Source code for vis.analyzers.indexers.cadence

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# -------------------------------------------------------------------- #
# Program Name:           vis
# Program Description:    Helps analyze music with computers.
# Filename:               analyzers/indexers/
# Purpose:                Cadence Indexer
# Copyright (C) 2016 Marina Borsodi-Benson, M. Ryan Bannon, Alexander 
# Morgan
# 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
# 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 
# <>.
# -------------------------------------------------------------------- #
.. codeauthor:: Marina Borsodi-Benson <>


from vis.analyzers import indexer
import pandas

[docs]class CadenceIndexer(indexer.Indexer): """ Using ``OverBassIndexer`` and ``FermataIndexer`` results, finds cadences as lists of events in the approach to a fermata. Call this indexer via the ``get_data()`` method of either an ``indexed_piece`` object or an ``aggregated_pieces`` object (see example below). :keyword 'length': The length of the cadence, or how many events happen before a fermata. :type 'length': int :keyword 'voice': The voice in which you want to look for fermatas. The default value for this is 'all'. :type 'voice': str or int **Example:** Prepare an indexed piece and import pandas: >>> from vis.models.indexed_piece import Importer >>> import pandas >>> ip = Importer('path_to_piece.xml') Prepare ``OverBassIndexer`` and ``FermataIndexer`` results. For more specific advice on how to do this, please see the documentation of those two indexers. These two DataFrames should be passed as a list. For simplicity, including the ``FermataIndexer`` results is optional, and this example shows how to use the ``CadenceIndexer`` without explicitly providing the ``FermataIndexer`` results, so the 'data' argument is a singleton list. >>> overbass_input_dfs = [ip.get_data('noterest'), ip.get_data('vertical_interval')] >>> ob_setts = { 'type': 'notes' } >>> overbass = ip.get_data('over_bass', data=overbass_input_dfs, settings=ob_setts) Get the ``CadenceIndexer`` results with specified settings: >>> ca_setts = {'length': 3} >>> ip.get_data('cadence', data=[overbass], settings=ca_setts) """ required_score_type = 'pandas.DataFrame' possible_settings = ['length', 'voice'] _MISSING_LENGTH = 'CadenceIndexer requires "length" setting.' _LOW_LENGTH = 'Setting "length" must have a value of at least 1.' _BAD_VOICE = 'voice setting must be a voice present in the piece' def __init__(self, score, settings=None): """ :param score: The OverBassIndexer results and FermataIndexer results to be used to find cadences. :type score: :class:`pandas.DataFrame` :param settings: The setting 'length' is required. :type settings: dict :raises: :exc:`RuntimeError` if the required setting 'length' is notgiven. :raises: :exc:`RuntimeError` if the value of 'length' is below 1 :raises: :exc:`RuntimeError` if the given voice is not a voice found in the piece. """ self._score = pandas.concat(score, axis=1) self.fig = self._score['over_bass.OverBassIndexer'] self.ferm = self._score['fermata.FermataIndexer'] if settings is None or 'length' not in settings: raise RuntimeError(self._MISSING_LENGTH) elif settings['length'] < 1: raise RuntimeError(self._LOW_LENGTH) elif 'voice' not in settings: self._settings = settings self._settings['voice'] = 'all' elif(type(settings['voice']) is int and settings['voice'] >= len(self.ferm.columns)): raise RuntimeError(self._BAD_VOICE) else: self._settings = settings super(CadenceIndexer, self).__init__(score, None)
[docs] def run(self): """ Makes a new index of the cadences in the piece. :returns: A :class:`DataFrame` of the cadences. :rtype: :class:`pandas.DataFrame` """ endings = [] if self._settings['voice'] is 'all': for part in self.ferm.columns: endings.extend(self.ferm[ self.ferm[part].notnull()].index.tolist()) else: endings.extend(self.ferm[self.ferm[ str(self._settings['voice'])].notnull()].index.tolist()) endings = list(set(endings)) beginnings = [] indices = self.ferm.index.tolist() for ind in endings: beginnings.append(indices[ indices.index(ind)-self._settings['length']+1]) beginnings.sort() endings.sort() locations = list(zip(beginnings, endings)) cadences = [] for x in range(len(beginnings)): my_cadence = [] for place in self.fig.loc[locations[x][0]:locations[x][1]].index.tolist(): my_cadence.extend(self.fig.loc[place].tolist()) cadences.append(my_cadence) result = pandas.DataFrame( {'Cadences': pandas.Series(cadences, index=beginnings)}) return self.make_return(result.columns.values, [result[name] for name in result.columns])