def test_observe_works_for_source_distributed_over_several_fovs(
self, cmds, im_src):
orig_sum = np.sum(im_src.fields[0].data)
cmds["SIM_PIXEL_SCALE"] = 0.02
opt = OpticalTrain(cmds)
opt.observe(im_src)
wave = np.arange(0.5, 2.51, 0.1) * u.um
unit = u.Unit("ph s-1 m-2 um-1")
print(opt.optics_manager.surfaces_table.emission(wave).to(unit))
print(opt.optics_manager.surfaces_table.table)
implane = opt.image_planes[0]
final_sum = np.sum(implane.image)
print(orig_sum, final_sum)
if PLOTS:
for fov in opt.fov_manager.fovs:
cnrs = fov.corners[1]
plt.plot(cnrs[0], cnrs[1])
plt.imshow(implane.image.T,
origin="lower",
norm=LogNorm(),
extent=(
-implane.hdu.header["NAXIS1"] / 2,
implane.hdu.header["NAXIS1"] / 2,
-implane.hdu.header["NAXIS2"] / 2,
implane.hdu.header["NAXIS2"] / 2,
))
plt.colorbar()
plt.show()
def test_observe_works_for_image(self, cmds, im_src):
opt = OpticalTrain(cmds)
opt.observe(im_src)
if PLOTS:
plt.imshow(opt.image_plane.image.T, origin="lower", norm=LogNorm())
plt.show()
def test_works_with_a_pointer_to_fits_imagehdu(self, cmds):
# Basically just checking to make sure observe doesn't throw an error
# when passed a Source object with a file pointer ImageHDU
fits_src = src_objs._fits_image_source()
array_src = src_objs._image_source()
src = fits_src + array_src
opt = OpticalTrain(cmds)
opt.observe(src)
assert np.sum(opt.image_planes[0].data) > 0
def yaml_file_can_be_loaded_into_optical_train(self):
# .. todo: get this working on Travis
filename = os.path.join(TEST_PATH, "MICADO_SCAO_WIDE_2.yaml")
cmds = UserCommands(yamls=[filename])
assert isinstance(cmds, UserCommands)
assert isinstance(cmds.yaml_dicts, list)
psf_file = cmds.yaml_dicts[1]["effects"][0]["kwargs"]["filename"]
if find_file(psf_file) is None:
new_file = "test_FVPSF.fits"
cmds.yaml_dicts[1]["effects"][0]["kwargs"]["filename"] = new_file
opt = OpticalTrain(cmds=cmds)
assert isinstance(opt, OpticalTrain)
assert isinstance(opt.optics_manager, OpticsManager)
assert isinstance(
opt.optics_manager.get_all(efs.FieldVaryingPSF)[0],
efs.FieldVaryingPSF)
assert isinstance(opt.image_plane, ImagePlane)
assert opt.image_plane.hdu.header["NAXIS1"] >= 4096
assert isinstance(opt.fov_manager, FOVManager)
assert len(opt.fov_manager.fovs) == 64
if PLOTS:
for fov in opt.fov_manager.fovs:
sky_cnrs, det_cnrs = fov.corners
plt.plot(sky_cnrs[0], sky_cnrs[1])
plt.show()
r = np.arange(-25, 25)
x, y = np.meshgrid(r, r)
x = x.flatten() * u.arcsec
y = y.flatten() * u.arcsec
ref = [0] * len(x)
weight = [1] * len(x)
spec = sp.SourceSpectrum(sp.Empirical1D,
points=[0.5, 3.0] * u.um,
lookup_table=[1e3, 1e3] *
u.Unit("ph s-1 m-2 um-1"))
src = Source(x=x, y=y, ref=ref, weight=weight, spectra=[spec])
opt.observe(src)
if PLOTS:
plt.imshow(opt.image_plane.image.T, origin="lower", norm=LogNorm())
plt.colorbar()
plt.show()
def test_flux_is_conserved_for_no_bg_emission(self, cmds, tbl_src):
opt = OpticalTrain(cmds)
opt.observe(tbl_src)
im = opt.image_planes[0].image
bg_flux = np.pi / 4 * np.prod(im.shape)
src_flux = tbl_src.photons_in_range(1, 2, 1)[0].value
if PLOTS:
implane = opt.image_planes[0]
plt.imshow(implane.image.T, origin="lower", norm=LogNorm())
plt.colorbar()
plt.show()
# given a 1 um bandpass
print(src_flux, bg_flux)
area = opt.optics_manager.surfaces_table.area.value
assert src_flux == approx(1) # u.Unit("ph s-1")
assert np.sum(im) == approx(src_flux * area, rel=2e-3)
def test_flux_is_conserved_for_yes_bg_emission(self, non_unity_cmds,
tbl_src):
"""
# originally all has 1 count. atmo TC=0.9, mirror TC=0.5. Hence:
# 0.5 counts from atmo, 1 count from mirror, 0.45 count from source
"""
opt = OpticalTrain(non_unity_cmds)
opt.observe(tbl_src)
im = opt.image_planes[0].image
src_flux = tbl_src.photons_in_range(1, 2, 1)[0].value
if PLOTS:
implane = opt.image_planes[0]
plt.imshow(implane.image.T, origin="lower", norm=LogNorm())
plt.colorbar()
plt.show()
# given a 1 um bandpass
assert src_flux == approx(1) # u.Unit("ph s-1")
assert np.max(im) - np.median(im) == approx(0.45, rel=1e-2)
assert np.median(im) == approx(1.5, abs=1e-2)
def test_observe_works_for_many_sources_distributed(self, cmds, im_src):
orig_sum = np.sum(im_src.fields[0].data)
im_src.fields[0].data += 1
im_src1 = deepcopy(im_src)
im_src2 = deepcopy(im_src)
im_src2.shift(7, 7)
im_src3 = deepcopy(im_src)
im_src3.shift(-10, 14)
im_src4 = deepcopy(im_src)
im_src4.shift(-4, -6)
im_src5 = deepcopy(im_src)
im_src5.shift(15, -15)
multi_img = im_src1 + im_src2 + im_src3 + im_src4 + im_src5
cmds["SIM_PIXEL_SCALE"] = 0.02
opt = OpticalTrain(cmds)
opt.observe(multi_img)
implane = opt.image_planes[0]
final_sum = np.sum(implane.image)
print(orig_sum, final_sum)
if PLOTS:
for fov in opt.fov_manager.fovs:
cnrs = fov.corners[1]
plt.plot(cnrs[0], cnrs[1])
plt.imshow(implane.image.T,
origin="lower",
norm=LogNorm(),
extent=(
-implane.hdu.header["NAXIS1"] / 2,
implane.hdu.header["NAXIS1"] / 2,
-implane.hdu.header["NAXIS2"] / 2,
implane.hdu.header["NAXIS2"] / 2,
))
plt.colorbar()
plt.show()
def test_readout_works_when_source_observed(self, unity_cmds, unity_src):
opt = OpticalTrain(unity_cmds)
opt.observe(unity_src)
hdus = opt.readout()
hdu = hdus[0]
if PLOTS:
plt.subplot(221)
plt.imshow(unity_src.fields[0].data)
plt.colorbar()
plt.subplot(222)
plt.imshow(opt.image_planes[0].image)
plt.colorbar()
plt.subplot(223)
plt.imshow(hdu[1].data)
plt.colorbar()
plt.show()
src_average = np.average(unity_src.fields[0].data)
assert np.average(hdu[1].data) == approx(np.pi / 4., rel=1e-3)