groover 0.2.2
A rhythm feature extractor and classifier for MIDI files
Requires Python: >=3.6

# groover 0.2.2

## Installation

`groover` is a beat-by-beat rhythm feature clustering and token generation tool for `.mid` files. You can download `groover` using pip:

```shell
pip install groover
```

To check if `groover` is successfully installed, type `python` in the terminal, and do the following:

```
>>> from groover import RhythmClassifier
>>> type(RhythmClassifier())
<class 'groover.classifier.RhythmClassifier'>
```

## `RhythmClassifier`
### `RhythmClassifier.__init__`
#### Returns: `None`
#### Parameters (all optional)
- `bins_per_beat`: `int`
   - the number of quantized time units within a beat
   - is set to 24 by default
- `n_beats_pitched`: `int`
   - the number of beats that consist a rhythmic pattern for pitched instruments
   - is set to 1 by default
- `n_beats_drums`: `int`
   - the number of beats that consist a rhythmic pattern for drum instruments
   - is set to 4 by default
- `drums`: `list`
   - a list of `str` that specifies which types of drums to consider
   - complete mapping from drum names to MIDI pitches can be found in `groover.drum_names_to_pitches`
- `notes_weight`: callable
   - a function that takes a list of `miditoolkit.midi.containers.Note` as input, and outputs an `numpy.ndarray` with shape `(len(notes),)`
- `similarity`: callable
   - a function that takes two `numpy.ndarray` and output the similarity score between the two arrays

### `RhythmClassifier.get_pitched_dataset(self, midi_objs, pitches=None, in_four=False)`
#### Returns: `numpy.ndarray`
- a dataset that contains rhythmic patterns extracted from pitched instruments of the MIDI objects
- has shape `(n, m)`, where `n` is the number of patterns and `m` is the number of quantized time units within a pattern
#### Parameters
- `midi_objs`: `list`
   - a list of `miditoolkit.midi.parser.MidiFile` to extract rhythmic patterns from
- `pitches`: iterable
   - an iterable that contains the pitches to be considered
   - is set to `range(128)` by default
- `in_four`: `bool`
   - will only consider MIDI files that has time signatures in power of two if set to `True`

### `RhythmClassifier.get_drum_dataset(self, midi_objs, in_four=False)`
#### Returns: `numpy.ndarray`
- a dataset that contains rhythmic patterns extracted from drum instruments of the MIDI objects
- has shape `(n, len(self.drums), m)`, where `n` is the number of patterns and `m` is the number of quantized time units within a pattern
#### Parameters
- `midi_objs`: `list`
   - a list of `miditoolkit.midi.parser.MidiFile` to extract rhythmic patterns from
- `in_four`: `bool`
   - will only consider MIDI files that has time signatures in power of two if set to `True`

### `RhythmClassifier.def fit_from_midi(self, midi_objs, k_pitched=200, k_all_drums=100, k_single_drum=20, quantize=True):`
#### Returns: `None`
The `RhythmClassifier` instance itself will be modified with updated rhythmic pattern clusters.
#### Parameters
- `midi_objs`: `list`
   - a list of `miditoolkit.midi.parser.MidiFile` to classify rhythmic patterns from
- `k_pitched`: `int`
   - the number of rhythmic pattern classes from pitched instruments
- `k_all_drums`: `int`
   - the number of rhythmic pattern classes from drum instruments
- `k_single_drum`: `int`
   - the number of rhythmic pattern classes from each drum
- `quantize`: `bool`
   - quantize drum patterns further down to 16th notes if set to `True`

### `RhythmClassifier.add_pitched_markers(self, midi_obj, pitches=None)`
#### Returns: `None`
`midi_obj` will be modified with rhythmic pattern markers from pitched instruments in `midi_obj.markers`
#### Parameters
- `midi_obj`: `miditoolkit.midi.parser.MidiFile`
   - the MIDI file to add rhythmic pattern markers from pitched instruments to
- `pitches`: iterable
   - an iterable that contains the pitches to be considered
   - is set to `range(128)` by default

### `RhythmClassifier.add_composite_drum_markers(self, midi_obj, rid_empty=True)`
#### Returns: `None`
`midi_obj` will be modified with rhythmic pattern markers from drum instruments in `midi_obj.markers`
#### Parameters
- `midi_obj`: `miditoolkit.midi.parser.MidiFile`
   - the MIDI file to add rhythmic pattern markers from drum instruments to
- `rid_empty`: `bool`
   - sections with no drums will not add markers to the MIDI files if set to `True`

### `RhythmClassifier.add_separate_drum_markers(self, midi_obj, rid_empty=True)`
#### Returns: `None`
`midi_obj` will be modified with rhythmic pattern markers from each drum instruments in `midi_obj.markers`
#### Parameters
- `midi_obj`: `miditoolkit.midi.parser.MidiFile`
   - the MIDI file to add rhythmic pattern markers from each drum instruments to
- `rid_empty`: `bool`
   - sections with no drums will not add markers to the MIDI files if set to `True`