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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')
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