diff --git a/brainwidemap/meta/bwm_figs.py b/brainwidemap/meta/bwm_figs.py
index b3a8cbe..c1d227f 100644
--- a/brainwidemap/meta/bwm_figs.py
+++ b/brainwidemap/meta/bwm_figs.py
@@ -30,11 +30,13 @@
 from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
 import matplotlib.ticker as tck
 import matplotlib.colors as mcolors
+import matplotlib.patches as mpatches
 
 from brainwidemap import download_aggregate_tables, bwm_units
 from brainwidemap.encoding.design import generate_design
 from brainwidemap.encoding.glm_predict import GLMPredictor, predict
-from brainwidemap.encoding.utils import load_regressors, single_cluster_raster, find_trial_ids
+from brainwidemap.encoding.utils import (load_regressors, 
+    single_cluster_raster, find_trial_ids)
 from brainbox.plot import peri_event_time_histogram
 from reproducible_ephys_functions import labs
 
@@ -1759,6 +1761,9 @@ def perf_scatter(rerun=False):
 #####
 '''
 
+dec_d = {'stim': 'stimside', 'choice': 'choice',
+            'fback': 'feedback'}
+
 def group_into_regions():
 
     ''' 
@@ -1811,9 +1816,6 @@ def significance_by_region(group):
             result['sig_combined'] = result['pval_combined'] < ALPHA_LEVEL
         return result
 
-    dec_d = {'stim': 'stimside', 'choice': 'choice',
-             'fback': 'feedback'} 
-
     # indicate in file name constraint
     exx = '' if MIN_TRIALS == 0 else ('_' + str(MIN_TRIALS))
     
@@ -2218,8 +2220,7 @@ def swansons_SI_dec(vari):
 
     fig, ax = plt.subplots(figsize=(5.67, 3.18))  
 
-    dec_d = {'stim': 'stimside', 'choice': 'choice',
-             'fback': 'feedback'}
+
              
     pqt_file = os.path.join(dec_pth,f"{dec_d[vari]}_stage2.pqt")
     df1 = pd.read_parquet(pqt_file)
@@ -2283,6 +2284,103 @@ def swansons_SI_dec(vari):
 
 
 
