everything for 1ms kernels

dataoverview.py

@@ -6,9 +6,6 @@ from scipy.stats import mannwhitneyu
 from thunderlab.tabledata import TableData
 from plotstyle import plot_style, lighter, significance_str
 
-
-data_path = Path('data')
-
 from noisesplit import model_cell as model_split_example
 from modelsusceptcontrasts import model_cells as model_contrast_examples
 from modelsusceptlown import model_cell as model_lown_example
@@ -18,6 +15,9 @@ from noisesplit import example_cell as punit_split_example
 from ampullaryexamplecell import example_cell as ampul_example
 from ampullaryexamplecell import example_cells as ampul_examples
 
+
+data_path = Path('data')
+
 model_examples = ([[model_lown_example, 0.01],
                    [model_lown_example, 0.03],
                    [model_lown_example, 0.1]],
@@ -26,6 +26,8 @@ model_examples = ([[model_lown_example, 0.01],
 punit_examples = (punit_example, [punit_split_example], punit_examples)
 ampul_examples = (ampul_example, [], ampul_examples)
 
+respmod = 'respmod1'
+
 
 def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
               si_thresh, example=[], split_example=[], examples=[]):
@@ -266,7 +268,7 @@ def si_stats(title, data, sicol, si_thresh, nsegscol):
     print(f'    total duration:     {np.min(duration):.1f}s - {np.max(duration):.1f}s, median={np.median(duration):.1f}s, mean={np.mean(duration):.1f}s, std={np.std(duration):.1f}s')
     duration = data['durationbase']
     print(f'    baseline duration:  {np.min(duration):.1f}s - {np.max(duration):.1f}s, median={np.median(duration):.1f}s, mean={np.mean(duration):.1f}s, std={np.std(duration):.1f}s')
-    cols = ['cvbase', 'respmod2', 'ratebase', 'vsbase', 'serialcorr1', 'burstfrac', 'ratestim', 'cvstim']
+    cols = ['cvbase', respmod, 'ratebase', 'vsbase', 'serialcorr1', 'burstfrac', 'ratestim', 'cvstim']
     for i in range(len(cols)):
         for j in range(i + 1, len(cols)):
             xcol = cols[i]
@@ -285,7 +287,7 @@ def plot_cvbase_si_punit(ax, data, ycol, si_thresh, color):
     ax.set_ylim(0, 6.5)
     ax.set_yticks_delta(2)
     examples = punit_examples if 'stimindex' in data else model_examples
-    #cax = plot_corr(ax, data, 'cvbase', ycol, 'respmod2', 0, 250, 3,
+    #cax = plot_corr(ax, data, 'cvbase', ycol, respmod, 0, 250, 3,
     #                'coolwarm', color, si_thresh, *examples)
     #cax.set_ylabel('Response mod.', 'Hz')
     plot_corr_contrast(ax, data, 'cvbase', ycol, 3, color,
@@ -300,7 +302,7 @@ def plot_cvstim_si_punit(ax, data, ycol, si_thresh, color):
     ax.set_ylim(0, 6.5)
     ax.set_yticks_delta(2)
     examples = punit_examples if 'stimindex' in data else model_examples
-    #cax = plot_corr(ax, data, 'cvstim', ycol, 'respmod2', 0, 250, 2,
+    #cax = plot_corr(ax, data, 'cvstim', ycol, respmod, 0, 250, 2,
     #                'coolwarm', color, si_thresh, *examples)
     #cax.set_ylabel('Response mod.', 'Hz')
     #cax = plot_corr(ax, data, 'cvstim', ycol, 'cvbase', 0, 1.5, 2,
@@ -318,16 +320,16 @@ def plot_cvstim_si_punit(ax, data, ycol, si_thresh, color):
     
 def plot_rmod_si_punit(ax, data, ycol, si_thresh, color):
     ax.set_xlabel('Response modulation', 'Hz')
-    ax.set_xlim(0, 250)
+    ax.set_xlim(0, 350)
     ax.set_xticks_delta(100)
     ax.set_ylabel('SI($r$)')
     ax.set_ylim(0, 6.5)
     ax.set_yticks_delta(2)
     examples = punit_examples if 'stimindex' in data else model_examples
-    #cax = plot_corr(ax, data, 'respmod2', ycol, 'cvbase', 0, 1.5, 0.016,
+    #cax = plot_corr(ax, data, respmod, ycol, 'cvbase', 0, 1.5, 0.016,
     #                'coolwarm', color, si_thresh, *examples)
     #cax.set_ylabel('CV$_{\\rm base}$')
-    plot_corr_contrast(ax, data, 'respmod2', ycol, 0.016, color,
+    plot_corr_contrast(ax, data, respmod, ycol, 0.016, color,
                        si_thresh, *examples)
 
