def test_trim_array_one_origin():
y, x = np.indices((100, 100))
a = x*y
ta, tx, ty = trim_array(a, 21, (50, 95), origin=1)
npt.assert_array_equal(ta, a[85:, 40:62])
check.equal(tx, 11)
check.equal(ty, 11)
def test_trim_array_even():
y, x = np.indices((100, 100))
a = x*y
ta, tx, ty = trim_array(a, 20, (50, 95))
npt.assert_array_equal(ta, a[85:, 40:61])
check.equal(tx, 10)
check.equal(ty, 10)
def test_trim_array_even(self):
y, x = np.indices((100, 100))
a = x*y
ta, tx, ty = trim_array(a, 20, (50, 95))
assert_equal(ta, a[85:, 40:61])
assert_equal(tx, 10)
assert_equal(ty, 10)
def test_trim_array_no_indices():
y, x = np.indices((100, 100))
a = x*y
ta, tx, ty = trim_array(a, 21, (50, 95))
npt.assert_array_equal(ta, a[84:, 39:61])
check.equal(tx, 11)
check.equal(ty, 11)
def test_trim_array_no_indices(self):
y, x = np.indices((100, 100))
a = x*y
ta, tx, ty = trim_array(a, 21, (50, 95))
assert_equal(ta, a[84:, 39:61])
assert_equal(tx, 11)
assert_equal(ty, 11)
def gen_stars_gaussian(size, x, y, flux, sigma, theta):
"""Generate stars image to add to background."""
im = np.zeros(size)
grid_y, grid_x = np.indices(size)
try:
sigma_x, sigma_y = sigma
except (TypeError, ValueError):
sigma_x = sigma_y = sigma
if check_number(sigma_x):
sigma_x = [sigma_x] * len(x)
if check_number(sigma_y):
sigma_y = [sigma_y] * len(x)
if check_number(theta):
theta = [theta] * len(x)
for xi, yi, fi, sxi, syi, ti in zip(x, y, flux, sigma_x, sigma_y, theta):
imi, gxi, gyi = trim_array(np.zeros_like(im),
box_size=10 * sxi,
position=(xi, yi),
indices=(grid_y, grid_x))
imi += gaussian_2d(gxi, gyi, xi, yi, sxi, syi, ti, fi, 0)
im[gyi.min():gyi.max() + 1, gxi.min():gxi.max() + 1] += imi
return im
def test_trim_array_left():
y, x = np.indices((100, 100))
a = x*y
ta, tx, ty = trim_array(a, 21, (5, 50), (y, x))
npt.assert_array_equal(ta, a[39:61, :16])
npt.assert_array_equal(tx, x[39:61, :16])
npt.assert_array_equal(ty, y[39:61, :16])
check.equal(np.min(tx), 0)
check.equal(np.max(tx), 15)
check.equal(np.min(ty), 39)
check.equal(np.max(ty), 60)
check.equal(np.min(ta), 0*39)
check.equal(np.max(ta), 15*60)
def test_trim_array_right():
y, x = np.indices((100, 100))
a = x*y
ta, tx, ty = trim_array(a, 21, (95, 50), (y, x))
npt.assert_array_equal(ta, a[39:61, 84:])
npt.assert_array_equal(tx, x[39:61, 84:])
npt.assert_array_equal(ty, y[39:61, 84:])
check.equal(np.min(tx), 84)
check.equal(np.max(tx), 99)
check.equal(np.min(ty), 39)
check.equal(np.max(ty), 60)
check.equal(np.min(ta), 84*39)
check.equal(np.max(ta), 99*60)
def test_trim_array_centered():
y, x = np.indices((100, 100))
a = x*y
ta, tx, ty = trim_array(a, 21, (50, 50), (y, x))
npt.assert_array_equal(ta, a[39:61, 39:61])
npt.assert_array_equal(tx, x[39:61, 39:61])
npt.assert_array_equal(ty, y[39:61, 39:61])
check.equal(np.min(tx), 39)
check.equal(np.max(tx), 60)
check.equal(np.min(ty), 39)
check.equal(np.max(ty), 60)
check.equal(np.min(ta), 39*39)
check.equal(np.max(ta), 60*60)
def test_trim_array_top():
y, x = np.indices((100, 100))
a = x*y
ta, tx, ty = trim_array(a, 21, (50, 95), (y, x))
npt.assert_array_equal(ta, a[84:, 39:61])
npt.assert_array_equal(tx, x[84:, 39:61])
npt.assert_array_equal(ty, y[84:, 39:61])
check.equal(np.min(ty), 84)
check.equal(np.max(ty), 99)
check.equal(np.min(tx), 39)
check.equal(np.max(tx), 60)
check.equal(np.min(ta), 84*39)
check.equal(np.max(ta), 99*60)
def test_trim_array_bottom():
y, x = np.indices((100, 100))
a = x*y
ta, tx, ty = trim_array(a, 21, (50, 5), (y, x))
npt.assert_array_equal(ta, a[:16, 39:61])
npt.assert_array_equal(tx, x[:16, 39:61])
npt.assert_array_equal(ty, y[:16, 39:61])
check.equal(np.min(ty), 0)
check.equal(np.max(ty), 15)
check.equal(np.min(tx), 39)
check.equal(np.max(tx), 60)
check.equal(np.min(ta), 0*39)
check.equal(np.max(ta), 15*60)
def gen_stars_moffat(size, x, y, flux, fwhm):
"""Generate stars image to add to background."""
beta = 1.5
alpha = fwhm / np.sqrt(2**(1 / beta) - 1)
im = np.zeros(size)
grid_y, grid_x = np.indices(size)
for xi, yi, fi in zip(x, y, flux):
imi, gxi, gyi = trim_array(np.zeros_like(im),
box_size=5 * fwhm,
position=(xi, yi),
indices=(grid_y, grid_x))
imi += moffat_2d(gxi, gyi, xi, yi, alpha, beta, fi, 0)
im[gyi.min():gyi.max() + 1, gxi.min():gxi.max() + 1] += imi
return im
