From 3185df782cf509a80d4c375bf5c55b2f06f69cdf Mon Sep 17 00:00:00 2001
From: Giovanni Asole <gasole@ssh-flomics.freeddns.org>
Date: Thu, 19 Jun 2025 19:28:05 +0200
Subject: [PATCH 1/4] ridgeline plot for gene coverage

---
 ...overage_profile_fig2_tmpH-checkpoint.ipynb | 1019 +++++++++++++++++
 src/gene_coverage_profile_fig2_tmpH.ipynb     |  786 ++++++++++++-
 2 files changed, 1780 insertions(+), 25 deletions(-)
 create mode 100644 src/.ipynb_checkpoints/gene_coverage_profile_fig2_tmpH-checkpoint.ipynb

diff --git a/src/.ipynb_checkpoints/gene_coverage_profile_fig2_tmpH-checkpoint.ipynb b/src/.ipynb_checkpoints/gene_coverage_profile_fig2_tmpH-checkpoint.ipynb
new file mode 100644
index 0000000..0127b83
--- /dev/null
+++ b/src/.ipynb_checkpoints/gene_coverage_profile_fig2_tmpH-checkpoint.ipynb
@@ -0,0 +1,1019 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "e3f32189-fd60-4cd4-830a-efdb73ce993f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc154ab0",
+   "metadata": {},
+   "source": [
+    "## LIBRARIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d40ba3ab-b568-4dfb-acdb-c8450c9eb50c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "530bed1f",
+   "metadata": {},
+   "source": [
+    "## FUNCTIONS/PAR_SETTING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3e51378d-bfcf-4d8c-ac17-af501ab2ad56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_results_folder(base_dataset_path, prefix):\n",
+    "    for folder in os.listdir(base_dataset_path):\n",
+    "        if os.path.isdir(os.path.join(base_dataset_path, folder)):\n",
+    "            if prefix in folder and 'results' in folder:\n",
+    "                return folder\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b701cdfa-4d91-49ad-a0ab-d27ec1f9a981",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dodged barplot function\n",
+    "script_dir = \"/mnt/efs/home/gasole/repos/personal/utils/plots\"\n",
+    "sys.path.append(script_dir)\n",
+    "\n",
+    "from ridgeline_from_known_density_plot import ridgeline_from_known_density_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a234ae7e",
+   "metadata": {},
+   "source": [
+    "## WORKFLOW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "253950be-269a-4adb-b97f-6a3800d37b73",
+   "metadata": {},
+   "source": [
+    "### Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e6bf5f86-330b-42a8-8dc5-1868b714ce44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_folder = '/mnt/s3fs/flomics-data/FL-cfRNA-meta'\n",
+    "output_folder = os.getcwd()\n",
+    "\n",
+    "datasets = {\n",
+    "    'block':         {'sliced': True,  'n_parts': 4,  'prefix': 'block'},\n",
+    "    'chalasani_merged': {'sliced': True,  'n_parts': 5,  'prefix': 'chalasani'},\n",
+    "    'chen':          {'sliced': True,  'n_parts': 21, 'prefix': 'chen'},\n",
+    "    'flomics_1': {'sliced': False, 'subfolder_path': 'Flomics_1/2025_05_16_11_26_41/n3abcb4a/'},\n",
+    "    'flomics_2': {'sliced': False, 'subfolder_path': 'Flomics_2/2025_06_05_09_32_11/n61e05f8/'},\n",
+    "    'decru':         {'sliced': True,  'n_parts': 6,  'prefix': 'decru'},\n",
+    "    'giraldez':      {'sliced': False,                 'prefix': 'giraldez'},\n",
+    "    'ibarra':        {'sliced': True,  'n_parts': 14, 'prefix': 'ibarra'},\n",
+    "    'moufarrej':     {'sliced': True,  'n_parts': 17, 'prefix': 'moufarrej'},\n",
+    "    'ngo':           {'sliced': False,                 'prefix': 'ngo'},\n",
+    "    'reggiardo':     {'sliced': False,                 'prefix': 'reggiardo'},\n",
+    "    'roskams':       {'sliced': True,  'n_parts': 6,  'prefix': 'roskams'},\n",
+    "    'rozowsky':      {'sliced': True,  'n_parts': 9,  'prefix': 'rozowsky'},\n",
+    "    'sun':           {'sliced': True,  'n_parts': 3,  'prefix': 'sun'},\n",
+    "    'tao':           {'sliced': True,  'n_parts': 12, 'prefix': 'tao'},\n",
+    "    #'taowei':        {'sliced': True,  'n_parts': 4,  'prefix': 'taowei'},\n",
+    "    'toden':         {'sliced': True,  'n_parts': 14, 'prefix': 'toden'},\n",
+    "    'wang':          {'sliced': True,  'n_parts': 4,  'prefix': 'wang'},\n",
+    "    'zhu':           {'sliced': True,  'n_parts': 5,  'prefix': 'zhu'},\n",
+    "}\n",
+    "\n",
+    "dataset_colors = {\n",
+    "    \"block\": \"#b3b3b3\", \"decru\": \"#009E73\", \"zhu\": \"#ffd633\", \"chen\": \"#997a00\", \"flomics_1\" : \"#9AB9D6\", \"flomics_2\": \"#144D6B\",\n",
+    "    \"ngo\": \"#fa8072\", \"roskams\": \"#944dff\", \"moufarrej\": \"#CC79A7\", \"sun\": \"#D55E00\",\n",
+    "    \"tao\": \"#0072B2\", \"toden\": \"#800099\", \"ibarra\": \"#800000\", \"chalasani_merged\": \"#800040\",\n",
+    "    \"rozowsky\": \"#006600\", \"taowei\": \"#B32400\", \"giraldez\": \"#B1CC71\", \"reggiardo\": \"#F1085C\", \"wang\": \"#FE8F42\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f40a95d-f889-4aec-b65c-cbd798946b35",
+   "metadata": {},
+   "source": [
+    "### Process dataset and individual plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "883c4a91-9ff1-4c2a-9e7b-9fdf6615d91d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Processing dataset: block\n",
+      "Saved: block_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: chalasani_merged\n",
+      "Saved: chalasani_merged_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: chen\n",
+      "Saved: chen_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: flomics_1\n",
+      "Saved: flomics_1_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: flomics_2\n",
+      "Saved: flomics_2_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: decru\n",
+      "Saved: decru_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: giraldez\n",
+      "Saved: giraldez_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: ibarra\n",
+      "Saved: ibarra_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: moufarrej\n",
+      "Saved: moufarrej_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: ngo\n",
+      "Saved: ngo_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: reggiardo\n",
+      "Saved: reggiardo_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: roskams\n",
+      "Saved: roskams_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: rozowsky\n",
+      "Saved: rozowsky_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: sun\n",
+      "Saved: sun_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: tao\n",
+      "Saved: tao_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: toden\n",
+      "Saved: toden_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: wang\n",
+      "Saved: wang_multiqc_coverage.png\n",
+      "\n",
+      "Processing dataset: zhu\n",
+      "Saved: zhu_multiqc_coverage.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ---- Process ----\n",
+    "all_profiles = {}\n",
+    "\n",
+    "for dataset, meta in datasets.items():\n",
+    "    sliced = meta['sliced']\n",
+    "    n_parts = meta.get('n_parts', 1)\n",
+    "    prefix = meta.get('prefix', None)  # Only exists if sliced or traditional unsliced\n",
+    "    print(f\"\\nProcessing dataset: {dataset}\")\n",
+    "    \n",
+    "    part_means = []\n",
+    "\n",
+    "    if sliced:\n",
+    "        for i in range(n_parts):\n",
+    "            part_folder = f\"{prefix}_results_{i:02d}.part/multiqc/star_salmon/multiqc_data\"\n",
+    "            file_path = os.path.join(base_folder, dataset, part_folder, \"mqc_qualimap_gene_coverage_profile_Normalised.txt\")\n",
+    "            if os.path.isfile(file_path):\n",
+    "                try:\n",
+    "                    df = pd.read_csv(file_path, sep=\"\\t\")\n",
+    "                    sample_only = df.drop(columns=[\"Sample\"])\n",
+    "                    part_means.append(sample_only.mean(axis=0))\n",
+    "                except Exception as e:\n",
+    "                    print(f\"Error reading {file_path}: {e}\")\n",
+    "            else:\n",
+    "                print(f\"Missing: {file_path}\")\n",
+    "    else:\n",
+    "        if \"subfolder_path\" in meta:\n",
+    "            part_path = os.path.join(base_folder, meta[\"subfolder_path\"], \"multiqc\", \"star_salmon\", \"multiqc_data\")\n",
+    "        else:\n",
+    "            base_dataset_path = os.path.join(base_folder, dataset)\n",
+    "            matched_folder = find_results_folder(base_dataset_path, prefix)\n",
+    "            if not matched_folder:\n",
+    "                print(f\"Could not find results folder for: {dataset}\")\n",
+    "                continue\n",
+    "            part_path = os.path.join(base_dataset_path, matched_folder, \"multiqc\", \"star_salmon\", \"multiqc_data\")\n",
+    "\n",
+    "        file_path = os.path.join(part_path, \"mqc_qualimap_gene_coverage_profile_Normalised.txt\")\n",
+    "        if os.path.isfile(file_path):\n",
+    "            try:\n",
+    "                df = pd.read_csv(file_path, sep=\"\\t\")\n",
+    "                sample_only = df.drop(columns=[\"Sample\"])\n",
+    "                part_means.append(sample_only.mean(axis=0))\n",
+    "            except Exception as e:\n",
+    "                print(f\"Error reading {file_path}: {e}\")\n",
+    "        else:\n",
+    "            print(f\"Missing: {file_path}\")\n",
+    "\n",
+    "\n",
+    "    # Average across all .part means\n",
+    "    if part_means:\n",
+    "        dataset_mean_df = pd.DataFrame(part_means).mean(axis=0).reset_index()\n",
+    "        dataset_mean_df.columns = [\"position\", \"coverage\"]\n",
+    "        dataset_mean_df[\"position\"] = dataset_mean_df[\"position\"].astype(int)\n",
+    "        dataset_mean_df.sort_values(\"position\", inplace=True)\n",
+    "        all_profiles[dataset] = dataset_mean_df\n",
+    "\n",
+    "        # Per-dataset plot\n",
+    "        plt.figure(figsize=(10, 4))\n",
+    "        plt.plot(dataset_mean_df[\"position\"], dataset_mean_df[\"coverage\"],\n",
+    "                 label=dataset, color=dataset_colors.get(dataset))\n",
+    "        plt.title(f\"Normalized Transcript Coverage: {dataset}\")\n",
+    "        plt.xlabel(\"Transcript position (%)\")\n",
+    "        plt.ylabel(\"Normalized coverage\")\n",
+    "        plt.tight_layout()\n",
+    "        plt.savefig(os.path.join(output_folder, f\"{dataset}_multiqc_coverage.png\"))\n",
+    "        plt.close()\n",
+    "        print(f\"Saved: {dataset}_multiqc_coverage.png\")\n",
+    "    else:\n",
+    "        print(f\"No data for {dataset}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3594b8e5-f53a-4b96-af29-cfae9a1e3ade",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All per-dataset and combined plots saved.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_labels = {\n",
+    "    \"block\": \"Block\",\n",
+    "    \"chalasani_merged\": \"Chalasani\",\n",
+    "    \"chen\": \"Chen\",\n",
+    "    \"flomics_1\" : \"Flomics 1\",\n",
+    "    \"flomics_2\": \"Flomics 2\",\n",
+    "    \"decru\": \"Decruyenaere\",\n",
+    "    \"giraldez\": \"Giráldez\",\n",
+    "    \"ibarra\": \"Ibarra\",\n",
+    "    \"moufarrej\": \"Moufarrej\",\n",
+    "    \"ngo\": \"Ngo\",\n",
+    "    \"reggiardo\": \"Reggiardo\",\n",
+    "    \"roskams\": \"Roskams-Hieter\",\n",
+    "    \"rozowsky\": \"Rozowsky\",\n",
+    "    \"sun\": \"Sun\",\n",
+    "    \"tao\": \"Tao\",\n",
+    "    \"taowei\": \"Wei\",\n",
+    "    \"toden\": \"Toden\",\n",
+    "    \"wang\": \"Wang\",\n",
+    "    \"zhu\": \"Zhu\"\n",
+    "}\n",
+    "\n",
+    "# ---- Combined Plot ----\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "for dataset, df in all_profiles.items():\n",
+    "    plt.plot(df[\"position\"], df[\"coverage\"], label=dataset_labels.get(dataset, dataset), color=dataset_colors.get(dataset))\n",
+    "plt.xlabel(\"Transcript position (%)\")\n",
+    "plt.ylabel(\"Normalized coverage\")\n",
+    "plt.title(\"Average Gene Coverage per Dataset (MultiQC normalized)\")\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(os.path.join(output_folder, \"multiqc_style_normalized_coverage_combined.png\"), dpi=600)\n",
+    "plt.close()\n",
+    "\n",
+    "print(\"All per-dataset and combined plots saved.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b507097c-114a-4ec6-9589-8cdbb7cbf324",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'block':     position  coverage\n",
+       " 0          0  0.751370\n",
+       " 1          1  0.948973\n",
+       " 2          2  1.042970\n",
+       " 3          3  1.101110\n",
+       " 4          4  1.137402\n",
+       " ..       ...       ...\n",
+       " 95        95  0.387874\n",
+       " 96        96  0.314820\n",
+       " 97        97  0.234645\n",
+       " 98        98  0.149621\n",
+       " 99        99  0.054939\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'chalasani_merged':     position  coverage\n",
+       " 0          0  0.893336\n",
+       " 1          1  0.992028\n",
+       " 2          2  1.068821\n",
+       " 3          3  1.125762\n",
+       " 4          4  1.172424\n",
+       " ..       ...       ...\n",
+       " 95        95  0.405052\n",
+       " 96        96  0.326549\n",
+       " 97        97  0.239147\n",
+       " 98        98  0.149661\n",
+       " 99        99  0.050280\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'chen':     position  coverage\n",
+       " 0          0  0.883808\n",
+       " 1          1  1.080623\n",
+       " 2          2  1.127518\n",
+       " 3          3  1.161813\n",
+       " 4          4  1.208590\n",
+       " ..       ...       ...\n",
+       " 95        95  0.288879\n",
+       " 96        96  0.242541\n",
+       " 97        97  0.178097\n",
+       " 98        98  0.115640\n",
+       " 99        99  0.038419\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'flomics_1':     position  coverage\n",
+       " 0          0  0.577109\n",
+       " 1          1  0.638392\n",
+       " 2          2  0.690236\n",
+       " 3          3  0.762369\n",
+       " 4          4  0.824304\n",
+       " ..       ...       ...\n",
+       " 95        95  0.337860\n",
+       " 96        96  0.288220\n",
+       " 97        97  0.234444\n",
+       " 98        98  0.176366\n",
+       " 99        99  0.081433\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'flomics_2':     position  coverage\n",
+       " 0          0  0.678069\n",
+       " 1          1  0.751549\n",
+       " 2          2  0.805104\n",
+       " 3          3  0.885331\n",
+       " 4          4  0.948078\n",
+       " ..       ...       ...\n",
+       " 95        95  0.331373\n",
+       " 96        96  0.278453\n",
+       " 97        97  0.223898\n",
+       " 98        98  0.155197\n",
+       " 99        99  0.062723\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'decru':     position  coverage\n",
+       " 0          0  0.594986\n",
+       " 1          1  0.673064\n",
+       " 2          2  0.728644\n",
+       " 3          3  0.816234\n",
+       " 4          4  0.906582\n",
+       " ..       ...       ...\n",
+       " 95        95  0.299129\n",
+       " 96        96  0.257159\n",
+       " 97        97  0.207672\n",
+       " 98        98  0.152542\n",
+       " 99        99  0.069510\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'giraldez':     position  coverage\n",
+       " 0          0  0.146943\n",
+       " 1          1  0.179056\n",
+       " 2          2  0.206957\n",
+       " 3          3  0.284656\n",
+       " 4          4  0.369986\n",
+       " ..       ...       ...\n",
+       " 95        95  0.144934\n",
+       " 96        96  0.174990\n",
+       " 97        97  0.030457\n",
+       " 98        98  0.018695\n",
+       " 99        99  0.005258\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'ibarra':     position  coverage\n",
+       " 0          0  0.811200\n",
+       " 1          1  0.921145\n",
+       " 2          2  0.990593\n",
+       " 3          3  1.035097\n",
+       " 4          4  1.071725\n",
+       " ..       ...       ...\n",
+       " 95        95  0.431216\n",
+       " 96        96  0.350551\n",
+       " 97        97  0.258570\n",
+       " 98        98  0.161554\n",
+       " 99        99  0.053056\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'moufarrej':     position  coverage\n",
+       " 0          0  0.922650\n",
+       " 1          1  1.289233\n",
+       " 2          2  1.376297\n",
+       " 3          3  1.501520\n",
+       " 4          4  1.510656\n",
+       " ..       ...       ...\n",
+       " 95        95  0.312282\n",
+       " 96        96  0.268549\n",
+       " 97        97  0.214269\n",
+       " 98        98  0.164626\n",
+       " 99        99  0.080866\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'ngo':     position  coverage\n",
+       " 0          0  0.683297\n",
+       " 1          1  0.853360\n",
+       " 2          2  0.905121\n",
+       " 3          3  0.955589\n",
+       " 4          4  1.046210\n",
+       " ..       ...       ...\n",
+       " 95        95  0.272191\n",
+       " 96        96  0.218318\n",
+       " 97        97  0.158297\n",
+       " 98        98  0.099380\n",
+       " 99        99  0.033786\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'reggiardo':     position  coverage\n",
+       " 0          0  0.456523\n",
+       " 1          1  0.471918\n",
+       " 2          2  0.491256\n",
+       " 3          3  0.501107\n",
+       " 4          4  0.515223\n",
+       " ..       ...       ...\n",
+       " 95        95  0.471508\n",
+       " 96        96  0.392591\n",
+       " 97        97  0.293598\n",
+       " 98        98  0.178205\n",
+       " 99        99  0.048939\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'roskams':     position  coverage\n",
+       " 0          0  0.836213\n",
+       " 1          1  0.958719\n",
+       " 2          2  1.006426\n",
+       " 3          3  1.049440\n",
+       " 4          4  1.083980\n",
+       " ..       ...       ...\n",
+       " 95        95  0.345281\n",
+       " 96        96  0.283676\n",
+       " 97        97  0.216060\n",
+       " 98        98  0.140177\n",
+       " 99        99  0.048876\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'rozowsky':     position  coverage\n",
+       " 0          0  0.664510\n",
+       " 1          1  0.823720\n",
+       " 2          2  0.986983\n",
+       " 3          3  1.143379\n",
