Datasets:

chuxuan
/

REPRO-Bench

import os
import sys
import shutil
import zipfile
import multiprocessing
import pandas as pd
from datetime import datetime,timedelta

from lib.data_helpers import data_utils
from lib.experiment_specs import study_config
from lib.data_helpers import test
from lib.data_helpers.builder_utils import BuilderUtils
from lib.data_helpers.gaming import Gaming

from lib.utilities import serialize

class PullEvents():

    port_dir = {"PCDashboard": os.path.join("data","external","dropbox","..","PCPort"),
                "PhoneDashboard":os.path.join("data","external","dropbox","..","PhoneDashboardPort")}

    #identifying_cols are a list of columns in the dataframe that identify a row. none of them can be empty
    def __init__(self,source: str, keyword: str, scratch: bool, test: bool,
                 time_cols: list, raw_timezone: str, appcode_col: str, identifying_cols: list, sort_cols: list,
                 drop_cols: list, cat_cols: list,
                 compress_type: str, processing_func = BuilderUtils.default_puller_process, file_reader = None):
        """
        Purpose
        -------
        This class appends the raw data files housed in self.zipped_directory, cleans them a bit with the self.processing func,
        and saves the merged data file in self.raw_file. Unless self.scratch == True, the puller will only process new raw data files
        and appends them to the dataframe saved in self.raw_file. The puller documents which files have been processed in the config located in self.config_file_path
        WARNING: if self.scratch==TRUE, it may take hours or days to reprocess all the data. Call Michael to discuss if this needs to happen.
        Parameters
        ----------
        source - source the app data source (either PC or PD)
        keyword - the kind of data coming in (e.g. Snooze, Use etc)
        scratch - whether or not to start data processing from scratch
        test - whether or not to test the pulling pipeline (it will reprocess that latest three zipfiles) (I wouldn't play with this)
        time_cols - list of columns that contain datetimes
        raw_timezone - the timezone of the raw data
        appcode_col - the raw column name containing the appcodes
        identifying_cols - list of columns that should identify a unique row
        sort_cols - list of columns that describes how the df should be ordered. The df will be sorted from smallest to largest value of the sort_cols
        drop_cols - list of columns that appear in the raw zipfile or after zipfile processing that should be dropped
        cat_cols - list of columns that can be converted into the categorical data type
        compress_type - how the zipfiles are compressed (either "folder", "csv", or "txt")
        processing_func - the function used to process the raw data (e.g. clean_master dates)
        file_reader - function that reads in the raw file into memory. only required for file stored in zip folders.
        """

        self.source = source
        self.keyword = keyword
        self.scratch = scratch
        self.pull_test = test
        self.time_cols = time_cols
        self.raw_timezone = raw_timezone
        self.appcode_col = appcode_col
        self.identifying_cols = identifying_cols
        self.sort_cols = sort_cols
        self.drop_cols = drop_cols
        self.cat_cols = cat_cols
        self.compress_type = compress_type
        self.processing_func = processing_func
        self.file_reader = file_reader


        self.zipped_directory = os.path.join("data","external", "input", source, f"Raw{self.keyword}Zipped")
        self.buggy_dir = os.path.join("data","external", "input", source, "BuggyFiles")
        self.config_file_path = os.path.join("data","external", "input", source, f"{self.source}{self.keyword}Config.csv")
        self.raw_file = os.path.join("data","external", "input", source, f"Raw{self.keyword}")
        self.test_file = os.path.join("data","external", "input_test", source, f"Raw{self.keyword}")

        self.config_user_dict = serialize.open_yaml("config_user.yaml")

        self.builder_utils = BuilderUtils()
        self.added_config = self.builder_utils.get_config(self.config_file_path) #dictionary of zipfiles in the raw_file
        self.new_config = {}


    def update_data(self):
        print(f"\n Updating {self.keyword} data! {datetime.now()}")
        if self.config_user_dict["local"]["test"]==True:
            df = serialize.soft_df_open(self.test_file)
            df = self._memory_redux(df)
            print(df.memory_usage(deep=True) / 1024 ** 2)
            return df

        else:
            self.builder_utils.transport_new_zip_files(PullEvents.port_dir[self.source], self.keyword, self.zipped_directory)
            new_zip_files = [x for x in os.listdir(self.zipped_directory) if (x.replace(".zip", "") not in self.added_config) & (".zip" in x)]
            print(f"new zip files {len(new_zip_files)}")


