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| import pandas as pd | |
| import numpy as np | |
| import geopandas as gpd | |
| import requests | |
| url = "https://Projects-by-IF-model-temp-api.hf.space/model-effect" | |
| headers = {"Content-Type": "application/json"} | |
| cell_area = 100*100 #in square meters | |
| def make_XYT(df, treatment_col = '%CoberturaVeg', target_col = 'LST'): | |
| Y = df[target_col].values | |
| T = df[treatment_col].values | |
| X = df.drop(columns=[treatment_col, target_col]).values | |
| return X, Y, T | |
| def prepare_data(og_file, training_file): | |
| original_df = pd.read_csv(og_file, index_col=0) | |
| df = pd.read_csv(training_file, index_col=0) | |
| original_df = original_df.round(2) | |
| df = df.drop(columns=['row','column','x','y']) | |
| df = df.astype(np.float32) | |
| X, Y, T = make_XYT(df) | |
| return original_df, df, X, Y, T | |
| def prepare_treatment_df(original_df, df): | |
| treatment_df = original_df[['x','y','%CoberturaVeg']].copy() | |
| treatment_df.loc[:, '%Construccion'] = original_df['%Construccion'].values | |
| treatment_df.loc[:, 'LST'] = df['LST'].values | |
| treatment_df = treatment_df.reset_index() | |
| return treatment_df | |
| def run_treatment_increase(X, T, original_df, df, value, name): | |
| treatment_df = prepare_treatment_df(original_df, df) | |
| T_sim = T.copy() | |
| value_num = 1+ (value/100) | |
| T_sim = T_sim*value_num | |
| data = {"X": X.tolist(), "T0":T.tolist(), "T1":(T_sim).tolist()} | |
| response = requests.post(url, headers=headers, json=data) | |
| if response.ok: | |
| result = response.json() | |
| # print("Model response:", result["effect"]) | |
| else: | |
| print("Request failed:", response.status_code, response.text) | |
| treatment_df[name] = result["effect"] | |
| treatment_df[f'%total_trees_treatment_{value}%'] = T_sim | |
| treatment_df[f'pp_trees_increase_treatment_{value}%'] = treatment_df[f'%total_trees_treatment_{value}%'] - treatment_df['%CoberturaVeg'] | |
| return treatment_df | |
| def process_data_trees_to_temp(value, original_df, df, X, Y, T): | |
| name = f'treatment_effect_{value}%' | |
| treatment_df = run_treatment_increase(X, T, original_df, df, value, name) | |
| treatment_df[f'simulated_temp_{value}%'] = treatment_df['LST'] + treatment_df[name] | |
| #add columns translated to square meters | |
| treatment_df[f'sqm_total_trees_treatment_{value}%'] = (treatment_df[f'%total_trees_treatment_{value}%']/100)*cell_area | |
| treatment_df[f'sqm_trees_increase_treatment_{value}%'] = (treatment_df[f'pp_trees_increase_treatment_{value}%']/100)*cell_area | |
| gdf = gpd.GeoDataFrame(treatment_df, geometry=gpd.points_from_xy(treatment_df.x, treatment_df.y)) | |
| return gdf | |
| def run_temp_decrease(X, original_df, df, goal, name): | |
| treatment_df = prepare_treatment_df(original_df, df) | |
| data = {"X": X.tolist()} | |
| response = requests.post(url, headers=headers, json=data) | |
| if response.ok: | |
| result = response.json() | |
| # print("Model response:", result["effect"]) | |
| else: | |
| print("Request failed:", response.status_code, response.text) | |
| treatment_effects = np.array(result["effect"]) | |
| trees_needed = -goal / treatment_effects | |
| treatment_df[name] = trees_needed | |
| treatment_df['goal_temp'] = treatment_df['LST'] - goal | |
| return treatment_df | |
| def process_data_temp_to_trees(goal, original_df, df, X, Y, T): | |
| name = f'pp_trees_increase_for_{goal}C' | |
| treatment_df = run_temp_decrease(X,original_df, df, goal, name) | |
| treatment_df[f'%total_trees_needed_for_{goal}C'] = treatment_df['%CoberturaVeg'] + treatment_df[name] | |
| #add columns translated to square meters | |
| treatment_df[f'sqm_total_trees_needed_for_{goal}C'] = (treatment_df[f'%total_trees_needed_for_{goal}C']/100)*cell_area | |
| treatment_df[f'sqm_trees_increase_for_{goal}C'] = (treatment_df[f'pp_trees_increase_for_{goal}C']/100)*cell_area | |
| gdf = gpd.GeoDataFrame(treatment_df, geometry=gpd.points_from_xy(treatment_df.x, treatment_df.y)) | |
| return gdf | |