Merge pull request #27 from glotzerlab/remove_py2

Remove support for Python < 3.6, Numpy < 1.15

ChangeLog.rst

@@ -2,7 +2,7 @@ The format is based on `Keep a Changelog <http://keepachangelog.com/en/1.0.0/>`_
 This project adheres to `Semantic Versioning <http://semver.org/spec/v2.0.0.html>`_.
 
 
-Unreleased
+v1.3.0 - xxxx-xx-xx
 ----------
 
 Fixed
@@ -11,6 +11,11 @@ Fixed
 * Docstring of geometry.angle was missing a factor of 2 in the comparison to intrinsic_distance.
 * Docstrings of functions using support1d decorator were losing their docstring (fixed with functools.wraps).
 
+Changed
++++++++
+
+* Drop Python 2 support.
+
 v1.2.2 - 2019-09-11
 -------------------
 

README.md

@@ -54,13 +54,13 @@ python setup.py install --user
 
 ### Requirements
 
-*   Python = 2.7, >= 3.3
-*   NumPy >= 1.10
+*   Python = >= 3.6
+*   NumPy >= 1.15
 
 ## Testing
 
-The package is currently tested for Python versions 2.7 and Python >= 3.3 on Unix-like systems.
-Continuous integrated testing is performed using CircleCI on these Python versions with NumPy versions 1.10 and above.
+The package is currently tested for Python >= 3.6 on Unix-like systems.
+Continuous integrated testing is performed using CircleCI on these Python versions with NumPy versions 1.15 and above.
 
 To run the packaged unit tests, execute the following line from the root of the repository:
 

doc/index.rst

@@ -57,12 +57,12 @@ Core features of rowan include (but are not limited to):
     package-interpolate
     package-mapping
     package-random
-    development
 
 .. toctree::
     :maxdepth: 1
     :caption: Reference:
 
+    development
     license
     changelog
     credits
@@ -75,8 +75,8 @@ Requirements
 
 The minimum requirements for using rowan are:
 
-* Python = 2.7, >= 3.3
-* NumPy >= 1.10
+* Python >= 3.6
+* NumPy >= 1.15
 
 Installation
 ------------
@@ -133,8 +133,8 @@ For example:
 Running Tests
 -------------
 
-The package is currently tested for Python versions 2.7 and Python >= 3.3 on Unix-like systems.
-Continuous integrated testing is performed using CircleCI on these Python versions with NumPy versions 1.10 and above.
+The package is currently tested for Python >= 3.6 on Unix-like systems.
+Continuous integrated testing is performed using CircleCI on these Python versions with NumPy versions 1.15 and above.
 
 To run the packaged unit tests, execute the following line from the root of the repository:
 

requirements.txt

@@ -1,4 +1,4 @@
 ###### Requirements without Version Specifiers ######
 
 ###### Requirements with Version Specifiers ######
-numpy>=1.10
+numpy>=1.15

rowan/__init__.py

@@ -16,8 +16,6 @@
 not match and return results according to NumPy's broadcasting rules.
 """
 
-from __future__ import division, print_function, absolute_import
-
 from . import calculus
 from . import geometry
 from . import interpolate
@@ -38,7 +36,7 @@
 __all__ = ['calculus', 'geometry', 'interpolate', 'mapping', 'random',
            'allclose', 'conjugate', 'divide', 'exp', 'expb', 'exp10', 'equal',
            'from_axis_angle', 'from_euler', 'from_matrix', 'from_mirror_plane',
-           'inverse', 'isclose', 'isinf', 'isfinite', 'isnan', 'is_unit', 'log',
-           'logb', 'log10', 'multiply', 'norm', 'normalize', 'not_equal',
-           'power', 'reflect', 'rotate', 'to_axis_angle', 'to_euler',
-           'to_matrix', 'vector_vector_rotation']
+           'inverse', 'isclose', 'isinf', 'isfinite', 'isnan', 'is_unit',
+           'log', 'logb', 'log10', 'multiply', 'norm', 'normalize',
+           'not_equal', 'power', 'reflect', 'rotate', 'to_axis_angle',
+           'to_euler', 'to_matrix', 'vector_vector_rotation']

rowan/calculus/__init__.py

@@ -4,7 +4,6 @@
 R"""This subpackage provides the ability to compute the derivative and
 integral of a quaternion.
 """
-from __future__ import division, print_function, absolute_import
 
 import numpy as np
 

rowan/functions.py

@@ -2,47 +2,10 @@
 # All rights reserved.
 # This software is licensed under the BSD 3-Clause License.
 R"""Submodule containing all standard functions"""
-from __future__ import division, print_function, absolute_import
 
