From 0d81e0068c42a08d3adf745ae534e277a46c6487 Mon Sep 17 00:00:00 2001 From: Chaitanya CHINTALURI Date: Tue, 7 Feb 2023 17:06:47 +0100 Subject: [PATCH] bioarxiv version with correct figure numbers --- README.md | 37 +++++++++++--- fig3_nw.py | 46 +++++++++++++----- fig4.py | 4 +- figure_odgs/Figure1.odg | Bin 258086 -> 269369 bytes figure_odgs/Figure1_supp.odg | Bin 340223 -> 434967 bytes figure_odgs/Figure2.odg | Bin 344108 -> 369169 bytes figure_odgs/Figure3.odg | Bin 738096 -> 851656 bytes figure_odgs/Figure3_supp.odg | Bin 200175 -> 196101 bytes figure_odgs/Figure4.odg | Bin 369091 -> 401835 bytes figure_odgs/Figure4_supp.odg | Bin 29027 -> 30124 bytes figure_odgs/Figure5_supp.odg | Bin 18990 -> 282937 bytes figure_odgs/Figure6_supp.odg | Bin 25859 -> 25900 bytes .../20/20_0.3_5_25_300_200_50_summary.pkl | Bin 0 -> 7046253 bytes netsim_results/20/20_0_300_200_50.npz | Bin 0 -> 13198 bytes netsim_results/20/20_25_300_200_50.npz | Bin 0 -> 13198 bytes ...20_0.3_5_25_300_200_50_poi_onoff_currs.pkl | Bin ..._20_0.3_5_25_300_200_50_poi_onoff_spks.pkl | Bin 10262734 -> 14335677 bytes 17 files changed, 66 insertions(+), 21 deletions(-) create mode 100644 netsim_results/20/20_0.3_5_25_300_200_50_summary.pkl create mode 100644 netsim_results/20/20_0_300_200_50.npz create mode 100644 netsim_results/20/20_25_300_200_50.npz rename netsim_results/{ => 20}/nw_20_0.3_5_25_300_200_50_poi_onoff_currs.pkl (100%) rename netsim_results/{ => 20}/nw_20_0.3_5_25_300_200_50_poi_onoff_spks.pkl (71%) diff --git a/README.md b/README.md index e26e90a..a1df5f6 100644 --- a/README.md +++ b/README.md @@ -10,9 +10,29 @@ After this, please activate this specific python environment by user@computer:~/metabolic_spiking$ source activate py36 (py36)user@computer:~/metabolic_spiking$ -## Generate figures from the paper - -### Figure 1 & supplementary +## Generate figures from the paper - +bioarxiv preprint (https://www.biorxiv.org/content/10.1101/2022.10.16.512428v1) +DOI: https://doi.org/10.1101/2022.10.16.512428 + +|Figure in MS|.odg file / |.py file| +|------------|-----------|--------| +| Figure 1 | /figure_odgs/Figure1.odg| fig1.py | +| Figure 2 | /figure_odgs/Figure2.odg| fig2.py | +| Figure 3 | /figure_odgs/Figure2.odg| fig2_v6_2.py | +| Figure 4 | /figure_odgs/Figure3.odg| fig3_dfb.py | +| Figure 5 | /figure_odgs/Figure3.odg| fig3_nw.py | +| Figure 6 | /figure_odgs/Figure4.odg| fig4.py | +| Figure 7 | /figure_odgs/Figure5_supp.odg| fig_predictions.py | +| Figure S1 |/figure_odgs/Figure1_supp.odg| fig1_supp.py | +| Figure S2 |/figure_odgs/Figure1_supp.odg| fig1_supp_b.py | +| Figure S3 |/figure_odgs/Figure2_supp.odg| fig2_supp.py | +| Figure S4 |/figure_odgs/Figure3_supp.odg| fig3_nw_supp.py | +| Figure S5 |/figure_odgs/Figure4_supp.odg| -NA- | +| Figure