Merge pull request #1310 from kohr-h/npy19

BLD: add travis test with Numpy 1.10 and drop 1.9 support

Author: kohr-h

.gitignore

@@ -1,3 +1,38 @@
+## Custom stuff
+
+# vim viles
+*.swp
+*.swo
+*~
+
+# Spyder project files
+.spyproject/
+.spyderproject
+.spyderworkspace
+.spyproject/
+
+# PyCharm project files
+.idea
+
+# VS Code files
+.vscode/
+
+# IPython / Jupyter notebook files
+*.ipynb
+.ipynb_checkpoints
+
+# caches
+.pytest_cache/
+
+# Sphinx documentation
+doc/_build/
+doc/source/generated
+doc/source/odl_interface
+doc/source/odl*.rst
+
+
+## Python
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -51,46 +86,18 @@ coverage.xml
 # Django stuff:
 *.log
 
-# Sphinx documentation
-doc/_build/
-doc/source/generated
-doc/source/odl_interface
-doc/source/odl*.rst
-
 # PyBuilder
 target/
 
 # Backups from futurize/pasteurize
 *.py.bak
 
-# Visual studio
+
+## Visual studio
 *.sdf
 *.sln
 *.suo
 *.pyproj
 *.pyproj.user
 *.pyperf
 *.psess
-
-# vim viles
-*.swp
-*.swo
-*~
-
-# Spyder project files
-.spyproject/
-.spyderproject
-.spyderworkspace
-.spyproject/
-
-# PyCharm project files
-.idea
-
-# VS Code files
-.vscode/
-
-# IPython / Jupyter notebook files
-*.ipynb
-.ipynb_checkpoints
-
-# Documentation build files

.travis.yml

@@ -5,6 +5,9 @@ matrix:
         - python: 2.7
           env: NUMPY_VERSION=1.13
 
+        - python: 3.6
+          env: NUMPY_VERSION=1.10.1
+
         - python: 3.6
           env: NUMPY_VERSION=1.12
 
@@ -46,9 +49,10 @@ install:
     # make it run faster and avoid downloading big stuff like mkl
     - conda create -n testenv python=$TRAVIS_PYTHON_VERSION nomkl pywavelets
     - source activate testenv
-    # We avoid annoying issues with building pyfftw wheels by using a conda-forge version
-    - pip install "numpy==$NUMPY_VERSION" scipy future scikit-image pytest pytest-cov  # faster than conda
-    - conda install -c conda-forge pyfftw  # compilation sometimes breaks on pip, using conda therefore
+    # Install packages with pip if possible, it's way faster
+    - pip install "numpy==$NUMPY_VERSION" scipy future scikit-image pytest pytest-cov;
+    # Building pyfftw wheels sometimes fails, using a conda-forge version instead
+    - conda install -c conda-forge pyfftw
 
     # Doc dependencies
     - if [[ "$BUILD_DOCS" == "true" ]]; then

conda/meta.yaml

@@ -19,13 +19,13 @@ requirements:
         - setuptools
         - nomkl # [not win]
         - future >=0.14
-        - numpy >=1.9
+        - numpy >=1.10
         - scipy >=0.14
     run:
         - python
         - future >=0.14
         - nomkl # [not win]
-        - numpy >=1.9
+        - numpy >=1.10
         - scipy >=0.14
         - matplotlib
         - pytest >=3.0.3

odl/space/fspace.py

@@ -21,8 +21,7 @@
     is_real_dtype, is_complex_floating_dtype, dtype_repr, dtype_str,
     complex_dtype, real_dtype, signature_string,
     is_valid_input_array, is_valid_input_meshgrid,
-    out_shape_from_array, out_shape_from_meshgrid, vectorize, broadcast_to,
-    writable_array)
+    out_shape_from_array, out_shape_from_meshgrid, vectorize, writable_array)
 from odl.util.utility import preload_first_arg, getargspec
 
 
@@ -93,7 +92,7 @@ def _default_in_place(func, x, out, **kwargs):
         else:
             raise RuntimeError('bad input')
 
-        bcast_results = [broadcast_to(res, scalar_out_shape)
+        bcast_results = [np.broadcast_to(res, scalar_out_shape)
                          for res in flat_results]
         # New array that is flat in the `out_shape` axes, reshape it
         # to the final `out_shape + scalar_shape`, using the same
@@ -569,7 +568,7 @@ def wrapper(x, out=None, **kwargs):
                             reshaped = np.reshape(res, scalar_out_shape)
                         except ValueError:
                             bcast_results.append(
-                                broadcast_to(res, scalar_out_shape))
+                                np.broadcast_to(res, scalar_out_shape))
                         else:
                             bcast_results.append(reshaped)
 
