def test_geometry_not_converted(self):
log = logging.getLogger(__name__)
geojson = get_static_file('simple_input.geojson')
geojson = json.loads(geojson)
geojson = StringIO(json.dumps(geojson))
output_csv = convert(geojson, log)
output_csv = pandas.read_csv(output_csv)
self.assertFalse('geometry' in output_csv.columns)
def test_convert_ghana(self):
log = logging.getLogger(__name__)
geojson = open(join_static_path('ghana_input.geojson'))
output_csv = convert(geojson, log)
output_csv = pandas.read_csv(output_csv)
expected_csv = pandas.read_csv(join_static_path('ghana_output.csv'))
self.assertEqual(set(output_csv.columns), set(expected_csv.columns))
output_csv = output_csv[expected_csv.columns]
pandas.util.testing.assert_frame_equal(output_csv, expected_csv)
def push_to_datastore(task_id, input, dry_run=False):
'''Download and parse a resource push its data into CKAN's DataStore.
An asynchronous job that gets a resource from CKAN, downloads the
resource's data file and, if the data file has changed since last time,
parses the data and posts it into CKAN's DataStore.
:param dry_run: Fetch and parse the data file but don't actually post the
data to the DataStore, instead return the data headers and rows that
would have been posted.
:type dry_run: boolean
'''
handler = util.StoringHandler(task_id, input)
logger = logging.getLogger(task_id)
logger.addHandler(handler)
logging_id = input.get("metadata", {}).get("resource_id", "None")
formatter = logging.Formatter(f'[%(asctime)s] %(levelname)s - resource_id:{logging_id} - %(message)s')
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(formatter)
logger.addHandler(stream_handler)
logger.propagate = False
logger.setLevel(logging.DEBUG)
validate_input(input)
data = input['metadata']
ckan_url = data['ckan_url']
resource_id = data['resource_id']
api_key = input.get('api_key')
try:
time.sleep(1) # HACK: Give CKAN time to update the resource
resource = get_resource(resource_id, ckan_url, api_key)
except util.JobError as e:
# try again in 5 seconds just incase CKAN is slow at adding resource
time.sleep(5)
resource = get_resource(resource_id, ckan_url, api_key)
# check if the resource url_type is a datastore
if resource.get('url_type') == 'datastore':
logger.info('Dump files are managed with the Datastore API')
return
# check scheme
url = resource.get('url')
scheme = urlsplit(url).scheme
if scheme not in ('http', 'https', 'ftp'):
raise util.JobError(
'Only http, https, and ftp resources may be fetched.'
)
# fetch the resource data
logger.info('Fetching from: {0}'.format(url))
headers = {}
if resource.get('url_type') == 'upload':
# If this is an uploaded file to CKAN, authenticate the request,
# otherwise we won't get file from private resources
headers['Authorization'] = api_key
try:
response = requests.get(
url,
headers=headers,
timeout=DOWNLOAD_TIMEOUT,
verify=SSL_VERIFY,
stream=True, # just gets the headers for now
)
response.raise_for_status()
cl = response.headers.get('content-length')
try:
if cl and int(cl) > MAX_CONTENT_LENGTH:
raise util.JobError(
'Resource too large to download: {cl} > max ({max_cl}).'
.format(cl=cl, max_cl=MAX_CONTENT_LENGTH))
except ValueError:
pass
tmp = tempfile.TemporaryFile()
length = 0
m = hashlib.md5()
for chunk in response.iter_content(CHUNK_SIZE):
length += len(chunk)
if length > MAX_CONTENT_LENGTH:
raise util.JobError(
'Resource too large to process: {cl} > max ({max_cl}).'
