diff --git a/notebooks/notebook-template.ipynb b/notebooks/notebook-template.ipynb
deleted file mode 100644
index dad9f26..0000000
--- a/notebooks/notebook-template.ipynb
+++ /dev/null
@@ -1,358 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start here! If you can directly link to an image relevant to your notebook, such as [canonical logos](https://github.com/numpy/numpy/blob/main/doc/source/_static/numpylogo.svg), do so here at the top of your notebook. You can do this with Markdown syntax,\n",
-    "\n",
-    "> `![<image title>](http://link.com/to/image.png \"image alt text\")`\n",
-    "\n",
-    "or edit this cell to see raw HTML `img` demonstration. This is preferred if you need to shrink your embedded image. **Either way be sure to include `alt` text for any embedded images to make your content more accessible.**\n",
-    "\n",
-    "<img src=\"images/ProjectPythia_Logo_Final-01-Blue.svg\" width=250 alt=\"Project Pythia Logo\"></img>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Project Pythia Notebook Template\n",
-    "\n",
-    "Next, title your notebook appropriately with a top-level Markdown header, `#`. Do not use this level header anywhere else in the notebook. Our book build process will use this title in the navbar, table of contents, etc. Keep it short, keep it descriptive. Follow this with a `---` cell to visually distinguish the transition to the prerequisites section."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Overview\n",
-    "If you have an introductory paragraph, lead with it here! Keep it short and tied to your material, then be sure to continue into the required list of topics below,\n",
-    "\n",
-    "1. This is a numbered list of the specific topics\n",
-    "1. These should map approximately to your main sections of content\n",
-    "1. Or each second-level, `##`, header in your notebook\n",
-    "1. Keep the size and scope of your notebook in check\n",
-    "1. And be sure to let the reader know up front the important concepts they'll be leaving with"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "This section was inspired by [this template](https://github.com/alan-turing-institute/the-turing-way/blob/master/book/templates/chapter-template/chapter-landing-page.md) of the wonderful [The Turing Way](https://the-turing-way.netlify.app) Jupyter Book.\n",
-    "\n",
-    "Following your overview, tell your reader what concepts, packages, or other background information they'll **need** before learning your material. Tie this explicitly with links to other pages here in Foundations or to relevant external resources. Remove this body text, then populate the Markdown table, denoted in this cell with `|` vertical brackets, below, and fill out the information following. In this table, lay out prerequisite concepts by explicitly linking to other Foundations material or external resources, or describe generally helpful concepts.\n",
-    "\n",
-    "Label the importance of each concept explicitly as **helpful/necessary**.\n",
-    "\n",
-    "| Concepts | Importance | Notes |\n",
-    "| --- | --- | --- |\n",
-    "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n",
-    "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
-    "| Project management | Helpful | |\n",
-    "\n",
-    "- **Time to learn**: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.\n",
-    "- **System requirements**:\n",
-    "    - Populate with any system, version, or non-Python software requirements if necessary\n",
-    "    - Otherwise use the concepts table above and the Imports section below to describe required packages as necessary\n",
-    "    - If no extra requirements, remove the **System requirements** point altogether"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports\n",
-    "Begin your body of content with another `---` divider before continuing into this section, then remove this body text and populate the following code cell with all necessary Python imports **up-front**:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Your first content section"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is where you begin your first section of material, loosely tied to your objectives stated up front. Tie together your notebook as a narrative, with interspersed Markdown text, images, and more as necessary,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# as well as any and all of your code cells\n",
-    "print(\"Hello world!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### A content subsection\n",
-    "Divide and conquer your objectives with Markdown subsections, which will populate the helpful navbar in Jupyter Lab and here on the Jupyter Book!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# some subsection code\n",
