def map_reduce_between(self,
other=None,
map_func_nb=None,
map_args=None,
reduce_func_nb=None,
reduce_args=None,
broadcast_kwargs=None):
"""See `vectorbt.signals.nb.map_reduce_between_nb`.
If `other` specified, see `vectorbt.signals.nb.map_reduce_between_two_nb`.
Both will be broadcast using `vectorbt.base.reshape_fns.broadcast`
with `broadcast_kwargs`.
Note that `map_args` and `reduce_args` won't be broadcast.
## Example
Get average distance between signals in `sig`:
```python-repl
>>> distance_map_nb = njit(lambda from_i, to_i, col: to_i - from_i)
>>> mean_reduce_nb = njit(lambda col, a: np.nanmean(a))
>>> sig.vbt.signals.map_reduce_between(
... map_func_nb=distance_map_nb,
... reduce_func_nb=mean_reduce_nb)
a NaN
b 2.0
c 1.0
dtype: float64
```
"""
if broadcast_kwargs is None:
broadcast_kwargs = {}
checks.assert_not_none(map_func_nb)
checks.assert_not_none(reduce_func_nb)
checks.assert_numba_func(map_func_nb)
checks.assert_numba_func(reduce_func_nb)
if map_args is None:
map_args = ()
if reduce_args is None:
reduce_args = ()
if other is None:
# One input array
result = nb.map_reduce_between_nb(self.to_2d_array(), map_func_nb,
map_args, reduce_func_nb,
reduce_args)
if isinstance(self._obj, pd.Series):
return result[0]
return pd.Series(result, index=self.wrapper.columns)
else:
# Two input arrays
obj, other = reshape_fns.broadcast(self._obj, other,
**broadcast_kwargs)
checks.assert_dtype(other, np.bool)
result = nb.map_reduce_between_two_nb(obj.vbt.to_2d_array(),
other.vbt.to_2d_array(),
map_func_nb, map_args,
reduce_func_nb, reduce_args)
return obj.vbt.wrapper.wrap_reduced(result)
def map_reduce_between(self,
*args,
other=None,
map_func_nb=None,
reduce_func_nb=None,
broadcast_kwargs={}):
"""See `vectorbt.signals.nb.map_reduce_between_nb`.
If `other` specified, see `vectorbt.signals.nb.map_reduce_between_two_nb`.
Arguments will be broadcasted using `vectorbt.utils.reshape_fns.broadcast`
with `broadcast_kwargs`.
Example:
Get maximum distance between signals in `signals`:
```python-repl
>>> distance_map_nb = njit(lambda col, prev_i, next_i: next_i - prev_i)
>>> max_reduce_nb = njit(lambda col, a: np.nanmax(a))
>>> print(signals.vbt.signals.map_reduce_between(
... map_func_nb=distance_map_nb, reduce_func_nb=max_reduce_nb))
a 3.0
b 3.0
c NaN
dtype: float64
```"""
checks.assert_not_none(map_func_nb)
checks.assert_not_none(reduce_func_nb)
checks.assert_numba_func(map_func_nb)
checks.assert_numba_func(reduce_func_nb)
if other is None:
# One input array
result = nb.map_reduce_between_nb(self.to_2d_array(), map_func_nb,
reduce_func_nb, *args)
if isinstance(self._obj, pd.Series):
return result[0]
return pd.Series(result, index=self.columns)
else:
# Two input arrays
obj, other = reshape_fns.broadcast(self._obj, other,
**broadcast_kwargs)
other.vbt.signals.validate()
result = nb.map_reduce_between_two_nb(self.to_2d_array(),
other.vbt.to_2d_array(),
map_func_nb, reduce_func_nb,
*args)
if isinstance(obj, pd.Series):
return result[0]
return pd.Series(result, index=obj.vbt.columns)
def map_reduce_between(self,
*args,
other=None,
map_func_nb=None,
reduce_func_nb=None,
broadcast_kwargs={}):
"""See `vectorbt.signals.nb.map_reduce_between_nb`.
