def fit(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments,
['domain', 'rcond', 'full', 'w', 'window'], {
'domain': IterableDataStructure,
'rcond': RealNumber,
'full': bool,
'w': IterableDataStructure,
'window': IterableDataStructure,
}, 'fit', 3)
if isinstance(dvar, StypyTypeError):
return dvar
ret = call_utilities.create_numpy_array_n_dimensions(
call_utilities.get_inner_type(localization, arguments[0]),
call_utilities.get_dimensions(localization, arguments[0]))
ret = numpy.polynomial.Chebyshev(ret.get_wrapped_type())
if 'full' in dvar.keys():
tup = wrap_contained_type(tuple())
ld = wrap_contained_type(list())
ld.set_contained_type(
call_utilities.get_inner_type(localization, arguments[0]))
un = UnionType.add(ret, ld)
tup.set_contained_type(un)
return tup
return ret
def __getitem__(localization, proxy_obj, arguments):
r = TypeWrapper.get_wrapper_of(proxy_obj.__self__)
index_selector = arguments[0]
if isinstance(index_selector, tuple):
index_selector = index_selector[0]
dims = call_utilities.get_dimensions(localization, r)
if dims > 1:
if call_utilities.is_iterable(arguments[0]):
if call_utilities.is_iterable(arguments[0].get_contained_type()):
return call_utilities.create_numpy_array_n_dimensions(
call_utilities.cast_to_numpy_type(call_utilities.get_inner_type(localization, r)), dims - 1)
contained = call_utilities.cast_to_numpy_type(call_utilities.get_inner_type(localization, r))
for i in range(dims - 1):
contained = UnionType.add(contained,
call_utilities.create_numpy_array_n_dimensions(r.get_contained_type(), i + 1))
else:
contained = r.get_contained_type()
if isinstance(index_selector, TypeWrapper):
if isinstance(index_selector.wrapped_type, slice) or (
call_utilities.is_iterable(index_selector) and not isinstance(index_selector.wrapped_type,
tuple)):
l = call_utilities.create_numpy_array(contained)
return l
return contained # proxy_obj.__self__.dtype.type()
def dot(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['out'], {
'out': numpy.ndarray,
}, 'dot')
if isinstance(dvar, StypyTypeError):
return dvar
if Number == type(arguments[0]) and Number == type(arguments[1]):
return call_utilities.cast_to_greater_numpy_type(
arguments[0], arguments[1])
if Number == type(arguments[0]) and call_utilities.is_iterable(
arguments[1]):
c_t = get_contained_elements_type(localization, arguments[1])
if not 'out' in dvar.keys():
return call_utilities.create_numpy_array(c_t)
else:
set_contained_elements_type(localization, dvar['out'], c_t)
return dvar['out']
if Number == type(arguments[1]) and call_utilities.is_iterable(
arguments[0]):
c_t = get_contained_elements_type(localization, arguments[0])
if not 'out' in dvar.keys():
return call_utilities.create_numpy_array(c_t)
else:
set_contained_elements_type(localization, dvar['out'], c_t)
return dvar['out']
if call_utilities.is_iterable(
arguments[0]) and call_utilities.is_iterable(arguments[1]):
if call_utilities.get_dimensions(
localization,
arguments[0]) == 1 and call_utilities.get_dimensions(
localization, arguments[1]) == 1:
return call_utilities.cast_to_greater_numpy_type(
call_utilities.get_inner_type(localization, arguments[0]),
call_utilities.get_inner_type(localization, arguments[1]))
typ = call_utilities.cast_to_greater_numpy_type(
call_utilities.get_inner_type(localization, arguments[0]),
call_utilities.get_inner_type(localization, arguments[1]))
for i in range(
call_utilities.get_dimensions(localization, arguments[0])):
typ = call_utilities.create_numpy_array(typ)
if not 'out' in dvar.keys():
return typ
else:
set_contained_elements_type(
localization, dvar['out'],
get_contained_elements_type(localization, typ))
return dvar['out']
return arguments[0]
def divide(localization, proxy_obj, arguments, func_name='divide'):
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['out', 'where'], {
