Duplicate from dredddddd/IntMap

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+Restaurants_and_canteens.xlsx filter=lfs diff=lfs merge=lfs -text
+Service.xlsx filter=lfs diff=lfs merge=lfs -text
+final_part_domrf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: IntMap
+emoji: 😻
+colorFrom: gray
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+duplicated_from: dredddddd/IntMap
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Restaurants_and_canteens.xlsx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6171cad661d3ad3ff648c0d3c8d5a99381bdf239d6c72d10abb2124c2c5b2a1d
+size 2867894

Service.xlsx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:92d9784a49eacde73cc85ba2931871c16da23e1de5918dcaed198de3bf327bab
+size 3431143

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import streamlit as st
+import pandas as pd 
+import gspread
+from math import log 
+from math import sqrt 
+import re
+import numpy as np 
+import osmnx as os 
+from osmnx.geocoder import geocode 
+from osmnx.distance import euclidean_dist_vec 
+from osmnx import geocode_to_gdf 
+import geocoder 
+import shapely as sh 
+import datetime 
+import pickle
+import geopandas
+from shapely.geometry import Point
+from shapely.ops import unary_union
+from shapely.ops import transform
+import folium
+from streamlit_folium import folium_static
+import pyproj
+    
+@st.cache
+def import_data():
+    Service = pd.read_excel('Service.xlsx')
+    Food = pd.read_excel('Restaurants_and_canteens.xlsx')
+
+    col2 = np.array(list(map(float, Food['Latitude_WGS84'].values[1:])))
+    col1 = np.array(list(map(float, Food['Longitude_WGS84'].values[1:])))
+
+    col2_S = np.array(list(map(float, Service['Latitude_WGS84'].values[1:])))
+    col1_S = np.array(list(map(float, Service['Longitude_WGS84'].values[1:])))
+
+#     col_S = np.column_stack([col2_S, col1_S])
+
+#     col = np.column_stack([col2, col1])
+    Data1 = geopandas.GeoDataFrame(pd.read_pickle('districts_moc.pickle')).sort_values('name')
+
+    Subway = os.geometries_from_place('Moscow', tags = {'railways':'station', 'station':'subway'})
+    Highways = os.geometries_from_place('Moscow', tags = {'highway':['motorway','trunk','primary']})
+    For = os.geometries_from_place('Moscow', tags = { 'natural':'wood',
+    'landuse':'forest'})
+    Schools = os.geometries_from_place('Moscow', tags = { 'amenity':'school'})
+    Railway = os.geometries_from_place('Moscow', tags = { 'railway':['rail','disused']})
+    locations_gpd = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(col1, col2),
+                                           crs='epsg:4326')
+    Eda = locations_gpd.to_crs("EPSG:25837")
+    locations_gpd1 = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(col1_S, col2_S),
+                                            crs='epsg:4326')
+    Uslugi = locations_gpd1.to_crs("EPSG:25837")
+    locations_gpd_m = geopandas.GeoDataFrame(Subway.geometry,
+                                             crs='epsg:4326')
+    Metro = locations_gpd_m.to_crs("EPSG:25837")
+    locations_gpd_H = geopandas.GeoDataFrame(Highways.geometry,
+                                             crs='epsg:4326')
+    Shosse = locations_gpd_H.to_crs("EPSG:25837")
+    Lesa = For.to_crs("EPSG:25837")
+    locations_gpd_Sc = geopandas.GeoDataFrame(Schools.geometry,
+                                              crs='epsg:4326')
+    Shkoly = locations_gpd_Sc.to_crs("EPSG:25837")
+    locations_gpd_R = geopandas.GeoDataFrame(Railway.geometry,
+                                             crs='epsg:4326')
+    Zhd = locations_gpd_R.to_crs("EPSG:25837")
+    # zhk = pd.read_pickle('coordsnovostroy')
+    # col2_Z = zhk['b']
+    # col1_Z = zhk['a']
+    zhk = pd.read_pickle('final_part_domrf')
+    # ZHK = pd.read_pickle('zhks_w_coords_v2.pickle')
+    # col2_Z = ZHK['Lat']
+    # col1_Z = ZHK['Long']
+    # A = pd.read_pickle('ZHKS_COORDS_DOMRF')
