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import argparse
import json
import numpy as np
import pandas as pd
import os
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,f1_score
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras import backend as K
from keras.utils.vis_utils import plot_model
from sklearn.externals import joblib
import time
def f1(y_true, y_pred):
def recall(y_true, y_pred):
"""Recall metric.
Only computes a batch-wise average of recall.
Computes the recall, a metric for multi-label classification of
how many relevant items are selected.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
"""Precision metric.
Only computes a batch-wise average of precision.
Computes the precision, a metric for multi-label classification of
how many selected items are relevant.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
precision = precision(y_true, y_pred)
recall = recall(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+K.epsilon()))
def get_embeddings(sentences_list,layer_json):
'''
:param sentences_list: the path o the sentences.txt
:param layer_json: the path of the json file that contains the embeddings of the sentences
:return: Dictionary with key each sentence of the sentences_list and as value the embedding
'''
sentences = dict()#dict with key the index of each line of the sentences_list.txt and as value the sentence
embeddings = dict()##dict with key the index of each sentence and as value the its embedding
sentence_emb = dict()#key:sentence,value:its embedding
with open(sentences_list,'r') as file:
for index,line in enumerate(file):
sentences[index] = line.strip()
with open(layer_json, 'r',encoding='utf-8') as f:
for line in f:
embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features'])
for key,value in sentences.items():
sentence_emb[value] = embeddings[key]
return sentence_emb
def train_classifier(sentences_list,layer_json,dataset_csv,filename):
'''
:param sentences_list: the path o the sentences.txt
:param layer_json: the path of the json file that contains the embeddings of the sentences
:param dataset_csv: the path of the dataset
:param filename: The path of the pickle file that the model will be stored
:return:
'''
dataset = pd.read_csv(dataset_csv)
bert_dict = get_embeddings(sentences_list,layer_json)
length = list()
sentence_emb = list()
previous_emb = list()
next_list = list()
section_list = list()
label = list()
errors = 0
for row in dataset.iterrows():
sentence = row[1][0].strip()
previous = row[1][1].strip()
nexts = row[1][2].strip()
section = row[1][3].strip()
if sentence in bert_dict:
sentence_emb.append(bert_dict[sentence])
else:
sentence_emb.append(np.zeros(768))
print(sentence)
errors += 1
if previous in bert_dict:
previous_emb.append(bert_dict[previous])
else:
previous_emb.append(np.zeros(768))
if nexts in bert_dict:
next_list.append(bert_dict[nexts])
else:
next_list.append(np.zeros(768))
if section in bert_dict:
section_list.append(bert_dict[section])
else:
section_list.append(np.zeros(768))
length.append(row[1][4])
label.append(row[1][5])
sentence_emb = np.asarray(sentence_emb)
print(sentence_emb.shape)
next_emb = np.asarray(next_list)
print(next_emb.shape)
previous_emb = np.asarray(previous_emb)
print(previous_emb.shape)
section_emb = np.asarray(section_list)
print(sentence_emb.shape)
length = np.asarray(length)
print(length.shape)
label = np.asarray(label)
print(errors)
features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1)
features = np.column_stack([features, length]) # np.append(features,length,axis=1)
print(features.shape)
X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42)
log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1)
log.fit(X_train, y_train)
#save the model
_ = joblib.dump(log, filename, compress=9)
predictions = log.predict(X_val)
print("###########################################")
print("Results using embeddings from the",layer_json,"file")
print(classification_report(y_val, predictions))
print("F1 score using Logistic Regression:",f1_score(y_val, predictions))
print("###########################################")
#train a DNN
f1_results = list()
for i in range(3):
model = Sequential()
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dense(128, activation='relu', trainable=True))
model.add(Dropout(0.30))
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dropout(0.25))
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dropout(0.35))
model.add(Dense(1, activation='sigmoid'))
# compile network
model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1])
# fit network
model.fit(X_train, y_train, epochs=100, batch_size=64)
loss, f_1 = model.evaluate(X_val, y_val, verbose=1)
print('\nTest F1: %f' % (f_1 * 100))
f1_results.append(f_1)
model = None
print("###########################################")
print("Results using embeddings from the", layer_json, "file")
# evaluate
print(np.mean(f1_results))
print("###########################################")
def parameter_tuning_LR(sentences_list,layer_json,dataset_csv):
'''
:param sentences_list: the path o the sentences.txt
:param layer_json: the path of the json file that contains the embeddings of the sentences
:param dataset_csv: the path of the dataset
:return:
'''
dataset = pd.read_csv(dataset_csv)
bert_dict = get_embeddings(sentences_list,layer_json)
length = list()
sentence_emb = list()
previous_emb = list()
next_list = list()
section_list = list()
label = list()
errors = 0
for row in dataset.iterrows():
sentence = row[1][0].strip()
previous = row[1][1].strip()
nexts = row[1][2].strip()
section = row[1][3].strip()
if sentence in bert_dict:
sentence_emb.append(bert_dict[sentence])
else:
sentence_emb.append(np.zeros(768))
print(sentence)
errors += 1
if previous in bert_dict:
previous_emb.append(bert_dict[previous])
else:
previous_emb.append(np.zeros(768))
if nexts in bert_dict:
next_list.append(bert_dict[nexts])
else:
next_list.append(np.zeros(768))
if section in bert_dict:
section_list.append(bert_dict[section])
else:
section_list.append(np.zeros(768))
length.append(row[1][4])
label.append(row[1][5])
sentence_emb = np.asarray(sentence_emb)
print(sentence_emb.shape)
next_emb = np.asarray(next_list)
print(next_emb.shape)
previous_emb = np.asarray(previous_emb)
print(previous_emb.shape)
section_emb = np.asarray(section_list)
print(sentence_emb.shape)
length = np.asarray(length)
print(length.shape)
label = np.asarray(label)
print(errors)
features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1)
features = np.column_stack([features, length])
print(features.shape)
X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42)
C = [0.1,1,2,5,10]
solver = ['newton-cg','saga','sag']
best_params = dict()
best_score = 0.0
for c in C:
for s in solver:
start = time.time()
log = LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c)
log.fit(X_train, y_train)
predictions = log.predict(X_val)
print("###########################################")
print("LR with C =",c,'and solver = ',s)
print("Results using embeddings from the", layer_json, "file")
print(classification_report(y_val, predictions))
f1 = f1_score(y_val, predictions)
if f1 > best_score:
best_score = f1
best_params['c'] = c
best_params['solver'] = s
print("F1 score using Logistic Regression:",f1)
print("###########################################")
end = time.time()
running_time = end - start
print("Running time:"+str(running_time))
def visualize_DNN(file_to_save):
'''
Save the DNN architecture to a png file. Better use the Visulize_DNN.ipynd
:param file_to_save: the png file that the architecture of the DNN will be saved.
:return: None
'''
model = Sequential()
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dense(128, activation='relu', trainable=True))
model.add(Dropout(0.30))
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dropout(0.25))
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dropout(0.35))
model.add(Dense(1, activation='sigmoid'))
plot_model(model, to_file=file_to_save, show_shapes=True)
def save_model(sentences_list,layer_json,dataset_csv,pkl):
dataset = pd.read_csv(dataset_csv)
bert_dict = get_embeddings(sentences_list, layer_json)
length = list()
sentence_emb = list()
previous_emb = list()
next_list = list()
section_list = list()
label = list()
errors = 0
for row in dataset.iterrows():
sentence = row[1][0].strip()
previous = row[1][1].strip()
nexts = row[1][2].strip()
section = row[1][3].strip()
if sentence in bert_dict:
sentence_emb.append(bert_dict[sentence])
else:
sentence_emb.append(np.zeros(768))
print(sentence)
errors += 1
if previous in bert_dict:
previous_emb.append(bert_dict[previous])
else:
previous_emb.append(np.zeros(768))
if nexts in bert_dict:
next_list.append(bert_dict[nexts])
else:
next_list.append(np.zeros(768))
if section in bert_dict:
section_list.append(bert_dict[section])
else:
section_list.append(np.zeros(768))
length.append(row[1][4])
label.append(row[1][5])
sentence_emb = np.asarray(sentence_emb)
print(sentence_emb.shape)
next_emb = np.asarray(next_list)
print(next_emb.shape)
previous_emb = np.asarray(previous_emb)
print(previous_emb.shape)
section_emb = np.asarray(section_list)
print(sentence_emb.shape)
length = np.asarray(length)
print(length.shape)
label = | np.asarray(label) | numpy.asarray |
import numpy
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
from src.support import support
class PhraseManager:
def __init__(self, configuration):
self.train_phrases, self.train_labels = self._read_train_phrases()
self.test_phrases, self.test_labels = self._read_test_phrases()
self.configuration = configuration
self.tokenizer = None
def get_phrases_train(self):
return self.train_phrases, self.train_labels
def get_phrases_test(self):
return self.test_phrases, self.test_labels
def get_dataset(self, level = None):
if level == support.WORD_LEVEL:
return self._word_process(self.configuration[support.WORD_MAX_LENGTH])
elif level == support.CHAR_LEVEL:
return self._char_process(self.configuration[support.CHAR_MAX_LENGTH])
else:
return self.train_phrases, self.train_labels, self.test_phrases, self.test_labels
def _word_process(self, word_max_length):
tokenizer = Tokenizer(num_words=self.configuration[support.QUANTITY_WORDS])
tokenizer.fit_on_texts(self.train_phrases)
x_train_sequence = tokenizer.texts_to_sequences(self.train_phrases)
x_test_sequence = tokenizer.texts_to_sequences(self.test_phrases)
x_train = sequence.pad_sequences(x_train_sequence, maxlen=word_max_length, padding='post', truncating='post')
x_test = sequence.pad_sequences(x_test_sequence, maxlen=word_max_length, padding='post', truncating='post')
y_train = numpy.array(self.train_labels)
y_test = numpy.array(self.test_labels)
return x_train, y_train, x_test, y_test
def _char_process(self, max_length):
embedding_w, embedding_dic = self._onehot_dic_build()
x_train = []
for i in range(len(self.train_phrases)):
doc_vec = self._doc_process(self.train_phrases[i].lower(), embedding_dic, max_length)
x_train.append(doc_vec)
x_train = numpy.asarray(x_train, dtype='int64')
y_train = numpy.array(self.train_labels, dtype='float32')
x_test = []
for i in range(len( self.test_phrases)):
doc_vec = self._doc_process( self.test_phrases[i].lower(), embedding_dic, max_length)
x_test.append(doc_vec)
x_test = numpy.asarray(x_test, dtype='int64')
y_test = numpy.array(self.test_labels, dtype='float32')
del embedding_w, embedding_dic
return x_train, y_train, x_test, y_test
def _doc_process(self, doc, embedding_dic, max_length):
min_length = min(max_length, len(doc))
doc_vec = numpy.zeros(max_length, dtype='int64')
for j in range(min_length):
if doc[j] in embedding_dic:
doc_vec[j] = embedding_dic[doc[j]]
else:
doc_vec[j] = embedding_dic['UNK']
return doc_vec
def _onehot_dic_build(self):
alphabet = "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}"
embedding_dic = {}
embedding_w = []
embedding_dic["UNK"] = 0
embedding_w.append(numpy.zeros(len(alphabet), dtype='float32'))
for i, alpha in enumerate(alphabet):
onehot = numpy.zeros(len(alphabet), dtype='float32')
embedding_dic[alpha] = i + 1
onehot[i] = 1
embedding_w.append(onehot)
embedding_w = numpy.array(embedding_w, dtype='float32')
return embedding_w, embedding_dic
def get_tokenizer(self):
if self.tokenizer is None:
self.tokenizer = Tokenizer(num_words=self.configuration[support.QUANTITY_WORDS])
self.tokenizer.fit_on_texts(self.train_phrases)
return self.tokenizer
def text_to_vector_word(self, text):
vector_sequence = self.get_tokenizer().texts_to_sequences([text])
result = sequence.pad_sequences(vector_sequence, maxlen=self.configuration[support.WORD_MAX_LENGTH], padding='post', truncating='post')
return result
def text_to_vector_word_all(self, texts):
vector_sequence = self.get_tokenizer().texts_to_sequences(texts)
result = sequence.pad_sequences(vector_sequence, maxlen=self.configuration[support.WORD_MAX_LENGTH], padding='post', truncating='post')
return result
def text_to_vector_char(self, text):
embedding_dictionary = self._get_embedding_dictionary()
max_length = self.configuration[support.CHAR_MAX_LENGTH]
min_length = min(max_length, len(text))
text_vector = numpy.zeros(max_length, dtype="int64")
for j in range(min_length):
if text[j] in embedding_dictionary:
text_vector[j] = embedding_dictionary[text[j]]
else:
text_vector[j] = embedding_dictionary["UNK"]
return text_vector
def text_to_vector_char_all(self, texts):
embedding_w, embedding_dic = self._onehot_dic_build()
result = []
for i in range(len(texts)):
doc_vec = self.text_to_vector_char(texts[i].lower())
result.append(doc_vec)
result = | numpy.asarray(result, dtype="int64") | numpy.asarray |
import numpy
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
from src.support import support
class PhraseManager:
def __init__(self, configuration):
self.train_phrases, self.train_labels = self._read_train_phrases()
self.test_phrases, self.test_labels = self._read_test_phrases()
self.configuration = configuration
self.tokenizer = None
def get_phrases_train(self):
return self.train_phrases, self.train_labels
def get_phrases_test(self):
return self.test_phrases, self.test_labels
def get_dataset(self, level = None):
if level == support.WORD_LEVEL:
return self._word_process(self.configuration[support.WORD_MAX_LENGTH])
elif level == support.CHAR_LEVEL:
return self._char_process(self.configuration[support.CHAR_MAX_LENGTH])
else:
return self.train_phrases, self.train_labels, self.test_phrases, self.test_labels
def _word_process(self, word_max_length):
tokenizer = Tokenizer(num_words=self.configuration[support.QUANTITY_WORDS])
tokenizer.fit_on_texts(self.train_phrases)
x_train_sequence = tokenizer.texts_to_sequences(self.train_phrases)
x_test_sequence = tokenizer.texts_to_sequences(self.test_phrases)
x_train = sequence.pad_sequences(x_train_sequence, maxlen=word_max_length, padding='post', truncating='post')
x_test = sequence.pad_sequences(x_test_sequence, maxlen=word_max_length, padding='post', truncating='post')
y_train = numpy.array(self.train_labels)
y_test = numpy.array(self.test_labels)
return x_train, y_train, x_test, y_test
def _char_process(self, max_length):
embedding_w, embedding_dic = self._onehot_dic_build()
x_train = []
for i in range(len(self.train_phrases)):
doc_vec = self._doc_process(self.train_phrases[i].lower(), embedding_dic, max_length)
x_train.append(doc_vec)
x_train = numpy.asarray(x_train, dtype='int64')
y_train = numpy.array(self.train_labels, dtype='float32')
x_test = []
for i in range(len( self.test_phrases)):
doc_vec = self._doc_process( self.test_phrases[i].lower(), embedding_dic, max_length)
x_test.append(doc_vec)
x_test = | numpy.asarray(x_test, dtype='int64') | numpy.asarray |
'''
<NAME>
set up :2020-1-9
intergrate img and label into one file
-- fiducial1024_v1
'''
import argparse
import sys, os
import pickle
import random
import collections
import json
import numpy as np
import scipy.io as io
import scipy.misc as m
import matplotlib.pyplot as plt
import glob
import math
import time
import threading
import multiprocessing as mp
from multiprocessing import Pool
import re
import cv2
# sys.path.append('/lustre/home/gwxie/hope/project/dewarp/datasets/') # /lustre/home/gwxie/program/project/unwarp/perturbed_imgaes/GAN
import utils
def getDatasets(dir):
return os.listdir(dir)
class perturbed(utils.BasePerturbed):
def __init__(self, path, bg_path, save_path, save_suffix):
self.path = path
self.bg_path = bg_path
self.save_path = save_path
self.save_suffix = save_suffix
def save_img(self, m, n, fold_curve='fold', repeat_time=4, fiducial_points = 16, relativeShift_position='relativeShift_v2'):
origin_img = cv2.imread(self.path, flags=cv2.IMREAD_COLOR)
save_img_shape = [512*2, 480*2] # 320
# reduce_value = np.random.choice([2**4, 2**5, 2**6, 2**7, 2**8], p=[0.01, 0.1, 0.4, 0.39, 0.1])
reduce_value = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.02, 0.18, 0.2, 0.3, 0.1, 0.1, 0.08, 0.02])
# reduce_value = np.random.choice([8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.01, 0.02, 0.2, 0.4, 0.19, 0.18])
# reduce_value = np.random.choice([16, 24, 32, 40, 48, 64], p=[0.01, 0.1, 0.2, 0.4, 0.2, 0.09])
base_img_shrink = save_img_shape[0] - reduce_value
# enlarge_img_shrink = [1024, 768]
# enlarge_img_shrink = [896, 672] # 420
enlarge_img_shrink = [512*4, 480*4] # 420
# enlarge_img_shrink = [896*2, 768*2] # 420
# enlarge_img_shrink = [896, 768] # 420
# enlarge_img_shrink = [768, 576] # 420
# enlarge_img_shrink = [640, 480] # 420
''''''
im_lr = origin_img.shape[0]
im_ud = origin_img.shape[1]
reduce_value_v2 = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 28*2, 32*2, 48*2], p=[0.02, 0.18, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1])
# reduce_value_v2 = np.random.choice([16, 24, 28, 32, 48, 64], p=[0.01, 0.1, 0.2, 0.3, 0.25, 0.14])
if im_lr > im_ud:
im_ud = min(int(im_ud / im_lr * base_img_shrink), save_img_shape[1] - reduce_value_v2)
im_lr = save_img_shape[0] - reduce_value
else:
base_img_shrink = save_img_shape[1] - reduce_value
im_lr = min(int(im_lr / im_ud * base_img_shrink), save_img_shape[0] - reduce_value_v2)
im_ud = base_img_shrink
if round(im_lr / im_ud, 2) < 0.5 or round(im_ud / im_lr, 2) < 0.5:
repeat_time = min(repeat_time, 8)
edge_padding = 3
im_lr -= im_lr % (fiducial_points-1) - (2*edge_padding) # im_lr % (fiducial_points-1) - 1
im_ud -= im_ud % (fiducial_points-1) - (2*edge_padding) # im_ud % (fiducial_points-1) - 1
im_hight = np.linspace(edge_padding, im_lr - edge_padding, fiducial_points, dtype=np.int64)
im_wide = np.linspace(edge_padding, im_ud - edge_padding, fiducial_points, dtype=np.int64)
# im_lr -= im_lr % (fiducial_points-1) - (1+2*edge_padding) # im_lr % (fiducial_points-1) - 1
# im_ud -= im_ud % (fiducial_points-1) - (1+2*edge_padding) # im_ud % (fiducial_points-1) - 1
# im_hight = np.linspace(edge_padding, im_lr - (1+edge_padding), fiducial_points, dtype=np.int64)
# im_wide = np.linspace(edge_padding, im_ud - (1+edge_padding), fiducial_points, dtype=np.int64)
im_x, im_y = np.meshgrid(im_hight, im_wide)
segment_x = (im_lr) // (fiducial_points-1)
segment_y = (im_ud) // (fiducial_points-1)
# plt.plot(im_x, im_y,
# color='limegreen',
# marker='.',
# linestyle='')
# plt.grid(True)
# plt.show()
self.origin_img = cv2.resize(origin_img, (im_ud, im_lr), interpolation=cv2.INTER_CUBIC)
perturbed_bg_ = getDatasets(self.bg_path)
perturbed_bg_img_ = self.bg_path+random.choice(perturbed_bg_)
perturbed_bg_img = cv2.imread(perturbed_bg_img_, flags=cv2.IMREAD_COLOR)
mesh_shape = self.origin_img.shape[:2]
self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 256, dtype=np.float32)#np.zeros_like(perturbed_bg_img)
# self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 0, dtype=np.int16)#np.zeros_like(perturbed_bg_img)
self.new_shape = self.synthesis_perturbed_img.shape[:2]
perturbed_bg_img = cv2.resize(perturbed_bg_img, (save_img_shape[1], save_img_shape[0]), cv2.INPAINT_TELEA)
origin_pixel_position = np.argwhere(np.zeros(mesh_shape, dtype=np.uint32) == 0).reshape(mesh_shape[0], mesh_shape[1], 2)
pixel_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(self.new_shape[0], self.new_shape[1], 2)
self.perturbed_xy_ = np.zeros((self.new_shape[0], self.new_shape[1], 2))
# self.perturbed_xy_ = pixel_position.copy().astype(np.float32)
# fiducial_points_grid = origin_pixel_position[im_x, im_y]
self.synthesis_perturbed_label = np.zeros((self.new_shape[0], self.new_shape[1], 2))
x_min, y_min, x_max, y_max = self.adjust_position_v2(0, 0, mesh_shape[0], mesh_shape[1], save_img_shape)
origin_pixel_position += [x_min, y_min]
x_min, y_min, x_max, y_max = self.adjust_position(0, 0, mesh_shape[0], mesh_shape[1])
x_shift = random.randint(-enlarge_img_shrink[0]//16, enlarge_img_shrink[0]//16)
y_shift = random.randint(-enlarge_img_shrink[1]//16, enlarge_img_shrink[1]//16)
x_min += x_shift
x_max += x_shift
y_min += y_shift
y_max += y_shift
'''im_x,y'''
im_x += x_min
im_y += y_min
self.synthesis_perturbed_img[x_min:x_max, y_min:y_max] = self.origin_img
self.synthesis_perturbed_label[x_min:x_max, y_min:y_max] = origin_pixel_position
synthesis_perturbed_img_map = self.synthesis_perturbed_img.copy()
synthesis_perturbed_label_map = self.synthesis_perturbed_label.copy()
foreORbackground_label = np.full((mesh_shape), 1, dtype=np.int16)
foreORbackground_label_map = np.full((self.new_shape), 0, dtype=np.int16)
foreORbackground_label_map[x_min:x_max, y_min:y_max] = foreORbackground_label
# synthesis_perturbed_img_map = self.pad(self.synthesis_perturbed_img.copy(), x_min, y_min, x_max, y_max)
# synthesis_perturbed_label_map = self.pad(synthesis_perturbed_label_map, x_min, y_min, x_max, y_max)
'''*****************************************************************'''
is_normalizationFun_mixture = self.is_perform(0.2, 0.8)
# if not is_normalizationFun_mixture:
normalizationFun_0_1 = False
# normalizationFun_0_1 = self.is_perform(0.5, 0.5)
if fold_curve == 'fold':
fold_curve_random = True
# is_normalizationFun_mixture = False
normalizationFun_0_1 = self.is_perform(0.2, 0.8)
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
fold_curve_random = self.is_perform(0.1, 0.9) # False # self.is_perform(0.01, 0.99)
alpha_perturbed = random.randint(80, 160) / 100
# is_normalizationFun_mixture = False # self.is_perform(0.01, 0.99)
synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 256)
# synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 0, dtype=np.int16)
synthesis_perturbed_label = np.zeros_like(self.synthesis_perturbed_label)
alpha_perturbed_change = self.is_perform(0.5, 0.5)
p_pp_choice = self.is_perform(0.8, 0.2) if fold_curve == 'fold' else self.is_perform(0.1, 0.9)
for repeat_i in range(repeat_time):
if alpha_perturbed_change:
if fold_curve == 'fold':
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
alpha_perturbed = random.randint(80, 160) / 100
''''''
linspace_x = [0, (self.new_shape[0] - im_lr) // 2 - 1,
self.new_shape[0] - (self.new_shape[0] - im_lr) // 2 - 1, self.new_shape[0] - 1]
linspace_y = [0, (self.new_shape[1] - im_ud) // 2 - 1,
self.new_shape[1] - (self.new_shape[1] - im_ud) // 2 - 1, self.new_shape[1] - 1]
linspace_x_seq = [1, 2, 3]
linspace_y_seq = [1, 2, 3]
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_p = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
if ((r_x == 1 or r_x == 3) and (r_y == 1 or r_y == 3)) and p_pp_choice:
linspace_x_seq.remove(r_x)
linspace_y_seq.remove(r_y)
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_pp = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
# perturbed_p, perturbed_pp = np.array(
# [random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10]) \
# , np.array([random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10])
# perturbed_p, perturbed_pp = np.array(
# [random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10]) \
# , np.array([random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10])
''''''
perturbed_vp = perturbed_pp - perturbed_p
perturbed_vp_norm = np.linalg.norm(perturbed_vp)
perturbed_distance_vertex_and_line = np.dot((perturbed_p - pixel_position), perturbed_vp) / perturbed_vp_norm
''''''
# perturbed_v = np.array([random.randint(-3000, 3000) / 100, random.randint(-3000, 3000) / 100])
# perturbed_v = np.array([random.randint(-4000, 4000) / 100, random.randint(-4000, 4000) / 100])
if fold_curve == 'fold' and self.is_perform(0.6, 0.4): # self.is_perform(0.3, 0.7):
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
perturbed_v = np.array([random.randint(-10000, 10000) / 100, random.randint(-10000, 10000) / 100])
# perturbed_v = np.array([random.randint(-11000, 11000) / 100, random.randint(-11000, 11000) / 100])
else:
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
# perturbed_v = np.array([random.randint(-16000, 16000) / 100, random.randint(-16000, 16000) / 100])
perturbed_v = np.array([random.randint(-8000, 8000) / 100, random.randint(-8000, 8000) / 100])
# perturbed_v = np.array([random.randint(-3500, 3500) / 100, random.randint(-3500, 3500) / 100])
# perturbed_v = np.array([random.randint(-600, 600) / 10, random.randint(-600, 600) / 10])
''''''
if fold_curve == 'fold':
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
''''''
if fold_curve_random:
# omega_perturbed = (alpha_perturbed+0.2) / (perturbed_d + alpha_perturbed)
# omega_perturbed = alpha_perturbed**perturbed_d
omega_perturbed = alpha_perturbed / (perturbed_d + alpha_perturbed)
else:
omega_perturbed = 1 - perturbed_d ** alpha_perturbed
'''shadow'''
if self.is_perform(0.6, 0.4):
synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] = np.minimum(np.maximum(synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] - np.int16(np.round(omega_perturbed[x_min:x_max, y_min:y_max].repeat(3).reshape(x_max-x_min, y_max-y_min, 3) * abs(np.linalg.norm(perturbed_v//2))*np.array([0.4-random.random()*0.1, 0.4-random.random()*0.1, 0.4-random.random()*0.1]))), 0), 255)
''''''
if relativeShift_position in ['position', 'relativeShift_v2']:
self.perturbed_xy_ += np.array([omega_perturbed * perturbed_v[0], omega_perturbed * perturbed_v[1]]).transpose(1, 2, 0)
else:
print('relativeShift_position error')
exit()
'''
flat_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(
self.new_shape[0] * self.new_shape[1], 2)
vtx, wts = self.interp_weights(self.perturbed_xy_.reshape(self.new_shape[0] * self.new_shape[1], 2), flat_position)
wts_sum = np.abs(wts).sum(-1)
# flat_img.reshape(flat_shape[0] * flat_shape[1], 3)[:] = interpolate(pixel, vtx, wts)
wts = wts[wts_sum <= 1, :]
vtx = vtx[wts_sum <= 1, :]
synthesis_perturbed_img.reshape(self.new_shape[0] * self.new_shape[1], 3)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_img_map.reshape(self.new_shape[0] * self.new_shape[1], 3), vtx, wts)
synthesis_perturbed_label.reshape(self.new_shape[0] * self.new_shape[1], 2)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_label_map.reshape(self.new_shape[0] * self.new_shape[1], 2), vtx, wts)
foreORbackground_label = np.zeros(self.new_shape)
foreORbackground_label.reshape(self.new_shape[0] * self.new_shape[1], 1)[wts_sum <= 1, :] = self.interpolate(foreORbackground_label_map.reshape(self.new_shape[0] * self.new_shape[1], 1), vtx, wts)
foreORbackground_label[foreORbackground_label < 0.99] = 0
foreORbackground_label[foreORbackground_label >= 0.99] = 1
# synthesis_perturbed_img = np.around(synthesis_perturbed_img).astype(np.uint8)
synthesis_perturbed_label[:, :, 0] *= foreORbackground_label
synthesis_perturbed_label[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 0] *= foreORbackground_label
synthesis_perturbed_img[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 2] *= foreORbackground_label
self.synthesis_perturbed_img = synthesis_perturbed_img
self.synthesis_perturbed_label = synthesis_perturbed_label
'''
'''perspective'''
perspective_shreshold = random.randint(26, 36)*10 # 280
x_min_per, y_min_per, x_max_per, y_max_per = self.adjust_position(perspective_shreshold, perspective_shreshold, self.new_shape[0]-perspective_shreshold, self.new_shape[1]-perspective_shreshold)
pts1 = np.float32([[x_min_per, y_min_per], [x_max_per, y_min_per], [x_min_per, y_max_per], [x_max_per, y_max_per]])
e_1_ = x_max_per - x_min_per
e_2_ = y_max_per - y_min_per
e_3_ = e_2_
e_4_ = e_1_
perspective_shreshold_h = e_1_*0.02
perspective_shreshold_w = e_2_*0.02
a_min_, a_max_ = 70, 110
# if self.is_perform(1, 0):
if fold_curve == 'curve' and self.is_perform(0.5, 0.5):
if self.is_perform(0.5, 0.5):
while True:
pts2 = np.around(
np.float32([[x_min_per - (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold],
[x_max_per - (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold],
[x_min_per + (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold],
[x_max_per + (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold]])) # right
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
else:
while True:
pts2 = np.around(
np.float32([[x_min_per + (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold],
[x_max_per + (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold],
[x_min_per - (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold],
[x_max_per - (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold]]))
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
else:
while True:
pts2 = np.around(np.float32([[x_min_per+(random.random()-0.5)*perspective_shreshold, y_min_per+(random.random()-0.5)*perspective_shreshold],
[x_max_per+(random.random()-0.5)*perspective_shreshold, y_min_per+(random.random()-0.5)*perspective_shreshold],
[x_min_per+(random.random()-0.5)*perspective_shreshold, y_max_per+(random.random()-0.5)*perspective_shreshold],
[x_max_per+(random.random()-0.5)*perspective_shreshold, y_max_per+(random.random()-0.5)*perspective_shreshold]]))
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
M = cv2.getPerspectiveTransform(pts1, pts2)
one = np.ones((self.new_shape[0], self.new_shape[1], 1), dtype=np.int16)
matr = np.dstack((pixel_position, one))
new = np.dot(M, matr.reshape(-1, 3).T).T.reshape(self.new_shape[0], self.new_shape[1], 3)
x = new[:, :, 0]/new[:, :, 2]
y = new[:, :, 1]/new[:, :, 2]
perturbed_xy_ = np.dstack((x, y))
# perturbed_xy_round_int = np.around(cv2.bilateralFilter(perturbed_xy_round_int, 9, 75, 75))
# perturbed_xy_round_int = np.around(cv2.blur(perturbed_xy_, (17, 17)))
# perturbed_xy_round_int = cv2.blur(perturbed_xy_round_int, (17, 17))
# perturbed_xy_round_int = cv2.GaussianBlur(perturbed_xy_round_int, (7, 7), 0)
perturbed_xy_ = perturbed_xy_-np.min(perturbed_xy_.T.reshape(2, -1), 1)
# perturbed_xy_round_int = np.around(perturbed_xy_round_int-np.min(perturbed_xy_round_int.T.reshape(2, -1), 1)).astype(np.int16)
self.perturbed_xy_ += perturbed_xy_
'''perspective end'''
'''to img'''
flat_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(
self.new_shape[0] * self.new_shape[1], 2)
# self.perturbed_xy_ = cv2.blur(self.perturbed_xy_, (7, 7))
self.perturbed_xy_ = cv2.GaussianBlur(self.perturbed_xy_, (7, 7), 0)
'''get fiducial points'''
fiducial_points_coordinate = self.perturbed_xy_[im_x, im_y]
vtx, wts = self.interp_weights(self.perturbed_xy_.reshape(self.new_shape[0] * self.new_shape[1], 2), flat_position)
wts_sum = np.abs(wts).sum(-1)
# flat_img.reshape(flat_shape[0] * flat_shape[1], 3)[:] = interpolate(pixel, vtx, wts)
wts = wts[wts_sum <= 1, :]
vtx = vtx[wts_sum <= 1, :]
synthesis_perturbed_img.reshape(self.new_shape[0] * self.new_shape[1], 3)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_img_map.reshape(self.new_shape[0] * self.new_shape[1], 3), vtx, wts)
synthesis_perturbed_label.reshape(self.new_shape[0] * self.new_shape[1], 2)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_label_map.reshape(self.new_shape[0] * self.new_shape[1], 2), vtx, wts)
foreORbackground_label = | np.zeros(self.new_shape) | numpy.zeros |
from abc import ABCMeta, abstractmethod
import os
from vmaf.tools.misc import make_absolute_path, run_process
from vmaf.tools.stats import ListStats
__copyright__ = "Copyright 2016-2018, Netflix, Inc."
__license__ = "Apache, Version 2.0"
import re
import numpy as np
import ast
from vmaf import ExternalProgramCaller, to_list
from vmaf.config import VmafConfig, VmafExternalConfig
from vmaf.core.executor import Executor
from vmaf.core.result import Result
from vmaf.tools.reader import YuvReader
class FeatureExtractor(Executor):
"""
FeatureExtractor takes in a list of assets, and run feature extraction on
them, and return a list of corresponding results. A FeatureExtractor must
specify a unique type and version combination (by the TYPE and VERSION
attribute), so that the Result generated by it can be identified.
A derived class of FeatureExtractor must:
1) Override TYPE and VERSION
2) Override _generate_result(self, asset), which call a
command-line executable and generate feature scores in a log file.
3) Override _get_feature_scores(self, asset), which read the feature
scores from the log file, and return the scores in a dictionary format.
For an example, follow VmafFeatureExtractor.
"""
__metaclass__ = ABCMeta
@property
@abstractmethod
def ATOM_FEATURES(self):
raise NotImplementedError
def _read_result(self, asset):
result = {}
result.update(self._get_feature_scores(asset))
executor_id = self.executor_id
return Result(asset, executor_id, result)
@classmethod
def get_scores_key(cls, atom_feature):
return "{type}_{atom_feature}_scores".format(
type=cls.TYPE, atom_feature=atom_feature)
@classmethod
def get_score_key(cls, atom_feature):
return "{type}_{atom_feature}_score".format(
type=cls.TYPE, atom_feature=atom_feature)
def _get_feature_scores(self, asset):
# routine to read the feature scores from the log file, and return
# the scores in a dictionary format.
log_file_path = self._get_log_file_path(asset)
atom_feature_scores_dict = {}
atom_feature_idx_dict = {}
for atom_feature in self.ATOM_FEATURES:
atom_feature_scores_dict[atom_feature] = []
atom_feature_idx_dict[atom_feature] = 0
with open(log_file_path, 'rt') as log_file:
for line in log_file.readlines():
for atom_feature in self.ATOM_FEATURES:
re_template = "{af}: ([0-9]+) ([a-zA-Z0-9.-]+)".format(af=atom_feature)
mo = re.match(re_template, line)
if mo:
cur_idx = int(mo.group(1))
assert cur_idx == atom_feature_idx_dict[atom_feature]
# parse value, allowing NaN and inf
val = float(mo.group(2))
if np.isnan(val) or np.isinf(val):
val = None
atom_feature_scores_dict[atom_feature].append(val)
atom_feature_idx_dict[atom_feature] += 1
continue
len_score = len(atom_feature_scores_dict[self.ATOM_FEATURES[0]])
assert len_score != 0
for atom_feature in self.ATOM_FEATURES[1:]:
assert len_score == len(atom_feature_scores_dict[atom_feature]), \
"Feature data possibly corrupt. Run cleanup script and try again."
feature_result = {}
for atom_feature in self.ATOM_FEATURES:
scores_key = self.get_scores_key(atom_feature)
feature_result[scores_key] = atom_feature_scores_dict[atom_feature]
return feature_result
class VmafFeatureExtractor(FeatureExtractor):
TYPE = "VMAF_feature"
# VERSION = '0.1' # vmaf_study; Anush's VIF fix
# VERSION = '0.2' # expose vif_num, vif_den, adm_num, adm_den, anpsnr
# VERSION = '0.2.1' # expose vif num/den of each scale
# VERSION = '0.2.2' # adm abs-->fabs, corrected border handling, uniform reading with option of offset for input YUV, updated VIF corner case
# VERSION = '0.2.2b' # expose adm_den/num_scalex
# VERSION = '0.2.3' # AVX for VMAF convolution; update adm features by folding noise floor into per coef
# VERSION = '0.2.4' # Fix a bug in adm feature passing scale into dwt_quant_step
# VERSION = '0.2.4b' # Modify by adding ADM noise floor outside cube root; add derived feature motion2
VERSION = '0.2.4c' # Modify by moving motion2 to c code
ATOM_FEATURES = ['vif', 'adm', 'ansnr', 'motion', 'motion2',
'vif_num', 'vif_den', 'adm_num', 'adm_den', 'anpsnr',
'vif_num_scale0', 'vif_den_scale0',
'vif_num_scale1', 'vif_den_scale1',
'vif_num_scale2', 'vif_den_scale2',
'vif_num_scale3', 'vif_den_scale3',
'adm_num_scale0', 'adm_den_scale0',
'adm_num_scale1', 'adm_den_scale1',
'adm_num_scale2', 'adm_den_scale2',
'adm_num_scale3', 'adm_den_scale3',
]
DERIVED_ATOM_FEATURES = ['vif_scale0', 'vif_scale1', 'vif_scale2', 'vif_scale3',
'vif2', 'adm2', 'adm3',
'adm_scale0', 'adm_scale1', 'adm_scale2', 'adm_scale3',
]
ADM2_CONSTANT = 0
ADM_SCALE_CONSTANT = 0
def _generate_result(self, asset):
# routine to call the command-line executable and generate feature
# scores in the log file.
quality_width, quality_height = asset.quality_width_height
log_file_path = self._get_log_file_path(asset)
yuv_type=self._get_workfile_yuv_type(asset)
ref_path=asset.ref_workfile_path
dis_path=asset.dis_workfile_path
w=quality_width
h=quality_height
logger = self.logger
ExternalProgramCaller.call_vmaf_feature(yuv_type, ref_path, dis_path, w, h, log_file_path, logger)
@classmethod
def _post_process_result(cls, result):
# override Executor._post_process_result
result = super(VmafFeatureExtractor, cls)._post_process_result(result)
# adm2 =
# (adm_num + ADM2_CONSTANT) / (adm_den + ADM2_CONSTANT)
adm2_scores_key = cls.get_scores_key('adm2')
adm_num_scores_key = cls.get_scores_key('adm_num')
adm_den_scores_key = cls.get_scores_key('adm_den')
result.result_dict[adm2_scores_key] = list(
(np.array(result.result_dict[adm_num_scores_key]) + cls.ADM2_CONSTANT) /
(np.array(result.result_dict[adm_den_scores_key]) + cls.ADM2_CONSTANT)
)
# vif_scalei = vif_num_scalei / vif_den_scalei, i = 0, 1, 2, 3
vif_num_scale0_scores_key = cls.get_scores_key('vif_num_scale0')
vif_den_scale0_scores_key = cls.get_scores_key('vif_den_scale0')
vif_num_scale1_scores_key = cls.get_scores_key('vif_num_scale1')
vif_den_scale1_scores_key = cls.get_scores_key('vif_den_scale1')
vif_num_scale2_scores_key = cls.get_scores_key('vif_num_scale2')
vif_den_scale2_scores_key = cls.get_scores_key('vif_den_scale2')
vif_num_scale3_scores_key = cls.get_scores_key('vif_num_scale3')
vif_den_scale3_scores_key = cls.get_scores_key('vif_den_scale3')
vif_scale0_scores_key = cls.get_scores_key('vif_scale0')
vif_scale1_scores_key = cls.get_scores_key('vif_scale1')
vif_scale2_scores_key = cls.get_scores_key('vif_scale2')
vif_scale3_scores_key = cls.get_scores_key('vif_scale3')
result.result_dict[vif_scale0_scores_key] = list(
(np.array(result.result_dict[vif_num_scale0_scores_key])
/ np.array(result.result_dict[vif_den_scale0_scores_key]))
)
result.result_dict[vif_scale1_scores_key] = list(
(np.array(result.result_dict[vif_num_scale1_scores_key])
/ np.array(result.result_dict[vif_den_scale1_scores_key]))
)
result.result_dict[vif_scale2_scores_key] = list(
(np.array(result.result_dict[vif_num_scale2_scores_key])
/ np.array(result.result_dict[vif_den_scale2_scores_key]))
)
result.result_dict[vif_scale3_scores_key] = list(
(np.array(result.result_dict[vif_num_scale3_scores_key])
/ np.array(result.result_dict[vif_den_scale3_scores_key]))
)
# vif2 =
# ((vif_num_scale0 / vif_den_scale0) + (vif_num_scale1 / vif_den_scale1) +
# (vif_num_scale2 / vif_den_scale2) + (vif_num_scale3 / vif_den_scale3)) / 4.0
vif_scores_key = cls.get_scores_key('vif2')
result.result_dict[vif_scores_key] = list(
(
(np.array(result.result_dict[vif_num_scale0_scores_key])
/ np.array(result.result_dict[vif_den_scale0_scores_key])) +
(np.array(result.result_dict[vif_num_scale1_scores_key])
/ np.array(result.result_dict[vif_den_scale1_scores_key])) +
(np.array(result.result_dict[vif_num_scale2_scores_key])
/ np.array(result.result_dict[vif_den_scale2_scores_key])) +
(np.array(result.result_dict[vif_num_scale3_scores_key])
/ np.array(result.result_dict[vif_den_scale3_scores_key]))
) / 4.0
)
# adm_scalei = adm_num_scalei / adm_den_scalei, i = 0, 1, 2, 3
adm_num_scale0_scores_key = cls.get_scores_key('adm_num_scale0')
adm_den_scale0_scores_key = cls.get_scores_key('adm_den_scale0')
adm_num_scale1_scores_key = cls.get_scores_key('adm_num_scale1')
adm_den_scale1_scores_key = cls.get_scores_key('adm_den_scale1')
adm_num_scale2_scores_key = cls.get_scores_key('adm_num_scale2')
adm_den_scale2_scores_key = cls.get_scores_key('adm_den_scale2')
adm_num_scale3_scores_key = cls.get_scores_key('adm_num_scale3')
adm_den_scale3_scores_key = cls.get_scores_key('adm_den_scale3')
adm_scale0_scores_key = cls.get_scores_key('adm_scale0')
adm_scale1_scores_key = cls.get_scores_key('adm_scale1')
adm_scale2_scores_key = cls.get_scores_key('adm_scale2')
adm_scale3_scores_key = cls.get_scores_key('adm_scale3')
result.result_dict[adm_scale0_scores_key] = list(
(np.array(result.result_dict[adm_num_scale0_scores_key]) + cls.ADM_SCALE_CONSTANT)
/ (np.array(result.result_dict[adm_den_scale0_scores_key]) + cls.ADM_SCALE_CONSTANT)
)
result.result_dict[adm_scale1_scores_key] = list(
(np.array(result.result_dict[adm_num_scale1_scores_key]) + cls.ADM_SCALE_CONSTANT)
/ (np.array(result.result_dict[adm_den_scale1_scores_key]) + cls.ADM_SCALE_CONSTANT)
)
result.result_dict[adm_scale2_scores_key] = list(
(np.array(result.result_dict[adm_num_scale2_scores_key]) + cls.ADM_SCALE_CONSTANT)
/ (np.array(result.result_dict[adm_den_scale2_scores_key]) + cls.ADM_SCALE_CONSTANT)
)
result.result_dict[adm_scale3_scores_key] = list(
(np.array(result.result_dict[adm_num_scale3_scores_key]) + cls.ADM_SCALE_CONSTANT)
/ (np.array(result.result_dict[adm_den_scale3_scores_key]) + cls.ADM_SCALE_CONSTANT)
)
# adm3 = \
# (((adm_num_scale0 + ADM_SCALE_CONSTANT) / (adm_den_scale0 + ADM_SCALE_CONSTANT))
# + ((adm_num_scale1 + ADM_SCALE_CONSTANT) / (adm_den_scale1 + ADM_SCALE_CONSTANT))
# + ((adm_num_scale2 + ADM_SCALE_CONSTANT) / (adm_den_scale2 + ADM_SCALE_CONSTANT))
# + ((adm_num_scale3 + ADM_SCALE_CONSTANT) / (adm_den_scale3 + ADM_SCALE_CONSTANT))) / 4.0
adm3_scores_key = cls.get_scores_key('adm3')
result.result_dict[adm3_scores_key] = list(
(
((np.array(result.result_dict[adm_num_scale0_scores_key]) + cls.ADM_SCALE_CONSTANT)
/ (np.array(result.result_dict[adm_den_scale0_scores_key]) + cls.ADM_SCALE_CONSTANT)) +
((np.array(result.result_dict[adm_num_scale1_scores_key]) + cls.ADM_SCALE_CONSTANT)
/ (np.array(result.result_dict[adm_den_scale1_scores_key]) + cls.ADM_SCALE_CONSTANT)) +
((np.array(result.result_dict[adm_num_scale2_scores_key]) + cls.ADM_SCALE_CONSTANT)
/ (np.array(result.result_dict[adm_den_scale2_scores_key]) + cls.ADM_SCALE_CONSTANT)) +
((np.array(result.result_dict[adm_num_scale3_scores_key]) + cls.ADM_SCALE_CONSTANT)
/ (np.array(result.result_dict[adm_den_scale3_scores_key]) + cls.ADM_SCALE_CONSTANT))
) / 4.0
)
# validate
for feature in cls.DERIVED_ATOM_FEATURES:
assert cls.get_scores_key(feature) in result.result_dict
return result
class VifFrameDifferenceFeatureExtractor(FeatureExtractor):
TYPE = "VifDiff_feature"
VERSION = '0.1'
ATOM_FEATURES = ['vifdiff',
'vifdiff_num', 'vifdiff_den',
'vifdiff_num_scale0', 'vifdiff_den_scale0',
'vifdiff_num_scale1', 'vifdiff_den_scale1',
'vifdiff_num_scale2', 'vifdiff_den_scale2',
'vifdiff_num_scale3', 'vifdiff_den_scale3',
]
DERIVED_ATOM_FEATURES = ['vifdiff_scale0', 'vifdiff_scale1', 'vifdiff_scale2', 'vifdiff_scale3',
]
ADM2_CONSTANT = 0
ADM_SCALE_CONSTANT = 0
def _generate_result(self, asset):
# routine to call the command-line executable and generate feature
# scores in the log file.
quality_width, quality_height = asset.quality_width_height
log_file_path = self._get_log_file_path(asset)
yuv_type=self._get_workfile_yuv_type(asset)
ref_path=asset.ref_workfile_path
dis_path=asset.dis_workfile_path
w=quality_width
h=quality_height
logger = self.logger
ExternalProgramCaller.call_vifdiff_feature(yuv_type, ref_path, dis_path, w, h, log_file_path, logger)
@classmethod
def _post_process_result(cls, result):
# override Executor._post_process_result
result = super(VifFrameDifferenceFeatureExtractor, cls)._post_process_result(result)
# vifdiff_scalei = vifdiff_num_scalei / vifdiff_den_scalei, i = 0, 1, 2, 3
vifdiff_num_scale0_scores_key = cls.get_scores_key('vifdiff_num_scale0')
vifdiff_den_scale0_scores_key = cls.get_scores_key('vifdiff_den_scale0')
vifdiff_num_scale1_scores_key = cls.get_scores_key('vifdiff_num_scale1')
vifdiff_den_scale1_scores_key = cls.get_scores_key('vifdiff_den_scale1')
vifdiff_num_scale2_scores_key = cls.get_scores_key('vifdiff_num_scale2')
vifdiff_den_scale2_scores_key = cls.get_scores_key('vifdiff_den_scale2')
vifdiff_num_scale3_scores_key = cls.get_scores_key('vifdiff_num_scale3')
vifdiff_den_scale3_scores_key = cls.get_scores_key('vifdiff_den_scale3')
vifdiff_scale0_scores_key = cls.get_scores_key('vifdiff_scale0')
vifdiff_scale1_scores_key = cls.get_scores_key('vifdiff_scale1')
vifdiff_scale2_scores_key = cls.get_scores_key('vifdiff_scale2')
vifdiff_scale3_scores_key = cls.get_scores_key('vifdiff_scale3')
result.result_dict[vifdiff_scale0_scores_key] = list(
(np.array(result.result_dict[vifdiff_num_scale0_scores_key])
/ np.array(result.result_dict[vifdiff_den_scale0_scores_key]))
)
result.result_dict[vifdiff_scale1_scores_key] = list(
(np.array(result.result_dict[vifdiff_num_scale1_scores_key])
/ np.array(result.result_dict[vifdiff_den_scale1_scores_key]))
)
result.result_dict[vifdiff_scale2_scores_key] = list(
(np.array(result.result_dict[vifdiff_num_scale2_scores_key])
/ | np.array(result.result_dict[vifdiff_den_scale2_scores_key]) | numpy.array |
# coding: utf-8
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Test the Logarithmic Units and Quantities
"""
from __future__ import (absolute_import, unicode_literals, division,
print_function)
from ...extern import six
from ...extern.six.moves import zip
import pickle
import itertools
import pytest
import numpy as np
from numpy.testing.utils import assert_allclose
from ...tests.helper import assert_quantity_allclose
from ... import units as u, constants as c
lu_units = [u.dex, u.mag, u.decibel]
lu_subclasses = [u.DexUnit, u.MagUnit, u.DecibelUnit]
lq_subclasses = [u.Dex, u.Magnitude, u.Decibel]
pu_sample = (u.dimensionless_unscaled, u.m, u.g/u.s**2, u.Jy)
class TestLogUnitCreation(object):
def test_logarithmic_units(self):
"""Check logarithmic units are set up correctly."""
assert u.dB.to(u.dex) == 0.1
assert u.dex.to(u.mag) == -2.5
assert u.mag.to(u.dB) == -4
@pytest.mark.parametrize('lu_unit, lu_cls', zip(lu_units, lu_subclasses))
def test_callable_units(self, lu_unit, lu_cls):
assert isinstance(lu_unit, u.UnitBase)
assert callable(lu_unit)
assert lu_unit._function_unit_class is lu_cls
@pytest.mark.parametrize('lu_unit', lu_units)
def test_equality_to_normal_unit_for_dimensionless(self, lu_unit):
lu = lu_unit()
assert lu == lu._default_function_unit # eg, MagUnit() == u.mag
assert lu._default_function_unit == lu # and u.mag == MagUnit()
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_call_units(self, lu_unit, physical_unit):
"""Create a LogUnit subclass using the callable unit and physical unit,
and do basic check that output is right."""
lu1 = lu_unit(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
def test_call_invalid_unit(self):
with pytest.raises(TypeError):
u.mag([])
with pytest.raises(ValueError):
u.mag(u.mag())
@pytest.mark.parametrize('lu_cls, physical_unit', itertools.product(
lu_subclasses + [u.LogUnit], pu_sample))
def test_subclass_creation(self, lu_cls, physical_unit):
"""Create a LogUnit subclass object for given physical unit,
and do basic check that output is right."""
lu1 = lu_cls(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
lu2 = lu_cls(physical_unit,
function_unit=2*lu1._default_function_unit)
assert lu2.physical_unit == physical_unit
assert lu2.function_unit == u.Unit(2*lu2._default_function_unit)
with pytest.raises(ValueError):
lu_cls(physical_unit, u.m)
def test_predefined_magnitudes():
assert_quantity_allclose((-21.1*u.STmag).physical,
1.*u.erg/u.cm**2/u.s/u.AA)
assert_quantity_allclose((-48.6*u.ABmag).physical,
1.*u.erg/u.cm**2/u.s/u.Hz)
assert_quantity_allclose((0*u.M_bol).physical, c.L_bol0)
assert_quantity_allclose((0*u.m_bol).physical,
c.L_bol0/(4.*np.pi*(10.*c.pc)**2))
def test_predefined_reinitialisation():
assert u.mag('ST') == u.STmag
assert u.mag('AB') == u.ABmag
assert u.mag('Bol') == u.M_bol
assert u.mag('bol') == u.m_bol
def test_predefined_string_roundtrip():
"""Ensure roundtripping; see #5015"""
with u.magnitude_zero_points.enable():
assert u.Unit(u.STmag.to_string()) == u.STmag
assert u.Unit(u.ABmag.to_string()) == u.ABmag
assert u.Unit(u.M_bol.to_string()) == u.M_bol
assert u.Unit(u.m_bol.to_string()) == u.m_bol
def test_inequality():
"""Check __ne__ works (regresssion for #5342)."""
lu1 = u.mag(u.Jy)
lu2 = u.dex(u.Jy)
lu3 = u.mag(u.Jy**2)
lu4 = lu3 - lu1
assert lu1 != lu2
assert lu1 != lu3
assert lu1 == lu4
class TestLogUnitStrings(object):
def test_str(self):
"""Do some spot checks that str, repr, etc. work as expected."""
lu1 = u.mag(u.Jy)
assert str(lu1) == 'mag(Jy)'
assert repr(lu1) == 'Unit("mag(Jy)")'
assert lu1.to_string('generic') == 'mag(Jy)'
with pytest.raises(ValueError):
lu1.to_string('fits')
lu2 = u.dex()
assert str(lu2) == 'dex'
assert repr(lu2) == 'Unit("dex(1)")'
assert lu2.to_string() == 'dex(1)'
lu3 = u.MagUnit(u.Jy, function_unit=2*u.mag)
assert str(lu3) == '2 mag(Jy)'
assert repr(lu3) == 'MagUnit("Jy", unit="2 mag")'
assert lu3.to_string() == '2 mag(Jy)'
lu4 = u.mag(u.ct)
assert lu4.to_string('generic') == 'mag(ct)'
assert lu4.to_string('latex') == ('$\\mathrm{mag}$$\\mathrm{\\left( '
'\\mathrm{ct} \\right)}$')
assert lu4._repr_latex_() == lu4.to_string('latex')
class TestLogUnitConversion(object):
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_physical_unit_conversion(self, lu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to their non-log counterparts."""
lu1 = lu_unit(physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(physical_unit, 0.) == 1.
assert physical_unit.is_equivalent(lu1)
assert physical_unit.to(lu1, 1.) == 0.
pu = u.Unit(8.*physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(pu, 0.) == 0.125
assert pu.is_equivalent(lu1)
assert_allclose(pu.to(lu1, 0.125), 0., atol=1.e-15)
# Check we round-trip.
value = np.linspace(0., 10., 6)
assert_allclose(pu.to(lu1, lu1.to(pu, value)), value, atol=1.e-15)
# And that we're not just returning True all the time.
pu2 = u.g
assert not lu1.is_equivalent(pu2)
with pytest.raises(u.UnitsError):
lu1.to(pu2)
assert not pu2.is_equivalent(lu1)
with pytest.raises(u.UnitsError):
pu2.to(lu1)
@pytest.mark.parametrize('lu_unit', lu_units)
def test_container_unit_conversion(self, lu_unit):
"""Check that conversion to logarithmic units (u.mag, u.dB, u.dex)
is only possible when the physical unit is dimensionless."""
values = np.linspace(0., 10., 6)
lu1 = lu_unit(u.dimensionless_unscaled)
assert lu1.is_equivalent(lu1.function_unit)
assert_allclose(lu1.to(lu1.function_unit, values), values)
lu2 = lu_unit(u.Jy)
assert not lu2.is_equivalent(lu2.function_unit)
with pytest.raises(u.UnitsError):
lu2.to(lu2.function_unit, values)
@pytest.mark.parametrize(
'flu_unit, tlu_unit, physical_unit',
itertools.product(lu_units, lu_units, pu_sample))
def test_subclass_conversion(self, flu_unit, tlu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to each other if they correspond to equivalent physical units."""
values = np.linspace(0., 10., 6)
flu = flu_unit(physical_unit)
tlu = tlu_unit(physical_unit)
assert flu.is_equivalent(tlu)
assert_allclose(flu.to(tlu), flu.function_unit.to(tlu.function_unit))
assert_allclose(flu.to(tlu, values),
values * flu.function_unit.to(tlu.function_unit))
tlu2 = tlu_unit(u.Unit(100.*physical_unit))
assert flu.is_equivalent(tlu2)
# Check that we round-trip.
assert_allclose(flu.to(tlu2, tlu2.to(flu, values)), values, atol=1.e-15)
tlu3 = tlu_unit(physical_unit.to_system(u.si)[0])
assert flu.is_equivalent(tlu3)
assert_allclose(flu.to(tlu3, tlu3.to(flu, values)), values, atol=1.e-15)
tlu4 = tlu_unit(u.g)
assert not flu.is_equivalent(tlu4)
with pytest.raises(u.UnitsError):
flu.to(tlu4, values)
def test_unit_decomposition(self):
lu = u.mag(u.Jy)
assert lu.decompose() == u.mag(u.Jy.decompose())
assert lu.decompose().physical_unit.bases == [u.kg, u.s]
assert lu.si == u.mag(u.Jy.si)
assert lu.si.physical_unit.bases == [u.kg, u.s]
assert lu.cgs == u.mag(u.Jy.cgs)
assert lu.cgs.physical_unit.bases == [u.g, u.s]
def test_unit_multiple_possible_equivalencies(self):
lu = u.mag(u.Jy)
assert lu.is_equivalent(pu_sample)
class TestLogUnitArithmetic(object):
def test_multiplication_division(self):
"""Check that multiplication/division with other units is only
possible when the physical unit is dimensionless, and that this
turns the unit into a normal one."""
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 * u.m
with pytest.raises(u.UnitsError):
u.m * lu1
with pytest.raises(u.UnitsError):
lu1 / lu1
for unit in (u.dimensionless_unscaled, u.m, u.mag, u.dex):
with pytest.raises(u.UnitsError):
lu1 / unit
lu2 = u.mag(u.dimensionless_unscaled)
with pytest.raises(u.UnitsError):
lu2 * lu1
with pytest.raises(u.UnitsError):
lu2 / lu1
# But dimensionless_unscaled can be cancelled.
assert lu2 / lu2 == u.dimensionless_unscaled
# With dimensionless, normal units are OK, but we return a plain unit.
tf = lu2 * u.m
tr = u.m * lu2
for t in (tf, tr):
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit * u.m
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(lu2.physical_unit)
# Now we essentially have a LogUnit with a prefactor of 100,
# so should be equivalent again.
t = tf / u.cm
with u.set_enabled_equivalencies(u.logarithmic()):
assert t.is_equivalent(lu2.function_unit)
assert_allclose(t.to(u.dimensionless_unscaled, np.arange(3.)/100.),
lu2.to(lu2.physical_unit, np.arange(3.)))
# If we effectively remove lu1, a normal unit should be returned.
t2 = tf / lu2
assert not isinstance(t2, type(lu2))
assert t2 == u.m
t3 = tf / lu2.function_unit
assert not isinstance(t3, type(lu2))
assert t3 == u.m
# For completeness, also ensure non-sensical operations fail
with pytest.raises(TypeError):
lu1 * object()
with pytest.raises(TypeError):
slice(None) * lu1
with pytest.raises(TypeError):
lu1 / []
with pytest.raises(TypeError):
1 / lu1
@pytest.mark.parametrize('power', (2, 0.5, 1, 0))
def test_raise_to_power(self, power):
"""Check that raising LogUnits to some power is only possible when the
physical unit is dimensionless, and that conversion is turned off when
the resulting logarithmic unit (such as mag**2) is incompatible."""
lu1 = u.mag(u.Jy)
if power == 0:
assert lu1 ** power == u.dimensionless_unscaled
elif power == 1:
assert lu1 ** power == lu1
else:
with pytest.raises(u.UnitsError):
lu1 ** power
# With dimensionless, though, it works, but returns a normal unit.
lu2 = u.mag(u.dimensionless_unscaled)
t = lu2**power
if power == 0:
assert t == u.dimensionless_unscaled
elif power == 1:
assert t == lu2
else:
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit**power
# also check we roundtrip
t2 = t**(1./power)
assert t2 == lu2.function_unit
with u.set_enabled_equivalencies(u.logarithmic()):
assert_allclose(t2.to(u.dimensionless_unscaled, np.arange(3.)),
lu2.to(lu2.physical_unit, np.arange(3.)))
@pytest.mark.parametrize('other', pu_sample)
def test_addition_subtraction_to_normal_units_fails(self, other):
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 + other
with pytest.raises(u.UnitsError):
lu1 - other
with pytest.raises(u.UnitsError):
other - lu1
def test_addition_subtraction_to_non_units_fails(self):
lu1 = u.mag(u.Jy)
with pytest.raises(TypeError):
lu1 + 1.
with pytest.raises(TypeError):
lu1 - [1., 2., 3.]
@pytest.mark.parametrize(
'other', (u.mag, u.mag(), u.mag(u.Jy), u.mag(u.m),
u.Unit(2*u.mag), u.MagUnit('', 2.*u.mag)))
def test_addition_subtraction(self, other):
"""Check physical units are changed appropriately"""
lu1 = u.mag(u.Jy)
other_pu = getattr(other, 'physical_unit', u.dimensionless_unscaled)
lu_sf = lu1 + other
assert lu_sf.is_equivalent(lu1.physical_unit * other_pu)
lu_sr = other + lu1
assert lu_sr.is_equivalent(lu1.physical_unit * other_pu)
lu_df = lu1 - other
assert lu_df.is_equivalent(lu1.physical_unit / other_pu)
lu_dr = other - lu1
assert lu_dr.is_equivalent(other_pu / lu1.physical_unit)
def test_complicated_addition_subtraction(self):
"""for fun, a more complicated example of addition and subtraction"""
dm0 = u.Unit('DM', 1./(4.*np.pi*(10.*u.pc)**2))
lu_dm = u.mag(dm0)
lu_absST = u.STmag - lu_dm
assert lu_absST.is_equivalent(u.erg/u.s/u.AA)
def test_neg_pos(self):
lu1 = u.mag(u.Jy)
neg_lu = -lu1
assert neg_lu != lu1
assert neg_lu.physical_unit == u.Jy**-1
assert -neg_lu == lu1
pos_lu = +lu1
assert pos_lu is not lu1
assert pos_lu == lu1
def test_pickle():
lu1 = u.dex(u.cm/u.s**2)
s = pickle.dumps(lu1)
lu2 = pickle.loads(s)
assert lu1 == lu2
def test_hashable():
lu1 = u.dB(u.mW)
lu2 = u.dB(u.m)
lu3 = u.dB(u.mW)
assert hash(lu1) != hash(lu2)
assert hash(lu1) == hash(lu3)
luset = {lu1, lu2, lu3}
assert len(luset) == 2
class TestLogQuantityCreation(object):
@pytest.mark.parametrize('lq, lu', zip(lq_subclasses + [u.LogQuantity],
lu_subclasses + [u.LogUnit]))
def test_logarithmic_quantities(self, lq, lu):
"""Check logarithmic quantities are all set up correctly"""
assert lq._unit_class == lu
assert type(lu()._quantity_class(1.)) is lq
@pytest.mark.parametrize('lq_cls, physical_unit',
itertools.product(lq_subclasses, pu_sample))
def test_subclass_creation(self, lq_cls, physical_unit):
"""Create LogQuantity subclass objects for some physical units,
and basic check on transformations"""
value = np.arange(1., 10.)
log_q = lq_cls(value * physical_unit)
assert log_q.unit.physical_unit == physical_unit
assert log_q.unit.function_unit == log_q.unit._default_function_unit
assert_allclose(log_q.physical.value, value)
with pytest.raises(ValueError):
lq_cls(value, physical_unit)
@pytest.mark.parametrize(
'unit', (u.mag, u.mag(), u.mag(u.Jy), u.mag(u.m),
u.Unit(2*u.mag), u.MagUnit('', 2.*u.mag),
u.MagUnit(u.Jy, -1*u.mag), u.MagUnit(u.m, -2.*u.mag)))
def test_different_units(self, unit):
q = u.Magnitude(1.23, unit)
assert q.unit.function_unit == getattr(unit, 'function_unit', unit)
assert q.unit.physical_unit is getattr(unit, 'physical_unit',
u.dimensionless_unscaled)
@pytest.mark.parametrize('value, unit', (
(1.*u.mag(u.Jy), None),
(1.*u.dex(u.Jy), None),
(1.*u.mag(u.W/u.m**2/u.Hz), u.mag(u.Jy)),
(1.*u.dex(u.W/u.m**2/u.Hz), u.mag(u.Jy))))
def test_function_values(self, value, unit):
lq = u.Magnitude(value, unit)
assert lq == value
assert lq.unit.function_unit == u.mag
assert lq.unit.physical_unit == getattr(unit, 'physical_unit',
value.unit.physical_unit)
@pytest.mark.parametrize(
'unit', (u.mag(), u.mag(u.Jy), u.mag(u.m), u.MagUnit('', 2.*u.mag),
u.MagUnit(u.Jy, -1*u.mag), u.MagUnit(u.m, -2.*u.mag)))
def test_indirect_creation(self, unit):
q1 = 2.5 * unit
assert isinstance(q1, u.Magnitude)
assert q1.value == 2.5
assert q1.unit == unit
pv = 100. * unit.physical_unit
q2 = unit * pv
assert q2.unit == unit
assert q2.unit.physical_unit == pv.unit
assert q2.to_value(unit.physical_unit) == 100.
assert (q2._function_view / u.mag).to_value(1) == -5.
q3 = unit / 0.4
assert q3 == q1
def test_from_view(self):
# Cannot view a physical quantity as a function quantity, since the
# values would change.
q = [100., 1000.] * u.cm/u.s**2
with pytest.raises(TypeError):
q.view(u.Dex)
# But fine if we have the right magnitude.
q = [2., 3.] * u.dex
lq = q.view(u.Dex)
assert isinstance(lq, u.Dex)
assert lq.unit.physical_unit == u.dimensionless_unscaled
assert np.all(q == lq)
def test_using_quantity_class(self):
"""Check that we can use Quantity if we have subok=True"""
# following issue #5851
lu = u.dex(u.AA)
with pytest.raises(u.UnitTypeError):
u.Quantity(1., lu)
q = u.Quantity(1., lu, subok=True)
assert type(q) is lu._quantity_class
def test_conversion_to_and_from_physical_quantities():
"""Ensures we can convert from regular quantities."""
mst = [10., 12., 14.] * u.STmag
flux_lambda = mst.physical
mst_roundtrip = flux_lambda.to(u.STmag)
# check we return a logquantity; see #5178.
assert isinstance(mst_roundtrip, u.Magnitude)
assert mst_roundtrip.unit == mst.unit
assert_allclose(mst_roundtrip.value, mst.value)
wave = [4956.8, 4959.55, 4962.3] * u.AA
flux_nu = mst.to(u.Jy, equivalencies=u.spectral_density(wave))
mst_roundtrip2 = flux_nu.to(u.STmag, u.spectral_density(wave))
assert isinstance(mst_roundtrip2, u.Magnitude)
assert mst_roundtrip2.unit == mst.unit
assert_allclose(mst_roundtrip2.value, mst.value)
def test_quantity_decomposition():
lq = 10.*u.mag(u.Jy)
assert lq.decompose() == lq
assert lq.decompose().unit.physical_unit.bases == [u.kg, u.s]
assert lq.si == lq
assert lq.si.unit.physical_unit.bases == [u.kg, u.s]
assert lq.cgs == lq
assert lq.cgs.unit.physical_unit.bases == [u.g, u.s]
class TestLogQuantityViews(object):
def setup(self):
self.lq = u.Magnitude(np.arange(10.) * u.Jy)
self.lq2 = u.Magnitude(np.arange(5.))
def test_value_view(self):
lq_value = self.lq.value
assert type(lq_value) is np.ndarray
lq_value[2] = -1.
assert np.all(self.lq.value == lq_value)
def test_function_view(self):
lq_fv = self.lq._function_view
assert type(lq_fv) is u.Quantity
assert lq_fv.unit is self.lq.unit.function_unit
lq_fv[3] = -2. * lq_fv.unit
assert np.all(self.lq.value == lq_fv.value)
def test_quantity_view(self):
# Cannot view as Quantity, since the unit cannot be represented.
with pytest.raises(TypeError):
self.lq.view(u.Quantity)
# But a dimensionless one is fine.
q2 = self.lq2.view(u.Quantity)
assert q2.unit is u.mag
assert np.all(q2.value == self.lq2.value)
lq3 = q2.view(u.Magnitude)
assert type(lq3.unit) is u.MagUnit
assert lq3.unit.physical_unit == u.dimensionless_unscaled
assert np.all(lq3 == self.lq2)
class TestLogQuantitySlicing(object):
def test_item_get_and_set(self):
lq1 = u.Magnitude(np.arange(1., 11.)*u.Jy)
assert lq1[9] == u.Magnitude(10.*u.Jy)
lq1[2] = 100.*u.Jy
assert lq1[2] == u.Magnitude(100.*u.Jy)
with pytest.raises(u.UnitsError):
lq1[2] = 100.*u.m
with pytest.raises(u.UnitsError):
lq1[2] = 100.*u.mag
with pytest.raises(u.UnitsError):
lq1[2] = u.Magnitude(100.*u.m)
assert lq1[2] == u.Magnitude(100.*u.Jy)
def test_slice_get_and_set(self):
lq1 = u.Magnitude(np.arange(1., 10.)*u.Jy)
lq1[2:4] = 100.*u.Jy
assert np.all(lq1[2:4] == u.Magnitude(100.*u.Jy))
with pytest.raises(u.UnitsError):
lq1[2:4] = 100.*u.m
with pytest.raises(u.UnitsError):
lq1[2:4] = 100.*u.mag
with pytest.raises(u.UnitsError):
lq1[2:4] = u.Magnitude(100.*u.m)
assert np.all(lq1[2] == u.Magnitude(100.*u.Jy))
class TestLogQuantityArithmetic(object):
def test_multiplication_division(self):
"""Check that multiplication/division with other quantities is only
possible when the physical unit is dimensionless, and that this turns
the result into a normal quantity."""
lq = u.Magnitude(np.arange(1., 11.)*u.Jy)
with pytest.raises(u.UnitsError):
lq * (1.*u.m)
with pytest.raises(u.UnitsError):
(1.*u.m) * lq
with pytest.raises(u.UnitsError):
lq / lq
for unit in (u.m, u.mag, u.dex):
with pytest.raises(u.UnitsError):
lq / unit
lq2 = u.Magnitude(np.arange(1, 11.))
with pytest.raises(u.UnitsError):
lq2 * lq
with pytest.raises(u.UnitsError):
lq2 / lq
with pytest.raises(u.UnitsError):
lq / lq2
# but dimensionless_unscaled can be cancelled
r = lq2 / u.Magnitude(2.)
assert r.unit == u.dimensionless_unscaled
assert np.all(r.value == lq2.value/2.)
# with dimensionless, normal units OK, but return normal quantities
tf = lq2 * u.m
tr = u.m * lq2
for t in (tf, tr):
assert not isinstance(t, type(lq2))
assert t.unit == lq2.unit.function_unit * u.m
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(lq2.unit.physical_unit)
t = tf / (50.*u.cm)
# now we essentially have the same quantity but with a prefactor of 2
assert t.unit.is_equivalent(lq2.unit.function_unit)
assert_allclose(t.to(lq2.unit.function_unit), lq2._function_view*2)
@pytest.mark.parametrize('power', (2, 0.5, 1, 0))
def test_raise_to_power(self, power):
"""Check that raising LogQuantities to some power is only possible when
the physical unit is dimensionless, and that conversion is turned off
when the resulting logarithmic unit (say, mag**2) is incompatible."""
lq = u.Magnitude(np.arange(1., 4.)*u.Jy)
if power == 0:
assert np.all(lq ** power == 1.)
elif power == 1:
assert np.all(lq ** power == lq)
else:
with pytest.raises(u.UnitsError):
lq ** power
# with dimensionless, it works, but falls back to normal quantity
# (except for power=1)
lq2 = u.Magnitude(np.arange(10.))
t = lq2**power
if power == 0:
assert t.unit is u.dimensionless_unscaled
assert np.all(t.value == 1.)
elif power == 1:
assert np.all(t == lq2)
else:
assert not isinstance(t, type(lq2))
assert t.unit == lq2.unit.function_unit ** power
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(u.dimensionless_unscaled)
def test_error_on_lq_as_power(self):
lq = u.Magnitude(np.arange(1., 4.)*u.Jy)
with pytest.raises(TypeError):
lq ** lq
@pytest.mark.parametrize('other', pu_sample)
def test_addition_subtraction_to_normal_units_fails(self, other):
lq = u.Magnitude(np.arange(1., 10.)*u.Jy)
q = 1.23 * other
with pytest.raises(u.UnitsError):
lq + q
with pytest.raises(u.UnitsError):
lq - q
with pytest.raises(u.UnitsError):
q - lq
@pytest.mark.parametrize(
'other', (1.23 * u.mag, 2.34 * u.mag(),
u.Magnitude(3.45 * u.Jy), u.Magnitude(4.56 * u.m),
5.67 * u.Unit(2*u.mag), u.Magnitude(6.78, 2.*u.mag)))
def test_addition_subtraction(self, other):
"""Check that addition/subtraction with quantities with magnitude or
MagUnit units works, and that it changes the physical units
appropriately."""
lq = u.Magnitude(np.arange(1., 10.)*u.Jy)
other_physical = other.to(getattr(other.unit, 'physical_unit',
u.dimensionless_unscaled),
equivalencies=u.logarithmic())
lq_sf = lq + other
assert_allclose(lq_sf.physical, lq.physical * other_physical)
lq_sr = other + lq
assert_allclose(lq_sr.physical, lq.physical * other_physical)
lq_df = lq - other
assert_allclose(lq_df.physical, lq.physical / other_physical)
lq_dr = other - lq
assert_allclose(lq_dr.physical, other_physical / lq.physical)
@pytest.mark.parametrize('other', pu_sample)
def test_inplace_addition_subtraction_unit_checks(self, other):
lu1 = u.mag(u.Jy)
lq1 = u.Magnitude(np.arange(1., 10.), lu1)
with pytest.raises(u.UnitsError):
lq1 += other
assert np.all(lq1.value == np.arange(1., 10.))
assert lq1.unit == lu1
with pytest.raises(u.UnitsError):
lq1 -= other
assert np.all(lq1.value == np.arange(1., 10.))
assert lq1.unit == lu1
@pytest.mark.parametrize(
'other', (1.23 * u.mag, 2.34 * u.mag(),
u.Magnitude(3.45 * u.Jy), u.Magnitude(4.56 * u.m),
5.67 * u.Unit(2*u.mag), u.Magnitude(6.78, 2.*u.mag)))
def test_inplace_addition_subtraction(self, other):
"""Check that inplace addition/subtraction with quantities with
magnitude or MagUnit units works, and that it changes the physical
units appropriately."""
lq = u.Magnitude(np.arange(1., 10.)*u.Jy)
other_physical = other.to(getattr(other.unit, 'physical_unit',
u.dimensionless_unscaled),
equivalencies=u.logarithmic())
lq_sf = lq.copy()
lq_sf += other
assert_allclose(lq_sf.physical, lq.physical * other_physical)
lq_df = lq.copy()
lq_df -= other
assert_allclose(lq_df.physical, lq.physical / other_physical)
def test_complicated_addition_subtraction(self):
"""For fun, a more complicated example of addition and subtraction."""
dm0 = u.Unit('DM', 1./(4.*np.pi*(10.*u.pc)**2))
DMmag = u.mag(dm0)
m_st = 10. * u.STmag
dm = 5. * DMmag
M_st = m_st - dm
assert M_st.unit.is_equivalent(u.erg/u.s/u.AA)
assert np.abs(M_st.physical /
(m_st.physical*4.*np.pi*(100.*u.pc)**2) - 1.) < 1.e-15
class TestLogQuantityComparisons(object):
def test_comparison_to_non_quantities_fails(self):
lq = u.Magnitude(np.arange(1., 10.)*u.Jy)
# On python2, ordering operations always succeed, given essentially
# meaningless results.
if not six.PY2:
with pytest.raises(TypeError):
lq > 'a'
assert not (lq == 'a')
assert lq != 'a'
def test_comparison(self):
lq1 = u.Magnitude(np.arange(1., 4.)*u.Jy)
lq2 = u.Magnitude(2.*u.Jy)
assert np.all((lq1 > lq2) == np.array([True, False, False]))
assert np.all((lq1 == lq2) == np.array([False, True, False]))
lq3 = u.Dex(2.*u.Jy)
assert np.all((lq1 > lq3) == np.array([True, False, False]))
assert np.all((lq1 == lq3) == np.array([False, True, False]))
lq4 = u.Magnitude(2.*u.m)
assert not (lq1 == lq4)
assert lq1 != lq4
with pytest.raises(u.UnitsError):
lq1 < lq4
q5 = 1.5 * u.Jy
assert np.all((lq1 > q5) == | np.array([True, False, False]) | numpy.array |
"""
Binary serialization
NPY format
==========
A simple format for saving numpy arrays to disk with the full
information about them.
The ``.npy`` format is the standard binary file format in NumPy for
persisting a *single* arbitrary NumPy array on disk. The format stores all
of the shape and dtype information necessary to reconstruct the array
correctly even on another machine with a different architecture.
The format is designed to be as simple as possible while achieving
its limited goals.
The ``.npz`` format is the standard format for persisting *multiple* NumPy
arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``
files, one for each array.
Capabilities
------------
- Can represent all NumPy arrays including nested record arrays and
object arrays.
- Represents the data in its native binary form.
- Supports Fortran-contiguous arrays directly.
- Stores all of the necessary information to reconstruct the array
including shape and dtype on a machine of a different
architecture. Both little-endian and big-endian arrays are
supported, and a file with little-endian numbers will yield
a little-endian array on any machine reading the file. The
types are described in terms of their actual sizes. For example,
if a machine with a 64-bit C "long int" writes out an array with
"long ints", a reading machine with 32-bit C "long ints" will yield
an array with 64-bit integers.
- Is straightforward to reverse engineer. Datasets often live longer than
the programs that created them. A competent developer should be
able to create a solution in their preferred programming language to
read most ``.npy`` files that they have been given without much
documentation.
- Allows memory-mapping of the data. See `open_memmap`.
- Can be read from a filelike stream object instead of an actual file.
- Stores object arrays, i.e. arrays containing elements that are arbitrary
Python objects. Files with object arrays are not to be mmapable, but
can be read and written to disk.
Limitations
-----------
- Arbitrary subclasses of numpy.ndarray are not completely preserved.
Subclasses will be accepted for writing, but only the array data will
be written out. A regular numpy.ndarray object will be created
upon reading the file.
.. warning::
Due to limitations in the interpretation of structured dtypes, dtypes
with fields with empty names will have the names replaced by 'f0', 'f1',
etc. Such arrays will not round-trip through the format entirely
accurately. The data is intact; only the field names will differ. We are
working on a fix for this. This fix will not require a change in the
file format. The arrays with such structures can still be saved and
restored, and the correct dtype may be restored by using the
``loadedarray.view(correct_dtype)`` method.
File extensions
---------------
We recommend using the ``.npy`` and ``.npz`` extensions for files saved
in this format. This is by no means a requirement; applications may wish
to use these file formats but use an extension specific to the
application. In the absence of an obvious alternative, however,
we suggest using ``.npy`` and ``.npz``.
Version numbering
-----------------
The version numbering of these formats is independent of NumPy version
numbering. If the format is upgraded, the code in `numpy.io` will still
be able to read and write Version 1.0 files.
Format Version 1.0
------------------
The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.
The next 1 byte is an unsigned byte: the major version number of the file
format, e.g. ``\\x01``.
The next 1 byte is an unsigned byte: the minor version number of the file
format, e.g. ``\\x00``. Note: the version of the file format is not tied
to the version of the numpy package.
The next 2 bytes form a little-endian unsigned short int: the length of
the header data HEADER_LEN.
The next HEADER_LEN bytes form the header data describing the array's
format. It is an ASCII string which contains a Python literal expression
of a dictionary. It is terminated by a newline (``\\n``) and padded with
spaces (``\\x20``) to make the total of
``len(magic string) + 2 + len(length) + HEADER_LEN`` be evenly divisible
by 64 for alignment purposes.
The dictionary contains three keys:
"descr" : dtype.descr
An object that can be passed as an argument to the `numpy.dtype`
constructor to create the array's dtype.
"fortran_order" : bool
Whether the array data is Fortran-contiguous or not. Since
Fortran-contiguous arrays are a common form of non-C-contiguity,
we allow them to be written directly to disk for efficiency.
"shape" : tuple of int
The shape of the array.
For repeatability and readability, the dictionary keys are sorted in
alphabetic order. This is for convenience only. A writer SHOULD implement
this if possible. A reader MUST NOT depend on this.
Following the header comes the array data. If the dtype contains Python
objects (i.e. ``dtype.hasobject is True``), then the data is a Python
pickle of the array. Otherwise the data is the contiguous (either C-
or Fortran-, depending on ``fortran_order``) bytes of the array.
Consumers can figure out the number of bytes by multiplying the number
of elements given by the shape (noting that ``shape=()`` means there is
1 element) by ``dtype.itemsize``.
Format Version 2.0
------------------
The version 1.0 format only allowed the array header to have a total size of
65535 bytes. This can be exceeded by structured arrays with a large number of
columns. The version 2.0 format extends the header size to 4 GiB.
`numpy.save` will automatically save in 2.0 format if the data requires it,
else it will always use the more compatible 1.0 format.
The description of the fourth element of the header therefore has become:
"The next 4 bytes form a little-endian unsigned int: the length of the header
data HEADER_LEN."
Format Version 3.0
------------------
This version replaces the ASCII string (which in practice was latin1) with
a utf8-encoded string, so supports structured types with any unicode field
names.
Notes
-----
The ``.npy`` format, including motivation for creating it and a comparison of
alternatives, is described in the
:doc:`"npy-format" NEP <neps:nep-0001-npy-format>`, however details have
evolved with time and this document is more current.
"""
import numpy
import io
import warnings
from numpy.lib.utils import safe_eval
from numpy.compat import (
isfileobj, os_fspath, pickle
)
__all__ = []
EXPECTED_KEYS = {'descr', 'fortran_order', 'shape'}
MAGIC_PREFIX = b'\x93NUMPY'
MAGIC_LEN = len(MAGIC_PREFIX) + 2
ARRAY_ALIGN = 64 # plausible values are powers of 2 between 16 and 4096
BUFFER_SIZE = 2**18 # size of buffer for reading npz files in bytes
# difference between version 1.0 and 2.0 is a 4 byte (I) header length
# instead of 2 bytes (H) allowing storage of large structured arrays
_header_size_info = {
(1, 0): ('<H', 'latin1'),
(2, 0): ('<I', 'latin1'),
(3, 0): ('<I', 'utf8'),
}
def _check_version(version):
if version not in [(1, 0), (2, 0), (3, 0), None]:
msg = "we only support format version (1,0), (2,0), and (3,0), not %s"
raise ValueError(msg % (version,))
def magic(major, minor):
""" Return the magic string for the given file format version.
Parameters
----------
major : int in [0, 255]
minor : int in [0, 255]
Returns
-------
magic : str
Raises
------
ValueError if the version cannot be formatted.
"""
if major < 0 or major > 255:
raise ValueError("major version must be 0 <= major < 256")
if minor < 0 or minor > 255:
raise ValueError("minor version must be 0 <= minor < 256")
return MAGIC_PREFIX + bytes([major, minor])
def read_magic(fp):
""" Read the magic string to get the version of the file format.
Parameters
----------
fp : filelike object
Returns
-------
major : int
minor : int
"""
magic_str = _read_bytes(fp, MAGIC_LEN, "magic string")
if magic_str[:-2] != MAGIC_PREFIX:
msg = "the magic string is not correct; expected %r, got %r"
raise ValueError(msg % (MAGIC_PREFIX, magic_str[:-2]))
major, minor = magic_str[-2:]
return major, minor
def _has_metadata(dt):
if dt.metadata is not None:
return True
elif dt.names is not None:
return any(_has_metadata(dt[k]) for k in dt.names)
elif dt.subdtype is not None:
return _has_metadata(dt.base)
else:
return False
def dtype_to_descr(dtype):
"""
Get a serializable descriptor from the dtype.
The .descr attribute of a dtype object cannot be round-tripped through
the dtype() constructor. Simple types, like dtype('float32'), have
a descr which looks like a record array with one field with '' as
a name. The dtype() constructor interprets this as a request to give
a default name. Instead, we construct descriptor that can be passed to
dtype().
Parameters
----------
dtype : dtype
The dtype of the array that will be written to disk.
Returns
-------
descr : object
An object that can be passed to `numpy.dtype()` in order to
replicate the input dtype.
"""
if _has_metadata(dtype):
warnings.warn("metadata on a dtype may be saved or ignored, but will "
"raise if saved when read. Use another form of storage.",
UserWarning, stacklevel=2)
if dtype.names is not None:
# This is a record array. The .descr is fine. XXX: parts of the
# record array with an empty name, like padding bytes, still get
# fiddled with. This needs to be fixed in the C implementation of
# dtype().
return dtype.descr
else:
return dtype.str
def descr_to_dtype(descr):
"""
Returns a dtype based off the given description.
This is essentially the reverse of `dtype_to_descr()`. It will remove
the valueless padding fields created by, i.e. simple fields like
dtype('float32'), and then convert the description to its corresponding
dtype.
Parameters
----------
descr : object
The object retreived by dtype.descr. Can be passed to
`numpy.dtype()` in order to replicate the input dtype.
Returns
-------
dtype : dtype
The dtype constructed by the description.
"""
if isinstance(descr, str):
# No padding removal needed
return numpy.dtype(descr)
elif isinstance(descr, tuple):
# subtype, will always have a shape descr[1]
dt = descr_to_dtype(descr[0])
return numpy.dtype((dt, descr[1]))
titles = []
names = []
formats = []
offsets = []
offset = 0
for field in descr:
if len(field) == 2:
name, descr_str = field
dt = descr_to_dtype(descr_str)
else:
name, descr_str, shape = field
dt = numpy.dtype((descr_to_dtype(descr_str), shape))
# Ignore padding bytes, which will be void bytes with '' as name
# Once support for blank names is removed, only "if name == ''" needed)
is_pad = (name == '' and dt.type is numpy.void and dt.names is None)
if not is_pad:
title, name = name if isinstance(name, tuple) else (None, name)
titles.append(title)
names.append(name)
formats.append(dt)
offsets.append(offset)
offset += dt.itemsize
return numpy.dtype({'names': names, 'formats': formats, 'titles': titles,
'offsets': offsets, 'itemsize': offset})
def header_data_from_array_1_0(array):
""" Get the dictionary of header metadata from a numpy.ndarray.
Parameters
----------
array : numpy.ndarray
Returns
-------
d : dict
This has the appropriate entries for writing its string representation
to the header of the file.
"""
d = {'shape': array.shape}
if array.flags.c_contiguous:
d['fortran_order'] = False
elif array.flags.f_contiguous:
d['fortran_order'] = True
else:
# Totally non-contiguous data. We will have to make it C-contiguous
# before writing. Note that we need to test for C_CONTIGUOUS first
# because a 1-D array is both C_CONTIGUOUS and F_CONTIGUOUS.
d['fortran_order'] = False
d['descr'] = dtype_to_descr(array.dtype)
return d
def _wrap_header(header, version):
"""
Takes a stringified header, and attaches the prefix and padding to it
"""
import struct
assert version is not None
fmt, encoding = _header_size_info[version]
if not isinstance(header, bytes): # always true on python 3
header = header.encode(encoding)
hlen = len(header) + 1
padlen = ARRAY_ALIGN - ((MAGIC_LEN + struct.calcsize(fmt) + hlen) % ARRAY_ALIGN)
try:
header_prefix = magic(*version) + struct.pack(fmt, hlen + padlen)
except struct.error:
msg = "Header length {} too big for version={}".format(hlen, version)
raise ValueError(msg) from None
# Pad the header with spaces and a final newline such that the magic
# string, the header-length short and the header are aligned on a
# ARRAY_ALIGN byte boundary. This supports memory mapping of dtypes
# aligned up to ARRAY_ALIGN on systems like Linux where mmap()
# offset must be page-aligned (i.e. the beginning of the file).
return header_prefix + header + b' '*padlen + b'\n'
def _wrap_header_guess_version(header):
"""
Like `_wrap_header`, but chooses an appropriate version given the contents
"""
try:
return _wrap_header(header, (1, 0))
except ValueError:
pass
try:
ret = _wrap_header(header, (2, 0))
except UnicodeEncodeError:
pass
else:
warnings.warn("Stored array in format 2.0. It can only be"
"read by NumPy >= 1.9", UserWarning, stacklevel=2)
return ret
header = _wrap_header(header, (3, 0))
warnings.warn("Stored array in format 3.0. It can only be "
"read by NumPy >= 1.17", UserWarning, stacklevel=2)
return header
def _write_array_header(fp, d, version=None):
""" Write the header for an array and returns the version used
Parameters
----------
fp : filelike object
d : dict
This has the appropriate entries for writing its string representation
to the header of the file.
version: tuple or None
None means use oldest that works
explicit version will raise a ValueError if the format does not
allow saving this data. Default: None
"""
header = ["{"]
for key, value in sorted(d.items()):
# Need to use repr here, since we eval these when reading
header.append("'%s': %s, " % (key, repr(value)))
header.append("}")
header = "".join(header)
if version is None:
header = _wrap_header_guess_version(header)
else:
header = _wrap_header(header, version)
fp.write(header)
def write_array_header_1_0(fp, d):
""" Write the header for an array using the 1.0 format.
Parameters
----------
fp : filelike object
d : dict
This has the appropriate entries for writing its string
representation to the header of the file.
"""
_write_array_header(fp, d, (1, 0))
def write_array_header_2_0(fp, d):
""" Write the header for an array using the 2.0 format.
The 2.0 format allows storing very large structured arrays.
.. versionadded:: 1.9.0
Parameters
----------
fp : filelike object
d : dict
This has the appropriate entries for writing its string
representation to the header of the file.
"""
_write_array_header(fp, d, (2, 0))
def read_array_header_1_0(fp):
"""
Read an array header from a filelike object using the 1.0 file format
version.
This will leave the file object located just after the header.
Parameters
----------
fp : filelike object
A file object or something with a `.read()` method like a file.
Returns
-------
shape : tuple of int
The shape of the array.
fortran_order : bool
The array data will be written out directly if it is either
C-contiguous or Fortran-contiguous. Otherwise, it will be made
contiguous before writing it out.
dtype : dtype
The dtype of the file's data.
Raises
------
ValueError
If the data is invalid.
"""
return _read_array_header(fp, version=(1, 0))
def read_array_header_2_0(fp):
"""
Read an array header from a filelike object using the 2.0 file format
version.
This will leave the file object located just after the header.
.. versionadded:: 1.9.0
Parameters
----------
fp : filelike object
A file object or something with a `.read()` method like a file.
Returns
-------
shape : tuple of int
The shape of the array.
fortran_order : bool
The array data will be written out directly if it is either
C-contiguous or Fortran-contiguous. Otherwise, it will be made
contiguous before writing it out.
dtype : dtype
The dtype of the file's data.
Raises
------
ValueError
If the data is invalid.
"""
return _read_array_header(fp, version=(2, 0))
def _filter_header(s):
"""Clean up 'L' in npz header ints.
Cleans up the 'L' in strings representing integers. Needed to allow npz
headers produced in Python2 to be read in Python3.
Parameters
----------
s : string
Npy file header.
Returns
-------
header : str
Cleaned up header.
"""
import tokenize
from io import StringIO
tokens = []
last_token_was_number = False
for token in tokenize.generate_tokens(StringIO(s).readline):
token_type = token[0]
token_string = token[1]
if (last_token_was_number and
token_type == tokenize.NAME and
token_string == "L"):
continue
else:
tokens.append(token)
last_token_was_number = (token_type == tokenize.NUMBER)
return tokenize.untokenize(tokens)
def _read_array_header(fp, version):
"""
see read_array_header_1_0
"""
# Read an unsigned, little-endian short int which has the length of the
# header.
import struct
hinfo = _header_size_info.get(version)
if hinfo is None:
raise ValueError("Invalid version {!r}".format(version))
hlength_type, encoding = hinfo
hlength_str = _read_bytes(fp, struct.calcsize(hlength_type), "array header length")
header_length = struct.unpack(hlength_type, hlength_str)[0]
header = _read_bytes(fp, header_length, "array header")
header = header.decode(encoding)
# The header is a pretty-printed string representation of a literal
# Python dictionary with trailing newlines padded to a ARRAY_ALIGN byte
# boundary. The keys are strings.
# "shape" : tuple of int
# "fortran_order" : bool
# "descr" : dtype.descr
# Versions (2, 0) and (1, 0) could have been created by a Python 2
# implementation before header filtering was implemented.
if version <= (2, 0):
header = _filter_header(header)
try:
d = | safe_eval(header) | numpy.lib.utils.safe_eval |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
from external.transformations import translation_matrix, rotation_matrix
import external.transformations as tf
from Trace import Photon
from Geometry import Box, Cylinder, FinitePlane, transform_point, transform_direction, rotation_matrix_from_vector_alignment, norm
from Materials import Spectrum
def random_spherecial_vector():
# This method of calculating isotropic vectors is taken from GNU Scientific Library
LOOP = True
while LOOP:
x = -1. + 2. * np.random.uniform()
y = -1. + 2. * np.random.uniform()
s = x**2 + y**2
if s <= 1.0:
LOOP = False
z = -1. + 2. * s
a = 2 * np.sqrt(1 - s)
x = a * x
y = a * y
return np.array([x,y,z])
class SimpleSource(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, use_random_polarisation=False):
super(SimpleSource, self).__init__()
self.position = position
self.direction = direction
self.wavelength = wavelength
self.use_random_polarisation = use_random_polarisation
self.throw = 0
self.source_id = "SimpleSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
# If use_polarisation is set generate a random polarisation vector of the photon
if self.use_random_polarisation:
# Randomise rotation angle around xy-plane, the transform from +z to the direction of the photon
vec = random_spherecial_vector()
vec[2] = 0.
vec = norm(vec)
R = rotation_matrix_from_vector_alignment(self.direction, [0,0,1])
photon.polarisation = transform_direction(vec, R)
else:
photon.polarisation = None
photon.id = self.throw
self.throw = self.throw + 1
return photon
class Laser(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, polarisation=None):
super(Laser, self).__init__()
self.position = np.array(position)
self.direction = np.array(direction)
self.wavelength = wavelength
assert polarisation != None, "Polarisation of the Laser is not set."
self.polarisation = np.array(polarisation)
self.throw = 0
self.source_id = "LaserSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
photon.polarisation = self.polarisation
photon.id = self.throw
self.throw = self.throw + 1
return photon
class PlanarSource(object):
"""A box that emits photons from the top surface (normal), sampled from the spectrum."""
def __init__(self, spectrum=None, wavelength=555, direction=(0,0,1), length=0.05, width=0.05):
super(PlanarSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.plane = FinitePlane(length=length, width=width)
self.length = length
self.width = width
# direction is the direction that photons are fired out of the plane in the GLOBAL FRAME.
# i.e. this is passed directly to the photon to set is's direction
self.direction = direction
self.throw = 0
self.source_id = "PlanarSource_" + str(id(self))
def translate(self, translation):
self.plane.append_transform(tf.translation_matrix(translation))
def rotate(self, angle, axis):
self.plane.append_transform(tf.rotation_matrix(angle, axis))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Create a point which is on the surface of the finite plane in it's local frame
x = np.random.uniform(0., self.length)
y = np.random.uniform(0., self.width)
local_point = (x, y, 0.)
# Transform the direciton
photon.position = transform_point(local_point, self.plane.transform)
photon.direction = self.direction
photon.active = True
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class LensSource(object):
"""
A source where photons generated in a plane are focused on a line with space tolerance given by variable "focussize".
The focus line should be perpendicular to the plane normal and aligned with the z-axis.
"""
def __init__(self, spectrum = None, wavelength = 555, linepoint=(0,0,0), linedirection=(0,0,1), focussize = 0, planeorigin = (-1,-1,-1), planeextent = (-1,1,1)):
super(LensSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.planeorigin = planeorigin
self.planeextent = planeextent
self.linepoint = np.array(linepoint)
self.linedirection = np.array(linedirection)
self.focussize = focussize
self.throw = 0
self.source_id = "LensSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Position
x = np.random.uniform(self.planeorigin[0],self.planeextent[0])
y = np.random.uniform(self.planeorigin[1],self.planeextent[1])
z = np.random.uniform(self.planeorigin[2],self.planeextent[2])
photon.position = np.array((x,y,z))
# Direction
focuspoint = np.array((0.,0.,0.))
focuspoint[0] = self.linepoint[0] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[1] = self.linepoint[1] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[2] = photon.position[2]
direction = focuspoint - photon.position
modulus = (direction[0]**2+direction[1]**2+direction[2]**2)**0.5
photon.direction = direction/modulus
# Wavelength
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class LensSourceAngle(object):
"""
A source where photons generated in a plane are focused on a line with space tolerance given by variable "focussize".
The focus line should be perpendicular to the plane normal and aligned with the z-axis.
For this lense an additional z-boost is added (Angle of incidence in z-direction).
"""
def __init__(self, spectrum = None, wavelength = 555, linepoint=(0,0,0), linedirection=(0,0,1), angle = 0, focussize = 0, planeorigin = (-1,-1,-1), planeextent = (-1,1,1)):
super(LensSourceAngle, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.planeorigin = planeorigin
self.planeextent = planeextent
self.linepoint = np.array(linepoint)
self.linedirection = np.array(linedirection)
self.focussize = focussize
self.angle = angle
self.throw = 0
self.source_id = "LensSourceAngle_" + str(id(self))
def photon(self):
photon = Photon()
photon.id = self.throw
self.throw = self.throw + 1
# Position
x = np.random.uniform(self.planeorigin[0],self.planeextent[0])
y = np.random.uniform(self.planeorigin[1],self.planeextent[1])
boost = y*np.tan(self.angle)
z = np.random.uniform(self.planeorigin[2],self.planeextent[2]) - boost
photon.position = np.array((x,y,z))
# Direction
focuspoint = np.array((0.,0.,0.))
focuspoint[0] = self.linepoint[0] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[1] = self.linepoint[1] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[2] = photon.position[2] + boost
direction = focuspoint - photon.position
modulus = (direction[0]**2+direction[1]**2+direction[2]**2)**0.5
photon.direction = direction/modulus
# Wavelength
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class CylindricalSource(object):
"""
A source for photons emitted in a random direction and position inside a cylinder(radius, length)
"""
def __init__(self, spectrum = None, wavelength = 555, radius = 1, length = 10):
super(CylindricalSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.shape = Cylinder(radius = radius, length = length)
self.radius = radius
self.length = length
self.throw = 0
self.source_id = "CylindricalSource_" + str(id(self))
def translate(self, translation):
self.shape.append_transform(tf.translation_matrix(translation))
def rotate(self, angle, axis):
self.shape.append_transform(tf.rotation_matrix(angle, axis))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Position of emission
phi = np.random.uniform(0., 2*np.pi)
r = np.random.uniform(0.,self.radius)
x = r*np.cos(phi)
y = r*np.sin(phi)
z = np.random.uniform(0.,self.length)
local_center = (x,y,z)
photon.position = transform_point(local_center, self.shape.transform)
# Direction of emission (no need to transform if meant to be isotropic)
phi = np.random.uniform(0.,2*np.pi)
theta = np.random.uniform(0.,np.pi)
x = np.cos(phi)* | np.sin(theta) | numpy.sin |
'''
<NAME>
set up :2020-1-9
intergrate img and label into one file
-- fiducial1024_v1
'''
import argparse
import sys, os
import pickle
import random
import collections
import json
import numpy as np
import scipy.io as io
import scipy.misc as m
import matplotlib.pyplot as plt
import glob
import math
import time
import threading
import multiprocessing as mp
from multiprocessing import Pool
import re
import cv2
# sys.path.append('/lustre/home/gwxie/hope/project/dewarp/datasets/') # /lustre/home/gwxie/program/project/unwarp/perturbed_imgaes/GAN
import utils
def getDatasets(dir):
return os.listdir(dir)
class perturbed(utils.BasePerturbed):
def __init__(self, path, bg_path, save_path, save_suffix):
self.path = path
self.bg_path = bg_path
self.save_path = save_path
self.save_suffix = save_suffix
def save_img(self, m, n, fold_curve='fold', repeat_time=4, fiducial_points = 16, relativeShift_position='relativeShift_v2'):
origin_img = cv2.imread(self.path, flags=cv2.IMREAD_COLOR)
save_img_shape = [512*2, 480*2] # 320
# reduce_value = np.random.choice([2**4, 2**5, 2**6, 2**7, 2**8], p=[0.01, 0.1, 0.4, 0.39, 0.1])
reduce_value = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.02, 0.18, 0.2, 0.3, 0.1, 0.1, 0.08, 0.02])
# reduce_value = np.random.choice([8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.01, 0.02, 0.2, 0.4, 0.19, 0.18])
# reduce_value = np.random.choice([16, 24, 32, 40, 48, 64], p=[0.01, 0.1, 0.2, 0.4, 0.2, 0.09])
base_img_shrink = save_img_shape[0] - reduce_value
# enlarge_img_shrink = [1024, 768]
# enlarge_img_shrink = [896, 672] # 420
enlarge_img_shrink = [512*4, 480*4] # 420
# enlarge_img_shrink = [896*2, 768*2] # 420
# enlarge_img_shrink = [896, 768] # 420
# enlarge_img_shrink = [768, 576] # 420
# enlarge_img_shrink = [640, 480] # 420
''''''
im_lr = origin_img.shape[0]
im_ud = origin_img.shape[1]
reduce_value_v2 = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 28*2, 32*2, 48*2], p=[0.02, 0.18, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1])
# reduce_value_v2 = np.random.choice([16, 24, 28, 32, 48, 64], p=[0.01, 0.1, 0.2, 0.3, 0.25, 0.14])
if im_lr > im_ud:
im_ud = min(int(im_ud / im_lr * base_img_shrink), save_img_shape[1] - reduce_value_v2)
im_lr = save_img_shape[0] - reduce_value
else:
base_img_shrink = save_img_shape[1] - reduce_value
im_lr = min(int(im_lr / im_ud * base_img_shrink), save_img_shape[0] - reduce_value_v2)
im_ud = base_img_shrink
if round(im_lr / im_ud, 2) < 0.5 or round(im_ud / im_lr, 2) < 0.5:
repeat_time = min(repeat_time, 8)
edge_padding = 3
im_lr -= im_lr % (fiducial_points-1) - (2*edge_padding) # im_lr % (fiducial_points-1) - 1
im_ud -= im_ud % (fiducial_points-1) - (2*edge_padding) # im_ud % (fiducial_points-1) - 1
im_hight = np.linspace(edge_padding, im_lr - edge_padding, fiducial_points, dtype=np.int64)
im_wide = np.linspace(edge_padding, im_ud - edge_padding, fiducial_points, dtype=np.int64)
# im_lr -= im_lr % (fiducial_points-1) - (1+2*edge_padding) # im_lr % (fiducial_points-1) - 1
# im_ud -= im_ud % (fiducial_points-1) - (1+2*edge_padding) # im_ud % (fiducial_points-1) - 1
# im_hight = np.linspace(edge_padding, im_lr - (1+edge_padding), fiducial_points, dtype=np.int64)
# im_wide = np.linspace(edge_padding, im_ud - (1+edge_padding), fiducial_points, dtype=np.int64)
im_x, im_y = np.meshgrid(im_hight, im_wide)
segment_x = (im_lr) // (fiducial_points-1)
segment_y = (im_ud) // (fiducial_points-1)
# plt.plot(im_x, im_y,
# color='limegreen',
# marker='.',
# linestyle='')
# plt.grid(True)
# plt.show()
self.origin_img = cv2.resize(origin_img, (im_ud, im_lr), interpolation=cv2.INTER_CUBIC)
perturbed_bg_ = getDatasets(self.bg_path)
perturbed_bg_img_ = self.bg_path+random.choice(perturbed_bg_)
perturbed_bg_img = cv2.imread(perturbed_bg_img_, flags=cv2.IMREAD_COLOR)
mesh_shape = self.origin_img.shape[:2]
self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 256, dtype=np.float32)#np.zeros_like(perturbed_bg_img)
# self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 0, dtype=np.int16)#np.zeros_like(perturbed_bg_img)
self.new_shape = self.synthesis_perturbed_img.shape[:2]
perturbed_bg_img = cv2.resize(perturbed_bg_img, (save_img_shape[1], save_img_shape[0]), cv2.INPAINT_TELEA)
origin_pixel_position = np.argwhere(np.zeros(mesh_shape, dtype=np.uint32) == 0).reshape(mesh_shape[0], mesh_shape[1], 2)
pixel_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(self.new_shape[0], self.new_shape[1], 2)
self.perturbed_xy_ = np.zeros((self.new_shape[0], self.new_shape[1], 2))
# self.perturbed_xy_ = pixel_position.copy().astype(np.float32)
# fiducial_points_grid = origin_pixel_position[im_x, im_y]
self.synthesis_perturbed_label = np.zeros((self.new_shape[0], self.new_shape[1], 2))
x_min, y_min, x_max, y_max = self.adjust_position_v2(0, 0, mesh_shape[0], mesh_shape[1], save_img_shape)
origin_pixel_position += [x_min, y_min]
x_min, y_min, x_max, y_max = self.adjust_position(0, 0, mesh_shape[0], mesh_shape[1])
x_shift = random.randint(-enlarge_img_shrink[0]//16, enlarge_img_shrink[0]//16)
y_shift = random.randint(-enlarge_img_shrink[1]//16, enlarge_img_shrink[1]//16)
x_min += x_shift
x_max += x_shift
y_min += y_shift
y_max += y_shift
'''im_x,y'''
im_x += x_min
im_y += y_min
self.synthesis_perturbed_img[x_min:x_max, y_min:y_max] = self.origin_img
self.synthesis_perturbed_label[x_min:x_max, y_min:y_max] = origin_pixel_position
synthesis_perturbed_img_map = self.synthesis_perturbed_img.copy()
synthesis_perturbed_label_map = self.synthesis_perturbed_label.copy()
foreORbackground_label = np.full((mesh_shape), 1, dtype=np.int16)
foreORbackground_label_map = np.full((self.new_shape), 0, dtype=np.int16)
foreORbackground_label_map[x_min:x_max, y_min:y_max] = foreORbackground_label
# synthesis_perturbed_img_map = self.pad(self.synthesis_perturbed_img.copy(), x_min, y_min, x_max, y_max)
# synthesis_perturbed_label_map = self.pad(synthesis_perturbed_label_map, x_min, y_min, x_max, y_max)
'''*****************************************************************'''
is_normalizationFun_mixture = self.is_perform(0.2, 0.8)
# if not is_normalizationFun_mixture:
normalizationFun_0_1 = False
# normalizationFun_0_1 = self.is_perform(0.5, 0.5)
if fold_curve == 'fold':
fold_curve_random = True
# is_normalizationFun_mixture = False
normalizationFun_0_1 = self.is_perform(0.2, 0.8)
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
fold_curve_random = self.is_perform(0.1, 0.9) # False # self.is_perform(0.01, 0.99)
alpha_perturbed = random.randint(80, 160) / 100
# is_normalizationFun_mixture = False # self.is_perform(0.01, 0.99)
synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 256)
# synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 0, dtype=np.int16)
synthesis_perturbed_label = np.zeros_like(self.synthesis_perturbed_label)
alpha_perturbed_change = self.is_perform(0.5, 0.5)
p_pp_choice = self.is_perform(0.8, 0.2) if fold_curve == 'fold' else self.is_perform(0.1, 0.9)
for repeat_i in range(repeat_time):
if alpha_perturbed_change:
if fold_curve == 'fold':
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
alpha_perturbed = random.randint(80, 160) / 100
''''''
linspace_x = [0, (self.new_shape[0] - im_lr) // 2 - 1,
self.new_shape[0] - (self.new_shape[0] - im_lr) // 2 - 1, self.new_shape[0] - 1]
linspace_y = [0, (self.new_shape[1] - im_ud) // 2 - 1,
self.new_shape[1] - (self.new_shape[1] - im_ud) // 2 - 1, self.new_shape[1] - 1]
linspace_x_seq = [1, 2, 3]
linspace_y_seq = [1, 2, 3]
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_p = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
if ((r_x == 1 or r_x == 3) and (r_y == 1 or r_y == 3)) and p_pp_choice:
linspace_x_seq.remove(r_x)
linspace_y_seq.remove(r_y)
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_pp = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
# perturbed_p, perturbed_pp = np.array(
# [random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10]) \
# , np.array([random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10])
# perturbed_p, perturbed_pp = np.array(
# [random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10]) \
# , np.array([random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10])
''''''
perturbed_vp = perturbed_pp - perturbed_p
perturbed_vp_norm = np.linalg.norm(perturbed_vp)
perturbed_distance_vertex_and_line = np.dot((perturbed_p - pixel_position), perturbed_vp) / perturbed_vp_norm
''''''
# perturbed_v = np.array([random.randint(-3000, 3000) / 100, random.randint(-3000, 3000) / 100])
# perturbed_v = np.array([random.randint(-4000, 4000) / 100, random.randint(-4000, 4000) / 100])
if fold_curve == 'fold' and self.is_perform(0.6, 0.4): # self.is_perform(0.3, 0.7):
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
perturbed_v = np.array([random.randint(-10000, 10000) / 100, random.randint(-10000, 10000) / 100])
# perturbed_v = np.array([random.randint(-11000, 11000) / 100, random.randint(-11000, 11000) / 100])
else:
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
# perturbed_v = np.array([random.randint(-16000, 16000) / 100, random.randint(-16000, 16000) / 100])
perturbed_v = np.array([random.randint(-8000, 8000) / 100, random.randint(-8000, 8000) / 100])
# perturbed_v = np.array([random.randint(-3500, 3500) / 100, random.randint(-3500, 3500) / 100])
# perturbed_v = np.array([random.randint(-600, 600) / 10, random.randint(-600, 600) / 10])
''''''
if fold_curve == 'fold':
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
''''''
if fold_curve_random:
# omega_perturbed = (alpha_perturbed+0.2) / (perturbed_d + alpha_perturbed)
# omega_perturbed = alpha_perturbed**perturbed_d
omega_perturbed = alpha_perturbed / (perturbed_d + alpha_perturbed)
else:
omega_perturbed = 1 - perturbed_d ** alpha_perturbed
'''shadow'''
if self.is_perform(0.6, 0.4):
synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] = np.minimum(np.maximum(synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] - np.int16(np.round(omega_perturbed[x_min:x_max, y_min:y_max].repeat(3).reshape(x_max-x_min, y_max-y_min, 3) * abs(np.linalg.norm(perturbed_v//2))*np.array([0.4-random.random()*0.1, 0.4-random.random()*0.1, 0.4-random.random()*0.1]))), 0), 255)
''''''
if relativeShift_position in ['position', 'relativeShift_v2']:
self.perturbed_xy_ += np.array([omega_perturbed * perturbed_v[0], omega_perturbed * perturbed_v[1]]).transpose(1, 2, 0)
else:
print('relativeShift_position error')
exit()
'''
flat_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(
self.new_shape[0] * self.new_shape[1], 2)
vtx, wts = self.interp_weights(self.perturbed_xy_.reshape(self.new_shape[0] * self.new_shape[1], 2), flat_position)
wts_sum = np.abs(wts).sum(-1)
# flat_img.reshape(flat_shape[0] * flat_shape[1], 3)[:] = interpolate(pixel, vtx, wts)
wts = wts[wts_sum <= 1, :]
vtx = vtx[wts_sum <= 1, :]
synthesis_perturbed_img.reshape(self.new_shape[0] * self.new_shape[1], 3)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_img_map.reshape(self.new_shape[0] * self.new_shape[1], 3), vtx, wts)
synthesis_perturbed_label.reshape(self.new_shape[0] * self.new_shape[1], 2)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_label_map.reshape(self.new_shape[0] * self.new_shape[1], 2), vtx, wts)
foreORbackground_label = np.zeros(self.new_shape)
foreORbackground_label.reshape(self.new_shape[0] * self.new_shape[1], 1)[wts_sum <= 1, :] = self.interpolate(foreORbackground_label_map.reshape(self.new_shape[0] * self.new_shape[1], 1), vtx, wts)
foreORbackground_label[foreORbackground_label < 0.99] = 0
foreORbackground_label[foreORbackground_label >= 0.99] = 1
# synthesis_perturbed_img = np.around(synthesis_perturbed_img).astype(np.uint8)
synthesis_perturbed_label[:, :, 0] *= foreORbackground_label
synthesis_perturbed_label[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 0] *= foreORbackground_label
synthesis_perturbed_img[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 2] *= foreORbackground_label
self.synthesis_perturbed_img = synthesis_perturbed_img
self.synthesis_perturbed_label = synthesis_perturbed_label
'''
'''perspective'''
perspective_shreshold = random.randint(26, 36)*10 # 280
x_min_per, y_min_per, x_max_per, y_max_per = self.adjust_position(perspective_shreshold, perspective_shreshold, self.new_shape[0]-perspective_shreshold, self.new_shape[1]-perspective_shreshold)
pts1 = np.float32([[x_min_per, y_min_per], [x_max_per, y_min_per], [x_min_per, y_max_per], [x_max_per, y_max_per]])
e_1_ = x_max_per - x_min_per
e_2_ = y_max_per - y_min_per
e_3_ = e_2_
e_4_ = e_1_
perspective_shreshold_h = e_1_*0.02
perspective_shreshold_w = e_2_*0.02
a_min_, a_max_ = 70, 110
# if self.is_perform(1, 0):
if fold_curve == 'curve' and self.is_perform(0.5, 0.5):
if self.is_perform(0.5, 0.5):
while True:
pts2 = np.around(
np.float32([[x_min_per - (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold],
[x_max_per - (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold],
[x_min_per + (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold],
[x_max_per + (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold]])) # right
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = | np.linalg.norm(pts2[0]-pts2[2]) | numpy.linalg.norm |
from gtrain import Model
import numpy as np
import tensorflow as tf
class NetForHypinv(Model):
"""
Implementaion of the crutial function for the HypINV algorithm.
