Metadata-Version: 1.0
Name: permuta
Version: 0.1.0
Summary: A comprehensive high performance permutation library.
Home-page: https://github.com/PermutaTriangle/Permuta
Author: Henning Ulfarsson
Author-email: henningu@ru.is
License: BSD-3
Description-Content-Type: UNKNOWN
Description: # permuta
        
        [![Build Status](https://travis-ci.org/PermutaTriangle/Permuta.svg?branch=master)](https://travis-ci.org/PermutaTriangle/Permuta)
        [![Coverage Status](https://coveralls.io/repos/github/PermutaTriangle/Permuta/badge.svg?branch=master)](https://coveralls.io/github/PermutaTriangle/Permuta?branch=master)
        
        Permuta is a Python library for working with perms (short for permutations),
        patts (patterns), and mesh patts.
        
        ## Installing
        
        To install Permuta on your system, simply run the following command as a superuser:
        
        ```
        # ./setup.py install
        ```
        
        It is also possible to install Permuta in development mode, in which case you
        run the following instead:
        
        ```
        # ./setup.py develop
        ```
        
        To run the unit tests using pytest run:
        
        ```
        # ./setup.py test
        ```
        
        This requires `pytest`, `pytest-cov`, `pytest-pep8` and `pytest-isort`.
        
        ## Demo
        
        Once you've installed Permuta, it can be imported by a Python script or an
        interactive Python session, just like any other Python library:
        
        ```python
        >>> from permuta.demo import *
        ```
        
        For this section we focus on a subset of modified features exposed in the `demo`
        submodule. Importing `*` from it supplies you with the Perm and PermClass
        classes (and their respective aliases Permutation and Av).
        
        ### Creating a single perm
        
        There are several ways of creating a perm:
        
        ```python
        >>> Perm()  # Empty perm
        ()
        >>> Perm([])  # Another empty perm
        ()
        >>> Perm(132)  # From number
        (1, 3, 2)
        >>> Perm(248)  # Attempted interpretation
        (1, 2, 3)
        >>> Perm("1234")  # From string
        (1, 2, 3, 4)
        >>> Perm("dcab")  # This is equivalent to ...
        (4, 3, 1, 2)
        >>> Perm(["d", "c", "a", "b"])  # ... this
        (4, 3, 1, 2)
        >>> Perm(0, 0, 2, 1)  # Index is tie-breaker
        (1, 2, 4, 3)
        >>> Perm("Ragnar", "Christian", "Henning")
        (3, 1, 2)
        >>> Perm.monotone_increasing(4)
        (1, 2, 3, 4)
        >>> Perm.monotone_decreasing(3)
        (3, 2, 1)
        >>> random_perm = Perm.random(7)
        ```
        
        You can visualize a perm using matplotlib/seaborn:
        
        ```python
        >>> import matplotlib.pyplot as plt
        >>> p = Perm((1, 3, 2, 4))
        >>> ax = p.plot()
        >>> plt.show()
        ```
        
        ![Plot of the perm 1324](README.d/1324.png?raw=true "Plot of the perm 1324")
        
        The avoids, contains, and occurrence\* methods enable working with patts:
        
        ```python
        >>> p.contains(321)
        False
        >>> p.avoids(12)
        False
        >>> p.occurrences_of(21)
        [[3, 2]]
        >>> Perm(12).occurrences_in(p)
        [[1, 3], [1, 2], [1, 4], [3, 4], [2, 4]]
        ```
        
        The basic symmetries are implemented:
        
        ```python
        >>> [p.reverse(), p.complement(), p.inverse()]
         [(4, 2, 3, 1), (4, 2, 3, 1), (1, 3, 2, 4)]
        ```
        
        To take direct sums and skew sums we use `+` and `-`:
        
        ```python
        >>> q = Perm((1, 2, 3, 4, 5))
        >>> p + q
        (1, 3, 2, 4, 5, 6, 7, 8, 9)
        >>> p - q
        (6, 8, 7, 9, 1, 2, 3, 4, 5)
        ```
        
