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 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 outer(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['out'],{
'out': numpy.ndarray,
}, 'outer', 2)
if isinstance(dvar, StypyTypeError):
return dvar
t1 = get_contained_elements_type(localization, arguments[0])
t2 = get_contained_elements_type(localization, arguments[1])
l = wrap_contained_type(list())
set_contained_elements_type(localization, l, call_utilities.cast_to_greater_numpy_type(t1, t2))
if 'out' in dvar:
set_contained_elements_type(localization, dvar['out'], l)
return call_utilities.create_numpy_array(l)
def convolve(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['mode'],{
'mode': Str,
}, 'convolve', 2)
dvar = call_utilities.check_possible_values(dvar, 'mode', ['full', 'same', 'valid'])
if isinstance(dvar, StypyTypeError):
return dvar
if Number == type(arguments[0]):
type1 = arguments[0]
else:
type1 = get_contained_elements_type(localization, arguments[0])
if Number == type(arguments[1]):
type2 = arguments[1]
else:
type2 = get_contained_elements_type(localization, arguments[1])
return call_utilities.create_numpy_array(call_utilities.cast_to_greater_numpy_type(type1, type2))
def cross(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['axisa', 'axisb','axisc', 'axis'],{
'axisa': Integer,
'axisb': Integer,
'axisc': Integer,
'axis': Integer,
}, 'cross', 2)
if isinstance(dvar, StypyTypeError):
return dvar
if Number == type(arguments[0]):
type1 = arguments[0]
else:
type1 = get_contained_elements_type(localization, arguments[0])
if Number == type(arguments[1]):
type2 = arguments[1]
else:
type2 = get_contained_elements_type(localization, arguments[1])
return call_utilities.create_numpy_array(call_utilities.cast_to_greater_numpy_type(type1, type2))
