From 4fbaf03a4f6853072ea14c4901f459e465f45869 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jochen=20Nie=C3=9Fer?= <j.niesser@fz-juelich.de>
Date: Sun, 13 Oct 2024 15:51:18 +0200
Subject: [PATCH 1/6] add and re-arrange raw data

---
 .../Create_results_in_figure_2.ipynb          | 1487 +++++++++++++++++
 ...Create_validation_plot_from_raw_data.ipynb |   20 +-
 .../Processing_test_1_raw_data.ipynb          |   56 +-
 .../Template.xlsx                             |  Bin 0 -> 14933 bytes
 ...mal model_normal data_noise level 0.6.xlsx |  Bin
 ...mal model_normal data_noise level 1.2.xlsx |  Bin
 ...odel_skew normal data_noise level 0.6.xlsx |  Bin
 ...mal model_normal data_noise level 0.6.xlsx |  Bin
 ...odel_skew normal data_noise level 0.6.xlsx |  Bin
 ...odel_skew normal data_noise level 1.2.xlsx |  Bin
 .../{ => paper raw data}/test1_all_data.txt   |    0
 .../{ => paper raw data}/test2_summary.xlsx   |  Bin
 .../test3_df_comparison.xlsx                  |  Bin
 13 files changed, 1525 insertions(+), 38 deletions(-)
 create mode 100644 docs/source/notebooks/Create_results_in_figure_2.ipynb
 create mode 100644 docs/source/notebooks/paper raw data/raw data set for validation with MultiQuant/Template.xlsx
 rename docs/source/notebooks/{ => paper raw data/synthetic data sets for validation}/Normal model_normal data_noise level 0.6.xlsx (100%)
 rename docs/source/notebooks/{ => paper raw data/synthetic data sets for validation}/Normal model_normal data_noise level 1.2.xlsx (100%)
 rename docs/source/notebooks/{ => paper raw data/synthetic data sets for validation}/Normal model_skew normal data_noise level 0.6.xlsx (100%)
 rename docs/source/notebooks/{ => paper raw data/synthetic data sets for validation}/Skew normal model_normal data_noise level 0.6.xlsx (100%)
 rename docs/source/notebooks/{ => paper raw data/synthetic data sets for validation}/Skew normal model_skew normal data_noise level 0.6.xlsx (100%)
 rename docs/source/notebooks/{ => paper raw data/synthetic data sets for validation}/Skew normal model_skew normal data_noise level 1.2.xlsx (100%)
 rename docs/source/notebooks/{ => paper raw data}/test1_all_data.txt (100%)
 rename docs/source/notebooks/{ => paper raw data}/test2_summary.xlsx (100%)
 rename docs/source/notebooks/{ => paper raw data}/test3_df_comparison.xlsx (100%)

diff --git a/docs/source/notebooks/Create_results_in_figure_2.ipynb b/docs/source/notebooks/Create_results_in_figure_2.ipynb
new file mode 100644
index 0000000..ea6af20
--- /dev/null
+++ b/docs/source/notebooks/Create_results_in_figure_2.ipynb
@@ -0,0 +1,1487 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create results and diagnostic plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import pandas\n",
+    "import numpy as np\n",
+    "import arviz as az\n",
+    "from pathlib import Path\n",
+    "from peak_performance import pipeline as pl, models, plots\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exemplary result with a single peak"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_result = Path(\"./paper raw data\")\n",
+    "path = Path(\"./paper raw data/exemplary results raw data/A1t1R1Part2_110_109.9_110.1.npy\")\n",
+    "timeseries = np.load(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2411d8356f0>]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(timeseries[0], timeseries[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\joche\\miniconda3\\envs\\pp_env\\lib\\site-packages\\pymc\\data.py:287: FutureWarning: ConstantData is deprecated. All Data variables are now mutable. Use Data instead.\n",
+      "  warnings.warn(\n",
+      "Sampling: [L, alpha, area, baseline_intercept, baseline_slope, mean, noise, std]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    :root {\n",
+       "        --column-width-1: 40%; /* Progress column width */\n",
+       "        --column-width-2: 15%; /* Chain column width */\n",
+       "        --column-width-3: 15%; /* Divergences column width */\n",
+       "        --column-width-4: 15%; /* Step Size column width */\n",
+       "        --column-width-5: 15%; /* Gradients/Draw column width */\n",
+       "    }\n",
+       "\n",
+       "    .nutpie {\n",
+       "        max-width: 800px;\n",
+       "        margin: 10px auto;\n",
+       "        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n",
+       "        //color: #333;\n",
+       "        //background-color: #fff;\n",
+       "        padding: 10px;\n",
+       "        box-shadow: 0 4px 6px rgba(0,0,0,0.1);\n",
+       "        border-radius: 8px;\n",
+       "        font-size: 14px; /* Smaller font size for a more compact look */\n",
+       "    }\n",
+       "    .nutpie table {\n",
+       "        width: 100%;\n",
+       "        border-collapse: collapse; /* Remove any extra space between borders */\n",
+       "    }\n",
+       "    .nutpie th, .nutpie td {\n",
+       "        padding: 8px 10px; /* Reduce padding to make table more compact */\n",
+       "        text-align: left;\n",
+       "        border-bottom: 1px solid #888;\n",
+       "    }\n",
+       "    .nutpie th {\n",
+       "        //background-color: #f0f0f0;\n",
+       "    }\n",
+       "\n",
+       "    .nutpie th:nth-child(1) { width: var(--column-width-1); }\n",
+       "    .nutpie th:nth-child(2) { width: var(--column-width-2); }\n",
+       "    .nutpie th:nth-child(3) { width: var(--column-width-3); }\n",
+       "    .nutpie th:nth-child(4) { width: var(--column-width-4); }\n",
+       "    .nutpie th:nth-child(5) { width: var(--column-width-5); }\n",
+       "\n",
+       "    .nutpie progress {\n",
+       "        width: 100%;\n",
+       "        height: 15px; /* Smaller progress bars */\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    progress::-webkit-progress-bar {\n",
+       "        background-color: #eee;\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    progress::-webkit-progress-value {\n",
+       "        background-color: #5cb85c;\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    progress::-moz-progress-bar {\n",
+       "        background-color: #5cb85c;\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    .nutpie .progress-cell {\n",
+       "        width: 100%;\n",
+       "    }\n",
+       "\n",
+       "    .nutpie p strong { font-size: 16px; font-weight: bold; }\n",
+       "\n",
+       "    @media (prefers-color-scheme: dark) {\n",
+       "        .nutpie {\n",
+       "            //color: #ddd;\n",
+       "            //background-color: #1e1e1e;\n",
+       "            box-shadow: 0 4px 6px rgba(0,0,0,0.2);\n",
+       "        }\n",
+       "        .nutpie table, .nutpie th, .nutpie td {\n",
+       "            border-color: #555;\n",
+       "            color: #ccc;\n",
+       "        }\n",
+       "        .nutpie th {\n",
+       "            background-color: #2a2a2a;\n",
+       "        }\n",
+       "        .nutpie progress::-webkit-progress-bar {\n",
+       "            background-color: #444;\n",
+       "        }\n",
+       "        .nutpie progress::-webkit-progress-value {\n",
+       "            background-color: #3178c6;\n",
+       "        }\n",
+       "        .nutpie progress::-moz-progress-bar {\n",
