def zeros(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['dtype', 'order'], {
'dtype': [type, IterableDataStructure],
'order': Str,
}, 'zeros')
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'])
if isinstance(t, StypyTypeError):
return t
if Number == type(arguments[0]):
return call_utilities.create_numpy_array(numpy.float64(),
dtype=dtype)
else:
if call_utilities.is_iterable(dtype):
tup = call_utilities.wrap_contained_type(tuple())
tup.set_contained_type(numpy.float64())
contents = call_utilities.create_numpy_array(tup)
return call_utilities.create_numpy_array(contents)
dims = call_utilities.get_dimensions(localization, arguments[0])
typ = call_utilities.create_numpy_array_n_dimensions(
numpy.float64(), dims, dtype=dtype)
return typ
def ones(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['dtype', 'order'],{
'dtype': type,
'order': Str,
}, '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'])
if isinstance(t, StypyTypeError):
return t
if Number == type(arguments[0]):
return call_utilities.create_numpy_array(numpy.float64(), dtype=dtype)
else:
dims = call_utilities.get_dimensions(localization, arguments[0])
typ = call_utilities.create_numpy_array(numpy.float64(), dtype=dtype)
for i in range(dims):
typ = call_utilities.create_numpy_array(typ)
return typ
def reshape(localization, proxy_obj, arguments):
if Number == type(arguments[0]):
return call_utilities.create_numpy_array(arguments[0], False)
shape_levels = len(arguments) - 1
if Str == type(arguments[-1]):
shape_levels -= 1
if shape_levels == 0:
return StypyTypeError(
localization,
"Invalid 'shape' parameter for reshape call: There must be at least one shape element"
)
if IterableDataStructure == type(arguments[1]):
shape_levels = 2
if len(arguments) > 2 and not Str == type(
arguments[-1]) and not Integer == type(arguments[-1]):
return StypyTypeError(
localization,
"Invalid 'order' parameter for reshape call: {0}".format(
str(arguments[-1])))
contained = get_contained_elements_type(localization, arguments[0])
for i in range(shape_levels):
contained = call_utilities.create_numpy_array(contained)
return contained
def uniform(localization, proxy_obj, arguments):
if len(arguments) == 0:
return float()
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['low', 'high', 'size'], {
'low': [
RealNumber,
IterableDataStructureWithTypedElements(RealNumber)
],
'high': [
RealNumber,
IterableDataStructureWithTypedElements(RealNumber)
],
'size':
[Integer,
IterableDataStructureWithTypedElements(Integer)],
}, 'uniform', 0)
if isinstance(dvar, StypyTypeError):
return dvar
if call_utilities.check_parameter_type(dvar, 'low', IterableDataStructureWithTypedElements(RealNumber)) or \
call_utilities.check_parameter_type(dvar, 'high', IterableDataStructureWithTypedElements(RealNumber)) or \
call_utilities.check_parameter_type(dvar, 'size', IterableDataStructureWithTypedElements(RealNumber)):
return call_utilities.create_numpy_array(numpy.float64())
if 'size' in dvar.keys():
return call_utilities.create_numpy_array(numpy.float64())
return float()
def random(localization, proxy_obj, arguments):
# if len(arguments) == 0:
# return float()
#
# dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['size'], {
# 'size': [Integer, IterableDataStructureWithTypedElements(Integer)],
# }, 'random', 0)
#
# if isinstance(dvar, StypyTypeError):
# return dvar
#
# if call_utilities.is_iterable(arguments[0]):
# inner_array = call_utilities.create_numpy_array(numpy.float64())
# return call_utilities.create_numpy_array(inner_array)
#
# return float()
if len(arguments) == 0:
return float()
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['size'], {
'size': [Integer, IterableDataStructureWithTypedElements(Integer)],
}, 'random', 0)
if isinstance(dvar, StypyTypeError):
return dvar
if call_utilities.is_iterable(arguments[0]):
inner_array = call_utilities.create_numpy_array_n_dimensions(numpy.float64(), call_utilities.get_dimensions(localization, arguments[0]))
return call_utilities.create_numpy_array(inner_array)
return call_utilities.create_numpy_array(numpy.float64())
def nan_to_num(localization, proxy_obj, arguments):
if RealNumber == type(arguments[0]):
return call_utilities.cast_to_numpy_type(arguments[0])
if can_store_elements(arguments[0]):
return call_utilities.create_numpy_array(
get_contained_elements_type(arguments[0]))