S6 |/figure_odgs/Figure6_supp.odg| -NA- | +| Figure S7 |/figure_odgs/Figure6_supp.odg| -NA- | + + +### Figures 1, S1 and S2 from the MS Please run the steady_state.py first if you want to regenerate everthing from the beginning, you can skip this line if you want to use the pre-generated data. (py36)user@computer:~/metabolic_spiking$ python steady_state.py @@ -25,6 +45,7 @@ This will refresh / re-populate the contents of these folders (i) ./reaction_rat You will have Figure1.png, Figure1_supp.png, Figure1_supp_b.png in your folder after this. + For this figure split in two parts type (py36)user@computer:~/metabolic_spiking$ python fig1a.py @@ -32,7 +53,7 @@ For this figure split in two parts type You will have Figure1a.png and Figure1b.png in your folder after this. -### Figure 2 & supplementary +### Figure 2, 3 and S3 from the MS Please run ret_intrinsic_summary.py and fet_intrinsic_summary.py for starting from the beginning, you can skip this if you don't want to use the pre-generated data. (py36)user@computer:~/metabolic_spiking$ python ret_intrinsic_summary.py @@ -45,14 +66,14 @@ This will refresh / re-populate the contents of ./spike_compensation/ folder wit You will have Figure2.png and Figure2_supp.png in your folder after this. The second line with also add some additional files to the above folder -### Figure 3 dfb neuron +### Figure 4 dfb neuron Please run the following (py36)user@computer:~/metabolic_spiking$ python fig3_dfb.py This will output Figure3_dfb.png file -### Figure 3 network model +### Figure 5 network model You have to run the model if you want for other seeds/parameter values than the ones shown in the figure. @@ -61,7 +82,7 @@ You have to run the model if you want for other seeds/parameter values than the This will output Figure3_nw.png file -### Figure 3 network model supplementary plot +### Figure 4S network model supplementary plot (py36)user@computer:~/metabolic_spiking$ python nw_va.py vogels2005 (py36)user@computer:~/metabolic_spiking$ python nw_va.py metabolic @@ -79,7 +100,7 @@ These commands will produce as many files with the summary data as pickles (./ne When the above is run for the first time, the summary values are calculated and dumped into a .npz file, which is used for the subsequent figure generation calls. In the end, you will have Figure3_nw_supp.png file in your folder. -### Figure 4 +### Figure 6 Please run the following command diff --git a/fig3_nw.py b/fig3_nw.py index ca317d1..01013a9 100644 --- a/fig3_nw.py +++ b/fig3_nw.py @@ -89,8 +89,10 @@ def summary_spikes(total_neurons, sim_time, times, nidx, M, dict_key, norm_1 = colors.BoundaryNorm(bounds, cmap_.N, clip=True) im = ax2.scatter(times_zoom, nidx_zoom, c=M_zoom, cmap=cmap_, s=1, linewidth=0.1, edgecolor='k', norm=norm_1) + # ax2.plot([0, sim_time/2/ms], [4050, 