def plot(datafiles, output_file, extra_annotation=None):
print(datafiles)
print(output_file)
# load data
blocks = [get_io(datafile).read_block() for datafile in datafiles]
# note: Neo needs a pretty printer that is not tied to IPython
# for block in blocks:
# print(block.describe())
# for now take only the first segment
segments = [block.segments[0] for block in blocks]
labels = [block.annotations['simulator'] for block in blocks]
# build annotations
script_name = blocks[0].annotations.get('script_name', '')
if script_name:
for block in blocks[1:]:
assert block.annotations['script_name'] == script_name
# also consider adding metadata to PNG file - see http://stackoverflow.com/questions/10532614/can-matplotlib-add-metadata-to-saved-figures
context = ["Generated by: %s" % __file__,
"Working directory: %s" % os.getcwd(),
"Timestamp: %s" % datetime.now().strftime("%Y-%m-%d %H:%M:%S%z"),
"Output file: %s" % output_file,
"Input file(s): %s" % "\n ".join(datafiles)]
if extra_annotation:
context.append(extra_annotation)
annotations = "\n".join(context)
# create and save plot
fig = comparison_plot(segments, labels, title=script_name,
annotations=annotations)
fig.save(output_file)
def get(self, request, format=None, **kwargs):
na_file = request.session['na_file']
block = get_io(na_file).read_block()
id_segment = int(request.GET['segment_id'])
segment = block.segments[id_segment]
seg_data_test = {
'name': "segment 1",
'description': "a first fake segment",
'file_origin': "nowhere",
'spiketrains': [{}, {}],
'analogsignals': [{}, {}, {}]
}
seg_data = {
'name':
segment.name or "",
'description':
segment.description or "",
'file_origin':
segment.file_origin or "",
'annotations':
segment.annotations,
# 'spiketrains': segment.spiketrains,
'analogsignals': [{} for a in segment.analogsignals],
'as_prop': [{
'size': e.size,
'name': e.name.decode('cp1252')
} for e in segment.analogsignals]
}
return JsonResponse(seg_data, safe=False)
def get(self, request, format=None, **kwargs):
na_file = request.session['na_file']
block = get_io(na_file).read_block()
id_segment = int(request.GET['segment_id'])
id_analog_signal = int(request.GET['analog_signal_id'])
segment = block.segments[id_segment]
analogsignal = segment.analogsignals[id_analog_signal]
analog_signal_values = []
for item in analogsignal:
try: # TODO find a better solution
analog_signal_values.append(item.item())
except ValueError:
analog_signal_values.append(item[1].item())
analog_signal_times = []
for item in analogsignal.times:
analog_signal_times.append(item.item())
graph_data = {
"values": analog_signal_values,
"values_units": str(analogsignal.units.dimensionality),
"times": analog_signal_times,
"times_dimensionality":
str(analogsignal.t_start.units.dimensionality),
"t_start": analogsignal.t_start.item(),
"t_stop": analogsignal.t_stop.item()
}
return JsonResponse(graph_data)
def end(compatible_output=True):
"""Do any necessary cleaning up before exiting."""
