def evaluate_transform(self, x, no_copy=False):
r"""Call transform on the provided message.
Args:
x (object): Message object to transform.
no_copy (bool, optional): If True, the transformation occurs in
place. Otherwise a copy is created and transformed. Defaults
to False.
Returns:
object: The transformed message.
"""
out = x
np_dtype = type2numpy(self.transformed_datatype)
if isinstance(x, pandas.DataFrame):
out = pandas2numpy(x).astype(np_dtype, copy=True)
elif isinstance(x, np.ndarray):
out = x.astype(np_dtype, copy=True)
elif np_dtype and isinstance(x, (list, tuple, dict, np.ndarray)):
if len(x) == 0:
out = np.zeros(0, np_dtype)
else:
if isinstance(x, dict):
x = dict2list(x, order=np_dtype.names)
out = consolidate_array(x, dtype=np_dtype)
else:
# warning?
raise TypeError(("Cannot consolidate object of type %s "
"into a structured numpy array.") % type(x))
if not no_copy:
out = copy.deepcopy(out)
return out
def consolidate_array(self, out):
r"""Consolidate message into a structure numpy array if possible.
Args:
out (list, tuple, np.ndarray): Object to consolidate into a
structured numpy array.
Returns:
np.ndarray: Structured numpy array containing consolidated message.
Raises:
ValueError: If the array cannot be consolidated.
"""
np_dtype = self.numpy_dtype
if np_dtype and isinstance(out, (list, tuple, np.ndarray)):
out = serialize.consolidate_array(out, dtype=np_dtype)
else:
warnings.warn(("Cannot consolidate message into a structured " +
"numpy array: %s") % str(out))
return out
def test_consolidate_array():
r"""Test consolidation of array information in different forms."""
names0 = ['f0', 'f1', 'f2', 'f3']
# names1 = ["name", "number", "value", "complex"]
dtypes = ['S5', 'i8', 'f8', 'c16']
dtype0 = np.dtype([(n, f) for n, f in zip(names0, dtypes)])
if platform._is_win: # pragma: windows
if backwards.PY2: # pragma: Python 2
dtype0_list = dtype0
else: # pragma: Python 3
dtype0_list = np.dtype({
'names': names0,
'formats': ['S5', 'i4', 'f8', 'c16']
})
else:
dtype0_list = dtype0
# dtype1 = np.dtype([(n, f) for n, f in zip(names1, dtypes)])
dtype2 = np.dtype([('f0', 'i8'), ('f1', 'f8')])
dtype3 = np.dtype([('f0', 'i4'), ('f1', 'f4')])
shape = (5, 5)
# Create list of input variables
dlist = []
x0list = []
x1list = []
for dtype in [np.dtype('float'), dtype0]:
x = np.zeros(shape, dtype=dtype)
if dtype == dtype0:
x['f0'][:] = 'hello'
x_flat = x.flatten()
dlist.append(dtype)
x0list.append(x)
x1list.append(x)
if len(dtype) > 0:
dlist += [dtype, dtype]
if dtype == dtype0:
dtype_list = dtype0_list
else: # pragma: debug
dtype_list = dtype
dlist.append(dtype_list)
x0list += [x, x_flat, x_flat.astype(dtype_list)]
# Tests with lists of arrays rows or columns
x1list.append([x[n] for n in dtype.names])
x1list.append([x_flat[i] for i in range(len(x_flat))])
x1list.append([x_flat[i].tolist() for i in range(len(x_flat))])
# Tests with single array mapped onto structured array
nrow = 4
x0 = np.zeros(nrow, dtype2)
x1 = np.zeros((nrow, len(dtype2)))
dlist += [None]
x0list += [x0]
x1list.append(x0)
x2 = serialize.consolidate_array(x1, dtype=dtype2)
np.testing.assert_array_equal(x0, x2)
np.testing.assert_array_equal(
x0, serialize.consolidate_array(np.zeros(nrow, dtype3), dtype=dtype2))
# Test with list of arrays
arr_void = np.ones(5, dtype0)
x0list.append(arr_void)
x1list.append([a for a in arr_void])
dlist.append(dtype0)
# Loop over different test inputs
i = 0
for x0, x1, dtype in zip(x0list, x1list, dlist):
i += 1
# Dtype provided
x2 = serialize.consolidate_array(x1, dtype=dtype)
np.testing.assert_array_equal(x0, x2)
# Dtype not provided
x2 = serialize.consolidate_array(x1)
np.testing.assert_array_equal(x0, x2)
# Error on incorrect data format
assert_raises(ValueError,
serialize.consolidate_array,
np.zeros((4, 1)),
dtype=dtype2)
# Error on incorrect type
assert_raises(TypeError, serialize.consolidate_array, None)
# Error on dtypes with differing numbers of fields
assert_raises(ValueError,
serialize.consolidate_array,
np.zeros(3, dtype0),
dtype=dtype3)