Metadata-Version: 1.0
Name: skidmarks
Version: 0.0.3
Summary: find runs (non-randomness) in sequences
Home-page: http://bitbucket.org/brentp/biostuff/
Author: brentp
Author-email: bpederse@gmail.com
License: MIT
Description: 
        Skid Marks: Check for runs in sequences
        ----------------------------------------
        
        Q: how do you check for runs?
        
        A: look for skidmarks.
        
        This module implements some functions to check a sequence for randomness.
        in some cases, it is assumed to be a binary sequence (not only 1's and 0's
        but containing only 2 distinct values).
        Any feedback, improvements, additions are welcomed.
        
        >>> from skidmarks import gap_test, wald_wolfowitz, auto_correlation, serial_test
        
        
        Wald-Wolfowitz
        ---------------
        
        http://en.wikipedia.org/wiki/Wald-Wolfowitz_runs_test
        
        http://support.sas.com/kb/33/092.html
        
        >>> r = wald_wolfowitz('1000001')
        >>> r['n_runs'] # should be 3, because 1, 0, 1
        3
        
        >>> r['p'] < 0.05 # not < 0.05 evidence to reject Ho of random sequence
        False
        
        # this should show significance for non-randomness
        >>> li = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
        >>> wald_wolfowitz(li)['p'] < 0.05
        True
        
        
        
        Autocorrelation
        ----------------
        
        >>> result = auto_correlation('00000001111111111100000000')
        >>> result['p'] < 0.05
        True
        
        >>> result['auto_correlation']
        0.83766233766233755
        
        
        Serial Test
        ------------
        
        http://books.google.com/books?id=EIbxfCGfzgcC&lpg=PA141&ots=o-8ymmqbs9&pg=PA142#v=onepage&q=&f=false
        
        >>> serial_test('101010101111000')
        {'chi': 1.4285714285714286, 'p': 0.69885130769248427}
        
        >>> serial_test('110000000000000111111111111')
        {'chi': 18.615384615384617, 'p': 0.00032831021826061683}
        
        
        Gap Test
        ---------
        
        http://books.google.com/books?id=EIbxfCGfzgcC&lpg=PA141&ots=o-8ymmqbs9&pg=PA142#v=onepage&q=&f=false
        
        >>> gap_test('100020001200000')
        {'chi': 756406.99909855379, 'item': '1', 'p': 0.0}
        
        >>> gap_test('101010111101000')
        {'chi': 11.684911193438811, 'item': '1', 'p': 0.23166089118674466}
        
        gap_test() will default to looking for gaps between the first value in
        the sequence (in this case '1') and each later occurrence. use the `item`
        kwarg to specify another value.
        
        >>> gap_test('101010111101000', item='0')
        {'chi': 11.028667632612191, 'item': '0', 'p': 0.27374903509732523}
        
Keywords: bioinformatics sequence randomness test
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
