diff --git a/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_2__varyvilocaparams_snvs.ipynb b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_2__varyvilocaparams_snvs.ipynb
new file mode 100644
index 00000000..9b593dee
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+++ b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_2__varyvilocaparams_snvs.ipynb
@@ -0,0 +1,504 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "57a0a953",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "import humanize\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "38db5bea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "method_palette = { \"LoFreq\": \"#9593D9\",\n",
+    "                   \"ShoRAH\": \"#ffe66d\",\n",
+    "                   \"CliqueSNV\":  \"#4ecdc4\",\n",
+    "                   \"VILOCA\": \"#ff6b6b\",\n",
+    "                   \"ground_truth\": \"grey\",}\n",
+    "\n",
+    "\n",
+    "method_palette = {\n",
+    "    'viloca_alpha_0.00001':  \"#ff6b6b\", \n",
+    "    'viloca_envp':  \"#ff6b6b\",  \n",
+    "    'viloca_envp_0.001':  \"#ff6b6b\", \n",
+    "    'viloca_uniform':  \"#ff6b6b\", \n",
+    "    'viloca_learn_error_params':  \"#ff6b6b\", \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8b41bcad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fname_viloca_posterior = \"results/varyvilocaparams/performance_measures/performance_mutation_calls_posterior_thres.csv\"\n",
+    "fname_local = \"results/varyvilocaparams/performance_measures/local/performance.csv\"\n",
+    "fname_runtime =  \"results/varyvilocaparams/performance_measures/local/runtime.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "db351c29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_local = pd.read_csv(fname_local).drop(columns=[\"Unnamed: 0\"])\n",
+    "df_local = df_local[(~df_local['method'].str.startswith(\"viloca\")) | (~df_local['method'].str.startswith(\"shorah\"))]\n",
+    "df_local = df_local.pivot(index=['method', 'params', 'replicate'], columns='variable', values='value').reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "017cfadf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_viloca = pd.read_csv(fname_viloca_posterior).drop(columns=[\"Unnamed: 0\"])\n",
+    "\n",
+    "df_viloca = df_viloca[df_viloca['posterior_threshold'].isin([0.0,0.5, 0.9])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "526c6942",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['viloca_alpha_0.00001', 'viloca_envp', 'viloca_envp_0.001',\n",
+       "       'shorah_default', 'viloca_uniform', 'viloca_learn_error_params'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_viloca['method'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ed8a4906",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat([df_viloca, df_local])\n",
+    "\n",
+    "# give other methods also a postiro_threshold vaues such that they apppear in plotting\n",
+    "df['posterior_threshold'] = df['posterior_threshold'].fillna(0.9)\n",
+    "\n",
+    "#df[\"Method\"] = df['method'].apply(lambda x: mapping_methods[x])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b8afc93",
+   "metadata": {},
+   "source": [
+    "## snv performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "cdef2427",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 874.775x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.5)\n",
+    "\n",
+    "df = pd.concat([df_viloca, df_local])\n",
+    "\n",
+    "# give other methods also a postiro_threshold vaues such that they apppear in plotting\n",
+    "df['posterior_threshold'] = df['posterior_threshold'].fillna(0.9)\n",
+    "df_long = df[df['posterior_threshold']>=0.9]\n",
+    "df_long = df_long.drop(columns=['posterior_threshold'])\n",
+    "df_long = pd.melt(df_long, id_vars=[\n",
+    "                                    \"method\",\n",
+    "                                    \"params\", \n",
+    "                                    \"replicate\", \n",
+    "                                    ]).assign(\n",
+    "        params=lambda x: x[\"params\"].str.replace(\"_\", \"\\n\")\n",
+    "    )\n",
+    "\n",
+    "df_long['seq_mode'] = df_long['params'].str.split(\"param~\").str[1].str.split('\\n').str[0]\n",
+    "df_long = df_long[df_long['method'].isin(['viloca_alpha_0.00001', \n",
+    "                                                    'viloca_envp', \n",
+    "                                                    'viloca_envp_0.001', \n",
+    "                                                    'viloca_uniform', \n",
+    "                                                    'viloca_learn_error_params'])]\n",
+    "\n",
+    "method_order  = ['viloca_alpha_0.00001', 'viloca_uniform', 'viloca_learn_error_params', \n",
+    "                 'viloca_envp', 'viloca_envp_0.001']\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "        data=df_long,\n",
+    "        x=\"method\",\n",
+    "        y=\"value\",\n",
+    "        col='variable',\n",
+    "        order = method_order, \n",
+    "        hue=\"seq_mode\",\n",
+    "        kind=\"box\",\n",
+    "        legend=True, \n",
+    "        width=0.5,\n",
+    "        height=7,\n",
+    "        aspect=0.5,\n",
+    "        linewidth=0.5,\n",
+    "        sharex=True,\n",
+    "        margin_titles=True,\n",
+    "        palette=['#ffc8c8', '#ff5858'],\n",
+    "    )\n",
+    "g.set_titles(col_template='{col_name}')\n",
+    "g.set(ylim=(0.48, 1.02))\n",
+    "g.set(xlabel =\"\")\n",
+    "g.set(ylabel =\"\")\n",
+    "g.set_xticklabels(rotation=70)\n",
+    "#g.add_legend(fontsize=20)\n",
+    "g.tight_layout()\n",
+    "\n",
+    "g.fig.savefig(\"varyvilocaparams__snvs.svg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "5353db80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7275751660352924"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[(df_long['method']=='viloca_alpha_0.00001') \n",
+    "        & (df_long['variable']=='recall')]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "b56d0e7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7349290953297077"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[(df_long['method']=='viloca_learn_error_params') \n",
+    "        & (df_long['variable']=='recall')]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "73f68f3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.714583393335505"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[(df_long['method']=='viloca_alpha_0.00001') \n",
+    "        & (df_long['seq_mode']=='400:100')\n",
+    "        & (df_long['variable']=='recall')]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "4d882519",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8936451151187216"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[(df_long['method']=='viloca_uniform') \n",
+    "        & (df_long['seq_mode']=='400:100')\n",
+    "        & (df_long['variable']=='recall')]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "8b362986",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7405669387350798"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[(df_long['method']=='viloca_alpha_0.00001') \n",
+    "        & (df_long['seq_mode']=='400:10')\n",
+    "        & (df_long['variable']=='recall')]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "344fdfc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5144389299421755"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[(df_long['method']=='viloca_uniform') \n",
+    "        & (df_long['seq_mode']=='400:10')\n",
+    "        & (df_long['variable']=='recall')]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1732d0d9",
+   "metadata": {},
+   "source": [
+    "## Runtime comparision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "37671183",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_1720/1974663772.py:6: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  g = sns.catplot(\n",
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_1720/1974663772.py:6: UserWarning: \n",
+      "The palette list has fewer values (1) than needed (5) and will cycle, which may produce an uninterpretable plot.\n",
+      "  g = sns.catplot(\n",
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_1720/1974663772.py:28: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  g.ax.set_yticklabels([datetime.timedelta(seconds=ti) for ti in ticks])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 559.3x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# runtime \n",
+    "df = pd.read_csv(fname_runtime)\n",
+    "\n",
+    "df = df[df['method'].isin(method_order)]\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "        data=df,\n",
+    "        x=\"method\",\n",
+    "        order = method_order,\n",
+    "        y=\"value\",\n",
+    "        kind=\"point\",\n",
+    "        linestyles='', \n",
+    "        margin_titles=True,\n",
+    "        height=7,\n",
+    "        aspect=0.6,\n",
+    "        errorbar='sd',\n",
+    "        palette=[\"#ff6b6b\"]\n",
+    "    )\n",
+    "\n",
+    "# set your date formatter whit this format '%H:%M'\n",
+    "#formatter = time.strftime('%H:%M:%S', time.gmtime)\n",
+    "#g.ax.yaxis.set_major_formatter(formatter)\n",
+    "ticks = g.ax.get_yticks()\n",
+    "#g.ax.set_yticklabels(pd.to_datetime(ticks, unit='s').strftime('%H:%M:%S'))\n",
+    "\n",
+    "import datetime\n",
+    "#str(datetime.timedelta(seconds=666))\n",
+    "g.ax.set_yticklabels([datetime.timedelta(seconds=ti) for ti in ticks])\n",
+    "\n",
+    "g.set_xticklabels(rotation=70)\n",
+    "\n",
+    "g.tight_layout()\n",
+    "g.savefig('varyvilocaparams__runtime.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "a90f7d42",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.356262333333333"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['method']=='viloca_alpha_0.00001']['value'].mean()/60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "e3c8448f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.511947666666667"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['method']=='viloca_envp_0.001']['value'].mean()/60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "7ccb66d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.041774666666667"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['method']=='viloca_envp']['value'].mean()/60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe35a044",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_3A+B__SupFig_X__distance__snvs_precision_recall_plots.ipynb b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_3A+B__SupFig_X__distance__snvs_precision_recall_plots.ipynb
new file mode 100644
index 00000000..4f77ce02
--- /dev/null
+++ b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_3A+B__SupFig_X__distance__snvs_precision_recall_plots.ipynb
@@ -0,0 +1,933 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "57a0a953",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "import humanize\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "38db5bea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "method_palette = { \"LoFreq\": \"#9593D9\",\n",
+    "                   \"ShoRAH\": \"#ffe66d\",\n",
+    "                   \"CliqueSNV\":  \"#4ecdc4\",\n",
+    "                   \"VILOCA\": \"#ff6b6b\",\n",
+    "                   \"PredictHaplo\": \"#227c9d\",\n",
+    "                   \"ground_truth\": \"grey\",}\n",
+    "\n",
+    "mapping_methods = {\n",
+    "    'cliquesnv_local_haplo_snv_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_snv': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo': \"CliqueSNV\",\n",
+    "    'shorah_default': 'ShoRAH',\n",
+    "    'lofreq_local_haplo': 'LoFreq',\n",
+    "    'viloca_alpha_0.00001': 'VILOCA',\n",
+    "    'viloca_alpha_0.00001_K100': 'VILOCA',\n",
+    "    'viloca_multi': 'VILOCA',\n",
+    "    'ground_truth': \"ground truth\",\n",
+    "    'predicthaplo_local_haplo': 'PredictHaplo'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d65ecad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping_haplos = {\n",
+    "    '2@3@30@20@20@geom@0.75': 'Population 1', \n",
+    "    '5@5@30@10@15@geom@0.75': 'Population 2',\n",
+    "    '5@10@30@10@10@geom@0.75': 'Population 3', \n",
+    "    '5@15@30@10@15@geom@0.75': 'Population 4', \n",
+    "    \n",
+    "    '2@3@600@400@400@geom@0.75': 'Population 5',\n",
+    "    '2@3@600@600@600@geom@0.75': 'Population 6',\n",
+    "    '5@5@600@200@300@geom@0.75': 'Population 7', \n",
+    "    '5@10@600@200@240@geom@0.75': 'Population 8',\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8b41bcad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fname_viloca_posterior = \"results/distance/performance_measures/performance_mutation_calls_posterior_thres.csv\"\n",
+    "fname_local = \"results/distance/performance_measures/local/performance.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "db351c29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_local = pd.read_csv(fname_local).drop(columns=[\"Unnamed: 0\"])\n",
+    "\n",
+    "df_local = df_local[(~df_local['method'].str.startswith(\"viloca\")) \n",
+    "                    & (~df_local['method'].str.startswith(\"shorah\"))\n",
+    "                    & (~df_local['method'].str.startswith(\"predicthaplo\"))] # predicthaplo does not provide snvs\n",
+    "\n",
+    "df_local = df_local.pivot(index=['method', 'params', 'replicate'], columns='variable', values='value').reset_index()\n",
+    "\n",
+    "df_local.loc[:, 'seq_tech'] = df_local['params'].str.split('~').str[1].str.split('\\n').str[0]\n",
+    "df_local.loc[:,'coverage'] = df_local['params'].str.split('~').str[6].str.split('\\n').str[0]\n",
+    "df_local.loc[:, 'haplos'] = df_local['params'].str.split('~').str[7].str.split('\\n').str[0]\n",
+    "df_local = df_local[df_local['haplos'].isin(mapping_haplos.keys())]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8cf2dfa8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['lofreq_local_haplo'], dtype=object)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_local['method'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "017cfadf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_viloca = pd.read_csv(fname_viloca_posterior).drop(columns=[\"Unnamed: 0\"])\n",
+    "\n",
+    "df_viloca.loc[:, 'seq_tech'] = df_viloca['params'].str.split('~').str[1].str.split('_').str[0]\n",
+    "df_viloca.loc[:,'coverage'] = df_viloca['params'].str.split('~').str[6].str.split('_').str[0]\n",
+    "df_viloca.loc[:, 'haplos'] = df_viloca['params'].str.split('~').str[7].str.split('_').str[0]\n",
+    "df_viloca = df_viloca[df_viloca['haplos'].isin(mapping_haplos.keys())]\n",
+    "\n",
+    "df_viloca = df_viloca[df_viloca['posterior_threshold'].isin([0.0,0.5, 0.9])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "526c6942",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['shorah_default', 'viloca_alpha_0.00001'], dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_viloca['method'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ed8a4906",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat([df_viloca, df_local])\n",
+    "\n",
+    "# give other methods also a postiro_threshold vaues such that they apppear in plotting\n",
+    "df['posterior_threshold'] = df['posterior_threshold'].fillna(0.9)\n",
+    "\n",
+    "df[\"Method\"] = df['method'].apply(lambda x: mapping_methods[x])\n",
+    "df[\"Population\"] = df['haplos'].apply(lambda x: mapping_haplos[x])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5e3e7134",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7fb3c74bcc10>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1320.65x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "g =sns.relplot(\n",
+    "    data=df,\n",
+    "    x='recall', \n",
+    "    y='precision', \n",
+    "    hue='Method',\n",
+    "    markers=[\"X\", \"P\", \"X\"],\n",
+    "    size='posterior_threshold',\n",
+    "    sizes=(50, 400),\n",
+    "    style='Method',\n",
+    "    col='seq_tech',\n",
+    "    legend=True,\n",
+    "    alpha=0.4, \n",
+    "    palette=method_palette,\n",
+    ")\n",
+    "\n",
+    "g.set_titles(col_template='{col_name}')\n",
+    "g.set(ylim=(0, 1.01))\n",
