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def calculate_desired_noise_rms(clean_rms, snr):
"""
Given the Root Mean Square (RMS) of a clean sound and a desired signal-to-noise ratio (SNR),
calculate the desired RMS of a noise sound to be mixed in.
Based on https://github.com/Sato-Kunihiko/audio-SNR/blob/8d2c933b6c0afe6f1203251f4877e7a1068a6130/create_mixed_audio_file.py#L20
:param clean_rms: Root Mean Square (RMS) - a value between 0.0 and 1.0
:param snr: Signal-to-Noise (SNR) Ratio in dB - typically somewhere between -20 and 60
:return:
"""
a = float(snr) / 20
noise_rms = clean_rms / (10 ** a)
return noise_rms | 0.930253 | 0.753603 |
def is_waveform_multichannel(samples):
"""
Return bool that answers the question: Is the given ndarray a multichannel waveform or not?
:param samples: numpy ndarray
:return:
"""
return len(samples.shape) > 1 | 0.772101 | 0.565959 |
def is_spectrogram_multichannel(spectrogram):
"""
Return bool that answers the question: Is the given ndarray a multichannel spectrogram?
:param samples: numpy ndarray
:return:
"""
return len(spectrogram.shape) > 2 and spectrogram.shape[-1] > 1 | 0.823577 | 0.777215 |
def normalize_timestamp(timestamp):
"""
Format a timestamp (string or numeric) into a standardized
xxxxxxxxxx.xxxxx (10.5) format.
Note that timestamps using values greater than or equal to November 20th,
2286 at 17:46 UTC will use 11 digits to represent the number of
seconds.
:param timestamp: unix timestamp
:returns: normalized timestamp as a string
"""
return "%016.05f" % (float(timestamp)) | 0.860472 | 0.780662 |
def unpack_str(byteseq):
"""Unpack a byte sequence into a string."""
return byteseq.decode() | 0.721743 | 0.609408 |
def num_model_detection_error(ground_truth_vps, detected_vps):
"""Measures error in the number of detected vanishing points.
Returns:
Integer, positive when there are too many VPs, negative
when there are too few.
"""
return len(detected_vps) - len(ground_truth_vps) | 0.785679 | 0.801237 |
def binary_search_iterative(array, item):
"""Time Complexity: O(log*n) because you are constantly dividing the length of array by 2 until array length is 1
Space Complexity: O(1) """
left, right = 0, len(array) - 1
if len(array) == 0:
return None
while left <= right:
middle = left + (right - left) // 2
if item == array[middle]:
return middle
elif item > array[middle]:
left = middle + 1
else:
right = middle - 1
return None | 0.755997 | 0.620765 |
import torch
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth l1 loss.
:param pred: predict
:param target: target
:param beta: beta
:return: loss
"""
assert beta > 0
assert pred.size() == target.size() and target.numel() > 0
diff = torch.abs(pred - target)
loss = torch.where(diff < beta, 0.5 * diff * diff / beta, diff - 0.5 * beta)
return loss | 0.768907 | 0.611295 |
import torch
def _expand_binary_labels(labels, label_weights, label_channels):
"""Expand binary labels.
:param labels: labels
:param label_weights: label weights
:param label_channels: label channels
:return: binary label and label weights
"""
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(labels >= 1).squeeze()
if inds.numel() > 0:
bin_labels[inds, labels[inds] - 1] = 1
if label_weights is None:
bin_label_weights = None
else:
bin_label_weights = label_weights.view(-1, 1).expand(label_weights.size(0), label_channels)
return bin_labels, bin_label_weights | 0.779154 | 0.550547 |
def normalize_pack_version(version):
"""
Normalize old, pre StackStorm v2.1 non valid semver version string (e.g. 0.2) to a valid
semver version string (0.2.0).
:rtype: ``str``
"""
version = str(version)
version_seperator_count = version.count('.')
if version_seperator_count == 1:
version = version + '.0'
return version | 0.719482 | 0.505249 |
def roundup_to_integer_multiple(x, factor):
"""Round up integer x to the nearest integer multiple of integer factor.
Returns x if factor is set to -1. Both x and factor must otherwise be
positive."""
# ensure integers
assert int(x) == x, "The input x is not an integer."
assert int(factor) == factor, "The input factor is not an integer."
# use -1 to indicate no padding needed
if factor == -1:
return x
# ensure positive values
assert factor > 0 and x > 0, "Factor and x are <= 0."
if x < factor:
return factor
else:
if x % factor == 0:
return x
else:
return x + (factor - (x % factor)) | 0.886039 | 0.563828 |
def calculate_matvec_accumulator_range(matrix, vec_dt):
"""Calculate the minimum and maximum possible result (accumulator) values
for a dot product x * A, given matrix A of dims (MW, MH), and vector (1, MW)
with datatype vec_dt. Returns (acc_min, acc_max).
"""
min_weight = matrix.min()
max_weight = matrix.max()
perceptive_field_elems = matrix.shape[0]
min_input = vec_dt.min()
max_input = vec_dt.max()
# calculate minimum and maximum values of accumulator
# assume inputs span the whole range of the input datatype
acc_min = perceptive_field_elems * min(
min_weight * max_input,
min_weight * min_input,
max_weight * max_input,
max_weight * min_input,
)
acc_max = perceptive_field_elems * max(
min_weight * max_input,
min_weight * min_input,
max_weight * max_input,
max_weight * min_input,
)
return (acc_min, acc_max) | 0.871146 | 0.762114 |
import torch
def cam2pixel(cam_coords, proj_c2p_rot, proj_c2p_tr, padding_mode):
"""Transform coordinates in the camera frame to the pixel frame.
Args:
cam_coords: pixel coordinates defined in the first camera coordinates system -- [B, 4, H, W]
proj_c2p_rot: rotation matrix of cameras -- [B, 3, 4]
proj_c2p_tr: translation vectors of cameras -- [B, 3, 1]
Returns:
array of [-1,1] coordinates -- [B, 2, H, W]
"""
b, _, h, w = cam_coords.size()
cam_coords_flat = cam_coords.view(b, 3, -1) # [B, 3, H*W]
if proj_c2p_rot is not None:
pcoords = proj_c2p_rot.bmm(cam_coords_flat)
else:
pcoords = cam_coords_flat
if proj_c2p_tr is not None:
pcoords = pcoords + proj_c2p_tr # [B, 3, H*W]
X = pcoords[:, 0]
Y = pcoords[:, 1]
Z = pcoords[:, 2].clamp(min=1e-3)
X_norm = 2*(X / Z)/(w-1) - 1 # Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]
Y_norm = 2*(Y / Z)/(h-1) - 1 # Idem [B, H*W]
if padding_mode == 'zeros':
X_mask = ((X_norm > 1)+(X_norm < -1)).detach()
X_norm[X_mask] = 2 # make sure that no point in warped image is a combinaison of im and gray
Y_mask = ((Y_norm > 1)+(Y_norm < -1)).detach()
Y_norm[Y_mask] = 2
pixel_coords = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]
return pixel_coords.view(b,h,w,2) | 0.805058 | 0.637045 |
import torch
def euler2mat(angle):
"""Convert euler angles to rotation matrix.
