[python-hdf5storage] 81/152: Added write readback tests to file with no python type information stored (either no information or just matlab information).
Ghislain Vaillant
ghisvail-guest at moszumanska.debian.org
Mon Feb 29 08:24:36 UTC 2016
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ghisvail-guest pushed a commit to annotated tag 0.1
in repository python-hdf5storage.
commit d46aae8df14a619a6510812816751cfa3987118f
Author: Freja Nordsiek <fnordsie at gmail.com>
Date: Sun Feb 2 05:22:06 2014 -0500
Added write readback tests to file with no python type information stored (either no information or just matlab information).
---
tests/test_write_readback.py | 166 ++++++++++++++++++++++++++++++++++++++++++-
1 file changed, 165 insertions(+), 1 deletion(-)
diff --git a/tests/test_write_readback.py b/tests/test_write_readback.py
index 4b9173c..3ad5956 100644
--- a/tests/test_write_readback.py
+++ b/tests/test_write_readback.py
@@ -160,7 +160,7 @@ class TestPythonMatlabFormat(object):
else:
assert a.dtype == b.dtype
assert a.shape == b.shape
- if a.dtype.name != 'object':
+ if b.dtype.name != 'object':
npt.assert_equal(a, b)
else:
for index, x in np.ndenumerate(a):
@@ -345,3 +345,167 @@ class TestPythonFormat(TestPythonMatlabFormat):
TestPythonMatlabFormat.__init__(self)
self.options = hdf5storage.Options(matlab_compatible=False)
self.filename = 'data.h5'
+
+
+class TestNoneFormat(TestPythonMatlabFormat):
+ def __init__(self):
+ # The parent does most of the setup. All that has to be changed
+ # is turning off the storage of type information as well as
+ # MATLAB compatibility.
+ TestPythonMatlabFormat.__init__(self)
+ self.options = hdf5storage.Options(store_type_information=False,
+ matlab_compatible=False)
+
+ def assert_equal(self, a, b):
+ # Compares a and b for equality. b is always the original. If
+ # the original is not a numpy type (isn't or doesn't inherit
+ # from np.generic or np.ndarray), then it is a matter of
+ # converting it to the appropriate numpy type. Otherwise, both
+ # are supposed to be numpy types. For object arrays, each
+ # element must be iterated over to be compared. Then, if it
+ # isn't a string type, then they must have the same dtype,
+ # shape, and all elements. If it is an empty string, then it
+ # would have been stored as just a null byte (recurse to do that
+ # comparison). If it is a bytes_ type, the dtype, shape, and
+ # elements must all be the same. If it is string_ type, we must
+ # convert to uint32 and then everything can be compared.
+ if not isinstance(b, (np.generic, np.ndarray)):
+ if b is None:
+ # It should be np.float64([])
+ assert type(a) == np.ndarray
+ assert a.dtype == np.float64([]).dtype
+ assert a.shape == (0, )
+ elif isinstance(b, (bytes, str, bytearray)):
+ assert a == np.bytes_(b)
+ else:
+ self.assert_equal(a, \
+ np.array(b)[()])
+ else:
+ if b.dtype.name != 'object':
+ if b.dtype.char in ('U', 'S'):
+ if b.shape == tuple() and len(b) == 0:
+ self.assert_equal(a, \
+ np.zeros(shape=tuple(), dtype=b.dtype.char))
+ elif b.dtype.char == 'U':
+ c = np.atleast_1d(b).view(np.uint32)
+ assert a.dtype == c.dtype
+ assert a.shape == c.shape
+ npt.assert_equal(a, c)
+ else:
+ assert a.dtype == b.dtype
+ assert a.shape == b.shape
+ npt.assert_equal(a, b)
+ else:
+ assert a.dtype == b.dtype
+ assert a.shape == b.shape
+ npt.assert_equal(a, b)
+ else:
+ assert a.dtype == b.dtype
+ assert a.shape == b.shape
+ for index, x in np.ndenumerate(a):
+ self.assert_equal(a[index], b[index])
+
+ def assert_equal_python_collection(self, a, b, tp):
+ # Compares two python collections that are supposed to be the
+ # specified type tp. As every collection is just getting turned
+ # into a list and then a numpy object array, b must be converted
+ # first before the comparison.