Warning: Do not use this class but implement its subclass, for example see FCNetForHypinv
"""
def __init__(self, weights):
self.eval_session = None
self.grad_session = None
self.initial_x = None
self.center = None
self.weights = weights
self.out_for_eval = None #(going to be filled in build_for_eval method)
self.boundary_out_for_eval = None
self.trained_x = None
self.training_class_index = None
self.x = None # tf variable for inversion (going to be filled in build method)
self.x_for_eval = None
self.out = None
self.boundary_out = None # list of tf tensorf for each class of softmax class vs others output
self.loss = None
self.boundary_loss = None
self.t = None #target
self.boundary_t = None
self.x1 = None # this attribute is used of purposes of modified loss function
def __del__(self):
# close arr sessions
if self.eval_session:
self.eval_session.close()
if self.grad_session:
self.grad_session.close()
def set_initial_x(self, initial_x):
# sets starting point for the search of the closest point
self.initial_x = initial_x
def set_center(self, center):
# sets center point
self.center = center / np.linalg.norm(center)
def set_x1(self, x1):
# sets x1 to which we want to found the cosest point x0
self.x1 = x1
def has_modified_loss(self):
pass # if uses modified loss then it returns true
def set_initial_x_in_session(self, x, session=None):
# sets initial x in certain session
if session is None:
self.set_initial_x(x)
else:
pass # overide this method
def eval(self, x):
if len(x.shape) == 1:
x = x.reshape((1,len(x)))
if not self.eval_session:
self.eval_session = tf.Session()
with self.eval_session.as_default():
self.build_for_eval()
self.eval_session.run(tf.global_variables_initializer())
return self.eval_session.run(self.out_for_eval, {self.x_for_eval: x})
def boundary_eval(self, x, class_index):
# evaluates binary classificaitons class_index and other classes
if not self.eval_session:
self.eval_session = tf.Session()
with self.eval_session.as_default():
self.build_for_eval()
self.eval_session.run(tf.global_variables_initializer())
return self.eval_session.run(self.boundary_out_for_eval[class_index], {self.x_for_eval: x})
def get_boundary_gradient(self, x, class_index):
# computes gradient of the boundary for specified class_index
if not self.grad_session:
self.grad_session = tf.Session()
with self.grad_session.as_default():
self.build_for_eval()
self.grad = list()
for i in range(len(self.weights[0][-1][0])):
self.grad.append(tf.gradients(self.boundary_out_for_eval[i], [self.x_for_eval])[0])
self.grad_x = self.x_for_eval
return self.grad_session.run(self.grad[class_index], {self.grad_x: x})
def build_for_eval(self):
# build model for evaluation
pass #override this method (fill self.out_for_eval)
def train_ended(self, session):
self.trained_x = session.run(self.x)
def build(self):
# build model for training
pass #override this method (fill self.x, self.out)
def set_train_class(self, class_index):
# sets class of the x1
self.training_class_index = class_index
# overided methods from gtrain.Model
def get_loss(self):
if self.training_class_index is None:
return self.loss
else:
return self.boundary_loss[self.training_class_index]
def get_hits(self):
return self.get_loss()
def get_count(self):
return self.get_loss()
def get_train_summaries(self):
return []
def get_dev_summaries(self):
return []
def get_placeholders(self):
if self.training_class_index is None:
return [self.t]
else:
return [self.boundary_t]
#________________________________________EXAMPLES_OF_NetForHypinv_CLASS_____________________________________________
class FCNetForHypinv(NetForHypinv):
"""
Implementation of multi layer perceptron to by used in HypINV rule extraction algorithm
"""
def __init__(self, weights, function=tf.sigmoid, use_modified_loss=False, mu = 0.01):
"""
:param weights: saved as [list of weights for layers][0 weight, 1 bias]
:param function: tf function for propagation. For example tf.nn.sigmoid, tf.atan
:param use_modified_loss: weather the modified loss should be used
:param mu: factor of the penalty terms that specified the distance between x0 and x1 and
the distance x1 from the boundary
"""
super(FCNetForHypinv, self).__init__(weights)
self.function = function
self.layer_sizes = [len(self.weights[0][0])]
for bias in weights[1]:
self.layer_sizes.append(len(bias))
self.num_classes = self.layer_sizes[-1]
self.initial_x = np.zeros([1, self.layer_sizes[0]])
self.use_modified_loss = use_modified_loss
self.mu = mu
def build(self):
with tf.name_scope("Input"):
if self.center is not None:
self.point_weights = tf.Variable(self.center.reshape((1, len(self.center))),
dtype=tf.float64, trainable=False, name="Boundary_point")
init_factor = self.center
init_factor[init_factor!=0] = self.initial_x[init_factor!=0] / self.center[init_factor!=0]
self.factor = tf.Variable(init_factor.reshape((1, len(self.center))),
dtype=tf.float64, name="factor")
else:
self.point_weights = tf.Variable(self.initial_x.reshape((1, len(self.initial_x))),
dtype=tf.float64, trainable=False, name="Boundary_point")
self.factor = tf.Variable(np.ones((1, len(self.center))),
dtype=tf.float64, name="factor")
self.x = self.point_weights * self.factor
with tf.name_scope("Target"):
if self.use_modified_loss:
x1_constant = tf.constant(self.x1.reshape((1, len(self.x1))), dtype=tf.float64)
self.t = tf.placeholder(tf.float64, shape=[None, self.num_classes], name="Target_output")
self.boundary_t = tf.placeholder(tf.float64, shape=[None, 2], name="Target_boundary_output")
with tf.name_scope("FC_net"):
flowing_x = self.x
for i, _ in enumerate(self.weights[0]):
with tf.name_scope("layer_{}".format(i)):
W = tf.constant(self.weights[0][i], name="Weight_{}".format(i), dtype=tf.float64)
b = tf.constant(self.weights[1][i], name="Bias_{}".format(i), dtype=tf.float64)
flowing_x = self.function(tf.nn.xw_plus_b(flowing_x, W, b))
y = flowing_x
self.out = tf.nn.softmax(y)
with tf.name_scope("Binary_class_output"):
self.boundary_out = list()
for i in range(self.num_classes):
mask = True+ | np.zeros(self.num_classes, dtype=np.bool) | numpy.zeros |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = | np.sin(knot_demonstrate_time) | numpy.sin |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot( | np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100) | numpy.linspace |
import numpy
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
from src.support import support
class PhraseManager:
def __init__(self, configuration):
self.train_phrases, self.train_labels = self._read_train_phrases()
self.test_phrases, self.test_labels = self._read_test_phrases()
self.configuration = configuration
self.tokenizer = None
def get_phrases_train(self):
return self.train_phrases, self.train_labels
def get_phrases_test(self):
return self.test_phrases, self.test_labels
def get_dataset(self, level = None):
if level == support.WORD_LEVEL:
return self._word_process(self.configuration[support.WORD_MAX_LENGTH])
elif level == support.CHAR_LEVEL:
return self._char_process(self.configuration[support.CHAR_MAX_LENGTH])
else:
return self.train_phrases, self.train_labels, self.test_phrases, self.test_labels
def _word_process(self, word_max_length):
tokenizer = Tokenizer(num_words=self.configuration[support.QUANTITY_WORDS])
tokenizer.fit_on_texts(self.train_phrases)
x_train_sequence = tokenizer.texts_to_sequences(self.train_phrases)
x_test_sequence = tokenizer.texts_to_sequences(self.test_phrases)
x_train = sequence.pad_sequences(x_train_sequence, maxlen=word_max_length, padding='post', truncating='post')
x_test = sequence.pad_sequences(x_test_sequence, maxlen=word_max_length, padding='post', truncating='post')
y_train = numpy.array(self.train_labels)
y_test = numpy.array(self.test_labels)
return x_train, y_train, x_test, y_test
def _char_process(self, max_length):
embedding_w, embedding_dic = self._onehot_dic_build()
x_train = []
for i in range(len(self.train_phrases)):
doc_vec = self._doc_process(self.train_phrases[i].lower(), embedding_dic, max_length)
x_train.append(doc_vec)
x_train = | numpy.asarray(x_train, dtype='int64') | numpy.asarray |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * | np.ones(101) | numpy.ones |
'''
<NAME>
set up :2020-1-9
intergrate img and label into one file
-- fiducial1024_v1
'''
import argparse
import sys, os
import pickle
import random
import collections
import json
import numpy as np
import scipy.io as io
import scipy.misc as m
import matplotlib.pyplot as plt
import glob
import math
import time
import threading
import multiprocessing as mp
from multiprocessing import Pool
import re
import cv2
# sys.path.append('/lustre/home/gwxie/hope/project/dewarp/datasets/') # /lustre/home/gwxie/program/project/unwarp/perturbed_imgaes/GAN
import utils
def getDatasets(dir):
return os.listdir(dir)
class perturbed(utils.BasePerturbed):
def __init__(self, path, bg_path, save_path, save_suffix):
self.path = path
self.bg_path = bg_path
self.save_path = save_path
self.save_suffix = save_suffix
def save_img(self, m, n, fold_curve='fold', repeat_time=4, fiducial_points = 16, relativeShift_position='relativeShift_v2'):
origin_img = cv2.imread(self.path, flags=cv2.IMREAD_COLOR)
save_img_shape = [512*2, 480*2] # 320
# reduce_value = np.random.choice([2**4, 2**5, 2**6, 2**7, 2**8], p=[0.01, 0.1, 0.4, 0.39, 0.1])
reduce_value = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.02, 0.18, 0.2, 0.3, 0.1, 0.1, 0.08, 0.02])
# reduce_value = np.random.choice([8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.01, 0.02, 0.2, 0.4, 0.19, 0.18])
# reduce_value = np.random.choice([16, 24, 32, 40, 48, 64], p=[0.01, 0.1, 0.2, 0.4, 0.2, 0.09])
base_img_shrink = save_img_shape[0] - reduce_value
# enlarge_img_shrink = [1024, 768]
# enlarge_img_shrink = [896, 672] # 420
enlarge_img_shrink = [512*4, 480*4] # 420
# enlarge_img_shrink = [896*2, 768*2] # 420
# enlarge_img_shrink = [896, 768] # 420
# enlarge_img_shrink = [768, 576] # 420
# enlarge_img_shrink = [640, 480] # 420
''''''
im_lr = origin_img.shape[0]
im_ud = origin_img.shape[1]
reduce_value_v2 = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 28*2, 32*2, 48*2], p=[0.02, 0.18, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1])
# reduce_value_v2 = np.random.choice([16, 24, 28, 32, 48, 64], p=[0.01, 0.1, 0.2, 0.3, 0.25, 0.14])
if im_lr > im_ud:
im_ud = min(int(im_ud / im_lr * base_img_shrink), save_img_shape[1] - reduce_value_v2)
im_lr = save_img_shape[0] - reduce_value
else:
base_img_shrink = save_img_shape[1] - reduce_value
im_lr = min(int(im_lr / im_ud * base_img_shrink), save_img_shape[0] - reduce_value_v2)
im_ud = base_img_shrink
if round(im_lr / im_ud, 2) < 0.5 or round(im_ud / im_lr, 2) < 0.5:
repeat_time = min(repeat_time, 8)
edge_padding = 3
im_lr -= im_lr % (fiducial_points-1) - (2*edge_padding) # im_lr % (fiducial_points-1) - 1
im_ud -= im_ud % (fiducial_points-1) - (2*edge_padding) # im_ud % (fiducial_points-1) - 1
im_hight = np.linspace(edge_padding, im_lr - edge_padding, fiducial_points, dtype=np.int64)
im_wide = np.linspace(edge_padding, im_ud - edge_padding, fiducial_points, dtype=np.int64)
# im_lr -= im_lr % (fiducial_points-1) - (1+2*edge_padding) # im_lr % (fiducial_points-1) - 1
# im_ud -= im_ud % (fiducial_points-1) - (1+2*edge_padding) # im_ud % (fiducial_points-1) - 1
# im_hight = np.linspace(edge_padding, im_lr - (1+edge_padding), fiducial_points, dtype=np.int64)
# im_wide = np.linspace(edge_padding, im_ud - (1+edge_padding), fiducial_points, dtype=np.int64)
im_x, im_y = np.meshgrid(im_hight, im_wide)
segment_x = (im_lr) // (fiducial_points-1)
segment_y = (im_ud) // (fiducial_points-1)
# plt.plot(im_x, im_y,
# color='limegreen',
# marker='.',
# linestyle='')
# plt.grid(True)
# plt.show()
self.origin_img = cv2.resize(origin_img, (im_ud, im_lr), interpolation=cv2.INTER_CUBIC)
perturbed_bg_ = getDatasets(self.bg_path)
perturbed_bg_img_ = self.bg_path+random.choice(perturbed_bg_)
perturbed_bg_img = cv2.imread(perturbed_bg_img_, flags=cv2.IMREAD_COLOR)
mesh_shape = self.origin_img.shape[:2]
self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 256, dtype=np.float32)#np.zeros_like(perturbed_bg_img)
# self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 0, dtype=np.int16)#np.zeros_like(perturbed_bg_img)
self.new_shape = self.synthesis_perturbed_img.shape[:2]
perturbed_bg_img = cv2.resize(perturbed_bg_img, (save_img_shape[1], save_img_shape[0]), cv2.INPAINT_TELEA)
origin_pixel_position = np.argwhere(np.zeros(mesh_shape, dtype=np.uint32) == 0).reshape(mesh_shape[0], mesh_shape[1], 2)
pixel_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(self.new_shape[0], self.new_shape[1], 2)
self.perturbed_xy_ = np.zeros((self.new_shape[0], self.new_shape[1], 2))
# self.perturbed_xy_ = pixel_position.copy().astype(np.float32)
# fiducial_points_grid = origin_pixel_position[im_x, im_y]
self.synthesis_perturbed_label = np.zeros((self.new_shape[0], self.new_shape[1], 2))
x_min, y_min, x_max, y_max = self.adjust_position_v2(0, 0, mesh_shape[0], mesh_shape[1], save_img_shape)
origin_pixel_position += [x_min, y_min]
x_min, y_min, x_max, y_max = self.adjust_position(0, 0, mesh_shape[0], mesh_shape[1])
x_shift = random.randint(-enlarge_img_shrink[0]//16, enlarge_img_shrink[0]//16)
y_shift = random.randint(-enlarge_img_shrink[1]//16, enlarge_img_shrink[1]//16)
x_min += x_shift
x_max += x_shift
y_min += y_shift
y_max += y_shift
'''im_x,y'''
im_x += x_min
im_y += y_min
self.synthesis_perturbed_img[x_min:x_max, y_min:y_max] = self.origin_img
self.synthesis_perturbed_label[x_min:x_max, y_min:y_max] = origin_pixel_position
synthesis_perturbed_img_map = self.synthesis_perturbed_img.copy()
synthesis_perturbed_label_map = self.synthesis_perturbed_label.copy()
foreORbackground_label = np.full((mesh_shape), 1, dtype=np.int16)
foreORbackground_label_map = np.full((self.new_shape), 0, dtype=np.int16)
foreORbackground_label_map[x_min:x_max, y_min:y_max] = foreORbackground_label
# synthesis_perturbed_img_map = self.pad(self.synthesis_perturbed_img.copy(), x_min, y_min, x_max, y_max)
# synthesis_perturbed_label_map = self.pad(synthesis_perturbed_label_map, x_min, y_min, x_max, y_max)
'''*****************************************************************'''
is_normalizationFun_mixture = self.is_perform(0.2, 0.8)
# if not is_normalizationFun_mixture:
normalizationFun_0_1 = False
# normalizationFun_0_1 = self.is_perform(0.5, 0.5)
if fold_curve == 'fold':
fold_curve_random = True
# is_normalizationFun_mixture = False
normalizationFun_0_1 = self.is_perform(0.2, 0.8)
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
fold_curve_random = self.is_perform(0.1, 0.9) # False # self.is_perform(0.01, 0.99)
alpha_perturbed = random.randint(80, 160) / 100
# is_normalizationFun_mixture = False # self.is_perform(0.01, 0.99)
synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 256)
# synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 0, dtype=np.int16)
synthesis_perturbed_label = np.zeros_like(self.synthesis_perturbed_label)
alpha_perturbed_change = self.is_perform(0.5, 0.5)
p_pp_choice = self.is_perform(0.8, 0.2) if fold_curve == 'fold' else self.is_perform(0.1, 0.9)
for repeat_i in range(repeat_time):
if alpha_perturbed_change:
if fold_curve == 'fold':
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
alpha_perturbed = random.randint(80, 160) / 100
''''''
linspace_x = [0, (self.new_shape[0] - im_lr) // 2 - 1,
self.new_shape[0] - (self.new_shape[0] - im_lr) // 2 - 1, self.new_shape[0] - 1]
linspace_y = [0, (self.new_shape[1] - im_ud) // 2 - 1,
self.new_shape[1] - (self.new_shape[1] - im_ud) // 2 - 1, self.new_shape[1] - 1]
linspace_x_seq = [1, 2, 3]
linspace_y_seq = [1, 2, 3]
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_p = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
if ((r_x == 1 or r_x == 3) and (r_y == 1 or r_y == 3)) and p_pp_choice:
linspace_x_seq.remove(r_x)
linspace_y_seq.remove(r_y)
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_pp = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
# perturbed_p, perturbed_pp = np.array(
# [random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10]) \
# , np.array([random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10])
# perturbed_p, perturbed_pp = np.array(
# [random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10]) \
# , np.array([random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10])
''''''
perturbed_vp = perturbed_pp - perturbed_p
perturbed_vp_norm = np.linalg.norm(perturbed_vp)
perturbed_distance_vertex_and_line = np.dot((perturbed_p - pixel_position), perturbed_vp) / perturbed_vp_norm
''''''
# perturbed_v = np.array([random.randint(-3000, 3000) / 100, random.randint(-3000, 3000) / 100])
# perturbed_v = np.array([random.randint(-4000, 4000) / 100, random.randint(-4000, 4000) / 100])
if fold_curve == 'fold' and self.is_perform(0.6, 0.4): # self.is_perform(0.3, 0.7):
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
perturbed_v = np.array([random.randint(-10000, 10000) / 100, random.randint(-10000, 10000) / 100])
# perturbed_v = np.array([random.randint(-11000, 11000) / 100, random.randint(-11000, 11000) / 100])
else:
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
# perturbed_v = np.array([random.randint(-16000, 16000) / 100, random.randint(-16000, 16000) / 100])
perturbed_v = np.array([random.randint(-8000, 8000) / 100, random.randint(-8000, 8000) / 100])
# perturbed_v = np.array([random.randint(-3500, 3500) / 100, random.randint(-3500, 3500) / 100])
# perturbed_v = np.array([random.randint(-600, 600) / 10, random.randint(-600, 600) / 10])
''''''
if fold_curve == 'fold':
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
''''''
if fold_curve_random:
# omega_perturbed = (alpha_perturbed+0.2) / (perturbed_d + alpha_perturbed)
# omega_perturbed = alpha_perturbed**perturbed_d
omega_perturbed = alpha_perturbed / (perturbed_d + alpha_perturbed)
else:
omega_perturbed = 1 - perturbed_d ** alpha_perturbed
'''shadow'''
if self.is_perform(0.6, 0.4):
synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] = np.minimum(np.maximum(synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] - np.int16(np.round(omega_perturbed[x_min:x_max, y_min:y_max].repeat(3).reshape(x_max-x_min, y_max-y_min, 3) * abs(np.linalg.norm(perturbed_v//2))*np.array([0.4-random.random()*0.1, 0.4-random.random()*0.1, 0.4-random.random()*0.1]))), 0), 255)
''''''
if relativeShift_position in ['position', 'relativeShift_v2']:
self.perturbed_xy_ += np.array([omega_perturbed * perturbed_v[0], omega_perturbed * perturbed_v[1]]).transpose(1, 2, 0)
else:
print('relativeShift_position error')
exit()
'''
flat_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(
self.new_shape[0] * self.new_shape[1], 2)
vtx, wts = self.interp_weights(self.perturbed_xy_.reshape(self.new_shape[0] * self.new_shape[1], 2), flat_position)
wts_sum = np.abs(wts).sum(-1)
# flat_img.reshape(flat_shape[0] * flat_shape[1], 3)[:] = interpolate(pixel, vtx, wts)
wts = wts[wts_sum <= 1, :]
vtx = vtx[wts_sum <= 1, :]
synthesis_perturbed_img.reshape(self.new_shape[0] * self.new_shape[1], 3)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_img_map.reshape(self.new_shape[0] * self.new_shape[1], 3), vtx, wts)
synthesis_perturbed_label.reshape(self.new_shape[0] * self.new_shape[1], 2)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_label_map.reshape(self.new_shape[0] * self.new_shape[1], 2), vtx, wts)
foreORbackground_label = np.zeros(self.new_shape)
foreORbackground_label.reshape(self.new_shape[0] * self.new_shape[1], 1)[wts_sum <= 1, :] = self.interpolate(foreORbackground_label_map.reshape(self.new_shape[0] * self.new_shape[1], 1), vtx, wts)
foreORbackground_label[foreORbackground_label < 0.99] = 0
foreORbackground_label[foreORbackground_label >= 0.99] = 1
# synthesis_perturbed_img = np.around(synthesis_perturbed_img).astype(np.uint8)
synthesis_perturbed_label[:, :, 0] *= foreORbackground_label
synthesis_perturbed_label[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 0] *= foreORbackground_label
synthesis_perturbed_img[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 2] *= foreORbackground_label
self.synthesis_perturbed_img = synthesis_perturbed_img
self.synthesis_perturbed_label = synthesis_perturbed_label
'''
'''perspective'''
perspective_shreshold = random.randint(26, 36)*10 # 280
x_min_per, y_min_per, x_max_per, y_max_per = self.adjust_position(perspective_shreshold, perspective_shreshold, self.new_shape[0]-perspective_shreshold, self.new_shape[1]-perspective_shreshold)
pts1 = np.float32([[x_min_per, y_min_per], [x_max_per, y_min_per], [x_min_per, y_max_per], [x_max_per, y_max_per]])
e_1_ = x_max_per - x_min_per
e_2_ = y_max_per - y_min_per
e_3_ = e_2_
e_4_ = e_1_
perspective_shreshold_h = e_1_*0.02
perspective_shreshold_w = e_2_*0.02
a_min_, a_max_ = 70, 110
# if self.is_perform(1, 0):
if fold_curve == 'curve' and self.is_perform(0.5, 0.5):
if self.is_perform(0.5, 0.5):
while True:
pts2 = np.around(
np.float32([[x_min_per - (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold],
[x_max_per - (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold],
[x_min_per + (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold],
[x_max_per + (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold]])) # right
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
else:
while True:
pts2 = np.around(
np.float32([[x_min_per + (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold],
[x_max_per + (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold],
[x_min_per - (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold],
[x_max_per - (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold]]))
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
else:
while True:
pts2 = np.around(np.float32([[x_min_per+(random.random()-0.5)*perspective_shreshold, y_min_per+(random.random()-0.5)*perspective_shreshold],
[x_max_per+(random.random()-0.5)*perspective_shreshold, y_min_per+(random.random()-0.5)*perspective_shreshold],
[x_min_per+(random.random()-0.5)*perspective_shreshold, y_max_per+(random.random()-0.5)*perspective_shreshold],
[x_max_per+(random.random()-0.5)*perspective_shreshold, y_max_per+(random.random()-0.5)*perspective_shreshold]]))
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
M = cv2.getPerspectiveTransform(pts1, pts2)
one = np.ones((self.new_shape[0], self.new_shape[1], 1), dtype=np.int16)
matr = | np.dstack((pixel_position, one)) | numpy.dstack |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot( | np.linspace(0.85 * np.pi, 1.15 * np.pi, 101) | numpy.linspace |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = | np.linspace(slope_based_maximum_time, slope_based_minimum_time) | numpy.linspace |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * | np.ones(100) | numpy.ones |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cntk as C
import numpy as np
from .common import floatx, epsilon, image_dim_ordering, image_data_format
from collections import defaultdict
from contextlib import contextmanager
import warnings
C.set_global_option('align_axis', 1)
b_any = any
dev = C.device.use_default_device()
if dev.type() == 0:
warnings.warn(
'CNTK backend warning: GPU is not detected. '
'CNTK\'s CPU version is not fully optimized,'
'please run with GPU to get better performance.')
# A learning phase is a bool tensor used to run Keras models in
# either train mode (learning_phase == 1) or test mode (learning_phase == 0).
# LEARNING_PHASE_PLACEHOLDER is the placeholder for dynamic learning phase
_LEARNING_PHASE_PLACEHOLDER = C.constant(shape=(), dtype=np.float32, value=1.0, name='_keras_learning_phase')
# static learning phase flag, if it is not 0 or 1, we will go with dynamic learning phase tensor.
_LEARNING_PHASE = -1
_UID_PREFIXES = defaultdict(int)
# cntk doesn't support gradient as symbolic op, to hook up with keras model,
# we will create gradient as a constant placeholder, here use this global
# map to keep the mapping from grad placeholder to parameter
grad_parameter_dict = {}
NAME_SCOPE_STACK = []
@contextmanager
def name_scope(name):
global NAME_SCOPE_STACK
NAME_SCOPE_STACK.append(name)
yield
NAME_SCOPE_STACK.pop()
def get_uid(prefix=''):
_UID_PREFIXES[prefix] += 1
return _UID_PREFIXES[prefix]
def learning_phase():
# If _LEARNING_PHASE is not 0 or 1, return dynamic learning phase tensor
return _LEARNING_PHASE if _LEARNING_PHASE in {0, 1} else _LEARNING_PHASE_PLACEHOLDER
def set_learning_phase(value):
global _LEARNING_PHASE
if value not in {0, 1}:
raise ValueError('CNTK Backend: Set learning phase '
'with value %s is not supported, '
'expected 0 or 1.' % value)
_LEARNING_PHASE = value
def clear_session():
"""Reset learning phase flag for cntk backend.
"""
global _LEARNING_PHASE
global _LEARNING_PHASE_PLACEHOLDER
_LEARNING_PHASE = -1
_LEARNING_PHASE_PLACEHOLDER.value = np.asarray(1.0)
def in_train_phase(x, alt, training=None):
global _LEARNING_PHASE
if training is None:
training = learning_phase()
uses_learning_phase = True
else:
uses_learning_phase = False
# CNTK currently don't support cond op, so here we use
# element_select approach as workaround. It may have
# perf issue, will resolve it later with cntk cond op.
if callable(x) and isinstance(x, C.cntk_py.Function) is False:
x = x()
if callable(alt) and isinstance(alt, C.cntk_py.Function) is False:
alt = alt()
if training is True:
x._uses_learning_phase = uses_learning_phase
return x
else:
# if _LEARNING_PHASE is static
if isinstance(training, int) or isinstance(training, bool):
result = x if training == 1 or training is True else alt
else:
result = C.element_select(training, x, alt)
result._uses_learning_phase = uses_learning_phase
return result
def in_test_phase(x, alt, training=None):
return in_train_phase(alt, x, training=training)
def _convert_string_dtype(dtype):
# cntk only support float32 and float64
if dtype == 'float32':
return np.float32
elif dtype == 'float64':
return np.float64
else:
# cntk only running with float,
# try to cast to float to run the model
return np.float32
def _convert_dtype_string(dtype):
if dtype == np.float32:
return 'float32'
elif dtype == np.float64:
return 'float64'
else:
raise ValueError('CNTK Backend: Unsupported dtype: %s. '
'CNTK only supports float32 and '
'float64.' % dtype)
def variable(value, dtype=None, name=None, constraint=None):
"""Instantiates a variable and returns it.
# Arguments
value: Numpy array, initial value of the tensor.
dtype: Tensor type.
name: Optional name string for the tensor.
constraint: Optional projection function to be
applied to the variable after an optimizer update.
# Returns
A variable instance (with Keras metadata included).
"""
if dtype is None:
dtype = floatx()
if name is None:
name = ''
if isinstance(
value,
C.variables.Constant) or isinstance(
value,
C.variables.Parameter):
value = value.value
# we don't support init parameter with symbolic op, so eval it first as
# workaround
if isinstance(value, C.cntk_py.Function):
value = eval(value)
shape = value.shape if hasattr(value, 'shape') else ()
if hasattr(value, 'dtype') and value.dtype != dtype and len(shape) > 0:
value = value.astype(dtype)
# TODO: remove the conversion when cntk supports int32, int64
# https://docs.microsoft.com/en-us/python/api/cntk.variables.parameter
dtype = 'float32' if 'int' in str(dtype) else dtype
v = C.parameter(shape=shape,
init=value,
dtype=dtype,
name=_prepare_name(name, 'variable'))
v._keras_shape = v.shape
v._uses_learning_phase = False
v.constraint = constraint
return v
def bias_add(x, bias, data_format=None):
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format ' + str(data_format))
dims = len(x.shape)
if dims > 0 and x.shape[0] == C.InferredDimension:
dims -= 1
bias_dims = len(bias.shape)
if bias_dims != 1 and bias_dims != dims:
raise ValueError('Unexpected bias dimensions %d, '
'expected 1 or %d dimensions' % (bias_dims, dims))
if dims == 4:
if data_format == 'channels_first':
if bias_dims == 1:
shape = (bias.shape[0], 1, 1, 1)
else:
shape = (bias.shape[3],) + bias.shape[:3]
elif data_format == 'channels_last':
if bias_dims == 1:
shape = (1, 1, 1, bias.shape[0])
else:
shape = bias.shape
elif dims == 3:
if data_format == 'channels_first':
if bias_dims == 1:
shape = (bias.shape[0], 1, 1)
else:
shape = (bias.shape[2],) + bias.shape[:2]
elif data_format == 'channels_last':
if bias_dims == 1:
shape = (1, 1, bias.shape[0])
else:
shape = bias.shape
elif dims == 2:
if data_format == 'channels_first':
if bias_dims == 1:
shape = (bias.shape[0], 1)
else:
shape = (bias.shape[1],) + bias.shape[:1]
elif data_format == 'channels_last':
if bias_dims == 1:
shape = (1, bias.shape[0])
else:
shape = bias.shape
else:
shape = bias.shape
return x + reshape(bias, shape)
def eval(x):
if isinstance(x, C.cntk_py.Function):
return x.eval()
elif isinstance(x, C.variables.Constant) or isinstance(x, C.variables.Parameter):
return x.value
else:
raise ValueError('CNTK Backend: `eval` method on '
'`%s` type is not supported. '
'CNTK only supports `eval` with '
'`Function`, `Constant` or '
'`Parameter`.' % type(x))
def placeholder(
shape=None,
ndim=None,
dtype=None,
sparse=False,
name=None,
dynamic_axis_num=1):
if dtype is None:
dtype = floatx()
if not shape:
if ndim:
shape = tuple([None for _ in range(ndim)])
dynamic_dimension = C.FreeDimension if _get_cntk_version() >= 2.2 else C.InferredDimension
cntk_shape = [dynamic_dimension if s is None else s for s in shape]
cntk_shape = tuple(cntk_shape)
if dynamic_axis_num > len(cntk_shape):
raise ValueError('CNTK backend: creating placeholder with '
'%d dimension is not supported, at least '
'%d dimensions are needed.'
% (len(cntk_shape, dynamic_axis_num)))
if name is None:
name = ''
cntk_shape = cntk_shape[dynamic_axis_num:]
x = C.input(
shape=cntk_shape,
dtype=_convert_string_dtype(dtype),
is_sparse=sparse,
name=name)
x._keras_shape = shape
x._uses_learning_phase = False
x._cntk_placeholder = True
return x
def is_placeholder(x):
"""Returns whether `x` is a placeholder.
# Arguments
x: A candidate placeholder.
# Returns
Boolean.
"""
return hasattr(x, '_cntk_placeholder') and x._cntk_placeholder
def is_keras_tensor(x):
if not is_tensor(x):
raise ValueError('Unexpectedly found an instance of type `' +
str(type(x)) + '`. '
'Expected a symbolic tensor instance.')
return hasattr(x, '_keras_history')
def is_tensor(x):
return isinstance(x, (C.variables.Constant,
C.variables.Variable,
C.variables.Parameter,
C.ops.functions.Function))
def shape(x):
shape = list(int_shape(x))
num_dynamic = _get_dynamic_axis_num(x)
non_dyn_shape = []
for i in range(len(x.shape)):
if shape[i + num_dynamic] is None:
non_dyn_shape.append(x.shape[i])
else:
non_dyn_shape.append(shape[i + num_dynamic])
return shape[:num_dynamic] + non_dyn_shape
def is_sparse(tensor):
return tensor.is_sparse
def int_shape(x):
if hasattr(x, '_keras_shape'):
return x._keras_shape
shape = x.shape
if hasattr(x, 'dynamic_axes'):
dynamic_shape = [None for a in x.dynamic_axes]
shape = tuple(dynamic_shape) + shape
return shape
def ndim(x):
shape = int_shape(x)
return len(shape)
def _prepare_name(name, default):
prefix = '_'.join(NAME_SCOPE_STACK)
if name is None or name == '':
return prefix + '/' + default
return prefix + '/' + name
def constant(value, dtype=None, shape=None, name=None):
if dtype is None:
dtype = floatx()
if shape is None:
shape = ()
np_value = value * np.ones(shape)
const = C.constant(np_value,
dtype=dtype,
name=_prepare_name(name, 'constant'))
const._keras_shape = const.shape
const._uses_learning_phase = False
return const
def random_binomial(shape, p=0.0, dtype=None, seed=None):
# use numpy workaround now
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e7)
np.random.seed(seed)
if dtype is None:
dtype = np.float32
else:
dtype = _convert_string_dtype(dtype)
size = 1
for _ in shape:
if _ is None:
raise ValueError('CNTK Backend: randomness op with '
'dynamic shape is not supported now. '
'Please provide fixed dimension '
'instead of `None`.')
size *= _
binomial = np.random.binomial(1, p, size).astype(dtype).reshape(shape)
return variable(value=binomial, dtype=dtype)
def random_uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
for _ in shape:
if _ is None:
raise ValueError('CNTK Backend: randomness op with '
'dynamic shape is not supported now. '
'Please provide fixed dimension '
'instead of `None`.')
return random_uniform_variable(shape, minval, maxval, dtype, seed)
def random_uniform_variable(shape, low, high,
dtype=None, name=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e3)
if dtype is None:
dtype = np.float32
else:
dtype = _convert_string_dtype(dtype)
if name is None:
name = ''
scale = (high - low) / 2
p = C.parameter(
shape,
init=C.initializer.uniform(
scale,
seed=seed),
dtype=dtype,
name=name)
return variable(value=p.value + low + scale)
def random_normal_variable(
shape,
mean,
scale,
dtype=None,
name=None,
seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e7)
if dtype is None:
dtype = np.float32
else:
dtype = _convert_string_dtype(dtype)
if name is None:
name = ''
return C.parameter(
shape=shape,
init=C.initializer.normal(
scale=scale,
seed=seed),
dtype=dtype,
name=name)
def random_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
for _ in shape:
if _ is None:
raise ValueError('CNTK Backend: randomness op with '
'dynamic shape is not supported now. '
'Please provide fixed dimension '
'instead of `None`.')
# how to apply mean and stddev
return random_normal_variable(shape=shape, mean=mean, scale=1.0, seed=seed)
def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
if seed is None:
seed = np.random.randint(1, 10e6)
if dtype is None:
dtype = np.float32
else:
dtype = _convert_string_dtype(dtype)
return C.parameter(
shape, init=C.initializer.truncated_normal(
stddev, seed=seed), dtype=dtype)
def dtype(x):
return _convert_dtype_string(x.dtype)
def zeros(shape, dtype=None, name=None):
if dtype is None:
dtype = floatx()
ctype = _convert_string_dtype(dtype)
return variable(value=np.zeros(shape, ctype), dtype=dtype, name=name)
def ones(shape, dtype=None, name=None):
if dtype is None:
dtype = floatx()
ctype = _convert_string_dtype(dtype)
return variable(value=np.ones(shape, ctype), dtype=dtype, name=name)
def eye(size, dtype=None, name=None):
if dtype is None:
dtype = floatx()
return variable( | np.eye(size) | numpy.eye |
'''
<NAME>
set up :2020-1-9
intergrate img and label into one file
-- fiducial1024_v1
'''
import argparse
import sys, os
import pickle
import random
import collections
import json
import numpy as np
import scipy.io as io
import scipy.misc as m
import matplotlib.pyplot as plt
import glob
import math
import time
import threading
import multiprocessing as mp
from multiprocessing import Pool
import re
import cv2
# sys.path.append('/lustre/home/gwxie/hope/project/dewarp/datasets/') # /lustre/home/gwxie/program/project/unwarp/perturbed_imgaes/GAN
import utils
def getDatasets(dir):
return os.listdir(dir)
class perturbed(utils.BasePerturbed):
def __init__(self, path, bg_path, save_path, save_suffix):
self.path = path
self.bg_path = bg_path
self.save_path = save_path
self.save_suffix = save_suffix
def save_img(self, m, n, fold_curve='fold', repeat_time=4, fiducial_points = 16, relativeShift_position='relativeShift_v2'):
origin_img = cv2.imread(self.path, flags=cv2.IMREAD_COLOR)
save_img_shape = [512*2, 480*2] # 320
# reduce_value = np.random.choice([2**4, 2**5, 2**6, 2**7, 2**8], p=[0.01, 0.1, 0.4, 0.39, 0.1])
reduce_value = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.02, 0.18, 0.2, 0.3, 0.1, 0.1, 0.08, 0.02])
# reduce_value = np.random.choice([8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.01, 0.02, 0.2, 0.4, 0.19, 0.18])
# reduce_value = np.random.choice([16, 24, 32, 40, 48, 64], p=[0.01, 0.1, 0.2, 0.4, 0.2, 0.09])
base_img_shrink = save_img_shape[0] - reduce_value
# enlarge_img_shrink = [1024, 768]
# enlarge_img_shrink = [896, 672] # 420
enlarge_img_shrink = [512*4, 480*4] # 420
# enlarge_img_shrink = [896*2, 768*2] # 420
# enlarge_img_shrink = [896, 768] # 420
# enlarge_img_shrink = [768, 576] # 420
# enlarge_img_shrink = [640, 480] # 420
''''''
im_lr = origin_img.shape[0]
im_ud = origin_img.shape[1]
reduce_value_v2 = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 28*2, 32*2, 48*2], p=[0.02, 0.18, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1])
# reduce_value_v2 = np.random.choice([16, 24, 28, 32, 48, 64], p=[0.01, 0.1, 0.2, 0.3, 0.25, 0.14])
if im_lr > im_ud:
im_ud = min(int(im_ud / im_lr * base_img_shrink), save_img_shape[1] - reduce_value_v2)
im_lr = save_img_shape[0] - reduce_value
else:
base_img_shrink = save_img_shape[1] - reduce_value
im_lr = min(int(im_lr / im_ud * base_img_shrink), save_img_shape[0] - reduce_value_v2)
im_ud = base_img_shrink
if round(im_lr / im_ud, 2) < 0.5 or round(im_ud / im_lr, 2) < 0.5:
repeat_time = min(repeat_time, 8)
edge_padding = 3
im_lr -= im_lr % (fiducial_points-1) - (2*edge_padding) # im_lr % (fiducial_points-1) - 1
im_ud -= im_ud % (fiducial_points-1) - (2*edge_padding) # im_ud % (fiducial_points-1) - 1
im_hight = np.linspace(edge_padding, im_lr - edge_padding, fiducial_points, dtype=np.int64)
im_wide = np.linspace(edge_padding, im_ud - edge_padding, fiducial_points, dtype=np.int64)
# im_lr -= im_lr % (fiducial_points-1) - (1+2*edge_padding) # im_lr % (fiducial_points-1) - 1
# im_ud -= im_ud % (fiducial_points-1) - (1+2*edge_padding) # im_ud % (fiducial_points-1) - 1
# im_hight = np.linspace(edge_padding, im_lr - (1+edge_padding), fiducial_points, dtype=np.int64)
# im_wide = np.linspace(edge_padding, im_ud - (1+edge_padding), fiducial_points, dtype=np.int64)
im_x, im_y = np.meshgrid(im_hight, im_wide)
segment_x = (im_lr) // (fiducial_points-1)
segment_y = (im_ud) // (fiducial_points-1)
# plt.plot(im_x, im_y,
# color='limegreen',
# marker='.',
# linestyle='')
# plt.grid(True)
# plt.show()
self.origin_img = cv2.resize(origin_img, (im_ud, im_lr), interpolation=cv2.INTER_CUBIC)
perturbed_bg_ = getDatasets(self.bg_path)
perturbed_bg_img_ = self.bg_path+random.choice(perturbed_bg_)
perturbed_bg_img = cv2.imread(perturbed_bg_img_, flags=cv2.IMREAD_COLOR)
mesh_shape = self.origin_img.shape[:2]
self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 256, dtype=np.float32)#np.zeros_like(perturbed_bg_img)
# self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 0, dtype=np.int16)#np.zeros_like(perturbed_bg_img)
self.new_shape = self.synthesis_perturbed_img.shape[:2]
perturbed_bg_img = cv2.resize(perturbed_bg_img, (save_img_shape[1], save_img_shape[0]), cv2.INPAINT_TELEA)
origin_pixel_position = np.argwhere(np.zeros(mesh_shape, dtype=np.uint32) == 0).reshape(mesh_shape[0], mesh_shape[1], 2)
pixel_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(self.new_shape[0], self.new_shape[1], 2)
self.perturbed_xy_ = np.zeros((self.new_shape[0], self.new_shape[1], 2))
# self.perturbed_xy_ = pixel_position.copy().astype(np.float32)
# fiducial_points_grid = origin_pixel_position[im_x, im_y]
self.synthesis_perturbed_label = np.zeros((self.new_shape[0], self.new_shape[1], 2))
x_min, y_min, x_max, y_max = self.adjust_position_v2(0, 0, mesh_shape[0], mesh_shape[1], save_img_shape)
origin_pixel_position += [x_min, y_min]
x_min, y_min, x_max, y_max = self.adjust_position(0, 0, mesh_shape[0], mesh_shape[1])
x_shift = random.randint(-enlarge_img_shrink[0]//16, enlarge_img_shrink[0]//16)
y_shift = random.randint(-enlarge_img_shrink[1]//16, enlarge_img_shrink[1]//16)
x_min += x_shift
x_max += x_shift
y_min += y_shift
y_max += y_shift
'''im_x,y'''
im_x += x_min
im_y += y_min
self.synthesis_perturbed_img[x_min:x_max, y_min:y_max] = self.origin_img
self.synthesis_perturbed_label[x_min:x_max, y_min:y_max] = origin_pixel_position
synthesis_perturbed_img_map = self.synthesis_perturbed_img.copy()
synthesis_perturbed_label_map = self.synthesis_perturbed_label.copy()
foreORbackground_label = np.full((mesh_shape), 1, dtype=np.int16)
foreORbackground_label_map = np.full((self.new_shape), 0, dtype=np.int16)
foreORbackground_label_map[x_min:x_max, y_min:y_max] = foreORbackground_label
# synthesis_perturbed_img_map = self.pad(self.synthesis_perturbed_img.copy(), x_min, y_min, x_max, y_max)
# synthesis_perturbed_label_map = self.pad(synthesis_perturbed_label_map, x_min, y_min, x_max, y_max)
'''*****************************************************************'''
is_normalizationFun_mixture = self.is_perform(0.2, 0.8)
# if not is_normalizationFun_mixture:
normalizationFun_0_1 = False
# normalizationFun_0_1 = self.is_perform(0.5, 0.5)
if fold_curve == 'fold':
fold_curve_random = True
# is_normalizationFun_mixture = False
normalizationFun_0_1 = self.is_perform(0.2, 0.8)
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
fold_curve_random = self.is_perform(0.1, 0.9) # False # self.is_perform(0.01, 0.99)
alpha_perturbed = random.randint(80, 160) / 100
# is_normalizationFun_mixture = False # self.is_perform(0.01, 0.99)
synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 256)
# synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 0, dtype=np.int16)
synthesis_perturbed_label = np.zeros_like(self.synthesis_perturbed_label)
alpha_perturbed_change = self.is_perform(0.5, 0.5)
p_pp_choice = self.is_perform(0.8, 0.2) if fold_curve == 'fold' else self.is_perform(0.1, 0.9)
for repeat_i in range(repeat_time):
if alpha_perturbed_change:
if fold_curve == 'fold':
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
alpha_perturbed = random.randint(80, 160) / 100
''''''
linspace_x = [0, (self.new_shape[0] - im_lr) // 2 - 1,
self.new_shape[0] - (self.new_shape[0] - im_lr) // 2 - 1, self.new_shape[0] - 1]
linspace_y = [0, (self.new_shape[1] - im_ud) // 2 - 1,
self.new_shape[1] - (self.new_shape[1] - im_ud) // 2 - 1, self.new_shape[1] - 1]
linspace_x_seq = [1, 2, 3]
linspace_y_seq = [1, 2, 3]
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_p = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
if ((r_x == 1 or r_x == 3) and (r_y == 1 or r_y == 3)) and p_pp_choice:
linspace_x_seq.remove(r_x)
linspace_y_seq.remove(r_y)
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_pp = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
# perturbed_p, perturbed_pp = np.array(
# [random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10]) \
# , np.array([random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10])
# perturbed_p, perturbed_pp = np.array(
# [random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10]) \
# , np.array([random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10])
''''''
perturbed_vp = perturbed_pp - perturbed_p
perturbed_vp_norm = np.linalg.norm(perturbed_vp)
perturbed_distance_vertex_and_line = np.dot((perturbed_p - pixel_position), perturbed_vp) / perturbed_vp_norm
''''''
# perturbed_v = np.array([random.randint(-3000, 3000) / 100, random.randint(-3000, 3000) / 100])
# perturbed_v = np.array([random.randint(-4000, 4000) / 100, random.randint(-4000, 4000) / 100])
if fold_curve == 'fold' and self.is_perform(0.6, 0.4): # self.is_perform(0.3, 0.7):
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
perturbed_v = np.array([random.randint(-10000, 10000) / 100, random.randint(-10000, 10000) / 100])
# perturbed_v = np.array([random.randint(-11000, 11000) / 100, random.randint(-11000, 11000) / 100])
else:
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
# perturbed_v = np.array([random.randint(-16000, 16000) / 100, random.randint(-16000, 16000) / 100])
perturbed_v = np.array([random.randint(-8000, 8000) / 100, random.randint(-8000, 8000) / 100])
# perturbed_v = np.array([random.randint(-3500, 3500) / 100, random.randint(-3500, 3500) / 100])
# perturbed_v = np.array([random.randint(-600, 600) / 10, random.randint(-600, 600) / 10])
''''''
if fold_curve == 'fold':
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
''''''
if fold_curve_random:
# omega_perturbed = (alpha_perturbed+0.2) / (perturbed_d + alpha_perturbed)
# omega_perturbed = alpha_perturbed**perturbed_d
omega_perturbed = alpha_perturbed / (perturbed_d + alpha_perturbed)
else:
omega_perturbed = 1 - perturbed_d ** alpha_perturbed
'''shadow'''
if self.is_perform(0.6, 0.4):
synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] = np.minimum(np.maximum(synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] - np.int16(np.round(omega_perturbed[x_min:x_max, y_min:y_max].repeat(3).reshape(x_max-x_min, y_max-y_min, 3) * abs(np.linalg.norm(perturbed_v//2))*np.array([0.4-random.random()*0.1, 0.4-random.random()*0.1, 0.4-random.random()*0.1]))), 0), 255)
''''''
if relativeShift_position in ['position', 'relativeShift_v2']:
self.perturbed_xy_ += np.array([omega_perturbed * perturbed_v[0], omega_perturbed * perturbed_v[1]]).transpose(1, 2, 0)
else:
print('relativeShift_position error')
exit()
'''
flat_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(
self.new_shape[0] * self.new_shape[1], 2)
vtx, wts = self.interp_weights(self.perturbed_xy_.reshape(self.new_shape[0] * self.new_shape[1], 2), flat_position)
wts_sum = np.abs(wts).sum(-1)
# flat_img.reshape(flat_shape[0] * flat_shape[1], 3)[:] = interpolate(pixel, vtx, wts)
wts = wts[wts_sum <= 1, :]
vtx = vtx[wts_sum <= 1, :]
synthesis_perturbed_img.reshape(self.new_shape[0] * self.new_shape[1], 3)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_img_map.reshape(self.new_shape[0] * self.new_shape[1], 3), vtx, wts)
synthesis_perturbed_label.reshape(self.new_shape[0] * self.new_shape[1], 2)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_label_map.reshape(self.new_shape[0] * self.new_shape[1], 2), vtx, wts)
foreORbackground_label = np.zeros(self.new_shape)
foreORbackground_label.reshape(self.new_shape[0] * self.new_shape[1], 1)[wts_sum <= 1, :] = self.interpolate(foreORbackground_label_map.reshape(self.new_shape[0] * self.new_shape[1], 1), vtx, wts)
foreORbackground_label[foreORbackground_label < 0.99] = 0
foreORbackground_label[foreORbackground_label >= 0.99] = 1
# synthesis_perturbed_img = np.around(synthesis_perturbed_img).astype(np.uint8)
synthesis_perturbed_label[:, :, 0] *= foreORbackground_label
synthesis_perturbed_label[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 0] *= foreORbackground_label
synthesis_perturbed_img[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 2] *= foreORbackground_label
self.synthesis_perturbed_img = synthesis_perturbed_img
self.synthesis_perturbed_label = synthesis_perturbed_label
'''
'''perspective'''
perspective_shreshold = random.randint(26, 36)*10 # 280
x_min_per, y_min_per, x_max_per, y_max_per = self.adjust_position(perspective_shreshold, perspective_shreshold, self.new_shape[0]-perspective_shreshold, self.new_shape[1]-perspective_shreshold)
pts1 = np.float32([[x_min_per, y_min_per], [x_max_per, y_min_per], [x_min_per, y_max_per], [x_max_per, y_max_per]])
e_1_ = x_max_per - x_min_per
e_2_ = y_max_per - y_min_per
e_3_ = e_2_
e_4_ = e_1_
perspective_shreshold_h = e_1_*0.02
perspective_shreshold_w = e_2_*0.02
a_min_, a_max_ = 70, 110
# if self.is_perform(1, 0):
if fold_curve == 'curve' and self.is_perform(0.5, 0.5):
if self.is_perform(0.5, 0.5):
while True:
pts2 = np.around(
np.float32([[x_min_per - (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold],
[x_max_per - (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold],
[x_min_per + (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold],
[x_max_per + (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold]])) # right
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
else:
while True:
pts2 = np.around(
np.float32([[x_min_per + (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold],
[x_max_per + (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold],
[x_min_per - (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold],
[x_max_per - (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold]]))
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = | np.linalg.norm(pts2[0]-pts2[2]) | numpy.linalg.norm |
import numpy as np
from typing import Tuple, Union, Optional
from autoarray.structures.arrays.two_d import array_2d_util
from autoarray.geometry import geometry_util
from autoarray import numba_util
from autoarray.mask import mask_2d_util
@numba_util.jit()
def grid_2d_centre_from(grid_2d_slim: np.ndarray) -> Tuple[float, float]:
"""
Returns the centre of a grid from a 1D grid.
Parameters
----------
grid_2d_slim
The 1D grid of values which are mapped to a 2D array.
Returns
-------
(float, float)
The (y,x) central coordinates of the grid.
"""
centre_y = (np.max(grid_2d_slim[:, 0]) + np.min(grid_2d_slim[:, 0])) / 2.0
centre_x = (np.max(grid_2d_slim[:, 1]) + np.min(grid_2d_slim[:, 1])) / 2.0
return centre_y, centre_x
@numba_util.jit()
def grid_2d_slim_via_mask_from(
mask_2d: np.ndarray,
pixel_scales: Union[float, Tuple[float, float]],
sub_size: int,
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
For a sub-grid, every unmasked pixel of its 2D mask with shape (total_y_pixels, total_x_pixels) is divided into
a finer uniform grid of shape (total_y_pixels*sub_size, total_x_pixels*sub_size). This routine computes the (y,x)
scaled coordinates a the centre of every sub-pixel defined by this 2D mask array.
The sub-grid is returned on an array of shape (total_unmasked_pixels*sub_size**2, 2). y coordinates are
stored in the 0 index of the second dimension, x coordinates in the 1 index. Masked coordinates are therefore
removed and not included in the slimmed grid.
Grid2D are defined from the top-left corner, where the first unmasked sub-pixel corresponds to index 0.
Sub-pixels that are part of the same mask array pixel are indexed next to one another, such that the second
sub-pixel in the first pixel has index 1, its next sub-pixel has index 2, and so forth.
Parameters
----------
mask_2d
A 2D array of bools, where `False` values are unmasked and therefore included as part of the calculated
sub-grid.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the 2D mask array.
sub_size
The size of the sub-grid that each pixel of the 2D mask array is divided into.
origin : (float, flloat)
The (y,x) origin of the 2D array, which the sub-grid is shifted around.
Returns
-------
ndarray
A slimmed sub grid of (y,x) scaled coordinates at the centre of every pixel unmasked pixel on the 2D mask
array. The sub grid array has dimensions (total_unmasked_pixels*sub_size**2, 2).
Examples
--------
mask = np.array([[True, False, True],
[False, False, False]
[True, False, True]])
grid_slim = grid_2d_slim_via_mask_from(mask=mask, pixel_scales=(0.5, 0.5), sub_size=1, origin=(0.0, 0.0))
"""
total_sub_pixels = mask_2d_util.total_sub_pixels_2d_from(mask_2d, sub_size)
grid_slim = np.zeros(shape=(total_sub_pixels, 2))
centres_scaled = geometry_util.central_scaled_coordinate_2d_from(
shape_native=mask_2d.shape, pixel_scales=pixel_scales, origin=origin
)
sub_index = 0
y_sub_half = pixel_scales[0] / 2
y_sub_step = pixel_scales[0] / (sub_size)
x_sub_half = pixel_scales[1] / 2
x_sub_step = pixel_scales[1] / (sub_size)
for y in range(mask_2d.shape[0]):
for x in range(mask_2d.shape[1]):
if not mask_2d[y, x]:
y_scaled = (y - centres_scaled[0]) * pixel_scales[0]
x_scaled = (x - centres_scaled[1]) * pixel_scales[1]
for y1 in range(sub_size):
for x1 in range(sub_size):
grid_slim[sub_index, 0] = -(
y_scaled - y_sub_half + y1 * y_sub_step + (y_sub_step / 2.0)
)
grid_slim[sub_index, 1] = (
x_scaled - x_sub_half + x1 * x_sub_step + (x_sub_step / 2.0)
)
sub_index += 1
return grid_slim
def grid_2d_via_mask_from(
mask_2d: np.ndarray,
pixel_scales: Union[float, Tuple[float, float]],
sub_size: int,
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
For a sub-grid, every unmasked pixel of its 2D mask with shape (total_y_pixels, total_x_pixels) is divided into a
finer uniform grid of shape (total_y_pixels*sub_size, total_x_pixels*sub_size). This routine computes the (y,x)
scaled coordinates at the centre of every sub-pixel defined by this 2D mask array.
The sub-grid is returned in its native dimensions with shape (total_y_pixels*sub_size, total_x_pixels*sub_size).
y coordinates are stored in the 0 index of the second dimension, x coordinates in the 1 index. Masked pixels are
given values (0.0, 0.0).
Grids are defined from the top-left corner, where the first unmasked sub-pixel corresponds to index 0.
Sub-pixels that are part of the same mask array pixel are indexed next to one another, such that the second
sub-pixel in the first pixel has index 1, its next sub-pixel has index 2, and so forth.
Parameters
----------
mask_2d
A 2D array of bools, where `False` values are unmasked and therefore included as part of the calculated
sub-grid.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the 2D mask array.
sub_size
The size of the sub-grid that each pixel of the 2D mask array is divided into.
origin : (float, flloat)
The (y,x) origin of the 2D array, which the sub-grid is shifted around.
Returns
-------
ndarray
A sub grid of (y,x) scaled coordinates at the centre of every pixel unmasked pixel on the 2D mask
array. The sub grid array has dimensions (total_y_pixels*sub_size, total_x_pixels*sub_size).
Examples
--------
mask = np.array([[True, False, True],
[False, False, False]
[True, False, True]])
grid_2d = grid_2d_via_mask_from(mask=mask, pixel_scales=(0.5, 0.5), sub_size=1, origin=(0.0, 0.0))
"""
grid_2d_slim = grid_2d_slim_via_mask_from(
mask_2d=mask_2d, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin
)
return grid_2d_native_from(
grid_2d_slim=grid_2d_slim, mask_2d=mask_2d, sub_size=sub_size
)
def grid_2d_slim_via_shape_native_from(
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
sub_size: int,
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
For a sub-grid, every unmasked pixel of its 2D mask with shape (total_y_pixels, total_x_pixels) is divided into a
finer uniform grid of shape (total_y_pixels*sub_size, total_x_pixels*sub_size). This routine computes the (y,x)
scaled coordinates at the centre of every sub-pixel defined by this 2D mask array.
The sub-grid is returned in its slimmed dimensions with shape (total_pixels**2*sub_size**2, 2). y coordinates are
stored in the 0 index of the second dimension, x coordinates in the 1 index.
Grid2D are defined from the top-left corner, where the first sub-pixel corresponds to index [0,0].
Sub-pixels that are part of the same mask array pixel are indexed next to one another, such that the second
sub-pixel in the first pixel has index 1, its next sub-pixel has index 2, and so forth.
Parameters
----------
shape_native
The (y,x) shape of the 2D array the sub-grid of coordinates is computed for.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the 2D mask array.
sub_size
The size of the sub-grid that each pixel of the 2D mask array is divided into.
origin
The (y,x) origin of the 2D array, which the sub-grid is shifted around.
Returns
-------
ndarray
A sub grid of (y,x) scaled coordinates at the centre of every pixel unmasked pixel on the 2D mask
array. The sub grid is slimmed and has dimensions (total_unmasked_pixels*sub_size**2, 2).
Examples
--------
mask = np.array([[True, False, True],
[False, False, False]
[True, False, True]])
grid_2d_slim = grid_2d_slim_via_shape_native_from(shape_native=(3,3), pixel_scales=(0.5, 0.5), sub_size=2, origin=(0.0, 0.0))
"""
return grid_2d_slim_via_mask_from(
mask_2d=np.full(fill_value=False, shape=shape_native),
pixel_scales=pixel_scales,
sub_size=sub_size,
origin=origin,
)
def grid_2d_via_shape_native_from(
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
sub_size: int,
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
For a sub-grid, every unmasked pixel of its 2D mask with shape (total_y_pixels, total_x_pixels) is divided
into a finer uniform grid of shape (total_y_pixels*sub_size, total_x_pixels*sub_size). This routine computes
the (y,x) scaled coordinates at the centre of every sub-pixel defined by this 2D mask array.
The sub-grid is returned in its native dimensions with shape (total_y_pixels*sub_size, total_x_pixels*sub_size).
y coordinates are stored in the 0 index of the second dimension, x coordinates in the 1 index.
Grids are defined from the top-left corner, where the first sub-pixel corresponds to index [0,0].
Sub-pixels that are part of the same mask array pixel are indexed next to one another, such that the second
sub-pixel in the first pixel has index 1, its next sub-pixel has index 2, and so forth.
Parameters
----------
shape_native
The (y,x) shape of the 2D array the sub-grid of coordinates is computed for.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the 2D mask array.
sub_size
The size of the sub-grid that each pixel of the 2D mask array is divided into.
origin : (float, flloat)
The (y,x) origin of the 2D array, which the sub-grid is shifted around.
Returns
-------
ndarray
A sub grid of (y,x) scaled coordinates at the centre of every pixel unmasked pixel on the 2D mask
array. The sub grid array has dimensions (total_y_pixels*sub_size, total_x_pixels*sub_size).
Examples
--------
grid_2d = grid_2d_via_shape_native_from(shape_native=(3, 3), pixel_scales=(1.0, 1.0), sub_size=2, origin=(0.0, 0.0))
"""
return grid_2d_via_mask_from(
mask_2d=np.full(fill_value=False, shape=shape_native),
pixel_scales=pixel_scales,
sub_size=sub_size,
origin=origin,
)
@numba_util.jit()
def grid_scaled_2d_slim_radial_projected_from(
extent: np.ndarray,
centre: Tuple[float, float],
pixel_scales: Union[float, Tuple[float, float]],
sub_size: int,
shape_slim: Optional[int] = 0,
) -> np.ndarray:
"""
Determine a projected radial grid of points from a 2D region of coordinates defined by an
extent [xmin, xmax, ymin, ymax] and with a (y,x) centre. This functions operates as follows:
1) Given the region defined by the extent [xmin, xmax, ymin, ymax], the algorithm finds the longest 1D distance of
the 4 paths from the (y,x) centre to the edge of the region (e.g. following the positive / negative y and x axes).
2) Use the pixel-scale corresponding to the direction chosen (e.g. if the positive x-axis was the longest, the
pixel_scale in the x dimension is used).
3) Determine the number of pixels between the centre and the edge of the region using the longest path between the
two chosen above.
4) Create a (y,x) grid of radial points where all points are at the centre's y value = 0.0 and the x values iterate
from the centre in increasing steps of the pixel-scale.
5) Rotate these radial coordinates by the input `angle` clockwise.
A schematric is shown below:
-------------------
| |
|<- - - - ->x | x = centre
| | <-> = longest radial path from centre to extent edge
| |
-------------------
Using the centre x above, this function finds the longest radial path to the edge of the extent window.
The returned `grid_radii` represents a radial set of points that in 1D sample the 2D grid outwards from its centre.
This grid stores the radial coordinates as (y,x) values (where all y values are the same) as opposed to a 1D data
structure so that it can be used in functions which require that a 2D grid structure is input.
Parameters
----------
extent
The extent of the grid the radii grid is computed using, with format [xmin, xmax, ymin, ymax]
centre : (float, flloat)
The (y,x) central coordinate which the radial grid is traced outwards from.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the 2D mask array.
sub_size
The size of the sub-grid that each pixel of the 2D mask array is divided into.
shape_slim
Manually choose the shape of the 1D projected grid that is returned. If 0, the border based on the 2D grid is
used (due to numba None cannot be used as a default value).
Returns
-------
ndarray
A radial set of points sampling the longest distance from the centre to the edge of the extent in along the
positive x-axis.
"""
distance_to_positive_x = extent[1] - centre[1]
distance_to_positive_y = extent[3] - centre[0]
distance_to_negative_x = centre[1] - extent[0]
distance_to_negative_y = centre[0] - extent[2]
scaled_distance = max(
[
distance_to_positive_x,
distance_to_positive_y,
distance_to_negative_x,
distance_to_negative_y,
]
)
if (scaled_distance == distance_to_positive_y) or (
scaled_distance == distance_to_negative_y
):
pixel_scale = pixel_scales[0]
else:
pixel_scale = pixel_scales[1]
if shape_slim == 0:
shape_slim = sub_size * int((scaled_distance / pixel_scale)) + 1
grid_scaled_2d_slim_radii = np.zeros((shape_slim, 2))
grid_scaled_2d_slim_radii[:, 0] += centre[0]
radii = centre[1]
for slim_index in range(shape_slim):
grid_scaled_2d_slim_radii[slim_index, 1] = radii
radii += pixel_scale / sub_size
return grid_scaled_2d_slim_radii
@numba_util.jit()
def grid_pixels_2d_slim_from(
grid_scaled_2d_slim: np.ndarray,
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
Convert a slimmed grid of 2d (y,x) scaled coordinates to a slimmed grid of 2d (y,x) pixel coordinate values. Pixel
coordinates are returned as floats such that they include the decimal offset from each pixel's top-left corner
relative to the input scaled coordinate.
The input and output grids are both slimmed and therefore shape (total_pixels, 2).
The pixel coordinate origin is at the top left corner of the grid, such that the pixel [0,0] corresponds to
the highest (most positive) y scaled coordinate and lowest (most negative) x scaled coordinate on the gird.
The scaled grid is defined by an origin and coordinates are shifted to this origin before computing their
1D grid pixel coordinate values.
Parameters
----------
grid_scaled_2d_slim: np.ndarray
The slimmed grid of 2D (y,x) coordinates in scaled units which are converted to pixel value coordinates.
shape_native
The (y,x) shape of the original 2D array the scaled coordinates were computed on.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the original 2D array.
origin : (float, flloat)
The (y,x) origin of the grid, which the scaled grid is shifted to.
Returns
-------
ndarray
A slimmed grid of 2D (y,x) pixel-value coordinates with dimensions (total_pixels, 2).
Examples
--------
grid_scaled_2d_slim = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])
grid_pixels_2d_slim = grid_scaled_2d_slim_from(grid_scaled_2d_slim=grid_scaled_2d_slim, shape=(2,2),
pixel_scales=(0.5, 0.5), origin=(0.0, 0.0))
"""
grid_pixels_2d_slim = np.zeros((grid_scaled_2d_slim.shape[0], 2))
centres_scaled = geometry_util.central_scaled_coordinate_2d_from(
shape_native=shape_native, pixel_scales=pixel_scales, origin=origin
)
for slim_index in range(grid_scaled_2d_slim.shape[0]):
grid_pixels_2d_slim[slim_index, 0] = (
(-grid_scaled_2d_slim[slim_index, 0] / pixel_scales[0])
+ centres_scaled[0]
+ 0.5
)
grid_pixels_2d_slim[slim_index, 1] = (
(grid_scaled_2d_slim[slim_index, 1] / pixel_scales[1])
+ centres_scaled[1]
+ 0.5
)
return grid_pixels_2d_slim
@numba_util.jit()
def grid_pixel_centres_2d_slim_from(
grid_scaled_2d_slim: np.ndarray,
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
Convert a slimmed grid of 2D (y,x) scaled coordinates to a slimmed grid of 2D (y,x) pixel values. Pixel coordinates
are returned as integers such that they map directly to the pixel they are contained within.
The input and output grids are both slimmed and therefore shape (total_pixels, 2).
The pixel coordinate origin is at the top left corner of the grid, such that the pixel [0,0] corresponds to
the highest (most positive) y scaled coordinate and lowest (most negative) x scaled coordinate on the gird.
The scaled coordinate grid is defined by the class attribute origin, and coordinates are shifted to this
origin before computing their 1D grid pixel indexes.
Parameters
----------
grid_scaled_2d_slim: np.ndarray
The slimmed grid of 2D (y,x) coordinates in scaled units which is converted to pixel indexes.
shape_native
The (y,x) shape of the original 2D array the scaled coordinates were computed on.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the original 2D array.
origin : (float, flloat)
The (y,x) origin of the grid, which the scaled grid is shifted
Returns
-------
ndarray
A slimmed grid of 2D (y,x) pixel indexes with dimensions (total_pixels, 2).
Examples
--------
grid_scaled_2d_slim = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])
grid_pixels_2d_slim = grid_scaled_2d_slim_from(grid_scaled_2d_slim=grid_scaled_2d_slim, shape=(2,2),
pixel_scales=(0.5, 0.5), origin=(0.0, 0.0))
"""
grid_pixels_2d_slim = np.zeros((grid_scaled_2d_slim.shape[0], 2))
centres_scaled = geometry_util.central_scaled_coordinate_2d_from(
shape_native=shape_native, pixel_scales=pixel_scales, origin=origin
)
for slim_index in range(grid_scaled_2d_slim.shape[0]):
grid_pixels_2d_slim[slim_index, 0] = int(
(-grid_scaled_2d_slim[slim_index, 0] / pixel_scales[0])
+ centres_scaled[0]
+ 0.5
)
grid_pixels_2d_slim[slim_index, 1] = int(
(grid_scaled_2d_slim[slim_index, 1] / pixel_scales[1])
+ centres_scaled[1]
+ 0.5
)
return grid_pixels_2d_slim
@numba_util.jit()
def grid_pixel_indexes_2d_slim_from(
grid_scaled_2d_slim: np.ndarray,
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
Convert a slimmed grid of 2D (y,x) scaled coordinates to a slimmed grid of pixel indexes. Pixel coordinates are
returned as integers such that they are the pixel from the top-left of the 2D grid going rights and then downwards.
The input and output grids are both slimmed and have shapes (total_pixels, 2) and (total_pixels,).
For example:
The pixel at the top-left, whose native index is [0,0], corresponds to slimmed pixel index 0.
The fifth pixel on the top row, whose native index is [0,5], corresponds to slimmed pixel index 4.
The first pixel on the second row, whose native index is [0,1], has slimmed pixel index 10 if a row has 10 pixels.
The scaled coordinate grid is defined by the class attribute origin, and coordinates are shifted to this
origin before computing their 1D grid pixel indexes.
The input and output grids are both of shape (total_pixels, 2).
Parameters
----------
grid_scaled_2d_slim: np.ndarray
The slimmed grid of 2D (y,x) coordinates in scaled units which is converted to slimmed pixel indexes.
shape_native
The (y,x) shape of the original 2D array the scaled coordinates were computed on.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the original 2D array.
origin : (float, flloat)
The (y,x) origin of the grid, which the scaled grid is shifted.
Returns
-------
ndarray
A grid of slimmed pixel indexes with dimensions (total_pixels,).
Examples
--------
grid_scaled_2d_slim = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])
grid_pixel_indexes_2d_slim = grid_pixel_indexes_2d_slim_from(grid_scaled_2d_slim=grid_scaled_2d_slim, shape=(2,2),
pixel_scales=(0.5, 0.5), origin=(0.0, 0.0))
"""
grid_pixels_2d_slim = grid_pixel_centres_2d_slim_from(
grid_scaled_2d_slim=grid_scaled_2d_slim,
shape_native=shape_native,
pixel_scales=pixel_scales,
origin=origin,
)
grid_pixel_indexes_2d_slim = np.zeros(grid_pixels_2d_slim.shape[0])
for slim_index in range(grid_pixels_2d_slim.shape[0]):
grid_pixel_indexes_2d_slim[slim_index] = int(
grid_pixels_2d_slim[slim_index, 0] * shape_native[1]
+ grid_pixels_2d_slim[slim_index, 1]
)
return grid_pixel_indexes_2d_slim
@numba_util.jit()
def grid_scaled_2d_slim_from(
grid_pixels_2d_slim: np.ndarray,
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
Convert a slimmed grid of 2D (y,x) pixel coordinates to a slimmed grid of 2D (y,x) scaled values.
The input and output grids are both slimmed and therefore shape (total_pixels, 2).
The pixel coordinate origin is at the top left corner of the grid, such that the pixel [0,0] corresponds to
the highest (most positive) y scaled coordinate and lowest (most negative) x scaled coordinate on the gird.
The scaled coordinate origin is defined by the class attribute origin, and coordinates are shifted to this
origin after computing their values from the 1D grid pixel indexes.
Parameters
----------
grid_pixels_2d_slim: np.ndarray
The slimmed grid of (y,x) coordinates in pixel values which is converted to scaled coordinates.
shape_native
The (y,x) shape of the original 2D array the scaled coordinates were computed on.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the original 2D array.
origin : (float, flloat)
The (y,x) origin of the grid, which the scaled grid is shifted.
Returns
-------
ndarray
A slimmed grid of 2d scaled coordinates with dimensions (total_pixels, 2).
Examples
--------
grid_pixels_2d_slim = np.array([[0,0], [0,1], [1,0], [1,1])
grid_pixels_2d_slim = grid_scaled_2d_slim_from(grid_pixels_2d_slim=grid_pixels_2d_slim, shape=(2,2),
pixel_scales=(0.5, 0.5), origin=(0.0, 0.0))
"""
grid_scaled_2d_slim = np.zeros((grid_pixels_2d_slim.shape[0], 2))
centres_scaled = geometry_util.central_scaled_coordinate_2d_from(
shape_native=shape_native, pixel_scales=pixel_scales, origin=origin
)
for slim_index in range(grid_scaled_2d_slim.shape[0]):
grid_scaled_2d_slim[slim_index, 0] = (
-(grid_pixels_2d_slim[slim_index, 0] - centres_scaled[0] - 0.5)
* pixel_scales[0]
)
grid_scaled_2d_slim[slim_index, 1] = (
grid_pixels_2d_slim[slim_index, 1] - centres_scaled[1] - 0.5
) * pixel_scales[1]
return grid_scaled_2d_slim
@numba_util.jit()
def grid_pixel_centres_2d_from(
grid_scaled_2d: np.ndarray,
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
Convert a native grid of 2D (y,x) scaled coordinates to a native grid of 2D (y,x) pixel values. Pixel coordinates
are returned as integers such that they map directly to the pixel they are contained within.
The input and output grids are both native resolution and therefore have shape (y_pixels, x_pixels, 2).
The pixel coordinate origin is at the top left corner of the grid, such that the pixel [0,0] corresponds to
the highest (most positive) y scaled coordinate and lowest (most negative) x scaled coordinate on the gird.
The scaled coordinate grid is defined by the class attribute origin, and coordinates are shifted to this
origin before computing their 1D grid pixel indexes.
Parameters
----------
grid_scaled_2d: np.ndarray
The native grid of 2D (y,x) coordinates in scaled units which is converted to pixel indexes.
shape_native
The (y,x) shape of the original 2D array the scaled coordinates were computed on.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the original 2D array.
origin : (float, flloat)
The (y,x) origin of the grid, which the scaled grid is shifted
Returns
-------
ndarray
A native grid of 2D (y,x) pixel indexes with dimensions (y_pixels, x_pixels, 2).
Examples
--------
grid_scaled_2d = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])
grid_pixel_centres_2d = grid_pixel_centres_2d_from(grid_scaled_2d=grid_scaled_2d, shape=(2,2),
pixel_scales=(0.5, 0.5), origin=(0.0, 0.0))
"""
grid_pixels_2d = np.zeros((grid_scaled_2d.shape[0], grid_scaled_2d.shape[1], 2))
centres_scaled = geometry_util.central_scaled_coordinate_2d_from(
shape_native=shape_native, pixel_scales=pixel_scales, origin=origin
)
for y in range(grid_scaled_2d.shape[0]):
for x in range(grid_scaled_2d.shape[1]):
grid_pixels_2d[y, x, 0] = int(
(-grid_scaled_2d[y, x, 0] / pixel_scales[0]) + centres_scaled[0] + 0.5
)
grid_pixels_2d[y, x, 1] = int(
(grid_scaled_2d[y, x, 1] / pixel_scales[1]) + centres_scaled[1] + 0.5
)
return grid_pixels_2d
@numba_util.jit()
def relocated_grid_via_jit_from(grid, border_grid):
"""
Relocate the coordinates of a grid to its border if they are outside the border, where the border is
defined as all pixels at the edge of the grid's mask (see *mask._border_1d_indexes*).
This is performed as follows:
1: Use the mean value of the grid's y and x coordinates to determine the origin of the grid.
2: Compute the radial distance of every grid coordinate from the origin.
3: For every coordinate, find its nearest pixel in the border.
4: Determine if it is outside the border, by comparing its radial distance from the origin to its paired
border pixel's radial distance.
5: If its radial distance is larger, use the ratio of radial distances to move the coordinate to the
border (if its inside the border, do nothing).
The method can be used on uniform or irregular grids, however for irregular grids the border of the
'image-plane' mask is used to define border pixels.
Parameters
----------
grid : Grid2D
The grid (uniform or irregular) whose pixels are to be relocated to the border edge if outside it.
border_grid : Grid2D
The grid of border (y,x) coordinates.
"""
grid_relocated = np.zeros(grid.shape)
grid_relocated[:, :] = grid[:, :]
border_origin = np.zeros(2)
border_origin[0] = np.mean(border_grid[:, 0])
border_origin[1] = np.mean(border_grid[:, 1])
border_grid_radii = np.sqrt(
np.add(
np.square(np.subtract(border_grid[:, 0], border_origin[0])),
np.square(np.subtract(border_grid[:, 1], border_origin[1])),
)
)
border_min_radii = np.min(border_grid_radii)
grid_radii = np.sqrt(
np.add(
np.square(np.subtract(grid[:, 0], border_origin[0])),
np.square(np.subtract(grid[:, 1], border_origin[1])),
)
)
for pixel_index in range(grid.shape[0]):
if grid_radii[pixel_index] > border_min_radii:
closest_pixel_index = np.argmin(
| np.square(grid[pixel_index, 0] - border_grid[:, 0]) | numpy.square |
# coding: utf-8
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Test the Logarithmic Units and Quantities
"""
from __future__ import (absolute_import, unicode_literals, division,
print_function)
from ...extern import six
from ...extern.six.moves import zip
import pickle
import itertools
import pytest
import numpy as np
from numpy.testing.utils import assert_allclose
from ...tests.helper import assert_quantity_allclose
from ... import units as u, constants as c
lu_units = [u.dex, u.mag, u.decibel]
lu_subclasses = [u.DexUnit, u.MagUnit, u.DecibelUnit]
lq_subclasses = [u.Dex, u.Magnitude, u.Decibel]
pu_sample = (u.dimensionless_unscaled, u.m, u.g/u.s**2, u.Jy)
class TestLogUnitCreation(object):
def test_logarithmic_units(self):
"""Check logarithmic units are set up correctly."""
assert u.dB.to(u.dex) == 0.1
assert u.dex.to(u.mag) == -2.5
assert u.mag.to(u.dB) == -4
@pytest.mark.parametrize('lu_unit, lu_cls', zip(lu_units, lu_subclasses))
def test_callable_units(self, lu_unit, lu_cls):
assert isinstance(lu_unit, u.UnitBase)
assert callable(lu_unit)
assert lu_unit._function_unit_class is lu_cls
@pytest.mark.parametrize('lu_unit', lu_units)
def test_equality_to_normal_unit_for_dimensionless(self, lu_unit):
lu = lu_unit()
assert lu == lu._default_function_unit # eg, MagUnit() == u.mag
assert lu._default_function_unit == lu # and u.mag == MagUnit()
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_call_units(self, lu_unit, physical_unit):
"""Create a LogUnit subclass using the callable unit and physical unit,
and do basic check that output is right."""
lu1 = lu_unit(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
def test_call_invalid_unit(self):
with pytest.raises(TypeError):
u.mag([])
with pytest.raises(ValueError):
u.mag(u.mag())
@pytest.mark.parametrize('lu_cls, physical_unit', itertools.product(
lu_subclasses + [u.LogUnit], pu_sample))
def test_subclass_creation(self, lu_cls, physical_unit):
"""Create a LogUnit subclass object for given physical unit,
and do basic check that output is right."""
lu1 = lu_cls(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
lu2 = lu_cls(physical_unit,
function_unit=2*lu1._default_function_unit)
assert lu2.physical_unit == physical_unit
assert lu2.function_unit == u.Unit(2*lu2._default_function_unit)
with pytest.raises(ValueError):
lu_cls(physical_unit, u.m)
def test_predefined_magnitudes():
assert_quantity_allclose((-21.1*u.STmag).physical,
1.*u.erg/u.cm**2/u.s/u.AA)
assert_quantity_allclose((-48.6*u.ABmag).physical,
1.*u.erg/u.cm**2/u.s/u.Hz)
assert_quantity_allclose((0*u.M_bol).physical, c.L_bol0)
assert_quantity_allclose((0*u.m_bol).physical,
c.L_bol0/(4.*np.pi*(10.*c.pc)**2))
def test_predefined_reinitialisation():
assert u.mag('ST') == u.STmag
assert u.mag('AB') == u.ABmag
assert u.mag('Bol') == u.M_bol
assert u.mag('bol') == u.m_bol
def test_predefined_string_roundtrip():
"""Ensure roundtripping; see #5015"""
with u.magnitude_zero_points.enable():
assert u.Unit(u.STmag.to_string()) == u.STmag
assert u.Unit(u.ABmag.to_string()) == u.ABmag
assert u.Unit(u.M_bol.to_string()) == u.M_bol
assert u.Unit(u.m_bol.to_string()) == u.m_bol
def test_inequality():
"""Check __ne__ works (regresssion for #5342)."""
lu1 = u.mag(u.Jy)
lu2 = u.dex(u.Jy)
lu3 = u.mag(u.Jy**2)
lu4 = lu3 - lu1
assert lu1 != lu2
assert lu1 != lu3
assert lu1 == lu4
class TestLogUnitStrings(object):
def test_str(self):
"""Do some spot checks that str, repr, etc. work as expected."""
lu1 = u.mag(u.Jy)
assert str(lu1) == 'mag(Jy)'
assert repr(lu1) == 'Unit("mag(Jy)")'
assert lu1.to_string('generic') == 'mag(Jy)'
with pytest.raises(ValueError):
lu1.to_string('fits')
lu2 = u.dex()
assert str(lu2) == 'dex'
assert repr(lu2) == 'Unit("dex(1)")'
assert lu2.to_string() == 'dex(1)'
lu3 = u.MagUnit(u.Jy, function_unit=2*u.mag)
assert str(lu3) == '2 mag(Jy)'
assert repr(lu3) == 'MagUnit("Jy", unit="2 mag")'
assert lu3.to_string() == '2 mag(Jy)'
lu4 = u.mag(u.ct)
assert lu4.to_string('generic') == 'mag(ct)'
assert lu4.to_string('latex') == ('$\\mathrm{mag}$$\\mathrm{\\left( '
'\\mathrm{ct} \\right)}$')
assert lu4._repr_latex_() == lu4.to_string('latex')
class TestLogUnitConversion(object):
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_physical_unit_conversion(self, lu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to their non-log counterparts."""
lu1 = lu_unit(physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(physical_unit, 0.) == 1.
assert physical_unit.is_equivalent(lu1)
assert physical_unit.to(lu1, 1.) == 0.
pu = u.Unit(8.*physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(pu, 0.) == 0.125
assert pu.is_equivalent(lu1)
assert_allclose(pu.to(lu1, 0.125), 0., atol=1.e-15)
# Check we round-trip.
value = np.linspace(0., 10., 6)
assert_allclose(pu.to(lu1, lu1.to(pu, value)), value, atol=1.e-15)
# And that we're not just returning True all the time.
pu2 = u.g
assert not lu1.is_equivalent(pu2)
with pytest.raises(u.UnitsError):
lu1.to(pu2)
assert not pu2.is_equivalent(lu1)
with pytest.raises(u.UnitsError):
pu2.to(lu1)
@pytest.mark.parametrize('lu_unit', lu_units)
def test_container_unit_conversion(self, lu_unit):
"""Check that conversion to logarithmic units (u.mag, u.dB, u.dex)
is only possible when the physical unit is dimensionless."""
values = np.linspace(0., 10., 6)
lu1 = lu_unit(u.dimensionless_unscaled)
assert lu1.is_equivalent(lu1.function_unit)
assert_allclose(lu1.to(lu1.function_unit, values), values)
lu2 = lu_unit(u.Jy)
assert not lu2.is_equivalent(lu2.function_unit)
with pytest.raises(u.UnitsError):
lu2.to(lu2.function_unit, values)
@pytest.mark.parametrize(
'flu_unit, tlu_unit, physical_unit',
itertools.product(lu_units, lu_units, pu_sample))
def test_subclass_conversion(self, flu_unit, tlu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to each other if they correspond to equivalent physical units."""
values = np.linspace(0., 10., 6)
flu = flu_unit(physical_unit)
tlu = tlu_unit(physical_unit)
assert flu.is_equivalent(tlu)
assert_allclose(flu.to(tlu), flu.function_unit.to(tlu.function_unit))
assert_allclose(flu.to(tlu, values),
values * flu.function_unit.to(tlu.function_unit))
tlu2 = tlu_unit(u.Unit(100.*physical_unit))
assert flu.is_equivalent(tlu2)
# Check that we round-trip.
assert_allclose(flu.to(tlu2, tlu2.to(flu, values)), values, atol=1.e-15)
tlu3 = tlu_unit(physical_unit.to_system(u.si)[0])
assert flu.is_equivalent(tlu3)
assert_allclose(flu.to(tlu3, tlu3.to(flu, values)), values, atol=1.e-15)
tlu4 = tlu_unit(u.g)
assert not flu.is_equivalent(tlu4)
with pytest.raises(u.UnitsError):
flu.to(tlu4, values)
def test_unit_decomposition(self):
lu = u.mag(u.Jy)
assert lu.decompose() == u.mag(u.Jy.decompose())
assert lu.decompose().physical_unit.bases == [u.kg, u.s]
assert lu.si == u.mag(u.Jy.si)
assert lu.si.physical_unit.bases == [u.kg, u.s]
assert lu.cgs == u.mag(u.Jy.cgs)
assert lu.cgs.physical_unit.bases == [u.g, u.s]
def test_unit_multiple_possible_equivalencies(self):
lu = u.mag(u.Jy)
assert lu.is_equivalent(pu_sample)
class TestLogUnitArithmetic(object):
def test_multiplication_division(self):
"""Check that multiplication/division with other units is only
possible when the physical unit is dimensionless, and that this
turns the unit into a normal one."""
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 * u.m
with pytest.raises(u.UnitsError):
u.m * lu1
with pytest.raises(u.UnitsError):
lu1 / lu1
for unit in (u.dimensionless_unscaled, u.m, u.mag, u.dex):
with pytest.raises(u.UnitsError):
lu1 / unit
lu2 = u.mag(u.dimensionless_unscaled)
with pytest.raises(u.UnitsError):
lu2 * lu1
with pytest.raises(u.UnitsError):
lu2 / lu1
# But dimensionless_unscaled can be cancelled.
assert lu2 / lu2 == u.dimensionless_unscaled
# With dimensionless, normal units are OK, but we return a plain unit.
tf = lu2 * u.m
tr = u.m * lu2
for t in (tf, tr):
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit * u.m
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(lu2.physical_unit)
# Now we essentially have a LogUnit with a prefactor of 100,
# so should be equivalent again.
t = tf / u.cm
with u.set_enabled_equivalencies(u.logarithmic()):
assert t.is_equivalent(lu2.function_unit)
assert_allclose(t.to(u.dimensionless_unscaled, np.arange(3.)/100.),
lu2.to(lu2.physical_unit, np.arange(3.)))
# If we effectively remove lu1, a normal unit should be returned.
t2 = tf / lu2
assert not isinstance(t2, type(lu2))
assert t2 == u.m
t3 = tf / lu2.function_unit
assert not isinstance(t3, type(lu2))
assert t3 == u.m
# For completeness, also ensure non-sensical operations fail
with pytest.raises(TypeError):
lu1 * object()
with pytest.raises(TypeError):
slice(None) * lu1
with pytest.raises(TypeError):
lu1 / []
with pytest.raises(TypeError):
1 / lu1
@pytest.mark.parametrize('power', (2, 0.5, 1, 0))
def test_raise_to_power(self, power):
"""Check that raising LogUnits to some power is only possible when the
physical unit is dimensionless, and that conversion is turned off when
the resulting logarithmic unit (such as mag**2) is incompatible."""
lu1 = u.mag(u.Jy)
if power == 0:
assert lu1 ** power == u.dimensionless_unscaled
elif power == 1:
assert lu1 ** power == lu1
else:
with pytest.raises(u.UnitsError):
lu1 ** power
# With dimensionless, though, it works, but returns a normal unit.
lu2 = u.mag(u.dimensionless_unscaled)
t = lu2**power
if power == 0:
assert t == u.dimensionless_unscaled
elif power == 1:
assert t == lu2
else:
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit**power
# also check we roundtrip
t2 = t**(1./power)
assert t2 == lu2.function_unit
with u.set_enabled_equivalencies(u.logarithmic()):
assert_allclose(t2.to(u.dimensionless_unscaled, np.arange(3.)),
lu2.to(lu2.physical_unit, np.arange(3.)))
@pytest.mark.parametrize('other', pu_sample)
def test_addition_subtraction_to_normal_units_fails(self, other):
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 + other
with pytest.raises(u.UnitsError):
lu1 - other
with pytest.raises(u.UnitsError):
other - lu1
def test_addition_subtraction_to_non_units_fails(self):
lu1 = u.mag(u.Jy)
with pytest.raises(TypeError):
lu1 + 1.
with pytest.raises(TypeError):
lu1 - [1., 2., 3.]
@pytest.mark.parametrize(
'other', (u.mag, u.mag(), u.mag(u.Jy), u.mag(u.m),
u.Unit(2*u.mag), u.MagUnit('', 2.*u.mag)))
def test_addition_subtraction(self, other):
"""Check physical units are changed appropriately"""
lu1 = u.mag(u.Jy)
other_pu = getattr(other, 'physical_unit', u.dimensionless_unscaled)
lu_sf = lu1 + other
assert lu_sf.is_equivalent(lu1.physical_unit * other_pu)
lu_sr = other + lu1
assert lu_sr.is_equivalent(lu1.physical_unit * other_pu)
lu_df = lu1 - other
assert lu_df.is_equivalent(lu1.physical_unit / other_pu)
lu_dr = other - lu1
assert lu_dr.is_equivalent(other_pu / lu1.physical_unit)
def test_complicated_addition_subtraction(self):
"""for fun, a more complicated example of addition and subtraction"""
dm0 = u.Unit('DM', 1./(4.*np.pi*(10.*u.pc)**2))
lu_dm = u.mag(dm0)
lu_absST = u.STmag - lu_dm
assert lu_absST.is_equivalent(u.erg/u.s/u.AA)
def test_neg_pos(self):
lu1 = u.mag(u.Jy)
neg_lu = -lu1
assert neg_lu != lu1
assert neg_lu.physical_unit == u.Jy**-1
assert -neg_lu == lu1
pos_lu = +lu1
assert pos_lu is not lu1
assert pos_lu == lu1
def test_pickle():
lu1 = u.dex(u.cm/u.s**2)
s = pickle.dumps(lu1)
lu2 = pickle.loads(s)
assert lu1 == lu2
def test_hashable():
lu1 = u.dB(u.mW)
lu2 = u.dB(u.m)
lu3 = u.dB(u.mW)
assert hash(lu1) != hash(lu2)
assert hash(lu1) == hash(lu3)
luset = {lu1, lu2, lu3}
assert len(luset) == 2
class TestLogQuantityCreation(object):
@pytest.mark.parametrize('lq, lu', zip(lq_subclasses + [u.LogQuantity],
lu_subclasses + [u.LogUnit]))
def test_logarithmic_quantities(self, lq, lu):
"""Check logarithmic quantities are all set up correctly"""
assert lq._unit_class == lu
assert type(lu()._quantity_class(1.)) is lq
@pytest.mark.parametrize('lq_cls, physical_unit',
itertools.product(lq_subclasses, pu_sample))
def test_subclass_creation(self, lq_cls, physical_unit):
"""Create LogQuantity subclass objects for some physical units,
and basic check on transformations"""
value = np.arange(1., 10.)
log_q = lq_cls(value * physical_unit)
assert log_q.unit.physical_unit == physical_unit
assert log_q.unit.function_unit == log_q.unit._default_function_unit
assert_allclose(log_q.physical.value, value)
with pytest.raises(ValueError):
lq_cls(value, physical_unit)
@pytest.mark.parametrize(
'unit', (u.mag, u.mag(), u.mag(u.Jy), u.mag(u.m),
u.Unit(2*u.mag), u.MagUnit('', 2.*u.mag),
u.MagUnit(u.Jy, -1*u.mag), u.MagUnit(u.m, -2.*u.mag)))
def test_different_units(self, unit):
q = u.Magnitude(1.23, unit)
assert q.unit.function_unit == getattr(unit, 'function_unit', unit)
assert q.unit.physical_unit is getattr(unit, 'physical_unit',
u.dimensionless_unscaled)
@pytest.mark.parametrize('value, unit', (
(1.*u.mag(u.Jy), None),
(1.*u.dex(u.Jy), None),
(1.*u.mag(u.W/u.m**2/u.Hz), u.mag(u.Jy)),
(1.*u.dex(u.W/u.m**2/u.Hz), u.mag(u.Jy))))
def test_function_values(self, value, unit):
lq = u.Magnitude(value, unit)
assert lq == value
assert lq.unit.function_unit == u.mag
assert lq.unit.physical_unit == getattr(unit, 'physical_unit',
value.unit.physical_unit)
@pytest.mark.parametrize(
'unit', (u.mag(), u.mag(u.Jy), u.mag(u.m), u.MagUnit('', 2.*u.mag),
u.MagUnit(u.Jy, -1*u.mag), u.MagUnit(u.m, -2.*u.mag)))
def test_indirect_creation(self, unit):
q1 = 2.5 * unit
assert isinstance(q1, u.Magnitude)
assert q1.value == 2.5
assert q1.unit == unit
pv = 100. * unit.physical_unit
q2 = unit * pv
assert q2.unit == unit
assert q2.unit.physical_unit == pv.unit
assert q2.to_value(unit.physical_unit) == 100.
assert (q2._function_view / u.mag).to_value(1) == -5.
q3 = unit / 0.4
assert q3 == q1
def test_from_view(self):
# Cannot view a physical quantity as a function quantity, since the
# values would change.
q = [100., 1000.] * u.cm/u.s**2
with pytest.raises(TypeError):
q.view(u.Dex)
# But fine if we have the right magnitude.
q = [2., 3.] * u.dex
lq = q.view(u.Dex)
assert isinstance(lq, u.Dex)
assert lq.unit.physical_unit == u.dimensionless_unscaled
assert np.all(q == lq)
def test_using_quantity_class(self):
"""Check that we can use Quantity if we have subok=True"""
# following issue #5851
lu = u.dex(u.AA)
with pytest.raises(u.UnitTypeError):
u.Quantity(1., lu)
q = u.Quantity(1., lu, subok=True)
assert type(q) is lu._quantity_class
def test_conversion_to_and_from_physical_quantities():
"""Ensures we can convert from regular quantities."""
mst = [10., 12., 14.] * u.STmag
flux_lambda = mst.physical
mst_roundtrip = flux_lambda.to(u.STmag)
# check we return a logquantity; see #5178.
assert isinstance(mst_roundtrip, u.Magnitude)
assert mst_roundtrip.unit == mst.unit
assert_allclose(mst_roundtrip.value, mst.value)
wave = [4956.8, 4959.55, 4962.3] * u.AA
flux_nu = mst.to(u.Jy, equivalencies=u.spectral_density(wave))
mst_roundtrip2 = flux_nu.to(u.STmag, u.spectral_density(wave))
assert isinstance(mst_roundtrip2, u.Magnitude)
assert mst_roundtrip2.unit == mst.unit
assert_allclose(mst_roundtrip2.value, mst.value)
def test_quantity_decomposition():
lq = 10.*u.mag(u.Jy)
assert lq.decompose() == lq
assert lq.decompose().unit.physical_unit.bases == [u.kg, u.s]
assert lq.si == lq
assert lq.si.unit.physical_unit.bases == [u.kg, u.s]
assert lq.cgs == lq
assert lq.cgs.unit.physical_unit.bases == [u.g, u.s]
class TestLogQuantityViews(object):
def setup(self):
self.lq = u.Magnitude(np.arange(10.) * u.Jy)
self.lq2 = u.Magnitude(np.arange(5.))
def test_value_view(self):
lq_value = self.lq.value
assert type(lq_value) is np.ndarray
lq_value[2] = -1.
assert np.all(self.lq.value == lq_value)
def test_function_view(self):
lq_fv = self.lq._function_view
assert type(lq_fv) is u.Quantity
assert lq_fv.unit is self.lq.unit.function_unit
lq_fv[3] = -2. * lq_fv.unit
assert np.all(self.lq.value == lq_fv.value)
def test_quantity_view(self):
# Cannot view as Quantity, since the unit cannot be represented.
with pytest.raises(TypeError):
self.lq.view(u.Quantity)
# But a dimensionless one is fine.
q2 = self.lq2.view(u.Quantity)
assert q2.unit is u.mag
assert np.all(q2.value == self.lq2.value)
lq3 = q2.view(u.Magnitude)
assert type(lq3.unit) is u.MagUnit
assert lq3.unit.physical_unit == u.dimensionless_unscaled
assert np.all(lq3 == self.lq2)
class TestLogQuantitySlicing(object):
def test_item_get_and_set(self):
lq1 = u.Magnitude( | np.arange(1., 11.) | numpy.arange |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[2].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[2].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
plt.savefig('jss_figures/DFA_different_trends.png')
plt.show()
# plot 6b
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences Zoomed Region', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[0].set_ylim(-5.5, 5.5)
axs[0].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].set_ylim(-5.5, 5.5)
axs[1].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([np.pi, (3 / 2) * np.pi])
axs[2].set_xticklabels([r'$\pi$', r'$\frac{3}{2}\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].set_ylim(-5.5, 5.5)
axs[2].set_xlim(0.95 * np.pi, 1.55 * np.pi)
plt.savefig('jss_figures/DFA_different_trends_zoomed.png')
plt.show()
hs_ouputs = hilbert_spectrum(time, imfs_51, hts_51, ifs_51, max_frequency=12, plot=False)
# plot 6c
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Simple Sinusoidal Time Seres with Added Noise', 50))
x_hs, y, z = hs_ouputs
z_min, z_max = 0, | np.abs(z) | numpy.abs |
# coding: utf-8
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Test the Logarithmic Units and Quantities
"""
from __future__ import (absolute_import, unicode_literals, division,
print_function)
from ...extern import six
from ...extern.six.moves import zip
import pickle
import itertools
import pytest
import numpy as np
from numpy.testing.utils import assert_allclose
from ...tests.helper import assert_quantity_allclose
from ... import units as u, constants as c
lu_units = [u.dex, u.mag, u.decibel]
lu_subclasses = [u.DexUnit, u.MagUnit, u.DecibelUnit]
lq_subclasses = [u.Dex, u.Magnitude, u.Decibel]
pu_sample = (u.dimensionless_unscaled, u.m, u.g/u.s**2, u.Jy)
class TestLogUnitCreation(object):
def test_logarithmic_units(self):
"""Check logarithmic units are set up correctly."""
assert u.dB.to(u.dex) == 0.1
assert u.dex.to(u.mag) == -2.5
assert u.mag.to(u.dB) == -4
@pytest.mark.parametrize('lu_unit, lu_cls', zip(lu_units, lu_subclasses))
def test_callable_units(self, lu_unit, lu_cls):
assert isinstance(lu_unit, u.UnitBase)
assert callable(lu_unit)
assert lu_unit._function_unit_class is lu_cls
@pytest.mark.parametrize('lu_unit', lu_units)
def test_equality_to_normal_unit_for_dimensionless(self, lu_unit):
lu = lu_unit()
assert lu == lu._default_function_unit # eg, MagUnit() == u.mag
assert lu._default_function_unit == lu # and u.mag == MagUnit()
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_call_units(self, lu_unit, physical_unit):
"""Create a LogUnit subclass using the callable unit and physical unit,
and do basic check that output is right."""
lu1 = lu_unit(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
def test_call_invalid_unit(self):
with pytest.raises(TypeError):
u.mag([])
with pytest.raises(ValueError):
u.mag(u.mag())
@pytest.mark.parametrize('lu_cls, physical_unit', itertools.product(
lu_subclasses + [u.LogUnit], pu_sample))
def test_subclass_creation(self, lu_cls, physical_unit):
"""Create a LogUnit subclass object for given physical unit,
and do basic check that output is right."""
lu1 = lu_cls(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
lu2 = lu_cls(physical_unit,
function_unit=2*lu1._default_function_unit)
assert lu2.physical_unit == physical_unit
assert lu2.function_unit == u.Unit(2*lu2._default_function_unit)
with pytest.raises(ValueError):
lu_cls(physical_unit, u.m)
def test_predefined_magnitudes():
assert_quantity_allclose((-21.1*u.STmag).physical,
1.*u.erg/u.cm**2/u.s/u.AA)
assert_quantity_allclose((-48.6*u.ABmag).physical,
1.*u.erg/u.cm**2/u.s/u.Hz)
assert_quantity_allclose((0*u.M_bol).physical, c.L_bol0)
assert_quantity_allclose((0*u.m_bol).physical,
c.L_bol0/(4.*np.pi*(10.*c.pc)**2))
def test_predefined_reinitialisation():
assert u.mag('ST') == u.STmag
assert u.mag('AB') == u.ABmag
assert u.mag('Bol') == u.M_bol
assert u.mag('bol') == u.m_bol
def test_predefined_string_roundtrip():
"""Ensure roundtripping; see #5015"""
with u.magnitude_zero_points.enable():
assert u.Unit(u.STmag.to_string()) == u.STmag
assert u.Unit(u.ABmag.to_string()) == u.ABmag
assert u.Unit(u.M_bol.to_string()) == u.M_bol
assert u.Unit(u.m_bol.to_string()) == u.m_bol
def test_inequality():
"""Check __ne__ works (regresssion for #5342)."""
lu1 = u.mag(u.Jy)
lu2 = u.dex(u.Jy)
lu3 = u.mag(u.Jy**2)
lu4 = lu3 - lu1
assert lu1 != lu2
assert lu1 != lu3
assert lu1 == lu4
class TestLogUnitStrings(object):
def test_str(self):
"""Do some spot checks that str, repr, etc. work as expected."""
lu1 = u.mag(u.Jy)
assert str(lu1) == 'mag(Jy)'
assert repr(lu1) == 'Unit("mag(Jy)")'
assert lu1.to_string('generic') == 'mag(Jy)'
with pytest.raises(ValueError):
lu1.to_string('fits')
lu2 = u.dex()
assert str(lu2) == 'dex'
assert repr(lu2) == 'Unit("dex(1)")'
assert lu2.to_string() == 'dex(1)'
lu3 = u.MagUnit(u.Jy, function_unit=2*u.mag)
assert str(lu3) == '2 mag(Jy)'
assert repr(lu3) == 'MagUnit("Jy", unit="2 mag")'
assert lu3.to_string() == '2 mag(Jy)'
lu4 = u.mag(u.ct)
assert lu4.to_string('generic') == 'mag(ct)'
assert lu4.to_string('latex') == ('$\\mathrm{mag}$$\\mathrm{\\left( '
'\\mathrm{ct} \\right)}$')
assert lu4._repr_latex_() == lu4.to_string('latex')
class TestLogUnitConversion(object):
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_physical_unit_conversion(self, lu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to their non-log counterparts."""
lu1 = lu_unit(physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(physical_unit, 0.) == 1.
assert physical_unit.is_equivalent(lu1)
assert physical_unit.to(lu1, 1.) == 0.
pu = u.Unit(8.*physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(pu, 0.) == 0.125
assert pu.is_equivalent(lu1)
assert_allclose(pu.to(lu1, 0.125), 0., atol=1.e-15)
# Check we round-trip.
value = np.linspace(0., 10., 6)
assert_allclose(pu.to(lu1, lu1.to(pu, value)), value, atol=1.e-15)
# And that we're not just returning True all the time.
pu2 = u.g
assert not lu1.is_equivalent(pu2)
with pytest.raises(u.UnitsError):
lu1.to(pu2)
assert not pu2.is_equivalent(lu1)
with pytest.raises(u.UnitsError):
pu2.to(lu1)
@pytest.mark.parametrize('lu_unit', lu_units)
def test_container_unit_conversion(self, lu_unit):
"""Check that conversion to logarithmic units (u.mag, u.dB, u.dex)
is only possible when the physical unit is dimensionless."""
values = np.linspace(0., 10., 6)
lu1 = lu_unit(u.dimensionless_unscaled)
assert lu1.is_equivalent(lu1.function_unit)
assert_allclose(lu1.to(lu1.function_unit, values), values)
lu2 = lu_unit(u.Jy)
assert not lu2.is_equivalent(lu2.function_unit)
with pytest.raises(u.UnitsError):
lu2.to(lu2.function_unit, values)
@pytest.mark.parametrize(
'flu_unit, tlu_unit, physical_unit',
itertools.product(lu_units, lu_units, pu_sample))
def test_subclass_conversion(self, flu_unit, tlu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to each other if they correspond to equivalent physical units."""
values = np.linspace(0., 10., 6)
flu = flu_unit(physical_unit)
tlu = tlu_unit(physical_unit)
assert flu.is_equivalent(tlu)
assert_allclose(flu.to(tlu), flu.function_unit.to(tlu.function_unit))
assert_allclose(flu.to(tlu, values),
values * flu.function_unit.to(tlu.function_unit))
tlu2 = tlu_unit(u.Unit(100.*physical_unit))
assert flu.is_equivalent(tlu2)
# Check that we round-trip.
assert_allclose(flu.to(tlu2, tlu2.to(flu, values)), values, atol=1.e-15)
tlu3 = tlu_unit(physical_unit.to_system(u.si)[0])
assert flu.is_equivalent(tlu3)
assert_allclose(flu.to(tlu3, tlu3.to(flu, values)), values, atol=1.e-15)
tlu4 = tlu_unit(u.g)
assert not flu.is_equivalent(tlu4)
with pytest.raises(u.UnitsError):
flu.to(tlu4, values)
def test_unit_decomposition(self):
lu = u.mag(u.Jy)
assert lu.decompose() == u.mag(u.Jy.decompose())
assert lu.decompose().physical_unit.bases == [u.kg, u.s]
assert lu.si == u.mag(u.Jy.si)
assert lu.si.physical_unit.bases == [u.kg, u.s]
assert lu.cgs == u.mag(u.Jy.cgs)
assert lu.cgs.physical_unit.bases == [u.g, u.s]
def test_unit_multiple_possible_equivalencies(self):
lu = u.mag(u.Jy)
assert lu.is_equivalent(pu_sample)
class TestLogUnitArithmetic(object):
def test_multiplication_division(self):
"""Check that multiplication/division with other units is only
possible when the physical unit is dimensionless, and that this
turns the unit into a normal one."""
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 * u.m
with pytest.raises(u.UnitsError):
u.m * lu1
with pytest.raises(u.UnitsError):
lu1 / lu1
for unit in (u.dimensionless_unscaled, u.m, u.mag, u.dex):
with pytest.raises(u.UnitsError):
lu1 / unit
lu2 = u.mag(u.dimensionless_unscaled)
with pytest.raises(u.UnitsError):
lu2 * lu1
with pytest.raises(u.UnitsError):
lu2 / lu1
# But dimensionless_unscaled can be cancelled.
assert lu2 / lu2 == u.dimensionless_unscaled
# With dimensionless, normal units are OK, but we return a plain unit.
tf = lu2 * u.m
tr = u.m * lu2
for t in (tf, tr):
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit * u.m
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(lu2.physical_unit)
# Now we essentially have a LogUnit with a prefactor of 100,
# so should be equivalent again.
t = tf / u.cm
with u.set_enabled_equivalencies(u.logarithmic()):
assert t.is_equivalent(lu2.function_unit)
assert_allclose(t.to(u.dimensionless_unscaled, np.arange(3.)/100.),
lu2.to(lu2.physical_unit, np.arange(3.)))
# If we effectively remove lu1, a normal unit should be returned.
t2 = tf / lu2
assert not isinstance(t2, type(lu2))
assert t2 == u.m
t3 = tf / lu2.function_unit
assert not isinstance(t3, type(lu2))
assert t3 == u.m
# For completeness, also ensure non-sensical operations fail
with pytest.raises(TypeError):
lu1 * object()
with pytest.raises(TypeError):
slice(None) * lu1
with pytest.raises(TypeError):
lu1 / []
with pytest.raises(TypeError):
1 / lu1
@pytest.mark.parametrize('power', (2, 0.5, 1, 0))
def test_raise_to_power(self, power):
"""Check that raising LogUnits to some power is only possible when the
physical unit is dimensionless, and that conversion is turned off when
the resulting logarithmic unit (such as mag**2) is incompatible."""
lu1 = u.mag(u.Jy)
if power == 0:
assert lu1 ** power == u.dimensionless_unscaled
elif power == 1:
assert lu1 ** power == lu1
else:
with pytest.raises(u.UnitsError):
lu1 ** power
# With dimensionless, though, it works, but returns a normal unit.
lu2 = u.mag(u.dimensionless_unscaled)
t = lu2**power
if power == 0:
assert t == u.dimensionless_unscaled
elif power == 1:
assert t == lu2
else:
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit**power
# also check we roundtrip
t2 = t**(1./power)
assert t2 == lu2.function_unit
with u.set_enabled_equivalencies(u.logarithmic()):
assert_allclose(t2.to(u.dimensionless_unscaled, np.arange(3.)),
lu2.to(lu2.physical_unit, np.arange(3.)))
@pytest.mark.parametrize('other', pu_sample)
def test_addition_subtraction_to_normal_units_fails(self, other):
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 + other
with pytest.raises(u.UnitsError):
lu1 - other
with pytest.raises(u.UnitsError):
other - lu1
def test_addition_subtraction_to_non_units_fails(self):
lu1 = u.mag(u.Jy)
with pytest.raises(TypeError):
lu1 + 1.
with pytest.raises(TypeError):
lu1 - [1., 2., 3.]
@pytest.mark.parametrize(
'other', (u.mag, u.mag(), u.mag(u.Jy), u.mag(u.m),
u.Unit(2*u.mag), u.MagUnit('', 2.*u.mag)))
def test_addition_subtraction(self, other):
"""Check physical units are changed appropriately"""
lu1 = u.mag(u.Jy)
other_pu = getattr(other, 'physical_unit', u.dimensionless_unscaled)
lu_sf = lu1 + other
assert lu_sf.is_equivalent(lu1.physical_unit * other_pu)
lu_sr = other + lu1
assert lu_sr.is_equivalent(lu1.physical_unit * other_pu)
lu_df = lu1 - other
assert lu_df.is_equivalent(lu1.physical_unit / other_pu)
lu_dr = other - lu1
assert lu_dr.is_equivalent(other_pu / lu1.physical_unit)
def test_complicated_addition_subtraction(self):
"""for fun, a more complicated example of addition and subtraction"""
dm0 = u.Unit('DM', 1./(4.*np.pi*(10.*u.pc)**2))
lu_dm = u.mag(dm0)
lu_absST = u.STmag - lu_dm
assert lu_absST.is_equivalent(u.erg/u.s/u.AA)
def test_neg_pos(self):
lu1 = u.mag(u.Jy)
neg_lu = -lu1
assert neg_lu != lu1
assert neg_lu.physical_unit == u.Jy**-1
assert -neg_lu == lu1
pos_lu = +lu1
assert pos_lu is not lu1
assert pos_lu == lu1
def test_pickle():
lu1 = u.dex(u.cm/u.s**2)
s = pickle.dumps(lu1)
lu2 = pickle.loads(s)
assert lu1 == lu2
def test_hashable():
lu1 = u.dB(u.mW)
lu2 = u.dB(u.m)
lu3 = u.dB(u.mW)
assert hash(lu1) != hash(lu2)
assert hash(lu1) == hash(lu3)
luset = {lu1, lu2, lu3}
assert len(luset) == 2
class TestLogQuantityCreation(object):
@pytest.mark.parametrize('lq, lu', zip(lq_subclasses + [u.LogQuantity],
lu_subclasses + [u.LogUnit]))
def test_logarithmic_quantities(self, lq, lu):
"""Check logarithmic quantities are all set up correctly"""
assert lq._unit_class == lu
assert type(lu()._quantity_class(1.)) is lq
@pytest.mark.parametrize('lq_cls, physical_unit',
itertools.product(lq_subclasses, pu_sample))
def test_subclass_creation(self, lq_cls, physical_unit):
"""Create LogQuantity subclass objects for some physical units,
and basic check on transformations"""
value = np.arange(1., 10.)
log_q = lq_cls(value * physical_unit)
assert log_q.unit.physical_unit == physical_unit
assert log_q.unit.function_unit == log_q.unit._default_function_unit
assert_allclose(log_q.physical.value, value)
with pytest.raises(ValueError):
lq_cls(value, physical_unit)
@pytest.mark.parametrize(
'unit', (u.mag, u.mag(), u.mag(u.Jy), u.mag(u.m),
u.Unit(2*u.mag), u.MagUnit('', 2.*u.mag),
u.MagUnit(u.Jy, -1*u.mag), u.MagUnit(u.m, -2.*u.mag)))
def test_different_units(self, unit):
q = u.Magnitude(1.23, unit)
assert q.unit.function_unit == getattr(unit, 'function_unit', unit)
assert q.unit.physical_unit is getattr(unit, 'physical_unit',
u.dimensionless_unscaled)
@pytest.mark.parametrize('value, unit', (
(1.*u.mag(u.Jy), None),
(1.*u.dex(u.Jy), None),
(1.*u.mag(u.W/u.m**2/u.Hz), u.mag(u.Jy)),
(1.*u.dex(u.W/u.m**2/u.Hz), u.mag(u.Jy))))
def test_function_values(self, value, unit):
lq = u.Magnitude(value, unit)
assert lq == value
assert lq.unit.function_unit == u.mag
assert lq.unit.physical_unit == getattr(unit, 'physical_unit',
value.unit.physical_unit)
@pytest.mark.parametrize(
'unit', (u.mag(), u.mag(u.Jy), u.mag(u.m), u.MagUnit('', 2.*u.mag),
u.MagUnit(u.Jy, -1*u.mag), u.MagUnit(u.m, -2.*u.mag)))
def test_indirect_creation(self, unit):
q1 = 2.5 * unit
assert isinstance(q1, u.Magnitude)
assert q1.value == 2.5
assert q1.unit == unit
pv = 100. * unit.physical_unit
q2 = unit * pv
assert q2.unit == unit
assert q2.unit.physical_unit == pv.unit
assert q2.to_value(unit.physical_unit) == 100.
assert (q2._function_view / u.mag).to_value(1) == -5.
q3 = unit / 0.4
assert q3 == q1
def test_from_view(self):
# Cannot view a physical quantity as a function quantity, since the
# values would change.
q = [100., 1000.] * u.cm/u.s**2
with pytest.raises(TypeError):
q.view(u.Dex)
# But fine if we have the right magnitude.
q = [2., 3.] * u.dex
lq = q.view(u.Dex)
assert isinstance(lq, u.Dex)
assert lq.unit.physical_unit == u.dimensionless_unscaled
assert np.all(q == lq)
def test_using_quantity_class(self):
"""Check that we can use Quantity if we have subok=True"""
# following issue #5851
lu = u.dex(u.AA)
with pytest.raises(u.UnitTypeError):
u.Quantity(1., lu)
q = u.Quantity(1., lu, subok=True)
assert type(q) is lu._quantity_class
def test_conversion_to_and_from_physical_quantities():
"""Ensures we can convert from regular quantities."""
mst = [10., 12., 14.] * u.STmag
flux_lambda = mst.physical
mst_roundtrip = flux_lambda.to(u.STmag)
# check we return a logquantity; see #5178.
assert isinstance(mst_roundtrip, u.Magnitude)
assert mst_roundtrip.unit == mst.unit
assert_allclose(mst_roundtrip.value, mst.value)
wave = [4956.8, 4959.55, 4962.3] * u.AA
flux_nu = mst.to(u.Jy, equivalencies=u.spectral_density(wave))
mst_roundtrip2 = flux_nu.to(u.STmag, u.spectral_density(wave))
assert isinstance(mst_roundtrip2, u.Magnitude)
assert mst_roundtrip2.unit == mst.unit
assert_allclose(mst_roundtrip2.value, mst.value)
def test_quantity_decomposition():
lq = 10.*u.mag(u.Jy)
assert lq.decompose() == lq
assert lq.decompose().unit.physical_unit.bases == [u.kg, u.s]
assert lq.si == lq
assert lq.si.unit.physical_unit.bases == [u.kg, u.s]
assert lq.cgs == lq
assert lq.cgs.unit.physical_unit.bases == [u.g, u.s]
class TestLogQuantityViews(object):
def setup(self):
self.lq = u.Magnitude(np.arange(10.) * u.Jy)
self.lq2 = u.Magnitude(np.arange(5.))
def test_value_view(self):
lq_value = self.lq.value
assert type(lq_value) is np.ndarray
lq_value[2] = -1.
assert np.all(self.lq.value == lq_value)
def test_function_view(self):
lq_fv = self.lq._function_view
assert type(lq_fv) is u.Quantity
assert lq_fv.unit is self.lq.unit.function_unit
lq_fv[3] = -2. * lq_fv.unit
assert np.all(self.lq.value == lq_fv.value)
def test_quantity_view(self):
# Cannot view as Quantity, since the unit cannot be represented.
with pytest.raises(TypeError):
self.lq.view(u.Quantity)
# But a dimensionless one is fine.
q2 = self.lq2.view(u.Quantity)
assert q2.unit is u.mag
assert np.all(q2.value == self.lq2.value)
lq3 = q2.view(u.Magnitude)
assert type(lq3.unit) is u.MagUnit
assert lq3.unit.physical_unit == u.dimensionless_unscaled
assert np.all(lq3 == self.lq2)
class TestLogQuantitySlicing(object):
def test_item_get_and_set(self):
lq1 = u.Magnitude(np.arange(1., 11.)*u.Jy)
assert lq1[9] == u.Magnitude(10.*u.Jy)
lq1[2] = 100.*u.Jy
assert lq1[2] == u.Magnitude(100.*u.Jy)
with pytest.raises(u.UnitsError):
lq1[2] = 100.*u.m
with pytest.raises(u.UnitsError):
lq1[2] = 100.*u.mag
with pytest.raises(u.UnitsError):
lq1[2] = u.Magnitude(100.*u.m)
assert lq1[2] == u.Magnitude(100.*u.Jy)
def test_slice_get_and_set(self):
lq1 = u.Magnitude(np.arange(1., 10.)*u.Jy)
lq1[2:4] = 100.*u.Jy
assert np.all(lq1[2:4] == u.Magnitude(100.*u.Jy))
with pytest.raises(u.UnitsError):
lq1[2:4] = 100.*u.m
with pytest.raises(u.UnitsError):
lq1[2:4] = 100.*u.mag
with pytest.raises(u.UnitsError):
lq1[2:4] = u.Magnitude(100.*u.m)
assert np.all(lq1[2] == u.Magnitude(100.*u.Jy))
class TestLogQuantityArithmetic(object):
def test_multiplication_division(self):
"""Check that multiplication/division with other quantities is only
possible when the physical unit is dimensionless, and that this turns
the result into a normal quantity."""
lq = u.Magnitude(np.arange(1., 11.)*u.Jy)
with pytest.raises(u.UnitsError):
lq * (1.*u.m)
with pytest.raises(u.UnitsError):
(1.*u.m) * lq
with pytest.raises(u.UnitsError):
lq / lq
for unit in (u.m, u.mag, u.dex):
with pytest.raises(u.UnitsError):
lq / unit
lq2 = u.Magnitude(np.arange(1, 11.))
with pytest.raises(u.UnitsError):
lq2 * lq
with pytest.raises(u.UnitsError):
lq2 / lq
with pytest.raises(u.UnitsError):
lq / lq2
# but dimensionless_unscaled can be cancelled
r = lq2 / u.Magnitude(2.)
assert r.unit == u.dimensionless_unscaled
assert np.all(r.value == lq2.value/2.)
