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remainder_correction.py
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#!/usr/bin/python
"""
Adjust track photometries based on persistent spots.
"""
import argparse
import csv
import cPickle
import os.path
import numpy as np
import MCsimlib
#define and parse arguments; use custom MyFormatter to do both ArgumentDefault
#and RawDescription Formatters via multiple inheritence, this is a trick to
#preserve docstring formatting in --help output
class MyFormatter(argparse.ArgumentDefaultsHelpFormatter,
argparse.RawDescriptionHelpFormatter):
pass
parser = argparse.ArgumentParser(description=__doc__,
formatter_class=MyFormatter)
#Script arguments
tracks_helpstring = "track_photometries_??????.csv file to adjust."
parser.add_argument('tracks', nargs=1, type=str, help=tracks_helpstring)
minimum_remainders_per_field_helpstring = \
("Discard fields without at least this many remainders in them.")
parser.add_argument('--min', type=int, default=5,
help=minimum_remainders_per_field_helpstring)
diff_median_helpstring = \
("Method 1: Whether to use remainder track median instead of mean as "
"benchmark.")
parser.add_argument('--M1_diff_median', action='store_true', default=False,
help=diff_median_helpstring)
print_adjustments_helpstring = "Print adjustments to screen."
parser.add_argument('--print_adjustments', action='store_true', default=False,
help=print_adjustments_helpstring)
save_adjustments_helpstring = "Save adjustments used to pkl file."
parser.add_argument('--save_adjustments', action='store_true', default=False,
help=save_adjustments_helpstring)
method_helpstring = \
("Which method to use. NOTE: Only method 4 available. Others are "
"nonsense.")
parser.add_argument('--method', type=int, default=4, help=method_helpstring)
args = parser.parse_args()
csv_path = os.path.abspath(args.tracks[0])
if args.method != 4:
raise Exception("Older methods not supported.")
photometries, row_photometries = \
MCsimlib.read_track_photometries_csv(csv_path,
head_truncate=0,
tail_truncate=0,
downstep_filtered=False)
def method_1(photometries, minimum, num_frames, use_median):
remainder_diffs = {}
for channel, cdict in photometries.iteritems():
for field, fdict in cdict.iteritems():
remainder_diffs.setdefault(channel, {}).setdefault(field,
[[] for f in range(num_frames)])
for (h, w), (category, intensities, row) in fdict.iteritems():
if set(category) != set([True]):
continue
else:
if use_median:
remainder_m = np.median(intensities)
else:
remainder_m = np.mean(intensities)
diffs = [intensity - remainder_m
for intensity in intensities]
for frame, diff in enumerate(diffs):
remainder_diffs[channel][field][frame].append(diff)
remainder_medians = {}
for channel, cdict in remainder_diffs.iteritems():
for field, diff_lists in cdict.iteritems():
if any([len(diffs) < minimum
for frame, diffs in enumerate(diff_lists)]):
continue
remainder_medians.setdefault(channel, {}).setdefault(field,
[np.median(diffs) for diffs in diff_lists])
adjusted_photometries = {}
for channel, cdict in remainder_medians.iteritems():
adjusted_photometries.setdefault(channel, {})
for field, medians in cdict.iteritems():
adjusted_photometries[channel].setdefault(field, {})
fdict = photometries[channel][field]
for (h, w), (category, intensities, row) in fdict.iteritems():
adjusted_intensities = [intensity - medians[frame]
for frame, intensity in enumerate(intensities)]
adjusted_photometries[channel][field].setdefault((h, w),
(category, adjusted_intensities))
return adjusted_photometries, remainder_medians
def method_2(photometries, minimum, num_frames):
remainder_values = {}
for channel, cdict in photometries.iteritems():
for field, fdict in cdict.iteritems():
for (h, w), (category, intensities, row) in fdict.iteritems():
if set(category) != set([True]):
continue
remainder_values.setdefault(channel, {}).setdefault(field,
[[] for f in range(num_frames)])
for frame, intensity in enumerate(intensities):
remainder_values[channel][field][frame].append(intensity)
remainder_adjustments = {}
