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import gradio as gr | |
import pandas as pd | |
import numpy as np | |
import json | |
from io import StringIO | |
from collections import OrderedDict | |
from notion_client import Client as client_notion | |
import os | |
# Accessing the secret variable | |
notionToken = os.getenv('notionToken') | |
if notionToken is None: | |
raise Exception("Secret token not found. Please check the environment variables.") | |
else: | |
print("Secret token found successfully!") | |
from config import landuseDatabaseId , subdomainAttributesDatabaseId | |
from imports_utils import fetch_all_database_pages | |
from imports_utils import get_property_value | |
from imports_utils import notion | |
landuse_attributes = fetch_all_database_pages(notion, landuseDatabaseId) | |
livability_attributes = fetch_all_database_pages(notion, subdomainAttributesDatabaseId) | |
# fetch the dictionary with landuse - domain pairs | |
landuseMapperDict ={} | |
subdomains_unique = [] | |
for page in landuse_attributes: | |
value_landuse = get_property_value(page, "LANDUSE") | |
value_subdomain = get_property_value(page, "SUBDOMAIN_LIVEABILITY") | |
if value_subdomain and value_landuse: | |
landuseMapperDict[value_landuse] = value_subdomain | |
if value_subdomain != "": | |
subdomains_unique.append(value_subdomain) | |
#subdomains_unique = list(set(subdomains_unique)) | |
# fetch the dictionary with subdomain attribute data | |
attributeMapperDict ={} | |
domains_unique = [] | |
for page in livability_attributes: | |
subdomain = get_property_value(page, "SUBDOMAIN_UNIQUE") | |
sqm_per_employee = get_property_value(page, "SQM PER EMPL") | |
thresholds = get_property_value(page, "MANHATTAN THRESHOLD") | |
max_points = get_property_value(page, "LIVABILITY MAX POINT") | |
domain = get_property_value(page, "DOMAIN") | |
if thresholds: | |
attributeMapperDict[subdomain] = { | |
'sqmPerEmpl': [sqm_per_employee if sqm_per_employee != "" else 0], | |
'thresholds': thresholds, | |
'max_points': max_points, | |
'domain': [domain if domain != "" else 0] | |
} | |
if domain != "": | |
domains_unique.append(domain) | |
#domains_unique = list(set(domains_unique)) | |
def test(input_json): | |
print("Received input") | |
# Parse the input JSON string | |
try: | |
inputs = json.loads(input_json) | |
except json.JSONDecodeError: | |
inputs = json.loads(input_json.replace("'", '"')) | |
# Accessing input data from Grasshopper | |
matrix = inputs['input']["matrix"] | |
landuses = inputs['input']["landuse_areas"] | |
transport_matrix = inputs['input']["transportMatrix"] | |
#attributeMapperDict = inputs['input']["attributeMapperDict"] | |
#landuseMapperDict = inputs['input']["landuseMapperDict"] | |
alpha = inputs['input']["alpha"] | |
alpha = float(alpha) | |
threshold = inputs['input']["threshold"] | |
threshold = float(threshold) | |
df_matrix = pd.DataFrame(matrix).T | |
df_matrix = df_matrix.round(0).astype(int) | |
df_landuses = pd.DataFrame(landuses).T | |
df_landuses = df_landuses.round(0).astype(int) | |
""" | |
# List containing the substrings to check against | |
tranportModes = ["DRT", "GMT", "HSR"] | |
# Initialize a dictionary to hold the categorized sub-dictionaries | |
result_dict = {mode: {} for mode in tranportModes} | |
# Iterate over the original dictionary to split into sub-dictionaries based on substrings | |
for key, value in transport_matrix.items(): | |
for mode in tranportModes: | |
if mode in key: # Check if the substring is in the dictionary key | |
result_dict[substring][key] = value | |
