Hello there,
here is a feature request for the possibility to wrap numpy random
distributions to be wrapped by a mechanism like __array_function__ or
__array_ufunc__. You can find the GH issue at :
https://github.com/numpy/numpy/issues/19382
<https://github.com/numpy/numpy/issues/19382>.
This post follows [this one](https://github.com/numpy/numpy/issues/18902
<https://github.com/numpy/numpy/issues/18902>). I figured I should open another
issue for `np.random.normal`, but this applies for all distributions I guess.
## Feature
Basicaly, I would like that `numpy.random.*` distributions could trigger an
interface when passed instances from custom classes, "à la" `__array_ufunc__`
or `__array_function__`. Here is an example concept :
```python
arr = np.arange(10)
# Reference result
print(np.mean(arr))
print(np.random.normal(arr))
custom_obj = MyArrayLike(arr)
print(np.mean(custom_obj)) # OK : np.mean will trigger
__array_function__ interface
print(np.random.normal(custom_obj)) # KO : np.random.normal will "only" try to
cast the object to float
```
And here is a MWE :
```python
import numpy as np
np.random.seed(1234)
HANDLED_FUNCTIONS = {}
class NumericalLabeled():
def __init__(self, value, label=""):
self.value = value
self.label = label
def __repr__(self):
return "NumericalLabelled<"+str(self.value) + "," + self.label+">"
def __array_function__(self, func, types, args, kwargs):
if func not in HANDLED_FUNCTIONS:
return NotImplemented
return HANDLED_FUNCTIONS[func](*args, **kwargs)
def make_numericallabelled(x, label=""):
"""
Helper function to cast anything into a NumericalLabelled object.
"""
if isinstance(x, NumericalLabeled):
return x
else:
return NumericalLabeled(x, label=label)
# Numpy functions
# Override functions - used with __array_function__
def implements(np_function):
def decorator(func):
HANDLED_FUNCTIONS[np_function] = func
return func
return decorator
@implements(np.random.normal)
def np_random_normal(loc=0.0, scale=1.0, **kwargs):
# cast both loc and scale into Numericallabelled
loc = make_numericallabelled(loc)
scale = make_numericallabelled(scale)
# check their label is "compatible"
if not loc.label == scale.label:
raise ValueError
return NumericalLabeled(np.random.rand(loc=loc.value,
scale=scale.value, **kwargs),
loc.label+scale.label)
@implements(np.mean)
def np_mean(a, *args, **kwargs):
return NumericalLabeled(np.mean(a.value, *args, **kwargs),
a.label)
def main():
# reference result for standard array
arr = np.arange(10)
print(np.mean(arr))
print(np.random.normal(arr))
# array-like object
num_labeled = NumericalLabeled(arr, "toto")
print(np.mean(num_labeled))
try:
print(np.random.normal(num_labeled))
except Exception as e:
print(e)
main()
```
which results in
```
4.5
[ 0.47143516 -0.19097569 3.43270697 2.6873481 3.27941127 5.88716294
6.85958841 6.3634765 8.01569637 6.75731505]
NumericalLabelled<4.5,toto>
float() argument must be a string or a number, not 'NumericalLabeled'
```
Since the distribution functions accept array as input, I would expect them to
be wrappable like many other array functions.
### Versions
```
Python : 3.8.5 (default, Sep 4 2020, 02:22:02)
[Clang 10.0.0 ]
Numpy : 1.21.0
```
Cheers_______________________________________________
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