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In [1]:



    


import sys



    











In [2]:



    


sys.path.append('/Users/spacecoffin/Development')



    











In [3]:



    


sys.path



    


















    
Out[3]:








['',
 '/Library/Frameworks/Python.framework/Versions/3.5/lib/python35.zip',
 '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5',
 '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/plat-darwin',
 '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/lib-dynload',
 '/Users/spacecoffin/Development/sprbrg/lib/python3.5/site-packages',
 '/Users/spacecoffin/Development/sprbrg/lib/python3.5/site-packages/supriya',
 '/Users/spacecoffin/Development/sprbrg/lib/python3.5/site-packages/BregmanToolkit',
 '/Users/spacecoffin/Development/sprbrg/lib/python3.5/site-packages/IPython/extensions',
 '/Users/spacecoffin/.ipython',
 '/Users/spacecoffin/Development']

















In [4]:



    


import GravelKicker as gk



    











In [5]:



    


dir(gk)



    


















    
Out[5]:








['__all__',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 'feature_extraction',
 'generator',
 'util']

















In [6]:



    


dir(gk.util)



    


















    
Out[6]:








['__all__',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 'get_cwd_parent_dir',
 'join_path_to_file',
 'make_cmd',
 'make_osc_file',
 'make_out_dirs',
 'render_session',
 'sc']

















In [7]:



    


dir(gk.feature_extraction)



    


















    
Out[7]:








['__all__',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 'featurize',
 'get_stats',
 'stats']

















In [8]:



    


dir(gk.generator)



    


















    
Out[8]:








['__all__',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 'gendy1']

















In [9]:



    


dir(gk.generator.gendy1)



    


















    
Out[9]:








['__all__',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__spec__',
 'build_out',
 'cp_to_sc',
 'format_params',
 'gen_min_max_freq',
 'gen_params',
 'hashlib',
 'librosa',
 'make_builder',
 'np',
 'pd',
 'stats',
 'synthdeftools',
 'ugentools']

















In [14]:



    


import GravelKicker as gk
import os
import pandas as pd

from datetime import datetime
from supriya.tools import nonrealtimetools



    











In [15]:



    


pmtx = gk.generator.gendy1.gen_params(rows=20)
df = gk.generator.gendy1.format_params(pmtx)
df.sort_values(["hash"])



    


















    
Out[15]:










  
    

      
      adparam
      ampscale
      ddparam
      durscale
      ampdist
      durdist
      knum
      minfrequency
      maxfrequency
      init_cps
      hash
    

  
  
    

      11
      0.174762
      0.221599
      0.073717
      0.397338
      5.0
      3.0
      13.0
      46.249303
      51.913087
      16.0
      005c1e45724d2af61b84def5b441bec9
    

    

      18
      0.524953
      0.459376
      0.614547
      0.765223
      3.0
      1.0
      14.0
      783.990872
      1661.218790
      16.0
      014e14b31011d8477cd2c2a4963553d5
    

    

      15
      0.743336
      0.924925
      0.801979
      0.144148
      0.0
      5.0
      15.0
      9.722718
      466.163762
      16.0
      0785bb918fa5f111a51b46bb098ef022
    

    

      19
      0.435284
      0.421904
      0.600644
      0.241138
      5.0
      3.0
      13.0
      261.625565
      554.365262
      16.0
      12aea483698ea62932324887b9067444
    

    

      10
      0.229079
      0.403972
      0.678791
      0.254824
      0.0
      0.0
      11.0
      20.601722
      184.997211
      16.0
      1809ca586e295df92d949a37dbada5b2
    

    

      4
      0.695884
      0.897465
      0.222027
      0.000964
      1.0
      1.0
      7.0
      554.365262
      659.255114
      16.0
      1abd6ebc0e59bb9e56e7b153121db105
    

    

      2
      0.264392
      0.046060
      0.569097
      0.639831
      5.0
      3.0
      10.0
      155.563492
      554.365262
      16.0
      21e57a94b2c037258bcf1fad573fe2fb
    

    

      9
      0.399641
      0.051536
      0.632386
      0.369661
      3.0
      0.0
      10.0
      38.890873
      155.563492
      16.0
      4c3a940fe9f5fa062791891292b3bd14
    

    