return call_utilities.create_numpy_array(arguments[0].wrapped_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 i0(localization, proxy_obj, arguments):
if call_utilities.is_numpy_array(arguments[0]):
return arguments[0]
else:
if Number == type(arguments[0]):
return call_utilities.create_numpy_array(arguments[0])
else:
return call_utilities.create_numpy_array(
get_contained_elements_type(arguments[0]))
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 ediff1d(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['to_end', 'to_begin'], {
'to_end': IterableDataStructure,
'to_begin': IterableDataStructure,
}, 'ediff1d')
if isinstance(dvar, StypyTypeError):
return dvar
if Number == type(arguments[0]):
return call_utilities.create_numpy_array(arguments[0])
else:
return call_utilities.create_numpy_array(get_contained_elements_type(localization, arguments[0]))
def any(localization, proxy_obj, arguments):
if Number == type(arguments[0]):
return numpy.bool_()
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['axis', 'out', 'keepdims'], {
'axis': [
types.NoneType, Integer,
IterableDataStructureWithTypedElements(Integer)
],
'out':
numpy.ndarray,
'keepdims':
bool
}, 'any')
if isinstance(dvar, StypyTypeError):
return dvar
if 'out' in dvar.keys():
set_contained_elements_type(localization, dvar['out'],
numpy.bool_())
return dvar['out']
if 'axis' in dvar.keys():
return call_utilities.create_numpy_array(numpy.bool_())
return numpy.bool_()
def clip(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, ['a_min', 'a_max', 'out'], {
'a_min': [
Integer,
IterableDataStructureWithTypedElements(Integer),
types.NoneType
],
'a_max': [
Integer,
IterableDataStructureWithTypedElements(Integer),
types.NoneType
],
'out':
numpy.ndarray,
}, 'clip')
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']
return call_utilities.create_numpy_array(
get_contained_elements_type(localization, arguments[0]))
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 __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 __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 astype(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments,
['dtype', 'order', 'casting', 'subok', 'copy'], {
'dtype': type,
'order': Str,
'casting': Str,
'subok': bool,
'copy': bool,
}, 'astype', 0)
if isinstance(dvar, StypyTypeError):
return dvar
dvar = call_utilities.check_possible_values(dvar, 'order', ['C', 'F', 'A', 'K'])
if isinstance(dvar, StypyTypeError):
return dvar
dvar = call_utilities.check_possible_values(dvar, 'casting', ['no', 'equiv', 'safe', 'same_kind', 'unsafe'])
if isinstance(dvar, StypyTypeError):
return dvar
r = TypeWrapper.get_wrapper_of(proxy_obj.__self__)
dims = call_utilities.get_dimensions(localization, r)
if dims > 1:
return call_utilities.create_numpy_array_n_dimensions(
call_utilities.get_contained_elements_type(localization, r),
dims - 1, dvar['dtype'])
else:
return call_utilities.create_numpy_array(call_utilities.get_contained_elements_type(localization, r),
dvar['dtype'])
def randint(localization, proxy_obj, arguments):
if len(arguments) == 1:
return int()
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['high', 'size', 'dtype'], {
'high': Integer,
'size':
[Integer,
IterableDataStructureWithTypedElements(Integer)],
'dtype': type,
}, 'randint', 1)
if isinstance(dvar, StypyTypeError):
return dvar
if 'dtype' in dvar:
if not Integer == dvar['dtype']:
return StypyTypeError(
localization, "Unsupported type {0} for randint".format(
str(dvar['dtype'])))
if 'size' in dvar:
return call_utilities.create_numpy_array(numpy.int32())
return int()
def nansum(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', 'keepdims'], {
'axis': int,
'dtype': type,
'out': numpy.ndarray,
'keepdims': bool
}, 'nansum')
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(
get_contained_elements_type(localization, arguments[0]))
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 sinc(localization, proxy_obj, arguments):
if Number == type(arguments[0]):
return numpy.float64()
if Number == type(get_contained_elements_type(arguments[0])):
return call_utilities.create_numpy_array(
get_contained_elements_type(arguments[0]))
return arguments[0]
def block(localization, proxy_obj, arguments):
union = None
for arg in arguments:
if call_utilities.is_iterable(arg):