Reference: https://github.com/pulkitag/pycaffe-utils/blob/master/rot_utils.py#L174
Args:
angle: rotation angle along 3 axis (in radians) -- size = [B, 3]
Returns:
Rotation matrix corresponding to the euler angles -- size = [B, 3, 3]
"""
B = angle.size(0)
x, y, z = angle[:,0], angle[:,1], angle[:,2]
cosz = torch.cos(z)
sinz = torch.sin(z)
zeros = z.detach()*0
ones = zeros.detach()+1
zmat = torch.stack([cosz, -sinz, zeros,
sinz, cosz, zeros,
zeros, zeros, ones], dim=1).view(B, 3, 3)
cosy = torch.cos(y)
siny = torch.sin(y)
ymat = torch.stack([cosy, zeros, siny,
zeros, ones, zeros,
-siny, zeros, cosy], dim=1).view(B, 3, 3)
cosx = torch.cos(x)
sinx = torch.sin(x)
xmat = torch.stack([ones, zeros, zeros,
zeros, cosx, -sinx,
zeros, sinx, cosx], dim=1).view(B, 3, 3)
rotMat = xmat.bmm(ymat).bmm(zmat)
return rotMat | 0.950353 | 0.861538 |
import torch
def quat2mat(quat):
"""Convert quaternion coefficients to rotation matrix.
Args:
quat: first three coeff of quaternion of rotation. fourht is then computed to have a norm of 1 -- size = [B, 3]
Returns:
Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
"""
norm_quat = torch.cat([quat[:,:1].detach()*0 + 1, quat], dim=1)
norm_quat = norm_quat/norm_quat.norm(p=2, dim=1, keepdim=True)
w, x, y, z = norm_quat[:,0], norm_quat[:,1], norm_quat[:,2], norm_quat[:,3]
B = quat.size(0)
w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
wx, wy, wz = w*x, w*y, w*z
xy, xz, yz = x*y, x*z, y*z
rotMat = torch.stack([w2 + x2 - y2 - z2, 2*xy - 2*wz, 2*wy + 2*xz,
2*wz + 2*xy, w2 - x2 + y2 - z2, 2*yz - 2*wx,
2*xz - 2*wy, 2*wx + 2*yz, w2 - x2 - y2 + z2], dim=1).view(B, 3, 3)
return rotMat | 0.914558 | 0.940024 |
def elemwise_mul(a, b):
"""
a: A theano matrix
b: A theano matrix
Returns the elementwise product of a and b
"""
return a * b | 0.713531 | 0.629276 |
import torch
def l2_norm(input, axis=1):
"""l2 normalization.
Args:
input (torch.Tensor): The input tensor.
axis (int, optional): Specifies which axis of input to calculate the
norm across. Defaults to 1.
Returns:
Tensor: Tensor after L2 normalization per-instance.
"""
norm = torch.norm(input, 2, axis, True)
output = torch.div(input, norm)
return output | 0.818193 | 0.500122 |
def rectangle_centroid(rectangle):
"""
get the centroid of the rectangle
Keyword arguments:
rectangle -- polygon geojson object
return centroid
"""
bbox = rectangle['coordinates'][0]
xmin = bbox[0][0]
ymin = bbox[0][1]
xmax = bbox[2][0]
ymax = bbox[2][1]
xwidth = xmax - xmin
ywidth = ymax - ymin
return {'type': 'Point', 'coordinates': [xmin + xwidth / 2, ymin + ywidth / 2]} | 0.817684 | 0.666021 |
def fixed_time_horizon(df, column='close', lookback=20):
"""
Fixed-time Horizon
As it relates to finance, virtually all ML papers label observations using the fixed-time horizon method.
Fixed-time horizon is presented as one of the main procedures to label data when it comes to processing
financial time series for machine learning.
Parameters
----------
df: pd.DataFrame
column: str
Choose from "open", "high", "low", and "close."
lookahead: str
The number of days to look ahead.
References
----------
1. https://mlfinlab.readthedocs.io/en/latest/labeling/labeling_fixed_time_horizon.html
2. https://arxiv.org/pdf/1603.08604.pdf
3. https://quantdare.com/4-simple-ways-to-label-financial-data-for-machine-learning/
4. De Prado, Advances in financial machine learning, 2018
5. Dixon et al., Classification-based financial markets prediction using deep neural networks, 2017
"""
price = df[column]
label = (price.shift(-lookback) / price > 1).astype(int)
return label | 0.937002 | 0.756942 |
def _make_cache_key(times, targets):
"""
Make a unique key to reference this combination of ``times`` and ``targets``.
Often, we wish to store expensive calculations for a combination of
``targets`` and ``times`` in a cache on an ``observer``` object. This
routine will provide an appropriate, hashable, key to store these
calculations in a dictionary.
Parameters
----------
times : `~astropy.time.Time`
Array of times on which to test the constraint.
targets : `~astropy.coordinates.SkyCoord`
Target or list of targets.
Returns
-------
cache_key : tuple
A hashable tuple for use as a cache key
"""
# make a tuple from times
try:
timekey = tuple(times.jd) + times.shape
except BaseException: # must be scalar
timekey = (times.jd,)
# make hashable thing from targets coords
try:
if hasattr(targets, 'frame'):
# treat as a SkyCoord object. Accessing the longitude
# attribute of the frame data should be unique and is
# quicker than accessing the ra attribute.
targkey = tuple(targets.frame.data.lon.value.ravel()) + targets.shape
else:
# assume targets is a string.
targkey = (targets,)
except BaseException:
targkey = (targets.frame.data.lon,)
return timekey + targkey | 0.903967 | 0.904693 |
def min_best_rescale(vals, min_val, max_val, less_than_min=1):
"""
rescales an input array ``vals`` to be a score (between zero and one),
where the ``min_val`` goes to one, and the ``max_val`` goes to zero.
Parameters
----------
vals : array-like
the values that need to be rescaled to be between 0 and 1
min_val : float
worst acceptable value (rescales to 0)
max_val : float
best value cared about (rescales to 1)
less_than_min : 0 or 1
what is returned for ``vals`` below ``min_val``. (in some cases
anything less than ``min_val`` should also return one,
in some cases it should return zero)
Returns
-------
array of floats between 0 and 1 inclusive rescaled so that
``vals`` equal to ``max_val`` equal 0 and those equal to
``min_val`` equal 1
Examples
--------
rescale airmasses to between 0 and 1, with the best (1)
and worst (2.25). All values outside the range should
return 0.
>>> from astroplan.constraints import min_best_rescale
>>> import numpy as np
>>> airmasses = np.array([1, 1.5, 2, 3, 0])
>>> min_best_rescale(airmasses, 1, 2.25, less_than_min = 0) # doctest: +FLOAT_CMP
array([ 1. , 0.6, 0.2, 0. , 0. ])
"""
rescaled = (vals - max_val) / (min_val - max_val)
below = vals < min_val
above = vals > max_val
rescaled[below] = less_than_min
rescaled[above] = 0
return rescaled | 0.937947 | 0.981095 |
def max_best_rescale(vals, min_val, max_val, greater_than_max=1):
"""
rescales an input array ``vals`` to be a score (between zero and one),
where the ``max_val`` goes to one, and the ``min_val`` goes to zero.
