def main():
# Initialization
path_data = ''
xmin = '50'
xsize = '100'
ymin = '0'
ysize = '-1'
zmin = '0'
zsize = '-1'
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
path_data = '/Volumes/folder_shared/template/t2'
path_out = '/Volumes/folder_shared/template/t2_crop'
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:o:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ("-i"):
path_data = arg
elif opt in ("-o"):
path_out = arg
# check input folder
sct.check_folder_exist(path_data)
# add slash
path_data = sct.slash_at_the_end(path_data, 1)
path_out = sct.slash_at_the_end(path_out, 1)
# create output folder
if os.path.exists(path_out):
sct.printv('WARNING: Output folder exists. Deleting it.', 1, 'warning')
# remove dir
shutil.rmtree(path_out)
# create dir
os.makedirs(path_out)
# list all files in folder
files = [f for f in glob.glob(path_data + '*.nii.gz')]
# for files in glob.glob(path_data+'*.nii.gz'):
# print files
# crop files one by one (to inform user)
for f in files:
path_f, file_f, ext_f = sct.extract_fname(f)
sct.run('fslroi ' + f + ' ' + path_out + file_f + ' ' + xmin + ' ' +
xsize + ' ' + ymin + ' ' + ysize + ' ' + zmin + ' ' + zsize)
# to view results
print '\nDone!'
def main():
# Initialization
path_data = ''
xmin = '50'
xsize = '100'
ymin = '0'
ysize = '-1'
zmin = '0'
zsize = '-1'
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
path_data = '/Volumes/folder_shared/template/t2'
path_out = '/Volumes/folder_shared/template/t2_crop'
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:o:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ("-i"):
path_data = arg
elif opt in ("-o"):
path_out = arg
# check input folder
sct.check_folder_exist(path_data)
# add slash
path_data = sct.slash_at_the_end(path_data, 1)
path_out = sct.slash_at_the_end(path_out, 1)
# create output folder
if os.path.exists(path_out):
sct.printv('WARNING: Output folder exists. Deleting it.', 1, 'warning')
# remove dir
shutil.rmtree(path_out)
# create dir
os.makedirs(path_out)
# list all files in folder
files = [f for f in glob.glob(path_data+'*.nii.gz')]
# for files in glob.glob(path_data+'*.nii.gz'):
# print files
# crop files one by one (to inform user)
for f in files:
path_f, file_f, ext_f = sct.extract_fname(f)
sct.run('fslroi '+f+' '+path_out+file_f+' '+xmin+' '+xsize+' '+ymin+' '+ysize+' '+zmin+' '+zsize)
# to view results
print '\nDone!'
def test_function(script_name):
if script_name == 'test_debug':
return test_debug() # JULIEN
else:
# Using the retest variable to recheck if we can perform tests after we downloaded the data
retest = 1
# while condition values are arbitrary and are present to prevent infinite loop
while 0 < retest < 3:
# build script name
fname_log = script_name + ".log"
tmp_script_name = script_name
result_folder = "results_"+script_name
script_name = "test_"+script_name
if retest == 1:
# create folder and go in it
sct.create_folder(result_folder)
os.chdir(result_folder)
# display script name
print_line('Checking '+script_name)
# import function as a module
script_tested = importlib.import_module(script_name)
# test function
status, output = script_tested.test(param.path_data)
# returning script_name to its original name
script_name = tmp_script_name
# manage status
if status == 0:
print_ok()
retest = 0
else:
print_fail()
print output
print "\nTest files missing, downloading them now \n"
os.chdir('../..')
downloaddata()
param.path_data = sct.slash_at_the_end(os.path.abspath(param.path_data), 1)
# check existence of testing data folder
sct.check_folder_exist(param.path_data)
os.chdir(param.path_tmp + result_folder)
retest += 1
# log file
write_to_log_file(fname_log, output, 'w')
# go back to parent folder
os.chdir('..')
# end while loop
# return
return status
def checkFolder(self, param):
# check if the folder exist. If not, create it.
sct.printv("Check folder existence...")
sct.check_folder_exist(param, 0)
return param
def main():
path_data = param.path_data
function_to_test = param.function_to_test
# function_to_avoid = param.function_to_avoid
remove_tmp_file = param.remove_tmp_file
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:],'h:d:p:f:r:a:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
sys.exit(0)
if opt == '-d':
param.download = int(arg)
if opt == '-p':
param.path_data = arg
if opt == '-f':
function_to_test = arg
# if opt == '-a':
# function_to_avoid = arg
if opt == '-r':
remove_tmp_file = int(arg)
start_time = time.time()
# if function_to_avoid:
# try:
# functions.remove(function_to_avoid)
# except ValueError:
# print 'The function you want to avoid does not figure in the functions to test list'
# download data
if param.download:
downloaddata()
param.path_data = 'sct_testing_data/data'
# get absolute path and add slash at the end
param.path_data = sct.slash_at_the_end(os.path.abspath(param.path_data), 1)
# check existence of testing data folder
if not sct.check_folder_exist(param.path_data, 0):
downloaddata()
# display path to data
sct.printv('\nPath to testing data:\n.. '+param.path_data, param.verbose)
# create temp folder that will have all results and go in it
param.path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.create_folder(param.path_tmp)
os.chdir(param.path_tmp)
# get list of all scripts to test
functions = fill_functions()
# loop across all functions and test them
status = []
[status.append(test_function(f)) for f in functions if function_to_test == f]
if not status:
for f in functions:
status.append(test_function(f))
print 'status: '+str(status)
# display elapsed time
elapsed_time = time.time() - start_time
print 'Finished! Elapsed time: '+str(int(round(elapsed_time)))+'s\n'
# remove temp files
if param.remove_tmp_file:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf '+param.path_tmp, param.verbose)
e = 0
if sum(status) != 0:
e = 1
print e
sys.exit(e)
def main():
#Initialization
directory = ""
fname_template = ''
n_l = 0
verbose = param.verbose
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:t:n:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ("-i"):
directory = arg
elif opt in ("-t"):
fname_template = arg
elif opt in ('-n'):
n_l = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_template == '' or directory == '':
usage()
# check existence of input files
print '\nCheck if file exists ...\n'
sct.check_file_exist(fname_template)
sct.check_folder_exist(directory)
path_template, file_template, ext_template = sct.extract_fname(
fname_template)
template_absolute_path = sct.get_absolute_path(fname_template)
os.chdir(directory)
n_i = len([
name for name in os.listdir('.')
if (os.path.isfile(name) and name.endswith(".nii.gz")
and name != 'template_landmarks.nii.gz')
]) # number of landmark images
average = zeros((n_i, n_l))
compteur = 0
for file in os.listdir('.'):
if file.endswith(".nii.gz") and file != 'template_landmarks.nii.gz':
print file
img = nibabel.load(file)
data = img.get_data()
X, Y, Z = (data > 0).nonzero()
Z = [Z[i] for i in Z.argsort()]
Z.reverse()
for i in xrange(n_l):
if i < len(Z):
average[compteur][i] = Z[i]
compteur = compteur + 1
average = array([
int(round(mean([average[average[:, i] > 0, i]]))) for i in xrange(n_l)
])
#print average
print template_absolute_path
print '\nGet dimensions of template...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(template_absolute_path)
print '.. matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)
print '.. voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(
pz) + 'mm'
img = nibabel.load(template_absolute_path)
data = img.get_data()
hdr = img.get_header()
data[:, :, :] = 0
compteur = 1
for i in average:
print int(round(nx / 2.0)), int(round(ny / 2.0)), int(round(i)), int(
round(compteur))
data[int(round(nx / 2.0)),
int(round(ny / 2.0)),
int(round(i))] = int(round(compteur))
compteur = compteur + 1
print '\nSave volume ...'
#hdr.set_data_dtype('float32') # set imagetype to uint8
# save volume
#data = data.astype(float32, copy =False)
img = nibabel.Nifti1Image(data, None, hdr)
file_name = 'template_landmarks.nii.gz'
nibabel.save(img, file_name)
print '\nFile created : ' + file_name
def main():
results_folder = param_default.results_folder
methods_to_display = param_default.methods_to_display
# Parameters for debug mode
if param_default.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
results_folder = "/Users/slevy_local/spinalcordtoolbox/dev/atlas/validate_atlas/results_20150210_200iter"#"C:/cygwin64/home/Simon_2/data_methods_comparison"
path_sct = '/Users/slevy_local/spinalcordtoolbox' #'C:/cygwin64/home/Simon_2/spinalcordtoolbox'
methods_to_display = 'bin,wa,wath,ml,map'
else:
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'i:m:') # define flags
except getopt.GetoptError as err: # check if the arguments are defined
print str(err) # error
# usage() # display usage
# if not opts:
# print 'Please enter the path to the result folder. Exit program.'
# sys.exit(1)
# # usage()
for opt, arg in opts: # explore flags
if opt in '-i':
results_folder = arg
if opt in '-m':
methods_to_display = arg
# Append path that contains scripts, to be able to load modules
sys.path.append(path_sct + '/scripts')
import sct_utils as sct
sct.printv("Working directory: " + os.getcwd())
results_folder_noise = results_folder + '/noise'
results_folder_tracts = results_folder + '/tracts'
sct.printv('\n\nData will be extracted from folder ' + results_folder_noise + ' and ' + results_folder_tracts + '.', 'warning')
sct.printv('\t\tCheck existence...')
sct.check_folder_exist(results_folder_noise)
sct.check_folder_exist(results_folder_tracts)
# Extract methods to display
methods_to_display = methods_to_display.strip().split(',')
# Extract file names of the results files
fname_results_noise = glob.glob(results_folder_noise + '/*.txt')
fname_results_tracts = glob.glob(results_folder_tracts + '/*.txt')
fname_results = fname_results_noise + fname_results_tracts
# Remove doublons (due to the two folders)
# for i_fname in range(0, len(fname_results)):
# for j_fname in range(0, len(fname_results)):
# if (i_fname != j_fname) & (os.path.basename(fname_results[i_fname]) == os.path.basename(fname_results[j_fname])):
# fname_results.remove(fname_results[j_fname])
file_results = []
for fname in fname_results:
file_results.append(os.path.basename(fname))
for file in file_results:
if file_results.count(file) > 1:
ind = file_results.index(file)
fname_results.remove(fname_results[ind])
file_results.remove(file)
nb_results_file = len(fname_results)
# 1st dim: SNR, 2nd dim: tract std, 3rd dim: mean abs error, 4th dim: std abs error
# result_array = numpy.empty((nb_results_file, nb_results_file, 3), dtype=object)
# SNR
snr = numpy.zeros((nb_results_file))
# Tracts std
tracts_std = numpy.zeros((nb_results_file))
# CSF value
csf_values = numpy.zeros((nb_results_file))
# methods' name
methods_name = [] #numpy.empty((nb_results_file, nb_method), dtype=object)
# labels
error_per_label = []
std_per_label = []
labels_id = []
# median
median_results = numpy.zeros((nb_results_file, 5))
# median std across bootstraps
median_std = numpy.zeros((nb_results_file, 5))
# min
min_results = numpy.zeros((nb_results_file, 5))
# max
max_results = numpy.zeros((nb_results_file, 5))
#
for i_file in range(0, nb_results_file):
# Open file
f = open(fname_results[i_file]) # open file
# Extract all lines in .txt file
lines = [line for line in f.readlines() if line.strip()]
# extract SNR
# find all index of lines containing the string "sigma noise"
ind_line_noise = [lines.index(line_noise) for line_noise in lines if "sigma noise" in line_noise]
if len(ind_line_noise) != 1:
sct.printv("ERROR: number of lines including \"sigma noise\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# result_array[:, i_file, i_file] = int(''.join(c for c in lines[ind_line_noise[0]] if c.isdigit()))
snr[i_file] = int(''.join(c for c in lines[ind_line_noise[0]] if c.isdigit()))
# extract tract std
ind_line_tract_std = [lines.index(line_tract_std) for line_tract_std in lines if
"range tracts" in line_tract_std]
if len(ind_line_tract_std) != 1:
sct.printv("ERROR: number of lines including \"range tracts\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# result_array[i_file, i_file, :] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# regex = re.compile(''('(.*)':) # re.I permet d'ignorer la case (majuscule/minuscule)
# match = regex.search(lines[ind_line_tract_std[0]])
# result_array[:, i_file, :, :] = match.group(1) # le groupe 1 correspond a '.*'
tracts_std[i_file] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# extract CSF value
ind_line_csf_value = [lines.index(line_csf_value) for line_csf_value in lines if
"# value CSF" in line_csf_value]
if len(ind_line_csf_value) != 1:
sct.printv("ERROR: number of lines including \"range tracts\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# result_array[i_file, i_file, :] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# regex = re.compile(''('(.*)':) # re.I permet d'ignorer la case (majuscule/minuscule)
# match = regex.search(lines[ind_line_tract_std[0]])
# result_array[:, i_file, :, :] = match.group(1) # le groupe 1 correspond a '.*'
csf_values[i_file] = int(''.join(c for c in lines[ind_line_csf_value[0]].split(':')[1] if c.isdigit()))
# extract method name
ind_line_label = [lines.index(line_label) for line_label in lines if "Label" in line_label]
if len(ind_line_label) != 1:
sct.printv("ERROR: number of lines including \"Label\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# methods_name[i_file, :] = numpy.array(lines[ind_line_label[0]].strip().split(',')[1:])
methods_name.append(lines[ind_line_label[0]].strip().replace(' ', '').split(',')[1:])
# extract median
ind_line_median = [lines.index(line_median) for line_median in lines if "median" in line_median]
if len(ind_line_median) != 1:
sct.printv("WARNING: number of lines including \"median\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
median = lines[ind_line_median[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 0] = [float(m.split('(')[0]) for m in median]
median_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in median])
median_std[i_file, :] = numpy.array([float(m.split('(')[1][:-1]) for m in median])
# extract min
ind_line_min = [lines.index(line_min) for line_min in lines if "min," in line_min]
if len(ind_line_min) != 1:
sct.printv("WARNING: number of lines including \"min\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
min = lines[ind_line_min[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in min]
min_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in min])
# extract max
ind_line_max = [lines.index(line_max) for line_max in lines if "max" in line_max]
if len(ind_line_max) != 1:
sct.printv("WARNING: number of lines including \"max\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
max = lines[ind_line_max[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in max]
max_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in max])
# extract error for each label
error_per_label_for_file_i = []
std_per_label_for_file_i = []
labels_id_for_file_i = []
# Due to 2 different kind of file structure, the number of the last label line must be adapted
if not ind_line_median:
ind_line_median = [len(lines) + 1]
for i_line in range(ind_line_label[0] + 1, ind_line_median[0] - 1):
line_label_i = lines[i_line].strip().split(',')
error_per_label_for_file_i.append([float(error.strip().split('(')[0]) for error in line_label_i[1:]])
std_per_label_for_file_i.append([float(error.strip().split('(')[1][:-1]) for error in line_label_i[1:]])
labels_id_for_file_i.append(line_label_i[0])
error_per_label.append(error_per_label_for_file_i)
std_per_label.append(std_per_label_for_file_i)
labels_id.append(labels_id_for_file_i)
# close file
f.close()
# check if all the files in the result folder were generated with the same number of methods
if not all(x == methods_name[0] for x in methods_name):
sct.printv(
'ERROR: All the generated files in folder ' + results_folder + ' have not been generated with the same number of methods. Exit program.',
'error')
sys.exit(1)
# check if all the files in the result folder were generated with the same labels
if not all(x == labels_id[0] for x in labels_id):
sct.printv(
'ERROR: All the generated files in folder ' + results_folder + ' have not been generated with the same labels. Exit program.',
'error')
sys.exit(1)
# convert the list "error_per_label" into a numpy array to ease further manipulations
error_per_label = numpy.array(error_per_label)
std_per_label = numpy.array(std_per_label)
# compute different stats
abs_error_per_labels = numpy.absolute(error_per_label)
max_abs_error_per_meth = numpy.amax(abs_error_per_labels, axis=1)
min_abs_error_per_meth = numpy.amin(abs_error_per_labels, axis=1)
mean_abs_error_per_meth = numpy.mean(abs_error_per_labels, axis=1)
std_abs_error_per_meth = numpy.std(abs_error_per_labels, axis=1)
nb_method = len(methods_to_display)
sct.printv('Noise std of the ' + str(nb_results_file) + ' generated files:')
print snr
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Tracts std of the ' + str(nb_results_file) + ' generated files:')
print tracts_std
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('CSF value of the ' + str(nb_results_file) + ' generated files:')
print csf_values
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Methods used to generate results for the ' + str(nb_results_file) + ' generated files:')
print methods_name
print '----------------------------------------------------------------------------------------------------------------'
sct.printv(
'Median obtained with each method (in colons) for the ' + str(nb_results_file) + ' generated files (in lines):')
print median_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Minimum obtained with each method (in colons) for the ' + str(
nb_results_file) + ' generated files (in lines):')
print min_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Maximum obtained with each method (in colons) for the ' + str(
nb_results_file) + ' generated files (in lines):')
print max_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Labels\' ID (in colons) for the ' + str(nb_results_file) + ' generated files (in lines):')
print labels_id
print '----------------------------------------------------------------------------------------------------------------'
sct.printv(
'Errors obtained with each method (in colons) for the ' + str(nb_results_file) + ' generated files (in lines):')
print error_per_label
# *********************************************** START PLOTTING HERE **********************************************
# # plot A (NOT GOOD)
# fig0 = plt.figure(0)
# fig0.suptitle('Absolute error within all tracts as a function of noise std')
#
# fig0_ax = fig0.add_subplot(111)
# fig0_ax.grid(True)
# fig0_ax.set_xlabel('Noise std')
# fig0_ax.set_ylabel('Absolute error')
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
#
# for meth, color in zip(methods_name[0], colors):
# if meth != 'mlwa':
# i_meth = methods_name[0].index(meth)
#
# # median
# plt.plot(snr[ind_tracts_std_10], median_results[ind_tracts_std_10, i_meth][0], label='median '+meth, color=color, marker='o', linestyle='None', markersize=5.0, linewidth=2.0)
# # min
# plt.plot(snr[ind_tracts_std_10], min_results[ind_tracts_std_10, i_meth][0], label='min '+meth, color=color, marker='_', linestyle='None', markersize=10.0, linewidth=20.0)
# # max
# plt.plot(snr[ind_tracts_std_10], max_results[ind_tracts_std_10, i_meth][0], label='max '+meth, color=color, marker='+', linestyle='None', markersize=10.0, linewidth=20.0)
#
# handles, labels = fig0_ax.get_legend_handles_labels()
# fig0_ax.legend(handles, labels, loc='best', handler_map={Line2D: HandlerLine2D(numpoints=1)}, fontsize=16)
#
#
# # plot B (NOT GOOD)
# fig1 = plt.figure(1)
# fig1.suptitle('Absolute error within all tracts as a function of tract std')
#
# fig1_ax = fig1.add_subplot(111)
# fig1_ax.grid(True)
# fig1_ax.set_xlabel('Tract std (percentage of the true value in tract)')
# fig1_ax.set_ylabel('Absolute error')
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
#
# for meth, color in zip(methods_name[0], colors):
# if meth != 'mlwa':
# i_meth = methods_name[0].index(meth)
#
# # median
# plt.plot(tracts_std[ind_snr_10], median_results[ind_snr_10, i_meth][0], label='median '+meth, color=color, marker='o', linestyle='None', markersize=5.0, linewidth=2.0)
# # min
# plt.plot(tracts_std[ind_snr_10], min_results[ind_snr_10, i_meth][0], label='min '+meth, color=color, marker='_', linestyle='None', markersize=10.0, linewidth=20.0)
# # max
# plt.plot(tracts_std[ind_snr_10], max_results[ind_snr_10, i_meth][0], label='max '+meth, color=color, marker='+', linestyle='None', markersize=10.0, linewidth=20.0)
#
# handles, labels = fig1_ax.get_legend_handles_labels()
# fig1_ax.legend(handles, labels, loc='best', handler_map={Line2D: HandlerLine2D(numpoints=1)}, fontsize=16)
# Plot A
ind_tracts_std_10 = numpy.where((tracts_std == 10) & (snr != 50)) # indexes where TRACTS STD=10
