Repository: myokit/myokit (language: python)

Issue:
cvodessim: Change `variables` in sensitivity list to also accept `dot(x)`
Allow logging of d/dp dot(x) (syntax is there in .py already)



Discussion / hints:

Fix patch:
diff --git a/examples/README.md b/examples/README.md
index a86591618..f657f8705 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -174,7 +174,8 @@ They have not been reviewed or checked extensively, so some errors may be presen
 6. [Markov channel models](https://nbviewer.jupyter.org/github/MichaelClerx/myokit/blob/main/examples/t6-markov-channels.ipynb)
 7. [Rush-Larsen updates](https://nbviewer.jupyter.org/github/MichaelClerx/myokit/blob/main/examples/t7-rush-larsen.ipynb)
 
+- [Simulation test case: Simple model](https://nbviewer.jupyter.org/github/MichaelClerx/myokit/blob/main/examples/tA-test-case-simple.ipynb)
 - Simulation test case: HH ion channel model
-- [Simulation test case: PK model](https://nbviewer.jupyter.org/github/MichaelClerx/myokit/blob/main/examples/tB-test-case-pk-model.ipynb)
+- [Simulation test case: PK model](https://nbviewer.jupyter.org/github/MichaelClerx/myokit/blob/main/examples/tC-test-case-pk-model.ipynb)
 - [Autodiff simulations](https://nbviewer.jupyter.org/github/MichaelClerx/myokit/blob/main/examples/tZ-autodiff.ipynb)
 


