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belarus_report.py
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belarus_report.py
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import csv
import datetime
import html
import io
import os
from collections import defaultdict
from urllib.parse import urlencode
import github
import psycopg2
import pandas as pd
import shapely.wkt
import shapely.strtree
from jinja2 import Template
from belarus_upd import Engine
from belarus_utils import PostgisSearchReadEngine, OverpassApiSearchEnigne, CYRILIC_REGEXP
POSTGRES_HOST = os.environ['POSTGRES_HOST']
POSTGRES_PORT = os.environ.get('POSTGRES_PORT', 5432)
POSTGRES_DB = os.environ['POSTGRES_DB']
POSTGRES_USER = os.environ['POSTGRES_USER']
POSTGRES_PASSWORD = os.environ['POSTGRES_PASSWORD']
OSM2PGSQL_CACHE = os.environ['OSM2PGSQL_CACHE']
GITHUB_TOKEN = os.environ['GITHUB_TOKEN']
REPORT_OUTPUT_API = bool(int(os.environ['REPORT_OUTPUT_API']))
REPO_NAME = 'tbicr/osm-name-migrate'
REPO = f'https://github.com/{REPO_NAME}'
SKIP_CSV_COLUMNS = {'be=ru', 'be+ru', 'ru+be', 'be'}
FLOAT_FORMAT = '{:.3f}'.format
TABLE_ATTRS = 'class="table table-sm table-hover caption-top"'
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', FLOAT_FORMAT)
# define rules
CATEGORIES_RULES = {
'admin': [
['admin_level', '2'],
['admin_level', '4'],
['admin_level', '6'],
['admin_level', '8'],
['admin_level', '9'],
],
'place': [
['place', 'city'],
['place', 'town'],
['place', 'village'],
['place', 'hamlet'],
['place', 'isolated_dwelling'],
['admin_level', None],
['boundary', 'administrative'],
['traffic_sign', 'city_limit'],
],
'allotments': [
['place', 'allotments'],
['landuse', 'allotments'],
],
'locality': [
['place', 'locality'],
['abandoned:place', None],
],
'suburb': [
['landuse', 'cemetery'],
['landuse', 'commercial'],
['landuse', 'construction'],
['landuse', 'farmland'],
['landuse', 'farmyard'],
['landuse', 'industrial'],
['landuse', 'residential'],
['landuse', 'retail'],
['place', None],
['residential', None],
['industrial', None],
['landuse', None],
],
'natural': [
['boundary', None],
['natural', None],
['place', 'island'],
['place', 'islet'],
['ele', None],
['landuse', 'forest'],
],
'water': [
['tunnel', None],
['waterway', 'drain'],
['waterway', 'ditch'],
['waterway', 'stream'],
['waterway', 'river'],
['waterway', 'canal'],
['waterway', None],
['type', 'waterway'],
['water', None],
['natural', 'water'],
['natural', 'spring'],
],
'highway': [
['highway', 'motorway'],
['highway', 'trunk'],
['highway', 'primary'],
['highway', 'secondary'],
['highway', 'tertiary'],
['highway', 'unclassified'],
['highway', 'residential'],
['highway', 'service'],
['highway', 'track'],
['highway', None],
['type', 'associatedStreet'],
['type', 'street'],
['bridge', None],
],
'public_transport': [
['highway', 'bus_stop'],
['public_transport', None],
['route', None],
['type', 'route'],
['railway', None],
['type', 'route_master'],
['route_master', None],
],
'infrastructure': [
['barrier', None],
['power', None],
['substation', None],
['man_made', None],
['embankment', None],
['amenity', 'fuel'],
],
'religion': [
['religion', None],
['amenity', 'place_of_worship'],
['amenity', 'monastery'],
['building', 'church'],
['building', 'cathedral'],
['building', 'chapel'],
],
'education': [
['landuse', 'education'],
['amenity', 'university'],
['amenity', 'college'],
['amenity', 'school'],
['amenity', 'kindergarten'],
['building', 'university'],
['building', 'college'],
['building', 'school'],
['building', 'kindergarten'],
],
'healthcare': [
['emergency', None],
['healthcare', None],
['amenity', 'hospital'],
['amenity', 'pharmacy'],
['amenity', 'clinic'],
['amenity', 'doctors'],
['amenity', 'dentist'],
['building', 'hospital'],
['building', 'clinic'],
],
'government': [
['amenity', 'post_office'],
['amenity', 'police'],
['amenity', 'library'],