ind_ind_snr_sort_tracts_std_10 = numpy.argsort(snr[ind_tracts_std_10]) # indexes of indexes where TRACTS STD=10 sorted according to SNR values (in ascending order)
ind_snr_sort_tracts_std_10 = ind_tracts_std_10[0][ind_ind_snr_sort_tracts_std_10] # indexes where TRACTS STD=10 sorted according to SNR values (in ascending order)
# fig2 = plt.figure(2)
# ind_fig2 = numpy.arange(len(snr[ind_snr_sort_tracts_std_10])) * 1.2
# width = 1.0 / (nb_method + 1)
# plt.ylabel('Error (%)')
# plt.xlabel('Noise std')
# plt.title('Error within all tracts as a function of noise std')
# plt.xticks(ind_fig2 + 0.5, snr[ind_snr_sort_tracts_std_10])
# plt.gca().set_xlim([-width / (nb_method + 1), numpy.max(ind_fig2) + 1])
# plt.gca().yaxis.grid(True)
#
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
# bar_plots = []
# for meth, color in zip(methods_name[0], colors):
# i_meth = methods_name[0].index(meth)
#
# plot_i = plt.bar(ind_fig2 + i_meth * width + (float(i_meth) * width) / (nb_method + 1),
# max_results[ind_snr_sort_tracts_std_10, i_meth] - min_results[
# ind_snr_sort_tracts_std_10, i_meth], width,
# min_results[ind_snr_sort_tracts_std_10, i_meth], edgecolor=color, color='white', linewidth=3)
# plt.plot(ind_fig2 + i_meth * width + width / 2 + (float(i_meth) * width) / (nb_method + 1),
# median_results[ind_snr_sort_tracts_std_10, i_meth], color=color, marker='_', linestyle='None',
# markersize=200 * width, markeredgewidth=3)
# bar_plots.append(plot_i[0])
#
# plt.legend(bar_plots, methods_name[0], bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
# Plot B
ind_snr_10 = numpy.where((snr == 10) & (tracts_std != 50)) # indexes where SNR=10
ind_ind_tracts_std_sort_snr_10 = numpy.argsort(tracts_std[
ind_snr_10]) # indexes of indexes where SNR=10 sorted according to tracts_std values (in ascending order)
ind_tracts_std_sort_snr_10 = ind_snr_10[0][
ind_ind_tracts_std_sort_snr_10] # indexes where SNR=10 sorted according to tracts_std values (in ascending order)
# fig3 = plt.figure(3)
# ind_fig3 = numpy.arange(len(tracts_std[ind_tracts_std_sort_snr_10])) * 1.2
# width = 1.0 / (nb_method + 1)
# plt.ylabel('Error (%)')
# plt.xlabel('Tracts std (in percentage of the mean value of the tracts)')
# plt.title('Error within all tracts as a function of tracts std')
# plt.xticks(ind_fig3 + 0.5, tracts_std[ind_tracts_std_sort_snr_10])
# plt.gca().set_xlim([-width / (nb_method + 1), numpy.max(ind_fig3) + 1])
# plt.gca().yaxis.grid(True)
#
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
# bar_plots = []
# for meth, color in zip(methods_name[0], colors):
# i_meth = methods_name[0].index(meth)
#
# plot_i = plt.bar(ind_fig3 + i_meth * width + (float(i_meth) * width) / (nb_method + 1),
# max_results[ind_tracts_std_sort_snr_10, i_meth] - min_results[
# ind_tracts_std_sort_snr_10, i_meth], width,
# min_results[ind_tracts_std_sort_snr_10, i_meth], edgecolor=color, color='white', linewidth=3)
# plt.plot(ind_fig2 + i_meth * width + width / 2 + (float(i_meth) * width) / (nb_method + 1),
# median_results[ind_tracts_std_sort_snr_10, i_meth], color=color, marker='_', linestyle='None',
# markersize=200 * width, markeredgewidth=3)
# bar_plots.append(plot_i[0])
#
# plt.legend(bar_plots, methods_name[0], bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
# # Plot A -- v2: Absolute error (min, max, mean)
#
# fig4 = plt.figure(4)
# ind_fig4 = numpy.arange(len(snr[ind_snr_sort_tracts_std_10])) * 1.2
# width = 1.0 / (nb_method + 1)
# plt.ylabel('Absolute error (%)')
# plt.xlabel('Noise std')
# plt.title('Absolute error within all tracts as a function of noise std')
# plt.xticks(ind_fig4 + 0.5, snr[ind_snr_sort_tracts_std_10])
# plt.gca().set_xlim([-width / (nb_method + 1), numpy.max(ind_fig4) + 1])
# plt.gca().yaxis.grid(True)
#
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
# bar_plots = []
# for meth, color in zip(methods_name[0], colors):
# i_meth = methods_name[0].index(meth)
#
# plot_i = plt.bar(ind_fig4 + i_meth * width + (float(i_meth) * width) / (nb_method + 1),
# max_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth] - min_abs_error_per_meth[
# ind_snr_sort_tracts_std_10, i_meth], width,
# min_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth], edgecolor=color, color='white',
# linewidth=3)
# plt.errorbar(ind_fig2 + i_meth * width + width / 2 + (float(i_meth) * width) / (nb_method + 1),
# mean_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth],
# std_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth], color=color, marker='_',
# linestyle='None', markersize=200 * width, markeredgewidth=3)
# bar_plots.append(plot_i[0])
#
# plt.legend(bar_plots, methods_name[0], bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
#
# # Plot B -- v2: Absolute error (min, max, mean)
# fig5 = plt.figure(5)
# ind_fig5 = numpy.arange(len(tracts_std[ind_tracts_std_sort_snr_10])) * 1.2
# width = 1.0 / (nb_method + 1)
# plt.ylabel('Absolute error (%)')
# plt.xlabel('Tracts std (in percentage of the mean value of the tracts)')
# plt.title('Absolute error within all tracts as a function of tracts std')
# plt.xticks(ind_fig5 + 0.5, tracts_std[ind_tracts_std_sort_snr_10])
# plt.gca().set_xlim([-width / (nb_method + 1), numpy.max(ind_fig5) + 1])
# plt.gca().yaxis.grid(True)
#
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
# bar_plots = []
# for meth, color in zip(methods_name[0], colors):
# i_meth = methods_name[0].index(meth)
#
# plot_i = plt.bar(ind_fig5 + i_meth * width + (float(i_meth) * width) / (nb_method + 1),
# max_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth] - min_abs_error_per_meth[
# ind_tracts_std_sort_snr_10, i_meth], width,
# min_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth], edgecolor=color, color='white',
# linewidth=3)
# plt.errorbar(ind_fig2 + i_meth * width + width / 2 + (float(i_meth) * width) / (nb_method + 1),
# mean_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth],
# std_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth], color=color, marker='_',
# linestyle='None', markersize=200 * width, markeredgewidth=3)
# bar_plots.append(plot_i[0])
#
# plt.legend(bar_plots, methods_name[0], bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
matplotlib.rcParams.update({'font.size': 45, 'font.family': 'trebuchet'})
plt.rcParams['xtick.major.pad'] = '9'
plt.rcParams['ytick.major.pad'] = '15'
# Plot A -- v3: Box plots absolute error
fig6 = plt.figure(6, figsize=(30, 16))
width = 1.0 / (nb_method + 1)
ind_fig6 = numpy.arange(len(snr[ind_snr_sort_tracts_std_10])) * (1.0 + width)
plt.ylabel('Absolute error (%)\n', fontsize=55)
plt.xlabel('Noise STD (% of true WM value)', fontsize=55)
plt.title('Absolute error within all tracts as a function of noise std\n', fontsize=65)
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
box_plots = []
for meth, color in zip(methods_to_display, colors):
i_meth = methods_name[0].index(meth)
i_meth_to_display = methods_to_display.index(meth)
boxprops = dict(linewidth=4, color=color)
flierprops = dict(color=color, markeredgewidth=0.7, markersize=15, marker='.')
whiskerprops = dict(color=color, linewidth=3)
capprops = dict(color=color, linewidth=3)
medianprops = dict(linewidth=4, color=color)
meanpointprops = dict(marker='D', markeredgecolor='black', markerfacecolor='firebrick')
meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')
plot_i = plt.boxplot(numpy.transpose(abs_error_per_labels[ind_snr_sort_tracts_std_10, :, i_meth]),
positions=ind_fig6 + i_meth_to_display * width + (float(i_meth_to_display) * width) / (
nb_method + 1), widths=width, boxprops=boxprops, medianprops=medianprops,
flierprops=flierprops, whiskerprops=whiskerprops, capprops=capprops)
# plt.errorbar(ind_fig2+i_meth*width+width/2+(float(i_meth)*width)/(nb_method+1), mean_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth], std_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth], color=color, marker='_', linestyle='None', markersize=200*width, markeredgewidth=3)
box_plots.append(plot_i['boxes'][0])
# add alternated vertical background colored bars
for i_xtick in range(0, len(ind_fig6), 2):
plt.axvspan(ind_fig6[i_xtick] - width - width / 4, ind_fig6[i_xtick] + (nb_method + 1) * width - width / 4, facecolor='grey', alpha=0.1)
# plt.legend(box_plots, methods_to_display, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
# plt.legend(box_plots, methods_to_display, loc='best')
# convert xtick labels into integers
xtick_labels = [int(xtick) for xtick in snr[ind_snr_sort_tracts_std_10]]
plt.xticks(ind_fig6 + (numpy.floor(nb_method / 2)) * width * (1.0 + 1.0 / (nb_method + 1)), xtick_labels)
plt.gca().set_xlim([-width, numpy.max(ind_fig6) + (nb_method + 0.5) * width])
plt.gca().set_ylim([0, 18])
plt.gca().yaxis.set_major_locator(plt.MultipleLocator(2))
plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(0.5))
plt.grid(b=True, axis='y', which='both', alpha=0.5)
plt.subplots_adjust(left=0.1)
plt.savefig(param_default.fname_folder_to_save_fig+'/absolute_error_vs_noise_std_Tracts_std_fixed_to_10.pdf', format='PDF')
# Plot B -- v3: Box plots absolute error
fig7 = plt.figure(7, figsize=(30, 16))
width = 1.0 / (nb_method + 1)
ind_fig7 = numpy.arange(len(tracts_std[ind_tracts_std_sort_snr_10])) * (1.0 + width)
plt.ylabel('Absolute error (%)\n', fontsize=55)
plt.xlabel('Tracts STD (% of true WM value)', fontsize=55)
plt.title('Absolute error within all tracts as a function of tracts std\n', fontsize=65)
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
box_plots = []
for meth, color in zip(methods_to_display, colors):
i_meth = methods_name[0].index(meth)
i_meth_to_display = methods_to_display.index(meth)
boxprops = dict(linewidth=4, color=color)
flierprops = dict(color=color, markeredgewidth=0.7, markersize=15, marker='.')
whiskerprops = dict(color=color, linewidth=3)
capprops = dict(color=color, linewidth=3)
medianprops = dict(linewidth=4, color=color)
meanpointprops = dict(marker='D', markeredgecolor='black', markerfacecolor='firebrick')
meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')
plot_i = plt.boxplot(numpy.transpose(abs_error_per_labels[ind_tracts_std_sort_snr_10, :, i_meth]),
positions=ind_fig7 + i_meth_to_display * width + (float(i_meth_to_display) * width) / (
nb_method + 1), widths=width, boxprops=boxprops, medianprops=medianprops,
flierprops=flierprops, whiskerprops=whiskerprops, capprops=capprops)
# plt.errorbar(ind_fig2+i_meth*width+width/2+(float(i_meth)*width)/(nb_method+1), mean_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth], std_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth], color=color, marker='_', linestyle='None', markersize=200*width, markeredgewidth=3)
box_plots.append(plot_i['boxes'][0])
# add alternated vertical background colored bars
for i_xtick in range(0, len(ind_fig7), 2):
plt.axvspan(ind_fig7[i_xtick] - width - width / 4, ind_fig7[i_xtick] + (nb_method + 1) * width - width / 4, facecolor='grey', alpha=0.1)
# plt.legend(box_plots, methods_to_display, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
# plt.legend(box_plots, methods_to_display, loc='best')
# convert xtick labels into integers
xtick_labels = [int(xtick) for xtick in tracts_std[ind_tracts_std_sort_snr_10]]
plt.xticks(ind_fig7 + (numpy.floor(nb_method / 2)) * width * (1.0 + 1.0 / (nb_method + 1)), xtick_labels)
plt.gca().set_xlim([-width, numpy.max(ind_fig7) + (nb_method + 0.5) * width])
plt.gca().set_ylim([0, 18])
plt.gca().yaxis.set_major_locator(plt.MultipleLocator(2))
plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(0.5))
plt.grid(b=True, axis='y', which='both', alpha=0.5)
plt.subplots_adjust(left=0.1)
plt.savefig(param_default.fname_folder_to_save_fig+'/absolute_error_vs_tracts_std_Noise_std_fixed_to_10.pdf', format='PDF')
plt.show(block=False)
def main():
# Initialization to defaults parameters
fname_data = '' # data is empty by default
path_label = '' # empty by default
method = param.method # extraction mode by default
labels_of_interest = param.labels_of_interest
slices_of_interest = param.slices_of_interest
vertebral_levels = param.vertebral_levels
average_all_labels = param.average_all_labels
fname_output = param.fname_output
fname_vertebral_labeling = param.fname_vertebral_labeling
fname_normalizing_label = '' # optional then default is empty
normalization_method = '' # optional then default is empty
actual_vert_levels = None # variable used in case the vertebral levels asked by the user don't correspond exactly to the vertebral levels available in the metric data
warning_vert_levels = None # variable used to warn the user in case the vertebral levels he asked don't correspond exactly to the vertebral levels available in the metric data
verbose = param.verbose
flag_h = 0
ml_clusters = param.ml_clusters
adv_param = param.adv_param
adv_param_user = ''
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput(
'echo $SCT_TESTING_DATA_DIR')
fname_data = '/Users/julien/data/temp/sct_example_data/mt/mtr.nii.gz'
path_label = '/Users/julien/data/temp/sct_example_data/mt/label/atlas/'
method = 'map'
ml_clusters = '0:29,30,31'
labels_of_interest = '0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29'
slices_of_interest = ''
vertebral_levels = ''
average_all_labels = 1
fname_normalizing_label = '' #path_sct+'/testing/data/errsm_23/mt/label/template/MNI-Poly-AMU_CSF.nii.gz'
normalization_method = '' #'whole'
else:
# Check input parameters
try:
opts, args = getopt.getopt(
sys.argv[1:], 'haf:i:l:m:n:o:p:v:w:z:') # define flags
except getopt.GetoptError as err: # check if the arguments are defined
print str(err) # error
usage() # display usage
if not opts:
usage()
for opt, arg in opts: # explore flags
if opt in '-a':
average_all_labels = 1
elif opt in '-f':
path_label = os.path.abspath(arg) # save path of labels folder
elif opt == '-h': # help option
flag_h = 1
elif opt in '-i':
fname_data = arg
elif opt in '-l':
labels_of_interest = arg
elif opt in '-m': # method for metric extraction
method = arg
elif opt in '-n': # filename of the label by which the user wants to normalize
fname_normalizing_label = arg
elif opt in '-o': # output option
fname_output = arg # fname of output file
elif opt in '-p':
adv_param_user = arg
elif opt in '-v':
# vertebral levels option, if the user wants to average the metric across specific vertebral levels
vertebral_levels = arg
elif opt in '-w':
# method used for the normalization by the metric estimation into the normalizing label (see flag -n): 'sbs' for slice-by-slice or 'whole' for normalization after estimation in the whole labels
normalization_method = arg
elif opt in '-z': # slices numbers option
slices_of_interest = arg # save labels numbers
# Display usage with tract parameters by default in case files aren't chosen in arguments inputs
if fname_data == '' or path_label == '' or flag_h:
param.path_label = path_label
usage()
# Check existence of data file
sct.printv('\ncheck existence of input files...', verbose)
sct.check_file_exist(fname_data)
sct.check_folder_exist(path_label)
if fname_normalizing_label:
sct.check_folder_exist(fname_normalizing_label)
# add slash at the end
path_label = sct.slash_at_the_end(path_label, 1)
# Find path to the vertebral labeling file if vertebral levels were specified by the user
if vertebral_levels:
if slices_of_interest: # impossible to select BOTH specific slices and specific vertebral levels
print '\nERROR: You cannot select BOTH vertebral levels AND slice numbers.'
usage()
else:
fname_vertebral_labeling_list = sct.find_file_within_folder(
fname_vertebral_labeling, path_label + '..')
if len(fname_vertebral_labeling_list) > 1:
print color.red + 'ERROR: More than one file named \'' + fname_vertebral_labeling + ' were found in ' + path_label + '. Exit program.' + color.end
sys.exit(2)
elif len(fname_vertebral_labeling_list) == 0:
print color.red + 'ERROR: No file named \'' + fname_vertebral_labeling + ' were found in ' + path_label + '. Exit program.' + color.end
sys.exit(2)
else:
fname_vertebral_labeling = os.path.abspath(
fname_vertebral_labeling_list[0])
# Check input parameters
check_method(method, fname_normalizing_label, normalization_method)
# parse argument for param
if not adv_param_user == '':
adv_param = adv_param_user.replace(' ', '').split(
',') # remove spaces and parse with comma
del adv_param_user # clean variable
# TODO: check integrity of input
# Extract label info
label_id, label_name, label_file = read_label_file(path_label,
param.file_info_label)
nb_labels_total = len(label_id)
# check consistency of label input parameter.
label_id_user, average_all_labels = check_labels(
labels_of_interest, nb_labels_total, average_all_labels,
method) # If 'labels_of_interest' is empty, then
# 'label_id_user' contains the index of all labels in the file info_label.txt
# print parameters
print '\nChecked parameters:'
print ' data ...................... ' + fname_data
print ' folder label .............. ' + path_label
print ' selected labels ........... ' + str(label_id_user)
print ' estimation method ......... ' + method
print ' slices of interest ........ ' + slices_of_interest
print ' vertebral levels .......... ' + vertebral_levels
print ' vertebral labeling file.... ' + fname_vertebral_labeling
print ' advanced parameters ....... ' + str(adv_param)
# Check if the orientation of the data is RPI
orientation_data = get_orientation(fname_data)
# If orientation is not RPI, change to RPI
if orientation_data != 'RPI':
sct.printv(
'\nCreate temporary folder to change the orientation of the NIFTI files into RPI...',
verbose)
path_tmp = sct.slash_at_the_end('tmp.' + time.strftime("%y%m%d%H%M%S"),
1)
sct.create_folder(path_tmp)
# change orientation and load data
sct.printv('\nChange image orientation and load it...', verbose)
data = nib.load(
set_orientation(fname_data, 'RPI',
path_tmp + 'orient_data.nii')).get_data()
# Do the same for labels
sct.printv('\nChange labels orientation and load them...', verbose)
labels = np.empty([nb_labels_total],
dtype=object) # labels(nb_labels_total, x, y, z)
for i_label in range(0, nb_labels_total):
labels[i_label] = nib.load(
set_orientation(path_label + label_file[i_label], 'RPI',
path_tmp + 'orient_' +
label_file[i_label])).get_data()
if fname_normalizing_label: # if the "normalization" option is wanted,
normalizing_label = np.empty(
[1], dtype=object
) # choose this kind of structure so as to keep easily the
# compatibility with the rest of the code (dimensions: (1, x, y, z))
normalizing_label[0] = nib.load(
set_orientation(fname_normalizing_label, 'RPI', path_tmp +
'orient_normalizing_volume.nii')).get_data()
if vertebral_levels: # if vertebral levels were selected,
data_vertebral_labeling = nib.load(
set_orientation(
fname_vertebral_labeling, 'RPI',
path_tmp + 'orient_vertebral_labeling.nii.gz')).get_data()
# Remove the temporary folder used to change the NIFTI files orientation into RPI
sct.printv('\nRemove the temporary folder...', verbose)
status, output = commands.getstatusoutput('rm -rf ' + path_tmp)
else:
# Load image
sct.printv('\nLoad image...', verbose)
data = nib.load(fname_data).get_data()
# Load labels
sct.printv('\nLoad labels...', verbose)
labels = np.empty([nb_labels_total],
dtype=object) # labels(nb_labels_total, x, y, z)
for i_label in range(0, nb_labels_total):
labels[i_label] = nib.load(path_label +
label_file[i_label]).get_data()
if fname_normalizing_label: # if the "normalization" option is wanted,
normalizing_label = np.empty(
[1], dtype=object
) # choose this kind of structure so as to keep easily the
# compatibility with the rest of the code (dimensions: (1, x, y, z))
normalizing_label[0] = nib.load(fname_normalizing_label).get_data(
) # load the data of the normalizing label
if vertebral_levels: # if vertebral levels were selected,
data_vertebral_labeling = nib.load(
fname_vertebral_labeling).get_data()
# Change metric data type into floats for future manipulations (normalization)
data = np.float64(data)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz = data.shape
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# Get dimensions of labels
sct.printv('\nGet dimensions of label...', verbose)
nx_atlas, ny_atlas, nz_atlas = labels[0].shape
sct.printv(
'.. ' + str(nx_atlas) + ' x ' + str(ny_atlas) + ' x ' + str(nz_atlas) +
' x ' + str(nb_labels_total), verbose)
# Check dimensions consistency between atlas and data
if (nx, ny, nz) != (nx_atlas, ny_atlas, nz_atlas):
print '\nERROR: Metric data and labels DO NOT HAVE SAME DIMENSIONS.'
