def historic_averages_handler(request, average_type):
"""
Controller for retrieving averages
"""
# handle the parameters from the user
try:
reach_id, region, units, return_format = handle_parameters(request)
units_title, units_title_long = get_units_title(units)
forcing = request.args.get('forcing', 'era_5')
hist_df = get_historical_dataframe(reach_id, region, units, forcing)
except Exception as e:
raise e
hist_df.index = pd.to_datetime(hist_df.index)
if average_type == 'daily':
hist_df = hd.daily_average(hist_df, rolling=True)
else:
hist_df = hd.monthly_average(hist_df)
hist_df.index.name = 'datetime'
if return_format == 'csv':
response = make_response(hist_df.to_csv())
response.headers['content-type'] = 'text/csv'
response.headers[
'Content-Disposition'] = f'attachment; filename=seasonal_average_{forcing}_{reach_id}.csv'
return response
if return_format == "json":
return jsonify({
'region':
region,
'simulation_forcing':
forcing,
'forcing_fullname':
forcing.replace('era_', 'ERA ').title(),
'comid':
reach_id,
'gendate':
datetime.datetime.utcnow().isoformat() + 'Z',
'time_series': {
'datetime': hist_df.index.tolist(),
'flow': hist_df[f'streamflow_{units_title}^3/s'].tolist(),
},
'units': {
'name': 'Streamflow',
'short': f'{units_title}3/s',
'long': f'Cubic {units_title_long} per Second'
}
})
else:
raise ValueError(f'Invalid return_format: {return_format}')
def historic_data_handler(request):
"""
Controller for retrieving simulated historic data
"""
# handle the parameters from the user
try:
reach_id, region, units, return_format = handle_parameters(request)
units_title, units_title_long = get_units_title(units)
forcing = request.args.get('forcing', 'era_5')
hist_df = get_historical_dataframe(reach_id, region, units, forcing)
except Exception as e:
raise e
# if csv, return the dataframe as csv
if return_format == 'csv':
response = make_response(hist_df.to_csv())
response.headers['content-type'] = 'text/csv'
response.headers[
'Content-Disposition'] = f'attachment; filename=historic_streamflow_{forcing}_{reach_id}.csv'
return response
# if you wanted json out, create and return json
# era_interim or era_5
if return_format == "json":
return {
'region': region,
'simulation_forcing': forcing,
'forcing_fullname': forcing.replace('era_', 'ERA ').title(),
'comid': reach_id,
'gendate': datetime.datetime.utcnow().isoformat() + 'Z',
'startdate': hist_df.index[0],
'enddate': hist_df.index[-1],
'time_series': {
'datetime': hist_df.index.tolist(),
'flow': hist_df[f'streamflow_{units_title}^3/s'].tolist(),
},
'units': {
'name': 'Streamflow',
'short': f'{units_title}3/s',
'long': f'Cubic {units_title_long} per Second'
}
}
else:
return jsonify({"error": "Invalid return_format."}), 422
def return_periods_handler(request):
"""
Controller for retrieving seasonal averages
"""
# handle the parameters from the user
try:
reach_id, region, units, return_format = handle_parameters(request)
except Exception as e:
raise e
forcing = request.args.get('forcing', 'era_5')
if forcing == 'era_interim':
forcing_fullname = 'ERA Interim'
historical_data_file = glob.glob(
os.path.join(PATH_TO_ERA_INTERIM, region,
'*return_periods*.nc*'))[0]
startdate = '1980-01-01T00:00:00Z'
enddate = '2014-12-31T00:00:00Z'
elif forcing == 'era_5':
forcing_fullname = 'ERA 5'
historical_data_file = glob.glob(
os.path.join(PATH_TO_ERA_5, region, '*return_periods*.nc*'))[0]
startdate = '1979-01-01T00:00:00Z'
enddate = '2018-12-31T00:00:00Z'
else:
return {
"error": "Invalid forcing specified, choose era_interim or era_5"
}, 422
