diff --git a/notebooks/witzit-plot-vanta.ipynb b/notebooks/witzit-plot-vanta.ipynb
index 31ac54a..90b4cd6 100644
--- a/notebooks/witzit-plot-vanta.ipynb
+++ b/notebooks/witzit-plot-vanta.ipynb
@@ -61,7 +61,7 @@
    "outputs": [],
    "source": [
     "df = pd.read_csv('examples/olympus-vanta.csv', header = 0,\n",
-    "                 skiprows=40,\n",
+    "                 skiprows=39,\n",
     "                 names=('eV', 'Counts'),\n",
     "                 usecols = [0, 1])\n",
     "\n",
@@ -85,13 +85,14 @@
    "source": [
     "sns.set_theme()\n",
     "plt.figure(facecolor='xkcd:off white', figsize=(15, 8))\n",
-    "# XXX y=\n",
-    "g=sns.lineplot(x=\"eV\", y=\"Counts\", data=df, linestyle='-', linewidth=1, color='xkcd:pale orange')\n",
+    "g=sns.lineplot(x=\"eV\", y=\"Counts\", data=df, linestyle='-', linewidth=1, color='xkcd:pale orange', legend=False)\n",
     "g.set_title('XRF Spectrum'.format('seaborn'), color='xkcd:cornflower blue', fontsize='large', fontweight='bold')\n",
     "g.set_xlabel('energy (eV)', color='xkcd:goldenrod', fontsize='large', fontweight='bold')\n",
     "g.set_ylabel('Counts', color='xkcd:cerulean', fontsize='large', fontweight='bold')\n",
     "g.patch.set_alpha(1.0)\n",
     "g.set_facecolor('xkcd:powder blue')\n",
+    "plt.xlim(0,50000)\n",
+    "plt.ylim(0,100000)\n",
     "plt.tight_layout()\n",
     "plt.savefig(\"xrf-spectrum-vanta.png\", dpi=72, transparent=False, facecolor='xkcd:off white', edgecolor=g.get_facecolor())\n",
     "plt.show()\n",
diff --git a/notebooks/witzit-plot-x555.ipynb b/notebooks/witzit-plot-x555.ipynb
index c6a90d6..3463faa 100644
--- a/notebooks/witzit-plot-x555.ipynb
+++ b/notebooks/witzit-plot-x555.ipynb
@@ -2,13 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# witzit-plot --- What In The Zap Is That? AI categorization of spectra from LIBS/XRF analyzers.\n",
+    "# witzit-plot-x555 --- What In The Zap Is That? AI categorization of spectra from LIBS/XRF analyzers.\n",
     "#\n",
     "# Copyright (C) 2019-2022, Jeff Moe\n",
     "#\n",
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,20 +75,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "sns.set_theme()\n",
     "plt.figure(facecolor='xkcd:off white', figsize=(15, 8))\n",
@@ -98,6 +96,8 @@
     "g.set_ylabel('Counts', color='xkcd:cerulean', fontsize='large', fontweight='bold')\n",
     "g.patch.set_alpha(1.0)\n",
     "g.set_facecolor('xkcd:powder blue')\n",
+    "plt.xlim(0,50000)\n",
+    "plt.ylim(0,100000)\n",
     "plt.tight_layout()\n",
     "plt.savefig(\"xrf-spectrum.png\", dpi=72, transparent=False, facecolor='xkcd:off white', edgecolor=g.get_facecolor())\n",
     "plt.show()\n",
diff --git a/notebooks/xrf-spectrum-vanta.png b/notebooks/xrf-spectrum-vanta.png
index 8cc49da..52016d7 100644
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diff --git a/notebooks/xrf-spectrum.png b/notebooks/xrf-spectrum.png
index a93df01..8d4c3b1 100644
Binary files a/notebooks/xrf-spectrum.png and b/notebooks/xrf-spectrum.png differ