-    "new = \"helpful information\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Another content subsection\n",
-    "Keep up the good work! A note, *try to avoid using code comments as narrative*, and instead let them only exist as brief clarifications where necessary."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Your second content section\n",
-    "Here we can move on to our second objective, and we can demonstrate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Subsection to the second section\n",
-    "\n",
-    "#### a quick demonstration\n",
-    "\n",
-    "##### of further and further\n",
-    "\n",
-    "###### header levels"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "as well $m = a * t / h$ text! Similarly, you have access to other $\\LaTeX$ equation [**functionality**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Typesetting%20Equations.html) via MathJax (demo below from link),\n",
-    "\n",
-    "\\begin{align}\n",
-    "\\dot{x} & = \\sigma(y-x) \\\\\n",
-    "\\dot{y} & = \\rho x - y - xz \\\\\n",
-    "\\dot{z} & = -\\beta z + xy\n",
-    "\\end{align}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check out [**any number of helpful Markdown resources**](https://www.markdownguide.org/basic-syntax/) for further customizing your notebooks and the [**Jupyter docs**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Working%20With%20Markdown%20Cells.html) for Jupyter-specific formatting information. Don't hesitate to ask questions if you have problems getting it to look *just right*."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Last Section\n",
-    "\n",
-    "If you're comfortable, and as we briefly used for our embedded logo up top, you can embed raw html into Jupyter Markdown cells (edit to see):"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-info\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Info</p>\n",
-    "    Your relevant information here!\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Feel free to copy this around and edit or play around with yourself. Some other `admonitions` you can put in:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-success\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Success</p>\n",
-    "    We got this done after all!\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-warning\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Warning</p>\n",
-    "    Be careful!\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-danger\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Danger</p>\n",
-    "    Scary stuff be here.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We also suggest checking out Jupyter Book's [brief demonstration](https://jupyterbook.org/content/metadata.html#jupyter-cell-tags) on adding cell tags to your cells in Jupyter Notebook, Lab, or manually. Using these cell tags can allow you to [customize](https://jupyterbook.org/interactive/hiding.html) how your code content is displayed and even [demonstrate errors](https://jupyterbook.org/content/execute.html#dealing-with-code-that-raises-errors) without altogether crashing our loyal army of machines!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "Add one final `---` marking the end of your body of content, and then conclude with a brief single paragraph summarizing at a high level the key pieces that were learned and how they tied to your objectives. Look to reiterate what the most important takeaways were.\n",
-    "\n",
-    "### What's next?\n",
-    "Let Jupyter book tie this to the next (sequential) piece of content that people could move on to down below and in the sidebar. However, if this page uniquely enables your reader to tackle other nonsequential concepts throughout this book, or even external content, link to it here!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Resources and references\n",
-    "Finally, be rigorous in your citations and references as necessary. Give credit where credit is due. Also, feel free to link to relevant external material, further reading, documentation, etc. Then you're done! Give yourself a quick review, a high five, and send us a pull request. A few final notes:\n",
-    " - `Kernel > Restart Kernel and Run All Cells...` to confirm that your notebook will cleanly run from start to finish\n",
-    " - `Kernel > Restart Kernel and Clear All Outputs...` before committing your notebook, our machines will do the heavy lifting\n",
-    " - Take credit! Provide author contact information if you'd like; if so, consider adding information here at the bottom of your notebook\n",
-    " - Give credit! Attribute appropriate authorship for referenced code, information, images, etc.\n",
-    " - Only include what you're legally allowed: **no copyright infringement or plagiarism**\n",
-    " \n",
-    "Thank you for your contribution!"
