def clean(self, *args, **kwargs):
# add custom validation here
print(self.geo_field, '\n New line')
geos_field = GEOSGeometry(self.geo_field)
print(self.geo_field, '\n New line')
farm_s = farm.objects.get(farm_name=self.farm)
geos_farm = GEOSGeometry(farm_s.geo_farm)
if not (
geos_field.within(geos_farm)
): # GEOS method check if field is within the farm, returns a T or F.
raise ValidationError(_('Field is not inside the selected farm'),
#params={'farm': farm_s.farm_name },
)
super(field, self).clean(*args, **kwargs)
def point_source_pollution(geojson):
"""
Given a GeoJSON shape, retrieve point source pollution data
from the `ms_pointsource` or `ms_pointsource_drb` table to display
in the Analyze tab.
Returns a dictionary to append to the outgoing JSON for analysis
results.
"""
geom = GEOSGeometry(geojson, srid=4326)
drb = geom.within(DRB)
table_name = get_point_source_table(drb)
sql = '''
SELECT city, state, npdes_id, mgd, kgn_yr, kgp_yr, latitude,
longitude, {facilityname}
FROM {table_name}
WHERE ST_Intersects(geom, ST_SetSRID(ST_GeomFromText(%s), 4326))
'''.format(facilityname='facilityname' if drb else 'null',
table_name=table_name)
with connection.cursor() as cursor:
cursor.execute(sql, [geom.wkt])
if cursor.rowcount != 0:
columns = [col[0] for col in cursor.description]
point_source_results = [
dict(
zip(columns, [
row[0], row[1], row[2],
float(row[3]) if row[3] else None,
float(row[4]) if row[4] else None,
float(row[5]) if row[5] else None,
float(row[6]) if row[6] else None,
float(row[7]) if row[7] else None, row[8]
])) for row in cursor.fetchall()
]
else:
point_source_results = []
return {
'displayName': 'Point Source',
'name': 'pointsource',
'categories': point_source_results
}
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
def start_analyze_drb_2100_land(request, key=None, format=None):
"""
Starts a job to get a land-use histogram for an area within DRB in 2100.
Uses simulations of change in land use over the 20-year period 2080-2099
based on two urban growth scenarios: centers and corridors. Generated by
Shippensburg University, serviced via APIs by Drexel University and the
Academy of Natural Sciences.
For more information, see the
[technical documentation](https://wikiwatershed.org/
documentation/mmw-tech/#overlays-tab-coverage).
## Response
You can use the URL provided in the response's `Location`
header to poll for the job's results.
<summary>
**Example of a completed job's `result`**
</summary>
<details>
{
"survey": {
"displayName": "DRB 2100 land forecast (corridors)",
"name": "drb_2100_land_corridors",
"categories": [
{
"area": 3572379,
"code": "open_water",
"coverage": 0.041698374846361526,
"nlcd": 11,
"type": "Open Water"
},
{
"area": 0,
"code": "perennial_ice",
"coverage": 0.0,
"nlcd": 12,
"type": "Perennial Ice/Snow"
},
{
"area": 10355769,
"code": "developed_open",
"coverage": 0.12087707871542477,
"nlcd": 21,
"type": "Developed, Open Space"
},
{
"area": 11455623,
"code": "developed_low",
"coverage": 0.13371505709573384,
"nlcd": 22,
"type": "Developed, Low Intensity"
},
{
"area": 27048582,
"code": "developed_med",
"coverage": 0.3157229149814583,
"nlcd": 23,
"type": "Developed, Medium Intensity"
},
{
"area": 29183382,
"code": "developed_high",
"coverage": 0.3406412370917419,
"nlcd": 24,
"type": "Developed, High Intensity"
},
{
"area": 11187,
"code": "barren_land",
"coverage": 0.00013057957159815528,
"nlcd": 31,
"type": "Barren Land (Rock/Sand/Clay)"
},
{
"area": 2684196,
"code": "deciduous_forest",
"coverage": 0.03133111323549495,
"nlcd": 41,
"type": "Deciduous Forest"
},
{
"area": 14301,
"code": "evergreen_forest",
"coverage": 0.00016692754567133446,
"nlcd": 42,
"type": "Evergreen Forest"
},
{
"area": 93402,
"code": "mixed_forest",
"coverage": 0.001090229118298999,
"nlcd": 43,
"type": "Mixed Forest"
},
{
"area": 114768,
"code": "shrub",
"coverage": 0.001339622443298211,
"nlcd": 52,
"type": "Shrub/Scrub"
},
{
"area": 55143,
"code": "grassland",
"coverage": 0.000643653286550199,
"nlcd": 71,
"type": "Grassland/Herbaceous"
},
{
"area": 59643,
"code": "pasture",
"coverage": 0.0006961792606443887,
"nlcd": 81,
"type": "Pasture/Hay"
},
{
"area": 14193,
"code": "cultivated_crops",
"coverage": 0.0001656669222930739,
"nlcd": 82,
"type": "Cultivated Crops"
},
{
"area": 574722,
"code": "woody_wetlands",
"coverage": 0.006708407307413516,
"nlcd": 90,
"type": "Woody Wetlands"
},
{
"area": 434610,
"code": "herbaceous_wetlands",
"coverage": 0.0050729585780168295,
"nlcd": 95,
"type": "Emergent Herbaceous Wetlands"
}
]
}
}
</details>
"""
user = request.user if request.user.is_authenticated else None
area_of_interest, wkaoi = _parse_input(request)
errs = []
if not key:
errs.append('`key` must be specified')
if key not in settings.DREXEL_FAST_ZONAL_API['keys']:
errs.append('`key` must be one of "{}".'.format('", "'.join(
settings.DREXEL_FAST_ZONAL_API['keys'])))
# A little redundant since GeoJSON -> GEOSGeometry is already done once
# within _parse_input, but it is not returned from there and changing
# that API could break many things.
geom = GEOSGeometry(area_of_interest, srid=4326)
# In the front-end, we use DRB_SIMPLE_PERIMETER. This is sent from the
# back-end on every page render, and is considerably lighter ~0.2% than
# the actual perimeter. We use the same here for consistency.
if not geom.within(settings.DRB_SIMPLE_PERIMETER):
errs.append('The area of interest must be within the'
' Delaware River Basin.')
if errs:
return Response({'errors': errs}, status=status.HTTP_400_BAD_REQUEST)
return start_celery_job(
[tasks.analyze_drb_2100_land.s(area_of_interest, key)],
area_of_interest, user)
