Begin with stress events in the ERS script.
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0b3e9226b3
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f3c6a66da9
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@ -259,7 +259,8 @@ rule extract_event_information_from_esm:
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stage = "extract",
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pid = "{pid}"
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output:
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"data/raw/ers/{pid}_ers.csv"
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"data/raw/ers/{pid}_ers.csv",
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"data/raw/ers/{pid}_stress_event_targets.csv"
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script:
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"../src/features/phone_esm/straw/process_user_event_related_segments.py"
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@ -31,6 +31,9 @@ def extract_ers_from_file(esm_df, device_id):
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config = yaml.load(stream, Loader=yaml.FullLoader)
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pd.DataFrame().to_csv(snakemake.output[1]) # Create an empty stress event file either way
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esm_preprocessed = clean_up_esm(preprocess_esm(esm_df))
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# Take only ema_completed sessions responses
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@ -69,13 +72,19 @@ def extract_ers_from_file(esm_df, device_id):
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extracted_ers["shift"] = extracted_ers["diffs"].apply(lambda x: format_timestamp(x))
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elif targets_method == "stress_event":
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pass
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pd.DataFrame().to_csv(snakemake.output[1])
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# TODO: generiranje ERS datoteke za stress_events
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# VV Testiranje različnih povpraševanj za VV
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# print(esm_df[esm_df.questionnaire_id == 87])
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# filter_esm = esm_df[(esm_df.esm_type == 7) & ((esm_df.questionnaire_id == 90.) | (esm_df.questionnaire_id == 91.))][['questionnaire_id', 'esm_user_answer', 'esm_session']]
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# print(filter_esm[filter_esm.esm_user_answer == "1 - Še vedno traja"].shape)
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# print(filter_esm.shape)
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# TODO: generiranje stress_events_targets datoteke (dodaj tudi stolpec s pid) + dodati moraš merge metodo, ki bo združila te datoteke
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# TODO: na koncu se mora v čistilni skripti ustrezno odstraniti vse targete in prilepiti nove targete zraven ustreznih segmentov (zna se zgoditi, da bodo overlap)
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else:
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raise Exception("Please select correct target method for the event-related segments.")
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extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
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