Merge pull request #865 from sunpy/doc

Author: nabobalis

examples/dev/example_template.py

@@ -366,8 +366,8 @@ def test_slice_drop_dimensions(time_lut, skycoord_2d_lut):
     assert u.allclose(sec['lon'].wcs.pixel_to_world_values(list(range(2))),
                       ec['lon'].wcs.pixel_to_world_values(list(range(2)), [0, 0]))
 
-    assert u.allclose(sec['exposure_time'].wcs.pixel_to_world_values(list(range(2)), list(range(2))),
-                      ec['exposure_time'].wcs.pixel_to_world_values(list(range(2)), list(range(2))))
+    assert u.allclose(sec['exposure_time'].wcs.pixel_to_world_values(list(range(2))),
+                      ec['exposure_time'].wcs.pixel_to_world_values(list(range(2))))
 
 
 def test_slice_extra_twice(time_lut, wave_lut):

ndcube/extra_coords/tests/test_extra_coords.py

@@ -1133,7 +1133,7 @@ def rebin(self, bin_shape, operation=np.mean, operation_ignores_mask=False, hand
         Downsample array by combining contiguous pixels into bins.
 
         Values in bins are determined by applying a function to the pixel values within it.
-        The number of pixels in each bin in each dimension is given by the bin_shape input.
+        The number of pixels in each bin in each dimension is given by the ``bin_shape`` input.
         This must be an integer fraction of the cube's array size in each dimension.
         If the NDCube instance has uncertainties attached, they are propagated
         depending on binning method chosen.
@@ -1148,6 +1148,7 @@ def rebin(self, bin_shape, operation=np.mean, operation_ignores_mask=False, hand
             units have to be convertible to pixels.
             The sentinel value ``-1`` can be passed for a dimension which means
             that no rebinning will occur along that dimension.
+            Please note the ``bin_shape`` follows array axis (NumPy) ordering of the axes.
         operation : function
             Function applied to the data to derive values of the bins.
             Default is `numpy.mean`

ndcube/ndcube.py

@@ -4,7 +4,7 @@
 import dask.array
 import numpy as np
 import pytest
-from specutils import Spectrum1D
+from specutils import Spectrum
 
 import astropy.units as u
 import astropy.wcs
@@ -311,6 +311,6 @@ def test_rebin_specutils():
     # Tests for https://github.com/sunpy/ndcube/issues/717
     y = np.arange(4000)*u.ct
     x = np.arange(200, 4200)*u.nm
-    spec = Spectrum1D(flux=y, spectral_axis=x, bin_specification='centers', mask=x > 2000*u.nm)
+    spec = Spectrum(flux=y, spectral_axis=x, bin_specification='centers', mask=x > 2000*u.nm)
     output = spec.rebin((10,), operation=np.sum, operation_ignores_mask=False)
     assert output.shape == (400,)