@@ -1331,7 +1330,7 @@ def __call__(self, x, out=None, **kwargs):
                     # Broadcast the returned element, but not in the
                     # scalar case. The resulting array may be read-only,
                     # in which case we copy.
-                    out = broadcast_to(out, out_shape)
+                    out = np.broadcast_to(out, out_shape)
                     if not out.flags.writeable:
                         out = out.copy()
 
@@ -1353,7 +1352,8 @@ def __call__(self, x, out=None, **kwargs):
                             shp = getattr(res, 'shape', ())
                             if shp and shp[0] == 1:
                                 res = res.reshape(res.shape[1:])
-                        bcast_res.append(broadcast_to(res, scalar_out_shape))
+                        bcast_res.append(
+                            np.broadcast_to(res, scalar_out_shape))
 
                     out_arr = np.array(bcast_res,
                                        dtype=self.space.scalar_out_dtype)

odl/test/space/tensors_test.py

@@ -1358,11 +1358,11 @@ def test_ufuncs(tspace, ufunc):
     data_elem = elements[0]
 
     out_elems = elements[nin:]
-    out_arr_kwargs = {'out': out_arrays[:nout]}
-
     if nout == 1:
+        out_arr_kwargs = {'out': out_arrays[0]}
         out_elem_kwargs = {'out': out_elems[0]}
     elif nout > 1:
+        out_arr_kwargs = {'out': out_arrays[:nout]}
         out_elem_kwargs = {'out': out_elems[:nout]}
 
     # Get function to call, using both interfaces:

odl/tomo/analytic/filtered_back_projection.py

@@ -11,7 +11,6 @@
 
 from odl.discr import ResizingOperator
 from odl.trafos import FourierTransform, PYFFTW_AVAILABLE
-from odl.util.npy_compat import broadcast_to
 
 
 __all__ = ('fbp_op', 'fbp_filter_op', 'tam_danielson_window',
@@ -293,7 +292,7 @@ def b(alpha):
 
     scale = 0.5 * alen / np.pi
     return ray_trafo.range.element(
-        broadcast_to(S_sum * scale, ray_trafo.range.shape))
+        np.broadcast_to(S_sum * scale, ray_trafo.range.shape))
 
 
 def fbp_filter_op(ray_trafo, padding=True, filter_type='Ram-Lak',

odl/tomo/backends/astra_setup.py

@@ -39,6 +39,7 @@
     Geometry, DivergentBeamGeometry, ParallelBeamGeometry,
     Flat1dDetector, Flat2dDetector)
 from odl.tomo.util.utility import euler_matrix
+from odl.util.npy_compat import moveaxis
 
 # Make sure that ASTRA >= 1.7 is used
 if ASTRA_AVAILABLE:
@@ -275,7 +276,7 @@ def astra_conebeam_3d_geom_to_vec(geometry):
 
     # Vectors from detector pixel (0, 0) to (1, 0) and (0, 0) to (0, 1)
     # `det_axes` gives shape (N, 2, 3), swap to get (2, N, 3)
-    det_axes = np.moveaxis(geometry.det_axes(angles), -2, 0)
+    det_axes = moveaxis(geometry.det_axes(angles), -2, 0)
     px_sizes = geometry.det_partition.cell_sides
     # Swap detector axes to have better memory layout in  projection data.
     # ASTRA produces `(v, theta, u)` layout, and to map to ODL layout
@@ -403,7 +404,7 @@ def astra_parallel_3d_geom_to_vec(geometry):
 
     # Vectors from detector pixel (0, 0) to (1, 0) and (0, 0) to (0, 1)
     # `det_axes` gives shape (N, 2, 3), swap to get (2, N, 3)
-    det_axes = np.moveaxis(geometry.det_axes(angles), -2, 0)
+    det_axes = moveaxis(geometry.det_axes(angles), -2, 0)
     px_sizes = geometry.det_partition.cell_sides
     # Swap detector axes to have better memory layout in  projection data.
     # ASTRA produces `(v, theta, u)` layout, and to map to ODL layout

odl/tomo/geometry/detector.py

@@ -15,6 +15,7 @@
 from odl.discr import RectPartition
 from odl.tomo.util import perpendicular_vector, is_inside_bounds
 from odl.util import indent, signature_string, array_str
+from odl.util.npy_compat import moveaxis
 
