Is there uint4 standard (4 bit) array in python?

Here is the list of numpy's data types, so no uint4.

For uints, you have the following:

(note: .itemsize returns the "length of one array element in bytes")

In [1]: numpy_dtypes = (
   ...:     (np.uint8, np.ubyte),
   ...:     (np.uint16, np.ushort),
   ...:     (np.uint32, np.uintc),
   ...:     (np.uint64, np.uintp, np.ulonglong),
   ...: )
   ...: for dtypes in numpy_dtypes:
   ...:     print([dt().itemsize for dt in dtypes])
[1, 1]
[2, 2]
[4, 4]
[8, 8, 8]

You can simulate any bit depth by just scaling into the correct dynamic range and rounding to integers:

import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable


def main():

    imh, imw = 512, 512

    data = np.arange(imh * imw).reshape(imh, imw)
    num_bits = [2, 4, 8, 16]

    images = {
        nbits: np.round(minmax_scale(data, vmin=0, vmax=2**nbits - 1))
        for nbits in num_bits
    }

    fig, axes = plt.subplots(ncols=len(images), figsize=(12, 4))

    for ax, (nbits, image) in zip(axes, images.items()):
        ax.set_title(f"{nbits = }")
        im = ax.imshow(image, vmin=0, vmax=2**nbits - 1, interpolation="none")
        add_colorbar(ax, im)

    plt.subplots_adjust(wspace=0.8)
    plt.show()


def add_colorbar(ax, im):
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    cax.get_figure().colorbar(im, cax=cax, orientation="vertical")


def minmax_scale(arr, *, vmin, vmax):
    amin, amax = arr.min(), arr.max()
    arr_std = (arr - amin) / (amax - amin)
    return arr_std * (vmax - vmin) + vmin


if __name__ == "__main__":
    main()