ndcube/tests/test_ndcube_reproject_and_rebin.py

@@ -3,20 +3,20 @@
   "ndcube.visualization.tests.test_plotting.test_plot_1D_cube_from_slice[ln_lt_l_t-cslice0-kwargs0]": "ea89b5d1c2fdcf34ba353dffe528f5ecd5ce364a77f43a08d00a6a7f11a869f4",
   "ndcube.visualization.tests.test_plotting.test_plot_1D_cube_from_slice[ln_lt_l_t-cslice1-kwargs1]": "f5d10df37509d1ba9429deb95ee29cf37dddea1a30f4c84cf2b10034c74c31bd",
   "ndcube.visualization.tests.test_plotting.test_plot_1D_cube_from_slice[ln_lt_l_t-cslice2-kwargs2]": "e48765a561ea0f43f5a80d3da27fd71654b18d276cbb7617e1918843012425f9",
-  "ndcube.visualization.tests.test_plotting.test_plot_1D_cube_from_slice[ln_lt_l_t-cslice3-kwargs3]": "b8d2d026c12a600b36ebe8cd943a5d443519443436f755e54fac12a681f1e3e4",
+  "ndcube.visualization.tests.test_plotting.test_plot_1D_cube_from_slice[ln_lt_l_t-cslice3-kwargs3]": "7d9c6a470077d7ea1e2a38160d9dd27e9c82e9106d8bfdcec789055e9089c8cd",
   "ndcube.visualization.tests.test_plotting.test_plot_1D_cube_from_slice[uncertainty-cslice4-kwargs4]": "04083a963e79e9aaf2d1771e9a86158ef4be51c4b8cde7b6b7b85f80e1452ea9",
   "ndcube.visualization.tests.test_plotting.test_plot_1D_cube_from_slice[unit_uncertainty-cslice5-kwargs5]": "53f90627c0612aac7edcca97e108069b915bc7edaf871e62ba747732510e7cb2",
   "ndcube.visualization.tests.test_plotting.test_plot_1D_cube_from_slice[mask-cslice6-kwargs6]": "b888887d05f9f7654968bd59a33fd86a12111503222904bfd369475210da7abb",
-  "ndcube.visualization.tests.test_plotting.test_plot_2D_cube": "cc6ff47be658bdcc2e8dd226f50ab04da89cc23dc834fa5b0ce2a5a724414ada",
-  "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_colorbar": "2cdc18629da36f96ddeb24631f70387bc3090c128ad994914816b1be7864af79",
-  "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_custom_axis": "cc6ff47be658bdcc2e8dd226f50ab04da89cc23dc834fa5b0ce2a5a724414ada",
-  "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_custom_axis_plot_axes": "c61173e4904ffd7c6a88a9a18e68014ee258d5d59fb226c021f49a57230dfefc",
+  "ndcube.visualization.tests.test_plotting.test_plot_2D_cube": "4b194dbc850bfd9ae983ec5f0e07e7295f537e591e61177aadca28e41d74bd98",
+  "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_colorbar": "8adf60402dbd71d066547a08c81757f99db4f55b85288212420cf3a70f967c3e",
+  "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_custom_axis": "4b194dbc850bfd9ae983ec5f0e07e7295f537e591e61177aadca28e41d74bd98",
+  "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_custom_axis_plot_axes": "712809a75e7d587c064d9631ed218214071a9dd2d8017d937ae73c613e4c2ab4",
   "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_from_slice[ln_lt_l_t-cslice0-kwargs0]": "77b0b9a0067897786777a78974cff240a3268ed38937047c3945473e82bc4cf0",
   "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_from_slice[ln_lt_l_t-cslice1-kwargs1]": "2af86ad3672ef70d51b3637325c5e7860cad26f70032b51877d45d0a9b647a44",
-  "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_from_slice[ln_lt_l_t-cslice2-kwargs2]": "b00ec9344620cd9f640bbb3410e1fc508b5a86d02826d378147b33b999606d05",
+  "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_from_slice[ln_lt_l_t-cslice2-kwargs2]": "af99b2460f2ed4a99f159fe854cdf63285eaffb55c09932f74f15f2198a7f1b1",
   "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_from_slice[unit_uncertainty-cslice3-kwargs3]": "77b0b9a0067897786777a78974cff240a3268ed38937047c3945473e82bc4cf0",
   "ndcube.visualization.tests.test_plotting.test_plot_2D_cube_from_slice[mask-cslice4-kwargs4]": "490f4d37a93ab800ab2eb4fa077c448bcd6ebc30d64ab374118f5a9288246fb3",
-  "ndcube.visualization.tests.test_plotting.test_animate_2D_cube": "5dbe5e20670b3809365605637157f46d11235d7a28358cd03db075899e7ff2ae",
+  "ndcube.visualization.tests.test_plotting.test_animate_2D_cube": "f0622456d02808c9845336e7bead8f4ac22e99d2450bf6f14bb74686a45fd5ed",
   "ndcube.visualization.tests.test_plotting.test_animate_cube_from_slice[ln_lt_l_t-cslice0-kwargs0]": "47a0279e3244139f5b7126625a1aeb2bd038b3b39b38829a6b5a9129d8bba09d",
   "ndcube.visualization.tests.test_plotting.test_animate_cube_from_slice[ln_lt_l_t-cslice1-kwargs1]": "8e9303bb77aef56991d2cf470de6840cea1994dd8ffb17bec48be9428ec5f7b8",
   "ndcube.visualization.tests.test_plotting.test_animate_cube_from_slice[ln_lt_l_t-None-kwargs2]": "4ae632474865d7df8434f66560c83205597437eadb61840266c334a836e4df7d",

…82_ft_261_astropy_600_animators_111.json …00_ft_261_astropy_710_animators_124.jsonndcube/visualization/tests/figure_hashes_mpl_382_ft_261_astropy_600_animators_111.json renamed to ndcube/visualization/tests/figure_hashes_mpl_3100_ft_261_astropy_710_animators_124.json ndcube/visualization/tests/figure_hashes_mpl_382_ft_261_astropy_600_animators_111.json renamed to ndcube/visualization/tests/figure_hashes_mpl_3100_ft_261_astropy_710_animators_124.json

@@ -55,10 +55,10 @@ deps =
     # Oldest Dependencies
     oldestdeps: minimum_dependencies
     # Figure tests need a tightly controlled environment
-    figure-!devdeps: astropy==6.0.0
+    figure-!devdeps: astropy==7.1.0
     figure-!devdeps: dask
-    figure-!devdeps: matplotlib==3.8.2
-    figure-!devdeps: mpl-animators==1.1.1
+    figure-!devdeps: matplotlib==3.10.0
+    figure-!devdeps: mpl-animators==1.2.4
     figure-!devdeps: scipy
 # The following indicates which extras_require will be installed
 extras =