def envelope(A,t):
"""
Wrapper for rb_gaussian
INPUT:
t: numpy.array
A[0]: sigma (sigma^2 = variance)
A[1]: mu (mean)
A[2]: offset
A[3]: scale, before offset
CHANGELOG:
20130408/RB: started function
"""
return EQ.rb_gaussian(A,t)
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]))
