diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1113e2030f..cb787a37bb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,10 @@
 -   Some features ("loss of active material" and "particle mechanics") can now be specified separately for the negative electrode and positive electrode by passing a 2-tuple ([#1490](https://github.com/pybamm-team/PyBaMM/pull/1490))
 -   `plot` and `plot2D` now take and return a matplotlib Axis to allow for easier customization ([#1472](https://github.com/pybamm-team/PyBaMM/pull/1472))
 -   `ParameterValues.evaluate` can now return arrays to allow function parameters to be easily evaluated ([#1472](https://github.com/pybamm-team/PyBaMM/pull/1472))
+-   Added option to save only specific cycle numbers when simulating an `Experiment` ([#1459](https://github.com/pybamm-team/PyBaMM/pull/1459))
+-   Added capacity-based termination conditions when simulating an `Experiment` ([#1459](https://github.com/pybamm-team/PyBaMM/pull/1459))
+-   Added "summary variables" to track degradation over several cycles ([#1459](https://github.com/pybamm-team/PyBaMM/pull/1459))
+-   Added `ElectrodeSOH` model for calculating capacities and stoichiometric limits ([#1459](https://github.com/pybamm-team/PyBaMM/pull/1459))
 -   Added Batch Study class ([#1455](https://github.com/pybamm-team/PyBaMM/pull/1455))
 -   Added `ConcatenationVariable`, which is automatically created when variables are concatenated ([#1453](https://github.com/pybamm-team/PyBaMM/pull/1453))
 -   Added "fast with events" mode for the CasADi solver, which solves a model and finds events more efficiently than "safe" mode. As of PR #1450 this feature is still being tested and "safe" mode remains the default ([#1450](https://github.com/pybamm-team/PyBaMM/pull/1450))
diff --git a/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb b/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb
index 85ab986d3f..655b6dba08 100644
--- a/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb	
+++ b/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb	
@@ -455,9 +455,9 @@
        " 'Current collector current density',\n",
        " 'Current collector current density [A.m-2]',\n",
        " 'Leading-order current collector current density',\n",
-       " 'Sei interfacial current density',\n",
-       " 'Sei interfacial current density [A.m-2]',\n",
-       " 'Sei interfacial current density per volume [A.m-3]',\n",
+       " 'SEI interfacial current density',\n",
+       " 'SEI interfacial current density [A.m-2]',\n",
+       " 'SEI interfacial current density per volume [A.m-3]',\n",
        " 'Negative electrode interfacial current density',\n",
        " 'X-averaged negative electrode interfacial current density',\n",
        " 'Negative electrode interfacial current density [A.m-2]',\n",
diff --git a/examples/notebooks/Getting Started/Tutorial 5 - Run experiments.ipynb b/examples/notebooks/Getting Started/Tutorial 5 - Run experiments.ipynb
index cd610aaad6..35eafc34a4 100644
--- a/examples/notebooks/Getting Started/Tutorial 5 - Run experiments.ipynb	
+++ b/examples/notebooks/Getting Started/Tutorial 5 - Run experiments.ipynb	
@@ -227,7 +227,20 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
   }
  },
  "nbformat": 4,
diff --git a/examples/notebooks/Validating_mechanical_models_Enertech_DFN.ipynb b/examples/notebooks/Validating_mechanical_models_Enertech_DFN.ipynb
deleted file mode 100644
index 3a90816866..0000000000
--- a/examples/notebooks/Validating_mechanical_models_Enertech_DFN.ipynb
+++ /dev/null
@@ -1,338 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test the parameter set of the Enertech cells\n",
-    "In this notebook, we show how to use pybamm to reproduce the experimental results for the Enertech cells (LCO-G). To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/models/index.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1.1 is available.\n",
-      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install pybamm -q    # install PyBaMM if it is not installed\n",
-    "import pybamm\n",
-    "import os\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you load a model in PyBaMM it builds by default. Building the model sets all of the model variables and sets up any variables which are coupled between different submodels: this is the process which couples the submodels together and allows one submodel to access variables from another. If you would like to swap out a submodel in an exisitng battery model you need to load it without building it by passing the keyword `build=False`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = pybamm.lithium_ion.DFN(\n",
-    "    options = {\n",
-    "        \"particle\": \"Fickian diffusion\", \n",
-    "        \"cell geometry\": \"arbitrary\", \n",
-    "        \"thermal\": \"lumped\", \n",
-    "        \"particle mechanics\": \"swelling only\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can get the parameter set `Ai2020` for the model, which includes the mechanical properties required by the mechanical model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "0.5 C\n",
-      "1 C\n",
-      "2 C\n"
-     ]
-    }
-   ],
-   "source": [
-    "chemistry = pybamm.parameter_sets.Ai2020\n",
-    "param = pybamm.ParameterValues(chemistry=chemistry)\n",
-    "capacity = param[\"Nominal cell capacity [A.h]\"]\n",
-    "param.update({\n",
-    "    \"Current function [A]\": capacity * pybamm.InputParameter(\"C-rate\")\n",
-    "})\n",
-    "# experiment05C = pybamm.Experiment([\"Discharge at 0.5C until 3 V\"])\n",
-    "# experiment1C = pybamm.Experiment([\"Discharge at 1C until 3 V\"])\n",
-    "# experiment2C = pybamm.Experiment([\"Discharge at 2C until 3 V\"])\n",
-    "var = pybamm.standard_spatial_vars\n",
-    "var_pts = {\n",
-    "  var.x_n: 50,\n",
-    "  var.x_s: 50,\n",
-    "  var.x_p: 50,\n",
-    "  var.r_n: 20,\n",
-    "  var.r_p: 20,\n",
-    "}\n",
-    "\n",
-    "sim = pybamm.Simulation(\n",
-    "        model,\n",
-    "        var_pts=var_pts,\n",
-    "        parameter_values=param,\n",
-    "        solver=pybamm.CasadiSolver(dt_max=600)\n",
-    "    )\n",
-    "Crates = [0.5, 1, 2]\n",
-    "solutions = []\n",
-    "\n",
-    "for Crate in Crates:\n",
-    "    print(f\"{Crate} C\")\n",
-    "    sol = sim.solve(t_eval=[0, 3600/Crate*1.05], inputs={\"C-rate\": Crate})\n",
-    "    solutions.append(sol)\n",
-    "\n",
-    "\n",
-    "solution05C, solution1C, solution2C = solutions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load experimental results of the Enertech cells (see [[1]](#References))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load experimental results\n",
-    "import pandas as pd\n",
-    "path = \"pybamm/input/discharge_data/Enertech_cells/\"\n",
-    "data_Disp_01C=pd.read_csv (path + \"0.1C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
-    "data_Disp_05C=pd.read_csv (path + \"0.5C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
-    "data_Disp_1C=pd.read_csv (path + \"1C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
-    "data_Disp_2C=pd.read_csv (path + \"2C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
-    "data_V_01C=pd.read_csv (path + \"0.1C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
-    "data_V_05C=pd.read_csv (path + \"0.5C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
-    "data_V_1C=pd.read_csv (path + \"1C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
-    "data_V_2C=pd.read_csv (path + \"2C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
-    "data_T_05C=pd.read_csv (path + \"0.5C_discharge_T.txt\", delimiter= '\\s+',header=None)\n",
-    "data_T_1C=pd.read_csv (path + \"1C_discharge_T.txt\", delimiter= '\\s+',header=None)\n",
-    "data_T_2C=pd.read_csv (path + \"2C_discharge_T.txt\", delimiter= '\\s+',header=None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "source": [
-    "t_all2C = solution2C[\"Time [h]\"].entries\n",
-    "V_n2C = solution2C[\"Terminal voltage [V]\"].entries\n",
-    "T_n2C = solution2C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
-    "L_x2C = solution2C[\"Cell thickness change [m]\"].entries\n",
-    "\n",
-    "t_all1C = solution1C[\"Time [h]\"].entries\n",
-    "V_n1C = solution1C[\"Terminal voltage [V]\"].entries\n",
-    "T_n1C = solution1C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
-    "L_x1C = solution1C[\"Cell thickness change [m]\"].entries\n",
-    "\n",
-    "t_all05C = solution05C[\"Time [h]\"].entries\n",
-    "V_n05C = solution05C[\"Terminal voltage [V]\"].entries\n",
-    "T_n05C = solution05C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
-    "L_x05C = solution05C[\"Cell thickness change [m]\"].entries\n",
-    "f, (ax1, ax2,ax3) = plt.subplots(1, 3 ,figsize=(14,4))\n",
-    "ax1.plot(t_all2C, V_n2C,'r-',label=\"Simulation\")\n",
-    "ax1.plot(data_V_2C.values[::30,0]/3600, data_V_2C.values[::30,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
-    "ax1.plot(t_all05C, V_n05C,'g-')\n",
-    "ax1.plot(data_V_05C.values[::100,0]/3600, data_V_05C.values[::100,1],'go',markerfacecolor='none')\n",
-    "ax1.plot(t_all1C, V_n1C,'b-')\n",
-    "ax1.plot(data_V_1C.values[::50,0]/3600, data_V_1C.values[::50,1],'bo',markerfacecolor='none')\n",
-    "ax1.legend()\n",
-    "#plt.xlim(0, 3600);\n",
-    "ax1.set_xlabel(\"Time [h]\")\n",
-    "ax1.set_ylabel(\"Terminal voltage [V]\")\n",
-    "ax1.text(0.1, 3.2, r'2 C', {'color': 'r', 'fontsize': 14})\n",
-    "ax1.text(1.1, 3.2, r'1 C', {'color': 'b', 'fontsize': 14})\n",
-    "ax1.text(1.6, 3.2, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
-    "\n",
-    "ax2.plot(t_all2C, T_n2C,'r-',label=\"Simulation\")\n",
-    "ax2.plot(data_T_2C.values[0:1754:50,0]/3600, data_T_2C.values[0:1754:50,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
-    "ax2.plot(t_all05C, T_n05C,'g-')\n",
-    "ax2.plot(data_T_05C.values[0:7301:200,0]/3600, data_T_05C.values[0:7301:200,1],'go',markerfacecolor='none')\n",
-    "ax2.plot(t_all1C, T_n1C,'b-')\n",
-    "ax2.plot(data_T_1C.values[0:3598:100,0]/3600, data_T_1C.values[0:3598:100,1],'bo',markerfacecolor='none')\n",
-    "ax2.legend()\n",
-    "ax2.set_xlabel(\"Time [h]\")\n",
-    "ax2.set_ylabel(\"Temperature rise [K]\")\n",
-    "#plt.xlim(0, 3600);\n",
-    "ax2.text(0.5, 8, r'2 C', {'color': 'r', 'fontsize': 14})\n",
-    "ax2.text(0.8, 4.4, r'1 C', {'color': 'b', 'fontsize': 14})\n",
-    "ax2.text(1.5, 2, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
-    "\n",
-    "ax3.plot(t_all2C, L_x2C,'r-',label=\"Simulation\")\n",
-    "ax3.plot(data_Disp_2C.values[0:1754:5,0]/3600, data_Disp_2C.values[0:1754:5,1]-data_Disp_2C.values[0,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
-    "ax3.plot(t_all05C, L_x05C,'g-')\n",
-    "ax3.plot(data_Disp_05C.values[0:1754:10,0]/3600, data_Disp_05C.values[0:1754:10,1]-data_Disp_05C.values[0,1],'go',markerfacecolor='none')\n",
-    "ax3.plot(t_all1C, L_x1C,'b-')\n",
-    "ax3.plot(data_Disp_1C.values[0:1754:10,0]/3600, data_Disp_1C.values[0:1754:10,1]-data_Disp_1C.values[0,1],'bo',markerfacecolor='none')\n",
-    "ax3.legend()\n",
-    "ax3.set_xlabel(\"Time [h]\")\n",
-    "ax3.set_ylabel(\"Thickness change [m]\")\n",
-    "ax3.text(0.1, -0.0001, r'2 C', {'color': 'r', 'fontsize': 14})\n",
-    "ax3.text(0.9, -0.0001, r'1 C', {'color': 'b', 'fontsize': 14})\n",
-    "ax3.text(1.8, -0.0001, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
-    "#plt.xlim(0, 3600);\n",
-    "f.tight_layout()\n",
-    "f.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Stress data below are from Fig. 6 in [[1]](#References)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "<Figure size 720x252 with 2 Axes>",
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "source": [
-    "cs_n_xav = solution2C[\"X-averaged negative particle concentration [mol.m-3]\"].entries\n",
-    "cs_p_xav = solution2C[\"X-averaged positive particle concentration [mol.m-3]\"].entries\n",
-    "st_surf_n =  solution2C[\"Negative particle surface tangential stress\"].entries\n",
-    "st_surf_p =  solution2C[\"Positive particle surface tangential stress\"].entries\n",
-    "\n",
-    "data_st_n_2C=pd.read_csv (path + \"stn_2C.txt\", delimiter= ',',header=3)\n",
-    "data_st_p_2C=pd.read_csv (path + \"stp_2C.txt\", delimiter= ',',header=3)\n",
-    "\n",
-    "f, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,3.5))\n",
-    "ax1.plot(t_all2C, st_surf_n[-1,:],'ro',markerfacecolor='none',label=\"Current\")\n",
-    "ax1.plot(data_st_n_2C.values[:,0]/3600, data_st_n_2C.values[:,1],'r-',label=\"Ai et al. 2020\")\n",
-    "ax1.legend()\n",
-    "#plt.xlim(0, 3600);\n",
-    "ax1.set_xlabel(\"Time [h]\")\n",
-    "ax1.set_ylabel(\"$\\sigma_{t,n}/E_n$\")\n",
-    "\n",
-    "ax2.plot(t_all2C, st_surf_p[0,:],'ro',markerfacecolor='none',label=\"Current\")\n",
-    "ax2.plot(data_st_p_2C.values[0:3601,0]/3600, data_st_p_2C.values[0:3601,1],'r-',label=\"Ai et al. 2020\")\n",
-    "ax2.legend()\n",
-    "ax2.set_xlabel(\"Time [h]\")\n",
-    "ax2.set_ylabel(\"$\\sigma_{t,p}/E_p$\")\n",
-    "#plt.xlim(0, 3600);\n",
-    "f.tight_layout()\n",
-    "#f.set_title(\"particle surface tangential stress close to the separator\")\n",
-    "f.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "\n",
-    "The relevant papers for this notebook are:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n[7] Robert Timms, Scott G. Marquis, Valentin Sulzer, Colin P. Please, and S. Jon Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. Submitted for publication, 2020. arXiv:2005.05127.\n\n"
-     ]
-    }
-   ],
-   "source": [
-    "pybamm.print_citations()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/examples/notebooks/electrode-state-of-health.ipynb b/examples/notebooks/electrode-state-of-health.ipynb
new file mode 100644
index 0000000000..a1ab16ef83
--- /dev/null
+++ b/examples/notebooks/electrode-state-of-health.ipynb
@@ -0,0 +1,376 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Electrode State of Health"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook demonstrates some utilities to work with electrode State of Health (also sometimes called electrode stoichiometry), using the algorithm from Mohtat et al [1]\n",
+    "\n",
+    "[1] Mohtat, P., Lee, S., Siegel, J. B., & Stefanopoulou, A. G. (2019). Towards better estimability of electrode-specific state of health: Decoding the cell expansion. Journal of Power Sources, 427, 101-111."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1 is available.\n",
+      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install pybamm -q\n",
+    "import pybamm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create and solve model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fe8f802d1e340b19641493c0f503db3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=2.324130562944902, step=0.02324130562944902)…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x146bb7580>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spm = pybamm.lithium_ion.SPM()\n",
+    "experiment = pybamm.Experiment([\n",
+    "    \"Charge at 1C until 4.2V\", \n",
+    "    \"Hold at 4.2V until C/50\",\n",
+    "    \"Discharge at 1C until 2.8V\",\n",
+    "    \"Hold at 2.8V until C/50\",\n",
+    "])\n",
+    "parameter_values = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Mohtat2020)\n",
+    "\n",
+    "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n",
+    "spm_sol = sim.solve()\n",
+    "spm_sol.plot([\n",
+    "    \"Terminal voltage [V]\", \n",
+    "    \"Current [A]\", \n",
+    "    \"Negative electrode SOC\",\n",
+    "    \"Positive electrode SOC\",\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solve for electrode SOH variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given a total amount of lithium, $n_{Li}$, electrode capacities, $C_n$ and $C_p$, and voltage limits, $V_{min}$ and $V_{max}$, we can solve for the min and max electrode SOCs, $x_0$, $x_{100}$, $y_0$, and $y_{100}$,  and the cell capacity, $C$, using the algorithm from Mohtat et al [1].\n",
+    "First, we find $x_{100}$ and $y_{100}$ using\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "n_{Li} &= \\frac{3600}{F}(y_{100}C_p + x_{100}C_n),\n",
+    "\\\\\n",
+    "V_{max} &= U_p(y_{100}) - U_n(x_{100}).\n",
+    "\\end{align}\n",
+    "$$\n",
+    "Then, we find $C$ using\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "V_{min} &= U_p(y_{0}) - U_n(x_{0})\n",
+    "\\\\\n",
+    "&= U_p\\left(y_{100} + \\frac{C}{C_p}\\right) - U_n\\left(x_{100} - \\frac{C}{C_n}\\right)\n",
+    "\\end{align}\n",
+    "$$\n",
+    "Finally, $x_0$ and $y_0$ are simply defined as\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "x_0 &= x_{100} - \\frac{C}{C_n},\n",
+    "\\\\\n",
+    "y_0 &= y_{100} + \\frac{C}{C_p}.\n",
+    "\\end{align}\n",
+    "$$\n",
+    "We implement this in pybamm as an algebraic model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param = pybamm.LithiumIonParameters()\n",
+    "\n",
+    "Vmin = 2.8\n",
+    "Vmax = 4.2\n",
+    "Cn = parameter_values.evaluate(param.C_n_init)\n",
+    "Cp = parameter_values.evaluate(param.C_p_init)\n",
+    "n_Li = parameter_values.evaluate(param.n_Li_particles_init)\n",
+    "\n",
+    "Un = param.U_n_dimensional\n",
+    "Up = param.U_p_dimensional\n",
+    "T_ref = param.T_ref"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x_100 : 0.8334162315444249\n",
+      "y_100 : 0.03350230659015535\n",
+      "C : 4.969342722024421\n",
+      "x_0 : 0.0014851546252120373\n",
+      "y_0 : 0.8909223888800355\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = pybamm.BaseModel()\n",
+    "x100 = pybamm.Variable(\"x100\")\n",
+    "C = pybamm.Variable(\"C\")\n",
+    "\n",
+    "y100 = (n_Li * param.F / 3600 - x100 * Cn) / Cp\n",
+    "x0 = x100 - C/Cn\n",
+    "y0 = y100 + C/Cp\n",
+    "\n",
+    "model.algebraic = {\n",
+    "    x100: Up(y100, T_ref) - Un(x100, T_ref) - Vmax,\n",
+    "    C: Up(y0, T_ref) - Un(x0, T_ref) - Vmin,\n",
+    "}\n",
+    "model.initial_conditions = {\n",
+    "    x100: 0.9,\n",
+    "    C: Cp,\n",
+    "}\n",
+    "model.variables = {\n",
+    "    \"x_100\": x100,\n",
+    "    \"y_100\": y100,\n",
+    "    \"C\": C,\n",
+    "    \"x_0\": x0,\n",
+    "    \"y_0\": y0,\n",
+    "}\n",
+    "\n",
+    "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n",
+    "sol = sim.solve([0])\n",
+    "for var in [\"x_100\", \"y_100\", \"C\", \"x_0\", \"y_0\"]:\n",
+    "    print(var, \":\", sol[var].data[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model is implemented in PyBaMM as the `ElectrodeSOH` model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x_100 : 0.8334162315444252\n",
+      "y_100 : 0.033502306590155045\n",
+      "C : 4.969342721609116\n",
+      "x_0 : 0.0014851546947397543\n",
+      "y_0 : 0.8909223888083776\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = pybamm.lithium_ion.ElectrodeSOH()\n",
+    "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n",
+    "esoh_sol = sim.solve(\n",
+    "    [0],\n",
+    "    inputs={ \"V_min\": Vmin, \"V_max\": Vmax, \"C_n\": Cn, \"C_p\": Cp, \"n_Li\": n_Li},\n",
+    ")\n",
+    "for var in [\"x_100\", \"y_100\", \"C\", \"x_0\", \"y_0\"]:\n",
+    "    print(var, \":\", esoh_sol[var].data[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check against simulations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting the SPM simulations against the eSOH calculations validates the min/max stoichiometry calculations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t = spm_sol[\"Time [h]\"].data\n",
+    "x_spm = spm_sol[\"Negative electrode SOC\"].data\n",
+    "y_spm = spm_sol[\"Positive electrode SOC\"].data\n",
+    "\n",
+    "x_0 = esoh_sol[\"x_0\"].data * np.ones_like(t)\n",
+    "y_0 = esoh_sol[\"y_0\"].data * np.ones_like(t)\n",
+    "x_100 = esoh_sol[\"x_100\"].data * np.ones_like(t)\n",
+    "y_100 = esoh_sol[\"y_100\"].data * np.ones_like(t)\n",
+    "\n",
+    "fig, axes = plt.subplots(1,2)\n",
+    "\n",
+    "axes[0].plot(t, x_spm, \"b\")\n",
+    "axes[0].plot(t, x_0, \"k:\")\n",
+    "axes[0].plot(t, x_100, \"k:\")\n",
+    "axes[0].set_ylabel(\"x\")\n",
+    "    \n",
+    "axes[1].plot(t, y_spm, \"r\")\n",
+    "axes[1].plot(t, y_0, \"k:\")\n",
+    "axes[1].plot(t, y_100, \"k:\")\n",
+    "axes[1].set_ylabel(\"y\")\n",
+    "    \n",
+    "for k in range(2):\n",
+    "    axes[k].set_xlim([t[0],t[-1]])\n",
+    "    axes[k].set_ylim([0,1])    \n",
+    "    axes[k].set_xlabel(\"Time [h]\")\n",
+    "    \n",
+    "fig.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[4] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n",
+      "[5] Peyman Mohtat, Suhak Lee, Valentin Sulzer, Jason B. Siegel, and Anna G. Stefanopoulou. Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates. Journal of The Electrochemical Society, 167(11):110561, 2020. doi:10.1149/1945-7111/aba5d1.\n",
+      "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
+      "[7] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/notebooks/lithium-plating.ipynb b/examples/notebooks/lithium-plating.ipynb
index f4c6bdbef7..64718a44e2 100644
--- a/examples/notebooks/lithium-plating.ipynb
+++ b/examples/notebooks/lithium-plating.ipynb
@@ -18,6 +18,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1 is available.\n",
+      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -73,7 +75,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x7fb9a92df190>"
+       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x10e4a5730>"
       ]
      },
      "execution_count": 3,
@@ -163,10 +165,18 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The linesearch algorithm failed with too small a step.\n",
+      "The linesearch algorithm failed with too small a step.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<pybamm.solvers.solution.Solution at 0x7fb9a1a52b50>"
+       "<pybamm.solvers.solution.Solution at 0x152d76d30>"
       ]
      },
      "execution_count": 5,
@@ -251,7 +261,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x648 with 4 Axes>"
       ]
@@ -324,9 +334,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The linesearch algorithm failed with too small a step.\n",
+      "The linesearch algorithm failed with too small a step.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.solvers.solution.Solution at 0x156525250>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sim2_2C = pybamm.Simulation(model2, experiment=experiment_2C, parameter_values=parameter_values)\n",
     "sim2_2C.solve()\n",
@@ -342,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -400,9 +429,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x648 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axs = plt.subplots(2, 2, figsize=(13,9))\n",
     "axs[0,0].plot(t_2C/60, V_2C, color='tab:purple', linestyle='solid')\n",
@@ -465,12 +507,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n",
+      "[3] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[4] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[5] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n",
+      "[6] Dongsheng Ren, Kandler Smith, Dongxu Guo, Xuebing Han, Xuning Feng, Languang Lu, and Minggao Ouyang. Investigation of lithium plating-stripping process in li-ion batteries at low temperature using an electrochemical model. Journal of the Electrochemistry Society, 165:A2167-A2178, 2018. doi:10.1149/2.0661810jes.\n",
+      "[7] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
+      "[8] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "pybamm.print_citations()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -489,7 +554,20 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
   }
  },
  "nbformat": 4,
diff --git a/examples/notebooks/models/SPM.ipynb b/examples/notebooks/models/SPM.ipynb
index 40b25e61d2..052c54a72e 100644
--- a/examples/notebooks/models/SPM.ipynb
+++ b/examples/notebooks/models/SPM.ipynb
@@ -696,6 +696,7 @@
       "\t- Total lithium in negative electrode [mol]\n",
       "\t- Positive particle flux\n",
       "\t- X-averaged positive particle flux\n",
+      "\t- Total lithium in electrolyte [mol]\n",
       "\t- Positive electrode volume-averaged concentration\n",
       "\t- Positive electrode volume-averaged concentration [mol.m-3]\n",
       "\t- Total lithium in positive electrode [mol]\n",
@@ -729,9 +730,9 @@
       "\t- Current collector current density\n",
       "\t- Current collector current density [A.m-2]\n",
       "\t- Leading-order current collector current density\n",
-      "\t- Sei interfacial current density\n",
-      "\t- Sei interfacial current density [A.m-2]\n",
-      "\t- Sei interfacial current density per volume [A.m-3]\n",
+      "\t- SEI interfacial current density\n",
+      "\t- SEI interfacial current density [A.m-2]\n",
+      "\t- SEI interfacial current density per volume [A.m-3]\n",
       "\t- X-averaged negative electrode total interfacial current density\n",
       "\t- X-averaged negative electrode total interfacial current density [A.m-2]\n",
       "\t- X-averaged negative electrode total interfacial current density per volume [A.m-3]\n",
diff --git a/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb b/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb
new file mode 100644
index 0000000000..298561281b
--- /dev/null
+++ b/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb
@@ -0,0 +1,360 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test the parameter set of the Enertech cells\n",
+    "In this notebook, we show how to use pybamm to reproduce the experimental results for the Enertech cells (LCO-G). To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/models/index.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1.1 is available.\n",
+      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install pybamm -q    # install PyBaMM if it is not installed\n",
+    "import pybamm\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "os.chdir(pybamm.__path__[0]+'/..')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you load a model in PyBaMM it builds by default. Building the model sets all of the model variables and sets up any variables which are coupled between different submodels: this is the process which couples the submodels together and allows one submodel to access variables from another. If you would like to swap out a submodel in an exisitng battery model you need to load it without building it by passing the keyword `build=False`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pybamm.lithium_ion.DFN(\n",
+    "    options = {\n",
+    "        \"particle\": \"Fickian diffusion\", \n",
+    "        \"cell geometry\": \"arbitrary\", \n",
+    "        \"thermal\": \"lumped\", \n",
+    "        \"particle mechanics\": \"swelling only\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can get the parameter set `Ai2020` for the model, which includes the mechanical properties required by the mechanical model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5 C\n",
+      "1 C\n",
+      "2 C\n"
+     ]
+    }
+   ],
+   "source": [
+    "chemistry = pybamm.parameter_sets.Ai2020\n",
+    "param = pybamm.ParameterValues(chemistry=chemistry)\n",
+    "capacity = param[\"Nominal cell capacity [A.h]\"]\n",
+    "param.update({\n",
+    "    \"Current function [A]\": capacity * pybamm.InputParameter(\"C-rate\")\n",
+    "})\n",
+    "# experiment05C = pybamm.Experiment([\"Discharge at 0.5C until 3 V\"])\n",
+    "# experiment1C = pybamm.Experiment([\"Discharge at 1C until 3 V\"])\n",
+    "# experiment2C = pybamm.Experiment([\"Discharge at 2C until 3 V\"])\n",
+    "var = pybamm.standard_spatial_vars\n",
+    "var_pts = {\n",
+    "  var.x_n: 50,\n",
+    "  var.x_s: 50,\n",
+    "  var.x_p: 50,\n",
+    "  var.r_n: 20,\n",
+    "  var.r_p: 20,\n",
+    "}\n",
+    "\n",
+    "sim = pybamm.Simulation(\n",
