def analysis_typespecific(ddata, opts=None):
# def analysis_typespecific(ddata, p_exp, opts=None):
# 'opts' input are the options in a dictionary
# if opts is defined, then make it well formed
# the valid keys of opts are in list_opts
opts_run = {
'type': 'dpl_laminar',
'f_max': 100.,
'save_data': 0,
'runtype': 'parallel',
}
# check if opts is supplied
if opts:
# assume opts is a dict
# iterate through provided opts and assign if the key is present
# otherwise, ignore
for key, val in opts.items():
if key in opts_run.keys():
opts_run[key] = val
# preallocate lists for use below
list_param, list_ts, list_spec = [], [], []
# aggregrate all files from individual expmts into lists
expmt_group = ddata.expmt_groups[0]
# get the list of params
# returns an alpha SORTED list
# add to list of all param files
param_tmp = ddata.file_match(expmt_group, 'param')
print('param_tmp:', param_tmp)
list_param.extend(param_tmp)
# get exp prefix for each trial in this expmt group
list_exp_prefix = [fio.strip_extprefix(fparam) for fparam in param_tmp]
# get the list of dipoles and create spec output filenames
if opts_run['type'] in ('dpl', 'dpl_laminar'):
list_ts.extend(ddata.file_match(expmt_group, 'rawdpl'))
list_spec.extend([
ddata.create_filename(expmt_group, 'rawspec', exp_prefix)
for exp_prefix in list_exp_prefix
])
elif opts_run['type'] == 'current':
list_ts.extend(ddata.file_match(expmt_group, 'rawcurrent'))
list_spec.extend(
ddata.create_filename(expmt_group, 'rawspeccurrent',
list_exp_prefix[-1]))
# create list of spec output names
# this is sorted because of file_match
# exp_prefix_list = [fio.strip_extprefix(fparam) for fparam in list_param]
# perform analysis on all runs from all exmpts at same time
if opts_run['type'] == 'current':
# list_spec.extend([ddata.create_filename(expmt_group, 'rawspeccurrent', exp_prefix) for exp_prefix in exp_prefix_list])
if opts_run['runtype'] == 'parallel':
pl = mp.Pool()
for fparam, fts, fspec in zip(list_param, list_ts, list_spec):
pl.apply_async(spec_current_kernel,
(fparam, fts, fspec, opts_run['f_max']))
pl.close()
pl.join()
elif opts_run['runtype'] == 'debug':
for fparam, fts, fspec in zip(list_param, list_ts, list_spec):
spec_current_kernel(fparam, fts, fspec, opts_run['f_max'])
elif opts_run['type'] == 'dpl_laminar':
# these should be OUTPUT filenames that are being generated
# list_spec.extend([ddata.create_filename(expmt_group, 'rawspec', exp_prefix) for exp_prefix in exp_prefix_list])
# also in this case, the original spec results will be overwritten
# and replaced by laminar specific ones and aggregate ones
# in this case, list_ts is a list of dipole
if opts_run['runtype'] == 'parallel':
pl = mp.Pool()
for fparam, fts, fspec in zip(list_param, list_ts, list_spec):
pl.apply_async(spec_dpl_kernel,
(fparam, fts, fspec, opts_run['f_max']))
pl.close()
pl.join()
elif opts_run['runtype'] == 'debug':
# spec_results_L2 and _L5
for fparam, fts, fspec in zip(list_param, list_ts, list_spec):
spec_dpl_kernel(fparam, fts, fspec, opts_run['f_max'])
def pkernel(dfig_dpl, f_dpl, f_spk, f_spec, f_current, f_spec_current, f_param, ax_handles, spec_cmap='jet'):
T = paramrw.find_param(f_param, 'tstop')
xlim = (50., T)
# into the pdipole directory, this will plot dipole, spec, and spikes
# create the axis handle
f = ac.FigDipoleExp(ax_handles)
# create the figure name
fprefix = fio.strip_extprefix(f_dpl) + '-dpl'
fname = os.path.join(dfig_dpl, fprefix + '.png')
# grab the dipole
dpl = dipolefn.Dipole(f_dpl)
dpl.convert_fAm_to_nAm()
# plot the dipole to the agg axes
dpl.plot(f.ax['dpl_agg'], xlim)
dpl.plot(f.ax['dpl_agg_L5'], xlim)
# f.ax['dpl_agg_L5'].hold(True)
# dpl.plot(f.ax['dpl_agg_L5'], xlim, 'L5')
# plot individual dipoles
dpl.plot(f.ax['dpl'], xlim, 'L2')
dpl.plot(f.ax['dpl_L5'], xlim, 'L5')
# f.ysymmetry(f.ax['dpl'])
# print dpl.max('L5', (0., -1)), dpl.max('L5', (50., -1))
# print f.ax['dpl_L5'].get_ylim()
# f.ax['dpl_L5'].set_ylim((-1e5, 1e5))
# f.ysymmetry(f.ax['dpl_L5'])
# plot the current
I_soma = currentfn.SynapticCurrent(f_current)
I_soma.plot_to_axis(f.ax['I_soma'], 'L2')
I_soma.plot_to_axis(f.ax['I_soma_L5'], 'L5')
# plot the dipole-based spec data
pc = specfn.pspec_ax(f.ax['spec_dpl'], f_spec, xlim, 'L2')
f.f.colorbar(pc, ax=f.ax['spec_dpl'])
pc = specfn.pspec_ax(f.ax['spec_dpl_L5'], f_spec, xlim, 'L5')
f.f.colorbar(pc, ax=f.ax['spec_dpl_L5'])
# grab the current spec and plot them
spec_L2, spec_L5 = data_spec_current = specfn.read(f_spec_current, type='current')
pc_L2 = f.ax['spec_I'].imshow(spec_L2['TFR'], aspect='auto', origin='upper',cmap=plt.get_cmap(spec_cmap))
pc_L5 = f.ax['spec_I_L5'].imshow(spec_L5['TFR'], aspect='auto', origin='upper',cmap=plt.get_cmap(spec_cmap))
# plot the current-based spec data
# pci = specfn.pspec_ax(f.ax['spec_I'], f_spec_current, type='current')
f.f.colorbar(pc_L2, ax=f.ax['spec_I'])
f.f.colorbar(pc_L5, ax=f.ax['spec_I_L5'])
# get all spikes
s = spikefn.spikes_from_file(f_param, f_spk)
# these work primarily because of how the keys are done
# in the spike dict s (consequence of spikefn.spikes_from_file())
s_L2 = spikefn.filter_spike_dict(s, 'L2_')
s_L5 = spikefn.filter_spike_dict(s, 'L5_')
# resize xlim based on our 50 ms cutoff thingy
xlim = (50., xlim[1])
# plot the spikes
spikefn.spike_png(f.ax['spk'], s_L2)
spikefn.spike_png(f.ax['spk_L5'], s_L5)
f.ax['dpl'].set_xlim(xlim)
# f.ax['dpl_L5'].set_xlim(xlim)
# f.ax['spec_dpl'].set_xlim(xlim)
f.ax['spk'].set_xlim(xlim)
f.ax['spk_L5'].set_xlim(xlim)
f.savepng(fname)
f.close()
return 0