def runThunderSTORM(filename):
# ---------------------------------------------
# Runs the ThunderSTORM plugin on the filename
# provided with the settings from the top of
# this file.
# ---------------------------------------------
global exportList
if virtualStack:
IJ.openVirtual(filename)
else:
IJ.open(filename)
IJ.run(stormAnalysisCmd, stormAnalysisParams)
dataStoreFilename = os.path.dirname(filename) + os.sep + os.path.splitext(
os.path.basename(filename))[0] + ".csv"
exportList.append(dataStoreFilename)
exportParams = stormExportParams.replace("%filename", dataStoreFilename)
print exportParams
IJ.run(stormExportCmd, exportParams)
WindowManager.closeAllWindows()
return exportList
def show_thresholded(event):
""" Show virtual stack of image thresholded to mu +/- 1x sigma of each Gaussian component
Parameters
----------
event : Event
Waits for show_thresholded_JB JButton to be pressed
Returns
-------
None
"""
# Get image filename
results_dir = get_results_dir()
with open(os.path.join(results_dir, "Users_Params.csv")) as csv_file:
reader = csv.DictReader(csv_file)
for row in reader:
img_fname = row['img_fname']
n_gaussians = int(row['n_gaussians'])
print("Displaying image {} with {} Gaussians fitted".format(
img_fname, n_gaussians))
# Read mu and sigma from results directory
with open(os.path.join(results_dir, "fitted_results.csv")) as csv_file:
reader = csv.reader(csv_file)
results = [] # list containing lines in fitted_results.csv
for row in reader:
results.append(row)
mu_all = []
sigma_all = []
for i in range(3, len(results)):
mu_all.append(float(results[i][0]))
sigma_all.append(float(results[i][1]))
# Calculate values for thresholding (mu +/- 1x sigma)
lower_threshold = map(lambda mu, sigma: mu - sigma, mu_all, sigma_all)
upper_threshold = map(lambda mu, sigma: mu + sigma, mu_all, sigma_all)
# Display each slice thresholded with the Gaussian number in title
for gaussian in range(n_gaussians):
imp = IJ.openVirtual(img_fname)
imp.setTitle("Gaussian {}".format(gaussian))
imp.show()
IJ.setThreshold(imp, lower_threshold[gaussian],
upper_threshold[gaussian])
print("Gaussian {} threshold values [{} - {}]".format(
gaussian, lower_threshold[gaussian], upper_threshold[gaussian]))