def infer_datashape(source):
if isinstance(source, np.ndarray):
return from_numpy(source.shape, source.dtype)
elif isinstance(source, list):
# TODO: um yeah, we'd don't actually want to do this
cast = np.array(source)
return from_numpy(cast.shape, cast.dtype)
else:
return dynamic
def infer_datashape(source):
if isinstance(source, np.ndarray):
return from_numpy(source.shape, source.dtype)
elif isinstance(source, list):
# TODO: um yeah, we'd don't actually want to do this
cast = np.array(source)
return from_numpy(cast.shape, cast.dtype)
else:
return dynamic
def infer_datashape(source):
"""
The user has only provided us with a Python object ( could be
a buffer interface, a string, a list, list of lists, etc) try
our best to infer what the datashape should be in the context of
what it would mean as a CArray.
"""
if isinstance(source, np.ndarray):
return from_numpy(source.shape, source.dtype)
elif isinstance(source, list):
# TODO: um yeah, we'd don't actually want to do this
cast = np.array(source)
return from_numpy(cast.shape, cast.dtype)
else:
return dynamic
def infer_datashape(source):
"""
The user has only provided us with a Python object (
could be a buffer interface, a string, a list, list of
lists, etc) try our best to infer what the datashape
should be in the context of this datasource.
"""
if isinstance(source, np.ndarray):
return from_numpy(source.shape, source.dtype)
elif isinstance(source, list):
# TODO: um yeah, we'd don't actually want to do this
cast = np.array(source)
return from_numpy(cast.shape, cast.dtype)
else:
return dynamic
def empty(self, dshape):
""" Create a CArraySource from a datashape specification,
downcasts into Numpy dtype and shape tuples if possible
otherwise raises an exception.
"""
shape, dtype = from_numpy(dshape)
return CArraySource(carray([], dtype))
def empty(self, dshape):
""" Create a CArraySource from a datashape specification,
downcasts into Numpy dtype and shape tuples if possible
otherwise raises an exception.
"""
shape, dtype = from_numpy(dshape)
return CArraySource(carray([], dtype))
def __init__(self, data=None, dshape=None, params=None):
# need at least one of the three
assert (data is not None) or (dshape is not None) or \
(params.get('storage'))
# Extract the relevant carray parameters from the more
# general Blaze params object.
if params:
cparams, rootdir, format_flavor = to_cparams(params)
else:
rootdir,cparams = None, None
if dshape:
dtype = to_numpy(dshape)
self.ca = carray.carray(data, dtype, rootdir=rootdir)
self.dshape = dshape
else:
self.ca = carray.carray(data, rootdir=rootdir, cparams=cparams)
self.dshape = from_numpy(self.ca.shape, self.ca.dtype)
def empty(self, dshape):
shape, dtype = from_numpy(dshape)
return CTableSource(carray([[]], dtype))
def test_from_numpy():
from_numpy((), np.int32) == blaze.int32
from_numpy((), np.int_) == blaze.int_
from_numpy((1,), np.int32) == blaze.dshape('1, int32')
from_numpy((1,2), np.int32) == blaze.dshape('1, 2, int32')
def empty(self, datashape):
shape, dtype = from_numpy(datashape)
return ArraySource(np.ndarray(shape, dtype))
def empty(self, datashape):
shape, dtype = from_numpy(datashape)
return ArraySource(np.ndarray(shape, dtype))
def test_from_numpy(self):
self.assertEqual(from_numpy((), np.int32), blaze.int32)
self.assertEqual(from_numpy((), np.int_), blaze.int_)
self.assertEqual(from_numpy((1,), np.int32), blaze.dshape('1, int32'))
self.assertEqual(from_numpy((1,2), np.int32), blaze.dshape('1, 2, int32'))
def test_from_numpy():
from_numpy((), np.int32) == blaze.int32
from_numpy((), np.int_) == blaze.int_
from_numpy((1, ), np.int32) == blaze.dshape('1, int32')
from_numpy((1, 2), np.int32) == blaze.dshape('1, 2, int32')
def empty(self, dshape):
shape, dtype = from_numpy(dshape)
return CTableSource(carray([[]], dtype))
def test_from_numpy(self):
self.assertEqual(from_numpy((), np.int32), blaze.int32)
self.assertEqual(from_numpy((), np.int_), blaze.int_)
self.assertEqual(from_numpy((1,), np.int32), blaze.dshape('1, int32'))
self.assertEqual(from_numpy((1,2), np.int32), blaze.dshape('1, 2, int32'))