['office', 'government'],
['government', None],
['landuse', 'military'],
['military', None],
],
'bank': [
['amenity', 'atm'],
['amenity', 'bank'],
],
'office': [
['office', None],
],
'tourism': [
['tourism', None],
['historic', None],
['memorial', None],
['ruins', None],
['information', None],
['attraction', None],
['resort', None],
['artwork_type', None],
],
'amenity': [
['amenity', 'cafe'],
['amenity', 'fast_food'],
['amenity', 'community_centre'],
['amenity', 'restaurant'],
['amenity', 'bar'],
['amenity', None],
['shop', 'convenience'],
['shop', 'clothes'],
['shop', 'car_repair'],
['shop', 'hairdresser'],
['shop', 'chemist'],
['shop', 'supermarket'],
['shop', 'car_parts'],
['shop', 'furniture'],
['shop', 'hardware'],
['shop', 'kiosk'],
['shop', 'doityourself'],
['shop', 'pet'],
['shop', 'florist'],
['shop', 'beauty'],
['shop', 'mobile_phone'],
['shop', 'shoes'],
['shop', 'newsagent'],
['shop', 'electronics'],
['shop', 'alcohol'],
['shop', 'jewelry'],
['shop', 'mall'],
['shop', 'butcher'],
['shop', 'cosmetics'],
['shop', None],
['leisure', None],
['sport', None],
['craft', 'shoemaker'],
['clothes', None],
],
'building': [
['building', 'industrial'],
['building', 'service'],
['building', 'retail'],
['building', 'commercial'],
['building', 'warehouse'],
['building', 'public'],
['building', 'dormitory'],
['building', 'warehouse'],
['building', None],
],
}
DEPENDANTS = [
'addr:region',
'addr:district',
'addr:subdistrict',
'addr:city',
'addr:place',
'addr:street',
'addr2:street',
'from',
'to',
'via',
'destination',
'destination:backward',
'destination:forward',
'water_tank:city',
]
LANGUAGE_TAGS = [
'name',
'name:prefix',
'was:name:prefix',
'short_name',
'official_name',
'official_status',
'official_short_type',
'operator',
'brand',
'network',
'description',
]
usage = defaultdict(set)
CATEGORIES_RULES2 = {}
for category, group in CATEGORIES_RULES.items():
if category not in CATEGORIES_RULES2:
CATEGORIES_RULES2[category] = []
for tag, value in group:
if value is not None:
CATEGORIES_RULES2[category].append([tag, True, {value}])
usage[tag].add(value)
for category, group in CATEGORIES_RULES.items():
if category not in CATEGORIES_RULES2:
CATEGORIES_RULES2[category] = []
for tag, value in group:
if value is None:
CATEGORIES_RULES2[category].append([tag, False, usage[tag]])
tag_star_category = {}
tag_star_values = {}
for category, group in CATEGORIES_RULES.items():
for tag, value in group:
if value is None:
tag_star_category[tag] = category
tag_star_values[tag] = set()
for category, group in CATEGORIES_RULES.items():
for tag, value in group:
if value is not None:
if tag in tag_star_category and category == tag_star_category[tag]:
tag_star_values[tag].add(value)
OVERPASS_QUERIES = {}
for category, group in CATEGORIES_RULES2.items():
conditions = []
for k, eq, vv in group:
if vv and eq and len(vv) == 1:
tag = f'{k} = {list(vv)[0]}'
condition = f'["{k}"="{list(vv)[0]}"]'
if k in tag_star_category and tag_star_category[k] == category:
category_condition = None
else:
category_condition = condition
elif vv and not eq:
tag = f'{k} = *'
condition = f'["{k}"]' + ''.join(
f'["{k}"!="{v}"]' for v in sorted(vv))
category_condition = f'["{k}"]' + ''.join(
f'["{k}"!="{v}"]' for v in sorted(vv) if v not in tag_star_values[k])
elif not eq:
tag = f'{k} = *'
condition = f'["{k}"]'
category_condition = condition
else:
raise ValueError()
OVERPASS_QUERIES[f'{category} - {tag}'] = [condition]
if category_condition:
conditions.append(category_condition)
OVERPASS_QUERIES[category] = conditions
def run_psql(command):
os.system(
f'PGPASSWORD={POSTGRES_PASSWORD} '
f'psql -h {POSTGRES_HOST} -p {POSTGRES_PORT} -U {POSTGRES_USER} -d {POSTGRES_DB} -c "{command}"'
)
print('load dump to postgis')
os.system('wget --backups=1 -N -nv https://download.geofabrik.de/europe/belarus-latest.osm.pbf')
run_psql('CREATE EXTENSION IF NOT EXISTS hstore')