sys.exit(2)
# Update the flag "slices_of_interest" according to the vertebral levels selected by user (if it's the case)
if vertebral_levels:
slices_of_interest, actual_vert_levels, warning_vert_levels = \
get_slices_matching_with_vertebral_levels(data, vertebral_levels, data_vertebral_labeling)
# select slice of interest by cropping data and labels
if slices_of_interest:
data = remove_slices(data, slices_of_interest)
for i_label in range(0, nb_labels_total):
labels[i_label] = remove_slices(labels[i_label],
slices_of_interest)
if fname_normalizing_label: # if the "normalization" option was selected,
normalizing_label[0] = remove_slices(normalizing_label[0],
slices_of_interest)
# if user wants to get unique value across labels, then combine all labels together
if average_all_labels == 1:
sum_labels_user = np.sum(
labels[label_id_user]) # sum the labels selected by user
if method == 'ml' or method == 'map': # in case the maximum likelihood and the average across different labels are wanted
labels_tmp = np.empty([nb_labels_total - len(label_id_user) + 1],
dtype=object)
labels = np.delete(
labels, label_id_user) # remove the labels selected by user
labels_tmp[
0] = sum_labels_user # put the sum of the labels selected by user in first position of the tmp
# variable
for i_label in range(1, len(labels_tmp)):
labels_tmp[i_label] = labels[
i_label -
1] # fill the temporary array with the values of the non-selected labels
labels = labels_tmp # replace the initial labels value by the updated ones (with the summed labels)
del labels_tmp # delete the temporary labels
else: # in other cases than the maximum likelihood, we can remove other labels (not needed for estimation)
labels = np.empty(1, dtype=object)
labels[
0] = sum_labels_user # we create a new label array that includes only the summed labels
if fname_normalizing_label: # if the "normalization" option is wanted
sct.printv('\nExtract normalization values...', verbose)
if normalization_method == 'sbs': # case: the user wants to normalize slice-by-slice
for z in range(0, data.shape[-1]):
normalizing_label_slice = np.empty(
[1], dtype=object
) # in order to keep compatibility with the function
# 'extract_metric_within_tract', define a new array for the slice z of the normalizing labels
normalizing_label_slice[0] = normalizing_label[0][..., z]
metric_normalizing_label = extract_metric_within_tract(
data[..., z], normalizing_label_slice, method, 0)
# estimate the metric mean in the normalizing label for the slice z
if metric_normalizing_label[0][0] != 0:
data[..., z] = data[..., z] / metric_normalizing_label[0][
0] # divide all the slice z by this value
elif normalization_method == 'whole': # case: the user wants to normalize after estimations in the whole labels
metric_mean_norm_label, metric_std_norm_label = extract_metric_within_tract(
data, normalizing_label, method,
param.verbose) # mean and std are lists
# identify cluster for each tract (for use with robust ML)
ml_clusters_array = get_clusters(ml_clusters, labels)
# extract metrics within labels
sct.printv('\nExtract metric within labels...', verbose)
metric_mean, metric_std = extract_metric_within_tract(
data, labels, method, verbose, ml_clusters_array,
adv_param) # mean and std are lists
if fname_normalizing_label and normalization_method == 'whole': # case: user wants to normalize after estimations in the whole labels
metric_mean, metric_std = np.divide(metric_mean,
metric_mean_norm_label), np.divide(
metric_std,
metric_std_norm_label)
# update label name if average
if average_all_labels == 1:
label_name[0] = 'AVERAGED' + ' -'.join(
label_name[i]
for i in label_id_user) # concatenate the names of the
# labels selected by the user if the average tag was asked
label_id_user = [
0
] # update "label_id_user" to select the "averaged" label (which is in first position)
metric_mean = metric_mean[label_id_user]
metric_std = metric_std[label_id_user]
# display metrics
sct.printv('\nEstimation results:', 1)
for i in range(0, metric_mean.size):
sct.printv(
str(label_id_user[i]) + ', ' + str(label_name[label_id_user[i]]) +
': ' + str(metric_mean[i]) + ' +/- ' + str(metric_std[i]), 1,
'info')
# save and display metrics
save_metrics(label_id_user, label_name, slices_of_interest, metric_mean,
metric_std, fname_output, fname_data, method,
fname_normalizing_label, actual_vert_levels,
warning_vert_levels)
def main():
results_folder = param_default.results_folder
methods_to_display = param_default.methods_to_display
# Parameters for debug mode
if param_default.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
results_folder = "/Users/slevy_local/spinalcordtoolbox/dev/atlas/validate_atlas/results_20150210_200iter" #"C:/cygwin64/home/Simon_2/data_methods_comparison"
path_sct = '/Users/slevy_local/spinalcordtoolbox' #'C:/cygwin64/home/Simon_2/spinalcordtoolbox'
methods_to_display = 'bin,wa,wath,ml,map'
else:
path_sct = os.environ.get("SCT_DIR",
os.path.dirname(os.path.dirname(__file__)))
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'i:m:') # define flags
except getopt.GetoptError as err: # check if the arguments are defined
print str(err) # error
# usage() # display usage
# if not opts:
# print 'Please enter the path to the result folder. Exit program.'
# sys.exit(1)
# # usage()
for opt, arg in opts: # explore flags
if opt in '-i':
results_folder = arg
if opt in '-m':
methods_to_display = arg
# Append path that contains scripts, to be able to load modules
sys.path.append(os.path.join(path_sct, "scripts"))
import sct_utils as sct
sct.printv("Working directory: " + os.getcwd())
results_folder_noise = results_folder + '/noise'
results_folder_tracts = results_folder + '/tracts'
sct.printv(
'\n\nData will be extracted from folder ' + results_folder_noise +
' and ' + results_folder_tracts, 'warning')
sct.printv('\t\tCheck existence...')
sct.check_folder_exist(results_folder_noise)
sct.check_folder_exist(results_folder_tracts)
# Extract methods to display
methods_to_display = methods_to_display.strip().split(',')
# Extract file names of the results files
fname_results_noise = glob.glob(os.path.join(results_folder_noise,
"*.txt"))
fname_results_tracts = glob.glob(
os.path.join(results_folder_tracts, "*.txt"))
fname_results = fname_results_noise + fname_results_tracts
# Remove doublons (due to the two folders)
# for i_fname in range(0, len(fname_results)):
# for j_fname in range(0, len(fname_results)):
# if (i_fname != j_fname) & (os.path.basename(fname_results[i_fname]) == os.path.basename(fname_results[j_fname])):
# fname_results.remove(fname_results[j_fname])
file_results = []
for fname in fname_results:
file_results.append(os.path.basename(fname))
for file in file_results:
if file_results.count(file) > 1:
ind = file_results.index(file)
fname_results.remove(fname_results[ind])
file_results.remove(file)
nb_results_file = len(fname_results)
# 1st dim: SNR, 2nd dim: tract std, 3rd dim: mean abs error, 4th dim: std abs error
# result_array = numpy.empty((nb_results_file, nb_results_file, 3), dtype=object)
# SNR
snr = numpy.zeros((nb_results_file))
# Tracts std
tracts_std = numpy.zeros((nb_results_file))
# CSF value
csf_values = numpy.zeros((nb_results_file))
# methods' name
methods_name = [] #numpy.empty((nb_results_file, nb_method), dtype=object)
# labels
error_per_label = []
std_per_label = []
labels_id = []
# median
median_results = numpy.zeros((nb_results_file, 5))
# median std across bootstraps
median_std = numpy.zeros((nb_results_file, 5))
# min
min_results = numpy.zeros((nb_results_file, 5))
# max
max_results = numpy.zeros((nb_results_file, 5))
#
for i_file in range(0, nb_results_file):
# Open file
f = open(fname_results[i_file]) # open file
# Extract all lines in .txt file
lines = [line for line in f.readlines() if line.strip()]
# extract SNR
# find all index of lines containing the string "sigma noise"
ind_line_noise = [
lines.index(line_noise) for line_noise in lines
if "sigma noise" in line_noise
]
if len(ind_line_noise) != 1:
sct.printv(
"ERROR: number of lines including \"sigma noise\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
# result_array[:, i_file, i_file] = int(''.join(c for c in lines[ind_line_noise[0]] if c.isdigit()))
snr[i_file] = int(''.join(c for c in lines[ind_line_noise[0]]
if c.isdigit()))
# extract tract std
ind_line_tract_std = [
lines.index(line_tract_std) for line_tract_std in lines
if "range tracts" in line_tract_std
]
if len(ind_line_tract_std) != 1:
sct.printv(
"ERROR: number of lines including \"range tracts\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
# result_array[i_file, i_file, :] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# regex = re.compile(''('(.*)':) # re.I permet d'ignorer la case (majuscule/minuscule)
# match = regex.search(lines[ind_line_tract_std[0]])
# result_array[:, i_file, :, :] = match.group(1) # le groupe 1 correspond a '.*'
tracts_std[i_file] = int(''.join(
c for c in lines[ind_line_tract_std[0]].split(':')[1]
if c.isdigit()))
# extract CSF value
ind_line_csf_value = [
lines.index(line_csf_value) for line_csf_value in lines
if "# value CSF" in line_csf_value
]
if len(ind_line_csf_value) != 1:
sct.printv(
"ERROR: number of lines including \"range tracts\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
# result_array[i_file, i_file, :] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# regex = re.compile(''('(.*)':) # re.I permet d'ignorer la case (majuscule/minuscule)
# match = regex.search(lines[ind_line_tract_std[0]])
# result_array[:, i_file, :, :] = match.group(1) # le groupe 1 correspond a '.*'
csf_values[i_file] = int(''.join(
c for c in lines[ind_line_csf_value[0]].split(':')[1]
if c.isdigit()))
# extract method name
ind_line_label = [
lines.index(line_label) for line_label in lines
if "Label" in line_label
]
if len(ind_line_label) != 1:
sct.printv(
"ERROR: number of lines including \"Label\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
# methods_name[i_file, :] = numpy.array(lines[ind_line_label[0]].strip().split(',')[1:])
methods_name.append(lines[ind_line_label[0]].strip().replace(
' ', '').split(',')[1:])
# extract median
ind_line_median = [
lines.index(line_median) for line_median in lines
if "median" in line_median
]
if len(ind_line_median) != 1:
sct.printv(
"WARNING: number of lines including \"median\" is different from 1. Exit program.",
'warning')
# sys.exit(1)
else:
median = lines[ind_line_median[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 0] = [float(m.split('(')[0]) for m in median]
median_results[i_file, :] = numpy.array(
[float(m.split('(')[0]) for m in median])
median_std[i_file, :] = numpy.array(
[float(m.split('(')[1][:-1]) for m in median])
# extract min
ind_line_min = [
lines.index(line_min) for line_min in lines if "min," in line_min
]
if len(ind_line_min) != 1:
sct.printv(
"WARNING: number of lines including \"min\" is different from 1. Exit program.",
'warning')
# sys.exit(1)
else:
min = lines[ind_line_min[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in min]
min_results[i_file, :] = numpy.array(
[float(m.split('(')[0]) for m in min])
# extract max
ind_line_max = [
lines.index(line_max) for line_max in lines if "max" in line_max
]
if len(ind_line_max) != 1:
sct.printv(
"WARNING: number of lines including \"max\" is different from 1. Exit program.",
'warning')
# sys.exit(1)
else:
max = lines[ind_line_max[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in max]
max_results[i_file, :] = numpy.array(
[float(m.split('(')[0]) for m in max])
# extract error for each label
error_per_label_for_file_i = []
std_per_label_for_file_i = []
labels_id_for_file_i = []
# Due to 2 different kind of file structure, the number of the last label line must be adapted
if not ind_line_median:
ind_line_median = [len(lines) + 1]
for i_line in range(ind_line_label[0] + 1, ind_line_median[0] - 1):
line_label_i = lines[i_line].strip().split(',')
error_per_label_for_file_i.append([
float(error.strip().split('(')[0])
for error in line_label_i[1:]
])
std_per_label_for_file_i.append([
float(error.strip().split('(')[1][:-1])
for error in line_label_i[1:]
])
labels_id_for_file_i.append(line_label_i[0])
error_per_label.append(error_per_label_for_file_i)
std_per_label.append(std_per_label_for_file_i)
labels_id.append(labels_id_for_file_i)
# close file
f.close()
# check if all the files in the result folder were generated with the same number of methods
if not all(x == methods_name[0] for x in methods_name):
sct.printv(
'ERROR: All the generated files in folder ' + results_folder +
' have not been generated with the same number of methods. Exit program.',
'error')
sys.exit(1)
# check if all the files in the result folder were generated with the same labels
if not all(x == labels_id[0] for x in labels_id):
sct.printv(
'ERROR: All the generated files in folder ' + results_folder +
' have not been generated with the same labels. Exit program.',
'error')
sys.exit(1)
# convert the list "error_per_label" into a numpy array to ease further manipulations
error_per_label = numpy.array(error_per_label)
std_per_label = numpy.array(std_per_label)
# compute different stats
abs_error_per_labels = numpy.absolute(error_per_label)
max_abs_error_per_meth = numpy.amax(abs_error_per_labels, axis=1)
min_abs_error_per_meth = numpy.amin(abs_error_per_labels, axis=1)
mean_abs_error_per_meth = numpy.mean(abs_error_per_labels, axis=1)
std_abs_error_per_meth = numpy.std(abs_error_per_labels, axis=1)
nb_method = len(methods_to_display)
sct.printv('Noise std of the ' + str(nb_results_file) +
' generated files:')
print snr
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Tracts std of the ' + str(nb_results_file) +
' generated files:')
print tracts_std
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('CSF value of the ' + str(nb_results_file) +
' generated files:')
print csf_values
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Methods used to generate results for the ' +
str(nb_results_file) + ' generated files:')
print methods_name
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Median obtained with each method (in colons) for the ' +
str(nb_results_file) + ' generated files (in lines):')
print median_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Minimum obtained with each method (in colons) for the ' +
str(nb_results_file) + ' generated files (in lines):')
print min_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Maximum obtained with each method (in colons) for the ' +
str(nb_results_file) + ' generated files (in lines):')
print max_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Labels\' ID (in colons) for the ' + str(nb_results_file) +
' generated files (in lines):')
print labels_id
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Errors obtained with each method (in colons) for the ' +
str(nb_results_file) + ' generated files (in lines):')
print error_per_label
# *********************************************** START PLOTTING HERE **********************************************
# # plot A (NOT GOOD)
# fig0 = plt.figure(0)
# fig0.suptitle('Absolute error within all tracts as a function of noise std')
#
# fig0_ax = fig0.add_subplot(111)
# fig0_ax.grid(True)
# fig0_ax.set_xlabel('Noise std')
# fig0_ax.set_ylabel('Absolute error')
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
#
# for meth, color in zip(methods_name[0], colors):
# if meth != 'mlwa':
# i_meth = methods_name[0].index(meth)
#
# # median
# plt.plot(snr[ind_tracts_std_10], median_results[ind_tracts_std_10, i_meth][0], label='median '+meth, color=color, marker='o', linestyle='None', markersize=5.0, linewidth=2.0)
# # min
# plt.plot(snr[ind_tracts_std_10], min_results[ind_tracts_std_10, i_meth][0], label='min '+meth, color=color, marker='_', linestyle='None', markersize=10.0, linewidth=20.0)
# # max
# plt.plot(snr[ind_tracts_std_10], max_results[ind_tracts_std_10, i_meth][0], label='max '+meth, color=color, marker='+', linestyle='None', markersize=10.0, linewidth=20.0)
#
# handles, labels = fig0_ax.get_legend_handles_labels()
# fig0_ax.legend(handles, labels, loc='best', handler_map={Line2D: HandlerLine2D(numpoints=1)}, fontsize=16)
#
#
# # plot B (NOT GOOD)
# fig1 = plt.figure(1)
# fig1.suptitle('Absolute error within all tracts as a function of tract std')
#
# fig1_ax = fig1.add_subplot(111)
# fig1_ax.grid(True)
# fig1_ax.set_xlabel('Tract std (percentage of the true value in tract)')
# fig1_ax.set_ylabel('Absolute error')
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
#
# for meth, color in zip(methods_name[0], colors):
# if meth != 'mlwa':
# i_meth = methods_name[0].index(meth)
#
# # median
# plt.plot(tracts_std[ind_snr_10], median_results[ind_snr_10, i_meth][0], label='median '+meth, color=color, marker='o', linestyle='None', markersize=5.0, linewidth=2.0)
# # min
# plt.plot(tracts_std[ind_snr_10], min_results[ind_snr_10, i_meth][0], label='min '+meth, color=color, marker='_', linestyle='None', markersize=10.0, linewidth=20.0)
# # max
# plt.plot(tracts_std[ind_snr_10], max_results[ind_snr_10, i_meth][0], label='max '+meth, color=color, marker='+', linestyle='None', markersize=10.0, linewidth=20.0)
#
# handles, labels = fig1_ax.get_legend_handles_labels()
# fig1_ax.legend(handles, labels, loc='best', handler_map={Line2D: HandlerLine2D(numpoints=1)}, fontsize=16)
# Plot A
ind_tracts_std_10 = numpy.where(
(tracts_std == 10) & (snr != 50)) # indexes where TRACTS STD=10
ind_ind_snr_sort_tracts_std_10 = numpy.argsort(
snr[ind_tracts_std_10]
) # indexes of indexes where TRACTS STD=10 sorted according to SNR values (in ascending order)
ind_snr_sort_tracts_std_10 = ind_tracts_std_10[0][
ind_ind_snr_sort_tracts_std_10] # indexes where TRACTS STD=10 sorted according to SNR values (in ascending order)
# fig2 = plt.figure(2)
# ind_fig2 = numpy.arange(len(snr[ind_snr_sort_tracts_std_10])) * 1.2
# width = 1.0 / (nb_method + 1)
# plt.ylabel('Error (%)')
# plt.xlabel('Noise std')
# plt.title('Error within all tracts as a function of noise std')
# plt.xticks(ind_fig2 + 0.5, snr[ind_snr_sort_tracts_std_10])
# plt.gca().set_xlim([-width / (nb_method + 1), numpy.max(ind_fig2) + 1])
# plt.gca().yaxis.grid(True)
#
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
# bar_plots = []
# for meth, color in zip(methods_name[0], colors):
# i_meth = methods_name[0].index(meth)
#
# plot_i = plt.bar(ind_fig2 + i_meth * width + (float(i_meth) * width) / (nb_method + 1),
# max_results[ind_snr_sort_tracts_std_10, i_meth] - min_results[
# ind_snr_sort_tracts_std_10, i_meth], width,
# min_results[ind_snr_sort_tracts_std_10, i_meth], edgecolor=color, color='white', linewidth=3)
# plt.plot(ind_fig2 + i_meth * width + width / 2 + (float(i_meth) * width) / (nb_method + 1),
# median_results[ind_snr_sort_tracts_std_10, i_meth], color=color, marker='_', linestyle='None',
# markersize=200 * width, markeredgewidth=3)
# bar_plots.append(plot_i[0])
#
# plt.legend(bar_plots, methods_name[0], bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
# Plot B
ind_snr_10 = numpy.where(
(snr == 10) & (tracts_std != 50)) # indexes where SNR=10
ind_ind_tracts_std_sort_snr_10 = numpy.argsort(
tracts_std[ind_snr_10]
) # indexes of indexes where SNR=10 sorted according to tracts_std values (in ascending order)
ind_tracts_std_sort_snr_10 = ind_snr_10[0][
ind_ind_tracts_std_sort_snr_10] # indexes where SNR=10 sorted according to tracts_std values (in ascending order)
# fig3 = plt.figure(3)
# ind_fig3 = numpy.arange(len(tracts_std[ind_tracts_std_sort_snr_10])) * 1.2
# width = 1.0 / (nb_method + 1)
# plt.ylabel('Error (%)')
# plt.xlabel('Tracts std (in percentage of the mean value of the tracts)')
# plt.title('Error within all tracts as a function of tracts std')
# plt.xticks(ind_fig3 + 0.5, tracts_std[ind_tracts_std_sort_snr_10])
# plt.gca().set_xlim([-width / (nb_method + 1), numpy.max(ind_fig3) + 1])
# plt.gca().yaxis.grid(True)
#
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
# bar_plots = []
# for meth, color in zip(methods_name[0], colors):
# i_meth = methods_name[0].index(meth)
#
# plot_i = plt.bar(ind_fig3 + i_meth * width + (float(i_meth) * width) / (nb_method + 1),
# max_results[ind_tracts_std_sort_snr_10, i_meth] - min_results[
# ind_tracts_std_sort_snr_10, i_meth], width,
# min_results[ind_tracts_std_sort_snr_10, i_meth], edgecolor=color, color='white', linewidth=3)
# plt.plot(ind_fig2 + i_meth * width + width / 2 + (float(i_meth) * width) / (nb_method + 1),
# median_results[ind_tracts_std_sort_snr_10, i_meth], color=color, marker='_', linestyle='None',
# markersize=200 * width, markeredgewidth=3)
# bar_plots.append(plot_i[0])
#
# plt.legend(bar_plots, methods_name[0], bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
# # Plot A -- v2: Absolute error (min, max, mean)
#
# fig4 = plt.figure(4)
# ind_fig4 = numpy.arange(len(snr[ind_snr_sort_tracts_std_10])) * 1.2
# width = 1.0 / (nb_method + 1)
# plt.ylabel('Absolute error (%)')
# plt.xlabel('Noise std')
# plt.title('Absolute error within all tracts as a function of noise std')
# plt.xticks(ind_fig4 + 0.5, snr[ind_snr_sort_tracts_std_10])
# plt.gca().set_xlim([-width / (nb_method + 1), numpy.max(ind_fig4) + 1])
# plt.gca().yaxis.grid(True)
#
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
# bar_plots = []
# for meth, color in zip(methods_name[0], colors):
# i_meth = methods_name[0].index(meth)
#
# plot_i = plt.bar(ind_fig4 + i_meth * width + (float(i_meth) * width) / (nb_method + 1),
# max_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth] - min_abs_error_per_meth[
# ind_snr_sort_tracts_std_10, i_meth], width,
# min_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth], edgecolor=color, color='white',
# linewidth=3)
# plt.errorbar(ind_fig2 + i_meth * width + width / 2 + (float(i_meth) * width) / (nb_method + 1),
# mean_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth],
# std_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth], color=color, marker='_',
# linestyle='None', markersize=200 * width, markeredgewidth=3)
# bar_plots.append(plot_i[0])
#
# plt.legend(bar_plots, methods_name[0], bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
#
# # Plot B -- v2: Absolute error (min, max, mean)
# fig5 = plt.figure(5)
# ind_fig5 = numpy.arange(len(tracts_std[ind_tracts_std_sort_snr_10])) * 1.2
# width = 1.0 / (nb_method + 1)
# plt.ylabel('Absolute error (%)')
# plt.xlabel('Tracts std (in percentage of the mean value of the tracts)')
# plt.title('Absolute error within all tracts as a function of tracts std')
# plt.xticks(ind_fig5 + 0.5, tracts_std[ind_tracts_std_sort_snr_10])
# plt.gca().set_xlim([-width / (nb_method + 1), numpy.max(ind_fig5) + 1])
# plt.gca().yaxis.grid(True)
#
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
# bar_plots = []
# for meth, color in zip(methods_name[0], colors):
# i_meth = methods_name[0].index(meth)
#
# plot_i = plt.bar(ind_fig5 + i_meth * width + (float(i_meth) * width) / (nb_method + 1),
# max_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth] - min_abs_error_per_meth[
# ind_tracts_std_sort_snr_10, i_meth], width,
# min_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth], edgecolor=color, color='white',
# linewidth=3)
# plt.errorbar(ind_fig2 + i_meth * width + width / 2 + (float(i_meth) * width) / (nb_method + 1),
# mean_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth],
# std_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth], color=color, marker='_',
# linestyle='None', markersize=200 * width, markeredgewidth=3)
# bar_plots.append(plot_i[0])
#
# plt.legend(bar_plots, methods_name[0], bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
matplotlib.rcParams.update({'font.size': 45, 'font.family': 'trebuchet'})
plt.rcParams['xtick.major.pad'] = '9'
plt.rcParams['ytick.major.pad'] = '15'
# Plot A -- v3: Box plots absolute error
fig6 = plt.figure(6, figsize=(30, 16))
width = 1.0 / (nb_method + 1)
ind_fig6 = numpy.arange(len(
snr[ind_snr_sort_tracts_std_10])) * (1.0 + width)
plt.ylabel('Absolute error (%)\n', fontsize=55)
plt.xlabel('Noise STD (% of true WM value)', fontsize=55)
plt.title('Absolute error within all tracts as a function of noise std\n',
fontsize=65)
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
box_plots = []
for meth, color in zip(methods_to_display, colors):
i_meth = methods_name[0].index(meth)
i_meth_to_display = methods_to_display.index(meth)
boxprops = dict(linewidth=4, color=color)
flierprops = dict(color=color,
markeredgewidth=0.7,
markersize=15,
marker='.')