+       "            background-color: #3178c6;\n",
+       "        }\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div class=\"nutpie\">\n",
+       "    <p><strong>Sampler Progress</strong></p>\n",
+       "    <p>Total Chains: <span id=\"total-chains\">4</span></p>\n",
+       "    <p>Active Chains: <span id=\"active-chains\">0</span></p>\n",
+       "    <p>\n",
+       "        Finished Chains:\n",
+       "        <span id=\"active-chains\">4</span>\n",
+       "    </p>\n",
+       "    <p>Sampling for now</p>\n",
+       "    <p>\n",
+       "        Estimated Time to Completion:\n",
+       "        <span id=\"eta\">now</span>\n",
+       "    </p>\n",
+       "\n",
+       "    <progress\n",
+       "        id=\"total-progress-bar\"\n",
+       "        max=\"32000\"\n",
+       "        value=\"32000\">\n",
+       "    </progress>\n",
+       "    <table>\n",
+       "        <thead>\n",
+       "            <tr>\n",
+       "                <th>Progress</th>\n",
+       "                <th>Draws</th>\n",
+       "                <th>Divergences</th>\n",
+       "                <th>Step Size</th>\n",
+       "                <th>Gradients/Draw</th>\n",
+       "            </tr>\n",
+       "        </thead>\n",
+       "        <tbody id=\"chain-details\">\n",
+       "            \n",
+       "                <tr>\n",
+       "                    <td class=\"progress-cell\">\n",
+       "                        <progress\n",
+       "                            max=\"8000\"\n",
+       "                            value=\"8000\">\n",
+       "                        </progress>\n",
+       "                    </td>\n",
+       "                    <td>8000</td>\n",
+       "                    <td>0</td>\n",
+       "                    <td>0.62</td>\n",
+       "                    <td>3</td>\n",
+       "                </tr>\n",
+       "            \n",
+       "                <tr>\n",
+       "                    <td class=\"progress-cell\">\n",
+       "                        <progress\n",
+       "                            max=\"8000\"\n",
+       "                            value=\"8000\">\n",
+       "                        </progress>\n",
+       "                    </td>\n",
+       "                    <td>8000</td>\n",
+       "                    <td>0</td>\n",
+       "                    <td>0.62</td>\n",
+       "                    <td>3</td>\n",
+       "                </tr>\n",
+       "            \n",
+       "                <tr>\n",
+       "                    <td class=\"progress-cell\">\n",
+       "                        <progress\n",
+       "                            max=\"8000\"\n",
+       "                            value=\"8000\">\n",
+       "                        </progress>\n",
+       "                    </td>\n",
+       "                    <td>8000</td>\n",
+       "                    <td>0</td>\n",
+       "                    <td>0.60</td>\n",
+       "                    <td>7</td>\n",
+       "                </tr>\n",
+       "            \n",
+       "                <tr>\n",
+       "                    <td class=\"progress-cell\">\n",
+       "                        <progress\n",
+       "                            max=\"8000\"\n",
+       "                            value=\"8000\">\n",
+       "                        </progress>\n",
+       "                    </td>\n",
+       "                    <td>8000</td>\n",
+       "                    <td>0</td>\n",
+       "                    <td>0.63</td>\n",
+       "                    <td>7</td>\n",
+       "                </tr>\n",
+       "            \n",
+       "            </tr>\n",
+       "        </tbody>\n",
+       "    </table>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "<nutpie.sample._BackgroundSampler at 0x2412e21ed70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [L]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65de9276e99248e8a1dedd3f35be1c50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>baseline_intercept</th>\n",
+       "      <td>-43.978</td>\n",
+       "      <td>7.255</td>\n",
+       "      <td>-57.960</td>\n",
+       "      <td>-30.508</td>\n",
+       "      <td>0.079</td>\n",
+       "      <td>0.056</td>\n",
+       "      <td>8511.0</td>\n",
+       "      <td>6241.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>baseline_slope</th>\n",
+       "      <td>6.657</td>\n",
+       "      <td>0.513</td>\n",
+       "      <td>5.757</td>\n",
+       "      <td>7.675</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>6862.0</td>\n",
+       "      <td>5687.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>noise_log__</th>\n",
+       "      <td>4.638</td>\n",
+       "      <td>0.072</td>\n",
+       "      <td>4.509</td>\n",
+       "      <td>4.778</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>9353.0</td>\n",
+       "      <td>5750.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>25.949</td>\n",
+       "      <td>0.013</td>\n",
+       "      <td>25.924</td>\n",
+       "      <td>25.972</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>2928.0</td>\n",
+       "      <td>3445.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std_log__</th>\n",
+       "      <td>-0.643</td>\n",
+       "      <td>0.041</td>\n",
+       "      <td>-0.722</td>\n",
+       "      <td>-0.570</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>2733.0</td>\n",
+       "      <td>3353.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alpha</th>\n",
+       "      <td>2.963</td>\n",
+       "      <td>0.396</td>\n",
+       "      <td>2.210</td>\n",
+       "      <td>3.674</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>3023.0</td>\n",
+       "      <td>3830.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>area_log__</th>\n",
+       "      <td>7.321</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>7.274</td>\n",
+       "      <td>7.368</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>5421.0</td>\n",
+       "      <td>5390.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>noise</th>\n",
+       "      <td>103.654</td>\n",
+       "      <td>7.525</td>\n",
+       "      <td>90.303</td>\n",
+       "      <td>118.270</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>0.056</td>\n",
+       "      <td>9353.0</td>\n",
+       "      <td>5750.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.526</td>\n",
+       "      <td>0.021</td>\n",
+       "      <td>0.486</td>\n",
+       "      <td>0.565</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>2733.0</td>\n",
+       "      <td>3353.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>area</th>\n",
+       "      <td>1512.656</td>\n",
+       "      <td>37.424</td>\n",
+       "      <td>1442.853</td>\n",
+       "      <td>1584.455</td>\n",
+       "      <td>0.508</td>\n",
+       "      <td>0.360</td>\n",
+       "      <td>5421.0</td>\n",
+       "      <td>5390.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std_skew</th>\n",
+       "      <td>0.345</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.327</td>\n",
+       "      <td>0.365</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>3575.0</td>\n",