# with dimensionless, normal units OK, but return normal quantities
tf = lq2 * u.m
tr = u.m * lq2
for t in (tf, tr):
assert not isinstance(t, type(lq2))
assert t.unit == lq2.unit.function_unit * u.m
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(lq2.unit.physical_unit)
t = tf / (50.*u.cm)
# now we essentially have the same quantity but with a prefactor of 2
assert t.unit.is_equivalent(lq2.unit.function_unit)
assert_allclose(t.to(lq2.unit.function_unit), lq2._function_view*2)
@pytest.mark.parametrize('power', (2, 0.5, 1, 0))
def test_raise_to_power(self, power):
"""Check that raising LogQuantities to some power is only possible when
the physical unit is dimensionless, and that conversion is turned off
when the resulting logarithmic unit (say, mag**2) is incompatible."""
lq = u.Magnitude(np.arange(1., 4.)*u.Jy)
if power == 0:
assert np.all(lq ** power == 1.)
elif power == 1:
assert np.all(lq ** power == lq)
else:
with pytest.raises(u.UnitsError):
lq ** power
# with dimensionless, it works, but falls back to normal quantity
# (except for power=1)
lq2 = u.Magnitude(np.arange(10.))
t = lq2**power
if power == 0:
assert t.unit is u.dimensionless_unscaled
assert np.all(t.value == 1.)
elif power == 1:
assert np.all(t == lq2)
else:
assert not isinstance(t, type(lq2))
assert t.unit == lq2.unit.function_unit ** power
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(u.dimensionless_unscaled)
def test_error_on_lq_as_power(self):
lq = u.Magnitude( | np.arange(1., 4.) | numpy.arange |
try:
import importlib.resources as pkg_resources
except ImportError:
# Try backported to PY<37 `importlib_resources`.
import importlib_resources as pkg_resources
from . import images
from gym import Env, spaces
from time import time
import numpy as np
from copy import copy
import colorsys
import pygame
from pygame.transform import scale
class MinesweeperEnv(Env):
def __init__(self, grid_shape=(10, 15), bombs_density=0.1, n_bombs=None, impact_size=3, max_time=999, chicken=False):
self.grid_shape = grid_shape
self.grid_size = np.prod(grid_shape)
self.n_bombs = max(1, int(bombs_density * self.grid_size)) if n_bombs is None else n_bombs
self.n_bombs = min(self.grid_size - 1, self.n_bombs)
self.flaged_bombs = 0
self.flaged_empty = 0
self.max_time = max_time
if impact_size % 2 == 0:
raise ValueError('Impact_size must be an odd number !')
self.impact_size = impact_size
# Define constants
self.HIDDEN = 0
self.REVEAL = 1
self.FLAG = 2
self.BOMB = self.impact_size ** 2
# Setting up gym Env conventions
nvec_observation = (self.BOMB + 2) * np.ones(self.grid_shape)
self.observation_space = spaces.MultiDiscrete(nvec_observation)
nvec_action = np.array(self.grid_shape + (2,))
self.action_space = spaces.MultiDiscrete(nvec_action)
# Initalize state
self.state = np.zeros(self.grid_shape + (2,), dtype=np.uint8)
## Setup bombs places
idx = np.indices(self.grid_shape).reshape(2, -1)
bombs_ids = np.random.choice(range(self.grid_size), size=self.n_bombs, replace=False)
self.bombs_positions = idx[0][bombs_ids], idx[1][bombs_ids]
## Place numbers
self.semi_impact_size = (self.impact_size-1)//2
bomb_impact = np.ones((self.impact_size, self.impact_size), dtype=np.uint8)
for bombs_id in bombs_ids:
bomb_x, bomb_y = idx[0][bombs_id], idx[1][bombs_id]
x_min, x_max, dx_min, dx_max = self.clip_index(bomb_x, 0)
y_min, y_max, dy_min, dy_max = self.clip_index(bomb_y, 1)
bomb_region = self.state[x_min:x_max, y_min:y_max, 0]
bomb_region += bomb_impact[dx_min:dx_max, dy_min:dy_max]
## Place bombs
self.state[self.bombs_positions + (0,)] = self.BOMB
self.start_time = time()
self.time_left = int(time() - self.start_time)
# Setup rendering
self.pygame_is_init = False
self.chicken = chicken
self.done = False
self.score = 0
def get_observation(self):
observation = copy(self.state[:, :, 1])
revealed = observation == 1
flaged = observation == 2
observation += self.impact_size ** 2 + 1
observation[revealed] = copy(self.state[:, :, 0][revealed])
observation[flaged] -= 1
return observation
def reveal_around(self, coords, reward, done, without_loss=False):
if not done:
x_min, x_max, _, _ = self.clip_index(coords[0], 0)
y_min, y_max, _, _ = self.clip_index(coords[1], 1)
region = self.state[x_min:x_max, y_min:y_max, :]
unseen_around = np.sum(region[..., 1] == 0)
if unseen_around == 0:
if not without_loss:
reward -= 0.001
return
flags_around = np.sum(region[..., 1] == 2)
if flags_around == self.state[coords + (0,)]:
unrevealed_zeros_around = np.logical_and(region[..., 0] == 0, region[..., 1] == self.HIDDEN)
if np.any(unrevealed_zeros_around):
zeros_coords = np.argwhere(unrevealed_zeros_around)
for zero in zeros_coords:
coord = (x_min + zero[0], y_min + zero[1])
self.state[coord + (1,)] = 1
self.reveal_around(coord, reward, done, without_loss=True)
self.state[x_min:x_max, y_min:y_max, 1][self.state[x_min:x_max, y_min:y_max, 1] != self.FLAG] = 1
unflagged_bombs_around = np.logical_and(region[..., 0] == self.BOMB, region[..., 1] != self.FLAG)
if np.any(unflagged_bombs_around):
self.done = True
reward, done = -1, True
else:
if not without_loss:
reward -= 0.001
def clip_index(self, x, axis):
max_idx = self.grid_shape[axis]
x_min, x_max = max(0, x-self.semi_impact_size), min(max_idx, x + self.semi_impact_size + 1)
dx_min, dx_max = x_min - (x - self.semi_impact_size), x_max - (x + self.semi_impact_size + 1) + self.impact_size
return x_min, x_max, dx_min, dx_max
def step(self, action):
coords = action[:2]
action_type = action[2] + 1 # 0 -> 1 = reveal; 1 -> 2 = toggle_flag
case_state = self.state[coords + (1,)]
case_content = self.state[coords + (0,)]
NO_BOMBS_AROUND = 0
reward, done = 0, False
self.time_left = self.max_time - time() + self.start_time
if self.time_left <= 0:
score = -(self.n_bombs - self.flaged_bombs + self.flaged_empty)/self.n_bombs
reward, done = score, True
return self.get_observation(), reward, done, {'passed':False}
if action_type == self.REVEAL:
if case_state == self.HIDDEN:
self.state[coords + (1,)] = action_type
if case_content == self.BOMB:
if self.pygame_is_init: self.done = True
reward, done = -1, True
return self.get_observation(), reward, done, {'passed':False}
elif case_content == NO_BOMBS_AROUND:
self.reveal_around(coords, reward, done)
elif case_state == self.REVEAL:
self.reveal_around(coords, reward, done)
reward -= 0.01
else:
reward -= 0.001
self.score += reward
return self.get_observation(), reward, done, {'passed':True}
elif action_type == self.FLAG:
if case_state == self.REVEAL:
reward -= 0.001
else:
flaging = 1
if case_state == self.FLAG:
flaging = -1
self.state[coords + (1,)] = self.HIDDEN
else:
self.state[coords + (1,)] = self.FLAG
if case_content == self.BOMB:
self.flaged_bombs += flaging
else:
self.flaged_empty += flaging
if self.flaged_bombs == self.n_bombs and self.flaged_empty == 0:
reward, done = 2 + self.time_left/self.max_time, True
if np.any(np.logical_and(self.state[..., 0]==9, self.state[..., 1]==1)) or self.done:
reward, done = -1 + self.time_left/self.max_time + (self.flaged_bombs - self.flaged_empty)/self.n_bombs, True
self.score += reward
return self.get_observation(), reward, done, {'passed':False}
def reset(self):
self.__init__(self.grid_shape, n_bombs=self.n_bombs, impact_size=self.impact_size, max_time=self.max_time, chicken=self.chicken)
return self.get_observation()
def render(self):
if not self.pygame_is_init:
self._init_pygame()
self.pygame_is_init = True
for event in pygame.event.get():
if event.type == pygame.QUIT: # pylint: disable=E1101
pygame.quit() # pylint: disable=E1101
# Plot background
pygame.draw.rect(self.window, (60, 56, 53), (0, 0, self.height, self.width))
# Plot grid
for index, state in np.ndenumerate(self.state[..., 1]):
self._plot_block(index, state)
# Plot infos
## Score
score_text = self.score_font.render("SCORE", 1, (255, 10, 10))
score = self.score_font.render(str(round(self.score, 4)), 1, (255, 10, 10))
self.window.blit(score_text, (0.1*self.header_size, 0.75*self.width))
self.window.blit(score, (0.1*self.header_size, 0.8*self.width))
## Time left
time_text = self.num_font.render("TIME", 1, (255, 10, 10))
self.time_left = self.max_time - time() + self.start_time
time_left = self.num_font.render(str(int(self.time_left+1)), 1, (255, 10, 10))
self.window.blit(time_text, (0.1*self.header_size, 0.03*self.width))
self.window.blit(time_left, (0.1*self.header_size, 0.1*self.width))
## Bombs left
bombs_text = self.num_font.render("BOMBS", 1, (255, 255, 10))
left_text = self.num_font.render("LEFT", 1, (255, 255, 10))
potential_bombs_left = self.n_bombs - self.flaged_bombs - self.flaged_empty
potential_bombs_left = self.num_font.render(str(int(potential_bombs_left)), 1, (255, 255, 10))
self.window.blit(bombs_text, (0.1*self.header_size, 0.4*self.width))
self.window.blit(left_text, (0.1*self.header_size, 0.45*self.width))
self.window.blit(potential_bombs_left, (0.1*self.header_size, 0.5*self.width))
pygame.display.flip()
pygame.time.wait(10)
if self.done:
pygame.time.wait(3000)
@staticmethod
def _get_color(n, max_n):
BLUE_HUE = 0.6
RED_HUE = 0.0
HUE = RED_HUE + (BLUE_HUE - RED_HUE) * ((max_n - n) / max_n)**3
color = 255 * np.array(colorsys.hsv_to_rgb(HUE, 1, 0.7))
return color
def _plot_block(self, index, state):
position = tuple(self.origin + self.scale_factor * self.BLOCK_SIZE * np.array((index[1], index[0])))
label = None
if state == self.HIDDEN and not self.done:
img_key = 'hidden'
elif state == self.FLAG:
if not self.done:
img_key = 'flag'
else:
content = self.state[index][0]
if content == self.BOMB:
img_key = 'disabled_mine' if not self.chicken else 'disabled_chicken'
else:
img_key = 'misplaced_flag'
else:
content = self.state[index][0]
if content == self.BOMB:
if state == self.HIDDEN:
img_key = 'mine' if not self.chicken else 'chicken'
else:
img_key = 'exploded_mine' if not self.chicken else 'exploded_chicken'
else:
img_key = 'revealed'
label = self.num_font.render(str(content), 1, self._get_color(content, self.BOMB))
self.window.blit(self.images[img_key], position)
if label: self.window.blit(label, position + self.font_offset - (content > 9) * self.decimal_font_offset)
def _init_pygame(self):
pygame.init() # pylint: disable=E1101
# Open Pygame window
self.scale_factor = 2 * min(12 / self.grid_shape[0], 25 / self.grid_shape[1])
self.BLOCK_SIZE = 32
self.header_size = self.scale_factor * 100
self.origin = | np.array([self.header_size, 0]) | numpy.array |
import argparse
import json
import numpy as np
import pandas as pd
import os
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,f1_score
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras import backend as K
from keras.utils.vis_utils import plot_model
from sklearn.externals import joblib
import time
def f1(y_true, y_pred):
def recall(y_true, y_pred):
"""Recall metric.
Only computes a batch-wise average of recall.
Computes the recall, a metric for multi-label classification of
how many relevant items are selected.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
"""Precision metric.
Only computes a batch-wise average of precision.
Computes the precision, a metric for multi-label classification of
how many selected items are relevant.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
precision = precision(y_true, y_pred)
recall = recall(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+K.epsilon()))
def get_embeddings(sentences_list,layer_json):
'''
:param sentences_list: the path o the sentences.txt
:param layer_json: the path of the json file that contains the embeddings of the sentences
:return: Dictionary with key each sentence of the sentences_list and as value the embedding
'''
sentences = dict()#dict with key the index of each line of the sentences_list.txt and as value the sentence
embeddings = dict()##dict with key the index of each sentence and as value the its embedding
sentence_emb = dict()#key:sentence,value:its embedding
with open(sentences_list,'r') as file:
for index,line in enumerate(file):
sentences[index] = line.strip()
with open(layer_json, 'r',encoding='utf-8') as f:
for line in f:
embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features'])
for key,value in sentences.items():
sentence_emb[value] = embeddings[key]
return sentence_emb
def train_classifier(sentences_list,layer_json,dataset_csv,filename):
'''
:param sentences_list: the path o the sentences.txt
:param layer_json: the path of the json file that contains the embeddings of the sentences
:param dataset_csv: the path of the dataset
:param filename: The path of the pickle file that the model will be stored
:return:
'''
dataset = pd.read_csv(dataset_csv)
bert_dict = get_embeddings(sentences_list,layer_json)
length = list()
sentence_emb = list()
previous_emb = list()
next_list = list()
section_list = list()
label = list()
errors = 0
for row in dataset.iterrows():
sentence = row[1][0].strip()
previous = row[1][1].strip()
nexts = row[1][2].strip()
section = row[1][3].strip()
if sentence in bert_dict:
sentence_emb.append(bert_dict[sentence])
else:
sentence_emb.append(np.zeros(768))
print(sentence)
errors += 1
if previous in bert_dict:
previous_emb.append(bert_dict[previous])
else:
previous_emb.append(np.zeros(768))
if nexts in bert_dict:
next_list.append(bert_dict[nexts])
else:
next_list.append(np.zeros(768))
if section in bert_dict:
section_list.append(bert_dict[section])
else:
section_list.append(np.zeros(768))
length.append(row[1][4])
label.append(row[1][5])
sentence_emb = np.asarray(sentence_emb)
print(sentence_emb.shape)
next_emb = np.asarray(next_list)
print(next_emb.shape)
previous_emb = np.asarray(previous_emb)
print(previous_emb.shape)
section_emb = np.asarray(section_list)
print(sentence_emb.shape)
length = np.asarray(length)
print(length.shape)
label = np.asarray(label)
print(errors)
features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1)
features = np.column_stack([features, length]) # np.append(features,length,axis=1)
print(features.shape)
X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42)
log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1)
log.fit(X_train, y_train)
#save the model
_ = joblib.dump(log, filename, compress=9)
predictions = log.predict(X_val)
print("###########################################")
print("Results using embeddings from the",layer_json,"file")
print(classification_report(y_val, predictions))
print("F1 score using Logistic Regression:",f1_score(y_val, predictions))
print("###########################################")
#train a DNN
f1_results = list()
for i in range(3):
model = Sequential()
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dense(128, activation='relu', trainable=True))
model.add(Dropout(0.30))
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dropout(0.25))
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dropout(0.35))
model.add(Dense(1, activation='sigmoid'))
# compile network
model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1])
# fit network
model.fit(X_train, y_train, epochs=100, batch_size=64)
loss, f_1 = model.evaluate(X_val, y_val, verbose=1)
print('\nTest F1: %f' % (f_1 * 100))
f1_results.append(f_1)
model = None
print("###########################################")
print("Results using embeddings from the", layer_json, "file")
# evaluate
print(np.mean(f1_results))
print("###########################################")
def parameter_tuning_LR(sentences_list,layer_json,dataset_csv):
'''
:param sentences_list: the path o the sentences.txt
:param layer_json: the path of the json file that contains the embeddings of the sentences
:param dataset_csv: the path of the dataset
:return:
'''
dataset = pd.read_csv(dataset_csv)
bert_dict = get_embeddings(sentences_list,layer_json)
length = list()
sentence_emb = list()
previous_emb = list()
next_list = list()
section_list = list()
label = list()
errors = 0
for row in dataset.iterrows():
sentence = row[1][0].strip()
previous = row[1][1].strip()
nexts = row[1][2].strip()
section = row[1][3].strip()
if sentence in bert_dict:
sentence_emb.append(bert_dict[sentence])
else:
sentence_emb.append(np.zeros(768))
print(sentence)
errors += 1
if previous in bert_dict:
previous_emb.append(bert_dict[previous])
else:
previous_emb.append(np.zeros(768))
if nexts in bert_dict:
next_list.append(bert_dict[nexts])
else:
next_list.append(np.zeros(768))
if section in bert_dict:
section_list.append(bert_dict[section])
else:
section_list.append(np.zeros(768))
length.append(row[1][4])
label.append(row[1][5])
sentence_emb = | np.asarray(sentence_emb) | numpy.asarray |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * | np.ones(101) | numpy.ones |
###############################################################################
# @todo add Pilot2-splash-app disclaimer
###############################################################################
""" Get's KRAS states """
import MDAnalysis as mda
from MDAnalysis.analysis import align
from MDAnalysis.lib.mdamath import make_whole
import os
import numpy as np
import math
############## Below section needs to be uncommented ############
import mummi_core
import mummi_ras
from mummi_core.utils import Naming
# # Logger has to be initialized the first thing in the script
from logging import getLogger
LOGGER = getLogger(__name__)
# # Innitilize MuMMI if it has not been done before
# MUMMI_ROOT = mummi.init(True)
# This is needed so the Naming works below
#@TODO fix this so we don't have these on import make them as an init
mummi_core.init()
dirKRASStates = Naming.dir_res('states')
dirKRASStructures = Naming.dir_res('structures')
# #RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-ONLY.microstates.txt"))
RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-states.txt"),comments='#')
# #RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-RAF.microstates.txt"))
RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-raf-states.txt"),comments='#') # Note diffrent number of columns so index change below
# TODO: CS, my edits to test
# RAS_ONLY_macrostate = np.loadtxt('ras-states.txt')
# RAS_RAF_macrostate = np.loadtxt('ras-raf-states.txt')
############## above section needs to be uncommented ############
# TODO: CS, my edits to test
# TODO: TSC, The reference structure has to currently be set as the 'RAS-ONLY-reference-structure.gro'
# TODO: TSC, path to the reference structure is: mummi_resources/structures/
kras_ref_universe = mda.Universe(os.path.join(dirKRASStructures, "RAS-ONLY-reference-structure.gro"))
# kras_ref_universe = mda.Universe("RAS-ONLY-reference-structure.gro")
# kras_ref_universe = mda.Universe('AA_pfpatch_000000004641_RAS_RAF2_411.gro')
# TODO: CS, not using these for x4 proteins; instead using protein_systems below to set num_res
######### Below hard codes the number of residues within RAS-only and RAS-RAF ##########
RAS_only_num_res = 184
RAS_RAF_num_res = 320
######### Above hard codes the number of residues within RAS-only and RAS-RAF ##########
####### This can be removed
# def get_kras(syst, kras_start):
# """Gets all atoms for a KRAS protein starting at 'kras_start'."""
# return syst.atoms[kras_start:kras_start+428]
####### This can be removed
def get_segids(u):
"""Identifies the list of segments within the system. Only needs to be called x1 time"""
segs = u.segments
segs = segs.segids
ras_segids = []
rasraf_segids = []
for i in range(len(segs)):
# print(segs[i])
if segs[i][-3:] == 'RAS':
ras_segids.append(segs[i])
if segs[i][-3:] == 'RAF':
rasraf_segids.append(segs[i])
return ras_segids, rasraf_segids
def get_protein_info(u,tag):
"""Uses the segments identified in get_segids to make a list of all proteins in the systems.\
Outputs a list of the first residue number of the protein, and whether it is 'RAS-ONLY', or 'RAS-RAF'.\
The 'tag' input defines what is used to identify the first residue of the protein. i.e. 'resname ACE1 and name BB'.\
Only needs to be called x1 time"""
ras_segids, rasraf_segids = get_segids(u)
if len(ras_segids) > 0:
RAS = u.select_atoms('segid '+ras_segids[0]+' and '+str(tag))
else:
RAS = []
if len(rasraf_segids) > 0:
RAF = u.select_atoms('segid '+rasraf_segids[0]+' and '+str(tag))
else:
RAF = []
protein_info = []#np.empty([len(RAS)+len(RAF),2])
for i in range(len(RAS)):
protein_info.append((RAS[i].resid,'RAS-ONLY'))
for i in range(len(RAF)):
protein_info.append((RAF[i].resid,'RAS-RAF'))
######## sort protein info
protein_info = sorted(protein_info)
######## sort protein info
return protein_info
def get_ref_kras():
"""Gets the reference KRAS struct. Only called x1 time when class is loaded"""
start_of_g_ref = kras_ref_universe.residues[0].resid
ref_selection = 'resid '+str(start_of_g_ref)+':'+str(start_of_g_ref+24)+' ' +\
str(start_of_g_ref+38)+':'+str(start_of_g_ref+54)+' ' +\
str(start_of_g_ref+67)+':'+str(start_of_g_ref+164)+' ' +\
'and (name CA or name BB)'
r2_26r40_56r69_166_ref = kras_ref_universe.select_atoms(str(ref_selection))
return kras_ref_universe.select_atoms(str(ref_selection)).positions - kras_ref_universe.select_atoms(str(ref_selection)).center_of_mass()
# Load inital ref frames (only need to do this once)
ref0 = get_ref_kras()
def getKRASstates(u,kras_indices):
"""Gets states for all KRAS proteins in path."""
# res_shift = 8
# all_glycine = u.select_atoms("resname GLY")
# kras_indices = []
# for i in range(0, len(all_glycine), 26):
# kras_indices.append(all_glycine[i].index)
########## Below is taken out of the function so it is only done once #########
# kras_indices = get_protein_info(u,'resname ACE1 and name BB')
########## Above is taken out of the function so it is only done once #########
# CS, for x4 cases:
# [{protein_x4: (protein_type, num_res)}]
protein_systems = [{'ras4a': ('RAS-ONLY', 185),
'ras4araf': ('RAS-RAF', 321),
'ras': ('RAS-ONLY', 184),
'rasraf': ('RAS-RAF', 320)}]
ALLOUT = []
for k in range(len(kras_indices)):
start_of_g = kras_indices[k][0]
protein_x4 = str(kras_indices[k][1])
try:
protein_type = [item[protein_x4] for item in protein_systems][0][0] # 'RAS-ONLY' OR 'RAS-RAF'
num_res = [item[protein_x4] for item in protein_systems][0][1]
except:
LOGGER.error('Check KRas naming between modules')
raise Exception('Error: unknown KRas name')
# TODO: CS, replacing this comment section with the above, to handle x4 protein types
# ---------------------------------------
# ALLOUT = []
# for k in range(len(kras_indices)):
# start_of_g = kras_indices[k][0]
# protein_type = str(kras_indices[k][1])
# ########## BELOW SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ##############
# ########## POTENTIALLY REDO WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) #######
# ########## HAS BEEN REDONE WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) ########
# # if len(kras_indices) == 1:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB') ####### HAS TO BE FIXED FOR BACKBONE ATOMS FOR SPECIFIC PROTEIN
# # elif len(kras_indices) > 1:
# # if k == len(kras_indices)-1:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB')
# # else:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(kras_indices[k+1][0])+' and name BB')
# ########## ABOVE SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ##############
#
# ########## Below hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations #########################
# if protein_type == 'RAS-ONLY':
# num_res = RAS_only_num_res
# elif protein_type == 'RAS-RAF':
# num_res = RAS_RAF_num_res
# ########## Above hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations #########################
# ---------------------------------------
# TODO: TSC, I changed the selection below, which can be used for the make_whole...
# krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res)+' and (name CA or name BB)')
krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res))
krases0_BB.guess_bonds()
r2_26r40_56r69_166 = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+24)+' ' +\
str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\
str(start_of_g+67)+':'+str(start_of_g+164)+\
' and (name CA or name BB)')
u_selection = \
'resid '+str(start_of_g)+':'+str(start_of_g+24)+' '+str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\
str(start_of_g+67)+':'+str(start_of_g+164)+' and (name CA or name BB)'
mobile0 = u.select_atoms(str(u_selection)).positions - u.select_atoms(str(u_selection)).center_of_mass()
# TODO: CS, something wrong with ref0 from get_kras_ref()
# just making ref0 = mobile0 to test for now
# ref0 = mobile0
# TSC removed this
R, RMSD_junk = align.rotation_matrix(mobile0, ref0)
######## TODO: TSC, Adjusted for AA lipid names ########
# lipids = u.select_atoms('resname POPX POPC PAPC POPE DIPE DPSM PAPS PAP6 CHOL')
lipids = u.select_atoms('resname POPC PAPC POPE DIPE SSM PAPS SAPI CHL1')
coords = ref0
RotMat = []
OS = []
r152_165 = krases0_BB.select_atoms('resid '+str(start_of_g+150)+':'+str(start_of_g+163)+' and (name CA or name BB)')
r65_74 = krases0_BB.select_atoms('resid '+str(start_of_g+63)+':'+str(start_of_g+72)+' and (name CA or name BB)')
timeframes = []
# TODO: CS, for AA need bonds to run make_whole()
# krases0_BB.guess_bonds()
# TODO: CS, turn off for now to test beyond this point
''' *** for AA, need to bring that back on once all else runs ***
'''
# @Tim and <NAME>. this was commented out - please check.
#make_whole(krases0_BB)
j, rmsd_junk = mda.analysis.align.rotation_matrix((r2_26r40_56r69_166.positions-r2_26r40_56r69_166.center_of_mass()), coords)
RotMat.append(j)
OS.append(r65_74.center_of_mass()-r152_165.center_of_mass())
timeframes.append(u.trajectory.time)
if protein_type == 'RAS-RAF':
z_pos = []
############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES BELOW ####################
############### TODO: TSC, zshifting is set to -1 (instead of -2), as there are ACE caps that are separate residues in AA
#zshifting=-1
if protein_x4 == 'rasraf':
zshifting = -1
elif protein_x4 == 'ras4araf':
zshifting = 0
else:
zshifting = 0
LOGGER.error('Found unsupported protein_x4 type')
raf_loops_selection = u.select_atoms('resid '+str(start_of_g+zshifting+291)+':'+str(start_of_g+zshifting+294)+' ' +\
str(start_of_g+zshifting+278)+':'+str(start_of_g+zshifting+281)+' ' +\
' and (name CA or name BB)')
############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES ABOVE ####################
diff = (lipids.center_of_mass()[2]-raf_loops_selection.center_of_mass(unwrap=True)[2])/10
if diff < 0:
diff = diff+(u.dimensions[2]/10)
z_pos.append(diff)
z_pos = np.array(z_pos)
RotMatNP = np.array(RotMat)
OS = np.array(OS)
OA = RotMatNP[:, 2, :]/(((RotMatNP[:, 2, 0]**2)+(RotMatNP[:, 2, 1]**2)+(RotMatNP[:, 2, 2]**2))**0.5)[:, None]
OWAS = np.arccos(RotMatNP[:, 2, 2])*180/math.pi
OC_temp = | np.concatenate((OA, OS), axis=1) | numpy.concatenate |
'''
<NAME>
set up :2020-1-9
intergrate img and label into one file
-- fiducial1024_v1
'''
import argparse
import sys, os
import pickle
import random
import collections
import json
import numpy as np
import scipy.io as io
import scipy.misc as m
import matplotlib.pyplot as plt
import glob
import math
import time
import threading
import multiprocessing as mp
from multiprocessing import Pool
import re
import cv2
# sys.path.append('/lustre/home/gwxie/hope/project/dewarp/datasets/') # /lustre/home/gwxie/program/project/unwarp/perturbed_imgaes/GAN
import utils
def getDatasets(dir):
return os.listdir(dir)
class perturbed(utils.BasePerturbed):
def __init__(self, path, bg_path, save_path, save_suffix):
self.path = path
self.bg_path = bg_path
self.save_path = save_path
self.save_suffix = save_suffix
def save_img(self, m, n, fold_curve='fold', repeat_time=4, fiducial_points = 16, relativeShift_position='relativeShift_v2'):
origin_img = cv2.imread(self.path, flags=cv2.IMREAD_COLOR)
save_img_shape = [512*2, 480*2] # 320
# reduce_value = np.random.choice([2**4, 2**5, 2**6, 2**7, 2**8], p=[0.01, 0.1, 0.4, 0.39, 0.1])
reduce_value = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.02, 0.18, 0.2, 0.3, 0.1, 0.1, 0.08, 0.02])
# reduce_value = np.random.choice([8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.01, 0.02, 0.2, 0.4, 0.19, 0.18])
# reduce_value = np.random.choice([16, 24, 32, 40, 48, 64], p=[0.01, 0.1, 0.2, 0.4, 0.2, 0.09])
base_img_shrink = save_img_shape[0] - reduce_value
# enlarge_img_shrink = [1024, 768]
# enlarge_img_shrink = [896, 672] # 420
enlarge_img_shrink = [512*4, 480*4] # 420
# enlarge_img_shrink = [896*2, 768*2] # 420
# enlarge_img_shrink = [896, 768] # 420
# enlarge_img_shrink = [768, 576] # 420
# enlarge_img_shrink = [640, 480] # 420
''''''
im_lr = origin_img.shape[0]
im_ud = origin_img.shape[1]
reduce_value_v2 = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 28*2, 32*2, 48*2], p=[0.02, 0.18, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1])
# reduce_value_v2 = np.random.choice([16, 24, 28, 32, 48, 64], p=[0.01, 0.1, 0.2, 0.3, 0.25, 0.14])
if im_lr > im_ud:
im_ud = min(int(im_ud / im_lr * base_img_shrink), save_img_shape[1] - reduce_value_v2)
im_lr = save_img_shape[0] - reduce_value
else:
base_img_shrink = save_img_shape[1] - reduce_value
im_lr = min(int(im_lr / im_ud * base_img_shrink), save_img_shape[0] - reduce_value_v2)
im_ud = base_img_shrink
if round(im_lr / im_ud, 2) < 0.5 or round(im_ud / im_lr, 2) < 0.5:
repeat_time = min(repeat_time, 8)
edge_padding = 3
im_lr -= im_lr % (fiducial_points-1) - (2*edge_padding) # im_lr % (fiducial_points-1) - 1
im_ud -= im_ud % (fiducial_points-1) - (2*edge_padding) # im_ud % (fiducial_points-1) - 1
im_hight = np.linspace(edge_padding, im_lr - edge_padding, fiducial_points, dtype=np.int64)
im_wide = np.linspace(edge_padding, im_ud - edge_padding, fiducial_points, dtype=np.int64)
# im_lr -= im_lr % (fiducial_points-1) - (1+2*edge_padding) # im_lr % (fiducial_points-1) - 1
# im_ud -= im_ud % (fiducial_points-1) - (1+2*edge_padding) # im_ud % (fiducial_points-1) - 1
# im_hight = np.linspace(edge_padding, im_lr - (1+edge_padding), fiducial_points, dtype=np.int64)
# im_wide = np.linspace(edge_padding, im_ud - (1+edge_padding), fiducial_points, dtype=np.int64)
im_x, im_y = np.meshgrid(im_hight, im_wide)
segment_x = (im_lr) // (fiducial_points-1)
segment_y = (im_ud) // (fiducial_points-1)
# plt.plot(im_x, im_y,
# color='limegreen',
# marker='.',
# linestyle='')
# plt.grid(True)
# plt.show()
self.origin_img = cv2.resize(origin_img, (im_ud, im_lr), interpolation=cv2.INTER_CUBIC)
perturbed_bg_ = getDatasets(self.bg_path)
perturbed_bg_img_ = self.bg_path+random.choice(perturbed_bg_)
perturbed_bg_img = cv2.imread(perturbed_bg_img_, flags=cv2.IMREAD_COLOR)
mesh_shape = self.origin_img.shape[:2]
self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 256, dtype=np.float32)#np.zeros_like(perturbed_bg_img)
# self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 0, dtype=np.int16)#np.zeros_like(perturbed_bg_img)
self.new_shape = self.synthesis_perturbed_img.shape[:2]
perturbed_bg_img = cv2.resize(perturbed_bg_img, (save_img_shape[1], save_img_shape[0]), cv2.INPAINT_TELEA)
origin_pixel_position = np.argwhere(np.zeros(mesh_shape, dtype=np.uint32) == 0).reshape(mesh_shape[0], mesh_shape[1], 2)
pixel_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(self.new_shape[0], self.new_shape[1], 2)
self.perturbed_xy_ = np.zeros((self.new_shape[0], self.new_shape[1], 2))
# self.perturbed_xy_ = pixel_position.copy().astype(np.float32)
# fiducial_points_grid = origin_pixel_position[im_x, im_y]
self.synthesis_perturbed_label = np.zeros((self.new_shape[0], self.new_shape[1], 2))
x_min, y_min, x_max, y_max = self.adjust_position_v2(0, 0, mesh_shape[0], mesh_shape[1], save_img_shape)
origin_pixel_position += [x_min, y_min]
x_min, y_min, x_max, y_max = self.adjust_position(0, 0, mesh_shape[0], mesh_shape[1])
x_shift = random.randint(-enlarge_img_shrink[0]//16, enlarge_img_shrink[0]//16)
y_shift = random.randint(-enlarge_img_shrink[1]//16, enlarge_img_shrink[1]//16)
x_min += x_shift
x_max += x_shift
y_min += y_shift
y_max += y_shift
'''im_x,y'''
im_x += x_min
im_y += y_min
self.synthesis_perturbed_img[x_min:x_max, y_min:y_max] = self.origin_img
self.synthesis_perturbed_label[x_min:x_max, y_min:y_max] = origin_pixel_position
synthesis_perturbed_img_map = self.synthesis_perturbed_img.copy()
synthesis_perturbed_label_map = self.synthesis_perturbed_label.copy()
foreORbackground_label = np.full((mesh_shape), 1, dtype=np.int16)
foreORbackground_label_map = np.full((self.new_shape), 0, dtype=np.int16)
foreORbackground_label_map[x_min:x_max, y_min:y_max] = foreORbackground_label
# synthesis_perturbed_img_map = self.pad(self.synthesis_perturbed_img.copy(), x_min, y_min, x_max, y_max)
# synthesis_perturbed_label_map = self.pad(synthesis_perturbed_label_map, x_min, y_min, x_max, y_max)
'''*****************************************************************'''
is_normalizationFun_mixture = self.is_perform(0.2, 0.8)
# if not is_normalizationFun_mixture:
normalizationFun_0_1 = False
# normalizationFun_0_1 = self.is_perform(0.5, 0.5)
if fold_curve == 'fold':
fold_curve_random = True
# is_normalizationFun_mixture = False
normalizationFun_0_1 = self.is_perform(0.2, 0.8)
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
fold_curve_random = self.is_perform(0.1, 0.9) # False # self.is_perform(0.01, 0.99)
alpha_perturbed = random.randint(80, 160) / 100
# is_normalizationFun_mixture = False # self.is_perform(0.01, 0.99)
synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 256)
# synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 0, dtype=np.int16)
synthesis_perturbed_label = np.zeros_like(self.synthesis_perturbed_label)
alpha_perturbed_change = self.is_perform(0.5, 0.5)
p_pp_choice = self.is_perform(0.8, 0.2) if fold_curve == 'fold' else self.is_perform(0.1, 0.9)
for repeat_i in range(repeat_time):
if alpha_perturbed_change:
if fold_curve == 'fold':
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
alpha_perturbed = random.randint(80, 160) / 100
''''''
linspace_x = [0, (self.new_shape[0] - im_lr) // 2 - 1,
self.new_shape[0] - (self.new_shape[0] - im_lr) // 2 - 1, self.new_shape[0] - 1]
linspace_y = [0, (self.new_shape[1] - im_ud) // 2 - 1,
self.new_shape[1] - (self.new_shape[1] - im_ud) // 2 - 1, self.new_shape[1] - 1]
linspace_x_seq = [1, 2, 3]
linspace_y_seq = [1, 2, 3]
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_p = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
if ((r_x == 1 or r_x == 3) and (r_y == 1 or r_y == 3)) and p_pp_choice:
linspace_x_seq.remove(r_x)
linspace_y_seq.remove(r_y)
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_pp = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
# perturbed_p, perturbed_pp = np.array(
# [random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10]) \
# , np.array([random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10])
# perturbed_p, perturbed_pp = np.array(
# [random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10]) \
# , np.array([random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10])
''''''
perturbed_vp = perturbed_pp - perturbed_p
perturbed_vp_norm = np.linalg.norm(perturbed_vp)
perturbed_distance_vertex_and_line = np.dot((perturbed_p - pixel_position), perturbed_vp) / perturbed_vp_norm
''''''
# perturbed_v = np.array([random.randint(-3000, 3000) / 100, random.randint(-3000, 3000) / 100])
# perturbed_v = np.array([random.randint(-4000, 4000) / 100, random.randint(-4000, 4000) / 100])
if fold_curve == 'fold' and self.is_perform(0.6, 0.4): # self.is_perform(0.3, 0.7):
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
perturbed_v = np.array([random.randint(-10000, 10000) / 100, random.randint(-10000, 10000) / 100])
# perturbed_v = np.array([random.randint(-11000, 11000) / 100, random.randint(-11000, 11000) / 100])
else:
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
# perturbed_v = np.array([random.randint(-16000, 16000) / 100, random.randint(-16000, 16000) / 100])
perturbed_v = np.array([random.randint(-8000, 8000) / 100, random.randint(-8000, 8000) / 100])
# perturbed_v = np.array([random.randint(-3500, 3500) / 100, random.randint(-3500, 3500) / 100])
# perturbed_v = np.array([random.randint(-600, 600) / 10, random.randint(-600, 600) / 10])
''''''
if fold_curve == 'fold':
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
''''''
if fold_curve_random:
# omega_perturbed = (alpha_perturbed+0.2) / (perturbed_d + alpha_perturbed)
# omega_perturbed = alpha_perturbed**perturbed_d
omega_perturbed = alpha_perturbed / (perturbed_d + alpha_perturbed)
else:
omega_perturbed = 1 - perturbed_d ** alpha_perturbed
'''shadow'''
if self.is_perform(0.6, 0.4):
synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] = np.minimum(np.maximum(synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] - np.int16(np.round(omega_perturbed[x_min:x_max, y_min:y_max].repeat(3).reshape(x_max-x_min, y_max-y_min, 3) * abs(np.linalg.norm(perturbed_v//2))*np.array([0.4-random.random()*0.1, 0.4-random.random()*0.1, 0.4-random.random()*0.1]))), 0), 255)
''''''
if relativeShift_position in ['position', 'relativeShift_v2']:
self.perturbed_xy_ += np.array([omega_perturbed * perturbed_v[0], omega_perturbed * perturbed_v[1]]).transpose(1, 2, 0)
else:
print('relativeShift_position error')
exit()
'''
flat_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(
self.new_shape[0] * self.new_shape[1], 2)
vtx, wts = self.interp_weights(self.perturbed_xy_.reshape(self.new_shape[0] * self.new_shape[1], 2), flat_position)
wts_sum = np.abs(wts).sum(-1)
# flat_img.reshape(flat_shape[0] * flat_shape[1], 3)[:] = interpolate(pixel, vtx, wts)
wts = wts[wts_sum <= 1, :]
vtx = vtx[wts_sum <= 1, :]
synthesis_perturbed_img.reshape(self.new_shape[0] * self.new_shape[1], 3)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_img_map.reshape(self.new_shape[0] * self.new_shape[1], 3), vtx, wts)
synthesis_perturbed_label.reshape(self.new_shape[0] * self.new_shape[1], 2)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_label_map.reshape(self.new_shape[0] * self.new_shape[1], 2), vtx, wts)
foreORbackground_label = np.zeros(self.new_shape)
foreORbackground_label.reshape(self.new_shape[0] * self.new_shape[1], 1)[wts_sum <= 1, :] = self.interpolate(foreORbackground_label_map.reshape(self.new_shape[0] * self.new_shape[1], 1), vtx, wts)
foreORbackground_label[foreORbackground_label < 0.99] = 0
foreORbackground_label[foreORbackground_label >= 0.99] = 1
# synthesis_perturbed_img = np.around(synthesis_perturbed_img).astype(np.uint8)
synthesis_perturbed_label[:, :, 0] *= foreORbackground_label
synthesis_perturbed_label[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 0] *= foreORbackground_label
synthesis_perturbed_img[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 2] *= foreORbackground_label
self.synthesis_perturbed_img = synthesis_perturbed_img
self.synthesis_perturbed_label = synthesis_perturbed_label
'''
'''perspective'''
perspective_shreshold = random.randint(26, 36)*10 # 280
x_min_per, y_min_per, x_max_per, y_max_per = self.adjust_position(perspective_shreshold, perspective_shreshold, self.new_shape[0]-perspective_shreshold, self.new_shape[1]-perspective_shreshold)
pts1 = np.float32([[x_min_per, y_min_per], [x_max_per, y_min_per], [x_min_per, y_max_per], [x_max_per, y_max_per]])
e_1_ = x_max_per - x_min_per
e_2_ = y_max_per - y_min_per
e_3_ = e_2_
e_4_ = e_1_
perspective_shreshold_h = e_1_*0.02
perspective_shreshold_w = e_2_*0.02
a_min_, a_max_ = 70, 110
# if self.is_perform(1, 0):
if fold_curve == 'curve' and self.is_perform(0.5, 0.5):
if self.is_perform(0.5, 0.5):
while True:
pts2 = np.around(
np.float32([[x_min_per - (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold],
[x_max_per - (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold],
[x_min_per + (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold],
[x_max_per + (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold]])) # right
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
else:
while True:
pts2 = np.around(
np.float32([[x_min_per + (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold],
[x_max_per + (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold],
[x_min_per - (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold],
[x_max_per - (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold]]))
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
else:
while True:
pts2 = np.around(np.float32([[x_min_per+(random.random()-0.5)*perspective_shreshold, y_min_per+(random.random()-0.5)*perspective_shreshold],
[x_max_per+(random.random()-0.5)*perspective_shreshold, y_min_per+(random.random()-0.5)*perspective_shreshold],
[x_min_per+(random.random()-0.5)*perspective_shreshold, y_max_per+(random.random()-0.5)*perspective_shreshold],
[x_max_per+(random.random()-0.5)*perspective_shreshold, y_max_per+(random.random()-0.5)*perspective_shreshold]]))
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
M = cv2.getPerspectiveTransform(pts1, pts2)
one = np.ones((self.new_shape[0], self.new_shape[1], 1), dtype=np.int16)
matr = np.dstack((pixel_position, one))
new = np.dot(M, matr.reshape(-1, 3).T).T.reshape(self.new_shape[0], self.new_shape[1], 3)
x = new[:, :, 0]/new[:, :, 2]
y = new[:, :, 1]/new[:, :, 2]
perturbed_xy_ = np.dstack((x, y))
# perturbed_xy_round_int = np.around(cv2.bilateralFilter(perturbed_xy_round_int, 9, 75, 75))
# perturbed_xy_round_int = np.around(cv2.blur(perturbed_xy_, (17, 17)))
# perturbed_xy_round_int = cv2.blur(perturbed_xy_round_int, (17, 17))
# perturbed_xy_round_int = cv2.GaussianBlur(perturbed_xy_round_int, (7, 7), 0)
perturbed_xy_ = perturbed_xy_-np.min(perturbed_xy_.T.reshape(2, -1), 1)
# perturbed_xy_round_int = np.around(perturbed_xy_round_int-np.min(perturbed_xy_round_int.T.reshape(2, -1), 1)).astype(np.int16)
self.perturbed_xy_ += perturbed_xy_
'''perspective end'''
'''to img'''
flat_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(
self.new_shape[0] * self.new_shape[1], 2)
# self.perturbed_xy_ = cv2.blur(self.perturbed_xy_, (7, 7))
self.perturbed_xy_ = cv2.GaussianBlur(self.perturbed_xy_, (7, 7), 0)
'''get fiducial points'''
fiducial_points_coordinate = self.perturbed_xy_[im_x, im_y]
vtx, wts = self.interp_weights(self.perturbed_xy_.reshape(self.new_shape[0] * self.new_shape[1], 2), flat_position)
wts_sum = np.abs(wts).sum(-1)
# flat_img.reshape(flat_shape[0] * flat_shape[1], 3)[:] = interpolate(pixel, vtx, wts)
wts = wts[wts_sum <= 1, :]
vtx = vtx[wts_sum <= 1, :]
synthesis_perturbed_img.reshape(self.new_shape[0] * self.new_shape[1], 3)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_img_map.reshape(self.new_shape[0] * self.new_shape[1], 3), vtx, wts)
synthesis_perturbed_label.reshape(self.new_shape[0] * self.new_shape[1], 2)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_label_map.reshape(self.new_shape[0] * self.new_shape[1], 2), vtx, wts)
foreORbackground_label = np.zeros(self.new_shape)
foreORbackground_label.reshape(self.new_shape[0] * self.new_shape[1], 1)[wts_sum <= 1, :] = self.interpolate(foreORbackground_label_map.reshape(self.new_shape[0] * self.new_shape[1], 1), vtx, wts)
foreORbackground_label[foreORbackground_label < 0.99] = 0
foreORbackground_label[foreORbackground_label >= 0.99] = 1
self.synthesis_perturbed_img = synthesis_perturbed_img
self.synthesis_perturbed_label = synthesis_perturbed_label
self.foreORbackground_label = foreORbackground_label
'''draw fiducial points
stepSize = 0
fiducial_points_synthesis_perturbed_img = self.synthesis_perturbed_img.copy()
for l in fiducial_points_coordinate.astype(np.int64).reshape(-1,2):
cv2.circle(fiducial_points_synthesis_perturbed_img, (l[1] + math.ceil(stepSize / 2), l[0] + math.ceil(stepSize / 2)), 5, (0, 0, 255), -1)
cv2.imwrite('/lustre/home/gwxie/program/project/unwarp/unwarp_perturbed/TPS/img/cv_TPS_large.jpg', fiducial_points_synthesis_perturbed_img)
'''
'''clip'''
perturbed_x_min, perturbed_y_min, perturbed_x_max, perturbed_y_max = -1, -1, self.new_shape[0], self.new_shape[1]
for x in range(self.new_shape[0] // 2, perturbed_x_max):
if np.sum(self.synthesis_perturbed_img[x, :]) == 768 * self.new_shape[1] and perturbed_x_max - 1 > x:
perturbed_x_max = x
break
for x in range(self.new_shape[0] // 2, perturbed_x_min, -1):
if np.sum(self.synthesis_perturbed_img[x, :]) == 768 * self.new_shape[1] and x > 0:
perturbed_x_min = x
break
for y in range(self.new_shape[1] // 2, perturbed_y_max):
if np.sum(self.synthesis_perturbed_img[:, y]) == 768 * self.new_shape[0] and perturbed_y_max - 1 > y:
perturbed_y_max = y
break
for y in range(self.new_shape[1] // 2, perturbed_y_min, -1):
if np.sum(self.synthesis_perturbed_img[:, y]) == 768 * self.new_shape[0] and y > 0:
perturbed_y_min = y
break
if perturbed_x_min == 0 or perturbed_x_max == self.new_shape[0] or perturbed_y_min == self.new_shape[1] or perturbed_y_max == self.new_shape[1]:
raise Exception('clip error')
if perturbed_x_max - perturbed_x_min < im_lr//2 or perturbed_y_max - perturbed_y_min < im_ud//2:
raise Exception('clip error')
perfix_ = self.save_suffix+'_'+str(m)+'_'+str(n)
is_shrink = False
if perturbed_x_max - perturbed_x_min > save_img_shape[0] or perturbed_y_max - perturbed_y_min > save_img_shape[1]:
is_shrink = True
synthesis_perturbed_img = cv2.resize(self.synthesis_perturbed_img[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max, :].copy(), (im_ud, im_lr), interpolation=cv2.INTER_LINEAR)
synthesis_perturbed_label = cv2.resize(self.synthesis_perturbed_label[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max, :].copy(), (im_ud, im_lr), interpolation=cv2.INTER_LINEAR)
foreORbackground_label = cv2.resize(self.foreORbackground_label[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max].copy(), (im_ud, im_lr), interpolation=cv2.INTER_LINEAR)
foreORbackground_label[foreORbackground_label < 0.99] = 0
foreORbackground_label[foreORbackground_label >= 0.99] = 1
'''shrink fiducial points'''
center_x_l, center_y_l = perturbed_x_min + (perturbed_x_max - perturbed_x_min) // 2, perturbed_y_min + (perturbed_y_max - perturbed_y_min) // 2
fiducial_points_coordinate_copy = fiducial_points_coordinate.copy()
shrink_x = im_lr/(perturbed_x_max - perturbed_x_min)
shrink_y = im_ud/(perturbed_y_max - perturbed_y_min)
fiducial_points_coordinate *= [shrink_x, shrink_y]
center_x_l *= shrink_x
center_y_l *= shrink_y
# fiducial_points_coordinate[1:, 1:] *= [shrink_x, shrink_y]
# fiducial_points_coordinate[1:, :1, 0] *= shrink_x
# fiducial_points_coordinate[:1, 1:, 1] *= shrink_y
# perturbed_x_min_copy, perturbed_y_min_copy, perturbed_x_max_copy, perturbed_y_max_copy = perturbed_x_min, perturbed_y_min, perturbed_x_max, perturbed_y_max
perturbed_x_min, perturbed_y_min, perturbed_x_max, perturbed_y_max = self.adjust_position_v2(0, 0, im_lr, im_ud, self.new_shape)
self.synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 256)
self.synthesis_perturbed_label = np.zeros_like(self.synthesis_perturbed_label)
self.foreORbackground_label = np.zeros_like(self.foreORbackground_label)
self.synthesis_perturbed_img[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max, :] = synthesis_perturbed_img
self.synthesis_perturbed_label[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max, :] = synthesis_perturbed_label
self.foreORbackground_label[perturbed_x_min:perturbed_x_max, perturbed_y_min:perturbed_y_max] = foreORbackground_label
center_x, center_y = perturbed_x_min + (perturbed_x_max - perturbed_x_min) // 2, perturbed_y_min + (perturbed_y_max - perturbed_y_min) // 2
if is_shrink:
fiducial_points_coordinate += [center_x-center_x_l, center_y-center_y_l]
'''draw fiducial points
stepSize = 0
fiducial_points_synthesis_perturbed_img = self.synthesis_perturbed_img.copy()
for l in fiducial_points_coordinate.astype(np.int64).reshape(-1, 2):
cv2.circle(fiducial_points_synthesis_perturbed_img,
(l[1] + math.ceil(stepSize / 2), l[0] + math.ceil(stepSize / 2)), 5, (0, 0, 255), -1)
cv2.imwrite('/lustre/home/gwxie/program/project/unwarp/unwarp_perturbed/TPS/img/cv_TPS_small.jpg',fiducial_points_synthesis_perturbed_img)
'''
self.new_shape = save_img_shape
self.synthesis_perturbed_img = self.synthesis_perturbed_img[
center_x - self.new_shape[0] // 2:center_x + self.new_shape[0] // 2,
center_y - self.new_shape[1] // 2:center_y + self.new_shape[1] // 2,
:].copy()
self.synthesis_perturbed_label = self.synthesis_perturbed_label[
center_x - self.new_shape[0] // 2:center_x + self.new_shape[0] // 2,
center_y - self.new_shape[1] // 2:center_y + self.new_shape[1] // 2,
:].copy()
self.foreORbackground_label = self.foreORbackground_label[
center_x - self.new_shape[0] // 2:center_x + self.new_shape[0] // 2,
center_y - self.new_shape[1] // 2:center_y + self.new_shape[1] // 2].copy()
perturbed_x_ = max(self.new_shape[0] - (perturbed_x_max - perturbed_x_min), 0)
perturbed_x_min = perturbed_x_ // 2
perturbed_x_max = self.new_shape[0] - perturbed_x_ // 2 if perturbed_x_%2 == 0 else self.new_shape[0] - (perturbed_x_ // 2 + 1)
perturbed_y_ = max(self.new_shape[1] - (perturbed_y_max - perturbed_y_min), 0)
perturbed_y_min = perturbed_y_ // 2
perturbed_y_max = self.new_shape[1] - perturbed_y_ // 2 if perturbed_y_%2 == 0 else self.new_shape[1] - (perturbed_y_ // 2 + 1)
'''clip
perturbed_x_min, perturbed_y_min, perturbed_x_max, perturbed_y_max = -1, -1, self.new_shape[0], self.new_shape[1]
for x in range(self.new_shape[0] // 2, perturbed_x_max):
if np.sum(self.synthesis_perturbed_img[x, :]) == 768 * self.new_shape[1] and perturbed_x_max - 1 > x:
perturbed_x_max = x
break
for x in range(self.new_shape[0] // 2, perturbed_x_min, -1):
if np.sum(self.synthesis_perturbed_img[x, :]) == 768 * self.new_shape[1] and x > 0:
perturbed_x_min = x
break
for y in range(self.new_shape[1] // 2, perturbed_y_max):
if np.sum(self.synthesis_perturbed_img[:, y]) == 768 * self.new_shape[0] and perturbed_y_max - 1 > y:
perturbed_y_max = y
break
for y in range(self.new_shape[1] // 2, perturbed_y_min, -1):
if np.sum(self.synthesis_perturbed_img[:, y]) == 768 * self.new_shape[0] and y > 0:
perturbed_y_min = y
break
center_x, center_y = perturbed_x_min+(perturbed_x_max - perturbed_x_min)//2, perturbed_y_min+(perturbed_y_max - perturbed_y_min)//2
perfix_ = self.save_suffix+'_'+str(m)+'_'+str(n)
self.new_shape = save_img_shape
perturbed_x_ = max(self.new_shape[0] - (perturbed_x_max - perturbed_x_min), 0)
perturbed_x_min = perturbed_x_ // 2
perturbed_x_max = self.new_shape[0] - perturbed_x_ // 2 if perturbed_x_%2 == 0 else self.new_shape[0] - (perturbed_x_ // 2 + 1)
perturbed_y_ = max(self.new_shape[1] - (perturbed_y_max - perturbed_y_min), 0)
perturbed_y_min = perturbed_y_ // 2
perturbed_y_max = self.new_shape[1] - perturbed_y_ // 2 if perturbed_y_%2 == 0 else self.new_shape[1] - (perturbed_y_ // 2 + 1)
self.synthesis_perturbed_img = self.synthesis_perturbed_img[center_x-self.new_shape[0]//2:center_x+self.new_shape[0]//2, center_y-self.new_shape[1]//2:center_y+self.new_shape[1]//2, :].copy()
self.synthesis_perturbed_label = self.synthesis_perturbed_label[center_x-self.new_shape[0]//2:center_x+self.new_shape[0]//2, center_y-self.new_shape[1]//2:center_y+self.new_shape[1]//2, :].copy()
self.foreORbackground_label = self.foreORbackground_label[center_x-self.new_shape[0]//2:center_x+self.new_shape[0]//2, center_y-self.new_shape[1]//2:center_y+self.new_shape[1]//2].copy()
'''
'''save'''
pixel_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(self.new_shape[0], self.new_shape[1], 2)
if relativeShift_position == 'relativeShift_v2':
self.synthesis_perturbed_label -= pixel_position
fiducial_points_coordinate -= [center_x - self.new_shape[0] // 2, center_y - self.new_shape[1] // 2]
self.synthesis_perturbed_label[:, :, 0] *= self.foreORbackground_label
self.synthesis_perturbed_label[:, :, 1] *= self.foreORbackground_label
self.synthesis_perturbed_img[:, :, 0] *= self.foreORbackground_label
self.synthesis_perturbed_img[:, :, 1] *= self.foreORbackground_label
self.synthesis_perturbed_img[:, :, 2] *= self.foreORbackground_label
'''
synthesis_perturbed_img_filter = self.synthesis_perturbed_img.copy()
synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (3, 3), 0)
# if self.is_perform(0.9, 0.1) or repeat_time > 5:
# # if self.is_perform(0.1, 0.9) and repeat_time > 9:
# # synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (7, 7), 0)
# # else:
# synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (5, 5), 0)
# else:
# synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (3, 3), 0)
self.synthesis_perturbed_img[self.foreORbackground_label == 1] = synthesis_perturbed_img_filter[self.foreORbackground_label == 1]
'''
'''
perturbed_bg_img = perturbed_bg_img.astype(np.float32)
perturbed_bg_img[:, :, 0] *= 1 - self.foreORbackground_label
perturbed_bg_img[:, :, 1] *= 1 - self.foreORbackground_label
perturbed_bg_img[:, :, 2] *= 1 - self.foreORbackground_label
self.synthesis_perturbed_img += perturbed_bg_img
HSV
perturbed_bg_img = perturbed_bg_img.astype(np.float32)
if self.is_perform(0.1, 0.9):
if self.is_perform(0.2, 0.8):
synthesis_perturbed_img_clip_HSV = self.synthesis_perturbed_img.copy()
synthesis_perturbed_img_clip_HSV = cv2.cvtColor(synthesis_perturbed_img_clip_HSV, cv2.COLOR_RGB2HSV)
H_, S_, V_ = (random.random()-0.2)*20, (random.random()-0.2)/8, (random.random()-0.2)*20
synthesis_perturbed_img_clip_HSV[:, :, 0], synthesis_perturbed_img_clip_HSV[:, :, 1], synthesis_perturbed_img_clip_HSV[:, :, 2] = synthesis_perturbed_img_clip_HSV[:, :, 0]-H_, synthesis_perturbed_img_clip_HSV[:, :, 1]-S_, synthesis_perturbed_img_clip_HSV[:, :, 2]-V_
synthesis_perturbed_img_clip_HSV = cv2.cvtColor(synthesis_perturbed_img_clip_HSV, cv2.COLOR_HSV2RGB)
perturbed_bg_img[:, :, 0] *= 1-self.foreORbackground_label
perturbed_bg_img[:, :, 1] *= 1-self.foreORbackground_label
perturbed_bg_img[:, :, 2] *= 1-self.foreORbackground_label
synthesis_perturbed_img_clip_HSV += perturbed_bg_img
self.synthesis_perturbed_img = synthesis_perturbed_img_clip_HSV
else:
perturbed_bg_img_HSV = perturbed_bg_img
perturbed_bg_img_HSV = cv2.cvtColor(perturbed_bg_img_HSV, cv2.COLOR_RGB2HSV)
H_, S_, V_ = (random.random()-0.5)*20, (random.random()-0.5)/8, (random.random()-0.2)*20
perturbed_bg_img_HSV[:, :, 0], perturbed_bg_img_HSV[:, :, 1], perturbed_bg_img_HSV[:, :, 2] = perturbed_bg_img_HSV[:, :, 0]-H_, perturbed_bg_img_HSV[:, :, 1]-S_, perturbed_bg_img_HSV[:, :, 2]-V_
perturbed_bg_img_HSV = cv2.cvtColor(perturbed_bg_img_HSV, cv2.COLOR_HSV2RGB)
perturbed_bg_img_HSV[:, :, 0] *= 1-self.foreORbackground_label
perturbed_bg_img_HSV[:, :, 1] *= 1-self.foreORbackground_label
perturbed_bg_img_HSV[:, :, 2] *= 1-self.foreORbackground_label
self.synthesis_perturbed_img += perturbed_bg_img_HSV
# self.synthesis_perturbed_img[np.sum(self.synthesis_perturbed_img, 2) == 771] = perturbed_bg_img_HSV[np.sum(self.synthesis_perturbed_img, 2) == 771]
else:
synthesis_perturbed_img_clip_HSV = self.synthesis_perturbed_img.copy()
perturbed_bg_img[:, :, 0] *= 1 - self.foreORbackground_label
perturbed_bg_img[:, :, 1] *= 1 - self.foreORbackground_label
perturbed_bg_img[:, :, 2] *= 1 - self.foreORbackground_label
synthesis_perturbed_img_clip_HSV += perturbed_bg_img
# synthesis_perturbed_img_clip_HSV[np.sum(self.synthesis_perturbed_img, 2) == 771] = perturbed_bg_img[np.sum(self.synthesis_perturbed_img, 2) == 771]
synthesis_perturbed_img_clip_HSV = cv2.cvtColor(synthesis_perturbed_img_clip_HSV, cv2.COLOR_RGB2HSV)
H_, S_, V_ = (random.random()-0.5)*20, (random.random()-0.5)/10, (random.random()-0.4)*20
synthesis_perturbed_img_clip_HSV[:, :, 0], synthesis_perturbed_img_clip_HSV[:, :, 1], synthesis_perturbed_img_clip_HSV[:, :, 2] = synthesis_perturbed_img_clip_HSV[:, :, 0]-H_, synthesis_perturbed_img_clip_HSV[:, :, 1]-S_, synthesis_perturbed_img_clip_HSV[:, :, 2]-V_
synthesis_perturbed_img_clip_HSV = cv2.cvtColor(synthesis_perturbed_img_clip_HSV, cv2.COLOR_HSV2RGB)
self.synthesis_perturbed_img = synthesis_perturbed_img_clip_HSV
'''
'''HSV_v2'''
perturbed_bg_img = perturbed_bg_img.astype(np.float32)
# if self.is_perform(1, 0):
# if self.is_perform(1, 0):
if self.is_perform(0.1, 0.9):
if self.is_perform(0.2, 0.8):
synthesis_perturbed_img_clip_HSV = self.synthesis_perturbed_img.copy()
synthesis_perturbed_img_clip_HSV = self.HSV_v1(synthesis_perturbed_img_clip_HSV)
perturbed_bg_img[:, :, 0] *= 1-self.foreORbackground_label
perturbed_bg_img[:, :, 1] *= 1-self.foreORbackground_label
perturbed_bg_img[:, :, 2] *= 1-self.foreORbackground_label
synthesis_perturbed_img_clip_HSV += perturbed_bg_img
self.synthesis_perturbed_img = synthesis_perturbed_img_clip_HSV
else:
perturbed_bg_img_HSV = perturbed_bg_img
perturbed_bg_img_HSV = self.HSV_v1(perturbed_bg_img_HSV)
perturbed_bg_img_HSV[:, :, 0] *= 1-self.foreORbackground_label
perturbed_bg_img_HSV[:, :, 1] *= 1-self.foreORbackground_label
perturbed_bg_img_HSV[:, :, 2] *= 1-self.foreORbackground_label
self.synthesis_perturbed_img += perturbed_bg_img_HSV
# self.synthesis_perturbed_img[np.sum(self.synthesis_perturbed_img, 2) == 771] = perturbed_bg_img_HSV[np.sum(self.synthesis_perturbed_img, 2) == 771]
else:
synthesis_perturbed_img_clip_HSV = self.synthesis_perturbed_img.copy()
perturbed_bg_img[:, :, 0] *= 1 - self.foreORbackground_label
perturbed_bg_img[:, :, 1] *= 1 - self.foreORbackground_label
perturbed_bg_img[:, :, 2] *= 1 - self.foreORbackground_label
synthesis_perturbed_img_clip_HSV += perturbed_bg_img
synthesis_perturbed_img_clip_HSV = self.HSV_v1(synthesis_perturbed_img_clip_HSV)
self.synthesis_perturbed_img = synthesis_perturbed_img_clip_HSV
''''''
# cv2.imwrite(self.save_path+'clip/'+perfix_+'_'+fold_curve+str(perturbed_time)+'-'+str(repeat_time)+'.png', synthesis_perturbed_img_clip)
self.synthesis_perturbed_img[self.synthesis_perturbed_img < 0] = 0
self.synthesis_perturbed_img[self.synthesis_perturbed_img > 255] = 255
self.synthesis_perturbed_img = np.around(self.synthesis_perturbed_img).astype(np.uint8)
label = np.zeros_like(self.synthesis_perturbed_img, dtype=np.float32)
label[:, :, :2] = self.synthesis_perturbed_label
label[:, :, 2] = self.foreORbackground_label
# grey = np.around(self.synthesis_perturbed_img[:, :, 0] * 0.2989 + self.synthesis_perturbed_img[:, :, 1] * 0.5870 + self.synthesis_perturbed_img[:, :, 0] * 0.1140).astype(np.int16)
# synthesis_perturbed_grey = np.concatenate((grey.reshape(self.new_shape[0], self.new_shape[1], 1), label), axis=2)
synthesis_perturbed_color = np.concatenate((self.synthesis_perturbed_img, label), axis=2)
self.synthesis_perturbed_color = np.zeros_like(synthesis_perturbed_color, dtype=np.float32)
# self.synthesis_perturbed_grey = np.zeros_like(synthesis_perturbed_grey, dtype=np.float32)
reduce_value_x = int(round(min((random.random() / 2) * (self.new_shape[0] - (perturbed_x_max - perturbed_x_min)), min(reduce_value, reduce_value_v2))))
reduce_value_y = int(round(min((random.random() / 2) * (self.new_shape[1] - (perturbed_y_max - perturbed_y_min)), min(reduce_value, reduce_value_v2))))
perturbed_x_min = max(perturbed_x_min - reduce_value_x, 0)
perturbed_x_max = min(perturbed_x_max + reduce_value_x, self.new_shape[0])
perturbed_y_min = max(perturbed_y_min - reduce_value_y, 0)
perturbed_y_max = min(perturbed_y_max + reduce_value_y, self.new_shape[1])
if im_lr >= im_ud:
self.synthesis_perturbed_color[:, perturbed_y_min:perturbed_y_max, :] = synthesis_perturbed_color[:, perturbed_y_min:perturbed_y_max, :]
# self.synthesis_perturbed_grey[:, perturbed_y_min:perturbed_y_max, :] = synthesis_perturbed_grey[:, perturbed_y_min:perturbed_y_max, :]
else:
self.synthesis_perturbed_color[perturbed_x_min:perturbed_x_max, :, :] = synthesis_perturbed_color[perturbed_x_min:perturbed_x_max, :, :]
# self.synthesis_perturbed_grey[perturbed_x_min:perturbed_x_max, :, :] = synthesis_perturbed_grey[perturbed_x_min:perturbed_x_max, :, :]
'''blur'''
if self.is_perform(0.1, 0.9):
synthesis_perturbed_img_filter = self.synthesis_perturbed_color[:, :, :3].copy()
if self.is_perform(0.1, 0.9):
synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (5, 5), 0)
else:
synthesis_perturbed_img_filter = cv2.GaussianBlur(synthesis_perturbed_img_filter, (3, 3), 0)
if self.is_perform(0.5, 0.5):
self.synthesis_perturbed_color[:, :, :3][self.synthesis_perturbed_color[:, :, 5] == 1] = synthesis_perturbed_img_filter[self.synthesis_perturbed_color[:, :, 5] == 1]
else:
self.synthesis_perturbed_color[:, :, :3] = synthesis_perturbed_img_filter
fiducial_points_coordinate = fiducial_points_coordinate[:, :, ::-1]
'''draw fiducial points'''
stepSize = 0
fiducial_points_synthesis_perturbed_img = self.synthesis_perturbed_color[:, :, :3].copy()
for l in fiducial_points_coordinate.astype(np.int64).reshape(-1, 2):
cv2.circle(fiducial_points_synthesis_perturbed_img, (l[0] + math.ceil(stepSize / 2), l[1] + math.ceil(stepSize / 2)), 2, (0, 0, 255), -1)
cv2.imwrite(self.save_path + 'fiducial_points/' + perfix_ + '_' + fold_curve + '.png', fiducial_points_synthesis_perturbed_img)
cv2.imwrite(self.save_path + 'png/' + perfix_ + '_' + fold_curve + '.png', self.synthesis_perturbed_color[:, :, :3])
'''forward-begin'''
self.forward_mapping = np.full((save_img_shape[0], save_img_shape[1], 2), 0, dtype=np.float32)
forward_mapping = np.full((save_img_shape[0], save_img_shape[1], 2), 0, dtype=np.float32)
forward_position = (self.synthesis_perturbed_color[:, :, 3:5] + pixel_position)[self.synthesis_perturbed_color[:, :, 5] != 0, :]
flat_position = np.argwhere(np.zeros(save_img_shape, dtype=np.uint32) == 0)
vtx, wts = self.interp_weights(forward_position, flat_position)
wts_sum = np.abs(wts).sum(-1)
wts = wts[wts_sum <= 1, :]
vtx = vtx[wts_sum <= 1, :]
flat_position_forward = flat_position.reshape(save_img_shape[0], save_img_shape[1], 2)[self.synthesis_perturbed_color[:, :, 5] != 0, :]
forward_mapping.reshape(save_img_shape[0] * save_img_shape[1], 2)[wts_sum <= 1, :] = self.interpolate(flat_position_forward, vtx, wts)
forward_mapping = forward_mapping.reshape(save_img_shape[0], save_img_shape[1], 2)
mapping_x_min_, mapping_y_min_, mapping_x_max_, mapping_y_max_ = self.adjust_position_v2(0, 0, im_lr, im_ud, self.new_shape)
shreshold_zoom_out = 2
mapping_x_min = mapping_x_min_ + shreshold_zoom_out
mapping_y_min = mapping_y_min_ + shreshold_zoom_out
mapping_x_max = mapping_x_max_ - shreshold_zoom_out
mapping_y_max = mapping_y_max_ - shreshold_zoom_out
self.forward_mapping[mapping_x_min:mapping_x_max, mapping_y_min:mapping_y_max] = forward_mapping[mapping_x_min:mapping_x_max, mapping_y_min:mapping_y_max]
self.scan_img = np.full((save_img_shape[0], save_img_shape[1], 3), 0, dtype=np.float32)
self.scan_img[mapping_x_min_:mapping_x_max_, mapping_y_min_:mapping_y_max_] = self.origin_img
self.origin_img = self.scan_img
# flat_img = np.full((save_img_shape[0], save_img_shape[1], 3), 0, dtype=np.float32)
# cv2.remap(self.synthesis_perturbed_color[:, :, :3], self.forward_mapping[:, :, 1], self.forward_mapping[:, :, 0], cv2.INTER_LINEAR, flat_img)
# cv2.imwrite(self.save_path + 'outputs/1.jpg', flat_img)
'''forward-end'''
synthesis_perturbed_data = {
'fiducial_points': fiducial_points_coordinate,
'segment': | np.array((segment_x, segment_y)) | numpy.array |
###############################################################################
# @todo add Pilot2-splash-app disclaimer
###############################################################################
""" Get's KRAS states """
import MDAnalysis as mda
from MDAnalysis.analysis import align
from MDAnalysis.lib.mdamath import make_whole
import os
import numpy as np
import math
############## Below section needs to be uncommented ############
import mummi_core
import mummi_ras
from mummi_core.utils import Naming
# # Logger has to be initialized the first thing in the script
from logging import getLogger
LOGGER = getLogger(__name__)
# # Innitilize MuMMI if it has not been done before
# MUMMI_ROOT = mummi.init(True)
# This is needed so the Naming works below
#@TODO fix this so we don't have these on import make them as an init
mummi_core.init()
dirKRASStates = Naming.dir_res('states')
dirKRASStructures = Naming.dir_res('structures')
# #RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-ONLY.microstates.txt"))
RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-states.txt"),comments='#')
# #RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-RAF.microstates.txt"))
RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-raf-states.txt"),comments='#') # Note diffrent number of columns so index change below
# TODO: CS, my edits to test
# RAS_ONLY_macrostate = np.loadtxt('ras-states.txt')
# RAS_RAF_macrostate = np.loadtxt('ras-raf-states.txt')
############## above section needs to be uncommented ############
# TODO: CS, my edits to test
# TODO: TSC, The reference structure has to currently be set as the 'RAS-ONLY-reference-structure.gro'
# TODO: TSC, path to the reference structure is: mummi_resources/structures/
kras_ref_universe = mda.Universe(os.path.join(dirKRASStructures, "RAS-ONLY-reference-structure.gro"))
# kras_ref_universe = mda.Universe("RAS-ONLY-reference-structure.gro")
# kras_ref_universe = mda.Universe('AA_pfpatch_000000004641_RAS_RAF2_411.gro')
# TODO: CS, not using these for x4 proteins; instead using protein_systems below to set num_res
######### Below hard codes the number of residues within RAS-only and RAS-RAF ##########
RAS_only_num_res = 184
RAS_RAF_num_res = 320
######### Above hard codes the number of residues within RAS-only and RAS-RAF ##########
####### This can be removed
# def get_kras(syst, kras_start):
# """Gets all atoms for a KRAS protein starting at 'kras_start'."""
# return syst.atoms[kras_start:kras_start+428]
####### This can be removed
def get_segids(u):
"""Identifies the list of segments within the system. Only needs to be called x1 time"""
segs = u.segments
segs = segs.segids
ras_segids = []
rasraf_segids = []
for i in range(len(segs)):
# print(segs[i])
if segs[i][-3:] == 'RAS':
ras_segids.append(segs[i])
if segs[i][-3:] == 'RAF':
rasraf_segids.append(segs[i])
return ras_segids, rasraf_segids
def get_protein_info(u,tag):
"""Uses the segments identified in get_segids to make a list of all proteins in the systems.\
Outputs a list of the first residue number of the protein, and whether it is 'RAS-ONLY', or 'RAS-RAF'.\
The 'tag' input defines what is used to identify the first residue of the protein. i.e. 'resname ACE1 and name BB'.\
Only needs to be called x1 time"""
ras_segids, rasraf_segids = get_segids(u)
if len(ras_segids) > 0:
RAS = u.select_atoms('segid '+ras_segids[0]+' and '+str(tag))
else:
RAS = []
if len(rasraf_segids) > 0:
RAF = u.select_atoms('segid '+rasraf_segids[0]+' and '+str(tag))
else:
RAF = []
protein_info = []#np.empty([len(RAS)+len(RAF),2])
for i in range(len(RAS)):
protein_info.append((RAS[i].resid,'RAS-ONLY'))
for i in range(len(RAF)):
protein_info.append((RAF[i].resid,'RAS-RAF'))
######## sort protein info
protein_info = sorted(protein_info)
######## sort protein info
return protein_info
def get_ref_kras():
"""Gets the reference KRAS struct. Only called x1 time when class is loaded"""
start_of_g_ref = kras_ref_universe.residues[0].resid
ref_selection = 'resid '+str(start_of_g_ref)+':'+str(start_of_g_ref+24)+' ' +\
str(start_of_g_ref+38)+':'+str(start_of_g_ref+54)+' ' +\
str(start_of_g_ref+67)+':'+str(start_of_g_ref+164)+' ' +\
'and (name CA or name BB)'
r2_26r40_56r69_166_ref = kras_ref_universe.select_atoms(str(ref_selection))
return kras_ref_universe.select_atoms(str(ref_selection)).positions - kras_ref_universe.select_atoms(str(ref_selection)).center_of_mass()
# Load inital ref frames (only need to do this once)
ref0 = get_ref_kras()
def getKRASstates(u,kras_indices):
"""Gets states for all KRAS proteins in path."""
# res_shift = 8
# all_glycine = u.select_atoms("resname GLY")
# kras_indices = []
# for i in range(0, len(all_glycine), 26):
# kras_indices.append(all_glycine[i].index)
########## Below is taken out of the function so it is only done once #########
# kras_indices = get_protein_info(u,'resname ACE1 and name BB')
########## Above is taken out of the function so it is only done once #########
# CS, for x4 cases:
# [{protein_x4: (protein_type, num_res)}]
protein_systems = [{'ras4a': ('RAS-ONLY', 185),
'ras4araf': ('RAS-RAF', 321),
'ras': ('RAS-ONLY', 184),
'rasraf': ('RAS-RAF', 320)}]
ALLOUT = []
for k in range(len(kras_indices)):
start_of_g = kras_indices[k][0]
protein_x4 = str(kras_indices[k][1])
try:
protein_type = [item[protein_x4] for item in protein_systems][0][0] # 'RAS-ONLY' OR 'RAS-RAF'
num_res = [item[protein_x4] for item in protein_systems][0][1]
except:
LOGGER.error('Check KRas naming between modules')
raise Exception('Error: unknown KRas name')
# TODO: CS, replacing this comment section with the above, to handle x4 protein types
# ---------------------------------------
# ALLOUT = []
# for k in range(len(kras_indices)):
# start_of_g = kras_indices[k][0]
# protein_type = str(kras_indices[k][1])
# ########## BELOW SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ##############
# ########## POTENTIALLY REDO WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) #######
# ########## HAS BEEN REDONE WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) ########
# # if len(kras_indices) == 1:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB') ####### HAS TO BE FIXED FOR BACKBONE ATOMS FOR SPECIFIC PROTEIN
# # elif len(kras_indices) > 1:
# # if k == len(kras_indices)-1:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB')
# # else:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(kras_indices[k+1][0])+' and name BB')
# ########## ABOVE SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ##############
#
# ########## Below hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations #########################
# if protein_type == 'RAS-ONLY':
# num_res = RAS_only_num_res
# elif protein_type == 'RAS-RAF':
# num_res = RAS_RAF_num_res
# ########## Above hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations #########################
# ---------------------------------------
# TODO: TSC, I changed the selection below, which can be used for the make_whole...
# krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res)+' and (name CA or name BB)')
krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res))
krases0_BB.guess_bonds()
r2_26r40_56r69_166 = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+24)+' ' +\
str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\
str(start_of_g+67)+':'+str(start_of_g+164)+\
' and (name CA or name BB)')
u_selection = \
'resid '+str(start_of_g)+':'+str(start_of_g+24)+' '+str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\
str(start_of_g+67)+':'+str(start_of_g+164)+' and (name CA or name BB)'
mobile0 = u.select_atoms(str(u_selection)).positions - u.select_atoms(str(u_selection)).center_of_mass()
# TODO: CS, something wrong with ref0 from get_kras_ref()
# just making ref0 = mobile0 to test for now
# ref0 = mobile0
# TSC removed this
R, RMSD_junk = align.rotation_matrix(mobile0, ref0)
######## TODO: TSC, Adjusted for AA lipid names ########
# lipids = u.select_atoms('resname POPX POPC PAPC POPE DIPE DPSM PAPS PAP6 CHOL')
lipids = u.select_atoms('resname POPC PAPC POPE DIPE SSM PAPS SAPI CHL1')
coords = ref0
RotMat = []
OS = []
r152_165 = krases0_BB.select_atoms('resid '+str(start_of_g+150)+':'+str(start_of_g+163)+' and (name CA or name BB)')
r65_74 = krases0_BB.select_atoms('resid '+str(start_of_g+63)+':'+str(start_of_g+72)+' and (name CA or name BB)')
timeframes = []
# TODO: CS, for AA need bonds to run make_whole()
# krases0_BB.guess_bonds()
# TODO: CS, turn off for now to test beyond this point
''' *** for AA, need to bring that back on once all else runs ***
'''
# @Tim and <NAME>. this was commented out - please check.
#make_whole(krases0_BB)
j, rmsd_junk = mda.analysis.align.rotation_matrix((r2_26r40_56r69_166.positions-r2_26r40_56r69_166.center_of_mass()), coords)
RotMat.append(j)
OS.append(r65_74.center_of_mass()-r152_165.center_of_mass())
timeframes.append(u.trajectory.time)
if protein_type == 'RAS-RAF':
z_pos = []
############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES BELOW ####################
############### TODO: TSC, zshifting is set to -1 (instead of -2), as there are ACE caps that are separate residues in AA
#zshifting=-1
if protein_x4 == 'rasraf':
zshifting = -1
elif protein_x4 == 'ras4araf':
zshifting = 0
else:
zshifting = 0
LOGGER.error('Found unsupported protein_x4 type')
raf_loops_selection = u.select_atoms('resid '+str(start_of_g+zshifting+291)+':'+str(start_of_g+zshifting+294)+' ' +\
str(start_of_g+zshifting+278)+':'+str(start_of_g+zshifting+281)+' ' +\
' and (name CA or name BB)')
############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES ABOVE ####################
diff = (lipids.center_of_mass()[2]-raf_loops_selection.center_of_mass(unwrap=True)[2])/10
if diff < 0:
diff = diff+(u.dimensions[2]/10)
z_pos.append(diff)
z_pos = np.array(z_pos)
RotMatNP = np.array(RotMat)
OS = np.array(OS)
OA = RotMatNP[:, 2, :]/(((RotMatNP[:, 2, 0]**2)+(RotMatNP[:, 2, 1]**2)+(RotMatNP[:, 2, 2]**2))**0.5)[:, None]
OWAS = np.arccos(RotMatNP[:, 2, 2])*180/math.pi
OC_temp = np.concatenate((OA, OS), axis=1)
t = ((OC_temp[:, 0]*OC_temp[:, 3])+(OC_temp[:, 1]*OC_temp[:, 4]) +
(OC_temp[:, 2]*OC_temp[:, 5]))/((OC_temp[:, 0]**2)+(OC_temp[:, 1]**2)+(OC_temp[:, 2]**2))
OC = OA*t[:, None]
ORS_tp = np.concatenate((OC, OS), axis=1)
ORS_norm = (((ORS_tp[:, 3]-ORS_tp[:, 0])**2)+((ORS_tp[:, 4]-ORS_tp[:, 1])**2)+((ORS_tp[:, 5]-ORS_tp[:, 2])**2))**0.5
ORS = (OS - OC)/ORS_norm[:, None]
OACRS = np.cross(OA, ORS)
OZCA = OA * OA[:, 2][:, None]
Z_unit = np.full([len(OZCA), 3], 1)
Z_adjust = np.array([0, 0, 1])
Z_unit = Z_unit*Z_adjust
Z_OZCA = Z_unit-OZCA
OZPACB = Z_OZCA/((Z_OZCA[:, 0]**2+Z_OZCA[:, 1]**2+Z_OZCA[:, 2]**2)**0.5)[:, None]
OROTNOTSIGNED = np.zeros([len(ORS)])
for i in range(len(ORS)):
OROTNOTSIGNED[i] = np.arccos(np.dot(OZPACB[i, :], ORS[i, :]) /
(np.sqrt(np.dot(OZPACB[i, :], OZPACB[i, :]))) *
(np.sqrt(np.dot(ORS[i, :], ORS[i, :]))))*180/math.pi
OZPACBCRS_cross = np.cross(OZPACB, ORS)
OZPACBCRS = OZPACBCRS_cross/((OZPACBCRS_cross[:, 0]**2+OZPACBCRS_cross[:, 1]**2+OZPACBCRS_cross[:, 2]**2)**0.5)[:, None]
OFORSIGN_temp = (OA - OZPACBCRS)**2
OFORSIGN = OFORSIGN_temp[:, 0]+OFORSIGN_temp[:, 1]+OFORSIGN_temp[:, 2]
OROT = OROTNOTSIGNED
for i in range(len(OROT)):
if OROT[i] < 0:
OROT[i] = -(OROT[i])
for i in range(len(OROT)):
if OFORSIGN[i] < 0.25:
OROT[i] = -(OROT[i])
###### Below introduces new shift to account for upper vs. lower leaflet #####
for i in range(len(OWAS)):
OWAS[i] = abs(-(OWAS[i])+180) # made this an absolute value so that the tilt remains positive
for i in range(len(OROT)):
if OROT[i] < 0:
OROT[i] = OROT[i]+180
elif OROT[i] > 0:
OROT[i] = OROT[i]-180
###### Above introduces new shift to account for upper vs. lower leaflet #####
###### Below might have to be updated to take into account the periodic nature of the rotation ######
if protein_type == 'RAS-ONLY':
states = np.zeros(len(OROT))
for j in range(len(OROT)):
diff0 = []
for i in range(len(RAS_ONLY_macrostate)):
#diff0.append([((RAS_ONLY_macrostate[i,0]-OWAS[j])**2+(RAS_ONLY_macrostate[i,1]-OROT[j])**2)**0.5, RAS_ONLY_macrostate[i,6]])
diff0.append([((RAS_ONLY_macrostate[i,1]-OWAS[j])**2+(RAS_ONLY_macrostate[i,0]-OROT[j])**2)**0.5, RAS_ONLY_macrostate[i,5]])
diff0.sort()
states[j] = diff0[0][1]
elif protein_type == 'RAS-RAF':
states = np.zeros(len(OROT))
for j in range(len(OROT)):
### below: adding in the requirements for the 'high-z' state ###
if (OROT[j] < -45 or OROT[j] > 140) and z_pos[j] > 4.8:
states[j] = 3
else:
### above: adding in the requirements for the 'high-z' state ###
diff0 = []
for i in range(len(RAS_RAF_macrostate)):
#diff0.append([((RAS_RAF_macrostate[i,0]-OWAS[j])**2+(RAS_RAF_macrostate[i,1]-OROT[j])**2)**0.5, RAS_RAF_macrostate[i,6]])
diff0.append([((RAS_RAF_macrostate[i,1]-OWAS[j])**2+(RAS_RAF_macrostate[i,0]-OROT[j])**2)**0.5, RAS_RAF_macrostate[i,4]])
diff0.sort()
states[j] = diff0[0][1]
###### Above might have to be updated to take into account the periodic nature of the rotation ######
###### Assume we want to remove this? Where is the code that reads this information? i.e. will there be knock-on effects? ######
###### If feedback code needs index 5 (two_states) from the output, deleting this four_states will shift that to index 4 #######
# four_states = np.zeros(len(OROT))
# for j in range(len(OROT)):
# diff0 = []
# for i in range(len(macrostate4)):
# diff0.append([((macrostate4[i,0]-OWAS[j])**2+(macrostate4[i,1]-OROT[j])**2)**0.5, macrostate4[i,6]])
# diff0.sort()
# four_states[j] = diff0[0][1]+1
###### below: old output details.... ######################################
###### Updated - RAS-only to NOT HAVE the Z-distance ######################
###### Updated - Added in the protein 'tag', i.e. RAS-ONLY or RAS-RAF #####
# OUTPUT = np.zeros([len(OROT), 6])
# for i in range(len(OROT)):
# OUTPUT[i] = timeframes[i], OWAS[i], OROT[i], z_pos[i], four_states[i], two_states[i]
###### above: old output details.... ######################################
###### below: NEW output details.... ######################################
if protein_type == 'RAS-ONLY':
OUTPUT = np.zeros([len(OROT), 6]).astype(object)
for i in range(len(OROT)):
OUTPUT[i] = str(protein_type), timeframes[i], OWAS[i], OROT[i], 'n/a', int(states[i])
elif protein_type == 'RAS-RAF':
OUTPUT = np.zeros([len(OROT), 6]).astype(object)
for i in range(len(OROT)):
OUTPUT[i] = str(protein_type), timeframes[i], OWAS[i], OROT[i], z_pos[i], int(states[i])
ALLOUT.append(OUTPUT)
return | np.asarray(ALLOUT) | numpy.asarray |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[2].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[2].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
plt.savefig('jss_figures/DFA_different_trends.png')
plt.show()
# plot 6b
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences Zoomed Region', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[0].set_ylim(-5.5, 5.5)
axs[0].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].set_ylim(-5.5, 5.5)
axs[1].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([np.pi, (3 / 2) * np.pi])
axs[2].set_xticklabels([r'$\pi$', r'$\frac{3}{2}\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].set_ylim(-5.5, 5.5)
axs[2].set_xlim(0.95 * np.pi, 1.55 * np.pi)
plt.savefig('jss_figures/DFA_different_trends_zoomed.png')
plt.show()
hs_ouputs = hilbert_spectrum(time, imfs_51, hts_51, ifs_51, max_frequency=12, plot=False)
# plot 6c
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Simple Sinusoidal Time Seres with Added Noise', 50))
x_hs, y, z = hs_ouputs
z_min, z_max = 0, np.abs(z).max()
ax.pcolormesh(x_hs, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max)
ax.plot(x_hs[0, :], 8 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 8$', Linewidth=3)
ax.plot(x_hs[0, :], 4 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 4$', Linewidth=3)
ax.plot(x_hs[0, :], 2 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 2$', Linewidth=3)
ax.set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
ax.set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$'])
plt.ylabel(r'Frequency (rad.s$^{-1}$)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.85, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/DFA_hilbert_spectrum.png')
plt.show()
# plot 6c
time = np.linspace(0, 5 * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 51)
fluc = Fluctuation(time=time, time_series=time_series)
max_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=False)
max_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=True)
min_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=False)
min_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=True)
util = Utility(time=time, time_series=time_series)
maxima = util.max_bool_func_1st_order_fd()
minima = util.min_bool_func_1st_order_fd()
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title(textwrap.fill('Plot Demonstrating Unsmoothed Extrema Envelopes if Schoenberg–Whitney Conditions are Not Satisfied', 50))
plt.plot(time, time_series, label='Time series', zorder=2, LineWidth=2)
plt.scatter(time[maxima], time_series[maxima], c='r', label='Maxima', zorder=10)
plt.scatter(time[minima], time_series[minima], c='b', label='Minima', zorder=10)
plt.plot(time, max_unsmoothed[0], label=textwrap.fill('Unsmoothed maxima envelope', 10), c='darkorange')
plt.plot(time, max_smoothed[0], label=textwrap.fill('Smoothed maxima envelope', 10), c='red')
plt.plot(time, min_unsmoothed[0], label=textwrap.fill('Unsmoothed minima envelope', 10), c='cyan')
plt.plot(time, min_smoothed[0], label=textwrap.fill('Smoothed minima envelope', 10), c='blue')
for knot in knots[:-1]:
plt.plot(knot * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', zorder=1)
plt.plot(knots[-1] * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', label='Knots', zorder=1)
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi),
(r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$', r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Schoenberg_Whitney_Conditions.png')
plt.show()
# plot 7
a = 0.25
width = 0.2
time = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 1001)
knots = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 11)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
inflection_bool = utils.inflection_point()
inflection_x = time[inflection_bool]
inflection_y = time_series[inflection_bool]
fluctuation = emd_mean.Fluctuation(time=time, time_series=time_series)
maxima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
maxima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
inflection_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='inflection_points')[0]
binomial_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='binomial_average', order=21,
increment=20)[0]
derivative_of_lsq = utils.derivative_forward_diff()
derivative_time = time[:-1]
derivative_knots = np.linspace(knots[0], knots[-1], 31)
# change (1) detrended_fluctuation_technique and (2) max_internal_iter and (3) debug (confusing with external debugging)
emd = AdvEMDpy.EMD(time=derivative_time, time_series=derivative_of_lsq)
imf_1_of_derivative = emd.empirical_mode_decomposition(knots=derivative_knots,
knot_time=derivative_time, text=False, verbose=False)[0][1, :]
utils = emd_utils.Utility(time=time[:-1], time_series=imf_1_of_derivative)
optimal_maxima = np.r_[False, utils.derivative_forward_diff() < 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
optimal_minima = np.r_[False, utils.derivative_forward_diff() > 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
EEMD_maxima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'maxima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
EEMD_minima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'minima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Detrended Fluctuation Analysis Examples')
plt.plot(time, time_series, LineWidth=2, label='Time series')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(time[optimal_maxima], time_series[optimal_maxima], c='darkred', zorder=4,
label=textwrap.fill('Optimal maxima', 10))
plt.scatter(time[optimal_minima], time_series[optimal_minima], c='darkblue', zorder=4,
label=textwrap.fill('Optimal minima', 10))
plt.scatter(inflection_x, inflection_y, c='magenta', zorder=4, label=textwrap.fill('Inflection points', 10))
plt.plot(time, maxima_envelope, c='darkblue', label=textwrap.fill('EMD envelope', 10))
plt.plot(time, minima_envelope, c='darkblue')
plt.plot(time, (maxima_envelope + minima_envelope) / 2, c='darkblue')
plt.plot(time, maxima_envelope_smooth, c='darkred', label=textwrap.fill('SEMD envelope', 10))
plt.plot(time, minima_envelope_smooth, c='darkred')
plt.plot(time, (maxima_envelope_smooth + minima_envelope_smooth) / 2, c='darkred')
plt.plot(time, EEMD_maxima_envelope, c='darkgreen', label=textwrap.fill('EEMD envelope', 10))
plt.plot(time, EEMD_minima_envelope, c='darkgreen')
plt.plot(time, (EEMD_maxima_envelope + EEMD_minima_envelope) / 2, c='darkgreen')
plt.plot(time, inflection_points_envelope, c='darkorange', label=textwrap.fill('Inflection point envelope', 10))
plt.plot(time, binomial_points_envelope, c='deeppink', label=textwrap.fill('Binomial average envelope', 10))
plt.plot(time, np.cos(time), c='black', label='True mean')
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi), (r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$',
r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/detrended_fluctuation_analysis.png')
plt.show()
# Duffing Equation Example
def duffing_equation(xy, ts):
gamma = 0.1
epsilon = 1
omega = ((2 * np.pi) / 25)
return [xy[1], xy[0] - epsilon * xy[0] ** 3 + gamma * np.cos(omega * ts)]
t = np.linspace(0, 150, 1501)
XY0 = [1, 1]
solution = odeint(duffing_equation, XY0, t)
x = solution[:, 0]
dxdt = solution[:, 1]
x_points = [0, 50, 100, 150]
x_names = {0, 50, 100, 150}
y_points_1 = [-2, 0, 2]
y_points_2 = [-1, 0, 1]
fig, axs = plt.subplots(2, 1)
plt.subplots_adjust(hspace=0.2)
axs[0].plot(t, x)
axs[0].set_title('Duffing Equation Displacement')
axs[0].set_ylim([-2, 2])
axs[0].set_xlim([0, 150])
axs[1].plot(t, dxdt)
axs[1].set_title('Duffing Equation Velocity')
axs[1].set_ylim([-1.5, 1.5])
axs[1].set_xlim([0, 150])
axis = 0
for ax in axs.flat:
ax.label_outer()
if axis == 0:
ax.set_ylabel('x(t)')
ax.set_yticks(y_points_1)
if axis == 1:
ax.set_ylabel(r'$ \dfrac{dx(t)}{dt} $')
ax.set(xlabel='t')
ax.set_yticks(y_points_2)
ax.set_xticks(x_points)
ax.set_xticklabels(x_names)
axis += 1
plt.savefig('jss_figures/Duffing_equation.png')
plt.show()
# compare other packages Duffing - top
pyemd = pyemd0215()
py_emd = pyemd(x)
IP, IF, IA = emd040.spectra.frequency_transform(py_emd.T, 10, 'hilbert')
freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 0.2, 100)
hht = emd040.spectra.hilberthuang(IF, IA, freq_edges)
hht = gaussian_filter(hht, sigma=1)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 1.0
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using PyEMD 0.2.10', 40))
plt.pcolormesh(t, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max( | np.abs(hht) | numpy.abs |
#!/usr/bin/env python
# encoding: utf-8
import numbers
import os
import re
import sys
from itertools import chain
import numpy as np
import scipy.sparse as sp
import six
import pickle
from .model import get_convo_nn2
from .stop_words import THAI_STOP_WORDS
from .utils import CHAR_TYPES_MAP, CHARS_MAP, create_feature_array
MODULE_PATH = os.path.dirname(__file__)
WEIGHT_PATH = os.path.join(MODULE_PATH, 'weight', 'cnn_without_ne_ab.h5')
TOKENIZER = None
def tokenize(text, custom_dict=None):
"""
Tokenize given Thai text string
Input
=====
text: str, Thai text string
custom_dict: str (or list), path to customized dictionary file
It allows the function not to tokenize given dictionary wrongly.
The file should contain custom words separated by line.
Alternatively, you can provide list of custom words too.
Output
======
tokens: list, list of tokenized words
Example
=======
>> deepcut.tokenize('ตัดคำได้ดีมาก')
>> ['ตัดคำ','ได้','ดี','มาก']
"""
global TOKENIZER
if not TOKENIZER:
TOKENIZER = DeepcutTokenizer()
return TOKENIZER.tokenize(text, custom_dict=custom_dict)
def _custom_dict(word, text, word_end):
word_length = len(word)
initial_loc = 0
while True:
try:
start_char = re.search(word, text).start()
first_char = start_char + initial_loc
last_char = first_char + word_length - 1
initial_loc += start_char + word_length
text = text[start_char + word_length:]
word_end[first_char:last_char] = (word_length - 1) * [0]
word_end[last_char] = 1
except:
break
return word_end
def _document_frequency(X):
"""
Count the number of non-zero values for each feature in sparse X.
"""
if sp.isspmatrix_csr(X):
return np.bincount(X.indices, minlength=X.shape[1])
return np.diff(sp.csc_matrix(X, copy=False).indptr)
def _check_stop_list(stop):
"""
Check stop words list
ref: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/text.py#L87-L95
"""
if stop == "thai":
return THAI_STOP_WORDS
elif isinstance(stop, six.string_types):
raise ValueError("not a built-in stop list: %s" % stop)
elif stop is None:
return None
# assume it's a collection
return frozenset(stop)
def load_model(file_path):
"""
Load saved pickle file of DeepcutTokenizer
Parameters
==========
file_path: str, path to saved model from ``save_model`` method in DeepcutTokenizer
"""
tokenizer = pickle.load(open(file_path, 'rb'))
tokenizer.model = get_convo_nn2()
tokenizer.model = tokenizer.model.load_weights(WEIGHT_PATH)
return tokenizer
class DeepcutTokenizer(object):
"""
Class for tokenizing given Thai text documents using deepcut library
Parameters
==========
ngram_range : tuple, tuple for ngram range for vocabulary, (1, 1) for unigram
and (1, 2) for bigram
stop_words : list or set, list or set of stop words to be removed
if None, max_df can be set to value [0.7, 1.0) to automatically remove
vocabulary. If using "thai", this will use list of pre-populated stop words
max_features : int or None, if provided, only consider number of vocabulary
ordered by term frequencies
max_df : float in range [0.0, 1.0] or int, default=1.0
ignore terms that have a document frequency higher than the given threshold
min_df : float in range [0.0, 1.0] or int, default=1
ignore terms that have a document frequency lower than the given threshold
dtype : type, optional
Example
=======
raw_documents = ['ฉันอยากกินข้าวของฉัน',
'ฉันอยากกินไก่',
'อยากนอนอย่างสงบ']
tokenizer = DeepcutTokenizer(ngram_range=(1, 1))
X = tokenizer.fit_tranform(raw_documents) # document-term matrix in sparse CSR format
>> X.todense()
>> [[0, 0, 1, 0, 1, 0, 2, 1],
[0, 1, 1, 0, 1, 0, 1, 0],
[1, 0, 0, 1, 1, 1, 0, 0]]
>> tokenizer.vocabulary_
>> {'นอน': 0, 'ไก่': 1, 'กิน': 2, 'อย่าง': 3, 'อยาก': 4, 'สงบ': 5, 'ฉัน': 6, 'ข้าว': 7}
"""
def __init__(self, ngram_range=(1, 1), stop_words=None,
max_df=1.0, min_df=1, max_features=None, dtype=np.dtype('float64')):
self.model = get_convo_nn2()
self.model.load_weights(WEIGHT_PATH)
self.vocabulary_ = {}
self.ngram_range = ngram_range
self.dtype = dtype
self.max_df = max_df
self.min_df = min_df
if max_df < 0 or min_df < 0:
raise ValueError("negative value for max_df or min_df")
self.max_features = max_features
self.stop_words = _check_stop_list(stop_words)
def _word_ngrams(self, tokens):
"""
Turn tokens into a tokens of n-grams
ref: https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/feature_extraction/text.py#L124-L153
"""
# handle stop words
if self.stop_words is not None:
tokens = [w for w in tokens if w not in self.stop_words]
# handle token n-grams
min_n, max_n = self.ngram_range
if max_n != 1:
original_tokens = tokens
if min_n == 1:
# no need to do any slicing for unigrams
# just iterate through the original tokens
tokens = list(original_tokens)
min_n += 1
else:
tokens = []
n_original_tokens = len(original_tokens)
# bind method outside of loop to reduce overhead
tokens_append = tokens.append
space_join = " ".join
for n in range(min_n,
min(max_n + 1, n_original_tokens + 1)):
for i in range(n_original_tokens - n + 1):
tokens_append(space_join(original_tokens[i: i + n]))
return tokens
def _limit_features(self, X, vocabulary,
high=None, low=None, limit=None):
"""Remove too rare or too common features.
ref: https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/feature_extraction/text.py#L734-L773
"""
if high is None and low is None and limit is None:
return X, set()
# Calculate a mask based on document frequencies
dfs = _document_frequency(X)
mask = np.ones(len(dfs), dtype=bool)
if high is not None:
mask &= dfs <= high
if low is not None:
mask &= dfs >= low
if limit is not None and mask.sum() > limit:
tfs = np.asarray(X.sum(axis=0)).ravel()
mask_inds = (-tfs[mask]).argsort()[:limit]
new_mask = np.zeros(len(dfs), dtype=bool)
new_mask[np.where(mask)[0][mask_inds]] = True
mask = new_mask
new_indices = np.cumsum(mask) - 1 # maps old indices to new
removed_terms = set()
for term, old_index in list(vocabulary.items()):
if mask[old_index]:
vocabulary[term] = new_indices[old_index]
else:
del vocabulary[term]
removed_terms.add(term)
kept_indices = | np.where(mask) | numpy.where |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot( | np.linspace(0.95 * np.pi, 1.55 * np.pi, 101) | numpy.linspace |
"""
This script will modulate the blinky lights using the following algorithm:
1) uses user-provided location to obtain row of pixel data from bathy image
2) samples a 'number of LEDs' number of pixels from that row
3) shifts the sampled row data to center it at the location specified by user
4) displays resulting pixels on Blinky Tape
5) shifts next row by a given latitude, also specified by user
6) sleeps for user-specified period of time
Uses the following arguments:
-l/--location: tuple
Location of the user in tuple(lat, lon). This represents the center of the LED strip. Defaults to (0, 0)
-u/--update-interval: int
Update interval of the script, in minutes. Defaults to 10.
-p/--port: str
Serial port of the BlinkyLight (e.g., 'ttyAMA0', 'COM3'). Defaults to 'COM5'.
-d/--delta_latitude: int
Vertical change in latitude every update rate. May be 0, but this will result in a never-changing LEDs.
-i/--image: str
Name of the PNG image that contains the color coded pathymetric data.
The file current named mapserv.png was obtained using the following API:
https://www.gebco.net/data_and_products/gebco_web_services/web_map_service/mapserv?request=getmap&service=wms&BBOX=-90,-180,90,180&format=image/png&height=600&width=1200&crs=EPSG:4326&layers=GEBCO_LATEST_SUB_ICE_TOPO&version=1.3.0
In lieu of providing command line arguments, you may alternatively edit the defaults in bath_config.json.
NOTE: runs via:
runfile('/BlinkyTape_Python/bathymetry_blink/bathymetry_blink.py', wdir='/BlinkyTape_Python/')
(C) 2021 <NAME> (https://joeycodes.dev)
MIT Licensed
"""
import optparse
import json
from blinkytape import BlinkyTape
from time import sleep
from PIL import Image
import numpy as np
import sys
MAX_ERRORS = 3
num_errors = 0
# Obtain default parameters
with open("./bathymetry_blink/bathy_config.json") as f:
config = json.load(f)
# Default Blinky Tape port on Raspberry Pi is /dev/ttyACM0
parser = optparse.OptionParser()
parser.add_option("-p", "--port", dest="portname",
help="serial port (ex: /dev/ttyACM0)", default=config["port"])
parser.add_option("-l", "--location", dest="location",
help="Location of the center of the LED strip (ex: 70,-110)", default=config["location"])
parser.add_option("-u", "--update-rate", dest="update_rate",
help="How often to update elevation profile (mins) (ex: 5)", default=config["update_rate"])
parser.add_option("-d", "--delta-latitude", dest="delta_latitude",
help="Change in latitude during update (ex: 5)", default=config["delta_latitude"])
parser.add_option("-n", "--num-leds", dest="num_leds",
help="Number of LEDs in strip (ex: 60)", default=config["num_leds"])
parser.add_option("-i", "--image", dest="image_name",
help="Name of the map/bathymetry image (ex: ./mapserv.png)", default=config["image"])
(options, args) = parser.parse_args()
if args:
print("Unknown parameters: " + args)
# grab the values provided by user (or defaults)
port = options.portname
loc = options.location
rate = options.update_rate
delta = options.delta_latitude
n_leds = options.num_leds
i_name = options.image_name
# Some visual indication that it works, for headless setups (green tape)
bt = BlinkyTape(port, n_leds)
bt.displayColor(0, 100, 0)
bt.show()
sleep(2)
while True:
try:
# first, load image
im = Image.open(i_name) # Can be many different formats.
cols, rows = im.size
a = np.asarray(im) # of shape (rows, cols, channels)
# map loc latitude to 0-based index
latitude_index = min(rows - 1, max(0, (int)(((loc[0] - -90) / (90 - -90)) * (rows - 0) + 0)))
longitude_index = min(cols - 1, max(0, (int)(((loc[1] - -180) / (180 - -180)) * (cols - 0) + 0)))
# update the location of the next row of elevation data to take
loc[0] += delta
loc[0] = ((loc[0] + 90) % 180) - 90 # wraps to next pole if overflow
print("Lat index: " + str(latitude_index))
print("Lon index: " + str(longitude_index))
print("Next latitude: " + str(loc[0]))
# grab the applicable pixel indices
indices = [(int)(x*(cols/n_leds)) for x in range(n_leds)]
# sample that row of pixel data
output_pixels = | np.take(a[latitude_index], indices, axis=0) | numpy.take |
"""
YTArray class.
"""
from __future__ import print_function
#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
import copy
import numpy as np
from distutils.version import LooseVersion
from functools import wraps
from numpy import \
add, subtract, multiply, divide, logaddexp, logaddexp2, true_divide, \
floor_divide, negative, power, remainder, mod, absolute, rint, \
sign, conj, exp, exp2, log, log2, log10, expm1, log1p, sqrt, square, \
reciprocal, sin, cos, tan, arcsin, arccos, arctan, arctan2, \
hypot, sinh, cosh, tanh, arcsinh, arccosh, arctanh, deg2rad, rad2deg, \
bitwise_and, bitwise_or, bitwise_xor, invert, left_shift, right_shift, \
greater, greater_equal, less, less_equal, not_equal, equal, logical_and, \
logical_or, logical_xor, logical_not, maximum, minimum, fmax, fmin, \
isreal, iscomplex, isfinite, isinf, isnan, signbit, copysign, nextafter, \
modf, ldexp, frexp, fmod, floor, ceil, trunc, fabs, spacing
try:
# numpy 1.13 or newer
from numpy import positive, divmod as divmod_, isnat, heaviside
except ImportError:
positive, divmod_, isnat, heaviside = (None,)*4
from yt.units.unit_object import Unit, UnitParseError
from yt.units.unit_registry import UnitRegistry
from yt.units.dimensions import \
angle, \
current_mks, \
dimensionless, \
em_dimensions
from yt.utilities.exceptions import \
YTUnitOperationError, YTUnitConversionError, \
YTUfuncUnitError, YTIterableUnitCoercionError, \
YTInvalidUnitEquivalence, YTEquivalentDimsError
from yt.utilities.lru_cache import lru_cache
from numbers import Number as numeric_type
from yt.utilities.on_demand_imports import _astropy
from sympy import Rational
from yt.units.unit_lookup_table import \
default_unit_symbol_lut
from yt.units.equivalencies import equivalence_registry
from yt.utilities.logger import ytLogger as mylog
from .pint_conversions import convert_pint_units
NULL_UNIT = Unit()
POWER_SIGN_MAPPING = {multiply: 1, divide: -1}
# redefine this here to avoid a circular import from yt.funcs
def iterable(obj):
try: len(obj)
except: return False
return True
def return_arr(func):
@wraps(func)
def wrapped(*args, **kwargs):
ret, units = func(*args, **kwargs)
if ret.shape == ():
return YTQuantity(ret, units)
else:
# This could be a subclass, so don't call YTArray directly.
return type(args[0])(ret, units)
return wrapped
@lru_cache(maxsize=128, typed=False)
def sqrt_unit(unit):
return unit**0.5
@lru_cache(maxsize=128, typed=False)
def multiply_units(unit1, unit2):
return unit1 * unit2
def preserve_units(unit1, unit2=None):
return unit1
@lru_cache(maxsize=128, typed=False)
def power_unit(unit, power):
return unit**power
@lru_cache(maxsize=128, typed=False)
def square_unit(unit):
return unit*unit
@lru_cache(maxsize=128, typed=False)
def divide_units(unit1, unit2):
return unit1/unit2
@lru_cache(maxsize=128, typed=False)
def reciprocal_unit(unit):
return unit**-1
def passthrough_unit(unit, unit2=None):
return unit
def return_without_unit(unit, unit2=None):
return None
def arctan2_unit(unit1, unit2):
return NULL_UNIT
def comparison_unit(unit1, unit2=None):
return None
def invert_units(unit):
raise TypeError(
"Bit-twiddling operators are not defined for YTArray instances")
def bitop_units(unit1, unit2):
raise TypeError(
"Bit-twiddling operators are not defined for YTArray instances")
def get_inp_u_unary(ufunc, inputs, out_arr=None):
inp = inputs[0]
u = getattr(inp, 'units', None)
if u is None:
u = NULL_UNIT
if u.dimensions is angle and ufunc in trigonometric_operators:
inp = inp.in_units('radian').v
if out_arr is not None:
out_arr = ufunc(inp).view(np.ndarray)
return out_arr, inp, u
def get_inp_u_binary(ufunc, inputs):
inp1 = coerce_iterable_units(inputs[0])
inp2 = coerce_iterable_units(inputs[1])
unit1 = getattr(inp1, 'units', None)
unit2 = getattr(inp2, 'units', None)
ret_class = get_binary_op_return_class(type(inp1), type(inp2))
if unit1 is None:
unit1 = Unit(registry=getattr(unit2, 'registry', None))
if unit2 is None and ufunc is not power:
unit2 = Unit(registry=getattr(unit1, 'registry', None))
elif ufunc is power:
unit2 = inp2
if isinstance(unit2, np.ndarray):
if isinstance(unit2, YTArray):
if unit2.units.is_dimensionless:
pass
else:
raise YTUnitOperationError(ufunc, unit1, unit2)
unit2 = 1.0
return (inp1, inp2), (unit1, unit2), ret_class
def handle_preserve_units(inps, units, ufunc, ret_class):
if units[0] != units[1]:
any_nonzero = [np.any(inps[0]), np.any(inps[1])]
if any_nonzero[0] == np.bool_(False):
units = (units[1], units[1])
elif any_nonzero[1] == np.bool_(False):
units = (units[0], units[0])
else:
if not units[0].same_dimensions_as(units[1]):
raise YTUnitOperationError(ufunc, *units)
inps = (inps[0], ret_class(inps[1]).to(
ret_class(inps[0]).units))
return inps, units
def handle_comparison_units(inps, units, ufunc, ret_class, raise_error=False):
if units[0] != units[1]:
u1d = units[0].is_dimensionless
u2d = units[1].is_dimensionless
any_nonzero = [np.any(inps[0]), np.any(inps[1])]
if any_nonzero[0] == np.bool_(False):
units = (units[1], units[1])
elif any_nonzero[1] == np.bool_(False):
units = (units[0], units[0])
elif not any([u1d, u2d]):
if not units[0].same_dimensions_as(units[1]):
raise YTUnitOperationError(ufunc, *units)
else:
if raise_error:
raise YTUfuncUnitError(ufunc, *units)
inps = (inps[0], ret_class(inps[1]).to(
ret_class(inps[0]).units))
return inps, units
def handle_multiply_divide_units(unit, units, out, out_arr):
if unit.is_dimensionless and unit.base_value != 1.0:
if not units[0].is_dimensionless:
if units[0].dimensions == units[1].dimensions:
out_arr = np.multiply(out_arr.view(np.ndarray),
unit.base_value, out=out)
unit = Unit(registry=unit.registry)
return out, out_arr, unit
def coerce_iterable_units(input_object):
if isinstance(input_object, np.ndarray):
return input_object
if iterable(input_object):
if any([isinstance(o, YTArray) for o in input_object]):
ff = getattr(input_object[0], 'units', NULL_UNIT, )
if any([ff != getattr(_, 'units', NULL_UNIT) for _ in input_object]):
raise YTIterableUnitCoercionError(input_object)
# This will create a copy of the data in the iterable.
return YTArray(input_object)
return input_object
else:
return input_object
def sanitize_units_mul(this_object, other_object):
inp = coerce_iterable_units(this_object)
ret = coerce_iterable_units(other_object)
# If the other object is a YTArray and has the same dimensions as the object
# under consideration, convert so we don't mix units with the same
# dimensions.
if isinstance(ret, YTArray):
if inp.units.same_dimensions_as(ret.units):
ret.in_units(inp.units)
return ret
def sanitize_units_add(this_object, other_object, op_string):
inp = coerce_iterable_units(this_object)
ret = coerce_iterable_units(other_object)
# Make sure the other object is a YTArray before we use the `units`
# attribute.
if isinstance(ret, YTArray):
if not inp.units.same_dimensions_as(ret.units):
# handle special case of adding or subtracting with zero or
# array filled with zero
if not np.any(other_object):
return ret.view(np.ndarray)
elif not np.any(this_object):
return ret
raise YTUnitOperationError(op_string, inp.units, ret.units)
ret = ret.in_units(inp.units)
else:
# If the other object is not a YTArray, then one of the arrays must be
# dimensionless or filled with zeros
if not inp.units.is_dimensionless and np.any(ret):
raise YTUnitOperationError(op_string, inp.units, dimensionless)
return ret
def validate_comparison_units(this, other, op_string):
# Check that other is a YTArray.
if hasattr(other, 'units'):
if this.units.expr is other.units.expr:
if this.units.base_value == other.units.base_value:
return other
if not this.units.same_dimensions_as(other.units):
raise YTUnitOperationError(op_string, this.units, other.units)
return other.in_units(this.units)
return other
@lru_cache(maxsize=128, typed=False)
def _unit_repr_check_same(my_units, other_units):
"""
Takes a Unit object, or string of known unit symbol, and check that it
is compatible with this quantity. Returns Unit object.
"""
# let Unit() handle units arg if it's not already a Unit obj.
if not isinstance(other_units, Unit):
other_units = Unit(other_units, registry=my_units.registry)
equiv_dims = em_dimensions.get(my_units.dimensions, None)
if equiv_dims == other_units.dimensions:
if current_mks in equiv_dims.free_symbols:
base = "SI"
else:
base = "CGS"
raise YTEquivalentDimsError(my_units, other_units, base)
if not my_units.same_dimensions_as(other_units):
raise YTUnitConversionError(
my_units, my_units.dimensions, other_units, other_units.dimensions)
return other_units
unary_operators = (
negative, absolute, rint, sign, conj, exp, exp2, log, log2,
log10, expm1, log1p, sqrt, square, reciprocal, sin, cos, tan, arcsin,
arccos, arctan, sinh, cosh, tanh, arcsinh, arccosh, arctanh, deg2rad,
rad2deg, invert, logical_not, isreal, iscomplex, isfinite, isinf, isnan,
signbit, floor, ceil, trunc, modf, frexp, fabs, spacing, positive, isnat,
)
binary_operators = (
add, subtract, multiply, divide, logaddexp, logaddexp2, true_divide, power,
remainder, mod, arctan2, hypot, bitwise_and, bitwise_or, bitwise_xor,
left_shift, right_shift, greater, greater_equal, less, less_equal,
not_equal, equal, logical_and, logical_or, logical_xor, maximum, minimum,
fmax, fmin, copysign, nextafter, ldexp, fmod, divmod_, heaviside
)
trigonometric_operators = (
sin, cos, tan,
)
class YTArray(np.ndarray):
"""
An ndarray subclass that attaches a symbolic unit object to the array data.
Parameters
----------
input_array : :obj:`!iterable`
A tuple, list, or array to attach units to
input_units : String unit specification, unit symbol object, or astropy units
The units of the array. Powers must be specified using python
syntax (cm**3, not cm^3).
registry : ~yt.units.unit_registry.UnitRegistry
The registry to create units from. If input_units is already associated
with a unit registry and this is specified, this will be used instead of
the registry associated with the unit object.
dtype : data-type
The dtype of the array data. Defaults to the dtype of the input data,
or, if none is found, uses np.float64
bypass_validation : boolean
If True, all input validation is skipped. Using this option may produce
corrupted, invalid units or array data, but can lead to significant
speedups in the input validation logic adds significant overhead. If set,
input_units *must* be a valid unit object. Defaults to False.