+    "        model,\n",
+    "        var_pts=var_pts,\n",
+    "        parameter_values=param,\n",
+    "        solver=pybamm.CasadiSolver(dt_max=600)\n",
+    "    )\n",
+    "Crates = [0.5, 1, 2]\n",
+    "solutions = []\n",
+    "\n",
+    "for Crate in Crates:\n",
+    "    print(f\"{Crate} C\")\n",
+    "    sol = sim.solve(t_eval=[0, 3600/Crate*1.05], inputs={\"C-rate\": Crate})\n",
+    "    solutions.append(sol)\n",
+    "\n",
+    "\n",
+    "solution05C, solution1C, solution2C = solutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load experimental results of the Enertech cells (see [[1]](#References))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load experimental results\n",
+    "import pandas as pd\n",
+    "path = \"pybamm/input/discharge_data/Enertech_cells/\"\n",
+    "data_Disp_01C=pd.read_csv (path + \"0.1C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
+    "data_Disp_05C=pd.read_csv (path + \"0.5C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
+    "data_Disp_1C=pd.read_csv (path + \"1C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
+    "data_Disp_2C=pd.read_csv (path + \"2C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
+    "data_V_01C=pd.read_csv (path + \"0.1C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
+    "data_V_05C=pd.read_csv (path + \"0.5C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
+    "data_V_1C=pd.read_csv (path + \"1C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
+    "data_V_2C=pd.read_csv (path + \"2C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
+    "data_T_05C=pd.read_csv (path + \"0.5C_discharge_T.txt\", delimiter= '\\s+',header=None)\n",
+    "data_T_1C=pd.read_csv (path + \"1C_discharge_T.txt\", delimiter= '\\s+',header=None)\n",
+    "data_T_2C=pd.read_csv (path + \"2C_discharge_T.txt\", delimiter= '\\s+',header=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t_all2C = solution2C[\"Time [h]\"].entries\n",
+    "V_n2C = solution2C[\"Terminal voltage [V]\"].entries\n",
+    "T_n2C = solution2C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
+    "L_x2C = solution2C[\"Cell thickness change [m]\"].entries\n",
+    "\n",
+    "t_all1C = solution1C[\"Time [h]\"].entries\n",
+    "V_n1C = solution1C[\"Terminal voltage [V]\"].entries\n",
+    "T_n1C = solution1C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
+    "L_x1C = solution1C[\"Cell thickness change [m]\"].entries\n",
+    "\n",
+    "t_all05C = solution05C[\"Time [h]\"].entries\n",
+    "V_n05C = solution05C[\"Terminal voltage [V]\"].entries\n",
+    "T_n05C = solution05C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
+    "L_x05C = solution05C[\"Cell thickness change [m]\"].entries\n",
+    "f, (ax1, ax2,ax3) = plt.subplots(1, 3 ,figsize=(14,4))\n",
+    "ax1.plot(t_all2C, V_n2C,'r-',label=\"Simulation\")\n",
+    "ax1.plot(data_V_2C.values[::30,0]/3600, data_V_2C.values[::30,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
+    "ax1.plot(t_all05C, V_n05C,'g-')\n",
+    "ax1.plot(data_V_05C.values[::100,0]/3600, data_V_05C.values[::100,1],'go',markerfacecolor='none')\n",
+    "ax1.plot(t_all1C, V_n1C,'b-')\n",
+    "ax1.plot(data_V_1C.values[::50,0]/3600, data_V_1C.values[::50,1],'bo',markerfacecolor='none')\n",
+    "ax1.legend()\n",
+    "#plt.xlim(0, 3600);\n",
+    "ax1.set_xlabel(\"Time [h]\")\n",
+    "ax1.set_ylabel(\"Terminal voltage [V]\")\n",
+    "ax1.text(0.1, 3.2, r'2 C', {'color': 'r', 'fontsize': 14})\n",
+    "ax1.text(1.1, 3.2, r'1 C', {'color': 'b', 'fontsize': 14})\n",
+    "ax1.text(1.6, 3.2, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
+    "\n",
+    "ax2.plot(t_all2C, T_n2C,'r-',label=\"Simulation\")\n",
+    "ax2.plot(data_T_2C.values[0:1754:50,0]/3600, data_T_2C.values[0:1754:50,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
+    "ax2.plot(t_all05C, T_n05C,'g-')\n",
+    "ax2.plot(data_T_05C.values[0:7301:200,0]/3600, data_T_05C.values[0:7301:200,1],'go',markerfacecolor='none')\n",
+    "ax2.plot(t_all1C, T_n1C,'b-')\n",
+    "ax2.plot(data_T_1C.values[0:3598:100,0]/3600, data_T_1C.values[0:3598:100,1],'bo',markerfacecolor='none')\n",
+    "ax2.legend()\n",
+    "ax2.set_xlabel(\"Time [h]\")\n",
+    "ax2.set_ylabel(\"Temperature rise [K]\")\n",
+    "#plt.xlim(0, 3600);\n",
+    "ax2.text(0.5, 8, r'2 C', {'color': 'r', 'fontsize': 14})\n",
+    "ax2.text(0.8, 4.4, r'1 C', {'color': 'b', 'fontsize': 14})\n",
+    "ax2.text(1.5, 2, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
+    "\n",
+    "ax3.plot(t_all2C, L_x2C,'r-',label=\"Simulation\")\n",
+    "ax3.plot(data_Disp_2C.values[0:1754:5,0]/3600, data_Disp_2C.values[0:1754:5,1]-data_Disp_2C.values[0,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
+    "ax3.plot(t_all05C, L_x05C,'g-')\n",
+    "ax3.plot(data_Disp_05C.values[0:1754:10,0]/3600, data_Disp_05C.values[0:1754:10,1]-data_Disp_05C.values[0,1],'go',markerfacecolor='none')\n",
+    "ax3.plot(t_all1C, L_x1C,'b-')\n",
+    "ax3.plot(data_Disp_1C.values[0:1754:10,0]/3600, data_Disp_1C.values[0:1754:10,1]-data_Disp_1C.values[0,1],'bo',markerfacecolor='none')\n",
+    "ax3.legend()\n",
+    "ax3.set_xlabel(\"Time [h]\")\n",
+    "ax3.set_ylabel(\"Thickness change [m]\")\n",
+    "ax3.text(0.1, -0.0001, r'2 C', {'color': 'r', 'fontsize': 14})\n",
+    "ax3.text(0.9, -0.0001, r'1 C', {'color': 'b', 'fontsize': 14})\n",
+    "ax3.text(1.8, -0.0001, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
+    "#plt.xlim(0, 3600);\n",
+    "f.tight_layout()\n",
+    "f.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Stress data below are from Fig. 6 in [[1]](#References)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x252 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cs_n_xav = solution2C[\"X-averaged negative particle concentration [mol.m-3]\"].entries\n",
+    "cs_p_xav = solution2C[\"X-averaged positive particle concentration [mol.m-3]\"].entries\n",
+    "st_surf_n =  solution2C[\"Negative particle surface tangential stress\"].entries\n",
+    "st_surf_p =  solution2C[\"Positive particle surface tangential stress\"].entries\n",
+    "\n",
+    "data_st_n_2C=pd.read_csv (path + \"stn_2C.txt\", delimiter= ',',header=3)\n",
+    "data_st_p_2C=pd.read_csv (path + \"stp_2C.txt\", delimiter= ',',header=3)\n",
+    "\n",
+    "f, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,3.5))\n",
+    "ax1.plot(t_all2C, st_surf_n[-1,:],'ro',markerfacecolor='none',label=\"Current\")\n",
+    "ax1.plot(data_st_n_2C.values[:,0]/3600, data_st_n_2C.values[:,1],'r-',label=\"Ai et al. 2020\")\n",
+    "ax1.legend()\n",
+    "#plt.xlim(0, 3600);\n",
+    "ax1.set_xlabel(\"Time [h]\")\n",
+    "ax1.set_ylabel(\"$\\sigma_{t,n}/E_n$\")\n",
+    "\n",
+    "ax2.plot(t_all2C, st_surf_p[0,:],'ro',markerfacecolor='none',label=\"Current\")\n",
+    "ax2.plot(data_st_p_2C.values[0:3601,0]/3600, data_st_p_2C.values[0:3601,1],'r-',label=\"Ai et al. 2020\")\n",
+    "ax2.legend()\n",
+    "ax2.set_xlabel(\"Time [h]\")\n",
+    "ax2.set_ylabel(\"$\\sigma_{t,p}/E_p$\")\n",
+    "#plt.xlim(0, 3600);\n",
+    "f.tight_layout()\n",
+    "#f.set_title(\"particle surface tangential stress close to the separator\")\n",
+    "f.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n",
+      "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n",
+      "[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
+      "[7] Robert Timms, Scott G. Marquis, Valentin Sulzer, Colin P. Please, and S. Jon Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. Submitted for publication, 2020. arXiv:2005.05127.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/notebooks/compare-particle-diffusion-models.ipynb b/examples/notebooks/models/compare-particle-diffusion-models.ipynb
similarity index 100%
rename from examples/notebooks/compare-particle-diffusion-models.ipynb
rename to examples/notebooks/models/compare-particle-diffusion-models.ipynb
diff --git a/examples/notebooks/using-model-options_thermal-example.ipynb b/examples/notebooks/models/using-model-options_thermal-example.ipynb
similarity index 100%
rename from examples/notebooks/using-model-options_thermal-example.ipynb
rename to examples/notebooks/models/using-model-options_thermal-example.ipynb
diff --git a/examples/notebooks/simulating-long-experiments.ipynb b/examples/notebooks/simulating-long-experiments.ipynb
new file mode 100644
index 0000000000..cc49eb9c35
--- /dev/null
+++ b/examples/notebooks/simulating-long-experiments.ipynb
@@ -0,0 +1,1964 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "regional-bedroom",
+   "metadata": {},
+   "source": [
+    "# Simulating long experiments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "quantitative-radar",
+   "metadata": {},
+   "source": [
+    "This notebook introduces functionality for simulating experiments over hundreds or even thousands of cycles. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "novel-spectacular",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1.1 is available.\n",
+      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install pybamm -q\n",
+    "import pybamm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mounted-seven",
+   "metadata": {},
+   "source": [
+    "## Simulating long experiments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "chronic-consensus",
+   "metadata": {},
+   "source": [
+    "In the interest of simplicity and running time, we consider a SPM with SEI effects leading to linear degradation, with parameter values chosen so that the capacity fades by 20% in just a few cycles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "limiting-making",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_values = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Mohtat2020)\n",
+    "parameter_values.update({\"SEI kinetic rate constant [m.s-1]\": 1e-14})\n",
+    "spm = pybamm.lithium_ion.SPM({\"SEI\": \"ec reaction limited\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "compact-teddy",
+   "metadata": {},
+   "source": [
+    "Using the \"Electrode SOH\" (eSOH) model, we initialize the concentration in each electrode at 100% State of Charge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "photographic-sussex",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial negative electrode SOC: 0.833\n",
+      "Initial positive electrode SOC: 0.034\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate stoichiometries at 100% SOC\n",
+    "esoh_model = pybamm.lithium_ion.ElectrodeSOH()\n",
+    "esoh_sim = pybamm.Simulation(esoh_model, parameter_values=parameter_values)\n",
+    "param = spm.param\n",
+    "\n",
+    "Vmin = 3.0\n",
+    "Vmax = 4.2\n",
+    "Cn = parameter_values.evaluate(param.C_n_init)\n",
+    "Cp = parameter_values.evaluate(param.C_p_init)\n",
+    "n_Li_init = parameter_values.evaluate(param.n_Li_particles_init)\n",
+    "\n",
+    "esoh_sol = esoh_sim.solve(\n",
+    "    [0],\n",
+    "    inputs={\"V_min\": Vmin, \"V_max\": Vmax, \"C_n\": Cn, \"C_p\": Cp, \"n_Li\": n_Li_init},\n",
+    ")\n",
+    "print(f\"Initial negative electrode SOC: {esoh_sol['x_100'].data[0]:.3f}\")\n",
+    "print(f\"Initial positive electrode SOC: {esoh_sol['y_100'].data[0]:.3f}\")\n",
+    "\n",
+    "# Update parameter values with initial conditions\n",
+    "c_n_max = parameter_values.evaluate(param.c_n_max)\n",
+    "c_p_max = parameter_values.evaluate(param.c_p_max)\n",
+    "parameter_values.update(\n",
+    "    {\n",
+    "        \"Initial concentration in negative electrode [mol.m-3]\": esoh_sol[\"x_100\"].data[0] * c_n_max,\n",
+    "        \"Initial concentration in positive electrode [mol.m-3]\": esoh_sol[\"y_100\"].data[0] * c_p_max,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "focused-substitute",
+   "metadata": {},
+   "source": [
+    "We can now simulate a single CCCV cycle using the `Experiment` class (see [this notebook](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/Getting%20Started/Tutorial%205%20-%20Run%20experiments.ipynb) for more details)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "religious-primary",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:03:27,605 - [NOTICE] simulation.solve(710): Cycle 1/1 (152.113 ms elapsed) --------------------\n",
+      "2021-05-24 09:03:27,606 - [NOTICE] simulation.solve(742): Cycle 1/1, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:27,778 - [NOTICE] simulation.solve(742): Cycle 1/1, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:27,923 - [NOTICE] simulation.solve(742): Cycle 1/1, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:28,101 - [NOTICE] simulation.solve(742): Cycle 1/1, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:28,606 - [NOTICE] simulation.solve(837): Finish experiment simulation, took 1.153 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.set_logging_level(\"NOTICE\")\n",
+    "\n",
+    "experiment = pybamm.Experiment([\n",
+    "    (f\"Discharge at 1C until {Vmin}V\",\n",
+    "     \"Rest for 1 hour\",\n",
+    "     f\"Charge at 1C until {Vmax}V\", \n",
+    "     f\"Hold at {Vmax}V until C/50\"\n",
+    "    )\n",
+    "])\n",
+    "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n",
+    "sol = sim.solve()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "heavy-crisis",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can simulate many CCCV cycles. Here we simulate either 100 cycles or until the capacity is 80% of the initial capacity, whichever is first. The capacity is calculated by the eSOH model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "stupid-abortion",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:03:31,209 - [NOTICE] simulation.solve(710): Cycle 1/500 (53.770 ms elapsed) --------------------\n",
+      "2021-05-24 09:03:31,210 - [NOTICE] simulation.solve(742): Cycle 1/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:31,400 - [NOTICE] simulation.solve(742): Cycle 1/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:31,571 - [NOTICE] simulation.solve(742): Cycle 1/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:31,757 - [NOTICE] simulation.solve(742): Cycle 1/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:32,406 - [NOTICE] simulation.solve(820): Capacity is now 4.941 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:32,407 - [NOTICE] simulation.solve(710): Cycle 2/500 (1.252 s elapsed) --------------------\n",
+      "2021-05-24 09:03:32,408 - [NOTICE] simulation.solve(742): Cycle 2/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:32,538 - [NOTICE] simulation.solve(742): Cycle 2/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:32,629 - [NOTICE] simulation.solve(742): Cycle 2/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:32,740 - [NOTICE] simulation.solve(742): Cycle 2/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:32,879 - [NOTICE] simulation.solve(820): Capacity is now 4.913 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:32,880 - [NOTICE] simulation.solve(710): Cycle 3/500 (1.725 s elapsed) --------------------\n",
+      "2021-05-24 09:03:32,881 - [NOTICE] simulation.solve(742): Cycle 3/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:32,996 - [NOTICE] simulation.solve(742): Cycle 3/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:33,089 - [NOTICE] simulation.solve(742): Cycle 3/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:33,209 - [NOTICE] simulation.solve(742): Cycle 3/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:33,343 - [NOTICE] simulation.solve(820): Capacity is now 4.886 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:33,344 - [NOTICE] simulation.solve(710): Cycle 4/500 (2.189 s elapsed) --------------------\n",
+      "2021-05-24 09:03:33,345 - [NOTICE] simulation.solve(742): Cycle 4/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:33,469 - [NOTICE] simulation.solve(742): Cycle 4/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:33,565 - [NOTICE] simulation.solve(742): Cycle 4/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:33,673 - [NOTICE] simulation.solve(742): Cycle 4/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:33,816 - [NOTICE] simulation.solve(820): Capacity is now 4.859 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:33,817 - [NOTICE] simulation.solve(710): Cycle 5/500 (2.662 s elapsed) --------------------\n",
+      "2021-05-24 09:03:33,817 - [NOTICE] simulation.solve(742): Cycle 5/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:33,940 - [NOTICE] simulation.solve(742): Cycle 5/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:34,036 - [NOTICE] simulation.solve(742): Cycle 5/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:34,156 - [NOTICE] simulation.solve(742): Cycle 5/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:34,294 - [NOTICE] simulation.solve(820): Capacity is now 4.833 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:34,295 - [NOTICE] simulation.solve(710): Cycle 6/500 (3.140 s elapsed) --------------------\n",
+      "2021-05-24 09:03:34,295 - [NOTICE] simulation.solve(742): Cycle 6/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:34,405 - [NOTICE] simulation.solve(742): Cycle 6/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:34,501 - [NOTICE] simulation.solve(742): Cycle 6/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:34,614 - [NOTICE] simulation.solve(742): Cycle 6/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:34,745 - [NOTICE] simulation.solve(820): Capacity is now 4.807 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:34,746 - [NOTICE] simulation.solve(710): Cycle 7/500 (3.591 s elapsed) --------------------\n",
+      "2021-05-24 09:03:34,746 - [NOTICE] simulation.solve(742): Cycle 7/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:34,854 - [NOTICE] simulation.solve(742): Cycle 7/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:34,951 - [NOTICE] simulation.solve(742): Cycle 7/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:35,059 - [NOTICE] simulation.solve(742): Cycle 7/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:35,189 - [NOTICE] simulation.solve(820): Capacity is now 4.781 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:35,190 - [NOTICE] simulation.solve(710): Cycle 8/500 (4.034 s elapsed) --------------------\n",
+      "2021-05-24 09:03:35,190 - [NOTICE] simulation.solve(742): Cycle 8/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:35,303 - [NOTICE] simulation.solve(742): Cycle 8/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:35,405 - [NOTICE] simulation.solve(742): Cycle 8/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:35,511 - [NOTICE] simulation.solve(742): Cycle 8/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:35,666 - [NOTICE] simulation.solve(820): Capacity is now 4.756 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:35,667 - [NOTICE] simulation.solve(710): Cycle 9/500 (4.512 s elapsed) --------------------\n",
+      "2021-05-24 09:03:35,668 - [NOTICE] simulation.solve(742): Cycle 9/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:35,777 - [NOTICE] simulation.solve(742): Cycle 9/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:35,862 - [NOTICE] simulation.solve(742): Cycle 9/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:35,970 - [NOTICE] simulation.solve(742): Cycle 9/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:36,111 - [NOTICE] simulation.solve(820): Capacity is now 4.732 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:36,112 - [NOTICE] simulation.solve(710): Cycle 10/500 (4.956 s elapsed) --------------------\n",
+      "2021-05-24 09:03:36,112 - [NOTICE] simulation.solve(742): Cycle 10/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:36,411 - [NOTICE] simulation.solve(742): Cycle 10/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:36,501 - [NOTICE] simulation.solve(742): Cycle 10/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:36,628 - [NOTICE] simulation.solve(742): Cycle 10/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:36,771 - [NOTICE] simulation.solve(820): Capacity is now 4.708 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:36,772 - [NOTICE] simulation.solve(710): Cycle 11/500 (5.617 s elapsed) --------------------\n",
+      "2021-05-24 09:03:36,773 - [NOTICE] simulation.solve(742): Cycle 11/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:36,902 - [NOTICE] simulation.solve(742): Cycle 11/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:36,997 - [NOTICE] simulation.solve(742): Cycle 11/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:37,111 - [NOTICE] simulation.solve(742): Cycle 11/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:37,250 - [NOTICE] simulation.solve(820): Capacity is now 4.684 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:37,250 - [NOTICE] simulation.solve(710): Cycle 12/500 (6.095 s elapsed) --------------------\n",
+      "2021-05-24 09:03:37,251 - [NOTICE] simulation.solve(742): Cycle 12/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:37,372 - [NOTICE] simulation.solve(742): Cycle 12/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:37,473 - [NOTICE] simulation.solve(742): Cycle 12/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:37,582 - [NOTICE] simulation.solve(742): Cycle 12/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:37,727 - [NOTICE] simulation.solve(820): Capacity is now 4.660 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:37,728 - [NOTICE] simulation.solve(710): Cycle 13/500 (6.573 s elapsed) --------------------\n",
+      "2021-05-24 09:03:37,729 - [NOTICE] simulation.solve(742): Cycle 13/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:37,841 - [NOTICE] simulation.solve(742): Cycle 13/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:37,938 - [NOTICE] simulation.solve(742): Cycle 13/500, step 3/4: Charge at 1C until 4.2V\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:03:38,042 - [NOTICE] simulation.solve(742): Cycle 13/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:38,175 - [NOTICE] simulation.solve(820): Capacity is now 4.637 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:38,176 - [NOTICE] simulation.solve(710): Cycle 14/500 (7.020 s elapsed) --------------------\n",
+      "2021-05-24 09:03:38,176 - [NOTICE] simulation.solve(742): Cycle 14/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:38,295 - [NOTICE] simulation.solve(742): Cycle 14/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:38,383 - [NOTICE] simulation.solve(742): Cycle 14/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:38,495 - [NOTICE] simulation.solve(742): Cycle 14/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:38,635 - [NOTICE] simulation.solve(820): Capacity is now 4.614 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:38,636 - [NOTICE] simulation.solve(710): Cycle 15/500 (7.481 s elapsed) --------------------\n",
+      "2021-05-24 09:03:38,637 - [NOTICE] simulation.solve(742): Cycle 15/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:38,758 - [NOTICE] simulation.solve(742): Cycle 15/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:38,862 - [NOTICE] simulation.solve(742): Cycle 15/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:38,966 - [NOTICE] simulation.solve(742): Cycle 15/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:39,108 - [NOTICE] simulation.solve(820): Capacity is now 4.592 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:39,108 - [NOTICE] simulation.solve(710): Cycle 16/500 (7.953 s elapsed) --------------------\n",
+      "2021-05-24 09:03:39,109 - [NOTICE] simulation.solve(742): Cycle 16/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:39,226 - [NOTICE] simulation.solve(742): Cycle 16/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:39,319 - [NOTICE] simulation.solve(742): Cycle 16/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:39,424 - [NOTICE] simulation.solve(742): Cycle 16/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:39,556 - [NOTICE] simulation.solve(820): Capacity is now 4.570 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:39,557 - [NOTICE] simulation.solve(710): Cycle 17/500 (8.402 s elapsed) --------------------\n",
+      "2021-05-24 09:03:39,558 - [NOTICE] simulation.solve(742): Cycle 17/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:39,677 - [NOTICE] simulation.solve(742): Cycle 17/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:39,773 - [NOTICE] simulation.solve(742): Cycle 17/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:39,888 - [NOTICE] simulation.solve(742): Cycle 17/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:40,032 - [NOTICE] simulation.solve(820): Capacity is now 4.548 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:40,033 - [NOTICE] simulation.solve(710): Cycle 18/500 (8.878 s elapsed) --------------------\n",
+      "2021-05-24 09:03:40,034 - [NOTICE] simulation.solve(742): Cycle 18/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:40,149 - [NOTICE] simulation.solve(742): Cycle 18/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:40,238 - [NOTICE] simulation.solve(742): Cycle 18/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:40,345 - [NOTICE] simulation.solve(742): Cycle 18/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:40,481 - [NOTICE] simulation.solve(820): Capacity is now 4.526 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:40,482 - [NOTICE] simulation.solve(710): Cycle 19/500 (9.326 s elapsed) --------------------\n",
+      "2021-05-24 09:03:40,482 - [NOTICE] simulation.solve(742): Cycle 19/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:40,589 - [NOTICE] simulation.solve(742): Cycle 19/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:40,677 - [NOTICE] simulation.solve(742): Cycle 19/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:40,773 - [NOTICE] simulation.solve(742): Cycle 19/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:40,912 - [NOTICE] simulation.solve(820): Capacity is now 4.505 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:40,913 - [NOTICE] simulation.solve(710): Cycle 20/500 (9.758 s elapsed) --------------------\n",
+      "2021-05-24 09:03:40,914 - [NOTICE] simulation.solve(742): Cycle 20/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:41,029 - [NOTICE] simulation.solve(742): Cycle 20/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:41,296 - [NOTICE] simulation.solve(742): Cycle 20/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:41,404 - [NOTICE] simulation.solve(742): Cycle 20/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:41,538 - [NOTICE] simulation.solve(820): Capacity is now 4.484 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:41,539 - [NOTICE] simulation.solve(710): Cycle 21/500 (10.384 s elapsed) --------------------\n",
+      "2021-05-24 09:03:41,540 - [NOTICE] simulation.solve(742): Cycle 21/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:41,646 - [NOTICE] simulation.solve(742): Cycle 21/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:41,741 - [NOTICE] simulation.solve(742): Cycle 21/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:41,846 - [NOTICE] simulation.solve(742): Cycle 21/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:41,985 - [NOTICE] simulation.solve(820): Capacity is now 4.463 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:41,986 - [NOTICE] simulation.solve(710): Cycle 22/500 (10.830 s elapsed) --------------------\n",
+      "2021-05-24 09:03:41,987 - [NOTICE] simulation.solve(742): Cycle 22/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:42,105 - [NOTICE] simulation.solve(742): Cycle 22/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:42,205 - [NOTICE] simulation.solve(742): Cycle 22/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:42,304 - [NOTICE] simulation.solve(742): Cycle 22/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:42,438 - [NOTICE] simulation.solve(820): Capacity is now 4.442 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:42,438 - [NOTICE] simulation.solve(710): Cycle 23/500 (11.283 s elapsed) --------------------\n",
