def recipe():
return scopesim_grid(N1d=16,
border=50,
perturbation=2,
magnitude=lambda N: rng.uniform(18, 24, N),
custom_subpixel_psf=anisocado_psf,
seed=seed)
def inner():
# These imports are necessary to be able to execute in a forkserver context; it does not copy the full memory space, so
# we'd have to rely on the target to know the imports
from thesis_lib.testdata.recipes import scopesim_grid
from thesis_lib.testdata.helpers import lowpass
import numpy as np
return scopesim_grid(seed=seed,
N1d=25,
perturbation=2.,
psf_transform=lowpass(),
magnitude=lambda N: np.random.uniform(18, 24, N))
def test_grid_mean_devation(printer, generator_args):
with work_in(Config.instance().scopesim_working_dir):
img, table = scopesim_grid(**generator_args)
xref, yref = table[INPUT_TABLE_NAMES[X]], table[INPUT_TABLE_NAMES[Y]]
x, y = centroid_sources(img, xref, yref, box_size=7, centroid_func=centroid_quadratic)
xdev_mean = np.mean(x-xref)
ydev_mean = np.mean(y-yref)
printer(f'{xdev_mean=} {ydev_mean=}')
assert xdev_mean < 0.002
assert ydev_mean < 0.002
def inner():
# These imports are necessary to be able to execute in a forkserver context; it does not copy the full memory space, so
# we'd have to rely on the target to know the imports
from thesis_lib.testdata.recipes import scopesim_grid
import numpy as np
from thesis_lib.scopesim_helper import make_anisocado_model
psf = make_anisocado_model(lowpass=5)
return scopesim_grid(seed=seed,
N1d=10,
perturbation=2.,
custom_subpixel_psf=psf,
magnitude=lambda N: np.random.uniform(18, 24, N))
def test_grid(printer, generator_args):
with work_in(Config.instance().scopesim_working_dir):
img, table = scopesim_grid(**generator_args)
xref, yref = table[INPUT_TABLE_NAMES[X]], table[INPUT_TABLE_NAMES[Y]]
x, y = centroid_sources(img, xref, yref, box_size=7, centroid_func=centroid_quadratic)
xdev = np.abs(x-xref)
ydev = np.abs(y-yref)
printer(f'{xdev=}\n{ydev=}')
#TODO this is pretty shit accuracy sometimes...
assert np.all(np.abs(xdev) < 0.15)
assert np.all(np.abs(ydev) < 0.15)