chi_squared_new = chi_squared(v1_45.independent_variables_array,
v1_45.relative_intensities,
v1_45.relative_std,
fit_params_new,
average_angle=0,
precision=-1)
plot_runs(v1_45,
title="Sample 1 (LUX), 178nm, Vacuum, 45 degrees",
label="data",
log=True)
plot_TSTR_fit(45.,
1.,
fit_params_old,
label="fitted with old fitter",
color="b",
average_angle=0,
precision=-1,
fit_text="Old Fit, chi^2=" + str(round(chi_squared_old, 4)) +
": ",
fit_text_offset=0.05)
plot_TSTR_fit(45.,
1.,
fit_params_new,
label="fitted with new fitter",
color="g",
average_angle=0,
precision=-1,
fit_text="New Fit, chi^2=" + str(round(chi_squared_new, 4)) +
": ")
plt.show()
1.7])) # theta_r shift range is +5 to -10 deg
#fit_params=fit_params_simple[ii]
# fit_params=[.051,2.47,.076,1]
print("Fit parameters (rho_L, n, gamma): " + str(fit_params))
# Plot BRIDF model from fits
n_LXe_178 = 1.69
sigma_theta_i = -1
precision = -1
average_angle = -1
plot_TSTR_fit(angles[ii],
n_LXe_178,
fit_params,
color=colors[ii],
average_angle=average_angle,
precision=precision,
sigma_theta_i=sigma_theta_i,
include_fit_text=False)
chi_sq = chi_squared(run_data[0],
run_data[1],
run_data[2],
fit_params,
average_angle=average_angle,
precision=precision,
sigma_theta_i=sigma_theta_i)
print("Chi squared from fit for angle={0} deg: {1:.3f}".format(
angles[ii], chi_sq))
plt.text(
0.02,
n=n_LXe
plt.xlim(0,85)
plt.ylim(1e-3,1e2)
plt.yscale("log")
#colors=["r","g","b","m","c","y"]
colors=[plt.cm.plasma(i) for i in np.linspace(0.2,0.90,len(runs))]
# colors=[cmap(i) for i in np.linspace(0,1,len(runs))]
label_x=[0.63,0.93,0.88,0.85,0.9,0.88] # Positions for s9 low_p
label_y=[0.27,0.31,0.45,0.63,0.75,0.88]
#label_x=[0.25,0.45,0.55,0.65,0.75,0.85] # Positions for s9 in vacuum
#label_y=[0.6,0.62,0.65,0.7,0.76,0.85]
angle_labels=[r"$\theta_i=30^{\circ}$",r"$45^{\circ}$",r"$52^{\circ}$",r"$60^{\circ}$",r"$67^{\circ}$",r"$75^{\circ}$"]
for ii in range(len(angle_shifts)):
plot_TSTR_fit(angle_shifts[ii], n, fit_params, color=colors[ii], label=None, average_angle=average_angle, precision=precision, sigma_theta_i=sigma_theta_i, include_fit_text=False)
plt.text(label_x[ii], label_y[ii], angle_labels[ii],transform=plt.gca().transAxes,fontsize=13,color=colors[ii])
# Set x-axis tick marks to every 10 deg
loc = matplotlib.ticker.MultipleLocator(base=10.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
# Add grid lines
plt.grid(b=True,which='major',color="lightgray",linestyle='--')
# plt.grid(b=True,which='major',axis='x',color="lightgray",linestyle=':')
# plt.grid(b=True,which='both',axis='y',color="lightgray",linestyle=':')
#plt.tight_layout()
t2=time.time()
# fit_params_lowp_215=[1.011,1.563,0.118] # 2.15 solid angle factor
print("Fit parameters (rho_L, n, gamma): "+str(fit_params))
#print("Fit angle: "+str(fit_ang))
t1=time.time()
print("Fitting time: {0}".format(t1-t0))
# phi_r_list = np.linspace(-np.pi,np.pi,10)
# BRIDF_test_list = [BRIDF(52., phi_r, 0., 1.69, 0.5, fit_params) for phi_r in phi_r_list]
# print("BRIDF at normal incidence and viewing: ",BRIDF_test_list)
# Plot BRIDF model from fits
n_LXe_178 = 1.69
sigma_theta_i=2.