run_psql('CREATE EXTENSION IF NOT EXISTS postgis')
run_psql('DROP MATERIALIZED VIEW IF EXISTS planet_osm_region CASCADE')
os.system(
f'PGPASSWORD={POSTGRES_PASSWORD} '
f'osm2pgsql -H {POSTGRES_HOST} -P {POSTGRES_PORT} -U {POSTGRES_USER} -d {POSTGRES_DB} '
f'-v -l -j -G -x --hstore-add-index -C {OSM2PGSQL_CACHE} -S /usr/share/osm2pgsql/default.style '
f'belarus-latest.osm.pbf'
)
postgres_params = {
'host': POSTGRES_HOST,
'port': POSTGRES_PORT,
'dbname': POSTGRES_DB,
'user': POSTGRES_USER,
'password': POSTGRES_PASSWORD,
}
print('add complex relations that was not loaded to postgis')
postgis_api = PostgisSearchReadEngine(**postgres_params)
overpass_api = OverpassApiSearchEnigne(cache=False)
print('type = street')
postgis_api.insert_extra_relations(overpass_api.search({'type': ['street']}))
print('type = associatedStreet')
postgis_api.insert_extra_relations(overpass_api.search({'type': ['associatedStreet']}))
print('type = route')
postgis_api.insert_extra_relations(overpass_api.search({'type': ['route']}))
print('type = route_master')
postgis_api.insert_extra_relations(overpass_api.search({'type': ['route_master']}))
print('type = waterway')
postgis_api.insert_extra_relations(overpass_api.search({'type': ['waterway']}))
postgis_api.create_materialized_views()
def precalculate_field_data(lang_tag):
lang_tag_sql = lang_tag.replace(':', '__')
data_table = f'{postgis_api.data_table}_{lang_tag_sql}'
OSM_DATA_VIEW_SQL = f"""
CREATE MATERIALIZED VIEW IF NOT EXISTS {data_table} AS
SELECT osm_type, osm_id, tags
FROM {postgis_api.data_table}
WHERE tags->'{lang_tag}' ~ '{CYRILIC_REGEXP}'
"""
OSM_DATA_TAGS_INDEX_SQL = f"""
CREATE INDEX IF NOT EXISTS "{data_table}_tags_idx" ON {data_table} USING GIN (tags)
"""
OSM_DATA_ANALYZE_SQL = f"ANALYZE {data_table}"
with psycopg2.connect(**postgres_params) as conn:
conn.autocommit = True
with conn.cursor() as cur:
cur.execute(OSM_DATA_VIEW_SQL)
cur.execute(OSM_DATA_TAGS_INDEX_SQL)
cur.execute(OSM_DATA_ANALYZE_SQL)
return data_table
def get_stat_query(lang_tag, data_table):
query_template = """
SELECT DISTINCT
osm_type,
osm_id,
'{category}' AS category,
{num} AS num,
tags->'{field}' AS value,
tags->'{field}:be' AS value_be,
tags->'{field}:ru' AS value_ru
FROM {data_table}
WHERE {condition}
"""
exclude = defaultdict(lambda: [[], []])
for category, group in CATEGORIES_RULES2.items():
conditions = []
for i, (k, eq, vv) in enumerate(group):
if vv:
eq_str = 'IN' if eq else 'NOT IN'
vv_str = ','.join(f"'{v}'" for v in vv)
condition = f"tags->'{k}' {eq_str} ({vv_str})"
elif not eq:
condition = f"tags->'{k}' IS NOT NULL"
else:
raise ValueError()
conditions.append(condition)
exclude[k][eq].append(condition)
query = query_template.format(
data_table=data_table, field=lang_tag, category=category, num=i,
condition=condition, cyrilic_regexp=CYRILIC_REGEXP)
yield query
condition = ' OR '.join(f'({c})' for c in conditions)
query = query_template.format(
data_table=data_table, field=lang_tag, category=category, num=-1,
condition=condition, cyrilic_regexp=CYRILIC_REGEXP)
yield query
condition = ' OR '.join(f"(tags->'{k}' IS NOT NULL)" for k, eq_c in exclude.items())
query = query_template.format(
data_table=data_table, field=lang_tag, category='other', num=-1,
condition=f'NOT ({condition})', cyrilic_regexp=CYRILIC_REGEXP)
yield query
query = query_template.format(
data_table=data_table, field=lang_tag, category='TOTAL', num=-1,
condition='TRUE', cyrilic_regexp=CYRILIC_REGEXP)
yield query
def get_df(lang_tag, data_table):
data = []
with psycopg2.connect(**postgres_params) as conn:
with conn.cursor() as cur:
for i, query in enumerate(get_stat_query(lang_tag, data_table), 1):
cur.execute(query)
records = cur.fetchall()
for osm_type, osm_id, category, num, value, value_be, value_ru in records:
if value == value_be == value_ru:
progress = [1, 0, 0, 0, 0, 0, 0, 0, 0] # be=ru