            # if no new data or scratch run, just load old data
            if ((self.scratch == False) &
                (len(new_zip_files) == 0) &
                (self.pull_test == False)):
                print("\t No New Data! Loading pickled raw data into memory")

                if(os.path.getsize(self.raw_file+".pickle") > 0) :
                    df = serialize.soft_df_open(self.raw_file)
                else:
                    print("Raw file is empty. Reading Backup Compression Zip")
                    df = serialize.read_gz(self.raw_file+".gz")
                df = self._memory_redux(df)

                # Archiving Recruitment Data IF Use Data
                if (self.keyword == "Alternative") or (self.keyword == "Use"):
                    #self._archive_recruitment(df)
                    df = df.loc[df["CreatedDatetimeHour"] >= study_config.surveys["Baseline"]["Start"]-timedelta(1)]

                return df

            else:
                print(f"Length of new zip files {len(new_zip_files)}")
                df = self.aggregate_data(new_zip_files)
                self.builder_utils.update_config(self.added_config,
                                                 self.new_config,
                                                 self.config_file_path)
                return df

    def aggregate_data(self,new_zip_files):
        old_df, new_zip_files = self._configure_data(new_zip_files)

        print(f"\t Going to add {len(new_zip_files)} files:")
        if self.config_user_dict['local']["parallel"] == False:
            df_dic = self._process_zips(new_zip_files)

        #if the number of files > # of cores, then multiprocess
        elif multiprocessing.cpu_count() < len(new_zip_files):
            df_dic = self._process_zips_mp(new_zip_files)

        else:
            print(f"not multiprocessing b/c number of cores {multiprocessing.cpu_count()} < {len(new_zip_files)}")
            df_dic = self._process_zips(new_zip_files)

        #get appcodes to keep
        print("\t get data survey")
        cl_file = os.path.join(os.path.join("data","external","dropbox_confidential","ContactLists","Used"),
                               study_config.kept_appcode_cl)
        a_df = pd.read_csv(cl_file)
        a_df = data_utils.add_A_to_appcode(a_df,"AppCode")
        appcodes = list(a_df["AppCode"])
        print(len(appcodes))
        assert len(appcodes) == study_config.number_of_kept_appcodes

        # combine new with old and keep relevant appcodes
        if len(df_dic)>0:
            print("\t adding new data to master")
            new_df = pd.concat(list(df_dic.values()), sort=True)
            new_df = new_df.loc[new_df["AppCode"].isin(appcodes)]
            parent_df = pd.concat([old_df,new_df], sort=True)
        else:
            print("\t no new data!")
            parent_df = old_df

        # minimal cleaning and save to disk: sort from smallest to largest values of sort_cols
        print(f"\t Len before duplicate drop {len(parent_df)}")
        parent_df = parent_df.sort_values(by = self.sort_cols)
        parent_df = parent_df.drop_duplicates(subset = self.identifying_cols, keep = 'last')
        print(f"\t Len after duplicate drop {len(parent_df)}")

        for col in parent_df.columns:
            if "Unnamed" in col:
                parent_df = parent_df.drop(columns= [col])
        parent_df = parent_df.reset_index(drop=True)
        print(parent_df.memory_usage(deep=True) / 1024 ** 2)

        try:
            os.rename(self.raw_file+".pickle",self.raw_file+"PrevRun.pickle")
        except:
            print('could not find old pickle file')

        try:
            serialize.save_pickle(parent_df, self.raw_file)
        except:
            print("Failed to save pickle!")