precision=0.25
average_angle=4
#plot_TSTR_fit(20., n_LXe_178, fit_params, color="r", average_angle=average_angle, precision=precision, sigma_theta_i=sigma_theta_i)
plot_TSTR_fit(30., n_LXe_178, fit_params, color="r", average_angle=average_angle, precision=precision, sigma_theta_i=sigma_theta_i)
plot_TSTR_fit(45., n_LXe_178, fit_params, color="g", average_angle=average_angle, precision=precision, sigma_theta_i=sigma_theta_i)
plot_TSTR_fit(52., n_LXe_178, fit_params, color="b", average_angle=average_angle, precision=precision, sigma_theta_i=sigma_theta_i)
plot_TSTR_fit(60., n_LXe_178, fit_params, color="m", average_angle=average_angle, precision=precision, sigma_theta_i=sigma_theta_i)
#independent_variables_array = get_independent_variables_and_relative_intensities(runs)[0]
#independent_variables_array = [list+[sigma_theta_i,precision,average_angle] for list in independent_variables_array]
#print([list[2] for list in independent_variables_array])
#theta_r_list = [list[0] for list in independent_variables_array]
#plt.plot(theta_r_list,fitter(independent_variables_array, np.log(fit_params[0]),np.log(fit_params[1]-1),np.log(fit_params[2])),color="y")
plot_TSTR_fit(67., n_LXe_178, fit_params, color="c", average_angle=average_angle, precision=precision, sigma_theta_i=sigma_theta_i)
plot_TSTR_fit(75., n_LXe_178, fit_params, color="y", average_angle=average_angle, precision=precision, sigma_theta_i=sigma_theta_i)
# plot_TSTR_fit(75., n_LXe_178, fit_params, color="y", average_angle=average_angle, precision=precision, sigma_theta_i=sigma_theta_i,phi_r=5)
# plot_TSTR_fit(75., n_LXe_178, fit_params, color="y", average_angle=average_angle, precision=precision, sigma_theta_i=sigma_theta_i,phi_r=10)
#plt.text(0.1,0.1,r"Fit: $\rho_L$={0:.3f}, n={1:.2f}, $\gamma$={2:.3f}".format(*fit_params),transform=plt.gca().transAxes,fontsize=13)
#plt.text(0.1,0.2,r"Fit: $\rho_L$={0:.3f}, n={1:.2f}, $\gamma$={2:.3f}, K={3:.3f}".format(*fit_params),transform=plt.gca().transAxes,fontsize=13)
t2=time.time()
fit_params = fit_parameters(
get_independent_variables_and_relative_intensities(v1_30),
p0=[0.24, 1.2, .1, 5.0],
average_angle=4.,
precision=0.25,
use_errs=True,
use_spike=True)
plot_runs(v1_30,
title="Sample 1 (LUX), 178nm, Vacuum, 30 degrees",
label="data",
log=True)
plot_TSTR_fit(30.,
1.,
fit_params,
label="fitted",
color="k",
average_angle=4.,
precision=0.25)
print("done with 30")
fit_params = fit_parameters(
get_independent_variables_and_relative_intensities(v1_45),
p0=[0.24, 1.2, .1, 5.0],
average_angle=4.,
precision=0.25,
use_errs=True,
use_spike=True)
plot_runs(v1_45,
title="Sample 1 (LUX), 178nm, Vacuum, 45 degrees",
label="data",
m18path = "vuv_height_comparison_and_first_data/M18 turn/center of sample/"
m1830 = Run(m18path + "2018_08_30__15_16_18.txt")
m1845 = Run(m18path + "2018_08_30__15_22_02.txt")
m1860 = Run(m18path + "2018_08_30__15_29_09.txt")
m1875 = Run(m18path + "2018_08_30__15_35_27.txt")
runs = [m1830, m1845, m1860, m1875]
