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outsource drop_leap_day in temporal aggregation

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Fabian Neumann 2024-03-04 19:37:02 +01:00
parent 9d913bcb38
commit 96f71d7e9c
1 changed files with 8 additions and 15 deletions
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23
scripts/prepare_sector_network.py
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@ -813,7 +813,7 @@ def add_co2limit(n, options, nyears=1.0, limit=0.0):
# TODO PyPSA-Eur merge issue # TODO PyPSA-Eur merge issue
def average_every_nhours(n, offset, drop_leap_day=False): def average_every_nhours(n, offset):
logger.info(f"Resampling the network to {offset}") logger.info(f"Resampling the network to {offset}")
m = n.copy(with_time=False) m = n.copy(with_time=False)
@ -832,10 +832,6 @@ def average_every_nhours(n, offset, drop_leap_day=False):
else: else:
pnl[k] = df.resample(offset).mean() pnl[k] = df.resample(offset).mean()
if drop_leap_day:
sns = m.snapshots[~((m.snapshots.month == 2) & (m.snapshots.day == 29))]
m.set_snapshots(sns)
return m return m
@ -3425,7 +3421,7 @@ def cluster_heat_buses(n):
def apply_time_segmentation( def apply_time_segmentation(
n, segments, solver_name="cbc", overwrite_time_dependent=True, drop_leap_day=False n, segments, solver_name="cbc", overwrite_time_dependent=True
): ):
""" """
Aggregating time series to segments with different lengths. Aggregating time series to segments with different lengths.
@ -3489,11 +3485,6 @@ def apply_time_segmentation(
for component, key in values_t.columns.droplevel(2).unique(): for component, key in values_t.columns.droplevel(2).unique():
n.pnl(component)[key] = values_t[component, key] n.pnl(component)[key] = values_t[component, key]
# TODO: need to check that this actually works as intended.
if drop_leap_day:
sns = n.snapshots[~((n.snapshots.month == 2) & (n.snapshots.day == 29))]
n.set_snapshots(sns)
return n return n
@ -3515,14 +3506,16 @@ def set_temporal_aggregation(n, resolution, solver_name, drop_leap_day=False):
elif "seg" in resolution.lower(): elif "seg" in resolution.lower():
segments = int(resolution[:-3]) segments = int(resolution[:-3])
logger.info("Use temporal segmentation with %s segments", segments) logger.info("Use temporal segmentation with %s segments", segments)
n = apply_time_segmentation( n = apply_time_segmentation(n, segments, solver_name=solver_name)
n, segments, solver_name=solver_name, drop_leap_day=drop_leap_day
)
# temporal averaging # temporal averaging
elif "h" in resolution.lower(): elif "h" in resolution.lower():
logger.info("Aggregate to frequency %s", resolution) logger.info("Aggregate to frequency %s", resolution)
n = average_every_nhours(n, resolution, drop_leap_day) n = average_every_nhours(n, resolution)
if drop_leap_day:
sns = n.snapshots[~((n.snapshots.month == 2) & (n.snapshots.day == 29))]
n.set_snapshots(sns)
return n return n