CI: add 32-bit Win to CI

azure-pipelines.yml

@@ -0,0 +1,49 @@
+trigger:
+  # start a new build for every push
+  batch: False
+  branches:
+    include:
+      - develop
+  paths:
+    include:
+      - '*'
+
+pr:
+  branches:
+    include:
+    - '*'  # must quote since "*" is a YAML reserved character; we want a string
+
+
+jobs:
+- job: Windows
+  condition: and(succeeded(), ne(variables['Build.SourceBranch'], 'refs/heads/master'))  # skip for PR merges
+  pool:
+    vmImage: 'VS2017-Win2016'
+  strategy:
+    maxParallel: 4
+    matrix:
+        Python37-32bit-full:
+          PYTHON_VERSION: '3.7'
+          PYTHON_ARCH: 'x86'
+          BITS: 32
+  steps:
+  - task: UsePythonVersion@0
+    inputs:
+      versionSpec: $(PYTHON_VERSION)
+      addToPath: true
+      architecture: $(PYTHON_ARCH)
+  - script: python -m pip install --upgrade pip setuptools wheel
+    displayName: 'Install tools'
+  - script: python -m pip install numpy scipy cython pytest pytest-xdist matplotlib
+    displayName: 'Install dependencies'
+  - powershell: |
+      cd package
+      python setup.py install
+      cd ..\testsuite
+      python setup.py install
+      cd ..
+    displayName: 'Build MDAnalysis'
+  - powershell: |
+      cd testsuite
+      pytest .\MDAnalysisTests --disable-pytest-warnings -n 2
+    displayName: 'Run MDAnalysis Test Suite'

package/MDAnalysis/core/selection.py

@@ -226,7 +226,7 @@ class ByResSelection(UnarySelection):
 
     def apply(self, group):
         res = self.sel.apply(group)
-        unique_res = unique_int_1d(res.resindices.astype(np.int64))
+        unique_res = unique_int_1d(res.resindices.astype(np.intp))
         mask = np.in1d(group.resindices, unique_res)
 
         return group[mask].unique

package/MDAnalysis/core/topologyattrs.py

@@ -1474,7 +1474,7 @@ class Molnums(ResidueAttr):
     attrname = 'molnums'
     singular = 'molnum'
     target_classes = [AtomGroup, ResidueGroup, Atom, Residue]
-    dtype = np.int64
+    dtype = np.intp
 
 # segment attributes
 
@@ -1730,7 +1730,7 @@ def fragindices(self):
         .. versionadded:: 0.20.0
         """
         fragdict = self.universe._fragdict
-        return np.array([fragdict[aix].ix for aix in self.ix], dtype=np.int64)
+        return np.array([fragdict[aix].ix for aix in self.ix], dtype=np.intp)
 
     def fragment(self):
         """An :class:`~MDAnalysis.core.groups.AtomGroup` representing the

package/MDAnalysis/lib/NeighborSearch.py

@@ -93,7 +93,7 @@ def search(self, atoms, radius, level='A'):
                                 radius, box=self._box, return_distances=False)
 
         if pairs.size > 0:
-            unique_idx = unique_int_1d(np.asarray(pairs[:, 1], dtype=np.int64))
+            unique_idx = unique_int_1d(np.asarray(pairs[:, 1], dtype=np.intp))
         return self._index2level(unique_idx, level)
 
     def _index2level(self, indices, level):

package/MDAnalysis/lib/_augment.pyx

@@ -294,12 +294,12 @@ def augment_coordinates(float[:, ::1] coordinates, float[:] box, float r):
                 output.push_back(coord[j] - shiftZ[j])
             indices.push_back(i)
     n = indices.size()
-    return np.asarray(output, dtype=np.float32).reshape(n, 3), np.asarray(indices, dtype=np.int64)
+    return np.asarray(output, dtype=np.float32).reshape(n, 3), np.asarray(indices, dtype=np.intp)
 
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-def undo_augment(np.int64_t[:] results, np.int64_t[:] translation, int nreal):
+def undo_augment(np.intp_t[:] results, np.intp_t[:] translation, int nreal):
     """Translate augmented indices back to original indices.
 
