better handle missing results (i.e. not enough units)

src/dynamic_routing_analysis/decoding_utils.py

@@ -1064,7 +1064,7 @@ def decode_context_with_linear_shift(session=None,params=None,trials=None,units=
 
         #loop through repeats
         for nunits in n_units_input:
-            if nunits>len(area_units):
+            if nunits!='all' and nunits>len(area_units):
                 continue
             decoder_results[session_id]['results'][aa]['shift'][nunits]={}
             for rr in range(n_repeats):
@@ -1243,6 +1243,8 @@ def concat_decoder_results(files,savepath=None,return_table=True,single_session=
                     all_bal_acc[session_id][aa]={}
                     ### ADD LOOP FOR NUNITS ###
                     for nu in nunits:
+                        if nu not in decoder_results[session_id]['results'][aa]['shift'].keys():
+                            continue
                         all_bal_acc[session_id][aa][nu]=[]
                         for rr in range(n_repeats):
                             if rr in decoder_results[session_id]['results'][aa]['shift'][nu].keys():
@@ -1271,19 +1273,28 @@ def concat_decoder_results(files,savepath=None,return_table=True,single_session=
                     ### LOOP THROUGH NUNITS TO APPEND TO DICT ###
 
                     for nu in nunits:
-                        true_acc_ind=np.where(half_shifts==1)[0][0]
-                        null_acc_ind=np.where(half_shifts!=1)[0]
-                        true_accuracy=all_bal_acc[session_id][aa][nu][true_acc_ind]
-                        null_accuracy_mean=np.mean(all_bal_acc[session_id][aa][nu][null_acc_ind])
-                        null_accuracy_median=np.median(all_bal_acc[session_id][aa][nu][null_acc_ind])
-                        null_accuracy_std=np.std(all_bal_acc[session_id][aa][nu][null_acc_ind])
-                        p_value=np.mean(all_bal_acc[session_id][aa][nu][null_acc_ind]>=true_accuracy)
-
-                        linear_shift_dict['true_accuracy_'+str(nu)].append(true_accuracy)
-                        linear_shift_dict['null_accuracy_mean_'+str(nu)].append(null_accuracy_mean)
-                        linear_shift_dict['null_accuracy_median_'+str(nu)].append(null_accuracy_median)
-                        linear_shift_dict['null_accuracy_std_'+str(nu)].append(null_accuracy_std)
-                        linear_shift_dict['p_value_'+str(nu)].append(p_value)
+                        if nu in all_bal_acc[session_id][aa].keys():
+
+                            true_acc_ind=np.where(half_shifts==1)[0][0]
+                            null_acc_ind=np.where(half_shifts!=1)[0]
+                            true_accuracy=all_bal_acc[session_id][aa][nu][true_acc_ind]
+                            null_accuracy_mean=np.mean(all_bal_acc[session_id][aa][nu][null_acc_ind])
+                            null_accuracy_median=np.median(all_bal_acc[session_id][aa][nu][null_acc_ind])
+                            null_accuracy_std=np.std(all_bal_acc[session_id][aa][nu][null_acc_ind])
+                            p_value=np.mean(all_bal_acc[session_id][aa][nu][null_acc_ind]>=true_accuracy)
+
+                            linear_shift_dict['true_accuracy_'+str(nu)].append(true_accuracy)
+                            linear_shift_dict['null_accuracy_mean_'+str(nu)].append(null_accuracy_mean)
+                            linear_shift_dict['null_accuracy_median_'+str(nu)].append(null_accuracy_median)
+                            linear_shift_dict['null_accuracy_std_'+str(nu)].append(null_accuracy_std)
+                            linear_shift_dict['p_value_'+str(nu)].append(p_value)
+
+                        else:
+                            linear_shift_dict['true_accuracy_'+str(nu)].append(np.nan)
+                            linear_shift_dict['null_accuracy_mean_'+str(nu)].append(np.nan)
+                            linear_shift_dict['null_accuracy_median_'+str(nu)].append(np.nan)
+                            linear_shift_dict['null_accuracy_std_'+str(nu)].append(np.nan)
+                            linear_shift_dict['p_value_'+str(nu)].append(np.nan)
 
                     #make big dict/dataframe for this:
                     #save true decoding, mean/median null decoding, and p value for each area/probe
@@ -1594,7 +1605,7 @@ def concat_trialwise_decoder_results(files,savepath=None,return_table=False,n_un
                         npc_lims.get_cache_path('performance',session_id,version='any')
                         )
             except:
-                print('skipping session:',session_id)
+                print('trials or performance not available; skipping session:',session_id)
                 continue
 
             trials_since_rewarded_target=[]
@@ -1681,6 +1692,8 @@ def concat_trialwise_decoder_results(files,savepath=None,return_table=False,n_un
                     temp_shifts=[]
                     for rr in range(n_repeats):
                         if n_units is not None:
+                            if n_units not in decoder_results[session_id]['results'][aa]['shift'].keys():
+                                continue
                             if n_units=='all' and rr>0:
                                 continue
                             if sh in list(decoder_results[session_id]['results'][aa]['shift'][n_units][rr].keys()):