+       "      <td>4874.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_skew</th>\n",
+       "      <td>26.346</td>\n",
+       "      <td>0.012</td>\n",
+       "      <td>26.322</td>\n",
+       "      <td>26.368</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>4179.0</td>\n",
+       "      <td>5737.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>delta</th>\n",
+       "      <td>0.945</td>\n",
+       "      <td>0.014</td>\n",
+       "      <td>0.919</td>\n",
+       "      <td>0.968</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>3023.0</td>\n",
+       "      <td>3830.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mue_z</th>\n",
+       "      <td>0.754</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>0.733</td>\n",
+       "      <td>0.772</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>3023.0</td>\n",
+       "      <td>3830.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sigma_z</th>\n",
+       "      <td>0.657</td>\n",
+       "      <td>0.012</td>\n",
+       "      <td>0.635</td>\n",
+       "      <td>0.680</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>3023.0</td>\n",
+       "      <td>3830.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mode_offset</th>\n",
+       "      <td>0.480</td>\n",
+       "      <td>0.023</td>\n",
+       "      <td>0.439</td>\n",
+       "      <td>0.523</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>3023.0</td>\n",
+       "      <td>3830.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mode_skew</th>\n",
+       "      <td>26.201</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>26.171</td>\n",
+       "      <td>26.228</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>3411.0</td>\n",
+       "      <td>3972.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>height</th>\n",
+       "      <td>1880.508</td>\n",
+       "      <td>38.756</td>\n",
+       "      <td>1811.139</td>\n",
+       "      <td>1958.103</td>\n",
+       "      <td>0.404</td>\n",
+       "      <td>0.286</td>\n",
+       "      <td>9193.0</td>\n",
+       "      <td>7168.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sn</th>\n",
+       "      <td>18.237</td>\n",
+       "      <td>1.366</td>\n",
+       "      <td>15.606</td>\n",
+       "      <td>20.692</td>\n",
+       "      <td>0.014</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>9724.0</td>\n",
+       "      <td>6250.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        mean      sd    hdi_3%   hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "baseline_intercept   -43.978   7.255   -57.960   -30.508      0.079    0.056   \n",
+       "baseline_slope         6.657   0.513     5.757     7.675      0.006    0.004   \n",
+       "noise_log__            4.638   0.072     4.509     4.778      0.001    0.001   \n",
+       "mean                  25.949   0.013    25.924    25.972      0.000    0.000   \n",
+       "std_log__             -0.643   0.041    -0.722    -0.570      0.001    0.001   \n",
+       "alpha                  2.963   0.396     2.210     3.674      0.007    0.005   \n",
+       "area_log__             7.321   0.025     7.274     7.368      0.000    0.000   \n",
+       "noise                103.654   7.525    90.303   118.270      0.078    0.056   \n",
+       "std                    0.526   0.021     0.486     0.565      0.000    0.000   \n",
+       "area                1512.656  37.424  1442.853  1584.455      0.508    0.360   \n",
+       "std_skew               0.345   0.010     0.327     0.365      0.000    0.000   \n",
+       "mean_skew             26.346   0.012    26.322    26.368      0.000    0.000   \n",
+       "delta                  0.945   0.014     0.919     0.968      0.000    0.000   \n",
+       "mue_z                  0.754   0.011     0.733     0.772      0.000    0.000   \n",
+       "sigma_z                0.657   0.012     0.635     0.680      0.000    0.000   \n",
+       "mode_offset            0.480   0.023     0.439     0.523      0.000    0.000   \n",
+       "mode_skew             26.201   0.015    26.171    26.228      0.000    0.000   \n",
+       "height              1880.508  38.756  1811.139  1958.103      0.404    0.286   \n",
+       "sn                    18.237   1.366    15.606    20.692      0.014    0.010   \n",
+       "\n",
+       "                    ess_bulk  ess_tail  r_hat  \n",
+       "baseline_intercept    8511.0    6241.0    1.0  \n",
+       "baseline_slope        6862.0    5687.0    1.0  \n",
+       "noise_log__           9353.0    5750.0    1.0  \n",
+       "mean                  2928.0    3445.0    1.0  \n",
+       "std_log__             2733.0    3353.0    1.0  \n",
+       "alpha                 3023.0    3830.0    1.0  \n",
+       "area_log__            5421.0    5390.0    1.0  \n",
+       "noise                 9353.0    5750.0    1.0  \n",
+       "std                   2733.0    3353.0    1.0  \n",
+       "area                  5421.0    5390.0    1.0  \n",
+       "std_skew              3575.0    4874.0    1.0  \n",
+       "mean_skew             4179.0    5737.0    1.0  \n",
+       "delta                 3023.0    3830.0    1.0  \n",
+       "mue_z                 3023.0    3830.0    1.0  \n",
+       "sigma_z               3023.0    3830.0    1.0  \n",
+       "mode_offset           3023.0    3830.0    1.0  \n",
+       "mode_skew             3411.0    3972.0    1.0  \n",
+       "height                9193.0    7168.0    1.0  \n",
+       "sn                    9724.0    6250.0    1.0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pmodel = models.define_model_skew(\n",
+    "    time=timeseries[0],\n",
+    "    intensity=timeseries[1]\n",
+    ")\n",
+    "idata = pl.sampling(pmodel, tune=6000, draws=2000)\n",
+    "idata = pl.posterior_predictive_sampling(pmodel, idata)\n",
+    "summary = az.summary(idata, var_names=[\"~y\", \"~baseline\", \"offset\"])\n",
+    "summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\joche\\AppData\\Local\\Temp\\ipykernel_5868\\1943866237.py:9: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs = az.plot_ppc(\n",
+    "    idata,\n",
+    "    # data_pairs={\"L\":\"L\"},\n",
+    "    var_names=[\"L\"],\n",
+    "    kind=\"cumulative\",\n",
+    "    # backend_kwargs=dict(sharey=True),\n",
+    ")\n",
+    "fig = plt.gcf()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plots.plot_posterior_predictive(\n",
+    "    identifier=\"peak_fit_skew_normal\",\n",
+    "    time=timeseries[0],\n",
+    "    intensity=timeseries[1],\n",
+    "    path=path_result,\n",
+    "    idata=idata,\n",
+    "    discarded=False,\n",
+    ")\n",
+    "\n",
+    "plots.plot_posterior(\n",
+    "    identifier=\"peak_fit_skew_normal\",\n",
+    "    time=timeseries[0],\n",
+    "    intensity=timeseries[1],\n",
+    "    path=path_result,\n",
+    "    idata=idata,\n",
+    "    discarded=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exemplary result with a double peak"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_result = Path(\"./paper raw data\")\n",
+    "path_d = Path(\"./paper raw data/exemplary results raw data/A2t2R1Part1_132_85.9_86.1.npy\")\n",
