Use array API in all cosmic ray tests

Author: mwcraig

ccdproc/combiner.py

@@ -500,8 +500,7 @@ def median_combine(
         Parameters
         ----------
         median_func : function, optional
-            Function that calculates median of a `numpy.ma.MaskedArray`.
-            Default is `numpy.ma.median`.
+            Function that calculates median of an array.
 
         scale_to : float or None, optional
             Scaling factor used in the average combined image. If given,
@@ -612,19 +611,18 @@ def average_combine(
         Parameters
         ----------
         scale_func : function, optional
-            Function to calculate the average. Defaults to
-            `numpy.nanmean`.
+            Function to calculate the average.
 
         scale_to : float or None, optional
             Scaling factor used in the average combined image. If given,
             it overrides `scaling`. Defaults to ``None``.
 
         uncertainty_func : function, optional
-            Function to calculate uncertainty. Defaults to `numpy.ma.std`.
+            Function to calculate uncertainty.
 
         sum_func : function, optional
             Function used to calculate sums, including the one done to
-            find the weighted average. Defaults to `numpy.nansum`.
+            find the weighted average.
 
         Returns
         -------
@@ -706,7 +704,7 @@ def sum_combine(
             it overrides `scaling`. Defaults to ``None``.
 
         uncertainty_func : function, optional
-            Function to calculate uncertainty. Defaults to `numpy.ma.std`.
+            Function to calculate uncertainty.
 
         Returns
         -------

ccdproc/core.py

@@ -8,6 +8,7 @@
 import warnings
 
 import array_api_compat
+import array_api_extra as xpx
 from astropy import nddata, stats
 from astropy import units as u
 from astropy.modeling import fitting
@@ -101,7 +102,7 @@ def _percentile_fallback(array, percentiles, xp=None):  # pragma: no cover
 
     Parameters
     ----------
-    array : array-like
+    array : array_like
         Array from which to calculate the percentile.
 
     percentiles : float or list-like
@@ -1205,7 +1206,7 @@ def setbox(x, y, mbox, xmax, ymax):
     return x1, x2, y1, y2
 
 
-def background_deviation_box(data, bbox):
+def background_deviation_box(data, bbox, xp=None):
     """
     Determine the background deviation with a box size of bbox. The algorithm
     steps through the image and calculates the deviation within each box.
@@ -1214,34 +1215,43 @@ def background_deviation_box(data, bbox):
 
     Parameters
     ----------
-    data : `numpy.ndarray` or `numpy.ma.MaskedArray`
+    data : `numpy.ndarray` or other array_like
         Data to measure background deviation.
 
     bbox : int
         Box size for calculating background deviation.
 
+    xp : array namespace, optional
+        Array namespace to use for calculations. If not provided, the
+        namespace will be determined from the array.
+
     Raises
     ------
     ValueError
         A value error is raised if bbox is less than 1.
 
     Returns
     -------
-    background : `numpy.ndarray` or `numpy.ma.MaskedArray`
+    background : array_like
         An array with the measured background deviation in each pixel.
     """
     # Check to make sure the background box is an appropriate size
     # If it is too small, then insufficient statistics are generated
     if bbox < 1:
         raise ValueError("bbox must be greater than 1.")
 
+    if xp is None:
+        # Get the array namespace
+        xp = array_api_compat.array_namespace(data)
     # make the background image
-    barr = data * 0.0 + data.std()
+    barr = data * 0.0 + xp.std(data)
     ylen, xlen = data.shape
     for i in range(int(0.5 * bbox), xlen, bbox):
         for j in range(int(0.5 * bbox), ylen, bbox):
             x1, x2, y1, y2 = setbox(i, j, bbox, xlen, ylen)
-            barr[y1:y2, x1:x2] = sigma_func(data[y1:y2, x1:x2])
+            xpx.at(barr)[y1:y2, x1:x2].set(
+                xp.astype(sigma_func(data[y1:y2, x1:x2]), data.dtype)
+            )
 
     return barr
 
@@ -1266,7 +1276,7 @@ def background_deviation_filter(data, bbox):
 
