def test_GetElement(self):
"""Check if GetElement returns proper lists."""
ldict = {'elements': 'iaf_neuron', 'rows': 4, 'columns': 5}
nest.ResetKernel()
l = topo.CreateLayer((ldict, ldict))
checkpos = [[0, 0], [1, 1], [4, 3]]
# single gid, single coord gives 1-elem gid list
n1 = topo.GetElement(l[:1], checkpos[0])
self.assertEqual(len(n1), 1)
self.assertTrue(not nest.is_sequencetype(n1[0]))
# multiple gid, single coord gives l-elem gid list
n2 = topo.GetElement(l, checkpos[0])
self.assertEqual(len(n2), len(l))
self.assertTrue(all([nest.is_sequencetype(n) for n in n2]))
# single gid, multiple coord gives len(checkpos)-elem gid list
n3 = topo.GetElement(l[:1], checkpos)
self.assertEqual(len(n3), len(checkpos))
self.assertTrue(all([nest.is_sequencetype(n) for n in n3]))
self.assertTrue(all([len(n) == 1 for n in n3]))
# multiple gid, multiple coord gives l*len(cp)-elem gid list
n4 = topo.GetElement(l, checkpos)
self.assertEqual(len(n4), len(l))
self.assertTrue(all([nest.is_sequencetype(n) for n in n4]))
self.assertTrue(all([len(n) == len(checkpos) for n in n4]))
self.assertTrue(all([nest.is_sequencetype(m) for n in n4 for m in n]))
def test_GetElement(self):
"""Check if GetElement returns proper lists."""
ldict = {'elements': 'iaf_neuron',
'rows': 4, 'columns': 5}
nest.ResetKernel()
l = topo.CreateLayer((ldict,ldict))
checkpos = [[0,0],[1,1],[4,3]]
# single gid, single coord gives 1-elem gid list
n1 = topo.GetElement(l[:1], checkpos[0])
self.assertEqual(len(n1), 1)
self.assertTrue(not nest.is_sequencetype(n1[0]))
# multiple gid, single coord gives l-elem gid list
n2 = topo.GetElement(l, checkpos[0])
self.assertEqual(len(n2), len(l))
self.assertTrue(all([nest.is_sequencetype(n) for n in n2]))
# single gid, multiple coord gives len(checkpos)-elem gid list
n3 = topo.GetElement(l[:1], checkpos)
self.assertEqual(len(n3), len(checkpos))
self.assertTrue(all([nest.is_sequencetype(n) for n in n3]))
self.assertTrue(all([len(n) == 1 for n in n3]))
# multiple gid, multiple coord gives l*len(cp)-elem gid list
n4 = topo.GetElement(l, checkpos)
self.assertEqual(len(n4), len(l))
self.assertTrue(all([nest.is_sequencetype(n) for n in n4]))
self.assertTrue(all([len(n) == len(checkpos) for n in n4]))
self.assertTrue(all([nest.is_sequencetype(m) for n in n4 for m in n]))
def load_spikedata_from_file(spikefile):
"""
Loads spike data from spikefile and returns it as a numpy array
Directly borrowed and trimmed from existing method in nest.raster_plot
Returns:
data data is a matrix such that
data[:,0] is a vector of all gids and
data[:,1] a vector with the corresponding time stamps.
