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ecell · Mar 14, 2018 · 08b37d6 · 08b37d6
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diff --git a/bioimaging/NEC_tirfm_script.py b/bioimaging/NEC_tirfm_script.py
@@ -12,57 +12,55 @@
 
 def test_tirfm(t0, t1, beam, dist=None) :
 
-	# create TIRF Microscopy
-	tirfm = TIRFMConfigs()
-	tirfm.set_LightSource(source_type='LASER', wave_length=532, flux_density=beam, angle=65.7)
-	tirfm.set_Fluorophore(fluorophore_type='Tetramethylrhodamine(TRITC)')
-	tirfm.set_DichroicMirror('FF562-Di03-25x36')
-	tirfm.set_EmissionFilter('FF01-593_40-25')
-	tirfm.set_Magnification(Mag=100)
-	#tirfm.set_Magnification(Mag=250)
-
-	# Detector : CMOS Camera
-	tirfm.set_Detector(detector='CMOS', image_size=(600,600), pixel_length=6.5e-6, \
-			focal_point=(0.0,0.5,0.5), exposure_time=30e-3, QE=0.73)
-	tirfm.set_ADConverter(bit=16, offset=100, fullwell=30000)
-#	# Detector : EMCCD Camera
-#	tirfm.set_Detector(detector='EMCCD', image_size=(512,512), pixel_length=16e-6, \
-#			focal_point=(0.0,0.5,0.5), exposure_time=30e-3, QE=0.92, readout_noise=100, emgain=300)
-#	tirfm.set_ADConverter(bit=16, offset=2000, fullwell=370000)
-
-	### Output data
-	#tifm.set_OutputData(image_file_dir='./images')
-	#tirfm.set_OutputData(image_file_dir='./numpys_test')
-	#tirfm.set_OutputData(image_file_dir='./numpys_nec/numpys_nec_2A_%02dw_cmos_%03dnm' % (beam, dist))
-	#tirfm.set_OutputData(image_file_dir='./numpys_nec/numpys_nec_2A_%02dw_emccd_%03dnm' % (beam, dist))
-	#tirfm.set_OutputData(image_file_dir='./numpys_nec/numpys_nec_100A_%02dw_cmos' % (beam))
-	#tirfm.set_OutputData(image_file_dir='./numpys_nec/numpys_nec_100A_%02dw_emccd' % (beam))
-	tirfm.set_OutputData(image_file_dir='./numpys_nec_03/numpys_nec_05000A_20w_cmos')
-
-	### Input data
-	#tirfm.set_InputData('/home/masaki/ecell3/latest/data/csv/beads_nec_2A', start=t0, end=t1, dist_nm=dist)
-	tirfm.set_InputData('/home/masaki/ecell3/latest/data/csv/beads_nec_05000A', start=t0, end=t1)
-
-	# create physical effects
-	physics = PhysicalEffects()
-	physics.set_background(mean=0.1)
-	physics.set_fluorescence(quantum_yield=1.00, abs_coefficient=100000)
-	#physics.set_photobleaching(tau0=1.8, alpha=0.73)
-	#physics.set_photoactivation(turn_on_ratio=1000, activation_yield=0.1, frac_preactivation=0.00)
-
-	# create image and movie
-	create = TIRFMVisualizer(configs=tirfm, effects=physics)
-	create.output_frames(num_div=16)
+    # create TIRF Microscopy
+    tirfm = TIRFMConfigs()
+    tirfm.set_LightSource(source_type='LASER', wave_length=532, flux_density=beam, angle=65.7)
+    tirfm.set_Fluorophore(fluorophore_type='Tetramethylrhodamine(TRITC)')
+    tirfm.set_DichroicMirror('FF562-Di03-25x36')
+    tirfm.set_EmissionFilter('FF01-593_40-25')
+    tirfm.set_Magnification(Mag=100)
+    #tirfm.set_Magnification(Mag=250)
+
+    # Detector : CMOS Camera
+    tirfm.set_Detector(detector='CMOS', image_size=(600,600), pixel_length=6.5e-6, \
+                    focal_point=(0.0,0.5,0.5), exposure_time=30e-3, QE=0.73)
+    tirfm.set_ADConverter(bit=16, offset=100, fullwell=30000)
+#       # Detector : EMCCD Camera
+#       tirfm.set_Detector(detector='EMCCD', image_size=(512,512), pixel_length=16e-6, \
+#                       focal_point=(0.0,0.5,0.5), exposure_time=30e-3, QE=0.92, readout_noise=100, emgain=300)
+#       tirfm.set_ADConverter(bit=16, offset=2000, fullwell=370000)
+
+    ### Output data
+    #tifm.set_OutputData(image_file_dir='./images')
+    #tirfm.set_OutputData(image_file_dir='./numpys_test')
+    #tirfm.set_OutputData(image_file_dir='./numpys_nec/numpys_nec_2A_%02dw_cmos_%03dnm' % (beam, dist))
+    #tirfm.set_OutputData(image_file_dir='./numpys_nec/numpys_nec_2A_%02dw_emccd_%03dnm' % (beam, dist))
+    #tirfm.set_OutputData(image_file_dir='./numpys_nec/numpys_nec_100A_%02dw_cmos' % (beam))
+    #tirfm.set_OutputData(image_file_dir='./numpys_nec/numpys_nec_100A_%02dw_emccd' % (beam))
+    tirfm.set_OutputData(image_file_dir='./numpys_nec_03/numpys_nec_05000A_20w_cmos')
+
+    ### Input data
+    #tirfm.set_InputData('/home/masaki/ecell3/latest/data/csv/beads_nec_2A', start=t0, end=t1, dist_nm=dist)
+    tirfm.set_InputData('/home/masaki/ecell3/latest/data/csv/beads_nec_05000A', start=t0, end=t1)
+
+    # create physical effects
+    physics = PhysicalEffects()
+    physics.set_background(mean=0.1)
+    physics.set_fluorescence(quantum_yield=1.00, abs_coefficient=100000)
+    #physics.set_photobleaching(tau0=1.8, alpha=0.73)
+    #physics.set_photoactivation(turn_on_ratio=1000, activation_yield=0.1, frac_preactivation=0.00)
+
+    # create image and movie
+    create = TIRFMVisualizer(configs=tirfm, effects=physics)
+    create.output_frames(num_div=16)
 