elif value == value_be and value_ru is not None:
progress = [0, 1, 0, 0, 0, 0, 0, 0, 0] # be+ru
elif value == value_be:
progress = [0, 0, 1, 0, 0, 0, 0, 0, 0] # be
elif value == value_ru and value_be is not None:
progress = [0, 0, 0, 1, 0, 0, 0, 0, 0] # ru+be
elif value == value_ru:
progress = [0, 0, 0, 0, 1, 0, 0, 0, 0] # ru
elif value_be is not None and value_ru is not None:
progress = [0, 0, 0, 0, 0, 1, 0, 0, 0] # other both
elif value_be is not None:
progress = [0, 0, 0, 0, 0, 0, 1, 0, 0] # other be
elif value_ru is not None:
progress = [0, 0, 0, 0, 0, 0, 0, 1, 0] # other ru
else:
progress = [0, 0, 0, 0, 0, 0, 0, 0, 1] # no lang
data.append([
category, num, osm_type, osm_id, value, value_be, value_ru, *(bool(v) for v in progress)
])
return pd.DataFrame(data, columns=[
'category', 'num', 'osm_type', 'osm_id', lang_tag, f'{lang_tag}:be', f'{lang_tag}:ru',
'be=ru', 'be+ru', 'be', 'ru+be', 'ru',
'other_both', 'other_be', 'other_ru', 'no_lang',
]).sort_values(by=['category', 'num', lang_tag, f'{lang_tag}:be', f'{lang_tag}:ru', 'osm_type', 'osm_id'])
def get_overpass_link(lang_tag, category, tag, column):
if tag is None:
parts = OVERPASS_QUERIES[category]
else:
parts = OVERPASS_QUERIES[f'{category} - {tag}']
base = f'nwr["{lang_tag}"]["{lang_tag}"!~"^[0123456789]+$"]'
if column == 'be=ru':
parts = [(
f'{base}["{lang_tag}:be"]["{lang_tag}:ru"]{part}(area.b)'
f'(if: t["{lang_tag}"] == t["{lang_tag}:be"] && t["{lang_tag}"] == t["{lang_tag}:ru"]);'
) for part in parts]
elif column == 'be+ru':
parts = [(
f'{base}["{lang_tag}:be"]["{lang_tag}:ru"]{part}(area.b)'
f'(if: t["{lang_tag}"] == t["{lang_tag}:be"] && t["{lang_tag}"] != t["{lang_tag}:ru"]);'
) for part in parts]
elif column == 'ru+be':
parts = [(
f'{base}["{lang_tag}:be"]["{lang_tag}:ru"]{part}(area.b)'
f'(if: t["{lang_tag}"] != t["{lang_tag}:be"] && t["{lang_tag}"] == t["{lang_tag}:ru"]);'
) for part in parts]
elif column == 'be':
parts = [(
f'{base}["{lang_tag}:be"][!"{lang_tag}:ru"]{part}(area.b)'
f'(if: t["{lang_tag}"] == t["{lang_tag}:be"]);'
) for part in parts]
elif column == 'ru':
parts = [(
f'{base}[!"{lang_tag}:be"]["{lang_tag}:ru"]{part}(area.b)'
f'(if: t["{lang_tag}"] == t["{lang_tag}:ru"]);'
) for part in parts]
elif column == 'other_both':
parts = [(
f'{base}["{lang_tag}:be"]["{lang_tag}:ru"]{part}(area.b)'
f'(if: t["{lang_tag}"] != t["{lang_tag}:be"] && t["{lang_tag}"] != t["{lang_tag}:ru"]);'
) for part in parts]
elif column == 'other_be':
parts = [(
f'{base}["{lang_tag}:be"][!"{lang_tag}:ru"]{part}(area.b)'
f'(if: t["{lang_tag}"] != t["{lang_tag}:be"]);'
) for part in parts]
elif column == 'other_ru':
parts = [(
f'{base}[!"{lang_tag}:be"]["{lang_tag}:ru"]{part}(area.b)'
f'(if: t["{lang_tag}"] != t["{lang_tag}:ru"]);'
) for part in parts]
elif column == 'no_lang':
parts = [(
f'{base}[!"{lang_tag}:be"][!"{lang_tag}:ru"]{part}(area.b);'
) for part in parts]
parts_str = '\n '.join(parts)
query = f"""
// tag: {lang_tag}
// category: {category}
// condition: {tag or '*'}
// column: {column}
[out:json][timeout:300];
(
area["admin_level"="2"]["name"="Беларусь"]->.b;
{parts_str}
);
out body;
>;
out skel qt;
""".replace(' ' * 8, '').strip()
return 'https://overpass-turbo.eu?' + urlencode({'Q': query})
def a_tag(link, name):
return f"<a href='{link}' target='_blank'>{name}</a>"
def wrap_hint_progress(lang_tag, category, tag, column, value):
if category == 'TOTAL':
return value
csv_link = None
if should_create_csv(lang_tag, category, tag, column):
if tag is None or category == 'other':
csv_link = f'{REPO}/blob/main/data/{lang_tag}/{category}/{column}.csv'
else:
csv_link = f'{REPO}/blob/main/data/{lang_tag}/{category} - {tag}/{column}.csv'
overpass_link = None
if category != 'other':
overpass_link = get_overpass_link(lang_tag, category, tag, column)
if csv_link and overpass_link:
title = f"{a_tag(csv_link, 'CSV')} or {a_tag(overpass_link, 'overpass')}"