-   ]
-  }
- ],
- "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": "3.10.8"
-  },
-  "nbdime-conflicts": {
-   "local_diff": [
-    {
-     "diff": [
-      {
-       "diff": [
-        {
-         "key": 0,
-         "op": "addrange",
-         "valuelist": [
-          "Python 3"
-         ]
-        },
-        {
-         "key": 0,
-         "length": 1,
-         "op": "removerange"
-        }
-       ],
-       "key": "display_name",
-       "op": "patch"
-      }
-     ],
-     "key": "kernelspec",
-     "op": "patch"
-    }
-   ],
-   "remote_diff": [
-    {
-     "diff": [
-      {
-       "diff": [
-        {
-         "key": 0,
-         "op": "addrange",
-         "valuelist": [
-          "Python3"
-         ]
-        },
-        {
-         "key": 0,
-         "length": 1,
-         "op": "removerange"
-        }
-       ],
-       "key": "display_name",
-       "op": "patch"
-      }
-     ],
-     "key": "kernelspec",
-     "op": "patch"
-    }
-   ]
-  },
-  "toc-autonumbering": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/notebooks/01_classification.ipynb b/notebooks/notebooks/01_classification.ipynb
deleted file mode 100644
index 2b40809..0000000
--- a/notebooks/notebooks/01_classification.ipynb
+++ /dev/null
@@ -1,59 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "title: Classification\n",
-        "author: Pikall Nikolas\n",
-        "jupyter: \n",
-        "  kernelspec:\n",
-        "    name: \"01_classification\"\n",
-        "    language: \"python\"\n",
-        "    display_name: \"01_classification\"\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "#| echo: true\n",
-        "#| code-fold: true\n",
-        "# Imports\n",
-        "import os\n",
-        "import xarray as xr\n",
-        "import numpy as np\n",
-        "import datetime as dt\n",
-        "import cmcrameri as cmc\n",
-        "import folium\n",
-        "from dotenv import dotenv_values\n",
-        "\n",
-        "from eodag import EODataAccessGateway, SearchResult, setup_logging\n",
-        "from rasterio.crs import CRS\n",
-        "\n",
-        "# Setup logging\n",
-        "setup_logging(0)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Lorum ipsum"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "name": "01_classification",
-      "language": "python",
-      "display_name": "01_classification"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/notebooks/notebooks/02_floodmapping.ipynb b/notebooks/notebooks/02_floodmapping.ipynb
deleted file mode 100644
index 3954014..0000000
--- a/notebooks/notebooks/02_floodmapping.ipynb
+++ /dev/null
@@ -1,350 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "title: Reverend Bayes updates our Belief in Flood Detection\n",
-        "subtitle: How an 275 year old idea helps map the extent of floods\n",
-        "jupyter: \n",
-        "  kernelspec:\n",
-        "    name: \"02_floodmapping\"\n",
-        "    language: \"python\"\n",
-        "    display_name: \"02_floodmapping\"\n",
-        "---\n",
-        "\n",
-        "![Image from [wikipedia](https://upload.wikimedia.org/wikipedia/commons/d/d4/Thomas_Bayes.gif)](https://upload.wikimedia.org/wikipedia/commons/d/d4/Thomas_Bayes.gif)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "%matplotlib widget\n",
-        "\n",
-        "import numpy as np\n",
-        "import xarray as xr\n",
-        "import numpy as np\n",
-        "import datetime\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "from scipy.stats import norm\n",