 
 
 if __name__ == "__main__":
 
-        t0 = float(sys.argv[1])
-        t1 = float(sys.argv[2])
-        beam = 20#float(sys.argv[3])
-        dist = 200#float(sys.argv[4])
-
-	test_tirfm(t0, t1, beam, dist)
-
+    t0 = float(sys.argv[1])
+    t1 = float(sys.argv[2])
+    beam = 20#float(sys.argv[3])
+    dist = 200#float(sys.argv[4])
 
+    test_tirfm(t0, t1, beam, dist)
diff --git a/bioimaging/analyses_correlation.py b/bioimaging/analyses_correlation.py
@@ -8,40 +8,36 @@
 
 def get_auto_corr(I0, It) :
 
-	length = len(It[:,2])
-	mu = It[:,2].sum()/length
-	s2 = (It[:,2] - mu)**2
-	s  = numpy.sqrt(s2.sum()/(length-1))
+    length = len(It[:,2])
+    mu = It[:,2].sum()/length
+    s2 = (It[:,2] - mu)**2
+    s  = numpy.sqrt(s2.sum()/(length-1))
 
-	cor = (I0[:,2] - mu)*(It[:,2] - mu)
-	avg = cor.sum()/len(cor)
+    cor = (I0[:,2] - mu)*(It[:,2] - mu)
+    avg = cor.sum()/len(cor)
 
-	C_auto = avg/(mu*mu)
+    C_auto = avg/(mu*mu)
 
-	return C_auto
+    return C_auto
 
 
 if __name__=='__main__':
 
-	file_in = '/home/masaki/bioimaging_4public/images_fcs_2StMD_A200'
-	#file_in = '/home/masaki/bioimaging_4public/images_fcs_D010_A100'
-	#file_in = '/home/masaki/bioimaging_4public/images_fcs_D010_A200'
-	#file_in = '/home/masaki/bioimaging_4public/images_fcs_D100_A100'
-	#file_in = '/home/masaki/bioimaging_4public/images_fcs_D100_A200'
+    file_in = '/home/masaki/bioimaging_4public/images_fcs_2StMD_A200'
+    #file_in = '/home/masaki/bioimaging_4public/images_fcs_D010_A100'
+    #file_in = '/home/masaki/bioimaging_4public/images_fcs_D010_A200'
+    #file_in = '/home/masaki/bioimaging_4public/images_fcs_D100_A100'
+    #file_in = '/home/masaki/bioimaging_4public/images_fcs_D100_A200'
 
