Metadata-Version: 1.2
Name: py-tlsh
Version: 4.7.2
Summary: TLSH (C++ Python extension)
Home-page: https://github.com/trendmicro/tlsh
Author: Jonathan Oliver / Chun Cheng / Yanggui Chen
Author-email: jon_oliver@trendmicro.com
License: Apache or BSD
Description-Content-Type: text/markdown
Description: # TLSH - C++ extension for Python

	[TLSH (Trend Micro Locality Sensitive Hash)](https://github.com/trendmicro/tlsh) is a fuzzy matching library.
	Given a byte stream with a minimum length of 50 bytes
	TLSH generates a hash value which can be used for similarity comparisons.
	Similar objects will have similar hash values which allows for
	the detection of similar objects by comparing their hash values.  Note that
	the byte stream should have a sufficient amount of complexity.  For example,
	a byte stream of identical bytes will not generate a hash value.

	## What's new in py-tlsh 4.7.2
	This Python module supercedes the python-tlsh package on PyPi.
	The improvements in 4.7.2, are that we added additional functions to Python
	* lvalue
	* q1ratio
	* q2ratio
	* checksum
	* bucket_value
	* is_valid

	The improvements 4.5.0 were:
	* fixed this package so that it works on Windows
	* compatibility with VirusTotal adoption of TLSH: updated to the T1 hash format with backwards compatibility for old hashes
	* fixed the q3=0 divide by zero bug [issue 79](https://github.com/trendmicro/tlsh/issues/79)

	## Usage

	```python
	import tlsh

	tlsh.hash(data)
	```

	Note data needs to be bytes - not a string.
	This is because TLSH is for binary data and binary data can contain a NULL (zero) byte.

	In default mode the data must contain at least 50 bytes to generate a hash value and that
	it must have a certain amount of randomness.
	To get the hash value of a file, try

	```python
	tlsh.hash(open(file, 'rb').read())
	```

	Note: the open statement has opened the file in binary mode.

	## Example
	```python
	import tlsh

	h1 = tlsh.hash(data)
	h2 = tlsh.hash(similar_data)
	score = tlsh.diff(h1, h2)

	h3 = tlsh.Tlsh()
	with open('file', 'rb') as f:
	    for buf in iter(lambda: f.read(512), b''):
		h3.update(buf)
	    h3.final()
	# this assertion is stating that the distance between a TLSH and itself must be zero
	assert h3.diff(h3) == 0
	score = h3.diff(h1)
	```

	## Extra Options

	The `diffxlen` function removes the file length component of the tlsh header from the comparison.

	```python
	tlsh.diffxlen(h1, h2)
	```

	If a file with a repeating pattern is compared to a file with only a single instance of the pattern,
	then the difference will be increased if the file lenght is included.
	But by using the `diffxlen` function, the file length will be removed from consideration.

	## Backwards Compatibility Options

	If you use the "conservative" option, then the data must contain at least 256 characters.
	For example,

	```python
	import os
	tlsh.conservativehash(os.urandom(256))
	```

	should generate a hash, but

	```python
	tlsh.conservativehash(os.urandom(100))
	```

	will generate TNULL as it is less than 256 bytes.

	If you need to generate old style hashes (without the "T1" prefix) then use

	```python
	tlsh.oldhash(os.urandom(100))
	```


	The old and conservative options may be combined:

	```python
	tlsh.oldconservativehash(os.urandom(500))
	```

Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Development Status :: 5 - Production/Stable
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Requires-Python: >=2.7