      0
      0.769618
      0.196847
      0.688058
      0.069917
      5.0
      2.0
      12.0
      10.300861
      123.470825
      16.0
      62d1647a3f349aa6f232ea9001fa8d93
    

    

      3
      0.166905
      0.426392
      0.067223
      0.950235
      3.0
      4.0
      10.0
      2637.020455
      2793.825851
      16.0
      6ba325edba9f428c62d46a952d10e776
    

    

      6
      0.211677
      0.716012
      0.776628
      0.249939
      2.0
      0.0
      10.0
      440.000000
      3951.066410
      16.0
      755812ae03e22cf0d4b1429cdbf0d7d4
    

    

      8
      0.999191
      0.380020
      0.203953
      0.248066
      1.0
      3.0
      11.0
      880.000000
      3729.310092
      16.0
      8aae91b8dd88f68b073b7e327b93449c
    

    

      16
      0.406652
      0.434271
      0.017588
      0.599756
      1.0
      3.0
      12.0
      1396.912926
      4186.009045
      16.0
      9243eb9b68203c383d283a48728e2d11
    

    

      13
      0.637114
      0.941036
      0.800288
      0.038992
      4.0
      3.0
      12.0
      69.295658
      195.997718
      16.0
      989e9f197eb34b3a92503f468becafb2
    

    

      1
      0.652324
      0.047935
      0.618170
      0.248597
      1.0
      0.0
      12.0
      880.000000
      1046.502261
      16.0
      9f4d97cc9b5cfa0baf92601facffacb9
    

    

      12
      0.566293
      0.635214
      0.189112
      0.301430
      5.0
      1.0
      11.0
      1760.000000
      2959.955382
      16.0
      a2f46fa9db3c589db004ed4b4856adf4
    

    

      14
      0.048555
      0.682584
      0.721583
      0.334873
      4.0
      1.0
      15.0
      233.081881
      987.766603
      16.0
      bcecc01fb3503dc246dcbe36544d9768
    

    

      17
      0.127376
      0.162602
      0.285705
      0.972035
      4.0
      3.0
      14.0
      103.826174
      880.000000
      16.0
      cb1a6248f10147bf29ed65969c41d516
    

    

      7
      0.584381
      0.581177
      0.563843
      0.343797
      5.0
      2.0
      14.0
      783.990872
      1046.502261
      16.0
      f7dd58a8829ffda9c60da001639493ae
    

    

      5
      0.536318
      0.878002
      0.918937
      0.308756
      4.0
      3.0
      9.0
      783.990872
      1396.912926
      16.0
      fed6d9c28a803eec577e8484f55fd46a
    

  




















In [20]:



    


#this_dir = gk.util.get_cwd_parent_dir()
this_dir = '/Users/spacecoffin/Development/GravelKicker/__gen_files'

for i, row in df.iterrows():
    
    session = nonrealtimetools.Session()
    
    builder = gk.generator.gendy1.make_builder(row)
    
    out = gk.generator.gendy1.build_out(builder)
    
    synthdef = builder.build()
    
    with session.at(0):
        synth_a = session.add_synth(duration=10, synthdef=synthdef)
    
    gk.util.render_session(session, this_dir, row["hash"])



    











In [47]:



    


dt = datetime.now().strftime("%Y_%m_%d") #_%H-%M-%S")
identifier = '{0}-len{1}'.format(dt, str(df.shape[0]))



    











In [53]:



    


df.to_pickle("{0}/df-{1}.p".format(this_dir, dt))



    











In [54]:



    


df.to_pickle("{0}/df.p".format(this_dir, dt))

Next, we need to:

  1. extract features from each file
  2. join features with parameter df (index on hash?)
  3. get it into a regressor




In [ ]:
def pickle_data(file_pickle, path): """Saves pickle file depending on object instance""" if isinstance(file_pickle, pd.DataFrame): file_pickle.to_pickle(path) elif isinstance(file_pickle, pyspark.RDD): file_pickle.saveAsPickleFile(path) else: with open(path, 'wb') as p_file: pickle.dump(file_pickle, p_file) pickle_data(data_pandas, path='{0}/pandas-df-{1}.p'.format(dat_path, identifier))

Content source: spacecoffin/GravelKicker

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