def optimize_z(x,y,z,image,n=None):
"""Optimize z for poly fit"""
if type(image) == str:
img = tf.imread(image)
elif type(image) == np.ndarray:
img = image
data_z = img[:,y,x]
if n is None:
n = getn(data_z)
x_opt_vals, y_opt_vals, z_opt_vals = [], [], []
x_opt,y_opt,z_opt = x,y,z
for i in range(5):
try:
print x_opt,y_opt,z_opt
x_opt,y_opt,z_opt = int(round(x_opt)),int(round(y_opt)),int(round(z_opt))
x_opt, y_opt = optimize_xy(x_opt,y_opt,z_opt,img,nx=None,ny=None)
data_z = img[:,round(y_opt),round(x_opt)]
except Exception as e:
if clrmsg and debug is True: print clrmsg.ERROR
print IndexError("Optimization failed, possibly due to low signal or low SNR. "+str(e))
return [x],[y],['failed']
n = getn(data_z)
z_opt, data_z_yp_poly = parabolic.parabolic_polyfit(data_z, np.argmax(data_z), n)
x_opt_vals.append(x_opt)
y_opt_vals.append(y_opt)
z_opt_vals.append(z_opt)
return x_opt_vals, y_opt_vals, z_opt_vals
def getzPoly(x, y, img, n=None, optimize=False):
"""x and y are coordinates
img is the path to the z-stack tiff file or a numpy.ndarray from tifffile.py imread function
n is the number of points around the max value that are used in the polyfit
leave n to use the maximum amount of points
If optimize is set to True, the algorithm will try to optimize the x,y,z position
!! if optimize is True, 3 values are returned: x,y,z"""
if not isinstance(img, str) and not isinstance(img, np.ndarray):
if clrmsg and debug is True: print clrmsg.ERROR
raise TypeError(
'I can only handle an image path as string or an image volume as numpy.ndarray imported from tifffile.py'
)
elif isinstance(img, str):
img = tf.imread(img)
data_z = img[:, y, x]
if n is None:
n = getn(data_z)
data_z_xp_poly, data_z_yp_poly = parabolic.parabolic_polyfit(
data_z, np.argmax(data_z), n)
if math.isnan(data_z_xp_poly):
if clrmsg and debug is True: print clrmsg.ERROR
print TypeError('Failed: Probably due to low SNR')
if optimize is True:
return x, y, 'failed'
else:
return 'failed'
if debug is True:
f, ax = plt.subplots()
ax.plot(range(0, len(data_z)), data_z, color='blue')
ax.plot(data_z_xp_poly, data_z_yp_poly, 'o', color='black')
ax.set_title("mid: " + str(data_z_xp_poly))
plt.draw()
plt.pause(1)
plt.close()
if optimize is True:
x_opt_vals, y_opt_vals, z_opt_vals = optimize_z(x,
y,
data_z_xp_poly,
img,
n=None)
return x_opt_vals[-1], y_opt_vals[-1], z_opt_vals[-1]
else:
return data_z_xp_poly
def getzPoly(x,y,img,n=None,optimize=False):
"""x and y are coordinates
img is the path to the z-stack tiff file or a numpy.ndarray from tifffile.py imread function
n is the number of points around the max value that are used in the polyfit
leave n to use the maximum amount of points
If optimize is set to True, the algorithm will try to optimize the x,y,z position
!! if optimize is True, 3 values are returned: x,y,z"""
if not isinstance(img, str) and not isinstance(img, np.ndarray):
if clrmsg and debug is True: print clrmsg.ERROR
raise TypeError('I can only handle an image path as string or an image volume as numpy.ndarray imported from tifffile.py')
elif isinstance(img, str):
img = tf.imread(img)
data_z = img[:,y,x]
if n is None:
n = getn(data_z)
data_z_xp_poly, data_z_yp_poly = parabolic.parabolic_polyfit(data_z, np.argmax(data_z), n)
if math.isnan(data_z_xp_poly):
if clrmsg and debug is True: print clrmsg.ERROR
print TypeError('Failed: Probably due to low SNR')
if optimize is True:
return x,y,'failed'
else:
return 'failed'
if debug is True:
f, ax = plt.subplots()
ax.plot(range(0,len(data_z)), data_z, color='blue')
