68 lines
3.5 KiB
Python
68 lines
3.5 KiB
Python
import pandas as pd
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import itertools
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def dailyFeaturesFromSummaryData(sleep_summary_data, sleep_type):
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if sleep_type == "main":
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sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 1]
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elif sleep_type == "nap":
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sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 0]
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elif sleep_type == "all":
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pass
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else:
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raise ValueError("sleep_type can only be one of ['main', 'nap', 'all'].")
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features_sum = sleep_summary_data[["minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed", "local_end_date"]].groupby(["local_end_date"]).sum()
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features_sum.index.rename("local_date", inplace=True)
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if "sumdurationafterwakeup" in daily_features_from_summary_data:
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sleep_daily_features["sleep_daily_sumdurationafterwakeup" + sleep_type] = features_sum["minutes_after_wakeup"]
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if "sumdurationasleep" in daily_features_from_summary_data:
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sleep_daily_features["sleep_daily_sumdurationasleep" + sleep_type] = features_sum["minutes_asleep"]
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if "sumdurationawake" in daily_features_from_summary_data:
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sleep_daily_features["sleep_daily_sumdurationawake" + sleep_type] = features_sum["minutes_awake"]
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if "sumdurationtofallasleep" in daily_features_from_summary_data:
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sleep_daily_features["sleep_daily_sumdurationtofallasleep" + sleep_type] = features_sum["minutes_to_fall_asleep"]
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if "sumdurationinbed" in daily_features_from_summary_data:
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sleep_daily_features["sleep_daily_sumdurationinbed" + sleep_type] = features_sum["minutes_in_bed"]
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features_avg = sleep_summary_data[["efficiency", "local_end_date"]].groupby(["local_end_date"]).mean()
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features_avg.index.rename("local_date", inplace=True)
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if "avgefficiency" in daily_features_from_summary_data:
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sleep_daily_features["sleep_daily_avgefficiency" + sleep_type] = features_avg["efficiency"]
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features_count = sleep_summary_data[["local_start_date_time", "local_end_date"]].groupby(["local_end_date"]).count()
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features_count.index.rename("local_date", inplace=True)
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if "countepisode" in daily_features_from_summary_data:
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sleep_daily_features["sleep_daily_count" + sleep_type] = features_count["local_start_date_time"]
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return sleep_daily_features
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sleep_summary_data = pd.read_csv(snakemake.input["sleep_summary_data"])
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sleep_types = snakemake.params["sleep_types"]
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daily_features_from_summary_data = snakemake.params["daily_features_from_summary_data"]
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day_segment = snakemake.params["day_segment"]
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daily_features_can_be_zero = list(set(daily_features_from_summary_data) - set(["avgefficiency"]))
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colnames_can_be_zero = ["sleep_daily_" + x for x in ["".join(feature) for feature in itertools.product(daily_features_can_be_zero, sleep_types)]]
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colnames = ["sleep_daily_" + x for x in ["".join(feature) for feature in itertools.product(daily_features_from_summary_data, sleep_types)]]
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if sleep_summary_data.empty:
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sleep_daily_features = pd.DataFrame(columns=["local_date"] + colnames)
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else:
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sleep_daily_features = pd.DataFrame(columns=colnames)
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for sleep_type in sleep_types:
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sleep_daily_features = dailyFeaturesFromSummaryData(sleep_summary_data, sleep_type)
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sleep_daily_features[colnames_can_be_zero] = sleep_daily_features[colnames_can_be_zero].fillna(0)
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if day_segment == "daily":
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sleep_daily_features.to_csv(snakemake.output[0])
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else:
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ValueError("Sleep summary features are only implemented for daily day segments")
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