42 lines
2.6 KiB
Python
42 lines
2.6 KiB
Python
import pandas as pd
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from datetime import datetime, timedelta, time
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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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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features = snakemake.params["features"]
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if battery_data.empty:
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battery_features = pd.DataFrame(columns=["local_date"] + ["battery_" + day_segment + "_" + x for x in features])
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else:
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battery_data = splitOvernightEpisodes(battery_data, ["battery_diff"], [])
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if day_segment != "daily":
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battery_data = splitMultiSegmentEpisodes(battery_data, day_segment, ["battery_diff"])
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battery_data["battery_consumption_rate"] = battery_data["battery_diff"] / battery_data["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 features:
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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 features:
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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 features:
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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 features:
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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_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 features:
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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 features:
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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 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) |