        There are numerous practical methods available:
        
        ```python
        >>> p.fixed_points()
        [1, 4]
        >>> p.ascents()
        [1, 3]
        >>> p.descents()
        [2]
        >>> p.inversions()
        [[3, 2]]
        >>> p.cycles()
        [[1], [3, 2], [4]]
        >>> p.major_index()
        2
        ```
        
        ### Creating a perm class
        
        You might want the set of all perms:
        
        ```python
        >>> all_perms = PermClass()
        >>> all_perms
        <All perms>
        ```
        
        Perm classes can be specified with a basis:
        
        ```python
        >>> basis = [213, Perm((2, 3, 1))]
        >>> basis
        [213, (2, 3, 1)]
        >>> perm_class = Av(basis)
        >>> perm_class
        <Perms avoiding: (2, 1, 3) and (2, 3, 1)>
        ```
        
        Recall that Av is just an alias of PermClass.
        
        You can ask whether a perm belongs to the perm class:
        
        ```python
        >>> 4321 in perm_class
        True
        >>> 1324 in perm_class
        False
        ```
        
        You can get the n-th perm of the class or iterate:
        
        ```python
        >> [perm_class[n] for n in range(10)]
        [(), (1), (1, 2), (2, 1), (1, 2, 3), (1, 3, 2), (3, 2, 1), (3, 1, 2), (4, 3, 2, 1), (4, 1, 3, 2)]
        >>> perm_class_iter = iter(perm_class)
        >>> [next(perm_class_iter) for _ in range(10)]
        [(), (1), (1, 2), (2, 1), (1, 2, 3), (1, 3, 2), (3, 2, 1), (3, 1, 2), (4, 3, 2, 1), (4, 1, 3, 2)]
        ```
        
        (BEWARE: Lexicographic order is not guaranteed at the moment!)
        
        ### The subset of a perm class where the perms are a specific length
        
        You can define a subset of perms of a specific length in the perm class:
        
        ```python
        >>> perm_class_14 = perm_class.of_length(14)
        >>> perm_class_14
        <Perms of length 14 avoiding: (2, 1, 3) and (2, 3, 1)>
        ```
        
        You can ask for the size of the subset because it is guaranteed to be finite:
        
        ```python
        >>> len(perm_class_14)
        8192
        ```
        
        The iterating and containment functionality is the same as with `perm_class`,
        but indexing has yet to be implemented:
        
        ```python
        >>> 321 in perm_class_14
        False
        >>> (1, 14, 2, 13, 3, 4, 5, 12, 6, 11, 7, 8, 9, 10) in perm_class_14
        True
        >>> Perm(range(10)) - Perm(range(4)) in perm_class_14
        False
        >>> next(iter(perm_class_14))
        (14, 1, 2, 3, 4, 5, 13, 12, 11, 10, 6, 9, 7, 8)
        ```
        
        To get a feeling for the perm class, you can plot a heatmap of this subset
         using matplotlib/seaborn:
        
        ```python
        >>> ax = perm_class_14.plot()
        >>> plt.show()
        ```
        
        ![A heatmap plot for the perms of length 14 avoiding 213 and 231](README.d/av_213_231_of_length_14_heatmap.png?raw=true "A heatmap plot for the perms of length 14 avoiding 213 and 231")
        
        ## Life in Permuta beyond the demo
        
        If your work has reached a place where your require functionality beyond
        that offered by the demo, it may be time to proceed to the non-demo version
        of Permuta. The first hurdle will be coming to terms with the zero based indexing.
        Here's how to get started:
        
        ```python
        >>> from permuta import Perm, PermSet, MeshPatt
        ```
        
        ## License
        BSD-3: see the [LICENSE](https://github.com/PermutaTriangle/Permuta/blob/master/LICENSE) file.
        
Keywords: permutation perm mesh pattern patt avoid contain occurrencestatistic
Platform: UNKNOWN