elif csv_link:
title = a_tag(csv_link, 'CSV')
elif overpass_link:
title = a_tag(overpass_link, 'overpass')
else:
return value
classes = 'text-decoration-none text-reset'
return f'<a href="##" class="{classes}" data-bs-toggle="tooltip" data-bs-html="true" title="{title}">{value}</a>'
def wrap_hint_dependant_progress(category, column, value):
if not should_create_csv('name', category, None, column):
return value
csv_link = f'{REPO}/blob/main/data/name/{category}/{column}.csv'
title = a_tag(csv_link, 'CSV')
classes = 'text-decoration-none text-reset'
return f'<a href="##" class="{classes}" data-bs-toggle="tooltip" data-bs-html="true" title="{title}">{value}</a>'
def should_create_csv(lang_tag, category, tag, column):
if lang_tag != 'name':
return False
if category in CATEGORIES_RULES and tag is None:
return False
if category in {'TOTAL', 'amenity', 'building'}:
return False
if column in SKIP_CSV_COLUMNS:
return False
return True
def statistic_report(lang_tag, df):
report_data = []
for c in list(CATEGORIES_RULES) + ['other', 'TOTAL']:
raw_df = df[(df['category'] == c) & (df['num'] == -1)]
field_df = raw_df[raw_df[lang_tag].notnull()]
field_be_df = raw_df[raw_df[f'{lang_tag}:be'].notnull()]
field_ru_df = raw_df[raw_df[f'{lang_tag}:ru'].notnull()]
field_cnt = field_df[lang_tag].count()
field_uniq = field_df[lang_tag].value_counts().count()
field_be_cnt = field_be_df[f'{lang_tag}:be'].count()
field_be_uniq = field_be_df[f'{lang_tag}:be'].value_counts().count()
field_ru_cnt = field_ru_df[f'{lang_tag}:ru'].count()
field_ru_uniq = field_ru_df[f'{lang_tag}:ru'].value_counts().count()
report_data.append([
'#', c,
field_cnt, field_be_cnt, field_ru_cnt,
field_be_cnt / (field_cnt or 1), field_ru_cnt / (field_cnt or 1),
field_uniq, field_be_uniq, field_ru_uniq,
field_be_uniq / (field_uniq or 1), field_ru_uniq / (field_uniq or 1),
])
if c in {'other', 'TOTAL'}:
continue
for i, (k, eq, vv) in enumerate(CATEGORIES_RULES2[c]):
if eq:
tag = f'{k} = {list(vv)[0]}'
else:
tag = f'{k} = *'
raw_df = df[(df['category'] == c) & (df['num'] == i)]
field_df = raw_df[raw_df[lang_tag].notnull()]
field_be_df = raw_df[raw_df[f'{lang_tag}:be'].notnull()]
field_ru_df = raw_df[raw_df[f'{lang_tag}:ru'].notnull()]
field_cnt = field_df[lang_tag].count()
field_uniq = field_df[lang_tag].value_counts().count()
field_be_cnt = field_be_df[f'{lang_tag}:be'].count()
field_be_uniq = field_be_df[f'{lang_tag}:be'].value_counts().count()
field_ru_cnt = field_ru_df[f'{lang_tag}:ru'].count()
field_ru_uniq = field_ru_df[f'{lang_tag}:ru'].value_counts().count()
report_data.append([
'', tag,
field_cnt, field_be_cnt, field_ru_cnt,
field_be_cnt / (field_cnt or 1), field_ru_cnt / (field_cnt or 1),
field_uniq, field_be_uniq, field_ru_uniq,
field_be_uniq / (field_uniq or 1), field_ru_uniq / (field_uniq or 1),
])
report_df = pd.DataFrame(report_data, columns=[
'lvl', 'category',
'all', 'all be', 'all ru',
'all be%', 'all ru%',
'uniq', 'uniq be', 'uniq ru',
'uniq be%', 'uniq ru%',
])
yield f'statistic-{lang_tag}.csv', report_df.to_csv(index=False, float_format=FLOAT_FORMAT)
yield f'statistic-{lang_tag}.html', (
report_df
.style
.set_properties(subset=['category'], **{'text-align': 'left'})
.set_properties(subset=[c for c in report_df.columns if c != 'category'], **{'text-align': 'right'})
.background_gradient('YlOrRd', subset=[c for c in report_df.columns if c.endswith('%')], vmin=0, vmax=1)
.format({f: '{:.3f}' for f in [c for c in report_df.columns if c.endswith('%')]})
.apply(lambda row: [("font-weight: bold" if row.loc['lvl'] == '#' else '') for _ in row], axis=1)
.to_html(table_attributes=TABLE_ATTRS)
)
def progress_report(lang_tag, df):
report_data = []
report_data_html = []
for c in list(CATEGORIES_RULES) + ['other', 'TOTAL']:
raw_df = df[(df['category'] == c) & (df['num'] == -1)]
field_both_df = raw_df[raw_df['be=ru']]
field_be_ru_df = raw_df[raw_df['be+ru']]
