def get_structure_demand(pods, structures):
"""Get a single table with structure demand estimates.
The structures file is expected to have the following fields:
- PARCEL_ID : The parcel ID on which the structure lies.
- STRUCTURE_ID : A unique identifier for each structure.
- SummerAF: The water demand in the summer (AF)
- WinterAF: The water demand in the winter (AF)
The POD file is expected to have the following fields:
PARCEL_ID : The parcel ID on which the POD lies.
FEATUREID : The NHD+V2 catchment ID in which the POD lies.
APPL_ID : The water right application ID.
"""
pod_data = util.read_dbf(pods)
structure_data = util.read_dbf(structures)
join = pd.merge(structure_data, pod_data,
left_on='PARCEL_ID', right_on='PARCEL_ID',
how='inner', suffixes=('_pod', '_structure'))
join['APPL_ID'] = join['APPL_ID'].map(lambda x: x[:-1] if x.endswith('R') else x)
columns = [
'APPL_ID',
'STRUCT_ID',
'SummerAF',
'WinterAF',
]
return join[columns].groupby('STRUCT_ID').agg({
'APPL_ID': lambda x: x.iloc[0],
'SummerAF': lambda x: x.iloc[0],
'WinterAF': lambda x: x.iloc[0]
}).groupby('APPL_ID').agg({
'SummerAF': np.sum,
'WinterAF': np.sum
})
def get_ag_demand(use_file, pod_file):
"""Get a single table with agricultural demand estimates.
The use file is expected to contain the following columns:
- POD_ID : A unique identifier for the point of diversion.
- APPL_ID : The water right application ID
- Vine_Water : The water used for the vineyard acreage
- Orch_Water : The water used for the orchard acreage
The POD file is expected to contain the following columns:
- POD_ID : A unique identifier for the point of diversion
- FEATUREID : The catchment feature ID (from the NHD+V2 dataset) in which the POD lies.
Parameters
----------
use_file : string
The name of the file with all of the use information.
pod_file : string
The name of the file with the POD locations joined to their catchment.
"""
use_data = pd.read_csv(use_file)
pod_data = util.read_dbf(pod_file)
pod_use = pd.merge(pod_data, use_data, left_on='POD_ID', right_on='POD_ID',
suffixes=('_pod', '_use'), how='left')
pod_use['APPL_ID_pod'] = pod_use['APPL_ID_pod'].map(lambda x: x[:-1] if x.endswith('R') else x)
columns = [
'APPL_ID_pod',
'FEATUREID',
'Vine_Water',
'Orch_Water',
]
first = lambda x: x.iloc[0]
return pod_use[columns].groupby('APPL_ID_pod').agg(first)
def get_structure_demand_prepared(database):
"""Read already prepared POD-Structure demand table."""
data = util.read_dbf(database)
columns = [
'APPL_ID',
'STRUCT_ID',
'SummerAF',
'WinterAF',
'FEATUREID',
]
return data[columns].groupby('APPL_ID').agg({
'SummerAF': np.sum,
'WinterAF': np.sum,
'FEATUREID': lambda x: x.iloc[0],
})
def create_use_reports(pod_file, ewrims_file):
"""Create use report stubs for hand entry.
This function does not apply any automatic demand estimates.
"""
ewrims_data = get_rights_data(ewrims_file)
pod_data = util.read_dbf(pod_file)
pod_right = pd.merge(pod_data, ewrims_data, left_on='APPL_ID',
right_on='Application Number', how='left')
columns = [
"APPL_ID",
"FEATUREID",
"Status Date",
"Riparian",
"Pre 1914",
"FACEAMT",
]
first = lambda x: x.iloc[0]
agg = {
"FEATUREID": first,
"Status Date": first,
"Riparian": first,
"Pre 1914": first,
"FACEAMT": np.sum
}
result = convert_ewrims_columns(pod_right[columns]).groupby("APPL_ID").agg(agg).sort("Status Date")
result['Demand Year'] = pd.Series()
for i in range(1, 13):
result[calendar.month_abbr[i]] = pd.Series()
return split_appropriative_riparian(result)