+    "timeseries_d = np.load(path_d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x241411204c0>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(timeseries_d[0], timeseries_d[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\joche\\miniconda3\\envs\\pp_env\\lib\\site-packages\\pymc\\data.py:287: FutureWarning: ConstantData is deprecated. All Data variables are now mutable. Use Data instead.\n",
+      "  warnings.warn(\n",
+      "Sampling: [L, baseline_intercept, baseline_slope, height, meanmean, noise, separation, std]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    :root {\n",
+       "        --column-width-1: 40%; /* Progress column width */\n",
+       "        --column-width-2: 15%; /* Chain column width */\n",
+       "        --column-width-3: 15%; /* Divergences column width */\n",
+       "        --column-width-4: 15%; /* Step Size column width */\n",
+       "        --column-width-5: 15%; /* Gradients/Draw column width */\n",
+       "    }\n",
+       "\n",
+       "    .nutpie {\n",
+       "        max-width: 800px;\n",
+       "        margin: 10px auto;\n",
+       "        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n",
+       "        //color: #333;\n",
+       "        //background-color: #fff;\n",
+       "        padding: 10px;\n",
+       "        box-shadow: 0 4px 6px rgba(0,0,0,0.1);\n",
+       "        border-radius: 8px;\n",
+       "        font-size: 14px; /* Smaller font size for a more compact look */\n",
+       "    }\n",
+       "    .nutpie table {\n",
+       "        width: 100%;\n",
+       "        border-collapse: collapse; /* Remove any extra space between borders */\n",
+       "    }\n",
+       "    .nutpie th, .nutpie td {\n",
+       "        padding: 8px 10px; /* Reduce padding to make table more compact */\n",
+       "        text-align: left;\n",
+       "        border-bottom: 1px solid #888;\n",
+       "    }\n",
+       "    .nutpie th {\n",
+       "        //background-color: #f0f0f0;\n",
+       "    }\n",
+       "\n",
+       "    .nutpie th:nth-child(1) { width: var(--column-width-1); }\n",
+       "    .nutpie th:nth-child(2) { width: var(--column-width-2); }\n",
+       "    .nutpie th:nth-child(3) { width: var(--column-width-3); }\n",
+       "    .nutpie th:nth-child(4) { width: var(--column-width-4); }\n",
+       "    .nutpie th:nth-child(5) { width: var(--column-width-5); }\n",
+       "\n",
+       "    .nutpie progress {\n",
+       "        width: 100%;\n",
+       "        height: 15px; /* Smaller progress bars */\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    progress::-webkit-progress-bar {\n",
+       "        background-color: #eee;\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    progress::-webkit-progress-value {\n",
+       "        background-color: #5cb85c;\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    progress::-moz-progress-bar {\n",
+       "        background-color: #5cb85c;\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    .nutpie .progress-cell {\n",
+       "        width: 100%;\n",
+       "    }\n",
+       "\n",
+       "    .nutpie p strong { font-size: 16px; font-weight: bold; }\n",
+       "\n",
+       "    @media (prefers-color-scheme: dark) {\n",
+       "        .nutpie {\n",
+       "            //color: #ddd;\n",
+       "            //background-color: #1e1e1e;\n",
+       "            box-shadow: 0 4px 6px rgba(0,0,0,0.2);\n",
+       "        }\n",
+       "        .nutpie table, .nutpie th, .nutpie td {\n",
+       "            border-color: #555;\n",
+       "            color: #ccc;\n",
+       "        }\n",
+       "        .nutpie th {\n",
+       "            background-color: #2a2a2a;\n",
+       "        }\n",
+       "        .nutpie progress::-webkit-progress-bar {\n",
+       "            background-color: #444;\n",
+       "        }\n",
+       "        .nutpie progress::-webkit-progress-value {\n",
+       "            background-color: #3178c6;\n",
+       "        }\n",
+       "        .nutpie progress::-moz-progress-bar {\n",
+       "            background-color: #3178c6;\n",
+       "        }\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div class=\"nutpie\">\n",
+       "    <p><strong>Sampler Progress</strong></p>\n",
+       "    <p>Total Chains: <span id=\"total-chains\">4</span></p>\n",
+       "    <p>Active Chains: <span id=\"active-chains\">0</span></p>\n",
+       "    <p>\n",
+       "        Finished Chains:\n",
+       "        <span id=\"active-chains\">4</span>\n",
+       "    </p>\n",
+       "    <p>Sampling for now</p>\n",
+       "    <p>\n",
+       "        Estimated Time to Completion:\n",
+       "        <span id=\"eta\">now</span>\n",
+       "    </p>\n",
+       "\n",
+       "    <progress\n",
+       "        id=\"total-progress-bar\"\n",
+       "        max=\"56000\"\n",
+       "        value=\"56000\">\n",
+       "    </progress>\n",
+       "    <table>\n",
+       "        <thead>\n",
+       "            <tr>\n",
+       "                <th>Progress</th>\n",
+       "                <th>Draws</th>\n",
+       "                <th>Divergences</th>\n",
+       "                <th>Step Size</th>\n",
+       "                <th>Gradients/Draw</th>\n",
+       "            </tr>\n",
+       "        </thead>\n",
+       "        <tbody id=\"chain-details\">\n",
+       "            \n",
+       "                <tr>\n",
+       "                    <td class=\"progress-cell\">\n",
+       "                        <progress\n",
+       "                            max=\"14000\"\n",
+       "                            value=\"14000\">\n",
+       "                        </progress>\n",
+       "                    </td>\n",
+       "                    <td>14000</td>\n",
+       "                    <td>0</td>\n",
+       "                    <td>0.55</td>\n",
+       "                    <td>15</td>\n",
+       "                </tr>\n",
+       "            \n",
+       "                <tr>\n",
+       "                    <td class=\"progress-cell\">\n",
+       "                        <progress\n",
+       "                            max=\"14000\"\n",
+       "                            value=\"14000\">\n",
+       "                        </progress>\n",
+       "                    </td>\n",
+       "                    <td>14000</td>\n",
+       "                    <td>0</td>\n",
+       "                    <td>0.54</td>\n",
+       "                    <td>15</td>\n",
+       "                </tr>\n",
+       "            \n",
+       "                <tr>\n",
+       "                    <td class=\"progress-cell\">\n",
+       "                        <progress\n",
+       "                            max=\"14000\"\n",
+       "                            value=\"14000\">\n",
+       "                        </progress>\n",
+       "                    </td>\n",
+       "                    <td>14000</td>\n",
+       "                    <td>0</td>\n",
+       "                    <td>0.52</td>\n",
+       "                    <td>15</td>\n",
+       "                </tr>\n",
+       "            \n",
+       "                <tr>\n",
+       "                    <td class=\"progress-cell\">\n",
+       "                        <progress\n",
+       "                            max=\"14000\"\n",