+      "2021-05-24 09:03:42,439 - [NOTICE] simulation.solve(742): Cycle 23/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:42,552 - [NOTICE] simulation.solve(742): Cycle 23/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:42,638 - [NOTICE] simulation.solve(742): Cycle 23/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:42,738 - [NOTICE] simulation.solve(742): Cycle 23/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:42,880 - [NOTICE] simulation.solve(820): Capacity is now 4.422 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:42,881 - [NOTICE] simulation.solve(710): Cycle 24/500 (11.726 s elapsed) --------------------\n",
+      "2021-05-24 09:03:42,882 - [NOTICE] simulation.solve(742): Cycle 24/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:42,985 - [NOTICE] simulation.solve(742): Cycle 24/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:43,077 - [NOTICE] simulation.solve(742): Cycle 24/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:43,174 - [NOTICE] simulation.solve(742): Cycle 24/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:43,310 - [NOTICE] simulation.solve(820): Capacity is now 4.402 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:43,311 - [NOTICE] simulation.solve(710): Cycle 25/500 (12.156 s elapsed) --------------------\n",
+      "2021-05-24 09:03:43,312 - [NOTICE] simulation.solve(742): Cycle 25/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:43,417 - [NOTICE] simulation.solve(742): Cycle 25/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:43,517 - [NOTICE] simulation.solve(742): Cycle 25/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:43,622 - [NOTICE] simulation.solve(742): Cycle 25/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:43,752 - [NOTICE] simulation.solve(820): Capacity is now 4.382 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:43,753 - [NOTICE] simulation.solve(710): Cycle 26/500 (12.597 s elapsed) --------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:03:43,753 - [NOTICE] simulation.solve(742): Cycle 26/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:43,859 - [NOTICE] simulation.solve(742): Cycle 26/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:43,945 - [NOTICE] simulation.solve(742): Cycle 26/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:44,044 - [NOTICE] simulation.solve(742): Cycle 26/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:44,182 - [NOTICE] simulation.solve(820): Capacity is now 4.362 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:44,183 - [NOTICE] simulation.solve(710): Cycle 27/500 (13.028 s elapsed) --------------------\n",
+      "2021-05-24 09:03:44,183 - [NOTICE] simulation.solve(742): Cycle 27/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:44,284 - [NOTICE] simulation.solve(742): Cycle 27/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:44,377 - [NOTICE] simulation.solve(742): Cycle 27/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:44,476 - [NOTICE] simulation.solve(742): Cycle 27/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:44,609 - [NOTICE] simulation.solve(820): Capacity is now 4.343 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:44,610 - [NOTICE] simulation.solve(710): Cycle 28/500 (13.455 s elapsed) --------------------\n",
+      "2021-05-24 09:03:44,611 - [NOTICE] simulation.solve(742): Cycle 28/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:44,722 - [NOTICE] simulation.solve(742): Cycle 28/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:44,813 - [NOTICE] simulation.solve(742): Cycle 28/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:44,910 - [NOTICE] simulation.solve(742): Cycle 28/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:45,042 - [NOTICE] simulation.solve(820): Capacity is now 4.324 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:45,043 - [NOTICE] simulation.solve(710): Cycle 29/500 (13.888 s elapsed) --------------------\n",
+      "2021-05-24 09:03:45,044 - [NOTICE] simulation.solve(742): Cycle 29/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:45,145 - [NOTICE] simulation.solve(742): Cycle 29/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:45,230 - [NOTICE] simulation.solve(742): Cycle 29/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:45,331 - [NOTICE] simulation.solve(742): Cycle 29/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:45,463 - [NOTICE] simulation.solve(820): Capacity is now 4.305 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:45,464 - [NOTICE] simulation.solve(710): Cycle 30/500 (14.309 s elapsed) --------------------\n",
+      "2021-05-24 09:03:45,464 - [NOTICE] simulation.solve(742): Cycle 30/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:45,566 - [NOTICE] simulation.solve(742): Cycle 30/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:45,656 - [NOTICE] simulation.solve(742): Cycle 30/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:45,753 - [NOTICE] simulation.solve(742): Cycle 30/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:45,895 - [NOTICE] simulation.solve(820): Capacity is now 4.286 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:45,895 - [NOTICE] simulation.solve(710): Cycle 31/500 (14.740 s elapsed) --------------------\n",
+      "2021-05-24 09:03:45,896 - [NOTICE] simulation.solve(742): Cycle 31/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:46,189 - [NOTICE] simulation.solve(742): Cycle 31/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:46,283 - [NOTICE] simulation.solve(742): Cycle 31/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:46,396 - [NOTICE] simulation.solve(742): Cycle 31/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:46,540 - [NOTICE] simulation.solve(820): Capacity is now 4.267 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:46,540 - [NOTICE] simulation.solve(710): Cycle 32/500 (15.385 s elapsed) --------------------\n",
+      "2021-05-24 09:03:46,541 - [NOTICE] simulation.solve(742): Cycle 32/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:46,660 - [NOTICE] simulation.solve(742): Cycle 32/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:46,758 - [NOTICE] simulation.solve(742): Cycle 32/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:46,859 - [NOTICE] simulation.solve(742): Cycle 32/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:47,001 - [NOTICE] simulation.solve(820): Capacity is now 4.249 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:47,002 - [NOTICE] simulation.solve(710): Cycle 33/500 (15.847 s elapsed) --------------------\n",
+      "2021-05-24 09:03:47,003 - [NOTICE] simulation.solve(742): Cycle 33/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:47,123 - [NOTICE] simulation.solve(742): Cycle 33/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:47,218 - [NOTICE] simulation.solve(742): Cycle 33/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:47,337 - [NOTICE] simulation.solve(742): Cycle 33/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:47,485 - [NOTICE] simulation.solve(820): Capacity is now 4.231 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:47,486 - [NOTICE] simulation.solve(710): Cycle 34/500 (16.331 s elapsed) --------------------\n",
+      "2021-05-24 09:03:47,486 - [NOTICE] simulation.solve(742): Cycle 34/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:47,601 - [NOTICE] simulation.solve(742): Cycle 34/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:47,702 - [NOTICE] simulation.solve(742): Cycle 34/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:47,815 - [NOTICE] simulation.solve(742): Cycle 34/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:47,959 - [NOTICE] simulation.solve(820): Capacity is now 4.213 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:47,960 - [NOTICE] simulation.solve(710): Cycle 35/500 (16.804 s elapsed) --------------------\n",
+      "2021-05-24 09:03:47,960 - [NOTICE] simulation.solve(742): Cycle 35/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:48,062 - [NOTICE] simulation.solve(742): Cycle 35/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:48,162 - [NOTICE] simulation.solve(742): Cycle 35/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:48,260 - [NOTICE] simulation.solve(742): Cycle 35/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:48,395 - [NOTICE] simulation.solve(820): Capacity is now 4.195 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:48,395 - [NOTICE] simulation.solve(710): Cycle 36/500 (17.240 s elapsed) --------------------\n",
+      "2021-05-24 09:03:48,396 - [NOTICE] simulation.solve(742): Cycle 36/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:48,504 - [NOTICE] simulation.solve(742): Cycle 36/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:48,620 - [NOTICE] simulation.solve(742): Cycle 36/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:48,719 - [NOTICE] simulation.solve(742): Cycle 36/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:48,871 - [NOTICE] simulation.solve(820): Capacity is now 4.177 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:48,871 - [NOTICE] simulation.solve(710): Cycle 37/500 (17.716 s elapsed) --------------------\n",
+      "2021-05-24 09:03:48,872 - [NOTICE] simulation.solve(742): Cycle 37/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:48,984 - [NOTICE] simulation.solve(742): Cycle 37/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:49,073 - [NOTICE] simulation.solve(742): Cycle 37/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:49,172 - [NOTICE] simulation.solve(742): Cycle 37/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:49,315 - [NOTICE] simulation.solve(820): Capacity is now 4.160 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:49,315 - [NOTICE] simulation.solve(710): Cycle 38/500 (18.160 s elapsed) --------------------\n",
+      "2021-05-24 09:03:49,316 - [NOTICE] simulation.solve(742): Cycle 38/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:49,421 - [NOTICE] simulation.solve(742): Cycle 38/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:49,515 - [NOTICE] simulation.solve(742): Cycle 38/500, step 3/4: Charge at 1C until 4.2V\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:03:49,619 - [NOTICE] simulation.solve(742): Cycle 38/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:49,754 - [NOTICE] simulation.solve(820): Capacity is now 4.143 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:49,754 - [NOTICE] simulation.solve(710): Cycle 39/500 (18.599 s elapsed) --------------------\n",
+      "2021-05-24 09:03:49,755 - [NOTICE] simulation.solve(742): Cycle 39/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:49,867 - [NOTICE] simulation.solve(742): Cycle 39/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:49,964 - [NOTICE] simulation.solve(742): Cycle 39/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:50,060 - [NOTICE] simulation.solve(742): Cycle 39/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:50,200 - [NOTICE] simulation.solve(820): Capacity is now 4.126 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:50,200 - [NOTICE] simulation.solve(710): Cycle 40/500 (19.045 s elapsed) --------------------\n",
+      "2021-05-24 09:03:50,201 - [NOTICE] simulation.solve(742): Cycle 40/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:50,301 - [NOTICE] simulation.solve(742): Cycle 40/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:50,389 - [NOTICE] simulation.solve(742): Cycle 40/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:50,483 - [NOTICE] simulation.solve(742): Cycle 40/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:50,637 - [NOTICE] simulation.solve(820): Capacity is now 4.109 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:50,638 - [NOTICE] simulation.solve(710): Cycle 41/500 (19.483 s elapsed) --------------------\n",
+      "2021-05-24 09:03:50,639 - [NOTICE] simulation.solve(742): Cycle 41/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:50,758 - [NOTICE] simulation.solve(742): Cycle 41/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:50,848 - [NOTICE] simulation.solve(742): Cycle 41/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:50,947 - [NOTICE] simulation.solve(742): Cycle 41/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:51,083 - [NOTICE] simulation.solve(820): Capacity is now 4.092 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:51,084 - [NOTICE] simulation.solve(710): Cycle 42/500 (19.929 s elapsed) --------------------\n",
+      "2021-05-24 09:03:51,085 - [NOTICE] simulation.solve(742): Cycle 42/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:51,199 - [NOTICE] simulation.solve(742): Cycle 42/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:51,296 - [NOTICE] simulation.solve(742): Cycle 42/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:51,600 - [NOTICE] simulation.solve(742): Cycle 42/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:51,735 - [NOTICE] simulation.solve(820): Capacity is now 4.075 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:51,736 - [NOTICE] simulation.solve(710): Cycle 43/500 (20.581 s elapsed) --------------------\n",
+      "2021-05-24 09:03:51,737 - [NOTICE] simulation.solve(742): Cycle 43/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:51,838 - [NOTICE] simulation.solve(742): Cycle 43/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:51,927 - [NOTICE] simulation.solve(742): Cycle 43/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:52,024 - [NOTICE] simulation.solve(742): Cycle 43/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:52,164 - [NOTICE] simulation.solve(820): Capacity is now 4.059 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:52,165 - [NOTICE] simulation.solve(710): Cycle 44/500 (21.009 s elapsed) --------------------\n",
+      "2021-05-24 09:03:52,165 - [NOTICE] simulation.solve(742): Cycle 44/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:52,266 - [NOTICE] simulation.solve(742): Cycle 44/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:52,360 - [NOTICE] simulation.solve(742): Cycle 44/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:52,455 - [NOTICE] simulation.solve(742): Cycle 44/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:52,594 - [NOTICE] simulation.solve(820): Capacity is now 4.042 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:52,595 - [NOTICE] simulation.solve(710): Cycle 45/500 (21.439 s elapsed) --------------------\n",
+      "2021-05-24 09:03:52,595 - [NOTICE] simulation.solve(742): Cycle 45/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:52,704 - [NOTICE] simulation.solve(742): Cycle 45/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:52,803 - [NOTICE] simulation.solve(742): Cycle 45/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:52,901 - [NOTICE] simulation.solve(742): Cycle 45/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:53,040 - [NOTICE] simulation.solve(820): Capacity is now 4.026 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:53,040 - [NOTICE] simulation.solve(710): Cycle 46/500 (21.885 s elapsed) --------------------\n",
+      "2021-05-24 09:03:53,041 - [NOTICE] simulation.solve(742): Cycle 46/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:53,145 - [NOTICE] simulation.solve(742): Cycle 46/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:53,234 - [NOTICE] simulation.solve(742): Cycle 46/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:53,329 - [NOTICE] simulation.solve(742): Cycle 46/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:53,467 - [NOTICE] simulation.solve(820): Capacity is now 4.010 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:53,467 - [NOTICE] simulation.solve(710): Cycle 47/500 (22.312 s elapsed) --------------------\n",
+      "2021-05-24 09:03:53,468 - [NOTICE] simulation.solve(742): Cycle 47/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:53,569 - [NOTICE] simulation.solve(742): Cycle 47/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:53,664 - [NOTICE] simulation.solve(742): Cycle 47/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:53,754 - [NOTICE] simulation.solve(742): Cycle 47/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:53,899 - [NOTICE] simulation.solve(820): Capacity is now 3.994 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:53,900 - [NOTICE] simulation.solve(710): Cycle 48/500 (22.745 s elapsed) --------------------\n",
+      "2021-05-24 09:03:53,901 - [NOTICE] simulation.solve(742): Cycle 48/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:53,998 - [NOTICE] simulation.solve(742): Cycle 48/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:54,088 - [NOTICE] simulation.solve(742): Cycle 48/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:54,188 - [NOTICE] simulation.solve(742): Cycle 48/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:54,333 - [NOTICE] simulation.solve(820): Capacity is now 3.978 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:54,334 - [NOTICE] simulation.solve(710): Cycle 49/500 (23.179 s elapsed) --------------------\n",
+      "2021-05-24 09:03:54,335 - [NOTICE] simulation.solve(742): Cycle 49/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:54,441 - [NOTICE] simulation.solve(742): Cycle 49/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:54,530 - [NOTICE] simulation.solve(742): Cycle 49/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:54,642 - [NOTICE] simulation.solve(742): Cycle 49/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:54,791 - [NOTICE] simulation.solve(820): Capacity is now 3.963 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:54,792 - [NOTICE] simulation.solve(710): Cycle 50/500 (23.637 s elapsed) --------------------\n",
+      "2021-05-24 09:03:54,793 - [NOTICE] simulation.solve(742): Cycle 50/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:54,896 - [NOTICE] simulation.solve(742): Cycle 50/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:54,981 - [NOTICE] simulation.solve(742): Cycle 50/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:55,084 - [NOTICE] simulation.solve(742): Cycle 50/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:55,226 - [NOTICE] simulation.solve(826): Stopping experiment since capacity (3.947 Ah) is below stopping capacity (3.952 Ah).\n",
+      "2021-05-24 09:03:55,229 - [NOTICE] simulation.solve(837): Finish experiment simulation, took 24.074 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "experiment = pybamm.Experiment([\n",
+    "    (f\"Discharge at 1C until {Vmin}V\",\n",
+    "     \"Rest for 1 hour\",\n",
+    "    f\"Charge at 1C until {Vmax}V\", \n",
+    "    f\"Hold at {Vmax}V until C/50\")\n",
+    "] * 500,\n",
+    "termination=\"80% capacity\"\n",
+    ")\n",
+    "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n",
+    "sol = sim.solve()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cloudy-lover",
+   "metadata": {},
+   "source": [
+    "### Summary variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "shared-practitioner",
+   "metadata": {},
+   "source": [
+    "We can plot standard variables like the current and voltage, but it isn't very instructive on these timescales"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "personalized-oracle",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2d45e28bd3243e097085f04d92deb2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=147.04264089559257, step=1.4704264089559258)…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x146e46070>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sol.plot([\"Current [A]\", \"Terminal voltage [V]\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intense-princeton",
+   "metadata": {},
+   "source": [
+    "Instead, we plot \"summary variables\", which show how the battery degrades over time by various metrics. Some of the variables also have \"Change in ...\", which is how much that variable changes over each cycle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "right-skiing",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['C',\n",
+       " 'C_n',\n",
+       " 'C_n * (x_100 - x_0)',\n",
+       " 'C_p',\n",
+       " 'C_p * (y_100 - y_0)',\n",
+       " 'Capacity [A.h]',\n",
+       " 'Change in local ECM resistance [Ohm]',\n",
+       " 'Change in loss of active material in negative electrode [%]',\n",
+       " 'Change in loss of active material in positive electrode [%]',\n",
+       " 'Change in loss of capacity to negative electrode SEI [A.h]',\n",
+       " 'Change in loss of capacity to negative electrode lithium plating [A.h]',\n",
+       " 'Change in loss of capacity to positive electrode SEI [A.h]',\n",
+       " 'Change in loss of capacity to positive electrode lithium plating [A.h]',\n",
+       " 'Change in loss of lithium inventory [%]',\n",
+       " 'Change in loss of lithium inventory, including electrolyte [%]',\n",
+       " 'Change in loss of lithium to negative electrode SEI [mol]',\n",
+       " 'Change in loss of lithium to negative electrode lithium plating [mol]',\n",
+       " 'Change in loss of lithium to positive electrode SEI [mol]',\n",
+       " 'Change in loss of lithium to positive electrode lithium plating [mol]',\n",
+       " 'Change in negative electrode capacity [A.h]',\n",
+       " 'Change in positive electrode capacity [A.h]',\n",
+       " 'Change in total capacity lost to side reactions [A.h]',\n",
+       " 'Change in total lithium [mol]',\n",
+       " 'Change in total lithium in electrolyte [mol]',\n",
+       " 'Change in total lithium in negative electrode [mol]',\n",
+       " 'Change in total lithium in particles [mol]',\n",
+       " 'Change in total lithium in positive electrode [mol]',\n",
+       " 'Change in total lithium lost [mol]',\n",
+       " 'Change in total lithium lost from electrolyte [mol]',\n",
+       " 'Change in total lithium lost from particles [mol]',\n",
+       " 'Change in total lithium lost to side reactions [mol]',\n",
+       " 'Cycle number',\n",
+       " 'Local ECM resistance [Ohm]',\n",
+       " 'Loss of active material in negative electrode [%]',\n",
+       " 'Loss of active material in positive electrode [%]',\n",
+       " 'Loss of capacity to negative electrode SEI [A.h]',\n",
+       " 'Loss of capacity to negative electrode lithium plating [A.h]',\n",
+       " 'Loss of capacity to positive electrode SEI [A.h]',\n",
+       " 'Loss of capacity to positive electrode lithium plating [A.h]',\n",
+       " 'Loss of lithium inventory [%]',\n",
+       " 'Loss of lithium inventory, including electrolyte [%]',\n",
+       " 'Loss of lithium to negative electrode SEI [mol]',\n",
+       " 'Loss of lithium to negative electrode lithium plating [mol]',\n",
+       " 'Loss of lithium to positive electrode SEI [mol]',\n",
+       " 'Loss of lithium to positive electrode lithium plating [mol]',\n",
+       " 'Maximum measured discharge capacity [A.h]',\n",
+       " 'Measured capacity [A.h]',\n",
+       " 'Minimum measured discharge capacity [A.h]',\n",
+       " 'Negative electrode capacity [A.h]',\n",
+       " 'Positive electrode capacity [A.h]',\n",
+       " 'Total capacity lost to side reactions [A.h]',\n",
+       " 'Total lithium [mol]',\n",
+       " 'Total lithium in electrolyte [mol]',\n",
+       " 'Total lithium in negative electrode [mol]',\n",
+       " 'Total lithium in particles [mol]',\n",
+       " 'Total lithium in positive electrode [mol]',\n",
+       " 'Total lithium lost [mol]',\n",
+       " 'Total lithium lost from electrolyte [mol]',\n",
+       " 'Total lithium lost from particles [mol]',\n",
+       " 'Total lithium lost to side reactions [mol]',\n",
+       " 'Un(x_0)',\n",
+       " 'Un(x_100)',\n",
+       " 'Up(y_0)',\n",
+       " 'Up(y_0) - Un(x_0)',\n",
+       " 'Up(y_100)',\n",
+       " 'Up(y_100) - Un(x_100)',\n",
+       " 'n_Li',\n",
+       " 'n_Li_0',\n",
+       " 'n_Li_100',\n",
+       " 'x_0',\n",
+       " 'x_100',\n",
+       " 'y_0',\n",
+       " 'y_100']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(sol.summary_variables.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "given-telephone",
+   "metadata": {},
+   "source": [
+    "Here the only degradation mechanism is one that causes loss of lithium, so we don't see loss of active material"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "little-remedy",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vars_to_plot = [\n",
+    "    \"Capacity [A.h]\",\n",
+    "    \"Loss of lithium inventory [%]\",\n",
+    "    \"Loss of capacity to negative electrode SEI [A.h]\",\n",
+    "    \"Loss of active material in negative electrode [%]\",\n",
+    "    \"Loss of active material in positive electrode [%]\",\n",
+    "    \"x_100\",\n",
+    "    \"x_0\",\n",
+    "    \"y_100\",\n",
+    "    \"y_0\"\n",
+    "]\n",
+    "l = len(vars_to_plot)\n",
+    "n = int(l//np.sqrt(l))\n",
+    "m = int(np.ceil(l/n))\n",
+    "\n",
+    "fig, axes = plt.subplots(n,m,figsize=(10,10))\n",
+    "for var, ax in zip(vars_to_plot,axes.flat):\n",
+    "    ax.plot(sol.summary_variables[\"Cycle number\"], sol.summary_variables[var])\n",
+    "    ax.set_xlabel(\"Cycle number\")\n",
+    "    ax.set_ylabel(var)\n",
+    "    ax.set_xlim([1,sol.summary_variables[\"Cycle number\"][-1]])\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "french-substance",
+   "metadata": {},
+   "source": [
+    "To suggest additional summary variables, open an issue!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accepting-canada",
+   "metadata": {},
+   "source": [
+    "## Choosing which cycles to save"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "employed-plate",
+   "metadata": {},
+   "source": [
+    "If the simulation contains thousands of cycles, saving each cycle in RAM might not be possible. To get around this, we can use `save_at_cycles`. If this is an integer `n`, every nth cycle is saved. If this is a list, all the cycles in the list are saved.\n",
+    "The first cycle is always saved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "polished-trout",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:03:59,225 - [NOTICE] simulation.solve(710): Cycle 1/500 (62.655 ms elapsed) --------------------\n",
+      "2021-05-24 09:03:59,226 - [NOTICE] simulation.solve(742): Cycle 1/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:03:59,368 - [NOTICE] simulation.solve(742): Cycle 1/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:03:59,485 - [NOTICE] simulation.solve(742): Cycle 1/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:03:59,610 - [NOTICE] simulation.solve(742): Cycle 1/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:03:59,975 - [NOTICE] simulation.solve(820): Capacity is now 4.941 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:03:59,976 - [NOTICE] simulation.solve(710): Cycle 2/500 (813.963 ms elapsed) --------------------\n",
+      "2021-05-24 09:03:59,977 - [NOTICE] simulation.solve(742): Cycle 2/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:00,118 - [NOTICE] simulation.solve(742): Cycle 2/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:00,210 - [NOTICE] simulation.solve(742): Cycle 2/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:00,349 - [NOTICE] simulation.solve(742): Cycle 2/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:00,494 - [NOTICE] simulation.solve(820): Capacity is now 4.913 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:00,495 - [NOTICE] simulation.solve(710): Cycle 3/500 (1.332 s elapsed) --------------------\n",
