Add day epochs to battery metrics and fix some of them
Meng Li
parent
d8421575ba
commit
99d387a7a4
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@ -23,7 +23,9 @@ rule all:
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segment = config["BLUETOOTH"]["DAY_SEGMENTS"]),
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expand("data/processed/{pid}/google_activity_recognition_{segment}.csv",pid=config["PIDS"],
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segment = config["GOOGLE_ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"]),
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expand("data/processed/{pid}/battery_daily.csv", pid=config["PIDS"]),
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expand("data/processed/{pid}/battery_{day_segment}.csv",
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pid = config["PIDS"],
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day_segment = config["BATTERY"]["DAY_SEGMENTS"]),
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# Reports
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expand("reports/figures/{pid}/{sensor}_heatmap_rows.html", pid=config["PIDS"], sensor=config["SENSORS"]),
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expand("reports/figures/{pid}/compliance_heatmap.html", pid=config["PIDS"], sensor=config["SENSORS"]),
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@ -57,3 +57,8 @@ BLUETOOTH:
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GOOGLE_ACTIVITY_RECOGNITION:
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DAY_SEGMENTS: *day_segments
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METRICS: ['count','most_common_activity','number_unique_activities','activity_change_count']
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BATTERY:
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DAY_SEGMENTS: *day_segments
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METRICS: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
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@ -66,7 +66,10 @@ rule activity_metrics:
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rule battery_metrics:
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input:
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"data/processed/{pid}/battery_deltas.csv"
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params:
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day_segment = "{day_segment}",
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metrics = config["BATTERY"]["METRICS"]
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output:
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"data/processed/{pid}/battery_daily.csv"
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"data/processed/{pid}/battery_{day_segment}.csv"
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script:
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"../src/features/battery_metrics.py"
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@ -1,60 +1,114 @@
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import pandas as pd
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import datetime
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from datetime import datetime, timedelta, time
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def truncateTime(df, segment_column, new_day_segment, datetime_column, date_column, new_time):
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df.loc[:, segment_column] = new_day_segment
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df.loc[:, datetime_column] = df[date_column].apply(lambda date: datetime.combine(date, new_time))
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return df
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def computeTruncatedBatteryTimeDifferences(df):
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df["truncated_time_diff"] = df["local_end_date_time"] - df["local_start_date_time"]
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df["truncated_time_diff"] = df["truncated_time_diff"].apply(lambda time: time.total_seconds()/3600)
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df["battery_diff"] = df["battery_diff"] * (df["truncated_time_diff"] / df["time_diff"])
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del df["time_diff"]
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df.rename(columns={"truncated_time_diff": "time_diff"}, inplace=True)
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return df
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def splitOvernightEpisodes(battery_data):
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overnight = battery_data[(battery_data["local_start_date"] + timedelta(days=1)) == battery_data["local_end_date"]]
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not_overnight = battery_data[battery_data["local_start_date"] == battery_data["local_end_date"]]
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if not overnight.empty:
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today = overnight[["battery_diff", "time_diff", "local_start_date_time", "local_start_date", "local_start_day_segment"]].copy()
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tomorrow = overnight[["battery_diff", "time_diff", "local_end_date_time", "local_end_date", "local_end_day_segment"]].copy()
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# truncate the end time of all overnight periods to midnight
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today = truncateTime(today, "local_end_day_segment", "evening", "local_end_date_time", "local_start_date", time(23,59,59))
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today["local_end_date"] = overnight["local_start_date"]
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# set the start time of all periods after midnight to midnight
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tomorrow = truncateTime(tomorrow, "local_start_day_segment", "night", "local_start_date_time", "local_end_date", time(0,0,0))
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tomorrow["local_start_date"] = overnight["local_end_date"]
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overnight = pd.concat([today, tomorrow], axis=0, sort=False)
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# calculate new battery_diff and time_diff for split overnight periods
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overnight = computeTruncatedBatteryTimeDifferences(overnight)
