os.path.basename(fpath).replace(".h5.npz", ".h5_integrated.refl"))
# integrated reflections on this shot (by DIALS)
_R_shot_int = flex.reflection_table.from_file(int_refl_path)
# strong reflections on this shot
_R_shot_strong = Rmaster.select(Rmaster['id'] == i_shot)
for _pid in range(len(DET)):
xstrong, ystrong = [], []
x0, y0 = [], []
x, y = [], []
# get nanoBragg spot prediction positions
rpp = prediction_utils.refls_by_panelname(refls_predict)
if _pid in rpp:
x, y, _ = map(lambda x: np.array(x) - 0.5,
prediction_utils.xyz_from_refl(rpp[_pid]))
# get strong spot pos
_R_shot_strong_panel = _R_shot_strong.select(
_R_shot_strong["panel"] == _pid)
if len(_R_shot_strong_panel) > 0:
xstrong, ystrong, _ = map(
lambda x: np.array(x) - 0.5,
prediction_utils.xyz_from_refl(_R_shot_strong_panel))
# get stills process prediction positions
_R_panel = _R_shot_int.select(_R_shot_int["panel"] == _pid)
if len(_R_panel) > 0:
x0, y0, _ = map(lambda x: np.array(x) - 0.5,
prediction_utils.xyz_from_refl(_R_panel))
alist_panels = list(panels_with_spots)
#alist_panels = list(range(64,72))
panels_with_spots = [i for i in panels_with_spots if i in alist_panels]
rois_perpanel = {}
panel_keys = {}
counts_perpanel = {}
refl_ids = {}
for ii, pid in enumerate(panels_with_spots):
rois_perpanel[ii] = []
refl_ids[ii] = []
counts_perpanel[ii] = np.zeros((sdim, fdim))
panel_keys[pid] = ii
centroid_x, centroid_y, _ = map(
lambda x: np.array(x) - 0.5,
prediction_utils.xyz_from_refl(exper_refls_strong))
for i_ref, (i, j, pid) in enumerate(zip(centroid_x, centroid_y,
panel_ids)):
if pid not in alist_panels:
continue
i1 = int(max(0, i - bb))
i2 = int(min(fdim, i + bb))
j1 = int(max(0, j - bb))
j2 = int(min(sdim, j + bb))
roi = (i1, i2), (j1, j2)
ii = panel_keys[pid]
rois_perpanel[ii].append(roi)
counts_perpanel[ii][j1:j2, i1:i2] += 1
refl_ids[ii].append(i_ref)
# <><><><><><><><><>
height=y2 - y1,
fc='none',
ec='r')
plt.gca().add_patch(patch)
plt.title("Panel=%d" % pid)
# get the ground truth background plane and plot
if args.savefigdir is not None:
plt.savefig(
os.path.join(args.savefigdir,
"_figure%d.png" % pid))
plt.draw()
plt.pause(args.plot)
r_on_panel = Rpp[pid]
x, y, _ = prediction_utils.xyz_from_refl(r_on_panel)
x = np.array(x) - 0.5
y = np.array(y) - 0.5
Hi_on_panel = np.array(Hi)[np.array(bbox_panel_ids) == pid]
for i_spot, (x1, x2, y1, y2) in enumerate(bb_on_panel):
inX = np.logical_and(x1 < x, x < x2)
inY = np.logical_and(y1 < y, y < y2)
in_bb = inX & inY
if not any(in_bb):
continue
pos = np.where(in_bb)[0]
for p in pos:
h_pred_nb = Hi_on_panel[i_spot]
h_pred_stills = r_on_panel[p]["miller_index"]
panels_with_spots = set(panel_ids)
alist_panels = list(panels_with_spots)
# alist_panels = list(range(64,72))
panels_with_spots = [i for i in panels_with_spots if i in alist_panels]
rois_perpanel = {}
panel_keys = {}
counts_perpanel = {}
refl_ids = {}
for ii, pid in enumerate(panels_with_spots):
rois_perpanel[ii] = []
refl_ids[ii] = []
counts_perpanel[ii] = np.zeros((sdim, fdim))
panel_keys[pid] = ii
centroid_x, centroid_y, _ = map(lambda x: np.array(x)-0.5, prediction_utils.xyz_from_refl(exper_refls_strong))
for i_ref, (i,j, pid) in enumerate(zip(centroid_x, centroid_y, panel_ids)):
if pid not in alist_panels:
continue
i1 = int(max(0, i-bb))
i2 = int(min(fdim, i+bb))
j1 = int(max(0, j-bb))
j2 = int(min(sdim, j+bb))
roi = (i1, i2), (j1, j2)
ii = panel_keys[pid]
rois_perpanel[ii].append(roi)
counts_perpanel[ii][j1:j2, i1:i2] += 1
refl_ids[ii].append(i_ref)
# <><><><><><><><><>
# DO THE SIMULATION