        try:
            test.save_test_df(parent_df, self.test_file)
        except:
            print("Failed to save testfile")

        # DONT PUT IN TRY BECAUSE IF BACKUP FAILS, WE WANT TO RE PROCESS THE NEW FILES
        try:
            os.rename(self.raw_file + ".gz", self.raw_file + "PrevRun.gz")
        except:
            print("no old gz file")
        parent_df.to_csv(self.raw_file + '.gz', index=False, compression='gzip')

        print(f"\t Done Saving {datetime.now()}")

        # add new guys to config in memory, now that everything has saved
        for zip_file, df in df_dic.items():
            try:
                latest_hour = str(df[self.time_cols[0]].max())
                earliest_hour = str(df[self.time_cols[0]].min())
            except:
                latest_hour = "NAN"
                earliest_hour = "NAN"
            self.new_config[zip_file.replace(".zip", "")] = {"EarliestHour": earliest_hour,
                                                             "LatestHour": latest_hour,
                                                             "ZipFile": zip_file}
        return parent_df

    """ modifes the reaggregation in the following way:
     - if self.scatch is True: all zipfiles in data dire are new, and old_df is empty
     - if self.test is True: reprocess the last three data files, and old_df is reloaded
     - else: process the new zip files with old df"""
    def _configure_data(self,new_zip_files):
        if self.scratch:
            all_zip_files = [x for x in os.listdir(self.zipped_directory) if ".zip" in x]
            new_zip_files = sorted(all_zip_files)

            #continue_ans = input("Warning: Do you want to reprocess all use files? Enter (Y or N) \n ")
            continue_ans = "Y"
            if continue_ans == "N":
                sys.exit()
            print(f"DELETING ADDED EVENT CONFIG, CAUSING REAGGREGATION OF ALL {self.keyword} ZIPFILES")
            self.added_config = {}
            if self.keyword == "Use":
                print("\t Also Deleting all Granular Gaming Files")
                for gran_folder in [os.path.join(Gaming.gaming_dir, "FirstLast"),
                                    os.path.join(Gaming.gaming_dir, "Granular")]:
                    shutil.rmtree(gran_folder)
                    os.mkdir(gran_folder)
            old_df = pd.DataFrame()

        else:
            if self.pull_test:
                try:
                    assert len(new_zip_files) == 0
                except:
                    print("There are actual new zip files to process! First process these files, then test")
                    sys.exit()

                print("TEST: Reprocessing 3 files from zipped directory")
                test_files = os.listdir(self.zipped_directory)[-3:]
                for zip_file in test_files:
                    del self.added_config[zip_file.replace(".zip", "")]
                new_zip_files = test_files

            if not os.path.exists((self.raw_file+".pickle")):
                old_df = pd.DataFrame()

            elif os.path.getsize(self.raw_file + ".pickle") > 0:
                old_df = serialize.soft_df_open(self.raw_file)
            else:
                print("Raw file is empty. Reading Backup Compression Zip")
                old_df = serialize.read_gz(self.raw_file + ".gz")

            if len(old_df)>1:
                old_df = self._memory_redux(old_df)

            # Archiving Recruitment Data IF Use Data
            if (self.keyword == "Alternative") or (self.keyword == "Use"):
                #self._archive_recruitment(old_df)
                old_df = old_df.loc[old_df["CreatedDatetimeHour"] >= study_config.surveys["Baseline"]["Start"]-timedelta(1)]

        return old_df, new_zip_files

    def _process_zips_mp(self, new_zip_files: list):

        # create the pool object for multiprocessing
        pool = multiprocessing.Pool(processes=study_config.cores)

        # split the files to add into n lists where n = cores
        chunks = [new_zip_files[i::study_config.cores] for i in range(study_config.cores)]

        print(f"Multiprocessing with {study_config.cores} cpus")
        df_list_of_dics = pool.map(func = self._process_zips, iterable = chunks)
        pool.close()
        print("Done Multiprocessing")

