Matplotlib - How to assign an alpha value to each point on a 3D scatter plot? Radar Data Included

Question

So I am trying to create a 3D scatter plot of radar data, where each point is assigned an alpha value based on the amplitude of the corresponding pixel.

I have done this looping through all x,y,z points and building the scatter plot point by point assigning the alpha values on each iteration. But once the scatter plot is created it is very slow and unable to manipulate the graph without considerable time spent waiting for the plot to update.

Points is a normalised (0 to 1) array.

Here is a link to my data Data

File preparation:

def normalise0to1(data):
    normalised = (data - np.min(data)) / (np.max(data) - np.min(data))
    return normalised
Data = np.loadtxt('filepath.txt')
points2D = normalise0to1(Data) #Is (101,400) size
points3D = np.reshape(points2D,(101,20,20)) #Is (101,20,20) size

And the first attempt at creating the 3D scatter plot:

def Scatter_Plot1(points3D):
    fig = plt.figure()
    ax = fig.add_subplot(projection='3d')

    for x in range(0,points3D[1]):
        for y in range(0,points3D[2]):
            for z in range(0,points3D[0]):
                val = points3D[z,x,y]
                ax.scatter(x, y, z, alpha=val,c='black',s=1)

    plt.show()

This takes a long time to run and is very slow once created.

In 2D, I can do something like this. Bear in mind this is the same array as the 3D 'points' array, but the (20x20) has been flattened to 400. I have provided an image of the flattened array, you can see how it creates an image where intensity is scaled to the alpha value.

def Scatter_Plot2(points2D):
    fig = plt.figure()
    ax = fig.add_subplot()
    x_=np.linspace(0,points2D.shape[1]-1,points2D.shape[1])
    y_=np.linspace(0,points2D.shape[0]-1,points2D.shape[0])
    x,y = np.meshgrid(x_,y_)
    ax.scatter(x,y,alpha=points2D.flatten(),s=1,c='black')
    plt.show()

This image is the flattened version of the 3D plot I want to create, where instead of 400 length, it would be (20x20) and overall the 3D shape is (101,20,20).

The problem comes when trying to assign the alpha to a 3D plot, in 2D it seems happy enough when I provide a flattened array to the alpha parameter.

I would like something like this, but whether that's possible is another question..

def Scatter_Plot3(points3D):
    fig = plt.figure()
    ax = fig.add_subplot(projection='3d')
    x_=np.linspace(0,points3D.shape[2]-1,points3D.shape[2])
    y_=np.linspace(0,points3D.shape[1]-1,points3D.shape[1])
    z_=np.linspace(0,points3D.shape[0]-1,points3D.shape[0])

    x,y,z = np.meshgrid(x_,y_,z_)
    ax.scatter(x,y,z,alpha=points3D,s=1,c='black')
    plt.show()

Results in this image, which seems a bit random in terms of how the alpha is assigned. You would expect to see the dark horizontal lines as in the first picture. What I want may not be possible, I'm open to using another package, perhaps pyqtgraph or mayavi etc. But matplotlib would be preferable.

Thank you!

Edit:

I have achieved something similar to what I would like, though not exactly. I have used the c and cmap inputs. This isn't ideal as I am dealing with a 3D cube and viewing the centre is still difficult but it has correctly mapped a variation to the data. But it doesn't work when I use the alpha parameter. Notice the 2 main horizontal bands and the dark bit in the centre which is hard to see.

What I need is the same mapping but rather opacity than colour.

c = (points2D.T.flatten())
ax.scatter(x,y,z,s=1,c=c,cmap='viridis_r')