 
 __all__ = ('Detector',
@@ -187,7 +188,7 @@ def surface_normal(self, param):
             deriv = self.surface_deriv(param)
             if deriv.ndim > 2:
                 # Vectorized, need to reshape (N, 2, 3) to (2, N, 3)
-                deriv = np.moveaxis(deriv, -2, 0)
+                deriv = moveaxis(deriv, -2, 0)
             normal = np.cross(*deriv, axis=-1)
             normal /= np.linalg.norm(normal, axis=-1, keepdims=True)
             return normal
@@ -243,7 +244,7 @@ def surface_measure(self, param):
             deriv = self.surface_deriv(param)
             if deriv.ndim > 2:
                 # Vectorized, need to reshape (N, 2, 3) to (2, N, 3)
-                deriv = np.moveaxis(deriv, -2, 0)
+                deriv = moveaxis(deriv, -2, 0)
             cross = np.cross(*deriv, axis=-1)
             measure = np.linalg.norm(cross, axis=-1)
             if scalar_out:
@@ -401,10 +402,9 @@ def surface_deriv(self, param):
             return self.axis
         else:
             # Produce array of shape `param.shape + (ndim,)` by broadcasting
-            axis_slc = (None,) * param.ndim + (slice(None),)
-            # TODO: use broadcast_to from Numpy when v1.10 is required
-            zeros = np.zeros(param.shape + (1,))
-            return self.axis[axis_slc] + zeros
+            bcast_slc = (None,) * param.ndim + (slice(None),)
+            return np.broadcast_to(
+                self.axis[bcast_slc], param.shape + self.axis.shape)
 
     def __repr__(self):
         """Return ``repr(self)``."""
@@ -611,13 +611,8 @@ def surface_deriv(self, param):
         if squeeze_out:
             return self.axes
         else:
-            # Produce array of shape `broadcast(*param).shape + (2, 3)`
-            # by explicit broadcasting.
-            # TODO: use broadcast_to from Numpy when v1.10 is required
-            axes_slc = ((None,) * len(np.broadcast(*param).shape) +
-                        (slice(None), slice(None)))
-            zeros = np.zeros(np.broadcast(*param).shape + (1, 1))
-            return self.axes[axes_slc] + zeros
+            return np.broadcast_to(
+                self.axes, np.broadcast(*param).shape + self.axes.shape)
 
     def __repr__(self):
         """Return ``repr(self)``."""

odl/util/normalize.py

@@ -106,29 +106,8 @@ def normalized_scalar_param_list(param, length, param_conv=None,
         raise ValueError('`length` must be nonnegative, got {}'
                          ''.format(length_in))
 
-    try:
-        # TODO: always use this when numpy >= 1.10 can be assumed
-        param = np.array(param, dtype=object, copy=True, ndmin=1)
-        nonconv_list = list(np.broadcast_to(param, (length,)))
-    except AttributeError:
-        # numpy.broadcast_to not available
-        if np.isscalar(param):
-            # Try this first, will work better with iterable input like '10'
-            nonconv_list = [param] * length
-        else:
-            try:
-                param_len = len(param)
-            except TypeError:
-                # Not a sequence -> single parameter
-                nonconv_list = [param] * length
-            else:
-                if param_len == 1:
-                    nonconv_list = list(param) * length
-                elif param_len == length:
-                    nonconv_list = list(param)
-                else:
-                    raise ValueError('sequence `param` has length {}, '
-                                     'expected {}'.format(param_len, length))
+    param = np.array(param, dtype=object, copy=True, ndmin=1)
+    nonconv_list = list(np.broadcast_to(param, (length,)))
 
     if len(nonconv_list) != length:
         raise ValueError('sequence `param` has length {}, expected {}'

odl/util/npy_compat.py

@@ -12,28 +12,7 @@
 import numpy as np
 
 
-__all__ = ('broadcast_to', 'moveaxis')
-
-
-# TODO: Remove when Numpy 1.10 is an ODL dependency
-def broadcast_to(array, shape):
-    """Broadcast an array to a new shape.
-
-    This function is a backport of `numpy.broadcast_to` introduced in
-    NumPy 1.10.
-
-    See Also
-    --------
-    numpy.broadcast_to
-    """
-    array = np.asarray(array)
-    try:
-        return np.broadcast_to(array, shape)
-    except AttributeError:
-        # The above requires numpy 1.10, fallback impl else
-        shape = [m if n == 1 and m != 1 else 1
-                 for n, m in zip(array.shape, shape)]
-        return array + np.zeros(shape, dtype=array.dtype)
+__all__ = ('moveaxis', 'flip')
 
 
 # TODO: Remove when Numpy 1.11 is an ODL dependency

odl/util/testutils.py

@@ -327,7 +327,8 @@ def noise_array(space):
         return np.array([noise_array(si) for si in space])
     else:
         if space.dtype == bool:
-            arr = np.random.randint(0, 2, size=space.shape, dtype=bool)
+            # TODO(kohr-h): use `randint(..., dtype=bool)` from Numpy 1.11 on
+            arr = np.random.randint(0, 2, size=space.shape).astype(bool)
         elif np.issubdtype(space.dtype, np.unsignedinteger):
             arr = np.random.randint(0, 10, space.shape)
         elif np.issubdtype(space.dtype, np.signedinteger):

requirements.txt

@@ -1,3 +1,3 @@
 future >=0.14
-numpy >=1.9,!=1.14.0
+numpy >=1.10,!=1.14.0
 scipy >=0.14