for (population, variables, filename) in simulator.state.write_on_end:
io = get_io(filename)
population.write_data(io, variables)
simulator.state.write_on_end = []
nml_doc = simulator._get_nml_doc()
import neuroml.writers as writers
if save_format == 'xml':
nml_file = '%s.net.nml' % nml_doc.id
writers.NeuroMLWriter.write(nml_doc, nml_file)
elif save_format == 'hdf5':
nml_file = '%s.net.nml.h5' % nml_doc.id
writers.NeuroMLHdf5Writer.write(nml_doc, nml_file)
logger.info("Written NeuroML 2 file out to: " + nml_file)
lems_sim = simulator._get_lems_sim()
lems_sim.include_neuroml2_file("PyNN.xml", include_included=False)
lems_sim.include_neuroml2_file(nml_file)
lems_file = lems_sim.save_to_file()
logger.info("Written LEMS file (to simulate NeuroML file) to: " +
lems_file)
def write_data(self, io, variables='all', gather=True, clear=False, annotations=None):
if isinstance(io, basestring):
io = get_io(io)
data = self.get_data(variables, gather, clear, annotations)
#if self._simulator.state.mpi_rank == 0 or gather is False:
if True: # tmp. Need to handle MPI
io.write(data)
def get(self, request, format=None, **kwargs):
na_file = _get_file_from_url(request)
if 'type' in request.GET and request.GET.get('type'):
iotype = request.GET.get('type')
method = getattr(io, iotype)
r = method(filename=na_file)
block = r.read_block()
else:
try:
block = get_io(na_file).read_block()
except IOError as err:
# todo: need to be more fine grained. There could be other reasons
# for an IOError
return JsonResponse(
{
'error': 'incorrect file type',
'message': str(err)
},
status=status.HTTP_415_UNSUPPORTED_MEDIA_TYPE)
block_data = {
'block': [{
'annotations':
_handle_dict(block.annotations),
# 'channel_indexes': block.channel_indexes,
'description':
block.description or "",
# 'file_datetime': block.file_datetime,
'file_origin':
block.file_origin or "",
# 'index': block.index,
'name':
block.name or "",
'rec_datetime':
block.rec_datetime,
'segments': [
{
'name': s.name or "",
'annotations': _handle_dict(s.annotations),
'description': s.description or "",
# 'epochs': s.epochs,
# 'events': s.events,
# 'spiketrains': s.spiketrains,
# 'spiketrains': [],
'rec_datetime': s.rec_datetime,
# 'irregularlysampledsignals': s.irregularlysampledsignals,
# 'index': s.index,
'file_origin': s.file_origin or "",
# 'block': s.block,
# 'analogsignals': s.analogsignals,
'analogsignals': [],
} for s in block.segments
],
}]
}
return JsonResponse(block_data)
def plot(datafiles, output_file, annotation=None):
print datafiles
print output_file
blocks = [get_io(datafile).read_block() for datafile in datafiles]
# note: Neo needs a pretty printer that is not tied to IPython
for block in blocks:
print (block.describe())
# for now take only the first segment
segments = [block.segments[0] for block in blocks]
labels = [block.annotations['simulator'] for block in blocks]
variables_to_plot = set.union(*(variable_names(s) for s in segments))
print "Plotting the following variables: %s" % ", ".join(variables_to_plot)
n_panels = sum(a.shape[1] for a in segments[0].analogsignalarrays) #+ bool(segments[0].spiketrains)
script_name = blocks[0].annotations.get('script_name', '')
if script_name:
for block in blocks[1:]:
assert block.annotations['script_name'] == script_name
fig_settings = { # pass these in a configuration file?
'lines.linewidth': 0.5,
'axes.linewidth': 0.5,
'axes.labelsize': 'small',
'legend.fontsize': 'small',
'font.size': 8,
}
plt.rcParams.update(fig_settings)
width, height = 6, 3*n_panels + 1.2
plt.figure(1, figsize=(width, height))
gs = gridspec.GridSpec(n_panels, 1)
gs.update(bottom=1.2/height) # leave space for annotations
panels = [plt.subplot(gs[i, 0]) for i in reversed(range(n_panels))]
n_seg = len(segments)
for k, (segment, label) in enumerate(zip(segments, labels)):
panel = 0
lw = 2*(n_seg - k) - 1
col = 'rbgmck'[k%6]
for array in segment.analogsignalarrays:
sorted_channels = sorted(array.channel_index)
for channel in sorted_channels:
i = array.channel_index.tolist().index(channel)
print "plotting '%s' for %s in panel %d" % (array.name, label, panel)
plot_signal(panels[panel], array, i, colour=col, linewidth=lw, label=label)
panel += 1
for panel in panels:
panel.legend()
plt.xlabel("time (%s)" % array.times.units._dimensionality.string)
plt.setp(plt.gca().get_xticklabels(), visible=True)
plt.title(script_name)
# also consider adding metadata to PNG file - see http://stackoverflow.com/questions/10532614/can-matplotlib-add-metadata-to-saved-figures
context = ["Generated by: %s" % __file__,
"Working directory: %s" % os.getcwd(),
"Timestamp: %s" % datetime.now().strftime("%Y-%m-%d %H:%M:%S%z"),
"Output file: %s" % output_file,
"Input file(s): %s" % "\n ".join(datafiles)]
if annotation:
context.append(annotation)
plt.figtext(0.01, 0.01, "\n".join(context), fontsize=6, verticalalignment='bottom')
plt.savefig(output_file)
def end(compatible_output=True):
"""Do any necessary cleaning up before exiting."""