union = UnionType.add(
union, get_contained_elements_type(localization, arg))
else:
union = UnionType.add(union, arg)
return call_utilities.create_numpy_array(union)
def angle(localization, proxy_obj, arguments):
if not call_utilities.is_iterable(arguments):
return numpy.float64()
r = TypeWrapper.get_wrapper_of(proxy_obj.__self__)
dims = call_utilities.get_dimensions(localization, r)
if dims > 1:
return call_utilities.create_numpy_array_n_dimensions(
numpy.float64(), dims - 1)
else:
return call_utilities.create_numpy_array(numpy.float64())
def bincount(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['weights', 'minlength'], {
'weights': IterableDataStructure,
'minlength': Integer,
}, 'bincount')
if isinstance(dvar, StypyTypeError):
return dvar
return call_utilities.create_numpy_array(numpy.int32())
def empty(localization, proxy_obj, arguments):
if Number == type(arguments[0]):
return call_utilities.create_numpy_array(numpy.float64(), False)
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments,
['dtype', 'order'], {
'dtype': type,
'order': Str,
}, 'array')
if isinstance(dvar, StypyTypeError):
return dvar
if 'dtype' in dvar:
dtype = dvar['dtype']
else:
dtype = None
return call_utilities.create_numpy_array(
get_contained_elements_type(localization, arguments[0]), dtype)
def argpartition(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['kth', 'axis', 'kind', 'order'], {
'kth':
[Integer,
IterableDataStructureWithTypedElements(Integer)],
'axis': Integer,
'kind': Str,
'order': [Str,
IterableDataStructureWithTypedElements(Str)]
}, 'argpartition')
if isinstance(dvar, StypyTypeError):
return dvar
if Number == type(arguments[0]):
return call_utilities.create_numpy_array(arguments[0])
if call_utilities.is_numpy_array(arguments[0]):
return arguments[0]
return call_utilities.create_numpy_array(arguments[0])
def gradient(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(
localization, arguments, ['vargargs', 'edge_order'], {
'varargs': IterableDataStructureWithTypedElements(RealNumber),
'edge_order': Integer,
}, 'gradient')
if isinstance(dvar, StypyTypeError):
return dvar
return call_utilities.create_numpy_array(
get_contained_elements_type(arguments[0]))
def diff(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments,
['n', 'axis'], {
'n': Integer,
'axis': Integer,
}, 'diff')
if isinstance(dvar, StypyTypeError):
return dvar
return call_utilities.create_numpy_array(
get_contained_elements_type(arguments[0]))
def tensordot(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['axes'],{
'axes': [Integer, IterableDataStructureWithTypedElements(Integer)],
}, 'tensordot', 2)
if isinstance(dvar, StypyTypeError):
return dvar
l = wrap_contained_type(list())
set_contained_elements_type(localization, l, DynamicType())
return call_utilities.create_numpy_array(l)
def unique(localization, proxy_obj, arguments):
dvar = call_utilities.parse_varargs_and_kwargs(localization, arguments, ['return_index', 'return_inverse', 'return_counts'], {
'return_index': bool,
'return_inverse': bool,
'return_counts': bool,
}, 'unique')
if isinstance(dvar, StypyTypeError):
return dvar
if Number == type(arguments[0]):
ret_arr = call_utilities.create_numpy_array(arguments[0])
else:
ret_arr = call_utilities.create_numpy_array(get_contained_elements_type(localization, arguments[0]))
if len(dvar.keys()) == 0:
return ret_arr
tup = wrap_type(tuple())
union = UnionType.add(ret_arr, call_utilities.create_numpy_array(numpy.int32()))
if len(dvar.keys()) == 1:
set_contained_elements_type(localization, tup, union)
if len(dvar.keys()) == 2:
union = UnionType.add(union, call_utilities.create_numpy_array(numpy.int32()))
set_contained_elements_type(localization, tup, union)
if len(dvar.keys()) == 3:
union = UnionType.add(union, call_utilities.create_numpy_array(numpy.int32()))
union = UnionType.add(union, call_utilities.create_numpy_array(numpy.int32()))
set_contained_elements_type(localization, tup, union)
return tup
def concatenate(localization, proxy_obj, arguments):
union = None
for arg in arguments:
if call_utilities.is_iterable(arg):
union = UnionType.add(
union, get_contained_elements_type(localization, arg))
else:
return StypyTypeError(
localization,
"A non-iterable parameter {0} was passed to the concatenate function"
.format(str(arg)))
return call_utilities.create_numpy_array(union)