     Returns
     -------
-    background : `numpy.ndarray` or `numpy.ma.MaskedArray`
+    background : `numpy.ndarray`
         An array with the measured background deviation in each pixel.
     """
     # Check to make sure the background box is an appropriate size
@@ -1861,7 +1871,7 @@ def cosmicray_median(ccd, error_image=None, thresh=5, mbox=11, gbox=0, rbox=0, x
 
     Parameters
     ----------
-    ccd : `~astropy.nddata.CCDData`, `numpy.ndarray` or `numpy.ma.MaskedArray`
+    ccd : `~astropy.nddata.CCDData`, `numpy.ndarray` or other array_like
         Data to have cosmic ray cleaned.
 
     thresh : float, optional
@@ -2079,7 +2089,7 @@ def ccdmask(
     -------
     mask : `numpy.ndarray`
         A boolean ndarray where the bad pixels have a value of 1 (True) and
-        valid pixels 0 (False), following the numpy.ma conventions.
+        valid pixels 0 (False), following the numpy convention for masking.
 
     Notes
     -----
@@ -2235,8 +2245,7 @@ def bitfield_to_boolean_mask(bitfield, ignore_bits=0, flip_bits=None):
     Returns
     -------
     mask : `numpy.ndarray` of boolean dtype
-        The bitfield converted to a boolean mask that can be used for
-        `numpy.ma.MaskedArray` or `~astropy.nddata.CCDData`.
+        The bitfield converted to a boolean mask.
 
     Examples
     --------

ccdproc/tests/test_cosmicray.py

@@ -1,6 +1,5 @@
 # Licensed under a 3-clause BSD style license - see LICENSE.rst
 
-import array_api_compat
 import pytest
 from astropy import units as u
 from astropy.utils.exceptions import AstropyDeprecationWarning
@@ -9,6 +8,8 @@
 from numpy.random import default_rng
 from numpy.testing import assert_allclose
 
+# Set up the array library to be used in tests
+from ccdproc.conftest import testing_array_library as xp
 from ccdproc.core import (
     background_deviation_box,
     background_deviation_filter,
@@ -26,7 +27,6 @@
 def add_cosmicrays(data, scale, threshold, ncrays=NCRAYS):
     size = data.shape[0]
     rng = default_rng(99)
-    xp = array_api_compat.array_namespace(data.data)
     crrays = xp.asarray(rng.integers(0, size, size=(ncrays, 2)))
     # use (threshold + 15) below to make sure cosmic ray is well above the
     # threshold no matter what the random number generator returns
@@ -58,7 +58,6 @@ def test_cosmicray_lacosmic():
 
 def test_cosmicray_lacosmic_ccddata():
     ccd_data = ccd_data_func(data_scale=DATA_SCALE)
-    xp = array_api_compat.array_namespace(ccd_data.data)
     threshold = 5
     add_cosmicrays(ccd_data, DATA_SCALE, threshold, ncrays=NCRAYS)
     noise = DATA_SCALE * xp.ones_like(ccd_data.data)
@@ -75,7 +74,6 @@ def test_cosmicray_lacosmic_ccddata():
 
 def test_cosmicray_lacosmic_check_data():
     ccd_data = ccd_data_func(data_scale=DATA_SCALE)
-    xp = array_api_compat.array_namespace(ccd_data.data)
     with pytest.raises(TypeError):
         noise = DATA_SCALE * xp.ones_like(ccd_data.data)
         cosmicray_lacosmic(10, noise)
@@ -90,7 +88,6 @@ def test_cosmicray_gain_correct(array_input, gain_correct_data):
     # data and returns that gain corrected data. That is not the
     # intent...
     ccd_data = ccd_data_func(data_scale=DATA_SCALE)
-    xp = array_api_compat.array_namespace(ccd_data.data)
     threshold = 5
     add_cosmicrays(ccd_data, DATA_SCALE, threshold, ncrays=NCRAYS)
     noise = DATA_SCALE * xp.ones_like(ccd_data.data)
@@ -123,7 +120,6 @@ def test_cosmicray_gain_correct(array_input, gain_correct_data):
 
 def test_cosmicray_lacosmic_accepts_quantity_gain():
     ccd_data = ccd_data_func(data_scale=DATA_SCALE)
-    xp = array_api_compat.array_namespace(ccd_data.data)
     threshold = 5
     add_cosmicrays(ccd_data, DATA_SCALE, threshold, ncrays=NCRAYS)
     noise = DATA_SCALE * xp.ones_like(ccd_data.data)
@@ -138,7 +134,6 @@ def test_cosmicray_lacosmic_accepts_quantity_gain():
 
