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BUG : add __hash__ to AffineBase #2909

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BUG : add __hash__ to AffineBase #2909

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BUG : add __hash__ to AffineBase
tacaswell Mar 19, 2014
c3bea7a
ENH : tweaked hash method and documentation
tacaswell Mar 19, 2014
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BUG : add __hash__ to AffineBase 
python3 requires that if a class defines __eq__ then in must define
__hash__ (so that if a == b then hash(a) == hash(b)).  Define 64bit
hash by pulling the first 8 characters out of a sha1 hash of the matrix

closes #2828
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@tacaswell
tacaswell committed Mar 19, 2014
commit c1ab6c6684e8bddfe0971e6b8d8cd2d66ab5acbe
9 changes: 8 additions & 1 deletion 9 lib/matplotlib/transforms.py
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Expand Up @@ -33,7 +33,7 @@
unicode_literals)

import six

import hashlib
import numpy as np
from numpy import ma
from matplotlib._path import (affine_transform, count_bboxes_overlapping_bbox,
Expand Down Expand Up @@ -1570,6 +1570,13 @@ def __eq__(self, other):
return np.all(self.get_matrix() == other.get_matrix())
return NotImplemented

# python3 requires that if a class defines __eq__ then in must define
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Where is that stated? The documentation states that it is perfectly valid for objects to define __eq__ and not __hash__ (http://docs.python.org/3.4/reference/datamodel.html#object.__hash__) as far as I can see.

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They must if they want to be hashable, clarified the comment.

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Why do transforms want to be hashable? They aren't currently AFAICT?

# __hash__ (so that if a == b then hash(a) == hash(b)). Define 64bit
# hash by pulling the first 8 characters out of a sha1 hash of the matrix
# see issue #2828
def __hash__(self):
return int(hashlib.sha1(self.get_matrix()).hexdigest()[:8], 16)
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Seems like a correct approach. But I wonder if it's faster to do:

hash(self.get_matrix().tostring())

(Don't know if it is, just though the code would spend more time in C that way...)

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indeed

In [51]: tt = np.random.rand(3, 3)

In [52]: %timeit int(hashlib.sha1(tt).hexdigest()[:8], 16)
1000000 loops, best of 3: 1.58 µs per loop

In [53]: %timeit hash(tt.tostring())
1000000 loops, best of 3: 270 ns per loop


def transform(self, values):
return self.transform_affine(values)
transform.__doc__ = Transform.transform.__doc__
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