def test_file_or_text_response_forged_response_format(monkeypatch,
mock_coordinates,
mock_dataset,
mock_response_query):
# TEST 1: Non-existing ResponseFormat is intercepted by Class
with pytest.raises(ValueError) as e:
assert file_or_text_response(mock_dataset,
ResponseFormat("mock_format"), 'knmi',
'pluim', mock_response_query,
[mock_coordinates])
assert str(
e.value.args[0]) == "'mock_format' is not a valid ResponseFormat"
# TEST 2: Forged non-existing ResponseFormat should be intercepted by file_response
monkeypatch.setattr(api_models, "ResponseFormat", MockResponseFormat)
with pytest.raises(NotImplementedError) as e:
assert file_response(mock_dataset,
api_models.ResponseFormat.mock_format, "knmi",
"pluim", mock_response_query, [mock_coordinates])
assert str(
e.value.args[0]
) == f"Cannot create file response for the mock_format response format"
controller = WeatherController()
ds_hist_day: xr.Dataset = controller.get_weather(source_id='knmi',
model_id='daggegevens',
fetch_async=False,
coords=coords,
begin=datetime(year=2018,
month=1,
day=1),
end=datetime(year=2018,
month=1,
day=31))
response_format = ResponseFormat.netcdf4
converted_weather_data = controller.convert_names_and_units(
'knmi', 'daggegevens', False, ds_hist_day, OutputUnit.si)
ret_args = WeatherContentRequestQuery(begin='2018-01-01 00:00',
end='2018-01-31 23:59',
lat=52.1,
lon=5.18,
factors=None)
response, optional_file_path = serializers.file_or_text_response(
converted_weather_data, response_format, 'knmi', 'daggegevens',
ret_args, coords)
print(response)
print(optional_file_path)
def test_file_or_text_response_regular(response_format, mock_coordinates,
mock_dataset, mock_response_query):
assert file_or_text_response(mock_dataset, ResponseFormat(response_format),
'knmi', 'pluim', mock_response_query,
mock_coordinates)
ds_hist_day: xr.Dataset = controller.get_weather(
source_id='cds',
model_id='era5sl',
fetch_async=False,
coords=coords,
begin=datetime(2018, 2, 1, 0, 0),
end=datetime(2018, 2, 28, 23, 59))
response_format = ResponseFormat.netcdf4
converted_weather_data = controller.convert_names_and_units(
'cds', 'era5sl', False, ds_hist_day, OutputUnit.si)
ret_args = WeatherContentRequestQuery(begin='2018-01-01 00:00',
end='2018-01-31 23:59',
lat=52.1,
lon=5.18,
factors=None)
coords = [(
float(np.mean([single_point[0] for single_point in single_polygon])),
float(np.mean([single_point[1] for single_point in single_polygon])),
) for single_polygon in coords
] # means to match the used coordinates for the request
response, optional_file_path = serializers.file_or_text_response(
converted_weather_data, response_format, 'cds', 'era5sl', ret_args,
coords)
print(response, optional_file_path)
]
ds_hist_day: xr.Dataset = controller.get_weather(source_id='knmi',
model_id='waarnemingen',
fetch_async=False,
coords=coords,
begin=datetime(year=2018,
month=1,
day=1),
end=datetime(year=2018,
month=1,
day=31))
response_format = ResponseFormat.netcdf4
converted_weather_data = controller.convert_names_and_units(
'knmi', 'waarnemingen', False, ds_hist_day, OutputUnit.si)
ret_args = WeatherContentRequestQuery(begin='2018-01-01 00:00',
end='2018-01-31 23:59',
lat=52.1,
lon=5.18,
factors=None)
response, optional_file_path = serializers.file_or_text_response(
converted_weather_data, response_format, 'knmi', 'waarnemingen',
ret_args, coords)
print(response)
print(optional_file_path)
async def get_sync_weather(
source_id: str,
model_id: str,
cleanup_tasks: BackgroundTasks,
ret_args: WeatherContentRequestQuery = Depends(),
fmt_args: WeatherFormattingRequestQuery = Depends(),
accept: str = Depends(header_accept_type),
): # pragma: no cover
"""
<B>Request weather data for a specific Model using the given settings (location, period, weather factors, e.g.). <BR>
This data is then formatted as the requested output format (output unit system and file format) before returning the requested data.</B>
<I>(Please note that as some models are predictive or otherwise restricted in the periods available for requests,
that sometimes the begin and end values will altered to match these restrictions.)</I>
"""
"""
An API function that retrieves specific weather data for a specific Weather Model and returns it as the
requested output format and units.
