def main(self, img):
"""Create dialog and image inside it
**Inputs**
* img: 2d-array, containing the image data
* roi: tuple of slices, contains two slice objects, one for each
image axis. The tuple can be used as a 2D slice object.
* name: string, the name of the plugin
**Notes**
This function is called from Odysseus.
"""
self.img = img
transimg, odimg, com, n0, a, bprime = norm_and_guess(self.img,
norm=False)
self.odimg = odimg
self.com = com
self.cpoint = com
self.set_cpoint()
self.defaultangles = self.anglesList.toPlainText()
cpoint = self.get_cpoint()
self.marker = self.imgView.ax.plot([cpoint[0]], [cpoint[1]], 'r+')[0]
self.imgView.ax.imshow(self.img, cmap=mpl.cm.gray, vmin=0, vmax=1.35)
self.imgView.ax.set_xticks([])
self.imgView.ax.set_yticks([])
self.compute_profile()
def main(self, img):
"""Create dialog and image inside it
**Inputs**
* img: 2d-array, containing the image data
* roi: tuple of slices, contains two slice objects, one for each
image axis. The tuple can be used as a 2D slice object.
* name: string, the name of the plugin
**Notes**
This function is called from Odysseus.
"""
self.img = img
transimg, odimg, com, n0, a, bprime = norm_and_guess(self.img, norm=False)
self.odimg = odimg
self.com = com
self.cpoint = com
self.set_cpoint()
self.defaultangles = self.anglesList.toPlainText()
cpoint = self.get_cpoint()
self.marker = self.imgView.ax.plot([cpoint[0]], [cpoint[1]], 'r+')[0]
self.imgView.ax.imshow(self.img, cmap=mpl.cm.gray, vmin=0, vmax=1.35)
self.imgView.ax.set_xticks([])
self.imgView.ax.set_yticks([])
self.compute_profile()
def main(self, img):
"""Integrate along one axis and display the resulting linear profile"""
transimg, odimg, com, n0, a, bprime = norm_and_guess(img)
#line_profile_x = np.sum(odimg, 0)
#line_coord_x = range(len(line_profile_x))
#N_count = np.sum(line_profile_x)
#peak_x = np.argmax(line_profile_x)
#ans = fit1dfunc(gaussian, line_coord_x, \
#line_profile_x, [n0, bprime, com[0]])
#fit_profile_x = gaussian(line_coord_x, ans[0], ans[1], ans[2])
line_profile_y = np.sum(odimg, 1)
line_coord_y = range(len(line_profile_y))
N_count = np.sum(line_profile_y)
peak_y = np.argmax(line_profile_y)
ans1 = fit1dfunc(gaussian, line_coord_y, \
line_profile_y, [n0, bprime, com[0]])
fit_profile_y = gaussian(line_coord_y, ans1[0], ans1[1], ans1[2])
#self.ax.plot(line_coord_x, line_profile_x)
#self.ax.plot(line_coord_x, fit_profile_x, 'r-')
#self.ax.set_xlabel(r'pix')
#self.ax.set_ylabel(r'OD')
#self.ax.set_title(r'X vs. Integrated OD')
#self.ax.text(len(line_profile_x)*0.65, line_coord_x[peak_x], 'width = %1.2f'%(ans[1]/1.414))
#self.ax.text(len(line_profile_x)*0.75, line_coord_x[peak_x]*0.9, 'N = %1.2f'%(N_count))
self.ax.plot(line_coord_y, line_profile_y)
self.ax.plot(line_coord_y, fit_profile_y, 'r-')
self.ax.set_xlabel(r'pix')
self.ax.set_ylabel(r'OD')
self.ax.set_title(r'Y vs. Integrated OD')
self.ax.text(len(line_profile_y)*0.65, line_coord_y[peak_y], 'width = %1.2f'%(ans1[1]/1.414))
self.ax.text(len(line_profile_y)*0.75, line_coord_y[peak_y]*0.9, 'N = %1.2f'%(N_count))
def main(self, img):
"""Calculate and display the Fourier transform of img"""
##transimg, odimg, com, n0, a, bprime = norm_and_guess(img)
##rad_coord, rad_profile = radial_interpolate(odimg, com, 0.3)
##self.ax.plot(rad_coord, rad_profile)
##self.ax.set_xlabel(r'pix')
##self.ax.set_ylabel(r'OD')
pixcal = 10e-6 # 10um / pix
# normalized the image
transimg, odimg, com, n0, q, bprime = norm_and_guess(img)
# choose starting values for fit
x, y, width_x, width_y = fitfuncs2D.gaussian_moments_2D(odimg)
guess = np.zeros(9.)
guess[0:4] = [com[0], com[1], width_x, width_y]
guess[4] = n0
guess[5:] = [q, 0., 0., 0.]