art = result_dict["DRT"] | |
df_art_matrix = pd.DataFrame(art).T | |
df_art_matrix = df_art_matrix.round(0).astype(int) | |
""" | |
# create a mask based on the matrix size and ids, crop activity nodes to the mask | |
mask_connected = df_matrix.index.tolist() | |
valid_indexes = [idx for idx in mask_connected if idx in df_landuses.index] | |
# Identify and report missing indexes | |
missing_indexes = set(mask_connected) - set(valid_indexes) | |
if missing_indexes: | |
print(f"Error: The following indexes were not found in the DataFrame: {missing_indexes}, length: {len(missing_indexes)}") | |
# Apply the filtered mask | |
df_landuses_filtered = df_landuses.loc[valid_indexes] | |
# find a set of unique domains, to which subdomains are aggregated | |
temp = [] | |
for key, values in attributeMapperDict.items(): | |
domain = attributeMapperDict[key]['domain'] | |
for item in domain: | |
if ',' in item: | |
domain_list = item.split(',') | |
attributeMapperDict[key]['domain'] = domain_list | |
for domain in domain_list: | |
temp.append(domain) | |
else: | |
if item != 0: | |
temp.append(item) | |
domainsUnique = list(set(temp)) | |
# find a list of unique subdomains, to which land uses are aggregated | |
temp = [] | |
for key, values in landuseMapperDict.items(): | |
subdomain = str(landuseMapperDict[key]) | |
if subdomain != 0: | |
temp.append(subdomain) | |
subdomainsUnique = list(set(temp)) | |
def landusesToSubdomains(DistanceMatrix, LanduseDf, LanduseToSubdomainDict, UniqueSubdomainsList): | |
df_LivabilitySubdomainsArea = pd.DataFrame(0, index=DistanceMatrix.index, columns=UniqueSubdomainsList) | |
for subdomain in UniqueSubdomainsList: | |
for lu, lu_subdomain in LanduseToSubdomainDict.items(): | |
if lu_subdomain == subdomain: | |
if lu in LanduseDf.columns: | |
df_LivabilitySubdomainsArea[subdomain] = df_LivabilitySubdomainsArea[subdomain].add(LanduseDf[lu], fill_value=0) | |
else: | |
print(f"Warning: Column '{lu}' not found in landuse database") | |
return df_LivabilitySubdomainsArea | |
LivabilitySubdomainsWeights = landusesToSubdomains(df_matrix,df_landuses_filtered,landuseMapperDict,subdomainsUnique) | |
def FindWorkplaces (DistanceMatrix,SubdomainAttributeDict,destinationWeights,UniqueSubdomainsList ): | |
df_LivabilitySubdomainsWorkplaces = pd.DataFrame(0, index=DistanceMatrix.index, columns=['jobs']) | |
for subdomain in UniqueSubdomainsList: | |
for key, value_list in SubdomainAttributeDict.items(): | |
sqm_per_empl = float(SubdomainAttributeDict[subdomain]['sqmPerEmpl'][0]) | |
if key in destinationWeights.columns and key == subdomain: | |
if sqm_per_empl > 0: | |
df_LivabilitySubdomainsWorkplaces['jobs'] += (round(destinationWeights[key] / sqm_per_empl,2)).fillna(0) | |
else: | |
df_LivabilitySubdomainsWorkplaces['jobs'] += 0 | |
return df_LivabilitySubdomainsWorkplaces | |
WorkplacesNumber = FindWorkplaces(df_matrix,attributeMapperDict,LivabilitySubdomainsWeights,subdomainsUnique) | |
# prepare an input weights dataframe for the parameter LivabilitySubdomainsInputs | |
LivabilitySubdomainsInputs =pd.concat([LivabilitySubdomainsWeights, WorkplacesNumber], axis=1) | |
def computeAccessibility (DistanceMatrix, destinationWeights=None,alpha = 0.0038, threshold = 600): | |
decay_factors = np.exp(-alpha * DistanceMatrix) * (DistanceMatrix <= threshold) | |
# for weighted accessibility (e. g. areas) | |
if destinationWeights is not None: #not destinationWeights.empty: | |
subdomainsAccessibility = pd.DataFrame(index=DistanceMatrix.index, columns=destinationWeights.columns) | |