+    "g.set(ylabel =\"\")\n",
+    "#g.legend(loc='center left', bbox_to_anchor=(1.25, 0.5), ncol=1)\n",
+    "g.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bc362dbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>method</th>\n",
+       "      <th>params</th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>posterior_threshold</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>seq_tech</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>haplos</th>\n",
+       "      <th>Method</th>\n",
+       "      <th>Population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.925158</td>\n",
+       "      <td>0.961124</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.925711</td>\n",
+       "      <td>0.961423</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1769</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.750999</td>\n",
+       "      <td>0.857795</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1770</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.925158</td>\n",
+       "      <td>0.961124</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1771</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.943461</td>\n",
+       "      <td>0.970908</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1772</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.919226</td>\n",
+       "      <td>0.957913</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1773</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.873476</td>\n",
+       "      <td>0.932466</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2965</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.717875</td>\n",
+       "      <td>0.835771</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2966</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.925158</td>\n",
+       "      <td>0.961124</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2967</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.775557</td>\n",
+       "      <td>0.873593</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2968</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.755973</td>\n",
+       "      <td>0.861030</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2969</th>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.697620</td>\n",
+       "      <td>0.821880</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>5@5@600@200@300@geom@0.75</td>\n",
+       "      <td>VILOCA</td>\n",
+       "      <td>Population 7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    method                                             params  \\\n",
+       "274   viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "275   viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "276   viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "277   viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "278   viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "1769  viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "1770  viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "1771  viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "1772  viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "1773  viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "2965  viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "2966  viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "2967  viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "2968  viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "2969  viloca_alpha_0.00001  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...   \n",
+       "\n",
+       "      replicate  posterior_threshold  precision    recall        f1 seq_tech  \\\n",
+       "274           0                  0.0        1.0  1.000000  1.000000   pacbio   \n",
+       "275           1                  0.0        1.0  0.925158  0.961124   pacbio   \n",
+       "276           2                  0.0        1.0  1.000000  1.000000   pacbio   \n",
+       "277           3                  0.0        1.0  1.000000  1.000000   pacbio   \n",
+       "278           4                  0.0        1.0  0.925711  0.961423   pacbio   \n",
+       "1769          0                  0.5        1.0  0.750999  0.857795   pacbio   \n",
+       "1770          1                  0.5        1.0  0.925158  0.961124   pacbio   \n",
+       "1771          2                  0.5        1.0  0.943461  0.970908   pacbio   \n",
+       "1772          3                  0.5        1.0  0.919226  0.957913   pacbio   \n",
+       "1773          4                  0.5        1.0  0.873476  0.932466   pacbio   \n",
+       "2965          0                  0.9        1.0  0.717875  0.835771   pacbio   \n",
+       "2966          1                  0.9        1.0  0.925158  0.961124   pacbio   \n",
+       "2967          2                  0.9        1.0  0.775557  0.873593   pacbio   \n",
+       "2968          3                  0.9        1.0  0.755973  0.861030   pacbio   \n",
+       "2969          4                  0.9        1.0  0.697620  0.821880   pacbio   \n",
+       "\n",
+       "     coverage                     haplos  Method    Population  \n",
+       "274      1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "275      1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "276      1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "277      1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "278      1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "1769     1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "1770     1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "1771     1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "1772     1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "1773     1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "2965     1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "2966     1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "2967     1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "2968     1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  \n",
+       "2969     1000  5@5@600@200@300@geom@0.75  VILOCA  Population 7  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df['coverage']=='1000') \n",
+    "   & (df['Method']=='VILOCA')\n",
+    "   & (df['Population']=='Population 7')\n",
+    "   & (df['seq_tech']=='pacbio')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c83d53b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 3120.66x360 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "g =sns.relplot(\n",
+    "    data=df[df['coverage']=='1000'],\n",
+    "    x='recall', \n",
+    "    y='precision', \n",
+    "    hue='Method',\n",
+    "    markers=[\"X\", \"P\", \"X\"],\n",
+    "    size='posterior_threshold',\n",
+    "    sizes=(100, 500),\n",
+    "    style='Method',\n",
+    "    #row='seq_tech', \n",
+    "    col='Population',\n",
+    "    col_order=[\"Population 1\", \"Population 2\", \"Population 3\", \"Population 4\", \"Population 5\", \"Population 6\", \"Population 7\", \"Population 8\"],\n",
+    "    legend=True,\n",
+    "    alpha=0.4, \n",
+    "    palette=method_palette,\n",
+    ")\n",
+    "\n",
+    "g.set_titles(col_template='{col_name}')\n",
+    "g.set(ylim=(0, 1.05))\n",
+    "#g.legend(loc='center left', bbox_to_anchor=(1.25, 0.5), ncol=1)\n",
+    "g.tight_layout()\n",
+    "g.fig.savefig(\"distance__precision-recall-plot-all-reads.svg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cdef2427",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1228.03x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "df_long = df[df['posterior_threshold']>=0.9]\n",
+    "df_long = df_long.drop(columns=['posterior_threshold'])\n",
+    "df_long = pd.melt(df_long, id_vars=[\"Method\",\n",
+    "                                    \"method\",\n",
+    "                                    \"params\", \n",
+    "                                    \"replicate\", \n",
+    "                                    \"seq_tech\", \n",
+    "                                    \"coverage\", \n",
+    "                                    \"haplos\", \n",
+    "                                    \"Population\"]).assign(\n",
+    "        params=lambda x: x[\"params\"].str.replace(\"_\", \"\\n\")\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "mapping_seqtech = {'illumina': 'Illumina', \n",
+    "                    'nanopore': 'ONT', \n",
+    "                  'pacbio': 'PacBio'}\n",
+    "\n",
+    "df_long[\"seq_tech\"] = df_long['seq_tech'].apply(lambda x: mapping_seqtech[x])\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "        data=df_long[df_long['coverage']=='1000'],\n",
+    "        x=\"variable\",\n",
+    "        y=\"value\",\n",
+    "        hue=\"Method\",\n",
+    "        col=\"seq_tech\",\n",
+    "        kind=\"box\",\n",
+    "        hue_order=[\n",
+    "                 'VILOCA',\n",
+    "                 'LoFreq',\n",
+    "                 'ShoRAH',\n",
+    "                ] ,\n",
+    "        legend=True, \n",
+    "        width=0.8,\n",
+    "        linewidth=0.5,\n",
+    "        sharex=True,\n",
+    "        margin_titles=True,\n",
+    "        palette=method_palette,\n",
+    "    )\n",
+    "g.set_titles(col_template='{col_name}')\n",
+    "g.set(ylim=(-0.01, 1))\n",
+    "g.set(xlabel =\"\")\n",
+    "g.set(ylabel =\"\")\n",
+    "#g.add_legend(fontsize=20)\n",
+    "g.tight_layout()\n",
+    "\n",
+    "g.fig.savefig(\"distance__snvs_seq_tech.svg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "50880496",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1228.03x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "df_long = df[df['posterior_threshold']>=0.9]\n",
+    "df_long = df_long.drop(columns=['posterior_threshold'])\n",
+    "df_long = pd.melt(df_long, id_vars=[\"Method\",\n",
+    "                                    \"method\",\n",
+    "                                    \"params\", \n",
+    "                                    \"replicate\", \n",
+    "                                    \"seq_tech\", \n",
+    "                                    \"coverage\", \n",
+    "                                    \"haplos\", \n",
+    "                                    \"Population\"]).assign(\n",
+    "        params=lambda x: x[\"params\"].str.replace(\"_\", \"\\n\")\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "        data=df_long[df_long['Population'].isin([\"Population 2\", \"Population 6\"])],\n",
+    "        x=\"coverage\",\n",
+    "        y=\"value\",\n",
+    "        hue=\"Method\",\n",
+    "        col=\"variable\",\n",
+    "        row='Population',\n",
+    "        kind=\"box\",\n",
+    "        order=['100', '200', '500', '800', '1000', '5000', '10000'],\n",
+    "        hue_order=[\n",
+    "                 'VILOCA',\n",
+    "                 'LoFreq',\n",
+    "                 'ShoRAH',\n",
+    "                ] ,\n",
+    "        legend=True, \n",
+    "        width=0.8,\n",
+    "        linewidth=0.5,\n",
+    "        sharex=False,\n",
+    "        margin_titles=True,\n",
+    "        palette=method_palette,\n",
+    "    )\n",
+    "g.set_titles(row_template='{row_name}')\n",
+    "g.set_titles(col_template='{col_name}')\n",
+    "\n",
+    "g.set(ylim=(-0.01, 1.03))\n",
+    "g.set(xlabel =\"\")\n",
+    "g.set(ylabel =\"\")\n",
+    "g.set_xticklabels(rotation=30)\n",
+    "#g.add_legend(fontsize=20)\n",
+    "g.tight_layout()\n",
+    "\n",
+    "g.fig.savefig(\"distance__snvs_coverage.svg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "16d5177b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.43415435521530904"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[\n",
+    "    (df_long['Method']=='LoFreq') \n",
+    "    & (df_long['variable']=='recall')\n",
+    "    & (df_long['seq_tech']!='Illumina')\n",
+    "]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "00051228",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9762829067302236"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[\n",
+    "    (df_long['Method']=='LoFreq') \n",
+    "    & (df_long['variable']=='precision')\n",
+    "    & (df_long['seq_tech']!='Illumina')\n",
+    "]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "23bd8ee7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4650991140341277"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[\n",
+    "    (df_long['Method']=='ShoRAH') \n",
+    "    & (df_long['variable']=='recall')\n",
+    "    & (df_long['seq_tech']!='Illumina')\n",
+    "]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a5d8596d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9355322954596151"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[\n",
+    "    (df_long['Method']=='ShoRAH') \n",
+    "    & (df_long['variable']=='precision')\n",
+    "    & (df_long['seq_tech']!='Illumina')\n",
+    "]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "49af2c7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8082377745264459"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[\n",
+    "    (df_long['Method']=='VILOCA') \n",
+    "    & (df_long['variable']=='recall')\n",
+    "    & (df_long['seq_tech']!='Illumina')\n",
+    "]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "94d40a67",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9955442966841386"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_long[\n",
+    "    (df_long['Method']=='VILOCA') \n",
+    "    & (df_long['variable']=='precision')\n",
+    "    & (df_long['seq_tech']!='Illumina')\n",
+    "]['value'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce6a1b78",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee39a4a2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_3A__distance__snvs_freq_scatter.ipynb b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_3A__distance__snvs_freq_scatter.ipynb
new file mode 100644
index 00000000..31174308
--- /dev/null
+++ b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_3A__distance__snvs_freq_scatter.ipynb
@@ -0,0 +1,763 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "57a0a953",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "import humanize\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['lines.markersize'] = 10              #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "38db5bea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "method_palette = { \"LoFreq\": \"#9593D9\",\n",
+    "                   \"ShoRAH\": \"#ffe66d\",\n",
+    "                   \"CliqueSNV\":  \"#4ecdc4\",\n",
+    "                   \"VILOCA\": \"#ff6b6b\",\n",
+    "                   \"PredictHaplo\": \"#227c9d\",\n",
+    "                   \"ground_truth\": \"grey\",}\n",
+    "\n",
+    "mapping_methods = {\n",
+    "    'cliquesnv_local_haplo_snv_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_snv': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo': \"CliqueSNV\",\n",
+    "    'shorah_default': 'ShoRAH',\n",
+    "    'lofreq_local_haplo': 'LoFreq',\n",
+    "    'viloca_alpha_0.00001': 'VILOCA',\n",
+    "    'viloca_alpha_0.00001_K100': 'VILOCA',\n",
+    "    'viloca_multi': 'VILOCA',\n",
+    "    'ground_truth': \"ground truth\",\n",
+    "    'predicthaplo_local_haplo': 'PredictHaplo'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0d65ecad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping_haplos = {\n",
+    "    '2@3@30@20@20@geom@0.75': 'Population 1', \n",
+    "    '5@5@30@10@15@geom@0.75': 'Population 2',\n",
+    "    '5@10@30@10@10@geom@0.75': 'Population 3', \n",
+    "    '5@15@30@10@15@geom@0.75': 'Population 4', \n",
+    "    \n",
+    "    '2@3@600@400@400@geom@0.75': 'Population 5',\n",
+    "    '2@3@600@600@600@geom@0.75': 'Population 6',\n",
+    "    '5@5@600@200@300@geom@0.75': 'Population 7', \n",
+    "    '5@10@600@200@240@geom@0.75': 'Population 8',\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8b41bcad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fname = \"results/distance/performance_measures/performance_mutation_calls_freqs.csv\"\n",
+    "df = pd.read_csv(fname).drop(columns=[\"Unnamed: 0\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "12a622b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>method</th>\n",
