Modalis API¶

Modalis: Basics of Modality¶

The module modalis focuses on the fundamental elements of modality in a melody. Its defines a Melodie class for getting :

• The Dominant : It is based on :

  ** The most present frequency (Todo). ** The most probable frequency.

class music22.modalis.melodia(file_path, transpose='No', freqref=300)[source]¶

Create the melodia object.

Paramètres:	path to a list of frequencies (f0) in a .txt file. (A) –

All attributes from music22.core.f0file.

clean_freqs¶

numpy.ndarray

The list of frequencies without Zeros neither NaNs.

pdf¶

numpy.ndarray

The Probability Density Function on the range 0-500 Hz.

xpeaks, xpeaks

numpy.ndarray

the Peaks of the PDF and their values.

ordredpeaks¶

pandas.core.frame.DataFrame

The Peaks ordered by their probability.

dominante¶

numpy.float64

the dominant frequency.

intervals¶

pandas.core.series.Series

the list of intervals in the choosen Unit (default is savarts).

scale¶

list

the Scale from the Dominant, compared to reference epimoric intervals.

pdf_show[source]¶

matplotlibfigure

the PDF plot with peaks.

Example

>>> import music22.modalis
>>> file = '/Users/anas/AUDIO/Barraq/P1.wav'
>>> Barraq = music22.modalis.melodia(file)
Instance created with the txt file : P1.wav
Instance created with the txt file : P1.txt
The detected tonic is 167.696 Hz.
>>> Barraq.file.basename
'P1.txt'
>>> Barraq.freqs
array([ nan,  nan,  nan, ...,  nan,  nan,  nan])
>>> Barraq.clean_freqs
array([ 155.564,  157.372,  160.123, ...,  159.201,  158.284,  156.466])
>>> Barraq.tonique
167.696
>>> Barraq.pdf
array([  1.55304474e-33,   5.65209492e-33,   2.03065388e-32,
         7.20217980e-32,   2.52170476e-31,   8.71620406e-31,
         [...]
         8.53621504e-26,   2.80105445e-26,   9.07308675e-27,
         2.90111852e-27,   9.15698236e-28])
>>> Barraq.pdf_show
<bound method melodia.pdf_show of <music22.modalis.melodia object at 0x102a32910>>
>>> Barraq.ordredpeaks
       xpeaks    ypeaks
1  243.486974  0.012244
0  201.402806  0.007772
3  324.649299  0.003007
2  278.557114  0.002709
4  375.751503  0.000785

get_scale(reference='tonic')[source]¶

Get the scale of the melody.

Paramètres:	reference (float) – the reference frequencies to calculate the scale. It could be the _tonic_ (self.tonique) or the _domin_ (self.dominante)
Retourne:	self.intervals – the intervals in the the choosen linear Unit. self.scale (): The scale compared to reference intervals.
Type retourné:	list

get_tonique(percent=0.5, method='pdf')[source]¶

Get the tonic of a melody.

Paramètres:	percent (float) – the percentage of the last considered frequencies. method (str) – The method to be used for the analysis : “pdf” or “mode”.
Retourne:	tonic – The detected tonic.
Type retourné:	float

pdf_show()[source]

Show the pdf.

plot(peaks='No', width=12, height=6)[source]¶

Plots the melody frequencies

Paramètres:	peaks (boolean, optional) – Draw the peak-lines. Default=”Yes” width (int,optional) – width of the figure. Default = 12 height (int,optional) – height of the figure. Default = 6

class music22.modalis.melodies(path, transpose='No', freqref=300)[source]¶

This class defines a set of melodia-a and compares them.

corr(metric='KL')[source]¶

Calculate distances between PDFs.

Paramètres:	self.melodies.pdf (list) – list of pdf lists. metric (str) – the metric method : euclidian, KL (Kullback-Leibler). Default is KL.
Retourne:	self.distances

matrix()[source]¶

Plot the matrix and heatmap of PDFS.

pdf_show(width=12, height=6)[source]¶

music22.modalis.transmode(freqlist, freqref=300)[source]¶

Transpose all the frequencies by setting the _mode_ on a given reference frequency.

Paramètres:	freqlist (numpy.array) – A list of frequencies to be transposed. freqref (int) – The frequency reference to be transposed to. Default = 300.
Retourne:	transfreq – a list of the transposed frequencies.
Type retourné:	numpy.array