'out': numpy.ndarray,
'where': numpy.ndarray,
}, func_name, 2)
if isinstance(dvar, StypyTypeError):
return dvar
if Number == type(arguments[0]) and Number == type(arguments[1]):
return call_utilities.cast_to_greater_numpy_type(
arguments[0], arguments[1])
try:
dims = 0
if call_utilities.is_iterable(arguments[0]):
param0 = call_utilities.get_inner_type(localization,
arguments[0])
dims = call_utilities.get_dimensions(localization,
arguments[0])
else:
param0 = arguments[0]
if call_utilities.is_iterable(arguments[1]):
param1 = call_utilities.get_inner_type(localization,
arguments[1])
temp = call_utilities.get_dimensions(localization,
arguments[1])
if temp > dims:
dims = temp
else:
param1 = arguments[1]
if dims > 0:
ret = call_utilities.create_numpy_array_n_dimensions(
call_utilities.cast_to_greater_numpy_type(param0, param1),
dims)
else:
ret = call_utilities.cast_to_greater_numpy_type(param0, param1)
except Exception as ex:
return StypyTypeError(localization, str(ex))
if 'out' in dvar.keys():
set_contained_elements_type(localization, dvar['out'], ret)
return dvar['out']
return ret
def zeros_like(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['dtype', 'order', 'subok'], {
'dtype': type,
'order': Str,
'subok': bool,
}, 'ones')
if isinstance(dvar, StypyTypeError):
return dvar
if 'dtype' in dvar.keys():
dtype = dvar['dtype']
else:
dtype = None
t = call_utilities.check_possible_values(dvar, 'order', ['C', 'F', 'A', 'K'])
if isinstance(t, StypyTypeError):
return t
if Number == type(arguments[0]):
return call_utilities.create_numpy_array(arguments[0], dtype=dtype)
else:
dims = call_utilities.get_dimensions(localization, arguments[0])
typ = call_utilities.create_numpy_array_n_dimensions(call_utilities.get_inner_type(localization, arguments[0]),
dims, dtype=dtype)
return typ
def trace(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments,
['offset', 'axis1', ' axis2', 'dtype', 'out'], {
'offset': Integer,
'axis1': Integer,
'axis2': Integer,
'dtype': type,
'out': numpy.ndarray,
}, 'trace')
if isinstance(dvar, StypyTypeError):
return dvar
dim = call_utilities.get_dimensions(localization, arguments[0])
if dim == 1:
return call_utilities.cast_to_numpy_type(
get_contained_elements_type(localization, arguments[0]))
else:
ret = call_utilities.create_numpy_array(
call_utilities.get_inner_type(localization, arguments[0]))
if 'out' in dvar.keys():
if dim == 1 or not (call_utilities.get_dimensions(
localization, dvar['out']) == 1):
return StypyTypeError(
localization,
"Wrong dimensions of out parameter in trace call")
set_contained_elements_type(
localization, dvar['out'],
get_contained_elements_type(localization, arguments[0]))
return dvar['out']
return ret
def reciprocal(localization, proxy_obj, arguments, func_name='reciprocal'):
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['out', 'where'], {
'out': numpy.ndarray,
'where': numpy.ndarray,
}, func_name)
if isinstance(dvar, StypyTypeError):
return dvar
if Number == type(arguments[0]):
ret = call_utilities.cast_to_numpy_type(numpy.float64())
else:
try:
ret = call_utilities.create_numpy_array_n_dimensions(
call_utilities.cast_to_numpy_type(
call_utilities.get_inner_type(localization,
arguments[0])),
call_utilities.get_dimensions(localization, arguments[0]))
except Exception as ex:
return StypyTypeError(localization, str(ex))
if 'out' in dvar.keys():
set_contained_elements_type(localization, dvar['out'], ret)
return dvar['out']
return ret
def __div__(localization, proxy_obj, arguments, func_name='__div__'):
# dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['out', 'where'], {
# 'out': numpy.ndarray,
# 'where': numpy.ndarray,
# }, func_name, 2)
#
# if isinstance(dvar, StypyTypeError):
# return dvar
try:
dims = 0
if call_utilities.is_iterable(arguments[0]):
param0 = call_utilities.get_inner_type(localization, arguments[0])
