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bb1e1900a4
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junos | bb1e1900a4 | |
junos | c35c75682f |
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@ -46,13 +46,15 @@ def extract_ers_from_file(esm_df, device_id): # TODO: session_id groupby -> spre
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# Extract time-relevant information
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extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # in rounded up seconds
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extracted_ers = extracted_ers[extracted_ers["timestamp"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min
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extracted_ers = extracted_ers[extracted_ers["timestamp"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min
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# TODO: Rename "timestamp" column meaningfully.
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time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
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extracted_ers["label"] = "straw_event_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
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extracted_ers["event_timestamp"] = esm_df.groupby("esm_session")['timestamp'].min().reset_index()['timestamp']
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extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x))
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# TODO: Think about adding questionnaire duration.
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extracted_ers["shift"] = time_before_questionnaire
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extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: format_timestamp(x))
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extracted_ers["shift_direction"] = -1
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