I tried to plot multiple contours then blend it to make it seem overlayed.
This is my code snippet, and this is how my figure looks like.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
X_2d = [[100, -400], [101, -404], [102, -408], [103, -412]]
torch_X_2d = torch.tensor(X_2d, dtype =torch.float)
HSIC_2d, (width_x_2d, width_x_2d_) = normalized_HSIC(torch_X_2d, torch_X_2d, return_width=True, kernel_param_average_method='mean') # ignore this
X_2d = np.asarray(X_2d)
plt.figure(figsize=(10, 10))
plt.xlabel('x1')
plt.ylabel('x2')
sigma_x_2d = width_x_2d # scalar case
n_grid = 51
x1linspace = np.linspace(-3*sigma_x_2d, 3*sigma_x_2d, n_grid)
x2linspace = np.linspace(-3*sigma_x_2d, 3*sigma_x_2d, n_grid)
x1x1, x2x2 = np.meshgrid(x1linspace, x2linspace)
print("x1x1 shpae", x1x1.shape)
print("x2x2 shpae", x2x2.shape)
kernel_Y = np.exp(- (x1x1*x1x1 + x2x2*x2x2)/ (sigma_x_2d)**2 ) # scalar case
x1_min = 0
x1_max = 0
x2_min = 0
x2_max = 0
step = 0.02
m = torch.amax(kernel_Y)
levels = np.arange(0.0, m, step) + step
cmap = ["Reds", "Blues", "Greens", "Purples"]
alphas = Normalize(0, .3, clip=True)(np.abs(kernel_Y))
alphas = np.clip(alphas, .4, 1) # alpha value clipped at the bottom at .4
print("alphas shape", alphas.shape)
print(alphas)
for i, x in enumerate(X_2d) :
print("x", x)
print("x1x1", x1x1)
contour = plt.contour(x[0]+x1x1, x[1]+x2x2, kernel_Y, cmap = "viridis_r", levels=7, alpha=0.6, linewidths=1, vmin=0)
contourf = plt.contourf(x[0] + x1x1, x[1] + x2x2, kernel_Y, cmap=cmap[i], levels=levels, alpha=0.5, vmin=0)
if i == 0 :
plt.clabel(contour, inline=True, fontsize=10)
plt.title('isotropic kernel')
x1_min = min(X_2d[:,0])
x1_max = max(X_2d[:,0])
x2_min = min(X_2d[:,1])
x2_max = max(X_2d[:,1])
x1_axis_min = min( x1_min - (x1_max - x1_min)*0.2, min(X_2d[:,0])-2*sigma_x_2d)
x1_axis_max = max( x1_max + (x1_max - x1_min)*0.2, max(X_2d[:,0])+2*sigma_x_2d)
x2_axis_min = min( x2_min - (x2_max - x2_min)*0.2, min(X_2d[:,1])-2*sigma_x_2d)
x2_axis_max = max( x2_max + (x2_max - x2_min)*0.2, max(X_2d[:,1])+2*sigma_x_2d)
plt.axis((x1_axis_min, x1_axis_max, x2_axis_min, x2_axis_max))
plt.scatter(X_2d[:,0],X_2d[:,1], color='red')
plt.colorbar(contourf)
plt.show()
The code is dirty, but what I am doing is basically ploting 4 contour plots In 2d plane, and blend them to show all of 4 contour plots in one plane.
So I would appreciate if I set alpha of contourf less than 1, It shows underlying, previous contour plot.
If realised, It would be something like a feature of Photoshop's layer blending mode.
Is there already a way to achieve this?
Problem
I tried to plot multiple contours then blend it to make it seem overlayed.
This is my code snippet, and this is how my figure looks like.
The code is dirty, but what I am doing is basically ploting 4 contour plots In 2d plane, and blend them to show all of 4 contour plots in one plane.
So I would appreciate if I set alpha of contourf less than 1, It shows underlying, previous contour plot.
If realised, It would be something like a feature of Photoshop's layer blending mode.
Is there already a way to achieve this?
Thanks!
Proposed solution
No response