for (population, variables, filename) in simulator.state.write_on_end:
io = get_io(filename)
population.write_data(io, variables)
simulator.state.write_on_end = []
# should have common implementation of end()
simulator.state.net.to_nineml().write(simulator.state.output_filename)
def end(compatible_output=True):
"""Do any necessary cleaning up before exiting."""
for (population, variables, filename) in simulator.state.write_on_end:
io = get_io(filename)
population.write_data(io, variables)
simulator.state.write_on_end = []
# should have common implementation of end()
simulator.state.net.to_nineml().write(simulator.state.output_filename)
def get(self, request, format=None, **kwargs):
if 'na_file' not in request.session:
url = request.GET.get('url')
request = _get_file_from_url(request, url)
na_file = request.session['na_file']
if 'type' in request.GET and request.GET.get('type'):
iotype = request.GET.get('type')
method = getattr(io, iotype)
r = method(filename=na_file)
block = r.read_block()
else:
try:
block = get_io(na_file).read_block()
except IOError:
return JsonResponse(
{'block': [{
'error': 'incorrect file type'
}]})
block_data = {
'block': [{
'annotations':
_handle_dict(block.annotations),
# 'channel_indexes': block.channel_indexes,
'description':
block.description or "",
# 'file_datetime': block.file_datetime,
'file_origin':
block.file_origin or "",
# 'index': block.index,
'name':
block.name or "",
'rec_datetime':
block.rec_datetime,
'segments': [
{
'name': s.name or "",
'annotations': _handle_dict(s.annotations),
'description': s.description or "",
# 'epochs': s.epochs,
# 'events': s.events,
# 'spiketrains': s.spiketrains,
# 'spiketrains': [],
'rec_datetime': s.rec_datetime,
# 'irregularlysampledsignals': s.irregularlysampledsignals,
# 'index': s.index,
'file_origin': s.file_origin or "",
# 'block': s.block,
# 'analogsignals': s.analogsignals,
'analogsignals': [],
} for s in block.segments
],
}]
}
return JsonResponse(block_data)
def test_mix_procedural_and_oo(sim):
# cf Issues #217, #234
fn_proc = "test_write_procedural.pkl"
fn_oo = "test_write_oo.pkl"
sim.setup(timestep=0.1, min_delay=0.1)
cells = sim.Population(5, sim.IF_cond_exp(i_offset=0.2))
sim.record('v', cells, fn_proc)
sim.run(10.0)
cells.write_data(fn_oo) # explicitly write data
sim.end() # implicitly write data using filename provided previously
data_proc = get_io(fn_proc).read()[0]
data_oo = get_io(fn_oo).read()[0]
assert_array_equal(data_proc.segments[0].analogsignals[0],
data_oo.segments[0].analogsignals[0])
os.remove(fn_proc)
os.remove(fn_oo)
def custom_get_io(filename):
try:
io = get_io(filename)
except AssertionError as err:
if "try_signal_grouping" in str(err):
io = neo.io.Spike2IO(filename, try_signal_grouping=False)
else:
raise
return io
def test_mix_procedural_and_oo(sim):
# cf Issues #217, #234
fn_proc = "test_write_procedural.pkl"
fn_oo = "test_write_oo.pkl"
sim.setup(timestep=0.1, min_delay=0.1)
cells = sim.Population(5, sim.IF_cond_exp(i_offset=0.2))
sim.record('v', cells, fn_proc)
sim.run(10.0)
cells.write_data(fn_oo) # explicitly write data
sim.end() # implicitly write data using filename provided previously
data_proc = get_io(fn_proc).read()[0]
data_oo = get_io(fn_oo).read()[0]
assert_arrays_equal(data_proc.segments[0].analogsignalarrays[0],
data_oo.segments[0].analogsignalarrays[0])
os.remove(fn_proc)
os.remove(fn_oo)
def write_data(self,
io,
variables='all',
gather=True,
clear=False,
annotations=None):
if isinstance(io, basestring):
io = get_io(io)
data = self.get_data(variables, gather, clear, annotations)
#if self._simulator.state.mpi_rank == 0 or gather is False:
if True: # tmp. Need to handle MPI
io.write(data)
def end(compatible_output=True):
"""Do any necessary cleaning up before exiting."""