Test patch:
diff --git a/examples/tA-test-case-simple.ipynb b/examples/tA-test-case-simple.ipynb
new file mode 100644
index 000000000..1c88f9ec3
--- /dev/null
+++ b/examples/tA-test-case-simple.ipynb
@@ -0,0 +1,366 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Technical note: Simulation with sensitivities test case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Derivative expressions\n",
+    "\n",
+    "In this notebook we set up a simple model and look at derivatives involving derivative expressions, e.g. the sensitivity of `dot(y)` or the sensitivity of an intermediary variable that depends on `dot(y)`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Model:\n",
+    "\n",
+    "\\begin{align}\n",
+    "t_0     &\\equiv 0 \\\\\n",
+    "p       &= 1 / 100 \\\\\n",
+    "q       &= 2 \\\\\n",
+    "r       &= 3 \\\\\n",
+    "x(t_0)  &= x_0 = 1.2 \\\\\n",
+    "y(t_0)  &= y_0 = 2.3 \\\\\n",
+    "\\dot{x} &= pq \\\\\n",
+    "\\dot{y} &= 2p - y \\\\\n",
+    "f_x     &= x^2 \\\\\n",
+    "f_y     &= r + p^2 + \\dot{y}\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Solutions:\n",
+    "\\begin{align}\n",
+    "x(t)       &= x_0 + pqt \\\\\n",
+    "y(t)       &= 2p + (y_0 - 2p) e^{-t} \\\\\n",
+    "\\dot{x}(t) &= pq \\\\\n",
+    "\\dot{x}(t) &= (2p - y_0) e^{-t} \\\\\n",
+    "f_x(t)     &= (pqt)^2 + 2pqtx_0 + x_0^2 \\\\\n",
+    "f_y(t)     &= r + p^2 + (2p - y_0) e^{-t}\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import myokit\n",
+    "import numpy as np\n",
+    "\n",
+    "m = myokit.parse_model('''\n",
+    "[[model]]\n",
+    "e.x = 1.2\n",
+    "e.y = 2.3\n",
+    "\n",
+    "[e]\n",
+    "t = 0 bind time\n",
+    "p = 1 / 100\n",
+    "q = 2\n",
+    "r = 3\n",
+    "dot(x) = p * q\n",
+    "dot(y) = 2 * p - y\n",
+    "fx = x^2\n",
+    "fy = r + p^2 + dot(y)\n",
+    "''')\n",
+    "m.validate()\n",
+    "\n",
+    "s = myokit.Simulation(m)\n",
+    "d = s.run(8).npview()\n",
+    "t = d.time()\n",
+    "\n",
+    "x0 = m.get('e.x').state_value()\n",
+    "y0 = m.get('e.y').state_value()\n",
+    "p = m.get('e.p').eval()\n",
+    "q = m.get('e.q').eval()\n",
+    "r = m.get('e.r').eval()\n",
+    "\n",
+    "fig = plt.figure(figsize=(16, 7))\n",
+    "ax = fig.add_subplot(1, 1, 1)\n",
+    "ax.plot(t, d['e.x'], lw=3, label='x')\n",
+    "ax.plot(t, x0 + p * q * t, 'k--')\n",
+    "ax.plot(t, d['e.y'], lw=3, label='y')\n",
+    "ax.plot(t, 2 * p + (y0 - 2 * p) * np.exp(-t), 'k--')\n",
+    "ax.plot(t, d['dot(e.x)'], lw=3, label=\"x'\")\n",
+    "ax.plot(t, np.ones(t.shape) * p * q, 'k--')\n",
+    "ax.plot(t, d['dot(e.y)'], lw=3, label=\"y'\")\n",
+    "ax.plot(t, (2 * p - y0) * np.exp(-t), 'k--')\n",
+    "ax.plot(t, d['e.fx'], lw=3, label='fx')\n",
+    "ax.plot(t, (p * q * t)**2 + 2 * p * q * t * x0 + x0**2, 'k--')\n",
+    "ax.plot(t, d['e.fy'], lw=3, label='fy')\n",
+    "ax.plot(t, r + p**2 + (2 * p - y0) * np.exp(-t), 'k--')\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sensitivities of $x = x_0 + pqt$:\n",
+    "\\begin{align}\n",
+    "\\left.\\frac{\\partial x}{\\partial p}\\right\\rvert_t &= qt \\\\\n",
+    "\\left.\\frac{\\partial x}{\\partial q}\\right\\rvert_t &= pt \\\\\n",
+    "\\left.\\frac{\\partial x}{\\partial x_0}\\right\\rvert_t &= 1 \\\\\n",
+    "\\end{align}\n",
+    "And of $\\dot{x} = pq$:\n",
+    "\\begin{align}\n",
+    "\\left.\\frac{\\partial \\dot{x}}{\\partial p}\\right\\rvert_t &= q \\\\\n",
+    "\\left.\\frac{\\partial \\dot{x}}{\\partial q}\\right\\rvert_t &= p \\\\\n",
+    "\\left.\\frac{\\partial \\dot{x}}{\\partial x_0}\\right\\rvert_t &= 0\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sens = (['e.x', 'dot(e.x)'], ['e.p', 'e.q', 'init(e.x)'])\n",
+    "\n",
+    "s = myokit.Simulation(m, sensitivities=sens)\n",
+    "d, e = s.run(8)\n",
+    "d, e = d.npview(), np.array(e)\n",
+    "t = d.time()\n",
+    "\n",
+    "fig = plt.figure(figsize=(16, 7))\n",
+    "ax = fig.add_subplot(1, 2, 1)\n",
+    "ax.plot(t, e[:, 0, 0], lw=3, label='dx/dp')\n",
+    "ax.plot(t, q * t, 'k--')\n",
+    "ax.plot(t, e[:, 0, 2], lw=3, label='dx/dx0')\n",
+    "ax.plot(t, np.ones(t.shape), 'k--')\n",
+    "ax.plot(t, e[:, 1, 0], lw=3, label=\"dx'/dp\")\n",
+    "ax.plot(t, q * np.ones(t.shape), 'k--')\n",
+    "ax.plot(t, e[:, 1, 2], lw=3, label=\"dx'/dx0\")\n",
+    "ax.plot(t, np.zeros(t.shape), 'k--')\n",
+    "ax.legend()\n",
+    "ax = fig.add_subplot(1, 2, 2)\n",
+    "ax.plot(t, e[:, 0, 1], lw=3, color='tab:purple', label='dx/dq')\n",
+    "ax.plot(t, p * t, 'k--')\n",
+    "ax.plot(t, e[:, 1, 1], lw=3, color='tab:brown', label=\"dx'/dq\")\n",
