diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 013acb74..bbb5196e 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -14,41 +14,84 @@ on:
 
 jobs:
   test:
+    name: ${{ matrix.name }}
     runs-on: ubuntu-latest
     strategy:
+      fail-fast: false
       matrix:
-        python-version: ['3.8', '3.9', '3.10']
+        include:
+          - name: Test suite with py38-ubuntu
+            python: "3.8"
+            toxenv: py38
+          - name: Test suite with py39-ubuntu
+            python: "3.9"
+            toxenv: py39
+          - name: Test suite with py310-ubuntu
+            python: "3.10"
+            toxenv: py310
+          - name: Formatting with black + isort
+            python: "3.9"
+            toxenv: format
+          - name: Linting with flake8
+            python: "3.9"
+            toxenv: lint
+          - name: Test suite for Jupyter Notebooks
+            python: "3.9"
+            toxenv: notebooks
+    env:
+      # Color Output
+      # Rich (pip)
+      FORCE_COLOR: 1
+      # Tox
+      PY_COLORS: 1
+      # Pytest
+      PYTEST_ADDOPTS: "--color=yes"
     steps:
       - uses: actions/checkout@v3
 
-      - name: Set up Python ${{ matrix.python-version }}
+      - name: Set up Python ${{ matrix.python }}
         uses: actions/setup-python@v4
         with:
-          python-version: ${{ matrix.python-version }}
-      
+          python-version: ${{ matrix.python }}
+          check-latest: true
+          cache: "pip"
+          cache-dependency-path: pyproject.toml
+
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install -e .[ci]
-      
-      - name: Lint with black
-        uses: psf/black@stable
+          pip install tox
+
+      - name: Setup tox environment
+        run: tox -e ${{ matrix.toxenv }} --notest
+
+      - name: Test
+        run: tox -e ${{ matrix.toxenv }} --skip-pkg-install
+
+      - name: Store coverage
+        uses: actions/upload-artifact@v3
         with:
-          options: "--check"
-          jupyter: true
-            
-      - name: Run tests and notebooks
-        if: ${{ github.event_name == 'pull_request' }}
-        run: |
-          py.test ./tests
-      
-      - name: Run tests only
-        if: ${{ github.event_name == 'push' }}
-        run: |
-          py.test ./tests -m "not notebooks"
-      
+          name: coverage
+          path: coverage.xml
+          if-no-files-found: ignore
+
+  coverage:
+    name: Coverage
+    runs-on: ubuntu-latest
+    needs: test
+    steps:
+      - name: Set up Python ${{ matrix.python }}
+        uses: actions/setup-python@v4
+        with:
+          check-latest: true
+
+      - name: Get coverage
+        uses: actions/download-artifact@v3
+        with:
+          name: coverage
+
       - name: Run codacy-coverage-reporter
-        if: github.repository == 'pastas/pastas'
+        if: ${{ github.repository == 'pastas/pastas' && success() }}
         uses: codacy/codacy-coverage-reporter-action@master
         with:
           project-token: ${{ secrets.CODACY_PROJECT_TOKEN }}
diff --git a/.gitignore b/.gitignore
index 1d84aa18..789fa63f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,6 +16,7 @@ examples/notebooks/.ipynb_checkpoints/
 gui/settings
 build
 dist
+.tox
 
 .idea/.name
 .cache/v/cache/lastfailed
@@ -32,3 +33,7 @@ dist
 
 .DS_Store
 /doc/api/generated/
+Test_Model.pdf
+test.pas
+doc/about/publications.bib
+doc/about/references.bib
diff --git a/README.rst b/README.rst
index 438ff0f0..60395307 100644
--- a/README.rst
+++ b/README.rst
@@ -13,16 +13,18 @@ Pastas: Analysis of Groundwater Time Series
    :target: https://mit-license.org/
 .. image:: https://img.shields.io/pypi/pyversions/pastas
    :target: https://pypi.python.org/pypi/pastas
+.. image:: https://img.shields.io/pypi/dm/pastas
+   :target: https://pypi.org/project/pastas/
+.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1465866.svg
+   :target: https://doi.org/10.5281/zenodo.1465866
 .. image:: https://api.codacy.com/project/badge/Grade/952f41c453854064ba0ee1fa0a0b4434
    :target: https://www.codacy.com/gh/pastas/pastas
 .. image:: https://api.codacy.com/project/badge/Coverage/952f41c453854064ba0ee1fa0a0b4434
    :target: https://www.codacy.com/gh/pastas/pastas
-.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1465866.svg
-   :target: https://doi.org/10.5281/zenodo.1465866
-.. image:: https://mybinder.org/badge_logo.svg
-   :target: https://mybinder.org/v2/gh/pastas/pastas/master?filepath=examples%2Fnotebooks%2F1_basic_model.ipynb
 .. image:: https://readthedocs.org/projects/pastas/badge/?version=latest
    :target: https://pastas.readthedocs.io/en/latest/?badge=latest
+.. image:: https://mybinder.org/badge_logo.svg
+   :target: https://mybinder.org/v2/gh/pastas/pastas/master?filepath=examples%2Fnotebooks%2F1_basic_model.ipynb
 
 Pastas: what is it?
 ~~~~~~~~~~~~~~~~~~~
@@ -34,10 +36,10 @@ built-in optimization, visualisation, and statistical analysis tools.
 
 Documentation & Examples
 ~~~~~~~~~~~~~~~~~~~~~~~~
-- Documentation is provided on a dedicated website: http://pastas.readthedocs.io/
+- Documentation is provided on the dedicated website `pastas.dev <http://www.pastas.dev/>`_
 - Examples can be found on the `examples directory on the documentation website <https://pastas.readthedocs.io/en/dev/examples/index.html>`_
 - View and edit a working example notebook of a Pastas model in `MyBinder <https://mybinder.org/v2/gh/pastas/pastas/master?filepath=examples%2Fnotebooks%2F1_basic_model.ipynb>`_
-- A list of Publications that used Pastas is available in a `dedicated GitHub repo <https://github.com/pastas/pastas_research>`_
+- A list of publications that use Pastas is available in a `dedicated Zotero group <https://www.zotero.org/groups/4846685/pastas/items/32FS5PTW/item-list>`_
 
 Get in Touch
 ~~~~~~~~~~~~
@@ -50,7 +52,7 @@ Get in Touch
 
 Quick installation guide
 ~~~~~~~~~~~~~~~~~~~~~~~~
-To install Pastas, a working version of Python 3.7, 3.8 or 3.9 has to be
+To install Pastas, a working version of Python 3.8, 3.9 or 3.10 has to be
 installed on your computer. We recommend using the `Anaconda Distribution
 <https://www.continuum.io/downloads>`_ as it includes most of the python
 package dependencies and the Jupyter Notebook software to run the notebooks.
@@ -90,7 +92,7 @@ Pastas
 - numpy>=1.7
 - matplotlib>=3.1
 - pandas>=1.1
-- scipy>=1.3
+- scipy>=1.8
 - numba>=0.51
 
 To install the most important optional dependencies (solver LmFit and function visualisation Latexify) at the same time with Pastas use::
diff --git a/doc/_static/Eawag_logo.png b/doc/_static/Eawag_logo.png
new file mode 100644
index 00000000..820befc3
Binary files /dev/null and b/doc/_static/Eawag_logo.png differ
diff --git a/doc/_static/TUD_logo.png b/doc/_static/TUD_logo.png
index d0b457cd..87d37719 100644
Binary files a/doc/_static/TUD_logo.png and b/doc/_static/TUD_logo.png differ
diff --git a/doc/_static/example_output.png b/doc/_static/example_output.png
index 2cfcf8df..48399a3f 100644
Binary files a/doc/_static/example_output.png and b/doc/_static/example_output.png differ
diff --git a/doc/_static/history_initial.jpg b/doc/_static/history_initial.jpg
new file mode 100644
index 00000000..3c283047
Binary files /dev/null and b/doc/_static/history_initial.jpg differ
diff --git a/doc/about/01_history.rst b/doc/about/01_history.rst
new file mode 100644
index 00000000..c53b06ff
--- /dev/null
+++ b/doc/about/01_history.rst
@@ -0,0 +1,36 @@
+History
+=======
+
+The Development of Pastas started in the spring of 2016 at the TU Delft and Artesia in the Netherlands. Researchers
+at the TU Delft required a flexible framework that could support future research on time series analysis of
+groundwater data. Consulting company `Artesia <https://www.artesia-water.nl>`_ on the other hand, needed a tool
+that allowed them to perform more complex analyses in a scripted environment. As such, Pastas was developed under
+a fruitful partnership between academia and private industry, a collaboration that continues to this day.
+
+.. figure:: ./../_static/history_initial.jpg
+    :figwidth: 600px
+
+    Drawing the initial design of Pastas on a whiteboard (5th of April, 2016)
+
+Python was chosen as the programming language for Pastas, being a flexible and open-source language that allows for
+quick prototyping. Moreover, many if not most hydrologists learn Python during their education, making the software
+available to many. From the start, all code was made completely open-source under MIT license, embracing open science
+and FAIR (Findability, Accessibility, Interoperability, and Reuse) data practices.
+
+International use of Pastas and research grew substantially after the publication of the Pastas article in the
+international journal Groundwater :cite:p:`collenteur_pastas_2019`. In addition, the University of Graz in Austria
+and now the Swiss research institute Eawag employed one of the maintainers, further pushing the international use and
+development of Pastas. Since its inception, Pastas has been applied in over a dozen countries worldwide. Supporting
+international research continues to be an important goal of Pastas. For a list of peer-reviewed publications using
+Pastas please see the :doc:`Publications page <05_publications>`.
+
+Pastas 1.0
+----------
+
+In february 2023, Pastas version 1.0 was released, the celebration of 7 years of research and development of Pastas.
+Pastas was already operational for a couple of years, but the a lot of features were still changing. In Pastas 1.0
+focus lies on Pastas main goal: time series analysis on groundwater levels. This was done by improving the
+documentation, removing unused features, unifying the API and relying more on high quality Python packages such as
+Pandas, NumPy and Numba. With version 1.0, Pastas is ready for further future developments!
+
+
diff --git a/doc/about/02_team.rst b/doc/about/02_team.rst
new file mode 100644
index 00000000..a6d67b6e
--- /dev/null
+++ b/doc/about/02_team.rst
@@ -0,0 +1,110 @@
+Pastas Team
+===========
+
+Active maintainers
+------------------
+
+Core team
+~~~~~~~~~
+The following people are part of the core team of Pastas and can merge pull requests on the Pastas GitHub.
+
+
+.. grid:: 1 2 3 4
+
+    .. grid-item::
+
+        .. figure:: https://avatars2.githubusercontent.com/u/12051167?s=460&u=ad0baaa3520ab942c93af44ea8e296b44ea459ac&v=4
+            :figwidth: 150px
+            :target: https://github.com/raoulcollenteur
+
+            Raoul Collenteur
+            (Eawag)
+
+    .. grid-item::
+        .. figure:: https://avatars3.githubusercontent.com/u/1363400?s=460&u=8d34d0824bd0f43a63f29c0b71fbfae435a15565&v=4
+            :figwidth: 150px
+            :target: https://github.com/mbakker7
+
+            Mark Bakker
+            (TU Delft)
+
+    .. grid-item::
+        .. figure:: https://avatars3.githubusercontent.com/u/18527362?s=460&u=3e1faab416c86e141ff0860017a5a02133830b08&v=4
+            :figwidth: 150px
+            :target: https://github.com/rubencalje
+
+            Ruben Caljé
+            (Artesia)
+
+    .. grid-item::
+        .. figure:: https://avatars1.githubusercontent.com/u/6552225?s=460&u=ba73dc4a63fe3d5707a2da9276143d16672ad413&v=4
+            :figwidth: 150px
+            :target: https://github.com/dbrakenhoff
+
+            Davíd Brakenhoff
+            (Artesia)
+
+Developers
+~~~~~~~~~~
+
+The following people are part of the development team of Pastas and help develop new features.
+
+.. grid:: 1 2 3 4
+
+    .. grid-item::
+        .. figure:: https://avatars.githubusercontent.com/u/10562903?v=4
+            :figwidth: 120px
+            :target: https://github.com/OnnoEbbens
+
+            Onno Ebbens
+            (Artesia)
+
+    .. grid-item::
+        .. figure:: https://avatars.githubusercontent.com/u/66305055?v=4
+            :figwidth: 120px
+            :target: https://github.com/martinvonk
+
+            Martin Vonk
+            (Artesia & TU Delft)
+
+All the people who have contributed code to the Pastas project can be found on `GitHub Contributor list <https://github
+.com/pastas/pastas/graphs/contributors>`_.
+
+
+Institutional partners
+----------------------
+Institutional partners are companies and universities that support the project by employing maintainers and developers.
+Current institutional partners include:
+
+.. grid:: 1 2 3 3
+
+    .. grid-item-card::
+
+
+        .. figure:: ./../_static/Eawag_logo.png
+            :figwidth: 150px
+            :target: https://www.eawag.ch/en/
+
+    .. grid-item-card::
+
+        .. figure:: ./../_static/TUD_logo.png
+            :figwidth: 150px
+            :target: https://www.tudelft.nl
+
+    .. grid-item-card::
+
+        .. figure:: ./../_static/Art_logo.jpg
+            :figwidth: 150px
+            :target: http://www.artesia-water.nl
+
+Past Institutional support
+--------------------------
+The following institutions have employed maintainers in the past or past maintainers:
+
+.. grid:: 1 2 3 3
+
+    .. grid-item-card::
+
+        .. figure:: ./../_static/logo_graz.png
+            :figwidth: 150px
+            :target: https://erdwissenschaften.uni-graz.at/en/
diff --git a/doc/about/03_citing.rst b/doc/about/03_citing.rst
new file mode 100644
index 00000000..9ec90ff1
--- /dev/null
+++ b/doc/about/03_citing.rst
@@ -0,0 +1,73 @@
+Citing and logo
+===============
+
+If you find Pastas useful in your work, please cite the `Pastas article published in Groundwater journal <https://doi.org/10.1111/gwat.12925>`_:
+
+Collenteur, R., Caljé, R., Klop, S., Schaars, F., & Bakker, M. (2019). Pastas: open source software for the analysis of groundwater time series. Groundwater, 57. https://doi.org/10.1111/gwat.12925
+
+.. code-block:: text
+
+    @article{Collenteur_Pastas_open_source_2019,
+      author        = {Collenteur, R.A. and
+                       Caljé, R. and
+                       Klop, S.A. and
+                       Schaars, F. and
+                       Bakker, M.},
+      doi           = {10.1111/gwat.12925},
+      journal       = {Groundwater},
+      month         = {7},
+      title         = {{Pastas: open source software for the analysis of groundwater time series}},
+      volume        = {57},
+      year          = {2019}
+    }
+
+To cite a specific version of Pastas, you can cite `one of the releases on Zenodo <https://doi.org/10.5281/zenodo
+.7585471>`_:
+
+.. code-block:: text
+
+    @software{raoul_collenteur_2023_7585471,
+      author        = {Raoul Collenteur and
+                       Mark Bakker and
+                       Ruben Caljé and
+                       Frans Schaars},
+      title         = {{Pastas: open-source software for the analysis of
+                        hydrogeological time series}},
+      month         = jan,
+      year          = 2023,
+      publisher     = {Zenodo},
+      version       = {v1.0.0},
+      doi           = {10.5281/zenodo.7585471},
+      url           = {https://doi.org/10.5281/zenodo.7585471}
+    }
+
+Naming
+------
+
+Pastas is written as one, non-capitalized word: "Pastas".
+
+Logo
+----
+
+.. figure::
+    ./../_static/logo.png
+    :figwidth: 200px
+
+Color
+-----
+
+.. raw:: html
+
+    <table class="table">
+        <tr>
+            <td style="text-align: center;">
+                <svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+                    <circle cx="50" cy="50" r="50" fill="#009BE0"/>
+                </svg>
+                <br/>
+                <b style="color: #009BE0;">Pastas Blue</b><br/>
+                RGB: R0 G155 B224<br/>
+                HEX: #009BE0
+            </td>
+        </tr>
+    </table>
\ No newline at end of file
diff --git a/doc/about/04_onsite_course.rst b/doc/about/04_onsite_course.rst
new file mode 100644
index 00000000..d12b9cb0
--- /dev/null
+++ b/doc/about/04_onsite_course.rst
@@ -0,0 +1,10 @@
+On-site-courses
+===============
+
+Every now and then on-site Pastas courses are held. Please check the `GitHub Discussion <https://github
+.com/pastas/pastas/discussions>`_ page for announcements of any public Pastas courses to be held. We can also provide
+private courses on request. To organize such a workshop please contact of the following.
+
+- For courses in the Netherlands, please contact Artesia (info[AT]artesia-water.nl)
+- For international courses, please contact Raoul Collenteur (Raoul.Collenteur[AT]eawag.ch)
+
diff --git a/doc/publications.rst b/doc/about/05_publications.rst
similarity index 100%
rename from doc/publications.rst
rename to doc/about/05_publications.rst
diff --git a/doc/references.rst b/doc/about/06_references.rst
similarity index 61%
rename from doc/references.rst
rename to doc/about/06_references.rst
index 2f6e13e3..11c063ee 100644
--- a/doc/references.rst
+++ b/doc/about/06_references.rst
@@ -1,10 +1,10 @@
-References
-==========
+Bibliography
+============
 
-Pastas is built on a lot of scientific literature. Here the references are listed for all the methods implemented in
+Pastas is based on a lot of scientific literature. Here the references are listed for all the methods implemented in
 Pastas. This list is automatically generated from a public
 `Zotero library <https://www.zotero.org/groups/4846685/pastas>`_ (collection `References`). For a list of
-publications using Pastas we refer to the Publications page of this website.
+studies using Pastas we refer to the Publications page of this website.
 
 
 .. bibliography:: references.bib
diff --git a/doc/about/index.rst b/doc/about/index.rst
new file mode 100644
index 00000000..d2835580
--- /dev/null
+++ b/doc/about/index.rst
@@ -0,0 +1,25 @@
+About Pastas
+============
+
+Pastas is an open source Python package to analyse hydro(geo)logical time series. The objective of Pastas is twofold:
+to provide a scientific framework to develop and test new methods, and to provide a reliable ready‐to‐use software
+tool for groundwater practitioners. All code is available from the `Pastas GitHub <https://github.com/pastas/pastas>`_.
+
+Mission
+-------
+
+Our mission is to improve the analysis and modeling of groundwater time
+series through research and development of new methods and accompanying
+computer code. We strive to maximize what we can learn from the information
+contained in groundwater time series. We believe in the power of open science,
+FAIR data and open software to achieve this, making code open and available to
+all. With the development of Pastas, we attempt to bridge the gap between
+science and practice and shorten the time between scientific discoveries
+and their real-world applications.
+
+.. toctree::
+    :hidden:
+    :maxdepth: 4
+    :glob:
+
+    ./*
\ No newline at end of file
diff --git a/doc/api/index.rst b/doc/api/index.rst
index 09a59e95..552e16df 100644
--- a/doc/api/index.rst
+++ b/doc/api/index.rst
@@ -19,9 +19,11 @@ left-hand menu you will find the different categories of the API documentation.
     recharge
     solver
     modelplots.Plotting
+    modelcompare.CompareModels
     timeseries.TimeSeries
-    utils
     timeseries_utils
+    utils
+    timer.SolveTimer
     plots
     stats
 