+       " 4          4  1.228875\n",
+       " ..       ...       ...\n",
+       " 95        95  0.526265\n",
+       " 96        96  0.390768\n",
+       " 97        97  0.241327\n",
+       " 98        98  0.147613\n",
+       " 99        99  0.048011\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'sun':     position  coverage\n",
+       " 0          0  0.827656\n",
+       " 1          1  1.024742\n",
+       " 2          2  1.238866\n",
+       " 3          3  1.111935\n",
+       " 4          4  1.164360\n",
+       " ..       ...       ...\n",
+       " 95        95  0.291605\n",
+       " 96        96  0.237043\n",
+       " 97        97  0.170098\n",
+       " 98        98  0.109589\n",
+       " 99        99  0.038476\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'tao':     position  coverage\n",
+       " 0          0  0.816322\n",
+       " 1          1  0.957243\n",
+       " 2          2  1.002606\n",
+       " 3          3  1.037284\n",
+       " 4          4  1.073102\n",
+       " ..       ...       ...\n",
+       " 95        95  0.284971\n",
+       " 96        96  0.236689\n",
+       " 97        97  0.183865\n",
+       " 98        98  0.121932\n",
+       " 99        99  0.040224\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'toden':     position  coverage\n",
+       " 0          0  0.794399\n",
+       " 1          1  0.889729\n",
+       " 2          2  0.952080\n",
+       " 3          3  0.994954\n",
+       " 4          4  1.036387\n",
+       " ..       ...       ...\n",
+       " 95        95  0.440365\n",
+       " 96        96  0.354897\n",
+       " 97        97  0.259633\n",
+       " 98        98  0.161428\n",
+       " 99        99  0.053321\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'wang':     position  coverage\n",
+       " 0          0  0.884646\n",
+       " 1          1  1.098436\n",
+       " 2          2  1.216367\n",
+       " 3          3  1.430555\n",
+       " 4          4  1.722352\n",
+       " ..       ...       ...\n",
+       " 95        95  0.265076\n",
+       " 96        96  0.202165\n",
+       " 97        97  0.142643\n",
+       " 98        98  0.162646\n",
+       " 99        99  0.075130\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'zhu':     position  coverage\n",
+       " 0          0  0.982054\n",
+       " 1          1  1.203608\n",
+       " 2          2  1.250519\n",
+       " 3          3  1.290315\n",
+       " 4          4  1.337304\n",
+       " ..       ...       ...\n",
+       " 95        95  0.283839\n",
+       " 96        96  0.220782\n",
+       " 97        97  0.164646\n",
+       " 98        98  0.103329\n",
+       " 99        99  0.031109\n",
+       " \n",
+       " [100 rows x 2 columns]}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_profiles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53a6c9e2-db95-4245-9cc5-7aaeb8f1e92d",
+   "metadata": {},
+   "source": [
+    "### data preparation for ridge plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6351b588-d2b9-4cba-8acf-16522ee1eb22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "block\n",
+      "chalasani_merged\n",
+      "chen\n",
+      "flomics_1\n",
+      "flomics_2\n",
+      "decru\n",
+      "giraldez\n",
+      "ibarra\n",
+      "moufarrej\n",
+      "ngo\n",
+      "reggiardo\n",
+      "roskams\n",
+      "rozowsky\n",
+      "sun\n",
+      "tao\n",
+      "toden\n",
+      "wang\n",
+      "zhu\n",
+      "(1800, 3)\n"
+     ]
+    },
+    {
+     "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></th>\n",
+       "      <th>position</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>dataset_short_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">block</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.751370</td>\n",
+       "      <td>block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.948973</td>\n",
+       "      <td>block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.042970</td>\n",
+       "      <td>block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.101110</td>\n",
+       "      <td>block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.137402</td>\n",
+       "      <td>block</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         position  coverage dataset_short_name\n",
+       "block 0         0  0.751370              block\n",
+       "      1         1  0.948973              block\n",
+       "      2         2  1.042970              block\n",
+       "      3         3  1.101110              block\n",
+       "      4         4  1.137402              block"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#add dataset_short_name column\n",
+    "for dataset, df in all_profiles.items():\n",
+    "    df['dataset_short_name'] = dataset\n",
+    "    print(dataset)\n",
+    "\n",
+    "#create a single dataset\n",
+    "all_dataset = pd.concat(all_profiles, ignore_index=False)\n",
+    "print(all_dataset.shape)\n",
+    "all_dataset.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c545fb94-cd41-47b2-a952-954d4349936e",
+   "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></th>\n",
+       "      <th>position</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>dataset_short_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">block</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.751370</td>\n",
+       "      <td>Block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.948973</td>\n",
+       "      <td>Block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.042970</td>\n",
+       "      <td>Block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.101110</td>\n",
+       "      <td>Block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.137402</td>\n",
+       "      <td>Block</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         position  coverage dataset_short_name\n",
+       "block 0         0  0.751370              Block\n",
+       "      1         1  0.948973              Block\n",
+       "      2         2  1.042970              Block\n",
+       "      3         3  1.101110              Block\n",
+       "      4         4  1.137402              Block"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#correct dataset labels in dataframe\n",
+    "all_dataset[\"dataset_short_name\"].replace(dataset_labels, inplace=True)\n",
+    "all_dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "5d3ca5e0-6cb9-4b19-88ba-8e04de9638cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Block': '#b3b3b3', 'Decruyenaere': '#009E73', 'Zhu': '#ffd633', 'Chen': '#997a00', 'Flomics 1': '#9AB9D6', 'Flomics 2': '#144D6B', 'Ngo': '#fa8072', 'Roskams-Hieter': '#944dff', 'Moufarrej': '#CC79A7', 'Sun': '#D55E00', 'Tao': '#0072B2', 'Toden': '#800099', 'Ibarra': '#800000', 'Chalasani': '#800040', 'Rozowsky': '#006600', 'Wei': '#B32400', 'Giráldez': '#B1CC71', 'Reggiardo': '#F1085C', 'Wang': '#FE8F42'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#correct dataset labels in color dict\n",
+    "dataset_short_name_colors = {dataset_labels.get(old_key, old_key): value \n",
+    "            for old_key, value in dataset_colors.items()}\n",
+    "\n",
+    "print(dataset_short_name_colors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7d205ca-1721-4fea-8ebf-596157f32803",
+   "metadata": {},
+   "source": [
+    "### ridge plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "28087f79-0fc9-4a31-9c77-067d0cef5f78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "    Generate a ridgeline plot from pre-calculated density values.\n",
+      "\n",
+      "    Args:\n",
+      "        dataset (pd.DataFrame): The input pandas DataFrame containing pre-calculated densities values\n",
+      "        x_column (str): Column containing the coordinates for the density curves.\n",
+      "        density_column (str): Column containing the density values for the curves.\n",
+      "        category_column (str): Column containing categories to group the curves.\n",
+      "        overlap (float, optional): The degree of overlap between the lines (from 0 to 1). Defaults to 0.5.\n",
+      "        fig_width (float, optional): The width of the figure in inches. Defaults to 10.\n",
+      "        fig_height (float, optional): The height of the figure in inches. Defaults to 7.\n",
+      "        output (str, optional): The full path filename (without extension) to save the plot.\n",
+      "        show_xlabel (bool, optional): Display the X-axis label. Defaults to True.\n",
+      "        xlabel (str, optional): Custom label for the X-axis. Defaults to None.\n",
+      "        xlabel_fontsize (float, optional): Font size for the X-axis label. Defaults to 12.\n",
+      "        show_ylabel (bool, optional): Display the Y-axis label. Defaults to True\n",
+      "        ylabel (str, optional): Custom label for the Y-axis. Defaults to None.\n",
+      "        ylabel_fontsize (float, optional): Font size for the Y-axis label. Defaults to 12.\n",
+      "        show_title (bool, optional): Display plot title. Defaults to True.\n",
+      "        title (str, optional): Custom title. Defaults to None.\n",
+      "        title_fontsize: float (optional): Font size for title. Defaults to 14.\n",
+      "        xticks_fontsize (float, optional): Font size for the X-axis tick labels. Defaults to 10.\n",
+      "        yticks_fontsize (float, optional): Font size for the Y-axis tick labels. Defaults to 10.\n",
+      "        fill_alpha (float, optional): Transparency of the filled area under the curves. Defaults to 0.7.\n",
+      "        line_color (str, optional): Color of the curve lines. Defaults to 'black'.\n",
+      "        fill_color (str or dict, optional): Color of the filled area under the curves.\n",
+      "                                            Can be a single color string for all categories,\n",
+      "                                            or a dictionary mapping category names\n",
+      "                                            Defaults to 'skyblue'.\n",
+      "        category_order (list, optional): Order in which categories should be plotted from bottom to top.\n",
+      "    \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ridgeline_from_known_density_plot.__doc__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "6fdcbbc1-399e-420c-b410-4d72949f43bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ridgeline plot saved to gene_coverage_merged.pdf\n",
+      "Ridgeline plot saved to gene_coverage_merged.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 700x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ridgeline_from_known_density_plot(\n",
+    "    dataset = all_dataset,\n",
+    "    x_column = \"position\",\n",
+    "    density_column = \"coverage\",\n",
+    "    category_column = \"dataset_short_name\",\n",
+    "    fig_width = 7,\n",
+    "    fig_height = 10,\n",
+    "    output = \"gene_coverage_merged\",\n",
+    "    show_ylabel = False,\n",
+    "    fill_color = dataset_short_name_colors\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d2930e-da88-4b9d-98e1-3d543eea0c76",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3ff92dc-162e-4cf7-8a79-bdc333dffacc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efb1701b-3c12-44c1-b10c-e5ccb720c531",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e901d9e",
+   "metadata": {},
+   "source": [
+    "## CITATIONS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e8e4aa",
+   "metadata": {},
+   "source": [
+    "write here citations for used packages/datasets etc.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "437eac95-8e6d-4623-9849-4c7c98780ede",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ee557aa-9a41-4784-94dd-b7dafba6ed84",
+   "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": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/gene_coverage_profile_fig2_tmpH.ipynb b/src/gene_coverage_profile_fig2_tmpH.ipynb
index 7ea2198..0127b83 100644
--- a/src/gene_coverage_profile_fig2_tmpH.ipynb
+++ b/src/gene_coverage_profile_fig2_tmpH.ipynb
@@ -2,40 +2,105 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "59017647-22aa-469c-b8fe-ba2d91c2a1d0",
+   "execution_count": 46,
+   "id": "e3f32189-fd60-4cd4-830a-efdb73ce993f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc154ab0",
+   "metadata": {},
+   "source": [
+    "## LIBRARIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d40ba3ab-b568-4dfb-acdb-c8450c9eb50c",
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
     "import pandas as pd\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "530bed1f",
+   "metadata": {},
+   "source": [
+    "## FUNCTIONS/PAR_SETTING"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "8c8c3748-333d-4c4c-aa70-7ec8cc49ee1e",
+   "execution_count": 18,
+   "id": "3e51378d-bfcf-4d8c-ac17-af501ab2ad56",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# ---- Helper ----\n",
     "def find_results_folder(base_dataset_path, prefix):\n",
     "    for folder in os.listdir(base_dataset_path):\n",
     "        if os.path.isdir(os.path.join(base_dataset_path, folder)):\n",
     "            if prefix in folder and 'results' in folder:\n",
     "                return folder\n",
-    "    return None\n"
+    "    return None"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ee4b7a1e-5910-455e-ae58-69d72ad0e184",
+   "execution_count": 34,
+   "id": "b701cdfa-4d91-49ad-a0ab-d27ec1f9a981",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dodged barplot function\n",
+    "script_dir = \"/mnt/efs/home/gasole/repos/personal/utils/plots\"\n",
+    "sys.path.append(script_dir)\n",
+    "\n",
+    "from ridgeline_from_known_density_plot import ridgeline_from_known_density_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a234ae7e",
+   "metadata": {},
+   "source": [
+    "## WORKFLOW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "253950be-269a-4adb-b97f-6a3800d37b73",
+   "metadata": {},
+   "source": [
+    "### Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e6bf5f86-330b-42a8-8dc5-1868b714ce44",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# ---- Setup ----\n",
     "base_folder = '/mnt/s3fs/flomics-data/FL-cfRNA-meta'\n",
     "output_folder = os.getcwd()\n",
     "\n",
@@ -69,10 +134,18 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2f40a95d-f889-4aec-b65c-cbd798946b35",
+   "metadata": {},
+   "source": [
+    "### Process dataset and individual plots"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "aa786704-48cc-41f8-849a-2b651259c74e",
+   "execution_count": 20,
+   "id": "883c4a91-9ff1-4c2a-9e7b-9fdf6615d91d",
    "metadata": {},
    "outputs": [
     {
@@ -132,16 +205,11 @@
       "Saved: wang_multiqc_coverage.png\n",
       "\n",
       "Processing dataset: zhu\n",
-      "Saved: zhu_multiqc_coverage.png\n",
-      "\n",
-      "✅ All per-dataset and combined plots saved.\n"
+      "Saved: zhu_multiqc_coverage.png\n"
      ]
     }
    ],
    "source": [
-    "\n",
-    "\n",
-    "\n",
     "# ---- Process ----\n",
     "all_profiles = {}\n",
     "\n",
@@ -209,17 +277,23 @@
     "        plt.close()\n",
     "        print(f\"Saved: {dataset}_multiqc_coverage.png\")\n",
     "    else:\n",
-    "        print(f\"No data for {dataset}\")\n",
-    "\n",
-    "\n"
+    "        print(f\"No data for {dataset}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1b0cfff9-37eb-4d43-aafb-6d7d3ee88397",
+   "execution_count": 21,
+   "id": "3594b8e5-f53a-4b96-af29-cfae9a1e3ade",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All per-dataset and combined plots saved.\n"
+     ]
+    }
+   ],
    "source": [
     "dataset_labels = {\n",
     "    \"block\": \"Block\",\n",
@@ -257,6 +331,668 @@
     "\n",
     "print(\"All per-dataset and combined plots saved.\")\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b507097c-114a-4ec6-9589-8cdbb7cbf324",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'block':     position  coverage\n",
+       " 0          0  0.751370\n",
+       " 1          1  0.948973\n",
+       " 2          2  1.042970\n",
+       " 3          3  1.101110\n",
+       " 4          4  1.137402\n",
+       " ..       ...       ...\n",
+       " 95        95  0.387874\n",
+       " 96        96  0.314820\n",
+       " 97        97  0.234645\n",
+       " 98        98  0.149621\n",
+       " 99        99  0.054939\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'chalasani_merged':     position  coverage\n",
+       " 0          0  0.893336\n",
+       " 1          1  0.992028\n",
+       " 2          2  1.068821\n",
+       " 3          3  1.125762\n",
+       " 4          4  1.172424\n",
+       " ..       ...       ...\n",
+       " 95        95  0.405052\n",
+       " 96        96  0.326549\n",
+       " 97        97  0.239147\n",
+       " 98        98  0.149661\n",
+       " 99        99  0.050280\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'chen':     position  coverage\n",
+       " 0          0  0.883808\n",
+       " 1          1  1.080623\n",
+       " 2          2  1.127518\n",
+       " 3          3  1.161813\n",
+       " 4          4  1.208590\n",
+       " ..       ...       ...\n",
+       " 95        95  0.288879\n",
+       " 96        96  0.242541\n",
+       " 97        97  0.178097\n",
+       " 98        98  0.115640\n",
+       " 99        99  0.038419\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'flomics_1':     position  coverage\n",
+       " 0          0  0.577109\n",
+       " 1          1  0.638392\n",
+       " 2          2  0.690236\n",
+       " 3          3  0.762369\n",
+       " 4          4  0.824304\n",
+       " ..       ...       ...\n",
+       " 95        95  0.337860\n",
+       " 96        96  0.288220\n",
+       " 97        97  0.234444\n",
+       " 98        98  0.176366\n",
+       " 99        99  0.081433\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'flomics_2':     position  coverage\n",
+       " 0          0  0.678069\n",
+       " 1          1  0.751549\n",
+       " 2          2  0.805104\n",
+       " 3          3  0.885331\n",
+       " 4          4  0.948078\n",
+       " ..       ...       ...\n",
+       " 95        95  0.331373\n",
+       " 96        96  0.278453\n",
+       " 97        97  0.223898\n",
+       " 98        98  0.155197\n",
+       " 99        99  0.062723\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'decru':     position  coverage\n",
