ENH: Suppress warnings by passing more reasonable data types

Author: effigies

nibabel/funcs.py

@@ -35,7 +35,7 @@ def squeeze_image(img):
     --------
     >>> import nibabel as nf
     >>> shape = (10,20,30,1,1)
-    >>> data = np.arange(np.prod(shape)).reshape(shape)
+    >>> data = np.arange(np.prod(shape), dtype='int32').reshape(shape)
     >>> affine = np.eye(4)
     >>> img = nf.Nifti1Image(data, affine)
     >>> img.shape == (10, 20, 30, 1, 1)
@@ -47,7 +47,7 @@ def squeeze_image(img):
     If the data are 3D then last dimensions of 1 are ignored
 
     >>> shape = (10,1,1)
-    >>> data = np.arange(np.prod(shape)).reshape(shape)
+    >>> data = np.arange(np.prod(shape), dtype='int32').reshape(shape)
     >>> img = nf.ni1.Nifti1Image(data, affine)
     >>> img.shape == (10, 1, 1)
     True

nibabel/tests/test_filehandles.py

@@ -26,7 +26,7 @@ def test_multiload():
     # Make a tiny image, save, load many times.  If we are leaking filehandles,
     # this will cause us to run out and generate an error
     N = SOFT_LIMIT + 100
-    arr = np.arange(24).reshape((2, 3, 4))
+    arr = np.arange(24, dtype='int32').reshape((2, 3, 4))
     img = Nifti1Image(arr, np.eye(4))
     imgs = []
     try:

nibabel/tests/test_funcs.py

@@ -51,12 +51,12 @@ def test_concat():
         #   second position.
         for data0_shape in all_shapes:
             data0_numel = np.asarray(data0_shape).prod()
-            data0 = np.arange(data0_numel).reshape(data0_shape)
+            data0 = np.arange(data0_numel, dtype='int32').reshape(data0_shape)
             img0_mem = Nifti1Image(data0, affine)
 
             for data1_shape in all_shapes:
                 data1_numel = np.asarray(data1_shape).prod()
-                data1 = np.arange(data1_numel).reshape(data1_shape)
+                data1 = np.arange(data1_numel, dtype='int32').reshape(data1_shape)
                 img1_mem = Nifti1Image(data1, affine)
                 img2_mem = Nifti1Image(data1, affine + 1)  # bad affine
 

nibabel/tests/test_processing.py

@@ -97,7 +97,7 @@ def test_adapt_affine():
 @needs_scipy
 def test_resample_from_to(caplog):
     # Test resampling from image to image / image space
-    data = np.arange(24).reshape((2, 3, 4))
+    data = np.arange(24, dtype='int32').reshape((2, 3, 4))
     affine = np.diag([-4, 5, 6, 1])
     img = Nifti1Image(data, affine)
     img.header['descrip'] = 'red shirt image'
@@ -186,7 +186,7 @@ def test_resample_from_to(caplog):
     out = resample_from_to(img, (img_2d.shape, img_2d.affine))
     assert_array_equal(out.dataobj, data[:, :, 0])
     # 4D input and output also OK
-    data_4d = np.arange(24 * 5).reshape((2, 3, 4, 5))
+    data_4d = np.arange(24 * 5, dtype='int32').reshape((2, 3, 4, 5))
     img_4d = Nifti1Image(data_4d, affine)
     out = resample_from_to(img_4d, img_4d)
     assert_almost_equal(data_4d, out.dataobj)
@@ -202,7 +202,7 @@ def test_resample_from_to(caplog):
 def test_resample_to_output(caplog):
     # Test routine to sample iamges to output space
     # Image aligned to output axes - no-op
-    data = np.arange(24).reshape((2, 3, 4))
+    data = np.arange(24, dtype='int32').reshape((2, 3, 4))
     img = Nifti1Image(data, np.eye(4))
     # Check default resampling
     img2 = resample_to_output(img)
@@ -305,7 +305,7 @@ def test_resample_to_output(caplog):
 @needs_scipy
 def test_smooth_image(caplog):
     # Test image smoothing
-    data = np.arange(24).reshape((2, 3, 4))
+    data = np.arange(24, dtype='int32').reshape((2, 3, 4))
     aff = np.diag([-4, 5, 6, 1])
     img = Nifti1Image(data, aff)
     # Zero smoothing is no-op
@@ -332,7 +332,7 @@ def test_smooth_image(caplog):
     with pytest.raises(ValueError):
         smooth_image(img_2d, [8, 8, 8])
     # Isotropic in 4D has zero for last dimension in scalar case
-    data_4d = np.arange(24 * 5).reshape((2, 3, 4, 5))
+    data_4d = np.arange(24 * 5, dtype='int32').reshape((2, 3, 4, 5))
     img_4d = Nifti1Image(data_4d, aff)
     exp_out = spnd.gaussian_filter(data_4d, list(sd) + [0], mode='nearest')
     assert_array_equal(smooth_image(img_4d, 8).dataobj, exp_out)

nibabel/tests/test_round_trip.py

@@ -17,9 +17,8 @@
 
 
 def round_trip(arr, out_dtype):
-    img = Nifti1Image(arr, np.eye(4))
+    img = Nifti1Image(arr, np.eye(4), dtype=out_dtype)
     img.file_map['image'].fileobj = BytesIO()
-    img.set_data_dtype(out_dtype)
     img.to_file_map()
     back = Nifti1Image.from_file_map(img.file_map)
     # Recover array and calculated scaling from array proxy object