+       "      <th>params</th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>true_positive</th>\n",
+       "      <th>false_negative</th>\n",
+       "      <th>haplotype</th>\n",
+       "      <th>frequency_groundtruth</th>\n",
+       "      <th>frequency_predicted</th>\n",
+       "      <th>posterior</th>\n",
+       "      <th>position</th>\n",
+       "      <th>variant</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0000</td>\n",
+       "      <td>0.327785</td>\n",
+       "      <td>0.568</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0000</td>\n",
+       "      <td>0.327785</td>\n",
+       "      <td>0.562</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0000</td>\n",
+       "      <td>0.327785</td>\n",
+       "      <td>0.574</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0000</td>\n",
+       "      <td>0.327785</td>\n",
+       "      <td>0.327</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>66</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0000</td>\n",
+       "      <td>0.327785</td>\n",
+       "      <td>0.566</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>67</td>\n",
+       "      <td>G</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               method                                             params  \\\n",
+       "0  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "1  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "2  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "3  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "4  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "\n",
+       "   replicate  true_positive  false_negative      haplotype  \\\n",
+       "0          0              1               0  haplotype0000   \n",
+       "1          0              1               0  haplotype0000   \n",
+       "2          0              1               0  haplotype0000   \n",
+       "3          0              1               0  haplotype0000   \n",
+       "4          0              1               0  haplotype0000   \n",
+       "\n",
+       "   frequency_groundtruth  frequency_predicted  posterior  position variant  \n",
+       "0               0.327785                0.568        1.0         6       T  \n",
+       "1               0.327785                0.562        1.0         9       A  \n",
+       "2               0.327785                0.574        1.0        60       A  \n",
+       "3               0.327785                0.327        1.0        66       A  \n",
+       "4               0.327785                0.566        1.0        67       G  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "4ea3233c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['lofreq_local_haplo', 'shorah_default', 'viloca_alpha_0.00001'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['method'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "04da708b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>method</th>\n",
+       "      <th>params</th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>true_positive</th>\n",
+       "      <th>false_negative</th>\n",
+       "      <th>haplotype</th>\n",
+       "      <th>frequency_groundtruth</th>\n",
+       "      <th>frequency_predicted</th>\n",
+       "      <th>posterior</th>\n",
+       "      <th>position</th>\n",
+       "      <th>variant</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>17660</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0002</td>\n",
+       "      <td>0.142530</td>\n",
+       "      <td>0.205</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17706</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0005</td>\n",
+       "      <td>0.060130</td>\n",
+       "      <td>0.205</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17720</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0006</td>\n",
+       "      <td>0.045097</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17735</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0007</td>\n",
+       "      <td>0.033823</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17752</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0008</td>\n",
+       "      <td>0.025367</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17767</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0009</td>\n",
+       "      <td>0.019025</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17779</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0010</td>\n",
+       "      <td>0.014269</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17790</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0011</td>\n",
+       "      <td>0.010702</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17800</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0012</td>\n",
+       "      <td>0.008026</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17811</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0013</td>\n",
+       "      <td>0.006020</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17812</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0013</td>\n",
+       "      <td>0.006020</td>\n",
+       "      <td>0.766</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>57</td>\n",
+       "      <td>G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17823</th>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>haplotype0014</td>\n",
+       "      <td>0.004515</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   method                                             params  \\\n",
+       "17660  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "17706  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "17720  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "17735  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "17752  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "17767  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "17779  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "17790  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "17800  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "17811  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "17812  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "17823  lofreq_local_haplo  seq_tech~illumina__seq_mode~shotgun__seq_mode_...   \n",
+       "\n",
+       "       replicate  true_positive  false_negative      haplotype  \\\n",
+       "17660          1              1               0  haplotype0002   \n",
+       "17706          1              1               0  haplotype0005   \n",
+       "17720          1              1               0  haplotype0006   \n",
+       "17735          1              1               0  haplotype0007   \n",
+       "17752          1              1               0  haplotype0008   \n",
+       "17767          1              1               0  haplotype0009   \n",
+       "17779          1              1               0  haplotype0010   \n",
+       "17790          1              1               0  haplotype0011   \n",
+       "17800          1              1               0  haplotype0012   \n",
+       "17811          1              1               0  haplotype0013   \n",
+       "17812          1              1               0  haplotype0013   \n",
+       "17823          1              1               0  haplotype0014   \n",
+       "\n",
+       "       frequency_groundtruth  frequency_predicted  posterior  position variant  \n",
+       "17660               0.142530                0.205        1.0        56       T  \n",
+       "17706               0.060130                0.205        1.0        56       T  \n",
+       "17720               0.045097                0.170        1.0        52       A  \n",
+       "17735               0.033823                0.170        1.0        52       A  \n",
+       "17752               0.025367                0.170        1.0        52       A  \n",
+       "17767               0.019025                0.170        1.0        52       A  \n",
+       "17779               0.014269                0.170        1.0        52       A  \n",
+       "17790               0.010702                0.170        1.0        52       A  \n",
+       "17800               0.008026                0.170        1.0        52       A  \n",
+       "17811               0.006020                0.170        1.0        52       A  \n",
+       "17812               0.006020                0.766        1.0        57       G  \n",
+       "17823               0.004515                0.170        1.0        52       A  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# What happens in Population 3 with LoFreq? Why are the frequency predictions so off? \n",
+    "df[(df['params'].str.startswith('seq_tech~illumina__seq_mode~shotgun__seq_mode'))\n",
+    "  & (df['method']==\"lofreq_local_haplo\")  & (df['replicate']==1)\n",
+    "  & (df['position'].isin([50,51,52,53,54,55,56,57,]))\n",
+    "  & (df['params']=='seq_tech~illumina__seq_mode~shotgun__seq_mode_param~single_amplicon__read_length~249__genome_size~249__coverage~1000__haplos~5@10@30@10@10@geom@0.75')]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "ee29c664",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# mutations in the ground truth are not unique, they occurr several per row with different frequencies as \n",
+    "# they \n",
+    "\n",
+    "df = df.pivot_table(values='frequency_groundtruth', \n",
+    "               index=['params', 'replicate', 'method', 'frequency_predicted', 'posterior', 'position', 'variant'],\n",
+    "               aggfunc='sum').reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "db351c29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[:, 'seq_tech'] = df['params'].str.split('~').str[1].str.split('__').str[0]\n",
+    "df.loc[:,'coverage'] = df['params'].str.split('~').str[6].str.split('\\n').str[0]\n",
+    "df.loc[:, 'haplos'] = df['params'].str.split('~').str[7].str.split('\\n').str[0]\n",
+    "df = df[df['haplos'].isin(mapping_haplos.keys())]\n",
+    "df[\"Method\"] = df['method'].apply(lambda x: mapping_methods[x])\n",
+    "df[\"Population\"] = df['haplos'].apply(lambda x: mapping_haplos[x])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "ed8a4906",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df[df['posterior']>=0.9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "f6aebf56",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_73835/373708036.py:4: UserWarning: \n",
+      "The markers list has fewer values (1) than needed (3) and will cycle, which may produce an uninterpretable plot.\n",
+      "  g =sns.relplot(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f7abba6c280>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 3028.03x360 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "mpl.rcParams['lines.markersize'] = 20\n",
+    "\n",
+    "g =sns.relplot(\n",
+    "    data=df,\n",
+    "    x='frequency_groundtruth', \n",
+    "    y='frequency_predicted', \n",
+    "    hue='Method',\n",
+    "    markers=\"X\",\n",
+    "    style='Method',\n",
+    "    col='Population',\n",
+    "    col_order=[\"Population 1\", \"Population 2\", \"Population 3\", \"Population 4\", \"Population 5\", \"Population 6\", \"Population 7\", \"Population 8\"],\n",
+    "    kind=\"scatter\",\n",
+    "    legend=True,\n",
+    "    palette=method_palette,\n",
+    "    alpha=0.2\n",
+    ")\n",
+    "\n",
+    "for ax in g.axes.flat:\n",
+    "    ax.plot(np.linspace(0, 1, 100), np.linspace(0, 1, 100), color='grey')\n",
+    "\n",
+    "g.set_titles(col_template='{col_name}')\n",
+    "# , ylabel='estimated frequency', xlabel='ground truth frequency'\n",
+    "#g.set(yscale='log', xscale='log', xlim = (0,1), ylim=(0,1))\n",
+    "g.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "57c31e2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['seq_tech~illumina__seq_mode~shotgun__seq_mode_param~single_amplicon__read_length~249__genome_size~249__coverage~1000__haplos~5@10@30@10@10@geom@0.75'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# What happens in Population 3 with LoFreq? Why are the frequency predictions so off? \n",
+    "df[(df['Population']==\"Population 3\")\n",
+    "  & (df['Method']==\"LoFreq\")  & (df['replicate']==1)\n",
+    "  & (df['position'].isin([50,51,52,53,54,55,56,57,58,59,60,61,62,63, 150,151,152,153,154,155,156,157,158,159,\n",
+    "                          160,161,162,163,218,219,220,221,222,223,224]))]['params'].unique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "40e0c515",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_73835/2791891421.py:10: UserWarning: \n",
+      "The markers list has fewer values (1) than needed (3) and will cycle, which may produce an uninterpretable plot.\n",
+      "  g =sns.relplot(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1228.03x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "mpl.rcParams['lines.markersize'] = 10\n",
+    "\n",
+    "mapping_seqtech = {'illumina': 'Illumina', \n",
+    "                    'nanopore': 'ONT', \n",
+    "                  'pacbio': 'PacBio'}\n",
+    "\n",
+    "df[\"seq_tech\"] = df['seq_tech'].apply(lambda x: mapping_seqtech[x])\n",
+    "\n",
+    "g =sns.relplot(\n",
+    "    data=df,\n",
+    "    x='frequency_groundtruth', \n",
+    "    y='frequency_predicted', \n",
+    "    hue='Method',\n",
+    "    markers=\"X\",\n",
+    "    style='Method',\n",
+    "    col='seq_tech',\n",
+    "    #col_order=[\"Population 1\", \"Population 2\", \"Population 3\", \"Population 4\", \"Population 5\", \"Population 6\", \"Population 7\", \"Population 8\"],\n",
+    "    kind=\"scatter\",\n",
+    "    legend=True,\n",
+    "    palette=method_palette,\n",
+    "    alpha=0.5\n",
+    ")\n",
+    "\n",
+    "for ax in g.axes.flat:\n",
+    "    ax.plot(np.linspace(0, 1, 100), np.linspace(0, 1, 100), color='grey')\n",
+    "\n",
+    "g.set_titles(col_template='{col_name}')\n",
+    "# , ylabel='estimated frequency', xlabel='ground truth frequency'\n",
+    "#g.set(yscale='log', xscale='log', xlim = (0,1), ylim=(0,1))\n",
+    "g.tight_layout()\n",
+    "g.fig.savefig(\"distance__snvs_freqs.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "67c0b2dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g.fig.savefig('distance__snvs_freqs.png', dpi=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "39c8b4cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g.fig.savefig('distance__snvs_freqs.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2ed44d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_4A+B__distance__haplos_populations_pr.ipynb b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_4A+B__distance__haplos_populations_pr.ipynb
new file mode 100644
index 00000000..1a86b378
--- /dev/null
+++ b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_4A+B__distance__haplos_populations_pr.ipynb
@@ -0,0 +1,1256 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "57a0a953",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "import humanize\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "import editdistance\n",
+    "from tqdm import tqdm\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "38db5bea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "method_palette = { \"LoFreq\": \"#9593D9\",\n",
+    "                   \"ShoRAH\": \"#ffe66d\",\n",
+    "                   \"CliqueSNV\":  \"#4ecdc4\",\n",
+    "                   \"VILOCA\": \"#ff6b6b\",\n",
+    "                   \"PredictHaplo\": \"#227c9d\",\n",
+    "                   \"ground_truth\": \"grey\",\n",
+    "                  \"ground truth\": \"grey\",\n",
+    "                  }\n",
+    "\n",
+    "mapping_methods = {\n",
+    "    'cliquesnv_local_haplo_snv_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_snv': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_tf0.1':\"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo': \"CliqueSNV\",\n",
+    "    'shorah_default': 'ShoRAH',\n",
+    "    'lofreq_local_haplo': 'LoFreq',\n",
+    "    'viloca_alpha_0.00001': 'VILOCA',\n",
+    "    'viloca_alpha_0.00001_K100': 'VILOCA',\n",