Examples
--------
>>> from yt import YTArray
>>> a = YTArray([1, 2, 3], 'cm')
>>> b = YTArray([4, 5, 6], 'm')
>>> a + b
YTArray([ 401., 502., 603.]) cm
>>> b + a
YTArray([ 4.01, 5.02, 6.03]) m
NumPy ufuncs will pass through units where appropriate.
>>> import numpy as np
>>> a = YTArray(np.arange(8) - 4, 'g/cm**3')
>>> np.abs(a)
YTArray([4, 3, 2, 1, 0, 1, 2, 3]) g/cm**3
and strip them when it would be annoying to deal with them.
>>> np.log10(a)
array([ -inf, 0. , 0.30103 , 0.47712125, 0.60205999,
0.69897 , 0.77815125, 0.84509804])
YTArray is tightly integrated with yt datasets:
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> a = ds.arr(np.ones(5), 'code_length')
>>> a.in_cgs()
YTArray([ 3.08600000e+24, 3.08600000e+24, 3.08600000e+24,
3.08600000e+24, 3.08600000e+24]) cm
This is equivalent to:
>>> b = YTArray(np.ones(5), 'code_length', registry=ds.unit_registry)
>>> np.all(a == b)
True
"""
_ufunc_registry = {
add: preserve_units,
subtract: preserve_units,
multiply: multiply_units,
divide: divide_units,
logaddexp: return_without_unit,
logaddexp2: return_without_unit,
true_divide: divide_units,
floor_divide: divide_units,
negative: passthrough_unit,
power: power_unit,
remainder: preserve_units,
mod: preserve_units,
fmod: preserve_units,
absolute: passthrough_unit,
fabs: passthrough_unit,
rint: return_without_unit,
sign: return_without_unit,
conj: passthrough_unit,
exp: return_without_unit,
exp2: return_without_unit,
log: return_without_unit,
log2: return_without_unit,
log10: return_without_unit,
expm1: return_without_unit,
log1p: return_without_unit,
sqrt: sqrt_unit,
square: square_unit,
reciprocal: reciprocal_unit,
sin: return_without_unit,
cos: return_without_unit,
tan: return_without_unit,
sinh: return_without_unit,
cosh: return_without_unit,
tanh: return_without_unit,
arcsin: return_without_unit,
arccos: return_without_unit,
arctan: return_without_unit,
arctan2: arctan2_unit,
arcsinh: return_without_unit,
arccosh: return_without_unit,
arctanh: return_without_unit,
hypot: preserve_units,
deg2rad: return_without_unit,
rad2deg: return_without_unit,
bitwise_and: bitop_units,
bitwise_or: bitop_units,
bitwise_xor: bitop_units,
invert: invert_units,
left_shift: bitop_units,
right_shift: bitop_units,
greater: comparison_unit,
greater_equal: comparison_unit,
less: comparison_unit,
less_equal: comparison_unit,
not_equal: comparison_unit,
equal: comparison_unit,
logical_and: comparison_unit,
logical_or: comparison_unit,
logical_xor: comparison_unit,
logical_not: return_without_unit,
maximum: preserve_units,
minimum: preserve_units,
fmax: preserve_units,
fmin: preserve_units,
isreal: return_without_unit,
iscomplex: return_without_unit,
isfinite: return_without_unit,
isinf: return_without_unit,
isnan: return_without_unit,
signbit: return_without_unit,
copysign: passthrough_unit,
nextafter: preserve_units,
modf: passthrough_unit,
ldexp: bitop_units,
frexp: return_without_unit,
floor: passthrough_unit,
ceil: passthrough_unit,
trunc: passthrough_unit,
spacing: passthrough_unit,
positive: passthrough_unit,
divmod_: passthrough_unit,
isnat: return_without_unit,
heaviside: preserve_units,
}
__array_priority__ = 2.0
def __new__(cls, input_array, input_units=None, registry=None, dtype=None,
bypass_validation=False):
if dtype is None:
dtype = getattr(input_array, 'dtype', np.float64)
if bypass_validation is True:
obj = np.asarray(input_array, dtype=dtype).view(cls)
obj.units = input_units
if registry is not None:
obj.units.registry = registry
return obj
if input_array is NotImplemented:
return input_array.view(cls)
if registry is None and isinstance(input_units, (str, bytes)):
if input_units.startswith('code_'):
raise UnitParseError(
"Code units used without referring to a dataset. \n"
"Perhaps you meant to do something like this instead: \n"
"ds.arr(%s, \"%s\")" % (input_array, input_units)
)
if isinstance(input_array, YTArray):
ret = input_array.view(cls)
if input_units is None:
if registry is None:
ret.units = input_array.units
else:
units = Unit(str(input_array.units), registry=registry)
ret.units = units
elif isinstance(input_units, Unit):
ret.units = input_units
else:
ret.units = Unit(input_units, registry=registry)
return ret
elif isinstance(input_array, np.ndarray):
pass
elif iterable(input_array) and input_array:
if isinstance(input_array[0], YTArray):
return YTArray(np.array(input_array, dtype=dtype),
input_array[0].units, registry=registry)
# Input array is an already formed ndarray instance
# We first cast to be our class type
obj = np.asarray(input_array, dtype=dtype).view(cls)
# Check units type
if input_units is None:
# Nothing provided. Make dimensionless...
units = Unit()
elif isinstance(input_units, Unit):
if registry and registry is not input_units.registry:
units = Unit(str(input_units), registry=registry)
else:
units = input_units
else:
# units kwarg set, but it's not a Unit object.
# don't handle all the cases here, let the Unit class handle if
# it's a str.
units = Unit(input_units, registry=registry)
# Attach the units
obj.units = units
return obj
def __repr__(self):
"""
"""
return super(YTArray, self).__repr__()+' '+self.units.__repr__()
def __str__(self):
"""
"""
return str(self.view(np.ndarray)) + ' ' + str(self.units)
#
# Start unit conversion methods
#
def convert_to_units(self, units):
"""
Convert the array and units to the given units.
Parameters
----------
units : Unit object or str
The units you want to convert to.
"""
new_units = _unit_repr_check_same(self.units, units)
(conversion_factor, offset) = self.units.get_conversion_factor(new_units)
self.units = new_units
values = self.d
values *= conversion_factor
if offset:
np.subtract(self, offset*self.uq, self)
return self
def convert_to_base(self, unit_system="cgs"):
"""
Convert the array and units to the equivalent base units in
the specified unit system.
Parameters
----------
unit_system : string, optional
The unit system to be used in the conversion. If not specified,
the default base units of cgs are used.
Examples
--------
>>> E = YTQuantity(2.5, "erg/s")
>>> E.convert_to_base(unit_system="galactic")
"""
return self.convert_to_units(self.units.get_base_equivalent(unit_system))
def convert_to_cgs(self):
"""
Convert the array and units to the equivalent cgs units.
"""
return self.convert_to_units(self.units.get_cgs_equivalent())
def convert_to_mks(self):
"""
Convert the array and units to the equivalent mks units.
"""
return self.convert_to_units(self.units.get_mks_equivalent())
def in_units(self, units, equivalence=None, **kwargs):
"""
Creates a copy of this array with the data in the supplied
units, and returns it.
Optionally, an equivalence can be specified to convert to an
equivalent quantity which is not in the same dimensions.
.. note::
All additional keyword arguments are passed to the
equivalency, which should be used if that particular
equivalency requires them.
Parameters
----------
units : Unit object or string
The units you want to get a new quantity in.
equivalence : string, optional
The equivalence you wish to use. To see which
equivalencies are supported for this unitful
quantity, try the :meth:`list_equivalencies`
method. Default: None
Returns
-------
YTArray
"""
if equivalence is None:
new_units = _unit_repr_check_same(self.units, units)
(conversion_factor, offset) = self.units.get_conversion_factor(new_units)
new_array = type(self)(self.ndview * conversion_factor, new_units)
if offset:
np.subtract(new_array, offset*new_array.uq, new_array)
return new_array
else:
return self.to_equivalent(units, equivalence, **kwargs)
def to(self, units, equivalence=None, **kwargs):
"""
An alias for YTArray.in_units().
See the docstrings of that function for details.
"""
return self.in_units(units, equivalence=equivalence, **kwargs)
def to_value(self, units=None, equivalence=None, **kwargs):
"""
Creates a copy of this array with the data in the supplied
units, and returns it without units. Output is therefore a
bare NumPy array.
Optionally, an equivalence can be specified to convert to an
equivalent quantity which is not in the same dimensions.
.. note::
All additional keyword arguments are passed to the
equivalency, which should be used if that particular
equivalency requires them.
Parameters
----------
units : Unit object or string, optional
The units you want to get the bare quantity in. If not
specified, the value will be returned in the current units.
equivalence : string, optional
The equivalence you wish to use. To see which
equivalencies are supported for this unitful
quantity, try the :meth:`list_equivalencies`
method. Default: None
Returns
-------
NumPy array
"""
if units is None:
v = self.value
else:
v = self.in_units(units, equivalence=equivalence, **kwargs).value
if isinstance(self, YTQuantity):
return float(v)
else:
return v
def in_base(self, unit_system="cgs"):
"""
Creates a copy of this array with the data in the specified unit system,
and returns it in that system's base units.
Parameters
----------
unit_system : string, optional
The unit system to be used in the conversion. If not specified,
the default base units of cgs are used.
Examples
--------
>>> E = YTQuantity(2.5, "erg/s")
>>> E_new = E.in_base(unit_system="galactic")
"""
return self.in_units(self.units.get_base_equivalent(unit_system))
def in_cgs(self):
"""
Creates a copy of this array with the data in the equivalent cgs units,
and returns it.
Returns
-------
Quantity object with data converted to cgs units.
"""
return self.in_units(self.units.get_cgs_equivalent())
def in_mks(self):
"""
Creates a copy of this array with the data in the equivalent mks units,
and returns it.
Returns
-------
Quantity object with data converted to mks units.
"""
return self.in_units(self.units.get_mks_equivalent())
def to_equivalent(self, unit, equiv, **kwargs):
"""
Convert a YTArray or YTQuantity to an equivalent, e.g., something that is
related by only a constant factor but not in the same units.
Parameters
----------
unit : string
The unit that you wish to convert to.
equiv : string
The equivalence you wish to use. To see which equivalencies are
supported for this unitful quantity, try the
:meth:`list_equivalencies` method.
Examples
--------
>>> a = yt.YTArray(1.0e7,"K")
>>> a.to_equivalent("keV", "thermal")
"""
conv_unit = Unit(unit, registry=self.units.registry)
if self.units.same_dimensions_as(conv_unit):
return self.in_units(conv_unit)
this_equiv = equivalence_registry[equiv]()
oneway_or_equivalent = (
conv_unit.has_equivalent(equiv) or this_equiv._one_way)
if self.has_equivalent(equiv) and oneway_or_equivalent:
new_arr = this_equiv.convert(
self, conv_unit.dimensions, **kwargs)
if isinstance(new_arr, tuple):
try:
return type(self)(new_arr[0], new_arr[1]).in_units(unit)
except YTUnitConversionError:
raise YTInvalidUnitEquivalence(equiv, self.units, unit)
else:
return new_arr.in_units(unit)
else:
raise YTInvalidUnitEquivalence(equiv, self.units, unit)
def list_equivalencies(self):
"""
Lists the possible equivalencies associated with this YTArray or
YTQuantity.
"""
self.units.list_equivalencies()
def has_equivalent(self, equiv):
"""
Check to see if this YTArray or YTQuantity has an equivalent unit in
*equiv*.
"""
return self.units.has_equivalent(equiv)
def ndarray_view(self):
"""
Returns a view into the array, but as an ndarray rather than ytarray.
Returns
-------
View of this array's data.
"""
return self.view(np.ndarray)
def to_ndarray(self):
"""
Creates a copy of this array with the unit information stripped
"""
return np.array(self)
@classmethod
def from_astropy(cls, arr, unit_registry=None):
"""
Convert an AstroPy "Quantity" to a YTArray or YTQuantity.
Parameters
----------
arr : AstroPy Quantity
The Quantity to convert from.
unit_registry : yt UnitRegistry, optional
A yt unit registry to use in the conversion. If one is not
supplied, the default one will be used.
"""
# Converting from AstroPy Quantity
u = arr.unit
ap_units = []
for base, exponent in zip(u.bases, u.powers):
unit_str = base.to_string()
# we have to do this because AstroPy is silly and defines
# hour as "h"
if unit_str == "h": unit_str = "hr"
ap_units.append("%s**(%s)" % (unit_str, Rational(exponent)))
ap_units = "*".join(ap_units)
if isinstance(arr.value, np.ndarray):
return YTArray(arr.value, ap_units, registry=unit_registry)
else:
return YTQuantity(arr.value, ap_units, registry=unit_registry)
def to_astropy(self, **kwargs):
"""
Creates a new AstroPy quantity with the same unit information.
"""
if _astropy.units is None:
raise ImportError("You don't have AstroPy installed, so you can't convert to " +
"an AstroPy quantity.")
return self.value*_astropy.units.Unit(str(self.units), **kwargs)
@classmethod
def from_pint(cls, arr, unit_registry=None):
"""
Convert a Pint "Quantity" to a YTArray or YTQuantity.
Parameters
----------
arr : Pint Quantity
The Quantity to convert from.
unit_registry : yt UnitRegistry, optional
A yt unit registry to use in the conversion. If one is not
supplied, the default one will be used.
Examples
--------
>>> from pint import UnitRegistry
>>> import numpy as np
>>> ureg = UnitRegistry()
>>> a = np.random.random(10)
>>> b = ureg.Quantity(a, "erg/cm**3")
>>> c = yt.YTArray.from_pint(b)
"""
p_units = []
for base, exponent in arr._units.items():
bs = convert_pint_units(base)
p_units.append("%s**(%s)" % (bs, Rational(exponent)))
p_units = "*".join(p_units)
if isinstance(arr.magnitude, np.ndarray):
return YTArray(arr.magnitude, p_units, registry=unit_registry)
else:
return YTQuantity(arr.magnitude, p_units, registry=unit_registry)
def to_pint(self, unit_registry=None):
"""
Convert a YTArray or YTQuantity to a Pint Quantity.
Parameters
----------
arr : YTArray or YTQuantity
The unitful quantity to convert from.
unit_registry : Pint UnitRegistry, optional
The Pint UnitRegistry to use in the conversion. If one is not
supplied, the default one will be used. NOTE: This is not
the same as a yt UnitRegistry object.
Examples
--------
>>> a = YTQuantity(4.0, "cm**2/s")
>>> b = a.to_pint()
"""
from pint import UnitRegistry
if unit_registry is None:
unit_registry = UnitRegistry()
powers_dict = self.units.expr.as_powers_dict()
units = []
for unit, pow in powers_dict.items():
# we have to do this because Pint doesn't recognize
# "yr" as "year"
if str(unit).endswith("yr") and len(str(unit)) in [2,3]:
unit = str(unit).replace("yr","year")
units.append("%s**(%s)" % (unit, Rational(pow)))
units = "*".join(units)
return unit_registry.Quantity(self.value, units)
#
# End unit conversion methods
#
def write_hdf5(self, filename, dataset_name=None, info=None, group_name=None):
r"""Writes a YTArray to hdf5 file.
Parameters
----------
filename: string
The filename to create and write a dataset to
dataset_name: string
The name of the dataset to create in the file.
info: dictionary
A dictionary of supplementary info to write to append as attributes
to the dataset.
group_name: string
An optional group to write the arrays to. If not specified, the arrays
are datasets at the top level by default.
Examples
--------
>>> a = YTArray([1,2,3], 'cm')
>>> myinfo = {'field':'dinosaurs', 'type':'field_data'}
>>> a.write_hdf5('test_array_data.h5', dataset_name='dinosaurs',
... info=myinfo)
"""
from yt.utilities.on_demand_imports import _h5py as h5py
from yt.extern.six.moves import cPickle as pickle
if info is None:
info = {}
info['units'] = str(self.units)
info['unit_registry'] = np.void(pickle.dumps(self.units.registry.lut))
if dataset_name is None:
dataset_name = 'array_data'
f = h5py.File(filename)
if group_name is not None:
if group_name in f:
g = f[group_name]
else:
g = f.create_group(group_name)
else:
g = f
if dataset_name in g.keys():
d = g[dataset_name]
# Overwrite without deleting if we can get away with it.
if d.shape == self.shape and d.dtype == self.dtype:
d[...] = self
for k in d.attrs.keys():
del d.attrs[k]
else:
del f[dataset_name]
d = g.create_dataset(dataset_name, data=self)
else:
d = g.create_dataset(dataset_name, data=self)
for k, v in info.items():
d.attrs[k] = v
f.close()
@classmethod
def from_hdf5(cls, filename, dataset_name=None, group_name=None):
r"""Attempts read in and convert a dataset in an hdf5 file into a
YTArray.
Parameters
----------
filename: string
The filename to of the hdf5 file.
dataset_name: string
The name of the dataset to read from. If the dataset has a units
attribute, attempt to infer units as well.
group_name: string
An optional group to read the arrays from. If not specified, the
arrays are datasets at the top level by default.
"""
import h5py
from yt.extern.six.moves import cPickle as pickle
if dataset_name is None:
dataset_name = 'array_data'
f = h5py.File(filename)
if group_name is not None:
g = f[group_name]
else:
g = f
dataset = g[dataset_name]
data = dataset[:]
units = dataset.attrs.get('units', '')
if 'unit_registry' in dataset.attrs.keys():
unit_lut = pickle.loads(dataset.attrs['unit_registry'].tostring())
else:
unit_lut = None
f.close()
registry = UnitRegistry(lut=unit_lut, add_default_symbols=False)
return cls(data, units, registry=registry)
#
# Start convenience methods
#
@property
def value(self):
"""Get a copy of the array data as a numpy ndarray"""
return np.array(self)
v = value
@property
def ndview(self):
"""Get a view of the array data."""
return self.ndarray_view()
d = ndview
@property
def unit_quantity(self):
"""Get a YTQuantity with the same unit as this array and a value of
1.0"""
return YTQuantity(1.0, self.units)
uq = unit_quantity
@property
def unit_array(self):
"""Get a YTArray filled with ones with the same unit and shape as this
array"""
return np.ones_like(self)
ua = unit_array
def __getitem__(self, item):
ret = super(YTArray, self).__getitem__(item)
if ret.shape == ():
return YTQuantity(ret, self.units, bypass_validation=True)
else:
if hasattr(self, 'units'):
ret.units = self.units
return ret
#
# Start operation methods
#
if LooseVersion(np.__version__) < LooseVersion('1.13.0'):
def __add__(self, right_object):
"""
Add this ytarray to the object on the right of the `+` operator.
Must check for the correct (same dimension) units.
"""
ro = sanitize_units_add(self, right_object, "addition")
return super(YTArray, self).__add__(ro)
def __radd__(self, left_object):
""" See __add__. """
lo = sanitize_units_add(self, left_object, "addition")
return super(YTArray, self).__radd__(lo)
def __iadd__(self, other):
""" See __add__. """
oth = sanitize_units_add(self, other, "addition")
np.add(self, oth, out=self)
return self
def __sub__(self, right_object):
"""
Subtract the object on the right of the `-` from this ytarray. Must
check for the correct (same dimension) units.
"""
ro = sanitize_units_add(self, right_object, "subtraction")
return super(YTArray, self).__sub__(ro)
def __rsub__(self, left_object):
""" See __sub__. """
lo = sanitize_units_add(self, left_object, "subtraction")
return super(YTArray, self).__rsub__(lo)
def __isub__(self, other):
""" See __sub__. """
oth = sanitize_units_add(self, other, "subtraction")
np.subtract(self, oth, out=self)
return self
def __neg__(self):
""" Negate the data. """
return super(YTArray, self).__neg__()
def __mul__(self, right_object):
"""
Multiply this YTArray by the object on the right of the `*`
operator. The unit objects handle being multiplied.
"""
ro = sanitize_units_mul(self, right_object)
return super(YTArray, self).__mul__(ro)
def __rmul__(self, left_object):
""" See __mul__. """
lo = sanitize_units_mul(self, left_object)
return super(YTArray, self).__rmul__(lo)
def __imul__(self, other):
""" See __mul__. """
oth = sanitize_units_mul(self, other)
np.multiply(self, oth, out=self)
return self
def __div__(self, right_object):
"""
Divide this YTArray by the object on the right of the `/` operator.
"""
ro = sanitize_units_mul(self, right_object)
return super(YTArray, self).__div__(ro)
def __rdiv__(self, left_object):
""" See __div__. """
lo = sanitize_units_mul(self, left_object)
return super(YTArray, self).__rdiv__(lo)
def __idiv__(self, other):
""" See __div__. """
oth = sanitize_units_mul(self, other)
np.divide(self, oth, out=self)
return self
def __truediv__(self, right_object):
ro = sanitize_units_mul(self, right_object)
return super(YTArray, self).__truediv__(ro)
def __rtruediv__(self, left_object):
""" See __div__. """
lo = sanitize_units_mul(self, left_object)
return super(YTArray, self).__rtruediv__(lo)
def __itruediv__(self, other):
""" See __div__. """
oth = sanitize_units_mul(self, other)
np.true_divide(self, oth, out=self)
return self
def __floordiv__(self, right_object):
ro = sanitize_units_mul(self, right_object)
return super(YTArray, self).__floordiv__(ro)
def __rfloordiv__(self, left_object):
""" See __div__. """
lo = sanitize_units_mul(self, left_object)
return super(YTArray, self).__rfloordiv__(lo)
def __ifloordiv__(self, other):
""" See __div__. """
oth = sanitize_units_mul(self, other)
np.floor_divide(self, oth, out=self)
return self
def __or__(self, right_object):
return super(YTArray, self).__or__(right_object)
def __ror__(self, left_object):
return super(YTArray, self).__ror__(left_object)
def __ior__(self, other):
np.bitwise_or(self, other, out=self)
return self
def __xor__(self, right_object):
return super(YTArray, self).__xor__(right_object)
def __rxor__(self, left_object):
return super(YTArray, self).__rxor__(left_object)
def __ixor__(self, other):
np.bitwise_xor(self, other, out=self)
return self
def __and__(self, right_object):
return super(YTArray, self).__and__(right_object)
def __rand__(self, left_object):
return super(YTArray, self).__rand__(left_object)
def __iand__(self, other):
np.bitwise_and(self, other, out=self)
return self
def __pow__(self, power):
"""
Raise this YTArray to some power.
Parameters
----------
power : float or dimensionless YTArray.
The pow value.
"""
if isinstance(power, YTArray):
if not power.units.is_dimensionless:
raise YTUnitOperationError('power', power.unit)
# Work around a sympy issue (I think?)
#
# If I don't do this, super(YTArray, self).__pow__ returns a YTArray
# with a unit attribute set to the sympy expression 1/1 rather than
# a dimensionless Unit object.
if self.units.is_dimensionless and power == -1:
ret = super(YTArray, self).__pow__(power)
return type(self)(ret, input_units='')
return super(YTArray, self).__pow__(power)
def __abs__(self):
""" Return a YTArray with the abs of the data. """
return super(YTArray, self).__abs__()
#
# Start comparison operators.
#
def __lt__(self, other):
""" Test if this is less than the object on the right. """
# converts if possible
oth = validate_comparison_units(self, other, 'less_than')
return super(YTArray, self).__lt__(oth)
def __le__(self, other):
"""Test if this is less than or equal to the object on the right.
"""
oth = validate_comparison_units(self, other, 'less_than or equal')
return super(YTArray, self).__le__(oth)
def __eq__(self, other):
""" Test if this is equal to the object on the right. """
# Check that other is a YTArray.
if other is None:
# self is a YTArray, so it can't be None.
return False
oth = validate_comparison_units(self, other, 'equal')
return super(YTArray, self).__eq__(oth)
def __ne__(self, other):
""" Test if this is not equal to the object on the right. """
# Check that the other is a YTArray.
if other is None:
return True
oth = validate_comparison_units(self, other, 'not equal')
return super(YTArray, self).__ne__(oth)
def __ge__(self, other):
""" Test if this is greater than or equal to other. """
# Check that the other is a YTArray.
oth = validate_comparison_units(
self, other, 'greater than or equal')
return super(YTArray, self).__ge__(oth)
def __gt__(self, other):
""" Test if this is greater than the object on the right. """
# Check that the other is a YTArray.
oth = validate_comparison_units(self, other, 'greater than')
return super(YTArray, self).__gt__(oth)
#
# End comparison operators
#
#
# Begin reduction operators
#
@return_arr
def prod(self, axis=None, dtype=None, out=None):
if axis is not None:
units = self.units**self.shape[axis]
else:
units = self.units**self.size
return super(YTArray, self).prod(axis, dtype, out), units
@return_arr
def mean(self, axis=None, dtype=None, out=None):
return super(YTArray, self).mean(axis, dtype, out), self.units
@return_arr
def sum(self, axis=None, dtype=None, out=None):
return super(YTArray, self).sum(axis, dtype, out), self.units
@return_arr
def std(self, axis=None, dtype=None, out=None, ddof=0):
return super(YTArray, self).std(axis, dtype, out, ddof), self.units
def __array_wrap__(self, out_arr, context=None):
ret = super(YTArray, self).__array_wrap__(out_arr, context)
if isinstance(ret, YTQuantity) and ret.shape != ():
ret = ret.view(YTArray)
if context is None:
if ret.shape == ():
return ret[()]
else:
return ret
ufunc = context[0]
inputs = context[1]
if ufunc in unary_operators:
out_arr, inp, u = get_inp_u_unary(ufunc, inputs, out_arr)
unit = self._ufunc_registry[context[0]](u)
ret_class = type(self)
elif ufunc in binary_operators:
unit_operator = self._ufunc_registry[context[0]]
inps, units, ret_class = get_inp_u_binary(ufunc, inputs)
if unit_operator in (preserve_units, comparison_unit,
arctan2_unit):
inps, units = handle_comparison_units(
inps, units, ufunc, ret_class, raise_error=True)
unit = unit_operator(*units)
if unit_operator in (multiply_units, divide_units):
out_arr, out_arr, unit = handle_multiply_divide_units(
unit, units, out_arr, out_arr)
else:
raise RuntimeError(
"Support for the %s ufunc has not been added "
"to YTArray." % str(context[0]))
if unit is None:
out_arr = np.array(out_arr, copy=False)
return out_arr
out_arr.units = unit
if out_arr.size == 1:
return YTQuantity(np.array(out_arr), unit)
else:
if ret_class is YTQuantity:
# This happens if you do ndarray * YTQuantity. Explicitly
# casting to YTArray avoids creating a YTQuantity with
# size > 1
return YTArray(np.array(out_arr), unit)
return ret_class(np.array(out_arr, copy=False), unit)
else: # numpy version equal to or newer than 1.13
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
func = getattr(ufunc, method)
if 'out' in kwargs:
out_orig = kwargs.pop('out')
out = np.asarray(out_orig[0])
else:
out = None
if len(inputs) == 1:
_, inp, u = get_inp_u_unary(ufunc, inputs)
out_arr = func(np.asarray(inp), out=out, **kwargs)
if ufunc in (multiply, divide) and method == 'reduce':
power_sign = POWER_SIGN_MAPPING[ufunc]
if 'axis' in kwargs and kwargs['axis'] is not None:
unit = u**(power_sign*inp.shape[kwargs['axis']])
else:
unit = u**(power_sign*inp.size)
else:
unit = self._ufunc_registry[ufunc](u)
ret_class = type(self)
elif len(inputs) == 2:
unit_operator = self._ufunc_registry[ufunc]
inps, units, ret_class = get_inp_u_binary(ufunc, inputs)
if unit_operator in (comparison_unit, arctan2_unit):
inps, units = handle_comparison_units(
inps, units, ufunc, ret_class)
elif unit_operator is preserve_units:
inps, units = handle_preserve_units(
inps, units, ufunc, ret_class)
unit = unit_operator(*units)
out_arr = func(np.asarray(inps[0]), np.asarray(inps[1]),
out=out, **kwargs)
if unit_operator in (multiply_units, divide_units):
out, out_arr, unit = handle_multiply_divide_units(
unit, units, out, out_arr)
else:
raise RuntimeError(
"Support for the %s ufunc with %i inputs has not been"
"added to YTArray." % (str(ufunc), len(inputs)))
if unit is None:
out_arr = np.array(out_arr, copy=False)
elif ufunc in (modf, divmod_):
out_arr = tuple((ret_class(o, unit) for o in out_arr))
elif out_arr.size == 1:
out_arr = YTQuantity(np.asarray(out_arr), unit)
else:
if ret_class is YTQuantity:
# This happens if you do ndarray * YTQuantity. Explicitly
# casting to YTArray avoids creating a YTQuantity with
# size > 1
out_arr = YTArray(np.asarray(out_arr), unit)
else:
out_arr = ret_class(np.asarray(out_arr), unit)
if out is not None:
out_orig[0].flat[:] = out.flat[:]
if isinstance(out_orig[0], YTArray):
out_orig[0].units = unit
return out_arr
def copy(self, order='C'):
return type(self)(np.copy(np.asarray(self)), self.units)
def __array_finalize__(self, obj):
if obj is None and hasattr(self, 'units'):
return
self.units = getattr(obj, 'units', NULL_UNIT)
def __pos__(self):
""" Posify the data. """
# this needs to be defined for all numpy versions, see
# numpy issue #9081
return type(self)(super(YTArray, self).__pos__(), self.units)
@return_arr
def dot(self, b, out=None):
return super(YTArray, self).dot(b), self.units*b.units
def __reduce__(self):
"""Pickle reduction method
See the documentation for the standard library pickle module:
http://docs.python.org/2/library/pickle.html
Unit metadata is encoded in the zeroth element of third element of the
returned tuple, itself a tuple used to restore the state of the ndarray.
This is always defined for numpy arrays.
"""
np_ret = super(YTArray, self).__reduce__()
obj_state = np_ret[2]
unit_state = (((str(self.units), self.units.registry.lut),) + obj_state[:],)
new_ret = np_ret[:2] + unit_state + np_ret[3:]
return new_ret
def __setstate__(self, state):
"""Pickle setstate method
This is called inside pickle.read() and restores the unit data from the
metadata extracted in __reduce__ and then serialized by pickle.
"""
super(YTArray, self).__setstate__(state[1:])
try:
unit, lut = state[0]
except TypeError:
# this case happens when we try to load an old pickle file
# created before we serialized the unit symbol lookup table
# into the pickle file
unit, lut = str(state[0]), default_unit_symbol_lut.copy()
# need to fix up the lut if the pickle was saved prior to PR #1728
# when the pickle format changed
if len(lut['m']) == 2:
lut.update(default_unit_symbol_lut)
for k, v in [(k, v) for k, v in lut.items() if len(v) == 2]:
lut[k] = v + (0.0, r'\rm{' + k.replace('_', '\ ') + '}')
registry = UnitRegistry(lut=lut, add_default_symbols=False)
self.units = Unit(unit, registry=registry)
def __deepcopy__(self, memodict=None):
"""copy.deepcopy implementation
This is necessary for stdlib deepcopy of arrays and quantities.
"""
if memodict is None:
memodict = {}
ret = super(YTArray, self).__deepcopy__(memodict)
return type(self)(ret, copy.deepcopy(self.units))
class YTQuantity(YTArray):
"""
A scalar associated with a unit.
Parameters
----------
input_scalar : an integer or floating point scalar
The scalar to attach units to
input_units : String unit specification, unit symbol object, or astropy units
The units of the quantity. Powers must be specified using python syntax
(cm**3, not cm^3).
registry : A UnitRegistry object
The registry to create units from. If input_units is already associated
with a unit registry and this is specified, this will be used instead of
the registry associated with the unit object.
dtype : data-type
The dtype of the array data.
Examples
--------
>>> from yt import YTQuantity
>>> a = YTQuantity(1, 'cm')
>>> b = YTQuantity(2, 'm')
>>> a + b
201.0 cm
>>> b + a
2.01 m
NumPy ufuncs will pass through units where appropriate.
>>> import numpy as np
>>> a = YTQuantity(12, 'g/cm**3')
>>> np.abs(a)
12 g/cm**3
and strip them when it would be annoying to deal with them.
>>> print(np.log10(a))
1.07918124605
YTQuantity is tightly integrated with yt datasets:
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> a = ds.quan(5, 'code_length')
>>> a.in_cgs()
1.543e+25 cm
This is equivalent to:
>>> b = YTQuantity(5, 'code_length', registry=ds.unit_registry)
>>> np.all(a == b)
True
"""
def __new__(cls, input_scalar, input_units=None, registry=None,
dtype=np.float64, bypass_validation=False):
if not isinstance(input_scalar, (numeric_type, np.number, np.ndarray)):
raise RuntimeError("YTQuantity values must be numeric")
ret = YTArray.__new__(cls, input_scalar, input_units, registry,
dtype=dtype, bypass_validation=bypass_validation)
if ret.size > 1:
raise RuntimeError("YTQuantity instances must be scalars")
return ret
def __repr__(self):
return str(self)
def validate_numpy_wrapper_units(v, arrs):
if not any(isinstance(a, YTArray) for a in arrs):
return v
if not all(isinstance(a, YTArray) for a in arrs):
raise RuntimeError("Not all of your arrays are YTArrays.")
a1 = arrs[0]
if not all(a.units == a1.units for a in arrs[1:]):
raise RuntimeError("Your arrays must have identical units.")
v.units = a1.units
return v
def uconcatenate(arrs, axis=0):
"""Concatenate a sequence of arrays.
This wrapper around numpy.concatenate preserves units. All input arrays must
have the same units. See the documentation of numpy.concatenate for full
details.
Examples
--------
>>> A = yt.YTArray([1, 2, 3], 'cm')
>>> B = yt.YTArray([2, 3, 4], 'cm')
>>> uconcatenate((A, B))
YTArray([ 1., 2., 3., 2., 3., 4.]) cm
"""
v = np.concatenate(arrs, axis=axis)
v = validate_numpy_wrapper_units(v, arrs)
return v
def ucross(arr1, arr2, registry=None, axisa=-1, axisb=-1, axisc=-1, axis=None):
"""Applies the cross product to two YT arrays.
This wrapper around numpy.cross preserves units.
See the documentation of numpy.cross for full
details.
"""
v = np.cross(arr1, arr2, axisa=axisa, axisb=axisb, axisc=axisc, axis=axis)
units = arr1.units * arr2.units
arr = YTArray(v, units, registry=registry)
return arr
def uintersect1d(arr1, arr2, assume_unique=False):
"""Find the sorted unique elements of the two input arrays.
A wrapper around numpy.intersect1d that preserves units. All input arrays
must have the same units. See the documentation of numpy.intersect1d for
full details.
Examples
--------
>>> A = yt.YTArray([1, 2, 3], 'cm')
>>> B = yt.YTArray([2, 3, 4], 'cm')
>>> uintersect1d(A, B)
YTArray([ 2., 3.]) cm
"""
v = np.intersect1d(arr1, arr2, assume_unique=assume_unique)
v = validate_numpy_wrapper_units(v, [arr1, arr2])
return v
def uunion1d(arr1, arr2):
"""Find the union of two arrays.
A wrapper around numpy.intersect1d that preserves units. All input arrays
must have the same units. See the documentation of numpy.intersect1d for
full details.
Examples
--------
>>> A = yt.YTArray([1, 2, 3], 'cm')
>>> B = yt.YTArray([2, 3, 4], 'cm')
>>> uunion1d(A, B)
YTArray([ 1., 2., 3., 4.]) cm
"""
v = np.union1d(arr1, arr2)
v = validate_numpy_wrapper_units(v, [arr1, arr2])
return v
def unorm(data, ord=None, axis=None, keepdims=False):
"""Matrix or vector norm that preserves units
This is a wrapper around np.linalg.norm that preserves units. See
the documentation for that function for descriptions of the keyword
arguments.
The keepdims argument is ignored if the version of numpy installed is
older than numpy 1.10.0.
"""
if LooseVersion(np.__version__) < LooseVersion('1.10.0'):
norm = np.linalg.norm(data, ord=ord, axis=axis)
else:
norm = np.linalg.norm(data, ord=ord, axis=axis, keepdims=keepdims)
if norm.shape == ():
return YTQuantity(norm, data.units)
return YTArray(norm, data.units)
def udot(op1, op2):
"""Matrix or vector dot product that preserves units
This is a wrapper around np.dot that preserves units.
"""
dot = np.dot(op1.d, op2.d)
units = op1.units*op2.units
if dot.shape == ():
return YTQuantity(dot, units)
return YTArray(dot, units)
def uvstack(arrs):
"""Stack arrays in sequence vertically (row wise) while preserving units
This is a wrapper around np.vstack that preserves units.
"""
v = | np.vstack(arrs) | numpy.vstack |
###############################################################################
# @todo add Pilot2-splash-app disclaimer
###############################################################################
""" Get's KRAS states """
import MDAnalysis as mda
from MDAnalysis.analysis import align
from MDAnalysis.lib.mdamath import make_whole
import os
import numpy as np
import math
############## Below section needs to be uncommented ############
import mummi_core
import mummi_ras
from mummi_core.utils import Naming
# # Logger has to be initialized the first thing in the script
from logging import getLogger
LOGGER = getLogger(__name__)
# # Innitilize MuMMI if it has not been done before
# MUMMI_ROOT = mummi.init(True)
# This is needed so the Naming works below
#@TODO fix this so we don't have these on import make them as an init
mummi_core.init()
dirKRASStates = Naming.dir_res('states')
dirKRASStructures = Naming.dir_res('structures')
# #RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-ONLY.microstates.txt"))
RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-states.txt"),comments='#')
# #RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-RAF.microstates.txt"))
RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-raf-states.txt"),comments='#') # Note diffrent number of columns so index change below
# TODO: CS, my edits to test
# RAS_ONLY_macrostate = np.loadtxt('ras-states.txt')
# RAS_RAF_macrostate = np.loadtxt('ras-raf-states.txt')
############## above section needs to be uncommented ############
# TODO: CS, my edits to test
# TODO: TSC, The reference structure has to currently be set as the 'RAS-ONLY-reference-structure.gro'
# TODO: TSC, path to the reference structure is: mummi_resources/structures/
kras_ref_universe = mda.Universe(os.path.join(dirKRASStructures, "RAS-ONLY-reference-structure.gro"))
# kras_ref_universe = mda.Universe("RAS-ONLY-reference-structure.gro")
# kras_ref_universe = mda.Universe('AA_pfpatch_000000004641_RAS_RAF2_411.gro')
# TODO: CS, not using these for x4 proteins; instead using protein_systems below to set num_res
######### Below hard codes the number of residues within RAS-only and RAS-RAF ##########
RAS_only_num_res = 184
RAS_RAF_num_res = 320
######### Above hard codes the number of residues within RAS-only and RAS-RAF ##########
####### This can be removed
# def get_kras(syst, kras_start):
# """Gets all atoms for a KRAS protein starting at 'kras_start'."""
# return syst.atoms[kras_start:kras_start+428]
####### This can be removed
def get_segids(u):
"""Identifies the list of segments within the system. Only needs to be called x1 time"""
segs = u.segments
segs = segs.segids
ras_segids = []
rasraf_segids = []
for i in range(len(segs)):
# print(segs[i])
if segs[i][-3:] == 'RAS':
ras_segids.append(segs[i])
if segs[i][-3:] == 'RAF':
rasraf_segids.append(segs[i])
return ras_segids, rasraf_segids
def get_protein_info(u,tag):
"""Uses the segments identified in get_segids to make a list of all proteins in the systems.\
Outputs a list of the first residue number of the protein, and whether it is 'RAS-ONLY', or 'RAS-RAF'.\
The 'tag' input defines what is used to identify the first residue of the protein. i.e. 'resname ACE1 and name BB'.\
Only needs to be called x1 time"""
ras_segids, rasraf_segids = get_segids(u)
if len(ras_segids) > 0:
RAS = u.select_atoms('segid '+ras_segids[0]+' and '+str(tag))
else:
RAS = []
if len(rasraf_segids) > 0:
RAF = u.select_atoms('segid '+rasraf_segids[0]+' and '+str(tag))
else:
RAF = []
protein_info = []#np.empty([len(RAS)+len(RAF),2])
for i in range(len(RAS)):
protein_info.append((RAS[i].resid,'RAS-ONLY'))
for i in range(len(RAF)):
protein_info.append((RAF[i].resid,'RAS-RAF'))
######## sort protein info
protein_info = sorted(protein_info)
######## sort protein info
return protein_info
def get_ref_kras():
"""Gets the reference KRAS struct. Only called x1 time when class is loaded"""
start_of_g_ref = kras_ref_universe.residues[0].resid
ref_selection = 'resid '+str(start_of_g_ref)+':'+str(start_of_g_ref+24)+' ' +\
str(start_of_g_ref+38)+':'+str(start_of_g_ref+54)+' ' +\
str(start_of_g_ref+67)+':'+str(start_of_g_ref+164)+' ' +\
'and (name CA or name BB)'
r2_26r40_56r69_166_ref = kras_ref_universe.select_atoms(str(ref_selection))
return kras_ref_universe.select_atoms(str(ref_selection)).positions - kras_ref_universe.select_atoms(str(ref_selection)).center_of_mass()
# Load inital ref frames (only need to do this once)
ref0 = get_ref_kras()
def getKRASstates(u,kras_indices):
"""Gets states for all KRAS proteins in path."""
# res_shift = 8
# all_glycine = u.select_atoms("resname GLY")
# kras_indices = []
# for i in range(0, len(all_glycine), 26):
# kras_indices.append(all_glycine[i].index)
########## Below is taken out of the function so it is only done once #########
# kras_indices = get_protein_info(u,'resname ACE1 and name BB')
########## Above is taken out of the function so it is only done once #########
# CS, for x4 cases:
# [{protein_x4: (protein_type, num_res)}]
protein_systems = [{'ras4a': ('RAS-ONLY', 185),
'ras4araf': ('RAS-RAF', 321),
'ras': ('RAS-ONLY', 184),
'rasraf': ('RAS-RAF', 320)}]
ALLOUT = []
for k in range(len(kras_indices)):
start_of_g = kras_indices[k][0]
protein_x4 = str(kras_indices[k][1])
try:
protein_type = [item[protein_x4] for item in protein_systems][0][0] # 'RAS-ONLY' OR 'RAS-RAF'
num_res = [item[protein_x4] for item in protein_systems][0][1]
except:
LOGGER.error('Check KRas naming between modules')
raise Exception('Error: unknown KRas name')
# TODO: CS, replacing this comment section with the above, to handle x4 protein types
# ---------------------------------------
# ALLOUT = []
# for k in range(len(kras_indices)):
# start_of_g = kras_indices[k][0]
# protein_type = str(kras_indices[k][1])
# ########## BELOW SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ##############
# ########## POTENTIALLY REDO WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) #######
# ########## HAS BEEN REDONE WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) ########
# # if len(kras_indices) == 1:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB') ####### HAS TO BE FIXED FOR BACKBONE ATOMS FOR SPECIFIC PROTEIN
# # elif len(kras_indices) > 1:
# # if k == len(kras_indices)-1:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB')
# # else:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(kras_indices[k+1][0])+' and name BB')
# ########## ABOVE SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ##############
#
# ########## Below hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations #########################
# if protein_type == 'RAS-ONLY':
# num_res = RAS_only_num_res
# elif protein_type == 'RAS-RAF':
# num_res = RAS_RAF_num_res
# ########## Above hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations #########################
# ---------------------------------------
# TODO: TSC, I changed the selection below, which can be used for the make_whole...
# krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res)+' and (name CA or name BB)')
krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res))
krases0_BB.guess_bonds()
r2_26r40_56r69_166 = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+24)+' ' +\
str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\
str(start_of_g+67)+':'+str(start_of_g+164)+\
' and (name CA or name BB)')
u_selection = \
'resid '+str(start_of_g)+':'+str(start_of_g+24)+' '+str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\
str(start_of_g+67)+':'+str(start_of_g+164)+' and (name CA or name BB)'
mobile0 = u.select_atoms(str(u_selection)).positions - u.select_atoms(str(u_selection)).center_of_mass()
# TODO: CS, something wrong with ref0 from get_kras_ref()
# just making ref0 = mobile0 to test for now
# ref0 = mobile0
# TSC removed this
R, RMSD_junk = align.rotation_matrix(mobile0, ref0)
######## TODO: TSC, Adjusted for AA lipid names ########
# lipids = u.select_atoms('resname POPX POPC PAPC POPE DIPE DPSM PAPS PAP6 CHOL')
lipids = u.select_atoms('resname POPC PAPC POPE DIPE SSM PAPS SAPI CHL1')
coords = ref0
RotMat = []
OS = []
r152_165 = krases0_BB.select_atoms('resid '+str(start_of_g+150)+':'+str(start_of_g+163)+' and (name CA or name BB)')
r65_74 = krases0_BB.select_atoms('resid '+str(start_of_g+63)+':'+str(start_of_g+72)+' and (name CA or name BB)')
timeframes = []
# TODO: CS, for AA need bonds to run make_whole()
# krases0_BB.guess_bonds()
# TODO: CS, turn off for now to test beyond this point
''' *** for AA, need to bring that back on once all else runs ***
'''
# @Tim and <NAME>. this was commented out - please check.
#make_whole(krases0_BB)
j, rmsd_junk = mda.analysis.align.rotation_matrix((r2_26r40_56r69_166.positions-r2_26r40_56r69_166.center_of_mass()), coords)
RotMat.append(j)
OS.append(r65_74.center_of_mass()-r152_165.center_of_mass())
timeframes.append(u.trajectory.time)
if protein_type == 'RAS-RAF':
z_pos = []
############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES BELOW ####################
############### TODO: TSC, zshifting is set to -1 (instead of -2), as there are ACE caps that are separate residues in AA
#zshifting=-1
if protein_x4 == 'rasraf':
zshifting = -1
elif protein_x4 == 'ras4araf':
zshifting = 0
else:
zshifting = 0
LOGGER.error('Found unsupported protein_x4 type')
raf_loops_selection = u.select_atoms('resid '+str(start_of_g+zshifting+291)+':'+str(start_of_g+zshifting+294)+' ' +\
str(start_of_g+zshifting+278)+':'+str(start_of_g+zshifting+281)+' ' +\
' and (name CA or name BB)')
############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES ABOVE ####################
diff = (lipids.center_of_mass()[2]-raf_loops_selection.center_of_mass(unwrap=True)[2])/10
if diff < 0:
diff = diff+(u.dimensions[2]/10)
z_pos.append(diff)
z_pos = np.array(z_pos)
RotMatNP = np.array(RotMat)
OS = np.array(OS)
OA = RotMatNP[:, 2, :]/(((RotMatNP[:, 2, 0]**2)+(RotMatNP[:, 2, 1]**2)+(RotMatNP[:, 2, 2]**2))**0.5)[:, None]
OWAS = np.arccos(RotMatNP[:, 2, 2])*180/math.pi
OC_temp = np.concatenate((OA, OS), axis=1)
t = ((OC_temp[:, 0]*OC_temp[:, 3])+(OC_temp[:, 1]*OC_temp[:, 4]) +
(OC_temp[:, 2]*OC_temp[:, 5]))/((OC_temp[:, 0]**2)+(OC_temp[:, 1]**2)+(OC_temp[:, 2]**2))
OC = OA*t[:, None]
ORS_tp = np.concatenate((OC, OS), axis=1)
ORS_norm = (((ORS_tp[:, 3]-ORS_tp[:, 0])**2)+((ORS_tp[:, 4]-ORS_tp[:, 1])**2)+((ORS_tp[:, 5]-ORS_tp[:, 2])**2))**0.5
ORS = (OS - OC)/ORS_norm[:, None]
OACRS = np.cross(OA, ORS)
OZCA = OA * OA[:, 2][:, None]
Z_unit = np.full([len(OZCA), 3], 1)
Z_adjust = np.array([0, 0, 1])
Z_unit = Z_unit*Z_adjust
Z_OZCA = Z_unit-OZCA
OZPACB = Z_OZCA/((Z_OZCA[:, 0]**2+Z_OZCA[:, 1]**2+Z_OZCA[:, 2]**2)**0.5)[:, None]
OROTNOTSIGNED = np.zeros([len(ORS)])
for i in range(len(ORS)):
OROTNOTSIGNED[i] = np.arccos( | np.dot(OZPACB[i, :], ORS[i, :]) | numpy.dot |
import numpy as np
import pytest
from astropy import convolution
from scipy.signal import medfilt
import astropy.units as u
from ..spectra.spectrum1d import Spectrum1D
from ..tests.spectral_examples import simulated_spectra
from ..manipulation.smoothing import (convolution_smooth, box_smooth,
gaussian_smooth, trapezoid_smooth,
median_smooth)
def compare_flux(flux_smooth1, flux_smooth2, flux_original, rtol=0.01):
"""
There are two things to compare for each set of smoothing:
1. Compare the smoothed flux from the astropy machinery vs
the smoothed flux from specutils. This is done by
comparing flux_smooth1 and flux_smooth2.
2. Next we want to compare the smoothed flux to the original
flux. This is a little more difficult as smoothing will
make a difference for median filter, but less so for
convolution based smoothing if the kernel is normalized
(area under the kernel = 1).
In this second case the rtol (relative tolerance) is used
judiciously.
"""
# Compare, element by element, the two smoothed fluxes.
assert | np.allclose(flux_smooth1, flux_smooth2) | numpy.allclose |
#!/usr/bin/env python
# encoding: utf-8 -*-
"""
This module contains unit tests of the rmgpy.reaction module.
"""
import numpy
import unittest
from external.wip import work_in_progress
from rmgpy.species import Species, TransitionState
from rmgpy.reaction import Reaction
from rmgpy.statmech.translation import Translation, IdealGasTranslation
from rmgpy.statmech.rotation import Rotation, LinearRotor, NonlinearRotor, KRotor, SphericalTopRotor
from rmgpy.statmech.vibration import Vibration, HarmonicOscillator
from rmgpy.statmech.torsion import Torsion, HinderedRotor
from rmgpy.statmech.conformer import Conformer
from rmgpy.kinetics import Arrhenius
from rmgpy.thermo import Wilhoit
import rmgpy.constants as constants
################################################################################
class PseudoSpecies:
"""
Can be used in place of a :class:`rmg.species.Species` for isomorphism checks.
PseudoSpecies('a') is isomorphic with PseudoSpecies('A')
but nothing else.
"""
def __init__(self, label):
self.label = label
def __repr__(self):
return "PseudoSpecies('{0}')".format(self.label)
def __str__(self):
return self.label
def isIsomorphic(self, other):
return self.label.lower() == other.label.lower()
class TestReactionIsomorphism(unittest.TestCase):
"""
Contains unit tests of the isomorphism testing of the Reaction class.
"""
def makeReaction(self,reaction_string):
""""
Make a Reaction (containing PseudoSpecies) of from a string like 'Ab=CD'
"""
reactants, products = reaction_string.split('=')
reactants = [PseudoSpecies(i) for i in reactants]
products = [PseudoSpecies(i) for i in products]
return Reaction(reactants=reactants, products=products)
def test1to1(self):
r1 = self.makeReaction('A=B')
self.assertTrue(r1.isIsomorphic(self.makeReaction('a=B')))
self.assertTrue(r1.isIsomorphic(self.makeReaction('b=A')))
self.assertFalse(r1.isIsomorphic(self.makeReaction('B=a'),eitherDirection=False))
self.assertFalse(r1.isIsomorphic(self.makeReaction('A=C')))
self.assertFalse(r1.isIsomorphic(self.makeReaction('A=BB')))
def test1to2(self):
r1 = self.makeReaction('A=BC')
self.assertTrue(r1.isIsomorphic(self.makeReaction('a=Bc')))
self.assertTrue(r1.isIsomorphic(self.makeReaction('cb=a')))
self.assertTrue(r1.isIsomorphic(self.makeReaction('a=cb'),eitherDirection=False))
self.assertFalse(r1.isIsomorphic(self.makeReaction('bc=a'),eitherDirection=False))
self.assertFalse(r1.isIsomorphic(self.makeReaction('a=c')))
self.assertFalse(r1.isIsomorphic(self.makeReaction('ab=c')))
def test2to2(self):
r1 = self.makeReaction('AB=CD')
self.assertTrue(r1.isIsomorphic(self.makeReaction('ab=cd')))
self.assertTrue(r1.isIsomorphic(self.makeReaction('ab=dc'),eitherDirection=False))
self.assertTrue(r1.isIsomorphic(self.makeReaction('dc=ba')))
self.assertFalse(r1.isIsomorphic(self.makeReaction('cd=ab'),eitherDirection=False))
self.assertFalse(r1.isIsomorphic(self.makeReaction('ab=ab')))
self.assertFalse(r1.isIsomorphic(self.makeReaction('ab=cde')))
def test2to3(self):
r1 = self.makeReaction('AB=CDE')
self.assertTrue(r1.isIsomorphic(self.makeReaction('ab=cde')))
self.assertTrue(r1.isIsomorphic(self.makeReaction('ba=edc'),eitherDirection=False))
self.assertTrue(r1.isIsomorphic(self.makeReaction('dec=ba')))
self.assertFalse(r1.isIsomorphic(self.makeReaction('cde=ab'),eitherDirection=False))
self.assertFalse(r1.isIsomorphic(self.makeReaction('ab=abc')))
self.assertFalse(r1.isIsomorphic(self.makeReaction('abe=cde')))
class TestReaction(unittest.TestCase):
"""
Contains unit tests of the Reaction class.
"""
def setUp(self):
"""
A method that is called prior to each unit test in this class.
"""
ethylene = Species(
label = 'C2H4',
conformer = Conformer(
E0 = (44.7127, 'kJ/mol'),
modes = [
IdealGasTranslation(
mass = (28.0313, 'amu'),
),
NonlinearRotor(
inertia = (
[3.41526, 16.6498, 20.065],
'amu*angstrom^2',
),
symmetry = 4,
),
HarmonicOscillator(
frequencies = (
[828.397, 970.652, 977.223, 1052.93, 1233.55, 1367.56, 1465.09, 1672.25, 3098.46, 3111.7, 3165.79, 3193.54],
'cm^-1',
),
),
],
spinMultiplicity = 1,
opticalIsomers = 1,
),
)
hydrogen = Species(
label = 'H',
conformer = Conformer(
E0 = (211.794, 'kJ/mol'),
modes = [
IdealGasTranslation(
mass = (1.00783, 'amu'),
),
],
spinMultiplicity = 2,
opticalIsomers = 1,
),
)
ethyl = Species(
label = 'C2H5',
conformer = Conformer(
E0 = (111.603, 'kJ/mol'),
modes = [
IdealGasTranslation(
mass = (29.0391, 'amu'),
),
NonlinearRotor(
inertia = (
[4.8709, 22.2353, 23.9925],
'amu*angstrom^2',
),
symmetry = 1,
),
HarmonicOscillator(
frequencies = (
[482.224, 791.876, 974.355, 1051.48, 1183.21, 1361.36, 1448.65, 1455.07, 1465.48, 2688.22, 2954.51, 3033.39, 3101.54, 3204.73],
'cm^-1',
),
),
HinderedRotor(
inertia = (1.11481, 'amu*angstrom^2'),
symmetry = 6,
barrier = (0.244029, 'kJ/mol'),
semiclassical = None,
),
],
spinMultiplicity = 2,
opticalIsomers = 1,
),
)
TS = TransitionState(
label = 'TS',
conformer = Conformer(
E0 = (266.694, 'kJ/mol'),
modes = [
IdealGasTranslation(
mass = (29.0391, 'amu'),
),
NonlinearRotor(
inertia = (
[6.78512, 22.1437, 22.2114],
'amu*angstrom^2',
),
symmetry = 1,
),
HarmonicOscillator(
frequencies = (
[412.75, 415.206, 821.495, 924.44, 982.714, 1024.16, 1224.21, 1326.36, 1455.06, 1600.35, 3101.46, 3110.55, 3175.34, 3201.88],
'cm^-1',
),
),
],
spinMultiplicity = 2,
opticalIsomers = 1,
),
frequency = (-750.232, 'cm^-1'),
)
self.reaction = Reaction(
reactants = [hydrogen, ethylene],
products = [ethyl],
kinetics = Arrhenius(
A = (501366000.0, 'cm^3/(mol*s)'),
n = 1.637,
Ea = (4.32508, 'kJ/mol'),
T0 = (1, 'K'),
Tmin = (300, 'K'),
Tmax = (2500, 'K'),
),
transitionState = TS,
)
# CC(=O)O[O]
acetylperoxy = Species(
label='acetylperoxy',
thermo=Wilhoit(Cp0=(4.0*constants.R,"J/(mol*K)"), CpInf=(21.0*constants.R,"J/(mol*K)"), a0=-3.95, a1=9.26, a2=-15.6, a3=8.55, B=(500.0,"K"), H0=(-6.151e+04,"J/mol"), S0=(-790.2,"J/(mol*K)")),
)
# C[C]=O
acetyl = Species(
label='acetyl',
thermo=Wilhoit(Cp0=(4.0*constants.R,"J/(mol*K)"), CpInf=(15.5*constants.R,"J/(mol*K)"), a0=0.2541, a1=-0.4712, a2=-4.434, a3=2.25, B=(500.0,"K"), H0=(-1.439e+05,"J/mol"), S0=(-524.6,"J/(mol*K)")),
)
# [O][O]
oxygen = Species(
label='oxygen',
thermo=Wilhoit(Cp0=(3.5*constants.R,"J/(mol*K)"), CpInf=(4.5*constants.R,"J/(mol*K)"), a0=-0.9324, a1=26.18, a2=-70.47, a3=44.12, B=(500.0,"K"), H0=(1.453e+04,"J/mol"), S0=(-12.19,"J/(mol*K)")),
)
self.reaction2 = Reaction(
reactants=[acetyl, oxygen],
products=[acetylperoxy],
kinetics = Arrhenius(
A = (2.65e12, 'cm^3/(mol*s)'),
n = 0.0,
Ea = (0.0, 'kJ/mol'),
T0 = (1, 'K'),
Tmin = (300, 'K'),
Tmax = (2000, 'K'),
),
)
def testIsIsomerization(self):
"""
Test the Reaction.isIsomerization() method.
"""
isomerization = Reaction(reactants=[Species()], products=[Species()])
association = Reaction(reactants=[Species(),Species()], products=[Species()])
dissociation = Reaction(reactants=[Species()], products=[Species(),Species()])
bimolecular = Reaction(reactants=[Species(),Species()], products=[Species(),Species()])
self.assertTrue(isomerization.isIsomerization())
self.assertFalse(association.isIsomerization())
self.assertFalse(dissociation.isIsomerization())
self.assertFalse(bimolecular.isIsomerization())
def testIsAssociation(self):
"""
Test the Reaction.isAssociation() method.
"""
isomerization = Reaction(reactants=[Species()], products=[Species()])
association = Reaction(reactants=[Species(),Species()], products=[Species()])
dissociation = Reaction(reactants=[Species()], products=[Species(),Species()])
bimolecular = Reaction(reactants=[Species(),Species()], products=[Species(),Species()])
self.assertFalse(isomerization.isAssociation())
self.assertTrue(association.isAssociation())
self.assertFalse(dissociation.isAssociation())
self.assertFalse(bimolecular.isAssociation())
def testIsDissociation(self):
"""
Test the Reaction.isDissociation() method.
"""
isomerization = Reaction(reactants=[Species()], products=[Species()])
association = Reaction(reactants=[Species(),Species()], products=[Species()])
dissociation = Reaction(reactants=[Species()], products=[Species(),Species()])
bimolecular = Reaction(reactants=[Species(),Species()], products=[Species(),Species()])
self.assertFalse(isomerization.isDissociation())
self.assertFalse(association.isDissociation())
self.assertTrue(dissociation.isDissociation())
self.assertFalse(bimolecular.isDissociation())
def testHasTemplate(self):
"""
Test the Reaction.hasTemplate() method.
"""
reactants = self.reaction.reactants[:]
products = self.reaction.products[:]
self.assertTrue(self.reaction.hasTemplate(reactants, products))
self.assertTrue(self.reaction.hasTemplate(products, reactants))
self.assertFalse(self.reaction2.hasTemplate(reactants, products))
self.assertFalse(self.reaction2.hasTemplate(products, reactants))
reactants.reverse()
products.reverse()
self.assertTrue(self.reaction.hasTemplate(reactants, products))
self.assertTrue(self.reaction.hasTemplate(products, reactants))
self.assertFalse(self.reaction2.hasTemplate(reactants, products))
self.assertFalse(self.reaction2.hasTemplate(products, reactants))
reactants = self.reaction2.reactants[:]
products = self.reaction2.products[:]
self.assertFalse(self.reaction.hasTemplate(reactants, products))
self.assertFalse(self.reaction.hasTemplate(products, reactants))
self.assertTrue(self.reaction2.hasTemplate(reactants, products))
self.assertTrue(self.reaction2.hasTemplate(products, reactants))
reactants.reverse()
products.reverse()
self.assertFalse(self.reaction.hasTemplate(reactants, products))
self.assertFalse(self.reaction.hasTemplate(products, reactants))
self.assertTrue(self.reaction2.hasTemplate(reactants, products))
self.assertTrue(self.reaction2.hasTemplate(products, reactants))
def testEnthalpyOfReaction(self):
"""
Test the Reaction.getEnthalpyOfReaction() method.
"""
Tlist = numpy.arange(200.0, 2001.0, 200.0, numpy.float64)
Hlist0 = [float(v) for v in ['-146007', '-145886', '-144195', '-141973', '-139633', '-137341', '-135155', '-133093', '-131150', '-129316']]
Hlist = self.reaction2.getEnthalpiesOfReaction(Tlist)
for i in range(len(Tlist)):
self.assertAlmostEqual(Hlist[i] / 1000., Hlist0[i] / 1000., 2)
def testEntropyOfReaction(self):
"""
Test the Reaction.getEntropyOfReaction() method.
"""
Tlist = numpy.arange(200.0, 2001.0, 200.0, numpy.float64)
Slist0 = [float(v) for v in ['-156.793', '-156.872', '-153.504', '-150.317', '-147.707', '-145.616', '-143.93', '-142.552', '-141.407', '-140.441']]
Slist = self.reaction2.getEntropiesOfReaction(Tlist)
for i in range(len(Tlist)):
self.assertAlmostEqual(Slist[i], Slist0[i], 2)
def testFreeEnergyOfReaction(self):
"""
Test the Reaction.getFreeEnergyOfReaction() method.
"""
Tlist = numpy.arange(200.0, 2001.0, 200.0, numpy.float64)
Glist0 = [float(v) for v in ['-114648', '-83137.2', '-52092.4', '-21719.3', '8073.53', '37398.1', '66346.8', '94990.6', '123383', '151565']]
Glist = self.reaction2.getFreeEnergiesOfReaction(Tlist)
for i in range(len(Tlist)):
self.assertAlmostEqual(Glist[i] / 1000., Glist0[i] / 1000., 2)
def testEquilibriumConstantKa(self):
"""
Test the Reaction.getEquilibriumConstant() method.
"""
Tlist = numpy.arange(200.0, 2001.0, 200.0, numpy.float64)
Kalist0 = [float(v) for v in ['8.75951e+29', '7.1843e+10', '34272.7', '26.1877', '0.378696', '0.0235579', '0.00334673', '0.000792389', '0.000262777', '0.000110053']]
Kalist = self.reaction2.getEquilibriumConstants(Tlist, type='Ka')
for i in range(len(Tlist)):
self.assertAlmostEqual(Kalist[i] / Kalist0[i], 1.0, 4)
def testEquilibriumConstantKc(self):
"""
Test the Reaction.getEquilibriumConstant() method.
"""
Tlist = numpy.arange(200.0, 2001.0, 200.0, numpy.float64)
Kclist0 = [float(v) for v in ['1.45661e+28', '2.38935e+09', '1709.76', '1.74189', '0.0314866', '0.00235045', '0.000389568', '0.000105413', '3.93273e-05', '1.83006e-05']]
Kclist = self.reaction2.getEquilibriumConstants(Tlist, type='Kc')
for i in range(len(Tlist)):
self.assertAlmostEqual(Kclist[i] / Kclist0[i], 1.0, 4)
def testEquilibriumConstantKp(self):
"""
Test the Reaction.getEquilibriumConstant() method.
"""
Tlist = numpy.arange(200.0, 2001.0, 200.0, numpy.float64)
Kplist0 = [float(v) for v in ['8.75951e+24', '718430', '0.342727', '0.000261877', '3.78696e-06', '2.35579e-07', '3.34673e-08', '7.92389e-09', '2.62777e-09', '1.10053e-09']]
Kplist = self.reaction2.getEquilibriumConstants(Tlist, type='Kp')
for i in range(len(Tlist)):
self.assertAlmostEqual(Kplist[i] / Kplist0[i], 1.0, 4)
def testStoichiometricCoefficient(self):
"""
Test the Reaction.getStoichiometricCoefficient() method.
"""
for reactant in self.reaction.reactants:
self.assertEqual(self.reaction.getStoichiometricCoefficient(reactant), -1)
for product in self.reaction.products:
self.assertEqual(self.reaction.getStoichiometricCoefficient(product), 1)
for reactant in self.reaction2.reactants:
self.assertEqual(self.reaction.getStoichiometricCoefficient(reactant), 0)
for product in self.reaction2.products:
self.assertEqual(self.reaction.getStoichiometricCoefficient(product), 0)
def testRateCoefficient(self):
"""
Test the Reaction.getRateCoefficient() method.
"""
Tlist = numpy.arange(200.0, 2001.0, 200.0, numpy.float64)
P = 1e5
for T in Tlist:
self.assertAlmostEqual(self.reaction.getRateCoefficient(T, P) / self.reaction.kinetics.getRateCoefficient(T), 1.0, 6)
def testGenerateReverseRateCoefficient(self):
"""
Test the Reaction.generateReverseRateCoefficient() method.
"""
Tlist = numpy.arange(200.0, 2001.0, 200.0, numpy.float64)
P = 1e5
reverseKinetics = self.reaction2.generateReverseRateCoefficient()
for T in Tlist:
kr0 = self.reaction2.getRateCoefficient(T, P) / self.reaction2.getEquilibriumConstant(T)
kr = reverseKinetics.getRateCoefficient(T)
self.assertAlmostEqual(kr0 / kr, 1.0, 0)
def testGenerateReverseRateCoefficientArrhenius(self):
"""
Test the Reaction.generateReverseRateCoefficient() method works for the Arrhenius format.
"""
original_kinetics = Arrhenius(
A = (2.65e12, 'cm^3/(mol*s)'),
n = 0.0,
Ea = (0.0, 'kJ/mol'),
T0 = (1, 'K'),
Tmin = (300, 'K'),
Tmax = (2000, 'K'),
)
self.reaction2.kinetics = original_kinetics
reverseKinetics = self.reaction2.generateReverseRateCoefficient()
self.reaction2.kinetics = reverseKinetics
# reverse reactants, products to ensure Keq is correctly computed
self.reaction2.reactants, self.reaction2.products = self.reaction2.products, self.reaction2.reactants
reversereverseKinetics = self.reaction2.generateReverseRateCoefficient()
# check that reverting the reverse yields the original
Tlist = numpy.arange(original_kinetics.Tmin.value_si, original_kinetics.Tmax.value_si, 200.0, numpy.float64)
P = 1e5
for T in Tlist:
korig = original_kinetics.getRateCoefficient(T, P)
krevrev = reversereverseKinetics.getRateCoefficient(T, P)
self.assertAlmostEqual(korig / krevrev, 1.0, 0)
@work_in_progress
def testGenerateReverseRateCoefficientArrheniusEP(self):
"""
Test the Reaction.generateReverseRateCoefficient() method works for the ArrheniusEP format.
"""
from rmgpy.kinetics import ArrheniusEP
original_kinetics = ArrheniusEP(
A = (2.65e12, 'cm^3/(mol*s)'),
n = 0.0,
alpha = 0.5,
E0 = (41.84, 'kJ/mol'),
Tmin = (300, 'K'),
Tmax = (2000, 'K'),
)
self.reaction2.kinetics = original_kinetics
reverseKinetics = self.reaction2.generateReverseRateCoefficient()
self.reaction2.kinetics = reverseKinetics
# reverse reactants, products to ensure Keq is correctly computed
self.reaction2.reactants, self.reaction2.products = self.reaction2.products, self.reaction2.reactants
reversereverseKinetics = self.reaction2.generateReverseRateCoefficient()
# check that reverting the reverse yields the original
Tlist = numpy.arange(original_kinetics.Tmin, original_kinetics.Tmax, 200.0, numpy.float64)
P = 1e5
for T in Tlist:
korig = original_kinetics.getRateCoefficient(T, P)
krevrev = reversereverseKinetics.getRateCoefficient(T, P)
self.assertAlmostEqual(korig / krevrev, 1.0, 0)
def testGenerateReverseRateCoefficientPDepArrhenius(self):
"""
Test the Reaction.generateReverseRateCoefficient() method works for the PDepArrhenius format.
"""
from rmgpy.kinetics import PDepArrhenius
arrhenius0 = Arrhenius(
A = (1.0e6,"s^-1"),
n = 1.0,
Ea = (10.0,"kJ/mol"),
T0 = (300.0,"K"),
Tmin = (300.0,"K"),
Tmax = (2000.0,"K"),
comment = """This data is completely made up""",
)
arrhenius1 = Arrhenius(
A = (1.0e12,"s^-1"),
n = 1.0,
Ea = (20.0,"kJ/mol"),
T0 = (300.0,"K"),
Tmin = (300.0,"K"),
Tmax = (2000.0,"K"),
comment = """This data is completely made up""",
)
pressures = numpy.array([0.1, 10.0])
arrhenius = [arrhenius0, arrhenius1]
Tmin = 300.0
Tmax = 2000.0
Pmin = 0.1
Pmax = 10.0
comment = """This data is completely made up"""
original_kinetics = PDepArrhenius(
pressures = (pressures,"bar"),
arrhenius = arrhenius,
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
Pmin = (Pmin,"bar"),
Pmax = (Pmax,"bar"),
comment = comment,
)
self.reaction2.kinetics = original_kinetics
reverseKinetics = self.reaction2.generateReverseRateCoefficient()
self.reaction2.kinetics = reverseKinetics
# reverse reactants, products to ensure Keq is correctly computed
self.reaction2.reactants, self.reaction2.products = self.reaction2.products, self.reaction2.reactants
reversereverseKinetics = self.reaction2.generateReverseRateCoefficient()
# check that reverting the reverse yields the original
Tlist = numpy.arange(Tmin, Tmax, 200.0, numpy.float64)
P = 1e5
for T in Tlist:
korig = original_kinetics.getRateCoefficient(T, P)
krevrev = reversereverseKinetics.getRateCoefficient(T, P)
self.assertAlmostEqual(korig / krevrev, 1.0, 0)
def testGenerateReverseRateCoefficientMultiArrhenius(self):
"""
Test the Reaction.generateReverseRateCoefficient() method works for the MultiArrhenius format.
"""
from rmgpy.kinetics import MultiArrhenius
pressures = numpy.array([0.1, 10.0])
Tmin = 300.0
Tmax = 2000.0
Pmin = 0.1
Pmax = 10.0
comment = """This data is completely made up"""
arrhenius = [
Arrhenius(
A = (9.3e-14,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (4740*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
comment = comment,
),
Arrhenius(
A = (1.4e-9,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (11200*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
comment = comment,
),
]
original_kinetics = MultiArrhenius(
arrhenius = arrhenius,
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
comment = comment,
)
self.reaction2.kinetics = original_kinetics
reverseKinetics = self.reaction2.generateReverseRateCoefficient()
self.reaction2.kinetics = reverseKinetics
# reverse reactants, products to ensure Keq is correctly computed
self.reaction2.reactants, self.reaction2.products = self.reaction2.products, self.reaction2.reactants
reversereverseKinetics = self.reaction2.generateReverseRateCoefficient()
# check that reverting the reverse yields the original
Tlist = numpy.arange(Tmin, Tmax, 200.0, numpy.float64)
P = 1e5
for T in Tlist:
korig = original_kinetics.getRateCoefficient(T, P)
krevrev = reversereverseKinetics.getRateCoefficient(T, P)
self.assertAlmostEqual(korig / krevrev, 1.0, 0)
def testGenerateReverseRateCoefficientMultiPDepArrhenius(self):
"""
Test the Reaction.generateReverseRateCoefficient() method works for the MultiPDepArrhenius format.
"""
from rmgpy.kinetics import PDepArrhenius, MultiPDepArrhenius
Tmin = 350.
Tmax = 1500.
Pmin = 1e-1
Pmax = 1e1
pressures = numpy.array([1e-1,1e1])
comment = 'CH3 + C2H6 <=> CH4 + C2H5 (Baulch 2005)'
arrhenius = [
PDepArrhenius(
pressures = (pressures,"bar"),
arrhenius = [
Arrhenius(
A = (9.3e-16,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (4740*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
comment = comment,
),
Arrhenius(
A = (9.3e-14,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (4740*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
comment = comment,
),
],
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
Pmin = (Pmin,"bar"),
Pmax = (Pmax,"bar"),
comment = comment,
),
PDepArrhenius(
pressures = (pressures,"bar"),
arrhenius = [
Arrhenius(
A = (1.4e-11,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (11200*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
comment = comment,
),
Arrhenius(
A = (1.4e-9,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (11200*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
comment = comment,
),
],
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
Pmin = (Pmin,"bar"),
Pmax = (Pmax,"bar"),
comment = comment,
),
]
original_kinetics = MultiPDepArrhenius(
arrhenius = arrhenius,
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
Pmin = (Pmin,"bar"),
Pmax = (Pmax,"bar"),
comment = comment,
)
self.reaction2.kinetics = original_kinetics
reverseKinetics = self.reaction2.generateReverseRateCoefficient()
self.reaction2.kinetics = reverseKinetics
# reverse reactants, products to ensure Keq is correctly computed
self.reaction2.reactants, self.reaction2.products = self.reaction2.products, self.reaction2.reactants
reversereverseKinetics = self.reaction2.generateReverseRateCoefficient()
# check that reverting the reverse yields the original
Tlist = numpy.arange(Tmin, Tmax, 200.0, numpy.float64)
P = 1e5
for T in Tlist:
korig = original_kinetics.getRateCoefficient(T, P)
krevrev = reversereverseKinetics.getRateCoefficient(T, P)
self.assertAlmostEqual(korig / krevrev, 1.0, 0)
def testGenerateReverseRateCoefficientThirdBody(self):
"""
Test the Reaction.generateReverseRateCoefficient() method works for the ThirdBody format.
"""
from rmgpy.kinetics import ThirdBody
arrheniusLow = Arrhenius(
A = (2.62e+33,"cm^6/(mol^2*s)"),
n = -4.76,
Ea = (10.21,"kJ/mol"),
T0 = (1,"K"),
)
efficiencies = {"C": 3, "C(=O)=O": 2, "CC": 3, "O": 6, "[Ar]": 0.7, "[C]=O": 1.5, "[H][H]": 2}
Tmin = 300.
Tmax = 2000.
Pmin = 0.01
Pmax = 100.
comment = """H + CH3 -> CH4"""
thirdBody = ThirdBody(
arrheniusLow = arrheniusLow,
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
Pmin = (Pmin,"bar"),
Pmax = (Pmax,"bar"),
efficiencies = efficiencies,
comment = comment,
)
original_kinetics = thirdBody
self.reaction2.kinetics = original_kinetics
reverseKinetics = self.reaction2.generateReverseRateCoefficient()
self.reaction2.kinetics = reverseKinetics
# reverse reactants, products to ensure Keq is correctly computed
self.reaction2.reactants, self.reaction2.products = self.reaction2.products, self.reaction2.reactants
reversereverseKinetics = self.reaction2.generateReverseRateCoefficient()
# check that reverting the reverse yields the original
Tlist = numpy.arange(Tmin, Tmax, 200.0, numpy.float64)
P = 1e5
for T in Tlist:
korig = original_kinetics.getRateCoefficient(T, P)
krevrev = reversereverseKinetics.getRateCoefficient(T, P)
self.assertAlmostEqual(korig / krevrev, 1.0, 0)
def testGenerateReverseRateCoefficientLindemann(self):
"""
Test the Reaction.generateReverseRateCoefficient() method works for the Lindemann format.
"""
from rmgpy.kinetics import Lindemann
arrheniusHigh = Arrhenius(
A = (1.39e+16,"cm^3/(mol*s)"),
n = -0.534,
Ea = (2.243,"kJ/mol"),
T0 = (1,"K"),
)
arrheniusLow = Arrhenius(
A = (2.62e+33,"cm^6/(mol^2*s)"),
n = -4.76,
Ea = (10.21,"kJ/mol"),
T0 = (1,"K"),
)
efficiencies = {"C": 3, "C(=O)=O": 2, "CC": 3, "O": 6, "[Ar]": 0.7, "[C]=O": 1.5, "[H][H]": 2}
Tmin = 300.
Tmax = 2000.
Pmin = 0.01
Pmax = 100.
comment = """H + CH3 -> CH4"""
lindemann = Lindemann(
arrheniusHigh = arrheniusHigh,
arrheniusLow = arrheniusLow,
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
Pmin = (Pmin,"bar"),
Pmax = (Pmax,"bar"),
efficiencies = efficiencies,
comment = comment,
)
original_kinetics = lindemann
self.reaction2.kinetics = original_kinetics
reverseKinetics = self.reaction2.generateReverseRateCoefficient()
self.reaction2.kinetics = reverseKinetics
# reverse reactants, products to ensure Keq is correctly computed
self.reaction2.reactants, self.reaction2.products = self.reaction2.products, self.reaction2.reactants
reversereverseKinetics = self.reaction2.generateReverseRateCoefficient()
# check that reverting the reverse yields the original
Tlist = numpy.arange(Tmin, Tmax, 200.0, numpy.float64)
P = 1e5
for T in Tlist:
korig = original_kinetics.getRateCoefficient(T, P)
krevrev = reversereverseKinetics.getRateCoefficient(T, P)
self.assertAlmostEqual(korig / krevrev, 1.0, 0)
def testGenerateReverseRateCoefficientTroe(self):
"""
Test the Reaction.generateReverseRateCoefficient() method works for the Troe format.
"""
from rmgpy.kinetics import Troe
arrheniusHigh = Arrhenius(
A = (1.39e+16,"cm^3/(mol*s)"),
n = -0.534,
Ea = (2.243,"kJ/mol"),
T0 = (1,"K"),
)
arrheniusLow = Arrhenius(
A = (2.62e+33,"cm^6/(mol^2*s)"),
n = -4.76,
Ea = (10.21,"kJ/mol"),
T0 = (1,"K"),
)
alpha = 0.783
T3 = 74
T1 = 2941
T2 = 6964
efficiencies = {"C": 3, "C(=O)=O": 2, "CC": 3, "O": 6, "[Ar]": 0.7, "[C]=O": 1.5, "[H][H]": 2}
Tmin = 300.
Tmax = 2000.
Pmin = 0.01
Pmax = 100.
comment = """H + CH3 -> CH4"""
troe = Troe(
arrheniusHigh = arrheniusHigh,
arrheniusLow = arrheniusLow,
alpha = alpha,
T3 = (T3,"K"),
T1 = (T1,"K"),
T2 = (T2,"K"),
Tmin = (Tmin,"K"),
Tmax = (Tmax,"K"),
Pmin = (Pmin,"bar"),
Pmax = (Pmax,"bar"),
efficiencies = efficiencies,
comment = comment,
)
original_kinetics = troe
self.reaction2.kinetics = original_kinetics
reverseKinetics = self.reaction2.generateReverseRateCoefficient()
self.reaction2.kinetics = reverseKinetics
# reverse reactants, products to ensure Keq is correctly computed
self.reaction2.reactants, self.reaction2.products = self.reaction2.products, self.reaction2.reactants
reversereverseKinetics = self.reaction2.generateReverseRateCoefficient()
# check that reverting the reverse yields the original
Tlist = | numpy.arange(Tmin, Tmax, 200.0, numpy.float64) | numpy.arange |
# coding: utf-8
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Test the Logarithmic Units and Quantities
"""
from __future__ import (absolute_import, unicode_literals, division,
print_function)
from ...extern import six
from ...extern.six.moves import zip
import pickle
import itertools
import pytest
import numpy as np
from numpy.testing.utils import assert_allclose
from ...tests.helper import assert_quantity_allclose
from ... import units as u, constants as c
lu_units = [u.dex, u.mag, u.decibel]
lu_subclasses = [u.DexUnit, u.MagUnit, u.DecibelUnit]
lq_subclasses = [u.Dex, u.Magnitude, u.Decibel]
pu_sample = (u.dimensionless_unscaled, u.m, u.g/u.s**2, u.Jy)
class TestLogUnitCreation(object):
def test_logarithmic_units(self):
"""Check logarithmic units are set up correctly."""
assert u.dB.to(u.dex) == 0.1
assert u.dex.to(u.mag) == -2.5
assert u.mag.to(u.dB) == -4
@pytest.mark.parametrize('lu_unit, lu_cls', zip(lu_units, lu_subclasses))
def test_callable_units(self, lu_unit, lu_cls):
assert isinstance(lu_unit, u.UnitBase)
assert callable(lu_unit)
assert lu_unit._function_unit_class is lu_cls
@pytest.mark.parametrize('lu_unit', lu_units)
def test_equality_to_normal_unit_for_dimensionless(self, lu_unit):
lu = lu_unit()
assert lu == lu._default_function_unit # eg, MagUnit() == u.mag
assert lu._default_function_unit == lu # and u.mag == MagUnit()
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_call_units(self, lu_unit, physical_unit):
"""Create a LogUnit subclass using the callable unit and physical unit,
and do basic check that output is right."""
lu1 = lu_unit(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
def test_call_invalid_unit(self):
with pytest.raises(TypeError):
u.mag([])
with pytest.raises(ValueError):
u.mag(u.mag())
@pytest.mark.parametrize('lu_cls, physical_unit', itertools.product(
lu_subclasses + [u.LogUnit], pu_sample))
def test_subclass_creation(self, lu_cls, physical_unit):
"""Create a LogUnit subclass object for given physical unit,
and do basic check that output is right."""
lu1 = lu_cls(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
lu2 = lu_cls(physical_unit,
function_unit=2*lu1._default_function_unit)
assert lu2.physical_unit == physical_unit
assert lu2.function_unit == u.Unit(2*lu2._default_function_unit)
with pytest.raises(ValueError):
lu_cls(physical_unit, u.m)
def test_predefined_magnitudes():
assert_quantity_allclose((-21.1*u.STmag).physical,
1.*u.erg/u.cm**2/u.s/u.AA)
assert_quantity_allclose((-48.6*u.ABmag).physical,
1.*u.erg/u.cm**2/u.s/u.Hz)
assert_quantity_allclose((0*u.M_bol).physical, c.L_bol0)
assert_quantity_allclose((0*u.m_bol).physical,
c.L_bol0/(4.*np.pi*(10.*c.pc)**2))
def test_predefined_reinitialisation():
assert u.mag('ST') == u.STmag
assert u.mag('AB') == u.ABmag
assert u.mag('Bol') == u.M_bol
assert u.mag('bol') == u.m_bol
def test_predefined_string_roundtrip():
"""Ensure roundtripping; see #5015"""
with u.magnitude_zero_points.enable():
assert u.Unit(u.STmag.to_string()) == u.STmag
assert u.Unit(u.ABmag.to_string()) == u.ABmag
assert u.Unit(u.M_bol.to_string()) == u.M_bol
assert u.Unit(u.m_bol.to_string()) == u.m_bol
def test_inequality():
"""Check __ne__ works (regresssion for #5342)."""
lu1 = u.mag(u.Jy)
lu2 = u.dex(u.Jy)
lu3 = u.mag(u.Jy**2)
lu4 = lu3 - lu1
assert lu1 != lu2
assert lu1 != lu3
assert lu1 == lu4
class TestLogUnitStrings(object):
def test_str(self):
"""Do some spot checks that str, repr, etc. work as expected."""
lu1 = u.mag(u.Jy)
assert str(lu1) == 'mag(Jy)'
assert repr(lu1) == 'Unit("mag(Jy)")'
assert lu1.to_string('generic') == 'mag(Jy)'
with pytest.raises(ValueError):
lu1.to_string('fits')
lu2 = u.dex()
assert str(lu2) == 'dex'
assert repr(lu2) == 'Unit("dex(1)")'
assert lu2.to_string() == 'dex(1)'
lu3 = u.MagUnit(u.Jy, function_unit=2*u.mag)
assert str(lu3) == '2 mag(Jy)'
assert repr(lu3) == 'MagUnit("Jy", unit="2 mag")'
assert lu3.to_string() == '2 mag(Jy)'
lu4 = u.mag(u.ct)
assert lu4.to_string('generic') == 'mag(ct)'
assert lu4.to_string('latex') == ('$\\mathrm{mag}$$\\mathrm{\\left( '
'\\mathrm{ct} \\right)}$')
assert lu4._repr_latex_() == lu4.to_string('latex')
class TestLogUnitConversion(object):
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_physical_unit_conversion(self, lu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to their non-log counterparts."""
lu1 = lu_unit(physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(physical_unit, 0.) == 1.
assert physical_unit.is_equivalent(lu1)
assert physical_unit.to(lu1, 1.) == 0.
pu = u.Unit(8.*physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(pu, 0.) == 0.125
assert pu.is_equivalent(lu1)
assert_allclose(pu.to(lu1, 0.125), 0., atol=1.e-15)
# Check we round-trip.
value = np.linspace(0., 10., 6)
assert_allclose(pu.to(lu1, lu1.to(pu, value)), value, atol=1.e-15)
# And that we're not just returning True all the time.
pu2 = u.g
assert not lu1.is_equivalent(pu2)
with pytest.raises(u.UnitsError):
lu1.to(pu2)
assert not pu2.is_equivalent(lu1)
with pytest.raises(u.UnitsError):
pu2.to(lu1)
@pytest.mark.parametrize('lu_unit', lu_units)
def test_container_unit_conversion(self, lu_unit):
"""Check that conversion to logarithmic units (u.mag, u.dB, u.dex)
is only possible when the physical unit is dimensionless."""
values = np.linspace(0., 10., 6)
lu1 = lu_unit(u.dimensionless_unscaled)
assert lu1.is_equivalent(lu1.function_unit)
assert_allclose(lu1.to(lu1.function_unit, values), values)
lu2 = lu_unit(u.Jy)
assert not lu2.is_equivalent(lu2.function_unit)
with pytest.raises(u.UnitsError):
lu2.to(lu2.function_unit, values)
@pytest.mark.parametrize(
'flu_unit, tlu_unit, physical_unit',
itertools.product(lu_units, lu_units, pu_sample))
def test_subclass_conversion(self, flu_unit, tlu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to each other if they correspond to equivalent physical units."""
values = np.linspace(0., 10., 6)
flu = flu_unit(physical_unit)
tlu = tlu_unit(physical_unit)
assert flu.is_equivalent(tlu)
assert_allclose(flu.to(tlu), flu.function_unit.to(tlu.function_unit))
assert_allclose(flu.to(tlu, values),
values * flu.function_unit.to(tlu.function_unit))
tlu2 = tlu_unit(u.Unit(100.*physical_unit))
assert flu.is_equivalent(tlu2)
# Check that we round-trip.
assert_allclose(flu.to(tlu2, tlu2.to(flu, values)), values, atol=1.e-15)
tlu3 = tlu_unit(physical_unit.to_system(u.si)[0])
assert flu.is_equivalent(tlu3)
assert_allclose(flu.to(tlu3, tlu3.to(flu, values)), values, atol=1.e-15)
tlu4 = tlu_unit(u.g)
assert not flu.is_equivalent(tlu4)
with pytest.raises(u.UnitsError):
flu.to(tlu4, values)
def test_unit_decomposition(self):
lu = u.mag(u.Jy)
assert lu.decompose() == u.mag(u.Jy.decompose())
assert lu.decompose().physical_unit.bases == [u.kg, u.s]
assert lu.si == u.mag(u.Jy.si)
assert lu.si.physical_unit.bases == [u.kg, u.s]
assert lu.cgs == u.mag(u.Jy.cgs)
assert lu.cgs.physical_unit.bases == [u.g, u.s]
def test_unit_multiple_possible_equivalencies(self):
lu = u.mag(u.Jy)
assert lu.is_equivalent(pu_sample)
class TestLogUnitArithmetic(object):
def test_multiplication_division(self):
"""Check that multiplication/division with other units is only
possible when the physical unit is dimensionless, and that this
turns the unit into a normal one."""
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 * u.m
with pytest.raises(u.UnitsError):
u.m * lu1
with pytest.raises(u.UnitsError):
lu1 / lu1
for unit in (u.dimensionless_unscaled, u.m, u.mag, u.dex):
with pytest.raises(u.UnitsError):
lu1 / unit
lu2 = u.mag(u.dimensionless_unscaled)
with pytest.raises(u.UnitsError):
lu2 * lu1
with pytest.raises(u.UnitsError):
lu2 / lu1
# But dimensionless_unscaled can be cancelled.
assert lu2 / lu2 == u.dimensionless_unscaled
# With dimensionless, normal units are OK, but we return a plain unit.
tf = lu2 * u.m
tr = u.m * lu2
for t in (tf, tr):
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit * u.m
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(lu2.physical_unit)
# Now we essentially have a LogUnit with a prefactor of 100,
# so should be equivalent again.
t = tf / u.cm
with u.set_enabled_equivalencies(u.logarithmic()):
assert t.is_equivalent(lu2.function_unit)
assert_allclose(t.to(u.dimensionless_unscaled, np.arange(3.)/100.),
lu2.to(lu2.physical_unit, np.arange(3.)))
# If we effectively remove lu1, a normal unit should be returned.
t2 = tf / lu2
assert not isinstance(t2, type(lu2))
assert t2 == u.m
t3 = tf / lu2.function_unit
assert not isinstance(t3, type(lu2))
assert t3 == u.m
# For completeness, also ensure non-sensical operations fail
with pytest.raises(TypeError):
lu1 * object()
with pytest.raises(TypeError):
slice(None) * lu1
with pytest.raises(TypeError):
lu1 / []
with pytest.raises(TypeError):
1 / lu1
@pytest.mark.parametrize('power', (2, 0.5, 1, 0))
def test_raise_to_power(self, power):
"""Check that raising LogUnits to some power is only possible when the
physical unit is dimensionless, and that conversion is turned off when
the resulting logarithmic unit (such as mag**2) is incompatible."""
lu1 = u.mag(u.Jy)
if power == 0:
assert lu1 ** power == u.dimensionless_unscaled
elif power == 1:
assert lu1 ** power == lu1
else:
with pytest.raises(u.UnitsError):
lu1 ** power
# With dimensionless, though, it works, but returns a normal unit.
lu2 = u.mag(u.dimensionless_unscaled)
t = lu2**power
if power == 0:
assert t == u.dimensionless_unscaled
elif power == 1:
assert t == lu2
else:
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit**power
# also check we roundtrip
t2 = t**(1./power)
assert t2 == lu2.function_unit
with u.set_enabled_equivalencies(u.logarithmic()):
assert_allclose(t2.to(u.dimensionless_unscaled, np.arange(3.)),
lu2.to(lu2.physical_unit, np.arange(3.)))
@pytest.mark.parametrize('other', pu_sample)
def test_addition_subtraction_to_normal_units_fails(self, other):
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 + other
with pytest.raises(u.UnitsError):
lu1 - other
with pytest.raises(u.UnitsError):
other - lu1
def test_addition_subtraction_to_non_units_fails(self):
lu1 = u.mag(u.Jy)
with pytest.raises(TypeError):
lu1 + 1.
with pytest.raises(TypeError):
lu1 - [1., 2., 3.]
@pytest.mark.parametrize(
'other', (u.mag, u.mag(), u.mag(u.Jy), u.mag(u.m),
u.Unit(2*u.mag), u.MagUnit('', 2.*u.mag)))
def test_addition_subtraction(self, other):
"""Check physical units are changed appropriately"""
lu1 = u.mag(u.Jy)
other_pu = getattr(other, 'physical_unit', u.dimensionless_unscaled)
lu_sf = lu1 + other
assert lu_sf.is_equivalent(lu1.physical_unit * other_pu)
lu_sr = other + lu1
assert lu_sr.is_equivalent(lu1.physical_unit * other_pu)
lu_df = lu1 - other
assert lu_df.is_equivalent(lu1.physical_unit / other_pu)
lu_dr = other - lu1
assert lu_dr.is_equivalent(other_pu / lu1.physical_unit)
def test_complicated_addition_subtraction(self):
"""for fun, a more complicated example of addition and subtraction"""
dm0 = u.Unit('DM', 1./(4.*np.pi*(10.*u.pc)**2))
lu_dm = u.mag(dm0)
lu_absST = u.STmag - lu_dm
assert lu_absST.is_equivalent(u.erg/u.s/u.AA)
def test_neg_pos(self):
lu1 = u.mag(u.Jy)
neg_lu = -lu1
assert neg_lu != lu1
assert neg_lu.physical_unit == u.Jy**-1
assert -neg_lu == lu1
pos_lu = +lu1
assert pos_lu is not lu1
assert pos_lu == lu1
def test_pickle():
lu1 = u.dex(u.cm/u.s**2)
s = pickle.dumps(lu1)
lu2 = pickle.loads(s)
assert lu1 == lu2
def test_hashable():
lu1 = u.dB(u.mW)
lu2 = u.dB(u.m)
lu3 = u.dB(u.mW)
assert hash(lu1) != hash(lu2)
assert hash(lu1) == hash(lu3)
luset = {lu1, lu2, lu3}
assert len(luset) == 2
class TestLogQuantityCreation(object):
@pytest.mark.parametrize('lq, lu', zip(lq_subclasses + [u.LogQuantity],
lu_subclasses + [u.LogUnit]))
def test_logarithmic_quantities(self, lq, lu):
"""Check logarithmic quantities are all set up correctly"""
assert lq._unit_class == lu
assert type(lu()._quantity_class(1.)) is lq
@pytest.mark.parametrize('lq_cls, physical_unit',
itertools.product(lq_subclasses, pu_sample))
def test_subclass_creation(self, lq_cls, physical_unit):
"""Create LogQuantity subclass objects for some physical units,
and basic check on transformations"""
value = np.arange(1., 10.)
log_q = lq_cls(value * physical_unit)
assert log_q.unit.physical_unit == physical_unit
assert log_q.unit.function_unit == log_q.unit._default_function_unit
assert_allclose(log_q.physical.value, value)
with pytest.raises(ValueError):
lq_cls(value, physical_unit)
@pytest.mark.parametrize(
'unit', (u.mag, u.mag(), u.mag(u.Jy), u.mag(u.m),
u.Unit(2*u.mag), u.MagUnit('', 2.*u.mag),
u.MagUnit(u.Jy, -1*u.mag), u.MagUnit(u.m, -2.*u.mag)))
def test_different_units(self, unit):
q = u.Magnitude(1.23, unit)
assert q.unit.function_unit == getattr(unit, 'function_unit', unit)
assert q.unit.physical_unit is getattr(unit, 'physical_unit',
u.dimensionless_unscaled)
@pytest.mark.parametrize('value, unit', (
(1.*u.mag(u.Jy), None),
(1.*u.dex(u.Jy), None),
(1.*u.mag(u.W/u.m**2/u.Hz), u.mag(u.Jy)),
(1.*u.dex(u.W/u.m**2/u.Hz), u.mag(u.Jy))))
def test_function_values(self, value, unit):
lq = u.Magnitude(value, unit)
assert lq == value
assert lq.unit.function_unit == u.mag
assert lq.unit.physical_unit == getattr(unit, 'physical_unit',
value.unit.physical_unit)
@pytest.mark.parametrize(
'unit', (u.mag(), u.mag(u.Jy), u.mag(u.m), u.MagUnit('', 2.*u.mag),
u.MagUnit(u.Jy, -1*u.mag), u.MagUnit(u.m, -2.*u.mag)))
def test_indirect_creation(self, unit):
q1 = 2.5 * unit
assert isinstance(q1, u.Magnitude)
assert q1.value == 2.5
assert q1.unit == unit
pv = 100. * unit.physical_unit
q2 = unit * pv
assert q2.unit == unit
assert q2.unit.physical_unit == pv.unit
assert q2.to_value(unit.physical_unit) == 100.
assert (q2._function_view / u.mag).to_value(1) == -5.
q3 = unit / 0.4
assert q3 == q1
def test_from_view(self):
# Cannot view a physical quantity as a function quantity, since the
# values would change.
q = [100., 1000.] * u.cm/u.s**2
with pytest.raises(TypeError):
q.view(u.Dex)
# But fine if we have the right magnitude.
q = [2., 3.] * u.dex
lq = q.view(u.Dex)
assert isinstance(lq, u.Dex)
assert lq.unit.physical_unit == u.dimensionless_unscaled
assert np.all(q == lq)
def test_using_quantity_class(self):
"""Check that we can use Quantity if we have subok=True"""
# following issue #5851
lu = u.dex(u.AA)
with pytest.raises(u.UnitTypeError):
u.Quantity(1., lu)
q = u.Quantity(1., lu, subok=True)
assert type(q) is lu._quantity_class
def test_conversion_to_and_from_physical_quantities():
"""Ensures we can convert from regular quantities."""
mst = [10., 12., 14.] * u.STmag
flux_lambda = mst.physical
mst_roundtrip = flux_lambda.to(u.STmag)
# check we return a logquantity; see #5178.
assert isinstance(mst_roundtrip, u.Magnitude)
assert mst_roundtrip.unit == mst.unit
assert_allclose(mst_roundtrip.value, mst.value)
wave = [4956.8, 4959.55, 4962.3] * u.AA
flux_nu = mst.to(u.Jy, equivalencies=u.spectral_density(wave))
mst_roundtrip2 = flux_nu.to(u.STmag, u.spectral_density(wave))
assert isinstance(mst_roundtrip2, u.Magnitude)
assert mst_roundtrip2.unit == mst.unit
assert_allclose(mst_roundtrip2.value, mst.value)
def test_quantity_decomposition():
lq = 10.*u.mag(u.Jy)
assert lq.decompose() == lq
assert lq.decompose().unit.physical_unit.bases == [u.kg, u.s]
assert lq.si == lq
assert lq.si.unit.physical_unit.bases == [u.kg, u.s]
assert lq.cgs == lq
assert lq.cgs.unit.physical_unit.bases == [u.g, u.s]
class TestLogQuantityViews(object):
def setup(self):
self.lq = u.Magnitude(np.arange(10.) * u.Jy)
self.lq2 = u.Magnitude(np.arange(5.))
def test_value_view(self):
lq_value = self.lq.value
assert type(lq_value) is np.ndarray
lq_value[2] = -1.
assert np.all(self.lq.value == lq_value)
def test_function_view(self):
lq_fv = self.lq._function_view
assert type(lq_fv) is u.Quantity
assert lq_fv.unit is self.lq.unit.function_unit
lq_fv[3] = -2. * lq_fv.unit
assert np.all(self.lq.value == lq_fv.value)
def test_quantity_view(self):
# Cannot view as Quantity, since the unit cannot be represented.
with pytest.raises(TypeError):
self.lq.view(u.Quantity)
# But a dimensionless one is fine.
q2 = self.lq2.view(u.Quantity)
assert q2.unit is u.mag
assert np.all(q2.value == self.lq2.value)
lq3 = q2.view(u.Magnitude)
assert type(lq3.unit) is u.MagUnit
assert lq3.unit.physical_unit == u.dimensionless_unscaled
assert np.all(lq3 == self.lq2)
class TestLogQuantitySlicing(object):
def test_item_get_and_set(self):
lq1 = u.Magnitude(np.arange(1., 11.)*u.Jy)
assert lq1[9] == u.Magnitude(10.*u.Jy)
lq1[2] = 100.*u.Jy
assert lq1[2] == u.Magnitude(100.*u.Jy)
with pytest.raises(u.UnitsError):
lq1[2] = 100.*u.m
with pytest.raises(u.UnitsError):
lq1[2] = 100.*u.mag
with pytest.raises(u.UnitsError):
lq1[2] = u.Magnitude(100.*u.m)
assert lq1[2] == u.Magnitude(100.*u.Jy)
def test_slice_get_and_set(self):
lq1 = u.Magnitude(np.arange(1., 10.)*u.Jy)
lq1[2:4] = 100.*u.Jy
assert np.all(lq1[2:4] == u.Magnitude(100.*u.Jy))
with pytest.raises(u.UnitsError):
lq1[2:4] = 100.*u.m
with pytest.raises(u.UnitsError):
lq1[2:4] = 100.*u.mag
with pytest.raises(u.UnitsError):
lq1[2:4] = u.Magnitude(100.*u.m)
assert np.all(lq1[2] == u.Magnitude(100.*u.Jy))
class TestLogQuantityArithmetic(object):
def test_multiplication_division(self):
"""Check that multiplication/division with other quantities is only
possible when the physical unit is dimensionless, and that this turns
the result into a normal quantity."""
lq = u.Magnitude(np.arange(1., 11.)*u.Jy)
with pytest.raises(u.UnitsError):
lq * (1.*u.m)
with pytest.raises(u.UnitsError):
(1.*u.m) * lq
with pytest.raises(u.UnitsError):
lq / lq
for unit in (u.m, u.mag, u.dex):
with pytest.raises(u.UnitsError):
lq / unit
lq2 = u.Magnitude(np.arange(1, 11.))
with pytest.raises(u.UnitsError):
lq2 * lq
with pytest.raises(u.UnitsError):
lq2 / lq
with pytest.raises(u.UnitsError):
lq / lq2
# but dimensionless_unscaled can be cancelled
r = lq2 / u.Magnitude(2.)
assert r.unit == u.dimensionless_unscaled
assert np.all(r.value == lq2.value/2.)
# with dimensionless, normal units OK, but return normal quantities
tf = lq2 * u.m
tr = u.m * lq2
for t in (tf, tr):
assert not isinstance(t, type(lq2))
assert t.unit == lq2.unit.function_unit * u.m
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(lq2.unit.physical_unit)
t = tf / (50.*u.cm)
# now we essentially have the same quantity but with a prefactor of 2
assert t.unit.is_equivalent(lq2.unit.function_unit)
assert_allclose(t.to(lq2.unit.function_unit), lq2._function_view*2)
@pytest.mark.parametrize('power', (2, 0.5, 1, 0))
def test_raise_to_power(self, power):
"""Check that raising LogQuantities to some power is only possible when
the physical unit is dimensionless, and that conversion is turned off
when the resulting logarithmic unit (say, mag**2) is incompatible."""
lq = u.Magnitude(np.arange(1., 4.)*u.Jy)
if power == 0:
assert np.all(lq ** power == 1.)
elif power == 1:
assert np.all(lq ** power == lq)
else:
with pytest.raises(u.UnitsError):
lq ** power
# with dimensionless, it works, but falls back to normal quantity
# (except for power=1)
lq2 = u.Magnitude(np.arange(10.))
t = lq2**power
if power == 0:
assert t.unit is u.dimensionless_unscaled
assert np.all(t.value == 1.)
elif power == 1:
assert np.all(t == lq2)
else:
assert not isinstance(t, type(lq2))
assert t.unit == lq2.unit.function_unit ** power
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(u.dimensionless_unscaled)
def test_error_on_lq_as_power(self):
lq = u.Magnitude(np.arange(1., 4.)*u.Jy)
with pytest.raises(TypeError):
lq ** lq
@pytest.mark.parametrize('other', pu_sample)
def test_addition_subtraction_to_normal_units_fails(self, other):
lq = u.Magnitude(np.arange(1., 10.)*u.Jy)
q = 1.23 * other
with pytest.raises(u.UnitsError):
lq + q
with pytest.raises(u.UnitsError):
lq - q
with pytest.raises(u.UnitsError):
q - lq
@pytest.mark.parametrize(
'other', (1.23 * u.mag, 2.34 * u.mag(),
u.Magnitude(3.45 * u.Jy), u.Magnitude(4.56 * u.m),
5.67 * u.Unit(2*u.mag), u.Magnitude(6.78, 2.*u.mag)))
def test_addition_subtraction(self, other):
"""Check that addition/subtraction with quantities with magnitude or
MagUnit units works, and that it changes the physical units
appropriately."""
lq = u.Magnitude(np.arange(1., 10.)*u.Jy)
other_physical = other.to(getattr(other.unit, 'physical_unit',
u.dimensionless_unscaled),
equivalencies=u.logarithmic())
lq_sf = lq + other
assert_allclose(lq_sf.physical, lq.physical * other_physical)
lq_sr = other + lq
assert_allclose(lq_sr.physical, lq.physical * other_physical)
lq_df = lq - other
assert_allclose(lq_df.physical, lq.physical / other_physical)
lq_dr = other - lq
assert_allclose(lq_dr.physical, other_physical / lq.physical)
@pytest.mark.parametrize('other', pu_sample)
def test_inplace_addition_subtraction_unit_checks(self, other):
lu1 = u.mag(u.Jy)
lq1 = u.Magnitude(np.arange(1., 10.), lu1)
with pytest.raises(u.UnitsError):
lq1 += other
assert np.all(lq1.value == np.arange(1., 10.))
assert lq1.unit == lu1
with pytest.raises(u.UnitsError):
lq1 -= other
assert np.all(lq1.value == np.arange(1., 10.))
assert lq1.unit == lu1
@pytest.mark.parametrize(
'other', (1.23 * u.mag, 2.34 * u.mag(),
u.Magnitude(3.45 * u.Jy), u.Magnitude(4.56 * u.m),
5.67 * u.Unit(2*u.mag), u.Magnitude(6.78, 2.*u.mag)))
def test_inplace_addition_subtraction(self, other):
"""Check that inplace addition/subtraction with quantities with
magnitude or MagUnit units works, and that it changes the physical
units appropriately."""
lq = u.Magnitude(np.arange(1., 10.)*u.Jy)
other_physical = other.to(getattr(other.unit, 'physical_unit',
u.dimensionless_unscaled),
equivalencies=u.logarithmic())
lq_sf = lq.copy()
lq_sf += other
assert_allclose(lq_sf.physical, lq.physical * other_physical)
lq_df = lq.copy()
lq_df -= other
assert_allclose(lq_df.physical, lq.physical / other_physical)
def test_complicated_addition_subtraction(self):
"""For fun, a more complicated example of addition and subtraction."""
dm0 = u.Unit('DM', 1./(4.*np.pi*(10.*u.pc)**2))
DMmag = u.mag(dm0)
m_st = 10. * u.STmag
dm = 5. * DMmag
M_st = m_st - dm
assert M_st.unit.is_equivalent(u.erg/u.s/u.AA)
assert np.abs(M_st.physical /
(m_st.physical*4.*np.pi*(100.*u.pc)**2) - 1.) < 1.e-15
class TestLogQuantityComparisons(object):
def test_comparison_to_non_quantities_fails(self):
lq = u.Magnitude(np.arange(1., 10.)*u.Jy)
# On python2, ordering operations always succeed, given essentially
# meaningless results.
if not six.PY2:
with pytest.raises(TypeError):
lq > 'a'
assert not (lq == 'a')
assert lq != 'a'
def test_comparison(self):
lq1 = u.Magnitude(np.arange(1., 4.)*u.Jy)
lq2 = u.Magnitude(2.*u.Jy)
assert np.all((lq1 > lq2) == np.array([True, False, False]))
assert np.all((lq1 == lq2) == np.array([False, True, False]))
lq3 = u.Dex(2.*u.Jy)
assert np.all((lq1 > lq3) == np.array([True, False, False]))
assert np.all((lq1 == lq3) == np.array([False, True, False]))
lq4 = u.Magnitude(2.*u.m)
assert not (lq1 == lq4)
assert lq1 != lq4
with pytest.raises(u.UnitsError):
lq1 < lq4
q5 = 1.5 * u.Jy
assert np.all((lq1 > q5) == np.array([True, False, False]))
assert np.all((q5 < lq1) == np.array([True, False, False]))
with pytest.raises(u.UnitsError):
lq1 >= 2.*u.m
with pytest.raises(u.UnitsError):
lq1 <= lq1.value * u.mag
# For physically dimensionless, we can compare with the function unit.
lq6 = u.Magnitude(np.arange(1., 4.))
fv6 = lq6.value * u.mag
assert np.all(lq6 == fv6)
# but not some arbitrary unit, of course.
with pytest.raises(u.UnitsError):
lq6 < 2.*u.m
class TestLogQuantityMethods(object):
def setup(self):
self.mJy = np.arange(1., 5.).reshape(2, 2) * u.mag(u.Jy)
self.m1 = np.arange(1., 5.5, 0.5).reshape(3, 3) * u.mag()
self.mags = (self.mJy, self.m1)
@pytest.mark.parametrize('method', ('mean', 'min', 'max', 'round', 'trace',
'std', 'var', 'ptp', 'diff', 'ediff1d'))
def test_always_ok(self, method):
for mag in self.mags:
res = getattr(mag, method)()
assert np.all(res.value ==
getattr(mag._function_view, method)().value)
if method in ('std', 'ptp', 'diff', 'ediff1d'):
assert res.unit == u.mag()
elif method == 'var':
assert res.unit == u.mag**2
else:
assert res.unit == mag.unit
def test_clip(self):
for mag in self.mags:
assert np.all(mag.clip(2. * mag.unit, 4. * mag.unit).value ==
mag.value.clip(2., 4.))
@pytest.mark.parametrize('method', ('sum', 'cumsum', 'nansum'))
def test_only_ok_if_dimensionless(self, method):
res = getattr(self.m1, method)()
assert np.all(res.value ==
getattr(self.m1._function_view, method)().value)
assert res.unit == self.m1.unit
with pytest.raises(TypeError):
getattr(self.mJy, method)()
def test_dot(self):
assert np.all(self.m1.dot(self.m1).value ==
self.m1.value.dot(self.m1.value))
@pytest.mark.parametrize('method', ('prod', 'cumprod'))
def test_never_ok(self, method):
with pytest.raises(ValueError):
getattr(self.mJy, method)()
with pytest.raises(ValueError):
getattr(self.m1, method)()
class TestLogQuantityUfuncs(object):
"""Spot checks on ufuncs."""
def setup(self):
self.mJy = np.arange(1., 5.).reshape(2, 2) * u.mag(u.Jy)
self.m1 = | np.arange(1., 5.5, 0.5) | numpy.arange |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
from external.transformations import translation_matrix, rotation_matrix
import external.transformations as tf
from Trace import Photon
from Geometry import Box, Cylinder, FinitePlane, transform_point, transform_direction, rotation_matrix_from_vector_alignment, norm
from Materials import Spectrum
def random_spherecial_vector():
# This method of calculating isotropic vectors is taken from GNU Scientific Library
LOOP = True
while LOOP:
x = -1. + 2. * np.random.uniform()
y = -1. + 2. * np.random.uniform()
s = x**2 + y**2
if s <= 1.0:
LOOP = False
z = -1. + 2. * s
a = 2 * np.sqrt(1 - s)
x = a * x
y = a * y
return np.array([x,y,z])
class SimpleSource(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, use_random_polarisation=False):
super(SimpleSource, self).__init__()
self.position = position
self.direction = direction
self.wavelength = wavelength
self.use_random_polarisation = use_random_polarisation
self.throw = 0
self.source_id = "SimpleSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
# If use_polarisation is set generate a random polarisation vector of the photon
if self.use_random_polarisation:
# Randomise rotation angle around xy-plane, the transform from +z to the direction of the photon
vec = random_spherecial_vector()
vec[2] = 0.
vec = norm(vec)
R = rotation_matrix_from_vector_alignment(self.direction, [0,0,1])
photon.polarisation = transform_direction(vec, R)
else:
photon.polarisation = None
photon.id = self.throw
self.throw = self.throw + 1
return photon
class Laser(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, polarisation=None):
super(Laser, self).__init__()
self.position = | np.array(position) | numpy.array |
# -*- coding: utf-8 -*-
"""
Script to execute example covarying MMGP regression forecasting model
with full Krhh.
Inputs: Data training and test sets (dictionary pickle)
Data for example:
- normalised solar data for 25 sites for 15 minute forecast
- N_train = 4200, N_test = 2276, P = 25, D = 51
- Xtr[:, :50] 2 recent lagged observations for each site in order
- Xtr[:, 50] time index
- link inputs is a 25x2 array (link inputs repeated for every group)
with normalised lat,long for each site in order
Model Options:
- Sparse or full x-function covariance prior Krhh (set bool SPARSE_PRIOR)
- Diagonal or Kronecker-structured variational posterior covariance Sr (set bool DIAG_POST)
- Sparse or full posterior covariance (when Kronecker posterior; set bool SPARSE_POST)
Current Settings (sparse covarying mmgp model with sparse Kronecker posterior):
DIAG_POST = False
SPARSE_PRIOR = False # set True for equivalent sparse scmmgp model
SPARSE_POST = True
Note on specifying group structure for F:
Grouping occurs via block_struct, a nested list of grouping order
Where functions [i] are independent i.e. in own block, set link_kernel[i] = link_inputs[i] = 1.0
See model class preamble and example below for further details.