diff --git a/doc/benchmarks/armamodel.ipynb b/doc/benchmarks/armamodel.ipynb
new file mode 100644
index 00000000..885c2ee7
--- /dev/null
+++ b/doc/benchmarks/armamodel.ipynb
@@ -0,0 +1,496 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ARMA(1,1) Noise Model for Pastas\n",
+    "*R.A. Collenteur, University of Graz, May 2020*\n",
+    "\n",
+    "In this notebook an Autoregressive-Moving-Average (ARMA(1,1)) noise model is developed for Pastas models. This new noise model is tested against synthetic data generated with NumPy or Statsmodels' ARMA model. This noise model is tested on head time series with a regular time step.\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "    \n",
+    "<b>Warning</b>\n",
+    "\n",
+    "It should be noted that the time step may be non-equidistant in this formulation, but this model is not yet tested for irregular time steps.\n",
+    "   \n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python version: 3.10.5\n",
+      "NumPy version: 1.22.3\n",
+      "Pandas version: 1.4.3\n",
+      "SciPy version: 1.9.3\n",
+      "Matplotlib version: 3.6.2\n",
+      "Numba version: 0.56.3\n",
+      "LMfit version: 1.0.3\n",
+      "Latexify version: 0.2.0\n",
+      "Pastas version: 1.0.0b\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.special import gammainc, gammaincinv\n",
+    "\n",
+    "import pastas as ps\n",
+    "\n",
+    "ps.set_log_level(\"ERROR\")\n",
+    "ps.show_versions()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Develop the ARMA(1,1) Noise Model for Pastas\n",
+    "\n",
+    "The following formula is used to calculate the noise according to the ARMA(1,1) process:\n",
+    "\n",
+    "$$\\upsilon(t_i) = r(t_i) - r(t_{i-1}) \\text{e}^{-\\Delta t_i / \\alpha} - \\upsilon(t_{i-1}) \\text{e}^{-\\Delta t_i / \\beta}$$\n",
+    "\n",
+    "where $\\upsilon$ is the noise, $\\Delta t_i$ the time step between the residuals ($r$), and respectively $\\alpha$ [days] and $\\beta$ [days] the parameters of the AR and MA parts of the model. The model is named `ArmaModel` and can be found in `noisemodel.py`. It is added to a Pastas model as follows: `ml.add_noisemodel(ps.ArmaModel())`\n",
+    "\n",
+    "## 2. Generate synthetic head time series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Read in some data\n",
+    "rain = (\n",
+    "    pd.read_csv(\"../examples/data/rain_260.csv\", index_col=0, parse_dates=[0]).squeeze()\n",
+    "    / 1000\n",
+    ")\n",
+    "\n",
+    "# Set the True parameters\n",
+    "Atrue = 800\n",
+    "ntrue = 1.1\n",
+    "atrue = 200\n",
+    "dtrue = 20\n",
+    "\n",
+    "# Generate the head\n",
+    "step = ps.Gamma().block([Atrue, ntrue, atrue])\n",
+    "h = dtrue * np.ones(len(rain) + step.size)\n",
+    "for i in range(len(rain)):\n",
+    "    h[i : i + step.size] += rain[i] * step\n",
+    "head = pd.DataFrame(\n",
+    "    index=rain.index,\n",
+    "    data=h[: len(rain)],\n",
+    ")\n",
+    "head = head[\"1990\":\"2015\"]\n",
+    "\n",
+    "# Plot the head without noise\n",
+    "plt.figure(figsize=(10, 2))\n",
+    "plt.plot(head, \"k.\", label=\"head\")\n",
+    "plt.legend(loc=0)\n",
+    "plt.ylabel(\"Head (m)\")\n",
+    "plt.xlabel(\"Time (years)\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Generate ARMA(1,1) noise and add it to the synthetic heads\n",
+    "In the following code-block, noise is generated using an ARMA(1,1) process using NumPy. An alternative procedure is available from Statsmodels (commented out now). More information about the ARMA model can be found on the [statsmodels website](https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima_process.ArmaProcess.html). The noise is added to the head series generated in the previous code-block."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reproduction of random numbers\n",
+    "np.random.seed(1234)\n",
+    "alpha = 0.95\n",
+    "beta = 0.1\n",
+    "\n",
+    "# generate samples using Statsmodels\n",
+    "# import statsmodels.api as stats\n",
+    "# ar = np.array([1, -alpha])\n",
+    "# ma = np.r_[1, beta]\n",
+    "# arma = stats.tsa.ArmaProcess(ar, ma)\n",
+    "# noise = arma.generate_sample(head[0].index.size)*np.std(head.values) * 0.1\n",
+    "\n",
+    "# generate samples using NumPy\n",
+    "random_seed = np.random.RandomState(1234)\n",
+    "\n",
+    "noise = random_seed.normal(0, 1, len(head)) * np.std(head.values) * 0.1\n",
+    "a = np.zeros_like(head[0])\n",
+    "\n",
+    "for i in range(1, noise.size):\n",
+    "    a[i] = noise[i] + noise[i - 1] * beta + a[i - 1] * alpha\n",
+    "\n",
+    "head_noise = head[0] + a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x16ac41810>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(a)\n",
+    "plt.plot(noise)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Create and solve a Pastas Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit report 0                       Fit Statistics\n",
+      "=================================================\n",
+      "nfev    23                     EVP          88.34\n",
+      "nobs    8946                   R2            0.88\n",
+      "noise   True                   RMSE          0.11\n",
+      "tmin    1991-01-01 00:00:00    AIC      -62720.76\n",
+      "tmax    2015-06-29 00:00:00    BIC      -62678.16\n",
+      "freq    D                      Obj           4.03\n",
+      "warmup  3650 days 00:00:00     ___               \n",
+      "solver  LeastSquares           Interp.         No\n",
+      "\n",
+      "Parameters (6 optimized)\n",
+      "=================================================\n",
+      "                optimal  stderr     initial  vary\n",
+      "recharge_A   735.028508  ±3.16%  217.313623  True\n",
+      "recharge_n     1.089017  ±0.88%    1.000000  True\n",
+      "recharge_a   191.760204  ±4.98%   10.000000  True\n",
+      "constant_d    20.175721  ±0.27%   21.873653  True\n",
+      "noise_alpha   20.507272  ±7.16%    1.000000  True\n",
+      "noise_beta     0.433955  ±4.62%    1.000000  True\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x16bf65f00>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml = ps.Model(head_noise)\n",
+    "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
+    "ml.add_stressmodel(sm)\n",
+    "ml.add_noisemodel(ps.ArmaModel())\n",
+    "\n",
+    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=True)\n",
+    "axes = ml.plots.results(figsize=(10, 5))\n",
+    "axes[-2].plot(ps.Gamma().step([Atrue, ntrue, atrue]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Did we find back the original ARMA parameters?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.95 vs 0.95\n",
+      "0.1 vs. 0.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_alpha\", \"optimal\"]).round(2), \"vs\", alpha)\n",
+    "print(\n",
+    "    np.exp(-1.0 / np.abs(ml.parameters.loc[\"noise_beta\", \"optimal\"])).round(2),\n",
+    "    \"vs.\",\n",
+    "    beta,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The estimated parameters for the noise model are almost the same as the true parameters, showing that the model works for regular time steps.\n",
+    "\n",
+    "## 6. So is the autocorrelation removed correctly?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml.plots.diagnostics(figsize=(10, 4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That seems okay. It is important to understand that this noisemodel will only help in removing autocorrelations at the first time lag, but not at larger time lags, compared to its AR(1) counterpart. \n",
+    "\n",
+    "## 7. What happens if we use an AR(1) model?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.96 vs 0.95\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml = ps.Model(head_noise)\n",
+    "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
+    "ml.add_stressmodel(sm)\n",
+    "\n",
+    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
+    "axes = ml.plots.results(figsize=(10, 5))\n",
+    "axes[-2].plot(ps.Gamma().step([Atrue, ntrue, atrue]))\n",
+    "\n",
+    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_alpha\", \"optimal\"]).round(2), \"vs\", alpha)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml.plots.diagnostics(figsize=(10, 4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Significant autocorrelation is still present at lag 1 and the parameter of the AR(1) is overestimated, trying to correct for the lack of an MA(1) part. This is to be expected, as the MA(1) process generates a strong autocorrelation at this first time lag. The negative autocorrelation in the first few time steps is a result of the overestimation of the AR(1) parameter.\n",
+    "\n",
+    "A possible effect of failing to remove the autocorrelation at lag 1 may be that the parameter standard errors are under- or overestimated. Although that does not seem the case for this synthetic, real life examples may suffer from this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Test the ArmaModel for irregular time steps\n",
+    "In this final step the ArmaModel is tested for irregular timesteps, using the indices from a real groundwater level time series. It is clear from the example below that the ArmaModel does not yet work for irregular time steps, as (unlike the AR(1) model in Pastas) no weights are applied yet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index = (\n",
+    "    pd.read_csv(\"../examples/data/test_index.csv\", parse_dates=True, index_col=0)\n",
+    "    .index.round(\"D\")\n",
+    "    .drop_duplicates()\n",
+    ")\n",
+    "head_irregular = head_noise.reindex(index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.95 vs 0.95\n",
+      "0.08 vs. 0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml = ps.Model(head_irregular)\n",
+    "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
+    "ml.add_stressmodel(sm)\n",
+    "ml.add_noisemodel(ps.ArmaModel())\n",
+    "\n",
+    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
+    "axes = ml.plots.results(figsize=(10, 5))\n",
+    "axes[-2].plot(ps.Gamma().step([Atrue, ntrue, atrue]))\n",
+    "\n",
+    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_alpha\", \"optimal\"]).round(2), \"vs\", alpha)\n",
+    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_beta\", \"optimal\"]).round(2), \"vs.\", beta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml.plots.diagnostics(figsize=(10, 4));"
+   ]
+  }
+ ],
+ "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": "3.10.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/doc/benchmarks/autocorrelation.ipynb b/doc/benchmarks/autocorrelation.ipynb
new file mode 100644
index 00000000..5f144e56
--- /dev/null
+++ b/doc/benchmarks/autocorrelation.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Autocorrelation for irregular time series\n",
+    "*R.A. Collenteur, Artesia Water & University of Graz, 2020*\n",
+    "\n",
+    "In this notebook the autocorrelation function for irregular time steps that is built-in in Pastas is tested and validated on synthetic data. The methodology for calculating the autocorrelation is based on [Rehfeld et al. (2011)](#References) and [Edelson and Krolik (1978)](#References). The methods are available through `pastas.stats` package (e.g. `pastas.stats.acf(series)`). The full report of the methods underlying this Notebook are available in [Collenteur (2018, in Dutch)](#References). \n",
+    "\n",
+    "1. [Create synthetic time series](#Create-synthetic-time-series)\n",
+    "2. [Compute the autocorrelation](#Computing-the-autocorrelation)\n",
+    "3. [Autocorrelation for regular time series](#Autocorrelation-for-regular-time-series)\n",
+    "4. [Autocorrelation for irregular time series](#Sine-wave-with-non-equidistant-timesteps)\n",
+    "5. [References](#References)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pastas as ps\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create synthetic time series\n",
+    "Two synthetic time series are created with a known autocorrelation and a regular time interval. The first is a sine wave with a wave period of one year. The second is a series of correlated noise generated through an AR(1) process. Both synthetic time series have a length of ten years and a daily observation time step ($n=3650$). From both time series three time series with irregular time steps are created:\n",
+    "\n",
+    "- **Time Series 1:** 3 years of monthly data, 3 years of bi-weekly data, and 4 years of daily data.\n",
+    "- **Time Series 2:** 3 years of monthly data, 3 years of bi-weekly data, a one year gap, and 4 years of daily data.\n",
+    "- **Time Series 3:** reindex time series using the indices from a real groundwater level time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/hs/1j43d1k53k38dlwr99tmgf780000gn/T/ipykernel_66733/2057735080.py:24: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  .append(v.iloc[-7 * 365 : -4 * 365].asfreq(\"14D\"))\n",
+      "/var/folders/hs/1j43d1k53k38dlwr99tmgf780000gn/T/ipykernel_66733/2057735080.py:25: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  .append(v.iloc[-4 * 365 :])\n",
+      "/var/folders/hs/1j43d1k53k38dlwr99tmgf780000gn/T/ipykernel_66733/2057735080.py:33: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  .append(v.iloc[-7 * 365 : -4 * 365].asfreq(\"14D\"))\n",
+      "/var/folders/hs/1j43d1k53k38dlwr99tmgf780000gn/T/ipykernel_66733/2057735080.py:34: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  .append(v.iloc[-3 * 365 :])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "index_test = (\n",
+    "    pd.read_csv(\n",
+    "        \"../examples/data/test_index.csv\", parse_dates=True, index_col=0, names=[\"Date\"]\n",
+    "    )\n",
+    "    .squeeze()\n",
+    "    .index.ceil(\"D\")\n",
+    "    .unique()\n",
+    ")\n",
+    "n_years = 10\n",
+    "index = pd.to_datetime(np.arange(0, n_years * 365, 1), unit=\"D\", origin=\"2005\")\n",
+    "index.name = \"Time [Years]\"\n",
+    "\n",
+    "# 1. Sine timeseries 1: equal timesteps\n",
+    "np.random.seed(0)\n",
+    "data = np.sin(\n",
+    "    np.linspace(0, n_years * 2 * np.pi, len(index))\n",
+    ")  # +np.random.rand(index.size)\n",
+    "v = pd.Series(data=data, index=index, name=\"STS_SIN\")\n",
+    "\n",
+    "# 2. Sine timeseries with three frequencies\n",
+    "v1 = (\n",
+    "    v.iloc[: -7 * 365]\n",
+    "    .asfreq(\"30D\")\n",
+    "    .append(v.iloc[-7 * 365 : -4 * 365].asfreq(\"14D\"))\n",
+    "    .append(v.iloc[-4 * 365 :])\n",
+    ")\n",
+    "v1.name = \"STS_SIN1\"\n",
+    "\n",
+    "# 3. Sine timeseries with three frequencies and a data gap\n",
+    "v2 = (\n",
+    "    v.iloc[: -7 * 365]\n",
+    "    .asfreq(\"30D\")\n",
+    "    .append(v.iloc[-7 * 365 : -4 * 365].asfreq(\"14D\"))\n",
+    "    .append(v.iloc[-3 * 365 :])\n",
+    ")\n",
+    "v2.name = \"STS_SIN2\"\n",
+    "\n",
+    "# 4. Sine timeseries with indices based on a true groundwater level measurement indices\n",
+    "v3 = v.reindex(index_test).dropna()\n",
+    "v3.name = \"STS_SIN3\"\n",
+    "\n",
+    "# Convoluting a random noise process with a exponential decay function to obtain a autocorrelation\n",
+    "# timeseries similar to an Auto Regressive model of order 1 (AR(1))\n",
+    "alpha = 10\n",
+    "np.random.seed(0)\n",
+    "n = np.random.rand(index.size + 365)\n",
+    "b = np.exp(-np.arange(366) / alpha)\n",
+    "n = np.convolve(n, b, mode=\"valid\")\n",
+    "n = n - n.mean()\n",
+    "\n",
+    "index = pd.to_datetime(np.arange(0, n.size, 1), unit=\"D\", origin=\"2005\")\n",
+    "index.name = \"Time [Years]\"\n",
+    "n = pd.Series(data=n, index=index, name=\"STS_EXP\")\n",
+    "\n",
+    "n1 = n.reindex(v1.index).dropna()\n",
+    "n1.name = \"STS_EXP1\"\n",
+    "n2 = n.reindex(v2.index).dropna()\n",
+    "n2.name = \"STS_EXP2\"\n",
+    "n3 = n.reindex(index_test).dropna()\n",
+    "n3.name = \"STS_EXP3\"\n",
+    "\n",
+    "# Create a DataFrame with all series and plot them all\n",
+    "d = pd.concat([v, v1, v2, v3, n, n1, n2, n3], axis=1)\n",
+    "d.plot(subplots=True, figsize=(10, 6), marker=\".\", markersize=2, color=\"steelblue\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Theoretical autocorrelation\n",
+    "In this codeblock a figure is created showing the time series with equidistant timesteps and their theoretical autocorrelation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute true autocorrelation functions\n",
+    "acf_v_true = pd.Series(np.cos(np.linspace(0, 2 * np.pi, 365)))\n",
+    "acf_n_true = pd.Series(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Computing the autocorrelation\n",
+    "\n",
+    "The autocorrelation function for all time series is calculated for a number of time lags. Two different methods are used:\n",
+    "\n",
+    "- binning in a rectangular bin\n",
+    "- weighting through a Gaussian kernel\n",
+    "\n",
+    "<div class=\"alert alert-warning\">\n",
+    "    \n",
+    "<b>Warning</b> \n",
+    "    \n",
+    "Calculation of all the autocorrelation functions can take several minutes!\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lags = np.arange(1.0, 366)\n",
+    "acf_df = pd.DataFrame(index=lags)\n",
+    "\n",
+    "for name, sts in d.items():\n",
+    "    for method in [\"rectangle\", \"gaussian\"]:\n",
+    "        acf = ps.stats.acf(sts.dropna(), bin_method=method, max_gap=30)\n",
+    "        acf.index = acf.index.days\n",
+    "        acf_df.loc[:, name + \"_\" + method] = acf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Autocorrelation for regular time series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Error [L]')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x700 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(3, 2, figsize=(10, 7), sharey=\"row\")\n",
+    "\n",
+    "for i, name in enumerate([\"STS_SIN\", \"STS_EXP\"]):\n",
+    "    sts = d.loc[:, name]\n",
+    "    sts.plot(ax=axes[0][i], style=\".\", legend=True, label=name)\n",
+    "\n",
+    "    if name == \"STS_SIN\":\n",
+    "        acf_true = acf_v_true\n",
+    "    else:\n",
+    "        acf_true = acf_n_true\n",
+    "\n",
+    "    acf_true.plot(ax=axes[1][i])\n",
+    "\n",
+    "    axes[2][i].plot([0.0, 365], [0, 0], label=\"true ACF\")\n",
+    "    for bm in [\"gaussian\", \"rectangle\"]:\n",
+    "        acf_name = name + \"_\" + bm\n",
+    "        acf = acf_df.loc[:, acf_name]\n",
+    "        if bm == \"gaussian\":\n",
+    "            kwargs = dict(marker=\"x\", markersize=10)\n",
+    "        else:\n",
+    "            kwargs = dict(marker=\"o\")\n",
+    "        acf.plot(label=bm, ax=axes[1][i], logx=True, linestyle=\"\", **kwargs)\n",
+    "        dif = acf.subtract(acf_true).dropna()\n",
+    "        rmse = \" (RMSE={:.2f})\".format(np.sqrt((dif.pow(2)).sum() / dif.size))\n",
+    "        dif.plot(label=bm + rmse, ax=axes[2][i], logx=True, **kwargs)\n",
+    "        axes[2][i].legend()\n",
+    "        axes[1][i].set_xticks([])\n",
+    "        axes[2][i].set_xlabel(\"Lags [Days]\")\n",
+    "        axes[2][i].set_xticks([1, 10, 30, 60, 180, 365])\n",
+    "        axes[2][i].set_xticklabels([1, 10, 30, 60, 180, 365])\n",
+    "\n",
+    "ax = axes[0][0].set_ylabel(\"Noise [L]\")\n",
+    "axes[1][0].set_ylabel(\"ACF [-]\")\n",
+    "axes[2][0].set_ylabel(\"Error [L]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sine wave with non-equidistant timesteps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Error [L]')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1600x900 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(3, 3, figsize=(16, 9), sharey=\"row\")\n",
+    "#\n",
+    "for i, name in enumerate([\"STS_SIN1\", \"STS_SIN2\", \"STS_SIN3\"]):\n",
+    "    sts = d.loc[:, name]\n",
+    "    sts.plot(ax=axes[0][i], style=\".\", label=name)\n",
+    "    axes[0][i].legend(loc=3)\n",
+    "    acf_v_true.plot(ax=axes[1][i])\n",
+    "    axes[2][i].plot([0.0, 365], [0, 0], label=\"true ACF\")\n",
+    "    for bm in [\"gaussian\", \"rectangle\"]:\n",
+    "        acf_name = name + \"_\" + bm\n",
+    "        acf = acf_df.loc[:, acf_name]\n",
+    "        if bm == \"gaussian\":\n",
+    "            kwargs = dict(marker=\"x\", markersize=10)\n",
+    "        else:\n",
+    "            kwargs = dict(marker=\"o\")\n",
+    "        acf.plot(label=bm, ax=axes[1][i], logx=True, linestyle=\"\", **kwargs)\n",
+    "        dif = acf.subtract(acf_v_true).dropna()\n",
+    "        rmse = \" (RMSE={:.2f})\".format(np.sqrt((dif.pow(2)).sum() / dif.size))\n",
+    "        dif.plot(label=bm + rmse, ax=axes[2][i], logx=True, **kwargs)\n",
+    "        axes[1][i].set_xticks([])\n",
+    "        axes[2][i].set_xticks([1, 10, 30, 60, 180, 365])\n",
+    "        axes[2][i].set_xticklabels([1, 10, 30, 60, 180, 365])\n",
+    "        axes[2][i].legend(loc=3)\n",
+    "        axes[2][i].set_xlabel(\"Lags [Days]\")\n",
+    "\n",
+    "axes[2][i].relim()\n",
+    "axes[0][0].set_ylabel(\"Noise [L]\")\n",
+    "axes[1][0].set_ylabel(\"ACF [-]\")\n",
+    "axes[2][0].set_ylabel(\"Error [L]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exponential decay with non-equidistant timesteps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Difference from true value [-]')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1600x900 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(3, 3, figsize=(16, 9), sharey=\"row\")\n",
+    "\n",
+    "for i, name in enumerate([\"STS_EXP1\", \"STS_EXP2\", \"STS_EXP3\"]):\n",
+    "    sts = d.loc[:, name]\n",
+    "    sts.plot(ax=axes[0][i], style=\".\", label=name)\n",
+    "    axes[0][i].legend(loc=3)\n",
+    "    acf_n_true.plot(ax=axes[1][i])\n",
+    "    axes[2][i].plot([0.0, 365], [0, 0], label=\"true ACF\")\n",
+    "    for j, bm in enumerate([\"gaussian\", \"rectangle\"]):\n",
+    "        acf_name = name + \"_\" + bm\n",
+    "        acf = acf_df.loc[:, acf_name]\n",
+    "        if bm == \"gaussian\":\n",
+    "            kwargs = dict(marker=\"x\", markersize=10)\n",
+    "        else:\n",
+    "            kwargs = dict(marker=\"o\")\n",
+    "        acf.plot(label=bm, ax=axes[1][i], logx=True, linestyle=\"\", **kwargs)\n",
+    "        dif = acf.subtract(acf_n_true).dropna()\n",
+    "        rmse = \" (RMSE={:.2f})\".format(np.sqrt((dif.pow(2)).sum() / dif.size))\n",
+    "        dif.plot(label=bm + rmse, ax=axes[2][i], logx=True, sharey=axes[2][0], **kwargs)\n",
+    "        axes[2][i].set_xticks([1, 10, 30, 60, 180, 365])\n",
+    "        axes[2][i].set_xticklabels([1, 10, 30, 60, 180, 365])\n",
+    "        axes[1][i].set_xticks([])\n",
+    "        axes[2][i].legend(loc=2)\n",
+    "        axes[2][i].set_xlabel(\"Lags [Days]\")\n",
+    "\n",
+    "axes[0][0].set_ylabel(\"Noise [L]\")\n",
+    "axes[1][0].set_ylabel(\"Autocorrelation [-]\")\n",
+    "axes[2][0].set_ylabel(\"Difference from true value [-]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "- Rehfeld, K., Marwan, N., Heitzig, J., Kurths, J. (2011). [Comparison of correlation analysis techniques for irregularly sampled time series](https://doi.org/10.5194/npg-18-389-2011). Nonlinear Processes in Geophysics. 18. 389-404.\n",
+    "- Edelson, R. A., & Krolik, J. H. (1988). The discrete correlation function-A new method for analyzing unevenly sampled variability data. The Astrophysical Journal, 333, 646-659.\n",
+    "- Collenteur, R.A. (2018) [Over autocorrelatie van tijdreeksmoddellen met niet-equidistante tijdstappen](http://www.artesia-water.nl/wp-content/uploads/Autocorrelatie_niet_gelijke_tijdstappen.pdf), Artesia, Schoonhoven, Nederland. In Dutch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "dace5e1b41a98a8e52d2a8eebc3b981caf2c12e7a76736ebfb89a489e3b62e79"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/doc/benchmarks/check_response_functions.ipynb b/doc/benchmarks/check_response_functions.ipynb
new file mode 100644
index 00000000..ed684bd7
--- /dev/null
+++ b/doc/benchmarks/check_response_functions.ipynb
@@ -0,0 +1,989 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "45d1dfdc",
+   "metadata": {},
+   "source": [
+    "# Check Response Functions\n",
+    "This notebook has two topics:\n",
+    "1. It checks the step response functions by numerically integrating the impulse response functions.\n",
+    "2. It compares the Polder, Hantush, and HantushWell response functions to their classical equivalents and gives relationships between the parameters used in Pastas and the aquifer parameters used in the classical functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "938b129f-c9ac-418d-879e-f2eba52298d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.integrate import quad\n",
+    "import pastas as ps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c0335c02-6e9b-4cb4-8f31-5378c8a769cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams[\"figure.figsize\"] = [3.6, 2.4]\n",
+    "plt.rcParams[\"font.size\"] = 8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ca33adb-b484-4836-894e-daaa3c560807",
+   "metadata": {},
+   "source": [
+    "## Comparison of step function with numerical integration of the impulse response function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a50691a-85cb-487b-87ad-a238224cdda2",
+   "metadata": {},
+   "source": [
+    "### Gamma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c5a802df-52d5-49c7-95c0-aa026b94b4f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$$ \\displaystyle \\begin{array}{l} \\left( A\\space,\\space n\\space,\\space a\\right)  = p \\\\ \\mathrm{theta}(t, p) = \\frac{A t^{n - {1}} \\exp{\\left({\\frac{-t}{a}}\\right)}}{a^{n} \\mathrm{{\\Gamma}}\\left(n\\right)} \\end{array} $$"
+      ],
+      "text/plain": [
+       "<latexify.frontend.LatexifiedFunction at 0x7fa35bd1f640>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ps.Gamma.impulse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7b8bf54b-44ac-444c-9f30-4fad27b5a546",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = 5\n",
+    "n = 1.5\n",
+    "a = 50\n",
+    "p = [A, n, a]\n",
+    "\n",
+    "gamma = ps.Gamma()\n",
+    "tmax = gamma.get_tmax(p)\n",
+    "t = np.arange(0, tmax)\n",
+    "\n",
+    "step = gamma.step(p)\n",
+    "stepnum = np.zeros(len(t))\n",
+    "for i in range(1, len(t)):\n",
+    "    stepnum[i] = quad(gamma.impulse, 0, t[i], args=(p))[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "41d2e835-bf3a-47e7-9f6b-7d0dce2d9c6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(t[1:], step, label=\"analytic\")\n",
+    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
+    "plt.xlabel(\"time (d)\")\n",
+    "plt.ylabel(\"step (m)\")\n",
+    "plt.grid()\n",
+    "_ = plt.legend()  # try to show figure in readthedocs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94fb2160-3637-43c7-b8f1-25d508c9b485",
+   "metadata": {},
+   "source": [
+    "### Exponential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c4508596-cf12-4ed4-b74a-567b25a88ee5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$$ \\displaystyle \\begin{array}{l} \\left( A\\space,\\space a\\right)  = p \\\\ \\mathrm{theta}(t, p) = \\frac{A}{a} \\exp{\\left({\\frac{-t}{a}}\\right)} \\end{array} $$"
+      ],
+      "text/plain": [
+       "<latexify.frontend.LatexifiedFunction at 0x7fa35bd3f160>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ps.Exponential.impulse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "77ca80bb-c678-46c1-aab1-f0e2ec60ebdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = 5\n",
+    "a = 50\n",
+    "p = [A, a]\n",
+    "\n",
+    "exponential = ps.Exponential()\n",
+    "tmax = exponential.get_tmax(p)\n",
+    "t = np.arange(0, tmax)\n",
+    "\n",
+    "step = exponential.step(p)\n",
+    "stepnum = np.zeros(len(t))\n",
+    "for i in range(1, len(t)):\n",
+    "    stepnum[i] = quad(exponential.impulse, 0, t[i], args=(p))[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6172dd00-763c-4f25-b4f0-3c34a15b3c33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(t[1:], step, label=\"analytic\")\n",
+    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
+    "plt.xlabel(\"time (d)\")\n",
+    "plt.ylabel(\"step (m)\")\n",
+    "plt.grid()\n",
+    "_ = plt.legend()  # try to show figure in readthedocs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d007aa2-7c99-4c3c-9c2a-f413a8f5a5a3",
+   "metadata": {},
+   "source": [
+    "### Hantush"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "750373d0-402a-478c-b1f4-b3457cdb4812",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$$ \\displaystyle \\begin{array}{l} \\left( A\\space,\\space a\\space,\\space b\\right)  = p \\\\ \\mathrm{theta}(t, p) = \\frac{A}{{2} t \\mathrm{K_0}\\left({2} \\sqrt{b}\\right)} \\exp{\\left({\\frac{-t}{a} - \\frac{a b}{t}}\\right)} \\end{array} $$"
+      ],
+      "text/plain": [
+       "<latexify.frontend.LatexifiedFunction at 0x7fa35bdc8a60>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ps.Hantush.impulse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "35790f67-b518-4367-90c3-2d5a425da0c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = 5\n",
+    "a = 50\n",
+    "b = 2\n",
+    "p = [A, a, b]\n",
+    "\n",
+    "hantush = ps.Hantush()\n",
+    "tmax = hantush.get_tmax(p)\n",
+    "t = np.arange(0, tmax)\n",
+    "\n",
+    "step = hantush.step(p)\n",
+    "stepnum = np.zeros(len(t))\n",
+    "for i in range(1, len(t)):\n",
+    "    stepnum[i] = quad(hantush.impulse, 0, t[i], args=(p))[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e63a25d6-1b30-41a2-a02d-d4d013925c30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(t[1:], step, label=\"analytic\")\n",
+    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
+    "plt.xlabel(\"time (d)\")\n",
+    "plt.ylabel(\"step (m)\")\n",
+    "plt.grid()\n",
+    "_ = plt.legend()  # try to show figure in readthedocs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5d822c8-f954-4e7c-96c6-de24895d4da8",
+   "metadata": {},
+   "source": [
+    "### Polder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "c2dd0ae6-fa59-482b-94b1-74b4b31e0a8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$$ \\displaystyle \\begin{array}{l} \\left( A\\space,\\space a\\space,\\space b\\right)  = p \\\\ \\mathrm{theta}(t, p) = A \\sqrt{\\frac{a b}{{\\pi}}} t^{-{1.5}} \\exp{\\left({\\frac{-t}{a} - \\frac{a b}{t}}\\right)} \\end{array} $$"
+      ],
+      "text/plain": [
+       "<latexify.frontend.LatexifiedFunction at 0x7fa35bdc8eb0>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ps.Polder.impulse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2eaa17db-8cba-4731-926e-4f8479987063",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = 5\n",
+    "a = 100\n",
+    "b = 0.25\n",
+    "p = [A, a, b]\n",
+    "\n",
+    "polder = ps.Polder()\n",
+    "tmax = polder.get_tmax(p)\n",
+    "t = np.arange(0, tmax)\n",
+    "\n",
+    "step = polder.step(p)\n",
+    "stepnum = np.zeros(len(t))\n",
+    "for i in range(1, len(t)):\n",
+    "    stepnum[i] = quad(polder.impulse, 0, t[i], args=(p))[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "419cf91b-9a53-440d-958d-fc085ec0a4b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(t[1:], step, label=\"analytic\")\n",
+    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
+    "plt.xlabel(\"time (d)\")\n",
+    "plt.ylabel(\"step (m)\")\n",
+    "plt.grid()\n",
+    "_ = plt.legend()  # try to show figure in readthedocs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd0facf9-763d-47a6-b722-ebf50b02a2d1",
+   "metadata": {},
+   "source": [
+    "### Four-parameter function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "57d08799-3632-4bb6-84a1-509f72a5861a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$$ \\displaystyle \\begin{array}{l} \\left( _\\space,\\space n\\space,\\space a\\space,\\space b\\right)  = p \\\\ \\mathrm{theta}(t, p) = t^{n - {1}} \\exp{\\left({\\frac{-t}{a} - \\frac{a b}{t}}\\right)} \\end{array} $$"
+      ],
+      "text/plain": [
+       "<latexify.frontend.LatexifiedFunction at 0x7fa35be1b400>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ps.FourParam.impulse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "83601f06-0737-4ae9-b7eb-0b113a90bc85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = 1  # impulse response implemented for A=1 only\n",
+    "n = 1.5\n",
+    "a = 50\n",
+    "b = 10\n",
+    "p = [A, n, a, b]\n",
+    "\n",
+    "fourparam = ps.FourParam(quad=False)  # use simple integration\n",
+    "tmax = fourparam.get_tmax(p)\n",
+    "t = np.arange(0, tmax)\n",
+    "\n",
+    "step = fourparam.step(p)\n",
+    "stepnum = np.zeros(len(t))\n",
+    "for i in range(1, len(t)):\n",
+    "    stepnum[i] = quad(fourparam.impulse, 0, t[i], args=(p))[0]\n",
+    "stepnum = (\n",
+    "    stepnum / quad(fourparam.impulse, 0, np.inf, args=p)[0]\n",
+    ")  # four param is scaled at the end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "095f83cb-9a04-4651-8031-7f2f84445c54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(t[1:], step, label=\"analytic\")\n",
+    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
+    "plt.xlabel(\"time (d)\")\n",
+    "plt.ylabel(\"step (m)\")\n",
+    "plt.grid()\n",
+    "_ = plt.legend()  # try to show figure in readthedocs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0396351-cd54-4666-b847-862ee442a032",
+   "metadata": {},
+   "source": [
+    "### Double exponential function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "07790a6a-8ec8-4b95-9160-a832b2aceed2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$$ \\displaystyle \\begin{array}{l} \\left( A\\space,\\space {\\alpha}\\space,\\space a_1\\space,\\space a_2\\right)  = p \\\\ \\mathrm{theta}(t, p) = A \\left( \\frac{{1} - {\\alpha}}{a_1} \\exp{\\left({\\frac{-t}{a_1}}\\right)} + \\frac{{\\alpha}}{a_2} \\exp{\\left({\\frac{-t}{a_2}}\\right)} \\right) \\end{array} $$"
+      ],
+      "text/plain": [
+       "<latexify.frontend.LatexifiedFunction at 0x7fa35bd3f3d0>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ps.DoubleExponential.impulse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "42e26e95-9afb-4595-b170-0f5ee6e0a186",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = 5  # impulse response implemented for A=1 only\n",
+    "a = 10\n",
+    "b = 50\n",
+    "f = 0.4\n",
+    "p = [A, f, a, b]\n",
+    "\n",
+    "doubexp = ps.DoubleExponential()\n",
+    "tmax = doubexp.get_tmax(p)\n",
+    "t = np.arange(0, tmax)\n",
+    "\n",
+    "step = doubexp.step(p)\n",
+    "stepnum = np.zeros(len(t))\n",
+    "for i in range(1, len(t)):\n",
+    "    stepnum[i] = quad(doubexp.impulse, 0, t[i], args=(p))[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "8299b68e-fe97-45cf-8b5b-7f61534d6a60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(t[1:], step, label=\"analytic\")\n",
+    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
+    "plt.xlabel(\"time (d)\")\n",
+    "plt.ylabel(\"step (m)\")\n",
+    "plt.grid()\n",
+    "_ = plt.legend()  # try to show figure in readthedocs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5b5fa2c",
+   "metadata": {},
+   "source": [
+    "### Kraijenhoff\n",
+    "\n",
+    "#### Kraijenhoff van de Leur\n",
+    "\n",
+    "##### Impulse Response\n",
+    "from [A study of non-steady groundwater flow with special reference to a reservoir-coefficient (1958)](https://edepot.wur.nl/422032) formula 2\n",
+    "\n",
+    "$ \\theta(t) = \\frac{4N}{\\pi S} \\sum_{n=1,3,5...}^\\infty \\left( \\frac{1}{n} \\exp{\\left( {-n^2\\frac{\\pi^2T}{SL^2} t} \\right)} \\sin \\left(\\frac{n\\pi x}{L}\\right) \\right) $\n",
+    "\n",
+    "##### Step Response\n",
+    "\n",
+    "The step response is obtained by taking the integral of the impulse response function\n",
+    "\n",
+    "$ \\Theta(t) = \\frac{4 N}{\\pi S} \\sum_{n=1,3,5...}^\\infty \\frac{1}{n^3} \\left(\\frac{SL^2}{\\pi^2 T} - \\frac{SL^2}{\\pi^2 T} \\exp\\left(-n^2\\frac{\\pi^2T}{SL^2}t\\right)\\right)  \\sin \\left(\\frac{n\\pi x}{L}\\right) $\n",
+    "\n",
+    "$ \\Theta(t) = \\frac{4 N L^2}{\\pi^3 T} \\sum_{n=1,3,5...}^\\infty \\frac{1}{n^3} \\left(1 - \\exp\\left(-n^2\\frac{\\pi^2T}{SL^2}t\\right)\\right)  \\sin \\left(\\frac{n\\pi x}{L}\\right)$\n",
+    "\n",
+    "And $\\sum_{n=1,3,5...}^\\infty n = \\sum_{n=0}^\\infty (2n+1)$ gives:\n",
+    "\n",
+    "$ \\Theta(t) = \\frac{4 N L^2}{\\pi^3 T} \\sum_{n=0}^\\infty \\frac{1}{(2n+1)^3} \\left(1 - \\exp\\left(-(2n+1)^2\\frac{\\pi^2T}{SL^2}t)\\right)\\right)  \\sin \\left(\\frac{(2n+1)\\pi x}{L}\\right)$\n",
+    "\n",
+    "\n",
+    "Kraijenhoff van de Leur takes $\\frac{x}{L}=\\frac{1}{2}$ as the middle of the domain. \n",
+    "\n",
+    "#### Bruggeman \n",
+    "\n",
+    "from Analytical Solutions of Geohydrological Problems (1999) formula 133.15\n",
+    "\n",
+    "##### Step Response\n",
+    "\n",
+    "$ \\Theta(t) = \\frac{-N}{2T}\\left(x^2 - \\frac{1}{4}L^2\\right) - \\frac{4NL^2}{\\pi^3T} \\sum_{n=0}^\\infty\n",
+    "\\frac{(-1)^n}{(2n + 1)^3} \\cos\\left(\\frac{(2n+1)\\pi x}{L}\\right)\n",
+    "\\exp\\left(-\\frac{(2n+1)^2\\pi^2 T}{SL^2}t\\right)\n",
+    "$\n",
+    "\n",
+    "$ \\Theta(t) = \\frac{-NL^2}{2T}\\left(\\left(\\frac{x}{L}\\right)^2 - \\frac{1}{4}\\right) - \\frac{4NL^2}{\\pi^3T} \\sum_{n=0}^\\infty\n",
+    "\\frac{(-1)^n}{(2n + 1)^3} \n",
+    "\\exp\\left(-\\frac{(2n+1)^2\\pi^2 T}{SL^2}t\\right) \\cos\\left(\\frac{(2n+1)\\pi x}{L}\\right)\n",
+    "$\n",
+    "\n",
+    "$ \\Theta(t) = \\frac{-NL^2}{2T}\\left(\\left(\\frac{x}{L}\\right)^2 - \\tfrac{1}{4}\\right) \\left(1 - \\frac{8}{\\pi^3 \\left(\\frac{1}{4} - \\left(\\frac{x}{L}\\right)^2\\right)} \\sum_{n=0}^\\infty\n",
+    "\\frac{(-1)^n}{(2n + 1)^3} \n",
+    "\\exp\\left(-\\frac{(2n+1)^2\\pi^2 T}{SL^2}t\\right) \\cos\\left(\\frac{(2n+1)\\pi x}{L}\\right) \\right)\n",
+    "$\n",
+    "\n",
+    "Note that $x=0$ is the middle of the domain for Bruggeman.\n",
+    "\n",
+    "#### Pastas Implementation\n",
+    "\n",
+    "In Pastas the Bruggeman response function is computed and the parameters are transformed to:\n",
+    "\n",
+    "Scale parameter (such that the gain is always $A$):\n",
+    "\n",
+    "$A = \\frac{-NL^2}{2T}\\left(\\left(\\frac{x}{L}\\right)^2 - \\tfrac{1}{4}\\right)$\n",
+    "\n",
+    "Reservoir coefficient (also known as $j$ in Kraijenhoff):\n",
+    "\n",
+    "$a = \\frac{SL^2}{\\pi^2 T}$\n",
+    "\n",
+    "Location in the domain:\n",
+    "\n",
+    "$b = \\frac{x}{L}$\n",
+    "\n",
+    "\n",
+    "Such that the step response becomes:\n",
+    "\n",
+    "$ \\Theta(t) = A\\left(1 - \\frac{8}{\\pi^3(\\frac{1}{4} - b^2)} \\sum_{n=0}^\\infty \\frac{(-1)^n}{(2n+1)^3} \\cos\\left((2n+1)\\pi b\\right)\\exp\\left(-\\frac{(2n+1)^2t}{a}\\right) \\right)$\n",
+    "\n",
+    "Taking the derivative gives the impulse response:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "798ab1b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$$ \\displaystyle \\begin{array}{l} \\left( A\\space,\\space a\\space,\\space b\\right)  = p \\\\ nterms = {10} \\\\ \\mathrm{theta}(t, p) = \\frac{A {8}}{{\\pi}^{{3}} \\left( \\frac{{1}}{{4}} - b^{{2}} \\right)} \\sum_{n = {0}}^{{nterms - 1}} \\left({\\frac{\\left( -{1} \\right)^{n}}{a \\left( {2} n + {1} \\right)} \\cos{\\left({\\left( {2} n + {1} \\right) {\\pi} b}\\right)} \\exp{\\left({\\frac{-\\left( \\left( {2} n + {1} \\right)^{{2}} t \\right)}{a}}\\right)}}\\right) \\end{array} $$"
+      ],
+      "text/plain": [
+       "<latexify.frontend.LatexifiedFunction at 0x7fa35be1bb20>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ps.Kraijenhoff.impulse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "c3835b9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = 5\n",
+    "a = 10\n",
+    "b = 0.25\n",
+    "p = [A, a, b]\n",
+    "\n",
+    "khoff = ps.Kraijenhoff()\n",
+    "tmax = khoff.get_tmax(p)\n",
+    "t = np.arange(0, tmax)\n",
+    "\n",
+    "step = khoff.step(p)\n",
+    "stepnum_brug = np.zeros(len(t))\n",
+    "\n",
+    "for i in range(1, len(t)):\n",
+    "    stepnum_brug[i] = quad(khoff.impulse, 0, t[i], args=(p))[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "e2c870cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(t[1:], step, label=\"analytic\")\n",
+    "plt.plot(t, stepnum_brug, \"--\", label=\"numerical Bruggeman\")\n",
+    "plt.xlabel(\"time (d)\")\n",
+    "plt.ylabel(\"step (m)\")\n",
+    "plt.grid()\n",
+    "_ = plt.legend()  # try to show figure in readthedocs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ee7967d-0699-428c-b9fe-23a3f2b65894",
+   "metadata": {},
+   "source": [
+    "## Comparison to classical analytical response functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c35cd520-32a0-4d17-8f66-ec7f34eadbaa",
+   "metadata": {},
+   "source": [
+    "### Polder step function compared to classic polder function\n",
+    "The classic polder function is (Eq. 123.32 in Bruggeman, 1999)\n",
+    "\n",
+    "$$\n",
+    "h(t) = \\Delta h \\text{P}\\left(\\frac{x}{2\\lambda}, \\sqrt{\\frac{t}{cS}}\\right)\n",
+    "$$\n",
+    "\n",
+    "where P is the polder function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "eabfe32f-5b8d-4ecc-a18b-6698e3ddc379",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fa35ccbbf70>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ieLQq3Rw8WdwVdVnPFXPmpVuIrvmBAjrh+J+P6NG7n9axhNBck61KC+9mq6og968/JbrmB0oIpmra+1KKQlwDKcZWosbpJP3le4mtPkSZCqD4zn/Qs2+S1rGEaJGkGFsB5XKxb+XPSKn4kmplIOeWVUQl3qB1LCFaLCnGVmDttj1EFX6GS+k4NOQFBtxwm9aRhGjRZHSdFu7jw/ks2VrMSp7md0lWbpowW+tIQrR4Uowt2JFTJTz67gGUgpuGpnLjTwdoHUmIVkFWpVuoonwL/q/fyJCafdwY05WnJsbKKDlCNBJZYmyBqu02ClfdS3+Vw1LjWjpM+aVc2lSIRiTfphbo29cfpr/jCGUqAN20NZjay1ktQjQmKcYWZt/mlQwpXA/A8ZF/IjImSdtAQrRCUowtSPb3+4j95kkAdoU/QNLN0zROJETrJMXYQlSWl8K6WbTT2TlkTCF11h+0jiREqyU7X1qI3314lAHVMQQaygl74G18DPLRCdFU5NvVAmw6cIq13xai182h77TnGRwarnUkIVo1WZX2crl5eTzx/kEAFoyJZvCAaI0TCdH6STF6sRqnE+sb9/BX1+/4SQ8nC8dEaR1JiDZBVqW92J53ljLMcZgKvT+9bouRg7iFaCbyTfNSWel7GXj8rwAcSVpCj979NU4kRNshxeiFHNV2ajbMx0/n5EDAUAb/dIHWkYRoU6QYvdC+NU8SVXOcUgIJv/9VdHr5mIRoTvKN8zInM/YzMPt1ADJTnqRLWE+NEwnR9kgxehGXS/Hip0fIVqF8F5DKwNvnaR1JiDbJ42LMzMxk+PDhxMTEkJqaSnp6ep1pdu3aRVJSEklJSQwYMID58+djt9sbJXBrtubrk2zI68QUnifkf96QVWghNOLxN2/+/PnMmzePY8eOsWjRIubMmVNnmsTERPbu3cuBAwc4dOgQhYWFpKWlNUrg1qrAauP3Hx8F4NHx8XTvLme3CKEVj4qxoKCA/fv3M2PGDAAmT55MVlYW2dnZl0zXrl07fH19Aaiurqaqqgr9ZZZ+7HY7Vqv1kltbdHzVbGY4N5LSox0zhsp2RSG05FExWiwWwsLCMPw4gIFOpyMyMpKcnJw602ZnZ5OUlESXLl0IDg5m3rz6t5ctW7YMk8nkvkVERFzD22jZDn2xiWGl/+ZXhnd5/kYjPnq5RIEQWvJ4Vbr2dUWUUvVOZzabOXDgAPn5+djtdjZu3FjvdIsXL6a0tNR9s1gsnkZq0artNkzbFwOwL2Qy0YnDNU4khPCoGCMiIsjNzcXpdALnS9FisRAZGXnZ57Rv356pU6eyZs2aeh83Go0EBwdfcmtL9q9/nkjXKYox0X/677WOI4TAw2IMCQkhOTmZ1atXA7BhwwbMZjNms/mS6Y4fP47D4QDOb2PcuHEjCQkJjZO4FSnKtzDg2CsAnEh4jOAOnTVOJISAa1iVTktLIy0tjZiYGJYvX86qVasAmDt3Lps3bwZg+/btJCcnk5iYSHJyMqGhoTzxxBONm7wVOPGPRQTpqsg0RDNQTvsTwmvo1OU2EmrEarViMpkoLS1t1avVR3/IxPz2UIw6Jxm3baDf4Ju1jiREq+ZJt8iwYxpQSrH082JOVz/PL8JPcJeUohBeRYpRA9syCth5vBg/nx4Mnnaf1nGEELXIOWfNzOmoZs0HnwIw+wYzEZ3aaZxICFGbLDE2s2//9Qqvlz/FO/4TmDj6La3jCCHqIUuMzchWVUHkwRXodYqoqL6YAny1jiSEqIcUYzM6sOEPhFJMPl1ImvwrreMIIS5DirGZlJWepe8P5wegzUn4f/gHBGqcSAhxOVKMzeTwxufpSBk5+h6kTHxQ6zhCiCuQYmwGpSVFDDj5NgAFAx/F4OuncSIhxJVIMTaDj7Zto0bpyNZHkjxuttZxhBBXIcXYxEorHTxz0MRI+5/J+clf8THIEVJCeDspxib2xs4syu1OwruFMmLYCK3jCCEaQIqxCZWVniXzqw2AYsGYKPQyMrcQLYKs1zWhw//8I3/hZe5uP4Ib4z7QOo4QooFkibGJVFWU0Tfr7wC0j79NruMiRAsixdhEvtv8Ep2wckoXSvKEuVrHEUJ4QIqxCTiq7fQ8+jcAcvv/TI5bFKKFkWJsAt99/De6U0gxJhJv/7nWcYQQHpJibGTK5aLzgZUAZJpn4N+uvcaJhBCekmJsZDsPpmN31lCh/Ok/6VGt4wghroEUYyN7ZV8F46uX82b83zF16qp1HCHENfC4GDMzMxk+fDgxMTGkpqaSnp5eZ5pt27YxZMgQYmNjiYuLY8mSJXjZxQibRHqela9+KEKv0/HTn4zUOo4Q4hp5XIzz589n3rx5HDt2jEWLFjFnzpw603Ts2JG1a9eSnp7Ovn372LFjB2vXrm2UwN7si4/XEYCNW+O7E95RruUiREvl0ZkvBQUF7N+/n08/PX8xp8mTJ7NgwQKys7Mxm83u6ZKTk93/9vf3JykpiRMnTtQ7T7vdjt1ud9+3Wq2eRPIaRXkneeDk40w1+mNJ+VzrOEKI6+DREqPFYiEsLAzDjyPE6HQ6IiMjycnJuexz8vPzWb9+PRMmTKj38WXLlmEymdy3iIgITyJ5jR/+vQI/XQ35fj2J79dX6zhCiOvg8aq0TnfpqW1X2nZotVqZOHEiixYtIiUlpd5pFi9eTGlpqftmsVg8jaQ5u62SmNx1AFQkz9M4jRDienm0Kh0REUFubi5OpxODwYBSCovFQmRkZJ1py8rKGD9+PJMmTeKXv/zlZedpNBoxGo2eJ/ciBz/+G4OxcobOJI6drnUcIcR18miJMSQkhOTkZFavXg3Ahg0bMJvNl2xfBCgvL2f8+PGMGzeOJ554otHCeiPlctHx0PnT/070mian/wnRCni8Kp2WlkZaWhoxMTEsX76cVatWATB37lw2b94MwIoVK9izZw/vv/8+SUlJJCUl8eyzzzZuci9xdO8WomqOY1O+9JuwQOs4QohGoFNedoCh1WrFZDJRWlpKcHCw1nGuatPLi5hY+Cr7Ot5K6iOt/5AkIVoqT7pFBqq9DoVldn51ehR/dPXjtfGpWscRQjQSOSXwOry7NwdHjaJzRF/69ovTOo4QopFIMV4jp6OaLbv3A3D/0J4apxFCNCZZlb5Ghz5/jw32BWwOGMOt8eu0jiOEaESyxHiNfPa/gY9O0a1bd/x9fbSOI4RoRFKM1yAvK4O4qm8AiBz7kMZphBCNTYrxGpzc8gp6neKQMYUevQdoHUcI0cikGD3kqLYTfeqf5/+dPFPbMEKIJiHF6KFD29bShXMU0YH4MdO0jiOEaAJSjB7SHXgHgMwed+Dr17IHvxBC1E8O1/GA5WwlM0vnMkkfy89vlsuiCtFayRKjB9bts2BVgWT3mkaPXjIYrRCtlRRjAzmdNby3LxeAqaktc5RxIUTDSDE20JEv1vGq7THuC9jN2NhQreMIIZqQbGNsINe+t0jWZ1EZUoDRIGe6CNGayRJjAxTl5xBfsRuA7qPkmi5CtHZSjA3ww2evY9C5OGroR8/+A7WOI4RoYlKMV6FcLsKy1gNQ2n+qxmmEEM1BivEqju7dQqTrFJXKSOzYWVrHEUI0AynGq7DuegOAIx3H0D64o8ZphBDNQfZKX0G53cnfziZQqvIJH/aA1nGEEM3E4yXGzMxMhg8fTkxMDKmpqaSnp9eZJjs7m1GjRmEymRg0aFCjBNXCvw+e5uPqBJaZnqJf6lit4wghmonHS4zz589n3rx5zJo1i/Xr1zNnzhx27dp1yTTBwcE888wzlJaW8tRTTzVa2Ob23j4LAPcMikCn02mcRjQXl8tFdXW11jHEdfDz80Ovv/YthR4VY0FBAfv37+fTTz8FYPLkySxYsIDs7GzMZrN7uk6dOjFixAi2b99+zcG0lpN5kBGnXqNAP4rJKT20jiOaSXV1NVlZWbhcLq2jiOug1+vp1asXfn5+1/R8j4rRYrEQFhaGwXD+aTqdjsjISHJyci4pRk/Y7Xbsdrv7vtVqvab5NLZT217jEcNGRgXkERIsA9K2BUopTp8+jY+PDxEREde1xCG043K5yMvL4/Tp00RGRl7T2p7Hq9K1X0Qp5fGLXmzZsmUsXbr0uubR2JyOaqJPbwZAJd+vcRrRXJxOJ5WVlYSFhdGuXTut44jr0LVrV/Ly8nA6nfj6+nr8fI/+S4yIiCA3Nxen0wmcL0WLxUJkZKTHL3zB4sWLKS0tdd8sFss1z6uxHN6+ni6coxgTcaPu0TqOaCY1NTUA17z6JbzHhc/wwmfqKY+KMSQkhOTkZFavXg3Ahg0bMJvN17waDWA0GgkODr7kpjX17dsAZHa7XUbpboNkR1vLd72foccbUdLS0khLSyMmJobly5ezatUqAObOncvmzedXP+12O+Hh4dxzzz0cPHiQ8PBwFi9efF1Bm0tR3sn/DhgxWgaMENoym83069ePpKQkYmNj+ctf/tKg5xw+fLjex0aNGsUHH3zQ2DHdZs2axcsvv9yo89y3bx/Tp09v1HlejcfbGPv27Vvn8ByA119/3f1vo9FIbm7u9SXTSOZnrzJM5yLDN5Z+fZO0jiME69evJy4uDovFQnx8PCNHjiQhIaHZXt/pdLp3uGph0KBBrFmzpllfU3a7XUQpRcapYsqVP2UyYESbppSistrZ5DdPdl5GREQQExPDsWPHOHPmDHfeeSfx8fHExcXx6quv1vuc9PR0hgwZQkpKCtOnT8dms7kfy8/PZ8qUKaSmppKQkMCTTz7pfsxsNvPss88yevRoZs6se1TGqVOnuPvuu0lISCAhIYEnnniizjRbt25l2LBhJCcnExcXxxtvvOF+7JlnnqF///4kJSWRlJTEyZMnqaqq4t577yU2NpbExERuueUWALZv337JiSIffvghgwcPJjExkaSkJL7++usG/w4bSk4JvMjuE2dZap3IX/zGs2PczVrHERqqctQQ++QnTf466U+Po51fw76Ghw4dIiMjg8TERBYuXEi/fv14//33KSgoYODAgSQlJZGamnrJc+6//34WLlzIzJkz2b17NzfccIP7sZkzZ7JkyRJuvPFGnE4nt99+O++//z533nknADk5OWzbtq3e7XUzZsxgwoQJrF9/fuSpwsLCOtOkpKTw1Vdf4ePjw9mzZ0lJSWH8+PH4+/vzwgsvcPr0aQICAqisrESv1/PRRx9RUlLiPpvu7NmzdeZ57Ngx5syZwxdffEFMTAwOh4PKysoG/f48IcV4kbV7cgC4JbkPgYHtNU4jxHl33303/v7+tGvXjr/97W9ER0ezZcsWvvvuO+D8TtG77rqLrVu3XlKMVquVw4cPc//95w85Gzp0KPHx8QBUVFSwbds2zpw5456+vLycjIwM9/3Zs2fXW4rl5eXs3LmTzz77zP2zrl271pmuuLiYOXPmcOzYMQwGA0VFRRw5coTRo0cTHR3NjBkzuOWWW7jtttsIDw8nMTGRjIwMHnroIW666SYmTJhQZ56fffYZEyZMICYmBgBfX19MJpNHv8+GkGL8UUnRGfKOfAn05r7Uaz/8SLQOAb4+pD89rlle52oubGOsrXZp1Vdil9s763K50Ol07N2797LH+bVvf30LBw8++CATJ05kw4YN6HQ6UlJSsNls+Pj4sHv3bnbu3Mn27dsZOnQoa9euZeTIkaSnp7Nt2za2bNnCokWLOHDgwHVluFayjfFHxz5+hfWGJ3gj+DXiejT+/0CiZdHpdLTzMzT57VoPK7n55pvd2xULCwt5//33GTNmzCXTBAcHExcX595xsWfPHg4dOgRAUFAQI0eOZPny5e7p8/LyGrTTtH379owYMYI//elP7p/VtypdUlJCz5490el0fPHFF+4l3LKyMs6cOcPIkSN54oknGDFiBN9++y25ubnodDomTZrECy+84D5O+mLjxo3jo48+4tixYwA4HA5KS0uvmtlTUoz8OEr38XcBCIy5UeM0Qlzdiy++yMGDB0lISGD06NEsWbKkzvZFgL///e+8/PLLpKSk8OqrrzJkyBD3Y2vWrOH7778nPj6e+Ph4Jk+eTHFxcYNe/+2332b37t0MGDCAxMTEeg/RWb58OY8//jhDhw7lzTffdL92aWkpd911F/Hx8SQkJOBwOJg5cyaHDh1i+PDhJCQkkJKSwv33319n73tUVBSrVq1i2rRpJCQkkJqaytGjRz351TWITl3vOX2NzGq1YjKZKC0tbbaDvQ9/tZm4LfdTofxRj2XIgLRtlM1mIysri169euHv7691HHEd6vssPekWWWIEHDtXAnC46wQpRSGEFOPpk0dJqNgJQLebf6FxGiGEN2jzxZj98Uv46BSHjcn07JeidRwhhBdo08Voq3YSdPr80qJj4FyN0wghvEWbLsZ13+QyybaUX/n9hoQxcgqgEOK8NluMzhoXaV+cwIWe+FH34KPhSfJCCO/SZotx285dFJaU0jnQjymDIrSOI4TwIm2yGF01NUR//nO+ND7C4oRyAvyuflqWEFrw1vEYZ82aRXh4OElJSfTv35/58+fjcDiuaV6//e1vve6qjG2yGA989ja9XCdph52xN47UOo7wZtUVl785bB5MW1V32gZav349Bw4c4JNPPmHJkiUcPHiwEd5Yw124lEltv/71rzlw4ADffvstBw8eZOXKldc0/6VLl3pdMba5DWt2WyUhXy8D4HDEfQzt2EXjRMKrPRd2+ceib4Hp6/57/w9R4LjMEFg9R8DsD/97/8/xsOiER1EuHo8xNDSUBx98kB9++AGlFAsXLmTevLojzqenpzN79mwcDgf9+/evMx7jwoULyc7Oxmazcccdd/D0008D55c6f/azn7FlyxbCwsKuOFCsv78/I0eO5OjRo2zdupXf/OY32Gw2HA4Hjz32GLNnzwbOj8G4Zs0ajMbzlwvZtGkTy5ad/y4OHz4cvV7Pp59+ypYtW1ixYgXV1dUopXjuueeYMGECLpeLhQsXsmXLFoxGIwaDgf/85z9NcpZSmyvGbzf8gaEqnyI6EH/vk1d/ghBewpvGY7xYSUkJn3zyCY888ojHYzCuXLmStLQ0du7c6R7NZ9y4cUybNg2dTkd2djbDhw/n5MmTHD58mK1bt5Keno5er6e0tLTJLlzWporxXFE+sZlpAGQl/JLBQR20DSS83//lXf4xXa1t04//cIVpa221euRQgyN423iMF1y45pNOp2Py5MnMmjWL48ePezQGY32ysrKYPn06ubm57nmcPHmS3r1743A4eOCBBxg9ejS33XZbk137u80Uo3K5yHpjDslUcNynFymTHtY6kmgJ/AI1n9Zbx2P89a9/zYIFCy752bWMwVjb1KlTeeGFF7jjjjsA6NSpEzabDZPJxJEjR9ixYweff/45ixcv5osvviAqKuqKOa9Fm9n58uZXP/B9qS/VyoDr9hfluEXRomk5HuOVeDoG44UsF4+pWFJS4r4k8+rVqykpKXG/z4qKCm655Raee+45zGaz+zIIjc3jYszMzGT48OHExMSQmpp62WCrVq0iOjqaPn36MG/evMvu2WoOO38o4rlPfuD/nD/jgxEbiU6WMRdFy6b1eIyX4+kYjACPPfYYY8aMISkpiYKCAlasWMGdd97JiBEj+O6774iMPD+ivsViYezYsSQkJLgvAnbrrbdeV97LUh4aPXq0euONN5RSSq1bt04NHTq0zjQnTpxQ3bt3V/n5+crlcqmJEyeqlStXNmj+paWlClClpaWeRqvXlxv+ohIXr1M9//cD9fPV+5TL5WqU+YrWp6qqSqWnp6uqqiqto4jrVN9n6Um3eLQ+WVBQwP79+/n0008BmDx5MgsWLCA7O9u96Avnt4nceeedhIaGAue3O/z+979n/vz5jdXnl8gqqoDiH9C5HDir7djKiinPOUinE5sY4TzGCz7J/CvuBZ6/J+mah5IXQrQdHhWjxWIhLCzMffFtnU5HZGQkOTk5lxRjTk4OPXv2dN83m83k5OTUO0+73Y7dbnfft1qtnkQCYNqru1lje5g++tN1HnMqPYFRN/DnqSnommgPlhCidfF4D0TtJS51mSsjXDzd5aYBWLZsGUuXLvU0xiU6tPOlojqIs1RQgw8V+iDKfDtTYb6FqNH3M6ybnAsthGg4j4oxIiKC3NxcnE4nBoPBfRWvCxtHL4iMjCQ7O9t9/+TJk3WmuWDx4sX88pe/dN+3Wq1ERHhWZB8/ciOw132/7hVuhWi4K/1HLlqG6/0MPSrGkJAQkpOTWb16NbNmzWLDhg2YzeZLVqPh/LbHESNG8OSTTxISEsLKlSuZOrX+8Q6NRqP7FCEhtOTr64tOp6OwsJCuXbvK9ugWSilFYWEhOp3ussdoXo3HVwk8evQos2bNori4mODgYN566y0GDBjA3LlzmTRpEpMmTQLgtdde4/nnn8flcjFmzBheeeWVBoXU4iqBQlxQXl5Obm6uLDW2cDqdjvDw8EsOUvekW+TyqULUUlNTc81DaAnv4Ovri4/PpadsetItcvqHELX4+PjU+VKJtkWOXxFCiFq8bonxwpr9tRzPKIQQl3OhUxqy9dDrirGsrAzA40N2hBCiIcrKyjCZTFecxut2vrhcLvLy8ggKCrrq4RIXjnm0WCxevaNGcjYuydm42kpOpRRlZWWEhYVddRxHr1ti1Ov1lx3A8nKCg4O9+gO9QHI2LsnZuNpCzqstKV4gO1+EEKIWKUYhhKilRRej0Wjkqaee8vpTCiVn45KcjUty1uV1O1+EEEJrLXqJUQghmoIUoxBC1CLFKIQQtUgxCiFELS22GBt6GdfmZrPZuOOOO4iJiSEpKYnx48e7RzMvKChg/PjxREdHExcXx1dffaVtWGDp0qXodDoOHz4MeF9Gu93OggULiI6OZsCAAcyYMcMrc37yyScMHDiQ5ORk4uLieOutt7wi58KFCzGbzZd8xlfLVVlZybRp04iKiiImJoaNGzdqlvOBBx6gb9++JCUlceONN3LgwIHmydl4FyxsXg25jKsWqqqq1Icffui+TOtLL72kxo4dq5RSavbs2eqpp55SSim1Z88eFRkZqRwOh1ZR1TfffKPGjx+vIiMj1aFDh7wy4yOPPKJ+8YtfuH+feXl5SinvyulyuVSnTp3Ud999p5RSKisrSxmNRmW1WjXPuWPHDmWxWFTPnj3dn7FSV/79LV26VM2cOVMpdf5SyKGhoers2bOa5Ny0aZM717/+9S8VHR3tfqwpc7bIYjxz5owymUzuX5jL5VKhoaEqKytL22D12Lt3r+rTp49SSqnAwEBVUFDgfmzw4MHq888/1ySXzWZTQ4cOVSdOnLjkj9GbMpaXlyuTyaTKysrqPOZNOS8U444dO5RSSn333XcqLCxM2e12r8lZu3CulCs2Nlbt2bPH/dg999zjXghp7pwXKywsVH5+fqqmpkYp1bQ5W+Sq9JUu4+ptXnzxRSZOnEhxcTEul4uuXf97qa4rXVa2qT355JPMmDGDXr16uX/mbRmPHz9O586deeaZZxg0aBAjR45k69atXpdTp9Px3nvvcdddd9GzZ09GjBjBW2+9RVlZmVflvOBqvz9PLn/cnFasWMGECRPcA0A0Zc4WWYzQ8Mu4aum5554jMzOTZ599FvCezLt27WLv3r089NBDdR7zlowADoeDEydOEBsby759+3j55ZeZOnUqTqfTq3I6nU6WLVvGpk2bOHnyJFu3bmXmzJmAd/0+L3a1XA29/HFzWb16Ne+99x5paWmX/LypcrbIYrz4Mq7AZS/jqqUXXniBjRs38tFHH9GuXTs6d+4MQGFhoXuaK11Wtint2LGDjIwMevXqhdlsJjc3l3HjxrFnzx6vyQjQs2dP9Ho906dPByAxMZFevXrx/fffe1XOAwcOkJeXxw033ADA4MGDCQsL4+DBg16V84Kr/S16cvnj5vDuu++ydOlSPvvsM0JCQtw/b9KcjbJCroGbbrrpkp0vQ4YM0TbQRf74xz+qlJSUOhuCZ86ceckG74iICE13bFxw8XYdb8s4duxY9eGHHyqllMrOzlZdunRReXl5XpUzPz9fBQUFqYyMDKWUUpmZmapjx44qNzfXa3LW3nZ3pVxPPfXUJTs1QkJCVHFxsSY53333XRUVFaWys7PrTNuUOVtsMWZkZKihQ4eq6OhoNXDgQHX48GGtIymllLJYLApQvXv3VomJiSoxMVGlpqYqpc5/gcaOHauioqJUbGys2r59u8Zpz7v4j9HbMh4/flzddNNNKi4uTiUmJqqNGzd6Zc533nlHxcXFqYSEBBUfH6/Wrl3rFTkfeugh1aNHD+Xj46NCQ0PdOwKvlKu8vFxNmTJF9enTR0VHR6t169ZpltNgMKjw8HD3dykxMVEVFRU1eU4ZREIIIWppkdsYhRCiKUkxCiFELVKMQghRixSjEELUIsUohBC1SDEKIUQtUoxCCFGLFKMQQtQixSiEELVIMQohRC3/H2aN9ugx2O2tAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 360x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.special import erfc\n",
+    "\n",
+    "\n",
+    "def polder_classic(t, x, T, S, c):\n",
+    "    X = x / (2 * np.sqrt(T * c))\n",
+    "    Y = np.sqrt(t / (c * S))\n",
+    "    rv = 0.5 * np.exp(2 * X) * erfc(X / Y + Y) + 0.5 * np.exp(-2 * X) * erfc(X / Y - Y)\n",
+    "    return rv\n",
+    "\n",
+    "\n",
+    "delh = 2\n",
+    "T = 20\n",
+    "c = 5000\n",
+    "S = 0.01\n",
+    "x = 400\n",
+    "x / np.sqrt(c * T)\n",
+    "t = np.arange(1, 121)\n",
+    "h_polder_classic = np.zeros(len(t))\n",
+    "for i in range(len(t)):\n",
+    "    h_polder_classic[i] = delh * polder_classic(t[i], x=x, T=T, S=S, c=c)\n",
+    "#\n",
+    "A = delh\n",
+    "a = c * S\n",
+    "b = x**2 / (4 * T * c)\n",
+    "hpd = polder.step([A, a, b], dt=1, cutoff=0.95)\n",
+    "#\n",
+    "plt.plot(t, h_polder_classic, label=\"Polder classic\")\n",
+    "plt.plot(np.arange(1, len(hpd) + 1), hpd, label=\"Polder Pastas\", linestyle=\"--\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f500c884-3efc-4312-bcda-a1a6b4c792e1",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Hantush step function compared to classic Hantush function\n",
+    "The classic Hantush function is \n",
+    "\n",
+    "$$\n",
+    "h(r, t) = \\frac{-Q}{4\\pi T}\\int_u  ^\\infty  \\exp\\left(-y - \\frac{r^2}{4  \\lambda^2 y} \\right) \\frac{\\text{d}y}{y} \n",
+    "$$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "$$\n",
+    "u=\\frac{r^2 S}{4 T t}\n",
+    "$$\n",
+    "\n",
+    "For large time, the classic Hantush function goes to\n",
+    "\n",
+    "$$\n",
+    "\\lim_{t\\to\\infty} h(r, t) = \\frac{-Q}{2\\pi T}\\text{K}_0(r/\\lambda)\n",
+    "$$\n",
+    "\n",
+    "where $\\lambda^2=cT$. \n",
+    "The classic Hantush function is a step function. The impulse response function $\\theta$ is obtained by differentiation\n",
+    "\n",
+    "$$\n",
+    "\\theta(t) = \\frac{\\partial h}{\\partial t} = \\frac{-Q}{4\\pi Tt} \\exp\\left(\\frac{-t}{cS} -\\frac{r^2cS}{4cTt}\\right)\n",
+    "$$\n",
+    "\n",
+    "The Hantush impulse response function used in Pastas is defined as\n",
+    "\n",
+    "$$\n",
+    "\\theta(t) = A\\frac{1}{2\\text{K}_0(2\\sqrt{b})} \\frac{1}{t} e^{-t/a - ab/t} \n",
+    "$$\n",
+    "\n",
+    "Hence, the parameters in Pastas are related to the classic Hantush function as\n",
+    "\n",
+    "$$\n",
+    "A = -\\frac{\\text{K}_0(2\\sqrt{b})}{2\\pi T}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "a = cS\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "b = \\frac{r^2}{4\\lambda^2}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "a7caf2eb-2d75-42ba-8fea-8b06eef189e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.integrate import quad\n",
+    "from scipy.special import k0\n",
+    "\n",
+    "\n",
+    "def integrand_hantush(y, r, lab):\n",
+    "    return np.exp(-y - r**2 / (4 * lab**2 * y)) / y\n",
+    "\n",
+    "\n",
+    "def hantush_classic(t=1, r=1, Q=1, T=100, S=1e-4, c=1000):\n",
+    "    lab = np.sqrt(T * c)\n",
+    "    u = r**2 * S / (4 * T * t)\n",
+    "    F = quad(integrand_hantush, u, np.inf, args=(r, lab))[0]\n",
+    "    return -Q / (4 * np.pi * T) * F\n",
+    "\n",
+    "\n",
+    "c = 1000  # d\n",
+    "S = 0.01  # -\n",
+    "T = 100  # m^2/d\n",
+    "r = 500  # m\n",
+    "Q = 20  # m^3/d\n",
+    "lab = np.sqrt(T * c)\n",
+    "#\n",
+    "A = k0(r / lab) / (2 * np.pi * T)\n",
+    "a = c * S\n",
+    "b = r**2 / (4 * T * c)\n",
+    "\n",
+    "#\n",
+    "ht = hantush.step([A, a, b], dt=1, cutoff=0.99) * -Q  # multiply by Q\n",
+    "\n",
+    "# calculate classic Hantush\n",
+    "t = np.arange(1, len(ht) + 1)\n",
+    "h_hantush_classic = np.zeros(len(t))\n",
+    "for i in range(len(t)):\n",
+    "    h_hantush_classic[i] = hantush_classic(t[i], r=r, Q=Q, T=T, S=S, c=c)\n",
+    "\n",
+    "# plot comparison\n",
+    "plt.plot(t, h_hantush_classic, label=\"Hantush classic\")\n",
+    "plt.plot(np.arange(1, len(ht) + 1), ht, \"--\", label=\"Hantush Pastas\")\n",
+    "plt.axhline(-Q * A, ls=\"dashed\", c=\"k\", label=\"Q*A\")\n",
+    "plt.grid()\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7de44a-2831-4094-b496-868dbabedac6",
+   "metadata": {},
+   "source": [
+    "### HantushWellModel step function in Pastas compared to classic Hantush function and Hantush function in Pastas\n",
+    "The impulse response of the classic Hantush function is\n",
+    "\n",
+    "$$\n",
+    "\\theta(t) = \\frac{\\partial h}{\\partial t} = \\frac{-Q}{4\\pi Tt} \\exp\\left(\\frac{-t}{cS} -\\frac{r^2cS}{4cTt}\\right)\n",
+    "$$\n",
+    "\n",
+    "The impulse response used for the HantushWellModel is\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28465bae-a2b3-44e0-aa11-c137aaa8bfd3",
+   "metadata": {},
+   "source": [
+    "The HantushWellModel impulse response function used in Pastas is defined as\n",
+    "\n",
+    "$$\n",
+    "\\theta(r, t) = \\frac{A^\\prime}{2t} e^{-t/a - a r^2 \\exp(b^\\prime) /t} \n",
+    "$$\n",
+    "\n",
+    "Where $ A^\\prime $, $a$, and $b^\\prime$ are fitting parameters. The parameters are named $A^\\prime $ and $b^\\prime$ to distinguish them from $A$ and $b$ in the original Hantush formulation. The fitting parameters are related to the geohydrological parameters as\n",
+    "\n",
+    "$$\n",
+    "A^\\prime = -\\frac{1}{2\\pi T}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "a = cS\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "b^\\prime = \\ln \\left( \\frac{1}{4\\lambda^2} \\right)\n",
+    "$$\n",
+    "\n",
+    "and the gain of the HantushWellModel is\n",
+    "\n",
+    "$$\n",
+    "\\text{gain} = A^\\prime\\text{K}_0 \\left( 2 r \\exp \\left( \\frac{b^\\prime}{2} \\right) \\right)\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "77ebd676-becd-4f0e-829f-fa5a038f777a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x240 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compare pastas.Hantush to pastas.HantushWellModel\n",
+    "\n",
+    "hantush = ps.Hantush()\n",
+    "hantush._update_rfunc_settings(up=-1)\n",
+    "hantushwm = ps.HantushWellModel()\n",
+    "hantushwm._update_rfunc_settings(up=-1)\n",
+    "hantushwm.set_distances = 1.0\n",
+    "\n",
+    "bprime = np.log(1 / (4 * T * c))\n",
+    "Aprime = A / k0(2 * r * np.exp(bprime / 2))\n",
+    "\n",
+    "h = hantush.step([A, a, b], dt=1, cutoff=0.99)\n",
+    "hwm = hantushwm.step([Aprime, a, bprime, r], dt=1, cutoff=0.99)\n",
+    "\n",
+    "plt.plot(hwm, \"-\", label=\"HantushWellModel\")\n",
+    "plt.plot(h, \".\", label=\"Hantush\")\n",
+    "plt.axhline(A, ls=\"dashed\", color=\"k\", label=\"Gain\")\n",
+    "plt.legend()\n",
+    "plt.grid();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/benchmarks/index.rst b/doc/benchmarks/index.rst
new file mode 100644
index 00000000..c5963f99
--- /dev/null
+++ b/doc/benchmarks/index.rst
@@ -0,0 +1,17 @@
+Benchmarks
+==========
+
+Pastas is built on numerous scientific works. This section contains a number of benchmark tests for Pastas .
+
+.. toctree::
+    :maxdepth: 1
+
+    check_response_functions
+    pastas_synthetic
+    autocorrelation
+    noisemodel
+    armamodel
+
+.. note::
+    The benchmark notebooks are currently not included in the continuous integration and are thus not
+    run on a regular basis like the other notebooks.
diff --git a/doc/benchmarks/noisemodel.ipynb b/doc/benchmarks/noisemodel.ipynb
new file mode 100644
index 00000000..289fd462
--- /dev/null
+++ b/doc/benchmarks/noisemodel.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Testing an AR(1) Noise Model for Pastas\n",
+    "*R.A. Collenteur, University of Graz, May 2020*\n",
+    "\n",
+    "In this notebook the classical Autoregressive AR1 noise model is tested for Pastas models. This noise model is tested against synthetic data generated with NumPy or Statsmodels' ARMA model. This noise model is tested on head time series with a regular time step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python version: 3.10.5\n",
+      "NumPy version: 1.22.3\n",
+      "Pandas version: 1.4.3\n",
+      "SciPy version: 1.9.3\n",
+      "Matplotlib version: 3.6.2\n",
+      "Numba version: 0.56.3\n",
+      "LMfit version: 1.0.3\n",
+      "Latexify version: 0.2.0\n",
+      "Pastas version: 1.0.0b\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pastas as ps\n",
+    "\n",
+    "ps.set_log_level(\"ERROR\")\n",
+    "ps.show_versions()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Develop the AR(1) Noise Model for Pastas\n",
+    "\n",
+    "The following formula is used to calculate the noise according to the ARMA(1,1) process:\n",
+    "\n",
+    "$$\\upsilon(t_i) = r(t_i) - r(t_{i-1}) \\text{e}^{-\\Delta t_i / \\alpha}$$\n",
+    "\n",
+    "where $\\upsilon$ is the noise, $\\Delta t_i$ the time step between the residuals ($r$), and $\\alpha$ [days] the AR parameter of the model. The model is named `NoiseModel` and can be found in `noisemodel.py`. This is the default noise model that is added to a Pastas model, but it can also be added manually as follows: `ml.add_noisemodel(ps.NoiseModel())`\n",
+    "\n",
+    "## 2. Generate synthetic head time series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Read in some data\n",
+    "rain = (\n",
+    "    pd.read_csv(\"../examples/data/rain_260.csv\", index_col=0, parse_dates=[0]).squeeze()\n",
+    "    / 1000\n",
+    ")\n",
+    "\n",
+    "# Set the True parameters\n",
+    "Atrue = 800\n",
+    "ntrue = 1.4\n",
+    "atrue = 200\n",
+    "dtrue = 20\n",
+    "\n",
+    "# Generate the head\n",
+    "step = ps.Gamma().block([Atrue, ntrue, atrue], cutoff=0.9999)\n",
+    "h = dtrue * np.ones(len(rain) + step.size)\n",
+    "for i in range(len(rain)):\n",
+    "    h[i : i + step.size] += rain[i] * step\n",
+    "head = pd.DataFrame(\n",
+    "    index=rain.index,\n",
+    "    data=h[: len(rain)],\n",
+    ")\n",
+    "head = head[\"1990\":\"2015\"]\n",
+    "\n",
+    "# Plot the head without noise\n",
+    "plt.figure(figsize=(10, 2))\n",
+    "plt.plot(head, \"k.\", label=\"head\")\n",
+    "plt.legend(loc=0)\n",
+    "plt.ylabel(\"Head (m)\")\n",
+    "plt.xlabel(\"Time (years)\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Generate AR(1) noise and add it to the synthetic heads\n",
+    "In the following code-block, noise is generated using an AR(1) process using Numpy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reproduction of random numbers\n",
+    "np.random.seed(1234)\n",
+    "alpha = 0.8\n",
+    "\n",
+    "# generate samples using NumPy\n",
+    "random_seed = np.random.RandomState(1234)\n",
+    "noise = random_seed.normal(0, 1, len(head)) * np.std(head.values) * 0.2\n",
+    "a = np.zeros_like(head[0])\n",
+    "\n",
+    "for i in range(1, noise.size):\n",
+    "    a[i] = noise[i] + a[i - 1] * alpha\n",
+    "\n",
+    "head_noise = head[0] + a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Create and solve a Pastas Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x16ccb8850>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml = ps.Model(head_noise)\n",
+    "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
+    "ml.add_stressmodel(sm)\n",
+    "ml.add_noisemodel(ps.NoiseModel())\n",
+    "\n",
+    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
+    "\n",
+    "# Plot the results\n",
+    "axes = ml.plots.results(figsize=(10, 5))\n",
+    "axes[-1].plot(ps.Gamma().step([Atrue, ntrue, atrue], cutoff=0.999))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Did we find back the original AR parameter?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8 vs 0.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_alpha\", \"optimal\"]).round(2), \"vs\", alpha)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The estimated parameters for the noise model are almost the same as the true parameters, showing that the model works for regular time steps.\n",
+    "\n",
+    "## 6. So is the autocorrelation removed correctly?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml.plots.diagnostics(figsize=(10, 4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That seems okay. It is important to understand that this noisemodel will only help in removing autocorrelations at the first time lag, but not at larger time lags, compared to its AR(1) counterpart."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Test the NoiseModel for irregular time steps\n",
+    "In this final step the ArmaModel is tested for irregular timesteps, using the indices from a real groundwater level time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index = (\n",
+    "    pd.read_csv(\"../examples/data/test_index.csv\", parse_dates=True, index_col=0)\n",
+    "    .index.round(\"D\")\n",
+    "    .drop_duplicates()\n",
+    ")\n",
+    "head_irregular = head_noise.reindex(index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "StressModel.__init__() got an unexpected keyword argument 'cutoff'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [8], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m ml \u001b[38;5;241m=\u001b[39m ps\u001b[38;5;241m.\u001b[39mModel(head_irregular)\n\u001b[0;32m----> 2\u001b[0m sm \u001b[38;5;241m=\u001b[39m \u001b[43mps\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mStressModel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mps\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGamma\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrecharge\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msettings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mprec\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcutoff\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.99\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m ml\u001b[38;5;241m.\u001b[39madd_stressmodel(sm)\n\u001b[1;32m      4\u001b[0m ml\u001b[38;5;241m.\u001b[39madd_noisemodel(ps\u001b[38;5;241m.\u001b[39mNoiseModel())\n",
+      "\u001b[0;31mTypeError\u001b[0m: StressModel.__init__() got an unexpected keyword argument 'cutoff'"
+     ]
+    }
+   ],
+   "source": [
+    "ml = ps.Model(head_irregular)\n",
+    "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\", cutoff=0.99)\n",
+    "ml.add_stressmodel(sm)\n",
+    "ml.add_noisemodel(ps.NoiseModel())\n",
+    "\n",
+    "# ml.set_parameter(\"noise_alpha\", 9.5, vary=False)\n",
+    "# ml.set_parameter(\"recharge_A\", 800, vary=False)\n",
+    "# ml.set_parameter(\"recharge_n\", 1.2, vary=False)\n",
+    "# ml.set_parameter(\"recharge_a\", 200, vary=False)\n",
+    "\n",
+    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
+    "axes = ml.plots.results(figsize=(10, 5))\n",
+    "axes[-1].plot(ps.Gamma().step([Atrue, ntrue, atrue]))\n",
+    "\n",
+    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_alpha\", \"optimal\"]).round(2), \"vs\", alpha)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml.plots.diagnostics(figsize=(10, 4), acf_options={\"bin_width\": 0.5});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above autocorrelation plot shows that there is some autocorrelation in the first time steps. However, this is probably more a numerical artifict due to estimating the ACF for irregular time steps than \"real\" autocorrelation. This can be shown when estimating the ACF for the period with more frequent observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = ml.noise()\n",
+    "print(\"\", v.loc[:\"2010-01-01\"].std())\n",
+    "print(\"\", v.loc[\"2010-01-01\":].std())"
+   ]
+  }
+ ],
+ "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": "3.10.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/doc/benchmarks/pastas_synthetic.ipynb b/doc/benchmarks/pastas_synthetic.ipynb
new file mode 100644
index 00000000..35af96e8
--- /dev/null
+++ b/doc/benchmarks/pastas_synthetic.ipynb
@@ -0,0 +1,532 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Synthetic Data\n",
+    "\n",
+    "*Developed by Stijn Klop and Mark Bakker*\n",
+    "\n",
+    "This Notebook contains a number of examples and tests with synthetic data. The purpose of this notebook is to demonstrate the noise model of Pastas.\n",
+    "\n",
+    "In this Notebook, heads are generated with a known response function. Next, Pastas is used to solve for the parameters of the model it is verified that Pastas finds the correct parameters back. Several different types of errors are introduced in the generated heads and it is tested whether the confidence intervals computed by Pastas are reasonable. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python version: 3.10.5\n",
+      "NumPy version: 1.22.3\n",
+      "Pandas version: 1.4.3\n",
+      "SciPy version: 1.9.3\n",
+      "Matplotlib version: 3.6.2\n",
+      "Numba version: 0.56.3\n",
+      "LMfit version: 1.0.3\n",
+      "Latexify version: 0.2.0\n",
+      "Pastas version: 1.0.0b\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.special import gammainc, gammaincinv\n",
+    "import pandas as pd\n",
+    "import pastas as ps\n",
+    "\n",
+    "ps.show_versions()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load data and define functions\n",
+    "The rainfall observations are read from file. The rainfall series is taken from KNMI station De Bilt. Heads are generated with a Gamma response function which is defined below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rain = (\n",
+    "    pd.read_csv(\"../examples/data/rain_260.csv\", index_col=0, parse_dates=[0]).squeeze()\n",
+    "    / 1000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gamma_tmax(A, n, a, cutoff=0.999):\n",
+    "    return gammaincinv(n, cutoff) * a\n",
+    "\n",
+    "\n",
+    "def gamma_step(A, n, a, cutoff=0.999):\n",
+    "    tmax = gamma_tmax(A, n, a, cutoff)\n",
+    "    t = np.arange(0, tmax, 1)\n",
+    "    s = A * gammainc(n, t / a)\n",
+    "    return s\n",
+    "\n",
+    "\n",
+    "def gamma_block(A, n, a, cutoff=0.999):\n",
+    "    # returns the gamma block response starting at t=0 with intervals of delt = 1\n",
+    "    s = gamma_step(A, n, a, cutoff)\n",
+    "    return np.append(s[0], s[1:] - s[:-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Gamma response function requires 3 input arguments; A, n and a. The values for these parameters are defined along with the parameter d, the base groundwater level. The response function is created using the functions defined above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Atrue = 800\n",
+    "ntrue = 1.1\n",
+    "atrue = 200\n",
+    "dtrue = 20\n",
+    "h = gamma_block(Atrue, ntrue, atrue)\n",
+    "tmax = gamma_tmax(Atrue, ntrue, atrue)\n",
+    "plt.plot(h)\n",
+    "plt.xlabel(\"Time (days)\")\n",
+    "plt.ylabel(\"Head response (m) due to 1 mm of rain in day 1\")\n",
+    "plt.title(\"Gamma block response with tmax=\" + str(int(tmax)));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create synthetic observations\n",
+    "Rainfall is used as input series for this example. No errors are introduced. A Pastas model is created to test whether Pastas is able to . The generated head series is purposely not generated with convolution.\n",
+    "Heads are computed for the period 1990 - 2000. Computations start in 1980 as a warm-up period. Convolution is not used so that it is clear how the head is computed. The computed head at day 1 is the head at the end of day 1 due to rainfall during day 1. No errors are introduced."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "step = gamma_block(Atrue, ntrue, atrue)[1:]\n",
+    "lenstep = len(step)\n",
+    "h = dtrue * np.ones(len(rain) + lenstep)\n",
+    "for i in range(len(rain)):\n",
+    "    h[i : i + lenstep] += rain[i] * step\n",
+    "head = pd.DataFrame(\n",
+    "    index=rain.index,\n",
+    "    data=h[: len(rain)],\n",
+    ")\n",
+    "head = head[\"1990\":\"1999\"]\n",
+    "\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "plt.plot(head, \"k.\", label=\"head\")\n",
+    "plt.legend(loc=0)\n",
+    "plt.ylabel(\"Head (m)\")\n",
+    "plt.xlabel(\"Time (years)\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Pastas model\n",
+    "The next step is to create a Pastas model. The head generated using the Gamma response function is used as input for the Pastas model. \n",
+    "\n",
+    "A `StressModel` instance is created and added to the Pastas model. The `StressModel` intance takes the rainfall series as input aswell as the type of response function, in this case the Gamma response function ( `ps.Gamma`).\n",
+    "\n",
+    "The Pastas model is solved without a noise model since there is no noise present in the data. The results of the Pastas model are plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: 1D-DataFrame was provided, automatically transformed to pandas.Series.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit report 0                   Fit Statistics\n",
+      "=============================================\n",
+      "nfev    14                     EVP      100.00\n",
+      "nobs    3652                   R2        1.00\n",
+      "noise   False                  RMSE      0.00\n",
+      "tmin    1990-01-01 00:00:00    AIC      -219737.53\n",
+      "tmax    1999-12-31 00:00:00    BIC      -219712.72\n",
+      "freq    D                      Obj       0.00\n",
+      "warmup  3650 days 00:00:00     ___           \n",
+      "solver  LeastSquares           Interp.     No\n",
+      "\n",
+      "Parameters (4 optimized)\n",
+      "=============================================\n",
+      "            optimal  stderr     initial  vary\n",
+      "recharge_A    800.0  ±0.00%  217.313623  True\n",
+      "recharge_n      1.1  ±0.00%    1.000000  True\n",
+      "recharge_a    200.0  ±0.00%   10.000000  True\n",
+      "constant_d     20.0  ±0.00%   21.798909  True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x800 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml = ps.Model(head)\n",
+    "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
+    "ml.add_stressmodel(sm)\n",
+    "ml.solve(noise=False, ftol=1e-8)\n",
+    "ml.plots.results();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results of the Pastas model show the calibrated parameters for the Gamma response function. The parameters calibrated using pastas are equal to the `Atrue`, `ntrue`, `atrue` and `dtrue` parameters defined above. The Explained Variance Percentage for this example model is 100%. \n",
+    "\n",
+    "The results plots show that the Pastas simulation is identical to the observed groundwater. The residuals of the simulation are shown in the plot together with the response function and the contribution for each stress.\n",
+    "\n",
+    "Below the Pastas block response and the true Gamma response function are plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(gamma_block(Atrue, ntrue, atrue), label=\"Synthetic response\")\n",
+    "plt.plot(ml.get_block_response(\"recharge\"), \"-.\", label=\"Pastas response\")\n",
+    "plt.legend(loc=0)\n",
+    "plt.ylabel(\"Head response (m) due to 1 m of rain in day 1\")\n",
+    "plt.xlabel(\"Time (days)\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test 1: Adding noise\n",
+    "In the next test example noise is added to the observations of the groundwater head. The noise is normally distributed noise with a mean of 0 and a standard deviation of 1 and is scaled with the standard deviation of the head. \n",
+    "\n",
+    "The noise series is added to the head series created in the previous example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random_seed = np.random.RandomState(15892)\n",
+    "\n",
+    "noise = random_seed.normal(0, 1, len(head)) * np.std(head.values) * 0.5\n",
+    "head_noise = head[0] + noise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Pastas model\n",
+    "\n",
+    "A pastas model is created using the head with noise. A stress model is added to the Pastas model and the model is solved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit report 0                        Fit Statistics\n",
+      "==================================================\n",
+      "nfev    14                     EVP           79.43\n",
+      "nobs    3652                   R2             0.79\n",
+      "noise   True                   RMSE           0.20\n",
+      "tmin    1990-01-01 00:00:00    AIC       -11660.89\n",
+      "tmax    1999-12-31 00:00:00    BIC       -11629.87\n",
+      "freq    D                      Obj           74.75\n",
+      "warmup  3650 days 00:00:00     ___                \n",
+      "solver  LeastSquares           Interp.          No\n",
+      "\n",
+      "Parameters (5 optimized)\n",
+      "==================================================\n",
+      "                optimal   stderr     initial  vary\n",
+      "recharge_A   811.846778   ±1.30%  217.313623  True\n",
+      "recharge_n     1.047627   ±1.66%    1.000000  True\n",
+      "recharge_a   218.803805   ±3.36%   10.000000  True\n",
+      "constant_d    19.979988   ±0.12%   21.804260  True\n",
+      "noise_alpha    0.186774  ±65.40%    1.000000  True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x800 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml2 = ps.Model(head_noise)\n",
+    "sm2 = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
+    "ml2.add_stressmodel(sm2)\n",
+    "ml2.solve(noise=True)\n",
+    "ml2.plots.results();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results of the simulation show that Pastas is able to filter the noise from the observed groundwater head. The simulated groundwater head and the generated synthetic head are plotted below. The parameters found with the Pastas optimization are similair to the original parameters of the Gamma response function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 5))\n",
+    "plt.plot(head_noise, \".k\", alpha=0.1, label=\"Head with noise\")\n",
+    "plt.plot(head, \".k\", label=\"Head true\")\n",
+    "plt.plot(ml2.simulate(), label=\"Pastas simulation\")\n",
+    "plt.title(\"Simulated Pastas head compared with synthetic head\")\n",
+    "plt.legend(loc=0)\n",
+    "plt.ylabel(\"Head (m)\")\n",
+    "plt.xlabel(\"Date (years)\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test 2: Adding correlated noise\n",
+    "In this example correlated noise is added to the observed head. The correlated noise is generated using the noise series created in the previous example. The correlated noise is implemented as exponential decay using the following formula:\n",
+    "\n",
+    "$$ n_{c}(t) = e^{-1/\\alpha} \\cdot n_{c}(t-1) + n(t)$$\n",
+    "\n",
+    "where $n_{c}$ is the correlated noise, $\\alpha$ is the noise decay parameter and $n$ is the uncorrelated noise. The noise series that is created is added to the observed groundwater head. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "noise_corr = np.zeros(len(noise))\n",
+    "noise_corr[0] = noise[0]\n",
+    "\n",
+    "alphatrue = 2\n",
+    "\n",
+    "for i in range(1, len(noise_corr)):\n",
+    "    noise_corr[i] = np.exp(-1 / alphatrue) * noise_corr[i - 1] + noise[i]\n",
+    "\n",
+    "head_noise_corr = head[0] + noise_corr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Pastas model\n",
+    "A Pastas model is created using the head with correlated noise as input. A stressmodel is added to the model and the Pastas model is solved. The results of the model are plotted. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit report 0                       Fit Statistics\n",
+      "=================================================\n",
+      "nfev    13                     EVP          70.22\n",
+      "nobs    3652                   R2            0.70\n",
+      "noise   True                   RMSE          0.26\n",
+      "tmin    1990-01-01 00:00:00    AIC      -11657.67\n",
+      "tmax    1999-12-31 00:00:00    BIC      -11626.65\n",
+      "freq    D                      Obj          74.81\n",
+      "warmup  3650 days 00:00:00     ___               \n",
+      "solver  LeastSquares           Interp.         No\n",
+      "\n",
+      "Parameters (5 optimized)\n",
+      "=================================================\n",
+      "                optimal  stderr     initial  vary\n",
+      "recharge_A   820.011253  ±3.42%  217.313623  True\n",
+      "recharge_n     1.017822  ±3.60%    1.000000  True\n",
+      "recharge_a   234.421669  ±8.13%   10.000000  True\n",
+      "constant_d    19.971140  ±0.31%   21.812483  True\n",
+      "noise_alpha    2.076617  ±4.38%    1.000000  True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x800 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml3 = ps.Model(head_noise_corr)\n",
+    "sm3 = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
+    "ml3.add_stressmodel(sm3)\n",
+    "ml3.solve(noise=True)\n",
+    "ml3.plots.results();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Pastas model is able to calibrate the model parameters fairly well. The calibrated parameters are close to the true values defined above. The `noise_alpha` parameter calibrated by Pastas is close the the `alphatrue` parameter defined for the correlated noise series.\n",
+    "\n",
+    "Below the head simulated with the Pastas model is plotted together with the head series and the head series with the correlated noise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 5))\n",
+    "plt.plot(head_noise_corr, \".k\", alpha=0.1, label=\"Head with correlated noise\")\n",
+    "plt.plot(head, \".k\", label=\"Head true\")\n",
+    "plt.plot(ml3.simulate(), label=\"Pastas simulation\")\n",
+    "plt.title(\"Simulated Pastas head compared with synthetic head\")\n",
+    "plt.legend(loc=0)\n",
+    "plt.ylabel(\"Head (m)\")\n",
+    "plt.xlabel(\"Date (years)\");"
+   ]
+  }
+ ],
+ "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": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/doc/concepts/armamodel.ipynb b/doc/concepts/armamodel.ipynb
deleted file mode 100644
index a3f0778a..00000000
--- a/doc/concepts/armamodel.ipynb
+++ /dev/null
@@ -1,323 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ARMA(1,1) Noise Model for Pastas\n",
-    "*R.A. Collenteur, University of Graz, May 2020*\n",
-    "\n",
-    "In this notebook an Autoregressive-Moving-Average (ARMA(1,1)) noise model is developed for Pastas models. This new noise model is tested against synthetic data generated with NumPy or Statsmodels' ARMA model. This noise model is tested on head time series with a regular time step.\n",
-    "\n",
-    "<div class=\"alert alert-info\">\n",
-    "    \n",
-    "<b>Warning</b>\n",
-    "\n",
-    "It should be noted that the time step may be non-equidistant in this formulation, but this model is not yet tested for irregular time steps.\n",
-    "   \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.special import gammainc, gammaincinv\n",
-    "\n",
-    "import pastas as ps\n",
-    "\n",
-    "ps.set_log_level(\"ERROR\")\n",
-    "ps.show_versions()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Develop the ARMA(1,1) Noise Model for Pastas\n",
-    "\n",
-    "The following formula is used to calculate the noise according to the ARMA(1,1) process:\n",
-    "\n",
-    "$$\\upsilon(t_i) = r(t_i) - r(t_{i-1}) \\text{e}^{-\\Delta t_i / \\alpha} - \\upsilon(t_{i-1}) \\text{e}^{-\\Delta t_i / \\beta}$$\n",
-    "\n",
-    "where $\\upsilon$ is the noise, $\\Delta t_i$ the time step between the residuals ($r$), and respectively $\\alpha$ [days] and $\\beta$ [days] the parameters of the AR and MA parts of the model. The model is named `ArmaModel` and can be found in `noisemodel.py`. It is added to a Pastas model as follows: `ml.add_noisemodel(ps.ArmaModel())`\n",
-    "\n",
-    "## 2. Generate synthetic head time series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Read in some data\n",
-    "rain = (\n",
-    "    pd.read_csv(\"../examples/data/rain_260.csv\", index_col=0, parse_dates=[0]).squeeze()\n",
-    "    / 1000\n",
-    ")\n",
-    "\n",
-    "# Set the True parameters\n",
-    "Atrue = 800\n",
-    "ntrue = 1.1\n",
-    "atrue = 200\n",
-    "dtrue = 20\n",
-    "\n",
-    "# Generate the head\n",
-    "step = ps.Gamma().block([Atrue, ntrue, atrue])\n",
-    "h = dtrue * np.ones(len(rain) + step.size)\n",
-    "for i in range(len(rain)):\n",
-    "    h[i : i + step.size] += rain[i] * step\n",
-    "head = pd.DataFrame(\n",
-    "    index=rain.index,\n",
-    "    data=h[: len(rain)],\n",
-    ")\n",
-    "head = head[\"1990\":\"2015\"]\n",
-    "\n",
-    "# Plot the head without noise\n",
-    "plt.figure(figsize=(10, 2))\n",
-    "plt.plot(head, \"k.\", label=\"head\")\n",
-    "plt.legend(loc=0)\n",
-    "plt.ylabel(\"Head (m)\")\n",
-    "plt.xlabel(\"Time (years)\");"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Generate ARMA(1,1) noise and add it to the synthetic heads\n",
-    "In the following code-block, noise is generated using an ARMA(1,1) process using NumPy. An alternative procedure is available from Statsmodels (commented out now). More information about the ARMA model can be found on the [statsmodels website](https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima_process.ArmaProcess.html). The noise is added to the head series generated in the previous code-block."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# reproduction of random numbers\n",
-    "np.random.seed(1234)\n",
-    "alpha = 0.95\n",
-    "beta = 0.1\n",
-    "\n",
-    "# generate samples using Statsmodels\n",
-    "# import statsmodels.api as stats\n",
-    "# ar = np.array([1, -alpha])\n",
-    "# ma = np.r_[1, beta]\n",
-    "# arma = stats.tsa.ArmaProcess(ar, ma)\n",
-    "# noise = arma.generate_sample(head[0].index.size)*np.std(head.values) * 0.1\n",
-    "\n",
-    "# generate samples using NumPy\n",
-    "random_seed = np.random.RandomState(1234)\n",
-    "\n",
-    "noise = random_seed.normal(0, 1, len(head)) * np.std(head.values) * 0.1\n",
-    "a = np.zeros_like(head[0])\n",
-    "\n",
-    "for i in range(1, noise.size):\n",
-    "    a[i] = noise[i] + noise[i - 1] * beta + a[i - 1] * alpha\n",
-    "\n",
-    "head_noise = head[0] + a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(a)\n",
-    "plt.plot(noise)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Create and solve a Pastas Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml = ps.Model(head_noise)\n",
-    "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
-    "ml.add_stressmodel(sm)\n",
-    "ml.add_noisemodel(ps.ArmaModel())\n",
-    "\n",
-    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=True)\n",
-    "axes = ml.plots.results(figsize=(10, 5))\n",
-    "axes[-2].plot(ps.Gamma().step([Atrue, ntrue, atrue]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Did we find back the original ARMA parameters?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_alpha\", \"optimal\"]).round(2), \"vs\", alpha)\n",
-    "print(\n",
-    "    np.exp(-1.0 / np.abs(ml.parameters.loc[\"noise_beta\", \"optimal\"])).round(2),\n",
-    "    \"vs.\",\n",
-    "    beta,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The estimated parameters for the noise model are almost the same as the true parameters, showing that the model works for regular time steps.\n",
-    "\n",
-    "## 6. So is the autocorrelation removed correctly?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml.plots.diagnostics(figsize=(10, 4));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That seems okay. It is important to understand that this noisemodel will only help in removing autocorrelations at the first time lag, but not at larger time lags, compared to its AR(1) counterpart. \n",
-    "\n",
-    "## 7. What happens if we use an AR(1) model?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml = ps.Model(head_noise)\n",
-    "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
-    "ml.add_stressmodel(sm)\n",
-    "\n",
-    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
-    "axes = ml.plots.results(figsize=(10, 5))\n",
-    "axes[-2].plot(ps.Gamma().step([Atrue, ntrue, atrue]))\n",
-    "\n",
-    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_alpha\", \"optimal\"]).round(2), \"vs\", alpha)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml.plots.diagnostics(figsize=(10, 4));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Significant autocorrelation is still present at lag 1 and the parameter of the AR(1) is overestimated, trying to correct for the lack of an MA(1) part. This is to be expected, as the MA(1) process generates a strong autocorrelation at this first time lag. The negative autocorrelation in the first few time steps is a result of the overestimation of the AR(1) parameter.\n",
-    "\n",
-    "A possible effect of failing to remove the autocorrelation at lag 1 may be that the parameter standard errors are under- or overestimated. Although that does not seem the case for this synthetic, real life examples may suffer from this."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 8. Test the ArmaModel for irregular time steps\n",
-    "In this final step the ArmaModel is tested for irregular timesteps, using the indices from a real groundwater level time series. It is clear from the example below that the ArmaModel does not yet work for irregular time steps, as (unlike the AR(1) model in Pastas) no weights are applied yet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "index = (\n",
-    "    pd.read_csv(\"../examples/data/test_index.csv\", parse_dates=True, index_col=0)\n",
-    "    .index.round(\"D\")\n",
-    "    .drop_duplicates()\n",
-    ")\n",
-    "head_irregular = head_noise.reindex(index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml = ps.Model(head_irregular)\n",
-    "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
-    "ml.add_stressmodel(sm)\n",
-    "ml.add_noisemodel(ps.ArmaModel())\n",
-    "\n",
-    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
-    "axes = ml.plots.results(figsize=(10, 5))\n",
-    "axes[-2].plot(ps.Gamma().step([Atrue, ntrue, atrue]))\n",
-    "\n",
-    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_alpha\", \"optimal\"]).round(2), \"vs\", alpha)\n",
-    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_beta\", \"optimal\"]).round(2), \"vs.\", beta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml.plots.diagnostics(figsize=(10, 4));"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pastas_dev",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
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-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/doc/concepts/autocorrelation.ipynb b/doc/concepts/autocorrelation.ipynb
deleted file mode 100644
index dfddddac..00000000
--- a/doc/concepts/autocorrelation.ipynb
+++ /dev/null
@@ -1,351 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Autocorrelation for irregular time series\n",
-    "*R.A. Collenteur, Artesia Water & University of Graz, 2020*\n",
-    "\n",
-    "In this notebook the autocorrelation function for irregular time steps that is built-in in Pastas is tested and validated on synthetic data. The methodology for calculating the autocorrelation is based on [Rehfeld et al. (2011)](#References) and [Edelson and Krolik (1978)](#References). The methods are available through `pastas.stats` package (e.g. `pastas.stats.acf(series)`). The full report of the methods underlying this Notebook are available in [Collenteur (2018, in Dutch)](#References). \n",
-    "\n",
-    "1. [Create synthetic time series](#Create-synthetic-time-series)\n",
-    "2. [Compute the autocorrelation](#Computing-the-autocorrelation)\n",
-    "3. [Autocorrelation for regular time series](#Autocorrelation-for-regular-time-series)\n",
-    "4. [Autocorrelation for irregular time series](#Sine-wave-with-non-equidistant-timesteps)\n",
-    "5. [References](#References)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import pastas as ps\n",
-    "\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create synthetic time series\n",
-    "Two synthetic time series are created with a known autocorrelation and a regular time interval. The first is a sine wave with a wave period of one year. The second is a series of correlated noise generated through an AR(1) process. Both synthetic time series have a length of ten years and a daily observation time step ($n=3650$). From both time series three time series with irregular time steps are created:\n",
-    "\n",
-    "- **Time Series 1:** 3 years of monthly data, 3 years of bi-weekly data, and 4 years of daily data.\n",
-    "- **Time Series 2:** 3 years of monthly data, 3 years of bi-weekly data, a one year gap, and 4 years of daily data.\n",
-    "- **Time Series 3:** reindex time series using the indices from a real groundwater level time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "index_test = (\n",
-    "    pd.read_csv(\n",
-    "        \"../examples/data/test_index.csv\", parse_dates=True, index_col=0, names=[\"Date\"]\n",
-    "    )\n",
-    "    .squeeze()\n",
-    "    .index.ceil(\"D\")\n",
-    "    .unique()\n",
-    ")\n",
-    "n_years = 10\n",
-    "index = pd.to_datetime(np.arange(0, n_years * 365, 1), unit=\"D\", origin=\"2005\")\n",
-    "index.name = \"Time [Years]\"\n",
-    "\n",
-    "# 1. Sine timeseries 1: equal timesteps\n",
-    "np.random.seed(0)\n",
-    "data = np.sin(\n",
-    "    np.linspace(0, n_years * 2 * np.pi, len(index))\n",
-    ")  # +np.random.rand(index.size)\n",
-    "v = pd.Series(data=data, index=index, name=\"STS_SIN\")\n",
-    "\n",
-    "# 2. Sine timeseries with three frequencies\n",
-    "v1 = (\n",
-    "    v.iloc[: -7 * 365]\n",
-    "    .asfreq(\"30D\")\n",
-    "    .append(v.iloc[-7 * 365 : -4 * 365].asfreq(\"14D\"))\n",
-    "    .append(v.iloc[-4 * 365 :])\n",
-    ")\n",
-    "v1.name = \"STS_SIN1\"\n",
-    "\n",
-    "# 3. Sine timeseries with three frequencies and a data gap\n",
-    "v2 = (\n",
-    "    v.iloc[: -7 * 365]\n",
-    "    .asfreq(\"30D\")\n",
-    "    .append(v.iloc[-7 * 365 : -4 * 365].asfreq(\"14D\"))\n",
-    "    .append(v.iloc[-3 * 365 :])\n",
-    ")\n",
-    "v2.name = \"STS_SIN2\"\n",
-    "\n",
-    "# 4. Sine timeseries with indices based on a true groundwater level measurement indices\n",
-    "v3 = v.reindex(index_test).dropna()\n",
-    "v3.name = \"STS_SIN3\"\n",
-    "\n",
-    "# Convoluting a random noise process with a exponential decay function to obtain a autocorrelation\n",
-    "# timeseries similar to an Auto Regressive model of order 1 (AR(1))\n",
-    "alpha = 10\n",
-    "np.random.seed(0)\n",
-    "n = np.random.rand(index.size + 365)\n",
-    "b = np.exp(-np.arange(366) / alpha)\n",
-    "n = np.convolve(n, b, mode=\"valid\")\n",
-    "n = n - n.mean()\n",
-    "\n",
-    "index = pd.to_datetime(np.arange(0, n.size, 1), unit=\"D\", origin=\"2005\")\n",
-    "index.name = \"Time [Years]\"\n",
-    "n = pd.Series(data=n, index=index, name=\"STS_EXP\")\n",
-    "\n",
-    "n1 = n.reindex(v1.index).dropna()\n",
-    "n1.name = \"STS_EXP1\"\n",
-    "n2 = n.reindex(v2.index).dropna()\n",
-    "n2.name = \"STS_EXP2\"\n",
-    "n3 = n.reindex(index_test).dropna()\n",
-    "n3.name = \"STS_EXP3\"\n",
-    "\n",
-    "# Create a DataFrame with all series and plot them all\n",
-    "d = pd.concat([v, v1, v2, v3, n, n1, n2, n3], axis=1)\n",
-    "d.plot(subplots=True, figsize=(10, 6), marker=\".\", markersize=2, color=\"steelblue\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Theoretical autocorrelation\n",
-    "In this codeblock a figure is created showing the time series with equidistant timesteps and their theoretical autocorrelation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Compute true autocorrelation functions\n",
-    "acf_v_true = pd.Series(np.cos(np.linspace(0, 2 * np.pi, 365)))\n",
-    "acf_n_true = pd.Series(b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Computing the autocorrelation\n",
-    "\n",
-    "The autocorrelation function for all time series is calculated for a number of time lags. Two different methods are used:\n",
-    "\n",
-    "- binning in a rectangular bin\n",
-    "- weighting through a Gaussian kernel\n",
-    "\n",
-    "<div class=\"alert alert-warning\">\n",
-    "    \n",
-    "<b>Warning</b> \n",
-    "    \n",
-    "Calculation of all the autocorrelation functions can take several minutes!\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lags = np.arange(1.0, 366)\n",
-    "acf_df = pd.DataFrame(index=lags)\n",
-    "\n",
-    "for name, sts in d.items():\n",
-    "    for method in [\"rectangle\", \"gaussian\"]:\n",
-    "        acf = ps.stats.acf(sts.dropna(), bin_method=method, max_gap=30)\n",
-    "        acf.index = acf.index.days\n",
-    "        acf_df.loc[:, name + \"_\" + method] = acf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Autocorrelation for regular time series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axes = plt.subplots(3, 2, figsize=(10, 7), sharey=\"row\")\n",
-    "\n",
-    "for i, name in enumerate([\"STS_SIN\", \"STS_EXP\"]):\n",
-    "    sts = d.loc[:, name]\n",
-    "    sts.plot(ax=axes[0][i], style=\".\", legend=True, label=name)\n",
-    "\n",
-    "    if name == \"STS_SIN\":\n",
-    "        acf_true = acf_v_true\n",
-    "    else:\n",
-    "        acf_true = acf_n_true\n",
-    "\n",
-    "    acf_true.plot(ax=axes[1][i])\n",
-    "\n",
-    "    axes[2][i].plot([0.0, 365], [0, 0], label=\"true ACF\")\n",
-    "    for bm in [\"gaussian\", \"rectangle\"]:\n",
-    "        acf_name = name + \"_\" + bm\n",
-    "        acf = acf_df.loc[:, acf_name]\n",
-    "        if bm == \"gaussian\":\n",
-    "            kwargs = dict(marker=\"x\", markersize=10)\n",
-    "        else:\n",
-    "            kwargs = dict(marker=\"o\")\n",
-    "        acf.plot(label=bm, ax=axes[1][i], logx=True, linestyle=\"\", **kwargs)\n",
-    "        dif = acf.subtract(acf_true).dropna()\n",
-    "        rmse = \" (RMSE={:.2f})\".format(np.sqrt((dif.pow(2)).sum() / dif.size))\n",
-    "        dif.plot(label=bm + rmse, ax=axes[2][i], logx=True, **kwargs)\n",
-    "        axes[2][i].legend()\n",
-    "        axes[1][i].set_xticks([])\n",
-    "        axes[2][i].set_xlabel(\"Lags [Days]\")\n",
-    "        axes[2][i].set_xticks([1, 10, 30, 60, 180, 365])\n",
-    "        axes[2][i].set_xticklabels([1, 10, 30, 60, 180, 365])\n",
-    "\n",
-    "ax = axes[0][0].set_ylabel(\"Noise [L]\")\n",
-    "axes[1][0].set_ylabel(\"ACF [-]\")\n",
-    "axes[2][0].set_ylabel(\"Error [L]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sine wave with non-equidistant timesteps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axes = plt.subplots(3, 3, figsize=(16, 9), sharey=\"row\")\n",
-    "#\n",
-    "for i, name in enumerate([\"STS_SIN1\", \"STS_SIN2\", \"STS_SIN3\"]):\n",
-    "    sts = d.loc[:, name]\n",
-    "    sts.plot(ax=axes[0][i], style=\".\", label=name)\n",
-    "    axes[0][i].legend(loc=3)\n",
-    "    acf_v_true.plot(ax=axes[1][i])\n",
-    "    axes[2][i].plot([0.0, 365], [0, 0], label=\"true ACF\")\n",
-    "    for bm in [\"gaussian\", \"rectangle\"]:\n",
-    "        acf_name = name + \"_\" + bm\n",
-    "        acf = acf_df.loc[:, acf_name]\n",
-    "        if bm == \"gaussian\":\n",
-    "            kwargs = dict(marker=\"x\", markersize=10)\n",
-    "        else:\n",
-    "            kwargs = dict(marker=\"o\")\n",
-    "        acf.plot(label=bm, ax=axes[1][i], logx=True, linestyle=\"\", **kwargs)\n",
-    "        dif = acf.subtract(acf_v_true).dropna()\n",
-    "        rmse = \" (RMSE={:.2f})\".format(np.sqrt((dif.pow(2)).sum() / dif.size))\n",
-    "        dif.plot(label=bm + rmse, ax=axes[2][i], logx=True, **kwargs)\n",
-    "        axes[1][i].set_xticks([])\n",
-    "        axes[2][i].set_xticks([1, 10, 30, 60, 180, 365])\n",
-    "        axes[2][i].set_xticklabels([1, 10, 30, 60, 180, 365])\n",
-    "        axes[2][i].legend(loc=3)\n",
-    "        axes[2][i].set_xlabel(\"Lags [Days]\")\n",
-    "\n",
-    "axes[2][i].relim()\n",
-    "axes[0][0].set_ylabel(\"Noise [L]\")\n",
-    "axes[1][0].set_ylabel(\"ACF [-]\")\n",
-    "axes[2][0].set_ylabel(\"Error [L]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exponential decay with non-equidistant timesteps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axes = plt.subplots(3, 3, figsize=(16, 9), sharey=\"row\")\n",
-    "\n",
-    "for i, name in enumerate([\"STS_EXP1\", \"STS_EXP2\", \"STS_EXP3\"]):\n",
-    "    sts = d.loc[:, name]\n",
-    "    sts.plot(ax=axes[0][i], style=\".\", label=name)\n",
-    "    axes[0][i].legend(loc=3)\n",
-    "    acf_n_true.plot(ax=axes[1][i])\n",
-    "    axes[2][i].plot([0.0, 365], [0, 0], label=\"true ACF\")\n",