+    # A = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(col1_Z, col2_Z),
+    #                                  crs='epsg:4326')
+    return Data1, Eda, Uslugi, Metro, Shosse, Lesa, Shkoly, Zhd, zhk
+    
+
+
+Data1, Eda, Uslugi, Metro, Shosse, Lesa, Shkoly, Zhd, zhk = import_data()
+
+st.write("""
+#Простая интерактивная карта v0.2 (Alfa)
+""")
+
+st.sidebar.header('User Input Parameters')
+
+def user_input_features():
+    serv = st.sidebar.slider('Distance from services (не больше)', 50, 5000, 500)
+    food = st.sidebar.slider('Distance from food markets (не больше)', 50, 5000, 200)
+    metro = st.sidebar.slider('Distance from metro (не больше)', 50, 5000, 1000)
+    highway = st.sidebar.slider('Distance from highway (не меньше)', 50, 5000, 100)
+    area_forest = st.sidebar.slider('Forest area (не меньше)', 1000, 20000, 1000)
+    forest = st.sidebar.slider('Distance from forest (не больше)', 50, 5000, 1000)
+    school = st.sidebar.slider('Distance from schools (не больше)', 50, 5000, 500)
+    railway = st.sidebar.slider('Distance from railway (не меньше)', 50, 5000, 100)
+    data = {'serv': serv,
+            'food': food,
+            'metro': metro,
+            'highway': highway,
+            'area_forest': area_forest,
+            'forest': forest,
+            'school': school,
+            'railway': railway}
+    features = pd.DataFrame(data, index=[0])
+    return features
+
+df = user_input_features()
+
+st.subheader('User Input parameters')
+st.write(df)
+
+
+# In[17]:
+
+
+# def PLOT(serv,food,metro,highway,area_forest,forest, school,railway):
+#     m = folium.Map(location=[55.87890, 37.71943], zoom_start=10, tiles='CartoDB positron')
+#     locations_gpd = geopandas.GeoDataFrame(Eda.geometry)
+#     locations_gpd.geometry = locations_gpd.geometry.buffer(serv,resolution=2)
+#     K = locations_gpd.geometry.unary_union
+#     locations_gpd1 = geopandas.GeoDataFrame(Uslugi.geometry)
+#     locations_gpd1.geometry = locations_gpd1.geometry.buffer(food,resolution=2)
+#     T = locations_gpd1.geometry.unary_union
+
+#     locations_gpd_m = geopandas.GeoDataFrame(Metro.geometry)
+#     locations_gpd_m.geometry = locations_gpd_m.geometry.buffer(metro,resolution=2)
+#     M = locations_gpd_m.geometry.unary_union
+#     locations_gpd_H = geopandas.GeoDataFrame(Shosse.geometry)
+#     locations_gpd_H.geometry = locations_gpd_H.geometry.buffer(highway,resolution=2)
+#     H = locations_gpd_H.geometry.unary_union
+
+#     Forests = geopandas.GeoDataFrame(Lesa.geometry)
+#     Forests = Forests[Forests.geometry.area > area_forest]
+#     Forests.geometry = Forests.geometry.buffer(forest,resolution=2)
+#     F = Forests.geometry.unary_union
+
+#     locations_gpd_Sc = geopandas.GeoDataFrame(Shkoly.geometry)
+#     locations_gpd_Sc.geometry = locations_gpd_Sc.geometry.buffer(school,resolution=2)
+#     Sc = locations_gpd_Sc.geometry.unary_union
+
+#     locations_gpd_R = geopandas.GeoDataFrame(Zhd.geometry)
+#     locations_gpd_R.geometry = locations_gpd_R.geometry.buffer(railway,resolution=2)
+#     Ra = locations_gpd_R.geometry.unary_union
+    
+#     url = "https://cdn-icons-png.flaticon.com/512/746/746859.png{}".format
+#     beerGlass_img = url("")
+#     custom_icon = folium.CustomIcon(beerGlass_img, icon_size=(35, 35), popup_anchor=(0, -22))
+#     insta_post = 'https://www.instagram.com/p/CjcvNysq8om/'
+#     website = 'vk.com'
+#     name = 'bebra'
+#     directions = 'https://yandex.ru/maps/213/moscow/stops/2057340510/?ll=37.593517%2C55.775694&tab=overview&z=12.32'
+#     realty_html = folium.Html(f"""<p style="text-align: center;"><span style="font-family: Didot, serif; font-size: 21px;">{name}</span></p>
+#     <p style="text-align: center;"><iframe src={insta_post}embed width="240" height="290" frameborder="0" scrolling="auto" allowtransparency="true"></iframe>
+#     <p style="text-align: center;"><a href={website} target="_blank" title="{name} Website"><span style="font-family: Didot, serif; font-size: 17px;">{name} Website</span></a></p>
