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import sys |
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from lib.experiment_specs import study_config |
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from lib.data_helpers import data_utils |
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from lib.data_helpers import test |
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from lib.utilities import codebook |
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from lib.utilities import serialize |
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from functools import reduce |
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import pandas as pd |
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import os |
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from datetime import datetime, timedelta |
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from lib.data_helpers.builder_utils import BuilderUtils |
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""" |
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Object that cleans phone dashboard and PC Dashbaord data, by doing the following: |
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""" |
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class CleanEvents(): |
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def __init__(self, source: str, keyword: str): |
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""" |
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establishes a bunch of paths |
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Parameters |
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---------- |
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source: either "PhoneDashboard" or "PCDashboard" |
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keyword: the kind of [PhoneDasboard] data, like "Use" or "Alternative |
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""" |
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self.user_event_file = os.path.join("data","external", "intermediate", source, f"{keyword}Summary.csv") |
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self.clean_file = os.path.join("data", "external", "intermediate", source, f"{keyword}") |
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self.clean_test_file = os.path.join("data","external", "intermediate_test", source, f"{keyword}") |
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self.keyword = keyword |
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self.config_user_dict = serialize.open_yaml("config_user.yaml") |
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def clean_events(self, raw_event_df: pd.DataFrame, date_col: str, cleaner, phase_data: bool = True): |
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""" |
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- subsets data: only appcodes in study, within the study dates |
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- adds phase label |
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- creates phase level data |
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- applies specific cleaning functions given as input |
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Parameters |
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---------- |
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raw_event_df: a dataframe that contains raw PD or PC data |
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date_col: the column name that gives the date of the row, used for dividing rows into phases |
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cleaner: custom cleaner object |
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phase_data: whether or not the cleaner should create phase level data |
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Returns |
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------- |
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""" |
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print(f"\t Cleaning {self.keyword} {datetime.now()}") |
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df_list = [] |
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print(len(raw_event_df)) |
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df_clean = cleaner.prep_clean(raw_event_df) |
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print(f"{len(df_clean)}: After Clean") |
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df_clean = df_clean.loc[(df_clean[date_col] >= study_config.first_pull.date()) & (df_clean[date_col] <= study_config.last_pull.date())] |
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df_clean = BuilderUtils.add_phase_label(raw_df = df_clean, raw_df_date = date_col) |
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print(f"Length of file before saving {len(df_clean)}") |
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print(df_clean.memory_usage(deep=True)/1024**2) |
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if self.config_user_dict["local"]["test"]: |
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test.save_test_df(df_clean, self.clean_test_file) |
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else: |
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try: |
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serialize.save_pickle(df_clean, self.clean_file) |
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except: |
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print("Couldn't save pickle!") |
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if phase_data ==True: |
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for phase,specs in study_config.phases.items(): |
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if datetime.now()<study_config.phases[phase]["StartSurvey"]["Start"]+timedelta(2): |
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continue |
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old_code = specs["StartSurvey"]["Code"] |
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df_p = df_clean.loc[df_clean["Phase"] == phase] |
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df_p = cleaner.phase_clean(df_p,phase) |
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df_p = data_utils.add_survey_code(df_p, old_code) |
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df_list.append(df_p) |
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p_df = reduce(lambda x, y: pd.merge(x, y, how= "outer", on = 'AppCode'), df_list) |
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if self.config_user_dict["local"]["test"]==False: |
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p_df.to_csv(self.user_event_file, index = False) |
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print(f"\t Done Cleaning {datetime.now()}") |
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return p_df, df_clean |
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else: |
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return df_clean |
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