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update on NHD to read reservoir_persistence_usgs #737

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update on NHD to read reservoir_persistence_usgs #737

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38 changes: 22 additions & 16 deletions src/troute-network/troute/DataAssimilation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -432,22 +432,23 @@ def __init__(self, network, from_files, value_dict, da_run=[]):
reset_index().
set_index(['usgs_gage_id']) # <- TODO use input parameter for this
)

gage_lake_df.usgs_lake_id = gage_lake_df.usgs_lake_id.astype(int)
gage_lake_df.index = gage_lake_df.index.astype(str).str.strip()
# build dataframe that crosswalks gageIDs to segmentIDs
gage_link_df = (
network.link_gage_df['gages'].
reset_index().
set_index(['gages'])
)

gage_link_df.index = gage_link_df.index.astype(str).str.strip()
# build dataframe that crosswalks segmentIDs to lakeIDs
link_lake_df = (
gage_lake_df.
join(gage_link_df, how = 'inner').
reset_index().set_index('link').
drop(['index'], axis = 1)
)

# resample `usgs_df` to 15 minute intervals
usgs_df_15min = (
self._usgs_df.
Expand All @@ -462,18 +463,22 @@ def __init__(self, network, from_files, value_dict, da_run=[]):
# containing all of the rows it needs. By using pd.concat here we add in
# the missing rows. But this should be fixed earlier, likely in the
# creation of the gages dictionary...
reservoir_usgs_df = pd.concat(
[
usgs_df_15min.join(link_lake_df, how = 'inner').
reset_index().
set_index('usgs_lake_id').
drop(['index'], axis = 1),
usgs_df_15min.join(network.usgs_lake_gage_crosswalk, how='inner').
drop(['usgs_gage_id'], axis = 1).
rename_axis('usgs_lake_id')
]
)


if not link_lake_df.empty:
reservoir_usgs_df = pd.concat(
[
usgs_df_15min.join(link_lake_df, how = 'inner').
reset_index().
set_index('usgs_lake_id').
drop(['index'], axis = 1),
usgs_df_15min.join(network.usgs_lake_gage_crosswalk, how='inner').
drop(['usgs_gage_id'], axis = 1).
rename_axis('usgs_lake_id')
]
)
else:
reservoir_usgs_df = pd.DataFrame()

# create reservoir hybrid DA initial parameters dataframe
if not reservoir_usgs_df.empty:
reservoir_usgs_param_df = pd.DataFrame(
Expand Down Expand Up @@ -532,7 +537,8 @@ def __init__(self, network, from_files, value_dict, da_run=[]):
# an error. Need to think through this more.
if not self._usgs_df.empty:
self._usgs_df = self._usgs_df.loc[:,network.t0:]

self._usgs_df.index = self._usgs_df.index.astype(str).str.strip()

def update_after_compute(self, run_results,):
'''
Function to update data assimilation object after running routing module.
Expand Down
17 changes: 11 additions & 6 deletions src/troute-network/troute/NHDNetwork.py
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Original file line number Diff line number Diff line change
Expand Up @@ -269,16 +269,16 @@ def read_geo_file(self,):
)

if reservoir_da:
usgs_hybrid = reservoir_da.get(
usgs_hybrid = reservoir_da['reservoir_persistence_da'].get(
'reservoir_persistence_usgs',
False
)
usace_hybrid = reservoir_da.get(
usace_hybrid = reservoir_da['reservoir_persistence_da'].get(
'reservoir_persistence_usace',
False
)
param_file = reservoir_da.get(
'gage_lakeID_crosswalk_file',
'reservoir_parameter_file',
None
)
else:
Expand All @@ -287,18 +287,20 @@ def read_geo_file(self,):
usgs_hybrid = False

# check if RFC-type reservoirs are set to true
rfc_params = self.waterbody_parameters.get('rfc')

rfc_params = reservoir_da['reservoir_rfc_da']
if rfc_params:
rfc_forecast = rfc_params.get(
'reservoir_rfc_forecasts',
False
)
param_file = rfc_params.get('reservoir_parameter_file',None)
param_file = reservoir_da.get('reservoir_parameter_file',None)
else:
rfc_forecast = False

if (param_file and reservoir_da) or (param_file and rfc_forecast):
self._waterbody_type_specified = True

(
self._waterbody_types_df,
self._usgs_lake_gage_crosswalk,
Expand All @@ -315,6 +317,9 @@ def read_geo_file(self,):
reservoir_da.get('crosswalk_usace_lakeID_field', 'usace_lake_id'),
self.waterbody_connections.values(),
)
self._usgs_lake_gage_crosswalk['usgs_gage_id'] = self._usgs_lake_gage_crosswalk['usgs_gage_id'].apply(lambda x: x.decode('utf-8'))
self._usgs_lake_gage_crosswalk.index = self._usgs_lake_gage_crosswalk.index.astype(str).str.strip()

else:
self._waterbody_type_specified = True
self._waterbody_types_df = pd.DataFrame(data = 1, index = self.waterbody_dataframe.index, columns = ['reservoir_type'])
Expand Down
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