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