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MAINT: remove NumPy dependency #12

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merged 5 commits into from
Dec 1, 2024
Merged

MAINT: remove NumPy dependency #12

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19b6785
MAINT: remove NumPy dependency
mdhaber Nov 21, 2024
480cb49
MAINT: simplify __repr__, __str__
mdhaber Nov 21, 2024
8703448
Merge remote-tracking branch 'origin/main' into no_numpy_dependency
mdhaber Nov 24, 2024
bb606d0
STY: use f-strings for simplicity
mdhaber Nov 25, 2024
5fcaf6f
Update marray/__init__.py
mdhaber Nov 25, 2024
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24 changes: 13 additions & 11 deletions marray/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@

__version__ = "0.0.4"

import numpy as np # temporarily used in __repr__ and __str__
import textwrap
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def masked_array(xp):
Expand Down Expand Up @@ -86,20 +86,20 @@ def __setitem__(self, key, other):
self.mask[key] = getattr(other, 'mask', False)
return self.data.__setitem__(key, getattr(other, 'data', other))

def _data_mask_string(self, fun):
data_str = fun(self.data)
mask_str = fun(self.mask)
if len(data_str) + len(mask_str) <= 66:
return f"MaskedArray({data_str}, {mask_str})"
else:
return f"MaskedArray(\n {data_str},\n {mask_str}\n)"

## Visualization ##
def __repr__(self):
# temporary: fix for CuPy
# eventually: rewrite to avoid masked array
data = np.asarray(self.data)
mask = np.asarray(self.mask)
return np.ma.masked_array(data, mask).__repr__()
return self._data_mask_string(repr)

def __str__(self):
# temporary: fix for CuPy
# eventually: rewrite to avoid masked array
data = np.asarray(self.data)
mask = np.asarray(self.mask)
return np.ma.masked_array(data, mask).__str__()
return self._data_mask_string(str)
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I wonder if this should always be repr? That would mean there's no difference between __str__ and __repr__, but given that at least numpy is always a pseudo-repr anyways (so recreating objects is not possible) this might not be too much of an issue?

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I wonder if this should always be repr?

But why not follow the convention of the underlying library, whatever it is?

import numpy as np
import torch
import jax.numpy as jnp
import cupy as cp
for xp in [np, torch, jnp, cp]:
    x = xp.asarray([1, 2, 3, 4])
    print(xp.__name__)
    print(str(x))
    print(repr(x))

# numpy
# [1 2 3 4]
# array([1, 2, 3, 4])
#
# torch
# tensor([1, 2, 3, 4])
# tensor([1, 2, 3, 4])
#
# jax.numpy
# [1 2 3 4]
# Array([1, 2, 3, 4], dtype=int32)
#
# cupy
# [1 2 3 4]
# array([1, 2, 3, 4])

Most of them have different str vs repr, so I don't see a reason not to do what they do.

but given that at least numpy is always a pseudo-repr anyways (so recreating objects is not possible) this might not be too much of an issue?

I don't think it's much of an issue, especially since this is just a placeholder until it's time to polish things.
But if we were to expose MaskedArray (and maybe accept dtype as a parameter), then with the repr output, the object could be recreated to the extent that the NumPy array object can be recreated.


## Linear Algebra Methods ##
def __matmul__(self, other):
Expand Down Expand Up @@ -188,6 +188,8 @@ class module:

mod = module()

mod.MaskedArray = MaskedArray

## Constants ##
constant_names = ['e', 'inf', 'nan', 'newaxis', 'pi']
for name in constant_names:
Expand Down
4 changes: 2 additions & 2 deletions pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,11 @@ classifiers = [
"Programming Language :: Python :: 3.12",
]
dynamic = ["version", "description"]
dependencies = ["numpy"]
dependencies = []
requires-python = ">=3.10"

[project.optional-dependencies]
test = ["pytest"]
test = ["numpy", "pytest"]

[project.urls]
Home = "https://github.com/mdhaber/marray"
Expand Down