Generalize reproject_and_coadd to N-dimensions and fix test failures

Author: astrofrog

Diff for: reproject/array_utils.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-__all__ = ["map_coordinates"]
+__all__ = ["map_coordinates", "sample_array_edges"]
 
 
 def map_coordinates(image, coords, **kwargs):
@@ -35,3 +35,22 @@ def map_coordinates(image, coords, **kwargs):
     values[reset] = kwargs.get("cval", 0.0)
 
     return values
+
+
+def sample_array_edges(shape, *, n_samples):
+    # Given an N-dimensional array shape, sample each edge of the array using
+    # the requested number of samples (which will include vertices). To do this
+    # we iterate through the dimensions and for each one we sample the points
+    # in that dimension and iterate over the combination of other vertices.
+    # Returns an array with dimensions (N, n_samples)
+    all_positions = []
+    ndim = len(shape)
+    shape = np.array(shape)
+    for idim in range(ndim):
+        for vertex in range(2**ndim):
+            positions = -0.5 + shape * ((vertex & (2 ** np.arange(ndim))) > 0).astype(int)
+            positions = np.broadcast_to(positions, (n_samples, ndim)).copy()
+            positions[:, idim] = np.linspace(-0.5, shape[idim] - 0.5, n_samples)
+            all_positions.append(positions)
+    positions = np.unique(np.vstack(all_positions), axis=0).T
+    return positions

Diff for: reproject/mosaicking/coadd.py

@@ -4,6 +4,7 @@
 from astropy.wcs import WCS
 from astropy.wcs.wcsapi import SlicedLowLevelWCS
 
+from ..array_utils import sample_array_edges
 from ..utils import parse_input_data, parse_input_weights, parse_output_projection
 from .background import determine_offset_matrix, solve_corrections_sgd
 from .subset_array import ReprojectedArraySubset
@@ -30,15 +31,13 @@ def reproject_and_coadd(
     output_footprint=None,
     block_sizes=None,
     progress_bar=None,
-    blank_pixel_value=np.nan,
+    blank_pixel_value=0,
     **kwargs,
 ):
     """
-    Given a set of input images, reproject and co-add these to a single
+    Given a set of input data, reproject and co-add these to a single
     final image.
 
-    This currently only works with 2-d images with celestial WCS.
-
     Parameters
     ----------
     input_data : iterable
@@ -149,24 +148,31 @@ def reproject_and_coadd(
 
     wcs_out, shape_out = parse_output_projection(output_projection, shape_out=shape_out)
 
-    if output_array is not None and output_array.shape != shape_out:
+    if output_array is None:
+        output_array = np.zeros(shape_out)
+    elif output_array.shape != shape_out:
         raise ValueError(
             "If you specify an output array, it must have a shape matching "
             f"the output shape {shape_out}"
         )
-    if output_footprint is not None and output_footprint.shape != shape_out:
+
+    if output_footprint is None:
+        output_footprint = np.zeros(shape_out)
+    elif output_footprint.shape != shape_out:
         raise ValueError(
             "If you specify an output footprint array, it must have a shape matching "
             f"the output shape {shape_out}"
         )
 
-    if output_array is None:
-        output_array = np.zeros(shape_out)
-    if output_footprint is None:
-        output_footprint = np.zeros(shape_out)
+    # Define 'on-the-fly' mode: in the case where we don't need to match
+    # the backgrounds and we are combining with 'mean' or 'sum', we don't
+    # have to keep track of the intermediate arrays and can just modify
+    # the output array on-the-fly
+    on_the_fly = not match_background and combine_function in ("mean", "sum")
 
     # Start off by reprojecting individual images to the final projection
-    if match_background:
+
+    if not on_the_fly:
         arrays = []
 
