def check_numpy_structured_array_empty(self, dimensions):
# Makes a random structured ndarray of the given type, writes it
# and reads it back, and then compares it.
shape = random_numpy_shape(dimensions, \
max_structured_ndarray_axis_length)
data = random_structured_numpy_array(shape, (1, 0))
out = self.write_readback(data, random_name(), self.options)
self.assert_equal(out, data)
def test_numpy_recarray_unicode_fields(self):
# Makes a random 1d structured ndarray with non-ascii characters
# in its fields, converts it to a recarray, writes it and reads
# it back, and then compares it.
shape = random_numpy_shape(1, \
max_structured_ndarray_axis_length)
data = random_structured_numpy_array(shape, nonascii_fields=True)
out = self.write_readback(data, random_name(), self.options)
self.assert_equal(out, data)
def check_numpy_recarray(self, dimensions):
# Makes a random structured ndarray of the given type, converts
# it to a recarray, writes it and reads it back, and then
# compares it.
shape = random_numpy_shape(dimensions, \
max_structured_ndarray_axis_length)
data = random_structured_numpy_array(shape).view(np.recarray).copy()
out = self.write_readback(data, random_name(), self.options)
self.assert_equal(out, data)
def check_numpy_recarray_field_special_char(self, ch, leading=False):
# Makes a random 1d structured ndarray with the character
# in one field, converts it to a recarray, writes it and reads
# it back, and then compares it.
field_names = [random_str_ascii(max_dict_key_length) for i in range(2)]
if leading:
field_names[1] = ch + field_names[1]
else:
field_names[1] = field_names[1][0] + ch + field_names[1][1:]
shape = random_numpy_shape(1, \
max_structured_ndarray_axis_length)
data = random_structured_numpy_array(shape, names=field_names).view(
np.recarray).copy()
out = self.write_readback(data, random_name(), self.options)
self.assert_equal(out, data)
to_julia['object'] = random_numpy_scalar('object', \
object_element_dtypes=dtypes_to_do)
to_julia['object_array_2'] = random_numpy( \
random_numpy_shape(2, 6, min_length=2), \
'object', object_element_dtypes=dtypes_to_do)
to_julia['object_array_3'] = random_numpy( \
random_numpy_shape(3, 6, min_length=2), \
'object', object_element_dtypes=dtypes_to_do)
# Julia MAT doesn't seem to read and then write back empty object
# types.
#to_julia['object_empty'] = np.array([], dtype='object')
to_julia['struct'] = random_structured_numpy_array((1,), \
nondigits_fields=True)
to_julia['struct_empty'] = random_structured_numpy_array(tuple(), \
nondigits_fields=True)
# Something goes wrong with 2 dimensional structure arrays that warrants
# further investigation.
#to_julia['struct_array_2'] = random_structured_numpy_array((3, 5), \
# nondigits_fields=True)
from_julia_v7_to_v7p3 = dict()
from_julia_v7p3_to_v7p3 = dict()
def julia_command(julia_file, fin, fout):
subprocess.check_call(['julia', julia_file, fin, fout],