# handle the units
units_title, units_title_long = get_units_title(units)
# collect the data in a dataframe
qout_nc = xarray.open_dataset(historical_data_file)
qout_data = qout_nc.to_dataframe()
try:
del qout_data['lon'], qout_data['lat']
except Exception:
pass
qout_data = qout_data[qout_data.index == reach_id]
if units == 'english':
for column in qout_data:
qout_data[column] *= M3_TO_FT3
# if csv, return the dataframe as csv
if return_format == 'csv':
response = make_response(qout_data.to_csv())
response.headers['content-type'] = 'text/csv'
response.headers[
'Content-Disposition'] = f'attachment; filename=return_periods_{forcing}_{reach_id}.csv'
return response
# create a json of the data
json_output = {
'return_periods': json.loads(qout_data.to_json(orient='records'))[0],
'region': region,
'comid': reach_id,
'simulation_forcing': forcing,
'forcing_fullname': forcing_fullname,
'gendate': datetime.datetime.utcnow().isoformat() + 'Z',
'startdate': startdate,
'enddate': enddate,
'units': {
'name': 'Streamflow',
'short': f'{units_title}3/s',
'long': f'Cubic {units_title_long} per Second'
}
}
# if you wanted json out, return json
if return_format == "json":
return jsonify(json_output)
# use the json to render a waterml document
if return_format == "waterml":
xml_response = make_response(
render_template('return_periods.xml', **json_output))
xml_response.headers.set('Content-Type', 'application/xml')
return xml_response
else:
return jsonify({"error": "Invalid return_format."}), 422
def forecast_records_handler(request):
# handle the parameters from the user
try:
reach_id, region, units, return_format = handle_parameters(request)
except Exception as e:
raise ValueError(e)
year = dt.utcnow().year
start_date = request.args.get(
'start_date',
dt(year=year, month=1, day=1).strftime('%Y%m%d'))
end_date = request.args.get(
'end_date',
dt(year=year, month=12, day=31).strftime('%Y%m%d'))
try:
start_date = dt.strptime(start_date, '%Y%m%d')
end_date = dt.strptime(end_date, '%Y%m%d')
except:
raise ValueError(
f'Unrecognized start_date "{start_date}" or end_date "{end_date}". Use YYYYMMDD format'
)
# handle the units
units_title, units_title_long = get_units_title(units)
# open and read the forecast record netcdf
record_path = os.path.join(PATH_TO_FORECAST_RECORDS, region,
f'forecast_record-{year}-{region}.nc')
forecast_record = xarray.open_dataset(record_path)
times = pd.to_datetime(pd.Series(forecast_record['time'].data,
name='datetime'),
unit='s',
origin='unix')
record_flows = forecast_record.sel(rivid=reach_id)['Qout']
forecast_record.close()
# create a dataframe and filter by date
df = times.to_frame().join(
pd.Series(record_flows, name=f'streamflow_{units_title}^3/s'))
df = df[df['datetime'].between(start_date, end_date)]
df.index = df['datetime']
del df['datetime']
df.index = df.index.strftime('%Y-%m-%dT%H:%M:%SZ')
df.index.name = 'datetime'
df[df[f'streamflow_{units_title}^3/s'] > 1000000000] = np.nan
df.dropna(inplace=True)
if units == 'english':
df[f'streamflow_{units_title}^3/s'] *= M3_TO_FT3
# create the http response
if return_format == 'csv':
response = make_response(df.to_csv())
response.headers['content-type'] = 'text/csv'
response.headers[
'Content-Disposition'] = f'attachment; filename=forecast_record_{reach_id}.csv'
return response
elif return_format == 'json':
return {
'region': region,
'comid': reach_id,
'gendate': dt.utcnow().isoformat() + 'Z',
'startdate': df.index[0],
'enddate': df.index[-1],
'units': {
'name': 'Streamflow',
'short': f'{units_title}^3/s',