+       " 0          0  0.594986\n",
+       " 1          1  0.673064\n",
+       " 2          2  0.728644\n",
+       " 3          3  0.816234\n",
+       " 4          4  0.906582\n",
+       " ..       ...       ...\n",
+       " 95        95  0.299129\n",
+       " 96        96  0.257159\n",
+       " 97        97  0.207672\n",
+       " 98        98  0.152542\n",
+       " 99        99  0.069510\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'giraldez':     position  coverage\n",
+       " 0          0  0.146943\n",
+       " 1          1  0.179056\n",
+       " 2          2  0.206957\n",
+       " 3          3  0.284656\n",
+       " 4          4  0.369986\n",
+       " ..       ...       ...\n",
+       " 95        95  0.144934\n",
+       " 96        96  0.174990\n",
+       " 97        97  0.030457\n",
+       " 98        98  0.018695\n",
+       " 99        99  0.005258\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'ibarra':     position  coverage\n",
+       " 0          0  0.811200\n",
+       " 1          1  0.921145\n",
+       " 2          2  0.990593\n",
+       " 3          3  1.035097\n",
+       " 4          4  1.071725\n",
+       " ..       ...       ...\n",
+       " 95        95  0.431216\n",
+       " 96        96  0.350551\n",
+       " 97        97  0.258570\n",
+       " 98        98  0.161554\n",
+       " 99        99  0.053056\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'moufarrej':     position  coverage\n",
+       " 0          0  0.922650\n",
+       " 1          1  1.289233\n",
+       " 2          2  1.376297\n",
+       " 3          3  1.501520\n",
+       " 4          4  1.510656\n",
+       " ..       ...       ...\n",
+       " 95        95  0.312282\n",
+       " 96        96  0.268549\n",
+       " 97        97  0.214269\n",
+       " 98        98  0.164626\n",
+       " 99        99  0.080866\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'ngo':     position  coverage\n",
+       " 0          0  0.683297\n",
+       " 1          1  0.853360\n",
+       " 2          2  0.905121\n",
+       " 3          3  0.955589\n",
+       " 4          4  1.046210\n",
+       " ..       ...       ...\n",
+       " 95        95  0.272191\n",
+       " 96        96  0.218318\n",
+       " 97        97  0.158297\n",
+       " 98        98  0.099380\n",
+       " 99        99  0.033786\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'reggiardo':     position  coverage\n",
+       " 0          0  0.456523\n",
+       " 1          1  0.471918\n",
+       " 2          2  0.491256\n",
+       " 3          3  0.501107\n",
+       " 4          4  0.515223\n",
+       " ..       ...       ...\n",
+       " 95        95  0.471508\n",
+       " 96        96  0.392591\n",
+       " 97        97  0.293598\n",
+       " 98        98  0.178205\n",
+       " 99        99  0.048939\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'roskams':     position  coverage\n",
+       " 0          0  0.836213\n",
+       " 1          1  0.958719\n",
+       " 2          2  1.006426\n",
+       " 3          3  1.049440\n",
+       " 4          4  1.083980\n",
+       " ..       ...       ...\n",
+       " 95        95  0.345281\n",
+       " 96        96  0.283676\n",
+       " 97        97  0.216060\n",
+       " 98        98  0.140177\n",
+       " 99        99  0.048876\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'rozowsky':     position  coverage\n",
+       " 0          0  0.664510\n",
+       " 1          1  0.823720\n",
+       " 2          2  0.986983\n",
+       " 3          3  1.143379\n",
+       " 4          4  1.228875\n",
+       " ..       ...       ...\n",
+       " 95        95  0.526265\n",
+       " 96        96  0.390768\n",
+       " 97        97  0.241327\n",
+       " 98        98  0.147613\n",
+       " 99        99  0.048011\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'sun':     position  coverage\n",
+       " 0          0  0.827656\n",
+       " 1          1  1.024742\n",
+       " 2          2  1.238866\n",
+       " 3          3  1.111935\n",
+       " 4          4  1.164360\n",
+       " ..       ...       ...\n",
+       " 95        95  0.291605\n",
+       " 96        96  0.237043\n",
+       " 97        97  0.170098\n",
+       " 98        98  0.109589\n",
+       " 99        99  0.038476\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'tao':     position  coverage\n",
+       " 0          0  0.816322\n",
+       " 1          1  0.957243\n",
+       " 2          2  1.002606\n",
+       " 3          3  1.037284\n",
+       " 4          4  1.073102\n",
+       " ..       ...       ...\n",
+       " 95        95  0.284971\n",
+       " 96        96  0.236689\n",
+       " 97        97  0.183865\n",
+       " 98        98  0.121932\n",
+       " 99        99  0.040224\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'toden':     position  coverage\n",
+       " 0          0  0.794399\n",
+       " 1          1  0.889729\n",
+       " 2          2  0.952080\n",
+       " 3          3  0.994954\n",
+       " 4          4  1.036387\n",
+       " ..       ...       ...\n",
+       " 95        95  0.440365\n",
+       " 96        96  0.354897\n",
+       " 97        97  0.259633\n",
+       " 98        98  0.161428\n",
+       " 99        99  0.053321\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'wang':     position  coverage\n",
+       " 0          0  0.884646\n",
+       " 1          1  1.098436\n",
+       " 2          2  1.216367\n",
+       " 3          3  1.430555\n",
+       " 4          4  1.722352\n",
+       " ..       ...       ...\n",
+       " 95        95  0.265076\n",
+       " 96        96  0.202165\n",
+       " 97        97  0.142643\n",
+       " 98        98  0.162646\n",
+       " 99        99  0.075130\n",
+       " \n",
+       " [100 rows x 2 columns],\n",
+       " 'zhu':     position  coverage\n",
+       " 0          0  0.982054\n",
+       " 1          1  1.203608\n",
+       " 2          2  1.250519\n",
+       " 3          3  1.290315\n",
+       " 4          4  1.337304\n",
+       " ..       ...       ...\n",
+       " 95        95  0.283839\n",
+       " 96        96  0.220782\n",
+       " 97        97  0.164646\n",
+       " 98        98  0.103329\n",
+       " 99        99  0.031109\n",
+       " \n",
+       " [100 rows x 2 columns]}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_profiles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53a6c9e2-db95-4245-9cc5-7aaeb8f1e92d",
+   "metadata": {},
+   "source": [
+    "### data preparation for ridge plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6351b588-d2b9-4cba-8acf-16522ee1eb22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "block\n",
+      "chalasani_merged\n",
+      "chen\n",
+      "flomics_1\n",
+      "flomics_2\n",
+      "decru\n",
+      "giraldez\n",
+      "ibarra\n",
+      "moufarrej\n",
+      "ngo\n",
+      "reggiardo\n",
+      "roskams\n",
+      "rozowsky\n",
+      "sun\n",
+      "tao\n",
+      "toden\n",
+      "wang\n",
+      "zhu\n",
+      "(1800, 3)\n"
+     ]
+    },
+    {
+     "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></th>\n",
+       "      <th>position</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>dataset_short_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">block</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.751370</td>\n",
+       "      <td>block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.948973</td>\n",
+       "      <td>block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.042970</td>\n",
+       "      <td>block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.101110</td>\n",
+       "      <td>block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.137402</td>\n",
+       "      <td>block</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         position  coverage dataset_short_name\n",
+       "block 0         0  0.751370              block\n",
+       "      1         1  0.948973              block\n",
+       "      2         2  1.042970              block\n",
+       "      3         3  1.101110              block\n",
+       "      4         4  1.137402              block"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#add dataset_short_name column\n",
+    "for dataset, df in all_profiles.items():\n",
+    "    df['dataset_short_name'] = dataset\n",
+    "    print(dataset)\n",
+    "\n",
+    "#create a single dataset\n",
+    "all_dataset = pd.concat(all_profiles, ignore_index=False)\n",
+    "print(all_dataset.shape)\n",
+    "all_dataset.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c545fb94-cd41-47b2-a952-954d4349936e",
+   "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></th>\n",
+       "      <th>position</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>dataset_short_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">block</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.751370</td>\n",
+       "      <td>Block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.948973</td>\n",
+       "      <td>Block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.042970</td>\n",
+       "      <td>Block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.101110</td>\n",
+       "      <td>Block</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.137402</td>\n",
+       "      <td>Block</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         position  coverage dataset_short_name\n",
+       "block 0         0  0.751370              Block\n",
+       "      1         1  0.948973              Block\n",
+       "      2         2  1.042970              Block\n",
+       "      3         3  1.101110              Block\n",
+       "      4         4  1.137402              Block"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#correct dataset labels in dataframe\n",
+    "all_dataset[\"dataset_short_name\"].replace(dataset_labels, inplace=True)\n",
+    "all_dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "5d3ca5e0-6cb9-4b19-88ba-8e04de9638cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Block': '#b3b3b3', 'Decruyenaere': '#009E73', 'Zhu': '#ffd633', 'Chen': '#997a00', 'Flomics 1': '#9AB9D6', 'Flomics 2': '#144D6B', 'Ngo': '#fa8072', 'Roskams-Hieter': '#944dff', 'Moufarrej': '#CC79A7', 'Sun': '#D55E00', 'Tao': '#0072B2', 'Toden': '#800099', 'Ibarra': '#800000', 'Chalasani': '#800040', 'Rozowsky': '#006600', 'Wei': '#B32400', 'Giráldez': '#B1CC71', 'Reggiardo': '#F1085C', 'Wang': '#FE8F42'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#correct dataset labels in color dict\n",
+    "dataset_short_name_colors = {dataset_labels.get(old_key, old_key): value \n",
+    "            for old_key, value in dataset_colors.items()}\n",
+    "\n",
+    "print(dataset_short_name_colors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7d205ca-1721-4fea-8ebf-596157f32803",
+   "metadata": {},
+   "source": [
+    "### ridge plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "28087f79-0fc9-4a31-9c77-067d0cef5f78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "    Generate a ridgeline plot from pre-calculated density values.\n",
+      "\n",
+      "    Args:\n",
+      "        dataset (pd.DataFrame): The input pandas DataFrame containing pre-calculated densities values\n",
+      "        x_column (str): Column containing the coordinates for the density curves.\n",
+      "        density_column (str): Column containing the density values for the curves.\n",
+      "        category_column (str): Column containing categories to group the curves.\n",
+      "        overlap (float, optional): The degree of overlap between the lines (from 0 to 1). Defaults to 0.5.\n",
+      "        fig_width (float, optional): The width of the figure in inches. Defaults to 10.\n",
+      "        fig_height (float, optional): The height of the figure in inches. Defaults to 7.\n",
+      "        output (str, optional): The full path filename (without extension) to save the plot.\n",
+      "        show_xlabel (bool, optional): Display the X-axis label. Defaults to True.\n",
+      "        xlabel (str, optional): Custom label for the X-axis. Defaults to None.\n",
+      "        xlabel_fontsize (float, optional): Font size for the X-axis label. Defaults to 12.\n",
+      "        show_ylabel (bool, optional): Display the Y-axis label. Defaults to True\n",
+      "        ylabel (str, optional): Custom label for the Y-axis. Defaults to None.\n",
+      "        ylabel_fontsize (float, optional): Font size for the Y-axis label. Defaults to 12.\n",
+      "        show_title (bool, optional): Display plot title. Defaults to True.\n",
+      "        title (str, optional): Custom title. Defaults to None.\n",
+      "        title_fontsize: float (optional): Font size for title. Defaults to 14.\n",
+      "        xticks_fontsize (float, optional): Font size for the X-axis tick labels. Defaults to 10.\n",
+      "        yticks_fontsize (float, optional): Font size for the Y-axis tick labels. Defaults to 10.\n",
+      "        fill_alpha (float, optional): Transparency of the filled area under the curves. Defaults to 0.7.\n",
+      "        line_color (str, optional): Color of the curve lines. Defaults to 'black'.\n",
+      "        fill_color (str or dict, optional): Color of the filled area under the curves.\n",
+      "                                            Can be a single color string for all categories,\n",
+      "                                            or a dictionary mapping category names\n",
+      "                                            Defaults to 'skyblue'.\n",
+      "        category_order (list, optional): Order in which categories should be plotted from bottom to top.\n",
+      "    \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ridgeline_from_known_density_plot.__doc__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "6fdcbbc1-399e-420c-b410-4d72949f43bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ridgeline plot saved to gene_coverage_merged.pdf\n",
+      "Ridgeline plot saved to gene_coverage_merged.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 700x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ridgeline_from_known_density_plot(\n",
+    "    dataset = all_dataset,\n",
+    "    x_column = \"position\",\n",
+    "    density_column = \"coverage\",\n",
+    "    category_column = \"dataset_short_name\",\n",
+    "    fig_width = 7,\n",
+    "    fig_height = 10,\n",
+    "    output = \"gene_coverage_merged\",\n",
+    "    show_ylabel = False,\n",
+    "    fill_color = dataset_short_name_colors\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d2930e-da88-4b9d-98e1-3d543eea0c76",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3ff92dc-162e-4cf7-8a79-bdc333dffacc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efb1701b-3c12-44c1-b10c-e5ccb720c531",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e901d9e",
+   "metadata": {},
+   "source": [
+    "## CITATIONS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e8e4aa",
+   "metadata": {},
+   "source": [
+    "write here citations for used packages/datasets etc.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "437eac95-8e6d-4623-9849-4c7c98780ede",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ee557aa-9a41-4784-94dd-b7dafba6ed84",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -275,7 +1011,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,

From db66cf2185b94e4b981d377a7eac4b3070a41fab Mon Sep 17 00:00:00 2001
From: Giovanni Asole <gasole@ssh-flomics.freeddns.org>
Date: Thu, 17 Jul 2025 15:31:22 +0200
Subject: [PATCH 2/4] scripts related to gene coverage analysis

---
 .gitignore                               |   1 +
 src/gene_coverage_plot.ipynb             | 560 +++++++++++++++++++++++
 src/ridgeline_from_known_density_plot.py | 312 +++++++++++++
 3 files changed, 873 insertions(+)
 create mode 100644 src/gene_coverage_plot.ipynb
 create mode 100644 src/ridgeline_from_known_density_plot.py

diff --git a/.gitignore b/.gitignore
index 4fc0837..bc2c79a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,7 @@ sra_metadata/GSE192902*
 sra_metadata/moufarrej_geo*
 
 src/__pycache__
+src/.ipynb_checkpoints/
 
 .Rproj.user
 *.Rproj
diff --git a/src/gene_coverage_plot.ipynb b/src/gene_coverage_plot.ipynb
new file mode 100644
index 0000000..cb5c870
--- /dev/null
+++ b/src/gene_coverage_plot.ipynb
@@ -0,0 +1,560 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5da38501",
+   "metadata": {},
+   "source": [
+    "## OWNER"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebcc104f",
+   "metadata": {},
+   "source": [
+    "Name: Flomics Biotech SL \n",
+    "\n",
+    "date: 19/06/2025"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ae957f0",
+   "metadata": {},
+   "source": [
+    "## MAIN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52e31236",
+   "metadata": {},
+   "source": [
+    "Generate gene coverage plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc154ab0",
+   "metadata": {},
+   "source": [
+    "## LIBRARIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d40ba3ab-b568-4dfb-acdb-c8450c9eb50c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import sys\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "530bed1f",
+   "metadata": {},
+   "source": [
+    "## FUNCTIONS/PAR_SETTING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2da9e8f-9e77-42a5-84ef-141702556023",
+   "metadata": {},
+   "source": [
+    "### Set font for plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2064c8d6-e67a-4e66-a909-89df7752e0ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['font.family'] = 'sans-serif'\n",
+    "plt.rcParams['font.sans-serif'] = ['Arial']\n",
+    "plt.rcParams['mathtext.fontset'] = 'custom'\n",
+    "plt.rcParams['mathtext.rm'] = 'Arial'\n",
+    "plt.rcParams['mathtext.it'] = 'Arial:italic'\n",
+    "plt.rcParams['mathtext.bf'] = 'Arial:bold'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40fe77e4-60f6-4132-a15b-f9d0c1458253",
+   "metadata": {},
+   "source": [
+    "### External scripts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b701cdfa-4d91-49ad-a0ab-d27ec1f9a981",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dodged barplot function\n",
+    "script_dir = \"./\"\n",
