32 lines
1.6 KiB
Python
32 lines
1.6 KiB
Python
import pandas as pd
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import numpy as np
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light_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
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day_segment = snakemake.params["day_segment"]
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metrics = snakemake.params["metrics"]
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light_features = pd.DataFrame(columns=["local_date"] + ["light_" + day_segment + "_" + x for x in metrics])
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if not light_data.empty:
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if day_segment != "daily":
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light_data =light_data[light_data["local_day_segment"] == day_segment]
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if not light_data.empty:
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light_features = pd.DataFrame()
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if "count" in metrics:
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light_features["light_" + day_segment + "_count"] = light_data.groupby(["local_date"]).count()["timestamp"]
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# get light ambient luminance related features
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if "maxlux" in metrics:
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light_features["light_" + day_segment + "_maxlux"] = light_data.groupby(["local_date"])["double_light_lux"].max()
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if "minlux" in metrics:
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light_features["light_" + day_segment + "_minlux"] = light_data.groupby(["local_date"])["double_light_lux"].min()
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if "avglux" in metrics:
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light_features["light_" + day_segment + "_avglux"] = light_data.groupby(["local_date"])["double_light_lux"].mean()
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if "medianlux" in metrics:
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light_features["light_" + day_segment + "_medianlux"] = light_data.groupby(["local_date"])["double_light_lux"].median()
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if "stdlux" in metrics:
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light_features["light_" + day_segment + "_stdlux"] = light_data.groupby(["local_date"])["double_light_lux"].std()
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light_features = light_features.fillna(0).reset_index()
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light_features.to_csv(snakemake.output[0], index=False) |