def plot_run_analysis_standard2_heatmap(runnumber,
analysis_name='coh',
savefigure=True):
"""
Plots a heatmap of coherence for given runnumber. Even though we can give other analysis_names,
it probably doesn't generalize very well because of sns.heatmap(..., vmin=0, vmax=1, ...)
:param runnumber:
:param analysis_name:
:param savefig:
:return: fig
"""
run_analysis_standard2_df = mlu.run_analysis_standard2_to_df(runnumber)
run_settings_loops = mlu.get_run_loops(runnumber)
sett = mlu.get_clean_settings(runnumber)['sett']
if run_settings_loops[2]['name'] == 'g_syn_loop':
region_from = run_settings_loops[2]['region_from']
region_to = run_settings_loops[2]['region_to']
fixed_conductance_ee = sett['g_syn_ee_r'][region_from, region_to]
fixed_conductance_ei = sett['g_syn_ei_r'][region_from, region_to]
analysis_df = run_analysis_standard2_df[analysis_name].unstack().transpose(
)
fig = plt.figure()
ax = fig.add_subplot(111)
sns.heatmap(analysis_df, vmin=0, vmax=1, cmap='inferno', ax=ax)
# x-axis
ax.set_xlabel(run_settings_loops[1]['name_readable'])
label_idxs = range(0, len(run_settings_loops[1]['values_inhibitory']), 3)
ax.set_xticks(label_idxs)
ax.set_xticklabels(
np.around(run_settings_loops[1]['values_inhibitory'][
ax.get_xticks().astype(int)],
decimals=2))
# y-axis
ax.invert_yaxis()
ax.set_ylabel(run_settings_loops[2]['name_readable'])
ylabels = run_settings_loops[2]['values']
ax.set_yticks(range(0, len(ylabels), 2))
ax.set_yticklabels(ylabels[ax.get_yticks()], rotation=0)
# The rest
ax.set_title(
f'Run{runnumber} - {mlu.analysis_standard2_names[analysis_name]}')
plt.show()
if savefigure:
name = f'run{runnumber}_{analysis_name}.png'
fig.savefig(os.path.join(settings.run_figures_dir(runnumber), name),
bbox_inches='tight',
pad_inches=0)
return fig
def plot_spiketrain_histogram(runnumber,
l1,
l2,
savefigure=True,
findpeaks=True,
cwt_widths=np.arange(4, 25)):
sett = mlu.get_clean_settings(runnumber)['sett']
spikes_excitatory, spikes_inhibitory = mlu.get_spiketrains_df(
runnumber, l1, l2)
bins = np.linspace(start=0,
stop=sett['Ttot'],
num=int(sett['Ttot'] / sett['dthist'] + 1),
endpoint=True)
region1_excitatory_spikes_binned, _ = np.histogram(
spikes_excitatory[spikes_excitatory['region'] == 1]['t'], bins=bins)
region2_excitatory_spikes_binned, _ = np.histogram(
spikes_excitatory[spikes_excitatory['region'] == 2]['t'], bins=bins)
fig, ax = plt.subplots(nrows=2, sharex=True, figsize=(40, 15))
ax[0].bar(x=bins[:-1], height=region1_excitatory_spikes_binned[:])
ax[1].bar(x=bins[:-1], height=region2_excitatory_spikes_binned[:])
ax[1].invert_yaxis()
if findpeaks:
region1_peaks = scipy.signal.find_peaks_cwt(
region1_excitatory_spikes_binned, widths=cwt_widths)
region2_peaks = scipy.signal.find_peaks_cwt(
region2_excitatory_spikes_binned, widths=cwt_widths)
ax[0].vlines(region1_peaks * sett['dthist'], 0, 10, colors='k')
ax[1].vlines(region2_peaks * sett['dthist'], 0, 10, colors='k')
fig.subplots_adjust(wspace=0, hspace=0)
ax[0].set_title(
f"Run {runnumber}, loop1={l1}, loop2={l2} - $reg_1$ (upper) and $reg_2$ (lower) excitatory cell spike histograms"
)
fig.show()
if savefigure:
name = 'run{}_l1{}_l2{}_excitatory_histograms.png'.format(
runnumber, l1, l2)
fig.savefig(os.path.join(settings.run_figures_dir(runnumber), name),
