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CalcInstrMag.py
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CalcInstrMag.py
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#!/usr/bin/env python
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx #
# xxxxxxxxxxxxx-----------Calculate Instrumental Magnitudes From TXDumped Photometry Files--------------xxxxxxxxxxxxx #
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx #
# ------------------------------------------------------------------------------------------------------------------- #
# Import Required Libraries
# ------------------------------------------------------------------------------------------------------------------- #
import os
import re
import glob
import numpy as np
import pandas as pd
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Global Variables To Be Used In The Code
# ------------------------------------------------------------------------------------------------------------------- #
precision = 3
filters = ['U', 'B', 'V', 'R', 'I']
list_magcol = ['ID', 'IMAGE', 'IFILTER', 'XCENTER', 'YCENTER', 'SKY_COUNTS', 'AIRMASS',
'APER_1', 'APER_2', 'MAG_1', 'MAG_2', 'ERR_1', 'ERR_2']
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Functions For Handling Files & Lists
# ------------------------------------------------------------------------------------------------------------------- #
def remove_file(file_name):
"""
Removes the file 'file_name' in the constituent directory.
Args:
file_name : Name of the file to be removed from the current directory
Returns:
None
"""
try:
os.remove(file_name)
except OSError:
pass
def remove_similar_files(common_text):
"""
Removes similar files based on the string 'common_text'.
Args:
common_text : String containing partial name of the files to be deleted
Returns:
None
"""
for residual_file in glob.glob(common_text):
remove_file(residual_file)
def group_similar_files(text_list, common_text, exceptions=''):
"""
Groups similar files based on the string 'common_text'. Writes the similar files
onto the list 'text_list' (only if this string is not empty) and appends the similar
files to a list 'python_list'.
Args:
text_list : Name of the output text file with names grouped based on the 'common_text'
common_text : String containing partial name of the files to be grouped
exceptions : String containing the partial name of the files that need to be excluded
Returns:
list_files : Python list containing the names of the grouped files
"""
list_files = glob.glob(common_text)
if exceptions != '':
list_exception = exceptions.split(',')
for file_name in glob.glob(common_text):
for text in list_exception:
test = re.search(text, file_name)
if test:
try:
list_files.remove(file_name)
except ValueError:
pass
list_files.sort()
if len(text_list) != 0:
with open(text_list, 'w') as f:
for file_name in list_files:
f.write(file_name + '\n')
return list_files
def list_statistics(list_values):
"""
Returns the statistics of the list of elements in the input 'list_values'.
Args:
list_values : Input list of elements
Returns:
value_mean : Mean of the list of elements
value_median: Median of the list of elements
value_std : Standard Deviation of the list of elements
"""
value_mean = np.mean(list_values)
value_median = np.median(list_values)
value_std = np.std(list_values)
return value_mean, value_median, value_std
def reject(list_values, iterations=2):
"""
Rejects outliers from the input 'list_values'.
Args:
list_values : Input list of elements
iterations : No. of iterations of rejection to be run on the input list
Returns:
list_reject : Output list after rejecting outliers from the input 'list_values'
"""
list_reject = filter(lambda x: x != np.nan, list_values)
list_reject = map(float, list_reject)
list_reject.sort()
for _ in range(0, iterations):
if len(list_values) > 2:
value_mean, value_median, value_std = list_statistics(list_reject)
if abs(list_reject[0] - value_median) < abs(list_reject[-1] - value_median):
remove_index = -1
else:
remove_index = 0
if abs(list_reject[remove_index] - value_median) > value_std:
list_reject.pop(remove_index)
return list_reject
def display_text(text_to_display):
"""
Displays text mentioned in the string 'text_to_display'
Args:
text_to_display : Text to be displayed
Returns:
None
"""
print ("\n" + "# " + "-" * (12 + len(text_to_display)) + " #")
print ("# " + "-" * 5 + " " + str(text_to_display) + " " + "-" * 5 + " #")
print ("# " + "-" * (12 + len(text_to_display)) + " #" + "\n")
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Function To Obtain Instrumental Magnitudes
# ------------------------------------------------------------------------------------------------------------------- #
def add_series(list_series, sub=False, err=False):
"""
Adds multiple Pandas Series Column wise and obtains
Args:
list_series : List of all Pandas Series to be added to obtain a single Pandas Series
sub : True, if the series needs to be subtracted
err : True, if the series contains error data
Returns:
output_series : Output Pandas Series obtained after adding all the series
"""
output_series = list_series[0]
list_indices = output_series.index.values
if err:
sub = False
for series in list_series[1:]:
if not err:
if not sub:
append_data = [val_1 + val_2 if val_1 != 'INDEF' and val_2 != 'INDEF' else 'INDEF'
for val_1, val_2 in zip(output_series, series)]
else:
append_data = [val_1 - val_2 if val_1 != 'INDEF' and val_2 != 'INDEF' else 'INDEF'
for val_1, val_2 in zip(output_series, series)]
else:
append_data = [round((val_1 ** 2 + val_2 ** 2) ** 0.5,
int(precision)) if val_1 != 'INDEF' and val_2 != 'INDEF' else 'INDEF'
for val_1, val_2 in zip(output_series, series)]
output_series = pd.Series(data=append_data, index=list_indices)
return output_series
def append_missing_data(input_df):
"""
Appends missing data for a filter as a column of 'INDEF' to the DataFrame.
