def output_error():
cputime = tickwatch()
target = 'A10008_TRAN'
mydb = manidb('G:/NCREE_GIS/2020_address/TGOS_NLSC_TWN22.sqlite')
df0 = mydb.get_alias(target).read()
cputime.tick('Calculation accomplished')
## export the outlier
df1 = df0[df0['check_town_geo'] == 0]
df2 = df0[df0['reCntycode'] == 0]
df3 = df0[df0['reTowncode'] == 0]
df4 = df0[df0['reNumber'] == 0]
frames = [df1, df2, df3, df4]
result = pd.concat(frames)
mydb.get_alias(target + '_out').write(result)
cputime.tick('Write down dataframe' + target)
df1 = df0[df0['check_town_geo'] == 0]
df2 = df0[df0['reCntycode'] == 0]
df3 = df0[df0['reTowncode'] == 0]
df4 = df0[df0['reNumber'] == 0]
frames = [df1, df2, df3, df4]
result = pd.concat(frames)
mydb.get_alias(target + '_out').write(result)
cputime.tick('Write down dataframe' + target)
if __name__ == '__console__' or __name__ == '__main__':
target = 'A10008_TRAN'
modify = 'A10008_TRAN_modify'
mydb = manidb('G:/NCREE_GIS/2020_address/TGOS_NLSC_TWN22.sqlite')
df0 = mydb.get_alias(target).read()
df1 = mydb.get_alias(modify).read()
df0 = df0.set_index(['fid'])
df1 = df1.set_index(['fid'])
# df_x = df0.loc[df1.index,:]
df0.loc[df1.index, :] = df1[:]
# df_y = df0.loc[df1.index, :]
sdf = df0[[
'origin_address', 'cnty_code', 'town_code', 'lie', 'lin', 'road',
'zone', 'lane', 'alley', 'number', 'floor'
def function_x():
cputime = tickwatch()
from osgeo import ogr
mydb = manidb('G:/NCREE_GIS/2020_address/TGOS_NLSC_TWN22.sqlite')
cnty = mydb.get_alias('metadata_nsg_cnty').read()
town = mydb.get_alias('metadata_nsg_town').read()
for key, row in cnty[cnty['ncity'] == '10008'].iterrows():
cntynum = row['ncity']
cnty_wkt = row['cnty_wkt']
target = 'A' + cntynum
#resource
tab0 = mydb.get_alias(target)
#outcome
tab1 = mydb.get_alias(target + '_TRAN')
df0 = tab0.read()
df0['point_wkt'] = df0[['TWD97_X', 'TWD97_Y'
]].astype(str).apply(lambda x: ' '.join(x),
axis=1)
df0['point_wkt'] = df0['point_wkt'].apply(lambda x: f'POINT({x})')
if df0.empty:
continue
cputime.tick()
#check county boundary
df0 = check_addr_column(cnty_wkt, df0, 'point_wkt', fun4checkgeo)
# check town boundary
town_wkt = lookup_value(df0, 'town_code', town)
ziparg = zip(town_wkt['town_wkt'].tolist(), df0['point_wkt'].tolist())
#-----------------------------------------
with Pool(8) as mpool:
check_town = mpool.map(fun4checkgeo, ziparg)
#-----------------------------------------
#debug
#check_town = []
#for arg in ziparg:
# check_town.append(fun4checkgeo(arg))
#------------------------------------
df_check_town = pd.DataFrame.from_dict(check_town, orient='columns')
df0['check_town_geo'] = df_check_town['checkgeo']
cputime.tick('Geometry checked')
#check county code
df0 = check_addr_column(cntynum, df0, 'cnty_code', fun4cntycode)
# check town code
df0 = check_addr_column(cntynum, df0, 'town_code', fun4towncode)
# check number
df0 = check_addr_column(cntynum, df0, 'num', fun4number)
df0 = trans_column(df0)
mydb.get_alias(target + '_TRAN').write(df0)
target_result['dc_json_report'] = json.dumps(dc_report)
#%%----------run-------------
step1(addr_text)
step2(target_result['dc_unusual_tail']) if dc_report['re'] else {}
step3() if dc_report['re'] and dc_report['num_tp'] else {}
return (ind, target_result)