bbox_inches='tight',
pad_inches=0)
return fig
def plot_spiketrains_scatter_region(runnumber,
l1,
l2,
region,
savefigure=True):
sett = mlu.get_clean_settings(runnumber)['sett']
spikes_excitatory, spikes_inhibitory = mlu.get_spiketrains_df(
runnumber, l1, l2)
spikes_excitatory[
'neuron_idx'] = spikes_excitatory['neuron_idx'] + sett['Ni']
fig, ax = plt.subplots(figsize=(20, 7.5))
sns.scatterplot(
x='t',
y='neuron_idx',
color='blue',
s=5,
data=spikes_excitatory[spikes_excitatory['region'] == region],
ax=ax,
label='Pyramidal Cells')
sns.scatterplot(
x='t',
y='neuron_idx',
color='red',
s=5,
data=spikes_inhibitory[spikes_inhibitory['region'] == region],
ax=ax,
label='Interneurons')
ax.set_ylabel('Neuron index')
ax.set_xlabel('Time ($s$)')
ax.legend(loc='upper right')
fig.show()
if savefigure:
name = f'run{runnumber}_l1{l1}_l2{l2}_region{region}_spiketrains.png'
fig.savefig(os.path.join(settings.run_figures_dir(runnumber), name),
bbox_inches='tight',
pad_inches=0)
return fig
def plot_spiketrains_scatter(runnumber, l1, l2, savefigure=True, regions=None):
sett = mlu.get_clean_settings(runnumber)['sett']
spikes_excitatory, spikes_inhibitory = mlu.get_spiketrains_df(
runnumber, l1, l2)
if regions is None:
regions = sorted(spikes_excitatory.region.unique().astype(int))
spikes_excitatory[
'neuron_idx'] = spikes_excitatory['neuron_idx'] + sett['Ni']
fig, axes = plt.subplots(nrows=len(regions),
sharex=False,
figsize=(40, 15))
for region, ax in zip(regions, axes):
sns.scatterplot(
x='t',
y='neuron_idx',
color='red',
s=5,
data=spikes_inhibitory[spikes_inhibitory['region'] == region],
ax=ax)
sns.scatterplot(
x='t',
y='neuron_idx',
color='blue',
s=5,
data=spikes_excitatory[spikes_excitatory['region'] == region],
ax=ax)
fig.show()
if savefigure:
name = f'run{runnumber}_l1{l1}_l2{l2}_spiketrains.png'
fig.savefig(os.path.join(settings.run_figures_dir(runnumber), name),
bbox_inches='tight',
pad_inches=0)
return fig
def __figure_results_correlation_boxplots_prepare_df(runnumbers_sett):
noisetypes = {1: "White", 2: "Brown", 3: "Pink"}
corr_df2 = pd.DataFrame()
for runnumbers in runnumbers_sett:
for runnumber in runnumbers:
sett = mlu.get_clean_settings(runnumber)['sett']
feedback_conductance_ee = sett['g_syn_ee_r'][0, 1]
feedback_conductance_ei = sett['g_syn_ei_r'][0, 1]
noisetype = noisetypes[sett['noisetype']]
noiseamp = sett['noiseamp']
analysis_standard2_df = mlu.run_analysis_standard2_to_df(runnumber)
correlations = mlu.get_max_correlations(runnumber)
new = pd.DataFrame({
'feedback':
feedback_conductance_ei,
'coh':
analysis_standard2_df['coh'],
'correlation':
correlations[analysis_info2_name],
'threshold':
list(analysis_standard2_df['coh'] >= coherence_threshold),
'Noiseseed':
sett['noiseseed'],
'noisetype':
noisetype,
'noiseamp':
noiseamp
})
for col in correlations.columns:
new[col] = correlations[col]
corr_df2 = corr_df2.append(new)
return corr_df2
def methods_analysis_spike_density_histogram(runnumber,
l1,
l2,
region,
start,
stop,
filename=None):
sett = mlu.get_clean_settings(runnumber)['sett']
spikes_excitatory, spikes_inhibitory = mlu.get_spiketrains_df(
runnumber, l1, l2)
spikes_excitatory[
'neuron_idx'] = spikes_excitatory['neuron_idx'] + sett['Ni']
# Filter for region
spikes_excitatory = spikes_excitatory[spikes_excitatory['region'] ==
