print '\tinhibitory', inhibit
print '\texcitatory', excite
# test whether variables are statistically different between inhibitory and excitatory neurons
_, std_err_p = manu(inhibit_df['std_err'].values, excite_df['std_err'].values)
_, length_p = manu(inhibit_df['length'].values, excite_df['length'].values)
_, recip_n_p = manu(inhibit_df['n'].values, excite_df['n'].values)
print '\np-values for significance between individual inhibitory and excitatory variables'
print '\tstd_err_p', std_err_p
print '\tlength_p', length_p
print '\tn_p', recip_n_p
# make plots
distribution_plot(cre_dict,
2,
6,
xlabel='spike cut length standard error',
ylabel='expVar GLIF3')
distribution_plot(cre_dict,
3,
6,
xlabel='spike cut length',
ylabel='expVar GLIF3')
distribution_plot(cre_dict,
4,
6,
xlabel='ave number of noise 1 spikes',
ylabel='expVar GLIF3')
distribution_plot(cre_dict,
5,
6,
specimen_ID = os.path.basename(folder)[:9]
pp_file = get_pp_path(folder)
pp_dict = ju.read(pp_file)
cre = os.path.basename(folder)[10:]
all_neurons.append([
specimen_ID, cre,
pp_dict['spike_cut_length']['no deltaV shift']['slope'],
pp_dict['spike_cut_length']['no deltaV shift']['intercept'] * 1000.,
pp_dict['spike_cut_length']['no deltaV shift']['length'] * 1000. *
pp_dict['dt_used_for_preprocessor_calculations']
])
(cre_dict) = check_and_organize_data(all_neurons)
distribution_plot(cre_dict,
3,
2,
ylabel='Slope (mV/mS)',
xlabel='Intercept (mV)')
distribution_plot(cre_dict,
4,
2,
ylabel='Slope (mV/mS)',
xlabel='Spike cut length (ms)')
specimen_id = 474637203 #htr3
make_plots(specimen_id)
specimen_id = 512322162 #ctgf
make_plots(specimen_id)
1.e12, #total charge
pp_dict['resistance']['R_from_lims']['value'], #7
pp_dict['capacitance']['C_from_lims']['value'], #8
pp_dict['capacitance']['C_test_list']['mean'] *
pp_dict['resistance']['R_test_list']['mean'] * 1.e3
])
(cre_dict) = check_and_organize_data(all_neurons)
#---------------------------------------------------
#---plotting simple cap versus resistance no asc----
#---------------------------------------------------
percentile_dict = distribution_plot(cre_dict,
2,
4,
xlabel='R (MOhms)',
ylabel='C (pF)')
plt.annotate('Resistance Fit Without ASC',
xy=(.5, .93),
xycoords='figure fraction',
horizontalalignment='center',
verticalalignment='bottom',
fontsize=22)
#------------------------------------------------------
#---plotting simple cap versus tau with no ASC---------
#------------------------------------------------------
percentile_dict = distribution_plot(cre_dict,
9,
4,
pp_file = get_pp_path(folder)
pp_dict = ju.read(pp_file)
if pp_dict['threshold_adaptation'][
'a_voltage_comp_of_thr_from_fitab'] is not None and pp_dict[
'threshold_adaptation'][
'b_voltage_comp_of_thr_from_fitab'] is not None:
all_neurons.append([
specimen_ID, cre,
pp_dict['threshold_adaptation']['a_voltage_comp_of_thr_from_fitab']
/ pp_dict['threshold_adaptation']
['b_voltage_comp_of_thr_from_fitab'],
np.log10(pp_dict['threshold_adaptation']
['b_voltage_comp_of_thr_from_fitab'])
])
else:
raise Exception('this should not have made it though the exclusions')
cre_dict = check_and_organize_data(all_neurons)
percentile_dict = distribution_plot(cre_dict,
2,
3,
xlabel=r'$a_v/b_v$',
ylabel=r'log$_{10}(b_v)$')
#plt.annotate('Voltage Component of Threshold', xy=(.5, .93),
# xycoords='figure fraction',
# horizontalalignment='center', verticalalignment='bottom',
# fontsize=22)
plt.show()
LIFASC_dict['th_opt']['absolute'] * 1e3, #9
LIFASC_dict['th_opt']['from_zero'] * 1e3, #10
LIFRASC_dict['th_opt']['absolute'] *
1e3, #11
LIFRASC_dict['th_opt']['from_zero'] * 1e3, #12
LIFRASCAT_dict['th_opt']['absolute'] *
1e3, #13
LIFRASCAT_dict['th_opt']['from_zero'] * 1e3
]) #14
(cre_dict) = check_and_organize_data(
all_neurons) #organizes data into format used for distribution plotting
percentile_dict = distribution_plot(cre_dict,
3,
2,
xlabel='Resting potential (mV)',
ylabel=r'Measured $\theta_{\infty}$ (mV)')
percentile_dict = distribution_plot(
cre_dict,
3,
4,
xlabel='Resting potential (mV)',
ylabel=r'Measured $\Delta V$ $\theta_{\infty}$ (mV)')
plt.figure()
for cre in cre_dict:
for neuron in cre_dict[cre]:
plt.plot(neuron[2], neuron[5], '.', ms=12, color=color_dict[cre])
plt.xlabel(r'Measured $\theta_{\infty}$ (mV)')
plt.ylabel(r'GLIF$_1$ $\theta_{\infty}$ (mV)')
# plt.title('absolute threshold')
folders = [os.path.join(data_path, f) for f in os.listdir(data_path)]
all_neurons = []
for folder in folders:
specimen_ID = os.path.basename(folder)[:9]
cre = os.path.basename(folder)[10:]
try:
file = get_file_path_endswith(folder, '_GLIF2_neuron_config.json')
except:
continue
neuron_dict = ju.read(file)
all_neurons.append([
specimen_ID, cre,
neuron_dict['threshold_reset_method']['params']['a_spike'] * 1.e3,
1. / neuron_dict['threshold_reset_method']['params']['b_spike'] * 1.e3
])
(cre_dict) = check_and_organize_data(all_neurons)
percentile_dict = distribution_plot(cre_dict,
2,
3,
xlabel=r'$\delta \Theta_s (mV)$',
ylabel=r'$1/b_s (ms)$')
##--------------plot examples-----------------------------
make_plots(474637203) #htr3
make_plots(512322162) #ctgf