 import numpy as np
 
 
-def _support_1d(func):
-    R"""Decorator to raise input array shape to 2D and flatten on output.
-
-    In order to support arbitrary-dimensional arrays, rowan makes liberal use
-    of fancy NumPy indexing tricks, in particular the '...' indexer. However,
-    NumPy versions older than 1.13 fail for inputs that are 1-dimensional that
-    use this approach. For backwards compatibility with older NumPy versions,
-    this decorator manually promotes input arrays to at least 2-dimensional
-    arrays, then flattens them back to the input shape if needed. This function
-    can be removed when we officially drop support for NumPy < 1.13
-
-    Currently the decorator is only designed for functions that take a single
-    argument.
-    """
-
-    from functools import wraps
-
-    # Wrapper function to ensure that input arrays are at least 2-dimensional.
-    @wraps(func)
-    def func_atleast_2d(q, *args, **kwargs):
-        q = np.asarray(q)
-        if len(q.shape) == 1:
-            flat = True
-            q = np.atleast_2d(q)
-        else:
-            flat = False
-        ret = func(q, *args, **kwargs)
-        if flat:
-            return ret.squeeze()
-        else:
-            return ret
-
-    return func_atleast_2d
-
-
-@_support_1d
 def exp(q):
     R"""Computes the natural exponential function :math:`e^q`.
 
@@ -136,7 +99,6 @@ def exp10(q):
     return expb(q, 10)
 
 
-@_support_1d
 def log(q):
     R"""Computes the quaternion natural logarithm.
 
@@ -246,7 +208,6 @@ def log10(q):
     return logb(q, 10)
 
 
-@_support_1d
 def power(q, n):
     R"""Computes the power of a quaternion :math:`q^n`.
 
@@ -308,7 +269,6 @@ def conjugate(q):
     return conjugate
 
 
-@_support_1d
 def inverse(q):
     R"""Computes the inverse of an array of quaternions.
 

rowan/geometry/__init__.py

@@ -9,7 +9,6 @@
 between rotations. An overview of distance measurements can be found in
 `this paper <https://link.springer.com/article/10.1007/s10851-009-0161-2>`_.
 """
-from __future__ import division, print_function, absolute_import
 
 import numpy as np
 
@@ -85,10 +84,10 @@ def riemann_exp_map(p, v):
     The nonzero quaternions form a Lie algebra :math:`\mathbb{H}^*` that
     is also a Riemannian manifold. In general, given a point :math:`p` on a
     Riemannian manifold :math:`\mathcal{M}` and an element of the tangent
-    space at :math:`p`, :math:`v \in T_p\mathcal{M}`, the Riemannian exponential
-    map is defined by the geodesic starting at :math:`p` and tracing out
-    an arc of length :math:`v` in the direction of :math:`v`. This function
-    computes the endpoint of that path (which is itself a quaternion).
+    space at :math:`p`, :math:`v \in T_p\mathcal{M}`, the Riemannian
+    exponential map is defined by the geodesic starting at :math:`p` and
+    tracing out an arc of length :math:`v` in the direction of :math:`v`. This
+    function computes the endpoint of that path (which is itself a quaternion).
 
     Explicitly, we define the exponential map as
 
@@ -117,9 +116,9 @@ def riemann_log_map(p, q):
     R"""Compute the log map on the Riemannian manifold :math:`\mathbb{H}^*` of
     nonzero quaterions.
 
-    This function inverts :py:func:`riemann_exp_map`. See that function for more
-    details. In brief, given two quaternions p and q, this method returns a
-    third quaternion parameterizing the geodesic passing from p to q. It is
+    This function inverts :py:func:`riemann_exp_map`. See that function for
+    more details. In brief, given two quaternions p and q, this method returns
+    a third quaternion parameterizing the geodesic passing from p to q. It is
     therefore an important measure of the distance between the two input
     quaternions.
 
@@ -128,8 +127,8 @@ def riemann_log_map(p, q):
         q ((..., 4) np.array): Endpoints (quaternions).
 
     Returns:
-        Array of shape (..., 4) containing quaternions pointing from p to q with
-        magnitudes equal to the length of the geodesics joining these
+        Array of shape (..., 4) containing quaternions pointing from p to q
+        with magnitudes equal to the length of the geodesics joining these
         quaternions.
 
     Example::

rowan/interpolate/__init__.py

@@ -5,7 +5,6 @@
 The rowan package provides a simple interface to slerp, the standard method
 of quaternion interpolation for two quaternions.
 """
-from __future__ import division, print_function, absolute_import
 
 import numpy as np
 

rowan/mapping/__init__.py

@@ -15,8 +15,8 @@
 If points in the two sets have a known correspondence, the problem is much
 simpler. Various precise formulations exist that admit analytical formulations,
 such as the `orthogonal Procrustes problem
-<https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem>`_ searching for an
-orthogonal transformation
+<https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem>`_ searching for
+an orthogonal transformation
 
 .. math::
     \begin{equation}
@@ -29,21 +29,21 @@
 .. math::
     \begin{equation}
         \min_{\boldsymbol{R} \in SO(3)} \frac{1}{2} \sum_{k=1}^N a_k \lvert
-        \lvert \boldsymbol{w}_k - \boldsymbol{R} \boldsymbol{v}_k \rvert\rvert^2
+        \lvert \boldsymbol{w}_k - \boldsymbol{R}\boldsymbol{v}_k\rvert\rvert^2
     \end{equation}
 