whiskerprops = dict(color=color, linewidth=3)
capprops = dict(color=color, linewidth=3)
medianprops = dict(linewidth=4, color=color)
meanpointprops = dict(marker='D',
markeredgecolor='black',
markerfacecolor='firebrick')
meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')
plot_i = plt.boxplot(
numpy.transpose(abs_error_per_labels[ind_snr_sort_tracts_std_10, :,
i_meth]),
positions=ind_fig6 + i_meth_to_display * width +
(float(i_meth_to_display) * width) / (nb_method + 1),
widths=width,
boxprops=boxprops,
medianprops=medianprops,
flierprops=flierprops,
whiskerprops=whiskerprops,
capprops=capprops)
# plt.errorbar(ind_fig2+i_meth*width+width/2+(float(i_meth)*width)/(nb_method+1), mean_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth], std_abs_error_per_meth[ind_snr_sort_tracts_std_10, i_meth], color=color, marker='_', linestyle='None', markersize=200*width, markeredgewidth=3)
box_plots.append(plot_i['boxes'][0])
# add alternated vertical background colored bars
for i_xtick in range(0, len(ind_fig6), 2):
plt.axvspan(ind_fig6[i_xtick] - width - width / 4,
ind_fig6[i_xtick] + (nb_method + 1) * width - width / 4,
facecolor='grey',
alpha=0.1)
# plt.legend(box_plots, methods_to_display, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
# plt.legend(box_plots, methods_to_display, loc='best')
# convert xtick labels into integers
xtick_labels = [int(xtick) for xtick in snr[ind_snr_sort_tracts_std_10]]
plt.xticks(
ind_fig6 + (numpy.floor(nb_method / 2)) * width *
(1.0 + 1.0 / (nb_method + 1)), xtick_labels)
plt.gca().set_xlim(
[-width, numpy.max(ind_fig6) + (nb_method + 0.5) * width])
plt.gca().set_ylim([0, 18])
plt.gca().yaxis.set_major_locator(plt.MultipleLocator(2))
plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(0.5))
plt.grid(b=True, axis='y', which='both', alpha=0.5)
plt.subplots_adjust(left=0.1)
plt.savefig(os.path.join(
param_default.fname_folder_to_save_fig,
'absolute_error_vs_noise_std_Tracts_std_fixed_to_10.pdf'),
format='PDF')
# Plot B -- v3: Box plots absolute error
fig7 = plt.figure(7, figsize=(30, 16))
width = 1.0 / (nb_method + 1)
ind_fig7 = numpy.arange(len(
tracts_std[ind_tracts_std_sort_snr_10])) * (1.0 + width)
plt.ylabel('Absolute error (%)\n', fontsize=55)
plt.xlabel('Tracts STD (% of true WM value)', fontsize=55)
plt.title('Absolute error within all tracts as a function of tracts std\n',
fontsize=65)
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
box_plots = []
for meth, color in zip(methods_to_display, colors):
i_meth = methods_name[0].index(meth)
i_meth_to_display = methods_to_display.index(meth)
boxprops = dict(linewidth=4, color=color)
flierprops = dict(color=color,
markeredgewidth=0.7,
markersize=15,
marker='.')
whiskerprops = dict(color=color, linewidth=3)
capprops = dict(color=color, linewidth=3)
medianprops = dict(linewidth=4, color=color)
meanpointprops = dict(marker='D',
markeredgecolor='black',
markerfacecolor='firebrick')
meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')
plot_i = plt.boxplot(
numpy.transpose(abs_error_per_labels[ind_tracts_std_sort_snr_10, :,
i_meth]),
positions=ind_fig7 + i_meth_to_display * width +
(float(i_meth_to_display) * width) / (nb_method + 1),
widths=width,
boxprops=boxprops,
medianprops=medianprops,
flierprops=flierprops,
whiskerprops=whiskerprops,
capprops=capprops)
# plt.errorbar(ind_fig2+i_meth*width+width/2+(float(i_meth)*width)/(nb_method+1), mean_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth], std_abs_error_per_meth[ind_tracts_std_sort_snr_10, i_meth], color=color, marker='_', linestyle='None', markersize=200*width, markeredgewidth=3)
box_plots.append(plot_i['boxes'][0])
# add alternated vertical background colored bars
for i_xtick in range(0, len(ind_fig7), 2):
plt.axvspan(ind_fig7[i_xtick] - width - width / 4,
ind_fig7[i_xtick] + (nb_method + 1) * width - width / 4,
facecolor='grey',
alpha=0.1)
# plt.legend(box_plots, methods_to_display, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
# plt.legend(box_plots, methods_to_display, loc='best')
# convert xtick labels into integers
xtick_labels = [
int(xtick) for xtick in tracts_std[ind_tracts_std_sort_snr_10]
]
plt.xticks(
ind_fig7 + (numpy.floor(nb_method / 2)) * width *
(1.0 + 1.0 / (nb_method + 1)), xtick_labels)
plt.gca().set_xlim(
[-width, numpy.max(ind_fig7) + (nb_method + 0.5) * width])
plt.gca().set_ylim([0, 18])
plt.gca().yaxis.set_major_locator(plt.MultipleLocator(2))
plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(0.5))
plt.grid(b=True, axis='y', which='both', alpha=0.5)
plt.subplots_adjust(left=0.1)
plt.savefig(os.path.join(
param_default.fname_folder_to_save_fig,
'absolute_error_vs_tracts_std_Noise_std_fixed_to_10.pdf'),
format='PDF')
plt.show(block=False)
def main():
# Initialization
path_atlas = ''
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:s:m:g:t:v:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
path_atlas = arg
elif opt in ('-s'):
param.fname_seg = arg
elif opt in ('-m'):
param.fname_GM = arg
elif opt in ('-t'):
param.threshold_atlas = float(arg)
elif opt in ('-g'):
param.threshold_GM = float(arg)
elif opt in ('-v'):
param.verbose = int(arg)
# display usage if a mandatory argument is not provided
if path_atlas == '' and not param.debug:
# get path of the toolbox
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
path_atlas = path_sct+'/data/atlas/'
param.fname_seg = path_sct+'/data/template/MNI-Poly-AMU_cord.nii.gz'
param.fname_GM = path_sct+'/data/template/MNI-Poly-AMU_GM.nii.gz'
# print arguments
if param.verbose:
print 'Check input parameters...'
print '.. Atlas folder path: '+path_atlas
print '.. Spinal cord segmentation path: '+param.fname_seg
print '.. Spinal cord gray matter path: '+param.fname_GM
print '.. Atlas threshold= '+str(param.threshold_atlas)
# Check for end-caracter of folder path
if path_atlas[-1] != "/": path_atlas=path_atlas+"/";
# Check folder existence
sct.printv('\nCheck atlas existence...', param.verbose)
sct.check_folder_exist(path_atlas)
# Extract atlas info
atlas_id, atlas_name, atlas_file = read_label_file(path_atlas)
nb_tracts_total = len(atlas_id)
# Load atlas
sct.printv('\nLoad atlas...', param.verbose)
atlas = np.empty([nb_tracts_total], dtype=object) # labels(nb_labels_total, x, y, z)
for i_atlas in range(0, nb_tracts_total):
atlas[i_atlas] = nib.load(path_atlas+atlas_file[i_atlas]).get_data()
# Check integrity
sct.printv('\nCheck atlas integrity...', param.verbose)
check_integrity(atlas, atlas_id, atlas_name)
example=["0", "1", "2"],
default_value="1",
)
return parser
if __name__ == "__main__":
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
ml_param = Param()
fname_gm = arguments["-gm"]
fname_wm = arguments["-wm"]
path_template = arguments["-t"]
if not sct.check_folder_exist(path_template):
sct.printv(
parser.usage.generate(
error="ERROR: label/ folder does not exist. Please specify the path to the template using flag -t"
)
)
fname_warp_template = arguments["-w"]
fname_warp_target2template = None
fname_manual_gmseg = None
fname_sc_seg = None
if "-param" in arguments:
ml_param.param_reg = arguments["-param"]
if "-manual-gm" in arguments:
fname_manual_gmseg = arguments["-manual-gm"]
def main():
results_folder = param_default.results_folder
methods_to_display = param_default.methods_to_display
noise_std_to_display = param_default.noise_std_to_display
tracts_std_to_display = param_default.tracts_std_to_display
csf_value_to_display = param_default.csf_value_to_display
nb_RL_labels = param_default.nb_RL_labels
# Parameters for debug mode
if param_default.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
results_folder = "/Users/slevy_local/spinalcordtoolbox/dev/atlas/validate_atlas/results_20150210_200iter" #"C:/cygwin64/home/Simon_2/data_methods_comparison"
path_sct = '/Users/slevy_local/spinalcordtoolbox' #'C:/cygwin64/home/Simon_2/spinalcordtoolbox'
else:
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'i:m:') # define flags
except getopt.GetoptError as err: # check if the arguments are defined
print str(err) # error
# usage() # display usage
# if not opts:
# print 'Please enter the path to the result folder. Exit program.'
# sys.exit(1)
# # usage()
for opt, arg in opts: # explore flags
if opt in '-i':
results_folder = arg
if opt in '-m':
methods_to_display = arg
# Append path that contains scripts, to be able to load modules
sys.path.append(path_sct + '/scripts')
import sct_utils as sct
import isct_get_fractional_volume
sct.printv("Working directory: " + os.getcwd())
results_folder_noise = results_folder + '/noise'
results_folder_tracts = results_folder + '/tracts'
results_folder_csf = results_folder + '/csf'
sct.printv(
'\n\nData will be extracted from folder ' + results_folder_noise +
' , ' + results_folder_tracts + ' and ' + results_folder_csf + '.',
'warning')
sct.printv('\t\tCheck existence...')
