BUG: Fix np.quantile([Fraction(2,1)], 0.5)

[eendebakpt]

On main:

import numpy as np
from fractions import Fraction
arr = np.array([Fraction(1, 1), Fraction(2, 1), Fraction(3, 1)])
np.quantile(arr, 0) # output: Fraction(1, 1)
np.quantile(arr, Fraction(1, 2)) # output: Fraction(2, 1)
np.quantile(arr, 0.5) # raises an error

In this PR we update the np.quantile to handle the case np.quantile(arr, 0.5) as well.

Also see #24592

[charris]

The debug test failure seems to be an Ubuntu problem, the requested file is missing from the upstream directory. I suspect it is due to a micro upgrade in the Ubuntu version.

[seberg]

This may sound weird, but we should probably also add dtype=type(lerp_interpolation.dtype).

If we have unsafe casting, this means that at least to some degree we ignore the precision of the weights of the quantiles for the output precision (promotion). Can you give me a hint in how things are currently? Does the same happen if we use np.longdouble for the quantile level (q)?

(Sorry, but I am a bit surprised how many subtleties there still seem to be along promotion in quantiles :(, and hoping we can clarify things a bit for me and everyone else.)

[eendebakpt]

@seberg I tested with

import numpy as np
print(np,np.__version__)

x = np.linspace(.1, .7, 5, dtype=np.longdouble)
for q in [0, 1, 0., 1., .5]:
    r = np.quantile(x, q)
    print(f'{q} ({type(q)}) -> {r} ({type(r)})')
    r = np.quantile(x, np.longdouble(q))
    print(f'{q} ({type(q)}) -> {r} ({type(r)})')
    
    
print('----')    
x = np.array([0, 1])
for q in [0, 1, 0., 1., .5]:
    r = np.quantile(x, q)
    print(f'{q} ({type(q)}) -> {r} ({type(r)})')
    r = np.quantile(x, np.longdouble(q))
    print(f'{q} ({type(q)}) -> {r} ({type(r)})')    

Output on 1.26:

<module 'numpy' from '/home/eendebakpt/py310/lib/python3.10/site-packages/numpy/__init__.py'> 1.26.0
0 (<class 'int'>) -> 0.1 (<class 'numpy.longdouble'>)
0 (<class 'int'>) -> 0.1 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 0.7 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 0.7 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.1 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.1 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 0.7 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 0.7 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.4 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.4 (<class 'numpy.longdouble'>)
----
0 (<class 'int'>) -> 0 (<class 'numpy.int64'>)
0 (<class 'int'>) -> 0.0 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 1 (<class 'numpy.int64'>)
1 (<class 'int'>) -> 1.0 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.0 (<class 'numpy.float64'>)
0.0 (<class 'float'>) -> 0.0 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 1.0 (<class 'numpy.float64'>)
1.0 (<class 'float'>) -> 1.0 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.5 (<class 'numpy.float64'>)
0.5 (<class 'float'>) -> 0.5 (<class 'numpy.longdouble'>)

Output with this PR:

<module 'numpy' from '/mnt/data/numpy/numpy/__init__.py'> 2.0.0.dev0+git20231107.a555e7d
0 (<class 'int'>) -> 0.1 (<class 'numpy.longdouble'>)
0 (<class 'int'>) -> 0.1 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 0.7 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 0.7 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.1 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.1 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 0.7 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 0.7 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.4 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.4 (<class 'numpy.longdouble'>)
----
0 (<class 'int'>) -> 0 (<class 'numpy.int64'>)
0 (<class 'int'>) -> 0.0 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 1 (<class 'numpy.int64'>)
1 (<class 'int'>) -> 1.0 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.0 (<class 'numpy.float64'>)
0.0 (<class 'float'>) -> 0.0 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 1.0 (<class 'numpy.float64'>)
1.0 (<class 'float'>) -> 1.0 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.5 (<class 'numpy.float64'>)
0.5 (<class 'float'>) -> 0.5 (<class 'numpy.longdouble'>)

If you want I can add tests for this case as well.

[charris]

@seberg ping.

[seberg]

Thanks @eendebakpt, sorry for forgetting to look over this again. I think it should be fine to do this cast and it seems like you gave it a spin with that test. So let's give it a shot.