def plot_spike_counts(ax, fname=None):
if fname is None:
for ii in range(len(SIX_SPIKES)):
spike_counts = []
for jj, sim_set in enumerate([SIX_SPIKES, THREE_SPIKES, ALL_SPIKES]):
sim_list = sim_set[ii][1:-1]
for kk, jid in enumerate(sim_list):
try:
fname = nda.find_h5_file(jid, DATA_DIR)
except:
# First entry is old data and moved to back up
# Template should still have all the data
print(f'JID {jid} not in datadir. Looking in template dir')
fname = nda.find_h5_file(jid, TEMPLATE_DIR)
with h5.File(fname, 'r') as fd:
kc_st, kc_id = nda.get_event_times(fd[nda.kc_st_path])
kc_sc = sum([len(st) for st in kc_st])
print('JID:', jid, 'total spikes:', kc_sc)
# For all cases, plot the result from the last successful KC removal, and first
spike_counts.append(kc_sc)
ax.plot(len(spike_counts)-1, spike_counts[-1], 'k|')
ax.plot(spike_counts, 'o-', fillstyle='none')
else:
total_spike_count = pd.read_csv(fname, sep=',')
for ii, (series_id, simgrp) in enumerate(total_spike_count.groupby('series')):
ax.plot(simgrp['total_spikes'].values, 'o-', fillstyle='none')
series_df = simgrp.reset_index()
for removal, remgrp in series_df.groupby('removal'):
print('#', removal)
print(remgrp)
# jj += len(remgrp)
ax.plot(remgrp.index.values[-1], remgrp['total_spikes'].values[-1], 'k.')
def make_psth_and_vm(ax_psth, ax_vm, ax_kc_hist):
binwidth = 100
datalist = (SIX_SPIKES[0][1:-1], THREE_SPIKES[0][1:-1],
ALL_SPIKES[0][1:-1])
colors = ['#e66101', '#5e3c99', '#009292']
ls = ['-', ':']
for ii, group in enumerate(datalist):
print(group)
for jj, jid in enumerate((group[0], group[-1])):
print(jid)
try:
fname = nda.find_h5_file(jid, DATA_DIR)
except:
# First entry is old data and moved to back up
# Template should still have all the data
print(f'JID {jid} not in datadir. Looking in template dir')
fname = nda.find_h5_file(jid, TEMPLATE_DIR)
with h5.File(fname, 'r') as fd:
kc_st, kc_id = nda.get_event_times(fd[nda.kc_st_path])
kc_sc = np.array([len(st) for st in kc_st])
try:
ax_kc_hist.hist(kc_sc,
bins=np.arange(1,
max(kc_sc) + 0.5, 1),
color=colors[ii],
ls=ls[jj],
label=f'{ii}: {jj}: {jid}',
histtype='step',
linewidth=1)
except IndexError:
print(jid, ':', kc_sc, '|')
pop_st = np.concatenate(kc_st)
try:
ax_psth.hist(pop_st,
bins=np.arange(500, 2100, binwidth),
color=colors[ii],
ls=ls[jj],
histtype='step',
label=jid)
except IndexError:
print(jid, pop_st)
ggn_vm, t = nda.get_ggn_vm(fd, 'basal')
ax_vm.plot(t,
ggn_vm[0, :],
label=jid,
color=colors[ii],
ls=ls[jj])
ax_psth.legend()
ax_vm.legend()
def main():
plt.close('all')
fig_ax = []
fig_ax_hist = []
for ii in range(len(SIX_SPIKES)):
fig, ax = plt.subplots(nrows=4, ncols=3, sharex='row', sharey='row')
fig_ax.append((fig, ax))
fig_hist, ax_hist = plt.subplots()
fig_ax_hist.append((fig_hist, ax_hist))
for jj, sim_set in enumerate([SIX_SPIKES, THREE_SPIKES, ALL_SPIKES]):
for kk, jid in enumerate(sim_set[ii][:-1]):
try:
fname = nda.find_h5_file(jid, DATA_DIR)
except:
# First entry is old data and moved to back up
# Template should still have all the data
print(f'JID {jid} not in datadir. Looking in template dir')
fname = nda.find_h5_file(jid, TEMPLATE_DIR)
with h5.File(fname, 'r') as fd:
kc_st, kc_id = nda.get_event_times(fd[nda.kc_st_path])
kc_sc = np.array([len(st) for st in kc_st])
spiking_kc_count = len(np.flatnonzero(kc_sc))
