def test_flatfield_pipeline():
L = ave_ff.sink_to_list()
mask_setting["setting"] = "none"
ff = np.ones((2048, 2048))
# ff = np.random.random((2048, 2048))
ff *= np.random.normal(1, .01, size=(2048, 2048))
geo = Geometry(
wavelength=.18e-10,
detector="perkin",
dist=.18,
poni1=.1024 * 2,
poni2=.1024 * 2,
rot1=0,
rot2=0,
rot3=0,
)
raw_foreground_dark.emit(0.0)
raw_background_dark.emit(0.0)
raw_background.emit(0.0)
is_calibration_img.emit(False)
geo_input.emit(geo.getPyFAI())
motors.emit((0, 0))
img_counter.emit(1)
q2 = geo.qArray((2048, 2048)) / 10.
img = np.exp(-q2 / 25) * 10000
img2 = img * ff
raw_foreground.emit(img2)
assert len(L) == 1
window_title="predicted flat field",
limit_func=lambda x: (
np.nanpercentile(x, .1),
np.nanpercentile(x, 99.9),
),
).update))
raw_foreground.map(np.nan_to_num).sink(
LiveImage(
"image",
cmap="viridis",
window_title="w/o flat field",
limit_func=lambda x: (
np.nanpercentile(x, .1),
np.nanpercentile(x, 99.9),
),
).update)
is_calibration_img.emit(False)
geo_input.emit(geo.getPyFAI())
for i, p in enumerate(np.linspace(.2, -.2, k)):
for j, pp in enumerate(np.linspace(.2, -.2, k)):
img_counter.emit(i * k + j + 1)
motors.emit((p, pp))
new_geo = move_center((p, pp), geo)
q2 = new_geo.qArray((2048, 2048)) / 10.
img = np.exp(-q2 / 25) * 10000
img2 = img * ff
raw_foreground.emit(img2)
plt.pause(.1)
plt.show()
