def lithology_geojson(request, **kwargs):
realtime = request.GET.get('realtime') in ('True', 'true')
if realtime:
sw_long = request.query_params.get('sw_long')
sw_lat = request.query_params.get('sw_lat')
ne_long = request.query_params.get('ne_long')
ne_lat = request.query_params.get('ne_lat')
bounds = None
bounds_sql = ''
if sw_long and sw_lat and ne_long and ne_lat:
bounds_sql = 'and geom @ ST_MakeEnvelope(%s, %s, %s, %s, 4326)'
bounds = (sw_long, sw_lat, ne_long, ne_lat)
iterator = GeoJSONIterator(LITHOLOGY_SQL.format(bounds=bounds_sql),
LITHOLOGY_CHUNK_SIZE, connection.cursor(),
bounds)
response = StreamingHttpResponse((item for item in iterator),
content_type='application/json')
response[
'Content-Disposition'] = 'attachment; filename="lithology.json"'
return response
else:
# Generating spatial data realtime is much too slow,
# so we have to redirect to a pre-generated instance.
url = 'https://{}/{}/{}'.format(
get_env_variable('S3_HOST'),
get_env_variable('S3_WELL_EXPORT_BUCKET'),
'api/v1/gis/lithology.json')
return HttpResponseRedirect(url)
def aquifer_geojson_v2(request, **kwargs):
realtime = request.GET.get('realtime') in ('True', 'true')
if realtime:
sw_long = request.query_params.get('sw_long')
sw_lat = request.query_params.get('sw_lat')
ne_long = request.query_params.get('ne_long')
ne_lat = request.query_params.get('ne_lat')
if sw_long and sw_lat and ne_long and ne_lat:
bounds_sql = 'and geom @ ST_Transform(ST_MakeEnvelope(%s, %s, %s, %s, 4326), 3005)'
bounds = (sw_long, sw_lat, ne_long, ne_lat)
else:
bounds = None
bounds_sql = ''
iterator = GeoJSONIterator(AQUIFERS_SQL_V2.format(bounds=bounds_sql),
AQUIFER_CHUNK_SIZE, connection.cursor(),
bounds)
response = StreamingHttpResponse((item for item in iterator),
content_type='application/json')
response[
'Content-Disposition'] = 'attachment; filename="aquifers.json"'
return response
else:
# TODO: Update export_databc command to upload a v2 version of the aquifers JSON:
# https://apps.nrs.gov.bc.ca/int/jira/browse/WATER-1049
# Generating spatial data realtime is much too slow,
# so we have to redirect to a pre-generated instance.
url = 'https://{}/{}/{}'.format(
get_env_variable('S3_HOST'),
get_env_variable('S3_WELL_EXPORT_BUCKET'),
'api/v1/gis/aquifers.json')
return HttpResponseRedirect(url)
def aquifer_geojson_simplified(request, **kwargs):
"""
Sadly, GeoDjango's ORM doesn't seem to directly support a call to
ST_AsGEOJSON, but the latter performs much better than processing WKT
in Python, so we must generate SQL here.
"""
SQL = """
SELECT
ST_AsGeoJSON(geom_simplified, 8) :: json AS "geometry",
aquifer.aquifer_id AS id
FROM aquifer;
"""
iterator = GeoJSONIterator(SQL, AQUIFER_CHUNK_SIZE, connection.cursor())
response = StreamingHttpResponse((item for item in iterator),
content_type='application/json')
return response
def aquifer_geojson_simplified_v1(request, **kwargs):
"""
Sadly, GeoDjango's ORM doesn't seem to directly support a call to
ST_AsGEOJSON, but the latter performs much better than processing WKT
in Python, so we must generate SQL here.
"""
sql = """
SELECT
ST_AsGeoJSON((ST_GeometryN(geom_simplified, 1)), 8) :: json AS "geometry",
aquifer.aquifer_id AS id
FROM aquifer
"""
if not request.user.groups.filter(name=AQUIFERS_EDIT_ROLE).exists():
sql += "WHERE effective_date <= NOW() AND expiry_date >= NOW() AND retire_date >= NOW()"
iterator = GeoJSONIterator(sql, AQUIFER_CHUNK_SIZE, connection.cursor())
response = StreamingHttpResponse((item for item in iterator),
content_type='application/json')
return response