+       "                            value=\"14000\">\n",
+       "                        </progress>\n",
+       "                    </td>\n",
+       "                    <td>14000</td>\n",
+       "                    <td>0</td>\n",
+       "                    <td>0.49</td>\n",
+       "                    <td>15</td>\n",
+       "                </tr>\n",
+       "            \n",
+       "            </tr>\n",
+       "        </tbody>\n",
+       "    </table>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "<nutpie.sample._BackgroundSampler at 0x2414673c160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [L]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7adad524a584934b7a6ff499da3717c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>baseline_intercept</th>\n",
+       "      <td>1116.578</td>\n",
+       "      <td>38.645</td>\n",
+       "      <td>1041.890</td>\n",
+       "      <td>1188.558</td>\n",
+       "      <td>0.738</td>\n",
+       "      <td>0.523</td>\n",
+       "      <td>2743.0</td>\n",
+       "      <td>3418.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>baseline_slope</th>\n",
+       "      <td>-21.756</td>\n",
+       "      <td>3.070</td>\n",
+       "      <td>-27.447</td>\n",
+       "      <td>-15.806</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>2712.0</td>\n",
+       "      <td>3492.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>noise_log__</th>\n",
+       "      <td>4.773</td>\n",
+       "      <td>0.067</td>\n",
+       "      <td>4.646</td>\n",
+       "      <td>4.894</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>8809.0</td>\n",
+       "      <td>5963.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std_log__[0]</th>\n",
+       "      <td>-1.813</td>\n",
+       "      <td>0.108</td>\n",
+       "      <td>-2.012</td>\n",
+       "      <td>-1.611</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>7129.0</td>\n",
+       "      <td>5764.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std_log__[1]</th>\n",
+       "      <td>-1.879</td>\n",
+       "      <td>0.044</td>\n",
+       "      <td>-1.963</td>\n",
+       "      <td>-1.798</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>7303.0</td>\n",
+       "      <td>6430.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>height_log__[0]</th>\n",
+       "      <td>6.650</td>\n",
+       "      <td>0.085</td>\n",
+       "      <td>6.493</td>\n",
+       "      <td>6.812</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>7345.0</td>\n",
+       "      <td>5516.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>height_log__[1]</th>\n",
+       "      <td>7.474</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>7.405</td>\n",
+       "      <td>7.544</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>7762.0</td>\n",
+       "      <td>6293.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>meanmean</th>\n",
+       "      <td>12.082</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>12.066</td>\n",
+       "      <td>12.098</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>6577.0</td>\n",
+       "      <td>5571.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>separation_log__</th>\n",
+       "      <td>-0.354</td>\n",
+       "      <td>0.023</td>\n",
+       "      <td>-0.397</td>\n",
+       "      <td>-0.312</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>6911.0</td>\n",
+       "      <td>5108.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>noise</th>\n",
+       "      <td>118.513</td>\n",
+       "      <td>7.938</td>\n",
+       "      <td>104.088</td>\n",
+       "      <td>133.441</td>\n",
+       "      <td>0.084</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>8809.0</td>\n",
+       "      <td>5963.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std[0]</th>\n",
+       "      <td>0.164</td>\n",
+       "      <td>0.018</td>\n",
+       "      <td>0.133</td>\n",
+       "      <td>0.199</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>7129.0</td>\n",
+       "      <td>5764.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std[1]</th>\n",
+       "      <td>0.153</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.140</td>\n",
+       "      <td>0.165</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>7303.0</td>\n",
+       "      <td>6430.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>height[0]</th>\n",
+       "      <td>775.188</td>\n",
+       "      <td>65.076</td>\n",
+       "      <td>656.037</td>\n",
+       "      <td>902.696</td>\n",
+       "      <td>0.758</td>\n",
+       "      <td>0.536</td>\n",
+       "      <td>7345.0</td>\n",
+       "      <td>5516.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>height[1]</th>\n",
+       "      <td>1762.620</td>\n",
+       "      <td>65.170</td>\n",
+       "      <td>1634.624</td>\n",
+       "      <td>1878.031</td>\n",
+       "      <td>0.740</td>\n",
+       "      <td>0.523</td>\n",
+       "      <td>7762.0</td>\n",
+       "      <td>6293.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>separation</th>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.016</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>0.732</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>6911.0</td>\n",
+       "      <td>5108.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>area[0]</th>\n",
+       "      <td>317.099</td>\n",
+       "      <td>28.666</td>\n",
+       "      <td>261.556</td>\n",
+       "      <td>368.253</td>\n",
+       "      <td>0.280</td>\n",
+       "      <td>0.199</td>\n",
+       "      <td>10562.0</td>\n",
+       "      <td>6227.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>area[1]</th>\n",
+       "      <td>674.799</td>\n",
+       "      <td>26.942</td>\n",
+       "      <td>623.811</td>\n",
+       "      <td>724.368</td>\n",
+       "      <td>0.242</td>\n",
+       "      <td>0.171</td>\n",
+       "      <td>12379.0</td>\n",
+       "      <td>6343.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sn[0]</th>\n",
+       "      <td>6.571</td>\n",
+       "      <td>0.707</td>\n",
+       "      <td>5.235</td>\n",
+       "      <td>7.853</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>6926.0</td>\n",
+       "      <td>5009.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sn[1]</th>\n",
+       "      <td>14.939</td>\n",
+       "      <td>1.135</td>\n",
+       "      <td>12.775</td>\n",
+       "      <td>17.015</td>\n",
+       "      <td>0.012</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>8383.0</td>\n",
+       "      <td>6378.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>offset[0]</th>\n",
+       "      <td>-0.351</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>-0.366</td>\n",
+       "      <td>-0.336</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>6911.0</td>\n",
+       "      <td>5108.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>offset[1]</th>\n",
+       "      <td>0.351</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.336</td>\n",
+       "      <td>0.366</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>6911.0</td>\n",
+       "      <td>5108.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean[0]</th>\n",
