R_1 = gaussian_parameters[1]
R_2 = gaussian_parameters[2]
TSTR_parameters = TSTR_fit.fit_parameters(points)
rho_L = TSTR_parameters[0]
n = TSTR_parameters[1]
gamma = TSTR_parameters[2]
semi_empirical_parameters = semi_empirical_fit.fit_parameters(points)
rho_L_ = semi_empirical_parameters[0]
n_ = semi_empirical_parameters[1]
K_ = semi_empirical_parameters[2]
gamma_ = semi_empirical_parameters[3]
semi_empirical_fitted_data = semi_empirical_fit.BRIDF_plotter(
theta_r_in_degrees_array, phi_r_in_degrees, theta_i_in_degrees, n_0,
polarization, semi_empirical_parameters)
gaussian_fitted_data = gaussian_fit.BRIDF_plotter(theta_r_in_degrees_array,
theta_i_in_degrees,
gaussian_parameters)
TSTR_fitted_data = TSTR_fit.BRIDF_plotter(theta_r_in_degrees_array,
phi_r_in_degrees,
theta_i_in_degrees, n_0,
polarization, TSTR_parameters)
plt.plot(theta_r_in_degrees_array, intensity_array, label="experimental")
plt.plot(theta_r_in_degrees_array,
gaussian_fitted_data,
label="Gaussian model")
def plot_with_semi_empirical_fit(points, title):
one_pass_x_data = []
run_name_list = []
points_by_run_name = []
for point in points:
if point.theta_r_in_degrees not in one_pass_x_data:
one_pass_x_data.append(point.theta_r_in_degrees)
if point.run_name not in run_name_list:
run_name_list.append(point.run_name)
points_by_run_name.append([point])
else:
index = 0.5 # to error if not overridden
for i in range(len(run_name_list)):
run_name = run_name_list[i]
if point.run_name == run_name:
index = i
points_by_run_name[index].append(point)
one_pass_x_data = sorted(one_pass_x_data)
phi_r_in_degrees = points[0].phi_r_in_degrees
semi_empirical_parameters = semi_empirical_fit.fit_parameters(points)
# make a plot for each angle
for i in range(len(run_name_list)):
run_name = run_name_list[i]
points = points_by_run_name[i]
n_0 = points[0].n_0
polarization = points[0].polarization
theta_i_in_degrees = points[0].theta_i_in_degrees
photodiode_angle = points[0].photodiode_angular_width
x_data = [point.theta_r_in_degrees for point in points]
y_data = [point.intensity for point in points]
max_y = max(y_data)
semi_empirical_y = semi_empirical_fit.BRIDF_plotter(
one_pass_x_data, phi_r_in_degrees, theta_i_in_degrees, n_0,
polarization, photodiode_angle, semi_empirical_parameters)
plt.figure()
plt.title(run_name)
plt.scatter(x_data, y_data, color="r", s=2, label="experimental")
plt.plot(one_pass_x_data, semi_empirical_y, label="Semi-Empirical Fit")
rho_L = semi_empirical_parameters[0]
n = semi_empirical_parameters[1]
K = semi_empirical_parameters[2]
gamma = semi_empirical_parameters[3]
string = "theta_i: " + str(theta_i_in_degrees) + "\n\nSemi-Empirical Parameters:\nrho_L: " + \
str(rho_L) + "\nn: " + str(n) + "\nK: " + str(K) + "\ngamma: " + str(gamma)
axes = plt.gca()
axes.set_ylim([0, 1.2 * max_y])
plt.legend()
plt.xlabel("viewing angle (degrees)")
plt.ylabel("intensity (flux/str)/(input flux)")
plt.annotate(string, xy=(0.05, 0.6), xycoords='axes fraction', size=6)
plt.figure()
current_min_y = 1000
current_max_y = 0
for i in range(len(run_name_list)):
run_name = run_name_list[i]
points = points_by_run_name[i]
n_0 = points[0].n_0
polarization = points[0].polarization
theta_i_in_degrees = points[0].theta_i_in_degrees
x_data = [point.theta_r_in_degrees for point in points]
y_data = [point.intensity for point in points]