region]
spikes_inhibitory = spikes_inhibitory[spikes_inhibitory['region'] ==
region]
# Filter for start <= t <= end
spikes_excitatory = spikes_excitatory[spikes_excitatory['t'] >= start]
spikes_excitatory = spikes_excitatory[spikes_excitatory['t'] <= stop]
spikes_inhibitory = spikes_inhibitory[spikes_inhibitory['t'] >= start]
spikes_inhibitory = spikes_inhibitory[spikes_inhibitory['t'] <= stop]
# Bin the spikes for histograms
bins = np.linspace(start=start,
stop=stop,
num=int((stop - start) / sett['dthist'] + 1),
endpoint=True)
excitatory_spikes_binned, _ = np.histogram(spikes_excitatory['t'],
bins=bins)
inhibitory_spikes_binned, _ = np.histogram(spikes_inhibitory['t'],
bins=bins)
excitatory_spikes_density = 1000 * 1 / (
sett['dthist'] * sett['Ne']) * excitatory_spikes_binned
inhibitory_spikes_density = 1000 * 1 / (
sett['dthist'] * sett['Ni']) * inhibitory_spikes_binned
fig, ax = plt.subplots(nrows=2, sharex=True, figsize=(10, 4.5))
ax[0].bar(x=bins[:-1],
height=excitatory_spikes_density,
color='red',
label='Pyramidal cells')
ax[1].bar(x=bins[:-1],
height=inhibitory_spikes_density,
color='blue',
label='Interneurons')
handles = [
mpatches.Patch(color='red', label='Pyramidal cells'),
mpatches.Patch(color='blue', label='Interneurons')
]
ax[0].legend(handles=handles, loc='upper right')
ax[1].invert_yaxis()
fig.subplots_adjust(wspace=0, hspace=0)
ax[1].set_xlabel('Time t ($ms$)')
ax[0].set_ylabel('Spikes/s')
ax[1].set_ylabel('Spikes/s')
# ax[0].set_title(
# f"Pyramidal cell (upper) and interneuron (lower) spike density plots")
fig.show()
ppc_value = mlu.run_analysis_standard_to_df(runnumber)[
region - 1]['Phaselocke'][l1 - 1, l2 - 1]
plu.save_figure(fig,
filename,
runnumbers=[runnumber],
l1=l1,
l2=l2,
region=region,
start=start,
stop=stop,
ppc_value=ppc_value)
def methods_analysis_spikes_scatterplot(runnumber,
l1,
l2,
region,
start,
stop,
filename=None):
sett = mlu.get_clean_settings(runnumber)['sett']
spikes_excitatory, spikes_inhibitory = mlu.get_spiketrains_df(
runnumber, l1, l2)
spikes_excitatory[
'neuron_idx'] = spikes_excitatory['neuron_idx'] + sett['Ni']
# Filter for region
spikes_excitatory = spikes_excitatory[spikes_excitatory['region'] ==
region]
spikes_inhibitory = spikes_inhibitory[spikes_inhibitory['region'] ==
region]
# Filter for start <= t <= end
spikes_excitatory = spikes_excitatory[spikes_excitatory['t'] >= start]
spikes_excitatory = spikes_excitatory[spikes_excitatory['t'] <= stop]
spikes_inhibitory = spikes_inhibitory[spikes_inhibitory['t'] >= start]
spikes_inhibitory = spikes_inhibitory[spikes_inhibitory['t'] <= stop]
# Plot spikes in scatterplot
fig, ax = plt.subplots(figsize=(10, 4))
sns.scatterplot(x='t',
y='neuron_idx',
color='red',
s=6,
data=spikes_excitatory,
ax=ax,
label='Pyramidal Cells')
sns.scatterplot(x='t',
y='neuron_idx',
color='blue',
s=6,
data=spikes_inhibitory,
ax=ax,
label='Interneurons')
ax.set_ylim(ymin=-20)
ax.set_ylabel('Neuron index')
ax.set_xlabel('Time t ($ms$)')
ax.legend(loc='upper left')
# ax.set_title('Pyramidal cell (red) and interneuron (blue) spiketrains')
# Create dummy Line2D objects for legend
h1 = Line2D([0], [0],
marker='o',
markersize=np.sqrt(30),
color='red',
linestyle='None')
h2 = Line2D([0], [0],
marker='o',
markersize=np.sqrt(30),
color='blue',