field_be_df = raw_df[raw_df['be']]
field_ru_be_df = raw_df[raw_df['ru+be']]
field_ru_df = raw_df[raw_df['ru']]
field_other_both_df = raw_df[raw_df['other_both']]
field_other_be_df = raw_df[raw_df['other_be']]
field_other_ru_df = raw_df[raw_df['other_ru']]
field_none_df = raw_df[raw_df['no_lang']]
raw_part_df_map = {
'be=ru': field_both_df,
'be+ru': field_be_ru_df,
'be': field_be_df,
'ru+be': field_ru_be_df,
'ru': field_ru_df,
'other_both': field_other_both_df,
'other_be': field_other_be_df,
'other_ru': field_other_ru_df,
'no_lang': field_none_df,
}
field_both_cnt = field_both_df['be=ru'].count()
field_be_ru_cnt = field_be_ru_df['be+ru'].count()
field_be_cnt = field_be_df['be'].count()
field_ru_be_cnt = field_ru_be_df['ru+be'].count()
field_ru_cnt = field_ru_df['ru'].count()
field_other_both_cnt = field_other_both_df['other_both'].count()
field_other_be_cnt = field_other_be_df['other_be'].count()
field_other_ru_cnt = field_other_ru_df['other_ru'].count()
field_none_cnt = field_none_df['no_lang'].count()
total = (
field_both_cnt + field_be_ru_cnt + field_be_cnt + field_ru_be_cnt + field_ru_cnt +
field_other_both_cnt + field_other_be_cnt + field_other_ru_cnt + field_none_cnt
)
report_data.append([
'#', c,
field_both_cnt,
field_be_ru_cnt, field_be_cnt,
field_ru_be_cnt, field_ru_cnt,
field_other_both_cnt, field_other_be_cnt, field_other_ru_cnt,
field_none_cnt,
field_both_cnt / (total or 1),
field_be_ru_cnt / (total or 1), field_be_cnt / (total or 1),
field_ru_be_cnt / (total or 1), field_ru_cnt / (total or 1),
field_other_both_cnt / (total or 1), field_other_be_cnt / (total or 1), field_other_ru_cnt / (total or 1),
field_none_cnt / (total or 1),
])
report_data_html.append([
'#', c,
wrap_hint_progress(lang_tag, c, None, 'be=ru', field_both_cnt),
wrap_hint_progress(lang_tag, c, None, 'be+ru', field_be_ru_cnt),
wrap_hint_progress(lang_tag, c, None, 'be', field_be_cnt),
wrap_hint_progress(lang_tag, c, None, 'ru+be', field_ru_be_cnt),
wrap_hint_progress(lang_tag, c, None, 'ru', field_ru_cnt),
wrap_hint_progress(lang_tag, c, None, 'other_both', field_other_both_cnt),
wrap_hint_progress(lang_tag, c, None, 'other_be', field_other_be_cnt),
wrap_hint_progress(lang_tag, c, None, 'other_ru', field_other_ru_cnt),
wrap_hint_progress(lang_tag, c, None, 'no_lang', field_none_cnt),
field_both_cnt / (total or 1),
field_be_ru_cnt / (total or 1), field_be_cnt / (total or 1),
field_ru_be_cnt / (total or 1), field_ru_cnt / (total or 1),
field_other_both_cnt / (total or 1), field_other_be_cnt / (total or 1), field_other_ru_cnt / (total or 1),
field_none_cnt / (total or 1),
])
if c != 'TOTAL':
for part, raw_part_df in raw_part_df_map.items():
if should_create_csv(lang_tag, c, None, part):
yield (
f'{lang_tag}/{c}/{part}.csv',
raw_part_df[[
'osm_type', 'osm_id', lang_tag, f'{lang_tag}:be', f'{lang_tag}:ru'
]].to_csv(index=False, float_format=FLOAT_FORMAT)
)
if c in {'other', 'TOTAL'}:
continue
for i, (k, eq, vv) in enumerate(CATEGORIES_RULES2[c]):
if eq:
tag = f'{k} = {list(vv)[0]}'
else:
tag = f'{k} = *'
raw_df = df[(df['category'] == c) & (df['num'] == i)]
field_both_df = raw_df[raw_df['be=ru']]
field_be_ru_df = raw_df[raw_df['be+ru']]
field_be_df = raw_df[raw_df['be']]
field_ru_be_df = raw_df[raw_df['ru+be']]
field_ru_df = raw_df[raw_df['ru']]
field_other_both_df = raw_df[raw_df['other_both']]
field_other_be_df = raw_df[raw_df['other_be']]
field_other_ru_df = raw_df[raw_df['other_ru']]
field_none_df = raw_df[raw_df['no_lang']]
raw_part_df_map = {
'be=ru': field_both_df,
'be+ru': field_be_ru_df,
'be': field_be_df,
'ru+be': field_ru_be_df,
'ru': field_ru_df,
'other_both': field_other_both_df,
'other_be': field_other_be_df,
'other_ru': field_other_ru_df,
'no_lang': field_none_df,
}
field_both_cnt = field_both_df['be=ru'].count()
field_be_ru_cnt = field_be_ru_df['be+ru'].count()
field_be_cnt = field_be_df['be'].count()