Parameters
----------
vals : array-like
the values that need to be rescaled to be between 0 and 1
min_val : float
worst acceptable value (rescales to 0)
max_val : float
best value cared about (rescales to 1)
greater_than_max : 0 or 1
what is returned for ``vals`` above ``max_val``. (in some cases
anything higher than ``max_val`` should also return one,
in some cases it should return zero)
Returns
-------
array of floats between 0 and 1 inclusive rescaled so that
``vals`` equal to ``min_val`` equal 0 and those equal to
``max_val`` equal 1
Examples
--------
rescale an array of altitudes to be between 0 and 1,
with the best (60) going to 1 and worst (35) going to
0. For values outside the range, the rescale should
return 0 below 35 and 1 above 60.
>>> from astroplan.constraints import max_best_rescale
>>> import numpy as np
>>> altitudes = np.array([20, 30, 40, 45, 55, 70])
>>> max_best_rescale(altitudes, 35, 60) # doctest: +FLOAT_CMP
array([ 0. , 0. , 0.2, 0.4, 0.8, 1. ])
"""
rescaled = (vals - min_val) / (max_val - min_val)
below = vals < min_val
above = vals > max_val
rescaled[below] = 0
rescaled[above] = greater_than_max
return rescaled | 0.942354 | 0.984796 |
def convert_qkv_weight(cfg, value):
"""
Convert qkv.weight to be compatible with LiBai transformer layer
Args:
cfg: config file
value: qkv.weight in the loaded checkpoint
"""
num_heads = cfg.model.num_heads
hidden_size = cfg.model.embed_dim
head_size = int(hidden_size / num_heads)
qkv_weight = (
value.view([3, num_heads, head_size, hidden_size])
.permute(1, 0, 2, 3)
.contiguous()
.view(hidden_size * 3, hidden_size)
)
return qkv_weight | 0.88136 | 0.689458 |
def convert_qkv_bias(cfg, value):
"""
Convert qkv.bias to be compatible with LiBai transformer layer
Args:
cfg: config file
value: qkv.bias in the loaded checkpoint
"""
num_heads = cfg.model.num_heads
hidden_size = cfg.model.embed_dim
head_size = int(hidden_size / num_heads)
qkv_bias = (
value.view(3, num_heads, head_size).permute(1, 0, 2).contiguous().view(hidden_size * 3)
)
return qkv_bias | 0.914901 | 0.817137 |
def get_supported_schedulers():
"""
Return a tuple of the scheduler supported by parallelcluster.
:return: a tuple of strings of the supported scheduler
"""
return "sge", "torque", "slurm", "awsbatch" | 0.744006 | 0.517937 |
def textBoxSize(txt, transformation=None, figure=None):
"""Get the width and height of a text object's bounding box transformed to the desired coordinates. Defaults to
figure coordinates if transformation is None."""
fig= txt.get_figure() if figure is None else figure
if transformation is None:
transformation = fig.transFigure
coordConvert = transformation.inverted().transform
bboxDisp = txt.get_window_extent(fig.canvas.renderer)
bboxConv = coordConvert(bboxDisp)
w = bboxConv[1,0] - bboxConv[0,0]
h = bboxConv[1,1] - bboxConv[0,1]
return w, h | 0.802517 | 0.693596 |
def pretty_date(ago):
""" Process a timedelta object.
From https://stackoverflow.com/questions/1551382/user-friendly-time-format-in-python
"""
second_diff = ago.seconds
day_diff = ago.days
if day_diff < 0:
return ''
if day_diff == 0:
if second_diff < 10:
return "just now"
if second_diff < 60:
return str(second_diff) + " seconds ago"
if second_diff < 120:
return "a minute ago"
if second_diff < 3600:
return str(second_diff / 60) + " minutes ago"
if second_diff < 7200:
return "an hour ago"
if second_diff < 86400:
return str(second_diff / 3600) + " hours ago"
if day_diff == 1:
return "Yesterday"
if day_diff < 7:
return str(day_diff) + " days ago"
if day_diff < 31:
if day_diff / 7 == 1:
return str(day_diff / 7) + " week ago"
return str(day_diff / 7) + " weeks ago"
if day_diff < 365:
if day_diff / 30 == 1:
return str(day_diff / 30) + " month ago"
return str(day_diff / 30) + " months ago"
if day_diff / 365 == 1:
return str(day_diff / 365) + " year ago"
return str(day_diff / 365) + " years ago" | 0.736401 | 0.514217 |
def combine_dict(d1,d2):
"""Creates a dictionary which has entries from both of them.
:param d1: dictionary 1
:param d2: dictionary 2
:return: resulting dictionary
"""
d = d1.copy()
d.update(d2)
return d | 0.723993 | 0.975414 |
def lift_to_dimension(A, dim):
"""Creates a view of A of dimension dim (by adding dummy dimensions if necessary).
:param A: numpy array
:param dim: desired dimension of view
:return: returns view of A of appropriate dimension
"""
current_dim = len(A.shape)
if current_dim > dim:
raise ValueError('Can only add dimensions, but not remove them')
if current_dim == dim:
return A
else:
return A.reshape([1]*(dim-current_dim)+list(A.shape)) | 0.769514 | 0.587647 |
def get_dim_of_affine_transform(Ab):
"""Returns the number of dimensions corresponding to an affine transformation of the
form y=Ax+b stored in a column vector. For A =[a1,a2,a3], the parameter vector is simply
[a1;a2;a3;b], i.e., all columns stacked on top of each other.
:param Ab: parameter vector
:return: dimensionality of transform (1,2,or 3)
"""
nr = len(Ab)
if nr==2:
return 1
elif nr==6:
return 2
elif nr==12:
return 3
else:
raise ValueError('Only supports dimensions 1, 2, and 3.') | 0.795975 | 0.727129 |
def t2np(v):
"""
Takes a torch array and returns it as a numpy array on the cpu
:param v: torch array
:return: numpy array
"""
return (v.detach()).cpu().numpy() | 0.77518 | 0.791821 |
def cxyz_to_xyzc( v ):
"""
Takes a torch array and returns it as a numpy array on the cpu
:param v: torch array
:return: numpy array
"""
dim = len(v.shape)-2
if dim ==2:
v = v.permute(0,2,3,1)
if dim ==3:
v = v.permute(0,2,3,4,1)
return v | 0.725357 | 0.735642 |
def best_scale(number):
"""Scale and units for a number with proper prefix."""
absnr = abs(number)
if absnr == 0:
return 1, ' '
if absnr < 0.99999999e-9:
return 1e12, 'p'
if absnr < 0.99999999e-6:
return 1e9, 'n'
if absnr < 0.99999999e-3:
return 1e6, 'µ'
if absnr < 0.99999999:
return 1e3, 'm'
if absnr < 0.99999999e3:
return 1, ' '
if absnr < 0.99999999e6:
return 1e-3, 'k'
if absnr < 0.999999991e9:
return 1e-6, 'M'
return 1e-9, 'G' | 0.702326 | 0.625495 |
def crop_img(img, relative_corners):
""" relative_corners are floats between 0 and 1 designating where the corners of a crop box
should be ([[top_left_x, top_left_y], [bottom_right_x, bottom_right_y]]).
e.g. [[0, 0], [1, 1]] would be the full image, [[0.5, 0.5], [1, 1]] would be bottom right."""
rc = relative_corners
raw_height, raw_width = img.shape[:2]
top_left_pix = [int(rc[0][0] * raw_width), int(rc[0][1] * raw_height)]
bottom_right_pix = [int(rc[1][0] * raw_width), int(rc[1][1] * raw_height)]
img_cropped = img[top_left_pix[1]:bottom_right_pix[1], top_left_pix[0]:bottom_right_pix[0]]
return img_cropped | 0.809765 | 0.708015 |
def loss(y_pred, y_true, metric):
"""Compute loss function between prediction and ground truth.