for (population, variables, filename) in simulator.state.write_on_end:
# Make directories if necessary
dir = os.path.dirname(filename)
if dir and not os.path.exists(dir):
os.makedirs(dir)
# Get NEO IO for filename
io = get_io(filename)
population.write_data(io, variables)
simulator.state.write_on_end = []
def plot(datafiles, output_file, sort_by='simulator', annotation=None):
blocks = [get_io(datafile).read_block() for datafile in datafiles]
# note: Neo needs a pretty printer that is not tied to IPython
#for block in blocks:
# print(block.describe())
script_name = blocks[0].annotations['script_name']
for block in blocks[1:]:
assert block.annotations['script_name'] == script_name
fig_settings = { # pass these in a configuration file?
'lines.linewidth': 0.5,
'axes.linewidth': 0.5,
'axes.labelsize': 'small',
'legend.fontsize': 'small',
'font.size': 8,
'savefig.dpi': 200,
}
plt.rcParams.update(fig_settings)
CM = 1 / 2.54
plt.figure(1, figsize=(15 * CM * len(blocks), 20 * CM))
gs = gridspec.GridSpec(4, 2 * len(blocks), hspace=0.25, wspace=0.25)
sorted_blocks = sort_by_annotation(sort_by, blocks)
hide_axis_labels = False
for k, (label, block_list) in enumerate(sorted_blocks.items()):
segments = {}
for block in block_list:
for name in ("exc", "inh"):
if name in block.name.lower():
segments[name] = block.segments[0]
# Plot membrane potential traces
plot_vm_traces(plt.subplot(gs[0, 2 * k:2 * k + 2]), segments['exc'],
label, hide_axis_labels)
# Plot spike rasters
plot_spiketrains(plt.subplot(gs[1, 2 * k:2 * k + 2]), segments['exc'],
label, hide_axis_labels)
# Inter-spike-interval histograms
# Histograms of coefficients of variation of ISI
plot_isi_hist(plt.subplot(gs[2, 2 * k]), segments['exc'], 'exc',
hide_axis_labels)
plot_cvisi_hist(plt.subplot(gs[3, 2 * k]), segments['exc'], 'exc',
hide_axis_labels)
hide_axis_labels = True
plot_isi_hist(plt.subplot(gs[2, 2 * k + 1]), segments['inh'], 'inh',
hide_axis_labels)
plot_cvisi_hist(plt.subplot(gs[3, 2 * k + 1]), segments['inh'], 'inh',
hide_axis_labels)
plt.savefig(output_file)
def get(self, request, format=None, **kwargs):
if 'na_file' not in request.session:
url = request.GET.get('url')
request = _get_file_from_url(request, url)
na_file = request.session['na_file']
try:
block = get_io(na_file).read_block()
except IOError:
return Response({'error': 'incorrect file type'},
status=status.HTTP_415_UNSUPPORTED_MEDIA_TYPE)
# read neo file from hd
block_data = {
'block': [{
'annotations':
block.annotations,
# 'channel_indexes': block.channel_indexes,
'description':
block.description or "",
# 'file_datetime': block.file_datetime,
'file_origin':
block.file_origin or "",
# 'index': block.index,
'name':
block.name or "",
'rec_datetime':
block.rec_datetime,
'segments': [
{
'name': s.name or "",
'annotations': s.annotations,
'description': s.description or "",
# 'epochs': s.epochs,
# 'events': s.events,
# 'spiketrains': s.spiketrains,
# 'spiketrains': [],
'rec_datetime': s.rec_datetime,
# 'irregularlysampledsignals': s.irregularlysampledsignals,
# 'index': s.index,
'file_origin': s.file_origin or "",
# 'block': s.block,
# 'analogsignals': s.analogsignals,
'analogsignals': [],
} for s in block.segments
],
}]
}
return JsonResponse(block_data)
def plot(datafiles, output_file, sort_by='simulator', annotation=None):
blocks = [get_io(datafile).read_block() for datafile in datafiles]
# note: Neo needs a pretty printer that is not tied to IPython
#for block in blocks:
# print(block.describe())
script_name = blocks[0].annotations['script_name']
for block in blocks[1:]:
assert block.annotations['script_name'] == script_name
fig_settings = { # pass these in a configuration file?
