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prox_list.py
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prox_list.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 8 13:56:51 2020
@author: pi
"""
#import geopandas as gpd
#from shapely.geometry import Point
from geopy.distance import geodesic
import pandas as pd
from sqlalchemy import create_engine, MetaData,Table, select,and_
import geopandas as gpd
from shapely.geometry import Point
import math, numpy as np
def get_bearing(lat1,lon1,lat2,lon2):
dLon = lon2 - lon1;
y = math.sin(dLon) * math.cos(lat2);
x = math.cos(lat1)*math.sin(lat2) - math.sin(lat1)*math.cos(lat2)*math.cos(dLon);
brng = np.rad2deg(math.atan2(y, x));
if brng < 0: brng+= 360
return brng
def read_poi(fname,tab=False):
sep_char=','
if tab:
sep_char ='\t'
df=pd.read_csv(fname,sep=sep_char)
return df
def gnaf_agil():
t_locality = Table('LOCALITY', metadata, autoload=True,
autoload_with=engine)
t_locality_point = Table('LOCALITY_POINT', metadata, autoload=True,
autoload_with=engine)
print('locality_point')
print(t_locality_point.columns.keys())
print('locality')
print(t_locality.columns.keys())
ilocs_stmt = select([t_locality.columns.locality_pid,
t_locality.columns.locality_name,
t_locality.columns.state_pid,
t_locality_point.columns.latitude,
t_locality_point.columns.longitude,
t_locality.columns.locality_class_code])
ilocs_stmt = ilocs_stmt.where(
and_(t_locality.columns.locality_pid == t_locality_point.columns.locality_pid,
t_locality.columns.locality_class_code == 'I'))
ilocs_stmt = ilocs_stmt.order_by(t_locality.columns.locality_pid)
results = connection.execute(ilocs_stmt).fetchall()
df = pd.DataFrame(results)
# Set Column names
df.columns = results[0].keys()
gdf = gpd.GeoDataFrame(
df, geometry=gpd.points_from_xy(df.longitude, df.latitude,))
return gdf
def closest_addresses(v_stmt,row,tolerance = 1):
v_address_view= Table('ADDRESS_TBL',metadata, autoload=True,
autoload_with=engine)
v_stmt_loop = v_stmt.where(
and_(v_address_view.columns.Longitude.between(int(row.Longitude)-tolerance,
int(row.Longitude)+tolerance),
v_address_view.columns.Latitude.between(int(row.Latitude)-tolerance,
int(row.Latitude)+tolerance)))
found = False
print('* query to dataframe *')
data=[]
for result in connection.execute(v_stmt_loop):
distance_km = geodesic((result.Latitude,result.Longitude),
(row.Latitude,row.Longitude)).km
data.append([result.Address_Detail_PID,result.AddressText.strip(),
result.Latitude,result.Longitude,
row.UCL_CODE_2016,row.UCL_NAME_2016,
row.Latitude,row.Longitude, distance_km])
data_df=pd.DataFrame(data,columns=['Address_Detail_PID','AddressText',
'address_latitude','address_longitude',
'UCL_CODE_2016','UCL_NAME_2016',
'locality_latitude',
'locality_longitude','distance_km'])
return data_df,found
def closest_address(v_stmt,row,tolerance = 1):
v_address_view= Table('ADDRESS_TBL',metadata, autoload=True,
autoload_with=engine)
v_stmt_loop = v_stmt.where(
and_(v_address_view.columns.Longitude.between(int(row.Longitude)-tolerance,
int(row.Longitude)+tolerance),
v_address_view.columns.Latitude.between(int(row.Latitude)-tolerance,
int(row.Latitude)+tolerance)))
found = False
small_data = ['0', 0, 0, 0, row.UCL_CODE_2016,\
row.UCL_NAME_2016,row.Latitude, row.Longitude,0]
smallest=99999
for result in connection.execute(v_stmt_loop):
distance_km=geodesic((result.Latitude,result.Longitude),
(row.Latitude,row.Longitude)).km
#if distance_km < 60:
if distance_km < smallest:
smallest=distance_km
small_data=[result.Address_Detail_PID,result.AddressText.strip(),
result.Latitude,result.Longitude, row.UCL_CODE_2016,