Loss function given by a Riemannian metric,
expressed as the squared geodesic distance between the prediction
and the ground truth.
Parameters
----------
y_pred
y_true
metric
Returns
-------
loss
"""
loss = metric.squared_dist(y_pred, y_true)
return loss | 0.947884 | 0.811265 |
def kl_to_prior(means, log_stds, stds):
"""
KL between a Gaussian and a standard Gaussian.
https://stats.stackexchange.com/questions/60680/kl-divergence-between-two-multivariate-gaussians
"""
return 0.5 * (
- 2 * log_stds # log std_prior = 0
- 1 # d = 1
+ stds ** 2
+ means ** 2
) | 0.72952 | 0.557243 |
def getConflictingAssignments(schedule):
""" Get list of assignments which exceeded rotation capacity
Parameters:
schedule (dict): overall assignments
Returns:
confictingAssignmentsByRotation (dict): overall schedule with conflicting assignments
"""
return {} | 0.777131 | 0.615203 |
def quadratic_formula(polynomial):
"""
input is single-variable polynomial of degree 2
returns zeros
"""
if len(polynomial.term_matrix) == 3:
if polynomial.term_matrix[2][1] == 1:
a, b = polynomial.term_matrix[1][0], polynomial.term_matrix[2][0]
return 0, -b/a
a, c = polynomial.term_matrix[1][0], polynomial.term_matrix[2][0]
return (-c/a)**.5, -(-c/a)**.5
if len(polynomial.term_matrix) == 2:
a, b, c, = polynomial.term_matrix[1][0], 0, 0
elif len(polynomial.term_matrix) == 3:
a, b, c = polynomial.term_matrix[1][0], polynomial.term_matrix[2][0], 0
else:
a, b, c = polynomial.term_matrix[1][0], polynomial.term_matrix[2][0], polynomial.term_matrix[3][0]
ans1 = (-b + (b**2 - 4*a*c)**.5)/2*a
ans2 = (-b - (b**2 - 4*a*c)**.5)/2*a
if ans1 == ans2:
return ans1
return ans1, ans2 | 0.739234 | 0.795221 |
def generate_coordinates(coords):
"""
A function that returns all possible triples of coords
Parameters:
coords: a numpy array of coordinates
Returns:
x: the first coordinate of possible triples
y: the second coordinate of possible triples
z the third coordinate of possible triples
"""
x = coords.reshape(-1, 1).repeat(1, len(coords) * len(coords)).flatten()
y = coords.reshape(-1, 1).repeat(1, len(coords)).flatten().repeat(len(coords))
z = coords.reshape(-1, 1).flatten().repeat(len(coords)*len(coords))
return x, y, z | 0.914827 | 0.929824 |
def midpoint_rule(f, M=100000):
"""Integrate f(x) over [0,1] using M intervals."""
from numpy import sum, linspace
dx = 1.0/M # interval length
x = linspace(dx/2, 1-dx/2, M) # integration points
return dx*sum(f(x)) | 0.700895 | 0.504639 |
def dollar(amount):
""" Given an amount as a number
Return a string formatted as a dollar amount
"""
amount = round(amount, 2)
return '${0:0.2f}'.format(amount) | 0.778986 | 0.924688 |
def dataqc_condcompress(p_orig, p_new, c_orig, cpcor=-9.57e-8):
"""
Description:
Implementation of the Sea-Bird conductivity compressibility correction,
scaling the input conductivity based on ratio of the original pressure
and the updated pressure.
Implemented by:
2013-04-07: Christopher Wingard. Initial python implementation.
Usage:
c_new = dataqc_condcompress(p_orig, p_new, c_orig, cpcor)
where
c_new = updated conductivity record [S/m]
p_orig = original pressure used to calculate original conductivity,
this typically the L1a PRESWAT [dbar]
p_new = updated pressure, typically L1b PRESWAT [dbar]
c_orig = original conductivty record, typically L1a CONDWAT [S/m]
cpcor = pressure correction coefficient used to calculate original
conductivity, default is -9.57e-8
References:
OOI (2012). Data Product Specification for Conductivity Compressibility
Correction. Document Control Number 1341-10030.
https://alfresco.oceanobservatories.org/ (See: Company Home >> OOI
>> Controlled >> 1000 System Level >>
1341-10030_Data_Product_SPEC_CNDCMPR_OOI.pdf)
"""
c_new = c_orig * (1 + cpcor * p_orig) / (1 + cpcor * p_new)
return c_new | 0.825906 | 0.751785 |
def delta(a, b):
"""
Return change in percent (or None if undefined).
The delta in percent is rounded to one decimal.
"""
if a is None or b is None:
return None
if a == 0.0 and b == 0.0:
return 0.0
assert a != 0.0 and b != 0.0
return round((b - a) * 1000.0 / a) / 10.0 | 0.741393 | 0.654322 |
def adjust_contrast(image, contrast_level):
"""Return the image scaled to a certain contrast level in [0, 1].
parameters:
- image: a numpy.ndarray
- contrast_level: a scalar in [0, 1]; with 1 -> full contrast
"""
assert(contrast_level >= 0.0), "contrast_level too low."
assert(contrast_level <= 1.0), "contrast_level too high."
return (1-contrast_level)/2.0 + image.dot(contrast_level) | 0.89753 | 0.78964 |
def to_list(data_in):
"""Convert the data into a list. Does not pack lists into a new one.
If your input is, for example, a string or a list of strings, or a
tuple filled with strings, you have, in general, a problem:
- just iterate through the object will fail because it iterates through the
characters of the string.
- using list(obj) converts the tuple, leaves the list but splits the strings
characters into single elements of a new list.
- using [obj] creates a list containing a string, but also a list containing
a list or a tuple, which you did not want to.
Solution: use to_list(obj), which creates a new list in case the object is
a single object (a string is a single object in this sence) or converts
to a list if the object is already a container for several objects.
Parameters
----------
data_in : any obj
So far, any object can be entered.
Returns
-------
out : list
Return a list containing the object or the object converted to a list.
"""
if isinstance(data_in, (str, int, float)):
data_in = [data_in]
data_in = list(data_in)
return data_in | 0.814754 | 0.701317 |
def dot(a, b, out=None):
"""Returns a dot product of two arrays.
For arrays with more than one axis, it computes the dot product along the
last axis of ``a`` and the second-to-last axis of ``b``. This is just a
matrix product if the both arrays are 2-D. For 1-D arrays, it uses their
unique axis as an axis to take dot product over.
Args:
a (cupy.ndarray): The left argument.
b (cupy.ndarray): The right argument.
out (cupy.ndarray): Output array.
