♟️ 💪🏻 🐡 莫斯科装修浪潮 🔩 ⚙️ 📄

美好的一天，habr的亲爱的读者们，在2020年8月12日，公布了翻新计划下的搬迁阶段（您可以在此处找到），我想知道如果这些阶段都可视化会怎样。在这里有必要澄清一下，我与莫斯科政府没有任何联系，但是我是一家装修建筑中的公寓的快乐主人，所以我很感兴趣地看到，也许甚至可以准确地猜出在我的情况下装修浪潮可能向何处移动（也许亲爱的读者，如果您对此感兴趣的话。当然，准确的预测是行不通的，但是至少可以从新的角度看图片。

UPD 2020年8月28日，我们

获得了一张完整的翻新地图，上面有翻新的波浪和发射场。

介绍

2017 . 350 , , .

, . 5174 .

… ( )

12 2020 . № 45/182/-335/20 ( ) 2032 ( ):

2020 — 2024., 930 , 3-29
2025 — 2028., 1636 , 30-76
2029 — 2032., 1809 , 77-128
( 1 2021.) — 688 , 129-148

github .

, . , .

wave1.ipynb (obsolete)

, .. — pdf , tabula pdf .

import pandas as pd
import numpy as np
import requests
from tabula import read_pdf
import json
import os

, , .

test = read_pdf('prikaz_grafikpereseleniya.pdf', pages='3', pandas_options={'header':None})

test.head()

	0	3	4	5
0	No /	NaN		unom
1	1	., .49 c.4	NaN	1316
2	2	., .77 c.3	NaN	1327
3	3	., .2/26	NaN	19328
4	4	., .3	NaN	31354

, , , parse_pdf_table.

def parse_pdf_table(pages, pdf_file='prikaz_grafikpereseleniya.pdf'):
    df = read_pdf(pdf_file, pages=pages, pandas_options={'header':None})

    #    
    df = df[~(df.iloc[:,0] == 'No /')]

    #    
    df = df.iloc[:,1:4]
    df.columns = ['AO', 'district', 'address']

    return df

, , .. , pdf . ( , .. )

wave_1 = parse_pdf_table('3-29') # 2020 - 2024
wave_1['wave'] = 1

wave_1.shape

(930, 4)

wave_2 = parse_pdf_table('30-76') # 2025 - 2028
wave_2['wave'] = 2

wave_2.shape

(1636, 4)

wave_3 = parse_pdf_table('77-128') # 2029 - 2032
wave_3['wave'] = 3

wave_3.shape

(1809, 4)

unknown = parse_pdf_table('129-148')
unknown['wave'] = 0

unknown.shape

(688, 4)

(pandas), df.

df = pd.concat([wave_1, wave_2, wave_3, unknown], ignore_index=True)

df['marker-color'] = df['wave'].map({1:'#0ACF00',  # 
                                     2:'#1142AA',  # 
                                     3:'#FFFD00',  # 
                                     0:'#FD0006'}) #

df['iconContent'] = df['wave'].map({1:'1',
                                    2:'2',
                                    3:'3',
                                    0:''})

df['description'] = df['address']

— , , , , , . ( ! :)

def add_city(x):
    if x['AO'] == '':
        return ', ' + x['address']

    return ', ' + x['address']

df['address'] = df[['AO', 'address']].apply(add_city, axis=1)

, , .. . , .

def geocoder(addr, key='  '):   
    url = 'https://geocode-maps.yandex.ru/1.x'
    params = {'format':'json', 'apikey': key, 'geocode': addr}
    response = requests.get(url, params=params)

    try:
        coordinates = response.json()["response"]["GeoObjectCollection"]["featureMember"][0]["GeoObject"]["Point"]["pos"]
        lon, lat = coordinates.split(' ')
    except:
        lon, lat = 0, 0

    return lon, lat

%%time
df['longitude'], df['latitude'] = zip(*df['address'].apply(geocoder))

CPU times: user 2min 11s, sys: 4.31 s, total: 2min 15s
Wall time: 15min 14s

( , .. , ), - - .

len(df[df['longitude'] == 0])

df.to_csv('waves.csv')

#df = pd.read_csv('waves.csv')

GeoJSON.

def df_to_geojson(df, properties, lat='latitude', lon='longitude'):
    geojson = {'type':'FeatureCollection', 'features':[]}
    for _, row in df.iterrows():
        feature = {'type':'Feature',
                   'properties':{},
                   'geometry':{'type':'Point',
                               'coordinates':[]}}
        feature['geometry']['coordinates'] = [row[lon],row[lat]]
        for prop in properties:
            feature['properties'][prop] = row[prop]
        geojson['features'].append(feature)
    return geojson

.. , , .

properties = ['marker-color', 'iconContent', 'description']

if not os.path.exists('data'):
    os.makedirs('data')

for ao, data in df.groupby('AO'):
    geojson = df_to_geojson(data, properties)

    with open('data/' + ao + '.geojson', 'w') as f:
        json.dump(geojson, f, indent=2)

.geojson data. _.geojson .

geojson = df_to_geojson(df, properties)

with open('data/_.geojson', 'w') as f:
    json.dump(geojson, f, indent=2)

( ) .

, , , , — (.), .1 - — . (. , .), .8//. ( , )

, :(

, . , , , , , , , . 39, , . 6, — , . 1, 2, 3, . 38.

( ), , , , .

— !

- , , / .

