#!/usr/bin/env python
# -*- coding: utf-8 -*-
#--------------------------------------------------------------------------------------------------
# Program Name: vis
# Program Description: Helps analyze music with computers.
#
# Filename: controllers/experimenters/frequency.py
# Purpose: Frequency experimenter
#
# 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>
Experimenters that deal with the frequencies (number of occurrences) of events.
"""
import pandas
from vis.analyzers import experimenter
[docs]def experimenter_func(obj):
"""
Used by the :class:`FrequencyExperimenter` to calculate the frequencies of things in an index.
:param obj: An identifier plus the results of an indexer.
:type obj: :obj:`tuple` of (anything, :class:`pandas.Series`)
:returns: An identifier plus the result of this indexation. In the series, the index is the \
names of objects found in the inputted series, and the value is the number of occurrences. \
The first element is the first element given here, used for identification purposes.
:rtype: :obj:`tuple` of (anything, :class:`pandas.Series`)
"""
thing_dict = {}
for each in obj[1]:
if each not in thing_dict:
thing_dict[each] = sum(obj[1] == each)
return obj[0], pandas.Series(thing_dict)
[docs]class FrequencyExperimenter(experimenter.Experimenter):
"""
Calculate the number of occurrences of things found in an index.
"""
possible_settings = []
default_settings = {}
def __init__(self, index, settings=None):
"""
.. note:: It is the caller's responsibility to provide indices with the proper settings.
:param index: A list of :class:`Series`, where each one is the result of an indexer for \
one of the parts in this score.
:type index: :obj:`list` or :obj:`dict` of :class:`pandas.Series`
:param settings: This indexer uses no settings, so this is ignored.
:type settings: :obj:`dict` or :obj:`None`
"""
super(FrequencyExperimenter, self).__init__(index, None)
[docs] def run(self):
"""
Run the :class:`FrequencyExperimenter`.
:returns: The result of the experiment. Data is stored such that column labels correspond \
to the part (combinations) totalled in the column, and row labels correspond to a type \
of the kind of objects found in the given index. Note that all columns are totalled in \
the "all" column, and that not every part combination will have every interval; in \
case an interval does not appear in a part combination, the value is :obj:`numpy.NaN`.
:rtype: :class:`pandas.DataFrame`
"""
# assemble results per-part
results = None
if isinstance(self._index, dict):
results = [[(x, self._index[x])] for x in self._index.iterkeys()]
else:
results = [[(i, x)] for i, x in enumerate(self._index)]
results = self._do_multiprocessing(experimenter_func, results)
post = {}
for result in results:
post[result[0]] = result[1]
# assemble all-part results
tokens = []
for part_i in post.iterkeys():
tokens.extend(list(post[part_i].index))
tokens = set(tokens)
for i in post.iterkeys():
post[i].reindex(index=tokens)
post = pandas.DataFrame(post)
post[u'all'] = post.sum(axis=1, skipna=True)
return post