if __name__ == '__console__' or __name__ == '__main__':
#machine argument
#%%----------read-------------
ax_dec_1 = ax_htax_decomposition()
origindb = manidb('G:/NCREE_GIS/htax/nsg_bldg_TTK_bm.sqlite')
target_tab = '63000'
df_all = origindb.get_alias(f'rawdata_hou_A{target_tab}_addr').read()
print('read')
df_bin = df_all.set_index(['HOU_LOSN'])
#df_bin['LOCAT_ADDR'] = df_bin['LOCAT_ADDR'].apply( lambda x : x.split() [0] )
## decomposition
##
onere = [
ax_dec_1.decomposition(item) for item in df_bin['LOCAT_ADDR'].items()
]
df_onere = pandas.DataFrame.from_dict(dict(onere), orient='index')
import pandas
import csv, re, math, json
import numpy as np
import pandas as pd
from itertools import zip_longest
import Lily.ctao2.ctao2_database_alias
from Lily.ctao2.ctao2_database_alias import manidb, alias, tickwatch
from multiprocessing import Pool
import osgeo, ogr
if __name__ == '__console__' or __name__ == '__main__':
cputime = tickwatch()
#%%----------read-------------
origindb = manidb( 'G:/NCREE_GIS/htax/nsg_bldg_taipei.sqlite' )
country = 'taipei'
df_tax = origindb.get_alias(f'htax_{country}').read()
df_tgo = origindb.get_alias(f'tgos_{country}_group').read()[['nsg_addr_key','geom']]#.head(200)
cputime.tick('Dataframe read')
#%%---------search-------------
df_tax = df_tax.merge(df_tgo, left_on='nsg_addr_key', right_on='nsg_addr_key')
#df_tgo = df_tgo.set_index(['nsg_addr_key'])
#tax_addr_list = df_tax['nsg_addr_key'].tolist()
#i = 0
#for addr in tax_addr_list:
# i += 1; print(i)
# if addr in df_tgo.index:
step2(target_result['dc_unusual_tail']) if dc_report['pattern'] else {}
step3() if dc_report['pattern'] and dc_report['check_num'] else {}
return (ind, target_result)
if __name__ == '__console__' or __name__ == '__main__':
cputime = tickwatch()
#%%---------target-------------
workdir = 'G:/NCREE_GIS/'
target_tab = '92000'
country = 'yilan'
db = manidb(workdir + 'htax/nsg_bldg_3826.sqlite')
#db = manidb( workdir + 'htax/nsg_bldg_3825.sqlite' )
cputime.tick('Dataframe read')
#%%------step1. read pickle-------------
#df = pd.read_pickle(workdir + 'data_pickle/bin_{target_tab}')
#try:
# df['LOCAT_ADDR'] = df['LOCAT_ADDR'].apply( lambda x : x.split()[0] )
#except:
# break
#db.get_alias('rawdata_hou_A{target_tab}_addr').write(df)
cputime.tick('read pickle done')
#%%-------step2. htax address decomposition-------------
return a
def get_df(df0, list):
df = pd.DataFrame.from_dict(list, orient='columns')
for colname in df.columns:
df0[colname] = df[colname].fillna(value='')
return df0
if __name__ == '__console__' or __name__ == '__main__':
cputime = tickwatch()
#%%-----------resource--------------
mydb = manidb('G:/NCREE_GIS/tgos_address/2021_TGOS_NLSC_TWN22_V1.sqlite')
output = manidb('G:/NCREE_GIS/tgos_address/nsg_bldg_TGOS.sqlite')
#cnty = mydb.get_alias('metadata_nsg_cnty').read()
cnty = mydb.get_alias('metadata_nsg_cnty_3825').read()
town = mydb.get_alias('metadata_nsg_town').read()
for key, row in cnty[cnty['ncity'] == '09020'].iterrows():
#for key, row in cnty.iterrows():
#%%----------read-------------
mpool = Pool(8)
cntynum = row['ncity']
cnty_wkt = row['cnty_wkt']
dbf = Dbf5(srcdb_file, codec='big5')
df = dbf.to_dataframe() #.head(20)
