experimenters Package

experimenters Package

aggregator Module

Aggregating experimenters.

class vis.analyzers.experimenters.aggregator.ColumnAggregator(index, settings=None)

Bases: vis.analyzers.experimenter.Experimenter

(Arguments for the constructor are listed below).

Experiment that aggregates data from columns of a DataFrame, or a list of DataFrame objects, by summing each row. Values from columns named 'all' will not be included in the aggregated results. You may provide a 'column' setting to guide the experimenter to include only certain results.

Example 1

Inputting single DataFrame like this:

Index	piece_1	piece_2
M3	12	24
m3	NaN	36
P5	3	9

Yields this DataFrame:

Index	‘aggregator.ColumnAggregator’
M3	36
m3	36
P5	12

Example 2

Inputting two DataFrame objects is similar.

Index	piece_1
M3	12
P5	3

Index	piece_2
M3	24
m3	36
P5	9

The result is the same DataFrame:

Index	‘aggregator.ColumnAggregator’
M3	36
m3	36
P5	12

Example 3

You may also give a DataFrame (or a list of DataFrame objects) that have a pandas.MultiIndex as produced by subclasses of Indexer. In this case, use the 'column' setting to indicate which indexer’s results you wish to aggregate.

Index	‘frequency.FrequencyExperimenter’		‘feelings.FeelingsExperimenter’
	‘0,1’	‘1,2’	‘Christopher’	‘Alex’
M3	12	24	‘delight’	‘exuberance’
m3	NaN	36	‘sheer joy’	‘nonchalance’
P5	3	9	‘emptiness’	‘serenity’

If 'column' is 'frequency.FrequencyExperimenter', yet again you will have this DataFrame:

Index	‘aggregator.ColumnAggregator’
M3	36
m3	36
P5	12

default_settings = {'column': None}

possible_settings = ['column']

Parameters:'column' (str) – The column name to use for aggregation. The default is None, which aggregates across all columns. If you set this to 'all', it will override the default behaviour of not including columns called 'all'.

run()

Run the ColumnAggregator experiment.

Returns:A Series with an index that is the combination of all indices of the provided pandas objects, and the value is the sum of all values in the pandas objects. Return type:pandas.Series

*Example:*

import music21 from vis.analyzers.indexers import noterest from vis.analyzers.experimenters import aggregator, frequency

score = music21.converter.parse(‘example.xml’) notes = noterest.NoteRestIndexer(score).run()

freqs = frequency.FrequencyExperimenter(notes).run() agg = aggregator.ColumnAggregator(freqs).run() print(agg)

barchart Module

Aggregating experimenters.

class vis.analyzers.experimenters.aggregator.ColumnAggregator(index, settings=None)

Bases: vis.analyzers.experimenter.Experimenter

(Arguments for the constructor are listed below).

Experiment that aggregates data from columns of a DataFrame, or a list of DataFrame objects, by summing each row. Values from columns named 'all' will not be included in the aggregated results. You may provide a 'column' setting to guide the experimenter to include only certain results.

Example 1

Inputting single DataFrame like this:

Index	piece_1	piece_2
M3	12	24
m3	NaN	36
P5	3	9

Yields this DataFrame:

Index	‘aggregator.ColumnAggregator’
M3	36
m3	36
P5	12

Example 2

Inputting two DataFrame objects is similar.

Index	piece_1
M3	12
P5	3

Index	piece_2
M3	24
m3	36
P5	9

The result is the same DataFrame:

Index	‘aggregator.ColumnAggregator’
M3	36
m3	36
P5	12

Example 3

You may also give a DataFrame (or a list of DataFrame objects) that have a pandas.MultiIndex as produced by subclasses of Indexer. In this case, use the 'column' setting to indicate which indexer’s results you wish to aggregate.

Index	‘frequency.FrequencyExperimenter’		‘feelings.FeelingsExperimenter’
	‘0,1’	‘1,2’	‘Christopher’	‘Alex’
M3	12	24	‘delight’	‘exuberance’
m3	NaN	36	‘sheer joy’	‘nonchalance’
P5	3	9	‘emptiness’	‘serenity’

If 'column' is 'frequency.FrequencyExperimenter', yet again you will have this DataFrame:

Index	‘aggregator.ColumnAggregator’
M3	36
m3	36
P5	12

default_settings = {'column': None}

possible_settings = ['column']

Parameters:'column' (str) – The column name to use for aggregation. The default is None, which aggregates across all columns. If you set this to 'all', it will override the default behaviour of not including columns called 'all'.

run()

Run the ColumnAggregator experiment.