+    "sys.path.append(script_dir)\n",
+    "\n",
+    "from ridgeline_from_known_density_plot import ridgeline_from_known_density_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e51378d-bfcf-4d8c-ac17-af501ab2ad56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_results_folder(base_dataset_path, prefix):\n",
+    "    for folder in os.listdir(base_dataset_path):\n",
+    "        if os.path.isdir(os.path.join(base_dataset_path, folder)):\n",
+    "            if prefix in folder and 'results' in folder:\n",
+    "                return folder\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a234ae7e",
+   "metadata": {},
+   "source": [
+    "## WORKFLOW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "253950be-269a-4adb-b97f-6a3800d37b73",
+   "metadata": {},
+   "source": [
+    "### Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6bf5f86-330b-42a8-8dc5-1868b714ce44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_folder = './'\n",
+    "chalasani_folder = './'\n",
+    "output_folder = os.getcwd()\n",
+    "\n",
+    "datasets = {\n",
+    "    'block':         {'sliced': True,  'n_parts': 4,  'prefix': 'block'},\n",
+    "    'chalasani': {'sliced': True,  'n_parts': 6,  'prefix': 'chalasani'},\n",
+    "    'chen':          {'sliced': True,  'n_parts': 21, 'prefix': 'chen'},\n",
+    "    'flomics_2': {'sliced': False, 'subfolder_path': 'Flomics_2'},\n",
+    "    'decru':         {'sliced': True,  'n_parts': 6,  'prefix': 'decru'},\n",
+    "    'giraldez':      {'sliced': False,                 'prefix': 'giraldez_results_full'},\n",
+    "    'ibarra':        {'sliced': True,  'n_parts': 14, 'prefix': 'ibarra'},\n",
+    "    'moufarrej':     {'sliced': True,  'n_parts': 17, 'prefix': 'moufarrej'},\n",
+    "    'ngo':           {'sliced': False,                 'prefix': 'ngo'},\n",
+    "    'reggiardo':     {'sliced': False,                 'prefix': 'reggiardo'},\n",
+    "    'roskams':       {'sliced': True,  'n_parts': 6,  'prefix': 'roskams'},\n",
+    "    'rozowsky':      {'sliced': True,  'n_parts': 9,  'prefix': 'rozowsky'},\n",
+    "    'sun':           {'sliced': True,  'n_parts': 3,  'prefix': 'sun'},\n",
+    "    'tao':           {'sliced': True,  'n_parts': 12, 'prefix': 'tao'},\n",
+    "    #'taowei':        {'sliced': True,  'n_parts': 5,  'prefix': 'taowei'},\n",
+    "    'toden':         {'sliced': True,  'n_parts': 5, 'prefix': 'toden'},\n",
+    "    'wang':          {'sliced': True,  'n_parts': 4,  'prefix': 'wang'},\n",
+    "    'zhu':           {'sliced': True,  'n_parts': 5,  'prefix': 'zhu'},\n",
+    "}\n",
+    "\n",
+    "dataset_colors = {\n",
+    "    \"block\": \"#b3b3b3\", \"decru\": \"#009E73\", \"zhu\": \"#ffd633\", \"chen\": \"#997a00\", \"flomics_2\": \"#144D6B\",\n",
+    "    \"ngo\": \"#fa8072\", \"roskams\": \"#944dff\", \"moufarrej\": \"#CC79A7\", \"sun\": \"#D55E00\",\n",
+    "    \"tao\": \"#0072B2\", \"toden\": \"#800099\", \"ibarra\": \"#800000\", \"chalasani_merged\": \"#800040\",\n",
+    "    \"rozowsky\": \"#006600\", \"taowei\": \"#B32400\", \"giraldez\": \"#B1CC71\", \"reggiardo\": \"#F1085C\", \"wang\": \"#FE8F42\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f40a95d-f889-4aec-b65c-cbd798946b35",
+   "metadata": {},
+   "source": [
+    "### Save per sample gene coverage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "883c4a91-9ff1-4c2a-9e7b-9fdf6615d91d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# ---- Process ----\n",
+    "all_profiles = {}\n",
+    "\n",
+    "for dataset, meta in datasets.items():\n",
+    "    sliced = meta['sliced']\n",
+    "    n_parts = meta.get('n_parts', 1)\n",
+    "    prefix = meta.get('prefix', None)  # Only exists if sliced or traditional unsliced\n",
+    "    print(f\"\\nProcessing dataset: {dataset}\")\n",
+    "\n",
+    "    if sliced:\n",
+    "        for i in range(n_parts):\n",
+    "            print(f\"\\nProcessing dataset: {dataset+\"_\"+str(i)}\")\n",
+    "            part_folder = f\"{prefix}_results_{i:02d}.part/multiqc/star_salmon/multiqc_data\"\n",
+    "            if dataset == \"chalasani\":\n",
+    "                part_folder = chalasani_folder+f\"{prefix}_results_{i:02d}.part/multiqc/star_salmon/multiqc_data\"\n",
+    "            file_path = os.path.join(base_folder, dataset, part_folder, \"mqc_qualimap_gene_coverage_profile_Normalised.txt\")\n",
+    "            if os.path.isfile(file_path):\n",
+    "                try:\n",
+    "                    df = pd.read_csv(file_path, sep=\"\\t\")\n",
+    "                    print(df.shape)\n",
+    "                    all_profiles[dataset+\"_\"+str(i)] = df\n",
+    "                    #handle special case (decru)\n",
+    "                    if dataset == \"decru\":\n",
+    "                        df[\"Sample\"] = df[\"Sample\"].str.split('_').str[0]\n",
+    "                    #handle special case (chalasani)\n",
+    "                    if dataset == \"chalasani_merged\":\n",
+    "                        df[\"Sample\"] = \"X\" + df[\"Sample\"].astype(str)\n",
+    "                    if dataset in [\"chalasani_merged\",\"decru\",\"toden\"]:\n",
+    "                        print(df.head())\n",
+    "                except Exception as e:\n",
+    "                    print(f\"Error reading {file_path}: {e}\")\n",
+    "            else:\n",
+    "                print(f\"Missing: {file_path}\")\n",
+    "    else:\n",
+    "        if \"subfolder_path\" in meta:\n",
+    "            part_path = os.path.join(base_folder, meta[\"subfolder_path\"], \"multiqc\", \"star_salmon\", \"multiqc_data\")\n",
+    "        else:\n",
+    "            base_dataset_path = os.path.join(base_folder, dataset)\n",
+    "            matched_folder = find_results_folder(base_dataset_path, prefix)\n",
+    "            if not matched_folder:\n",
+    "                print(f\"Could not find results folder for: {dataset}\")\n",
+    "                continue\n",
+    "            part_path = os.path.join(base_dataset_path, matched_folder, \"multiqc\", \"star_salmon\", \"multiqc_data\")\n",
+    "\n",
+    "        file_path = os.path.join(part_path, \"mqc_qualimap_gene_coverage_profile_Normalised.txt\")\n",
+    "        if os.path.isfile(file_path):\n",
+    "            try:\n",
+    "                df = pd.read_csv(file_path, sep=\"\\t\")\n",
+    "                print(df.shape)\n",
+    "                print(df.head())\n",
+    "                all_profiles[dataset+\"_\"+str(part_path)] = df\n",
+    "            except Exception as e:\n",
+    "                print(f\"Error reading {file_path}: {e}\")\n",
+    "        else:\n",
+    "            print(f\"Missing: {file_path}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8caf53b-a5e8-48be-8b33-be5aec20e062",
+   "metadata": {},
+   "source": [
+    "### preparing dataset for ridge plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6dbcf992-ed2c-4cc4-a431-e9e3135d03c4",
+   "metadata": {},
+   "source": [
+    "#### Merge gene coverage in one dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3594b8e5-f53a-4b96-af29-cfae9a1e3ade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# List to hold individual DataFrames\n",
+    "all_dataframes = []\n",
+    "\n",
+    "# Iterate through the dictionary and append each DataFrame to the list\n",
+    "for key, df in all_profiles.items():\n",
+    "    all_dataframes.append(df)\n",
+    "    \n",
+    "\n",
+    "# Concatenate all DataFrames into a single, unique dataset\n",
+    "sample_gene_coverage = pd.concat(all_dataframes)\n",
+    "sample_gene_coverage = sample_gene_coverage.set_index('Sample')\n",
+    "print(sample_gene_coverage.shape)\n",
+    "sample_gene_coverage.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af7b6a41-64ba-43dd-b806-edff48a450ce",
+   "metadata": {},
+   "source": [
+    "#### Merge gene coverage with metadata (handle specific sample names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f93ab25-b5d2-4acc-89a1-b02ef7d07f58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load metadata\n",
+    "metadata = pd.read_csv(\"../tables/cfRNA-meta_per_sample_metadata.tsv\",sep=\"\\t\")\n",
+    "#handle special case (Flomics_1 and Flomics2)\n",
+    "condition = metadata['dataset_batch'].str.contains(r'^flomics_.*', na=False)\n",
+    "metadata.loc[condition, 'run'] = metadata['sample_display_name']\n",
+    "#set index\n",
+    "metadata = metadata.set_index(\"run\")\n",
+    "print(metadata.shape)\n",
+    "metadata.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "587b0e7b-17f6-40d3-911b-9bf62775f082",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#merge with metadata\n",
+    "full_gene_coverage_data = sample_gene_coverage.join(metadata, how='inner')\n",
+    "print(full_gene_coverage_data.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fc674d2-f9d7-4222-be2f-928f55c611e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(set(sample_gene_coverage.index) - set(full_gene_coverage_data.index))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "404ecf53-32e5-40d0-8f75-83ebc22fb5db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(set(metadata[\"dataset_short_name\"].unique()) - set(full_gene_coverage_data[\"dataset_short_name\"].unique()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d50cd9f-530a-4f12-910a-f40576100669",
+   "metadata": {},
+   "source": [
+    "#### rename batches name with standard names and load batches proper colors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "875bc923-3c44-4511-9bc3-f056f1707220",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load JSON file\n",
+    "with open(\"./dataset_mappings.json\") as json_file:\n",
+    "    data_mapping = json.load(json_file)\n",
+    "\n",
+    "order_label = data_mapping['datasetVisualOrder']\n",
+    "color_dataset = data_mapping['datasetsPalette']\n",
+    "data_mapping = data_mapping['datasetsLabels']\n",
+    "order_label = [data_mapping.get(item, item) for item in order_label]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f20bb0a3-2304-457b-a9d9-ab04d0562daa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#replace names in batches column\n",
+    "full_gene_coverage_data[\"dataset_batch\"].replace(data_mapping, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a1a9d15-f5f3-4b13-b79b-9826dcea4bd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_gene_coverage_data[\"dataset_batch\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca19bc3f-c58c-48dd-8338-ddaf40e5ef4e",
+   "metadata": {},
+   "source": [
+    "#### melt dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6e8fc3f-ab65-4935-97c3-2f76e9e56d58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reset index\n",
+    "full_gene_coverage_data['Sample'] = full_gene_coverage_data.index\n",
+    "full_gene_coverage_data = full_gene_coverage_data.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d0b06e8-148a-4f04-b39b-5d5c66426304",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "position_list = [str(i) for i in range(0, 99)]\n",
+    "print(position_list)\n",
+    "print(full_gene_coverage_data.columns)\n",
+    "melt_dataset = full_gene_coverage_data.melt(id_vars=['dataset_batch'], value_vars=position_list)\n",
+    "melt_dataset[\"variable\"] = pd.to_numeric(melt_dataset[\"variable\"])\n",
+    "print(melt_dataset.shape)\n",
+    "melt_dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d3ca5e0-6cb9-4b19-88ba-8e04de9638cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#correct dataset labels in color dict\n",
+    "dataset_batch_colors = {data_mapping.get(old_key, old_key): value \n",
+    "            for old_key, value in color_dataset.items()}\n",
+    "\n",
+    "print(dataset_batch_colors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7d205ca-1721-4fea-8ebf-596157f32803",
+   "metadata": {},
+   "source": [
+    "### ridge plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28f42c14-2791-41d6-85f2-10e6811ee46a",
+   "metadata": {},
+   "source": [
+    "#### default plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fdcbbc1-399e-420c-b410-4d72949f43bf",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "reload(heatmap_plot)\n",
+    "\n",
+    "ridgeline_from_known_density_plot(\n",
+    "    dataset = melt_dataset,\n",
+    "    x_column = \"variable\",\n",
+    "    density_column = \"value\",\n",
+    "    category_column = \"dataset_batch\",\n",
+    "    fig_width = 10,\n",
+    "    fig_height = 10,\n",
+    "    output = \"gene_coverage_merged\",\n",
+    "    show_ylabel = False,\n",
+    "    xlabel = \"Transcript position (%)\",\n",
+    "    xlabel_va = 0.51,\n",
+    "    fill_color = dataset_batch_colors,\n",
+    "    show_title = False,\n",
+    "    line_width = 0.5,\n",
+    "    normalization = 'mean'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e13e62a-3b9c-4518-960f-ad431935a05e",
+   "metadata": {},
+   "source": [
+    "#### Y-axis plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d2930e-da88-4b9d-98e1-3d543eea0c76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reload(heatmap_plot)\n",
+    "\n",
+    "ridgeline_from_known_density_plot(\n",
+    "    dataset = melt_dataset,\n",
+    "    x_column = \"variable\",\n",
+    "    density_column = \"value\",\n",
+    "    category_column = \"dataset_batch\",\n",
+    "    category_order = order_label[:-1],\n",
+    "    fig_width = 10,\n",
+    "    fig_height = 15,\n",
+    "    output = \"gene_coverage_merged_y_axis\",\n",
+    "    #layout\n",
+    "    show_ylabel = True,\n",
+    "    xlabel = \"Transcript position (%)\",\n",
+    "    xlabel_va = 0.51,\n",
+    "    fill_color = dataset_batch_colors,\n",
+    "    show_title = False,\n",
+    "    line_width = 0.5,\n",
+    "    y_ticks_frequency = 1,\n",
+    "    normalization = 'mean',\n",
+    "    #individual sub-plots\n",
+    "    show_individual_yaxis = True,\n",
+    "    show_individual_hspace = 0.1,\n",
+    "    consistent_y_scale = False,\n",
+    "    show_yticks = False,\n",
+    "    ylabel_fontsize = 10\n",
+    ")"
+   ]
+  }
+ ],
+ "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": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/ridgeline_from_known_density_plot.py b/src/ridgeline_from_known_density_plot.py
new file mode 100644
index 0000000..7d1dbea
--- /dev/null
+++ b/src/ridgeline_from_known_density_plot.py
@@ -0,0 +1,312 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import warnings
+import seaborn as sns
+
+def ridgeline_from_known_density_plot(# ~ Basic input ~ #
+                                  dataset: pd.DataFrame,
+                                  x_column: str,
+                                  density_column: str,
+                                  category_column: str,
+                                  category_order: list = None,
+                                  overlap: float = 0.5,
+                                  fig_width: float = 10,
+                                  fig_height: float = 7,
+                                  output: str = None,
+                                  # ~ Layout ~ #
+                                  show_xlabel: bool = True,
+                                  xlabel: str = None,
+                                  xlabel_va: float = 0.5,
+                                  xlabel_fontsize: float = 12,
+                                  show_ylabel: bool = True,
+                                  ylabel: str = None,
+                                  ylabel_fontsize: float = 12,
+                                  show_title: bool = True,
+                                  title: str = None,
+                                  title_fontsize: float = 14,
+                                  xticks_fontsize: float = 10,
+                                  show_yticks: bool = True,
+                                  yticks_fontsize: float = 10,
+                                  y_ticks_frequency: float = 1,
+                                  fill_alpha: float = 0.7,
+                                  line_color: str = 'black',
+                                  fill_color: str = 'skyblue',
+                                  line_width: float = 0.5,
+                                  # ~ Normalization ~ #
+                                  normalization: str = None,
+                                  # ~ Individual sub-plots ~ #
+                                  show_individual_yaxis: bool = False,
+                                  show_individual_hspace: float = 0.4,
+                                  consistent_y_scale: bool = True): # New parameter
+    """
+    Generate a ridgeline plot from pre-calculated density values.
+
+    Args:
+
+        # ~ Basic input ~ #
+        dataset (pd.DataFrame): The input pandas DataFrame containing pre-calculated densities values
+        x_column (str): Column containing the coordinates for the density curves.
+        density_column (str): Column containing the density values for the curves.
+        category_column (str): Column containing categories to group the curves.
+        category_order (list, optional): Order in which categories should be plotted from bottom to top.
+        overlap (float, optional): The degree of overlap between the lines (from 0 to 1). Defaults to 0.5.
+        fig_width (float, optional): The width of the figure in inches. Defaults to 10.
+        fig_height (float, optional): The height of the figure in inches. Defaults to 7.
+        output (str, optional): The full path filename (without extension) to save the plot.
+
+        # ~ Layout ~ #
+        show_xlabel (bool, optional): Display the X-axis label. Defaults to True.
+        xlabel (str, optional): Custom label for the X-axis. Defaults to None.
+        xlabel_va (float, optional): X-axis label vertical adjustment. Default to 0.5.
+        xlabel_fontsize (float, optional): Font size for the X-axis label. Defaults to 12.
+        show_ylabel (bool, optional): Display the Y-axis label. Defaults to True
+        ylabel (str, optional): Custom label for the Y-axis. Defaults to None.
+        ylabel_fontsize (float, optional): Font size for the Y-axis label. Defaults to 12.
+        show_title (bool, optional): Display plot title. Defaults to True.
+        title (str, optional): Custom title. Defaults to None.
+        title_fontsize: float (optional): Font size for title. Defaults to 14.
