Wraps hashlib.hash instances for easily computing multiple hashes at the same time.

See the full documentation at https://bnb-MultiHash.readthedocs.io

A convenience class for computing multiple hashes at the same time from a single source.

This class optimizes disk reads per computation, but does not implement threading or multiprocessing.

Provides the same interface as hashlib.hash classes with properties returning dictionaries of results using the hash's name as the key, excluding .name, which returns a MultiHash specific name.

The class provides an additional_hashers set where other classes conforming the hashlib.hash interface, or classes which override existing hashlib.hash classes functionalities may be placed in order for instances of MultiHash to utilize them.

Computing the hash(es) of some data in RAM

>>> from multihash import MultiHash
>>> MultiHash(b"This is a test", hashers=['md5', 'sha256']).hexdigest()
{'sha256': 'c7be1ed902fb8dd4d48997c6452f5d7e509fbcdbe2808b16bcf4edce4c07d14e', 'md5': 'ce114e4501d2f4e2dcea3e17b546f339'}

Utilizing the .update() method

>>> from multihash import MultiHash
>>> x = MultiHash(hashers=['md5', 'sha256'])
>>> x.update(b"this is some data")
>>> x.update(b"and this is some more")
>>> x.hexdigest()
{'md5': '8dcdbfc187e97b7f8817b7fe857c7635', 'sha256': 'a88897eb17993ace17bdb55f60bce7afc37156a939c3b217d4cb31fd2ade5848'}

Computing the hash(es) of a file

>>> from multihash import MultiHash
>>> with open('test_file', 'wb') as f:
...     f.write(b"blahblahblahblahblahblahblah")
>>> MultiHash.from_filepath('test_file', hashers=['md5', 'sha256']).hexdigest()
{'md5': '4ca3f52a8a3c1643708cce5e9a919b43', 'sha256': 'a2c56fb21bee151494b5d3bb40ed3e3d357b2014c82bb937f0d181dd43124fea'}

Computing the hash(es) of a stream/file like object/thing that implements .read()

>>> from os import urandom
>>> from io import BytesIO
>>> MultiHash.from_Stream(BytesIO(urandom(256)), hashers=['md5', 'sha256']).hexdigest()
{'md5': 'bd5c3a82f88ed4d903f4c30a21b827b6', 'sha256': 'a799b79935c54af47704d3b8421c83989b0cbc4078dd5a94aa8036a4912ae27e'}

• $ git clone https://github.com/bnbalsamo/MultiHash.git
• $ cd MultiHash
  • If you would like to install the pinned dependencies, run pip install -r requirements.txt
• $ python -m pip install .

To quickly install + configure a development environment...

Install pyenv, pyenv-virtualenv, and xxenv-latest and copy the following into your terminal while in the repository root.

[[ `type -t pyenv` ]] && \
[ -s "$PYENV_ROOT/plugins/pyenv-virtualenv" ] && \
[ -s "$PYENV_ROOT/plugins/xxenv-latest" ] && \
pyenv latest install -s 3.8 && \
PYENV_LATEST_38=$(pyenv latest -p 3.8) && \
pyenv latest install -s 3.7 && \
PYENV_LATEST_37=$(pyenv latest -p 3.7) && \
pyenv latest install -s 3.6 && \
PYENV_LATEST_36=$(pyenv latest -p 3.6) && \
pyenv virtualenv "$PYENV_LATEST_38" "MultiHash" && \
pyenv local "MultiHash" "$PYENV_LATEST_38" "$PYENV_LATEST_37" "$PYENV_LATEST_36" && \
pip install -e .[dev,tests,docs]

If you choose not to use the quickstart script you will need to...

• Create a virtual environment
• Install the development dependencies
  • pip install -e .[dev,tests,docs]
• Configure tox so that it can access all relevant python interpreters

$ inv run.tests

$ inv run.autoformatters

$ inv pindeps

Brian Balsamo [email protected]

Created using bnbalsamo/cookiecutter-pypackage v0.38.0