Returns:
cupy.ndarray: The dot product of ``a`` and ``b``.
.. seealso:: :func:`numpy.dot`
"""
# TODO(okuta): check type
return a.dot(b, out) | 0.814422 | 0.750736 |
def coord_to_index(coord, sl):
"""
Takes a 3D coordinate in a cube and the cube side length.
Returns index in flattened 3D array.
"""
return coord[0] * sl * sl + coord[1] * sl + coord[2] | 0.749546 | 0.992327 |
def index_to_coord(index, sl):
"""
Takes an index into a flattened 3D array and its side length.
Returns the coordinate in the cube.
"""
coord = []
two_d_slice_size = sl * sl
coord.append(index // two_d_slice_size)
remaining = index % two_d_slice_size
coord.append(remaining // sl)
coord.append(remaining % sl)
return coord | 0.748076 | 0.868102 |
def use_node_def_or_str(given_value, default_func):
"""Transform a value of type (None, str, Callable) to a node annotation function."""
# Default: use pre-defined function from this module
if given_value is None:
func = default_func
# Transform: value to function that returns the value
elif isinstance(given_value, str):
given_value = str(given_value)
def func(atom):
return given_value
# Passthrough: value itself is a function
else:
func = given_value
return func | 0.745306 | 0.540378 |
def use_node_def_or_num(given_value, default_func):
"""Transform a value of type (None, int, float, Callable) to a node annotation function."""
# Default: use pre-defined function from this module
if given_value is None:
func = default_func
# Transform: value to function that returns the value
elif isinstance(given_value, (int, float)):
given_value = float(given_value)
def func(atom):
return given_value
# Passthrough: value itself is a function
else:
func = given_value
return func | 0.737725 | 0.556882 |
def use_edge_def_or_str(given_value, default_func):
"""Transform a value of type (None, str, Callable) to an edge annotation function."""
# Default: use pre-defined function from this module
if given_value is None:
func = default_func
# Transform: value to function that returns the value
elif isinstance(given_value, str):
given_value = str(given_value)
def func(atom1, atom2):
return given_value
# Passthrough: value itself is a function
else:
func = given_value
return func | 0.752559 | 0.546436 |
def use_edge_def_or_num(given_value, default_func):
"""Transform a value of type (None, int, float, Callable) to an edge annotation function."""
# Default: use pre-defined function from this module
if given_value is None:
func = default_func
# Transform: value to function that returns the value
elif isinstance(given_value, (int, float)):
given_value = float(given_value)
def func(atom1, atom2):
return given_value
# Passthrough: value itself is a function
else:
func = given_value
return func | 0.761361 | 0.562177 |
import torch
def gnmt_length_penalty(lengths, alpha=0.8):
"""Calculate a length penalty from https://arxiv.org/pdf/1609.08144.pdf
The paper states the penalty as (5 + |Y|)^a / (5 + 1)^a. This is implemented
as ((5 + |Y|) / 6)^a for a (very) tiny performance boost
:param lengths: `torch.LongTensor`: [B, K] The lengths of the beams.
:param alpha: `float`: A hyperparameter. See Table 2 for a search on this
parameter.
:returns:
`torch.FloatTensor`: [B, K, 1] The penalties.
"""
lengths = lengths.to(torch.float)
penalty = torch.pow(((5 + lengths) / 6), alpha)
return penalty.unsqueeze(-1) | 0.913342 | 0.563798 |
def repeat_batch(t, K, dim=0):
"""Repeat a tensor while keeping the concept of a batch.
:param t: `torch.Tensor`: The tensor to repeat.
:param K: `int`: The number of times to repeat the tensor.
:param dim: `int`: The dimension to repeat in. This should be the
batch dimension.
:returns: `torch.Tensor`: The repeated tensor. The new shape will be
batch size * K at dim, the rest of the shapes will be the same.
Example::
>>> a = torch.arange(10).view(2, -1)
>>> a
tensor([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> a.repeat(2, 1)
tensor([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> repeat_batch(a, 2)
tensor([[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[5, 6, 7, 8, 9]])
"""
shape = t.shape
tiling = [1] * (len(shape) + 1)
tiling[dim + 1] = K
tiled = t.unsqueeze(dim + 1).repeat(tiling)
old_bsz = shape[dim]
new_bsz = old_bsz * K
new_shape = list(shape[:dim]) + [new_bsz] + list(shape[dim + 1 :])
return tiled.view(new_shape) | 0.946076 | 0.911928 |
import torch
def bilinear_interpolate_torch(im, x, y):
"""
Args:
im: (H, W, C) [y, x]
x: (N)
y: (N)
Returns:
"""
x0 = torch.floor(x).long()
x1 = x0 + 1
y0 = torch.floor(y).long()
y1 = y0 + 1
x0 = torch.clamp(x0, 0, im.shape[1] - 1)
x1 = torch.clamp(x1, 0, im.shape[1] - 1)
y0 = torch.clamp(y0, 0, im.shape[0] - 1)
y1 = torch.clamp(y1, 0, im.shape[0] - 1)
Ia = im[y0, x0]
Ib = im[y1, x0]
Ic = im[y0, x1]
Id = im[y1, x1]
wa = (x1.type_as(x) - x) * (y1.type_as(y) - y)
wb = (x1.type_as(x) - x) * (y - y0.type_as(y))
wc = (x - x0.type_as(x)) * (y1.type_as(y) - y)
wd = (x - x0.type_as(x)) * (y - y0.type_as(y))
ans = torch.t((torch.t(Ia) * wa)) + torch.t(torch.t(Ib) * wb) + torch.t(torch.t(Ic) * wc) + torch.t(torch.t(Id) * wd)
return ans | 0.806396 | 0.580798 |
def int_parameter(level, maxval):
"""Helper function to scale `val` between 0 and maxval .
Args:
level: Level of the operation that will be between [0, `PARAMETER_MAX`].
maxval: Maximum value that the operation can have. This will be scaled to
level/PARAMETER_MAX.
Returns:
An int that results from scaling `maxval` according to `level`.
"""
return int(level * maxval / 10) | 0.768386 | 0.610802 |
def float_parameter(level, maxval):
"""Helper function to scale `val` between 0 and maxval.
Args:
level: Level of the operation that will be between [0, `PARAMETER_MAX`].
maxval: Maximum value that the operation can have. This will be scaled to
level/PARAMETER_MAX.
Returns:
A float that results from scaling `maxval` according to `level`.
"""
return float(level) * maxval / 10. | 0.862207 | 0.820397 |
def normalize(image):
"""Normalize input image channel-wise to zero mean and unit variance."""
return image - 127 | 0.792223 | 0.963746 |
def MakeMetadataLine(label, value, indent=1):
"""Returns a string with a vertically aligned label and value.
Labels of the same indentation level will start at the same column. Values
will all start at the same column (unless the combined left-indent and
label length is excessively long). If a value spans multiple lines,
indentation will only be applied to the first line. Example output from
several calls:
Label1: Value (default indent of 1 was used)
Sublabel1: Value (used indent of 2 here)
Label2: Value
Args:
label: The label to print in the first column.
value: The value to print in the second column.
indent: (4 * indent) spaces will be placed before the label.
Returns:
A string with a vertically aligned label and value.