addr = df['CNTY_NAME'].fillna(value='') + df['TOWN_NAME'].fillna(
value='') + df['ATRACTNAME'].fillna(value='') + df["AROAD"].fillna(
value='') + df["AAREA"].fillna(value='') + df["ALANE"].fillna(
value='') + df["AALLEY"].fillna(value='') + df["ANO"].fillna(
value='')
api = mpool.map(google_map_api, addr)
list_lat = []
list_lng = []
list_location_type = []
for i in api:
if i != {}:
print(i)
list_lat.append(i['lat'])
list_lng.append(i['lng'])
list_location_type.append(i['location_type'])
else:
list_lat.append('')
list_lng.append('')
list_location_type.append('')
df['GOOGLE_LAT'] = list_lat
df['GOOGLE_LON'] = list_lng
df['location_type'] = list_location_type
output = manidb('G:/NCREE_GIS/2020_address/_Total_err3_s2.sqlite')
output.get_alias('_Total_err3_s2_V1').write(df)
#
#
step1(addr_text)
step2(target_result['dc_unusual_tail']) if dc_report['re'] else {}
step3() if dc_report['re'] and dc_report['num_tp'] else {}
return (ind, target_result)
if __name__ == '__console__' or __name__ == '__main__':
#machine argument
uAnswer = answer()
ax_dec_1 = ax_htax_decomposition()
workdir = uAnswer.host.home + '/Desktop/crying_freeman/data_nsg/'
origindb = manidb(workdir + 'nsg_bldg_TTK_bm.sqlite')
target_tab = '10017'
#read data
df_all = origindb.get_alias(f'rawdata_hou_A{target_tab}_addr').read()
#for debug and development
#df_bin = df_all.head(10000)
df_bin = df_all
df_bin = df_bin.set_index(['HOU_LOSN'])
#df_bin['LOCAT_ADDR'] = df_bin['LOCAT_ADDR'].apply( lambda x : x.split() [0] )
## decomposition
##
lambda x: x.split()[0])
df_10002['LOCAT_ADDR'] = df_10002['LOCAT_ADDR'].apply(
lambda x: x.split()[0])
#df_10008['LOCAT_ADDR'] = df_10008['LOCAT_ADDR'].apply( lambda x : x.split()[0] )
#df_10009['LOCAT_ADDR'] = df_10009['LOCAT_ADDR'].apply( lambda x : x.split()[0] )
#df_10010['LOCAT_ADDR'] = df_10010['LOCAT_ADDR'].apply( lambda x : x.split()[0] )
#df_10013['LOCAT_ADDR'] = df_10013['LOCAT_ADDR'].apply( lambda x : x.split()[0] )
#df_10014['LOCAT_ADDR'] = df_10014['LOCAT_ADDR'].apply( lambda x : x.split()[0] )
#df_10015['LOCAT_ADDR'] = df_10015['LOCAT_ADDR'].apply( lambda x : x.split()[0] )
#df_10016['LOCAT_ADDR'] = df_10016['LOCAT_ADDR'].apply( lambda x : x.split()[0] )
#df_10017['LOCAT_ADDR'] = df_10017['LOCAT_ADDR'].apply( lambda x : x.split()[0] )
#df_10018['LOCAT_ADDR'] = df_10018['LOCAT_ADDR'].apply( lambda x : x.split()[0] )
#df_10020['LOCAT_ADDR'] = df_10020['LOCAT_ADDR'].apply( lambda x : x.split()[0] )
with manidb(workdir + 'nsg_bldg_3826.sqlite') as db:
#db.get_alias('rawdata_hou_A66000_addr').write(df_66000)
#db.get_alias('rawdata_hou_A67000_addr').write(df_67000)
#db.get_alias('rawdata_hou_A68000_addr').write(df_68000)
db.get_alias('rawdata_hou_A91000_addr').write(df_91000)
db.get_alias('rawdata_hou_A92000_addr').write(df_92000)
db.get_alias('rawdata_hou_A10002_addr').write(df_10002)
#db.get_alias('rawdata_hou_A10008_addr').write(df_10008)
#db.get_alias('rawdata_hou_A10009_addr').write(df_10009)
#db.get_alias('rawdata_hou_A10010_addr').write(df_10010)
#db.get_alias('rawdata_hou_A10013_addr').write(df_10013)
#db.get_alias('rawdata_hou_A10014_addr').write(df_10014)
#db.get_alias('rawdata_hou_A10015_addr').write(df_10015)
#db.get_alias('rawdata_hou_A10016_addr').write(df_10016)
