34 lines
1.9 KiB
Python
34 lines
1.9 KiB
Python
import pandas as pd
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def base_light_features(light_data, day_segment, requested_features):
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# name of the features this function can compute
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base_features_names = ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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if light_data.empty:
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light_features = pd.DataFrame(columns=["local_date"] + ["light_" + day_segment + "_" + x for x in features_to_compute])
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else:
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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 features_to_compute:
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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 features_to_compute:
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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 features_to_compute:
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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 features_to_compute:
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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 features_to_compute:
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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 features_to_compute:
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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.reset_index()
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return light_features |