71 lines
4.0 KiB
Python
71 lines
4.0 KiB
Python
import pandas as pd
|
|
import itertools
|
|
|
|
|
|
|
|
def dailyFeaturesFromSummaryData(sleep_daily_features, sleep_summary_data, summary_features, sleep_type):
|
|
if sleep_type == "main":
|
|
sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 1]
|
|
elif sleep_type == "nap":
|
|
sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 0]
|
|
elif sleep_type == "all":
|
|
pass
|
|
else:
|
|
raise ValueError("sleep_type can only be one of ['main', 'nap', 'all'].")
|
|
|
|
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()
|
|
features_sum.index.rename("local_date", inplace=True)
|
|
if "sumdurationafterwakeup" in summary_features:
|
|
sleep_daily_features["sleep_daily_sumdurationafterwakeup" + sleep_type] = features_sum["minutes_after_wakeup"]
|
|
if "sumdurationasleep" in summary_features:
|
|
sleep_daily_features["sleep_daily_sumdurationasleep" + sleep_type] = features_sum["minutes_asleep"]
|
|
if "sumdurationawake" in summary_features:
|
|
sleep_daily_features["sleep_daily_sumdurationawake" + sleep_type] = features_sum["minutes_awake"]
|
|
if "sumdurationtofallasleep" in summary_features:
|
|
sleep_daily_features["sleep_daily_sumdurationtofallasleep" + sleep_type] = features_sum["minutes_to_fall_asleep"]
|
|
if "sumdurationinbed" in summary_features:
|
|
sleep_daily_features["sleep_daily_sumdurationinbed" + sleep_type] = features_sum["minutes_in_bed"]
|
|
|
|
features_avg = sleep_summary_data[["efficiency", "local_end_date"]].groupby(["local_end_date"]).mean()
|
|
features_avg.index.rename("local_date", inplace=True)
|
|
if "avgefficiency" in summary_features:
|
|
sleep_daily_features["sleep_daily_avgefficiency" + sleep_type] = features_avg["efficiency"]
|
|
|
|
features_count = sleep_summary_data[["local_start_date_time", "local_end_date"]].groupby(["local_end_date"]).count()
|
|
features_count.index.rename("local_date", inplace=True)
|
|
if "countepisode" in summary_features:
|
|
sleep_daily_features["sleep_daily_countepisode" + sleep_type] = features_count["local_start_date_time"]
|
|
|
|
return sleep_daily_features
|
|
|
|
def base_fitbit_sleep_features(sleep_summary_data, day_segment, requested_summary_features, requested_sleep_type):
|
|
if not day_segment == "daily":
|
|
return pd.DataFrame(columns=["local_date"])
|
|
else:
|
|
# name of the features this function can compute
|
|
base_summary_features_names = ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"]
|
|
base_sleep_type = ["main", "nap", "all"]
|
|
# the subset of requested features this function can compute
|
|
summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names))
|
|
sleep_type_to_compute = list(set(requested_sleep_type) & set(base_sleep_type))
|
|
# full names
|
|
features_fullnames_to_compute = ["".join(feature) for feature in itertools.product(summary_features_to_compute, sleep_type_to_compute)]
|
|
|
|
colnames_can_be_zero = ["sleep_daily_" + x for x in [col for col in features_fullnames_to_compute if "avgefficiency" not in col]]
|
|
|
|
if sleep_summary_data.empty:
|
|
sleep_summary_features = pd.DataFrame(columns=["local_date"] + ["sleep_daily_" + x for x in features_fullnames_to_compute])
|
|
else:
|
|
|
|
sleep_summary_features = pd.DataFrame(columns=["sleep_daily_" + x for x in features_fullnames_to_compute])
|
|
|
|
for sleep_type in sleep_type_to_compute:
|
|
sleep_summary_features = dailyFeaturesFromSummaryData(sleep_summary_features, sleep_summary_data, summary_features_to_compute, sleep_type)
|
|
|
|
sleep_summary_features[colnames_can_be_zero] = sleep_summary_features[colnames_can_be_zero].fillna(0)
|
|
|
|
sleep_summary_features = sleep_summary_features.reset_index()
|
|
|
|
return sleep_summary_features
|
|
|