+    "    'viloca_multi': 'VILOCA',\n",
+    "    'ground_truth': \"ground truth\",\n",
+    "    'predicthaplo_local_haplo': 'PredictHaplo'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d65ecad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping_haplos = {\n",
+    "    '2@3@30@20@20@geom@0.75': 'Population 1', \n",
+    "    '5@5@30@10@15@geom@0.75': 'Population 2',\n",
+    "    '5@10@30@10@10@geom@0.75': 'Population 3', \n",
+    "    '5@15@30@10@15@geom@0.75': 'Population 4', \n",
+    "    \n",
+    "    '2@3@600@400@400@geom@0.75': 'Population 5',\n",
+    "    '2@3@600@600@600@geom@0.75': 'Population 6',\n",
+    "    '5@5@600@200@300@geom@0.75': 'Population 7', \n",
+    "    '5@10@600@200@240@geom@0.75': 'Population 8',\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8b41bcad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fname_global =  \"results/distance/performance_measures/global/csv_files/pr_results.csv\"\n",
+    "fname_mds = 'results/distance/performance_measures/global/csv_files/mds_results.csv.gz'\n",
+    "\n",
+    "# for computation of precision recall for differnt posterior thresholds\n",
+    "fname_pred = 'results/distance/performance_measures/global/csv_files/predictions.csv.gz'\n",
+    "df_pred = pd.read_csv(fname_pred)\n",
+    "\n",
+    "fname_true = 'results/distance/performance_measures/global/csv_files/ground_truth.csv.gz'\n",
+    "df_true = pd.read_csv(fname_true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "059490a3",
+   "metadata": {},
+   "source": [
+    "## mds plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bb58dbf9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_mds = pd.read_csv(fname_mds)\n",
+    "df_mds.loc[:,'coverage'] = df_mds['params'].str.split('~').str[6].str.split('__').str[0]\n",
+    "df_mds.loc[:, 'haplos'] = df_mds['params'].str.split('~').str[7].str.split('\\n').str[0]\n",
+    "df_mds.loc[:, 'seq_tech'] = df_mds['params'].str.split('~').str[1].str.split('__').str[0]\n",
+    "\n",
+    "#df_mds = df_mds[df_mds['method'].isin(methods_order)]\n",
+    "df_mds[\"Method\"] = df_mds['method'].apply(lambda x: mapping_methods[x])\n",
+    "#df_mds = df_mds[df_mds['haplos'].isin(populations_long + populations_short)]\n",
+    "df_mds[\"Population\"] = df_mds['haplos'].apply(lambda x: mapping_haplos[x])\n",
+    "\n",
+    "# prepare mds plot\n",
+    "\n",
+    "def f_freq_bins(x):\n",
+    "    if x>0.2:\n",
+    "        return 0.3\n",
+    "    elif x>0.1:\n",
+    "        return 0.2\n",
+    "    elif x>0.05:\n",
+    "        return 0.1\n",
+    "    elif x>0.01:\n",
+    "        return 0.05\n",
+    "    elif x<=0.01:\n",
+    "        return 0.01\n",
+    "    \n",
+    "df_mds[\"Frequency\"] = df_mds[\"frequency\"].apply(f_freq_bins)\n",
+    "\n",
+    "# filter out only results with high posterior for viloca samples\n",
+    "df_mds = df_mds[(df_mds['posterior'].isnull()) | (df_mds['posterior']>=0.90)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ad4e40cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['cliquesnv_local_haplo_tf0.01', 'viloca_alpha_0.00001',\n",
+       "       'ground_truth', 'cliquesnv_local_haplo_tf0.1',\n",
+       "       'predicthaplo_local_haplo'], dtype=object)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_mds['method'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "7d75981d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1617.78x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "rep_id = 1\n",
+    "marker_viloca = \"1\"\n",
+    "marker_cliquesnv = \"3\"\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['seq_tech'].isin([\"illumina\"]))\n",
+    "                 & (df_mds['Method'].isin([\"ground truth\"]))]\n",
+    "\n",
+    "g = sns.relplot(data=df_plot.sort_values(\"Method\",  key=np.vectorize(method_order.index)), \n",
+    "            kind=\"scatter\", \n",
+    "            x=\"MDS0\", \n",
+    "            y=\"MDS1\", \n",
+    "            col=\"Population\",\n",
+    "            size=\"Frequency\",\n",
+    "            hue=\"Method\", \n",
+    "            col_order=['Population 1', 'Population 2', 'Population 3', 'Population 4'] ,\n",
+    "            markers=markers,\n",
+    "            hue_order= method_order, \n",
+    "            style_order = method_order, \n",
+    "            style=\"Method\", \n",
+    "            sizes=(300, 600),\n",
+    "            palette=method_palette,\n",
+    "            alpha=0.3,\n",
+    "            linewidth=0.5,\n",
+    "            legend=True,  \n",
+    "            )\n",
+    "\n",
+    "ax0 = g.fig.axes[0]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 1\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"illumina\"]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax0.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca,s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 1\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"illumina\"]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax0.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv, c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "ax1 = g.fig.axes[1]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 2\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"illumina\"]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax1.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca, s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 2\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"illumina\"]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax1.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv, c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "ax2 = g.fig.axes[2]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 3\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"illumina\"]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax2.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca, s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 3\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"illumina\"]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax2.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv,c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "ax3 = g.fig.axes[3]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 4\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"illumina\"]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax3.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca, s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 4\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"illumina\"]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax3.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv, c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "g.set_titles(col_template='{col_name}', fontsize=35)\n",
+    "g.set_xticklabels(\"\")\n",
+    "g.set_yticklabels(\"\")\n",
+    "g.set(xlabel =\"\")\n",
+    "g.set(ylabel =\"sequence space (MDS plot)\")\n",
+    "\n",
+    "g.set(ylim=(-25,25), xlim=(-30, 30))\n",
+    "g.tight_layout()\n",
+    "\n",
+    "g.savefig(\"distance_haplos_illumina_mds.svg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e2686e8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1617.78x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "rep_id = 0\n",
+    "marker_viloca = \"1\"\n",
+    "marker_cliquesnv = \"3\"\n",
+    "marker_predicthaplo = \"4\"\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"ground truth\"]))]\n",
+    "\n",
+    "g = sns.relplot(data=df_plot, \n",
+    "            kind=\"scatter\", \n",
+    "            x=\"MDS0\", \n",
+    "            y=\"MDS1\", \n",
+    "            col=\"Population\",\n",
+    "            size=\"Frequency\",\n",
+    "            hue=\"Method\", \n",
+    "            col_order=['Population 5', 'Population 6', 'Population 7', 'Population 8'] ,\n",
+    "            markers=[\"o\"],\n",
+    "            #hue_order= , \n",
+    "            #style_order = method_order, \n",
+    "            style=\"Method\", \n",
+    "            sizes=(300, 600),\n",
+    "            palette=method_palette,\n",
+    "            alpha=0.3,\n",
+    "            linewidth=0.5,\n",
+    "            legend=True,  \n",
+    "            )\n",
+    "\n",
+    "ax0 = g.fig.axes[0]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 5\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax0.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca,s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 5\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax0.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv, c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 5\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"PredictHaplo\"]))]\n",
+    "ax0.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_predicthaplo, c=\"#227c9d\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax1 = g.fig.axes[1]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 6\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax1.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca, s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 6\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax1.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv, c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 6\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"PredictHaplo\"]))]\n",
+    "ax1.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_predicthaplo, c=\"#227c9d\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax2 = g.fig.axes[2]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 7\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax2.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca, s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 7\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax2.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv,c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 7\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"PredictHaplo\"]))]\n",
+    "ax2.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_predicthaplo,c=\"#227c9d\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax3 = g.fig.axes[3]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 8\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax3.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca, s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 8\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax3.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv, c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 8\")\n",
+    "                 & (df_mds['seq_tech'].isin([\"pacbio\"]))\n",
+    "                 & (df_mds['Method'].isin([\"PredictHaplo\"]))]\n",
+    "ax3.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_predicthaplo, c=\"#227c9d\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "\n",
+    "\n",
+    "g.set_titles(col_template='{col_name}', fontsize=35)\n",
+    "g.set_xticklabels(\"\")\n",
+    "g.set_yticklabels(\"\")\n",
+    "g.set(xlabel =\"\")\n",
+    "g.set(ylabel =\"sequence space (MDS plot)\")\n",
+    "\n",
+    "g.tight_layout()\n",
+    "\n",
+    "g.savefig(\"distance_haplos_pacbio_mds.svg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "91d22584",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1617.78x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "rep_id = 0\n",
+    "marker_viloca = \"1\"\n",
+    "marker_cliquesnv = \"3\"\n",
+    "marker_predicthaplo = \"4\"\n",
+    "\n",
+    "seq_tech = 'nanopore'\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"ground truth\"]))]\n",
+    "\n",
+    "g = sns.relplot(data=df_plot, \n",
+    "            kind=\"scatter\", \n",
+    "            x=\"MDS0\", \n",
+    "            y=\"MDS1\", \n",
+    "            col=\"Population\",\n",
+    "            size=\"Frequency\",\n",
+    "            hue=\"Method\", \n",
+    "            col_order=['Population 5', 'Population 6', 'Population 7', 'Population 8'] ,\n",
+    "            markers='o',\n",
+    "            style=\"Method\", \n",
+    "            sizes=(300, 600),\n",
+    "            palette=method_palette,\n",
+    "            alpha=0.3,\n",
+    "            linewidth=0.5,\n",
+    "            legend=True,  \n",
+    "            )\n",
+    "\n",
+    "ax0 = g.fig.axes[0]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 5\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax0.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca,s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 5\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax0.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv, c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 5\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"PredictHaplo\"]))]\n",
+    "ax0.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_predicthaplo, c=\"#227c9d\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax1 = g.fig.axes[1]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 6\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax1.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca, s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 6\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax1.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv, c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 6\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"PredictHaplo\"]))]\n",
+    "ax1.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_predicthaplo, c=\"#227c9d\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax2 = g.fig.axes[2]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 7\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax2.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca, s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 7\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax2.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv,c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 7\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"PredictHaplo\"]))]\n",
+    "ax2.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_predicthaplo,c=\"#227c9d\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax3 = g.fig.axes[3]\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 8\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax3.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], color=\"#ff6b6b\", marker=marker_viloca, s=600, linewidth=3)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 8\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax3.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_cliquesnv, c=\"#4ecdc4\", s=600, linewidth=3, alpha =0.8)\n",
+    "df_plot = df_mds[(df_mds[\"replicate\"]==rep_id) \n",
+    "                 & (df_mds['coverage'].isin([\"1000\"]))\n",
+    "                 & (df_mds['Population']==\"Population 8\")\n",
+    "                 & (df_mds['seq_tech'].isin([seq_tech]))\n",
+    "                 & (df_mds['Method'].isin([\"PredictHaplo\"]))]\n",
+    "ax3.scatter(df_plot[\"MDS0\"], df_plot[\"MDS1\"], marker=marker_predicthaplo, c=\"#227c9d\", s=600, linewidth=3, alpha =0.8)\n",
+    "\n",
+    "\n",
+    "\n",
+    "g.set_titles(col_template='{col_name}', fontsize=35)\n",
+    "g.set_xticklabels(\"\")\n",
+    "g.set_yticklabels(\"\")\n",
+    "g.set(xlabel =\"\")\n",
+    "g.set(ylabel =\"sequence space (MDS plot)\")\n",
+    "\n",
+    "g.tight_layout()\n",
+    "\n",
+    "g.savefig(\"distance_haplos_ont_mds.svg\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bf57e50",
+   "metadata": {},
+   "source": [
+    "## for viloca: compute posterior threshold pr-values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "36e6b97c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def relative_edit_distance(seq1, seq2):\n",
+    "    dist = editdistance.eval(seq1, seq2)\n",
+    "    rel = dist / max(len(seq1), len(seq2))\n",
+    "    return rel\n",
+    "\n",
+    "\n",
+    "def pr_worker(index, df_group, df_true, thres):\n",
+    "    (method, params, replicate) = index\n",
+    "\n",
+    "    tp = 0\n",
+    "    fp = 0\n",
+    "    fn = 0\n",
+    "\n",
+    "    df_true_grpd = df_true[\n",
+    "        (df_true[\"params\"] == params) & (df_true[\"replicate\"] == replicate)\n",