'lines.linewidth': 0.5,
'axes.linewidth': 0.5,
'axes.labelsize': 'small',
'legend.fontsize': 'small',
'font.size': 8,
'savefig.dpi': 200,
}
plt.rcParams.update(fig_settings)
CM=1/2.54
plt.figure(1, figsize=(15*CM*len(blocks), 20*CM))
gs = gridspec.GridSpec(4, 2*len(blocks), hspace=0.25, wspace=0.25)
sorted_blocks = sort_by_annotation(sort_by, blocks)
hide_axis_labels = False
for k, (label, block_list) in enumerate(sorted_blocks.items()):
segments = {}
for block in block_list:
for name in ("exc", "inh"):
if name in block.name.lower():
segments[name] = block.segments[0]
# Plot membrane potential traces
plot_vm_traces(plt.subplot(gs[0, 2*k:2*k+2]), segments['exc'], label, hide_axis_labels)
# Plot spike rasters
plot_spiketrains(plt.subplot(gs[1, 2*k:2*k+2]), segments['exc'], label, hide_axis_labels)
# Inter-spike-interval histograms
# Histograms of coefficients of variation of ISI
plot_isi_hist(plt.subplot(gs[2, 2*k]), segments['exc'], 'exc', hide_axis_labels)
plot_cvisi_hist(plt.subplot(gs[3, 2*k]), segments['exc'], 'exc', hide_axis_labels)
hide_axis_labels = True
plot_isi_hist(plt.subplot(gs[2, 2*k+1]), segments['inh'], 'inh', hide_axis_labels)
plot_cvisi_hist(plt.subplot(gs[3, 2*k+1]), segments['inh'], 'inh', hide_axis_labels)
plt.savefig(output_file)
def get(self, request, format=None, **kwargs):
na_file = _get_file_from_url(request)
block = get_io(na_file).read_block()
id_segment = int(request.GET['segment_id'])
# todo, catch MultiValueDictKeyError in case segment_id isn't given, and return a 400 Bad Request response
segment = block.segments[id_segment]
# todo, catch IndexError, and return a 404 response
seg_data_test = {
'name': "segment 1",
'description': "a first fake segment",
'file_origin': "nowhere",
'spiketrains': [{}, {}],
'analogsignals': [{}, {}, {}]
}
seg_data = {
'name':
segment.name or "",
'description':
segment.description or "",
'file_origin':
segment.file_origin or "",
'annotations':
_handle_dict(segment.annotations),
# 'spiketrains': segment.spiketrains,
'analogsignals': [{} for a in segment.analogsignals],
'as_prop': [{
'size': e.size,
'name': e.name
} for e in segment.analogsignals]
}
return JsonResponse(seg_data, safe=False)
def end(compatible_output=True):
"""Do any necessary cleaning up before exiting."""
for (population, variables, filename) in simulator.state.write_on_end:
io = get_io(filename)
population.write_data(io, variables)
simulator.state.write_on_end = []
nml_doc = simulator._get_nml_doc()
import neuroml.writers as writers
if save_format == 'xml':
nml_file = '%s.net.nml'%nml_doc.id
writers.NeuroMLWriter.write(nml_doc, nml_file)
elif save_format == 'hdf5':
nml_file = '%s.net.nml.h5'%nml_doc.id
writers.NeuroMLHdf5Writer.write(nml_doc, nml_file)
logger.info("Written NeuroML 2 file out to: "+nml_file)
lems_sim = simulator._get_lems_sim()
lems_sim.include_neuroml2_file("PyNN.xml", include_included=False)
lems_sim.include_neuroml2_file(nml_file)
lems_file = lems_sim.save_to_file()
logger.info("Written LEMS file (to simulate NeuroML file) to: "+lems_file)
def end(compatible_output=True):
"""Do any necessary cleaning up before exiting."""