"""
import os
import numpy as np
import pickle
import pandas as pd
import traceback
import time
import sklearn.cluster
import csv
import sys
import mmgp
from mmgp import likelihoods
from mmgp import kernels
import tensorflow as tf
from mmgp import datasets
from mmgp import losses
from mmgp import util
dpath = '/experiments/datasets/'
dfile = 'p25_inputsdict.pickle'
dlinkfile = 'p25_linkinputsarray.pickle'
outdir = '/experiments/results/p25_nonsparse_cmmgp/'
try:
os.makedirs(outdir)
except FileExistsError:
pass
def get_inputs():
"""
inputsdict contains {'Yte': Yte, 'Ytr': Ytr, 'Xtr': Xtr, 'Xte': Xte} where values are np.arrays
np. arrays are truncated to evenly split into batches of size = batchsize
returns inputsdict, Xtr_link (ndarray, shape = [P, D_link_features])
"""
with open(os.path.join(dpath, dfile), 'rb') as f:
d_all = pickle.load(f)
with open(os.path.join(dpath, dlinkfile), 'rb') as f:
d_link = pickle.load(f)
return d_all, d_link
def init_z(train_inputs, num_inducing):
# Initialize inducing points using clustering.
mini_batch = sklearn.cluster.MiniBatchKMeans(num_inducing)
cluster_indices = mini_batch.fit_predict(train_inputs)
inducing_locations = mini_batch.cluster_centers_
return inducing_locations
FLAGS = util.util.get_flags()
BATCH_SIZE = FLAGS.batch_size
LEARNING_RATE = FLAGS.learning_rate
DISPLAY_STEP = FLAGS.display_step
EPOCHS = FLAGS.n_epochs
NUM_SAMPLES = FLAGS.mc_train
PRED_SAMPLES = FLAGS.mc_test
NUM_INDUCING = FLAGS.n_inducing
NUM_COMPONENTS = FLAGS.num_components
IS_ARD = FLAGS.is_ard
TOL = FLAGS.opt_tol
VAR_STEPS = FLAGS.var_steps
DIAG_POST = False
SPARSE_PRIOR = False
SPARSE_POST = True # option for non-diag post
MAXTIME = 1200
print("settings done")
# define GPRN P and Q
output_dim = 25 #P
node_dim = 25 #Q
lag_dim = 2
save_nlpds = False # If True saves samples of nlpds for n,p,s
# extract dataset
d, d_link = get_inputs()
Ytr, Yte, Xtr, Xte = d['Ytr'], d['Yte'], d['Xtr'], d['Xte']
data = datasets.DataSet(Xtr.astype(np.float32), Ytr.astype(np.float32), shuffle=False)
test = datasets.DataSet(Xte.astype(np.float32), Yte.astype(np.float32), shuffle=False)
print("dataset created")
# model config block rows (where P=Q): block all w.1, w.2 etc, leave f independent
# order of block_struct is rows, node functions
# lists required: block_struct, link_inputs, kern_link, kern
#block_struct nested list of grouping order
weight_struct = [[] for _ in range(output_dim)]
for i in range(output_dim):
row = list(range(i, i+output_dim*(node_dim-1)+1, output_dim))
row_0 = row.pop(i) # bring diag to pivot position
weight_struct[i] = [row_0] + row
nodes = [[x] for x in list(range(output_dim * node_dim, output_dim * node_dim + output_dim))]
block_struct = weight_struct + nodes
# create link inputs (link inputs used repeatedly but can have link input per group)
# permute to bring diagonal to first position
link_inputs = [[] for _ in range(output_dim)]
for i in range(output_dim):
idx = list(range(d_link.shape[0]))
link_inputs[i] = d_link[[idx.pop(i)] + idx, :]
link_inputs = link_inputs + [1.0 for i in range(output_dim)] # for full W row blocks, independent nodes
# create 'between' kernel list
klink_rows = [kernels.CompositeKernel('mul',[kernels.RadialBasis(2, std_dev=2.0, lengthscale=1.0, white=0.01, input_scaling = IS_ARD),
kernels.CompactSlice(2, active_dims=[0,1], lengthscale = 2.0, input_scaling = IS_ARD)] )
for i in range(output_dim) ]
klink_f = [1.0 for i in range(node_dim)]
kernlink = klink_rows + klink_f
# create 'within' kernel
# kern
lag_active_dims_s = [ [] for _ in range(output_dim)]
for i in range(output_dim):
lag_active_dims_s[i] = list(range(lag_dim*i, lag_dim*(i+1)))
k_rows = [kernels.CompositeKernel('mul',[kernels.RadialBasisSlice(lag_dim, active_dims=lag_active_dims_s[i],
std_dev = 1.0, white = 0.01, input_scaling = IS_ARD),
kernels.PeriodicSliceFixed(1, active_dims=[Xtr.shape[1]-1],
lengthscale=0.5, std_dev=1.0, period = 144) ])
for i in range(output_dim)]
k_f = [kernels.RadialBasisSlice(lag_dim, active_dims=lag_active_dims_s[i], std_dev = 1.0, white = 0.01, input_scaling = IS_ARD)
for i in range(output_dim)]
kern = k_rows + k_f
print('len link_inputs ',len(link_inputs))
print('len kernlink ',len(kernlink))
print('len kern ', len(kern))
print('no. groups = ', len(block_struct), 'no. latent functions =', len([i for b in block_struct for i in b]))
print('number latent functions', node_dim*(output_dim+1))
likelihood = likelihoods.CovaryingRegressionNetwork(output_dim, node_dim, std_dev = 0.2) # p, q, lik_noise
print("likelihood and kernels set")
Z = init_z(data.X, NUM_INDUCING)
print('inducing points set')
m = mmgp.ExplicitSCMMGP(output_dim, likelihood, kern, kernlink, block_struct, Z, link_inputs,
num_components=NUM_COMPONENTS, diag_post=DIAG_POST, sparse_prior=SPARSE_PRIOR,
sparse_post=SPARSE_POST, num_samples=NUM_SAMPLES, predict_samples=PRED_SAMPLES)
print("model set")
# initialise losses and logging
error_rate = losses.RootMeanSqError(data.Dout)
os.chdir(outdir)
with open("log_results.csv", 'w', newline='') as f:
csv.writer(f).writerow(['epoch', 'fit_runtime', 'nelbo', error_rate.get_name(),'generalised_nlpd'])
with open("log_params.csv", 'w', newline='') as f:
csv.writer(f).writerow(['epoch', 'raw_kernel_params', 'raw_kernlink_params', 'raw_likelihood_params', 'raw_weights'])
with open("log_comp_time.csv", 'w', newline='') as f:
csv.writer(f).writerow(['epoch', 'batch_time', 'nelbo_time', 'pred_time', 'gen_nlpd_time', error_rate.get_name()+'_time'])
# optimise
o = tf.train.AdamOptimizer(LEARNING_RATE, beta1=0.9,beta2=0.99)
print("start time = ", time.strftime('%X %x %Z'))
m.fit(data, o, var_steps = VAR_STEPS, epochs = EPOCHS, batch_size = BATCH_SIZE, display_step=DISPLAY_STEP,
test = test, loss = error_rate, tolerance = TOL, max_time=MAXTIME )
print("optimisation complete")
# export final predicted values and loss metrics
ypred = m.predict(test.X, batch_size = BATCH_SIZE) #same batchsize used for convenience
np.savetxt("predictions.csv", np.concatenate(ypred, axis=1), delimiter=",")
if save_nlpds == True:
nlpd_samples, nlpd_meanvar = m.nlpd_samples(test.X, test.Y, batch_size = BATCH_SIZE)
try:
np.savetxt("nlpd_meanvar.csv", nlpd_meanvar, delimiter=",") # N x 2P as for predictions
except:
print('nlpd_meanvar export fail')
try:
| np.savetxt("nlpd_samples.csv", nlpd_samples, delimiter=",") | numpy.savetxt |
"""Bindings for the Barnes Hut TSNE algorithm with fast nearest neighbors
Refs:
References
[1] <NAME>, L.J.P.; Hinton, G.E. Visualizing High-Dimensional Data
Using t-SNE. Journal of Machine Learning Research 9:2579-2605, 2008.
[2] <NAME>, L.J.P. t-Distributed Stochastic Neighbor Embedding
http://homepage.tudelft.nl/19j49/t-SNE.html
"""
import numpy as N
import ctypes
import os
import pkg_resources
def ord_string(s):
b = bytearray()
arr = b.extend(map(ord, s))
return N.array([x for x in b] + [0]).astype(N.uint8)
class TSNE(object):
def __init__(self,
n_components=2,
perplexity=50.0,
early_exaggeration=2.0,
learning_rate=200.0,
num_neighbors=1023,
force_magnify_iters=250,
pre_momentum=0.5,
post_momentum=0.8,
theta=0.5,
epssq=0.0025,
n_iter=1000,
n_iter_without_progress=1000,
min_grad_norm=1e-7,
perplexity_epsilon=1e-3,
metric='euclidean',
init='random',
return_style='once',
num_snapshots=5,
verbose=0,
random_seed=None,
use_interactive=False,
viz_timeout=10000,
viz_server="tcp://localhost:5556",
dump_points=False,
dump_file="dump.txt",
dump_interval=1,
print_interval=10,
device=0,
):
"""Initialization method for barnes hut T-SNE class.
"""
# Initialize the variables
self.n_components = int(n_components)
if self.n_components != 2:
raise ValueError('The current barnes-hut implementation does not support projection into dimensions other than 2 for now.')
self.perplexity = float(perplexity)
self.early_exaggeration = float(early_exaggeration)
self.learning_rate = float(learning_rate)
self.n_iter = int(n_iter)
self.n_iter_without_progress = int(n_iter_without_progress)
self.min_grad_norm = float(min_grad_norm)
if metric not in ['euclidean']:
raise ValueError('Non-Euclidean metrics are not currently supported. Please use metric=\'euclidean\' for now.')
else:
self.metric = metric
if init not in ['random']:
raise ValueError('Non-Random initialization is not currently supported. Please use init=\'random\' for now.')
else:
self.init = init
self.verbose = int(verbose)
# Initialize non-sklearn variables
self.num_neighbors = int(num_neighbors)
self.force_magnify_iters = int(force_magnify_iters)
self.perplexity_epsilon = float(perplexity_epsilon)
self.pre_momentum = float(pre_momentum)
self.post_momentum = float(post_momentum)
self.theta = float(theta)
self.epssq =float(epssq)
self.device = int(device)
self.print_interval = int(print_interval)
# Point dumpoing
self.dump_file = str(dump_file)
self.dump_points = bool(dump_points)
self.dump_interval = int(dump_interval)
# Viz
self.use_interactive = bool(use_interactive)
self.viz_server = str(viz_server)
self.viz_timeout = int(viz_timeout)
# Return style
if return_style not in ['once','snapshots']:
raise ValueError('Invalid return style...')
elif return_style == 'once':
self.return_style = 0
elif return_style == 'snapshots':
self.return_style = 1
self.num_snapshots = int(num_snapshots)
# Build the hooks for the BH T-SNE library
self._path = pkg_resources.resource_filename('tsnecuda','') # Load from current location
# self._faiss_lib = N.ctypeslib.load_library('libfaiss', self._path) # Load the ctypes library
# self._gpufaiss_lib = N.ctypeslib.load_library('libgpufaiss', self._path) # Load the ctypes library
self._lib = N.ctypeslib.load_library('libtsnecuda', self._path) # Load the ctypes library
# Hook the BH T-SNE function
self._lib.pymodule_bh_tsne.restype = None
self._lib.pymodule_bh_tsne.argtypes = [
N.ctypeslib.ndpointer(N.float32, ndim=2, flags='ALIGNED, F_CONTIGUOUS, WRITEABLE'), # result
N.ctypeslib.ndpointer(N.float32, ndim=2, flags='ALIGNED, CONTIGUOUS'), # points
ctypes.POINTER(N.ctypeslib.c_intp), # dims
ctypes.c_float, # Perplexity
ctypes.c_float, # Learning Rate
ctypes.c_float, # Magnitude Factor
ctypes.c_int, # Num Neighbors
ctypes.c_int, # Iterations
ctypes.c_int, # Iterations no progress
ctypes.c_int, # Force Magnify iterations
ctypes.c_float, # Perplexity search epsilon
ctypes.c_float, # pre-exaggeration momentum
ctypes.c_float, # post-exaggeration momentum
ctypes.c_float, # Theta
ctypes.c_float, # epssq
ctypes.c_float, # Minimum gradient norm
ctypes.c_int, # Initialization types
N.ctypeslib.ndpointer(N.float32, ndim=2, flags='ALIGNED, F_CONTIGUOUS'), # Initialization Data
ctypes.c_bool, # Dump points
N.ctypeslib.ndpointer(N.uint8, flags='ALIGNED, CONTIGUOUS'), # Dump File
ctypes.c_int, # Dump interval
ctypes.c_bool, # Use interactive
| N.ctypeslib.ndpointer(N.uint8, flags='ALIGNED, CONTIGUOUS') | numpy.ctypeslib.ndpointer |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * | np.ones(100) | numpy.ones |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
from external.transformations import translation_matrix, rotation_matrix
import external.transformations as tf
from Trace import Photon
from Geometry import Box, Cylinder, FinitePlane, transform_point, transform_direction, rotation_matrix_from_vector_alignment, norm
from Materials import Spectrum
def random_spherecial_vector():
# This method of calculating isotropic vectors is taken from GNU Scientific Library
LOOP = True
while LOOP:
x = -1. + 2. * np.random.uniform()
y = -1. + 2. * np.random.uniform()
s = x**2 + y**2
if s <= 1.0:
LOOP = False
z = -1. + 2. * s
a = 2 * np.sqrt(1 - s)
x = a * x
y = a * y
return np.array([x,y,z])
class SimpleSource(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, use_random_polarisation=False):
super(SimpleSource, self).__init__()
self.position = position
self.direction = direction
self.wavelength = wavelength
self.use_random_polarisation = use_random_polarisation
self.throw = 0
self.source_id = "SimpleSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
# If use_polarisation is set generate a random polarisation vector of the photon
if self.use_random_polarisation:
# Randomise rotation angle around xy-plane, the transform from +z to the direction of the photon
vec = random_spherecial_vector()
vec[2] = 0.
vec = norm(vec)
R = rotation_matrix_from_vector_alignment(self.direction, [0,0,1])
photon.polarisation = transform_direction(vec, R)
else:
photon.polarisation = None
photon.id = self.throw
self.throw = self.throw + 1
return photon
class Laser(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, polarisation=None):
super(Laser, self).__init__()
self.position = np.array(position)
self.direction = np.array(direction)
self.wavelength = wavelength
assert polarisation != None, "Polarisation of the Laser is not set."
self.polarisation = np.array(polarisation)
self.throw = 0
self.source_id = "LaserSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = | np.array(self.direction) | numpy.array |
import numpy
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
from src.support import support
class PhraseManager:
def __init__(self, configuration):
self.train_phrases, self.train_labels = self._read_train_phrases()
self.test_phrases, self.test_labels = self._read_test_phrases()
self.configuration = configuration
self.tokenizer = None
def get_phrases_train(self):
return self.train_phrases, self.train_labels
def get_phrases_test(self):
return self.test_phrases, self.test_labels
def get_dataset(self, level = None):
if level == support.WORD_LEVEL:
return self._word_process(self.configuration[support.WORD_MAX_LENGTH])
elif level == support.CHAR_LEVEL:
return self._char_process(self.configuration[support.CHAR_MAX_LENGTH])
else:
return self.train_phrases, self.train_labels, self.test_phrases, self.test_labels
def _word_process(self, word_max_length):
tokenizer = Tokenizer(num_words=self.configuration[support.QUANTITY_WORDS])
tokenizer.fit_on_texts(self.train_phrases)
x_train_sequence = tokenizer.texts_to_sequences(self.train_phrases)
x_test_sequence = tokenizer.texts_to_sequences(self.test_phrases)
x_train = sequence.pad_sequences(x_train_sequence, maxlen=word_max_length, padding='post', truncating='post')
x_test = sequence.pad_sequences(x_test_sequence, maxlen=word_max_length, padding='post', truncating='post')
y_train = numpy.array(self.train_labels)
y_test = numpy.array(self.test_labels)
return x_train, y_train, x_test, y_test
def _char_process(self, max_length):
embedding_w, embedding_dic = self._onehot_dic_build()
x_train = []
for i in range(len(self.train_phrases)):
doc_vec = self._doc_process(self.train_phrases[i].lower(), embedding_dic, max_length)
x_train.append(doc_vec)
x_train = numpy.asarray(x_train, dtype='int64')
y_train = numpy.array(self.train_labels, dtype='float32')
x_test = []
for i in range(len( self.test_phrases)):
doc_vec = self._doc_process( self.test_phrases[i].lower(), embedding_dic, max_length)
x_test.append(doc_vec)
x_test = numpy.asarray(x_test, dtype='int64')
y_test = numpy.array(self.test_labels, dtype='float32')
del embedding_w, embedding_dic
return x_train, y_train, x_test, y_test
def _doc_process(self, doc, embedding_dic, max_length):
min_length = min(max_length, len(doc))
doc_vec = numpy.zeros(max_length, dtype='int64')
for j in range(min_length):
if doc[j] in embedding_dic:
doc_vec[j] = embedding_dic[doc[j]]
else:
doc_vec[j] = embedding_dic['UNK']
return doc_vec
def _onehot_dic_build(self):
alphabet = "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}"
embedding_dic = {}
embedding_w = []
embedding_dic["UNK"] = 0
embedding_w.append(numpy.zeros(len(alphabet), dtype='float32'))
for i, alpha in enumerate(alphabet):
onehot = numpy.zeros(len(alphabet), dtype='float32')
embedding_dic[alpha] = i + 1
onehot[i] = 1
embedding_w.append(onehot)
embedding_w = | numpy.array(embedding_w, dtype='float32') | numpy.array |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * | np.ones_like(dash_max_min_1_x) | numpy.ones_like |
# pylint: disable=protected-access
"""
Test the wrappers for the C API.
"""
import os
from contextlib import contextmanager
import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
import xarray as xr
from packaging.version import Version
from pygmt import Figure, clib
from pygmt.clib.conversion import dataarray_to_matrix
from pygmt.clib.session import FAMILIES, VIAS
from pygmt.exceptions import (
GMTCLibError,
GMTCLibNoSessionError,
GMTInvalidInput,
GMTVersionError,
)
from pygmt.helpers import GMTTempFile
TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
with clib.Session() as _lib:
gmt_version = Version(_lib.info["version"])
@contextmanager
def mock(session, func, returns=None, mock_func=None):
"""
Mock a GMT C API function to make it always return a given value.
Used to test that exceptions are raised when API functions fail by
producing a NULL pointer as output or non-zero status codes.
Needed because it's not easy to get some API functions to fail without
inducing a Segmentation Fault (which is a good thing because libgmt usually
only fails with errors).
"""
if mock_func is None:
def mock_api_function(*args): # pylint: disable=unused-argument
"""
A mock GMT API function that always returns a given value.
"""
return returns
mock_func = mock_api_function
get_libgmt_func = session.get_libgmt_func
def mock_get_libgmt_func(name, argtypes=None, restype=None):
"""
Return our mock function.
"""
if name == func:
return mock_func
return get_libgmt_func(name, argtypes, restype)
setattr(session, "get_libgmt_func", mock_get_libgmt_func)
yield
setattr(session, "get_libgmt_func", get_libgmt_func)
def test_getitem():
"""
Test that I can get correct constants from the C lib.
"""
ses = clib.Session()
assert ses["GMT_SESSION_EXTERNAL"] != -99999
assert ses["GMT_MODULE_CMD"] != -99999
assert ses["GMT_PAD_DEFAULT"] != -99999
assert ses["GMT_DOUBLE"] != -99999
with pytest.raises(GMTCLibError):
ses["A_WHOLE_LOT_OF_JUNK"] # pylint: disable=pointless-statement
def test_create_destroy_session():
"""
Test that create and destroy session are called without errors.
"""
# Create two session and make sure they are not pointing to the same memory
session1 = clib.Session()
session1.create(name="test_session1")
assert session1.session_pointer is not None
session2 = clib.Session()
session2.create(name="test_session2")
assert session2.session_pointer is not None
assert session2.session_pointer != session1.session_pointer
session1.destroy()
session2.destroy()
# Create and destroy a session twice
ses = clib.Session()
for __ in range(2):
with pytest.raises(GMTCLibNoSessionError):
ses.session_pointer # pylint: disable=pointless-statement
ses.create("session1")
assert ses.session_pointer is not None
ses.destroy()
with pytest.raises(GMTCLibNoSessionError):
ses.session_pointer # pylint: disable=pointless-statement
def test_create_session_fails():
"""
Check that an exception is raised when failing to create a session.
"""
ses = clib.Session()
with mock(ses, "GMT_Create_Session", returns=None):
with pytest.raises(GMTCLibError):
ses.create("test-session-name")
# Should fail if trying to create a session before destroying the old one.
ses.create("test1")
with pytest.raises(GMTCLibError):
ses.create("test2")
def test_destroy_session_fails():
"""
Fail to destroy session when given bad input.
"""
ses = clib.Session()
with pytest.raises(GMTCLibNoSessionError):
ses.destroy()
ses.create("test-session")
with mock(ses, "GMT_Destroy_Session", returns=1):
with pytest.raises(GMTCLibError):
ses.destroy()
ses.destroy()
def test_call_module():
"""
Run a command to see if call_module works.
"""
data_fname = os.path.join(TEST_DATA_DIR, "points.txt")
out_fname = "test_call_module.txt"
with clib.Session() as lib:
with GMTTempFile() as out_fname:
lib.call_module("info", "{} -C ->{}".format(data_fname, out_fname.name))
assert os.path.exists(out_fname.name)
output = out_fname.read().strip()
assert output == "11.5309 61.7074 -2.9289 7.8648 0.1412 0.9338"
def test_call_module_invalid_arguments():
"""
Fails for invalid module arguments.
"""
with clib.Session() as lib:
with pytest.raises(GMTCLibError):
lib.call_module("info", "bogus-data.bla")
def test_call_module_invalid_name():
"""
Fails when given bad input.
"""
with clib.Session() as lib:
with pytest.raises(GMTCLibError):
lib.call_module("meh", "")
def test_call_module_error_message():
"""
Check is the GMT error message was captured.
"""
with clib.Session() as lib:
try:
lib.call_module("info", "bogus-data.bla")
except GMTCLibError as error:
assert "Module 'info' failed with status code" in str(error)
assert "gmtinfo [ERROR]: Cannot find file bogus-data.bla" in str(error)
def test_method_no_session():
"""
Fails when not in a session.
"""
# Create an instance of Session without "with" so no session is created.
lib = clib.Session()
with pytest.raises(GMTCLibNoSessionError):
lib.call_module("gmtdefaults", "")
with pytest.raises(GMTCLibNoSessionError):
lib.session_pointer # pylint: disable=pointless-statement
def test_parse_constant_single():
"""
Parsing a single family argument correctly.
"""
lib = clib.Session()
for family in FAMILIES:
parsed = lib._parse_constant(family, valid=FAMILIES)
assert parsed == lib[family]
def test_parse_constant_composite():
"""
Parsing a composite constant argument (separated by |) correctly.
"""
lib = clib.Session()
test_cases = ((family, via) for family in FAMILIES for via in VIAS)
for family, via in test_cases:
composite = "|".join([family, via])
expected = lib[family] + lib[via]
parsed = lib._parse_constant(composite, valid=FAMILIES, valid_modifiers=VIAS)
assert parsed == expected
def test_parse_constant_fails():
"""
Check if the function fails when given bad input.
"""
lib = clib.Session()
test_cases = [
"SOME_random_STRING",
"GMT_IS_DATASET|GMT_VIA_MATRIX|GMT_VIA_VECTOR",
"GMT_IS_DATASET|NOT_A_PROPER_VIA",
"NOT_A_PROPER_FAMILY|GMT_VIA_MATRIX",
"NOT_A_PROPER_FAMILY|ALSO_INVALID",
]
for test_case in test_cases:
with pytest.raises(GMTInvalidInput):
lib._parse_constant(test_case, valid=FAMILIES, valid_modifiers=VIAS)
# Should also fail if not given valid modifiers but is using them anyway.
# This should work...
lib._parse_constant(
"GMT_IS_DATASET|GMT_VIA_MATRIX", valid=FAMILIES, valid_modifiers=VIAS
)
# But this shouldn't.
with pytest.raises(GMTInvalidInput):
lib._parse_constant(
"GMT_IS_DATASET|GMT_VIA_MATRIX", valid=FAMILIES, valid_modifiers=None
)
def test_create_data_dataset():
"""
Run the function to make sure it doesn't fail badly.
"""
with clib.Session() as lib:
# Dataset from vectors
data_vector = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_VECTOR",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[10, 20, 1, 0], # columns, rows, layers, dtype
)
# Dataset from matrices
data_matrix = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_MATRIX",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[10, 20, 1, 0],
)
assert data_vector != data_matrix
def test_create_data_grid_dim():
"""
Create a grid ignoring range and inc.
"""
with clib.Session() as lib:
# Grids from matrices using dim
lib.create_data(
family="GMT_IS_GRID|GMT_VIA_MATRIX",
geometry="GMT_IS_SURFACE",
mode="GMT_CONTAINER_ONLY",
dim=[10, 20, 1, 0],
)
def test_create_data_grid_range():
"""
Create a grid specifying range and inc instead of dim.
"""
with clib.Session() as lib:
# Grids from matrices using range and int
lib.create_data(
family="GMT_IS_GRID|GMT_VIA_MATRIX",
geometry="GMT_IS_SURFACE",
mode="GMT_CONTAINER_ONLY",
ranges=[150.0, 250.0, -20.0, 20.0],
inc=[0.1, 0.2],
)
def test_create_data_fails():
"""
Check that create_data raises exceptions for invalid input and output.
"""
# Passing in invalid mode
with pytest.raises(GMTInvalidInput):
with clib.Session() as lib:
lib.create_data(
family="GMT_IS_DATASET",
geometry="GMT_IS_SURFACE",
mode="Not_a_valid_mode",
dim=[0, 0, 1, 0],
ranges=[150.0, 250.0, -20.0, 20.0],
inc=[0.1, 0.2],
)
# Passing in invalid geometry
with pytest.raises(GMTInvalidInput):
with clib.Session() as lib:
lib.create_data(
family="GMT_IS_GRID",
geometry="Not_a_valid_geometry",
mode="GMT_CONTAINER_ONLY",
dim=[0, 0, 1, 0],
ranges=[150.0, 250.0, -20.0, 20.0],
inc=[0.1, 0.2],
)
# If the data pointer returned is None (NULL pointer)
with pytest.raises(GMTCLibError):
with clib.Session() as lib:
with mock(lib, "GMT_Create_Data", returns=None):
lib.create_data(
family="GMT_IS_DATASET",
geometry="GMT_IS_SURFACE",
mode="GMT_CONTAINER_ONLY",
dim=[11, 10, 2, 0],
)
def test_virtual_file():
"""
Test passing in data via a virtual file with a Dataset.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
shape = (5, 3)
for dtype in dtypes:
with clib.Session() as lib:
family = "GMT_IS_DATASET|GMT_VIA_MATRIX"
geometry = "GMT_IS_POINT"
dataset = lib.create_data(
family=family,
geometry=geometry,
mode="GMT_CONTAINER_ONLY",
dim=[shape[1], shape[0], 1, 0], # columns, rows, layers, dtype
)
data = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
lib.put_matrix(dataset, matrix=data)
# Add the dataset to a virtual file and pass it along to gmt info
vfargs = (family, geometry, "GMT_IN|GMT_IS_REFERENCE", dataset)
with lib.open_virtual_file(*vfargs) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", "{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
["<{:.0f}/{:.0f}>".format(col.min(), col.max()) for col in data.T]
)
expected = "<matrix memory>: N = {}\t{}\n".format(shape[0], bounds)
assert output == expected
def test_virtual_file_fails():
"""
Check that opening and closing virtual files raises an exception for non-
zero return codes.
"""
vfargs = (
"GMT_IS_DATASET|GMT_VIA_MATRIX",
"GMT_IS_POINT",
"GMT_IN|GMT_IS_REFERENCE",
None,
)
# Mock Open_VirtualFile to test the status check when entering the context.
# If the exception is raised, the code won't get to the closing of the
# virtual file.
with clib.Session() as lib, mock(lib, "GMT_Open_VirtualFile", returns=1):
with pytest.raises(GMTCLibError):
with lib.open_virtual_file(*vfargs):
print("Should not get to this code")
# Test the status check when closing the virtual file
# Mock the opening to return 0 (success) so that we don't open a file that
# we won't close later.
with clib.Session() as lib, mock(lib, "GMT_Open_VirtualFile", returns=0), mock(
lib, "GMT_Close_VirtualFile", returns=1
):
with pytest.raises(GMTCLibError):
with lib.open_virtual_file(*vfargs):
pass
print("Shouldn't get to this code either")
def test_virtual_file_bad_direction():
"""
Test passing an invalid direction argument.
"""
with clib.Session() as lib:
vfargs = (
"GMT_IS_DATASET|GMT_VIA_MATRIX",
"GMT_IS_POINT",
"GMT_IS_GRID", # The invalid direction argument
0,
)
with pytest.raises(GMTInvalidInput):
with lib.open_virtual_file(*vfargs):
print("This should have failed")
def test_virtualfile_from_vectors():
"""
Test the automation for transforming vectors to virtual file dataset.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
size = 10
for dtype in dtypes:
x = np.arange(size, dtype=dtype)
y = np.arange(size, size * 2, 1, dtype=dtype)
z = np.arange(size * 2, size * 3, 1, dtype=dtype)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, z) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", "{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
["<{:.0f}/{:.0f}>".format(i.min(), i.max()) for i in (x, y, z)]
)
expected = "<vector memory>: N = {}\t{}\n".format(size, bounds)
assert output == expected
@pytest.mark.parametrize("dtype", [str, object])
def test_virtualfile_from_vectors_one_string_or_object_column(dtype):
"""
Test passing in one column with string or object dtype into virtual file
dataset.
"""
size = 5
x = np.arange(size, dtype=np.int32)
y = np.arange(size, size * 2, 1, dtype=np.int32)
strings = np.array(["a", "bc", "defg", "hijklmn", "opqrst"], dtype=dtype)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, strings) as vfile:
with GMTTempFile() as outfile:
lib.call_module("convert", f"{vfile} ->{outfile.name}")
output = outfile.read(keep_tabs=True)
expected = "".join(f"{i}\t{j}\t{k}\n" for i, j, k in zip(x, y, strings))
assert output == expected
@pytest.mark.parametrize("dtype", [str, object])
def test_virtualfile_from_vectors_two_string_or_object_columns(dtype):
"""
Test passing in two columns of string or object dtype into virtual file
dataset.
"""
size = 5
x = np.arange(size, dtype=np.int32)
y = np.arange(size, size * 2, 1, dtype=np.int32)
strings1 = np.array(["a", "bc", "def", "ghij", "klmno"], dtype=dtype)
strings2 = np.array(["pqrst", "uvwx", "yz!", "@#", "$"], dtype=dtype)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, strings1, strings2) as vfile:
with GMTTempFile() as outfile:
lib.call_module("convert", f"{vfile} ->{outfile.name}")
output = outfile.read(keep_tabs=True)
expected = "".join(
f"{h}\t{i}\t{j} {k}\n" for h, i, j, k in zip(x, y, strings1, strings2)
)
assert output == expected
def test_virtualfile_from_vectors_transpose():
"""
Test transforming matrix columns to virtual file dataset.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
shape = (7, 5)
for dtype in dtypes:
data = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(*data.T) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", "{} -C ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
["{:.0f}\t{:.0f}".format(col.min(), col.max()) for col in data.T]
)
expected = "{}\n".format(bounds)
assert output == expected
def test_virtualfile_from_vectors_diff_size():
"""
Test the function fails for arrays of different sizes.
"""
x = | np.arange(5) | numpy.arange |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cntk as C
import numpy as np
from .common import floatx, epsilon, image_dim_ordering, image_data_format
from collections import defaultdict
from contextlib import contextmanager
import warnings
C.set_global_option('align_axis', 1)
b_any = any
dev = C.device.use_default_device()
if dev.type() == 0:
warnings.warn(
'CNTK backend warning: GPU is not detected. '
'CNTK\'s CPU version is not fully optimized,'
'please run with GPU to get better performance.')
# A learning phase is a bool tensor used to run Keras models in
# either train mode (learning_phase == 1) or test mode (learning_phase == 0).
# LEARNING_PHASE_PLACEHOLDER is the placeholder for dynamic learning phase
_LEARNING_PHASE_PLACEHOLDER = C.constant(shape=(), dtype=np.float32, value=1.0, name='_keras_learning_phase')
# static learning phase flag, if it is not 0 or 1, we will go with dynamic learning phase tensor.
_LEARNING_PHASE = -1
_UID_PREFIXES = defaultdict(int)
# cntk doesn't support gradient as symbolic op, to hook up with keras model,
# we will create gradient as a constant placeholder, here use this global
# map to keep the mapping from grad placeholder to parameter
grad_parameter_dict = {}
NAME_SCOPE_STACK = []
@contextmanager
def name_scope(name):
global NAME_SCOPE_STACK
NAME_SCOPE_STACK.append(name)
yield
NAME_SCOPE_STACK.pop()
def get_uid(prefix=''):
_UID_PREFIXES[prefix] += 1
return _UID_PREFIXES[prefix]
def learning_phase():
# If _LEARNING_PHASE is not 0 or 1, return dynamic learning phase tensor
return _LEARNING_PHASE if _LEARNING_PHASE in {0, 1} else _LEARNING_PHASE_PLACEHOLDER
def set_learning_phase(value):
global _LEARNING_PHASE
if value not in {0, 1}:
raise ValueError('CNTK Backend: Set learning phase '
'with value %s is not supported, '
'expected 0 or 1.' % value)
_LEARNING_PHASE = value
def clear_session():
"""Reset learning phase flag for cntk backend.
"""
global _LEARNING_PHASE
global _LEARNING_PHASE_PLACEHOLDER
_LEARNING_PHASE = -1
_LEARNING_PHASE_PLACEHOLDER.value = np.asarray(1.0)
def in_train_phase(x, alt, training=None):
global _LEARNING_PHASE
if training is None:
training = learning_phase()
uses_learning_phase = True
else:
uses_learning_phase = False
# CNTK currently don't support cond op, so here we use
# element_select approach as workaround. It may have
# perf issue, will resolve it later with cntk cond op.
if callable(x) and isinstance(x, C.cntk_py.Function) is False:
x = x()
if callable(alt) and isinstance(alt, C.cntk_py.Function) is False:
alt = alt()
if training is True:
x._uses_learning_phase = uses_learning_phase
return x
else:
# if _LEARNING_PHASE is static
if isinstance(training, int) or isinstance(training, bool):
result = x if training == 1 or training is True else alt
else:
result = C.element_select(training, x, alt)
result._uses_learning_phase = uses_learning_phase
return result
def in_test_phase(x, alt, training=None):
return in_train_phase(alt, x, training=training)
def _convert_string_dtype(dtype):
# cntk only support float32 and float64
if dtype == 'float32':
return np.float32
elif dtype == 'float64':
return np.float64
else:
# cntk only running with float,
# try to cast to float to run the model
return np.float32
def _convert_dtype_string(dtype):
if dtype == np.float32:
return 'float32'
elif dtype == np.float64:
return 'float64'
else:
raise ValueError('CNTK Backend: Unsupported dtype: %s. '
'CNTK only supports float32 and '
'float64.' % dtype)
def variable(value, dtype=None, name=None, constraint=None):
"""Instantiates a variable and returns it.
# Arguments
value: Numpy array, initial value of the tensor.
dtype: Tensor type.
name: Optional name string for the tensor.
constraint: Optional projection function to be
applied to the variable after an optimizer update.
# Returns
A variable instance (with Keras metadata included).
"""
if dtype is None:
dtype = floatx()
if name is None:
name = ''
if isinstance(
value,
C.variables.Constant) or isinstance(
value,
C.variables.Parameter):
value = value.value
# we don't support init parameter with symbolic op, so eval it first as
# workaround
if isinstance(value, C.cntk_py.Function):
value = eval(value)
shape = value.shape if hasattr(value, 'shape') else ()
if hasattr(value, 'dtype') and value.dtype != dtype and len(shape) > 0:
value = value.astype(dtype)
# TODO: remove the conversion when cntk supports int32, int64
# https://docs.microsoft.com/en-us/python/api/cntk.variables.parameter
dtype = 'float32' if 'int' in str(dtype) else dtype
v = C.parameter(shape=shape,
init=value,
dtype=dtype,
name=_prepare_name(name, 'variable'))
v._keras_shape = v.shape
v._uses_learning_phase = False
v.constraint = constraint
return v
def bias_add(x, bias, data_format=None):
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format ' + str(data_format))
dims = len(x.shape)
if dims > 0 and x.shape[0] == C.InferredDimension:
dims -= 1
bias_dims = len(bias.shape)
if bias_dims != 1 and bias_dims != dims:
raise ValueError('Unexpected bias dimensions %d, '
'expected 1 or %d dimensions' % (bias_dims, dims))
if dims == 4:
if data_format == 'channels_first':
if bias_dims == 1:
shape = (bias.shape[0], 1, 1, 1)
else:
shape = (bias.shape[3],) + bias.shape[:3]
elif data_format == 'channels_last':
if bias_dims == 1:
shape = (1, 1, 1, bias.shape[0])
else:
shape = bias.shape
elif dims == 3:
if data_format == 'channels_first':
if bias_dims == 1:
shape = (bias.shape[0], 1, 1)
else:
shape = (bias.shape[2],) + bias.shape[:2]
elif data_format == 'channels_last':
if bias_dims == 1:
shape = (1, 1, bias.shape[0])
else:
shape = bias.shape
elif dims == 2:
if data_format == 'channels_first':
if bias_dims == 1:
shape = (bias.shape[0], 1)
else:
shape = (bias.shape[1],) + bias.shape[:1]
elif data_format == 'channels_last':
if bias_dims == 1:
shape = (1, bias.shape[0])
else:
shape = bias.shape
else:
shape = bias.shape
return x + reshape(bias, shape)
def eval(x):
if isinstance(x, C.cntk_py.Function):
return x.eval()
elif isinstance(x, C.variables.Constant) or isinstance(x, C.variables.Parameter):
return x.value
else:
raise ValueError('CNTK Backend: `eval` method on '
'`%s` type is not supported. '
'CNTK only supports `eval` with '
'`Function`, `Constant` or '
'`Parameter`.' % type(x))
def placeholder(
shape=None,
ndim=None,
dtype=None,
sparse=False,
name=None,
dynamic_axis_num=1):
if dtype is None:
dtype = floatx()
if not shape:
if ndim:
shape = tuple([None for _ in range(ndim)])
dynamic_dimension = C.FreeDimension if _get_cntk_version() >= 2.2 else C.InferredDimension
cntk_shape = [dynamic_dimension if s is None else s for s in shape]
cntk_shape = tuple(cntk_shape)
if dynamic_axis_num > len(cntk_shape):
raise ValueError('CNTK backend: creating placeholder with '
'%d dimension is not supported, at least '
'%d dimensions are needed.'
% (len(cntk_shape, dynamic_axis_num)))
if name is None:
name = ''
cntk_shape = cntk_shape[dynamic_axis_num:]
x = C.input(
shape=cntk_shape,
dtype=_convert_string_dtype(dtype),
is_sparse=sparse,
name=name)
x._keras_shape = shape
x._uses_learning_phase = False
x._cntk_placeholder = True
return x
def is_placeholder(x):
"""Returns whether `x` is a placeholder.
# Arguments
x: A candidate placeholder.
# Returns
Boolean.
"""
return hasattr(x, '_cntk_placeholder') and x._cntk_placeholder
def is_keras_tensor(x):
if not is_tensor(x):
raise ValueError('Unexpectedly found an instance of type `' +
str(type(x)) + '`. '
'Expected a symbolic tensor instance.')
return hasattr(x, '_keras_history')
def is_tensor(x):
return isinstance(x, (C.variables.Constant,
C.variables.Variable,
C.variables.Parameter,
C.ops.functions.Function))
def shape(x):
shape = list(int_shape(x))
num_dynamic = _get_dynamic_axis_num(x)
non_dyn_shape = []
for i in range(len(x.shape)):
if shape[i + num_dynamic] is None:
non_dyn_shape.append(x.shape[i])
else:
non_dyn_shape.append(shape[i + num_dynamic])
return shape[:num_dynamic] + non_dyn_shape
def is_sparse(tensor):
return tensor.is_sparse
def int_shape(x):
if hasattr(x, '_keras_shape'):
return x._keras_shape
shape = x.shape
if hasattr(x, 'dynamic_axes'):
dynamic_shape = [None for a in x.dynamic_axes]
shape = tuple(dynamic_shape) + shape
return shape
def ndim(x):
shape = int_shape(x)
return len(shape)
def _prepare_name(name, default):
prefix = '_'.join(NAME_SCOPE_STACK)
if name is None or name == '':
return prefix + '/' + default
return prefix + '/' + name
def constant(value, dtype=None, shape=None, name=None):
if dtype is None:
dtype = floatx()
if shape is None:
shape = ()
np_value = value * np.ones(shape)
const = C.constant(np_value,
dtype=dtype,
name=_prepare_name(name, 'constant'))
const._keras_shape = const.shape
const._uses_learning_phase = False
return const
def random_binomial(shape, p=0.0, dtype=None, seed=None):
# use numpy workaround now
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e7)
np.random.seed(seed)
if dtype is None:
dtype = np.float32
else:
dtype = _convert_string_dtype(dtype)
size = 1
for _ in shape:
if _ is None:
raise ValueError('CNTK Backend: randomness op with '
'dynamic shape is not supported now. '
'Please provide fixed dimension '
'instead of `None`.')
size *= _
binomial = np.random.binomial(1, p, size).astype(dtype).reshape(shape)
return variable(value=binomial, dtype=dtype)
def random_uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
for _ in shape:
if _ is None:
raise ValueError('CNTK Backend: randomness op with '
'dynamic shape is not supported now. '
'Please provide fixed dimension '
'instead of `None`.')
return random_uniform_variable(shape, minval, maxval, dtype, seed)
def random_uniform_variable(shape, low, high,
dtype=None, name=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e3)
if dtype is None:
dtype = np.float32
else:
dtype = _convert_string_dtype(dtype)
if name is None:
name = ''
scale = (high - low) / 2
p = C.parameter(
shape,
init=C.initializer.uniform(
scale,
seed=seed),
dtype=dtype,
name=name)
return variable(value=p.value + low + scale)
def random_normal_variable(
shape,
mean,
scale,
dtype=None,
name=None,
seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e7)
if dtype is None:
dtype = np.float32
else:
dtype = _convert_string_dtype(dtype)
if name is None:
name = ''
return C.parameter(
shape=shape,
init=C.initializer.normal(
scale=scale,
seed=seed),
dtype=dtype,
name=name)
def random_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
for _ in shape:
if _ is None:
raise ValueError('CNTK Backend: randomness op with '
'dynamic shape is not supported now. '
'Please provide fixed dimension '
'instead of `None`.')
# how to apply mean and stddev
return random_normal_variable(shape=shape, mean=mean, scale=1.0, seed=seed)
def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
if seed is None:
seed = | np.random.randint(1, 10e6) | numpy.random.randint |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + | np.sin(5 * knot_demonstrate_time) | numpy.sin |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[2].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[2].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
plt.savefig('jss_figures/DFA_different_trends.png')
plt.show()
# plot 6b
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences Zoomed Region', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[0].set_ylim(-5.5, 5.5)
axs[0].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].set_ylim(-5.5, 5.5)
axs[1].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([np.pi, (3 / 2) * np.pi])
axs[2].set_xticklabels([r'$\pi$', r'$\frac{3}{2}\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].set_ylim(-5.5, 5.5)
axs[2].set_xlim(0.95 * np.pi, 1.55 * np.pi)
plt.savefig('jss_figures/DFA_different_trends_zoomed.png')
plt.show()
hs_ouputs = hilbert_spectrum(time, imfs_51, hts_51, ifs_51, max_frequency=12, plot=False)
# plot 6c
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Simple Sinusoidal Time Seres with Added Noise', 50))
x_hs, y, z = hs_ouputs
z_min, z_max = 0, np.abs(z).max()
ax.pcolormesh(x_hs, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max)
ax.plot(x_hs[0, :], 8 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 8$', Linewidth=3)
ax.plot(x_hs[0, :], 4 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 4$', Linewidth=3)
ax.plot(x_hs[0, :], 2 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 2$', Linewidth=3)
ax.set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
ax.set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$'])
plt.ylabel(r'Frequency (rad.s$^{-1}$)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.85, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/DFA_hilbert_spectrum.png')
plt.show()
# plot 6c
time = np.linspace(0, 5 * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 51)
fluc = Fluctuation(time=time, time_series=time_series)
max_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=False)
max_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=True)
min_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=False)
min_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=True)
util = Utility(time=time, time_series=time_series)
maxima = util.max_bool_func_1st_order_fd()
minima = util.min_bool_func_1st_order_fd()
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title(textwrap.fill('Plot Demonstrating Unsmoothed Extrema Envelopes if Schoenberg–Whitney Conditions are Not Satisfied', 50))
plt.plot(time, time_series, label='Time series', zorder=2, LineWidth=2)
plt.scatter(time[maxima], time_series[maxima], c='r', label='Maxima', zorder=10)
plt.scatter(time[minima], time_series[minima], c='b', label='Minima', zorder=10)
plt.plot(time, max_unsmoothed[0], label=textwrap.fill('Unsmoothed maxima envelope', 10), c='darkorange')
plt.plot(time, max_smoothed[0], label=textwrap.fill('Smoothed maxima envelope', 10), c='red')
plt.plot(time, min_unsmoothed[0], label=textwrap.fill('Unsmoothed minima envelope', 10), c='cyan')
plt.plot(time, min_smoothed[0], label=textwrap.fill('Smoothed minima envelope', 10), c='blue')
for knot in knots[:-1]:
plt.plot(knot * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', zorder=1)
plt.plot(knots[-1] * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', label='Knots', zorder=1)
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi),
(r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$', r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Schoenberg_Whitney_Conditions.png')
plt.show()
# plot 7
a = 0.25
width = 0.2
time = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 1001)
knots = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 11)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
inflection_bool = utils.inflection_point()
inflection_x = time[inflection_bool]
inflection_y = time_series[inflection_bool]
fluctuation = emd_mean.Fluctuation(time=time, time_series=time_series)
maxima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
maxima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
inflection_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='inflection_points')[0]
binomial_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='binomial_average', order=21,
increment=20)[0]
derivative_of_lsq = utils.derivative_forward_diff()
derivative_time = time[:-1]
derivative_knots = np.linspace(knots[0], knots[-1], 31)
# change (1) detrended_fluctuation_technique and (2) max_internal_iter and (3) debug (confusing with external debugging)
emd = AdvEMDpy.EMD(time=derivative_time, time_series=derivative_of_lsq)
imf_1_of_derivative = emd.empirical_mode_decomposition(knots=derivative_knots,
knot_time=derivative_time, text=False, verbose=False)[0][1, :]
utils = emd_utils.Utility(time=time[:-1], time_series=imf_1_of_derivative)
optimal_maxima = np.r_[False, utils.derivative_forward_diff() < 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
optimal_minima = np.r_[False, utils.derivative_forward_diff() > 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
EEMD_maxima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'maxima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
EEMD_minima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'minima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Detrended Fluctuation Analysis Examples')
plt.plot(time, time_series, LineWidth=2, label='Time series')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(time[optimal_maxima], time_series[optimal_maxima], c='darkred', zorder=4,
label=textwrap.fill('Optimal maxima', 10))
plt.scatter(time[optimal_minima], time_series[optimal_minima], c='darkblue', zorder=4,
label=textwrap.fill('Optimal minima', 10))
plt.scatter(inflection_x, inflection_y, c='magenta', zorder=4, label=textwrap.fill('Inflection points', 10))
plt.plot(time, maxima_envelope, c='darkblue', label=textwrap.fill('EMD envelope', 10))
plt.plot(time, minima_envelope, c='darkblue')
plt.plot(time, (maxima_envelope + minima_envelope) / 2, c='darkblue')
plt.plot(time, maxima_envelope_smooth, c='darkred', label=textwrap.fill('SEMD envelope', 10))
plt.plot(time, minima_envelope_smooth, c='darkred')
plt.plot(time, (maxima_envelope_smooth + minima_envelope_smooth) / 2, c='darkred')
plt.plot(time, EEMD_maxima_envelope, c='darkgreen', label=textwrap.fill('EEMD envelope', 10))
plt.plot(time, EEMD_minima_envelope, c='darkgreen')
plt.plot(time, (EEMD_maxima_envelope + EEMD_minima_envelope) / 2, c='darkgreen')
plt.plot(time, inflection_points_envelope, c='darkorange', label=textwrap.fill('Inflection point envelope', 10))
plt.plot(time, binomial_points_envelope, c='deeppink', label=textwrap.fill('Binomial average envelope', 10))
plt.plot(time, np.cos(time), c='black', label='True mean')
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi), (r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$',
r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/detrended_fluctuation_analysis.png')
plt.show()
# Duffing Equation Example
def duffing_equation(xy, ts):
gamma = 0.1
epsilon = 1
omega = ((2 * np.pi) / 25)
return [xy[1], xy[0] - epsilon * xy[0] ** 3 + gamma * np.cos(omega * ts)]
t = | np.linspace(0, 150, 1501) | numpy.linspace |
# -*- encoding:utf-8 -*-
# @Time : 2021/1/3 15:15
# @Author : gfjiang
import os.path as osp
import mmcv
import numpy as np
import cvtools
import matplotlib.pyplot as plt
import cv2.cv2 as cv
from functools import partial
import torch
import math
from cvtools.utils.path import add_prefix_filename_suffix
from mmdet.ops import nms
from mmdet.apis import init_detector, inference_detector
def draw_features(module, input, output, work_dir='./'):
x = output.cpu().numpy()
out_channels = list(output.shape)[1]
height = int(math.sqrt(out_channels))
width = height
if list(output.shape)[2] < 128:
return
fig = plt.figure(figsize=(32, 32))
fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05)
for i in range(height * width):
plt.subplot(height, width, i + 1)
plt.axis('off')
img = x[0, i, :, :]
pmin = | np.min(img) | numpy.min |
# coding: utf-8
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Test the Logarithmic Units and Quantities
"""
from __future__ import (absolute_import, unicode_literals, division,
print_function)
from ...extern import six
from ...extern.six.moves import zip
import pickle
import itertools
import pytest
import numpy as np
from numpy.testing.utils import assert_allclose
from ...tests.helper import assert_quantity_allclose
from ... import units as u, constants as c
lu_units = [u.dex, u.mag, u.decibel]
lu_subclasses = [u.DexUnit, u.MagUnit, u.DecibelUnit]
lq_subclasses = [u.Dex, u.Magnitude, u.Decibel]
pu_sample = (u.dimensionless_unscaled, u.m, u.g/u.s**2, u.Jy)
class TestLogUnitCreation(object):
def test_logarithmic_units(self):
"""Check logarithmic units are set up correctly."""
assert u.dB.to(u.dex) == 0.1
assert u.dex.to(u.mag) == -2.5
assert u.mag.to(u.dB) == -4
@pytest.mark.parametrize('lu_unit, lu_cls', zip(lu_units, lu_subclasses))
def test_callable_units(self, lu_unit, lu_cls):
assert isinstance(lu_unit, u.UnitBase)
assert callable(lu_unit)
assert lu_unit._function_unit_class is lu_cls
@pytest.mark.parametrize('lu_unit', lu_units)
def test_equality_to_normal_unit_for_dimensionless(self, lu_unit):
lu = lu_unit()
assert lu == lu._default_function_unit # eg, MagUnit() == u.mag
assert lu._default_function_unit == lu # and u.mag == MagUnit()
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_call_units(self, lu_unit, physical_unit):
"""Create a LogUnit subclass using the callable unit and physical unit,
and do basic check that output is right."""
lu1 = lu_unit(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
def test_call_invalid_unit(self):
with pytest.raises(TypeError):
u.mag([])
with pytest.raises(ValueError):
u.mag(u.mag())
@pytest.mark.parametrize('lu_cls, physical_unit', itertools.product(
lu_subclasses + [u.LogUnit], pu_sample))
def test_subclass_creation(self, lu_cls, physical_unit):
"""Create a LogUnit subclass object for given physical unit,
and do basic check that output is right."""
lu1 = lu_cls(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
lu2 = lu_cls(physical_unit,
function_unit=2*lu1._default_function_unit)
assert lu2.physical_unit == physical_unit
assert lu2.function_unit == u.Unit(2*lu2._default_function_unit)
with pytest.raises(ValueError):
lu_cls(physical_unit, u.m)
def test_predefined_magnitudes():
assert_quantity_allclose((-21.1*u.STmag).physical,
1.*u.erg/u.cm**2/u.s/u.AA)
assert_quantity_allclose((-48.6*u.ABmag).physical,
1.*u.erg/u.cm**2/u.s/u.Hz)
assert_quantity_allclose((0*u.M_bol).physical, c.L_bol0)
assert_quantity_allclose((0*u.m_bol).physical,
c.L_bol0/(4.*np.pi*(10.*c.pc)**2))
def test_predefined_reinitialisation():
assert u.mag('ST') == u.STmag
assert u.mag('AB') == u.ABmag
assert u.mag('Bol') == u.M_bol
assert u.mag('bol') == u.m_bol
def test_predefined_string_roundtrip():
"""Ensure roundtripping; see #5015"""
with u.magnitude_zero_points.enable():
assert u.Unit(u.STmag.to_string()) == u.STmag
assert u.Unit(u.ABmag.to_string()) == u.ABmag
assert u.Unit(u.M_bol.to_string()) == u.M_bol
assert u.Unit(u.m_bol.to_string()) == u.m_bol
def test_inequality():
"""Check __ne__ works (regresssion for #5342)."""
lu1 = u.mag(u.Jy)
lu2 = u.dex(u.Jy)
lu3 = u.mag(u.Jy**2)
lu4 = lu3 - lu1
assert lu1 != lu2
assert lu1 != lu3
assert lu1 == lu4
class TestLogUnitStrings(object):
def test_str(self):
"""Do some spot checks that str, repr, etc. work as expected."""
lu1 = u.mag(u.Jy)
assert str(lu1) == 'mag(Jy)'
assert repr(lu1) == 'Unit("mag(Jy)")'
assert lu1.to_string('generic') == 'mag(Jy)'
with pytest.raises(ValueError):
lu1.to_string('fits')
lu2 = u.dex()
assert str(lu2) == 'dex'
assert repr(lu2) == 'Unit("dex(1)")'
assert lu2.to_string() == 'dex(1)'
lu3 = u.MagUnit(u.Jy, function_unit=2*u.mag)
assert str(lu3) == '2 mag(Jy)'
assert repr(lu3) == 'MagUnit("Jy", unit="2 mag")'
assert lu3.to_string() == '2 mag(Jy)'
lu4 = u.mag(u.ct)
assert lu4.to_string('generic') == 'mag(ct)'
assert lu4.to_string('latex') == ('$\\mathrm{mag}$$\\mathrm{\\left( '
'\\mathrm{ct} \\right)}$')
assert lu4._repr_latex_() == lu4.to_string('latex')
class TestLogUnitConversion(object):
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_physical_unit_conversion(self, lu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to their non-log counterparts."""
lu1 = lu_unit(physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(physical_unit, 0.) == 1.
assert physical_unit.is_equivalent(lu1)
assert physical_unit.to(lu1, 1.) == 0.
pu = u.Unit(8.*physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(pu, 0.) == 0.125
assert pu.is_equivalent(lu1)
assert_allclose(pu.to(lu1, 0.125), 0., atol=1.e-15)
# Check we round-trip.
value = np.linspace(0., 10., 6)
assert_allclose(pu.to(lu1, lu1.to(pu, value)), value, atol=1.e-15)
# And that we're not just returning True all the time.
pu2 = u.g
assert not lu1.is_equivalent(pu2)
with pytest.raises(u.UnitsError):
lu1.to(pu2)
assert not pu2.is_equivalent(lu1)
with pytest.raises(u.UnitsError):
pu2.to(lu1)
@pytest.mark.parametrize('lu_unit', lu_units)
def test_container_unit_conversion(self, lu_unit):
"""Check that conversion to logarithmic units (u.mag, u.dB, u.dex)
is only possible when the physical unit is dimensionless."""
values = np.linspace(0., 10., 6)
lu1 = lu_unit(u.dimensionless_unscaled)
assert lu1.is_equivalent(lu1.function_unit)
assert_allclose(lu1.to(lu1.function_unit, values), values)
lu2 = lu_unit(u.Jy)
assert not lu2.is_equivalent(lu2.function_unit)
with pytest.raises(u.UnitsError):
lu2.to(lu2.function_unit, values)
@pytest.mark.parametrize(
'flu_unit, tlu_unit, physical_unit',
itertools.product(lu_units, lu_units, pu_sample))
def test_subclass_conversion(self, flu_unit, tlu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to each other if they correspond to equivalent physical units."""
values = np.linspace(0., 10., 6)
flu = flu_unit(physical_unit)
tlu = tlu_unit(physical_unit)
assert flu.is_equivalent(tlu)
assert_allclose(flu.to(tlu), flu.function_unit.to(tlu.function_unit))
assert_allclose(flu.to(tlu, values),
values * flu.function_unit.to(tlu.function_unit))
tlu2 = tlu_unit(u.Unit(100.*physical_unit))
assert flu.is_equivalent(tlu2)
# Check that we round-trip.
assert_allclose(flu.to(tlu2, tlu2.to(flu, values)), values, atol=1.e-15)
tlu3 = tlu_unit(physical_unit.to_system(u.si)[0])
assert flu.is_equivalent(tlu3)
assert_allclose(flu.to(tlu3, tlu3.to(flu, values)), values, atol=1.e-15)
tlu4 = tlu_unit(u.g)
assert not flu.is_equivalent(tlu4)
with pytest.raises(u.UnitsError):
flu.to(tlu4, values)
def test_unit_decomposition(self):
lu = u.mag(u.Jy)
assert lu.decompose() == u.mag(u.Jy.decompose())
assert lu.decompose().physical_unit.bases == [u.kg, u.s]
assert lu.si == u.mag(u.Jy.si)
assert lu.si.physical_unit.bases == [u.kg, u.s]
assert lu.cgs == u.mag(u.Jy.cgs)
assert lu.cgs.physical_unit.bases == [u.g, u.s]
def test_unit_multiple_possible_equivalencies(self):
lu = u.mag(u.Jy)
assert lu.is_equivalent(pu_sample)
class TestLogUnitArithmetic(object):
def test_multiplication_division(self):
"""Check that multiplication/division with other units is only
possible when the physical unit is dimensionless, and that this
turns the unit into a normal one."""
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 * u.m
with pytest.raises(u.UnitsError):
u.m * lu1
with pytest.raises(u.UnitsError):
lu1 / lu1
for unit in (u.dimensionless_unscaled, u.m, u.mag, u.dex):
with pytest.raises(u.UnitsError):
lu1 / unit
lu2 = u.mag(u.dimensionless_unscaled)
with pytest.raises(u.UnitsError):
lu2 * lu1
with pytest.raises(u.UnitsError):
lu2 / lu1
# But dimensionless_unscaled can be cancelled.
assert lu2 / lu2 == u.dimensionless_unscaled
# With dimensionless, normal units are OK, but we return a plain unit.
tf = lu2 * u.m
tr = u.m * lu2
for t in (tf, tr):
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit * u.m
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(lu2.physical_unit)
# Now we essentially have a LogUnit with a prefactor of 100,
# so should be equivalent again.
t = tf / u.cm
with u.set_enabled_equivalencies(u.logarithmic()):
assert t.is_equivalent(lu2.function_unit)
assert_allclose(t.to(u.dimensionless_unscaled, np.arange(3.)/100.),
lu2.to(lu2.physical_unit, np.arange(3.)))
# If we effectively remove lu1, a normal unit should be returned.
t2 = tf / lu2
assert not isinstance(t2, type(lu2))
assert t2 == u.m
t3 = tf / lu2.function_unit
assert not isinstance(t3, type(lu2))
assert t3 == u.m
# For completeness, also ensure non-sensical operations fail
with pytest.raises(TypeError):
lu1 * object()
with pytest.raises(TypeError):
slice(None) * lu1
with pytest.raises(TypeError):
lu1 / []
with pytest.raises(TypeError):
1 / lu1
@pytest.mark.parametrize('power', (2, 0.5, 1, 0))
def test_raise_to_power(self, power):
"""Check that raising LogUnits to some power is only possible when the
physical unit is dimensionless, and that conversion is turned off when
the resulting logarithmic unit (such as mag**2) is incompatible."""
lu1 = u.mag(u.Jy)
if power == 0:
assert lu1 ** power == u.dimensionless_unscaled
elif power == 1:
assert lu1 ** power == lu1
else:
with pytest.raises(u.UnitsError):
lu1 ** power
# With dimensionless, though, it works, but returns a normal unit.
lu2 = u.mag(u.dimensionless_unscaled)
t = lu2**power
if power == 0:
assert t == u.dimensionless_unscaled
elif power == 1:
assert t == lu2
else:
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit**power
# also check we roundtrip
t2 = t**(1./power)
assert t2 == lu2.function_unit
with u.set_enabled_equivalencies(u.logarithmic()):
assert_allclose(t2.to(u.dimensionless_unscaled, np.arange(3.)),
lu2.to(lu2.physical_unit, np.arange(3.)))
@pytest.mark.parametrize('other', pu_sample)
def test_addition_subtraction_to_normal_units_fails(self, other):
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 + other
with pytest.raises(u.UnitsError):
lu1 - other
with pytest.raises(u.UnitsError):
other - lu1
def test_addition_subtraction_to_non_units_fails(self):
lu1 = u.mag(u.Jy)
with pytest.raises(TypeError):
lu1 + 1.
with pytest.raises(TypeError):
lu1 - [1., 2., 3.]
@pytest.mark.parametrize(
'other', (u.mag, u.mag(), u.mag(u.Jy), u.mag(u.m),
u.Unit(2*u.mag), u.MagUnit('', 2.*u.mag)))
def test_addition_subtraction(self, other):
"""Check physical units are changed appropriately"""
lu1 = u.mag(u.Jy)
other_pu = getattr(other, 'physical_unit', u.dimensionless_unscaled)
lu_sf = lu1 + other
assert lu_sf.is_equivalent(lu1.physical_unit * other_pu)
lu_sr = other + lu1
assert lu_sr.is_equivalent(lu1.physical_unit * other_pu)
lu_df = lu1 - other
assert lu_df.is_equivalent(lu1.physical_unit / other_pu)
lu_dr = other - lu1
assert lu_dr.is_equivalent(other_pu / lu1.physical_unit)
def test_complicated_addition_subtraction(self):
"""for fun, a more complicated example of addition and subtraction"""
dm0 = u.Unit('DM', 1./(4.*np.pi*(10.*u.pc)**2))
lu_dm = u.mag(dm0)
lu_absST = u.STmag - lu_dm
assert lu_absST.is_equivalent(u.erg/u.s/u.AA)
def test_neg_pos(self):
lu1 = u.mag(u.Jy)
neg_lu = -lu1
assert neg_lu != lu1
assert neg_lu.physical_unit == u.Jy**-1
assert -neg_lu == lu1
pos_lu = +lu1
assert pos_lu is not lu1
assert pos_lu == lu1
def test_pickle():
lu1 = u.dex(u.cm/u.s**2)
s = pickle.dumps(lu1)
lu2 = pickle.loads(s)
assert lu1 == lu2
def test_hashable():
lu1 = u.dB(u.mW)
lu2 = u.dB(u.m)
lu3 = u.dB(u.mW)
assert hash(lu1) != hash(lu2)
assert hash(lu1) == hash(lu3)
luset = {lu1, lu2, lu3}
assert len(luset) == 2
class TestLogQuantityCreation(object):
@pytest.mark.parametrize('lq, lu', zip(lq_subclasses + [u.LogQuantity],
lu_subclasses + [u.LogUnit]))
def test_logarithmic_quantities(self, lq, lu):
"""Check logarithmic quantities are all set up correctly"""
assert lq._unit_class == lu
assert type(lu()._quantity_class(1.)) is lq
@pytest.mark.parametrize('lq_cls, physical_unit',
itertools.product(lq_subclasses, pu_sample))
def test_subclass_creation(self, lq_cls, physical_unit):
"""Create LogQuantity subclass objects for some physical units,
and basic check on transformations"""
value = np.arange(1., 10.)
log_q = lq_cls(value * physical_unit)
assert log_q.unit.physical_unit == physical_unit
assert log_q.unit.function_unit == log_q.unit._default_function_unit
assert_allclose(log_q.physical.value, value)
with pytest.raises(ValueError):
lq_cls(value, physical_unit)
@pytest.mark.parametrize(
'unit', (u.mag, u.mag(), u.mag(u.Jy), u.mag(u.m),
u.Unit(2*u.mag), u.MagUnit('', 2.*u.mag),
u.MagUnit(u.Jy, -1*u.mag), u.MagUnit(u.m, -2.*u.mag)))
def test_different_units(self, unit):
q = u.Magnitude(1.23, unit)
assert q.unit.function_unit == getattr(unit, 'function_unit', unit)
assert q.unit.physical_unit is getattr(unit, 'physical_unit',
u.dimensionless_unscaled)
@pytest.mark.parametrize('value, unit', (
(1.*u.mag(u.Jy), None),
(1.*u.dex(u.Jy), None),
(1.*u.mag(u.W/u.m**2/u.Hz), u.mag(u.Jy)),
(1.*u.dex(u.W/u.m**2/u.Hz), u.mag(u.Jy))))
def test_function_values(self, value, unit):
lq = u.Magnitude(value, unit)
assert lq == value
assert lq.unit.function_unit == u.mag
assert lq.unit.physical_unit == getattr(unit, 'physical_unit',
value.unit.physical_unit)
@pytest.mark.parametrize(
'unit', (u.mag(), u.mag(u.Jy), u.mag(u.m), u.MagUnit('', 2.*u.mag),
u.MagUnit(u.Jy, -1*u.mag), u.MagUnit(u.m, -2.*u.mag)))
def test_indirect_creation(self, unit):
q1 = 2.5 * unit
assert isinstance(q1, u.Magnitude)
assert q1.value == 2.5
assert q1.unit == unit
pv = 100. * unit.physical_unit
q2 = unit * pv
assert q2.unit == unit
assert q2.unit.physical_unit == pv.unit
assert q2.to_value(unit.physical_unit) == 100.
assert (q2._function_view / u.mag).to_value(1) == -5.
q3 = unit / 0.4
assert q3 == q1
def test_from_view(self):
# Cannot view a physical quantity as a function quantity, since the
# values would change.
q = [100., 1000.] * u.cm/u.s**2
with pytest.raises(TypeError):
q.view(u.Dex)
# But fine if we have the right magnitude.
q = [2., 3.] * u.dex
lq = q.view(u.Dex)
assert isinstance(lq, u.Dex)
assert lq.unit.physical_unit == u.dimensionless_unscaled
assert np.all(q == lq)
def test_using_quantity_class(self):
"""Check that we can use Quantity if we have subok=True"""
# following issue #5851
lu = u.dex(u.AA)
with pytest.raises(u.UnitTypeError):
u.Quantity(1., lu)
q = u.Quantity(1., lu, subok=True)
assert type(q) is lu._quantity_class
def test_conversion_to_and_from_physical_quantities():
"""Ensures we can convert from regular quantities."""
mst = [10., 12., 14.] * u.STmag
flux_lambda = mst.physical
mst_roundtrip = flux_lambda.to(u.STmag)
# check we return a logquantity; see #5178.
assert isinstance(mst_roundtrip, u.Magnitude)
assert mst_roundtrip.unit == mst.unit
assert_allclose(mst_roundtrip.value, mst.value)
wave = [4956.8, 4959.55, 4962.3] * u.AA
flux_nu = mst.to(u.Jy, equivalencies=u.spectral_density(wave))
mst_roundtrip2 = flux_nu.to(u.STmag, u.spectral_density(wave))
assert isinstance(mst_roundtrip2, u.Magnitude)
assert mst_roundtrip2.unit == mst.unit
assert_allclose(mst_roundtrip2.value, mst.value)
def test_quantity_decomposition():
lq = 10.*u.mag(u.Jy)
assert lq.decompose() == lq
assert lq.decompose().unit.physical_unit.bases == [u.kg, u.s]
assert lq.si == lq
assert lq.si.unit.physical_unit.bases == [u.kg, u.s]
assert lq.cgs == lq
assert lq.cgs.unit.physical_unit.bases == [u.g, u.s]
class TestLogQuantityViews(object):
def setup(self):
self.lq = u.Magnitude(np.arange(10.) * u.Jy)
self.lq2 = u.Magnitude(np.arange(5.))
def test_value_view(self):
lq_value = self.lq.value
assert type(lq_value) is np.ndarray
lq_value[2] = -1.
assert np.all(self.lq.value == lq_value)
def test_function_view(self):
lq_fv = self.lq._function_view
assert type(lq_fv) is u.Quantity
assert lq_fv.unit is self.lq.unit.function_unit
lq_fv[3] = -2. * lq_fv.unit
assert np.all(self.lq.value == lq_fv.value)
def test_quantity_view(self):
# Cannot view as Quantity, since the unit cannot be represented.
with pytest.raises(TypeError):
self.lq.view(u.Quantity)
# But a dimensionless one is fine.
q2 = self.lq2.view(u.Quantity)
assert q2.unit is u.mag
assert np.all(q2.value == self.lq2.value)
lq3 = q2.view(u.Magnitude)
assert type(lq3.unit) is u.MagUnit
assert lq3.unit.physical_unit == u.dimensionless_unscaled
assert np.all(lq3 == self.lq2)
class TestLogQuantitySlicing(object):
def test_item_get_and_set(self):
lq1 = u.Magnitude(np.arange(1., 11.)*u.Jy)
assert lq1[9] == u.Magnitude(10.*u.Jy)
lq1[2] = 100.*u.Jy
assert lq1[2] == u.Magnitude(100.*u.Jy)
with pytest.raises(u.UnitsError):
lq1[2] = 100.*u.m
with pytest.raises(u.UnitsError):
lq1[2] = 100.*u.mag
with pytest.raises(u.UnitsError):
lq1[2] = u.Magnitude(100.*u.m)
assert lq1[2] == u.Magnitude(100.*u.Jy)
def test_slice_get_and_set(self):
lq1 = u.Magnitude(np.arange(1., 10.)*u.Jy)
lq1[2:4] = 100.*u.Jy
assert np.all(lq1[2:4] == u.Magnitude(100.*u.Jy))
with pytest.raises(u.UnitsError):
lq1[2:4] = 100.*u.m
with pytest.raises(u.UnitsError):
lq1[2:4] = 100.*u.mag
with pytest.raises(u.UnitsError):
lq1[2:4] = u.Magnitude(100.*u.m)
assert np.all(lq1[2] == u.Magnitude(100.*u.Jy))
class TestLogQuantityArithmetic(object):
def test_multiplication_division(self):
"""Check that multiplication/division with other quantities is only
possible when the physical unit is dimensionless, and that this turns
the result into a normal quantity."""
lq = u.Magnitude(np.arange(1., 11.)*u.Jy)
with pytest.raises(u.UnitsError):
lq * (1.*u.m)
with pytest.raises(u.UnitsError):
(1.*u.m) * lq
with pytest.raises(u.UnitsError):
lq / lq
for unit in (u.m, u.mag, u.dex):
with pytest.raises(u.UnitsError):
lq / unit
lq2 = u.Magnitude(np.arange(1, 11.))
with pytest.raises(u.UnitsError):
lq2 * lq
with pytest.raises(u.UnitsError):
lq2 / lq
with pytest.raises(u.UnitsError):
lq / lq2
# but dimensionless_unscaled can be cancelled
r = lq2 / u.Magnitude(2.)
assert r.unit == u.dimensionless_unscaled
assert np.all(r.value == lq2.value/2.)
# with dimensionless, normal units OK, but return normal quantities
tf = lq2 * u.m
tr = u.m * lq2
for t in (tf, tr):
assert not isinstance(t, type(lq2))
assert t.unit == lq2.unit.function_unit * u.m
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(lq2.unit.physical_unit)
t = tf / (50.*u.cm)
# now we essentially have the same quantity but with a prefactor of 2
assert t.unit.is_equivalent(lq2.unit.function_unit)
assert_allclose(t.to(lq2.unit.function_unit), lq2._function_view*2)
@pytest.mark.parametrize('power', (2, 0.5, 1, 0))
def test_raise_to_power(self, power):
"""Check that raising LogQuantities to some power is only possible when
the physical unit is dimensionless, and that conversion is turned off
when the resulting logarithmic unit (say, mag**2) is incompatible."""
lq = u.Magnitude(np.arange(1., 4.)*u.Jy)
if power == 0:
assert np.all(lq ** power == 1.)
elif power == 1:
assert np.all(lq ** power == lq)
else:
with pytest.raises(u.UnitsError):
lq ** power
# with dimensionless, it works, but falls back to normal quantity
# (except for power=1)
lq2 = u.Magnitude(np.arange(10.))
t = lq2**power
if power == 0:
assert t.unit is u.dimensionless_unscaled
assert np.all(t.value == 1.)
elif power == 1:
assert | np.all(t == lq2) | numpy.all |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[2].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[2].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
plt.savefig('jss_figures/DFA_different_trends.png')
plt.show()
# plot 6b
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences Zoomed Region', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[0].set_ylim(-5.5, 5.5)
axs[0].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].set_ylim(-5.5, 5.5)
axs[1].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([np.pi, (3 / 2) * np.pi])
axs[2].set_xticklabels([r'$\pi$', r'$\frac{3}{2}\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].set_ylim(-5.5, 5.5)
axs[2].set_xlim(0.95 * np.pi, 1.55 * np.pi)
plt.savefig('jss_figures/DFA_different_trends_zoomed.png')
plt.show()
hs_ouputs = hilbert_spectrum(time, imfs_51, hts_51, ifs_51, max_frequency=12, plot=False)
# plot 6c
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Simple Sinusoidal Time Seres with Added Noise', 50))
x_hs, y, z = hs_ouputs
z_min, z_max = 0, np.abs(z).max()
ax.pcolormesh(x_hs, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max)
ax.plot(x_hs[0, :], 8 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 8$', Linewidth=3)
ax.plot(x_hs[0, :], 4 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 4$', Linewidth=3)
ax.plot(x_hs[0, :], 2 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 2$', Linewidth=3)
ax.set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
ax.set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$'])
plt.ylabel(r'Frequency (rad.s$^{-1}$)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.85, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/DFA_hilbert_spectrum.png')
plt.show()
# plot 6c
time = np.linspace(0, 5 * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 51)
fluc = Fluctuation(time=time, time_series=time_series)
max_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=False)
max_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=True)
min_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=False)
min_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=True)
util = Utility(time=time, time_series=time_series)
maxima = util.max_bool_func_1st_order_fd()
minima = util.min_bool_func_1st_order_fd()
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title(textwrap.fill('Plot Demonstrating Unsmoothed Extrema Envelopes if Schoenberg–Whitney Conditions are Not Satisfied', 50))
plt.plot(time, time_series, label='Time series', zorder=2, LineWidth=2)
plt.scatter(time[maxima], time_series[maxima], c='r', label='Maxima', zorder=10)
plt.scatter(time[minima], time_series[minima], c='b', label='Minima', zorder=10)
plt.plot(time, max_unsmoothed[0], label=textwrap.fill('Unsmoothed maxima envelope', 10), c='darkorange')
plt.plot(time, max_smoothed[0], label=textwrap.fill('Smoothed maxima envelope', 10), c='red')
plt.plot(time, min_unsmoothed[0], label=textwrap.fill('Unsmoothed minima envelope', 10), c='cyan')
plt.plot(time, min_smoothed[0], label=textwrap.fill('Smoothed minima envelope', 10), c='blue')
for knot in knots[:-1]:
plt.plot(knot * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', zorder=1)
plt.plot(knots[-1] * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', label='Knots', zorder=1)
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi),
(r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$', r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Schoenberg_Whitney_Conditions.png')
plt.show()
# plot 7
a = 0.25
width = 0.2
time = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 1001)
knots = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 11)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
inflection_bool = utils.inflection_point()
inflection_x = time[inflection_bool]
inflection_y = time_series[inflection_bool]
fluctuation = emd_mean.Fluctuation(time=time, time_series=time_series)
maxima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
maxima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
inflection_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='inflection_points')[0]
binomial_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='binomial_average', order=21,
increment=20)[0]
derivative_of_lsq = utils.derivative_forward_diff()
derivative_time = time[:-1]
derivative_knots = np.linspace(knots[0], knots[-1], 31)
# change (1) detrended_fluctuation_technique and (2) max_internal_iter and (3) debug (confusing with external debugging)
emd = AdvEMDpy.EMD(time=derivative_time, time_series=derivative_of_lsq)
imf_1_of_derivative = emd.empirical_mode_decomposition(knots=derivative_knots,
knot_time=derivative_time, text=False, verbose=False)[0][1, :]
utils = emd_utils.Utility(time=time[:-1], time_series=imf_1_of_derivative)
optimal_maxima = np.r_[False, utils.derivative_forward_diff() < 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
optimal_minima = np.r_[False, utils.derivative_forward_diff() > 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
EEMD_maxima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'maxima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
EEMD_minima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'minima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Detrended Fluctuation Analysis Examples')
plt.plot(time, time_series, LineWidth=2, label='Time series')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(time[optimal_maxima], time_series[optimal_maxima], c='darkred', zorder=4,
label=textwrap.fill('Optimal maxima', 10))
plt.scatter(time[optimal_minima], time_series[optimal_minima], c='darkblue', zorder=4,
label=textwrap.fill('Optimal minima', 10))
plt.scatter(inflection_x, inflection_y, c='magenta', zorder=4, label=textwrap.fill('Inflection points', 10))
plt.plot(time, maxima_envelope, c='darkblue', label=textwrap.fill('EMD envelope', 10))
plt.plot(time, minima_envelope, c='darkblue')
plt.plot(time, (maxima_envelope + minima_envelope) / 2, c='darkblue')
plt.plot(time, maxima_envelope_smooth, c='darkred', label=textwrap.fill('SEMD envelope', 10))
plt.plot(time, minima_envelope_smooth, c='darkred')
plt.plot(time, (maxima_envelope_smooth + minima_envelope_smooth) / 2, c='darkred')
plt.plot(time, EEMD_maxima_envelope, c='darkgreen', label=textwrap.fill('EEMD envelope', 10))
plt.plot(time, EEMD_minima_envelope, c='darkgreen')
plt.plot(time, (EEMD_maxima_envelope + EEMD_minima_envelope) / 2, c='darkgreen')
plt.plot(time, inflection_points_envelope, c='darkorange', label=textwrap.fill('Inflection point envelope', 10))
plt.plot(time, binomial_points_envelope, c='deeppink', label=textwrap.fill('Binomial average envelope', 10))
plt.plot(time, np.cos(time), c='black', label='True mean')
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi), (r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$',
r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/detrended_fluctuation_analysis.png')
plt.show()
# Duffing Equation Example
def duffing_equation(xy, ts):
gamma = 0.1
epsilon = 1
omega = ((2 * np.pi) / 25)
return [xy[1], xy[0] - epsilon * xy[0] ** 3 + gamma * | np.cos(omega * ts) | numpy.cos |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cntk as C
import numpy as np
from .common import floatx, epsilon, image_dim_ordering, image_data_format
from collections import defaultdict
from contextlib import contextmanager
import warnings
C.set_global_option('align_axis', 1)
b_any = any
dev = C.device.use_default_device()
if dev.type() == 0:
warnings.warn(
'CNTK backend warning: GPU is not detected. '
'CNTK\'s CPU version is not fully optimized,'
'please run with GPU to get better performance.')
# A learning phase is a bool tensor used to run Keras models in
# either train mode (learning_phase == 1) or test mode (learning_phase == 0).
# LEARNING_PHASE_PLACEHOLDER is the placeholder for dynamic learning phase
_LEARNING_PHASE_PLACEHOLDER = C.constant(shape=(), dtype=np.float32, value=1.0, name='_keras_learning_phase')
# static learning phase flag, if it is not 0 or 1, we will go with dynamic learning phase tensor.
_LEARNING_PHASE = -1
_UID_PREFIXES = defaultdict(int)
# cntk doesn't support gradient as symbolic op, to hook up with keras model,
# we will create gradient as a constant placeholder, here use this global
# map to keep the mapping from grad placeholder to parameter
grad_parameter_dict = {}
NAME_SCOPE_STACK = []
@contextmanager
def name_scope(name):
global NAME_SCOPE_STACK
NAME_SCOPE_STACK.append(name)
yield
NAME_SCOPE_STACK.pop()
def get_uid(prefix=''):
_UID_PREFIXES[prefix] += 1
return _UID_PREFIXES[prefix]
def learning_phase():
# If _LEARNING_PHASE is not 0 or 1, return dynamic learning phase tensor
return _LEARNING_PHASE if _LEARNING_PHASE in {0, 1} else _LEARNING_PHASE_PLACEHOLDER
def set_learning_phase(value):
global _LEARNING_PHASE
if value not in {0, 1}:
raise ValueError('CNTK Backend: Set learning phase '
'with value %s is not supported, '
'expected 0 or 1.' % value)
_LEARNING_PHASE = value
def clear_session():
"""Reset learning phase flag for cntk backend.
"""
global _LEARNING_PHASE
global _LEARNING_PHASE_PLACEHOLDER
_LEARNING_PHASE = -1
_LEARNING_PHASE_PLACEHOLDER.value = | np.asarray(1.0) | numpy.asarray |
import os
import numpy as np
import pandas as pd
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import img_to_array, load_img
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preprocessing import LabelEncoder, StandardScaler
def load_numeric_training(standardize=True):
data = pd.read_csv('../train.csv')
ID = data.pop('id')
y = data.pop('species')
y = LabelEncoder().fit(y).transform(y)
X = StandardScaler().fit(data).transform(data) if standardize else data.values
return ID.values, X, y
def load_numeric_test(standardize=True):
data = pd.read_csv('../test.csv')
ID = data.pop('id')
test = StandardScaler().fit(data).transform(data) if standardize else data.values
return ID.values, test
def resize_img(img, max_dim=96):
max_axis = np.argmax(img.size)
scale = max_dim / img.size[max_axis]
return img.resize((int(img.size[0] * scale), int(img.size[1] * scale)))
def load_img_data(ids, max_dim=96, center=True):
X = np.empty((len(ids), max_dim, max_dim, 1))
for i, id in enumerate(ids):
img = load_img('../images/{}.jpg'.format(id), grayscale=True)
img = resize_img(img, max_dim=max_dim)
x = img_to_array(img)
h, w = x.shape[:2]
if center:
h1 = (max_dim - h) >> 1
h2 = h1 + h
w1 = (max_dim - w) >> 1
w2 = w1 + w
else:
h1, h2, w1, w2 = 0, h, 0, w
X[i][h1:h2, w1:w2][:] = x
return np.around(X / 255)
def load_train_data(split=0.9, random_state=7):
ID, X_num_train, y = load_numeric_training()
X_img_train = load_img_data(ID)
sss = StratifiedShuffleSplit(n_splits=1, train_size=split, test_size=1 - split, random_state=random_state)
train_idx, val_idx = next(sss.split(X_num_train, y))
ID_tr, X_num_tr, X_img_tr, y_tr = ID[train_idx], X_num_train[train_idx], X_img_train[train_idx], y[train_idx]
ID_val, X_num_val, X_img_val, y_val = ID[val_idx], X_num_train[val_idx], X_img_train[val_idx], y[val_idx]
return (ID_tr, X_num_tr, X_img_tr, y_tr), (ID_val, X_num_val, X_img_val, y_val)
def load_test_data():
ID, X_num_test = load_numeric_test()
X_img_test = load_img_data(ID)
return ID, X_num_test, X_img_test
print('Loading train data ...')
(ID_train, X_num_tr, X_img_tr, y_tr), (ID_val, X_num_val, X_img_val, y_val) = load_train_data()
# Prepare ID-to-label and ID-to-numerical dictionary
ID_y_dic, ID_num_dic = {}, {}
for i in range(len(ID_train)):
ID_y_dic[ID_train[i]] = y_tr[i]
ID_num_dic[ID_train[i]] = X_num_tr[i, :]
print('Loading test data ...')
ID_test, X_num_test, X_img_test = load_test_data()
# Convert label to categorical/one-hot
ID_train, y_tr, y_val = to_categorical(ID_train), to_categorical(y_tr), to_categorical((y_val))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _float32_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def write_val_data():
val_data_path = '../tfrecords/val_data_1.tfrecords'
if os.path.exists(val_data_path):
print('Warning: old file exists, removed.')
os.remove(val_data_path)
val_image, val_num, val_label = X_img_val.astype(np.bool), X_num_val.astype(np.float64), y_val.astype(np.bool)
print(val_image.shape, val_num.shape, val_label.shape)
val_writer = tf.python_io.TFRecordWriter(val_data_path)
print('Writing data into tfrecord ...')
for i in range(len(val_image)):
image, num, label = val_image[i], val_num[i], val_label[i]
feature = {'image': _bytes_feature(image.tostring()),
'num': _bytes_feature(num.tostring()),
'label': _bytes_feature(label.tostring())}
example = tf.train.Example(features=tf.train.Features(feature=feature))
val_writer.write(example.SerializeToString())
print('Done!')
def write_train_data():
imgen = ImageDataGenerator(rotation_range=20, zoom_range=0.2, horizontal_flip=True,
vertical_flip=True, fill_mode='nearest')
imgen_train = imgen.flow(X_img_tr, ID_train, batch_size=32, seed=7)
print('Generating augmented images')
all_images = []
all_ID = []
p = True
for i in range(28 * 200):
print('Generating augmented images for epoch {}, batch {}'.format(i // 28, i % 28))
X, ID = imgen_train.next()
all_images.append(X)
all_ID.append( | np.argmax(ID, axis=1) | numpy.argmax |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
from external.transformations import translation_matrix, rotation_matrix
import external.transformations as tf
from Trace import Photon
from Geometry import Box, Cylinder, FinitePlane, transform_point, transform_direction, rotation_matrix_from_vector_alignment, norm
from Materials import Spectrum
def random_spherecial_vector():
# This method of calculating isotropic vectors is taken from GNU Scientific Library
LOOP = True
while LOOP:
x = -1. + 2. * np.random.uniform()
y = -1. + 2. * np.random.uniform()
s = x**2 + y**2
if s <= 1.0:
LOOP = False
z = -1. + 2. * s
a = 2 * np.sqrt(1 - s)
x = a * x
y = a * y
return np.array([x,y,z])
class SimpleSource(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, use_random_polarisation=False):
super(SimpleSource, self).__init__()
self.position = position
self.direction = direction
self.wavelength = wavelength
self.use_random_polarisation = use_random_polarisation
self.throw = 0
self.source_id = "SimpleSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
# If use_polarisation is set generate a random polarisation vector of the photon
if self.use_random_polarisation:
# Randomise rotation angle around xy-plane, the transform from +z to the direction of the photon
vec = random_spherecial_vector()
vec[2] = 0.
vec = norm(vec)
R = rotation_matrix_from_vector_alignment(self.direction, [0,0,1])
photon.polarisation = transform_direction(vec, R)
else:
photon.polarisation = None
photon.id = self.throw
self.throw = self.throw + 1
return photon
class Laser(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, polarisation=None):
super(Laser, self).__init__()
self.position = np.array(position)
self.direction = np.array(direction)
self.wavelength = wavelength
assert polarisation != None, "Polarisation of the Laser is not set."
self.polarisation = np.array(polarisation)
self.throw = 0
self.source_id = "LaserSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
photon.polarisation = self.polarisation
photon.id = self.throw
self.throw = self.throw + 1
return photon
class PlanarSource(object):
"""A box that emits photons from the top surface (normal), sampled from the spectrum."""
def __init__(self, spectrum=None, wavelength=555, direction=(0,0,1), length=0.05, width=0.05):
super(PlanarSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.plane = FinitePlane(length=length, width=width)
self.length = length
self.width = width
# direction is the direction that photons are fired out of the plane in the GLOBAL FRAME.
# i.e. this is passed directly to the photon to set is's direction
self.direction = direction
self.throw = 0
self.source_id = "PlanarSource_" + str(id(self))
def translate(self, translation):
self.plane.append_transform(tf.translation_matrix(translation))
def rotate(self, angle, axis):
self.plane.append_transform(tf.rotation_matrix(angle, axis))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Create a point which is on the surface of the finite plane in it's local frame
x = np.random.uniform(0., self.length)
y = np.random.uniform(0., self.width)
local_point = (x, y, 0.)
# Transform the direciton
photon.position = transform_point(local_point, self.plane.transform)
photon.direction = self.direction
photon.active = True
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class LensSource(object):
"""
A source where photons generated in a plane are focused on a line with space tolerance given by variable "focussize".
The focus line should be perpendicular to the plane normal and aligned with the z-axis.
"""
def __init__(self, spectrum = None, wavelength = 555, linepoint=(0,0,0), linedirection=(0,0,1), focussize = 0, planeorigin = (-1,-1,-1), planeextent = (-1,1,1)):
super(LensSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.planeorigin = planeorigin
self.planeextent = planeextent
self.linepoint = np.array(linepoint)
self.linedirection = np.array(linedirection)
self.focussize = focussize
self.throw = 0
self.source_id = "LensSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Position
x = np.random.uniform(self.planeorigin[0],self.planeextent[0])
y = np.random.uniform(self.planeorigin[1],self.planeextent[1])
z = np.random.uniform(self.planeorigin[2],self.planeextent[2])
photon.position = np.array((x,y,z))
# Direction
focuspoint = np.array((0.,0.,0.))
focuspoint[0] = self.linepoint[0] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[1] = self.linepoint[1] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[2] = photon.position[2]
direction = focuspoint - photon.position
modulus = (direction[0]**2+direction[1]**2+direction[2]**2)**0.5
photon.direction = direction/modulus
# Wavelength
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class LensSourceAngle(object):
"""
A source where photons generated in a plane are focused on a line with space tolerance given by variable "focussize".
The focus line should be perpendicular to the plane normal and aligned with the z-axis.
For this lense an additional z-boost is added (Angle of incidence in z-direction).
"""
def __init__(self, spectrum = None, wavelength = 555, linepoint=(0,0,0), linedirection=(0,0,1), angle = 0, focussize = 0, planeorigin = (-1,-1,-1), planeextent = (-1,1,1)):
super(LensSourceAngle, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.planeorigin = planeorigin
self.planeextent = planeextent
self.linepoint = np.array(linepoint)
self.linedirection = np.array(linedirection)
self.focussize = focussize
self.angle = angle
self.throw = 0
self.source_id = "LensSourceAngle_" + str(id(self))
def photon(self):
photon = Photon()
photon.id = self.throw
self.throw = self.throw + 1
# Position
x = np.random.uniform(self.planeorigin[0],self.planeextent[0])
y = np.random.uniform(self.planeorigin[1],self.planeextent[1])
boost = y*np.tan(self.angle)
z = np.random.uniform(self.planeorigin[2],self.planeextent[2]) - boost
photon.position = np.array((x,y,z))
# Direction
focuspoint = np.array((0.,0.,0.))
focuspoint[0] = self.linepoint[0] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[1] = self.linepoint[1] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[2] = photon.position[2] + boost
direction = focuspoint - photon.position
modulus = (direction[0]**2+direction[1]**2+direction[2]**2)**0.5
photon.direction = direction/modulus
# Wavelength
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class CylindricalSource(object):
"""
A source for photons emitted in a random direction and position inside a cylinder(radius, length)
"""
def __init__(self, spectrum = None, wavelength = 555, radius = 1, length = 10):
super(CylindricalSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.shape = Cylinder(radius = radius, length = length)
self.radius = radius
self.length = length
self.throw = 0
self.source_id = "CylindricalSource_" + str(id(self))
def translate(self, translation):
self.shape.append_transform(tf.translation_matrix(translation))
def rotate(self, angle, axis):
self.shape.append_transform(tf.rotation_matrix(angle, axis))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Position of emission
phi = | np.random.uniform(0., 2*np.pi) | numpy.random.uniform |
"""Test the search module"""
from collections.abc import Iterable, Sized
from io import StringIO
from itertools import chain, product
from functools import partial
import pickle
import sys
from types import GeneratorType
import re
import numpy as np
import scipy.sparse as sp
import pytest
from sklearn.utils.fixes import sp_version
from sklearn.utils._testing import assert_raises
from sklearn.utils._testing import assert_warns
from sklearn.utils._testing import assert_warns_message
from sklearn.utils._testing import assert_raise_message
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_allclose
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import ignore_warnings
from sklearn.utils._mocking import CheckingClassifier, MockDataFrame
from scipy.stats import bernoulli, expon, uniform
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.base import clone
from sklearn.exceptions import NotFittedError
from sklearn.datasets import make_classification
from sklearn.datasets import make_blobs
from sklearn.datasets import make_multilabel_classification
from sklearn.model_selection import fit_grid_point
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.model_selection import LeaveOneGroupOut
from sklearn.model_selection import LeavePGroupsOut
from sklearn.model_selection import GroupKFold
from sklearn.model_selection import GroupShuffleSplit
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import ParameterGrid
from sklearn.model_selection import ParameterSampler
from sklearn.model_selection._search import BaseSearchCV
from sklearn.model_selection._validation import FitFailedWarning
from sklearn.svm import LinearSVC, SVC
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeClassifier
from sklearn.cluster import KMeans
from sklearn.neighbors import KernelDensity
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import f1_score
from sklearn.metrics import recall_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import make_scorer
from sklearn.metrics import roc_auc_score
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression
from sklearn.experimental import enable_hist_gradient_boosting # noqa
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.model_selection.tests.common import OneTimeSplitter
# Neither of the following two estimators inherit from BaseEstimator,
# to test hyperparameter search on user-defined classifiers.
class MockClassifier:
"""Dummy classifier to test the parameter search algorithms"""
def __init__(self, foo_param=0):
self.foo_param = foo_param
def fit(self, X, Y):
assert len(X) == len(Y)
self.classes_ = np.unique(Y)
return self
def predict(self, T):
return T.shape[0]
def transform(self, X):
return X + self.foo_param
def inverse_transform(self, X):
return X - self.foo_param
predict_proba = predict
predict_log_proba = predict
decision_function = predict
def score(self, X=None, Y=None):
if self.foo_param > 1:
score = 1.
else:
score = 0.
return score
def get_params(self, deep=False):
return {'foo_param': self.foo_param}
def set_params(self, **params):
self.foo_param = params['foo_param']
return self
class LinearSVCNoScore(LinearSVC):
"""An LinearSVC classifier that has no score method."""
@property
def score(self):
raise AttributeError
X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
y = np.array([1, 1, 2, 2])
def assert_grid_iter_equals_getitem(grid):
assert list(grid) == [grid[i] for i in range(len(grid))]
@pytest.mark.parametrize("klass", [ParameterGrid,
partial(ParameterSampler, n_iter=10)])
@pytest.mark.parametrize(
"input, error_type, error_message",
[(0, TypeError, r'Parameter .* is not a dict or a list \(0\)'),
([{'foo': [0]}, 0], TypeError, r'Parameter .* is not a dict \(0\)'),
({'foo': 0}, TypeError, "Parameter.* value is not iterable .*"
r"\(key='foo', value=0\)")]
)
def test_validate_parameter_input(klass, input, error_type, error_message):
with pytest.raises(error_type, match=error_message):
klass(input)
def test_parameter_grid():
# Test basic properties of ParameterGrid.
params1 = {"foo": [1, 2, 3]}
grid1 = ParameterGrid(params1)
assert isinstance(grid1, Iterable)
assert isinstance(grid1, Sized)
assert len(grid1) == 3
assert_grid_iter_equals_getitem(grid1)
params2 = {"foo": [4, 2],
"bar": ["ham", "spam", "eggs"]}
grid2 = ParameterGrid(params2)
assert len(grid2) == 6
# loop to assert we can iterate over the grid multiple times
for i in range(2):
# tuple + chain transforms {"a": 1, "b": 2} to ("a", 1, "b", 2)
points = set(tuple(chain(*(sorted(p.items())))) for p in grid2)
assert (points ==
set(("bar", x, "foo", y)
for x, y in product(params2["bar"], params2["foo"])))
assert_grid_iter_equals_getitem(grid2)
# Special case: empty grid (useful to get default estimator settings)
empty = ParameterGrid({})
assert len(empty) == 1
assert list(empty) == [{}]
assert_grid_iter_equals_getitem(empty)
assert_raises(IndexError, lambda: empty[1])
has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}])
assert len(has_empty) == 4
assert list(has_empty) == [{'C': 1}, {'C': 10}, {}, {'C': .5}]
assert_grid_iter_equals_getitem(has_empty)
def test_grid_search():
# Test that the best estimator contains the right value for foo_param
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3)
# make sure it selects the smallest parameter in case of ties
old_stdout = sys.stdout
sys.stdout = StringIO()
grid_search.fit(X, y)
sys.stdout = old_stdout
assert grid_search.best_estimator_.foo_param == 2
assert_array_equal(grid_search.cv_results_["param_foo_param"].data,
[1, 2, 3])
# Smoke test the score etc:
grid_search.score(X, y)
grid_search.predict_proba(X)
grid_search.decision_function(X)
grid_search.transform(X)
# Test exception handling on scoring
grid_search.scoring = 'sklearn'
assert_raises(ValueError, grid_search.fit, X, y)
def test_grid_search_pipeline_steps():
# check that parameters that are estimators are cloned before fitting
pipe = Pipeline([('regressor', LinearRegression())])
param_grid = {'regressor': [LinearRegression(), Ridge()]}
grid_search = GridSearchCV(pipe, param_grid, cv=2)
grid_search.fit(X, y)
regressor_results = grid_search.cv_results_['param_regressor']
assert isinstance(regressor_results[0], LinearRegression)
assert isinstance(regressor_results[1], Ridge)
assert not hasattr(regressor_results[0], 'coef_')
assert not hasattr(regressor_results[1], 'coef_')
assert regressor_results[0] is not grid_search.best_estimator_
assert regressor_results[1] is not grid_search.best_estimator_
# check that we didn't modify the parameter grid that was passed
assert not hasattr(param_grid['regressor'][0], 'coef_')
assert not hasattr(param_grid['regressor'][1], 'coef_')
@pytest.mark.parametrize("SearchCV", [GridSearchCV, RandomizedSearchCV])
def test_SearchCV_with_fit_params(SearchCV):
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = CheckingClassifier(expected_fit_params=['spam', 'eggs'])
searcher = SearchCV(
clf, {'foo_param': [1, 2, 3]}, cv=2, error_score="raise"
)
# The CheckingClassifier generates an assertion error if
# a parameter is missing or has length != len(X).
err_msg = r"Expected fit parameter\(s\) \['eggs'\] not seen."
with pytest.raises(AssertionError, match=err_msg):
searcher.fit(X, y, spam=np.ones(10))
err_msg = "Fit parameter spam has length 1; expected"
with pytest.raises(AssertionError, match=err_msg):
searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10))
searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10))
@ignore_warnings
def test_grid_search_no_score():
# Test grid-search on classifier that has no score function.
clf = LinearSVC(random_state=0)
X, y = make_blobs(random_state=0, centers=2)
Cs = [.1, 1, 10]
clf_no_score = LinearSVCNoScore(random_state=0)
grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy')
grid_search.fit(X, y)
grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs},
scoring='accuracy')
# smoketest grid search
grid_search_no_score.fit(X, y)
# check that best params are equal
assert grid_search_no_score.best_params_ == grid_search.best_params_
# check that we can call score and that it gives the correct result
assert grid_search.score(X, y) == grid_search_no_score.score(X, y)
# giving no scoring function raises an error
grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs})
assert_raise_message(TypeError, "no scoring", grid_search_no_score.fit,
[[1]])
def test_grid_search_score_method():
X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2,
random_state=0)
clf = LinearSVC(random_state=0)
grid = {'C': [.1]}
search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y)
search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y)
search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid,
scoring='roc_auc'
).fit(X, y)
search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y)
# Check warning only occurs in situation where behavior changed:
# estimator requires score method to compete with scoring parameter
score_no_scoring = search_no_scoring.score(X, y)
score_accuracy = search_accuracy.score(X, y)
score_no_score_auc = search_no_score_method_auc.score(X, y)
score_auc = search_auc.score(X, y)
# ensure the test is sane
assert score_auc < 1.0
assert score_accuracy < 1.0
assert score_auc != score_accuracy
assert_almost_equal(score_accuracy, score_no_scoring)
assert_almost_equal(score_auc, score_no_score_auc)
def test_grid_search_groups():
# Check if ValueError (when groups is None) propagates to GridSearchCV
# And also check if groups is correctly passed to the cv object
rng = np.random.RandomState(0)
X, y = make_classification(n_samples=15, n_classes=2, random_state=0)
groups = rng.randint(0, 3, 15)
clf = LinearSVC(random_state=0)
grid = {'C': [1]}
group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2),
GroupKFold(n_splits=3), GroupShuffleSplit()]
for cv in group_cvs:
gs = GridSearchCV(clf, grid, cv=cv)
assert_raise_message(ValueError,
"The 'groups' parameter should not be None.",
gs.fit, X, y)
gs.fit(X, y, groups=groups)
non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()]
for cv in non_group_cvs:
gs = GridSearchCV(clf, grid, cv=cv)
# Should not raise an error
gs.fit(X, y)
def test_classes__property():
# Test that classes_ property matches best_estimator_.classes_
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
Cs = [.1, 1, 10]
grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs})
grid_search.fit(X, y)
assert_array_equal(grid_search.best_estimator_.classes_,
grid_search.classes_)
# Test that regressors do not have a classes_ attribute
grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]})
grid_search.fit(X, y)
assert not hasattr(grid_search, 'classes_')
# Test that the grid searcher has no classes_ attribute before it's fit
grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs})
assert not hasattr(grid_search, 'classes_')
# Test that the grid searcher has no classes_ attribute without a refit
grid_search = GridSearchCV(LinearSVC(random_state=0),
{'C': Cs}, refit=False)
grid_search.fit(X, y)
assert not hasattr(grid_search, 'classes_')
def test_trivial_cv_results_attr():
# Test search over a "grid" with only one point.
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3)
grid_search.fit(X, y)
assert hasattr(grid_search, "cv_results_")
random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3)
random_search.fit(X, y)
assert hasattr(grid_search, "cv_results_")
def test_no_refit():
# Test that GSCV can be used for model selection alone without refitting
clf = MockClassifier()
for scoring in [None, ['accuracy', 'precision']]:
grid_search = GridSearchCV(
clf, {'foo_param': [1, 2, 3]}, refit=False, cv=3
)
grid_search.fit(X, y)
assert not hasattr(grid_search, "best_estimator_") and \
hasattr(grid_search, "best_index_") and \
hasattr(grid_search, "best_params_")
# Make sure the functions predict/transform etc raise meaningful
# error messages
for fn_name in ('predict', 'predict_proba', 'predict_log_proba',
'transform', 'inverse_transform'):
assert_raise_message(NotFittedError,
('refit=False. %s is available only after '
'refitting on the best parameters'
% fn_name), getattr(grid_search, fn_name), X)
# Test that an invalid refit param raises appropriate error messages
for refit in ["", 5, True, 'recall', 'accuracy']:
assert_raise_message(ValueError, "For multi-metric scoring, the "
"parameter refit must be set to a scorer key",
GridSearchCV(clf, {}, refit=refit,
scoring={'acc': 'accuracy',
'prec': 'precision'}
).fit,
X, y)
def test_grid_search_error():
# Test that grid search will capture errors on data with different length
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
assert_raises(ValueError, cv.fit, X_[:180], y_)
def test_grid_search_one_grid_point():
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
param_dict = {"C": [1.0], "kernel": ["rbf"], "gamma": [0.1]}
clf = SVC(gamma='auto')
cv = GridSearchCV(clf, param_dict)
cv.fit(X_, y_)
clf = SVC(C=1.0, kernel="rbf", gamma=0.1)
clf.fit(X_, y_)
assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_)
def test_grid_search_when_param_grid_includes_range():
# Test that the best estimator contains the right value for foo_param
clf = MockClassifier()
grid_search = None
grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3)
grid_search.fit(X, y)
assert grid_search.best_estimator_.foo_param == 2
def test_grid_search_bad_param_grid():
param_dict = {"C": 1}
clf = SVC(gamma='auto')
assert_raise_message(
ValueError,
"Parameter grid for parameter (C) needs to"
" be a list or numpy array, but got (<class 'int'>)."
" Single values need to be wrapped in a list"
" with one element.",
GridSearchCV, clf, param_dict)
param_dict = {"C": []}
clf = SVC()
assert_raise_message(
ValueError,
"Parameter values for parameter (C) need to be a non-empty sequence.",
GridSearchCV, clf, param_dict)
param_dict = {"C": "1,2,3"}
clf = SVC(gamma='auto')
assert_raise_message(
ValueError,
"Parameter grid for parameter (C) needs to"
" be a list or numpy array, but got (<class 'str'>)."
" Single values need to be wrapped in a list"
" with one element.",
GridSearchCV, clf, param_dict)
param_dict = {"C": np.ones((3, 2))}
clf = SVC()
assert_raises(ValueError, GridSearchCV, clf, param_dict)
def test_grid_search_sparse():
# Test that grid search works with both dense and sparse matrices
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
cv.fit(X_[:180], y_[:180])
y_pred = cv.predict(X_[180:])
C = cv.best_estimator_.C
X_ = sp.csr_matrix(X_)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
cv.fit(X_[:180].tocoo(), y_[:180])
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator_.C
assert np.mean(y_pred == y_pred2) >= .9
assert C == C2
def test_grid_search_sparse_scoring():
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring="f1")
cv.fit(X_[:180], y_[:180])
y_pred = cv.predict(X_[180:])
C = cv.best_estimator_.C
X_ = sp.csr_matrix(X_)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring="f1")
cv.fit(X_[:180], y_[:180])
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator_.C
assert_array_equal(y_pred, y_pred2)
assert C == C2
# Smoke test the score
# np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]),
# cv.score(X_[:180], y[:180]))
# test loss where greater is worse
def f1_loss(y_true_, y_pred_):
return -f1_score(y_true_, y_pred_)
F1Loss = make_scorer(f1_loss, greater_is_better=False)
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss)
cv.fit(X_[:180], y_[:180])
y_pred3 = cv.predict(X_[180:])
C3 = cv.best_estimator_.C
assert C == C3
assert_array_equal(y_pred, y_pred3)
def test_grid_search_precomputed_kernel():
# Test that grid search works when the input features are given in the
# form of a precomputed kernel matrix
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
# compute the training kernel matrix corresponding to the linear kernel
K_train = np.dot(X_[:180], X_[:180].T)
y_train = y_[:180]
clf = SVC(kernel='precomputed')
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
cv.fit(K_train, y_train)
assert cv.best_score_ >= 0
# compute the test kernel matrix
K_test = np.dot(X_[180:], X_[:180].T)
y_test = y_[180:]
y_pred = cv.predict(K_test)
assert np.mean(y_pred == y_test) >= 0
# test error is raised when the precomputed kernel is not array-like
# or sparse
assert_raises(ValueError, cv.fit, K_train.tolist(), y_train)
def test_grid_search_precomputed_kernel_error_nonsquare():
# Test that grid search returns an error with a non-square precomputed
# training kernel matrix
K_train = np.zeros((10, 20))
y_train = np.ones((10, ))
clf = SVC(kernel='precomputed')
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
assert_raises(ValueError, cv.fit, K_train, y_train)
class BrokenClassifier(BaseEstimator):
"""Broken classifier that cannot be fit twice"""
def __init__(self, parameter=None):
self.parameter = parameter
def fit(self, X, y):
assert not hasattr(self, 'has_been_fit_')
self.has_been_fit_ = True
def predict(self, X):
return np.zeros(X.shape[0])
@ignore_warnings
def test_refit():
# Regression test for bug in refitting
# Simulates re-fitting a broken estimator; this used to break with
# sparse SVMs.
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}],
scoring="precision", refit=True)
clf.fit(X, y)
def test_refit_callable():
"""
Test refit=callable, which adds flexibility in identifying the
"best" estimator.
"""
def refit_callable(cv_results):
"""
A dummy function tests `refit=callable` interface.
Return the index of a model that has the least
`mean_test_score`.
"""
# Fit a dummy clf with `refit=True` to get a list of keys in
# clf.cv_results_.
X, y = make_classification(n_samples=100, n_features=4,
random_state=42)
clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]},
scoring='precision', refit=True)
clf.fit(X, y)
# Ensure that `best_index_ != 0` for this dummy clf
assert clf.best_index_ != 0
# Assert every key matches those in `cv_results`
for key in clf.cv_results_.keys():
assert key in cv_results
return cv_results['mean_test_score'].argmin()
X, y = make_classification(n_samples=100, n_features=4,
random_state=42)
clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]},
scoring='precision', refit=refit_callable)
clf.fit(X, y)
assert clf.best_index_ == 0
# Ensure `best_score_` is disabled when using `refit=callable`
assert not hasattr(clf, 'best_score_')
def test_refit_callable_invalid_type():
"""
Test implementation catches the errors when 'best_index_' returns an
invalid result.
"""
def refit_callable_invalid_type(cv_results):
"""
A dummy function tests when returned 'best_index_' is not integer.
"""
return None
X, y = make_classification(n_samples=100, n_features=4,
random_state=42)
clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]},
scoring='precision', refit=refit_callable_invalid_type)
with pytest.raises(TypeError,
match='best_index_ returned is not an integer'):
clf.fit(X, y)
@pytest.mark.parametrize('out_bound_value', [-1, 2])
@pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV])
def test_refit_callable_out_bound(out_bound_value, search_cv):
"""
Test implementation catches the errors when 'best_index_' returns an
out of bound result.
"""
def refit_callable_out_bound(cv_results):
"""
A dummy function tests when returned 'best_index_' is out of bounds.
"""
return out_bound_value
X, y = make_classification(n_samples=100, n_features=4,
random_state=42)
clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]},
scoring='precision', refit=refit_callable_out_bound)
with pytest.raises(IndexError, match='best_index_ index out of range'):
clf.fit(X, y)
def test_refit_callable_multi_metric():
"""
Test refit=callable in multiple metric evaluation setting
"""
def refit_callable(cv_results):
"""
A dummy function tests `refit=callable` interface.
Return the index of a model that has the least
`mean_test_prec`.
