Python: remove buffer protocol from docs, tests and examples

because nanobind doesn't support it

docs/grid.rst

@@ -121,35 +121,21 @@ Values are accessed with functions get_value() and set_value():
   >>> grid.get_value(-11, 13, 25)
   7.0
 
-.. _buffer_protocol:
-
-The data can be also accessed through the
-`buffer protocol <https://docs.python.org/3/c-api/buffer.html>`_.
-It means that you can use it as a NumPy array (Fortran-style contiguous)
-without copying the data:
+The data can be also accessed as a NumPy array (Fortran-style contiguous),
+without copying the data, through the `array` property:
 
 .. doctest::
   :skipif: numpy is None or sys.platform == 'win32'
 
   >>> import numpy
-  >>> array = numpy.array(grid, copy=False)
+  >>> array = grid.array
   >>> array.dtype
   dtype('float32')
   >>> array.shape
   (12, 12, 12)
   >>> numpy.argwhere(array == 7.0)
   array([[1, 1, 1]])
 
-The buffer protocol is not specific to NumPy -- any other Python library
-that supports this protocol can directly access the grid data.
-Alternatively, the grid can be viewed as a NumPy array through
-the `array` property:
-
-.. doctest::
-  :skipif: numpy is None or sys.platform == 'win32'
-
-  >>> grid.array.shape
-  (12, 12, 12)
 
 Symmetry
 --------

docs/hkl.rst

@@ -392,23 +392,14 @@ It provides access to all the MTZ data as 2D NumPy array
   >>> mtz.array.shape
   (441, 8)
 
-Another way to access MTZ data in Python is through the
-`buffer protocol <https://docs.python.org/3/c-api/buffer.html>`_.
-For example, here is equivalent of `mtz.array`:
-
-.. doctest::
-  :skipif: numpy is None
-
-  >>> all_data = numpy.array(mtz, copy=False)
-
 It helps to have labels on the columns. A good data structure for this
 is Pandas DataFrame:
 
 .. doctest::
   :skipif: pandas is None
 
   >>> import pandas
-  >>> df = pandas.DataFrame(data=all_data, columns=mtz.column_labels())
+  >>> df = pandas.DataFrame(data=mtz.array, columns=mtz.column_labels())
   >>> # now we can handle columns using their labels:
   >>> I_over_sigma = df['I'] / df['SIGI']
 
@@ -965,7 +956,7 @@ into a single DataFrame:
 
 .. literalinclude:: ../examples/merge_mtz_mmcif.py
   :language: python
-  :lines: 4-28
+  :lines: 4-27
 
 We want to compare the FP column from the MTZ file and the F_meas_au column
 from mmCIF. We start with plotting one against the other:
@@ -976,7 +967,7 @@ from mmCIF. We start with plotting one against the other:
 
 .. literalinclude:: ../examples/merge_mtz_mmcif.py
   :language: python
-  :lines: 31-38
+  :lines: 30-37
 
 The numbers are similar, but not exactly equal.
 Let us check how the difference between the two values depends on the
@@ -990,7 +981,7 @@ so we can use it for coloring.
 
 .. literalinclude:: ../examples/merge_mtz_mmcif.py
   :language: python
-  :lines: 41-
+  :lines: 40-
 
 Apparently, some scaling has been applied. The scaling is anisotropic
 and is the strongest along the *k* axis.
@@ -1457,8 +1448,7 @@ To check if the size is big enough you can call:
   >>> rblock.data_fits_into([52,6,18])
   False
 
-To access the data you can use either the buffer protocol
-(:ref:`in the same way <buffer_protocol>` as in the Grid class),
+To access the data you can use either the NumPy interface (`grid.array`),
 or getter and setter:
 
 .. doctest::
@@ -1572,8 +1562,7 @@ tool.
 
   >>> size = rblock.get_size_for_hkl(sample_rate=2.6)
   >>> full = rblock.get_f_phi_on_grid('pdbx_FWT', 'pdbx_PHWT', size)
-  >>> array = numpy.array(full, copy=False)
-  >>> complex_map = numpy.fft.ifftn(array.conj())
+  >>> complex_map = numpy.fft.ifftn(full.array.conj())
   >>> scale_factor = complex_map.size / full.unit_cell.volume
   >>> real_map = numpy.real(complex_map) * scale_factor
   >>> round(real_map[1][2][3], 5)
@@ -1591,8 +1580,7 @@ using complex-to-real FFT on a half of the data:
   :skipif: numpy is None
 
   >>> half = rblock.get_f_phi_on_grid('pdbx_FWT', 'pdbx_PHWT', size, half_l=True)
-  >>> array = numpy.array(half, copy=False)
-  >>> real_map = numpy.fft.irfftn(array.conj()) * scale_factor
+  >>> real_map = numpy.fft.irfftn(half.array.conj()) * scale_factor
   >>> round(real_map[1][2][3], 5)
   -0.40554
   >>> round(real_map.std(), 5)
@@ -1646,8 +1634,7 @@ The grid can be accessed as NumPy 3D array:
 .. doctest::
   :skipif: numpy is None
 
-  >>> array = numpy.array(_, copy=False)
-  >>> round(array.std(), 5)
+  >>> round(_.array.std(), 5)
   0.66338
 
