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Remove unnecessary variables, clean up warp_image #625

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Remove unnecessary variables, clean up warp_image #625

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29 changes: 11 additions & 18 deletions hexrd/projections/polar.py
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Original file line number Diff line number Diff line change
Expand Up @@ -233,42 +233,35 @@ def warp_image(self, image_dict, pad_with_nans=False,

"""

angpts = self.angular_grid
dummy_ome = np.zeros((self.ntth*self.neta))
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Removing unnecessary variables isn't always helpful, because they can help with readability (in this case, dummy_ome indicates that we are making dummy omega values).

gvec_angs = np.vstack([
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Moving gvec_angs outside the loop is a great idea here.

self.angular_grid[1].flatten(),
self.angular_grid[0].flatten(),
Comment on lines +236 to +238
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Suggested change
gvec_angs = np.vstack([
self.angular_grid[1].flatten(),
self.angular_grid[0].flatten(),
angular_grid = self.angular_grid
gvec_angs = np.vstack([
angular_grid[1].flatten(),
angular_grid[0].flatten(),

Since self.angular_grid is a computed property, you will probably get better performance by adding back the variable so it is only computed once.

np.zeros((self.ntth*self.neta))]).T
xypts = np.empty((len(gvec_angs), 2))

# lcount = 0
img_dict = dict.fromkeys(self.detectors)
for detector_id, panel in self.detectors.items():
_project_on_detector = self._func_project_on_detector(panel)
img = image_dict[detector_id]

gvec_angs = np.vstack([
angpts[1].flatten(),
angpts[0].flatten(),
dummy_ome]).T
args, kwargs = self._args_project_on_detector(gvec_angs, panel)

args, kwargs = self._args_project_on_detector(gvec_angs,
panel)

xypts = np.nan*np.ones((len(gvec_angs), 2))
valid_xys, rmats_s, on_plane = _project_on_detector(*args,
**kwargs)
valid_xys, _, on_plane = _project_on_detector(*args, **kwargs)
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@psavery psavery Apr 4, 2024 •
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Could we just pass valid_xys into the interpolation function directly, rather than setting xypts[on_plane, :] = valid_xys below and passing xypts? It appears that the interpolation functions ignore invalid xys, and only operate on a subset of the valid xys (in their functions, they check which xys are on the panel, and ignore xys not on the panel, and they also apply the panel buffer mask to remove even more xys).

Can you verify that you get the same result that way? It might be faster also...

More thoughts: the interpolation functions call clip_to_panel(xy, buffer_edges=True), which consists of two parts:
~~ 1. Removing any xys that are not on the panel ~~
~~ 2. Applying the panel buffer mask to remove any masked xys

Since we already know that valid_xys are on the panel, it would be nice if we could just skip the check for whether the xys are on the panel. But we still need to apply the panel buffer mask (it hasn't been applied yet). Maybe we should make a separate way to apply the panel buffer mask so we can skip the extra check that clip_to_panel() performs... but only if we verify that the result stays the same.

Correction: it looks like the original valid_xys are on the detector plane, but not necessarily on the detector itself. So we do actually need to keep the clip_to_panel() call.

However, I think it still might be better to just pass the valid_xys into the interpolation function directly, since we don't need to pass in any xys that are not valid (i. e., nan).

xypts[on_plane, :] = valid_xys

if do_interpolation:
this_img = panel.interpolate_bilinear(
xypts, img,
xypts, image_dict[detector_id],
pad_with_nans=pad_with_nans).reshape(self.shape)
else:
this_img = panel.interpolate_nearest(
xypts, img,
xypts, image_dict[detector_id],
pad_with_nans=pad_with_nans).reshape(self.shape)
nan_mask = np.isnan(this_img)
img_dict[detector_id] = np.ma.masked_array(
data=this_img, mask=nan_mask, fill_value=0.
)
maimg = np.ma.sum(np.ma.stack(img_dict.values()), axis=0)
return maimg

return np.ma.sum(np.ma.stack(img_dict.values()), axis=0)

def tth_to_pixel(self, tth):
"""
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