'long': f'Cubic {units_title_long} per Second',
},
'time_series': {
'datetime': df.index.tolist(),
'flow': df[f'streamflow_{units_title}^3/s'].tolist(),
}
}
else:
raise ValueError(f'Invalid return_format "{return_format}"')
def forecast_stats_handler(request):
"""
Controller that will retrieve forecast statistics data in different formats
"""
# handle the parameters from the user
try:
reach_id, region, units, return_format = handle_parameters(request)
except Exception as e:
raise e
forecast_folder = request.args.get('date', 'most_recent')
# handle the units
units_title, units_title_long = get_units_title(units)
# find/check current output datasets
path_to_output_files = os.path.join(PATH_TO_FORECASTS, region)
forecast_nc_list, start_date = ecmwf_find_most_current_files(
path_to_output_files, forecast_folder)
forecast_nc_list = sorted(forecast_nc_list)
if not forecast_nc_list or not start_date:
raise ValueError(
f'ECMWF forecast for region "{region}" and date "{start_date}" not found'
)
try:
# combine 52 ensembles
qout_datasets = []
ensemble_index_list = []
for forecast_nc in forecast_nc_list:
ensemble_index_list.append(
int(os.path.basename(forecast_nc)[:-3].split("_")[-1]))
qout_datasets.append(
xarray.open_dataset(forecast_nc).sel(rivid=reach_id).Qout)
merged_ds = xarray.concat(
qout_datasets, pd.Index(ensemble_index_list, name='ensemble'))
# get an array of all the ensembles, delete the high res before doing averages
merged_array = merged_ds.data
merged_array = np.delete(merged_array,
list(merged_ds.ensemble.data).index(52),
axis=0)
except:
raise ValueError('Error while reading data from the netCDF files')
# replace any values that went negative because of the muskingham routing
merged_array[merged_array <= 0] = 0
# load all the series into a dataframe
df = pd.DataFrame(
{
f'flow_max_{units_title}^3/s': np.amax(merged_array, axis=0),
f'flow_75%_{units_title}^3/s': np.percentile(
merged_array, 75, axis=0),
f'flow_avg_{units_title}^3/s': np.mean(merged_array, axis=0),
f'flow_25%_{units_title}^3/s': np.percentile(
merged_array, 25, axis=0),
f'flow_min_{units_title}^3/s': np.min(merged_array, axis=0),
f'high_res_{units_title}^3/s': merged_ds.sel(ensemble=52).data
},
index=merged_ds.time.data)
df.index = df.index.strftime('%Y-%m-%dT%H:%M:%SZ')
df.index.name = 'datetime'
# handle units conversion
if units_title == 'ft':
for column in df.columns:
df[column] *= M3_TO_FT3
if return_format == 'csv':
response = make_response(df.to_csv())
response.headers['content-type'] = 'text/csv'
response.headers['Content-Disposition'] = \
f'attachment; filename=forecasted_streamflow_{region}_{reach_id}_{units_title}^3/s.csv'
return response
# split the df so that we can use dropna to lists of dates and values without na entries
high_res_data = df[f'high_res_{units_title}^3/s'].dropna()
del df[f'high_res_{units_title}^3/s']
df.dropna(inplace=True)
# create a dictionary with the metadata and series of values
context = {
'region': region,
'comid': reach_id,
'gendate': dt.utcnow().isoformat() + 'Z',
'startdate': df.index[0],
'enddate': df.index[-1],
'units': {
'name': 'Streamflow',
'short': f'{units_title}^3/s',
'long': f'Cubic {units_title_long} per Second',
},
'time_series': {
'datetime': df.index.tolist(),
'datetime_high_res': high_res_data.index.tolist(),
'high_res': high_res_data.to_list(),
}
}
context['time_series'].update(df.to_dict(orient='list'))
if return_format == "json":
return jsonify(context)
else:
raise ValueError('Invalid return_format')
def forecast_ensembles_handler(request):
"""