"""
return '{}{}'.format(((' ' * indent * 4) + label + ':').ljust(28), value) | 0.90532 | 0.670947 |
def to_label(name, capitalize=True):
"""Converts `name` into label by replacing underscores by spaces. If
`capitalize` is ``True`` (default) then the first letter of the label is
capitalized."""
label = name.replace("_", " ")
if capitalize:
label = label.capitalize()
return label | 0.731538 | 0.581273 |
def closest_ref_length(ref_lens, hyp_len):
"""
This function finds the reference that is the closest length to the
hypothesis. The closest reference length is referred to as *r* variable
from the brevity penalty formula in Papineni et. al. (2002)
:param references: A list of reference translations.
:type references: list(list(str))
:param hyp_len: The length of the hypothesis.
:type hyp_len: int
:return: The length of the reference that's closest to the hypothesis.
:rtype: int
"""
closest_ref_len = min(
ref_lens, key=lambda ref_len: (abs(ref_len - hyp_len), ref_len)
)
return closest_ref_len | 0.776708 | 0.756897 |
def firing_rate(spike_train, duration):
"""Calculate firing rate for a spike train.
If either temporal bound is not specified, the first and last spike time are used by default.
Inputs:
-------
spike_train : array of spike times (in seconds)
duration : length of recording (in seconds)
Outputs:
--------
fr : float
Firing rate in Hz
"""
fr = spike_train.size / duration
return fr | 0.788176 | 0.967132 |
def get_unit_pcs(these_pc_features, index_mask, channel_mask):
""" Use the index_mask and channel_mask to return PC features for one unit
Inputs:
-------
these_pc_features : numpy.ndarray (float)
Array of pre-computed PC features (num_spikes x num_PCs x num_channels)
index_mask : numpy.ndarray (boolean)
Mask for spike index dimension of pc_features array
channel_mask : numpy.ndarray (boolean)
Mask for channel index dimension of pc_features array
Output:
-------
unit_PCs : numpy.ndarray (float)
PCs for one unit (num_spikes x num_PCs x num_channels)
"""
unit_PCs = these_pc_features[index_mask, :, :]
unit_PCs = unit_PCs[:, :, channel_mask]
return unit_PCs | 0.794863 | 0.76291 |
def capitalize(text):
"""capitalizes a word, for use in rendering template
Args:
text (str): word to capitalize
Returns:
capitalized (str): capitalized word
"""
return text[0].upper() + text[1:] | 0.821116 | 0.759839 |
def rule_separation(value: float, layer1: str, layer2: str):
"""Min space between different layers"""
error = f"min {layer1} {layer2} separation {value}um"
return f"{layer1}.separation({layer2}, {value})" f".output('{error}', '{error}')" | 0.76454 | 0.723187 |
def remove_alpha(pic):
"""
Removes the alpha channel from an image, if it exists. Necessary for OCR.
Args:
pic: PIL.Image object to convert.
Returns:
The PIL.Image object in RGB format.
"""
return pic.convert("RGB") | 0.805288 | 0.673809 |
def is_array(signature):
"""Return True if this argument is an array. A dictionary is considered an array."""
return signature[0] == "a" | 0.758332 | 0.624007 |
def to_row_vec(col_vec):
"""
:param col_vec: 2d np array
:return:
"""
return col_vec.reshape(1, -1) | 0.758689 | 0.970688 |
def concatenate_rounds(rounds_1, rounds_2):
"""
:param rounds_1: list - first rounds played.
:param rounds_2: list - second set of rounds played.
:return: list - all rounds played.
"""
return rounds_1 + rounds_2 | 0.799403 | 0.693077 |
def list_contains_round(rounds, number):
"""
:param rounds: list - rounds played.
:param number: int - round number.
:return: bool - was the round played?
"""
return number in rounds | 0.702836 | 0.50116 |
def card_average(hand):
"""
:param hand: list - cards in hand.
:return: float - average value of the cards in the hand.
"""
return sum(hand) / len(hand) | 0.702938 | 0.841044 |
def split_num(line, chars=' ', maxsplits=1, empty=''):
"""/lazy/ wrapper, to stop us having to bounds-check when splitting.
Arguments:
line -- line to split
chars -- character(s) to split line on
maxsplits -- how many split items are returned
empty -- character to put in place of nothing
Returns:
line.split(chars, items); return value is padded until `maxsplits + 1` number of values
are present"""
line = line.split(chars, maxsplits)
while len(line) <= maxsplits:
line.append(empty)
return line | 0.756178 | 0.555797 |
def const_rate(n, p1=0.0, p2=1.0, p3=1.0):
"""
Constant rate function.
:param n: int - allele number (unused)
:param p1: float - constant parameter
:param p2: float - linear parameter (unused)
:param p3: float - additional parameter (unused)
:return: float - p1
"""
return p1 | 0.76708 | 0.553143 |
def linear_rate(n, p1=0.0, p2=1.0, p3=1.0):
"""
Linear rate function.
:param n: int - allele number
:param p1: float - constant parameter
:param p2: float - linear parameter
:param p3: float - additional parameter (unused)
:return: float - p1 + p2 * n
"""
return p1 + p2 * n | 0.821939 | 0.580293 |
def n2_rate(n, p1=0.0, p2=1.0, p3=1.0):
"""
Quadratic rate function.
:param n: int - allele number
:param p1: float - constant parameter
:param p2: float - linear parameter
:param p3: float - quadratic parameter
:return: float - p1 + p2 * n + p3 * n * n
"""
return p1 + p2 * n + p3 * n * n | 0.752104 | 0.734881 |
def set_axis(ax, x, y, letter=None):
"""
Formats the plot's caption.
Parameters
----------
ax: Axes object.
x: float
X-position of caption.
y: float
Y-position of caption.
letter: string
Caption of the plot.
Default: None.
Returns
-------
ax: modyfied Axes object.
"""
ax.text(
x,
y,
letter,
fontsize=15,
weight='bold',
transform=ax.transAxes)
return ax | 0.923394 | 0.536374 |
def _airtovac(w):
"""Convert air wavelengths to vacuum wavelengths. Don't convert less than 2000 Å.
Parameters
----------
w : :class:`float`
Wavelength [Å] of the line in air.
Returns
-------
:class:`float`
Wavelength [Å] of the line in vacuum.
"""
if w < 2000.0:
return w;
vac = w
for iter in range(2):
sigma2 = (1.0e4/vac)*(1.0e4/vac)
fact = 1.0 + 5.792105e-2/(238.0185 - sigma2) + 1.67917e-3/(57.362 - sigma2)
vac = w*fact
return vac | 0.943796 | 0.520862 |
def dict_zero(first_level_keys):
"""Initialise a dictionary with one level
Parameters
----------
first_level_keys : list
First level data
Returns
-------
one_level_dict : dict
dictionary
"""
one_level_dict = dict.fromkeys(first_level_keys, 0) # set zero as argument
return one_level_dict | 0.761184 | 0.743215 |
import torch
def get_dihedral_torch(c1, c2, c3, c4):
""" Returns the dihedral angle in radians.
