apply_poisson_noise

photutils.datasets.apply_poisson_noise(data, seed=None)

Apply Poisson noise to an array, where the value of each element in the input array represents the expected number of counts.

Each pixel in the output array is generated by drawing a random sample from a Poisson distribution whose expectation value is given by the pixel value in the input array.

Parameters:

dataarray_like

The array on which to apply Poisson noise. Every pixel in the array must have a positive value (i.e., counts).

seedint, optional

A seed to initialize the numpy.random.BitGenerator. If None, then fresh, unpredictable entropy will be pulled from the OS.

Returns:

resultndarray

The data array after applying Poisson noise.

See also

make_noise_image

Examples

import matplotlib.pyplot as plt
from photutils.datasets import (apply_poisson_noise,
                                make_4gaussians_image)

data1 = make_4gaussians_image(noise=False)
data2 = apply_poisson_noise(data1, seed=0)

# plot the images
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8))
ax1.imshow(data1, origin='lower', interpolation='nearest')
ax1.set_title('Original image')
ax2.imshow(data2, origin='lower', interpolation='nearest')
ax2.set_title('Original image with Poisson noise applied')

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