mirror of
https://github.com/vale981/binfootprint
synced 2025-03-04 16:51:39 -05:00
63 lines
1.9 KiB
Markdown
63 lines
1.9 KiB
Markdown
# binfootprint
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[](https://badge.fury.io/py/binfootprint)
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[](https://travis-ci.org/cimatosa/binfootprint)
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[](https://codecov.io/gh/cimatosa/binfootprint)
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Calculate a unique binary representation (binary footprint) for simple data structures
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with the intension to use this binary footprint as a loop up key for example in a data base.
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The following atomic types are supported:
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* integer (32bit and python integer)
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* float (the usual 64bit)
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* complex (the usual 2 times 64bit)
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* string
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* bytes
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* numpy ndarray (see note below)
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These atomic types can be structured arbitrarily nested using the following python standard containers:
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* tuple
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* named tuple
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* list
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* dict
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Generating the binary footprint and reconstruction is done as follows:
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```python
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import binfootprint as bf
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data = ['hallo', 42]
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bin_key = bf.dump(data)
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data_prime = bf.load(bin_key)
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print(data_prime)
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```
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Further any class that implements `__getstate__` may be used as a container as well. When reconstructing, the class needs to have the `__setstate__` method implemented.
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Additionally the `bf.load` function required a mapping from the class name to the class object, like this:
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```python
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import binfootprint as bf
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class T(object):
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def __init__(self, a):
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self.a = a
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def __getstate__(self):
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return [self.a]
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def __setstate__(self, state):
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self.a = state[0]
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ob = T(4)
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bin_ob = bf.dump(ob)
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# reconstruction
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classes = {}
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classes['T'] = T
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ob_prime = bf.load(bin_ob, classes)
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```
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### Note on numpy ndarrays
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As it has not been clarified/tested yet whether the buffer of the numpy ndarray is really unique also on different machines and architectures
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is it not assured that the binary footprint serves as a valid key.
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