row.UCL_NAME_2016,row.Latitude, row.Longitude,
distance_km]
found = True
return small_data, found
def get_agil():
agil_names_url="https://data.gov.au/data/dataset/34b1c164-fbe8-44a0-84fd-467dba645aa7/resource/6947c036-6383-44f3-a867-0fa0c2a48d6b/download/agil_names20190208.csv"
agil_locations_url="https://data.gov.au/data/dataset/34b1c164-fbe8-44a0-84fd-467dba645aa7/resource/625e0a41-6a30-4c11-9a20-ac64ba5a1d1f/download/agil_locations20190208.csv"
agil_names = pd.read_csv(agil_names_url)
agil_locations = pd.read_csv(agil_locations_url)
agil_joined = pd.merge(agil_locations,agil_names,on='LCODE')
geom=[Point(xy) for xy in zip(agil_joined.LONGITUDE, agil_joined.LATITUDE)]
agil_joined_gdf=gpd.GeoDataFrame(agil_joined,geometry=geom,crs="EPSG:4283")
return agil_joined_gdf
def add_cols(df,id_col,id_name):
rafile = gpd.read_file('shape/RA_2016_AUST.shp')
rafile['RA_NAME16'] = rafile['RA_NAME16'].str.replace('.\(.*\)','')
geom=[Point(xy) for xy in zip(df.locality_longitude, df.locality_latitude)]
df_poi_gdf=gpd.GeoDataFrame(df[['Address_Detail_PID',
'locality_latitude',
'locality_longitude']], geometry = geom, crs = "EPSG:4283")
df_poi_ra_gdf = gpd.sjoin(df_poi_gdf, rafile[['RA_NAME16','geometry']][rafile.geometry!=None], how='left', op='within')
df_poi_ra = df_poi_ra_gdf.drop(columns=['geometry','index_right','locality_latitude', 'locality_longitude'])
df_poi_ra.columns = ['Address_Detail_PID', 'locality_longlat_RA16']
geom=[Point(xy) for xy in zip(df.address_longitude, df.address_latitude)]
df_address_gdf=gpd.GeoDataFrame(df,geometry=geom,crs="EPSG:4283")
df_address_ra_gdf = gpd.sjoin(df_address_gdf, rafile[['RA_NAME16','geometry']][rafile.geometry!=None], how='left', op='within')
df_address_ra_gdf.rename(columns={'RA_NAME16': 'address_longlat_RA16'}, inplace=True)
df_out = pd.merge(df_address_ra_gdf[['Address_Detail_PID', 'AddressText', 'address_latitude',
'address_longitude', id_col, id_name,
'locality_latitude', 'locality_longitude', 'distance_km','address_longlat_RA16']],df_poi_ra,on='Address_Detail_PID')
df_out['RA16_Diff_Flag'] = df_out['address_longlat_RA16'] != df_out['locality_longlat_RA16']
df_out['RA16_Diff_Flag'] = df_out.RA16_Diff_Flag.apply(lambda x:int(x))
df_out= df_out.drop_duplicates(id_col)
return df_out
def post_agil():
agil_names_url="https://data.gov.au/data/dataset/34b1c164-fbe8-44a0-84fd-467dba645aa7/resource/6947c036-6383-44f3-a867-0fa0c2a48d6b/download/agil_names20190208.csv"
agil_locations_url="https://data.gov.au/data/dataset/34b1c164-fbe8-44a0-84fd-467dba645aa7/resource/625e0a41-6a30-4c11-9a20-ac64ba5a1d1f/download/agil_locations20190208.csv"
agil_names = pd.read_csv(agil_names_url)
agil_locations = pd.read_csv(agil_locations_url)
agil_joined = pd.merge(agil_locations,agil_names,on='LCODE')
geom=[Point(xy) for xy in zip(agil_joined.LONGITUDE, agil_joined.LATITUDE)]
agil_joined_gdf=gpd.GeoDataFrame(agil_joined,geometry=geom,crs="EPSG:4283")
rafile = gpd.read_file('shape/RA_2016_AUST.shp')
agil_remote_gdf = gpd.sjoin(agil_joined_gdf, rafile[['RA_NAME16','geometry']][rafile.geometry!=None], how='left', op='within')
p_agil = agil_remote_gdf.loc[agil_remote_gdf['NFLAG']=='P',['LCODE','NAME','STATE','LONGITUDE','LATITUDE','RA_NAME16']]
a_agil = agil_remote_gdf[agil_remote_gdf.NFLAG=='A']
# stages 1 and 2 - convert to inputs to geospatial and base process
ref_agil= pd.merge(df_closest, p_agil, left_on='locality_name', right_on='NAME',
how='left')
ref_agil['locality_longlat_RA16'] = ref_agil.RA_NAME16.str.replace('.\(.*\)','')
process_dataset_1 = ref_agil[['LCODE','locality_pid','locality_name','locality_latitude',