     Parameters
@@ -339,4 +339,4 @@ def undo_augment(np.int64_t[:] results, np.int64_t[:] translation, int nreal):
     for i in range(N):
         if results[i] >= nreal:
             results[i] = translation[results[i] - nreal]
-    return np.asarray(results, dtype=np.int64)
+    return np.asarray(results, dtype=np.intp)

package/MDAnalysis/lib/_cutil.pyx

@@ -49,7 +49,7 @@ ctypedef cmap[int, intset] intmap
 
 @cython.boundscheck(False) # turn off bounds-checking for entire function
 @cython.wraparound(False)  # turn off negative index wrapping for entire function
-def unique_int_1d(np.int64_t[:] values):
+def unique_int_1d(np.intp_t[:] values):
     """Find the unique elements of a 1D array of integers.
 
     This function is optimal on sorted arrays.
@@ -71,7 +71,7 @@ def unique_int_1d(np.int64_t[:] values):
     cdef int i = 0
     cdef int j = 0
     cdef int n_values = values.shape[0]
-    cdef np.int64_t[:] result = np.empty(n_values, dtype=np.int64)
+    cdef np.intp_t[:] result = np.empty(n_values, dtype=np.intp)
 
     if n_values == 0:
         return np.array(result)

package/MDAnalysis/lib/distances.py

@@ -526,7 +526,7 @@ def _bruteforce_capped(reference, configuration, max_cutoff, min_cutoff=None,
         ``configuration[pairs[k, 1]]``.
     """
     # Default return values (will be overwritten only if pairs are found):
-    pairs = np.empty((0, 2), dtype=np.int64)
+    pairs = np.empty((0, 2), dtype=np.intp)
     distances = np.empty((0,), dtype=np.float64)
 
     if len(reference) > 0 and len(configuration) > 0:
@@ -605,7 +605,7 @@ def _pkdtree_capped(reference, configuration, max_cutoff, min_cutoff=None,
     from .pkdtree import PeriodicKDTree  # must be here to avoid circular import
 
     # Default return values (will be overwritten only if pairs are found):
-    pairs = np.empty((0, 2), dtype=np.int64)
+    pairs = np.empty((0, 2), dtype=np.intp)
     distances = np.empty((0,), dtype=np.float64)
 
     if len(reference) > 0 and len(configuration) > 0:
@@ -685,7 +685,7 @@ def _nsgrid_capped(reference, configuration, max_cutoff, min_cutoff=None,
         ``configuration[pairs[k, 1]]``.
     """
     # Default return values (will be overwritten only if pairs are found):
-    pairs = np.empty((0, 2), dtype=np.int64)
+    pairs = np.empty((0, 2), dtype=np.intp)
     distances = np.empty((0,), dtype=np.float64)
 
     if len(reference) > 0 and len(configuration) > 0:
@@ -919,7 +919,7 @@ def _bruteforce_capped_self(reference, max_cutoff, min_cutoff=None, box=None,
        Added `return_distances` keyword.
     """
     # Default return values (will be overwritten only if pairs are found):
-    pairs = np.empty((0, 2), dtype=np.int64)
+    pairs = np.empty((0, 2), dtype=np.intp)
     distances = np.empty((0,), dtype=np.float64)
 
     N = len(reference)
@@ -996,7 +996,7 @@ def _pkdtree_capped_self(reference, max_cutoff, min_cutoff=None, box=None,
     from .pkdtree import PeriodicKDTree  # must be here to avoid circular import
 
     # Default return values (will be overwritten only if pairs are found):
-    pairs = np.empty((0, 2), dtype=np.int64)
+    pairs = np.empty((0, 2), dtype=np.intp)
     distances = np.empty((0,), dtype=np.float64)
 
