Code source de music22.scale

Scale : Analyse the scale of a melody.

This module provides function to analyse the scale of a melody. 

* Evaluate the PDF of the frequencies
* Get Peaks of the main frequencies
* Plots the PDF and the Peaks
* Gives the scale and compare intervals to reference epimoric intervals


import numpy
from pandas import Series,DataFrame

from music22 import core,diastema

from scipy.stats.kde import gaussian_kde
from scipy.stats.mstats import mode

import matplotlib.pyplot as plt

xmin = 0
xmax = 500
x = numpy.linspace(xmin,xmax,xmax-xmin)

bw_method = 'scott'

[docs]def set_xrange(min,max): """Set the limits of x for plots and pdfs. Args : xmin (int): x-minimum xmax (int): x-maximum """ global xmin,xmax,x xmin = min xmax = max x = numpy.linspace(xmin,xmax,xmax-xmin)
[docs]def set_bw_method(method): """Set the bw_method for gaussian.kde. Args : bw_method (str or scalar) : 'scott' (default), 'silvermann' """ global bw_method bw_method = method
[docs]def kde(freqs): """Estimate the pdf of the freqs using a Kernel Density Estimation. The estimation is done on the frequencies (0,500). Args: freqs (numpy.ndarray) : A list of frequencies in Hz. Returns: pdf (scipy.stats.kde.gaussian_kde) : the pdf function of freqs. Exemple: >>> from music22 import diastema,scale >>> import matplotlib.pyplot as plt >>> file_path = "/Users/anas/AUDIO/Barraq/txt/P0.txt" >>> freqs = numpy.loadtxt(file_path) >>> freqs = music22.core.clean_list(freqs) >>> pdf = music22.scale.kde(freqs) >>> plt.plot(pdf) >>> """ global x, bw_method kde = gaussian_kde(freqs,bw_method) pdf = kde.evaluate(x) return pdf
[docs]def peaks(pdf): """Get the peaks of the estimated Probability Density Function. The estimation is done on the frequencies (0,500). Args: pdf (numpy.array) : the probability of the frequencies in the range 0-500 Hz. Returns: xpeaks (numpy.array) : the peaks frequencies. ypeaks (numpy.array) : the probability of the peaks. """ global x c = (numpy.diff(numpy.sign(numpy.diff(pdf))) < 0).nonzero()[0] + 1 # local max peaks = x[c] peakspdf = pdf[c] xpeaks = numpy.array(peaks, float) ypeaks = numpy.array(peakspdf, float) return xpeaks, ypeaks
[docs]def pdf_show(pdf,xpeaks,ypeaks,label="pdf"): """Plot the PDF and the peaks of the frequencies. Args: pdf (numpy.ndarray) : the PDF of the frequencies given by kde() xpeaks (numpy.ndarray) : frequencies of the peaks, given by peaks() ypeaks (numpy.ndarray) : pdf values of the peaks, given by peaks() label (str) : label to give to the figure (`optional`) Returns : A plot """ global x plt.plot(x,pdf,linewidth=3,alpha=.6,label=label) plt.plot(xpeaks, ypeaks, "ro") for i in range(0,len(xpeaks)): plt.annotate( "%.2f" % xpeaks[i], xy=(xpeaks[i], ypeaks[i]), xytext=(xpeaks[i], (ypeaks[i]+0.0001))) plt.legend()
[docs]def order_peaks(xpeaks,ypeaks): """Orders the peaks following their probability Args: xpeaks (numpy.array) : list of peaks (Hz.) ypeaks (numpy.array) : probability of the peaks Returns: ordredpeaks (pandas.core.frame.DataFrame) : a table with ordered frequencies """ data = {'xpeaks': xpeaks,'ypeaks': ypeaks} table = DataFrame(data) ordredpeaks = table.sort_index(by='ypeaks',ascending=False) return ordredpeaks
if __name__ == "__main__": import doctest doctest.testmod()