/usr/lib/python2.7/dist-packages/photutils/datasets/tests/test_make.py is in python-photutils 0.2.1-2.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
from numpy.testing import assert_allclose
from astropy.tests.helper import pytest, assert_quantity_allclose
from astropy.table import Table
import astropy.units as u
from .. import (make_noise_image, make_poisson_noise, make_gaussian_sources,
make_random_gaussians, make_4gaussians_image,
make_100gaussians_image)
TABLE = Table()
TABLE['flux'] = [1, 2, 3]
TABLE['x_mean'] = [30, 50, 70.5]
TABLE['y_mean'] = [50, 50, 50.5]
TABLE['x_stddev'] = [1, 2, 3.5]
TABLE['y_stddev'] = [2, 1, 3.5]
TABLE['theta'] = np.array([0., 30, 50]) * np.pi / 180.
def test_make_noise_image():
shape = (100, 100)
image = make_noise_image(shape, 'gaussian', mean=0., stddev=2.)
assert image.shape == shape
assert_allclose(image.mean(), 0., atol=1.)
def test_make_noise_image_poisson():
shape = (100, 100)
image = make_noise_image(shape, 'poisson', mean=1.)
assert image.shape == shape
assert_allclose(image.mean(), 1., atol=1.)
def test_make_noise_image_nomean():
"""
Test if ValueError raises if mean is not input.
"""
with pytest.raises(ValueError):
shape = (100, 100)
make_noise_image(shape, 'gaussian', stddev=2.)
def test_make_noise_image_nostddev():
"""
Test if ValueError raises if stddev is not input for Gaussian noise.
"""
with pytest.raises(ValueError):
shape = (100, 100)
make_noise_image(shape, 'gaussian', mean=2.)
def test_make_noise_image_unit():
shape = (100, 100)
unit = u.electron / u.s
image = make_noise_image(shape, 'gaussian', mean=0., stddev=2., unit=unit)
assert image.shape == shape
assert image.unit == unit
assert_quantity_allclose(image.mean(), 0.*unit, atol=1.*unit)
def test_make_poisson_noise():
shape = (100, 100)
data = np.ones(shape)
result = make_poisson_noise(data)
assert result.shape == shape
assert_allclose(result.mean(), 1., atol=1.)
def test_make_poisson_noise_negative():
"""Test if negative image values raises ValueError."""
with pytest.raises(ValueError):
shape = (100, 100)
data = np.zeros(shape) - 1.
make_poisson_noise(data)
def test_make_poisson_noise_unit():
shape = (100, 100)
unit = u.electron / u.s
data = np.ones(shape) * unit
result = make_poisson_noise(data)
assert result.shape == shape
assert result.unit == unit
assert_quantity_allclose(result.mean(), 1.*unit, atol=1.*unit)
def test_make_gaussian_sources():
shape = (100, 100)
image = make_gaussian_sources(shape, TABLE)
assert image.shape == shape
assert_allclose(image.sum(), TABLE['flux'].sum())
def test_make_gaussian_sources_amplitude():
table = TABLE.copy()
table.remove_column('flux')
table['amplitude'] = [1, 2, 3]
shape = (100, 100)
image = make_gaussian_sources(shape, table)
assert image.shape == shape
def test_make_gaussian_sources_oversample():
shape = (100, 100)
image = make_gaussian_sources(shape, TABLE, oversample=10)
assert image.shape == shape
assert_allclose(image.sum(), TABLE['flux'].sum())
def test_make_gaussian_sources_parameters():
with pytest.raises(ValueError):
table = TABLE.copy()
table.remove_column('flux')
shape = (100, 100)
make_gaussian_sources(shape, table)
def test_make_gaussian_sources_unit():
shape = (100, 100)
unit = u.electron / u.s
image = make_gaussian_sources(shape, TABLE, unit=unit)
assert image.shape == shape
assert image.unit == unit
assert_quantity_allclose(image.sum(), TABLE['flux'].sum()*unit)
def test_make_random_gaussians():
n_sources = 5
bounds = [0, 1]
table = make_random_gaussians(n_sources, bounds, bounds, bounds, bounds,
bounds)
assert len(table) == n_sources
def test_make_random_gaussians_amplitude():
n_sources = 5
bounds = [0, 1]
table = make_random_gaussians(n_sources, bounds, bounds, bounds, bounds,
bounds, amplitude_range=bounds)
assert len(table) == n_sources
def test_make_4gaussians_image():
shape = (100, 200)
data_sum = 176219.18059091491
image = make_4gaussians_image()
assert image.shape == shape
assert_allclose(image.sum(), data_sum, rtol=1.e-6)
def test_make_100gaussians_image():
shape = (300, 500)
data_sum = 826182.24501251709
image = make_100gaussians_image()
assert image.shape == shape
assert_allclose(image.sum(), data_sum, rtol=1.e-6)
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