for channel, cdict in remainder_values.iteritems():
for field, remainder_lists in cdict.iteritems():
if len(remainder_lists[0]) < minimum:
continue
remainder_medians = [np.median(remainder_list)
for remainder_list in remainder_lists]
adjustments = [median - remainder_medians[0]
for median in remainder_medians]
remainder_adjustments.setdefault(channel, {}).setdefault(field,
adjustments)
adjusted_photometries = {}
for channel, cdict in remainder_adjustments.iteritems():
adjusted_photometries.setdefault(channel, {})
for field, adjustments in cdict.iteritems():
adjusted_photometries[channel].setdefault(field, {})
fdict = photometries[channel][field]
for (h, w), (category, intensities, row) in fdict.iteritems():
adjusted_intensities = [intensity - adjustments[frame]
for frame, intensity in enumerate(intensities)]
adjusted_photometries[channel][field].setdefault((h, w),
(category, adjusted_intensities))
return adjusted_photometries, remainder_adjustments
def method_3(photometries, minimum, num_frames):
remainder_values = {}
for channel, cdict in photometries.iteritems():
for field, fdict in cdict.iteritems():
for (h, w), (category, intensities, row) in fdict.iteritems():
if set(category) != set([True]):
continue
remainder_values.setdefault(channel, {}).setdefault(field,
[[] for f in range(num_frames)])
for frame, intensity in enumerate(intensities):
remainder_values[channel][field][frame].append(intensity)
remainder_adjustments = {}
for channel, cdict in remainder_values.iteritems():
for field, remainder_lists in cdict.iteritems():
if len(remainder_lists[0]) < minimum:
continue
remainder_medians = [np.median(remainder_list)
for remainder_list in remainder_lists]
adjustments = [remainder_medians[0] / float(median)
for median in remainder_medians]
remainder_adjustments.setdefault(channel, {}).setdefault(field,
adjustments)
adjusted_photometries = {}
for channel, cdict in remainder_adjustments.iteritems():
adjusted_photometries.setdefault(channel, {})
for field, adjustments in cdict.iteritems():
adjusted_photometries[channel].setdefault(field, {})
fdict = photometries[channel][field]
for (h, w), (category, intensities, row) in fdict.iteritems():
adjusted_intensities = [intensity * adjustments[frame]
for frame, intensity in enumerate(intensities)]
adjusted_photometries[channel][field].setdefault((h, w),
(category, adjusted_intensities))
return adjusted_photometries, remainder_adjustments
num_frames = len(row_photometries.popitem()[1][4])
#Deleting row_photometries because we have modified it by popping. Want to
#avoid bugs caused by assumption that this dictionary still has everything.
del row_photometries
if args.method == 1:
adjusted_photometries, remainder_adjustments = method_1(photometries,
args.min, num_frames, args.M1_diff_median)
elif args.method == 2:
adjusted_photometries, remainder_adjustments = method_2(photometries,
args.min, num_frames)
elif args.method == 3:
adjusted_photometries, remainder_adjustments = method_3(photometries,
args.min, num_frames)
elif args.method == 4:
adjusted_photometries, adjustment_ratio_medians = \
MCsimlib._remainder_adjust_2(photometries=photometries,
num_frames=num_frames,
minimum_r_per_field=args.min)
remainder_adjustments = adjustment_ratio_medians
else:
raise ValueError("Unknown method.")
if args.print_adjustments:
print(remainder_adjustments)
output_filepath = csv_path + '_adjusted.csv'
if args.save_adjustments:
adjustments_output_filepath = csv_path + '_adjustments.pkl'
cPickle.dump(remainder_adjustments, open(adjustments_output_filepath, 'w'))
csv_writer = csv.writer(open(output_filepath, 'w'))
header_row = (["CHANNEL", "FIELD", "H", "W", "CATEGORY"] +
["FRAME " + str(frame) for frame in range(num_frames)])
csv_writer.writerow(header_row)
for channel, cdict in adjusted_photometries.iteritems():
for field, fdict in cdict.iteritems():
for (h, w), (parsed_category, adjusted_intensities, row) in fdict.iteritems():
row = [str(channel), str(field), str(h), str(w),
str(parsed_category)]
row += [str(intensity) for intensity in adjusted_intensities]
csv_writer.writerow(row)