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return pd.concat([not_overnight, overnight], axis=0, sort=False)
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def splitMultiSegmentEpisodes(battery_data, day_segment):
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# extract episodes that start and end at the same epochs
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exact_segments = battery_data.query("local_start_day_segment == local_end_day_segment and local_start_day_segment == @day_segment").copy()
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# extract episodes that start and end at different epochs
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across_segments = battery_data.query("local_start_day_segment != local_end_day_segment").copy()
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# 1) if start time is in current day_segment
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start_segment = across_segments[across_segments["local_start_day_segment"] == day_segment].copy()
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if not start_segment.empty:
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start_segment = truncateTime(start_segment, "local_end_day_segment", day_segment, "local_end_date_time", "local_end_date", time(EPOCH_TIMES[day_segment][1],59,59))
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# 2) if end time is in current day_segment
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end_segment = across_segments[across_segments["local_end_day_segment"] == day_segment].copy()
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if not end_segment.empty:
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end_segment = truncateTime(end_segment, "local_start_day_segment", day_segment, "local_start_date_time", "local_start_date", time(EPOCH_TIMES[day_segment][0],0,0))
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# 3) if current episode comtains day_segment
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across_segments.loc[:,"start_segment"] = across_segments["local_start_day_segment"].apply(lambda seg: SEGMENT[seg])
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across_segments.loc[:,"end_segment"] = across_segments["local_end_day_segment"].apply(lambda seg: SEGMENT[seg])
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day_segment_num = SEGMENT[day_segment]
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within_segments = across_segments.query("start_segment < @day_segment_num and end_segment > @day_segment_num")
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del across_segments["start_segment"], across_segments["end_segment"]
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del within_segments["start_segment"], within_segments["end_segment"]
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if not within_segments.empty:
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within_segments = truncateTime(within_segments, "local_start_day_segment", day_segment, "local_start_date_time", "local_start_date", time(EPOCH_TIMES[day_segment][0],0,0))
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within_segments = truncateTime(within_segments, "local_end_day_segment", day_segment, "local_end_date_time", "local_end_date", time(EPOCH_TIMES[day_segment][1],59,59))
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across_segments = pd.concat([start_segment, end_segment, within_segments], axis=0, sort=False)
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if not across_segments.empty:
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accross_segments = computeTruncatedBatteryTimeDifferences(across_segments)
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return pd.concat([exact_segments, across_segments], axis=0, sort=False)
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battery_data = pd.read_csv(snakemake.input[0])
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battery_data = pd.read_csv(snakemake.input[0], parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
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day_segment = snakemake.params["day_segment"]
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metrics = snakemake.params["metrics"]
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SEGMENT = {"night": 0, "morning": 1, "afternoon": 2, "evening": 3}
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EPOCH_TIMES = {"night": [0,5], "morning": [6,11], "afternoon": [12,17], "evening": [18,23]}
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if battery_data.empty:
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battery_features = pd.DataFrame(columns=["battery_diff", "time_diff", "battery_decrease_times","battery_consumption_rate", "local_date"])
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battery_features = pd.DataFrame(columns=["local_date"] + ["battery_" + day_segment + "_" + x for x in metrics])
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else:
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for col in ["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"]:
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battery_data[col] = pd.to_datetime(battery_data[col])
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battery_data = splitOvernightEpisodes(battery_data)
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# split the row into 2 rows when local_start_date + 1 = local_end_date
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battery_data_overnight = battery_data[battery_data["local_start_date"] + datetime.timedelta(days=1) == battery_data["local_end_date"]]
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if not battery_data_overnight.empty:
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battery_data_overnight_first, battery_data_overnight_second = pd.DataFrame(columns=battery_data.columns), pd.DataFrame(columns=battery_data.columns)
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battery_data_overnight_first["total_battery_diff"], battery_data_overnight_second["total_battery_diff"] = battery_data_overnight["battery_diff"], battery_data_overnight["battery_diff"]
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battery_data_overnight_first["total_time_diff"], battery_data_overnight_second["total_time_diff"] = battery_data_overnight["time_diff"], battery_data_overnight["time_diff"]
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# let start = start OR end = end, then fill the left col with the start+23:59:59.