+      "2021-05-24 09:04:00,495 - [NOTICE] simulation.solve(742): Cycle 3/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:00,620 - [NOTICE] simulation.solve(742): Cycle 3/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:00,713 - [NOTICE] simulation.solve(742): Cycle 3/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:00,825 - [NOTICE] simulation.solve(742): Cycle 3/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:00,968 - [NOTICE] simulation.solve(820): Capacity is now 4.886 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:00,969 - [NOTICE] simulation.solve(710): Cycle 4/500 (1.806 s elapsed) --------------------\n",
+      "2021-05-24 09:04:00,970 - [NOTICE] simulation.solve(742): Cycle 4/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:01,092 - [NOTICE] simulation.solve(742): Cycle 4/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:01,189 - [NOTICE] simulation.solve(742): Cycle 4/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:01,308 - [NOTICE] simulation.solve(742): Cycle 4/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:01,453 - [NOTICE] simulation.solve(820): Capacity is now 4.859 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:01,454 - [NOTICE] simulation.solve(710): Cycle 5/500 (2.292 s elapsed) --------------------\n",
+      "2021-05-24 09:04:01,455 - [NOTICE] simulation.solve(742): Cycle 5/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:01,569 - [NOTICE] simulation.solve(742): Cycle 5/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:01,668 - [NOTICE] simulation.solve(742): Cycle 5/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:01,785 - [NOTICE] simulation.solve(742): Cycle 5/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:01,922 - [NOTICE] simulation.solve(820): Capacity is now 4.833 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:01,923 - [NOTICE] simulation.solve(710): Cycle 6/500 (2.760 s elapsed) --------------------\n",
+      "2021-05-24 09:04:01,923 - [NOTICE] simulation.solve(742): Cycle 6/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:02,044 - [NOTICE] simulation.solve(742): Cycle 6/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:02,142 - [NOTICE] simulation.solve(742): Cycle 6/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:02,256 - [NOTICE] simulation.solve(742): Cycle 6/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:02,394 - [NOTICE] simulation.solve(820): Capacity is now 4.807 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:02,395 - [NOTICE] simulation.solve(710): Cycle 7/500 (3.232 s elapsed) --------------------\n",
+      "2021-05-24 09:04:02,395 - [NOTICE] simulation.solve(742): Cycle 7/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:02,514 - [NOTICE] simulation.solve(742): Cycle 7/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:02,613 - [NOTICE] simulation.solve(742): Cycle 7/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:02,723 - [NOTICE] simulation.solve(742): Cycle 7/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:02,865 - [NOTICE] simulation.solve(820): Capacity is now 4.781 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:02,866 - [NOTICE] simulation.solve(710): Cycle 8/500 (3.704 s elapsed) --------------------\n",
+      "2021-05-24 09:04:02,867 - [NOTICE] simulation.solve(742): Cycle 8/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:02,985 - [NOTICE] simulation.solve(742): Cycle 8/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:03,087 - [NOTICE] simulation.solve(742): Cycle 8/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:03,194 - [NOTICE] simulation.solve(742): Cycle 8/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:03,337 - [NOTICE] simulation.solve(820): Capacity is now 4.756 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:03,337 - [NOTICE] simulation.solve(710): Cycle 9/500 (4.175 s elapsed) --------------------\n",
+      "2021-05-24 09:04:03,338 - [NOTICE] simulation.solve(742): Cycle 9/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:03,460 - [NOTICE] simulation.solve(742): Cycle 9/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:03,553 - [NOTICE] simulation.solve(742): Cycle 9/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:03,669 - [NOTICE] simulation.solve(742): Cycle 9/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:03,814 - [NOTICE] simulation.solve(820): Capacity is now 4.732 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:03,815 - [NOTICE] simulation.solve(710): Cycle 10/500 (4.652 s elapsed) --------------------\n",
+      "2021-05-24 09:04:03,815 - [NOTICE] simulation.solve(742): Cycle 10/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:03,932 - [NOTICE] simulation.solve(742): Cycle 10/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:04,037 - [NOTICE] simulation.solve(742): Cycle 10/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:04,146 - [NOTICE] simulation.solve(742): Cycle 10/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:04,287 - [NOTICE] simulation.solve(820): Capacity is now 4.708 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:04,288 - [NOTICE] simulation.solve(710): Cycle 11/500 (5.125 s elapsed) --------------------\n",
+      "2021-05-24 09:04:04,288 - [NOTICE] simulation.solve(742): Cycle 11/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:04,654 - [NOTICE] simulation.solve(742): Cycle 11/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:04,755 - [NOTICE] simulation.solve(742): Cycle 11/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:04,867 - [NOTICE] simulation.solve(742): Cycle 11/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:05,017 - [NOTICE] simulation.solve(820): Capacity is now 4.684 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:05,018 - [NOTICE] simulation.solve(710): Cycle 12/500 (5.856 s elapsed) --------------------\n",
+      "2021-05-24 09:04:05,019 - [NOTICE] simulation.solve(742): Cycle 12/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:05,131 - [NOTICE] simulation.solve(742): Cycle 12/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:05,225 - [NOTICE] simulation.solve(742): Cycle 12/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:05,330 - [NOTICE] simulation.solve(742): Cycle 12/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:05,466 - [NOTICE] simulation.solve(820): Capacity is now 4.660 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:05,467 - [NOTICE] simulation.solve(710): Cycle 13/500 (6.304 s elapsed) --------------------\n",
+      "2021-05-24 09:04:05,467 - [NOTICE] simulation.solve(742): Cycle 13/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:05,583 - [NOTICE] simulation.solve(742): Cycle 13/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:05,675 - [NOTICE] simulation.solve(742): Cycle 13/500, step 3/4: Charge at 1C until 4.2V\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:04:05,780 - [NOTICE] simulation.solve(742): Cycle 13/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:05,916 - [NOTICE] simulation.solve(820): Capacity is now 4.637 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:05,917 - [NOTICE] simulation.solve(710): Cycle 14/500 (6.755 s elapsed) --------------------\n",
+      "2021-05-24 09:04:05,918 - [NOTICE] simulation.solve(742): Cycle 14/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:06,034 - [NOTICE] simulation.solve(742): Cycle 14/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:06,133 - [NOTICE] simulation.solve(742): Cycle 14/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:06,245 - [NOTICE] simulation.solve(742): Cycle 14/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:06,386 - [NOTICE] simulation.solve(820): Capacity is now 4.614 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:06,387 - [NOTICE] simulation.solve(710): Cycle 15/500 (7.225 s elapsed) --------------------\n",
+      "2021-05-24 09:04:06,388 - [NOTICE] simulation.solve(742): Cycle 15/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:06,510 - [NOTICE] simulation.solve(742): Cycle 15/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:06,608 - [NOTICE] simulation.solve(742): Cycle 15/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:06,710 - [NOTICE] simulation.solve(742): Cycle 15/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:06,857 - [NOTICE] simulation.solve(820): Capacity is now 4.592 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:06,858 - [NOTICE] simulation.solve(710): Cycle 16/500 (7.696 s elapsed) --------------------\n",
+      "2021-05-24 09:04:06,859 - [NOTICE] simulation.solve(742): Cycle 16/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:06,972 - [NOTICE] simulation.solve(742): Cycle 16/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:07,068 - [NOTICE] simulation.solve(742): Cycle 16/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:07,169 - [NOTICE] simulation.solve(742): Cycle 16/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:07,309 - [NOTICE] simulation.solve(820): Capacity is now 4.570 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:07,309 - [NOTICE] simulation.solve(710): Cycle 17/500 (8.147 s elapsed) --------------------\n",
+      "2021-05-24 09:04:07,310 - [NOTICE] simulation.solve(742): Cycle 17/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:07,417 - [NOTICE] simulation.solve(742): Cycle 17/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:07,508 - [NOTICE] simulation.solve(742): Cycle 17/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:07,617 - [NOTICE] simulation.solve(742): Cycle 17/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:07,762 - [NOTICE] simulation.solve(820): Capacity is now 4.548 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:07,763 - [NOTICE] simulation.solve(710): Cycle 18/500 (8.601 s elapsed) --------------------\n",
+      "2021-05-24 09:04:07,763 - [NOTICE] simulation.solve(742): Cycle 18/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:07,877 - [NOTICE] simulation.solve(742): Cycle 18/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:07,962 - [NOTICE] simulation.solve(742): Cycle 18/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:08,070 - [NOTICE] simulation.solve(742): Cycle 18/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:08,212 - [NOTICE] simulation.solve(820): Capacity is now 4.526 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:08,213 - [NOTICE] simulation.solve(710): Cycle 19/500 (9.050 s elapsed) --------------------\n",
+      "2021-05-24 09:04:08,213 - [NOTICE] simulation.solve(742): Cycle 19/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:08,328 - [NOTICE] simulation.solve(742): Cycle 19/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:08,429 - [NOTICE] simulation.solve(742): Cycle 19/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:08,544 - [NOTICE] simulation.solve(742): Cycle 19/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:08,693 - [NOTICE] simulation.solve(820): Capacity is now 4.505 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:08,693 - [NOTICE] simulation.solve(710): Cycle 20/500 (9.531 s elapsed) --------------------\n",
+      "2021-05-24 09:04:08,694 - [NOTICE] simulation.solve(742): Cycle 20/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:08,814 - [NOTICE] simulation.solve(742): Cycle 20/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:08,916 - [NOTICE] simulation.solve(742): Cycle 20/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:09,029 - [NOTICE] simulation.solve(742): Cycle 20/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:09,165 - [NOTICE] simulation.solve(820): Capacity is now 4.484 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:09,166 - [NOTICE] simulation.solve(710): Cycle 21/500 (10.004 s elapsed) --------------------\n",
+      "2021-05-24 09:04:09,167 - [NOTICE] simulation.solve(742): Cycle 21/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:09,278 - [NOTICE] simulation.solve(742): Cycle 21/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:09,373 - [NOTICE] simulation.solve(742): Cycle 21/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:09,478 - [NOTICE] simulation.solve(742): Cycle 21/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:09,622 - [NOTICE] simulation.solve(820): Capacity is now 4.463 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:09,623 - [NOTICE] simulation.solve(710): Cycle 22/500 (10.461 s elapsed) --------------------\n",
+      "2021-05-24 09:04:09,624 - [NOTICE] simulation.solve(742): Cycle 22/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:09,737 - [NOTICE] simulation.solve(742): Cycle 22/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:09,838 - [NOTICE] simulation.solve(742): Cycle 22/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:09,943 - [NOTICE] simulation.solve(742): Cycle 22/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:10,083 - [NOTICE] simulation.solve(820): Capacity is now 4.442 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:10,083 - [NOTICE] simulation.solve(710): Cycle 23/500 (10.921 s elapsed) --------------------\n",
+      "2021-05-24 09:04:10,084 - [NOTICE] simulation.solve(742): Cycle 23/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:10,195 - [NOTICE] simulation.solve(742): Cycle 23/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:10,285 - [NOTICE] simulation.solve(742): Cycle 23/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:10,387 - [NOTICE] simulation.solve(742): Cycle 23/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:10,753 - [NOTICE] simulation.solve(820): Capacity is now 4.422 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:10,754 - [NOTICE] simulation.solve(710): Cycle 24/500 (11.592 s elapsed) --------------------\n",
+      "2021-05-24 09:04:10,755 - [NOTICE] simulation.solve(742): Cycle 24/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:10,859 - [NOTICE] simulation.solve(742): Cycle 24/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:10,949 - [NOTICE] simulation.solve(742): Cycle 24/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:11,047 - [NOTICE] simulation.solve(742): Cycle 24/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:11,187 - [NOTICE] simulation.solve(820): Capacity is now 4.402 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:11,188 - [NOTICE] simulation.solve(710): Cycle 25/500 (12.026 s elapsed) --------------------\n",
+      "2021-05-24 09:04:11,189 - [NOTICE] simulation.solve(742): Cycle 25/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:11,308 - [NOTICE] simulation.solve(742): Cycle 25/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:11,402 - [NOTICE] simulation.solve(742): Cycle 25/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:11,505 - [NOTICE] simulation.solve(742): Cycle 25/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:11,651 - [NOTICE] simulation.solve(820): Capacity is now 4.382 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:11,652 - [NOTICE] simulation.solve(710): Cycle 26/500 (12.490 s elapsed) --------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:04:11,653 - [NOTICE] simulation.solve(742): Cycle 26/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:11,760 - [NOTICE] simulation.solve(742): Cycle 26/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:11,855 - [NOTICE] simulation.solve(742): Cycle 26/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:11,959 - [NOTICE] simulation.solve(742): Cycle 26/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:12,100 - [NOTICE] simulation.solve(820): Capacity is now 4.362 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:12,101 - [NOTICE] simulation.solve(710): Cycle 27/500 (12.939 s elapsed) --------------------\n",
+      "2021-05-24 09:04:12,102 - [NOTICE] simulation.solve(742): Cycle 27/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:12,209 - [NOTICE] simulation.solve(742): Cycle 27/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:12,304 - [NOTICE] simulation.solve(742): Cycle 27/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:12,407 - [NOTICE] simulation.solve(742): Cycle 27/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:12,550 - [NOTICE] simulation.solve(820): Capacity is now 4.343 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:12,551 - [NOTICE] simulation.solve(710): Cycle 28/500 (13.389 s elapsed) --------------------\n",
+      "2021-05-24 09:04:12,552 - [NOTICE] simulation.solve(742): Cycle 28/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:12,670 - [NOTICE] simulation.solve(742): Cycle 28/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:12,761 - [NOTICE] simulation.solve(742): Cycle 28/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:12,864 - [NOTICE] simulation.solve(742): Cycle 28/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:13,002 - [NOTICE] simulation.solve(820): Capacity is now 4.324 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:13,003 - [NOTICE] simulation.solve(710): Cycle 29/500 (13.840 s elapsed) --------------------\n",
+      "2021-05-24 09:04:13,003 - [NOTICE] simulation.solve(742): Cycle 29/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:13,111 - [NOTICE] simulation.solve(742): Cycle 29/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:13,204 - [NOTICE] simulation.solve(742): Cycle 29/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:13,307 - [NOTICE] simulation.solve(742): Cycle 29/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:13,466 - [NOTICE] simulation.solve(820): Capacity is now 4.305 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:13,467 - [NOTICE] simulation.solve(710): Cycle 30/500 (14.305 s elapsed) --------------------\n",
+      "2021-05-24 09:04:13,468 - [NOTICE] simulation.solve(742): Cycle 30/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:13,591 - [NOTICE] simulation.solve(742): Cycle 30/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:13,692 - [NOTICE] simulation.solve(742): Cycle 30/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:13,793 - [NOTICE] simulation.solve(742): Cycle 30/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:13,936 - [NOTICE] simulation.solve(820): Capacity is now 4.286 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:13,937 - [NOTICE] simulation.solve(710): Cycle 31/500 (14.775 s elapsed) --------------------\n",
+      "2021-05-24 09:04:13,938 - [NOTICE] simulation.solve(742): Cycle 31/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:14,051 - [NOTICE] simulation.solve(742): Cycle 31/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:14,153 - [NOTICE] simulation.solve(742): Cycle 31/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:14,252 - [NOTICE] simulation.solve(742): Cycle 31/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:14,385 - [NOTICE] simulation.solve(820): Capacity is now 4.267 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:14,385 - [NOTICE] simulation.solve(710): Cycle 32/500 (15.223 s elapsed) --------------------\n",
+      "2021-05-24 09:04:14,386 - [NOTICE] simulation.solve(742): Cycle 32/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:14,495 - [NOTICE] simulation.solve(742): Cycle 32/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:14,588 - [NOTICE] simulation.solve(742): Cycle 32/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:14,689 - [NOTICE] simulation.solve(742): Cycle 32/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:14,836 - [NOTICE] simulation.solve(820): Capacity is now 4.249 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:14,837 - [NOTICE] simulation.solve(710): Cycle 33/500 (15.675 s elapsed) --------------------\n",
+      "2021-05-24 09:04:14,838 - [NOTICE] simulation.solve(742): Cycle 33/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:14,947 - [NOTICE] simulation.solve(742): Cycle 33/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:15,041 - [NOTICE] simulation.solve(742): Cycle 33/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:15,142 - [NOTICE] simulation.solve(742): Cycle 33/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:15,272 - [NOTICE] simulation.solve(820): Capacity is now 4.231 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:15,274 - [NOTICE] simulation.solve(710): Cycle 34/500 (16.111 s elapsed) --------------------\n",
+      "2021-05-24 09:04:15,275 - [NOTICE] simulation.solve(742): Cycle 34/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:15,384 - [NOTICE] simulation.solve(742): Cycle 34/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:15,478 - [NOTICE] simulation.solve(742): Cycle 34/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:15,583 - [NOTICE] simulation.solve(742): Cycle 34/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:15,719 - [NOTICE] simulation.solve(820): Capacity is now 4.213 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:15,720 - [NOTICE] simulation.solve(710): Cycle 35/500 (16.557 s elapsed) --------------------\n",
+      "2021-05-24 09:04:15,720 - [NOTICE] simulation.solve(742): Cycle 35/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:15,833 - [NOTICE] simulation.solve(742): Cycle 35/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:15,923 - [NOTICE] simulation.solve(742): Cycle 35/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:16,023 - [NOTICE] simulation.solve(742): Cycle 35/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:16,160 - [NOTICE] simulation.solve(820): Capacity is now 4.195 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:16,161 - [NOTICE] simulation.solve(710): Cycle 36/500 (16.999 s elapsed) --------------------\n",
+      "2021-05-24 09:04:16,161 - [NOTICE] simulation.solve(742): Cycle 36/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:16,264 - [NOTICE] simulation.solve(742): Cycle 36/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:16,372 - [NOTICE] simulation.solve(742): Cycle 36/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:16,720 - [NOTICE] simulation.solve(742): Cycle 36/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:16,853 - [NOTICE] simulation.solve(820): Capacity is now 4.177 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:16,854 - [NOTICE] simulation.solve(710): Cycle 37/500 (17.692 s elapsed) --------------------\n",
+      "2021-05-24 09:04:16,854 - [NOTICE] simulation.solve(742): Cycle 37/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:16,967 - [NOTICE] simulation.solve(742): Cycle 37/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:17,066 - [NOTICE] simulation.solve(742): Cycle 37/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:17,170 - [NOTICE] simulation.solve(742): Cycle 37/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:17,308 - [NOTICE] simulation.solve(820): Capacity is now 4.160 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:17,309 - [NOTICE] simulation.solve(710): Cycle 38/500 (18.147 s elapsed) --------------------\n",
+      "2021-05-24 09:04:17,310 - [NOTICE] simulation.solve(742): Cycle 38/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:17,417 - [NOTICE] simulation.solve(742): Cycle 38/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:17,513 - [NOTICE] simulation.solve(742): Cycle 38/500, step 3/4: Charge at 1C until 4.2V\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:04:17,619 - [NOTICE] simulation.solve(742): Cycle 38/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:17,754 - [NOTICE] simulation.solve(820): Capacity is now 4.143 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:17,755 - [NOTICE] simulation.solve(710): Cycle 39/500 (18.593 s elapsed) --------------------\n",
+      "2021-05-24 09:04:17,756 - [NOTICE] simulation.solve(742): Cycle 39/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:17,865 - [NOTICE] simulation.solve(742): Cycle 39/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:17,967 - [NOTICE] simulation.solve(742): Cycle 39/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:18,080 - [NOTICE] simulation.solve(742): Cycle 39/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:18,219 - [NOTICE] simulation.solve(820): Capacity is now 4.126 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:18,220 - [NOTICE] simulation.solve(710): Cycle 40/500 (19.058 s elapsed) --------------------\n",
+      "2021-05-24 09:04:18,221 - [NOTICE] simulation.solve(742): Cycle 40/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:18,334 - [NOTICE] simulation.solve(742): Cycle 40/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:18,431 - [NOTICE] simulation.solve(742): Cycle 40/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:18,527 - [NOTICE] simulation.solve(742): Cycle 40/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:18,667 - [NOTICE] simulation.solve(820): Capacity is now 4.109 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:18,668 - [NOTICE] simulation.solve(710): Cycle 41/500 (19.506 s elapsed) --------------------\n",
+      "2021-05-24 09:04:18,669 - [NOTICE] simulation.solve(742): Cycle 41/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:18,778 - [NOTICE] simulation.solve(742): Cycle 41/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:18,872 - [NOTICE] simulation.solve(742): Cycle 41/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:18,976 - [NOTICE] simulation.solve(742): Cycle 41/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:19,122 - [NOTICE] simulation.solve(820): Capacity is now 4.092 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:19,123 - [NOTICE] simulation.solve(710): Cycle 42/500 (19.961 s elapsed) --------------------\n",
+      "2021-05-24 09:04:19,124 - [NOTICE] simulation.solve(742): Cycle 42/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:19,233 - [NOTICE] simulation.solve(742): Cycle 42/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:19,333 - [NOTICE] simulation.solve(742): Cycle 42/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:19,435 - [NOTICE] simulation.solve(742): Cycle 42/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:19,567 - [NOTICE] simulation.solve(820): Capacity is now 4.075 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:19,567 - [NOTICE] simulation.solve(710): Cycle 43/500 (20.405 s elapsed) --------------------\n",
+      "2021-05-24 09:04:19,568 - [NOTICE] simulation.solve(742): Cycle 43/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:19,670 - [NOTICE] simulation.solve(742): Cycle 43/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:19,764 - [NOTICE] simulation.solve(742): Cycle 43/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:19,859 - [NOTICE] simulation.solve(742): Cycle 43/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:19,992 - [NOTICE] simulation.solve(820): Capacity is now 4.059 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:19,993 - [NOTICE] simulation.solve(710): Cycle 44/500 (20.830 s elapsed) --------------------\n",
+      "2021-05-24 09:04:19,993 - [NOTICE] simulation.solve(742): Cycle 44/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:20,100 - [NOTICE] simulation.solve(742): Cycle 44/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:20,188 - [NOTICE] simulation.solve(742): Cycle 44/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:20,289 - [NOTICE] simulation.solve(742): Cycle 44/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:20,422 - [NOTICE] simulation.solve(820): Capacity is now 4.042 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:20,423 - [NOTICE] simulation.solve(710): Cycle 45/500 (21.261 s elapsed) --------------------\n",
+      "2021-05-24 09:04:20,424 - [NOTICE] simulation.solve(742): Cycle 45/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:20,523 - [NOTICE] simulation.solve(742): Cycle 45/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:20,620 - [NOTICE] simulation.solve(742): Cycle 45/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:20,715 - [NOTICE] simulation.solve(742): Cycle 45/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:20,857 - [NOTICE] simulation.solve(820): Capacity is now 4.026 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:20,859 - [NOTICE] simulation.solve(710): Cycle 46/500 (21.697 s elapsed) --------------------\n",