current_min_y = min(current_min_y, min(y_data))
current_max_y = max(current_max_y, max(y_data))
semi_empirical_y = semi_empirical_fit.BRIDF_plotter(
one_pass_x_data, phi_r_in_degrees, theta_i_in_degrees, n_0,
polarization, photodiode_angle, semi_empirical_parameters)
plt.scatter(x_data, y_data, s=2, label=run_name + " experimental")
plt.plot(one_pass_x_data,
semi_empirical_y,
label=run_name + " TSTR Fit")
plt.title(title)
axes = plt.gca()
axes.set_ylim([current_min_y, 1.2 * current_max_y])
plt.legend()
plt.xlabel("viewing angle (degrees)")
plt.ylabel("intensity (flux/str)/(input flux)")
rho_L = semi_empirical_parameters[0]
n = semi_empirical_parameters[1]
K = semi_empirical_parameters[2]
gamma = semi_empirical_parameters[3]
string = "\n\nSemi-Empirical Parameters:\nrho_L: " + str(
rho_L) + "\nn: " + str(n) + "\nK: " + str(K) + "\ngamma: " + str(gamma)
plt.annotate(string, xy=(0.05, 0.6), xycoords='axes fraction', size=6)
plt.show()
def plot_with_semi_empirical_and_gaussian_fits(points):
one_pass_x_data = []
run_name_list = []
points_by_run_name = []
for point in points:
if point.theta_r_in_degrees not in one_pass_x_data:
one_pass_x_data.append(point.theta_r_in_degrees)
if point.run_name not in run_name_list:
run_name_list.append(point.run_name)
points_by_run_name.append([point])
else:
index = 0.5 # to error if not overridden
for i in range(len(run_name_list)):
run_name = run_name_list[i]
if point.run_name == run_name:
index = i
points_by_run_name[index].append(point)
plot_gaussian = len(run_name_list) == 1
phi_r_in_degrees = points[0].phi_r_in_degrees
n_0 = points[0].n_0
polarization = points[0].polarization
photodiode_solid_angle = points[0].photodiode_solid_angle
semi_empirical_parameters = semi_empirical_fit.fit_parameters(points)
TSTR_parameters = TSTR_fit.fit_parameters(points)
if plot_gaussian:
gaussian_parameters = gaussian_fit.fit_parameters(points)
for i in range(len(run_name_list)):
run_name = run_name_list[i]
points = points_by_run_name[i]
theta_i_in_degrees = points[0].theta_i_in_degrees
x_data = [point.theta_r_in_degrees for point in points]
y_data = [point.intensity for point in points]
max_y = max(y_data)
semi_empirical_y = semi_empirical_fit.BRIDF_plotter(
one_pass_x_data, phi_r_in_degrees, theta_i_in_degrees, n_0,
polarization, photodiode_solid_angle, semi_empirical_parameters)
TSTR_y = TSTR_fit.BRIDF_plotter(one_pass_x_data, phi_r_in_degrees,
theta_i_in_degrees, n_0, polarization,
TSTR_parameters)
if plot_gaussian:
gaussian_y = gaussian_fit.BRIDF_plotter(one_pass_x_data,
theta_i_in_degrees,
gaussian_parameters)
plt.figure()
plt.title(run_name)
plt.scatter(x_data,
y_data,
marker="x",
color="r",
label="experimental")
plt.plot(one_pass_x_data, semi_empirical_y, label="Semi-Empirical Fit")
plt.plot(one_pass_x_data, TSTR_y, label="TSTR Fit")
if plot_gaussian:
plt.plot(one_pass_x_data, gaussian_y, label="Gaussian Fit")
rho_L_ = semi_empirical_parameters[0]
n_ = semi_empirical_parameters[1]
K_ = semi_empirical_parameters[2]
gamma_ = semi_empirical_parameters[3]
rho_L = TSTR_parameters[0]
n = TSTR_parameters[1]
gamma = TSTR_parameters[2]
if plot_gaussian:
sigma = gaussian_parameters[0]
R_1 = gaussian_parameters[1]
R_2 = gaussian_parameters[2]
string = "theta_i: " + str(theta_i_in_degrees) + "\n\nSemi-Empirical Parameters:\nrho_L: " + str(rho_L_) + \