.format(cl=length, max_cl=MAX_CONTENT_LENGTH))
tmp.write(chunk)
m.update(chunk)
ct = response.headers.get('content-type', '').split(';', 1)[0]
except requests.HTTPError as e:
raise HTTPError(
"DataPusher received a bad HTTP response when trying to download "
"the data file", status_code=e.response.status_code,
request_url=url, response=e.response.content)
except requests.RequestException as e:
raise HTTPError(
message=str(e), status_code=None,
request_url=url, response=None)
file_hash = m.hexdigest()
tmp.seek(0)
if (resource.get('hash') == file_hash
and not data.get('ignore_hash')):
logger.info("The file hash hasn't changed: {hash}.".format(
hash=file_hash))
return
resource['hash'] = file_hash
if resource.get('format').lower() == 'geojson':
logger.info('Converting geojson to csv')
tmp = geojson2csv.convert(tmp, logger)
logger.info('Done.')
ct = 'application/csv'
try:
table_set = messytables.any_tableset(tmp, mimetype=ct, extension=ct)
except messytables.ReadError as e:
logger.warning("First attempt to read table failed: {}".format(e))
# try again with format inferred from url
if not table_set or not table_set.tables:
tmp.seek(0)
try:
extension = resource.get('format')
format = mimetypes.guess_type(url)[0]
table_set = messytables.any_tableset(tmp, mimetype=format, extension=extension)
except messytables.ReadError as e:
raise util.JobError(e)
if not table_set or not table_set.tables:
raise util.JobError("Unable to read any tabular data from the file.")
get_row_set = web.app.config.get('GET_ROW_SET',
lambda table_set: table_set.tables.pop())
row_set = get_row_set(table_set)
offset, headers = messytables.headers_guess(row_set.sample)
existing = datastore_resource_exists(resource_id, api_key, ckan_url)
existing_info = None
if existing:
existing_info = dict((f['id'], f['info'])
for f in existing.get('fields', []) if 'info' in f)
# Some headers might have been converted from strings to floats and such.
headers = [str(header) for header in headers]
row_set.register_processor(messytables.headers_processor(headers))
row_set.register_processor(messytables.offset_processor(offset + 1))
types = messytables.type_guess(row_set.sample, types=TYPES, strict=True)
# override with types user requested
if existing_info:
types = [{
'text': messytables.StringType(),
'numeric': messytables.DecimalType(),
'timestamp': messytables.DateUtilType(),
}.get(existing_info.get(h, {}).get('type_override'), t)
for t, h in zip(types, headers)]
row_set.register_processor(messytables.types_processor(types))
headers = [header.strip() for header in headers if header.strip()]
headers_set = set(headers)
def row_iterator():
for row in row_set:
data_row = {}
for index, cell in enumerate(row):
column_name = cell.column.strip()
if column_name not in headers_set:
continue
if isinstance(cell.value, str):
try:
data_row[column_name] = cell.value.encode('latin-1').decode('utf-8')
except (UnicodeDecodeError, UnicodeEncodeError):
data_row[column_name] = cell.value
else:
data_row[column_name] = cell.value
yield data_row
result = row_iterator()
'''
Delete existing datstore resource before proceeding. Otherwise
'datastore_create' will append to the existing datastore. And if
the fields have significantly changed, it may also fail.
'''
if existing:
logger.info('Deleting "{res_id}" from datastore.'.format(
res_id=resource_id))
delete_datastore_resource(resource_id, api_key, ckan_url)
headers_dicts = [dict(id=field[0], type=TYPE_MAPPING[str(field[1])])
for field in zip(headers, types)]
# Maintain data dictionaries from matching column names
if existing_info:
for h in headers_dicts:
if h['id'] in existing_info:
h['info'] = existing_info[h['id']]
# create columns with types user requested
type_override = existing_info[h['id']].get('type_override')
if type_override in list(_TYPE_MAPPING.values()):
h['type'] = type_override
logger.info('Determined headers and types: {headers}'.format(
headers=headers_dicts))
if dry_run:
return headers_dicts, result
count = 0
for i, chunk in enumerate(chunky(result, 250)):
records, is_it_the_last_chunk = chunk
count += len(records)
logger.info('Saving chunk {number} {is_last}'.format(
number=i, is_last='(last)' if is_it_the_last_chunk else ''))
send_resource_to_datastore(resource, headers_dicts, records,
is_it_the_last_chunk, api_key, ckan_url)
logger.info('Successfully pushed {n} entries to "{res_id}".'.format(
n=count, res_id=resource_id))
if data.get('set_url_type', False):
update_resource(resource, api_key, ckan_url)