[h5py] 202/455: Correct test version

[Ghislain Vaillant]

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ghisvail-guest pushed a commit to annotated tag 1.3.0
in repository h5py.

commit 01a90e35df7def181b0e4cb1664181306b30a2ed
Author: andrewcollette <andrew.collette at gmail.com>
Date:   Wed Jan 28 01:50:33 2009 +0000

    Correct test version
---
 h5py/tests/test_selections.py |   2 -
 h5py/tests/test_slicing.py    | 116 ++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 116 insertions(+), 2 deletions(-)

diff --git a/h5py/tests/test_selections.py b/h5py/tests/test_selections.py
deleted file mode 100644
index 139597f..0000000
--- a/h5py/tests/test_selections.py
+++ /dev/null
@@ -1,2 +0,0 @@
-
-
diff --git a/h5py/tests/test_slicing.py b/h5py/tests/test_slicing.py
new file mode 100644
index 0000000..e025e47
--- /dev/null
+++ b/h5py/tests/test_slicing.py
@@ -0,0 +1,116 @@
+import numpy as np
+import h5py
+import tempfile
+import os
+from nose.tools import assert_equal
+
+def check_arr_equal(dset, arr):
+    """ Make sure dset and arr have the same shape, dtype and contents.
+
+        Note that dset may be a NumPy array or an HDF5 dataset
+    """
+    if np.isscalar(dset) or np.isscalar(arr):
+        assert np.isscalar(dset) and np.isscalar(arr)
+        assert dset == arr
+        return
+
+    assert_equal(dset.shape, arr.shape)
+    assert_equal(dset.dtype, arr.dtype)
+    assert np.all(dset[...] == arr[...]), "%s %s" % (dset[...], arr[...])
+
+class SliceFreezer(object):
+    """ Necessary because numpy.s_ clips slices > 2**32 """
+    def __getitem__(self, args):
+        return args
+
+s = SliceFreezer()
+
+class TestSlicing(object):
+
+    def setUp(self):
+        fname = tempfile.mktemp('.hdf5')
+        self.f = h5py.File(fname, 'w')
+        self.fname = fname
+
+    def tearDown(self):
+        self.f.close()
+        os.unlink(self.fname)
+
+    def generate(self, shape, dtype):
+        if 'dset' in self.f:
+            del self.f['dset']
+
+        size = np.product(shape)
+        dset = self.f.create_dataset('dset', shape, dtype)
+        arr = np.arange(size, dtype=dtype).reshape(shape)
+        return dset, arr
+
+    def generate_rand(self, shape, dtype='f'):
+        return np.random.random(shape).astype(dtype)
+
+
+    def test_slices(self):
+        dset, arr = self.generate((10,10,50),'f')
+
+        slices = [s[0,0,0], s[0,0,:], s[0,:,0], s[0,:,:]]
+        slices += [s[0:1,:,4:5], s[2:3,0,4:5], s[:,0,0:1], s[0,:,0:1]]
+        slices += [ s[9,9,49], s[9,:,49], s[9,:,:] ]
+        slices += [ s[0, ..., 49], s[...], s[..., 49], s[9,...] ]
+        slices += [ s[0:7:2,0:9:3,15:43:5], s[2:8:2,...] ]
+        slices += [ s[0], s[1], s[9], s[0,0], s[4,5], s[:] ]
+        slices += [ s[3,...], s[3,2,...] ]
+        slices += [ np.random.random((10,10,50)) > 0.5 ]  # Truth array
+        slices += [ np.zeros((10,10,50), dtype='bool') ]
+        slices += [ s[0, 1, [2,3,6,7]], s[:,[1,2]], s[[1,2]], s[3:7,[1]]]
+        
+        dset[...] = arr[...]
+
+        for slc in slices:
+
+            arr[slc] += np.random.rand()
+            dset[slc] = arr[slc]
+            
+            print "check write %s" % (slc,)
+            check_arr_equal(dset, arr)
+
+            out = dset[slc]
+
+            print "check read %s" % (slc,)
+            check_arr_equal(out, arr[slc])
+
+
+    def test_scalars(self):
+
+        dset, arr = self.generate((),'i')
+        dset[...] = arr[...] = 42
+        assert dset[...] == dset[()] == arr[()] == 42
+        assert dset.shape == ()
+        assert np.isscalar(dset[...])
+
+
+    def test_broadcast(self):
+
+        dset, arr = self.generate((20,10,30),'f')
+        dset[...] = arr[...]
+
+        slices = [(s[...], (30,)),
+                  (s[...], (10,30)),
+                  (s[:,5,:], (20,30)),
+                  (s[:,4,:], (30,)),
+                  (s[:,3,...], (1,30,))]
+
+        for slc, shape in slices:
+
+            subarr = np.random.random(shape)
+
+            print "broadcast %s %s" % (slc, shape)
+            dset[slc] = subarr
+            arr[slc] = subarr
+            check_arr_equal(dset, arr)
+
+
+
+
+
+
+        

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