+#     <p style="text-align: center;"><a href={directions} target="_blank" title="Directions to {name}"><span style="font-family: Didot, serif; font-size: 17px;">Directions to {name}</span></a></p>
+#     """, script=True)
+    
+#     popup = folium.Popup(realty_html, max_width=700)
+    
+#     custom_marker = folium.Marker(location=[55.87890,37.71943], icon=custom_icon, tooltip=name, popup=popup)
+    
+    
+#     R = K.intersection(T)
+#     R = R.intersection(M)
+#     R= R.difference(H)
+#     R = R.intersection(F)
+#     R = R.intersection(Sc)
+#     R= R.difference(Ra)
+#     wgs84 = pyproj.CRS('EPSG:25837')
+#     utm = pyproj.CRS('EPSG:4326')
+
+#     project = pyproj.Transformer.from_crs(wgs84, utm, always_xy=True).transform
+#     utm_point = transform(project, R)
+#     R = folium.GeoJson(data=utm_point,  style_function=lambda x: {'fillColor': 'orange'})
+#     b = folium.GeoJson(data=M,  style_function=lambda x: {'fillColor': '#00000000', 'color': '#00000000'})
+#     AH = folium.GeoJson(data=(Data1),  style_function=lambda x: {'fillColor': '#00000000', 'color': 'black'})
+ 
+#     fg1 = folium.map.FeatureGroup(name='Metro').add_to(m)
+#     fg2 = folium.map.FeatureGroup(name='Plot').add_to(m)
+#     fg3 = folium.map.FeatureGroup(name='Districts').add_to(m)
+#     fg4 = folium.map.FeatureGroup(name='rightzhk').add_to(m)
+#     fg5 = folium.map.FeatureGroup(name='badzhk').add_to(m)
+#     R.add_child(folium.Popup('Plot'))
+#     b.add_child(folium.Popup('Метро'))
+#     AH.add_child(folium.Popup('Районы'))
+#     custom_marker.add_to(fg2)
+#     fg1.add_child(b)
+#     fg2.add_child(R)
+#     fg3.add_child(AH)
+#     G = np.array(A.intersects(utm_point))
+#     zhk['G']=G
+#     zhk_1 = zhk.query('G == True').copy()
+#     zhk_2 = zhk.query('G == False').copy()
+#     del(zhk['G'])
+#     for i,row in zhk_1.iterrows():
+#         iframe = folium.IFrame('ЖК:' + str(row[2]))
+#         popup = folium.Popup(iframe, min_width=100, max_width=100)
+#         Z=folium.Marker(location=[row[1],row[0]],
+#                           popup = popup, icon=folium.Icon(color='red', icon=''))
+#         fg4.add_child(Z)
+#     for i,row in zhk_2.iterrows():
+#         iframe = folium.IFrame('ЖК:' + str(row[2]))
+#         popup = folium.Popup(iframe, min_width=100, max_width=100)
+#         Z=folium.Marker(location=[row[1],row[0]],
+#                           popup = popup, icon=folium.Icon(color='gray', icon=''))
+#         fg4.add_child(Z)
+#     del(zhk_1)
+#     del(zhk_2)
+#     folium.LayerControl().add_to(m)
+    
+    
+#     folium_static(m)
+
+
+# In[ ]:
+
+
+st.subheader('Интерактивная карта')
+# PLOT(df['serv'][0],df['food'][0],df['metro'][0],df['highway'][0],df['area_forest'][0],df['forest'][0],df['school'][0],df['railway'][0])
+m = folium.Map(location=[55.87890, 37.71943], zoom_start=10, tiles='CartoDB positron')
+locations_gpd = geopandas.GeoDataFrame(Eda.geometry)
+locations_gpd.geometry = locations_gpd.geometry.buffer(df['serv'][0],resolution=2)
+K = locations_gpd.geometry.unary_union
+locations_gpd1 = geopandas.GeoDataFrame(Uslugi.geometry)
+locations_gpd1.geometry = locations_gpd1.geometry.buffer(df['food'][0],resolution=2)
+T = locations_gpd1.geometry.unary_union
+
+locations_gpd_m = geopandas.GeoDataFrame(Metro.geometry)
+locations_gpd_m.geometry = locations_gpd_m.geometry.buffer(df['metro'][0],resolution=2)
+M = locations_gpd_m.geometry.unary_union
+locations_gpd_H = geopandas.GeoDataFrame(Shosse.geometry)
+locations_gpd_H.geometry = locations_gpd_H.geometry.buffer(df['highway'][0],resolution=2)
+H = locations_gpd_H.geometry.unary_union
+
+Forests = geopandas.GeoDataFrame(Lesa.geometry)
+Forests = Forests[Forests.geometry.area > df['area_forest'][0]]
+Forests.geometry = Forests.geometry.buffer(df['forest'][0],resolution=2)
+F = Forests.geometry.unary_union
+
+locations_gpd_Sc = geopandas.GeoDataFrame(Shkoly.geometry)
