Pixel Size/Scaling Cleanup

src/aspire/image/xform.py

@@ -217,21 +217,12 @@ def __init__(self, resolution, zero_nyquist=True, centered_fft=True):
         super().__init__()
 
     def _forward(self, im, indices):
-        original_stack_shape = im.stack_shape
-        data = im.stack_reshape(-1)._data
-        im_ds = Image._downsample(
-            data,
+        return im.downsample(
             self.resolution,
             zero_nyquist=self.zero_nyquist,
             centered_fft=self.centered_fft,
         )
 
-        # pixel_size has already been adjusted in the ImageSource and passed
-        # to `im`, so we instantiate the new Image with im.pixel_size.
-        return Image(im_ds, pixel_size=im.pixel_size).stack_reshape(
-            original_stack_shape
-        )
-
     def _adjoint(self, im, indices):
         # TODO: Implement up-sampling with zero-padding
         raise NotImplementedError("Adjoint of downsampling not implemented yet.")

src/aspire/operators/filters.py

@@ -505,6 +505,12 @@ def _evaluate(self, omega, **kwargs):
 
         return h
 
+    def scale(self, c=1):
+        """
+        Override internal scaling for CTFFilter because they are passed pixel size explicitly.
+        """
+        return self
+
 
 class RadialCTFFilter(CTFFilter):
     def __init__(self, voltage=200, defocus=15000, Cs=2.26, alpha=0.07, B=0):

src/aspire/source/image.py

@@ -776,12 +776,6 @@ def _apply_filters(
         :param filters: A list of `Filter` objects
         :param indices: A list of indices indicating the corresponding filter in `filters`
         """
-        if not isinstance(im_orig, Image):
-            logger.warning(
-                f"_apply_filters() passed {type(im_orig)} instead of Image instance"
-            )
-            # for now just convert it
-            im_orig = Image(im_orig, pixel_size=self.pixel_size)
 
         im = im_orig.copy()
 

src/aspire/source/simulation.py

@@ -320,7 +320,6 @@ def _images(self, indices, clean_images=False):
         if not clean_images and self.noise_adder is not None:
             im = self.noise_adder.forward(im, indices=indices)
 
-        # scaling pixel_size in source, scaling filter, and scaling in IMage.downsample in conflict...
         # Finally, apply transforms to resulting Image
         return self.generation_pipeline.forward(im, indices)
 
@@ -331,9 +330,6 @@ def _apply_sim_filters(self, im, indices):
             self.filter_indices[indices],
         )
 
-        # Assign correct pixel_size
-        im.pixel_size = self.pixel_size
-
         return im
 
     def vol_coords(self, mean_vol=None, eig_vols=None):

tests/test_downsample.py

@@ -98,7 +98,9 @@ def test_downsample_2d_case(L, L_ds):
     assert checkCenterPoint(imgs_org, imgs_ds)
     # Confirm default `pixel_size`
     assert np.allclose(imgs_org.pixel_size, 1.0)
-    assert np.allclose(imgs_ds.pixel_size, imgs_org.pixel_size * (L / L_ds))
+    assert np.allclose(
+        imgs_ds.pixel_size, imgs_org.pixel_size * (L / L_ds)
+    ), f"{imgs_ds.pixel_size}, {imgs_org.pixel_size*(L / L_ds)}"
 
 
 @pytest.mark.parametrize("L", [65, 66])

tests/test_filters.py

@@ -5,6 +5,7 @@
 import numpy as np
 import pytest
 
+from aspire.downloader import emdb_2660
 from aspire.operators import (
     ArrayFilter,
     CTFFilter,
@@ -16,9 +17,11 @@
     ScaledFilter,
     ZeroFilter,
 )
+from aspire.source import ArrayImageSource, Simulation
 from aspire.utils import utest_tolerance
 
 DATA_DIR = os.path.join(os.path.dirname(__file__), "saved_test_data")
+SEED = 707
 
 
 class SimTestCase(TestCase):
@@ -139,11 +142,21 @@ def testCTFScale(self):
         result1 = filt.evaluate(self.omega, **self.filter_eval_kwargs)
         scale_value = 2.5
         filt = filt.scale(scale_value)
-        # scaling a CTFFilter scales the pixel size which cancels out
-        # a corresponding scaling in omega
-        result2 = filt.evaluate(self.omega * scale_value, **self.filter_eval_kwargs)
+        # Scaling a CTFFilter is a no op; as of v15.0 scaling controlled by the pixel size.
+        result2 = filt.evaluate(self.omega, **self.filter_eval_kwargs)
         self.assertTrue(np.allclose(result1, result2, atol=utest_tolerance(self.dtype)))
 
+        # However, we can still test scaling pixel_size against scaling omega grid
+        px_sz = self.filter_eval_kwargs["pixel_size"]
+        # Scaling a CTFFilter pixel size should match a corresponding scaling in omega.
+        result3 = filt.evaluate(
+            self.omega / scale_value, pixel_size=px_sz
+        )  # scale omega
+        result4 = filt.evaluate(
+            self.omega, pixel_size=px_sz * scale_value
+        )  # scale pixel size
+        self.assertTrue(np.allclose(result4, result3, atol=utest_tolerance(self.dtype)))
+
 
 DTYPES = [np.float32, np.float64]
 EPS = [None, 0.01]
@@ -245,3 +258,60 @@ def test_ctf_reference():
 
     # Test match all significant digits above
     np.testing.assert_allclose(h, ref_h, atol=5e-5)
+
+
+def testCTFdownsample():
+    """
+    Compare CTF Phaseflip -> Downsample vs Downsample -> Phaseflip
+    """
+    n = 10  # number of filters in stack
+    K = 179  # simulation pixel downsampled
+
+    angs = np.linspace(0, 2 * np.pi, n)
+    filter_stack = [
+        CTFFilter(defocus_u=10000, defocus_v=15000, defocus_ang=ang) for ang in angs
+    ]
+
+    vol = emdb_2660().astype(np.float64)
+    sim = Simulation(
+        n=n,
+        vols=vol,
+        offsets=0,
+        amplitudes=1,
+        unique_filters=filter_stack,
+        filter_indices=np.arange(n),
+        seed=SEED,
+    )
+    # Reduce possibility of simulation generation code interacting with the test.
+    src = ArrayImageSource(sim.images[:])
+    src.unique_filters = sim.unique_filters
+    src.filter_indices = sim.filter_indices
+
+    sim_pf_ds = src.phase_flip().downsample(K).images[:]
+    sim_ds_pf = src.downsample(K).phase_flip().images[:]
+    np.testing.assert_allclose(sim_ds_pf, sim_pf_ds)
+
+    sim_dsc_pf = src.downsample(K).cache().phase_flip().images[:]
+    np.testing.assert_allclose(sim_dsc_pf, sim_pf_ds)
+
+
+def test_downsample_cache():
+    """
+    Compare Downsample Cache vs Downsample
+    """
+    n = 10  # number of filters in stack
+    K = 179  # simulation pixel downsampled
+
+    vol = emdb_2660().astype(np.float64)
+    src = Simulation(
+        n=n,
+        vols=vol,
+        offsets=0,
+        amplitudes=1,
+        seed=SEED,
+    )
+
+    sim_ds = src.downsample(K).images[:]
+    sim_dsc = src.downsample(K).cache().images[:]
+
+    np.testing.assert_allclose(sim_dsc, sim_ds)