+def plot_female_male_repro():
+
+    '''
+    For the 5 repro regions, and all variables (stimulus, choice, feedback),
+    plot results split by female and male mice with two strips of dots 
+    (blue for male, red for female) for each region.
+    '''
+    repro_regs = ["VISa/am", "CA1", "DG", "LP", "PO"]
+
+    
+    # subject = one.get_details(eid)['subject']
+    # info = one.alyx.rest('subjects', 'read', id=subject)
+    # sex = info['sex']  # 'M', 'F', or 'U'
+
+    # Load the male/female subject dictionary
+    ds = np.load(os.path.join(dec_pth, 'male_female.npy'), allow_pickle=True).flat[0]
+
+    # Set up the plot structure (3 rows for each variable, 1 column)
+    fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(8.26, 4.37), 
+                             sharey=True, sharex=True)
+
+    for row_idx, vari in enumerate(variables):
+        pqt_file = os.path.join(dec_pth, f"{dec_d[vari]}_stage2.pqt")
+        df = pd.read_parquet(pqt_file)
+
+        # Combine 'VISa' and 'VISam' into 'VISa/am'
+        df['region'] = df['region'].replace({'VISa': 'VISa/am', 'VISam': 'VISa/am'})
+
+        # Restrict the DataFrame to the 5 repro regions
+        df = df[df['region'].isin(repro_regs)]
+
+        # Map the sex information to the DataFrame
+        df['sex'] = df['subject'].map(ds)
+
+        # Separate by sex
+        female_df = df[df['sex'] == 'F']
+        male_df = df[df['sex'] == 'M']
+
+        # Combine male and female data into a new DataFrame for better comparison in the plot
+        df_combined = pd.concat([female_df.assign(gender='Female'), male_df.assign(gender='Male')])
+
+        # Plot in the corresponding row
+        ax = axes[row_idx]
+        
+        # Strip plot for both male (blue) and female (red) side by side for each region
+        sns.stripplot(data=df_combined, x='region', y='score', hue='gender', 
+                      palette={'Male': 'blue', 'Female': 'red'}, 
+                      dodge=True, jitter=True, size=3, ax=ax)
+
+        # Add mean null score (white dot) for each gender and region
+        # Ensure the x-alignment is consistent (no dodge for null scores)
+        for region in repro_regs:
+            # Get the x position for this region (for both male and female)
+            x_female = repro_regs.index(region) - 0.2  # Slight left shift for female (consistent with dodge)
+            x_male = repro_regs.index(region) + 0.2    # Slight right shift for male (consistent with dodge)
+
+            # Plot null score for females (red)
+            female_null = female_df[female_df['region'] == region]['median-null'].mean()
+            ax.scatter(x_female, female_null, color='white', 
+                edgecolor='red', zorder=3, s=30)
+
+            # Plot null score for males (blue)
+            male_null = male_df[male_df['region'] == region]['median-null'].mean()
+            ax.scatter(x_male, male_null, color='white', 
+                edgecolor='blue', zorder=3, s=30)
+
+        ax.spines['top'].set_visible(False)
+        ax.spines['right'].set_visible(False)
+
+        # Set labels and titles for each row
+        ax.set_xlabel("Brain Regions")
+        ax.set_ylabel(f"{variverb[vari]} \n decoding accuracy")
+        
+        # Remove legend from individual plots to keep the layout clean
+        ax.get_legend().remove()
+        for i in range(1, len(repro_regs)):
+            ax.axvline(x=i-0.5, color='grey', linestyle='--', linewidth=1)
+
+    legend_handles = [
+        plt.Line2D([0], [0], marker='o', color='white', label='Male',
+                   markerfacecolor='blue', markeredgecolor='blue', markersize=5),
+        plt.Line2D([0], [0], marker='o', color='white', label='Female',
+                   markerfacecolor='red', markeredgecolor='red', markersize=5),
+        plt.Line2D([0], [0], marker='o', color='white', label='Null (Male)',
+                   markerfacecolor='white', markeredgecolor='blue', markersize=5),
+        plt.Line2D([0], [0], marker='o', color='white', label='Null (Female)',
+                   markerfacecolor='white', markeredgecolor='red', markersize=5)
+    ]
+
+    fig.legend(handles=legend_handles, loc='upper center', 
+            frameon=False, ncols=2).set_draggable(True)
+
+    # Adjust layout
+    plt.tight_layout()
+    fig.savefig(Path(imgs_pth, 'si', 
+                f'n6_supp_dec_repro_male_female.pdf'), dpi=150)    
+
 
 '''
 ##########
@@ -2475,9 +2573,9 @@ def get_example_results():
             "stimOn_times",  # Alignset key
         ),
         "choice": (
-            "671c7ea7-6726-4fbe-adeb-f89c2c8e489b",
-            "04c9890f-2276-4c20-854f-305ff5c9b6cf",
-            143, # was 143
+            "a7763417-e0d6-4f2a-aa55-e382fd9b5fb8",#"671c7ea7-6726-4fbe-adeb-f89c2c8e489b"
+            "57c5856a-c7bd-4d0f-87c6-37005b1484aa",#"04c9890f-2276-4c20-854f-305ff5c9b6cf"
+            74, # was 143
             "GRN",
             0.000992895,  # drsq
             "firstMovement_times",
@@ -2500,7 +2598,7 @@ def get_example_results():
     return targetunits, alignsets, sortlookup
     
     
-def ecoding_raster_lines(variable, clu_id0=None, axs=None,
+def encoding_raster_lines(variable, clu_id0=None, axs=None,
                          frac_tr=3):    
 
     '''
@@ -3028,8 +3126,6 @@ def plot_curves_scatter(variable, ga_pcs=False, curve='euc',
                     color=palette[reg],
                     label=f"{reg} {d[reg]['nclus']}")
 
-        axs[k].set_xscale('log')
-
         # put region labels
         y = yy[-1]
         x = xx[-1]
@@ -3282,12 +3378,12 @@ def ax_str(x):
 
     # encoding panels
     if not save_pans:   
-        ecoding_raster_lines(variable,clu_id0= clu_id0, 
+        encoding_raster_lines(variable,clu_id0= clu_id0, 
                              axs=[ax_str(x) for x in 
                              ['ras', 'enc0', 'enc1']])
                          
     else:
-        ecoding_raster_lines(variable, clu_id0=clu_id0)
+        encoding_raster_lines(variable, clu_id0=clu_id0)
     
     
     '''