36 lines
1.9 KiB
Python
36 lines
1.9 KiB
Python
import pandas as pd
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import numpy as np
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def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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light_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_features = provider["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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light_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
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if not light_data.empty:
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light_data = filter_data_by_segment(light_data, time_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["count"] = light_data.groupby(["local_segment"]).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["maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max()
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if "minlux" in features_to_compute:
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light_features["minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min()
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if "avglux" in features_to_compute:
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light_features["avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean()
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if "medianlux" in features_to_compute:
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light_features["medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median()
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if "stdlux" in features_to_compute:
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light_features["stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std().fillna(0)
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light_features = light_features.reset_index()
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return light_features |