for (population, variables, filename) in simulator.state.write_on_end:
io = get_io(filename)
population.write_data(io, variables)
simulator.state.write_on_end = []
def plot(datafiles, output_file, annotation=None):
print datafiles
print output_file
blocks = [get_io(datafile).read_block() for datafile in datafiles]
# note: Neo needs a pretty printer that is not tied to IPython
for block in blocks:
print(block.describe())
# for now take only the first segment
segments = [block.segments[0] for block in blocks]
labels = [block.annotations['simulator'] for block in blocks]
variables_to_plot = set.union(*(variable_names(s) for s in segments))
print "Plotting the following variables: %s" % ", ".join(variables_to_plot)
n_panels = sum(
a.shape[1] for a in
segments[0].analogsignalarrays) #+ bool(segments[0].spiketrains)
script_name = blocks[0].annotations.get('script_name', '')
if script_name:
for block in blocks[1:]:
assert block.annotations['script_name'] == script_name
fig_settings = { # pass these in a configuration file?
'lines.linewidth': 0.5,
'axes.linewidth': 0.5,
'axes.labelsize': 'small',
'legend.fontsize': 'small',
'font.size': 8,
}
plt.rcParams.update(fig_settings)
width, height = 6, 3 * n_panels + 1.2
plt.figure(1, figsize=(width, height))
gs = gridspec.GridSpec(n_panels, 1)
gs.update(bottom=1.2 / height) # leave space for annotations
panels = [plt.subplot(gs[i, 0]) for i in reversed(range(n_panels))]
n_seg = len(segments)
for k, (segment, label) in enumerate(zip(segments, labels)):
panel = 0
lw = 2 * (n_seg - k) - 1
col = 'rbgmck'[k % 6]
for array in segment.analogsignalarrays:
sorted_channels = sorted(array.channel_index)
for channel in sorted_channels:
i = array.channel_index.tolist().index(channel)
print "plotting '%s' for %s in panel %d" % (array.name, label,
panel)
plot_signal(panels[panel],
array,
i,
colour=col,
linewidth=lw,
label=label)
panel += 1
for panel in panels:
panel.legend()
plt.xlabel("time (%s)" % array.times.units._dimensionality.string)
plt.setp(plt.gca().get_xticklabels(), visible=True)
plt.title(script_name)
# also consider adding metadata to PNG file - see http://stackoverflow.com/questions/10532614/can-matplotlib-add-metadata-to-saved-figures
context = [
"Generated by: %s" % __file__,
"Working directory: %s" % os.getcwd(),
"Timestamp: %s" % datetime.now().strftime("%Y-%m-%d %H:%M:%S%z"),
"Output file: %s" % output_file,
"Input file(s): %s" % "\n ".join(datafiles)
]
if annotation:
context.append(annotation)
plt.figtext(0.01,
0.01,
"\n".join(context),
fontsize=6,
verticalalignment='bottom')
plt.savefig(output_file)
def get(self, request, format=None, **kwargs):
na_file = _get_file_from_url(request)
block = get_io(na_file).read_block()
id_segment = int(request.GET['segment_id'])
id_analog_signal = int(request.GET['analog_signal_id'])
# todo, catch MultiValueDictKeyError in case segment_id or analog_signal_id aren't given, and return a 400 Bad Request response
segment = block.segments[id_segment]
analogsignal = segment.analogsignals[id_analog_signal]
# todo, catch any IndexErrors, and return a 404 response
# unit = analogsignal.units
# t_start = analogsignal.t_start
# sampling_rate = analogsignal.sampling_rate
# time_laps = 1/sampling_rate
print("anaolgsignals", analogsignal, len(analogsignal))
print("units", analogsignal.units)
print("t_start", analogsignal.t_start)
print("sampling_rate", analogsignal.sampling_rate)
print("t_stop", analogsignal.t_stop)
print("times", analogsignal.times)
print("duration", analogsignal.duration)
print("name", analogsignal.name)
print("size", analogsignal.size)
# print('analogsignal', analogsignal[0], analogsignal[0].sampling_rate)
# analog_signal = block.segments[id_segment].analogsignals[id_analog_signal]
# print(analog_signal)
analog_signal_values = []
for item in analogsignal:
try: # TODO find a better solution
analog_signal_values.append(item.item())
except ValueError:
analog_signal_values.append(item[1].item())
analog_signal_times = []
for item in analogsignal.times:
analog_signal_times.append(item.item())
graph_data = {
"name":
analogsignal.name,
"values":
analog_signal_values,
"values_units":
str(analogsignal.units.dimensionality),
"times":
analog_signal_times,
"times_dimensionality":
str(analogsignal.t_start.units.dimensionality),
"t_start":
analogsignal.t_start.item(),
"t_stop":
analogsignal.t_stop.item(),
"sampling_rate":
float(analogsignal.sampling_rate.magnitude),
"sampling_rate_units":
str(analogsignal.sampling_rate.units.dimensionality)
}
return JsonResponse(graph_data)
def end(compatible_output=True):
"""Do any necessary cleaning up before exiting."""
