bioarxiv version with correct figure numbers

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
@@ -25,14 +45,15 @@ 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
        (py36)user@computer:~/metabolic_spiking$ python fig1b.py
 
 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
 

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',

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, r_cell_typ3.out['v'],
+    #          c='gold', lw=0.35)
     ax4.plot(t, r_cell_typ3.out['v'],
-             c='gold', lw=0.35)
+             c='#e6ab02', lw=0.35)
 
     ax1_m.plot(t, r_cell_cntrl.out['atp'],
                c=fp.def_colors['atp'], lw=0.5, label='ATP')