+       "      <td>11.731</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>11.704</td>\n",
+       "      <td>11.760</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>6241.0</td>\n",
+       "      <td>5139.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean[1]</th>\n",
+       "      <td>12.433</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>12.421</td>\n",
+       "      <td>12.445</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>11078.0</td>\n",
+       "      <td>6184.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        mean      sd    hdi_3%   hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "baseline_intercept  1116.578  38.645  1041.890  1188.558      0.738    0.523   \n",
+       "baseline_slope       -21.756   3.070   -27.447   -15.806      0.059    0.042   \n",
+       "noise_log__            4.773   0.067     4.646     4.894      0.001    0.001   \n",
+       "std_log__[0]          -1.813   0.108    -2.012    -1.611      0.001    0.001   \n",
+       "std_log__[1]          -1.879   0.044    -1.963    -1.798      0.001    0.000   \n",
+       "height_log__[0]        6.650   0.085     6.493     6.812      0.001    0.001   \n",
+       "height_log__[1]        7.474   0.037     7.405     7.544      0.000    0.000   \n",
+       "meanmean              12.082   0.008    12.066    12.098      0.000    0.000   \n",
+       "separation_log__      -0.354   0.023    -0.397    -0.312      0.000    0.000   \n",
+       "noise                118.513   7.938   104.088   133.441      0.084    0.059   \n",
+       "std[0]                 0.164   0.018     0.133     0.199      0.000    0.000   \n",
+       "std[1]                 0.153   0.007     0.140     0.165      0.000    0.000   \n",
+       "height[0]            775.188  65.076   656.037   902.696      0.758    0.536   \n",
+       "height[1]           1762.620  65.170  1634.624  1878.031      0.740    0.523   \n",
+       "separation             0.702   0.016     0.672     0.732      0.000    0.000   \n",
+       "area[0]              317.099  28.666   261.556   368.253      0.280    0.199   \n",
+       "area[1]              674.799  26.942   623.811   724.368      0.242    0.171   \n",
+       "sn[0]                  6.571   0.707     5.235     7.853      0.009    0.006   \n",
+       "sn[1]                 14.939   1.135    12.775    17.015      0.012    0.009   \n",
+       "offset[0]             -0.351   0.008    -0.366    -0.336      0.000    0.000   \n",
+       "offset[1]              0.351   0.008     0.336     0.366      0.000    0.000   \n",
+       "mean[0]               11.731   0.015    11.704    11.760      0.000    0.000   \n",
+       "mean[1]               12.433   0.006    12.421    12.445      0.000    0.000   \n",
+       "\n",
+       "                    ess_bulk  ess_tail  r_hat  \n",
+       "baseline_intercept    2743.0    3418.0    1.0  \n",
+       "baseline_slope        2712.0    3492.0    1.0  \n",
+       "noise_log__           8809.0    5963.0    1.0  \n",
+       "std_log__[0]          7129.0    5764.0    1.0  \n",
+       "std_log__[1]          7303.0    6430.0    1.0  \n",
+       "height_log__[0]       7345.0    5516.0    1.0  \n",
+       "height_log__[1]       7762.0    6293.0    1.0  \n",
+       "meanmean              6577.0    5571.0    1.0  \n",
+       "separation_log__      6911.0    5108.0    1.0  \n",
+       "noise                 8809.0    5963.0    1.0  \n",
+       "std[0]                7129.0    5764.0    1.0  \n",
+       "std[1]                7303.0    6430.0    1.0  \n",
+       "height[0]             7345.0    5516.0    1.0  \n",
+       "height[1]             7762.0    6293.0    1.0  \n",
+       "separation            6911.0    5108.0    1.0  \n",
+       "area[0]              10562.0    6227.0    1.0  \n",
+       "area[1]              12379.0    6343.0    1.0  \n",
+       "sn[0]                 6926.0    5009.0    1.0  \n",
+       "sn[1]                 8383.0    6378.0    1.0  \n",
+       "offset[0]             6911.0    5108.0    1.0  \n",
+       "offset[1]             6911.0    5108.0    1.0  \n",
+       "mean[0]               6241.0    5139.0    1.0  \n",
+       "mean[1]              11078.0    6184.0    1.0  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pmodel_d = models.define_model_double_normal(\n",
+    "    time=timeseries_d[0],\n",
+    "    intensity=timeseries_d[1]\n",
+    ")\n",
+    "idata_d = pl.sampling(pmodel_d, tune=12000, draws=2000)\n",
+    "idata_d = pl.posterior_predictive_sampling(pmodel_d, idata_d)\n",
+    "summary_d = az.summary(idata_d, var_names=[\"~y\", \"~baseline\", \"offset\"])\n",
+    "summary_d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\joche\\AppData\\Local\\Temp\\ipykernel_5868\\1105783876.py:9: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  plt.tight_layout()\n",
+      "c:\\Users\\joche\\miniconda3\\envs\\pp_env\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  func(*args, **kwargs)\n",
+      "c:\\Users\\joche\\miniconda3\\envs\\pp_env\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs = az.plot_ppc(\n",
+    "    idata_d,\n",
+    "    # data_pairs={\"L\":\"L\"},\n",
+    "    var_names=[\"L\"],\n",
+    "    kind=\"cumulative\",\n",
+    "    # backend_kwargs=dict(sharey=True),\n",
+    ")\n",
+    "fig = plt.gcf()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plots.plot_posterior_predictive(\n",
+    "    identifier=\"peak_fit_double_normal\",\n",
+    "    time=timeseries_d[0],\n",
+    "    intensity=timeseries_d[1],\n",
+    "    path=path_result,\n",
+    "    idata=idata_d,\n",
+    "    discarded=False,\n",
+    ")\n",
+    "\n",
+    "plots.plot_posterior(\n",
+    "    identifier=\"peak_fit_double_normal\",\n",
+    "    time=timeseries_d[0],\n",
+    "    intensity=timeseries_d[1],\n",
+    "    path=path_result,\n",
+    "    idata=idata_d,\n",
+    "    discarded=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: 2024-10-13T15:50:13.319852+02:00\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -idu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pp_env",
+   "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": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/notebooks/Create_validation_plot_from_raw_data.ipynb b/docs/source/notebooks/Create_validation_plot_from_raw_data.ipynb
index 5fc93d0..516b397 100644
--- a/docs/source/notebooks/Create_validation_plot_from_raw_data.ipynb
+++ b/docs/source/notebooks/Create_validation_plot_from_raw_data.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,11 +36,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open('test1_all_data.txt', 'r') as file:\n",
+    "with open(Path(\"./paper raw data/test1_all_data.txt\"), \"r\") as file:\n",
     "    all_data = json.loads(file.read())"
    ]
   },
@@ -53,11 +53,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pandas.read_excel(\"test2_summary.xlsx\")\n",
+    "df = pandas.read_excel(Path(\"./paper raw data/test2_summary.xlsx\"))\n",
     "df_normal = df[(df.loc[:, \"model\"] == \"normal\") & (df.loc[:, \"Unnamed: 0\"].isin([\"area\", \"height\"]))]\n",
     "df_normal.reset_index(inplace=True)\n",