-    "    for j, bm in enumerate([\"gaussian\", \"rectangle\"]):\n",
-    "        acf_name = name + \"_\" + bm\n",
-    "        acf = acf_df.loc[:, acf_name]\n",
-    "        if bm == \"gaussian\":\n",
-    "            kwargs = dict(marker=\"x\", markersize=10)\n",
-    "        else:\n",
-    "            kwargs = dict(marker=\"o\")\n",
-    "        acf.plot(label=bm, ax=axes[1][i], logx=True, linestyle=\"\", **kwargs)\n",
-    "        dif = acf.subtract(acf_n_true).dropna()\n",
-    "        rmse = \" (RMSE={:.2f})\".format(np.sqrt((dif.pow(2)).sum() / dif.size))\n",
-    "        dif.plot(label=bm + rmse, ax=axes[2][i], logx=True, sharey=axes[2][0], **kwargs)\n",
-    "        axes[2][i].set_xticks([1, 10, 30, 60, 180, 365])\n",
-    "        axes[2][i].set_xticklabels([1, 10, 30, 60, 180, 365])\n",
-    "        axes[1][i].set_xticks([])\n",
-    "        axes[2][i].legend(loc=2)\n",
-    "        axes[2][i].set_xlabel(\"Lags [Days]\")\n",
-    "\n",
-    "axes[0][0].set_ylabel(\"Noise [L]\")\n",
-    "axes[1][0].set_ylabel(\"Autocorrelation [-]\")\n",
-    "axes[2][0].set_ylabel(\"Difference from true value [-]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "- Rehfeld, K., Marwan, N., Heitzig, J., Kurths, J. (2011). [Comparison of correlation analysis techniques for irregularly sampled time series](https://doi.org/10.5194/npg-18-389-2011). Nonlinear Processes in Geophysics. 18. 389-404.\n",
-    "- Edelson, R. A., & Krolik, J. H. (1988). The discrete correlation function-A new method for analyzing unevenly sampled variability data. The Astrophysical Journal, 333, 646-659.\n",
-    "- Collenteur, R.A. (2018) [Over autocorrelatie van tijdreeksmoddellen met niet-equidistante tijdstappen](http://www.artesia-water.nl/wp-content/uploads/Autocorrelatie_niet_gelijke_tijdstappen.pdf), Artesia, Schoonhoven, Nederland. In Dutch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "artesia",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "dace5e1b41a98a8e52d2a8eebc3b981caf2c12e7a76736ebfb89a489e3b62e79"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/doc/concepts/check_response_functions.ipynb b/doc/concepts/check_response_functions.ipynb
deleted file mode 100644
index 79ef0a0e..00000000
--- a/doc/concepts/check_response_functions.ipynb
+++ /dev/null
@@ -1,510 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "45d1dfdc",
-   "metadata": {},
-   "source": [
-    "# Check Response Functions\n",
-    "This notebooks checks the step response functions by numerically integrating the impulse response functions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "938b129f-c9ac-418d-879e-f2eba52298d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.integrate import quad\n",
-    "import pastas as ps"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a50691a-85cb-487b-87ad-a238224cdda2",
-   "metadata": {},
-   "source": [
-    "### Gamma"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c5a802df-52d5-49c7-95c0-aa026b94b4f6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ps.Gamma.impulse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7b8bf54b-44ac-444c-9f30-4fad27b5a546",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = 5\n",
-    "n = 1.5\n",
-    "a = 50\n",
-    "p = [A, n, a]\n",
-    "\n",
-    "gamma = ps.Gamma()\n",
-    "tmax = gamma.get_tmax(p)\n",
-    "t = np.arange(0, tmax)\n",
-    "\n",
-    "step = gamma.step(p)\n",
-    "stepnum = np.zeros(len(t))\n",
-    "for i in range(1, len(t)):\n",
-    "    stepnum[i] = quad(gamma.impulse, 0, t[i], args=(p))[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "41d2e835-bf3a-47e7-9f6b-7d0dce2d9c6b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(t[1:], step, label=\"analytic\")\n",
-    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
-    "plt.xlabel(\"time (d)\")\n",
-    "plt.ylabel(\"step (m)\")\n",
-    "plt.grid()\n",
-    "_ = plt.legend()  # try to show figure in readthedocs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94fb2160-3637-43c7-b8f1-25d508c9b485",
-   "metadata": {},
-   "source": [
-    "### Exponential"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c4508596-cf12-4ed4-b74a-567b25a88ee5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ps.Exponential.impulse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "77ca80bb-c678-46c1-aab1-f0e2ec60ebdf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = 5\n",
-    "a = 50\n",
-    "p = [A, a]\n",
-    "\n",
-    "exponential = ps.Exponential()\n",
-    "tmax = exponential.get_tmax(p)\n",
-    "t = np.arange(0, tmax)\n",
-    "\n",
-    "step = exponential.step(p)\n",
-    "stepnum = np.zeros(len(t))\n",
-    "for i in range(1, len(t)):\n",
-    "    stepnum[i] = quad(exponential.impulse, 0, t[i], args=(p))[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6172dd00-763c-4f25-b4f0-3c34a15b3c33",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(t[1:], step, label=\"analytic\")\n",
-    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
-    "plt.xlabel(\"time (d)\")\n",
-    "plt.ylabel(\"step (m)\")\n",
-    "plt.grid()\n",
-    "_ = plt.legend()  # try to show figure in readthedocs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d007aa2-7c99-4c3c-9c2a-f413a8f5a5a3",
-   "metadata": {},
-   "source": [
-    "### Hantush"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "750373d0-402a-478c-b1f4-b3457cdb4812",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ps.Hantush.impulse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "35790f67-b518-4367-90c3-2d5a425da0c2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = 5\n",
-    "a = 50\n",
-    "b = 2\n",
-    "p = [A, a, b]\n",
-    "\n",
-    "hantush = ps.Hantush()\n",
-    "tmax = hantush.get_tmax(p)\n",
-    "t = np.arange(0, tmax)\n",
-    "\n",
-    "step = hantush.step(p)\n",
-    "stepnum = np.zeros(len(t))\n",
-    "for i in range(1, len(t)):\n",
-    "    stepnum[i] = quad(hantush.impulse, 0, t[i], args=(p))[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e63a25d6-1b30-41a2-a02d-d4d013925c30",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(t[1:], step, label=\"analytic\")\n",
-    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
-    "plt.xlabel(\"time (d)\")\n",
-    "plt.ylabel(\"step (m)\")\n",
-    "plt.grid()\n",
-    "_ = plt.legend()  # try to show figure in readthedocs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d5d822c8-f954-4e7c-96c6-de24895d4da8",
-   "metadata": {},
-   "source": [
-    "### Polder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2dd0ae6-fa59-482b-94b1-74b4b31e0a8a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ps.Polder.impulse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2eaa17db-8cba-4731-926e-4f8479987063",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = 5\n",
-    "a = 100\n",
-    "b = 0.25\n",
-    "p = [A, a, b]\n",
-    "\n",
-    "polder = ps.Polder()\n",
-    "tmax = polder.get_tmax(p)\n",
-    "t = np.arange(0, tmax)\n",
-    "\n",
-    "step = polder.step(p)\n",
-    "stepnum = np.zeros(len(t))\n",
-    "for i in range(1, len(t)):\n",
-    "    stepnum[i] = quad(polder.impulse, 0, t[i], args=(p))[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "419cf91b-9a53-440d-958d-fc085ec0a4b8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(t[1:], step, label=\"analytic\")\n",
-    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
-    "plt.xlabel(\"time (d)\")\n",
-    "plt.ylabel(\"step (m)\")\n",
-    "plt.grid()\n",
-    "_ = plt.legend()  # try to show figure in readthedocs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd0facf9-763d-47a6-b722-ebf50b02a2d1",
-   "metadata": {},
-   "source": [
-    "### Four-parameter function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "57d08799-3632-4bb6-84a1-509f72a5861a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ps.FourParam.impulse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "83601f06-0737-4ae9-b7eb-0b113a90bc85",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = 1  # impulse response implemented for A=1 only\n",
-    "n = 1.5\n",
-    "a = 50\n",
-    "b = 10\n",
-    "p = [A, n, a, b]\n",
-    "\n",
-    "fourparam = ps.FourParam(quad=False)  # use simple integration\n",
-    "tmax = fourparam.get_tmax(p)\n",
-    "t = np.arange(0, tmax)\n",
-    "\n",
-    "step = fourparam.step(p)\n",
-    "stepnum = np.zeros(len(t))\n",
-    "for i in range(1, len(t)):\n",
-    "    stepnum[i] = quad(fourparam.impulse, 0, t[i], args=(p))[0]\n",
-    "stepnum = (\n",
-    "    stepnum / quad(fourparam.impulse, 0, np.inf, args=p)[0]\n",
-    ")  # four param is scaled at the end"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "095f83cb-9a04-4651-8031-7f2f84445c54",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(t[1:], step, label=\"analytic\")\n",
-    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
-    "plt.xlabel(\"time (d)\")\n",
-    "plt.ylabel(\"step (m)\")\n",
-    "plt.grid()\n",
-    "_ = plt.legend()  # try to show figure in readthedocs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d0396351-cd54-4666-b847-862ee442a032",
-   "metadata": {},
-   "source": [
-    "### Double exponential function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "07790a6a-8ec8-4b95-9160-a832b2aceed2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ps.DoubleExponential.impulse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "42e26e95-9afb-4595-b170-0f5ee6e0a186",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = 5  # impulse response implemented for A=1 only\n",
-    "a = 10\n",
-    "b = 50\n",
-    "f = 0.4\n",
-    "p = [A, f, a, b]\n",
-    "\n",
-    "doubexp = ps.DoubleExponential()\n",
-    "tmax = doubexp.get_tmax(p)\n",
-    "t = np.arange(0, tmax)\n",
-    "\n",
-    "step = doubexp.step(p)\n",
-    "stepnum = np.zeros(len(t))\n",
-    "for i in range(1, len(t)):\n",
-    "    stepnum[i] = quad(doubexp.impulse, 0, t[i], args=(p))[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8299b68e-fe97-45cf-8b5b-7f61534d6a60",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(t[1:], step, label=\"analytic\")\n",
-    "plt.plot(t, stepnum, \"--\", label=\"numerical\")\n",
-    "plt.xlabel(\"time (d)\")\n",
-    "plt.ylabel(\"step (m)\")\n",
-    "plt.grid()\n",
-    "_ = plt.legend()  # try to show figure in readthedocs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5b5fa2c",
-   "metadata": {},
-   "source": [
-    "### Kraijenhoff\n",
-    "\n",
-    "#### Kraijenhoff van de Leur\n",
-    "\n",
-    "##### Impulse Response\n",
-    "from [A study of non-steady groundwater flow with special reference to a reservoir-coefficient (1958)](https://edepot.wur.nl/422032) formula 2\n",
-    "\n",
-    "$ \\theta(t) = \\frac{4N}{\\pi S} \\sum_{n=1,3,5...}^\\infty \\left( \\frac{1}{n} \\exp{\\left( {-n^2\\frac{\\pi^2T}{SL^2} t} \\right)} \\sin \\left(\\frac{n\\pi x}{L}\\right) \\right) $\n",
-    "\n",
-    "##### Step Response\n",
-    "\n",
-    "The step response is obtained by taking the integral of the impulse response function\n",
-    "\n",
-    "$ \\Theta(t) = \\frac{4 N}{\\pi S} \\sum_{n=1,3,5...}^\\infty \\frac{1}{n^3} \\left(\\frac{SL^2}{\\pi^2 T} - \\frac{SL^2}{\\pi^2 T} \\exp\\left(-n^2\\frac{\\pi^2T}{SL^2}t\\right)\\right)  \\sin \\left(\\frac{n\\pi x}{L}\\right) $\n",
-    "\n",
-    "$ \\Theta(t) = \\frac{4 N L^2}{\\pi^3 T} \\sum_{n=1,3,5...}^\\infty \\frac{1}{n^3} \\left(1 - \\exp\\left(-n^2\\frac{\\pi^2T}{SL^2}t\\right)\\right)  \\sin \\left(\\frac{n\\pi x}{L}\\right)$\n",
-    "\n",
-    "And $\\sum_{n=1,3,5...}^\\infty n = \\sum_{n=0}^\\infty (2n+1)$ gives:\n",
-    "\n",
-    "$ \\Theta(t) = \\frac{4 N L^2}{\\pi^3 T} \\sum_{n=0}^\\infty \\frac{1}{(2n+1)^3} \\left(1 - \\exp\\left(-(2n+1)^2\\frac{\\pi^2T}{SL^2}t)\\right)\\right)  \\sin \\left(\\frac{(2n+1)\\pi x}{L}\\right)$\n",
-    "\n",
-    "\n",
-    "Kraijenhoff van de Leur takes $\\frac{x}{L}=\\frac{1}{2}$ as the middle of the domain. \n",
-    "\n",
-    "#### Bruggeman \n",
-    "\n",
-    "from Analytical Solutions of Geohydrological Problems (1999) formula 133.15\n",
-    "\n",
-    "##### Step Response\n",
-    "\n",
-    "$ \\Theta(t) = \\frac{-N}{2T}\\left(x^2 - \\frac{1}{4}L^2\\right) - \\frac{4NL^2}{\\pi^3T} \\sum_{n=0}^\\infty\n",
-    "\\frac{(-1)^n}{(2n + 1)^3} \\cos\\left(\\frac{(2n+1)\\pi x}{L}\\right)\n",
-    "\\exp\\left(-\\frac{(2n+1)^2\\pi^2 T}{SL^2}t\\right)\n",
-    "$\n",
-    "\n",
-    "$ \\Theta(t) = \\frac{-NL^2}{2T}\\left(\\left(\\frac{x}{L}\\right)^2 - \\frac{1}{4}\\right) - \\frac{4NL^2}{\\pi^3T} \\sum_{n=0}^\\infty\n",
-    "\\frac{(-1)^n}{(2n + 1)^3} \n",
-    "\\exp\\left(-\\frac{(2n+1)^2\\pi^2 T}{SL^2}t\\right) \\cos\\left(\\frac{(2n+1)\\pi x}{L}\\right)\n",
-    "$\n",
-    "\n",
-    "$ \\Theta(t) = \\frac{-NL^2}{2T}\\left(\\left(\\frac{x}{L}\\right)^2 - \\tfrac{1}{4}\\right) \\left(1 - \\frac{8}{\\pi^3 \\left(\\frac{1}{4} - \\left(\\frac{x}{L}\\right)^2\\right)} \\sum_{n=0}^\\infty\n",
-    "\\frac{(-1)^n}{(2n + 1)^3} \n",
-    "\\exp\\left(-\\frac{(2n+1)^2\\pi^2 T}{SL^2}t\\right) \\cos\\left(\\frac{(2n+1)\\pi x}{L}\\right) \\right)\n",
-    "$\n",
-    "\n",
-    "Note that $x=0$ is the middle of the domain for Bruggeman.\n",
-    "\n",
-    "#### Pastas Implementation\n",
-    "\n",
-    "In Pastas the Bruggeman response function is computed and the parameters are transformed to:\n",
-    "\n",
-    "Scale parameter (such that the gain is always $A$):\n",
-    "\n",
-    "$A = \\frac{-NL^2}{2T}\\left(\\left(\\frac{x}{L}\\right)^2 - \\tfrac{1}{4}\\right)$\n",
-    "\n",
-    "Reservoir coefficient (also known as $j$ in Kraijenhoff):\n",
-    "\n",
-    "$a = \\frac{SL^2}{\\pi^2 T}$\n",
-    "\n",
-    "Location in the domain:\n",
-    "\n",
-    "$b = \\frac{x}{L}$\n",
-    "\n",
-    "\n",
-    "Such that the step response becomes:\n",
-    "\n",
-    "$ \\Theta(t) = A\\left(1 - \\frac{8}{\\pi^3(\\frac{1}{4} - b^2)} \\sum_{n=0}^\\infty \\frac{(-1)^n}{(2n+1)^3} \\cos\\left((2n+1)\\pi b\\right)\\exp\\left(-\\frac{(2n+1)^2t}{a}\\right) \\right)$\n",
-    "\n",
-    "Taking the derivative gives the impulse response:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "798ab1b1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ps.Kraijenhoff.impulse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c3835b9e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = 5\n",
-    "a = 10\n",
-    "b = 0.25\n",
-    "p = [A, a, b]\n",
-    "\n",
-    "khoff = ps.Kraijenhoff()\n",
-    "tmax = khoff.get_tmax(p)\n",
-    "t = np.arange(0, tmax)\n",
-    "\n",
-    "step = khoff.step(p)\n",
-    "stepnum_brug = np.zeros(len(t))\n",
-    "\n",
-    "for i in range(1, len(t)):\n",
-    "    stepnum_brug[i] = quad(khoff.impulse, 0, t[i], args=(p))[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e2c870cd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(t[1:], step, label=\"analytic\")\n",
-    "plt.plot(t, stepnum_brug, \"--\", label=\"numerical bruggeman\")\n",
-    "plt.xlabel(\"time (d)\")\n",
-    "plt.ylabel(\"step (m)\")\n",
-    "plt.grid()\n",
-    "_ = plt.legend()  # try to show figure in readthedocs"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/doc/concepts/handling_timeseries.ipynb b/doc/concepts/handling_timeseries.ipynb
deleted file mode 100644
index ed95fcda..00000000
--- a/doc/concepts/handling_timeseries.ipynb
+++ /dev/null
@@ -1,452 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Time series in Pastas\n",
-    "*R.A. Collenteur, University of Graz, 2020*\n",
-    "\n",
-    "Time series are at the heart of time series analysis, and therefore need to be considered carefully when dealing with time series models. In this notebook more background information is provided on important characteristics of time series and how these may influence your modeling results. In general, Pastas depends heavily on Pandas for dealing with time series, but adds capabilities to deal with irregular time series and missing data.\n",
-    "\n",
-    "All time series should be provided to Pastas as `pandas.Series` with a `pandas.DatetimeIndex`. Internally these time series are stored in a `pastas.TimeSeries` object. The goal of this object is to validate the user-provided time series and enable resampling (changing frequencies) of the independent time series. The TimeSeries object also has  capabilities to deal with missing data in the user-provided time series. As much of these operations occur internally, this notebook is meant to explain users what is happening and how to check for this.\n",
-    "\n",
-    "<div class=\"alert alert-info\">\n",
-    "\n",
-    "<b>Note</b>\n",
-    "    \n",
-    "* The standard Pastas data type for a date is the `pandas.Timestamp`.\n",
-    "* The standard Pastas data type for a sequence of dates is the `pandas.DatetimeIndex` with `pandas.Timestamp`.\n",
-    "* The standard  Pastas data type for a time series is a `pandas.Series` with a `pandas.DatetimeIndex`\n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pastas as ps\n",
-    "\n",
-    "ps.show_versions()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Different types of time series\n",
-    "Time series data may generally be defined as a set of data values observed at certain times, ordered in a way that the time indices are increasing. Many time series analysis method assume that the time step between the observations is regular, the time series has evenly-spaced observations. These evenly spaced time series may have missing data, but it will still be possible to lay the values on a time-grid with constant time steps. \n",
-    "\n",
-    "This is generally also assumed to be the case for the independent time series in hydrological studies. For example, the precipitation records may have some missing data but the precipitation is reported as the total rainfall over one day. In the case of missing data, we may impute a zero (no rain) or the rainfall amount from a nearby measurement station.\n",
-    "\n",
-    "Groundwater level time series do generally not share these characteristics with other hydrological time series, and are measured at irregular time intervals. This is especially true for historic time series that were measured by hand. The result is that the measurements can not be laid on a regular time grid. The figure below graphically shows the difference between the three types of time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "regular = pd.Series(\n",
-    "    index=pd.date_range(\"2000-01-01\", \"2000-01-10\", freq=\"D\"), data=np.ones(10)\n",
-    ")\n",
-    "missing_data = regular.copy()\n",
-    "missing_data.loc[[\"2000-01-03\", \"2000-01-08\"]] = np.nan\n",
-    "\n",
-    "index = [t + pd.Timedelta(np.random.rand() * 24, unit=\"H\") for t in missing_data.index]\n",
-    "irregular = missing_data.copy()\n",
-    "irregular.index = index\n",
-    "\n",
-    "fig, axes = plt.subplots(3, 1, figsize=(6, 5), sharex=True, sharey=True)\n",
-    "\n",
-    "regular.plot(ax=axes[0], linestyle=\" \", marker=\"o\", x_compat=True)\n",
-    "missing_data.plot(ax=axes[1], linestyle=\" \", marker=\"o\", x_compat=True)\n",
-    "irregular.plot(ax=axes[2], linestyle=\" \", marker=\"o\", x_compat=True)\n",
-    "\n",
-    "for i, name in enumerate(\n",
-    "    [\"(a) Regular time steps\", \"(b) Missing Data\", \"(c) Irregular time steps\"]\n",
-    "):\n",
-    "    axes[i].grid()\n",
-    "    axes[i].set_title(name)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Independent and dependent time series \n",
-    "We can differentiate between two types of input time series for Pastas models: the dependent and independent time series. The dependent time series are those that we want to explain (e.g., the groundwater levels) and the independent time series are those that we use to explain the dependent time series (e.g., precipitation or evaporation). The requirements for these time series are different:\n",
-    "\n",
-    "- The dependent time series may be of any kind: regular, missing data or irregular.\n",
-    "- The independent time series has to have regular time steps.\n",
-    "\n",
-    "In practice, this means that the time series provided to `pastas.Model` may be of any kind, and that the time series used by the stressmodels (e.g., `pastas.RerchargeModel`) need to have regular time steps. The regular time steps  are required to simulate contributions to the groundwater level fluctuations. As there are virtually no restrictions on the dependent time series, the remainder of this notebook will discuss primarily the independent time series.\n",
-    "\n",
-    "## How does the TimeSeries object validate a time series?\n",
-    "To ensure that a time series can be used for simulation a number of things are checked and changed:\n",
-    "\n",
-    "1. Make sure the values are floats. Values are change to dtype=float if not.\n",
-    "2. Make sure the index is a `pandas.DatetimeIndex`. Index is changed if not.\n",
-    "3. Make sure the timestamps in the index are increasing. Index is sorted if not.\n",
-    "4. Make sure there are no nan-values at the start and end of a time series.\n",
-    "5. Determine the frequency of the time series.\n",
-    "6. Make sure there are no duplicate indices. Values are averaged if this is the case.\n",
-    "7. Remove or fill up nan-values, depending on the settings.\n",
-    "\n",
-    "For each of these steps an INFO message will be returned by Pastas to inform the user if a change is made. The first four steps generally do not have a large impact and are there to prevent some basic issues. Preferably, no changes are reported. \n",
-    "\n",
-    "### Frequency of the input data\n",
-    "Pastas tries to determine the frequency in step 5, and will **always** report the result. It is generally good practice to double-check if the reported frequency agrees with what you know about the time series. Pastas will also report if no frequency can be inferred. If no frequency is reported there is probably some wrong and the user should fix either fix the input time series or provide Pastas with more information.\n",
-    "\n",
-    "Below we consider a time series with precipitation data, measured every day. We will use `settings=\"prec` as a shortcut for the settings to fill nans and resample. We will come back to those settings later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rain = pd.read_csv(\n",
-    "    \"../examples/data/rain_nb1.csv\", parse_dates=[\"date\"], index_col=\"date\"\n",
-    ").squeeze()\n",
-    "ps.TimeSeries(rain, settings=\"prec\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pastas correctly report the frequency and we can continue with this time series. Note that the input time series thus agrees with all the checks for the time series validation. Let's now introduce a nan-value and see what happens."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rain[\"1989-01-01\"] = np.nan\n",
-    "ps.TimeSeries(rain, settings=\"prec\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This also works fine. The frequency was inferred (stored as freq_original) and one nan-value was filled up with 0.0. Now we take the same time series, but drop the nan-value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ps.TimeSeries(rain.dropna(), settings=\"prec\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The above result is probably not what we want. Pastas could not infer the frequency and therefore resorts to the `timestep_weighted_resample` method. Documentation for this method is available in utils.py.\n",
-    "\n",
-    "If we know the original frequency of the time series, we can tell this to Pastas through the `freq_original` argument. As we can see below, the user-provided frequency is used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rain = pd.read_csv(\n",
-    "    \"../examples/data/rain_nb1.csv\", parse_dates=[\"date\"], index_col=\"date\"\n",
-    ").squeeze()\n",
-    "rain[\"1989-01-01\"] = np.nan\n",
-    "ps.TimeSeries(rain.dropna(), settings=\"prec\", freq_original=\"D\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The above example shows how to obtain the same or different result with four different methods. Some of these methods requires good knowledge about the TimeSeries object and how it processes your time series. It is often preferred to provide Pastas with a better initial time series by resampling it yourself. This has the additional benefit that you are interacting more closely with the data. Most of the examples also follow this pattern.\n",
-    "\n",
-    "<div class=\"alert alert-info\">\n",
-    "\n",
-    "<b>Best practice</b>\n",
-    "\n",
-    "Try and modify your original time series such that Pastas returns a message that it was able to infer the frequency from the time series itself: **INFO: Inferred frequency for time series rain: freq=D**\n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Time series settings\n",
-    "In the examples above we used the `settings` keyword when creating the TimeSeries. This is a shortcut method to select a number of settings from a predefined set of options. These predefined options can accessed through `ps.rcParams[\"timeseries\"]`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame.from_dict(ps.rcParams[\"timeseries\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each column name is a valid option for the `settings` argument. The rows shows the settings that may be chosen for changing the original time series. Once a TimeSeries is created, we can access the existing settings as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ts = ps.TimeSeries(rain, settings=\"prec\")\n",
-    "ts.settings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This settings dictionary now includes both settings used to resample (sample_up, sample_down), extend (fill_before, fill_after), normalize (norm), and fill nans in the time series, but also dynamic settings such as the start and end date (tmin, tmax), the frequency (freq) and the time offset. \n",
-    "\n",
-    "To update these settings you the `update_series` method is available. For example, if we want to resample the above time series to a 7-day frequency and sum up the values we can use:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ts.update_series(freq=\"7D\", sample_down=\"sum\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Because the original series are stored in the TimeSeries object as well, it is also possible to go back again. The changing made to the time series are always started from the original validated time series again. For more information on the possible settings see the API-docs for the [TimeSeries and update_series method](https://pastas.readthedocs.io/en/latest/api/timeseries.html) on the documentation website."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## An example with a Pastas Model\n",
-    "By now you may be wondering why all these settings exist in the first place. The main reason (apart from validating the user-provided time series) is to change the time step of the simulation of the independent time series. It may also be used to extend the time series in time.\n",
-    "\n",
-    "Below we load some time series, visualize them and create a Pastas model with precipitation and evaporation to explain the groundwater level fluctuations. It is generally recommended to plot your time series for a quick visual check of the input data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "head = pd.read_csv(\n",
-    "    \"../examples/data/B32C0639001.csv\", parse_dates=[\"date\"], index_col=\"date\"\n",
-    ").squeeze()\n",
-    "rain = pd.read_csv(\n",
-    "    \"../examples/data/rain_nb1.csv\", parse_dates=[\"date\"], index_col=\"date\"\n",
-    ").squeeze()\n",
-    "evap = pd.read_csv(\n",
-    "    \"../examples/data/evap_nb1.csv\", parse_dates=[\"date\"], index_col=\"date\"\n",
-    ").squeeze()\n",
-    "\n",
-    "fig, axes = plt.subplots(3, 1, figsize=(10, 6), sharex=True)\n",
-    "head.plot(ax=axes[0], x_compat=True, linestyle=\" \", marker=\".\")\n",
-    "evap.plot(ax=axes[1], x_compat=True)\n",
-    "rain.plot(ax=axes[2], x_compat=True)\n",
-    "axes[0].set_ylabel(\"Head [m]\")\n",
-    "axes[1].set_ylabel(\"Evap [mm/d]\")\n",
-    "axes[2].set_ylabel(\"Rain [mm/d]\")\n",
-    "\n",
-    "plt.xlim(\"1985\", \"2005\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml = ps.Model(head)\n",
-    "\n",
-    "rch = ps.rch.Linear()\n",
-    "rm = ps.RechargeModel(rain, evap, recharge=rch, rfunc=ps.Gamma(), name=\"rch\")\n",
-    "ml.add_stressmodel(rm)\n",
-    "\n",
-    "ml.solve(noise=True, tmin=\"1990\", report=\"basic\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## What is the model freq?\n",
-    "The output below shows that the time series have frequencies of `freq=D`. The fit report also shows a frequency of `freq=D`. The frequency reported in the fit_report is the time step of the simulation for the independent time series, and is internally passed on to the stressmodels. The user-provided dependent time series are stored in the stressmodel object and can be accessed as follows. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml.stressmodels[\"rch\"].stress"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want to change the resample method, for example we want to sum the precipitation and evaporation when sampling down (e.g., daily to weekly) we may do the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for stress in ml.stressmodels[\"rch\"].stress:\n",
-    "    stress.update_series(sample_down=\"sum\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After changing the methods for sampling down, we now solve the model with a simulation time step of 14 days. The precipitation and evaporation are then summed up over 14 day intervals, before being translated to a groundwater fluctuation using a respons function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml.settings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml.solve(freq=\"14D\", tmin=\"1980\", report=\"basic\")\n",
-    "ml.plots.results(figsize=(10, 6), tmin=\"1970\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml.stressmodels[\"rch\"].stress[1].update_series(tmin=\"1960\")\n",
-    "ml.stressmodels[\"rch\"].stress[1].settings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another method to obtain the settings of the time series used in a stressmodel is as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml.get_stressmodel_settings(\"rch\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Warnings\n",
-    "Because the TimeSeries object is a relatively complicated object that can potentially change model results extra care has to be taken in some cases. Below is a number of outstanding warnings and the related GitHub issues. \n",
-    "\n",
-    "<div class=\"alert alert-warning\">\n",
-    "\n",
-    "<b>A note on dependent time series</b>\n",
-    "\n",
-    "The dependent time series (stored as `ml.oseries`) are also stored in a TimeSeries object and therefore have the same capabilities. Usage of these methods on the dependent time series is however experimental and not recommended for real world use cases. See also [Issue #68](https://github.com/pastas/pastas/issues/68) and [Discussion #199](https://github.com/pastas/pastas/discussions/199)\n",
-    "    \n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "<div class=\"alert alert-warning\">\n",
-    "\n",
-    "<b>A note on monthly data</b>\n",
-    "\n",
-    "Monthly data is strictly irregular data, and poses additional challenges when resampling to regular frequencies. Pastas does not differentiate between monthly data reported at months end (`freq=M`) or months beginning (`freq=MS`) and the default settings are selected for `freq=M`. There may also be issues with extending the time series. See also [Issue #239](https://github.com/pastas/pastas/issues/239)\n",
-    "    \n",
-    "</div>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/doc/concepts/hantush_response.ipynb b/doc/concepts/hantush_response.ipynb
deleted file mode 100644
index 7be7a1a3..00000000
--- a/doc/concepts/hantush_response.ipynb
+++ /dev/null
@@ -1,727 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hantush response functions\n",
-    "\n",
-    "This notebook compares the two implementations of the Hantush response function in Pastas.\n",
-    "\n",
-    "*Developed by D.A. Brakenhoff (Artesia, 2021)*\n",
-    "\n",
-    "\n",
-    "## Contents\n",
-    "\n",
-    "- [Hantush versus HantushWellModel](#Hantush-versus-HantushWellModel)\n",
-    "- [Which Hantush should I use?](#Which-Hantush-should-I-use?)\n",
-    "- [Options](#Options)\n",
-    "- [Synthetic example](#Synthetic-example)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import pastas as ps\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "ps.show_versions()\n",
-    "ps.logger.setLevel(\"WARNING\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hantush versus HantushWellModel\n",
-    "\n",
-    "There are two implementations of the Hantush response functions in Pastas. The two implementations are very similar, but they differ in their intended application and their definition of the parameters. The table below shows the formulas for both implementations.\n",
-    "\n",
-    "\n",
-    "| Name             | Fitting parameters  | Formula                                                                 | Description                                                                    |\n",
-    "|------------------|-------------|:------------------------------------------------------------------------|--------------------------------------------------------------------------------|\n",
-    "| Hantush          | 3 - A, a, b | $$ \\theta(t) = \\frac{A}{2t \\text{K}_0 \\left(2 \\sqrt{b} \\right)} e^{-t/a - ab/t} $$                               | Response function commonly used for groundwater abstraction wells.             |\n",
-    "| HantushWellModel | 3 - A', a, b' | $$ \\theta(r,t) = \\frac{A^\\prime}{2t} e^{-t/a - a r^2 \\exp (b^\\prime) /t} $$ | Implementation of the Hantush well function that allows scaling with distance. |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Hantush\n",
-    "The Hantush response function is intended for the simulation of the effect of a\n",
-    "single pumping well. The Hantush implementation has three parameters: $A$, $a$,\n",
-    "and $b$. The parameter $A$ is also known as the \"gain\", which is equal to the\n",
-    "steady-state contribution of a stress with unit 1. For example, the drawdown\n",
-    "caused by a well with a continuous extraction rate of 1.0 (the units are\n",
-    "determined by the units of the stress and head used in the model).\n",
-    "\n",
-    "The relationship between the parameters $A$, $a$, and $b$ and the physical\n",
-    "parameters of the classic Hantush function are given in the notebook on\n",
-    "[response functions](https://pastas.readthedocs.io/en/latest/concepts/response_functions.ipynb.html#Hantush-step-function-compared-to-classic-Hantush-function)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### HantushWellModel\n",
-    "\n",
-    "The HantushWellModel also has three parameters: $A^\\prime$, $a$, and\n",
-    "$b^\\prime$. The HantushWellModel response function includes the distance $r$\n",
-    "between an extraction well and an observation well, which must be defined by\n",
-    "the user as an input variable. This allows multiple wells to have the same\n",
-    "response function, scaled by the distance $r$, which can be useful to reduce\n",
-    "the number of parameters in a model with multiple extraction wells. Note that\n",
-    "$r$ is a variable that must be provided by the user and is not a parameter that\n",
-    "is optimized. The gain of the HantushWellModel function is\n",
-    "\n",
-    "$$\n",
-    "A^\\prime \\text{K}_0 \\left( 2 r \\exp \\left( \\frac{b^\\prime}{2} \\right) \\right)\n",
-    "$$\n",
-    "\n",
-    "The relationship between the parameters of the Hantush function and the\n",
-    "HantushWellModel function are:\n",
-    "\n",
-    "$$\n",
-    "\\begin{align*}\n",
-    "A &= A^\\prime \\text{K}_0 \\left( 2 r \\exp \\left( \\frac{b^\\prime}{2} \\right) \\right)\\\\\n",
-    "a &= a \\\\\n",
-    "b &= r^2 \\exp \\left( b^\\prime \\right)\n",
-    "\\end{align*}\n",
-    "$$\n",
-    "\n",
-    "The log-transform of parameter $b$ is used in the implementation because\n",
-    "taking out $r^2$ causes parameter $b$ to become very small which can\n",
-    "cause issues in the optimization process. Note that this also requires the\n",
-    "uncertainty of $b^\\prime$ to be transformed to obtain the uncertainty of\n",
-    "$b$.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pros and cons of both functions\n",
-    "\n",
-    "There advantages and disadvantages of both implementations are listed below.\n",
-    "\n",
-    "### Hantush\n",
-    "\n",
-    "**Pro**:\n",
-    "- Parameter A is the gain, which makes it easier to interpret the results.\n",
-    "- Estimates the uncertainty of the gain directly.\n",
-    "\n",
-    "**Con**:\n",
-    "- Cannot be used to simulate multiple wells with a single response function.\n",
-    "\n",
-    "### HantushWellModel\n",
-    "\n",
-    "**Pro**:\n",
-    "- Can be used with WellModel to simulate multiple wells with a single response function.\n",
-    "\n",
-    "**Con**:\n",
-    "- Does not directly estimate the uncertainty of the gain; the uncertainty of the gain must be calculated using special methods.\n",
-    "- More sensitive to the initial value of parameters. (The initial parameter values may have to be tweaked to get a good fit result.)\n",
-    "\n",
-    "\n",
-    "So which one should you use? It depends on your use-case:\n",
-    "\n",
-    "- Use `Hantush` if you are considering a single extraction well or multiple\n",
-    "  wells in different aquifers.\n",
-    "- Use `HantushWellModel` if you are simulating multiple extraction wells in a\n",
-    "  single aquifer or want to pass the distance between an extraction and\n",
-    "  observation well as a known parameter."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Options\n",
-    "\n",
-    "Both Hantush implementations in Pastas include two options:\n",
-    "\n",
-    "- `quad`: numerically integrates the Hantush integrand using `scipy.integrate.quad`. This is relatively slow! (_Note: if available, numba is used to speed up calculation of the integrand_).\n",
-    "- `use_numba`: uses [`numba-scipy`](https://github.com/numba/numba-scipy) to speed up calculation of the default Hantush implementation under certain conditions.\n",
-    "\n",
-    "This yields the following options:\n",
-    "- `rf = ps.Hantush()`, default implementation using fast Hantush approximation with numpy (**fast**)\n",
-    "- `rf = ps.Hantush(quad=True)`, uses `quad` to numerically integrate Hantush integrand (**slow**)\n",
-    "- `rf = ps.Hantush(use_numba=True)`, speeds up fast Hantush approximation with numba-scipy (**fastest**)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The performance is calculated below for the different options listed above\n",
-    "(timing may differ depending on your hardware):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rf_numpy = ps.Hantush()\n",
-    "rf_quad = ps.Hantush(quad=True)\n",
-    "\n",
-    "print(\"Hantush approximation (numpy):\")\n",
-    "%timeit rf_numpy.step([-0.05, 200, 0.5])\n",
-    "print(\"Hantush numerical integration (quad):\")\n",
-    "%timeit rf_quad.step([-0.05, 200, 0.5])\n",
-    "\n",
-    "if ps.version.check_numba_scipy():\n",
-    "    rf_numba = ps.Hantush(use_numba=True)\n",
-    "    print(\"Hantush approximation (numba):\")\n",
-    "    %timeit rf_numba.step([-0.05, 200, 0.5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Synthetic example \n",
-    "\n",
-    "A synthetic example is used to show both Hantush implementations. First, we\n",
-    "create a synthetic time series generated with the Hantush response function to\n",
-    "which we add autocorrelated residuals. We set the parameter values for the\n",
-    "Hantush response function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# A defined so that 100 m3/day results in 5 m drawdown\n",
-    "Q = 100.0  # m3/day\n",
-    "A = -5 / Q\n",
-    "a = 200\n",
-    "b = 0.5\n",
-    "\n",
-    "d = 0.0  # reference level"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# auto-correlated residuals AR(1)\n",
-    "sigma_n = 0.05\n",
-    "alpha = 50\n",
-    "sigma_r = sigma_n / np.sqrt(1 - np.exp(-2 * 14 / alpha))\n",
-    "print(f\"sigma_r = {sigma_r:.2f} m\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a head observations time series and a time series with the well extraction rate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# head observations between 2000 and 2010\n",
-    "idx = pd.date_range(\"2000\", \"2010\", freq=\"D\")\n",
-    "ho = pd.Series(index=idx, data=0.0)\n",
-    "\n",
-    "# extraction of 100 m3/day between 2002 and 2006\n",
-    "well = pd.Series(index=idx, data=0.0)\n",
-    "well.loc[\"2002\":\"2006\"] = 100.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the synthetic head timeseries based on the extraction rate and the parameters we defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml0 = ps.Model(ho)  # alleen de tijdstippen waarop gemeten is worden gebruikt\n",
-    "rm = ps.StressModel(well, ps.Hantush(quad=True), name=\"well\", up=False)\n",
-    "ml0.add_stressmodel(rm)\n",
-    "ml0.set_parameter(\"well_A\", initial=A)\n",
-    "ml0.set_parameter(\"well_a\", initial=a)\n",
-    "ml0.set_parameter(\"well_b\", initial=b)\n",
-    "ml0.set_parameter(\"constant_d\", initial=d)\n",
-    "hsynthetic_no_error = ml0.simulate()[ho.index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Model settings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solver = ps.LmfitSolve  # solver to use\n",
-    "add_noise = True  # add correlated noise to synthetic head?\n",
-    "fit_constant = True  # fit constant separately\n",
-    "report = False  # print fit reports"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Add the auto-correlated residuals."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "delt = (ho.index[1:] - ho.index[:-1]).values / pd.Timedelta(\"1d\")\n",
-    "np.random.seed(1)\n",
-    "noise = sigma_n * np.random.randn(len(ho))\n",
-    "residuals = np.zeros_like(noise)\n",
-    "residuals[0] = noise[0]\n",
-    "for i in range(1, len(ho)):\n",
-    "    residuals[i] = np.exp(-delt[i - 1] / alpha) * residuals[i - 1] + noise[i]\n",
-    "\n",
-    "if add_noise:\n",
-    "    hsynthetic = hsynthetic_no_error + residuals\n",
-    "else:\n",
-    "    hsynthetic = hsynthetic_no_error"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ax = hsynthetic_no_error.plot(label=\"synthetic heads (no error)\", figsize=(10, 5))\n",
-    "hsynthetic.plot(ax=ax, color=\"C1\", label=\"synthetic heads (with error)\")\n",
-    "ax.legend(loc=\"best\")\n",
-    "ax.set_ylabel(\"head (m+ref)\")\n",
-    "ax.grid(visible=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create three models:\n",
-    "\n",
-    "1. Model with `Hantush` response function.\n",
-    "2. Model with `HantushWellModel` response function with $r$ set to 1.0 m.\n",
-    "3. Model with `WellModel`, which uses `HantushWellModel` and $r$ is set to 1.0 m in `WellModel`.\n",
-    "\n",
-    "All three models should yield the similar results and be able to estimate the true values of the parameters reasonably well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Hantush\n",
-    "ml_h1 = ps.Model(hsynthetic, name=\"gain\")\n",
-    "wm_h1 = ps.StressModel(well, ps.Hantush(), name=\"well\", up=False)\n",
-    "ml_h1.add_stressmodel(wm_h1)\n",
-    "ml_h1.set_parameter(\"constant_d\", initial=0.0)\n",
-    "ml_h1.solve(report=report, noise=add_noise, fit_constant=fit_constant, solver=solver())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Solve with noise model and HantushWellModel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# HantushWellModel\n",
-    "ml_h2 = ps.Model(hsynthetic, name=\"scaled\")\n",
-    "rfunc = ps.HantushWellModel()\n",
-    "rfunc.set_distances(1.0)\n",
-    "wm_h2 = ps.StressModel(well, rfunc, name=\"well\", up=False)\n",
-    "ml_h2.add_stressmodel(wm_h2)\n",
-    "ml_h2.set_parameter(\"constant_d\", initial=0.0)\n",
-    "ml_h2.solve(report=report, noise=add_noise, fit_constant=fit_constant, solver=solver())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# WellModel\n",
-    "r = np.array([1.0])  # parameter r\n",
-    "well.name = \"well\"\n",
-    "\n",
-    "ml_h3 = ps.Model(hsynthetic, name=\"wellmodel\")\n",
-    "wm_h3 = ps.WellModel([well], ps.HantushWellModel(), \"well\", r, up=False)\n",
-    "ml_h3.add_stressmodel(wm_h3)\n",
-    "ml_h3.set_parameter(\"constant_d\", initial=0.0)\n",
-    "ml_h3.solve(report=report, noise=add_noise, fit_constant=fit_constant, solver=solver())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot a comparison of all three models. The three models all yield similar results (all the lines overlap). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "axes = ps.plots.compare([ml_h1, ml_h2, ml_h3], adjust_height=True, figsize=(10, 8))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compare the optimized parameters for each model with the true values we defined at the beginning of this example. Note that we're comparing the value of the gain (not parameter $A$) and that each model has its own method for calculating the gain. As expected, the parameter estimates are reasonably close to the true values defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(\n",
-    "    index=[\"well_gain\", \"well_a\", \"well_b\"],\n",
-    "    columns=[\"True value\", \"Hantush\", \"HantushWellModel\", \"WellModel\"],\n",
-    ")\n",
-    "\n",
-    "df[\"True value\"] = A, a, b\n",
-    "\n",
-    "df[\"Hantush\"] = (\n",
-    "    # gain (same as A in this case)\n",
-    "    wm_h1.rfunc.gain(ml_h1.get_parameters(\"well\")),\n",
-    "    # a\n",
-    "    ml_h1.parameters.loc[\"well_a\", \"optimal\"],\n",
-    "    # b\n",
-    "    ml_h1.parameters.loc[\"well_b\", \"optimal\"],\n",
-    ")\n",
-    "\n",
-    "df[\"HantushWellModel\"] = (\n",
-    "    # gain (not same as A)\n",
-    "    wm_h2.rfunc.gain(ml_h2.get_parameters(\"well\")),\n",
-    "    # a\n",
-    "    ml_h2.parameters.loc[\"well_a\", \"optimal\"],\n",
-    "    # b\n",
-    "    np.exp(ml_h2.parameters.loc[\"well_b\", \"optimal\"]),\n",
-    ")\n",
-    "\n",
-    "df[\"WellModel\"] = (\n",
-    "    # gain, use WellModel.get_parameters() to get params: A, a, b and r\n",
-    "    wm_h3.rfunc.gain(wm_h3.get_parameters(model=ml_h3, istress=0)),\n",
-    "    # a\n",
-    "    ml_h3.parameters.loc[\"well_a\", \"optimal\"],\n",
-    "    # b (multiply parameter value by r^2 for comparison)\n",
-    "    np.exp(ml_h3.parameters.loc[\"well_b\", \"optimal\"] * r[0] ** 2),\n",
-    ")\n",
-    "\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Recall from earlier that when using `ps.Hantush` the gain and uncertainty of\n",
-    "the gain are calculated directly. This is not the case for\n",
-    "`ps.HantushWellModel`, so to obtain the uncertainty of the gain when using that\n",
-    "response function there is a method called\n",
-    "`ps.HantushWellModel.variance_gain()` that computes the variance based on the\n",
-    "optimal values and (co)variance of parameters $A'$ and $b'$. There is also a\n",
-    "convenience method `ps.WellModel.variance_gain()` that picks up the required\n",
-    "variances and covariances from the parent model.\n",
-    "\n",
-    "The code below shows the calculated gain for each model, and how to calculate\n",
-    "the variance and standard deviation of the gain for each model. The results\n",
-    "show that the calculated values are all very close, as would be expected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create dataframe\n",
-    "var_gain = pd.DataFrame(index=df.columns[1:])\n",
-    "\n",
-    "# add calculated gain\n",
-    "var_gain[\"gain\"] = df.iloc[0, 1:].values\n",
-    "\n",
-    "# Hantush: variance gain is computed directly\n",
-    "var_gain.loc[\"Hantush\", \"var gain\"] = ml_h1.fit.pcov.loc[\"well_A\", \"well_A\"]\n",
-    "\n",
-    "# HantushWellModel: calculate variance gain explicitly providing values\n",
-    "var_gain.loc[\"HantushWellModel\", \"var gain\"] = wm_h2.rfunc.variance_gain(\n",
-    "    ml_h2.parameters.loc[\"well_A\", \"optimal\"],  # A\n",
-    "    ml_h2.parameters.loc[\"well_b\", \"optimal\"],  # b\n",
-    "    ml_h2.fit.pcov.loc[\"well_A\", \"well_A\"],  # var_A\n",
-    "    ml_h2.fit.pcov.loc[\"well_b\", \"well_b\"],  # var_b\n",
-    "    ml_h2.fit.pcov.loc[\"well_A\", \"well_b\"],  # cov_Ab\n",
-    ")\n",
-    "\n",
-    "# WellModel: calculate variance gain providing only the parent model\n",
-    "var_gain.loc[\"WellModel\", \"var gain\"] = wm_h3.variance_gain(ml_h3, istress=0)\n",
-    "\n",
-    "# calculate std dev gain\n",
-    "var_gain[\"std gain\"] = np.sqrt(var_gain[\"var gain\"])\n",
-    "\n",
-    "# show table\n",
-    "var_gain.style.format(\"{:.5e}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the true parametrs, the estimated parameters and uncertainty ranges \n",
-    "($\\pm 2 \\sigma$):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models = [ml_h1, ml_h2, ml_h3]\n",
-    "\n",
-    "fig, axes = plt.subplots(5, 1, sharex=True, figsize=(6, 6))\n",
-    "\n",
-    "# Gain\n",
-    "axes[0].axhline(A, linestyle=\"dashed\", color=\"k\", label=\"True value\")\n",
-    "axes[0].errorbar(\n",
-    "    range(len(models)),\n",
-    "    df.loc[\"well_gain\", \"Hantush\":],\n",
-    "    marker=\"o\",\n",
-    "    mec=\"k\",\n",
-    "    ls=\"none\",\n",
-    "    mew=0.5,\n",
-    "    yerr=2 * var_gain.loc[:, \"std gain\"],\n",
-    "    capsize=3,\n",
-    "    ecolor=\"C0\",\n",
-    "    label=\"Estimated value\",\n",
-    ")\n",
-    "axes[0].set_ylabel(\"gain\")\n",
-    "\n",
-    "# a\n",
-    "axes[1].axhline(a, linestyle=\"dashed\", color=\"k\", label=\"True value\")\n",
-    "axes[1].errorbar(\n",
-    "    range(len(models)),\n",
-    "    df.loc[\"well_a\", \"Hantush\":],\n",
-    "    marker=\"o\",\n",
-    "    mec=\"k\",\n",
-    "    ls=\"none\",\n",
-    "    mew=0.5,\n",
-    "    yerr=2 * np.array([iml.parameters.loc[\"well_a\", \"stderr\"] for iml in models]),\n",
-    "    capsize=3,\n",
-    "    ecolor=\"C0\",\n",
-    "    label=\"Estimated value\",\n",
-    ")\n",
-    "axes[1].set_ylabel(\"a\")\n",
-    "\n",
-    "# b (NOTE: transformation of uncertainty necessary for log-transformed parameter)\n",
-    "axes[2].axhline(b, linestyle=\"dashed\", color=\"k\", label=\"True value\")\n",
-    "# transform log(param) uncertainty to linear uncertainty for HantushWellModel\n",
-    "b_stderr = np.array(\n",
-    "    [\n",
-    "        ml_h1.parameters.loc[\"well_b\", \"stderr\"],\n",
-    "        np.exp(ml_h2.parameters.loc[\"well_b\", \"optimal\"])\n",
-    "        * ml_h2.parameters.loc[\"well_b\", \"stderr\"],\n",
-    "        np.exp(ml_h3.parameters.loc[\"well_b\", \"optimal\"])\n",
-    "        * ml_h3.parameters.loc[\"well_b\", \"stderr\"],\n",
-    "    ]\n",
-    ")\n",
-    "axes[2].errorbar(\n",
-    "    range(len(models)),\n",
-    "    df.loc[\"well_b\", \"Hantush\":],\n",
-    "    marker=\"o\",\n",
-    "    mec=\"k\",\n",
-    "    ls=\"none\",\n",
-    "    mew=0.5,\n",
-    "    yerr=2 * b_stderr,\n",
-    "    capsize=3,\n",
-    "    ecolor=\"C0\",\n",
-    "    label=\"Estimated value\",\n",
-    ")\n",
-    "axes[2].set_ylabel(\"b\")\n",
-    "\n",
-    "# constant_d\n",
-    "axes[3].axhline(d, linestyle=\"dashed\", color=\"k\", label=\"True value\")\n",
-    "axes[3].errorbar(\n",
-    "    range(len(models)),\n",
-    "    np.array([iml.parameters.loc[\"constant_d\", \"optimal\"] for iml in models]),\n",
-    "    marker=\"o\",\n",
-    "    mec=\"k\",\n",
-    "    ls=\"none\",\n",
-    "    mew=0.5,\n",
-    "    yerr=2 * np.array([iml.parameters.loc[\"constant_d\", \"stderr\"] for iml in models]),\n",
-    "    capsize=3,\n",
-    "    ecolor=\"C0\",\n",
-    "    label=\"Estimated value\",\n",
-    ")\n",
-    "axes[3].set_ylabel(\"constant_d\")\n",
-    "\n",
-    "# noise_alpha\n",
-    "axes[4].axhline(alpha, linestyle=\"dashed\", color=\"k\", label=\"True value\")\n",
-    "axes[4].errorbar(\n",
-    "    range(len(models)),\n",
-    "    np.array([iml.parameters.loc[\"noise_alpha\", \"optimal\"] for iml in models]),\n",
-    "    marker=\"o\",\n",
-    "    mec=\"k\",\n",
-    "    ls=\"none\",\n",
-    "    mew=0.5,\n",
-    "    yerr=2 * np.array([iml.parameters.loc[\"noise_alpha\", \"stderr\"] for iml in models]),\n",
-    "    capsize=3,\n",
-    "    ecolor=\"C0\",\n",
-    "    label=\"Estimated value\",\n",
-    ")\n",
-    "axes[4].set_ylabel(\"noise_alpha\")\n",
-    "\n",
-    "# axes settings\n",
-    "axes[-1].set_xticks(range(len(models)))\n",
-    "axes[-1].set_xticklabels(df.columns[1:], ha=\"center\")\n",
-    "for iax in axes.flat:\n",
-    "    iax.grid(True)\n",
-    "axes[0].legend(loc=(0, 1), ncol=2, frameon=False)\n",
-    "\n",
-    "# figure settings\n",
-    "fig.align_ylabels()\n",
-    "fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Checking the calculation of the uncertainty of log-transformed parameter $b$**\n",
-    "\n",
-    "Relation between $b$ and the transformed parameter $b^\\prime$ is given by:\n",
-    "$$\n",
-    "b = r^2 \\exp \\left( b^\\prime \\right)\n",
-    "$$\n",
-    "\n",
-    "The formula for transforming the uncertainty (through propagation of uncertainty)\n",
-    "$$\n",
-    "\\sigma^2_{b} = \\left( \\frac{d b}{d b^\\prime} \\right)^2 \\sigma^2_{b^\\prime}\n",
-    "$$\n",
-    "\n",
-    "The derivative with respect to $b^\\prime$ is obviously:\n",
-    "$$\n",
-    "\\frac{d b}{d b^\\prime} = r^2 \\exp \\left( b^\\prime \\right)\n",
-    "$$\n",
-    "Which yields:\n",
-    "$$\n",
-    "\\sigma^2_b = r^4 \\exp \\left( 2b^\\prime \\right) \\sigma^2_{b^\\prime}\n",
-    "$$\n",
-    "\n",
-    "Testing these formulas on the results obtained with the pastas Models:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# parameter b\n",
-    "b = ml_h1.parameters.loc[\"well_b\", \"optimal\"]\n",
-    "sigma_b = ml_h1.parameters.loc[\"well_b\", \"stderr\"]\n",
-    "upper_b = b + 2 * sigma_b\n",
-    "lower_b = b - 2 * sigma_b\n",
-    "\n",
-    "# bprime = log transformed b\n",
-    "bp = ml_h2.parameters.loc[\"well_b\", \"optimal\"]\n",
-    "sigma_bp = ml_h2.parameters.loc[\"well_b\", \"stderr\"]\n",
-    "\n",
-    "print(f\"{b=:.4f}, {r[0]**2 * np.exp(bp)=:.4f}\")\n",
-    "sigma_b_transformed = r[0] ** 2 * np.exp(bp) * sigma_bp\n",
-    "print(f\"{sigma_b=:.4f}, {sigma_b_transformed=:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "dace5e1b41a98a8e52d2a8eebc3b981caf2c12e7a76736ebfb89a489e3b62e79"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/doc/concepts/index.rst b/doc/concepts/index.rst
deleted file mode 100644
index c2d0ee7e..00000000
--- a/doc/concepts/index.rst
+++ /dev/null
@@ -1,31 +0,0 @@
-Concepts of Pastas
-==================
-Pastas is an open source Python package for the analysis of hydrogeological time series.
-In Pastas, transfer function noise modeling is applied using predefined response functions.
-For example, the head response to rainfall is simulated through the convolution of measured rainfall with a Gamma response function.
-A Pastas model can be constructed in seven simple steps:
-
-#. import Pastas
-#. read the time series
-#. create a model
-#. specify the stresses and the types of response functions
-#. estimate the model parameters
-#. visualize output
-#. analyze the results.
-
-A time-series Model consists of one or more StressModels which together with a Constant produce the simulated time series.
-Most StressModels use a response function that transform one or more stress-series into its contribution in the simulation.
-Examples of response-functions are Exponential and Gamma. Each StressModel has a number of parameters, which are optimized by the Solver.
-The Solver minimizes the noise, which a NoiseModel calculates from the residuals (the difference between the simulation and the observations).
-
-This section holds notebooks describing some of the methods underlying Pastas.
-
-.. note::
-    This section is still being developed (November, 2020)
-
-.. toctree::
-    :maxdepth: 1
-    :numbered:
-    :glob:
-
-    ./*
diff --git a/doc/concepts/noisemodel.ipynb b/doc/concepts/noisemodel.ipynb
deleted file mode 100644
index b6609ed0..00000000
--- a/doc/concepts/noisemodel.ipynb
+++ /dev/null
@@ -1,276 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Testing an AR(1) Noise Model for Pastas\n",
-    "*R.A. Collenteur, University of Graz, May 2020*\n",
-    "\n",
-    "In this notebook the classical Autoregressive AR1 noise model is tested for Pastas models. This noise model is tested against synthetic data generated with NumPy or Statsmodels' ARMA model. This noise model is tested on head time series with a regular time step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import pastas as ps\n",
-    "\n",
-    "ps.set_log_level(\"ERROR\")\n",
-    "ps.show_versions()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Develop the AR(1) Noise Model for Pastas\n",
-    "\n",
-    "The following formula is used to calculate the noise according to the ARMA(1,1) process:\n",
-    "\n",
-    "$$\\upsilon(t_i) = r(t_i) - r(t_{i-1}) \\text{e}^{-\\Delta t_i / \\alpha}$$\n",
-    "\n",
-    "where $\\upsilon$ is the noise, $\\Delta t_i$ the time step between the residuals ($r$), and $\\alpha$ [days] the AR parameter of the model. The model is named `NoiseModel` and can be found in `noisemodel.py`. This is the default noise model that is added to a Pastas model, but it can also be added manually as follows: `ml.add_noisemodel(ps.NoiseModel())`\n",
-    "\n",
-    "## 2. Generate synthetic head time series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Read in some data\n",
-    "rain = (\n",
-    "    pd.read_csv(\"../examples/data/rain_260.csv\", index_col=0, parse_dates=[0]).squeeze()\n",
-    "    / 1000\n",
-    ")\n",
-    "\n",
-    "# Set the True parameters\n",
-    "Atrue = 800\n",
-    "ntrue = 1.4\n",
-    "atrue = 200\n",
-    "dtrue = 20\n",
-    "\n",
-    "# Generate the head\n",
-    "step = ps.Gamma().block([Atrue, ntrue, atrue], cutoff=0.9999)\n",
-    "h = dtrue * np.ones(len(rain) + step.size)\n",
-    "for i in range(len(rain)):\n",
-    "    h[i : i + step.size] += rain[i] * step\n",
-    "head = pd.DataFrame(\n",
-    "    index=rain.index,\n",
-    "    data=h[: len(rain)],\n",
-    ")\n",
-    "head = head[\"1990\":\"2015\"]\n",
-    "\n",
-    "# Plot the head without noise\n",
-    "plt.figure(figsize=(10, 2))\n",
-    "plt.plot(head, \"k.\", label=\"head\")\n",
-    "plt.legend(loc=0)\n",
-    "plt.ylabel(\"Head (m)\")\n",
-    "plt.xlabel(\"Time (years)\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Generate AR(1) noise and add it to the synthetic heads\n",
-    "In the following code-block, noise is generated using an AR(1) process using Numpy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# reproduction of random numbers\n",
-    "np.random.seed(1234)\n",
-    "alpha = 0.8\n",
-    "\n",
-    "# generate samples using NumPy\n",
-    "random_seed = np.random.RandomState(1234)\n",
-    "noise = random_seed.normal(0, 1, len(head)) * np.std(head.values) * 0.2\n",
-    "a = np.zeros_like(head[0])\n",
-    "\n",
-    "for i in range(1, noise.size):\n",
-    "    a[i] = noise[i] + a[i - 1] * alpha\n",
-    "\n",
-    "head_noise = head[0] + a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Create and solve a Pastas Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml = ps.Model(head_noise)\n",
-    "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
-    "ml.add_stressmodel(sm)\n",
-    "ml.add_noisemodel(ps.NoiseModel())\n",
-    "\n",
-    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
-    "\n",
-    "# Plot the results\n",
-    "axes = ml.plots.results(figsize=(10, 5))\n",
-    "axes[-1].plot(ps.Gamma().step([Atrue, ntrue, atrue], cutoff=0.999))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Did we find back the original AR parameter?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_alpha\", \"optimal\"]).round(2), \"vs\", alpha)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The estimated parameters for the noise model are almost the same as the true parameters, showing that the model works for regular time steps.\n",
-    "\n",
-    "## 6. So is the autocorrelation removed correctly?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml.plots.diagnostics(figsize=(10, 4));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That seems okay. It is important to understand that this noisemodel will only help in removing autocorrelations at the first time lag, but not at larger time lags, compared to its AR(1) counterpart."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 7. Test the NoiseModel for irregular time steps\n",
-    "In this final step the ArmaModel is tested for irregular timesteps, using the indices from a real groundwater level time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "index = (\n",
-    "    pd.read_csv(\"../examples/data/test_index.csv\", parse_dates=True, index_col=0)\n",
-    "    .index.round(\"D\")\n",
-    "    .drop_duplicates()\n",
-    ")\n",
-    "head_irregular = head_noise.reindex(index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml = ps.Model(head_irregular)\n",
-    "sm = ps.StressModel(rain, ps.Gamma(cutoff=0.99), name=\"recharge\", settings=\"prec\")\n",
-    "ml.add_stressmodel(sm)\n",
-    "ml.add_noisemodel(ps.NoiseModel())\n",
-    "\n",
-    "# ml.set_parameter(\"noise_alpha\", 9.5, vary=False)\n",
-    "# ml.set_parameter(\"recharge_A\", 800, vary=False)\n",
-    "# ml.set_parameter(\"recharge_n\", 1.2, vary=False)\n",
-    "# ml.set_parameter(\"recharge_a\", 200, vary=False)\n",
-    "\n",
-    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
-    "axes = ml.plots.results(figsize=(10, 5))\n",
-    "axes[-2].plot(ps.Gamma().step([Atrue, ntrue, atrue]))\n",
-    "\n",
-    "print(np.exp(-1.0 / ml.parameters.loc[\"noise_alpha\", \"optimal\"]).round(2), \"vs\", alpha)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml.plots.diagnostics(figsize=(10, 4), acf_options={\"bin_width\": 0.5});"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The above autocorrelation plot shows that there is some autocorrelation in the first time steps. However, this is probably more a numerical artifict due to estimating the ACF for irregular time steps than \"real\" autocorrelation. This can be shown when estimating the ACF for the period with more frequent observations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v = ml.noise()\n",
-    "print(\"\", v.loc[:\"2010-01-01\"].std())\n",
-    "print(\"\", v.loc[\"2010-01-01\":].std())"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "artesia",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "dace5e1b41a98a8e52d2a8eebc3b981caf2c12e7a76736ebfb89a489e3b62e79"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/doc/concepts/pastas_synthetic.ipynb b/doc/concepts/pastas_synthetic.ipynb
deleted file mode 100644
index c875084a..00000000
--- a/doc/concepts/pastas_synthetic.ipynb
+++ /dev/null
@@ -1,347 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Synthetic Data\n",
-    "\n",
-    "*Developed by Stijn Klop and Mark Bakker*\n",
-    "\n",
-    "This Notebook contains a number of examples and tests with synthetic data. The purpose of this notebook is to demonstrate the noise model of Pastas.\n",
-    "\n",
-    "In this Notebook, heads are generated with a known response function. Next, Pastas is used to solve for the parameters of the model it is verified that Pastas finds the correct parameters back. Several different types of errors are introduced in the generated heads and it is tested whether the confidence intervals computed by Pastas are reasonable. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.special import gammainc, gammaincinv\n",
-    "import pandas as pd\n",
-    "import pastas as ps\n",
-    "\n",
-    "ps.show_versions()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load data and define functions\n",
-    "The rainfall observations are read from file. The rainfall series is taken from KNMI station De Bilt. Heads are generated with a Gamma response function which is defined below. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rain = (\n",
-    "    pd.read_csv(\"../examples/data/rain_260.csv\", index_col=0, parse_dates=[0]).squeeze()\n",
-    "    / 1000\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def gamma_tmax(A, n, a, cutoff=0.999):\n",
-    "    return gammaincinv(n, cutoff) * a\n",
-    "\n",
-    "\n",
-    "def gamma_step(A, n, a, cutoff=0.999):\n",
-    "    tmax = gamma_tmax(A, n, a, cutoff)\n",
-    "    t = np.arange(0, tmax, 1)\n",
-    "    s = A * gammainc(n, t / a)\n",
-    "    return s\n",
-    "\n",
-    "\n",
-    "def gamma_block(A, n, a, cutoff=0.999):\n",
-    "    # returns the gamma block response starting at t=0 with intervals of delt = 1\n",
-    "    s = gamma_step(A, n, a, cutoff)\n",
-    "    return np.append(s[0], s[1:] - s[:-1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Gamma response function requires 3 input arguments; A, n and a. The values for these parameters are defined along with the parameter d, the base groundwater level. The response function is created using the functions defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Atrue = 800\n",
-    "ntrue = 1.1\n",
-    "atrue = 200\n",
-    "dtrue = 20\n",
-    "h = gamma_block(Atrue, ntrue, atrue)\n",
-    "tmax = gamma_tmax(Atrue, ntrue, atrue)\n",
-    "plt.plot(h)\n",
-    "plt.xlabel(\"Time (days)\")\n",
-    "plt.ylabel(\"Head response (m) due to 1 mm of rain in day 1\")\n",
-    "plt.title(\"Gamma block response with tmax=\" + str(int(tmax)));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create synthetic observations\n",
-    "Rainfall is used as input series for this example. No errors are introduced. A Pastas model is created to test whether Pastas is able to . The generated head series is purposely not generated with convolution.\n",
-    "Heads are computed for the period 1990 - 2000. Computations start in 1980 as a warm-up period. Convolution is not used so that it is clear how the head is computed. The computed head at day 1 is the head at the end of day 1 due to rainfall during day 1. No errors are introduced."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "step = gamma_block(Atrue, ntrue, atrue)[1:]\n",
-    "lenstep = len(step)\n",
-    "h = dtrue * np.ones(len(rain) + lenstep)\n",
-    "for i in range(len(rain)):\n",
-    "    h[i : i + lenstep] += rain[i] * step\n",
-    "head = pd.DataFrame(\n",
-    "    index=rain.index,\n",
-    "    data=h[: len(rain)],\n",
-    ")\n",
-    "head = head[\"1990\":\"1999\"]\n",
-    "\n",
-    "plt.figure(figsize=(12, 5))\n",
-    "plt.plot(head, \"k.\", label=\"head\")\n",
-    "plt.legend(loc=0)\n",
-    "plt.ylabel(\"Head (m)\")\n",
-    "plt.xlabel(\"Time (years)\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create Pastas model\n",
-    "The next step is to create a Pastas model. The head generated using the Gamma response function is used as input for the Pastas model. \n",
-    "\n",
-    "A `StressModel` instance is created and added to the Pastas model. The `StressModel` intance takes the rainfall series as input aswell as the type of response function, in this case the Gamma response function ( `ps.Gamma`).\n",
-    "\n",
-    "The Pastas model is solved without a noise model since there is no noise present in the data. The results of the Pastas model are plotted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml = ps.Model(head)\n",
-    "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
-    "ml.add_stressmodel(sm)\n",
-    "ml.solve(noise=False, ftol=1e-8)\n",
-    "ml.plots.results();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The results of the Pastas model show the calibrated parameters for the Gamma response function. The parameters calibrated using pastas are equal to the `Atrue`, `ntrue`, `atrue` and `dtrue` parameters defined above. The Explained Variance Percentage for this example model is 100%. \n",
-    "\n",
-    "The results plots show that the Pastas simulation is identical to the observed groundwater. The residuals of the simulation are shown in the plot together with the response function and the contribution for each stress.\n",
-    "\n",
-    "Below the Pastas block response and the true Gamma response function are plotted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "plt.plot(gamma_block(Atrue, ntrue, atrue), label=\"Synthetic response\")\n",
-    "plt.plot(ml.get_block_response(\"recharge\"), \"-.\", label=\"Pastas response\")\n",
-    "plt.legend(loc=0)\n",
-    "plt.ylabel(\"Head response (m) due to 1 m of rain in day 1\")\n",
-    "plt.xlabel(\"Time (days)\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test 1: Adding noise\n",
-    "In the next test example noise is added to the observations of the groundwater head. The noise is normally distributed noise with a mean of 0 and a standard deviation of 1 and is scaled with the standard deviation of the head. \n",
-    "\n",
-    "The noise series is added to the head series created in the previous example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "random_seed = np.random.RandomState(15892)\n",
-    "\n",
-    "noise = random_seed.normal(0, 1, len(head)) * np.std(head.values) * 0.5\n",
-    "head_noise = head[0] + noise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create Pastas model\n",
-    "\n",
-    "A pastas model is created using the head with noise. A stress model is added to the Pastas model and the model is solved."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml2 = ps.Model(head_noise)\n",
-    "sm2 = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
-    "ml2.add_stressmodel(sm2)\n",
-    "ml2.solve(noise=True)\n",
-    "ml2.plots.results();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The results of the simulation show that Pastas is able to filter the noise from the observed groundwater head. The simulated groundwater head and the generated synthetic head are plotted below. The parameters found with the Pastas optimization are similair to the original parameters of the Gamma response function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(12, 5))\n",
-    "plt.plot(head_noise, \".k\", alpha=0.1, label=\"Head with noise\")\n",
-    "plt.plot(head, \".k\", label=\"Head true\")\n",
-    "plt.plot(ml2.simulate(), label=\"Pastas simulation\")\n",
-    "plt.title(\"Simulated Pastas head compared with synthetic head\")\n",
-    "plt.legend(loc=0)\n",
-    "plt.ylabel(\"Head (m)\")\n",
-    "plt.xlabel(\"Date (years)\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test 2: Adding correlated noise\n",
-    "In this example correlated noise is added to the observed head. The correlated noise is generated using the noise series created in the previous example. The correlated noise is implemented as exponential decay using the following formula:\n",
-    "\n",
-    "$$ n_{c}(t) = e^{-1/\\alpha} \\cdot n_{c}(t-1) + n(t)$$\n",
-    "\n",
-    "where $n_{c}$ is the correlated noise, $\\alpha$ is the noise decay parameter and $n$ is the uncorrelated noise. The noise series that is created is added to the observed groundwater head. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "noise_corr = np.zeros(len(noise))\n",
-    "noise_corr[0] = noise[0]\n",
-    "\n",
-    "alphatrue = 2\n",
-    "\n",
-    "for i in range(1, len(noise_corr)):\n",
-    "    noise_corr[i] = np.exp(-1 / alphatrue) * noise_corr[i - 1] + noise[i]\n",
-    "\n",
-    "head_noise_corr = head[0] + noise_corr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create Pastas model\n",
-    "A Pastas model is created using the head with correlated noise as input. A stressmodel is added to the model and the Pastas model is solved. The results of the model are plotted. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml3 = ps.Model(head_noise_corr)\n",
-    "sm3 = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
-    "ml3.add_stressmodel(sm3)\n",
-    "ml3.solve(noise=True)\n",
-    "ml3.plots.results();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Pastas model is able to calibrate the model parameters fairly well. The calibrated parameters are close to the true values defined above. The `noise_alpha` parameter calibrated by Pastas is close the the `alphatrue` parameter defined for the correlated noise series.\n",
-    "\n",
-    "Below the head simulated with the Pastas model is plotted together with the head series and the head series with the correlated noise."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(12, 5))\n",
-    "plt.plot(head_noise_corr, \".k\", alpha=0.1, label=\"Head with correlated noise\")\n",
-    "plt.plot(head, \".k\", label=\"Head true\")\n",
-    "plt.plot(ml3.simulate(), label=\"Pastas simulation\")\n",
-    "plt.title(\"Simulated Pastas head compared with synthetic head\")\n",
-    "plt.legend(loc=0)\n",
-    "plt.ylabel(\"Head (m)\")\n",
-    "plt.xlabel(\"Date (years)\");"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/doc/concepts/pastas_timeseries.ipynb b/doc/concepts/pastas_timeseries.ipynb
deleted file mode 100644
index e51a7e9d..00000000
--- a/doc/concepts/pastas_timeseries.ipynb
+++ /dev/null
@@ -1,304 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pastas TimeSeries\n",
-    "    \n",
-    "*Developed by Raoul Collenteur* \n",
-    "\n",
-    "In this Jupyter Notebook, the concept of the `pastas.TimeSeries` class is explained in full detail. \n",
-    "\n",
-    "**Objective of the Pastas TimeSeries class:**\n",
-    "\n",
-    "\"*To create one class that deals with all user-provided time series and the manipulations of the series while maintaining the original series.*\"\n",
-    "\n",
-    "**Desired Capabilities:**\n",
-    "The central idea behind the TimeSeries class is to solve all data manipulations in a single class while maintaining the original time series. While manipulating the TimeSeries when working with your Pastas model, the original data are to be maintained such that only the settings and the original series can be stored. \n",
-    "- Validate user-provided time series\n",
-    "- Extend before and after\n",
-    "- Fill nan-values\n",
-    "- Change frequency\n",
-    "    - Upsample\n",
-    "    - Downsample \n",
-    "- Normalize values\n",
-    "\n",
-    "**Resources**\n",
-    "The definition of the class can be found on Github (https://github.com/pastas/pastas/blob/master/pastas/timeseries.py)\n",
-    "Documentation on the Pandas Series can be found here: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import some packages\n",
-    "import pastas as ps\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline\n",
-    "\n",
-    "ps.show_versions()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##  1. Importing groundwater time series\n",
-    "Let's first import some time series so we have some data to play around with. We use Pandas read_csv method and obtain a Pandas Series object, pandas data structure to efficiently deal with 1D Time Series data. By default, Pandas adds a wealth of functionalities to a Series object, such as descriptive statistics (e.g. `series.describe()`) and plotting funtionality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gwdata = pd.read_csv(\n",
-    "    \"../examples/data/head_nb1.csv\", parse_dates=[\"date\"], index_col=\"date\"\n",
-    ").squeeze()\n",
-    "gwdata.plot(figsize=(15, 4));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Creating a Pastas TimeSeries object\n",
-    "The user will provide time series data when creating a model instance, or one of the stressmodels found in stressmodels.py. Pastas expects Pandas Series as a standard format in which time series are provided, but will internally transform these to Pastas TimeSeries objects to add the necessary funtionality. It is therefore also possible to provide a TimeSeries object directly instead of a Pandas Series object.\n",
-    "\n",
-    "We will now create a TimeSeries object for the groundwater level (gwdata). When creating a TimeSeries object the time series that are provided are validated, such that Pastas can use the provided time series for simulation without errors. The time series are checked for:\n",
-    "\n",
-    "1. Being actual Pandas Series object;\n",
-    "2. Making sure the indices are all TimeStamps;\n",
-    "3. Making sure the indices are ordered in time;\n",
-    "4. Dropping any nan-values before and after the first and final valid value;\n",
-    "5. Frequency of the Series is inferred, or otherwise the user-provided value for \"freq\" is applied;\n",
-    "6. Nan-values within the series are handled, depending on the value for the \"fill_nan\" argument;\n",
-    "7. Duplicate indices are dropped from the series.\n",
-    "\n",
-    "If all of the above is OK, a TimeSeries object is returned. When valid time series are provided all of the above checks are no problem and no settings are required. However, all too often this is not the case and at least \"fill_nan\" and \"freq\" are required. The first argument tells the TimeSeries object how to handle nan-values, and the freq argument provides the frequency of the original time series (by default, freq=D, fill_nan=\"interpolate\")."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "oseries = ps.TimeSeries(gwdata, name=\"Groundwater Level\")\n",
-    "\n",
-    "# Plot the new time series and the original\n",
-    "plt.figure(figsize=(10, 4))\n",
-    "oseries.plot(label=\"pastas timeseries\")\n",
-    "gwdata.plot(label=\"original\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Configuring a TimeSeries object\n",
-    "So let's see how we can configure a TimeSeries object. In the case of the observed groundwater levels (oseries) as in the example above, interpolating between observations might not be the preffered method to deal with gaps in your data. In fact, the do not have to be constant for simulation, one of the benefits of the method of impulse response functions. The nan-values can simply be dropped. To configure a TimeSeries object the user has three options:\n",
-    "\n",
-    "1. Use a predefined configuration by providing a string to the settings argument\n",
-    "2. Manually set all or some of the settings by providing a dictonary to the settings argument\n",
-    "3. Providing the arguments as keyword arguments to the TimeSeries object (not recommended)\n",
-    "\n",
-    "For example, when creating a TimeSeries object for the groundwater levels consider the three following examples for setting the fill_nan option:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Options 1\n",
-    "oseries = ps.TimeSeries(gwdata, name=\"Groundwater Level\", settings=\"oseries\")\n",
-    "print(oseries.settings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Option 2\n",
-    "oseries = ps.TimeSeries(\n",
-    "    gwdata, name=\"Groundwater Level\", settings=dict(fill_nan=\"drop\")\n",
-    ")\n",
-    "print(oseries.settings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Options 3\n",
-    "oseries = ps.TimeSeries(gwdata, name=\"Groundwater Level\", fill_nan=\"drop\")\n",
-    "print(oseries.settings)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Predefined settings\n",
-    "All of the above methods yield the same result. It is up to the user which one is preferred. \n",
-    "\n",
-    "A question that may arise with options 1, is what the possible strings for `settings` are and what configuration is then used.\n",
-    "The TimeSeries class contains a dictionary with predefined settings that are used often. You can ask the TimeSeries class this question:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(ps.TimeSeries._predefined_settings).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Let's explore the possibilities\n",
-    "As said, Pastas TimeSeries are capable of handling time series in a way that is convenient for Pastas. \n",
-    "\n",
-    "- Changing the frequency of the time series (sample_up, sameple_down)\n",
-    "- Extending the time series (fill_before and fill_after)\n",
-    "- Normalizing the time series (norm *not fully supported yet)\n",
-    "\n",
-    "We will now import some precipitation series measured at a daily frequency and show how the above methods work"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import observed precipitation series\n",
-    "precip = pd.read_csv(\n",
-    "    \"../examples/data/rain_nb1.csv\", parse_dates=[\"date\"], index_col=\"date\"\n",
-    ").squeeze()\n",
-    "prec = ps.TimeSeries(precip, name=\"Precipitation\", settings=\"prec\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fig, ax = plt.subplots(2, 1, figsize=(10,8))\n",
-    "# prec.update_series(freq=\"D\")\n",
-    "# prec.series.plot.bar(ax=ax[0])\n",
-    "# prec.update_series(freq=\"7D\")\n",
-    "# prec.series.plot.bar(ax=ax[1])\n",
-    "\n",
-    "# import matplotlib.dates as mdates\n",
-    "# ax[1].fmt_xdata = mdates.DateFormatter('%m')\n",
-    "# fig.autofmt_xdate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Wait, what?\n",
-    "We just changed the frequency of the TimeSeries. When reducing the frequency, the values were summed into the new bins. Conveniently, all pandas methods are still available and functional, such as the great plotting functionalities of Pandas.\n",
-    "\n",
-    "All this happened `inplace`, meaning the same object just took another shape based on the new settings. Moreover, it performed those new settings (`freq=\"W\"` weekly values) on the original series. This means that going back and forth between frequencies does not lead to any information loss. \n",
-    "\n",
-    "Why is this so important? Because when solving or simulating a model, the Model will ask every member of the TimeSeries family to prepare itself with the necessary settings (e.g. new freq) and perform that operation only once. When asked for a time series, the TimeSeries object will \"be\" in that new shape.\n",
-    "\n",
-    "### Some more action\n",
-    "Let's say, we want to simulate the groundwater series for a period where no data is available for the time series, but we need some kind of value for the warmup period to prevent things from getting messy. The TimeSeries object can easily extend itself, as the following example shows. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prec.update_series(tmin=\"2011\")\n",
-    "prec.plot()\n",
-    "prec.settings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Exporting the TimeSeries\n",
-    "When done, we might want to store the TimeSeries object for later use. A `to_dict` method is built-in to export the original time series to a json format, along with its current settings and name. This way the original data is maintained and can easily be recreated from a json file. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = prec.to_dict()\n",
-    "print(data.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Tadaa, we have our extended time series in weekly frequency back!\n",
-    "ts = ps.TimeSeries(**data)\n",
-    "ts.plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
-   }
-  },
-  "widgets": {
-   "state": {},
-   "version": "1.1.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/doc/concepts/response_functions.ipynb b/doc/concepts/response_functions.ipynb
deleted file mode 100644
index a0d886b1..00000000
--- a/doc/concepts/response_functions.ipynb
+++ /dev/null
@@ -1,474 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Response functions\n",
-    "This notebook provides an overview of the response functions that are available in Pastas. Response functions describe the response of the dependent variable (e.g., groundwater levels) to an independent variable (e.g., groundwater pumping) and form a fundamental part in the transfer function noise models implemented in Pastas. Depending on the problem under investigation, a less or more complex response function may be required, where the complexity is quantified by the number of parameters. Response function are generally used in combination with a stressmodel, but in this notebook the response functions are studied independently to provide an overview of the different response functions and what they represent."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import pastas as ps\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "ps.show_versions()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The use of response functions\n",
-    "Depending on the stress type (e.g., recharge, river levels or groundwater pumping) different response function may be used. All response functions that are tested and supported in Pastas are summarized in the table below for reference. The equation in the third column is the formula for the impulse response function ($\\theta(t)$).\n",
-    "\n",
-    "| Name             | Fitting parameters  | Formula                                                                 | Description                                                                    |\n",
-    "|------------------|-------------|:------------------------------------------------------------------------|--------------------------------------------------------------------------------|\n",
-    "| FourParam | 4 - A, n, a, b | $$ \\theta(t) = A \\frac{t^{n-1}}{a^n \\Gamma \\left(n \\right)} e^{-t/a- ab/t} $$ | Response function with four parameters that may be used for many purposes. Many other response function are a simplification of this function. |\n",
-    "| Gamma |3 - A, a, n | $$ \\theta(t) = A \\frac{t^{n-1}}{a^n \\Gamma \\left(n \\right)} e^{-t/a} $$ | Three parameter version of FourParam, used for all sorts of stresses ($b=0$) |\n",
-    "| Exponential | 2 - A, a | $$ \\theta(t) = \\frac{A}{a} e^{-t/a} $$ | Response function that can be used for stresses that have an (almost) instant effect. ($n=1$ and $b=0$) |\n",
-    "| Hantush | 3 - A, a, b | $$ \\theta(t) = \\frac{A}{2t \\text{K}_0 \\left(2 \\sqrt{b} \\right)} e^{-t/a - ab/t} $$ | Response function commonly used for groundwater extraction wells ($n=0$) |\n",
-    "| Polder | 3 - a, b, c | $$ \\theta(t) = At^{-3/2} e^{-t/a -b/t} $$ | Response function commonly used to simulate the effects of (river) water levels on the groundwater levels ($n=-1/2$) |\n",
-    "| DoubleExponential | 4 - A, $\\alpha$, $a_1$, $a_2$ | $$ \\theta(t) = A \\left(1 - \\alpha \\right) e^{-t/a_1} + A \\alpha e^{-t/a_2} $$ | Response Function with a double exponential, simulating a fast and slow response. |\n",
-    "| Edelman | 1 - $\\beta$ | $$ \\theta(t) = \\text{?} $$ | The function of Edelman, describing the propagation of an instantaneous water level change into an adjacent half-infinite aquifer. | \n",
-    "| HantushWellModel | 3 - $A^\\prime$, a, $b^\\prime$ | $$ \\theta(r,t) = \\frac{A^\\prime}{2t} e^{-t/a - a r^2 \\exp(b^\\prime) /t} $$ | A special implementation of the Hantush well function for multiple wells. |\n",
-    "\n",
-    "Below the different response functions are plotted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Default Settings\n",
-    "cutoff = 0.999\n",
-    "gain_scale_factor = 1\n",
-    "up = True\n",
-    "\n",
-    "responses = {}\n",
-    "exp = ps.Exponential()\n",
-    "responses[\"Exponential\"] = exp\n",
-    "\n",
-    "gamma = ps.Gamma()\n",
-    "responses[\"Gamma\"] = gamma\n",
-    "\n",
-    "hantush = ps.Hantush()\n",
-    "responses[\"Hantush\"] = hantush\n",
-    "\n",
-    "polder = ps.Polder()\n",
-    "responses[\"Polder\"] = polder\n",
-    "\n",
-    "fourp = ps.FourParam()\n",
-    "responses[\"FourParam\"] = fourp\n",
-    "\n",
-    "DoubleExp = ps.DoubleExponential()\n",
-    "responses[\"DoubleExponential\"] = DoubleExp\n",
-    "\n",
-    "parameters = pd.DataFrame()\n",
-    "\n",
-    "fig, [ax1, ax2] = plt.subplots(1, 2, sharex=True, figsize=(10, 3))\n",
-    "\n",
-    "for name, response in responses.items():\n",
-    "    p = response.get_init_parameters(name)\n",
-    "    parameters = pd.concat([parameters, p], axis=0)\n",
-    "    ax1.plot(response.block(p.initial), label=name)\n",
-    "    ax2.plot(response.step(p.initial), label=name)\n",
-    "\n",
-    "ax1.set_title(\"Block response\")\n",
-    "ax2.set_title(\"Step responses\")\n",
-    "ax1.set_xlabel(\"Time [days]\")\n",
-    "ax2.set_xlabel(\"Time [days]\")\n",
-    "ax1.legend()\n",
-    "plt.xlim(1e-1, 500)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scaling of the step response functions\n",
-    "An important characteristic is the so-called \"gain\" of a response function. The gain is the final increase or decrease that results from a unit increase or decrease in a stress that continues infinitely in time (e.g., pumping at a constant rate forever). This can be visually inspected by the value of the step response function for large values of $t$ but can also be inferred from the parameters as follows:\n",
-    "\n",
-    "- The FourParam, Gamma, Exponential, and Hantush step functions are scaled such that the gain equals $A$\n",
-    "- The Polder function is scaled such that the gain equals $\\exp\\left(-2\\sqrt{b}\\right)$\n",
-    "- The gain of the Edelman function always equals 1, but this will take an infinite amount of time.\n",
-    "\n",
-    "## Comparison of the different response functions\n",
-    "The `Gamma`, `Exponential`, `Polder`, and `Hantush` response function can all be derived from the more general `FourParam` response function by fixing the parameters $n$ and/or $b$ to a specific value. The `DoubleExponential`, `Edelman`, and `HantushWellModel` cannot be written as some form of the `FourParam` function. Below the response function that are special forms of the four parameter function are are shown for different values of $n$ and $b$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = 1\n",
-    "a = 50\n",
-    "b = 0.4\n",
-    "plt.figure(figsize=(16, 8))\n",
-    "for i, n in enumerate([-0.5, 1e-6, 0.5, 1, 1.5]):\n",
-    "    plt.subplot(2, 3, i + 1)\n",
-    "    plt.title(f\"n={n:0.1f}\")\n",
-    "    fp = fourp.step([A, n, a, b], dt=1, cutoff=0.95)\n",
-    "    plt.plot(np.arange(1, len(fp) + 1), fp, \"C0\", label=\"4-param\")\n",
-    "    e = exp.step([A, a], dt=1, cutoff=0.95)\n",
-    "    plt.plot(np.arange(1, len(e) + 1), e, \"C1\", label=\"exp\")\n",
-    "    if n > 0:\n",
-    "        g = gamma.step([A, n, a], dt=1, cutoff=0.95)\n",
-    "        plt.plot(np.arange(1, len(g) + 1), g, \"C2\", label=\"gamma\")\n",
-    "    h = hantush.step([A, a, b], dt=1, cutoff=0.95) / hantush.gain([A, a, b])\n",
-    "    plt.plot(np.arange(1, len(h) + 1), h, \"C3\", label=\"hantush\")\n",
-    "    p = polder.step([A, a, b], dt=1, cutoff=0.95) / polder.gain([A, a, b])\n",
-    "    plt.plot(np.arange(1, len(p) + 1), p, \"C4\", label=\"polder\")\n",
-    "    plt.xlim(0, 200)\n",
-    "    plt.legend()\n",
-    "    if n > 0:\n",
-    "        print(\"fp, e, g, h, p:\", fp[-1], e[-1], g[-1], h[-1], p[-1])\n",
-    "    else:\n",
-    "        print(\"fp, e, h, p:\", fp[-1], e[-1], h[-1], p[-1])\n",
-    "    plt.axhline(0.95, linestyle=\":\", label=\"95\\% cutoff\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Parameter settings\n",
-    "- **up** : This parameters determines whether the influence of the stress goes up or down, hence a positive or a negative response function. For example, when groundwater pumping is defined as a positive flux, `up=False` because we want the groundwater levels to decrease as a result of pumping.\n",
-    "- **gain_scale_factor** : This parameter is used to estimate the initial value of the stationary effect of a stress. Hence the effect when a stress stays at an unit level for infinite amount of time. This parameter is usually referred from the stress time series and does not have to be provided by the user.\n",
-    "- **cutoff** : This parameter determines for how many time steps the response is calculated. This reduces calculation times as it reduces the length of the array the stress is convolved with. The default value is 0.999, meaning that the response is cutoff after 99.9% of the effect of the stress impulse has occurred. A minimum of length of three times the simulation time step is applied. \n",
-    "\n",
-    "The default parameter values for each of the response function are as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%% \n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Comparison to classical analytical response functions\n",
-    "\n",
-    "### Polder step function compared to classic polder function\n",
-    "The classic polder function is (Eq. 123.32 in Bruggeman, 1999)\n",
-    "\n",
-    "$$\n",
-    "h(t) = \\Delta h \\text{P}\\left(\\frac{x}{2\\lambda}, \\sqrt{\\frac{t}{cS}}\\right)\n",
-    "$$\n",
-    "\n",
-    "where P is the polder function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.special import erfc\n",
-    "\n",
-    "\n",
-    "def polder_classic(t, x, T, S, c):\n",
-    "    X = x / (2 * np.sqrt(T * c))\n",
-    "    Y = np.sqrt(t / (c * S))\n",
-    "    rv = 0.5 * np.exp(2 * X) * erfc(X / Y + Y) + 0.5 * np.exp(-2 * X) * erfc(X / Y - Y)\n",
-    "    return rv\n",
-    "\n",
-    "\n",
-    "delh = 2\n",
-    "T = 20\n",
-    "c = 5000\n",
-    "S = 0.01\n",
-    "x = 400\n",
-    "x / np.sqrt(c * T)\n",
-    "t = np.arange(1, 121)\n",
-    "h_polder_classic = np.zeros(len(t))\n",
-    "for i in range(len(t)):\n",
-    "    h_polder_classic[i] = delh * polder_classic(t[i], x=x, T=T, S=S, c=c)\n",
-    "#\n",
-    "A = delh\n",
-    "a = c * S\n",
-    "b = x**2 / (4 * T * c)\n",
-    "hpd = polder.step([A, a, b], dt=1, cutoff=0.95)\n",
-    "#\n",
-    "plt.plot(t, h_polder_classic, label=\"Polder classic\")\n",
-    "plt.plot(np.arange(1, len(hpd) + 1), hpd, label=\"Polder Pastas\", linestyle=\"--\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Hantush step function compared to classic Hantush function\n",
-    "The classic Hantush function is \n",
-    "\n",
-    "$$\n",
-    "h(r, t) = \\frac{-Q}{4\\pi T}\\int_u  ^\\infty  \\exp\\left(-y - \\frac{r^2}{4  \\lambda^2 y} \\right) \\frac{\\text{d}y}{y} \n",
-    "$$\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$$\n",
-    "u=\\frac{r^2 S}{4 T t}\n",
-    "$$\n",
-    "\n",
-    "For large time, the classic Hantush function goes to\n",
-    "\n",
-    "$$\n",
-    "\\lim_{t\\to\\infty} h(r, t) = \\frac{-Q}{2\\pi T}\\text{K}_0(r/\\lambda)\n",
-    "$$\n",
-    "\n",
-    "where $\\lambda^2=cT$. \n",
-    "The classic Hantush function is a step function. The impulse response function $\\theta$ is obtained by differentiation\n",
-    "\n",
-    "$$\n",
-    "\\theta(t) = \\frac{\\partial h}{\\partial t} = \\frac{-Q}{4\\pi Tt} \\exp\\left(\\frac{-t}{cS} -\\frac{r^2cS}{4cTt}\\right)\n",
-    "$$\n",
-    "\n",
-    "The Hantush impulse response function used in Pastas is defined as\n",
-    "\n",
-    "$$\n",
-    "\\theta(t) = A\\frac{1}{2\\text{K}_0(2\\sqrt{b})} \\frac{1}{t} e^{-t/a - ab/t} \n",
-    "$$\n",
-    "\n",
-    "Hence, the parameters in Pastas are related to the classic Hantush function as\n",
-    "\n",
-    "$$\n",
-    "A = -\\frac{\\text{K}_0(2\\sqrt{b})}{2\\pi T}\n",
-    "$$\n",
-    "\n",
-    "$$\n",
-    "a = cS\n",
-    "$$\n",
-    "\n",
-    "$$\n",
-    "b = \\frac{r^2}{4\\lambda^2}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.integrate import quad\n",
-    "from scipy.special import k0\n",
-    "\n",
-    "\n",
-    "def integrand_hantush(y, r, lab):\n",
-    "    return np.exp(-y - r**2 / (4 * lab**2 * y)) / y\n",
-    "\n",
-    "\n",
-    "def hantush_classic(t=1, r=1, Q=1, T=100, S=1e-4, c=1000):\n",
-    "    lab = np.sqrt(T * c)\n",
-    "    u = r**2 * S / (4 * T * t)\n",
-    "    F = quad(integrand_hantush, u, np.inf, args=(r, lab))[0]\n",
-    "    return -Q / (4 * np.pi * T) * F\n",
-    "\n",
-    "\n",
-    "c = 1000  # d\n",
-    "S = 0.01  # -\n",
-    "T = 100  # m^2/d\n",
-    "r = 500  # m\n",
-    "Q = 20  # m^3/d\n",
-    "lab = np.sqrt(T * c)\n",
-    "#\n",
-    "A = k0(r / lab) / (2 * np.pi * T)\n",
-    "a = c * S\n",
-    "b = r**2 / (4 * T * c)\n",
-    "\n",
-    "#\n",
-    "ht = hantush.step([A, a, b], dt=1, cutoff=0.99) * -Q  # multiply by Q\n",
-    "\n",
-    "# calculate classic Hantush\n",
-    "t = np.arange(1, len(ht) + 1)\n",
-    "h_hantush_classic = np.zeros(len(t))\n",
-    "for i in range(len(t)):\n",
-    "    h_hantush_classic[i] = hantush_classic(t[i], r=r, Q=Q, T=T, S=S, c=c)\n",
-    "\n",
-    "# plot comparison\n",
-    "plt.plot(t, h_hantush_classic, label=\"Hantush classic\")\n",
-    "plt.plot(np.arange(1, len(ht) + 1), ht, \"--\", label=\"Hantush Pastas\")\n",
-    "plt.axhline(-Q * A, ls=\"dashed\", c=\"k\", label=\"Q*A\")\n",
-    "plt.grid()\n",
-    "plt.legend();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### HantushWellModel step function in Pastas compared to classic Hantush function and Hantush function in Pastas\n",
-    "The impulse response of the classic Hantush function is\n",
-    "\n",
-    "$$\n",
-    "\\theta(t) = \\frac{\\partial h}{\\partial t} = \\frac{-Q}{4\\pi Tt} \\exp\\left(\\frac{-t}{cS} -\\frac{r^2cS}{4cTt}\\right)\n",
-    "$$\n",
-    "\n",
-    "The impulse response used for the HantushWellModel is\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The HantushWellModel impulse response function used in Pastas is defined as\n",
-    "\n",
-    "$$\n",
-    "\\theta(r, t) = \\frac{A^\\prime}{2t} e^{-t/a - a r^2 \\exp(b^\\prime) /t} \n",
-    "$$\n",
-    "\n",
-    "Where $ A^\\prime $, $a$, and $b^\\prime$ are fitting parameters. The parameters are named $A^\\prime $ and $b^\\prime$ to distinguish them from $A$ and $b$ in the original Hantush formulation. The fitting parameters are related to the geohydrological parameters as\n",
-    "\n",
-    "$$\n",
-    "A^\\prime = -\\frac{1}{2\\pi T}\n",
-    "$$\n",
-    "\n",
-    "$$\n",
-    "a = cS\n",
-    "$$\n",
-    "\n",
-    "$$\n",
-    "b^\\prime = \\ln \\left( \\frac{1}{4\\lambda^2} \\right)\n",
-    "$$\n",
-    "\n",
-    "and the gain of the HantushWellModel is\n",
-    "\n",
-    "$$\n",
-    "\\text{gain} = A^\\prime\\text{K}_0 \\left( 2 r \\exp \\left( \\frac{b^\\prime}{2} \\right) \\right)\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# compare pastas.Hantush to pastas.HantushWellModel\n",
-    "\n",
-    "hantush = ps.Hantush()\n",
-    "hantush._update_rfunc_settings(up=-1)\n",
-    "hantushwm = ps.HantushWellModel()\n",
-    "hantushwm._update_rfunc_settings(up=-1)\n",
-    "hantushwm.set_distances = 1.0\n",
-    "\n",
-    "bprime = np.log(1 / (4 * T * c))\n",
-    "Aprime = A / k0(2 * r * np.exp(bprime / 2))\n",
-    "\n",
-    "h = hantush.step([A, a, b], dt=1, cutoff=0.99)\n",
-    "hwm = hantushwm.step([Aprime, a, bprime, r], dt=1, cutoff=0.99)\n",
-    "\n",
-    "plt.plot(hwm, \"-\", label=\"HantushWellModel\")\n",
-    "plt.plot(h, \".\", label=\"Hantush\")\n",
-    "plt.axhline(A, ls=\"dashed\", color=\"k\", label=\"Gain\")\n",
-    "plt.legend()\n",
-    "plt.grid();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pastas_dev",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
-  },
-  "latex_envs": {
-   "LaTeX_envs_menu_present": true,
-   "autoclose": false,
-   "autocomplete": true,
-   "bibliofile": "biblio.bib",
-   "cite_by": "apalike",
-   "current_citInitial": 1,
-   "eqLabelWithNumbers": true,
-   "eqNumInitial": 1,
-   "hotkeys": {
-    "equation": "Ctrl-E",
-    "itemize": "Ctrl-I"
-   },
-   "labels_anchors": false,
-   "latex_user_defs": false,
-   "report_style_numbering": false,
-   "user_envs_cfg": false
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "metadata": {
-     "collapsed": false
-    },
-    "source": []
-   }
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/doc/conf.py b/doc/conf.py
index 38ffc732..6dd39613 100755
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -3,19 +3,11 @@
 # Pastas documentation build configuration file, created by
 # sphinx-quickstart on Wed May 11 12:38:06 2016.
 