dims = call_utilities.get_dimensions(localization, arguments[0])
else:
param0 = arguments[0]
if dims > 0:
ret = call_utilities.create_numpy_array_n_dimensions(
call_utilities.cast_to_numpy_type(param0),
dims)
else:
ret = call_utilities.create_numpy_array(call_utilities.cast_to_numpy_type(param0))
except Exception as ex:
return StypyTypeError(localization, str(ex))
# if 'out' in dvar.keys():
# set_contained_elements_type(localization, dvar['out'],
# ret)
# return dvar['out']
return ret
def sum(localization, proxy_obj, arguments):
if Number == type(arguments[0]):
return call_utilities.cast_to_numpy_type(arguments[0])
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['axis', 'dtype', 'out'], {
'axis': [
types.NoneType, Integer,
IterableDataStructureWithTypedElements(Integer)
],
'dtype':
type,
'out':
numpy.ndarray,
'keepdims':
bool
}, 'sum')
if isinstance(dvar, StypyTypeError):
return dvar
if 'out' in dvar.keys():
set_contained_elements_type(
localization, dvar['out'],
get_contained_elements_type(localization, arguments[0]))
return dvar['out']
if 'axis' in dvar.keys():
return call_utilities.create_numpy_array(
get_contained_elements_type(localization, arguments[0]))
return call_utilities.cast_to_numpy_type(
call_utilities.get_inner_type(localization, arguments[0]))
def det(localization, proxy_obj, arguments):
param0 = call_utilities.get_inner_type(localization, arguments[0])
dims = call_utilities.get_dimensions(localization, arguments[0])
if dims == 1:
return call_utilities.cast_to_numpy_type(param0)
else:
return call_utilities.create_numpy_array_n_dimensions(
call_utilities.cast_to_numpy_type(param0),
dims)
def einsum(localization, proxy_obj, arguments):
if isinstance(arguments[-1], dict):
if Str == type(arguments[0]):
arg_num = 2
else:
arg_num = 1
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments,
['out', 'dtype', 'order', 'casting', 'optimize'], {
'out': IterableDataStructure,
'dtype': type,
'order': Str,
'casting': Str,
'optimize': [bool, Str]
}, 'einsum', arg_num)
if isinstance(dvar, StypyTypeError):
return dvar
val_temp = call_utilities.check_possible_values(dvar, 'order', ['C', 'F', 'A', 'K'])
if isinstance(val_temp, StypyTypeError):
return val_temp
val_temp = call_utilities.check_possible_values(dvar, 'casting', ['no', 'equiv', 'safe', 'same_kind', 'unsafe'])
if isinstance(val_temp, StypyTypeError):
return val_temp
val_temp = call_utilities.check_possible_values(dvar, 'optimize', ['greedy', 'optimal', False, True])
if isinstance(val_temp, StypyTypeError):
return val_temp
arguments = arguments[:-1]
else:
dvar = dict()
typ = None
if Str == type(arguments[0]):
arg_list = arguments[1:]
if Number == type(arguments[1]) and 'out' in dvar:
return dvar['out']
else:
arg_list = arguments
for arg in arg_list:
if call_utilities.is_iterable(arg):
typ_temp = call_utilities.cast_to_numpy_type(call_utilities.get_inner_type(localization, arg))
typ = call_utilities.cast_to_greater_numpy_type(typ, typ_temp)
union = UnionType.add(typ, call_utilities.create_numpy_array(DynamicType))
if 'out' in dvar:
set_contained_elements_type(localization, dvar['out'], DynamicType)
return call_utilities.create_numpy_array(DynamicType)
return union
def hstack(localization, proxy_obj, arguments):
union = None
for arg in arguments:
if call_utilities.is_iterable(arg):
union = UnionType.add(
union, call_utilities.get_inner_type(localization, arg))
else:
return StypyTypeError(
localization,
"A non-iterable parameter {0} was passed to the hstack function"
.format(str(arg)))
return call_utilities.create_numpy_array(union)
def rollaxis(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['shift', 'axis'], {
'shift': [Integer, IterableDataStructureWithTypedElements(Integer)],
'axis': [Integer, IterableDataStructureWithTypedElements(Integer)],
}, 'rollaxis', 2)