+locations_gpd_Sc.geometry = locations_gpd_Sc.geometry.buffer(df['school'][0],resolution=2)
+Sc = locations_gpd_Sc.geometry.unary_union
+
+locations_gpd_R = geopandas.GeoDataFrame(Zhd.geometry)
+locations_gpd_R.geometry = locations_gpd_R.geometry.buffer(df['railway'][0],resolution=2)
+Ra = locations_gpd_R.geometry.unary_union
+
+url = "https://cdn-icons-png.flaticon.com/512/746/746859.png{}".format
+beerGlass_img = url("")
+custom_icon = folium.CustomIcon(beerGlass_img, icon_size=(35, 35), popup_anchor=(0, -22))
+insta_post = 'https://www.instagram.com/p/CjcvNysq8om/'
+website = 'vk.com'
+name = 'bebra'
+directions = 'https://yandex.ru/maps/213/moscow/stops/2057340510/?ll=37.593517%2C55.775694&tab=overview&z=12.32'
+realty_html = folium.Html(f"""<p style="text-align: center;"><span style="font-family: Didot, serif; font-size: 21px;">{name}</span></p>
+<p style="text-align: center;"><iframe src={insta_post}embed width="240" height="290" frameborder="0" scrolling="auto" allowtransparency="true"></iframe>
+<p style="text-align: center;"><a href={website} target="_blank" title="{name} Website"><span style="font-family: Didot, serif; font-size: 17px;">{name} Website</span></a></p>
+<p style="text-align: center;"><a href={directions} target="_blank" title="Directions to {name}"><span style="font-family: Didot, serif; font-size: 17px;">Directions to {name}</span></a></p> 
+# """, script=True)
+
+popup = folium.Popup(realty_html, max_width=700)
+
+custom_marker = folium.Marker(location=[55.87890,37.71943], icon=custom_icon, tooltip=name, popup=popup)
+
+
+R = K.intersection(T)
+R = R.intersection(M)
+R= R.difference(H)
+R = R.intersection(F)
+R = R.intersection(Sc)
+R= R.difference(Ra)
+wgs84 = pyproj.CRS('EPSG:25837')
+utm = pyproj.CRS('EPSG:4326')
+
+project = pyproj.Transformer.from_crs(wgs84, utm, always_xy=True).transform
+utm_point = transform(project, R)
+R = folium.GeoJson(data=utm_point,  style_function=lambda x: {'fillColor': 'orange'})
+b = folium.GeoJson(data=M,  style_function=lambda x: {'fillColor': '#00000000', 'color': '#00000000'})
+AH = folium.GeoJson(data=(Data1),  style_function=lambda x: {'fillColor': '#00000000', 'color': 'black'})
+
+fg1 = folium.map.FeatureGroup(name='Metro').add_to(m)
+fg2 = folium.map.FeatureGroup(name='Plot').add_to(m)
+fg3 = folium.map.FeatureGroup(name='Districts').add_to(m)
+# fg4 = folium.map.FeatureGroup(name='rightzhk').add_to(m)
+# fg5 = folium.map.FeatureGroup(name='badzhk').add_to(m)
+R.add_child(folium.Popup('Plot'))
+b.add_child(folium.Popup('Метро'))
+AH.add_child(folium.Popup('Районы'))
+custom_marker.add_to(fg2)
+fg1.add_child(b)
+fg2.add_child(R)
+fg3.add_child(AH)
+# G = np.array(A.intersects(utm_point))
+# for i,row in zhk.iterrows():
+#         iframe = folium.IFrame('ЖК:' + str(row[3]))
+#         popup = folium.Popup(iframe, min_width=100, max_width=100)
+#         # if (G[i]):
+#         Z=folium.Marker(location=[row[11],row[12]],
+#                       popup = popup, icon=folium.Icon(color='red', icon=''))
+#         fg4.add_child(Z)
+#         # else:
+#         #     Z=folium.Marker(location=[row[11],row[12]],
+#         #                   popup = popup, icon=folium.Icon(color='gray', icon=''))
+#         #     fg5.add_child(Z)
+# folium.LayerControl().add_to(m)
+
+
+folium_static(m)

districts_moc.pickle

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+oid sha256:50e1d18d4a219f9c91aa3ad172d1091dc46141df387df950d357f93130d66f40
+size 1377531

final_part_domrf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0514ed22af616844e377e6eb4ade606d66d1d479b456c9c15970982be417e6d6
+size 5519673

requirements.txt

@@ -0,0 +1,13 @@
+streamlit 
+pandas
+gspread  
+numpy 
+osmnx
+geocoder 
+shapely 
+datetime 
+geopandas
+folium
+streamlit_folium
+pyproj
+openpyxl

zhks_w_coords_v2.pickle

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+oid sha256:51495238f8955de4c75a6744c7948d288585a1d14ac897fb8235debb7ea69b25
+size 20835