136 lines
4.0 KiB
Python
136 lines
4.0 KiB
Python
"""Join csv data to buildings
|
|
|
|
Example usage (replace URL with test/staging/localhost as necessary, API key with real key for
|
|
the appropriate site):
|
|
|
|
python load_csv.py \
|
|
https://colouring.london \
|
|
a0a00000-0a00-0aaa-a0a0-0000aaaa0000 \
|
|
data.csv
|
|
|
|
The optional last argument specifies which columns should be parsed as JSON values.
|
|
This is required for example for columns of array type to be processed by the API correctly.
|
|
Otherwise, those values would be treated as a string and not an array.
|
|
|
|
An example usage with the json_columns argument (other values in the example are placeholders):
|
|
python load_csv.py \
|
|
https://colouring.london \
|
|
a0a00000-0a00-0aaa-a0a0-0000aaaa0000 \
|
|
data.csv \
|
|
current_landuse_group,date_url
|
|
|
|
This script uses the HTTP API, and can process CSV files which identify buildings by id, TOID,
|
|
UPRN.
|
|
|
|
The process:
|
|
- assume first line of the CSV is a header, where column names are either
|
|
- building identifiers - one of:
|
|
- building_id
|
|
- toid
|
|
- uprn
|
|
- building data field names
|
|
- read through lines of CSV:
|
|
- use building id if provided
|
|
- else lookup by toid
|
|
- else lookup by uprn
|
|
- else locate building by representative point
|
|
- (optional) parse JSON column values
|
|
- update building
|
|
|
|
TODO extend to allow latitude,longitude or easting,northing columns and lookup by location.
|
|
|
|
"""
|
|
import csv
|
|
import json
|
|
import os
|
|
import sys
|
|
|
|
import requests
|
|
|
|
|
|
def main(base_url, api_key, source_file, json_columns):
|
|
"""Read from file, update buildings
|
|
"""
|
|
with open(source_file, 'r') as source:
|
|
reader = csv.DictReader(source)
|
|
for line in reader:
|
|
building_id = find_building(line, base_url)
|
|
line = parse_json_columns(line, json_columns)
|
|
|
|
if building_id is None:
|
|
continue
|
|
|
|
response_code, response_data = update_building(building_id, line, api_key, base_url)
|
|
if response_code != 200:
|
|
print('ERROR', building_id, response_code, response_data)
|
|
|
|
|
|
def update_building(building_id, data, api_key, base_url):
|
|
"""Save data to a building
|
|
"""
|
|
r = requests.post(
|
|
"{}/api/buildings/{}.json".format(base_url, building_id),
|
|
params={'api_key': api_key},
|
|
json=data
|
|
)
|
|
return r.status_code, r.json()
|
|
|
|
|
|
def find_building(data, base_url):
|
|
if 'building_id' in data:
|
|
building_id = data['building_id']
|
|
if building_id is not None:
|
|
print("match_by_building_id", building_id)
|
|
return building_id
|
|
if 'toid' in data:
|
|
building_id = find_by_reference(base_url, 'toid', data['toid'])
|
|
if building_id is not None:
|
|
print("match_by_toid", data['toid'], building_id)
|
|
return building_id
|
|
|
|
if 'uprn' in data:
|
|
building_id = find_by_reference(base_url, 'uprn', data['uprn'])
|
|
if building_id is not None:
|
|
print("match_by_uprn", data['uprn'], building_id)
|
|
return building_id
|
|
|
|
print("no_match", data)
|
|
return None
|
|
|
|
|
|
def find_by_reference(base_url, ref_key, ref_id):
|
|
"""Find building_id by TOID or UPRN
|
|
"""
|
|
r = requests.get("{}/api/buildings/reference".format(base_url), params={
|
|
'key': ref_key,
|
|
'id': ref_id
|
|
})
|
|
buildings = r.json()
|
|
|
|
if buildings and 'error' not in buildings and len(buildings) == 1:
|
|
building_id = buildings[0]['building_id']
|
|
else:
|
|
building_id = None
|
|
|
|
return building_id
|
|
|
|
def parse_json_columns(row, json_columns):
|
|
for col in json_columns:
|
|
row[col] = json.loads(row[col])
|
|
|
|
return row
|
|
|
|
if __name__ == '__main__':
|
|
try:
|
|
url, api_key, filename = sys.argv[1], sys.argv[2], sys.argv[3]
|
|
except IndexError:
|
|
print(
|
|
"Usage: {} <URL> <api_key> ./path/to/data.csv [<json_columns>]".format(
|
|
os.path.basename(__file__)
|
|
))
|
|
exit()
|
|
|
|
json_columns = sys.argv[4].split(',') if len(sys.argv) > 4 else []
|
|
|
|
main(url, api_key, filename, json_columns)
|