If `other` specified, see `vectorbt.signals.nb.map_reduce_between_two_nb`.
Arguments will be broadcasted using `vectorbt.base.reshape_fns.broadcast`
with `broadcast_kwargs`.
Example:
Get average distance between signals in `sig`:
```python-repl
>>> distance_map_nb = njit(lambda col, from_i, to_i: to_i - from_i)
>>> mean_reduce_nb = njit(lambda col, a: np.nanmean(a))
>>> print(sig.vbt.signals.map_reduce_between(
... map_func_nb=distance_map_nb, reduce_func_nb=mean_reduce_nb))
a NaN
b 2.0
c 1.0
dtype: float64
```"""
checks.assert_not_none(map_func_nb)
checks.assert_not_none(reduce_func_nb)
checks.assert_numba_func(map_func_nb)
checks.assert_numba_func(reduce_func_nb)
if other is None:
# One input array
result = nb.map_reduce_between_nb(self.to_2d_array(), map_func_nb,
reduce_func_nb, *args)
if isinstance(self._obj, pd.Series):
return result[0]
return pd.Series(result, index=self.columns)
else:
# Two input arrays
obj, other = reshape_fns.broadcast(self._obj, other,
**broadcast_kwargs)
checks.assert_dtype(other, np.bool_)
result = nb.map_reduce_between_two_nb(self.to_2d_array(),
other.vbt.to_2d_array(),
map_func_nb, reduce_func_nb,
*args)
return self.wrap_reduced(result)
def map_reduce_between(
self,
other: tp.Optional[tp.ArrayLike] = None,
map_func_nb: tp.Optional[tp.SignalMapFunc] = None,
map_args: tp.Optional[tp.Args] = None,
reduce_func_nb: tp.Optional[tp.SignalReduceFunc] = None,
reduce_args: tp.Optional[tp.Args] = None,
broadcast_kwargs: tp.KwargsLike = None,
wrap_kwargs: tp.KwargsLike = None) -> tp.MaybeSeries:
"""See `vectorbt.signals.nb.map_reduce_between_nb`.
If `other` specified, see `vectorbt.signals.nb.map_reduce_between_two_nb`.
Both will broadcast using `vectorbt.base.reshape_fns.broadcast`
with `broadcast_kwargs`.
Note that `map_args` and `reduce_args` won't be broadcast.
## Example
Get average distance between signals in `sig`:
```python-repl
>>> distance_map_nb = njit(lambda from_i, to_i, col: to_i - from_i)
>>> mean_reduce_nb = njit(lambda col, a: np.nanmean(a))
>>> sig.vbt.signals.map_reduce_between(
... map_func_nb=distance_map_nb,
... reduce_func_nb=mean_reduce_nb)
a NaN
b 2.0
c 1.0
dtype: float64
```
"""
if broadcast_kwargs is None:
broadcast_kwargs = {}
checks.assert_not_none(map_func_nb)
checks.assert_not_none(reduce_func_nb)
checks.assert_numba_func(map_func_nb)
checks.assert_numba_func(reduce_func_nb)
if map_args is None:
map_args = ()
if reduce_args is None:
reduce_args = ()
wrap_kwargs = merge_dicts(dict(name_or_index='map_reduce_between'),
wrap_kwargs)
if other is None:
# One input array
result = nb.map_reduce_between_nb(self.to_2d_array(), map_func_nb,
map_args, reduce_func_nb,
reduce_args)
return self.wrapper.wrap_reduced(result, **wrap_kwargs)
else:
# Two input arrays
obj, other = reshape_fns.broadcast(self._obj, other,
**broadcast_kwargs)
checks.assert_dtype(other, np.bool_)
result = nb.map_reduce_between_two_nb(obj.vbt.to_2d_array(),
other.vbt.to_2d_array(),
map_func_nb, map_args,
reduce_func_nb, reduce_args)
return obj.vbt.wrapper.wrap_reduced(result, **wrap_kwargs)