-        "from eomaps import Maps"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "sig0_dc = xr.open_dataset('../data/s1_parameters/S1_CSAR_IWGRDH/SIG0/V1M1R1/EQUI7_EU020M/E054N006T3/SIG0_20180228T043908__VV_D080_E054N006T3_EU020M_V1M1R1_S1AIWGRDH_TUWIEN.nc')"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## From Backscattering to Flood Mapping\n",
-        "\n",
-        "This notebook explains how microwave ($\\sigma^0$) backscattering (@fig-area) can be used to map the extent of a flood. We replicate in this exercise the work of @bauer-marschallinger_satellite-based_2022 on the TU Wien Bayesian-based flood mapping algorithm.\n",
-        "\n",
-        "In the following lines we create a map with EOmaps [@quast_getting_2024] of the $\\sigma^0$ backscattering values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "#| label: fig-area\n",
-        "#| fig-cap: 'Area targeted for $\\sigma^0$ backscattering is the Greek region of Thessaly, which experienced a major flood in February of 2018.'\n",
-        "m = Maps(ax=121, crs=3857)\n",
-        "m.set_data(data=sig0_dc, x=\"x\", y=\"y\", parameter=\"SIG0\", crs=Maps.CRS.Equi7_EU)\n",
-        "m.plot_map()\n",
-        "m.add_colorbar(label=\"$\\sigma^0$ (dB)\", orientation=\"vertical\", hist_bins=30)\n",
-        "m.add_scalebar(n=5)\n",
-        "m2 = m.new_map(ax=122, crs=3857)\n",
-        "m2.set_extent(m.get_extent())\n",
-        "m2.add_wms.OpenStreetMap.add_layer.default()\n",
-        "m.apply_layout(\n",
-        "    {\n",
-        "        'figsize': [7.32, 4.59],\n",
-        "        '0_map': [0.05, 0.18, 0.35, 0.64],\n",
-        "        '1_cb': [0.8125, 0.1, 0.1, 0.8],\n",
-        "        '1_cb_histogram_size': 0.8,\n",
-        "        '2_map': [0.4375, 0.18, 0.35, 0.64]\n",
-        "        }\n",
-        "    )\n",
-        "m.show()"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Microwave Backscattering over Land and Water\n",
-        "\n",
-        "Reverend Bayes was concerned with two events, one (the *hypothesis*) occurring before the other (the *evidence*). If we know its cause, it is easy to logically deduce the probability of an effect. However, in this case we want to deduce the probability of a cause from an observed effect, also known as \"reversed probability\". In the case of flood mapping, we have $\\sigma^0$ backscatter observations over land (the effect) and we want to deduce the probability of flooding ($F$) and non-flooding ($NF$). \n",
-        "\n",
-        "In other words, we want to know the probability of flooding $P(F)$ given a pixel's $\\sigma^0$:\n",
-        "\n",
-        "$$P(F|\\sigma^0)$$\n",
-        "\n",
-        "and the probability of a pixel being not flooded $P(NF)$ given a certain $\\sigma^0$:\n",
-        "\n",
-        "$$P(NF|\\sigma^0).$$\n",
-        "\n",
-        "Bayes showed that these can be deduced from the observation that forward and reversed probability are equal, so that:\n",
-        "\n",
-        "$$P(F|\\sigma^0)P(\\sigma^0) = P(\\sigma^0|F)P(F)$$\n",
-        "\n",
-        "and\n",
-        "\n",
-        "$$P(NF|\\sigma^0)P(\\sigma^0) = P(\\sigma^0|NF)P(NF).$$\n",
-        "\n",
-        "\n",
-        "The forward probability of $\\sigma^0$ given the occurrence of flooding ($P(\\sigma^0|F)$) and $\\sigma^0$ given no flooding ($P(\\sigma^0|NF)$) can be extracted from past information on backscattering over land and water surfaces. As seen in the sketch below (@fig-sat), the characteristics of backscattering over land and water differ considerably.\n",
-        "\n",
-        "![Schematic backscattering over land and water. Image from [Geological Survey Ireland](https://www.gsi.ie/images/images/SAR_mapping_land_water.jpg)](https://www.gsi.ie/images/images/SAR_mapping_land_water.jpg){#fig-sat}\n",