Returns:A Series with an index that is the combination of all indices of the provided pandas objects, and the value is the sum of all values in the pandas objects. Return type:pandas.Series

*Example:*

import music21 from vis.analyzers.indexers import noterest from vis.analyzers.experimenters import aggregator, frequency

score = music21.converter.parse(‘example.xml’) notes = noterest.NoteRestIndexer(score).run()

freqs = frequency.FrequencyExperimenter(notes).run() agg = aggregator.ColumnAggregator(freqs).run() print(agg)

dendrogram Module

Aggregating experimenters.

class vis.analyzers.experimenters.aggregator.ColumnAggregator(index, settings=None)

Bases: vis.analyzers.experimenter.Experimenter

(Arguments for the constructor are listed below).

Experiment that aggregates data from columns of a DataFrame, or a list of DataFrame objects, by summing each row. Values from columns named 'all' will not be included in the aggregated results. You may provide a 'column' setting to guide the experimenter to include only certain results.

Example 1

Inputting single DataFrame like this:

Index	piece_1	piece_2
M3	12	24
m3	NaN	36
P5	3	9

Yields this DataFrame:

Index	‘aggregator.ColumnAggregator’
M3	36
m3	36
P5	12

Example 2

Inputting two DataFrame objects is similar.

Index	piece_1
M3	12
P5	3

Index	piece_2
M3	24
m3	36
P5	9

The result is the same DataFrame:

Index	‘aggregator.ColumnAggregator’
M3	36
m3	36
P5	12

Example 3

You may also give a DataFrame (or a list of DataFrame objects) that have a pandas.MultiIndex as produced by subclasses of Indexer. In this case, use the 'column' setting to indicate which indexer’s results you wish to aggregate.

Index	‘frequency.FrequencyExperimenter’		‘feelings.FeelingsExperimenter’
	‘0,1’	‘1,2’	‘Christopher’	‘Alex’
M3	12	24	‘delight’	‘exuberance’
m3	NaN	36	‘sheer joy’	‘nonchalance’
P5	3	9	‘emptiness’	‘serenity’

If 'column' is 'frequency.FrequencyExperimenter', yet again you will have this DataFrame:

Index	‘aggregator.ColumnAggregator’
M3	36
m3	36
P5	12

default_settings = {'column': None}

possible_settings = ['column']

Parameters:'column' (str) – The column name to use for aggregation. The default is None, which aggregates across all columns. If you set this to 'all', it will override the default behaviour of not including columns called 'all'.

run()

Run the ColumnAggregator experiment.

Returns:A Series with an index that is the combination of all indices of the provided pandas objects, and the value is the sum of all values in the pandas objects. Return type:pandas.Series

*Example:*

import music21 from vis.analyzers.indexers import noterest from vis.analyzers.experimenters import aggregator, frequency

score = music21.converter.parse(‘example.xml’) notes = noterest.NoteRestIndexer(score).run()

freqs = frequency.FrequencyExperimenter(notes).run() agg = aggregator.ColumnAggregator(freqs).run() print(agg)

frequency Module

Experimenters that deal with the frequencies (number of occurrences) of events.

class vis.analyzers.experimenters.frequency.FrequencyExperimenter(index, settings=None)

Bases: vis.analyzers.experimenter.Experimenter

Calculate the number of occurrences of objects in an index.

Use the 'column' setting to choose only the results of one previous analyzer. For example, if you wanted to calculate the frequency of vertical intervals, you would specify 'interval.IntervalIndexer'. This would avoid counting, for example, the horizontal intervals if they were also present.

default_settings = {'column': None}

possible_settings = ['column']

Parameters:'column' (str) – The column name to use for counting frequency. The default is None, which counts all columns. Use this to count only the frequency of one previous analyzer.

run()

Run the 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 numpy.NaN. Return type:list of pandas.DataFrame

*Example:*

import music21 from vis.analyzers.indexers import noterest from vis.analyzers.experimenters import frequency

score = music21.converter.parse(‘example.xml’) notes = noterest.NoteRestIndexer(score).run()

freqs = frequency.FrequencyExperimenter(notes).run() print(freqs)

template Module

Template for writing a new experimenter. Use this class to help write a new :class`Experimenter` subclass. The TemplateExperimenter does nothing, and should only be used by programmers.

class vis.analyzers.experimenters.template.TemplateExperimenter(index, settings=None)

Bases: vis.analyzers.experimenter.Experimenter

Template for an Experimenter subclass.

default_settings = {}

The default values for settings named in possible_settings. If a setting doesn’t have a value in this constant, then it must be specified to the constructor at runtime, or the constructor should raise a RuntimeException.

possible_settings = ['fake_setting']

This is a list of strings that are the names of the settings used in this experimenter. Specify the types and reasons for each setting as though it were an argument list, like this:

Parameters:'fake_setting' (boolean) – This is a fake setting.

run()

Run an experiment on a piece.

Returns:The result of the experiment. Each experiment should describe its data storage. Return type:pandas.Series or pandas.DataFrame