noisesplit.py

@@ -228,7 +228,7 @@ if __name__ == '__main__':
     cell_name = ['2017-07-18-ai-invivo-1', 1]  # Take this! at 3% model, 5% data
     nsmall = 100
     nlarge = 1000000
-    contrast = 0.05
+    contrast = 0.03
 
     wdt = 0.0001
     wnoise = whitenoise(0, 300, wdt, 0.05, rng=np.random.default_rng(51234))
@@ -237,26 +237,24 @@ if __name__ == '__main__':
     s = plot_style()
     fig, axs = plt.subplots(3, 4, cmsize=(s.plot_width, 0.7*s.plot_width),
                             width_ratios=[1, 0, 1, 1, 0.15, 1])
-    fig.subplots_adjust(leftm=8, rightm=1.5, topm=4, bottomm=4,
+    fig.subplots_adjust(leftm=8, rightm=1.5, topm=3, bottomm=4,
                         wspace=0.25, hspace=0.8)
     axs[0, 2].set_visible(False)
     axs[0, 3].set_visible(False)
-    xt = -2.2
-    yt = 1.3
     
     # data:
-    axs[0, 1].text(xt, yt, 'P-unit data', fontsize='large',
+    axs[0, 1].text(-2.42, 1.2, 'P-unit data', fontsize='large',
                    transform=axs[0, 1].transAxes, color=s.punit_color1)
     data_contrast, ratebase, eodf = plot_chi2_data(axs[0, 1], s, cell_name[0],
                                                    cell_name[1])
     plot_ram(axs[0, 0], data_contrast, eodf, wtime, wnoise)
-    axs[0, 1].text(xt + 0.9, yt, f'$r={ratebase:.0f}$\\,Hz',
+    axs[0, 1].text(-1.5, 1.2, f'$r={ratebase:.0f}$\\,Hz',
                    transform=axs[0, 1].transAxes, fontsize='large')
     
     # model:
     data_files = sims_path.glob(f'chi2-noisen-{cell_name[0]}-{1000*contrast:03.0f}-*.npz')
     files, nums = sort_files(cell_name[0], data_files, 2)
-    axs[1, 1].text(xt, yt, 'P-unit model', fontsize='large',
+    axs[1, 1].text(-2.42, 1.2, 'P-unit model', fontsize='large',
                    transform=axs[1, 1].transAxes, color=s.model_color1)
     plot_chi2_contrast(axs[1, 1], axs[1, 2], s, files, nums, nsmall, nlarge)
     axr1 = plot_noise_split(axs[1, 0], contrast, 0, 1, wtime, wnoise)
@@ -267,9 +265,9 @@ if __name__ == '__main__':
     # model noise split:
     data_files = sims_path.glob(f'chi2-split-{cell_name[0]}-*.npz')
     files, nums = sort_files(cell_name[0], data_files, 1)
-    axs[2, 1].text(xt, yt, 'P-unit model', fontsize='large',
+    axs[2, 1].text(-2.42, 1.2, 'P-unit model', fontsize='large',
                    transform=axs[2, 1].transAxes, color=s.model_color1)
-    axs[2, 1].text(xt + 0.9, yt, f'(noise split)', fontsize='large',
+    axs[2, 1].text(-1.5, 1.2, f'(noise split)', fontsize='large',
                    transform=axs[2, 1].transAxes)
     noise_contrast, noise_frac = plot_chi2_split(axs[2, 1], axs[2, 2], s,
                                                  files, nums, nsmall, nlarge)
@@ -285,6 +283,6 @@ if __name__ == '__main__':
     fig.tag([axs[0, :2],
              [axr1] + axs[1, 1:].tolist(),
              [axr2] + axs[2, 1:].tolist()],
-            xoffs=[-4.5, 1, 1, -4.5], yoffs=2)
+            xoffs=-4.5, yoffs=2)
     fig.savefig()
     print()