for (population, variables, filename) in simulator.state.write_on_end:
io = get_io(filename)
population.write_data(io, variables)
simulator.state.write_on_end = []
from binascii import hexlify
repo = hg.repository(ui.ui(), "..")
ctx = repo.parents()[0]
return hexlify(ctx.node()[:6])
except ImportError:
return "unknown"
__version__ = get_version()
spikes_and_vm_file, spike_counts_file, neuron_id = sys.argv[1:]
fig = plt.figure(figsize=(8, 3))
fig.dpi = 120
# Plot spike times
data = get_io(spikes_and_vm_file).read()[0].segments[0]
ax = fig.add_axes((0.1, 0.12, 0.6, 0.55), frameon=False)
ax.set_xlim([0, TSTOP])
for i, spiketrain in enumerate(data.spiketrains):
ax.plot(spiketrain.times, i*numpy.ones_like(spiketrain), 'b.', markersize=0.2)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.set_xlabel("Time (ms)")
ax.set_ylabel("Cell number")
# Plot firing rate histogram
with open(spike_counts_file) as fp:
fp.readline() # first line is metadata
n_spikes = numpy.loadtxt(fp) * 1000.0/TSTOP # calculate firing rate
def get(self, request, format=None, **kwargs):
if 'na_file' not in request.session:
url = request.GET.get('url')
request = _get_file_from_url(request, url)
na_file = request.session['na_file']
block = get_io(na_file).read_block()
id_segment = int(request.GET['segment_id'])
id_analog_signal = int(request.GET['analog_signal_id'])
segment = block.segments[id_segment]
analogsignal = segment.analogsignals[id_analog_signal]
# unit = analogsignal.units
# t_start = analogsignal.t_start
# sampling_rate = analogsignal.sampling_rate
# time_laps = 1/sampling_rate
print("anaolgsignals", analogsignal, len(analogsignal))
print("units", analogsignal.units)
print("t_start", analogsignal.t_start)
print("sampling_rate", analogsignal.sampling_rate)
print("t_stop", analogsignal.t_stop)
print("times", analogsignal.times)
print("duration", analogsignal.duration)
print("name", analogsignal.name)
print("size", analogsignal.size)
# print('analogsignal', analogsignal[0], analogsignal[0].sampling_rate)
# analog_signal = block.segments[id_segment].analogsignals[id_analog_signal]
# print(analog_signal)
analog_signal_values = []
for item in analogsignal:
try: # TODO find a better solution
analog_signal_values.append(item.item())
except ValueError:
analog_signal_values.append(item[1].item())
analog_signal_times = []
for item in analogsignal.times:
analog_signal_times.append(item.item())
graph_data = {
"name":
analogsignal.name,
"values":
analog_signal_values,
"values_units":
str(analogsignal.units.dimensionality),
"times":
analog_signal_times,
"times_dimensionality":
str(analogsignal.t_start.units.dimensionality),
"t_start":
analogsignal.t_start.item(),
"t_stop":
analogsignal.t_stop.item(),
"sampling_rate":
float(analogsignal.sampling_rate.magnitude),
"sampling_rate_units":
str(analogsignal.sampling_rate.units.dimensionality)
}
return JsonResponse(graph_data)