+ c = np.object_(list(b))
+ self.assert_equal(a, c)
+
+
+class TestMatlabFormat(TestNoneFormat):
+ def __init__(self):
+ # The parent does most of the setup. All that has to be changed
+ # is turning on the matlab compatibility, changing the filename,
+ # and removing 'float16' from the dtype list (its not supported
+ # by matlab).
+ TestNoneFormat.__init__(self)
+ self.options = hdf5storage.Options(store_type_information=False,
+ matlab_compatible=True)
+ self.filename = 'data.mat'
+ self.dtypes.remove('float16')
+
+ def assert_equal(self, a, b):
+ # Compares a and b for equality. b is always the original. If
+ # the original is not a numpy type (isn't or doesn't inherit
+ # from np.generic or np.ndarray), then it is a matter of
+ # converting it to the appropriate numpy type. Otherwise, both
+ # are supposed to be numpy types. For object arrays, each
+ # element must be iterated over to be compared. Then, if it
+ # isn't a string type, then they must have the same dtype,
+ # shape, and all elements. All strings are converted to
+ # numpy.str_ on read. If it is empty, it has shape (1, 0). A
+ # numpy.str_ has all of its strings per row compacted
+ # together. A numpy.bytes_ string has to have the same thing
+ # done, but then it needs to be converted up to UTF-32 and to
+ # numpy.str_ through uint32.
+ #
+ # In all cases, we expect things to be at least two dimensional
+ # arrays.
+ if not isinstance(b, (np.generic, np.ndarray)):
+ if b is None:
+ # It should be np.zeros(shape=(0, 1), dtype='float64'))
+ assert type(a) == np.ndarray
+ assert a.dtype == np.dtype('float64')
+ assert a.shape == (1, 0)
+ elif isinstance(b, (bytes, str, bytearray)):
+ if len(b) == 0:
+ TestPythonMatlabFormat.assert_equal(self, a, \
+ np.zeros(shape=(1, 0), dtype='U'))
+ elif isinstance(b, (bytes, bytearray)):
+ TestPythonMatlabFormat.assert_equal(self, a, \
+ np.atleast_2d(np.str_(b.decode())))
+ else:
+ TestPythonMatlabFormat.assert_equal(self, a, \
+ np.atleast_2d(np.str_(b)))
+ else:
+ TestPythonMatlabFormat.assert_equal(self, a, \
+ np.atleast_2d(np.array(b)))
+ else:
+ if b.dtype.name != 'object':
+ if b.dtype.char in ('U', 'S'):
+ if len(b) == 0 and (b.shape == tuple() \
+ or b.shape == (0, )):
+ TestPythonMatlabFormat.assert_equal(self, a, \
+ np.zeros(shape=(1, 0), \
+ dtype='U'))
+ elif b.dtype.char == 'U':
+ c = np.atleast_1d(b)
+ c = np.atleast_2d(c.view(np.dtype('U' \
+ + str(c.shape[-1]*c.dtype.itemsize//4))))
+ assert a.dtype == c.dtype
+ assert a.shape == c.shape
+ npt.assert_equal(a, c)
+ elif b.dtype.char == 'S':
+ c = np.atleast_1d(b)
+ c = c.view(np.dtype('S' \
+ + str(c.shape[-1]*c.dtype.itemsize)))
+ c = np.uint32(c.view(np.dtype('uint8')))
+ c = c.view(np.dtype('U' + str(c.shape[-1])))
+ c = np.atleast_2d(c)
+ assert a.dtype == c.dtype
+ assert a.shape == c.shape
+ npt.assert_equal(a, c)
+ pass
+ else:
+ c = np.atleast_2d(b)
+ assert a.dtype == c.dtype
+ assert a.shape == c.shape
+ npt.assert_equal(a, c)
+ else:
+ c = np.atleast_2d(b)
+ # An empty complex number gets turned into a real
+ # number when it is stored.
+ if np.prod(c.shape) == 0 \
+ and b.dtype.name.startswith('complex'):
+ c = np.real(c)
+ assert a.dtype == c.dtype
+ assert a.shape == c.shape
+ npt.assert_equal(a, c)
+ else:
+ c = np.atleast_2d(b)
+ assert a.dtype == c.dtype
+ assert a.shape == c.shape
+ for index, x in np.ndenumerate(a):
+ self.assert_equal(a[index], c[index])
--
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