Will use atan2 formula from:
https://en.wikipedia.org/wiki/Dihedral_angle#In_polymer_physics
Can't use torch.dot bc it does not broadcast
Inputs:
* c1: (batch, 3) or (3,)
* c1: (batch, 3) or (3,)
* c1: (batch, 3) or (3,)
* c1: (batch, 3) or (3,)
"""
u1 = c2 - c1
u2 = c3 - c2
u3 = c4 - c3
return torch.atan2( ( (torch.norm(u2, dim=-1, keepdim=True) * u1) * torch.cross(u2,u3, dim=-1) ).sum(dim=-1) ,
( torch.cross(u1,u2, dim=-1) * torch.cross(u2, u3, dim=-1) ).sum(dim=-1) ) | 0.86852 | 0.701728 |
import torch
def distmat_loss_torch(X=None, Y=None, X_mat=None, Y_mat=None, p=2, q=2, custom=None, distmat_mask=None):
""" Calculates a loss on the distance matrix - no need to align structs.
Inputs:
* X: (N, d) tensor. the predicted structure. One of (X, X_mat) is needed.
* X_mat: (N, N) tensor. the predicted distance matrix. Optional ()
* Y: (N, d) tensor. the true structure. One of (Y, Y_mat) is needed.
* Y_mat: (N, N) tensor. the predicted distance matrix. Optional ()
* p: int. power for the distance calculation (2 for euclidean)
* q: float. power for the scaling of the loss (2 for MSE, 1 for MAE, etc)
* custom: func or None. custom loss over distance matrices.
ex: lambda x,y: 1 - 1/ (1 + ((x-y))**2) (1 is very bad. 0 is good)
* distmat_mask: (N, N) mask (boolean or weights for each ij pos). optional.
"""
assert (X is not None or X_mat is not None) and \
(Y is not None or Y_mat is not None), "The true and predicted coords or dist mats must be provided"
# calculate distance matrices
if X_mat is None:
X_mat = torch.cdist(X, X, p=p)
if Y_mat is None:
Y_mat = torch.cdist(Y, Y, p=p)
if distmat_mask is None:
distmat_mask = torch.ones_like(Y_mat).bool()
# do custom expression if passed
if custom is not None:
loss = custom(X_mat, Y_mat).mean()
# **2 ensures always positive. Later scale back to desired power
else:
loss = ( X_mat - Y_mat )**2
if q != 2:
loss = loss**(q/2)
return loss[distmat_mask].mean() | 0.859752 | 0.665988 |
def Kabsch(A, B):
""" Returns Kabsch-rotated matrices resulting
from aligning A into B.
Adapted from: https://github.com/charnley/rmsd/
* Inputs:
* A,B are (3 x N)
* backend: one of ["numpy", "torch", "auto"] for backend choice
* Outputs: tensor/array of shape (3 x N)
"""
# run calcs - pick the 0th bc an additional dim was created
return A, B | 0.735167 | 0.770249 |
def RMSD(A, B):
""" Returns RMSD score as defined here (lower is better):
https://en.wikipedia.org/wiki/
Root-mean-square_deviation_of_atomic_positions
* Inputs:
* A,B are (B x 3 x N) or (3 x N)
* backend: one of ["numpy", "torch", "auto"] for backend choice
* Outputs: tensor/array of size (B,)
"""
return A, B | 0.820073 | 0.903081 |
def TMscore(A, B):
""" Returns TMscore as defined here (higher is better):
>0.5 (likely) >0.6 (highly likely) same folding.
= 0.2. https://en.wikipedia.org/wiki/Template_modeling_score
Warning! It's not exactly the code in:
https://zhanglab.ccmb.med.umich.edu/TM-score/TMscore.cpp
but will suffice for now.
Inputs:
* A,B are (B x 3 x N) (np.array or torch.tensor)
* mode: one of ["numpy", "torch", "auto"] for backend
Outputs: tensor/array of size (B,)
"""
return A, B | 0.788827 | 0.580352 |
def get_square(tracks, position):
"""Get square from tracks with position."""
row, col = position
return tracks[row][col] | 0.707203 | 0.526404 |
def split_history_and_current(windowed_ts):
"""
Returns the first n-1 columns as X, and the last column as y. Useful mainly for forecasting scenarios
:param windowed_ts: a pd.DataFrame with a date index and a column per timestamp. see get_windowed_ts
:return:
"""
X = windowed_ts.iloc[:, :-1].values
y = windowed_ts.iloc[:, -1].values
return (X, y) | 0.744099 | 0.775009 |
def calc_accuracy(pred, real):
"""
A function to calculate the accuracy of a CNN when given a list of predicted classes and a list of the real classes
Param:
- pred, a numpy array of predicted classes
- real, a numpy array of the real classes
Return:
- Accuracy as a decimal
"""
return sum(pred==real) / len(pred) | 0.753104 | 0.988313 |
def convert_to_physical(a_coeffs, b_coeffs, logic_x, logic_y):
"""
Convert to physical coordinates from logical coordinates.
Parameters
----------
a_coeffs : array
Perspective transformation coefficients for alpha.
b_coeffs : array
Perspective transformation coefficients for beta.
logic_x : float
Logical point in the x direction.
logic_y : float
Logical point in the y direction.
Returns
-------
x, y : tuple
The x and y physical values on the specified grid.
"""
# x = a(1) + a(2)*l + a(3)*m + a(4)*l*m
x = (a_coeffs[0] + a_coeffs[1] * logic_x + a_coeffs[2]
* logic_y + a_coeffs[3] * logic_x * logic_y)
# y = b(1) + b(2)*l + b(3)*m + b(4)*l*m
y = (b_coeffs[0] + b_coeffs[1] * logic_x +
b_coeffs[2] * logic_y + b_coeffs[3] * logic_x * logic_y)
return x, y | 0.926877 | 0.884888 |
def drop_disregard(df):
"""
If one token in a note is marked 'disregard', remove the whole note from df.
Parameters
----------
df: DataFrame
parsed token-level annotations df (created by `parse_annotations.py`)
Returns
-------
DataFrame
df without 'disregard' notes
"""
df['disregard_note'] = df.groupby('NotitieID').disregard.transform('any')
return df.query(
"not disregard_note"
).drop(columns=['disregard', 'disregard_note']) | 0.802517 | 0.611527 |
def fix_week_14(df):
"""
For annotations from week 14:
- Replace MBW values with `False`
- Replace MBW-lvl values with NaN
We remove this domain from week 14 since the guidelines for it were changed after this week.
Parameters
----------
df: DataFrame
parsed token-level annotations df (created by `parse_annotations.py`)
Returns
-------
DataFrame
df without MBW and MBW_lvl labels for week 14
"""
df['MBW'] = df.MBW.mask(df.batch == 'week_14', other=False)
df['MBW_lvl'] = df.MBW_lvl.mask(df.batch == 'week_14')
return df | 0.868576 | 0.586079 |
def gaussian_product_center(alpha1,A,alpha2,B):
"""
The center of the Gaussian resulting from the product of two Gaussians:
>>> gaussian_product_center(1,array((0,0,0),'d'),1,array((0,0,0),'d'))
array([ 0., 0., 0.])
"""
return (alpha1*A+alpha2*B)/(alpha1+alpha2) | 0.734501 | 0.534005 |
def smoothing_error(x, x_a, A):
"""Return the smoothing error through the averaging kernel.
