149 lines
8.1 KiB
Python
149 lines
8.1 KiB
Python
import pandas as pd
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import numpy as np
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import datetime
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import math, sys, yaml
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from esm_preprocess import clean_up_esm
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from esm import classify_sessions_by_completion_time, preprocess_esm
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input_data_files = dict(snakemake.input)
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def format_timestamp(x):
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tstring=""
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space = False
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if x//3600 > 0:
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tstring += f"{x//3600}H"
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space = True
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if x % 3600 // 60 > 0:
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tstring += f" {x % 3600 // 60}M" if "H" in tstring else f"{x % 3600 // 60}M"
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if x % 60 > 0:
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tstring += f" {x % 60}S" if "M" in tstring or "H" in tstring else f"{x % 60}S"
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return tstring
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def extract_ers(esm_df, device_id):
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pd.set_option("display.max_rows", 20)
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pd.set_option("display.max_columns", None)
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with open('config.yaml', 'r') as stream:
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config = yaml.load(stream, Loader=yaml.FullLoader)
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pd.DataFrame(columns=["label", "intensity"]).to_csv(snakemake.output[1]) # Create an empty stress_events_targets file
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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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classified = classify_sessions_by_completion_time(esm_preprocessed)
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esm_filtered_sessions = classified[classified["session_response"] == 'ema_completed'].reset_index()[['device_id', 'esm_session']]
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esm_df = esm_preprocessed.loc[(esm_preprocessed['device_id'].isin(esm_filtered_sessions['device_id'])) & (esm_preprocessed['esm_session'].isin(esm_filtered_sessions['esm_session']))]
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targets_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"]
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if targets_method in ["30_before", "90_before"]: # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
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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() # questionnaire length
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extracted_ers["label"] = f"straw_event_{targets_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
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extracted_ers[['event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].min().reset_index()[['timestamp', 'device_id']]
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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["shift_direction"] = -1
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if targets_method == "30_before":
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time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
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extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x))
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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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elif targets_method == "90_before":
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time_before_questionnaire = 90 * 60 # in seconds (90 minutes)
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extracted_ers[['end_event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].max().reset_index()[['timestamp', 'device_id']]
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extracted_ers['diffs'] = extracted_ers['event_timestamp'].astype('int64') - extracted_ers['end_event_timestamp'].shift(1, fill_value=0).astype('int64')
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extracted_ers.loc[extracted_ers['diffs'] > time_before_questionnaire * 1000, 'diffs'] = time_before_questionnaire * 1000
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extracted_ers["diffs"] = (extracted_ers["diffs"] / 1000).apply(lambda x: math.ceil(x))
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extracted_ers["length"] = (extracted_ers["timestamp"] + extracted_ers["diffs"]).apply(lambda x: format_timestamp(x))
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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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# Get and join required data
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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().rename(columns={'timestamp': 'session_length'}) # questionnaire end timestamp
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session_end_timestamp = esm_df.groupby(['device_id', 'esm_session'])['timestamp'].max().to_frame().rename(columns={'timestamp': 'session_end_timestamp'}) # questionnaire end timestamp
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se_time = esm_df[esm_df.questionnaire_id == 90.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_time'})
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se_duration = esm_df[esm_df.questionnaire_id == 91.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_duration'})
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se_intensity = esm_df[esm_df.questionnaire_id == 87.].set_index(['device_id', 'esm_session'])['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'intensity'})
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extracted_ers = extracted_ers.join(session_end_timestamp, on=['device_id', 'esm_session'], how='inner') \
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.join(se_time, on=['device_id', 'esm_session'], how='inner') \
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.join(se_duration, on=['device_id', 'esm_session'], how='inner') \
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.join(se_intensity, on=['device_id', 'esm_session'], how='inner')
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# Filter sessions that are not useful
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extracted_ers = extracted_ers[(extracted_ers.se_time != "0 - Ne spomnim se") & (extracted_ers.se_duration != "0 - Ne spomnim se")]
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# Transform data into its final form, ready for the extraction
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extracted_ers.reset_index(inplace=True)
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extracted_ers["label"] = f"straw_event_{targets_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
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time_before_event = 10 * 60 # in seconds (10 minutes)
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extracted_ers['event_timestamp'] = pd.to_datetime(extracted_ers['se_time']).apply(lambda x: x.timestamp() * 1000).astype('int64')
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extracted_ers['shift_direction'] = -1
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extracted_ers['se_duration'] = \
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np.where(
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extracted_ers['se_duration'] == "1 - Še vedno traja",
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extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'],
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extracted_ers['se_duration']
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)
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extracted_ers['se_duration'] = \
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extracted_ers['se_duration'].apply(lambda x: math.ceil(x / 1000) if isinstance(x, int) else (pd.to_datetime(x).hour * 60 + pd.to_datetime(x).minute) * 60) + time_before_event
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extracted_ers['shift'] = format_timestamp(time_before_event)
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extracted_ers['length'] = extracted_ers['se_duration'].apply(lambda x: format_timestamp(x))
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extracted_ers[["label", "intensity"]].to_csv(snakemake.output[1], index=False)
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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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return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]
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# Actual code execution
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if snakemake.params["stage"] == "extract":
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esm_df = pd.read_csv(input_data_files['esm_raw_input'])
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with open(input_data_files['pid_file'], 'r') as stream:
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pid_file = yaml.load(stream, Loader=yaml.FullLoader)
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extracted_ers = extract_ers(esm_df, pid_file["PHONE"]["DEVICE_IDS"][0])
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extracted_ers.to_csv(snakemake.output[0], index=False)
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elif snakemake.params["stage"] == "merge":
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input_data_files = dict(snakemake.input)
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straw_events = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
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stress_events_targets = pd.DataFrame(columns=["label", "intensity"])
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for input_file in input_data_files["ers_files"]:
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ers_df = pd.read_csv(input_file)
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straw_events = pd.concat([straw_events, ers_df], axis=0, ignore_index=True)
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straw_events.to_csv(snakemake.output[0], index=False)
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for input_file in input_data_files["se_files"]:
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se_df = pd.read_csv(input_file)
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stress_events_targets = pd.concat([stress_events_targets, se_df], axis=0, ignore_index=True)
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stress_events_targets.to_csv(snakemake.output[1], index=False)
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