+        xticks_fontsize (float, optional): Font size for the X-axis tick labels. Defaults to 10.
+        show_yticks (bool, optional): Show Y-axis ticks and label. Default to
+        yticks_fontsize (float, optional): Font size for the Y-axis tick labels. Defaults to 10.
+        y_ticks_frequency (float, optional): Frequency of Y-axis ticks. Default to 1
+        fill_alpha (float, optional): Transparency of the filled area under the curves. Defaults to 0.7.
+        line_color (str, optional): Color of the curve lines. Defaults to 'black'.
+        fill_color (str or dict, optional): Color of the filled area under the curves.
+                                            Can be a single color string for all categories,
+                                            or a dictionary mapping category names
+                                            Defaults to 'skyblue'.
+        line_width (float, otiopnal): Size of the line in the curve. Default to 0.5.
+
+        # ~ Normalization ~ #
+
+        normalization (str, optional): normalization method for the curve. Default to None.
+                                        If "mean", {category_column} and {x_column} will be grouped to calculate the {density_column} mean
+                                        If "median", {category_column} and {x_column} will be grouped to calculate the {density_column} median
+
+        # ~ Individual sub-plots ~ #
+
+        show_individual_yaxis (bool, optional): If True, each category will have its own y-axis.
+                                                This overrides 'overlap' to 0.
+                                                Defaults to False.
+        show_individual_hspace (float, optional): The height spacing between subplots when `show_individual_yaxis` is True. Defaults to 0.4.
+        consistent_y_scale (bool, optional): If False, The y scale will be different for each subplot. Default to True
+    """
+
+    # Input Validation
+    if not isinstance(dataset, pd.DataFrame):
+        raise TypeError("Input dataset must be a pandas DataFrame.")
+    if x_column not in dataset.columns:
+        raise ValueError(f"Column '{x_column}' not found in the dataset.")
+    if density_column not in dataset.columns:
+        raise ValueError(f"Column '{density_column}' not found in the dataset.")
+    if category_column not in dataset.columns:
+        raise ValueError(f"Column '{category_column}' not found in the dataset.")
+    if not pd.api.types.is_numeric_dtype(dataset[x_column]):
+        raise TypeError(f"Column '{x_column}' must be numeric.")
+    if not pd.api.types.is_numeric_dtype(dataset[density_column]):
+        raise TypeError(f"Column '{density_column}' must be numeric.")
+    if not (0 <= overlap <= 1):
+        raise ValueError("Overlap must be between 0 and 1.")
+    if category_order is not None and not isinstance(category_order, list):
+        raise TypeError("category_order must be a list.")
+    if category_order is not None:
+        if not all(cat in dataset[category_column].unique() for cat in category_order):
+            raise ValueError("All categories in category_order must be present in category_column.")
+    if not isinstance(fill_color, (str, dict)):
+        raise TypeError("fill_color must be a string or a dictionary.")
+    if normalization is not None and normalization not in ["mean","median"]:
+        raise ValueError("Invalid value for `normalization`. Choose from 'mean' or 'median'.")
+    if show_individual_yaxis:
+        if overlap != 0:
+            warnings.warn("`show_individual_yaxis` is True, so `overlap` is being set to 0 to prevent overlap.", UserWarning)
+        overlap = 0
+
+    # ~ Normalization ~ #
+    if normalization == "mean":
+        dataset = dataset.groupby([category_column, x_column])[density_column].mean().reset_index()
+    if normalization == "median":
+        dataset = dataset.groupby([category_column, x_column])[density_column].median().reset_index()
+
+    # ~ Get unique categories and define order ~ #
+    categories = dataset[category_column].unique()
+    if category_order is None:
+        categories = sorted(categories)
+    else:
+        # Filter and order categories based on category_order, ensuring they exist in the data
+        categories = [cat for cat in category_order if cat in categories]
+
+    # ~ Assigning colors based on fill_color type ~ #
+    category_fill_colors = {}
+    if isinstance(fill_color, str):
+        # If a single color string, apply to all categories
+        for cat in categories:
+            category_fill_colors[cat] = fill_color
+    else: # It's a dictionary
+        prop_cycle = plt.rcParams['axes.prop_cycle']
+        default_colors = prop_cycle.by_key()['color']
+        color_idx = 0
+        for cat in categories:
+            if cat in fill_color:
+                category_fill_colors[cat] = fill_color[cat]
+            else:
+                # Use default cycle color if not specified in the dictionary
+                category_fill_colors[cat] = default_colors[color_idx % len(default_colors)]
+                warnings.warn(f"No fill_color specified for category '{cat}'. Using default color: {category_fill_colors[cat]}", UserWarning)
+                color_idx += 1
+
+    if show_individual_yaxis:
+        # Create subplots
+        fig, axes = plt.subplots(len(categories), 1, figsize=(fig_width, fig_height), sharex=True, gridspec_kw={'hspace': show_individual_hspace})
+        if len(categories) == 1: # If only one subplot, axes is not an array
+            axes = [axes]
+    else:
+        fig, ax = plt.subplots(figsize=(fig_width, fig_height))
+
+    # ~ Calculate the maximum density across all plots to determine the appropriate vertical shift ~ #
+    if not show_individual_yaxis:
+        max_overall_density = dataset[density_column].max()
+        if pd.isna(max_overall_density) or max_overall_density == 0:
+            vertical_shift_amount = 0.5 # A default small shift if no density is found
+            warnings.warn("No density found in the density_column. Plots may appear flat or empty.", UserWarning)
+        else:
+            vertical_shift_amount = max_overall_density * (1 - overlap)
+    else:
+        if consistent_y_scale:
+            vertical_shift_amount = 0 # No vertical shift for individual y-axes
+            # Determine max density for consistent Y-axis limit across subplots
+            global_max_density = dataset[density_column].max()
+            if pd.isna(global_max_density) or global_max_density == 0:
+                global_max_density = 1.0
+                
+
+    # ~ Plot each density curve ~ #
+    for i, category in enumerate(categories):
+        subset = dataset[dataset[category_column] == category].sort_values(by=x_column)
+        x_kde = subset[x_column].values
+        y_kde = subset[density_column].values
+
+        if len(y_kde) == 0:
+            warnings.warn(f"Category '{category}' has no valid density data. Skipping plot for this category.", UserWarning)
+            continue
+
+        if show_individual_yaxis:
+            current_ax = axes[i]
+            y_shifted = y_kde # No shift needed, each has its own axis
+        else:
+            current_ax = ax
+            y_shifted = y_kde + (i * vertical_shift_amount)
+
+        # Get the appropriate fill color for the current category
+        current_fill_color = category_fill_colors.get(category, 'gray') # Fallback to gray if not mapped
+
+        # Plot the filled area
+        if show_individual_yaxis:
+            current_ax.fill_between(x_kde, 0, y_shifted, # Start fill from 0 for individual y-axis
+                                    color=current_fill_color, alpha=fill_alpha)
+            if consistent_y_scale:
+                current_ax.yaxis.set_ticks(np.arange(0, global_max_density * 1.1, y_ticks_frequency)) # Set consistent Y-axis limits
+            else:
+                max_density = dataset[dataset[category_column] == category][density_column].max()
+                current_ax.yaxis.set_ticks(np.arange(0, max_density * 1.1, y_ticks_frequency)) # Set variable Y-axis limit
+        else:
+            current_ax.fill_between(x_kde, i * vertical_shift_amount, y_shifted,
+                                    color=current_fill_color, alpha=fill_alpha)
+
+
+        # Plot the outline
+        current_ax.plot(x_kde, y_shifted, color=line_color, linewidth=line_width)
+
+        if show_individual_yaxis:
+            current_ax.tick_params(axis='x', labelsize=xticks_fontsize)
+            if show_yticks:
+                current_ax.tick_params(axis='y', labelsize=yticks_fontsize)
+            else:
+                current_ax.set_yticks([])
+            current_ax.spines['right'].set_visible(False)
+            current_ax.spines['top'].set_visible(False)
+            current_ax.grid(axis='x', linestyle='--', alpha=0.7)
+            if show_ylabel:
+                current_ax.set_ylabel(category, fontsize=ylabel_fontsize, rotation=0, ha='right', va='center')
+                current_ax.yaxis.set_label_coords(-0.05, 0.5)
+            else:
+                current_ax.set_ylabel('')
+
+        else:
+            # Add category label as a custom y-tick for ridgeline plot
+            current_ax.text(-0.02, i * vertical_shift_amount, str(category), va='center', ha='right',
+                            fontsize=yticks_fontsize, color='black', transform=current_ax.get_yaxis_transform())
+
+
+    # ~ Set x-axis label ~ #
+    if show_xlabel:
+        if show_individual_yaxis:
+            # Place x-label on the bottom-most subplot
+            bottom_ax = axes[-1]
+            bottom_ax.set_xlabel(xlabel if xlabel is not None else x_column, fontsize=xlabel_fontsize, x = xlabel_va, ha='center')
+            # Ensure ticks are visible on the bottom-most axis
+            bottom_ax.tick_params(axis='x', labelbottom=True)
+        else:
+            ax.set_xlabel(xlabel if xlabel is not None else x_column, fontsize=xlabel_fontsize, x = xlabel_va, ha='center')
+    else:
+        if show_individual_yaxis:
+            axes[-1].fig.supxlabel('')
+        else:
+            ax.set_xlabel('')
+
+    # ~ Set y-axis label and ticks (hide default numerical y-axis for ridgeline) ~ #
+    if not show_individual_yaxis:
+        if show_ylabel:
+            ax.set_ylabel(ylabel if ylabel is not None else 'Category', fontsize=ylabel_fontsize)
+        else:
+            ax.set_ylabel('')
+
+        ax.set_yticks([])
+        ax.tick_params(axis='y', length=0)
+        ax.tick_params(axis='x', labelsize=xticks_fontsize)
+        # ~ Remove top, right, and left plot lines for a cleaner look ~ #
+        ax.spines['left'].set_visible(False)
+        ax.spines['right'].set_visible(False)
+        ax.spines['top'].set_visible(False)
+        ax.yaxis.set_ticks_position('none')
+        ax.grid(axis='x', linestyle='--', alpha=0.7)
+
+
+    # ~ Set title ~ #
+    if show_title:
+        if show_individual_yaxis:
+            fig.suptitle(title if title is not None else f'Density Plots of {x_column} by {category_column}',
+                         fontsize=title_fontsize)
+        else:
+            ax.set_title(title if title is not None else f'Ridgeline Plot of {x_column} Densities by {category_column}',
+                         fontsize=title_fontsize)
+
+    #normal layout for regular plot
+    if not show_individual_yaxis:
+        plt.tight_layout()
+    # Use fig.subplots_adjust for individual curve plots
+    #TO DO: make it automatic
+    if show_individual_yaxis:
+        fig.subplots_adjust(left = 0.3, bottom=0.03, top=1)
+
+    # ~ Saving Plot (if output path is provided) ~
+    if output:
+        dir_name = os.path.dirname(output)
+        if dir_name and not os.path.exists(dir_name):
+            os.makedirs(dir_name)
+
+        filename_pdf = output + ".pdf"
+        plt.savefig(filename_pdf)
+        print(f"Plot saved to {filename_pdf}")
+
+        filename_png = output + ".png"
+        plt.savefig(filename_png)
+        print(f"Plot saved to {filename_png}")
+
+        filename_svg = output + ".svg"
+        plt.rcParams["svg.fonttype"] = "none"
+        plt.savefig(filename_svg)
+        print(f"Box plot saved to {filename_svg}")
+
+    plt.show()
\ No newline at end of file

From 4c7fd4b95314140e4e5dd8a85a3523b21f8229ab Mon Sep 17 00:00:00 2001
From: Giovanni Asole <gasole@ssh-flomics.freeddns.org>
Date: Thu, 17 Jul 2025 15:35:41 +0200
Subject: [PATCH 3/4] remove duplicated code

---
 src/gene_coverage_profile_fig2_tmpH.ipynb | 1019 ---------------------
 1 file changed, 1019 deletions(-)
 delete mode 100644 src/gene_coverage_profile_fig2_tmpH.ipynb

diff --git a/src/gene_coverage_profile_fig2_tmpH.ipynb b/src/gene_coverage_profile_fig2_tmpH.ipynb
deleted file mode 100644
index 0127b83..0000000
--- a/src/gene_coverage_profile_fig2_tmpH.ipynb
+++ /dev/null
@@ -1,1019 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "e3f32189-fd60-4cd4-830a-efdb73ce993f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc154ab0",
-   "metadata": {},
-   "source": [
-    "## LIBRARIES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "d40ba3ab-b568-4dfb-acdb-c8450c9eb50c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "530bed1f",
-   "metadata": {},
-   "source": [
-    "## FUNCTIONS/PAR_SETTING"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "3e51378d-bfcf-4d8c-ac17-af501ab2ad56",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_results_folder(base_dataset_path, prefix):\n",
-    "    for folder in os.listdir(base_dataset_path):\n",
-    "        if os.path.isdir(os.path.join(base_dataset_path, folder)):\n",
-    "            if prefix in folder and 'results' in folder:\n",
-    "                return folder\n",
-    "    return None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "b701cdfa-4d91-49ad-a0ab-d27ec1f9a981",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#dodged barplot function\n",
-    "script_dir = \"/mnt/efs/home/gasole/repos/personal/utils/plots\"\n",
-    "sys.path.append(script_dir)\n",
-    "\n",
-    "from ridgeline_from_known_density_plot import ridgeline_from_known_density_plot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a234ae7e",
-   "metadata": {},
-   "source": [
-    "## WORKFLOW"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "253950be-269a-4adb-b97f-6a3800d37b73",
-   "metadata": {},
-   "source": [
-    "### Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "e6bf5f86-330b-42a8-8dc5-1868b714ce44",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base_folder = '/mnt/s3fs/flomics-data/FL-cfRNA-meta'\n",
-    "output_folder = os.getcwd()\n",
-    "\n",
-    "datasets = {\n",
-    "    'block':         {'sliced': True,  'n_parts': 4,  'prefix': 'block'},\n",
-    "    'chalasani_merged': {'sliced': True,  'n_parts': 5,  'prefix': 'chalasani'},\n",
-    "    'chen':          {'sliced': True,  'n_parts': 21, 'prefix': 'chen'},\n",
-    "    'flomics_1': {'sliced': False, 'subfolder_path': 'Flomics_1/2025_05_16_11_26_41/n3abcb4a/'},\n",
-    "    'flomics_2': {'sliced': False, 'subfolder_path': 'Flomics_2/2025_06_05_09_32_11/n61e05f8/'},\n",
-    "    'decru':         {'sliced': True,  'n_parts': 6,  'prefix': 'decru'},\n",
-    "    'giraldez':      {'sliced': False,                 'prefix': 'giraldez'},\n",
-    "    'ibarra':        {'sliced': True,  'n_parts': 14, 'prefix': 'ibarra'},\n",
-    "    'moufarrej':     {'sliced': True,  'n_parts': 17, 'prefix': 'moufarrej'},\n",
-    "    'ngo':           {'sliced': False,                 'prefix': 'ngo'},\n",
-    "    'reggiardo':     {'sliced': False,                 'prefix': 'reggiardo'},\n",
-    "    'roskams':       {'sliced': True,  'n_parts': 6,  'prefix': 'roskams'},\n",
-    "    'rozowsky':      {'sliced': True,  'n_parts': 9,  'prefix': 'rozowsky'},\n",
-    "    'sun':           {'sliced': True,  'n_parts': 3,  'prefix': 'sun'},\n",
-    "    'tao':           {'sliced': True,  'n_parts': 12, 'prefix': 'tao'},\n",
-    "    #'taowei':        {'sliced': True,  'n_parts': 4,  'prefix': 'taowei'},\n",
-    "    'toden':         {'sliced': True,  'n_parts': 14, 'prefix': 'toden'},\n",
-    "    'wang':          {'sliced': True,  'n_parts': 4,  'prefix': 'wang'},\n",
-    "    'zhu':           {'sliced': True,  'n_parts': 5,  'prefix': 'zhu'},\n",
-    "}\n",
-    "\n",
-    "dataset_colors = {\n",
-    "    \"block\": \"#b3b3b3\", \"decru\": \"#009E73\", \"zhu\": \"#ffd633\", \"chen\": \"#997a00\", \"flomics_1\" : \"#9AB9D6\", \"flomics_2\": \"#144D6B\",\n",
-    "    \"ngo\": \"#fa8072\", \"roskams\": \"#944dff\", \"moufarrej\": \"#CC79A7\", \"sun\": \"#D55E00\",\n",
-    "    \"tao\": \"#0072B2\", \"toden\": \"#800099\", \"ibarra\": \"#800000\", \"chalasani_merged\": \"#800040\",\n",
-    "    \"rozowsky\": \"#006600\", \"taowei\": \"#B32400\", \"giraldez\": \"#B1CC71\", \"reggiardo\": \"#F1085C\", \"wang\": \"#FE8F42\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f40a95d-f889-4aec-b65c-cbd798946b35",
-   "metadata": {},
-   "source": [
-    "### Process dataset and individual plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "883c4a91-9ff1-4c2a-9e7b-9fdf6615d91d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Processing dataset: block\n",
-      "Saved: block_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: chalasani_merged\n",
-      "Saved: chalasani_merged_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: chen\n",
-      "Saved: chen_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: flomics_1\n",
-      "Saved: flomics_1_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: flomics_2\n",
-      "Saved: flomics_2_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: decru\n",
-      "Saved: decru_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: giraldez\n",
-      "Saved: giraldez_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: ibarra\n",
-      "Saved: ibarra_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: moufarrej\n",
-      "Saved: moufarrej_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: ngo\n",
-      "Saved: ngo_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: reggiardo\n",