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-
 import os
 import re
 import sys
 from datetime import date
+
 import requests
 
 year = date.today().strftime("%Y")
@@ -24,19 +16,14 @@
 
 use("agg")
 
-from dataclasses import dataclass, field
-import sphinxcontrib.bibtex.plugin
-from sphinxcontrib.bibtex.style.referencing import BracketStyle
-from sphinxcontrib.bibtex.style.referencing.author_year import AuthorYearReferenceStyle
-
-from pastas.version import __version__
+from pastas import __version__
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 sys.path.insert(0, os.path.abspath("."))
 
-# -- General configuration ------------------------------------------------
+# -- Load extensions ------------------------------------------------------------------
 
 extensions = [
     "sphinx.ext.autodoc",
@@ -50,17 +37,18 @@
     "IPython.sphinxext.ipython_console_highlighting",  # lowercase didn't work
     "nbsphinx",
     "numpydoc",
-    "sphinx_gallery.load_style",
     "sphinxcontrib.bibtex",
+    "sphinx_design",
+    "sphinx.ext.autosectionlabel",
 ]
 
-# Add any paths that contain templates here, relative to this directory.
+# -- General configuration ------------------------------------------------------------
+
 templates_path = ["_templates"]
 source_suffix = ".rst"
 source_encoding = "utf-8"
 
-# The master toctree document.
-master_doc = "index"
+master_doc = "index"  # The master toctree document.
 
 # General information about the project.
 project = "Pastas"
@@ -74,17 +62,18 @@
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
-exclude_patterns = ["_build", "**groundwater_paper", "**.ipynb_checkpoints"]
+exclude_patterns = [
+    "_build",
+    "**groundwater_paper",
+    "**.ipynb_checkpoints",
+]
 
 add_function_parentheses = False
 add_module_names = False
 show_authors = False  # section and module author directives will not be shown
-
-pygments_style = "sphinx"  # The name of the Pygments (syntax highlighting) style to use
-
 todo_include_todos = False  # Do not show TODOs in docs
 
-# -- Options for HTML output ----------------------------------------------
+# -- Options for HTML output ----------------------------------------------------------
 
 html_theme = "pydata_sphinx_theme"
 html_logo = "_static/logo.png"
@@ -101,6 +90,7 @@
 html_theme_options = {
     "github_url": "https://github.com/pastas/pastas",
     "use_edit_page_button": True,
+    "header_links_before_dropdown": 6,
 }
 
 html_context = {
@@ -110,54 +100,42 @@
     "doc_path": "doc",
 }
 
+# -- Napoleon settings ----------------------------------------------------------------
+
+napoleon_include_init_with_doc = False
 napoleon_use_param = True
 napoleon_type_aliases = {
     "array-like": ":term:`array-like <array_like>`",
     "array_like": ":term:`array_like`",
+    "ps": "pastas",
+    "ml": "pastas.model.Model",
+    "TimestampType": "pandas.Timestamp",
 }
+
+# -- Autodoc, autosummary, and autosectionlabel settings ------------------------------
+
 autodoc_typehints = "description"
 autodoc_typehints_format = "short"
+
 autosummary_generate = True
 
-numpydoc_class_members_toctree = True
-numpydoc_show_class_members = False
 autoclass_content = "class"
 
+autosectionlabel_prefix_document = True
 
-# If true, links to the reST sources are added to the pages.
-# html_show_sourcelink = True
-
-# -- Generating references and publications lists -------------------------
-
-
-# support Round brackets
-def bracket_style() -> BracketStyle:
-    return BracketStyle(
-        left="(",
-        right=")",
-    )
-
-
-@dataclass
-class MyReferenceStyle(AuthorYearReferenceStyle):
-    bracket_parenthetical: BracketStyle = field(default_factory=bracket_style)
-    bracket_textual: BracketStyle = field(default_factory=bracket_style)
-    bracket_author: BracketStyle = field(default_factory=bracket_style)
-    bracket_label: BracketStyle = field(default_factory=bracket_style)
-    bracket_year: BracketStyle = field(default_factory=bracket_style)
+# -- Numpydoc settings ----------------------------------------------------------------
 
+numpydoc_class_members_toctree = True
+numpydoc_show_class_members = False
 
-sphinxcontrib.bibtex.plugin.register_plugin(
-    "sphinxcontrib.bibtex.style.referencing", "author_year_round", MyReferenceStyle
-)
+# -- Generating references and publications lists with bibtex -------------------------
 
-# Generate bibliography-files from Zotero library
 # Get a Bibtex reference file from the Zotero group for referencing
 url = "https://api.zotero.org/groups/4846685/collections/8UG7PVLY/items/"
 params = {"format": "bibtex", "style": "apa", "limit": 100}
 
 r = requests.get(url=url, params=params)
-with open("references.bib", mode="w") as file:
+with open("about/references.bib", mode="w", encoding="utf-8") as file:
     file.write(r.text)
 
 # Get a Bibtex reference file from the Zotero group for publications list
@@ -165,90 +143,17 @@ class MyReferenceStyle(AuthorYearReferenceStyle):
 params = {"format": "bibtex", "style": "apa", "limit": 100}
 
 r = requests.get(url=url, params=params)
-with open("publications.bib", mode="w") as file:
+with open("about/publications.bib", mode="w", encoding="utf-8") as file:
     # Replace citation key to prevent duplicate labels and article now shown
     text = re.sub(r"(@([a-z]*){)", r"\1X_", r.text)
     file.write(text)
 
 # Add some settings for bibtex
-bibtex_bibfiles = ["references.bib", "publications.bib"]
-bibtex_reference_style = "author_year_round"
-
-# -- Options for LaTeX output ---------------------------------------------
-
-latex_elements = {
-    # The paper size ('letterpaper' or 'a4paper').
-    # 'papersize': 'letterpaper',
-    # The font size ('10pt', '11pt' or '12pt').
-    # 'pointsize': '10pt',
-    # Additional stuff for the LaTeX preamble.
-    # 'preamble': '',
-    # Latex figure (float) alignment
-    # 'figure_align': 'htbp',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-    (
-        master_doc,
-        "Pastas.tex",
-        "Pastas Documentation",
-        "R.A. Collenteur, M. Bakker, R. Calje, F. Schaars",
-        "manual",
-    ),
-]
-
-# The name of an image file (relative to this directory) to place at the top of
-# the title page.
-# latex_logo = '_static\\logo.png'
-
-# For "manual" documents, if this is true, then toplevel headings are parts,
-# not chapters.
-# latex_use_parts = False
-
-# If true, show page references after internal links.
-# latex_show_pagerefs = False
-
-# If true, show URL addresses after external links.
-# latex_show_urls = False
-
-# Documents to append as an appendix to all manuals.
-# latex_appendices = []
+bibtex_bibfiles = ["about/references.bib", "about/publications.bib"]
+bibtex_reference_style = "author_year"
 
-# If false, no module index is generated.
-# latex_domain_indices = True
+# -- Set intersphinx Directories ------------------------------------------------------
 
-
-# -- Options for manual page output ---------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [(master_doc, "pastas", "Pastas Documentation", [author], 1)]
-
-# If true, show URL addresses after external links.
-# man_show_urls = False
-
-
-# -- Options for Texinfo output -------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-#  dir menu entry, description, category)
-texinfo_documents = [
-    (
-        master_doc,
-        "Pastas",
-        "Pastas Documentation",
-        author,
-        "Pastas",
-        "One line description of project.",
-        "Miscellaneous",
-    ),
-]
-
-# Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
     "python": ("https://docs.python.org/3", None),
     "numpy": ("https://numpy.org/devdocs", None),
@@ -257,5 +162,7 @@ class MyReferenceStyle(AuthorYearReferenceStyle):
     "matplotlib": ("https://matplotlib.org/stable", None),
 }
 
-# Allow errors in notebooks, so we can see the error online
-nbsphinx_allow_errors = True
+# -- nbsphinx options---------- -------------------------------------------------------
+
+nbsphinx_allow_errors = True  # Allow errors in notebooks, to see the error online
+nbsphinx_execute = "auto"
diff --git a/doc/developers/automated_tests.png b/doc/developers/automated_tests.png
deleted file mode 100644
index 0fd3fd50..00000000
Binary files a/doc/developers/automated_tests.png and /dev/null differ
diff --git a/doc/developers/checklist.png b/doc/developers/checklist.png
deleted file mode 100644
index d9849317..00000000
Binary files a/doc/developers/checklist.png and /dev/null differ
diff --git a/doc/developers/code_style.rst b/doc/developers/code_style.rst
index 7d6a8100..b42c84c8 100644
--- a/doc/developers/code_style.rst
+++ b/doc/developers/code_style.rst
@@ -1,5 +1,34 @@
 Pastas Code Style
 =================
+This page provides information on the code style to use when writing code for Pastas.
+
+Black formatting
+----------------
+To ensure high quality code that is easy to read and maintain we follow the
+`Black <https://black.readthedocs.io/en/stable/index.html>`_ code formatting standard.
+Please checkout the `Black Documentation <https://black.readthedocs.io/en/stable/index.html>`_
+on how to format Python code this way.
+
+Type Hints
+----------
+Pastas uses TypeHinting, which is used to check user-provided input options. Please provide
+TypeHints when creating new methods and classes.
+
+Docstrings
+----------
+Documentation is
+created using `Sphinxdoc <http://www.sphinx-doc.org>`_. Docstrings within
+the method or class need to be written in `NumPy docformat <https://numpydoc
+.readthedocs.io/en/latest/format.html#docstring-standard>`_ to enable
+automatic documentation on this website.
+
+Optimization
+------------
+Much of the Pastas code is highly optimized to main high speed and productivity. Please
+make sure to optimize your code in terms of performance, particularly when changing or
+adding methods that are called by `ml.simulate` and `ml.solve`. It may also be
+possible to use Numba for speed-ups.
+
 
 Private Methods
 ---------------
diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst
index fdda71c8..e94281c0 100644
--- a/doc/developers/contributing.rst
+++ b/doc/developers/contributing.rst
@@ -11,7 +11,7 @@ to commit code changes to GitHub open source projects. Let's start!
 ------------------------
 Before you start you can start a GitHub Issue describing the changes you
 propose to make and why these are necessary. This is an easy way to inform
-the Pastas communnity in an early stage of any issues that needs to be solved
+the Pastas community in an early stage of any issues that needs to be solved
 and allows others to help you work out a solution.
 