# do the fit, and find temperature and number of atoms
ans = fitfuncs2D.fit2dfunc(fitfuncs2D.idealfermi_2D, odimg, guess,
tol=1e-10)
ToverTF, N = fitfuncs2D.ideal_fermi_numbers_2D(ans, pixcal)
#print ans
#print 'T/TF = ', ToverTF[0]
#print 'N = %1.2f million'%(N * 1e-6)
self.ax.text(0.1, 0.8, str(ans))
self.ax.text(0.1, 0.4, 'T/TF = %1.3f'%(ToverTF[0]))
self.ax.text(0.1, 0.2, 'N = %1.2f million'%(N * 1e-6))
def main(self, img):
"""Calculate and display the Fourier transform of img"""
transimg, odimg, com, n0, a, bprime = norm_and_guess(img)
rad_coord, rad_profile = radial_interpolate(odimg, com, 0.3)
self.ax.plot(rad_coord, rad_profile)
self.ax.set_xlabel(r'pix')
self.ax.set_ylabel(r'OD')
def main(self, img):
"""Calculate and display the Fourier transform of img"""
transimg, odimg, com, n0, a, bprime = norm_and_guess(img)
rad_coord, rad_profile = radial_interpolate(odimg, com, 0.3)
self.ax.plot(rad_coord, rad_profile)
self.ax.set_xlabel(r'pix')
self.ax.set_ylabel(r'OD')
def main(self, rawimg, img, roi, name, path):
"""Calculate and display the Fourier transform of img"""
pwa = rawimg[:,:,1]
pwoa = rawimg[:,:,2]
df = rawimg[:,:,3]
pwa=pwa[roi]
pwoa=pwoa[roi]
df=df[roi]
img=img[roi]
##transimg, odimg, com, n0, a, bprime = norm_and_guess(img)
##rad_coord, rad_profile = radial_interpolate(odimg, com, 0.3)
##self.ax.plot(rad_coord, rad_profile)
##self.ax.set_xlabel(r'pix')
##self.ax.set_ylabel(r'OD')
pixcal = 10e-6 # 10um / pix
# normalized the image
transimg, odimg, com, n0, q, bprime = norm_and_guess(img)
# choose starting values for fit
x, y, width_x, width_y = fitfuncs2D.gaussian_moments_2D(odimg)
guess = np.zeros(10.)
guess[0:4] = [com[0], com[1], width_x, width_y]
guess[4] = n0
guess[5:] = [q, 0., 0., 0., 0.]
ans1 = fitfuncs2D.fit2dfunc(fitfuncs2D.idealfermi_2D_angled, odimg, guess,
tol=1e-10)
# do the fit, and find temperature and number of atoms
ans = fitfuncs2D.fit2dfuncraw(fitfuncs2D.idealfermi_2D_angled, pwa, pwoa, df, ans1, tol=1e-10)
ToverTF, N = fitfuncs2D.ideal_fermi_numbers_2D_angled(ans, pixcal)
print ans
print 'T/TF = ', ToverTF[0]
print 'N = %1.2f million'%(N * 1e-6)
self.ax.text(0.1, 0.8, str(ans))
self.ax.text(0.1, 0.4, 'T/TF = %1.3f'%(ToverTF[0]))
self.ax.text(0.1, 0.2, 'N = %1.2f million'%(N * 1e-6))
residuals = fitfuncs2D.residuals_2D(odimg, ans, func=fitfuncs2D.idealfermi_2D_angled, smooth=4, showfig=True)
def main(self, img):
"""Calculate and display the Fourier transform of img"""
##transimg, odimg, com, n0, a, bprime = norm_and_guess(img)
##rad_coord, rad_profile = radial_interpolate(odimg, com, 0.3)
##self.ax.plot(rad_coord, rad_profile)
##self.ax.set_xlabel(r'pix')
##self.ax.set_ylabel(r'OD')
pixcal = 10e-6 # 10um / pix
# normalized the image
transimg, odimg, com, n0, q, bprime = norm_and_guess(img)
# choose starting values for fit
x, y, width_x, width_y = fitfuncs2D.gaussian_moments_2D(odimg)
guess = np.zeros(9.)
guess[0:4] = [com[0], com[1], width_x, width_y]
guess[4] = n0
guess[5:] = [q, 0., 0., 0.]
# do the fit, and find temperature and number of atoms
ans = fitfuncs2D.fit2dfunc(fitfuncs2D.idealfermi_2D,
odimg,
guess,
tol=1e-10)
ToverTF, N = fitfuncs2D.ideal_fermi_numbers_2D(ans, pixcal)
#print ans
#print 'T/TF = ', ToverTF[0]
#print 'N = %1.2f million'%(N * 1e-6)
self.ax.text(0.1, 0.8, str(ans))
self.ax.text(0.1, 0.4, 'T/TF = %1.3f' % (ToverTF[0]))
self.ax.text(0.1, 0.2, 'N = %1.2f million' % (N * 1e-6))
