initial commit

compliance.py

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+import streamlit as st
+import pandas as pd
+import geopandas as gpd
+import numpy as np
+import itertools
+from scipy.spatial import cKDTree
+import geopy.distance
+import matplotlib.pyplot as plt
+from operator import itemgetter
+
+# Title of the app
+st.title("Automatic Compliance Monitoring for Brick Kilns")
+
+# Dropdown for selecting the state
+state = st.selectbox("Select State", ["Punjab", "Haryana", "Bihar"])  # Update the list as needed
+
+# "Uttar_pradesh"
+
+# Checkboxes for different compliance criteria
+distance_kilns = st.checkbox("Inter-brick kiln distance < 1km")
+distance_hospitals = st.checkbox("Distance to Hospitals < 800m")
+distance_water_bodies = st.checkbox("Distance to Water bodies < 500m")
+fp2 = "/home/shataxi.dubey/shataxi_work/India_State_Shapefile/India_State_Shapefile/India_State_Boundary.shp"
+
+
+
+
+# Read file using gpd.read_file()
+data2 = gpd.read_file(fp2)
+# Function to calculate the nearest distances to water bodies
+def ckdnearest(brick_kilns, rivers, gdfB_cols=['geometry']):
+    A = np.vstack([np.array(geom) for geom in brick_kilns[['lon','lat']].values])
+    B = [np.array(geom.coords) for geom in rivers.geometry.to_list()]
+    B_ix = tuple(itertools.chain.from_iterable(
+        [itertools.repeat(i, x) for i, x in enumerate(list(map(len, B)))]))
+    B = np.concatenate(B)
+    ckd_tree = cKDTree(B)
+    dist, river_point_idx = ckd_tree.query(A, k=1)
+    closest_river_point = B[river_point_idx]
+    river_origin_idx = itemgetter(*river_point_idx)(B_ix)
+    gdf = pd.concat(
+        [brick_kilns, rivers.loc[river_origin_idx, gdfB_cols].reset_index(drop=True),
+         pd.DataFrame(closest_river_point, columns = ['closest_river_point_long','closest_river_point_lat']),
+         pd.Series(dist, name='dist')], axis=1)
+    return gdf
+# Function to calculate the nearest distances to hospitals
+
+def ckdnearest_hospital(brick_kilns, hospital_df):
+    A = np.vstack([np.array(geom) for geom in brick_kilns[['lon','lat']].values])
+    B = np.vstack([np.array(geom) for geom in hospital_df[['Longitude','Latitude']].values])
+    ckd_tree = cKDTree(B)
+    dist, hospital_idx = ckd_tree.query(A, k=1)
+    closest_hospital_point = B[hospital_idx]
+    gdf = pd.concat(
+        [brick_kilns,
+         pd.DataFrame(closest_hospital_point, columns=['closest_hospital_long', 'closest_hospital_lat']),
+         pd.Series(dist, name='dist')], axis=1)
+    return gdf
+# Function to calculate distances between brick kilns and nearest hospitals
+def cal_bk_hosp_dist(path, hospital_df):
+    state_bk = pd.read_csv(path)
+    bk_hospital_mapping = ckdnearest_hospital(state_bk, hospital_df)
+    bk_hospital_mapping['distance_km'] = 0
+    for i in range(len(bk_hospital_mapping)):
+        bk_hospital_mapping['distance_km'][i] = geopy.distance.distance(
+            (bk_hospital_mapping['lat'][i], bk_hospital_mapping['lon'][i]),
+            (bk_hospital_mapping['closest_hospital_lat'][i], bk_hospital_mapping['closest_hospital_long'][i])
+        ).km
+    return bk_hospital_mapping
+
+# Load hospitals data
+hospital_df = pd.read_csv('/home/rishabh.mondal/bkdb/India_Hospital_Data.csv')
+hospital_df = hospital_df.rename(columns = {'lon' : 'Longitude', 'lat' : 'Latitude'})
+
+# Function to calculate distances between brick kilns and nearest rivers
+def cal_bk_river_dist(path, waterways):
+    state_bk = pd.read_csv(path)
+    bk_river_mapping = ckdnearest(state_bk, waterways)
+    bk_river_mapping['distance'] = 0
+    for i in range(len(state_bk)):
+        bk_river_mapping['distance'][i] = geopy.distance.distance(
+            (bk_river_mapping['lat'][i], bk_river_mapping['lon'][i]),
+            (bk_river_mapping['closest_river_point_lat'][i], bk_river_mapping['closest_river_point_long'][i])
+        ).km