+      "2021-05-24 09:04:20,860 - [NOTICE] simulation.solve(742): Cycle 46/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:20,963 - [NOTICE] simulation.solve(742): Cycle 46/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:21,062 - [NOTICE] simulation.solve(742): Cycle 46/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:21,167 - [NOTICE] simulation.solve(742): Cycle 46/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:21,318 - [NOTICE] simulation.solve(820): Capacity is now 4.010 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:21,319 - [NOTICE] simulation.solve(710): Cycle 47/500 (22.157 s elapsed) --------------------\n",
+      "2021-05-24 09:04:21,320 - [NOTICE] simulation.solve(742): Cycle 47/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:21,426 - [NOTICE] simulation.solve(742): Cycle 47/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:21,517 - [NOTICE] simulation.solve(742): Cycle 47/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:21,612 - [NOTICE] simulation.solve(742): Cycle 47/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:21,744 - [NOTICE] simulation.solve(820): Capacity is now 3.994 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:21,745 - [NOTICE] simulation.solve(710): Cycle 48/500 (22.583 s elapsed) --------------------\n",
+      "2021-05-24 09:04:21,746 - [NOTICE] simulation.solve(742): Cycle 48/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:21,849 - [NOTICE] simulation.solve(742): Cycle 48/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:21,944 - [NOTICE] simulation.solve(742): Cycle 48/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:22,039 - [NOTICE] simulation.solve(742): Cycle 48/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:22,189 - [NOTICE] simulation.solve(820): Capacity is now 3.978 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:22,190 - [NOTICE] simulation.solve(710): Cycle 49/500 (23.028 s elapsed) --------------------\n",
+      "2021-05-24 09:04:22,191 - [NOTICE] simulation.solve(742): Cycle 49/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:22,294 - [NOTICE] simulation.solve(742): Cycle 49/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:22,383 - [NOTICE] simulation.solve(742): Cycle 49/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:22,487 - [NOTICE] simulation.solve(742): Cycle 49/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:22,629 - [NOTICE] simulation.solve(820): Capacity is now 3.963 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:22,630 - [NOTICE] simulation.solve(710): Cycle 50/500 (23.468 s elapsed) --------------------\n",
+      "2021-05-24 09:04:22,631 - [NOTICE] simulation.solve(742): Cycle 50/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:22,734 - [NOTICE] simulation.solve(742): Cycle 50/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:22,830 - [NOTICE] simulation.solve(742): Cycle 50/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:23,168 - [NOTICE] simulation.solve(742): Cycle 50/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:23,314 - [NOTICE] simulation.solve(826): Stopping experiment since capacity (3.947 Ah) is below stopping capacity (3.952 Ah).\n",
+      "2021-05-24 09:04:23,317 - [NOTICE] simulation.solve(837): Finish experiment simulation, took 24.154 s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:04:23,377 - [NOTICE] simulation.solve(710): Cycle 1/500 (59.597 ms elapsed) --------------------\n",
+      "2021-05-24 09:04:23,378 - [NOTICE] simulation.solve(742): Cycle 1/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:23,510 - [NOTICE] simulation.solve(742): Cycle 1/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:23,604 - [NOTICE] simulation.solve(742): Cycle 1/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:23,720 - [NOTICE] simulation.solve(742): Cycle 1/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:24,058 - [NOTICE] simulation.solve(820): Capacity is now 4.941 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:24,059 - [NOTICE] simulation.solve(710): Cycle 2/500 (741.293 ms elapsed) --------------------\n",
+      "2021-05-24 09:04:24,060 - [NOTICE] simulation.solve(742): Cycle 2/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:24,175 - [NOTICE] simulation.solve(742): Cycle 2/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:24,268 - [NOTICE] simulation.solve(742): Cycle 2/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:24,378 - [NOTICE] simulation.solve(742): Cycle 2/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:24,511 - [NOTICE] simulation.solve(820): Capacity is now 4.913 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:24,512 - [NOTICE] simulation.solve(710): Cycle 3/500 (1.194 s elapsed) --------------------\n",
+      "2021-05-24 09:04:24,513 - [NOTICE] simulation.solve(742): Cycle 3/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:24,630 - [NOTICE] simulation.solve(742): Cycle 3/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:24,735 - [NOTICE] simulation.solve(742): Cycle 3/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:24,845 - [NOTICE] simulation.solve(742): Cycle 3/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:24,985 - [NOTICE] simulation.solve(820): Capacity is now 4.886 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:24,986 - [NOTICE] simulation.solve(710): Cycle 4/500 (1.668 s elapsed) --------------------\n",
+      "2021-05-24 09:04:24,987 - [NOTICE] simulation.solve(742): Cycle 4/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:25,108 - [NOTICE] simulation.solve(742): Cycle 4/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:25,203 - [NOTICE] simulation.solve(742): Cycle 4/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:25,306 - [NOTICE] simulation.solve(742): Cycle 4/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:25,440 - [NOTICE] simulation.solve(820): Capacity is now 4.859 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:25,441 - [NOTICE] simulation.solve(710): Cycle 5/500 (2.123 s elapsed) --------------------\n",
+      "2021-05-24 09:04:25,442 - [NOTICE] simulation.solve(742): Cycle 5/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:25,559 - [NOTICE] simulation.solve(742): Cycle 5/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:25,652 - [NOTICE] simulation.solve(742): Cycle 5/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:25,765 - [NOTICE] simulation.solve(742): Cycle 5/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:25,899 - [NOTICE] simulation.solve(820): Capacity is now 4.833 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:25,900 - [NOTICE] simulation.solve(710): Cycle 6/500 (2.582 s elapsed) --------------------\n",
+      "2021-05-24 09:04:25,901 - [NOTICE] simulation.solve(742): Cycle 6/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:26,025 - [NOTICE] simulation.solve(742): Cycle 6/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:26,121 - [NOTICE] simulation.solve(742): Cycle 6/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:26,238 - [NOTICE] simulation.solve(742): Cycle 6/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:26,386 - [NOTICE] simulation.solve(820): Capacity is now 4.807 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:26,387 - [NOTICE] simulation.solve(710): Cycle 7/500 (3.069 s elapsed) --------------------\n",
+      "2021-05-24 09:04:26,388 - [NOTICE] simulation.solve(742): Cycle 7/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:26,504 - [NOTICE] simulation.solve(742): Cycle 7/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:26,598 - [NOTICE] simulation.solve(742): Cycle 7/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:26,717 - [NOTICE] simulation.solve(742): Cycle 7/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:26,861 - [NOTICE] simulation.solve(820): Capacity is now 4.781 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:26,862 - [NOTICE] simulation.solve(710): Cycle 8/500 (3.544 s elapsed) --------------------\n",
+      "2021-05-24 09:04:26,863 - [NOTICE] simulation.solve(742): Cycle 8/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:26,980 - [NOTICE] simulation.solve(742): Cycle 8/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:27,086 - [NOTICE] simulation.solve(742): Cycle 8/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:27,203 - [NOTICE] simulation.solve(742): Cycle 8/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:27,342 - [NOTICE] simulation.solve(820): Capacity is now 4.756 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:27,343 - [NOTICE] simulation.solve(710): Cycle 9/500 (4.025 s elapsed) --------------------\n",
+      "2021-05-24 09:04:27,343 - [NOTICE] simulation.solve(742): Cycle 9/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:27,463 - [NOTICE] simulation.solve(742): Cycle 9/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:27,558 - [NOTICE] simulation.solve(742): Cycle 9/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:27,666 - [NOTICE] simulation.solve(742): Cycle 9/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:27,810 - [NOTICE] simulation.solve(820): Capacity is now 4.732 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:27,811 - [NOTICE] simulation.solve(710): Cycle 10/500 (4.494 s elapsed) --------------------\n",
+      "2021-05-24 09:04:27,812 - [NOTICE] simulation.solve(742): Cycle 10/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:27,938 - [NOTICE] simulation.solve(742): Cycle 10/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:28,027 - [NOTICE] simulation.solve(742): Cycle 10/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:28,134 - [NOTICE] simulation.solve(742): Cycle 10/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:28,278 - [NOTICE] simulation.solve(820): Capacity is now 4.708 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:28,279 - [NOTICE] simulation.solve(710): Cycle 11/500 (4.961 s elapsed) --------------------\n",
+      "2021-05-24 09:04:28,280 - [NOTICE] simulation.solve(742): Cycle 11/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:28,669 - [NOTICE] simulation.solve(742): Cycle 11/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:28,761 - [NOTICE] simulation.solve(742): Cycle 11/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:28,868 - [NOTICE] simulation.solve(742): Cycle 11/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:29,009 - [NOTICE] simulation.solve(820): Capacity is now 4.684 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:29,010 - [NOTICE] simulation.solve(710): Cycle 12/500 (5.693 s elapsed) --------------------\n",
+      "2021-05-24 09:04:29,011 - [NOTICE] simulation.solve(742): Cycle 12/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:29,121 - [NOTICE] simulation.solve(742): Cycle 12/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:29,213 - [NOTICE] simulation.solve(742): Cycle 12/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:29,320 - [NOTICE] simulation.solve(742): Cycle 12/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:29,459 - [NOTICE] simulation.solve(820): Capacity is now 4.660 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:29,460 - [NOTICE] simulation.solve(710): Cycle 13/500 (6.142 s elapsed) --------------------\n",
+      "2021-05-24 09:04:29,461 - [NOTICE] simulation.solve(742): Cycle 13/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:29,565 - [NOTICE] simulation.solve(742): Cycle 13/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:29,656 - [NOTICE] simulation.solve(742): Cycle 13/500, step 3/4: Charge at 1C until 4.2V\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:04:29,761 - [NOTICE] simulation.solve(742): Cycle 13/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:29,903 - [NOTICE] simulation.solve(820): Capacity is now 4.637 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:29,903 - [NOTICE] simulation.solve(710): Cycle 14/500 (6.586 s elapsed) --------------------\n",
+      "2021-05-24 09:04:29,904 - [NOTICE] simulation.solve(742): Cycle 14/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:30,014 - [NOTICE] simulation.solve(742): Cycle 14/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:30,107 - [NOTICE] simulation.solve(742): Cycle 14/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:30,208 - [NOTICE] simulation.solve(742): Cycle 14/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:30,361 - [NOTICE] simulation.solve(820): Capacity is now 4.614 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:30,362 - [NOTICE] simulation.solve(710): Cycle 15/500 (7.045 s elapsed) --------------------\n",
+      "2021-05-24 09:04:30,363 - [NOTICE] simulation.solve(742): Cycle 15/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:30,482 - [NOTICE] simulation.solve(742): Cycle 15/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:30,575 - [NOTICE] simulation.solve(742): Cycle 15/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:30,674 - [NOTICE] simulation.solve(742): Cycle 15/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:30,816 - [NOTICE] simulation.solve(820): Capacity is now 4.592 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:30,817 - [NOTICE] simulation.solve(710): Cycle 16/500 (7.499 s elapsed) --------------------\n",
+      "2021-05-24 09:04:30,818 - [NOTICE] simulation.solve(742): Cycle 16/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:30,929 - [NOTICE] simulation.solve(742): Cycle 16/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:31,020 - [NOTICE] simulation.solve(742): Cycle 16/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:31,129 - [NOTICE] simulation.solve(742): Cycle 16/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:31,257 - [NOTICE] simulation.solve(820): Capacity is now 4.570 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:31,258 - [NOTICE] simulation.solve(710): Cycle 17/500 (7.941 s elapsed) --------------------\n",
+      "2021-05-24 09:04:31,260 - [NOTICE] simulation.solve(742): Cycle 17/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:31,376 - [NOTICE] simulation.solve(742): Cycle 17/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:31,475 - [NOTICE] simulation.solve(742): Cycle 17/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:31,588 - [NOTICE] simulation.solve(742): Cycle 17/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:31,726 - [NOTICE] simulation.solve(820): Capacity is now 4.548 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:31,726 - [NOTICE] simulation.solve(710): Cycle 18/500 (8.409 s elapsed) --------------------\n",
+      "2021-05-24 09:04:31,727 - [NOTICE] simulation.solve(742): Cycle 18/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:31,838 - [NOTICE] simulation.solve(742): Cycle 18/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:31,933 - [NOTICE] simulation.solve(742): Cycle 18/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:32,047 - [NOTICE] simulation.solve(742): Cycle 18/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:32,208 - [NOTICE] simulation.solve(820): Capacity is now 4.526 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:32,209 - [NOTICE] simulation.solve(710): Cycle 19/500 (8.892 s elapsed) --------------------\n",
+      "2021-05-24 09:04:32,211 - [NOTICE] simulation.solve(742): Cycle 19/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:32,329 - [NOTICE] simulation.solve(742): Cycle 19/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:32,445 - [NOTICE] simulation.solve(742): Cycle 19/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:32,561 - [NOTICE] simulation.solve(742): Cycle 19/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:32,700 - [NOTICE] simulation.solve(820): Capacity is now 4.505 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:32,700 - [NOTICE] simulation.solve(710): Cycle 20/500 (9.383 s elapsed) --------------------\n",
+      "2021-05-24 09:04:32,701 - [NOTICE] simulation.solve(742): Cycle 20/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:32,815 - [NOTICE] simulation.solve(742): Cycle 20/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:32,902 - [NOTICE] simulation.solve(742): Cycle 20/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:33,008 - [NOTICE] simulation.solve(742): Cycle 20/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:33,159 - [NOTICE] simulation.solve(820): Capacity is now 4.484 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:33,160 - [NOTICE] simulation.solve(710): Cycle 21/500 (9.842 s elapsed) --------------------\n",
+      "2021-05-24 09:04:33,160 - [NOTICE] simulation.solve(742): Cycle 21/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:33,273 - [NOTICE] simulation.solve(742): Cycle 21/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:33,368 - [NOTICE] simulation.solve(742): Cycle 21/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:33,468 - [NOTICE] simulation.solve(742): Cycle 21/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:33,608 - [NOTICE] simulation.solve(820): Capacity is now 4.463 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:33,609 - [NOTICE] simulation.solve(710): Cycle 22/500 (10.291 s elapsed) --------------------\n",
+      "2021-05-24 09:04:33,610 - [NOTICE] simulation.solve(742): Cycle 22/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:33,724 - [NOTICE] simulation.solve(742): Cycle 22/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:33,816 - [NOTICE] simulation.solve(742): Cycle 22/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:33,919 - [NOTICE] simulation.solve(742): Cycle 22/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:34,061 - [NOTICE] simulation.solve(820): Capacity is now 4.442 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:34,062 - [NOTICE] simulation.solve(710): Cycle 23/500 (10.745 s elapsed) --------------------\n",
+      "2021-05-24 09:04:34,064 - [NOTICE] simulation.solve(742): Cycle 23/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:34,175 - [NOTICE] simulation.solve(742): Cycle 23/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:34,263 - [NOTICE] simulation.solve(742): Cycle 23/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:34,365 - [NOTICE] simulation.solve(742): Cycle 23/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:34,495 - [NOTICE] simulation.solve(820): Capacity is now 4.422 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:34,496 - [NOTICE] simulation.solve(710): Cycle 24/500 (11.179 s elapsed) --------------------\n",
+      "2021-05-24 09:04:34,497 - [NOTICE] simulation.solve(742): Cycle 24/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:34,600 - [NOTICE] simulation.solve(742): Cycle 24/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:34,953 - [NOTICE] simulation.solve(742): Cycle 24/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:35,067 - [NOTICE] simulation.solve(742): Cycle 24/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:35,213 - [NOTICE] simulation.solve(820): Capacity is now 4.402 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:35,214 - [NOTICE] simulation.solve(710): Cycle 25/500 (11.896 s elapsed) --------------------\n",
+      "2021-05-24 09:04:35,215 - [NOTICE] simulation.solve(742): Cycle 25/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:35,323 - [NOTICE] simulation.solve(742): Cycle 25/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:35,419 - [NOTICE] simulation.solve(742): Cycle 25/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:35,528 - [NOTICE] simulation.solve(742): Cycle 25/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:35,681 - [NOTICE] simulation.solve(820): Capacity is now 4.382 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:35,682 - [NOTICE] simulation.solve(710): Cycle 26/500 (12.364 s elapsed) --------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:04:35,683 - [NOTICE] simulation.solve(742): Cycle 26/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:35,806 - [NOTICE] simulation.solve(742): Cycle 26/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:35,915 - [NOTICE] simulation.solve(742): Cycle 26/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:36,029 - [NOTICE] simulation.solve(742): Cycle 26/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:36,178 - [NOTICE] simulation.solve(820): Capacity is now 4.362 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:36,179 - [NOTICE] simulation.solve(710): Cycle 27/500 (12.861 s elapsed) --------------------\n",
+      "2021-05-24 09:04:36,180 - [NOTICE] simulation.solve(742): Cycle 27/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:36,304 - [NOTICE] simulation.solve(742): Cycle 27/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:36,400 - [NOTICE] simulation.solve(742): Cycle 27/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:36,501 - [NOTICE] simulation.solve(742): Cycle 27/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:36,641 - [NOTICE] simulation.solve(820): Capacity is now 4.343 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:36,642 - [NOTICE] simulation.solve(710): Cycle 28/500 (13.324 s elapsed) --------------------\n",
+      "2021-05-24 09:04:36,643 - [NOTICE] simulation.solve(742): Cycle 28/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:36,756 - [NOTICE] simulation.solve(742): Cycle 28/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:36,857 - [NOTICE] simulation.solve(742): Cycle 28/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:36,958 - [NOTICE] simulation.solve(742): Cycle 28/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:37,089 - [NOTICE] simulation.solve(820): Capacity is now 4.324 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:37,090 - [NOTICE] simulation.solve(710): Cycle 29/500 (13.773 s elapsed) --------------------\n",
+      "2021-05-24 09:04:37,091 - [NOTICE] simulation.solve(742): Cycle 29/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:37,196 - [NOTICE] simulation.solve(742): Cycle 29/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:37,292 - [NOTICE] simulation.solve(742): Cycle 29/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:37,389 - [NOTICE] simulation.solve(742): Cycle 29/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:37,519 - [NOTICE] simulation.solve(820): Capacity is now 4.305 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:37,520 - [NOTICE] simulation.solve(710): Cycle 30/500 (14.203 s elapsed) --------------------\n",
+      "2021-05-24 09:04:37,521 - [NOTICE] simulation.solve(742): Cycle 30/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:37,626 - [NOTICE] simulation.solve(742): Cycle 30/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:37,716 - [NOTICE] simulation.solve(742): Cycle 30/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:37,818 - [NOTICE] simulation.solve(742): Cycle 30/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:37,946 - [NOTICE] simulation.solve(820): Capacity is now 4.286 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:37,947 - [NOTICE] simulation.solve(710): Cycle 31/500 (14.629 s elapsed) --------------------\n",
+      "2021-05-24 09:04:37,947 - [NOTICE] simulation.solve(742): Cycle 31/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:38,051 - [NOTICE] simulation.solve(742): Cycle 31/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:38,150 - [NOTICE] simulation.solve(742): Cycle 31/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:38,248 - [NOTICE] simulation.solve(742): Cycle 31/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:38,384 - [NOTICE] simulation.solve(820): Capacity is now 4.267 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:38,385 - [NOTICE] simulation.solve(710): Cycle 32/500 (15.067 s elapsed) --------------------\n",
+      "2021-05-24 09:04:38,386 - [NOTICE] simulation.solve(742): Cycle 32/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:38,494 - [NOTICE] simulation.solve(742): Cycle 32/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:38,588 - [NOTICE] simulation.solve(742): Cycle 32/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:38,683 - [NOTICE] simulation.solve(742): Cycle 32/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:38,814 - [NOTICE] simulation.solve(820): Capacity is now 4.249 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:38,815 - [NOTICE] simulation.solve(710): Cycle 33/500 (15.497 s elapsed) --------------------\n",
+      "2021-05-24 09:04:38,816 - [NOTICE] simulation.solve(742): Cycle 33/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:38,922 - [NOTICE] simulation.solve(742): Cycle 33/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:39,012 - [NOTICE] simulation.solve(742): Cycle 33/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:39,110 - [NOTICE] simulation.solve(742): Cycle 33/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:39,240 - [NOTICE] simulation.solve(820): Capacity is now 4.231 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:39,241 - [NOTICE] simulation.solve(710): Cycle 34/500 (15.923 s elapsed) --------------------\n",
+      "2021-05-24 09:04:39,242 - [NOTICE] simulation.solve(742): Cycle 34/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:39,345 - [NOTICE] simulation.solve(742): Cycle 34/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:39,435 - [NOTICE] simulation.solve(742): Cycle 34/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:39,533 - [NOTICE] simulation.solve(742): Cycle 34/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:39,664 - [NOTICE] simulation.solve(820): Capacity is now 4.213 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:39,665 - [NOTICE] simulation.solve(710): Cycle 35/500 (16.347 s elapsed) --------------------\n",
+      "2021-05-24 09:04:39,665 - [NOTICE] simulation.solve(742): Cycle 35/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:39,772 - [NOTICE] simulation.solve(742): Cycle 35/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:39,866 - [NOTICE] simulation.solve(742): Cycle 35/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:39,961 - [NOTICE] simulation.solve(742): Cycle 35/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:40,097 - [NOTICE] simulation.solve(820): Capacity is now 4.195 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:40,098 - [NOTICE] simulation.solve(710): Cycle 36/500 (16.781 s elapsed) --------------------\n",
+      "2021-05-24 09:04:40,099 - [NOTICE] simulation.solve(742): Cycle 36/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:40,205 - [NOTICE] simulation.solve(742): Cycle 36/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:40,295 - [NOTICE] simulation.solve(742): Cycle 36/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:40,650 - [NOTICE] simulation.solve(742): Cycle 36/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:40,787 - [NOTICE] simulation.solve(820): Capacity is now 4.177 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:40,788 - [NOTICE] simulation.solve(710): Cycle 37/500 (17.470 s elapsed) --------------------\n",
+      "2021-05-24 09:04:40,789 - [NOTICE] simulation.solve(742): Cycle 37/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:40,894 - [NOTICE] simulation.solve(742): Cycle 37/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:40,990 - [NOTICE] simulation.solve(742): Cycle 37/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:41,085 - [NOTICE] simulation.solve(742): Cycle 37/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:41,235 - [NOTICE] simulation.solve(820): Capacity is now 4.160 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:41,235 - [NOTICE] simulation.solve(710): Cycle 38/500 (17.918 s elapsed) --------------------\n",
+      "2021-05-24 09:04:41,236 - [NOTICE] simulation.solve(742): Cycle 38/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:41,369 - [NOTICE] simulation.solve(742): Cycle 38/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:41,459 - [NOTICE] simulation.solve(742): Cycle 38/500, step 3/4: Charge at 1C until 4.2V\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:04:41,584 - [NOTICE] simulation.solve(742): Cycle 38/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:41,732 - [NOTICE] simulation.solve(820): Capacity is now 4.143 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:41,733 - [NOTICE] simulation.solve(710): Cycle 39/500 (18.415 s elapsed) --------------------\n",
+      "2021-05-24 09:04:41,733 - [NOTICE] simulation.solve(742): Cycle 39/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:41,832 - [NOTICE] simulation.solve(742): Cycle 39/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:41,934 - [NOTICE] simulation.solve(742): Cycle 39/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:42,033 - [NOTICE] simulation.solve(742): Cycle 39/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:42,174 - [NOTICE] simulation.solve(820): Capacity is now 4.126 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:42,175 - [NOTICE] simulation.solve(710): Cycle 40/500 (18.858 s elapsed) --------------------\n",
+      "2021-05-24 09:04:42,176 - [NOTICE] simulation.solve(742): Cycle 40/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:42,285 - [NOTICE] simulation.solve(742): Cycle 40/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:42,383 - [NOTICE] simulation.solve(742): Cycle 40/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:42,480 - [NOTICE] simulation.solve(742): Cycle 40/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:42,620 - [NOTICE] simulation.solve(820): Capacity is now 4.109 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:42,621 - [NOTICE] simulation.solve(710): Cycle 41/500 (19.303 s elapsed) --------------------\n",
+      "2021-05-24 09:04:42,621 - [NOTICE] simulation.solve(742): Cycle 41/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:42,726 - [NOTICE] simulation.solve(742): Cycle 41/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:42,816 - [NOTICE] simulation.solve(742): Cycle 41/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:42,921 - [NOTICE] simulation.solve(742): Cycle 41/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:43,063 - [NOTICE] simulation.solve(820): Capacity is now 4.092 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:43,064 - [NOTICE] simulation.solve(710): Cycle 42/500 (19.746 s elapsed) --------------------\n",
+      "2021-05-24 09:04:43,065 - [NOTICE] simulation.solve(742): Cycle 42/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:43,167 - [NOTICE] simulation.solve(742): Cycle 42/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:43,262 - [NOTICE] simulation.solve(742): Cycle 42/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:43,352 - [NOTICE] simulation.solve(742): Cycle 42/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:43,497 - [NOTICE] simulation.solve(820): Capacity is now 4.075 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:43,498 - [NOTICE] simulation.solve(710): Cycle 43/500 (20.180 s elapsed) --------------------\n",
+      "2021-05-24 09:04:43,499 - [NOTICE] simulation.solve(742): Cycle 43/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:43,615 - [NOTICE] simulation.solve(742): Cycle 43/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:43,710 - [NOTICE] simulation.solve(742): Cycle 43/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:43,804 - [NOTICE] simulation.solve(742): Cycle 43/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:43,945 - [NOTICE] simulation.solve(820): Capacity is now 4.059 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:43,946 - [NOTICE] simulation.solve(710): Cycle 44/500 (20.628 s elapsed) --------------------\n",
+      "2021-05-24 09:04:43,947 - [NOTICE] simulation.solve(742): Cycle 44/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:44,050 - [NOTICE] simulation.solve(742): Cycle 44/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:44,147 - [NOTICE] simulation.solve(742): Cycle 44/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:44,246 - [NOTICE] simulation.solve(742): Cycle 44/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:44,376 - [NOTICE] simulation.solve(820): Capacity is now 4.042 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:44,377 - [NOTICE] simulation.solve(710): Cycle 45/500 (21.060 s elapsed) --------------------\n",
+      "2021-05-24 09:04:44,379 - [NOTICE] simulation.solve(742): Cycle 45/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:44,487 - [NOTICE] simulation.solve(742): Cycle 45/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:44,599 - [NOTICE] simulation.solve(742): Cycle 45/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:44,695 - [NOTICE] simulation.solve(742): Cycle 45/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:44,831 - [NOTICE] simulation.solve(820): Capacity is now 4.026 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:44,832 - [NOTICE] simulation.solve(710): Cycle 46/500 (21.514 s elapsed) --------------------\n",
+      "2021-05-24 09:04:44,833 - [NOTICE] simulation.solve(742): Cycle 46/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:44,944 - [NOTICE] simulation.solve(742): Cycle 46/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:45,044 - [NOTICE] simulation.solve(742): Cycle 46/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:45,143 - [NOTICE] simulation.solve(742): Cycle 46/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:45,281 - [NOTICE] simulation.solve(820): Capacity is now 4.010 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:45,282 - [NOTICE] simulation.solve(710): Cycle 47/500 (21.965 s elapsed) --------------------\n",
+      "2021-05-24 09:04:45,283 - [NOTICE] simulation.solve(742): Cycle 47/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:45,393 - [NOTICE] simulation.solve(742): Cycle 47/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:45,481 - [NOTICE] simulation.solve(742): Cycle 47/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:45,584 - [NOTICE] simulation.solve(742): Cycle 47/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:45,722 - [NOTICE] simulation.solve(820): Capacity is now 3.994 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:45,723 - [NOTICE] simulation.solve(710): Cycle 48/500 (22.405 s elapsed) --------------------\n",
+      "2021-05-24 09:04:45,724 - [NOTICE] simulation.solve(742): Cycle 48/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:45,829 - [NOTICE] simulation.solve(742): Cycle 48/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:45,916 - [NOTICE] simulation.solve(742): Cycle 48/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:46,009 - [NOTICE] simulation.solve(742): Cycle 48/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:46,145 - [NOTICE] simulation.solve(820): Capacity is now 3.978 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:46,146 - [NOTICE] simulation.solve(710): Cycle 49/500 (22.828 s elapsed) --------------------\n",
+      "2021-05-24 09:04:46,147 - [NOTICE] simulation.solve(742): Cycle 49/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:46,248 - [NOTICE] simulation.solve(742): Cycle 49/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:46,347 - [NOTICE] simulation.solve(742): Cycle 49/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:46,446 - [NOTICE] simulation.solve(742): Cycle 49/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:46,585 - [NOTICE] simulation.solve(820): Capacity is now 3.963 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:46,586 - [NOTICE] simulation.solve(710): Cycle 50/500 (23.268 s elapsed) --------------------\n",
+      "2021-05-24 09:04:46,587 - [NOTICE] simulation.solve(742): Cycle 50/500, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:46,699 - [NOTICE] simulation.solve(742): Cycle 50/500, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:46,792 - [NOTICE] simulation.solve(742): Cycle 50/500, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:46,896 - [NOTICE] simulation.solve(742): Cycle 50/500, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:47,031 - [NOTICE] simulation.solve(826): Stopping experiment since capacity (3.947 Ah) is below stopping capacity (3.952 Ah).\n",
+      "2021-05-24 09:04:47,034 - [NOTICE] simulation.solve(837): Finish experiment simulation, took 23.716 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "# With integer\n",
+    "sol_int = sim.solve(save_at_cycles=5)\n",
+    "# With list\n",
+    "sol_list = sim.solve(save_at_cycles=[30,45,55])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "severe-yorkshire",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<pybamm.solvers.solution.Solution at 0x14bbf81f0>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x14b876f40>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x14bcc1c40>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x149ef8760>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x14bc63be0>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x14bc41640>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x14b77a5b0>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x14a31fa30>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x149443b20>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x14becd6a0>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x14ae71fd0>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sol_int.cycles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "unavailable-fetish",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<pybamm.solvers.solution.Solution at 0x149123190>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x1496e86d0>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " <pybamm.solvers.solution.Solution at 0x149d0d1f0>,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sol_list.cycles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "guilty-nylon",
+   "metadata": {},
+   "source": [
+    "For the cycles that are saved, you can plot as usual (note off-by-1 indexing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "architectural-signal",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2baf8ee77c64aeba0917d4316759932",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=130.31321105030034, description='t', max=133.11607429479875, min=130.3…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x14926ac70>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sol_list.cycles[44].plot([\"Current [A]\",\"Terminal voltage [V]\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "played-hundred",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lSHkT16fvTu2BLpbcqT2ny5EyYEAWzG9wb+MSUiq1p1AoFIr/CHdaL7llMok5ifSo3oPlS5cD0KlTb9avhxo1ICbpD0hLg+7dWZGRgRPoHRpK5pZMHBkOwnqGIYVk+bHlBPsE06JiC5a5HKxevfRicg9DC/bsgYQE6NIFDIarO+h/iBJSXsTtSJWU2jM5HDiFLp6MsgRHSqX2FAqFQvEfIqUkZXEKmr/GinA9zdarei+WLVtGaGgomZktsFqhd29g0SJ9p759WepK6/UKCyNlif44tFcoO8/vJDEnkW4x3TBqxgIh1bOnnuZbvhx8fKBDBwrTet27X7Xx/luUkPImf+FIGaDAkdKkhhO3kHJtI1w7K0dKoVAoFP8B2XuzsZ6xEtotlEVxulCqmFuR5ORkevTowfLl+g19r14UCCnZqxdLUlLw1TQ6BgeTstQlpHqGsuxYYVrPbrezYsUKIiMjady4McePw9Gjuojy86PQnirl9VGghJRXKV4j5e4H6MCAyeksLDYvqUbKXd6mHCmFQqFQ/Ae403q+3X3ZFLeJ+pH12bFmB1BYH2U0QtcGiXqVePPmHA4K4mR+Pp2CgzGmO8n8IxNLYws+5X0KhFTP6j3ZunUr6enp9OjRA03TWK5nC3XdlJcHq1ZBlSpQp44XRv7PUELKi7hn7WlSu9CRcjqRHjVSzoJOsYXb6A+UI6VQKBSKK49bSO2puweHdNCnRh/mz58PQN26fTl4UHeQAjcs1ncoltZLXZEKTgjrHUa2NZt1J9cRExJDbFgsS5cuBQrTekXqo9atg9xc6NOnTKzcoYSUN3GVOWlSc7lTHo6UlDhcNVIGWSiknE5VI6VQKBSK/xZ7lp30den41fRjYb7e9qB1RGu2bNlC06ZN2bpVb3g5cCBF6qOWpOqtDnqFhhbUR4X1CmPViVXkO/LpW6MvQJH6KLtdF1IREdCkCbDYJcx6974qY71clJDyIm5HSkiBEKKw/sntSLm2M0hDQY2UcqQUijLE+fN6iiJX9ftUlC3SVqUhrZKw3mEsProYf5M/aTvTkFLSv39/XMYUA/o6YMkSCAsjt0ULVqSmUsnHh/r+/qQuTUXz0whqH8SCQwsA6F+rP+np6WzatIm6detSqVIlNm/WJ/z16eOaoLd4sf6gDBSagxJSXqVg1p7TcIEjZfSokfJM7SlHSqEoO9zS7V2Cb/+Q9VPXezsUheIfkTw/GYD0NumczjhN12pdWbZId5G6dh3AmjVQqxbUSv8DUlKgVy9WZWaS63QyIDycnL055MflE9I1BM1HY+GRhfgafelSrQtLlizB4XDQr18/ABbqhhd9+wJxcbBvH7Rrp/eQKgMoIeVNPGbt6e6UhyPl2UfKI7Xn6VrpD5QjpVCUVrRavoxp3YbZC5WQUpQdpFOSPC8ZzaKxKFJP2w2oMYAlS5YQGRlJUlJLbDYYMIAiab35ybr46h8WRvI8/XH4wHD2Je7jVPopusV0w9/kX1BnNWDAAEAXUkK4MnnuDp99+ly18V4uSkh5kYJZe1KftefZI8rodBb0kTJIg1oiRqEog2zfcZyv9y/l8LFkb4eiUFwymVszsZ6zEtY7jFnHZyEQRKZGkpGRQb9+/ViwQJcOAwcCc+eCEMjevZmfnIyvptEtNJSkuUkAhA8IZ8FhPa3Xr0Y/HA4HCxcuJCQkhPbt23PmDOzcCW3aQHg4hfVRSkgpLgnXp69JrWRHyrWZQRZ2dVWOlEJRdhhMbWbvexV7Tp63Q1EoLpmkOboIoidsP7+dtpXbsnHpRgD69u3PwoV61q19pZOwYwe0a8cei4W4/Hy6h4RgTHKQuSWTgGYB+FbyZeFhPXfXr2Y/Nm/eTHJyMn369MFkMhXopn79AJtN78oZGemqOi8bKCHlRTz7SAlNXOBIedZI6RsqR0qhKEtkm60A+DjUpVZRdkiakwQGWFN9DQCDaw9m/vz5GI1GQkN7kZSk1zOZFs7Rdxg8uCCtNyA8nOQFySD1tF5aXhrrT62nbkRdYkJjCtJ6AwcOBIrVR61fD+np+hOt7Hxnyk6k1yCeqT1PR8qBAYOUBevrFThSQjlSCkVZwlTRB4Agh4+XI1EoLo3co7nk7M0huEMw089NB6CBoQGHDh2iS5curF6tF4APGADMuVBI9Q8PL0jrRQyKYOnRpTikg3419cLy+fPno2kaffr0wWbT2x5ERUHTpuhpQoBBg67aeK8ESkh5E4/Unqfb5EQr4khp0l2V7jlrz/WacqQUilJLw251AQh3BGJPSvVyNArF3+MWQX59/Fh7ci31I+uzbdk2AIYNG8a8eXpngr5t02DNGqhTh6Rq1dickUFji4WKmEhdloo52kxA0wDmHNTF1oBaAzhx4gR79uyhffv2hIWFsW4dZGa6DCghdWFmNrvWnCk7KCHlRYqk9jz6SLkdqYIaKZQjpVCURXre1BGA8tZwNs373cvRKBR/j7s+alv9bTilkyF1hjBz5kyEEDRvPoQ9e6B9ewjbtEC/kR88mHnJyTiBgRERpK1Mw5njJHxAODanjQWHFhDuF06HKh0uSOvNnq2fc/Bg9JYHx49Dt24QGHj1B34ZKCHlRYosEVPckXI4cLhTe87CGqkCR0qqGimForRToWZ5ck25VMyqwPwlm70djkLxl9iSbaSvT8e/vj/Tc/S0XovAFuzYsYP27dvz++/lgQvTejOTdPF1Q0REQduDiEERrDmxhvT8dAbWHohRMxZpeyClLqR8fV3Lwsybpx+vjKX1QAkp7+K5RExJNVLu1B6FqT3lSCkUZQchBOcCzlM+I4ojR5O8HY5C8ZckL0wGBwQPCGbJkSVEB0ZzePVhAG644QZmzdK3G9jbqvePioois3lzlqWkUNXHh8YWC0nzktD8NUK6hTD7wGwAhtYZSlZWFqtWrSI2NpY6deqwY4fee7NXL7BYKKyPcrlVZYkrIqSEELWEEDYhRIcrcbzrBc8lYoo7UganE7fXVOBIedZISVUjpVCUBc77J2KQBnLzVAsERenGndY73PQwufZchtQZwiyXeurYcSirV0P9+lDn7ErIyoKBA1mUlka+lNwQGUn2jmysZ6yE9gxF+AjmHJyDv8mfntV7smzZMqxWKwMGDEAIUZDWGzIEiI+HzZv1ivNKlbwx9MviSjlSLwFrrtCxrhs8l4jR3aZi7Q+Kz9rTVLG5QlHWSPXLAsBsF8hE5UopSieOPAcpi1MwVzAzwzwDgA4RHdi0aRPNmzdn27ZqOJ1w440UTeslJgIXpvW2nt3Kmcwz9I7tjZ/Jj3mu1J27m/ns2XqHgwEDgAULQMoymdaDKyCkhBCtgfPA6csP5zqj2BIxnmk7zWOJGM9ZewXbSJXaUyjKAv4xFv2nw8jqL5d4ORqFomTS16TjzHYS2j+UeYfnEeIbQsKWBEBP602bpm934zCnnobz9yevSxcWpKRQzmSibXAwiTMTQUB4//CCtN6QOkOw2+3MnTuX4OBgOnXqxPHjsGuXXrQeGUmZbXvg5ko4Ui8A//d3Gwkh7hdCbBVCbE10KdjrnSJLxJTgSAE4RaEjpRk15UgpFGWMfmP0Fexr2Mrz9ZJ1Xo5GoSgZ9yLF8a3iSc5NZkCtAcyZpTtPXbsOY/VqqFcP6uVshbNnoXdvVuTnk+VwMCQigvzDuWTvyia4UzDmKDOzD8zGIAwMqDWANWvWkJyczKBBgzCbzQWG1pAhQG4uLF0K0dGuZlJlj8sSUkKI/sBWKeXfLiQlpRwvpWwhpWwRGRl5Oae9drigj5SHI+USUlK4aqgATdOUI6VQlDFaDmhGljmbxokNyLUZ9RSGQlGKkFKSPD8Z4SOYH6nPrOtWsRtr1qyhXr167NlTG4cDhg8HpkzRdxo2rDCtFxlJ4nT9ceTwSA4mHWR/0n46V+tMmF8YM2boqcLhw4cDxdoerFihi6lBg/SVi8sgl+tINQG6CCEWAz2B94UQVS87qusEzz5SRZttFjpSDoOHI2XSVLG5QlHG0EwaByIOE54dTl52Nrm/7/R2SApFEXL255B3Io+QriFMPzEdH4MPubtzcTgcF6b1pk4FX1/sAwYwNzmZYIOBriEhupASEHlDZEETziG1h+BwOJg5cyYBAQH06qUvL7NuHTRoALGxlPm0HlymkJJSviml7Cal7AMsA56WUp68MqFd+xTpIyUAJCALaqRAd6TcNVKapuF0p/xwKXflSCkUpZ68OvoNT4jDyOdvzvJyNApFUdxpvbxOeZxMP0nP2J4snK0vgtet2zBWroQ6daB+xiY4fRr692e900mSzcaA8HAcx/PJ2p5FcPtgfCr6MOuA/js+pM4QNm7cSHx8PAMGDMDX15cFC/Q/W0OGAHY7zJqlN+Ds0sU7g78CXLE+UlLKO6WU66/U8a4LPPpIFf6fcBatkdIKhZTBaFCOlEJRBun7WDcA6lkrszJRzctRlC7cQmpVzCoAelftzbJly4iJieHo0cYFaT0x1ZXWGzmysAlnsbTemYwzbD69mWYVmlE5uDLTp+uNPYcNGwYUS+utWQNJSbob5et7dQb7H6AacnqRIn2kClLDziI1Up5CShhEoSMllSOlUJQVmvVvQppvOi3ONcOU74s8c9bbISkUANhSbKRv1LuZT06bjCY0jEeNWK1WbrjhBqZP1//W3HiDA6ZNA4sFR79+zExMxE/T6B0WRsI0fXZfxLAIpu3T84Aj6o3A6XQyc+ZM/Pz86Nu3L5mZsHix3iqqeXMozBne6I2hXzGUkPIiBX2kpMFDSDn0zuYeQspdI6UcKYWibCIMgn3RhwnIDyA/J4PF78/3dkgKBQApS1LAAcYeRnbG76R95fasmLsCgJ49h7FiBdSqBQ1T18L58zBwIOusVs5YrQwMD0c7ZSVrWxZBbYPwreTL5D2TARhRfwRbtmzh9OnT9O3bF4vFwty5kJcHI0aAcNhh5kw9rde7tzc/gstGCSlv4vr0iwopPbXnrpFyaoWz9oTm6UipWXsKRVmi2ohqAMQ6I/l8zUbvBqNQuEhdmgrArrq7AOgd05uFCxdSsWJF4uJaY7frhlFBWu+mm5iUoDtQt0RFFab1bozkeOpxfj/zO62jWxMTGlMwW8+d1ptSmBmEtWshMVFfEqYMp/VACSmvcjFHyr1oMbgcKWcJfaTcqT3lSCkUZYIbnhmAXbPTOqUx/j7BkJ3t7ZAUCtLXpyN8BLN89QLxwPOB5OTkMHToUGbM0CXC8ME2mD4dgoLI79WL6YmJhBqN9A0LKxRSwyKZuncqACPrj0RKyfTp0zGbzQwYMIC0ND2tFxMDLVtyzaT1QAkpr1KykCrqSHnO2jMYDMqRUijKKL6hvuyPPETVlKokp6Vx6LvF3g5JcZ1jjbeSeySXgBYBLDu9jMpBlflj4R8A9Ox5A8uXQ40a0Dh5JSQnw5AhLMnOJtVuZ3hkJM44K5lbMglsHYhvFV8m752MQDCi/gi2b9/OiRMn6NWrF0FBQcyeDTabK63ndOhpvYCAMp/WAyWkvItHQ04p3E36HAWLFoPeR6ogtWcQypFSKMow1pb697yc04eXJiz0cjSK6530DekApNZPJdeeS5/YPsyfN5+wsDCSkjqVmNabGB8PwC3lypEwWU/xuZtw7ji/g45VOxIdFM1vv/0GFDbhvCCtl5Cgp/X8/K7egP8jlJDyIn/pSHl0Ni/oI2XQlCOlUJRhhr2sL9jaPKcWuSF+Kr2n8Crp63UhtaPSDgAq5VciLS2NAQMGMHOmEYDhg6y6exQWRmaXLsxNTibabKZDcDDnfzkPAqJujmLKXl0pjaw/EofDwW+//Yavry9Dhw4lORmWL4eaNaFJE66ptB4oIeVdPJeIKVYj5TlrT3MWtj9wOJQjpVCUVaq2qMKZoLM0OduY/GQbR75TixgrvIfbkVoUtAiA83+cB6BLl/4sWwbVq0PThCWQng433MDs9HTynE5uKleO3J3Z5OzNIbRHKOaKZibv0VsnDK83nLVr13LmzBkGDRpEUFAQM2fqvTdHjiyW1uvTx2tjv5IoIeVFLqVGyrOPVFFHSvWRUiguBSFELSGETQjRodjrYUKI+UKIdUKIz4T47xf6EkJwunY8JqcJkz2Pl76f91+fUqEoEUeOg6w/s/Cr68e6jHXUDq/NqnmrMBqN5OX1wmYrltYbObLIbL34X/QUX9SoKPYk7GF/0n66xXSjnKUcEydOBODWW28FiqX11q2D+HgYMOCaSOuBElJexbMhZ/EaKc1j1p5wFrY/KHCknMqRUigukZeANSW8/iwwRUrZEbAAV6XqtdcLepfzltYaZIX4Q1bW1TitQlGEjC0ZSLskt0kuNqeNJqFNOHDgAB06dGDx4hAAhg/IgzlzoFw5Etq1Y1lKCrX9/Gjs60/8pHg0f42IoREFvaNuqn8TeXl5TJ8+nbCwMPr06UN8PKxaBfXq6evr8euvegAjR3pn4P8BSkh5E9cSMQZpKCKkijfkLHCkjBoOhwODQTXkVCguBSFEa+A8UNK6LJ0Bd2fMea7n/zlNBjYi0ZJE69MtsSfZVXpP4RXc9VGHqh0CICAlAIAuXXqybJmr+/j5BbrQHz6caampONDdqLTladjibUTeEIlm0Zi4eyImzcTQukNZuHAh6enp3HjjjZjNZmbM0BMnI0cCubl6fVRYGPTr56WRX3mUkPIiRRYtLuAvlohxNeTUNHA6VWpPobgEXgD+7yLvhQFprsdprucXRQhxvxBiqxBia2Ji4r8OSGiCY7Xi8LX7Ihy5vPS9mr2nuPpkbMgAYEWE3sU8bVcaAL6+ncnN1SfUFUnruWbr3VyuXGFa7/YoVp9Yzcn0kwyqPYgwv7AL0no//6wf4qabgPnzISNDV1Vm81UY5dVBCSkv4lkj9ZeOlLOwRqrQkVKpPYXirxBC9Ae2SimTL7JJKhDsehwMpPzV8aSU46WULaSULSIjIy8rtq5jOwLQ3FqdzGCzSu8prirSIUnfmI65gplFeYuoEFCBP1f+iY+PD4cOtQBgUM9cXfhUrMjx5s3ZmJFBy8BAYuxmkmYnYa5oJrRbKD/s+AGAu5rcRVpaGvPnz6dKlSq0b9+egwfh99+hbVt9mRl++UUPYNQoL438v0EJKW/i+vRLWrRY/MUSMZqmis0VikugCdBFCLEY6Am8L4So6vH+GsCdX+hHyXVU/wkthzcnxS+Vtmda4UwVKr2nuKpk/J6BI8OBs7WTTFsmLSJbcPzYcdq0acPixT5YLNAlc56eihsxgslJSQDcGhVF4oxEnLlOom6JItOeyYx9M6gQUIHeNXozc+ZMrFYrt9xyC5qmFbhRd9yBvhzMokUQGwtt2nhv8P8BSkh5kSKOFMUcqRKKzTWDy5nSnKrYXKH4G6SUb0opu0kp+wDLgKeBukII9+3wu8CtQoh1gA1YerViEwbB4Zon8Lf6I+2ZvPTdgqt1aoWClMW6+Xqs8TEAwrPDAahfvxNnz0KnTuC7cKa+8bBhTEpIQANGREYWSetN3TuVXHsuoxqNwqgZi6T1nE7dgPLxcdVHTZmi90C47Tb47yfIXlWUkPIm7j5STs/O5hdfIqagpkpzqhopheIfIKW8U0q5Xkq5WEr5i+u1ZCllfyllRynlQ1LKq/pl6vCkflfe1FaN9FAzpKVdzdMrrmNSFulCann0cgCy9+uNYc3mTgB07mDX3aPISHY1bMie7Gy6hYYSmiBJW52GpbGFgIYBBWm9O5vcycmTJ1m1ahWNGjWiQYMGrFoFcXEweDCEhFA4W++2267qWK8GSkh5EbcjpUmtiCPl2f7AYfBof+B2sAwOVSOlUJRx2t7amnTfdNqdaY2WamTfp/P/fieF4jKxJljJ3JpJQPMAFqctJtgnmH2r9mEwGDh7Vhf3nS3b9KLwgQOZ5Err3VKuHPET40FC+VHlOZh0kI1xG2kd3Zq6kXX5/vvvkVJy9913A/DTT/r57rgDOHSosFiqRg1vDPs/RQkpL+Lu/1e0s7nuSAkPR8qzRgrcjpR7c+VIKRRlEc2ocTD2GAH5AdhsaTz3q6qTUvz3JM3WhVFexzwScxLpUrkLe/fspXnz5mzcGIDFAs0PTgJADhrE5IQEfIRgaEQE8T/HgwblbinHjzt+BPQic4fDwffff4+Pjw+jRo0iMxNmzIDy5aFXLwrdqGusyNyNElLexqALKSduQeTAKYxFaqQ8l4gB0DSHqpFSKK4BWj2qz5Bq4qgKFYOQp+K8HJHiWifhN707+cbGGwGokl8FgEaNOnH6NLRrB6a1K8BgYHuLFpzMz6d3WBiGXXnk7M8htGcoxigjP+/6GV+jLzc1uInFixdz+vRphg0bRlhYGDNmQE4O3HorGDWnLqRMJhgxwmvj/i9RQsrLCE0UCCUdJw5hvOisPXA5UmrWnkJR5ul4RzsyfTJpf6Y19kRY/sYcb4ekuIbJP5tP2po0LI0sTLPpCwdb91kB8Pd31Uc1z4K9e6FlS6bn5wMwLDJSX6AYPa237Ngyzmae5Ya6NxDsG8y3334LwH333QcUS+utXg3Hj+tLwoSHX6WRXl2UkPIywiAQFFsiRjNcdIkYcNVIKUdKoSjzGHwMHK55gqC8IPLsKbyxZr23Q1JcwyRMTgAJvkN92Xp2K80rNGfdonUYDAYSE/XeZp39t+gbd+nCzMREjELQLziUhN8S0CwaEUMiivSOOnfuHPPnz6dGjRp07tyZ48d17dSkCTRsCIwfrx/v/vuv+nivFkpIeRuP1J1eJ+XEgbHIosXFHSkhVI2UQnGt0PaJlgA0l1WIiq6IfftuL0ekuBaRUnJuwjkQsKrxKgC6l+/Ovn376NixI5s2heDrCy3P6K7o6TZtOJibS5eQEMSqLGwJNiKHR5KupTP7wGwqB1WmW0w3fvjhBxwOB/feey9CiALddM896L2jZs2CKlWgZ08vjfy/RwkpLyMMonCJGAG6I2Us2ZEyKEdKobjWaH1bK7LMWXQ43Y6UuBx+eXmet0NSXINkbM4gZ18OYX3C+DHhRwD8j/kD0K5df06c0CfV+axbDkYji+rWBaBPWBgJU/S6qqhbo5i8ZzJWh5U7Gt8BEiZMmIDRaOTOO+8kPx+++w4sFldd+c8/g9UK994LBoMXRn11UELKywiDuNCR8qiRkuLC1J6atadQXDtoZo0T9U4TnBuM1ZjM18d3qu+14opz7rtzADhudLD9/HY6Ve3EhoUbAAgIGABA5xZZsG8ftGzJUpsNgC6WYJLmJGEMNxLSNaRI76hVq1Zx7NgxBg0aRFRUFDNmQFKS3ioqOEjqaT1Ng7vu8sKIrx5KSHkbj9Sd/n/DgVMYSu4jVZDaU46UQnEt0e7xVgC0clajVkRVspeoWinFlcOeaSdhSgKmciYmRurdxwdVHcTKlSupVasWW7fWBqBvyGYAZOfOrEpNJdRopMpmK450B5FDI9mXso+tZ7fSqWonYsNiLygy//JL/XxjxgBr1+r9o/r3h0qVru6ArzJKSHmZizlSmuuOtKQ+UgaDcqQUimuJJjc1IducTYfT7TlxKoX3/qeacyquHAlTEnBmO4kYFcEP+34g0ByIY6cDh8PBzTePYulSQfny0CJuFgDH2rQh2W6na0gIyTP1vlORwyP5YXthkXlSUhKzZs2iSpUq9OzZk127YMMGvX1C48aAS2Rdy0XmbpSQ8jaejpS7RkoYEC6B5DQWOlIFixwrR0qhuKbQfDRONzxHSE4IVr8kFjnOQmamt8NSXCOc+1ZP6+3otIOU3BRubXgrU3+dCkBMzG1kZendCbQ1q/T6qDp1AOgSEkLq0lQ0f43AToFM2jMJi8nC8HrD+fnnn7Fardxzzz0YDAa++ko/14MPAsnJMH06REdDnz7eGPJVRQkpL3NhsXkxR6qE1J6qkVIorj3aPKDP3mtiLE89/2oc/myulyNSXAtk/JFB5pZMgjsF82WKnnvrGtSVbdu2uWbrVQNgYJtE2L8fWrdmqVXvLdUu3Y/8uHxCOoWw/vx6zmedZ1DtQVhMFr799ls0TeOuu+4iI0NfoDgiAoYPR3+Sn69P3TMavTTyq4cSUl5GaKKY4+TAKbQijlRB03P3Iseao9CIUo6UQnFNUG9YPRzCQav4luyJi+Ppb9WSMYrL58znZ/QHd8Dak2tpFd2KlZNWAnD33aOZPBlCQ6FXxnQAHAMGsCYtjSiTiai1uQCEdA9h8p7JAIysP5INGzZw4MAB+vTpQ+XKlfnlF8jO1nWTj8mpF0tpGrjW3bvWUULK27iWiAEKUnsOPNofGEUJjpQDKYW+zLESUgrFNYEpzERyTCqx8bGciThIdhUTzr37vR2WogxjTbSSMDkBc7SZX8r9AsCoeqP49ddfiYyMxGgcRno63Hwz+C6YAcDenj3JcDjoFhpK2oo0AIK6BjFj/wyCfYLpU6NPkSJzKeGrr0AIeOABYNEiOHwYhgyBqlW9MOqrjxJSXqaII+XZ/sAztecuNheFDTn1LTWV2lMoriFiR8QA0MQcTmxOZaaNU+k9xb/n3LfnkFZJxH0RfL/ne4J9gsnflk92djb33nsvv/ziA8CdN2TAmjVQpw6LIiMB6BIUQtrKNIzhRvZG7CU5N5n+tfqTl53HtGnTKF++PP3792fdOn1Fmb59ISYG+OQT/eSPP+6dQXsBJaS8jDCIEtofFEvtuU0nj9SevqVBOVIKxTVEjYE1AGiU3ZBtR87w/t4t4Orno1D8E5x2J2e/OoswC1a0XkF6fjr3NruXCV9NQAhBjx4PsHQpNGoELY5MBrsdhg5lfnIyAG3PmbGn2gntFsry48sB6FW9FzNmzCA3N5fbb78dk8lU0PLgwQfRFdWyZdC0KXTo4KWRX32UkPI2F1kipsCR0ooLLeVIKRTXKoEtArGb7DQ61Yid5bdRK7YSKVOXeTssRRkkaXYS+afziRwZyYeHP0QTGs0dzdm/fz8DBgxg9mw97fbooyC+n6Dvc/PNbEhPp0lAAMGrcgAI7R7KsmP672CP6j2YOFHvQ3Xbbbdx/jzMnAnVqrkm53m6Ua4MyvWAElJeRhiKp/aKNuR0GkWBI+VO7SlHSqG4NtHMGqKZoGJaRUKizxF6LoqX3ljs7bAUZQwpJXHvxQFwYsgJjqQc4Ya6NzDlmykA3Hnng/zwA4SFwS2N98KWLdChAzPDw5HA0IgIUpenAmDqaGLz6c3Ui6wHmbBq1SoaNmxIw4YNmTBBN0wfeAAMacn6bL2oKBg50ltD9wpKSHkbj7X0Ch0pQ+ESMR41UsqRUiiufar0qAJAI0M9th/K5Q9zPJw75+WoFGWJ9A3pZG7JJKRrCB9mfQjADRVvYO7cudSrV49Tp3qRlQX33Qd+P7hyc/fcw4zERAAGB4WRvj4dn6o+bBQbcUgHvar3Yvbs2UgpueWWW3A44JtvwGx2Tc4bPx7y8vQcn4+Pl0buHZSQ8jIl10gZEG5HysAF7Q+EUI6UQnGtEtE1AoBGKY3YYj5F+wpVWf/SdC9HpShLxL2vu1H2e+ysOL6CFhVbsObXNUgpefzxJ/nySw1NgzE3p8GPP0JkJHGDB7MsNZV6/v5U3JiPM9dJWO8wlh3X03o9Y3uyZInekmPAgAEsXgxxcXrfqHKhNvjiC11VPfCAl0btPZSQ8jJCK2GJGAxFZ+051aw9heJ6IahNEE6Dk0YnG2GvugRLnJlnlqzTi4EVir8h52AOyXOT8a/nz4dm3Y26t/69/PTjT0RFRREefiuHD8PgwVB10deQkwNjxvBdWhoSuL9iRZKmFy4Ls+zYMkyaibYV2rJy5Uqio6OpX78+P/6on+/ee4EZM+DMGbjlFj21d52hhJS3Mbh+SjxqpDQ9tSelXiPl1kpu40rVSCkU1ywGiwEaQExiDEEVcjl+yJ+QagGk/rbI26EpygBxH8SBBN/RvkzZP4XqodU5s+wMeXl5PPLII3z4oS8ATz9mg88+A7MZ2+jRTDh3Dh8huDU0kuQ5yRjDjGQ0yeBQ8iHaVW7Hjj92kJ2dTa9evUhNFcydq7eJ6twZ+Phj/eSPPea1cXsTJaS8TEGTTadWxJHSX3MiNS4QUsqRUiiubSp0rwBAQ2NDltr9aBNQgRdfUbP3FH9N3uk8zv90HnMFM+Mrj8cpnTze4nG+/uJr/Pz8aNJkNBs2QMeO0O70VDh7Fm67jamaxhmrlZvKlYNVmdjT7EQMiWB5nN72oGf1wrRe7969mTIFrFa4/XbQtmyG33/XFVWTJl4cvfdQQsrLCINLSEmtsEbKJaQMTieOEvpIFdRIaWblSCkU1yCRXfSmiI3SG5Hkvx1LcjKrfY4iDx7ycmSK0kzcu3FIqyT08VC+2/MdUZYocjfnkpiYyH333cfXX4cD8NxYCR98AIB8/HHePXUKgKcrV+bs12cBiBoVxcz9MwHoV7MfS5YscfWf6sG8efr5broJChpJXaduFCgh5X1cqT1NejhSwuVISYnULpy1p2kuR8pgUo6UQnENEtwhGCkkTU40gSrLce71p1G1lsx6epa3Q1OUUvLP5XN2/FlM5Uz8Uu8X8h35PNziYT5+/2PMZjMDBz7L/PnQsCH0ZRFs3w59+rCkYkV2ZWfTPyyM2CQDKQtT8K/nj7OVk2XHllEjrAblKc+OHTto1aoVfn7hrFqlp/XqVkiDadOgQgUYONDbH4HXuCwhJYRoKoTYIIRYK4RYKYSofqUCu14oSO0VCCmHnrIDDA4H0lDYR4qC/mZuR8qkHCmF4hrEFGrCWd9JrXO1CC9vZX1OTSoFhvDits16cbBCUYy49+KQ+ZLQx0L5dNenhPqG4rfHj3PnznH33Xfzww/RgO5GiTde13d68UXejdNn+D1bpQpnvzgLEqIfjGb2gdnYnXZG1h/JsmV6Wrl3796sXq13OejXD8Rvk/Qnd90FRqM3hl0quFxH6hzQR0rZCXgfePXyQ7q+KEjtlVQjJaXe/kC6N9Z/FDhSmlE5UgrFNUr0MP0PXztHO9YgCBE7qVzBwOkvZ3s3MEWpwxpv5ezXZzFFmPi5/s9k27J5ovUTfPLuJxiNRoYNG8tvv0GNGjAichVs3gxdu/JHw4asSkujTVAQbez+nPnqDMYwI1G3R/Hr7l8BGFF/RJH6qEWuOQ/9+gE//aQ/uftuL4y69HBZQkpKeV5Kmel6mg+o+bn/FHe6zqNGyuFRI+U0oBwpheI6pNLwSgC0P9eeDL9NVNqeRoVq/Xjq/bUg5d/srbieOPnmSZy5TkIeCeHTPZ8S7hdO8IFg4uLiGDVqFN99Vw0p4eWXwfjWa/pOL73E6ydPAvBs5cqc+fQMzmwnlR6rxL6cfaw+sZoWFVtQM6gm8+fPJzw8nJYtW7Fwod5vs2u9eL0jevPmEBvrxdF7nytSIyWEsABvAO/9xTb3CyG2CiG2Jrq6pyqKFZu7HClnkVl7JbU/UI6UQnGtY6lvIbtCNs2ONcMSu460I5VJrl6BdVG7yF+6xtvhKUoJucdzOfv1WcwVzPzQ6Ady7bk80eoJ3nn9HYxGIzfe+AJTp0Lt2nBz9FpYswbat2dLs2bMS06mscVCf1MIpz89jSHQQPQj0Xz2+2cAPNb6MRYvXkxGRgY33ngjx44ZOXYMunQBy+oFegADBnhv8KWEyxZSQggTMAV4R0q572LbSSnHSylbSClbREZGXu5prx3cjpSzsEbK4a6RcjpxGqQupCRI4b4LVY6UQnGtI4QgZFgIJqeJbr4NWOpsiI/vXjr5VeXzx5d4OzxFKeHEyyeQNknQs0F8vudzIv0jEX8Izp49y3333cf338ciJfzvZYnx5ef1nf73P/7ncqNejYnh3JdncaQ7iH44mkRjIr/s+oUoSxQ31ruRSZMmAXDzzTezcKG+e79+wPz5+pPruMjczeUWm2vAr8BsKeXsKxLRdYbbkRJSeDhS+v8WzenUi82LvO/RR0o5UgrFNU2TR5oA0PdMX1abM2h6dAf17L14M3MXct9+7wan8DpZu7KInxiPX00/3ot+j3xHPk+1fIoP/u8D/Pz8GDz4JaZPh7p1YYRlAWzYAF27srFlSxanpNA8IIB+pmBOf3QazU+j0hOVeHvd2+Q78nm2/bNkpmUyf/58KlWqRIcOHQqEVN/uVli6VJ+t17Spdz+EUsDlOlI3AP2B24QQq4UQn12BmK4r3LP2DNJQYo2Uw7OGqkBIuRwpYVSOlEJxDWOpZeFs7bPUPVeXqKorqbYpg+TKMYj6f7L86RneDk/hRaSUHH32qD4Z6Sn4ed/P1AyrSfqKdFJSUnj00cf4v//TG7u++boTw0u6GyXffJOXT5wA4LWYGE5/eBpbko3oh6KJN8cz/s/xlA8oz+gWo/nqq6/Iy8vjgQceIDVVY80aqFkTap5ZDdnZ0L8/aKqL0uUWm0+XUgZIKbu4/j1ypQK7bnD1kSp0nDzaH7iLzdFTfxek9gwqtadQXOtUH613lelrjOHUiWgO1suitakpzy4/BQkJXo5O4S2S5yeTuiSVwDaBPGd+DoBnmjzDxx98TEhICE2aPMvq1dChAwzJ/Q1274YhQ1hSqxYrXDP1utsDiPsgDkOwgSrjqvDWurewOqyM6zAOzaHx+eef4+fnx5gxY5gyBWw2uPlmCtN6qj4KUA05vU6JS8TIYkvE4HKk3Pu4U3tCpfYUioshhAgSQmx0ueVbhBDdi71f3dUDb7UQYpUQopK3Yv0rWt7XklxzLr1O9OTHUI0Aw25anmjDjhpLOfbGRG+Hp/ACznwnR544AsD5p86z+tRqOlTpwMqvVpKbm8tLL73Ca6+FAvD+G3mIF18AIbC/9hpPHz0KwAexsZx68xTObCdVxlbhrHaWCdsnUDGwIvc3v5+vvvqKhIQE7rrrLsLDw/lV74bAbbdKXUj5+ED37iXGd72hhJSXKXmJmBIcKVnoSBUuEaMcKYXiL8gCOkkpuwA3Af9X7P0HgQmu938CSqWjbrAYSOqYRHBuMOUrJdBw+xGCtZYYap/mhW/OQG6ut0NUXGXiPooj72gekXdE8siZRxAIRpUbxeTfJlO3bl18fB5k/34YORJar30PTp6E++7jx/Bw9ubkMDwykqYp5oLZfpUeq8QLK1/A5rTxQscXyM/O580338TX15fnn3+e/fth0yZo3RpqWvfC8ePQtSsEBHj7oygVKCHlbS6ogXIWzNrTl4gp3h5B3wbAKQzKkVIoLoKU0imldPe2CwJ2FdtkLxDiehwKlNo8WfeX9Dv/weltSVyvsa+hL7UCazHdkUTm+N+8HJ3iapJ3Ko+Tb5zEEGRg5oCZHE87zr1N7+Wbl78B4H//+4iXXjLh4wNvPXwW3n4bgoPJeuUVXjpxApMQvB0Tw9FnjiJtkmr/q8a21G1M3D2R2uG1ua/Zfbz//vskJyfz2GOPER0dzTvv6OcePRr45Rf9yfDh3vkASiFKSHmZC/tIOXDKwiViSqqRUo6UQnFpCCGihRDrgaVA8YXqlgMPCCF2AaOB7652fJdKhU4VOFXxNM1ONWN9aArpFU/Q7Ewz7NWm8+3rJ9R14DpBSsmh0YdwZjuxPGvhjQNvEOEfQc24mvz5558MHDiQpUt7k5oK48ZB9a+e0R3L//2PN3NzOW+18mDFioRvyidpRhKWRhbK312eJ5Y8AcD7vd4nKSGJDz/8kJCQEMaOHcupUzBxIlSpAreOtOtCys8PbrzRy59G6UEJKW9TrI+UEEUdKafr/YKFiwHlSCkUl4aU8oyUsgPQCvi82NvvAC9KKRsBrwBv/dWxvNlUWAhBpccqAtDdWJ3IPfupe645xGbzbnIUjulqMePrgfiJ8aQsSiGwTSBjo8didVh5rvlzvD7udfz9/bn99k/4/nu90fjYdutg0iSoU4d9d93F+3FxlDOZeLlSVQ4/dhiAmp/WZPqh6WyM20j3mO70r9mfV155hZycHMaNG0doaCgvvQR2OzzzDJhWL4Nz52DoUAgK8vKnUXpQQsrLFOkjpeluU5EaKdf/IYM0KEdKofgHCCF8PJ5mAJnFNwGSXI8TgLC/Op63mwp3frQzqf5p9DzenR07z5NVsT6maibi2cqCFzepZWOucawJVo48dgRhFux6YhfLTy6nQ+UOrPlkDZmZmbzyyuu88UYMAJ99YMX34XsBkJ9+ykMnTmCXkg9iY8n5NoGcvTlEjozE2NbIM8ueQRMaH/b+kG3btvHtt99StWpVHnnkEbZsgZ9/1lse3H8/FOT47r3XS59C6UQJKS9TpI+U25GShQ05PR0pWbB6sXKkFIpLoIFrVt4qYA7wuBCiiRDiGdf7bwBvCSFWA68Bb3opzkvC4GsgrX8GvnZfqoaaSSl/lCY5TSBoAV8f6QKrV3s5QsV/yeFHD2NPsRP0VBCPHHkEf5M/I3xGMG/uPJo3b05m5qPs3AnDhkHfP16DQ4fgzjuZ2LAhq9PS6BISwjBbMMdfOo7mpxH7XiyvrH6FU+mnGNNiDA0iG/Dggw8ipeSTTz7B19ePxx/Xz/3BB2DetEZfXqZdO32NGEUBRm8HcN1TrI/UxRypEvtIKUdKobgoUsptQKcS3trhen8v0OFqxnS53PjhDWyesZnBZ3rwS/5aWpXrxB81/mDpn1Ekv/E+4V27ejtExX9A0pwkEqckYmlo4cmYJ8k+m82b7d/k1ZtexWAw8PTT3zFqlJGICPjyob3Q6x2IjCThrbd44uhRjELwRY0aHLn5CI4MB7Hvx3LAdICPN39MhYAKvNntTSZMmMAff/xBv379GDRoEFOm6DP1evSAAf2c0EHvVcXLL4MQfx3wdYZypLxM8T5SxR2pkvpIKUdKobg+CagUwOFGxymfXp4c30RC/OshqgscrGDmymA4dszbISquMPYsO4cfPgwa/PnYn6w+u5ruMd1Z+c5KkpOTeemlV3jrrSbY7fDlJzbKPTUK7HbkJ5/wQHIySTYbL1WtSvj8bJLnJxPYMpAKj1TggfkP4JAOPunzCfkZ+Tz33HP4+Pjw6aefkpEhePppvWn5Rx+B+PEH2LwZevXS/ymKoISUl3HXSLmXiBHCUSCkLqmPFCgxpVBcR/T9oCcAAzPacipxN7X9a4O2kMncBN+V2omHin/JyddPkn86H/+7/Xns3GME+QTRJr4NK5avoGPHjmRkjGP3br1n1I3bn4ft22HYMH7t0oXZSUk0DwjgKUt5jjx6BGEU1J5Qm0+2fsKWM1voV7Mfw+sN56GHHiIlJYVx48YRGxvL2LFw5gw89hg0qJAMY8eC2Qyff67cqBJQQsrbXJDacxa0P9CkxOn6ndWkVnKNFCghpVBcR1TrVo0TFeJoFteM3fmHaZk6GKI3soZGpE+Yrk+xUlwTZO/P5vSHpzFFmRjXeBz5jnyeqPsE777wLiEhIdx++698+KGBSpXgixFr4P33ITqaM59+yiNHjmAWgh/r1OHYPYexJdqo8nwVTkSd4PmVzxPkE8RX/b9i2rRpTJ8+ncaNGzNu3DjWrIFvvoGYGHj9deD55yE5GZ59Vq86V1yAElJe5sLUnocj5XAUrZGiWI2UcJW4qTopheK6ou4LtQEY4GiGT4I/PjEmHKxhZUJ92LDBy9EprgRSSg4/dBhpl5x88CQrk1fSvVp3fhv7Gzabjddf/4ZnnqmC0QhTv04h/OGbQQhsP/3E8PPnSXc4eCMmhtCf0kmel0xgq0AqjKvAbTNvw+qw8nnfz/HN9+Whhx7CaDTy448/4nCYue8+/fzffguWrWv0B9Wq6Y2pFCWihJSXKd6QUwgnTtyvyYIaKSGFR7G5y5HSXEJKOVIKxXVFywdacD44ni5HO7MtfhftwgeCWMhi+sDcud4OT3EFSJ6bTNqqNPza+jHGNAY/ox/R26M5dPAQt99+Fz/9NIK0NHjnTTtt/2+w3t9p7FjGVq3K5owM+oWF8UBaKEeeOoIh0EC9SfX437r/sTN+J8PqDuPWhrdyzz33kJSUxAsvvECTJk14+mk4fBjuvhu6t8yAO+7Q22qMHw/+/t7+SEotSkh5G89ictdae541Ug7PPlLFHSl3XlA5UgrFdYVm1Ah4OBCDNNDFXI36qY0QVWaziB7IOXNVT6kyjnRKjr98HICfB/5MhjWDO6rewc+f/EzlypXx9f2YrVth8GB44sRjsH499O7NzMce46PTp6ni48P30TXZP2IfMl9S88uaLLMv492N71IxsCJfD/iajz/+mPnz59OqVSuef/555s6FL7+E6tX1AnOeeEJfo+/hh6FnT+9+IKUcJaS8TImOlHSn+5xFHaniNVLKkVIorlv6vtibFEsKfY72YvPxPbSIbUEcR9h91A8OHvR2eIrLIGluEtm7sqEHfGn9kvoR9Vn22jIA7r77W8aPDyImBn7o+jPiqy8hNpYd48cz6tAhTEIwpU494m8/TO7BXMrfU56sflncPut2jJqRqcOncmzPMcaOHUtISAhTpkwhMdHM3XeDwaA3Qw9aORu+/x5q1Spswqm4KEpIeZtiS8QUd6Q8a6ScuAWTcqQUiusdg68B+20Ss8NMs4AI2shOEPw1sxgK8+Z5OzzFZXB+wnkAvmmpL0RcN64uRw8f5eab7+arr3qjaTBp3G5Cn70PAgI4N3Uq/U6fJsfp5NvatSn3bjIpC1MIahdExY8qMmzqMNLz03m/5/vU8qvFiBEjsNvt/PDDD0RHV+OWW/R68tdeg9aRx+DOO8Fo1Nuaq5Te36KElJcpcYkYlyP1t6k9d/sDJaQUiuuSoe8OIssni0GH+7Hu2C6aNklnOj1UnVQZxppoJWVxCrKqZLJ5Mq3LtWbWB7MoX7486ekfkJAALzyRQ5tX+4LVSs4PP9Db4eCc1crLVavSc66TU/93CnO0mTrT6nDLvFvYFb+LkfVH8kCTBxg6dCgnT57kiSeeYMiQITz+OKxdq7eHGvtort4aPT1dnwHYurW3P44ygRJSXqb4EjFQrCGnq/1Biak99/8+ldpTKK5LTEEmcobnYbFaqB9soa2pK3uCF3BswzlISvr7AyhKHQlTEpB2yfY220GA4XcDDruD/v3fZOHCEJo3l7x04FY4cwbn009zQ82a7M7O5tZy5XjwD38OjT6EIdBAg7kNePLPJ1lweAFtK7Xl+0Hfc//997Nhwwb69u3Le++9x7ff6nVRsbEw+TeJ4ZEHYccOGDECHn3U2x9FmUEJKW9T0hIxJThSJmkqSO1JqdofKBQKncEfDiTPmM+Nh4ew4eAOmjbbyXh5GyxY4O3QFP8QKSXnxp8D4IvoL4j0jWTjLxupV68Bc+fegckEv/SeiGnBbGjZknF33cWS1FQ6BQfzXlw5Dtx2AM1Ho+G8hnyU8RHfbPuGmmE1mXvzXN5+/W1++eUX6tevz+TJk1m50sBDD0FAAMyZA6Hj34Eff4Q6dfTGrqrx5iWjhJSXKd5H6mKOlAGDasipUCguwKecDxlDMgnODaZBcBDtRRs+D8oh7+ep3g5N8Q9JW5NG9u5sctrncCzoGDFZMeCEypUfJjHRwKO3JlP3/XsgMJBFn3/OuwkJVPHx4YfcKhy+YR/SKak3rR6fi895Zc0rRFmiWHTrIr779DveeOMNKlasyLx58zhwIIihQ/U/Hb/9BvV3TtL7REVGwvz5EBjo7Y+iTKGElJcpMmvvL2qkjBhVQ06FQlEig74YQK4pl5FHbmDtgX00b3mSb1eGwfHj3g5N8Q848+kZAH5u+jMAx2cex9fXl02bbiIkRPLSsbvAauXce+8xMj8fsxD8GlKDs4P248xxUvenuowPHs9Lq14iyhLFqjtWMf+X+YwbN47IyEhWrFhBbm4M/fpBdrY+MW+A73K46y7w89MnKcTGevMjKJMoIeVtPPtIuR0pp0dDTg9HyindqT23I6VqpBQKBfiW8yXzxmwC8wJpHhZEA2sDXqpgI+frn70dmuISyT2RS9KcJGSMZGrYVJoGNiVxXyJNm95ARkYwo9ofI3jtPGSHDvRr1oxMh4MPK1fHd8QJ7Ml2qr9TnZ+q/cQLK18gyhLFittXMO2raTz++OOEhoaybNky8vLq0KWLPkPvgw/g9ugVMHCgfjP+22+quPxfooSUlylwpDzaHzilhsS1RIxLSJmkqYSGnMqRUigUOkM+HUS2OZthh4aw4uROOrQI5uVPMvUZWIpSz9kvzoITVnZeidQktrU2AHJzHwbg3sT/A2DiM8+wIzuboeERdHwylew92UTdFsWMDjMYu3wskf6RLLttGd+88Q3/+9//iIqKYtWqVeTmNqZrV0hMhP/7P3iysUtE2WwwcaLe3VPxr1BCytuU4EgBSIRr0WLp2kzzKDZXNVIKhaIo5nAz8g6JxWqhe2AFos+E80PlPZx/bby3Q1P8DY4cB+e+O4cIFHxQ4QOqB1Rnz5w9NGnSnh072tKyYR6NtnyHvWlTRoeEEGgwMG5tAClzUghsFcimBzfx5LInCfMLY/Eti3nv2ff47LPPiImJYcOGDRw71pju3SEtDb74AsZWmwL9+kF+vi6iRo709kdQplFCyssUr5HybLbp2ZDTJE0FqT3VkFOhUJREnw/7kGpJY8j+QaxO28PAeq2554tzEBfn7dAUf0HSnCTsaXYOdzpMrk8uoQdDAbBYngbg6arTAFgzYgTZTidjLFHkPnMKQ4CBnI9zGL18NAHmABaMXMAbj7xRMDtv/foNTJkSyw03gN0Ov/wseTDrXbjpJtA0mD5diagrgBJSXqZg1l4xR8qJhuZ0FqT2NLSC1J5ypBQKRUkYA4yEjwvD5DRxk9aU3L05HGqyhQ23fOzt0BR/Qfwv8QB8UeULQn1C+fOnP6lWrSYbNw4kNsbBsFUPI0NCeKZVKzSg93dWnLlOyr9enps33YzNaeOnQT/x1iNvMWvWLFq2bMmyZWt44YUKvPCCPhlv5YJcblsyCsaOhYgIWLUKhg717sCvEZSQ8jYuLeSukRKiqCPl8KiRUkvEKBSKv6PD2A6cj4in65Gu7PXfTe/ygxi6LwXrDLVsTGkk/3w+KUtSsMXY2FFuB1XSqiCtkipVnkJKA8/WX4ghO4Oj99zDdk2jf2AYhgnJmKPNvFnpTc5knuHJNk+y4ssVzJs3j7Zt2zJz5gpGjQrnxx+hXj34/bdjtH+8pZ7Ga9AANm2CNm28PfRrBiWkvIzbkXI35CzuSElPR0oqR0qhUPw1mlGjyWeNARidMoC9Gw7RuKWNcXeth6wsL0enKE78T/HghHXN1oGAo9OPEhwcwubNt1M+ysnt6+9HWiw81K8fALdv9EFaJb53+PLT3p+oFV6LRkmN+PLLL6lRowa//DKXgQMDWbECunaFDf9bSswNTWHvXrjtNti8GWrU8PKory2UkPIy7hopgzSA5tG1HAMmpxOHposnozTicDlRntvoD5QjpVAoCqk9sjZn6p2jwZkG+FRKol5+LD9VXsv+e97zdmgKD6RTcnb8WTDCF9W+oKpPVbKOZlGr1i1YrX483mwdvmnnOXnXXSzVNLoGBlPhjSSEj+C3Br8BcF+9+3j0wUcxm81MmjSdO++McK3yIlnc431CbuqjN4367DN9EWKLxbuDvgZRQsrbXLBETKEjZfDoI2XEWOBIqc7mCoXirxBC0G9qH2yajQcO3cG8wwcYUmswPRYlYZuz0NvhKVykrkgl71ge8W3iSQlMwXefLwCnTt2F2Sy5Z+sYpI8Pjw0YAMDoP/yxJdgIvyecr098TbhfOPHL48nIyODll1/mp58as3499OkDv9Z4FfMLz0BYGCxfDg8/rJZ9+Y9QQsrLFF8ixtNtMnrUSBmkZ0NO5UgpFIq/JrR+KLm35RKaE8qA0PLYtiVSuc0xnrxpPSQkeDs8BXD2m7MAjK8zHn+jPwenHiQmpiHx8c0Z0uwUEYn7OXvTTcz18aFjQBDRLyWh+WrM6DyDLGsW9za+lwlfTyAwMJB69R7miy+genX4rccETG+9ChUr6qm8Ll28O9BrHCWkvMyF7Q8KHSmjLGzBacBwYR8pVGdzhUJxcfp/2Z+UoFQG7x3E5rBttPPpwMTqS1k75B113fAy+efySZ6TjL2SnVUVVxGbEwv5EBLyECC4J+dzAN7u0weA0Vv8sSXZCLkvhPeOvEeIbwjB+4JJTU3l3nvv5ZNPggH4/qm9hDw3GoKCdCdK1UP95ygh5W08GnLq7pRypBQKxZXBYDFQ77O6GKSBR5KGs2jjQW6MvYn++5NIefUzb4d3XXP++/NIu2RlG72T+fFpxwkMDGLnzluJrWKlx64PyG/ShC/Kl6eBnz+VX0pE89f4rd1vZNuyebT5o3z09kf4+PjQteuTrFkD7dpJOv94l9406qefoG5dbw/zukAJKS/jdqSEFC4hVdSRcnc2V2vtKRSKf0PtUbVJbpJCgzMNqFo7CdvuBFq3zafPm5k4V6zydnjXJdl7szn59knwga+qfUWsOZasw1nUrn0XTmcAD1edj4ZkwfDhANx83IIjxUHQ3UF8dOQjIv0jcWx0kJiYyEMPPcT48ZUAeKb3bvjjD+jRA4YM8eIIry+UkPI2rv8DBqcBoYkLaqTcDTkNzhIcKakcKYVC8dcIIej9Wy/smp37d93LNL/VNBR1SWg9mecHLYfTp70d4nVF+sZ0dvXbhTPbyeJRi0kLSMO2UV9X78iRB7FYJHdtfxQZGsoTbdrgr2m0/TAbgKlNp5LvyOfRpo/y6fufEhgYSLdu45g/H5o2hUFrntJP8txz3hredYkSUl6miCNlKEztOdGKpPaMGHG4BJTTqVJ7CoXi0gmoE4DlPgth2WHcEtCU+SsyGVL+AT6ptIDZHd+CzExvh3jN47Q7OfbCMbZ33E7+qXz2D9vPO5XeoU5AHU4tOkX9+r1IS6vFHU13EZx1ht233MIpg4GbCUP8kUNQzyC+SPiCUN9QEhcnkpmZyVNPPcUHH0QA8NaIHWgrl0Pr1tCtm5dHe32hhJSXKb5EjDtt58CAQUqcrnJzTWoea+2pYnOFQvHPaPlOS3KDchm+bTi5LWdiX3mGW2uMYHjWeXb2f16vq1H8Zxwbe4xTb52CCPh8zOc82PBByvuX5/zH5xFOQX7+WAAePvks0mDgKVeR+ZBf9Rvl/b33k5GfwS31bmHC1xMIDw+nc+cnWbUK2rSR9J5xv36it99WbQ6uMkpIeRuXqWRwGoo4UsVTe0ZpLFhrTzlSCoXin2IMNlLnnTqYnCbGJNzDl+bdVMs4S++W/nT6I5Bz97wIBb3qFFeS1BWpnP7wNLKCZPgdw5kRNYOmWlNS30ol7XQa3bq9w5Ej3ejdNIG6cUuJHziQ5QEB9PYLIWBCKn41/HjX910AQo+Ekp2dzf33389XXwUC8EyHzYitf0Dv3no7c8VVRQkpL+O5RIwQokhrA4OURRYt1jdU7Q8UCsW/o9r91bDWttL+UHvqtjzBpPWxxJarT2yHpbT9NZy0Z970dojXJOd/Og/AO33fIcWSQswfMWx/eTs+Tl9at/6GFSuepkJ5J9/m3ArA+67FhG9eYQQnZN2Wxa6kXfSv2Z9p30xD0zT69RvN9OlQo4Zk8Nx79BO9/bZXxne9o4SUl7mwj1QxR8rlQhncheWaan+gUCj+HUITNHu/GQD37L2H/XXeIXhKKi2insHW6TM6fBBO9psfezfIaxBHtn6N3hG4g7opdTm+4DhNmvTH4TjA77/fT+3qNhZXvJvKB5eTOnw4H1SuTANff2L+LwVjqJGPoz8GoIOhAwcPHmTgwIFMnVoFpxOe7PAHhkP74aab9IpzxVVHCSlvU6yP1EUdKVmSI+V6UzlSCoXiEgnvH47WXKPZiWbUbhDC21Zfmu+dwU2Gdzje/CV6vmgh780PvB3mNYUzT79GW41W9i3aR/Xq9dm9eypOZxT/13khu06F0ujPn8jt1o2WrqVcHtgXgDPTiWmUifln5tOkfBOWf7ccgHvueZIJEyAiAu7c87R+knHjvDW86x4lpLxMgSPl1C5sf+BwL1Os10jpO3jUSKn2BwqF4h8ihKDO2DoADDk8BEe9d5m6YzDWOn/yoHEsm2s8T58Xw8l7/jVVM3WFcOa6hJTJCnZISxuMw+HPpJYfM3ZNf4xhAcx++20iXnyRo1LypH95mjydgjAJfm7yMwA3Rt/IiuUraN68OTt2dCQnBx4eehq/reugc2do1MibQ7yuuWwhJYS4UwixUQixQQjR7EoEdV3h4UgVXyJGKzZrD1A1UgrFJSKECHJdm1YLIbYIIbqXsM1YIcRy1zbXzZzxiCERiPKCbnu6EdTIh5X8ScfvjrJlUCx3Rg1hTdQr9Hm7NrmPjlVi6grg6Uhhh5QUP5pUSWHI2iex16tHi4kTGdqmDb4mE2+lRzF0UBL2VDshr4Xw3ZnviA6M5sD0AwA88shTfP65wNcXHkx9C9eL3hqaAjBezs5CiFDgUaANEA38AnS4AnFdN7gdKUu+hVBrKE5fMxbDVjZTjvhTTiov38OmSEnKOTsNrO0gCgLsYTSIHk98fjpvRHeDX7bCqnNeHsl/h9Qkdn8HgTKYQEswgRaJj78kPyiEIAeYTEAQBBqD8NV8KRdeDpNJo1yEE2Hxh5AQbw9B4R2ygE5SSrsQojowBWjpflMI0RcIllL28FaA3kIzaVR5oAonXz1Jl/QuzK0wntfOLeWZVf/jj/LP07LcTtYse5P+n7/KgswH8ZvwORgM3g67zOLMdSKFxGawgR3AF0tOIgBjn3uO7UYjI/3DeeB5K2JFPHYNwl8I58aAG7EmWxnTcAyvPvcqlSpVIidnOAkJMOaOHCInfQeVK8PgwV4d3/XOZQkpoBWwTkppBY4LIQKFED5SyvwrENt1gTDqQmrgtoEM3DbQ9WoW8Bz11kLXtforbRMbcBP99Cdxrs3OALR0/by2+bTvp8zSZsGCJviykQPU4f1HBjOnxQ38fBu8Pux1VsashI8B+03Ab7zMq7xqeB1Wr4YOSt9fb0jdunXbtUHArmKbjABShRArgLPAw1LK9KsYolcpd1M5Tr56ks77OjO36Vz2nvuTCsth05fHuO/dsRwYcherJn9M/5+eZX767fhP/h58fLwddpnEmefEYXKAAGwAfviJXACmmc20sATw2L255O/KIaR7CJvv2szYuLFkJGdwT9N72PfTPmw2G48++hiffGJCCHgy5Huw2WDMGDBe7p9yxeVwuZ9+OJDq8TwNCAMusEeEEPcD9wNUqVLlMk977RDYMpDyd5dn175dJKQnkJubj8OOy053WerFnPXryWgPsFlomFiPqolVoYEG7CMPP05QjYZHj/P5DeAUUPVsDDQE/MCS8yfZDtgX1BYyHHDokBJS1ylCiGh0J6oWcHextysCyVLK7kKIh4FxwEXX1rjWrmGWOhb86vnReH9jgvsGky6+4T35Ac9Me5ufb3yZ+1bex4f9PmTV3PIMnH0vi/oNwTxvBvj7ezv0Mocj14Hd5Gp4agfww5c8APLMZhrvFuTvyiHi5gge7PEg646sw9/kz2udXyN+ZjwTJk6gfv36VKkymoMH4YYhTmpMfUsXtvfd57VxKXQuV0ilACEez4Ndr12AlHI8MB6gRYsW15MW+EsMvgbqTKhDHep4O5RSScbWDP5s+Sea1PAP8CVPs+J06vVhET4mAKSA2lE1ATCajETHmjl0CJzVa8AOrosaMluKjXPfnyPtzzQ2PbmJOQfncI//PXw3/jueeuoLZs+uTp2aDh4O/gUmT+b81Kl8kJhIqNHIoxnhnPq/U5S7qRynW57mnQ3vcEfjO6jvU5+PPvqIChUq8PTTzzB/Pnz8McyaBUF7N8Grr8KHH0K9et4e/kWRUp4BOgghqgGrgfkeb6cAi12PFwOf/s2xrrlrWLkby5H7ai6dTnViXtV5LD9RifGrjvHi3WcZPr0/dZou5ECNqaw80o1HVw7m65Ej9V8A5YD8I5x5TmxmfT09d2rPT+qOVK6PD+KMFYDJgyez7sA6ulbryoMVH+SFB17g0KFDREdHM3HiAoYNCwDgmWYrYPY5uOsufeqewqtc7rfhd+ANIYQJqABkqbSe4kriOauxyIxFV58tAIehsM+WZtI8ZjW6ivGv4VmN9iw7ce/GEfd+HM5cJ3bNzstRL5MQksCiCYsgDpYsmYGUz9DCvJeHrXcBcPe337KoeXOi0zRaDzuOwQlrj65lTN8xAGzbuo3j7x/HbrdTqVItpkx5im3b9M9zXof/49bdrqnWEyfCm6WziWOxMoMMoPiCcquBFsBy188jVy+60oE7vdd/W3/mNZ8HJ6YwlZHcvHI58wfczp2L7mTcgHHITx/nG+d+2s/fwKinnoJPPvF26GWKC4WUH34yB9CFlI/+kPd2v0fFsIo8VvExbuhzA06nkzvvvJNnnvmA++8P4+hReOhBSZsFL+k7qCLzUsFlCSkpZaoQ4ktgDXrG6bErEpVC4aL4WoT6r5ksmNUIuiMlpGs7Tbtu+mwlTE3gyONHsJ6zku2fzbTO01jYbCGJ+xNhC4jUQUjGUtVUhZesdzPSNoXJvXvz6s03k1ShOqPHOxg424m1Wi4fNfuIFXVXEOsfS9KcJA5vPEzFCtFUrvwMv/9+D6dPa/SueYxXj42i9e6N0KKF7kj17evtj+GvaCCE+Ai9y60ReFwI0QToKaV8D/gR+FYIsQq9cuV2bwXqLSx1LAR1CaL26trUDq/NQfOvTLJOY9qKIbT6fBSjv25FlIzifP3zaHuf4iH5Ix0/bUi1jh1h+HBvh19mcOY6sVlcQspdI+XMwGk04jAYMOdKEPqsvnYV2/HovY/idDqZPn06fn7D6NwZkpL0FWA+qvgefPk7dO+uGnCWEi7bn5VSfg98fwViUSguxN3QvaA9BICziCPl1AodKYPRcM07Uk6bkyOPHuHs12eRBsmMNjP4udPPWE9Zyf8+nxDRhrS0Dwi2tORtx7Pck/c5h7t0oMk933CqcmXu3+DHgPtzsAQYWXbPMl4Pe51g/2BuNtzMxGcnIhA8/NBYNm16id9/t1CzhuS7Kq/SaeUrEB4On/8GI0eW+oVRpZTbgE4lvLXD9X4+16F4Kk6lByuxb/U+Bu0axHuN32PXH0nkngqhxtlDbGlVmwHbBvBrj1/J2z2TTJ7iBfE2Ex98EHr0UDNiLwHplEir1FsfSFyLV/ji6zyH3dcXAFO2BF9AwPnT5zl16hRjxoxB04bRv78+YfL11yTjgr7A8OQ4/Xv444/eG5SiCKohp6JU47kWIQV/t526I+UhpNx9toRB4HS/Lq89R8qR52DP0D2c/fos2RWzuf/e+/l5wM/kzsslf3I+0QGvkZa2njY1a7MnrwYP2D9n8ovPUf/ll7HHxvLT1xaGvZBDeKMAPvvfZ7wW8RqNKzbmQfEgE5+ZSEhwCDNmLGPNmv9j2zYLgwc5+bPebbqIatwY/vxTX4qilIsoxaUTMSQCLUyj075OmJqbgE/4mMcZM2cOCwfBkF1DsFvsUAUCAsYzmZEcSQyCF1/0duhlAncPqXxTPsYC78IPP0dWgZAyu4UU4LTq2zdu3J4xY3QRtWyhjRdP3Y/h8UcgMBBmzIBKla72UBQXQQkpRanGXSNlcBo8hJQDBwYMnkLKqf8qX8uOlNPuZP/N+0lZkEJ6/XRuvu1msmKyyP8iHw5D7dpTOXPmJQa3S2HVsapEG+OZ8M033N69O/UtFr7/0kL5yVmE9Ajhyye/ZGrqVNpXbs9D/g/x1ti3CA8PZ+XKdXz0UXd274bbboMZ1Z4mYO4kPZW3ejVcA7PVFEXRTBpRN0QRkB9A8/TmUH4BE2lLz2W7ORabQrrFQscDHfFr54fVOg2nzORdy6vwzTeQkODt8Es9biGVZ8gruE6BH372LGyudhKmLIn00UsVhF2/0K1e7Ud8PDzzDHSdMhq++w5q14YtW/RO5opSgxJSitKNu/M72oWOlKtGqogjpXk6UtdW5/djzx0jaXYStoY2bhp0E/4R/li/tWI7b6NTp0kcPHgjvTvlMHVfA3xlLqu//Zb7qlenio8PP6wJR/yaQmCLQDa/uJkfDv1Ag3INeL/Z+zw2+jF8fX1ZtGgRv/zSgHXroFcv+KHfNAyffgRVq8KiRSqNcw0TOSISgK57u0IDsDGLSfZbGT1vLrOHwLDtw7DXtmMlh8DA35hovZEMux/88ot3Ay8DuJeHyTfk6zeEAPjiZ8/E6k7tZTkvEFIZGXqbiSHVd8H330PNmrB5M9SqdXUHoPhblJBSlGoKHCl5oSNldDlNnkJKM3rO2nPtcA04Uklzkjj9wWkM0QbuGHgH0l8StSqKlKMpjBz5OitX3kjt2pIp1hswpyWQ8uabDK5WDR8hmCRiyB4XhzHciN/3fjy+7nECzAFMuWEKD93zEDk5OXz++efk57fko49002nSp0kYHxmjt42fNUtNsb7GCekagiHcQPuD7THVNgGTmCRu5Z5Fi1ja10H1M/WpHF8ZmoHZ/Ck5NiO/iVt1l0QtIfOXuB2pXGMuwuG+iPnha8/EajYDYM6UOHz065S06p+nw+EHgP+3rhmSn36qbmZKKUpIKUo3rt/QokLKecGsvQIhpWnXnCNlS7Vx8IGDYIBv7vqGc+ZzDDQPZNeiXXTu3JOlS5/HaITJAycRvHkJ9OzJHX36kOFw8HZMdXwePA0OqP1dbR7d/ij5jnw+6fMJy6cs588//2TQoEGMGnU399+vn2/8eAh/+2lIToYXXlAzg64DNKNGuWHlsORbaJncEqL3sFtqpCWUo/X+bSzvASN/H4lvN1+S0/cDSxgf9BTywAFYscLb4ZdqHLm6QMrVPIWUL37kku+ukcoDp49+nXILKbtdF1J+Z49CcDD06XN1A1dcMkpIKUo1BX2kpIYU7jvfojVSDkNRR8rhuLYcqWNjj2GLt5E+Kp2Jxom0r9ieha8tJCAggCpVviMlRfD84zk0+e5h8PNj1XvvMT8tjRaBgdy4SCNrexbhg8NZV3sdK46voH3l9gypNoSXX34Zi8XCZ599xo8/Cvbv1+vIe1fcDT//DLGxMG6ct4evuEqUG1EOgM77OkN9gMlM4hbuXrSI2SME3XZ3x5JngUYQFvYRf6bXYDk94N13vRp3aaegRsqYV5C2Az9dSLlqpHzywW7WO5878vXrlc2mp/b881JUN/lSjhJSilJNQWqPEhwpl5Aq4kgZPPpIXQOOVPa+bM5NOIdPNR+eqPMEBmGgyu4q5GTncP/9LzBpUhUqVYKxOf+DtDTkM8/wqFXvkvxRleqceu0kwiyo/kF1Xln9CqCvW/jJx5+Qnp7OE088QblyVXj9db1Z9ZtvAi+9pKdrXn8dXKkHxbVPcOdgjJFGOhzsgKmOCcQkJmq3MWjjZhLDsjgSa6DH7h5YOlhISVkG7OG1gHeRy5bpszkVJeIWUlaj1dWME9yOVF4JQso9a89qdTlS+WlKSJVylJBSlG7cxeZO7ZJm7QmDuKYcqeMvHQcnHBx1kON5xxlZcyTTvppGdHQ0Z88+hsMBbzybgf8PX0BkJAvvvps92dkMCg8nZkYO1rNWyt9ZnqXWpexO2M2g2oOI9Y/l448/JigoiCeeeIJffoHTp+Huu6G67SDMmQP16+u9ohTXDZpRI/KGSPzz/WmZ0BIqHSPOGc82a3MGb9jA8p4w6MAgssOyIQyioj5ifVZTttEcPvvM2+GXWgqKzY35SJtE04yAET9yyXXdqPjkg82kN+x05BVzpHKSlJAq5SghpSjVlFxsrjfk9Jy1V9DZ3FCY2nOWcSGVvT+bpJlJ+Nf356WglzAbzPj/4Y/dbueBB55j+nQ/qlWDW+M/hNxceOwx3khOBuC5ylWIezcODFBlbBU+3PwhAC92fJFffvmFjIwMxowZQ2hoGF98oZ/v6aeBL7/Unzz+OGjq8nC9ETlMn73XaX8nV3rvNyZyK7euWsXq3oKoMxWpllCNgPYBpKRMAjL4xX80TJ6s19QpLsDTkZI2idGoO02+5JHjcqTM1kIhZc/Vnan8fD+EkJidueDn54XIFZeKulIqSjWeS8R41kg50dA8Z+05L2x/4HCW7YacZz49A8DZm85yOvs0w2oNY/K3kwkNDSUp6S7sdnjiETvGrz8Hi4Wdo0axOSODTsHB1N7iIO94HpFDIzkWcIz1p9bTOro1LSq24KuvvkIIwejRo9m4EXbu1Nsd1KyQBT/8AKGhcMstXh69whuEdAnBEGqg3cF2GGsbQUxlqriRLn9sRzrS+LMZ9NzTE0NTAzZ7Hj4+M/mNm7Hl2fXfHcUFuB0pq9GK0+rEYHCl7Mglx8ORspr1lLwtVxdUeXl++Pu77h+VI1WqUUJKUbpxLxFTrI+UAwOGEvpIuR0pTfOokSqDjpQt1cb5n85jKmfi/fD3AahytgpZWVncd99oJk2yEBgId0fO1Z2AW29lfF4eAA9UrMi5b88BUOH+CozfNl5/vfkDbNiwgX379tG3b1+qVavGhAn6+caMAebOhcxMuPNOdeG+TtFMGpGDIwnMC6RpUlOIPk2KPMJaRyeGrV3Lil7Qf39/0kU6VIawsF9JzLGwlN6qp9RFKHCkTFac+U40TZ+pVzy1l2/U19e25djQNI3cXDN+rpl8ypEq3SghpSjVuB0pg9OA5OKOlHC5T+4lYjStbDtSCb8l4Mx14nObDxsSNtCucjtWTloJQEzMvSQlwYgREPDbdwDk3XEHE+PjCTEa6S+CSJqThG+ML4FdA5m0ZxJBPkGMbDCSyZMnA3D33XdjteotokJDoV8/9PQMKDfqOidypJ7e672ztyu9N40pjGTU6tWs6yzwyQimQVwDQruGcv78SuAMv0Q+Cbt2wb593gy9VOLpSDnyHWhaoSOVZTZjkGB0FAopa7YVPz8/8vIE/r6ua5e6sSnVKCGlKNVcSvuDkhwpg6FsF5vH/xoPwJLGSwDoWa4nf/zxBx06dGDx4uoA3DkgCZYsgbp1WVCjBukOB7eUK0fOvDSkTRJ1WxTr4taRlJPE4NqD8dF8mDFjBhaLhX79+rFsGaSlwdChYM5KgcWL9ZYHzZt7a9iKUkBYzzBMlUx0PNCRgNoBYJzETG0ILf7cS3BGIhvbQf99/cmJyUEaJEFBk5mT1ol0guC337wdfqmjYK09Y34RIeVLHllmM76uP8N5Jt1Rzs/Ox8/Pj5wc8DMrIVUWUEJKUbpxz9qTFy4RI0ooNnfXSOmpvbLpSOUezSVjUwaBrQP5NuVbzAYzGZsyABgx4g4WLoSYGGh/bro+tltuYbar0Hd4ZCSJMxMBvXB4+r7pAAyrO4wNGzZw/vx5BgwYgJ+fH1On6ucbORKYNw9sNv2JWpD4ukYYBBXvrIjZbqbn4Z5Q+zxpznXMYQi3Ll/Oij76UjJ2hx1qgK/vr+TZjMzQRuhCSnU6L0KR9gc2cK9O7HakfF3Oea4hF4D8rHz8/f3JzQV/H1e/BJXaK9UoIaUo1XjO2vNM7TkwYCih2FzvIyXRNFmY2itjjlTCZH0h2PwB+ZxMP0m/mv1YOmcpBoMBf/+h2Gy6iyTmzgHANngw85OTCTcaaSsCSF2eim91X/wa+jHrwCwCzAH0iu3FrFmzABg+fDhOp758XnAwdO2K7kYBDBzojSErShnl7y4PRrht7W1YmlqArxnP/YxetIgtzSV5woeWR1sS2S2ShIQdwB4mhT4ER4/C1q1ejr504e5snm/Kd/WRKkztZZpM+MqiQiovMw9fX9c2JpeQUo5UqUYJKUXpxvUbKqTwSO1duESMpyMFoGlOnGW0Rip5vu4urayl10S1DWvL3r176dChA6tWhQMwuEe2vjRHbCyrK1YkzW5nYEQEGUtSkVZJxNAIdpzfwfms8/SO7Y2fyY+lS3Ux1rNnT3buhMRE6N4dTJoDli3Ti6VatvTauBWlB78YPyo9Xomw7DDuOnwXNFrCKvzIPRVIq317WNMZBu4fSHpUOpghPHwiq1IaEU85ld4rxoUNOQtTexlmM76uZWNyDDmALqR8fFzr7LlaIighVbpRQkpRqilwpJwXOlLuYnOHoWhDTgCDwVEma6SsCVYyfs8goFkAM1JnIBBY9+jTovv1G8iCBRAWBu3SF+mpuMGDWZyaCsCg8HBSV+iPw/uFs+K4vgZar9henD17ln379tG6dWuCg4NZulQ/X69ewPbt+sy/Hj3AYLggJsX1SbWXq6FV1Bi6ZSiN6jUCy12M5hNem/ADs0ZIWuxrTVB6EMbmRuz2STglzDDdDFOmlKnv3H+NZ0NObCClL0bNgREH6SYTPi4hla1l6zvYwcdHF05+BpeQUqm9Uo0SUopSjXDV6wjERWukSnKkDAZnoRFVhhyplEUpIMHU3cSO8ztoU6kNaxauAaBKlYGkpUHfvmBcvVzfoW9fVqaloQFdQ0JIXZGK8BEEtQ1i+TF9m+4x3Vm+XH/cs2dPgKJCaskSjycKhY4x0EiDHxugSY3nFj+HT78TbGAZh3a2puPepSzoa+C2dbfh28WX9IxTwHomBY+Gs2dh3Tpvh19qKO5IOZ1+BSm7XB8ffF3r72UbsvE16PVTJpPLkTLoM/mUI1W6UUJKUfox6I6UE7cgcuAQxiI1Up7tDwA0zVEma6SSF+ppvT0N9gDQK6YXGzZsoFq1ahw9WgvQ03GsWgUmE8ktW7IjK4tmgYH4nLGTdyyP4PbB2Ew21p9aT7WQalQPrc6yZcsAXUjl58P69VC9ul60XvBHr3v3qz1cRSknrGcYUfdFUSGtAiMTR0LDd3icoYz5ZDkrBpyjxZkBhGWEQV0IC/uCDUl12EVDld7zwLP9QYGQcjlNeWYzPi7TKUvLwseodzp3Cyk/t5BSjlSpRgkpRalHaKLAcdJx4hTGIrP23O0PitRIlbFZe1JK0lanYQwxssyiC59yOeXIzc2lS5curF6tb9elbjwcOgRt2rDatUBxt5AQ0lamARDaPZTfT/9Orj2X7jHdEUKwbt06/P39adWqFdu3g9UKHTuifzabN0NUFFSrdtXHrCj9xL4ViwgW3Lz+ZiLaRGA1P8bDuZ/x9Qdv8X/Pwdg5z2PubyYlbyqwmc/NT8L06fovmaJIQ07s4HD44mfUP5tcHx98bPp1KkNk4Ku5HSnXOnua3hJBOVKlGyWkFKUeYRBozmJ9pISx5IacWtl1pHIO5mBLsBHcIZjVcavxM/qRuENvZdC+fRc2bICqVSHmqCut16ULK131Ud1CQ0lbmwZASNcQNp3eBECHKh2Ij4/n5MmTtGjRApPJxObN+u5t2gAHDkB6OrRtq9oeKErEHGEm5oUYfO2+9D3SF1oe4E/+YO/+dnQ6MpedHWpz6++3Qn8wGB/hZ/sITqVYYNo0b4deKijiSNnA4fDDV/MQUi69mall4qPpjlTBMjJCCamygBJSitKPh+Ok10k5cYqSFy0uOmvPtX8ZcaTS16YDIFtJTqafpG3ltqxfsx6A4ODO5OZCly7A2rX6Dl26sCkjAwG0Cwoic0smwiQIbBbI72d+B6B1dGt+/931uHVrgKJCapMuuGjb9iqMUFFWKX+n3g6h746+iCYC+JAXeZVXvvuZucPS6Xr0NuoG1cVRZyv5zo94lE/hlVf0CRHXOZ4NObGD3e6Ln6an7HLN5kIhJTIxCRNQKKT80VsiqNRe6UYJKUWpRxhEMSFVgiPlKPs1Umlr0gDYF6Mvs9Gxckc2btxI1apVOXGimv5aR2DLFtA08po3Z3d2NvX8/fHLhpwDOQQ0CUCYBb+f/p0gnyBqR9QuUUhZLNCgAcVUlUJRMuZIMxEDI6iQWoFGWY2g0gmSOMianO7cvHIJU0dqPLT0IUQfAeZXmENd5hypR8FijtcxJbU/8BMuIeXjg4+rDCrHkOMhpFyz9qTeEkE5UqUbJaQUpR8Px8ntSDk0Y8mz9kShkCowosqIkEpfl45m0VgboDtOlZyVyMnJoU2bNvzxh75Nq0Z5sHs31K3LDsAuJS2DgsjcmgkSAlsFcibzDOeyztGyYks0obHZJZbatGnDuXNw8qTeLspoBH7/XW950KKFV8asKDtE3RYFQJvDbaAxwI9M4F4emjePZb0lldLr0yixEbSxA4/xMJ+R9fxbkJTkzbC9jrshZ1EhpTtNuT4+mF3ZO6vJigldSAnhcqSkqyWCElKlGqO3A1Ao/g53jRSgS3+HA2exPlIXztpzlqlFi63xVvLj8gnuHMzWRL0zdP5x/Va1ZcuWfPGF7u7XzduuC8OWLdmSmam/HxhIxhZ9CZmgVkFsOL0B0NN6TqeTP/74g+joaKKjo1m4UD9fixboxcD790Pt2upCrfhbgtoGAVDrfC0MzQxIbRaLnd9iOW2jzb6dLOzXhFEbR2FuaSZWCyXEeZQ5qW/TvMM85BVwPIVnDZ9nOV/x0j7PzS62z98c40qeK+9EHg6jA6nJgiVifNHVky6k9BvCfGM+Rqn/SdY0lyPldAkpldor1SghpSj1CIMoSN15OlKaSyCV3Nm8bDlSmdt0UeTf1J+d8TupHV6bPdv0Fgg1arTg+HFo3x6M213WVMuW/OESUq0CA8nccgrQHamtp3Uh1jK6JSdPniQzM5POnTsDupkF0KgRcPAg2O3QsOFVGqWiLONTwQdzeTO1z9fGGeZEaungPMkWWtHtzz/ZGtOUmyY3o/lhz0Wvozl/EDgY762wSwXxYa7xux0p6XKkzGbMrjKofFM+BuluiOtypJxZ+lN1o1OqUUJKUfrRQLMVrZFyYkC4lJLTw5EqWFJGlC1Hyi2k0mqkYU2y0rxic7b+tBUhBA5HU8DlIv3hIaQyMjALQaOAAP7ckYVm0fCv5c+e7boAaxzVmD3r9ccNXWLJLaQaNqSYqlIo/p6AZgFYF1qJSo/ifOR5OLeDbTSn654NNFggEQhW1l/J5lqbSQ5MJlfLxSqtOB1OuIy1jD3bnxRtzkuR43q+V7RlSrH9isVysf0EnjdwJZyr+DE84/V4Oy4sDuKATABLQe1TntmMT45+UKvRiubQL2BSumbt2fXrgnKkSjdKSClKPcIgLqyREh6OlBHcvToLO5uXMUdqq37B3F9hPyRBk8gmTNs5jdq1a7Nvn55SadkSeOtPMBrJq1+fw1u20CggAC3bSd6JPAJbBSI0wZ6EPVhMFqqGVOW33XpjxAYNGgC6djIYoE4dYPIu/eTKkVJcIoHNAklZmEKtc7U4X+E8nNvOn+ZODNvjBBtM6vAb33X7FrlEwh4guzpQDwgELFz+nxx5kcf/xXtX8vjum5UQoCfhOb/hMJlwGgyYciT4AqJwqSspXX2k7HrKXjlSpRslpBSlHqEVq5HCgVNcgiPl1k9lxJEyBBrYZNDbEYTlhWGz2WjevDm7XHqnaQOr3oizdm0OOBw4gQYWCzn79LtbSwMLmfmZnEg7Qevo1mhCY8+eQkfKZtNLomrWBF9flCOl+McENAsAoOa5mqwtvxajcTt/isfJtGaT7ZvHhG7fYVxqxPaHHZyzgP50ZzWdWEsYKVjIxszlNeqUxZwgz+eX+t7FtxP/8fGdGHiGoXmzOBddDgBztktIwcUdKSWkSjVKSClKP4YSZu0JY4GQkkZRYo2UlAIJiFLuSFnjrVjPWAnuHMzOxJ0A2M/oa3E1atSIn34CkwlqysN6TVP9+uzJ1otQ6/v7k71Jf2ypb2Ffot46oUE5twO1G6PRSO3atTl0SG/rU2BA7d4NQUFQpcpVHK2iLBPQRBdSMfExGOoa0LQdmPN9sRHK7qpbcApw/mkDw1hwDuRnhjKKucxr2p6pfTpxNjyGbD8/8sxmnJfZANY9axdKqAH3fO8ij4vvd8F7FzvGJZ7rr/b7mYYcrFwZgAoHHEgf13autlsOh0tIWdP1Rrk+PihKL0pIKUo9RRwpd42U0IrO2nP3kXIvcixcbhUahlLuSGXv1YVQQKMA9iXuo2pwVY4fOA5ArVr13SYUpkN79R3q12dvju5C1bdYyN6TBuiO1J4E3YFqUK4BVquVAwcOULt2bcxmc9H6qIwMiItTHc0V/wifaP0Pevnc8ohIgdV6lubEAbCl+mZEqgVpzQRG0SFgOqOy5vJC9wd468WbaLHnFH2WVqXSQYklG3zywWi/vHjkRX51i7/u+fxS9vmr/S9ln4ttX/w9v1wIT5Gcr+8qRi8QUq7Uni1dr49S39FSjRJSilJPkRopV2rPgacjRUGNlDu1p2kukYWhYHHj0kr2Pl1I5VfPJzM9kw5VOrB3hS6a/P3rYbdDvXrAXg8h5XKkGlgsZOw5A+iO1J7dhULq8OHD2O126tevD+iT9MB1rKNH9Se1av33A1RcM2hmDUOwgdCcUOwmO2jQ1KkvtL09ZjvyQB5+RJJLPUbmf0CywcDbz4/A/4Afbz5VHh/p5MwN5/jO7zvifOLIM+VhNVpxin9/s3NB0XdJxeJw0ULxEovS/8E+xc//j/eRcCZM/w4b8vRZe3a7y5HKS1VpvTKAElKK0o/HWnqeS8QIh0tIXSC09D5S+pZaqa+Rctc4nY06C+lQL7Ies/bNwt/fn9TUqgDUrw/sLhRSe9LS8Nc0qvr6snlvNsYQI+aKZvau1LepH1mfzcv1Rpx16tQBCrVTjRrAoSMeTxSKS8ccaSYgTk/x4Q+xWTbyjPmciDwBC8AsOpMrBYNsy1lRvSpS07h5EpitZk4MncWddT8l2CeYTmGdOLX1FCd3nSQtIa3wZuhK8FcGzsXe+6evX+l97LDu8DoAbDZX+4P8VAhQM/ZKO0pIKUo9RRpyulJ7DuFTuESMEXCZToWpvUJHqrTP2nOn9vaH7gcgNjiW48eP06xZM/bv18ddvz4weS+YzWTHxHB80yZaBgbizHRgPWslqF0QQgiOpBwhwBxA+YDyHHUppxousXTEpZ1iY4GF176QEkIEAYsBK+APjJNSrihhu1eBW6WU1+6HcQUxlTNhPmLGx+pDvn8+hiwneUar/t1MhEzZk0qmM1SxxfFm+1vBKRmwPRebFs/djT4lxDeEZ8Oe5fn7nwcgJCSEJtWaomkBCOGPlAacTpDFJ7/9LYU7yAt2LnlWXdHt/n4bKWVBlu1i+0opPZ4WjeNi+7sfi5oQEVGZ9HTdRfbLS4VygShKN0pIKUo/JSwR48RQuETMNeBImcqZ2G3Vi5j8s/yRUlKvXr2CbF69mlY4fBjq1eOIayHYOv7+5B7Vu/n51fDD6rByMv0kDcs11EWVSznFxsYCupAqXx4CAihUVdewkAKygE5SSrsQojowBWjpuYEQIgpQ+c1/gClSX8YkJCeEeEs8Bpw4XN83nOAkmg5S73e2vHdfgo8mEpJVjl0V4nAKuKXKLfzvjv9hsVj4/vsprFrVj59/FrjK/hQujEaJJTcR/Mp5OxTF36CElKLUIwyiWHsDBw5hKJiN5zRwQY1UWXGkrIlWbEk2QrqEFMy4yz+tLw1Tv359fv1VXxOvpumkPo6aNTmaq4unWD8/cve6hFSsHyfTTuKUTmLDdOHk6UhlZEBiot4dHShmT12bSCmdFP5mBAG7StjsJeBtYObViqusY440AxCcHUy8fzwGJPnC5a44AYyEOc6Q6mfkWMWKtJp8FCjHqnr673fiihPYbDa++WYC33zTn5UrITIShg6wUinvCAEZ5/CzZaDZ8vVZqv+QS2lJUKQ9gRB4zq37230EyIIbO1HonBV/XOSYf3/84s+bhMdhXmJTNVJlACWkFKUeoZXQkNOz2Nxzrb1iqT0nWqkWUu76KP+6/uxP2k90YDSnj54GoFat2hw7BlWrgumUq8ApNrZASFX39SXvqL5ml1+sHwdSD+ibhLodqCMEBgYSGRnJjh367gUG1JEjEB4OoaH//SC9iBAiGt2JqgXcXey9mkCAlHKXuIRZUUKI+4H7Aapcxy0jTOV0Ryo0OxT8wQBFHCkwYJJ28sxGEILmh4OxIVjeZCHhOf/f3rkHSZJd9fk7mVlV3VX9qO6Z7pnZ7p3dmVljQEIMMJKQENJaCCFh/EIQiMCAACOIwAiFCWEZA6EwBmTrDzAPh0PG8gqMkcLGkmIlIpAV0uoZSKzEIoFgLc3s7uz07mzPox/Tr+rKzOs/7s2szKrqx/SrsqrPF9HTVZWZVbdysm79+nfOPQce+9MvUCqVeOKJ7+WjH4VXvhI+9P0PMfSWn4WVlV69reJy3329HoGyAyqklOKT1JEyZFrEeDa0ZwyxL2mOVPpHo1sFFOEXOrS39rgVUuaCYXFlkYunL3LlC1cAqNcvsLYG58/TyhTPCKkLw8OsX14CYOjCEJdv230uTFxgc3OTq1ev8oIXvMCF+ezhDzwArK7CM8/Ai198ZO+zVxhj5oCXicj9wCPABzOb3wb8yl081zuBdwJcunRpHw1P+ptsaI+qNYHDREgZgACfkEjsCrQTYZ3rpSYro8/xir+GDz17nQcffJCHH64hAv/zR/+MoR/7Ceu8/PzPW9t0agpqNVs/aa9L/7M5TAd1+7Ced7vXu3gRpdiokFIKT1pk03humXScTtJeHGN8WjmdSQTQ6w9Hav2KFUW3p2/DihVBf3PZJkaF4TnARd+uWHHFhQtc3rAu1IXhYZ693ArtXf6CE1KTF3jyySeJ47h7onnyXIOdH4WIVIwxDXd3GdfpLMN54PecG3VGRH7bGPOmoxxjP5IN7VEDHyGSrCMVENDEuM9oKfIwQ/YPhtKcD0S89KWv5td/HV70IsOpX/wJ27fo4YfhwQeP/P0oyn7xej0ARdkJ8Z2Qir1WixjsJO3HMZGdmy2ZFjEAkVcutCO1ccWKorm6rSNzfuI8V65cYXp6muvX7RLzCxdoOVLnz3N5fZ2a5zFdKrF+eR1/1Kd0ssTlBZcTNflAmh+VJJpnDC148kl75/77D/vt9Zrni8gnRORjwAeAN4vIRRF5C4Ax5iXGmNcYY14DPKsiandkQ3veiIePR+RF9o8ZA+BToknk2c9oOfIxvl0gcSW0x5ZKrwTguy7Ow9wcvPa1KqKUvkUdKaX4JOIo7age25Ad4BnT1ZFKc6S8oPiOlMBXhr8CwJmhM8zPz/OSl7wkq53g3ZchCGjOznL16lW+vlbDNA2NpxuMvGAEEeHy7cuUvBL3jt3LBy/bCFYipJ62xaetdvq8zcHCtagYVIwxnwde3mXTY132HWx77gBJQnvja+OUxkt4CJEXIXHyMQwI2EyFlB9BJFZINWL72GOPnQTg1b6rRvFd33WUb0FRDpR9OVIi8lsi8ufu560HNShFyZI4Ur7xczlSAH4U2abFUWfTYoDIL7gjdXmDykyFy2tWNZVXbdjk/PnzrWjeeUOSdX41ioiwYb2NJzcgtvlRxhiuLFzhvvp9+J7P0045JUnR165BuQwnT7o7MPBCSjkcktDexNoEXs3DwyP2YlrFyW2OVOzZv9P9CGLPNipuxvaxr37V/n7x3z5kD1EhpfQx+w3t/Z4x5luBlwL/REQGdy210jMSIZVzpCTjSHnSl45Uc6FJuBgydH6IKwtWNUU37VgvXLjQcqRG5mFtLZ9oPjRkhRQwfG6YhY0F1sN17h2z4uiaE0uzs7PuPszMgOfRsqfcNkW5GxJH6sT6CaRiP3CRF7ZKlOBTYhPjhJQXQ4gVUqHYY6MoIAgMpc983MabB7gMhzL47EtIGWO+4n7HQEgrU0VRDo6kyGYuR8o5UnFs60httWrPKxXWkUryo4bPD3Nl4QpjlTHmn5wHWo7U1BSMzrdifFddovl9Q0M05mwedeXeCteWnXAaS4RTS0gtL9sexaluShwpFVLKHsj224t9V/hWokw33oASTWK/5UglQirCCanQJwiAzU3bkVtR+pgDSTYXkR8CrhhjntxmnzeKyKMi8uiNGzcO4mWVY0KabG68zhypZNVeopX6yJFKVuyVzpV4evlpzk+c54knngBgZuY816/DuXO0HKSzZ7nWsOLp3kolLdxZme0UUnNzc4yMjDA2Ntapm65dsx3lB7yGlHJ4lCZLjKyNpPXdIi+EuCWkfCKiTGivib1WQ5eWG21C4DsbOdBUXaW/2fEKFhEf+HSXTR8yxvyqiLwK+DHgH233PFqDRdkziSOVCqkujlQMGDCuwnI/OVIrp1aIr8ecq5/jqaeeAqBSuR+As2exq5oAZmdTITVbqdC4dtvu2yakjDFcu3aNc+fOISL5lChjrDA7e3bv9XmUY4837BGEAb5LHo/8vJAK2nKkQmOTzZvG7b8pKqSUgWHHK9gYEwHf2m2biLwY+FXgtcaY9QMem6IAu3CkvEwOVVuLmEI7Uk/Yj8ytE7fgOtw3fh8fu/YxPM9jc/MMYPOaspbS3KYNkcxUKtzIOlKPt4TUrVu3aDQaufwodzjcvg0bGxrWU/aFN+QRNFtCKvaijJDyrZCS1qq9UGyrl8g1Hw+bHoHvdlchpfQ5+w3t/TdgFHi/iDwiIt9yAGNSlBxpQc4tcqQiNyG3hFbGkZKgsI5UEpq7PnodsCJobm6O06dPc/26/XKZnSWnhK41GgQiTJfL9ngfyqfKzC3Ppc/RLdG823Mpyl7xhuznb2hzCOgM7QWEudBeIqRCt0/U9NSRUgaG/SabP9/9POh+Pn9QA1OUlA6hFBGZVkHOONkee2loL8k+j/xSYR2pzblN8OBq+SoAU0NT3Lx5k9nZ2bzeSUJ7MzNcazS4p1zGF6Ex16BypoL4wrU7LUdKhZRy2CRCarg5DFghZTpypLJ1pNzn0fUgDpuBCillYNDK5krhybaISUJ7iSNlQ3u0tifHSLKaqNiOVPl0mWvrVtxUGhUAZmZmUr2ThvYmJlitVFgMQ2YrFaL1iPBWSGXWHnNt+Rplv8zJ6slUSM3MzNhtWe2UJK5rDSllH6RCajMRUlGrBIkL7WUdqci1bEr+pgkjn8Ct+FMhpfQ7KqSUwtOtRUzUnmyOFVIdjpRXTEcq2oho3mxSmWklirNsf83Ozrbyy89EtsHw7CxzLtF8plJplT7ICKmZ0Rk88bo6UkEA09PAs8/aJz5z5vDfpDKwtAup2I+6hPaSorkZIRXa5k6R8SkF6kgpg4EKKaX47NQipsOxyiSbi19IIbX5jE0az664a9x0K/Iyob17gnkIwzSsB8mKvZaQWm4ss9xYZmYscaDyjtT163DqlO0Ly7ytU8WpU4f+HpXBpTNHqouQkkxl88QhDg0BEMYegeccqVLpKIeuKAeOCiml8HRtEWNaLWIiz/5lm8+RKnb5g6wQmrszx3RtmutzNuk8Ce1NTUHl5lalDzLHu0TzmVErnJ577jkAzpw5QxTBrVv2uQBIarilDyjK3dMR2vNDTCa05xMResnK2pYjRRxlhJQ6UspgoEJKKT5tjpRInIb2bNPiTPkDR9EdqUQI+Wd8nlt5Ll2xB3D69CzXr3dfsQdWSCWOVvmeMvOr1mU6PXIagHnnOp04cYLbt62OnJ52L5w4UukDinL3eMNdcqSivCPV9DPJ5qmQivERQuO3HCkVUkqfo0JKKTztOVIiGUcqjnGlafCN3z+OlMtxWj+xjsEwMzqThuTK5VmM6RRSz7oaUmfKZTbnnZA61RJS0zUrjm7cuMHk5CSlUqnTgLpxAyoVGBk5/DepDCwdOVISdyab+505UsQRHh4GT5PNlYFBhZRSeNpX7eUcqYyQEiMYksrm/eFILUwsAPmyBVF0D+BW7F234T5On2beCalT5TLNeVspujRd4saaVUtT1SmMMczPzzPtHKcOA2p+3t7RqubKPui2as/EYGPvng3tdXWkIsTlN6ojpQwKKqSU4tNWR2orR6ofc6SeG7H5TLNjszzzzDNMTk6ytGS/nE6fppXTND3NfNOKp+lSieYNe7s8lXekVlZWaDQaTDkLKhFSU1PYpPXbtzU/Stk3abJ5M5tsDp77SgkI80LKzwopK5wCUSGlDAYqpJTCs5Mj1a2OVFL+oLCOlAvtPVN9BrD5TfPz85w6dSrvImXu3NjcpCTCeBDY0J5AcCJIhdRUbSrNj0ocqYwOg5s3M3cUZe90hPa8uENIJTlS7cnmuNV86kgpg4IKKaXwdM+Rciv5so5Upo5UrmkxkFlSVAiazzXxx32ei6wjNSIjbG5uMj09nc9rylhK880mU6USIkJzvknpRAkv8NLQ3nRtOhVS7Y7U9DRtqkpR9k7XgpwxSM6RauVIxYloiiMkFVJOXGn5A6XPUSGlFJ9uq/ZMq45UTkiRL8iZNE4tmiu1Ob9JeboVlgsa9stlamqq05Gq1YiGh7nphJQxhs0bm5Sm7BdQNrR3w4mldkeqXZQpyn7oXkfKpPlPPhFh4COxwTNZRyoEsdethvaUQUGFlFJ42utIiUTErvKmH0X5HCnacqTcX79FypOKViPitZjSdCkVQazYX9PT050u0vQ0t5tNDDBdLhPdiTANQ2nafiHdWL1B2S8zWh5VR0o5EhIhVd2sAm7VXmxIEhoDQpqBjxfbz2PX0B6u8Z4KKaXPUSGlFB7J1olKHalMHansqr22FjGx6/dVJEdq84YrXZBxpJpLLpE8K6TGG7C01JFonpY+mCoD1pGark0jIupIKUeCP2wFU77Xnmklkrtk8ySil3OkcI6UsdexCiml31EhpRSfjOOU9NrL5khF2TpStOVIJUv+CuRIZUsXzK/OM1IeYenmEtASUr4P9WZmxZ4rfTBVLqcr9krTJcI45Nb6LaaqiQOVTzafn4ehIVc2Sh0p5YDoHtoD44SUT0Qz8EkW67VW7ZlWaC9SIaUMBiqklMKTJptnV+2ZzKq9pL9epo6UMQV2pObzjlQ2STxJNp+aAu9Wy066kS194IRYebrMrbVb9vFMMU57SCu0NzXlykapI6UcEN2TzU0qpAJCwsBrCanUkQKDdVKDyK5cVSGl9DsqpJTiky1v4HrtRdk6UkmdqdgjJnGeXLd5ipdsngghTsCdzTs5IXXy5HSSFpVLcEocqelyOQ0NlqZKudIHkHekkrJRqQGlffaUA6J7HSmDScJ2hDRKLUcqDtzn0oBJHakN+5gKKaXPUSGlFJ5ujlQS2vPiOG06n68j5brNe8VLNk8cqfWxdSC/2q5SmSKKuggp50hNZRypbFXz6WrLkRIRJicnuX3bHn7ypHvhxUX7e3Ly8N6ccizo2iKmzZHaqPj4rv9eFLQcqThxpELnSGn5A6XPUSGlFJ60IGcmRyrqVtnc9JcjtTyyDFgRlDhJxlhBlBNSbaG9bLJ5e5+9mzdvMjk5ie/7qW6amHAvvLhoY3yjo4f35pRjQfuqPetIxWnYzidibTiTI1XKCinnSIX2Dwl1pJR+R4WUUny6tojJJJs7RyowQSqkjClu+YNECC3WFoFWRfIgCGg06vaxKfLtYTKhvTTZfKqU5kidqJ6wz7m4yIRTTomQqtfdCy8uwtgYePqxV/ZHIqQS7Kq9uOU2ERL5GUeqFNmmxgZi4/bZVCGlDAY6oyqFp71FDLQlm7urOCDoi4KciSN1YyhfkXxqaoqbN+2b6WgPkwnthbdt/Z3SyRKLG4sA1IfqhGHIysoKdaecEiE1Pu5eeGkpo6oUZe+0C6mkRUy2jlToZZLNy1ESbSc2zpFqqpBSBgMVUkrh2alFTOJIZcsfFLkg5+b8JnhwPbgOwMnhk9y8ebN7MU6AkydZCEMCEUZ8n3DRCqlgPMgJqaUlW0IhEVLurtVOxtgHUlWlKHvHG+7iSMVAWx2pXGgvqSnlHKlSc80+oEJK6XNUSCnFp61FDMREceJStQpyBhIQmyS0lzhSbmPBHKnSyRLzG1Y1DUVDxHHM1NQUt2ykjhMnyCWHL4Yh9SBARAgXQ7xhD6/isdRw4ikjpMadWMqF9lZX7TlQR0o5ADocKYlzQsonygmp2I9bQip2jtSmCillMFAhpRSe9hYxtiBn0hA1InLVzAMTdDpSFMuRMsbQvNGkNNVacVdq2i+WyclJFhbsfhMTWCVUKsHwcCqkAMLFkKBub2cdqUWnnLqG9joSphRl73iVrRypVmgvaq9s3uZIBY1V+4AKKaXPUSGlFJ8ujpT9V6wjlYT28DPJ5sXMkYqWI0xoKJ0ssbDhVJNLFZmYmMjrnYUFqNcJjWEliu5aSOVCex0JU4qyd8QXpCTpfZtsDluG9jJCqrWPq6em5Q+UPkeFlFJ42nOk0kRyvFz5g8C0QnstR6pYLWKaC27F3UQrUTxes2Or1+v5kgWLi1CvsxjanKh6EBBtRMQbcYeQGq+Mb+lI1eu0qSpF2T/Z8F4r2bx7aK+7kNKmxcpgoEJKKTzdVu2BFUnZgpw+fmeLmII5UuGCSxSfCFhYX8AXn40lW+F5YmIiDe3Va02b1zQxkQqpiSAgWrLvIyukaqUaJb+0u9DeMXKkRGRMRD4jIo+IyOdE5Dvatv+CiHxWRD4tIr8jIrLVcymdZIVUe7K5CinlOKFCSik+XepIQcuRSlbtlUwpU5CzmI5UuuJuImBhYyGXJJ6E9kRgNFq0B7Q5UunxGSFVH6rb29s5UsczR2oFeLkx5kHg9cDb27a/zxjzYmPMtwGngFce8fj6mu5CyscnRIDI81RIKccCFVJK4dnJkUpypDw8jOkTR6puSxdMDE+w4Gyoer2epEXhLS/iHtxSSBljckJq2/IHxzC0Z4yJjTHu25ox4Itt27+SuduA5Jtd2Q05ISUtRyoRSFs7Uq2EdHtDhZTS36iQUgpPe45UKpLwKMUxkddatReRCKaCOlJOSHnjHiubK7kk8cSRaneQskIqybEK6gHr4TrNuNnhSGXLHwQBDA9zLEN7ACIyIyKfAj4MvG+LfV4BnAE+scNzvVFEHhWRR5PeiMeZbC2pbGjPd5/BnJASdaSUwUWFlFJ8nBZKVu2JtESSb1oFD3z8wjtSiRBqjNiGrRNDLUdqbKzO8nIm0RxyOVLtjtTSRquGFHQP7dXrNlR4HB0pAGPMnDHmZcCLgN9t3y4iL8CG/F5vkotn6+d6pzHmkjHm0tTU1OEMuI/oCO0ZAH8XjpQKKWWwUCGlFJ4ktJfUkUpEUoRPEMdEXVbtZfexN4ohpBJHaqPqEsyHJ1IBFAQTGJMpfQDbhvaypQ+gU0jlOsIcQ0dKRCqZu8vAnbbtDwDvwoqom0c5tkFgq1V7iUDadY6Ulj9Q+hwVUkrhSUJ7YsRdsUkdKY8g40h5eB1Ni+PkEi9KaM8JoZXqCgD1Sj11pERss+GtQnsTWwip8UoSylt0h9QJQ7hzp4uQOl6O1PNF5BMi8jHgA8CbReSiiLzFbf8toA68263s+4c9GmdfstWqva6hPXWklAFGr2Cl+Lj52os9VwSwzZFyyeb95EjdKVtzJJtsHoZWELWH9hZ26UgtLS3heR4jIyOpoZVrWAzHSkgZYz4PvLzLpsfc9u850gENGDlHKm0Rkw/t5Sqbp6n8KqSUwUIdKaXwpMnmxnNhvrwjFbsWMR5eRki5faRgjpQTUktDruTBkA3tjY6OsrJiv1D2E9qr1+uISKcBdQxDe8rhspUjpTlSynFDhZRSfBJHygmp9hyppCBnEHdxpEyxHKkk2fx2+TbQcqSS0gfQ5kjdpZBKVux1GFCLi3b5Xrl8OG9MOXZ0tIjZJkeqlUNl97H/