Implement advanced features of the emulated cameras

[bnl-atf]

Implement advanced features of the emulated cameras

[thomaswmorris]

Done!

[mrakitin]

I think we can keep it open for now - I'd like to add an option to display predefined images from a directory (first bullet point).

For the second one, maybe we need to test it more at ATF and see if there are any missing buffers, or force this situation with the emulated cameras.

[mrakitin]

Oh, actually, we should port this issue to the https://github.com/BNL-ATF/ophyd-basler repo now. I'll do that.

[mrakitin]

For the 1st task, here is a starting point to generate a number of images:

import numpy as np
import matplotlib.pyplot as plt

plt.ion()

# sigma(standard deviation) and muu(mean) are the parameters of gaussian

def gaussian_filter(kernel_size, sigma=0.1, muu=0.0):

    # Initializing value of x,y as grid of kernel size
    # in the range of kernel size

    x, y = np.meshgrid(np.linspace(-1, 1, kernel_size),
        np.linspace(-1, 1, kernel_size))
    dst = np.sqrt((x+0.1)**2+(y-0.5)**2)

    # lower normal part of gaussian
    normal = 1/(2.0 * np.pi * sigma**2)

    # Calculating Gaussian filter
    gauss = np.exp(-((dst-muu)**2 / (2.0 * sigma**2))) * normal
    return gauss

kernel_size=1000
gaussian = gaussian_filter(kernel_size)
print("gaussian filter of {} X {} :".format(kernel_size, kernel_size))
print(gaussian)
plt.imshow(gaussian)
plt.show()

[mrakitin]

Useful example: basler/pypylon#73.