Parameters:
x (ndarray): Atmospherice profile.
x_a (ndarray): A priori profile.
A (ndarray): Averaging kernel matrix.
Returns:
ndarray: Smoothing error due to correlation between layers.
"""
return A @ (x - x_a) | 0.917935 | 0.960584 |
def get_f_min(f_max, cents_per_value, v_min, v_max):
"""
This function takes in a y value max and min, a maximum frequency and a y scale parameter in units of cents/y value, and returns the minimum frequency that fits to such a scale.
Cents are a logarithmic unit of tone intervals (https://en.wikipedia.org/wiki/Cent_(music)).
Parameters
----------
f_max : float
Maximum frequency.
cents_per_value : float
A y scale parameter in units of cents/y value.
v_min : float
Minimum y value.
v_max : float
Maximum y value.
Returns
-------
float
Minimum frequency.
"""
f_min = f_max / (2 ** ((v_max - v_min) * cents_per_value / 1200))
return f_min | 0.937168 | 0.675141 |
def get_f_max(f_min, cents_per_value, v_min, v_max):
"""
This function takes in a y value max and min, a minimum frequency and a y scale parameter in units of cents/y value, and returns the maximum frequency that fits to such a scale.
Cents are a logarithmic unit of tone intervals (https://en.wikipedia.org/wiki/Cent_(music)).
Parameters
----------
f_min : float
Minimum frequency.
cents_per_value : float
A y scale parameter in units of cents/y value.
v_min : float
Minimum y value.
v_max : float
Maximum y value.
Returns
-------
float
Maximum frequency.
"""
f_max = f_min * (2 ** ((v_max - v_min) * cents_per_value / 1200))
return f_max | 0.934189 | 0.710622 |
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
# number of channels
C = tensor.size(1)
# new axis order
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
# Transpose: (N, C, D, H, W) -> (C, N, H, W)
transposed = tensor.permute(axis_order)
# Flatten: (C, N, D, H, W) -> (C, N * H * W)
return transposed.contiguous().view(C, -1) | 0.85741 | 0.681264 |
import torch
def expand_as_one_hot(input, C, ignore_index=None):
"""
Converts NxHxW label image to NxCxDxHxW, where each label gets converted to its corresponding one-hot vector
:param input: 4D input image (NxDxHxW)
:param C: number of channels/labels
:param ignore_index: ignore index to be kept during the expansion
:return: 5D output image (NxCxDxHxW)
"""
assert input.dim() == 3
# expand the input tensor to Nx1xHxW before scattering
input = input.unsqueeze(1)
# create result tensor shape (NxCxDxHxW)
shape = list(input.size())
shape[1] = C
if ignore_index is not None:
# create ignore_index mask for the result
mask = input.expand(shape) == ignore_index
# clone the src tensor and zero out ignore_index in the input
input = input.clone()
input[input == ignore_index] = 0
# scatter to get the one-hot tensor
result = torch.zeros(shape).to(input.device).scatter_(1, input, 1)
# bring back the ignore_index in the result
result[mask] = ignore_index
return result
else:
# scatter to get the one-hot tensor
return torch.zeros(shape).to(input.device).scatter_(1, input, 1) | 0.766992 | 0.659302 |
def batch_quat_to_rotmat(q, out=None):
"""
quaternion a + bi + cj + dk should be given in the form [a,b,c,d]
:param q:
:param out:
:return:
"""
import torch
batchsize = q.size(0)
if out is None:
out = q.new_empty(batchsize, 3, 3)
# 2 / squared quaternion 2-norm
s = 2 / torch.sum(q.pow(2), 1)
# coefficients of the Hamilton product of the quaternion with itself
h = torch.bmm(q.unsqueeze(2), q.unsqueeze(1))
out[:, 0, 0] = 1 - (h[:, 2, 2] + h[:, 3, 3]).mul(s)
out[:, 0, 1] = (h[:, 1, 2] - h[:, 3, 0]).mul(s)
out[:, 0, 2] = (h[:, 1, 3] + h[:, 2, 0]).mul(s)
out[:, 1, 0] = (h[:, 1, 2] + h[:, 3, 0]).mul(s)
out[:, 1, 1] = 1 - (h[:, 1, 1] + h[:, 3, 3]).mul(s)
out[:, 1, 2] = (h[:, 2, 3] - h[:, 1, 0]).mul(s)
out[:, 2, 0] = (h[:, 1, 3] - h[:, 2, 0]).mul(s)
out[:, 2, 1] = (h[:, 2, 3] + h[:, 1, 0]).mul(s)
out[:, 2, 2] = 1 - (h[:, 1, 1] + h[:, 2, 2]).mul(s)
return out | 0.855127 | 0.810216 |
import torch
def cosine_distance(memory_matrix, cos_keys):
"""
compute the cosine similarity between keys to each of the
memory slot.
Parameters:
----------
memory_matrix: Tensor (batch_size, mem_slot, mem_size)
the memory matrix to lookup in
keys: Tensor (batch_size, mem_size, number_of_keys)
the keys to query the memory with
strengths: Tensor (batch_size, number_of_keys, )
the list of strengths for each lookup key
Returns: Tensor (batch_size, mem_slot, number_of_keys)
The list of lookup weightings for each provided key
"""
memory_norm = torch.norm(memory_matrix, 2, 2, keepdim=True)
keys_norm = torch.norm(cos_keys, 2, 1, keepdim=True)
normalized_mem = torch.div(
memory_matrix, memory_norm.expand_as(memory_matrix) + 1e-8)
normalized_keys = torch.div(cos_keys, keys_norm.expand_as(cos_keys) + 1e-8)
out = torch.bmm(normalized_mem, normalized_keys)
# print(normalized_keys)
# print(out)
# apply_dict(locals())
return out | 0.803174 | 0.748168 |
def center_to_corner(boxes):
""" Convert bounding boxes from center format (cx, cy, width, height) to corner format (xmin, ymin, xmax, ymax)
Args:
- boxes: numpy array of tensor containing all the boxes to be converted
Returns:
- A numpy array or tensor of converted boxes
"""
temp = boxes.copy()
temp[..., 0] = boxes[..., 0] - (boxes[..., 2] / 2) # xmin
temp[..., 1] = boxes[..., 1] - (boxes[..., 3] / 2) # ymin
temp[..., 2] = boxes[..., 0] + (boxes[..., 2] / 2) # xmax
temp[..., 3] = boxes[..., 1] + (boxes[..., 3] / 2) # ymax
return temp | 0.908541 | 0.767123 |
def exact_match(gt_s, gt_e, pr_s, pr_e):
"""
Evaluate exact match of a predicted span over a ground truth span.
Args:
gt_s: index of the ground truth start position
gt_e: index of the ground truth end position
pr_s: index of the predicted start position
pr_e: index of the predicted end position
"""
return gt_s == pr_s and gt_e == pr_e | 0.779741 | 0.658424 |
def Pluralize(num, word, plural=None):
"""Pluralize word based on num.
Args:
num: int, the number of objects to count.
word: str, the word to pluralize.
plural: str, the plural form of word if not "add s"
Returns:
str: the plural or singular form of word in accord with num.
"""
if num == 1:
return word
return plural or word + 's' | 0.708213 | 0.614539 |
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