def detect_simulated_blips(amplitude=1):
tmpdir = os.path.join(os.path.dirname(g.settings.config_file), 'tmp')
flika_fn = 'simulated_blips.flika'
if os.path.isdir(tmpdir):
if flika_fn in os.listdir(tmpdir):
pass
else:
shutil.rmtree(tmpdir)
os.mkdir(tmpdir)
else:
os.mkdir(tmpdir)
data_window = Window(generateBlips(amplitude),
filename=os.path.join(tmpdir, 'simulated_blips.tif'))
mt, mx, my = data_window.image.shape
data_window.setWindowTitle('Data Window')
norm_window = data_window
medB = 3
gaussian_blur(medB, keepSourceWindow=True)
makeROI('rectangle',
pts=np.array([[medB * 2, medB * 2], [medB * 2, my - medB * 2],
[mx - medB * 2, my - medB * 2],
[mx - medB * 2, medB * 2], [medB * 2, medB * 2]]))
set_value(0, 0, mt - 1, restrictToOutside=True)
g.m.currentWindow.setWindowTitle('Lightly Blurred')
binary_window2 = threshold(.3, keepSourceWindow=True)
norm_window.setAsCurrentWindow()
binary_window3 = threshold(1.5, keepSourceWindow=True)
binary_window = Window(binary_window2.image + binary_window3.image)
close([binary_window2, binary_window3])
binary_window = threshold(1.5)
binary_window.setWindowTitle('Binary Window')
threshold_cluster(binary_window,
data_window,
norm_window,
rotatedfit=False,
density_threshold=2,
time_factor=.2,
roi_width=5,
maxPuffLen=20,
maxSigmaForGaussianFit=20,
load_flika_file=True)
def detect_simulated_blips(amplitude=1):
tmpdir=os.path.join(os.path.dirname(g.settings.config_file),'tmp')
flika_fn='simulated_blips.flika'
if os.path.isdir(tmpdir):
if flika_fn in os.listdir(tmpdir):
pass
else:
shutil.rmtree(tmpdir)
os.mkdir(tmpdir)
else:
os.mkdir(tmpdir)
data_window=Window(generateBlips(amplitude),filename=os.path.join(tmpdir,'simulated_blips.tif'))
mt,mx,my=data_window.image.shape
data_window.setWindowTitle('Data Window')
norm_window=data_window
medB=3
gaussian_blur(medB,keepSourceWindow=True)
makeROI('rectangle',pts=np.array([[medB*2,medB*2],[medB*2,my-medB*2],[mx-medB*2,my-medB*2],[mx-medB*2,medB*2],[medB*2,medB*2]]))
set_value(0,0,mt-1,restrictToOutside=True)
g.m.currentWindow.setWindowTitle('Lightly Blurred')
binary_window2=threshold(.3,keepSourceWindow=True)
norm_window.setAsCurrentWindow()
binary_window3=threshold(1.5,keepSourceWindow=True)
binary_window=Window(binary_window2.image+binary_window3.image)
close([binary_window2,binary_window3])
binary_window=threshold(1.5)
binary_window.setWindowTitle('Binary Window')
threshold_cluster(binary_window,
data_window,
norm_window,
rotatedfit=False,
density_threshold=2,
time_factor=.2,
roi_width=5,
maxPuffLen=20,
maxSigmaForGaussianFit=20,
load_flika_file=True)
Created on Fri Dec 04 13:23:33 2015
@author: Kyle Ellefsen
"""
import numpy as np
from plugins.detect_puffs.puff_simulator.puff_simulator import simulate_puffs
from plugins.detect_puffs.threshold_cluster import threshold_cluster
data_window = simulate_puffs(nFrames=1000, nPuffs=20)
data_window = g.m.currentWindow
norm_window = data_window
norm_window.setName('Normalized Window')
binary_window = threshold(1.1, keepSourceWindow=True)
binary_window.setName('Binary Window')
threshold_cluster(binary_window,
data_window,
norm_window,
density_threshold=5.8)
## run through manual steps
## once threshold_cluster has run, see what the percentage of puffs it detected, and the false positive rate
detected_puffs = g.m.puffAnalyzer.puffs.puffs
simulated_puffs = simulate_puffs.puffs
nearest_puffs = []
