Hello

I have several datapoints in space. I’m binning and taking the average and I’m trying to plot these in different ways: contourf, streamplot and quiver. But before plotting I am making and interpolation with LinearTriInterpolator.

However some bins are empty and with the interpolation these bins will magically have data and will be plotted. To make this distinction I thought of using alpha=1 for occupied bins and something else for vacant bins.

I managed to do this with scatterplots as follows:

```
import numpy as np
from matplotlib import cm
from matplotlib.colors import Normalize
import matplotlib.pyplot as plt
color_array = np.array([1,2,3,4,5])
alpha_array = np.array([2,10,0,0,2], dtype=np.float32)
alpha_array[alpha_array != 0] = 1
alpha_array[np.isclose(alpha_array,0)] = 0.3
norm = Normalize(vmin=color_array.min(),
vmax=color_array.max())
cmap = cm.get_cmap('viridis')
color_array = norm(color_array)
color_array = cmap(color_array)
color_array[...,-1] = alpha_array
plt.scatter([1,2,3,4,5], [1,2,3,4,5], c=color_array)
```

In this toy example I have 5 bins and alpha_array contains the occupation number of each bin and because of the normalization, the alphas will be [0.2, 1. , 0. , 0. , 0.2]

This works very nicely with scatterplots since I can pass “A 2D array in which the rows are RGB or RGBA.” but for the contourf, quiver and streamplot I can’t find this option. (and if I try to force it using the colors arguments on contourf for example, it complains because it’s not expecting RGB or RGBA)

Am I missing something? Is it possible to do it?

I am using ubuntu 20.04 LTS and matplotlib 3.4.1 on python 3.8

Thanks in advance

Pedro