-	array = numpy.load(file_in + '/image_all.npy')
+    array = numpy.load(file_in + '/image_all.npy')
 
-	N = len(array)/2
-
-	for i in range(N) :
-
-	    I0 = array[0:N]
-	    It = array[i:i+N]
-
-	    C_auto= get_auto_corr(I0, It)
-
-	    print(It[i,0], It[i,2], C_auto)
+    N = len(array)/2
 
+    for i in range(N) :
 
+        I0 = array[0:N]
+        It = array[i:i+N]
 
+        C_auto= get_auto_corr(I0, It)
 
+        print(It[i,0], It[i,2], C_auto)
diff --git a/bioimaging/analyses_moriga_fitting.py b/bioimaging/analyses_moriga_fitting.py
@@ -18,126 +18,123 @@
 
 
 def gaussian(A, x0, y0, width_x, width_y) :
-	"""Returns a gaussian function with the given parameters"""
-	width_x = float(width_x)
-	width_y = float(width_y)
+    """Returns a gaussian function with the given parameters"""
+    width_x = float(width_x)
+    width_y = float(width_y)
 
-	return lambda x,y: A*numpy.exp(-(((x0-x)/width_x)**2+((y0-y)/width_y)**2)/2)
+    return lambda x,y: A*numpy.exp(-(((x0-x)/width_x)**2+((y0-y)/width_y)**2)/2)
 
 
 def moments(data) :
-	"""Returns (A, x, y, width_x, width_y)
-	the gaussian parameters of a 2D distribution by calculating its
-	moments """
-	total = data.sum()
+    """Returns (A, x, y, width_x, width_y)
+    the gaussian parameters of a 2D distribution by calculating its
+    moments """
+    total = data.sum()
 
-	X, Y = numpy.indices(data.shape)
-	x = (X*data).sum()/total
-	y = (Y*data).sum()/total
+    X, Y = numpy.indices(data.shape)
+    x = (X*data).sum()/total
+    y = (Y*data).sum()/total
 
-	col = data[:, int(y)]
-	width_x = numpy.sqrt(abs((numpy.arange(col.size)-y)**2*col).sum()/col.sum())
+    col = data[:, int(y)]
+    width_x = numpy.sqrt(abs((numpy.arange(col.size)-y)**2*col).sum()/col.sum())
 
-	row = data[int(x), :]
-	width_y = numpy.sqrt(abs((numpy.arange(row.size)-x)**2*row).sum()/row.sum())
+    row = data[int(x), :]
+    width_y = numpy.sqrt(abs((numpy.arange(row.size)-x)**2*row).sum()/row.sum())
 
-	height = data.max()
+    height = data.max()
 
-	return height, x, y, width_x, width_y
+    return height, x, y, width_x, width_y
 
 
 
 def convert(file_in, file_out, index=None) :
 
-	i = 1
+    i = 1
 
-	#image = numpy.zeros(shape=(512,512))
-	signal = []
-	background = []
-	error  = []
+    #image = numpy.zeros(shape=(512,512))
+    signal = []
+    background = []
+    error  = []
 
-	while (True) :
-	    try :
-		#input_image  = numpy.load(file_in + '/image_%07d.npy' % (i))
-	        input_image = (imread(file_in + '/image_%07d.tif' % (i-1))).astype('int')
-	    except Exception :
-		break
+    while (True) :
+        try :
+            #input_image  = numpy.load(file_in + '/image_%07d.npy' % (i))
+            input_image = (imread(file_in + '/image_%07d.tif' % (i-1))).astype('int')
+        except Exception :
+            break
 
-	    image0 = numpy.array(input_image)
+        image0 = numpy.array(input_image)
 
-	    for j in range(len(coord)) :
+        for j in range(len(coord)) :
 
-	      if (i-1 == coord[j][0]) :
+            if (i-1 == coord[j][0]) :
 
-		x, y = coord[j][1], coord[j][2]
+                x, y = coord[j][1], coord[j][2]
 