     
@@ -355,7 +357,7 @@ def plot_cvbase_si_ampul(ax, data, ycol, si_thresh, color):
     ax.set_ylabel('SI($r$)')
     ax.set_ylim(0, 10)
     ax.set_yticks_delta(2)
-    #cax = plot_corr(ax, data, 'cvbase', ycol, 'respmod2', 0, 80, 20,
+    #cax = plot_corr(ax, data, 'cvbase', ycol, respmod, 0, 80, 20,
     #                'coolwarm', color, si_thresh, *ampul_examples)
     #cax.set_ylabel('Response mod.', 'Hz')
     plot_corr_contrast(ax, data, 'cvbase', ycol, 20, color,
@@ -369,7 +371,7 @@ def plot_cvstim_si_ampul(ax, data, ycol, si_thresh, color):
     ax.set_ylabel('SI($r$)')
     ax.set_ylim(0, 10)
     ax.set_yticks_delta(2)
-    #cax = plot_corr(ax, data, 'cvstim', ycol, 'respmod2', 0, 80, 6,
+    #cax = plot_corr(ax, data, 'cvstim', ycol, respmod, 0, 80, 6,
     #                'coolwarm', color, si_thresh, *ampul_examples)
     #cax.set_ylabel('Response mod.', 'Hz')
     #cax = plot_corr(ax, data, 'cvstim', ycol, 'cvbase', 0, 0.2, 6,
@@ -386,16 +388,16 @@ def plot_cvstim_si_ampul(ax, data, ycol, si_thresh, color):
     
 def plot_rmod_si_ampul(ax, data, ycol, si_thresh, color):
     ax.set_xlabel('Response modulation', 'Hz')
-    ax.set_xlim(0, 80)
-    ax.set_xticks_delta(20)
+    ax.set_xlim(0, 100)
+    ax.set_xticks_delta(30)
     ax.set_ylabel('SI($r$)')
     ax.set_ylim(0, 10)
     ax.set_yticks_delta(2)
-    #cax = plot_corr(ax, data, 'respmod2', ycol, 'cvbase', 0, 0.2, 0.06,
+    #cax = plot_corr(ax, data, respmod, ycol, 'cvbase', 0, 0.2, 0.06,
     #                'coolwarm', color, si_thresh, *ampul_examples)
     #cax.set_ylabel('CV$_{\\rm base}$')
     #cax.set_yticks_delta(0.1)
-    plot_corr_contrast(ax, data, 'respmod2', ycol, 0.06, color,
+    plot_corr_contrast(ax, data, respmod, ycol, 0.06, color,
                        si_thresh, *ampul_examples)
 
         
@@ -412,7 +414,7 @@ def plot_rate_si_ampul(ax, data, ycol, si_thresh, color):
     #cax.set_yticks_delta(0.1)
     plot_corr_contrast(ax, data, 'ratebase', ycol, 0.06, color,
                        si_thresh, *ampul_examples)
-    ax.legend(title='contrast', loc='upper right', bbox_to_anchor=(1.95, 1),
+    ax.legend(title='contrast', loc='upper right', bbox_to_anchor=(1.9, 1),
               markerfirst=False, handletextpad=0.5)
 
     
@@ -453,8 +455,8 @@ if __name__ == '__main__':
     print(f'    baseline rate model: min={np.min(ratemodel):3.0f}Hz max={np.max(ratemodel):3.0f}Hz median={np.median(ratemodel):3.0f}Hz')
     print(f'    baseline rate data:  min={np.min(ratedata):3.0f}Hz max={np.max(ratedata):3.0f}Hz median={np.median(ratedata):3.0f}Hz')
     print()
-    rmmodel = punit_model['respmod2']
-    rmdata = punit_data['respmod2']
+    rmmodel = punit_model[respmod]
+    rmdata = punit_data[respmod]
     u, p = mannwhitneyu(rmmodel, rmdata)
     print('Response modulation differs between P-unit models and data:')
     print(f'    U={u:g}, p={p:.2g}')
@@ -489,7 +491,7 @@ if __name__ == '__main__':
     s = plot_style()
     fig, axs = plt.subplots(3, 3, cmsize=(s.plot_width, 0.75*s.plot_width),
                             height_ratios=[1, 0, 1, 0.3, 1])
-    fig.subplots_adjust(leftm=6.5, rightm=18, topm=4.5, bottomm=4,
+    fig.subplots_adjust(leftm=6.5, rightm=17.5, topm=4.5, bottomm=4,
                         wspace=0.6, hspace=0.6)
     
     si_stats('P-unit model:', punit_model, 'dsinorm100', si_thresh,