'locality_longitude','locality_longlat_RA16','distance_km',
'Address_Detail_PID','AddressText','address_latitude',
'address_longitude']]
geom=[Point(xy) for xy in zip(process_dataset_1.address_longitude,
process_dataset_1.address_latitude)]
process_dataset_1_gdf = gpd.GeoDataFrame(process_dataset_1,geometry=geom,
crs="EPSG:4283")
process_dataset_2_gdf = gpd.sjoin(process_dataset_1_gdf,
rafile[['RA_NAME16','geometry']][rafile.geometry!=None],
how='left', op='within')
process_dataset_2_gdf['AddressText'] = process_dataset_2_gdf.AddressText.str.strip()
process_dataset_2_gdf['RA16_Diff_Flag'] = process_dataset_2_gdf['address_longlat_RA16'] != process_dataset_2_gdf['locality_longlat_RA16']
process_dataset_2_gdf['RA16_Diff_Flag'] = process_dataset_2_gdf.RA16_Diff_Flag.apply(lambda x:int(x))
#stage 3 - drop working columns and output primary list
process_dataset_3 = process_dataset_2_gdf.drop(['geometry','index_right','RA_NAME16'], axis = 1)
process_dataset_3.to_csv('csv/new_closest_wout_alts.csv')
agil_joined_ref = process_dataset_3[['LCODE','NCODE','NAME','NFLAG']]
#stage 4 and 5 - replace with alternate name references and output complete list
process_dataset_4 = pd.merge(process_dataset_3, agil_joined_ref, on='LCODE',
how='left')
process_dataset_4['locality_name'] = process_dataset_4.NAME
process_dataset_5 = process_dataset_4.drop(['NCODE','NFLAG','NAME'],axis=1)
#process_dataset_5 = process_dataset_4.drop(['NCODE','NAME'],axis=1)
process_dataset_5.to_csv('csv/new_closest_w_alts.csv')
def mb_aust():
mbact = gpd.read_file('shape/MB_2016_ACT.shp')
mbnsw = gpd.read_file('shape/MB_2016_NSW.shp')
mbnt = gpd.read_file('shape/MB_2016_NT.shp')
mbot = gpd.read_file('shape/MB_2016_OT.shp')
mbqld = gpd.read_file('shape/MB_2016_QLD.shp')
mbsa = gpd.read_file('shape/MB_2016_SA.shp')
mbtas = gpd.read_file('shape/MB_2016_TAS.shp')
mbvic = gpd.read_file('shape/MB_2016_VIC.shp')
mbwa = gpd.read_file('shape/MB_2016_WA.shp')
gdf = pd.concat([mbact, mbnsw, mbnt, mbnt, mbot, mbqld, mbsa, mbtas, mbvic,
mbwa])
gdf.STE_NAME16.astype('category').unique()
return gdf
engine = create_engine('sqlite:///spatialite_db/gnaf_may_2020_new.sqlite')
print(engine.table_names())
connection = engine.connect()
#tables
metadata = MetaData()
#ADDRESS_DETAIL -> ADDRESS_DETAIL_PID (in v_address_view)
#ADDRESS_MESH_BLOCK_2016 -> ADDRESS_MESH_BLOCK_2016_PID,ADDRESS_DETAIL_PID,MB_2016_PID
#MB_2016 -> MB_2016_PID,MB_2016_CODE
print('address view')
v_address_view= Table('ADDRESS_TBL',metadata, autoload=True,
autoload_with=engine)
print(v_address_view.columns.keys())
v_address_view= Table('ADDRESS_TBL',metadata, autoload=True,
autoload_with=engine)
closest=[]
test=read_poi('csv/ucl.txt',tab=True)
df=test[test.Latitude!=0]
v_stmt = select([v_address_view.columns.Address_Detail_PID,
v_address_view.columns.Latitude, v_address_view.columns.Longitude,
v_address_view.columns.AddressText])
##
for index,row in df.iterrows():
#limit by bounding box
tolerance = 1
while True:
print('trying tolerance', tolerance,' degree and row \n',row)
small_data, found = closest_address(v_stmt,row,tolerance)
print(index,small_data)
if found == True:
break
else:
tolerance += 1
closest.append(small_data)
connection.close()
print(closest)
df_closest=pd.DataFrame(closest,columns=['Address_Detail_PID','AddressText',
'address_latitude','address_longitude',
'UCL_CODE_2016','UCL_NAME_2016',
'locality_latitude',
'locality_longitude','distance_km'])
df_closest.to_csv('closest3.csv')
#df_closest = read_poi('closest3.csv')
df_out = add_cols(df_closest,'UCL_CODE_2016','UCL_NAME_2016')