sct.check_folder_exist(results_folder_noise)
sct.check_folder_exist(results_folder_tracts)
sct.check_folder_exist(results_folder_csf)
# Extract methods to display
methods_to_display = methods_to_display.strip().split(',')
# Extract file names of the results files
fname_results_noise = glob.glob(results_folder_noise + '/*.txt')
fname_results_tracts = glob.glob(results_folder_tracts + '/*.txt')
fname_results_csf = glob.glob(results_folder_csf + '/*.txt')
fname_results = fname_results_noise + fname_results_tracts + fname_results_csf
# Remove doublons (due to the two folders)
# for i_fname in range(0, len(fname_results)):
# for j_fname in range(0, len(fname_results)):
# if (i_fname != j_fname) & (os.path.basename(fname_results[i_fname]) == os.path.basename(fname_results[j_fname])):
# fname_results.remove(fname_results[j_fname])
file_results = []
for fname in fname_results:
file_results.append(os.path.basename(fname))
for file in file_results:
if file_results.count(file) > 1:
ind = file_results.index(file)
fname_results.remove(fname_results[ind])
file_results.remove(file)
nb_results_file = len(fname_results)
# 1st dim: SNR, 2nd dim: tract std, 3rd dim: mean abs error, 4th dim: std abs error
# result_array = numpy.empty((nb_results_file, nb_results_file, 3), dtype=object)
# SNR
snr = numpy.zeros((nb_results_file))
# Tracts std
tracts_std = numpy.zeros((nb_results_file))
# CSF value
csf_values = numpy.zeros((nb_results_file))
# methods' name
methods_name = [] #numpy.empty((nb_results_file, nb_method), dtype=object)
# labels
error_per_label = []
std_per_label = []
labels_id = []
# median
median_results = numpy.zeros((nb_results_file, 5))
# median std across bootstraps
median_std = numpy.zeros((nb_results_file, 5))
# min
min_results = numpy.zeros((nb_results_file, 5))
# max
max_results = numpy.zeros((nb_results_file, 5))
#
for i_file in range(0, nb_results_file):
# Open file
f = open(fname_results[i_file]) # open file
# Extract all lines in .txt file
lines = [line for line in f.readlines() if line.strip()]
# extract SNR
# find all index of lines containing the string "sigma noise"
ind_line_noise = [
lines.index(line_noise) for line_noise in lines
if "sigma noise" in line_noise
]
if len(ind_line_noise) != 1:
sct.printv(
"ERROR: number of lines including \"sigma noise\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
# result_array[:, i_file, i_file] = int(''.join(c for c in lines[ind_line_noise[0]] if c.isdigit()))
snr[i_file] = int(''.join(c for c in lines[ind_line_noise[0]]
if c.isdigit()))
# extract tract std
ind_line_tract_std = [
lines.index(line_tract_std) for line_tract_std in lines
if "range tracts" in line_tract_std
]
if len(ind_line_tract_std) != 1:
sct.printv(
"ERROR: number of lines including \"range tracts\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
# result_array[i_file, i_file, :] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# regex = re.compile(''('(.*)':) # re.I permet d'ignorer la case (majuscule/minuscule)
# match = regex.search(lines[ind_line_tract_std[0]])
# result_array[:, i_file, :, :] = match.group(1) # le groupe 1 correspond a '.*'
tracts_std[i_file] = int(''.join(
c for c in lines[ind_line_tract_std[0]].split(':')[1]
if c.isdigit()))
# extract CSF value
ind_line_csf_value = [
lines.index(line_csf_value) for line_csf_value in lines
if "# value CSF" in line_csf_value
]
if len(ind_line_csf_value) != 1:
sct.printv(
"ERROR: number of lines including \"range tracts\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
# result_array[i_file, i_file, :] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# regex = re.compile(''('(.*)':) # re.I permet d'ignorer la case (majuscule/minuscule)
# match = regex.search(lines[ind_line_tract_std[0]])
# result_array[:, i_file, :, :] = match.group(1) # le groupe 1 correspond a '.*'
csf_values[i_file] = int(''.join(
c for c in lines[ind_line_csf_value[0]].split(':')[1]
if c.isdigit()))
# extract method name
ind_line_label = [
lines.index(line_label) for line_label in lines
if "Label" in line_label
]
if len(ind_line_label) != 1:
sct.printv(
"ERROR: number of lines including \"Label\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
# methods_name[i_file, :] = numpy.array(lines[ind_line_label[0]].strip().split(',')[1:])
methods_name.append(lines[ind_line_label[0]].strip().replace(
' ', '').split(',')[1:])
# extract median
ind_line_median = [
lines.index(line_median) for line_median in lines
if "median" in line_median
]
if len(ind_line_median) != 1:
sct.printv(
"WARNING: number of lines including \"median\" is different from 1. Exit program.",
'warning')
# sys.exit(1)
else:
median = lines[ind_line_median[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 0] = [float(m.split('(')[0]) for m in median]
median_results[i_file, :] = numpy.array(
[float(m.split('(')[0]) for m in median])
median_std[i_file, :] = numpy.array(
[float(m.split('(')[1][:-1]) for m in median])
# extract min
ind_line_min = [
lines.index(line_min) for line_min in lines if "min," in line_min
]
if len(ind_line_min) != 1:
sct.printv(
"WARNING: number of lines including \"min\" is different from 1. Exit program.",
'warning')
# sys.exit(1)
else:
min = lines[ind_line_min[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in min]
min_results[i_file, :] = numpy.array(
[float(m.split('(')[0]) for m in min])
# extract max
ind_line_max = [
lines.index(line_max) for line_max in lines if "max" in line_max
]
if len(ind_line_max) != 1:
sct.printv(
"WARNING: number of lines including \"max\" is different from 1. Exit program.",
'warning')
# sys.exit(1)
else:
max = lines[ind_line_max[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in max]
max_results[i_file, :] = numpy.array(
[float(m.split('(')[0]) for m in max])
# extract error for each label
error_per_label_for_file_i = []
std_per_label_for_file_i = []
labels_id_for_file_i = []
# Due to 2 different kind of file structure, the number of the last label line must be adapted
if not ind_line_median:
ind_line_median = [len(lines) + 1]
for i_line in range(ind_line_label[0] + 1, ind_line_median[0] - 1):
line_label_i = lines[i_line].strip().split(',')
error_per_label_for_file_i.append([
float(error.strip().split('(')[0])
for error in line_label_i[1:]
])
std_per_label_for_file_i.append([
float(error.strip().split('(')[1][:-1])
for error in line_label_i[1:]
])
labels_id_for_file_i.append(int(line_label_i[0]))
error_per_label.append(error_per_label_for_file_i)
std_per_label.append(std_per_label_for_file_i)
labels_id.append(labels_id_for_file_i)
# close file
f.close()
# check if all the files in the result folder were generated with the same number of methods
if not all(x == methods_name[0] for x in methods_name):
sct.printv(
'ERROR: All the generated files in folder ' + results_folder +
' have not been generated with the same number of methods. Exit program.',
'error')
sys.exit(1)
# check if all the files in the result folder were generated with the same labels
if not all(x == labels_id[0] for x in labels_id):
sct.printv(
'ERROR: All the generated files in folder ' + results_folder +
' have not been generated with the same labels. Exit program.',
'error')
sys.exit(1)
# convert the list "error_per_label" into a numpy array to ease further manipulations
error_per_label = numpy.array(error_per_label)
std_per_label = numpy.array(std_per_label)
# compute different stats
abs_error_per_labels = numpy.absolute(error_per_label)
max_abs_error_per_meth = numpy.amax(abs_error_per_labels, axis=1)
min_abs_error_per_meth = numpy.amin(abs_error_per_labels, axis=1)
mean_abs_error_per_meth = numpy.mean(abs_error_per_labels, axis=1)
std_abs_error_per_meth = numpy.std(abs_error_per_labels, axis=1)
# average error and std across sides
meanRL_abs_error_per_labels = numpy.zeros(
(error_per_label.shape[0], nb_RL_labels, error_per_label.shape[2]))
meanRL_std_abs_error_per_labels = numpy.zeros(
(std_per_label.shape[0], nb_RL_labels, std_per_label.shape[2]))
for i_file in range(0, nb_results_file):
for i_meth in range(0, len(methods_name[i_file])):
for i_label in range(0, nb_RL_labels):
# find indexes of corresponding labels
ind_ID_first_side = labels_id[i_file].index(i_label)
ind_ID_other_side = labels_id[i_file].index(i_label +
nb_RL_labels)
# compute mean across 2 sides
meanRL_abs_error_per_labels[i_file, i_label, i_meth] = float(
error_per_label[i_file, ind_ID_first_side, i_meth] +
error_per_label[i_file, ind_ID_other_side, i_meth]) / 2
meanRL_std_abs_error_per_labels[
i_file, i_label, i_meth] = float(
std_per_label[i_file, ind_ID_first_side, i_meth] +
std_per_label[i_file, ind_ID_other_side, i_meth]) / 2
nb_method = len(methods_to_display)
sct.printv('Noise std of the ' + str(nb_results_file) +
' generated files:')
print snr
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Tracts std of the ' + str(nb_results_file) +
' generated files:')
print tracts_std
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('CSF value of the ' + str(nb_results_file) +
' generated files:')
print csf_values
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Methods used to generate results for the ' +
str(nb_results_file) + ' generated files:')
print methods_name
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Median obtained with each method (in colons) for the ' +
str(nb_results_file) + ' generated files (in lines):')
print median_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Minimum obtained with each method (in colons) for the ' +
str(nb_results_file) + ' generated files (in lines):')
print min_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Maximum obtained with each method (in colons) for the ' +
str(nb_results_file) + ' generated files (in lines):')
print max_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Labels\' ID (in colons) for the ' + str(nb_results_file) +
' generated files (in lines):')
print labels_id
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Errors obtained with each method (in colons) for the ' +
str(nb_results_file) + ' generated files (in lines):')
print error_per_label
print '----------------------------------------------------------------------------------------------------------------'
sct.printv(
'Mean errors across both sides obtained with each method (in colons) for the '
+ str(nb_results_file) + ' generated files (in lines):')
print meanRL_abs_error_per_labels
# Compute fractional volume per label
labels_id_FV, labels_name_FV, fract_vol_per_lab, labels_name_FV_RL_gathered, fract_vol_per_lab_RL_gathered = isct_get_fractional_volume.get_fractional_volume_per_label(
'./cropped_atlas/', 'info_label.txt')
# # Get the number of voxels including at least one tract
# nb_voxels_in_WM = isct_get_fractional_volume.get_nb_voxel_in_WM('./cropped_atlas/', 'info_label.txt')
# normalize by the number of voxels in WM and express it as a percentage
fract_vol_norm = numpy.divide(
fract_vol_per_lab_RL_gathered,
numpy.sum(fract_vol_per_lab_RL_gathered) / 100)
# NOT NECESSARY NOW WE AVERAGE ACROSS BOTH SIDES (which orders the labels)
# # check if the order of the labels returned by the function computing the fractional volumes is the same (which should be the case)
# if labels_id_FV != labels_id[0]:
# sct.printv('\n\nERROR: the labels IDs returned by the function \'i_sct_get_fractional_volume\' are different from the labels IDs of the results files\n\n', 'error')
# # Remove labels #30 and #31
# labels_id_FV_29, labels_name_FV_29, fract_vol_per_lab_29 = labels_id_FV[:-2], labels_name_FV[:-2], fract_vol_per_lab[:-2]
# indexes of labels sort according to the fractional volume
ind_labels_sort = numpy.argsort(fract_vol_norm)
# Find index of the file generated with noise variance = 10 and tracts std = 10
ind_file_to_display = numpy.where((snr == noise_std_to_display)
& (tracts_std == tracts_std_to_display)
& (csf_values == csf_value_to_display))
# sort arrays in this order
meanRL_abs_error_per_labels_sort = meanRL_abs_error_per_labels[
ind_file_to_display[0], ind_labels_sort, :]
meanRL_std_abs_error_per_labels_sort = meanRL_std_abs_error_per_labels[
ind_file_to_display[0], ind_labels_sort, :]
labels_name_sort = numpy.array(labels_name_FV_RL_gathered)[ind_labels_sort]
# *********************************************** START PLOTTING HERE **********************************************
# stringColor = Color()
matplotlib.rcParams.update({'font.size': 50, 'font.family': 'trebuchet'})
# plt.rcParams['xtick.major.pad'] = '11'
plt.rcParams['ytick.major.pad'] = '15'
fig = plt.figure(figsize=(60, 37))
width = 1.0 / (nb_method + 1)
ind_fig = numpy.arange(len(labels_name_sort)) * (1.0 + width)
plt.ylabel('Absolute error (%)\n', fontsize=65)
plt.xlabel('Fractional volume (% of the total number of voxels in WM)',
fontsize=65)
plt.title(
'Absolute error per tract as a function of their fractional volume\n\n',
fontsize=30)
plt.suptitle('(Noise std=' + str(snr[ind_file_to_display[0]][0]) +
', Tracts std=' + str(tracts_std[ind_file_to_display[0]][0]) +
', CSF value=' + str(csf_values[ind_file_to_display[0]][0]) +
')',
fontsize=30)
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
markers = ['o', 's', '^', 'D']
errorbar_plots = []
for meth, color, marker in zip(methods_to_display, colors, markers):
i_meth = methods_name[0].index(meth)
i_meth_to_display = methods_to_display.index(meth)
plot_i = plt.errorbar(ind_fig + i_meth_to_display * width,
meanRL_abs_error_per_labels_sort[:, i_meth],
meanRL_std_abs_error_per_labels_sort[:, i_meth],
color=color,
marker=marker,
markersize=35,
lw=7,
elinewidth=1,
capthick=5,
capsize=10)
# plot_i = plt.boxplot(numpy.transpose(abs_error_per_labels[ind_files_csf_sort, :, i_meth]), positions=ind_fig + i_meth_to_display * width + (float(i_meth_to_display) * width) / (nb_method + 1), widths=width, boxprops=boxprops, medianprops=medianprops, flierprops=flierprops, whiskerprops=whiskerprops, capprops=capprops)
errorbar_plots.append(plot_i)
# add alternated vertical background colored bars
for i_xtick in range(0, len(ind_fig), 2):
plt.axvspan(ind_fig[i_xtick] - width - width / 2,
ind_fig[i_xtick] + (nb_method + 1) * width - width / 2,
facecolor='grey',
alpha=0.1)
# concatenate value of fractional volume to labels'name
xtick_labels = [
labels_name_sort[i_lab] + '\n' + r'$\bf{[' +
str(round(fract_vol_norm[ind_labels_sort][i_lab], 2)) + ']}$'
for i_lab in range(0, len(labels_name_sort))
]
ind_lemniscus = numpy.where(
labels_name_sort ==
'spinal lemniscus (spinothalamic and spinoreticular tracts)')[0][0]
xtick_labels[ind_lemniscus] = 'spinal lemniscus\n' + r'$\bf{[' + str(
round(fract_vol_norm[ind_labels_sort][ind_lemniscus], 2)) + ']}$'
# plt.legend(box_plots, methods_to_display, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
plt.legend(errorbar_plots,
methods_to_display,
loc=1,
fontsize=50,
numpoints=1)
plt.xticks(ind_fig + (numpy.floor(float(nb_method - 1) / 2)) * width,
xtick_labels,
fontsize=45)
# Tweak spacing to prevent clipping of tick-labels
plt.subplots_adjust(bottom=0, top=0.95, right=0.96)
plt.gca().set_xlim(
[-width, numpy.max(ind_fig) + (nb_method + 0.5) * width])
plt.gca().set_ylim([0, 17])
plt.gca().yaxis.set_major_locator(plt.MultipleLocator(1.0))
plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(0.5))
plt.grid(b=True, axis='y', which='both')
fig.autofmt_xdate()
plt.savefig(param_default.fname_folder_to_save_fig +
'/absolute_error_vs_fractional_volume.pdf',
format='PDF')
plt.show(block=False)
def checkFolder(self, param):
# check if the folder exist. If not, create it.
sct.printv("Check folder existence...")
sct.check_folder_exist(param, 0)
return param
def main():
# Initialization to defaults parameters
fname_data = '' # data is empty by default
path_label = '' # empty by default
method = param.method # extraction mode by default
labels_of_interest = param.labels_of_interest
slices_of_interest = param.slices_of_interest
vertebral_levels = param.vertebral_levels
average_all_labels = param.average_all_labels
fname_output = param.fname_output
fname_vertebral_labeling = param.fname_vertebral_labeling
fname_normalizing_label = '' # optional then default is empty
normalization_method = '' # optional then default is empty
actual_vert_levels = None # variable used in case the vertebral levels asked by the user don't correspond exactly to the vertebral levels available in the metric data
warning_vert_levels = None # variable used to warn the user in case the vertebral levels he asked don't correspond exactly to the vertebral levels available in the metric data
verbose = param.verbose
flag_h = 0
ml_clusters = param.ml_clusters
adv_param = param.adv_param
adv_param_user = ''
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR')
fname_data = '/Users/julien/data/temp/sct_example_data/mt/mtr.nii.gz'
path_label = '/Users/julien/data/temp/sct_example_data/mt/label/atlas/'
method = 'map'
ml_clusters = '0:29,30,31'
labels_of_interest = '0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29'
slices_of_interest = ''
vertebral_levels = ''
average_all_labels = 1
fname_normalizing_label = '' #path_sct+'/testing/data/errsm_23/mt/label/template/MNI-Poly-AMU_CSF.nii.gz'
normalization_method = '' #'whole'
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'haf:i:l:m:n:o:p:v:w:z:') # define flags
except getopt.GetoptError as err: # check if the arguments are defined
print str(err) # error
usage() # display usage
if not opts:
usage()
for opt, arg in opts: # explore flags
if opt in '-a':
average_all_labels = 1
elif opt in '-f':
path_label = os.path.abspath(arg) # save path of labels folder
elif opt == '-h': # help option
flag_h = 1
elif opt in '-i':
fname_data = arg
elif opt in '-l':
labels_of_interest = arg
elif opt in '-m': # method for metric extraction
method = arg
elif opt in '-n': # filename of the label by which the user wants to normalize
fname_normalizing_label = arg
elif opt in '-o': # output option
fname_output = arg # fname of output file
elif opt in '-p':
adv_param_user = arg
elif opt in '-v':
# vertebral levels option, if the user wants to average the metric across specific vertebral levels
vertebral_levels = arg
elif opt in '-w':
# method used for the normalization by the metric estimation into the normalizing label (see flag -n): 'sbs' for slice-by-slice or 'whole' for normalization after estimation in the whole labels
normalization_method = arg
elif opt in '-z': # slices numbers option
slices_of_interest = arg # save labels numbers
# Display usage with tract parameters by default in case files aren't chosen in arguments inputs
if fname_data == '' or path_label == '' or flag_h:
param.path_label = path_label
usage()
# Check existence of data file
sct.printv('\ncheck existence of input files...', verbose)
sct.check_file_exist(fname_data)
sct.check_folder_exist(path_label)
if fname_normalizing_label:
sct.check_folder_exist(fname_normalizing_label)
# add slash at the end
path_label = sct.slash_at_the_end(path_label, 1)
# Find path to the vertebral labeling file if vertebral levels were specified by the user
if vertebral_levels:
if slices_of_interest: # impossible to select BOTH specific slices and specific vertebral levels
print '\nERROR: You cannot select BOTH vertebral levels AND slice numbers.'
usage()
else:
fname_vertebral_labeling_list = sct.find_file_within_folder(fname_vertebral_labeling, path_label + '..')
if len(fname_vertebral_labeling_list) > 1:
print color.red + 'ERROR: More than one file named \'' + fname_vertebral_labeling + ' were found in ' + path_label + '. Exit program.' + color.end
sys.exit(2)
elif len(fname_vertebral_labeling_list) == 0:
print color.red + 'ERROR: No file named \'' + fname_vertebral_labeling + ' were found in ' + path_label + '. Exit program.' + color.end
sys.exit(2)
else:
fname_vertebral_labeling = os.path.abspath(fname_vertebral_labeling_list[0])
# Check input parameters
check_method(method, fname_normalizing_label, normalization_method)
# parse argument for param
if not adv_param_user == '':
adv_param = adv_param_user.replace(' ', '').split(',') # remove spaces and parse with comma
del adv_param_user # clean variable
# TODO: check integrity of input
# Extract label info
label_id, label_name, label_file = read_label_file(path_label, param.file_info_label)
nb_labels_total = len(label_id)
# check consistency of label input parameter.
label_id_user, average_all_labels = check_labels(labels_of_interest, nb_labels_total, average_all_labels, method) # If 'labels_of_interest' is empty, then
# 'label_id_user' contains the index of all labels in the file info_label.txt
# print parameters
print '\nChecked parameters:'
print ' data ...................... '+fname_data
print ' folder label .............. '+path_label
print ' selected labels ........... '+str(label_id_user)
print ' estimation method ......... '+method
print ' slices of interest ........ '+slices_of_interest
print ' vertebral levels .......... '+vertebral_levels
print ' vertebral labeling file.... '+fname_vertebral_labeling
print ' advanced parameters ....... '+str(adv_param)
# Check if the orientation of the data is RPI
orientation_data = get_orientation(fname_data)
# If orientation is not RPI, change to RPI
if orientation_data != 'RPI':
sct.printv('\nCreate temporary folder to change the orientation of the NIFTI files into RPI...', verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.create_folder(path_tmp)
# change orientation and load data
sct.printv('\nChange image orientation and load it...', verbose)
data = nib.load(set_orientation(fname_data, 'RPI', path_tmp+'orient_data.nii')).get_data()
# Do the same for labels
sct.printv('\nChange labels orientation and load them...', verbose)
labels = np.empty([nb_labels_total], dtype=object) # labels(nb_labels_total, x, y, z)
for i_label in range(0, nb_labels_total):
labels[i_label] = nib.load(set_orientation(path_label+label_file[i_label], 'RPI', path_tmp+'orient_'+label_file[i_label])).get_data()
if fname_normalizing_label: # if the "normalization" option is wanted,
normalizing_label = np.empty([1], dtype=object) # choose this kind of structure so as to keep easily the
# compatibility with the rest of the code (dimensions: (1, x, y, z))
normalizing_label[0] = nib.load(set_orientation(fname_normalizing_label, 'RPI', path_tmp+'orient_normalizing_volume.nii')).get_data()
if vertebral_levels: # if vertebral levels were selected,
data_vertebral_labeling = nib.load(set_orientation(fname_vertebral_labeling, 'RPI', path_tmp+'orient_vertebral_labeling.nii.gz')).get_data()
# Remove the temporary folder used to change the NIFTI files orientation into RPI
sct.printv('\nRemove the temporary folder...', verbose)
status, output = commands.getstatusoutput('rm -rf ' + path_tmp)
else:
# Load image
sct.printv('\nLoad image...', verbose)
data = nib.load(fname_data).get_data()
# Load labels
sct.printv('\nLoad labels...', verbose)
labels = np.empty([nb_labels_total], dtype=object) # labels(nb_labels_total, x, y, z)
for i_label in range(0, nb_labels_total):
labels[i_label] = nib.load(path_label+label_file[i_label]).get_data()
if fname_normalizing_label: # if the "normalization" option is wanted,
normalizing_label = np.empty([1], dtype=object) # choose this kind of structure so as to keep easily the
# compatibility with the rest of the code (dimensions: (1, x, y, z))
normalizing_label[0] = nib.load(fname_normalizing_label).get_data() # load the data of the normalizing label
if vertebral_levels: # if vertebral levels were selected,
data_vertebral_labeling = nib.load(fname_vertebral_labeling).get_data()
# Change metric data type into floats for future manipulations (normalization)
data = np.float64(data)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz = data.shape
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# Get dimensions of labels
sct.printv('\nGet dimensions of label...', verbose)
nx_atlas, ny_atlas, nz_atlas = labels[0].shape
sct.printv('.. '+str(nx_atlas)+' x '+str(ny_atlas)+' x '+str(nz_atlas)+' x '+str(nb_labels_total), verbose)
# Check dimensions consistency between atlas and data
if (nx, ny, nz) != (nx_atlas, ny_atlas, nz_atlas):
print '\nERROR: Metric data and labels DO NOT HAVE SAME DIMENSIONS.'