spike_count = kc_sc.sum()
print('JID:', jid, ', spiking KCs:', spiking_kc_count, 'total spikes:', spike_count)
# For all cases, plot the result from the last successful KC removal, and first
if (kk == len(sim_set[ii]) - 2) or (kk == 1):
pn_st, pn_id = nda.get_event_times(fd[nda.pn_st_path])
ggn_vm, t = nda.get_ggn_vm(fd, 'basal')
ax[0, jj].plot(np.concatenate(pn_st), np.concatenate(pn_id), ',')
ax[1, jj].plot(np.concatenate(kc_st), np.concatenate(kc_id), ',')
ax[2, jj].plot(t, ggn_vm[0, :], label=f'{ii}: jj: {jid}')
kc_pop_st = np.concatenate(kc_st)
kc_pop_st.sort()
ax[3, jj].hist(kc_pop_st, bins=np.arange(0, t[-1], 100e-3),
alpha=0.5, label=f'{ii}: {jj}: {jid}',
histtype='step', linewidth=2)
scounts = [len(st) for st in kc_st]
if max(scounts) == 0:
continue
# bin starting with 1 to remove nonspiking KCs
ax_hist.hist(kc_sc, bins=np.arange(1, max(kc_sc)+0.5, 1),
alpha=0.5, label=f'{ii}: {jj}: {jid}',
histtype='step', linewidth=2)
fig.set_size_inches(210/25.4, 290/25.4)
fig.savefig(f'fig_kc_removal_jid_{SIX_SPIKES[ii][0]}.svg', transparent=True)
fig_hist.savefig(f'fig_kc_hist_kc_removal_jid_{SIX_SPIKES[ii][0]}.svg', transparent=True)
plt.show()
def find_jid_spiking_kcs(jid_list, datadir):
ret = []
for jid in jid_list:
fname = nda.find_h5_file(str(jid), datadir)
with h5.File(fname, 'r') as fd:
ret.append(nda.get_spiking_kcs(fd))
return ret
def remove_high_firing_kcs(jid, limit, sdir, tdir):
"""Remove KCs firing more than `limit` from dataset of `jid`. `sdir`
points to directory containing the data file, `tdir` is where the
output file will be written. If source file is called `x.h5`,
output file will be `x_kc{limit}.h5`
"""
fpath = nda.find_h5_file(jid, sdir)
kc_spike_count = []
with h5.File(fpath, 'r') as fd:
for kc, spikes in fd[nda.kc_st_path].items():
kc_spike_count.append((spikes.attrs['source'], len(spikes)))
try:
forig_path = fd.attrs['original']
print(jid, ': original template:', forig_path)
except KeyError:
forig_path = fpath
syn = fd[nda.pn_kc_syn_path]
if len(syn.shape) == 2:
syn_orig = pd.DataFrame(data=syn[:, 0])
else:
syn_orig = pd.DataFrame(data=syn[:])
kc_spikes = pd.DataFrame(kc_spike_count, columns=['kc', 'spikes'])
print('# kcs > 5 spikes:', len(kc_spikes[kc_spikes['spikes'] > 5]))
print('# kcs > 10 spikes:', len(kc_spikes[kc_spikes['spikes'] > 10]))
print('# spiking kcs:', len(kc_spikes[kc_spikes['spikes'] > 0]))
orig_0 = set(np.where(syn_orig['gmax'] == 0)[0])
over = kc_spikes[kc_spikes['spikes'] > limit]
print('{} kcs spiked more than {} spikes'.format(len(over), limit))
if len(over) == 0:
print('No KCs meet criterion. Nothing to do')
return 0
over_syn = pd.merge(syn_orig, over, left_on='post', right_on='kc')
print('Synapses to > limit kcs', len(over_syn))
print('Synapse to these kcs set to 0?',
len(np.flatnonzero(over_syn['gmax'].values == 0)))
# This is tricky - a simulation based on a template used to
# generate a datafile with external reference to the synapse
# datasets in the template. So attempt to generate another
# template by updating synapses in the produced datafile
# referred back to the original template.
fname = os.path.basename(fpath).rpartition('.')[0]
out_fname = '{}_kc{}.h5'.format(fname, limit)
outfile = os.path.join(tdir, out_fname)
if os.path.exists(outfile):
print(f'File already exists: {outfile}')