field_ru_be_cnt = field_ru_be_df['ru+be'].count()
field_ru_cnt = field_ru_df['ru'].count()
field_other_both_cnt = field_other_both_df['other_both'].count()
field_other_be_cnt = field_other_be_df['other_be'].count()
field_other_ru_cnt = field_other_ru_df['other_ru'].count()
field_none_cnt = field_none_df['no_lang'].count()
total = (
field_both_cnt + field_be_ru_cnt + field_be_cnt + field_ru_be_cnt + field_ru_cnt +
field_other_both_cnt + field_other_be_cnt + field_other_ru_cnt + field_none_cnt
)
report_data.append([
'', tag,
field_both_cnt,
field_be_ru_cnt, field_be_cnt,
field_ru_be_cnt, field_ru_cnt,
field_other_both_cnt, field_other_be_cnt, field_other_ru_cnt,
field_none_cnt,
field_both_cnt / (total or 1),
field_be_ru_cnt / (total or 1), field_be_cnt / (total or 1),
field_ru_be_cnt / (total or 1), field_ru_cnt / (total or 1),
field_other_both_cnt / (total or 1), field_other_be_cnt / (total or 1),
field_other_ru_cnt / (total or 1),
field_none_cnt / (total or 1),
])
report_data_html.append([
'', tag,
wrap_hint_progress(lang_tag, c, tag, 'be=ru', field_both_cnt),
wrap_hint_progress(lang_tag, c, tag, 'be+ru', field_be_ru_cnt),
wrap_hint_progress(lang_tag, c, tag, 'be', field_be_cnt),
wrap_hint_progress(lang_tag, c, tag, 'ru+be', field_ru_be_cnt),
wrap_hint_progress(lang_tag, c, tag, 'ru', field_ru_cnt),
wrap_hint_progress(lang_tag, c, tag, 'other_both', field_other_both_cnt),
wrap_hint_progress(lang_tag, c, tag, 'other_be', field_other_be_cnt),
wrap_hint_progress(lang_tag, c, tag, 'other_ru', field_other_ru_cnt),
wrap_hint_progress(lang_tag, c, tag, 'no_lang', field_none_cnt),
field_both_cnt / (total or 1),
field_be_ru_cnt / (total or 1), field_be_cnt / (total or 1),
field_ru_be_cnt / (total or 1), field_ru_cnt / (total or 1),
field_other_both_cnt / (total or 1), field_other_be_cnt / (total or 1),
field_other_ru_cnt / (total or 1),
field_none_cnt / (total or 1),
])
for part, raw_part_df in raw_part_df_map.items():
if should_create_csv(lang_tag, c, tag, part):
yield (
f'{lang_tag}/{c} - {tag}/{part}.csv',
raw_part_df[[
'osm_type', 'osm_id', lang_tag, f'{lang_tag}:be', f'{lang_tag}:ru'
]].to_csv(index=False, float_format=FLOAT_FORMAT)
)
report_df = pd.DataFrame(report_data, columns=[
'lvl', 'category',
'be=ru', 'be+ru', 'be', 'ru+be', 'ru',
'other both', 'other be', 'other ru', 'no lang',
'be=ru%', 'be+ru%', 'be%', 'ru+be%', 'ru%',
'other both%', 'other be%', 'other ru%', 'no lang%',
])
report_df_html = pd.DataFrame(report_data_html, columns=[
'lvl', 'category',
'be=ru', 'be+ru', 'be', 'ru+be', 'ru',
'other both', 'other be', 'other ru', 'no lang',
'be=ru%', 'be+ru%', 'be%', 'ru+be%', 'ru%',
'other both%', 'other be%', 'other ru%', 'no lang%',
])
yield f'progress-{lang_tag}.csv', report_df.to_csv(index=False, float_format=FLOAT_FORMAT)
yield f'progress-{lang_tag}.html', (
report_df_html
.style
.set_properties(subset=['category'], **{'text-align': 'left'})
.set_properties(subset=[c for c in report_df_html.columns if c != 'category'], **{'text-align': 'right'})
.set_properties(subset=['be=ru', 'be+ru', 'be'],
**{'background-color': '#d9ead3'})
.set_properties(subset=['ru+be'],
**{'background-color': '#fff2cc'})
.set_properties(subset=['ru', 'other both', 'other be', 'other ru', 'no lang'],
**{'background-color': '#f4cccc'})
.background_gradient('YlOrRd', subset=[c for c in report_df_html.columns if c.endswith('%')], vmin=0, vmax=1)
.format({f: '{:.3f}' for f in [c for c in report_df_html.columns if c.endswith('%')]})
.apply(lambda row: [("font-weight: bold" if row.loc['lvl'] == '#' else '') for _ in row], axis=1)
.to_html(table_attributes=TABLE_ATTRS)
)
def get_dependant_df():
data = []
with psycopg2.connect(**postgres_params) as conn:
with conn.cursor() as cur:
cur.execute(f"""
SELECT osm_type, osm_id, name, name_be, name_ru, ST_AsText(way)
FROM {postgis_api.data_table}
