diff --git a/examples/notebook/calculation_of_V_average.ipynb b/examples/notebook/calculation_of_V_average.ipynb
index 9e3065e..23a774b 100644
--- a/examples/notebook/calculation_of_V_average.ipynb
+++ b/examples/notebook/calculation_of_V_average.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mu_vol(mu, sigma):\n",
+    "    return (mu**4 + 6*mu**2*sigma**2 + 3*sigma**4)/(mu*(mu**2 + 3*sigma**2))\n",
+    "\n",
+    "def sigma_vol(mu, sigma):\n",
+    "    return np.sqrt((mu**4 + 10 * mu**2 * sigma**2 +  15 * sigma**4)/(mu**2 + 3*sigma**2) - mu_vol(mu, sigma)**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,16 +73,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x16dea9f10>]"
+       "[<matplotlib.lines.Line2D at 0x1373c26d0>]"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -55,16 +103,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x16caf6d10>]"
+       "[<matplotlib.lines.Line2D at 0x1374b52d0>]"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -88,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -109,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,7 +186,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25.244301699129714"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mu_vol(19, 7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -147,7 +215,7 @@
        "5.342180091555236"
       ]
      },
-     "execution_count": 160,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -158,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -185,7 +253,7 @@
        "5.342180091555236"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -196,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -205,7 +273,7 @@
        "22.200770484516223"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -216,22 +284,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x29080ab10>]"
+       "(0.0, 70.0)"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -241,9 +309,13 @@
     }
    ],
    "source": [
-    "plt.plot(D, PV)\n",
-    "plt.plot(D, PD)\n",
-    "plt.plot(D, theortical_PD)"
+    "plt.plot(D, PV, label=\"PDF (TEM)\")\n",
+    "plt.plot(D, PD, label=\"PDF (TEM, scaled with volume)\")\n",
+    "plt.plot(D, theortical_PD, label=\"N(μ, σ) (analitycal solution)\")\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"Diameter (Å)\")\n",
+    "plt.ylabel(\"Probability density\")\n",
+    "plt.xlim(0, 70)"
    ]
   },
   {
diff --git a/examples/notebook/plotting_hollow_shell.ipynb b/examples/notebook/plotting_hollow_shell.ipynb
index 66cb685..12f2eb7 100644
--- a/examples/notebook/plotting_hollow_shell.ipynb
+++ b/examples/notebook/plotting_hollow_shell.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,6 +364,83 @@
     "## Plot and save the data to PowerPoint slides"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1/1 [00:00<00:00, 102.21it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 124.24it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 91.88it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 247.47it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 216.97it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 333.86it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 218.04it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 148.77it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 330x250 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plot the histogram of the input data and save to pptx slides\n",
+    "adn = Analyze(\"../sweep/Pt40Au60_AgBP1_hollow_shell_TEM_correction_results.csv\")\n",
+    "adn.analyze_all(\"Pt40Au60_AgBP1_hollow_shell\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1/1 [00:00<00:00, 97.23it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 119.97it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 89.79it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 224.47it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 224.37it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 338.28it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 167.93it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 146.84it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 330x250 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plot the histogram of the input data and save to pptx slides\n",
+    "adn = Analyze(\"../sweep/Pt20Au80_AgBP1_hollow_shell_TEM_correction_results.csv\")\n",
+    "adn.analyze_all(\"Pt20Au80_AgBP1_hollow_shell\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -437,26 +514,83 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Plot the histogram of the input data and save to pptx slides\n",
+    "adn = Analyze(\"../sweep/Pt20Au80_AgBP1_fix_bulk_fraction_hollow_shell_results.csv\")\n",
+    "adn.analyze_all(\"Pt20Au80_AgBP1_fix_bulk_fraction_hollow_shell\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'float' object has no attribute 'round'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[8], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m#Plot the histogram of the input data and save to pptx slides\u001b[39;00m\n\u001b[1;32m      2\u001b[0m adn \u001b[39m=\u001b[39m Analyze(\u001b[39m\"\u001b[39m\u001b[39m../sweep/Pt40Au60_AgBP1_hollow_shell_results.csv\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m adn\u001b[39m.\u001b[39;49manalyze_all(\u001b[39m\"\u001b[39;49m\u001b[39mPt40Au60_AgBP1_hollow_shell\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
