def make_lda_features_from_spks(SpkFileNames):
"all files should have been generated by the same parameters"
clu_means, clu_covs = [], []
pars0 = n_ch, sample_rate, s_before, s_after = get_pars_from_xml2(
switch_ext(SpkFileNames[0], "xml"))
print "parameters: %s" % str(pars0[1:])
for fname in SpkFileNames:
pars = get_pars_from_xml2(switch_ext(fname, "xml"))
if pars[1:] != pars0[1:]:
print "%s has wrong parameters. skipping" % fname
continue
n_ch = pars[0]
X_nsc = read_spk(fname, n_ch, s_after + s_before)
Clu_n = read_clu(switch_ext(fname, "clu.1"))
for i_ch in xrange(n_ch):
Mean_ms, Cov_mss, Count_m = get_clu_params(Clu_n, X_nsc[:, :,
i_ch])
for mean, cov, count in zip(Mean_ms, Cov_mss, Count_m):
if count > 100:
clu_means.append(mean)
clu_covs.append(cov)
Feat_ss = compute_feats_lda(clu_means, clu_covs)
save_feature_info(Feat_ss, s_before, s_after, sample_rate)
def plot_probe(ProbeFileName,output_dir=None):
load_probe(ProbeFileName)
if PROBE_DIM == 0:
x = [np.cos(2*np.pi*i_site/N_SITES) for i_site in xrange(N_SITES)]
y = [np.sin(2*np.pi*i_site/N_SITES) for i_site in xrange(N_SITES)]
elif PROBE_DIM == 1:
x = [site.x for site in PROBE_SITES]
y = [0 for site in PROBE_SITES]
elif PROBE_DIM == 2:
x = [site.x for site in PROBE_SITES]
y = [site.y for site in PROBE_SITES]
#if max2min(x) > max2min(y):
#figsize = (6,1+max2min(y)/max2min(x)*6)
#else:
#figsize = (1+max2min(x)/max2min(y)*6,6)
plt.plot(x,y,'go')
ax = plt.gca()
ax.set_xticks([])
ax.set_yticks([])
for ind_src,ind_targ in edges(PROBE_GRAPH):
ax.add_line(matplotlib.lines.Line2D([x[ind_src],x[ind_targ]],[y[ind_src],y[ind_targ]],lw=1))
for ind_node in nodes(PROBE_GRAPH):
site = PROBE_SITES[ind_node]
ax.text(x[ind_node],y[ind_node],site.name,color='r')
img_filename = join(output_dir or dirname(ProbeFileName),switch_ext(basename(ProbeFileName),"png"))
print("Saving figure as %s"%abspath(img_filename))
plt.savefig(img_filename)
def get_dat_pars(DatFileName):
xmlpath = switch_ext(DatFileName, 'xml')
if os.path.exists(xmlpath):
n_ch_dat, sample_rate = get_pars_from_xml(xmlpath)
else:
n_ch_dat, sample_rate = get_pars_from_prompt()
write_xml(n_ch_dat, 0, 0, sample_rate, xmlpath)
print("writing parameters in xml file %s" % xmlpath)
return n_ch_dat, sample_rate
def get_dat_pars(DatFileName):
xmlpath = switch_ext(DatFileName,'xml')
if os.path.exists(xmlpath):
n_ch_dat,sample_rate = get_pars_from_xml(xmlpath)
else:
n_ch_dat,sample_rate = get_pars_from_prompt()
write_xml(n_ch_dat,0,0,sample_rate,xmlpath)
print("writing parameters in xml file %s"%xmlpath)
return n_ch_dat,sample_rate
def make_lda_features_from_spks(SpkFileNames):
"all files should have been generated by the same parameters"
clu_means, clu_covs = [],[]
pars0 = n_ch,sample_rate,s_before,s_after = get_pars_from_xml2(switch_ext(SpkFileNames[0],"xml"))
print "parameters: %s"%str(pars0[1:])
for fname in SpkFileNames:
pars = get_pars_from_xml2(switch_ext(fname,"xml"))
if pars[1:] != pars0[1:]:
print "%s has wrong parameters. skipping"%fname
continue
n_ch = pars[0]
X_nsc = read_spk(fname,n_ch,s_after+s_before)
Clu_n = read_clu(switch_ext(fname,"clu.1"))
for i_ch in xrange(n_ch):
Mean_ms, Cov_mss, Count_m = get_clu_params(Clu_n,X_nsc[:,:,i_ch])
for mean,cov,count in zip(Mean_ms, Cov_mss, Count_m):
if count > 100:
clu_means.append(mean)
clu_covs.append(cov)
Feat_ss = compute_feats_lda(clu_means,clu_covs)
save_feature_info(Feat_ss,s_before,s_after,sample_rate)
def plot_probe(ProbeFileName, output_dir=None):
load_probe(ProbeFileName)
if PROBE_DIM == 0:
x = [
np.cos(2 * np.pi * i_site / N_SITES) for i_site in xrange(N_SITES)
]
y = [
np.sin(2 * np.pi * i_site / N_SITES) for i_site in xrange(N_SITES)
]
elif PROBE_DIM == 1:
x = [site.x for site in PROBE_SITES]
y = [0 for site in PROBE_SITES]
elif PROBE_DIM == 2:
x = [site.x for site in PROBE_SITES]
y = [site.y for site in PROBE_SITES]
#if max2min(x) > max2min(y):
#figsize = (6,1+max2min(y)/max2min(x)*6)
#else:
#figsize = (1+max2min(x)/max2min(y)*6,6)
plt.plot(x, y, 'go')
ax = plt.gca()
ax.set_xticks([])
ax.set_yticks([])
for ind_src, ind_targ in edges(PROBE_GRAPH):
ax.add_line(
matplotlib.lines.Line2D([x[ind_src], x[ind_targ]],
[y[ind_src], y[ind_targ]],
lw=1))
for ind_node in nodes(PROBE_GRAPH):
site = PROBE_SITES[ind_node]
ax.text(x[ind_node], y[ind_node], site.name, color='r')
img_filename = join(output_dir or dirname(ProbeFileName),
switch_ext(basename(ProbeFileName), "png"))
print("Saving figure as %s" % abspath(img_filename))
plt.savefig(img_filename)