ax.plot(data_z_xp_poly, data_z_yp_poly, 'o', color='black')
ax.set_title("mid: "+str(data_z_xp_poly))
plt.draw()
plt.pause(1)
plt.close()
if optimize is True:
x_opt_vals, y_opt_vals, z_opt_vals = optimize_z(x,y,data_z_xp_poly,img,n=None)
return x_opt_vals[-1], y_opt_vals[-1], z_opt_vals[-1]
else:
return data_z_xp_poly
def optimize_xy(x, y, z, image, nx=None, ny=None):
"""x and y are coordinates, z is the layer in the z-stack tiff file
image can be either the path to the z-stack tiff file or the np.array data of itself
n is the number of points around the max value that are used in the polyfit
leave n to use the maximum amount of points"""
get_nx, get_ny = False, False
if type(image) == str:
img = tf.imread(image)
elif type(image) == np.ndarray:
img = image
## amount of data points around coordinate
samplewidth = 10
data_x = img[z, y, x - samplewidth:x + samplewidth]
data_y = img[z, y - samplewidth:y + samplewidth, x]
if debug is True: f, axarr = plt.subplots(2, sharex=True)
if nx is None:
get_nx = True
if ny is None:
get_ny = True
## optimize x
xmaxvals = np.array([], dtype=np.int32)
for offset in range(10):
data_x = img[z, y - offset, x - samplewidth:x + samplewidth]
if data_x.max() < data_x.mean() * 1.1:
# print "breaking at ",offset
# print data_x.max(), data_x.mean(), data_x.mean()*1.1
break
if get_nx is True:
nx = getn(data_x)
data_x_xp_poly, data_x_yp_poly = parabolic.parabolic_polyfit(
data_x, np.argmax(data_x), nx)
xmaxvals = np.append(xmaxvals, [data_x_xp_poly])
c = np.random.rand(3, 1)
if debug is True:
axarr[0].plot(range(0, len(data_x)), data_x, color=c)
axarr[0].plot(data_x_xp_poly, data_x_yp_poly, 'o', color=c)
for offset in range(10):
data_x = img[z, y + offset, x - samplewidth:x + samplewidth]
if data_x.max() < data_x.mean() * 1.1:
# print "breaking at ",offset
# print data_x.max(), data_x.mean(), data_x.mean()*1.1
break
if get_nx is True:
nx = getn(data_x)
data_x_xp_poly, data_x_yp_poly = parabolic.parabolic_polyfit(
data_x, np.argmax(data_x), nx)
xmaxvals = np.append(xmaxvals, [data_x_xp_poly])
c = np.random.rand(3, 1)
if debug is True:
axarr[0].plot(range(0, len(data_x)), data_x, color=c)
axarr[0].plot(data_x_xp_poly, data_x_yp_poly, 'o', color=c)
if debug is True: axarr[0].set_title("mid-mean: " + str(xmaxvals.mean()))
## optimize y
ymaxvals = np.array([], dtype=np.int32)
for offset in range(10):
data_y = img[z, y - samplewidth:y + samplewidth, x - offset]
if data_y.max() < data_y.mean() * 1.1:
# print "breaking at ",offset
# print data_y.max(), data_y.mean(), data_y.mean()*1.1
break
if get_ny is True:
ny = getn(data_y)
data_y_xp_poly, data_y_yp_poly = parabolic.parabolic_polyfit(
data_y, np.argmax(data_y), ny)
ymaxvals = np.append(ymaxvals, [data_y_xp_poly])
c = np.random.rand(3, 1)
if debug is True:
axarr[1].plot(range(0, len(data_y)), data_y, color=c)
axarr[1].plot(data_y_xp_poly, data_y_yp_poly, 'o', color=c)
for offset in range(10):
data_y = img[z, y - samplewidth:y + samplewidth, x + offset]
if data_y.max() < data_y.mean() * 1.1:
# print "breaking at ",offset
# print data_y.max(), data_y.mean(), data_y.mean()*1.1
break
if get_ny is True:
ny = getn(data_y)
data_y_xp_poly, data_y_yp_poly = parabolic.parabolic_polyfit(
data_y, np.argmax(data_y), ny)
ymaxvals = np.append(ymaxvals, [data_y_xp_poly])
c = np.random.rand(3, 1)
if debug is True:
axarr[1].plot(range(0, len(data_y)), data_y, color=c)
axarr[1].plot(data_y_xp_poly, data_y_yp_poly, 'o', color=c)
if debug is True: axarr[1].set_title("mid-mean: " + str(ymaxvals.mean()))
