Code source de music22.modalis

# -*- coding: utf-8 -*-
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.


import os, glob, time, numpy
import matplotlib.pyplot as plt
from scipy.stats.mstats import mode

from music22 import core, diastema, scale, audio, stats

[docs]class melodies(object): """This class defines a set of `melodia`-a and compares them. """ def __init__(self,path,transpose='No',freqref=300): self.path = path self.transpose = transpose self.freqref=freqref print "Reading the content of :", self.path print '\n' # Information about files in path #print "Audios files are :" self.wavfiles = glob.glob(self.path+'*.wav') self.melodies = [] self.labels = [] for file in self.wavfiles: self.melodies.append(melodia(file,transpose,freqref)) print file,'appended' self.labels.append(melodia(file).name) print '\n' ##
[docs] def pdf_show(self,width=12,height=6): plt.figure(figsize=(width,height)) for i in range(0,len(self.melodies)): self.melodies[i].pdf_show()
[docs] def corr(self,metric='KL'): """Calculate distances between PDFs. Args: self.melodies.pdf (list) : list of pdf lists. metric (str) : the metric method : euclidian, KL (Kullback-Leibler). Default is KL. Return: self.distances """ PDFS = [] for i in range(0,len(self.melodies)): PDFS.append(self.melodies[i].pdf) self.distances = stats.corr(PDFS,metric) return
[docs] def matrix(self): """Plot the matrix and heatmap of PDFS.""" try: stats.matrix(self.distances,self.labels) except AttributeError: self.corr(metric='KL') stats.matrix(self.distances,self.labels) return
[docs]class melodia(object): def __init__(self,file_path,transpose='No',freqref=300): """Create the melodia object. Args: A path to a list of frequencies (f0) in a .txt file. Attributes: 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 (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 """ file = core.file(file_path) if file.extension == 'wav': self.file = core.wavfile(file_path) txt_file = self.file.dirname+'/f0/''.txt' if not os.path.isfile(txt_file): self.file.pitch_extract() self.file = core.f0file(txt_file) elif file.extension == 'txt': self.file = core.f0file(file_path) = self.file.get_data() self.file.clean_data() if transpose=="No": self.freqs = self.clean_freqs = self.file.clean_data if transpose=="Yes": self.freqs = transmode(,freqref) self.clean_freqs = transmode(self.file.clean_data,freqref) self.xmin = numpy.min(self.clean_freqs) self.xmax = numpy.max(self.clean_freqs) self.mode = mode(self.clean_freqs)[0] self.get_tonique() self.pdf = scale.kde(self.clean_freqs) self.xpeaks, self.ypeaks = scale.peaks(self.pdf) self.ordredpeaks = scale.order_peaks(self.xpeaks,self.ypeaks) self.dominante = self.ordredpeaks['xpeaks'].iloc[0] self.get_scale()
[docs] def get_scale(self,reference="tonic"): """Get the scale of the melody. Args: reference (float): the reference frequencies to calculate the scale. It could be the _tonic_ (self.tonique) or the _domin_ (self.dominante) Return: self.intervals (list) : the intervals in the the choosen linear Unit. self.scale (): The scale compared to reference intervals. """ if reference == 'tonic': self.intervals = diastema.dias(self.ordredpeaks['xpeaks']/self.tonique) if reference == 'domin': self.intervals = diastema.dias(self.ordredpeaks['xpeaks']/self.dominante) scale = [] x = 0 for interval in self.intervals: var = (self.ordredpeaks['xpeaks'].iloc[x].astype('str'), diastema.get_inter_ref(interval)) scale.append(var) x = x + 1 self.scale = scale
[docs] def plot(self,peaks="No",width=12,height=6): """ Plots the melody frequencies Args: 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 """ hopSize = 128 frameSize = 2048 sampleRate = 44100 n_frames = len(self.freqs) fig = plt.figure(figsize=(width,height)) plt.plot(range(n_frames), self.freqs, 'bo') n_ticks = 10 xtick_locs = [i * (n_frames / 10.0) for i in range(n_ticks)] xtick_lbls = [i * (n_frames / 10.0) * hopSize / sampleRate for i in range(n_ticks)] xtick_lbls = ["%.2f" % round(x,2) for x in xtick_lbls] plt.xticks(xtick_locs, xtick_lbls) ax = fig.add_subplot(111) ax.set_xlabel('Time (s)') ax.set_ylabel('Pitch (Hz)') plt.suptitle( plt.axhline(y=self.tonique,color='r') if peaks=="Yes" : plt.yticks(self.ordredpeaks['xpeaks'].tolist()) c = ['r','g','y','b','b','b','b','b','b','b','b','b'] for i in range(0,len(self.ordredpeaks)): l = plt.axhline(y=self.ordredpeaks['xpeaks'].tolist()[i],color=c[i]) plt.text(len(self.freqs), self.ordredpeaks['xpeaks'].tolist()[i], self.scale[i][1]) elif peaks=="No" : pass
[docs] def pdf_show(self): """Show the pdf.""" self.pdf = scale.kde(self.clean_freqs) scale.pdf_show(self.pdf,self.xpeaks,self.ypeaks,
[docs] def get_tonique(self,percent=0.5,method="pdf"): """Get the tonic of a melody. Args: percent (float): the percentage of the last considered frequencies. method (str): The method to be used for the analysis : "pdf" or "mode". Return: tonic (float): The detected tonic. """ self.percent = percent self.method = method L = len(self.clean_freqs) Nb_Frames = L*self.percent/100 Final_Freqs = self.clean_freqs[(L-Nb_Frames):L] self.final_freqs = Final_Freqs if method=='mode': self.tonique = self._tonique_mode(Final_Freqs) elif method=='pdf': self.tonique = self._tonique_pdf(Final_Freqs) #print 'The detected tonic is', self.tonique, 'Hz.'
def _tonique_mode(self,Final_Freqs): from scipy.stats.mstats import mode return float(mode(Final_Freqs)[0]) def _tonique_pdf(self,Final_Freqs): from scipy.stats.kde import gaussian_kde final_pdf = gaussian_kde(Final_Freqs) xmin_range = numpy.min(Final_Freqs) xmax_range = numpy.max(Final_Freqs) step = 1 x = numpy.arange(xmin_range,xmax_range,step) y = final_pdf(x) pdfmax = numpy.argmax(final_pdf(x)) lmax = x[pdfmax] return float("%0.3f" % lmax)
[docs]def transmode(freqlist,freqref=300): """Transpose all the frequencies by setting the _mode_ on a given reference frequency. Args: freqlist (numpy.array) : A list of frequencies to be transposed. freqref (int): The frequency reference to be transposed to. Default = 300. Return: transfreq (numpy.array): a list of the transposed frequencies. """ transfreq = freqlist*float(freqref)/mode(freqlist)[0] return transfreq
if __name__ == "__main__": import doctest doctest.testmod()