def get_weather(watershed_id):
ws = [w for w in huc12_ws if w.watershed_id == watershed_id]
stations = [w.station for w in ws]
temps_by_station = [unique_wd[0][station] for station in stations]
prcps_by_station = [unique_wd[1][station] for station in stations]
return (ws, (average_weather_data(temps_by_station),
average_weather_data(prcps_by_station)))
def get_weather_simulation_for_project(project, category):
wss = nearest_weather_stations([(None, None, project.area_of_interest)])
data = []
errs = []
for ws in wss:
url = settings.WEATHER_DATA_BUCKET_URL.format(category=category,
station=ws.station)
# Ensure the station exists, if not exit quickly
res = requests.head(url)
if not res.ok:
errs.append(f'Error {res.status_code} while getting data for'
f' {category}/{ws.station}')
return {}, errs
# Fetch and parse station weather data, noting any errors
with closing(requests.get(url, stream=True)) as r:
ws_data, ws_errs = get_weather_modifications(r.raw)
data.append(ws_data)
errs += ws_errs
# Respond with errors, if any
if errs:
return {}, errs
# Check that the datasets have the same characteristics
for c in ['WxYrBeg', 'WxYrEnd', 'WxYrs']:
s = set([d[c] for d in data])
if len(s) > 1:
errs.append(f'{c} does not match in dataset: {s}')
# Respond with errors, if any
if errs:
return {}, errs
# Final result has characteristics of the first station (which would be
# identical to all other stations), and the precipitation and temperature
# data which is the mean of all stations.
return {
'WxYrBeg': data[0]['WxYrBeg'],
'WxYrEnd': data[0]['WxYrEnd'],
'WxYrs': data[0]['WxYrs'],
'Prec': average_weather_data([d['Prec'] for d in data]),
'Temp': average_weather_data([d['Temp'] for d in data]),
}, errs
def collect_data(geop_results, geojson, watershed_id=None, weather=None):
geop_result = {k: v for r in geop_results for k, v in r.items()}
geom = GEOSGeometry(geojson, srid=4326)
area = geom.transform(5070, clone=True).area # Square Meters
# Data Model is called z by convention
z = settings.GWLFE_DEFAULTS.copy()
z['watershed_id'] = watershed_id
# Statically calculated lookup values
z['DayHrs'] = day_lengths(geom)
# Data from the Weather Stations dataset
if weather is not None:
ws, wd = weather
else:
ws = nearest_weather_stations([(None, watershed_id, geojson)])
z['Grow'] = growing_season(ws)
z['Acoef'] = erosion_coeff(ws, z['Grow'])
z['PcntET'] = et_adjustment(ws)
z['WxYrBeg'] = int(max([w.begyear for w in ws]))
z['WxYrEnd'] = int(min([w.endyear for w in ws]))
z['WxYrs'] = z['WxYrEnd'] - z['WxYrBeg'] + 1
# Data from the County Animals dataset
ag_lscp = ag_ls_c_p(geom)
z['C'][0] = ag_lscp.hp_c
z['C'][1] = ag_lscp.crop_c
livestock_aeu, poultry_aeu, population = animal_energy_units(geom)
z['AEU'] = livestock_aeu / (area * ACRES_PER_SQM)
z['n41j'] = livestock_aeu
z['n41k'] = poultry_aeu
z['n41l'] = livestock_aeu + poultry_aeu
z['NumAnimals'] = [int(population.get(animal, 0))
for animal in ANIMAL_KEYS]
z['ManNitr'], z['ManPhos'] = manure_spread(z['AEU'])
# Data from Streams dataset
z['StreamLength'] = stream_length(geom) or 10 # Meters
z['n42b'] = round(z['StreamLength'] / 1000, 1) # Kilometers
# Data from Point Source Discharge dataset
n_load, p_load, discharge = point_source_discharge(geom, area,
drb=geom.within(DRB))
z['PointNitr'] = n_load
z['PointPhos'] = p_load
z['PointFlow'] = discharge
# Data from National Weather dataset
if weather is None:
wd = weather_data(ws, z['WxYrBeg'], z['WxYrEnd'])
temps_dict, prcps_dict = wd
temps = average_weather_data(temps_dict.values())
prcps = average_weather_data(prcps_dict.values())
else:
temps, prcps = wd
z['Temp'] = temps
z['Prec'] = prcps
# Begin processing geop_result
# Set stream related variables to zero if AoI does not contain
# any streams.
if 'ag_stream_pct' in geop_result:
z['AgLength'] = geop_result['ag_stream_pct'] * z['StreamLength']
z['UrbLength'] = z['StreamLength'] - z['AgLength']
z['n42'] = round(z['AgLength'] / 1000, 1)
z['n46e'] = (geop_result['med_high_urban_stream_pct'] *
z['StreamLength'] / 1000)
z['n46f'] = (geop_result['low_urban_stream_pct'] *
z['StreamLength'] / 1000)
else:
z['AgLength'] = 0
z['UrbLength'] = 0
z['n42'] = 0
z['n46e'] = 0
z['n46f'] = 0
z['CN'] = geop_result['cn']
z['SedPhos'] = geop_result['soilp']
z['Area'] = [percent * area * HECTARES_PER_SQM
for percent in geop_result['landuse_pcts']]
# Immediately return an error if z['Area'] is a list of 0s
if sum(z['Area']) == 0:
raise Exception(NO_LAND_COVER)
z['UrbAreaTotal'] = sum(z['Area'][NRur:])
z['PhosConc'] = phosphorus_conc(z['SedPhos'])
z['NumNormalSys'] = num_normal_sys(z['Area'])
z['AgSlope3'] = geop_result['ag_slope_3_pct'] * area * HECTARES_PER_SQM
z['AgSlope3To8'] = (geop_result['ag_slope_3_8_pct'] *
area * HECTARES_PER_SQM)
z['n41'] = geop_result['n41']
z['AvSlope'] = geop_result['avg_slope']
z['AvKF'] = geop_result['avg_kf']
z['KF'] = geop_result['kf']
z['KV'] = kv_coefficient(geop_result['landuse_pcts'], z['Grow'])
# Original at [email protected]:9803-9807
z['n23'] = z['Area'][1] # Row Crops Area
z['n23b'] = z['Area'][13] # High Density Mixed Urban Area
z['n24'] = z['Area'][0] # Hay/Pasture Area
z['n24b'] = z['Area'][11] # Low Density Mixed Urban Area
z['SedDelivRatio'] = sediment_delivery_ratio(area * SQKM_PER_SQM)
z['TotArea'] = area * HECTARES_PER_SQM
z['GrNitrConc'] = geop_result['gr_nitr_conc']
z['GrPhosConc'] = geop_result['gr_phos_conc']
z['MaxWaterCap'] = geop_result['avg_awc']
z['SedAFactor'] = sed_a_factor(geop_result['landuse_pcts'],
z['CN'], z['AEU'], z['AvKF'], z['AvSlope'])
# Use zeroed out stream variables if there are no streams in the AoI
if 'lu_stream_pct' in geop_result:
z['LS'] = ls_factors(geop_result['lu_stream_pct'], z['StreamLength'],
z['Area'], z['AvSlope'], ag_lscp)
else:
zeroed_lu_stream_pct = [0.0] * 16
z['LS'] = ls_factors(zeroed_lu_stream_pct, 0,
z['Area'], z['AvSlope'], ag_lscp)
z['P'] = p_factors(z['AvSlope'], ag_lscp)
z['SedNitr'] = geop_result['soiln']
z['RecessionCoef'] = geop_result['recess_coef']
return z