return 0
# 2019-07-15 - copying forig_path resulted in losing KC spike
# information from new simulation (fpath). Now that I save the
# synapses directly, instead of an external ref, there is no need
# to copy forig.
print('Copying data from {} as {} to update using {} KC spiking'.format(
forig_path, outfile, fpath))
shutil.copyfile(forig_path, outfile)
print('Disabling PN synapses to KCs firing > {} spikes'.format(limit))
changed_syn_count = 0
with h5.File(outfile, 'a') as ofd:
syndf = ofd[nda.pn_kc_syn_path]
# Set the conductances to each KC spiking more than 5 spikes to 0
for row in over.itertuples():
idx = np.where(syn_orig['post'] == row.kc)[0]
# print('Common 0 syn:', len(set(idx).intersection(orig_0)))
changed_syn_count += len(idx)
syndf[idx, 0, 'gmax'] = 0.0
print('Modified: synapses set to 0 conductance:', changed_syn_count)
ofd.close()
print('Original: synapses with 0 conductance:',
len(syn_orig[syn_orig['gmax'] == 0.0]))
with h5.File(outfile, 'r') as o2:
syn_new = pd.DataFrame(data=o2[nda.pn_kc_syn_path][:, 0])
print('# synapses in updated file', len(syn_new))
print('# shape of synapse data in updated file', syn_new.shape)
print('# synapses with 0 conductance',
len(syn_new[syn_new['gmax'] == 0.0]))
assert (len(syn_new[syn_new['gmax'] == 0.0]) -
len(syn_orig[syn_orig['gmax'] == 0.0])) == changed_syn_count
print('Finished checking updated file')
return changed_syn_count
import matplotlib.gridspec as gridspec
import pn_kc_ggn_plot_mpl as myplot
plt.rc('font', size=8)
_ur = pint.UnitRegistry()
Q_ = _ur.Quantity
# datadir = 'Z:/Subhasis/ggn_model_data/olfactory_network'
# datadir = '/data/rays3/ggn/olfactory_network/'
datadir = 'D:/biowulf_stage/olfactory_network'
#** jid_sc for only shifting PN, constant GGN->KC inhibition - but this did not
# have PN-KC clustered conn
jid_sc = '22087969'
fname_sc = nda.find_h5_file(jid_sc, datadir)
fd_sc = h5.File(fname_sc, 'r')
print('shifting PN, jid: {}, spiking KCs {}'.format(
jid_sc, len(nda.get_spiking_kcs(fd_sc))))
print(yaml.dump(nda.load_config(fd_sc), default_style=''))
print('-' * 20)
stiminfo = nda.get_stimtime(fd_sc)
pn_st = []
pn_id = []
for pn in fd_sc[nda.pn_st_path].values():
pn_st.append(pn[:])
pn_id.append([int(pn.name.rpartition('_')[-1])] * len(pn))
kc_st = []
kc_id = []
jids = [
30184869,
30184873,
30184874,
30184876,
30184878,
30184880,
30184882,
30184884,
30184886,
30184888,
]
for jid in jids:
fpath = nda.find_h5_file(jid, datadir)
forig_path = fpath
kc_spike_count = []
with h5.File(fpath, 'r') as fd:
for kc, spikes in fd[nda.kc_st_path].items():
kc_spike_count.append((spikes.attrs['source'], len(spikes)))
try:
forig_path = fd.attrs['original']
except KeyError:
forig_path = fpath
syn_orig = pd.DataFrame(data=fd[nda.pn_kc_syn_path][:, 0])
kc_spikes = pd.DataFrame(kc_spike_count, columns=['kc', 'spikes'])
#kc_spikes['spikes'].plot('hist')
print('# kcs > 5 spikes:', len(kc_spikes[kc_spikes['spikes'] > 5]))
print('# kcs > 10 spikes:', len(kc_spikes[kc_spikes['spikes'] > 10]))
counts.append(len(jid_spiking_kcs[jid]))
jid_kc_count.loc[jid_kc_count.jid == jid, 'spiking_kcs'] \
= counts[-1]
if common is None:
common = set(jid_spiking_kcs[jid])
else:
common = common.intersection(jid_spiking_kcs[jid])
pos = (common_kcs.odor
== odor) & (common_kcs.connection
== conn) & (common_kcs.template_jid == template)
common_kcs.loc[pos, 'common_kcs'] = len(common)
common_kcs.loc[pos, 'avg_kcs'] = np.mean(counts)
templates = odor_trials_data.template_jid.unique()
for t in templates:
fname = nda.find_h5_file(str(t), datadir)
print(fname)
with h5.File(fname, 'r') as fd:
print(yaml.dump(nda.load_config(fd), default_flow_style=''))
tmp_color_map = {
10829002: '#e66101', # iid
10829014: '#b2abd2', # clus
