notebook.community

Edit and run



In [2]:

    
%matplotlib notebook

from scipy.io.wavfile import write
from numpy import linspace,sin,pi,int16,concatenate
from pylab import plot,show,axis

import pandas as pd
import os
import random as rand
from os.path import isfile, join



In [3]:

    
fileList = os.listdir('gg/')
noteDir = os.listdir('scl/')

def isDataFile(filename):
    return filename.find('.dat')>0

dataFiles = filter(isDataFile,fileList)

def isNoteFile(filename):
    return filename.find('.txt')>0

noteFiles = filter(isNoteFile,noteDir) #Pulls all .txt files containing the various scales, same as data files func



In [27]:

    
h = 50
h/5









    Out[27]:





10



In [4]:

    
scales = {} #Initialize overall scale dictionary
all_grains = [] #Initialize overall list of grain snapshots

print 'Building note list...'
for scale in noteFiles:
    notes = open('scl/' + scale,'r')
    note_list = []
    note_file = notes.readlines()
    for line in note_file:
        line = line.split("\t",3)
        line[2] = line[2].strip('\n')
        note_list.append(line)
    scales[scale.strip('.txt')] = note_list

print 'Note list built.'

print 'Building grain list...'

for data in dataFiles:
    grain_list = []
    grains = open("gg/" + data, "r")
    grain_file = grains.readlines()
    for line in grain_file:
        line = line.split("\t",3)
        line[2] = line[2].strip('\n')
        grain_list.append(line)
    all_grains.append(grain_list)

print 'Grain list built.'









    



Building note list...
Note list built.
Building grain list...
Grain list built.



In [5]:

    
len(all_grains[0])









    Out[5]:





50



In [30]:

    
pd.read_csv('gg/'+dataFiles[0],sep='\t',header=None,names=('grain-ID','area','sides'))



In [5]:

    
# tone synthesis
def note(freq, len, amp=1, rate=44100):
 t = linspace(0,len,len*rate)
 data = sin(2*pi*freq*t)*amp
 return data.astype(int16) # two byte integers



In [8]:

    
# A tone, 2 seconds, 44100 samples per second (PH note)

tone_total = []
notefile = 0
i = 0
for data in all_grains:
    i += 1
    scale_selection = rand.randint(0,3)
    note_len = rand.choice([0.25,0.5,0.75,1.0,1.25,1.5,1.75,2.0])
    tones = []
    for grain in data:
        for tone in scales[noteFiles[notefile].strip('.txt')]:
            if grain[2] == tone[2]:
                output_note = note(float(tone[1]),float(2)/len(data),amp=100000*float(grain[1]))
                tone_total.append(output_note)
    if i%5 == 0:
        notefile += 1
    if i/10 == 2:
        notefile = 0
        i = 0
    print i
    print notefile
    #tone_total.append(sum(tones))

chords = []
chordfile = 0
w = 0
for data in all_grains:
    w += 1
    scale_selection = rand.randint(0,3)
    tones = []
    for grain in data:
        for tone in scales[noteFiles[chordfile].strip('.txt')]:
            if grain[2] == tone[2]:
                output_note = note(float(tone[1]),2,amp=1000*float(grain[1]))
                tones.append(output_note)
    chords.append(sum(tones))
    if w%5 == 0:
        chordfile += 1
    if w/10 == 2:
        chordfile = 0
        w = 0
    
print 'Finished building notes'




#concatenate((tone_total))









    



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Finished building notes



In [10]:

    
write('test_noteProgression.wav',44100,concatenate((tone_total))) # writing the sound to a file
len(tone_total[:])
write('test_chords.wav',44100,concatenate((chords))) # writing the sound to a file
print len(tone_total[:])
print len(chords[:])
#plot(linspace(0,2,2*44100),tone_total)
#axis([0,0.4,15000,-15000])
#show()



In [ ]:

	grain-ID	area	sides
0	1	0.002307	4
1	2	0.041458	5
2	3	0.011237	5
3	4	0.014604	6
4	5	0.027024	8
5	6	0.017106	5
6	7	0.034995	7
7	8	0.012546	6
8	9	0.074316	5
9	10	0.028605	5
10	11	0.006609	5
11	12	0.043620	7
12	13	0.017081	4
13	14	0.002516	3
14	15	0.041003	7
15	16	0.004306	5
16	17	0.024224	6
17	18	0.029528	5
18	19	0.029660	3
19	20	0.019782	5
20	21	0.004285	4
21	22	0.002741	4
22	23	0.005267	4
23	24	0.004443	6
24	25	0.020166	7
25	26	0.010164	4
26	27	0.035461	7
27	28	0.035187	5
28	29	0.032246	8
29	30	0.014116	9
30	31	0.013792	4
31	32	0.027356	7
32	33	0.042277	9
33	34	0.003075	4
34	35	0.010560	5
35	36	0.035285	6
36	37	0.005527	5
37	38	0.012483	5
38	39	0.007960	4
39	40	0.000837	3
40	41	0.033747	7
41	42	0.032337	6
42	43	0.002720	3
43	44	0.001259	4
44	45	0.027278	8
45	46	0.029346	7
46	47	0.012089	4
47	48	0.003810	4
48	49	0.049907	5
49	50	0.001755	5