 2. Fork Pastas
@@ -32,7 +32,7 @@ After you forked Pastas, you can start making changes to the code or add new
 features to it. To ensure high quality code that is easy to read and maintain
 we follow the `Black <https://black.readthedocs.io/en/stable/index.html>`_ code
 formatting standard. Check out the Pastas Code Style section to learn
-more.
+more about the formatting of code and docstrings.
 
 4. Test Code
 ------------
@@ -49,7 +49,8 @@ before the new code will be pulled into the dev branch. Documentation is
 created using `Sphinxdoc <http://www.sphinx-doc.org>`_. Docstrings within
 the method or class need to be written in `NumPy docformat <https://numpydoc
 .readthedocs.io/en/latest/format.html#docstring-standard>`_ to enable
-automatic documentation on this website.
+automatic documentation on this website. In the case of a new module, the module
+needs to be added to `index.rst` in the api-folder.
 
 A Jupyter Notebook explaining the use of your new code can be added the to
 examples folder. This Notebook will also be automatically converted and
@@ -65,13 +66,11 @@ checklist to go through to check if your PR is up to standards. Pastas will
 run automatic code tests to ensure that the code works, is documented and
 has a good code style.
 
-.. figure:: checklist.png
-    :figwidth: 300px
-
-.. figure:: automated_tests.png
-    :figwidth: 400px
-
+7. Share and enjoy your work!
+-----------------------------
 After you have create a Pull Request the Core Development Team will
 review your code and discuss potential improvements on GitHub before merging
 your code into the development branch. After a successful Pull Request your
-code will be included in the next release!
+code will be included in the next release of Pastas when the master-branch is
+updated. Congratulations, you are now officially a contributor to the Pastas
+project!
diff --git a/doc/developers/history.rst b/doc/developers/history.rst
deleted file mode 100644
index a22d8ca3..00000000
--- a/doc/developers/history.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-History
-=======
-The Development of Pastas started in the spring of 2016 at the TU Delft (The
-Netherlands) and Artesia (The Netherlands). The main goal was to create a
-framework that could support future research on time series analysis of
-hydrogeological time series. As the project progressed the software was
-developed into a stable Python package used by drinking water companies,
-water boards, and engineering companies. The project is currently actively
-being developed by the TU Delft, Artesia, and the University of Graz
-(Austria).
-
-.. figure:: ./../_static/logo_graz.png
-    :figwidth: 200px
-    :target: https://erdwissenschaften.uni-graz.at/en/
-
-.. figure:: ./../_static/TUD_logo.png
-    :figwidth: 200px
-    :target: https://www.tudelft.nl
-
-.. figure:: ./../_static/Art_logo.jpg
-    :figwidth: 200px
-    :target: http://www.artesia-water.nl
diff --git a/doc/developers/index.rst b/doc/developers/index.rst
index f7881c02..66632836 100644
--- a/doc/developers/index.rst
+++ b/doc/developers/index.rst
@@ -1,10 +1,10 @@
 Development
 ===========
-
-As an open source project, Pastas depends on the open source community for
-maintaining and developing the software. Any help in maintaining the code,
-writing or updating documentation is more than welcome. This section serves
-to help you in contributing code to the Pastas software.
+As an open source software project, Pastas depends on the open source community for
+maintaining and developing the software. Any help in maintaining the code, developing
+new features, or writing and updating documentation is more than welcome. This section
+serves to help you in contributing code to Pastas. Additionally, this section
+serves to document code conventions and quick how-to's for maintainers and developers.
 
 .. toctree::
     :maxdepth: 1
@@ -15,60 +15,24 @@ to help you in contributing code to the Pastas software.
 
 Bug reports / Feature requests
 ------------------------------
-If you have found a bug in Pastas, or if you would like to suggest
-an improvement or enhancement, please `submit a new Issue <https://github
-.com/pastas/pastas/issues>`_ through the Github Issue tracker.
+If you have found a bug in Pastas, or if you would like to suggest an improvement or
+enhancement, please `submit a new Issue <https://github.com/pastas/pastas/issues>`_
+through the Github Issue tracker.
+
+User questions
+--------------
+If you have question about the use of Pastas, feel free to ask questions in the
+`GitHub Discussions <https://github.com/pastas/pastas/discussions>`_. Also feel free to
+answer questions here and help each other!
 
 Release Cycle
 -------------
-Pastas does not have a fixed release cycle but it is tried to release a new
-version every three months. You can check when a new release is
-approximately scheduled at the `GitHub Milestones page <https://github
-.com/pastas/pastas/milestones>`_.
-
-Core Development Team
----------------------
-The following people are part of the core development team of Pastas and can
-merge pull requests on the Pastas GitHub.
-
-
-.. figure:: https://avatars2.githubusercontent.com/u/12051167?s=460&u=ad0baaa3520ab942c93af44ea8e296b44ea459ac&v=4
-    :figwidth: 150px
-    :target: https://github.com/raoulcollenteur
-
-    Raoul Collenteur.
-    `Uni Graz <https://www.uni-graz.at>`_
-
-.. figure:: https://avatars3.githubusercontent.com/u/1363400?s=460&u=8d34d0824bd0f43a63f29c0b71fbfae435a15565&v=4
-    :figwidth: 150px
-    :target: https://github.com/mbakker7
+Pastas does not have a fixed regular release cycle but we strive to release a new
+version every three months. You can check when a new release is approximately scheduled on
+the `GitHub Milestones page <https://github.com/pastas/pastas/milestones>`_.
 
-    Mark Bakker.
-    `TU Delft <https://www.tudelft.nl>`_
-
-.. figure:: https://avatars3.githubusercontent.com/u/18527362?s=460&u=3e1faab416c86e141ff0860017a5a02133830b08&v=4
-    :figwidth: 150px
-    :target: https://github.com/rubencalje
-
-    Ruben Caljé.
-    `Artesia. <http://www.artesia-water.nl>`_
-
-.. figure:: https://avatars1.githubusercontent.com/u/6552225?s=460&u=ba73dc4a63fe3d5707a2da9276143d16672ad413&v=4
-    :figwidth: 150px
-    :target: https://github.com/dbrakenhoff
-
-    Davíd Brakenhoff.
-    `Artesia <http://www.artesia-water.nl>`_.
-
-Contributors
-------------
-The following people have contributed code to the Pastas project. Please
-notify us when contributors are missing from this list.
-
-- `Onno Ebbens <https://github.com/OnnoEbbens>`_
-- `Martin Vonk <https://github.com/martinvonk>`_
-- `Stijn Klop <https://github.com/saklop>`_
-- `Tom van Steijn <https://github.com/tomvansteijn>`_
-- `Hugo van de Berg <https://github.com/Hugovdberg>`_
-- `Eit van der Meulen <https://github.com/EitvanderMeulen>`_
-- `Pytrik Graafstra <https://github.com/pgraafstra>`_
+Release Notes
+-------------
+Release notes for all Pastas version can be found on the
+`Github Release page <https://github.com/pastas/pastas/releases>`_. We do not maintain
+release notes on the documentation website here.
diff --git a/doc/developers/new_release.rst b/doc/developers/new_release.rst
index 5ca36f00..a1f23b05 100644
--- a/doc/developers/new_release.rst
+++ b/doc/developers/new_release.rst
@@ -3,14 +3,18 @@ Make a Release
 
 To create a new release of Pastas follow the following steps:
 
-1. Update version.py to the correct version.
+1. Update version.py to the correct version ("X.X.X").
 2. Commit these changes to the Dev-branch and sync.
 3. Create a pull request to merge Dev into Master.
-4. Merge commit the pull request. (Don't do squash and merge!).
-5. Make a GitHub release from the master branch. A Pypi release will be
+4. Check the documentation website if all is correct.
+5. Merge commit the pull request. (Don't do squash and merge!).
+6. Make a GitHub release from the master branch. A Pypi release will be
    created automatically and the Pastas version will receive a unique DOI at
    Zenodo.
-6. Switch back to the Dev-branch and update the version.py file ("X.X.Xb").
+7. Switch back to the Dev-branch and update the version.py file ("X.X.Xb").
+
+.. warning::
+    When making a release, do not use "Squash and merge", just do merge.
 
 Dependency policy
 -----------------
diff --git a/doc/examples/01_basic_model.ipynb b/doc/examples/01_basic_model.ipynb
index bdc7dc98..d1621792 100644
--- a/doc/examples/01_basic_model.ipynb
+++ b/doc/examples/01_basic_model.ipynb
@@ -50,7 +50,7 @@
     "# Plot the observed groundwater levels\n",
     "gwdata.plot(style=\".\", figsize=(10, 4))\n",
     "plt.ylabel(\"Head [m]\")\n",
-    "plt.xlabel(\"Time [years]\");"
+    "plt.xlabel(\"Time [years]\")"
    ]
   },
   {
@@ -86,13 +86,14 @@
     "\n",
     "# Calculate the recharge to the groundwater\n",
     "recharge = precip - evap\n",
+    "recharge.name = \"recharge\"  # set name if pandas series\n",
     "print(\"The data type of the recharge series is: %s\" % type(recharge))\n",
     "\n",
     "# Plot the time series of the precipitation and evaporation\n",
     "plt.figure()\n",
     "recharge.plot(label=\"Recharge\", figsize=(10, 4))\n",
     "plt.xlabel(\"Time [years]\")\n",
-    "plt.ylabel(\"Recharge (m/year)\");"
+    "plt.ylabel(\"Recharge (m/year)\")"
    ]
   },
   {
@@ -247,7 +248,7 @@
   },
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "pastas_dev",
    "language": "python",
    "name": "python3"
   },
@@ -261,7 +262,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
   },
   "widgets": {
    "state": {},
diff --git a/doc/examples/02_fix_parameters.ipynb b/doc/examples/02_fix_parameters.ipynb
index f4288a50..b47843d2 100644
--- a/doc/examples/02_fix_parameters.ipynb
+++ b/doc/examples/02_fix_parameters.ipynb
@@ -1,10 +1,11 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Time Series Analysis with Pastas\n",
+    "# Fixating parameters while fitting\n",
     "    \n",
     "*Developed by Mark Bakker, TU Delft*"
    ]
@@ -228,7 +229,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "pastas_dev",
    "language": "python",
    "name": "python3"
   },
@@ -242,7 +243,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
   },
   "widgets": {
    "state": {},
diff --git a/doc/examples/09_calibration_options.ipynb b/doc/examples/03_calibration_options.ipynb
similarity index 97%
rename from doc/examples/09_calibration_options.ipynb
rename to doc/examples/03_calibration_options.ipynb
index 4c372dca..e9bf3019 100644
--- a/doc/examples/09_calibration_options.ipynb
+++ b/doc/examples/03_calibration_options.ipynb
@@ -1,10 +1,12 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Calibrating Pastas models\n",
+    "# Calibration\n",
+    "\n",
     "*R.A. Collenteur, University of Graz*\n",
     "\n",
     "After a model is constructed, the model parameters can be estimated using the `ml.solve` method. It can (and will) happen that the model fit after solving is not as good as expected. This may be the result of the settings that are used to solve the model or the way the model was constructed. In this notebook common pitfalls and various tips and tricks that may help to improve the calibration of Pastas models are shared. \n",
@@ -255,7 +257,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "artesia",
+   "display_name": "pastas_dev",
    "language": "python",
    "name": "python3"
   },
@@ -269,11 +271,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7 | packaged by conda-forge | (default, Sep 29 2021, 19:20:46) \n[GCC 9.4.0]"
+   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
   },
   "vscode": {
    "interpreter": {
-    "hash": "dace5e1b41a98a8e52d2a8eebc3b981caf2c12e7a76736ebfb89a489e3b62e79"
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
    }
   }
  },
diff --git a/doc/examples/04_adding_rivers.ipynb b/doc/examples/04_adding_rivers.ipynb
index c0336b36..e050ba5e 100644
--- a/doc/examples/04_adding_rivers.ipynb
+++ b/doc/examples/04_adding_rivers.ipynb
@@ -1,13 +1,14 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Adding river levels\n",
+    "# Adding surface water levels\n",
     "*Developed by R.A. Collenteur & D. Brakenhoff*\n",
     "\n",
-    "In this example it is shown how to create a Pastas model that not only includes precipitation and evaporation, but also observed river levels. We will consider observed heads that are strongly influenced  by river level, based on a visual interpretation of the raw data."
+    "In this example it is shown how to create a Pastas model that not only includes precipitation and evaporation, but also observed river levels. We will consider observed heads that are strongly influenced by river level, based on a visual interpretation of the raw data."
    ]
   },
   {
@@ -95,7 +96,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "pastas_dev",
    "language": "python",
    "name": "python3"
   },
@@ -109,7 +110,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
   }
  },
  "nbformat": 4,
diff --git a/doc/examples/05_adding_wells.ipynb b/doc/examples/05_adding_wells.ipynb
index f8ee6e44..6dcc16a0 100644
--- a/doc/examples/05_adding_wells.ipynb
+++ b/doc/examples/05_adding_wells.ipynb
@@ -1,10 +1,11 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Adding groundwater pumping\n",
+    "# Adding pumping wells\n",
     "*Developed by R.A. Collenteur & M. Bakker*\n",
     "\n",
     "In this example notebook it is shown how to simulate the effect of a pumping well on the groundwater levels. We will first create a TFN model with the net recharge as the single stress used to explain the observed heads. Second, this model is extended to include the effect of a pumping well on the heads by adding another stress model. The simulated heads are compared and it can be clearly seen how the addition of the pumping well improves the simulation of the heads.\n",
diff --git a/doc/examples/10_multiple_wells.ipynb b/doc/examples/06_multiple_wells.ipynb
similarity index 99%
rename from doc/examples/10_multiple_wells.ipynb
rename to doc/examples/06_multiple_wells.ipynb
index 5e2a189c..0934585f 100644
--- a/doc/examples/10_multiple_wells.ipynb
+++ b/doc/examples/06_multiple_wells.ipynb
@@ -1,10 +1,11 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Adding Multiple Wells\n",
+    "# Adding multiple wells\n",
     "\n",
     "This notebook shows how a WellModel can be used to fit multiple wells with one response function. The influence of the individual wells is scaled by the distance to the observation point. \n",
     "\n",
diff --git a/doc/examples/06_adding_trends.ipynb b/doc/examples/07_adding_trends.ipynb
similarity index 99%
rename from doc/examples/06_adding_trends.ipynb
rename to doc/examples/07_adding_trends.ipynb
index 3fa85b59..92489928 100644
--- a/doc/examples/06_adding_trends.ipynb
+++ b/doc/examples/07_adding_trends.ipynb
@@ -1,10 +1,11 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Adding Trends\n",
+    "# Adding trends\n",
     "*R.A. Collenteur (University of Graz), O.N. Ebbens (Artesia)*\n",
     "\n",
     "In this notebook it is explained how to use linear and step trend models to improve the simulation of groundwater levels."
diff --git a/doc/examples/16_changing_responses.ipynb b/doc/examples/08_changing_responses.ipynb
similarity index 82%
rename from doc/examples/16_changing_responses.ipynb
rename to doc/examples/08_changing_responses.ipynb
index c9b7b295..9f5c346c 100644
--- a/doc/examples/16_changing_responses.ipynb
+++ b/doc/examples/08_changing_responses.ipynb
@@ -116,18 +116,22 @@
     "step = ps.StepModel(\"2012-01-01\", rfunc=ps.One(), name=\"step\")\n",
     "\n",
     "ml.add_stressmodel([sm, step])\n",
-    "ml.solve(report=False, tmin=\"2004\", tmax=\"2018\", noise=True)\n",
+    "ml.solve(report=False, tmin=\"2004\", tmax=\"2017-12-31\", noise=True)\n",
     "ml.plots.results(figsize=(10, 6))\n",
     "\n",
     "# ChangeModel\n",
     "ml2 = ps.Model(head, name=\"linear\")\n",
     "\n",
     "cm = ps.ChangeModel(\n",
-    "    river, ps.Exponential(), ps.Exponential(), name=\"test\", tchange=\"2012-01-01\"\n",
+    "    river,\n",
+    "    ps.Exponential(),\n",
+    "    ps.Exponential(),\n",
+    "    name=\"test\",\n",
+    "    tchange=\"2012-01-01\",\n",
     ")\n",
     "\n",
     "ml2.add_stressmodel([cm, step])\n",
-    "ml2.solve(report=False, tmin=\"2004\", tmax=\"2018\", noise=True)\n",
+    "ml2.solve(report=False, tmin=\"2004\", tmax=\"2017-12-31\", noise=True)\n",
     "ml2.plots.results(figsize=(10, 6));"
    ]
   },
@@ -176,14 +180,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.plot(\n",
-    "    cm.rfunc1.step(p=ml2.parameters.loc[[\"test_1_A\", \"test_1_a\"], \"optimal\"].values)\n",
+    "cm_rf1 = cm.rfunc1.step(\n",
+    "    p=ml2.parameters.loc[[\"test_1_A\", \"test_1_a\"], \"optimal\"].values\n",
     ")\n",
-    "plt.plot(\n",
-    "    cm.rfunc2.step(p=ml2.parameters.loc[[\"test_2_A\", \"test_2_a\"], \"optimal\"].values)\n",
+    "cm_rf2 = cm.rfunc2.step(\n",
+    "    p=ml2.parameters.loc[[\"test_2_A\", \"test_2_a\"], \"optimal\"].values\n",
     ")\n",
     "\n",
-    "plt.legend([\"Before\", \"After\"])"
+    "plt.plot(np.arange(1, len(cm_rf1) + 1), cm_rf1)\n",
+    "plt.plot(np.arange(1, len(cm_rf2) + 1), cm_rf2)\n",
+    "\n",
+    "plt.legend([\"Before\", \"After\"]);"
    ]
   },
   {
@@ -210,12 +217,12 @@
     "river2 = river.copy()\n",
     "river2.loc[:\"2011\"] = 0\n",
     "\n",
-    "r1 = ps.StressModel(river1, rfunc=ps.Exponential(), name=\"river\", settings=\"level\")\n",
-    "r2 = ps.StressModel(river2, rfunc=ps.Exponential(), name=\"river2\", settings=\"level\")\n",
+    "r1 = ps.StressModel(river1, rfunc=ps.Exponential(), name=\"river\")\n",
+    "r2 = ps.StressModel(river2, rfunc=ps.Exponential(), name=\"river2\")\n",
     "step = ps.StepModel(\"2012-01-01\", rfunc=ps.One(), name=\"step\")\n",
     "\n",
     "ml3.add_stressmodel([r1, r2, step])\n",
-    "ml3.solve(report=False, tmin=\"2004\", tmax=\"2018\", noise=True)\n",
+    "ml3.solve(report=False, tmin=\"2004\", tmax=\"2017-12-31\", noise=True)\n",
     "ml3.plots.results(figsize=(10, 6));"
    ]
   },
@@ -234,17 +241,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.plot(\n",
-    "    cm.rfunc1.step(p=ml2.parameters.loc[[\"test_1_A\", \"test_1_a\"], \"optimal\"].values)\n",
+    "# change model\n",
+    "cm_rf1 = cm.rfunc1.step(\n",
+    "    p=ml2.parameters.loc[[\"test_1_A\", \"test_1_a\"], \"optimal\"].values\n",
     ")\n",
-    "plt.plot(\n",
-    "    cm.rfunc2.step(p=ml2.parameters.loc[[\"test_2_A\", \"test_2_a\"], \"optimal\"].values)\n",
+    "cm_rf2 = cm.rfunc2.step(\n",
+    "    p=ml2.parameters.loc[[\"test_2_A\", \"test_2_a\"], \"optimal\"].values\n",
     ")\n",
+    "plt.plot(np.arange(1, len(cm_rf1) + 1), cm_rf1)\n",
+    "plt.plot(np.arange(1, len(cm_rf2) + 1), cm_rf2)\n",
     "\n",
+    "# 2 stressmodels\n",
     "ml3.get_step_response(\"river\").plot()\n",
     "ml3.get_step_response(\"river2\").plot()\n",
     "\n",
-    "plt.legend([\"Before\", \"After\", \"Before (method 2)\", \"After (method 2)\"])"
+    "plt.legend(\n",
+    "    [\n",
+    "        \"Before (ChangeModel)\",\n",
+    "        \"After (ChangeModel)\",\n",
+    "        \"Before (method 2)\",\n",
+    "        \"After (method 2)\",\n",
+    "    ]\n",
+    ")"
    ]
   },
   {
diff --git a/doc/examples/08_threshold_non_linear.ipynb b/doc/examples/09_threshold_non_linear.ipynb
similarity index 99%
rename from doc/examples/08_threshold_non_linear.ipynb
rename to doc/examples/09_threshold_non_linear.ipynb
index 5888cc91..54d6cd65 100644
--- a/doc/examples/08_threshold_non_linear.ipynb
+++ b/doc/examples/09_threshold_non_linear.ipynb
@@ -1,10 +1,11 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Threshold Non-linearities\n",
+    "# Threshold non-linearities\n",
     "*Developed by Ruben Caljé*\n",
     "\n",
     "This notebook compares two different options in Pastas for modeling threshold non-linear groundwater systems. We start with a basic model that contains a `StressModel2` to model the influence of precipitation and evaporation on groundwater head.\n",
@@ -21,10 +22,10 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "import pastas as pt\n",
+    "import pastas as ps\n",
     "\n",
-    "pt.set_log_level(\"WARNING\")\n",
-    "pt.show_versions()"
+    "ps.set_log_level(\"WARNING\")\n",
+    "ps.show_versions()"
    ]
   },
   {
@@ -33,7 +34,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ml = pt.io.load(\"data_notebook_8/B28H1804_2.pas\")\n",
+    "ml = ps.io.load(\"data_notebook_8/B28H1804_2.pas\")\n",
     "# the model is already solved, but we solve it again to be certain\n",
     "ml.solve(noise=False, report=False)\n",
     "# and we plot the results\n",
@@ -54,7 +55,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ml.add_transform(pt.ThresholdTransform())\n",
+    "ml.add_transform(ps.ThresholdTransform())\n",
     "\n",
     "ml.solve(noise=False, report=False)\n",
     "ml.plots.results();"
@@ -86,7 +87,7 @@
     "ml.del_transform()\n",
     "\n",
     "# then add a TarsoModel\n",
-    "sm = pt.TarsoModel(prec, evap, ml.oseries.series)\n",
+    "sm = ps.TarsoModel(prec, evap, ml.oseries.series)\n",
     "ml.add_stressmodel(sm)\n",
     "\n",
     "# and solve and plot the results again\n",
@@ -175,7 +176,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "pastas_dev",
    "language": "python",
    "name": "python3"
   },
@@ -189,7 +190,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
   }
  },
  "nbformat": 4,
diff --git a/doc/examples/07_non_linear_recharge.ipynb b/doc/examples/10_non_linear_recharge.ipynb
similarity index 100%
rename from doc/examples/07_non_linear_recharge.ipynb
rename to doc/examples/10_non_linear_recharge.ipynb
diff --git a/doc/examples/14_recharge_estimation.ipynb b/doc/examples/11_recharge_estimation.ipynb
similarity index 99%
rename from doc/examples/14_recharge_estimation.ipynb
rename to doc/examples/11_recharge_estimation.ipynb
index 314f5a03..a76ada18 100644
--- a/doc/examples/14_recharge_estimation.ipynb
+++ b/doc/examples/11_recharge_estimation.ipynb
@@ -430,7 +430,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
   },
   "vscode": {
    "interpreter": {
diff --git a/doc/examples/18_snowmodel.ipynb b/doc/examples/12_snowmodel.ipynb
similarity index 99%
rename from doc/examples/18_snowmodel.ipynb
rename to doc/examples/12_snowmodel.ipynb
index 420cabdc..e125914b 100644
--- a/doc/examples/18_snowmodel.ipynb
+++ b/doc/examples/12_snowmodel.ipynb
@@ -1,11 +1,12 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "9f197b14",
    "metadata": {},
    "source": [
-    "# Snow model\n",
+    "# Snow\n",
     "*R.A. Collenteur, University of Graz / Eawag, November 2021*\n",
     "\n",
     "In this notebook it is shown how to account for snowfall and smowmelt on groundwater recharge and groundwater levels, using a degree-day snow model. This notebook is work in progress and will be extended in the future."
diff --git a/doc/examples/22_comparing_models.ipynb b/doc/examples/13_comparing_models.ipynb
similarity index 100%
rename from doc/examples/22_comparing_models.ipynb
rename to doc/examples/13_comparing_models.ipynb
diff --git a/doc/examples/03_diagnostic_checking.ipynb b/doc/examples/14_diagnostic_checking.ipynb
similarity index 97%
rename from doc/examples/03_diagnostic_checking.ipynb
rename to doc/examples/14_diagnostic_checking.ipynb
index b2b20e62..06933390 100644
--- a/doc/examples/03_diagnostic_checking.ipynb
+++ b/doc/examples/14_diagnostic_checking.ipynb
@@ -1,10 +1,11 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Model Diagnostic Checking\n",
+    "# Diagnostic checking\n",
     "*R.A. Collenteur, University of Graz, July 2020*.\n",
     "\n",
     "This notebook provides an overview of the different methods that are available for diagnostic checking of the models residuals in Pastas. Readers who want to get a quick overview of how to perform diagnostic checks on Pastas models are referred to section 2, while sections 3 to 6 are recommended for readers looking for in-depth discussions of the individual methods.\n",
@@ -167,6 +168,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -175,7 +177,7 @@
     "\n",
     "| Name | Pastas method | Description | Non-equidistant |\n",
     "|:-----|:--------------|:-----------------------------------------|----------------:|\n",
-    "| Visualization | `ps.stats.plot_acf` | Visualization of the autocorrelation and its confidence intervals. | Yes |\n",
+    "| Visualization | `ps.plots.acf` | Visualization of the autocorrelation and its confidence intervals. | Yes |\n",
     "| Ljung-Box test| `ps.stats.ljung_box`| This test checks whether the autocorrelations of a time series are significantly different from zero.| No |\n",
     "| Durbin-Watson test | `ps.stats.durbin_watson` | This tests diagnoses for autocorrelation at a lag of one time step. | No |\n",
     "| Stoffer-Toloi test |`ps.stats.stoffer_toloi`| This test is similar to the Ljung-Box test, but is adapted for missing values | Yes |\n",
@@ -184,7 +186,7 @@
     "Whereas many time series models have equidistant time steps, the residuals of Pastas models may have non-equidistant time steps. To deal with this property, functions have been implemented in Pastas that can deal with non-equidistant time steps [(Rehfeld et al., 2011)](#References). We therefore recommend to use the statistical methods supplied in Pastas, unless the modeler is sure he/she is dealing with equidistant time steps. See the additional Notebook on the autocorrelation function for more details and a proof of concept.\n",
     "\n",
     "### Visual interpretation of the autocorrelation\n",
-    "To diagnose the model residuals for autocorrelation we first plot the autocorrelation function (ACF) using the `ps.stats.plot_acf` method and perform a visual interpretation of the models residuals. The created plot shows the autocorrelation function up to a time lag of 250 days. The blue-shaded area denotes the 95\\% confidence interval (1-$\\alpha$). If 95\\% of the autocorrelations fall within this confidence interval (that is, 0.95 $\\cdot$ 250 = ±237 of them), we may conclude that there is no significant autocorrelation in the residuals. "
+    "To diagnose the model residuals for autocorrelation we first plot the autocorrelation function (ACF) using the `ps.plots.acf` method and perform a visual interpretation of the models residuals. The created plot shows the autocorrelation function up to a time lag of 250 days. The blue-shaded area denotes the 95\\% confidence interval (1-$\\alpha$). If 95\\% of the autocorrelations fall within this confidence interval (that is, 0.95 $\\cdot$ 250 = ±237 of them), we may conclude that there is no significant autocorrelation in the residuals. "
    ]
   },
   {
diff --git a/doc/examples/13_timestep_analysis.ipynb b/doc/examples/15_timestep_analysis.ipynb
similarity index 99%
rename from doc/examples/13_timestep_analysis.ipynb
rename to doc/examples/15_timestep_analysis.ipynb
index dbdcc123..19534ebb 100644
--- a/doc/examples/13_timestep_analysis.ipynb
+++ b/doc/examples/15_timestep_analysis.ipynb
@@ -1,11 +1,12 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "2e3d942c",
    "metadata": {},
    "source": [
-    "# Reducing Autocorrelation\n",
+    "# Reducing autocorrelation\n",
     "*R.A. Collenteur, University of Graz*\n",
     "\n",
     "In this notebook we look at two strategies that may help to reduce the autocorrelation in the noise, such that the estimated standard errors of the parameters may be used for further analysis. \n",
diff --git a/doc/examples/15_uncertainty.ipynb b/doc/examples/16_uncertainty.ipynb
similarity index 100%
rename from doc/examples/15_uncertainty.ipynb
rename to doc/examples/16_uncertainty.ipynb
diff --git a/doc/examples/12_emcee_uncertainty.ipynb b/doc/examples/17_emcee_uncertainty.ipynb
similarity index 97%
rename from doc/examples/12_emcee_uncertainty.ipynb
rename to doc/examples/17_emcee_uncertainty.ipynb
index fff37366..49243db7 100644
--- a/doc/examples/12_emcee_uncertainty.ipynb
+++ b/doc/examples/17_emcee_uncertainty.ipynb
@@ -1,10 +1,11 @@
 {
     "cells": [
         {
+            "attachments": {},
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "# Testing MCMC on Pastas Models\n",
+                "# MCMC uncertainty\n",
                 "*R.A. Collenteur, University of Graz, November 2019*\n",
                 "\n",
                 "In this notebook it is shown how the MCMC-algorithm can be used to estimate the model parameters for a Pastas model. Besides Pastas the following Python Packages have to be installed to run this notebook:\n",
@@ -191,7 +192,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
         },
         "vscode": {
             "interpreter": {
diff --git a/doc/examples/11_sgi_example.ipynb b/doc/examples/18_sgi_example.ipynb
similarity index 97%
rename from doc/examples/11_sgi_example.ipynb
rename to doc/examples/18_sgi_example.ipynb
index ce0acff6..4966a0ae 100644
--- a/doc/examples/11_sgi_example.ipynb
+++ b/doc/examples/18_sgi_example.ipynb
@@ -1,10 +1,11 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Standardized Groundwater Index (SGI)\n",
+    "# Standardized Groundwater Index\n",
     "*R.A. Collenteur, University of Graz, November 2020*\n",
     "\n",
     "To study the occurrence of groundwater droughts [Bloomfield and Marchant (2013)](#References) developed the Standardized Groundwater Index (SGI). More and more SGI values are being used to study and quantify groundwater droughts. In this Notebook it is shown how to compute the SGI using Pastas, and how Pastas models may be used to obtain groundwater level time series with regular time steps. The SGI implemented in Pastas (`ps.stats.sgi`) is based on the description in [Bloomfield and Marchant (2013)](#References).\n",
@@ -361,7 +362,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "pastas_dev",
    "language": "python",
    "name": "python3"
   },
@@ -375,7 +376,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
   }
  },
  "nbformat": 4,
diff --git a/doc/examples/20_signatures.ipynb b/doc/examples/19_signatures.ipynb
similarity index 100%
rename from doc/examples/20_signatures.ipynb
rename to doc/examples/19_signatures.ipynb
diff --git a/doc/examples/data/B28H1808_2_pastas-0-23-0.pas b/doc/examples/data/B28H1808_2_pastas-0-23-0.pas
new file mode 100644
index 00000000..299357ff
--- /dev/null
+++ b/doc/examples/data/B28H1808_2_pastas-0-23-0.pas
@@ -0,0 +1,155 @@
+{
+    "name": "B28H1808_2",
+    "oseries": {
+        "series": 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+    "settings": {
+        "tmin": "2012-06-07T00:00:00",
+        "tmax": "2017-02-01T00:00:00",
+        "freq": "D",
+        "warmup": "315360000000000000 nanoseconds",
+        "time_offset": "0 nanoseconds",
+        "noise": true,
+        "solver": "LeastSquares",
+        "fit_constant": true,
+        "weights": null
+    },
+    "stressmodels": {
+        "Recharge": {
+            "class": "RechargeModel",
+            "prec": {
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+                "name": "Exctraction",
+                "settings": {
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+                    "tmin": "2002-06-10T00:00:00",
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+                    "time_offset": "0 nanoseconds"
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+        "value": 20.0325,
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+    "noisemodel": {
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+    "fit": {
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+}
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diff --git a/doc/examples/data_notebook_8/B28H1804_2.pas b/doc/examples/data_notebook_8/B28H1804_2.pas
index ac41c612..b500fc8e 100644
--- a/doc/examples/data_notebook_8/B28H1804_2.pas
+++ b/doc/examples/data_notebook_8/B28H1804_2.pas
@@ -1,7 +1,7 @@
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         "name": "B28H1804_2",
         "settings": {
             "freq": null,
@@ -12,7 +12,6 @@
             "fill_after": null,
             "tmin": "2012-06-06T20:00:00",
             "tmax": "2019-09-17T20:00:00",
-            "norm": null,
             "time_offset": "0 nanoseconds"
         },
         "metadata": {
@@ -23,13 +22,12 @@
             "lowfiltlev": 15.917,
             "upfiltlev": 16.417,
             "surflev": 19.44
-        },
-        "freq_original": "D"
+        }
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+    "parameters": "{\n\"recharge_A\": {\n\"initial\": 214.5114941633,\n\"name\": \"recharge\",\n\"optimal\": 567.8365332647097,\n\"pmin\": 1e-05,\n\"pmax\": 21766.41143940185,\n\"vary\": true,\n\"stderr\": 16.10271249445721\n},\n\"recharge_n\": {\n\"initial\": 1.0,\n\"name\": \"recharge\",\n\"optimal\": 0.7666340616683066,\n\"pmin\": 0.01,\n\"pmax\": 100.0,\n\"vary\": true,\n\"stderr\": 0.008812404580026337\n},\n\"recharge_a\": {\n\"initial\": 10.0,\n\"name\": \"recharge\",\n\"optimal\": 323.1865535253526,\n\"pmin\": 0.01,\n\"pmax\": 10000.0,\n\"vary\": true,\n\"stderr\": 20.13031774553277\n},\n\"recharge_f\": {\n\"initial\": -1.0,\n\"name\": \"recharge\",\n\"optimal\": -1.1771603895716105,\n\"pmin\": -2.0,\n\"pmax\": 0.0,\n\"vary\": true,\n\"stderr\": 0.026105526073289323\n},\n\"constant_d\": {\n\"initial\": 19.3681834586,\n\"name\": \"constant\",\n\"optimal\": 19.244479399343444,\n\"pmin\": NaN,\n\"pmax\": NaN,\n\"vary\": true,\n\"stderr\": 0.024391437447678355\n}\n}",
     "settings": {
-        "tmin": "2012-06-06T00:00:00",
-        "tmax": "2019-09-17T00:00:00",
+        "tmin": "2012-06-06T20:00:00",
+        "tmax": "2019-09-17T20:00:00",
         "freq": "D",
         "warmup": "315360000000000000 nanoseconds",
         "time_offset": "0 nanoseconds",
@@ -40,9 +38,9 @@
     },
     "stressmodels": {
         "recharge": {
-            "stressmodel": "RechargeModel",
+            "class": "RechargeModel",
             "prec": {
-                "series": 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                 "name": "RD_Weerselo",
                 "settings": {
                     "freq": "D",
@@ -52,8 +50,7 @@
                     "fill_before": "mean",
                     "fill_after": "mean",
                     "tmin": "2002-06-09T00:00:00",
-                    "tmax": "2019-09-17T00:00:00",
-                    "norm": null,
+                    "tmax": "2019-09-17T20:00:00",
                     "time_offset": "0 nanoseconds"
                 },
                 "metadata": {
@@ -61,11 +58,10 @@
                     "y": 485592.853335713,
                     "z": 0.0,
                     "projection": null
-                },
-                "freq_original": "D"
+                }
             },
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,0.0033,0.0042,0.0029,0.0031,0.002,0.0014,0.0034,0.0028,0.0033,0.003,0.0031,0.0012,0.0029,0.0028,0.0032,0.0025,0.0035,0.0019,0.0032,0.0008,0.0014,0.0026,0.0027,0.0033,0.0038,0.0038,0.0041,0.0041,0.0039,0.0038,0.0035,0.0028,0.0035,0.0037,0.0018,0.0028,0.001,0.0015,0.002,0.0022,0.0019,0.0014,0.0025,0.0024,0.001,0.0022,0.0022,0.0026,0.0023,0.0007]}",
                 "name": "EV24_Twenthe",
                 "settings": {
                     "freq": "D",
@@ -75,8 +71,7 @@
                     "fill_before": "mean",
                     "fill_after": "mean",
                     "tmin": "2002-06-09T00:00:00",
-                    "tmax": "2019-09-17T00:00:00",
-                    "norm": null,
+                    "tmax": "2019-09-17T20:00:00",
                     "time_offset": "0 nanoseconds"
                 },
                 "metadata": {
@@ -84,31 +79,36 @@
                     "y": 476370.2740831941,
                     "z": 0.0,
                     "projection": null
-                },
-                "freq_original": "D"
+                }
+            },
+            "rfunc": {
+                "class": "Gamma",
+                "up": true,
+                "gain_scale_factor": 0.004594234574606022,
+                "cutoff": 0.999
             },
-            "rfunc": "Gamma",
             "name": "recharge",
-            "recharge": "Linear",
-            "recharge_kwargs": {},
-            "cutoff": 0.999,
+            "recharge": {
+                "class": "Linear"
+            },
             "temp": null
         }
     },
     "constant": true,
     "noisemodel": {
-        "type": "NoiseModel"
+        "class": "NoiseModel",
+        "norm": true
     },
     "fit": {
-        "name": "LeastSquares",
-        "pcov": "{\n\"recharge_A\": {\n\"recharge_A\": 259.3255657832,\n\"recharge_n\": -0.0625238767,\n\"recharge_a\": 254.4239611148,\n\"recharge_f\": 0.0183569445,\n\"constant_d\": -0.0941175074\n},\n\"recharge_n\": {\n\"recharge_A\": -0.0625238767,\n\"recharge_n\": 7.76629e-05,\n\"recharge_a\": -0.1472907701,\n\"recharge_f\": 0.0001205817,\n\"constant_d\": -8.94566e-05\n},\n\"recharge_a\": {\n\"recharge_A\": 254.4239611148,\n\"recharge_n\": -0.1472907701,\n\"recharge_a\": 405.3011387501,\n\"recharge_f\": -0.2624216766,\n\"constant_d\": 0.1595833292\n},\n\"recharge_f\": {\n\"recharge_A\": 0.0183569445,\n\"recharge_n\": 0.0001205817,\n\"recharge_a\": -0.2624216766,\n\"recharge_f\": 0.0006815509,\n\"constant_d\": -0.0006220789\n},\n\"constant_d\": {\n\"recharge_A\": -0.0941175074,\n\"recharge_n\": -8.94566e-05,\n\"recharge_a\": 0.1595833292,\n\"recharge_f\": -0.0006220789,\n\"constant_d\": 0.0005949885\n}\n}",
-        "nfev": 12,
-        "obj_func": null
+        "class": "LeastSquares",
+        "pcov": "{\n\"recharge_A\": {\n\"recharge_A\": 259.2973496791484,\n\"recharge_n\": -0.06251519357231868,\n\"recharge_a\": 254.38400201145575,\n\"recharge_f\": 0.018367629170703097,\n\"constant_d\": -0.09412559115420296\n},\n\"recharge_n\": {\n\"recharge_A\": -0.06251519357231869,\n\"recharge_n\": 7.765847448206918e-05,\n\"recharge_a\": -0.14727202441367138,\n\"recharge_f\": 0.00012057049626422292,\n\"constant_d\": -8.944607880908175e-05\n},\n\"recharge_a\": {\n\"recharge_A\": 254.38400201145578,\n\"recharge_n\": -0.14727202441367138,\n\"recharge_a\": 405.22969253611166,\n\"recharge_f\": -0.26238155370354366,\n\"constant_d\": 0.1595496353671956\n},\n\"recharge_f\": {\n\"recharge_A\": 0.018367629170703097,\n\"recharge_n\": 0.00012057049626422292,\n\"recharge_a\": -0.26238155370354366,\n\"recharge_f\": 0.0006814984915631887,\n\"constant_d\": -0.0006220287842186511\n},\n\"constant_d\": {\n\"recharge_A\": -0.094125591154203,\n\"recharge_n\": -8.944607880908176e-05,\n\"recharge_a\": 0.15954963536719563,\n\"recharge_f\": -0.0006220287842186512,\n\"constant_d\": 0.000594942220764006\n}\n}",
+        "nfev": 14,
+        "obj_func": 13.963333131765367
     },
     "file_info": {
         "date_created": "2020-03-05T16:39:15.536406",
-        "date_modified": "2022-08-17T08:29:11.512352",
-        "pastas_version": "0.22.0b",
+        "date_modified": "2023-01-30T16:17:23.910508",
+        "pastas_version": "0.23.0",
         "owner": "raoulcollenteur"
     }
 }
\ No newline at end of file
diff --git a/doc/examples/example_spline.py b/doc/examples/example_spline.py
index 0ae09dc2..b06b6638 100644
--- a/doc/examples/example_spline.py
+++ b/doc/examples/example_spline.py
@@ -23,7 +23,7 @@
     "columns"
 )
 
-# Solve with a Exponential response function
+# Solve with an Exponential response function
 ml1 = ps.Model(obs, name="Exp")
 sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Exponential())
 ml1.add_stressmodel(sm)
diff --git a/doc/examples/example_tarso.py b/doc/examples/example_tarso.py
index 40c20f74..b2378a6d 100644
--- a/doc/examples/example_tarso.py
+++ b/doc/examples/example_tarso.py
@@ -1,8 +1,10 @@
 # %%
-import pastas as pt
+import pastas as ps
 
 # load a model with precipitation, evaporation and a well
-ml = pt.io.load("data/B28H1808_2_pastas-0-22-0.pas")
+ml = ps.io.load("data/B28H1808_2_pastas-0-23-0.pas")
+if ml.transform is not None:
+    ml.del_transform()
 
 # first solve and plot to see the model-performance
 ml.solve(noise=False)
@@ -19,7 +21,7 @@
 ml.del_constant()
 
 # then add a TarsoModel
-sm = pt.TarsoModel(
+sm = ps.TarsoModel(
     prec, evap, dmin=ml.oseries.series.min(), dmax=ml.oseries.series.max()
 )
 ml.add_stressmodel(sm)
diff --git a/doc/examples/example_timestep_weighted_resample.py b/doc/examples/example_timestep_weighted_resample.py
index 15c3a68e..6bee1098 100644
--- a/doc/examples/example_timestep_weighted_resample.py
+++ b/doc/examples/example_timestep_weighted_resample.py
@@ -2,9 +2,7 @@
 import numpy as np
 import pandas as pd
 
-from pastas.timeseries_utils import (
-    timestep_weighted_resample,
-)
+from pastas.timeseries_utils import timestep_weighted_resample
 
 # make a daily series from monthly (mostly) values, without specifying the
 # frequency of the original series
diff --git a/doc/examples/example_transform.py b/doc/examples/example_transform.py
index 1472865f..c616a530 100644
--- a/doc/examples/example_transform.py
+++ b/doc/examples/example_transform.py
@@ -2,7 +2,7 @@
 import pastas as ps
 
 # load a model with precipitation, evaporation and a well
-ml = ps.io.load("data/B28H1808_2_pastas-0-22-0.pas")
+ml = ps.io.load("data/B28H1808_2_pastas-0-23-0.pas")
 if ml.transform is not None:
     ml.del_transform()
 
diff --git a/doc/examples/index.rst b/doc/examples/index.rst
index 4b47fbe9..254792ed 100644
--- a/doc/examples/index.rst
+++ b/doc/examples/index.rst
@@ -1,24 +1,96 @@
-Example Gallery
-===============
+Examples
+========
 
 Below you can find examples of how Pastas models are used for the analysis
 of groundwater levels. Examples in the form of Python scripts can also be found
-on the `examples directory on GitHub <https://github
-.com/pastas/pastas/tree/master/examples>`_.
+on the `examples directory on GitHub <https://github.com/pastas/pastas/tree/master/doc/examples>`_.
 
 .. toctree::
-    :maxdepth: 2
-    :numbered:
+    :maxdepth: 4
     :hidden:
+    :glob:
 
-.. tip::
-    The latest versions of the Jupyter Notebooks can be found in the
-    examples folder on GitHub!
+    ./*
 
+Basics
+------
 
-.. nbgallery::
-    :caption: Click on one of the examples below:
-    :name: rst-gallery
-    :glob:
+`Preprocessing user-provided time series`_
 
-    ./*
+`A basic model`_
+
+`Fixating parameters while fitting`_
+
+`Calibration`_
+
+.. _Preprocessing user-provided time series: 00_prepare_timeseries.ipynb
+.. _A basic model: 01_basic_model.ipynb
+.. _Fixating parameters while fitting: 02_fix_parameters.ipynb
+.. _Calibration: 03_calibration_options.ipynb
+
+
+Stressmodels
+------------
+
+`Adding surface water levels`_
+
+`Adding pumping wells`_
+
+`Adding multiple wells`_
+
+`Adding trends`_
+
+`Changing response functions`_
+
+.. _Adding surface water levels: 04_adding_rivers.ipynb
+.. _Adding pumping wells: 05_adding_wells.ipynb
+.. _Adding multiple wells: 06_multiple_wells.ipynb
+.. _Adding trends: 07_adding_trends.ipynb
+.. _Changing response functions: 08_changing_responses.ipynb
+
+Non-linear (Recharge) Models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`Threshold non-linearities`_
+
+`Non-linear recharge models`_
+
+`Estimating recharge`_
+
+`Modeling snow`_
+
+.. _Threshold non-linearities: 09_threshold_non_linear.ipynb
+.. _Non-linear recharge models: 10_non_linear.ipynb
+.. _Estimating recharge: 11_recharge_estimation.ipynb
+.. _Modeling snow: 12_snowmodel.ipynb
+
+
+Model Evaluation
+----------------
+
+`Comparing models visually`_
+
+`Diagnostics checking`_
+
+`Reducing autocorrelation`_
+
+`Uncertainty quantification`_
+
+`MCMC uncertainty`_
+
+.. _Comparing models visually: 13_comparing_models.ipynb
+.. _Diagnostics checking: 14_diagnostics_checking.ipynb
+.. _`Reducing autocorrelation`: 15_timestep_analysis.ipynb
+.. _Uncertainty quantification: 16_uncertainty.ipynb
+.. _MCMC uncertainty: 17_emcee_uncertainty.ipynb
+
+
+Applications
+------------
+
+`Standardized Groundwater Index`_
+
+`Groundwater signatures`_
+
+.. _Standardized Groundwater Index: 18_sgi_example.ipynb
+.. _Groundwater signatures: 19_signatures.ipynb
diff --git a/doc/index.rst b/doc/index.rst
index 0b5734b1..ab454da9 100755
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -1,14 +1,89 @@
 Introduction
 ============
+
 Pastas is an open source Python package to analyse hydro(geo)logical time
 series. The objective of Pastas is twofold: to provide a scientific
 framework to develop and test new methods, and to provide a reliable
 ready‐to‐use software tool for groundwater practitioners. All code is
 available from the `Pastas GitHub <https://github.com/pastas/pastas>`_. Want
-to contribute to the project? Check out the :ref:`Development` section.
+to contribute to the project? Check out the :doc:`Developers <developers/index>` section.
+
+.. grid::
+
+    .. grid-item-card:: User Guide
+        :link: userguide/index
+        :link-type: doc
+
+        User guide on the basic concepts of Pastas.
+
+    .. grid-item-card:: Examples
+        :link: examples/index
+        :link-type: doc
+
+        Examples of Pastas usage.
+
+    .. grid-item-card:: Code Reference
+        :link: api/index
+        :link-type: doc
+
+        Pastas code reference.
+
+
+.. grid::
+
+    .. grid-item-card:: Contribute
+        :link: developers/index
+        :link-type: doc
+
+        Want to contribute to Pastas? Find resources and guides for developers here.
+
+    .. grid-item-card:: Publications
+        :link: about/05_publications
+        :link-type: doc
+
+        Find an overview of scientific peer-reviewed studies that used Pastas.
+
+    .. grid-item-card:: More Pastas
+        :link: https://github.com/pastas/
 
-.. figure:: _static/example_output.png
-    :figwidth: 500px
+        Find out more useful resources developed by the Pastas community!
+
+
+Quick Example
+-------------
+
+.. tab-set::
+
+    .. tab-item:: Python
+
+        In this example a head time series is modelled in just a few lines of Python code.
+
+        .. code-block:: python
+
+            # Import python packages
+            import pandas as pd
+            import pastas as ps
+
+            # Read head and stress data
+            obs = pd.read_csv("head.csv", index_col=0, parse_dates=True).squeeze("columns")
+            rain = pd.read_csv("rain.csv", index_col=0, parse_dates=True).squeeze("columns")
+            evap = pd.read_csv("evap.csv", index_col=0, parse_dates=True).squeeze("columns")
+
+            # Create and calibrate model
+            ml = ps.Model(obs, name="head")
+            sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Exponential(), name="recharge")
+            ml.add_stressmodel(sm)
+            ml.solve()
+            ml.plots.results()
+
+    .. tab-item:: Result
+
+        .. figure:: _static/example_output.png
+            :figwidth: 500px
+
+
+Using Pastas? Please cite us!
+-----------------------------
 
 If you find Pastas useful and use it in your research or project, we kindly
 ask you to cite the Pastas article published in Groundwater journal as follows:
@@ -22,12 +97,11 @@ ask you to cite the Pastas article published in Groundwater journal as follows:
     :maxdepth: 2
     :hidden:
 
-    User Guide <userguide/getting_started>
-    Concepts <concepts/index>
+    User Guide <userguide/index>
     Examples <examples/index>
-    Development <developers/index>
     API Docs <api/index>
-    Release Notes <releases/index>
-    Publications <publications>
-    References <references>
+    Benchmarks <benchmarks/index>
+    Developers <developers/index>
+    About <about/index>
+
 
diff --git a/doc/releases/index.rst b/doc/releases/index.rst
deleted file mode 100644
index d7bc66de..00000000
--- a/doc/releases/index.rst
+++ /dev/null
@@ -1,16 +0,0 @@
-Release Notes
-=============
-
-Starting with the release of Pastas 0.15 changes to the API are
-reported here. The release notes for previous releases up to 0.14 can be found
-at the `GitHub Release page <https://github.com/pastas/pastas/releases>`_.
-For full details of all changes check the commit log.
-
-.. toctree::
-    :maxdepth: 1
-    :glob:
-    :reversed:
-
-    ./*
-
-
diff --git a/doc/releases/release_0.15.rst b/doc/releases/release_0.15.rst
deleted file mode 100644
index 85af1693..00000000
--- a/doc/releases/release_0.15.rst
+++ /dev/null
@@ -1,66 +0,0 @@
-Version 0.15 (31st of July 2020)
---------------------------------
-
-.. py:currentmodule:: pastas
-
-.. note::
-    This release will introduce backward incompatible changes to Pastas, most
-    notably due to the weighting of the first value of the noise. This will
-    cause the calibrated values to be slightly different but better for most
-    models. It is highly recommended to upgrade to this new version of Pastas.
-
-New Features / Enhancements
-***************************
-
-- :meth:`Model.noise()` now returns the noise and not the weighted noise.
-  Weights may now be obtained through :meth:`Model.noise_weights()`.
-- Private methods are now identified by a leading underscore issue 74.
-- :meth:`Model.set_parameter` method on the Model class is introduced to set
-  the initial, minimum, maximum and vary settings for a parameters in one line.
-- the ps.stats subpackage has been completely restructured. All methods may now
-  also be used as separate methods.
-
-    - :func:`ps.stats.diagnostics`: perform multiple diagnostic tests at once.
-    - :func:`ps.stats.stoffer_toloi`: Ljung-box test adapted for missing data.
-    - :func:`ps.stats.plot_diagnostics`: stand-alone version of the plot for
-      diagnostic checking
-    - :func:`ps.stats.plot_acf`: convenience method to plot the
-      autocorrelation function.
-    - all goodness-of-fit metrics are now available as separate functions e.g.,
-      :func:`ps.stats.nse()`. See the API docs for all available methods.
-
-- A new experimental noise model is added: :class:`ArmaModel`. This model
-  computes the noise from the residuals according to a
-  autoregressive-moving-average model (ARMA(1,1)). Currently this method is
-  experimental and only applicable to time series with equidistant time steps.
-- The response functions have been standardized to all fit the same formula
-  for the impulse response function, when some parameters are fixed to certain
-  values.
-- new function :func:`ps.show_versions()` is introduced. This function may
-  be used to show the version of package dependencies that are installed.
-- New method :meth:`ml.get_response_tmax` is introduced. This method may be
-  used to obtain the tmax of the response function.
-
-Deprecations
-************
-
-- :meth:`ml.set_vary`, :meth:`ml.set_initial`, :meth:`ml.set_pmin`, and
-  :meth:`ml.set_pmax` are deprecated and will be removed in a future release
-  . The use of :meth:`ml.set_parameter` method is now recommended.
-
-Backwards incompatible API changes
-**********************************
-
-- The parameters of the Hantush response function have new names. This will
-  cause problems when loading models using this function to be loaded from
-  .pas-file. No fix is available for this.
-- The first value of the noise series has changes (see issue 152 for
-  details), causing changes in the optimal parameter values.
-
-New Example (Notebooks)
-***********************
-
-- Notebook on diagnostic checking of Pastas models.
-- Notebook on the new ArmaModel noise model.
-- Notebook on reading Dutch datasets.
-- Notebook on the autocorrelation function with irregular time steps.
diff --git a/doc/releases/release_0.16.rst b/doc/releases/release_0.16.rst
deleted file mode 100644
index 2a4b3c2a..00000000
--- a/doc/releases/release_0.16.rst
+++ /dev/null
@@ -1,45 +0,0 @@
-Version 0.16 (16th of November 2020)
-------------------------------------
-
-.. note::
-    This release will introduce backward incompatible changes to Pastas, most
-    notably due to the renaming of the parameter input argument. This change
-    is mostly internally and will only affect users that explicitly pass
-    parameters into a method.
-
-New Features / Enhancements
-***************************
-
-- A new stress model (:class:`ps.LinearTrend`) to simulate linear trends is
-  added to the list of stable stress models.
-- New method to compute the Standardized Groundwater Index. See
-  (:meth:`ps.stats.sgi`) for more details.
-- Most of the goodness-of-fit metrics now allow providing a "weighted"
-  argument. This may result in more realistic values for time series with
-  irregular time steps.
-- The documentation website is further improved. We now separate 'Examples'
-  and 'Concepts'. The First are worked-out example notebooks using Pastas to
-  analyse a problem, the second are notebooks showing a underlying methods.
-
-Deprecations
-************
-
-- The following methods to set parameter properties are now deprecated
-  and replaced by the single method `ml.set_parameter`: `ml.set_vary`,
-  `ml.set_pmin`, `ml.set_pmax`, `ml.set_initial`.
-- The name of the input argument for the parameters was made consistent
-  throughout Pastas. If the input argument is named `p` an array-like object
-  is expected, whereas if the input is `parameters` a Pandas DataFrame object
-  is expected.
-- :class:`ps.FactorModel` is deprecated and will be removed in a future
-  version. Use :class:`ps.StressModel` with `rfunc=ps.One` instead.
-
-Backwards incompatible API changes
-**********************************
-
-
-New Example (Notebooks)
-***********************
-
-- New notebook on computing Standardized Groundwater Index using Pastas.
-- New Notebook on simulated step and linear trend.
\ No newline at end of file
diff --git a/doc/releases/release_0.17.rst b/doc/releases/release_0.17.rst
deleted file mode 100644
index 2b355bfc..00000000
--- a/doc/releases/release_0.17.rst
+++ /dev/null
@@ -1,59 +0,0 @@
-
-Version 0.17.1 (12th of May 2021)
----------------------------------
-
-.. note::
-    Intermediate release to solve an Issue with the LmFitSolver that is
-    experienced by many users. See
-    `Issue 295 <https://github.com/pastas/pastas/issues/295>`_.
-
-
-Version 0.17 (2nd of April 2021)
---------------------------------
-
-This version primarily fixes a number of Bugs and introduced some
-performance improvements. This version should be backward-compatible with
-the previous release.
-
-.. note::
-    The :class:`Pastas.Project` class has been removed from Pastas in this
-    release. `Pastastore <https://github.com/pastas/pastastore>`_ is now
-    recommended to deal with multiple time series models.
-
-New Features / Enhancements
-***************************
-
-- :meth:`ml.set_parameter` now also takes an `optimal` argument.
-- :meth:`ml.get_stressmodel_settings` has been added. This method can be
-  used to obtains the TimeSeries settings in a stressmodel.
-- Predefined TimeSeries settings can now be accessed through `ps.rcParams`
-- The Parameters of the response function :class:`ps.Hantush` have been
-  rescaled such that parameter 'A' is the gain.
-- The metrics :meth:`AIC` and :meth:`BIC` now use the noise when available,
-  and residuals otherwise.
-- The use of f-strings is now the preferred method for strings. For the
-  logger messages s-strings are now consistently applied to further speed-up
-  the code.
-- The method :meth:`ml.plots.cum_frequency` is added to the plotting library.
-
-
-Deprecations
-************
-
-No methods were deprecated in this version.
-
-Backwards incompatible API changes
-**********************************
-
-- :class:`pastas.Project` class is removed from pastas. Use `Pastastore
-  <https://github.com/pastas/pastastore>`_ instead.
-- :class:`ps.FactorModel` stressmodel is removed. Use :class:`ps.StressModel`
-  with :class:`ps.One` response function instead.
-
-
-New Example Notebooks
-*********************
-
-- A new example notebook on model calibration has been added.
-- A new example notebook on recharge estimation has been added.
-- A new example notebook on changing the time step has been added.
diff --git a/doc/releases/release_0.18.rst b/doc/releases/release_0.18.rst
deleted file mode 100644
index 1170af3c..00000000
--- a/doc/releases/release_0.18.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-Version 0.18 (2nd of September 2021)
-------------------------------------
-Minor update introducing a few new features and fixes some smaller bugs.
-
-New Features / Enhancements
-***************************
-
-- Improved documentation of the uncertainty quantification methods
-- Add section to fit report if parameters hit or are close to parameter bounds after optimization.
-- Improve method :meth:`pastas.Model._check_parameters_bounds()` (used by
-  above).
-- Improve parameter bounds for HantushWellModel, no longer limited by maximum distance.
-- `pastas.Model.__init__()` now accepts the `freq` argument, which can be
-  used to set the simulation frequency of the Model at creating.
-- :meth:`ps.plots.series` is added to quickly visualise the input time series.
-
-Deprecations
-************
-- No methods have been deprecated in this version.
-
-Backwards incompatible API changes
-**********************************
-- Official support for Python 3.6 is dropped in this version. Python 3.9
-  support is added.
-
-New Example Notebooks
-*********************
-
-- An example notebook on uncertainty quantification has been added.
diff --git a/doc/releases/release_0.19.rst b/doc/releases/release_0.19.rst
deleted file mode 100644
index eef9c56b..00000000
--- a/doc/releases/release_0.19.rst
+++ /dev/null
@@ -1,32 +0,0 @@
-Version 0.19 (Expected 1st of December 2021)
---------------------------------------------
-Minor update introducing a few new features and fixes some smaller bugs.
-
-New Features / Enhancements
-***************************
-
-- :class:`ps.ChangeModel` is added, simulating a response that changes over
-  time (PR #332).
-- :meth:`ps.utils.get_equidistant_series` is added, a method to get
-  equidistant timeseries using nearest reindexing.
-- :class:`ps.rfunc.Kraijenhoff` is added, simulating the response in a 
-  domain between two drainage channels.
-- :class:`ps.rch.FlexModel` now has an optional snow and interception
-  bucket (PR #343).
-
-Deprecations
-************
-- No methods have been deprecated in this version.
-
-Backwards incompatible API changes
-**********************************
-
-
-New Example Notebooks
-*********************
-
-- An example notebook for the :class:`ps.ChangeModel` is added.
-- An example notebook showcasing methods for creating equidistant timeseries is
-  added.
-- An example notebook for the new snow model option in the
-  :class:`ps.rch.FlexModel` is added.
diff --git a/doc/releases/release_0.20.rst b/doc/releases/release_0.20.rst
deleted file mode 100644
index 9075d01f..00000000
--- a/doc/releases/release_0.20.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-Version 0.20 (Expected 28th of February 2022)
----------------------------------------------
-
-New Features / Enhancements
-***************************
-
-- The model fit report now includes a warnings section. This includes warnings 
-  when the optimization is not successful, when parameters hit bounds or when the 
-  length of the response function exceeds the length of the calibration period.
-- The release of Scipy 1.8 has solved a potential issue with small parameter 
-  values during optimization. This means `ps.Leastsquares` is preferred solver in 
-  most situations. The WellModel will no longer warn the user to use
-  `ps.LmFitSolve` if scipy>=1.8.
-
-
-Deprecations
-************
-
-- The `warnbounds` argument in `ml.fit_report()` is deprecated. Replaced with 
-  argument `warnings`.
-
-Backwards incompatible API changes
-**********************************
-
diff --git a/doc/userguide/faqs.rst b/doc/userguide/faqs.rst
index bd7f8f0e..a7698069 100644
--- a/doc/userguide/faqs.rst
+++ b/doc/userguide/faqs.rst
@@ -1,4 +1,4 @@
-FAQS (Frequently Asked Questions)
+FAQs (Frequently Asked Questions)
 =================================
 
 This section contains a list of frequently asked questions.
@@ -22,6 +22,6 @@ DOI proivded through Zenodo for every major release of the software.
 