        # flatten the list of dictionaries
        df_dic = {}
        for d in df_list_of_dics:
            for k, v in d.items():
                df_dic[k] = v
        return df_dic

    def _process_zips(self,new_zip_files):
        df_dic = {}
        i = 1
        for zip_file in new_zip_files:
            print(f"processing {zip_file} ({round((i * 100) / len(new_zip_files))}%)", end = "\r")
            df = self._process_zip(zip_file)
            if len(df) == 0:
                continue
            df_dic[zip_file] = df
            i = i + 1
        return df_dic

    def _process_zip(self, zip_file):
        if ".zip" not in zip_file:
            return pd.DataFrame()

        # open zipfile
        if self.compress_type != "folder":
            df, problem = self._open_zipfile(zip_file)

        # open json zip folder
        else:
            df = self._open_zipfolder(zip_file)
            problem = ""

        if len(df) == 0:
            if problem != "Corrupt":
                problem = "Empty"
                print(f"{zip_file} is empty ...archiving in {self.buggy_dir}/{problem}")

            old_file = os.path.join(self.zipped_directory, zip_file)
            new_file = os.path.join(self.buggy_dir, problem, zip_file)
            os.rename(old_file, new_file)
            return df

        df.columns = df.columns.str.replace(' ', '')
        df = df.rename(columns={self.appcode_col: "AppCode"})
        df["AppCode"] = df["AppCode"].astype(str).apply(lambda x: "A" + x if x != "nan" and x.isnumeric() else x)
        df = self.processing_func(df, zip_file, self)

        df["Zipfile"] = zip_file
        df = self._memory_redux(df)

        return df

    def _open_zipfile(self,zip_file):
        if self.compress_type == "txt":
            separator = '\t'
        elif self.compress_type == "csv":
            separator = ','
        else:
            separator = ""
            print("Illegal seperator")
            sys.exit()
        try:
            df = pd.read_csv(os.path.join(self.zipped_directory, zip_file), compression='zip', header=0,
                             sep=separator, quotechar='"')
            problem = "None"
        except:
            print(f"{zip_file} is corrupt. Investigate in archive")
            df = pd.DataFrame()
            problem = "Corrupt"
        return df, problem

    def _open_zipfolder(self,zip_folder):
        #print(zip_folder)
        #print(self.zipped_directory)
        zip_ref = zipfile.ZipFile(os.path.join(self.zipped_directory, zip_folder))
        temp_folder = os.path.join(self.zipped_directory, zip_folder.replace(".zip", "FOLDER"))
        zip_ref.extractall(path=temp_folder)
        df_list = []
        for file in os.listdir(temp_folder):
            file_path = os.path.join(temp_folder, file)
            clean_data = self.file_reader(file_path)
            df_list.append(clean_data)
        shutil.rmtree(temp_folder)
        df = pd.concat(df_list)
        return df

    def _open_zipfolders_install(self,zip_folders):
        """for recovering install data"""
        for zip_folder in zip_folders:
            df = self._open_zipfolder(zip_folder)

    def _memory_redux(self,df):
        # drop columns that were initially in raw data or processing byproduct
        for drop_col in self.drop_cols + ["Server","AsOf"]:
            if drop_col in df.columns.values:
                df = df.drop(columns = [drop_col])

        # make columns categorical
        for cat_col in self.cat_cols + ["Zipfile"]:
            if cat_col in df.columns.values:
                try:
                    df[cat_col] = df[cat_col].astype('category')
                except:
                    print(f"\t categorizing {cat_col} failed!")

