diff --git a/demos/mdPCA_maasMDS.ipynb b/demos/mdPCA_maasMDS.ipynb
index c098a9c..5b54212 100644
--- a/demos/mdPCA_maasMDS.ipynb
+++ b/demos/mdPCA_maasMDS.ipynb
@@ -75,27 +75,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[INFO] 2024-11-20 08:49:32: ------ Array 1 Processing: ------\n",
-      "[INFO] 2024-11-20 08:49:32: VCF Processing Time: --- 0.08896374702453613 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: TSV Processing Time: --- 0.07820534706115723 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Masking for ancestry 0 --- 0.0006721019744873047 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Masking for ancestry 1 --- 0.0010004043579101562 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Masking for ancestry 2 --- 0.0010309219360351562 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Masking for ancestry 3 --- 0.0024771690368652344 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Masking for ancestry 4 --- 0.0014414787292480469 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Covariance Matrix --- 0.12268495559692383 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Percent variance explained by the principal component 1: 40.66990976545803\n",
-      "[INFO] 2024-11-20 08:49:32: Percent variance explained by the principal component 2: 35.323312747684334\n"
+      "[INFO] 2025-01-03 05:18:59: ------ Array 1 Processing: ------\n",
+      "[INFO] 2025-01-03 05:18:59: VCF Processing Time: --- 0.09156608581542969 seconds ---\n",
+      "[INFO] 2025-01-03 05:18:59: TSV Processing Time: --- 0.08198070526123047 seconds ---\n",
+      "[INFO] 2025-01-03 05:18:59: Masking for ancestry 0 --- 0.0006837844848632812 seconds ---\n",
+      "[INFO] 2025-01-03 05:18:59: Masking for ancestry 1 --- 0.0010597705841064453 seconds ---\n",
+      "[INFO] 2025-01-03 05:18:59: Masking for ancestry 2 --- 0.0010416507720947266 seconds ---\n",
+      "[INFO] 2025-01-03 05:18:59: Masking for ancestry 3 --- 0.002353668212890625 seconds ---\n",
+      "[INFO] 2025-01-03 05:18:59: Masking for ancestry 4 --- 0.0014388561248779297 seconds ---\n",
+      "[INFO] 2025-01-03 05:18:59: Covariance Matrix --- 0.1636357307434082 seconds ---\n",
+      "[INFO] 2025-01-03 05:18:59: Percent variance explained by the principal component 1: 40.669909765458044\n",
+      "[INFO] 2025-01-03 05:18:59: Percent variance explained by the principal component 2: 35.32331274768433\n"
      ]
     }
    ],
    "source": [
     "# Initialize the mdPCA object with SNP and LAI data, labels file, and selected ancestry\n",
     "mdpca = mdPCA(\n",
-    "    snpobj=snpobj,         # SNP data object\n",
-    "    laiobj=laiobj,         # LAI data object\n",
-    "    labels_file=labels_file, # File containing sample labels\n",
-    "    ancestry=\"4\"           # Ancestry component to analyze (e.g., '4' for East Asia)\n",
+    "    snpobj=snpobj,            # SNP data object\n",
+    "    laiobj=laiobj,            # LAI data object\n",
+    "    labels_file=labels_file,  # File containing sample labels\n",
+    "    ancestry=\"4\",             # Ancestry component to analyze (e.g., '4' for East Asia)\n",
+    "    average_strands=False     # Diploid data\n",
     ")"
    ]
   },
@@ -124,10 +125,33 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "samples ['GA000856_GA000856', 'GA000856_GA000856', 'GA000857_GA000857', 'GA000858_GA000858', 'GA000858_GA000858']\n",
+      "haplotypes ['GA000856_GA000856_A', 'GA000856_GA000856_B', 'GA000857_GA000857_A', 'GA000858_GA000858_A', 'GA000858_GA000858_B']\n",
+      "n_samples 3\n",
+      "n_haplotypes 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"samples\", mdpca.samples_)\n",
+    "print(\"haplotypes\", mdpca.haplotypes_)\n",
+    "print(\"n_samples\", mdpca.n_samples)\n",
+    "print(\"n_haplotypes\", mdpca.n_haplotypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
@@ -151,22 +175,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[INFO] 2024-11-20 08:49:32: ------ Array 1 Processing: ------\n",