-		image1 = input_image[y-8:y+8,x-8:x+8]
-		image0[y-8:y+8,x-8:x+8] = image0[y-8:y+8,x-8:x+8] - image1
+                image1 = input_image[y-8:y+8,x-8:x+8]
+                image0[y-8:y+8,x-8:x+8] = image0[y-8:y+8,x-8:x+8] - image1
 
-		I_all = float(image1.sum())
-		signal.append(I_all)
+                I_all = float(image1.sum())
+                signal.append(I_all)
 
-#	        params = moments(image0)
-#	        errorfunction = lambda p : numpy.ravel(gaussian(*p)(*numpy.indices(image0.shape)) - image0)
-#	        p, success = optimize.leastsq(errorfunction, params)
+#               params = moments(image0)
+#               errorfunction = lambda p : numpy.ravel(gaussian(*p)(*numpy.indices(image0.shape)) - image0)
+#               p, success = optimize.leastsq(errorfunction, params)
 
-	    I_bg = []
-	    array0 = image0.reshape((512*512))
+        I_bg = []
+        array0 = image0.reshape((512*512))
 
-	    for k in range(len(array0)) :
+        for k in range(len(array0)) :
 
-		if (array0[k] > 0) :
-		    I_bg.append(float(array0[k]))
+            if (array0[k] > 0) :
+                I_bg.append(float(array0[k]))
 
-	    I_bg = numpy.array(I_bg)
+        I_bg = numpy.array(I_bg)
 
-	    length = len(I_bg)
+        length = len(I_bg)
 
-	    b_avg = I_bg.sum()/length
-	    bdev2 = (I_bg - b_avg)**2
-	    dev_b = numpy.sqrt(bdev2.sum()/length)
+        b_avg = I_bg.sum()/length
+        bdev2 = (I_bg - b_avg)**2
+        dev_b = numpy.sqrt(bdev2.sum()/length)
 
-	    background.append(b_avg)
-	    error.append(dev_b)
+        background.append(b_avg)
+        error.append(dev_b)
 
-	    # 16-bit data format
-	    #image_array.astype('uint16')
-	    #toimage(image_array, high=cmax, low=cmin, mode='I').save(output_image)
+        # 16-bit data format
+        #image_array.astype('uint16')
+        #toimage(image_array, high=cmax, low=cmin, mode='I').save(output_image)
 
-	    # 8-bit data format (for making movie)
-	    #toimage(image, cmin=amin, cmax=410).save(output_image)
+        # 8-bit data format (for making movie)
+        #toimage(image, cmin=amin, cmax=410).save(output_image)
 
-	    i += 1
+        i += 1
 
-	# background
-	background = numpy.array(background)
-	length = len(background)
-	b_avg = background.sum()/length
+    # background
+    background = numpy.array(background)
+    length = len(background)
+    b_avg = background.sum()/length
 
-	error = numpy.array(error)
-	length = len(error)
-	err_b = error.sum()/length
+    error = numpy.array(error)
+    length = len(error)
+    err_b = error.sum()/length
 
-	# signal
-	signal = numpy.array(signal - 16*16*b_avg)
+    # signal
+    signal = numpy.array(signal - 16*16*b_avg)
 
-	length = len(signal)
-	s_avg = signal.sum()/length
-	dev2  = (signal - s_avg)**2
-	err_s = numpy.sqrt(dev2.sum()/length)
+    length = len(signal)
+    s_avg = signal.sum()/length
+    dev2  = (signal - s_avg)**2
+    err_s = numpy.sqrt(dev2.sum()/length)
 
-	print(210, s_avg, err_s, b_avg, err_b)
+    print(210, s_avg, err_s, b_avg, err_b)
 
-	# 8-bit data format (for making movie)
-	#toimage(image, cmin=100, cmax=1000).save(file_out + '/image_summed.png')
+    # 8-bit data format (for making movie)
+    #toimage(image, cmin=100, cmax=1000).save(file_out + '/image_summed.png')
 
 
 if __name__=='__main__':
 
-	file_in = '/home/masaki/bioimaging_4public/Data_fromMoriga_08-05-2015/images_0805/images_tif_210mW'
-	file_out = '/home/masaki/bioimaging_4public/images_png'
-
-	convert(file_in, file_out)
-
-
+    file_in = '/home/masaki/bioimaging_4public/Data_fromMoriga_08-05-2015/images_0805/images_tif_210mW'
+    file_out = '/home/masaki/bioimaging_4public/images_png'
 
+    convert(file_in, file_out)