        return df

    def _archive_recruitment(self,df):
        if os.path.exists(self.raw_file + "RecruitmentPhase.pickle"):
            pass
        else:
            r_df_raw = df.loc[df["CreatedDatetimeHour"] < study_config.surveys["Baseline"]["Start"]-timedelta(1)]
            try:
                serialize.save_pickle(r_df_raw, self.raw_file + "RecruitmentPhase.pickle")
            except:
                r_df_raw.to_csv(self.raw_file + 'RecruitmentPhase.gz', index=False, compression='gzip')

	import os
	import sys
	import shutil
	import zipfile
	import multiprocessing
	import pandas as pd
	from datetime import datetime,timedelta

	from lib.data_helpers import data_utils
	from lib.experiment_specs import study_config
	from lib.data_helpers import test
	from lib.data_helpers.builder_utils import BuilderUtils
	from lib.data_helpers.gaming import Gaming

	from lib.utilities import serialize

	class PullEvents():

	port_dir = {"PCDashboard": os.path.join("data","external","dropbox","..","PCPort"),
	"PhoneDashboard":os.path.join("data","external","dropbox","..","PhoneDashboardPort")}

	#identifying_cols are a list of columns in the dataframe that identify a row. none of them can be empty
	def __init__(self,source: str, keyword: str, scratch: bool, test: bool,
	time_cols: list, raw_timezone: str, appcode_col: str, identifying_cols: list, sort_cols: list,
	drop_cols: list, cat_cols: list,
	compress_type: str, processing_func = BuilderUtils.default_puller_process, file_reader = None):
	"""

	Purpose
	-------

	This class appends the raw data files housed in self.zipped_directory, cleans them a bit with the self.processing func,
	and saves the merged data file in self.raw_file. Unless self.scratch == True, the puller will only process new raw data files
	and appends them to the dataframe saved in self.raw_file. The puller documents which files have been processed in the config located in self.config_file_path

	WARNING: if self.scratch==TRUE, it may take hours or days to reprocess all the data. Call Michael to discuss if this needs to happen.

	Parameters
	----------
	source - source the app data source (either PC or PD)
	keyword - the kind of data coming in (e.g. Snooze, Use etc)
	scratch - whether or not to start data processing from scratch
	test - whether or not to test the pulling pipeline (it will reprocess that latest three zipfiles) (I wouldn't play with this)
	time_cols - list of columns that contain datetimes
	raw_timezone - the timezone of the raw data
	appcode_col - the raw column name containing the appcodes
	identifying_cols - list of columns that should identify a unique row
	sort_cols - list of columns that describes how the df should be ordered. The df will be sorted from smallest to largest value of the sort_cols
	drop_cols - list of columns that appear in the raw zipfile or after zipfile processing that should be dropped
	cat_cols - list of columns that can be converted into the categorical data type
	compress_type - how the zipfiles are compressed (either "folder", "csv", or "txt")
	processing_func - the function used to process the raw data (e.g. clean_master dates)
	file_reader - function that reads in the raw file into memory. only required for file stored in zip folders.
	"""

	self.source = source
	self.keyword = keyword
	self.scratch = scratch
	self.pull_test = test
	self.time_cols = time_cols
	self.raw_timezone = raw_timezone
	self.appcode_col = appcode_col
	self.identifying_cols = identifying_cols
	self.sort_cols = sort_cols
	self.drop_cols = drop_cols
	self.cat_cols = cat_cols
	self.compress_type = compress_type
	self.processing_func = processing_func
	self.file_reader = file_reader


	self.zipped_directory = os.path.join("data","external", "input", source, f"Raw{self.keyword}Zipped")
	self.buggy_dir = os.path.join("data","external", "input", source, "BuggyFiles")
	self.config_file_path = os.path.join("data","external", "input", source, f"{self.source}{self.keyword}Config.csv")
	self.raw_file = os.path.join("data","external", "input", source, f"Raw{self.keyword}")
	self.test_file = os.path.join("data","external", "input_test", source, f"Raw{self.keyword}")

	self.config_user_dict = serialize.open_yaml("config_user.yaml")

	self.builder_utils = BuilderUtils()
	self.added_config = self.builder_utils.get_config(self.config_file_path) #dictionary of zipfiles in the raw_file
	self.new_config = {}


	def update_data(self):
	print(f"\n Updating {self.keyword} data! {datetime.now()}")
	if self.config_user_dict["local"]["test"]==True:
	df = serialize.soft_df_open(self.test_file)
	df = self._memory_redux(df)
	print(df.memory_usage(deep=True) / 1024 ** 2)
	return df

	else:
	self.builder_utils.transport_new_zip_files(PullEvents.port_dir[self.source], self.keyword, self.zipped_directory)
	new_zip_files = [x for x in os.listdir(self.zipped_directory) if (x.replace(".zip", "") not in self.added_config) & (".zip" in x)]
	print(f"new zip files {len(new_zip_files)}")


