import gradio as gr import pandas as pd import numpy as np import json from io import StringIO from collections import OrderedDict # define variables # distance matrices branch_name_dm = "graph_geometry/distance_matrix" commit_id_dm = "cfde6f4ba4" # ebcfc50abe/commits/cfde6f4ba4 dm_activityNodes = "activity_node+distance_matrix_ped_mm_noEntr" dm_transportStops = "an_stations+distance_matrix_ped_mm_art_noEntr" # land use attributes branch_name_lu = "graph_geometry/activity_nodes_with_land_use" commit_id_lu = "13ae6cdd30" # livability attributes notion_lu_domains = "407c2fce664f4dde8940bb416780a86d" notion_domain_attributes = "01401b78420f4296a2449f587d4ed9c9" 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"] 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_landuses = pd.DataFrame(landuses).T df_matrix = df_matrix.round(0).astype(int) df_landuses = df_landuses.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 domain in UniqueSubdomainsList: for key, value_list in SubdomainAttributeDict.items(): sqm_per_empl = float(SubdomainAttributeDict[domain]['sqmPerEmpl'][0]) if key in destinationWeights.columns and key == domain: 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) subdomainsAccessibility = pd.DataFrame(index=DistanceMatrix.index, columns=destinationWeights.columns) # for weighted accessibility (e. g. areas) if not destinationWeights.empty: for col in destinationWeights.columns: subdomainsAccessibility[col] = (decay_factors * destinationWeights[col].values).sum(axis=1) # for unweighted accessibility (e. g. points of interest) else: for col in DistanceMatrix.columns: subdomainsAccessibility[col] = (decay_factors * 1).sum(axis=1) return subdomainsAccessibility subdomainsAccessibility = computeAccessibility(df_matrix,LivabilitySubdomainsInputs,alpha,threshold) 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,subdomainsAccessibility, SubdomainAttributeDict,UniqueDomainsList): livability = pd.DataFrame(index=DistanceMatrix.index, columns=subdomainsAccessibility.columns) livability.drop(columns='jobs', inplace=True) livability["Workplaces"] = 0 livability.fillna(0, inplace=True) for domain in UniqueDomainsList: livability[domain] = 0 # remap accessibility to livability points for key, values in SubdomainAttributeDict.items(): if key == 'commercial': threshold = float(SubdomainAttributeDict[key]['thresholds']) max_livability = float(SubdomainAttributeDict[key]['max_points']) livability_score = remap(subdomainsAccessibility['jobs'], 0, threshold, 0, max_livability) livability.loc[subdomainsAccessibility['jobs'] >= threshold, 'Workplaces'] = max_livability livability.loc[subdomainsAccessibility['jobs'] < threshold, 'Workplaces'] = livability_score elif key in subdomainsAccessibility.columns and key != 'commercial': domain = [str(item) for item in SubdomainAttributeDict[key]['domain']] threshold = float(SubdomainAttributeDict[key]['thresholds']) max_livability = float(SubdomainAttributeDict[key]['max_points']) sqm_per_employee = SubdomainAttributeDict[key]['sqmPerEmpl'] livability_score = remap(subdomainsAccessibility[key], 0, threshold, 0, max_livability) livability.loc[subdomainsAccessibility[key] >= threshold, key] = max_livability livability.loc[subdomainsAccessibility[key] < threshold, key] = livability_score if any(domain): for item in domain: if domain != 'Workplaces': livability.loc[subdomainsAccessibility[key] >= threshold, item] += max_livability livability.loc[subdomainsAccessibility[key] < threshold, item] += 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') # Prepare the output output = { "subdomainsAccessibility_dictionary": subdomainsAccessibility_dictionary, "livability_dictionary": livability_dictionary, "subdomainsWeights_dictionary": LivabilitySubdomainsInputs_dictionary } 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()