-        "\n",
-        "## Likelihoods\n",
-        "\n",
-        "The so-called likelihoods of $P(\\sigma^0|F)$ and $P(\\sigma^0|NF)$ can thus be calculated from past backscattering information. Without going into the details of how these likelihoods are calculated, you can **click** on a pixel of the map to plot the likelihoods of $\\sigma^0$ being governed by land or water."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "#| label: fig-lik\n",
-        "#| fig-cap: 'Likelihoods for $\\sigma^0$ being associated with land or water for 1 pixel in the Greek area of Thessaly. Likelihoods are calculated over a range of $\\sigma^0$. The pixel''s observed $\\sigma^0$ is given with a vertical line. Click on the map to re-calculate and update this figure for another pixel in the study area. Map created with EOmaps [@quast_getting_2024].'\n",
-        "\n",
-        "RANGE = np.arange(-30, 0, 0.1)\n",
-        "hparam_dc = xr.open_dataset('../data/tuw_s1_harpar/S1_CSAR_IWGRDH/SIG0-HPAR/V0M2R3/EQUI7_EU020M/E054N006T3/D080.nc')\n",
-        "plia_dc = xr.open_dataset('../data/s1_parameters/S1_CSAR_IWGRDH/PLIA-TAG/V01R03/EQUI7_EU020M/E054N006T3/PLIA-TAG-MEAN_20200101T000000_20201231T235959__D080_E054N006T3_EU020M_V01R03_S1IWGRDH.nc')\n",
-        "sig0_dc['id'] = (('y', 'x'), np.arange(sig0_dc.SIG0.size).reshape(sig0_dc.SIG0.shape))\n",
-        "hparam_dc['id'] = (('y', 'x'), np.arange(sig0_dc.SIG0.size).reshape(sig0_dc.SIG0.shape))\n",
-        "plia_dc['id'] = (('y', 'x'), np.arange(sig0_dc.SIG0.size).reshape(sig0_dc.SIG0.shape))\n",
-        "\n",
-        "def calc_water_likelihood(id, x):\n",
-        "    point = plia_dc.where(plia_dc.id == id, drop=True)\n",
-        "    wbsc_mean = point.PLIA * -0.394181 + -4.142015\n",
-        "    wbsc_std = 2.754041\n",
-        "    return norm.pdf(x, wbsc_mean.to_numpy(), wbsc_std).flatten()\n",
-        "\n",
-        "def expected_land_backscatter(data, dtime_str):\n",
-        "    w = np.pi * 2 / 365\n",
-        "    dt = datetime.datetime.strptime(dtime_str, \"%Y-%m-%d\")\n",
-        "    t = dt.timetuple().tm_yday\n",
-        "    wt = w * t\n",
-        "\n",
-        "    M0 = data.M0\n",
-        "    S1 = data.S1\n",
-        "    S2 = data.S2\n",
-        "    S3 = data.S3\n",
-        "    C1 = data.C1\n",
-        "    C2 = data.C2\n",
-        "    C3 = data.C3\n",
-        "    hm_c1 = (M0 + S1 * np.sin(wt)) + (C1 * np.cos(wt))\n",
-        "    hm_c2 = ((hm_c1 + S2 * np.sin(2 * wt)) + C2 * np.cos(2 * wt))\n",
-        "    hm_c3 = ((hm_c2 + S3 * np.sin(3 * wt)) + C3 * np.cos(3 * wt))\n",
-        "    return hm_c3\n",
-        "\n",
-        "def calc_land_likelihood(id, x):\n",
-        "    point = hparam_dc.where(hparam_dc.id == id, drop=True)\n",
-        "    lbsc_mean = expected_land_backscatter(point, '2018-02-01')\n",
-        "    lbsc_std = point.STD\n",
-        "    return norm.pdf(x, lbsc_mean.to_numpy(), lbsc_std.to_numpy()).flatten()\n",
-        "\n",
-        "def calc_likelihoods(id, x):\n",
-        "    if isinstance(x, list):\n",
-        "        x = np.arange(x[0], x[1], 0.1)\n",
-        "    water_likelihood, land_likelihood = calc_water_likelihood(id=id, x=x), calc_land_likelihood(id=id, x=x)\n",
-        "    return water_likelihood, land_likelihood\n",
-        "\n",
-        "def view_bayes_flood(sig0_dc, calc_posteriors=None, bayesian_flood_decision=None):\n",
-        "\n",
-        "    # initialize a map on top\n",
-        "    m = Maps(ax=122, layer=\"data\", crs=Maps.CRS.Equi7_EU)\n",