 .. rubric:: What does "Pastas" stand for?
 
-Pastas is not an acronym for something. However we like a good Pasta every
-once in a while, and as that name was already taken in the Python Packaging
-Index, we chose for Pastas as the name of the package.
+Pastas is not an acronym for something. That is why it is not written as PASTAS.
+However, we like a good pasta every once in a while, and as that name was already
+taken in the Python Packaging Index, we chose for Pastas as the name of the package.
diff --git a/doc/userguide/getting_started.rst b/doc/userguide/getting_started.rst
index 010b7772..cca2f153 100644
--- a/doc/userguide/getting_started.rst
+++ b/doc/userguide/getting_started.rst
@@ -1,4 +1,4 @@
-Getting Started with Pastas
+Getting started with Pastas
 ===========================
 On this page you will find all the information to get started with Pastas.
 A basic knowledge of programming in Python is assumed, but nothing more than
@@ -6,40 +6,42 @@ that. See the Examples section for some example-code.
 
 Installing Python
 -----------------
-To install Pastas, a working version of Python 3.7 or higher has to be
+To install Pastas, a working version of Python 3.8 or higher has to be
 installed on your computer. We recommend using the `Anaconda Distribution
-<https://www.continuum.io/downloads>`_ of Python. This Python distribution
-includes most of the python package dependencies and the Jupyter Notebook
-software to run the notebooks. Moreover, it includes the Graphical User
-Interface (GUI) Spyder to start scripting in Python. However, you are free
-to install any Python distribution you want.
+<https://www.anaconda.com/products/distribution>`_ of Python. This Python
+distribution includes most of the python package dependencies and the
+Jupyter Lab integrated development environment (IDE) to run the notebooks.
+However, you are free to install any Python IDE distribution you want.
 
 Installing Pastas
 -----------------
-To get Pastas, there are several options available. The easiest is to use
-the Python Package Index (`PyPI <https://pypi.python.org/pypi>`_), where
-many official python packages are gathered. To get the latest version of
-Pastas, open the Anaconda Prompt, a Windows Command Prompt (also called
+To install Pastas, there are several options available. The easiest is to
+use the Python Package Index (`PyPI <https://pypi.python.org/pypi>`_),
+where many official python packages are gathered. To get the latest version
+of Pastas, open the Anaconda Prompt, a Windows Command Prompt (also called
 command window) or a Mac/Linux terminal and type::
 
->>> pip install pastas
+    pip install pastas
 
 Pastas will now be installed on your computer, including the packages
 necessary for Pastas to work properly (called dependencies in Python
-language).
+language). To install Pastas with the optional dependencies use::
 
-It sometimes occurs that the automatic installation of the
-dependencies does not work. A safe method to update another package if you are
+    pip install pastas[full]
+
+It sometimes occurs that the automatic installation of the dependencies
+does not work. A safe method to update another package if you are
 using Anaconda is to install a package with the follow command line::
 
->>> conda install package
+    conda install package
 
 Updating Pastas
 ---------------
 If you have already installed Pastas, it is possible to update Pastas
-easily. To update, open a Windows command screen or a Mac terminal and type::
+easily. To update, open a Anaconda/Windows command prompt or a Mac
+terminal and type::
 
->>> pip install pastas --upgrade
+    pip install pastas --upgrade
 
 Dependencies
 ------------
@@ -49,25 +51,13 @@ following packages are necessary for a minimal function installation of
 Pastas::
 
     numpy
-    scipy
     pandas
+    scipy
     matplotlib
-    numba  #(large speed-up, highly recommended)
+    numba  #(significant speed-up)
 
 Other optional, but recommended dependencies include::
 
     jupyter  #(for running notebooks)
     lmfit  #(alternative solver)
-
-.. tip::
-    Numba is highly recommended when using Pastas. Not only this
-    package, but also Pandas makes use of Numba. Since Pastas partly
-    depends on Pandas, installing Numba will also speed up Pastas.
-
-
-.. toctree::
-    :maxdepth: 4
-    :hidden:
-    :glob:
-
-    ./*
+    latexify #(visualising formula's of functions)
diff --git a/doc/userguide/index.rst b/doc/userguide/index.rst
new file mode 100644
index 00000000..c1537934
--- /dev/null
+++ b/doc/userguide/index.rst
@@ -0,0 +1,16 @@
+User Guide
+==========
+
+In this section the basic concepts of analyzing and modeling hydraulic head time series with Pastas is explained.
+
+
+.. toctree::
+    :maxdepth: 1
+
+    getting_started
+    time_series
+    pastas_modeling
+    response_functions
+    stressmodels
+    post_processing
+    faqs
\ No newline at end of file
diff --git a/doc/userguide/pastas_modeling.ipynb b/doc/userguide/pastas_modeling.ipynb
new file mode 100644
index 00000000..eea79493
--- /dev/null
+++ b/doc/userguide/pastas_modeling.ipynb
@@ -0,0 +1,296 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "46806847",
+   "metadata": {},
+   "source": [
+    "# Modeling workflow\n",
+    "\n",
+    "In this section the general workflow for modeling and analyzing hydraulic head time series with Pastas is explained. \n",
+    "\n",
+    "- Modeling workflow\n",
+    "- Loading the data\n",
+    "- Time Series Model\n",
+    "    - Creating a Model\n",
+    "    - Adding StressModels\n",
+    "    - Solving the model\n",
+    "- Analyzing the results\n",
+    "    - Visualisation"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "1ae047e0",
+   "metadata": {},
+   "source": [
+    "## Pastas\n",
+    "Pastas is a computer program for hydrological time series analysis and is available from the [Pastas Github](https://github.com/pastas/pastas) . Pastas makes heavy use of `pandas` `timeseries`. An introduction to `pandas` `timeseries` can be found, for example, [here](http://nbviewer.jupyter.org/github/mbakker7/exploratory_computing_with_python/blob/master/notebook8_pandas/py_exploratory_comp_8_sol.ipynb). The Pastas documentation is available [here](http://pastas.readthedocs.io)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "042754d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import pastas as ps\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "ps.set_log_level(\"ERROR\")\n",
+    "ps.show_versions()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "09065e71",
+   "metadata": {},
+   "source": [
+    "## Loading the data\n",
+    "### Head observations\n",
+    "The first step in time series analysis is to load a time series of head observations. The time series needs to be stored as a `pandas.Series` object where the index is the date (and time, if desired). `pandas` provides many options to load time series data, depending on the format of the file that contains the time series. In this example, measured heads are stored in the csv file `head_nb1.csv`. \n",
+    "The heads are read from a csv file with the `read_csv` function of `pandas` and are then squeezed to create a `pandas Series` object.  To check if you have the correct data type, use the `type` command as shown below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52fdb043",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ho = pd.read_csv(\n",
+    "    \"../examples/data/head_nb1.csv\", parse_dates=[\"date\"], index_col=\"date\"\n",
+    ").squeeze()\n",
+    "print(\"The data type of the oseries is:\", type(ho))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b2cebe2",
+   "metadata": {},
+   "source": [
+    "The variable `ho` is now a `pandas Series` object. To see the first five lines, type `ho.head()`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32674605",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ho.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7920bd69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ho.plot(style=\".\", figsize=(12, 4))\n",
+    "plt.ylabel(\"Head [m]\")\n",
+    "plt.xlabel(\"Time [years]\");"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "acbe8ac0",
+   "metadata": {},
+   "source": [
+    "### Stress observations\n",
+    "The head variation shown above is believed to be caused by two stresses: rainfall and evaporation. Measured rainfall is stored in the file `rain_nb1.csv` and measured potential evaporation is stored in the file `evap_nb1.csv`. \n",
+    "The rainfall and potential evaporation are loaded and plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c87a2046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rain = pd.read_csv(\n",
+    "    \"../examples/data/rain_nb1.csv\", parse_dates=[\"date\"], index_col=\"date\"\n",
+    ").squeeze()\n",
+    "print(\"The data type of the rain series is:\", type(rain))\n",
+    "\n",
+    "evap = pd.read_csv(\n",
+    "    \"../examples/data/evap_nb1.csv\", parse_dates=[\"date\"], index_col=\"date\"\n",
+    ").squeeze()\n",
+    "print(\"The data type of the evap series is\", type(evap))\n",
+    "\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "rain.plot(label=\"rain\")\n",
+    "evap.plot(label=\"evap\")\n",
+    "plt.xlabel(\"Time [years]\")\n",
+    "plt.ylabel(\"Rainfall/Evaporation (m/d)\")\n",
+    "plt.legend(loc=\"best\");"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "da904ecd",
+   "metadata": {},
+   "source": [
+    "### Computing Recharge\n",
+    "As a first simple model, the recharge is approximated as the measured rainfall minus the measured potential evaporation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d382ed8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recharge = rain - evap\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "recharge.plot()\n",
+    "plt.xlabel(\"Time [years]\")\n",
+    "plt.ylabel(\"Recharge (m/d)\");"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "9ccb8c6c",
+   "metadata": {},
+   "source": [
+    "## Time Series Model\n",
+    "Once the time series are read from the data files, a time series model can be constructed by going through the following three steps:\n",
+    "\n",
+    "Create a `Model` object by passing it the observed head series. Store your model in a variable so that you can use it later on. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04ca9b7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml = ps.Model(ho, name=\"first_model\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "1ae95667",
+   "metadata": {},
+   "source": [
+    "### Adding a StressModel\n",
+    "\n",
+    "Add the stresses that are expected to cause the observed head variation to the model. In this example, this is only the recharge series. For each stess, a `StressModel` object needs to be created. Each `StressModel` object needs three input arguments: the time series of the stress, the response function that is used to simulate the effect of the stress, and a name. In addition, it is recommended to specified the `kind` of series, which is used to perform a number of checks on the series and fix problems when needed. This checking and fixing of problems (for example, what to substitute for a missing value) depends on the kind of series. In this case, the time series of the stress is stored in the variable `recharge`, the Gamma function is used to simulate the response, the series will be called `'recharge'`, and the kind is `prec` which stands for precipitation. One of the other keyword arguments of the `StressModel` class is `up`, which means that a positive stress results in an increase (up) of the head. The default value is `True`, which we use in this case as a positive recharge will result in the heads going up. Each `StressModel` object needs to be stored in a variable, after which it can be added to the model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d58b437",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sm1 = ps.StressModel(recharge, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
+    "ml.add_stressmodel(sm1)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "045671f3",
+   "metadata": {},
+   "source": [
+    "### Solving the Model\n",
+    "\n",
+    "When everything is added, the model can be solved. The default option is to minimize the sum of the squares of the errors between the observed and modeled heads `ps.LeastSquares()`\n",
+    "\n",
+    "The `solve` function has a number of default options that can be specified with keyword arguments. One of these options is that by default a fit report is printed to the screen. The fit report includes a summary of the fitting procedure, the optimal values obtained by the fitting routine, and some basic statistics. The model contains five parameters: the parameters $A$, $n$, and $a$ of the Gamma function used as the response function for the recharge, the parameter $d$, which is a constant base level, and the parameter $\\alpha$ of the noise model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "536ad84b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml.solve(tmin=\"1985\", tmax=\"2010\", solver=ps.LeastSquares())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "d6ff6dea",
+   "metadata": {},
+   "source": [
+    "### Visualisation\n",
+    "The results of the model are plotted below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a04ff88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml.plot(figsize=(12, 4));"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "6a7a22c5",
+   "metadata": {},
+   "source": [
+    "Pastas also has a way to plot the most important information in one plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca9d6c92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml.plots.results();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pastas_dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/userguide/post_processing.ipynb b/doc/userguide/post_processing.ipynb
new file mode 100644
index 00000000..c5209535
--- /dev/null
+++ b/doc/userguide/post_processing.ipynb
@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ba6dd28a",
+   "metadata": {},
+   "source": [
+    "# Analyzing the results\n",
+    "\n",
+    "In this section we dive a bit deeper into the different post-processing options.\n",
+    "\n",
+    "- Visualisation\n",
+    "- Metrics\n",
+    "- Diagnostics"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "a3ed54a5",
+   "metadata": {},
+   "source": [
+    "This notebook is not finished yet. For now we refer to the [Example notebooks](https://pastas.readthedocs.io/en/latest/examples/index.html) under Model Evaluation:\n",
+    "\n",
+    "- Comparing models visually\n",
+    "- Diagnostics checking\n",
+    "- Reducing autocorrelation\n",
+    "- Uncertainty quantification\n",
+    "- MCMC Uncertainty"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pastas_dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/userguide/response_functions.ipynb b/doc/userguide/response_functions.ipynb
new file mode 100644
index 00000000..44e1ff50
--- /dev/null
+++ b/doc/userguide/response_functions.ipynb
@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c87cbaf6",
+   "metadata": {},
+   "source": [
+    "# Response functions\n",
+    "This notebook provides an overview of the response functions that are available in Pastas. Response functions describe the response of the dependent variable (an `oseries` like groundwater heads) to an independent variable (a `stress` like precipitation or groundwater pumping) and form a fundamental part in the approach implemented in Pastas. Depending on the problem under investigation, a response function may be required with a certain shape or with more degrees of freedom (parameters). Response functions are generally used in combination with a stress model, but in this notebook the response functions are discussed independently to provide an overview of the different response functions and what they represent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "292ce7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pastas as ps\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "ps.show_versions()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "819e135d",
+   "metadata": {},
+   "source": [
+    "## Response functions available in Pastas\n",
+    "The following response functions are available in Pastas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1cb1927",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps.rfunc.__all__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddf4372c",
+   "metadata": {},
+   "source": [
+    "Below we discuss the some of the most used respons functions and what they represent:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16490ab3",
+   "metadata": {},
+   "source": [
+    "### Exponential response function\n",
+    "The exponential response function is the simplest response function with only two parameters: a scaling parameter $A$ and a shape parameter $a$. The response function may be used for stresses that start to have an immediate effect on the head, for example precipitation in a shallow aquifer. The step response function plateaus at $A$ (the gain is $A$). \n",
+    "The equation for the impulse response is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "850d287f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps.Exponential.impulse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cee1e82",
+   "metadata": {},
+   "source": [
+    "### Gamma response function\n",
+    "The Gamma response function is slightly more complicated than the exponential response function and has one additional shape parameter $n$. When $n>1$, there is a delay in the response. When $n=1$, the Gamma response is the same as the Exponential response. When $n<1$ there is an immediate response. The response function is the most used response function in Pastas, as its shape is versatile and able to mimic many responses found in the field (it is also used in many other parts of hydrology to simulate a hydrological response).  The step response plateaus at $A$ (the gain is $A$). The equation for the impulse response is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "995242ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps.Gamma.impulse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "456085a7",
+   "metadata": {},
+   "source": [
+    "### Hantush response function\n",
+    "The Hantush response function is intended for the simulation of the response of pumping wells. It is based on the classic Hantush function for pumping in a semi-confined aquifer. The response function has one scale parameter $A$ and two shape parameters $a$ and $b$.  \n",
+    "The step response plateaus at $A$ (the gain is $A$). The equation for the impulse response is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7a08f3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps.Hantush.impulse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4dd9926",
+   "metadata": {},
+   "source": [
+    "### Double exponential function\n",
+    "The Double exponential response function is intended for the simulation in a multi-aquifer system, with an initially a quick response and after that a slower response. It consists of two exponential functions and contains four parameters: the scale parameter $A$, two shape parameters $a_1$ and $a_2$, and a parameter $\\alpha$ that distributes the response between the two exponential functions. The step response plateaus at $A$ (the gain is $A$). The Double exponential function is not applied very often in practice. The equation for the impulse response is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "926b3266",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps.DoubleExponential.impulse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "429e5bc2",
+   "metadata": {},
+   "source": [
+    "### Polder function\n",
+    "The Polder response function is intended for the simulation of the response to variations of surface water levels. It is based on the response of a semi-confined aquifer to a variation in the surface water boundary. The response function has one scale parameter $A$ and two shape parameters $a$ and $b$. The step response plateaus at $A\\exp(-2\\sqrt{b})$ (the gain is $A\\exp(-2\\sqrt{b})$. The equation for the impulse response is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dae6fed9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps.Polder.impulse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce6ff544",
+   "metadata": {},
+   "source": [
+    "### Four parameter function\n",
+    "The four parameter function is a function with four parameters that includes the Exponential, Gamma, Hantush and Polder functions as a special case. It is the most general function, but it is relatively slow to compute. This function needs further documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35fd788a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps.FourParam.impulse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "353490d4",
+   "metadata": {},
+   "source": [
+    "### Examples of response functions\n",
+    "A few example response functions are plotted below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb87fab9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Default Settings\n",
+    "cutoff = 0.999\n",
+    "gain_scale_factor = 1\n",
+    "up = True\n",
+    "\n",
+    "responses = {}\n",
+    "exp = ps.Exponential()\n",
+    "responses[\"Exponential\"] = exp\n",
+    "\n",
+    "gamma = ps.Gamma()\n",
+    "responses[\"Gamma\"] = gamma\n",
+    "\n",
+    "hantush = ps.Hantush()\n",
+    "responses[\"Hantush\"] = hantush\n",
+    "\n",
+    "polder = ps.Polder()\n",
+    "responses[\"Polder\"] = polder\n",
+    "\n",
+    "fourp = ps.FourParam()\n",
+    "responses[\"FourParam\"] = fourp\n",
+    "\n",
+    "DoubleExp = ps.DoubleExponential()\n",
+    "responses[\"DoubleExponential\"] = DoubleExp\n",
+    "\n",
+    "parameters = pd.DataFrame()\n",
+    "\n",
+    "fig, [ax1, ax2] = plt.subplots(1, 2, sharex=True, figsize=(10, 3))\n",
+    "\n",
+    "for name, response in responses.items():\n",
+    "    p = response.get_init_parameters(name)\n",
+    "    parameters = pd.concat([parameters, p], axis=0)\n",
+    "    ax1.plot(response.block(p.initial), label=name)\n",
+    "    ax2.plot(response.step(p.initial), label=name)\n",
+    "\n",
+    "ax1.set_title(\"Block response\")\n",
+    "ax1.grid()\n",
+    "ax2.set_title(\"Step responses\")\n",
+    "ax2.grid()\n",
+    "ax1.set_xlabel(\"Time [days]\")\n",
+    "ax2.set_xlabel(\"Time [days]\")\n",
+    "ax1.legend()\n",
+    "plt.xlim(1e-1, 500)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.10.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/userguide/stressmodels.ipynb b/doc/userguide/stressmodels.ipynb
new file mode 100644
index 00000000..4507a0d8
--- /dev/null
+++ b/doc/userguide/stressmodels.ipynb
@@ -0,0 +1,331 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1c4c287b",
+   "metadata": {},
+   "source": [
+    "# Stress models\n",
+    "\n",
+    "In this notebook we give a quick start guide to StressModels in Pastas. Pastas uses smaller submodels to translate one or multiple hydrological stress(es) (e.g., precipitation, river stages) to a contribution to the head fluctuations. The submodels are named \"stress models\" in Pastas. All stress models are contained in the `stressmodels` module. There are different stress models for a variety of use-cases. It is the task of the hydrologist to select a specific stress model to simulate the head contribution from a particular stress. \n",
+    "\n",
+    "The following stress models are available:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0de9140",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pastas as ps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eecadfb5",
+   "metadata": {},
+   "source": [
+    "## Stresses\n",
+    "\n",
+    "To start, let's read some common stresses and visualize them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "578ef880",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "head = pd.read_csv(\n",
+    "    \"../examples/data/head_nb1.csv\", index_col=0, parse_dates=True\n",
+    ").squeeze()\n",
+    "prec = pd.read_csv(\n",
+    "    \"../examples/data/rain_nb1.csv\", index_col=0, parse_dates=True\n",
+    ").squeeze()\n",
+    "evap = pd.read_csv(\n",
+    "    \"../examples/data/evap_nb1.csv\", index_col=0, parse_dates=True\n",
+    ").squeeze()\n",
+    "\n",
+    "# quick visualization\n",
+    "ps.plots.series(head=head, stresses=[prec, evap], hist=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21178f65",
+   "metadata": {},
+   "source": [
+    "__Figure 1: Observed head and stresses, in this case precipitation and evaporation (both positive)__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf0697c5",
+   "metadata": {},
+   "source": [
+    "## Creating a StressModel\n",
+    "\n",
+    "One of the most commonly used stress models is the `ps.StressModel`. This model takes a single stress (e.g., precipitation), and convolutes that stress with an impulse response function to compute the contribution of the precipitation to the head fluctuations. Here is how to create a stress model instance. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c59d2ff9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sm = ps.StressModel(\n",
+    "    stress=prec,\n",
+    "    rfunc=ps.Gamma(),\n",
+    "    name=\"precipitation\",\n",
+    "    up=True,  # default (head goes up if it rains)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c83b497",
+   "metadata": {},
+   "source": [
+    "This stress model takes in a few required arguments, the `stress` time series, the response function `rfunc`, and the `name` of the stress model. This name has to be unique among the stress models added to the model. In the example above, the argument `up=True` is provided as well, which means that the head response to precipitation is positive (it rains, heads increase). Depending on the stress model, more or less input arguments are required. For each stress model, these arguments are described in the docstring.  \n",
+    "\n",
+    "As the stress models are actually mini models, each stress model has a `simulate` method that simulates the head contributions. One does typically not use these methods, as these are called internally by the `Model`. However, for illustrative purposes we simulate the contribution here, using the initial parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e034c4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p = sm.parameters.initial.values\n",
+    "\n",
+    "sm.simulate(p=p, tmin=\"2005\", tmax=\"2010\").plot(figsize=(6, 2));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caba0529",
+   "metadata": {},
+   "source": [
+    "### Add stress models to the main Model\n",
+    "\n",
+    "As stated before, stress models are typically not used on their own, but as part of a larger model, the pastas Model. Stress models are added to a Model using the `.add_stressmodel()` method and can be parsed seperately or as a list of stress models. This means that you can add multiple stress models if you want. Below we add a single stress model to the main model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d95ce18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml = ps.Model(head)\n",
+    "ml.add_stressmodel(sm)  # Adding the stress model\n",
+    "\n",
+    "ml.simulate(tmin=\"2005\", tmax=\"2010\").plot(figsize=(6, 2));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f91a4d1",
+   "metadata": {},
+   "source": [
+    "Note how the simulated head is similar to that simulated by the stress model created above, plus the constant. \n",
+    "\n",
+    "### Creating and adding more stress models\n",
+    "\n",
+    "Often multiple stresses are causing the head fluctuations. For example, if evaporation is affecting the head fluctuations, we can create a second stress model that simulate that contribution. As evaporation has a lowering effect on the heads, we set `up=False`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49e492a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sm2 = ps.StressModel(\n",
+    "    stress=evap,\n",
+    "    rfunc=ps.Gamma(),\n",
+    "    name=\"evaporation\",\n",
+    "    up=False,\n",
+    ")\n",
+    "\n",
+    "p = sm2.parameters.initial.values\n",
+    "\n",
+    "sm2.simulate(p=p, tmin=\"2005\", tmax=\"2010\").plot(figsize=(6, 2));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "542c44ef",
+   "metadata": {},
+   "source": [
+    "Now we add the second stress model to the main model. We do not have to create a new Model instance again, but can just add it to the one we created earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4dfa5bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml.add_stressmodel(sm2)  # Adding the second stress model\n",
+    "ml.simulate(tmin=\"2005\", tmax=\"2010\").plot(figsize=(6, 2));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "356a4b21",
+   "metadata": {},
+   "source": [
+    "This start to look like a real model, let's estimate the parameters by calling `ml.solve` and plot the estimated contributions of the two stresses to the head fluctuations using the `ml.plots.decomposition` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e08bcd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml.solve(tmin=\"2005\", tmax=\"2010\", report=False)\n",
+    "ml.plots.decomposition(tmin=\"2005\", tmax=\"2010\", figsize=(5, 4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c494faa",
+   "metadata": {},
+   "source": [
+    "### Stress model settings\n",
+    "\n",
+    "We now understand what stress models are, how they work, and how to add them to the model. Before we discuss other stress models, let's dive a bit deeper into one particular argument that each stress model has: the `settings` argument. \n",
+    "\n",
+    "The settings determine how the stress time series are internally updated if necessary for simulation, for example if a different simulation frequency is chosen or if the time series need to be extended in time (for example to create data for a warmup period). The creation of the stress model is where these settings are chosen. \n",
+    "\n",
+    "Some stress models have default settings for this, and use one of the predefined settings dictionaries (defined in `ps.rcParams[\"timeseries\"]`). Others have no default settings. In all cases, the user can always provide user-defined settings. Here's how to use and set the settings:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe6a4fe3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "settings = ps.rcParams[\"timeseries\"][\"prec\"]  # Select from predefined settings options\n",
+    "settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d32b301",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sm = ps.StressModel(\n",
+    "    stress=prec,\n",
+    "    rfunc=ps.Gamma(),\n",
+    "    name=\"prec\",\n",
+    "    settings=\"prec\",  # Is similar to settings=ps.rcParams[\"timeseries\"][\"prec\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dea3f8ba",
+   "metadata": {},
+   "source": [
+    "## Other stress models in Pastas\n",
+    "\n",
+    "Pastas contains a number of other stress models for different hydrological stresses. The following stress models are available:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c95e9955",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps.stressmodels.__all__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "576d0698",
+   "metadata": {},
+   "source": [
+    "From these, the `LinearTrend` and `StepModel` stress models add a specific kind of trend that do not require a stress. In these cases, the modeller should have a clear idea of what is causing the trend or step. The `ChangeModel` simulates a changing response function over time, to translate a single stress. The `WellModel` deals with one or multiple wells which are pumping groundwater and cause a lowering head. For all of these stress models examples can be found in the examples section on [readthedocs](https://pastas.readthedocs.io/en/latest/examples/index.html).\n",
+    "\n",
+    "Two other models deal with precipitation and evaporation in a single stress model: `RechargeModel` and `TarsoModel`. The `TarsoModel` simulates a threshold-nonlinearity where the head responds differrently to precipitation and evaporation depending on the head itself.\n",
+    "\n",
+    "Perhaps the most used model is the `RechargeModel`, which takes in precipitation and potential evaporation and simulates recharge from these two stresses and finally the head contribution. How the model simulates the recharge depends on the `recharge` model which is chosen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a917b73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sm3 = ps.RechargeModel(\n",
+    "    prec=prec,\n",
+    "    evap=evap,\n",
+    "    rfunc=ps.Exponential(),  # default\n",
+    "    name=\"recharge\",  # default\n",
+    "    recharge=ps.rch.FlexModel(),  # other options are ps.rch.Linear(), ps.rch.Peterson(), ps.rch.Berendrecht()\n",
+    "    settings=(\"prec\", \"evap\"),  # default\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "130a2fe1",
+   "metadata": {},
+   "source": [
+    "### Other use cases for stressmodels\n",
+    "\n",
+    "There are many other use cases for the different stress models in Pastas. For more example check out the examples section."
+   ]
+  }
+ ],
+ "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": "3.10.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/userguide/time_series.ipynb b/doc/userguide/time_series.ipynb
new file mode 100644
index 00000000..eedbf57c
--- /dev/null
+++ b/doc/userguide/time_series.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Time series in Pastas\n",
+    "\n",
+    "Time series are at the heart of Pastas and modeling hydraulic head fluctuations. In this section background information is provided on important characteristics of time series and how these may influence your modeling results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pastas as ps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Different types of time series\n",
+    "\n",
+    "### Regular and irregular time series\n",
+    "\n",
+    "Time series data are a set of data values measured at certain times, ordered in a way that the time indices are increasing. Many time series analysis and modeling methods require that the time step between the measurements is always the same or in other words, equidistant. Such regular time series may have missing data, but those may be filled so that computations can be done with a constant time step. Hydraulic heads are often measured at irregular time intervals; such time series are called irregular. This is especially true for historic time series that were measured by hand. The figure below graphically shows the difference between the three types of time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "regular = pd.Series(\n",
+    "    index=pd.date_range(\"2000-01-01\", \"2000-01-10\", freq=\"D\"), data=np.ones(10)\n",
+    ")\n",
+    "missing_data = regular.copy()\n",
+    "missing_data.loc[[\"2000-01-03\", \"2000-01-08\"]] = np.nan\n",
+    "\n",
+    "index = [t + pd.Timedelta(np.random.rand() * 24, unit=\"H\") for t in missing_data.index]\n",
+    "irregular = missing_data.copy()\n",
+    "irregular.index = index\n",
+    "\n",
+    "fig, axes = plt.subplots(3, 1, figsize=(6, 3), sharex=True, sharey=True)\n",
+    "\n",
+    "regular.plot(ax=axes[0], linestyle=\" \", marker=\"o\", x_compat=True)\n",
+    "missing_data.plot(ax=axes[1], linestyle=\" \", marker=\"o\", x_compat=True)\n",
+    "irregular.plot(ax=axes[2], linestyle=\" \", marker=\"o\", x_compat=True)\n",
+    "\n",
+    "for i, name in enumerate(\n",
+    "    [\"(a) Regular time steps\", \"(b) Missing Data\", \"(c) Irregular time steps\"]\n",
+    "):\n",
+    "    axes[i].grid()\n",
+    "    axes[i].set_title(name)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Independent and dependent time series \n",
+    "\n",
+    "We can differentiate between two types of input time series for Pastas models: the dependent and independent time series. The dependent time series are those that we want to explain/model (e.g., the groundwater levels), and are referred to as the `oseries` in pastas (short for observation series). The independent time series are those that we use to model the dependent time series (e.g., precipitation or evaporation), and are referred to as `stresses` in Pastas. The requirements for these time series are different:\n",
+    "\n",
+    "- The dependent time series may be of any kind: regular, missing data or irregular.\n",
+    "- The stress time series must have regular time steps.\n",
+    "\n",
+    "### A word on timestamps and measurements\n",
+    "\n",
+    "Stresses often represent a flux measured over a certain time period. For example, precipitation is often provided in mm/day, and the value represents the cumulative precipitation amount for a day. This is recorded at the end of the day, e.g., a measurement with a time stamp of 2000-01-01 represents the total precipitation that fell on the first of January in the year 2000.\n",
+    "\n",
+    "## The Python package for time series data: `pandas`\n",
+    "\n",
+    "The `pandas` package provides a lot of methods to deal with time series data, such as resampling, gap-filling, and computing descriptive statistics. Another important functionality of `pandas` are the `pandas.read_csv` and related methods, which facilitate the loading of data from csv-files and other popular data storage formats.  Pastas requires all time series to be provided as `pandas.Series` with a `pandas.DatetimeIndex`; examples are provided. For more information and user guidance on `pandas` please see their documentation website (https://pandas.pydata.org).\n",
+    "\n",
+    "## Validating user-provided time series\n",
+    "\n",
+    "As is clear from the descriptions above, the user is required to provide time series in a certain format and with certain characteristics, depending on the type of time series. To prevent issues later in the modeling chain, all user-provided time series are internally checked to make sure all requirements are met. This is done using the `pastas.validate_stress` and `pastas.validate_oseries` methods, which can also be called directly by the user. Let's look at the docstring of these methods to see what is checked:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "?ps.validate_stress"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The last check (equidistant time steps) is not required for `oseries`. If any of these checks fail, the `pastas.validate_stress` and `pastas.validate_oseries` methods will return an Error with pointers on how to solve the problem and fix the time series. We refer to the Examples-section for more examples on how to pre-process the user-provided time series."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Settings for user-provided time series\n",
+    "\n",
+    "User-provided time series must be long enough and of the right frequency. If this is not the case, Pastas will adjust the provided time series. Internally, time series are stored in a `pastas.TimeSeries` object, which has functions to extend the time series forward and backward in time (when needed) and/or resample the time series to a different frequency (when needed). How these two operations are performed depends on the `settings` that are provided. An appropriate setting must be specified when creating a `StressModel` object. \n",
+    "For example, specify the setting as `prec` for a `StressModel` that simulates the effect of precipitation. The predefined settings and their associated operations can be accessed through `ps.rcParams[\"timeseries\"]` (see code cell below). It can be seen, for example, that when the setting is `prec` (precipitation), a missing value is replaced by the value zero (`fill_nan=0.0` in the table) and when the series has to be extended in the past, it is filled with the mean value of the provided series (`fill_before=mean` in the table). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame.from_dict(ps.rcParams[\"timeseries\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each column name is a valid option for the `settings` argument. For example, the default setting for the precipitation stress provided to the `ps.RechargeModel` object is \"prec\" (see the docstring of `ps.RechargeModel`). This means that the following settings is used:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps.rcParams[\"timeseries\"][\"prec\"]\n",
+    "\n",
+    "# sm = ps.Stressmodel(stress, settings=\"prec\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, one may provide a dictionary to a stress model object with the settings. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "settings = {\n",
+    "    \"fill_before\": 0.0,\n",
+    "    \"fill_after\": 0.0,\n",
+    "    # Etcetera\n",
+    "}\n",
+    "# sm = ps.Stressmodel(stress, settings=settings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If Pastas does an operation on the time series that is provided by the user, it will **always** output an INFO message describing what is done. To see these, make sure the log_level of Pastas is set to \"INFO\" by running `ps.set_log_level(\"INFO\")` before."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Formal requirements time series\n",
+    "\n",
+    "The formal requirements for a time series are listed below. When using, for example, `pandas.read_csv` correctly, these requirements are automatically met.\n",
+    "\n",
+    "* The dtype for a date must be `pandas.Timestamp`.\n",
+    "* The dtype for a sequence of dates must be `pandas.DatetimeIndex` with `pandas.Timestamp`s.\n",
+    "* The dtype for a time series must be a `pandas.Series` with a `pandas.DatetimeIndex`.\n",
+    "* The dtype for the values of a `pandas.Series` must be `float`.    "
+   ]
+  }
+ ],
+ "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": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pastas/__init__.py b/pastas/__init__.py
index 60026e66..42ca3cce 100644
--- a/pastas/__init__.py
+++ b/pastas/__init__.py
@@ -1,3 +1,4 @@
+# flake8: noqa
 import logging
 
 from pandas.plotting import register_matplotlib_converters
@@ -13,7 +14,6 @@
 from .noisemodels import ArmaModel, NoiseModel
 from .plots import TrackSolve
 from .rcparams import rcParams
-from .read import read_dino, read_dino_level_gauge, read_knmi, read_meny, read_waterbase
 from .rfunc import (
     DoubleExponential,
     Exponential,
@@ -34,11 +34,10 @@
     RechargeModel,
     StepModel,
     StressModel,
-    StressModel2,
     TarsoModel,
     WellModel,
 )
-from .timeseries import validate_stress, validate_oseries
+from .timeseries import validate_oseries, validate_stress
 from .transform import ThresholdTransform
 from .utils import initialize_logger, set_log_level
 from .version import __version__, show_versions
diff --git a/pastas/decorators.py b/pastas/decorators.py
index df190e89..1d3fca9b 100644
--- a/pastas/decorators.py
+++ b/pastas/decorators.py
@@ -1,7 +1,7 @@
 from functools import wraps
 from logging import getLogger
+from typing import Dict, Optional
 
-from typing import Optional, Dict
 from pastas.typing import Function, TimestampType
 
 logger = getLogger(__name__)
@@ -67,7 +67,7 @@ def PastasDeprecationWarning(function: Function) -> Function:
     @wraps(function)
     def _function(*args, **kwargs):
         logger.warning(
-            "Method is deprecated and will be removed in Pastas version 1.0."
+            "Method is deprecated and will be removed in Pastas version 1.2."
         )
         return function(*args, **kwargs)
 
diff --git a/pastas/io/__init__.py b/pastas/io/__init__.py
index d819eff1..a18367cd 100644
--- a/pastas/io/__init__.py
+++ b/pastas/io/__init__.py
@@ -1 +1,2 @@
+# flake8: noqa
 from .base import dump, load
diff --git a/pastas/io/base.py b/pastas/io/base.py
index eb8bffbc..eed7059e 100644
--- a/pastas/io/base.py
+++ b/pastas/io/base.py
@@ -4,7 +4,6 @@
 from logging import getLogger
 from os import path
 
-from numpy import log
 from packaging import version
 from pandas import to_numeric
 
@@ -12,7 +11,6 @@
 
 # Type Hinting
 from pastas.typing import Model
-from .timeseries_legacy import TimeSeriesOld
 
 logger = getLogger(__name__)
 
@@ -36,6 +34,7 @@ def load(fname: str, **kwargs) -> Model:
     --------
     >>> import pastas as ps
     >>> ml = ps.io.load("model.pas")
+
     """
     if not path.exists(fname):
         logger.error("File not found: %s", fname)
@@ -46,9 +45,19 @@ def load(fname: str, **kwargs) -> Model:
     # Get dicts for all data sources
     data = load_mod.load(fname, **kwargs)
 
+    file_version = data["file_info"]["pastas_version"]
+
+    # A single catch for old pas-files, no longer supported
+    if version.parse(file_version) < version.parse("0.23.0"):
+        raise UserWarning(
+            "This file was created with a Pastas version prior to 0.23 "
+            "and cannot be loaded with Pastas >= 1.0. Please load and "
+            "save the file with Pastas 0.23 first to update the file "
+            "format."
+        )
+
     ml = _load_model(data)
 
-    file_version = data["file_info"]["pastas_version"]
     logger.info(
         "Pastas Model from file %s successfully loaded. This file was created with "
         "Pastas %s. Your current version of Pastas is: %s",
@@ -100,29 +109,17 @@ def _load_model(data: dict) -> Model:
 
     # Add transform
     if "transform" in data.keys():
-        # Todo Deal with old files. Remove in pastas 1.0
-        if "transform" in data["transform"].keys():
-            data["transform"]["class"] = data["transform"].pop("transform")
-
         transform = getattr(ps.transform, data["transform"].pop("class"))
         transform = transform(**data["transform"])
         ml.add_transform(transform)
 
     # Add noisemodel if present
     if "noisemodel" in data.keys():
-        # Todo Deal with old files. Remove in pastas 1.0
-        if "type" in data["noisemodel"].keys():
-            data["noisemodel"]["class"] = data["noisemodel"].pop("type")
-
         n = getattr(ps.noisemodels, data["noisemodel"].pop("class"))()
         ml.add_noisemodel(n)
 
     # Add fit object to the model
     if "fit" in data.keys():
-        # Todo Deal with old files. Remove in pastas 1.0
-        if "name" in data["fit"].keys():
-            data["fit"]["class"] = data["fit"].pop("name")
-
         solver = getattr(ps.solver, data["fit"].pop("class"))
         ml.fit = solver(**data["fit"])
         ml.fit.set_model(ml)
@@ -140,51 +137,6 @@ def _load_model(data: dict) -> Model:
 
 
 def _load_stressmodel(ts, data):
-    # Todo Deal with old files. Remove in pastas 1.0
-    if "stressmodel" in ts.keys():
-        ts["class"] = ts.pop("stressmodel")
-
-    # TODO Deal with old StressModel2 files for version 0.22.0. Remove in 0.23.0.
-    if ts["class"] == "StressModel2":
-        msg = (
-            "StressModel2 is removed since Pastas 0.22.0 and is replaced by the "
-            "RechargeModel using a Linear recharge model. Make sure to save "
-            "this file first using Pastas version 0.22.0 as this file cannot be "
-            "loaded in newer Pastas versions. This will automatically update "
-            "your model to the newer RechargeModel stress model."
-        )
-        logger.error(msg=msg)
-        raise NotImplementedError(msg)
-
-    # TODO Deal with old parameter value b in HantushWellModel: b_new = np.log(b_old)
-    if (ts["class"] == "WellModel") and (
-        version.parse(data["file_info"]["pastas_version"]) < version.parse("0.22.0")
-    ):
-        logger.warning(
-            "The value of parameter 'b' in HantushWellModel was modified in 0.22.0: "
-            "b_new = log(b_old). The value of 'b' is automatically updated on load."
-        )
-        wnam = ts["name"]
-        for pcol in ["initial", "optimal", "pmin", "pmax"]:
-            if wnam + "_b" in data["parameters"].index:
-                if data["parameters"].loc[wnam + "_b", pcol] > 0:
-                    data["parameters"].loc[wnam + "_b", pcol] = log(
-                        data["parameters"].loc[wnam + "_b", pcol]
-                    )
-
-    # Deal with old-style response functions (TODO remove in 1.0)
-    if version.parse(data["file_info"]["pastas_version"]) < version.parse("0.23.0"):
-        if "rfunc" in ts.keys():
-            rfunc_kwargs = ts.pop("rfunc_kwargs", {})
-            rfunc_kwargs["class"] = ts["rfunc"]
-            if "cutoff" in ts.keys():
-                rfunc_kwargs["cutoff"] = ts.pop("cutoff")
-            ts["rfunc"] = rfunc_kwargs
-        if "recharge" in ts.keys():
-            recharge_kwargs = ts.pop("recharge_kwargs", {})
-            recharge_kwargs["class"] = ts["recharge"]
-            ts["recharge"] = recharge_kwargs
-
     # Create and add stress model
     stressmodel = getattr(ps.stressmodels, ts.pop("class"))
 
@@ -258,28 +210,6 @@ def _unpack_series(data: dict):
     metadata = data["metadata"]
     settings = data["settings"]
 
-    # Deal with pas-files from Pastas version 0.22. Pastas 0.22.0 was very loose on
-    # the input data and would internally fix a lot. Here we choose to recreate the
-    # old TimeSeries object, and use the TimeSeries.series.
-
-    if "freq_original" in data.keys():
-        msg = (
-            "Whoops, looks like an old pas-file using the old TimeSeries format. "
-            "Pastas will convert to the new TimeSeries format. However, it can not "
-            "be guaranteed that the conversion will result in the exact same results. "
-            "If you have the Python scripts used to generate this pas-file, it is "
-            "highly recommended to rerun the script using a newer Pastas version ("
-            "0.23 or higher). "
-        )
-        logger.warning(msg)
-
-        # Create an old TimeSeries object
-        series = TimeSeriesOld(**data).series
-
-        # Remove deprecated keywords
-        settings.pop("norm")
-        data.pop("freq_original")
-
     return series, metadata, settings
 
 
diff --git a/pastas/io/men.py b/pastas/io/men.py
deleted file mode 100644
index 64576c60..00000000
--- a/pastas/io/men.py
+++ /dev/null
@@ -1,15 +0,0 @@
-"""This module is deprecated.
-"""
-
-msg = (
-    "Pastas no longer supports export to Menyanthes files and this functionality "
-    "will be removed in Pastas 1.0."
-)
-
-
-def load(*args, **kwargs):
-    raise DeprecationWarning(msg)
-
-
-def dump(*args, **kwargs):
-    raise DeprecationWarning(msg)
diff --git a/pastas/io/men/2.x.g.t (beta).men b/pastas/io/men/2.x.g.t (beta).men
deleted file mode 100644
index 3cfba5c8..00000000
Binary files a/pastas/io/men/2.x.g.t (beta).men and /dev/null differ
diff --git a/pastas/io/men/3.x.b.c (gamma).men b/pastas/io/men/3.x.b.c (gamma).men
deleted file mode 100644
index ea038d85..00000000
Binary files a/pastas/io/men/3.x.b.c (gamma).men and /dev/null differ
diff --git a/pastas/model.py b/pastas/model.py
index e872edec..e5b89638 100644
--- a/pastas/model.py
+++ b/pastas/model.py
@@ -5,7 +5,6 @@
 from itertools import combinations
 from logging import getLogger
 from os import getlogin
-import inspect
 
 # Type Hinting
 from typing import List, Optional, Tuple, Union
@@ -32,20 +31,18 @@
 from pastas.solver import LeastSquares
 from pastas.stressmodels import Constant
 from pastas.timeseries import TimeSeries
-from pastas.transform import ThresholdTransform
-from pastas.typing import ArrayLike
-from pastas.typing import Model as ModelType
-from pastas.typing import NoiseModel as NoiseModelType
-from pastas.typing import Solver, StressModel
-from pastas.typing import TimestampType
 from pastas.timeseries_utils import (
+    _frequency_is_supported,
     _get_dt,
     _get_time_offset,
-    _frequency_is_supported,
     get_sample,
 )
+from pastas.transform import ThresholdTransform
+from pastas.typing import ArrayLike
+from pastas.typing import Model as ModelType
+from pastas.typing import NoiseModel as NoiseModelType
+from pastas.typing import Solver, StressModel, TimestampType
 from pastas.utils import validate_name
-
 from pastas.version import __version__
 
 
@@ -193,7 +190,7 @@ def add_stressmodel(self, stressmodel: StressModel, replace: bool = False) -> No
         To add multiple stress models at once you can do the following:
 
         >>> sm1 = ps.StressModel(stress, rfunc=ps.Gamma(), name="stress1")
-        >>> sm1 = ps.StressModel(stress, rfunc=ps.Gamma(), name="stress2")
+        >>> sm2 = ps.StressModel(stress, rfunc=ps.Gamma(), name="stress2")
         >>> ml.add_stressmodel([sm1, sm2])
 
         See Also
@@ -245,7 +242,7 @@ def add_transform(self, transform: ThresholdTransform):
 
         Parameters
         ----------
-        transform: pastas.transform instance
+        transform: ps.ThresholdTransform
             An instance of a pastas.transform class.
 
         Examples
@@ -420,7 +417,9 @@ def simulate(
 
         if sim.hasnans:
             sim = sim.dropna()
-            self.logger.warning("Nan-values were removed from the simulation.")
+            msg = "Simulation contains Nan-values. Check stresses time series settings!"
+            self.logger.error(msg)
+            raise ValueError(msg)
 
         sim.name = "Simulation"
         return sim
@@ -617,7 +616,7 @@ def observations(
         -----
         This method makes sure the simulation is compared to the nearest observation.
         It finds the index closest to sim_index, and then returns a selection of the
-        oseries. in the residuals method, the simulation is interpolated to the
+        oseries. In the `residuals` method, the simulation is interpolated to the
         observation-timestamps.
         """
         if tmin is None and self.settings["tmin"]:
@@ -798,13 +797,6 @@ def solve(
 
         # If a solver is provided, use that one
         if solver is not None:
-            # Check if a solver instance is provided, not a class
-            if inspect.isclass(solver):
-                raise DeprecationWarning(
-                    "As of Pastas 0.23, Solvers should be provided as an instance "
-                    "(e.g., ps.LeastSquares()), and not as a class (e.g., "
-                    "ps.LeastSquares). Please provide an instance of the solver."
-                )
             self.fit = solver
             self.fit.set_model(self)
         # Create the default solver is None is provided or already present
@@ -1699,7 +1691,6 @@ def fit_report(
         self,
         output: str = "basic",
         warnings: bool = True,
-        warnbounds: Optional[bool] = None,
     ) -> str:
         """Method that reports on the fit after a model is optimized.
 
@@ -1711,8 +1702,6 @@ def fit_report(
         warnings : bool, optional
             print warnings in case of optimization failure, parameters hitting
             bounds, or length of responses exceeding calibration period.
-        warnbounds: bool, optional
-            Deprecated.
 
         Returns
         -------
@@ -1754,11 +1743,6 @@ def fit_report(
             "Interp.": "Yes" if self.interpolate_simulation else "No",
         }
 
-        if warnbounds:
-            raise KeyError(
-                "The 'warnbounds' argument is deprecated. Use warnings=True instead."
-            )
-
         parameters = self.parameters.loc[:, ["optimal", "stderr", "initial", "vary"]]
         stderr = parameters.loc[:, "stderr"] / parameters.loc[:, "optimal"]
         parameters.loc[:, "stderr"] = stderr.abs().apply("±{:.2%}".format)
diff --git a/pastas/modelcompare.py b/pastas/modelcompare.py
index 14c4b873..7930710e 100644
--- a/pastas/modelcompare.py
+++ b/pastas/modelcompare.py
@@ -1,3 +1,5 @@
+"""This module contains tools for visually comparing multiple models.
+"""
 from itertools import combinations
 from typing import List, Optional, Tuple
 from warnings import warn
@@ -36,7 +38,7 @@ class CompareModels:
     Users can either use the default mosaic or provide their own.
 
     Additional logic is available to control plotting of multiple contributions of
-    stresses on the same set of axes. Additionally some helper methods are defined to
+    stresses on the same set of axes. Additionally, some helper methods are defined to
     obtain relevant information from the models passed to CompareModels.
 
     Example usage::
@@ -542,7 +544,7 @@ def plot_contribution(
             in the first contribution window and 'rech' in the second. By default,
             None, which creates a separate subplot for each stressmodel.
         axn : str, optional
-            name of labeled axes to plot the contributions on, by default "con{i}". If
+            name of labeled axes to plot the contributions to, by default "con{i}". If
             smdict is not None, keys of that dictionary are used to fill in axes
             label, e.g. key 0 indicates the contributions will be plotted on axes
             with label 'con0'. Otherwise, each contribution will be plotted on a
@@ -820,7 +822,7 @@ def plot(
         figsize : tuple, optional
             figure size, by default (10, 8).
         grid : bool, optional
-            grid in each subplots, by default True.
+            grid in each subplot, by default True.
         legend : bool, optional
             add legend in each subplot, by default True.
         adjust_height : bool, optional
diff --git a/pastas/modelplots.py b/pastas/modelplots.py
index d9d40b7e..bf013186 100644
--- a/pastas/modelplots.py
+++ b/pastas/modelplots.py
@@ -67,7 +67,7 @@ def plot(
             True to plot the observed time series.
         simulation: bool, optional
             True to plot the simulated time series.
-        ax: Matplotlib.axes instance, optional
+        ax: matplotlib.axes.Axes, optional
             Axes to add the plot to.
         figsize: tuple, optional
             Tuple with the height and width of the figure in inches.
@@ -141,10 +141,10 @@ def results(
             Show the warmup-period. Default is false.
         block_or_step: str, optional
             Plot the block- or step-response on the right. Default is 'step'.
-        fig: Matplotib.Figure instance, optional
-            Optionally provide a Matplotib.Figure instance to plot onto.
+        fig: matplotib.Figure instance, optional
+            Optionally provide a matplotib.Figure instance to plot onto.
         **kwargs: dict, optional
-            Optional arguments, passed on to the plt.figure method.
+            Optional arguments, passed on to the matplotlib.pyplot.figure method.
 
         Returns
         -------
@@ -356,7 +356,7 @@ def decomposition(
         min_ylim_diff: float, optional
             Float with the difference in the ylimits. Default is None
         **kwargs: dict, optional
-            Optional arguments, passed on to the plt.subplots method.
+            Optional arguments, passed on to the matplotlib.pyplot.subplots method.
 
         Returns
         -------
@@ -505,12 +505,12 @@ def diagnostics(
             number of bins used for the histogram. 50 is default.
         acf_options: dict, optional
             dictionary with keyword arguments that are passed on to pastas.stats.acf.
-        fig: Matplotib.Figure instance, optional
-            Optionally provide a Matplotib.Figure instance to plot onto.
+        fig: matplotlib.pyplot.Figure, optional
+            Optionally provide a matplotlib.pyplot.Figure instance to plot onto.
         alpha: float, optional
             Significance level to calculate the (1-alpha)-confidence intervals.
         **kwargs: dict, optional
-            Optional keyword arguments, passed on to plt.figure.
+            Optional keyword arguments, passed on to matplotlib.pyplot.figure method.
 
         Returns
         -------
@@ -570,7 +570,7 @@ def cum_frequency(
         ----------
         tmin: str or pandas.Timestamp, optional
         tmax: str or pandas.Timestamp, optional
-        ax: Matplotlib.axes instance, optional
+        ax: matplotlib.axes.Axes, optional
             Axes to add the plot to.
         figsize: tuple, optional
             Tuple with the height and width of the figure in inches.
@@ -602,7 +602,7 @@ def block_response(
         ----------
         stressmodels: list, optional
             List with the stressmodels to plot the block response for.
-        ax: Matplotlib.axes instance, optional
+        ax: matplotlib.axes.Axes, optional
             Axes to add the plot to.
         figsize: tuple, optional
             Tuple with the height and width of the figure in inches.
@@ -705,7 +705,7 @@ def stresses(
         stresses = _get_stress_series(self.ml, split=split)
 
         rows = len(stresses)
-        rows = -(-rows // cols)  # round up with out additional import
+        rows = -(-rows // cols)  # round up without additional import
 
         fig, axes = plt.subplots(rows, cols, sharex=sharex, figsize=figsize, **kwargs)
 
@@ -744,8 +744,8 @@ def contributions_pie(
         ----------
         tmin: str or pandas.Timestamp, optional.
         tmax: str or pandas.Timestamp, optional.
-        ax: matplotlib.axes, optional
-            Axes to plot the pie chart on. A new figure and axes will be created of
+        ax: matplotlib.axes.Axes, optional
+            The Axes to plot the pie chart on. A new figure and axes will be created of
             not provided.
         figsize: tuple, optional
             tuple of size 2 to determine the figure size in inches.
@@ -766,7 +766,7 @@ def contributions_pie(
 
         Returns
         -------
-        ax: matplotlib.axes
+        ax: matplotlib.axes.Axes
         """
         if ax is None:
             _, ax = plt.subplots(figsize=figsize)
@@ -921,7 +921,7 @@ def series(
 
         Returns
         -------
-        matplotlib.Axes
+        matplotlib.axes.Axes
         """
         obs = self.ml.observations(tmin=tmin, tmax=tmax)
         stresses = _get_stress_series(self.ml, split=split)
@@ -955,7 +955,7 @@ def summary_pdf(
 
         Returns
         -------
-        fig: matplotlib.Figure instance
+        fig: matplotlib.pyplot.Figure instance
         """
         if fname is None:
             fname = "{}.pdf".format(self.ml.name)
diff --git a/pastas/modelstats.py b/pastas/modelstats.py
index b627e7a1..0af90cb8 100644
--- a/pastas/modelstats.py
+++ b/pastas/modelstats.py
@@ -29,7 +29,6 @@
 from pandas import DataFrame
 
 from pastas.typing import Model, TimestampType
-
 from .decorators import model_tmin_tmax
 from .stats import diagnostics, metrics
 
@@ -49,7 +48,7 @@ class Statistics:
     ]
 
     def __init__(self, ml: Model):
-        """This class provides statistics to to pastas Model class.
+        """This class provides statistics to pastas Model class.
 
         Parameters
         ----------
diff --git a/pastas/plots.py b/pastas/plots.py
index d8a4ffdd..2f12a170 100644
--- a/pastas/plots.py
+++ b/pastas/plots.py
@@ -37,9 +37,9 @@ def compare(models: List[Model], adjust_height: bool = True, **kwargs) -> Axes:
         Adjust the height of the graphs, so that the vertical scale of all the
         subplots on the left is equal. Default is False, in which case the axes are
         not rescaled to include all data, so certain data might not be visible. Set
-        to False to ensure you can see all data.
+        False to ensure you can see all data.
     **kwargs
-        Kwargs are passed to the CompareModels.plot() function.
+        The kwargs are passed to the CompareModels.plot() function.
 