Args:
input_df : Pandas DataFrame containing star magnitudes
Returns:
output_df : Pandas DataFrame containing appended columns for missing data
"""
star_id = set(input_df.index.values)
for band in filters:
if band not in set(input_df['FILTER'].values):
data_ext = [[band] + ['INDEF'] * (len(input_df.columns.values) - 1) for _ in range(0, len(star_id))]
input_df = pd.concat([pd.DataFrame(data_ext, columns=input_df.columns.values, index=star_id), input_df])
output_df = input_df.sort_values(by='FILTER').sort_index(kind='mergesort')
output_df = output_df.replace('INDEF', np.nan, regex=True)
return output_df
def unorgmag_to_ubvriframe(input_df):
"""
Creates a Pandas DataFrame with broadband magnitudes from an input DataFrame with unorganised magnitudes.
Args:
input_df : Pandas DataFrame containing magnitudes and color terms
Returns:
output_df : Pandas DataFrame containing broadband magnitudes
"""
dict_stars = {}
for index, row in input_df.iterrows():
if index not in dict_stars.keys():
dict_stars[index] = {}
dict_stars[index][row[0]] = row[1]
output_df = pd.DataFrame(data=dict_stars).T[filters]
output_df = output_df.apply(pd.to_numeric, errors='coerce').round(decimals=int(precision))
output_df = output_df.replace(np.nan, 'INDEF', regex=True)
return output_df
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Function To Obtain Instrumental Magnitudes From MAG Files
# ------------------------------------------------------------------------------------------------------------------- #
def calculate_instrmag(file_mag):
"""
Calculates instrumental magnitudes from Tx'dumped mag files obtained after photometry.
Arg
file_mag : File containing Tx'dumped magnitudes obtained from MAG files generated by IRAF
Returns:
mag_df : Pandas DataFrame containing broadband magnitudes
err_df : Pandas DataFrame containing errors in magnitudes
"""
file_df = pd.read_csv(filepath_or_buffer=file_mag, sep="\s+", names=list_magcol, index_col=0, engine='python')
file_df = file_df.replace('INDEF', np.nan)
file_df[['MAG_1', 'MAG_2']] = file_df[['MAG_1', 'MAG_2']].astype('float64')
indexes = file_df.index.values
star_count = len(set(indexes))
file_df['FILTER'] = file_df['IFILTER'].apply(lambda x: str(x)[-1])
file_df['APCOR'] = file_df['MAG_1'] - file_df['MAG_2']
data_grouped = file_df[['APCOR', 'FILTER']].groupby(['FILTER'])
mean = {}
stdev = {}
for band in set(file_df['FILTER'].values):
temp_list = reject(data_grouped.get_group(name=band)['APCOR'].tolist(), iterations=int(star_count / 3) + 1)
mean[band] = np.mean(temp_list)
stdev[band] = np.std(temp_list)
file_df['COR_MEAN'] = file_df['FILTER'].apply(lambda x: mean[x])
file_df['COR_STD'] = file_df['FILTER'].apply(lambda x: stdev[x])
file_df['INSTR_MAG'] = file_df['MAG_1'] - file_df['COR_MEAN']
file_df['INSTR_ERR'] = add_series([file_df['COR_STD'], file_df['ERR_1']], err=True)
file_df = file_df.round(int(precision))
file_df = file_df[['FILTER', 'INSTR_MAG', 'INSTR_ERR']]
file_df = append_missing_data(file_df)
mag_df = unorgmag_to_ubvriframe(file_df[['FILTER', 'INSTR_MAG']])
err_df = unorgmag_to_ubvriframe(file_df[['FILTER', 'INSTR_ERR']])
date = file_mag.split("_")[1]
mag_df.to_csv("OUTPUT_instrmag_" + date, sep=" ", index=True)
err_df.to_csv("OUTPUT_instrerr_" + date, sep=" ", index=True)
net_df = pd.DataFrame()
for column in mag_df:
net_df[column] = mag_df[column].apply(lambda x: str(x) + "+/-") + err_df[column].apply(lambda x: str(x))
net_df.to_csv("OUTPUT_instr_" + date, sep=" ", index=True)
return mag_df, err_df
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Calculate Instrumental Magnitudes For Each Day From The Mag Files
# ------------------------------------------------------------------------------------------------------------------- #
list_mag = group_similar_files("", "*_mag4")
for file_name in list_mag:
calculate_instrmag(file_name)
display_text("Instrumental Magnitudes Have Been Computed From MAG Files")
# ------------------------------------------------------------------------------------------------------------------- #