     # We're searching within a single coordinate set, so we need at least two
@@ -1068,7 +1068,7 @@ def _nsgrid_capped_self(reference, max_cutoff, min_cutoff=None, box=None,
        Added `return_distances` keyword.
     """
     # Default return values (will be overwritten only if pairs are found):
-    pairs = np.empty((0, 2), dtype=np.int64)
+    pairs = np.empty((0, 2), dtype=np.intp)
     distances = np.empty((0,), dtype=np.float64)
 
     # We're searching within a single coordinate set, so we need at least two

package/MDAnalysis/lib/nsgrid.pyx

@@ -385,7 +385,7 @@ cdef class NSResults(object):
             and initial atom coordinates of shape ``(N, 2)``
         """
 
-        return np.asarray(self.pairs_buffer, dtype=np.int64).reshape(self.npairs, 2)
+        return np.asarray(self.pairs_buffer, dtype=np.intp).reshape(self.npairs, 2)
 
     def get_pair_distances(self):
         """Returns all the distances corresponding to each pair of neighbors

package/MDAnalysis/lib/pkdtree.py

@@ -189,7 +189,7 @@ def search(self, centers, radius):
                                                       radius))
             self._indices = np.array(list(
                                      itertools.chain.from_iterable(indices)),
-                                     dtype=np.int64)
+                                     dtype=np.intp)
             if self._indices.size > 0:
                 self._indices = undo_augment(self._indices,
                                              self.mapping,
@@ -200,7 +200,7 @@ def search(self, centers, radius):
                                                       radius))
             self._indices = np.array(list(
                                      itertools.chain.from_iterable(indices)),
-                                     dtype=np.int64)
+                                     dtype=np.intp)
         self._indices = np.asarray(unique_int_1d(self._indices))
         return self._indices
 
@@ -234,7 +234,7 @@ def search_pairs(self, radius):
             if self.cutoff < radius:
                 raise RuntimeError('Set cutoff greater or equal to the radius.')
 
-        pairs = np.array(list(self.ckdt.query_pairs(radius)), dtype=np.int64)
+        pairs = np.array(list(self.ckdt.query_pairs(radius)), dtype=np.intp)
         if self.pbc:
             if len(pairs) > 1:
                 pairs[:, 0] = undo_augment(pairs[:, 0], self.mapping,
@@ -294,7 +294,7 @@ class initialization
             other_tree = cKDTree(wrapped_centers, leafsize=self.leafsize)
             pairs = other_tree.query_ball_tree(self.ckdt, radius)
             pairs = np.array([[i, j] for i, lst in enumerate(pairs) for j in lst],
-                                   dtype=np.int64)
+                                   dtype=np.intp)
             if pairs.size > 0:
                 pairs[:, 1] = undo_augment(pairs[:, 1],
                                              self.mapping,
@@ -303,7 +303,7 @@ class initialization
             other_tree = cKDTree(centers, leafsize=self.leafsize)
             pairs = other_tree.query_ball_tree(self.ckdt, radius)
             pairs = np.array([[i, j] for i, lst in enumerate(pairs) for j in lst],
-                                   dtype=np.int64)
+                                   dtype=np.intp)
         if pairs.size > 0:
             pairs = unique_rows(pairs)
         return pairs

testsuite/MDAnalysisTests/analysis/test_density.py

@@ -398,6 +398,8 @@ class TestNotWithin(object):
     def u():
         return mda.Universe(GRO)
 
+    @pytest.mark.skipif(sys.maxsize <= 2**32,
+                        reason="non-kdtree density too large for 32-bit")
     def test_within(self, u):
         vers1 = density.notwithin_coordinates_factory(u, 'resname SOL',
                                                       'protein', 2,
@@ -410,6 +412,8 @@ def test_within(self, u):
 
         assert_equal(vers1, vers2)
 
+    @pytest.mark.skipif(sys.maxsize <= 2**32,
+                        reason="non-kdtree density too large for 32-bit")
     def test_not_within(self, u):
         vers1 = density.notwithin_coordinates_factory(u, 'resname SOL',
                                                      'protein', 2,

testsuite/MDAnalysisTests/analysis/test_hole2.py

@@ -73,6 +73,8 @@ def test_relative(self):
             fixed = check_and_fix_long_filename(abspath)
             assert fixed == self.filename
 