Controller that will retrieve forecast ensemble data in different formats
"""
# handle the parameters from the user
try:
reach_id, region, units, return_format = handle_parameters(request)
except Exception as e:
raise ValueError(e)
ensemble = request.args.get('ensemble', 'all')
forecast_folder = request.args.get('date', 'most_recent')
# handle the units
units_title, units_title_long = get_units_title(units)
# find/check current output datasets
path_to_output_files = os.path.join(PATH_TO_FORECASTS, region)
forecast_nc_list, start_date = ecmwf_find_most_current_files(
path_to_output_files, forecast_folder)
forecast_nc_list = sorted(forecast_nc_list)
if not forecast_nc_list or not start_date:
raise ValueError(
f'ECMWF forecast for region "{region}" and date "{start_date}" not found'
)
try:
# combine 52 ensembles with xarray
qout_datasets = []
ensemble_index_list = []
for forecast_nc in forecast_nc_list:
ensemble_index_list.append(
int(os.path.basename(forecast_nc)[:-3].split("_")[-1]))
qout_datasets.append(
xarray.open_dataset(forecast_nc).sel(rivid=reach_id).Qout)
merged_ds = xarray.concat(
qout_datasets, pd.Index(ensemble_index_list, name='ensemble'))
except:
raise ValueError('Error while reading data from the netCDF files')
# make a list column names (with zero padded numbers) for the pandas DataFrame
ensemble_column_names = []
for i in ensemble_index_list:
ensemble_column_names.append(f'ensemble_{i:02}_{units_title}^3/s')
# make the data into a pandas dataframe
df = pd.DataFrame(data=np.transpose(merged_ds.data),
columns=ensemble_column_names,
index=merged_ds.time.data)
df.index = df.index.strftime('%Y-%m-%dT%H:%M:%SZ')
df.index.name = 'datetime'
# handle units conversion
if units_title == 'ft':
for column in df.columns:
df[column] *= M3_TO_FT3
# filtering which ensembles you want to get out of the dataframe of them all
if ensemble != 'all':
requested_ensembles = []
for ens in ensemble.split(','):
# if there was a range requested with a '-', generate a list of numbers between the 2
if '-' in ens:
start, end = ens.split('-')
for i in range(int(start), int(end) + 1):
requested_ensembles.append(
f'ensemble_{int(i):02}_{units_title}^3/s')
else:
requested_ensembles.append(
f'ensemble_{int(ens):02}_{units_title}^3/s')
# make a list of columns to remove from the dataframe deleting the requested ens from all ens columns
for ens in requested_ensembles:
if ens in ensemble_column_names:
ensemble_column_names.remove(ens)
# delete the dataframe columns we aren't interested
for ens in ensemble_column_names:
del df[ens]
if return_format == 'csv':
response = make_response(df.to_csv())
response.headers['content-type'] = 'text/csv'
response.headers['Content-Disposition'] = \
f'attachment; filename=forecasted_ensembles_{region}_{reach_id}_{units_title}^3/s.csv'
return response
# for any column in the dataframe (e.g. each ensemble)
ensemble_ts_dict = {
'datetime':
df[f'ensemble_01_{units_title}^3/s'].dropna(
inplace=False).index.tolist(),
'datetime_high_res':
df[f'ensemble_52_{units_title}^3/s'].dropna(
inplace=False).index.tolist(),
}
for column in df.columns:
ensemble_ts_dict[column] = df[column].dropna(inplace=False).tolist()
context = {
'region': region,
'comid': reach_id,
'startdate': df.index[0],
'enddate': df.index[-1],
'gendate': dt.utcnow().isoformat() + 'Z',
'time_series': ensemble_ts_dict,
'units': {
'name': 'Streamflow',
'short': f'{units_title}3/s',
'long': f'Cubic {units_title_long} per Second'
}
}
if return_format == 'json':
return jsonify(context)
else:
raise ValueError('Invalid return_format')