"\nn: " + str(n_) + "\nK: " + str(K_) + "\ngamma: " + str(gamma_) + "\n\nTSTR Parameters:\nrho_L: " + \
str(rho_L) + "\nn: " + str(n) + "\ngamma: " + str(gamma)
if plot_gaussian:
string += "\n\nsigma: " + str(sigma) + "\nR_1: " + str(
R_1) + "\nR_2: " + str(R_2)
axes = plt.gca()
axes.set_ylim([0, 1.2 * max_y])
plt.legend()
plt.xlabel("viewing angle (degrees)")
plt.ylabel("intensity (flux/str)/(input flux)")
plt.annotate(string, xy=(0.05, 0.6), xycoords='axes fraction', size=6)
plt.show()
def plot_semi_empirical_components(points):
one_pass_x_data = []
run_name_list = []
points_by_run_name = []
for point in points:
if point.theta_r_in_degrees not in one_pass_x_data:
one_pass_x_data.append(point.theta_r_in_degrees)
if point.run_name not in run_name_list:
run_name_list.append(point.run_name)
points_by_run_name.append([point])
else:
index = 0.5 # to error if not overridden
for i in range(len(run_name_list)):
run_name = run_name_list[i]
if point.run_name == run_name:
index = i
points_by_run_name[index].append(point)
phi_r_in_degrees = points[0].phi_r_in_degrees
n_0 = points[0].n_0
polarization = points[0].polarization
photodiode_solid_angle = points[0].photodiode_solid_angle
semi_empirical_parameters = semi_empirical_fit.fit_parameters(points)
for i in range(len(run_name_list)):
run_name = run_name_list[i]
points = points_by_run_name[i]
theta_i_in_degrees = points[0].theta_i_in_degrees
x_data = [point.theta_r_in_degrees for point in points]
y_data = [point.intensity for point in points]
max_y = max(y_data)
semi_empirical_y_diffuse = semi_empirical_fit.BRIDF_diffuse_plotter(
one_pass_x_data, phi_r_in_degrees, theta_i_in_degrees, n_0,
polarization, photodiode_solid_angle, semi_empirical_parameters)
semi_empirical_y_specular_lobe = semi_empirical_fit.BRIDF_specular_lobe_plotter(
one_pass_x_data, phi_r_in_degrees, theta_i_in_degrees, n_0,
polarization, photodiode_solid_angle, semi_empirical_parameters)
semi_empirical_y_specular_spike = semi_empirical_fit.BRIDF_specular_spike_plotter(
one_pass_x_data, phi_r_in_degrees, theta_i_in_degrees, n_0,
polarization, photodiode_solid_angle, semi_empirical_parameters)
semi_empirical_y_total = semi_empirical_fit.BRIDF_plotter(
one_pass_x_data, phi_r_in_degrees, theta_i_in_degrees, n_0,
polarization, photodiode_solid_angle, semi_empirical_parameters)
plt.figure()
plt.title(run_name + "\nSemi-Empirical Fit Components")
plt.scatter(x_data,
y_data,
marker="x",
color="r",
label="experimental")
plt.plot(one_pass_x_data, semi_empirical_y_diffuse, label="diffuse")
plt.plot(one_pass_x_data,
semi_empirical_y_specular_lobe,
label="specular_lobe")
plt.plot(one_pass_x_data,
semi_empirical_y_specular_spike,
label="specular_spike")
plt.plot(one_pass_x_data, semi_empirical_y_total, label="total")
rho_L_ = semi_empirical_parameters[0]
n_ = semi_empirical_parameters[1]
K_ = semi_empirical_parameters[2]
gamma_ = semi_empirical_parameters[3]
string = "theta_i: " + str(theta_i_in_degrees) + "\n\nSemi-Empirical Parameters:\nrho_L: " + str(rho_L_) + \
"\nn: " + str(n_) + "\nK: " + str(K_) + "\ngamma: " + str(gamma_)
axes = plt.gca()
axes.set_ylim([0, 1.2 * max_y])
plt.legend()
plt.xlabel("viewing angle (degrees)")
plt.ylabel("intensity (flux/str)/(input flux)")
plt.annotate(string, xy=(0.05, 0.8), xycoords='axes fraction', size=6)
plt.show()