	# if no new data or scratch run, just load old data
	if ((self.scratch == False) &
	(len(new_zip_files) == 0) &
	(self.pull_test == False)):
	print("\t No New Data! Loading pickled raw data into memory")

	if(os.path.getsize(self.raw_file+".pickle") > 0) :
	df = serialize.soft_df_open(self.raw_file)
	else:
	print("Raw file is empty. Reading Backup Compression Zip")
	df = serialize.read_gz(self.raw_file+".gz")
	df = self._memory_redux(df)

	# Archiving Recruitment Data IF Use Data
	if (self.keyword == "Alternative") or (self.keyword == "Use"):
	#self._archive_recruitment(df)
	df = df.loc[df["CreatedDatetimeHour"] >= study_config.surveys["Baseline"]["Start"]-timedelta(1)]

	return df

	else:
	print(f"Length of new zip files {len(new_zip_files)}")
	df = self.aggregate_data(new_zip_files)
	self.builder_utils.update_config(self.added_config,
	self.new_config,
	self.config_file_path)
	return df

	def aggregate_data(self,new_zip_files):
	old_df, new_zip_files = self._configure_data(new_zip_files)

	print(f"\t Going to add {len(new_zip_files)} files:")
	if self.config_user_dict['local']["parallel"] == False:
	df_dic = self._process_zips(new_zip_files)

	#if the number of files > # of cores, then multiprocess
	elif multiprocessing.cpu_count() < len(new_zip_files):
	df_dic = self._process_zips_mp(new_zip_files)

	else:
	print(f"not multiprocessing b/c number of cores {multiprocessing.cpu_count()} < {len(new_zip_files)}")
	df_dic = self._process_zips(new_zip_files)

	#get appcodes to keep
	print("\t get data survey")
	cl_file = os.path.join(os.path.join("data","external","dropbox_confidential","ContactLists","Used"),
	study_config.kept_appcode_cl)
	a_df = pd.read_csv(cl_file)
	a_df = data_utils.add_A_to_appcode(a_df,"AppCode")
	appcodes = list(a_df["AppCode"])
	print(len(appcodes))
	assert len(appcodes) == study_config.number_of_kept_appcodes

	# combine new with old and keep relevant appcodes
	if len(df_dic)>0:
	print("\t adding new data to master")
	new_df = pd.concat(list(df_dic.values()), sort=True)
	new_df = new_df.loc[new_df["AppCode"].isin(appcodes)]
	parent_df = pd.concat([old_df,new_df], sort=True)
	else:
	print("\t no new data!")
	parent_df = old_df

	# minimal cleaning and save to disk: sort from smallest to largest values of sort_cols
	print(f"\t Len before duplicate drop {len(parent_df)}")
	parent_df = parent_df.sort_values(by = self.sort_cols)
	parent_df = parent_df.drop_duplicates(subset = self.identifying_cols, keep = 'last')
	print(f"\t Len after duplicate drop {len(parent_df)}")

	for col in parent_df.columns:
	if "Unnamed" in col:
	parent_df = parent_df.drop(columns= [col])
	parent_df = parent_df.reset_index(drop=True)
	print(parent_df.memory_usage(deep=True) / 1024 ** 2)

	try:
	os.rename(self.raw_file+".pickle",self.raw_file+"PrevRun.pickle")
	except:
	print('could not find old pickle file')

	try:
	serialize.save_pickle(parent_df, self.raw_file)
	except:
	print("Failed to save pickle!")