 def test_cosmicray_lacosmic_accepts_quantity_readnoise():
     ccd_data = ccd_data_func(data_scale=DATA_SCALE)
-    xp = array_api_compat.array_namespace(ccd_data.data)
     threshold = 5
     add_cosmicrays(ccd_data, DATA_SCALE, threshold, ncrays=NCRAYS)
     noise = DATA_SCALE * xp.ones_like(ccd_data.data)
@@ -156,7 +151,6 @@ def test_cosmicray_lacosmic_detects_inconsistent_units():
     # of adu, a readnoise in electrons and a gain in adu / electron.
     # That is not internally inconsistent.
     ccd_data = ccd_data_func(data_scale=DATA_SCALE)
-    xp = array_api_compat.array_namespace(ccd_data.data)
     ccd_data.unit = "adu"
     threshold = 5
     add_cosmicrays(ccd_data, DATA_SCALE, threshold, ncrays=NCRAYS)
@@ -176,7 +170,6 @@ def test_cosmicray_lacosmic_detects_inconsistent_units():
 def test_cosmicray_lacosmic_warns_on_ccd_in_electrons():
     # Check that an input ccd in electrons raises a warning.
     ccd_data = ccd_data_func(data_scale=DATA_SCALE)
-    xp = array_api_compat.array_namespace(ccd_data.data)
     # The unit below is important for the test; this unit on
     # input is supposed to raise an error.
     ccd_data.unit = u.electron
@@ -206,7 +199,6 @@ def test_cosmicray_lacosmic_invar_inbkg(new_args):
     # that calling with the new keyword arguments to astroscrappy
     # 1.1.0 raises no error.
     ccd_data = ccd_data_func(data_scale=DATA_SCALE)
-    xp = array_api_compat.array_namespace(ccd_data.data)
     threshold = 5
     add_cosmicrays(ccd_data, DATA_SCALE, threshold, ncrays=NCRAYS)
     noise = DATA_SCALE * xp.ones_like(ccd_data.data)
@@ -304,28 +296,28 @@ def test_cosmicray_median_background_deviation():
 
 def test_background_deviation_box():
     scale = 5.3
-    cd = default_rng(seed=123).normal(loc=0, size=(100, 100), scale=scale)
+    cd = xp.asarray(default_rng(seed=123).normal(loc=0, size=(100, 100), scale=scale))
     bd = background_deviation_box(cd, 25)
     assert abs(bd.mean() - scale) < 0.10
 
 
 def test_background_deviation_box_fail():
     scale = 5.3
-    cd = default_rng(seed=123).normal(loc=0, size=(100, 100), scale=scale)
+    cd = xp.asarray(default_rng(seed=123).normal(loc=0, size=(100, 100), scale=scale))
     with pytest.raises(ValueError):
         background_deviation_box(cd, 0.5)
 
 
 def test_background_deviation_filter():
     scale = 5.3
-    cd = default_rng(seed=123).normal(loc=0, size=(100, 100), scale=scale)
+    cd = xp.asarray(default_rng(seed=123).normal(loc=0, size=(100, 100), scale=scale))
     bd = background_deviation_filter(cd, 25)
     assert abs(bd.mean() - scale) < 0.10
 
 
 def test_background_deviation_filter_fail():
     scale = 5.3
-    cd = default_rng(seed=123).normal(loc=0, size=(100, 100), scale=scale)
+    cd = xp.asarray(default_rng(seed=123).normal(loc=0, size=(100, 100), scale=scale))
     with pytest.raises(ValueError):
         background_deviation_filter(cd, 0.5)
 
@@ -356,7 +348,6 @@ def test_cosmicray_lacosmic_pssl_does_not_fail():
     # to make sure that passing in pssl does not lead to an error
     # since the new interface does not include pssl.
     ccd_data = ccd_data_func(data_scale=DATA_SCALE)
-    xp = array_api_compat.array_namespace(ccd_data.data)
     threshold = 5
     add_cosmicrays(ccd_data, DATA_SCALE, threshold, ncrays=NCRAYS)
     noise = DATA_SCALE * xp.ones_like(ccd_data.data)