79 lines
3.4 KiB
Python
79 lines
3.4 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_from_file(esm_df, device_id):
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# pd.set_option("display.max_rows", None)
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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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targets_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"]
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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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# Kako ugotoviti, kje je bilo vprašanje na distressed?
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# Extract time-relevant information
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time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
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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 in seconds
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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["label"] = "straw_event_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
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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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extracted_ers["shift_direction"] = -1
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# sys.exit()
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return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]
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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_from_file(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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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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