4050], + # lw=2, c='gold', zorder=10) ax2.plot([0, sim_time/2/ms], [4050, 4050], - lw=2, c='gold', zorder=10) + lw=2, c='#e6ab02', zorder=10) ax2.plot([sim_time/2/ms, sim_time/ms], [4050, 4050], lw=2, c='k', zorder=10) cax = inset_axes(ax2, width='100%', height='100%', @@ -149,8 +151,10 @@ def summary_spikes(total_neurons, sim_time, times, nidx, M, dict_key, size_vertical=(ymax-ymin)/1000) ax0.add_artist(asb) ax0.set_ylabel('Pop. act. (Hz)') + # ax0.plot([0, sim_time/2/ms], [0.02, 0.02], + # lw=1.5, c='gold') ax0.plot([0, sim_time/2/ms], [0.02, 0.02], - lw=1.5, c='gold') + lw=1.5, c='#e6ab02') ax0.text(sim_time/4/ms, 0.03, s='Poisson input (3Hz)', color='k', va='center', ha='center') ax0.plot([sim_time/2/ms, sim_time/ms], [0.02, 0.02], lw=1.5, c='k') @@ -248,10 +252,13 @@ def summary_spikes(total_neurons, sim_time, times, nidx, M, dict_key, if opt_dicts: Nfh, B = np.histogram(np.array(avg_fr_fh), bins=np.linspace(0, 40, 41)) Nsh, B = np.histogram(np.array(avg_fr_sh), bins=np.linspace(0, 40, 41)) - ax4.plot(B[:-1], Nfh / np.sum(Nfh), c='gold', lw=.75) + # ax4.plot(B[:-1], Nfh / np.sum(Nfh), c='gold', lw=.75) + ax4.plot(B[:-1], Nfh / np.sum(Nfh), c='#e6ab02', lw=.75) ax4.plot(B[:-1], Nsh / np.sum(Nsh), c='k', lw=0.75) + # ax4.plot((np.mean(avg_fr_fh)*Hz, np.mean(avg_fr_fh)*Hz), + # (0, np.max(N)/np.sum(N)), c='gold', lw=.75, ls='--') ax4.plot((np.mean(avg_fr_fh)*Hz, np.mean(avg_fr_fh)*Hz), - (0, np.max(N)/np.sum(N)), c='gold', lw=.75, ls='--') + (0, np.max(N)/np.sum(N)), c='#e6ab02', lw=.75, ls='--') ax4.plot((np.mean(avg_fr_sh)*Hz, np.mean(avg_fr_sh)*Hz), (0, np.max(N)/np.sum(N)), c='k', lw=.75, ls='--') ax4.set_xlabel('Avg firing rate(Hz)') @@ -267,10 +274,13 @@ def summary_spikes(total_neurons, sim_time, times, nidx, M, dict_key, if opt_dicts: Nfh, B = np.histogram(all_isi_fh, bins=np.logspace(0, 3.1, 30)) Nsh, B = np.histogram(all_isi_sh, bins=np.logspace(0, 3.1, 30)) - ax5.plot(B[:-1], Nfh / np.sum(Nfh), c='gold', lw=.75) + # ax5.plot(B[:-1], Nfh / np.sum(Nfh), c='gold', lw=.75) + ax5.plot(B[:-1], Nfh / np.sum(Nfh), c='#e6ab02', lw=.75) ax5.plot(B[:-1], Nsh / np.sum(Nsh), c='k', lw=.75) + # ax5.plot((np.mean(all_isi_fh), np.mean(all_isi_fh)), + # (0, np.max(N)/np.sum(N)), c='gold', lw=.75, ls='--') ax5.plot((np.mean(all_isi_fh), np.mean(all_isi_fh)), - (0, np.max(N)/np.sum(N)), c='gold', lw=.75, ls='--') + (0, np.max(N)/np.sum(N)), c='#e6ab02', lw=.75, ls='--') ax5.plot((np.mean(all_isi_sh), np.mean(all_isi_sh)), (0, np.max(N)/np.sum(N)), c='k', lw=.75, ls='--') ax5.set_xscale('symlog') @@ -283,10 +293,14 @@ def summary_spikes(total_neurons, sim_time, times, nidx, M, dict_key, if opt_dicts: Nfh, B = np.histogram(cvs_fh, bins=np.linspace(0, 3, 30)) Nsh, B = np.histogram(cvs_sh, bins=np.linspace(0, 3, 30)) - ax6.plot(B[:-1], Nfh / np.sum(Nfh), c='gold', lw=0.75) + # ax6.plot(B[:-1], Nfh / np.sum(Nfh), c='gold', lw=0.75) + ax6.plot(B[:-1], Nfh / np.sum(Nfh), c='#e6ab02', lw=0.75) ax6.plot(B[:-1], Nsh / np.sum(Nsh), c='k', lw=0.75) + # ax6.plot((np.mean(cvs_fh), np.mean(cvs_fh)), + # (0, np.max(N)/np.sum(N)), c='gold', + # lw=0.75, ls='--') ax6.plot((np.mean(cvs_fh), np.mean(cvs_fh)), - (0, np.max(N)/np.sum(N)), c='gold', + (0, np.max(N)/np.sum(N)), c='#e6ab02', lw=0.75, ls='--') ax6.plot((np.mean(cvs_sh), np.mean(cvs_sh)), (0, np.max(N)/np.sum(N)), c='k', @@ -302,11 +316,15 @@ def summary_spikes(total_neurons, sim_time, times, nidx, M, dict_key, if opt_dicts: Mfh, B = np.histogram(opt_dicts[2], bins=np.linspace(-2, 1, 31)) Msh, B = np.histogram(opt_dicts[3], bins=np.linspace(-2, 1, 31)) - ax7.plot(B[:-1], Mfh / sum(Mfh), c='gold', lw=0.75) + # ax7.plot(B[:-1], Mfh / sum(Mfh), c='gold', lw=0.75) + ax7.plot(B[:-1], Mfh / sum(Mfh), c='#e6ab02', lw=0.75) ax7.plot(B[:-1], Msh / sum(Msh), c='k', lw=0.75) + # ax7.plot((np.mean(opt_dicts[2]), + # np.mean(opt_dicts[2])), (0, np.max(N)/np.sum(N)), + # c='gold', lw=0.75, ls='--') ax7.plot((np.mean(opt_dicts[2]), np.mean(opt_dicts[2])), (0, np.max(N)/np.sum(N)), - c='gold', lw=0.75, ls='--') + c='#e6ab02', lw=0.75, ls='--') ax7.plot((np.mean(opt_dicts[3]), np.mean(opt_dicts[3])), (0, np.max(N)/np.sum(N)), c='k', lw=0.75, ls='--') @@ -323,7 +341,9 @@ def summary_spikes(total_neurons, sim_time, times, nidx, M, dict_key, linestyle='None') fits_t_sh.power_law.plot_pdf(ax=ax8, lw=0.5, color='k') fits_t_fh = opt_dicts[0]['fit_t'] - fits_t_fh.plot_pdf(ax=ax8, color='gold', markersize=1, + # fits_t_fh.plot_pdf(ax=ax8, color='gold', markersize=1, + # marker='o') + fits_t_fh.plot_pdf(ax=ax8, color='#e6ab02', markersize=1, marker='o') ax8.text(0.6, 0.95, s=r'$\alpha_{D}=$'+str(opt_dicts[1]['talpha'])[:4], horizontalalignment='center', @@ -340,7 +360,9 @@ def summary_spikes(total_neurons, sim_time, times, nidx, M, dict_key, linestyle='None') fits_sh.power_law.plot_pdf(ax=ax9, lw=0.5, color='k') fits_fh = opt_dicts[0]['fit_s'] - fits_fh.plot_pdf(ax=ax9, color='gold', markersize=1, + # fits_fh.plot_pdf(ax=ax9, color='gold', markersize=1, + # marker='o') + fits_fh.plot_pdf(ax=ax9, color='#e6ab02', markersize=1, marker='o') ax9.text(0.6, 0.95, s=r'$\alpha_{S}=$'+str(opt_dicts[1]['palpha'])[:4], horizontalalignment='center', diff --git a/fig4.py b/fig4.py index e8ac43c..f9c87e9 100644 --- a/fig4.py +++ b/fig4.py @@ -99,8 +99,10 @@ def upper_run(axs, axs_meta, mito_baseline, spike_quanta): c=fp.def_colors['park1'], lw=0.25) ax3.plot(t, r_cell_typ2.out['v'], c=fp.def_colors['park2'], lw=0.25) + # ax4.plot(t, 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