	try:
	test.save_test_df(parent_df, self.test_file)
	except:
	print("Failed to save testfile")

	# DONT PUT IN TRY BECAUSE IF BACKUP FAILS, WE WANT TO RE PROCESS THE NEW FILES
	try:
	os.rename(self.raw_file + ".gz", self.raw_file + "PrevRun.gz")
	except:
	print("no old gz file")
	parent_df.to_csv(self.raw_file + '.gz', index=False, compression='gzip')

	print(f"\t Done Saving {datetime.now()}")

	# add new guys to config in memory, now that everything has saved
	for zip_file, df in df_dic.items():
	try:
	latest_hour = str(df[self.time_cols[0]].max())
	earliest_hour = str(df[self.time_cols[0]].min())
	except:
	latest_hour = "NAN"
	earliest_hour = "NAN"
	self.new_config[zip_file.replace(".zip", "")] = {"EarliestHour": earliest_hour,
	"LatestHour": latest_hour,
	"ZipFile": zip_file}
	return parent_df

	""" modifes the reaggregation in the following way:
	- if self.scatch is True: all zipfiles in data dire are new, and old_df is empty
	- if self.test is True: reprocess the last three data files, and old_df is reloaded
	- else: process the new zip files with old df"""
	def _configure_data(self,new_zip_files):
	if self.scratch:
	all_zip_files = [x for x in os.listdir(self.zipped_directory) if ".zip" in x]
	new_zip_files = sorted(all_zip_files)

	#continue_ans = input("Warning: Do you want to reprocess all use files? Enter (Y or N) \n ")
	continue_ans = "Y"
	if continue_ans == "N":
	sys.exit()
	print(f"DELETING ADDED EVENT CONFIG, CAUSING REAGGREGATION OF ALL {self.keyword} ZIPFILES")
	self.added_config = {}
	if self.keyword == "Use":
	print("\t Also Deleting all Granular Gaming Files")
	for gran_folder in [os.path.join(Gaming.gaming_dir, "FirstLast"),
	os.path.join(Gaming.gaming_dir, "Granular")]:
	shutil.rmtree(gran_folder)
	os.mkdir(gran_folder)
	old_df = pd.DataFrame()

	else:
	if self.pull_test:
	try:
	assert len(new_zip_files) == 0
	except:
	print("There are actual new zip files to process! First process these files, then test")
	sys.exit()

	print("TEST: Reprocessing 3 files from zipped directory")
	test_files = os.listdir(self.zipped_directory)[-3:]
	for zip_file in test_files:
	del self.added_config[zip_file.replace(".zip", "")]
	new_zip_files = test_files

	if not os.path.exists((self.raw_file+".pickle")):
	old_df = pd.DataFrame()

	elif os.path.getsize(self.raw_file + ".pickle") > 0:
	old_df = serialize.soft_df_open(self.raw_file)
	else:
	print("Raw file is empty. Reading Backup Compression Zip")
	old_df = serialize.read_gz(self.raw_file + ".gz")

	if len(old_df)>1:
	old_df = self._memory_redux(old_df)

	# Archiving Recruitment Data IF Use Data
	if (self.keyword == "Alternative") or (self.keyword == "Use"):
	#self._archive_recruitment(old_df)
	old_df = old_df.loc[old_df["CreatedDatetimeHour"] >= study_config.surveys["Baseline"]["Start"]-timedelta(1)]

	return old_df, new_zip_files

	def _process_zips_mp(self, new_zip_files: list):

	# create the pool object for multiprocessing
	pool = multiprocessing.Pool(processes=study_config.cores)

	# split the files to add into n lists where n = cores
	chunks = [new_zip_files[i::study_config.cores] for i in range(study_config.cores)]

	print(f"Multiprocessing with {study_config.cores} cpus")
	df_list_of_dics = pool.map(func = self._process_zips, iterable = chunks)
	pool.close()
	print("Done Multiprocessing")