Args:
source_id: The Source ID of the Source to request the weather data from.
model_id: The Model ID for the Model to request the weather data from.
cleanup_tasks: A BackgroundTasks object to hold any pending cleanup tasks for when the data request is
finished.
ret_args: A WeatherContentRequestQuery object holding the parameters for the weather data request to
use.
fmt_args: A WeatherFormattingRequestQuery object holding the parameters for the output format and
units to use.
accept: Header type to use for the output file.
Rounded to the most likely value using header_accept_type().
Returns:
The weather data in the requested format for the requested parameters.
"""
source_id = source_id.lower()
model_id = model_id.lower()
coords = controller.lat_lon_to_coords(ret_args.lat, ret_args.lon)
begin = parse_datetime(ret_args.begin, raise_errors=True, loc=["query", "begin"])
end = parse_datetime(
ret_args.end,
round_missing_time_up=True,
raise_errors=True,
loc=["query", "end"],
)
try:
weather_data = controller.get_weather(
source_id,
model_id,
fetch_async=False,
coords=coords,
begin=begin,
end=end,
factors=ret_args.factors,
)
except FileNotFoundError as e:
raise HTTPException(status_code=404, detail=e.args[0])
if weather_data is None:
raise HTTPException(
status_code=404, detail="No data was found for the given period"
)
response_format = fmt_args.response_format or accept
converted_weather_data = controller.convert_names_and_units(
source_id, model_id, False, weather_data, fmt_args.units
)
coords = [
(lat_val, lon_val)
for (lat_val, lon_val) in zip(
converted_weather_data.coords["lat"].values,
converted_weather_data.coords["lon"].values,
)
]
response, optional_file_path = serializers.file_or_text_response(
converted_weather_data, response_format, source_id, model_id, ret_args, coords
)
cleanup_tasks.add_task(remove_file, optional_file_path)
return response
async def get_sync_weather(
source_id: str,
model_id: str,
cleanup_tasks: BackgroundTasks,
ret_args: WeatherContentRequestQuery = Depends(),
fmt_args: WeatherFormattingRequestQuery = Depends(),
accept: str = Depends(header_accept_type),
): # pragma: no cover
"""
Function to gather data for a specific model using specific settings (location, period, factors, e.g.).
The function then formats this data into the requested output format (file-format and selected output unit) and
returns it.
Args:
source_id: The identifier for the chosen source
model_id: The identifier for the chosen model
cleanup_tasks: Holder for background tasks, in get_sync_weather solely used for file cleanup, hence the name
ret_args: Contains a WeatherContentRequestQuery item with all of the settings to be used for data collection
fmt_args: Contains a WeatherFormattingRequestQuery item with all of the setting to be used for formatting the
output
accept:
Returns:
Returns a file in the requested output format, containing any weather data that matched the specifications from
the WeatherContentRequestQuery.
"""
# TODO: Append a proper definition of the accept arg
source_id = source_id.lower()
model_id = model_id.lower()
coords = controller.lat_lon_to_coords(ret_args.lat, ret_args.lon)
begin = parse_datetime(ret_args.begin, raise_errors=True, loc=["query", "begin"])
end = parse_datetime(
ret_args.end,
round_missing_time_up=True,
raise_errors=True,
loc=["query", "end"],
)
try:
weather_data = controller.get_weather(
source_id,
model_id,
fetch_async=False,
coords=coords,
begin=begin,
end=end,
factors=ret_args.factors,
)
except FileNotFoundError as e:
raise HTTPException(status_code=404, detail=e.args[0])
if weather_data is None:
raise HTTPException(
status_code=404, detail="No data was found for the given period"
)
response_format = fmt_args.response_format or accept
converted_weather_data = controller.convert_names_and_units(
source_id, model_id, False, weather_data, fmt_args.units
)
coords = [
(lat_val, lon_val)
for (lat_val, lon_val) in zip(
converted_weather_data.coords["lat"].values,
converted_weather_data.coords["lon"].values,
)
]
response, optional_file_path = serializers.file_or_text_response(
converted_weather_data, response_format, source_id, model_id, ret_args, coords
)
cleanup_tasks.add_task(remove_file, optional_file_path)
return response