61 lines
2.8 KiB
Python
61 lines
2.8 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 getSampleRate(data):
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try:
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timestamps_diff = data['timestamp'].diff().dropna().mean()
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except:
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raise Exception("Error occured while trying to get the mean sample rate from the data.")
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return 1000/timestamps_diff
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def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, window_length, 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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sample_rate = getSampleRate(temperature_intraday_data)
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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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if window_length is None:
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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=int(sample_rate), featureNames=features))
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else:
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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'], window_length*int(sample_rate)), fs=int(sample_rate), 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 cr_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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if provider["WINDOWS"]["COMPUTE"]:
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requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
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else:
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requested_window_length = None
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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, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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return temperature_intraday_features |