+    "    ]\n",
+    "\n",
+    "    # subsample large results\n",
+    "    max_num = 500\n",
+    "    df_group = df_group.sample(n=min(df_group.shape[0], max_num))\n",
+    "\n",
+    "    # true positive: predicted seq appears in ground truth\n",
+    "    # false positive: predicted seq does not appear in ground truth\n",
+    "    df_cur = df_true_grpd.copy()\n",
+    "    for row in tqdm(df_group.itertuples(), total=df_group.shape[0], leave=False):\n",
+    "        ser_dist = df_cur[\"sequence\"].apply(\n",
+    "            lambda x: relative_edit_distance(x, row.sequence)\n",
+    "        )\n",
+    "        passed_thres = (ser_dist <= thres).any()\n",
+    "\n",
+    "        if passed_thres:\n",
+    "            tp += 1\n",
+    "\n",
+    "            # remove ground truth because it was predicted\n",
+    "            df_cur = df_cur.drop(ser_dist.idxmin())\n",
+    "        else:\n",
+    "            fp += 1\n",
+    "\n",
+    "    # false negative: ground truth sequence was not predicted\n",
+    "    # single prediction should not map to multiple ground truth seqs\n",
+    "    df_cur = df_group.copy()\n",
+    "    for row in tqdm(\n",
+    "        df_true_grpd.itertuples(), total=df_true_grpd.shape[0], leave=False\n",
+    "    ):\n",
+    "        ser_dist = df_cur[\"sequence\"].apply(\n",
+    "            lambda x: relative_edit_distance(x, row.sequence)\n",
+    "        )\n",
+    "        passed_thres = (ser_dist <= thres).any()\n",
+    "\n",
+    "        if not passed_thres:\n",
+    "            fn += 1\n",
+    "        else:\n",
+    "            # remove current prediction\n",
+    "            df_cur = df_cur.drop(ser_dist.idxmin())\n",
+    "    \n",
+    "    recall = tp / (tp + fn)\n",
+    "    precision = tp / (tp + fp)\n",
+    "    # finalize\n",
+    "    return {\n",
+    "        \"method\": method,\n",
+    "        \"params\": params,\n",
+    "        \"replicate\": replicate,\n",
+    "        \"tp\": tp,\n",
+    "        \"fp\": fp,\n",
+    "        \"fn\": fn,\n",
+    "        \"precision\": tp / (tp + fp),\n",
+    "        \"recall\": tp / (tp + fn),\n",
+    "        \"f1\": 0 if precision + recall == 0 else 2 * (precision * recall) / (precision + recall), \n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "852eaf74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                                                                                                                                            \r"
+     ]
+    }
+   ],
+   "source": [
+    "distance_threshold = 0.01\n",
+    "\n",
+    "posterior_thresholds = [0.0, 0.5, 0.9]\n",
+    "\n",
+    "# Goal dataframe with the following columns\n",
+    "# params, method, replicate, posterior_threshold, precison, recall, f1\n",
+    "\n",
+    "tmp = []\n",
+    "\n",
+    "for posterior_threshold in posterior_thresholds:\n",
+    "    df_pred_tmp = df_pred[df_pred['posterior']>posterior_threshold]\n",
+    "    for index, df_group in df_pred_tmp.groupby([\"method\", \"params\", \"replicate\"]):\n",
+    "\n",
+    "        pr_list = pr_worker(index, df_group, df_true, distance_threshold)\n",
+    "        pr_list['posterior_threshold'] = posterior_threshold\n",
+    "        tmp.append(pr_list)\n",
+    "                                                   \n",
+    "df_pr = pd.DataFrame(tmp) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "71902da1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_8414/1643034023.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_pr_precision['value']= df_pr_precision['precision']\n",
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_8414/1643034023.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_pr_precision['variable']='precision'\n",
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_8414/1643034023.py:8: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_pr_recall['value']= df_pr_recall['recall']\n",
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_8414/1643034023.py:9: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_pr_recall['variable']='recall'\n",
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_8414/1643034023.py:13: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_pr_f1['value']= df_pr_f1['f1']\n",
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_8414/1643034023.py:14: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_pr_f1['variable']='f1'\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_pr_precision = df_pr[['precision', 'method', 'replicate', 'params', 'posterior_threshold']]\n",
+    "df_pr_precision['value']= df_pr_precision['precision']\n",
+    "df_pr_precision['variable']='precision'\n",
+    "df_pr_precision = df_pr_precision[['variable','value', 'method', 'replicate', 'params', 'posterior_threshold']]\n",
+    "\n",
+    "\n",
+    "df_pr_recall = df_pr[['recall', 'method', 'replicate', 'params', 'posterior_threshold']]\n",
+    "df_pr_recall['value']= df_pr_recall['recall']\n",
+    "df_pr_recall['variable']='recall'\n",
+    "df_pr_recall = df_pr_recall[['variable','value', 'method', 'replicate', 'params', 'posterior_threshold']]\n",
+    "\n",
+    "df_pr_f1 = df_pr[['f1', 'method', 'replicate', 'params', 'posterior_threshold']]\n",
+    "df_pr_f1['value']= df_pr_f1['f1']\n",
+    "df_pr_f1['variable']='f1'\n",
+    "df_pr_f1 = df_pr_f1[['variable','value', 'method', 'replicate', 'params', 'posterior_threshold']]\n",
+    "\n",
+    "\n",
+    "df_pr_long = pd.concat([df_pr_recall, df_pr_precision, df_pr_f1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "09add8cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_viloca = df_pr_long[df_pr_long['posterior_threshold']>=0.9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "55d61381",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>variable</th>\n",
+       "      <th>value</th>\n",
+       "      <th>method</th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>params</th>\n",
+       "      <th>posterior_threshold</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>recall</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>0.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>recall</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>0.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>recall</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>2</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>0.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>recall</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>4</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>0.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>recall</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>viloca_alpha_0.00001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>seq_tech~illumina__seq_mode~shotgun__seq_mode_...</td>\n",
+       "      <td>0.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    variable  value                method  replicate  \\\n",
+       "278   recall    1.0  viloca_alpha_0.00001          0   \n",
+       "279   recall    1.0  viloca_alpha_0.00001          1   \n",
+       "280   recall    1.0  viloca_alpha_0.00001          2   \n",
+       "281   recall    1.0  viloca_alpha_0.00001          4   \n",
+       "282   recall    1.0  viloca_alpha_0.00001          0   \n",
+       "\n",
+       "                                                params  posterior_threshold  \n",
+       "278  seq_tech~illumina__seq_mode~shotgun__seq_mode_...                  0.9  \n",
+       "279  seq_tech~illumina__seq_mode~shotgun__seq_mode_...                  0.9  \n",
+       "280  seq_tech~illumina__seq_mode~shotgun__seq_mode_...                  0.9  \n",
+       "281  seq_tech~illumina__seq_mode~shotgun__seq_mode_...                  0.9  \n",
+       "282  seq_tech~illumina__seq_mode~shotgun__seq_mode_...                  0.9  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_viloca.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8d92479b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_haplo = pd.read_csv(fname_global)\n",
+    "df_haplo = df_haplo[df_haplo['method'].isin(['cliquesnv_local_haplo_tf0.01', 'predicthaplo_local_haplo'])]\n",
+    "\n",
+    "\n",
+    "# compute f1 score\n",
+    "df_haplo[\"f1\"] = 2 * (df_haplo[\"precision\"] * df_haplo[\"recall\"]) / (df_haplo[\"precision\"] + df_haplo[\"recall\"]) \n",
+    "df_haplo[\"f1\"] = df_haplo[\"f1\"].fillna(0)\n",
+    "\n",
+    "# transform dataframe in right format\n",
+    "df_haplo = pd.melt( df_haplo, \n",
+    "                    id_vars=[\"params\", \"replicate\",  \"method\"], \n",
+    "                    value_vars= [\"precision\", \"recall\", \"f1\"], \n",
+    "                    var_name=\"variable\", \n",
+    "                    value_name=\"value\", \n",
+    "                    ignore_index=True)\n",
+    "\n",
+    "df = pd.concat([df_viloca, df_haplo])\n",
+    "\n",
+    "df.loc[:,'coverage'] = df['params'].str.split('~').str[6].str.split('__').str[0]\n",
+    "df.loc[:, 'haplos'] = df['params'].str.split('~').str[7].str.split('\\n').str[0]\n",
+    "df.loc[:, 'seq_tech'] = df['params'].str.split('~').str[1].str.split('__').str[0]\n",
+    "\n",
+    "df[\"Method\"] = df['method'].apply(lambda x: mapping_methods[x])\n",
+    "df[\"Population\"] = df['haplos'].apply(lambda x: mapping_haplos[x])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "1b96adeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df['Method']=='PredictHaplo') \n",
+    "   & (df['Population'].isin([\"Population 5\", \"Population 6\"])) \n",
+    "   & (df['coverage']==\"1000\")\n",
+    "   & (df['variable']==\"recall\")]['value'].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "83a3eb44",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1624.53x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "        data=df,\n",
+    "        x=\"variable\",\n",
+    "        y=\"value\",\n",
+    "        hue=\"Method\",\n",
+    "        col=\"Population\",\n",
+    "        kind=\"box\",\n",
+    "        col_order=[\"Population 1\", \"Population 2\", \"Population 3\", \"Population 4\"],\n",
+    "        legend=True, \n",
+    "        palette=method_palette,\n",
+    "        sharex=True,\n",
+    "        sharey=True,\n",
+    "        margin_titles=True,\n",
+    "    )\n",
+    "g.set_titles(col_template='{col_name}')\n",
+    "\n",
+    "\n",
+    "g.set(ylim=(-0.1, 1.1))\n",
+    "g.set_xticklabels(size=20)\n",
+    "g.set(xlabel =\"\")\n",
+    "g.set(ylabel =\"\")\n",
+    "\n",
+    "g.tight_layout()\n",
+    "g.savefig(\"distance_haplos_pr_illumina.svg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "510a1c0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1624.53x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "        data=df[df['seq_tech']=='pacbio'],\n",
+    "        x=\"variable\",\n",
+    "        y=\"value\",\n",
+    "        hue=\"Method\",\n",
+    "        col=\"Population\",\n",
+    "        kind=\"box\",\n",
+    "        col_order=[\"Population 5\", \"Population 6\", \"Population 7\", \"Population 8\"],\n",
+    "        legend=True, \n",
+    "        palette=method_palette,\n",
+    "        sharex=True,\n",
+    "        sharey=True,\n",
+    "        margin_titles=True,\n",
+    "    )\n",
+    "g.set_titles(col_template='{col_name}')\n",
+    "\n",
+    "\n",
+    "g.set(ylim=(-0.1, 1.1))\n",
+    "g.set_xticklabels(size=20)\n",
+    "g.set(xlabel =\"\")\n",
+    "g.set(ylabel =\"\")\n",
+    "\n",
+    "g.tight_layout()\n",
+    "g.savefig(\"distance_haplos_pr_pacbio.svg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "a793f4da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1624.53x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "        data=df[df['seq_tech']=='nanopore'],\n",
+    "        x=\"variable\",\n",
+    "        y=\"value\",\n",
+    "        hue=\"Method\",\n",
+    "        col=\"Population\",\n",
+    "        kind=\"box\",\n",
+    "        col_order=[\"Population 5\", \"Population 6\", \"Population 7\", \"Population 8\"],\n",
+    "        legend=True, \n",
+    "        palette=method_palette,\n",
+    "        sharex=True,\n",
+    "        sharey=True,\n",
+    "        margin_titles=True,\n",
+    "    )\n",
+    "g.set_titles(col_template='{col_name}')\n",
+    "\n",
+    "\n",
+    "g.set(ylim=(-0.1, 1.1))\n",
+    "g.set_xticklabels(size=20)\n",
+    "g.set(xlabel =\"\")\n",
+    "g.set(ylabel =\"\")\n",
+    "\n",
+    "g.tight_layout()\n",
+    "g.savefig(\"distance_haplos_pr_ont.svg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "1cc775b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>variable</th>\n",
+       "      <th>value</th>\n",
+       "      <th>method</th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>params</th>\n",
+       "      <th>posterior_threshold</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>haplos</th>\n",
+       "      <th>seq_tech</th>\n",
+       "      <th>Method</th>\n",
+       "      <th>Population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>recall</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>predicthaplo_local_haplo</td>\n",
+       "      <td>0</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>2@3@600@400@400@geom@0.75</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>PredictHaplo</td>\n",
+       "      <td>Population 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>recall</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>predicthaplo_local_haplo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>2@3@600@400@400@geom@0.75</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>PredictHaplo</td>\n",
+       "      <td>Population 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>recall</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>predicthaplo_local_haplo</td>\n",
+       "      <td>2</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>2@3@600@400@400@geom@0.75</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>PredictHaplo</td>\n",
+       "      <td>Population 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>recall</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>predicthaplo_local_haplo</td>\n",
+       "      <td>3</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>2@3@600@400@400@geom@0.75</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>PredictHaplo</td>\n",
+       "      <td>Population 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>recall</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>predicthaplo_local_haplo</td>\n",
+       "      <td>4</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>2@3@600@400@400@geom@0.75</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>PredictHaplo</td>\n",
+       "      <td>Population 5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    variable  value                    method  replicate  \\\n",
+       "407   recall    0.8  predicthaplo_local_haplo          0   \n",
+       "408   recall    0.2  predicthaplo_local_haplo          1   \n",
+       "409   recall    0.2  predicthaplo_local_haplo          2   \n",
+       "410   recall    0.6  predicthaplo_local_haplo          3   \n",
+       "411   recall    0.4  predicthaplo_local_haplo          4   \n",
+       "\n",
+       "                                                params  posterior_threshold  \\\n",
+       "407  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...                  NaN   \n",
+       "408  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...                  NaN   \n",
+       "409  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...                  NaN   \n",
+       "410  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...                  NaN   \n",
+       "411  seq_tech~pacbio__seq_mode~shotgun__seq_mode_pa...                  NaN   \n",
+       "\n",
+       "    coverage                     haplos seq_tech        Method    Population  \n",
+       "407     1000  2@3@600@400@400@geom@0.75   pacbio  PredictHaplo  Population 5  \n",
+       "408     1000  2@3@600@400@400@geom@0.75   pacbio  PredictHaplo  Population 5  \n",
+       "409     1000  2@3@600@400@400@geom@0.75   pacbio  PredictHaplo  Population 5  \n",