-      "Saved: reggiardo_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: roskams\n",
-      "Saved: roskams_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: rozowsky\n",
-      "Saved: rozowsky_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: sun\n",
-      "Saved: sun_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: tao\n",
-      "Saved: tao_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: toden\n",
-      "Saved: toden_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: wang\n",
-      "Saved: wang_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: zhu\n",
-      "Saved: zhu_multiqc_coverage.png\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ---- Process ----\n",
-    "all_profiles = {}\n",
-    "\n",
-    "for dataset, meta in datasets.items():\n",
-    "    sliced = meta['sliced']\n",
-    "    n_parts = meta.get('n_parts', 1)\n",
-    "    prefix = meta.get('prefix', None)  # Only exists if sliced or traditional unsliced\n",
-    "    print(f\"\\nProcessing dataset: {dataset}\")\n",
-    "    \n",
-    "    part_means = []\n",
-    "\n",
-    "    if sliced:\n",
-    "        for i in range(n_parts):\n",
-    "            part_folder = f\"{prefix}_results_{i:02d}.part/multiqc/star_salmon/multiqc_data\"\n",
-    "            file_path = os.path.join(base_folder, dataset, part_folder, \"mqc_qualimap_gene_coverage_profile_Normalised.txt\")\n",
-    "            if os.path.isfile(file_path):\n",
-    "                try:\n",
-    "                    df = pd.read_csv(file_path, sep=\"\\t\")\n",
-    "                    sample_only = df.drop(columns=[\"Sample\"])\n",
-    "                    part_means.append(sample_only.mean(axis=0))\n",
-    "                except Exception as e:\n",
-    "                    print(f\"Error reading {file_path}: {e}\")\n",
-    "            else:\n",
-    "                print(f\"Missing: {file_path}\")\n",
-    "    else:\n",
-    "        if \"subfolder_path\" in meta:\n",
-    "            part_path = os.path.join(base_folder, meta[\"subfolder_path\"], \"multiqc\", \"star_salmon\", \"multiqc_data\")\n",
-    "        else:\n",
-    "            base_dataset_path = os.path.join(base_folder, dataset)\n",
-    "            matched_folder = find_results_folder(base_dataset_path, prefix)\n",
-    "            if not matched_folder:\n",
-    "                print(f\"Could not find results folder for: {dataset}\")\n",
-    "                continue\n",
-    "            part_path = os.path.join(base_dataset_path, matched_folder, \"multiqc\", \"star_salmon\", \"multiqc_data\")\n",
-    "\n",
-    "        file_path = os.path.join(part_path, \"mqc_qualimap_gene_coverage_profile_Normalised.txt\")\n",
-    "        if os.path.isfile(file_path):\n",
-    "            try:\n",
-    "                df = pd.read_csv(file_path, sep=\"\\t\")\n",
-    "                sample_only = df.drop(columns=[\"Sample\"])\n",
-    "                part_means.append(sample_only.mean(axis=0))\n",
-    "            except Exception as e:\n",
-    "                print(f\"Error reading {file_path}: {e}\")\n",
-    "        else:\n",
-    "            print(f\"Missing: {file_path}\")\n",
-    "\n",
-    "\n",
-    "    # Average across all .part means\n",
-    "    if part_means:\n",
-    "        dataset_mean_df = pd.DataFrame(part_means).mean(axis=0).reset_index()\n",
-    "        dataset_mean_df.columns = [\"position\", \"coverage\"]\n",
-    "        dataset_mean_df[\"position\"] = dataset_mean_df[\"position\"].astype(int)\n",
-    "        dataset_mean_df.sort_values(\"position\", inplace=True)\n",
-    "        all_profiles[dataset] = dataset_mean_df\n",
-    "\n",
-    "        # Per-dataset plot\n",
-    "        plt.figure(figsize=(10, 4))\n",
-    "        plt.plot(dataset_mean_df[\"position\"], dataset_mean_df[\"coverage\"],\n",
-    "                 label=dataset, color=dataset_colors.get(dataset))\n",
-    "        plt.title(f\"Normalized Transcript Coverage: {dataset}\")\n",
-    "        plt.xlabel(\"Transcript position (%)\")\n",
-    "        plt.ylabel(\"Normalized coverage\")\n",
-    "        plt.tight_layout()\n",
-    "        plt.savefig(os.path.join(output_folder, f\"{dataset}_multiqc_coverage.png\"))\n",
-    "        plt.close()\n",
-    "        print(f\"Saved: {dataset}_multiqc_coverage.png\")\n",
-    "    else:\n",
-    "        print(f\"No data for {dataset}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "3594b8e5-f53a-4b96-af29-cfae9a1e3ade",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All per-dataset and combined plots saved.\n"
-     ]
-    }
-   ],
-   "source": [
-    "dataset_labels = {\n",
-    "    \"block\": \"Block\",\n",
-    "    \"chalasani_merged\": \"Chalasani\",\n",
-    "    \"chen\": \"Chen\",\n",
-    "    \"flomics_1\" : \"Flomics 1\",\n",
-    "    \"flomics_2\": \"Flomics 2\",\n",
-    "    \"decru\": \"Decruyenaere\",\n",
-    "    \"giraldez\": \"Giráldez\",\n",
-    "    \"ibarra\": \"Ibarra\",\n",
-    "    \"moufarrej\": \"Moufarrej\",\n",
-    "    \"ngo\": \"Ngo\",\n",
-    "    \"reggiardo\": \"Reggiardo\",\n",
-    "    \"roskams\": \"Roskams-Hieter\",\n",
-    "    \"rozowsky\": \"Rozowsky\",\n",
-    "    \"sun\": \"Sun\",\n",
-    "    \"tao\": \"Tao\",\n",
-    "    \"taowei\": \"Wei\",\n",
-    "    \"toden\": \"Toden\",\n",
-    "    \"wang\": \"Wang\",\n",
-    "    \"zhu\": \"Zhu\"\n",
-    "}\n",
-    "\n",
-    "# ---- Combined Plot ----\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "for dataset, df in all_profiles.items():\n",
-    "    plt.plot(df[\"position\"], df[\"coverage\"], label=dataset_labels.get(dataset, dataset), color=dataset_colors.get(dataset))\n",
-    "plt.xlabel(\"Transcript position (%)\")\n",
-    "plt.ylabel(\"Normalized coverage\")\n",
-    "plt.title(\"Average Gene Coverage per Dataset (MultiQC normalized)\")\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(os.path.join(output_folder, \"multiqc_style_normalized_coverage_combined.png\"), dpi=600)\n",
-    "plt.close()\n",
-    "\n",
-    "print(\"All per-dataset and combined plots saved.\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "b507097c-114a-4ec6-9589-8cdbb7cbf324",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'block':     position  coverage\n",
-       " 0          0  0.751370\n",
-       " 1          1  0.948973\n",
-       " 2          2  1.042970\n",
-       " 3          3  1.101110\n",
-       " 4          4  1.137402\n",
-       " ..       ...       ...\n",
-       " 95        95  0.387874\n",
-       " 96        96  0.314820\n",
-       " 97        97  0.234645\n",
-       " 98        98  0.149621\n",
-       " 99        99  0.054939\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'chalasani_merged':     position  coverage\n",
-       " 0          0  0.893336\n",
-       " 1          1  0.992028\n",
-       " 2          2  1.068821\n",
-       " 3          3  1.125762\n",
-       " 4          4  1.172424\n",
-       " ..       ...       ...\n",
-       " 95        95  0.405052\n",
-       " 96        96  0.326549\n",
-       " 97        97  0.239147\n",
-       " 98        98  0.149661\n",
-       " 99        99  0.050280\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'chen':     position  coverage\n",
-       " 0          0  0.883808\n",
-       " 1          1  1.080623\n",
-       " 2          2  1.127518\n",
-       " 3          3  1.161813\n",
-       " 4          4  1.208590\n",
-       " ..       ...       ...\n",
-       " 95        95  0.288879\n",
-       " 96        96  0.242541\n",
-       " 97        97  0.178097\n",
-       " 98        98  0.115640\n",
-       " 99        99  0.038419\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'flomics_1':     position  coverage\n",
-       " 0          0  0.577109\n",
-       " 1          1  0.638392\n",
-       " 2          2  0.690236\n",
-       " 3          3  0.762369\n",
-       " 4          4  0.824304\n",
-       " ..       ...       ...\n",
-       " 95        95  0.337860\n",
-       " 96        96  0.288220\n",
-       " 97        97  0.234444\n",
-       " 98        98  0.176366\n",
-       " 99        99  0.081433\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'flomics_2':     position  coverage\n",
-       " 0          0  0.678069\n",
-       " 1          1  0.751549\n",
-       " 2          2  0.805104\n",
-       " 3          3  0.885331\n",
-       " 4          4  0.948078\n",
-       " ..       ...       ...\n",
-       " 95        95  0.331373\n",
-       " 96        96  0.278453\n",
-       " 97        97  0.223898\n",
-       " 98        98  0.155197\n",
-       " 99        99  0.062723\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'decru':     position  coverage\n",
-       " 0          0  0.594986\n",
-       " 1          1  0.673064\n",
-       " 2          2  0.728644\n",
-       " 3          3  0.816234\n",
-       " 4          4  0.906582\n",
-       " ..       ...       ...\n",
-       " 95        95  0.299129\n",
-       " 96        96  0.257159\n",
-       " 97        97  0.207672\n",
-       " 98        98  0.152542\n",
-       " 99        99  0.069510\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'giraldez':     position  coverage\n",
-       " 0          0  0.146943\n",
-       " 1          1  0.179056\n",
-       " 2          2  0.206957\n",
-       " 3          3  0.284656\n",
-       " 4          4  0.369986\n",
-       " ..       ...       ...\n",
-       " 95        95  0.144934\n",
-       " 96        96  0.174990\n",
-       " 97        97  0.030457\n",
-       " 98        98  0.018695\n",
-       " 99        99  0.005258\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'ibarra':     position  coverage\n",
-       " 0          0  0.811200\n",
-       " 1          1  0.921145\n",
-       " 2          2  0.990593\n",
-       " 3          3  1.035097\n",
-       " 4          4  1.071725\n",
-       " ..       ...       ...\n",
-       " 95        95  0.431216\n",
-       " 96        96  0.350551\n",
-       " 97        97  0.258570\n",
-       " 98        98  0.161554\n",
-       " 99        99  0.053056\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'moufarrej':     position  coverage\n",
-       " 0          0  0.922650\n",
-       " 1          1  1.289233\n",
-       " 2          2  1.376297\n",
-       " 3          3  1.501520\n",
-       " 4          4  1.510656\n",
-       " ..       ...       ...\n",
-       " 95        95  0.312282\n",
-       " 96        96  0.268549\n",
-       " 97        97  0.214269\n",
-       " 98        98  0.164626\n",
-       " 99        99  0.080866\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'ngo':     position  coverage\n",
-       " 0          0  0.683297\n",
-       " 1          1  0.853360\n",
-       " 2          2  0.905121\n",
-       " 3          3  0.955589\n",
-       " 4          4  1.046210\n",
-       " ..       ...       ...\n",
-       " 95        95  0.272191\n",
-       " 96        96  0.218318\n",
-       " 97        97  0.158297\n",
-       " 98        98  0.099380\n",
-       " 99        99  0.033786\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'reggiardo':     position  coverage\n",
-       " 0          0  0.456523\n",
-       " 1          1  0.471918\n",
-       " 2          2  0.491256\n",
-       " 3          3  0.501107\n",
-       " 4          4  0.515223\n",
-       " ..       ...       ...\n",
-       " 95        95  0.471508\n",
-       " 96        96  0.392591\n",
-       " 97        97  0.293598\n",
-       " 98        98  0.178205\n",
-       " 99        99  0.048939\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'roskams':     position  coverage\n",
-       " 0          0  0.836213\n",
-       " 1          1  0.958719\n",
-       " 2          2  1.006426\n",
-       " 3          3  1.049440\n",
-       " 4          4  1.083980\n",
-       " ..       ...       ...\n",
-       " 95        95  0.345281\n",
-       " 96        96  0.283676\n",
-       " 97        97  0.216060\n",
-       " 98        98  0.140177\n",
-       " 99        99  0.048876\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'rozowsky':     position  coverage\n",
-       " 0          0  0.664510\n",
-       " 1          1  0.823720\n",
-       " 2          2  0.986983\n",
-       " 3          3  1.143379\n",
-       " 4          4  1.228875\n",
-       " ..       ...       ...\n",
-       " 95        95  0.526265\n",
-       " 96        96  0.390768\n",
-       " 97        97  0.241327\n",
-       " 98        98  0.147613\n",
-       " 99        99  0.048011\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'sun':     position  coverage\n",
-       " 0          0  0.827656\n",
-       " 1          1  1.024742\n",
-       " 2          2  1.238866\n",
-       " 3          3  1.111935\n",
-       " 4          4  1.164360\n",
-       " ..       ...       ...\n",
-       " 95        95  0.291605\n",
-       " 96        96  0.237043\n",
-       " 97        97  0.170098\n",
-       " 98        98  0.109589\n",
-       " 99        99  0.038476\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'tao':     position  coverage\n",
-       " 0          0  0.816322\n",
-       " 1          1  0.957243\n",
-       " 2          2  1.002606\n",
-       " 3          3  1.037284\n",
-       " 4          4  1.073102\n",
-       " ..       ...       ...\n",
-       " 95        95  0.284971\n",
-       " 96        96  0.236689\n",
-       " 97        97  0.183865\n",
-       " 98        98  0.121932\n",
-       " 99        99  0.040224\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'toden':     position  coverage\n",
-       " 0          0  0.794399\n",
-       " 1          1  0.889729\n",
-       " 2          2  0.952080\n",
-       " 3          3  0.994954\n",
-       " 4          4  1.036387\n",
-       " ..       ...       ...\n",
-       " 95        95  0.440365\n",
-       " 96        96  0.354897\n",
-       " 97        97  0.259633\n",
-       " 98        98  0.161428\n",
-       " 99        99  0.053321\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'wang':     position  coverage\n",
-       " 0          0  0.884646\n",
-       " 1          1  1.098436\n",
-       " 2          2  1.216367\n",
-       " 3          3  1.430555\n",
-       " 4          4  1.722352\n",
-       " ..       ...       ...\n",
-       " 95        95  0.265076\n",
-       " 96        96  0.202165\n",
-       " 97        97  0.142643\n",
-       " 98        98  0.162646\n",
-       " 99        99  0.075130\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'zhu':     position  coverage\n",
-       " 0          0  0.982054\n",
-       " 1          1  1.203608\n",
-       " 2          2  1.250519\n",
-       " 3          3  1.290315\n",
-       " 4          4  1.337304\n",
-       " ..       ...       ...\n",
-       " 95        95  0.283839\n",
-       " 96        96  0.220782\n",
-       " 97        97  0.164646\n",
-       " 98        98  0.103329\n",
-       " 99        99  0.031109\n",
-       " \n",
-       " [100 rows x 2 columns]}"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_profiles"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53a6c9e2-db95-4245-9cc5-7aaeb8f1e92d",
-   "metadata": {},
-   "source": [
-    "### data preparation for ridge plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "6351b588-d2b9-4cba-8acf-16522ee1eb22",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "block\n",
-      "chalasani_merged\n",
-      "chen\n",
-      "flomics_1\n",
-      "flomics_2\n",
-      "decru\n",
-      "giraldez\n",
-      "ibarra\n",
-      "moufarrej\n",
-      "ngo\n",
-      "reggiardo\n",
-      "roskams\n",
-      "rozowsky\n",
-      "sun\n",
-      "tao\n",
-      "toden\n",
-      "wang\n",
-      "zhu\n",
-      "(1800, 3)\n"
-     ]
-    },
-    {
-     "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></th>\n",
-       "      <th>position</th>\n",
-       "      <th>coverage</th>\n",
-       "      <th>dataset_short_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">block</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.751370</td>\n",
-       "      <td>block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.948973</td>\n",
-       "      <td>block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1.042970</td>\n",
-       "      <td>block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.101110</td>\n",
-       "      <td>block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1.137402</td>\n",
-       "      <td>block</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         position  coverage dataset_short_name\n",
-       "block 0         0  0.751370              block\n",
-       "      1         1  0.948973              block\n",
-       "      2         2  1.042970              block\n",
-       "      3         3  1.101110              block\n",
-       "      4         4  1.137402              block"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#add dataset_short_name column\n",
-    "for dataset, df in all_profiles.items():\n",
-    "    df['dataset_short_name'] = dataset\n",
-    "    print(dataset)\n",
-    "\n",
-    "#create a single dataset\n",
-    "all_dataset = pd.concat(all_profiles, ignore_index=False)\n",
-    "print(all_dataset.shape)\n",
-    "all_dataset.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "c545fb94-cd41-47b2-a952-954d4349936e",
-   "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></th>\n",
-       "      <th>position</th>\n",
-       "      <th>coverage</th>\n",
-       "      <th>dataset_short_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">block</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.751370</td>\n",
-       "      <td>Block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.948973</td>\n",
-       "      <td>Block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1.042970</td>\n",
-       "      <td>Block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.101110</td>\n",
-       "      <td>Block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1.137402</td>\n",
-       "      <td>Block</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         position  coverage dataset_short_name\n",