+    return bk_river_mapping
+
+# Calculate inter-brick kiln distances
+
+def ckdnearest_brick_kilns(brick_kilns):
+    A = np.vstack([np.array(geom) for geom in brick_kilns[['lon','lat']].values])
+    ckd_tree = cKDTree(A)
+    dist, idx = ckd_tree.query(A, k=2)  # k=2 because the closest point will be itself
+    closest_kiln_point = A[idx[:, 1]]  # idx[:, 1] to get the second closest point
+    gdf = pd.concat(
+        [brick_kilns,
+         pd.DataFrame(closest_kiln_point, columns=['closest_kiln_long', 'closest_kiln_lat']),
+         pd.Series(dist[:, 1], name='dist')], axis=1)
+    return gdf
+# Load waterways shapefile
+waterways_path = '/home/shataxi.dubey/shataxi_work/compliance_analysis/waterways/waterways.shp'
+waterways = gpd.read_file(waterways_path)
+
+# Load brick kilns data (this should be the path to your brick kilns CSV file)
+brick_kilns_path = '/home/patel_zeel/compass24/exact_latlon/haryana.csv'
+brick_kilns_paths = {
+    "Punjab": '/home/patel_zeel/compass24/exact_latlon/punjab.csv',
+    "Haryana": '/home/patel_zeel/compass24/exact_latlon/haryana.csv',
+    # "Uttar Pradesh": '/home/patel_zeel/kilns_neurips24/exact_latlon/uttar_pradesh.csv',  
+    "Bihar": '/home/patel_zeel/compass24/exact_latlon/bihar.csv',
+}
+
+# Load brick kilns data for the selected state
+brick_kilns_path = brick_kilns_paths[state]
+brick_kilns = pd.read_csv(brick_kilns_path)
+
+bk_river_mapping = cal_bk_river_dist(brick_kilns_path, waterways)
+bk_hospital_mapping = cal_bk_hosp_dist(brick_kilns_path, hospital_df)
+bk_kiln_mapping = ckdnearest_brick_kilns(pd.read_csv(brick_kilns_path))
+
+
+brick_kilns['compliant'] = True
+if distance_kilns:
+    brick_kilns['compliant'] &= bk_kiln_mapping['dist'] >= 1
+if distance_hospitals:
+    brick_kilns['compliant'] &= bk_hospital_mapping['distance_km'] >= 0.8
+if distance_water_bodies:
+    brick_kilns['compliant'] &= bk_river_mapping['distance'] >= 0.5
+
+# Plotting the results
+fig, ax = plt.subplots(figsize=(8, 6))
+# data2 = gpd.read_file(waterways_path)  # Replace this with the appropriate shapefile for the state map
+data2.plot(ax=ax, cmap='Pastel2', edgecolor='black', linewidth=0.1)  # State map
+waterways.plot(ax=ax, color='blue', linewidth=0.2)  # Water bodies
+# Plot all brick kilns in green
+brick_kilns_compliant = brick_kilns[brick_kilns['compliant']]
+ax.scatter(brick_kilns_compliant['lon'], brick_kilns_compliant['lat'], color='green', s=10, marker='o', label='Compliant Brick Kilns')
+
+# Plot non-compliant brick kilns in red
+brick_kilns_non_compliant = brick_kilns[~brick_kilns['compliant']]
+ax.scatter(brick_kilns_non_compliant['lon'], brick_kilns_non_compliant['lat'], color='red', s=10, marker='o', label='Non-compliant Brick Kilns')
+if state == 'Bihar':
+    ax.text(83, 25.8, 'Uttar\n Pradesh')
+    ax.text(85.5, 25.5, 'Bihar')
+    ax.text(87.9, 25.3, 'West\n Bengal')
+    ax.set_xlim(83,89)
+    ax.set_ylim(24.25,27)
+    
+elif state == 'Haryana':
+    ax.text(77.3, 29.5, 'Uttar \nPradesh')
+    ax.text(74.5, 28.5, 'Rajasthan')
+    ax.text(75.5, 30.5, 'Punjab')
+    ax.text(76, 29, 'Haryana')
+    ax.text(77, 28.6, 'New Delhi')
+    ax.set_xlim(74,78)
+    ax.set_ylim(27.5,31)
+elif state == 'Punjab':
+    ax.text(76, 32, 'Himachal\n Pradesh')
+    ax.text(75.5, 31, 'Punjab')
+    ax.text(74, 29.6, 'Rajasthan')
+    ax.text(76, 29.6, 'Haryana')    
+    ax.set_xlim(73.5,77)
+    ax.set_ylim(29.5,32.5)
+
+
+
+plt.legend(loc='upper left')
+ax.set_axis_off()
+plt.tight_layout(pad=0)
+
+st.pyplot(fig)
+
+# Display the number of non-compliant kilns
+num_non_compliant = len(brick_kilns_non_compliant)
+st.write(f"Number of non-compliant brick kilns: {num_non_compliant}")
+

requirements.txt

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+folium
+streamlit==1.33.0
+pandas
+numpy
+scipy
+matplotlib
+geopandas==0.10.2
+itertools
+operator
+huggingface-hub