sys.exit(2)
# Update the flag "slices_of_interest" according to the vertebral levels selected by user (if it's the case)
if vertebral_levels:
slices_of_interest, actual_vert_levels, warning_vert_levels = \
get_slices_matching_with_vertebral_levels(data, vertebral_levels, data_vertebral_labeling)
# select slice of interest by cropping data and labels
if slices_of_interest:
data = remove_slices(data, slices_of_interest)
for i_label in range(0, nb_labels_total):
labels[i_label] = remove_slices(labels[i_label], slices_of_interest)
if fname_normalizing_label: # if the "normalization" option was selected,
normalizing_label[0] = remove_slices(normalizing_label[0], slices_of_interest)
# if user wants to get unique value across labels, then combine all labels together
if average_all_labels == 1:
sum_labels_user = np.sum(labels[label_id_user]) # sum the labels selected by user
if method == 'ml' or method == 'map': # in case the maximum likelihood and the average across different labels are wanted
labels_tmp = np.empty([nb_labels_total - len(label_id_user) + 1], dtype=object)
labels = np.delete(labels, label_id_user) # remove the labels selected by user
labels_tmp[0] = sum_labels_user # put the sum of the labels selected by user in first position of the tmp
# variable
for i_label in range(1, len(labels_tmp)):
labels_tmp[i_label] = labels[i_label-1] # fill the temporary array with the values of the non-selected labels
labels = labels_tmp # replace the initial labels value by the updated ones (with the summed labels)
del labels_tmp # delete the temporary labels
else: # in other cases than the maximum likelihood, we can remove other labels (not needed for estimation)
labels = np.empty(1, dtype=object)
labels[0] = sum_labels_user # we create a new label array that includes only the summed labels
if fname_normalizing_label: # if the "normalization" option is wanted
sct.printv('\nExtract normalization values...', verbose)
if normalization_method == 'sbs': # case: the user wants to normalize slice-by-slice
for z in range(0, data.shape[-1]):
normalizing_label_slice = np.empty([1], dtype=object) # in order to keep compatibility with the function
# 'extract_metric_within_tract', define a new array for the slice z of the normalizing labels
normalizing_label_slice[0] = normalizing_label[0][..., z]
metric_normalizing_label = extract_metric_within_tract(data[..., z], normalizing_label_slice, method, 0)
# estimate the metric mean in the normalizing label for the slice z
if metric_normalizing_label[0][0] != 0:
data[..., z] = data[..., z]/metric_normalizing_label[0][0] # divide all the slice z by this value
elif normalization_method == 'whole': # case: the user wants to normalize after estimations in the whole labels
metric_mean_norm_label, metric_std_norm_label = extract_metric_within_tract(data, normalizing_label, method, param.verbose) # mean and std are lists
# identify cluster for each tract (for use with robust ML)
ml_clusters_array = get_clusters(ml_clusters, labels)
# extract metrics within labels
sct.printv('\nExtract metric within labels...', verbose)
metric_mean, metric_std = extract_metric_within_tract(data, labels, method, verbose, ml_clusters_array, adv_param) # mean and std are lists
if fname_normalizing_label and normalization_method == 'whole': # case: user wants to normalize after estimations in the whole labels
metric_mean, metric_std = np.divide(metric_mean, metric_mean_norm_label), np.divide(metric_std, metric_std_norm_label)
# update label name if average
if average_all_labels == 1:
label_name[0] = 'AVERAGED'+' -'.join(label_name[i] for i in label_id_user) # concatenate the names of the
# labels selected by the user if the average tag was asked
label_id_user = [0] # update "label_id_user" to select the "averaged" label (which is in first position)
metric_mean = metric_mean[label_id_user]
metric_std = metric_std[label_id_user]
# display metrics
sct.printv('\nEstimation results:', 1)
for i in range(0, metric_mean.size):
sct.printv(str(label_id_user[i])+', '+str(label_name[label_id_user[i]])+': '+str(metric_mean[i])+' +/- '+str(metric_std[i]), 1, 'info')
# save and display metrics
save_metrics(label_id_user, label_name, slices_of_interest, metric_mean, metric_std, fname_output, fname_data,
method, fname_normalizing_label, actual_vert_levels, warning_vert_levels)
def main():
path_data = param.path_data
function_to_test = param.function_to_test
# function_to_avoid = param.function_to_avoid
remove_tmp_file = param.remove_tmp_file
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'h:d:p:f:r:a:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
sys.exit(0)
if opt == '-d':
param.download = int(arg)
if opt == '-p':
param.path_data = arg
if opt == '-f':
function_to_test = arg
# if opt == '-a':
# function_to_avoid = arg
if opt == '-r':
remove_tmp_file = int(arg)
start_time = time.time()
# if function_to_avoid:
# try:
# functions.remove(function_to_avoid)
# except ValueError:
# print 'The function you want to avoid does not figure in the functions to test list'
# download data
if param.download:
downloaddata()
param.path_data = 'sct_testing_data/data'
# get absolute path and add slash at the end
param.path_data = sct.slash_at_the_end(os.path.abspath(param.path_data), 1)
# check existence of testing data folder
if not sct.check_folder_exist(param.path_data, 0):
downloaddata()
# display path to data
sct.printv('\nPath to testing data:\n.. ' + param.path_data, param.verbose)
# create temp folder that will have all results and go in it
param.path_tmp = sct.slash_at_the_end(
'tmp.' + time.strftime("%y%m%d%H%M%S"), 1)
sct.create_folder(param.path_tmp)
os.chdir(param.path_tmp)
# get list of all scripts to test
functions = fill_functions()
# loop across all functions and test them
status = []
[
status.append(test_function(f)) for f in functions
if function_to_test == f
]
if not status:
for f in functions:
status.append(test_function(f))
print 'status: ' + str(status)
# display elapsed time
elapsed_time = time.time() - start_time
print 'Finished! Elapsed time: ' + str(int(round(elapsed_time))) + 's\n'
# remove temp files
if param.remove_tmp_file:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf ' + param.path_tmp, param.verbose)
e = 0
if sum(status) != 0:
e = 1
print e
sys.exit(e)
example=['0', '1', '2'],
default_value='1')
return parser
if __name__ == "__main__":
sct.start_stream_logger()
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
ml_param = Param()
fname_gm = arguments['-gm']
fname_wm = arguments['-wm']
path_template = arguments['-t']
if not sct.check_folder_exist(path_template):
sct.printv(
parser.usage.generate(
error=
'ERROR: label/ folder does not exist. Please specify the path to the template using flag -t'
))
fname_warp_template = arguments['-w']
fname_warp_target2template = None
fname_manual_gmseg = None
fname_sc_seg = None
fname_template_dest = None
if '-param' in arguments:
ml_param.param_reg = arguments['-param']
if '-manual-gm' in arguments:
def checkFolder(self, param):
# check if the folder exist. If not, create it.
if self.parser.check_file_exist:
sct.check_folder_exist(param, 0)
return param
def main():
#Initialization
directory = ""
fname_template = ''
n_l = 0
verbose = param.verbose
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:t:n:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts :
if opt == '-h':
usage()
elif opt in ("-i"):
directory = arg
elif opt in ("-t"):
fname_template = arg
elif opt in ('-n'):
n_l = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_template == '' or directory == '':
usage()
# check existence of input files
print'\nCheck if file exists ...\n'
sct.check_file_exist(fname_template)
sct.check_folder_exist(directory)
path_template, file_template, ext_template = sct.extract_fname(fname_template)
template_absolute_path = sct.get_absolute_path(fname_template)
os.chdir(directory)
n_i = len([name for name in os.listdir('.') if (os.path.isfile(name) and name.endswith(".nii.gz") and name!='template_landmarks.nii.gz')]) # number of landmark images
average = zeros((n_i,n_l))
compteur = 0
for file in os.listdir('.'):
if file.endswith(".nii.gz") and file != 'template_landmarks.nii.gz':
print file
img = nibabel.load(file)
data = img.get_data()
X,Y,Z = (data>0).nonzero()
Z = [Z[i] for i in Z.argsort()]
Z.reverse()
for i in xrange(n_l):
if i < len(Z):
average[compteur][i] = Z[i]
compteur = compteur + 1
average = array([int(round(mean([average[average[:,i]>0,i]]))) for i in xrange(n_l)])
#print average
print template_absolute_path
print '\nGet dimensions of template...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(template_absolute_path)
print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz)
print '.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm'
img = nibabel.load(template_absolute_path)
data = img.get_data()
hdr = img.get_header()
data[:,:,:] = 0
compteur = 1
for i in average:
print int(round(nx/2.0)),int(round(ny/2.0)),int(round(i)),int(round(compteur))
data[int(round(nx/2.0)),int(round(ny/2.0)),int(round(i))] = int(round(compteur))
compteur = compteur + 1
print '\nSave volume ...'
#hdr.set_data_dtype('float32') # set imagetype to uint8
# save volume
#data = data.astype(float32, copy =False)
img = nibabel.Nifti1Image(data, None, hdr)
file_name = 'template_landmarks.nii.gz'
nibabel.save(img,file_name)
print '\nFile created : ' + file_name
def main():
results_folder = param_default.results_folder
methods_to_display = param_default.methods_to_display
# Parameters for debug mode
if param_default.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
results_folder = "/Users/slevy_local/spinalcordtoolbox/dev/atlas/validate_atlas/results_20150210_200iter" #"C:/cygwin64/home/Simon_2/data_auto_vs_manual"
path_sct = '/Users/slevy_local/spinalcordtoolbox' #'C:/cygwin64/home/Simon_2/spinalcordtoolbox'
methods_to_display = 'bin,man0,man1,man2,man3'
else:
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'i:m:') # define flags
except getopt.GetoptError as err: # check if the arguments are defined
print str(err) # error
# usage() # display usage
# if not opts:
# print 'Please enter the path to the result folder. Exit program.'
# sys.exit(1)
# # usage()
for opt, arg in opts: # explore flags
if opt in '-i':
results_folder = arg
if opt in '-m':
methods_to_display = arg
# Append path that contains scripts, to be able to load modules
sys.path.append(path_sct + '/scripts')
import sct_utils as sct
sct.printv("Working directory: " + os.getcwd())
# Folder including data "automatic vs manual"
folder_auto_vs_manual = results_folder + '/manual_mask/sub'
sct.printv(
'\n\nData will be extracted from folder ' + folder_auto_vs_manual +
' .', 'warning')
sct.printv('\t\tCheck existence...')
sct.check_folder_exist(folder_auto_vs_manual)
# Extract methods to display
methods_to_display = methods_to_display.strip().split(',')
fname_results = glob.glob(folder_auto_vs_manual + '/*.txt')
nb_results_file = len(fname_results)
# 1st dim: SNR, 2nd dim: tract std, 3rd dim: mean abs error, 4th dim: std abs error
# result_array = numpy.empty((nb_results_file, nb_results_file, 3), dtype=object)
# SNR
snr = numpy.zeros((nb_results_file))
# Tracts std
tracts_std = numpy.zeros((nb_results_file))
# CSF value
csf_values = numpy.zeros((nb_results_file))
# methods' name
methods_name = [] #numpy.empty((nb_results_file, nb_method), dtype=object)
# labels
error_per_label = []
std_per_label = []
labels_id = []
# median
median_results = numpy.zeros((nb_results_file, 6))
# median std across bootstraps
median_std = numpy.zeros((nb_results_file, 6))
# min
min_results = numpy.zeros((nb_results_file, 6))
# max
max_results = numpy.zeros((nb_results_file, 6))
#
for i_file in range(0, nb_results_file):
# Open file
f = open(fname_results[i_file]) # open file
# Extract all lines in .txt file
lines = [line for line in f.readlines() if line.strip()]
# extract SNR
# find all index of lines containing the string "sigma noise"
ind_line_noise = [
lines.index(line_noise) for line_noise in lines
if "sigma noise" in line_noise
]
if len(ind_line_noise) != 1:
sct.printv(
"ERROR: number of lines including \"sigma noise\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
# result_array[:, i_file, i_file] = int(''.join(c for c in lines[ind_line_noise[0]] if c.isdigit()))
snr[i_file] = int(''.join(c for c in lines[ind_line_noise[0]]
if c.isdigit()))
# extract tract std
ind_line_tract_std = [
lines.index(line_tract_std) for line_tract_std in lines
if "range tracts" in line_tract_std
]
if len(ind_line_tract_std) != 1:
sct.printv(
"ERROR: number of lines including \"range tracts\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
# result_array[i_file, i_file, :] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# regex = re.compile(''('(.*)':) # re.I permet d'ignorer la case (majuscule/minuscule)
# match = regex.search(lines[ind_line_tract_std[0]])
# result_array[:, i_file, :, :] = match.group(1) # le groupe 1 correspond a '.*'
tracts_std[i_file] = int(''.join(
c for c in lines[ind_line_tract_std[0]].split(':')[1]
if c.isdigit()))
# extract CSF value
ind_line_csf_value = [
lines.index(line_csf_value) for line_csf_value in lines
if "# value CSF" in line_csf_value
]
if len(ind_line_csf_value) != 1:
sct.printv(
"ERROR: number of lines including \"range tracts\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
csf_values[i_file] = int(''.join(
c for c in lines[ind_line_csf_value[0]].split(':')[1]
if c.isdigit()))
# extract method name
ind_line_label = [
lines.index(line_label) for line_label in lines
if "Label" in line_label
]
if len(ind_line_label) != 1:
sct.printv(
"ERROR: number of lines including \"Label\" is different from 1. Exit program.",
'error')
sys.exit(1)
else:
# methods_name[i_file, :] = numpy.array(lines[ind_line_label[0]].strip().split(',')[1:])
methods_name.append(lines[ind_line_label[0]].strip().replace(
' ', '').split(',')[1:])
# extract median
ind_line_median = [
lines.index(line_median) for line_median in lines
if "median" in line_median
]
if len(ind_line_median) != 1:
sct.printv(
"WARNING: number of lines including \"median\" is different from 1. Exit program.",
'warning')
# sys.exit(1)
else:
median = lines[ind_line_median[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 0] = [float(m.split('(')[0]) for m in median]
median_results[i_file, :] = numpy.array(
[float(m.split('(')[0]) for m in median])
median_std[i_file, :] = numpy.array(
[float(m.split('(')[1][:-1]) for m in median])
# extract min
ind_line_min = [
lines.index(line_min) for line_min in lines if "min," in line_min
]
if len(ind_line_min) != 1:
sct.printv(
"WARNING: number of lines including \"min\" is different from 1. Exit program.",
'warning')
# sys.exit(1)
else:
min = lines[ind_line_min[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in min]
min_results[i_file, :] = numpy.array(
[float(m.split('(')[0]) for m in min])
# extract max
ind_line_max = [
lines.index(line_max) for line_max in lines if "max" in line_max
]
if len(ind_line_max) != 1:
sct.printv(
"WARNING: number of lines including \"max\" is different from 1. Exit program.",
'warning')
# sys.exit(1)
else:
max = lines[ind_line_max[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in max]
max_results[i_file, :] = numpy.array(
[float(m.split('(')[0]) for m in max])
# extract error for each label
error_per_label_for_file_i = []
std_per_label_for_file_i = []
labels_id_for_file_i = []
# Due to 2 different kind of file structure, the number of the last label line must be adapted
if not ind_line_median:
ind_line_median = [len(lines) + 1]
for i_line in range(ind_line_label[0] + 1, ind_line_median[0] - 1):
line_label_i = lines[i_line].strip().split(',')
error_per_label_for_file_i.append([
float(error.strip().split('(')[0])
for error in line_label_i[1:]
])
std_per_label_for_file_i.append([
float(error.strip().split('(')[1][:-1])
for error in line_label_i[1:]
])
labels_id_for_file_i.append(line_label_i[0])
error_per_label.append(error_per_label_for_file_i)
std_per_label.append(std_per_label_for_file_i)
labels_id.append(labels_id_for_file_i)
# close file
f.close()
# check if all the files in the result folder were generated with the same number of methods
if not all(x == methods_name[0] for x in methods_name):
sct.printv(
'ERROR: All the generated files in folder ' + results_folder +
' have not been generated with the same number of methods. Exit program.',
'error')
sys.exit(1)
# check if all the files in the result folder were generated with the same labels
if not all(x == labels_id[0] for x in labels_id):
sct.printv(
'ERROR: All the generated files in folder ' + results_folder +
' have not been generated with the same labels. Exit program.',
'error')
sys.exit(1)
# convert the list "error_per_label" into a numpy array to ease further manipulations
error_per_label = numpy.array(error_per_label)
std_per_label = numpy.array(std_per_label)
# compute different stats
abs_error_per_labels = numpy.absolute(error_per_label)
max_abs_error_per_meth = numpy.amax(abs_error_per_labels, axis=1)
min_abs_error_per_meth = numpy.amin(abs_error_per_labels, axis=1)
mean_abs_error_per_meth = numpy.mean(abs_error_per_labels, axis=1)
std_abs_error_per_meth = numpy.std(abs_error_per_labels, axis=1)
nb_method = len(methods_to_display)
sct.printv('Noise std of the ' + str(nb_results_file) +
' generated files:')
print snr
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Tracts std of the ' + str(nb_results_file) +
' generated files:')
print tracts_std
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Methods used to generate results for the ' +
str(nb_results_file) + ' generated files:')
print methods_name
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Median obtained with each method (in colons) for the ' +
str(nb_results_file) + ' generated files (in lines):')
print median_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Minimum obtained with each method (in colons) for the ' +
str(nb_results_file) + ' generated files (in lines):')
print min_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Maximum obtained with each method (in colons) for the ' +
str(nb_results_file) + ' generated files (in lines):')
print max_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Labels\' ID (in colons) for the ' + str(nb_results_file) +
' generated files (in lines):')
print labels_id
print '----------------------------------------------------------------------------------------------------------------'
sct.printv(
'Mean errors (across bootstraps) obtained with each method (in colons) for the '
+ str(nb_results_file) + ' generated files (in lines):')
print error_per_label
print '----------------------------------------------------------------------------------------------------------------'
sct.printv(
'Errors std (across bootstraps) obtained with each method (in colons) for the '
+ str(nb_results_file) + ' generated files (in lines):')
print std_per_label
# ********************************** START PLOTTING HERE ***********************************************************
# find index of the file generated with sigma noise = 10 and range tracts = -10:+10
ind_file_noise10_tracts_std10 = numpy.where((snr == 10)
& (tracts_std == 10))[0][0]
matplotlib.rcParams.update({'font.size': 45, 'font.family': 'trebuchet'})
plt.rcParams['xtick.major.pad'] = '9'
plt.rcParams['ytick.major.pad'] = '15'
fig0 = plt.figure(0, figsize=(34, 17))
width = 0.5 / (nb_method + 1)
ind_fig0 = numpy.arange(len(labels_id[0]))
plt.ylabel('Absolute error (%)', fontsize=55)
plt.xlabel('Labels', fontsize=55)
plt.suptitle('Automatic estimation vs. manual estimation', fontsize=65)
plt.title('(Noise std=' + str(snr[0]) + ', Tracts std=' +
str(tracts_std[0]) + ', CSF value=' + str(csf_values[0]) + ')\n',
fontsize=65)
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
plots = []
marker_size = 30
line_width = 3.0
cap_size = 15
eline_width = 7.0
cap_thick = 5.0
for meth, color in zip(methods_to_display, colors):
i_meth = methods_name[0].index(meth)
i_meth_to_display = methods_to_display.index(meth)
plot = plt.errorbar(
ind_fig0 + i_meth_to_display * width +
(float(i_meth_to_display) * width) / (nb_method + 1),
error_per_label[ind_file_noise10_tracts_std10, :, i_meth],
std_per_label[ind_file_noise10_tracts_std10, :, i_meth],
color=color,
linestyle='None',
marker='o',
ms=marker_size,
lw=line_width,
capsize=cap_size,
capthick=cap_thick,
elinewidth=eline_width)
plots.append(plot[0])
# add alternated vertical background colored bars
for i_xtick in range(0, len(ind_fig0), 2):
plt.axvspan(ind_fig0[i_xtick] - 3 * width,
ind_fig0[i_xtick] + (nb_method + 1) * width + 2 * width,
facecolor='grey',
alpha=0.1)
# plt.legend(plots, methods_to_display, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0., handler_map={Line2D: HandlerLine2D(numpoints=1)})
plt.legend(plots,
methods_to_display,
loc='best',
handler_map={Line2D: HandlerLine2D(numpoints=1)})
# plt.xticks(ind_fig0 + (numpy.floor(nb_method/2))*width*(1.0+1.0/(nb_method+1)), labels_id[0], fontsize=45)
plt.xticks(ind_fig0 + (numpy.floor(nb_method / 2)) * width *
(1.0 + 1.0 / (nb_method + 1)), ['l-cst', 'r-cst', 'dc'],
fontsize=45)
plt.gca().set_xlim(
[-3 * width, numpy.max(ind_fig0) + (nb_method + 3) * width])
# plt.gca().set_ylim([0, 2])
plt.gca().yaxis.set_major_locator(plt.MultipleLocator(2.5))
plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(0.5))
plt.grid(b=True, axis='y', which='both')
# adjust the size of the frame
# plt.subplots_adjust(bottom=0.15, top=0.86, right=0.7, left=0.2)
plt.savefig(param_default.fname_folder_to_save_fig +
'/automatic_method_vs_manual_methods.pdf',
format='PDF')
plt.show(block=False)
def main():
results_folder = param_default.results_folder
methods_to_display = param_default.methods_to_display
noise_std_to_display = param_default.noise_std_to_display
tracts_std_to_display = param_default.tracts_std_to_display
csf_value_to_display = param_default.csf_value_to_display
nb_RL_labels = param_default.nb_RL_labels
# Parameters for debug mode
if param_default.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
results_folder = "/Users/slevy_local/spinalcordtoolbox/dev/atlas/validate_atlas/results_20150210_200iter"#"C:/cygwin64/home/Simon_2/data_methods_comparison"
path_sct = '/Users/slevy_local/spinalcordtoolbox' #'C:/cygwin64/home/Simon_2/spinalcordtoolbox'
else:
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'i:m:') # define flags
except getopt.GetoptError as err: # check if the arguments are defined
print str(err) # error
# usage() # display usage
# if not opts:
# print 'Please enter the path to the result folder. Exit program.'
# sys.exit(1)
# # usage()
for opt, arg in opts: # explore flags
if opt in '-i':
results_folder = arg
if opt in '-m':
methods_to_display = arg
# Append path that contains scripts, to be able to load modules
sys.path.append(path_sct + '/scripts')
import sct_utils as sct
import isct_get_fractional_volume
sct.printv("Working directory: " + os.getcwd())
results_folder_noise = results_folder + '/noise'
results_folder_tracts = results_folder + '/tracts'
results_folder_csf = results_folder + '/csf'
sct.printv('\n\nData will be extracted from folder ' + results_folder_noise + ' , ' + results_folder_tracts + ' and ' + results_folder_csf + '.', 'warning')
sct.printv('\t\tCheck existence...')