linestyle='None')
ax.legend([h1, h2], ['Pyramidal cells', 'Interneurons'], loc='upper right')
fig.show()
ppc_value = mlu.run_analysis_standard_to_df(runnumber)[
region - 1]['Phaselocke'][l1 - 1, l2 - 1]
print('ppc_value: ', ppc_value)
plu.save_figure(fig,
filename,
runnumbers=[runnumber],
l1=l1,
l2=l2,
region=region,
start=start,
stop=stop,
ppc_value=ppc_value)
import matlab_utils as mlu
runnumber1 = 81
runnumber2 = 103
print_settings = False
if print_settings:
print(mlu.get_clean_settings(runnumber1))
print(mlu.get_clean_settings(runnumber2))
print(mlu.compare_settings(runnumber1, runnumber2))
def figure_results_coherence_heatmap_with_feedback(runnumbers,
filename_format,
show_info_fb_inh=True,
debug=False):
"""
:param runnumbers: All runnumbers need to have the same settings except seed!
:param filename: needs {feedback_conductance_ee} and {feedback_conductance_ei}
:return:
"""
sett = mlu.get_clean_settings(runnumbers[0])['sett']
feedback_conductance_ee = sett['g_syn_ee_r'][0, 1]
feedback_conductance_ei = sett['g_syn_ei_r'][0, 1]
# Compute the mean over all runnumbers for standard2 analysis
analyses_standard2_dfs = [
mlu.run_analysis_standard2_to_df(runnumber) for runnumber in runnumbers
]
analysis_standard2_mean = pd.concat(analyses_standard2_dfs).groupby(
level=[0, 1]).mean()
coherence_mean = analysis_standard2_mean['coh']
print(
f"Runs {runnumbers} coherence >= {coherence_threshold}: {sum(coherence_mean >= coherence_threshold)}"
)
## Plotting coherence
fig, ax = plt.subplots()
sns.heatmap(coherence_mean.unstack().transpose(),
cmap='inferno',
ax=ax,
vmin=0,
vmax=1)
ax.invert_yaxis()
plu.set_heatmap_axes(ax)
# ax.set_title(f'Coherence - pyramidal neurons')
# Insert a textbox listing the feedback conductance
text = mlu.get_infobox_text(
feedback_conductance_ee=feedback_conductance_ee,
feedback_conductance_ei=feedback_conductance_ei,
debug=debug,
show_info_fb_inh=show_info_fb_inh)
plu.set_textbox(ax, text)
fig.show()
if filename_format:
# Convert the float into a string. We are always <1, so cut off everything in front of decimal
feedback_conductance_ee_str = "{:4.3f}".format(
feedback_conductance_ee).replace(".", "_")[2:]
feedback_conductance_ei_str = "{:4.3f}".format(
feedback_conductance_ei).replace(".", "_")[2:]
filename = filename_format.format(
feedback_conductance_ee=feedback_conductance_ee_str,
feedback_conductance_ei=feedback_conductance_ei_str)
plu.save_figure(fig,
filename,
runnumbers=runnumbers,
feedback_conductance_ee=feedback_conductance_ee,
feedback_conductance_ei=feedback_conductance_ei)
def figure_results_feedforward_conductance_frequency(runnumber, l1,
filename_format):
"""
Plots the change in region 2 frequency in dependence on the total synaptic conductance from region 1 to region 2
:param runnumber:
:param l1:
:param filename_format:
:return:
"""
sett = mlu.get_clean_settings(runnumber)['sett']
feedback_conductance_ei = sett['g_syn_ei_r'][0, 1]
run_settings_loops = mlu.get_run_loops(runnumber)
analysis_standard_reg1_df, analysis_standard_reg2_df = mlu.run_analysis_standard_to_df(
runnumber)
avgmorfreqs1 = analysis_standard_reg1_df['avgmorfreq'][l1]
avgmorfreqs2 = analysis_standard_reg2_df['avgmorfreq'][l1]
i_inj_exc = run_settings_loops[1]['values_excitatory'][l1].round(2)