 Numerous algorithms to solve this problem exist, particularly in the field of
-aerospace engineering and robotics where this problem must be solved on embedded
-systems with limited processing. Since that constraint does not apply here, this
-package simply implements some of the most stable known methods irrespective of
-cost. In particular, this package contains the Kabsch algorithm, which solves
-Wahba's problem using an SVD in the vein of `Peter Schonemann's original
-solution <https://link.springer.com/article/10.1007/BF02289451>`_ to
-the orthogonal Procrustes problem. Additionally this package contains the
-`Davenport q method <https://ntrs.nasa.gov/search.jsp?R=19670009376>`_, which
-works directly with quaternions. The most popular algorithms for Wahba's problem
-are variants of the q method that are faster at the cost of some stability; we
-omit these here.
+aerospace engineering and robotics where this problem must be solved on
+embedded systems with limited processing. Since that constraint does not apply
+here, this package simply implements some of the most stable known methods
+irrespective of cost. In particular, this package contains the Kabsch
+algorithm, which solves Wahba's problem using an SVD in the vein of `Peter
+Schonemann's original solution
+<https://link.springer.com/article/10.1007/BF02289451>`_ to the orthogonal
+Procrustes problem. Additionally this package contains the `Davenport q method
+<https://ntrs.nasa.gov/search.jsp?R=19670009376>`_, which works directly with
+quaternions. The most popular algorithms for Wahba's problem are variants of
+the q method that are faster at the cost of some stability; we omit these here.
 
 In addition, :py:mod:`rowan.mapping` also includes some functionality for
 more general point set registration. If a point cloud has a set of known
@@ -52,7 +52,6 @@
 correspondence is known at all, then the iterative closest point algorithm can
 be used to approximate the mapping.
 """
-from __future__ import division, print_function, absolute_import
 
 import numpy as np
 
@@ -465,7 +464,8 @@ def icp(X, Y, method='best', unique_match=True, max_iterations=20,
             row_ind, indices = optimize.linear_sum_assignment(pair_distances)
             distances = pair_distances[row_ind, indices]
         else:
-            distances, indices = nn.kneighbors(cur_points, return_distance=True)
+            distances, indices = nn.kneighbors(
+                cur_points, return_distance=True)
             distances = distances.ravel()
             indices = indices.ravel()
 

rowan/random/__init__.py

@@ -14,7 +14,6 @@
 #
 #    other alternatives:
 #    http://home.lu.lv/~sd20008/papers/essays/Random%20unitary%20[paper].pdf
-from __future__ import division, print_function, absolute_import
 
 import numpy as np
 
@@ -24,7 +23,7 @@
 
 
 def rand(*args):
-    R"""Generate random rotations that are uniformly distributed on a unit sphere.
+    R"""Generate random rotations uniformly distributed on a unit sphere.
 
     This is a convenience function *a la* ``np.random.rand``. If you want a
     function that takes a tuple as input, use :py:func:`random_sample` instead.

setup.py

@@ -15,6 +15,9 @@
 except ImportError:
     readme = desc
 
+# Supported versions are determined according to NEP 29.
+# https://numpy.org/neps/nep-0029-deprecation_policy.html
+
 setup(name='rowan',
       version=version,
       description=desc,
@@ -26,18 +29,15 @@
       packages=find_packages(exclude=["tests"]),
       zip_safe=True,
       install_requires=[
-          'numpy>=1.10'
+          'numpy>=1.15'
       ],
-      python_requires='>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, <4',
+      python_requires='>=3.6, <4',
       classifiers=[
-          'Development Status :: 3 - Alpha',
+          'Development Status :: 6 - Mature',
           'License :: OSI Approved :: BSD License',
-          'Programming Language :: Python :: 2.7',
-          'Programming Language :: Python :: 3.3',
-          'Programming Language :: Python :: 3.4',
-          'Programming Language :: Python :: 3.5',
           'Programming Language :: Python :: 3.6',
           'Programming Language :: Python :: 3.7',
+          'Programming Language :: Python :: 3.8',
           'Topic :: Scientific/Engineering :: Mathematics',
       ],
       )

tox.ini

@@ -1,14 +1,12 @@
 [tox]
-envlist=py{27,34,35,36}-numpy{10,11,12,13,14}
+envlist=py{36,37,38}-numpy{15,16,17,18}
 
 [testenv]
 deps=
-  setuptools==38.3.0
-  numpy10: numpy==1.10.4
-  numpy11: numpy==1.11.3
-  numpy12: numpy==1.12.1
-  numpy13: numpy==1.13.3
-  numpy14: numpy==1.14.1
+  numpy15: numpy==1.15.4
+  numpy16: numpy==1.16.6
+  numpy17: numpy==1.17.5
+  numpy18: numpy==1.18.5
   scipy==1.2.1
 commands=
   python -m unittest discover tests