sct.check_folder_exist(results_folder_noise)
sct.check_folder_exist(results_folder_tracts)
sct.check_folder_exist(results_folder_csf)
# Extract methods to display
methods_to_display = methods_to_display.strip().split(',')
# Extract file names of the results files
fname_results_noise = glob.glob(results_folder_noise + '/*.txt')
fname_results_tracts = glob.glob(results_folder_tracts + '/*.txt')
fname_results_csf = glob.glob(results_folder_csf + '/*.txt')
fname_results = fname_results_noise + fname_results_tracts + fname_results_csf
# Remove doublons (due to the two folders)
# for i_fname in range(0, len(fname_results)):
# for j_fname in range(0, len(fname_results)):
# if (i_fname != j_fname) & (os.path.basename(fname_results[i_fname]) == os.path.basename(fname_results[j_fname])):
# fname_results.remove(fname_results[j_fname])
file_results = []
for fname in fname_results:
file_results.append(os.path.basename(fname))
for file in file_results:
if file_results.count(file) > 1:
ind = file_results.index(file)
fname_results.remove(fname_results[ind])
file_results.remove(file)
nb_results_file = len(fname_results)
# 1st dim: SNR, 2nd dim: tract std, 3rd dim: mean abs error, 4th dim: std abs error
# result_array = numpy.empty((nb_results_file, nb_results_file, 3), dtype=object)
# SNR
snr = numpy.zeros((nb_results_file))
# Tracts std
tracts_std = numpy.zeros((nb_results_file))
# CSF value
csf_values = numpy.zeros((nb_results_file))
# methods' name
methods_name = [] #numpy.empty((nb_results_file, nb_method), dtype=object)
# labels
error_per_label = []
std_per_label = []
labels_id = []
# median
median_results = numpy.zeros((nb_results_file, 5))
# median std across bootstraps
median_std = numpy.zeros((nb_results_file, 5))
# min
min_results = numpy.zeros((nb_results_file, 5))
# max
max_results = numpy.zeros((nb_results_file, 5))
#
for i_file in range(0, nb_results_file):
# Open file
f = open(fname_results[i_file]) # open file
# Extract all lines in .txt file
lines = [line for line in f.readlines() if line.strip()]
# extract SNR
# find all index of lines containing the string "sigma noise"
ind_line_noise = [lines.index(line_noise) for line_noise in lines if "sigma noise" in line_noise]
if len(ind_line_noise) != 1:
sct.printv("ERROR: number of lines including \"sigma noise\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# result_array[:, i_file, i_file] = int(''.join(c for c in lines[ind_line_noise[0]] if c.isdigit()))
snr[i_file] = int(''.join(c for c in lines[ind_line_noise[0]] if c.isdigit()))
# extract tract std
ind_line_tract_std = [lines.index(line_tract_std) for line_tract_std in lines if
"range tracts" in line_tract_std]
if len(ind_line_tract_std) != 1:
sct.printv("ERROR: number of lines including \"range tracts\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# result_array[i_file, i_file, :] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# regex = re.compile(''('(.*)':) # re.I permet d'ignorer la case (majuscule/minuscule)
# match = regex.search(lines[ind_line_tract_std[0]])
# result_array[:, i_file, :, :] = match.group(1) # le groupe 1 correspond a '.*'
tracts_std[i_file] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# extract CSF value
ind_line_csf_value = [lines.index(line_csf_value) for line_csf_value in lines if
"# value CSF" in line_csf_value]
if len(ind_line_csf_value) != 1:
sct.printv("ERROR: number of lines including \"range tracts\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# result_array[i_file, i_file, :] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# regex = re.compile(''('(.*)':) # re.I permet d'ignorer la case (majuscule/minuscule)
# match = regex.search(lines[ind_line_tract_std[0]])
# result_array[:, i_file, :, :] = match.group(1) # le groupe 1 correspond a '.*'
csf_values[i_file] = int(''.join(c for c in lines[ind_line_csf_value[0]].split(':')[1] if c.isdigit()))
# extract method name
ind_line_label = [lines.index(line_label) for line_label in lines if "Label" in line_label]
if len(ind_line_label) != 1:
sct.printv("ERROR: number of lines including \"Label\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# methods_name[i_file, :] = numpy.array(lines[ind_line_label[0]].strip().split(',')[1:])
methods_name.append(lines[ind_line_label[0]].strip().replace(' ', '').split(',')[1:])
# extract median
ind_line_median = [lines.index(line_median) for line_median in lines if "median" in line_median]
if len(ind_line_median) != 1:
sct.printv("WARNING: number of lines including \"median\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
median = lines[ind_line_median[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 0] = [float(m.split('(')[0]) for m in median]
median_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in median])
median_std[i_file, :] = numpy.array([float(m.split('(')[1][:-1]) for m in median])
# extract min
ind_line_min = [lines.index(line_min) for line_min in lines if "min," in line_min]
if len(ind_line_min) != 1:
sct.printv("WARNING: number of lines including \"min\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
min = lines[ind_line_min[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in min]
min_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in min])
# extract max
ind_line_max = [lines.index(line_max) for line_max in lines if "max" in line_max]
if len(ind_line_max) != 1:
sct.printv("WARNING: number of lines including \"max\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
max = lines[ind_line_max[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in max]
max_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in max])
# extract error for each label
error_per_label_for_file_i = []
std_per_label_for_file_i = []
labels_id_for_file_i = []
# Due to 2 different kind of file structure, the number of the last label line must be adapted
if not ind_line_median:
ind_line_median = [len(lines) + 1]
for i_line in range(ind_line_label[0] + 1, ind_line_median[0] - 1):
line_label_i = lines[i_line].strip().split(',')
error_per_label_for_file_i.append([float(error.strip().split('(')[0]) for error in line_label_i[1:]])
std_per_label_for_file_i.append([float(error.strip().split('(')[1][:-1]) for error in line_label_i[1:]])
labels_id_for_file_i.append(int(line_label_i[0]))
error_per_label.append(error_per_label_for_file_i)
std_per_label.append(std_per_label_for_file_i)
labels_id.append(labels_id_for_file_i)
# close file
f.close()
# check if all the files in the result folder were generated with the same number of methods
if not all(x == methods_name[0] for x in methods_name):
sct.printv(
'ERROR: All the generated files in folder ' + results_folder + ' have not been generated with the same number of methods. Exit program.',
'error')
sys.exit(1)
# check if all the files in the result folder were generated with the same labels
if not all(x == labels_id[0] for x in labels_id):
sct.printv(
'ERROR: All the generated files in folder ' + results_folder + ' have not been generated with the same labels. Exit program.',
'error')
sys.exit(1)
# convert the list "error_per_label" into a numpy array to ease further manipulations
error_per_label = numpy.array(error_per_label)
std_per_label = numpy.array(std_per_label)
# compute different stats
abs_error_per_labels = numpy.absolute(error_per_label)
max_abs_error_per_meth = numpy.amax(abs_error_per_labels, axis=1)
min_abs_error_per_meth = numpy.amin(abs_error_per_labels, axis=1)
mean_abs_error_per_meth = numpy.mean(abs_error_per_labels, axis=1)
std_abs_error_per_meth = numpy.std(abs_error_per_labels, axis=1)
# average error and std across sides
meanRL_abs_error_per_labels = numpy.zeros((error_per_label.shape[0], nb_RL_labels, error_per_label.shape[2]))
meanRL_std_abs_error_per_labels = numpy.zeros((std_per_label.shape[0], nb_RL_labels, std_per_label.shape[2]))
for i_file in range(0, nb_results_file):
for i_meth in range(0, len(methods_name[i_file])):
for i_label in range(0, nb_RL_labels):
# find indexes of corresponding labels
ind_ID_first_side = labels_id[i_file].index(i_label)
ind_ID_other_side = labels_id[i_file].index(i_label + nb_RL_labels)
# compute mean across 2 sides
meanRL_abs_error_per_labels[i_file, i_label, i_meth] = float(error_per_label[i_file, ind_ID_first_side, i_meth] + error_per_label[i_file, ind_ID_other_side, i_meth]) / 2
meanRL_std_abs_error_per_labels[i_file, i_label, i_meth] = float(std_per_label[i_file, ind_ID_first_side, i_meth] + std_per_label[i_file, ind_ID_other_side, i_meth]) / 2
nb_method = len(methods_to_display)
sct.printv('Noise std of the ' + str(nb_results_file) + ' generated files:')
print snr
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Tracts std of the ' + str(nb_results_file) + ' generated files:')
print tracts_std
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('CSF value of the ' + str(nb_results_file) + ' generated files:')
print csf_values
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Methods used to generate results for the ' + str(nb_results_file) + ' generated files:')
print methods_name
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Median obtained with each method (in colons) for the ' + str(nb_results_file) + ' generated files (in lines):')
print median_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Minimum obtained with each method (in colons) for the ' + str(
nb_results_file) + ' generated files (in lines):')
print min_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Maximum obtained with each method (in colons) for the ' + str(
nb_results_file) + ' generated files (in lines):')
print max_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Labels\' ID (in colons) for the ' + str(nb_results_file) + ' generated files (in lines):')
print labels_id
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Errors obtained with each method (in colons) for the ' + str(nb_results_file) + ' generated files (in lines):')
print error_per_label
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Mean errors across both sides obtained with each method (in colons) for the ' + str(nb_results_file) + ' generated files (in lines):')
print meanRL_abs_error_per_labels
# Compute fractional volume per label
labels_id_FV, labels_name_FV, fract_vol_per_lab, labels_name_FV_RL_gathered, fract_vol_per_lab_RL_gathered = isct_get_fractional_volume.get_fractional_volume_per_label('./cropped_atlas/', 'info_label.txt')
# # Get the number of voxels including at least one tract
# nb_voxels_in_WM = isct_get_fractional_volume.get_nb_voxel_in_WM('./cropped_atlas/', 'info_label.txt')
# normalize by the number of voxels in WM and express it as a percentage
fract_vol_norm = numpy.divide(fract_vol_per_lab_RL_gathered, numpy.sum(fract_vol_per_lab_RL_gathered)/100)
# NOT NECESSARY NOW WE AVERAGE ACROSS BOTH SIDES (which orders the labels)
# # check if the order of the labels returned by the function computing the fractional volumes is the same (which should be the case)
# if labels_id_FV != labels_id[0]:
# sct.printv('\n\nERROR: the labels IDs returned by the function \'i_sct_get_fractional_volume\' are different from the labels IDs of the results files\n\n', 'error')
# # Remove labels #30 and #31
# labels_id_FV_29, labels_name_FV_29, fract_vol_per_lab_29 = labels_id_FV[:-2], labels_name_FV[:-2], fract_vol_per_lab[:-2]
# indexes of labels sort according to the fractional volume
ind_labels_sort = numpy.argsort(fract_vol_norm)
# Find index of the file generated with noise variance = 10 and tracts std = 10
ind_file_to_display = numpy.where((snr == noise_std_to_display) & (tracts_std == tracts_std_to_display) & (csf_values == csf_value_to_display))
# sort arrays in this order
meanRL_abs_error_per_labels_sort = meanRL_abs_error_per_labels[ind_file_to_display[0], ind_labels_sort, :]
meanRL_std_abs_error_per_labels_sort = meanRL_std_abs_error_per_labels[ind_file_to_display[0], ind_labels_sort, :]
labels_name_sort = numpy.array(labels_name_FV_RL_gathered)[ind_labels_sort]
# *********************************************** START PLOTTING HERE **********************************************
# stringColor = Color()
matplotlib.rcParams.update({'font.size': 50, 'font.family': 'trebuchet'})
# plt.rcParams['xtick.major.pad'] = '11'
plt.rcParams['ytick.major.pad'] = '15'
fig = plt.figure(figsize=(60, 37))
width = 1.0 / (nb_method + 1)
ind_fig = numpy.arange(len(labels_name_sort)) * (1.0 + width)
plt.ylabel('Absolute error (%)\n', fontsize=65)
plt.xlabel('Fractional volume (% of the total number of voxels in WM)', fontsize=65)
plt.title('Absolute error per tract as a function of their fractional volume\n\n', fontsize=30)
plt.suptitle('(Noise std='+str(snr[ind_file_to_display[0]][0])+', Tracts std='+str(tracts_std[ind_file_to_display[0]][0])+', CSF value='+str(csf_values[ind_file_to_display[0]][0])+')', fontsize=30)
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
markers = ['o', 's', '^', 'D']
errorbar_plots = []
for meth, color, marker in zip(methods_to_display, colors, markers):
i_meth = methods_name[0].index(meth)
i_meth_to_display = methods_to_display.index(meth)
plot_i = plt.errorbar(ind_fig + i_meth_to_display * width, meanRL_abs_error_per_labels_sort[:, i_meth], meanRL_std_abs_error_per_labels_sort[:, i_meth], color=color, marker=marker, markersize=35, lw=7, elinewidth=1, capthick=5, capsize=10)
# plot_i = plt.boxplot(numpy.transpose(abs_error_per_labels[ind_files_csf_sort, :, i_meth]), positions=ind_fig + i_meth_to_display * width + (float(i_meth_to_display) * width) / (nb_method + 1), widths=width, boxprops=boxprops, medianprops=medianprops, flierprops=flierprops, whiskerprops=whiskerprops, capprops=capprops)
errorbar_plots.append(plot_i)
# add alternated vertical background colored bars
for i_xtick in range(0, len(ind_fig), 2):
plt.axvspan(ind_fig[i_xtick] - width - width / 2, ind_fig[i_xtick] + (nb_method + 1) * width - width / 2, facecolor='grey', alpha=0.1)
# concatenate value of fractional volume to labels'name
xtick_labels = [labels_name_sort[i_lab]+'\n'+r'$\bf{['+str(round(fract_vol_norm[ind_labels_sort][i_lab], 2))+']}$' for i_lab in range(0, len(labels_name_sort))]
ind_lemniscus = numpy.where(labels_name_sort == 'spinal lemniscus (spinothalamic and spinoreticular tracts)')[0][0]
xtick_labels[ind_lemniscus] = 'spinal lemniscus\n'+r'$\bf{['+str(round(fract_vol_norm[ind_labels_sort][ind_lemniscus], 2))+']}$'
# plt.legend(box_plots, methods_to_display, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
plt.legend(errorbar_plots, methods_to_display, loc=1, fontsize=50, numpoints=1)
plt.xticks(ind_fig + (numpy.floor(float(nb_method-1)/2)) * width, xtick_labels, fontsize=45)
# Tweak spacing to prevent clipping of tick-labels
plt.subplots_adjust(bottom=0, top=0.95, right=0.96)
plt.gca().set_xlim([-width, numpy.max(ind_fig) + (nb_method + 0.5) * width])
plt.gca().set_ylim([0, 17])
plt.gca().yaxis.set_major_locator(plt.MultipleLocator(1.0))
plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(0.5))
plt.grid(b=True, axis='y', which='both')
fig.autofmt_xdate()
plt.savefig(param_default.fname_folder_to_save_fig+'/absolute_error_vs_fractional_volume.pdf', format='PDF')
plt.show(block=False)
def main():
results_folder = param_default.results_folder
methods_to_display = param_default.methods_to_display
# Parameters for debug mode
if param_default.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
results_folder = "/Users/slevy_local/spinalcordtoolbox/dev/atlas/validate_atlas/results_20150210_200iter/map" #"C:/cygwin64/home/Simon_2/data_map"
methods_to_display = 'map'
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'i:m:') # define flags
except getopt.GetoptError as err: # check if the arguments are defined
print str(err) # error
# usage() # display usage
# if not opts:
# print 'Please enter the path to the result folder. Exit program.'
# sys.exit(1)
# # usage()
for opt, arg in opts: # explore flags
if opt in '-i':
results_folder = arg
if opt in '-m':
methods_to_display = arg
sct.printv("Working directory: "+os.getcwd())
sct.printv('\n\nData will be extracted from folder '+results_folder+' .', 'warning')
sct.printv('\t\tCheck existence...')
