def test_wave_reader(n=10000):
#plt.interactive(True)
try:
update = 0
FIG = plt.figure()
rb = None
Q = Queue()
bs_reader = BS3Reader(WAVEProtocolHandler, Q, verbose=False,
ident='TestWAVE')
bs_reader.start()
for _ in xrange(n):
#print Q.qsize()
try:
item = Q.get(block=True, timeout=2)
for wave in item.event_lst:
gid, uid, tv, nc, ns, wf = wave
if rb is None:
rb = MxRingBuffer(dimension=(ns, nc), capacity=2000)
rb.append(wf)
#print 'rb:', len(rb)
update += 1
if update > 1000:
print 'plotting enter'
FIG.clear()
waveforms({0: rb[:]}, tf=ns, plot_handle=FIG,
plot_separate=False, show=False)
#plt.draw()
save_figure(FIG, 'wave', file_dir='E:\SpiDAQ')
update = 0
print 'plotting exit'
except Empty:
continue
except Exception, ex:
print ex
def plot_filter_set(self, ph=None, show=False):
"""plot the filter set in a waveform plot"""
# get plotting tools
try:
from spikeplot import waveforms
except ImportError:
return None
# checks
if self.nf == 0:
warnings.warn("skipping plot, no active units!")
return None
# init
units = {}
for k in self._idx_active_set:
units[k] = sp.atleast_2d(self.bank[k].f_conc)
return waveforms(
units,
tf=self._tf,
plot_separate=True,
plot_mean=False,
plot_single_waveforms=False,
plot_handle=ph,
show=show,
)
def plot_template_set(self, ph=None, show=False):
"""plot the template set in a waveform plot"""
# get plotting tools
try:
from spikeplot import waveforms, plt
except ImportError:
return None
# checks
if self.nf == 0:
logging.warn('skipping plot, no active units!')
return None
# init
units = {}
for k in self._idx_active_set:
units[k] = self.bank[k]._xi_buf[:]
return waveforms(
units,
tf=self._tf,
plot_separate=True,
plot_mean=True,
plot_single_waveforms=True,
plot_handle=ph, show=show)
def plot_sorting_waveforms(self, ph=None, show=False):
"""plot the waveforms of the sorting of the last data chunk
:type ph: plot handle
:param ph: plot handle to use for the
:type show: bool
:param show: if True, call plt.show()
"""
# get plotting tools
try:
from spikeplot import waveforms
except ImportError:
return None
# check
if self._data is None or self.rval is None or len(
self._idx_active_set) == 0:
warnings.warn('not initialised properly to plot a sorting!')
return None
# inits
wf = {}
temps = {}
#if self._data is None or self.rval is None:
# return
cut = get_cut(self._tf)
# adapt filters with found waveforms
nunits = 0
for u in self.rval:
spks_u = self.spikes_u(u, mc=False)
if spks_u.size > 0:
wf[u] = self.spikes_u(u)
temps[u] = self.bank[u].xi_conc
nunits += 1
print 'waveforms for units:', nunits
"""
waveforms(waveforms, samples_per_second=None, tf=None, plot_mean=False,
plot_single_waveforms=True, set_y_range=False,
plot_separate=True, plot_handle=None, colours=None, title=None,
filename=None, show=True):
"""
return waveforms(wf, samples_per_second=None, tf=self._tf,
plot_mean=True, templates=temps,
plot_single_waveforms=True, set_y_range=False,
plot_separate=True, plot_handle=ph, show=show)
def main():
TF, SNR, PCADIM = 65, 0.5, 8
NTRL = 10
LOAD = False
if LOAD is True:
spks, spks_info, ndet = load_data()
else:
# spks, spks_info, ndet = get_data(tf=TF, trials=NTRL, snr=SNR,
# mean_correct=False, save=True)
pass
# plot.waveforms(spks, tf=TF, show=False)
input_obs = pre_processing(spks, ndet, TF, pca_dim=PCADIM)
plot.cluster(input_obs, show=False)
# kmeans
labels_km = cluster_kmeans(input_obs)
obs_km = {}
wf_km = {}
for i in xrange(labels_km.max() + 1):
obs_km[i] = input_obs[labels_km == i]
wf_km[i] = spks[labels_km == i]
if WITH_PLOT:
plot.cluster(obs_km, title='kmeans', show=False)
plot.waveforms(obs_km, tf=TF, title='kmeans', show=False)
# gmm
labels_gmm = cluster_gmm(input_obs)
obs_gmm = {}
wf_gmm = {}
for i in xrange(labels_km.max() + 1):
obs_gmm[i] = input_obs[labels_gmm == i]
wf_gmm[i] = spks[labels_gmm == i]
if WITH_PLOT:
plot.cluster(obs_gmm, title='gmm', show=False)
plot.waveforms(wf_gmm, tf=TF, title='gmm', show=False)
# ward
labels_ward = cluster_ward(input_obs)
obs_ward = {}
wf_ward = {}
for i in xrange(labels_km.max() + 1):
obs_ward[i] = input_obs[labels_ward == i]
wf_ward[i] = spks[labels_ward == i]
if WITH_PLOT:
plot.cluster(obs_ward, title='ward', show=False)
plot.waveforms(wf_ward, tf=TF, title='ward', show=False)
# spectral
#cluster_spectral(spks)
if WITH_PLOT:
plot.plt.show()
def main():
TF, SNR, PCADIM = 65, 0.5, 8
NTRL = 10
LOAD = False
if LOAD is True:
spks, spks_info, ndet = load_data()
else:
# spks, spks_info, ndet = get_data(tf=TF, trials=NTRL, snr=SNR,
# mean_correct=False, save=True)
pass
# plot.waveforms(spks, tf=TF, show=False)
input_obs = pre_processing(spks, ndet, TF, pca_dim=PCADIM)
plot.cluster(input_obs, show=False)
# kmeans
labels_km = cluster_kmeans(input_obs)
obs_km = {}
wf_km = {}
for i in xrange(labels_km.max() + 1):
obs_km[i] = input_obs[labels_km == i]
wf_km[i] = spks[labels_km == i]
if WITH_PLOT:
plot.cluster(obs_km, title='kmeans', show=False)
plot.waveforms(obs_km, tf=TF, title='kmeans', show=False)
# gmm
labels_gmm = cluster_gmm(input_obs)
obs_gmm = {}
wf_gmm = {}
for i in xrange(labels_km.max() + 1):
obs_gmm[i] = input_obs[labels_gmm == i]
wf_gmm[i] = spks[labels_gmm == i]
if WITH_PLOT:
plot.cluster(obs_gmm, title='gmm', show=False)
plot.waveforms(wf_gmm, tf=TF, title='gmm', show=False)
# ward
labels_ward = cluster_ward(input_obs)
obs_ward = {}
wf_ward = {}
for i in xrange(labels_km.max() + 1):
obs_ward[i] = input_obs[labels_ward == i]
wf_ward[i] = spks[labels_ward == i]
if WITH_PLOT:
plot.cluster(obs_ward, title='ward', show=False)
plot.waveforms(wf_ward, tf=TF, title='ward', show=False)
# spectral
#cluster_spectral(spks)
if WITH_PLOT:
plot.plt.show()
import scipy as sp
from spikeplot import waveforms
# get some data
my_data = {0: sp.randn(20, 50, 4), 1: sp.randn(10, 200) + 2}
# call the plot function on the axes
waveforms(my_data, tf=50, title="Test Plot", plot_mean=True, plot_single_waveforms=True, plot_separate=True)