 .. _map_from_rblock:

examples/maskcheck.py

@@ -51,8 +51,8 @@ def maskcheck(mask_path, coor_path, output_diff_map=None, verbose=False):
 
 
 def compare_mask_arrays(grid1, grid2, verbose):
-    arr1 = numpy.array(grid1, copy=False)
-    arr2 = numpy.array(grid2, copy=False)
+    arr1 = grid1.array
+    arr2 = grid2.array
     if arr1.shape != arr2.shape:
         sys.exit('Different grid sizes %s and %s. Exiting.' %
                  (arr1.shape, arr2.shape))

examples/maskdiff.py

@@ -6,9 +6,9 @@
 
 def maskdiff(path1, path2):
     mask1 = gemmi.read_ccp4_mask(path1, setup=True)
-    arr1 = numpy.array(mask1.grid, copy=False)
+    arr1 = mask1.grid.array
     mask2 = gemmi.read_ccp4_mask(path2, setup=True)
-    arr2 = numpy.array(mask2.grid, copy=False)
+    arr2 = mask2.grid.array
     print("Size: %d x %d x %d  and  %d x %d x %d" % (arr1.shape + arr2.shape))
     if arr1.shape != arr2.shape:
         sys.exit("Different sizes. Exiting.")

examples/merge_mtz_mmcif.py

@@ -11,8 +11,7 @@
 
 # make DataFrame from MTZ file
 mtz = gemmi.read_mtz_file(MTZ_PATH)
-mtz_data = numpy.array(mtz, copy=False)
-mtz_df = pandas.DataFrame(data=mtz_data, columns=mtz.column_labels())
+mtz_df = pandas.DataFrame(data=mtz.array, columns=mtz.column_labels())
 # (optional) store Miller indices as integers
 mtz_df = mtz_df.astype({label: 'int32' for label in 'HKL'})
 

examples/patterson_slice.py

@@ -6,7 +6,7 @@
 
 # toxd_aupatt.map is generated by $CCP4/examples/unix/runnable/patterson
 ccp4 = gemmi.read_ccp4_map('/tmp/wojdyr/toxd_aupatt.map', setup=True)
-arr = numpy.array(ccp4.grid, copy=False)
+arr = ccp4.grid.array
 x = numpy.linspace(0, ccp4.grid.unit_cell.a, num=arr.shape[0], endpoint=False)
 y = numpy.linspace(0, ccp4.grid.unit_cell.b, num=arr.shape[1], endpoint=False)
 X, Y = numpy.meshgrid(x, y, indexing='ij')

tests/test_grid.py

@@ -57,7 +57,7 @@ def test_reading(self):
         m.grid.symmetrize_max()
         self.assertEqual(m.grid.get_value(60-3, 24//2+4, 60-5), 90)
         if numpy:
-            arr = numpy.array(m.grid, copy=False)
+            arr = m.grid.array
             self.assertEqual(arr.shape, (60, 24, 60))
             self.assertEqual(arr[3][4][5], 90)
             grid2 = gemmi.FloatGrid(arr)

tests/test_hkl.py

@@ -31,7 +31,7 @@ def fft_test(self, data, f, phi, size, order=gemmi.AxisOrder.XYZ):
     self.assertTrue(data.data_fits_into(size))
     grid_full = data.get_f_phi_on_grid(f, phi, size, half_l=False, order=order)
     self.assertEqual(grid_full.axis_order, order)
-    array_full = numpy.array(grid_full, copy=False)
+    array_full = grid_full.array
     map1 = gemmi.transform_f_phi_grid_to_map(grid_full)
     self.assertEqual(map1.axis_order, order)
     map2 = numpy.fft.ifftn(array_full.conj())
@@ -84,7 +84,6 @@ def test_read_write(self):
         os.remove(out_name)
         self.assertEqual(mtz2.spacegroup.hm, 'P 1 21 1')
         if numpy is not None:
-            assert_numpy_equal(self, numpy.array(mtz, copy=False), mtz.array)
             assert_numpy_equal(self, mtz.array, mtz2.array)
 
     def test_remove_and_add_column(self):
@@ -96,17 +95,16 @@ def test_remove_and_add_column(self):
         ncol = len(mtz.columns)
         if numpy is None:
             return
-        assert_numpy_equal(self, col.array, numpy.array(col, copy=False))
         arr = col.array.copy()
-        mtz_data = numpy.array(mtz, copy=True)
+        mtz_data = mtz.array.copy()
         self.assertEqual(mtz_data.shape, (mtz.nreflections, ncol))
         mtz.remove_column(col_idx)
         self.assertEqual(len(mtz.columns), ncol-1)
-        self.assertEqual(numpy.array(mtz, copy=False).shape,
+        self.assertEqual(mtz.array.shape,
                          (mtz.nreflections, ncol-1))
         col = mtz.add_column(col_name, 'I', dataset_id=0, pos=col_idx)
-        numpy.array(col, copy=False)[:] = arr
-        assert_numpy_equal(self, mtz_data, numpy.array(mtz, copy=False))
+        col.array[:] = arr
+        assert_numpy_equal(self, mtz_data, mtz.array)
 
     def asu_data_test(self, grid):
         asu = grid.prepare_asu_data()
@@ -130,8 +128,8 @@ def test_f_phi_grid(self):
                                           order=gemmi.AxisOrder.ZYX)
             if numpy is None:
                 continue
-            array1 = numpy.array(grid1, copy=False)
-            array2 = numpy.array(grid2, copy=False)
+            array1 = grid1.array
+            array2 = grid2.array
             self.assertTrue((array2 == array1.transpose(2,1,0)).all())
             self.asu_data_test(grid1)
 
@@ -146,7 +144,7 @@ def test_value_grid(self):
         if numpy is None:
             return
         asu = gemmi.ReciprocalAsu(mtz.spacegroup)
-        mtz_data = numpy.array(mtz, copy=False)
+        mtz_data = mtz.array
         fp_idx = mtz.column_labels().index('FP')
         fp_map = {}
         for row in mtz_data: