Repository: ICB-DCM/pyABC (language: python)

Issue:
Interface COPASI for model simulation
Add a model wrapper to use COPASI for model simulation. In particular this would allow to use its SSA simulator and complement the current AMICI ODE interface.
* Read model from SBML
* Run via CopasiSE
* [Maybe] Read whole problem definition via PEtab


Discussion / hints:

Fix patch:
diff --git a/doc/api.rst b/doc/api.rst
index 13d1f26e9..bbcffa0dd 100644
--- a/doc/api.rst
+++ b/doc/api.rst
@@ -23,5 +23,7 @@ API reference
    api_random_variables
    api_sge
    api_external
+   api_petab
+   api_copasi
    api_visualization
    api_weightedstatistics
diff --git a/doc/api_copasi.rst b/doc/api_copasi.rst
new file mode 100644
index 000000000..4eb504fc4
--- /dev/null
+++ b/doc/api_copasi.rst
@@ -0,0 +1,4 @@
+.. _api_copasi:
+
+.. automodule:: pyabc.copasi
+   :imported-members:
diff --git a/doc/api_petab.rst b/doc/api_petab.rst
new file mode 100644
index 000000000..fab966c35
--- /dev/null
+++ b/doc/api_petab.rst
@@ -0,0 +1,4 @@
+.. _api_petab:
+
+.. automodule:: pyabc.petab
+   :imported-members:
diff --git a/doc/examples.rst b/doc/examples.rst
index 768efffcf..f088d3ed9 100644
--- a/doc/examples.rst
+++ b/doc/examples.rst
@@ -45,8 +45,8 @@ Algorithms and features
    examples/discrete_parameters.ipynb
    examples/look_ahead.ipynb
 
-Languages
----------
+External interfaces
+-------------------
 
 .. toctree::
    :maxdepth: 2
@@ -55,6 +55,7 @@ Languages
    examples/using_julia.ipynb
    examples/external_simulators.ipynb
    examples/petab_yaml2sbml.ipynb
+   examples/using_copasi.ipynb
 
 Application examples
 --------------------
@@ -75,8 +76,9 @@ Download the examples as notebooks
 * :download:`Model selection <examples/model_selection.ipynb>`
 * :download:`Early stopping of model simulations <examples/early_stopping.ipynb>`
 * :download:`Resuming stored ABC runs <examples/resuming.ipynb>`
-* :download:`Using R with pyABC <examples/using_R.ipynb>`
-* :download:`Using Julia with pyABC <examples/using_julia.ipynb>`
+* :download:`Using R via rpy2 <examples/using_R.ipynb>`
+* :download:`Using Julia via pyjulia <examples/using_julia.ipynb>`
+* :download:`Using COPASI via BasiCO <examples/using_copasi.ipynb>`
 * :download:`Ordinary differential equations: Conversion reaction <examples/conversion_reaction.ipynb>`
 * :download:`Markov jump process: Reaction network <examples/chemical_reaction.ipynb>`
 * :download:`Multi-scale model: Tumor spheroid growth <examples/multiscale_agent_based.ipynb>`
diff --git a/doc/examples/models/model1.xml b/doc/examples/models/model1.xml
new file mode 100644
index 000000000..bbcb5faa2
--- /dev/null
+++ b/doc/examples/models/model1.xml
@@ -0,0 +1,154 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!-- Created by COPASI version 4.30 (Build 240) on 2021-03-03 15:27 with libSBML version 5.19.0. -->
+<sbml xmlns="http://www.sbml.org/sbml/level2/version4" level="2" version="4">
+  <model metaid="COPASI0" id="ABC_Example" name="ABC Example">
+    <notes>
+      <body xmlns="http://www.w3.org/1999/xhtml">
+        <pre>Example from:
+
+https://pyabc.readthedocs.io/en/latest/examples/chemical_reaction.html</pre>
+      </body>
+    </notes>
+    <annotation>
+      <COPASI xmlns="http://www.copasi.org/static/sbml">
+        <rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
+          <rdf:Description rdf:about="#COPASI0">
+            <dcterms:created>
+              <rdf:Description>
+                <dcterms:W3CDTF>2021-03-03T11:20:00Z</dcterms:W3CDTF>
+              </rdf:Description>
+            </dcterms:created>
+          </rdf:Description>
+        </rdf:RDF>
+      </COPASI>
+    </annotation>
+    <listOfUnitDefinitions>
+      <unitDefinition id="substance" name="substance">
+        <listOfUnits>
+          <unit kind="mole" exponent="1" scale="-3" multiplier="1"/>
+        </listOfUnits>
+      </unitDefinition>
+      <unitDefinition id="length" name="length">
+        <listOfUnits>
+          <unit kind="metre" exponent="1" scale="0" multiplier="1"/>
+        </listOfUnits>
+      </unitDefinition>
+      <unitDefinition id="area" name="area">
+        <listOfUnits>
+          <unit kind="metre" exponent="2" scale="0" multiplier="1"/>
+        </listOfUnits>
+      </unitDefinition>
+      <unitDefinition id="volume" name="volume">
+        <listOfUnits>
+          <unit kind="litre" exponent="1" scale="0" multiplier="1"/>
+        </listOfUnits>
+      </unitDefinition>
+      <unitDefinition id="time" name="time">
+        <listOfUnits>
+          <unit kind="second" exponent="1" scale="0" multiplier="1"/>
+        </listOfUnits>
+      </unitDefinition>
+    </listOfUnitDefinitions>
+    <listOfCompartments>
+      <compartment metaid="COPASI1" id="compartment" name="compartment" spatialDimensions="3" size="1" constant="true">
+        <annotation>
+          <COPASI xmlns="http://www.copasi.org/static/sbml">
+            <rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
+              <rdf:Description rdf:about="#COPASI1">
+                <dcterms:created>
+                  <rdf:Description>
+                    <dcterms:W3CDTF>2021-03-03T11:20:53Z</dcterms:W3CDTF>
+                  </rdf:Description>
+                </dcterms:created>
+              </rdf:Description>
+            </rdf:RDF>
+          </COPASI>
+        </annotation>
+      </compartment>
+    </listOfCompartments>
+    <listOfSpecies>
+      <species metaid="COPASI2" id="X" name="X" compartment="compartment" initialConcentration="40" boundaryCondition="false" constant="false">
+        <annotation>
+          <COPASI xmlns="http://www.copasi.org/static/sbml">
+            <rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
+              <rdf:Description rdf:about="#COPASI2">
+                <dcterms:created>
+                  <rdf:Description>
+                    <dcterms:W3CDTF>2021-03-03T11:20:53Z</dcterms:W3CDTF>
+                  </rdf:Description>
+                </dcterms:created>
+              </rdf:Description>
+            </rdf:RDF>
+          </COPASI>
+        </annotation>
+      </species>
+      <species metaid="COPASI3" id="Y" name="Y" compartment="compartment" initialConcentration="3" boundaryCondition="false" constant="false">
+        <annotation>
+          <COPASI xmlns="http://www.copasi.org/static/sbml">
+            <rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
+              <rdf:Description rdf:about="#Metabolite_0">
+                <dcterms:created>
+                  <rdf:Description>
+                    <dcterms:W3CDTF>2021-03-03T11:20:53Z</dcterms:W3CDTF>
+                  </rdf:Description>
+                </dcterms:created>
+              </rdf:Description>
+            </rdf:RDF>
+          </COPASI>
+        </annotation>
+      </species>
+    </listOfSpecies>
+    <listOfParameters>
+      <parameter metaid="COPASI5" id="rate" name="rate" value="2.3" constant="true">
+        <annotation>
+          <COPASI xmlns="http://www.copasi.org/static/sbml">
+            <rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
+              <rdf:Description rdf:about="#COPASI5">
+                <dcterms:created>
+                  <rdf:Description>
+                    <dcterms:W3CDTF>2021-03-03T14:26:22Z</dcterms:W3CDTF>
+                  </rdf:Description>
+                </dcterms:created>
+              </rdf:Description>
+            </rdf:RDF>
+          </COPASI>
+        </annotation>
+      </parameter>
+    </listOfParameters>
+    <listOfReactions>
+      <reaction metaid="COPASI4" id="reaction" name="R1" reversible="false">
+        <annotation>
+          <COPASI xmlns="http://www.copasi.org/static/sbml">
+            <rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
+              <rdf:Description rdf:about="#COPASI4">
+                <dcterms:created>
+                  <rdf:Description>
+                    <dcterms:W3CDTF>2021-03-03T11:20:53Z</dcterms:W3CDTF>
+                  </rdf:Description>
+                </dcterms:created>
+              </rdf:Description>
+            </rdf:RDF>
+          </COPASI>
+        </annotation>
+        <listOfReactants>
+          <speciesReference species="X" stoichiometry="1"/>
+          <speciesReference species="Y" stoichiometry="1"/>
+        </listOfReactants>
+        <listOfProducts>
+          <speciesReference species="Y" stoichiometry="2"/>
+        </listOfProducts>
+        <kineticLaw>
+          <math xmlns="http://www.w3.org/1998/Math/MathML">
+            <apply>
+              <times/>
+              <ci> compartment </ci>
+              <ci> rate </ci>
+              <ci> X </ci>
+              <ci> Y </ci>
+            </apply>
+          </math>
+        </kineticLaw>
+      </reaction>
+    </listOfReactions>
+  </model>
+</sbml>
diff --git a/doc/examples/using_R.ipynb b/doc/examples/using_R.ipynb
index 3e993983f..c38bb7850 100644
--- a/doc/examples/using_R.ipynb
+++ b/doc/examples/using_R.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Using R with pyABC"
+    "# Using R via rpy2"
    ]
   },
   {
diff --git a/doc/examples/using_copasi.ipynb b/doc/examples/using_copasi.ipynb
new file mode 100644
index 000000000..2091604b7
--- /dev/null
+++ b/doc/examples/using_copasi.ipynb
@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8d1ec590-4bd5-4171-9c26-31786393e991",
+   "metadata": {},
+   "source": [
+    "# Using COPASI via BasiCO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdfb8a0f-94fd-4d2e-a285-fd447639cc4e",
+   "metadata": {},
+   "source": [
+    "This notebook demonstrates how to apply pyABC to models simulated via [COPASI](http://copasi.org/), using the Python interface [BasiCO](https://basico.readthedocs.io/en/latest/index.html)."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "d7de7f55-37c5-4908-8200-e7f6d0f359ab",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext",
+    "tags": []
+   },
+   "source": [
+    "pyABC's COPASI/BasiCO interface is the class :class:`pyabc.copasi.BasicoModel <pyabc.copasi.BasicoModel>`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36f9ceb8-fdd9-43a8-b2c5-17aa1cbc86be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install if not done yet\n",
+    "!pip install pyabc[copasi] --quiet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b7c4df4e-0e64-43da-88c9-31a9ee12a9fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyabc.copasi import BasicoModel\n",
+    "import pyabc\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tempfile\n",
+    "\n",
+    "pyabc.settings.set_figure_params('pyabc')  # for beautified plots"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "dbe059e9-3086-4b22-9112-3b1538ca756d",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext",
+    "tags": []
+   },
+   "source": [
+    "We consider a simple reaction :math:`m_1: X + Y \\xrightarrow{k_1} 2Y`\n",
+    "(used already in the `model selection <model_selection.ipynb>`_ notebook),\n",
+    "simulated as a discrete jump process.\n",
+    "Note that other simulation methods are available, see the `BasicoModel` API documentation for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ad80311a-d53a-43bb-af8f-1d62d3a61486",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "max_t = 0.1\n",
+    "model = BasicoModel(\"models/model1.xml\", duration=max_t)\n",
+    "\n",
+    "true_par = {\"rate\": 2.3}\n",
+    "obs = model(true_par)\n",
+    "plt.plot(obs[\"t\"], obs[\"X\"]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d469ccb0-5b3d-42b9-9b16-7109de5ef98d",
+   "metadata": {},
+   "source": [
+    "As usual, we define a distance and a prior distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3d0a93a9-8be2-4ffd-a3d9-dca411edf614",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_test_times = 20\n",
+    "t_test_times = np.linspace(0, max_t, n_test_times)\n",
+    "\n",
+    "def distance(x, y):\n",
+    "    xt_ind = np.searchsorted(x[\"t\"], t_test_times) - 1\n",
+    "    yt_ind = np.searchsorted(y[\"t\"], t_test_times) - 1\n",
+    "    error = (\n",
+    "        np.absolute(x[\"X\"][:, 1][xt_ind] - y[\"X\"][:, 1][yt_ind]).sum()\n",
+    "        / t_test_times.size\n",
+    "    )\n",
+    "    return error\n",
+    "\n",
+    "prior = pyabc.Distribution(rate=pyabc.RV(\"uniform\", 0, 50))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9927993f-fa27-4bac-87cb-081248026ba0",
+   "metadata": {},
+   "source": [
+    "We are all set to run an analysis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "38f52bdf-e12b-4cba-9e39-fcad767d258c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ABC.Sampler INFO: Parallelize sampling on 4 processes.\n",
+      "INFO:ABC.Sampler:Parallelize sampling on 4 processes.\n",
+      "ABC.History INFO: Start <ABCSMC id=1, start_time=2021-12-05 12:15:57>\n",
+      "INFO:ABC.History:Start <ABCSMC id=1, start_time=2021-12-05 12:15:57>\n",
+      "ABC INFO: Calibration sample t = -1.\n",
+      "INFO:ABC:Calibration sample t = -1.\n",
+      "ABC INFO: t: 0, eps: 8.95000000e+00.\n",
+      "INFO:ABC:t: 0, eps: 8.95000000e+00.\n",
+      "ABC INFO: Accepted: 100 / 208 = 4.8077e-01, ESS: 1.0000e+02.\n",
+      "INFO:ABC:Accepted: 100 / 208 = 4.8077e-01, ESS: 1.0000e+02.\n",
+      "ABC INFO: t: 1, eps: 8.25000000e+00.\n",
+      "INFO:ABC:t: 1, eps: 8.25000000e+00.\n",
+      "ABC INFO: Accepted: 100 / 206 = 4.8544e-01, ESS: 9.6966e+01.\n",
+      "INFO:ABC:Accepted: 100 / 206 = 4.8544e-01, ESS: 9.6966e+01.\n",
+      "ABC INFO: t: 2, eps: 6.55009972e+00.\n",
+      "INFO:ABC:t: 2, eps: 6.55009972e+00.\n",
+      "ABC INFO: Accepted: 100 / 250 = 4.0000e-01, ESS: 9.5430e+01.\n",
+      "INFO:ABC:Accepted: 100 / 250 = 4.0000e-01, ESS: 9.5430e+01.\n",
+      "ABC INFO: t: 3, eps: 5.09217796e+00.\n",
+      "INFO:ABC:t: 3, eps: 5.09217796e+00.\n",
+      "ABC INFO: Accepted: 100 / 226 = 4.4248e-01, ESS: 9.8080e+01.\n",
+      "INFO:ABC:Accepted: 100 / 226 = 4.4248e-01, ESS: 9.8080e+01.\n",
+      "ABC INFO: t: 4, eps: 3.08808133e+00.\n",
+      "INFO:ABC:t: 4, eps: 3.08808133e+00.\n",
+      "ABC INFO: Accepted: 100 / 307 = 3.2573e-01, ESS: 9.6889e+01.\n",
+      "INFO:ABC:Accepted: 100 / 307 = 3.2573e-01, ESS: 9.6889e+01.\n",
+      "ABC INFO: t: 5, eps: 1.92108500e+00.\n",
+      "INFO:ABC:t: 5, eps: 1.92108500e+00.\n",
+      "ABC INFO: Accepted: 100 / 355 = 2.8169e-01, ESS: 9.2695e+01.\n",
+      "INFO:ABC:Accepted: 100 / 355 = 2.8169e-01, ESS: 9.2695e+01.\n",
+      "ABC INFO: t: 6, eps: 1.47554023e+00.\n",
+      "INFO:ABC:t: 6, eps: 1.47554023e+00.\n",
+      "ABC INFO: Accepted: 100 / 489 = 2.0450e-01, ESS: 5.8759e+01.\n",
+      "INFO:ABC:Accepted: 100 / 489 = 2.0450e-01, ESS: 5.8759e+01.\n",
+      "ABC INFO: t: 7, eps: 1.20000000e+00.\n",
+      "INFO:ABC:t: 7, eps: 1.20000000e+00.\n",
+      "ABC INFO: Accepted: 100 / 1043 = 9.5877e-02, ESS: 8.4161e+01.\n",
+      "INFO:ABC:Accepted: 100 / 1043 = 9.5877e-02, ESS: 8.4161e+01.\n",
+      "ABC INFO: t: 8, eps: 1.05000000e+00.\n",
+      "INFO:ABC:t: 8, eps: 1.05000000e+00.\n",
+      "ABC INFO: Accepted: 100 / 1556 = 6.4267e-02, ESS: 8.3430e+01.\n",
+      "INFO:ABC:Accepted: 100 / 1556 = 6.4267e-02, ESS: 8.3430e+01.\n",
+      "ABC INFO: t: 9, eps: 9.50000000e-01.\n",
+      "INFO:ABC:t: 9, eps: 9.50000000e-01.\n",
+      "ABC INFO: Accepted: 100 / 2819 = 3.5474e-02, ESS: 9.1430e+01.\n",
+      "INFO:ABC:Accepted: 100 / 2819 = 3.5474e-02, ESS: 9.1430e+01.\n",
+      "ABC INFO: Stop: Maximum number of generations.\n",
+      "INFO:ABC:Stop: Maximum number of generations.\n",
+      "ABC.History INFO: Done <ABCSMC id=1, duration=0:00:16.694937, end_time=2021-12-05 12:16:14>\n",
+      "INFO:ABC.History:Done <ABCSMC id=1, duration=0:00:16.694937, end_time=2021-12-05 12:16:14>\n"
+     ]
+    }
+   ],
+   "source": [
+    "abc = pyabc.ABCSMC(model, prior, distance, population_size=100)\n",
+    "db = tempfile.mkstemp(suffix=\".db\")[1]\n",
+    "abc.new(\"sqlite:///\" + db, obs)\n",
+    "h = abc.run(max_nr_populations=10, min_acceptance_rate=1e-2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b6556aa-8a89-4fb1-ab64-1b69f0ef9d28",
+   "metadata": {},
+   "source": [
+    "As usual, we can now analyze the results. Posterior approximation over time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cd82ec23-7274-480a-af99-5c1fd8a7e518",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "for t in range(h.max_t):\n",
+    "    pyabc.visualization.plot_kde_1d_highlevel(\n",
+    "        h, x=\"rate\", xmin=0, xmax=20, t=t,\n",
+    "        refval=true_par, refval_color=\"grey\",\n",
+    "        label=f\"t={t}\", ax=ax,\n",
+    "    )\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f17271d0-b436-4d1b-8e36-60d69ea64b61",
+   "metadata": {},
+   "source": [
+    "Accepted simulations at the beginning and at the end:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ac278fb4-5edf-4aba-a9f9-006d46ae64e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_data(sumstat, weight, ax, **kwargs):\n",
+    "    \"\"\"Plot a single trajectory\"\"\"\n",
+    "    for i in range(2):\n",
+    "        ax.plot(sumstat[\"t\"], sumstat['X'][:, i], color=f\"C{i}\", alpha=0.1)\n",
+    "\n",
+    "for t in [0, h.max_t]:\n",
+    "    _, ax = plt.subplots()\n",
+    "    pyabc.visualization.plot_data_callback(\n",
+    "        h, plot_data, t=t, ax=ax,\n",
+    "    )\n",
+    "    ax.plot(obs[\"t\"], obs[\"X\"])\n",
+    "    ax.set_title(f\"Simulations at t={t}\"); "
+   ]
+  }
+ ],
+ "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": 5
+}
diff --git a/doc/examples/using_julia.ipynb b/doc/examples/using_julia.ipynb
index 09ccafea7..c891a4ece 100644
--- a/doc/examples/using_julia.ipynb
+++ b/doc/examples/using_julia.ipynb
@@ -5,7 +5,7 @@
    "id": "11bed717-fd94-4e66-a839-285e30377757",
    "metadata": {},
    "source": [
-    "# Using Julia with pyABC"
+    "# Using Julia via pyjulia"
    ]
   },
   {
diff --git a/pyabc/acceptor/__init__.py b/pyabc/acceptor/__init__.py
index 1dc277612..a08ea609e 100644
--- a/pyabc/acceptor/__init__.py
+++ b/pyabc/acceptor/__init__.py
@@ -3,7 +3,6 @@
 =========
 
 Acceptors handle the acceptance step.
-
 """
 
 
diff --git a/pyabc/copasi/__init__.py b/pyabc/copasi/__init__.py
new file mode 100644
index 000000000..b168505cb
--- /dev/null
+++ b/pyabc/copasi/__init__.py
@@ -0,0 +1,8 @@
+"""
+Copasi
+======
+
+Simulations via COPASI (http://copasi.org).
+"""
+
+from .model import BasicoModel
diff --git a/pyabc/copasi/model.py b/pyabc/copasi/model.py
new file mode 100644
index 000000000..ab70acd56
--- /dev/null
+++ b/pyabc/copasi/model.py
@@ -0,0 +1,197 @@
+"""Interface pyABC with COPASI via BasiCO."""
+
+import logging
+import os
+from typing import Dict, List
+
+from .. import Parameter
+from ..model import Model
+
+logger = logging.getLogger("ABC.Copasi")
+
+try:
+    import basico
+except ImportError:
+    basico = None
+    logger.error(
+        "Install BasiCO (see https://basico.rtfd.io) to use the BasiCO model, "
+        "e.g. via `pip install pyabc[copasi]`"
+    )
+
+
+class BasicoModel(Model):
+    """COPASI time series simulations via BasiCO.
+
+    This class is a :class:`pyabc.Model` compliant wrapper around
+    `basico.run_time_course`, allowing to update model parameters and use
+    various simulation methods.
+    BasiCO (https://basico.rtfd.io) is a simple Python interface to
+    COPASI (http://copasi.org).
+
+    The implementation is derived from an implementation by Frank Bergmann at
+    https://github.com/fbergmann/pyabc-copasi.
+    """
+
+    def __init__(
+        self,
+        sbml_file: str,
+        changes: Dict[str, float] = None,
+        change_unit: bool = True,
+        method: str = "stochastic",
+        t0: float = None,
+        duration: float = None,
+        num_steps: int = None,
+        automatic: bool = True,
+        use_numbers: bool = False,
+        output: List[str] = None,
+        model_name: str = None,
+    ):
+        """
+        Parameters
+        ----------
+        sbml_file:
+            SBML file containing the model definition.
+        changes:
+            Parametric changes to apply to the SBML model.
+        change_unit:
+            Whether to change units to 1, useful for discrete simulations
+            (particle numbers).
+        method:
+            Simulation method, can be any method supported by
+            `basico.run_time_course`, in particular:
+
+            * deterministic, lsoda: the LSODA implementation
+            * stochastic: the Gibson & Bruck Gillespie implementation
+            * directMethod: Gillespie Direct Method
+            * others: hybridode45, hybridlsoda, adaptivesa, tauleap, radau5,
+              sde
+        t0, duration, num_steps:
+            Initial time point, duration, number of steps. Definition and
+            combination as in `basico.run_time_course`.
+        automatic:
+            Whether to use automatic steps, or the specified interval / number
+            of steps.
+        use_numbers:
+            Whether to return all elements collected.
+        output:
+            Species to output. Defaults to all.
+        model_name:
+            Model name, for identification e.g. in the database.
+        """
+        # a maybe informative model name
+        if model_name is None:
+            model_name = os.path.splitext(os.path.basename(sbml_file))[0]
+        self.model_name = model_name
+
+        super().__init__(
+            name=f"BasicoModel_{model_name}",
+        )
+
+        self.sbml_file = sbml_file
+        self.dm = basico.load_model(sbml_file)
+
+        # many sbml models do not define realistic units, and
+        # since we compute in particle numbers, we usually do not run
+        # for  particles, so set substance unit to 1
+        if change_unit:
+            basico.set_model_unit(substance_unit='1', model=self.dm)
+        self.change_unit = change_unit
+
+        # allow to override parameters
+        if changes is not None:
+            self.apply_parameters(changes)
+        self.changes = changes
+
+        self.t0 = t0
+        self.duration = duration
+        self.num_steps = num_steps
+        self.automatic = automatic
+        self.use_numbers = use_numbers
+        self.output = output
+        self.method = method
+
+    def __call__(self, pars: Dict[str, float], return_raw: bool = False):
+        """Simulate data for given parameters.
+
+        Calls the time course and returns the selected result.
+        """
+        # apply parameters to model
+        self.apply_parameters(pars)
+
+        # parse time args by basico's logic
+        if self.t0 is not None:
+            args = self.t0, self.duration, self.num_steps
+        elif self.num_steps is not None:
+            args = self.duration, self.num_steps
+        else:
+            args = (self.duration,)
+
+        # simulate
+        tc = basico.run_time_course(
+            *args,
+            model=self.dm,
+            method=self.method,
+            automatic=self.automatic,
+            use_seed=False,
+            use_numbers=self.use_numbers,
+        ).reset_index()
+
+        if return_raw:
+            return tc
+
+        # cache output columns
+        if self.output is None:
+            self.output = list(set(tc.columns) - {"Time"})
+
+        return {
+            "t": tc.Time.to_numpy(),
+            "X": tc[self.output].to_numpy(),
+        }
+
+    def sample(self, pars: Parameter):
+        """Sample for parameters.
+
+        This is the method called by pyABC. It calls `__call__` and reduces
+        the output.
+        """
+        return self(pars, return_raw=False)
+
+    def apply_parameters(self, pars: Dict[str, float]):
+        """Set the parameters of the model.
+
+        Parameters
+        ----------
+        pars:
+            Parameters to apply, id-value dictionary.
+            Local parameters are assumed to be named something like
+            `(reaction).local_parameter`, where `reaction` is the name of the
+            reaction, and `local_parameter` the local parameter.
+            Specifically, local parameters are identified by the presence of
+            brackets.
+            Otherwise the parameter is expected to be a global one.
+        """
+        for key, val in pars.items():
+            if '(' in key:
+                basico.set_reaction_parameters(key, value=val, model=self.dm)
+            else:
+                basico.set_parameters(key, initial_value=val, model=self.dm)
+
+    def __getstate__(self):
+        # all arguments
+        state = {
+            "sbml_file": self.sbml_file,
+            "changes": self.changes,
+            "change_unit": self.change_unit,
+            "method": self.method,
+            "t0": self.t0,
+            "duration": self.duration,
+            "num_steps": self.num_steps,
+            "automatic": self.automatic,
+            "use_numbers": self.use_numbers,
+            "output": self.output,
+            "model_name": self.model_name,
+        }
+        return state
+
+    def __setstate__(self, state):
+        self.__init__(**state)
diff --git a/pyabc/petab/__init__.py b/pyabc/petab/__init__.py
index c70add5b4..ee4b4dcf0 100644
--- a/pyabc/petab/__init__.py
+++ b/pyabc/petab/__init__.py
@@ -1,1 +1,8 @@
+"""
+PEtab
+=====
+
+Problem definitions in the PEtab format (https://petab.rtfd.io).
+"""
+
 from .amici import AmiciPetabImporter
diff --git a/pyabc/petab/amici.py b/pyabc/petab/amici.py
index cc583ed30..3279fa713 100644
--- a/pyabc/petab/amici.py
+++ b/pyabc/petab/amici.py
@@ -1,3 +1,5 @@
+"""PEtab import with AMICI simulator."""
+
 import copy
 import logging
 import os
@@ -17,19 +19,19 @@
 except ImportError:
     petab = C = None
     logger.error(
-        "Install petab (see https://github.com/icb-dcm/petab) to use "
-        "the petab functionality."
+        "Install PEtab (see https://github.com/icb-dcm/petab) to use "
+        "the petab functionality, e.g. via `pip install pyabc[petab]`"
     )
 
 try:
     import amici
-    import amici.petab_import
+    from amici import petab_import as amici_petab_import
     from amici.petab_objective import LLH, RDATAS, simulate_petab
 except ImportError:
-    amici = amici.petab_import = simulate_petab = LLH = RDATAS = None
+    amici = amici_petab_import = simulate_petab = LLH = RDATAS = None
     logger.error(
         "Install amici (see https://github.com/icb-dcm/amici) to use "
-        "the amici functionality."
+        "the amici functionality, e.g. via `pip install pyabc[amici]`"
     )
 
 
@@ -132,7 +134,7 @@ def __getstate__(self) -> Dict:
     def __setstate__(self, state: Dict):
         self.__dict__.update(state)
 
-        model = amici.petab_import.import_petab_problem(self.petab_problem)
+        model = amici_petab_import.import_petab_problem(self.petab_problem)
         solver = model.getSolver()
 
         _fd, _file = tempfile.mkstemp()
@@ -181,13 +183,13 @@ class AmiciPetabImporter(PetabImporter):
     def __init__(
         self,
         petab_problem: petab.Problem,
-        amici_model: amici.Model = None,
-        amici_solver: amici.Solver = None,
+        amici_model: "amici.Model" = None,
+        amici_solver: "amici.Solver" = None,
     ):
         super().__init__(petab_problem=petab_problem)
 
         if amici_model is None:
-            amici_model = amici.petab_import.import_petab_problem(
+            amici_model = amici_petab_import.import_petab_problem(
                 petab_problem
             )
         self.amici_model = amici_model
diff --git a/pyabc/petab/base.py b/pyabc/petab/base.py
index 166915950..37aecea01 100644
--- a/pyabc/petab/base.py
+++ b/pyabc/petab/base.py
@@ -1,3 +1,5 @@
+"""Simulator-independent PEtab handling."""
+
 import abc
 import logging
 from collections.abc import Mapping, Sequence
@@ -17,8 +19,8 @@
 except ImportError:
     petab = C = None
     logger.error(
-        "Install petab (see https://github.com/icb-dcm/petab) to use "
-        "the petab functionality."
+        "Install PEtab (see https://github.com/icb-dcm/petab) to use "
+        "the petab functionality, e.g. via `pip install pyabc[petab]`"
     )
 
 
diff --git a/setup.cfg b/setup.cfg
index 7c32a9bc3..592ac0a3d 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -88,6 +88,8 @@ R =
 julia =
     julia >= 0.5.7
     pygments >= 2.6.1
+copasi =
+    copasi-basico >= 0.8
 ot =
     pot >= 0.7.0
 petab =
diff --git a/tox.ini b/tox.ini
index ab15cbdbf..b2bada774 100644
--- a/tox.ini
+++ b/tox.ini
@@ -65,7 +65,7 @@ description =
     Test visualization
 
 [testenv:external]
-extras = test,R,julia
+extras = test,R,julia,copasi
 commands =
     # General
     pytest --cov=pyabc --cov-report=xml --cov-append \
@@ -77,6 +77,9 @@ commands =
     python -c "import julia; julia.install()"
     python -m pytest --cov=pyabc --cov-report=xml --cov-append \
         test/external/test_pyjulia.py -s
+    # Copasi
+    pytest --cov=pyabc --cov-report=xml --cov-append \
+        test/copasi -s
 description =
     Test external model simulators
 
@@ -162,7 +165,7 @@ description =
 
 [testenv:doc]
 extras =
-    doc
+    doc,petab
 commands =
     sphinx-build -W -b html doc/ doc/_build/html
 description =


Test patch:
diff --git a/test/copasi/__init__.py b/test/copasi/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/copasi/conftest.py b/test/copasi/conftest.py
new file mode 100644
index 000000000..03b0d088e
--- /dev/null
+++ b/test/copasi/conftest.py
@@ -0,0 +1,19 @@
+import os
+import tempfile
+
+import pytest
+
+REMOVE_DB = True
+
+
+@pytest.fixture
+def db_path():
+    db_file_location = os.path.join(tempfile.gettempdir(), "abc_unittest.db")
+    db = "sqlite:///" + db_file_location
+    yield db
+    if REMOVE_DB:
+        try:
+            if REMOVE_DB:
+                os.remove(db_file_location)
+        except OSError:
+            pass
diff --git a/test/copasi/test_copasi.py b/test/copasi/test_copasi.py
new file mode 100644
index 000000000..145ba18d8
--- /dev/null
+++ b/test/copasi/test_copasi.py
@@ -0,0 +1,56 @@
+import cloudpickle as pickle
+import numpy as np
+import pytest
+
+import pyabc
+from pyabc.copasi import BasicoModel
+
+
+@pytest.fixture(params=["stochastic", "deterministic", "directMethod", "sde"])
+def method(request):
+    return request.param
+
+
+MAX_T = 0.1
+TRUE_PAR = {"rate": 2.3}
+MODEL1_PATH = "doc/examples/models/model1.xml"
+
+
+def test_basic(method):
+    model = BasicoModel(MODEL1_PATH, duration=MAX_T, method=method)
+    data = model(TRUE_PAR)
+
+    assert data.keys() == {"t", "X"}
+    assert MAX_T / 2 < data["t"].max() <= MAX_T
+
+
+def test_pickling():
+    model = BasicoModel(MODEL1_PATH, duration=MAX_T, method="deterministic")
+
+    model_re = pickle.loads(pickle.dumps(model))
+
+    ret = model(TRUE_PAR)
+    ret_re = model_re(TRUE_PAR)
+    assert np.allclose(ret["t"], ret_re["t"])
+
+
+def test_pipeline(db_path):
+    model = BasicoModel(MODEL1_PATH, duration=MAX_T, method="deterministic")
+    data = model(TRUE_PAR)
+    prior = pyabc.Distribution(rate=pyabc.RV("uniform", 0, 100))
+
+    n_test_times = 20
+    t_test_times = np.linspace(0, MAX_T, n_test_times)
+
+    def distance(x, y):
+        xt_ind = np.searchsorted(x["t"], t_test_times) - 1
+        yt_ind = np.searchsorted(y["t"], t_test_times) - 1
+        error = (
+            np.absolute(x["X"][:, 1][xt_ind] - y["X"][:, 1][yt_ind]).sum()
+            / t_test_times.size
+        )
+        return error
+
+    abc = pyabc.ABCSMC(model, prior, distance)
+    abc.new(db_path, data)
+    abc.run(max_nr_populations=3)