-        "\n",
-        "    # initialize 2 matplotlib plot-axes next to the map\n",
-        "    ax_upper = m.f.add_subplot(221)\n",
-        "    ax_upper.set_ylabel(\"likelihood\")\n",
-        "    ax_upper.set_xlabel(\"$\\sigma^0 (dB)$\")\n",
-        "\n",
-        "    ax_lower = m.f.add_subplot(223)\n",
-        "    ax_lower.set_ylabel(\"probability\")\n",
-        "    ax_lower.set_xlabel(\"$\\sigma^0 (dB)$\")\n",
-        "\n",
-        "    # -------- assign data to the map and plot it\n",
-        "    if bayesian_flood_decision is not None:\n",
-        "        # add map\n",
-        "        m2 = m.new_layer(layer=\"map\")\n",
-        "        m2.add_wms.OpenStreetMap.add_layer.default()\n",
-        "        flood_classification =  bayesian_flood_decision(sig0_dc.id, sig0_dc.SIG0)\n",
-        "        sig0_dc[\"decision\"] = (('y', 'x'), flood_classification.reshape(sig0_dc.SIG0.shape))\n",
-        "        sig0_dc[\"decision\"] = sig0_dc.decision.where(sig0_dc.SIG0.notnull())\n",
-        "        sig0_dc[\"decision\"] = sig0_dc.decision.where(sig0_dc.decision==0)\n",
-        "        m.set_data(data=sig0_dc, x=\"x\", y=\"y\", parameter=\"decision\", crs=Maps.CRS.Equi7_EU)\n",
-        "        m.plot_map()\n",
-        "        m.show_layer(\"map\", (\"data\", 0.5))\n",
-        "        m.apply_layout(\n",
-        "            {\n",
-        "                'figsize': [7.32, 4.59],\n",
-        "                '0_map': [0.44573, 0.11961, 0.3375, 0.75237],\n",
-        "                '1_': [0.10625, 0.5781, 0.3125, 0.29902],\n",
-        "                '2_': [0.10625, 0.11961, 0.3125, 0.29902],\n",
-        "            }\n",
-        "        )\n",
-        "\n",
-        "    else:\n",
-        "        m.set_data(data=sig0_dc, x=\"x\", y=\"y\", parameter=\"SIG0\", crs=Maps.CRS.Equi7_EU)\n",
-        "        m.plot_map()\n",
-        "        m.add_colorbar(label=\"$\\sigma^0$ (dB)\", orientation=\"vertical\", hist_bins=30)\n",
-        "        m.apply_layout(\n",
-        "            {\n",
-        "                'figsize': [7.32, 4.59],\n",
-        "                '0_map': [0.44573, 0.11961, 0.3375, 0.75237],\n",
-        "                '1_': [0.10625, 0.5781, 0.3125, 0.29902],\n",
-        "                '2_': [0.10625, 0.11961, 0.3125, 0.29902],\n",
-        "                '3_cb': [0.8, 0.09034, 0.1, 0.85],\n",
-        "                '3_cb_histogram_size': 0.8\n",
-        "            }\n",
-        "        )\n",
-        "\n",
-        "    def update_plots(ID, **kwargs):\n",
-        "        \n",
-        "        # get the data\n",
-        "        value = sig0_dc.where(sig0_dc.id == ID, drop=True).SIG0.to_numpy()\n",
-        "        y1_pdf, y2_pdf = calc_water_likelihood(ID, RANGE), calc_land_likelihood(ID, RANGE)\n",
-        "\n",
-        "        # plot the lines and vline\n",
-        "        (water,) = ax_upper.plot(RANGE, y1_pdf, 'k-', lw=2, label=\"water\")\n",
-        "        (land,) = ax_upper.plot(RANGE, y2_pdf,'r-', lw=5, alpha=0.6, label=\"land\")\n",
-        "        value_left = ax_upper.vlines(x=value, ymin=0, ymax=np.max((y1_pdf, y2_pdf)), lw=3, label=\"observed\")\n",
-        "        ax_upper.legend(loc=\"upper left\")\n",
-        "\n",
-        "        # add all artists as \"temporary pick artists\" so that they\n",
-        "        # are removed when the next datapoint is selected\n",
-        "        for a in [water, land, value_left]:\n",
-        "            m.cb.pick.add_temporary_artist(a)\n",
-        "\n",
-        "        if calc_posteriors is not None:\n",
