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SNPCC_DAT_to_pd.py
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SNPCC_DAT_to_pd.py
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import re
import os
import glob
import argparse
import numpy as np
import pandas as pd
import multiprocessing
from io import StringIO
from functools import partial
from concurrent.futures import ProcessPoolExecutor
def read_dat(fname):
"""Load SNPCC formatted data and cast it to a PANDAS dataframe
Args:
fname (str): path + name to .DAT file
Returns:
(pandas.DataFrame) dataframe with data and metadata
"""
# Read photometry
#
# Identify header rows
with open(fname, 'r') as fin:
idx = next(i for i, j in enumerate(fin) if j.startswith('VARLIST'))
# read DataFrame without header
df = pd.read_csv(fname, skiprows=idx, delimiter=" ",
index_col=False, skipinitialspace=True, skipfooter=1, engine='python')
# eliminate rows that are not observations
df = df[df['VARLIST:'] == 'OBS:']
cols_to_keep = [k for k in df.keys() if k not in ['FIELD', 'VARLIST:']]
df = df[cols_to_keep]
# Read metadata and save info
# TODO: save also errors
with open(fname, 'r') as fhin:
for line in fhin:
words = line.strip().split(':')
if len(words) > 1 and len(words[1]) > 1:
# formatting
val = re.findall(r'\S+', words[1])[0]
df[words[0].strip(" ")] = np.ones(len(df))*float(val) if re.match(
r'^-?\d+(?:\.\d+)?$', val) is not None else [str(val) for i in range(len(df))]
if 'NOBS:' in line:
break
# some reformatting
df['SNID'] = df['SNID'].astype(int)
keys_ibc = [1, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16,
18, 22, 23, 29, 45, 28]
keys_ii = [2, 3, 4, 12, 15, 17, 19, 20, 21, 24, 25, 26, 27,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]
df['TYPE'] = df['SNTYPE'].apply(
lambda x: 'Ia' if x == 0 else (
'Ibc' if x in keys_ibc else ('II' if x in keys_ii else 'unknown'))
)
return df
def only_metadata(fname):
"""Load SNPCC formatted data and cast its metadata to a PANDAS dataframe
Args:
fname (str): path + name to .DAT file
Returns:
(pandas.DataFrame) dataframe with metadata only
"""
dic = {}
# Read metadata and save info
# TODO: save also errors
with open(fname, 'r') as fhin:
for line in fhin:
words = line.strip().split(':')
if len(words) > 1 and len(words[1]) > 1:
# formatting
val = re.findall(r'\S+', words[1])[0]
dic[words[0].strip(" ")] = [float(val) if re.match(
r'^-?\d+(?:\.\d+)?$', val) is not None else str(val)]
if 'NOBS:' in line:
break
# some reformatting
keys_ibc = [1, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16,
18, 22, 23, 29, 45, 28]
keys_ii = [2, 3, 4, 12, 15, 17, 19, 20, 21, 24, 25, 26, 27,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]
dic['SNID'] = [int(i) for i in dic['SNID']]
dic['TYPE'] = 'Ia' if dic['SNTYPE'] == 0 else (
'Ibc' if dic['SNTYPE'] in keys_ibc else ('II' if dic['SNTYPE'] in keys_ii else 'unknown'))
df = pd.DataFrame.from_dict(dic)
return df
if __name__ == '__main__':
'''Read SNPCC data format DAT and convert to csv with all light-curves
Not optimized for very large number of light-curves == number of DAT files
'''
parser = argparse.ArgumentParser(
description='Selection function data vs simulations')
parser.add_argument('--path_data', type=str,
default='./SIMGEN_PUBLIC_DES/DES_*DAT',
help="Path to data files in .DAT SPCC format")
parser.add_argument('--path_dump', type=str,
default='./dump/',
help="Path to dump a csv database")
parser.add_argument('--test', action="store_true",
help="Only load one file to test it works")
parser.add_argument('--only_metadata', action="store_true",
help="Only load one file to test it works")
# Init
args = parser.parse_args()
path_data = args.path_data
path_dump = args.path_dump
os.makedirs(path_dump, exist_ok=True)
# Init parallization
max_workers = multiprocessing.cpu_count()
# Get all files
list_files = glob.glob(path_data)
print(f'Files to process {len(list_files)}')
if args.test:
list_files = list_files[:10]
print(f'Files to process shortened to {len(list_files)}')
if not args.only_metadata:
list_df = []
if not args.test:
# Process the whole data
process_fn = partial(read_dat)
with ProcessPoolExecutor(max_workers=max_workers) as executor:
list_df = executor.map(process_fn, list_files)
else:
for i in range(len(list_files)):
list_df.append(read_dat(list_files[i]))
df = pd.concat(list_df, sort=False)
df.to_csv(f'{path_dump}/database.csv')
else:
# Fetch only metadata
list_df = []
if not args.test:
process_fn = partial(only_metadata)
with ProcessPoolExecutor(max_workers=max_workers) as executor:
list_df = executor.map(process_fn, list_files)
else:
for i in range(len(list_files)):
list_df.append(only_metadata(list_files[i]))
df = pd.concat(list_df, sort=False)
df.to_csv(f'{path_dump}/metadata.csv')