"""
assert 'mean_test_prec' in cv_results
return cv_results['mean_test_prec'].argmin()
X, y = make_classification(n_samples=100, n_features=4,
random_state=42)
scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'}
clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]},
scoring=scoring, refit=refit_callable)
clf.fit(X, y)
assert clf.best_index_ == 0
# Ensure `best_score_` is disabled when using `refit=callable`
assert not hasattr(clf, 'best_score_')
def test_gridsearch_nd():
# Pass X as list in GridSearchCV
X_4d = np.arange(10 * 5 * 3 * 2).reshape(10, 5, 3, 2)
y_3d = np.arange(10 * 7 * 11).reshape(10, 7, 11)
check_X = lambda x: x.shape[1:] == (5, 3, 2)
check_y = lambda x: x.shape[1:] == (7, 11)
clf = CheckingClassifier(
check_X=check_X, check_y=check_y, methods_to_check=["fit"],
)
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]})
grid_search.fit(X_4d, y_3d).score(X, y)
assert hasattr(grid_search, "cv_results_")
def test_X_as_list():
# Pass X as list in GridSearchCV
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = CheckingClassifier(
check_X=lambda x: isinstance(x, list), methods_to_check=["fit"],
)
cv = KFold(n_splits=3)
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv)
grid_search.fit(X.tolist(), y).score(X, y)
assert hasattr(grid_search, "cv_results_")
def test_y_as_list():
# Pass y as list in GridSearchCV
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = CheckingClassifier(
check_y=lambda x: isinstance(x, list), methods_to_check=["fit"],
)
cv = KFold(n_splits=3)
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv)
grid_search.fit(X, y.tolist()).score(X, y)
assert hasattr(grid_search, "cv_results_")
@ignore_warnings
def test_pandas_input():
# check cross_val_score doesn't destroy pandas dataframe
types = [(MockDataFrame, MockDataFrame)]
try:
from pandas import Series, DataFrame
types.append((DataFrame, Series))
except ImportError:
pass
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
for InputFeatureType, TargetType in types:
# X dataframe, y series
X_df, y_ser = InputFeatureType(X), TargetType(y)
def check_df(x):
return isinstance(x, InputFeatureType)
def check_series(x):
return isinstance(x, TargetType)
clf = CheckingClassifier(check_X=check_df, check_y=check_series)
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]})
grid_search.fit(X_df, y_ser).score(X_df, y_ser)
grid_search.predict(X_df)
assert hasattr(grid_search, "cv_results_")
def test_unsupervised_grid_search():
# test grid-search with unsupervised estimator
X, y = make_blobs(n_samples=50, random_state=0)
km = KMeans(random_state=0, init="random", n_init=1)
# Multi-metric evaluation unsupervised
scoring = ['adjusted_rand_score', 'fowlkes_mallows_score']
for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']:
grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]),
scoring=scoring, refit=refit)
grid_search.fit(X, y)
# Both ARI and FMS can find the right number :)
assert grid_search.best_params_["n_clusters"] == 3
# Single metric evaluation unsupervised
grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]),
scoring='fowlkes_mallows_score')
grid_search.fit(X, y)
assert grid_search.best_params_["n_clusters"] == 3
# Now without a score, and without y
grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]))
grid_search.fit(X)
assert grid_search.best_params_["n_clusters"] == 4
def test_gridsearch_no_predict():
# test grid-search with an estimator without predict.
# slight duplication of a test from KDE
def custom_scoring(estimator, X):
return 42 if estimator.bandwidth == .1 else 0
X, _ = make_blobs(cluster_std=.1, random_state=1,
centers=[[0, 1], [1, 0], [0, 0]])
search = GridSearchCV(KernelDensity(),
param_grid=dict(bandwidth=[.01, .1, 1]),
scoring=custom_scoring)
search.fit(X)
assert search.best_params_['bandwidth'] == .1
assert search.best_score_ == 42
def test_param_sampler():
# test basic properties of param sampler
param_distributions = {"kernel": ["rbf", "linear"],
"C": uniform(0, 1)}
sampler = ParameterSampler(param_distributions=param_distributions,
n_iter=10, random_state=0)
samples = [x for x in sampler]
assert len(samples) == 10
for sample in samples:
assert sample["kernel"] in ["rbf", "linear"]
assert 0 <= sample["C"] <= 1
# test that repeated calls yield identical parameters
param_distributions = {"C": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}
sampler = ParameterSampler(param_distributions=param_distributions,
n_iter=3, random_state=0)
assert [x for x in sampler] == [x for x in sampler]
if sp_version >= (0, 16):
param_distributions = {"C": uniform(0, 1)}
sampler = ParameterSampler(param_distributions=param_distributions,
n_iter=10, random_state=0)
assert [x for x in sampler] == [x for x in sampler]
def check_cv_results_array_types(search, param_keys, score_keys):
# Check if the search `cv_results`'s array are of correct types
cv_results = search.cv_results_
assert all(isinstance(cv_results[param], np.ma.MaskedArray)
for param in param_keys)
assert all(cv_results[key].dtype == object for key in param_keys)
assert not any(isinstance(cv_results[key], np.ma.MaskedArray)
for key in score_keys)
assert all(cv_results[key].dtype == np.float64
for key in score_keys if not key.startswith('rank'))
scorer_keys = search.scorer_.keys() if search.multimetric_ else ['score']
for key in scorer_keys:
assert cv_results['rank_test_%s' % key].dtype == np.int32
def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand):
# Test the search.cv_results_ contains all the required results
assert_array_equal(sorted(cv_results.keys()),
sorted(param_keys + score_keys + ('params',)))
assert all(cv_results[key].shape == (n_cand,)
for key in param_keys + score_keys)
def test_grid_search_cv_results():
X, y = make_classification(n_samples=50, n_features=4,
random_state=42)
n_splits = 3
n_grid_points = 6
params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]),
dict(kernel=['poly', ], degree=[1, 2])]
param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel')
score_keys = ('mean_test_score', 'mean_train_score',
'rank_test_score',
'split0_test_score', 'split1_test_score',
'split2_test_score',
'split0_train_score', 'split1_train_score',
'split2_train_score',
'std_test_score', 'std_train_score',
'mean_fit_time', 'std_fit_time',
'mean_score_time', 'std_score_time')
n_candidates = n_grid_points
search = GridSearchCV(SVC(), cv=n_splits, param_grid=params,
return_train_score=True)
search.fit(X, y)
cv_results = search.cv_results_
# Check if score and timing are reasonable
assert all(cv_results['rank_test_score'] >= 1)
assert (all(cv_results[k] >= 0) for k in score_keys
if k != 'rank_test_score')
assert (all(cv_results[k] <= 1) for k in score_keys
if 'time' not in k and
k != 'rank_test_score')
# Check cv_results structure
check_cv_results_array_types(search, param_keys, score_keys)
check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates)
# Check masking
cv_results = search.cv_results_
n_candidates = len(search.cv_results_['params'])
assert all((cv_results['param_C'].mask[i] and
cv_results['param_gamma'].mask[i] and
not cv_results['param_degree'].mask[i])
for i in range(n_candidates)
if cv_results['param_kernel'][i] == 'linear')
assert all((not cv_results['param_C'].mask[i] and
not cv_results['param_gamma'].mask[i] and
cv_results['param_degree'].mask[i])
for i in range(n_candidates)
if cv_results['param_kernel'][i] == 'rbf')
def test_random_search_cv_results():
X, y = make_classification(n_samples=50, n_features=4, random_state=42)
n_splits = 3
n_search_iter = 30
params = [{'kernel': ['rbf'], 'C': expon(scale=10),
'gamma': expon(scale=0.1)},
{'kernel': ['poly'], 'degree': [2, 3]}]
param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel')
score_keys = ('mean_test_score', 'mean_train_score',
'rank_test_score',
'split0_test_score', 'split1_test_score',
'split2_test_score',
'split0_train_score', 'split1_train_score',
'split2_train_score',
'std_test_score', 'std_train_score',
'mean_fit_time', 'std_fit_time',
'mean_score_time', 'std_score_time')
n_cand = n_search_iter
search = RandomizedSearchCV(SVC(), n_iter=n_search_iter,
cv=n_splits,
param_distributions=params,
return_train_score=True)
search.fit(X, y)
cv_results = search.cv_results_
# Check results structure
check_cv_results_array_types(search, param_keys, score_keys)
check_cv_results_keys(cv_results, param_keys, score_keys, n_cand)
n_candidates = len(search.cv_results_['params'])
assert all((cv_results['param_C'].mask[i] and
cv_results['param_gamma'].mask[i] and
not cv_results['param_degree'].mask[i])
for i in range(n_candidates)
if cv_results['param_kernel'][i] == 'linear')
assert all((not cv_results['param_C'].mask[i] and
not cv_results['param_gamma'].mask[i] and
cv_results['param_degree'].mask[i])
for i in range(n_candidates)
if cv_results['param_kernel'][i] == 'rbf')
@pytest.mark.parametrize(
"SearchCV, specialized_params",
[(GridSearchCV, {'param_grid': {'C': [1, 10]}}),
(RandomizedSearchCV,
{'param_distributions': {'C': [1, 10]}, 'n_iter': 2})]
)
def test_search_default_iid(SearchCV, specialized_params):
# Test the IID parameter TODO: Clearly this test does something else???
# noise-free simple 2d-data
X, y = make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]], random_state=0,
cluster_std=0.1, shuffle=False, n_samples=80)
# split dataset into two folds that are not iid
# first one contains data of all 4 blobs, second only from two.
mask = np.ones(X.shape[0], dtype=np.bool)
mask[np.where(y == 1)[0][::2]] = 0
mask[np.where(y == 2)[0][::2]] = 0
# this leads to perfect classification on one fold and a score of 1/3 on
# the other
# create "cv" for splits
cv = [[mask, ~mask], [~mask, mask]]
common_params = {'estimator': SVC(), 'cv': cv,
'return_train_score': True}
search = SearchCV(**common_params, **specialized_params)
search.fit(X, y)
test_cv_scores = np.array(
[search.cv_results_['split%d_test_score' % s][0]
for s in range(search.n_splits_)]
)
test_mean = search.cv_results_['mean_test_score'][0]
test_std = search.cv_results_['std_test_score'][0]
train_cv_scores = np.array(
[search.cv_results_['split%d_train_score' % s][0]
for s in range(search.n_splits_)]
)
train_mean = search.cv_results_['mean_train_score'][0]
train_std = search.cv_results_['std_train_score'][0]
assert search.cv_results_['param_C'][0] == 1
# scores are the same as above
assert_allclose(test_cv_scores, [1, 1. / 3.])
assert_allclose(train_cv_scores, [1, 1])
# Unweighted mean/std is used
assert test_mean == pytest.approx(np.mean(test_cv_scores))
assert test_std == pytest.approx(np.std(test_cv_scores))
# For the train scores, we do not take a weighted mean irrespective of
# i.i.d. or not
assert train_mean == pytest.approx(1)
assert train_std == pytest.approx(0)
def test_grid_search_cv_results_multimetric():
X, y = make_classification(n_samples=50, n_features=4, random_state=42)
n_splits = 3
params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]),
dict(kernel=['poly', ], degree=[1, 2])]
grid_searches = []
for scoring in ({'accuracy': make_scorer(accuracy_score),
'recall': make_scorer(recall_score)},
'accuracy', 'recall'):
grid_search = GridSearchCV(SVC(), cv=n_splits,
param_grid=params,
scoring=scoring, refit=False)
grid_search.fit(X, y)
grid_searches.append(grid_search)
compare_cv_results_multimetric_with_single(*grid_searches)
def test_random_search_cv_results_multimetric():
X, y = make_classification(n_samples=50, n_features=4, random_state=42)
n_splits = 3
n_search_iter = 30
# Scipy 0.12's stats dists do not accept seed, hence we use param grid
params = dict(C=np.logspace(-4, 1, 3),
gamma=np.logspace(-5, 0, 3, base=0.1))
for refit in (True, False):
random_searches = []
for scoring in (('accuracy', 'recall'), 'accuracy', 'recall'):
# If True, for multi-metric pass refit='accuracy'
if refit:
probability = True
refit = 'accuracy' if isinstance(scoring, tuple) else refit
else:
probability = False
clf = SVC(probability=probability, random_state=42)
random_search = RandomizedSearchCV(clf, n_iter=n_search_iter,
cv=n_splits,
param_distributions=params,
scoring=scoring,
refit=refit, random_state=0)
random_search.fit(X, y)
random_searches.append(random_search)
compare_cv_results_multimetric_with_single(*random_searches)
compare_refit_methods_when_refit_with_acc(
random_searches[0], random_searches[1], refit)
def compare_cv_results_multimetric_with_single(
search_multi, search_acc, search_rec):
"""Compare multi-metric cv_results with the ensemble of multiple
single metric cv_results from single metric grid/random search"""
assert search_multi.multimetric_
assert_array_equal(sorted(search_multi.scorer_),
('accuracy', 'recall'))
cv_results_multi = search_multi.cv_results_
cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v
for k, v in search_acc.cv_results_.items()}
cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v
for k, v in search_rec.cv_results_.items()})
# Check if score and timing are reasonable, also checks if the keys
# are present
assert all((np.all(cv_results_multi[k] <= 1) for k in (
'mean_score_time', 'std_score_time', 'mean_fit_time',
'std_fit_time')))
# Compare the keys, other than time keys, among multi-metric and
# single metric grid search results. np.testing.assert_equal performs a
# deep nested comparison of the two cv_results dicts
np.testing.assert_equal({k: v for k, v in cv_results_multi.items()
if not k.endswith('_time')},
{k: v for k, v in cv_results_acc_rec.items()
if not k.endswith('_time')})
def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit):
"""Compare refit multi-metric search methods with single metric methods"""
assert search_acc.refit == refit
if refit:
assert search_multi.refit == 'accuracy'
else:
assert not search_multi.refit
return # search cannot predict/score without refit
X, y = make_blobs(n_samples=100, n_features=4, random_state=42)
for method in ('predict', 'predict_proba', 'predict_log_proba'):
assert_almost_equal(getattr(search_multi, method)(X),
getattr(search_acc, method)(X))
assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y))
for key in ('best_index_', 'best_score_', 'best_params_'):
assert getattr(search_multi, key) == getattr(search_acc, key)
def test_search_cv_results_rank_tie_breaking():
X, y = make_blobs(n_samples=50, random_state=42)
# The two C values are close enough to give similar models
# which would result in a tie of their mean cv-scores
param_grid = {'C': [1, 1.001, 0.001]}
grid_search = GridSearchCV(SVC(), param_grid=param_grid,
return_train_score=True)
random_search = RandomizedSearchCV(SVC(), n_iter=3,
param_distributions=param_grid,
return_train_score=True)
for search in (grid_search, random_search):
search.fit(X, y)
cv_results = search.cv_results_
# Check tie breaking strategy -
# Check that there is a tie in the mean scores between
# candidates 1 and 2 alone
assert_almost_equal(cv_results['mean_test_score'][0],
cv_results['mean_test_score'][1])
assert_almost_equal(cv_results['mean_train_score'][0],
cv_results['mean_train_score'][1])
assert not np.allclose(cv_results['mean_test_score'][1],
cv_results['mean_test_score'][2])
assert not np.allclose(cv_results['mean_train_score'][1],
cv_results['mean_train_score'][2])
# 'min' rank should be assigned to the tied candidates
assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3])
def test_search_cv_results_none_param():
X, y = [[1], [2], [3], [4], [5]], [0, 0, 0, 0, 1]
estimators = (DecisionTreeRegressor(), DecisionTreeClassifier())
est_parameters = {"random_state": [0, None]}
cv = KFold()
for est in estimators:
grid_search = GridSearchCV(est, est_parameters, cv=cv,
).fit(X, y)
assert_array_equal(grid_search.cv_results_['param_random_state'],
[0, None])
@ignore_warnings()
def test_search_cv_timing():
svc = LinearSVC(random_state=0)
X = [[1, ], [2, ], [3, ], [4, ]]
y = [0, 1, 1, 0]
gs = GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0)
rs = RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0, n_iter=2)
for search in (gs, rs):
search.fit(X, y)
for key in ['mean_fit_time', 'std_fit_time']:
# NOTE The precision of time.time in windows is not high
# enough for the fit/score times to be non-zero for trivial X and y
assert np.all(search.cv_results_[key] >= 0)
assert np.all(search.cv_results_[key] < 1)
for key in ['mean_score_time', 'std_score_time']:
assert search.cv_results_[key][1] >= 0
assert search.cv_results_[key][0] == 0.0
assert np.all(search.cv_results_[key] < 1)
assert hasattr(search, "refit_time_")
assert isinstance(search.refit_time_, float)
assert search.refit_time_ >= 0
def test_grid_search_correct_score_results():
# test that correct scores are used
n_splits = 3
clf = LinearSVC(random_state=0)
X, y = make_blobs(random_state=0, centers=2)
Cs = [.1, 1, 10]
for score in ['f1', 'roc_auc']:
grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits)
cv_results = grid_search.fit(X, y).cv_results_
# Test scorer names
result_keys = list(cv_results.keys())
expected_keys = (("mean_test_score", "rank_test_score") +
tuple("split%d_test_score" % cv_i
for cv_i in range(n_splits)))
assert all(np.in1d(expected_keys, result_keys))
cv = StratifiedKFold(n_splits=n_splits)
n_splits = grid_search.n_splits_
for candidate_i, C in enumerate(Cs):
clf.set_params(C=C)
cv_scores = np.array(
list(grid_search.cv_results_['split%d_test_score'
% s][candidate_i]
for s in range(n_splits)))
for i, (train, test) in enumerate(cv.split(X, y)):
clf.fit(X[train], y[train])
if score == "f1":
correct_score = f1_score(y[test], clf.predict(X[test]))
elif score == "roc_auc":
dec = clf.decision_function(X[test])
correct_score = roc_auc_score(y[test], dec)
assert_almost_equal(correct_score, cv_scores[i])
# FIXME remove test_fit_grid_point as the function will be removed on 0.25
@ignore_warnings(category=FutureWarning)
def test_fit_grid_point():
X, y = make_classification(random_state=0)
cv = StratifiedKFold()
svc = LinearSVC(random_state=0)
scorer = make_scorer(accuracy_score)
for params in ({'C': 0.1}, {'C': 0.01}, {'C': 0.001}):
for train, test in cv.split(X, y):
this_scores, this_params, n_test_samples = fit_grid_point(
X, y, clone(svc), params, train, test,
scorer, verbose=False)
est = clone(svc).set_params(**params)
est.fit(X[train], y[train])
expected_score = scorer(est, X[test], y[test])
# Test the return values of fit_grid_point
assert_almost_equal(this_scores, expected_score)
assert params == this_params
assert n_test_samples == test.size
# Should raise an error upon multimetric scorer
assert_raise_message(ValueError, "For evaluating multiple scores, use "
"sklearn.model_selection.cross_validate instead.",
fit_grid_point, X, y, svc, params, train, test,
{'score': scorer}, verbose=True)
# FIXME remove test_fit_grid_point_deprecated as
# fit_grid_point will be removed on 0.25
def test_fit_grid_point_deprecated():
X, y = make_classification(random_state=0)
svc = LinearSVC(random_state=0)
scorer = make_scorer(accuracy_score)
msg = ("fit_grid_point is deprecated in version 0.23 "
"and will be removed in version 0.25")
params = {'C': 0.1}
train, test = next(StratifiedKFold().split(X, y))
with pytest.warns(FutureWarning, match=msg):
fit_grid_point(X, y, svc, params, train, test, scorer, verbose=False)
def test_pickle():
# Test that a fit search can be pickled
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, cv=3)
grid_search.fit(X, y)
grid_search_pickled = pickle.loads(pickle.dumps(grid_search))
assert_array_almost_equal(grid_search.predict(X),
grid_search_pickled.predict(X))
random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]},
refit=True, n_iter=3, cv=3)
random_search.fit(X, y)
random_search_pickled = pickle.loads(pickle.dumps(random_search))
assert_array_almost_equal(random_search.predict(X),
random_search_pickled.predict(X))
def test_grid_search_with_multioutput_data():
# Test search with multi-output estimator
X, y = make_multilabel_classification(return_indicator=True,
random_state=0)
est_parameters = {"max_depth": [1, 2, 3, 4]}
cv = KFold()
estimators = [DecisionTreeRegressor(random_state=0),
DecisionTreeClassifier(random_state=0)]
# Test with grid search cv
for est in estimators:
grid_search = GridSearchCV(est, est_parameters, cv=cv)
grid_search.fit(X, y)
res_params = grid_search.cv_results_['params']
for cand_i in range(len(res_params)):
est.set_params(**res_params[cand_i])
for i, (train, test) in enumerate(cv.split(X, y)):
est.fit(X[train], y[train])
correct_score = est.score(X[test], y[test])
assert_almost_equal(
correct_score,
grid_search.cv_results_['split%d_test_score' % i][cand_i])
# Test with a randomized search
for est in estimators:
random_search = RandomizedSearchCV(est, est_parameters,
cv=cv, n_iter=3)
random_search.fit(X, y)
res_params = random_search.cv_results_['params']
for cand_i in range(len(res_params)):
est.set_params(**res_params[cand_i])
for i, (train, test) in enumerate(cv.split(X, y)):
est.fit(X[train], y[train])
correct_score = est.score(X[test], y[test])
assert_almost_equal(
correct_score,
random_search.cv_results_['split%d_test_score'
% i][cand_i])
def test_predict_proba_disabled():
# Test predict_proba when disabled on estimator.
X = np.arange(20).reshape(5, -1)
y = [0, 0, 1, 1, 1]
clf = SVC(probability=False)
gs = GridSearchCV(clf, {}, cv=2).fit(X, y)
assert not hasattr(gs, "predict_proba")
def test_grid_search_allows_nans():
# Test GridSearchCV with SimpleImputer
X = np.arange(20, dtype=np.float64).reshape(5, -1)
X[2, :] = np.nan
y = [0, 0, 1, 1, 1]
p = Pipeline([
('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)),
('classifier', MockClassifier()),
])
GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y)
class FailingClassifier(BaseEstimator):
"""Classifier that raises a ValueError on fit()"""
FAILING_PARAMETER = 2
def __init__(self, parameter=None):
self.parameter = parameter
def fit(self, X, y=None):
if self.parameter == FailingClassifier.FAILING_PARAMETER:
raise ValueError("Failing classifier failed as required")
def predict(self, X):
return np.zeros(X.shape[0])
def score(self, X=None, Y=None):
return 0.
def test_grid_search_failing_classifier():
# GridSearchCV with on_error != 'raise'
# Ensures that a warning is raised and score reset where appropriate.
X, y = make_classification(n_samples=20, n_features=10, random_state=0)
clf = FailingClassifier()
# refit=False because we only want to check that errors caused by fits
# to individual folds will be caught and warnings raised instead. If
# refit was done, then an exception would be raised on refit and not
# caught by grid_search (expected behavior), and this would cause an
# error in this test.
gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',
refit=False, error_score=0.0)
assert_warns(FitFailedWarning, gs.fit, X, y)
n_candidates = len(gs.cv_results_['params'])
# Ensure that grid scores were set to zero as required for those fits
# that are expected to fail.
def get_cand_scores(i):
return np.array(list(gs.cv_results_['split%d_test_score' % s][i]
for s in range(gs.n_splits_)))
assert all((np.all(get_cand_scores(cand_i) == 0.0)
for cand_i in range(n_candidates)
if gs.cv_results_['param_parameter'][cand_i] ==
FailingClassifier.FAILING_PARAMETER))
gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',
refit=False, error_score=float('nan'))
assert_warns(FitFailedWarning, gs.fit, X, y)
n_candidates = len(gs.cv_results_['params'])
assert all(np.all(np.isnan(get_cand_scores(cand_i)))
for cand_i in range(n_candidates)
if gs.cv_results_['param_parameter'][cand_i] ==
FailingClassifier.FAILING_PARAMETER)
ranks = gs.cv_results_['rank_test_score']
# Check that succeeded estimators have lower ranks
assert ranks[0] <= 2 and ranks[1] <= 2
# Check that failed estimator has the highest rank
assert ranks[clf.FAILING_PARAMETER] == 3
assert gs.best_index_ != clf.FAILING_PARAMETER
def test_grid_search_failing_classifier_raise():
# GridSearchCV with on_error == 'raise' raises the error
X, y = make_classification(n_samples=20, n_features=10, random_state=0)
clf = FailingClassifier()
# refit=False because we want to test the behaviour of the grid search part
gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',
refit=False, error_score='raise')
# FailingClassifier issues a ValueError so this is what we look for.
assert_raises(ValueError, gs.fit, X, y)
def test_parameters_sampler_replacement():
# raise warning if n_iter is bigger than total parameter space
params = [{'first': [0, 1], 'second': ['a', 'b', 'c']},
{'third': ['two', 'values']}]
sampler = ParameterSampler(params, n_iter=9)
n_iter = 9
grid_size = 8
expected_warning = ('The total space of parameters %d is smaller '
'than n_iter=%d. Running %d iterations. For '
'exhaustive searches, use GridSearchCV.'
% (grid_size, n_iter, grid_size))
assert_warns_message(UserWarning, expected_warning,
list, sampler)
# degenerates to GridSearchCV if n_iter the same as grid_size
sampler = ParameterSampler(params, n_iter=8)
samples = list(sampler)
assert len(samples) == 8
for values in ParameterGrid(params):
assert values in samples
# test sampling without replacement in a large grid
params = {'a': range(10), 'b': range(10), 'c': range(10)}
sampler = ParameterSampler(params, n_iter=99, random_state=42)
samples = list(sampler)
assert len(samples) == 99
hashable_samples = ["a%db%dc%d" % (p['a'], p['b'], p['c'])
for p in samples]
assert len(set(hashable_samples)) == 99
# doesn't go into infinite loops
params_distribution = {'first': bernoulli(.5), 'second': ['a', 'b', 'c']}
sampler = ParameterSampler(params_distribution, n_iter=7)
samples = list(sampler)
assert len(samples) == 7
def test_stochastic_gradient_loss_param():
# Make sure the predict_proba works when loss is specified
# as one of the parameters in the param_grid.
param_grid = {
'loss': ['log'],
}
X = np.arange(24).reshape(6, -1)
y = [0, 0, 0, 1, 1, 1]
clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),
param_grid=param_grid, cv=3)
# When the estimator is not fitted, `predict_proba` is not available as the
# loss is 'hinge'.
assert not hasattr(clf, "predict_proba")
clf.fit(X, y)
clf.predict_proba(X)
clf.predict_log_proba(X)
# Make sure `predict_proba` is not available when setting loss=['hinge']
# in param_grid
param_grid = {
'loss': ['hinge'],
}
clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),
param_grid=param_grid, cv=3)
assert not hasattr(clf, "predict_proba")
clf.fit(X, y)
assert not hasattr(clf, "predict_proba")
def test_search_train_scores_set_to_false():
X = | np.arange(6) | numpy.arange |
import numpy as np
from scipy import ndimage
def erode_value_blobs(array, steps=1, values_to_ignore=tuple(), new_value=0):
unique_values = list(np.unique(array))
all_entries_to_keep = np.zeros(shape=array.shape, dtype=np.bool)
for unique_value in unique_values:
entries_of_this_value = array == unique_value
if unique_value in values_to_ignore:
all_entries_to_keep = | np.logical_or(entries_of_this_value, all_entries_to_keep) | numpy.logical_or |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[2].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[2].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
plt.savefig('jss_figures/DFA_different_trends.png')
plt.show()
# plot 6b
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences Zoomed Region', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[0].set_ylim(-5.5, 5.5)
axs[0].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].set_ylim(-5.5, 5.5)
axs[1].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([np.pi, (3 / 2) * np.pi])
axs[2].set_xticklabels([r'$\pi$', r'$\frac{3}{2}\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].set_ylim(-5.5, 5.5)
axs[2].set_xlim(0.95 * np.pi, 1.55 * np.pi)
plt.savefig('jss_figures/DFA_different_trends_zoomed.png')
plt.show()
hs_ouputs = hilbert_spectrum(time, imfs_51, hts_51, ifs_51, max_frequency=12, plot=False)
# plot 6c
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Simple Sinusoidal Time Seres with Added Noise', 50))
x_hs, y, z = hs_ouputs
z_min, z_max = 0, np.abs(z).max()
ax.pcolormesh(x_hs, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max)
ax.plot(x_hs[0, :], 8 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 8$', Linewidth=3)
ax.plot(x_hs[0, :], 4 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 4$', Linewidth=3)
ax.plot(x_hs[0, :], 2 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 2$', Linewidth=3)
ax.set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
ax.set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$'])
plt.ylabel(r'Frequency (rad.s$^{-1}$)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.85, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/DFA_hilbert_spectrum.png')
plt.show()
# plot 6c
time = np.linspace(0, 5 * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 51)
fluc = Fluctuation(time=time, time_series=time_series)
max_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=False)
max_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=True)
min_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=False)
min_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=True)
util = Utility(time=time, time_series=time_series)
maxima = util.max_bool_func_1st_order_fd()
minima = util.min_bool_func_1st_order_fd()
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title(textwrap.fill('Plot Demonstrating Unsmoothed Extrema Envelopes if Schoenberg–Whitney Conditions are Not Satisfied', 50))
plt.plot(time, time_series, label='Time series', zorder=2, LineWidth=2)
plt.scatter(time[maxima], time_series[maxima], c='r', label='Maxima', zorder=10)
plt.scatter(time[minima], time_series[minima], c='b', label='Minima', zorder=10)
plt.plot(time, max_unsmoothed[0], label=textwrap.fill('Unsmoothed maxima envelope', 10), c='darkorange')
plt.plot(time, max_smoothed[0], label=textwrap.fill('Smoothed maxima envelope', 10), c='red')
plt.plot(time, min_unsmoothed[0], label=textwrap.fill('Unsmoothed minima envelope', 10), c='cyan')
plt.plot(time, min_smoothed[0], label=textwrap.fill('Smoothed minima envelope', 10), c='blue')
for knot in knots[:-1]:
plt.plot(knot * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', zorder=1)
plt.plot(knots[-1] * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', label='Knots', zorder=1)
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi),
(r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$', r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Schoenberg_Whitney_Conditions.png')
plt.show()
# plot 7
a = 0.25
width = 0.2
time = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 1001)
knots = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 11)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
inflection_bool = utils.inflection_point()
inflection_x = time[inflection_bool]
inflection_y = time_series[inflection_bool]
fluctuation = emd_mean.Fluctuation(time=time, time_series=time_series)
maxima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
maxima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
inflection_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='inflection_points')[0]
binomial_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='binomial_average', order=21,
increment=20)[0]
derivative_of_lsq = utils.derivative_forward_diff()
derivative_time = time[:-1]
derivative_knots = np.linspace(knots[0], knots[-1], 31)
# change (1) detrended_fluctuation_technique and (2) max_internal_iter and (3) debug (confusing with external debugging)
emd = AdvEMDpy.EMD(time=derivative_time, time_series=derivative_of_lsq)
imf_1_of_derivative = emd.empirical_mode_decomposition(knots=derivative_knots,
knot_time=derivative_time, text=False, verbose=False)[0][1, :]
utils = emd_utils.Utility(time=time[:-1], time_series=imf_1_of_derivative)
optimal_maxima = np.r_[False, utils.derivative_forward_diff() < 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
optimal_minima = np.r_[False, utils.derivative_forward_diff() > 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
EEMD_maxima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'maxima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
EEMD_minima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'minima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Detrended Fluctuation Analysis Examples')
plt.plot(time, time_series, LineWidth=2, label='Time series')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(time[optimal_maxima], time_series[optimal_maxima], c='darkred', zorder=4,
label=textwrap.fill('Optimal maxima', 10))
plt.scatter(time[optimal_minima], time_series[optimal_minima], c='darkblue', zorder=4,
label=textwrap.fill('Optimal minima', 10))
plt.scatter(inflection_x, inflection_y, c='magenta', zorder=4, label=textwrap.fill('Inflection points', 10))
plt.plot(time, maxima_envelope, c='darkblue', label=textwrap.fill('EMD envelope', 10))
plt.plot(time, minima_envelope, c='darkblue')
plt.plot(time, (maxima_envelope + minima_envelope) / 2, c='darkblue')
plt.plot(time, maxima_envelope_smooth, c='darkred', label=textwrap.fill('SEMD envelope', 10))
plt.plot(time, minima_envelope_smooth, c='darkred')
plt.plot(time, (maxima_envelope_smooth + minima_envelope_smooth) / 2, c='darkred')
plt.plot(time, EEMD_maxima_envelope, c='darkgreen', label=textwrap.fill('EEMD envelope', 10))
plt.plot(time, EEMD_minima_envelope, c='darkgreen')
plt.plot(time, (EEMD_maxima_envelope + EEMD_minima_envelope) / 2, c='darkgreen')
plt.plot(time, inflection_points_envelope, c='darkorange', label=textwrap.fill('Inflection point envelope', 10))
plt.plot(time, binomial_points_envelope, c='deeppink', label=textwrap.fill('Binomial average envelope', 10))
plt.plot(time, np.cos(time), c='black', label='True mean')
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi), (r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$',
r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/detrended_fluctuation_analysis.png')
plt.show()
# Duffing Equation Example
def duffing_equation(xy, ts):
gamma = 0.1
epsilon = 1
omega = ((2 * np.pi) / 25)
return [xy[1], xy[0] - epsilon * xy[0] ** 3 + gamma * np.cos(omega * ts)]
t = np.linspace(0, 150, 1501)
XY0 = [1, 1]
solution = odeint(duffing_equation, XY0, t)
x = solution[:, 0]
dxdt = solution[:, 1]
x_points = [0, 50, 100, 150]
x_names = {0, 50, 100, 150}
y_points_1 = [-2, 0, 2]
y_points_2 = [-1, 0, 1]
fig, axs = plt.subplots(2, 1)
plt.subplots_adjust(hspace=0.2)
axs[0].plot(t, x)
axs[0].set_title('Duffing Equation Displacement')
axs[0].set_ylim([-2, 2])
axs[0].set_xlim([0, 150])
axs[1].plot(t, dxdt)
axs[1].set_title('Duffing Equation Velocity')
axs[1].set_ylim([-1.5, 1.5])
axs[1].set_xlim([0, 150])
axis = 0
for ax in axs.flat:
ax.label_outer()
if axis == 0:
ax.set_ylabel('x(t)')
ax.set_yticks(y_points_1)
if axis == 1:
ax.set_ylabel(r'$ \dfrac{dx(t)}{dt} $')
ax.set(xlabel='t')
ax.set_yticks(y_points_2)
ax.set_xticks(x_points)
ax.set_xticklabels(x_names)
axis += 1
plt.savefig('jss_figures/Duffing_equation.png')
plt.show()
# compare other packages Duffing - top
pyemd = pyemd0215()
py_emd = pyemd(x)
IP, IF, IA = emd040.spectra.frequency_transform(py_emd.T, 10, 'hilbert')
freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 0.2, 100)
hht = emd040.spectra.hilberthuang(IF, IA, freq_edges)
hht = gaussian_filter(hht, sigma=1)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 1.0
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using PyEMD 0.2.10', 40))
plt.pcolormesh(t, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht))))
plt.plot(t[:-1], 0.124 * np.ones_like(t[:-1]), '--', label=textwrap.fill('Hamiltonian frequency approximation', 15))
plt.plot(t[:-1], 0.04 * np.ones_like(t[:-1]), 'g--', label=textwrap.fill('Driving function frequency', 15))
plt.xticks([0, 50, 100, 150])
plt.yticks([0, 0.1, 0.2])
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.75, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Duffing_equation_ht_pyemd.png')
plt.show()
plt.show()
emd_sift = emd040.sift.sift(x)
IP, IF, IA = emd040.spectra.frequency_transform(emd_sift, 10, 'hilbert')
freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 0.2, 100)
hht = emd040.spectra.hilberthuang(IF, IA, freq_edges)
hht = gaussian_filter(hht, sigma=1)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 1.0
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using emd 0.3.3', 40))
plt.pcolormesh(t, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht))))
plt.plot(t[:-1], 0.124 * np.ones_like(t[:-1]), '--', label=textwrap.fill('Hamiltonian frequency approximation', 15))
plt.plot(t[:-1], 0.04 * np.ones_like(t[:-1]), 'g--', label=textwrap.fill('Driving function frequency', 15))
plt.xticks([0, 50, 100, 150])
plt.yticks([0, 0.1, 0.2])
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.75, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Duffing_equation_ht_emd.png')
plt.show()
# compare other packages Duffing - bottom
emd_duffing = AdvEMDpy.EMD(time=t, time_series=x)
emd_duff, emd_ht_duff, emd_if_duff, _, _, _, _ = emd_duffing.empirical_mode_decomposition(verbose=False)
fig, axs = plt.subplots(2, 1)
plt.subplots_adjust(hspace=0.3)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
axs[0].plot(t, emd_duff[1, :], label='AdvEMDpy')
axs[0].plot(t, py_emd[0, :], '--', label='PyEMD 0.2.10')
axs[0].plot(t, emd_sift[:, 0], '--', label='emd 0.3.3')
axs[0].set_title('IMF 1')
axs[0].set_ylim([-2, 2])
axs[0].set_xlim([0, 150])
axs[1].plot(t, emd_duff[2, :], label='AdvEMDpy')
print(f'AdvEMDpy driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - emd_duff[2, :])), 3)}')
axs[1].plot(t, py_emd[1, :], '--', label='PyEMD 0.2.10')
print(f'PyEMD driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - py_emd[1, :])), 3)}')
axs[1].plot(t, emd_sift[:, 1], '--', label='emd 0.3.3')
print(f'emd driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - emd_sift[:, 1])), 3)}')
axs[1].plot(t, 0.1 * np.cos(0.04 * 2 * np.pi * t), '--', label=r'$0.1$cos$(0.08{\pi}t)$')
axs[1].set_title('IMF 2')
axs[1].set_ylim([-0.2, 0.4])
axs[1].set_xlim([0, 150])
axis = 0
for ax in axs.flat:
ax.label_outer()
if axis == 0:
ax.set_ylabel(r'$\gamma_1(t)$')
ax.set_yticks([-2, 0, 2])
if axis == 1:
ax.set_ylabel(r'$\gamma_2(t)$')
ax.set_yticks([-0.2, 0, 0.2])
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
ax.set_xticks(x_points)
ax.set_xticklabels(x_names)
axis += 1
plt.savefig('jss_figures/Duffing_equation_imfs.png')
plt.show()
hs_ouputs = hilbert_spectrum(t, emd_duff, emd_ht_duff, emd_if_duff, max_frequency=1.3, plot=False)
ax = plt.subplot(111)
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using AdvEMDpy', 40))
x, y, z = hs_ouputs
y = y / (2 * np.pi)
z_min, z_max = 0, np.abs(z).max()
figure_size = plt.gcf().get_size_inches()
factor = 1.0
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
ax.pcolormesh(x, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max)
plt.plot(t[:-1], 0.124 * np.ones_like(t[:-1]), '--', label=textwrap.fill('Hamiltonian frequency approximation', 15))
plt.plot(t[:-1], 0.04 * np.ones_like(t[:-1]), 'g--', label=textwrap.fill('Driving function frequency', 15))
plt.xticks([0, 50, 100, 150])
plt.yticks([0, 0.1, 0.2])
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.75, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Duffing_equation_ht.png')
plt.show()
# Carbon Dioxide Concentration Example
CO2_data = pd.read_csv('Data/co2_mm_mlo.csv', header=51)
plt.plot(CO2_data['month'], CO2_data['decimal date'])
plt.title(textwrap.fill('Mean Monthly Concentration of Carbon Dioxide in the Atmosphere', 35))
plt.ylabel('Parts per million')
plt.xlabel('Time (years)')
plt.savefig('jss_figures/CO2_concentration.png')
plt.show()
signal = CO2_data['decimal date']
signal = np.asarray(signal)
time = CO2_data['month']
time = np.asarray(time)
# compare other packages Carbon Dioxide - top
pyemd = pyemd0215()
py_emd = pyemd(signal)
IP, IF, IA = emd040.spectra.frequency_transform(py_emd[:2, :].T, 12, 'hilbert')
print(f'PyEMD annual frequency error: {np.round(sum(np.abs(IF[:, 0] - | np.ones_like(IF[:, 0]) | numpy.ones_like |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[2].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[2].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
plt.savefig('jss_figures/DFA_different_trends.png')
plt.show()
# plot 6b
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences Zoomed Region', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[0].set_ylim(-5.5, 5.5)
axs[0].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].set_ylim(-5.5, 5.5)
axs[1].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([np.pi, (3 / 2) * np.pi])
axs[2].set_xticklabels([r'$\pi$', r'$\frac{3}{2}\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].set_ylim(-5.5, 5.5)
axs[2].set_xlim(0.95 * np.pi, 1.55 * np.pi)
plt.savefig('jss_figures/DFA_different_trends_zoomed.png')
plt.show()
hs_ouputs = hilbert_spectrum(time, imfs_51, hts_51, ifs_51, max_frequency=12, plot=False)
# plot 6c
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Simple Sinusoidal Time Seres with Added Noise', 50))
x_hs, y, z = hs_ouputs
z_min, z_max = 0, np.abs(z).max()
ax.pcolormesh(x_hs, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max)
ax.plot(x_hs[0, :], 8 * | np.ones_like(x_hs[0, :]) | numpy.ones_like |
import numpy as np
from typing import Tuple, Union, Optional
from autoarray.structures.arrays.two_d import array_2d_util
from autoarray.geometry import geometry_util
from autoarray import numba_util
from autoarray.mask import mask_2d_util
@numba_util.jit()
def grid_2d_centre_from(grid_2d_slim: np.ndarray) -> Tuple[float, float]:
"""
Returns the centre of a grid from a 1D grid.
Parameters
----------
grid_2d_slim
The 1D grid of values which are mapped to a 2D array.
Returns
-------
(float, float)
The (y,x) central coordinates of the grid.
"""
centre_y = (np.max(grid_2d_slim[:, 0]) + np.min(grid_2d_slim[:, 0])) / 2.0
centre_x = (np.max(grid_2d_slim[:, 1]) + np.min(grid_2d_slim[:, 1])) / 2.0
return centre_y, centre_x
@numba_util.jit()
def grid_2d_slim_via_mask_from(
mask_2d: np.ndarray,
pixel_scales: Union[float, Tuple[float, float]],
sub_size: int,
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
For a sub-grid, every unmasked pixel of its 2D mask with shape (total_y_pixels, total_x_pixels) is divided into
a finer uniform grid of shape (total_y_pixels*sub_size, total_x_pixels*sub_size). This routine computes the (y,x)
scaled coordinates a the centre of every sub-pixel defined by this 2D mask array.
The sub-grid is returned on an array of shape (total_unmasked_pixels*sub_size**2, 2). y coordinates are
stored in the 0 index of the second dimension, x coordinates in the 1 index. Masked coordinates are therefore
removed and not included in the slimmed grid.
Grid2D are defined from the top-left corner, where the first unmasked sub-pixel corresponds to index 0.
Sub-pixels that are part of the same mask array pixel are indexed next to one another, such that the second
sub-pixel in the first pixel has index 1, its next sub-pixel has index 2, and so forth.
Parameters
----------
mask_2d
A 2D array of bools, where `False` values are unmasked and therefore included as part of the calculated
sub-grid.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the 2D mask array.
sub_size
The size of the sub-grid that each pixel of the 2D mask array is divided into.
origin : (float, flloat)
The (y,x) origin of the 2D array, which the sub-grid is shifted around.
Returns
-------
ndarray
A slimmed sub grid of (y,x) scaled coordinates at the centre of every pixel unmasked pixel on the 2D mask
array. The sub grid array has dimensions (total_unmasked_pixels*sub_size**2, 2).
Examples
--------
mask = np.array([[True, False, True],
[False, False, False]
[True, False, True]])
grid_slim = grid_2d_slim_via_mask_from(mask=mask, pixel_scales=(0.5, 0.5), sub_size=1, origin=(0.0, 0.0))
"""
total_sub_pixels = mask_2d_util.total_sub_pixels_2d_from(mask_2d, sub_size)
grid_slim = np.zeros(shape=(total_sub_pixels, 2))
centres_scaled = geometry_util.central_scaled_coordinate_2d_from(
shape_native=mask_2d.shape, pixel_scales=pixel_scales, origin=origin
)
sub_index = 0
y_sub_half = pixel_scales[0] / 2
y_sub_step = pixel_scales[0] / (sub_size)
x_sub_half = pixel_scales[1] / 2
x_sub_step = pixel_scales[1] / (sub_size)
for y in range(mask_2d.shape[0]):
for x in range(mask_2d.shape[1]):
if not mask_2d[y, x]:
y_scaled = (y - centres_scaled[0]) * pixel_scales[0]
x_scaled = (x - centres_scaled[1]) * pixel_scales[1]
for y1 in range(sub_size):
for x1 in range(sub_size):
grid_slim[sub_index, 0] = -(
y_scaled - y_sub_half + y1 * y_sub_step + (y_sub_step / 2.0)
)
grid_slim[sub_index, 1] = (
x_scaled - x_sub_half + x1 * x_sub_step + (x_sub_step / 2.0)
)
sub_index += 1
return grid_slim
def grid_2d_via_mask_from(
mask_2d: np.ndarray,
pixel_scales: Union[float, Tuple[float, float]],
sub_size: int,
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
For a sub-grid, every unmasked pixel of its 2D mask with shape (total_y_pixels, total_x_pixels) is divided into a
finer uniform grid of shape (total_y_pixels*sub_size, total_x_pixels*sub_size). This routine computes the (y,x)
scaled coordinates at the centre of every sub-pixel defined by this 2D mask array.
The sub-grid is returned in its native dimensions with shape (total_y_pixels*sub_size, total_x_pixels*sub_size).
y coordinates are stored in the 0 index of the second dimension, x coordinates in the 1 index. Masked pixels are
given values (0.0, 0.0).
Grids are defined from the top-left corner, where the first unmasked sub-pixel corresponds to index 0.
Sub-pixels that are part of the same mask array pixel are indexed next to one another, such that the second
sub-pixel in the first pixel has index 1, its next sub-pixel has index 2, and so forth.
Parameters
----------
mask_2d
A 2D array of bools, where `False` values are unmasked and therefore included as part of the calculated
sub-grid.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the 2D mask array.
sub_size
The size of the sub-grid that each pixel of the 2D mask array is divided into.
origin : (float, flloat)
The (y,x) origin of the 2D array, which the sub-grid is shifted around.
Returns
-------
ndarray
A sub grid of (y,x) scaled coordinates at the centre of every pixel unmasked pixel on the 2D mask
array. The sub grid array has dimensions (total_y_pixels*sub_size, total_x_pixels*sub_size).
Examples
--------
mask = np.array([[True, False, True],
[False, False, False]
[True, False, True]])
grid_2d = grid_2d_via_mask_from(mask=mask, pixel_scales=(0.5, 0.5), sub_size=1, origin=(0.0, 0.0))
"""
grid_2d_slim = grid_2d_slim_via_mask_from(
mask_2d=mask_2d, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin
)
return grid_2d_native_from(
grid_2d_slim=grid_2d_slim, mask_2d=mask_2d, sub_size=sub_size
)
def grid_2d_slim_via_shape_native_from(
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
sub_size: int,
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
For a sub-grid, every unmasked pixel of its 2D mask with shape (total_y_pixels, total_x_pixels) is divided into a
finer uniform grid of shape (total_y_pixels*sub_size, total_x_pixels*sub_size). This routine computes the (y,x)
scaled coordinates at the centre of every sub-pixel defined by this 2D mask array.
The sub-grid is returned in its slimmed dimensions with shape (total_pixels**2*sub_size**2, 2). y coordinates are
stored in the 0 index of the second dimension, x coordinates in the 1 index.
Grid2D are defined from the top-left corner, where the first sub-pixel corresponds to index [0,0].
Sub-pixels that are part of the same mask array pixel are indexed next to one another, such that the second
sub-pixel in the first pixel has index 1, its next sub-pixel has index 2, and so forth.
Parameters
----------
shape_native
The (y,x) shape of the 2D array the sub-grid of coordinates is computed for.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the 2D mask array.
sub_size
The size of the sub-grid that each pixel of the 2D mask array is divided into.
origin
The (y,x) origin of the 2D array, which the sub-grid is shifted around.
Returns
-------
ndarray
A sub grid of (y,x) scaled coordinates at the centre of every pixel unmasked pixel on the 2D mask
array. The sub grid is slimmed and has dimensions (total_unmasked_pixels*sub_size**2, 2).
Examples
--------
mask = np.array([[True, False, True],
[False, False, False]
[True, False, True]])
grid_2d_slim = grid_2d_slim_via_shape_native_from(shape_native=(3,3), pixel_scales=(0.5, 0.5), sub_size=2, origin=(0.0, 0.0))
"""
return grid_2d_slim_via_mask_from(
mask_2d=np.full(fill_value=False, shape=shape_native),
pixel_scales=pixel_scales,
sub_size=sub_size,
origin=origin,
)
def grid_2d_via_shape_native_from(
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
sub_size: int,
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
For a sub-grid, every unmasked pixel of its 2D mask with shape (total_y_pixels, total_x_pixels) is divided
into a finer uniform grid of shape (total_y_pixels*sub_size, total_x_pixels*sub_size). This routine computes
the (y,x) scaled coordinates at the centre of every sub-pixel defined by this 2D mask array.
The sub-grid is returned in its native dimensions with shape (total_y_pixels*sub_size, total_x_pixels*sub_size).
y coordinates are stored in the 0 index of the second dimension, x coordinates in the 1 index.
Grids are defined from the top-left corner, where the first sub-pixel corresponds to index [0,0].
Sub-pixels that are part of the same mask array pixel are indexed next to one another, such that the second
sub-pixel in the first pixel has index 1, its next sub-pixel has index 2, and so forth.
Parameters
----------
shape_native
The (y,x) shape of the 2D array the sub-grid of coordinates is computed for.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the 2D mask array.
sub_size
The size of the sub-grid that each pixel of the 2D mask array is divided into.
origin : (float, flloat)
The (y,x) origin of the 2D array, which the sub-grid is shifted around.
Returns
-------
ndarray
A sub grid of (y,x) scaled coordinates at the centre of every pixel unmasked pixel on the 2D mask
array. The sub grid array has dimensions (total_y_pixels*sub_size, total_x_pixels*sub_size).
Examples
--------
grid_2d = grid_2d_via_shape_native_from(shape_native=(3, 3), pixel_scales=(1.0, 1.0), sub_size=2, origin=(0.0, 0.0))
"""
return grid_2d_via_mask_from(
mask_2d=np.full(fill_value=False, shape=shape_native),
pixel_scales=pixel_scales,
sub_size=sub_size,
origin=origin,
)
@numba_util.jit()
def grid_scaled_2d_slim_radial_projected_from(
extent: np.ndarray,
centre: Tuple[float, float],
pixel_scales: Union[float, Tuple[float, float]],
sub_size: int,
shape_slim: Optional[int] = 0,
) -> np.ndarray:
"""
Determine a projected radial grid of points from a 2D region of coordinates defined by an
extent [xmin, xmax, ymin, ymax] and with a (y,x) centre. This functions operates as follows:
1) Given the region defined by the extent [xmin, xmax, ymin, ymax], the algorithm finds the longest 1D distance of
the 4 paths from the (y,x) centre to the edge of the region (e.g. following the positive / negative y and x axes).
2) Use the pixel-scale corresponding to the direction chosen (e.g. if the positive x-axis was the longest, the
pixel_scale in the x dimension is used).
3) Determine the number of pixels between the centre and the edge of the region using the longest path between the
two chosen above.
4) Create a (y,x) grid of radial points where all points are at the centre's y value = 0.0 and the x values iterate
from the centre in increasing steps of the pixel-scale.
5) Rotate these radial coordinates by the input `angle` clockwise.
A schematric is shown below:
-------------------
| |
|<- - - - ->x | x = centre
| | <-> = longest radial path from centre to extent edge
| |
-------------------
Using the centre x above, this function finds the longest radial path to the edge of the extent window.
The returned `grid_radii` represents a radial set of points that in 1D sample the 2D grid outwards from its centre.
This grid stores the radial coordinates as (y,x) values (where all y values are the same) as opposed to a 1D data
structure so that it can be used in functions which require that a 2D grid structure is input.
Parameters
----------
extent
The extent of the grid the radii grid is computed using, with format [xmin, xmax, ymin, ymax]
centre : (float, flloat)
The (y,x) central coordinate which the radial grid is traced outwards from.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the 2D mask array.
sub_size
The size of the sub-grid that each pixel of the 2D mask array is divided into.
shape_slim
Manually choose the shape of the 1D projected grid that is returned. If 0, the border based on the 2D grid is
used (due to numba None cannot be used as a default value).
Returns
-------
ndarray
A radial set of points sampling the longest distance from the centre to the edge of the extent in along the
positive x-axis.
"""
distance_to_positive_x = extent[1] - centre[1]
distance_to_positive_y = extent[3] - centre[0]
distance_to_negative_x = centre[1] - extent[0]
distance_to_negative_y = centre[0] - extent[2]
scaled_distance = max(
[
distance_to_positive_x,
distance_to_positive_y,
distance_to_negative_x,
distance_to_negative_y,
]
)
if (scaled_distance == distance_to_positive_y) or (
scaled_distance == distance_to_negative_y
):
pixel_scale = pixel_scales[0]
else:
pixel_scale = pixel_scales[1]
if shape_slim == 0:
shape_slim = sub_size * int((scaled_distance / pixel_scale)) + 1
grid_scaled_2d_slim_radii = np.zeros((shape_slim, 2))
grid_scaled_2d_slim_radii[:, 0] += centre[0]
radii = centre[1]
for slim_index in range(shape_slim):
grid_scaled_2d_slim_radii[slim_index, 1] = radii
radii += pixel_scale / sub_size
return grid_scaled_2d_slim_radii
@numba_util.jit()
def grid_pixels_2d_slim_from(
grid_scaled_2d_slim: np.ndarray,
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
Convert a slimmed grid of 2d (y,x) scaled coordinates to a slimmed grid of 2d (y,x) pixel coordinate values. Pixel
coordinates are returned as floats such that they include the decimal offset from each pixel's top-left corner
relative to the input scaled coordinate.
The input and output grids are both slimmed and therefore shape (total_pixels, 2).
The pixel coordinate origin is at the top left corner of the grid, such that the pixel [0,0] corresponds to
the highest (most positive) y scaled coordinate and lowest (most negative) x scaled coordinate on the gird.
The scaled grid is defined by an origin and coordinates are shifted to this origin before computing their
1D grid pixel coordinate values.
Parameters
----------
grid_scaled_2d_slim: np.ndarray
The slimmed grid of 2D (y,x) coordinates in scaled units which are converted to pixel value coordinates.
shape_native
The (y,x) shape of the original 2D array the scaled coordinates were computed on.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the original 2D array.
origin : (float, flloat)
The (y,x) origin of the grid, which the scaled grid is shifted to.
Returns
-------
ndarray
A slimmed grid of 2D (y,x) pixel-value coordinates with dimensions (total_pixels, 2).
Examples
--------
grid_scaled_2d_slim = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])
grid_pixels_2d_slim = grid_scaled_2d_slim_from(grid_scaled_2d_slim=grid_scaled_2d_slim, shape=(2,2),
pixel_scales=(0.5, 0.5), origin=(0.0, 0.0))
"""
grid_pixels_2d_slim = np.zeros((grid_scaled_2d_slim.shape[0], 2))
centres_scaled = geometry_util.central_scaled_coordinate_2d_from(
shape_native=shape_native, pixel_scales=pixel_scales, origin=origin
)
for slim_index in range(grid_scaled_2d_slim.shape[0]):
grid_pixels_2d_slim[slim_index, 0] = (
(-grid_scaled_2d_slim[slim_index, 0] / pixel_scales[0])
+ centres_scaled[0]
+ 0.5
)
grid_pixels_2d_slim[slim_index, 1] = (
(grid_scaled_2d_slim[slim_index, 1] / pixel_scales[1])
+ centres_scaled[1]
+ 0.5
)
return grid_pixels_2d_slim
@numba_util.jit()
def grid_pixel_centres_2d_slim_from(
grid_scaled_2d_slim: np.ndarray,
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
Convert a slimmed grid of 2D (y,x) scaled coordinates to a slimmed grid of 2D (y,x) pixel values. Pixel coordinates
are returned as integers such that they map directly to the pixel they are contained within.
The input and output grids are both slimmed and therefore shape (total_pixels, 2).
The pixel coordinate origin is at the top left corner of the grid, such that the pixel [0,0] corresponds to
the highest (most positive) y scaled coordinate and lowest (most negative) x scaled coordinate on the gird.
The scaled coordinate grid is defined by the class attribute origin, and coordinates are shifted to this
origin before computing their 1D grid pixel indexes.
Parameters
----------
grid_scaled_2d_slim: np.ndarray
The slimmed grid of 2D (y,x) coordinates in scaled units which is converted to pixel indexes.
shape_native
The (y,x) shape of the original 2D array the scaled coordinates were computed on.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the original 2D array.
origin : (float, flloat)
The (y,x) origin of the grid, which the scaled grid is shifted
Returns
-------
ndarray
A slimmed grid of 2D (y,x) pixel indexes with dimensions (total_pixels, 2).
Examples
--------
grid_scaled_2d_slim = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])
grid_pixels_2d_slim = grid_scaled_2d_slim_from(grid_scaled_2d_slim=grid_scaled_2d_slim, shape=(2,2),
pixel_scales=(0.5, 0.5), origin=(0.0, 0.0))
"""
grid_pixels_2d_slim = np.zeros((grid_scaled_2d_slim.shape[0], 2))
centres_scaled = geometry_util.central_scaled_coordinate_2d_from(
shape_native=shape_native, pixel_scales=pixel_scales, origin=origin
)
for slim_index in range(grid_scaled_2d_slim.shape[0]):
grid_pixels_2d_slim[slim_index, 0] = int(
(-grid_scaled_2d_slim[slim_index, 0] / pixel_scales[0])
+ centres_scaled[0]
+ 0.5
)
grid_pixels_2d_slim[slim_index, 1] = int(
(grid_scaled_2d_slim[slim_index, 1] / pixel_scales[1])
+ centres_scaled[1]
+ 0.5
)
return grid_pixels_2d_slim
@numba_util.jit()
def grid_pixel_indexes_2d_slim_from(
grid_scaled_2d_slim: np.ndarray,
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
Convert a slimmed grid of 2D (y,x) scaled coordinates to a slimmed grid of pixel indexes. Pixel coordinates are
returned as integers such that they are the pixel from the top-left of the 2D grid going rights and then downwards.
The input and output grids are both slimmed and have shapes (total_pixels, 2) and (total_pixels,).
For example:
The pixel at the top-left, whose native index is [0,0], corresponds to slimmed pixel index 0.
The fifth pixel on the top row, whose native index is [0,5], corresponds to slimmed pixel index 4.
The first pixel on the second row, whose native index is [0,1], has slimmed pixel index 10 if a row has 10 pixels.
The scaled coordinate grid is defined by the class attribute origin, and coordinates are shifted to this
origin before computing their 1D grid pixel indexes.
The input and output grids are both of shape (total_pixels, 2).
Parameters
----------
grid_scaled_2d_slim: np.ndarray
The slimmed grid of 2D (y,x) coordinates in scaled units which is converted to slimmed pixel indexes.
shape_native
The (y,x) shape of the original 2D array the scaled coordinates were computed on.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the original 2D array.
origin : (float, flloat)
The (y,x) origin of the grid, which the scaled grid is shifted.
Returns
-------
ndarray
A grid of slimmed pixel indexes with dimensions (total_pixels,).
Examples
--------
grid_scaled_2d_slim = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])
grid_pixel_indexes_2d_slim = grid_pixel_indexes_2d_slim_from(grid_scaled_2d_slim=grid_scaled_2d_slim, shape=(2,2),
pixel_scales=(0.5, 0.5), origin=(0.0, 0.0))
"""
grid_pixels_2d_slim = grid_pixel_centres_2d_slim_from(
grid_scaled_2d_slim=grid_scaled_2d_slim,
shape_native=shape_native,
pixel_scales=pixel_scales,
origin=origin,
)
grid_pixel_indexes_2d_slim = np.zeros(grid_pixels_2d_slim.shape[0])
for slim_index in range(grid_pixels_2d_slim.shape[0]):
grid_pixel_indexes_2d_slim[slim_index] = int(
grid_pixels_2d_slim[slim_index, 0] * shape_native[1]
+ grid_pixels_2d_slim[slim_index, 1]
)
return grid_pixel_indexes_2d_slim
@numba_util.jit()
def grid_scaled_2d_slim_from(
grid_pixels_2d_slim: np.ndarray,
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
Convert a slimmed grid of 2D (y,x) pixel coordinates to a slimmed grid of 2D (y,x) scaled values.
The input and output grids are both slimmed and therefore shape (total_pixels, 2).
The pixel coordinate origin is at the top left corner of the grid, such that the pixel [0,0] corresponds to
the highest (most positive) y scaled coordinate and lowest (most negative) x scaled coordinate on the gird.
The scaled coordinate origin is defined by the class attribute origin, and coordinates are shifted to this
origin after computing their values from the 1D grid pixel indexes.
Parameters
----------
grid_pixels_2d_slim: np.ndarray
The slimmed grid of (y,x) coordinates in pixel values which is converted to scaled coordinates.
shape_native
The (y,x) shape of the original 2D array the scaled coordinates were computed on.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the original 2D array.
origin : (float, flloat)
The (y,x) origin of the grid, which the scaled grid is shifted.
Returns
-------
ndarray
A slimmed grid of 2d scaled coordinates with dimensions (total_pixels, 2).
Examples
--------
grid_pixels_2d_slim = np.array([[0,0], [0,1], [1,0], [1,1])
grid_pixels_2d_slim = grid_scaled_2d_slim_from(grid_pixels_2d_slim=grid_pixels_2d_slim, shape=(2,2),
pixel_scales=(0.5, 0.5), origin=(0.0, 0.0))
"""
grid_scaled_2d_slim = np.zeros((grid_pixels_2d_slim.shape[0], 2))
centres_scaled = geometry_util.central_scaled_coordinate_2d_from(
shape_native=shape_native, pixel_scales=pixel_scales, origin=origin
)
for slim_index in range(grid_scaled_2d_slim.shape[0]):
grid_scaled_2d_slim[slim_index, 0] = (
-(grid_pixels_2d_slim[slim_index, 0] - centres_scaled[0] - 0.5)
* pixel_scales[0]
)
grid_scaled_2d_slim[slim_index, 1] = (
grid_pixels_2d_slim[slim_index, 1] - centres_scaled[1] - 0.5
) * pixel_scales[1]
return grid_scaled_2d_slim
@numba_util.jit()
def grid_pixel_centres_2d_from(
grid_scaled_2d: np.ndarray,
shape_native: Tuple[int, int],
pixel_scales: Union[float, Tuple[float, float]],
origin: Tuple[float, float] = (0.0, 0.0),
) -> np.ndarray:
"""
Convert a native grid of 2D (y,x) scaled coordinates to a native grid of 2D (y,x) pixel values. Pixel coordinates
are returned as integers such that they map directly to the pixel they are contained within.
The input and output grids are both native resolution and therefore have shape (y_pixels, x_pixels, 2).
The pixel coordinate origin is at the top left corner of the grid, such that the pixel [0,0] corresponds to
the highest (most positive) y scaled coordinate and lowest (most negative) x scaled coordinate on the gird.
The scaled coordinate grid is defined by the class attribute origin, and coordinates are shifted to this
origin before computing their 1D grid pixel indexes.
Parameters
----------
grid_scaled_2d: np.ndarray
The native grid of 2D (y,x) coordinates in scaled units which is converted to pixel indexes.
shape_native
The (y,x) shape of the original 2D array the scaled coordinates were computed on.
pixel_scales
The (y,x) scaled units to pixel units conversion factor of the original 2D array.
origin : (float, flloat)
The (y,x) origin of the grid, which the scaled grid is shifted
Returns
-------
ndarray
A native grid of 2D (y,x) pixel indexes with dimensions (y_pixels, x_pixels, 2).
Examples
--------
grid_scaled_2d = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])
grid_pixel_centres_2d = grid_pixel_centres_2d_from(grid_scaled_2d=grid_scaled_2d, shape=(2,2),
pixel_scales=(0.5, 0.5), origin=(0.0, 0.0))
"""
grid_pixels_2d = np.zeros((grid_scaled_2d.shape[0], grid_scaled_2d.shape[1], 2))
centres_scaled = geometry_util.central_scaled_coordinate_2d_from(
shape_native=shape_native, pixel_scales=pixel_scales, origin=origin
)
for y in range(grid_scaled_2d.shape[0]):
for x in range(grid_scaled_2d.shape[1]):
grid_pixels_2d[y, x, 0] = int(
(-grid_scaled_2d[y, x, 0] / pixel_scales[0]) + centres_scaled[0] + 0.5
)
grid_pixels_2d[y, x, 1] = int(
(grid_scaled_2d[y, x, 1] / pixel_scales[1]) + centres_scaled[1] + 0.5
)
return grid_pixels_2d
@numba_util.jit()
def relocated_grid_via_jit_from(grid, border_grid):
"""
Relocate the coordinates of a grid to its border if they are outside the border, where the border is
defined as all pixels at the edge of the grid's mask (see *mask._border_1d_indexes*).
This is performed as follows:
1: Use the mean value of the grid's y and x coordinates to determine the origin of the grid.
2: Compute the radial distance of every grid coordinate from the origin.
3: For every coordinate, find its nearest pixel in the border.
4: Determine if it is outside the border, by comparing its radial distance from the origin to its paired
border pixel's radial distance.
5: If its radial distance is larger, use the ratio of radial distances to move the coordinate to the
border (if its inside the border, do nothing).
The method can be used on uniform or irregular grids, however for irregular grids the border of the
'image-plane' mask is used to define border pixels.
Parameters
----------
grid : Grid2D
The grid (uniform or irregular) whose pixels are to be relocated to the border edge if outside it.
border_grid : Grid2D
The grid of border (y,x) coordinates.
"""
grid_relocated = np.zeros(grid.shape)
grid_relocated[:, :] = grid[:, :]
border_origin = np.zeros(2)
border_origin[0] = np.mean(border_grid[:, 0])
border_origin[1] = np.mean(border_grid[:, 1])
border_grid_radii = np.sqrt(
np.add(
np.square(np.subtract(border_grid[:, 0], border_origin[0])),
np.square(np.subtract(border_grid[:, 1], border_origin[1])),
)
)
border_min_radii = np.min(border_grid_radii)
grid_radii = np.sqrt(
np.add(
np.square(np.subtract(grid[:, 0], border_origin[0])),
np.square(np.subtract(grid[:, 1], border_origin[1])),
)
)
for pixel_index in range(grid.shape[0]):
if grid_radii[pixel_index] > border_min_radii:
closest_pixel_index = np.argmin(
np.square(grid[pixel_index, 0] - border_grid[:, 0])
+ np.square(grid[pixel_index, 1] - border_grid[:, 1])
)
move_factor = (
border_grid_radii[closest_pixel_index] / grid_radii[pixel_index]
)
if move_factor < 1.0:
grid_relocated[pixel_index, :] = (
move_factor * (grid[pixel_index, :] - border_origin[:])
+ border_origin[:]
)
return grid_relocated
@numba_util.jit()
def furthest_grid_2d_slim_index_from(
grid_2d_slim: np.ndarray, slim_indexes: np.ndarray, coordinate: Tuple[float, float]
) -> int:
distance_to_centre = 0.0
for slim_index in slim_indexes:
y = grid_2d_slim[slim_index, 0]
x = grid_2d_slim[slim_index, 1]
distance_to_centre_new = (x - coordinate[1]) ** 2 + (y - coordinate[0]) ** 2
if distance_to_centre_new >= distance_to_centre:
distance_to_centre = distance_to_centre_new
furthest_grid_2d_slim_index = slim_index
return furthest_grid_2d_slim_index
def grid_2d_slim_from(
grid_2d_native: np.ndarray, mask: np.ndarray, sub_size: int
) -> np.ndarray:
"""
For a native 2D grid and mask of shape [total_y_pixels, total_x_pixels, 2], map the values of all unmasked
pixels to a slimmed grid of shape [total_unmasked_pixels, 2].
The pixel coordinate origin is at the top left corner of the native grid and goes right-wards and downwards, such
that for an grid of shape (3,3) where all pixels are unmasked:
- pixel [0,0] of the 2D grid will correspond to index 0 of the 1D grid.
- pixel [0,1] of the 2D grid will correspond to index 1 of the 1D grid.
- pixel [1,0] of the 2D grid will correspond to index 4 of the 1D grid.
Parameters
----------
grid_2d_native : ndarray
The native grid of (y,x) values which are mapped to the slimmed grid.
mask_2d
A 2D array of bools, where `False` values mean unmasked and are included in the mapping.
sub_size
The size (sub_size x sub_size) of each unmasked pixels sub-array.
Returns
-------
ndarray
A 1D grid of values mapped from the 2D grid with dimensions (total_unmasked_pixels).
"""
grid_1d_slim_y = array_2d_util.array_2d_slim_from(
array_2d_native=grid_2d_native[:, :, 0], mask_2d=mask, sub_size=sub_size
)
grid_1d_slim_x = array_2d_util.array_2d_slim_from(
array_2d_native=grid_2d_native[:, :, 1], mask_2d=mask, sub_size=sub_size
)
return | np.stack((grid_1d_slim_y, grid_1d_slim_x), axis=-1) | numpy.stack |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
from external.transformations import translation_matrix, rotation_matrix
import external.transformations as tf
from Trace import Photon
from Geometry import Box, Cylinder, FinitePlane, transform_point, transform_direction, rotation_matrix_from_vector_alignment, norm
from Materials import Spectrum
def random_spherecial_vector():
# This method of calculating isotropic vectors is taken from GNU Scientific Library
LOOP = True
while LOOP:
x = -1. + 2. * np.random.uniform()
y = -1. + 2. * np.random.uniform()
s = x**2 + y**2
if s <= 1.0:
LOOP = False
z = -1. + 2. * s
a = 2 * np.sqrt(1 - s)
x = a * x
y = a * y
return np.array([x,y,z])
class SimpleSource(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, use_random_polarisation=False):
super(SimpleSource, self).__init__()
self.position = position
self.direction = direction
self.wavelength = wavelength
self.use_random_polarisation = use_random_polarisation
self.throw = 0
self.source_id = "SimpleSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
# If use_polarisation is set generate a random polarisation vector of the photon
if self.use_random_polarisation:
# Randomise rotation angle around xy-plane, the transform from +z to the direction of the photon
vec = random_spherecial_vector()
vec[2] = 0.
vec = norm(vec)
R = rotation_matrix_from_vector_alignment(self.direction, [0,0,1])
photon.polarisation = transform_direction(vec, R)
else:
photon.polarisation = None
photon.id = self.throw
self.throw = self.throw + 1
return photon
class Laser(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, polarisation=None):
super(Laser, self).__init__()
self.position = np.array(position)
self.direction = np.array(direction)
self.wavelength = wavelength
assert polarisation != None, "Polarisation of the Laser is not set."
self.polarisation = np.array(polarisation)
self.throw = 0
self.source_id = "LaserSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
photon.polarisation = self.polarisation
photon.id = self.throw
self.throw = self.throw + 1
return photon
class PlanarSource(object):
"""A box that emits photons from the top surface (normal), sampled from the spectrum."""
def __init__(self, spectrum=None, wavelength=555, direction=(0,0,1), length=0.05, width=0.05):
super(PlanarSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.plane = FinitePlane(length=length, width=width)
self.length = length
self.width = width
# direction is the direction that photons are fired out of the plane in the GLOBAL FRAME.
# i.e. this is passed directly to the photon to set is's direction
self.direction = direction
self.throw = 0
self.source_id = "PlanarSource_" + str(id(self))
def translate(self, translation):
self.plane.append_transform(tf.translation_matrix(translation))
def rotate(self, angle, axis):
self.plane.append_transform(tf.rotation_matrix(angle, axis))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Create a point which is on the surface of the finite plane in it's local frame
x = np.random.uniform(0., self.length)
y = np.random.uniform(0., self.width)
local_point = (x, y, 0.)
# Transform the direciton
photon.position = transform_point(local_point, self.plane.transform)
photon.direction = self.direction
photon.active = True
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class LensSource(object):
"""
A source where photons generated in a plane are focused on a line with space tolerance given by variable "focussize".
The focus line should be perpendicular to the plane normal and aligned with the z-axis.
"""
def __init__(self, spectrum = None, wavelength = 555, linepoint=(0,0,0), linedirection=(0,0,1), focussize = 0, planeorigin = (-1,-1,-1), planeextent = (-1,1,1)):
super(LensSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.planeorigin = planeorigin
self.planeextent = planeextent
self.linepoint = np.array(linepoint)
self.linedirection = np.array(linedirection)
self.focussize = focussize
self.throw = 0
self.source_id = "LensSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Position
x = np.random.uniform(self.planeorigin[0],self.planeextent[0])
y = np.random.uniform(self.planeorigin[1],self.planeextent[1])
z = np.random.uniform(self.planeorigin[2],self.planeextent[2])
photon.position = np.array((x,y,z))
# Direction
focuspoint = np.array((0.,0.,0.))
focuspoint[0] = self.linepoint[0] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[1] = self.linepoint[1] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[2] = photon.position[2]
direction = focuspoint - photon.position
modulus = (direction[0]**2+direction[1]**2+direction[2]**2)**0.5
photon.direction = direction/modulus
# Wavelength
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class LensSourceAngle(object):
"""
A source where photons generated in a plane are focused on a line with space tolerance given by variable "focussize".
The focus line should be perpendicular to the plane normal and aligned with the z-axis.
For this lense an additional z-boost is added (Angle of incidence in z-direction).
"""
def __init__(self, spectrum = None, wavelength = 555, linepoint=(0,0,0), linedirection=(0,0,1), angle = 0, focussize = 0, planeorigin = (-1,-1,-1), planeextent = (-1,1,1)):
super(LensSourceAngle, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.planeorigin = planeorigin
self.planeextent = planeextent
self.linepoint = np.array(linepoint)
self.linedirection = np.array(linedirection)
self.focussize = focussize
self.angle = angle
self.throw = 0
self.source_id = "LensSourceAngle_" + str(id(self))
def photon(self):
photon = Photon()
photon.id = self.throw
self.throw = self.throw + 1
# Position
x = np.random.uniform(self.planeorigin[0],self.planeextent[0])
y = np.random.uniform(self.planeorigin[1],self.planeextent[1])
boost = y*np.tan(self.angle)
z = np.random.uniform(self.planeorigin[2],self.planeextent[2]) - boost
photon.position = np.array((x,y,z))
# Direction
focuspoint = np.array((0.,0.,0.))
focuspoint[0] = self.linepoint[0] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[1] = self.linepoint[1] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[2] = photon.position[2] + boost
direction = focuspoint - photon.position
modulus = (direction[0]**2+direction[1]**2+direction[2]**2)**0.5
photon.direction = direction/modulus
# Wavelength
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class CylindricalSource(object):
"""
A source for photons emitted in a random direction and position inside a cylinder(radius, length)
"""
def __init__(self, spectrum = None, wavelength = 555, radius = 1, length = 10):
super(CylindricalSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.shape = Cylinder(radius = radius, length = length)
self.radius = radius
self.length = length
self.throw = 0
self.source_id = "CylindricalSource_" + str(id(self))
def translate(self, translation):
self.shape.append_transform(tf.translation_matrix(translation))
def rotate(self, angle, axis):
self.shape.append_transform(tf.rotation_matrix(angle, axis))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Position of emission
phi = np.random.uniform(0., 2*np.pi)
r = np.random.uniform(0.,self.radius)
x = r*np.cos(phi)
y = r*np.sin(phi)
z = np.random.uniform(0.,self.length)
local_center = (x,y,z)
photon.position = transform_point(local_center, self.shape.transform)
# Direction of emission (no need to transform if meant to be isotropic)
phi = np.random.uniform(0.,2*np.pi)
theta = np.random.uniform(0.,np.pi)
x = np.cos(phi)*np.sin(theta)
y = np.sin(phi)*np.sin(theta)
z = np.cos(theta)
local_direction = (x,y,z)
photon.direction = local_direction
# Set wavelength of photon
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
# Further initialisation
photon.active = True
return photon
class PointSource(object):
"""
A point source that emits randomly in solid angle specified by phimin, ..., thetamax
"""
def __init__(self, spectrum = None, wavelength = 555, center = (0.,0.,0.), phimin = 0, phimax = 2*np.pi, thetamin = 0, thetamax = np.pi):
super(PointSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.center = center
self.phimin = phimin
self.phimax = phimax
self.thetamin = thetamin
self.thetamax = thetamax
self.throw = 0
self.source_id = "PointSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
phi = np.random.uniform(self.phimin, self.phimax)
theta = np.random.uniform(self.thetamin, self.thetamax)
x = np.cos(phi)*np.sin(theta)
y = np.sin(phi)*np.sin(theta)
z = np.cos(theta)
direction = (x,y,z)
transform = tf.translation_matrix((0,0,0))
point = transform_point(self.center, transform)
photon.direction = direction
photon.position = point
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability( | np.random.uniform() | numpy.random.uniform |
import os
import sys
import click
import pickle
import sncosmo
import numpy as np
from astropy.table import Table
DATA_PATH = '/home/samdixon/jla_light_curves/'
def modify_error(lc, error_floor=0.):
"""Add an error floor of `error_floor` times the maximum flux of the band
to each observation
"""
data = sncosmo.photdata.photometric_data(lc).normalized(zp=25., zpsys='ab')
new_lc = {'time': data.time,
'band': data.band,
'flux': data.flux,
'fluxerr': data.fluxerr,
'zp': data.zp,
'zpsys': data.zpsys}
for band in set(data.band):
band_cut = data.band==band
max_flux_in_band = np.max(data.flux[band_cut])
new_lc['fluxerr'][band_cut] = | np.sqrt((error_floor*max_flux_in_band)**2+data.fluxerr[band_cut]**2) | numpy.sqrt |
# coding: utf-8
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Test the Logarithmic Units and Quantities
"""
from __future__ import (absolute_import, unicode_literals, division,
print_function)
from ...extern import six
from ...extern.six.moves import zip
import pickle
import itertools
import pytest
import numpy as np
from numpy.testing.utils import assert_allclose
from ...tests.helper import assert_quantity_allclose
from ... import units as u, constants as c
lu_units = [u.dex, u.mag, u.decibel]
lu_subclasses = [u.DexUnit, u.MagUnit, u.DecibelUnit]
lq_subclasses = [u.Dex, u.Magnitude, u.Decibel]
pu_sample = (u.dimensionless_unscaled, u.m, u.g/u.s**2, u.Jy)
class TestLogUnitCreation(object):
def test_logarithmic_units(self):
"""Check logarithmic units are set up correctly."""
assert u.dB.to(u.dex) == 0.1
assert u.dex.to(u.mag) == -2.5
assert u.mag.to(u.dB) == -4
@pytest.mark.parametrize('lu_unit, lu_cls', zip(lu_units, lu_subclasses))
def test_callable_units(self, lu_unit, lu_cls):
assert isinstance(lu_unit, u.UnitBase)
assert callable(lu_unit)
assert lu_unit._function_unit_class is lu_cls
@pytest.mark.parametrize('lu_unit', lu_units)
def test_equality_to_normal_unit_for_dimensionless(self, lu_unit):
lu = lu_unit()
assert lu == lu._default_function_unit # eg, MagUnit() == u.mag
assert lu._default_function_unit == lu # and u.mag == MagUnit()
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_call_units(self, lu_unit, physical_unit):
"""Create a LogUnit subclass using the callable unit and physical unit,
and do basic check that output is right."""
lu1 = lu_unit(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
def test_call_invalid_unit(self):
with pytest.raises(TypeError):
u.mag([])
with pytest.raises(ValueError):
u.mag(u.mag())
@pytest.mark.parametrize('lu_cls, physical_unit', itertools.product(
lu_subclasses + [u.LogUnit], pu_sample))
def test_subclass_creation(self, lu_cls, physical_unit):
"""Create a LogUnit subclass object for given physical unit,
and do basic check that output is right."""
lu1 = lu_cls(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
lu2 = lu_cls(physical_unit,
function_unit=2*lu1._default_function_unit)
assert lu2.physical_unit == physical_unit
assert lu2.function_unit == u.Unit(2*lu2._default_function_unit)
with pytest.raises(ValueError):
lu_cls(physical_unit, u.m)
def test_predefined_magnitudes():
assert_quantity_allclose((-21.1*u.STmag).physical,
1.*u.erg/u.cm**2/u.s/u.AA)
assert_quantity_allclose((-48.6*u.ABmag).physical,
1.*u.erg/u.cm**2/u.s/u.Hz)
assert_quantity_allclose((0*u.M_bol).physical, c.L_bol0)
assert_quantity_allclose((0*u.m_bol).physical,
c.L_bol0/(4.*np.pi*(10.*c.pc)**2))
def test_predefined_reinitialisation():
assert u.mag('ST') == u.STmag
assert u.mag('AB') == u.ABmag
assert u.mag('Bol') == u.M_bol
assert u.mag('bol') == u.m_bol
def test_predefined_string_roundtrip():
"""Ensure roundtripping; see #5015"""
with u.magnitude_zero_points.enable():
assert u.Unit(u.STmag.to_string()) == u.STmag
assert u.Unit(u.ABmag.to_string()) == u.ABmag
assert u.Unit(u.M_bol.to_string()) == u.M_bol
assert u.Unit(u.m_bol.to_string()) == u.m_bol
def test_inequality():
"""Check __ne__ works (regresssion for #5342)."""
lu1 = u.mag(u.Jy)
lu2 = u.dex(u.Jy)
lu3 = u.mag(u.Jy**2)
lu4 = lu3 - lu1
assert lu1 != lu2
assert lu1 != lu3
assert lu1 == lu4
class TestLogUnitStrings(object):
def test_str(self):
"""Do some spot checks that str, repr, etc. work as expected."""
lu1 = u.mag(u.Jy)
assert str(lu1) == 'mag(Jy)'
assert repr(lu1) == 'Unit("mag(Jy)")'
assert lu1.to_string('generic') == 'mag(Jy)'
with pytest.raises(ValueError):
lu1.to_string('fits')
lu2 = u.dex()
assert str(lu2) == 'dex'
assert repr(lu2) == 'Unit("dex(1)")'
assert lu2.to_string() == 'dex(1)'
lu3 = u.MagUnit(u.Jy, function_unit=2*u.mag)
assert str(lu3) == '2 mag(Jy)'
assert repr(lu3) == 'MagUnit("Jy", unit="2 mag")'
assert lu3.to_string() == '2 mag(Jy)'
lu4 = u.mag(u.ct)
assert lu4.to_string('generic') == 'mag(ct)'
assert lu4.to_string('latex') == ('$\\mathrm{mag}$$\\mathrm{\\left( '
'\\mathrm{ct} \\right)}$')
assert lu4._repr_latex_() == lu4.to_string('latex')
class TestLogUnitConversion(object):
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_physical_unit_conversion(self, lu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to their non-log counterparts."""
lu1 = lu_unit(physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(physical_unit, 0.) == 1.
assert physical_unit.is_equivalent(lu1)
assert physical_unit.to(lu1, 1.) == 0.
pu = u.Unit(8.*physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(pu, 0.) == 0.125
assert pu.is_equivalent(lu1)
assert_allclose(pu.to(lu1, 0.125), 0., atol=1.e-15)
# Check we round-trip.
value = | np.linspace(0., 10., 6) | numpy.linspace |
# pylint: disable=protected-access
"""
Test the wrappers for the C API.
"""
import os
from contextlib import contextmanager
import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
import xarray as xr
from packaging.version import Version
from pygmt import Figure, clib
from pygmt.clib.conversion import dataarray_to_matrix
from pygmt.clib.session import FAMILIES, VIAS
from pygmt.exceptions import (
GMTCLibError,
GMTCLibNoSessionError,
GMTInvalidInput,
GMTVersionError,
)
from pygmt.helpers import GMTTempFile
TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
with clib.Session() as _lib:
gmt_version = Version(_lib.info["version"])
@contextmanager
def mock(session, func, returns=None, mock_func=None):
"""
Mock a GMT C API function to make it always return a given value.
Used to test that exceptions are raised when API functions fail by
producing a NULL pointer as output or non-zero status codes.
Needed because it's not easy to get some API functions to fail without
inducing a Segmentation Fault (which is a good thing because libgmt usually
only fails with errors).
"""
if mock_func is None:
def mock_api_function(*args): # pylint: disable=unused-argument
"""
A mock GMT API function that always returns a given value.
"""
return returns
mock_func = mock_api_function
get_libgmt_func = session.get_libgmt_func
def mock_get_libgmt_func(name, argtypes=None, restype=None):
"""
Return our mock function.
"""
if name == func:
return mock_func
return get_libgmt_func(name, argtypes, restype)
setattr(session, "get_libgmt_func", mock_get_libgmt_func)
yield
setattr(session, "get_libgmt_func", get_libgmt_func)
def test_getitem():
"""
Test that I can get correct constants from the C lib.
"""
ses = clib.Session()
assert ses["GMT_SESSION_EXTERNAL"] != -99999
assert ses["GMT_MODULE_CMD"] != -99999
assert ses["GMT_PAD_DEFAULT"] != -99999
assert ses["GMT_DOUBLE"] != -99999
with pytest.raises(GMTCLibError):
ses["A_WHOLE_LOT_OF_JUNK"] # pylint: disable=pointless-statement
def test_create_destroy_session():
"""
Test that create and destroy session are called without errors.
"""
# Create two session and make sure they are not pointing to the same memory
session1 = clib.Session()
session1.create(name="test_session1")
assert session1.session_pointer is not None
session2 = clib.Session()
session2.create(name="test_session2")
assert session2.session_pointer is not None
assert session2.session_pointer != session1.session_pointer
session1.destroy()
session2.destroy()
# Create and destroy a session twice
ses = clib.Session()
for __ in range(2):
with pytest.raises(GMTCLibNoSessionError):
ses.session_pointer # pylint: disable=pointless-statement
ses.create("session1")
assert ses.session_pointer is not None
ses.destroy()
with pytest.raises(GMTCLibNoSessionError):
ses.session_pointer # pylint: disable=pointless-statement
def test_create_session_fails():
"""
Check that an exception is raised when failing to create a session.
"""
ses = clib.Session()
with mock(ses, "GMT_Create_Session", returns=None):
with pytest.raises(GMTCLibError):
ses.create("test-session-name")
# Should fail if trying to create a session before destroying the old one.
ses.create("test1")
with pytest.raises(GMTCLibError):
ses.create("test2")
def test_destroy_session_fails():
"""
Fail to destroy session when given bad input.
"""
ses = clib.Session()
with pytest.raises(GMTCLibNoSessionError):
ses.destroy()
ses.create("test-session")
with mock(ses, "GMT_Destroy_Session", returns=1):
with pytest.raises(GMTCLibError):
ses.destroy()
ses.destroy()
def test_call_module():
"""
Run a command to see if call_module works.
"""
data_fname = os.path.join(TEST_DATA_DIR, "points.txt")
out_fname = "test_call_module.txt"
with clib.Session() as lib:
with GMTTempFile() as out_fname:
lib.call_module("info", "{} -C ->{}".format(data_fname, out_fname.name))
assert os.path.exists(out_fname.name)
output = out_fname.read().strip()
assert output == "11.5309 61.7074 -2.9289 7.8648 0.1412 0.9338"
def test_call_module_invalid_arguments():
"""
Fails for invalid module arguments.
"""
with clib.Session() as lib:
with pytest.raises(GMTCLibError):
lib.call_module("info", "bogus-data.bla")
def test_call_module_invalid_name():
"""
Fails when given bad input.
"""
with clib.Session() as lib:
with pytest.raises(GMTCLibError):
lib.call_module("meh", "")
def test_call_module_error_message():
"""
Check is the GMT error message was captured.
"""
with clib.Session() as lib:
try:
lib.call_module("info", "bogus-data.bla")
except GMTCLibError as error:
assert "Module 'info' failed with status code" in str(error)
assert "gmtinfo [ERROR]: Cannot find file bogus-data.bla" in str(error)
def test_method_no_session():
"""
Fails when not in a session.
"""
# Create an instance of Session without "with" so no session is created.
lib = clib.Session()
with pytest.raises(GMTCLibNoSessionError):
lib.call_module("gmtdefaults", "")
with pytest.raises(GMTCLibNoSessionError):
lib.session_pointer # pylint: disable=pointless-statement
def test_parse_constant_single():
"""
Parsing a single family argument correctly.
"""
lib = clib.Session()
for family in FAMILIES:
parsed = lib._parse_constant(family, valid=FAMILIES)
assert parsed == lib[family]
def test_parse_constant_composite():
"""
Parsing a composite constant argument (separated by |) correctly.
"""
lib = clib.Session()
test_cases = ((family, via) for family in FAMILIES for via in VIAS)
for family, via in test_cases:
composite = "|".join([family, via])
expected = lib[family] + lib[via]
parsed = lib._parse_constant(composite, valid=FAMILIES, valid_modifiers=VIAS)
assert parsed == expected
def test_parse_constant_fails():
"""
Check if the function fails when given bad input.
"""
lib = clib.Session()
test_cases = [
"SOME_random_STRING",
"GMT_IS_DATASET|GMT_VIA_MATRIX|GMT_VIA_VECTOR",
"GMT_IS_DATASET|NOT_A_PROPER_VIA",
"NOT_A_PROPER_FAMILY|GMT_VIA_MATRIX",
"NOT_A_PROPER_FAMILY|ALSO_INVALID",
]
for test_case in test_cases:
with pytest.raises(GMTInvalidInput):
lib._parse_constant(test_case, valid=FAMILIES, valid_modifiers=VIAS)
# Should also fail if not given valid modifiers but is using them anyway.
# This should work...
lib._parse_constant(
"GMT_IS_DATASET|GMT_VIA_MATRIX", valid=FAMILIES, valid_modifiers=VIAS
)
# But this shouldn't.
with pytest.raises(GMTInvalidInput):
lib._parse_constant(
"GMT_IS_DATASET|GMT_VIA_MATRIX", valid=FAMILIES, valid_modifiers=None
)
def test_create_data_dataset():
"""
Run the function to make sure it doesn't fail badly.
"""
with clib.Session() as lib:
# Dataset from vectors
data_vector = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_VECTOR",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[10, 20, 1, 0], # columns, rows, layers, dtype
)
# Dataset from matrices
data_matrix = lib.create_data(
family="GMT_IS_DATASET|GMT_VIA_MATRIX",
geometry="GMT_IS_POINT",
mode="GMT_CONTAINER_ONLY",
dim=[10, 20, 1, 0],
)
assert data_vector != data_matrix
def test_create_data_grid_dim():
"""
Create a grid ignoring range and inc.
"""
with clib.Session() as lib:
# Grids from matrices using dim
lib.create_data(
family="GMT_IS_GRID|GMT_VIA_MATRIX",
geometry="GMT_IS_SURFACE",
mode="GMT_CONTAINER_ONLY",
dim=[10, 20, 1, 0],
)
def test_create_data_grid_range():
"""
Create a grid specifying range and inc instead of dim.
"""
with clib.Session() as lib:
# Grids from matrices using range and int
lib.create_data(
family="GMT_IS_GRID|GMT_VIA_MATRIX",
geometry="GMT_IS_SURFACE",
mode="GMT_CONTAINER_ONLY",
ranges=[150.0, 250.0, -20.0, 20.0],
inc=[0.1, 0.2],
)
def test_create_data_fails():
"""
Check that create_data raises exceptions for invalid input and output.
"""
# Passing in invalid mode
with pytest.raises(GMTInvalidInput):
with clib.Session() as lib:
lib.create_data(
family="GMT_IS_DATASET",
geometry="GMT_IS_SURFACE",
mode="Not_a_valid_mode",
dim=[0, 0, 1, 0],
ranges=[150.0, 250.0, -20.0, 20.0],
inc=[0.1, 0.2],
)
# Passing in invalid geometry
with pytest.raises(GMTInvalidInput):
with clib.Session() as lib:
lib.create_data(
family="GMT_IS_GRID",
geometry="Not_a_valid_geometry",
mode="GMT_CONTAINER_ONLY",
dim=[0, 0, 1, 0],
ranges=[150.0, 250.0, -20.0, 20.0],
inc=[0.1, 0.2],
)
# If the data pointer returned is None (NULL pointer)
with pytest.raises(GMTCLibError):
with clib.Session() as lib:
with mock(lib, "GMT_Create_Data", returns=None):
lib.create_data(
family="GMT_IS_DATASET",
geometry="GMT_IS_SURFACE",
mode="GMT_CONTAINER_ONLY",
dim=[11, 10, 2, 0],
)
def test_virtual_file():
"""
Test passing in data via a virtual file with a Dataset.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
shape = (5, 3)
for dtype in dtypes:
with clib.Session() as lib:
family = "GMT_IS_DATASET|GMT_VIA_MATRIX"
geometry = "GMT_IS_POINT"
dataset = lib.create_data(
family=family,
geometry=geometry,
mode="GMT_CONTAINER_ONLY",
dim=[shape[1], shape[0], 1, 0], # columns, rows, layers, dtype
)
data = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
lib.put_matrix(dataset, matrix=data)
# Add the dataset to a virtual file and pass it along to gmt info
vfargs = (family, geometry, "GMT_IN|GMT_IS_REFERENCE", dataset)
with lib.open_virtual_file(*vfargs) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", "{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
["<{:.0f}/{:.0f}>".format(col.min(), col.max()) for col in data.T]
)
expected = "<matrix memory>: N = {}\t{}\n".format(shape[0], bounds)
assert output == expected
def test_virtual_file_fails():
"""
Check that opening and closing virtual files raises an exception for non-
zero return codes.
"""
vfargs = (
"GMT_IS_DATASET|GMT_VIA_MATRIX",
"GMT_IS_POINT",
"GMT_IN|GMT_IS_REFERENCE",
None,
)
# Mock Open_VirtualFile to test the status check when entering the context.
# If the exception is raised, the code won't get to the closing of the
# virtual file.
with clib.Session() as lib, mock(lib, "GMT_Open_VirtualFile", returns=1):
with pytest.raises(GMTCLibError):
with lib.open_virtual_file(*vfargs):
print("Should not get to this code")
# Test the status check when closing the virtual file
# Mock the opening to return 0 (success) so that we don't open a file that
# we won't close later.
with clib.Session() as lib, mock(lib, "GMT_Open_VirtualFile", returns=0), mock(
lib, "GMT_Close_VirtualFile", returns=1
):
with pytest.raises(GMTCLibError):
with lib.open_virtual_file(*vfargs):
pass
print("Shouldn't get to this code either")
def test_virtual_file_bad_direction():
"""
Test passing an invalid direction argument.
"""
with clib.Session() as lib:
vfargs = (
"GMT_IS_DATASET|GMT_VIA_MATRIX",
"GMT_IS_POINT",
"GMT_IS_GRID", # The invalid direction argument
0,
)
with pytest.raises(GMTInvalidInput):
with lib.open_virtual_file(*vfargs):
print("This should have failed")
def test_virtualfile_from_vectors():
"""
Test the automation for transforming vectors to virtual file dataset.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
size = 10
for dtype in dtypes:
x = np.arange(size, dtype=dtype)
y = np.arange(size, size * 2, 1, dtype=dtype)
z = np.arange(size * 2, size * 3, 1, dtype=dtype)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, z) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", "{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
["<{:.0f}/{:.0f}>".format(i.min(), i.max()) for i in (x, y, z)]
)
expected = "<vector memory>: N = {}\t{}\n".format(size, bounds)
assert output == expected
@pytest.mark.parametrize("dtype", [str, object])
def test_virtualfile_from_vectors_one_string_or_object_column(dtype):
"""
Test passing in one column with string or object dtype into virtual file
dataset.
"""
size = 5
x = np.arange(size, dtype=np.int32)
y = np.arange(size, size * 2, 1, dtype=np.int32)
strings = np.array(["a", "bc", "defg", "hijklmn", "opqrst"], dtype=dtype)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, strings) as vfile:
with GMTTempFile() as outfile:
lib.call_module("convert", f"{vfile} ->{outfile.name}")
output = outfile.read(keep_tabs=True)
expected = "".join(f"{i}\t{j}\t{k}\n" for i, j, k in zip(x, y, strings))
assert output == expected
@pytest.mark.parametrize("dtype", [str, object])
def test_virtualfile_from_vectors_two_string_or_object_columns(dtype):
"""
Test passing in two columns of string or object dtype into virtual file
dataset.
"""
size = 5
x = np.arange(size, dtype=np.int32)
y = np.arange(size, size * 2, 1, dtype=np.int32)
strings1 = np.array(["a", "bc", "def", "ghij", "klmno"], dtype=dtype)
strings2 = np.array(["pqrst", "uvwx", "yz!", "@#", "$"], dtype=dtype)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, strings1, strings2) as vfile:
with GMTTempFile() as outfile:
lib.call_module("convert", f"{vfile} ->{outfile.name}")
output = outfile.read(keep_tabs=True)
expected = "".join(
f"{h}\t{i}\t{j} {k}\n" for h, i, j, k in zip(x, y, strings1, strings2)
)
assert output == expected
def test_virtualfile_from_vectors_transpose():
"""
Test transforming matrix columns to virtual file dataset.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
shape = (7, 5)
for dtype in dtypes:
data = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(*data.T) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", "{} -C ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
["{:.0f}\t{:.0f}".format(col.min(), col.max()) for col in data.T]
)
expected = "{}\n".format(bounds)
assert output == expected
def test_virtualfile_from_vectors_diff_size():
"""
Test the function fails for arrays of different sizes.
"""
x = np.arange(5)
y = np.arange(6)
with clib.Session() as lib:
with pytest.raises(GMTInvalidInput):
with lib.virtualfile_from_vectors(x, y):
print("This should have failed")
def test_virtualfile_from_matrix():
"""
Test transforming a matrix to virtual file dataset.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
shape = (7, 5)
for dtype in dtypes:
data = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
with clib.Session() as lib:
with lib.virtualfile_from_matrix(data) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", "{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
["<{:.0f}/{:.0f}>".format(col.min(), col.max()) for col in data.T]
)
expected = "<matrix memory>: N = {}\t{}\n".format(shape[0], bounds)
assert output == expected
def test_virtualfile_from_matrix_slice():
"""
Test transforming a slice of a larger array to virtual file dataset.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
shape = (10, 6)
for dtype in dtypes:
full_data = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
rows = 5
cols = 3
data = full_data[:rows, :cols]
with clib.Session() as lib:
with lib.virtualfile_from_matrix(data) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", "{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
["<{:.0f}/{:.0f}>".format(col.min(), col.max()) for col in data.T]
)
expected = "<matrix memory>: N = {}\t{}\n".format(rows, bounds)
assert output == expected
def test_virtualfile_from_vectors_pandas():
"""
Pass vectors to a dataset using pandas Series.
"""
dtypes = "float32 float64 int32 int64 uint32 uint64".split()
size = 13
for dtype in dtypes:
data = pd.DataFrame(
data=dict(
x=np.arange(size, dtype=dtype),
y=np.arange(size, size * 2, 1, dtype=dtype),
z=np.arange(size * 2, size * 3, 1, dtype=dtype),
)
)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(data.x, data.y, data.z) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", "{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
[
"<{:.0f}/{:.0f}>".format(i.min(), i.max())
for i in (data.x, data.y, data.z)
]
)
expected = "<vector memory>: N = {}\t{}\n".format(size, bounds)
assert output == expected
def test_virtualfile_from_vectors_arraylike():
"""
Pass array-like vectors to a dataset.
"""
size = 13
x = list(range(0, size, 1))
y = tuple(range(size, size * 2, 1))
z = range(size * 2, size * 3, 1)
with clib.Session() as lib:
with lib.virtualfile_from_vectors(x, y, z) as vfile:
with GMTTempFile() as outfile:
lib.call_module("info", "{} ->{}".format(vfile, outfile.name))
output = outfile.read(keep_tabs=True)
bounds = "\t".join(
["<{:.0f}/{:.0f}>".format(min(i), max(i)) for i in (x, y, z)]
)
expected = "<vector memory>: N = {}\t{}\n".format(size, bounds)
assert output == expected
def test_extract_region_fails():
"""
Check that extract region fails if nothing has been plotted.
"""
Figure()
with pytest.raises(GMTCLibError):
with clib.Session() as lib:
lib.extract_region()
def test_extract_region_two_figures():
"""
Extract region should handle multiple figures existing at the same time.
"""
# Make two figures before calling extract_region to make sure that it's
# getting from the current figure, not the last figure.
fig1 = Figure()
region1 = np.array([0, 10, -20, -10])
fig1.coast(region=region1, projection="M6i", frame=True, land="black")
fig2 = Figure()
fig2.basemap(region="US.HI+r5", projection="M6i", frame=True)
# Activate the first figure and extract the region from it
# Use in a different session to avoid any memory problems.
with clib.Session() as lib:
lib.call_module("figure", "{} -".format(fig1._name))
with clib.Session() as lib:
wesn1 = lib.extract_region()
npt.assert_allclose(wesn1, region1)
# Now try it with the second one
with clib.Session() as lib:
lib.call_module("figure", "{} -".format(fig2._name))
with clib.Session() as lib:
wesn2 = lib.extract_region()
npt.assert_allclose(wesn2, np.array([-165.0, -150.0, 15.0, 25.0]))
def test_write_data_fails():
"""
Check that write data raises an exception for non-zero return codes.
"""
# It's hard to make the C API function fail without causing a Segmentation
# Fault. Can't test this if by giving a bad file name because if
# output=='', GMT will just write to stdout and spaces are valid file
# names. Use a mock instead just to exercise this part of the code.
with clib.Session() as lib:
with mock(lib, "GMT_Write_Data", returns=1):
with pytest.raises(GMTCLibError):
lib.write_data(
"GMT_IS_VECTOR",
"GMT_IS_POINT",
"GMT_WRITE_SET",
[1] * 6,
"some-file-name",
None,
)
def test_dataarray_to_matrix_works():
"""
Check that dataarray_to_matrix returns correct output.
"""
data = np.diag(v=np.arange(3))
x = np.linspace(start=0, stop=4, num=3)
y = np.linspace(start=5, stop=9, num=3)
grid = xr.DataArray(data, coords=[("y", y), ("x", x)])
matrix, region, inc = dataarray_to_matrix(grid)
npt.assert_allclose(actual=matrix, desired=np.flipud(data))
npt.assert_allclose(actual=region, desired=[x.min(), x.max(), y.min(), y.max()])
npt.assert_allclose(actual=inc, desired=[x[1] - x[0], y[1] - y[0]])
def test_dataarray_to_matrix_negative_x_increment():
"""
Check if dataarray_to_matrix returns correct output with flipped x.
"""
data = np.diag(v=np.arange(3))
x = np.linspace(start=4, stop=0, num=3)
y = | np.linspace(start=5, stop=9, num=3) | numpy.linspace |
"""
Binary serialization
NPY format
==========
A simple format for saving numpy arrays to disk with the full
information about them.
The ``.npy`` format is the standard binary file format in NumPy for
persisting a *single* arbitrary NumPy array on disk. The format stores all
of the shape and dtype information necessary to reconstruct the array
correctly even on another machine with a different architecture.
The format is designed to be as simple as possible while achieving
its limited goals.
The ``.npz`` format is the standard format for persisting *multiple* NumPy
arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``
files, one for each array.
Capabilities
------------
- Can represent all NumPy arrays including nested record arrays and
object arrays.
- Represents the data in its native binary form.
- Supports Fortran-contiguous arrays directly.
- Stores all of the necessary information to reconstruct the array
including shape and dtype on a machine of a different
architecture. Both little-endian and big-endian arrays are
supported, and a file with little-endian numbers will yield
a little-endian array on any machine reading the file. The
types are described in terms of their actual sizes. For example,
if a machine with a 64-bit C "long int" writes out an array with
"long ints", a reading machine with 32-bit C "long ints" will yield
an array with 64-bit integers.
- Is straightforward to reverse engineer. Datasets often live longer than
the programs that created them. A competent developer should be
able to create a solution in their preferred programming language to
read most ``.npy`` files that they have been given without much
documentation.
- Allows memory-mapping of the data. See `open_memmap`.
- Can be read from a filelike stream object instead of an actual file.
- Stores object arrays, i.e. arrays containing elements that are arbitrary
Python objects. Files with object arrays are not to be mmapable, but
can be read and written to disk.
Limitations
-----------
- Arbitrary subclasses of numpy.ndarray are not completely preserved.
Subclasses will be accepted for writing, but only the array data will
be written out. A regular numpy.ndarray object will be created
upon reading the file.
.. warning::
Due to limitations in the interpretation of structured dtypes, dtypes
with fields with empty names will have the names replaced by 'f0', 'f1',
etc. Such arrays will not round-trip through the format entirely
accurately. The data is intact; only the field names will differ. We are
working on a fix for this. This fix will not require a change in the
file format. The arrays with such structures can still be saved and
restored, and the correct dtype may be restored by using the
``loadedarray.view(correct_dtype)`` method.
File extensions
---------------
We recommend using the ``.npy`` and ``.npz`` extensions for files saved
in this format. This is by no means a requirement; applications may wish
to use these file formats but use an extension specific to the
application. In the absence of an obvious alternative, however,
we suggest using ``.npy`` and ``.npz``.
Version numbering
-----------------
The version numbering of these formats is independent of NumPy version
numbering. If the format is upgraded, the code in `numpy.io` will still
be able to read and write Version 1.0 files.
Format Version 1.0
------------------
The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.
The next 1 byte is an unsigned byte: the major version number of the file
format, e.g. ``\\x01``.
The next 1 byte is an unsigned byte: the minor version number of the file
format, e.g. ``\\x00``. Note: the version of the file format is not tied
to the version of the numpy package.
The next 2 bytes form a little-endian unsigned short int: the length of
the header data HEADER_LEN.
The next HEADER_LEN bytes form the header data describing the array's
format. It is an ASCII string which contains a Python literal expression
of a dictionary. It is terminated by a newline (``\\n``) and padded with
spaces (``\\x20``) to make the total of
``len(magic string) + 2 + len(length) + HEADER_LEN`` be evenly divisible
by 64 for alignment purposes.
The dictionary contains three keys:
"descr" : dtype.descr
An object that can be passed as an argument to the `numpy.dtype`
constructor to create the array's dtype.
"fortran_order" : bool
Whether the array data is Fortran-contiguous or not. Since
Fortran-contiguous arrays are a common form of non-C-contiguity,
we allow them to be written directly to disk for efficiency.
"shape" : tuple of int
The shape of the array.
For repeatability and readability, the dictionary keys are sorted in
alphabetic order. This is for convenience only. A writer SHOULD implement
this if possible. A reader MUST NOT depend on this.
Following the header comes the array data. If the dtype contains Python
objects (i.e. ``dtype.hasobject is True``), then the data is a Python
pickle of the array. Otherwise the data is the contiguous (either C-
or Fortran-, depending on ``fortran_order``) bytes of the array.
Consumers can figure out the number of bytes by multiplying the number
of elements given by the shape (noting that ``shape=()`` means there is
1 element) by ``dtype.itemsize``.
Format Version 2.0
------------------
The version 1.0 format only allowed the array header to have a total size of
65535 bytes. This can be exceeded by structured arrays with a large number of
columns. The version 2.0 format extends the header size to 4 GiB.
`numpy.save` will automatically save in 2.0 format if the data requires it,
else it will always use the more compatible 1.0 format.
The description of the fourth element of the header therefore has become:
"The next 4 bytes form a little-endian unsigned int: the length of the header
data HEADER_LEN."
Format Version 3.0
------------------
This version replaces the ASCII string (which in practice was latin1) with
a utf8-encoded string, so supports structured types with any unicode field
names.
Notes
-----
The ``.npy`` format, including motivation for creating it and a comparison of
alternatives, is described in the
:doc:`"npy-format" NEP <neps:nep-0001-npy-format>`, however details have
evolved with time and this document is more current.
"""
import numpy
import io
import warnings
from numpy.lib.utils import safe_eval
from numpy.compat import (
isfileobj, os_fspath, pickle
)
__all__ = []
EXPECTED_KEYS = {'descr', 'fortran_order', 'shape'}
MAGIC_PREFIX = b'\x93NUMPY'
MAGIC_LEN = len(MAGIC_PREFIX) + 2
ARRAY_ALIGN = 64 # plausible values are powers of 2 between 16 and 4096
BUFFER_SIZE = 2**18 # size of buffer for reading npz files in bytes
# difference between version 1.0 and 2.0 is a 4 byte (I) header length
# instead of 2 bytes (H) allowing storage of large structured arrays
_header_size_info = {
(1, 0): ('<H', 'latin1'),
(2, 0): ('<I', 'latin1'),
(3, 0): ('<I', 'utf8'),
}
def _check_version(version):
if version not in [(1, 0), (2, 0), (3, 0), None]:
msg = "we only support format version (1,0), (2,0), and (3,0), not %s"
raise ValueError(msg % (version,))
def magic(major, minor):
""" Return the magic string for the given file format version.
Parameters
----------
major : int in [0, 255]
minor : int in [0, 255]
Returns
-------
magic : str
Raises
------
ValueError if the version cannot be formatted.
"""
if major < 0 or major > 255:
raise ValueError("major version must be 0 <= major < 256")
if minor < 0 or minor > 255:
raise ValueError("minor version must be 0 <= minor < 256")
return MAGIC_PREFIX + bytes([major, minor])
def read_magic(fp):
""" Read the magic string to get the version of the file format.
Parameters
----------
fp : filelike object
Returns
-------
major : int
minor : int
"""
magic_str = _read_bytes(fp, MAGIC_LEN, "magic string")
if magic_str[:-2] != MAGIC_PREFIX:
msg = "the magic string is not correct; expected %r, got %r"
raise ValueError(msg % (MAGIC_PREFIX, magic_str[:-2]))
major, minor = magic_str[-2:]
return major, minor
def _has_metadata(dt):
if dt.metadata is not None:
return True
elif dt.names is not None:
return any(_has_metadata(dt[k]) for k in dt.names)
elif dt.subdtype is not None:
return _has_metadata(dt.base)
else:
return False
def dtype_to_descr(dtype):
"""
Get a serializable descriptor from the dtype.
The .descr attribute of a dtype object cannot be round-tripped through
the dtype() constructor. Simple types, like dtype('float32'), have
a descr which looks like a record array with one field with '' as
a name. The dtype() constructor interprets this as a request to give
a default name. Instead, we construct descriptor that can be passed to
dtype().
Parameters
----------
dtype : dtype
The dtype of the array that will be written to disk.
Returns
-------
descr : object
An object that can be passed to `numpy.dtype()` in order to
replicate the input dtype.
"""
if _has_metadata(dtype):
warnings.warn("metadata on a dtype may be saved or ignored, but will "
"raise if saved when read. Use another form of storage.",
UserWarning, stacklevel=2)
if dtype.names is not None:
# This is a record array. The .descr is fine. XXX: parts of the
# record array with an empty name, like padding bytes, still get
# fiddled with. This needs to be fixed in the C implementation of
# dtype().
return dtype.descr
else:
return dtype.str
def descr_to_dtype(descr):
"""
Returns a dtype based off the given description.
This is essentially the reverse of `dtype_to_descr()`. It will remove
the valueless padding fields created by, i.e. simple fields like
dtype('float32'), and then convert the description to its corresponding
dtype.
Parameters
----------
descr : object
The object retreived by dtype.descr. Can be passed to
`numpy.dtype()` in order to replicate the input dtype.
Returns
-------
dtype : dtype
The dtype constructed by the description.
"""
if isinstance(descr, str):
# No padding removal needed
return numpy.dtype(descr)
elif isinstance(descr, tuple):
# subtype, will always have a shape descr[1]
dt = descr_to_dtype(descr[0])
return numpy.dtype((dt, descr[1]))
titles = []
names = []
formats = []
offsets = []
offset = 0
for field in descr:
if len(field) == 2:
name, descr_str = field
dt = descr_to_dtype(descr_str)
else:
name, descr_str, shape = field
dt = numpy.dtype((descr_to_dtype(descr_str), shape))
# Ignore padding bytes, which will be void bytes with '' as name
# Once support for blank names is removed, only "if name == ''" needed)
is_pad = (name == '' and dt.type is numpy.void and dt.names is None)
if not is_pad:
title, name = name if isinstance(name, tuple) else (None, name)
titles.append(title)
names.append(name)
formats.append(dt)
offsets.append(offset)
offset += dt.itemsize
return numpy.dtype({'names': names, 'formats': formats, 'titles': titles,
'offsets': offsets, 'itemsize': offset})
def header_data_from_array_1_0(array):
""" Get the dictionary of header metadata from a numpy.ndarray.
Parameters
----------
array : numpy.ndarray
Returns
-------
d : dict
This has the appropriate entries for writing its string representation
to the header of the file.
"""
d = {'shape': array.shape}
if array.flags.c_contiguous:
d['fortran_order'] = False
elif array.flags.f_contiguous:
d['fortran_order'] = True
else:
# Totally non-contiguous data. We will have to make it C-contiguous
# before writing. Note that we need to test for C_CONTIGUOUS first
# because a 1-D array is both C_CONTIGUOUS and F_CONTIGUOUS.
d['fortran_order'] = False
d['descr'] = dtype_to_descr(array.dtype)
return d
def _wrap_header(header, version):
"""
Takes a stringified header, and attaches the prefix and padding to it
"""
import struct
assert version is not None
fmt, encoding = _header_size_info[version]
if not isinstance(header, bytes): # always true on python 3
header = header.encode(encoding)
hlen = len(header) + 1
padlen = ARRAY_ALIGN - ((MAGIC_LEN + struct.calcsize(fmt) + hlen) % ARRAY_ALIGN)
try:
header_prefix = magic(*version) + struct.pack(fmt, hlen + padlen)
except struct.error:
msg = "Header length {} too big for version={}".format(hlen, version)
raise ValueError(msg) from None
# Pad the header with spaces and a final newline such that the magic
# string, the header-length short and the header are aligned on a
# ARRAY_ALIGN byte boundary. This supports memory mapping of dtypes
# aligned up to ARRAY_ALIGN on systems like Linux where mmap()
# offset must be page-aligned (i.e. the beginning of the file).
return header_prefix + header + b' '*padlen + b'\n'
def _wrap_header_guess_version(header):
"""
Like `_wrap_header`, but chooses an appropriate version given the contents
"""
try:
return _wrap_header(header, (1, 0))
except ValueError:
pass
try:
ret = _wrap_header(header, (2, 0))
except UnicodeEncodeError:
pass
else:
warnings.warn("Stored array in format 2.0. It can only be"
"read by NumPy >= 1.9", UserWarning, stacklevel=2)
return ret
header = _wrap_header(header, (3, 0))
warnings.warn("Stored array in format 3.0. It can only be "
"read by NumPy >= 1.17", UserWarning, stacklevel=2)
return header
def _write_array_header(fp, d, version=None):
""" Write the header for an array and returns the version used
Parameters
----------
fp : filelike object
d : dict
This has the appropriate entries for writing its string representation
to the header of the file.
version: tuple or None
None means use oldest that works
explicit version will raise a ValueError if the format does not
allow saving this data. Default: None
"""
header = ["{"]
for key, value in sorted(d.items()):
# Need to use repr here, since we eval these when reading
header.append("'%s': %s, " % (key, repr(value)))
header.append("}")
header = "".join(header)
if version is None:
header = _wrap_header_guess_version(header)
else:
header = _wrap_header(header, version)
fp.write(header)
def write_array_header_1_0(fp, d):
""" Write the header for an array using the 1.0 format.
Parameters
----------
fp : filelike object
d : dict
This has the appropriate entries for writing its string
representation to the header of the file.
"""
_write_array_header(fp, d, (1, 0))
def write_array_header_2_0(fp, d):
""" Write the header for an array using the 2.0 format.
The 2.0 format allows storing very large structured arrays.
.. versionadded:: 1.9.0
Parameters
----------
fp : filelike object
d : dict
This has the appropriate entries for writing its string
representation to the header of the file.
"""
_write_array_header(fp, d, (2, 0))
def read_array_header_1_0(fp):
"""
Read an array header from a filelike object using the 1.0 file format
version.
This will leave the file object located just after the header.
Parameters
----------
fp : filelike object
A file object or something with a `.read()` method like a file.
Returns
-------
shape : tuple of int
The shape of the array.
fortran_order : bool
The array data will be written out directly if it is either
C-contiguous or Fortran-contiguous. Otherwise, it will be made
contiguous before writing it out.
dtype : dtype
The dtype of the file's data.
Raises
------
ValueError
If the data is invalid.
"""
return _read_array_header(fp, version=(1, 0))
def read_array_header_2_0(fp):
"""
Read an array header from a filelike object using the 2.0 file format
version.
This will leave the file object located just after the header.
.. versionadded:: 1.9.0
Parameters
----------
fp : filelike object
A file object or something with a `.read()` method like a file.
Returns
-------
shape : tuple of int
The shape of the array.
fortran_order : bool
The array data will be written out directly if it is either
C-contiguous or Fortran-contiguous. Otherwise, it will be made
contiguous before writing it out.
dtype : dtype
The dtype of the file's data.
Raises
------
ValueError
If the data is invalid.
"""
return _read_array_header(fp, version=(2, 0))
def _filter_header(s):
"""Clean up 'L' in npz header ints.
Cleans up the 'L' in strings representing integers. Needed to allow npz
headers produced in Python2 to be read in Python3.
Parameters
----------
s : string
Npy file header.
Returns
-------
header : str
Cleaned up header.
"""
import tokenize
from io import StringIO
tokens = []
last_token_was_number = False
for token in tokenize.generate_tokens(StringIO(s).readline):
token_type = token[0]
token_string = token[1]
if (last_token_was_number and
token_type == tokenize.NAME and
token_string == "L"):
continue
else:
tokens.append(token)
last_token_was_number = (token_type == tokenize.NUMBER)
return tokenize.untokenize(tokens)
def _read_array_header(fp, version):
"""
see read_array_header_1_0
"""
# Read an unsigned, little-endian short int which has the length of the
# header.
import struct
hinfo = _header_size_info.get(version)
if hinfo is None:
raise ValueError("Invalid version {!r}".format(version))
hlength_type, encoding = hinfo
hlength_str = _read_bytes(fp, struct.calcsize(hlength_type), "array header length")
header_length = struct.unpack(hlength_type, hlength_str)[0]
header = _read_bytes(fp, header_length, "array header")
header = header.decode(encoding)
# The header is a pretty-printed string representation of a literal
# Python dictionary with trailing newlines padded to a ARRAY_ALIGN byte
# boundary. The keys are strings.
# "shape" : tuple of int
# "fortran_order" : bool
# "descr" : dtype.descr
# Versions (2, 0) and (1, 0) could have been created by a Python 2
# implementation before header filtering was implemented.
if version <= (2, 0):
header = _filter_header(header)
try:
d = safe_eval(header)
except SyntaxError as e:
msg = "Cannot parse header: {!r}"
raise ValueError(msg.format(header)) from e
if not isinstance(d, dict):
msg = "Header is not a dictionary: {!r}"
raise ValueError(msg.format(d))
if EXPECTED_KEYS != d.keys():
keys = sorted(d.keys())
msg = "Header does not contain the correct keys: {!r}"
raise ValueError(msg.format(keys))
# Sanity-check the values.
if (not isinstance(d['shape'], tuple) or
not all(isinstance(x, int) for x in d['shape'])):
msg = "shape is not valid: {!r}"
raise ValueError(msg.format(d['shape']))
if not isinstance(d['fortran_order'], bool):
msg = "fortran_order is not a valid bool: {!r}"
raise ValueError(msg.format(d['fortran_order']))
try:
dtype = descr_to_dtype(d['descr'])
except TypeError as e:
msg = "descr is not a valid dtype descriptor: {!r}"
raise ValueError(msg.format(d['descr'])) from e
return d['shape'], d['fortran_order'], dtype
def write_array(fp, array, version=None, allow_pickle=True, pickle_kwargs=None):
"""
Write an array to an NPY file, including a header.
If the array is neither C-contiguous nor Fortran-contiguous AND the
file_like object is not a real file object, this function will have to
copy data in memory.
Parameters
----------
fp : file_like object
An open, writable file object, or similar object with a
``.write()`` method.
array : ndarray
The array to write to disk.
version : (int, int) or None, optional
The version number of the format. None means use the oldest
supported version that is able to store the data. Default: None
allow_pickle : bool, optional
Whether to allow writing pickled data. Default: True
pickle_kwargs : dict, optional
Additional keyword arguments to pass to pickle.dump, excluding
'protocol'. These are only useful when pickling objects in object
arrays on Python 3 to Python 2 compatible format.
Raises
------
ValueError
If the array cannot be persisted. This includes the case of
allow_pickle=False and array being an object array.
Various other errors
If the array contains Python objects as part of its dtype, the
process of pickling them may raise various errors if the objects
are not picklable.
"""
_check_version(version)
_write_array_header(fp, header_data_from_array_1_0(array), version)
if array.itemsize == 0:
buffersize = 0
else:
# Set buffer size to 16 MiB to hide the Python loop overhead.
buffersize = max(16 * 1024 ** 2 // array.itemsize, 1)
if array.dtype.hasobject:
# We contain Python objects so we cannot write out the data
# directly. Instead, we will pickle it out
if not allow_pickle:
raise ValueError("Object arrays cannot be saved when "
"allow_pickle=False")
if pickle_kwargs is None:
pickle_kwargs = {}
pickle.dump(array, fp, protocol=3, **pickle_kwargs)
elif array.flags.f_contiguous and not array.flags.c_contiguous:
if isfileobj(fp):
array.T.tofile(fp)
else:
for chunk in numpy.nditer(
array, flags=['external_loop', 'buffered', 'zerosize_ok'],
buffersize=buffersize, order='F'):
fp.write(chunk.tobytes('C'))
else:
if isfileobj(fp):
array.tofile(fp)
else:
for chunk in numpy.nditer(
array, flags=['external_loop', 'buffered', 'zerosize_ok'],
buffersize=buffersize, order='C'):
fp.write(chunk.tobytes('C'))
def read_array(fp, allow_pickle=False, pickle_kwargs=None):
"""
Read an array from an NPY file.
Parameters
----------
fp : file_like object
If this is not a real file object, then this may take extra memory
and time.
allow_pickle : bool, optional
Whether to allow writing pickled data. Default: False
.. versionchanged:: 1.16.3
Made default False in response to CVE-2019-6446.
pickle_kwargs : dict
Additional keyword arguments to pass to pickle.load. These are only
useful when loading object arrays saved on Python 2 when using
Python 3.
Returns
-------
array : ndarray
The array from the data on disk.
Raises
------
ValueError
If the data is invalid, or allow_pickle=False and the file contains
an object array.
"""
version = read_magic(fp)
_check_version(version)
shape, fortran_order, dtype = _read_array_header(fp, version)
if len(shape) == 0:
count = 1
else:
count = numpy.multiply.reduce(shape, dtype=numpy.int64)
# Now read the actual data.
if dtype.hasobject:
# The array contained Python objects. We need to unpickle the data.
if not allow_pickle:
raise ValueError("Object arrays cannot be loaded when "
"allow_pickle=False")
if pickle_kwargs is None:
pickle_kwargs = {}
try:
array = pickle.load(fp, **pickle_kwargs)
except UnicodeError as err:
# Friendlier error message
raise UnicodeError("Unpickling a python object failed: %r\n"
"You may need to pass the encoding= option "
"to numpy.load" % (err,)) from err
else:
if isfileobj(fp):
# We can use the fast fromfile() function.
array = numpy.fromfile(fp, dtype=dtype, count=count)
else:
# This is not a real file. We have to read it the
# memory-intensive way.
# crc32 module fails on reads greater than 2 ** 32 bytes,
# breaking large reads from gzip streams. Chunk reads to
# BUFFER_SIZE bytes to avoid issue and reduce memory overhead
# of the read. In non-chunked case count < max_read_count, so
# only one read is performed.
# Use np.ndarray instead of np.empty since the latter does
# not correctly instantiate zero-width string dtypes; see
# https://github.com/numpy/numpy/pull/6430
array = numpy.ndarray(count, dtype=dtype)
if dtype.itemsize > 0:
# If dtype.itemsize == 0 then there's nothing more to read
max_read_count = BUFFER_SIZE // min(BUFFER_SIZE, dtype.itemsize)
for i in range(0, count, max_read_count):
read_count = min(max_read_count, count - i)
read_size = int(read_count * dtype.itemsize)
data = _read_bytes(fp, read_size, "array data")
array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype,
count=read_count)
if fortran_order:
array.shape = shape[::-1]
array = array.transpose()
else:
array.shape = shape
return array
def open_memmap(filename, mode='r+', dtype=None, shape=None,
fortran_order=False, version=None):
"""
Open a .npy file as a memory-mapped array.
