44 lines
2.0 KiB
Python
44 lines
2.0 KiB
Python
import pandas as pd
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from scipy.stats import entropy
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from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames
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from CalculatingFeatures.calculate_features import calculateFeatures
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import sys
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def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment):
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temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not temperature_intraday_data.empty:
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temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment)
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if not temperature_intraday_data.empty:
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temperature_intraday_features = pd.DataFrame()
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# apply methods from calculate features module
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temperature_intraday_features = \
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temperature_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=4, featureNames=features))
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temperature_intraday_features.reset_index(inplace=True)
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return temperature_intraday_features
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def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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temperature_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 = genericFeatureNames
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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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temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_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 temperature_intraday_features |