WHERE name IS NOT NULL OR name_be IS NOT NULL OR name_ru IS NOT NULL
""")
name_idx = defaultdict(list)
geom_idx = {}
geoms = defaultdict(list)
for osm_type, osm_id, name, name_be, name_ru, geom_wkt in cur.fetchall():
names = {name, name_be, name_ru}
geom = shapely.wkt.loads(geom_wkt)
for name in names:
if not name:
continue
name_idx[name].append((osm_type, osm_id, name, name_be, name_ru))
geoms[name].append(geom)
for name, name_geoms in geoms.items():
geom_idx[name] = shapely.strtree.STRtree(name_geoms)
empty = (None, None, None, None, None)
for dependant in DEPENDANTS:
cur.execute(f"""
SELECT osm_type, osm_id, tags->'{dependant}', ST_AsText(way)
FROM {postgis_api.data_table}
WHERE tags->'{dependant}' IS NOT NULL
""")
for osm_type, osm_id, name, geom_wkt in cur.fetchall():
geom = shapely.wkt.loads(geom_wkt)
for name_part in name.split(';'):
name_part = name_part.strip()
if name_part not in geom_idx:
v_osm_type, v_osm_id, value, value_be, value_ru = empty
else:
idx = geom_idx[name_part].nearest_item(geom)
v_osm_type, v_osm_id, value, value_be, value_ru = name_idx[name_part][idx]
if value and value_be and value_ru and name_part == value_be == value_ru:
progress = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0] # be=ru
elif value and value_be and value_ru and name_part == value_be:
progress = [0, 1, 0, 0, 0, 0, 0, 0, 0, 0] # be+ru
elif value and value_be and value_ru and name_part == value_ru:
progress = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0] # ru+be
elif value and value_be and value_ru:
progress = [0, 0, 0, 0, 0, 1, 0, 0, 0, 0] # other both
elif value and value_be and not value_ru and name_part == value_be:
progress = [0, 0, 1, 0, 0, 0, 0, 0, 0, 0] # be
elif value and value_be and not value_ru:
progress = [0, 0, 0, 0, 0, 0, 1, 0, 0, 0] # other be
elif value and not value_be and value_ru and name_part == value_ru:
progress = [0, 0, 0, 0, 1, 0, 0, 0, 0, 0] # ru
elif value and not value_be and value_ru:
progress = [0, 0, 0, 0, 0, 0, 0, 1, 0, 0] # other ru
elif value:
progress = [0, 0, 0, 0, 0, 0, 0, 0, 1, 0] # no lang
else:
progress = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1] # not found
data.append((
dependant, osm_type, osm_id, name_part,
v_osm_type, str(v_osm_id) if v_osm_id else None, value, value_be, value_ru,
*(bool(v) for v in progress),
))
return pd.DataFrame(data, columns=[
'category', 'dep_osm_type', 'dep_osm_id', 'dependant',
'osm_type', 'osm_id', 'name', 'name:be', 'name:ru',
'be=ru', 'be+ru', 'be', 'ru+be', 'ru',
'other_both', 'other_be', 'other_ru', 'no_lang', 'not_found',
]).sort_values(by=[
'category', 'dependant', 'name', 'name:be', 'name:ru', 'dep_osm_type', 'dep_osm_id'
])
def dependant_statistic_report(df):
report_data = []
for c in DEPENDANTS:
raw_df = df[df['category'] == c]
dependant_df = raw_df[raw_df['dependant'].notnull()]
field_df = raw_df[raw_df['name'].notnull()]
field_be_df = raw_df[raw_df['name:be'].notnull()]
field_ru_df = raw_df[raw_df['name:ru'].notnull()]
dependant_cnt = dependant_df['dependant'].count()
dependant_uniq = dependant_df['dependant'].value_counts().count()
field_cnt = field_df['name'].count()
field_uniq = field_df['name'].value_counts().count()
field_be_cnt = field_be_df['name:be'].count()
field_be_uniq = field_be_df['name:be'].value_counts().count()
field_ru_cnt = field_ru_df['name:ru'].count()
field_ru_uniq = field_ru_df['name:ru'].value_counts().count()
report_data.append([
c,
dependant_cnt, field_cnt, field_be_cnt, field_ru_cnt,
field_cnt / (dependant_cnt or 1),
field_be_cnt / (dependant_cnt or 1), field_ru_cnt / (dependant_cnt or 1),
dependant_uniq, field_uniq, field_be_uniq, field_ru_uniq,
field_uniq / (dependant_uniq or 1),
field_be_uniq / (dependant_uniq or 1), field_ru_uniq / (dependant_uniq or 1),
])
report_df = pd.DataFrame(report_data, columns=[
'category',
'dep', 'all', 'all be', 'all ru',
'all%', 'all be%', 'all ru%',