Source code for vis.analyzers.indexers.repeat

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#--------------------------------------------------------------------------------------------------
# Program Name:           vis
# Program Description:    Helps analyze music with computers.
#
# Filename:               controllers/indexers/repeat.py
# Purpose:                Indexers that somehow consider repetition.
#
# Copyright (C) 2013 Christopher Antila
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#--------------------------------------------------------------------------------------------------
"""
.. codeauthor:: Christopher Antila <crantila@fedoraproject.org>

Indexers that consider repetition in any way.
"""

import numpy
import pandas
from vis.analyzers import indexer


class FilterByRepeatIndexer(indexer.Indexer):
    """
    If the same event occurs many times in a row, remove all occurrences but the one with the \
    lowest ``offset`` value (i.e., the "first" event).
    """

    required_score_type = pandas.Series
    "The :class:`FilterByRepeatIndexer` requires :class:`pandas.Series` as input."

    def __init__(self, score, settings=None):
        """
        :param score: The indices from which to remove consecutive identical events.
        :type score: :obj:`list` of :obj:`pandas.Series`
        :param settings: This indexer uses no settings, so this is ignored.
        :type settings: :obj:`dict` or :obj:`None`

        :raises: :exc:`RuntimeError` if :obj:`score` is the wrong type.
        :raises: :exc:`RuntimeError` if :obj:`score` is not a list of the same types.
        """
        super(FilterByRepeatIndexer, self).__init__(score, None)

        # If self._score is a Stream (subclass), change to a list of types you want to process
        self._types = []

        # This Indexer uses pandas magic, not an _indexer_func().
        self._indexer_func = None

    def run(self):
        """
        Make a new index of the piece, removing any event that is identical to the preceding.

        :returns: A list of the new indices. The index of each Series corresponds to the index of
            the Part used to generate it, in the order specified to the constructor. Each element
            in the Series is a :obj:`basestring`.
        :rtype: :obj:`list` of :obj:`pandas.Series`
        """
        # I'm relying on pandas' efficiency. In the future, maybe we should use multiprocessing?
        post = []
        for part in self._score:
            if len(part.index) < 2:
                post.append(part)
                continue
            axe_me = []
            prev_off = None
            for offset in list(part.index):
                if prev_off is None:
                    pass  # prevent the other tests from being tried
                elif part[offset] == part[prev_off]:
                    axe_me.append(offset)
                prev_off = offset
            for axed in axe_me:
                part[axed] = numpy.nan
            post.append(part.dropna())
        return post