     for idata in progress_bar(range(len(input_data))):
@@ -192,71 +198,42 @@ def reproject_and_coadd(
         # significant distortion (when the edges of the input image become
         # convex in the output projection), and transforming every edge pixel,
         # which provides a lot of redundant information.
-        if array_in.ndim == 2:
-            ny, nx = array_in.shape
-            n_per_edge = 11
-            xs = np.linspace(-0.5, nx - 0.5, n_per_edge)
-            ys = np.linspace(-0.5, ny - 0.5, n_per_edge)
-            xs = np.concatenate((xs, np.full(n_per_edge, xs[-1]), xs, np.full(n_per_edge, xs[0])))
-            ys = np.concatenate((np.full(n_per_edge, ys[0]), ys, np.full(n_per_edge, ys[-1]), ys))
-            xc_out, yc_out = wcs_out.world_to_pixel(wcs_in.pixel_to_world(xs, ys))
-            shape_out_cel = shape_out
-        elif array_in.ndim == 3:
-            # for cubes, we only handle single corners now
-            nz, ny, nx = array_in.shape
-            xc = np.array([-0.5, nx - 0.5, nx - 0.5, -0.5])
-            yc = np.array([-0.5, -0.5, ny - 0.5, ny - 0.5])
-            zc = np.array([-0.5, nz - 0.5])
-            # TODO: figure out what to do here if the low_level_wcs doesn't  support subsetting
-            xc_out, yc_out = wcs_out.low_level_wcs.celestial.world_to_pixel(
-                wcs_in.celestial.pixel_to_world(xc, yc)
-            )
-            zc_out = wcs_out.low_level_wcs.spectral.world_to_pixel(
-                wcs_in.spectral.pixel_to_world(zc)
-            )
-            shape_out_cel = shape_out[1:]
-        else:
-            raise ValueError(f"Wrong number of dimensions: {array_in.ndim}")
+
+        edges = sample_array_edges(array_in.shape, n_samples=11)[::-1]
+        edges_out = wcs_out.world_to_pixel(wcs_in.pixel_to_world(*edges))[::-1]
 
         # Determine the cutout parameters
 
         # In some cases, images might not have valid coordinates in the corners,
         # such as all-sky images or full solar disk views. In this case we skip
         # this step and just use the full output WCS for reprojection.
 
-        if np.any(np.isnan(xc_out)) or np.any(np.isnan(yc_out)):
-            imin = 0
-            imax = shape_out_cel[1]
-            jmin = 0
-            jmax = shape_out_cel[0]
-        else:
-            imin = max(0, int(np.floor(xc_out.min() + 0.5)))
-            imax = min(shape_out_cel[1], int(np.ceil(xc_out.max() + 0.5)))
-            jmin = max(0, int(np.floor(yc_out.min() + 0.5)))
-            jmax = min(shape_out_cel[0], int(np.ceil(yc_out.max() + 0.5)))
+        ndim_out = len(shape_out)
 
-        if imax < imin or jmax < jmin:
+        skip_data = False
+        if np.any(np.isnan(edges_out)):
+            bounds = list(zip([0] * ndim_out, shape_out))
+        else:
+            bounds = []
+            for idim in range(ndim_out):
+                imin = max(0, int(np.floor(edges_out[idim].min() + 0.5)))
+                imax = min(shape_out[idim], int(np.ceil(edges_out[idim].max() + 0.5)))
+                bounds.append((imin, imax))
+                if imax < imin:
+                    skip_data = True
+                    break
+
+        if skip_data:
             continue
 
-        if array_in.ndim == 2:
-            if isinstance(wcs_out, WCS):
-                wcs_out_indiv = wcs_out[jmin:jmax, imin:imax]
-            else:
-                wcs_out_indiv = SlicedLowLevelWCS(
-                    wcs_out.low_level_wcs, (slice(jmin, jmax), slice(imin, imax))
-                )
-            shape_out_indiv = (jmax - jmin, imax - imin)
-            kmin, kmax = None, None  # for reprojectedarraysubset below
-        elif array_in.ndim == 3:
-            kmin = max(0, int(np.floor(zc_out.min() + 0.5)))
-            kmax = min(shape_out[0], int(np.ceil(zc_out.max() + 0.5)))
-            if isinstance(wcs_out, WCS):
-                wcs_out_indiv = wcs_out[kmin:kmax, jmin:jmax, imin:imax]
-            else:
-                wcs_out_indiv = SlicedLowLevelWCS(
-                    wcs_out.low_level_wcs, (slice(kmin, kmax), slice(jmin, jmax), slice(imin, imax))
-                )
-            shape_out_indiv = (kmax - kmin, jmax - jmin, imax - imin)
+        slice_out = tuple([slice(imin, imax) for (imin, imax) in bounds])
+
+        if isinstance(wcs_out, WCS):
+            wcs_out_indiv = wcs_out[slice_out]
+        else:
+            wcs_out_indiv = SlicedLowLevelWCS(wcs_out.low_level_wcs, slice_out)
+
+        shape_out_indiv = [imax - imin for (imin, imax) in bounds]
 
         if block_sizes is not None:
             if len(block_sizes) == len(input_data) and len(block_sizes[idata]) == len(shape_out):
@@ -296,22 +273,20 @@ def reproject_and_coadd(
             weights[reset] = 0.0
             footprint *= weights
 