+    "ax.plot(t, p * np.ones(t.shape), 'k--')\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sensitivities of $y = 2p + (y_0 - 2p) e^{-t}$:\n",
+    "\\begin{align}\n",
+    "\\left.\\frac{\\partial y}{\\partial p}\\right\\rvert_t &= 2 - 2e^{-t} \\\\\n",
+    "\\left.\\frac{\\partial y}{\\partial y_0}\\right\\rvert_t &= e^{-t} \\\\\n",
+    "\\end{align}\n",
+    "And of $\\dot{y} = (2p - y_0) e^{-t}$:\n",
+    "\\begin{align}\n",
+    "\\left.\\frac{\\partial \\dot{y}}{\\partial p}\\right\\rvert_t &= 2e^{-t} \\\\\n",
+    "\\left.\\frac{\\partial \\dot{y}}{\\partial y_0}\\right\\rvert_t &= -e^{-t} \n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sens = (['e.y', 'dot(e.y)'], ['e.p', 'init(e.y)'])\n",
+    "\n",
+    "s = myokit.Simulation(m, sensitivities=sens)\n",
+    "d, e = s.run(8)\n",
+    "d, e = d.npview(), np.array(e)\n",
+    "t = d.time()\n",
+    "\n",
+    "fig = plt.figure(figsize=(16, 7))\n",
+    "ax = fig.add_subplot(1, 1, 1)\n",
+    "ax.plot(t, e[:, 0, 0], lw=3, label='dy/dp')\n",
+    "ax.plot(t, 2 - 2 * np.exp(-t), 'k--')\n",
+    "ax.plot(t, e[:, 0, 1], lw=3, label='dy/dy0')\n",
+    "ax.plot(t, np.exp(-t), 'k--')\n",
+    "ax.plot(t, e[:, 1, 0], lw=3, label=\"dy'/dp\")\n",
+    "ax.plot(t, 2 * np.exp(-t), 'k--')\n",
+    "ax.plot(t, e[:, 1, 1], lw=3, label=\"dy'/dy0\")\n",
+    "ax.plot(t, -np.exp(-t), 'k--')\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sensitivities of $f_x= (pqt)^2 + 2pqtx_0 + x_0^2$:\n",
+    "\\begin{align}\n",
+    "\\left.\\frac{\\partial f_x}{\\partial p}\\right\\rvert_t &= 2p(qt)^2 + 2qtx_0 \\\\\n",
+    "\\left.\\frac{\\partial f_x}{\\partial q}\\right\\rvert_t &= 2p(qt)^2 + 2ptx_0 \\\\\n",
+    "\\left.\\frac{\\partial f_x}{\\partial x_0}\\right\\rvert_t &= 2pqt + 2x_0\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sens = (['e.fx'], ['e.p', 'e.q', 'init(e.x)'])\n",
+    "\n",
+    "s = myokit.Simulation(m, sensitivities=sens)\n",
+    "d, e = s.run(8)\n",
+    "d, e = d.npview(), np.array(e)\n",
+    "t = d.time()\n",
+    "\n",
+    "fig = plt.figure(figsize=(16, 7))\n",
+    "ax = fig.add_subplot(1, 2, 1)\n",
+    "ax.plot(t, e[:, 0, 0], lw=3, label='dfx/dp')\n",
+    "ax.plot(t, 2 * p * (q * t)**2 + 2 * q * t * x0, 'k--')\n",
+    "ax.legend()\n",
+    "ax = fig.add_subplot(1, 2, 2)\n",
+    "ax.plot(t, e[:, 0, 1], lw=3, color='tab:orange', label='dfx/dq')\n",
+    "ax.plot(t, 2 * q * (p * t)**2 + 2 * p * t * x0, 'k--')\n",
+    "ax.plot(t, e[:, 0, 2], color='tab:green', lw=3, label='dfx/dx0')\n",
+    "ax.plot(t, 2 * p * q * t + 2 * x0, 'k--')\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sensitivities of $f_y= r + p^2 + (2p - y_0) e^{-t}$:\n",
+    "\\begin{align}\n",
+    "\\left.\\frac{\\partial f_y}{\\partial p}\\right\\rvert_t &= 2p + 2e^{-t} \\\\\n",
+    "\\left.\\frac{\\partial f_y}{\\partial p}\\right\\rvert_t &= 1 \\\\\n",
+    "\\left.\\frac{\\partial f_y}{\\partial y_0}\\right\\rvert_t &= -e^{-t}\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sens = (['e.fy'], ['e.p', 'e.r', 'init(e.y)'])\n",
+    "\n",
+    "s = myokit.Simulation(m, sensitivities=sens)\n",
+    "d, e = s.run(8)\n",
+    "d, e = d.npview(), np.array(e)\n",
+    "t = d.time()\n",
+    "\n",
+    "fig = plt.figure(figsize=(16, 7))\n",
+    "ax = fig.add_subplot(1, 1, 1)\n",
+    "ax.plot(t, e[:, 0, 0], lw=3, label='dfy/dp')\n",
+    "ax.plot(t, 2 * p + 2 * np.exp(-t), 'k--')\n",
+    "ax.plot(t, e[:, 0, 1], lw=3, label='dfy/dr')\n",
+    "ax.plot(t, np.ones(t.shape), 'k--')\n",
+    "ax.plot(t, e[:, 0, 2], lw=3, label='dfy/dy0')\n",
+    "ax.plot(t, -np.exp(-t), 'k--')\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/tB-test-case-pk-model.ipynb b/examples/tC-test-case-pk-model.ipynb
similarity index 99%
rename from examples/tB-test-case-pk-model.ipynb
rename to examples/tC-test-case-pk-model.ipynb
index cff9c5fad..ce1c07c3c 100644
--- a/examples/tB-test-case-pk-model.ipynb
+++ b/examples/tC-test-case-pk-model.ipynb
@@ -18869,13 +18869,6 @@
     "plt.legend()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -18894,7 +18887,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,
diff --git a/myokit/tests/publications/pbmb-2016/test-example-multi-model-testing.mmt b/myokit/tests/publications/pbmb-2016/test-example-multi-model-testing.mmt
index 2be5237f6..bd5982690 100644
--- a/myokit/tests/publications/pbmb-2016/test-example-multi-model-testing.mmt
+++ b/myokit/tests/publications/pbmb-2016/test-example-multi-model-testing.mmt
@@ -1,7 +1,3 @@
-
-
-
-
 [[script]]
 import myokit
 import myokit.lib.multi
@@ -43,7 +39,7 @@ for model, protocol in myokit.lib.multi.iterdir(model_dir):
     protocol = myokit.pacing.blocktrain(1000, duration)
     
     # Create a simulation with APD tracking enabled
-    s = myokit.Simulation(model, protocol, apd_var=vm)
+    s = myokit.Simulation(model, protocol)
     
     # Pre-pace the model for 50 beats
     #print('  Pre-pacing...')
@@ -54,7 +50,8 @@ for model, protocol in myokit.lib.multi.iterdir(model_dir):
     
     # Measure the baseline APD at V=-70mV
     print('  Measuring baseline apd...')
-    x, apds = s.run(600, log=myokit.LOG_NONE, apd_threshold=-70)
+    x, apds = s.run(
+        600, log=myokit.LOG_NONE, apd_variable=vm, apd_threshold=-70)
     
     # The returned object `apds` contains more info that we need, extract only
     # the duration of the first AP
@@ -65,8 +62,9 @@ for model, protocol in myokit.lib.multi.iterdir(model_dir):
     
     # Create a new simulation and measure the new apd
     print('  Measuring modified apd...')
-    s = myokit.Simulation(model, protocol, apd_var=vm)
-    x, apds = s.run(600, log=myokit.LOG_NONE, apd_threshold=-70)
+    s = myokit.Simulation(model, protocol)
+    x, apds = s.run(
+        600, log=myokit.LOG_NONE, apd_variable=vm, apd_threshold=-70)
     apd_modified = apds['duration'][0]
     
     # Store the difference between the APDs
diff --git a/myokit/tests/test_simulation_cvodes.py b/myokit/tests/test_simulation_cvodes.py
index 7f6d5ef16..f49a686f0 100755
--- a/myokit/tests/test_simulation_cvodes.py
+++ b/myokit/tests/test_simulation_cvodes.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 #
-# Tests the CVODE simulation class.
+# Tests the CVODES simulation class.
 #
 # This file is part of Myokit.
 # See http://myokit.org for copyright, sharing, and licensing details.
@@ -34,7 +34,7 @@
 
 class SimulationTest(unittest.TestCase):
     """
-    Tests the CVode simulation class.
+    Tests the CVODES simulation class.
     """
 
     @classmethod
@@ -334,7 +334,29 @@ def test_eval_derivatives(self):
         self.sim.set_state(s1)
         self.assertEqual(d1, self.sim.eval_derivatives())
 
-    def test_order(self):
+    def test_sensitivites_initial(self):
+        # Test setting initial sensitivity values.
+
+        m = myokit.parse_model('''
+            [[model]]
+            e.y = 2.3
+
+            [e]
+            t = 0 bind time
+            p = 1 / 100
+            dot(y) = 2 * p - y
+            ''')
+        m.validate()
+
+        #TODO: Test results
+        s = myokit.Simulation(m, sensitivities=(['e.y'], ['e.p', 'init(e.y)']))
+
+        # Test bad initial matrix
+        self.assertRaisesRegex(
+            ValueError, 'None or a list',
+            s.run, 10, sensitivities='hello')
+
+    def test_sensitivity_ordering(self):
         # Tests that sensitivities are returned in the correct order
 
         # Define model
@@ -401,49 +423,7 @@ def test_order(self):
         s2 = s2[:, ::-1, ::-1]
         self.assertTrue(np.all(s1 == s2))
 
-    def test_run_bolus_infusion(self):
-        # Test :meth:`Simulation.run()` with a PKPD bolus infusion model
-
-        # Create bolus infusion model with linear clearance
-        model = myokit.Model()
-        comp = model.add_component('myokit')
-
-        amount = comp.add_variable('amount')
-        time = comp.add_variable('time')
-        dose_rate = comp.add_variable('dose_rate')
-        elimination_rate = comp.add_variable('elimination_rate')
-
-        time.set_binding('time')
-        dose_rate.set_binding('pace')
-
-        amount.promote(10)
-        amount.set_rhs(
-            myokit.Minus(
-                myokit.Name(dose_rate),
-                myokit.Multiply(
-                    myokit.Name(elimination_rate),
-                    myokit.Name(amount))))
-        elimination_rate.set_rhs(myokit.Number(1))
-        time.set_rhs(myokit.Number(0))
-        dose_rate.set_rhs(myokit.Number(0))
-
-        # Set sensitivies
-        sensitivities = (
-            ['myokit.amount'],
-            ['init(myokit.amount)', 'myokit.elimination_rate'])
-
-        sim = myokit.Simulation(model, sensitivities=sensitivities)
-
-        # Bad sensitivity input
-        with self.assertRaisesRegex(
-                ValueError, 'The argument `sensitivities` must be'):
-            sim.run(10, sensitivities='weird input')
-
-        # Return apds
-        result = sim.run(1, apd_variable='myokit.amount', apd_threshold=0.3)
-        self.assertEqual(len(result), 3)
-
-    def test_run_accuracy_bolus_infusion(self):
+    def test_sensitivities_bolus_infusion(self):
         # Test :meth:`Simulation.run()` accuracy by comparing to
         # analytic solution in a PKPD bolus infusion model.
 
@@ -529,6 +509,140 @@ def test_run_accuracy_bolus_infusion(self):
         self.assertAlmostEqual(partials[11, 0], ref_partials[0, 11], 6)
         self.assertAlmostEqual(partials[12, 0], ref_partials[0, 12], 6)
 
