76 lines
3.9 KiB
Python
76 lines
3.9 KiB
Python
import pandas as pd
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from scipy.stats import entropy
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def statsFeatures(bvp_data, features, bvp_features):
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col_name = "blood_volume_pulse"
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if "sumbvp" in features:
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bvp_features["sumbvp"] = bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].sum()
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if "maxbvp" in features:
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bvp_features["maxbvp"] = bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].max()
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if "minbvp" in features:
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bvp_features["minbvp"] = bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].min()
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if "avgbvp" in features:
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bvp_features["avgbvp"] = bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].mean()
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if "medianbvp" in features:
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bvp_features["medianbvp"] = bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].median()
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if "modebvp" in features:
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bvp_features["modebvp"] = bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].agg(lambda x: pd.Series.mode(x)[0])
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if "stdbvp" in features:
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bvp_features["stdbvp"] = bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].std()
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if "diffmaxmodebvp" in features:
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bvp_features["diffmaxmodebvp"] = bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].max() - \
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bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].agg(lambda x: pd.Series.mode(x)[0])
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if "diffminmodebvp" in features:
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bvp_features["diffminmodebvp"] = bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].agg(lambda x: pd.Series.mode(x)[0]) - \
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bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].min()
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if "entropybvp" in features:
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bvp_features["entropybvp"] = bvp_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].agg(entropy)
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return bvp_features
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def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, time_segment, filter_data_by_segment):
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bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not bvp_intraday_data.empty:
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bvp_intraday_data = filter_data_by_segment(bvp_intraday_data, time_segment)
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if not bvp_intraday_data.empty:
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bvp_intraday_features = pd.DataFrame()
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# get stats of bvp
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bvp_intraday_features = statsFeatures(bvp_intraday_data, features, bvp_intraday_features)
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bvp_intraday_features.reset_index(inplace=True)
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return bvp_intraday_features
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def dbdp_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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bvp_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_intraday_features = provider["FEATURES"]
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# name of the features this function can compute
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base_intraday_features_names = ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp",
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"diffminmodebvp", "entropybvp"]
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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bvp_intraday_features = extractBVPFeaturesFromIntradayData(bvp_intraday_data,
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intraday_features_to_compute, time_segment,
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filter_data_by_segment)
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return bvp_intraday_features |