-        "            f_post, nf_post = calc_posteriors(y1_pdf, y2_pdf)\n",
-        "            (f,) = ax_lower.plot(RANGE, f_post, 'k-', lw=2, label=\"flood\")\n",
-        "            (nf,) = ax_lower.plot(RANGE, nf_post,'r-', lw=5, alpha=0.6, label=\"non-flood\")\n",
-        "            value_right = ax_lower.vlines(x=value, ymin=-0.1, ymax=1.1, lw=3, label=\"observed\")\n",
-        "            ax_lower.legend(loc=\"upper left\")\n",
-        "            for a in [f, nf, value_right]:\n",
-        "                m.cb.pick.add_temporary_artist(a)\n",
-        "\n",
-        "        # re-compute axis limits based on the new artists\n",
-        "        ax_upper.relim()\n",
-        "        ax_upper.autoscale()\n",
-        "\n",
-        "    m.cb.pick.attach(update_plots)\n",
-        "    m.cb.pick.attach.mark(permanent=False, buffer=1, fc=\"none\", ec=\"r\")\n",
-        "    m.cb.pick.attach.mark(permanent=False, buffer=2, fc=\"none\", ec=\"r\", ls=\":\")\n",
-        "    m.show()\n",
-        "\n",
-        "view_bayes_flood(sig0_dc)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Posteriors\n",
-        "\n",
-        "Having calculated the likelihoods, we can now move on to calculate the probability of (non-)flooding given a pixel's $\\sigma^0$. These so-called *posteriors* need one more piece of information, as can be seen in the equation above. We need the probability that a pixel is flooded $P(F)$ or not flooded $P(NF)$. Of course, these are the figures we've been trying to find this whole time. We don't actually have them yet, so what can we do? In Bayesian statistics, we can just start with our best guess. These guesses are called our \"priors\", because they are the beliefs we hold *prior* to looking at the data. This subjective prior belief is the foundation Bayesian statistics, and we use the likelihoods we just calculated to update our belief in this particular hypothesis. This updated belief is called the \"posterior\".\n",
-        "\n",
-        "Let's say that our best estimate for the chance of flooding versus non-flooding of a pixel is 50-50: a coin flip.  We now can also calculate the probability of backscattering $P(\\sigma^0)$, as the weighted average of the water and land likelihoods, ensuring that our posteriors range between 0 to 1.\n",
-        "\n",
-        "The following code block shows how we calculate the priors."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "def calc_posteriors(water_likelihood, land_likelihood):\n",
-        "    evidence = (water_likelihood * 0.5) + (land_likelihood * 0.5)\n",
-        "    return (water_likelihood * 0.5) / evidence,  (land_likelihood * 0.5) / evidence"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can plot the posterior probabilities of flooding and non-flooding again and compare these to pixel's measured $\\sigma^0$. **Click** on a pixel to calculate the posterior probability."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "#| label: fig-post\n",
-        "#| fig-cap: 'Posterior probabilities for $\\sigma^0$ of 1 pixel being associated with land for water in the Greek area of Thessaly. Click on the map to re-calculate and update this figure for another pixel in the study area. Map created with EOmaps [@quast_getting_2024].'\n",
-        "view_bayes_flood(sig0_dc, calc_posteriors)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Flood Classification\n",
-        "\n",
-        "We are now ready to combine all this information and classify the pixels according to the probability of flooding given the backscatter value of each pixel. Here we just look whether the probability of flooding is higher than non-flooding:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "def bayesian_flood_decision(id, sig0_dc):\n",
