time_aggregation: handle case where resolution is bool false
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@ -78,13 +78,13 @@ if __name__ == "__main__":
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resolution = snakemake.params.time_resolution
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resolution = snakemake.params.time_resolution
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# Representative snapshots
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# Representative snapshots
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if "sn" in resolution.lower():
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if isinstance(resolution, str) and "sn" in resolution.lower():
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logger.info("Use representative snapshots")
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logger.info("Use representative snapshots")
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# Output an empty csv; this is taken care of in prepare_sector_network.py
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# Output an empty csv; this is taken care of in prepare_sector_network.py
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pd.DataFrame().to_csv(snakemake.output.snapshot_weightings)
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pd.DataFrame().to_csv(snakemake.output.snapshot_weightings)
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# Plain resampling
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# Plain resampling
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elif "h" in resolution.lower():
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elif isinstance(resolution, str) and "h" in resolution.lower():
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offset = resolution.lower()
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offset = resolution.lower()
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logger.info(f"Averaging every {offset} hours")
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logger.info(f"Averaging every {offset} hours")
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snapshot_weightings = n.snapshot_weightings.resample(offset).sum()
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snapshot_weightings = n.snapshot_weightings.resample(offset).sum()
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@ -95,7 +95,7 @@ if __name__ == "__main__":
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snapshot_weightings.to_csv(snakemake.output.snapshot_weightings)
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snapshot_weightings.to_csv(snakemake.output.snapshot_weightings)
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# Temporal segmentation
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# Temporal segmentation
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elif "seg" in resolution.lower():
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elif isinstance(resolution, str) and "seg" in resolution.lower():
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segments = int(resolution[:-3])
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segments = int(resolution[:-3])
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logger.info(f"Use temporal segmentation with {segments} segments")
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logger.info(f"Use temporal segmentation with {segments} segments")
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