diff --git a/notebooks/cornerplot.ipynb b/notebooks/cornerplot.ipynb
index 9b46140..4d91837 100644
--- a/notebooks/cornerplot.ipynb
+++ b/notebooks/cornerplot.ipynb
@@ -151,7 +151,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "69583866bb7a40c49fa072b51d3e89b8",
+       "model_id": "55d785b12c93485d88b405a7347f3cf9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -164,7 +164,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x500 with 4 Axes>"
       ]
@@ -202,7 +202,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "89795e47abde4cb98402178aef759b62",
+       "model_id": "f7f8ed4822634d728f0904714e6771a6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -215,7 +215,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x500 with 4 Axes>"
       ]
@@ -254,12 +254,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Removed no burn in\n"
+      "tensor([ 8.3111, -3.2867])\n",
+      "Removed no burn in\n",
+      "i =  0\n",
+      "j =  0\n",
+      "placing a v line\n",
+      "attempting to drop a point at axes 1 0\n",
+      "value is tensor(8.3111) tensor(8.3111)\n",
+      "i =  0\n",
+      "j =  1\n",
+      "attempting to drop a point at axes 1 1\n",
+      "value is tensor(8.3111) tensor(-3.2867)\n",
+      "i =  1\n",
+      "j =  0\n",
+      "attempting to drop a point at axes 2 0\n",
+      "value is tensor(-3.2867) tensor(8.3111)\n",
+      "i =  1\n",
+      "j =  1\n",
+      "placing a v line\n",
+      "attempting to drop a point at axes 2 1\n",
+      "value is tensor(-3.2867) tensor(-3.2867)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x400 with 3 Axes>"
       ]
@@ -269,6 +288,7 @@
     }
    ],
    "source": [
+    "print(theta_true)\n",
     "display.getdist_corner_plot(posterior_samples_static.numpy(),\n",
     "                            labels_list = ['m','b'],\n",
     "                            truth_list = theta_true,\n",
@@ -288,30 +308,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "9f9ebbf3-76ca-4a86-9293-b351066da24c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([ 8.3111, -3.2867])\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "'NoneType' object is not iterable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[10], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(theta_true)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mdisplay\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetdist_corner_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mposterior_samples_static\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                             \u001b[49m\u001b[43mposterior_samples_generative\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m                            \u001b[49m\u001b[43mlabels_list\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mm\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m                            \u001b[49m\u001b[38;5;66;43;03m#limit_list = [],\u001b[39;49;00m\n\u001b[1;32m      6\u001b[0m \u001b[43m                            \u001b[49m\u001b[43mtruth_list\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtheta_true\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m                            \u001b[49m\u001b[43mtruth_color\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43morange\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m                            \u001b[49m\u001b[43mplot\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m                            \u001b[49m\u001b[43msave\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m                            \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m../plots/static/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/DeepDiagnostics/src/scripts/plot.py:91\u001b[0m, in \u001b[0;36mDisplay.getdist_corner_plot\u001b[0;34m(self, posterior_samples, labels_list, limit_list, truth_list, truth_color, plot, save, path)\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;66;03m# Check if 'posterior_samples' is a list\u001b[39;00m\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(posterior_samples, \u001b[38;5;28mlist\u001b[39m):\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;66;03m# Handle the case where 'posterior_samples' is a list of samples\u001b[39;00m\n\u001b[1;32m     88\u001b[0m     \u001b[38;5;66;03m# You may want to customize this part based on your requirements\u001b[39;00m\n\u001b[1;32m     89\u001b[0m     samples_list \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     90\u001b[0m         MCSamples(samples\u001b[38;5;241m=\u001b[39msamps, names\u001b[38;5;241m=\u001b[39mlabels_list, labels\u001b[38;5;241m=\u001b[39mlabels_list, ranges\u001b[38;5;241m=\u001b[39mlimit_list)\n\u001b[0;32m---> 91\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m samps, limits \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mposterior_samples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimit_list\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     92\u001b[0m     ]\n\u001b[1;32m     94\u001b[0m     \u001b[38;5;66;03m# Create a getdist Plotter\u001b[39;00m\n\u001b[1;32m     95\u001b[0m     g \u001b[38;5;241m=\u001b[39m plots\u001b[38;5;241m.\u001b[39mget_subplot_plotter()\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not iterable"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(theta_true)\n",
     "display.getdist_corner_plot([posterior_samples_static.numpy(),\n",
diff --git a/src/scripts/plot.py b/src/scripts/plot.py
index c768d57..b198823 100644
--- a/src/scripts/plot.py
+++ b/src/scripts/plot.py
@@ -65,7 +65,7 @@ def getdist_corner_plot(
         labels_list: List[str] = None,
         limit_list: List[List[float]] = None,  # Each inner list contains [lower_limit, upper_limit]
         truth_list: List[float] = None,
-        truth_color: str = 'red',
+        truth_color: str = 'orange',
         plot: bool = False,
         save: bool = True,
         path: str = 'plots/',
@@ -97,16 +97,7 @@ def getdist_corner_plot(
             # Plot the triangle plot for each set of samples in the list
             g.triangle_plot(samples_list, filled=True)
 
-            # Add vertical truth line on the first subplot
-            if truth_list is not None:
-                for i in range(len(truth_list)):
-                    try:
-                        g.subplots[0, i].axvline(x=truth_list[i],
-                                                 color=truth_color)
-                        g.subplots[i, 0].axvline(x=truth_list[i],
-                                                 color=truth_color)
-                    except AttributeError:
-                        continue
+            
         else:
             # Assume 'posterior_samples' is a 2D numpy array or similar
             samples = MCSamples(samples=posterior_samples, names=labels_list, labels=labels_list, ranges=limit_list)
@@ -117,15 +108,24 @@ def getdist_corner_plot(
             # Plot the triangle plot
             g.triangle_plot(samples, filled=True)
 
-            # Add vertical truth line on the first subplot
-            if truth_list is not None:
-                for i in range(len(truth_list)):
-                    try:
-                        g.subplots[0, i].axvline(x=truth_list[i], color=truth_color)
-                        g.subplots[i, 0].axvline(x=truth_list[i],
+        # Add vertical truth line on the first subplot
+        if truth_list is not None:
+            for i in range(len(truth_list)):
+                for j in range(len(truth_list)):
+                    if i == j:
+                        # this is for the axvlines on the marginals
+                        # which is on the diagnoal
+                        g.subplots[i, j].axvline(x=truth_list[i],
                                                  color=truth_color)
-                    except AttributeError:
+                    
+                    try:
+                        # plot as a point for the posteriors
+                        g.subplots[int(1 + i), int(0 + j)].scatter(truth_list[0+i],
+                                                               truth_list[1+i],
+                                                               color=truth_color)
+                    except IndexError:
                         continue
+        
         # Save or show the plot
         if save:
             plt.savefig(path + "getdist_cornerplot.pdf")