This may be used to read an existing file or create a new one.
Parameters
----------
filename : str or path-like
The name of the file on disk. This may *not* be a file-like
object.
mode : str, optional
The mode in which to open the file; the default is 'r+'. In
addition to the standard file modes, 'c' is also accepted to mean
"copy on write." See `memmap` for the available mode strings.
dtype : data-type, optional
The data type of the array if we are creating a new file in "write"
mode, if not, `dtype` is ignored. The default value is None, which
results in a data-type of `float64`.
shape : tuple of int
The shape of the array if we are creating a new file in "write"
mode, in which case this parameter is required. Otherwise, this
parameter is ignored and is thus optional.
fortran_order : bool, optional
Whether the array should be Fortran-contiguous (True) or
C-contiguous (False, the default) if we are creating a new file in
"write" mode.
version : tuple of int (major, minor) or None
If the mode is a "write" mode, then this is the version of the file
format used to create the file. None means use the oldest
supported version that is able to store the data. Default: None
Returns
-------
marray : memmap
The memory-mapped array.
Raises
------
ValueError
If the data or the mode is invalid.
IOError
If the file is not found or cannot be opened correctly.
See Also
--------
numpy.memmap
"""
if isfileobj(filename):
raise ValueError("Filename must be a string or a path-like object."
" Memmap cannot use existing file handles.")
if 'w' in mode:
# We are creating the file, not reading it.
# Check if we ought to create the file.
_check_version(version)
# Ensure that the given dtype is an authentic dtype object rather
# than just something that can be interpreted as a dtype object.
dtype = | numpy.dtype(dtype) | numpy.dtype |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[2].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[2].plot(1.55 * np.pi * | np.ones(101) | numpy.ones |
import argparse
import json
import numpy as np
import pandas as pd
import os
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,f1_score
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras import backend as K
from keras.utils.vis_utils import plot_model
from sklearn.externals import joblib
import time
def f1(y_true, y_pred):
def recall(y_true, y_pred):
"""Recall metric.
Only computes a batch-wise average of recall.
Computes the recall, a metric for multi-label classification of
how many relevant items are selected.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
"""Precision metric.
Only computes a batch-wise average of precision.
Computes the precision, a metric for multi-label classification of
how many selected items are relevant.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
precision = precision(y_true, y_pred)
recall = recall(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+K.epsilon()))
def get_embeddings(sentences_list,layer_json):
'''
:param sentences_list: the path o the sentences.txt
:param layer_json: the path of the json file that contains the embeddings of the sentences
:return: Dictionary with key each sentence of the sentences_list and as value the embedding
'''
sentences = dict()#dict with key the index of each line of the sentences_list.txt and as value the sentence
embeddings = dict()##dict with key the index of each sentence and as value the its embedding
sentence_emb = dict()#key:sentence,value:its embedding
with open(sentences_list,'r') as file:
for index,line in enumerate(file):
sentences[index] = line.strip()
with open(layer_json, 'r',encoding='utf-8') as f:
for line in f:
embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features'])
for key,value in sentences.items():
sentence_emb[value] = embeddings[key]
return sentence_emb
def train_classifier(sentences_list,layer_json,dataset_csv,filename):
'''
:param sentences_list: the path o the sentences.txt
:param layer_json: the path of the json file that contains the embeddings of the sentences
:param dataset_csv: the path of the dataset
:param filename: The path of the pickle file that the model will be stored
:return:
'''
dataset = pd.read_csv(dataset_csv)
bert_dict = get_embeddings(sentences_list,layer_json)
length = list()
sentence_emb = list()
previous_emb = list()
next_list = list()
section_list = list()
label = list()
errors = 0
for row in dataset.iterrows():
sentence = row[1][0].strip()
previous = row[1][1].strip()
nexts = row[1][2].strip()
section = row[1][3].strip()
if sentence in bert_dict:
sentence_emb.append(bert_dict[sentence])
else:
sentence_emb.append(np.zeros(768))
print(sentence)
errors += 1
if previous in bert_dict:
previous_emb.append(bert_dict[previous])
else:
previous_emb.append(np.zeros(768))
if nexts in bert_dict:
next_list.append(bert_dict[nexts])
else:
next_list.append(np.zeros(768))
if section in bert_dict:
section_list.append(bert_dict[section])
else:
section_list.append(np.zeros(768))
length.append(row[1][4])
label.append(row[1][5])
sentence_emb = np.asarray(sentence_emb)
print(sentence_emb.shape)
next_emb = np.asarray(next_list)
print(next_emb.shape)
previous_emb = np.asarray(previous_emb)
print(previous_emb.shape)
section_emb = np.asarray(section_list)
print(sentence_emb.shape)
length = np.asarray(length)
print(length.shape)
label = np.asarray(label)
print(errors)
features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1)
features = np.column_stack([features, length]) # np.append(features,length,axis=1)
print(features.shape)
X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42)
log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1)
log.fit(X_train, y_train)
#save the model
_ = joblib.dump(log, filename, compress=9)
predictions = log.predict(X_val)
print("###########################################")
print("Results using embeddings from the",layer_json,"file")
print(classification_report(y_val, predictions))
print("F1 score using Logistic Regression:",f1_score(y_val, predictions))
print("###########################################")
#train a DNN
f1_results = list()
for i in range(3):
model = Sequential()
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dense(128, activation='relu', trainable=True))
model.add(Dropout(0.30))
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dropout(0.25))
model.add(Dense(64, activation='relu', trainable=True))
model.add(Dropout(0.35))
model.add(Dense(1, activation='sigmoid'))
# compile network
model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1])
# fit network
model.fit(X_train, y_train, epochs=100, batch_size=64)
loss, f_1 = model.evaluate(X_val, y_val, verbose=1)
print('\nTest F1: %f' % (f_1 * 100))
f1_results.append(f_1)
model = None
print("###########################################")
print("Results using embeddings from the", layer_json, "file")
# evaluate
print(np.mean(f1_results))
print("###########################################")
def parameter_tuning_LR(sentences_list,layer_json,dataset_csv):
'''
:param sentences_list: the path o the sentences.txt
:param layer_json: the path of the json file that contains the embeddings of the sentences
:param dataset_csv: the path of the dataset
:return:
'''
dataset = pd.read_csv(dataset_csv)
bert_dict = get_embeddings(sentences_list,layer_json)
length = list()
sentence_emb = list()
previous_emb = list()
next_list = list()
section_list = list()
label = list()
errors = 0
for row in dataset.iterrows():
sentence = row[1][0].strip()
previous = row[1][1].strip()
nexts = row[1][2].strip()
section = row[1][3].strip()
if sentence in bert_dict:
sentence_emb.append(bert_dict[sentence])
else:
sentence_emb.append(np.zeros(768))
print(sentence)
errors += 1
if previous in bert_dict:
previous_emb.append(bert_dict[previous])
else:
previous_emb.append(np.zeros(768))
if nexts in bert_dict:
next_list.append(bert_dict[nexts])
else:
next_list.append(np.zeros(768))
if section in bert_dict:
section_list.append(bert_dict[section])
else:
section_list.append( | np.zeros(768) | numpy.zeros |
import copy
import functools
import itertools
import numbers
import warnings
from collections import defaultdict
from datetime import timedelta
from distutils.version import LooseVersion
from typing import (
Any,
Dict,
Hashable,
Mapping,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
)
import numpy as np
import pandas as pd
import xarray as xr # only for Dataset and DataArray
from . import arithmetic, common, dtypes, duck_array_ops, indexing, nputils, ops, utils
from .indexing import (
BasicIndexer,
OuterIndexer,
PandasIndexAdapter,
VectorizedIndexer,
as_indexable,
)
from .npcompat import IS_NEP18_ACTIVE
from .options import _get_keep_attrs
from .pycompat import (
cupy_array_type,
dask_array_type,
integer_types,
is_duck_dask_array,
)
from .utils import (
OrderedSet,
_default,
decode_numpy_dict_values,
drop_dims_from_indexers,
either_dict_or_kwargs,
ensure_us_time_resolution,
infix_dims,
is_duck_array,
)
NON_NUMPY_SUPPORTED_ARRAY_TYPES = (
(
indexing.ExplicitlyIndexed,
pd.Index,
)
+ dask_array_type
+ cupy_array_type
)
# https://github.com/python/mypy/issues/224
BASIC_INDEXING_TYPES = integer_types + (slice,) # type: ignore
VariableType = TypeVar("VariableType", bound="Variable")
"""Type annotation to be used when methods of Variable return self or a copy of self.
When called from an instance of a subclass, e.g. IndexVariable, mypy identifies the
output as an instance of the subclass.
Usage::
class Variable:
def f(self: VariableType, ...) -> VariableType:
...
"""
class MissingDimensionsError(ValueError):
"""Error class used when we can't safely guess a dimension name."""
# inherits from ValueError for backward compatibility
# TODO: move this to an xarray.exceptions module?
def as_variable(obj, name=None) -> "Union[Variable, IndexVariable]":
"""Convert an object into a Variable.
Parameters
----------
obj : object
Object to convert into a Variable.
- If the object is already a Variable, return a shallow copy.
- Otherwise, if the object has 'dims' and 'data' attributes, convert
it into a new Variable.
- If all else fails, attempt to convert the object into a Variable by
unpacking it into the arguments for creating a new Variable.
name : str, optional
If provided:
- `obj` can be a 1D array, which is assumed to label coordinate values
along a dimension of this given name.
- Variables with name matching one of their dimensions are converted
into `IndexVariable` objects.
Returns
-------
var : Variable
The newly created variable.
"""
from .dataarray import DataArray
# TODO: consider extending this method to automatically handle Iris and
if isinstance(obj, DataArray):
# extract the primary Variable from DataArrays
obj = obj.variable
if isinstance(obj, Variable):
obj = obj.copy(deep=False)
elif isinstance(obj, tuple):
try:
obj = Variable(*obj)
except (TypeError, ValueError) as error:
# use .format() instead of % because it handles tuples consistently
raise error.__class__(
"Could not convert tuple of form "
"(dims, data[, attrs, encoding]): "
"{} to Variable.".format(obj)
)
elif utils.is_scalar(obj):
obj = Variable([], obj)
elif isinstance(obj, (pd.Index, IndexVariable)) and obj.name is not None:
obj = Variable(obj.name, obj)
elif isinstance(obj, (set, dict)):
raise TypeError("variable {!r} has invalid type {!r}".format(name, type(obj)))
elif name is not None:
data = as_compatible_data(obj)
if data.ndim != 1:
raise MissingDimensionsError(
"cannot set variable %r with %r-dimensional data "
"without explicit dimension names. Pass a tuple of "
"(dims, data) instead." % (name, data.ndim)
)
obj = Variable(name, data, fastpath=True)
else:
raise TypeError(
"unable to convert object into a variable without an "
"explicit list of dimensions: %r" % obj
)
if name is not None and name in obj.dims:
# convert the Variable into an Index
if obj.ndim != 1:
raise MissingDimensionsError(
"%r has more than 1-dimension and the same name as one of its "
"dimensions %r. xarray disallows such variables because they "
"conflict with the coordinates used to label "
"dimensions." % (name, obj.dims)
)
obj = obj.to_index_variable()
return obj
def _maybe_wrap_data(data):
"""
Put pandas.Index and numpy.ndarray arguments in adapter objects to ensure
they can be indexed properly.
NumpyArrayAdapter, PandasIndexAdapter and LazilyOuterIndexedArray should
all pass through unmodified.
"""
if isinstance(data, pd.Index):
return PandasIndexAdapter(data)
return data
def _possibly_convert_objects(values):
"""Convert arrays of datetime.datetime and datetime.timedelta objects into
datetime64 and timedelta64, according to the pandas convention. Also used for
validating that datetime64 and timedelta64 objects are within the valid date
range for ns precision, as pandas will raise an error if they are not.
"""
return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
def as_compatible_data(data, fastpath=False):
"""Prepare and wrap data to put in a Variable.
- If data does not have the necessary attributes, convert it to ndarray.
- If data has dtype=datetime64, ensure that it has ns precision. If it's a
pandas.Timestamp, convert it to datetime64.
- If data is already a pandas or xarray object (other than an Index), just
use the values.
Finally, wrap it up with an adapter if necessary.
"""
if fastpath and getattr(data, "ndim", 0) > 0:
# can't use fastpath (yet) for scalars
return _maybe_wrap_data(data)
if isinstance(data, Variable):
return data.data
if isinstance(data, NON_NUMPY_SUPPORTED_ARRAY_TYPES):
return _maybe_wrap_data(data)
if isinstance(data, tuple):
data = utils.to_0d_object_array(data)
if isinstance(data, pd.Timestamp):
# TODO: convert, handle datetime objects, too
data = np.datetime64(data.value, "ns")
if isinstance(data, timedelta):
data = np.timedelta64(getattr(data, "value", data), "ns")
# we don't want nested self-described arrays
data = getattr(data, "values", data)
if isinstance(data, np.ma.MaskedArray):
mask = np.ma.getmaskarray(data)
if mask.any():
dtype, fill_value = dtypes.maybe_promote(data.dtype)
data = np.asarray(data, dtype=dtype)
data[mask] = fill_value
else:
data = np.asarray(data)
if not isinstance(data, np.ndarray):
if hasattr(data, "__array_function__"):
if IS_NEP18_ACTIVE:
return data
else:
raise TypeError(
"Got an NumPy-like array type providing the "
"__array_function__ protocol but NEP18 is not enabled. "
"Check that numpy >= v1.16 and that the environment "
'variable "NUMPY_EXPERIMENTAL_ARRAY_FUNCTION" is set to '
'"1"'
)
# validate whether the data is valid data types.
data = np.asarray(data)
if isinstance(data, np.ndarray):
if data.dtype.kind == "O":
data = _possibly_convert_objects(data)
elif data.dtype.kind == "M":
data = _possibly_convert_objects(data)
elif data.dtype.kind == "m":
data = _possibly_convert_objects(data)
return _maybe_wrap_data(data)
def _as_array_or_item(data):
"""Return the given values as a numpy array, or as an individual item if
it's a 0d datetime64 or timedelta64 array.
Importantly, this function does not copy data if it is already an ndarray -
otherwise, it will not be possible to update Variable values in place.
This function mostly exists because 0-dimensional ndarrays with
dtype=datetime64 are broken :(
https://github.com/numpy/numpy/issues/4337
https://github.com/numpy/numpy/issues/7619
TODO: remove this (replace with np.asarray) once these issues are fixed
"""
if isinstance(data, cupy_array_type):
data = data.get()
else:
data = np.asarray(data)
if data.ndim == 0:
if data.dtype.kind == "M":
data = np.datetime64(data, "ns")
elif data.dtype.kind == "m":
data = np.timedelta64(data, "ns")
return data
class Variable(
common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin
):
"""A netcdf-like variable consisting of dimensions, data and attributes
which describe a single Array. A single Variable object is not fully
described outside the context of its parent Dataset (if you want such a
fully described object, use a DataArray instead).
The main functional difference between Variables and numpy arrays is that
numerical operations on Variables implement array broadcasting by dimension
name. For example, adding an Variable with dimensions `('time',)` to
another Variable with dimensions `('space',)` results in a new Variable
with dimensions `('time', 'space')`. Furthermore, numpy reduce operations
like ``mean`` or ``sum`` are overwritten to take a "dimension" argument
instead of an "axis".
Variables are light-weight objects used as the building block for datasets.
They are more primitive objects, so operations with them provide marginally
higher performance than using DataArrays. However, manipulating data in the
form of a Dataset or DataArray should almost always be preferred, because
they can use more complete metadata in context of coordinate labels.
"""
__slots__ = ("_dims", "_data", "_attrs", "_encoding")
def __init__(self, dims, data, attrs=None, encoding=None, fastpath=False):
"""
Parameters
----------
dims : str or sequence of str
Name(s) of the the data dimension(s). Must be either a string (only
for 1D data) or a sequence of strings with length equal to the
number of dimensions.
data : array_like
Data array which supports numpy-like data access.
attrs : dict_like or None, optional
Attributes to assign to the new variable. If None (default), an
empty attribute dictionary is initialized.
encoding : dict_like or None, optional
Dictionary specifying how to encode this array's data into a
serialized format like netCDF4. Currently used keys (for netCDF)
include '_FillValue', 'scale_factor', 'add_offset' and 'dtype'.
Well-behaved code to serialize a Variable should ignore
unrecognized encoding items.
"""
self._data = as_compatible_data(data, fastpath=fastpath)
self._dims = self._parse_dimensions(dims)
self._attrs = None
self._encoding = None
if attrs is not None:
self.attrs = attrs
if encoding is not None:
self.encoding = encoding
@property
def dtype(self):
return self._data.dtype
@property
def shape(self):
return self._data.shape
@property
def nbytes(self):
return self.size * self.dtype.itemsize
@property
def _in_memory(self):
return isinstance(self._data, (np.ndarray, np.number, PandasIndexAdapter)) or (
isinstance(self._data, indexing.MemoryCachedArray)
and isinstance(self._data.array, indexing.NumpyIndexingAdapter)
)
@property
def data(self):
if is_duck_array(self._data):
return self._data
else:
return self.values
@data.setter
def data(self, data):
data = as_compatible_data(data)
if data.shape != self.shape:
raise ValueError(
f"replacement data must match the Variable's shape. "
f"replacement data has shape {data.shape}; Variable has shape {self.shape}"
)
self._data = data
def astype(
self: VariableType,
dtype,
*,
order=None,
casting=None,
subok=None,
copy=None,
keep_attrs=True,
) -> VariableType:
"""
Copy of the Variable object, with data cast to a specified type.
Parameters
----------
dtype : str or dtype
Typecode or data-type to which the array is cast.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout order of the result. ‘C’ means C order,
‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are
Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to
the order the array elements appear in memory as possible.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise the
returned array will be forced to be a base-class array.
copy : bool, optional
By default, astype always returns a newly allocated array. If this
is set to False and the `dtype` requirement is satisfied, the input
array is returned instead of a copy.
keep_attrs : bool, optional
By default, astype keeps attributes. Set to False to remove
attributes in the returned object.
Returns
-------
out : same as object
New object with data cast to the specified type.
Notes
-----
The ``order``, ``casting``, ``subok`` and ``copy`` arguments are only passed
through to the ``astype`` method of the underlying array when a value
different than ``None`` is supplied.
Make sure to only supply these arguments if the underlying array class
supports them.
See also
--------
numpy.ndarray.astype
dask.array.Array.astype
sparse.COO.astype
"""
from .computation import apply_ufunc
kwargs = dict(order=order, casting=casting, subok=subok, copy=copy)
kwargs = {k: v for k, v in kwargs.items() if v is not None}
return apply_ufunc(
duck_array_ops.astype,
self,
dtype,
kwargs=kwargs,
keep_attrs=keep_attrs,
dask="allowed",
)
def load(self, **kwargs):
"""Manually trigger loading of this variable's data from disk or a
remote source into memory and return this variable.
Normally, it should not be necessary to call this method in user code,
because all xarray functions should either work on deferred data or
load data automatically.
Parameters
----------
**kwargs : dict
Additional keyword arguments passed on to ``dask.array.compute``.
See Also
--------
dask.array.compute
"""
if is_duck_dask_array(self._data):
self._data = as_compatible_data(self._data.compute(**kwargs))
elif not is_duck_array(self._data):
self._data = | np.asarray(self._data) | numpy.asarray |
"""
Greedy Word Swap with Word Importance Ranking
===================================================
When WIR method is set to ``unk``, this is a reimplementation of the search
method from the paper: Is BERT Really Robust?
A Strong Baseline for Natural Language Attack on Text Classification and
Entailment by Jin et. al, 2019. See https://arxiv.org/abs/1907.11932 and
https://github.com/jind11/TextFooler.
"""
import numpy as np
import torch
from torch.nn.functional import softmax
from textattack.goal_function_results import GoalFunctionResultStatus
from textattack.search_methods import SearchMethod
from textattack.shared.validators import (
transformation_consists_of_word_swaps_and_deletions,
)
class GreedyWordSwapWIR(SearchMethod):
"""An attack that greedily chooses from a list of possible perturbations in
order of index, after ranking indices by importance.
Args:
wir_method: method for ranking most important words
"""
def __init__(self, wir_method="unk"):
self.wir_method = wir_method
def _get_index_order(self, initial_text):
"""Returns word indices of ``initial_text`` in descending order of
importance."""
len_text = len(initial_text.words)
if self.wir_method == "unk":
leave_one_texts = [
initial_text.replace_word_at_index(i, "[UNK]") for i in range(len_text)
]
leave_one_results, search_over = self.get_goal_results(leave_one_texts)
index_scores = np.array([result.score for result in leave_one_results])
elif self.wir_method == "weighted-saliency":
# first, compute word saliency
leave_one_texts = [
initial_text.replace_word_at_index(i, "[UNK]") for i in range(len_text)
]
leave_one_results, search_over = self.get_goal_results(leave_one_texts)
saliency_scores = | np.array([result.score for result in leave_one_results]) | numpy.array |
import numpy as np
import pytest
import theano
import theano.tensor as tt
# Don't import test classes otherwise they get tested as part of the file
from tests import unittest_tools as utt
from tests.gpuarray.config import mode_with_gpu, mode_without_gpu, test_ctx_name
from tests.tensor.test_basic import (
TestAlloc,
TestComparison,
TestJoinAndSplit,
TestReshape,
)
from tests.tensor.utils import rand, safe_make_node
from theano.gpuarray.basic_ops import (
GpuAlloc,
GpuAllocEmpty,
GpuContiguous,
GpuEye,
GpuFromHost,
GpuJoin,
GpuReshape,
GpuSplit,
GpuToGpu,
GpuTri,
HostFromGpu,
gpu_contiguous,
gpu_join,
host_from_gpu,
)
from theano.gpuarray.elemwise import GpuDimShuffle, GpuElemwise
from theano.gpuarray.subtensor import GpuSubtensor
from theano.gpuarray.type import GpuArrayType, get_context, gpuarray_shared_constructor
from theano.tensor import TensorType
from theano.tensor.basic import alloc
pygpu = pytest.importorskip("pygpu")
gpuarray = pygpu.gpuarray
utt.seed_rng()
rng = np.random.RandomState(seed=utt.fetch_seed())
def inplace_func(
inputs,
outputs,
mode=None,
allow_input_downcast=False,
on_unused_input="raise",
name=None,
):
if mode is None:
mode = mode_with_gpu
return theano.function(
inputs,
outputs,
mode=mode,
allow_input_downcast=allow_input_downcast,
accept_inplace=True,
on_unused_input=on_unused_input,
name=name,
)
def fake_shared(value, name=None, strict=False, allow_downcast=None, **kwargs):
from theano.tensor.sharedvar import scalar_constructor, tensor_constructor
for c in (gpuarray_shared_constructor, tensor_constructor, scalar_constructor):
try:
return c(
value, name=name, strict=strict, allow_downcast=allow_downcast, **kwargs
)
except TypeError:
continue
def rand_gpuarray(*shape, **kwargs):
r = rng.rand(*shape) * 2 - 1
dtype = kwargs.pop("dtype", theano.config.floatX)
cls = kwargs.pop("cls", None)
if len(kwargs) != 0:
raise TypeError("Unexpected argument %s", list(kwargs.keys())[0])
return gpuarray.array(r, dtype=dtype, cls=cls, context=get_context(test_ctx_name))
def makeTester(
name,
op,
gpu_op,
cases,
checks=None,
mode_gpu=mode_with_gpu,
mode_nogpu=mode_without_gpu,
skip=False,
eps=1e-10,
):
if checks is None:
checks = {}
_op = op
_gpu_op = gpu_op
_cases = cases
_skip = skip
_checks = checks
class Checker(utt.OptimizationTestMixin):
op = staticmethod(_op)
gpu_op = staticmethod(_gpu_op)
cases = _cases
skip = _skip
checks = _checks
def setup_method(self):
eval(self.__class__.__module__ + "." + self.__class__.__name__)
def test_all(self):
if skip:
pytest.skip(skip)
for testname, inputs in cases.items():
for _ in range(len(inputs)):
if type(inputs[_]) is float:
inputs[_] = np.asarray(inputs[_], dtype=theano.config.floatX)
self.run_case(testname, inputs)
def run_case(self, testname, inputs):
inputs_ref = [theano.shared(inp) for inp in inputs]
inputs_tst = [theano.shared(inp) for inp in inputs]
try:
node_ref = safe_make_node(self.op, *inputs_ref)
node_tst = safe_make_node(self.op, *inputs_tst)
except Exception as exc:
err_msg = (
"Test %s::%s: Error occurred while making " "a node with inputs %s"
) % (self.gpu_op, testname, inputs)
exc.args += (err_msg,)
raise
try:
f_ref = inplace_func([], node_ref.outputs, mode=mode_nogpu)
f_tst = inplace_func([], node_tst.outputs, mode=mode_gpu)
except Exception as exc:
err_msg = (
"Test %s::%s: Error occurred while trying to " "make a Function"
) % (self.gpu_op, testname)
exc.args += (err_msg,)
raise
self.assertFunctionContains1(f_tst, self.gpu_op)
ref_e = None
try:
expecteds = f_ref()
except Exception as exc:
ref_e = exc
try:
variables = f_tst()
except Exception as exc:
if ref_e is None:
err_msg = (
"Test %s::%s: exception when calling the " "Function"
) % (self.gpu_op, testname)
exc.args += (err_msg,)
raise
else:
# if we raised an exception of the same type we're good.
if isinstance(exc, type(ref_e)):
return
else:
err_msg = (
"Test %s::%s: exception raised during test "
"call was not the same as the reference "
"call (got: %s, expected %s)"
% (self.gpu_op, testname, type(exc), type(ref_e))
)
exc.args += (err_msg,)
raise
for i, (variable, expected) in enumerate(zip(variables, expecteds)):
condition = (
variable.dtype != expected.dtype
or variable.shape != expected.shape
or not TensorType.values_eq_approx(variable, expected)
)
assert not condition, (
"Test %s::%s: Output %s gave the wrong "
"value. With inputs %s, expected %s "
"(dtype %s), got %s (dtype %s)."
% (
self.op,
testname,
i,
inputs,
expected,
expected.dtype,
variable,
variable.dtype,
)
)
for description, check in self.checks.items():
assert check(inputs, variables), (
"Test %s::%s: Failed check: %s " "(inputs were %s, ouputs were %s)"
) % (self.op, testname, description, inputs, variables)
Checker.__name__ = name
if hasattr(Checker, "__qualname__"):
Checker.__qualname__ = name
return Checker
def test_transfer_cpu_gpu():
a = tt.fmatrix("a")
g = GpuArrayType(dtype="float32", broadcastable=(False, False))("g")
av = np.asarray(rng.rand(5, 4), dtype="float32")
gv = gpuarray.array(av, context=get_context(test_ctx_name))
f = theano.function([a], GpuFromHost(test_ctx_name)(a))
fv = f(av)
assert GpuArrayType.values_eq(fv, gv)
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert np.all(fv == av)
def test_transfer_gpu_gpu():
g = GpuArrayType(
dtype="float32", broadcastable=(False, False), context_name=test_ctx_name
)()
av = np.asarray(rng.rand(5, 4), dtype="float32")
gv = gpuarray.array(av, context=get_context(test_ctx_name))
mode = mode_with_gpu.excluding(
"cut_gpua_host_transfers", "local_cut_gpua_host_gpua"
)
f = theano.function([g], GpuToGpu(test_ctx_name)(g), mode=mode)
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert isinstance(topo[0].op, GpuToGpu)
fv = f(gv)
assert GpuArrayType.values_eq(fv, gv)
def test_transfer_strided():
# This is just to ensure that it works in theano
# libgpuarray has a much more comprehensive suit of tests to
# ensure correctness
a = tt.fmatrix("a")
g = GpuArrayType(dtype="float32", broadcastable=(False, False))("g")
av = np.asarray(rng.rand(5, 8), dtype="float32")
gv = gpuarray.array(av, context=get_context(test_ctx_name))
av = av[:, ::2]
gv = gv[:, ::2]
f = theano.function([a], GpuFromHost(test_ctx_name)(a))
fv = f(av)
assert GpuArrayType.values_eq(fv, gv)
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert np.all(fv == av)
def gpu_alloc_expected(x, *shp):
g = gpuarray.empty(shp, dtype=x.dtype, context=get_context(test_ctx_name))
g[:] = x
return g
TestGpuAlloc = makeTester(
name="GpuAllocTester",
# The +1 is there to allow the lift to the GPU.
op=lambda *args: alloc(*args) + 1,
gpu_op=GpuAlloc(test_ctx_name),
cases=dict(
correct01=(rand(), np.int32(7)),
# just gives a DeepCopyOp with possibly wrong results on the CPU
# correct01_bcast=(rand(1), np.int32(7)),
correct02=(rand(), np.int32(4), np.int32(7)),
correct12=(rand(7), np.int32(4), np.int32(7)),
correct13=(rand(7), | np.int32(2) | numpy.int32 |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
from external.transformations import translation_matrix, rotation_matrix
import external.transformations as tf
from Trace import Photon
from Geometry import Box, Cylinder, FinitePlane, transform_point, transform_direction, rotation_matrix_from_vector_alignment, norm
from Materials import Spectrum
def random_spherecial_vector():
# This method of calculating isotropic vectors is taken from GNU Scientific Library
LOOP = True
while LOOP:
x = -1. + 2. * np.random.uniform()
y = -1. + 2. * np.random.uniform()
s = x**2 + y**2
if s <= 1.0:
LOOP = False
z = -1. + 2. * s
a = 2 * np.sqrt(1 - s)
x = a * x
y = a * y
return np.array([x,y,z])
class SimpleSource(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, use_random_polarisation=False):
super(SimpleSource, self).__init__()
self.position = position
self.direction = direction
self.wavelength = wavelength
self.use_random_polarisation = use_random_polarisation
self.throw = 0
self.source_id = "SimpleSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
# If use_polarisation is set generate a random polarisation vector of the photon
if self.use_random_polarisation:
# Randomise rotation angle around xy-plane, the transform from +z to the direction of the photon
vec = random_spherecial_vector()
vec[2] = 0.
vec = norm(vec)
R = rotation_matrix_from_vector_alignment(self.direction, [0,0,1])
photon.polarisation = transform_direction(vec, R)
else:
photon.polarisation = None
photon.id = self.throw
self.throw = self.throw + 1
return photon
class Laser(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, polarisation=None):
super(Laser, self).__init__()
self.position = np.array(position)
self.direction = np.array(direction)
self.wavelength = wavelength
assert polarisation != None, "Polarisation of the Laser is not set."
self.polarisation = np.array(polarisation)
self.throw = 0
self.source_id = "LaserSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
photon.polarisation = self.polarisation
photon.id = self.throw
self.throw = self.throw + 1
return photon
class PlanarSource(object):
"""A box that emits photons from the top surface (normal), sampled from the spectrum."""
def __init__(self, spectrum=None, wavelength=555, direction=(0,0,1), length=0.05, width=0.05):
super(PlanarSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.plane = FinitePlane(length=length, width=width)
self.length = length
self.width = width
# direction is the direction that photons are fired out of the plane in the GLOBAL FRAME.
# i.e. this is passed directly to the photon to set is's direction
self.direction = direction
self.throw = 0
self.source_id = "PlanarSource_" + str(id(self))
def translate(self, translation):
self.plane.append_transform(tf.translation_matrix(translation))
def rotate(self, angle, axis):
self.plane.append_transform(tf.rotation_matrix(angle, axis))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Create a point which is on the surface of the finite plane in it's local frame
x = np.random.uniform(0., self.length)
y = | np.random.uniform(0., self.width) | numpy.random.uniform |
# -*- coding: utf-8 -*-
"""
Script to execute example covarying MMGP regression forecasting model
with full Krhh.
Inputs: Data training and test sets (dictionary pickle)
Data for example:
- normalised solar data for 25 sites for 15 minute forecast
- N_train = 4200, N_test = 2276, P = 25, D = 51
- Xtr[:, :50] 2 recent lagged observations for each site in order
- Xtr[:, 50] time index
- link inputs is a 25x2 array (link inputs repeated for every group)
with normalised lat,long for each site in order
Model Options:
- Sparse or full x-function covariance prior Krhh (set bool SPARSE_PRIOR)
- Diagonal or Kronecker-structured variational posterior covariance Sr (set bool DIAG_POST)
- Sparse or full posterior covariance (when Kronecker posterior; set bool SPARSE_POST)
Current Settings (sparse covarying mmgp model with sparse Kronecker posterior):
DIAG_POST = False
SPARSE_PRIOR = False # set True for equivalent sparse scmmgp model
SPARSE_POST = True
Note on specifying group structure for F:
Grouping occurs via block_struct, a nested list of grouping order
Where functions [i] are independent i.e. in own block, set link_kernel[i] = link_inputs[i] = 1.0
See model class preamble and example below for further details.
"""
import os
import numpy as np
import pickle
import pandas as pd
import traceback
import time
import sklearn.cluster
import csv
import sys
import mmgp
from mmgp import likelihoods
from mmgp import kernels
import tensorflow as tf
from mmgp import datasets
from mmgp import losses
from mmgp import util
dpath = '/experiments/datasets/'
dfile = 'p25_inputsdict.pickle'
dlinkfile = 'p25_linkinputsarray.pickle'
outdir = '/experiments/results/p25_nonsparse_cmmgp/'
try:
os.makedirs(outdir)
except FileExistsError:
pass
def get_inputs():
"""
inputsdict contains {'Yte': Yte, 'Ytr': Ytr, 'Xtr': Xtr, 'Xte': Xte} where values are np.arrays
np. arrays are truncated to evenly split into batches of size = batchsize
returns inputsdict, Xtr_link (ndarray, shape = [P, D_link_features])
"""
with open(os.path.join(dpath, dfile), 'rb') as f:
d_all = pickle.load(f)
with open(os.path.join(dpath, dlinkfile), 'rb') as f:
d_link = pickle.load(f)
return d_all, d_link
def init_z(train_inputs, num_inducing):
# Initialize inducing points using clustering.
mini_batch = sklearn.cluster.MiniBatchKMeans(num_inducing)
cluster_indices = mini_batch.fit_predict(train_inputs)
inducing_locations = mini_batch.cluster_centers_
return inducing_locations
FLAGS = util.util.get_flags()
BATCH_SIZE = FLAGS.batch_size
LEARNING_RATE = FLAGS.learning_rate
DISPLAY_STEP = FLAGS.display_step
EPOCHS = FLAGS.n_epochs
NUM_SAMPLES = FLAGS.mc_train
PRED_SAMPLES = FLAGS.mc_test
NUM_INDUCING = FLAGS.n_inducing
NUM_COMPONENTS = FLAGS.num_components
IS_ARD = FLAGS.is_ard
TOL = FLAGS.opt_tol
VAR_STEPS = FLAGS.var_steps
DIAG_POST = False
SPARSE_PRIOR = False
SPARSE_POST = True # option for non-diag post
MAXTIME = 1200
print("settings done")
# define GPRN P and Q
output_dim = 25 #P
node_dim = 25 #Q
lag_dim = 2
save_nlpds = False # If True saves samples of nlpds for n,p,s
# extract dataset
d, d_link = get_inputs()
Ytr, Yte, Xtr, Xte = d['Ytr'], d['Yte'], d['Xtr'], d['Xte']
data = datasets.DataSet(Xtr.astype(np.float32), Ytr.astype(np.float32), shuffle=False)
test = datasets.DataSet(Xte.astype(np.float32), Yte.astype(np.float32), shuffle=False)
print("dataset created")
# model config block rows (where P=Q): block all w.1, w.2 etc, leave f independent
# order of block_struct is rows, node functions
# lists required: block_struct, link_inputs, kern_link, kern
#block_struct nested list of grouping order
weight_struct = [[] for _ in range(output_dim)]
for i in range(output_dim):
row = list(range(i, i+output_dim*(node_dim-1)+1, output_dim))
row_0 = row.pop(i) # bring diag to pivot position
weight_struct[i] = [row_0] + row
nodes = [[x] for x in list(range(output_dim * node_dim, output_dim * node_dim + output_dim))]
block_struct = weight_struct + nodes
# create link inputs (link inputs used repeatedly but can have link input per group)
# permute to bring diagonal to first position
link_inputs = [[] for _ in range(output_dim)]
for i in range(output_dim):
idx = list(range(d_link.shape[0]))
link_inputs[i] = d_link[[idx.pop(i)] + idx, :]
link_inputs = link_inputs + [1.0 for i in range(output_dim)] # for full W row blocks, independent nodes
# create 'between' kernel list
klink_rows = [kernels.CompositeKernel('mul',[kernels.RadialBasis(2, std_dev=2.0, lengthscale=1.0, white=0.01, input_scaling = IS_ARD),
kernels.CompactSlice(2, active_dims=[0,1], lengthscale = 2.0, input_scaling = IS_ARD)] )
for i in range(output_dim) ]
klink_f = [1.0 for i in range(node_dim)]
kernlink = klink_rows + klink_f
# create 'within' kernel
# kern
lag_active_dims_s = [ [] for _ in range(output_dim)]
for i in range(output_dim):
lag_active_dims_s[i] = list(range(lag_dim*i, lag_dim*(i+1)))
k_rows = [kernels.CompositeKernel('mul',[kernels.RadialBasisSlice(lag_dim, active_dims=lag_active_dims_s[i],
std_dev = 1.0, white = 0.01, input_scaling = IS_ARD),
kernels.PeriodicSliceFixed(1, active_dims=[Xtr.shape[1]-1],
lengthscale=0.5, std_dev=1.0, period = 144) ])
for i in range(output_dim)]
k_f = [kernels.RadialBasisSlice(lag_dim, active_dims=lag_active_dims_s[i], std_dev = 1.0, white = 0.01, input_scaling = IS_ARD)
for i in range(output_dim)]
kern = k_rows + k_f
print('len link_inputs ',len(link_inputs))
print('len kernlink ',len(kernlink))
print('len kern ', len(kern))
print('no. groups = ', len(block_struct), 'no. latent functions =', len([i for b in block_struct for i in b]))
print('number latent functions', node_dim*(output_dim+1))
likelihood = likelihoods.CovaryingRegressionNetwork(output_dim, node_dim, std_dev = 0.2) # p, q, lik_noise
print("likelihood and kernels set")
Z = init_z(data.X, NUM_INDUCING)
print('inducing points set')
m = mmgp.ExplicitSCMMGP(output_dim, likelihood, kern, kernlink, block_struct, Z, link_inputs,
num_components=NUM_COMPONENTS, diag_post=DIAG_POST, sparse_prior=SPARSE_PRIOR,
sparse_post=SPARSE_POST, num_samples=NUM_SAMPLES, predict_samples=PRED_SAMPLES)
print("model set")
# initialise losses and logging
error_rate = losses.RootMeanSqError(data.Dout)
os.chdir(outdir)
with open("log_results.csv", 'w', newline='') as f:
csv.writer(f).writerow(['epoch', 'fit_runtime', 'nelbo', error_rate.get_name(),'generalised_nlpd'])
with open("log_params.csv", 'w', newline='') as f:
csv.writer(f).writerow(['epoch', 'raw_kernel_params', 'raw_kernlink_params', 'raw_likelihood_params', 'raw_weights'])
with open("log_comp_time.csv", 'w', newline='') as f:
csv.writer(f).writerow(['epoch', 'batch_time', 'nelbo_time', 'pred_time', 'gen_nlpd_time', error_rate.get_name()+'_time'])
# optimise
o = tf.train.AdamOptimizer(LEARNING_RATE, beta1=0.9,beta2=0.99)
print("start time = ", time.strftime('%X %x %Z'))
m.fit(data, o, var_steps = VAR_STEPS, epochs = EPOCHS, batch_size = BATCH_SIZE, display_step=DISPLAY_STEP,
test = test, loss = error_rate, tolerance = TOL, max_time=MAXTIME )
print("optimisation complete")
# export final predicted values and loss metrics
ypred = m.predict(test.X, batch_size = BATCH_SIZE) #same batchsize used for convenience
np.savetxt("predictions.csv", | np.concatenate(ypred, axis=1) | numpy.concatenate |
import numpy as np
import tensorflow as tf
H = 2
N = 2
M = 3
BS = 10
def my_softmax(arr):
max_elements = np.reshape(np.max(arr, axis = 2), (BS, N, 1))
arr = arr - max_elements
exp_array = np.exp(arr)
print (exp_array)
sum_array = np.reshape( | np.sum(exp_array, axis=2) | numpy.sum |
# Credit to https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0
import gym
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
env = gym.make('FrozenLake-v0')
# NEURAL NETWORK IMPLEMENTATION
tf.reset_default_graph()
# Feature vector for current state representation
input1 = tf.placeholder(shape=[1, env.observation_space.n], dtype=tf.float32)
# tf.Variable(<initial-value>, name=<optional-name>)
# tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)
# Weighting W vector in range 0 - 0.01 (like the way Andrew Ng did with *0.01
W = tf.Variable(tf.random_uniform([env.observation_space.n, env.action_space.n], 0, 0.01))
# Qout with shape [1, env.action_space.n] - Action state value for Q[s, a] with every a available at a state
Qout = tf.matmul(input1, W)
# Greedy action at a state
predict = tf.argmax(Qout, axis=1)
# Feature vector for next state representation
nextQ = tf.placeholder(shape=[1, env.action_space.n], dtype=tf.float32)
# Entropy loss
loss = tf.reduce_sum(tf.square(Qout - nextQ))
trainer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
updateModel = trainer.minimize(loss)
# TRAIN THE NETWORK
init = tf.global_variables_initializer()
# Set learning parameters
y = 0.99
e = 0.1
number_episodes = 2000
# List to store total rewards and steps per episode
jList = []
rList = []
with tf.Session() as sess:
sess.run(init)
for i in range(number_episodes):
print("Episode #{} is running!".format(i))
# First state
s = env.reset()
rAll = 0
d = False
j = 0
# Q network
while j < 200: # or While not d:
j += 1
# Choose action by epsilon (e) greedy
# print("s = ", s," --> Identity s:s+1: ", np.identity(env.observation_space.n)[s:s+1])
# s = 0 --> Identity s: s + 1: [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
# s = 1 --> Identity s: s + 1: [[0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
# Identity [s:s+1] is a one-hot vector
# Therefore W is the actual Q value
a, allQ = sess.run([predict, Qout], feed_dict={input1: np.identity(env.observation_space.n)[s:s+1]})
if | np.random.rand(1) | numpy.random.rand |
from __future__ import division
from timeit import default_timer as timer
import csv
import numpy as np
import itertools
from munkres import Munkres, print_matrix, make_cost_matrix
import sys
from classes import *
from functions import *
from math import sqrt
import Tkinter as tk
import tkFileDialog as filedialog
root = tk.Tk()
root.withdraw()
p_file = filedialog.askopenfilename(title='Please select the posting file')
c_file = filedialog.askopenfilename(title='Please select the candidate file')
"""for use with /users/java_jonathan/postings_lge.csv and
/Users/java_jonathan/candidates_lge.csv"""
# p_file = raw_input("Please enter the path for the postings file: ")
# p_file = p_file.strip()
# c_file = raw_input("Please enter the path for the candidate file: ")
# c_file = c_file.strip()
start = timer()
with open(p_file,'r') as f:
#with open('/Users/Jonathan/Google Drive/CPD/Python/postings.csv','r') as f:
reader = csv.reader(f)
postingsAll = list(reader)
with open(c_file,'r') as f:
reader = csv.reader(f)
candidatesAll = list(reader)
"""create empty lists to fill with lists of lists output by iterating function
below"""
names = []
totalMatrix = []
for list in candidatesAll:
candidate = Candidate(*list)
names.append(candidate.name)
n = 0
for list in postingsAll:
posting = Posting(*list)
totalMatrix.append(matchDept(posting,candidate) + matchAnchor(posting,candidate)
+matchLocation(posting,candidate) + matchCompetency(posting,candidate) +
matchSkill(posting,candidate)+matchCohort(posting,candidate))
n += 1
l = len(names)
names.extend([0] * (n-l))
totalMatrix.extend([0] * (n**2 - len(totalMatrix)))
totalMatrix = np.asarray(totalMatrix)
totalMatrix = np.reshape(totalMatrix,(n,-1))
#at this point the matrix is structured as candidates down and jobs across
totalMatrix = np.transpose(totalMatrix)
#now it's switched!
totalMatrix = np.subtract(np.amax(totalMatrix),totalMatrix)
totalMatrix = | np.array(totalMatrix) | numpy.array |
import os
import numpy as np
import pandas as pd
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import img_to_array, load_img
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preprocessing import LabelEncoder, StandardScaler
def load_numeric_training(standardize=True):
data = pd.read_csv('../train.csv')
ID = data.pop('id')
y = data.pop('species')
y = LabelEncoder().fit(y).transform(y)
X = StandardScaler().fit(data).transform(data) if standardize else data.values
return ID.values, X, y
def load_numeric_test(standardize=True):
data = pd.read_csv('../test.csv')
ID = data.pop('id')
test = StandardScaler().fit(data).transform(data) if standardize else data.values
return ID.values, test
def resize_img(img, max_dim=96):
max_axis = np.argmax(img.size)
scale = max_dim / img.size[max_axis]
return img.resize((int(img.size[0] * scale), int(img.size[1] * scale)))
def load_img_data(ids, max_dim=96, center=True):
X = np.empty((len(ids), max_dim, max_dim, 1))
for i, id in enumerate(ids):
img = load_img('../images/{}.jpg'.format(id), grayscale=True)
img = resize_img(img, max_dim=max_dim)
x = img_to_array(img)
h, w = x.shape[:2]
if center:
h1 = (max_dim - h) >> 1
h2 = h1 + h
w1 = (max_dim - w) >> 1
w2 = w1 + w
else:
h1, h2, w1, w2 = 0, h, 0, w
X[i][h1:h2, w1:w2][:] = x
return np.around(X / 255)
def load_train_data(split=0.9, random_state=7):
ID, X_num_train, y = load_numeric_training()
X_img_train = load_img_data(ID)
sss = StratifiedShuffleSplit(n_splits=1, train_size=split, test_size=1 - split, random_state=random_state)
train_idx, val_idx = next(sss.split(X_num_train, y))
ID_tr, X_num_tr, X_img_tr, y_tr = ID[train_idx], X_num_train[train_idx], X_img_train[train_idx], y[train_idx]
ID_val, X_num_val, X_img_val, y_val = ID[val_idx], X_num_train[val_idx], X_img_train[val_idx], y[val_idx]
return (ID_tr, X_num_tr, X_img_tr, y_tr), (ID_val, X_num_val, X_img_val, y_val)
def load_test_data():
ID, X_num_test = load_numeric_test()
X_img_test = load_img_data(ID)
return ID, X_num_test, X_img_test
print('Loading train data ...')
(ID_train, X_num_tr, X_img_tr, y_tr), (ID_val, X_num_val, X_img_val, y_val) = load_train_data()
# Prepare ID-to-label and ID-to-numerical dictionary
ID_y_dic, ID_num_dic = {}, {}
for i in range(len(ID_train)):
ID_y_dic[ID_train[i]] = y_tr[i]
ID_num_dic[ID_train[i]] = X_num_tr[i, :]
print('Loading test data ...')
ID_test, X_num_test, X_img_test = load_test_data()
# Convert label to categorical/one-hot
ID_train, y_tr, y_val = to_categorical(ID_train), to_categorical(y_tr), to_categorical((y_val))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _float32_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def write_val_data():
val_data_path = '../tfrecords/val_data_1.tfrecords'
if os.path.exists(val_data_path):
print('Warning: old file exists, removed.')
os.remove(val_data_path)
val_image, val_num, val_label = X_img_val.astype(np.bool), X_num_val.astype(np.float64), y_val.astype(np.bool)
print(val_image.shape, val_num.shape, val_label.shape)
val_writer = tf.python_io.TFRecordWriter(val_data_path)
print('Writing data into tfrecord ...')
for i in range(len(val_image)):
image, num, label = val_image[i], val_num[i], val_label[i]
feature = {'image': _bytes_feature(image.tostring()),
'num': _bytes_feature(num.tostring()),
'label': _bytes_feature(label.tostring())}
example = tf.train.Example(features=tf.train.Features(feature=feature))
val_writer.write(example.SerializeToString())
print('Done!')
def write_train_data():
imgen = ImageDataGenerator(rotation_range=20, zoom_range=0.2, horizontal_flip=True,
vertical_flip=True, fill_mode='nearest')
imgen_train = imgen.flow(X_img_tr, ID_train, batch_size=32, seed=7)
print('Generating augmented images')
all_images = []
all_ID = []
p = True
for i in range(28 * 200):
print('Generating augmented images for epoch {}, batch {}'.format(i // 28, i % 28))
X, ID = imgen_train.next()
all_images.append(X)
all_ID.append(np.argmax(ID, axis=1))
all_images = np.concatenate(all_images).astype(np.bool)
all_ID = np.concatenate(all_ID)
all_y = np.zeros(all_ID.shape)
all_nums = | np.zeros((all_ID.shape[0], X_num_tr.shape[1])) | numpy.zeros |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * | np.ones(101) | numpy.ones |
import copy
import functools
import itertools
import numbers
import warnings
from collections import defaultdict
from datetime import timedelta
from distutils.version import LooseVersion
from typing import (
Any,
Dict,
Hashable,
Mapping,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
)
import numpy as np
import pandas as pd
import xarray as xr # only for Dataset and DataArray
from . import arithmetic, common, dtypes, duck_array_ops, indexing, nputils, ops, utils
from .indexing import (
BasicIndexer,
OuterIndexer,
PandasIndexAdapter,
VectorizedIndexer,
as_indexable,
)
from .npcompat import IS_NEP18_ACTIVE
from .options import _get_keep_attrs
from .pycompat import (
cupy_array_type,
dask_array_type,
integer_types,
is_duck_dask_array,
)
from .utils import (
OrderedSet,
_default,
decode_numpy_dict_values,
drop_dims_from_indexers,
either_dict_or_kwargs,
ensure_us_time_resolution,
infix_dims,
is_duck_array,
)
NON_NUMPY_SUPPORTED_ARRAY_TYPES = (
(
indexing.ExplicitlyIndexed,
pd.Index,
)
+ dask_array_type
+ cupy_array_type
)
# https://github.com/python/mypy/issues/224
BASIC_INDEXING_TYPES = integer_types + (slice,) # type: ignore
VariableType = TypeVar("VariableType", bound="Variable")
"""Type annotation to be used when methods of Variable return self or a copy of self.
When called from an instance of a subclass, e.g. IndexVariable, mypy identifies the
output as an instance of the subclass.
Usage::
class Variable:
def f(self: VariableType, ...) -> VariableType:
...
"""
class MissingDimensionsError(ValueError):
"""Error class used when we can't safely guess a dimension name."""
# inherits from ValueError for backward compatibility
# TODO: move this to an xarray.exceptions module?
def as_variable(obj, name=None) -> "Union[Variable, IndexVariable]":
"""Convert an object into a Variable.
Parameters
----------
obj : object
Object to convert into a Variable.
- If the object is already a Variable, return a shallow copy.
- Otherwise, if the object has 'dims' and 'data' attributes, convert
it into a new Variable.
- If all else fails, attempt to convert the object into a Variable by
unpacking it into the arguments for creating a new Variable.
name : str, optional
If provided:
- `obj` can be a 1D array, which is assumed to label coordinate values
along a dimension of this given name.
- Variables with name matching one of their dimensions are converted
into `IndexVariable` objects.
Returns
-------
var : Variable
The newly created variable.
"""
from .dataarray import DataArray
# TODO: consider extending this method to automatically handle Iris and
if isinstance(obj, DataArray):
# extract the primary Variable from DataArrays
obj = obj.variable
if isinstance(obj, Variable):
obj = obj.copy(deep=False)
elif isinstance(obj, tuple):
try:
obj = Variable(*obj)
except (TypeError, ValueError) as error:
# use .format() instead of % because it handles tuples consistently
raise error.__class__(
"Could not convert tuple of form "
"(dims, data[, attrs, encoding]): "
"{} to Variable.".format(obj)
)
elif utils.is_scalar(obj):
obj = Variable([], obj)
elif isinstance(obj, (pd.Index, IndexVariable)) and obj.name is not None:
obj = Variable(obj.name, obj)
elif isinstance(obj, (set, dict)):
raise TypeError("variable {!r} has invalid type {!r}".format(name, type(obj)))
elif name is not None:
data = as_compatible_data(obj)
if data.ndim != 1:
raise MissingDimensionsError(
"cannot set variable %r with %r-dimensional data "
"without explicit dimension names. Pass a tuple of "
"(dims, data) instead." % (name, data.ndim)
)
obj = Variable(name, data, fastpath=True)
else:
raise TypeError(
"unable to convert object into a variable without an "
"explicit list of dimensions: %r" % obj
)
if name is not None and name in obj.dims:
# convert the Variable into an Index
if obj.ndim != 1:
raise MissingDimensionsError(
"%r has more than 1-dimension and the same name as one of its "
"dimensions %r. xarray disallows such variables because they "
"conflict with the coordinates used to label "
"dimensions." % (name, obj.dims)
)
obj = obj.to_index_variable()
return obj
def _maybe_wrap_data(data):
"""
Put pandas.Index and numpy.ndarray arguments in adapter objects to ensure
they can be indexed properly.
NumpyArrayAdapter, PandasIndexAdapter and LazilyOuterIndexedArray should
all pass through unmodified.
"""
if isinstance(data, pd.Index):
return PandasIndexAdapter(data)
return data
def _possibly_convert_objects(values):
"""Convert arrays of datetime.datetime and datetime.timedelta objects into
datetime64 and timedelta64, according to the pandas convention. Also used for
validating that datetime64 and timedelta64 objects are within the valid date
range for ns precision, as pandas will raise an error if they are not.
"""
return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
def as_compatible_data(data, fastpath=False):
"""Prepare and wrap data to put in a Variable.
- If data does not have the necessary attributes, convert it to ndarray.
- If data has dtype=datetime64, ensure that it has ns precision. If it's a
pandas.Timestamp, convert it to datetime64.
- If data is already a pandas or xarray object (other than an Index), just
use the values.
Finally, wrap it up with an adapter if necessary.
"""
if fastpath and getattr(data, "ndim", 0) > 0:
# can't use fastpath (yet) for scalars
return _maybe_wrap_data(data)
if isinstance(data, Variable):
return data.data
if isinstance(data, NON_NUMPY_SUPPORTED_ARRAY_TYPES):
return _maybe_wrap_data(data)
if isinstance(data, tuple):
data = utils.to_0d_object_array(data)
if isinstance(data, pd.Timestamp):
# TODO: convert, handle datetime objects, too
data = np.datetime64(data.value, "ns")
if isinstance(data, timedelta):
data = np.timedelta64(getattr(data, "value", data), "ns")
# we don't want nested self-described arrays
data = getattr(data, "values", data)
if isinstance(data, np.ma.MaskedArray):
mask = np.ma.getmaskarray(data)
if mask.any():
dtype, fill_value = dtypes.maybe_promote(data.dtype)
data = np.asarray(data, dtype=dtype)
data[mask] = fill_value
else:
data = np.asarray(data)
if not isinstance(data, np.ndarray):
if hasattr(data, "__array_function__"):
if IS_NEP18_ACTIVE:
return data
else:
raise TypeError(
"Got an NumPy-like array type providing the "
"__array_function__ protocol but NEP18 is not enabled. "
"Check that numpy >= v1.16 and that the environment "
'variable "NUMPY_EXPERIMENTAL_ARRAY_FUNCTION" is set to '
'"1"'
)
# validate whether the data is valid data types.
data = np.asarray(data)
if isinstance(data, np.ndarray):
if data.dtype.kind == "O":
data = _possibly_convert_objects(data)
elif data.dtype.kind == "M":
data = _possibly_convert_objects(data)
elif data.dtype.kind == "m":
data = _possibly_convert_objects(data)
return _maybe_wrap_data(data)
def _as_array_or_item(data):
"""Return the given values as a numpy array, or as an individual item if
it's a 0d datetime64 or timedelta64 array.
Importantly, this function does not copy data if it is already an ndarray -
otherwise, it will not be possible to update Variable values in place.
This function mostly exists because 0-dimensional ndarrays with
dtype=datetime64 are broken :(
https://github.com/numpy/numpy/issues/4337
https://github.com/numpy/numpy/issues/7619
TODO: remove this (replace with np.asarray) once these issues are fixed
"""
if isinstance(data, cupy_array_type):
data = data.get()
else:
data = np.asarray(data)
if data.ndim == 0:
if data.dtype.kind == "M":
data = np.datetime64(data, "ns")
elif data.dtype.kind == "m":
data = np.timedelta64(data, "ns")
return data
class Variable(
common.AbstractArray, arithmetic.SupportsArithmetic, utils.NdimSizeLenMixin
):
"""A netcdf-like variable consisting of dimensions, data and attributes
which describe a single Array. A single Variable object is not fully
described outside the context of its parent Dataset (if you want such a
fully described object, use a DataArray instead).
The main functional difference between Variables and numpy arrays is that
numerical operations on Variables implement array broadcasting by dimension
name. For example, adding an Variable with dimensions `('time',)` to
another Variable with dimensions `('space',)` results in a new Variable
with dimensions `('time', 'space')`. Furthermore, numpy reduce operations
like ``mean`` or ``sum`` are overwritten to take a "dimension" argument
instead of an "axis".
Variables are light-weight objects used as the building block for datasets.
They are more primitive objects, so operations with them provide marginally
higher performance than using DataArrays. However, manipulating data in the
form of a Dataset or DataArray should almost always be preferred, because
they can use more complete metadata in context of coordinate labels.
"""
__slots__ = ("_dims", "_data", "_attrs", "_encoding")
def __init__(self, dims, data, attrs=None, encoding=None, fastpath=False):
"""
Parameters
----------
dims : str or sequence of str
Name(s) of the the data dimension(s). Must be either a string (only
for 1D data) or a sequence of strings with length equal to the
number of dimensions.
data : array_like
Data array which supports numpy-like data access.
attrs : dict_like or None, optional
Attributes to assign to the new variable. If None (default), an
empty attribute dictionary is initialized.
encoding : dict_like or None, optional
Dictionary specifying how to encode this array's data into a
serialized format like netCDF4. Currently used keys (for netCDF)
include '_FillValue', 'scale_factor', 'add_offset' and 'dtype'.
Well-behaved code to serialize a Variable should ignore
unrecognized encoding items.
"""
self._data = as_compatible_data(data, fastpath=fastpath)
self._dims = self._parse_dimensions(dims)
self._attrs = None
self._encoding = None
if attrs is not None:
self.attrs = attrs
if encoding is not None:
self.encoding = encoding
@property
def dtype(self):
return self._data.dtype
@property
def shape(self):
return self._data.shape
@property
def nbytes(self):
return self.size * self.dtype.itemsize
@property
def _in_memory(self):
return isinstance(self._data, (np.ndarray, np.number, PandasIndexAdapter)) or (
isinstance(self._data, indexing.MemoryCachedArray)
and isinstance(self._data.array, indexing.NumpyIndexingAdapter)
)
@property
def data(self):
if is_duck_array(self._data):
return self._data
else:
return self.values
@data.setter
def data(self, data):
data = as_compatible_data(data)
if data.shape != self.shape:
raise ValueError(
f"replacement data must match the Variable's shape. "
f"replacement data has shape {data.shape}; Variable has shape {self.shape}"
)
self._data = data
def astype(
self: VariableType,
dtype,
*,
order=None,
casting=None,
subok=None,
copy=None,
keep_attrs=True,
) -> VariableType:
"""
Copy of the Variable object, with data cast to a specified type.
Parameters
----------
dtype : str or dtype
Typecode or data-type to which the array is cast.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout order of the result. ‘C’ means C order,
‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are
Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to
the order the array elements appear in memory as possible.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise the
returned array will be forced to be a base-class array.
copy : bool, optional
By default, astype always returns a newly allocated array. If this
is set to False and the `dtype` requirement is satisfied, the input
array is returned instead of a copy.
keep_attrs : bool, optional
By default, astype keeps attributes. Set to False to remove
attributes in the returned object.
Returns
-------
out : same as object
New object with data cast to the specified type.
Notes
-----
The ``order``, ``casting``, ``subok`` and ``copy`` arguments are only passed
through to the ``astype`` method of the underlying array when a value
different than ``None`` is supplied.
Make sure to only supply these arguments if the underlying array class
supports them.
See also
--------
numpy.ndarray.astype
dask.array.Array.astype
sparse.COO.astype
"""
from .computation import apply_ufunc
kwargs = dict(order=order, casting=casting, subok=subok, copy=copy)
kwargs = {k: v for k, v in kwargs.items() if v is not None}
return apply_ufunc(
duck_array_ops.astype,
self,
dtype,
kwargs=kwargs,
keep_attrs=keep_attrs,
dask="allowed",
)
def load(self, **kwargs):
"""Manually trigger loading of this variable's data from disk or a
remote source into memory and return this variable.
Normally, it should not be necessary to call this method in user code,
because all xarray functions should either work on deferred data or
load data automatically.
Parameters
----------
**kwargs : dict
Additional keyword arguments passed on to ``dask.array.compute``.
See Also
--------
dask.array.compute
"""
if is_duck_dask_array(self._data):
self._data = as_compatible_data(self._data.compute(**kwargs))
elif not is_duck_array(self._data):
self._data = np.asarray(self._data)
return self
def compute(self, **kwargs):
"""Manually trigger loading of this variable's data from disk or a
remote source into memory and return a new variable. The original is
left unaltered.
Normally, it should not be necessary to call this method in user code,
because all xarray functions should either work on deferred data or
load data automatically.
Parameters
----------
**kwargs : dict
Additional keyword arguments passed on to ``dask.array.compute``.
See Also
--------
dask.array.compute
"""
new = self.copy(deep=False)
return new.load(**kwargs)
def __dask_tokenize__(self):
# Use v.data, instead of v._data, in order to cope with the wrappers
# around NetCDF and the like
from dask.base import normalize_token
return normalize_token((type(self), self._dims, self.data, self._attrs))
def __dask_graph__(self):
if is_duck_dask_array(self._data):
return self._data.__dask_graph__()
else:
return None
def __dask_keys__(self):
return self._data.__dask_keys__()
def __dask_layers__(self):
return self._data.__dask_layers__()
@property
def __dask_optimize__(self):
return self._data.__dask_optimize__
@property
def __dask_scheduler__(self):
return self._data.__dask_scheduler__
def __dask_postcompute__(self):
array_func, array_args = self._data.__dask_postcompute__()
return (
self._dask_finalize,
(array_func, array_args, self._dims, self._attrs, self._encoding),
)
def __dask_postpersist__(self):
array_func, array_args = self._data.__dask_postpersist__()
return (
self._dask_finalize,
(array_func, array_args, self._dims, self._attrs, self._encoding),
)
@staticmethod
def _dask_finalize(results, array_func, array_args, dims, attrs, encoding):
data = array_func(results, *array_args)
return Variable(dims, data, attrs=attrs, encoding=encoding)
@property
def values(self):
"""The variable's data as a numpy.ndarray"""
return _as_array_or_item(self._data)
@values.setter
def values(self, values):
self.data = values
def to_base_variable(self):
"""Return this variable as a base xarray.Variable"""
return Variable(
self.dims, self._data, self._attrs, encoding=self._encoding, fastpath=True
)
to_variable = utils.alias(to_base_variable, "to_variable")
def to_index_variable(self):
"""Return this variable as an xarray.IndexVariable"""
return IndexVariable(
self.dims, self._data, self._attrs, encoding=self._encoding, fastpath=True
)
to_coord = utils.alias(to_index_variable, "to_coord")
def to_index(self):
"""Convert this variable to a pandas.Index"""
return self.to_index_variable().to_index()
def to_dict(self, data=True):
"""Dictionary representation of variable."""
item = {"dims": self.dims, "attrs": decode_numpy_dict_values(self.attrs)}
if data:
item["data"] = ensure_us_time_resolution(self.values).tolist()
else:
item.update({"dtype": str(self.dtype), "shape": self.shape})
return item
@property
def dims(self):
"""Tuple of dimension names with which this variable is associated."""
return self._dims
@dims.setter
def dims(self, value):
self._dims = self._parse_dimensions(value)
def _parse_dimensions(self, dims):
if isinstance(dims, str):
dims = (dims,)
dims = tuple(dims)
if len(dims) != self.ndim:
raise ValueError(
"dimensions %s must have the same length as the "
"number of data dimensions, ndim=%s" % (dims, self.ndim)
)
return dims
def _item_key_to_tuple(self, key):
if utils.is_dict_like(key):
return tuple(key.get(dim, slice(None)) for dim in self.dims)
else:
return key
def _broadcast_indexes(self, key):
"""Prepare an indexing key for an indexing operation.
Parameters
-----------
key: int, slice, array-like, dict or tuple of integer, slice and array-like
Any valid input for indexing.
Returns
-------
dims : tuple
Dimension of the resultant variable.
indexers : IndexingTuple subclass
Tuple of integer, array-like, or slices to use when indexing
self._data. The type of this argument indicates the type of
indexing to perform, either basic, outer or vectorized.
new_order : Optional[Sequence[int]]
Optional reordering to do on the result of indexing. If not None,
the first len(new_order) indexing should be moved to these
positions.
"""
key = self._item_key_to_tuple(key) # key is a tuple
# key is a tuple of full size
key = indexing.expanded_indexer(key, self.ndim)
# Convert a scalar Variable to an integer
key = tuple(
k.data.item() if isinstance(k, Variable) and k.ndim == 0 else k for k in key
)
# Convert a 0d-array to an integer
key = tuple(
k.item() if isinstance(k, np.ndarray) and k.ndim == 0 else k for k in key
)
if all(isinstance(k, BASIC_INDEXING_TYPES) for k in key):
return self._broadcast_indexes_basic(key)
self._validate_indexers(key)
# Detect it can be mapped as an outer indexer
# If all key is unlabeled, or
# key can be mapped as an OuterIndexer.
if all(not isinstance(k, Variable) for k in key):
return self._broadcast_indexes_outer(key)
# If all key is 1-dimensional and there are no duplicate labels,
# key can be mapped as an OuterIndexer.
dims = []
for k, d in zip(key, self.dims):
if isinstance(k, Variable):
if len(k.dims) > 1:
return self._broadcast_indexes_vectorized(key)
dims.append(k.dims[0])
elif not isinstance(k, integer_types):
dims.append(d)
if len(set(dims)) == len(dims):
return self._broadcast_indexes_outer(key)
return self._broadcast_indexes_vectorized(key)
def _broadcast_indexes_basic(self, key):
dims = tuple(
dim for k, dim in zip(key, self.dims) if not isinstance(k, integer_types)
)
return dims, BasicIndexer(key), None
def _validate_indexers(self, key):
""" Make sanity checks """
for dim, k in zip(self.dims, key):
if isinstance(k, BASIC_INDEXING_TYPES):
pass
else:
if not isinstance(k, Variable):
k = np.asarray(k)
if k.ndim > 1:
raise IndexError(
"Unlabeled multi-dimensional array cannot be "
"used for indexing: {}".format(k)
)
if k.dtype.kind == "b":
if self.shape[self.get_axis_num(dim)] != len(k):
raise IndexError(
"Boolean array size {:d} is used to index array "
"with shape {:s}.".format(len(k), str(self.shape))
)
if k.ndim > 1:
raise IndexError(
"{}-dimensional boolean indexing is "
"not supported. ".format(k.ndim)
)
if getattr(k, "dims", (dim,)) != (dim,):
raise IndexError(
"Boolean indexer should be unlabeled or on the "
"same dimension to the indexed array. Indexer is "
"on {:s} but the target dimension is {:s}.".format(
str(k.dims), dim
)
)
def _broadcast_indexes_outer(self, key):
dims = tuple(
k.dims[0] if isinstance(k, Variable) else dim
for k, dim in zip(key, self.dims)
if not isinstance(k, integer_types)
)
new_key = []
for k in key:
if isinstance(k, Variable):
k = k.data
if not isinstance(k, BASIC_INDEXING_TYPES):
k = np.asarray(k)
if k.size == 0:
# Slice by empty list; numpy could not infer the dtype
k = k.astype(int)
elif k.dtype.kind == "b":
(k,) = np.nonzero(k)
new_key.append(k)
return dims, OuterIndexer(tuple(new_key)), None
def _nonzero(self):
""" Equivalent numpy's nonzero but returns a tuple of Varibles. """
# TODO we should replace dask's native nonzero
# after https://github.com/dask/dask/issues/1076 is implemented.
nonzeros = np.nonzero(self.data)
return tuple(Variable((dim), nz) for nz, dim in zip(nonzeros, self.dims))
def _broadcast_indexes_vectorized(self, key):
variables = []
out_dims_set = OrderedSet()
for dim, value in zip(self.dims, key):
if isinstance(value, slice):
out_dims_set.add(dim)
else:
variable = (
value
if isinstance(value, Variable)
else as_variable(value, name=dim)
)
if variable.dtype.kind == "b": # boolean indexing case
(variable,) = variable._nonzero()
variables.append(variable)
out_dims_set.update(variable.dims)
variable_dims = set()
for variable in variables:
variable_dims.update(variable.dims)
slices = []
for i, (dim, value) in enumerate(zip(self.dims, key)):
if isinstance(value, slice):
if dim in variable_dims:
# We only convert slice objects to variables if they share
# a dimension with at least one other variable. Otherwise,
# we can equivalently leave them as slices aknd transpose
# the result. This is significantly faster/more efficient
# for most array backends.
values = np.arange(*value.indices(self.sizes[dim]))
variables.insert(i - len(slices), Variable((dim,), values))
else:
slices.append((i, value))
try:
variables = _broadcast_compat_variables(*variables)
except ValueError:
raise IndexError(f"Dimensions of indexers mismatch: {key}")
out_key = [variable.data for variable in variables]
out_dims = tuple(out_dims_set)
slice_positions = set()
for i, value in slices:
out_key.insert(i, value)
new_position = out_dims.index(self.dims[i])
slice_positions.add(new_position)
if slice_positions:
new_order = [i for i in range(len(out_dims)) if i not in slice_positions]
else:
new_order = None
return out_dims, VectorizedIndexer(tuple(out_key)), new_order
def __getitem__(self: VariableType, key) -> VariableType:
"""Return a new Variable object whose contents are consistent with
getting the provided key from the underlying data.
NB. __getitem__ and __setitem__ implement xarray-style indexing,
where if keys are unlabeled arrays, we index the array orthogonally
with them. If keys are labeled array (such as Variables), they are
broadcasted with our usual scheme and then the array is indexed with
the broadcasted key, like numpy's fancy indexing.
If you really want to do indexing like `x[x > 0]`, manipulate the numpy
array `x.values` directly.
"""
dims, indexer, new_order = self._broadcast_indexes(key)
data = as_indexable(self._data)[indexer]
if new_order:
data = duck_array_ops.moveaxis(data, range(len(new_order)), new_order)
return self._finalize_indexing_result(dims, data)
def _finalize_indexing_result(self: VariableType, dims, data) -> VariableType:
"""Used by IndexVariable to return IndexVariable objects when possible."""
return type(self)(dims, data, self._attrs, self._encoding, fastpath=True)
def _getitem_with_mask(self, key, fill_value=dtypes.NA):
"""Index this Variable with -1 remapped to fill_value."""
# TODO(shoyer): expose this method in public API somewhere (isel?) and
# use it for reindex.
# TODO(shoyer): add a sanity check that all other integers are
# non-negative
# TODO(shoyer): add an optimization, remapping -1 to an adjacent value
# that is actually indexed rather than mapping it to the last value
# along each axis.
if fill_value is dtypes.NA:
fill_value = dtypes.get_fill_value(self.dtype)
dims, indexer, new_order = self._broadcast_indexes(key)
if self.size:
if is_duck_dask_array(self._data):
# dask's indexing is faster this way; also vindex does not
# support negative indices yet:
# https://github.com/dask/dask/pull/2967
actual_indexer = indexing.posify_mask_indexer(indexer)
else:
actual_indexer = indexer
data = as_indexable(self._data)[actual_indexer]
mask = indexing.create_mask(indexer, self.shape, data)
# we need to invert the mask in order to pass data first. This helps
# pint to choose the correct unit
# TODO: revert after https://github.com/hgrecco/pint/issues/1019 is fixed
data = duck_array_ops.where( | np.logical_not(mask) | numpy.logical_not |
"""
This code is used to scrape ScienceDirect of publication urls and write them to
a text file in the current directory for later use.
"""
import selenium
from selenium import webdriver
import numpy as np
import pandas as pd
import bs4
from bs4 import BeautifulSoup
import time
from sklearn.utils import shuffle
def scrape_page(driver):
"""
This method finds all the publication result web elements on the webpage.
Parameters
----------
driver (Selenium webdriver object) : Instance of the webdriver class e.g.
webdriver.Chrome()
Returns
-------
elems (list) : A list of all scraped hrefs from the page
"""
elems = driver.find_elements_by_class_name('ResultItem')
return elems
def clean(elems):
"""
This method takes a list of scraped selenium web elements
and filters/ returns only the hrefs leading to publications.
Filtering includes removing all urls with keywords that are indicative of
non-html links.
Parameters
----------
elems (list) : The list of hrefs to be filtered
Returns
-------
urls (list) : The new list of hrefs, which should be the same as the list
displayed on gui ScienceDirect
"""
titles = []
urls = []
for elem in elems:
href_child = elem.find_element_by_css_selector('a[href]')
url = href_child.get_attribute('href')
title = href_child.text
titles.append(title)
urls.append(url)
return urls, titles
def build_url_list(gui_prefix,search_terms,journal_list):
"""
This method takes the list of journals and creates a tiple nested dictionary
containing all accessible urls to each page, in each year, for each journal,
for a given search on sciencedirect.
"""
dict1 = {}
years = np.arange(1995,2020)
for journal in journal_list:
dict2 = {}
for year in years:
dict3 = {}
for i in range(60):
url = gui_prefix + search_terms + '&show=100'+ '&articleTypes=FLA%2CREV' + '&years='+ str(year)
if i != 0:
url = url + '&offset=' + str(i) +'00'
url = url + '&pub=' + journal
dict3[i] = url
dict2[year] = dict3
dict1[journal] = dict2
return dict1
def proxify(scraped_urls,uw_prefix):
"""
This method takes a list of scraped urls and turns them into urls that
go through the UW Library proxy so that all of them are full access.
Parameters
----------
scraped_urls (list) : The list of URLs to be converted
uw_prefix (str) : The string that all URLs which go through the UW Library
Proxy start with.
Returns
-------
proxy_urls (list) : The list of converted URLs which go through UW Library
proxy
"""
proxy_urls = []
for url in scraped_urls:
sd_id = url[-17:]
newlink = uw_prefix + sd_id
if sd_id.startswith('S'):
proxy_urls.append(newlink)
return proxy_urls
def write_urls(urls,titles,file,journal,year):
"""
This method takes a list of urls and writes them to a desired text file.
Parameters
----------
urls (list) : The list of URLs to be saved.
file (file object) : The opened .txt file which will be written to.
year (str or int) : The year associated with the publication date.
Returns
-------
Does not return anything
"""
for link,title in zip(urls,titles):
line = link + ',' + title + ',' + journal + ',' + str(year)
file.write(line)
file.write('\n')
def find_pubTitle(driver,journal):
"""
This method finds the identifying number for a specific journal. This
identifying number is added to the gui query URL to ensure only publciations
from the desired journal are being found.
"""
pub_elems = driver.find_elements_by_css_selector('input[id*=publicationTitles]')
pub_names = []
for elem in pub_elems:
pub_name = elem.get_attribute("name")
if pub_name == journal:
return elem.get_attribute('id')[-6:] #returns the identifying number
#for that journal
df = pd.read_excel('elsevier_journals.xls')
df.Full_Category = df.Full_Category.str.lower() # lowercase topics for searching
df = df.drop_duplicates(subset = 'Journal_Title') # drop any duplicate journals
df = shuffle(df,random_state = 42)
# The set of default strings that will be used to sort which journals we want
journal_strings = ['chemistry','energy','molecular','atomic','chemical','biochem'
,'organic','polymer','chemical engineering','biotech','coloid']
name = df.Full_Category.str.contains # making this an easier command to type
# new dataframe full of only journals who's topic description contained the
# desired keywords
df2 = df[name('polymer') | name('chemistry') | name('energy')
| name('molecular') | name('colloid') | name('biochem')
| name('organic') | name('biotech') | name('chemical')]
journal_list = df2.Journal_Title # Series of only the journals to be searched
gui_prefix = 'https://www.sciencedirect.com/search/advanced?qs='
search_terms = 'chemistry%20OR%20molecule%20OR%20polymer%20OR%20organic'
url_dict = build_url_list(gui_prefix,search_terms,journal_list)
driver = webdriver.Chrome()
uw_prefix = 'https://www-sciencedirect-com.offcampus.lib.washington.edu/science/article/pii/'
filename = input("Input filename with .txt extension for URL storage: ")
url_counter = 0
master_list = []
file = open(filename,'a+')
for journal in journal_list:
for year in np.arange(1995,2020):
for offset in | np.arange(60) | numpy.arange |
"""Routines for numerical differentiation."""
from __future__ import division
import numpy as np
from numpy.linalg import norm
from scipy.sparse.linalg import LinearOperator
from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
from ._group_columns import group_dense, group_sparse
EPS = np.finfo(np.float64).eps
def _adjust_scheme_to_bounds(x0, h, num_steps, scheme, lb, ub):
"""Adjust final difference scheme to the presence of bounds.
Parameters
----------
x0 : ndarray, shape (n,)
Point at which we wish to estimate derivative.
h : ndarray, shape (n,)
Desired finite difference steps.
num_steps : int
Number of `h` steps in one direction required to implement finite
difference scheme. For example, 2 means that we need to evaluate
f(x0 + 2 * h) or f(x0 - 2 * h)
scheme : {'1-sided', '2-sided'}
Whether steps in one or both directions are required. In other
words '1-sided' applies to forward and backward schemes, '2-sided'
applies to center schemes.
lb : ndarray, shape (n,)
Lower bounds on independent variables.
ub : ndarray, shape (n,)
Upper bounds on independent variables.
Returns
-------
h_adjusted : ndarray, shape (n,)
Adjusted step sizes. Step size decreases only if a sign flip or
switching to one-sided scheme doesn't allow to take a full step.
use_one_sided : ndarray of bool, shape (n,)
Whether to switch to one-sided scheme. Informative only for
``scheme='2-sided'``.
"""
if scheme == '1-sided':
use_one_sided = np.ones_like(h, dtype=bool)
elif scheme == '2-sided':
h = np.abs(h)
use_one_sided = np.zeros_like(h, dtype=bool)
else:
raise ValueError("`scheme` must be '1-sided' or '2-sided'.")
if np.all((lb == -np.inf) & (ub == np.inf)):
return h, use_one_sided
h_total = h * num_steps
h_adjusted = h.copy()
lower_dist = x0 - lb
upper_dist = ub - x0
if scheme == '1-sided':
x = x0 + h_total
violated = (x < lb) | (x > ub)
fitting = np.abs(h_total) <= np.maximum(lower_dist, upper_dist)
h_adjusted[violated & fitting] *= -1
forward = (upper_dist >= lower_dist) & ~fitting
h_adjusted[forward] = upper_dist[forward] / num_steps
backward = (upper_dist < lower_dist) & ~fitting
h_adjusted[backward] = -lower_dist[backward] / num_steps
elif scheme == '2-sided':
central = (lower_dist >= h_total) & (upper_dist >= h_total)
forward = (upper_dist >= lower_dist) & ~central
h_adjusted[forward] = np.minimum(
h[forward], 0.5 * upper_dist[forward] / num_steps)
use_one_sided[forward] = True
backward = (upper_dist < lower_dist) & ~central
h_adjusted[backward] = -np.minimum(
h[backward], 0.5 * lower_dist[backward] / num_steps)
use_one_sided[backward] = True
min_dist = np.minimum(upper_dist, lower_dist) / num_steps
adjusted_central = (~central & (np.abs(h_adjusted) <= min_dist))
h_adjusted[adjusted_central] = min_dist[adjusted_central]
use_one_sided[adjusted_central] = False
return h_adjusted, use_one_sided
relative_step = {"2-point": EPS**0.5,
"3-point": EPS**(1/3),
"cs": EPS**0.5}
def _compute_absolute_step(rel_step, x0, method):
if rel_step is None:
rel_step = relative_step[method]
sign_x0 = (x0 >= 0).astype(float) * 2 - 1
return rel_step * sign_x0 * np.maximum(1.0, np.abs(x0))
def _prepare_bounds(bounds, x0):
lb, ub = [np.asarray(b, dtype=float) for b in bounds]
if lb.ndim == 0:
lb = np.resize(lb, x0.shape)
if ub.ndim == 0:
ub = np.resize(ub, x0.shape)
return lb, ub
def group_columns(A, order=0):
"""Group columns of a 2-D matrix for sparse finite differencing [1]_.
Two columns are in the same group if in each row at least one of them
has zero. A greedy sequential algorithm is used to construct groups.
Parameters
----------
A : array_like or sparse matrix, shape (m, n)
Matrix of which to group columns.
order : int, iterable of int with shape (n,) or None
Permutation array which defines the order of columns enumeration.
If int or None, a random permutation is used with `order` used as
a random seed. Default is 0, that is use a random permutation but
guarantee repeatability.
Returns
-------
groups : ndarray of int, shape (n,)
Contains values from 0 to n_groups-1, where n_groups is the number
of found groups. Each value ``groups[i]`` is an index of a group to
which ith column assigned. The procedure was helpful only if
n_groups is significantly less than n.
References
----------
.. [1] <NAME>, <NAME>, and <NAME>, "On the estimation of
sparse Jacobian matrices", Journal of the Institute of Mathematics
and its Applications, 13 (1974), pp. 117-120.
"""
if issparse(A):
A = csc_matrix(A)
else:
A = np.atleast_2d(A)
A = (A != 0).astype(np.int32)
if A.ndim != 2:
raise ValueError("`A` must be 2-dimensional.")
m, n = A.shape
if order is None or np.isscalar(order):
rng = np.random.RandomState(order)
order = rng.permutation(n)
else:
order = np.asarray(order)
if order.shape != (n,):
raise ValueError("`order` has incorrect shape.")
A = A[:, order]
if issparse(A):
groups = group_sparse(m, n, A.indices, A.indptr)
else:
groups = group_dense(m, n, A)
groups[order] = groups.copy()
return groups
def approx_derivative(fun, x0, method='3-point', rel_step=None, f0=None,
bounds=(-np.inf, np.inf), sparsity=None,
as_linear_operator=False, args=(), kwargs={}):
"""Compute finite difference approximation of the derivatives of a
vector-valued function.
If a function maps from R^n to R^m, its derivatives form m-by-n matrix
called the Jacobian, where an element (i, j) is a partial derivative of
f[i] with respect to x[j].
Parameters
----------
fun : callable
Function of which to estimate the derivatives. The argument x
passed to this function is ndarray of shape (n,) (never a scalar
even if n=1). It must return 1-D array_like of shape (m,) or a scalar.
x0 : array_like of shape (n,) or float
Point at which to estimate the derivatives. Float will be converted
to a 1-D array.
method : {'3-point', '2-point', 'cs'}, optional
Finite difference method to use:
- '2-point' - use the first order accuracy forward or backward
difference.
- '3-point' - use central difference in interior points and the
second order accuracy forward or backward difference
near the boundary.
- 'cs' - use a complex-step finite difference scheme. This assumes
that the user function is real-valued and can be
analytically continued to the complex plane. Otherwise,
produces bogus results.
rel_step : None or array_like, optional
Relative step size to use. The absolute step size is computed as
``h = rel_step * sign(x0) * max(1, abs(x0))``, possibly adjusted to
fit into the bounds. For ``method='3-point'`` the sign of `h` is
ignored. If None (default) then step is selected automatically,
see Notes.
f0 : None or array_like, optional
If not None it is assumed to be equal to ``fun(x0)``, in this case
the ``fun(x0)`` is not called. Default is None.
bounds : tuple of array_like, optional
Lower and upper bounds on independent variables. Defaults to no bounds.
Each bound must match the size of `x0` or be a scalar, in the latter
case the bound will be the same for all variables. Use it to limit the
range of function evaluation. Bounds checking is not implemented
when `as_linear_operator` is True.
sparsity : {None, array_like, sparse matrix, 2-tuple}, optional
Defines a sparsity structure of the Jacobian matrix. If the Jacobian
matrix is known to have only few non-zero elements in each row, then
it's possible to estimate its several columns by a single function
evaluation [3]_. To perform such economic computations two ingredients
are required:
* structure : array_like or sparse matrix of shape (m, n). A zero
element means that a corresponding element of the Jacobian
identically equals to zero.
* groups : array_like of shape (n,). A column grouping for a given
sparsity structure, use `group_columns` to obtain it.
A single array or a sparse matrix is interpreted as a sparsity
structure, and groups are computed inside the function. A tuple is
interpreted as (structure, groups). If None (default), a standard
dense differencing will be used.
Note, that sparse differencing makes sense only for large Jacobian
matrices where each row contains few non-zero elements.
as_linear_operator : bool, optional
When True the function returns an `scipy.sparse.linalg.LinearOperator`.
Otherwise it returns a dense array or a sparse matrix depending on
`sparsity`. The linear operator provides an efficient way of computing
``J.dot(p)`` for any vector ``p`` of shape (n,), but does not allow
direct access to individual elements of the matrix. By default
`as_linear_operator` is False.
args, kwargs : tuple and dict, optional
Additional arguments passed to `fun`. Both empty by default.
The calling signature is ``fun(x, *args, **kwargs)``.
Returns
-------
J : {ndarray, sparse matrix, LinearOperator}
Finite difference approximation of the Jacobian matrix.
If `as_linear_operator` is True returns a LinearOperator
with shape (m, n). Otherwise it returns a dense array or sparse
matrix depending on how `sparsity` is defined. If `sparsity`
is None then a ndarray with shape (m, n) is returned. If
`sparsity` is not None returns a csr_matrix with shape (m, n).
For sparse matrices and linear operators it is always returned as
a 2-D structure, for ndarrays, if m=1 it is returned
as a 1-D gradient array with shape (n,).
See Also
--------
check_derivative : Check correctness of a function computing derivatives.
Notes
-----
If `rel_step` is not provided, it assigned to ``EPS**(1/s)``, where EPS is
machine epsilon for float64 numbers, s=2 for '2-point' method and s=3 for
'3-point' method. Such relative step approximately minimizes a sum of
truncation and round-off errors, see [1]_.
A finite difference scheme for '3-point' method is selected automatically.
The well-known central difference scheme is used for points sufficiently
far from the boundary, and 3-point forward or backward scheme is used for
points near the boundary. Both schemes have the second-order accuracy in
terms of Taylor expansion. Refer to [2]_ for the formulas of 3-point
forward and backward difference schemes.
For dense differencing when m=1 Jacobian is returned with a shape (n,),
on the other hand when n=1 Jacobian is returned with a shape (m, 1).
Our motivation is the following: a) It handles a case of gradient
computation (m=1) in a conventional way. b) It clearly separates these two
different cases. b) In all cases np.atleast_2d can be called to get 2-D
Jacobian with correct dimensions.
References
----------
.. [1] W. H. Press et. al. "Numerical Recipes. The Art of Scientific
Computing. 3rd edition", sec. 5.7.
.. [2] <NAME>, <NAME>, and <NAME>, "On the estimation of
sparse Jacobian matrices", Journal of the Institute of Mathematics
and its Applications, 13 (1974), pp. 117-120.
.. [3] <NAME>, "Generation of Finite Difference Formulas on
Arbitrarily Spaced Grids", Mathematics of Computation 51, 1988.
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import approx_derivative
>>>
>>> def f(x, c1, c2):
... return np.array([x[0] * np.sin(c1 * x[1]),
... x[0] * np.cos(c2 * x[1])])
...
>>> x0 = np.array([1.0, 0.5 * np.pi])
>>> approx_derivative(f, x0, args=(1, 2))
array([[ 1., 0.],
[-1., 0.]])
Bounds can be used to limit the region of function evaluation.
In the example below we compute left and right derivative at point 1.0.
>>> def g(x):
... return x**2 if x >= 1 else x
...
>>> x0 = 1.0
>>> approx_derivative(g, x0, bounds=(-np.inf, 1.0))
array([ 1.])
>>> approx_derivative(g, x0, bounds=(1.0, np.inf))
array([ 2.])
"""
if method not in ['2-point', '3-point', 'cs']:
raise ValueError("Unknown method '%s'. " % method)
x0 = np.atleast_1d(x0)
if x0.ndim > 1:
raise ValueError("`x0` must have at most 1 dimension.")
lb, ub = _prepare_bounds(bounds, x0)
if lb.shape != x0.shape or ub.shape != x0.shape:
raise ValueError("Inconsistent shapes between bounds and `x0`.")
if as_linear_operator and not (np.all(np.isinf(lb))
and np.all(np.isinf(ub))):
raise ValueError("Bounds not supported when "
"`as_linear_operator` is True.")
def fun_wrapped(x):
f = np.atleast_1d(fun(x, *args, **kwargs))
if f.ndim > 1:
raise RuntimeError("`fun` return value has "
"more than 1 dimension.")
return f
if f0 is None:
f0 = fun_wrapped(x0)
else:
f0 = np.atleast_1d(f0)
if f0.ndim > 1:
raise ValueError("`f0` passed has more than 1 dimension.")
if np.any((x0 < lb) | (x0 > ub)):
raise ValueError("`x0` violates bound constraints.")
if as_linear_operator:
if rel_step is None:
rel_step = relative_step[method]
return _linear_operator_difference(fun_wrapped, x0,
f0, rel_step, method)
else:
h = _compute_absolute_step(rel_step, x0, method)
if method == '2-point':
h, use_one_sided = _adjust_scheme_to_bounds(
x0, h, 1, '1-sided', lb, ub)
elif method == '3-point':
h, use_one_sided = _adjust_scheme_to_bounds(
x0, h, 1, '2-sided', lb, ub)
elif method == 'cs':
use_one_sided = False
if sparsity is None:
return _dense_difference(fun_wrapped, x0, f0, h,
use_one_sided, method)
else:
if not issparse(sparsity) and len(sparsity) == 2:
structure, groups = sparsity
else:
structure = sparsity
groups = group_columns(sparsity)
if issparse(structure):
structure = csc_matrix(structure)
else:
structure = np.atleast_2d(structure)
groups = np.atleast_1d(groups)
return _sparse_difference(fun_wrapped, x0, f0, h,
use_one_sided, structure,
groups, method)
def _linear_operator_difference(fun, x0, f0, h, method):
m = f0.size
n = x0.size
if method == '2-point':
def matvec(p):
if np.array_equal(p, np.zeros_like(p)):
return np.zeros(m)
dx = h / norm(p)
x = x0 + dx*p
df = fun(x) - f0
return df / dx
elif method == '3-point':
def matvec(p):
if np.array_equal(p, np.zeros_like(p)):
return np.zeros(m)
dx = 2*h / norm(p)
x1 = x0 - (dx/2)*p
x2 = x0 + (dx/2)*p
f1 = fun(x1)
f2 = fun(x2)
df = f2 - f1
return df / dx
elif method == 'cs':
def matvec(p):
if np.array_equal(p, np.zeros_like(p)):
return np.zeros(m)
dx = h / norm(p)
x = x0 + dx*p*1.j
f1 = fun(x)
df = f1.imag
return df / dx
else:
raise RuntimeError("Never be here.")
return LinearOperator((m, n), matvec)
def _dense_difference(fun, x0, f0, h, use_one_sided, method):
m = f0.size
n = x0.size
J_transposed = np.empty((n, m))
h_vecs = np.diag(h)
for i in range(h.size):
if method == '2-point':
x = x0 + h_vecs[i]
dx = x[i] - x0[i] # Recompute dx as exactly representable number.
df = fun(x) - f0
elif method == '3-point' and use_one_sided[i]:
x1 = x0 + h_vecs[i]
x2 = x0 + 2 * h_vecs[i]
dx = x2[i] - x0[i]
f1 = fun(x1)
f2 = fun(x2)
df = -3.0 * f0 + 4 * f1 - f2
elif method == '3-point' and not use_one_sided[i]:
x1 = x0 - h_vecs[i]
x2 = x0 + h_vecs[i]
dx = x2[i] - x1[i]
f1 = fun(x1)
f2 = fun(x2)
df = f2 - f1
elif method == 'cs':
f1 = fun(x0 + h_vecs[i]*1.j)
df = f1.imag
dx = h_vecs[i, i]
else:
raise RuntimeError("Never be here.")
J_transposed[i] = df / dx
if m == 1:
J_transposed = np.ravel(J_transposed)
return J_transposed.T
def _sparse_difference(fun, x0, f0, h, use_one_sided,
structure, groups, method):
m = f0.size
n = x0.size
row_indices = []
col_indices = []
fractions = []
n_groups = np.max(groups) + 1
for group in range(n_groups):
# Perturb variables which are in the same group simultaneously.
e = np.equal(group, groups)
h_vec = h * e
if method == '2-point':
x = x0 + h_vec
dx = x - x0
df = fun(x) - f0
# The result is written to columns which correspond to perturbed
# variables.
cols, = np.nonzero(e)
# Find all non-zero elements in selected columns of Jacobian.
i, j, _ = find(structure[:, cols])
# Restore column indices in the full array.
j = cols[j]
elif method == '3-point':
# Here we do conceptually the same but separate one-sided
# and two-sided schemes.
x1 = x0.copy()
x2 = x0.copy()
mask_1 = use_one_sided & e
x1[mask_1] += h_vec[mask_1]
x2[mask_1] += 2 * h_vec[mask_1]
mask_2 = ~use_one_sided & e
x1[mask_2] -= h_vec[mask_2]
x2[mask_2] += h_vec[mask_2]
dx = np.zeros(n)
dx[mask_1] = x2[mask_1] - x0[mask_1]
dx[mask_2] = x2[mask_2] - x1[mask_2]
f1 = fun(x1)
f2 = fun(x2)
cols, = | np.nonzero(e) | numpy.nonzero |
"""Bindings for the Barnes Hut TSNE algorithm with fast nearest neighbors
Refs:
References
[1] <NAME>, L.J.P.; Hinton, G.E. Visualizing High-Dimensional Data
Using t-SNE. Journal of Machine Learning Research 9:2579-2605, 2008.
[2] <NAME>, L.J.P. t-Distributed Stochastic Neighbor Embedding
http://homepage.tudelft.nl/19j49/t-SNE.html
"""
import numpy as N
import ctypes
import os
import pkg_resources
def ord_string(s):
b = bytearray()
arr = b.extend(map(ord, s))
return N.array([x for x in b] + [0]).astype(N.uint8)
class TSNE(object):
def __init__(self,
n_components=2,
perplexity=50.0,
early_exaggeration=2.0,
learning_rate=200.0,
num_neighbors=1023,
force_magnify_iters=250,
pre_momentum=0.5,
post_momentum=0.8,
theta=0.5,
epssq=0.0025,
n_iter=1000,
n_iter_without_progress=1000,
min_grad_norm=1e-7,
perplexity_epsilon=1e-3,
metric='euclidean',
init='random',
return_style='once',
num_snapshots=5,
verbose=0,
random_seed=None,
use_interactive=False,
viz_timeout=10000,
viz_server="tcp://localhost:5556",
dump_points=False,
dump_file="dump.txt",
dump_interval=1,
print_interval=10,
device=0,
):
"""Initialization method for barnes hut T-SNE class.
"""
# Initialize the variables
self.n_components = int(n_components)
if self.n_components != 2:
raise ValueError('The current barnes-hut implementation does not support projection into dimensions other than 2 for now.')
self.perplexity = float(perplexity)
self.early_exaggeration = float(early_exaggeration)
self.learning_rate = float(learning_rate)
self.n_iter = int(n_iter)
self.n_iter_without_progress = int(n_iter_without_progress)
self.min_grad_norm = float(min_grad_norm)
if metric not in ['euclidean']:
raise ValueError('Non-Euclidean metrics are not currently supported. Please use metric=\'euclidean\' for now.')
else:
self.metric = metric
if init not in ['random']:
raise ValueError('Non-Random initialization is not currently supported. Please use init=\'random\' for now.')
else:
self.init = init
self.verbose = int(verbose)
# Initialize non-sklearn variables
self.num_neighbors = int(num_neighbors)
self.force_magnify_iters = int(force_magnify_iters)
self.perplexity_epsilon = float(perplexity_epsilon)
self.pre_momentum = float(pre_momentum)
self.post_momentum = float(post_momentum)
self.theta = float(theta)
self.epssq =float(epssq)
self.device = int(device)
self.print_interval = int(print_interval)
# Point dumpoing
self.dump_file = str(dump_file)
self.dump_points = bool(dump_points)
self.dump_interval = int(dump_interval)
# Viz
self.use_interactive = bool(use_interactive)
self.viz_server = str(viz_server)
self.viz_timeout = int(viz_timeout)
# Return style
if return_style not in ['once','snapshots']:
raise ValueError('Invalid return style...')
elif return_style == 'once':
self.return_style = 0
elif return_style == 'snapshots':
self.return_style = 1
self.num_snapshots = int(num_snapshots)
# Build the hooks for the BH T-SNE library
self._path = pkg_resources.resource_filename('tsnecuda','') # Load from current location
# self._faiss_lib = N.ctypeslib.load_library('libfaiss', self._path) # Load the ctypes library
# self._gpufaiss_lib = N.ctypeslib.load_library('libgpufaiss', self._path) # Load the ctypes library
self._lib = N.ctypeslib.load_library('libtsnecuda', self._path) # Load the ctypes library
# Hook the BH T-SNE function
self._lib.pymodule_bh_tsne.restype = None
self._lib.pymodule_bh_tsne.argtypes = [
N.ctypeslib.ndpointer(N.float32, ndim=2, flags='ALIGNED, F_CONTIGUOUS, WRITEABLE'), # result
N.ctypeslib.ndpointer(N.float32, ndim=2, flags='ALIGNED, CONTIGUOUS'), # points
ctypes.POINTER(N.ctypeslib.c_intp), # dims
ctypes.c_float, # Perplexity
ctypes.c_float, # Learning Rate
ctypes.c_float, # Magnitude Factor
ctypes.c_int, # Num Neighbors
ctypes.c_int, # Iterations
ctypes.c_int, # Iterations no progress
ctypes.c_int, # Force Magnify iterations
ctypes.c_float, # Perplexity search epsilon
ctypes.c_float, # pre-exaggeration momentum
ctypes.c_float, # post-exaggeration momentum
ctypes.c_float, # Theta
ctypes.c_float, # epssq
ctypes.c_float, # Minimum gradient norm
ctypes.c_int, # Initialization types
N.ctypeslib.ndpointer(N.float32, ndim=2, flags='ALIGNED, F_CONTIGUOUS'), # Initialization Data
ctypes.c_bool, # Dump points
N.ctypeslib.ndpointer(N.uint8, flags='ALIGNED, CONTIGUOUS'), # Dump File
ctypes.c_int, # Dump interval
ctypes.c_bool, # Use interactive
N.ctypeslib.ndpointer(N.uint8, flags='ALIGNED, CONTIGUOUS'), # Viz Server
ctypes.c_int, # Viz timeout
ctypes.c_int, # Verbosity
ctypes.c_int, # Print interval
ctypes.c_int, # GPU Device
ctypes.c_int, # Return style
ctypes.c_int ] # Number of snapshots
def fit_transform(self, X, y=None):
"""Fit X into an embedded space and return that transformed output.
Arguments:
X {array} -- Input array, shape: (n_points, n_dimensions)
Keyword Arguments:
y {None} -- Ignored (default: {None})
"""
# Setup points/embedding requirements
self.points = N.require(X, N.float32, ['CONTIGUOUS', 'ALIGNED'])
self.embedding = N.zeros(shape=(X.shape[0],self.n_components))
self.embedding = N.require(self.embedding , N.float32, ['F_CONTIGUOUS', 'ALIGNED', 'WRITEABLE'])
# Handle Initialization
if y is None:
self.initialization_type = 1
self.init_data = N.require(N.zeros((1,1)),N.float32,['CONTIGUOUS','ALIGNED'])
else:
self.initialization_type = 3
self.init_data = | N.require(y, N.float32, ['F_CONTIGUOUS', 'ALIGNED']) | numpy.require |
import numpy as np
import tensorflow as tf
H = 2
N = 2
M = 3
BS = 10
def my_softmax(arr):
max_elements = np.reshape(np.max(arr, axis = 2), (BS, N, 1))
arr = arr - max_elements
exp_array = np.exp(arr)
print (exp_array)
sum_array = np.reshape(np.sum(exp_array, axis=2), (BS, N, 1))
return exp_array /sum_array
def masked_softmax(logits, mask, dim):
"""
Takes masked softmax over given dimension of logits.
Inputs:
logits: Numpy array. We want to take softmax over dimension dim.
mask: Numpy array of same shape as logits.
Has 1s where there's real data in logits, 0 where there's padding
dim: int. dimension over which to take softmax
Returns:
masked_logits: Numpy array same shape as logits.
This is the same as logits, but with 1e30 subtracted
(i.e. very large negative number) in the padding locations.
prob_dist: Numpy array same shape as logits.
The result of taking softmax over masked_logits in given dimension.
Should be 0 in padding locations.
Should sum to 1 over given dimension.
"""
exp_mask = (1 - tf.cast(mask, 'float64')) * (-1e30) # -large where there's padding, 0 elsewhere
print (exp_mask)
masked_logits = tf.add(logits, exp_mask) # where there's padding, set logits to -large
prob_dist = tf.nn.softmax(masked_logits, dim)
return masked_logits, prob_dist
def test_build_similarity(contexts, questions):
w_sim_1 = tf.get_variable('w_sim_1',
initializer=w_1) # 2 * H
w_sim_2 = tf.get_variable('w_sim_2',
initializer=w_2) # 2 * self.hidden_size
w_sim_3 = tf.get_variable('w_sim_3',
initializer=w_3) # 2 * self.hidden_size
q_tile = tf.tile(tf.expand_dims(questions, 0), [N, 1, 1, 1]) # N x BS x M x 2H
q_tile = tf.transpose(q_tile, (1, 0, 3, 2)) # BS x N x 2H x M
contexts = tf.expand_dims(contexts, -1) # BS x N x 2H x 1
result = (contexts * q_tile) # BS x N x 2H x M
tf.assert_equal(tf.shape(result), [BS, N, 2 * H, M])
result = tf.transpose(result, (0, 1, 3, 2)) # BS x N x M x 2H
result = tf.reshape(result, (-1, N * M, 2 * H)) # BS x (NxM) x 2H
tf.assert_equal(tf.shape(result), [BS, N*M, 2*H])
# w_sim_1 = tf.tile(tf.expand_dims(w_sim_1, 0), [BS, 1])
# w_sim_2 = tf.tile(tf.expand_dims(w_sim_2, 0), [BS, 1])
# w_sim_3 = tf.tile(tf.expand_dims(w_sim_3, 0), [BS, 1])
term1 = tf.matmul(tf.reshape(contexts, (BS * N, 2*H)), tf.expand_dims(w_sim_1, -1)) # BS x N
term1 = tf.reshape(term1, (-1, N))
term2 = tf.matmul(tf.reshape(questions, (BS * M, 2*H)), tf.expand_dims(w_sim_2, -1)) # BS x M
term2 = tf.reshape(term2, (-1, M))
term3 = tf.matmul(tf.reshape(result, (BS * N * M, 2* H)), tf.expand_dims(w_sim_3, -1))
term3 = tf.reshape(term3, (-1, N, M)) # BS x N x M
S = tf.reshape(term1,(-1, N, 1)) + term3 + tf.reshape(term2, (-1, 1, M))
return S
def test_build_sim_mask():
context_mask = np.array([True, True]) # BS x N
question_mask = np.array([True, True, False]) # BS x M
context_mask = np.tile(context_mask, [BS, 1])
question_mask = np.tile(question_mask, [BS, 1])
context_mask = tf.get_variable('context_mask', initializer=context_mask)
question_mask = tf.get_variable('question_mask', initializer=question_mask)
context_mask = tf.expand_dims(context_mask, -1) # BS x N x 1
question_mask = tf.expand_dims(question_mask, -1) # BS x M x 1
question_mask = tf.transpose(question_mask, (0, 2, 1)) # BS x 1 x M
sim_mask = tf.matmul(tf.cast(context_mask, dtype=tf.int32),
tf.cast(question_mask, dtype=tf.int32)) # BS x N x M
return sim_mask
def test_build_c2q(S, S_mask, questions):
_, alpha = masked_softmax(S, mask, 2) # BS x N x M
return tf.matmul(alpha, questions)
def test_build_q2c(S, S_mask, contexts):
# S = BS x N x M
# contexts = BS x N x 2H
m = tf.reduce_max(S * tf.cast(S_mask, dtype=tf.float64), axis=2) # BS x N
beta = tf.expand_dims(tf.nn.softmax(m), -1) # BS x N x 1
beta = tf.transpose(beta, (0, 2, 1))
q2c = tf.matmul(beta, contexts)
return m, beta, q2c
def test_concatenation(c2q, q2c):
q2c = tf.tile(q2c, (1, N, 1))
output = tf.concat([c2q, q2c], axis=2)
tf.assert_equal(tf.shape(output), [BS, N, 4*H])
return output
if __name__== "__main__":
w_1 = np.array([1., 2., 3., 4.])
w_2 = | np.array([5., 6., 7., 8.]) | numpy.array |
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
class TwoLayerNet(object):
"""
A two-layer fully-connected neural network. The net has an input dimension
of N, a hidden layer dimension of H, and performs classification over C
classes.
We train the network with a softmax loss function and L2 regularization on
the weight matrices. The network uses a ReLU nonlinearity after the first
fully connected layer.
In other words, the network has the following architecture:
input - fully connected layer - ReLU - fully connected layer - softmax
The outputs of the second fully-connected layer are the scores for each
class.
"""
def __init__(self, input_size, hidden_size, output_size, std=1e-4):
"""
Initialize the model. Weights are initialized to small random values
and biases are initialized to zero. Weights and biases are stored in
the variable self.params, which is a dictionary with the following keys
W1: First layer weights; has shape (D, H)
b1: First layer biases; has shape (H,)
W2: Second layer weights; has shape (H, C)
b2: Second layer biases; has shape (C,)
Inputs:
- input_size: The dimension D of the input data.
- hidden_size: The number of neurons H in the hidden layer.
- output_size: The number of classes C.
"""
self.params = {}
self.params['W1'] = std * np.random.randn(input_size, hidden_size)
self.params['b1'] = np.zeros(hidden_size)
self.params['W2'] = std * np.random.randn(hidden_size, output_size)
self.params['b2'] = np.zeros(output_size)
def loss(self, X, y=None, reg=0.0):
"""
Compute the loss and gradients for a two layer fully connected neural
network.
Inputs:
- X: Input data of shape (N, D). Each X[i] is a training sample.
- y: Vector of training labels. y[i] is the label for X[i], and each
y[i] is an integer in the range 0 <= y[i] < C. This parameter is
optional; if it is not passed then we only return scores, and if it
is passed then we instead return the loss and gradients.
- reg: Regularization strength.
Returns:
If y is None, return a matrix scores of shape (N, C) where scores[i, c]
is the score for class c on input X[i].
If y is not None, instead return a tuple of:
- loss: Loss (data loss and regularization loss) for this batch of
training samples.
- grads: Dictionary mapping parameter names to gradients of those
parameters with respect to the loss function; has the same keys as
self.params.
