def wrapper(*args, **kwargs):
try:
# Get the index of the first Series object, if any
index = next(arg.index
for arg in chain(args, kwargs.values())
if isinstance(arg, _pd_Series))
except StopIteration:
# No pandas.Series passed in; short-circuit
index = None
if index is None:
return func(*args, **kwargs)
# Use Series' float64 values if pandas, else use values as passed
args = [arg.values.astype(float) if isinstance(arg, _pd_Series) else arg
for arg in args]
kwargs = {k: v.values.astype(float) if isinstance(v, _pd_Series) else v
for k, v in kwargs.items()}
result = func(*args, **kwargs)
# check to see if we got a streaming result
first_result = result[0] if isinstance(result, tuple) else result
is_streaming_fn_result = not hasattr(first_result, '__len__')
if is_streaming_fn_result:
return result
# Series was passed in, Series gets out; re-apply index
if isinstance(result, tuple):
# Handle multi-array results such as BBANDS
return tuple(_pd_Series(arr, index=index)
for arr in result)
return _pd_Series(result, index=index)
def wrapper(*args, **kwargs):
try:
# Get the index of the first Series object, if any
index = next(arg.index for arg in chain(args, kwargs.values())
if isinstance(arg, _pd_Series))
except StopIteration:
# No pandas.Series passed in; short-circuit
index = None
if index is None:
return func(*args, **kwargs)
# Use Series' float64 values if pandas, else use values as passed
args = [
arg.values.astype(float)
if isinstance(arg, _pd_Series) else arg for arg in args
]
kwargs = {
k: v.values.astype(float) if isinstance(v, _pd_Series) else v
for k, v in kwargs.items()
}
result = func(*args, **kwargs)
# check to see if we got a streaming result
first_result = result[0] if isinstance(result, tuple) else result
is_streaming_fn_result = not hasattr(first_result, '__len__')
if is_streaming_fn_result:
return result
# Series was passed in, Series gets out; re-apply index
if isinstance(result, tuple):
# Handle multi-array results such as BBANDS
return tuple(_pd_Series(arr, index=index) for arr in result)
return _pd_Series(result, index=index)
def wrapper(*args, **kwds):
if _pl_Series is not None:
use_pl = any(isinstance(arg, _pl_Series) for arg in args) or \
any(isinstance(v, _pl_Series) for v in kwds.values())
else:
use_pl = False
if _pd_Series is not None:
use_pd = any(isinstance(arg, _pd_Series) for arg in args) or \
any(isinstance(v, _pd_Series) for v in kwds.values())
else:
use_pd = False
if use_pl and use_pd:
raise Exception("Cannot mix polars and pandas")
# Use float64 values if polars or pandas, else use values as passed
if use_pl:
_args = [
arg.to_numpy().astype(float) if isinstance(
arg, _pl_Series) else arg for arg in args
]
_kwds = {
k: v.to_numpy().astype(float)
if isinstance(v, _pl_Series) else v
for k, v in kwds.items()
}
elif use_pd:
index = next(arg.index for arg in chain(args, kwds.values())
if isinstance(arg, _pd_Series))
_args = [
arg.to_numpy().astype(float) if isinstance(
arg, _pd_Series) else arg for arg in args
]
_kwds = {
k: v.to_numpy().astype(float)
if isinstance(v, _pd_Series) else v
for k, v in kwds.items()
}
else:
_args = args
_kwds = kwds
result = func(*_args, **_kwds)
# check to see if we got a streaming result
first_result = result[0] if isinstance(result, tuple) else result
is_streaming_fn_result = not hasattr(first_result, '__len__')
if is_streaming_fn_result:
return result
# Series was passed in, Series gets out
if use_pl:
if isinstance(result, tuple):
return tuple(_pl_Series(arr) for arr in result)
else:
return _pl_Series(result)
elif use_pd:
if isinstance(result, tuple):
return tuple(
_pd_Series(arr, index=index) for arr in result)
else:
return _pd_Series(result, index=index)
else:
return result