-      "[INFO] 2024-11-20 08:49:32: VCF Processing Time: --- 0.0840766429901123 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: TSV Processing Time: --- 0.07722902297973633 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Masking for ancestry 0 --- 0.0006477832794189453 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Masking for ancestry 1 --- 0.0006830692291259766 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Masking for ancestry 2 --- 0.0007250308990478516 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Masking for ancestry 3 --- 0.002404451370239258 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Masking for ancestry 4 --- 0.001440286636352539 seconds ---\n",
-      "[INFO] 2024-11-20 08:49:32: Distance Matrix building: --- 0.004311800003051758 seconds ---\n"
+      "[INFO] 2025-01-03 05:19:06: ------ Array 1 Processing: ------\n",
+      "[INFO] 2025-01-03 05:19:06: VCF Processing Time: --- 0.16530919075012207 seconds ---\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INFO] 2025-01-03 05:19:06: TSV Processing Time: --- 0.12661957740783691 seconds ---\n",
+      "[INFO] 2025-01-03 05:19:06: Masking for ancestry 0 --- 0.0012772083282470703 seconds ---\n",
+      "[INFO] 2025-01-03 05:19:06: Masking for ancestry 1 --- 0.0015435218811035156 seconds ---\n",
+      "[INFO] 2025-01-03 05:19:06: Masking for ancestry 2 --- 0.0015606880187988281 seconds ---\n",
+      "[INFO] 2025-01-03 05:19:06: Masking for ancestry 3 --- 0.003694772720336914 seconds ---\n",
+      "[INFO] 2025-01-03 05:19:06: Masking for ancestry 4 --- 0.002327442169189453 seconds ---\n",
+      "[INFO] 2025-01-03 05:19:06: Distance Matrix building: --- 0.006646871566772461 seconds ---\n"
      ]
     }
    ],
@@ -176,42 +206,66 @@
     "\n",
     "# Initialize the maasMDS object with SNP and LAI data, labels file, and selected ancestry\n",
     "mds = maasMDS(\n",
-    "    snpobj=snpobj,         # SNP data object\n",
-    "    laiobj=laiobj,         # LAI data object\n",
+    "    snpobj=snpobj,           # SNP data object\n",
+    "    laiobj=laiobj,           # LAI data object\n",
     "    labels_file=labels_file, # File containing sample labels\n",
-    "    ancestry=\"4\"           # Ancestry component to analyze (e.g., '4' for East Asia)\n",
+    "    ancestry=\"4\",            # Ancestry component to analyze (e.g., '4' for East Asia)\n",
+    "    average_strands=False    # Diploid data\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "X_new [[ 0.05753786 -0.07705574]\n",
-      " [ 0.31123924  0.07635565]\n",
-      " [-0.14243854 -0.03442202]\n",
-      " [-0.07117519 -0.22365777]\n",
-      " [-0.15516337  0.25877988]]\n"
+      "X_new [[-0.4475931  -0.02684126]\n",
+      " [-0.53592473  0.33056938]\n",
+      " [-0.00128624 -0.34214979]\n",
+      " [ 0.37509062 -0.60215815]\n",
+      " [ 0.60971344  0.64057983]]\n"
      ]
     }
    ],
    "source": [
-    "print(\"X_new\", mdpca.X_new_)"
+    "print(\"X_new\", mds.X_new_)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "samples ['GA000856_GA000856', 'GA000856_GA000856', 'GA000857_GA000857', 'GA000858_GA000858', 'GA000858_GA000858']\n",
+      "haplotypes ['GA000856_GA000856_A', 'GA000856_GA000856_B', 'GA000857_GA000857_A', 'GA000858_GA000858_A', 'GA000858_GA000858_B']\n",
+      "n_samples 3\n",
+      "n_haplotypes 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"samples\", mdpca.samples_)\n",
+    "print(\"haplotypes\", mdpca.haplotypes_)\n",
+    "print(\"n_samples\", mdpca.n_samples)\n",
+    "print(\"n_haplotypes\", mdpca.n_haplotypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
@@ -221,7 +275,7 @@
     }
    ],
    "source": [
-    "scatter(mdpca, labels_file)"
+    "scatter(mds, labels_file)"
    ]
   },
   {
@@ -234,7 +288,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "galaxybio",
+   "display_name": "snputils",
    "language": "python",
    "name": "python3"
   },