i_inj_inh = run_settings_loops[1]['values_inhibitory'][l1].round(2)
synaptic_conductances = run_settings_loops[2]['values'].round(2)
print(f"Frequency of region 1: {avgmorfreqs1[0]}")
print(f"Starting frequency of region 2: {avgmorfreqs2[0]}")
print(f"Ending frequency of region 2: {list(avgmorfreqs2)[-1]}")
fig, ax = plt.subplots(figsize=(6.4, 4.4))
sns.scatterplot(x=synaptic_conductances,
y=avgmorfreqs1,
s=30,
ax=ax,
label="Region 1")
sns.scatterplot(x=synaptic_conductances,
y=avgmorfreqs2,
s=30,
ax=ax,
label="Region 2")
ax.set_xlabel(
r"Total synaptic conductance $g_{1 \to 2}^{e \to e, i}$ ($mS/cm^2$)")
ax.set_ylim([39, 71])
ax.set_ylabel('Frequency ($Hz$)')
# ax.set_title(f"Frequency modulation through synaptic conductance")
ax.legend(loc='upper left')
if feedback_conductance_ei != 0:
text = mlu.get_infobox_text(
feedback_conductance_ee=None,
feedback_conductance_ei=feedback_conductance_ei)
# text = f"$reg_{2}^{{exc}} \\rightarrow reg_{1}^{{inh}}={feedback_conductance_ei} mS/cm^2$"
plu.set_textbox(ax, text, loc='upper right')
fig.show()
if filename_format:
filename = filename_format.format(runnumber=runnumber, l1=l1)
plu.save_figure(fig,
filename,
runnumbers=[runnumber],
l1=l1,
i_inj_exc=i_inj_exc,
i_inj_inh=i_inj_inh,
reg1_freq=avgmorfreqs1[0],
reg2_freq_start=avgmorfreqs2[0],
reg2_freq_end=list(avgmorfreqs2)[-1])
def figure_appendix_analysis_standard_heatmap_with_feedback(
runnumbers,
region,
analysis_standard_name,
filename_format,
debug=False):
"""
:param runnumbers: All runnumbers need to have the same settings except seed!
:param filename: needs {analysis_standard_name} {feedback_conductance_ee} and {feedback_conductance_ei} and {region}
:return:
"""
sett = mlu.get_clean_settings(runnumbers[0])['sett']
feedback_conductance_ee = sett['g_syn_ee_r'][0, 1]
feedback_conductance_ei = sett['g_syn_ei_r'][0, 1]
# Compute the means for standard analyses
analyses_standard = {}
group_analyses_standard_reg = [
mlu.run_analysis_standard_to_df(runnumber)[region]
for runnumber in runnumbers
]
analyses_standard[region] = pd.concat(group_analyses_standard_reg).groupby(
level=[0, 1]).mean()
# # Compute the mean over all runnumbers for standard2 analysis
# analyses_standard2_dfs = [mlu.run_analysis_standard2_to_df(runnumber) for runnumber in runnumbers]
# analysis_standard2_mean = pd.concat(analyses_standard2_dfs).groupby(level=[0, 1]).mean()
if analysis_standard_name in ['Phaselocke', 'Phaselocki']:
vmin = 0
vmax = 1
elif analysis_standard_name in [
'freqe', 'freqi', 'avgmorfreq', 'avgmorfreqi'
]:
vmin = 35
vmax = 80
elif analysis_standard_name in ['re', 'ri']:
vmin = 10
vmax = 100
else:
vmin = None
vmax = None
## Plotting coherence
fig, ax = plt.subplots()
sns.heatmap(analyses_standard[region]
[analysis_standard_name].unstack().transpose(),
ax=ax,
vmin=vmin,
vmax=vmax,
cmap='inferno')
ax.invert_yaxis()
plu.set_heatmap_axes(ax)
analysis_title = mlu.analysis_standard_names[
analysis_standard_name] if not mlu.analysis_standard_names[
analysis_standard_name] == "" else analysis_standard_name
ax.set_title(f'{analysis_title} of region {region+1}')
text = mlu.get_infobox_text(
feedback_conductance_ei=feedback_conductance_ei,
feedback_conductance_ee=feedback_conductance_ee,
runnumbers=runnumbers,
debug=debug,
show_info_fb_inh=True)