     Returns
     -------
diff --git a/pastas/read/__init__.py b/pastas/read/__init__.py
deleted file mode 100644
index 84df9731..00000000
--- a/pastas/read/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .dinoloket import read_dino, read_dino_level_gauge
-from .knmi import read_knmi
-from .menyanthes import read_meny
-from .waterbase import read_waterbase
diff --git a/pastas/read/dinoloket.py b/pastas/read/dinoloket.py
deleted file mode 100644
index 9182c012..00000000
--- a/pastas/read/dinoloket.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""The read module of pastas is deprecated please use hydropandas instead ->
-https://hydropandas.readthedocs.io
-"""
-
-msg = (
-    "The read module of pastas is deprecated please use hydropandas instead -> "
-    "https://hydropandas.readthedocs.io",
-)
-
-
-def read_dino(*args, **kwargs):
-    raise DeprecationWarning(msg)
-
-
-def read_dino_level_gauge(*args, **kwargs):
-    raise DeprecationWarning(msg)
diff --git a/pastas/read/knmi.py b/pastas/read/knmi.py
deleted file mode 100644
index b4d50bb4..00000000
--- a/pastas/read/knmi.py
+++ /dev/null
@@ -1,12 +0,0 @@
-"""The read module of pastas is deprecated please use hydropandas instead ->
-https://hydropandas.readthedocs.io
-"""
-
-msg = (
-    "The read module of pastas is deprecated please use hydropandas instead -> "
-    "https://hydropandas.readthedocs.io",
-)
-
-
-def read_knmi(*args, **kwargs):
-    raise DeprecationWarning(msg)
diff --git a/pastas/read/menyanthes.py b/pastas/read/menyanthes.py
deleted file mode 100644
index 67349e49..00000000
--- a/pastas/read/menyanthes.py
+++ /dev/null
@@ -1,12 +0,0 @@
-"""The read module of pastas is deprecated please use hydropandas instead ->
-https://hydropandas.readthedocs.io
-"""
-
-msg = (
-    "The read module of pastas is deprecated please use hydropandas instead -> "
-    "https://hydropandas.readthedocs.io",
-)
-
-
-def read_meny(*args, **kwargs):
-    raise DeprecationWarning(msg)
diff --git a/pastas/read/waterbase.py b/pastas/read/waterbase.py
deleted file mode 100644
index 71dc6bfb..00000000
--- a/pastas/read/waterbase.py
+++ /dev/null
@@ -1,12 +0,0 @@
-"""The read module of pastas is deprecated please use hydropandas instead ->
-https://hydropandas.readthedocs.io
-"""
-
-msg = (
-    "The read module of pastas is deprecated please use hydropandas instead -> "
-    "https://hydropandas.readthedocs.io",
-)
-
-
-def read_waterbase(*args, **kwargs):
-    raise DeprecationWarning(msg)
diff --git a/pastas/recharge.py b/pastas/recharge.py
index 26dce2c3..4ae8b0f4 100644
--- a/pastas/recharge.py
+++ b/pastas/recharge.py
@@ -43,7 +43,6 @@
 from pandas import DataFrame
 
 from pastas.typing import ArrayLike
-
 from .decorators import njit
 
 logger = getLogger(__name__)
@@ -666,12 +665,7 @@ def get_recharge(
         ks: float = 50.0,
         dt: float = 1.0,
     ) -> Tuple[ArrayLike, ArrayLike, ArrayLike, ArrayLike]:
-        """Internal method used for the recharge calculation.
-
-        Notes
-        -----
-        If Numba is available, this method is significantly faster.
-        """
+        """Internal method used for the recharge calculation."""
         n = prec.size
         # Create an empty arrays to store the fluxes and states
         pe = fi * prec  # Effective precipitation flux
@@ -805,12 +799,7 @@ def get_recharge(
         gamma: float = 1.0,
         dt: float = 1.0,
     ) -> Tuple[ArrayLike, ArrayLike, ArrayLike, ArrayLike]:
-        """Internal method used for the recharge calculation.
-
-        Notes
-        -----
-        If Numba is available, this method is significantly faster.
-        """
+        """Internal method used for the recharge calculation."""
         n = len(prec)
         # Create an empty arrays to store the fluxes and states
         pe = zeros(n, dtype=float64)  # Effective precipitation flux
diff --git a/pastas/rfunc.py b/pastas/rfunc.py
index f5d8db2c..dd21c45e 100644
--- a/pastas/rfunc.py
+++ b/pastas/rfunc.py
@@ -4,6 +4,7 @@
 from logging import getLogger
 
 import numpy as np
+from numpy import pi
 from pandas import DataFrame
 from scipy.integrate import quad
 from scipy.interpolate import interp1d
@@ -18,9 +19,8 @@
     k1,
     lambertw,
 )
-from numpy import pi
 
-from .decorators import njit, latexfun
+from .decorators import latexfun, njit
 from .version import check_numba_scipy
 
 try:
@@ -30,6 +30,7 @@
 
 # Type Hinting
 from typing import Optional, Union
+
 from pastas.typing import ArrayLike
 
 logger = getLogger(__name__)
@@ -60,13 +61,6 @@ def __init__(
         **kwargs,
     ) -> None:
         self.up = up
-        if "meanstress" in kwargs.keys():
-            logger.warning(
-                "The mean_stress argument is deprecated and will throw an error "
-                "in Pastas 1.0. Please use the `gain_scale_factor` argument instead."
-            )
-            gain_scale_factor = kwargs["meanstress"]
-
         self.gain_scale_factor = gain_scale_factor
         self.cutoff = cutoff
 
@@ -570,7 +564,7 @@ def get_init_parameters(self, name: str) -> DataFrame:
             )
         parameters.loc[name + "_a"] = (100, 1e-3, 1e4, True, name)
         # set initial and bounds for b taking into account distances
-        # note log transform to avoid extremely small values for b
+        # note log transform to avoid tiny values for b
         binit = np.log(1.0 / np.mean(self.distances) ** 2)
         bmin = np.log(1e-6 / np.max(self.distances) ** 2)
         bmax = np.log(25.0 / np.min(self.distances) ** 2)
@@ -1224,7 +1218,7 @@ def get_init_parameters(self, name: str) -> DataFrame:
     @staticmethod
     @latexfun(identifiers={"impulse": "theta"})
     def impulse(t: ArrayLike, p: ArrayLike) -> ArrayLike:
-        _A, n, a, b = p
+        _, n, a, b = p
         return (t ** (n - 1)) * np.exp(-t / a - a * b / t)
 
     def get_tmax(self, p: ArrayLike, cutoff: Optional[float] = None) -> float:
@@ -1447,8 +1441,10 @@ def gain(self, p: ArrayLike) -> float:
     @staticmethod
     @latexfun(identifiers={"impulse": "theta"})
     def impulse(t: ArrayLike, p: ArrayLike) -> ArrayLike:
-        A, f, a, b = p
-        return A * ((1 - f) / a * np.exp(-t / a) + f / b * np.exp(-t / b))
+        A, alpha, a_1, a_2 = p
+        return A * (
+            (1 - alpha) / a_1 * np.exp(-t / a_1) + alpha / a_2 * np.exp(-t / a_2)
+        )
 
     def step(
         self,
diff --git a/pastas/stats/__init__.py b/pastas/stats/__init__.py
index 40bdcf1e..7530f6ec 100644
--- a/pastas/stats/__init__.py
+++ b/pastas/stats/__init__.py
@@ -14,6 +14,7 @@
     tests
 
 """
+# flake8: noqa
 
 import pastas.stats.metrics as metrics
 import pastas.stats.signatures as signatures
@@ -22,13 +23,4 @@
 from .dutch import ghg, glg, gvg, q_ghg, q_glg, q_gvg
 from .metrics import aic, bic, evp, kge_2012, mae, nse, pearsonr, rmse, rsq, sse
 from .sgi import sgi
-from .tests import (
-    diagnostics,
-    durbin_watson,
-    ljung_box,
-    plot_acf,
-    plot_cum_frequency,
-    plot_diagnostics,
-    runs_test,
-    stoffer_toloi,
-)
+from .tests import diagnostics, durbin_watson, ljung_box, runs_test, stoffer_toloi
diff --git a/pastas/stats/core.py b/pastas/stats/core.py
index ef00f171..e6eae0a4 100644
--- a/pastas/stats/core.py
+++ b/pastas/stats/core.py
@@ -27,7 +27,6 @@
 from scipy.stats import norm
 
 from pastas.typing import ArrayLike
-
 from ..decorators import njit
 
 
@@ -50,16 +49,12 @@ def acf(
         The index has to be a Pandas.DatetimeIndex.
     lags: array_like, optional
         numpy array containing the lags in days for which the cross-correlation if
-        calculated. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 30, 61, 90, 120, 150,
-        180, 210, 240, 270, 300, 330, 365].
+        calculated. Defaults is all lags from 1 to 365 days.
     bin_method: str, optional
         method to determine the type of bin. Options are "rectangle" (default),
-        and  "gaussian".
+        "gaussian" and "regular" (for regular timesteps).
     bin_width: float, optional
         number of days used as the width for the bin to calculate the correlation.
-        By default, these values are chosen based on the bin_method and the average
-        time step (dt_mu). That is 0.5dt_mu when bin_method="rectangle" and 0.25dt_mu
-        when bin_method="gaussian".
     max_gap: float, optional
         Maximum time step gap in the data. All time steps above this gap value are
         not used for calculating the average time step. This can be helpful when
@@ -68,7 +63,7 @@ def acf(
         Minimum number of observations in a bin to determine the correlation.
     full_output: bool, optional
         If True, also estimated uncertainties are returned. Default is False.
-    alpha: float
+    alpha: float, optional
         alpha level to compute the confidence interval (e.g., 1-alpha).
 
     Returns
@@ -138,16 +133,12 @@ def ccf(
         The index has to be a Pandas.DatetimeIndex.
     lags: array_like, optional
         numpy array containing the lags in days for which the cross-correlation is
-        calculated. Default [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 30, 61, 90,
-        120, 150, 180, 210, 240, 270, 300, 330, 365].
+        calculated. Defaults is all lags from 1 to 365 days.
     bin_method: str, optional
         method to determine the type of bin. Options are "rectangle" (default),
         "gaussian" and "regular" (for regular timesteps).
     bin_width: float, optional
-        number of days used as the width for the bin to calculate the correlation. By
-        default, these values are chosen based on the bin_method and the average time
-        step (dt_mu). That is 0.5dt_mu when bin_method="rectangle" and 0.25dt_mu when
-        bin_method="gaussian".
+        number of days used as the width for the bin to calculate the correlation.
     max_gap: float, optional
         Maximum timestep gap in the data. All timesteps above this gap value are not
         used for calculating the average timestep. This can be helpful when there is
@@ -191,12 +182,8 @@ def ccf(
         lags = array(lags, dtype=float)
 
     if bin_method == "rectangle":
-        if bin_width is None:
-            bin_width = 0.5 * dt_mu
         c, b = _compute_ccf_rectangle(lags, t_x, x, t_y, y, bin_width)
     elif bin_method == "gaussian":
-        if bin_width is None:
-            bin_width = 0.25 * dt_mu
         c, b = _compute_ccf_gaussian(lags, t_x, x, t_y, y, bin_width)
     elif bin_method == "regular":
         c, b = _compute_ccf_regular(arange(1.0, len(lags) + 1), x, y)
@@ -206,6 +193,7 @@ def ccf(
     conf = norm.ppf(1 - alpha / 2.0) / sqrt(b)
     result = DataFrame(
         data={"ccf": c, "conf": conf, "n": b},
+        dtype=float,
         index=TimedeltaIndex(lags, unit="D", name="Lags"),
     )
 
diff --git a/pastas/stats/dutch.py b/pastas/stats/dutch.py
index 4ca25b33..44f03739 100644
--- a/pastas/stats/dutch.py
+++ b/pastas/stats/dutch.py
@@ -10,8 +10,8 @@
 from numpy import nan
 from pandas import Series, Timedelta, concat, date_range
 
-from pastas.typing import Function, TimestampType
 from pastas.timeseries_utils import get_sample
+from pastas.typing import Function, TimestampType
 
 
 def q_ghg(
@@ -560,7 +560,7 @@ def _gxg(
         series = series.bfill(limit=limit)
     elif fill_method == "nearest":
         if limit == 0:
-            # limit=0 is a trick to only use each measurements once
+            # limit=0 is a trick to only use each measurement once
             # only keep days with measurements
             series = series.dropna()
             # generate an index at the 14th and 28th of every month
diff --git a/pastas/stats/metrics.py b/pastas/stats/metrics.py
index 078b82c7..4c0dccee 100644
--- a/pastas/stats/metrics.py
+++ b/pastas/stats/metrics.py
@@ -52,13 +52,14 @@ def mae(
 
     Parameters
     ----------
-    sim: pandas.Series
+    sim: pandas.Series, optional
         Series with the simulated values.
-    obs: pandas.Series
+    obs: pandas.Series, optional
         The Series with the observed values.
-    res: pandas.Series
+    res: pandas.Series, optional
         The Series with the residual values. If time series for the residuals are
-        provided, the sim and obs arguments are ignored.
+        provided, the sim and obs arguments are ignored. Note that the residuals
+        must be computed as `obs - sim` here.
     missing: str, optional
         string with the rule to deal with missing values. Only "drop" is supported now.
     weighted: bool, optional
@@ -71,26 +72,22 @@ def mae(
 
     Notes
     -----
-    The Mean Absolute Error (MAE) between two time series x and y is computed as
-    follows:
+    The Mean Absolute Error (MAE) between the observed (:math:`y_o`) and simulated
+    (:math:`y_s`) time series is computed as follows:
 
-    .. math:: \\text{MAE} = \\sum_{i=1}^{N} w_i |x_i - y_i|
+    .. math:: \\text{MAE} = \\sum_{i=1}^{N} w_i |y_s - y_o|
 
     where :math:`N` is the number of observations in the observed time series.
     """
-    if res is None:
-        res = sim - obs
-
-    if missing == "drop":
-        res = res.dropna()
+    err = _compute_err(obs=obs, sim=sim, res=res, missing=missing)
 
     # Return nan if the time indices of the sim and obs don't match
-    if res.index.size == 0:
+    if err.index.size == 0:
         logger.warning("Time indices of the sim and obs don't match.")
         return nan
 
-    w = _get_weights(res, weighted=weighted, max_gap=max_gap)
-    return (w * abs(res.to_numpy())).sum()
+    w = _get_weights(err, weighted=weighted, max_gap=max_gap)
+    return (w * abs(err.to_numpy())).sum()
 
 
 def rmse(
@@ -105,13 +102,14 @@ def rmse(
 
     Parameters
     ----------
-    sim: pandas.Series
+    sim: pandas.Series, optional
         Series with the simulated values.
-    obs: pandas.Series
+    obs: pandas.Series, optional
         The Series with the observed values.
-    res: pandas.Series
+    res: pandas.Series, optional
         The Series with the residual values. If time series for the residuals are
-        provided, the sim and obs arguments are ignored.
+        provided, the sim and obs arguments are ignored. Note that the residuals
+        must be computed as `obs - sim` here.
     missing: str, optional
         string with the rule to deal with missing values. Only "drop" is supported now.
     weighted: bool, optional
@@ -126,23 +124,19 @@ def rmse(
     -----
     Computes the Root Mean Squared Error (RMSE) as follows:
 
-    .. math:: \\text{RMSE} = \\sqrt{\\sum_{i=1}^{N} w_i n_i^2}
+    .. math:: \\text{RMSE} = \\sqrt{\\sum_{i=1}^{N} w_i \\epsilon_i^2}
 
-    where :math:`N` is the number of residuals :math:`n`.
+    where :math:`N` is the number of error :math:`\\epsilon`.
     """
-    if res is None:
-        res = sim - obs
-
-    if missing == "drop":
-        res = res.dropna()
+    err = _compute_err(obs=obs, sim=sim, res=res, missing=missing)
 
     # Return nan if the time indices of the sim and obs don't match
-    if res.index.size == 0:
+    if err.index.size == 0:
         logger.warning("Time indices of the sim and obs don't match.")
         return nan
 
-    w = _get_weights(res, weighted=weighted, max_gap=max_gap)
-    return sqrt((w * res.to_numpy() ** 2).sum())
+    w = _get_weights(err, weighted=weighted, max_gap=max_gap)
+    return sqrt((w * err.to_numpy() ** 2).sum())
 
 
 def sse(
@@ -155,13 +149,14 @@ def sse(
 
     Parameters
     ----------
-    sim: pandas.Series
+    sim: pandas.Series, optional
         Series with the simulated values.
-    obs: pandas.Series
+    obs: pandas.Series, optional
         The Series with the observed values.
-    res: pandas.Series
+    res: pandas.Series, optional
         The Series with the residual values. If time series for the residuals are
-        provided, the sim and obs arguments are ignored.
+        provided, the sim and obs arguments are ignored. Note that the residuals
+        must be computed as `obs - sim` here.
     missing: str, optional
         string with the rule to deal with missing values. Only "drop" is supported now.
 
@@ -169,22 +164,18 @@ def sse(
     -----
     The Sum of the Squared Errors (SSE) is calculated as follows:
 
-    .. math:: \\text{SSE} = \\sum(r^2)
+    .. math:: \\text{SSE} = \\sum(\\epsilon^2)
 
-    where :math:`r` are the residuals.
+    where :math:`\\epsilon` are the errors.
     """
-    if res is None:
-        res = sim - obs
-
-    if missing == "drop":
-        res = res.dropna()
+    err = _compute_err(obs=obs, sim=sim, res=res, missing=missing)
 
     # Return nan if the time indices of the sim and obs don't match
-    if res.index.size == 0:
+    if err.index.size == 0:
         logger.warning("Time indices of the sim and obs don't match.")
         return nan
 
-    return (res.to_numpy() ** 2).sum()
+    return (err.to_numpy() ** 2).sum()
 
 
 # Percentage Error Metrics
@@ -220,11 +211,11 @@ def pearsonr(
     -----
     The Pearson correlation (r) is computed as follows:
 
-    .. math:: r = \\frac{\\sum_{i=1}^{N}w_i (x_i - \\bar{x})(y_i - \\bar{y})}
-        {\\sqrt{\\sum_{i=1}^{N} w_i(x_i-\\bar{x})^2 \\sum_{i=1}^{N}
-        w_i(y_i-\\bar{y})^2}}
+    .. math:: r = \\frac{\\sum_{i=1}^{N}w_i (y_{o,i} - \\bar{y_o})(y_{s,i} - \\bar{
+        y_s})} {\\sqrt{\\sum_{i=1}^{N} w_i(y_{o,i}-\\bar{y_o})^2 \\sum_{i=1}^{N}w_i(
+        y_{s,i} -\\bar{y_s})^2}}
 
-    Where :math:`x` is observed time series, :math:`y` the simulated time series,
+    Where :math:`y_o` is observed time series, :math:`y_s` the simulated time series,
     and :math:`N` the number of observations in the observed time series.
     """
     if missing == "drop":
@@ -260,11 +251,12 @@ def evp(
     ----------
     obs: pandas.Series
         Series with the observed values.
-    sim: pandas.Series
+    sim: pandas.Series, optional
         The Series with the simulated values.
-    res: pandas.Series
+    res: pandas.Series, optional
         The Series with the residual values. If time series for the residuals are
-        provided, the sim and obs arguments are ignored.
+        provided, the sim and obs arguments are ignored. Note that the residuals
+        must be computed as `obs - sim` here.
     missing: str, optional
         string with the rule to deal with missing values. Only "drop" is supported now.
     weighted: bool, optional
@@ -284,17 +276,13 @@ def evp(
         * 100
 
     where :math:`\\sigma_h^2` is the variance of the observations and
-    :math:`\\sigma_r^2` is the variance of the residuals. The returned value is
+    :math:`\\sigma_r^2` is the variance of the errors. The returned value is
     bounded between 0% and 100%.
     """
-    if res is None:
-        res = sim - obs
-
-    if missing == "drop":
-        res = res.dropna()
+    err = _compute_err(obs=obs, sim=sim, res=res, missing=missing)
 
     # Return nan if the time indices of the sim and obs don't match
-    if res.index.size == 0:
+    if err.index.size == 0:
         logger.warning("Time indices of the sim and obs don't match.")
         return nan
 
@@ -306,7 +294,7 @@ def evp(
                 0.0,
                 (
                     1
-                    - var(res, weighted=weighted, max_gap=max_gap)
+                    - var(err, weighted=weighted, max_gap=max_gap)
                     / var(obs, weighted=weighted, max_gap=max_gap)
                 ),
             )
@@ -328,11 +316,12 @@ def nse(
     ----------
     obs: pandas.Series
         Series with the observed values.
-    sim: pandas.Series
+    sim: pandas.Series, optional
         The Series with the simulated values.
-    res: pandas.Series
+    res: pandas.Series, optional
         The Series with the residual values. If time series for the residuals are
-        provided, the sim and obs arguments are ignored.
+        provided, the sim and obs arguments are ignored. Note that the residuals
+        must be computed as `obs - sim` here.
     missing: str, optional
         string with the rule to deal with missing values. Only "drop" is supported now.
     weighted: bool, optional
@@ -349,21 +338,17 @@ def nse(
 
     .. math:: \\text{NSE} = 1 - \\frac{\\sum(h_s-h_o)^2}{\\sum(h_o-\\mu_{h,o})}
     """
-    if res is None:
-        res = sim - obs
-
-    if missing == "drop":
-        res = res.dropna()
+    err = _compute_err(obs=obs, sim=sim, res=res, missing=missing)
 
     # Return nan if the time indices of the sim and obs don't match
-    if res.index.size == 0:
+    if err.index.size == 0:
         logger.warning("Time indices of the sim and obs don't match.")
         return nan
 
-    w = _get_weights(res, weighted=weighted, max_gap=max_gap)
+    w = _get_weights(err, weighted=weighted, max_gap=max_gap)
     mu = average(obs.to_numpy(), weights=w)
 
-    return 1 - (w * res.to_numpy() ** 2).sum() / (w * (obs.to_numpy() - mu) ** 2).sum()
+    return 1 - (w * err.to_numpy() ** 2).sum() / (w * (obs.to_numpy() - mu) ** 2).sum()
 
 
 def rsq(
@@ -381,11 +366,12 @@ def rsq(
     ----------
     obs: pandas.Series
         Series with the observed values.
-    sim: pandas.Series
+    sim: pandas.Series, optional
         The Series with the simulated values.
-    res: pandas.Series
+    res: pandas.Series, optional
         The Series with the residual values. If time series for the residuals are
-        provided, the sim and obs arguments are ignored.
+        provided, the sim and obs arguments are ignored. Note that the residuals
+        must be computed as `obs - sim` here.
     missing: str, optional
         string with the rule to deal with missing values. Only "drop" is supported now.
     weighted: bool, optional
@@ -403,26 +389,22 @@ def rsq(
     .. math:: \\rho_{adj} = 1-  \\frac{n-1}{n-n_{param}}*\\frac{rss}{tss}
 
     Where n is the number of observations, :math:`n_{param}` the number of free
-    parameters, rss the sum of the squared residuals, and tss the total sum of
-    squared residuals.
+    parameters, rss the sum of the squared errors, and tss the total sum of
+    squared errors.
 
     When nparam is provided, the :math:`\\rho` is adjusted for the number of
     calibration parameters.
     """
-    if res is None:
-        res = sim - obs
-
-    if missing == "drop":
-        res = res.dropna()
+    err = _compute_err(obs=obs, sim=sim, res=res, missing=missing)
 
     # Return nan if the time indices of the sim and obs don't match
-    if res.index.size == 0:
+    if err.index.size == 0:
         logger.warning("Time indices of the sim and obs don't match.")
         return nan
 
-    w = _get_weights(res, weighted=weighted, max_gap=max_gap)
+    w = _get_weights(err, weighted=weighted, max_gap=max_gap)
     mu = average(obs.to_numpy(), weights=w)
-    rss = (w * res.to_numpy() ** 2.0).sum()
+    rss = (w * err.to_numpy() ** 2.0).sum()
     tss = (w * (obs.to_numpy() - mu) ** 2.0).sum()
 
     if nparam:
@@ -442,13 +424,14 @@ def bic(
 
     Parameters
     ----------
-    obs: pandas.Series
+    obs: pandas.Series, optional
         Series with the observed values.
-    sim: pandas.Series
+    sim: pandas.Series, optional
         The Series with the simulated values.
-    res: pandas.Series
+    res: pandas.Series, optional
         The Series with the residual values. If time series for the residuals are
-        provided, the sim and obs arguments are ignored.
+        provided, the sim and obs arguments are ignored. Note that the residuals
+        must be computed as `obs - sim` here.
     nparam: int, optional
         number of calibrated parameters.
     missing: str, optional
@@ -463,20 +446,16 @@ def bic(
 
     where :math:`n_{param}` is the number of calibration parameters.
     """
-    if res is None:
-        res = sim - obs
-
-    if missing == "drop":
-        res = res.dropna()
+    err = _compute_err(obs=obs, sim=sim, res=res, missing=missing)
 
     # Return nan if the time indices of the sim and obs don't match
-    if res.index.size == 0:
+    if err.index.size == 0:
         logger.warning("Time indices of the sim and obs don't match.")
         return nan
 
-    return res.index.size * log(
-        (res.to_numpy() ** 2.0).sum() / res.index.size
-    ) + nparam * log(res.index.size)
+    n = err.index.size
+
+    return n * log((err.to_numpy() ** 2.0).sum() / n) + nparam * log(n)
 
 
 def aic(
@@ -490,13 +469,14 @@ def aic(
 
     Parameters
     ----------
-    obs: pandas.Series
+    obs: pandas.Series, optional
         Series with the observed values.
-    sim: pandas.Series
+    sim: pandas.Series, optional
         The Series with the simulated values.
-    res: pandas.Series
+    res: pandas.Series, optional
         The Series with the residual values. If time series for the residuals are
-        provided, the sim and obs arguments are ignored.
+        provided, the sim and obs arguments are ignored. Note that the residuals
+        must be computed as `obs - sim` here.
     nparam: int, optional
         number of calibrated parameters.
     missing: str, optional
@@ -512,21 +492,16 @@ def aic(
     where :math:`n_{param}` is the number of calibration parameters and L is the
     likelihood function for the model.
     """
-    if res is None:
-        res = sim - obs
-
-    if missing == "drop":
-        res = res.dropna()
+    err = _compute_err(obs=obs, sim=sim, res=res, missing=missing)
 
     # Return nan if the time indices of the sim and obs don't match
-    if res.index.size == 0:
+    if err.index.size == 0:
         logger.warning("Time indices of the sim and obs don't match.")
         return nan
 
-    return (
-        res.index.size * log((res.to_numpy() ** 2.0).sum() / res.index.size)
-        + 2.0 * nparam
-    )
+    n = err.index.size
+
+    return n * log((err.to_numpy() ** 2.0).sum() / n) + 2.0 * nparam
 
 
 # Forecast Error Metrics
@@ -544,7 +519,7 @@ def kge_2012(
     sim: pandas.Series
         Series with the simulated values.
     obs: pandas.Series
-        Series with the observed values.
+        The Series with the observed values.
     missing: str, optional
         string with the rule to deal with missing values. Only "drop" is
         supported now.
@@ -590,3 +565,50 @@ def kge_2012(
 
     kge = 1 - sqrt((r - 1) ** 2 + (beta - 1) ** 2 + (gamma - 1) ** 2)
     return kge
+
+
+def _compute_err(
+    obs: Optional[Series] = None,
+    sim: Optional[Series] = None,
+    res: Optional[Series] = None,
+    missing: str = "drop",
+):
+    """
+
+    Parameters
+    ----------
+    Parameters
+    ----------
+    sim: pandas.Series, optional
+        Series with the simulated values.
+    obs: pandas.Series, optional
+        The Series with the observed values.
+    res: pandas.Series, optional
+        The Series with the residual values. If time series for the residuals are
+        provided, the sim and obs arguments are ignored. Note that the residuals
+        must be computed as `obs - sim` here.
+    missing: str, optional
+        string with the rule to deal with missing values. Only "drop" is supported now.
+
+    Returns
+    -------
+    err: pandas.Series
+        The pandas.Series with the errors, computed as
+
+    """
+    if (obs is not None) and (sim is not None):
+        err = sim.subtract(obs)
+    elif res is not None:
+        err = -res
+    else:
+        msg = (
+            "Either the residuals, or the simulation and observations have to be "
+            "provided. Please provide one of these two input options."
+        )
+        logger.error(msg)
+        raise ValueError(msg)
+
+    if missing == "drop":
+        err = err.dropna()
+
+    return err
diff --git a/pastas/stats/signatures.py b/pastas/stats/signatures.py
index 8c073216..b391fb8c 100644
--- a/pastas/stats/signatures.py
+++ b/pastas/stats/signatures.py
@@ -363,7 +363,7 @@ def colwell_contingency(
     method: str = "mean",
     normalize: bool = True,
 ) -> Tuple[float, float, float]:
-    """Colwell contingency :cite:t:`colwell_predictability_1974`
+    """Colwell's contingency :cite:t:`colwell_predictability_1974`
 
     Parameters
     ----------
@@ -852,7 +852,7 @@ def duration_curve_slope(
     series: pandas.Series
         Pandas Series with DatetimeIndex and head values.
     l: float
-        lower percentile, a float between 0 and 1, lower than u
+        lower percentile, a float between 0 and 1, lower than u.
     u: float, optional
         upper percentile, a float between 0 and 1, higher than l.
     normalize: bool, optional
@@ -889,7 +889,7 @@ def duration_curve_range(
     series: pandas.Series
         Pandas Series with DatetimeIndex and head values.
     l: float
-        lower percentile, a float between 0 and 1, lower than u
+        lower percentile, a float between 0 and 1, lower than u.
     u: float, optional
         upper percentile, a float between 0 and 1, higher than l.
     normalize: bool, optional
@@ -987,10 +987,10 @@ def _baseflow(series: Series, normalize: bool = True) -> Tuple[Series, Series]:
     if normalize:
         series = _normalize(series)
 
-    # A/B. Selecting minima hm over 5 day periods
+    # A/B. Selecting minima hm over 5-day periods
     hm = series.resample("5D").min().dropna()
 
-    # C. define the turning points ht (0.9 * head < adjacent heads)
+    # C. define the turning point ht (0.9 * head < adjacent heads)
     ht = pd.Series(dtype=float)
     for i, h in enumerate(hm.iloc[1:-1], start=1):
         if (h < hm.iloc[i - 1]) & (h < hm.iloc[i + 1]):
diff --git a/pastas/stats/tests.py b/pastas/stats/tests.py
index b1e0b6bc..a4c27956 100644
--- a/pastas/stats/tests.py
+++ b/pastas/stats/tests.py
@@ -21,8 +21,6 @@
     "runs_test",
     "stoffer_toloi",
     "diagnostics",
-    "plot_acf",
-    "plot_diagnostics",
 ]
 
 
@@ -515,24 +513,3 @@ def diagnostics(
     )
 
     return df
-
-
-def plot_acf():
-    raise DeprecationWarning(
-        "The method plot_acf is deprecated since 0.23 and will be removed "
-        "in Pastas 1.0. Use 'ps.plots.acf' instead."
-    )
-
-
-def plot_diagnostics():
-    raise DeprecationWarning(
-        "The method plot_diagnostics is deprecated since 0.23 and will be removed "
-        "in Pastas 1.0. Use 'ps.plots.diagnostics' instead."
-    )
-
-
-def plot_cum_frequency():
-    raise DeprecationWarning(
-        "The method plot_cum_frequency is deprecated since 0.23 and will be removed "
-        "in Pastas 1.0. Use 'ps.plots.cum_frequency' instead."
-    )
diff --git a/pastas/stressmodels.py b/pastas/stressmodels.py
index 558a756a..da242ed9 100644
--- a/pastas/stressmodels.py
+++ b/pastas/stressmodels.py
@@ -14,9 +14,7 @@
 pastas.model.Model.add_stressmodel
 """
 
-import inspect
 from logging import getLogger
-from importlib.metadata import version
 
 # Type Hinting
 from typing import List, Optional, Tuple, Union
@@ -26,7 +24,6 @@
 from scipy.signal import fftconvolve
 
 from pastas.typing import ArrayLike, Model, Recharge, RFunc, TimestampType
-
 from .decorators import njit, set_parameter
 from .recharge import Linear
 from .rfunc import Exponential, HantushWellModel, One
@@ -75,12 +72,6 @@ def __init__(
         self.freq = None
 
         if rfunc is not None:
-            if inspect.isclass(rfunc):
-                raise DeprecationWarning(
-                    "Response functions should be added to a stress model as an "
-                    "instance (e.g., ps.One()), and not as a class (e.g., ps.One). "
-                    "Please provide an instance of the response function."
-                )
             rfunc._update_rfunc_settings(up=up, gain_scale_factor=gain_scale_factor)
         self.rfunc = rfunc
 
@@ -252,21 +243,17 @@ class StressModel(StressModelBase):
     up: bool or None, optional
         True if response function is positive (default), False if negative. None if
         you don't want to define if response is positive or negative.
-    cutoff: float, optional
-        This argument is deprecated since 0.23. Directly provide this to directly to
-        the response function instance (e.g., ps.Gamma(cutoff=0.999).
     settings: dict or str, optional
         The settings of the stress. This can be a string referring to a predefined
-        settings dict, or a dict with the settings to apply. Refer to the docstring
-        of pastas.Timeseries for further information.
+        settings dict (defined in ps.rcParams["timeseries"]), or a dict with the
+        settings to apply. Refer to the docs of pastas.Timeseries for further
+        information.
     metadata: dict, optional
         dictionary containing metadata about the stress. This is passed onto the
         TimeSeries object.
     gain_scale_factor: float, optional
         the scale factor is used to set the initial value and the bounds of the gain
         parameter, computed as 1 / gain_scale_factor.
-    meanstress: float, optional
-        This argument is deprecated since 0.23. Use `gain_scale_factor` instead.
 
     Examples
     --------
@@ -289,21 +276,10 @@ def __init__(
         rfunc: RFunc,
         name: str,
         up: bool = True,
-        cutoff=None,
         settings: Optional[Union[dict, str]] = None,
         metadata: Optional[dict] = None,
         gain_scale_factor: Optional[float] = None,
-        meanstress: Optional[float] = None,
     ) -> None:
-        raise_deprecations(meanstress=meanstress, cutoff=cutoff)
-
-        if isinstance(stress, list):
-            logger.warning(
-                "providing a stress as list is deprecated and will results "
-                "in an Error in Pastas 1.0."
-            )
-            stress = stress[0]  # TODO Remove in Pastas 1.0
-
         stress = TimeSeries(stress, settings=settings, metadata=metadata)
 
         StressModelBase.__init__(
@@ -405,9 +381,6 @@ class StepModel(StressModelBase):
         ps.rfunc.One(), an instant effect.
     up: bool, optional
         Force a direction of the step. Default is None.
-    cutoff: float, optional
-        This argument is deprecated since 0.23. Directly provide this to directly to
-        the response function instance (e.g., ps.Gamma(cutoff=0.999).
 
     Notes
     -----
@@ -424,10 +397,7 @@ def __init__(
         name: str,
         rfunc: Optional[RFunc] = None,
         up: bool = True,
-        cutoff=None,
     ) -> None:
-        raise_deprecations(cutoff=cutoff)
-
         if rfunc is None:
             rfunc = One()
         StressModelBase.__init__(
@@ -663,11 +633,11 @@ class WellModel(StressModelBase):
         whether a positive stress has an increasing or decreasing effect on the model,
         by default False, in which case positive stress lowers e.g., the groundwater
         level.
-    cutoff: float, optional
-        This argument is deprecated since 0.23. Directly provide this to directly to
-        the response function instance (e.g., ps.Gamma(cutoff=0.999).
     settings: str, list of dict, optional
-        settings of the time series, by default "well".
+        The settings of the stress. This can be a string referring to a predefined
+        settings dict (defined in ps.rcParams["timeseries"]), or a dict with the
+        settings to apply. Refer to the docs of pastas.Timeseries for further
+        information.
     sort_wells: bool, optional
         sort wells from closest to furthest, by default True.
 
@@ -692,12 +662,10 @@ def __init__(
         name: str,
         distances: ArrayLike,
         up: bool = False,
-        cutoff=None,
         settings: str = "well",
         sort_wells: bool = True,
         metadata: Optional[list] = None,
     ) -> None:
-        raise_deprecations(cutoff=cutoff)
         if not isinstance(rfunc, HantushWellModel):
             raise NotImplementedError(
                 "WellModel only supports the rfunc HantushWellModel!"
@@ -1016,14 +984,11 @@ class RechargeModel(StressModelBase):
     temp: pandas.Series, optional
         pandas.Series with pandas.DatetimeIndex containing the temperature series.
         It depends on the recharge model is this argument is required or not.
-    cutoff: float, optional
-        This argument is deprecated since 0.23. Directly provide this to directly to
-        the response function instance (e.g., ps.Gamma(cutoff=0.999).
     settings: list of dicts or str, optional
         The settings of the precipitation and evaporation time series, in this order.
-        This can be a string referring to a predefined settings dict, or a dict with
-        the settings to apply. Refer to the docstring of pastas.Timeseries for
-        further information. Default is ("prec", "evap").
+        This can be a string referring to a predefined settings dict (defined in
+        ps.rcParams["timeseries"]), or a dict with the settings to apply. Refer to
+        the docs of pastas.Timeseries for further information.
     metadata: tuple of dicts or list of dicts, optional
         dictionary containing metadata about the stress. This is passed onto the
         TimeSeries object.
@@ -1064,7 +1029,6 @@ def __init__(
         name: str = "recharge",
         recharge: Optional[Recharge] = None,
         temp: Optional[Series] = None,
-        cutoff=None,
         settings: Tuple[Union[str, dict], Union[str, dict], Union[str, dict]] = (
             "prec",
             "evap",
@@ -1072,7 +1036,6 @@ def __init__(
         ),
         metadata: Optional[Tuple[dict, dict, dict]] = (None, None, None),
     ) -> None:
-        raise_deprecations(cutoff=cutoff)
         if rfunc is None:
             rfunc = Exponential()
 
@@ -1432,16 +1395,15 @@ def __init__(
         oseries: Optional[Series] = None,
         dmin: Optional[float] = None,
         dmax: Optional[float] = None,
-        rfunc: Optional[RFunc] = Exponential(),
+        rfunc: Optional[RFunc] = None,
         **kwargs,
     ) -> None:
         if oseries is not None:
             if dmin is not None or dmax is not None:
                 msg = "Please specify either oseries or dmin and dmax"
                 raise (Exception(msg))
-            o = TimeSeries(oseries).series
-            dmin = o.min()
-            dmax = o.max()
+            dmin = oseries.min()
+            dmax = oseries.max()
         elif dmin is None or dmax is None:
             msg = "Please specify either oseries or dmin and dmax"
             raise (Exception(msg))
@@ -1457,10 +1419,16 @@ def __init__(
     def set_init_parameters(self) -> None:
         # parameters for the first drainage level
         p0 = self.rfunc.get_init_parameters(self.name)
-        one = One()
-        gain_scale_factor = self.dmin + 0.5 * (self.dmax - self.dmin)
-        one._update_rfunc_settings(gain_scale_factor=gain_scale_factor)
-        pd0 = one.get_init_parameters(self.name).squeeze()
+        initial = self.dmin + 0.5 * (self.dmax - self.dmin)
+        pd0 = Series(
+            {
+                "initial": initial,
+                "pmin": np.NaN,
+                "pmax": np.NaN,
+                "vary": True,
+                "name": self.name,
+            }
+        )
         p0.loc[f"{self.name}_d"] = pd0
         p0.index = [f"{x}0" for x in p0.index]
 
@@ -1600,13 +1568,11 @@ class ChangeModel(StressModelBase):
     up: bool or None, optional
         True if response function is positive (default), False if negative. None if
         you don't want to define if response is positive or negative.
-    cutoff: float, optional
-        This argument is deprecated since 0.23. Directly provide this to directly to
-        the response function instance (e.g., ps.Gamma(cutoff=0.999).
     settings: dict or str, optional
-        the settings of the stress. This can be a string referring to a predefined
-        settings dict, or a dict with the settings to apply. Refer to the docstring
-        of pastas.Timeseries for further information.
+        The settings of the stress. This can be a string referring to a predefined
+        settings dict (defined in ps.rcParams["timeseries"]), or a dict with the
+        settings to apply. Refer to the docs of pastas.Timeseries for further
+        information.
     metadata: dict, optional
         dictionary containing metadata about the stress. This is passed onto the
         TimeSeries object.
@@ -1626,19 +1592,9 @@ def __init__(
         name: str,
         tchange: Union[str, TimestampType],
         up: bool = True,
-        cutoff=None,
         settings: Optional[Union[dict, str]] = None,
         metadata: Optional[dict] = None,
     ) -> None:
-        raise_deprecations(cutoff=cutoff)
-
-        if isinstance(stress, list):
-            logger.warning(
-                "providing a stress as list is deprecated and will results "
-                "in an Error in Pastas 1.0."
-            )
-            stress = stress[0]  # TODO Remove in Pastas 1.0
-
         stress = TimeSeries(stress, settings=settings, metadata=metadata)
 
         StressModelBase.__init__(
@@ -1648,11 +1604,6 @@ def __init__(
             tmin=stress.series.index.min(),
             tmax=stress.series.index.max(),
         )
-        if inspect.isclass(rfunc1) or inspect.isclass(rfunc2):
-            raise DeprecationWarning(
-                "Response functions should be provided as an instance (e.g., ps.One()),"
-                " not as a class (e.g., ps.One). Please provide an instance."
-            )
 
         rfunc1._update_rfunc_settings(up=up)
         self.rfunc1 = rfunc1
@@ -1754,25 +1705,3 @@ def to_dict(self, series: bool = True):
             "up": self.rfunc1.up,
         }
         return data
-
-
-def StressModel2(*args, **kwargs):
-    msg = (
-        "StressModel2 is removed in Pastas 0.23 and replaced by the RechargeModel "
-        "stress model. Use ps.RechargeModel(prec, evap, recharge=ps.rch.Linear) for "
-        "the same stress model in future codes."
-    )
-    logger.error(msg)
-
-
-def raise_deprecations(meanstress=None, cutoff=None):
-    if meanstress is not None:
-        raise DeprecationWarning(
-            "The argument `meanstress` is deprecated since Pastas 0.23. Please "
-            "use the new argument `gain_scale_factor` instead."
-        )
-    if cutoff is not None:
-        raise DeprecationWarning(
-            "The argument `cutoff` is deprecated since Pastas 0.23. Please "
-            "apply this directly to the response function (e.g., ps.Gamma(cutoff=0.99))"
-        )
diff --git a/pastas/timer.py b/pastas/timer.py
index 54a5ca2e..1934be93 100644
--- a/pastas/timer.py
+++ b/pastas/timer.py
@@ -1,3 +1,22 @@
+"""This module contains a timer for model optimization.
+
+The timer prints the time elapsed and number of iterations. Optionally, a maximum solve
+time can be specified, to abort long optimizations. This class is not automatically
+imported in pastas and requires the tqdm module (pip install tqdm).
+
+Usage::
+
+    from pastas.timer import SolveTimer
+
+    with SolveTimer(max_time=60) as t:  # max time in seconds
+        ml.solve(callback=t.timer)
+
+This will print the following to the console::
+
+    Optimization progress: 73it [00:01, 67.68it/s]
+
+"""
+
 try:
     from tqdm.auto import tqdm
 except ModuleNotFoundError:
@@ -16,8 +35,8 @@ class ExceededMaxSolveTime(Exception):
 class SolveTimer(tqdm):
     """Progress indicator for model optimization.
 
-    Usage
-    -----
+    Examples
+    --------
     Print timer and number of iterations in console while running `ml.solve()`::
 
         with SolveTimer() as t:
@@ -33,8 +52,8 @@ class SolveTimer(tqdm):
         with SolveTimer(max_time=60) as t:
             ml.solve(callback=t.timer)
 
-    Note
-    ----
+    Notes
+    -----
     If the logger is also printing messages to the console the timer will not be
     updated quite as nicely.
     """
diff --git a/pastas/timeseries.py b/pastas/timeseries.py
index 956532d0..8d1fc614 100644
--- a/pastas/timeseries.py
+++ b/pastas/timeseries.py
@@ -7,13 +7,8 @@
 from pandas import Series
 from pandas.tseries.frequencies import to_offset
 
-from pastas.typing import Axes
-
 from .rcparams import rcParams
-from .timeseries_utils import (
-    _get_dt,
-    _get_time_offset,
-)
+from .timeseries_utils import _get_dt, _get_time_offset
 from .utils import validate_name
 
 logger = getLogger(__name__)
@@ -62,11 +57,7 @@ def __init__(
         name: Optional[str] = None,
         settings: Optional[Union[str, dict]] = None,
         metadata: Optional[dict] = None,
-        **kwargs,  # TODO remove in Pastas 1.0
     ) -> None:
-        # First, deal with all deprecated features and raise errors
-        check_deprecated_input(series, settings, **kwargs)  # TODO remove in Pastas 1.0
-
         # Make sure we have a Pandas Series and not a 1D-DataFrame
         if isinstance(series, pd.DataFrame):
             if len(series.columns) == 1:
@@ -94,7 +85,7 @@ def __init__(
         self._series = None  #
         self.freq_original = pd.infer_freq(self._series_original.index)
         self.settings = {
-            "freq": None,
+            "freq": self.freq_original,
             "sample_up": None,
             "sample_down": None,
             "fill_nan": "interpolate",
@@ -167,13 +158,6 @@ def series(self, value):
             "series_original. Please set series_original to update the series."
         )
 
-    @property
-    def series_validated(self):
-        raise DeprecationWarning(
-            "TimeSeries objects no longer have a validated time series. Use the "
-            "_series_original instead."
-        )
-
     def update_series(self, force_update: bool = False, **kwargs) -> None:
         """Method to update the series with new options.
 
@@ -194,9 +178,8 @@ def update_series(self, force_update: bool = False, **kwargs) -> None:
             daily to weekly values). Possible values are: "mean", "drop", "sum",
             "min", "max".
         fill_nan: str or float, optional
-            Method to use when there ar nan-values in the time series.
-            Possible values are: "mean", "drop", "interpolate" (default) or a
-            float value.
+            Method to use when there ar nan-values in the time series. Possible
+            values are: "mean", "drop", "interpolate" (default) or a float value.
         fill_before: str or float, optional
             Method used to extend a time series before any measurements are
             available. possible values are: "mean" or a float value.
@@ -223,7 +206,10 @@ def update_series(self, force_update: bool = False, **kwargs) -> None:
 
             # Get the original series to start with
             series = self._series_original.copy(deep=True)
-            series = self._fill_nan(series)
+
+            # Only fill_nans if necessary
+            if series.hasnans:
+                series = self._fill_nan(series)
 
             # Update the series with the new settings
             series = self._change_frequency(series)
@@ -233,12 +219,6 @@ def update_series(self, force_update: bool = False, **kwargs) -> None:
 
             self._series = series
 
-    def multiply(self, other: float) -> None:
-        raise DeprecationWarning(
-            "TimeSeries objects no longer have the multiply method. Provide a new "
-            "_series_original that is multiplied instead."
-        )
-
     def _update_settings(self, **kwargs) -> bool:
         """Internal method that check if an update is actually necessary.
 
@@ -266,7 +246,10 @@ def _change_frequency(self, series: Series) -> Series:
         # 1. If no freq string is present or is provided (e.g. Oseries)
         if not freq:
             return series
-        # 2. If new frequency is required (only up or down sampling allowed)
+        # 2. If new frequency is the same
+        elif freq == self.freq_original:
+            return series
+        # 3. If new frequency is required (only up or down sampling allowed)
         else:
             dt_new = _get_dt(freq)
             dt_org = _get_dt(self.freq_original)
@@ -278,10 +261,10 @@ def _change_frequency(self, series: Series) -> Series:
             elif dt_new > dt_org:
                 series = self._sample_down(series)
 
-        # Drop nan-values at the beginning and end of the time series
-        series = series.loc[series.first_valid_index() : series.last_valid_index()]
+            # Drop nan-values at the beginning and end of the time series
+            series = series.loc[series.first_valid_index() : series.last_valid_index()]
 
-        return series
+            return series
 
     def _sample_up(self, series: Series) -> Series:
         """Resample the time series when the frequency increases (e.g. from weekly to
@@ -289,10 +272,11 @@ def _sample_up(self, series: Series) -> Series:
         method = self.settings["sample_up"]
         freq = self.settings["freq"]
 
+        success = True
         if method in ["backfill", "bfill", "pad", "ffill"]:
             series = series.asfreq(freq, method=method)
         elif method is None:
-            pass
+            success = False
         else:
             if method == "mean":
                 series = series.asfreq(freq).fillna(series.mean())
@@ -305,14 +289,17 @@ def _sample_up(self, series: Series) -> Series:
             elif isinstance(method, float):
                 series = series.asfreq(freq).fillna(method)
             else:
-                logger.warning(
-                    "Time Series %s: User-defined option for sample_up %s is not "
-                    "supported",
-                    self.name,
-                    method,
-                )
+                success = False
 
-        logger.info("Time Series %s were sampled up using %s.", self.name, method)
+        if success:
+            logger.info("Time Series %s were sampled up using %s.", self.name, method)
+        else:
+            logger.warning(
+                "Time Series %s: User-defined option for sample_up %s is not "
+                "supported",
+                self.name,
+                method,
+            )
 
         return series
 
@@ -343,6 +330,7 @@ def _sample_down(self, series: Series) -> Series:
         # Provide some standard pandas arguments for all options
         kwargs = {"label": "right", "closed": "right"}
 
+        success = True
         if method == "mean":
             series = series.resample(freq, **kwargs).mean()
         elif method == "drop":
@@ -354,24 +342,27 @@ def _sample_down(self, series: Series) -> Series:
         elif method == "max":
             series = series.resample(freq, **kwargs).max()
         else:
-            logger.warning(
-                "Time Series %s: User-defined option for sample down %s is not "
-                "supported",
-                self.name,
-                method,
-            )
+            success = False
 
         # TODO: replace by adding offset to resample method with pandas 1.1.0
         if self.settings["time_offset"] > pd.Timedelta(0):
             # The offset is removed by the resample-method, so we add it again
             series = series.shift(1, freq=self.settings["time_offset"])
 
-        logger.info(
-            "Time Series %s was sampled down to freq %s with method " "%s.",
-            self.name,
-            freq,
-            method,
-        )
+        if success:
+            logger.info(
+                "Time Series %s was sampled down to freq %s with method " "%s.",
+                self.name,
+                freq,
+                method,
+            )
+        else:
+            logger.warning(
+                "Time Series %s: User-defined option for sample down %s is not "
+                "supported",
+                self.name,
+                method,
+            )
 
         return series
 
@@ -379,11 +370,10 @@ def _fill_nan(self, series: Series) -> Series:
         """Fill up the nan-values when present."""
 
         method = self.settings["fill_nan"]
-
         n = series.isnull().values.sum()
-        if n == 0:
-            pass
-        elif method == "drop":
+
+        success = True
+        if method == "drop":
             series = series.dropna()
         elif method == "mean":
             series = series.fillna(series.mean())
@@ -392,19 +382,21 @@ def _fill_nan(self, series: Series) -> Series:
         elif isinstance(method, float):
             series = series.fillna(method)
         else:
-            logger.warning(
-                "Time Series %s: User-defined option for fill_nan %s is not supported.",
-                self.name,
-                method,
-            )
+            success = False
 
-        if n > 0:
+        if success:
             logger.info(
                 "Time Series %s: %s nan-value(s) was/were found and filled with: %s.",
                 self.name,
                 n,
                 method,
             )
+        else:
+            logger.warning(
+                "Time Series %s: User-defined option for fill_nan %s is not supported.",
+                self.name,
+                method,
+            )
 
         return series
 
@@ -414,8 +406,13 @@ def _fill_before(self, series: Series) -> Series:
         method = self.settings["fill_before"]
         tmin = self.settings["tmin"]
 
-        if tmin is None or method is None:
+        if tmin is None:
             pass
+        elif pd.Timestamp(tmin) > series.index.max():
+            logger.error(
+                "The tmin is later than the last value of the time series. Pastas "
+                "does not support this. Please extend time series manually."
+            )
         elif pd.Timestamp(tmin) >= series.index.min():
             series = series.loc[pd.Timestamp(tmin) :]
         else:
@@ -445,7 +442,7 @@ def _fill_before(self, series: Series) -> Series:
                 )
             else:
                 logger.info(
-                    "Time Series %s: User-defined option for fill_before %s is not "
+                    "Time Series %s: User-defined option for fill_before '%s' is not "
                     "supported.",
                     self.name,
                     method,
@@ -459,8 +456,13 @@ def _fill_after(self, series: Series) -> Series:
         method = self.settings["fill_after"]
         tmax = self.settings["tmax"]
 
-        if tmax is None or method is None:
+        if tmax is None:
             pass
+        elif pd.Timestamp(tmax) <= series.index.min():
+            logger.error(
+                "The tmax is before the first value of the time series. Pastas does "
+                "not support this. Please extend time series manually."
+            )
         elif pd.Timestamp(tmax) <= series.index.max():
             series = series.loc[: pd.Timestamp(tmax)]
         else:
@@ -490,7 +492,7 @@ def _fill_after(self, series: Series) -> Series:
                 )
             else:
                 logger.info(
-                    "Time Series %s: User-defined option for fill_after %s is not "
+                    "Time Series %s: User-defined option for fill_after '%s' is not "
                     "supported",
                     self.name,
                     method,
@@ -526,43 +528,6 @@ def to_dict(self, series: Optional[bool] = True) -> dict:
 
         return data
 
-    def plot(self, original: bool = False, **kwargs) -> Axes:
-        raise DeprecationWarning(
-            "The plot method is deprecated since 0.23 and will be removed in Pastas "
-            "1.0. Use the series.plot function from Pandas instead."
-        )
-
-
-def check_deprecated_input(series, settings, **kwargs):
-    """Method to check input data for Pastas version 0.23 and raise errors.
-
-    Parameters
-    ----------
-    series: pandas.Series
-    settings: dict
-
-    """
-    if "freq_original" in kwargs.keys():
-        raise DeprecationWarning(
-            "Freq_original is no longer supported. Please provide an equidistant "
-            "time series."
-        )
-
-    if isinstance(series, TimeSeries):
-        raise DeprecationWarning(
-            "TimeSeries are no longer allowed as input for to create new "
-            "TimeSeries objects. Please use the original pandas.Series object "
-            "and provide the settings and name."
-        )
-
-    if isinstance(settings, dict):
-        for key in settings.keys():
-            if key in ["norm"]:
-                raise DeprecationWarning(
-                    "Key %s is no longer supported. Please remove this keyword from "
-                    "the settings dictionary."
-                )
-
 
 def validate_stress(series: Series):
     """Method to validate user-provided stress input time series.
diff --git a/pastas/timeseries_utils.py b/pastas/timeseries_utils.py
index d4af3667..4a57f41a 100644
--- a/pastas/timeseries_utils.py
+++ b/pastas/timeseries_utils.py
@@ -6,7 +6,7 @@
 from typing import Optional
 
 import numpy as np
-from pandas import Index, Series, Timedelta, Timestamp, date_range, api
+from pandas import Index, Series, Timedelta, Timestamp, api, date_range
 from pandas.tseries.frequencies import to_offset
 from scipy import interpolate
 
@@ -160,14 +160,14 @@ def get_sample(tindex: Index, ref_tindex: Index) -> Index:
 
     Parameters
     ----------
-    tindex: pandas.index
+    tindex: pandas.Index
         Pandas index object.
-    ref_tindex: pandas.index
+    ref_tindex: pandas.Index
         Pandas index object.
 
     Returns
     -------
-    series: pandas.index
+    series: pandas.Index
 
     Notes
     -----
@@ -211,7 +211,7 @@ def timestep_weighted_resample(s: Series, index: Index, fast: bool = False) -> S
     ----------
     s : pandas.Series
         The original series to be resampled
-    index : pandas.index
+    index : pandas.Index
         The index to which to resample the series
     fast : bool, optional
         use fast implementation, default is False
@@ -317,7 +317,7 @@ def get_equidistant_series_nearest(
 
     Notes
     -----
-    This method creates an equidistant time series with specified freq using nearest
+    This method creates an equidistant time series with specified freq using the nearest
     sampling (meaning observations can be shifted in time), with additional filling
     logic that ensures each original measurement is only included once in the new
     time series. Values are filled as close as possible to their original timestamp
@@ -339,7 +339,7 @@ def get_equidistant_series_nearest(
     )
     ind_linear = fl(idx.asi8)
 
-    # get nearest index from original series
+    # get the nearest index from original series
     f = interpolate.interp1d(
         series.index.asi8,
         np.arange(0, series.index.size),
@@ -352,7 +352,7 @@ def get_equidistant_series_nearest(
     # create a new equidistant series
     s = Series(index=idx, data=np.nan)
 
-    # fill in nearest value for each timestamp in equidistant series
+    # fill in the nearest value for each timestamp in equidistant series
     s.loc[idx] = series.values[ind]
 
     # remove duplicates, each observation can only be used once
@@ -360,7 +360,7 @@ def get_equidistant_series_nearest(
     # mask all duplicates and set to NaN
     s.loc[mask] = np.nan
 
-    # look through duplicates which equidistant timestamp is closest
+    # look through duplicates which equidistant timestamp is the closest
     # then fill value from original series for closest timestamp
     for i in np.unique(ind[mask]):
         # mask duplicates
@@ -490,14 +490,3 @@ def pandas_equidistant_asfreq(series: Series, freq: str) -> Series:
         .squeeze()
     )
     return spandas
-
-
-def to_daily_unit():
-    raise DeprecationWarning("This function is deprecated and will be removed in v1.0")
-
-
-def timestep_weighted_resample_fast():
-    raise DeprecationWarning(
-        "This function is deprecated and will be removed in v1.0."
-        "Method is merged into `timestep_weighted_resample()`."
-    )
diff --git a/pastas/typing/__init__.py b/pastas/typing/__init__.py
index cb8e105e..16fc1689 100644
--- a/pastas/typing/__init__.py
+++ b/pastas/typing/__init__.py
@@ -1,3 +1,4 @@
+# flake8: noqa
 from .types import (
     ArrayLike,
     Axes,
diff --git a/pastas/typing/types.py b/pastas/typing/types.py
index 135584e3..bdd46823 100644
--- a/pastas/typing/types.py
+++ b/pastas/typing/types.py
@@ -1,6 +1,7 @@
+# flake8: noqa
 # Type hinting for Pastas library
 # Typing
-from typing import TYPE_CHECKING, Any, TypeVar, Union
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union
 
 # External libraries
 # Matplotlib
@@ -34,5 +35,5 @@
 Recharge = TypeVar("Recharge", bound="ps.recharge.RechargeBase")  # Recharge Base
 Reservoir = TypeVar("Reservoir", bound="ps.reservoir.ReservoirBase")  # Reservoir Base
 CallBack = TypeVar("CallBack", bound=Any)  # Callback
-Function = TypeVar("Function", bound=Any)  # Function (e.g. Objective Function)
+Function = Callable[..., Any]  # Function (e.g. Objective Function)
 RFunc = TypeVar("RFunc", bound="ps.rfunc.RfuncBase")  # rFunc Base
diff --git a/pastas/utils.py b/pastas/utils.py
index 9e9fa262..d909e00b 100644
--- a/pastas/utils.py
+++ b/pastas/utils.py
@@ -1,34 +1,20 @@
 """This module contains utility functions for working with Pastas models."""
 
 import logging
-from datetime import datetime
 from logging import handlers
 from platform import platform
 
 # Type Hinting
 from typing import Any, Optional, Tuple
 
-from pandas import DatetimeIndex, Timestamp
+from pandas import Timestamp
 
-from pastas.typing import ArrayLike
 from pastas.typing import Model as ModelType
 from pastas.typing import TimestampType
 
 logger = logging.getLogger(__name__)
 
 
-def excel_to_datetime(tindex: DatetimeIndex, freq="D") -> DatetimeIndex:
-    raise DeprecationWarning("This function is deprecated and will be removed in v1.0.")
-
-
-def matlab_datenum_to_datetime(datenum: float) -> datetime:
-    raise DeprecationWarning("This function is deprecated and will be removed in v1.0.")
-
-
-def datetime_to_matlab_datenum(tindex: DatetimeIndex) -> ArrayLike:
-    raise DeprecationWarning("This function is deprecated and will be removed in v1.0.")
-
-
 def get_stress_tmin_tmax(ml: ModelType) -> Tuple[TimestampType, TimestampType]:
     """Get the minimum and maximum time that all the stresses have data."""
     from pastas import Model
@@ -53,8 +39,8 @@ def initialize_logger(
     Parameters
     ----------
     logger : logging.Logger
-        A Logger-instance. Use ps.logger to initialise the Logging instance that
-        handles all logging throughout pastas,  including all submodules and packages.
+        A Logger-instance. Use pastas.logger to initialise the Logging instance that
+        handles all logging throughout pastas, including all submodules and packages.
     """
     if logger is None:
         logger = logging.getLogger("pastas")
@@ -113,7 +99,7 @@ def remove_console_handler(logger: Optional[Any] = None) -> None:
     ----------
     logger : logging.Logger
         A Logger-instance. Use ps.logger to initialise the Logging instance that
-        handles all logging throughout pastas, including all sub modules and packages.
+        handles all logging throughout pastas, including all submodules and packages.
     """
     if logger is None:
         logger = logging.getLogger("pastas")
@@ -138,7 +124,7 @@ def add_file_handlers(
     ----------
     logger : logging.Logger
         A Logger-instance. Use ps.logger to initialise the Logging instance that
-        handles all logging throughout pastas, including all sub modules and packages.
+        handles all logging throughout pastas, including all submodules and packages.
     """
     if logger is None:
         logger = logging.getLogger("pastas")
@@ -221,26 +207,3 @@ def validate_name(name: str, raise_error: bool = False) -> str:
                 logger.warning(msg)
 
     return name
-
-
-def get_sample(tindex, ref_tindex):
-    raise DeprecationWarning("This method was moved to `pastas.ts.get_sample()`!")
-
-
-def timestep_weighted_resample(series0, tindex):
-    raise DeprecationWarning(
-        "This method was moved to `pastas.ts.timestep_weighted_resample()`!"
-    )
-
-
-def timestep_weighted_resample_fase(series0, tindex):
-    raise DeprecationWarning(
-        "This method is deprecated and merged into "
-        "`pastas.ts.timestep_weighted_resample()`!"
-    )
-
-
-def get_equidistant_series(series, freq, minimize_data_loss=False):
-    raise DeprecationWarning(
-        "This method was moved to `pastas.ts.get_equidistant_series()`!"
-    )
diff --git a/pastas/version.py b/pastas/version.py
index a9cff324..7717fbf3 100644
--- a/pastas/version.py
+++ b/pastas/version.py
@@ -1,10 +1,11 @@
 import logging
-from platform import python_version
 from importlib import import_module, metadata
+from platform import python_version
 
-__version__ = metadata.version("pastas")
 logger = logging.getLogger(__name__)
 
+__version__ = "1.0.0"
+
 
 def check_numba_scipy() -> bool:
     try:
@@ -26,13 +27,16 @@ def check_numba_scipy() -> bool:
     return True
 
 
-def show_versions(lmfit: bool = True) -> None:
+def show_versions(lmfit: bool = True, latexify: bool = True) -> None:
     """Method to print the version of dependencies.
 