+       "410     1000  2@3@600@400@400@geom@0.75   pacbio  PredictHaplo  Population 5  \n",
+       "411     1000  2@3@600@400@400@geom@0.75   pacbio  PredictHaplo  Population 5  "
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df['seq_tech']=='pacbio') \n",
+    "   & (df['Method']=='PredictHaplo') \n",
+    "   & (df['Population']=='Population 5')\n",
+    "   & (df['variable']=='recall')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6537582e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_4C__distance__runtime.ipynb b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_4C__distance__runtime.ipynb
new file mode 100644
index 00000000..bd61acd5
--- /dev/null
+++ b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_4C__distance__runtime.ipynb
@@ -0,0 +1,576 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "57a0a953",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "import humanize\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b155874e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fname_runtime =  \"results/distance/performance_measures/runtime.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "38db5bea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "method_palette = { \"LoFreq\": \"#9593D9\",\n",
+    "                   \"ShoRAH\": \"#ffe66d\",\n",
+    "                   \"CliqueSNV\":  \"#4ecdc4\",\n",
+    "                   \"VILOCA\": \"#ff6b6b\",\n",
+    "                   \"ground_truth\": \"grey\",\n",
+    "                  \"ground truth\": \"grey\",\n",
+    "                  \"PredictHaplo\": \"#227c9d\",\n",
+    "                  }\n",
+    "\n",
+    "mapping_methods = {\n",
+    "    'cliquesnv_local_haplo_snv_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_snv': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo': \"CliqueSNV\",\n",
+    "    'shorah_default': 'ShoRAH',\n",
+    "    'lofreq_local_haplo': 'LoFreq',\n",
+    "    'viloca_alpha_0.00001': 'VILOCA',\n",
+    "    'viloca_alpha_0.00001_K100': 'VILOCA',\n",
+    "    'viloca_multi': 'VILOCA',\n",
+    "    'ground_truth': \"ground truth\",\n",
+    "    'predicthaplo_local_haplo': 'PredictHaplo'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0d65ecad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping_haplos = {\n",
+    "    '2@3@30@20@20@geom@0.75': 'Population 1', \n",
+    "    '5@5@30@10@15@geom@0.75': 'Population 2',\n",
+    "    '5@10@30@10@10@geom@0.75': 'Population 3', \n",
+    "    '5@15@30@10@15@geom@0.75': 'Population 4', \n",
+    "    \n",
+    "    '2@3@600@400@400@geom@0.75': 'Population 5',\n",
+    "    '2@3@600@600@600@geom@0.75': 'Population 6',\n",
+    "    '5@5@600@200@300@geom@0.75': 'Population 7', \n",
+    "    '5@10@600@200@240@geom@0.75': 'Population 8',\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "685446e6",
+   "metadata": {},
+   "source": [
+    "## runtime plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bb58dbf9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(fname_runtime)\n",
+    "df.loc[:,'coverage'] = df['params'].str.split('~').str[6].str.split('__').str[0]\n",
+    "df.loc[:, 'haplos'] = df['params'].str.split('~').str[7].str.split('\\n').str[0]\n",
+    "df.loc[:, 'seq_tech'] = df['params'].str.split('~').str[1].str.split('__').str[0]\n",
+    "\n",
+    "df = df[df['method'].isin(mapping_methods.keys())]\n",
+    "df[\"Method\"] = df['method'].apply(lambda x: mapping_methods[x])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5910486a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>method</th>\n",
+       "      <th>params</th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>value</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>haplos</th>\n",
+       "      <th>seq_tech</th>\n",
+       "      <th>Method</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>59</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~illumina\\n\\nseq\\nmode~shotgun\\n\\nseq...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>s</td>\n",
+       "      <td>0.9461</td>\n",
+       "      <td>200\\n\\nhaplos</td>\n",
+       "      <td>5@5@30@10@15@geom@0.75</td>\n",
+       "      <td>illumina\\n\\nseq\\nmode</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>63</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~illumina\\n\\nseq\\nmode~shotgun\\n\\nseq...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>s</td>\n",
+       "      <td>1.0466</td>\n",
+       "      <td>200\\n\\nhaplos</td>\n",
+       "      <td>5@5@30@10@15@geom@0.75</td>\n",
+       "      <td>illumina\\n\\nseq\\nmode</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>86</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~illumina\\n\\nseq\\nmode~shotgun\\n\\nseq...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>s</td>\n",
+       "      <td>1.3214</td>\n",
+       "      <td>500\\n\\nhaplos</td>\n",
+       "      <td>5@5@30@10@15@geom@0.75</td>\n",
+       "      <td>illumina\\n\\nseq\\nmode</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>87</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~illumina\\n\\nseq\\nmode~shotgun\\n\\nseq...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>s</td>\n",
+       "      <td>1.4040</td>\n",
+       "      <td>500\\n\\nhaplos</td>\n",
+       "      <td>5@5@30@10@15@geom@0.75</td>\n",
+       "      <td>illumina\\n\\nseq\\nmode</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>235</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~illumina\\n\\nseq\\nmode~shotgun\\n\\nseq...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>s</td>\n",
+       "      <td>1.7906</td>\n",
+       "      <td>1000\\n\\nhaplos</td>\n",
+       "      <td>5@10@30@10@10@geom@0.75</td>\n",
+       "      <td>illumina\\n\\nseq\\nmode</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>692</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~nanopore\\n\\nseq\\nmode~shotgun\\n\\nseq...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>s</td>\n",
+       "      <td>1710.4339</td>\n",
+       "      <td>10000\\n\\nhaplos</td>\n",
+       "      <td>2@3@600@600@600@geom@0.75</td>\n",
+       "      <td>nanopore\\n\\nseq\\nmode</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>696</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~nanopore\\n\\nseq\\nmode~shotgun\\n\\nseq...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>s</td>\n",
+       "      <td>1760.8780</td>\n",
+       "      <td>10000\\n\\nhaplos</td>\n",
+       "      <td>2@3@600@600@600@geom@0.75</td>\n",
+       "      <td>nanopore\\n\\nseq\\nmode</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>694</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~nanopore\\n\\nseq\\nmode~shotgun\\n\\nseq...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>s</td>\n",
+       "      <td>1798.8014</td>\n",
+       "      <td>10000\\n\\nhaplos</td>\n",
+       "      <td>2@3@600@600@600@geom@0.75</td>\n",
+       "      <td>nanopore\\n\\nseq\\nmode</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>693</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~nanopore\\n\\nseq\\nmode~shotgun\\n\\nseq...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>s</td>\n",
+       "      <td>1861.9009</td>\n",
+       "      <td>10000\\n\\nhaplos</td>\n",
+       "      <td>2@3@600@600@600@geom@0.75</td>\n",
+       "      <td>nanopore\\n\\nseq\\nmode</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>695</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~nanopore\\n\\nseq\\nmode~shotgun\\n\\nseq...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>s</td>\n",
+       "      <td>1963.2333</td>\n",
+       "      <td>10000\\n\\nhaplos</td>\n",
+       "      <td>2@3@600@600@600@geom@0.75</td>\n",
+       "      <td>nanopore\\n\\nseq\\nmode</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0              method  \\\n",
+       "59           59  lofreq_local_haplo   \n",
+       "63           63  lofreq_local_haplo   \n",
+       "86           86  lofreq_local_haplo   \n",
+       "87           87  lofreq_local_haplo   \n",
+       "235         235  lofreq_local_haplo   \n",
+       "..          ...                 ...   \n",
+       "692         692  lofreq_local_haplo   \n",
+       "696         696  lofreq_local_haplo   \n",
+       "694         694  lofreq_local_haplo   \n",
+       "693         693  lofreq_local_haplo   \n",
+       "695         695  lofreq_local_haplo   \n",
+       "\n",
+       "                                                params  replicate variable  \\\n",
+       "59   seq\\ntech~illumina\\n\\nseq\\nmode~shotgun\\n\\nseq...          0        s   \n",
+       "63   seq\\ntech~illumina\\n\\nseq\\nmode~shotgun\\n\\nseq...          4        s   \n",
+       "86   seq\\ntech~illumina\\n\\nseq\\nmode~shotgun\\n\\nseq...          2        s   \n",
+       "87   seq\\ntech~illumina\\n\\nseq\\nmode~shotgun\\n\\nseq...          3        s   \n",
+       "235  seq\\ntech~illumina\\n\\nseq\\nmode~shotgun\\n\\nseq...          1        s   \n",
+       "..                                                 ...        ...      ...   \n",
+       "692  seq\\ntech~nanopore\\n\\nseq\\nmode~shotgun\\n\\nseq...          0        s   \n",
+       "696  seq\\ntech~nanopore\\n\\nseq\\nmode~shotgun\\n\\nseq...          4        s   \n",
+       "694  seq\\ntech~nanopore\\n\\nseq\\nmode~shotgun\\n\\nseq...          2        s   \n",
+       "693  seq\\ntech~nanopore\\n\\nseq\\nmode~shotgun\\n\\nseq...          1        s   \n",
+       "695  seq\\ntech~nanopore\\n\\nseq\\nmode~shotgun\\n\\nseq...          3        s   \n",
+       "\n",
+       "         value         coverage                     haplos  \\\n",
+       "59      0.9461    200\\n\\nhaplos     5@5@30@10@15@geom@0.75   \n",
+       "63      1.0466    200\\n\\nhaplos     5@5@30@10@15@geom@0.75   \n",
+       "86      1.3214    500\\n\\nhaplos     5@5@30@10@15@geom@0.75   \n",
+       "87      1.4040    500\\n\\nhaplos     5@5@30@10@15@geom@0.75   \n",
+       "235     1.7906   1000\\n\\nhaplos    5@10@30@10@10@geom@0.75   \n",
+       "..         ...              ...                        ...   \n",
+       "692  1710.4339  10000\\n\\nhaplos  2@3@600@600@600@geom@0.75   \n",
+       "696  1760.8780  10000\\n\\nhaplos  2@3@600@600@600@geom@0.75   \n",
+       "694  1798.8014  10000\\n\\nhaplos  2@3@600@600@600@geom@0.75   \n",
+       "693  1861.9009  10000\\n\\nhaplos  2@3@600@600@600@geom@0.75   \n",
+       "695  1963.2333  10000\\n\\nhaplos  2@3@600@600@600@geom@0.75   \n",
+       "\n",
+       "                  seq_tech  Method  \n",
+       "59   illumina\\n\\nseq\\nmode  LoFreq  \n",
+       "63   illumina\\n\\nseq\\nmode  LoFreq  \n",
+       "86   illumina\\n\\nseq\\nmode  LoFreq  \n",
+       "87   illumina\\n\\nseq\\nmode  LoFreq  \n",
+       "235  illumina\\n\\nseq\\nmode  LoFreq  \n",
+       "..                     ...     ...  \n",
+       "692  nanopore\\n\\nseq\\nmode  LoFreq  \n",
+       "696  nanopore\\n\\nseq\\nmode  LoFreq  \n",
+       "694  nanopore\\n\\nseq\\nmode  LoFreq  \n",
+       "693  nanopore\\n\\nseq\\nmode  LoFreq  \n",
+       "695  nanopore\\n\\nseq\\nmode  LoFreq  \n",
+       "\n",
+       "[150 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['Method']=='LoFreq'].sort_values('value')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6ec4f4be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.684266388888888"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['Method']=='PredictHaplo']['value'].max()\n",
+    "34863.359/60/60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b7964fe1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.958333333333334"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['Method']=='CliqueSNV']['value'].max() # = 359.0311111 Stunden\n",
+    "359/24 # --> nearly 15 days"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "60c68344",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "15*24 - 359"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9f56764d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1292512.3887"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['Method']=='CliqueSNV']['value'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1b6954c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90682.641"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "90682.6410"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bc052b39",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_4085/3553202358.py:30: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  g.ax.set_yticklabels([datetime.timedelta(seconds=ti) for ti in ticks])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f7c278e52b0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 459.25x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "        data=df,\n",
+    "        x=\"seq_tech\",\n",
+    "        y=\"value\",\n",
+    "        hue=\"Method\",\n",
+    "        kind=\"point\",\n",
+    "        legend=True, \n",
+    "        sharex=True,\n",
+    "        sharey=False,\n",
+    "        linestyles='', \n",
+    "        margin_titles=True,\n",
+    "        palette=method_palette,\n",
+    "        hue_order=['VILOCA', 'LoFreq', 'ShoRAH', 'CliqueSNV', 'PredictHaplo'],\n",
+    "        #width=0.5,\n",
+    "        #linewidth=0.2,\n",
+    "        dodge=0.5,\n",
+    "        errorbar='sd'\n",
+    "    )\n",
+    "\n",
+    "# set your date formatter whit this format '%H:%M'\n",
+    "#formatter = time.strftime('%H:%M:%S', time.gmtime)\n",
+    "#g.ax.yaxis.set_major_formatter(formatter)\n",
+    "ticks = g.ax.get_yticks()\n",
+    "#g.ax.set_yticklabels(pd.to_datetime(ticks, unit='s').strftime('%H:%M:%S'))\n",
+    "\n",
+    "import datetime\n",
+    "#str(datetime.timedelta(seconds=666))\n",
+    "g.ax.set_yticklabels([datetime.timedelta(seconds=ti) for ti in ticks])\n",
+    "\n",
+    "g.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bdaeeb7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g.savefig('distance__runtime.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd9c56d7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_5A__realdata__analyse_real_data_mutation_calls.ipynb b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_5A__realdata__analyse_real_data_mutation_calls.ipynb
new file mode 100644
index 00000000..08b0d517
--- /dev/null
+++ b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_5A__realdata__analyse_real_data_mutation_calls.ipynb
@@ -0,0 +1,470 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f243dfe3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "import humanize\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "@FuncFormatter\n",
+    "def duration_fmt(x, pos):\n",
+    "    return humanize.precisedelta(x, minimum_unit='minutes',format='%0.0f')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f68745d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "method_palette = {\"lofreq_local_haplo\": \"#9593D9\",\n",
+    "                  \"LoFreq\": \"#9593D9\",\n",
+    "                   \"shorah_default\": \"#ffe66d\",\n",
+    "                   \"ShoRAH\": \"#ffe66d\",\n",
+    "                   \"cliquesnv_local_haplo_snv\": \"#4ecdc4\",\n",
+    "                   \"cliquesnv_local_haplo_snv_tf0.01\": \"#4ecdc4\",\n",
+    "                   \"cliquesnv_local_haplo_tf0.01\": \"#4ecdc4\",\n",
+    "                   \"cliquesnv_local_haplo\":\"#4ecdc4\",\n",
+    "                   \"CliqueSNV\":  \"#4ecdc4\",\n",
+    "                   \"viloca_alpha_0.000001\":  \"#ff6b6b\",\n",
+    "                   \"viloca_alpha_0.00001\":  \"#ff6b6b\",\n",
+    "                   \"viloca_alpha_3\":  \"#ff6b6b\",\n",
+    "                   \"viloca_alpha_0.00001_K100\": \"#ff6b6b\",\n",
+    "                   \"VILOCA\": \"#ff6b6b\",\n",
+    "                   \"viloca_multi\" : \"#e76f51\",\n",
+    "                   \"ground_truth\": \"grey\",\n",
+    "                   \"ground truth\": \"grey\",\n",
+    "                   \"cliquesnv_local_haplo_tf\": \"#ffe66d\",\n",
+    "                   \"CliqueSNV - extra params\": \"#ffe66d\",\n",
+    "                   \"cliquesnv_local_haplo_tf0.1\": \"#ffe66d\", \n",
