diff --git a/analysis/Python_scripts/scatter_plot.png b/analysis/Python_scripts/scatter_plot.png
new file mode 100644
index 0000000..e1808d9
Binary files /dev/null and b/analysis/Python_scripts/scatter_plot.png differ
diff --git a/analysis/Python_scripts/scatterplot.ipynb b/analysis/Python_scripts/scatterplot.ipynb
index a9f0f01..f2cbe54 100644
--- a/analysis/Python_scripts/scatterplot.ipynb
+++ b/analysis/Python_scripts/scatterplot.ipynb
@@ -2,29 +2,25 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import plotly.graph_objs as go\n",
     "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "from matplotlib import pyplot as plt\n",
     "from rdkit import Chem\n",
-    "import os\n",
-    "import plotly.graph_objs as go\n",
-    "from plotly.subplots import make_subplots\n",
     "from matchms.importing import load_from_msp\n",
     "from matchms.logging_functions import set_matchms_logger_level\n",
+    "\n",
     "from utils import is_spectrum_for_compound\n",
     "\n",
-    "set_matchms_logger_level('ERROR')\n"
+    "set_matchms_logger_level('ERROR')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,23 +29,46 @@
     "matchms_scores = matchms_scores[matchms_scores.apply(lambda x: is_spectrum_for_compound(x['query'], x['reference']), axis=1)]\n",
     "\n",
     "molecules = Chem.SDMolSupplier(\"../../data/RECETOX_GC-EI-MS_20201028.sdf\")\n",
+    "\n",
     "predicted_spectra = list(load_from_msp(\"../data/filtered/simulated_matchms_filter_1%I_all_peaks.msp\"))\n",
-    "spectra_metadata= pd.DataFrame.from_dict([x.metadata for x in predicted_spectra])\n",
-    "spectra_metadata.rename(columns={'compound_name': 'query'}, inplace=True)"
+    "spectra_metadata = pd.DataFrame.from_dict([x.metadata for x in predicted_spectra])\n",
+    "spectra_metadata.rename(columns={'compound_name': 'query'}, inplace=True)\n",
+    "\n",
+    "reference_spectra = list(load_from_msp(\"../data/experimental/RECETOX_GC-EI_MS_20201028.msp\"))\n",
+    "reference_spectra_metadata = pd.DataFrame.from_dict([x.metadata for x in reference_spectra])\n",
+    "reference_spectra_metadata.rename(columns={'compound_name': 'reference'}, inplace=True)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "merged = matchms_scores.merge(spectra_metadata, on=\"query\", how=\"inner\")"
+    "merged = matchms_scores.merge(spectra_metadata, on=\"query\", how=\"inner\")\n",
+    "merged.rename(columns={'num_peaks': 'n_peaks_query'}, inplace=True)\n",
+    "\n",
+    "merged = merged.merge(reference_spectra_metadata, on=\"reference\", how=\"inner\")\n",
+    "merged.rename(columns={'num_peaks': 'n_peaks_reference'}, inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged['CosineHungarian_0.01_0.0_1.0_matches'] = pd.to_numeric(merged['CosineHungarian_0.01_0.0_1.0_matches'], errors='coerce')\n",
+    "merged['n_peaks_query'] = pd.to_numeric(merged['n_peaks_query'], errors='coerce')\n",
+    "merged['n_peaks_reference'] = pd.to_numeric(merged['n_peaks_reference'], errors='coerce')\n",
+    "\n",
+    "merged['FractionQuery'] = merged['CosineHungarian_0.01_0.0_1.0_matches'] / merged['n_peaks_query']\n",
+    "merged['FractionReference'] = merged['CosineHungarian_0.01_0.0_1.0_matches'] / merged['n_peaks_reference']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
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{\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenu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plot of matched ions and scores\"},\"xaxis\":{\"title\":{\"text\":\"Score\"}},\"yaxis\":{\"title\":{\"text\":\"Matches\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('d8e85df5-f477-4fa0-80dc-f696ed9cd26d');\n",
+       "var gd = document.getElementById('af270972-fbde-46d3-a692-9dd1b336ae4a');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -1602,23 +2342,25 @@
     "# Create a scatter plot\n",
     "fig = go.Figure()\n",
     "fig.add_trace(go.Scatter(\n",
-    "    y=matchms_scores['CosineHungarian_0.01_0.0_1.0_matches'],\n",
-    "    x=matchms_scores['CosineHungarian_0.01_0.0_1.0_scores'],\n",
+    "    x=merged['CosineHungarian_0.01_0.0_1.0_scores'],\n",
+    "    y=merged['CosineHungarian_0.01_0.0_1.0_matches'],\n",
     "    mode='markers',\n",
     "    marker=dict(\n",
-    "        size=5,\n",
-    "        color='blue',\n",
+    "        size=merged['FractionQuery'] * 20,  # Adjust the size scaling factor as needed\n",
+    "        color=merged['FractionReference'],\n",
+    "        colorscale='Viridis',  # change the colorscale as needed\n",
+    "        colorbar=dict(title='Reference Matched %'),\n",
     "        opacity=0.5\n",
     "    ),\n",
-    "    name='Matched ions'\n",
+    "    name='Matched Ions'\n",
     "))\n",
     "fig.update_layout(\n",
     "    title='Scatter plot of matched ions and scores',\n",
-    "    xaxis_title='score',\n",
-    "    yaxis_title='matches'\n",
+    "    xaxis_title='Score',\n",
+    "    yaxis_title='Matches'\n",
     ")\n",
     "fig.show()\n",
-    "# node size should be the experimental spectra, color the fraction of predicted spectra"
+    "fig.write_image(\"scatter_plot.png\")"
    ]
   }
  ],
diff --git a/analysis_environment.yaml b/analysis_environment.yaml
index dffa2a3..ff8995b 100644
--- a/analysis_environment.yaml
+++ b/analysis_environment.yaml
@@ -15,4 +15,5 @@ dependencies:
 - python==3.10
 - rdkit
 - seaborn
+- python-kaleido