-        array = ReprojectedArraySubset(array, footprint, imin, imax, jmin, jmax, kmin, kmax)
+        array = ReprojectedArraySubset(array, footprint, bounds)
 
         # TODO: make sure we gracefully handle the case where the
         # output image is empty (due e.g. to no overlap).
 
-        if match_background:
-            arrays.append(array)
+        if on_the_fly:
+            # By default, values outside of the footprint are set to NaN
+            # but we set these to 0 here to avoid getting NaNs in the
+            # means/sums.
+            array.array[array.footprint == 0] = 0
+            output_array[array.view_in_original_array] += array.array * array.footprint
+            output_footprint[array.view_in_original_array] += array.footprint
         else:
-            if combine_function in ("mean", "sum"):
-                # By default, values outside of the footprint are set to NaN
-                # but we set these to 0 here to avoid getting NaNs in the
-                # means/sums.
-                array.array[array.footprint == 0] = 0
-
-                output_array[array.view_in_original_array] += array.array * array.footprint
-                output_footprint[array.view_in_original_array] += array.footprint
+            arrays.append(array)
 
     # If requested, try and match the backgrounds.
     if match_background and len(arrays) > 1:
@@ -322,11 +297,6 @@ def reproject_and_coadd(
         for array, correction in zip(arrays, corrections, strict=True):
             array.array -= correction
 
-    if combine_function == "min":
-        output_array[...] = np.inf
-    elif combine_function == "max":
-        output_array[...] = -np.inf
-
     if combine_function in ("mean", "sum"):
         if match_background:
             # if we're not matching the background, this part has already been done
@@ -336,37 +306,41 @@ def reproject_and_coadd(
                 # means/sums.
                 array.array[array.footprint == 0] = 0
 
-        output_array[array.view_in_original_array] += array.array * array.footprint
-        output_footprint[array.view_in_original_array] += array.footprint
+                output_array[array.view_in_original_array] += array.array * array.footprint
+                output_footprint[array.view_in_original_array] += array.footprint
 
         if combine_function == "mean":
             with np.errstate(invalid="ignore"):
                 output_array /= output_footprint
                 output_array[output_footprint == 0] = blank_pixel_value
 
     elif combine_function in ("first", "last", "min", "max"):
-        if match_background:
-            for array in arrays:
-                if combine_function == "first":
-                    mask = output_footprint[array.view_in_original_array] == 0
-                elif combine_function == "last":
-                    mask = array.footprint > 0
-                elif combine_function == "min":
-                    mask = (array.footprint > 0) & (
-                        array.array < output_array[array.view_in_original_array]
-                    )
-                elif combine_function == "max":
-                    mask = (array.footprint > 0) & (
-                        array.array > output_array[array.view_in_original_array]
-                    )
-
-                output_footprint[array.view_in_original_array] = np.where(
-                    mask, array.footprint, output_footprint[array.view_in_original_array]
+        if combine_function == "min":
+            output_array[...] = np.inf
+        elif combine_function == "max":
+            output_array[...] = -np.inf
+
+        for array in arrays:
+            if combine_function == "first":
+                mask = output_footprint[array.view_in_original_array] == 0
+            elif combine_function == "last":
+                mask = array.footprint > 0
+            elif combine_function == "min":
+                mask = (array.footprint > 0) & (
+                    array.array < output_array[array.view_in_original_array]
                 )
-                output_array[array.view_in_original_array] = np.where(
-                    mask, array.array, output_array[array.view_in_original_array]
+            elif combine_function == "max":
+                mask = (array.footprint > 0) & (
+                    array.array > output_array[array.view_in_original_array]
                 )
 
+            output_footprint[array.view_in_original_array] = np.where(
+                mask, array.footprint, output_footprint[array.view_in_original_array]
+            )
+            output_array[array.view_in_original_array] = np.where(
+                mask, array.array, output_array[array.view_in_original_array]
+            )
+
     output_array[output_footprint == 0] = blank_pixel_value
 
     return output_array, output_footprint