+        # Return apds
+        result = sim.run(1, apd_variable='myokit.amount', apd_threshold=0.3)
+        self.assertEqual(len(result), 3)
+
+    def test_sensitivities_with_derivatives(self):
+        # Test various inputs to the `sensitivities` argument are handled
+        # correctly, including sensitivites of derivatives and intermediary
+        # variables depending on derivatives.
+
+        # Note: passing the wrong values into the sensitivities constructor arg
+        # is tested in the CModel tests.
+
+        m = myokit.parse_model('''
+            [[model]]
+            e.x = 1.2
+            e.y = 2.3
+
+            [e]
+            t = 0 bind time
+            p = 1 / 100
+            q = 2
+            r = 3
+            dot(x) = p * q
+            dot(y) = 2 * p - y
+            fx = x^2
+            fy = r + p^2 + dot(y)
+            ''')
+        m.validate()
+
+        x0 = m.get('e.x').state_value()
+        y0 = m.get('e.y').state_value()
+        p = m.get('e.p').eval()
+        q = m.get('e.q').eval()
+        r = m.get('e.r').eval()
+
+        # Dependents is a list of y in dy/dx.
+        # Must refer to state, derivative, or intermediary, given as:
+        #  - Variable
+        #  - Name
+        #  - Derivative
+        #  - "qname"
+        #  - "dot(qname)"
+        dependents = [
+            'e.x',                  # qname
+            'e.y',                  # qname
+            'dot(e.x)',             # dot(qname)
+            m.get('e.y').lhs(),     # Derivative
+            m.get('e.fx'),          # Variable
+            m.get('e.fy').lhs(),    # Name
+        ]
+
+        # Independents is a list of x in dy/dx
+        # Must refer to literals or initial values, given as:
+        #  - Variable
+        #  - Name
+        #  - InitialValue
+        #  - "qname"
+        #  - "init(qname)"
+        independents = [
+            'e.p',                  # qname
+            m.get('e.q'),           # qname
+            m.get('e.r').lhs(),     # Name
+            'init(e.x)',            # init(qname)
+            myokit.InitialValue(myokit.Name(m.get('e.y'))),  # InitialValue
+        ]
+
+        # Run, get output
+        s = myokit.Simulation(m, sensitivities=(dependents, independents))
+        s.set_tolerance(1e-9, 1e-9)
+        d, e = s.run(8)
+        d, e = d.npview(), np.array(e)
+        t = d.time()
+
+        # Check solution
+        self.assertTrue(
+            np.allclose(d['e.x'], x0 + p * q * t))
+        self.assertTrue(np.allclose(
+            d['e.y'], 2 * p + (y0 - 2 * p) * np.exp(-t)))
+        self.assertTrue(np.allclose(
+            d['e.fx'], (p * q * t)**2 + 2 * p * q * t * x0 + x0**2))
+        self.assertTrue(np.allclose(
+            d['e.fy'], r + p**2 + (2 * p - y0) * np.exp(-t)))
+        self.assertTrue(np.allclose(
+            d['dot(e.x)'], p * q))
+        self.assertTrue(np.allclose(
+            d['dot(e.y)'], (2 * p - y0) * np.exp(-t)))
+
+        # Sensitivities of  x  to (p, q, r, x0, y0)
+        self.assertTrue(np.allclose(e[:, 0, 0], q * t))
+        self.assertTrue(np.allclose(e[:, 0, 1], p * t))
+        self.assertTrue(np.allclose(e[:, 0, 2], 0))
+        self.assertTrue(np.allclose(e[:, 0, 3], 1))
+        self.assertTrue(np.allclose(e[:, 0, 4], 0))
+
+        # Sensitivities of  y  to (p, q, r, x0, y0)
+        self.assertTrue(np.allclose(e[:, 1, 0], 2 - 2 * np.exp(-t)))
+        self.assertTrue(np.allclose(e[:, 1, 1], 0))
+        self.assertTrue(np.allclose(e[:, 1, 2], 0))
+        self.assertTrue(np.allclose(e[:, 1, 3], 0))
+        self.assertTrue(np.allclose(e[:, 1, 4], np.exp(-t)))
+
+        # Sensitivities of  dot(x)  to (p, q, r, x0, y0)
+        self.assertTrue(np.allclose(e[:, 2, 0], q))
+        self.assertTrue(np.allclose(e[:, 2, 1], p))
+        self.assertTrue(np.allclose(e[:, 2, 2], 0))
+        self.assertTrue(np.allclose(e[:, 2, 3], 0))
+        self.assertTrue(np.allclose(e[:, 2, 4], 0))
+
+        # Sensitivities of  dot(y)  to (p, q, r, x0, y0)
+        self.assertTrue(np.allclose(e[:, 3, 0], 2 * np.exp(-t)))
+        self.assertTrue(np.allclose(e[:, 3, 1], 0))
+        self.assertTrue(np.allclose(e[:, 3, 2], 0))
+        self.assertTrue(np.allclose(e[:, 3, 3], 0))
+        self.assertTrue(np.allclose(e[:, 3, 4], -np.exp(-t)))
+
+        # Sensitivities of  fx  to (p, q, r, x0, y0)
+        self.assertTrue(np.allclose(
+            e[:, 4, 0], 2 * p * (q * t)**2 + 2 * q * t * x0))
+        self.assertTrue(np.allclose(
+            e[:, 4, 1], 2 * q * (p * t)**2 + 2 * p * t * x0))
+        self.assertTrue(np.allclose(
+            e[:, 4, 2], 0))
+        self.assertTrue(np.allclose(
+            e[:, 4, 3], 2 * p * q * t + 2 * x0))
+        self.assertTrue(np.allclose(
+            e[:, 4, 4], 0))
+
+        # Sensitivities of  fy  to (p, q, r, x0, y0)
+        self.assertTrue(np.allclose(e[:, 5, 0], 2 * p + 2 * np.exp(-t)))
+        self.assertTrue(np.allclose(e[:, 5, 1], 0))
+        self.assertTrue(np.allclose(e[:, 5, 2], 1))
+        self.assertTrue(np.allclose(e[:, 5, 3], 0))
+        self.assertTrue(np.allclose(e[:, 5, 4], -np.exp(-t)))
+
     def test_set_tolerance(self):
         # Test :meth:`Simulation.set_tolerance()`.