#for c in range(nsb):
# if (sb.mask[c]&MaskCode.Valid == MaskCode.Valid and sb.mask[c]&MaskCode.Foreground == MaskCode.Foreground):
# values[c]=0.
v0 = values.set_selected(values <= 0, 0.)
v1 = v0.set_selected(v0 > 255, 255)
v2 = (256. - v1) / 256.
np_v2 = np.ndarray(shape=(
B[3] - B[2],
B[1] - B[0],
),
dtype=np.float32,
buffer=v2.as_numpy_array())
# insert code here to estimate the partiality response
pr_value = get_partiality_response(key,
one_index,
spectra_simulation=SS,
ROI=ROI)
print("NSB2", nsb)
for c in range(nsb):
intensity_lookup_1[(int(sb.coords()[c][1]),
int(sb.coords()[c][0]))] = pr_value[c]
assert len(intensity_lookup_1) == len(intensity_lookup)
assert len(pr_value) == len(sb.data)
values_1 = pr_value # sb.data # ADU
v0_1 = values_1.set_selected(values_1 <= 0, 0.)
v1_1 = v0_1.set_selected(v0_1 > 255, 255)
v2_1 = (256. - v1_1) / 256.
np_v2_1 = np.ndarray(shape=(
B[3] - B[2],
B[1] - B[0],
# intensity_lookup consists of the "observed" data from shoeboxes
v0 = values.set_selected(values <= 0, 0.)
v1 = v0.set_selected(v0 > 255, 255)
v2 = (256. - v1) / 256.
np_v2 = np.ndarray(shape=(
B[3] - B[2],
B[1] - B[0],
),
dtype=np.float32,
buffer=v2.as_numpy_array())
# insert code here to estimate the partiality response
PRD = get_partiality_response(
key,
hkl[x],
spectra_simulation=transmitted_info["spectra"],
ROI=ROI)
pr_value = PRD["roi_pixels"]
miller = PRD["miller"]
intensity = PRD["intensity"]
for c in range(nsb):
intensity_lookup_1[(int(sb.coords()[c][1]),
int(sb.coords()[c][0]))] = pr_value[c]
assert len(intensity_lookup_1) == len(intensity_lookup)
assert len(pr_value) == len(sb.data)
# intensity_lookup_1 consists of partiality model data from posthoc simulator (partiality x Icalc)
values_1 = pr_value # sb.data # ADU
v0_1 = values_1.set_selected(values_1 <= 0, 0.)