def populate_fields(self):
"""
This method does all of the work for the Learner, analyzing
the audio and coming out with ngrams for generation.
"""
for f in self.files:
audio = putil.load_pickle(f)
if not audio:
audio = putil.load_pickle(putil.make_pickle_audio(f))
self.songs.append({"data": audio, "bpm": audio.analysis.tempo,
"time": audio.analysis.time_signature,
"file": f})
self.find_pitch_sequences()
self.combine_same_pitch_for_length()
# Populate the bigram structure
self.ngrams["notes"] = self.count_ngrams(self.get_notes())
self.ngrams["rhythms"] = self.count_ngrams(self.get_rhythms())
def main():
if "--help" in sys.argv:
print """
Usage: ./HearBayes.py [audiofile_out]
[length of generation in seconds][audiofiles_in or Learner pickle
file, ..., ]
#TODO optional: --bpm=[number], --key=[C-B,+ for sharp, - for flat]
--time_sig=[number 1-4, only common time sigs allowed here]
In place of [audiofiles_in] as the last argument, you can also
specify a pickle file with extension ".pickle" to use as the Learner
if it has already been trained.
"""
return 0
arguments = sys.argv
file_out = arguments[1]
length = int(arguments[2])
files_in = arguments[3:]
if len(files_in) == 1 and "pickle" in files_in[0].split("."):
print "Loading learner from pickle..."
learner = putil.load_pickle(files_in[0])
else:
learner = l.Learner(list(files_in))
print "Learning songs..."
learner.populate_fields()
learner.save(files_in[0] + ".learner")
print("%d progressions learned" % len(learner.note_list))
print "Generating new audio..."
generator = g.Generator(learner, file_out)
generator.write_music(length, bpm=120, initial_key="D+")
print "Finished!"
# Debugging information about target statistics to more accurately
# set the ngram length.
print("hit notes: %d" % generator.FOUND_NOTE)
print("missed notes: %d" % generator.NOT_FOUND_NOTE)
print("hit rhythms: %d" % generator.FOUND_RHYTHM)
print("missed rhythms: %d" % generator.NOT_FOUND_RHYTHM)
print("not in key: %d" % generator.NOT_IN_KEY)
return 0
self.vmax = self.grid.max()
else:
self.vmax = vmax
if show:
self.show_grid(ax=ax)
## Define arguments for plotting P(z) grid
## The panel grid should have this arrangement:
## -------------------
## 1 | 2 |
## -------------------
## 3 | 4 |
## -------------------
dkgrid_dir = '/Users/khuang/Dropbox/Research/bivariate/bivariate_fit/dropsim_kernels'
dkgrid3 = pu.load_pickle(
os.path.join(dkgrid_dir, 'udrops_gds_deep_kgrid_m2h_140815.p'))
dkgrid4 = pu.load_pickle(
os.path.join(dkgrid_dir, 'bdrops_goods_kgrid_140317.p'))
dkgrid5 = pu.load_pickle(
os.path.join(dkgrid_dir, 'vdrops_goods_kgrid_140402.p'))
dkgrid6 = pu.load_pickle(
os.path.join(dkgrid_dir, 'idrops_gds_deep_kgrid_140228.p'))
dkgrid_list = [dkgrid3, dkgrid4, dkgrid5, dkgrid6]
pms = pmscolors.pmscolors()
colors = map(pms, ['Blue Purples', 'Periwinkle', 'Olive Green', 'Bright Red'])
m1500_lims = np.array([[-23., -21.], [-21., -19.], [-23., -21.], [-21., -19]])
logr_lims = np.array([[-0.5, 0.5], [-0.5, 0.5], [-1.5, 0.5], [-1.5, 0.5]])
def plot_dkgrid_multiple(dkgrids=dkgrid_list,
colors=colors,
from peer_spider import Spider
from pickle_util import load_pickle
val_perc = load_pickle("data/account_percent.pkl")
s = Spider(['3.14.161.135', '3.12.207.193', '3.142.224.108'], 'casper')
s.get_all_nodes()
perc = s.get_weight_active_percent(val_perc)
print(f"{round(perc, 2)}% weight active.")
from pickle_util import load_pickle, save_bz2_pickle
from pathlib import Path
SCRIPT_DIR = Path(__file__).parent.absolute()
DATA_DIR = SCRIPT_DIR / "data"
for file in DATA_DIR.glob('*.pickle'):
data = load_pickle(file)
save_bz2_pickle(data, file.with_suffix(".pbz2"))
