def main(argv):
"""
:param argv:
"""
directory = util.get_directory_from_command_line(argv, os.path.basename(__file__))
for filename in util.gen_img_paths(directory):
path = os.path.join(directory, filename)
# Open with Bioformats
im = pims.Bioformats(path + '.tif')
# Set scale
micron_per_pixel = im.calibration
# Calculate size range
min_size = max(0.3 / micron_per_pixel, 3)
max_size = min(2.0 / micron_per_pixel, im.frame_shape[0] * 0.5)
im = im[0][:-64]
im = np.flipud(im)
# Locate and refine circles
finder = pf.ParticleFinder(im)
f = finder.locate_particles(size_range=(min_size, max_size))
# Save fit images
plot.save_fits(f, im, path)
# Generate data files and save
report.save_circles_to_csv(f, path, micron_per_pixel)
report.save_circles_to_csv_grouped(f, path, micron_per_pixel)
def main(argv):
"""
:param argv:
"""
directory = util.get_directory_from_command_line(argv, os.path.basename(__file__))
# Open all .tif's in the directory
for filename in util.gen_img_paths(directory):
path = os.path.join(directory, filename)
# Open with Bioformats
im = pims.Bioformats(path + '.tif')
# Set scale
micron_per_pixel = im.calibration
im = im[0][:-64]
im = np.flipud(im)
# Report filename
report_file = os.path.abspath(
os.path.normpath(directory + os.path.sep + 'report' + os.path.sep + filename + '_frame.csv'))
if not os.path.isfile(report_file):
continue
checker = usercheckfits.UserCheckFits(report_file, micron_per_pixel)
f = checker.user_check_fits(im)
# Remove old csv files
summary_file = os.path.abspath(
os.path.normpath(directory + os.path.sep + 'report' + os.path.sep + filename + '_summary.csv'))
if os.path.isfile(summary_file):
os.remove(summary_file)
os.remove(report_file)
# Remove old grouped files
file_path_grouped = os.path.abspath(
os.path.normpath(directory + os.path.sep + 'report' + os.path.sep + re.sub("_\d+$", "", filename)))
report_file_grouped = file_path_grouped + '_grouped_report.csv'
summary_file_grouped = file_path_grouped + '_grouped_summary.csv'
if os.path.isfile(report_file_grouped):
os.remove(report_file_grouped)
if os.path.isfile(summary_file_grouped):
os.remove(summary_file_grouped)
# Remove old fit .tif file
fits_directory = os.path.abspath(os.path.normpath(directory + os.path.sep + 'fits'))
fit_file = os.path.abspath(os.path.normpath(fits_directory + os.path.sep + filename)) + '_fit.tif'
if os.path.isfile(fit_file):
os.remove(fit_file)
# Save fit images
plot.save_fits(f, im, path)
# Generate data files and save
report.save_circles_to_csv(f, path, micron_per_pixel)
report.save_circles_to_csv_grouped(f, path, micron_per_pixel, suffix='_grouped_checked')
def main_only_plot(argv):
"""
:param argv:
"""
directory = util.get_directory_from_command_line(argv, os.path.basename(__file__))
path = os.path.join(directory, 'plot.pdf')
filepath = os.path.join(directory, 'errors.csv')
errors = pandas.DataFrame.from_csv(filepath)
plot_frame(errors, round(errors['runs'][0]), path)
def main(argv):
"""
:param argv:
"""
directory = util.get_directory_from_command_line(argv, os.path.basename(__file__))
path = os.path.join(directory, 'plot.pdf')
filepath = os.path.join(directory, 'errors.csv')
errors = pandas.DataFrame(
columns=['r', 'runs', 'num_diff', 'r_diff', 'x_diff', 'y_diff', 'r_diff_std', 'x_diff_std', 'y_diff_std'])
radii = numpy.arange(5.0, 31.0, 1.0)
width = 1024
height = 943
runs = 10
set_latex_params()
for index, r in enumerate(radii):
for run in numpy.arange(0, runs, 1):
num = round(min([height / r, 100]))
test = TestImage(num, r, noise=0.3, shape=(width, height))
generated_image = test.image()
finder = particlefinder.ParticleFinder(generated_image)
fits = finder.locate_particles(n=test.number * 1.5, size_range=(
int(numpy.ceil(0.5 * min(radii))), int(numpy.ceil(2.0 * max(radii)))))
coords_df = test.get_coords_dataframe(True)
unmatched, value_diff = test.check_frames_difference(fits, expected=coords_df)
errors = errors.append({
'runs': runs,
'r': r,
'num_diff': float(len(coords_df['r']) - unmatched) / float(len(coords_df['r'])),
'r_diff': numpy.mean(value_diff['r']) / r,
'x_diff': numpy.mean(value_diff['x']),
'y_diff': numpy.mean(value_diff['y']),
'r_diff_std': numpy.std(value_diff['r']) / r,
'x_diff_std': numpy.std(value_diff['x']),
'y_diff_std': numpy.std(value_diff['y'])
}, ignore_index=True)
print str(float(index * runs + run) / float(len(radii) * runs) * 100.0) + '%'
# Save
errors.to_csv(filepath, encoding='utf-8')
plot_frame(errors, runs, path)