if debug is True:
plt.draw()
plt.pause(0.5)
plt.close()
## calculate offset into coordinates
x_opt = x + xmaxvals.mean() - samplewidth
y_opt = y + ymaxvals.mean() - samplewidth
return x_opt, y_opt
def optimize_xy(x,y,z,image,nx=None,ny=None):
"""x and y are coordinates, z is the layer in the z-stack tiff file
image can be either the path to the z-stack tiff file or the np.array data of itself
n is the number of points around the max value that are used in the polyfit
leave n to use the maximum amount of points"""
get_nx, get_ny = False, False
if type(image) == str:
img = tf.imread(image)
elif type(image) == np.ndarray:
img = image
## amount of data points around coordinate
samplewidth = 10
data_x = img[z,y,x-samplewidth:x+samplewidth]
data_y = img[z,y-samplewidth:y+samplewidth,x]
if debug is True: f, axarr = plt.subplots(2, sharex=True)
if nx is None:
get_nx = True
if ny is None:
get_ny = True
## optimize x
xmaxvals = np.array([], dtype=np.int32)
for offset in range(10):
data_x = img[z,y-offset,x-samplewidth:x+samplewidth]
if data_x.max() < data_x.mean()*1.1:
# print "breaking at ",offset
# print data_x.max(), data_x.mean(), data_x.mean()*1.1
break
if get_nx is True:
nx = getn(data_x)
data_x_xp_poly, data_x_yp_poly = parabolic.parabolic_polyfit(data_x, np.argmax(data_x), nx)
xmaxvals = np.append(xmaxvals,[data_x_xp_poly])
c = np.random.rand(3,1)
if debug is True:
axarr[0].plot(range(0,len(data_x)), data_x, color=c)
axarr[0].plot(data_x_xp_poly, data_x_yp_poly, 'o', color=c)
for offset in range(10):
data_x = img[z,y+offset,x-samplewidth:x+samplewidth]
if data_x.max() < data_x.mean()*1.1:
# print "breaking at ",offset
# print data_x.max(), data_x.mean(), data_x.mean()*1.1
break
if get_nx is True:
nx = getn(data_x)
data_x_xp_poly, data_x_yp_poly = parabolic.parabolic_polyfit(data_x, np.argmax(data_x), nx)
xmaxvals = np.append(xmaxvals,[data_x_xp_poly])
c = np.random.rand(3,1)
if debug is True:
axarr[0].plot(range(0,len(data_x)), data_x, color=c)
axarr[0].plot(data_x_xp_poly, data_x_yp_poly, 'o', color=c)
if debug is True: axarr[0].set_title("mid-mean: "+str(xmaxvals.mean()))
## optimize y
ymaxvals = np.array([], dtype=np.int32)
for offset in range(10):
data_y = img[z,y-samplewidth:y+samplewidth,x-offset]
if data_y.max() < data_y.mean()*1.1:
# print "breaking at ",offset
# print data_y.max(), data_y.mean(), data_y.mean()*1.1
break
if get_ny is True:
ny = getn(data_y)
data_y_xp_poly, data_y_yp_poly = parabolic.parabolic_polyfit(data_y, np.argmax(data_y), ny)
ymaxvals = np.append(ymaxvals,[data_y_xp_poly])
c = np.random.rand(3,1)
if debug is True:
axarr[1].plot(range(0,len(data_y)), data_y, color=c)
axarr[1].plot(data_y_xp_poly, data_y_yp_poly, 'o', color=c)
for offset in range(10):
data_y = img[z,y-samplewidth:y+samplewidth,x+offset]
if data_y.max() < data_y.mean()*1.1:
# print "breaking at ",offset
# print data_y.max(), data_y.mean(), data_y.mean()*1.1
break
if get_ny is True:
ny = getn(data_y)
data_y_xp_poly, data_y_yp_poly = parabolic.parabolic_polyfit(data_y, np.argmax(data_y), ny)
ymaxvals = np.append(ymaxvals,[data_y_xp_poly])
c = np.random.rand(3,1)
if debug is True:
axarr[1].plot(range(0,len(data_y)), data_y, color=c)
axarr[1].plot(data_y_xp_poly, data_y_yp_poly, 'o', color=c)
if debug is True: axarr[1].set_title("mid-mean: "+str(ymaxvals.mean()))
if debug is True:
plt.draw()
plt.pause(0.5)
plt.close()
## calculate offset into coordinates
x_opt = x+xmaxvals.mean()-samplewidth
y_opt = y+ymaxvals.mean()-samplewidth
return x_opt, y_opt