     "df_skew = df[(df.loc[:, \"model\"] == \"skew_normal\") & (df.loc[:, \"Unnamed: 0\"].isin([\"area\", \"height\"]))]\n",
@@ -83,11 +83,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_comparison_total = pandas.read_excel(\"test3_df_comparison.xlsx\")\n",
+    "df_comparison_total = pandas.read_excel(Path(\"./paper raw data/test3_df_comparison.xlsx\"))\n",
     "df_comparison_single = df_comparison_total[~df_comparison_total[\"PP experiment\"].isin([23, 24])]\n",
     "df_comparison_double = df_comparison_total[df_comparison_total[\"PP experiment\"].isin([23, 24])]"
    ]
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -224,14 +224,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 2024-10-11T17:56:34.996952+02:00\n",
+      "Last updated: 2024-10-13T15:10:41.315414+02:00\n",
       "\n"
      ]
     }
diff --git a/docs/source/notebooks/Processing_test_1_raw_data.ipynb b/docs/source/notebooks/Processing_test_1_raw_data.ipynb
index 5518ff1..2810339 100644
--- a/docs/source/notebooks/Processing_test_1_raw_data.ipynb
+++ b/docs/source/notebooks/Processing_test_1_raw_data.ipynb
@@ -20,17 +20,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "raw_data_files = [\n",
-    "    \"Normal model_normal data_noise level 0.6.xlsx\",\n",
-    "    \"Normal model_normal data_noise level 1.2.xlsx\",\n",
-    "    \"Normal model_skew normal data_noise level 0.6.xlsx\",\n",
-    "    \"Skew normal model_skew normal data_noise level 0.6.xlsx\",\n",
-    "    \"Skew normal model_skew normal data_noise level 1.2.xlsx\",\n",
-    "    \"Skew normal model_normal data_noise level 0.6.xlsx\",\n",
+    "    Path(\"./paper raw data/synthetic data sets for validation/Normal model_normal data_noise level 0.6.xlsx\"),\n",
+    "    Path(\"./paper raw data/synthetic data sets for validation/Normal model_normal data_noise level 1.2.xlsx\"),\n",
+    "    Path(\"./paper raw data/synthetic data sets for validation/Normal model_skew normal data_noise level 0.6.xlsx\"),\n",
+    "    Path(\"./paper raw data/synthetic data sets for validation/Skew normal model_skew normal data_noise level 0.6.xlsx\"),\n",
+    "    Path(\"./paper raw data/synthetic data sets for validation/Skew normal model_skew normal data_noise level 1.2.xlsx\"),\n",
+    "    Path(\"./paper raw data/synthetic data sets for validation/Skew normal model_normal data_noise level 0.6.xlsx\"),\n",
     "]\n",
     "\n",
     "parameters = [\"mean\", \"std\", \"area\", \"height\", \"alpha\", \"baseline_intercept\", \"baseline_slope\"]"
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -159,9 +159,9 @@
       "model: normal, data: normal, noise level: 0.6\n",
       "model: normal, data: normal, noise level: 1.2\n",
       "model: normal, data: skew normal, noise level: 0.6\n",
-      "model: skew normal, data: normal, noise level: 0.6\n",
       "model: skew normal, data: skew normal, noise level: 0.6\n",
-      "model: skew normal, data: skew normal, noise level: 1.2\n"
+      "model: skew normal, data: skew normal, noise level: 1.2\n",
+      "model: skew normal, data: normal, noise level: 0.6\n"
      ]
     }
    ],
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -221,14 +221,6 @@
        "   0.1425229338854569,\n",
        "   0.029251994462966387,\n",
        "   0.02178598822049324]],\n",
-       " 'normal data, skew normal model': [[0.9993873333333333,\n",
-       "   1.145324094260921,\n",
-       "   1.0038603930164334,\n",
-       "   1.0021702322498285],\n",
-       "  [0.025492314214288193,\n",
-       "   0.06460165579288266,\n",
-       "   0.0295645094605588,\n",
-       "   0.022277250178015084]],\n",
        " 'skew normal data, skew normal model': [[1.0003276666666665,\n",
        "   1.0178059537564914,\n",
        "   0.9995769654521169,\n",
@@ -244,10 +236,18 @@
        "  [0.029588612507556917,\n",
        "   0.13828870506270582,\n",
        "   0.050852728197426554,\n",
-       "   0.03782158437972263]]}"
+       "   0.03782158437972263]],\n",
+       " 'normal data, skew normal model': [[0.9993873333333333,\n",
+       "   1.145324094260921,\n",
+       "   1.0038603930164334,\n",
+       "   1.0021702322498285],\n",
+       "  [0.025492314214288193,\n",
+       "   0.06460165579288266,\n",
+       "   0.0295645094605588,\n",
+       "   0.022277250178015084]]}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -284,7 +284,7 @@
        "dict_keys(['normal data, normal model', 'normal data (higher noise), normal model', 'skew normal data, normal model', 'skew normal data, skew normal model', 'skew normal data (higher noise), skew normal model', 'normal data, skew normal model'])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -297,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -309,14 +309,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 2024-10-11T18:34:57.629742+02:00\n",
+      "Last updated: 2024-10-13T15:03:43.532805+02:00\n",
       "\n"
      ]
     }
diff --git a/docs/source/notebooks/paper raw data/raw data set for validation with MultiQuant/Template.xlsx b/docs/source/notebooks/paper raw data/raw data set for validation with MultiQuant/Template.xlsx
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diff --git a/docs/source/notebooks/Skew normal model_normal data_noise level 0.6.xlsx b/docs/source/notebooks/paper raw data/synthetic data sets for validation/Skew normal model_normal data_noise level 0.6.xlsx
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diff --git a/docs/source/notebooks/Skew normal model_skew normal data_noise level 1.2.xlsx b/docs/source/notebooks/paper raw data/synthetic data sets for validation/Skew normal model_skew normal data_noise level 1.2.xlsx
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similarity index 100%
rename from docs/source/notebooks/test1_all_data.txt
rename to docs/source/notebooks/paper raw data/test1_all_data.txt
diff --git a/docs/source/notebooks/test2_summary.xlsx b/docs/source/notebooks/paper raw data/test2_summary.xlsx
similarity index 100%
rename from docs/source/notebooks/test2_summary.xlsx
rename to docs/source/notebooks/paper raw data/test2_summary.xlsx
diff --git a/docs/source/notebooks/test3_df_comparison.xlsx b/docs/source/notebooks/paper raw data/test3_df_comparison.xlsx
similarity index 100%
rename from docs/source/notebooks/test3_df_comparison.xlsx
rename to docs/source/notebooks/paper raw data/test3_df_comparison.xlsx

From 54bd0cba5f491b7796201a4b182e348e47951a4b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jochen=20Nie=C3=9Fer?= <j.niesser@fz-juelich.de>
Date: Sun, 13 Oct 2024 16:08:56 +0200
Subject: [PATCH 2/6] Fix heading levels in raw data notebooks

---
 .../Create_results_in_figure_2.ipynb          |  8 ++------
 ...Create_validation_plot_from_raw_data.ipynb | 13 ++++--------
 .../Processing_test_1_raw_data.ipynb          | 20 +++++++------------
 3 files changed, 13 insertions(+), 28 deletions(-)

diff --git a/docs/source/notebooks/Create_results_in_figure_2.ipynb b/docs/source/notebooks/Create_results_in_figure_2.ipynb
index ea6af20..8d3292f 100644
--- a/docs/source/notebooks/Create_results_in_figure_2.ipynb
+++ b/docs/source/notebooks/Create_results_in_figure_2.ipynb
@@ -13,10 +13,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import pandas\n",
     "import numpy as np\n",
     "import arviz as az\n",