plot_runs(runs,
title="M18 turn, 175nm",
log=True,
labels=["30 degrees", "45 degrees", "60 degrees", "75 degrees"])
fit_params = fit_parameters(
get_independent_variables_and_relative_intensities(runs))
plot_TSTR_fit(30., 1., fit_params, label="fitted", color="k")
plot_TSTR_fit(45., 1., fit_params, color="k")
plot_TSTR_fit(60., 1., fit_params, color="k")
plot_TSTR_fit(75., 1., fit_params, color="k")
plt.figure()
x = [30, 45, 60, 75]
y_diffuse = [reflectance_diffuse(theta, 1., 0.5, fit_params) for theta in x]
y_specular = [reflectance_specular(theta, 1., 0.5, fit_params) for theta in x]
y_total = [y_diffuse[i] + y_specular[i] for i in range(len(y_specular))]
plt.plot(x, y_diffuse, label="diffuse")
plt.plot(x, y_specular, label="specular")
plt.plot(x, y_total, label="total")
import matplotlib.pyplot as plt
import numpy as np
from file_reader import Run, get_independent_variables_and_relative_intensities
from plotting import plot_runs, plot_TSTR_fit
from TSTR_fit_new import fit_parameters, fit_parameters_and_angle, BRIDF_plotter, reflectance_diffuse, reflectance_specular, BRIDF
import time
# Plots BRIDF at a few angles w/ and w/o specular spike, as well as hemispherical reflectances
# Used for testing effects of the specular spike model params
fit_params = [.74, 1.45, 0.049] # From Coimbra fit
fit_params_spec = [.74, 1.45, 0.049, 1.7] # From Coimbra fit
plot_TSTR_fit(45.,
1.,
fit_params,
color="k",
label="no spike",
average_angle=4.,
precision=0.25)
plot_TSTR_fit(65., 1., fit_params, color="k", average_angle=4., precision=0.25)
plot_TSTR_fit(80., 1., fit_params, color="k", average_angle=4., precision=0.25)
plot_TSTR_fit(45.,
1.,
fit_params_spec,
color="r",
label="with spike",
average_angle=4.,
precision=0.25)
plot_TSTR_fit(65.,
1.,
fit_params_spec,
"""
fit_params_30 = [0.99,1.54,.15]
fit_params_45 = [0.99,1.54,.15]
fit_params_52 = [0.99,1.54,.15]
fit_params_60 = [0.99,1.54,.15]
fit_params_67 = [0.99,1.54,.15]
fit_params_75 = [0.99,1.54,.15]
"""
t1 = time.time()
print("Fitting time: {0}".format(t1 - t0))
plot_TSTR_fit(30,
n_LXe_178,
fit_params_all,
color="r",
average_angle=average_angle,
precision=precision,
sigma_theta_i=sigma_theta_i,
include_fit_text=True)
plot_TSTR_fit(45,
n_LXe_178,
fit_params_all,
color="g",
average_angle=average_angle,
precision=precision,
sigma_theta_i=sigma_theta_i,
include_fit_text=False)
plot_TSTR_fit(52,
n_LXe_178,
fit_params_all,
color="b",
#labels=["30 degrees", "45 degrees", "52 degrees", "60 degrees", "67 degrees", "75 degrees"]
labels = ["30 degrees", "45 degrees", "60 degrees", "75 degrees"]
# Plot BRIDF data
sample_name = "Sample 1 (LUX)"
#plot_runs(runs, title=sample_name+", 178nm", log=True, labels=labels, errorbars=True)
#plt.show()
# In a loop: ask for input params, plot model overlaid on data
get_input = True
params = [0.24, 1.27, 0.1, 5.0, 0.1, 0.5]
plot_TSTR_fit(30.,
1.69,
params,
label="total",
color="k",
average_angle=4.,
precision=0.25,
sigma_theta_i=2.)
