remove non significant colors from plots, add filtering of fits for cv

Figures_results.py

@@ -29,12 +29,13 @@ def main():
     #
     behaviour_correlations_plot(fits_info)
     #
-    # labels, corr_values, corrected_p_values = parameter_correlations(fits_info)
+    labels, corr_values, corrected_p_values = parameter_correlations(fits_info)
-    # par_labels = [parameter_titles[l] for l in labels]
+    par_labels = [parameter_titles[l] for l in labels]
-    # fig, ax = plt.subplots(1, 1)
+    fig, ax = plt.subplots(1, 1)
-    # create_correlation_plot(ax, par_labels, corr_values, corrected_p_values, "", colorbar=True)
+    #ax, labels, correlations, p_values, title, y_label=True
-    # plt.savefig(consts.SAVE_FOLDER + "parameter_correlations.png")
+    create_correlation_plot(ax, par_labels, corr_values, corrected_p_values, "")
-    # plt.close()
+    plt.savefig(consts.SAVE_FOLDER + "parameter_correlations.png")
+    plt.close()
 
     # create_parameter_distributions(get_parameter_values(fits_info))
 
@@ -102,8 +103,9 @@ def create_correlation_plot(ax, labels, correlations, p_values, title, y_label=T
         for j in range(correlations.shape[1]):
             if abs(p_values[i, j]) < 0.05:
                 cleaned_cors[i, j] = correlations[i, j]
-
+            else:
-    im = ax.imshow(correlations, vmin=-1, vmax=1)
+                cleaned_cors[i, j] = np.NAN
+    im = ax.imshow(cleaned_cors, vmin=-1, vmax=1)
 
     # We want to show all ticks...
     ax.set_xticks(np.arange(len(labels)))

Fitter.py

@@ -552,6 +552,7 @@ def calculate_list_error(fit, reference):
 
     return norm_error
 
+
 def calculate_histogram_bins(isis):
     isis = np.array(isis) * 1000
     step = 0.1

analysis.py

@@ -63,8 +63,13 @@ def get_filtered_fit_info(folder):
         if model_behaviour["f_zero_slope"] > 50000 or cell_behaviour["f_zero_slope"] > 50000:
             print("f_zero_slope used to filter a fit")
             continue
-        if 1 - abs(model_behaviour["f_inf_slope"] - cell_behaviour["f_inf_slope"]) > 0.1:
+        # if (abs(model_behaviour["f_inf_slope"] - cell_behaviour["f_inf_slope"]) / cell_behaviour["f_inf_slope"]) > 0.25:
-            print("f_inf_slope used to filter a fit")
+        #     print("f_inf_slope used to filter a fit")
+        #     print((abs(model_behaviour["f_inf_slope"] - cell_behaviour["f_inf_slope"]) / cell_behaviour["f_inf_slope"]))
+        #     continue
+        if abs((model_behaviour["coefficient_of_variation"] - cell_behaviour["coefficient_of_variation"]) /
+                   cell_behaviour["coefficient_of_variation"]) > 0.25:
+            print("CV used to filter a fit")
             continue
 
         fits_info[item] = [results.get_final_parameters(), model_behaviour, cell_behaviour]