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battery_data_overnight_first["local_start_date_time"] = battery_data_overnight["local_start_date_time"]
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battery_data_overnight_first["local_end_date_time"] = battery_data_overnight["local_start_date"].apply(lambda x: datetime.datetime.combine(x, datetime.time(23,59,59)))
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battery_data_overnight_first["local_start_date"] = battery_data_overnight["local_start_date"]
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battery_data_overnight_second["local_end_date_time"] = battery_data_overnight["local_end_date_time"]
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battery_data_overnight_second["local_start_date_time"] = battery_data_overnight["local_start_date"].apply(lambda x: datetime.datetime.combine(x, datetime.time(23,59,59)))
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battery_data_overnight_second["local_start_date"] = battery_data_overnight["local_end_date"]
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battery_data_overnight = pd.concat([battery_data_overnight_first, battery_data_overnight_second])
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# calculate battery_diff and time_diff
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battery_data_overnight["time_diff"] = (battery_data_overnight["local_end_date_time"]-battery_data_overnight["local_start_date_time"]).apply(lambda x: x.total_seconds()/3600)
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battery_data_overnight["battery_diff"] = battery_data_overnight["total_battery_diff"]*(battery_data_overnight["time_diff"]/battery_data_overnight["total_time_diff"])
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del battery_data_overnight["total_battery_diff"], battery_data_overnight["total_time_diff"]
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if day_segment != "daily":
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battery_data = splitMultiSegmentEpisodes(battery_data, day_segment)
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# filter out the rows when local_start_date + 1 < local_end_date
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battery_data = battery_data[battery_data["local_start_date"] == battery_data["local_end_date"]]
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battery_data["battery_consumption_rate"] = battery_data["battery_diff"] / battery_data["time_diff"]
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# combine
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battery_data = pd.concat([battery_data, battery_data_overnight])
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# split into decrease table and charge table
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battery_data_decrease = battery_data[battery_data["battery_diff"] > 0]
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battery_data_charge = battery_data[battery_data["battery_diff"] <= 0]
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# for battery_data_decrease:
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battery_decrease_count = battery_data_decrease.groupby(["local_start_date"])["local_start_date"].count()
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battery_data_decrease = battery_data_decrease.groupby(["local_start_date"]).sum()
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battery_data_decrease["battery_decrease_count"] = battery_decrease_count
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battery_data_decrease["battery_decrease_duration"] = battery_data_decrease["time_diff"]
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battery_data_decrease["battery_consumption_rate"] = battery_data_decrease["battery_diff"]/battery_data_decrease["time_diff"]
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del battery_data_decrease["battery_diff"], battery_data_decrease["time_diff"]
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# for battery_data_discharge:
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battery_data_discharge = battery_data[battery_data["battery_diff"] > 0]
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battery_discharge_features = pd.DataFrame()
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if "countdischarge" in metrics:
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battery_discharge_features["battery_"+day_segment+"_countdischarge"] = battery_data_discharge.groupby(["local_start_date"])["local_start_date"].count()
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if "sumdurationdischarge" in metrics:
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battery_discharge_features["battery_"+day_segment+"_sumdurationdischarge"] = battery_data_discharge.groupby(["local_start_date"])["time_diff"].sum()
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if "avgconsumptionrate" in metrics:
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battery_discharge_features["battery_"+day_segment+"_avgconsumptionrate"] = battery_data_discharge.groupby(["local_start_date"])["battery_consumption_rate"].mean()
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if "maxconsumptionrate" in metrics:
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battery_discharge_features["battery_"+day_segment+"_maxconsumptionrate"] = battery_data_discharge.groupby(["local_start_date"])["battery_consumption_rate"].max()
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# for battery_data_charge:
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battery_charge_count = battery_data_charge.groupby(["local_start_date"])["local_start_date"].count()
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battery_data_charge = battery_data_charge.groupby(["local_start_date"]).sum()
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battery_data_charge["battery_charge_count"] = battery_charge_count
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battery_data_charge["battery_charge_duration"] = battery_data_charge["time_diff"]
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del battery_data_charge["battery_diff"], battery_data_charge["time_diff"]
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battery_data_charge = battery_data[battery_data["battery_diff"] <= 0]
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battery_charge_features = pd.DataFrame()
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if "countcharge" in metrics:
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battery_charge_features["battery_"+day_segment+"_countcharge"] = battery_data_charge.groupby(["local_start_date"])["local_start_date"].count()
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if "sumdurationcharge" in metrics:
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battery_charge_features["battery_"+day_segment+"_sumdurationcharge"] = battery_data_charge.groupby(["local_start_date"])["time_diff"].sum()
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# combine decrease features and charge features
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battery_features = pd.concat([battery_data_decrease, battery_data_charge], axis=1, sort=True)
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battery_features["local_date"] = battery_features.index
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battery_features.reset_index(inplace=True, drop=True)
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battery_features.to_csv(snakemake.output[0], index=False)
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# combine discharge features and charge features; fill the missing values with ZERO
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battery_features = pd.concat([battery_discharge_features, battery_charge_features], axis=1, sort=True).fillna(0)
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battery_features.index.rename("local_date", inplace=True)
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battery_features.to_csv(snakemake.output[0], index=True)
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