def testing_boundary_flow_noisy_parallel():
data = pd.read_csv('Sample_Data/data13.csv')
data_np = data.to_numpy()
data_np = data_np.T[1:]
noisy_data = noising_data(data_np, 0.03,
seed=998) # good seed, don't change!!
noisy_data = put_on_sphere(noisy_data)
final_p = sphere_centroid_finder_vecs(noisy_data, 3, 0.05, 0.01)
#print(final_p)
h = choose_h_gaussian(
noisy_data, final_p,
50) # needs to be very high! # needs to be very high!
radius = choose_h_binary(noisy_data, final_p, 30)
upper, curve, lower, upper_directions, lower_directions = principal_boundary(
noisy_data,
3,
0.02,
h,
radius,
start_point=final_p,
kernel_type="gaussian",
max_iter=10,
parallel_transport=True)
#print(curve)
x_upper, y_upper, z_upper = upper.T
x_curve, y_curve, z_curve = curve.T
x_lower, y_lower, z_lower = lower.T
x_upperv, y_upperv, z_upperv = upper_directions.T
x_lowerv, y_lowerv, z_lowerv = lower_directions.T
phi = np.linspace(0, np.pi, 20)
theta = np.linspace(0, 2 * np.pi, 40)
x = np.outer(np.sin(theta), np.cos(phi))
y = np.outer(np.sin(theta), np.sin(phi))
z = np.outer(np.cos(theta), np.ones_like(phi))
fig, ax = plt.subplots(1, 1, subplot_kw={'projection': '3d'})
ax.plot_surface(x, y, z, color='k', rstride=1, cstride=1,
alpha=0.1) # alpha affects transparency of the plot
xx, yy, zz = noisy_data.T
ax.scatter(xx, yy, zz, color="k", s=50)
ax.plot(x_curve, y_curve, z_curve, color="r", s=50)
ax.quiver(x_upper,
y_upper,
z_upper,
x_upperv,
y_upperv,
z_upperv,
length=0.05,
color="b")
ax.quiver(x_lower,
y_lower,
z_lower,
x_lowerv,
y_lowerv,
z_lowerv,
length=0.05,
color="g")
plt.show()
def testing_boundary_flow():
data = pd.read_csv('Sample_Data/data5.csv')
data_np = data.to_numpy()
data_np = data_np.T[1:]
final_p = sphere_centroid_finder_vecs(data_np, 3, 0.05, 0.01)
#print(final_p)
h = choose_h_gaussian(data_np, final_p, 20) # needs to be very high! # needs to be very high!
radius = choose_h_binary(data_np, final_p, 50)
upper, curve, lower = principal_boundary(data_np, 3, 0.02, h, radius, start_point=final_p, kernel_type="gaussian", max_iter=10)
#print(curve)
x_upper, y_upper, z_upper = upper.T
x_curve, y_curve, z_curve = curve.T
x_lower, y_lower, z_lower = lower.T
phi = np.linspace(0, np.pi, 20)
theta = np.linspace(0, 2 * np.pi, 40)
x = np.outer(np.sin(theta), np.cos(phi))
y = np.outer(np.sin(theta), np.sin(phi))
z = np.outer(np.cos(theta), np.ones_like(phi))
fig, ax = plt.subplots(1, 1, subplot_kw={'projection':'3d'})
ax.plot_surface(x, y, z, color='k', rstride=1, cstride=1, alpha=0.1) # alpha affects transparency of the plot
xx, yy, zz = data_np.T
ax.scatter(xx, yy, zz, color="k", s=50)
ax.scatter(x_curve, y_curve, z_curve, color="r", s=50)
ax.scatter(x_upper, y_upper, z_upper, color="b", s=50)
ax.scatter(x_lower, y_lower, z_lower, color="g", s=50)
plt.show()
#testing_boundary_flow()
sampled_X_on_sphere = put_on_sphere(sampled_X)
# print(sampled_X_on_sphere[0])
# Find centroid of data #
final_p = sphere_centroid_finder_vecs(sampled_X_on_sphere, sampled_X.shape[1],
0.05, 0.01)
# print(final_p)
final_p_img = final_p.reshape(m, n)
plt.imshow(final_p_img, cmap=plt.get_cmap('gray'))
plt.show()
h = choose_h_gaussian(sampled_X_on_sphere, final_p,
85) # needs to be very high!
radius = choose_h_binary(sampled_X_on_sphere, final_p, 40)
upper, curve, lower = principal_boundary(sampled_X_on_sphere, sampled_X.shape[1], 0.02, h, radius, \
start_point=final_p, kernel_type="gaussian", max_iter=40)
print("upper")
for j in range(5):
for i in range(9):
plt.subplot(330 + 1 + i)
plt.imshow(upper[i + 9 * j].reshape(m, n), cmap=plt.get_cmap('gray'))
plt.show()
print("curve")
for j in range(5):
for i in range(9):
plt.subplot(330 + 1 + i)
plt.imshow(curve[i + 9 * j].reshape(m, n), cmap=plt.get_cmap('gray'))
plt.show()
print("lower")