-       "block 0         0  0.751370              Block\n",
-       "      1         1  0.948973              Block\n",
-       "      2         2  1.042970              Block\n",
-       "      3         3  1.101110              Block\n",
-       "      4         4  1.137402              Block"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#correct dataset labels in dataframe\n",
-    "all_dataset[\"dataset_short_name\"].replace(dataset_labels, inplace=True)\n",
-    "all_dataset.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "5d3ca5e0-6cb9-4b19-88ba-8e04de9638cc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'Block': '#b3b3b3', 'Decruyenaere': '#009E73', 'Zhu': '#ffd633', 'Chen': '#997a00', 'Flomics 1': '#9AB9D6', 'Flomics 2': '#144D6B', 'Ngo': '#fa8072', 'Roskams-Hieter': '#944dff', 'Moufarrej': '#CC79A7', 'Sun': '#D55E00', 'Tao': '#0072B2', 'Toden': '#800099', 'Ibarra': '#800000', 'Chalasani': '#800040', 'Rozowsky': '#006600', 'Wei': '#B32400', 'Giráldez': '#B1CC71', 'Reggiardo': '#F1085C', 'Wang': '#FE8F42'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "#correct dataset labels in color dict\n",
-    "dataset_short_name_colors = {dataset_labels.get(old_key, old_key): value \n",
-    "            for old_key, value in dataset_colors.items()}\n",
-    "\n",
-    "print(dataset_short_name_colors)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7d205ca-1721-4fea-8ebf-596157f32803",
-   "metadata": {},
-   "source": [
-    "### ridge plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "28087f79-0fc9-4a31-9c77-067d0cef5f78",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "    Generate a ridgeline plot from pre-calculated density values.\n",
-      "\n",
-      "    Args:\n",
-      "        dataset (pd.DataFrame): The input pandas DataFrame containing pre-calculated densities values\n",
-      "        x_column (str): Column containing the coordinates for the density curves.\n",
-      "        density_column (str): Column containing the density values for the curves.\n",
-      "        category_column (str): Column containing categories to group the curves.\n",
-      "        overlap (float, optional): The degree of overlap between the lines (from 0 to 1). Defaults to 0.5.\n",
-      "        fig_width (float, optional): The width of the figure in inches. Defaults to 10.\n",
-      "        fig_height (float, optional): The height of the figure in inches. Defaults to 7.\n",
-      "        output (str, optional): The full path filename (without extension) to save the plot.\n",
-      "        show_xlabel (bool, optional): Display the X-axis label. Defaults to True.\n",
-      "        xlabel (str, optional): Custom label for the X-axis. Defaults to None.\n",
-      "        xlabel_fontsize (float, optional): Font size for the X-axis label. Defaults to 12.\n",
-      "        show_ylabel (bool, optional): Display the Y-axis label. Defaults to True\n",
-      "        ylabel (str, optional): Custom label for the Y-axis. Defaults to None.\n",
-      "        ylabel_fontsize (float, optional): Font size for the Y-axis label. Defaults to 12.\n",
-      "        show_title (bool, optional): Display plot title. Defaults to True.\n",
-      "        title (str, optional): Custom title. Defaults to None.\n",
-      "        title_fontsize: float (optional): Font size for title. Defaults to 14.\n",
-      "        xticks_fontsize (float, optional): Font size for the X-axis tick labels. Defaults to 10.\n",
-      "        yticks_fontsize (float, optional): Font size for the Y-axis tick labels. Defaults to 10.\n",
-      "        fill_alpha (float, optional): Transparency of the filled area under the curves. Defaults to 0.7.\n",
-      "        line_color (str, optional): Color of the curve lines. Defaults to 'black'.\n",
-      "        fill_color (str or dict, optional): Color of the filled area under the curves.\n",
-      "                                            Can be a single color string for all categories,\n",
-      "                                            or a dictionary mapping category names\n",
-      "                                            Defaults to 'skyblue'.\n",
-      "        category_order (list, optional): Order in which categories should be plotted from bottom to top.\n",
-      "    \n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ridgeline_from_known_density_plot.__doc__)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "6fdcbbc1-399e-420c-b410-4d72949f43bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ridgeline plot saved to gene_coverage_merged.pdf\n",
-      "Ridgeline plot saved to gene_coverage_merged.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 700x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ridgeline_from_known_density_plot(\n",
-    "    dataset = all_dataset,\n",
-    "    x_column = \"position\",\n",
-    "    density_column = \"coverage\",\n",
-    "    category_column = \"dataset_short_name\",\n",
-    "    fig_width = 7,\n",
-    "    fig_height = 10,\n",
-    "    output = \"gene_coverage_merged\",\n",
-    "    show_ylabel = False,\n",
-    "    fill_color = dataset_short_name_colors\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "45d2930e-da88-4b9d-98e1-3d543eea0c76",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c3ff92dc-162e-4cf7-8a79-bdc333dffacc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "efb1701b-3c12-44c1-b10c-e5ccb720c531",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e901d9e",
-   "metadata": {},
-   "source": [
-    "## CITATIONS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e8e4aa",
-   "metadata": {},
-   "source": [
-    "write here citations for used packages/datasets etc.."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "437eac95-8e6d-4623-9849-4c7c98780ede",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5ee557aa-9a41-4784-94dd-b7dafba6ed84",
-   "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": "3.12.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 7937a6887e19b05d4dfa11e48d781b7bc53555cf Mon Sep 17 00:00:00 2001
From: Giovanni Asole <gasole@ssh-flomics.freeddns.org>
Date: Thu, 17 Jul 2025 15:37:23 +0200
Subject: [PATCH 4/4] remove checkpoint files

---
 ...overage_profile_fig2_tmpH-checkpoint.ipynb | 1019 -----------------
 1 file changed, 1019 deletions(-)
 delete mode 100644 src/.ipynb_checkpoints/gene_coverage_profile_fig2_tmpH-checkpoint.ipynb

diff --git a/src/.ipynb_checkpoints/gene_coverage_profile_fig2_tmpH-checkpoint.ipynb b/src/.ipynb_checkpoints/gene_coverage_profile_fig2_tmpH-checkpoint.ipynb
deleted file mode 100644
index 0127b83..0000000
--- a/src/.ipynb_checkpoints/gene_coverage_profile_fig2_tmpH-checkpoint.ipynb
+++ /dev/null
@@ -1,1019 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "e3f32189-fd60-4cd4-830a-efdb73ce993f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc154ab0",
-   "metadata": {},
-   "source": [
-    "## LIBRARIES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "d40ba3ab-b568-4dfb-acdb-c8450c9eb50c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "530bed1f",
-   "metadata": {},
-   "source": [
-    "## FUNCTIONS/PAR_SETTING"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "3e51378d-bfcf-4d8c-ac17-af501ab2ad56",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_results_folder(base_dataset_path, prefix):\n",
-    "    for folder in os.listdir(base_dataset_path):\n",
-    "        if os.path.isdir(os.path.join(base_dataset_path, folder)):\n",
-    "            if prefix in folder and 'results' in folder:\n",
-    "                return folder\n",
-    "    return None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "b701cdfa-4d91-49ad-a0ab-d27ec1f9a981",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#dodged barplot function\n",
-    "script_dir = \"/mnt/efs/home/gasole/repos/personal/utils/plots\"\n",
-    "sys.path.append(script_dir)\n",
-    "\n",
-    "from ridgeline_from_known_density_plot import ridgeline_from_known_density_plot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a234ae7e",
-   "metadata": {},
-   "source": [
-    "## WORKFLOW"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "253950be-269a-4adb-b97f-6a3800d37b73",
-   "metadata": {},
-   "source": [
-    "### Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "e6bf5f86-330b-42a8-8dc5-1868b714ce44",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base_folder = '/mnt/s3fs/flomics-data/FL-cfRNA-meta'\n",
-    "output_folder = os.getcwd()\n",
-    "\n",
-    "datasets = {\n",
-    "    'block':         {'sliced': True,  'n_parts': 4,  'prefix': 'block'},\n",
-    "    'chalasani_merged': {'sliced': True,  'n_parts': 5,  'prefix': 'chalasani'},\n",
-    "    'chen':          {'sliced': True,  'n_parts': 21, 'prefix': 'chen'},\n",
-    "    'flomics_1': {'sliced': False, 'subfolder_path': 'Flomics_1/2025_05_16_11_26_41/n3abcb4a/'},\n",
-    "    'flomics_2': {'sliced': False, 'subfolder_path': 'Flomics_2/2025_06_05_09_32_11/n61e05f8/'},\n",
-    "    'decru':         {'sliced': True,  'n_parts': 6,  'prefix': 'decru'},\n",
-    "    'giraldez':      {'sliced': False,                 'prefix': 'giraldez'},\n",
-    "    'ibarra':        {'sliced': True,  'n_parts': 14, 'prefix': 'ibarra'},\n",
-    "    'moufarrej':     {'sliced': True,  'n_parts': 17, 'prefix': 'moufarrej'},\n",
-    "    'ngo':           {'sliced': False,                 'prefix': 'ngo'},\n",
-    "    'reggiardo':     {'sliced': False,                 'prefix': 'reggiardo'},\n",
-    "    'roskams':       {'sliced': True,  'n_parts': 6,  'prefix': 'roskams'},\n",
-    "    'rozowsky':      {'sliced': True,  'n_parts': 9,  'prefix': 'rozowsky'},\n",
-    "    'sun':           {'sliced': True,  'n_parts': 3,  'prefix': 'sun'},\n",
-    "    'tao':           {'sliced': True,  'n_parts': 12, 'prefix': 'tao'},\n",
-    "    #'taowei':        {'sliced': True,  'n_parts': 4,  'prefix': 'taowei'},\n",
-    "    'toden':         {'sliced': True,  'n_parts': 14, 'prefix': 'toden'},\n",
-    "    'wang':          {'sliced': True,  'n_parts': 4,  'prefix': 'wang'},\n",
-    "    'zhu':           {'sliced': True,  'n_parts': 5,  'prefix': 'zhu'},\n",
-    "}\n",
-    "\n",
-    "dataset_colors = {\n",
-    "    \"block\": \"#b3b3b3\", \"decru\": \"#009E73\", \"zhu\": \"#ffd633\", \"chen\": \"#997a00\", \"flomics_1\" : \"#9AB9D6\", \"flomics_2\": \"#144D6B\",\n",
-    "    \"ngo\": \"#fa8072\", \"roskams\": \"#944dff\", \"moufarrej\": \"#CC79A7\", \"sun\": \"#D55E00\",\n",
-    "    \"tao\": \"#0072B2\", \"toden\": \"#800099\", \"ibarra\": \"#800000\", \"chalasani_merged\": \"#800040\",\n",
-    "    \"rozowsky\": \"#006600\", \"taowei\": \"#B32400\", \"giraldez\": \"#B1CC71\", \"reggiardo\": \"#F1085C\", \"wang\": \"#FE8F42\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f40a95d-f889-4aec-b65c-cbd798946b35",
-   "metadata": {},
-   "source": [
-    "### Process dataset and individual plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "883c4a91-9ff1-4c2a-9e7b-9fdf6615d91d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Processing dataset: block\n",
-      "Saved: block_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: chalasani_merged\n",
-      "Saved: chalasani_merged_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: chen\n",
-      "Saved: chen_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: flomics_1\n",
-      "Saved: flomics_1_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: flomics_2\n",
-      "Saved: flomics_2_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: decru\n",
-      "Saved: decru_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: giraldez\n",
-      "Saved: giraldez_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: ibarra\n",
-      "Saved: ibarra_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: moufarrej\n",
-      "Saved: moufarrej_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: ngo\n",
-      "Saved: ngo_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: reggiardo\n",
-      "Saved: reggiardo_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: roskams\n",
-      "Saved: roskams_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: rozowsky\n",
-      "Saved: rozowsky_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: sun\n",
-      "Saved: sun_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: tao\n",
-      "Saved: tao_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: toden\n",
-      "Saved: toden_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: wang\n",
-      "Saved: wang_multiqc_coverage.png\n",
-      "\n",
-      "Processing dataset: zhu\n",
-      "Saved: zhu_multiqc_coverage.png\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ---- Process ----\n",
-    "all_profiles = {}\n",
-    "\n",
-    "for dataset, meta in datasets.items():\n",
-    "    sliced = meta['sliced']\n",
-    "    n_parts = meta.get('n_parts', 1)\n",
-    "    prefix = meta.get('prefix', None)  # Only exists if sliced or traditional unsliced\n",
-    "    print(f\"\\nProcessing dataset: {dataset}\")\n",
-    "    \n",
-    "    part_means = []\n",
-    "\n",
-    "    if sliced:\n",
-    "        for i in range(n_parts):\n",
-    "            part_folder = f\"{prefix}_results_{i:02d}.part/multiqc/star_salmon/multiqc_data\"\n",
-    "            file_path = os.path.join(base_folder, dataset, part_folder, \"mqc_qualimap_gene_coverage_profile_Normalised.txt\")\n",
-    "            if os.path.isfile(file_path):\n",
-    "                try:\n",
-    "                    df = pd.read_csv(file_path, sep=\"\\t\")\n",
-    "                    sample_only = df.drop(columns=[\"Sample\"])\n",
-    "                    part_means.append(sample_only.mean(axis=0))\n",
-    "                except Exception as e:\n",
-    "                    print(f\"Error reading {file_path}: {e}\")\n",
-    "            else:\n",
-    "                print(f\"Missing: {file_path}\")\n",
-    "    else:\n",
-    "        if \"subfolder_path\" in meta:\n",
-    "            part_path = os.path.join(base_folder, meta[\"subfolder_path\"], \"multiqc\", \"star_salmon\", \"multiqc_data\")\n",
-    "        else:\n",
-    "            base_dataset_path = os.path.join(base_folder, dataset)\n",
-    "            matched_folder = find_results_folder(base_dataset_path, prefix)\n",
-    "            if not matched_folder:\n",
-    "                print(f\"Could not find results folder for: {dataset}\")\n",
-    "                continue\n",
-    "            part_path = os.path.join(base_dataset_path, matched_folder, \"multiqc\", \"star_salmon\", \"multiqc_data\")\n",
-    "\n",
-    "        file_path = os.path.join(part_path, \"mqc_qualimap_gene_coverage_profile_Normalised.txt\")\n",
-    "        if os.path.isfile(file_path):\n",
-    "            try:\n",
-    "                df = pd.read_csv(file_path, sep=\"\\t\")\n",
-    "                sample_only = df.drop(columns=[\"Sample\"])\n",
-    "                part_means.append(sample_only.mean(axis=0))\n",
-    "            except Exception as e:\n",
-    "                print(f\"Error reading {file_path}: {e}\")\n",
-    "        else:\n",
-    "            print(f\"Missing: {file_path}\")\n",
-    "\n",
-    "\n",
-    "    # Average across all .part means\n",
-    "    if part_means:\n",
-    "        dataset_mean_df = pd.DataFrame(part_means).mean(axis=0).reset_index()\n",
-    "        dataset_mean_df.columns = [\"position\", \"coverage\"]\n",
-    "        dataset_mean_df[\"position\"] = dataset_mean_df[\"position\"].astype(int)\n",
-    "        dataset_mean_df.sort_values(\"position\", inplace=True)\n",
-    "        all_profiles[dataset] = dataset_mean_df\n",
-    "\n",
-    "        # Per-dataset plot\n",
-    "        plt.figure(figsize=(10, 4))\n",
-    "        plt.plot(dataset_mean_df[\"position\"], dataset_mean_df[\"coverage\"],\n",
-    "                 label=dataset, color=dataset_colors.get(dataset))\n",
-    "        plt.title(f\"Normalized Transcript Coverage: {dataset}\")\n",
-    "        plt.xlabel(\"Transcript position (%)\")\n",
-    "        plt.ylabel(\"Normalized coverage\")\n",
-    "        plt.tight_layout()\n",
-    "        plt.savefig(os.path.join(output_folder, f\"{dataset}_multiqc_coverage.png\"))\n",
-    "        plt.close()\n",
-    "        print(f\"Saved: {dataset}_multiqc_coverage.png\")\n",
-    "    else:\n",
-    "        print(f\"No data for {dataset}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "3594b8e5-f53a-4b96-af29-cfae9a1e3ade",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All per-dataset and combined plots saved.\n"
-     ]
-    }
-   ],
-   "source": [
-    "dataset_labels = {\n",
-    "    \"block\": \"Block\",\n",
-    "    \"chalasani_merged\": \"Chalasani\",\n",
-    "    \"chen\": \"Chen\",\n",
-    "    \"flomics_1\" : \"Flomics 1\",\n",
-    "    \"flomics_2\": \"Flomics 2\",\n",
-    "    \"decru\": \"Decruyenaere\",\n",
-    "    \"giraldez\": \"Giráldez\",\n",
-    "    \"ibarra\": \"Ibarra\",\n",
-    "    \"moufarrej\": \"Moufarrej\",\n",
-    "    \"ngo\": \"Ngo\",\n",
-    "    \"reggiardo\": \"Reggiardo\",\n",
-    "    \"roskams\": \"Roskams-Hieter\",\n",
-    "    \"rozowsky\": \"Rozowsky\",\n",
-    "    \"sun\": \"Sun\",\n",
-    "    \"tao\": \"Tao\",\n",
-    "    \"taowei\": \"Wei\",\n",
-    "    \"toden\": \"Toden\",\n",
-    "    \"wang\": \"Wang\",\n",
-    "    \"zhu\": \"Zhu\"\n",
-    "}\n",
-    "\n",
-    "# ---- Combined Plot ----\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "for dataset, df in all_profiles.items():\n",
-    "    plt.plot(df[\"position\"], df[\"coverage\"], label=dataset_labels.get(dataset, dataset), color=dataset_colors.get(dataset))\n",
-    "plt.xlabel(\"Transcript position (%)\")\n",
-    "plt.ylabel(\"Normalized coverage\")\n",
-    "plt.title(\"Average Gene Coverage per Dataset (MultiQC normalized)\")\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(os.path.join(output_folder, \"multiqc_style_normalized_coverage_combined.png\"), dpi=600)\n",
-    "plt.close()\n",
-    "\n",
