def to_value(cls, instance):
"""Convert to a value to send to Octave."""
if not isinstance(instance, OctaveUserClass) or not instance._attrs:
return dict()
# Bootstrap a MatlabObject from scipy.io
# From https://github.com/scipy/scipy/blob/93a0ea9e5d4aba1f661b6bb0e18f9c2d1fce436a/scipy/io/matlab/mio5.py#L435-L443
# and https://github.com/scipy/scipy/blob/93a0ea9e5d4aba1f661b6bb0e18f9c2d1fce436a/scipy/io/matlab/mio5_params.py#L224
dtype = []
values = []
for attr in instance._attrs:
dtype.append((str(attr), object))
values.append(getattr(instance, attr))
struct = np.array([tuple(values)], dtype)
return MatlabObject(struct, instance._name)
'expected': {'teststructnest': st2}
})
a = np.empty((1,2), dtype=[(n, object) for n in ['one', 'two']])
a[0,0]['one'] = mlarr(1)
a[0,0]['two'] = mlarr(2)
a[0,1]['one'] = array([u'number 1'])
a[0,1]['two'] = array([u'number 2'])
case_table5.append(
{'name': 'structarr',
'classes': {'teststructarr': 'struct'},
'expected': {'teststructarr': a}
})
ODT = np.dtype([(n, object) for n in
['expr', 'inputExpr', 'args',
'isEmpty', 'numArgs', 'version']])
MO = MatlabObject(np.zeros((1,1), dtype=ODT), 'inline')
m0 = MO[0,0]
m0['expr'] = array([u'x'])
m0['inputExpr'] = array([u' x = INLINE_INPUTS_{1};'])
m0['args'] = array([u'x'])
m0['isEmpty'] = mlarr(0)
m0['numArgs'] = mlarr(1)
m0['version'] = mlarr(1)
case_table5.append(
{'name': 'object',
'classes': {'testobject': 'object'},
'expected': {'testobject': MO}
})
fp_u_str = open(pjoin(test_data_path, 'japanese_utf8.txt'), 'rb')
u_str = fp_u_str.read().decode('utf-8')
fp_u_str.close()
def _encode(data, convert_to_float):
"""Convert the Python values to values suitable to send to Octave.
"""
ctf = convert_to_float
# Handle variable pointer.
if isinstance(data, (OctaveVariablePtr)):
return _encode(data.value, ctf)
# Handle a user defined object.
if isinstance(data, OctaveUserClass):
return _encode(OctaveUserClass.to_value(data), ctf)
# Handle a function pointer.
if isinstance(data, (OctaveFunctionPtr, MatlabFunction)):
raise Oct2PyError('Cannot write Octave functions')
# Handle matlab objects.
if isinstance(data, MatlabObject):
view = data.view(np.ndarray)
out = MatlabObject(data, data.classname)
for name in out.dtype.names:
out[name] = _encode(view[name], ctf)
return out
# Integer objects should be converted to floats
if isinstance(data, int):
return float(data)
# Handle pandas series and dataframes
if isinstance(data, (DataFrame, Series)):
return _encode(data.values, ctf)
# Extract and encode values from dict-like objects.
if isinstance(data, dict):
out = dict()
for (key, value) in data.items():
out[key] = _encode(value, ctf)
return out
# Send None as nan.
if data is None:
return np.NaN
# Sets are treated like lists.
if isinstance(data, set):
return _encode(list(data), ctf)
# Lists can be interpreted as numeric arrays or cell arrays.
if isinstance(data, list):
if _is_simple_numeric(data):
return _encode(np.array(data), ctf)
return _encode(tuple(data), ctf)
# Tuples are handled as cells.
if isinstance(data, tuple):
obj = np.empty(len(data), dtype=object)
for (i, item) in enumerate(data):
obj[i] = _encode(item, ctf)
return obj
# Sparse data must be floating type.
if isinstance(data, spmatrix):
return data.astype(np.float64)
# Return other data types unchanged.
if not isinstance(data, np.ndarray):
return data
# Extract and encode data from object-like arrays.
if data.dtype.kind in 'OV':
out = np.empty(data.size, dtype=data.dtype)
for (i, item) in enumerate(data.ravel()):
if data.dtype.names:
for name in data.dtype.names:
out[i][name] = _encode(item[name], ctf)
else:
out[i] = _encode(item, ctf)
return out.reshape(data.shape)
# Complex 128 is the highest supported by savemat.
if data.dtype.name == 'complex256':
return data.astype(np.complex128)
# Convert to float if applicable.
if ctf and data.dtype.kind in 'ui':
return data.astype(np.float64)
# Return standard array.
return data