-      "Cell \u001b[0;32mIn[6], line 166\u001b[0m, in \u001b[0;36mAnalyze.analyze_all\u001b[0;34m(self, title)\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdf\u001b[39m.\u001b[39mdropna(inplace\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m    163\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdf \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdf[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdf[\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m]\n\u001b[0;32m--> 166\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_table_drop \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgen_df_table(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_df_input())\n\u001b[1;32m    167\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_drop_len \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_df_len()\n\u001b[1;32m    168\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_table \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgen_df_table(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_df_output())\n",
-      "Cell \u001b[0;32mIn[6], line 98\u001b[0m, in \u001b[0;36mAnalyze.gen_df_table\u001b[0;34m(self, df)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     91\u001b[0m         digits \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m     92\u001b[0m     table \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mconcat([table,\n\u001b[1;32m     93\u001b[0m                         pd\u001b[39m.\u001b[39mDataFrame([[column,\n\u001b[1;32m     94\u001b[0m                                         \u001b[39m# str(df[column].min()),\u001b[39;00m\n\u001b[1;32m     95\u001b[0m                                         \u001b[39m# str(df[column].max()),\u001b[39;00m\n\u001b[1;32m     96\u001b[0m                                         \u001b[39m# str(df[column].mean()),\u001b[39;00m\n\u001b[1;32m     97\u001b[0m                                         \u001b[39m# str(df[column].std())]],columns = [\"Parameter\", \"Min\", \"Max\", \"Mean\", \"Standard Deviation\"])])\u001b[39;00m\n\u001b[0;32m---> 98\u001b[0m                                         df[column]\u001b[39m.\u001b[39;49mmin()\u001b[39m.\u001b[39;49mround(digits)\u001b[39m.\u001b[39mastype(\u001b[39m\"\u001b[39m\u001b[39mstr\u001b[39m\u001b[39m\"\u001b[39m),\n\u001b[1;32m     99\u001b[0m                                         df[column]\u001b[39m.\u001b[39mmax()\u001b[39m.\u001b[39mround(digits)\u001b[39m.\u001b[39mastype(\u001b[39m\"\u001b[39m\u001b[39mstr\u001b[39m\u001b[39m\"\u001b[39m),\n\u001b[1;32m    100\u001b[0m                                         df[column]\u001b[39m.\u001b[39mmean()\u001b[39m.\u001b[39mround(digits)\u001b[39m.\u001b[39mastype(\u001b[39m\"\u001b[39m\u001b[39mstr\u001b[39m\u001b[39m\"\u001b[39m),\n\u001b[1;32m    101\u001b[0m                                         df[column]\u001b[39m.\u001b[39mstd()\u001b[39m.\u001b[39mround(digits)\u001b[39m.\u001b[39mastype(\u001b[39m\"\u001b[39m\u001b[39mstr\u001b[39m\u001b[39m\"\u001b[39m)]],columns \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mParameter\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mMin\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mMax\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mMean\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mStandard Deviation\u001b[39m\u001b[39m\"\u001b[39m])])\n\u001b[1;32m    103\u001b[0m \u001b[39mreturn\u001b[39;00m table\u001b[39m.\u001b[39mreset_index(drop\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'round'"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1/1 [00:00<00:00, 96.57it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 110.88it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 73.49it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 205.97it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 190.11it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 196.05it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 139.66it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 117.85it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 330x250 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plot the histogram of the input data and save to pptx slides\n",
+    "adn = Analyze(\"../sweep/Pt20Au80_AgBP1_fix_bulk_fraction_hollow_shell_results.csv\")\n",
+    "adn.df = adn.df[adn.df[\"XA\"] > adn.df[\"XAP\"]]\n",
+    "adn.analyze_all(\"Pt20Au80_AgBP1_fix_bulk_fraction_hollow_shell_XA>XAP\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1/1 [00:00<00:00, 79.64it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 119.20it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 86.27it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 199.24it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 236.01it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 322.69it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 176.62it/s]\n",
+      "100%|██████████| 1/1 [00:00<00:00, 130.45it/s]\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 330x250 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "#Plot the histogram of the input data and save to pptx slides\n",
-    "adn = Analyze(\"../sweep/Pt40Au60_AgBP1_hollow_shell_results.csv\")\n",
+    "adn = Analyze(\"../sweep/Pt40Au60_AgBP1_hollow_shell_TEM_correction_results.csv\")\n",
     "adn.analyze_all(\"Pt40Au60_AgBP1_hollow_shell\")"
    ]
   },