+    "                  }\n",
+    "\n",
+    "mapping_methods = {\n",
+    "    'cliquesnv_local_haplo_snv_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_snv': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo': \"CliqueSNV\",\n",
+    "    'shorah_default': 'ShoRAH',\n",
+    "    'lofreq_local_haplo': 'LoFreq',\n",
+    "    'viloca_alpha_0.00001': 'VILOCA',\n",
+    "    'viloca_alpha_0.00001_K100': 'VILOCA',\n",
+    "    'viloca_alpha_0.00001_K100_multib2w': 'VILOCA',\n",
+    "    'viloca_multi': 'VILOCA',\n",
+    "    'ground_truth': \"ground truth\",\n",
+    "    'cliquesnv_local_haplo_tf0.1': \"CliqueSNV\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d36222de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import snv calls\n",
+    "fname_snv = \"results/realdata/performance_measures/local/performance.csv\"\n",
+    "\n",
+    "# import runtime\n",
+    "fname_runtime = \"results/realdata/performance_measures/local/runtime.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9338d7c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(fname_snv)\n",
+    "\n",
+    "df['seq_tech'] = df['params'].str.split(\"~\").str[1].str.split(\"\\n\\n\").str[0]\n",
+    "df['seq_mode'] = df['params'].str.split(\"~\").str[2].str.split(\"\\n\").str[0]\n",
+    "df['sample'] = df['params'].str.split(\"~\").str[3].str.split(\"\\n\").str[0]\n",
+    "\n",
+    "def f_sample_name(row):\n",
+    "    if row['seq_tech']=='pacbio':\n",
+    "        return \"Ten-strain-IAV-Mix \"\n",
+    "    if row['seq_tech']=='nanopore':\n",
+    "        return \"Five-strain-PVY-Mix\"\n",
+    "    if (row['seq_tech']=='illumina') & (row['sample']==\"5-virus-mix\"):\n",
+    "        return \"Five-strain-HIV-Mix\"\n",
+    "\n",
+    "df['sample'] = df.apply(f_sample_name, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9935b323",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['lofreq_local_haplo', 'shorah_default',\n",
+       "       'viloca_alpha_0.00001_K100_multib2w', 'viloca_alpha_0.00001_K100',\n",
+       "       'cliquesnv_local_haplo_tf0.1'], dtype=object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['method'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6ed5b3c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#methods_to_plot = [\n",
+    "#    'lofreq_local_haplo', \n",
+    "#       'shorah_default', \n",
+    "#       #'cliquesnv_local_haplo_snv_tf0.01',  # exclude because it actually cannot do snv wrt to reference\n",
+    "#        'viloca_alpha_0.00001_K100_multib2w', \n",
+    "#       'viloca_alpha_0.00001_K100']\n",
+    "\n",
+    "#df = df[df['method'].isin(methods_to_plot)]\n",
+    "df['Method'] = df['method'].apply(lambda x: mapping_methods[x])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ddb79ef4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>method</th>\n",
+       "      <th>params</th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>value</th>\n",
+       "      <th>seq_tech</th>\n",
+       "      <th>seq_mode</th>\n",
+       "      <th>sample</th>\n",
+       "      <th>Method</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~illumina\\n\\nseq\\nmode~real\\ndata\\n\\n...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>precision</td>\n",
+       "      <td>0.513337</td>\n",
+       "      <td>illumina</td>\n",
+       "      <td>real</td>\n",
+       "      <td>Five-strain-HIV-Mix</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~nanopore\\n\\nseq\\nmode~real\\n\\nseq\\nm...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>precision</td>\n",
+       "      <td>0.989474</td>\n",
+       "      <td>nanopore</td>\n",
+       "      <td>real</td>\n",
+       "      <td>Five-strain-PVY-Mix</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~pacbio\\n\\nseq\\nmode~amplicon\\n\\nseq\\...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>precision</td>\n",
+       "      <td>0.964286</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>amplicon</td>\n",
+       "      <td>Ten-strain-IAV-Mix</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~illumina\\n\\nseq\\nmode~real\\ndata\\n\\n...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>recall</td>\n",
+       "      <td>0.889623</td>\n",
+       "      <td>illumina</td>\n",
+       "      <td>real</td>\n",
+       "      <td>Five-strain-HIV-Mix</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~nanopore\\n\\nseq\\nmode~real\\n\\nseq\\nm...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>recall</td>\n",
+       "      <td>0.609071</td>\n",
+       "      <td>nanopore</td>\n",
+       "      <td>real</td>\n",
+       "      <td>Five-strain-PVY-Mix</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~pacbio\\n\\nseq\\nmode~amplicon\\n\\nseq\\...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>recall</td>\n",
+       "      <td>0.435484</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>amplicon</td>\n",
+       "      <td>Ten-strain-IAV-Mix</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>20</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~illumina\\n\\nseq\\nmode~real\\ndata\\n\\n...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>f1</td>\n",
+       "      <td>0.651018</td>\n",
+       "      <td>illumina</td>\n",
+       "      <td>real</td>\n",
+       "      <td>Five-strain-HIV-Mix</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>23</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~nanopore\\n\\nseq\\nmode~real\\n\\nseq\\nm...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>f1</td>\n",
+       "      <td>0.754011</td>\n",
+       "      <td>nanopore</td>\n",
+       "      <td>real</td>\n",
+       "      <td>Five-strain-PVY-Mix</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>26</td>\n",
+       "      <td>lofreq_local_haplo</td>\n",
+       "      <td>seq\\ntech~pacbio\\n\\nseq\\nmode~amplicon\\n\\nseq\\...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>f1</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>pacbio</td>\n",
+       "      <td>amplicon</td>\n",
+       "      <td>Ten-strain-IAV-Mix</td>\n",
+       "      <td>LoFreq</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0              method  \\\n",
+       "0            0  lofreq_local_haplo   \n",
+       "3            3  lofreq_local_haplo   \n",
+       "6            6  lofreq_local_haplo   \n",
+       "10          10  lofreq_local_haplo   \n",
+       "13          13  lofreq_local_haplo   \n",
+       "16          16  lofreq_local_haplo   \n",
+       "20          20  lofreq_local_haplo   \n",
+       "23          23  lofreq_local_haplo   \n",
+       "26          26  lofreq_local_haplo   \n",
+       "\n",
+       "                                               params  replicate   variable  \\\n",
+       "0   seq\\ntech~illumina\\n\\nseq\\nmode~real\\ndata\\n\\n...          0  precision   \n",
+       "3   seq\\ntech~nanopore\\n\\nseq\\nmode~real\\n\\nseq\\nm...          0  precision   \n",
+       "6   seq\\ntech~pacbio\\n\\nseq\\nmode~amplicon\\n\\nseq\\...          0  precision   \n",
+       "10  seq\\ntech~illumina\\n\\nseq\\nmode~real\\ndata\\n\\n...          0     recall   \n",
+       "13  seq\\ntech~nanopore\\n\\nseq\\nmode~real\\n\\nseq\\nm...          0     recall   \n",
+       "16  seq\\ntech~pacbio\\n\\nseq\\nmode~amplicon\\n\\nseq\\...          0     recall   \n",
+       "20  seq\\ntech~illumina\\n\\nseq\\nmode~real\\ndata\\n\\n...          0         f1   \n",
+       "23  seq\\ntech~nanopore\\n\\nseq\\nmode~real\\n\\nseq\\nm...          0         f1   \n",
+       "26  seq\\ntech~pacbio\\n\\nseq\\nmode~amplicon\\n\\nseq\\...          0         f1   \n",
+       "\n",
+       "       value  seq_tech  seq_mode               sample  Method  \n",
+       "0   0.513337  illumina      real  Five-strain-HIV-Mix  LoFreq  \n",
+       "3   0.989474  nanopore      real  Five-strain-PVY-Mix  LoFreq  \n",
+       "6   0.964286    pacbio  amplicon  Ten-strain-IAV-Mix   LoFreq  \n",
+       "10  0.889623  illumina      real  Five-strain-HIV-Mix  LoFreq  \n",
+       "13  0.609071  nanopore      real  Five-strain-PVY-Mix  LoFreq  \n",
+       "16  0.435484    pacbio  amplicon  Ten-strain-IAV-Mix   LoFreq  \n",
+       "20  0.651018  illumina      real  Five-strain-HIV-Mix  LoFreq  \n",
+       "23  0.754011  nanopore      real  Five-strain-PVY-Mix  LoFreq  \n",
+       "26  0.600000    pacbio  amplicon  Ten-strain-IAV-Mix   LoFreq  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['Method']=='LoFreq']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "46a5cbe5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Five-strain-HIV-Mix', 'Five-strain-PVY-Mix',\n",
+       "       'Ten-strain-IAV-Mix '], dtype=object)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['sample'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "752254e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7fb95294f070>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1228.03x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style = 'whitegrid')\n",
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "        data=df,\n",
+    "        x=\"variable\",\n",
+    "        y=\"value\",\n",
+    "        hue=\"Method\",\n",
+    "        kind=\"bar\",\n",
+    "        col=\"sample\", \n",
+    "        hue_order =[\"VILOCA\",\"LoFreq\", \"ShoRAH\"],\n",
+    "        col_order = ['Five-strain-PVY-Mix', 'Ten-strain-IAV-Mix ', 'Five-strain-HIV-Mix' ], \n",
+    "        legend=True, \n",
+    "        linewidth=2,\n",
+    "        sharex=True,\n",
+    "        margin_titles=True,\n",
+    "        palette=method_palette,\n",
+    "    )\n",
+    "\n",
+    "g.set_titles(col_template='{col_name}', size=23)\n",
+    "g.set_xticklabels(size=20)\n",
+    "#g.set_yticklabels(size=20)\n",
+    "g.set(ylabel =\"\")\n",
+    "g.set(xlabel =\"\")\n",
+    "g.set(ylim=(-0.01, 1))\n",
+    "g.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "bac9fdfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g.savefig(\"realdata_mutation_calls.svg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c140f3f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_5B__realdata__iav_mix.ipynb b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_5B__realdata__iav_mix.ipynb
new file mode 100644
index 00000000..62cc5a63
--- /dev/null
+++ b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/Figure_5B__realdata__iav_mix.ipynb
@@ -0,0 +1,749 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27af307e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "import humanize\n",
+    "import matplotlib.pyplot as plt\n",
+    "import editdistance\n",
+    "from tqdm import tqdm\n",
+    "from sklearn import manifold\n",
+    "\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "@FuncFormatter\n",
+    "def duration_fmt(x, pos):\n",
+    "    return humanize.precisedelta(x, minimum_unit='minutes',format='%0.0f')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0afbbc2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fname_global = 'results/realdata/performance_measures/global/csv_files/mds_results.csv.gz'\n",
+    "#df_mds = pd.read_csv(fname_global)\n",
+    "\n",
+    "fname_pred = 'results/realdata/performance_measures/global/csv_files//predictions.csv.gz'\n",
+    "df_pred = pd.read_csv(fname_pred)\n",
+    "\n",
+    "fname_true = 'results/realdata/performance_measures/global/csv_files//ground_truth.csv.gz'\n",
+    "df_true = pd.read_csv(fname_true)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7189139e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0          predicthaplo_local_haplo\n",
+       "1     cliquesnv_local_haplo_tf0.001\n",
+       "2     cliquesnv_local_haplo_tf0.001\n",
+       "3     cliquesnv_local_haplo_tf0.001\n",
+       "4     cliquesnv_local_haplo_tf0.001\n",
+       "5     cliquesnv_local_haplo_tf0.001\n",
+       "6     cliquesnv_local_haplo_tf0.001\n",
+       "7     cliquesnv_local_haplo_tf0.001\n",
+       "8     cliquesnv_local_haplo_tf0.001\n",
+       "9     cliquesnv_local_haplo_tf0.001\n",
+       "10    cliquesnv_local_haplo_tf0.001\n",
+       "11    cliquesnv_local_haplo_tf0.001\n",
+       "12    cliquesnv_local_haplo_tf0.001\n",
+       "13    cliquesnv_local_haplo_tf0.001\n",
+       "14        viloca_alpha_0.00001_K100\n",
+       "15        viloca_alpha_0.00001_K100\n",
+       "16        viloca_alpha_0.00001_K100\n",
+       "17        viloca_alpha_0.00001_K100\n",
+       "18        viloca_alpha_0.00001_K100\n",
+       "Name: method, dtype: object"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pred['method']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5754dca8",
+   "metadata": {},
+   "source": [
+    "### Recompute precision and recall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8a6676c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def relative_edit_distance(seq1, seq2):\n",
+    "    dist = editdistance.eval(seq1[:2262], seq2[:2262])\n",
+    "    rel = dist / max(len(seq1), len(seq2))\n",
+    "    return rel\n",
+    "\n",
+    "\n",
+    "def pr_worker(index, df_group, df_true, thres):\n",
+    "    (method, params, replicate) = index\n",
+    "\n",
+    "    tp = 0\n",
+    "    fp = 0\n",
+    "    fn = 0\n",
+    "\n",
+    "    df_true_grpd = df_true[\n",
+    "        (df_true[\"params\"] == params) & (df_true[\"replicate\"] == replicate)\n",
+    "    ]\n",
+    "\n",
+    "    # subsample large results\n",
+    "    max_num = 500\n",
+    "    df_group = df_group.sample(n=min(df_group.shape[0], max_num))\n",
+    "\n",
+    "    # true positive: predicted seq appears in ground truth\n",
+    "    # false positive: predicted seq does not appear in ground truth\n",
+    "    df_cur = df_true_grpd.copy()\n",
+    "    for row in tqdm(df_group.itertuples(), total=df_group.shape[0], leave=False):\n",
+    "        ser_dist = df_cur[\"sequence\"].apply(\n",
+    "            lambda x: relative_edit_distance(x, row.sequence)\n",
+    "        )\n",
+    "        passed_thres = (ser_dist <= thres).any()\n",
+    "\n",
+    "        if passed_thres:\n",
+    "            tp += 1\n",
+    "\n",
+    "            # remove ground truth because it was predicted\n",
+    "            df_cur = df_cur.drop(ser_dist.idxmin())\n",
+    "        else:\n",
+    "            fp += 1\n",
+    "\n",
+    "    # false negative: ground truth sequence was not predicted\n",
+    "    # single prediction should not map to multiple ground truth seqs\n",
+    "    df_cur = df_group.copy()\n",
+    "    for row in tqdm(\n",
+    "        df_true_grpd.itertuples(), total=df_true_grpd.shape[0], leave=False\n",
+    "    ):\n",
+    "        ser_dist = df_cur[\"sequence\"].apply(\n",
+    "            lambda x: relative_edit_distance(x, row.sequence)\n",
+    "        )\n",
+    "        passed_thres = (ser_dist <= thres).any()\n",
+    "\n",
+    "        if not passed_thres:\n",
+    "            fn += 1\n",
+    "        else:\n",
+    "            # remove current prediction\n",
+    "            df_cur = df_cur.drop(ser_dist.idxmin())\n",
+    "    \n",
+    "    recall = tp / (tp + fn)\n",
+    "    precision = tp / (tp + fp)\n",
+    "    # finalize\n",
+    "    return {\n",
+    "        \"method\": method,\n",
+    "        \"params\": params,\n",
+    "        \"replicate\": replicate,\n",
+    "        \"tp\": tp,\n",
+    "        \"fp\": fp,\n",
+    "        \"fn\": fn,\n",
+    "        \"precision\": tp / (tp + fp),\n",
+    "        \"recall\": tp / (tp + fn),\n",
+    "        \"f1\": 2 * (precision * recall) / (precision + recall), \n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5b5df8e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "distance_threshold = 0.001"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5399aa26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.262"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.001*2262"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b3a213b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_pred = df_pred[df_pred['method'].isin(['cliquesnv_local_haplo_tf0.001', \n",
+    "                                          'predicthaplo_local_haplo', \n",