, , . , PbIXTOP, .

wave2.ipynb ( 2.0)

2.0

import pandas as pd
import numpy as np
import json
from tabula import read_pdf
from tqdm.notebook import tqdm
import os

with open('renovation_address.txt') as f:
    bounded_addresses = json.load(f)

def parse_pdf_table(pages, pdf_file='prikaz_grafikpereseleniya.pdf'):
    df = read_pdf(pdf_file, pages=pages, pandas_options={'header':None})

    #    
    df = df[~(df.iloc[:,0] == 'No /')]

    df['unom'] = df.iloc[:,-1].combine_first(df.iloc[:,-2])

    #    
    df = df.iloc[:,[1, 2, 3, -1]]
    df.columns = ['AO', 'district', 'description', 'unom']

    return df

wave_1 = parse_pdf_table('3-29') # 2020 - 2024
wave_1['wave'] = 1

wave_2 = parse_pdf_table('30-76') # 2025 - 2028
wave_2['wave'] = 2

wave_3 = parse_pdf_table('77-128') # 2029 - 2032
wave_3['wave'] = 3

unknown = parse_pdf_table('129-148')
unknown['wave'] = 0

df = pd.concat([wave_1, wave_2, wave_3, unknown], ignore_index=True)

df['marker-color'] = df['wave'].map({1:'#0ACF00',  # 
                                     2:'#1142AA',  # 
                                     3:'#FFFD00',  # 
                                     0:'#FD0006'}) # 

df['iconContent'] = df['wave'].map({1:'1',
                                    2:'2',
                                    3:'3',
                                    0:''})

df['longitude'] = 0
df['latitude'] = 0

for i in tqdm(bounded_addresses):
    unom = i['unom']
    coordinates = i['center']['coordinates']

    df.loc[df['unom']==unom, 'longitude'] = coordinates[1]
    df.loc[df['unom']==unom, 'latitude'] = coordinates[0]

HBox(children=(FloatProgress(value=0.0, max=5152.0), HTML(value='')))

#      , ..      
df.loc[(df['AO'] == '') | (df['AO'] == ''), 'AO'] = ''

df[df['longitude'] == 0]

	district	description	unom	wave	marker-color	iconContent
917	-	. (.-), .11	15000016	1	#0ACF00	1
918	-	. (.-), .13	15000015	1	#0ACF00	1
919	-	. (.-), .3	15000013	1	#0ACF00	1
925	-	. (.-), .4	15000012	1	#0ACF00	1
926	-	. (.-), .6	15000014	1	#0ACF00	1
4883		. (. , .)...	4405823	0	#FD0006
4945		. (., /), .51	20000002	0	#FD0006
4946		. (., /), .52	20000003	0	#FD0006
4947		. (., /), .53	20000001	0	#FD0006
4948		. (., /), .85	20000000	0	#FD0006
4995		(.), .1	20000004	0	#FD0006

df.loc[917, ['longitude', 'latitude']] = 37.204805, 55.385382 
df.loc[918, ['longitude', 'latitude']] = 37.205255, 55.385367 
df.loc[919, ['longitude', 'latitude']] = 37.201518, 55.385265 
df.loc[925, ['longitude', 'latitude']] = 37.201545, 55.384927 
df.loc[926, ['longitude', 'latitude']] = 37.204151, 55.384576
df.loc[4883, ['longitude', 'latitude']] = 37.321218, 55.661308 
df.loc[4945, ['longitude', 'latitude']] = 37.476896, 55.604153 
df.loc[4946, ['longitude', 'latitude']] = 37.477406, 55.603895 
df.loc[4947, ['longitude', 'latitude']] = 37.476546, 55.602729 
df.loc[4948, ['longitude', 'latitude']] = 37.477568, 55.604659
df.loc[4995, ['longitude', 'latitude']] = 37.176806, 55.341541

with open('start_area.txt') as f:
    end = json.load(f)

data = {
    'AO':[],
    'district':[],
    'longitude':[],
    'latitude':[],
    'description':[]
}

for i in end['response']:

    data['AO'].append(i['OKRUG'])

    data['district'] = i['AREA']

    coordinates = i['geoData']['coordinates']

    data['longitude'].append(coordinates[1])
    data['latitude'].append(coordinates[0])

    description = i['Address']

    if 'StartOfRelocation' in i:
        if i['StartOfRelocation'] is not None:
            description += '\n' + i['StartOfRelocation']

    data['description'].append(description)

df_start_area = pd.DataFrame(data)
df_start_area['marker-color'] = '#7D3E00' #  
df_start_area['iconContent'] = '0'
df_start_area['unom'] = None
df_start_area['wave'] = -1

df = pd.concat([df, df_start_area], ignore_index=True)

def df_to_geojson(df, properties, lat='latitude', lon='longitude'):
    geojson = {'type':'FeatureCollection', 'features':[]}
    for _, row in df.iterrows():
        feature = {'type':'Feature',
                   'properties':{},
                   'geometry':{'type':'Point',
                               'coordinates':[]}}
        feature['geometry']['coordinates'] = [row[lon],row[lat]]
        for prop in properties:
            feature['properties'][prop] = row[prop]
        geojson['features'].append(feature)
    return geojson

properties = ['marker-color', 'iconContent', 'description']

if not os.path.exists('data'):
    os.makedirs('data')

for ao, data in df.groupby('AO'):
    geojson = df_to_geojson(data, properties)

    with open('data/' + ao + '.geojson', 'w') as f:
        json.dump(geojson, f, indent=2)

( )

geojson = df_to_geojson(df, properties)

with open('data/_.geojson', 'w') as f:
    json.dump(geojson, f, indent=2)

, , , , , , , .

UPD 28 2020

PbIXTOP , .

( )

UPD 1 2020

隐藏了实现，添加了用于形成地图的最新代码，因为本文的大多数读者只对地图感兴趣。

感谢您的关注。

莫斯科装修浪潮

介绍

, :(

2.0

UPD 28 2020

UPD 1 2020

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