# these parts of the plot do not currently average over the aperture
theta_r_in_degrees_array = np.linspace(0., 85., 100)
#plt.plot(theta_r_in_degrees_array, BRIDF_plotter(theta_r_in_degrees_array, 0., 30., 1.69, 0.5, params[:5], average_angle=4, precision=0.25, sigma_theta_i=2.), label="total, averaged over aperture")
plt.plot(theta_r_in_degrees_array,
BRIDF_plotter(theta_r_in_degrees_array, 0., 30., 1.69, 0.5,
params[:5]),
label="total, not averaged over aperture")
plt.plot(theta_r_in_degrees_array,
BRIDF_specular_plotter(theta_r_in_degrees_array, 0., 30., 1.69, 0.5,
params[:5]),
label="specular")
plot_runs(runs, title=sample_name+", 175 nm", log=True, labels=labels, errorbars=True,legend_loc=6)
t0=time.time()
# Fit data
fit_params = fit_parameters(get_independent_variables_and_relative_intensities(runs),p0=[0.75,1.57,0.2],average_angle=4., precision=-1,use_errs=True,use_spike=False)
#fit_params_ang = fit_parameters_and_angle(get_independent_variables_and_relative_intensities(runs),average_angle=4.)
#fit_ang = fit_params_ang[0]
#fit_params = fit_params_ang[1:]
print("Fit parameters (rho_L, n, gamma): "+str(fit_params))
#print("Fit angle: "+str(fit_ang))
t1=time.time()
print("Fitting time: {0}".format(t1-t0))
# Plot BRIDF model from fits
#plot_TSTR_fit(8., 1., fit_params, label="fitted", color="k", average_angle=4., precision=-1)
plot_TSTR_fit(33., 1., fit_params, color="k", average_angle=4., precision=-1)
plot_TSTR_fit(45., 1., fit_params, color="k", average_angle=4., precision=-1)
plot_TSTR_fit(61., 1., fit_params, color="k", average_angle=4., precision=-1)
plot_TSTR_fit(76, 1., fit_params, color="k", average_angle=4., precision=-1)
#plt.text(0.1,0.1,r"Fit: $\rho_L$={0:.3f}, n={1:.2f}, $\gamma$={2:.3f}".format(*fit_params),transform=plt.gca().transAxes,fontsize=13)
#plt.text(0.1,0.2,r"Fit: $\rho_L$={0:.3f}, n={1:.2f}, $\gamma$={2:.3f}, K={3:.3f}".format(*fit_params),transform=plt.gca().transAxes,fontsize=13)
t2=time.time()
print("Plotting time: {0}".format(t2-t1))
# Now calculate hemispherical reflectance
# plt.figure()
# # Different incident angles to calculate for
# x = [8,30,45,60]#[30, 45, 60, 75]#[0,10,20,30, 45, 55, 60, 65, 70, 75, 80, 85]#
# y_diffuse = [reflectance_diffuse(theta, 1., 0.5, fit_params) for theta in x]
#plt.figure(fig_num)
plot_runs(runs,
title=sample_name + ", 175 nm",
log=True,
labels=labels,
errorbars=True)
# Trying setting incident angles by hand, for better fits to specular spike
n_LXe_178 = 1.69
n = 1.0 #n_LXe_178
#sigma_theta_i=2.0
precision = 0.25
average_angle = 4.
plot_TSTR_fit(25.,
n,
fit_params,
label="fitted",
color="r",
average_angle=average_angle,
precision=precision,
sigma_theta_i=sigma_theta_i)
plot_TSTR_fit(40.,
n,
fit_params,
color="g",
average_angle=average_angle,
precision=precision,
sigma_theta_i=sigma_theta_i)
plot_TSTR_fit(48.,
n,
fit_params,
color="b",
average_angle=average_angle,
