def ABSORPTIVE1():
"""
CHANGELOG:
- 20110912 RB: init
"""
n = 100
window_fxn = [
["none", n],
["ones", n],
["triangle", n],
["gaussian", n],
["none", n/2],
["ones", n/2],
["triangle", n/2],
["gaussian", n/2]
]
array = numpy.ones(n)
plt.figure()
for i in range(len(window_fxn)):
w_array = M.window_functions(array, window_fxn[i][0], window_fxn[i][1])
plt.subplot(2, 4, i+1)
plt.plot(w_array)
plt.title(window_fxn[i][0] + " " + str(window_fxn[i][1]))
plt.ylim(0,1.1)
plt.show()
def find_w1_peaks(self, 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]]
# x_axis = self.mess.s_axis[2][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, 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.
"""
if verbose:
print("Fit double Lorentzian for " + self.objectname)
print(" x_min", self.dl_x_i[0], self.mess.s_axis[2][self.dl_x_i[0]])
print(" x_max", self.dl_x_i[1], self.mess.s_axis[2][self.dl_x_i[1]])
print(" y_min", self.dl_y_i[0], self.mess.s_axis[0][self.dl_y_i[0]])
print(" y_max", self.dl_y_i[1], self.mess.s_axis[0][self.dl_y_i[1]])
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]]
n_y, n_x = numpy.shape(data)
y_max = numpy.zeros(n_y)
y_min = numpy.zeros(n_y)
y_out_array = numpy.zeros((n_y, 8))
if flag_plot:
plt.figure()
color_array = ["b", "g", "r", "c", "m", "y", "k"]
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, verbose = False):
if verbose:
print("Fit tilt for " + self.objectname)
print(" x_min", self.dl_x_i[0], self.mess.s_axis[2][self.dl_x_i[0]])
print(" x_max", self.dl_x_i[1], self.mess.s_axis[2][self.dl_x_i[1]])
print(" y_min", self.dl_y_i[0] + self.l_i[0], self.mess.s_axis[0][self.dl_y_i[0] + self.l_i[0]])
print(" y_max", self.dl_y_i[0] + self.l_i[1], self.mess.s_axis[0][self.dl_y_i[0] + self.l_i[1]])
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]