sct.check_folder_exist(results_folder)
# Extract methods to display
methods_to_display = methods_to_display.strip().split(',')
fname_results = glob.glob(results_folder + '/*.txt')
nb_results_file = len(fname_results)
# 1st dim: SNR, 2nd dim: tract std, 3rd dim: mean abs error, 4th dim: std abs error
# result_array = numpy.empty((nb_results_file, nb_results_file, 3), dtype=object)
# SNR
snr = numpy.zeros((nb_results_file))
# Tracts std
tracts_std = numpy.zeros((nb_results_file))
# methods' name
methods_name = [] #numpy.empty((nb_results_file, nb_method), dtype=object)
# labels
error_per_label = []
std_per_label = []
labels_id = []
# median
median_results = numpy.zeros((nb_results_file, 6))
# median std across bootstraps
median_std = numpy.zeros((nb_results_file, 6))
# min
min_results = numpy.zeros((nb_results_file, 6))
# max
max_results = numpy.zeros((nb_results_file, 6))
# Extract variance within labels and variance of noise
map_var_params = numpy.zeros((nb_results_file, 2))
for i_file in range(0, nb_results_file):
fname = fname_results[i_file].strip()
ind_start, ind_end = fname.index('results_map')+11, fname.index('_all.txt')
var = fname[ind_start:ind_end]
map_var_params[i_file, 0] = float(var.split(",")[0])
map_var_params[i_file, 1] = float(var.split(",")[1])
# Read each file and extract data
for i_file in range(0, nb_results_file):
# Open file
f = open(fname_results[i_file]) # open file
# Extract all lines in .txt file
lines = [line for line in f.readlines() if line.strip()]
# extract SNR
# find all index of lines containing the string "sigma noise"
ind_line_noise = [lines.index(line_noise) for line_noise in lines if "sigma noise" in line_noise]
if len(ind_line_noise) != 1:
sct.printv("ERROR: number of lines including \"sigma noise\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# result_array[:, i_file, i_file] = int(''.join(c for c in lines[ind_line_noise[0]] if c.isdigit()))
snr[i_file] = int(''.join(c for c in lines[ind_line_noise[0]] if c.isdigit()))
# extract tract std
ind_line_tract_std = [lines.index(line_tract_std) for line_tract_std in lines if "range tracts" in line_tract_std]
if len(ind_line_tract_std) != 1:
sct.printv("ERROR: number of lines including \"range tracts\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# result_array[i_file, i_file, :] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# regex = re.compile(''('(.*)':) # re.I permet d'ignorer la case (majuscule/minuscule)
# match = regex.search(lines[ind_line_tract_std[0]])
# result_array[:, i_file, :, :] = match.group(1) # le groupe 1 correspond a '.*'
tracts_std[i_file] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# extract method name
ind_line_label = [lines.index(line_label) for line_label in lines if "Label" in line_label]
if len(ind_line_label) != 1:
sct.printv("ERROR: number of lines including \"Label\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# methods_name[i_file, :] = numpy.array(lines[ind_line_label[0]].strip().split(',')[1:])
methods_name.append(lines[ind_line_label[0]].strip().replace(' ', '').split(',')[1:])
# extract median
ind_line_median = [lines.index(line_median) for line_median in lines if "median" in line_median]
if len(ind_line_median) != 1:
sct.printv("WARNING: number of lines including \"median\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
median = lines[ind_line_median[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 0] = [float(m.split('(')[0]) for m in median]
median_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in median])
median_std[i_file, :] = numpy.array([float(m.split('(')[1][:-1]) for m in median])
# extract min
ind_line_min = [lines.index(line_min) for line_min in lines if "min," in line_min]
if len(ind_line_min) != 1:
sct.printv("WARNING: number of lines including \"min\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
min = lines[ind_line_min[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in min]
min_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in min])
# extract max
ind_line_max = [lines.index(line_max) for line_max in lines if "max" in line_max]
if len(ind_line_max) != 1:
sct.printv("WARNING: number of lines including \"max\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
max = lines[ind_line_max[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in max]
max_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in max])
# extract error for each label
error_per_label_for_file_i = []
std_per_label_for_file_i = []
labels_id_for_file_i = []
# Due to 2 different kind of file structure, the number of the last label line must be adapted
if not ind_line_median:
ind_line_median = [len(lines)+1]
for i_line in range(ind_line_label[0]+1, ind_line_median[0]-1):
line_label_i = lines[i_line].strip().split(',')
error_per_label_for_file_i.append([float(error.strip().split('(')[0]) for error in line_label_i[1:]])
std_per_label_for_file_i.append([float(error.strip().split('(')[1][:-1]) for error in line_label_i[1:]])
labels_id_for_file_i.append(line_label_i[0])
error_per_label.append(error_per_label_for_file_i)
std_per_label.append(std_per_label_for_file_i)
labels_id.append(labels_id_for_file_i)
# close file
f.close()
# check if all the files in the result folder were generated with the same number of methods
if not all(x == methods_name[0] for x in methods_name):
sct.printv('ERROR: All the generated files in folder '+results_folder+' have not been generated with the same number of methods. Exit program.', 'error')
sys.exit(1)
# check if all the files in the result folder were generated with the same labels
if not all(x == labels_id[0] for x in labels_id):
sct.printv('ERROR: All the generated files in folder '+results_folder+' have not been generated with the same labels. Exit program.', 'error')
sys.exit(1)
# convert the list "error_per_label" into a numpy array to ease further manipulations
error_per_label = numpy.array(error_per_label)
std_per_label = numpy.array(std_per_label)
# compute different stats
abs_error_per_labels = numpy.absolute(error_per_label)
max_abs_error_per_meth = numpy.amax(abs_error_per_labels, axis=1)
min_abs_error_per_meth = numpy.amin(abs_error_per_labels, axis=1)
mean_abs_error_per_meth = numpy.mean(abs_error_per_labels, axis=1)
std_abs_error_per_meth = numpy.std(abs_error_per_labels, axis=1)
sct.printv('Noise std of the '+str(nb_results_file)+' generated files:')
print snr
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Tracts std of the '+str(nb_results_file)+' generated files:')
print tracts_std
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Methods used to generate results for the '+str(nb_results_file)+' generated files:')
print methods_name
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Median obtained with each method (in colons) for the '+str(nb_results_file)+' generated files (in lines):')
print median_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Minimum obtained with each method (in colons) for the '+str(nb_results_file)+' generated files (in lines):')
print min_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Maximum obtained with each method (in colons) for the '+str(nb_results_file)+' generated files (in lines):')
print max_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Labels\' ID (in colons) for the '+str(nb_results_file)+' generated files (in lines):')
print labels_id
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Errors obtained with each method (in colons) for the '+str(nb_results_file)+' generated files (in lines):')
print error_per_label
# ********************************** START PLOTTING HERE ***********************************************************
matplotlib.rcParams.update({'font.size': 45, 'font.family': 'Trebuchet'})
plt.rcParams['xtick.major.pad'] = '9'
plt.rcParams['ytick.major.pad'] = '15'
# matplotlib.rcParams['legend.handlelength'] = 0
# find indexes of files to be plotted
ind_var_noise20 = numpy.where(map_var_params[:, 1] == 20) # indexes where noise variance = 20
ind_ind_var_label_sort_var_noise20 = numpy.argsort(map_var_params[ind_var_noise20, 0]) # indexes of indexes where noise variance=20 sorted according to values of variance within labels (in ascending order)
ind_var_label_sort_var_noise20 = ind_var_noise20[0][ind_ind_var_label_sort_var_noise20][0] # indexes where noise variance=20 sorted according to values of variance within labels (in ascending order)
ind_var_label20 = numpy.where(map_var_params[:, 0] == 20) # indexes where variance within labels = 20
ind_ind_var_noise_sort_var_label20 = numpy.argsort(map_var_params[ind_var_label20, 1]) # indexes of indexes where label variance=20 sorted according to values of noise variance (in ascending order)
ind_var_noise_sort_var_label20 = ind_var_label20[0][ind_ind_var_noise_sort_var_label20][0] # indexes where noise variance=20 sorted according to values of variance within labels (in ascending order)
plt.close('all')
# Errorbar plot
plt.figure()
plt.ylabel('Mean absolute error (%)', fontsize=55)
plt.xlabel('Variance within labels (in percentage of the mean)', fontsize=55)
plt.title('Sensitivity of the method \"MAP\" to the variance within labels and to the SNR\n', fontsize=65)
plt.errorbar(map_var_params[ind_var_label_sort_var_noise20, 0], mean_abs_error_per_meth[ind_var_label_sort_var_noise20, 0], std_abs_error_per_meth[ind_var_label_sort_var_noise20, 0], color='blue', marker='o', linestyle='--', markersize=8, elinewidth=2, capthick=2, capsize=10)
plt.errorbar(map_var_params[ind_var_noise_sort_var_label20, 1], mean_abs_error_per_meth[ind_var_noise_sort_var_label20, 0], std_abs_error_per_meth[ind_var_noise_sort_var_label20, 0], color='red', marker='o', linestyle='--', markersize=8, elinewidth=2, capthick=2, capsize=10)
# plt.legend(plots, methods_to_display, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0., handler_map={Line2D: HandlerLine2D(numpoints=1)})
plt.legend(['noise variance = 20', 'variance within labels = 20% of the mean'], loc='best', handler_map={Line2D: HandlerLine2D(numpoints=1)})
plt.gca().set_xlim([numpy.min(map_var_params[ind_var_label_sort_var_noise20, 0]) - 1, numpy.max(map_var_params[ind_var_label_sort_var_noise20, 0]) + 1])
plt.grid(b=True, axis='both')
# plt.gca().yaxis.set_major_locator(plt.MultipleLocator(2.5))
# Box-and-whisker plots
nb_box = 2
plt.figure(figsize=(30, 15))
width = 1.0 / (nb_box + 1)
ind_fig = numpy.arange(len(map_var_params[ind_var_label_sort_var_noise20, 0])) * (1.0 + width)
plt.ylabel('Absolute error (%)\n', fontsize=55)
plt.xlabel('Variance', fontsize=55)
plt.title('Sensitivity of the method \"MAP\" to the variance within labels and to the SNR\n', fontsize=65)
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_box))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
box_plots = []
boxprops = dict(linewidth=6, color='b')
flierprops = dict(markeredgewidth=0.7, markersize=15, marker='.', color='b')
whiskerprops = dict(linewidth=5, color='b')
capprops = dict(linewidth=5, color='b')
medianprops = dict(linewidth=6, color='b')
meanpointprops = dict(marker='D', markeredgecolor='black', markerfacecolor='firebrick')
meanlineprops = dict(linestyle='--', linewidth=2.5)
plot_constant_noise_var = plt.boxplot(numpy.transpose(abs_error_per_labels[ind_var_label_sort_var_noise20, :, 0]), positions=ind_fig, widths=width, boxprops=boxprops, medianprops=medianprops, flierprops=flierprops, whiskerprops=whiskerprops, capprops=capprops)
box_plots.append(plot_constant_noise_var['boxes'][0])
boxprops = dict(linewidth=6, color='r')
flierprops = dict(markeredgewidth=0.7, markersize=15, marker='.', color='r')
whiskerprops = dict(linewidth=5, color='r')
capprops = dict(linewidth=5, color='r')
medianprops = dict(linewidth=6, color='r')
meanpointprops = dict(marker='D', markeredgecolor='black', markerfacecolor='firebrick')
meanlineprops = dict(linestyle='--', linewidth=2.5)
plot_constant_label_var = plt.boxplot(numpy.transpose(abs_error_per_labels[ind_var_noise_sort_var_label20, :, 0]), positions=ind_fig + width + width / (nb_box + 1), widths=width, boxprops=boxprops, medianprops=medianprops, flierprops=flierprops, whiskerprops=whiskerprops, capprops=capprops)
box_plots.append(plot_constant_label_var['boxes'][0])
# add alternated vertical background colored bars
for i_xtick in range(0, len(ind_fig), 2):
plt.axvspan(ind_fig[i_xtick] - width - width / 4, ind_fig[i_xtick] + (nb_box+1) * width - width / 4, facecolor='grey', alpha=0.1)
# plt.legend(box_plots, methods_to_display, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
# leg = plt.legend(box_plots, [r'$\mathrm{\mathsf{noise\ variance\ =\ 20\ voxels^2}}$', r'$\mathrm{\mathsf{variance\ within\ labels\ =\ 20\%\ of\ the\ mean\ value}}$'], loc=1, handletextpad=-2)
# color_legend_texts(leg)
# convert xtick labels into int
xtick_labels = [int(xtick) for xtick in map_var_params[ind_var_label_sort_var_noise20, 0]]
plt.xticks(ind_fig + (numpy.floor(nb_box / 2)) * (width/2) * (1.0 + 1.0 / (nb_box + 1)), xtick_labels)
plt.gca().set_xlim([-width, numpy.max(ind_fig) + (nb_box + 0.5) * width])
plt.gca().yaxis.set_major_locator(plt.MultipleLocator(1.0))
plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(0.25))
plt.grid(b=True, axis='y', which='both')
plt.savefig(os.path.join(param_default.fname_folder_to_save_fig, 'absolute_error_as_a_function_of_MAP_parameters.pdf'), format='PDF')
plt.show(block=False)
def main():
results_folder = param_default.results_folder
methods_to_display = param_default.methods_to_display
# Parameters for debug mode
if param_default.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
results_folder = "/Users/slevy_local/spinalcordtoolbox/dev/atlas/validate_atlas/results_20150210_200iter"#"C:/cygwin64/home/Simon_2/data_auto_vs_manual"
path_sct = '/Users/slevy_local/spinalcordtoolbox' #'C:/cygwin64/home/Simon_2/spinalcordtoolbox'
methods_to_display = 'bin,man0,man1,man2,man3'
else:
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'i:m:') # define flags
except getopt.GetoptError as err: # check if the arguments are defined
print str(err) # error
# usage() # display usage
# if not opts:
# print 'Please enter the path to the result folder. Exit program.'
# sys.exit(1)
# # usage()
for opt, arg in opts: # explore flags
if opt in '-i':
results_folder = arg
if opt in '-m':
methods_to_display = arg
# Append path that contains scripts, to be able to load modules
sys.path.append(path_sct + '/scripts')
import sct_utils as sct
sct.printv("Working directory: "+os.getcwd())
# Folder including data "automatic vs manual"
folder_auto_vs_manual = results_folder+'/manual_mask/sub'
sct.printv('\n\nData will be extracted from folder '+folder_auto_vs_manual+' .', 'warning')
sct.printv('\t\tCheck existence...')
sct.check_folder_exist(folder_auto_vs_manual)
# Extract methods to display
methods_to_display = methods_to_display.strip().split(',')
fname_results = glob.glob(folder_auto_vs_manual + '/*.txt')
nb_results_file = len(fname_results)
# 1st dim: SNR, 2nd dim: tract std, 3rd dim: mean abs error, 4th dim: std abs error
# result_array = numpy.empty((nb_results_file, nb_results_file, 3), dtype=object)
# SNR
snr = numpy.zeros((nb_results_file))
# Tracts std
tracts_std = numpy.zeros((nb_results_file))
# CSF value
csf_values = numpy.zeros((nb_results_file))
# methods' name
methods_name = [] #numpy.empty((nb_results_file, nb_method), dtype=object)
# labels
error_per_label = []
std_per_label = []
labels_id = []
# median
median_results = numpy.zeros((nb_results_file, 6))
# median std across bootstraps
median_std = numpy.zeros((nb_results_file, 6))
# min
min_results = numpy.zeros((nb_results_file, 6))
# max
max_results = numpy.zeros((nb_results_file, 6))
#
for i_file in range(0, nb_results_file):
# Open file
f = open(fname_results[i_file]) # open file
# Extract all lines in .txt file
lines = [line for line in f.readlines() if line.strip()]
# extract SNR
# find all index of lines containing the string "sigma noise"
ind_line_noise = [lines.index(line_noise) for line_noise in lines if "sigma noise" in line_noise]
if len(ind_line_noise) != 1:
sct.printv("ERROR: number of lines including \"sigma noise\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# result_array[:, i_file, i_file] = int(''.join(c for c in lines[ind_line_noise[0]] if c.isdigit()))
snr[i_file] = int(''.join(c for c in lines[ind_line_noise[0]] if c.isdigit()))
# extract tract std
ind_line_tract_std = [lines.index(line_tract_std) for line_tract_std in lines if "range tracts" in line_tract_std]
if len(ind_line_tract_std) != 1:
sct.printv("ERROR: number of lines including \"range tracts\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# result_array[i_file, i_file, :] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# regex = re.compile(''('(.*)':) # re.I permet d'ignorer la case (majuscule/minuscule)
# match = regex.search(lines[ind_line_tract_std[0]])
# result_array[:, i_file, :, :] = match.group(1) # le groupe 1 correspond a '.*'
tracts_std[i_file] = int(''.join(c for c in lines[ind_line_tract_std[0]].split(':')[1] if c.isdigit()))
# extract CSF value
ind_line_csf_value = [lines.index(line_csf_value) for line_csf_value in lines if
"# value CSF" in line_csf_value]
if len(ind_line_csf_value) != 1:
sct.printv("ERROR: number of lines including \"range tracts\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
csf_values[i_file] = int(''.join(c for c in lines[ind_line_csf_value[0]].split(':')[1] if c.isdigit()))
# extract method name
ind_line_label = [lines.index(line_label) for line_label in lines if "Label" in line_label]
if len(ind_line_label) != 1:
sct.printv("ERROR: number of lines including \"Label\" is different from 1. Exit program.", 'error')
sys.exit(1)
else:
# methods_name[i_file, :] = numpy.array(lines[ind_line_label[0]].strip().split(',')[1:])
methods_name.append(lines[ind_line_label[0]].strip().replace(' ', '').split(',')[1:])
# extract median
ind_line_median = [lines.index(line_median) for line_median in lines if "median" in line_median]
if len(ind_line_median) != 1:
sct.printv("WARNING: number of lines including \"median\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
median = lines[ind_line_median[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 0] = [float(m.split('(')[0]) for m in median]
median_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in median])
median_std[i_file, :] = numpy.array([float(m.split('(')[1][:-1]) for m in median])
# extract min
ind_line_min = [lines.index(line_min) for line_min in lines if "min," in line_min]
if len(ind_line_min) != 1:
sct.printv("WARNING: number of lines including \"min\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
min = lines[ind_line_min[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in min]
min_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in min])
# extract max
ind_line_max = [lines.index(line_max) for line_max in lines if "max" in line_max]
if len(ind_line_max) != 1:
sct.printv("WARNING: number of lines including \"max\" is different from 1. Exit program.", 'warning')
# sys.exit(1)
else:
max = lines[ind_line_max[0]].strip().split(',')[1:]
# result_array[i_file, i_file, 1] = [float(m.split('(')[0]) for m in max]
max_results[i_file, :] = numpy.array([float(m.split('(')[0]) for m in max])
# extract error for each label
error_per_label_for_file_i = []
std_per_label_for_file_i = []
labels_id_for_file_i = []
# Due to 2 different kind of file structure, the number of the last label line must be adapted
if not ind_line_median:
ind_line_median = [len(lines)+1]
for i_line in range(ind_line_label[0]+1, ind_line_median[0]-1):
line_label_i = lines[i_line].strip().split(',')
error_per_label_for_file_i.append([float(error.strip().split('(')[0]) for error in line_label_i[1:]])
std_per_label_for_file_i.append([float(error.strip().split('(')[1][:-1]) for error in line_label_i[1:]])
labels_id_for_file_i.append(line_label_i[0])
error_per_label.append(error_per_label_for_file_i)
std_per_label.append(std_per_label_for_file_i)
labels_id.append(labels_id_for_file_i)
# close file
f.close()
# check if all the files in the result folder were generated with the same number of methods
if not all(x == methods_name[0] for x in methods_name):
sct.printv('ERROR: All the generated files in folder '+results_folder+' have not been generated with the same number of methods. Exit program.', 'error')
sys.exit(1)
# check if all the files in the result folder were generated with the same labels
if not all(x == labels_id[0] for x in labels_id):
sct.printv('ERROR: All the generated files in folder '+results_folder+' have not been generated with the same labels. Exit program.', 'error')
sys.exit(1)
# convert the list "error_per_label" into a numpy array to ease further manipulations
error_per_label = numpy.array(error_per_label)
std_per_label = numpy.array(std_per_label)
# compute different stats
abs_error_per_labels = numpy.absolute(error_per_label)
max_abs_error_per_meth = numpy.amax(abs_error_per_labels, axis=1)
min_abs_error_per_meth = numpy.amin(abs_error_per_labels, axis=1)
mean_abs_error_per_meth = numpy.mean(abs_error_per_labels, axis=1)
std_abs_error_per_meth = numpy.std(abs_error_per_labels, axis=1)
nb_method = len(methods_to_display)
sct.printv('Noise std of the '+str(nb_results_file)+' generated files:')
print snr
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Tracts std of the '+str(nb_results_file)+' generated files:')
print tracts_std
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Methods used to generate results for the '+str(nb_results_file)+' generated files:')
print methods_name
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Median obtained with each method (in colons) for the '+str(nb_results_file)+' generated files (in lines):')
print median_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Minimum obtained with each method (in colons) for the '+str(nb_results_file)+' generated files (in lines):')
print min_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Maximum obtained with each method (in colons) for the '+str(nb_results_file)+' generated files (in lines):')
print max_results
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Labels\' ID (in colons) for the '+str(nb_results_file)+' generated files (in lines):')
print labels_id
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Mean errors (across bootstraps) obtained with each method (in colons) for the '+str(nb_results_file)+' generated files (in lines):')
print error_per_label
print '----------------------------------------------------------------------------------------------------------------'
sct.printv('Errors std (across bootstraps) obtained with each method (in colons) for the '+str(nb_results_file)+' generated files (in lines):')
print std_per_label
# ********************************** START PLOTTING HERE ***********************************************************
# find index of the file generated with sigma noise = 10 and range tracts = -10:+10
ind_file_noise10_tracts_std10 = numpy.where((snr == 10) & (tracts_std == 10))[0][0]
matplotlib.rcParams.update({'font.size': 45, 'font.family': 'trebuchet'})
plt.rcParams['xtick.major.pad'] = '9'
plt.rcParams['ytick.major.pad'] = '15'
fig0 = plt.figure(0, figsize=(34, 17))
width = 0.5/(nb_method+1)
ind_fig0 = numpy.arange(len(labels_id[0]))
plt.ylabel('Absolute error (%)', fontsize=55)
plt.xlabel('Labels', fontsize=55)
plt.suptitle('Automatic estimation vs. manual estimation', fontsize=65)
plt.title('(Noise std='+str(snr[0])+', Tracts std='+str(tracts_std[0])+', CSF value='+str(csf_values[0])+')\n', fontsize=65)
# colors = plt.get_cmap('jet')(np.linspace(0, 1.0, nb_method))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
plots = []
marker_size = 30
line_width = 3.0
cap_size = 15
eline_width = 7.0
cap_thick = 5.0
for meth, color in zip(methods_to_display, colors):
i_meth = methods_name[0].index(meth)
i_meth_to_display = methods_to_display.index(meth)
plot = plt.errorbar(ind_fig0+i_meth_to_display*width+(float(i_meth_to_display)*width)/(nb_method+1), error_per_label[ind_file_noise10_tracts_std10, :, i_meth], std_per_label[ind_file_noise10_tracts_std10, :, i_meth], color=color, linestyle='None', marker='o', ms=marker_size, lw=line_width, capsize=cap_size, capthick=cap_thick, elinewidth=eline_width)
plots.append(plot[0])
# add alternated vertical background colored bars
for i_xtick in range(0, len(ind_fig0), 2):
plt.axvspan(ind_fig0[i_xtick] - 3*width, ind_fig0[i_xtick] + (nb_method + 1) * width + 2*width, facecolor='grey', alpha=0.1)
# plt.legend(plots, methods_to_display, bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0., handler_map={Line2D: HandlerLine2D(numpoints=1)})
plt.legend(plots, methods_to_display, loc='best', handler_map={Line2D: HandlerLine2D(numpoints=1)})
# plt.xticks(ind_fig0 + (numpy.floor(nb_method/2))*width*(1.0+1.0/(nb_method+1)), labels_id[0], fontsize=45)
plt.xticks(ind_fig0 + (numpy.floor(nb_method/2))*width*(1.0+1.0/(nb_method+1)), ['l-cst', 'r-cst', 'dc'], fontsize=45)
plt.gca().set_xlim([-3*width, numpy.max(ind_fig0)+(nb_method+3)*width])
# plt.gca().set_ylim([0, 2])
plt.gca().yaxis.set_major_locator(plt.MultipleLocator(2.5))
plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(0.5))
plt.grid(b=True, axis='y', which='both')
# adjust the size of the frame
# plt.subplots_adjust(bottom=0.15, top=0.86, right=0.7, left=0.2)
plt.savefig(param_default.fname_folder_to_save_fig +'/automatic_method_vs_manual_methods.pdf', format='PDF')
plt.show(block=False)