     "from pathlib import Path\n",
@@ -28,7 +24,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Exemplary result with a single peak"
+    "## Exemplary result with a single peak"
    ]
   },
   {
@@ -704,7 +700,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Exemplary result with a double peak"
+    "## Exemplary result with a double peak"
    ]
   },
   {
diff --git a/docs/source/notebooks/Create_validation_plot_from_raw_data.ipynb b/docs/source/notebooks/Create_validation_plot_from_raw_data.ipynb
index 516b397..0ec543b 100644
--- a/docs/source/notebooks/Create_validation_plot_from_raw_data.ipynb
+++ b/docs/source/notebooks/Create_validation_plot_from_raw_data.ipynb
@@ -15,14 +15,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import arviz as az\n",
     "import json\n",
     "import numpy as np\n",
     "import pandas\n",
-    "import pymc as pm\n",
     "from matplotlib import pyplot as plt\n",
     "from pathlib import Path"
    ]
@@ -31,7 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 1) Preparation of evaluation of synthetic data (test 1)"
+    "# Preparation of evaluation of synthetic data (test 1)"
    ]
   },
   {
@@ -48,7 +43,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 2) Prepartion of border-line cases normal vs. skew normal (test 2)"
+    "# Prepartion of border-line cases normal vs. skew normal (test 2)"
    ]
   },
   {
@@ -78,7 +73,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 3) Prepartion of comparison to MultiQuant (test 3)"
+    "# Prepartion of comparison to MultiQuant (test 3)"
    ]
   },
   {
@@ -96,7 +91,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 4) Plotting in one graph (for PeakPerformance paper)"
+    "# Plotting in one graph (for PeakPerformance paper)"
    ]
   },
   {
diff --git a/docs/source/notebooks/Processing_test_1_raw_data.ipynb b/docs/source/notebooks/Processing_test_1_raw_data.ipynb
index 2810339..e29fcba 100644
--- a/docs/source/notebooks/Processing_test_1_raw_data.ipynb
+++ b/docs/source/notebooks/Processing_test_1_raw_data.ipynb
@@ -1,19 +1,20 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Process raw data from synthetic tests"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import arviz as az\n",
-    "import json\n",
     "import numpy as np\n",
     "import pandas\n",
-    "import pymc as pm\n",
     "from matplotlib import pyplot as plt\n",
     "from pathlib import Path"
    ]
@@ -36,13 +37,6 @@
     "parameters = [\"mean\", \"std\", \"area\", \"height\", \"alpha\", \"baseline_intercept\", \"baseline_slope\"]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Prepare data in df_results"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 5,

From 395b89c922a967676ac20d7cabafc8b88e9aca46 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jochen=20Nie=C3=9Fer?= <j.niesser@fz-juelich.de>
Date: Sun, 13 Oct 2024 16:15:33 +0200
Subject: [PATCH 3/6] add environment.yml

---
 environment.yml | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 environment.yml

diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000..8debee2
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,16 @@
+name: pp_env
+channels:
+  - conda-forge
+dependencies:
+  - arviz
+  - jupyter
+  - matplotlib
+  - numba
+  - numpy
+  - nutpie
+  - openpyxl
+  - pip
+  - pymc
+  - python=3.11
+  - pip:
+    - peak-performance

From 7b8861da72b28df05fa8ff6b62c9da6e1eb5046d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jochen=20Nie=C3=9Fer?= <j.niesser@fz-juelich.de>
Date: Sun, 13 Oct 2024 16:21:33 +0200
Subject: [PATCH 4/6] update installation.md

---
 docs/source/markdown/Installation.md | 17 ++---------------
 1 file changed, 2 insertions(+), 15 deletions(-)

diff --git a/docs/source/markdown/Installation.md b/docs/source/markdown/Installation.md
index 0477d10..162bf72 100644
--- a/docs/source/markdown/Installation.md
+++ b/docs/source/markdown/Installation.md
@@ -11,20 +11,7 @@ If you have already installed Miniconda, you can install Mamba on top of it but
 The newest conda version should also work, just replace `mamba` with `conda` in step 2.)
 ```
 
-2. Create a new Python environment (replace "name_of_environment" with your desired name) in the command line via
+2. Create a new Python environment in the command line using the provided environment.yml file in the repo. You have to download environment.yml first and navigate to its location within the command line interface. Then execute the following command:
 ```
-mamba create -c conda-forge -n name_of_environment pymc nutpie arviz jupyter matplotlib openpyxl "python=3.10"
-```
-3. Install PeakPerformance:
-- __Recommended__: Clone the PeakPerformance repository, then open the command line, navigate to your local clone, activate the Python environment created in the previous step, and install PeakPerformance via
-```
-pip install -e .
-```
-- __Alternative a__: Activate the Python environment created in the previous step and install PeakPerformance via PyPI using
-```
-pip install peak-performance
-```
-- __Alternative b__: Download the latest Python wheel, then open the command line, navigate to the directory containing the wheel, activate the Python environment created above, and install PeakPerformance via
-```
-pip install name_of_wheel.whl
+mamba env create -f environment.yml
 ```

From 0d08bd7475e452a72cba81014b36201bd6d43677 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jochen=20Nie=C3=9Fer?= <j.niesser@fz-juelich.de>
Date: Sun, 13 Oct 2024 16:29:37 +0200
Subject: [PATCH 5/6] add raw data and update .gitignore

---
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diff --git a/.gitignore b/.gitignore
index 03a0001..38096fc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -605,3 +605,4 @@ idata_summary_*.xlsx
 idata_*
 *Sandbox*.ipynb
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From 5617d6c71b0993cc92ae77eee6229fae984b4d19 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jochen=20Nie=C3=9Fer?=
 <104903134+Y0dler@users.noreply.github.com>
Date: Sun, 13 Oct 2024 16:36:24 +0200
Subject: [PATCH 6/6] Update docs/source/markdown/Installation.md

Co-authored-by: Michael Osthege <m.osthege@fz-juelich.de>
---
 docs/source/markdown/Installation.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/source/markdown/Installation.md b/docs/source/markdown/Installation.md
index 162bf72..4c928fb 100644
--- a/docs/source/markdown/Installation.md
+++ b/docs/source/markdown/Installation.md
@@ -11,7 +11,8 @@ If you have already installed Miniconda, you can install Mamba on top of it but
 The newest conda version should also work, just replace `mamba` with `conda` in step 2.)
 ```
 
-2. Create a new Python environment in the command line using the provided environment.yml file in the repo. You have to download environment.yml first and navigate to its location within the command line interface. Then execute the following command:
+2. Create a new Python environment in the command line using the provided [`environment.yml`](https://github.com/JuBiotech/peak-performance/blob/main/environment.yml) file from the repo.
+   Download `environment.yml` first, then navigate to its location on the command line interface and run the following command:
 ```
 mamba env create -f environment.yml
 ```