Commit b20b5f31 authored by Cynthia Crowley's avatar Cynthia Crowley

Clean up comments and spaces

parent 9cda32b2
......@@ -461,5 +461,4 @@ class CFSConfig(ConfigBase):
return self._workspace
config = CFSConfig
......@@ -157,7 +157,6 @@ class ConfigBase(metaclass=abc.ABCMeta):
'RO_mm',
'Sa',
'Sm',
#'Snowpack',
'Ws'
]
......
......@@ -47,7 +47,6 @@ def spinup(config, meta_steps):
dependencies=forcing_1mo.targets)
forcing_1mo.replace_targets_with_tag_file(config.workspace().tag('spinup_1mo_forcing'))
steps.append(forcing_1mo)
steps += run_lsm_from_final_norm_state(config)
for month in all_months:
......@@ -70,7 +69,6 @@ def spinup(config, meta_steps):
steps += time_integrate_forcing(config, window)
steps += time_integrate_results(config, window)
# Compute monthly fits (and then anomalies) over the fit period
for param in config.lsm_rp_vars() + config.forcing_rp_vars() + config.state_rp_vars():
for month in all_months:
......@@ -331,7 +329,7 @@ def time_integrate_forcing(config:Config, window: int, *, basis: Optional[Basis]
def time_integrate_results(config: Config, window: int, *, basis: Optional[Basis]=None) -> List[Step]:
"""
Integrate specified LSM results (and any included forcing variables) over the given time window
Integrate specified LSM results over the given time window
"""
yearmons_in = config.historical_yearmons()
yearmons_out = yearmons_in[window-1:]
......
......@@ -41,15 +41,12 @@ make_results <- function(
PET,
PETmE,
P_net,
# Pr,
RO_m3,
RO_mm,
Runoff_m3,
Runoff_mm,
Sa,
Sm,
# Snowpack,
# T,
Ws,
dWdt,
extent
......@@ -63,15 +60,12 @@ make_results <- function(
PET= PET,
PETmE= PETmE,
P_net= P_net,
# Pr=Pr,
RO_m3= RO_m3,
RO_mm= RO_mm,
Runoff_m3= Runoff_m3,
Runoff_mm= Runoff_mm,
Sa= Sa,
Sm= Sm,
# Snowpack=Snowpack,
# T= T,
Ws= Ws,
dWdt= dWdt
)
......
......@@ -89,22 +89,18 @@ run <- function(static, state, forcing) {
obs <- list(
extent= state$extent,
#dayLength= forcing$daylength,
dWdt= dWdt,
E= E,
EmPET= E - E0,
P_net= P,
PET= E0,
PETmE= E0 - E,
#Pr= forcing$Pr,
RO_mm= revised_runoff,
RO_m3= revised_runoff*area_m2/1000,
Runoff_mm= R,
Runoff_m3= R*area_m2/1000,
Sa= Sa,
Sm= ifelse(is.na(Sa), NA, Sm),
#Snowpack= state$Snowpack,
#T= forcing$T,
Ws= Ws_ave
)
......
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