-        "    nf_post_prob, f_post_prob = calc_posteriors(*calc_likelihoods(id,  sig0_dc))\n",
-        "    return np.greater(f_post_prob, nf_post_prob)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "**Click** on a point in the below map to see the likelihoods and posterior distributions (in the left-hand subplots)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "#| label: fig-clas\n",
-        "#| fig-cap: 'Flood extent of the Greek region of Thessaly based on Bayesian probabilities are shown on the map superimposed on an open street map. Click on a pixel to generate the point''s water and land likelihoods as well as the posterior probabilities. Map created with EOmaps [@quast_getting_2024].'\n",
-        "view_bayes_flood(sig0_dc, calc_posteriors, bayesian_flood_decision)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "name": "02_floodmapping",
-      "language": "python",
-      "display_name": "02_floodmapping"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/notebooks/notebooks/Makefile b/notebooks/notebooks/Makefile
deleted file mode 100644
index a50f195..0000000
--- a/notebooks/notebooks/Makefile
+++ /dev/null
@@ -1,45 +0,0 @@
-.ONESHELL:
-SHELL = /bin/bash
-.PHONY: help clean environment kernel post-render data
-YML = $(wildcard chapters/*.yml)
-QMD = $(wildcard chapters/*.qmd)
-REQ = $(basename $(notdir $(YML)))
-CONDA_ENV_DIR := $(foreach i,$(REQ),$(shell conda info --base)/envs/$(i))
-KERNEL_DIR := $(foreach i,$(REQ),$(shell jupyter --data-dir)/kernels/$(i))
-CONDA_ACTIVATE = source $$(conda info --base)/etc/profile.d/conda.sh ; conda activate ; conda activate
-
-help:
-	@echo "make clean"
-	@echo " clean all jupyter checkpoints"
-	@echo "make environment"
-	@echo " create a conda environment"
-	@echo "make kernel"
-	@echo " make ipykernel based on conda lock file"
-
-clean:
-	rm --force --recursive .ipynb_checkpoints/
-	for i in $(REQ); do conda remove -n $$i --all -y ; jupyter kernelspec uninstall -y $$i ; done
-
-$(CONDA_ENV_DIR):
-	for i in $(YML); do conda env create -f $$i; done
-
-environment: $(CONDA_ENV_DIR)
-	@echo -e "conda environments are ready."
-
-$(KERNEL_DIR): $(CONDA_ENV_DIR)
-	pip install --upgrade pip
-	pip install jupyter
-	for i in $(REQ); do $(CONDA_ACTIVATE) $$i ; python -m ipykernel install --user --name $$i --display-name $$i ; conda deactivate; done
-
-kernel: $(KERNEL_DIR)
-	@echo -e "conda jupyter kernel is ready."
-
-post-render:
-	for i in $(QMD); do quarto convert $$i; done
-	- mv chapters/*.ipynb notebooks/ >/dev/null 2>&1
-	- for f in chapters/*.quarto_ipynb ; do mv -- "$f" "${f%.quarto_ipynb}.ipynb"  >/dev/null 2>&1; done
-	cp Makefile notebooks/
-
-data:
-	wget -q -P ./data https://cloud.geo.tuwien.ac.at/s/AezWMFwmFQJsKyr/download/floodmap.zip
-	cd data && unzip -n floodmap.zip && rm floodmap.zip
\ No newline at end of file
diff --git a/notebooks/notebooks/references.ipynb b/notebooks/notebooks/references.ipynb
deleted file mode 100644
index 8d814cc..0000000
--- a/notebooks/notebooks/references.ipynb
+++ /dev/null
@@ -1,24 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# References {.unnumbered}\n",
-        "\n",
-        "::: {#refs}\n",
-        ":::"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "name": "01_classification",
-      "language": "python",
-      "display_name": "01_classification",
-      "path": "/home/runner/.local/share/jupyter/kernels/01_classification"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
\ No newline at end of file