for puff in detected_puffs:
k = puff.kinetics
detected_puff = np.array([k['t_peak'], k['x'], k['y']])
difference = np.sqrt(np.sum((simulated_puffs - detected_puff)**2, 1))
closest_idx = np.argmin(difference)
#distance=difference[closest_idx]
"""
Created on Fri Dec 04 13:23:33 2015
@author: Kyle Ellefsen
"""
import numpy as np
from plugins.detect_puffs.puff_simulator.puff_simulator import simulate_puffs
from plugins.detect_puffs.threshold_cluster import threshold_cluster
data_window=simulate_puffs(nFrames=1000,nPuffs=20)
data_window=g.m.currentWindow
norm_window=data_window
norm_window.setName('Normalized Window')
binary_window=threshold(1.1, keepSourceWindow=True)
binary_window.setName('Binary Window')
threshold_cluster(binary_window,data_window,norm_window,density_threshold=5.8)
## run through manual steps
## once threshold_cluster has run, see what the percentage of puffs it detected, and the false positive rate
detected_puffs=g.m.puffAnalyzer.puffs.puffs
simulated_puffs=simulate_puffs.puffs
nearest_puffs=[]
for puff in detected_puffs:
k=puff.kinetics
detected_puff=np.array([k['t_peak'],k['x'],k['y']])
difference=np.sqrt(np.sum((simulated_puffs-detected_puff)**2,1))
closest_idx=np.argmin(difference)
#distance=difference[closest_idx]
@author: Kyle Ellefsen
"""
if __name__ == '__main__':
import os, sys
flika_dir = os.path.join(os.path.expanduser('~'), 'Documents', 'GitHub',
'flika')
sys.path.append(flika_dir)
from flika import *
start_flika()
from plugins.detect_puffs.threshold_cluster import threshold_cluster
from plugins.detect_puffs.puff_simulator.puff_simulator import simulate_puffs
simulate_puffs(nFrames=1000, nPuffs=20)
baseline = -5 # This is the mean pixel value in the absence of photons.
subtract(baseline)
data_window = ratio(
0, 30, 'average'
) # ratio(first_frame, nFrames, ratio_type). Now we are in F/F0
data_window.setName('Data Window (F/F0)')
norm_image = data_window.image - 1
norm_window = Window(norm_image)
norm_window.setName('Normalized Window')
blurred_window = gaussian_blur(2, norm_edges=True, keepSourceWindow=True)
blurred_window.setName('Blurred Window')
threshold_cluster(data_window,
blurred_window,
blurred_window,
blur_thresh=.3)
sys.exit(
g.app.exec_()) # This is required to run outside of Spyder or PyCharm
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 06 11:24:36 2015
@author: Kyle Ellefsen
"""
if __name__ == '__main__':
import os, sys; flika_dir = os.path.join(os.path.expanduser('~'),'Documents', 'GitHub', 'flika'); sys.path.append(flika_dir); from flika import *; start_flika()
from plugins.detect_puffs.threshold_cluster import threshold_cluster
from plugins.detect_puffs.puff_simulator.puff_simulator import simulate_puffs
simulate_puffs(nFrames=1000,nPuffs=20)
baseline = -5 # This is the mean pixel value in the absence of photons.
subtract(baseline)
data_window=ratio(0, 30, 'average') # ratio(first_frame, nFrames, ratio_type). Now we are in F/F0
data_window.setName('Data Window (F/F0)')
norm_image = data_window.image - 1
norm_window = Window(norm_image)
norm_window.setName('Normalized Window')
blurred_window = gaussian_blur(2, norm_edges=True, keepSourceWindow=True)
blurred_window.setName('Blurred Window')
threshold_cluster(data_window, blurred_window, blurred_window, blur_thresh=.3)
sys.exit(g.app.exec_()) # This is required to run outside of Spyder or PyCharm
