def plot_mat(self, mat, fn):
plt.matshow(asarray(mat.todense()))
plt.axis('equal')
sh = mat.shape
plt.gca().set_yticks(range(0, sh[0]))
plt.gca().set_xticks(range(0, sh[1]))
plt.grid('on')
plt.colorbar()
plt.savefig(join(self.outs_dir, fn))
plt.close()
def plot_mat(self, mat, fn):
plt.matshow(asarray(mat.todense()))
plt.axis('equal')
sh = mat.shape
plt.gca().set_yticks(range(0,sh[0]))
plt.gca().set_xticks(range(0,sh[1]))
plt.grid('on')
plt.colorbar()
plt.savefig(join(self.outs_dir, fn))
plt.close()
def test1(self):
file_G = join(self.share_dir, 'G0_He50km_VisM6.3E18_Rake83.h5')
G = vj.inv.ep.EpochGNoRaslip(file_G, mask_sites=['J550'])
out = G.get_data_at_epoch(0)
stacked = G.stack([0, 60, 120])
plt.matshow(abs(stacked) * 100, aspect=5)
# plt.show()
plt.close()
def test1(self):
file_G = join(self.share_dir,'G0_He50km_VisM6.3E18_Rake83.h5')
G = vj.inv.ep.EpochGNoRaslip(file_G,
mask_sites= ['J550']
)
out = G.get_data_at_epoch(0)
stacked = G.stack([0,60,120])
plt.matshow(abs(stacked)*100, aspect=5)
# plt.show()
plt.close()
def plot_convictions(self):
# plt.figure()
plt.matshow(self.get_matrix_of_agents_convictions())
plt.colorbar()
plt.clim(-1, 1) # Sets the min/max limits of colorbar
plt.title("Convictions")
def plot_culture(self):
# plt.figure()
plt.matshow(self.get_matrix_of_agents_culture())
plt.colorbar()
plt.clim(0, 1) # Sets the min/max limits of colorbar
plt.title("Culture")
from sigfeat import Extractor
from sigfeat.feature import MFCC
extmfcc = Extractor(MFCC())
if __name__ == '__main__':
from pylab import plt, np
from sigfeat.source.soundfile import SoundFileSource
from sigfeat.preprocess import MeanMix
from sigfeat.sink import DefaultDictSink
src = MeanMix(SoundFileSource('Test.wav', blocksize=4096, overlap=2048))
sink = DefaultDictSink()
extmfcc.extract(src, sink)
plt.matshow(np.array(sink['results']['MFCC']).T)
plt.axis('tight')
plt.show()