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import os
import json
import gzip
import pandas as pd
from tqdm import tqdm
from multiprocessing import Pool
def process_file(file_info):
input_path, output_path, clusters_file = file_info
# Read line numbers to avoid from clusters file
if(clusters_file != "<NOT_EXISTS>"):
try:
clusters_df = pd.read_parquet(clusters_file)
except FileNotFoundError:
with open("temp.txt", "a+") as f:
f.write(clusters_file + '\n')
clusters_df = pd.DataFrame()
clusters_df['id'] = []
clusters_df['id_int'] = []
clusters_df['clusters_id'] = []
else:
with open("temp.txt", "a+") as f:
f.write(clusters_file + '\n')
clusters_df = pd.DataFrame()
clusters_df['id'] = []
clusters_df['id_int'] = []
clusters_df['clusters_id'] = []
clusters_df['line_number'] = clusters_df['id'].apply(lambda x: int(x.split('/')[-1]))
duplicate_docs = set()
for _, row in clusters_df.iterrows():
if(row['id_int'] != row['cluster_id']):
duplicate_docs.add(int(row['id'].split('/')[-1]))
# Read JSON objects and filter them
with gzip.open(input_path, 'rt') as input_file:
try:
filtered_documents = [json.loads(line) for idx, line in enumerate(input_file) if idx not in duplicate_docs]
except:
print(input_file)
return
# Write filtered JSON objects to new file
with gzip.open(output_path, 'wt') as output_file:
for doc in filtered_documents:
output_file.write(json.dumps(doc, ensure_ascii=False) + '\n')
def filter_json_files(input_folder, output_folder, clusters_folder, snap):
# Create output folder if it doesn't exist
if not os.path.exists(output_folder):
os.makedirs(output_folder)
file_infos = []
clusters_file = "<NOT_EXISTS>"
# Iterate through each JSON file in the input folder
for filename in sorted(os.listdir(input_folder)):
if filename.endswith(".json.gz"):
input_path = os.path.join(input_folder, filename)
output_path = os.path.join(output_folder, filename)
# Determine the corresponding clusters file
for i in range(1, 11):
clusters_file_path = os.path.join(clusters_folder, f"total_{i}" , filename.split('_')[0], f"{snap}_{filename.split('_')[-1].split('.')[0]}.clusters.parquet")
if(os.path.exists(clusters_file_path)):
clusters_file = clusters_file_path
break
# print((input_path, output_path, clusters_file))
file_infos.append((input_path, output_path, clusters_file))
# Initialize tqdm with the total number of files
with tqdm(total=len(file_infos)) as pbar:
# Create a pool of workers
with Pool(processes=160) as pool:
# Use tqdm as a context manager to automatically close the pool and update the progress bar
for _ in pool.imap_unordered(process_file, file_infos):
pbar.update()
print("Filtering done for ", input_folder.split('/')[-1])
snapshots = ["2018-17", "2018-22", "2018-26", "2018-30", "2018-34", "2018-39", "2018-43", "2018-47", "2018-51", "2019-04", "2019-09", "2019-13", "2019-18", "2019-22", "2019-26", "2019-30", "2019-35", "2019-39", "2019-43", "2019-47", "2019-51", "2020-05", "2020-10", "2020-16", "2020-24", "2020-29", "2020-34", "2020-40", "2020-45", "2020-50", "2021-04", "2021-10", "2021-17", "2021-21", "2021-25", "2021-31", "2021-39", "2021-43", "2021-49", "2022-05", "2022-21", "2022-27", "2022-33", "2022-40", "2022-49", "2023-06", "2023-14", "2023-23", "2023-40", "2023-50", "2024-10"]
# snapshots = ["2023-06", "2023-14", "2023-23", "2023-40", "2023-50", "2024-10"]
for snap in snapshots:
input_folder = f"/mnt/weka/peacock/wet-data/output/local_filtered_without_bloom/{snap}"
output_folder = f"/mnt/weka/peacock/wet-data/output/global_filtered_without_bloom_new/{snap}"
clusters_folder = f"/mnt/weka/peacock/wet-data/output/fuzzy-clusters"
filter_json_files(input_folder, output_folder, clusters_folder, snap)
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