def plot_template_set2(self, show=False):
"""plot the template set in a waveform plot"""
# get plotting tools
try:
from spikeplot import plt
except ImportError:
return None
# checks
if self.nf == 0:
warnings.warn("skipping plot, no active units!")
return None
# init
f = plt.figure()
y_min, y_max = 0, 0
share = None
for k, i in enumerate(self._idx_active_set):
ax = f.add_subplot(self.nf, 1, k + 1, sharex=share, sharey=share)
a, b = self.bank[i].plot_buffer_to_axis(axis=ax, idx=i)
y_min = min(y_min, a)
y_max = max(y_max, b)
share = ax
f.axes[0].set_ylim(y_min, y_max)
if show is True:
plt.show()
return f
def test_visualize():
indata = open('C:\\Dev\\blockstream_runtimes\\test_file1_res.cove',
'rb').read()
bh = BS3CoveBlockHeader.from_data(indata[38:40])
print bh.version, bh.block_type
bk = BS3CoveDataBlock.from_data(bh, indata[40:])
print bk.data_lst
from spikeplot import plt
plt.matshow(bk.data_lst[-1])
plt.show()
def plot(self, data, views=2, show=False, filename=None):
"""plot clustering"""
# get plotting tools
try:
from spikeplot import plt, cluster, save_figure
except ImportError:
return None
# init
views = min(views, int(data.shape[1] / 2))
fig = plt.figure()
fig.suptitle('clustering [%s]' % self.clus_type)
ax = [fig.add_subplot(2, views, v + 1) for v in xrange(views)]
axg = fig.add_subplot(212)
ncmp = int(self.labels.max() + 1)
cdata = dict(
zip(xrange(ncmp), [data[self.labels == c] for c in xrange(ncmp)]))
# plot clustering
for v in xrange(views):
cluster(cdata,
data_dim=(2 * v, 2 * v + 1),
plot_handle=ax[v],
plot_mean=sp.sqrt(self.sigma_factor),
xlabel='PC %d' % int(2 * v),
ylabel='PC %d' % int(2 * v + 1),
show=False)
# plot gof
axg.plot(self._gof, ls='steps')
for i in xrange(1, len(self.crange)):
axg.axvline(i * self.repeats - 0.5, c='y', ls='--')
axg.axvspan(self._winner - 0.5,
self._winner + 0.5,
fc='gray',
alpha=0.2)
labels = []
for k in self.crange:
labels += ['%d' % k]
labels += ['.'] * (self.repeats - 1)
axg.set_xticks(sp.arange(len(labels)))
axg.set_xticklabels(labels)
axg.set_xlabel('cluster count and repeats')
axg.set_ylabel(str(self.gof_type).upper())
axg.set_xlim(-1, len(labels))
# handle the resulting plot
if filename is not None:
save_figure(fig, filename, '')
if show is True:
plt.show()
return True
def plot(self, data, views=2, show=False, filename=None):
"""plot clustering"""
# get plotting tools
try:
from spikeplot import plt, cluster, save_figure
except ImportError:
return None
# init
views = min(views, int(data.shape[1] / 2))
fig = plt.figure()
fig.suptitle('clustering [%s]' % self.clus_type)
ax = [fig.add_subplot(2, views, v + 1) for v in xrange(views)]
axg = fig.add_subplot(212)
ncmp = int(self.labels.max() + 1)
cdata = dict(zip(xrange(ncmp),
[data[self.labels == c] for c in xrange(ncmp)]))
# plot clustering
for v in xrange(views):
cluster(
cdata,
data_dim=(2 * v, 2 * v + 1),
plot_handle=ax[v],
plot_mean=sp.sqrt(self.sigma_factor),
xlabel='PC %d' % int(2 * v),
ylabel='PC %d' % int(2 * v + 1),
show=False)
# plot gof
axg.plot(self._gof, ls='steps')
for i in xrange(1, len(self.crange)):
axg.axvline(i * self.repeats - 0.5, c='y', ls='--')
axg.axvspan(self._winner - 0.5, self._winner + 0.5, fc='gray',
alpha=0.2)
labels = []
for k in self.crange:
labels += ['%d' % k]
labels += ['.'] * (self.repeats - 1)
axg.set_xticks(sp.arange(len(labels)))
axg.set_xticklabels(labels)
axg.set_xlabel('cluster count and repeats')
axg.set_ylabel(str(self.gof_type).upper())
axg.set_xlim(-1, len(labels))
# handle the resulting plot
if filename is not None:
save_figure(fig, filename, '')
if show is True:
plt.show()
return True
def plot(self, show=False):
"""plot detection in mcdata plot"""
try:
from spikeplot import plt, mcdata, COLOURS
except ImportError:
return None
fig = mcdata(self.data, other=self.energy, events={0: self.events},
show=False)
for i, th in enumerate(self.threshold):
fig.axes[-1].axhline(th, c=COLOURS[i % len(COLOURS)])
self._plot_additional(fig)
if show is True:
plt.show()
return fig
def test(debug=True):
from spikeplot import plt, mcdata
# setup
TF = 47
signal, noise, ce, temps = load_input_data(TF)
FB = BOTMNode(
templates=temps,
ce=ce,
adapt_templates=10,
learn_noise=False,
verbose=VERBOSE(debug * 10),
ovlp_taus=None,
chunk_size=500)
x = sp.ascontiguousarray(signal + noise, dtype=sp.float32)
# sort
FB.plot_xvft()
FB(x)
FB.plot_sorting()
print FB.rval
plt.show()
try:
import unittest2 as ut
except ImportError:
import unittest as ut
from botmpy.common import mad_scaling
from botmpy.common.datafile import XpdFile
import scipy as sp
try:
from spikeplot import mcdata, plt
WITH_PLOT = True
except ImportError:
WITH_PLOT = False
if __name__ == '__main__':
xpd = XpdFile('/home/pmeier/Data/Munk/Louis/L014/L0140001.xpd')
data = xpd.get_data(item=1)
if WITH_PLOT:
mcdata(data, show=False)
data_scaled, scale = mad_scaling(data)
if WITH_PLOT:
mcdata(data_scaled, show=False)
print 'magic eq', sp.stats.norm.ppf(0.75)
print 'STD:', sp.std(data, 0), sp.std(data_scaled, 0)
if WITH_PLOT:
plt.show()
def test_visualize_block(block):
from spikeplot import plt
plt.matshow(block.data_lst[-2])
plt.matshow(block.data_lst[-1])
plt.show()