if isinstance(dvar, StypyTypeError):
return dvar
t = call_utilities.get_inner_type(localization, arguments[0])
if not Number == type(t):
return StypyTypeError(localization, "The contents of the passed array are not numeric")
return arguments[0]
def repeat(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments,
['repeats', 'axis'], {
'repeats': [Integer,
IterableDataStructureWithTypedElements(Integer)],
'axis': Integer,
}, 'repeat')
if isinstance(dvar, StypyTypeError):
return dvar
r = TypeWrapper.get_wrapper_of(proxy_obj.__self__)
if 'axis' in dvar.keys():
return r
else:
typ = call_utilities.get_inner_type(localization, r)
return call_utilities.create_numpy_array(typ)
def unpackbits(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments,
['axis'], {
'axis': Integer,
}, 'unpackbits')
if isinstance(dvar, StypyTypeError):
return dvar
if 'axis' in dvar.keys():
contained = call_utilities.get_contained_elements_type(
localization, arguments[0])
else:
contained = call_utilities.get_inner_type(localization,
arguments[0])
return call_utilities.create_numpy_array(contained)
def atleast_2d(localization, proxy_obj, arguments):
rets = list()
for arg in arguments:
if call_utilities.is_iterable(arg):
if call_utilities.get_dimensions(localization, arg) >= 2:
rets.append(arg)
else:
rets.append(
call_utilities.create_numpy_array_n_dimensions(
call_utilities.get_inner_type(localization, arg),
2))
else:
return StypyTypeError(
localization,
"A non-iterable parameter {0} was passed to the atleast_2d function"
.format(str(arg)))
if len(rets) == 1:
return rets[0]
return tuple(rets)
def bitwise_and(localization,
proxy_obj,
arguments,
func_name='bitwise_and'):
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['where'], {
'where': [Str,
IterableDataStructureWithTypedElements(bool)],
}, func_name, 2)
if isinstance(dvar, StypyTypeError):
return dvar
if call_utilities.is_iterable(
arguments[0]) and call_utilities.is_iterable(arguments[1]):
if not call_utilities.check_possible_types(
call_utilities.get_inner_type(localization, arguments[0]),
[bool, Integer]) or not call_utilities.check_possible_types(
call_utilities.get_inner_type(localization, arguments[1]),
[bool, Integer]):
return StypyTypeError(
localization, " ufunc '" + func_name +
"' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
)
if call_utilities.is_numpy_array(arguments[0]):
return call_utilities.create_numpy_array(
call_utilities.get_inner_type(localization, arguments[0]))
if call_utilities.is_numpy_array(arguments[1]):
return call_utilities.create_numpy_array(
call_utilities.get_inner_type(localization, arguments[1]))
return arguments[0]
else:
if call_utilities.is_iterable(
arguments[0]) and not call_utilities.is_iterable(
arguments[1]):
if not call_utilities.check_possible_types(
call_utilities.get_inner_type(localization,
arguments[0]),
[bool, Integer
]) or not call_utilities.check_possible_types(
arguments[1], [bool, Integer]):
return StypyTypeError(
localization, " ufunc '" + func_name +
"' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
)
if call_utilities.is_numpy_array(arguments[0]):
return call_utilities.create_numpy_array(
call_utilities.get_inner_type(localization,
arguments[0]))
return arguments[0]
else:
if not call_utilities.is_iterable(
arguments[0]) and call_utilities.is_iterable(
arguments[1]):
if not call_utilities.check_possible_types(
call_utilities.get_inner_type(
localization, arguments[1]),
[bool, Integer
]) or not call_utilities.check_possible_types(
arguments[0], [bool, Integer]):
return StypyTypeError(
localization, " ufunc '" + func_name +
"' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
)
if call_utilities.is_numpy_array(arguments[1]):
return call_utilities.create_numpy_array(
call_utilities.get_inner_type(
localization, arguments[1]))
return arguments[1]
else:
return arguments[0]