# -*- coding: utf-8 -*-
"""
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']])
"""
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)
# -*- coding: utf-8 -*-
"""
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)
# -*- 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