RF: Remove crop_image (for now)

Author: effigies

nibabel/funcs.py

@@ -219,69 +219,3 @@ def _aff_is_diag(aff):
     ''' Utility function returning True if affine is nearly diagonal '''
     rzs_aff = aff[:3, :3]
     return np.allclose(rzs_aff, np.diag(np.diag(rzs_aff)))
-
-
-def crop_image(img, bounds, margin=0):
-    ''' Crop ``img`` to specified bounds
-
-    The image is cropped in the current orientation; no rotation or resampling
-    is performed.
-    The affine matrix is updated with the new intercept, so that all values are
-    found at the same RAS locations.
-
-    Parameters
-    ----------
-    img : ``spatialimage``
-        Image to be cropped
-    bounds : (3, 2) array-like
-        Minimum and maximum (inclusive) voxel indices, in each dimension, to be
-        included in cropped image
-    margin : int, optional
-        Margin, in voxels, to include on each side of cropped image
-
-    Returns
-    -------
-    cropped_img : ``spatialimage``
-       Version of `img` with cropped data array and updated affine matrix
-    '''
-
-    shape = np.reshape(img.shape[:3], (3, 1))
-    bounds = np.asanyarray(bounds)
-    if bounds.shape != (3, 2):
-        raise ValueError("bounds must be interpretable as a 3x2 array")
-    elif np.any(bounds > shape):
-        raise ValueError("bounds must not exceed image dimensions")
-
-    # Permit negative bounds
-    if np.any(bounds < 0):
-        bounds = (bounds + shape) % shape
-
-    if np.any(bounds < 0):
-        raise ValueError("negative bounds may not exceed image dimensions")
-    elif np.any(bounds[:, 0] > bounds[:, 1]):
-        raise ValueError("degenerate (0 width) crops are not permitted")
-
-    # Add margin in all directions
-    bounds += np.array([-margin, margin])
-
-    # Set min/max
-    bounds[bounds < 0] = 0
-    over = bounds[:, 1] > shape.reshape(-1) - 1
-    bounds[over, 1] = shape[over, 0] - 1
-
-    # Include upper bounds
-    bounds[:, 1] += 1
-
-    # Return original image if no cropping required
-    if np.array_equal(bounds, np.hstack(([[0], [0], [0]], shape))):
-        return img
-
-    x, y, z = bounds
-    new_origin = np.vstack((bounds[:, [0]], 1))
-
-    bounded_data = img.get_data()[x[0]:x[1], y[0]:y[1], z[0]:z[1]]
-
-    new_aff = img.affine.copy()
-    new_aff[:, [3]] = img.affine.dot(new_origin)
-
-    return img.__class__(bounded_data, new_aff, img.header)

nibabel/tests/test_funcs.py

@@ -11,7 +11,7 @@
 
 import numpy as np
 
-from ..funcs import concat_images, as_closest_canonical, OrientationError, crop_image
+from ..funcs import concat_images, as_closest_canonical, OrientationError
 from ..analyze import AnalyzeImage
 from ..nifti1 import Nifti1Image
 from ..loadsave import save
@@ -193,22 +193,3 @@ def test_closest_canonical():
     img.header.set_dim_info(None, None, 2)
     xyz_img = as_closest_canonical(img)
     assert_true(xyz_img.header.get_dim_info() == (None, None, 1))
-
-
-def test_crop_image():
-    # Use 32-bit data so that the AnalyzeImage class doesn't complain
-    arr = np.arange(60).reshape((5, 3, 4, 1)).astype(np.int32)
-
-    img = AnalyzeImage(arr, np.eye(4))
-
-    cropped_img = crop_image(img, [[1, 3], [1, 1], [1, 2]])
-    assert_equal(cropped_img.shape, (3, 1, 2, 1))
-    assert_array_equal(cropped_img.affine, [[1, 0, 0, 1],
-                                            [0, 1, 0, 1],
-                                            [0, 0, 1, 1],
-                                            [0, 0, 0, 1]])
-
-    cropped_img = crop_image(img, [[1, 3], [1, 1], [1, 2]], margin=1)
-    assert_equal(cropped_img.shape, (5, 3, 4, 1))
-    assert_array_equal(cropped_img.affine, img.affine)
-    assert_true(cropped_img is img)