def find_w1_peaks(self, flag_verbose = False):
data = self.mess.s[self.w1_peaks_y_i[0]:self.w1_peaks_y_i[1], self.w1_peaks_x_i[0]:self.w1_peaks_x_i[1]]
y_axis = self.mess.s_axis[0][self.w1_peaks_y_i[0]:self.w1_peaks_y_i[1]]
y = numpy.sum(data,1)
A_out = MATH.fit(y_axis, y, EQ.rb_lorentzian, self.w1_peaks_A_in)
self.w1_peaks[0] = A_out[1]
def fit_double_lorentzian(self, flag_plot = False, flag_verbose = False):
"""
For a selection of points on the w1-axis, take a cut (giving w3 vs z (intensity) plot) and fit it with a double Lorentzian.
self.dl_x_i[0] etc are the min/max indices to be fitted
"""
if flag_verbose:
self.verbose("Fit double Lorentzian for " + self.objectname, flag_verbose = flag_verbose)
self.verbose(" x_min: " + str(self.dl_x_i[0]) + " " + str(self.mess.s_axis[2][self.dl_x_i[0]]), flag_verbose = flag_verbose)
self.verbose(" x_max: " + str(self.dl_x_i[1]) + " " + str(self.mess.s_axis[2][self.dl_x_i[1]]), flag_verbose = flag_verbose)
self.verbose(" y_min: " + str(self.dl_y_i[0]) + " " + str(self.mess.s_axis[0][self.dl_y_i[0]]), flag_verbose = flag_verbose)
self.verbose(" y_max: " + str(self.dl_y_i[1]) + " " + str(self.mess.s_axis[0][self.dl_y_i[1]]), flag_verbose = flag_verbose)
# select the part of the data to be fitted
data = self.mess.s[self.dl_y_i[0]:self.dl_y_i[1], self.dl_x_i[0]:self.dl_x_i[1]]
x_axis = self.mess.s_axis[2][self.dl_x_i[0]:self.dl_x_i[1]]
y_axis = self.mess.s_axis[0][self.dl_y_i[0]:self.dl_y_i[1]]
# arrays for the results
n_y, n_x = numpy.shape(data)
y_max = numpy.zeros(n_y) # index of the maximum
y_min = numpy.zeros(n_y) # index of the minimum
y_out_array = numpy.zeros((n_y, 8)) # fitting parameters
if flag_plot:
plt.figure()
color_array = ["b", "g", "r", "c", "m", "y", "k"]
# calculate the fit for the cut of w1
for i in range(n_y):
y = data[i,:]
A_out = MATH.fit(x_axis, y, EQ.rb_two_lorentzians, self.dl_A_in)
y_out_array[i,:] = A_out
x_fit = numpy.arange(x_axis[0], x_axis[-1], 0.1)
y_fit = EQ.rb_two_lorentzians(A_out, x_fit)
if flag_plot:
plt.plot(x_fit, y_fit, c = color_array[i%len(color_array)])
plt.plot(x_axis, y, ":", c = color_array[i%len(color_array)])
y_max[i] = x_fit[numpy.argmax(y_fit)]
y_min[i] = x_fit[numpy.argmin(y_fit)]
self.dl_ble = y_min
self.dl_esa = y_max
self.dl_A = y_out_array
if flag_plot:
plt.show()
def fit_tilt(self, flag_verbose = False):
if flag_verbose:
self.verbose("Fit tilt for " + self.objectname, flag_verbose = flag_verbose)
self.verbose(" x_min: " + str(self.dl_x_i[0]) + " " + str(self.mess.s_axis[2][self.dl_x_i[0]]), flag_verbose = flag_verbose)
self.verbose(" x_max: " + str(self.dl_x_i[1]) + " " + str(self.mess.s_axis[2][self.dl_x_i[1]]), flag_verbose = flag_verbose)
self.verbose(" y_min: " + str(self.dl_y_i[0] + self.l_i[0]) + " " + str(self.mess.s_axis[0][self.dl_y_i[0] + self.l_i[0]]), flag_verbose = flag_verbose)
self.verbose(" y_max: " + str(self.dl_y_i[0] + self.l_i[1]) + " " + str(self.mess.s_axis[0][self.dl_y_i[0] + self.l_i[1]]), flag_verbose = flag_verbose)
y = self.mess.s_axis[0][self.dl_y_i[0] + self.l_i[0]:self.dl_y_i[0] + self.l_i[1]]
x = self.dl_ble[self.l_i[0]:self.l_i[1]]
self.l_A_ble = MATH.fit(x, y, EQ.linear, self.l_A_in)
x = self.dl_esa[self.l_i[0]:self.l_i[1]]
self.l_A_esa = MATH.fit(x, y, EQ.linear, self.l_A_in)
self.l_angle_ble = 90 - numpy.arctan(self.l_A_ble[1]) * 180 / numpy.pi
self.l_angle_esa = 90 - numpy.arctan(self.l_A_esa[1]) * 180 / numpy.pi
self.l_slope_ble = 1 / self.l_A_ble[1]
self.l_slope_esa = 1 / self.l_A_esa[1]
def make_plot(self, ax = False, normalize = False, fit = False):
"""
Make a plot of scan spectrum data.
INPUT:
- ax (plt axis instance, or False): If False, a new figure and axis instance will be made.
- normalize (bool, False): If True, the minimum is subtract from the data, then it is divided by the maximum.
- fit (Bool, False): If True, a fit will be made and will also be plotted. The fitting parameters are written to the terminal.
CHANGELOG:
201604-RB: started function
"""
if ax == False:
fig = plt.figure()
ax = fig.add_subplot(111)
if normalize:
for ds in range(self.r_n[2]):
self.r[:,0,ds,0,0,0,0,0] -= numpy.nanmin(self.r[:,0,ds,0,0,0,0,0])
self.r[:,0,ds,0,0,0,0,0] /= numpy.nanmax(self.r[:,0,ds,0,0,0,0,0])
ax.plot(self.r_axes[0], self.r[:,0,0,0,0,0,0,0], color = "g")
ax.plot(self.r_axes[0], self.r[:,0,1,0,0,0,0,0], color = "r")
if fit:
colors = ["lightgreen", "orange"]
labels = ["probe", "reference"]
sigma = (self.r_axes[0][0] - self.r_axes[0][-1]) / 4
print(" mu sigma offset scale")
for ds in range(self.r_n[2]):
A = [sigma, self.r_axes[0][numpy.argmax(self.r[:,0,ds,0,0,0,0,0])], 0, 1] # initial guess
A_final = M.fit(self.r_axes[0], self.r[:,0,ds,0,0,0,0,0], EQ.rb_gaussian, A)
ax.plot(self.r_axes[0], EQ.rb_gaussian(A_final, self.r_axes[0]), color = colors[ds])
print("{label:10} {mu:.5} {sigma:.3} {offset:.3} {scale:.3}".format(label = labels[ds], mu = A_final[1], sigma = A_final[0], offset = A_final[2], scale = A_final[3]))
def calculate_pulse_length(data, A = [], dt = CONST.hene_fringe_fs, plot_results = True, print_results = True, fwhm_limit = 200, mu_shift = 10, flag_recursive_limit = False):
"""
Calculate the pulse length
INPUT:
data (array): the result of a measure phase measurement for one pulse pair. Usually measure phase is saved as two measurements of two pulse pairs.
A (list, default = []): [sigma, mu, offset, scale, frequency, phase] if A is empty, some reasonable default values are used.
dt (float, HeNe-fringe in fs): to convert from indices to time
plot (BOOL, True): plot the results
print_res (BOOL, True): print the results
fwhm_limit (float, 200): if FWHM is above this limit, it will try again by shifting the mean a bit (by mu_shift)
mu_shift (float, 10): shift the mean a bit
flag_recursive_limit (BOOL, False): function will try again if set to False. To prevent endless recursion.
OUTPUT:
- the output can be plotted to confirm the fit
- the fitting results can be printed
- the FWHM is returned
CHANGELOG:
20130408/RB: started function
"""
# input for a single IR pulse
if A == []:
A = default_A()
data /= numpy.amax(data)
# make the time axis
t = numpy.arange(len(data)) - len(data)/2
t *= dt
# fit the single pulse by convoluting it with itself and fitting it to the data
A_out = MA.fit(t, data, fit_convolve, A)
# calculate the fitted pulse and convoluted pulse
y_single = pulse(A_out, t)
y_conv = fit_convolve(A_out, t)
# calculate the envelope
sigma = A_out[0]
mu = A_out[1]
offset = A_out[2]
scale = A_out[3]
A = [sigma, mu, offset, scale]
y_env = envelope(A,t)
# for the FWHM we need a higher time resolution
t2 = numpy.arange(len(data)*10) - 5*len(data)
t2 *= dt / 10
y_fwhm = envelope(A,t2)
# use the envelope to calculate the FWHM
# make it positive
if y_fwhm[int(len(t2)/2)] < 0:
y_fwhm = -y_fwhm
max_fwhm = numpy.amax(y_fwhm)
# select the part of the list
l = numpy.where(y_fwhm > max_fwhm/2)[0]
# the length of the list in indices, multiply with time distance
FWHM = len(l) * dt / 10
if FWHM > fwhm_limit and flag_recursive_limit == False:
# FWHM is above the limit, try again by shifting the mean a bit
print("Trying again...")
A = default_A()
A[1] = mu_shift
FWHM = calculate_pulse_length(data, A = A, dt = dt, plot_results = plot_results, print_results = print_results, flag_recursive_limit = True)
else:
# yay! a reasonable result. plot and print the results.
if plot_results:
plot_res(t, data, y_conv, y_single, y_env)
if print_results:
print_res(A_out, FWHM, dt)
return FWHM
def test_fit(self):
x = numpy.arange(10)
y = numpy.sin(x)
A = [0,1,2,3]
A = MATH.fit(x, y, EQ.rb_cos, A)
print(A)