9932209: '#fdb863', # iid
9932198: '#5e3c99' # clus
}
tmp_label_map = {
10829002: 'Diffuse 2', # iid
10829014: 'Clustered 2', # clus
9932209: 'Diffuse 1', # iid
import network_data_analysis as nda
# from matplotlib.backends.backend_pdf import PdfPages
import pn_kc_ggn_plot_mpl as myplot
import clust_fixed_net_multi_trial as cl
plt.rc('font', size=11)
analysis_dir = '/home/rays3/projects/ggn/analysis'
# datadir = '/data/rays3/ggn/olfactory_network'
datadir = '/data/rays3/ggn/fixed_net'
os.chdir(analysis_dir)
fig, ax = plt.subplots(nrows=3, ncols=2, sharex='all', sharey='row')
# jid = '13081213' # this reproduces GGN Vm well, PN->KC clustered, same KC cluster receives coactive PNs
jid = '21841553' # new simulation
fname = nda.find_h5_file(jid, datadir)
originals = []
with h5.File(fname, 'r') as fd:
ax[0, 0].set_title(f'{jid}')
print(fname)
orig = fd.attrs['original']
originals.append(orig)
print(f'original:{orig}')
with h5.File(orig, 'r') as forig:
print(yaml.dump(nda.load_config(forig), default_flow_style=False))
# fig, ax = plt.subplots(nrows=2, ncols=1, sharex='all')
myplot.plot_ggn_vm(ax[1, 0], fd, fd['/data/uniform/ggn_output/GGN_output_Vm'], 'LCA', 1, color='black', alpha=1.0)
ax[1, 0].set_ylim(-60, -45)
ax[1, 0].set_yticks([-55, -50])
ig_vm = fd['/data/uniform/ig/IG_Vm']
dt = ig_vm.attrs['dt']
datadir = '/data/rays3/ggn/olfactory_network/'
#* setup figure and axes
gs = gridspec.GridSpec(nrows=3, ncols=1, height_ratios=[2, 1, 1], hspace=0.05)
fig = plt.figure()
ax0 = fig.add_subplot(gs[0])
ax1 = fig.add_subplot(gs[1], sharex=ax0)
ax2 = fig.add_subplot(gs[2], sharex=ax0, sharey=ax1)
#* open file descriptors
#** jid_shifting_lognorm for shifting PNs with lognorm distribution of synapses
# jid = '3047110' # this was old version
# jid_lognorm = '16240941' # newer simulation with 0.2 s off response
# jid_lognorm = '22087970' # This is with lognorm distribution of all synaptic strengths, pn-kc gmax=2.4pS, 5K KCs spiked
jid_lognorm = '22087969'
fname_lognorm = nda.find_h5_file(jid_lognorm, datadir)
fd_lognorm = h5.File(fname_lognorm, 'r')
print('shifting PN, jid: {}, spiking KCs {}'.format(
jid_lognorm, len(nda.get_spiking_kcs(fd_lognorm))))
print(yaml.dump(nda.load_config(fd_lognorm), default_style=''))
print('-' * 20)
#** jid_constant for only shifting PN, constant GGN->KC inhibition - but this did not
# have PN-KC clustered conn
# jid_constant = '16562835' # Thu Mar 14 13:49:49 EDT 2019 - this file was somehow lost from backup.
# jid_constant = '22295183' # Redid simulations with constant synaptic conductances on 2019-03-13 PN-KC gmax=3 pS, spiking KCs 1707
jid_constant = '24211204' # The jid '22295183' in fig 5 had off-time 0.2 s, this one has 0.5s
fname_constant = nda.find_h5_file(jid_constant, datadir)
fd_constant = h5.File(fname_constant, 'r')
print('shifting PN, jid: {}, spiking KCs {}'.format(
jid_constant, len(nda.get_spiking_kcs(fd_constant))))
print(yaml.dump(nda.load_config(fd_constant), default_style=''))
siminfo = pd.read_csv(
StringIO("""jid,pn_kc_gmax,shifting,lognorm,spiking_kcs
22072442,2.5pS,y,all,6627
22087964,2.5pS,y,all,6497
22087965,2.5pS,y,all,7933
22087966,2.5pS,y,all,6833
22087967,2.5pS,y,all,7591
22087969,2.4pS,y,all,
22087970,2.4pS,y,all,
22087971,2.4pS,y,all,
22087972,2.4pS,y,all,
22087973,2.4pS,y,all,
"""))
for row in siminfo.itertuples():
filename = nda.find_h5_file(row.jid, datadir)
# fig, ax = plt.subplots()
with h5.File(filename, 'r') as fd:
config = yaml.load(fd.attrs['config'])
# ax0 = fig.add_subplot(rows, cols, nax)
spiking_kcs = 0
hyper_kcs = 0
spike_counts = []
for kc, st in fd['/data/event/kc/kc_spiketime'].items():
spike_counts.append(len(st))
if len(st) > 0:
spiking_kcs += 1
if len(st) > 3:
hyper_kcs += 1
print('=' * 20)
