From bc2cd1660d9cfc1993b9eb1d2be7d1c5a2c0f8e7 Mon Sep 17 00:00:00 2001 From: Patrick Avery Date: Thu, 26 Sep 2024 17:24:41 -0500 Subject: [PATCH 1/3] Add several speedups for polar view generation Perhaps the most important speedup added here is the ability to generate and cache a mapping of the xy coordinates on each detector to the angular pixels in the polar view. This is extremely valuable when you must process many images that have identical instrument configurations, since you can generate this mapping and re-use it on all subsequent polar view generations. This also implements several other speedups to polar view generation as well. Signed-off-by: Patrick Avery --- hexrd/instrument/detector.py | 14 +++++-- hexrd/projections/polar.py | 72 ++++++++++++++++++++++++++++++------ 2 files changed, 72 insertions(+), 14 deletions(-) diff --git a/hexrd/instrument/detector.py b/hexrd/instrument/detector.py index eb534c5b2..085790b43 100644 --- a/hexrd/instrument/detector.py +++ b/hexrd/instrument/detector.py @@ -1025,7 +1025,9 @@ def interpolate_nearest(self, xy, img, pad_with_nans=True): int_xy[on_panel] = int_vals return int_xy - def interpolate_bilinear(self, xy, img, pad_with_nans=True): + def interpolate_bilinear(self, xy, img, pad_with_nans=True, + clip_to_panel=True, + on_panel: Optional[np.ndarray] = None): """ Interpolate an image array at the specified cartesian points. @@ -1039,6 +1041,9 @@ def interpolate_bilinear(self, xy, img, pad_with_nans=True): pad_with_nans : bool, optional Toggle for assigning NaN to points that fall off the detector. The default is True. + on_panel : np.ndarray, optional + If you want to skip clip_to_panel() for performance reasons, + just provide an array of which pixels are on the panel. Returns ------- @@ -1066,8 +1071,11 @@ def interpolate_bilinear(self, xy, img, pad_with_nans=True): else: int_xy = np.zeros(len(xy)) - # clip away points too close to or off the edges of the detector - xy_clip, on_panel = self.clip_to_panel(xy, buffer_edges=True) + if on_panel is None: + # clip away points too close to or off the edges of the detector + xy_clip, on_panel = self.clip_to_panel(xy, buffer_edges=True) + else: + xy_clip = xy[on_panel] # grab fractional pixel indices of clipped points ij_frac = self.cartToPixel(xy_clip) diff --git a/hexrd/projections/polar.py b/hexrd/projections/polar.py index f2b87c762..007564808 100644 --- a/hexrd/projections/polar.py +++ b/hexrd/projections/polar.py @@ -232,16 +232,35 @@ def warp_image(self, image_dict, pad_with_nans=False, Tested ouput using Maud. """ + mapping = self.generate_warp_mapping() + return self.warp_image_from_mapping(image_dict, mapping, + pad_with_nans=pad_with_nans, do_interpolation=do_interpolation) + def generate_warp_mapping(self) -> dict[str, dict[str, np.ndarray]]: + """Generate mapping of detector coordinates to generate polar view + + If you plan to generate the polar view many times in a row using the + same instrument configuration, but different data files, this + function can be called once at the beginning to generate a mapping + of the detectors to the cartesian coordinates for each angular pixel, + followed by warp_image_from_mapping() to create the polar view. + + This can be significantly faster than calling `warp_image()` every + time + + The dictionary that returns has detector IDs as the first key, and + another dict as the second key. + + The nested dict has "xypts" and "on_panel" as keys, and the + respective arrays as the values. + """ angpts = self.angular_grid dummy_ome = np.zeros((self.ntth*self.neta)) - # lcount = 0 - img_dict = dict.fromkeys(self.detectors) + mapping = {} + on_panel_mapping = {} 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(), @@ -255,20 +274,51 @@ def warp_image(self, image_dict, pad_with_nans=False, **kwargs) xypts[on_plane, :] = valid_xys + _, on_panel = panel.clip_to_panel(xypts, buffer_edges=True) + + mapping[detector_id] = { + 'xypts': xypts, + 'on_panel': on_panel, + } + + return mapping + + def warp_image_from_mapping(self, image_dict: dict[str, np.ndarray], + mapping: dict[str, dict[str, np.ndarray]], + pad_with_nans: bool = False, + do_interpolation=True): + img_dict = dict.fromkeys(self.detectors) + nan_mask = None + for detector_id, panel in self.detectors.items(): + img = image_dict[detector_id] + xypts = mapping[detector_id]['xypts'] + on_panel = mapping[detector_id]['on_panel'] + if do_interpolation: this_img = panel.interpolate_bilinear( xypts, img, - pad_with_nans=pad_with_nans).reshape(self.shape) + pad_with_nans=pad_with_nans, + on_panel=on_panel).reshape(self.shape) else: this_img = panel.interpolate_nearest( xypts, img, 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 + + # It is faster to keep track of the global nans like this + # rather than the previous way we were doing it... + img_nans = np.isnan(this_img) + if nan_mask is None: + nan_mask = img_nans + else: + nan_mask = np.logical_and(img_nans, nan_mask) + + this_img[img_nans] = 0 + img_dict[detector_id] = this_img + + summed_img = np.sum(list(img_dict.values()), axis=0) + return np.ma.masked_array( + data=summed_img, mask=nan_mask, fill_value=0. + ) def tth_to_pixel(self, tth): """ From 7166559a96e887866ed320b13c1b5099b21111ee Mon Sep 17 00:00:00 2001 From: Patrick Avery Date: Sat, 28 Sep 2024 12:38:16 -0500 Subject: [PATCH 2/3] Add a polar projection test This will help us ensure that the values do not change as we make modifications to the code. 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z?PD~^(Z}UM`GDPuYc@34tW9}+66aN@Y`EQ`g7=p+z3U=e7cq{)$5=VTiXciWCygl~ zuls}R8fOCyPWy&R7h6-oW~yp*-#%?{ju!lx8@e0MQ}Y$#jV{2MuDl{U>;W!E3!Hw} zd4lEYSYiH5XXq{SKS;V_1=EAmMJ)o@kKae0eHX$3+o}oXM)Z;&6H1*^7^i}_Ml?tx z=dRPJ`*vV83wmX0#Oyr;(t`z(Z<>#o}};p#Vmsbu`#O4zOW@eJNi+Kr3eG}LPX$0~ul z_Lea$wI1bZ;k5%R>l37UKYLJ}72hdo=>$j0bn6m!?gqv|1-*isrod&tX;o_+K$@=p zFK<=+F4oSP)-;tIpyOk)AIN&EP^@0KrTx?;UBnvl2_?#M?%d|iSgZq!bzrd$EY^X=I`F?&2mTL;_d5{) literal 0 HcmV?d00001 diff --git a/tests/test_polar_view.py b/tests/test_polar_view.py new file mode 100644 index 000000000..e25b6152a --- /dev/null +++ b/tests/test_polar_view.py @@ -0,0 +1,90 @@ +from pathlib import Path + +import h5py +import numpy as np +import pytest + +from hexrd import imageseries +from hexrd.imageseries.process import ProcessedImageSeries +from hexrd.instrument import HEDMInstrument +from hexrd.projections.polar import PolarView + + +@pytest.fixture +def eiger_examples_path(example_repo_path: Path) -> Path: + return Path(example_repo_path) / 'eiger' + + +@pytest.fixture +def ceria_examples_path(eiger_examples_path: Path) -> Path: + return eiger_examples_path / 'first_ceria' + + +@pytest.fixture +def ceria_example_data(ceria_examples_path: Path) -> np.ndarray: + data_path = ceria_examples_path / 'ff_000_data_000001.h5' + with h5py.File(data_path, 'r') as rf: + # Just return the first frame + return rf['/entry/data/data'][0] + + +@pytest.fixture +def ceria_composite_instrument(ceria_examples_path: Path) -> HEDMInstrument: + instr_path = ( + ceria_examples_path / 'eiger_ceria_uncalibrated_composite.hexrd' + ) + with h5py.File(instr_path, 'r') as rf: + return HEDMInstrument(rf) + + +def test_caking( + ceria_composite_instrument: HEDMInstrument, + ceria_example_data: np.ndarray, + test_data_dir: Path, +): + instr = ceria_composite_instrument + image_data = ceria_example_data + + # Break up the image data into separate images for each detector + # It's easiest to do this using hexrd's imageseries and + # ProcessedImageSeries + ims_dict = {} + ims = imageseries.open(None, format='array', data=image_data) + for det_key, panel in