"""
# Unpack variables from the params dictionary
W1, b1 = self.params['W1'], self.params['b1']
W2, b2 = self.params['W2'], self.params['b2']
N, D = X.shape
# Compute the forward pass
scores = None
#######################################################################
# TODO: Perform the forward pass, computing the class scores for the #
# input. Store the result in the scores variable, which should be an #
# array of shape (N, C). #
#######################################################################
scores1 = X.dot(W1) + b1 # FC1
X2 = np.maximum(0, scores1) # ReLU FC1
scores = X2.dot(W2) + b2 # FC2
#######################################################################
# END OF YOUR CODE #
#######################################################################
# If the targets are not given then jump out, we're done
if y is None:
return scores
scores -= np.max(scores) # Fix Number instability
scores_exp = np.exp(scores)
probs = scores_exp / np.sum(scores_exp, axis=1, keepdims=True)
# Compute the loss
loss = None
#######################################################################
# TODO: Finish the forward pass, and compute the loss. This should #
# include both the data loss and L2 regularization for W1 and W2. #
# Store the result in the variable loss, which should be a scalar. Use#
# the Softmax classifier loss. #
#######################################################################
correct_probs = -np.log(probs[np.arange(N), y])
# L_i = -log(e^correct_score/sum(e^scores))) = -log(correct_probs)
loss = np.sum(correct_probs)
loss /= N
# L2 regularization WRT W1 and W2
loss += reg * ( | np.sum(W1 * W1) | numpy.sum |
'''
<NAME>
set up :2020-1-9
intergrate img and label into one file
-- fiducial1024_v1
'''
import argparse
import sys, os
import pickle
import random
import collections
import json
import numpy as np
import scipy.io as io
import scipy.misc as m
import matplotlib.pyplot as plt
import glob
import math
import time
import threading
import multiprocessing as mp
from multiprocessing import Pool
import re
import cv2
# sys.path.append('/lustre/home/gwxie/hope/project/dewarp/datasets/') # /lustre/home/gwxie/program/project/unwarp/perturbed_imgaes/GAN
import utils
def getDatasets(dir):
return os.listdir(dir)
class perturbed(utils.BasePerturbed):
def __init__(self, path, bg_path, save_path, save_suffix):
self.path = path
self.bg_path = bg_path
self.save_path = save_path
self.save_suffix = save_suffix
def save_img(self, m, n, fold_curve='fold', repeat_time=4, fiducial_points = 16, relativeShift_position='relativeShift_v2'):
origin_img = cv2.imread(self.path, flags=cv2.IMREAD_COLOR)
save_img_shape = [512*2, 480*2] # 320
# reduce_value = np.random.choice([2**4, 2**5, 2**6, 2**7, 2**8], p=[0.01, 0.1, 0.4, 0.39, 0.1])
reduce_value = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.02, 0.18, 0.2, 0.3, 0.1, 0.1, 0.08, 0.02])
# reduce_value = np.random.choice([8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.01, 0.02, 0.2, 0.4, 0.19, 0.18])
# reduce_value = np.random.choice([16, 24, 32, 40, 48, 64], p=[0.01, 0.1, 0.2, 0.4, 0.2, 0.09])
base_img_shrink = save_img_shape[0] - reduce_value
# enlarge_img_shrink = [1024, 768]
# enlarge_img_shrink = [896, 672] # 420
enlarge_img_shrink = [512*4, 480*4] # 420
# enlarge_img_shrink = [896*2, 768*2] # 420
# enlarge_img_shrink = [896, 768] # 420
# enlarge_img_shrink = [768, 576] # 420
# enlarge_img_shrink = [640, 480] # 420
''''''
im_lr = origin_img.shape[0]
im_ud = origin_img.shape[1]
reduce_value_v2 = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 28*2, 32*2, 48*2], p=[0.02, 0.18, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1])
# reduce_value_v2 = np.random.choice([16, 24, 28, 32, 48, 64], p=[0.01, 0.1, 0.2, 0.3, 0.25, 0.14])
if im_lr > im_ud:
im_ud = min(int(im_ud / im_lr * base_img_shrink), save_img_shape[1] - reduce_value_v2)
im_lr = save_img_shape[0] - reduce_value
else:
base_img_shrink = save_img_shape[1] - reduce_value
im_lr = min(int(im_lr / im_ud * base_img_shrink), save_img_shape[0] - reduce_value_v2)
im_ud = base_img_shrink
if round(im_lr / im_ud, 2) < 0.5 or round(im_ud / im_lr, 2) < 0.5:
repeat_time = min(repeat_time, 8)
edge_padding = 3
im_lr -= im_lr % (fiducial_points-1) - (2*edge_padding) # im_lr % (fiducial_points-1) - 1
im_ud -= im_ud % (fiducial_points-1) - (2*edge_padding) # im_ud % (fiducial_points-1) - 1
im_hight = np.linspace(edge_padding, im_lr - edge_padding, fiducial_points, dtype=np.int64)
im_wide = np.linspace(edge_padding, im_ud - edge_padding, fiducial_points, dtype=np.int64)
# im_lr -= im_lr % (fiducial_points-1) - (1+2*edge_padding) # im_lr % (fiducial_points-1) - 1
# im_ud -= im_ud % (fiducial_points-1) - (1+2*edge_padding) # im_ud % (fiducial_points-1) - 1
# im_hight = np.linspace(edge_padding, im_lr - (1+edge_padding), fiducial_points, dtype=np.int64)
# im_wide = np.linspace(edge_padding, im_ud - (1+edge_padding), fiducial_points, dtype=np.int64)
im_x, im_y = np.meshgrid(im_hight, im_wide)
segment_x = (im_lr) // (fiducial_points-1)
segment_y = (im_ud) // (fiducial_points-1)
# plt.plot(im_x, im_y,
# color='limegreen',
# marker='.',
# linestyle='')
# plt.grid(True)
# plt.show()
self.origin_img = cv2.resize(origin_img, (im_ud, im_lr), interpolation=cv2.INTER_CUBIC)
perturbed_bg_ = getDatasets(self.bg_path)
perturbed_bg_img_ = self.bg_path+random.choice(perturbed_bg_)
perturbed_bg_img = cv2.imread(perturbed_bg_img_, flags=cv2.IMREAD_COLOR)
mesh_shape = self.origin_img.shape[:2]
self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 256, dtype=np.float32)#np.zeros_like(perturbed_bg_img)
# self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 0, dtype=np.int16)#np.zeros_like(perturbed_bg_img)
self.new_shape = self.synthesis_perturbed_img.shape[:2]
perturbed_bg_img = cv2.resize(perturbed_bg_img, (save_img_shape[1], save_img_shape[0]), cv2.INPAINT_TELEA)
origin_pixel_position = np.argwhere(np.zeros(mesh_shape, dtype=np.uint32) == 0).reshape(mesh_shape[0], mesh_shape[1], 2)
pixel_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(self.new_shape[0], self.new_shape[1], 2)
self.perturbed_xy_ = np.zeros((self.new_shape[0], self.new_shape[1], 2))
# self.perturbed_xy_ = pixel_position.copy().astype(np.float32)
# fiducial_points_grid = origin_pixel_position[im_x, im_y]
self.synthesis_perturbed_label = np.zeros((self.new_shape[0], self.new_shape[1], 2))
x_min, y_min, x_max, y_max = self.adjust_position_v2(0, 0, mesh_shape[0], mesh_shape[1], save_img_shape)
origin_pixel_position += [x_min, y_min]
x_min, y_min, x_max, y_max = self.adjust_position(0, 0, mesh_shape[0], mesh_shape[1])
x_shift = random.randint(-enlarge_img_shrink[0]//16, enlarge_img_shrink[0]//16)
y_shift = random.randint(-enlarge_img_shrink[1]//16, enlarge_img_shrink[1]//16)
x_min += x_shift
x_max += x_shift
y_min += y_shift
y_max += y_shift
'''im_x,y'''
im_x += x_min
im_y += y_min
self.synthesis_perturbed_img[x_min:x_max, y_min:y_max] = self.origin_img
self.synthesis_perturbed_label[x_min:x_max, y_min:y_max] = origin_pixel_position
synthesis_perturbed_img_map = self.synthesis_perturbed_img.copy()
synthesis_perturbed_label_map = self.synthesis_perturbed_label.copy()
foreORbackground_label = np.full((mesh_shape), 1, dtype=np.int16)
foreORbackground_label_map = np.full((self.new_shape), 0, dtype=np.int16)
foreORbackground_label_map[x_min:x_max, y_min:y_max] = foreORbackground_label
# synthesis_perturbed_img_map = self.pad(self.synthesis_perturbed_img.copy(), x_min, y_min, x_max, y_max)
# synthesis_perturbed_label_map = self.pad(synthesis_perturbed_label_map, x_min, y_min, x_max, y_max)
'''*****************************************************************'''
is_normalizationFun_mixture = self.is_perform(0.2, 0.8)
# if not is_normalizationFun_mixture:
normalizationFun_0_1 = False
# normalizationFun_0_1 = self.is_perform(0.5, 0.5)
if fold_curve == 'fold':
fold_curve_random = True
# is_normalizationFun_mixture = False
normalizationFun_0_1 = self.is_perform(0.2, 0.8)
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
fold_curve_random = self.is_perform(0.1, 0.9) # False # self.is_perform(0.01, 0.99)
alpha_perturbed = random.randint(80, 160) / 100
# is_normalizationFun_mixture = False # self.is_perform(0.01, 0.99)
synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 256)
# synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 0, dtype=np.int16)
synthesis_perturbed_label = np.zeros_like(self.synthesis_perturbed_label)
alpha_perturbed_change = self.is_perform(0.5, 0.5)
p_pp_choice = self.is_perform(0.8, 0.2) if fold_curve == 'fold' else self.is_perform(0.1, 0.9)
for repeat_i in range(repeat_time):
if alpha_perturbed_change:
if fold_curve == 'fold':
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
alpha_perturbed = random.randint(80, 160) / 100
''''''
linspace_x = [0, (self.new_shape[0] - im_lr) // 2 - 1,
self.new_shape[0] - (self.new_shape[0] - im_lr) // 2 - 1, self.new_shape[0] - 1]
linspace_y = [0, (self.new_shape[1] - im_ud) // 2 - 1,
self.new_shape[1] - (self.new_shape[1] - im_ud) // 2 - 1, self.new_shape[1] - 1]
linspace_x_seq = [1, 2, 3]
linspace_y_seq = [1, 2, 3]
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_p = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
if ((r_x == 1 or r_x == 3) and (r_y == 1 or r_y == 3)) and p_pp_choice:
linspace_x_seq.remove(r_x)
linspace_y_seq.remove(r_y)
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_pp = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
# perturbed_p, perturbed_pp = np.array(
# [random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10]) \
# , np.array([random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10])
# perturbed_p, perturbed_pp = np.array(
# [random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10]) \
# , np.array([random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10])
''''''
perturbed_vp = perturbed_pp - perturbed_p
perturbed_vp_norm = np.linalg.norm(perturbed_vp)
perturbed_distance_vertex_and_line = np.dot((perturbed_p - pixel_position), perturbed_vp) / perturbed_vp_norm
''''''
# perturbed_v = np.array([random.randint(-3000, 3000) / 100, random.randint(-3000, 3000) / 100])
# perturbed_v = np.array([random.randint(-4000, 4000) / 100, random.randint(-4000, 4000) / 100])
if fold_curve == 'fold' and self.is_perform(0.6, 0.4): # self.is_perform(0.3, 0.7):
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
perturbed_v = np.array([random.randint(-10000, 10000) / 100, random.randint(-10000, 10000) / 100])
# perturbed_v = np.array([random.randint(-11000, 11000) / 100, random.randint(-11000, 11000) / 100])
else:
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
# perturbed_v = np.array([random.randint(-16000, 16000) / 100, random.randint(-16000, 16000) / 100])
perturbed_v = np.array([random.randint(-8000, 8000) / 100, random.randint(-8000, 8000) / 100])
# perturbed_v = np.array([random.randint(-3500, 3500) / 100, random.randint(-3500, 3500) / 100])
# perturbed_v = np.array([random.randint(-600, 600) / 10, random.randint(-600, 600) / 10])
''''''
if fold_curve == 'fold':
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), random.randint(1, 2))
else:
if normalizationFun_0_1:
perturbed_d = self.get_0_1_d(np.abs(perturbed_distance_vertex_and_line), 2)
else:
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
''''''
if fold_curve_random:
# omega_perturbed = (alpha_perturbed+0.2) / (perturbed_d + alpha_perturbed)
# omega_perturbed = alpha_perturbed**perturbed_d
omega_perturbed = alpha_perturbed / (perturbed_d + alpha_perturbed)
else:
omega_perturbed = 1 - perturbed_d ** alpha_perturbed
'''shadow'''
if self.is_perform(0.6, 0.4):
synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] = np.minimum(np.maximum(synthesis_perturbed_img_map[x_min:x_max, y_min:y_max] - np.int16(np.round(omega_perturbed[x_min:x_max, y_min:y_max].repeat(3).reshape(x_max-x_min, y_max-y_min, 3) * abs(np.linalg.norm(perturbed_v//2))*np.array([0.4-random.random()*0.1, 0.4-random.random()*0.1, 0.4-random.random()*0.1]))), 0), 255)
''''''
if relativeShift_position in ['position', 'relativeShift_v2']:
self.perturbed_xy_ += np.array([omega_perturbed * perturbed_v[0], omega_perturbed * perturbed_v[1]]).transpose(1, 2, 0)
else:
print('relativeShift_position error')
exit()
'''
flat_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(
self.new_shape[0] * self.new_shape[1], 2)
vtx, wts = self.interp_weights(self.perturbed_xy_.reshape(self.new_shape[0] * self.new_shape[1], 2), flat_position)
wts_sum = np.abs(wts).sum(-1)
# flat_img.reshape(flat_shape[0] * flat_shape[1], 3)[:] = interpolate(pixel, vtx, wts)
wts = wts[wts_sum <= 1, :]
vtx = vtx[wts_sum <= 1, :]
synthesis_perturbed_img.reshape(self.new_shape[0] * self.new_shape[1], 3)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_img_map.reshape(self.new_shape[0] * self.new_shape[1], 3), vtx, wts)
synthesis_perturbed_label.reshape(self.new_shape[0] * self.new_shape[1], 2)[wts_sum <= 1,
:] = self.interpolate(synthesis_perturbed_label_map.reshape(self.new_shape[0] * self.new_shape[1], 2), vtx, wts)
foreORbackground_label = np.zeros(self.new_shape)
foreORbackground_label.reshape(self.new_shape[0] * self.new_shape[1], 1)[wts_sum <= 1, :] = self.interpolate(foreORbackground_label_map.reshape(self.new_shape[0] * self.new_shape[1], 1), vtx, wts)
foreORbackground_label[foreORbackground_label < 0.99] = 0
foreORbackground_label[foreORbackground_label >= 0.99] = 1
# synthesis_perturbed_img = np.around(synthesis_perturbed_img).astype(np.uint8)
synthesis_perturbed_label[:, :, 0] *= foreORbackground_label
synthesis_perturbed_label[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 0] *= foreORbackground_label
synthesis_perturbed_img[:, :, 1] *= foreORbackground_label
synthesis_perturbed_img[:, :, 2] *= foreORbackground_label
self.synthesis_perturbed_img = synthesis_perturbed_img
self.synthesis_perturbed_label = synthesis_perturbed_label
'''
'''perspective'''
perspective_shreshold = random.randint(26, 36)*10 # 280
x_min_per, y_min_per, x_max_per, y_max_per = self.adjust_position(perspective_shreshold, perspective_shreshold, self.new_shape[0]-perspective_shreshold, self.new_shape[1]-perspective_shreshold)
pts1 = np.float32([[x_min_per, y_min_per], [x_max_per, y_min_per], [x_min_per, y_max_per], [x_max_per, y_max_per]])
e_1_ = x_max_per - x_min_per
e_2_ = y_max_per - y_min_per
e_3_ = e_2_
e_4_ = e_1_
perspective_shreshold_h = e_1_*0.02
perspective_shreshold_w = e_2_*0.02
a_min_, a_max_ = 70, 110
# if self.is_perform(1, 0):
if fold_curve == 'curve' and self.is_perform(0.5, 0.5):
if self.is_perform(0.5, 0.5):
while True:
pts2 = np.around(
np.float32([[x_min_per - (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold],
[x_max_per - (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold],
[x_min_per + (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold],
[x_max_per + (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold]])) # right
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
else:
while True:
pts2 = np.around(
np.float32([[x_min_per + (random.random()) * perspective_shreshold, y_min_per - (random.random()) * perspective_shreshold],
[x_max_per + (random.random()) * perspective_shreshold, y_min_per + (random.random()) * perspective_shreshold],
[x_min_per - (random.random()) * perspective_shreshold, y_max_per - (random.random()) * perspective_shreshold],
[x_max_per - (random.random()) * perspective_shreshold, y_max_per + (random.random()) * perspective_shreshold]]))
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
else:
while True:
pts2 = np.around(np.float32([[x_min_per+(random.random()-0.5)*perspective_shreshold, y_min_per+(random.random()-0.5)*perspective_shreshold],
[x_max_per+(random.random()-0.5)*perspective_shreshold, y_min_per+(random.random()-0.5)*perspective_shreshold],
[x_min_per+(random.random()-0.5)*perspective_shreshold, y_max_per+(random.random()-0.5)*perspective_shreshold],
[x_max_per+(random.random()-0.5)*perspective_shreshold, y_max_per+(random.random()-0.5)*perspective_shreshold]]))
e_1 = np.linalg.norm(pts2[0]-pts2[1])
e_2 = np.linalg.norm(pts2[0]-pts2[2])
e_3 = np.linalg.norm(pts2[1]-pts2[3])
e_4 = np.linalg.norm(pts2[2]-pts2[3])
if e_1_+perspective_shreshold_h > e_1 and e_2_+perspective_shreshold_w > e_2 and e_3_+perspective_shreshold_w > e_3 and e_4_+perspective_shreshold_h > e_4 and \
e_1_ - perspective_shreshold_h < e_1 and e_2_ - perspective_shreshold_w < e_2 and e_3_ - perspective_shreshold_w < e_3 and e_4_ - perspective_shreshold_h < e_4 and \
abs(e_1-e_4) < perspective_shreshold_h and abs(e_2-e_3) < perspective_shreshold_w:
a0_, a1_, a2_, a3_ = self.get_angle_4(pts2)
if (a0_ > a_min_ and a0_ < a_max_) or (a1_ > a_min_ and a1_ < a_max_) or (a2_ > a_min_ and a2_ < a_max_) or (a3_ > a_min_ and a3_ < a_max_):
break
M = cv2.getPerspectiveTransform(pts1, pts2)
one = np.ones((self.new_shape[0], self.new_shape[1], 1), dtype=np.int16)
matr = np.dstack((pixel_position, one))
new = np.dot(M, matr.reshape(-1, 3).T).T.reshape(self.new_shape[0], self.new_shape[1], 3)
x = new[:, :, 0]/new[:, :, 2]
y = new[:, :, 1]/new[:, :, 2]
perturbed_xy_ = np.dstack((x, y))
# perturbed_xy_round_int = np.around(cv2.bilateralFilter(perturbed_xy_round_int, 9, 75, 75))
# perturbed_xy_round_int = np.around(cv2.blur(perturbed_xy_, (17, 17)))
# perturbed_xy_round_int = cv2.blur(perturbed_xy_round_int, (17, 17))
# perturbed_xy_round_int = cv2.GaussianBlur(perturbed_xy_round_int, (7, 7), 0)
perturbed_xy_ = perturbed_xy_-np.min(perturbed_xy_.T.reshape(2, -1), 1)
# perturbed_xy_round_int = np.around(perturbed_xy_round_int-np.min(perturbed_xy_round_int.T.reshape(2, -1), 1)).astype(np.int16)
self.perturbed_xy_ += perturbed_xy_
'''perspective end'''
'''to img'''
flat_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(
self.new_shape[0] * self.new_shape[1], 2)
# self.perturbed_xy_ = cv2.blur(self.perturbed_xy_, (7, 7))
self.perturbed_xy_ = cv2.GaussianBlur(self.perturbed_xy_, (7, 7), 0)
'''get fiducial points'''
fiducial_points_coordinate = self.perturbed_xy_[im_x, im_y]
vtx, wts = self.interp_weights(self.perturbed_xy_.reshape(self.new_shape[0] * self.new_shape[1], 2), flat_position)
wts_sum = | np.abs(wts) | numpy.abs |
# coding: utf-8
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Test the Logarithmic Units and Quantities
"""
from __future__ import (absolute_import, unicode_literals, division,
print_function)
from ...extern import six
from ...extern.six.moves import zip
import pickle
import itertools
import pytest
import numpy as np
from numpy.testing.utils import assert_allclose
from ...tests.helper import assert_quantity_allclose
from ... import units as u, constants as c
lu_units = [u.dex, u.mag, u.decibel]
lu_subclasses = [u.DexUnit, u.MagUnit, u.DecibelUnit]
lq_subclasses = [u.Dex, u.Magnitude, u.Decibel]
pu_sample = (u.dimensionless_unscaled, u.m, u.g/u.s**2, u.Jy)
class TestLogUnitCreation(object):
def test_logarithmic_units(self):
"""Check logarithmic units are set up correctly."""
assert u.dB.to(u.dex) == 0.1
assert u.dex.to(u.mag) == -2.5
assert u.mag.to(u.dB) == -4
@pytest.mark.parametrize('lu_unit, lu_cls', zip(lu_units, lu_subclasses))
def test_callable_units(self, lu_unit, lu_cls):
assert isinstance(lu_unit, u.UnitBase)
assert callable(lu_unit)
assert lu_unit._function_unit_class is lu_cls
@pytest.mark.parametrize('lu_unit', lu_units)
def test_equality_to_normal_unit_for_dimensionless(self, lu_unit):
lu = lu_unit()
assert lu == lu._default_function_unit # eg, MagUnit() == u.mag
assert lu._default_function_unit == lu # and u.mag == MagUnit()
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_call_units(self, lu_unit, physical_unit):
"""Create a LogUnit subclass using the callable unit and physical unit,
and do basic check that output is right."""
lu1 = lu_unit(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
def test_call_invalid_unit(self):
with pytest.raises(TypeError):
u.mag([])
with pytest.raises(ValueError):
u.mag(u.mag())
@pytest.mark.parametrize('lu_cls, physical_unit', itertools.product(
lu_subclasses + [u.LogUnit], pu_sample))
def test_subclass_creation(self, lu_cls, physical_unit):
"""Create a LogUnit subclass object for given physical unit,
and do basic check that output is right."""
lu1 = lu_cls(physical_unit)
assert lu1.physical_unit == physical_unit
assert lu1.function_unit == lu1._default_function_unit
lu2 = lu_cls(physical_unit,
function_unit=2*lu1._default_function_unit)
assert lu2.physical_unit == physical_unit
assert lu2.function_unit == u.Unit(2*lu2._default_function_unit)
with pytest.raises(ValueError):
lu_cls(physical_unit, u.m)
def test_predefined_magnitudes():
assert_quantity_allclose((-21.1*u.STmag).physical,
1.*u.erg/u.cm**2/u.s/u.AA)
assert_quantity_allclose((-48.6*u.ABmag).physical,
1.*u.erg/u.cm**2/u.s/u.Hz)
assert_quantity_allclose((0*u.M_bol).physical, c.L_bol0)
assert_quantity_allclose((0*u.m_bol).physical,
c.L_bol0/(4.*np.pi*(10.*c.pc)**2))
def test_predefined_reinitialisation():
assert u.mag('ST') == u.STmag
assert u.mag('AB') == u.ABmag
assert u.mag('Bol') == u.M_bol
assert u.mag('bol') == u.m_bol
def test_predefined_string_roundtrip():
"""Ensure roundtripping; see #5015"""
with u.magnitude_zero_points.enable():
assert u.Unit(u.STmag.to_string()) == u.STmag
assert u.Unit(u.ABmag.to_string()) == u.ABmag
assert u.Unit(u.M_bol.to_string()) == u.M_bol
assert u.Unit(u.m_bol.to_string()) == u.m_bol
def test_inequality():
"""Check __ne__ works (regresssion for #5342)."""
lu1 = u.mag(u.Jy)
lu2 = u.dex(u.Jy)
lu3 = u.mag(u.Jy**2)
lu4 = lu3 - lu1
assert lu1 != lu2
assert lu1 != lu3
assert lu1 == lu4
class TestLogUnitStrings(object):
def test_str(self):
"""Do some spot checks that str, repr, etc. work as expected."""
lu1 = u.mag(u.Jy)
assert str(lu1) == 'mag(Jy)'
assert repr(lu1) == 'Unit("mag(Jy)")'
assert lu1.to_string('generic') == 'mag(Jy)'
with pytest.raises(ValueError):
lu1.to_string('fits')
lu2 = u.dex()
assert str(lu2) == 'dex'
assert repr(lu2) == 'Unit("dex(1)")'
assert lu2.to_string() == 'dex(1)'
lu3 = u.MagUnit(u.Jy, function_unit=2*u.mag)
assert str(lu3) == '2 mag(Jy)'
assert repr(lu3) == 'MagUnit("Jy", unit="2 mag")'
assert lu3.to_string() == '2 mag(Jy)'
lu4 = u.mag(u.ct)
assert lu4.to_string('generic') == 'mag(ct)'
assert lu4.to_string('latex') == ('$\\mathrm{mag}$$\\mathrm{\\left( '
'\\mathrm{ct} \\right)}$')
assert lu4._repr_latex_() == lu4.to_string('latex')
class TestLogUnitConversion(object):
@pytest.mark.parametrize('lu_unit, physical_unit',
itertools.product(lu_units, pu_sample))
def test_physical_unit_conversion(self, lu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to their non-log counterparts."""
lu1 = lu_unit(physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(physical_unit, 0.) == 1.
assert physical_unit.is_equivalent(lu1)
assert physical_unit.to(lu1, 1.) == 0.
pu = u.Unit(8.*physical_unit)
assert lu1.is_equivalent(physical_unit)
assert lu1.to(pu, 0.) == 0.125
assert pu.is_equivalent(lu1)
assert_allclose(pu.to(lu1, 0.125), 0., atol=1.e-15)
# Check we round-trip.
value = np.linspace(0., 10., 6)
assert_allclose(pu.to(lu1, lu1.to(pu, value)), value, atol=1.e-15)
# And that we're not just returning True all the time.
pu2 = u.g
assert not lu1.is_equivalent(pu2)
with pytest.raises(u.UnitsError):
lu1.to(pu2)
assert not pu2.is_equivalent(lu1)
with pytest.raises(u.UnitsError):
pu2.to(lu1)
@pytest.mark.parametrize('lu_unit', lu_units)
def test_container_unit_conversion(self, lu_unit):
"""Check that conversion to logarithmic units (u.mag, u.dB, u.dex)
is only possible when the physical unit is dimensionless."""
values = np.linspace(0., 10., 6)
lu1 = lu_unit(u.dimensionless_unscaled)
assert lu1.is_equivalent(lu1.function_unit)
assert_allclose(lu1.to(lu1.function_unit, values), values)
lu2 = lu_unit(u.Jy)
assert not lu2.is_equivalent(lu2.function_unit)
with pytest.raises(u.UnitsError):
lu2.to(lu2.function_unit, values)
@pytest.mark.parametrize(
'flu_unit, tlu_unit, physical_unit',
itertools.product(lu_units, lu_units, pu_sample))
def test_subclass_conversion(self, flu_unit, tlu_unit, physical_unit):
"""Check various LogUnit subclasses are equivalent and convertible
to each other if they correspond to equivalent physical units."""
values = np.linspace(0., 10., 6)
flu = flu_unit(physical_unit)
tlu = tlu_unit(physical_unit)
assert flu.is_equivalent(tlu)
assert_allclose(flu.to(tlu), flu.function_unit.to(tlu.function_unit))
assert_allclose(flu.to(tlu, values),
values * flu.function_unit.to(tlu.function_unit))
tlu2 = tlu_unit(u.Unit(100.*physical_unit))
assert flu.is_equivalent(tlu2)
# Check that we round-trip.
assert_allclose(flu.to(tlu2, tlu2.to(flu, values)), values, atol=1.e-15)
tlu3 = tlu_unit(physical_unit.to_system(u.si)[0])
assert flu.is_equivalent(tlu3)
assert_allclose(flu.to(tlu3, tlu3.to(flu, values)), values, atol=1.e-15)
tlu4 = tlu_unit(u.g)
assert not flu.is_equivalent(tlu4)
with pytest.raises(u.UnitsError):
flu.to(tlu4, values)
def test_unit_decomposition(self):
lu = u.mag(u.Jy)
assert lu.decompose() == u.mag(u.Jy.decompose())
assert lu.decompose().physical_unit.bases == [u.kg, u.s]
assert lu.si == u.mag(u.Jy.si)
assert lu.si.physical_unit.bases == [u.kg, u.s]
assert lu.cgs == u.mag(u.Jy.cgs)
assert lu.cgs.physical_unit.bases == [u.g, u.s]
def test_unit_multiple_possible_equivalencies(self):
lu = u.mag(u.Jy)
assert lu.is_equivalent(pu_sample)
class TestLogUnitArithmetic(object):
def test_multiplication_division(self):
"""Check that multiplication/division with other units is only
possible when the physical unit is dimensionless, and that this
turns the unit into a normal one."""
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 * u.m
with pytest.raises(u.UnitsError):
u.m * lu1
with pytest.raises(u.UnitsError):
lu1 / lu1
for unit in (u.dimensionless_unscaled, u.m, u.mag, u.dex):
with pytest.raises(u.UnitsError):
lu1 / unit
lu2 = u.mag(u.dimensionless_unscaled)
with pytest.raises(u.UnitsError):
lu2 * lu1
with pytest.raises(u.UnitsError):
lu2 / lu1
# But dimensionless_unscaled can be cancelled.
assert lu2 / lu2 == u.dimensionless_unscaled
# With dimensionless, normal units are OK, but we return a plain unit.
tf = lu2 * u.m
tr = u.m * lu2
for t in (tf, tr):
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit * u.m
with u.set_enabled_equivalencies(u.logarithmic()):
with pytest.raises(u.UnitsError):
t.to(lu2.physical_unit)
# Now we essentially have a LogUnit with a prefactor of 100,
# so should be equivalent again.
t = tf / u.cm
with u.set_enabled_equivalencies(u.logarithmic()):
assert t.is_equivalent(lu2.function_unit)
assert_allclose(t.to(u.dimensionless_unscaled, np.arange(3.)/100.),
lu2.to(lu2.physical_unit, np.arange(3.)))
# If we effectively remove lu1, a normal unit should be returned.
t2 = tf / lu2
assert not isinstance(t2, type(lu2))
assert t2 == u.m
t3 = tf / lu2.function_unit
assert not isinstance(t3, type(lu2))
assert t3 == u.m
# For completeness, also ensure non-sensical operations fail
with pytest.raises(TypeError):
lu1 * object()
with pytest.raises(TypeError):
slice(None) * lu1
with pytest.raises(TypeError):
lu1 / []
with pytest.raises(TypeError):
1 / lu1
@pytest.mark.parametrize('power', (2, 0.5, 1, 0))
def test_raise_to_power(self, power):
"""Check that raising LogUnits to some power is only possible when the
physical unit is dimensionless, and that conversion is turned off when
the resulting logarithmic unit (such as mag**2) is incompatible."""
lu1 = u.mag(u.Jy)
if power == 0:
assert lu1 ** power == u.dimensionless_unscaled
elif power == 1:
assert lu1 ** power == lu1
else:
with pytest.raises(u.UnitsError):
lu1 ** power
# With dimensionless, though, it works, but returns a normal unit.
lu2 = u.mag(u.dimensionless_unscaled)
t = lu2**power
if power == 0:
assert t == u.dimensionless_unscaled
elif power == 1:
assert t == lu2
else:
assert not isinstance(t, type(lu2))
assert t == lu2.function_unit**power
# also check we roundtrip
t2 = t**(1./power)
assert t2 == lu2.function_unit
with u.set_enabled_equivalencies(u.logarithmic()):
assert_allclose(t2.to(u.dimensionless_unscaled, np.arange(3.)),
lu2.to(lu2.physical_unit, np.arange(3.)))
@pytest.mark.parametrize('other', pu_sample)
def test_addition_subtraction_to_normal_units_fails(self, other):
lu1 = u.mag(u.Jy)
with pytest.raises(u.UnitsError):
lu1 + other
with pytest.raises(u.UnitsError):
lu1 - other
with pytest.raises(u.UnitsError):
other - lu1
def test_addition_subtraction_to_non_units_fails(self):
lu1 = u.mag(u.Jy)
with pytest.raises(TypeError):
lu1 + 1.
with pytest.raises(TypeError):
lu1 - [1., 2., 3.]
@pytest.mark.parametrize(
'other', (u.mag, u.mag(), u.mag(u.Jy), u.mag(u.m),
u.Unit(2*u.mag), u.MagUnit('', 2.*u.mag)))
def test_addition_subtraction(self, other):
"""Check physical units are changed appropriately"""
lu1 = u.mag(u.Jy)
other_pu = getattr(other, 'physical_unit', u.dimensionless_unscaled)
lu_sf = lu1 + other
assert lu_sf.is_equivalent(lu1.physical_unit * other_pu)
lu_sr = other + lu1
assert lu_sr.is_equivalent(lu1.physical_unit * other_pu)
lu_df = lu1 - other
assert lu_df.is_equivalent(lu1.physical_unit / other_pu)
lu_dr = other - lu1
assert lu_dr.is_equivalent(other_pu / lu1.physical_unit)
def test_complicated_addition_subtraction(self):
"""for fun, a more complicated example of addition and subtraction"""
dm0 = u.Unit('DM', 1./(4.*np.pi*(10.*u.pc)**2))
lu_dm = u.mag(dm0)
lu_absST = u.STmag - lu_dm
assert lu_absST.is_equivalent(u.erg/u.s/u.AA)
def test_neg_pos(self):
lu1 = u.mag(u.Jy)
neg_lu = -lu1
assert neg_lu != lu1
assert neg_lu.physical_unit == u.Jy**-1
assert -neg_lu == lu1
pos_lu = +lu1
assert pos_lu is not lu1
assert pos_lu == lu1
def test_pickle():
lu1 = u.dex(u.cm/u.s**2)
s = pickle.dumps(lu1)
lu2 = pickle.loads(s)
assert lu1 == lu2
def test_hashable():
lu1 = u.dB(u.mW)
lu2 = u.dB(u.m)
lu3 = u.dB(u.mW)
assert hash(lu1) != hash(lu2)
assert hash(lu1) == hash(lu3)
luset = {lu1, lu2, lu3}
assert len(luset) == 2
class TestLogQuantityCreation(object):
@pytest.mark.parametrize('lq, lu', zip(lq_subclasses + [u.LogQuantity],
lu_subclasses + [u.LogUnit]))
def test_logarithmic_quantities(self, lq, lu):
"""Check logarithmic quantities are all set up correctly"""
assert lq._unit_class == lu
assert type(lu()._quantity_class(1.)) is lq
@pytest.mark.parametrize('lq_cls, physical_unit',
itertools.product(lq_subclasses, pu_sample))
def test_subclass_creation(self, lq_cls, physical_unit):
"""Create LogQuantity subclass objects for some physical units,
and basic check on transformations"""
value = np.arange(1., 10.)
log_q = lq_cls(value * physical_unit)
assert log_q.unit.physical_unit == physical_unit
assert log_q.unit.function_unit == log_q.unit._default_function_unit
| assert_allclose(log_q.physical.value, value) | numpy.testing.utils.assert_allclose |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
from external.transformations import translation_matrix, rotation_matrix
import external.transformations as tf
from Trace import Photon
from Geometry import Box, Cylinder, FinitePlane, transform_point, transform_direction, rotation_matrix_from_vector_alignment, norm
from Materials import Spectrum
def random_spherecial_vector():
# This method of calculating isotropic vectors is taken from GNU Scientific Library
LOOP = True
while LOOP:
x = -1. + 2. * np.random.uniform()
y = -1. + 2. * np.random.uniform()
s = x**2 + y**2
if s <= 1.0:
LOOP = False
z = -1. + 2. * s
a = 2 * np.sqrt(1 - s)
x = a * x
y = a * y
return np.array([x,y,z])
class SimpleSource(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, use_random_polarisation=False):
super(SimpleSource, self).__init__()
self.position = position
self.direction = direction
self.wavelength = wavelength
self.use_random_polarisation = use_random_polarisation
self.throw = 0
self.source_id = "SimpleSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
# If use_polarisation is set generate a random polarisation vector of the photon
if self.use_random_polarisation:
# Randomise rotation angle around xy-plane, the transform from +z to the direction of the photon
vec = random_spherecial_vector()
vec[2] = 0.
vec = norm(vec)
R = rotation_matrix_from_vector_alignment(self.direction, [0,0,1])
photon.polarisation = transform_direction(vec, R)
else:
photon.polarisation = None
photon.id = self.throw
self.throw = self.throw + 1
return photon
class Laser(object):
"""A light source that will generate photons of a single colour, direction and position."""
def __init__(self, position=[0,0,0], direction=[0,0,1], wavelength=555, polarisation=None):
super(Laser, self).__init__()
self.position = np.array(position)
self.direction = np.array(direction)
self.wavelength = wavelength
assert polarisation != None, "Polarisation of the Laser is not set."
self.polarisation = np.array(polarisation)
self.throw = 0
self.source_id = "LaserSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.position = np.array(self.position)
photon.direction = np.array(self.direction)
photon.active = True
photon.wavelength = self.wavelength
photon.polarisation = self.polarisation
photon.id = self.throw
self.throw = self.throw + 1
return photon
class PlanarSource(object):
"""A box that emits photons from the top surface (normal), sampled from the spectrum."""
def __init__(self, spectrum=None, wavelength=555, direction=(0,0,1), length=0.05, width=0.05):
super(PlanarSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.plane = FinitePlane(length=length, width=width)
self.length = length
self.width = width
# direction is the direction that photons are fired out of the plane in the GLOBAL FRAME.
# i.e. this is passed directly to the photon to set is's direction
self.direction = direction
self.throw = 0
self.source_id = "PlanarSource_" + str(id(self))
def translate(self, translation):
self.plane.append_transform(tf.translation_matrix(translation))
def rotate(self, angle, axis):
self.plane.append_transform(tf.rotation_matrix(angle, axis))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Create a point which is on the surface of the finite plane in it's local frame
x = np.random.uniform(0., self.length)
y = np.random.uniform(0., self.width)
local_point = (x, y, 0.)
# Transform the direciton
photon.position = transform_point(local_point, self.plane.transform)
photon.direction = self.direction
photon.active = True
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class LensSource(object):
"""
A source where photons generated in a plane are focused on a line with space tolerance given by variable "focussize".
The focus line should be perpendicular to the plane normal and aligned with the z-axis.
"""
def __init__(self, spectrum = None, wavelength = 555, linepoint=(0,0,0), linedirection=(0,0,1), focussize = 0, planeorigin = (-1,-1,-1), planeextent = (-1,1,1)):
super(LensSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.planeorigin = planeorigin
self.planeextent = planeextent
self.linepoint = np.array(linepoint)
self.linedirection = np.array(linedirection)
self.focussize = focussize
self.throw = 0
self.source_id = "LensSource_" + str(id(self))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Position
x = np.random.uniform(self.planeorigin[0],self.planeextent[0])
y = np.random.uniform(self.planeorigin[1],self.planeextent[1])
z = np.random.uniform(self.planeorigin[2],self.planeextent[2])
photon.position = np.array((x,y,z))
# Direction
focuspoint = np.array((0.,0.,0.))
focuspoint[0] = self.linepoint[0] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[1] = self.linepoint[1] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[2] = photon.position[2]
direction = focuspoint - photon.position
modulus = (direction[0]**2+direction[1]**2+direction[2]**2)**0.5
photon.direction = direction/modulus
# Wavelength
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class LensSourceAngle(object):
"""
A source where photons generated in a plane are focused on a line with space tolerance given by variable "focussize".
The focus line should be perpendicular to the plane normal and aligned with the z-axis.
For this lense an additional z-boost is added (Angle of incidence in z-direction).
"""
def __init__(self, spectrum = None, wavelength = 555, linepoint=(0,0,0), linedirection=(0,0,1), angle = 0, focussize = 0, planeorigin = (-1,-1,-1), planeextent = (-1,1,1)):
super(LensSourceAngle, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.planeorigin = planeorigin
self.planeextent = planeextent
self.linepoint = np.array(linepoint)
self.linedirection = np.array(linedirection)
self.focussize = focussize
self.angle = angle
self.throw = 0
self.source_id = "LensSourceAngle_" + str(id(self))
def photon(self):
photon = Photon()
photon.id = self.throw
self.throw = self.throw + 1
# Position
x = np.random.uniform(self.planeorigin[0],self.planeextent[0])
y = np.random.uniform(self.planeorigin[1],self.planeextent[1])
boost = y*np.tan(self.angle)
z = np.random.uniform(self.planeorigin[2],self.planeextent[2]) - boost
photon.position = np.array((x,y,z))
# Direction
focuspoint = np.array((0.,0.,0.))
focuspoint[0] = self.linepoint[0] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[1] = self.linepoint[1] + np.random.uniform(-self.focussize,self.focussize)
focuspoint[2] = photon.position[2] + boost
direction = focuspoint - photon.position
modulus = (direction[0]**2+direction[1]**2+direction[2]**2)**0.5
photon.direction = direction/modulus
# Wavelength
if self.spectrum != None:
photon.wavelength = self.spectrum.wavelength_at_probability(np.random.uniform())
else:
photon.wavelength = self.wavelength
return photon
class CylindricalSource(object):
"""
A source for photons emitted in a random direction and position inside a cylinder(radius, length)
"""
def __init__(self, spectrum = None, wavelength = 555, radius = 1, length = 10):
super(CylindricalSource, self).__init__()
self.spectrum = spectrum
self.wavelength = wavelength
self.shape = Cylinder(radius = radius, length = length)
self.radius = radius
self.length = length
self.throw = 0
self.source_id = "CylindricalSource_" + str(id(self))
def translate(self, translation):
self.shape.append_transform(tf.translation_matrix(translation))
def rotate(self, angle, axis):
self.shape.append_transform(tf.rotation_matrix(angle, axis))
def photon(self):
photon = Photon()
photon.source = self.source_id
photon.id = self.throw
self.throw = self.throw + 1
# Position of emission
phi = np.random.uniform(0., 2*np.pi)
r = np.random.uniform(0.,self.radius)
x = r*np.cos(phi)
y = r*np.sin(phi)
z = np.random.uniform(0.,self.length)
local_center = (x,y,z)
photon.position = transform_point(local_center, self.shape.transform)
# Direction of emission (no need to transform if meant to be isotropic)
phi = np.random.uniform(0.,2*np.pi)
theta = np.random.uniform(0.,np.pi)
x = np.cos(phi)*np.sin(theta)
y = np.sin(phi)* | np.sin(theta) | numpy.sin |
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 28 12:10:11 2019
@author: Omer
"""
## File handler
## This file was initially intended purely to generate the matrices for the near earth code found in: https://public.ccsds.org/Pubs/131x1o2e2s.pdf
## The values from the above pdf were copied manually to a txt file, and it is the purpose of this file to parse it.
## The emphasis here is on correctness, I currently do not see a reason to generalise this file, since matrices will be saved in either json or some matrix friendly format.
import numpy as np
from scipy.linalg import circulant
#import matplotlib.pyplot as plt
import scipy.io
import common
import hashlib
import os
projectDir = os.environ.get('LDPC')
if projectDir == None:
import pathlib
projectDir = pathlib.Path(__file__).parent.absolute()
## <NAME>: added on 01/12/2020, need to make sure this doesn't break anything.
import sys
sys.path.insert(1, projectDir)
FILE_HANDLER_INT_DATA_TYPE = np.int32
GENERAL_CODE_MATRIX_DATA_TYPE = np.int32
NIBBLE_CONVERTER = np.array([8, 4, 2, 1], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
def nibbleToHex(inputArray):
n = NIBBLE_CONVERTER.dot(inputArray)
if n == 10:
h = 'A'
elif n== 11:
h = 'B'
elif n== 12:
h = 'C'
elif n== 13:
h = 'D'
elif n== 14:
h = 'E'
elif n== 15:
h = 'F'
else:
h = str(n)
return h
def binaryArraytoHex(inputArray):
d1 = len(inputArray)
assert (d1 % 4 == 0)
outputArray = np.zeros(d1//4, dtype = str)
outputString = ''
for j in range(d1//4):
nibble = inputArray[4 * j : 4 * j + 4]
h = nibbleToHex(nibble)
outputArray[j] = h
outputString = outputString + h
return outputArray, outputString
def hexStringToBinaryArray(hexString):
outputBinary = np.array([], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
for i in hexString:
if i == '0':
nibble = np.array([0,0,0,0], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == '1':
nibble = np.array([0,0,0,1], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == '2':
nibble = np.array([0,0,1,0], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == '3':
nibble = np.array([0,0,1,1], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == '4':
nibble = np.array([0,1,0,0], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == '5':
nibble = np.array([0,1,0,1], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == '6':
nibble = np.array([0,1,1,0], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == '7':
nibble = np.array([0,1,1,1], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == '8':
nibble = np.array([1,0,0,0], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == '9':
nibble = np.array([1,0,0,1], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == 'A':
nibble = np.array([1,0,1,0], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == 'B':
nibble = np.array([1,0,1,1], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == 'C':
nibble = np.array([1,1,0,0], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == 'D':
nibble = np.array([1,1,0,1], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == 'E':
nibble = np.array([1,1,1,0], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
elif i == 'F':
nibble = np.array([1,1,1,1], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
else:
#print('Error, 0-9 or A-F')
pass
nibble = np.array([], dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
outputBinary = np.hstack((outputBinary, nibble))
return outputBinary
def hexToCirculant(hexStr, circulantSize):
binaryArray = hexStringToBinaryArray(hexStr)
if len(binaryArray) < circulantSize:
binaryArray = np.hstack(np.zeros(circulantSize-len(binaryArray), dtype = GENERAL_CODE_MATRIX_DATA_TYPE))
else:
binaryArray = binaryArray[1:]
circulantMatrix = circulant(binaryArray)
circulantMatrix = circulantMatrix.T
return circulantMatrix
def hotLocationsToCirculant(locationList, circulantSize):
generatingVector = np.zeros(circulantSize, dtype = GENERAL_CODE_MATRIX_DATA_TYPE)
generatingVector[locationList] = 1
newCirculant = circulant(generatingVector)
newCirculant = newCirculant.T
return newCirculant
def readMatrixFromFile(fileName, dim0, dim1, circulantSize, isRow = True, isHex = True, isGenerator = True ):
# This function assumes that each line in the file contains the non zero locations of the first row of a circulant.
# Each line in the file then defines a circulant, and the order in which they are defined is top to bottom left to right, i.e.:
# line 0 defines circulant 0,0
with open(fileName) as fid:
lines = fid.readlines()
if isGenerator:
for i in range((dim0 // circulantSize) ):
bLeft = hexToCirculant(lines[2 * i], circulantSize)
bRight = hexToCirculant(lines[2 * i + 1], circulantSize)
newBlock = np.hstack((bLeft, bRight))
if i == 0:
accumulatedBlock = newBlock
else:
accumulatedBlock = np.vstack((accumulatedBlock, newBlock))
newMatrix = np.hstack((np.eye(dim0, dtype = GENERAL_CODE_MATRIX_DATA_TYPE), accumulatedBlock))
else:
for i in range((dim1 // circulantSize)):
locationList1 = list(lines[ i].rstrip('\n').split(','))
locationList1 = list(map(int, locationList1))
upBlock = hotLocationsToCirculant(locationList1, circulantSize)
if i == 0:
accumulatedUpBlock1 = upBlock
else:
accumulatedUpBlock1 = np.hstack((accumulatedUpBlock1, upBlock))
for i in range((dim1 // circulantSize)):
locationList = list(lines[(dim1 // circulantSize) + i].rstrip('\n').split(','))
locationList = list(map(int, locationList))
newBlock = hotLocationsToCirculant(locationList, circulantSize)
if i == 0:
accumulatedBlock2 = newBlock
else:
accumulatedBlock2 = np.hstack((accumulatedBlock2, newBlock))
newMatrix = np.vstack((accumulatedUpBlock1, accumulatedBlock2))
return newMatrix
def binaryMatrixToHexString(binaryMatrix, circulantSize):
leftPadding = np.array(4 - (circulantSize % 4))
m,n = binaryMatrix.shape
#print(m)
#print(n)
assert( m % circulantSize == 0)
assert (n % circulantSize == 0)
M = m // circulantSize
N = n // circulantSize
hexName = ''
for r in range(M):
for k in range(N):
nextLine = | np.hstack((leftPadding, binaryMatrix[ r * circulantSize , k * circulantSize : (k + 1) * circulantSize])) | numpy.hstack |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[2].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[2].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
plt.savefig('jss_figures/DFA_different_trends.png')
plt.show()
# plot 6b
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences Zoomed Region', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[0].set_ylim(-5.5, 5.5)
axs[0].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].set_ylim(-5.5, 5.5)
axs[1].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([np.pi, (3 / 2) * np.pi])
axs[2].set_xticklabels([r'$\pi$', r'$\frac{3}{2}\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].set_ylim(-5.5, 5.5)
axs[2].set_xlim(0.95 * np.pi, 1.55 * np.pi)
plt.savefig('jss_figures/DFA_different_trends_zoomed.png')
plt.show()
hs_ouputs = hilbert_spectrum(time, imfs_51, hts_51, ifs_51, max_frequency=12, plot=False)
# plot 6c
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Simple Sinusoidal Time Seres with Added Noise', 50))
x_hs, y, z = hs_ouputs
z_min, z_max = 0, np.abs(z).max()
ax.pcolormesh(x_hs, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max)
ax.plot(x_hs[0, :], 8 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 8$', Linewidth=3)
ax.plot(x_hs[0, :], 4 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 4$', Linewidth=3)
ax.plot(x_hs[0, :], 2 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 2$', Linewidth=3)
ax.set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
ax.set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$'])
plt.ylabel(r'Frequency (rad.s$^{-1}$)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.85, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/DFA_hilbert_spectrum.png')
plt.show()
# plot 6c
time = np.linspace(0, 5 * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 51)
fluc = Fluctuation(time=time, time_series=time_series)
max_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=False)
max_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=True)
min_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=False)
min_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=True)
util = Utility(time=time, time_series=time_series)
maxima = util.max_bool_func_1st_order_fd()
minima = util.min_bool_func_1st_order_fd()
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title(textwrap.fill('Plot Demonstrating Unsmoothed Extrema Envelopes if Schoenberg–Whitney Conditions are Not Satisfied', 50))
plt.plot(time, time_series, label='Time series', zorder=2, LineWidth=2)
plt.scatter(time[maxima], time_series[maxima], c='r', label='Maxima', zorder=10)
plt.scatter(time[minima], time_series[minima], c='b', label='Minima', zorder=10)
plt.plot(time, max_unsmoothed[0], label=textwrap.fill('Unsmoothed maxima envelope', 10), c='darkorange')
plt.plot(time, max_smoothed[0], label=textwrap.fill('Smoothed maxima envelope', 10), c='red')
plt.plot(time, min_unsmoothed[0], label=textwrap.fill('Unsmoothed minima envelope', 10), c='cyan')
plt.plot(time, min_smoothed[0], label=textwrap.fill('Smoothed minima envelope', 10), c='blue')
for knot in knots[:-1]:
plt.plot(knot * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', zorder=1)
plt.plot(knots[-1] * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', label='Knots', zorder=1)
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi),
(r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$', r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Schoenberg_Whitney_Conditions.png')
plt.show()
# plot 7
a = 0.25
width = 0.2
time = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 1001)
knots = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 11)
time_series = | np.cos(time) | numpy.cos |
import numpy as np
import cv2
import os
import json
import glob
from PIL import Image, ImageDraw
plate_diameter = 25 #cm
plate_depth = 1.5 #cm
plate_thickness = 0.2 #cm
def Max(x, y):
if (x >= y):
return x
else:
return y
def polygons_to_mask(img_shape, polygons):
mask = np.zeros(img_shape, dtype=np.uint8)
mask = Image.fromarray(mask)
xy = list(map(tuple, polygons))
ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)
mask = np.array(mask, dtype=bool)
return mask
def mask2box(mask):
index = np.argwhere(mask == 1)
rows = index[:, 0]
clos = index[:, 1]
left_top_r = np.min(rows)
left_top_c = np.min(clos)
right_bottom_r = np.max(rows)
right_bottom_c = np.max(clos)
return [left_top_c, left_top_r, right_bottom_c, right_bottom_r]
def get_bbox(points, h, w):
polygons = points
mask = polygons_to_mask([h,w], polygons)
return mask2box(mask)
def get_scale(points, img, lowest):
bbox = get_bbox(points, img.shape[0], img.shape[1])
diameter = (bbox[2]-bbox[0]+1+bbox[3]-bbox[1]+1)/2
len_per_pix = plate_diameter/float(diameter)
avg = 0
k = 0
for point in points:
avg += img[point[1]][point[0]]
k += 1
avg = avg/float(k)
depth = lowest - avg
depth_per_pix = plate_depth/depth
return len_per_pix, depth_per_pix
def cal_volume(points, img, len_per_pix, depth_per_pix, lowest):
volume = 0.0
bbox = get_bbox(points, img.shape[0], img.shape[1])
points = np.array(points)
shape = points.shape
points = points.reshape(shape[0], 1, shape[1])
for i in range(bbox[0], bbox[2]+1):
for j in range(bbox[1], bbox[3]+1):
if (cv2.pointPolygonTest(points, (i,j), False) >= 0):
volume += Max(0, (lowest - img[j][i]) * depth_per_pix - plate_thickness) * len_per_pix * len_per_pix
return volume
def get_volume(img, json_path):
lowest = | np.max(img) | numpy.max |
#
# Copyright (c) 2021 The GPflux Contributors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import abc
import numpy as np
import pytest
import tensorflow as tf
import tensorflow_probability as tfp
from gpflow.kullback_leiblers import gauss_kl
from gpflux.encoders import DirectlyParameterizedNormalDiag
from gpflux.layers import LatentVariableLayer, LayerWithObservations, TrackableLayer
tf.keras.backend.set_floatx("float64")
############
# Utilities
############
def _zero_one_normal_prior(w_dim):
""" N(0, I) prior """
return tfp.distributions.MultivariateNormalDiag(loc=np.zeros(w_dim), scale_diag=np.ones(w_dim))
def get_distributions_with_w_dim():
distributions = []
for d in [1, 5]:
mean = np.zeros(d)
scale_tri_l = np.eye(d)
mvn = tfp.distributions.MultivariateNormalTriL(mean, scale_tri_l)
std = np.ones(d)
mvn_diag = tfp.distributions.MultivariateNormalDiag(mean, std)
distributions.append((mvn, d))
distributions.append((mvn_diag, d))
return distributions
############
# Tests
############
@pytest.mark.parametrize("distribution, w_dim", get_distributions_with_w_dim())
def test_local_kls(distribution, w_dim):
lv = LatentVariableLayer(encoder=None, prior=distribution)
# test kl is 0 when posteriors == priors
posterior = distribution
assert lv._local_kls(posterior) == 0
# test kl > 0 when posteriors != priors
batch_size = 10
params = distribution.parameters
posterior_params = {
k: [v + 0.5 for _ in range(batch_size)]
for k, v in params.items()
if isinstance(v, np.ndarray)
}
posterior = lv.distribution_class(**posterior_params)
local_kls = lv._local_kls(posterior)
assert np.all(local_kls > 0)
assert local_kls.shape == (batch_size,)
@pytest.mark.parametrize("w_dim", [1, 5])
def test_local_kl_gpflow_consistency(w_dim):
num_data = 400
means = np.random.randn(num_data, w_dim)
encoder = DirectlyParameterizedNormalDiag(num_data, w_dim, means)
lv = LatentVariableLayer(encoder=encoder, prior=_zero_one_normal_prior(w_dim))
posteriors = lv._inference_posteriors(
[np.random.randn(num_data, 3), np.random.randn(num_data, 2)]
)
q_mu = posteriors.parameters["loc"]
q_sqrt = posteriors.parameters["scale_diag"]
gpflow_local_kls = gauss_kl(q_mu, q_sqrt)
tfp_local_kls = tf.reduce_sum(lv._local_kls(posteriors))
np.testing.assert_allclose(tfp_local_kls, gpflow_local_kls, rtol=1e-10)
class ArrayMatcher:
def __init__(self, expected):
self.expected = expected
def __eq__(self, actual):
return np.allclose(actual, self.expected, equal_nan=True)
@pytest.mark.parametrize("w_dim", [1, 5])
def test_latent_variable_layer_losses(mocker, w_dim):
num_data, x_dim, y_dim = 43, 3, 1
prior_shape = (w_dim,)
posteriors_shape = (num_data, w_dim)
prior = tfp.distributions.MultivariateNormalDiag(
loc=np.random.randn(*prior_shape),
scale_diag=np.random.randn(*prior_shape) ** 2,
)
posteriors = tfp.distributions.MultivariateNormalDiag(
loc=np.random.randn(*posteriors_shape),
scale_diag=np.random.randn(*posteriors_shape) ** 2,
)
encoder = mocker.Mock(return_value=(posteriors.loc, posteriors.scale.diag))
lv = LatentVariableLayer(encoder=encoder, prior=prior)
inputs = | np.full((num_data, x_dim), np.nan) | numpy.full |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import matplotlib.pyplot as plt
import CurveFit
import shutil
#find all DIRECTORIES containing non-hidden files ending in FILENAME
def getDataDirectories(DIRECTORY, FILENAME="valLoss.txt"):
directories=[]
for directory in os.scandir(DIRECTORY):
for item in os.scandir(directory):
if item.name.endswith(FILENAME) and not item.name.startswith("."):
directories.append(directory.path)
return directories
#get all non-hidden data files in DIRECTORY with extension EXT
def getDataFiles(DIRECTORY, EXT='txt'):
datafiles=[]
for item in os.scandir(DIRECTORY):
if item.name.endswith("."+EXT) and not item.name.startswith("."):
datafiles.append(item.path)
return datafiles
#checking if loss ever doesn't decrease for numEpochs epochs in a row.
def stopsDecreasing(loss, epoch, numEpochs):
minLoss=np.inf
epochMin=0
for i in range(0,loss.size):
if loss[i] < minLoss:
minLoss=loss[i]
epochMin=epoch[i]
elif (epoch[i]-epochMin) >= numEpochs:
return i, minLoss
return i, minLoss
#dirpath is where the accuracy and loss files are stored. want to move the files into the same format expected by grabNNData.
def createFolders(SEARCHDIR, SAVEDIR):
for item in os.scandir(SEARCHDIR):
name=str(item.name)
files=name.split('-')
SAVEFULLDIR=SAVEDIR+str(files[0])
if not os.path.exists(SAVEFULLDIR):
try:
os.makedirs(SAVEFULLDIR)
except FileExistsError:
#directory already exists--must have been created between the if statement & our attempt at making directory
pass
shutil.move(item.path, SAVEFULLDIR+"/"+str(files[1]))
#a function to read in information (e.g. accuracy, loss) stored at FILENAME
def grabNNData(FILENAME, header='infer', sep=' '):
data = pd.read_csv(FILENAME, sep, header=header)
if ('epochs' in data.columns) and ('trainLoss' in data.columns) and ('valLoss' in data.columns) and ('valAcc' in data.columns) and ('batch_size' in data.columns) and ('learning_rate' in data.columns):
sortedData=data.sort_values(by="epochs", axis=0, ascending=True)
epoch=np.array(sortedData['epochs'])
trainLoss=np.array(sortedData['trainLoss'])
valLoss= | np.array(sortedData['valLoss']) | numpy.array |
"""Routines for numerical differentiation."""
from __future__ import division
import numpy as np
from numpy.linalg import norm
from scipy.sparse.linalg import LinearOperator
from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
from ._group_columns import group_dense, group_sparse
EPS = np.finfo(np.float64).eps
def _adjust_scheme_to_bounds(x0, h, num_steps, scheme, lb, ub):
"""Adjust final difference scheme to the presence of bounds.
Parameters
----------
x0 : ndarray, shape (n,)
Point at which we wish to estimate derivative.
h : ndarray, shape (n,)
Desired finite difference steps.
num_steps : int
Number of `h` steps in one direction required to implement finite
difference scheme. For example, 2 means that we need to evaluate
f(x0 + 2 * h) or f(x0 - 2 * h)
scheme : {'1-sided', '2-sided'}
Whether steps in one or both directions are required. In other
words '1-sided' applies to forward and backward schemes, '2-sided'
applies to center schemes.
lb : ndarray, shape (n,)
Lower bounds on independent variables.
ub : ndarray, shape (n,)
Upper bounds on independent variables.
Returns
-------
h_adjusted : ndarray, shape (n,)
Adjusted step sizes. Step size decreases only if a sign flip or
switching to one-sided scheme doesn't allow to take a full step.
use_one_sided : ndarray of bool, shape (n,)
Whether to switch to one-sided scheme. Informative only for
``scheme='2-sided'``.
"""
if scheme == '1-sided':
use_one_sided = np.ones_like(h, dtype=bool)
elif scheme == '2-sided':
h = np.abs(h)
use_one_sided = np.zeros_like(h, dtype=bool)
else:
raise ValueError("`scheme` must be '1-sided' or '2-sided'.")
if | np.all((lb == -np.inf) & (ub == np.inf)) | numpy.all |
###############################################################################
# @todo add Pilot2-splash-app disclaimer
###############################################################################
""" Get's KRAS states """
import MDAnalysis as mda
from MDAnalysis.analysis import align
from MDAnalysis.lib.mdamath import make_whole
import os
import numpy as np
import math
############## Below section needs to be uncommented ############
import mummi_core
import mummi_ras
from mummi_core.utils import Naming
# # Logger has to be initialized the first thing in the script
from logging import getLogger
LOGGER = getLogger(__name__)
# # Innitilize MuMMI if it has not been done before
# MUMMI_ROOT = mummi.init(True)
# This is needed so the Naming works below
#@TODO fix this so we don't have these on import make them as an init
mummi_core.init()
dirKRASStates = Naming.dir_res('states')
dirKRASStructures = Naming.dir_res('structures')
# #RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-ONLY.microstates.txt"))
RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-states.txt"),comments='#')
# #RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-RAF.microstates.txt"))
RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-raf-states.txt"),comments='#') # Note diffrent number of columns so index change below
# TODO: CS, my edits to test
# RAS_ONLY_macrostate = np.loadtxt('ras-states.txt')
# RAS_RAF_macrostate = np.loadtxt('ras-raf-states.txt')
############## above section needs to be uncommented ############
# TODO: CS, my edits to test
# TODO: TSC, The reference structure has to currently be set as the 'RAS-ONLY-reference-structure.gro'
# TODO: TSC, path to the reference structure is: mummi_resources/structures/
kras_ref_universe = mda.Universe(os.path.join(dirKRASStructures, "RAS-ONLY-reference-structure.gro"))
# kras_ref_universe = mda.Universe("RAS-ONLY-reference-structure.gro")
# kras_ref_universe = mda.Universe('AA_pfpatch_000000004641_RAS_RAF2_411.gro')
# TODO: CS, not using these for x4 proteins; instead using protein_systems below to set num_res
######### Below hard codes the number of residues within RAS-only and RAS-RAF ##########
RAS_only_num_res = 184
RAS_RAF_num_res = 320
######### Above hard codes the number of residues within RAS-only and RAS-RAF ##########
####### This can be removed
# def get_kras(syst, kras_start):
# """Gets all atoms for a KRAS protein starting at 'kras_start'."""
# return syst.atoms[kras_start:kras_start+428]
####### This can be removed
def get_segids(u):
"""Identifies the list of segments within the system. Only needs to be called x1 time"""
segs = u.segments
segs = segs.segids
ras_segids = []
rasraf_segids = []
for i in range(len(segs)):
# print(segs[i])
if segs[i][-3:] == 'RAS':
ras_segids.append(segs[i])
if segs[i][-3:] == 'RAF':
rasraf_segids.append(segs[i])
return ras_segids, rasraf_segids
def get_protein_info(u,tag):
"""Uses the segments identified in get_segids to make a list of all proteins in the systems.\
Outputs a list of the first residue number of the protein, and whether it is 'RAS-ONLY', or 'RAS-RAF'.\
The 'tag' input defines what is used to identify the first residue of the protein. i.e. 'resname ACE1 and name BB'.\
Only needs to be called x1 time"""
ras_segids, rasraf_segids = get_segids(u)
if len(ras_segids) > 0:
RAS = u.select_atoms('segid '+ras_segids[0]+' and '+str(tag))
else:
RAS = []
if len(rasraf_segids) > 0:
RAF = u.select_atoms('segid '+rasraf_segids[0]+' and '+str(tag))
else:
RAF = []
protein_info = []#np.empty([len(RAS)+len(RAF),2])
for i in range(len(RAS)):
protein_info.append((RAS[i].resid,'RAS-ONLY'))
for i in range(len(RAF)):
protein_info.append((RAF[i].resid,'RAS-RAF'))
######## sort protein info
protein_info = sorted(protein_info)
######## sort protein info
return protein_info
def get_ref_kras():
"""Gets the reference KRAS struct. Only called x1 time when class is loaded"""
start_of_g_ref = kras_ref_universe.residues[0].resid
ref_selection = 'resid '+str(start_of_g_ref)+':'+str(start_of_g_ref+24)+' ' +\
str(start_of_g_ref+38)+':'+str(start_of_g_ref+54)+' ' +\
str(start_of_g_ref+67)+':'+str(start_of_g_ref+164)+' ' +\
'and (name CA or name BB)'
r2_26r40_56r69_166_ref = kras_ref_universe.select_atoms(str(ref_selection))
return kras_ref_universe.select_atoms(str(ref_selection)).positions - kras_ref_universe.select_atoms(str(ref_selection)).center_of_mass()
# Load inital ref frames (only need to do this once)
ref0 = get_ref_kras()
def getKRASstates(u,kras_indices):
"""Gets states for all KRAS proteins in path."""
# res_shift = 8
# all_glycine = u.select_atoms("resname GLY")
# kras_indices = []
# for i in range(0, len(all_glycine), 26):
# kras_indices.append(all_glycine[i].index)
########## Below is taken out of the function so it is only done once #########
# kras_indices = get_protein_info(u,'resname ACE1 and name BB')
########## Above is taken out of the function so it is only done once #########
# CS, for x4 cases:
# [{protein_x4: (protein_type, num_res)}]
protein_systems = [{'ras4a': ('RAS-ONLY', 185),
'ras4araf': ('RAS-RAF', 321),
'ras': ('RAS-ONLY', 184),
'rasraf': ('RAS-RAF', 320)}]
ALLOUT = []
for k in range(len(kras_indices)):
start_of_g = kras_indices[k][0]
protein_x4 = str(kras_indices[k][1])
try:
protein_type = [item[protein_x4] for item in protein_systems][0][0] # 'RAS-ONLY' OR 'RAS-RAF'
num_res = [item[protein_x4] for item in protein_systems][0][1]
except:
LOGGER.error('Check KRas naming between modules')
raise Exception('Error: unknown KRas name')
# TODO: CS, replacing this comment section with the above, to handle x4 protein types
# ---------------------------------------
# ALLOUT = []
# for k in range(len(kras_indices)):
# start_of_g = kras_indices[k][0]
# protein_type = str(kras_indices[k][1])
# ########## BELOW SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ##############
# ########## POTENTIALLY REDO WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) #######
# ########## HAS BEEN REDONE WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) ########
# # if len(kras_indices) == 1:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB') ####### HAS TO BE FIXED FOR BACKBONE ATOMS FOR SPECIFIC PROTEIN
# # elif len(kras_indices) > 1:
# # if k == len(kras_indices)-1:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB')
# # else:
# # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(kras_indices[k+1][0])+' and name BB')
# ########## ABOVE SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ##############
#
# ########## Below hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations #########################
# if protein_type == 'RAS-ONLY':
# num_res = RAS_only_num_res
# elif protein_type == 'RAS-RAF':
# num_res = RAS_RAF_num_res
# ########## Above hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations #########################
# ---------------------------------------
# TODO: TSC, I changed the selection below, which can be used for the make_whole...
# krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res)+' and (name CA or name BB)')
krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res))
krases0_BB.guess_bonds()
r2_26r40_56r69_166 = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+24)+' ' +\
str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\
str(start_of_g+67)+':'+str(start_of_g+164)+\
' and (name CA or name BB)')
u_selection = \
'resid '+str(start_of_g)+':'+str(start_of_g+24)+' '+str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\
str(start_of_g+67)+':'+str(start_of_g+164)+' and (name CA or name BB)'
mobile0 = u.select_atoms(str(u_selection)).positions - u.select_atoms(str(u_selection)).center_of_mass()
# TODO: CS, something wrong with ref0 from get_kras_ref()
# just making ref0 = mobile0 to test for now
# ref0 = mobile0
# TSC removed this
R, RMSD_junk = align.rotation_matrix(mobile0, ref0)
######## TODO: TSC, Adjusted for AA lipid names ########
# lipids = u.select_atoms('resname POPX POPC PAPC POPE DIPE DPSM PAPS PAP6 CHOL')
lipids = u.select_atoms('resname POPC PAPC POPE DIPE SSM PAPS SAPI CHL1')
coords = ref0
RotMat = []
OS = []
r152_165 = krases0_BB.select_atoms('resid '+str(start_of_g+150)+':'+str(start_of_g+163)+' and (name CA or name BB)')
r65_74 = krases0_BB.select_atoms('resid '+str(start_of_g+63)+':'+str(start_of_g+72)+' and (name CA or name BB)')
timeframes = []
# TODO: CS, for AA need bonds to run make_whole()
# krases0_BB.guess_bonds()
# TODO: CS, turn off for now to test beyond this point
''' *** for AA, need to bring that back on once all else runs ***
'''
# @Tim and <NAME>. this was commented out - please check.
#make_whole(krases0_BB)
j, rmsd_junk = mda.analysis.align.rotation_matrix((r2_26r40_56r69_166.positions-r2_26r40_56r69_166.center_of_mass()), coords)
RotMat.append(j)
OS.append(r65_74.center_of_mass()-r152_165.center_of_mass())
timeframes.append(u.trajectory.time)
if protein_type == 'RAS-RAF':
z_pos = []
############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES BELOW ####################
############### TODO: TSC, zshifting is set to -1 (instead of -2), as there are ACE caps that are separate residues in AA
#zshifting=-1
if protein_x4 == 'rasraf':
zshifting = -1
elif protein_x4 == 'ras4araf':
zshifting = 0
else:
zshifting = 0
LOGGER.error('Found unsupported protein_x4 type')
raf_loops_selection = u.select_atoms('resid '+str(start_of_g+zshifting+291)+':'+str(start_of_g+zshifting+294)+' ' +\
str(start_of_g+zshifting+278)+':'+str(start_of_g+zshifting+281)+' ' +\
' and (name CA or name BB)')
############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES ABOVE ####################
diff = (lipids.center_of_mass()[2]-raf_loops_selection.center_of_mass(unwrap=True)[2])/10
if diff < 0:
diff = diff+(u.dimensions[2]/10)
z_pos.append(diff)
z_pos = np.array(z_pos)
RotMatNP = np.array(RotMat)
OS = np.array(OS)
OA = RotMatNP[:, 2, :]/(((RotMatNP[:, 2, 0]**2)+(RotMatNP[:, 2, 1]**2)+(RotMatNP[:, 2, 2]**2))**0.5)[:, None]
OWAS = np.arccos(RotMatNP[:, 2, 2])*180/math.pi
OC_temp = np.concatenate((OA, OS), axis=1)
t = ((OC_temp[:, 0]*OC_temp[:, 3])+(OC_temp[:, 1]*OC_temp[:, 4]) +
(OC_temp[:, 2]*OC_temp[:, 5]))/((OC_temp[:, 0]**2)+(OC_temp[:, 1]**2)+(OC_temp[:, 2]**2))
OC = OA*t[:, None]
ORS_tp = np.concatenate((OC, OS), axis=1)
ORS_norm = (((ORS_tp[:, 3]-ORS_tp[:, 0])**2)+((ORS_tp[:, 4]-ORS_tp[:, 1])**2)+((ORS_tp[:, 5]-ORS_tp[:, 2])**2))**0.5
ORS = (OS - OC)/ORS_norm[:, None]
OACRS = np.cross(OA, ORS)
OZCA = OA * OA[:, 2][:, None]
Z_unit = np.full([len(OZCA), 3], 1)
Z_adjust = np.array([0, 0, 1])
Z_unit = Z_unit*Z_adjust
Z_OZCA = Z_unit-OZCA
OZPACB = Z_OZCA/((Z_OZCA[:, 0]**2+Z_OZCA[:, 1]**2+Z_OZCA[:, 2]**2)**0.5)[:, None]
OROTNOTSIGNED = np.zeros([len(ORS)])
for i in range(len(ORS)):
OROTNOTSIGNED[i] = np.arccos(np.dot(OZPACB[i, :], ORS[i, :]) /
(np.sqrt(np.dot(OZPACB[i, :], OZPACB[i, :]))) *
(np.sqrt( | np.dot(ORS[i, :], ORS[i, :]) | numpy.dot |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[2].plot( | np.linspace(0.95 * np.pi, 1.55 * np.pi, 101) | numpy.linspace |
"""Routines for numerical differentiation."""
from __future__ import division
import numpy as np
from numpy.linalg import norm
from scipy.sparse.linalg import LinearOperator
from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
from ._group_columns import group_dense, group_sparse
EPS = np.finfo(np.float64).eps
def _adjust_scheme_to_bounds(x0, h, num_steps, scheme, lb, ub):
"""Adjust final difference scheme to the presence of bounds.
Parameters
----------
x0 : ndarray, shape (n,)
Point at which we wish to estimate derivative.
h : ndarray, shape (n,)
Desired finite difference steps.
num_steps : int
Number of `h` steps in one direction required to implement finite
difference scheme. For example, 2 means that we need to evaluate
f(x0 + 2 * h) or f(x0 - 2 * h)
scheme : {'1-sided', '2-sided'}
Whether steps in one or both directions are required. In other
words '1-sided' applies to forward and backward schemes, '2-sided'
applies to center schemes.
lb : ndarray, shape (n,)
Lower bounds on independent variables.
ub : ndarray, shape (n,)
Upper bounds on independent variables.
Returns
-------
h_adjusted : ndarray, shape (n,)
Adjusted step sizes. Step size decreases only if a sign flip or
switching to one-sided scheme doesn't allow to take a full step.
use_one_sided : ndarray of bool, shape (n,)
Whether to switch to one-sided scheme. Informative only for
``scheme='2-sided'``.
"""
if scheme == '1-sided':
use_one_sided = np.ones_like(h, dtype=bool)
elif scheme == '2-sided':
h = np.abs(h)
use_one_sided = np.zeros_like(h, dtype=bool)
else:
raise ValueError("`scheme` must be '1-sided' or '2-sided'.")
if np.all((lb == -np.inf) & (ub == np.inf)):
return h, use_one_sided
h_total = h * num_steps
h_adjusted = h.copy()
lower_dist = x0 - lb
upper_dist = ub - x0
if scheme == '1-sided':
x = x0 + h_total
violated = (x < lb) | (x > ub)
fitting = np.abs(h_total) <= np.maximum(lower_dist, upper_dist)
h_adjusted[violated & fitting] *= -1
forward = (upper_dist >= lower_dist) & ~fitting
h_adjusted[forward] = upper_dist[forward] / num_steps
backward = (upper_dist < lower_dist) & ~fitting
h_adjusted[backward] = -lower_dist[backward] / num_steps
elif scheme == '2-sided':
central = (lower_dist >= h_total) & (upper_dist >= h_total)
forward = (upper_dist >= lower_dist) & ~central
h_adjusted[forward] = np.minimum(
h[forward], 0.5 * upper_dist[forward] / num_steps)
use_one_sided[forward] = True
backward = (upper_dist < lower_dist) & ~central
h_adjusted[backward] = -np.minimum(
h[backward], 0.5 * lower_dist[backward] / num_steps)
use_one_sided[backward] = True
min_dist = np.minimum(upper_dist, lower_dist) / num_steps
adjusted_central = (~central & (np.abs(h_adjusted) <= min_dist))
h_adjusted[adjusted_central] = min_dist[adjusted_central]
use_one_sided[adjusted_central] = False
return h_adjusted, use_one_sided
relative_step = {"2-point": EPS**0.5,
"3-point": EPS**(1/3),
"cs": EPS**0.5}
def _compute_absolute_step(rel_step, x0, method):
if rel_step is None:
rel_step = relative_step[method]
sign_x0 = (x0 >= 0).astype(float) * 2 - 1
return rel_step * sign_x0 * np.maximum(1.0, np.abs(x0))
def _prepare_bounds(bounds, x0):
lb, ub = [np.asarray(b, dtype=float) for b in bounds]
if lb.ndim == 0:
lb = np.resize(lb, x0.shape)
if ub.ndim == 0:
ub = np.resize(ub, x0.shape)
return lb, ub
def group_columns(A, order=0):
"""Group columns of a 2-D matrix for sparse finite differencing [1]_.
Two columns are in the same group if in each row at least one of them
has zero. A greedy sequential algorithm is used to construct groups.
Parameters
----------
A : array_like or sparse matrix, shape (m, n)
Matrix of which to group columns.
order : int, iterable of int with shape (n,) or None
Permutation array which defines the order of columns enumeration.
If int or None, a random permutation is used with `order` used as
a random seed. Default is 0, that is use a random permutation but
guarantee repeatability.
Returns
-------
groups : ndarray of int, shape (n,)
Contains values from 0 to n_groups-1, where n_groups is the number
of found groups. Each value ``groups[i]`` is an index of a group to
which ith column assigned. The procedure was helpful only if
n_groups is significantly less than n.
References
----------
.. [1] <NAME>, <NAME>, and <NAME>, "On the estimation of
sparse Jacobian matrices", Journal of the Institute of Mathematics
and its Applications, 13 (1974), pp. 117-120.
"""
if issparse(A):
A = csc_matrix(A)
else:
A = np.atleast_2d(A)
A = (A != 0).astype(np.int32)
if A.ndim != 2:
raise ValueError("`A` must be 2-dimensional.")
m, n = A.shape
if order is None or np.isscalar(order):
rng = | np.random.RandomState(order) | numpy.random.RandomState |
'''
<NAME>
set up :2020-1-9
intergrate img and label into one file
-- fiducial1024_v1
'''
import argparse
import sys, os
import pickle
import random
import collections
import json
import numpy as np
import scipy.io as io
import scipy.misc as m
import matplotlib.pyplot as plt
import glob
import math
import time
import threading
import multiprocessing as mp
from multiprocessing import Pool
import re
import cv2
# sys.path.append('/lustre/home/gwxie/hope/project/dewarp/datasets/') # /lustre/home/gwxie/program/project/unwarp/perturbed_imgaes/GAN
import utils
def getDatasets(dir):
return os.listdir(dir)
class perturbed(utils.BasePerturbed):
def __init__(self, path, bg_path, save_path, save_suffix):
self.path = path
self.bg_path = bg_path
self.save_path = save_path
self.save_suffix = save_suffix
def save_img(self, m, n, fold_curve='fold', repeat_time=4, fiducial_points = 16, relativeShift_position='relativeShift_v2'):
origin_img = cv2.imread(self.path, flags=cv2.IMREAD_COLOR)
save_img_shape = [512*2, 480*2] # 320
# reduce_value = np.random.choice([2**4, 2**5, 2**6, 2**7, 2**8], p=[0.01, 0.1, 0.4, 0.39, 0.1])
reduce_value = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.02, 0.18, 0.2, 0.3, 0.1, 0.1, 0.08, 0.02])
# reduce_value = np.random.choice([8*2, 16*2, 24*2, 32*2, 40*2, 48*2], p=[0.01, 0.02, 0.2, 0.4, 0.19, 0.18])
# reduce_value = np.random.choice([16, 24, 32, 40, 48, 64], p=[0.01, 0.1, 0.2, 0.4, 0.2, 0.09])
base_img_shrink = save_img_shape[0] - reduce_value
# enlarge_img_shrink = [1024, 768]
# enlarge_img_shrink = [896, 672] # 420
enlarge_img_shrink = [512*4, 480*4] # 420
# enlarge_img_shrink = [896*2, 768*2] # 420
# enlarge_img_shrink = [896, 768] # 420
# enlarge_img_shrink = [768, 576] # 420
# enlarge_img_shrink = [640, 480] # 420
''''''
im_lr = origin_img.shape[0]
im_ud = origin_img.shape[1]
reduce_value_v2 = np.random.choice([2*2, 4*2, 8*2, 16*2, 24*2, 28*2, 32*2, 48*2], p=[0.02, 0.18, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1])
# reduce_value_v2 = np.random.choice([16, 24, 28, 32, 48, 64], p=[0.01, 0.1, 0.2, 0.3, 0.25, 0.14])
if im_lr > im_ud:
im_ud = min(int(im_ud / im_lr * base_img_shrink), save_img_shape[1] - reduce_value_v2)
im_lr = save_img_shape[0] - reduce_value
else:
base_img_shrink = save_img_shape[1] - reduce_value
im_lr = min(int(im_lr / im_ud * base_img_shrink), save_img_shape[0] - reduce_value_v2)
im_ud = base_img_shrink
if round(im_lr / im_ud, 2) < 0.5 or round(im_ud / im_lr, 2) < 0.5:
repeat_time = min(repeat_time, 8)
edge_padding = 3
im_lr -= im_lr % (fiducial_points-1) - (2*edge_padding) # im_lr % (fiducial_points-1) - 1
im_ud -= im_ud % (fiducial_points-1) - (2*edge_padding) # im_ud % (fiducial_points-1) - 1
im_hight = np.linspace(edge_padding, im_lr - edge_padding, fiducial_points, dtype=np.int64)
im_wide = np.linspace(edge_padding, im_ud - edge_padding, fiducial_points, dtype=np.int64)
# im_lr -= im_lr % (fiducial_points-1) - (1+2*edge_padding) # im_lr % (fiducial_points-1) - 1
# im_ud -= im_ud % (fiducial_points-1) - (1+2*edge_padding) # im_ud % (fiducial_points-1) - 1
# im_hight = np.linspace(edge_padding, im_lr - (1+edge_padding), fiducial_points, dtype=np.int64)
# im_wide = np.linspace(edge_padding, im_ud - (1+edge_padding), fiducial_points, dtype=np.int64)
im_x, im_y = np.meshgrid(im_hight, im_wide)
segment_x = (im_lr) // (fiducial_points-1)
segment_y = (im_ud) // (fiducial_points-1)
# plt.plot(im_x, im_y,
# color='limegreen',
# marker='.',
# linestyle='')
# plt.grid(True)
# plt.show()
self.origin_img = cv2.resize(origin_img, (im_ud, im_lr), interpolation=cv2.INTER_CUBIC)
perturbed_bg_ = getDatasets(self.bg_path)
perturbed_bg_img_ = self.bg_path+random.choice(perturbed_bg_)
perturbed_bg_img = cv2.imread(perturbed_bg_img_, flags=cv2.IMREAD_COLOR)
mesh_shape = self.origin_img.shape[:2]
self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 256, dtype=np.float32)#np.zeros_like(perturbed_bg_img)
# self.synthesis_perturbed_img = np.full((enlarge_img_shrink[0], enlarge_img_shrink[1], 3), 0, dtype=np.int16)#np.zeros_like(perturbed_bg_img)
self.new_shape = self.synthesis_perturbed_img.shape[:2]
perturbed_bg_img = cv2.resize(perturbed_bg_img, (save_img_shape[1], save_img_shape[0]), cv2.INPAINT_TELEA)
origin_pixel_position = np.argwhere(np.zeros(mesh_shape, dtype=np.uint32) == 0).reshape(mesh_shape[0], mesh_shape[1], 2)
pixel_position = np.argwhere(np.zeros(self.new_shape, dtype=np.uint32) == 0).reshape(self.new_shape[0], self.new_shape[1], 2)
self.perturbed_xy_ = np.zeros((self.new_shape[0], self.new_shape[1], 2))
# self.perturbed_xy_ = pixel_position.copy().astype(np.float32)
# fiducial_points_grid = origin_pixel_position[im_x, im_y]
self.synthesis_perturbed_label = np.zeros((self.new_shape[0], self.new_shape[1], 2))
x_min, y_min, x_max, y_max = self.adjust_position_v2(0, 0, mesh_shape[0], mesh_shape[1], save_img_shape)
origin_pixel_position += [x_min, y_min]
x_min, y_min, x_max, y_max = self.adjust_position(0, 0, mesh_shape[0], mesh_shape[1])
x_shift = random.randint(-enlarge_img_shrink[0]//16, enlarge_img_shrink[0]//16)
y_shift = random.randint(-enlarge_img_shrink[1]//16, enlarge_img_shrink[1]//16)
x_min += x_shift
x_max += x_shift
y_min += y_shift
y_max += y_shift
'''im_x,y'''
im_x += x_min
im_y += y_min
self.synthesis_perturbed_img[x_min:x_max, y_min:y_max] = self.origin_img
self.synthesis_perturbed_label[x_min:x_max, y_min:y_max] = origin_pixel_position
synthesis_perturbed_img_map = self.synthesis_perturbed_img.copy()
synthesis_perturbed_label_map = self.synthesis_perturbed_label.copy()
foreORbackground_label = np.full((mesh_shape), 1, dtype=np.int16)
foreORbackground_label_map = np.full((self.new_shape), 0, dtype=np.int16)
foreORbackground_label_map[x_min:x_max, y_min:y_max] = foreORbackground_label
# synthesis_perturbed_img_map = self.pad(self.synthesis_perturbed_img.copy(), x_min, y_min, x_max, y_max)
# synthesis_perturbed_label_map = self.pad(synthesis_perturbed_label_map, x_min, y_min, x_max, y_max)
'''*****************************************************************'''
is_normalizationFun_mixture = self.is_perform(0.2, 0.8)
# if not is_normalizationFun_mixture:
normalizationFun_0_1 = False
# normalizationFun_0_1 = self.is_perform(0.5, 0.5)
if fold_curve == 'fold':
fold_curve_random = True
# is_normalizationFun_mixture = False
normalizationFun_0_1 = self.is_perform(0.2, 0.8)
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
fold_curve_random = self.is_perform(0.1, 0.9) # False # self.is_perform(0.01, 0.99)
alpha_perturbed = random.randint(80, 160) / 100
# is_normalizationFun_mixture = False # self.is_perform(0.01, 0.99)
synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 256)
# synthesis_perturbed_img = np.full_like(self.synthesis_perturbed_img, 0, dtype=np.int16)
synthesis_perturbed_label = np.zeros_like(self.synthesis_perturbed_label)
alpha_perturbed_change = self.is_perform(0.5, 0.5)
p_pp_choice = self.is_perform(0.8, 0.2) if fold_curve == 'fold' else self.is_perform(0.1, 0.9)
for repeat_i in range(repeat_time):
if alpha_perturbed_change:
if fold_curve == 'fold':
if is_normalizationFun_mixture:
alpha_perturbed = random.randint(80, 120) / 100
else:
if normalizationFun_0_1 and repeat_time < 8:
alpha_perturbed = random.randint(50, 70) / 100
else:
alpha_perturbed = random.randint(70, 130) / 100
else:
alpha_perturbed = random.randint(80, 160) / 100
''''''
linspace_x = [0, (self.new_shape[0] - im_lr) // 2 - 1,
self.new_shape[0] - (self.new_shape[0] - im_lr) // 2 - 1, self.new_shape[0] - 1]
linspace_y = [0, (self.new_shape[1] - im_ud) // 2 - 1,
self.new_shape[1] - (self.new_shape[1] - im_ud) // 2 - 1, self.new_shape[1] - 1]
linspace_x_seq = [1, 2, 3]
linspace_y_seq = [1, 2, 3]
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_p = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
if ((r_x == 1 or r_x == 3) and (r_y == 1 or r_y == 3)) and p_pp_choice:
linspace_x_seq.remove(r_x)
linspace_y_seq.remove(r_y)
r_x = random.choice(linspace_x_seq)
r_y = random.choice(linspace_y_seq)
perturbed_pp = np.array(
[random.randint(linspace_x[r_x-1] * 10, linspace_x[r_x] * 10),
random.randint(linspace_y[r_y-1] * 10, linspace_y[r_y] * 10)])/10
# perturbed_p, perturbed_pp = np.array(
# [random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10]) \
# , np.array([random.randint(0, self.new_shape[0] * 10) / 10,
# random.randint(0, self.new_shape[1] * 10) / 10])
# perturbed_p, perturbed_pp = np.array(
# [random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10]) \
# , np.array([random.randint((self.new_shape[0]-im_lr)//2*10, (self.new_shape[0]-(self.new_shape[0]-im_lr)//2) * 10) / 10,
# random.randint((self.new_shape[1]-im_ud)//2*10, (self.new_shape[1]-(self.new_shape[1]-im_ud)//2) * 10) / 10])
''''''
perturbed_vp = perturbed_pp - perturbed_p
perturbed_vp_norm = np.linalg.norm(perturbed_vp)
perturbed_distance_vertex_and_line = np.dot((perturbed_p - pixel_position), perturbed_vp) / perturbed_vp_norm
''''''
# perturbed_v = np.array([random.randint(-3000, 3000) / 100, random.randint(-3000, 3000) / 100])
# perturbed_v = np.array([random.randint(-4000, 4000) / 100, random.randint(-4000, 4000) / 100])
if fold_curve == 'fold' and self.is_perform(0.6, 0.4): # self.is_perform(0.3, 0.7):
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
perturbed_v = np.array([random.randint(-10000, 10000) / 100, random.randint(-10000, 10000) / 100])
# perturbed_v = np.array([random.randint(-11000, 11000) / 100, random.randint(-11000, 11000) / 100])
else:
# perturbed_v = np.array([random.randint(-9000, 9000) / 100, random.randint(-9000, 9000) / 100])
# perturbed_v = np.array([random.randint(-16000, 16000) / 100, random.randint(-16000, 16000) / 100])
perturbed_v = np.array([random.randint(-8000, 8000) / 100, random.randint(-8000, 8000) / 100])
# perturbed_v = np.array([random.randint(-3500, 3500) / 100, random.randint(-3500, 3500) / 100])
# perturbed_v = np.array([random.randint(-600, 600) / 10, random.randint(-600, 600) / 10])
''''''
if fold_curve == 'fold':
if is_normalizationFun_mixture:
if self.is_perform(0.5, 0.5):
perturbed_d = np.abs(self.get_normalize(perturbed_distance_vertex_and_line))
else:
perturbed_d = self.get_0_1_d( | np.abs(perturbed_distance_vertex_and_line) | numpy.abs |
"""Routines for numerical differentiation."""
from __future__ import division
import numpy as np
from numpy.linalg import norm
from scipy.sparse.linalg import LinearOperator
from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
from ._group_columns import group_dense, group_sparse
EPS = np.finfo(np.float64).eps
def _adjust_scheme_to_bounds(x0, h, num_steps, scheme, lb, ub):
"""Adjust final difference scheme to the presence of bounds.
Parameters
----------
x0 : ndarray, shape (n,)
Point at which we wish to estimate derivative.
h : ndarray, shape (n,)
Desired finite difference steps.
num_steps : int
Number of `h` steps in one direction required to implement finite
difference scheme. For example, 2 means that we need to evaluate
f(x0 + 2 * h) or f(x0 - 2 * h)
scheme : {'1-sided', '2-sided'}
Whether steps in one or both directions are required. In other
words '1-sided' applies to forward and backward schemes, '2-sided'
applies to center schemes.
lb : ndarray, shape (n,)
Lower bounds on independent variables.
ub : ndarray, shape (n,)
Upper bounds on independent variables.
Returns
-------
h_adjusted : ndarray, shape (n,)
Adjusted step sizes. Step size decreases only if a sign flip or
switching to one-sided scheme doesn't allow to take a full step.
use_one_sided : ndarray of bool, shape (n,)
Whether to switch to one-sided scheme. Informative only for
``scheme='2-sided'``.
"""
if scheme == '1-sided':
use_one_sided = np.ones_like(h, dtype=bool)
elif scheme == '2-sided':
h = np.abs(h)
use_one_sided = np.zeros_like(h, dtype=bool)
else:
raise ValueError("`scheme` must be '1-sided' or '2-sided'.")
if np.all((lb == -np.inf) & (ub == np.inf)):
return h, use_one_sided
h_total = h * num_steps
h_adjusted = h.copy()
lower_dist = x0 - lb
upper_dist = ub - x0
if scheme == '1-sided':
x = x0 + h_total
violated = (x < lb) | (x > ub)
fitting = np.abs(h_total) <= np.maximum(lower_dist, upper_dist)
h_adjusted[violated & fitting] *= -1
forward = (upper_dist >= lower_dist) & ~fitting
h_adjusted[forward] = upper_dist[forward] / num_steps
backward = (upper_dist < lower_dist) & ~fitting
h_adjusted[backward] = -lower_dist[backward] / num_steps
elif scheme == '2-sided':
central = (lower_dist >= h_total) & (upper_dist >= h_total)
forward = (upper_dist >= lower_dist) & ~central
h_adjusted[forward] = np.minimum(
h[forward], 0.5 * upper_dist[forward] / num_steps)
use_one_sided[forward] = True
backward = (upper_dist < lower_dist) & ~central
h_adjusted[backward] = -np.minimum(
h[backward], 0.5 * lower_dist[backward] / num_steps)
use_one_sided[backward] = True
min_dist = np.minimum(upper_dist, lower_dist) / num_steps
adjusted_central = (~central & (np.abs(h_adjusted) <= min_dist))
h_adjusted[adjusted_central] = min_dist[adjusted_central]
use_one_sided[adjusted_central] = False
return h_adjusted, use_one_sided
relative_step = {"2-point": EPS**0.5,
"3-point": EPS**(1/3),
"cs": EPS**0.5}
def _compute_absolute_step(rel_step, x0, method):
if rel_step is None:
rel_step = relative_step[method]
sign_x0 = (x0 >= 0).astype(float) * 2 - 1
return rel_step * sign_x0 * np.maximum(1.0, np.abs(x0))
def _prepare_bounds(bounds, x0):
lb, ub = [np.asarray(b, dtype=float) for b in bounds]
if lb.ndim == 0:
lb = np.resize(lb, x0.shape)
if ub.ndim == 0:
ub = np.resize(ub, x0.shape)
return lb, ub
def group_columns(A, order=0):
"""Group columns of a 2-D matrix for sparse finite differencing [1]_.
Two columns are in the same group if in each row at least one of them
has zero. A greedy sequential algorithm is used to construct groups.
Parameters
----------
A : array_like or sparse matrix, shape (m, n)
Matrix of which to group columns.
order : int, iterable of int with shape (n,) or None
Permutation array which defines the order of columns enumeration.
If int or None, a random permutation is used with `order` used as
a random seed. Default is 0, that is use a random permutation but
guarantee repeatability.
Returns
-------
groups : ndarray of int, shape (n,)
Contains values from 0 to n_groups-1, where n_groups is the number
of found groups. Each value ``groups[i]`` is an index of a group to
which ith column assigned. The procedure was helpful only if
n_groups is significantly less than n.
References
----------
.. [1] <NAME>, <NAME>, and <NAME>, "On the estimation of
sparse Jacobian matrices", Journal of the Institute of Mathematics
and its Applications, 13 (1974), pp. 117-120.
"""
if issparse(A):
A = csc_matrix(A)
else:
A = np.atleast_2d(A)
A = (A != 0).astype(np.int32)
if A.ndim != 2:
raise ValueError("`A` must be 2-dimensional.")
m, n = A.shape
if order is None or np.isscalar(order):
rng = np.random.RandomState(order)
order = rng.permutation(n)
else:
order = np.asarray(order)
if order.shape != (n,):
raise ValueError("`order` has incorrect shape.")
A = A[:, order]
if issparse(A):
groups = group_sparse(m, n, A.indices, A.indptr)
else:
groups = group_dense(m, n, A)
groups[order] = groups.copy()
return groups
def approx_derivative(fun, x0, method='3-point', rel_step=None, f0=None,
bounds=(-np.inf, np.inf), sparsity=None,
as_linear_operator=False, args=(), kwargs={}):
"""Compute finite difference approximation of the derivatives of a
vector-valued function.
If a function maps from R^n to R^m, its derivatives form m-by-n matrix
called the Jacobian, where an element (i, j) is a partial derivative of
f[i] with respect to x[j].
Parameters
----------
fun : callable
Function of which to estimate the derivatives. The argument x
passed to this function is ndarray of shape (n,) (never a scalar
even if n=1). It must return 1-D array_like of shape (m,) or a scalar.
x0 : array_like of shape (n,) or float
Point at which to estimate the derivatives. Float will be converted
to a 1-D array.
method : {'3-point', '2-point', 'cs'}, optional
Finite difference method to use:
- '2-point' - use the first order accuracy forward or backward
difference.
- '3-point' - use central difference in interior points and the
second order accuracy forward or backward difference
near the boundary.
- 'cs' - use a complex-step finite difference scheme. This assumes
that the user function is real-valued and can be
analytically continued to the complex plane. Otherwise,
produces bogus results.
rel_step : None or array_like, optional
Relative step size to use. The absolute step size is computed as
``h = rel_step * sign(x0) * max(1, abs(x0))``, possibly adjusted to
fit into the bounds. For ``method='3-point'`` the sign of `h` is
ignored. If None (default) then step is selected automatically,
see Notes.
f0 : None or array_like, optional
If not None it is assumed to be equal to ``fun(x0)``, in this case
the ``fun(x0)`` is not called. Default is None.
bounds : tuple of array_like, optional
Lower and upper bounds on independent variables. Defaults to no bounds.
Each bound must match the size of `x0` or be a scalar, in the latter
case the bound will be the same for all variables. Use it to limit the
range of function evaluation. Bounds checking is not implemented
when `as_linear_operator` is True.
sparsity : {None, array_like, sparse matrix, 2-tuple}, optional
Defines a sparsity structure of the Jacobian matrix. If the Jacobian
matrix is known to have only few non-zero elements in each row, then
it's possible to estimate its several columns by a single function
evaluation [3]_. To perform such economic computations two ingredients
are required:
* structure : array_like or sparse matrix of shape (m, n). A zero
element means that a corresponding element of the Jacobian
identically equals to zero.
* groups : array_like of shape (n,). A column grouping for a given
sparsity structure, use `group_columns` to obtain it.
A single array or a sparse matrix is interpreted as a sparsity
structure, and groups are computed inside the function. A tuple is
interpreted as (structure, groups). If None (default), a standard
dense differencing will be used.
Note, that sparse differencing makes sense only for large Jacobian
matrices where each row contains few non-zero elements.
as_linear_operator : bool, optional
When True the function returns an `scipy.sparse.linalg.LinearOperator`.
Otherwise it returns a dense array or a sparse matrix depending on
`sparsity`. The linear operator provides an efficient way of computing
``J.dot(p)`` for any vector ``p`` of shape (n,), but does not allow
direct access to individual elements of the matrix. By default
`as_linear_operator` is False.
args, kwargs : tuple and dict, optional
Additional arguments passed to `fun`. Both empty by default.
The calling signature is ``fun(x, *args, **kwargs)``.
Returns
-------
J : {ndarray, sparse matrix, LinearOperator}
Finite difference approximation of the Jacobian matrix.
If `as_linear_operator` is True returns a LinearOperator
with shape (m, n). Otherwise it returns a dense array or sparse
matrix depending on how `sparsity` is defined. If `sparsity`
is None then a ndarray with shape (m, n) is returned. If
`sparsity` is not None returns a csr_matrix with shape (m, n).
For sparse matrices and linear operators it is always returned as
a 2-D structure, for ndarrays, if m=1 it is returned
as a 1-D gradient array with shape (n,).
See Also
--------
check_derivative : Check correctness of a function computing derivatives.
Notes
-----
If `rel_step` is not provided, it assigned to ``EPS**(1/s)``, where EPS is
machine epsilon for float64 numbers, s=2 for '2-point' method and s=3 for
'3-point' method. Such relative step approximately minimizes a sum of
truncation and round-off errors, see [1]_.
A finite difference scheme for '3-point' method is selected automatically.
The well-known central difference scheme is used for points sufficiently
far from the boundary, and 3-point forward or backward scheme is used for
points near the boundary. Both schemes have the second-order accuracy in
terms of Taylor expansion. Refer to [2]_ for the formulas of 3-point
forward and backward difference schemes.
For dense differencing when m=1 Jacobian is returned with a shape (n,),
on the other hand when n=1 Jacobian is returned with a shape (m, 1).
Our motivation is the following: a) It handles a case of gradient
computation (m=1) in a conventional way. b) It clearly separates these two
different cases. b) In all cases np.atleast_2d can be called to get 2-D
Jacobian with correct dimensions.
References
----------
.. [1] W. H. Press et. al. "Numerical Recipes. The Art of Scientific
Computing. 3rd edition", sec. 5.7.
.. [2] <NAME>, <NAME>, and <NAME>, "On the estimation of
sparse Jacobian matrices", Journal of the Institute of Mathematics
and its Applications, 13 (1974), pp. 117-120.
.. [3] <NAME>, "Generation of Finite Difference Formulas on
Arbitrarily Spaced Grids", Mathematics of Computation 51, 1988.
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import approx_derivative
>>>
>>> def f(x, c1, c2):
... return np.array([x[0] * np.sin(c1 * x[1]),
... x[0] * np.cos(c2 * x[1])])
...
>>> x0 = np.array([1.0, 0.5 * np.pi])
>>> approx_derivative(f, x0, args=(1, 2))
array([[ 1., 0.],
[-1., 0.]])
Bounds can be used to limit the region of function evaluation.
In the example below we compute left and right derivative at point 1.0.
>>> def g(x):
... return x**2 if x >= 1 else x
...
>>> x0 = 1.0
>>> approx_derivative(g, x0, bounds=(-np.inf, 1.0))
array([ 1.])
>>> approx_derivative(g, x0, bounds=(1.0, np.inf))
array([ 2.])
"""
if method not in ['2-point', '3-point', 'cs']:
raise ValueError("Unknown method '%s'. " % method)
x0 = np.atleast_1d(x0)
if x0.ndim > 1:
raise ValueError("`x0` must have at most 1 dimension.")
lb, ub = _prepare_bounds(bounds, x0)
if lb.shape != x0.shape or ub.shape != x0.shape:
raise ValueError("Inconsistent shapes between bounds and `x0`.")
if as_linear_operator and not (np.all(np.isinf(lb))
and np.all(np.isinf(ub))):
raise ValueError("Bounds not supported when "
"`as_linear_operator` is True.")
def fun_wrapped(x):
f = np.atleast_1d(fun(x, *args, **kwargs))
if f.ndim > 1:
raise RuntimeError("`fun` return value has "
"more than 1 dimension.")
return f
if f0 is None:
f0 = fun_wrapped(x0)
else:
f0 = np.atleast_1d(f0)
if f0.ndim > 1:
raise ValueError("`f0` passed has more than 1 dimension.")
if np.any((x0 < lb) | (x0 > ub)):
raise ValueError("`x0` violates bound constraints.")
if as_linear_operator:
if rel_step is None:
rel_step = relative_step[method]
return _linear_operator_difference(fun_wrapped, x0,
f0, rel_step, method)
else:
h = _compute_absolute_step(rel_step, x0, method)
if method == '2-point':
h, use_one_sided = _adjust_scheme_to_bounds(
x0, h, 1, '1-sided', lb, ub)
elif method == '3-point':
h, use_one_sided = _adjust_scheme_to_bounds(
x0, h, 1, '2-sided', lb, ub)
elif method == 'cs':
use_one_sided = False
if sparsity is None:
return _dense_difference(fun_wrapped, x0, f0, h,
use_one_sided, method)
else:
if not issparse(sparsity) and len(sparsity) == 2:
structure, groups = sparsity
else:
structure = sparsity
groups = group_columns(sparsity)
if issparse(structure):
structure = csc_matrix(structure)
else:
structure = np.atleast_2d(structure)
groups = np.atleast_1d(groups)
return _sparse_difference(fun_wrapped, x0, f0, h,
use_one_sided, structure,
groups, method)
def _linear_operator_difference(fun, x0, f0, h, method):
m = f0.size
n = x0.size
if method == '2-point':
def matvec(p):
if np.array_equal(p, np.zeros_like(p)):
return np.zeros(m)
dx = h / norm(p)
x = x0 + dx*p
df = fun(x) - f0
return df / dx
elif method == '3-point':
def matvec(p):
if np.array_equal(p, np.zeros_like(p)):
return np.zeros(m)
dx = 2*h / norm(p)
x1 = x0 - (dx/2)*p
x2 = x0 + (dx/2)*p
f1 = fun(x1)
f2 = fun(x2)
df = f2 - f1
return df / dx
elif method == 'cs':
def matvec(p):
if np.array_equal(p, np.zeros_like(p)):
return | np.zeros(m) | numpy.zeros |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[2].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[2].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
plt.savefig('jss_figures/DFA_different_trends.png')
plt.show()
# plot 6b
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences Zoomed Region', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[0].set_ylim(-5.5, 5.5)
axs[0].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].set_ylim(-5.5, 5.5)
axs[1].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([np.pi, (3 / 2) * np.pi])
axs[2].set_xticklabels([r'$\pi$', r'$\frac{3}{2}\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].set_ylim(-5.5, 5.5)
axs[2].set_xlim(0.95 * np.pi, 1.55 * np.pi)
plt.savefig('jss_figures/DFA_different_trends_zoomed.png')
plt.show()
hs_ouputs = hilbert_spectrum(time, imfs_51, hts_51, ifs_51, max_frequency=12, plot=False)
# plot 6c
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Simple Sinusoidal Time Seres with Added Noise', 50))
x_hs, y, z = hs_ouputs
z_min, z_max = 0, np.abs(z).max()
ax.pcolormesh(x_hs, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max)
ax.plot(x_hs[0, :], 8 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 8$', Linewidth=3)
ax.plot(x_hs[0, :], 4 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 4$', Linewidth=3)
ax.plot(x_hs[0, :], 2 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 2$', Linewidth=3)
ax.set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
ax.set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$'])
plt.ylabel(r'Frequency (rad.s$^{-1}$)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.85, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/DFA_hilbert_spectrum.png')
plt.show()
# plot 6c
time = np.linspace(0, 5 * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 51)
fluc = Fluctuation(time=time, time_series=time_series)
max_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=False)
max_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=True)
min_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=False)
min_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=True)
util = Utility(time=time, time_series=time_series)
maxima = util.max_bool_func_1st_order_fd()
minima = util.min_bool_func_1st_order_fd()
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title(textwrap.fill('Plot Demonstrating Unsmoothed Extrema Envelopes if Schoenberg–Whitney Conditions are Not Satisfied', 50))
plt.plot(time, time_series, label='Time series', zorder=2, LineWidth=2)
plt.scatter(time[maxima], time_series[maxima], c='r', label='Maxima', zorder=10)
plt.scatter(time[minima], time_series[minima], c='b', label='Minima', zorder=10)
plt.plot(time, max_unsmoothed[0], label=textwrap.fill('Unsmoothed maxima envelope', 10), c='darkorange')
plt.plot(time, max_smoothed[0], label=textwrap.fill('Smoothed maxima envelope', 10), c='red')
plt.plot(time, min_unsmoothed[0], label=textwrap.fill('Unsmoothed minima envelope', 10), c='cyan')
plt.plot(time, min_smoothed[0], label=textwrap.fill('Smoothed minima envelope', 10), c='blue')
for knot in knots[:-1]:
plt.plot(knot * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', zorder=1)
plt.plot(knots[-1] * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', label='Knots', zorder=1)
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi),
(r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$', r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Schoenberg_Whitney_Conditions.png')
plt.show()
# plot 7
a = 0.25
width = 0.2
time = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 1001)
knots = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 11)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
inflection_bool = utils.inflection_point()
inflection_x = time[inflection_bool]
inflection_y = time_series[inflection_bool]
fluctuation = emd_mean.Fluctuation(time=time, time_series=time_series)
maxima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
maxima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
inflection_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='inflection_points')[0]
binomial_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='binomial_average', order=21,
increment=20)[0]
derivative_of_lsq = utils.derivative_forward_diff()
derivative_time = time[:-1]
derivative_knots = np.linspace(knots[0], knots[-1], 31)
# change (1) detrended_fluctuation_technique and (2) max_internal_iter and (3) debug (confusing with external debugging)
emd = AdvEMDpy.EMD(time=derivative_time, time_series=derivative_of_lsq)
imf_1_of_derivative = emd.empirical_mode_decomposition(knots=derivative_knots,
knot_time=derivative_time, text=False, verbose=False)[0][1, :]
utils = emd_utils.Utility(time=time[:-1], time_series=imf_1_of_derivative)
optimal_maxima = np.r_[False, utils.derivative_forward_diff() < 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
optimal_minima = np.r_[False, utils.derivative_forward_diff() > 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
EEMD_maxima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'maxima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
EEMD_minima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'minima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Detrended Fluctuation Analysis Examples')
plt.plot(time, time_series, LineWidth=2, label='Time series')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(time[optimal_maxima], time_series[optimal_maxima], c='darkred', zorder=4,
label=textwrap.fill('Optimal maxima', 10))
plt.scatter(time[optimal_minima], time_series[optimal_minima], c='darkblue', zorder=4,
label=textwrap.fill('Optimal minima', 10))
plt.scatter(inflection_x, inflection_y, c='magenta', zorder=4, label=textwrap.fill('Inflection points', 10))
plt.plot(time, maxima_envelope, c='darkblue', label=textwrap.fill('EMD envelope', 10))
plt.plot(time, minima_envelope, c='darkblue')
plt.plot(time, (maxima_envelope + minima_envelope) / 2, c='darkblue')
plt.plot(time, maxima_envelope_smooth, c='darkred', label=textwrap.fill('SEMD envelope', 10))
plt.plot(time, minima_envelope_smooth, c='darkred')
plt.plot(time, (maxima_envelope_smooth + minima_envelope_smooth) / 2, c='darkred')
plt.plot(time, EEMD_maxima_envelope, c='darkgreen', label=textwrap.fill('EEMD envelope', 10))
plt.plot(time, EEMD_minima_envelope, c='darkgreen')
plt.plot(time, (EEMD_maxima_envelope + EEMD_minima_envelope) / 2, c='darkgreen')
plt.plot(time, inflection_points_envelope, c='darkorange', label=textwrap.fill('Inflection point envelope', 10))
plt.plot(time, binomial_points_envelope, c='deeppink', label=textwrap.fill('Binomial average envelope', 10))
plt.plot(time, np.cos(time), c='black', label='True mean')
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi), (r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$',
r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/detrended_fluctuation_analysis.png')
plt.show()
# Duffing Equation Example
def duffing_equation(xy, ts):
gamma = 0.1
epsilon = 1
omega = ((2 * np.pi) / 25)
return [xy[1], xy[0] - epsilon * xy[0] ** 3 + gamma * np.cos(omega * ts)]
t = np.linspace(0, 150, 1501)
XY0 = [1, 1]
solution = odeint(duffing_equation, XY0, t)
x = solution[:, 0]
dxdt = solution[:, 1]
x_points = [0, 50, 100, 150]
x_names = {0, 50, 100, 150}
y_points_1 = [-2, 0, 2]
y_points_2 = [-1, 0, 1]
fig, axs = plt.subplots(2, 1)
plt.subplots_adjust(hspace=0.2)
axs[0].plot(t, x)
axs[0].set_title('Duffing Equation Displacement')
axs[0].set_ylim([-2, 2])
axs[0].set_xlim([0, 150])
axs[1].plot(t, dxdt)
axs[1].set_title('Duffing Equation Velocity')
axs[1].set_ylim([-1.5, 1.5])
axs[1].set_xlim([0, 150])
axis = 0
for ax in axs.flat:
ax.label_outer()
if axis == 0:
ax.set_ylabel('x(t)')
ax.set_yticks(y_points_1)
if axis == 1:
ax.set_ylabel(r'$ \dfrac{dx(t)}{dt} $')
ax.set(xlabel='t')
ax.set_yticks(y_points_2)
ax.set_xticks(x_points)
ax.set_xticklabels(x_names)
axis += 1
plt.savefig('jss_figures/Duffing_equation.png')
plt.show()
# compare other packages Duffing - top
pyemd = pyemd0215()
py_emd = pyemd(x)
IP, IF, IA = emd040.spectra.frequency_transform(py_emd.T, 10, 'hilbert')
freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 0.2, 100)
hht = emd040.spectra.hilberthuang(IF, IA, freq_edges)
hht = gaussian_filter(hht, sigma=1)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 1.0
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using PyEMD 0.2.10', 40))
plt.pcolormesh(t, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht))))
plt.plot(t[:-1], 0.124 * np.ones_like(t[:-1]), '--', label=textwrap.fill('Hamiltonian frequency approximation', 15))
plt.plot(t[:-1], 0.04 * np.ones_like(t[:-1]), 'g--', label=textwrap.fill('Driving function frequency', 15))
plt.xticks([0, 50, 100, 150])
plt.yticks([0, 0.1, 0.2])
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.75, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Duffing_equation_ht_pyemd.png')
plt.show()
plt.show()
emd_sift = emd040.sift.sift(x)
IP, IF, IA = emd040.spectra.frequency_transform(emd_sift, 10, 'hilbert')
freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 0.2, 100)
hht = emd040.spectra.hilberthuang(IF, IA, freq_edges)
hht = gaussian_filter(hht, sigma=1)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 1.0
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using emd 0.3.3', 40))
plt.pcolormesh(t, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht))))
plt.plot(t[:-1], 0.124 * np.ones_like(t[:-1]), '--', label=textwrap.fill('Hamiltonian frequency approximation', 15))
plt.plot(t[:-1], 0.04 * np.ones_like(t[:-1]), 'g--', label=textwrap.fill('Driving function frequency', 15))
plt.xticks([0, 50, 100, 150])
plt.yticks([0, 0.1, 0.2])
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.75, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Duffing_equation_ht_emd.png')
plt.show()
# compare other packages Duffing - bottom
emd_duffing = AdvEMDpy.EMD(time=t, time_series=x)
emd_duff, emd_ht_duff, emd_if_duff, _, _, _, _ = emd_duffing.empirical_mode_decomposition(verbose=False)
fig, axs = plt.subplots(2, 1)
plt.subplots_adjust(hspace=0.3)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
axs[0].plot(t, emd_duff[1, :], label='AdvEMDpy')
axs[0].plot(t, py_emd[0, :], '--', label='PyEMD 0.2.10')
axs[0].plot(t, emd_sift[:, 0], '--', label='emd 0.3.3')
axs[0].set_title('IMF 1')
axs[0].set_ylim([-2, 2])
axs[0].set_xlim([0, 150])
axs[1].plot(t, emd_duff[2, :], label='AdvEMDpy')
print(f'AdvEMDpy driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - emd_duff[2, :])), 3)}')
axs[1].plot(t, py_emd[1, :], '--', label='PyEMD 0.2.10')
print(f'PyEMD driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - py_emd[1, :])), 3)}')
axs[1].plot(t, emd_sift[:, 1], '--', label='emd 0.3.3')
print(f'emd driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - emd_sift[:, 1])), 3)}')
axs[1].plot(t, 0.1 * np.cos(0.04 * 2 * np.pi * t), '--', label=r'$0.1$cos$(0.08{\pi}t)$')
axs[1].set_title('IMF 2')
axs[1].set_ylim([-0.2, 0.4])
axs[1].set_xlim([0, 150])
axis = 0
for ax in axs.flat:
ax.label_outer()
if axis == 0:
ax.set_ylabel(r'$\gamma_1(t)$')
ax.set_yticks([-2, 0, 2])
if axis == 1:
ax.set_ylabel(r'$\gamma_2(t)$')
ax.set_yticks([-0.2, 0, 0.2])
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
ax.set_xticks(x_points)
ax.set_xticklabels(x_names)
axis += 1
plt.savefig('jss_figures/Duffing_equation_imfs.png')
plt.show()
hs_ouputs = hilbert_spectrum(t, emd_duff, emd_ht_duff, emd_if_duff, max_frequency=1.3, plot=False)
ax = plt.subplot(111)
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using AdvEMDpy', 40))
x, y, z = hs_ouputs
y = y / (2 * np.pi)
z_min, z_max = 0, np.abs(z).max()
figure_size = plt.gcf().get_size_inches()
factor = 1.0
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
ax.pcolormesh(x, y, | np.abs(z) | numpy.abs |
import numpy as np
import pytest
import theano
import theano.tensor as tt
# Don't import test classes otherwise they get tested as part of the file
from tests import unittest_tools as utt
from tests.gpuarray.config import mode_with_gpu, mode_without_gpu, test_ctx_name
from tests.tensor.test_basic import (
TestAlloc,
TestComparison,
TestJoinAndSplit,
TestReshape,
)
from tests.tensor.utils import rand, safe_make_node
from theano.gpuarray.basic_ops import (
GpuAlloc,
GpuAllocEmpty,
GpuContiguous,
GpuEye,
GpuFromHost,
GpuJoin,
GpuReshape,
GpuSplit,
GpuToGpu,
GpuTri,
HostFromGpu,
gpu_contiguous,
gpu_join,
host_from_gpu,
)
from theano.gpuarray.elemwise import GpuDimShuffle, GpuElemwise
from theano.gpuarray.subtensor import GpuSubtensor
from theano.gpuarray.type import GpuArrayType, get_context, gpuarray_shared_constructor
from theano.tensor import TensorType
from theano.tensor.basic import alloc
pygpu = pytest.importorskip("pygpu")
gpuarray = pygpu.gpuarray
utt.seed_rng()
rng = np.random.RandomState(seed=utt.fetch_seed())
def inplace_func(
inputs,
outputs,
mode=None,
allow_input_downcast=False,
on_unused_input="raise",
name=None,
):
if mode is None:
mode = mode_with_gpu
return theano.function(
inputs,
outputs,
mode=mode,
allow_input_downcast=allow_input_downcast,
accept_inplace=True,
on_unused_input=on_unused_input,
name=name,
)
def fake_shared(value, name=None, strict=False, allow_downcast=None, **kwargs):
from theano.tensor.sharedvar import scalar_constructor, tensor_constructor
for c in (gpuarray_shared_constructor, tensor_constructor, scalar_constructor):
try:
return c(
value, name=name, strict=strict, allow_downcast=allow_downcast, **kwargs
)
except TypeError:
continue
def rand_gpuarray(*shape, **kwargs):
r = rng.rand(*shape) * 2 - 1
dtype = kwargs.pop("dtype", theano.config.floatX)
cls = kwargs.pop("cls", None)
if len(kwargs) != 0:
raise TypeError("Unexpected argument %s", list(kwargs.keys())[0])
return gpuarray.array(r, dtype=dtype, cls=cls, context=get_context(test_ctx_name))
def makeTester(
name,
op,
gpu_op,
cases,
checks=None,
mode_gpu=mode_with_gpu,
mode_nogpu=mode_without_gpu,
skip=False,
eps=1e-10,
):
if checks is None:
checks = {}
_op = op
_gpu_op = gpu_op
_cases = cases
_skip = skip
_checks = checks
class Checker(utt.OptimizationTestMixin):
op = staticmethod(_op)
gpu_op = staticmethod(_gpu_op)
cases = _cases
skip = _skip
checks = _checks
def setup_method(self):
eval(self.__class__.__module__ + "." + self.__class__.__name__)
def test_all(self):
if skip:
pytest.skip(skip)
for testname, inputs in cases.items():
for _ in range(len(inputs)):
if type(inputs[_]) is float:
inputs[_] = np.asarray(inputs[_], dtype=theano.config.floatX)
self.run_case(testname, inputs)
def run_case(self, testname, inputs):
inputs_ref = [theano.shared(inp) for inp in inputs]
inputs_tst = [theano.shared(inp) for inp in inputs]
try:
node_ref = safe_make_node(self.op, *inputs_ref)
node_tst = safe_make_node(self.op, *inputs_tst)
except Exception as exc:
err_msg = (
"Test %s::%s: Error occurred while making " "a node with inputs %s"
) % (self.gpu_op, testname, inputs)
exc.args += (err_msg,)
raise
try:
f_ref = inplace_func([], node_ref.outputs, mode=mode_nogpu)
f_tst = inplace_func([], node_tst.outputs, mode=mode_gpu)
except Exception as exc:
err_msg = (
"Test %s::%s: Error occurred while trying to " "make a Function"
) % (self.gpu_op, testname)
exc.args += (err_msg,)
raise
self.assertFunctionContains1(f_tst, self.gpu_op)
ref_e = None
try:
expecteds = f_ref()
except Exception as exc:
ref_e = exc
try:
variables = f_tst()
except Exception as exc:
if ref_e is None:
err_msg = (
"Test %s::%s: exception when calling the " "Function"
) % (self.gpu_op, testname)
exc.args += (err_msg,)
raise
else:
# if we raised an exception of the same type we're good.
if isinstance(exc, type(ref_e)):
return
else:
err_msg = (
"Test %s::%s: exception raised during test "
"call was not the same as the reference "
"call (got: %s, expected %s)"
% (self.gpu_op, testname, type(exc), type(ref_e))
)
exc.args += (err_msg,)
raise
for i, (variable, expected) in enumerate(zip(variables, expecteds)):
condition = (
variable.dtype != expected.dtype
or variable.shape != expected.shape
or not TensorType.values_eq_approx(variable, expected)
)
assert not condition, (
"Test %s::%s: Output %s gave the wrong "
"value. With inputs %s, expected %s "
"(dtype %s), got %s (dtype %s)."
% (
self.op,
testname,
i,
inputs,
expected,
expected.dtype,
variable,
variable.dtype,
)
)
for description, check in self.checks.items():
assert check(inputs, variables), (
"Test %s::%s: Failed check: %s " "(inputs were %s, ouputs were %s)"
) % (self.op, testname, description, inputs, variables)
Checker.__name__ = name
if hasattr(Checker, "__qualname__"):
Checker.__qualname__ = name
return Checker
def test_transfer_cpu_gpu():
a = tt.fmatrix("a")
g = GpuArrayType(dtype="float32", broadcastable=(False, False))("g")
av = np.asarray(rng.rand(5, 4), dtype="float32")
gv = gpuarray.array(av, context=get_context(test_ctx_name))
f = theano.function([a], GpuFromHost(test_ctx_name)(a))
fv = f(av)
assert GpuArrayType.values_eq(fv, gv)
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert np.all(fv == av)
def test_transfer_gpu_gpu():
g = GpuArrayType(
dtype="float32", broadcastable=(False, False), context_name=test_ctx_name
)()
av = np.asarray(rng.rand(5, 4), dtype="float32")
gv = gpuarray.array(av, context=get_context(test_ctx_name))
mode = mode_with_gpu.excluding(
"cut_gpua_host_transfers", "local_cut_gpua_host_gpua"
)
f = theano.function([g], GpuToGpu(test_ctx_name)(g), mode=mode)
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert isinstance(topo[0].op, GpuToGpu)
fv = f(gv)
assert GpuArrayType.values_eq(fv, gv)
def test_transfer_strided():
# This is just to ensure that it works in theano
# libgpuarray has a much more comprehensive suit of tests to
# ensure correctness
a = tt.fmatrix("a")
g = GpuArrayType(dtype="float32", broadcastable=(False, False))("g")
av = np.asarray(rng.rand(5, 8), dtype="float32")
gv = gpuarray.array(av, context=get_context(test_ctx_name))
av = av[:, ::2]
gv = gv[:, ::2]
f = theano.function([a], GpuFromHost(test_ctx_name)(a))
fv = f(av)
assert GpuArrayType.values_eq(fv, gv)
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert np.all(fv == av)
def gpu_alloc_expected(x, *shp):
g = gpuarray.empty(shp, dtype=x.dtype, context=get_context(test_ctx_name))
g[:] = x
return g
TestGpuAlloc = makeTester(
name="GpuAllocTester",
# The +1 is there to allow the lift to the GPU.
op=lambda *args: alloc(*args) + 1,
gpu_op=GpuAlloc(test_ctx_name),
cases=dict(
correct01=(rand(), np.int32(7)),
# just gives a DeepCopyOp with possibly wrong results on the CPU
# correct01_bcast=(rand(1), np.int32(7)),
correct02=(rand(), np.int32(4), np.int32(7)),
correct12=(rand(7), np.int32(4), np.int32(7)),
correct13=(rand(7), np.int32(2), np.int32(4), np.int32(7)),
correct23=(rand(4, 7), np.int32(2), np.int32(4), np.int32(7)),
bad_shape12=(rand(7), | np.int32(7) | numpy.int32 |