-    "print(\"All per-dataset and combined plots saved.\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "b507097c-114a-4ec6-9589-8cdbb7cbf324",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'block':     position  coverage\n",
-       " 0          0  0.751370\n",
-       " 1          1  0.948973\n",
-       " 2          2  1.042970\n",
-       " 3          3  1.101110\n",
-       " 4          4  1.137402\n",
-       " ..       ...       ...\n",
-       " 95        95  0.387874\n",
-       " 96        96  0.314820\n",
-       " 97        97  0.234645\n",
-       " 98        98  0.149621\n",
-       " 99        99  0.054939\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'chalasani_merged':     position  coverage\n",
-       " 0          0  0.893336\n",
-       " 1          1  0.992028\n",
-       " 2          2  1.068821\n",
-       " 3          3  1.125762\n",
-       " 4          4  1.172424\n",
-       " ..       ...       ...\n",
-       " 95        95  0.405052\n",
-       " 96        96  0.326549\n",
-       " 97        97  0.239147\n",
-       " 98        98  0.149661\n",
-       " 99        99  0.050280\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'chen':     position  coverage\n",
-       " 0          0  0.883808\n",
-       " 1          1  1.080623\n",
-       " 2          2  1.127518\n",
-       " 3          3  1.161813\n",
-       " 4          4  1.208590\n",
-       " ..       ...       ...\n",
-       " 95        95  0.288879\n",
-       " 96        96  0.242541\n",
-       " 97        97  0.178097\n",
-       " 98        98  0.115640\n",
-       " 99        99  0.038419\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'flomics_1':     position  coverage\n",
-       " 0          0  0.577109\n",
-       " 1          1  0.638392\n",
-       " 2          2  0.690236\n",
-       " 3          3  0.762369\n",
-       " 4          4  0.824304\n",
-       " ..       ...       ...\n",
-       " 95        95  0.337860\n",
-       " 96        96  0.288220\n",
-       " 97        97  0.234444\n",
-       " 98        98  0.176366\n",
-       " 99        99  0.081433\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'flomics_2':     position  coverage\n",
-       " 0          0  0.678069\n",
-       " 1          1  0.751549\n",
-       " 2          2  0.805104\n",
-       " 3          3  0.885331\n",
-       " 4          4  0.948078\n",
-       " ..       ...       ...\n",
-       " 95        95  0.331373\n",
-       " 96        96  0.278453\n",
-       " 97        97  0.223898\n",
-       " 98        98  0.155197\n",
-       " 99        99  0.062723\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'decru':     position  coverage\n",
-       " 0          0  0.594986\n",
-       " 1          1  0.673064\n",
-       " 2          2  0.728644\n",
-       " 3          3  0.816234\n",
-       " 4          4  0.906582\n",
-       " ..       ...       ...\n",
-       " 95        95  0.299129\n",
-       " 96        96  0.257159\n",
-       " 97        97  0.207672\n",
-       " 98        98  0.152542\n",
-       " 99        99  0.069510\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'giraldez':     position  coverage\n",
-       " 0          0  0.146943\n",
-       " 1          1  0.179056\n",
-       " 2          2  0.206957\n",
-       " 3          3  0.284656\n",
-       " 4          4  0.369986\n",
-       " ..       ...       ...\n",
-       " 95        95  0.144934\n",
-       " 96        96  0.174990\n",
-       " 97        97  0.030457\n",
-       " 98        98  0.018695\n",
-       " 99        99  0.005258\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'ibarra':     position  coverage\n",
-       " 0          0  0.811200\n",
-       " 1          1  0.921145\n",
-       " 2          2  0.990593\n",
-       " 3          3  1.035097\n",
-       " 4          4  1.071725\n",
-       " ..       ...       ...\n",
-       " 95        95  0.431216\n",
-       " 96        96  0.350551\n",
-       " 97        97  0.258570\n",
-       " 98        98  0.161554\n",
-       " 99        99  0.053056\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'moufarrej':     position  coverage\n",
-       " 0          0  0.922650\n",
-       " 1          1  1.289233\n",
-       " 2          2  1.376297\n",
-       " 3          3  1.501520\n",
-       " 4          4  1.510656\n",
-       " ..       ...       ...\n",
-       " 95        95  0.312282\n",
-       " 96        96  0.268549\n",
-       " 97        97  0.214269\n",
-       " 98        98  0.164626\n",
-       " 99        99  0.080866\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'ngo':     position  coverage\n",
-       " 0          0  0.683297\n",
-       " 1          1  0.853360\n",
-       " 2          2  0.905121\n",
-       " 3          3  0.955589\n",
-       " 4          4  1.046210\n",
-       " ..       ...       ...\n",
-       " 95        95  0.272191\n",
-       " 96        96  0.218318\n",
-       " 97        97  0.158297\n",
-       " 98        98  0.099380\n",
-       " 99        99  0.033786\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'reggiardo':     position  coverage\n",
-       " 0          0  0.456523\n",
-       " 1          1  0.471918\n",
-       " 2          2  0.491256\n",
-       " 3          3  0.501107\n",
-       " 4          4  0.515223\n",
-       " ..       ...       ...\n",
-       " 95        95  0.471508\n",
-       " 96        96  0.392591\n",
-       " 97        97  0.293598\n",
-       " 98        98  0.178205\n",
-       " 99        99  0.048939\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'roskams':     position  coverage\n",
-       " 0          0  0.836213\n",
-       " 1          1  0.958719\n",
-       " 2          2  1.006426\n",
-       " 3          3  1.049440\n",
-       " 4          4  1.083980\n",
-       " ..       ...       ...\n",
-       " 95        95  0.345281\n",
-       " 96        96  0.283676\n",
-       " 97        97  0.216060\n",
-       " 98        98  0.140177\n",
-       " 99        99  0.048876\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'rozowsky':     position  coverage\n",
-       " 0          0  0.664510\n",
-       " 1          1  0.823720\n",
-       " 2          2  0.986983\n",
-       " 3          3  1.143379\n",
-       " 4          4  1.228875\n",
-       " ..       ...       ...\n",
-       " 95        95  0.526265\n",
-       " 96        96  0.390768\n",
-       " 97        97  0.241327\n",
-       " 98        98  0.147613\n",
-       " 99        99  0.048011\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'sun':     position  coverage\n",
-       " 0          0  0.827656\n",
-       " 1          1  1.024742\n",
-       " 2          2  1.238866\n",
-       " 3          3  1.111935\n",
-       " 4          4  1.164360\n",
-       " ..       ...       ...\n",
-       " 95        95  0.291605\n",
-       " 96        96  0.237043\n",
-       " 97        97  0.170098\n",
-       " 98        98  0.109589\n",
-       " 99        99  0.038476\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'tao':     position  coverage\n",
-       " 0          0  0.816322\n",
-       " 1          1  0.957243\n",
-       " 2          2  1.002606\n",
-       " 3          3  1.037284\n",
-       " 4          4  1.073102\n",
-       " ..       ...       ...\n",
-       " 95        95  0.284971\n",
-       " 96        96  0.236689\n",
-       " 97        97  0.183865\n",
-       " 98        98  0.121932\n",
-       " 99        99  0.040224\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'toden':     position  coverage\n",
-       " 0          0  0.794399\n",
-       " 1          1  0.889729\n",
-       " 2          2  0.952080\n",
-       " 3          3  0.994954\n",
-       " 4          4  1.036387\n",
-       " ..       ...       ...\n",
-       " 95        95  0.440365\n",
-       " 96        96  0.354897\n",
-       " 97        97  0.259633\n",
-       " 98        98  0.161428\n",
-       " 99        99  0.053321\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'wang':     position  coverage\n",
-       " 0          0  0.884646\n",
-       " 1          1  1.098436\n",
-       " 2          2  1.216367\n",
-       " 3          3  1.430555\n",
-       " 4          4  1.722352\n",
-       " ..       ...       ...\n",
-       " 95        95  0.265076\n",
-       " 96        96  0.202165\n",
-       " 97        97  0.142643\n",
-       " 98        98  0.162646\n",
-       " 99        99  0.075130\n",
-       " \n",
-       " [100 rows x 2 columns],\n",
-       " 'zhu':     position  coverage\n",
-       " 0          0  0.982054\n",
-       " 1          1  1.203608\n",
-       " 2          2  1.250519\n",
-       " 3          3  1.290315\n",
-       " 4          4  1.337304\n",
-       " ..       ...       ...\n",
-       " 95        95  0.283839\n",
-       " 96        96  0.220782\n",
-       " 97        97  0.164646\n",
-       " 98        98  0.103329\n",
-       " 99        99  0.031109\n",
-       " \n",
-       " [100 rows x 2 columns]}"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_profiles"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53a6c9e2-db95-4245-9cc5-7aaeb8f1e92d",
-   "metadata": {},
-   "source": [
-    "### data preparation for ridge plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "6351b588-d2b9-4cba-8acf-16522ee1eb22",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "block\n",
-      "chalasani_merged\n",
-      "chen\n",
-      "flomics_1\n",
-      "flomics_2\n",
-      "decru\n",
-      "giraldez\n",
-      "ibarra\n",
-      "moufarrej\n",
-      "ngo\n",
-      "reggiardo\n",
-      "roskams\n",
-      "rozowsky\n",
-      "sun\n",
-      "tao\n",
-      "toden\n",
-      "wang\n",
-      "zhu\n",
-      "(1800, 3)\n"
-     ]
-    },
-    {
-     "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></th>\n",
-       "      <th>position</th>\n",
-       "      <th>coverage</th>\n",
-       "      <th>dataset_short_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">block</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.751370</td>\n",
-       "      <td>block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.948973</td>\n",
-       "      <td>block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1.042970</td>\n",
-       "      <td>block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.101110</td>\n",
-       "      <td>block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1.137402</td>\n",
-       "      <td>block</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         position  coverage dataset_short_name\n",
-       "block 0         0  0.751370              block\n",
-       "      1         1  0.948973              block\n",
-       "      2         2  1.042970              block\n",
-       "      3         3  1.101110              block\n",
-       "      4         4  1.137402              block"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#add dataset_short_name column\n",
-    "for dataset, df in all_profiles.items():\n",
-    "    df['dataset_short_name'] = dataset\n",
-    "    print(dataset)\n",
-    "\n",
-    "#create a single dataset\n",
-    "all_dataset = pd.concat(all_profiles, ignore_index=False)\n",
-    "print(all_dataset.shape)\n",
-    "all_dataset.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "c545fb94-cd41-47b2-a952-954d4349936e",
-   "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></th>\n",
-       "      <th>position</th>\n",
-       "      <th>coverage</th>\n",
-       "      <th>dataset_short_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">block</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.751370</td>\n",
-       "      <td>Block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.948973</td>\n",
-       "      <td>Block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1.042970</td>\n",
-       "      <td>Block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.101110</td>\n",
-       "      <td>Block</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1.137402</td>\n",
-       "      <td>Block</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         position  coverage dataset_short_name\n",
-       "block 0         0  0.751370              Block\n",
-       "      1         1  0.948973              Block\n",
-       "      2         2  1.042970              Block\n",
-       "      3         3  1.101110              Block\n",
-       "      4         4  1.137402              Block"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#correct dataset labels in dataframe\n",
-    "all_dataset[\"dataset_short_name\"].replace(dataset_labels, inplace=True)\n",
-    "all_dataset.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "5d3ca5e0-6cb9-4b19-88ba-8e04de9638cc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'Block': '#b3b3b3', 'Decruyenaere': '#009E73', 'Zhu': '#ffd633', 'Chen': '#997a00', 'Flomics 1': '#9AB9D6', 'Flomics 2': '#144D6B', 'Ngo': '#fa8072', 'Roskams-Hieter': '#944dff', 'Moufarrej': '#CC79A7', 'Sun': '#D55E00', 'Tao': '#0072B2', 'Toden': '#800099', 'Ibarra': '#800000', 'Chalasani': '#800040', 'Rozowsky': '#006600', 'Wei': '#B32400', 'Giráldez': '#B1CC71', 'Reggiardo': '#F1085C', 'Wang': '#FE8F42'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "#correct dataset labels in color dict\n",
-    "dataset_short_name_colors = {dataset_labels.get(old_key, old_key): value \n",
-    "            for old_key, value in dataset_colors.items()}\n",
-    "\n",
-    "print(dataset_short_name_colors)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7d205ca-1721-4fea-8ebf-596157f32803",
-   "metadata": {},
-   "source": [
-    "### ridge plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "28087f79-0fc9-4a31-9c77-067d0cef5f78",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "    Generate a ridgeline plot from pre-calculated density values.\n",
-      "\n",
-      "    Args:\n",
-      "        dataset (pd.DataFrame): The input pandas DataFrame containing pre-calculated densities values\n",
-      "        x_column (str): Column containing the coordinates for the density curves.\n",
-      "        density_column (str): Column containing the density values for the curves.\n",
-      "        category_column (str): Column containing categories to group the curves.\n",
-      "        overlap (float, optional): The degree of overlap between the lines (from 0 to 1). Defaults to 0.5.\n",
-      "        fig_width (float, optional): The width of the figure in inches. Defaults to 10.\n",
-      "        fig_height (float, optional): The height of the figure in inches. Defaults to 7.\n",
-      "        output (str, optional): The full path filename (without extension) to save the plot.\n",
-      "        show_xlabel (bool, optional): Display the X-axis label. Defaults to True.\n",
-      "        xlabel (str, optional): Custom label for the X-axis. Defaults to None.\n",
-      "        xlabel_fontsize (float, optional): Font size for the X-axis label. Defaults to 12.\n",
-      "        show_ylabel (bool, optional): Display the Y-axis label. Defaults to True\n",
-      "        ylabel (str, optional): Custom label for the Y-axis. Defaults to None.\n",
-      "        ylabel_fontsize (float, optional): Font size for the Y-axis label. Defaults to 12.\n",
-      "        show_title (bool, optional): Display plot title. Defaults to True.\n",
-      "        title (str, optional): Custom title. Defaults to None.\n",
-      "        title_fontsize: float (optional): Font size for title. Defaults to 14.\n",
-      "        xticks_fontsize (float, optional): Font size for the X-axis tick labels. Defaults to 10.\n",
-      "        yticks_fontsize (float, optional): Font size for the Y-axis tick labels. Defaults to 10.\n",
-      "        fill_alpha (float, optional): Transparency of the filled area under the curves. Defaults to 0.7.\n",
-      "        line_color (str, optional): Color of the curve lines. Defaults to 'black'.\n",
-      "        fill_color (str or dict, optional): Color of the filled area under the curves.\n",
-      "                                            Can be a single color string for all categories,\n",
-      "                                            or a dictionary mapping category names\n",
-      "                                            Defaults to 'skyblue'.\n",
-      "        category_order (list, optional): Order in which categories should be plotted from bottom to top.\n",
-      "    \n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ridgeline_from_known_density_plot.__doc__)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "6fdcbbc1-399e-420c-b410-4d72949f43bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ridgeline plot saved to gene_coverage_merged.pdf\n",
-      "Ridgeline plot saved to gene_coverage_merged.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 700x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ridgeline_from_known_density_plot(\n",
-    "    dataset = all_dataset,\n",
-    "    x_column = \"position\",\n",
-    "    density_column = \"coverage\",\n",
-    "    category_column = \"dataset_short_name\",\n",
-    "    fig_width = 7,\n",
-    "    fig_height = 10,\n",
-    "    output = \"gene_coverage_merged\",\n",
-    "    show_ylabel = False,\n",
-    "    fill_color = dataset_short_name_colors\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "45d2930e-da88-4b9d-98e1-3d543eea0c76",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c3ff92dc-162e-4cf7-8a79-bdc333dffacc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "efb1701b-3c12-44c1-b10c-e5ccb720c531",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e901d9e",
-   "metadata": {},
-   "source": [
-    "## CITATIONS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e8e4aa",
-   "metadata": {},
-   "source": [
-    "write here citations for used packages/datasets etc.."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "437eac95-8e6d-4623-9849-4c7c98780ede",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5ee557aa-9a41-4784-94dd-b7dafba6ed84",
-   "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": "3.12.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}