21 lines
1.3 KiB
Python
21 lines
1.3 KiB
Python
import pandas as pd
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import numpy as np
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from fitbit_step.fitbit_step_base import base_fitbit_step_features
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step_data = pd.read_csv(snakemake.input["step_data"], parse_dates=["local_date_time"])
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day_segment = snakemake.params["day_segment"]
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threshold_active_bout = snakemake.params["threshold_active_bout"]
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include_zero_step_rows = snakemake.params["include_zero_step_rows"]
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step_features = pd.DataFrame(columns=["local_date"])
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requested_features = {}
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requested_features["features_all_steps"] = snakemake.params["features_all_steps"]
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requested_features["features_sedentary_bout"] = snakemake.params["features_sedentary_bout"]
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requested_features["features_active_bout"] = snakemake.params["features_active_bout"]
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step_features = step_features.merge(base_fitbit_step_features(step_data, day_segment, requested_features, threshold_active_bout, include_zero_step_rows), on="local_date", how="outer")
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assert np.sum([len(x) for x in requested_features.values()]) + 1 == step_features.shape[1], "The number of features in the output dataframe (=" + str(step_features.shape[1]) + ") does not match the expected value (=" + str(np.sum([len(x) for x in requested_features.values()])) + " + 1). Verify your fitbit step feature extraction functions"
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step_features.to_csv(snakemake.output[0], index=False) |