def group_by_filter(f_ext, object, filters=['I', 'R', 'V', 'B'], target_dir='Sorted', parent_dir=''):
"""
Preconditions: Must have the filter type letter as the last letter of the
filename. Requires the directory location of the csv files and the name of
the object being observed for naming the output file. Can also take the
filter types to look for as a list and the output directory to put the
files in.
Postconditions: Creates one csv file (named for observed object and
filter) for each filter type with the csv files for each image with
columns DataNum (matched by RA/Dec) and SourceFile.
"""
mk_fldr(target_dir, parent_dir)
write_location = os.path.join(parent_dir, target_dir)
# f_ext is where I'm trying to grab the files from
pattern = f_ext + '\*{}.csv'
# send all the files to f_group for each filter
for filter in filters:
big_file = f_group(pattern.format(filter))
# outputs table of located object's info in .csv format
big_file.write(os.path.join(write_location, object+filter+'Filt.csv'))
return write_location
def avg_flux(location, target_dir='Averaged', parent_dir='', identColumn='DataNum', flux_name='flux', srcs=0):
mk_fldr(target_dir, parent_dir)
files = glob.glob(location)
# print (files)
# what will be the names of the columns in the table created when
column_names = [identColumn, 'NumSources', 'AvgRA', 'AvgDec', 'AvgFlux', 'FluxErr', 'InstruMag', 'MaxPeak', 'a_Avg', 'b_Avg', 'thetaAvg']
write_location = os.path.join(parent_dir, target_dir)
print (files)
for file in files:
print (file)
# creates a new empty table to put the averaged data in
new_table = Table(names=column_names)
# print (file)
# retrieve file name minus extension and location for later use
base_name = os.path.basename(file)
first_part, _ = os.path.splitext(base_name)
# print (first_part)
# read file as table
file = Table.read(file)
num = 1
while num in file[identColumn]:
# finds the rows in the file that match the current DataNum
matches = (file[identColumn] == num)
# print (matches)
# makes a copy of the file that only contains the rows that match
# the desired DataNum
file2 = file[matches]
num_in_avg = len(file2)
# print(num_in_avg)
# makes sure there are enough points to matter
if num_in_avg > srcs:
# averages the data for the current DataNum
flux_avg = sum(file2[flux_name])/num_in_avg
max_peak = max(file2['peak'])
ra_avg = sum(file2['RA'])/num_in_avg
dec_avg = sum(file2['Dec'])/num_in_avg
# make fractional square add root divide de-fractionalize
frac_errs = sum((file2['FluxErr']/file2['flux'])**2)
flux_err = ((sqrt(frac_errs))/num_in_avg)*flux_avg
inst_mag = -2.5*log10(flux_avg)
a_avg = sum(file2['a'])/num_in_avg
b_avg = sum(file2['b'])/num_in_avg
theta_avg = sum(file2['theta'])/num_in_avg
# adds the new row to the table
# print (num, ra_avg, dec_avg, flux_avg, flux_err, num_in_avg)
row = [num, num_in_avg, ra_avg, dec_avg, flux_avg, flux_err, inst_mag, max_peak, a_avg, b_avg, theta_avg]
new_table.add_row(row)
num += 1 # increment counter
# write averaged table out to disk
# print (new_table)
new_table.write(os.path.join(write_location, first_part+'(Avg).csv'))
return write_location