for col in destinationWeights.columns: | |
subdomainsAccessibility[col] = (decay_factors * destinationWeights[col].values).sum(axis=1) | |
# for unweighted accessibility (e. g. points of interest) | |
else: | |
subdomainsAccessibility = pd.DataFrame(index=DistanceMatrix.index, columns=['ART']) | |
for col in subdomainsAccessibility.columns: | |
subdomainsAccessibility[col] = (decay_factors * 1).sum(axis=1) | |
return subdomainsAccessibility | |
subdomainsAccessibility = computeAccessibility(df_matrix,LivabilitySubdomainsInputs,alpha,threshold) | |
#transportAccessibility = computeAccessibility(df_art_matrix,None,alpha,threshold) | |
#AccessibilityInputs = pd.concat([subdomainsAccessibility, transportAccessibility], axis=1) | |
def remap(value, B_min, B_max, C_min, C_max): | |
return C_min + (((value - B_min) / (B_max - B_min))* (C_max - C_min)) | |
if 'jobs' not in subdomainsAccessibility.columns: | |
print("Error: Column 'jobs' does not exist in the subdomainsAccessibility.") | |
def accessibilityToLivability (DistanceMatrix,AccessibilityInputs, SubdomainAttributeDict,UniqueDomainsList): | |
livability = pd.DataFrame(index=DistanceMatrix.index, columns=AccessibilityInputs.columns) | |
for domain in UniqueDomainsList: | |
livability[domain] = 0 | |
livability.fillna(0, inplace=True) | |
templist = [] | |
# remap accessibility to livability points | |
for key, values in SubdomainAttributeDict.items(): | |
threshold = float(SubdomainAttributeDict[key]['thresholds']) | |
max_livability = float(SubdomainAttributeDict[key]['max_points']) | |
domains = [str(item) for item in SubdomainAttributeDict[key]['domain']] | |
if key in subdomainsAccessibility.columns and key != 'commercial': | |
livability_score = remap(AccessibilityInputs[key], 0, threshold, 0, max_livability) | |
livability.loc[AccessibilityInputs[key] >= threshold, key] = max_livability | |
livability.loc[AccessibilityInputs[key] < threshold, key] = livability_score | |
if any(domains): | |
for domain in domains: | |
if domain != 'Workplaces': | |
livability.loc[AccessibilityInputs[key] >= threshold, domain] += max_livability | |
livability.loc[AccessibilityInputs[key] < threshold, domain] += livability_score | |
elif key == 'commercial': | |
livability_score = remap(AccessibilityInputs['jobs'], 0, threshold, 0, max_livability) | |
livability.loc[AccessibilityInputs['jobs'] >= threshold, domains[0]] = max_livability | |
livability.loc[AccessibilityInputs['jobs'] < threshold, domains[0]] = livability_score | |
return livability | |
livability = accessibilityToLivability(df_matrix,subdomainsAccessibility,attributeMapperDict,domainsUnique) | |
livability_dictionary = livability.to_dict('index') | |
LivabilitySubdomainsInputs_dictionary = LivabilitySubdomainsInputs.to_dict('index') | |
subdomainsAccessibility_dictionary = subdomainsAccessibility.to_dict('index') | |
df_art_matrix_dict = df_art_matrix.to_dict('index') | |
# Prepare the output | |
output = { | |
"subdomainsAccessibility_dictionary": subdomainsAccessibility_dictionary, | |
"livability_dictionary": livability_dictionary, | |
"subdomainsWeights_dictionary": LivabilitySubdomainsInputs_dictionary, | |
"luDomainMapper": landuseMapperDict, | |
"attributeMapper": attributeMapperDict | |
} | |
return json.dumps(output) | |
# Define the Gradio interface with a single JSON input | |
iface = gr.Interface( | |
fn=test, | |
inputs=gr.Textbox(label="Input JSON", lines=20, placeholder="Enter JSON with all parameters here..."), | |
outputs=gr.JSON(label="Output JSON"), | |
title="testspace" | |
) | |
iface.launch() |