# ------------------------------------------------------------------------- #
# now call DF functions to fit the
# intensity histogram to a mixture of von-Mises-Fischer distribution
angles = ResultsFFT.X
values = ResultsFFT.Y
# convert the angles from degrees to radians:
r_X = np.radians( angles )
X_samples = r_X
# normalize the light intensity data (FFT):
n_X = dC.normalizeIntensity( angles, values, YNplot='Yes' )
Int = n_X[:,1]
# make points of angles based on the light intensity:
p_X = dC.makePoints( n_X, YNplot='Yes' )
# select model to fit:
#model_test = '1vM'
#model_test = '2vM'
#model_test = '3vM'
#model_test = '2vM1U'
model_test = '1vM1U'
#%% for a 1vM1U model:
#model_test = '1vM1U'
if model_test == '1vM1U':
# parameters for the von Mises member:
p1_ = 0.7 # weight contribution of the 1st von Mises
pu_ = 1. - p1_ # weight contribution of Uniform distribut
kappa1_ = np.array((12.0)) # concentration for the 1st von Mises member
loc1_ = -np.pi/9.0 # location for the 1st von Mises member
# ------------------------------------------------------------------------- #
# read the data from the csv file:
angles, values, mydat = dC.imageCVS2Data( csv_path )
# convert the angles from degrees to radians:
angles = angles
r_X = np.radians( angles )
X_samples = r_X
# normalize the light intensity data (FFT):
n_X = dC.normalizeIntensity( angles, values, YNplot='Yes' )
Int = n_X[:,1]
# make points of angles based on the light intensity:
p_X = ( dC.makePoints( n_X, YNplot='Yes' ) + np.pi/2 )/np.pi
#p_X = p_X + np.pi/2
nn = len(p_X)
u_X = np.random.uniform(0.0,np.pi,nn)
u_X = np.sort(np.unique(u_X))/np.pi
hh = np.arange(1/(nn+1), nn/(nn+1), 1/(nn+1))
#hh = np.arange(0, nn/(nn+1), 1/(nn+1))
#hh = np.arange(1/(nn+1), 1, 1/(nn+1))
fig, ax00 = plt.subplots(1, 1, figsize=(6, 6))
ax00.plot(hh, p_X[1::], color='r', linestyle=':', label='data')
ax00.plot(hh, u_X[1::], color='b', linestyle=':', label='uniform')
ax00.plot([0, 1], [0, 1], color='g', linestyle='--', label='45$^o$ line')
ax00.plot(hh, hh, color='m')