instr.detectors.items(): + ims_dict[det_key] = ProcessedImageSeries( + ims, oplist=[('rectangle', panel.roi)] + ) + + # Create the img_dict + img_dict = {k: v[0] for k, v in ims_dict.items()} + + # Create the PolarView + tth_range = [0, 14.0] + eta_min = -180.0 + eta_max = 180.0 + pixel_size = (0.01, 5.0) + + pv = PolarView(tth_range, instr, eta_min, eta_max, pixel_size) + img = pv.warp_image(img_dict, pad_with_nans=True, + do_interpolation=True) + + # This is a masked array. Just fill it with nans. + img = img.filled(np.nan) + + # Verify that the image is identical to a reference image + ref = np.load( + test_data_dir / 'cake_test_polar_view_expected.npy', allow_pickle=True + ) + assert np.allclose(img, ref, equal_nan=True) + + # The actual caking next consists of performing a mean. + # If the images were already identical, then the mean will + # definitely be identical, but we can just add this to the + # test anyways just to make it clear... + # Set nans to zero. + img[np.isnan(img)] = 0 + ref[np.isnan(ref)] = 0 + + cake = np.mean(img, axis=0) + cake_ref = np.mean(ref, axis=0) + assert np.allclose(cake, cake_ref) From 3be4204c8a2be343c42c202ab32240e1f01cdcb1 Mon Sep 17 00:00:00 2001 From: Patrick Avery Date: Sat, 28 Sep 2024 12:57:21 -0500 Subject: [PATCH 3/3] Refactor polar speedup to cache internally We were caching externally before, but caching internally is easier for users to work with. To make sure we don't break any old scripts, and to also force users to acknowledge that they will not change any instrument settings, we default to this caching being turned off. Signed-off-by: Patrick Avery --- hexrd/projections/polar.py | 62 ++++++++++++++++++++++++++++++-------- tests/test_polar_view.py | 24 +++++++-------- 2 files changed, 61 insertions(+), 25 deletions(-) diff --git a/hexrd/projections/polar.py b/hexrd/projections/polar.py index 007564808..30cd8ba4c 100644 --- a/hexrd/projections/polar.py +++ b/hexrd/projections/polar.py @@ -15,7 +15,8 @@ class PolarView: def __init__(self, plane_data, instrument, eta_min=0., eta_max=360., - pixel_size=(0.1, 0.25)): + pixel_size=(0.1, 0.25), + cache_coordinate_map=False): """ Instantiates a PolarView class. @@ -36,6 +37,13 @@ def __init__(self, plane_data, instrument, pixel_size : array_like, optional The angular pixels sizes (2theta, eta) in degrees. The default is (0.1, 0.25). + cache_coordinate_map : bool, optional + If True, the coordinate map will be cached so that calls to + `warp_image()` will be *significantly* faster. + If set to True, the caller *must* ensure that no parameters + on the instrument that would affect polar view generation, + and no parameters set on this class, will be modified, + because doing so would result in an incorrect `warp_image()`. Returns ------- @@ -67,6 +75,15 @@ def __init__(self, plane_data, instrument, self._instrument = instrument + self._coordinate_mapping = None + self._cache_coordinate_map = cache_coordinate_map + if cache_coordinate_map: + # It is important to generate the cached map now, rather than + # later, because this object might be sent to other processes + # for parallelization, and it will be faster if the mapping + # is already generated. + self._coordinate_mapping = self._generate_coordinate_mapping() + @property def instrument(self): return self._instrument @@ -75,6 +92,10 @@ def instrument(self): def detectors(self): return self._instrument.detectors + @property + def cache_coordinate_map(self): + return self._cache_coordinate_map + @property def tvec(self): return self._instrument.tvec @@ -216,6 +237,10 @@ def warp_image(self, image_dict, pad_with_nans=False, """ Performs the polar mapping of the input images. + Note: this function has the potential to run much faster if + `cache_coordinate_map` is set to `True` on the `PolarView` + initialization. + Parameters ---------- image_dict : dict @@ -232,13 +257,25 @@ def warp_image(self, image_dict, pad_with_nans=False, Tested ouput using Maud. """ - mapping = self.generate_warp_mapping() - return self.warp_image_from_mapping(image_dict, mapping, - pad_with_nans=pad_with_nans, do_interpolation=do_interpolation) + if self.cache_coordinate_map: + # The mapping should have already been generated. + mapping = self._coordinate_mapping + else: + # Otherwise, we must generate it every time + mapping = self._generate_coordinate_mapping() + + return self._warp_image_from_coordinate_map( + image_dict, + mapping, + pad_with_nans=pad_with_nans, + do_interpolation=do_interpolation, + ) - def generate_warp_mapping(self) -> dict[str, dict[str, np.ndarray]]: + def _generate_coordinate_mapping(self) -> dict[str, dict[str, np.ndarray]]: """Generate mapping of detector coordinates to generate polar view + This function is, in general, the most time consuming part of creating + the polar view. Its results can be cached If you plan to generate the polar view many times in a row using the same instrument configuration, but different data files, this function can be called once at the beginning to generate a mapping @@ -258,7 +295,6 @@ def generate_warp_mapping(self) -> dict[str, dict[str, np.ndarray]]: dummy_ome = np.zeros((self.ntth*self.neta)) mapping = {} - on_panel_mapping = {} for detector_id, panel in self.detectors.items(): _project_on_detector = self._func_project_on_detector(panel) gvec_angs = np.vstack([ @@ -283,16 +319,18 @@ def generate_warp_mapping(self) -> dict[str, dict[str, np.ndarray]]: return mapping - def warp_image_from_mapping(self, image_dict: dict[str, np.ndarray], - mapping: dict[str, dict[str, np.ndarray]], - pad_with_nans: bool = False, - do_interpolation=True): + def _warp_image_from_coordinate_map( + self, + image_dict: dict[str, np.ndarray], + coordinate_map: dict[str, dict[str, np.ndarray]], + pad_with_nans: bool = False, + do_interpolation=True) -> np.ma.MaskedArray: img_dict = dict.fromkeys(self.detectors) nan_mask = None for detector_id, panel in self.detectors.items(): img = image_dict[detector_id] - xypts = mapping[detector_id]['xypts'] - on_panel = mapping[detector_id]['on_panel'] + xypts = coordinate_map[detector_id]['xypts'] + on_panel = coordinate_map[detector_id]['on_panel'] if do_interpolation: this_img = panel.interpolate_bilinear( diff --git a/tests/test_polar_view.py b/tests/test_polar_view.py index e25b6152a..1b52edbcb 100644 --- a/tests/test_polar_view.py +++ b/tests/test_polar_view.py @@ -37,7 +37,7 @@ def ceria_composite_instrument(ceria_examples_path: Path) -> HEDMInstrument: return HEDMInstrument(rf) -def test_caking( +def test_polar_view( ceria_composite_instrument: HEDMInstrument, ceria_example_data: np.ndarray, test_data_dir: Path, @@ -73,18 +73,16 @@ def test_caking( # Verify that the image is identical to a reference image ref = np.load( - test_data_dir / 'cake_test_polar_view_expected.npy', allow_pickle=True + test_data_dir / 'test_polar_view_expected.npy', allow_pickle=True ) assert np.allclose(img, ref, equal_nan=True) - # The actual caking next consists of performing a mean. - # If the images were already identical, then the mean will - # definitely be identical, but we can just add this to the - # test anyways just to make it clear... - # Set nans to zero. - img[np.isnan(img)] = 0 - ref[np.isnan(ref)] = 0 - - cake = np.mean(img, axis=0) - cake_ref = np.mean(ref, axis=0) - assert np.allclose(cake, cake_ref) + # Also generate it using the cache + pv = PolarView(tth_range, instr, eta_min, eta_max, pixel_size, + cache_coordinate_map=True) + fast_img = pv.warp_image(img_dict, pad_with_nans=True, + do_interpolation=True) + + # This should also be identical + fast_img = fast_img.filled(np.nan) + assert np.allclose(fast_img, ref, equal_nan=True)