plu.set_textbox(ax, text)
fig.show()
if filename_format:
# Convert the float into a string. We are always <1, so cut off everything in front of decimal
feedback_conductance_ee_str = "{:4.3f}".format(
feedback_conductance_ee).replace(".", "_")[2:]
feedback_conductance_ei_str = "{:4.3f}".format(
feedback_conductance_ei).replace(".", "_")[2:]
filename = filename_format.format(
feedback_conductance_ee=feedback_conductance_ee_str,
feedback_conductance_ei=feedback_conductance_ei_str,
region=region,
analysis_standard_name=analysis_standard_name.lower())
plu.save_figure(fig,
filename,
runnumbers=runnumbers,
feedback_conductance_ee=feedback_conductance_ee,
feedback_conductance_ei=feedback_conductance_ei)
def figure_results_correlation_heatmap(runnumbers,
analysis_info2_name,
coherence_threshold,
filename_format,
debug=False,
show_info_fb_inh=True):
analysis_info2_title = mlu.derive_analysis_info2_title(analysis_info2_name)
sett = mlu.get_clean_settings(runnumbers[0])['sett']
feedback_conductance_ee = sett['g_syn_ee_r'][0, 1]
feedback_conductance_ei = sett['g_syn_ei_r'][0, 1]
noise_title = mlu.get_noise_title(sett['noiseamp'], sett['noisetype'])
analysis_standard2_df = mlu.run_analysis_standard2_to_df(runnumbers[0])
coherences_all = [
mlu.run_analysis_standard2_to_df(runnumber) for runnumber in runnumbers
]
coherences = pd.concat(coherences_all).groupby(level=[0, 1]).mean()['coh']
# print(analysis_standard2_df[analysis_standard2_df['coh'] >= coherence_threshold])
correlations_all = [
mlu.get_max_correlations(runnumber) for runnumber in runnumbers
]
correlations = pd.concat(correlations_all).groupby(level=[0, 1]).mean()
correlations = correlations[analysis_info2_name]
correlations[coherences < coherence_threshold] = np.nan
print(
f"{analysis_info2_name}, feedback {feedback_conductance_ei} mS/cm^2. Correlation limits (min, max): ({correlations.min()},{correlations.max()})"
)
fig, ax = plt.subplots()
sns.heatmap(correlations.unstack().transpose(),
cmap='coolwarm',
vmin=-0.3,
vmax=1,
center=0,
ax=ax)
ax.invert_yaxis()
plu.set_heatmap_axes(ax)
# Insert a textbox listing the feedback conductance
text = mlu.get_infobox_text(
feedback_conductance_ee=feedback_conductance_ee,
feedback_conductance_ei=feedback_conductance_ei,
runnumbers=runnumbers,
debug=debug,
show_info_fb_inh=True)
plu.set_textbox(ax, text, loc='lower right')
# ax.set_title(f"{analysis_info2_title}")
ax.legend(loc='lower right')
fig.show()
if filename_format:
# Convert the float into a string. We are always <1, so cut off everything in front of decimal
feedback_conductance_ee_str = "{:4.3f}".format(
feedback_conductance_ee).replace(".", "_")[2:]
feedback_conductance_ei_str = "{:4.3f}".format(
feedback_conductance_ei).replace(".", "_")[2:]
coherence_threshold_str = "{:3.2f}".format(
coherence_threshold).replace('.', "")
filename = filename_format.format(
analysis_info2_name=analysis_info2_name,
feedback_conductance_ee=feedback_conductance_ee_str,
feedback_conductance_ei=feedback_conductance_ei_str,
coherence_threshold=coherence_threshold_str)
plu.save_figure(fig,
filename,
runnumbers=runnumbers,
feedback_conductance_ee=feedback_conductance_ee,
feedback_conductance_ei=feedback_conductance_ei,
analysis_info2_name=analysis_info2_name,
coherence_threshold=coherence_threshold)