     Parameters
     ----------
     lmfit: bool, optional
-        Print the version of LMfit. Needs to be installed.
+        Print the version of LMfit if installed.
+    latexify: bool, optional
+        Print the version of Latexify if installed.
+
     """
 
     msg = (
@@ -52,6 +56,14 @@ def show_versions(lmfit: bool = True) -> None:
         except ModuleNotFoundError:
             msg += "Not Installed"
 
-    msg += f"\nPastas version: {metadata.version('pastas')}"
+    if latexify:
+        msg += "\nLatexify version: "
+        try:
+            import_module("latexify")
+            msg += f"{metadata.version('latexify-py')}"
+        except ModuleNotFoundError:
+            msg += "Not Installed"
+
+    msg += f"\nPastas version: {__version__}"
 
     return print(msg)
diff --git a/pyproject.toml b/pyproject.toml
index 1c53a047..14cc55d5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,11 +1,11 @@
 [build-system]
-requires = ["setuptools>=64.0.0", "wheel", "setuptools-scm"]
+requires = ["setuptools>=64.0.0", "wheel"]
 build-backend = "setuptools.build_meta"
 
 
 [project]
 name = "pastas"
-version = "0.23.1"
+dynamic = ["version"]
 description = "Pastas is an open-source Python framework for the analysis of groundwater time series."
 license = { file = "LICENSE" }
 readme = "README.rst"
@@ -19,7 +19,6 @@ maintainers = [
     { name = "O. Ebbens", email = "o.ebbens@artesia-water.nl" },
     { name = "M.A. Vonk", email = "m.vonk@artesia-water.nl" },
 ]
-
 requires-python = ">= 3.8"
 dependencies = [
     "numpy >= 1.17, < 1.24",
@@ -28,10 +27,9 @@ dependencies = [
     "scipy >= 1.8",
     "numba >= 0.51",
 ]
-
 keywords = ["hydrology", "groundwater", "timeseries", "analysis"]
 classifiers = [
-    'Development Status :: 4 - Beta',
+    'Development Status :: 5 - Production/Stable',
     'Intended Audience :: Science/Research',
     'Intended Audience :: Other Audience',
     'License :: OSI Approved :: MIT License',
@@ -49,11 +47,12 @@ documentation = "https://pastas.readthedocs.io/en/latest/"
 
 [project.optional-dependencies]
 full = ["lmfit >= 1.0.0", "latexify-py"]
+formatting = ["isort", "black[jupyter]"]
+linting = ["flake8"]
+pytesting = ["pytest>=7", "pytest-cov", "pytest-sugar"]
 ci = [
-    "pastas[full]",
+    "pastas[full,pytesting]",
     "jupyter",
-    "pytest",
-    "pytest-cov",
     "codacy-coverage",
     "corner",
     "emcee",
@@ -64,14 +63,19 @@ rtd = [
     "Ipython",
     "ipykernel",
     "pydata-sphinx-theme",
-    "sphinx-gallery >= 0.6, <0.11",
     "sphinx>=3.1, <6.0",
     "sphinxcontrib-bibtex",
     "requests",
     "numpydoc",
+    "sphinx-design",
 ]
+dev = ["tox", "pastas[formatting,linting,ci,rtd]"]
 numbascipy = ["numba-scipy >= 0.3.1"]
 
+
+[tool.setuptools.dynamic]
+version = { attr = "pastas.version.__version__" }
+
 [tool.black]
 line-length = 88
 
@@ -81,3 +85,39 @@ profile = "black"
 [tool.pytest.ini_options]
 addopts = "--strict-markers --durations=0 --cov-report xml:coverage.xml --cov pastas"
 markers = ["notebooks: run notebooks"]
+
+[tool.tox]
+legacy_tox_ini = """
+    [tox]
+    requires = tox>=4
+    env_list = format, lint, notebooks, py{38,39,310}
+
+    [testenv]
+    description = run unit tests
+    extras = ci
+    commands =
+            pytest tests -m "not notebooks"
+
+    [testenv:notebooks]
+    description = run unit tests
+    extras = ci
+    commands =
+            pytest tests
+
+    [testenv:format]
+    description = run formatters
+    basepython = python3.9
+    extras = formatting
+    commands =
+            black pastas --check --diff
+            isort pastas --diff --profile=black
+
+    [testenv:lint]
+    description = run linters
+    basepython = python3.9
+    extras = linting
+    commands =
+            flake8 pastas --count --show-source --exit-zero --max-line-length=88
+            flake8 pastas --count --exit-zero --max-line-length=88 --statistics
+
+"""
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/data/evap.csv b/tests/data/evap.csv
index bd9f566c..ede09344 100644
--- a/tests/data/evap.csv
+++ b/tests/data/evap.csv
@@ -1,4 +1,4 @@
-YYYYMMDD,EV24 380
+YYYYMMDD,EV24_380
 2010-01-01,0.0
 2010-01-02,0.0
 2010-01-03,0.00030000000000000003
diff --git a/tests/data/wellmodel_old.pas b/tests/data/wellmodel_old.pas
index 7e9fdb57..e463f3c6 100644
--- a/tests/data/wellmodel_old.pas
+++ b/tests/data/wellmodel_old.pas
@@ -1,7 +1,7 @@
 {
     "name": "WellModel",
     "oseries": {
-        "series": "{\"name\":\"Observation_well\",\"index\":[\"1960-04-28T12:00:00.000Z\",\"1960-05-16T12:00:00.000Z\",\"1960-05-30T12:00:00.000Z\",\"1960-06-15T12:00:00.000Z\",\"1960-06-29T12:00:00.000Z\",\"1960-07-15T12:00:00.000Z\",\"1960-07-29T12:00:00.000Z\",\"1960-08-17T12:00:00.000Z\",\"1960-08-30T12:00:00.000Z\",\"1960-09-15T12:00:00.000Z\",\"1960-09-29T12:00:00.000Z\",\"1960-10-17T12:00:00.000Z\",\"1960-10-31T12:00:00.000Z\",\"1960-11-15T12:00:00.000Z\",\"1960-11-28T12:00:00.000Z\",\"1960-12-14T12:00:00.000Z\",\"1960-12-28T12:00:00.000Z\",\"1961-01-14T12:00:00.000Z\",\"1961-01-30T12:00:00.000Z\",\"1961-02-14T12:00:00.000Z\",\"1961-02-28T12:00:00.000Z\",\"1961-03-14T12:00:00.000Z\",\"1961-03-28T12:00:00.000Z\",\"1961-04-14T12:00:00.000Z\",\"1961-04-28T12:00:00.000Z\",\"1961-05-15T12:00:00.000Z\",\"1961-05-29T12:00:00.000Z\",\"1961-06-14T12:00:00.000Z\",\"1961-06-28T12:00:00.000Z\",\"1961-07-14T12:00:00.000Z\",\"1961-07-28T12:00:00.000Z\",\"1961-08-14T12:00:00.000Z\",\"1961-08-29T12:00:00.000Z\",\"1961-09-14T12:00:00.000Z\",\"1961-09-28T12:00:00.000Z\",\"1961-10-16T12:00:00.000Z\",\"1961-10-30T12:00:00.000Z\",\"1961-11-15T12:00:00.000Z\",\"1961-11-28T12:00:00.000Z\",\"1961-12-14T12:00:00.000Z\",\"1961-12-28T12:00:00.000Z\",\"1962-01-15T12:00:00.000Z\",\"1962-01-29T12:00:00.000Z\",\"1962-02-14T12:00:00.000Z\",\"1962-02-28T12:00:00.000Z\",\"1962-03-14T12:00:00.000Z\",\"1962-03-28T12:00:00.000Z\",\"1962-04-13T12:00:00.000Z\",\"1962-04-27T12:00:00.000Z\",\"1962-05-14T12:00:00.000Z\",\"1962-05-28T12:00:00.000Z\",\"1962-06-14T12:00:00.000Z\",\"1962-06-28T12:00:00.000Z\",\"1962-07-16T12:00:00.000Z\",\"1962-07-27T12:00:00.000Z\",\"1962-08-14T12:00:00.000Z\",\"1962-08-28T12:00:00.000Z\",\"1962-09-14T12:00:00.000Z\",\"1962-09-28T12:00:00.000Z\",\"1962-10-17T12:00:00.000Z\",\"1962-10-29T12:00:00.000Z\",\"1962-11-15T12:00:00.000Z\",\"1962-11-28T12:00:00.000Z\",\"1962-12-14T12:00:00.000Z\",\"1963-03-14T12:00:00.000Z\",\"1963-03-29T12:00:00.000Z\",\"1963-04-17T12:00:00.000Z\",\"1963-04-29T12:00:00.000Z\",\"1963-05-14T12:00:00.000Z\",\"1963-05-28T12:00:00.000Z\",\"1963-06-14T12:00:00.000Z\",\"1963-06-28T12:00:00.000Z\",\"1963-07-15T12:00:00.000Z\",\"1963-07-29T12:00:00.000Z\",\"1963-08-16T12:00:00.000Z\",\"1963-08-28T12:00:00.000Z\",\"1963-09-16T12:00:00.000Z\",\"1963-09-27T12:00:00.000Z\",\"1963-10-14T12:00:00.000Z\",\"1963-10-29T12:00:00.000Z\",\"1963-11-14T12:00:00.000Z\",\"1963-11-29T12:00:00.000Z\",\"1963-12-17T12:00:00.000Z\",\"1963-12-28T12:00:00.000Z\",\"1964-01-14T12:00:00.000Z\",\"1964-01-29T12:00:00.000Z\",\"1964-02-14T12:00:00.000Z\",\"1964-02-28T12:00:00.000Z\",\"1964-03-13T12:00:00.000Z\",\"1964-03-26T12:00:00.000Z\",\"1964-04-14T12:00:00.000Z\",\"1964-04-28T12:00:00.000Z\",\"1964-05-28T12:00:00.000Z\",\"1964-06-29T12:00:00.000Z\",\"1964-07-14T12:00:00.000Z\",\"1964-07-28T12:00:00.000Z\",\"1964-08-28T12:00:00.000Z\",\"1964-09-14T12:00:00.000Z\",\"1964-09-28T12:00:00.000Z\",\"1964-10-14T12:00:00.000Z\",\"1964-10-28T12:00:00.000Z\",\"1964-11-13T12:00:00.000Z\",\"1964-11-27T12:00:00.000Z\",\"1964-12-14T12:00:00.000Z\",\"1965-01-14T12:00:00.000Z\",\"1965-01-28T12:00:00.000Z\",\"1965-02-15T12:00:00.000Z\",\"1965-02-28T12:00:00.000Z\",\"1965-03-01T12:00:00.000Z\",\"1965-03-15T12:00:00.000Z\",\"1965-04-14T12:00:00.000Z\",\"1965-04-29T12:00:00.000Z\",\"1965-05-14T12:00:00.000Z\",\"1965-05-28T12:00:00.000Z\",\"1965-06-14T12:00:00.000Z\",\"1965-06-28T12:00:00.000Z\",\"1965-07-14T12:00:00.000Z\",\"1965-08-13T12:00:00.000Z\",\"1965-08-27T12:00:00.000Z\",\"1965-09-14T12:00:00.000Z\",\"1965-09-29T12:00:00.000Z\",\"1965-10-14T12:00:00.000Z\",\"1965-10-28T12:00:00.000Z\",\"1965-11-15T12:00:00.000Z\",\"1965-11-30T12:00:00.000Z\",\"1965-12-14T12:00:00.000Z\",\"1965-12-28T12:00:00.000Z\",\"1966-01-17T12:00:00.000Z\",\"1966-01-28T12:00:00.000Z\",\"1966-02-14T12:00:00.000Z\",\"1966-03-14T12:00:00.000Z\",\"1966-03-28T12:00:00.000Z\",\"1966-04-14T12:00:00.000Z\",\"1966-04-28T12:00:00.000Z\",\"1966-05-14T12:00:00.000Z\",\"1966-05-28T12:00:00.000Z\",\"1966-06-15T12:00:00.000Z\",\"1966-06-30T12:00:00.000Z\",\"1966-07-14T12:00:00.000Z\",\"1966-07-28T12:00:00.000Z\",\"1966-08-15T12:00:00.000Z\",\"1966-08-29T12:00:00.000Z\",\"1966-09-15T12:00:00.000Z\",\"1966-09-28T12:00:00.000Z\",\"1966-10-14T12:00:00.000Z\",\"1966-11-02T12:00:00.000Z\",\"1966-11-14T12:00:00.000Z\",\"1966-11-28T12:00:00.000Z\",\"1966-12-14T12:00:00.000Z\",\"1966-12-28T12:00:00.000Z\",\"1967-01-16T12:00:00.000Z\",\"1967-02-14T12:00:00.000Z\",\"1967-02-28T12:00:00.000Z\",\"1967-03-14T12:00:00.000Z\",\"1967-03-30T12:00:00.000Z\",\"1967-04-13T12:00:00.000Z\",\"1967-06-28T12:00:00.000Z\",\"1967-07-15T12:00:00.000Z\",\"1967-07-28T12:00:00.000Z\",\"1967-08-14T12:00:00.000Z\",\"1967-08-28T12:00:00.000Z\",\"1967-09-14T12:00:00.000Z\",\"1967-09-28T12:00:00.000Z\",\"1967-10-13T12:00:00.000Z\",\"1967-12-28T12:00:00.000Z\",\"1968-01-15T12:00:00.000Z\",\"1968-01-29T12:00:00.000Z\",\"1968-02-14T12:00:00.000Z\",\"1968-02-28T12:00:00.000Z\",\"1968-03-14T12:00:00.000Z\",\"1968-03-28T12:00:00.000Z\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         "name": "Observation_well",
         "settings": {
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@@ -12,7 +12,6 @@
             "fill_after": null,
             "tmin": "1960-04-28T12:00:00",
             "tmax": "2015-06-29T09:00:00",
-            "norm": null,
             "time_offset": "0 nanoseconds"
         },
         "metadata": {
@@ -20,10 +19,9 @@
             "y": 0.0,
             "z": 0.0,
             "projection": null
-        },
-        "freq_original": null
+        }
     },
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+    "parameters": "{\n\"Recharge_A\": {\n\"initial\": 210.49852631334267,\n\"name\": \"Recharge\",\n\"optimal\": 1370.53366176721,\n\"pmin\": 1e-05,\n\"pmax\": 21049.852631334266,\n\"vary\": true,\n\"stderr\": 254.81749182046352\n},\n\"Recharge_n\": {\n\"initial\": 1.0,\n\"name\": \"Recharge\",\n\"optimal\": 0.9986682279367055,\n\"pmin\": 0.01,\n\"pmax\": 100.0,\n\"vary\": true,\n\"stderr\": 0.035768095898865405\n},\n\"Recharge_a\": {\n\"initial\": 10.0,\n\"name\": \"Recharge\",\n\"optimal\": 904.3182564067769,\n\"pmin\": 0.01,\n\"pmax\": 10000.0,\n\"vary\": true,\n\"stderr\": 266.06584456351686\n},\n\"Recharge_f\": {\n\"initial\": -1.0,\n\"name\": \"Recharge\",\n\"optimal\": -1.9999400783387697,\n\"pmin\": -2.0,\n\"pmax\": 0.0,\n\"vary\": true,\n\"stderr\": 0.2611423661668962\n},\n\"Wells_A\": {\n\"initial\": -0.0007559848400919654,\n\"name\": \"Wells\",\n\"optimal\": -0.00024534274288612323,\n\"pmin\": NaN,\n\"pmax\": 0.0,\n\"vary\": true,\n\"stderr\": 0.00013074446994138273\n},\n\"Wells_a\": {\n\"initial\": 100.0,\n\"name\": \"Wells\",\n\"optimal\": 618.6431444247911,\n\"pmin\": 0.001,\n\"pmax\": 10000.0,\n\"vary\": true,\n\"stderr\": 222.12499090242193\n},\n\"Wells_b\": {\n\"initial\": -15.6742618143276,\n\"name\": \"Wells\",\n\"optimal\": -16.61272957814348,\n\"pmin\": -29.67864298781234,\n\"pmax\": -12.246801452725835,\n\"vary\": true,\n\"stderr\": 0.6445051253428182\n},\n\"constant_d\": {\n\"initial\": 8.557529866479284,\n\"name\": \"constant\",\n\"optimal\": 12.058159761362845,\n\"pmin\": NaN,\n\"pmax\": NaN,\n\"vary\": true,\n\"stderr\": 0.6486204365976854\n},\n\"noise_alpha\": {\n\"initial\": 1.0,\n\"name\": \"noise\",\n\"optimal\": 57.00735161665356,\n\"pmin\": 1e-05,\n\"pmax\": 5000.0,\n\"vary\": true,\n\"stderr\": 4.823655554089738\n}\n}",
     "settings": {
         "tmin": "1960-04-28T12:00:00",
         "tmax": "2015-06-29T09:00:00",
@@ -37,9 +35,9 @@
     },
     "stressmodels": {
         "Recharge": {
-            "stressmodel": "RechargeModel",
+            "class": "RechargeModel",
             "prec": {
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                 "name": "prec",
                 "settings": {
                     "freq": "D",
@@ -49,8 +47,7 @@
                     "fill_before": "mean",
                     "fill_after": "mean",
                     "tmin": "1950-05-01T00:00:00",
-                    "tmax": "2015-06-29T09:00:00",
-                    "norm": null,
+                    "tmax": "2015-06-29T00:00:00",
                     "time_offset": "0 nanoseconds"
                 },
                 "metadata": {
@@ -58,11 +55,10 @@
                     "y": 0.0,
                     "z": 0.0,
                     "projection": null
-                },
-                "freq_original": "D"
+                }
             },
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                 "name": "evap",
                 "settings": {
                     "freq": "D",
@@ -72,8 +68,7 @@
                     "fill_before": "mean",
                     "fill_after": "mean",
                     "tmin": "1950-05-01T00:00:00",
-                    "tmax": "2015-06-29T09:00:00",
-                    "norm": null,
+                    "tmax": "2015-06-29T00:00:00",
                     "time_offset": "0 nanoseconds"
                 },
                 "metadata": {
@@ -81,34 +76,25 @@
                     "y": 0.0,
                     "z": 0.0,
                     "projection": null
-                },
-                "freq_original": "D"
+                }
+            },
+            "rfunc": {
+                "class": "Gamma",
+                "up": true,
+                "gain_scale_factor": 0.004832016915622979,
+                "cutoff": 0.999
             },
-            "rfunc": "Gamma",
-            "rfunc_kwargs": {},
             "name": "Recharge",
-            "recharge": "Linear",
-            "recharge_kwargs": {},
-            "cutoff": 0.999,
+            "recharge": {
+                "class": "Linear"
+            },
             "temp": null
         },
         "Wells": {
-            "stressmodel": "WellModel",
-            "rfunc": "HantushWellModel",
-            "rfunc_kwargs": {
-                "use_numba": false,
-                "quad": false
-            },
-            "name": "Wells",
-            "up": false,
-            "distances": [
-                2282.0709892551545,
-                2783.783396746234
-            ],
-            "cutoff": 0.999,
+            "class": "WellModel",
             "stress": [
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                     "name": "Extraction_2",
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@@ -119,7 +105,6 @@
                         "fill_after": 0.0,
                         "tmin": "1950-05-01T00:00:00",
                         "tmax": "2015-06-29T09:00:00",
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                         "time_offset": "0 nanoseconds"
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@@ -127,11 +112,10 @@
                         "y": 0.0,
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                         "projection": null
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-                    "freq_original": "D"
+                    }
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                     "name": "Extraction_3",
                     "settings": {
                         "freq": "D",
@@ -142,7 +126,6 @@
                         "fill_after": 0.0,
                         "tmin": "1950-05-01T00:00:00",
                         "tmax": "2015-06-29T09:00:00",
-                        "norm": null,
                         "time_offset": "0 nanoseconds"
                     },
                     "metadata": {
@@ -150,27 +133,41 @@
                         "y": 0.0,
                         "z": 0.0,
                         "projection": null
-                    },
-                    "freq_original": "D"
+                    }
                 }
             ],
+            "rfunc": {
+                "class": "HantushWellModel",
+                "up": false,
+                "gain_scale_factor": 10351.212913307714,
+                "cutoff": 0.999,
+                "use_numba": false,
+                "quad": false
+            },
+            "name": "Wells",
+            "distances": [
+                2282.0709892551545,
+                2783.783396746234
+            ],
+            "up": false,
             "sort_wells": true
         }
     },
     "constant": true,
     "noisemodel": {
-        "type": "NoiseModel"
+        "class": "NoiseModel",
+        "norm": true
     },
     "fit": {
-        "name": "LmfitSolve",
+        "class": "LmfitSolve",
         "pcov": "{\n\"Recharge_A\": {\n\"Recharge_A\": 64931.954137671986,\n\"Recharge_n\": -3.115146705757059,\n\"Recharge_a\": 56546.779071967976,\n\"Recharge_f\": -17.13969376259114,\n\"Wells_A\": -0.01357440229295682,\n\"Wells_a\": -30426.284293911755,\n\"Wells_b\": 67.2711916705495,\n\"constant_d\": 90.80308040407996,\n\"noise_alpha\": -54.24999563319319\n},\n\"Recharge_n\": {\n\"Recharge_A\": -3.1151467057573385,\n\"Recharge_n\": 0.0012793566842304325,\n\"Recharge_a\": -6.685032756008025,\n\"Recharge_f\": 0.00382309151008825,\n\"Wells_A\": 6.607607338638654e-07,\n\"Wells_a\": 2.118676214139587,\n\"Wells_b\": -0.003166521513949216,\n\"constant_d\": -0.01098155491936445,\n\"noise_alpha\": 0.002224296549152688\n},\n\"Recharge_a\": {\n\"Recharge_A\": 56546.77907197093,\n\"Recharge_n\": -6.685032756007907,\n\"Recharge_a\": 70791.0336432975,\n\"Recharge_f\": -41.66184125367875,\n\"Wells_A\": -0.01215578634760762,\n\"Wells_a\": -31370.3473238606,\n\"Wells_b\": 59.794107115657866,\n\"constant_d\": 137.06179513583342,\n\"noise_alpha\": -62.84172759552135\n},\n\"Recharge_f\": {\n\"Recharge_A\": -17.139693762596448,\n\"Recharge_n\": 0.0038230915100883763,\n\"Recharge_a\": -41.66184125368228,\n\"Recharge_f\": 0.06819533540724527,\n\"Wells_A\": 5.147319355662015e-06,\n\"Wells_a\": 18.389650397705612,\n\"Wells_b\": -0.025504806919609182,\n\"constant_d\": -0.15991435583120542,\n\"noise_alpha\": 0.1559293091778529\n},\n\"Wells_A\": {\n\"Recharge_A\": -0.013574402292954965,\n\"Recharge_n\": 6.607607338633802e-07,\n\"Recharge_a\": -0.01215578634760186,\n\"Recharge_f\": 5.147319355653647e-06,\n\"Wells_A\": 1.709411642025313e-08,\n\"Wells_a\": 0.027233030710046846,\n\"Wells_b\": -8.419944597639996e-05,\n\"constant_d\": -2.419610872092437e-05,\n\"noise_alpha\": -3.1159240679773226e-06\n},\n\"Wells_a\": {\n\"Recharge_A\": -30426.284293909775,\n\"Recharge_n\": 2.1186762141388624,\n\"Recharge_a\": -31370.347323852337,\n\"Recharge_f\": 18.38965039769277,\n\"Wells_A\": 0.027233030710048192,\n\"Wells_a\": 49339.51158340102,\n\"Wells_b\": -134.19492417704436,\n\"constant_d\": -67.70583523313263,\n\"noise_alpha\": 35.32303652847398\n},\n\"Wells_b\": {\n\"Recharge_A\": 67.27119167054028,\n\"Recharge_n\": -0.0031665215139468185,\n\"Recharge_a\": 59.79410711562938,\n\"Recharge_f\": -0.025504806919567843,\n\"Wells_A\": -8.419944597639989e-05,\n\"Wells_a\": -134.19492417703762,\n\"Wells_b\": 0.4153868565931618,\n\"constant_d\": 0.1185134173195892,\n\"noise_alpha\": 0.014705845235495544\n},\n\"constant_d\": {\n\"Recharge_A\": 90.80308040409139,\n\"Recharge_n\": -0.010981554919364447,\n\"Recharge_a\": 137.06179513583814,\n\"Recharge_f\": -0.15991435583120026,\n\"Wells_A\": -2.4196108720944147e-05,\n\"Wells_a\": -67.70583523316213,\n\"Wells_b\": 0.11851341731968694,\n\"constant_d\": 0.4207084707721721,\n\"noise_alpha\": -0.3774388912649904\n},\n\"noise_alpha\": {\n\"Recharge_A\": -54.249995633207384,\n\"Recharge_n\": 0.002224296549153451,\n\"Recharge_a\": -62.841727595535765,\n\"Recharge_f\": 0.15592930917786116,\n\"Wells_A\": -3.1159240679577574e-06,\n\"Wells_a\": 35.323036528505895,\n\"Wells_b\": 0.014705845235398368,\n\"constant_d\": -0.37743889126502417,\n\"noise_alpha\": 23.267652904500775\n}\n}",
         "nfev": 199,
         "obj_func": 23.066877109239798
     },
     "file_info": {
         "date_created": "2022-11-10T16:42:26.390008",
-        "date_modified": "2022-11-10T16:46:56.317284",
-        "pastas_version": "0.21.0",
-        "owner": "david"
+        "date_modified": "2023-01-30T16:21:10.402052",
+        "pastas_version": "0.23.0",
+        "owner": "raoulcollenteur"
     }
 }
\ No newline at end of file
diff --git a/tests/fixtures.py b/tests/fixtures.py
new file mode 100644
index 00000000..cb06c50f
--- /dev/null
+++ b/tests/fixtures.py
@@ -0,0 +1,75 @@
+import pytest
+from pandas import Series, read_csv, to_datetime
+
+import pastas as ps
+
+rain = read_csv("tests/data/rain.csv", index_col=0, parse_dates=True).squeeze("columns")
+evap = read_csv("tests/data/evap.csv", index_col=0, parse_dates=True).squeeze("columns")
+obs = (
+    read_csv("tests/data/obs.csv", index_col=0, parse_dates=True)
+    .squeeze("columns")
+    .dropna()
+)
+
+
+@pytest.fixture
+def ml_empty() -> ps.Model:
+    ml_empty = ps.Model(obs, name="Test_Model")
+    return ml_empty
+
+
+@pytest.fixture
+def ml() -> ps.Model:
+    ml = ps.Model(obs, name="Test_Model")
+    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
+    ml.add_stressmodel(sm)
+    return ml
+
+
+@pytest.fixture
+def ml_sm() -> ps.Model:
+    ml_sm = ps.Model(obs.dropna(), name="Test_Model")
+    sm1 = ps.StressModel(rain, rfunc=ps.Exponential(), name="prec", settings="prec")
+    sm2 = ps.StressModel(evap, rfunc=ps.Exponential(), name="evap", settings="evap")
+    ml_sm.add_stressmodel([sm1, sm2])
+    return ml_sm
+
+
+@pytest.fixture
+def ml_no_settings() -> ps.Model:
+    ml_no_settings = ps.Model(obs.dropna(), name="Test_Model")
+    sm1 = ps.StressModel(rain, rfunc=ps.Exponential(), name="prec")
+    sm2 = ps.StressModel(evap, rfunc=ps.Exponential(), name="evap")
+    ml_no_settings.add_stressmodel([sm1, sm2])
+    return ml_no_settings
+
+
+@pytest.fixture
+def rm() -> ps.RechargeModel:
+    rm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
+    return rm
+
+
+@pytest.fixture
+def sm_prec() -> ps.StressModel:
+    sm_prec = ps.StressModel(rain, rfunc=ps.Exponential(), name="prec")
+    return sm_prec
+
+
+@pytest.fixture
+def sm_evap() -> ps.StressModel:
+    sm_evap = ps.StressModel(evap, rfunc=ps.Exponential(), name="evap")
+    return sm_evap
+
+
+@pytest.fixture
+def collwell_data() -> Series:
+    """Example Tree C from the publication."""
+    n = 9
+    x = (
+        ["200{}-04-01".format(t) for t in range(0, n)]
+        + ["200{}-08-02".format(t) for t in range(0, n)]
+        + ["201{}-08-01".format(t) for t in range(0, n)]
+    )
+    y = [1] * n + [2] * n + [3] * n
+    return Series(y, index=to_datetime(x))
diff --git a/tests/test_001.py b/tests/test_001.py
index 0e34c66a..2dd9c93b 100644
--- a/tests/test_001.py
+++ b/tests/test_001.py
@@ -1,4 +1,2 @@
-def test_import():
+def test_import() -> None:
     import pastas as ps
-
-    return
diff --git a/tests/test_comparison.py b/tests/test_comparison.py
index ddd413e3..5e19bf8b 100644
--- a/tests/test_comparison.py
+++ b/tests/test_comparison.py
@@ -1,31 +1,19 @@
-from pandas import read_csv
-
 import pastas as ps
 
-rain = read_csv("tests/data/rain.csv", index_col=0, parse_dates=True).squeeze("columns")
-evap = read_csv("tests/data/evap.csv", index_col=0, parse_dates=True).squeeze("columns")
-obs = read_csv("tests/data/obs.csv", index_col=0, parse_dates=True).squeeze("columns")
-
-ml1 = ps.Model(obs.dropna(), name="Test_Model")
-sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-ml1.add_stressmodel(sm)
-ml1.solve(report=False)
-
-ml2 = ps.Model(obs.dropna(), name="Test_Model")
-sm1 = ps.StressModel(rain, rfunc=ps.Exponential(), name="prec")
-sm2 = ps.StressModel(evap, rfunc=ps.Exponential(), name="evap")
-ml2.add_stressmodel([sm1, sm2])
-ml2.solve(report=False)
+from .fixtures import ml, ml_sm
 
 
-def test_comparison_plot():
-    mc = ps.CompareModels(models=[ml1, ml2])
-    mc.plot(legend_kwargs={"ncol": 2})
-    return
+def test_comparison_plot(ml, ml_sm) -> None:
+    ml.solve()
+    ml_sm.solve()
+    mc = ps.CompareModels(models=[ml, ml_sm])
+    _ = mc.plot(legend_kwargs={"ncol": 2})
 
 
-def test_comparison_plot_custom():
-    mc = ps.CompareModels(models=[ml1, ml2])
+def test_comparison_plot_custom(ml, ml_sm) -> None:
+    ml.solve()
+    ml_sm.solve()
+    mc = ps.CompareModels(models=[ml, ml_sm])
     mosaic = [
         ["ose", "ose", "met"],
         ["sim", "sim", "tab"],
@@ -56,4 +44,3 @@ def test_comparison_plot_custom():
             mc.axes["con1"],
         ]
     )
-    return
diff --git a/tests/test_examples.py b/tests/test_examples.py
index 438551ee..3758db50 100644
--- a/tests/test_examples.py
+++ b/tests/test_examples.py
@@ -15,7 +15,7 @@
 
 
 @pytest.mark.parametrize("file", files)
-def test_example(file):
+def test_example(file) -> None:
     cwd = os.getcwd()
     os.chdir(pathname)
     try:
diff --git a/tests/test_gxg.py b/tests/test_gxg.py
index ac643aaa..4e6a0473 100644
--- a/tests/test_gxg.py
+++ b/tests/test_gxg.py
@@ -12,39 +12,39 @@
 
 
 class TestGXG(object):
-    def test_ghg(self):
+    def test_ghg(self) -> None:
         idx = pd.to_datetime(["20160114", "20160115", "20160128", "20160214"])
         s = pd.Series([10.0, 3.0, 30.0, 20.0], index=idx)
         v = ps.stats.ghg(s, min_n_meas=1, min_n_years=1)
         assert v == 30.0
 
-    def test_ghg_ffill(self):
+    def test_ghg_ffill(self) -> None:
         idx = pd.to_datetime(["20160101", "20160115", "20160130"])
         s = pd.Series([0.0, 0.0, 10.0], index=idx)
         v = ps.stats.ghg(s, fill_method="ffill", limit=15, min_n_meas=1, min_n_years=1)
         assert v == 0.0
 
-    def test_ghg_bfill(self):
+    def test_ghg_bfill(self) -> None:
         idx = pd.to_datetime(["20160101", "20160115", "20160130"])
         s = pd.Series([0.0, 0.0, 10.0], index=idx)
         v = ps.stats.ghg(s, fill_method="bfill", limit=15, min_n_meas=1, min_n_years=1)
         # TODO is this correct?
         assert v == 10.0
 
-    def test_ghg_linear(self):
+    def test_ghg_linear(self) -> None:
         idx = pd.to_datetime(["20160101", "20160110", "20160120", "20160130"])
         s = pd.Series([0.0, 0.0, 10.0, 10.0], index=idx)
         v = ps.stats.ghg(s, fill_method="linear", min_n_meas=1, min_n_years=1, limit=8)
         # TODO is this correct?
         assert v == 10.0
 
-    def test_ghg_len_yearly(self):
+    def test_ghg_len_yearly(self) -> None:
         idx = pd.date_range("20000101", "20550101", freq="d")
         s = pd.Series(np.ones(len(idx)), index=idx)
         v = ps.stats.ghg(s, output="yearly")
         assert v.notna().sum() == 55
 
-    def test_glg(self):
+    def test_glg(self) -> None:
         idx = pd.date_range("20000101", "20550101", freq="d")
         s = pd.Series(
             [x.month + x.day for x in idx],
@@ -53,7 +53,7 @@ def test_glg(self):
         v = ps.stats.glg(s, year_offset="a")
         assert v == 16.0
 
-    def test_glg_fill_limit(self):
+    def test_glg_fill_limit(self) -> None:
         idx = pd.to_datetime(["20170115", "20170130", "20200101"])
         s = pd.Series(np.ones(len(idx)), index=idx)
         v = ps.stats.glg(
@@ -66,7 +66,7 @@ def test_glg_fill_limit(self):
         )
         assert v.notna().sum() == 2
 
-    def test_glg_fill_limit_null(self):
+    def test_glg_fill_limit_null(self) -> None:
         idx = pd.to_datetime(["20170101", "20170131", "20200101"])
         s = pd.Series(np.ones(len(idx)), index=idx)
         v = ps.stats.glg(
@@ -79,7 +79,7 @@ def test_glg_fill_limit_null(self):
         )
         assert v.notna().sum() == 3
 
-    def test_gvg(self):
+    def test_gvg(self) -> None:
         idx = pd.to_datetime(["20170314", "20170328", "20170414", "20170428"])
         s = pd.Series([1.0, 2.0, 3.0, 4], index=idx)
         v = ps.stats.gvg(
@@ -87,7 +87,7 @@ def test_gvg(self):
         )
         assert v == 2.0
 
-    def test_gvg_nan(self):
+    def test_gvg_nan(self) -> None:
         idx = pd.to_datetime(["20170228", "20170428", "20170429"])
         s = pd.Series([1.0, 2.0, 3.0], index=idx)
         v = ps.stats.gvg(
@@ -95,7 +95,7 @@ def test_gvg_nan(self):
         )
         assert np.isnan(v)
 
-        # def test_gxg_series(self):
+        # def test_gxg_series(self) -> None:
         #     s = pd.read_csv('data\\hseries_gxg.csv', index_col=0, header=0,
         #                     parse_dates=True, dayfirst=True).squeeze()
         #     ps = Model(s)
@@ -115,7 +115,7 @@ def test_gvg_nan(self):
         #            'GVG: {gvg:.2f} m+NAP\n').format(
         #         ghg=-3.23, glg=-3.82, gvg=-3.43))
 
-        # def test_gxg_series(self, capsys):
+        # def test_gxg_series(self, capsys) -> None:
         #     s = pd.read_csv(r'data/hseries_gxg.csv', index_col=0, header=0,
         #         parse_dates=True, dayfirst=True).squeeze()
         #     ps = Model(s)
diff --git a/tests/test_model.py b/tests/test_model.py
index 6ff13c51..d270e01c 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -1,10 +1,10 @@
+import pytest
 from pandas import read_csv
-from pandas.testing import assert_series_equal
 
 import pastas as ps
 
-rain = read_csv("tests/data/rain.csv", index_col=0, parse_dates=True).squeeze("columns")
-evap = read_csv("tests/data/evap.csv", index_col=0, parse_dates=True).squeeze("columns")
+from .fixtures import ml, ml_empty, ml_no_settings, sm_evap, sm_prec
+
 obs = (
     read_csv("tests/data/obs.csv", index_col=0, parse_dates=True)
     .squeeze("columns")
@@ -12,98 +12,63 @@
 )
 
 
-def test_create_model():
+def test_create_model() -> None:
     ml = ps.Model(obs, name="Test_Model")
-    return None
 
 
-def test_add_stressmodel():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
-    return None
+def test_add_stressmodel(ml_empty, sm_prec) -> None:
+    ml_empty.add_stressmodel(sm_prec)
 
 
-def test_add_stressmodels():
-    ml = ps.Model(obs, name="Test_Model")
-    sm1 = ps.StressModel(rain, rfunc=ps.Exponential(), name="prec")
-    sm2 = ps.StressModel(evap, rfunc=ps.Exponential(), name="evap")
-    ml.add_stressmodel([sm1, sm2])
-    return None
+def test_add_stressmodels(ml_empty, sm_prec, sm_evap) -> None:
+    ml_empty.add_stressmodel([sm_prec, sm_evap])
 
 
-def test_del_stressmodel():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_del_stressmodel(ml) -> None:
     ml.del_stressmodel("rch")
-    return None
 
 
-def test_add_constant():
-    ml = ps.Model(obs, name="Test_Model")
-    ml.add_constant(ps.Constant())
-    return None
+def test_add_constant(ml_empty) -> None:
+    ml_empty.add_constant(ps.Constant())
 
 
-def test_del_constant():
-    ml = ps.Model(obs, name="Test_Model")
-    ml.del_constant()
-    return None
+def test_del_constant(ml_empty) -> None:
+    ml_empty.del_constant()
 
 
-def test_add_noisemodel():
-    ml = ps.Model(obs, name="Test_Model")
-    ml.add_noisemodel(ps.NoiseModel())
-    return None
+def test_add_noisemodel(ml_empty) -> None:
+    ml_empty.add_noisemodel(ps.NoiseModel())
 
 
-def test_armamodel():
+def test_armamodel() -> None:
     ml = ps.Model(obs, name="Test_Model", noisemodel=False)
     ml.add_noisemodel(ps.ArmaModel())
     ml.solve()
-    return None
 
 
-def test_del_noisemodel():
-    ml = ps.Model(obs, name="Test_Model")
-    ml.del_noisemodel()
-    return None
+def test_del_noisemodel(ml_empty) -> None:
+    ml_empty.del_noisemodel()
 
 
-def test_solve_model():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_solve_model(ml) -> None:
     ml.solve()
-    return None
 
 
-def test_solve_empty_model():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_solve_empty_model(ml) -> None:
     try:
         ml.solve(tmin="2016")
     except ValueError as e:
-        if e.args[0].startswith("Calibration series "):
+        if e.args[0].startswith("Calibration series"):
             return None
         else:
             raise e
 
 
-def test_save_model():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_save_model(ml) -> None:
     ml.to_file("test.pas")
-    return None
 
 
-def test_load_model():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_load_model(ml) -> None:
     ml.solve()
     # add some fictitious tiny value for testing float precision
     ml.parameters.loc["rch_f", "pmax"] = 1.23456789e-10
@@ -120,97 +85,52 @@ def test_load_model():
     # check if parameters and pcov dataframes are equal
     assert ml.parameters.equals(ml2.parameters)
     assert ml.fit.pcov.equals(ml2.fit.pcov)
-    return None
 
 
-def test_model_copy():
-    ml = ps.Model(obs, name="Test_Model")
-    ml.copy()
-    return None
+def test_model_copy(ml_empty) -> None:
+    ml_empty.copy()
 
 
-def test_get_block():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_get_block(ml) -> None:
     ml.get_block_response("rch")
-    return None
 
 
-def test_get_step():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_get_step(ml) -> None:
     ml.get_step_response("rch")
-    return None
 
 
-def test_get_contribution():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_get_contribution(ml) -> None:
     ml.get_contribution("rch")
-    return None
 
 
-def test_get_stress():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_get_stress(ml) -> None:
     ml.get_stress("rch")
-    return None
 
 
-def test_simulate():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_simulate(ml) -> None:
     ml.simulate()
-    return None
 
 
-def test_residuals():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_residuals(ml) -> None:
     ml.residuals()
-    return None
 
 
-def test_noise():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_noise(ml) -> None:
     ml.noise()
-    return None
 
 
-def test_observations():
-    ml = ps.Model(obs, name="Test_Model")
-    ml.observations()
-    return None
+def test_observations(ml_empty) -> None:
+    ml_empty.observations()
 
 
-def test_get_output_series():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_get_output_series(ml) -> None:
     ml.get_output_series()
-    return None
 
 
-def test_get_output_series_arguments():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
+def test_get_output_series_arguments(ml) -> None:
     ml.get_output_series(split=False, add_contributions=False)
-    return None
 
 
-def test_load_old_wellmodel():
-    # model with new parameter b_new = np.log(b_old)
-    ml_new = ps.io.load("./tests/data/wellmodel_new.pas")
-    # model with old parameter b_old
-    ml_old = ps.io.load("./tests/data/wellmodel_old.pas")
-    assert_series_equal(ml_new.simulate(), ml_old.simulate())
-    return
+def test_model_sim_w_nans_error(ml_no_settings):
+    with pytest.raises(ValueError) as e_info:
+        ml_no_settings.solve()
diff --git a/tests/test_notebooks.py b/tests/test_notebooks.py
index 6a3f506b..6f63bb98 100644
--- a/tests/test_notebooks.py
+++ b/tests/test_notebooks.py
@@ -21,14 +21,13 @@
 
 @pytest.mark.notebooks
 @pytest.mark.parametrize("file", files)
-def test_notebook(file):
+def test_notebook(file) -> None:
     cwd = os.getcwd()
 
     os.chdir(pathname)
     if file not in [
         "00_prepare_timeseries.ipynb",
         "12_emcee_uncertainty.ipynb",
-        "19_reservoir.ipynb",
     ]:
         try:
             # run autotest on each notebook
diff --git a/tests/test_plots.py b/tests/test_plots.py
index fec1eb76..30d6e7ee 100644
--- a/tests/test_plots.py
+++ b/tests/test_plots.py
@@ -1,83 +1,59 @@
-from pandas import read_csv
+import matplotlib as mpl
 
-import pastas as ps
 from pastas.plots import TrackSolve, compare
 
-rain = read_csv("tests/data/rain.csv", index_col=0, parse_dates=True).squeeze("columns")
-evap = read_csv("tests/data/evap.csv", index_col=0, parse_dates=True).squeeze("columns")
-obs = (
-    read_csv("tests/data/obs.csv", index_col=0, parse_dates=True)
-    .squeeze("columns")
-    .dropna()
-)
+from .fixtures import ml
 
+mpl.use("Agg")  # prevent _tkinter.TclError: Can't find a usable tk.tcl error
 
-def create_model():
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Gamma(), name="rch")
-    ml.add_stressmodel(sm)
-    return ml
 
+def test_plot(ml) -> None:
+    _ = ml.plot()
 
-def test_plot():
-    ml = create_model()
-    ml.plot()
 
+def test_decomposition(ml) -> None:
+    _ = ml.plots.decomposition(min_ylim_diff=0.1)
 
-def test_decomposition():
-    ml = create_model()
-    ml.plots.decomposition(min_ylim_diff=0.1)
 
+def test_results(ml) -> None:
+    _ = ml.plots.results()
 
-def test_results():
-    ml = create_model()
-    ml.plots.results()
 
+def test_stacked_results(ml) -> None:
+    _ = ml.plots.stacked_results()
 
-def test_stacked_results():
-    ml = create_model()
-    ml.plots.stacked_results()
 
+def test_block_response(ml) -> None:
+    _ = ml.plots.block_response()
 
-def test_block_response():
-    ml = create_model()
-    ml.plots.block_response()
 
+def test_step_response(ml) -> None:
+    _ = ml.plots.step_response()
 
-def test_step_response():
-    ml = create_model()
-    ml.plots.step_response()
 
+def test_diagnostics(ml) -> None:
+    _ = ml.plots.diagnostics(acf_options=dict(min_obs=10))
 
-def test_diagnostics():
-    ml = create_model()
-    ml.plots.diagnostics(acf_options=dict(min_obs=10))
 
+def test_stresses(ml) -> None:
+    _ = ml.plots.stresses()
 
-def test_stresses():
-    ml = create_model()
-    ml.plots.stresses()
 
+def test_contributions_pie(ml) -> None:
+    _ = ml.plots.contributions_pie()
 
-def test_contributions_pie():
-    ml = create_model()
-    ml.plots.contributions_pie()
 
-
-def test_compare():
-    ml = create_model()
+def test_compare(ml) -> None:
     ml2 = ml.copy()
     ml2.name = "Test_Model2"
     models = [ml, ml2]
-    compare(models)
+    _ = compare(models)
 
 
-def test_tracksolve():
-    ml = create_model()
+def test_tracksolve(ml) -> None:
     track = TrackSolve(ml)
-    ml.solve(callback=track.plot_track_solve)
+    _ = ml.solve(callback=track.plot_track_solve)
 
 
-def test_summary_pdf():
-    ml = create_model()
-    ml.plots.summary_pdf()
+def test_summary_pdf(ml) -> None:
+    _ = ml.plots.summary_pdf()
diff --git a/tests/test_qgxg.py b/tests/test_qgxg.py
index d04ae1ba..0aa281a3 100644
--- a/tests/test_qgxg.py
+++ b/tests/test_qgxg.py
@@ -12,46 +12,46 @@
 
 
 class TestQGXG(object):
-    def test_q_ghg(self):
+    def test_q_ghg(self) -> None:
         n = 101
         idx = pd.date_range("20160101", freq="d", periods=n)
         s = pd.Series(np.arange(n), index=idx)
         v = ps.stats.q_ghg(s, q=0.94)
         assert v == 94.0
 
-    def test_q_glg(self):
+    def test_q_glg(self) -> None:
         n = 101
         idx = pd.date_range("20160101", freq="d", periods=n)
         s = pd.Series(np.arange(n), index=idx)
         v = ps.stats.q_glg(s, q=0.06)
         assert v == 6.0
 
-    def test_q_ghg_nan(self):
+    def test_q_ghg_nan(self) -> None:
         idx = pd.date_range("20160101", freq="d", periods=4)
         s = pd.Series([1, np.nan, 3, np.nan], index=idx)
         v = ps.stats.q_ghg(s, q=0.5)
         assert v == 2.0
 
-    def test_q_gvg(self):
+    def test_q_gvg(self) -> None:
         idx = pd.to_datetime(["20160320", "20160401", "20160420"])
         s = pd.Series([0, 5, 10], index=idx)
         v = ps.stats.q_gvg(s)
         assert v == 2.5
 
-    def test_q_gvg_nan(self):
+    def test_q_gvg_nan(self) -> None:
         idx = pd.to_datetime(["20160820", "20160901", "20161120"])
         s = pd.Series([0, 5, 10], index=idx)
         v = ps.stats.q_gvg(s)
         assert np.isnan(v)
 
-    def test_q_glg_tmin(self):
+    def test_q_glg_tmin(self) -> None:
         tmin = "20160301"
         idx = pd.date_range("20160101", "20160331", freq="d")
         s = pd.Series(np.arange(len(idx)), index=idx)
         v = ps.stats.q_glg(s, q=0.06, tmin=tmin)
         assert v == 61.8
 
-    def test_q_ghg_tmax(self):
+    def test_q_ghg_tmax(self) -> None:
         n = 101
         tmax = "20160301"
         idx = pd.date_range("20160101", freq="d", periods=n)
@@ -59,7 +59,7 @@ def test_q_ghg_tmax(self):
         v = ps.stats.q_ghg(s, q=0.94, tmax=tmax)
         assert v == 56.4
 
-    def test_q_gvg_tmin_tmax(self):
+    def test_q_gvg_tmin_tmax(self) -> None:
         tmin = "20170301"
         tmax = "20170401"
         idx = pd.to_datetime(["20160401", "20170401", "20180401"])
@@ -67,7 +67,7 @@ def test_q_gvg_tmin_tmax(self):
         v = ps.stats.q_gvg(s, tmin=tmin, tmax=tmax)
         assert v == 5
 
-    def test_q_gxg_series(self):
+    def test_q_gxg_series(self) -> None:
         s = pd.read_csv(
             "tests/data/hseries_gxg.csv",
             index_col=0,
diff --git a/tests/test_recharge.py b/tests/test_recharge.py
index 40190251..20f754f2 100644
--- a/tests/test_recharge.py
+++ b/tests/test_recharge.py
@@ -3,6 +3,8 @@
 
 import pastas as ps
 
+from .fixtures import ml, ml_empty
+
 # Load series before
 rain = (
     read_csv("tests/data/rain.csv", index_col=0, parse_dates=True)
@@ -16,93 +18,66 @@
     .loc["2005":]
     * 1e3
 )
-obs = (
-    read_csv("tests/data/obs.csv", index_col=0, parse_dates=True)
-    .squeeze("columns")
-    .dropna()
-)
 temp = Series(index=evap.index, data=sin(arange(evap.size) / 365 * 6), dtype=float)
 
 
-def create_rechargemodel():
-    rm = ps.RechargeModel(prec=rain, evap=evap)
-    return rm
-
-
-def default_model():
-    ml = ps.Model(obs, name="rch_model")
-    rm = create_rechargemodel()
-    ml.add_stressmodel(rm)
-    return ml
-
-
-def test_model_solve():
-    ml = default_model()
+def test_model_solve(ml) -> None:
     ml.solve()
-    return
 
 
-def test_model_copy():
-    ml = default_model()
+def test_model_copy(ml) -> None:
     ml.copy()
-    return
 
 
-def test_berendrecht():
-    ml = ps.Model(obs, name="rch_model")
+def test_berendrecht(ml_empty) -> None:
     rm = ps.RechargeModel(prec=rain, evap=evap, recharge=ps.rch.Berendrecht())
-    ml.add_stressmodel(rm)
-    ml.solve()
-    return
+    ml_empty.add_stressmodel(rm)
+    ml_empty.solve()
+
+
+def test_peterson(ml_empty) -> None:
+    rm = ps.RechargeModel(prec=rain, evap=evap, recharge=ps.rch.Peterson())
+    ml_empty.add_stressmodel(rm)
+    ml_empty.solve()
 
 
-def test_linear():
-    ml = ps.Model(obs, name="rch_model")
+def test_linear(ml_empty) -> None:
     rm = ps.RechargeModel(prec=rain, evap=evap, recharge=ps.rch.Linear())
-    ml.add_stressmodel(rm)
-    ml.solve()
-    return
+    ml_empty.add_stressmodel(rm)
+    ml_empty.solve()
 
 
-def test_flexmodel():
-    ml = ps.Model(obs, name="rch_model")
+def test_flexmodel(ml_empty) -> None:
     rm = ps.RechargeModel(prec=rain, evap=evap, recharge=ps.rch.FlexModel())
-    ml.add_stressmodel(rm)
-    ml.solve()
-    return
+    ml_empty.add_stressmodel(rm)
+    ml_empty.solve()
 
 
-def test_flexmodel_no_interception():
-    ml = ps.Model(obs, name="rch_model")
+def test_flexmodel_no_interception(ml_empty) -> None:
     rm = ps.RechargeModel(
         prec=rain, evap=evap, recharge=ps.rch.FlexModel(interception=False)
     )
-    ml.add_stressmodel(rm)
-    ml.solve()
-    return
+    ml_empty.add_stressmodel(rm)
+    ml_empty.solve()
 
 
-def test_flexmodel_gw_uptake():
-    ml = ps.Model(obs, name="rch_model")
+def test_flexmodel_gw_uptake(ml_empty) -> None:
     rm = ps.RechargeModel(
         prec=rain, evap=evap, recharge=ps.rch.FlexModel(gw_uptake=True)
     )
-    ml.add_stressmodel(rm)
-    ml.solve()
-    return
+    ml_empty.add_stressmodel(rm)
+    ml_empty.solve()
 
 
-def test_flexmodel_snow():
-    ml = ps.Model(obs, name="rch_model")
+def test_flexmodel_snow(ml_empty) -> None:
     rm = ps.RechargeModel(
         prec=rain, evap=evap, temp=temp, recharge=ps.rch.FlexModel(snow=True)
     )
-    ml.add_stressmodel(rm)
-    ml.solve()
-    return
+    ml_empty.add_stressmodel(rm)
+    ml_empty.solve()
 
 
-def test_flexmodel_water_balance_rootzone():
+def test_flexmodel_water_balance_rootzone() -> None:
     rch = ps.rch.FlexModel()
     e = evap.to_numpy()
     p = rain.to_numpy()
@@ -111,7 +86,7 @@ def test_flexmodel_water_balance_rootzone():
     assert isclose(error, 0)
 
 
-def test_flexmodel_water_balance_snow():
+def test_flexmodel_water_balance_snow() -> None:
     rch = ps.rch.FlexModel()
     p = rain.to_numpy()
     t = temp.to_numpy()
@@ -120,28 +95,10 @@ def test_flexmodel_water_balance_snow():
     assert isclose(error, 0)
 
 
-def test_flexmodel_water_balance_interception():
+def test_flexmodel_water_balance_interception() -> None:
     rch = ps.rch.FlexModel()
     e = evap.to_numpy()
     p = rain.to_numpy()
     si, ei, pi = rch.get_interception_balance(p, e)
     error = si[0] - si[-1] + (pi + ei)[:-1].sum()
     assert isclose(error, 0)
-
-
-def test_peterson():
-    ml = ps.Model(obs, name="rch_model")
-    rm = ps.RechargeModel(prec=rain, evap=evap, recharge=ps.rch.Peterson())
-    ml.add_stressmodel(rm)
-    ml.solve()
-    return
-
-
-# don't test water balance because of forward Euler
-# def test_peterson_water_balance():
-#     rch = ps.rch.Peterson()
-#     e = evap.to_numpy()
-#     p = rain.to_numpy()
-#     r, sm, ea, pe = rch.get_recharge(p, e)
-#     error = (sm[0] - sm[-1] + (r + ea + pe)[:-1].sum())
-#     assert isclose(error, 0)
diff --git a/tests/test_rfuncs.py b/tests/test_rfuncs.py
index 8bcba84d..aacaca89 100644
--- a/tests/test_rfuncs.py
+++ b/tests/test_rfuncs.py
@@ -4,7 +4,7 @@
 
 
 @pytest.mark.parametrize("rfunc_name", ps.rfunc.__all__)
-def test_rfunc(rfunc_name):
+def test_rfunc(rfunc_name) -> None:
     if rfunc_name not in []:
         rfunc = getattr(ps.rfunc, rfunc_name)()
         if rfunc_name == "HantushWellModel":
@@ -15,7 +15,7 @@ def test_rfunc(rfunc_name):
 
 
 @pytest.mark.parametrize("rfunc_name", ps.rfunc.__all__)
-def test_to_dict_rfuncs(rfunc_name):
+def test_to_dict_rfuncs(rfunc_name) -> None:
     rfunc1 = getattr(ps.rfunc, rfunc_name)(cutoff=0.5)
 
     # Create the exact same instance using to_dict
diff --git a/tests/test_signatures.py b/tests/test_signatures.py
index f9a726dc..c54d14dd 100644
--- a/tests/test_signatures.py
+++ b/tests/test_signatures.py
@@ -3,6 +3,8 @@
 
 import pastas as ps
 
+from .fixtures import collwell_data
+
 
 def test_summary():
     """Test all signatures for minimal functioning."""
@@ -11,41 +13,24 @@ def test_summary():
     ps.stats.signatures.summary(head)
 
 
-def collwell_data():
-    """Example Tree C from the publication."""
-    n = 9
-    x = (
-        ["200{}-04-01".format(t) for t in range(0, n)]
-        + ["200{}-08-02".format(t) for t in range(0, n)]
-        + ["201{}-08-01".format(t) for t in range(0, n)]
-    )
-    y = [1] * n + [2] * n + [3] * n
-    obs = pd.Series(y, index=pd.to_datetime(x))
-    return obs
-
-
-def test_colwell_components():
-    obs = collwell_data()
-    ps.stats.signatures.colwell_components(obs, freq="4M", bins=3)
+def test_colwell_components(collwell_data) -> None:
+    ps.stats.signatures.colwell_components(collwell_data, freq="4M", bins=3)
     return
 
 
-def test_colwell_predictability():
+def test_colwell_predictability(collwell_data) -> None:
     """Example Tree C from the publication."""
-    obs = collwell_data()
-    p = ps.stats.signatures.colwell_components(obs, freq="4M", bins=3)[0]
+    p = ps.stats.signatures.colwell_components(collwell_data, freq="4M", bins=3)[0]
     assert p.round(2) == 1.0
 
 
-def test_colwell_constancy():
+def test_colwell_constancy(collwell_data) -> None:
     """Example Tree C from the publication."""
-    obs = collwell_data()
-    c = ps.stats.signatures.colwell_components(obs, freq="4M", bins=3)[1]
+    c = ps.stats.signatures.colwell_components(collwell_data, freq="4M", bins=3)[1]
     assert c.round(2) == 0.42
 
 
-def test_colwell_contingency():
+def test_colwell_contingency(collwell_data) -> None:
     """Example Tree C from the publication."""
-    obs = collwell_data()
-    m = ps.stats.signatures.colwell_components(obs, freq="4M", bins=3)[2]
+    m = ps.stats.signatures.colwell_components(collwell_data, freq="4M", bins=3)[2]
     assert m.round(2) == 0.58
diff --git a/tests/test_solvers.py b/tests/test_solvers.py
index d656ca8f..c13c6f78 100644
--- a/tests/test_solvers.py
+++ b/tests/test_solvers.py
@@ -1,67 +1,41 @@
-from pandas import read_csv
-
 import pastas as ps
 
+from .fixtures import ml
+
 
-def create_model():
-    obs = (
-        read_csv("tests/data/obs.csv", index_col=0, parse_dates=True)
-        .squeeze("columns")
-        .dropna()
-    )
-    rain = read_csv("tests/data/rain.csv", index_col=0, parse_dates=True).squeeze(
-        "columns"
-    )
-    evap = read_csv("tests/data/evap.csv", index_col=0, parse_dates=True).squeeze(
-        "columns"
-    )
-    ml = ps.Model(obs, name="Test_Model")
-    sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Exponential(), name="recharge")
-    ml.add_stressmodel(sm)
-    return ml
-
-
-def test_least_squares():
-    ml = create_model()
+def test_least_squares(ml):
     ml.solve(solver=ps.LeastSquares())
 
 
-def test_fit_constant():
-    ml = create_model()
+def test_fit_constant(ml):
     ml.solve(fit_constant=False)
 
 
-def test_no_noise():
-    ml = create_model()
+def test_no_noise(ml):
     ml.solve(noise=False)
 
 
 # test the uncertainty method here
-def test_pred_interval():
-    ml = create_model()
+def test_pred_interval(ml):
     ml.solve(solver=ps.LeastSquares())
     ml.fit.prediction_interval(n=10)
 
 
-def test_ci_simulation():
-    ml = create_model()
+def test_ci_simulation(ml):
     ml.solve(solver=ps.LeastSquares())
     ml.fit.ci_simulation(n=10)
 
 
-def test_ci_block_response():
-    ml = create_model()
+def test_ci_block_response(ml):
     ml.solve(solver=ps.LeastSquares())
-    ml.fit.ci_block_response(name="recharge", n=10)
+    ml.fit.ci_block_response(name="rch", n=10)
 
 
-def test_ci_step_response():
-    ml = create_model()
+def test_ci_step_response(ml):
     ml.solve(solver=ps.LeastSquares())
-    ml.fit.ci_step_response(name="recharge", n=10)
+    ml.fit.ci_step_response(name="rch", n=10)
 
 
-def test_ci_contribution():
-    ml = create_model()
+def test_ci_contribution(ml):
     ml.solve(solver=ps.LeastSquares())
-    ml.fit.ci_contribution(name="recharge", n=10)
+    ml.fit.ci_contribution(name="rch", n=10)
diff --git a/tests/test_stats.py b/tests/test_stats.py
index 34c92463..603027f0 100644
--- a/tests/test_stats.py
+++ b/tests/test_stats.py
@@ -13,7 +13,7 @@ def acf_func(**kwargs):
     return acf, acf_true
 
 
-def test_acf_rectangle():
+def test_acf_rectangle() -> None:
     acf, acf_true = acf_func(bin_method="rectangle")
     assert abs((acf - acf_true)).max() < 0.05