+    @pytest.mark.skipif(os.name == 'nt' and sys.maxsize <= 2**32,
+                        reason="FileNotFoundError on Win 32-bit")
     def test_symlink_dir(self, tmpdir):
         dirname = 'really_'*20 +'long_name'
         short_name = self.filename[-20:]
@@ -86,6 +88,8 @@ def test_symlink_dir(self, tmpdir):
             assert os.path.islink(fixed)
             assert fixed.endswith(short_name)
 
+    @pytest.mark.skipif(os.name == 'nt' and sys.maxsize <= 2**32,
+                        reason="OSError: symbolic link privilege not held")
     def test_symlink_file(self, tmpdir):
         long_name = 'a'*10 + self.filename
 

testsuite/MDAnalysisTests/core/test_fragments.py

@@ -129,7 +129,7 @@ def test_total_frags(self, u):
         # number of unique fragindices must correspond to number of fragments:
         assert len(np.unique(fragindices)) == len(fragments)
         # check fragindices dtype:
-        assert fragindices.dtype == np.int64
+        assert fragindices.dtype == np.intp
         #check n_fragments
         assert u.atoms.n_fragments == len(fragments)
 

testsuite/MDAnalysisTests/formats/test_libdcd.py

@@ -19,6 +19,7 @@
 
 from collections import namedtuple
 import os
+import sys
 import string
 import struct
 
@@ -316,6 +317,8 @@ def write_dcd(in_name, out_name, remarks='testing', header=None):
             f_out.write(xyz=frame.xyz, box=frame.unitcell)
 
 
+@pytest.mark.xfail(os.name == 'nt' and sys.maxsize <= 2**32,
+                   reason="occasional fail on 32-bit windows")
 @given(remarks=strategies.text(
     alphabet=string.printable, min_size=0,
     max_size=239))  # handle the printable ASCII strings

testsuite/MDAnalysisTests/lib/test_augment.py

@@ -105,5 +105,5 @@ def test_undoaugment(b, qres):
     q = apply_PBC(q, b)
     aug, mapping = augment_coordinates(q, b, radius)
     for idx, val in enumerate(aug):
-        imageid = np.asarray([len(q) + idx], dtype=np.int64)
+        imageid = np.asarray([len(q) + idx], dtype=np.intp)
         assert_equal(mapping[idx], undo_augment(imageid, mapping, len(q))[0])

testsuite/MDAnalysisTests/lib/test_cutil.py

@@ -38,7 +38,7 @@
     [1, 2, 2, 6, 4, 4, ],  # duplicates, non-monotonic
 ))
 def test_unique_int_1d(values):
-    array = np.array(values, dtype=np.int64)
+    array = np.array(values, dtype=np.intp)
     ref = np.unique(array)
     res = unique_int_1d(array)
     assert_equal(res, ref)

testsuite/MDAnalysisTests/lib/test_distances.py

@@ -1245,7 +1245,7 @@ def test_output_type_capped_distance(self, incoords, min_cut, box, met,
         else:
             pairs = res
         assert type(pairs) == np.ndarray
-        assert pairs.dtype.type == np.int64
+        assert pairs.dtype.type == np.intp
         assert pairs.ndim == 2
         assert pairs.shape[1] == 2
         if ret_dist:
@@ -1270,7 +1270,7 @@ def test_output_type_self_capped_distance(self, incoords, min_cut, box,
         else:
             pairs = res
         assert type(pairs) == np.ndarray
-        assert pairs.dtype.type == np.int64
+        assert pairs.dtype.type == np.intp
         assert pairs.ndim == 2
         assert pairs.shape[1] == 2
         if ret_dist:

testsuite/MDAnalysisTests/topology/test_tprparser.py

@@ -55,7 +55,7 @@ def test_moltypes(self, top):
     def test_molnums(self, top):
         molnums = top.molnums.values
         assert_equal(molnums, self.ref_molnums)
-        assert molnums.dtype == np.int64
+        assert molnums.dtype == np.intp
 
 
 class TestTPR(TPRAttrs):