"""
#try:
if True:
if nest.is_sequencetype(spikefile):
data = None
for f in fname:
if data == None:
print "Using loadtxt"
data = np.loadtxt(f)
else:
print "Using concatenate"
data = np.concatenate((data, np.loadtxt(f)))
else:
print "Loading spike data for file: %s" % spikefile
data = np.loadtxt(spikefile)
return data
#except:
print "Error with loading spike data for file: %s" % spikefile
return None
def from_file(fname, title=None, hist=False, hist_binwidth=5.0, grayscale=False):
"""
Plot raster from file
"""
if nest.is_sequencetype(fname):
data = None
for f in fname:
if data is None:
data = numpy.loadtxt(f)
else:
data = numpy.concatenate((data, numpy.loadtxt(f)))
else:
data = numpy.loadtxt(fname)
return from_data(data, title, hist, hist_binwidth, grayscale)
def from_file(fname,
title=None,
hist=False,
hist_binwidth=5.0,
grayscale=False):
"""
Plot raster from file
"""
if nest.is_sequencetype(fname):
data = None
for f in fname:
if data is None:
data = numpy.loadtxt(f)
else:
data = numpy.concatenate((data, numpy.loadtxt(f)))
else:
data = numpy.loadtxt(fname)
return from_data(data, title, hist, hist_binwidth, grayscale)
def from_file(fname, title=None, grayscale=False):
if nest.is_sequencetype(fname):
data = None
for f in fname:
if data is None:
data = numpy.loadtxt(f)
else:
data = numpy.concatenate((data, numpy.loadtxt(f)))
else:
data = numpy.loadtxt(fname)
if grayscale:
line_style = "k"
else:
line_style = ""
if len(data.shape) == 1:
print "INFO: only found 1 column in the file. Assuming that only one neuron was recorded."
plotid = pylab.plot(data, line_style)
pylab.xlabel("Time (steps of length interval)")
elif data.shape[1] == 2:
print "INFO: found 2 columns in the file. Assuming them to be gid, pot."
plotid = []
data_dict = {}
for d in data:
if not d[0] in data_dict:
data_dict[d[0]] = [d[1]]
else:
data_dict[d[0]].append(d[1])
for d in data_dict:
plotid.append(
pylab.plot(data_dict[d], line_style, label="Neuron %i" % d))
pylab.xlabel("Time (steps of length interval)")
pylab.legend()
elif data.shape[1] == 3:
plotid = []
data_dict = {}
g = data[0][0]
t = []
for d in data:
if not d[0] in data_dict:
data_dict[d[0]] = [d[2]]
else:
data_dict[d[0]].append(d[2])
if d[0] == g:
t.append(d[1])
for d in data_dict:
plotid.append(
pylab.plot(t, data_dict[d], line_style, label="Neuron %i" % d))
pylab.xlabel("Time (ms)")
pylab.legend()
else:
raise ValueError("Inappropriate data shape %i!" % data.shape)
if not title:
title = "Membrane potential from file '%s'" % fname
pylab.title(title)
pylab.ylabel("Membrane potential (mV)")
pylab.draw()
return plotid
def from_file(fname, title=None, grayscale=False):
if nest.is_sequencetype(fname):
data = None
for f in fname:
if data is None:
data = numpy.loadtxt(f)
else:
data = numpy.concatenate((data, numpy.loadtxt(f)))
else:
data = numpy.loadtxt(fname)
if grayscale:
line_style = "k"
else:
line_style = ""
if len(data.shape) == 1:
print "INFO: only found 1 column in the file. Assuming that only one neuron was recorded."
plotid = pylab.plot(data, line_style)
pylab.xlabel("Time (steps of length interval)")
elif data.shape[1] == 2:
print "INFO: found 2 columns in the file. Assuming them to be gid, pot."
plotid = []
data_dict = {}
for d in data:
if not d[0] in data_dict:
data_dict[d[0]] = [d[1]]
else:
data_dict[d[0]].append(d[1])
for d in data_dict:
plotid.append(pylab.plot(data_dict[d], line_style, label="Neuron %i" % d))
pylab.xlabel("Time (steps of length interval)")
pylab.legend()
elif data.shape[1] == 3:
plotid = []
data_dict = {}
g = data[0][0]
t = []
for d in data:
if not d[0] in data_dict:
data_dict[d[0]] = [d[2]]
else:
data_dict[d[0]].append(d[2])
if d[0] == g:
t.append(d[1])
for d in data_dict:
plotid.append(pylab.plot(t, data_dict[d], line_style, label="Neuron %i" % d))
pylab.xlabel("Time (ms)")
pylab.legend()
else:
raise ValueError("Inappropriate data shape %i!" % data.shape)
if not title:
title = "Membrane potential from file '%s'" % fname
pylab.title(title)
pylab.ylabel("Membrane potential (mV)")
pylab.draw()
return plotid