	# flatten the list of dictionaries
	df_dic = {}
	for d in df_list_of_dics:
	for k, v in d.items():
	df_dic[k] = v
	return df_dic

	def _process_zips(self,new_zip_files):
	df_dic = {}
	i = 1
	for zip_file in new_zip_files:
	print(f"processing {zip_file} ({round((i * 100) / len(new_zip_files))}%)", end = "\r")
	df = self._process_zip(zip_file)
	if len(df) == 0:
	continue
	df_dic[zip_file] = df
	i = i + 1
	return df_dic

	def _process_zip(self, zip_file):
	if ".zip" not in zip_file:
	return pd.DataFrame()

	# open zipfile
	if self.compress_type != "folder":
	df, problem = self._open_zipfile(zip_file)

	# open json zip folder
	else:
	df = self._open_zipfolder(zip_file)
	problem = ""

	if len(df) == 0:
	if problem != "Corrupt":
	problem = "Empty"
	print(f"{zip_file} is empty ...archiving in {self.buggy_dir}/{problem}")

	old_file = os.path.join(self.zipped_directory, zip_file)
	new_file = os.path.join(self.buggy_dir, problem, zip_file)
	os.rename(old_file, new_file)
	return df

	df.columns = df.columns.str.replace(' ', '')
	df = df.rename(columns={self.appcode_col: "AppCode"})
	df["AppCode"] = df["AppCode"].astype(str).apply(lambda x: "A" + x if x != "nan" and x.isnumeric() else x)
	df = self.processing_func(df, zip_file, self)

	df["Zipfile"] = zip_file
	df = self._memory_redux(df)

	return df

	def _open_zipfile(self,zip_file):
	if self.compress_type == "txt":
	separator = '\t'
	elif self.compress_type == "csv":
	separator = ','
	else:
	separator = ""
	print("Illegal seperator")
	sys.exit()
	try:
	df = pd.read_csv(os.path.join(self.zipped_directory, zip_file), compression='zip', header=0,
	sep=separator, quotechar='"')
	problem = "None"
	except:
	print(f"{zip_file} is corrupt. Investigate in archive")
	df = pd.DataFrame()
	problem = "Corrupt"
	return df, problem

	def _open_zipfolder(self,zip_folder):
	#print(zip_folder)
	#print(self.zipped_directory)
	zip_ref = zipfile.ZipFile(os.path.join(self.zipped_directory, zip_folder))
	temp_folder = os.path.join(self.zipped_directory, zip_folder.replace(".zip", "FOLDER"))
	zip_ref.extractall(path=temp_folder)
	df_list = []
	for file in os.listdir(temp_folder):
	file_path = os.path.join(temp_folder, file)
	clean_data = self.file_reader(file_path)
	df_list.append(clean_data)
	shutil.rmtree(temp_folder)
	df = pd.concat(df_list)
	return df

	def _open_zipfolders_install(self,zip_folders):
	"""for recovering install data"""
	for zip_folder in zip_folders:
	df = self._open_zipfolder(zip_folder)

	def _memory_redux(self,df):
	# drop columns that were initially in raw data or processing byproduct
	for drop_col in self.drop_cols + ["Server","AsOf"]:
	if drop_col in df.columns.values:
	df = df.drop(columns = [drop_col])

	# make columns categorical
	for cat_col in self.cat_cols + ["Zipfile"]:
	if cat_col in df.columns.values:
	try:
	df[cat_col] = df[cat_col].astype('category')
	except:
	print(f"\t categorizing {cat_col} failed!")

	return df

	def _archive_recruitment(self,df):
	if os.path.exists(self.raw_file + "RecruitmentPhase.pickle"):
	pass
	else:
	r_df_raw = df.loc[df["CreatedDatetimeHour"] < study_config.surveys["Baseline"]["Start"]-timedelta(1)]
	try:
	serialize.save_pickle(r_df_raw, self.raw_file + "RecruitmentPhase.pickle")
	except:
	r_df_raw.to_csv(self.raw_file + 'RecruitmentPhase.gz', index=False, compression='gzip')