+    "                                          'viloca_alpha_0.00001_K100'])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "58e8bd20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                                                                                                                                            \r"
+     ]
+    }
+   ],
+   "source": [
+    "tmp = []\n",
+    "\n",
+    "for index, df_group in df_pred.groupby([\"method\", \"params\", \"replicate\"]):\n",
+    "\n",
+    "    pr_list = pr_worker(index, df_group, df_true, distance_threshold)\n",
+    "    tmp.append(pr_list)\n",
+    "                                                   \n",
+    "df_pr = pd.DataFrame(tmp)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cfe02226",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>method</th>\n",
+       "      <th>params</th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>tp</th>\n",
+       "      <th>fp</th>\n",
+       "      <th>fn</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>cliquesnv_local_haplo_tf0.001</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~amplicon__seq_mode_p...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.692308</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.782609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>predicthaplo_local_haplo</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~amplicon__seq_mode_p...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.181818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>viloca_alpha_0.00001_K100</td>\n",
+       "      <td>seq_tech~pacbio__seq_mode~amplicon__seq_mode_p...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          method  \\\n",
+       "0  cliquesnv_local_haplo_tf0.001   \n",
+       "1       predicthaplo_local_haplo   \n",
+       "2      viloca_alpha_0.00001_K100   \n",
+       "\n",
+       "                                              params  replicate  tp  fp  fn  \\\n",
+       "0  seq_tech~pacbio__seq_mode~amplicon__seq_mode_p...          0   9   4   1   \n",
+       "1  seq_tech~pacbio__seq_mode~amplicon__seq_mode_p...          0   1   0   9   \n",
+       "2  seq_tech~pacbio__seq_mode~amplicon__seq_mode_p...          0   5   0   5   \n",
+       "\n",
+       "   precision  recall        f1  \n",
+       "0   0.692308     0.9  0.782609  \n",
+       "1   1.000000     0.1  0.181818  \n",
+       "2   1.000000     0.5  0.666667  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32fefe24",
+   "metadata": {},
+   "source": [
+    "## Recompute mds plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9716207f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                                                                                                                | 0/29 [00:00<?, ?it/s]\u001b[A\n",
+      "                                                                                                                                                                                                            \u001b[A\n",
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+      " 90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                 | 26/29 [00:05<00:00, 11.41it/s]\u001b[A\n",
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+      "                                                                                                                                                                                                            \u001b[A\n",
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+      "                                                                                                                                                                                                            \u001b[A\n",
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+      "                                                                                                                                                                                                            \u001b[A\r"
+     ]
+    }
+   ],
+   "source": [
+    "def mds_worker(index, df_pred_grpd, df_true):\n",
+    "    params, replicate = index\n",
+    "\n",
+    "    df_true_grpd = df_true[\n",
+    "        (df_true[\"params\"] == params) & (df_true[\"replicate\"] == replicate)\n",
+    "    ]\n",
+    "\n",
+    "    # subsample large results\n",
+    "    max_num = 50\n",
+    "\n",
+    "    df_pred_grpd = df_pred_grpd.copy()\n",
+    "    df_pred_grpd = (\n",
+    "        df_pred_grpd.groupby(\"method\")\n",
+    "        .apply(lambda x: x.sample(n=min(len(x), max_num)))\n",
+    "        .reset_index(drop=True)\n",
+    "    )\n",
+    "\n",
+    "    # compute dissimilarities\n",
+    "    sequence_list = (\n",
+    "        df_pred_grpd[\"sequence\"].tolist() + df_true_grpd[\"sequence\"].tolist()\n",
+    "    )\n",
+    "    \n",
+    "    sequence_list = [seq[:2262] for seq in sequence_list]\n",
+    "\n",
+    "    mat = np.zeros(shape=(len(sequence_list), len(sequence_list)))\n",
+    "    for i, seq1 in enumerate(tqdm(sequence_list, leave=False)):\n",
+    "        for j, seq2 in enumerate(tqdm(sequence_list, leave=False)):\n",
+    "            if i >= j:\n",
+    "                continue\n",
+    "\n",
+    "            mat[i, j] = editdistance.eval(seq1, seq2)\n",
+    "\n",
+    "    mat = np.triu(mat) + np.tril(mat.T, 1)  # mirror to make symmetric\n",
+    "\n",
+    "    # do MDS\n",
+    "    embedding = manifold.MDS(n_components=2, dissimilarity=\"precomputed\")\n",
+    "    mat_trans = embedding.fit_transform(mat)\n",
+    "\n",
+    "    df = pd.concat(\n",
+    "        [\n",
+    "            pd.DataFrame(mat_trans, columns=[\"MDS0\", \"MDS1\"]),\n",
+    "            pd.concat([df_pred_grpd, df_true_grpd], axis=0, ignore_index=True),\n",
+    "        ],\n",
+    "        axis=1,\n",
+    "    )\n",
+    "    df[\"method\"] = df[\"method\"].apply(lambda x: \"ground_truth\" if x is None else x)\n",
+    "    df[\"params\"] = params\n",
+    "    df[\"replicate\"] = replicate\n",
+    "\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def sequence_embedding(df_pred, df_true):\n",
+    "    \n",
+    "    df_list=[]\n",
+    "    for index, df_group in df_pred.groupby([\"params\", \"replicate\"]): \n",
+    "        df_list.append(mds_worker(index, df_group, df_true))\n",
+    "\n",
+    "    # finalize\n",
+    "    return pd.concat(df_list, ignore_index=True)\n",
+    "\n",
+    "\n",
+    "df_mds = sequence_embedding(df_pred, df_true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5844e978",
+   "metadata": {},
+   "source": [
+    "## Plot mds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ba34a63d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "method_palette = { \"LoFreq\": \"#9593D9\",\n",
+    "                   \"ShoRAH\": \"#ffe66d\",\n",
+    "                   \"CliqueSNV\":  \"#4ecdc4\",\n",
+    "                   \"VILOCA\": \"#ff6b6b\",\n",
+    "                  \"PredictHaplo\": \"#227c9d\",\n",
+    "                   \"ground_truth\": \"grey\",\n",
+    "                   \"ground truth\": \"grey\",}\n",
+    "\n",
+    "mapping_methods = {\n",
+    "    'cliquesnv_local_haplo_snv_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_snv': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo_tf0.01': \"CliqueSNV\",\n",
+    "    'cliquesnv_local_haplo': \"CliqueSNV\",\n",
+    "    'shorah_default': 'ShoRAH',\n",
+    "    'lofreq_local_haplo': 'LoFreq',\n",
+    "    'viloca_alpha_0.00001': 'VILOCA',\n",
+    "    'viloca_alpha_0.00001_K100': 'VILOCA',\n",
+    "    'viloca_multi': 'VILOCA',\n",
+    "    'ground_truth': \"ground truth\",\n",
+    "    'cliquesnv_local_haplo_tf0.001': 'CliqueSNV',\n",
+    "    'predicthaplo_local_haplo': 'PredictHaplo'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a7a292dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.set_theme(style=\"white\", font_scale=1.8)\n",
+    "\n",
+    "\n",
+    "def f_freq_bins(x):\n",
+    "    if x >=0.2:\n",
+    "        return 0.3\n",
+    "    elif x>0.1:\n",
+    "        return 0.2\n",
+    "    elif x>0.05:\n",
+    "        return 0.1\n",
+    "    elif x>0.01:\n",
+    "        return 0.05\n",
+    "    elif x<=0.01:\n",
+    "        return 0.01\n",
+    "    \n",
+    "    \n",
+    "\n",
+    "df_mds[\"Frequency\"] = df_mds[\"frequency\"].apply(f_freq_bins)\n",
+    "df_mds[\"Method\"] = df_mds['method'].apply(lambda x: mapping_methods[x])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "659b02f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 537.78x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"white\", font_scale=1.8)\n",
+    "\n",
+    "df_mds = df_mds[df_mds['method'].isin(['cliquesnv_local_haplo_tf0.001',\n",
+    "                                       'predicthaplo_local_haplo',\n",
+    "                                       'viloca_alpha_0.00001_K100', \n",
+    "                                       'ground_truth'])]\n",
+    "\n",
+    "\n",
+    "\n",
+    "marker_viloca = \"1\"\n",
+    "marker_cliquesnv = \"3\"\n",
+    "marker_predicthaplo = \"4\"\n",
+    "\n",
+    "\n",
+    "g = sns.relplot(data=df_mds[df_mds['method']=='ground_truth'], \n",
+    "            kind=\"scatter\", \n",
+    "            x=\"MDS1\", \n",
+    "            y=\"MDS0\", \n",
+    "            size=\"Frequency\",\n",
+    "            hue='Method',\n",
+    "            sizes=(200,500),\n",
+    "            palette={'ground truth': 'gray'},\n",
+    "            alpha=0.5, \n",
+    "            height=5, \n",
+    "            aspect=1.0\n",
+    "            )\n",
+    "\n",
+    "ax0 = g.fig.axes[0]\n",
+    "\n",
+    "df_plot = df_mds[(df_mds['Method'].isin([\"CliqueSNV\"]))]\n",
+    "ax0.scatter(df_plot[\"MDS1\"], df_plot[\"MDS0\"], marker=marker_cliquesnv, c=\"#4ecdc4\", s=600, linewidth=4, alpha =0.7)\n",
+    "\n",
+    "df_plot = df_mds[(df_mds['Method'].isin([\"VILOCA\"]))]\n",
+    "ax0.scatter(df_plot[\"MDS1\"], df_plot[\"MDS0\"], color=\"#ff6b6b\", marker=marker_viloca,s=600, linewidth=4)\n",
+    "\n",
+    "df_plot = df_mds[(df_mds['Method'].isin([\"PredictHaplo\"]))]\n",
+    "ax0.scatter(df_plot[\"MDS1\"], df_plot[\"MDS0\"], marker=marker_predicthaplo, c=\"#227c9d\", s=600, linewidth=4, alpha =0.7)\n",
+    "\n",
+    "\n",
+    "g.set_titles(col_template='{col_name}', fontsize=35)\n",
+    "\n",
+    "#g.set_xticklabels(\"\")\n",
+    "#g.set_yticklabels(\"\")\n",
+    "g.set(xlabel =\"\")\n",
+    "g.set(ylabel =\"sequence space (MDS plot)\")\n",
+    "\n",
+    "#g.set(ylim=(-7.5,11), xlim=(-3, 10))\n",
+    "g.tight_layout()\n",
+    "\n",
+    "g.savefig(\"realdata__10iav_mix.svg\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b3882708",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     cliquesnv_local_haplo_tf0.001\n",
+       "1     cliquesnv_local_haplo_tf0.001\n",
+       "2     cliquesnv_local_haplo_tf0.001\n",
+       "3     cliquesnv_local_haplo_tf0.001\n",
+       "4     cliquesnv_local_haplo_tf0.001\n",
+       "5     cliquesnv_local_haplo_tf0.001\n",
+       "6     cliquesnv_local_haplo_tf0.001\n",
+       "7     cliquesnv_local_haplo_tf0.001\n",
+       "8     cliquesnv_local_haplo_tf0.001\n",
+       "9     cliquesnv_local_haplo_tf0.001\n",
+       "10    cliquesnv_local_haplo_tf0.001\n",
+       "11    cliquesnv_local_haplo_tf0.001\n",
+       "12    cliquesnv_local_haplo_tf0.001\n",
+       "13         predicthaplo_local_haplo\n",
+       "14        viloca_alpha_0.00001_K100\n",
+       "15        viloca_alpha_0.00001_K100\n",
+       "16        viloca_alpha_0.00001_K100\n",
+       "17        viloca_alpha_0.00001_K100\n",
+       "18        viloca_alpha_0.00001_K100\n",
+       "19                     ground_truth\n",
+       "20                     ground_truth\n",
+       "21                     ground_truth\n",
+       "22                     ground_truth\n",
+       "23                     ground_truth\n",
+       "24                     ground_truth\n",
+       "25                     ground_truth\n",
+       "26                     ground_truth\n",
+       "27                     ground_truth\n",
+       "28                     ground_truth\n",
+       "Name: method, dtype: object"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_mds['method']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c71f66d2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/SuppFigure_S3__distance__snvs_precision_recall_curve_viloca_supplement.ipynb b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/SuppFigure_S3__distance__snvs_precision_recall_curve_viloca_supplement.ipynb
new file mode 100644
index 00000000..545dfb03
--- /dev/null
+++ b/resources/auxiliary_workflows/benchmark/resources/local_haplotype_setup/workflow/notebooks/SuppFigure_S3__distance__snvs_precision_recall_curve_viloca_supplement.ipynb
@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "27af307e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "import humanize\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "814038c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# \"VILOCA\": \"#ff6b6b\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "2893c8d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fname_viloca_posterior = \"results/distance/performance_measures/performance_mutation_calls_posterior_thres.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0afbbc2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_viloca = pd.read_csv(fname_viloca_posterior).drop(columns=[\"Unnamed: 0\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "0d5d27da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['shorah_default', 'viloca_alpha_0.00001'], dtype=object)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_viloca['method'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "44c8f6cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_viloca = df_viloca[df_viloca['method']=='viloca_alpha_0.00001']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "7cebcb02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_long = pd.melt(df_viloca, id_vars=[\"method\", \"params\", \"replicate\", \"posterior_threshold\"]).assign(\n",
+    "        params=lambda x: x[\"params\"].str.replace(\"_\", \"\\n\")\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "df_long.loc[:, 'seq_tech'] = df_long['params'].str.split('~').str[1].str.split('\\n').str[0]\n",
+    "\n",
+    "mapping_seqtech = {'illumina': 'Illumina', \n",
+    "                    'nanopore': 'ONT', \n",
+    "                  'pacbio': 'PacBio'}\n",
+    "\n",
+    "df_long[\"seq_tech\"] = df_long['seq_tech'].apply(lambda x: mapping_seqtech[x])\n",
+    "\n",
+    "df_long.loc[:,'coverage'] = df_long['params'].str.split('~').str[6].str.split('_').str[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "a7a292dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7fc246c1a610>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1096x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", font_scale=1.8)\n",
+    "\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "        data=df_long,\n",
+    "        x=\"posterior_threshold\",\n",
+    "        y=\"value\",\n",
+    "        hue=\"method\",\n",
+    "        col=\"variable\",\n",
+    "        col_order=['recall', 'precision', 'f1'],\n",
+    "        #row_order=['illumina', 'nanopore', 'pacbio'],\n",
+    "        row='seq_tech',\n",
+    "        kind=\"point\",\n",
+    "        legend=False, \n",
+    "        width=0.8,\n",
+    "        linewidth=1.5,\n",
+    "        sharex=True,\n",
+    "        palette=[\"#ff6b6b\"],\n",
+    "        margin_titles=True,\n",
+    "    )\n",
+    "g.set_titles(col_template='{col_name}', row_template='{row_name}')\n",
+    "g.tick_params(axis='x', rotation=90)\n",
+    "g.set(ylim=(0.60, 1.03))\n",
+    "g.set(ylabel =\"\")\n",
+    "g.add_legend(fontsize=20)\n",
+    "g.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "fcee0d24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g.savefig(\"distance__snvs_posteriorxprecisionrecall__supp.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ebd2ee7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}