qpBSBgMVUkrhyeZItTtSvjHp/Ozjd3Gkilf+wBv2uB1ZIZW0dunos3eXQiqOY5aXl7tXNQerrNSNUg4QCVpCKvbidNXeljlS6ZpIFVLKYKFCSik86aq92M/VkWrlSNkZ2jc+kdvWcqTcJV4QRypcDAkmWiKoYirEcdy9z567sxiGlEUY8ryWkBrPC6nl5WWMMamQWl62h4+NuRdeW4Na7ZDfnXKcyAopDe0pxxkVUkrxybSIse5UqyBnNrTnm2yvveIV5DTGEC6EaXsYgKBpv0RyffbqkM0WXwxD6kGAiBAuWkfLq3gsNWz8bnxonDt3bPL66OgoYHUTQLXqXnxtzVXmVJSDYTdCKpdsruUPlAFFhZRSeBJHSowgIjmR5BuTa1psd4Q4boktu3PvHal4LcY0DUG9JaTMuhV+XUN7w8NQqbAYhky4v9qjpYhg3N5e2bQlFEbLo6yvrwNQdcrJ3bVCKgztjwop5QDJCam0cnlr1V60W0fK068hpb/RK1gpPEmOVBLa63CkaIX2ABf+K54jlSSalyZKaVguEVJdk83rdSJjWItjRnz7PqK1CK9mP7broVVLw6Vh1pwFlQipxJEaHqalqlRIKQfIVo7UXdWRCgJXel9R+hcVUkrxcVdpkmy+lSPlJflQku3H5zYWwJFKGxZPBCysu9pRKy6RvF7P5zWtrsLICGtu3DUnpOK1GL9qb681nXgqVVMhNezEUs6Ryt1RlINhvzlSJZqaH6UMBAcipETk3SLykYN4LkVpp72OVLZpcRC12hQHxk3KknGkTHEcqeyKu+XGMrVSjfVVK3JGRkZSF6lWNVZIVausuXHXfJ+4GWNCg1e1H9uskGoP7eUcqdwdRTkYuq/a6y6kWgU77T7231CFlDIQ7FtIuaaf9f0PRVG2IFNHKtsiJsLHMyYN7WUdqSLmSEUrdgz+qM9qc5Vaucbq6ioAtVoNd5Oq3wBjoFZLHamq5xGtuuPbHKnhoDO0l4vmaWhPOQS6O1Kt8geR5+2cbO6cVkXpZw7Ckfpl4NcP4HkUpSupIxV7XcsfdCSbey0hFRVJSCVCqOazurlKrdQppIIAys1EUVVZTYSU7xOvxenxYIVUxa/ge35HaC+3ak9De8ohkHOkUsfpLssfaH6UMgDsS0iJyIPA/wOeO4jBKEo3klV7nvFyq/baC3Lmc6TcPsljBQjtxastIdTNkUpLPaUxvloutBet2W+lbGivWkocqO6r9jS0pxwWu82RiokxnukupIxBUfqdHQPUIuIDn+6y6UPAtwGvZxehPRF5I/BGgLNnz97VIJVjTqaOVNeCnMmqvbiz/EERHSkzbAgXw66OVLUKrRhfNRfaSx2pTGgvEVJbhfaqVeC2hvaUg2er0F67kIq8JFEq2VuFlDJY7CikjP3W+tb2x0VkFHgd8B5gGHieiPxbY8yvbfE87wTeCXDp0iX99Ci7JltHKuc24eHFccuRotiOVCKkNiubAF1zpE6fJheXSxyp6haO1JmRM/b2FqE9zZFSDouOZPO28gdJr71thZSiDAB7XjJhjLkDXAQQkfuB399KRCnKfsjWkerWtNiIXTCU1JESX4rpSLlk82bF1pOqlWoZAVRthfYSR6pWS3Okam2OlDGG9eb6jqE9zZFSDosOR8qt2ktIKpvHSca5OlLKgHIga0+NMU8CrzqI51KUDpKIXRdHyncTcey57YAf+MRxax97oziOVKPSAGCkPJI6UqVSjTh2WmcXjlQzbhKZaMvQnpY/UA6b7snmrVV4GtpTjgtakFMpPNlk8/YcKT8RTF4rR8rzvZYjFRfHkUqSzRslK6SyOVIitqFwuyO1VY5UtoYUdIb2tPyBcthkhRRCGtozrgiu2U1oT4WUMgCokFIKT7YgZ0eOlJuIjbRypLzAy+1jbxTHkVovW2GTzZGCjJDKOFKrmcrmWUcqrSFVSoRTZ0HOUsnVO1QhpRwCOSEFqZDKojlSynFAhZRSfJKCnLEVUpY4zZECiPwtHClTnBYxSY7UeskJqYwjZYwVUrlVe5nyB7k6Uts4UtkcqVQ35YpKKcrB0F1I5QtsqpBSjgMqpJTCk3Ok0is2TlftgXWkxKksK6SK60itltxKPedIBUFAs1m2j7U5Ut0qm2cdqa1Ce2trGd2kjpRyCOzGkfJiDe0pg48KKaX4ZFvEpHN31Hc5UqmQ8p2Qco5UUowTuqza28mRCrZetZfqJhVSyiHQIaTcqj3T+pDagpyiq/aUwUaFlFJ40vIHxs8JqRgvFVKR36ps7gUtRyoqUB2peDUGgRV/BbCO1NraGtVqNVuDs6sjVfO8rjlS263aU0dKOUzuOkcq1Uwa2lMGCxVSSuFJVu3lhVScc6SMZIRUxpGKC5Yj5VU9VpudjlTGhNpXjtTw8DBhCM2m5kgph8td50i1CSmfSB0pZSBQIaUUHzc3b+dIxZ5LRiefI1UkRypajfBH/FRIVUvV7qG9Lqv2qllHquax3lxPnwNsaK9cLuP7fmf9TXWklEPgrh2p1qP2eFAhpQwEKqSUwpOrI9XmSHlZIeVEk/hCFBXQkVqNbMPiTVeEkxLGmJwj1bFqL1P+YCdHqmvD4q4PKMr+2UpItedIdQqpjNhSIaUMACqklMKT5kjhYySZeDuTzbuF9opS/sAY0xJSzpHyQjvejtDeLiqbdxNS7X32UkdKK5srh8Bdh/ZSDqShhqIUBhVSSvHpumovzhXkzLaIsb32kqbF7oAeh/bMpoGInJCS0I5tu1V7a5HtFlgW2daRWl9f39mR0hwp5QDZatVeFhVSynFAhZRSeLJNizscqSSE15YjlYT20hypHjtSSTFOr+aloT027a+O0F42RyqOqfk+IrJtZfNsaK/DgFpfBxEolw/t/SnHj12XP/Da/4jR0J4yWKiQUoqPu0oF6XCkJNsiJgnteV7hHKmkhlQ22Zym/ZUtf5A6UiIwNMRaFFH17PuK12z5BK+SD+01m03CMOzos5dLNh8ets+pKAdEh5ACwNccKeXYoUJKKTzZOlJb5UhFfsuRksAmm3seRHExcqRSIZVJNo837Ni7rtqrVkGEtTim6vvpc3hVDxHJCaluxTihrfyB5kcpB0zyucyzTWXzLfZRlH5HhZRSeHKr9lK6tIhx7lPiSHleAR2pTI5UtOFW5HVbtVezvfdWo4haIqTWIvyqvZ0VUt2KcabPBVZZaX6UctB0NTj9jke2qiNlH1NHSul/VEgpxSepI9UtR2qb8ge+X5wWMUmOVOJIDQfDrK+55sXdVu1VqzTjmKYxudCeV7W318JOIdUe2svlSKkjpRww0jVU7OVCe6ChPWXwUSGlFJ7EkUocJ4vLkcoIqXTVnicZRyrZvbeOVLzqVty5HKmkYTFssWqvVmM9U/oA8o5UtiBne2gv50gZo6E9pWcYyQip9OOrXzvKYKFXtFJ4dpMj1b5qzxiD55niOFKr+VV7SXsYaAvtlZq2v0umqnnN8zCRwTRMy5HaJrSXc6Qajcyd44WIjInIZ0TkERH5nIh8R9v2IRH5IxH5pPs91Kux9iW7WLtgJNO0WFEGFBVSSvFxob3d1pHyfHtZ+35cyByplc2VDkdqdRWCAMphy5rKFeNcd8dvkyPVXpBzeJjjXkNqBXi5MeZB4PXA29u2vwH4O2PMtwOPu/vKvjC7CO1ld9fQntL/6PIJpfAkob0Tt0/wkx/+SbgP4I95iir/efE1/NxPfJi/M8B1+Ll3v9UedA7glwB487m3wp8C5//N0Q/ecW75Xr6Rr+dNv/+LNL+3yVe/OMdvvOtdALzjp/+KKyvfQxn4k3/wT3kd8Kmnb/NTv/k+eOk9fPn9f8Wv/P7/4tU8yCOPf5rX/LMf4PbXXIUqvGz2R7izOQ/AZ/74Bv/qE4/w2WfvA85Rfc+74OOftQM4ho6UMSbG1dsGxoAvtu3yCuA/utsPA78A/JejGd3xwAhE0p5snt1BhZTS/6iQUgqPeEJpusT4/Djf/+ff3+vh7IunLv4NAJu3l9hsLgHwhaXXAyN8A1/kdX/5CABfet79fPml9wDwzZfv4dVP2ttzs08yf/Er9smW4C/n/yR97s899zI+99yD6f17H/494Avuzr2H9p6KjIjMAO8Fvgb48bbNJ4AFd3sRmNzhud4IvBHg7NmzBzrOfqRytgLA05NPZx49w+v4Tzzpzs89z8LmS9xpzexWGwphA/iZnzmawSrKISKmB38RXLp0yTz66KNH/rpK/9J4tsGNv77B4/OP89TVp5ifj2ksbRDfucNKGBE1Y+JGTCNsEJmI5uYmxkAUYjPOexzaA1gvbXK9dhMExhemCaISldIIk7UzAJwau06tskoswhOn7iMMSnixYWZ+BT82GDEsnFoiDux7mY5OM2bGASgHFb727PPw3Aq/k+NNnnfOxfg8D174QhjqbQqQiHzeGHOpR699P/CIMeb+zGPvAd5ujHlMRC4C/9oY84O7eT6dwywbVzdYqi4R+RHhnYhG414eKF+lcWKS+SBg6lmDd9pjbmOOCSYIw5Bmc4qxMRi5/lU4dw78zpIJilI0tpu/1JFS+oLKmQqzZ2aZZbbXQ1H6BBGpGGNctj3LwJ22XT4OfDfwmPv98aMb3WAwdHaIIZxAn0gePcsQcBZciB3OD5/vPPiBBw59fIpyFGiyuaIog8rzReQTIvIx4APAm0Xkooi8xW1/CPgGEfkk8A3uvqIoyl2hjpSiKAOJMebzwMu7bHrMbV8HdhXKUxRF2Qp1pBRFURRFUfaICilFURRFUZQ9okJKURRFURRlj6iQUhRFURRF2SMqpBRFURRFUfaICilFURRFUZQ9okJKURRFURRlj6iQUhRFURRF2SM96bUnIjeAp+7ikJPAzUMazl4p2piKNh4o3piKNh44XmO6zxgzdQjPe+Tc5Rx2nP6P90PRxlS08UDxxlS08UAP5q+eCKm7RUQe7VWz060o2piKNh4o3piKNh7QMR0Hing+dUw7U7TxQPHGVLTxQG/GpKE9RVEURVGUPaJCSlEURVEUZY/0i5B6Z68H0IWijalo44Hijalo4wEd03GgiOdTx7QzRRsPFG9MRRsP9GBMfZEjpSiKoiiKUkT6xZFSFEVRFEUpHCqkFEVRFEVR9ogKKUVRFEVRlD1SKCElIm8Qkc+IyKdF5Jvbtg2JyB+JyCfd76ECjOkNIvKEiDzifmaOYDx/JiI3ROSXumzr1Tnabky9OEff5P6/PiEiHxWR823bJ0Xkg+48/Y6ISAHG9DYR+dvMefIPeTxj7rp+REQ+JyLf0ba9J9dSv1O0OUznr32PSeev3Y3pSOcv95rFmcOMMYX4ASaALwBl4BzwqbbtPw38srv9K8BPF2BMbwB+6YjP0+xWr9uLc7SLMfXiHJ0GRt3t7wb+sG3724EfdrffBbymAGN6G/DPj/AceUDgbp8H/qII11I//xRtDtP560DGpPPX7sZ0pPOXe83CzGFFcqReBHzSGLNpjHkCGBWRSmb7K4APutsPu/u9HhPAj4jIp0TkV0Xk0M+nMebaNpt7cY52GhMc/Tm6boy54+42gLBtlyM/T7sYE8AvuPP0piMYT2yMScYwBnyxbZeeXEt9TtHmMJ2/doHOXwcyJjjC+cuNqTBzWJGE1AlgIXN/EZjcYnv7tl6N6QPA12H/g+4DfugIxrQdvThHO9GzcyQiNeDfA+9o2zSJPT9wxOdpmzH9DvCNwHcC/1hEvv0IxjIjIp8CPgy8r21zEa+lolO0OUznr/2j89fuxnTk85cbTyHmsCIJqdtAPXN/3D3WbXv7tp6MyRizYIyJjDER8B6g1z2HenGOtqVX50hESsB7gf9gjPly2+YF7PmBIzxP243JGHPLWNaB/8MRnCdjzJwx5mVY5+J32zYX7lrqA4o2h+n8tU90/trdmHoxf7nXLcQcViQh9VngZSJSEpGzwIoxppHZ/nFsbBb3++O9HpOI1DP7vhJ4/AjGtB29OEfb0otz5Oz3/wG83xjz/i67HPl52mlMyXlyiaMPcsjnqS3EswzcadulcNdSH1C0OUznr32i89fuxnTU85d7rcLMYYWqbC4iPw78C8AAP4eNw36nMeYdIjKMTaybBa4BP2aM2ejxmH4NeJV77HHgp4wxzUMez38FXgpUgL/GJvn1+hxtN6ZenKPvAx4CHnUPfQn4EDBljPlDETkB/AGtuPrPGmPiHo/pIeDvAwI8Yox56yGP51uA3wQiIMD+n92ix9dSv1O0OUznr32PSeev3Y3pIY5w/nJjKswcVighpSiKoiiK0k8UKbSnKIqiKIrSV6iQUhRFURRF2SMqpBRFURRFUfaICilFURRFUZQ9okJKURRFURRlj6iQUhRFURRF2SMqpJQOROSzIvKYiHxZREJ3+zER+e8i8u9E5AcO8bXfICKLIvKZzGNGREa22P8jInJbRP7lYY1JUZT+Qecv5agJej0ApXgYY14MICL3A48aYy4e8RA+Yoz5vt3saIx5lSsGpyiKovOXcuSoI6XcFSLyUPLXk4i8TUTeIyJ/KiJfFZH3isg3ichHReSyiLwjc9wZEfnfIvI5EfmSiPziXb70m0TkL0Tkioi87kDflKIoxwKdv5TDQIWUsl++BfhBbHuArwXeDrwWeAHwoyLy99x+fwD8tjHmRe6Y14rId97F6ywbY14I/DDw2wc1eEVRjjU6fyn7RkN7yn75M2PMEoCIfBH4K9cYtSEijwMXROQZbCPLKdvTEoBR4OuA/7vL13mP+/3nwD0iMqS93xRF2Sc6fyn7RoWUsl+yk0HU5X6AdT4N8MJ9NPzcADDGRG4y02tXUZT9ovOXsm80tKccOsaYO8AngbQjuIjcKyKnezcqRVGUndH5S9kJFVLKUfFDwNe7RM0vAe8F6r0dkqIoyq7Q+UvZEjHG9HoMipIiIm8Avme3y4fdMQ9hlzn/7mGNS1EUZSd0/jqeqCOlFI114FK2oN12iMhHgFcAq4c6KkVRlJ3R+esYoo6UoiiKoijKHlFHSlEURVEUZY+okFIURVEURdkjKqQURVEURVH2iAopRVEURVGUPaJCSlEURVEUZY/8fxjA5Id4H2MMAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2,figsize=(10,5))\n",
+    "for cycle in sol_int.cycles:\n",
+    "    if cycle is not None:\n",
+    "        t = cycle[\"Time [h]\"].data - cycle[\"Time [h]\"].data[0]\n",
+    "        ax[0].plot(t, cycle[\"Current [A]\"].data)\n",
+    "        ax[0].set_xlabel(\"Time [h]\")\n",
+    "        ax[0].set_title(\"Current [A]\")\n",
+    "        ax[1].plot(t, cycle[\"Terminal voltage [V]\"].data)\n",
+    "        ax[1].set_xlabel(\"Time [h]\")\n",
+    "        ax[1].set_title(\"Terminal voltage [V]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "considered-rescue",
+   "metadata": {},
+   "source": [
+    "All summary variables are always available for every cycle, since these are much less memory-intensive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "miniature-stone",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(n,m,figsize=(10,10))\n",
+    "for var, ax in zip(vars_to_plot,axes.flat):\n",
+    "    ax.plot(sol_list.summary_variables[\"Cycle number\"], sol_list.summary_variables[var])\n",
+    "    ax.set_xlabel(\"Cycle number\")\n",
+    "    ax.set_ylabel(var)\n",
+    "    ax.set_xlim([1,sol_list.summary_variables[\"Cycle number\"][-1]])\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convinced-winter",
+   "metadata": {},
+   "source": [
+    "## Starting solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unauthorized-fundamental",
+   "metadata": {},
+   "source": [
+    "A simulation can be performed iteratively by using the `starting_solution` feature. For example, we first solve for 10 cycles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "posted-plastic",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:04:52,836 - [NOTICE] simulation.solve(710): Cycle 1/10 (50.983 ms elapsed) --------------------\n",
+      "2021-05-24 09:04:52,837 - [NOTICE] simulation.solve(742): Cycle 1/10, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:52,993 - [NOTICE] simulation.solve(742): Cycle 1/10, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:53,172 - [NOTICE] simulation.solve(742): Cycle 1/10, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:53,387 - [NOTICE] simulation.solve(742): Cycle 1/10, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:53,911 - [NOTICE] simulation.solve(820): Capacity is now 4.941 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:53,912 - [NOTICE] simulation.solve(710): Cycle 2/10 (1.127 s elapsed) --------------------\n",
+      "2021-05-24 09:04:53,912 - [NOTICE] simulation.solve(742): Cycle 2/10, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:54,040 - [NOTICE] simulation.solve(742): Cycle 2/10, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:54,136 - [NOTICE] simulation.solve(742): Cycle 2/10, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:54,248 - [NOTICE] simulation.solve(742): Cycle 2/10, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:54,388 - [NOTICE] simulation.solve(820): Capacity is now 4.913 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:54,389 - [NOTICE] simulation.solve(710): Cycle 3/10 (1.604 s elapsed) --------------------\n",
+      "2021-05-24 09:04:54,390 - [NOTICE] simulation.solve(742): Cycle 3/10, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:54,504 - [NOTICE] simulation.solve(742): Cycle 3/10, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:54,599 - [NOTICE] simulation.solve(742): Cycle 3/10, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:54,735 - [NOTICE] simulation.solve(742): Cycle 3/10, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:54,867 - [NOTICE] simulation.solve(820): Capacity is now 4.886 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:54,868 - [NOTICE] simulation.solve(710): Cycle 4/10 (2.083 s elapsed) --------------------\n",
+      "2021-05-24 09:04:54,869 - [NOTICE] simulation.solve(742): Cycle 4/10, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:54,987 - [NOTICE] simulation.solve(742): Cycle 4/10, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:55,089 - [NOTICE] simulation.solve(742): Cycle 4/10, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:55,201 - [NOTICE] simulation.solve(742): Cycle 4/10, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:55,341 - [NOTICE] simulation.solve(820): Capacity is now 4.859 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:55,341 - [NOTICE] simulation.solve(710): Cycle 5/10 (2.556 s elapsed) --------------------\n",
+      "2021-05-24 09:04:55,342 - [NOTICE] simulation.solve(742): Cycle 5/10, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:55,455 - [NOTICE] simulation.solve(742): Cycle 5/10, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:55,566 - [NOTICE] simulation.solve(742): Cycle 5/10, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:55,686 - [NOTICE] simulation.solve(742): Cycle 5/10, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:55,832 - [NOTICE] simulation.solve(820): Capacity is now 4.833 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:55,833 - [NOTICE] simulation.solve(710): Cycle 6/10 (3.048 s elapsed) --------------------\n",
+      "2021-05-24 09:04:55,833 - [NOTICE] simulation.solve(742): Cycle 6/10, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:55,953 - [NOTICE] simulation.solve(742): Cycle 6/10, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:56,057 - [NOTICE] simulation.solve(742): Cycle 6/10, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:56,169 - [NOTICE] simulation.solve(742): Cycle 6/10, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:56,310 - [NOTICE] simulation.solve(820): Capacity is now 4.807 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:56,312 - [NOTICE] simulation.solve(710): Cycle 7/10 (3.527 s elapsed) --------------------\n",
+      "2021-05-24 09:04:56,313 - [NOTICE] simulation.solve(742): Cycle 7/10, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:56,440 - [NOTICE] simulation.solve(742): Cycle 7/10, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:56,531 - [NOTICE] simulation.solve(742): Cycle 7/10, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:56,644 - [NOTICE] simulation.solve(742): Cycle 7/10, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:56,782 - [NOTICE] simulation.solve(820): Capacity is now 4.781 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:56,783 - [NOTICE] simulation.solve(710): Cycle 8/10 (3.998 s elapsed) --------------------\n",
+      "2021-05-24 09:04:56,783 - [NOTICE] simulation.solve(742): Cycle 8/10, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:56,908 - [NOTICE] simulation.solve(742): Cycle 8/10, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:57,000 - [NOTICE] simulation.solve(742): Cycle 8/10, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:57,406 - [NOTICE] simulation.solve(742): Cycle 8/10, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:57,558 - [NOTICE] simulation.solve(820): Capacity is now 4.756 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:57,559 - [NOTICE] simulation.solve(710): Cycle 9/10 (4.774 s elapsed) --------------------\n",
+      "2021-05-24 09:04:57,560 - [NOTICE] simulation.solve(742): Cycle 9/10, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:57,697 - [NOTICE] simulation.solve(742): Cycle 9/10, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:57,789 - [NOTICE] simulation.solve(742): Cycle 9/10, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:57,904 - [NOTICE] simulation.solve(742): Cycle 9/10, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:58,056 - [NOTICE] simulation.solve(820): Capacity is now 4.732 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:58,057 - [NOTICE] simulation.solve(710): Cycle 10/10 (5.272 s elapsed) --------------------\n",
+      "2021-05-24 09:04:58,058 - [NOTICE] simulation.solve(742): Cycle 10/10, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:58,178 - [NOTICE] simulation.solve(742): Cycle 10/10, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:58,294 - [NOTICE] simulation.solve(742): Cycle 10/10, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:58,417 - [NOTICE] simulation.solve(742): Cycle 10/10, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:58,586 - [NOTICE] simulation.solve(820): Capacity is now 4.708 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:58,588 - [NOTICE] simulation.solve(837): Finish experiment simulation, took 5.802 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "experiment = pybamm.Experiment([\n",
+    "    (f\"Discharge at 1C until {Vmin}V\",\n",
+    "     \"Rest for 1 hour\",\n",
+    "    f\"Charge at 1C until {Vmax}V\", \n",
+    "    f\"Hold at {Vmax}V until C/50\")\n",
+    "] * 10,\n",
+    "termination=\"80% capacity\"\n",
+    ")\n",
+    "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n",
+    "sol = sim.solve()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "weird-darkness",
+   "metadata": {},
+   "source": [
+    "If we give `sol` as the starting solution this will then solve for the next 10 cycles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "moderate-pipeline",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-05-24 09:04:58,686 - [NOTICE] simulation.solve(710): Cycle 11/20 (75.591 ms elapsed) --------------------\n",
+      "2021-05-24 09:04:58,687 - [NOTICE] simulation.solve(742): Cycle 11/20, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:58,804 - [NOTICE] simulation.solve(742): Cycle 11/20, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:58,907 - [NOTICE] simulation.solve(742): Cycle 11/20, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:59,022 - [NOTICE] simulation.solve(742): Cycle 11/20, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:59,385 - [NOTICE] simulation.solve(820): Capacity is now 4.684 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:59,386 - [NOTICE] simulation.solve(710): Cycle 12/20 (775.627 ms elapsed) --------------------\n",
+      "2021-05-24 09:04:59,387 - [NOTICE] simulation.solve(742): Cycle 12/20, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:59,505 - [NOTICE] simulation.solve(742): Cycle 12/20, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:04:59,607 - [NOTICE] simulation.solve(742): Cycle 12/20, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:04:59,718 - [NOTICE] simulation.solve(742): Cycle 12/20, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:04:59,865 - [NOTICE] simulation.solve(820): Capacity is now 4.660 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:04:59,865 - [NOTICE] simulation.solve(710): Cycle 13/20 (1.255 s elapsed) --------------------\n",
+      "2021-05-24 09:04:59,866 - [NOTICE] simulation.solve(742): Cycle 13/20, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:04:59,986 - [NOTICE] simulation.solve(742): Cycle 13/20, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:05:00,099 - [NOTICE] simulation.solve(742): Cycle 13/20, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:05:00,218 - [NOTICE] simulation.solve(742): Cycle 13/20, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:05:00,354 - [NOTICE] simulation.solve(820): Capacity is now 4.637 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:05:00,355 - [NOTICE] simulation.solve(710): Cycle 14/20 (1.745 s elapsed) --------------------\n",
+      "2021-05-24 09:05:00,356 - [NOTICE] simulation.solve(742): Cycle 14/20, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:05:00,465 - [NOTICE] simulation.solve(742): Cycle 14/20, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:05:00,567 - [NOTICE] simulation.solve(742): Cycle 14/20, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:05:00,685 - [NOTICE] simulation.solve(742): Cycle 14/20, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:05:00,820 - [NOTICE] simulation.solve(820): Capacity is now 4.614 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:05:00,821 - [NOTICE] simulation.solve(710): Cycle 15/20 (2.210 s elapsed) --------------------\n",
+      "2021-05-24 09:05:00,822 - [NOTICE] simulation.solve(742): Cycle 15/20, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:05:00,934 - [NOTICE] simulation.solve(742): Cycle 15/20, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:05:01,031 - [NOTICE] simulation.solve(742): Cycle 15/20, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:05:01,149 - [NOTICE] simulation.solve(742): Cycle 15/20, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:05:01,289 - [NOTICE] simulation.solve(820): Capacity is now 4.592 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:05:01,290 - [NOTICE] simulation.solve(710): Cycle 16/20 (2.679 s elapsed) --------------------\n",
+      "2021-05-24 09:05:01,291 - [NOTICE] simulation.solve(742): Cycle 16/20, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:05:01,396 - [NOTICE] simulation.solve(742): Cycle 16/20, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:05:01,487 - [NOTICE] simulation.solve(742): Cycle 16/20, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:05:01,590 - [NOTICE] simulation.solve(742): Cycle 16/20, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:05:01,732 - [NOTICE] simulation.solve(820): Capacity is now 4.570 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:05:01,733 - [NOTICE] simulation.solve(710): Cycle 17/20 (3.122 s elapsed) --------------------\n",
+      "2021-05-24 09:05:01,733 - [NOTICE] simulation.solve(742): Cycle 17/20, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:05:01,847 - [NOTICE] simulation.solve(742): Cycle 17/20, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:05:01,939 - [NOTICE] simulation.solve(742): Cycle 17/20, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:05:02,061 - [NOTICE] simulation.solve(742): Cycle 17/20, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:05:02,218 - [NOTICE] simulation.solve(820): Capacity is now 4.548 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:05:02,219 - [NOTICE] simulation.solve(710): Cycle 18/20 (3.609 s elapsed) --------------------\n",
+      "2021-05-24 09:05:02,220 - [NOTICE] simulation.solve(742): Cycle 18/20, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:05:02,352 - [NOTICE] simulation.solve(742): Cycle 18/20, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:05:02,448 - [NOTICE] simulation.solve(742): Cycle 18/20, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:05:02,576 - [NOTICE] simulation.solve(742): Cycle 18/20, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:05:02,718 - [NOTICE] simulation.solve(820): Capacity is now 4.526 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:05:02,719 - [NOTICE] simulation.solve(710): Cycle 19/20 (4.109 s elapsed) --------------------\n",
+      "2021-05-24 09:05:02,720 - [NOTICE] simulation.solve(742): Cycle 19/20, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:05:02,851 - [NOTICE] simulation.solve(742): Cycle 19/20, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:05:02,951 - [NOTICE] simulation.solve(742): Cycle 19/20, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:05:03,060 - [NOTICE] simulation.solve(742): Cycle 19/20, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:05:03,202 - [NOTICE] simulation.solve(820): Capacity is now 4.505 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:05:03,203 - [NOTICE] simulation.solve(710): Cycle 20/20 (4.592 s elapsed) --------------------\n",
+      "2021-05-24 09:05:03,204 - [NOTICE] simulation.solve(742): Cycle 20/20, step 1/4: Discharge at 1C until 3.0V\n",
+      "2021-05-24 09:05:03,318 - [NOTICE] simulation.solve(742): Cycle 20/20, step 2/4: Rest for 1 hour\n",
+      "2021-05-24 09:05:03,406 - [NOTICE] simulation.solve(742): Cycle 20/20, step 3/4: Charge at 1C until 4.2V\n",
+      "2021-05-24 09:05:03,520 - [NOTICE] simulation.solve(742): Cycle 20/20, step 4/4: Hold at 4.2V until C/50\n",
+      "2021-05-24 09:05:04,011 - [NOTICE] simulation.solve(820): Capacity is now 4.484 Ah (originally 4.941 Ah, will stop at 3.952 Ah)\n",
+      "2021-05-24 09:05:04,013 - [NOTICE] simulation.solve(837): Finish experiment simulation, took 5.402 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "sol2 = sim.solve(starting_solution=sol)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "leading-passport",
+   "metadata": {},
+   "source": [
+    "We have now simulated 20 cycles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "higher-covering",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(sol2.cycles)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-framework",
+   "metadata": {},
+   "source": [
+    "## References"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "drawn-fifty",
+   "metadata": {},
+   "source": [
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "driven-sensitivity",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[4] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n",
+      "[5] Peyman Mohtat, Suhak Lee, Valentin Sulzer, Jason B. Siegel, and Anna G. Stefanopoulou. Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates. Journal of The Electrochemical Society, 167(11):110561, 2020. doi:10.1149/1945-7111/aba5d1.\n",
+      "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
+      "[7] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "selective-comfort",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/notebooks/speed-up-solver.ipynb b/examples/notebooks/speed-up-solver.ipynb
index 86b6f30954..059d4eb5c8 100644
--- a/examples/notebooks/speed-up-solver.ipynb
+++ b/examples/notebooks/speed-up-solver.ipynb
@@ -23,7 +23,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: You are using pip version 20.3.4; however, version 21.0.1 is available.\n",
+      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1 is available.\n",
       "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
@@ -115,13 +115,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Safe: 409.386 ms\n",
-      "Fast: 135.592 ms\n"
+      "Safe: 392.261 ms\n",
+      "Fast: 131.810 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -163,534 +163,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Safe: 381.616 ms\n"
+      "Safe: 476.941 ms\n",
+      "Solving fast mode, error occured: .../casadi/interfaces/sundials/idas_interface.cpp:591: IDASolve returned \"IDA_CONV_FAIL\". Consult IDAS documentation.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Solving fast mode, error occured: .../casadi/interfaces/sundials/idas_interface.cpp:591: IDASolve returned \"IDA_TOO_MUCH_WORK\". Consult IDAS documentation.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      ".../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "At t = 0.179234, , mxstep steps taken before reaching tout.\n"
+      "At t = 0.179194 and h = 3.39292e-28, the corrector convergence failed repeatedly or with |h| = hmin.\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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Xhrn4t96C+fPD1xs27NznkENCsPfuHUb0xx4bllEq5EUSQoEu8eEeLnCaPz8sm6x43Lhx537Nm0OvXnDMMSHgjzkGOneGarflEpG9p0CXxHEPUzMLF8I774SAX7gwjO6ratgQevYMQV8R9kcdFW4YIiIxU6BL8n3+eQj2imPRop23DoawH02XLiHcjzmmMugPPjgVFYukBQW61Axffw3vvhvCveJYunTX2/QddljlfHzv3uFD2BYtUlCwSM2jQJeaq7gY3nuvciT/zjsh6L/9dud+HTqEYO/XL1zteswxoBswSC2kQJf0UloaNiN7553K1TULFuy8nUHDhiHcCwrClsL9+umOT1IrKNAl/ZWUwLJlYY38nDnhqLpOvnHjEO4nnhhu69exY8pKFUkkBbpkptWrYcaMsGf89OmwZk1lW7ducNppcPrpYb8abWMgGUKBLpnPPczFv/QSvPhiGMFX3K+1efMwaj/99HDXp6ZNU1qqyP5QoEvts3EjTJsWbsj90kuwaVP4fp06YZfJk0+Gk04KWwnrdn6SRhToUrtt3x52mZw6NdyM+733KtuaNQtz78cfHx61y6TUcAp0kao+/TTciPtf/wo3Bam6P02jRmHjsf79w+PRR8Phh2sULzWGAl1kd8rKwsVNM2aEbYTffBM++2znPo0ahW0LuncPH7YeeWR4bNtWQS9Jp0AX2Ruffx52mFy4sPLK1k8/3bVfgwZh9N6pU7hva4cOlUf79mGtvEic7Vegm1k28ApQDBwIXO/ur1Xr82tgGFAXGO/ur8dSmAJd0samTfD++zsfH38M//nPzjcGqeqQQ8JWwhVHmzaVR04OHHpo+E9BZC/sKdBj+fRnC5Dv7iVm1hGYAvSt8uInAwe5+9C4VCtSEzVtGtazDxiw8/dLSsIUzb//DStWhIBfsSJc9PTZZ+FK1z0NWlq0gNatw428Dz00PP7gB9Cq1c5H8+aa3pGooga6u5cBZeVPs4HF1bqcA2w0s9eANcDl7v717l7PzEYDowHatWu3LzWL1Bx164arUnd3Zer334fb/a1atfOxZk3lsWxZuOfrnmRlQcuWYdRf9aj4XvX/ADTdUyvFNIduZjmEkXln4GJ3n1qlbRqwxN3/n5ldDrRx99/E8uaachEhXAC1YQN88UWYv//ii7CffNVj/XpYty4cFRdM7UmTJpWj/YrRf9Wjdevw2Ly57i6VZvZ3ygV3Xw3kmVl7oBCYWqX5K8IcO+WPd+1zpSK1UZ06lSPunj333LesLFw0VRHwa9fu+rh2bfhPYdWqMM+/Jw0ahHBv3TrM6+fkVM7xV8z3t26tjc/SRNRAN7MG7r6t/Olm4JtqXQqBPsD08sfl8SxQRKrIygo3ADn4YOjadc993eGbb0K4V4z+K441a8Lj6tXh6xUrdv86ZmE036ZN5Qe8bduGx8MOC8fBB2ukXwPEMkLvYWZ3AKXl/a8ys17AMHefADwC3G9mM4DtwIUJqlVE9oYZZGeHo3PnPffdsiWEe8WxalWY+1+9OjyuXAnz5oUjkkaNQrBXXbrZsWNY1tmxY2iXhNM6dBGJzdatIeg/+ywcn35a+fjJJyH0q999qsIPfhDW6nfuXHl07Rq+p+mcvbLfc+giIjRsGC6i6tQpcntpaeX0TcUSzn//OxzLl1fuY19V3brh9Y48Mtw0vGfPsN1C+/ZaprkPNEIXkeTYtCl8SPvRR/Dhh/DBB2GjtI8+2nVk36RJuKdsbm649WBubpiz1zy9Lv0XkRps+/Ywil+yJGy1ULHdwqpVO/dr2xby88OumIMHh+maWhjwCnQRST9ffBHuJ/v222HjtDfegO++q2w//HA45ZRwDB4MBxyQslKTSYEuIulv+/awYdrMmeGWg4WFUFwc2po0gR/9CM4/H044IaP3tFegi0jm2bIlbHs8dSo88wx8+WX4/iGHwEUXweWXh3n3DLOnQNfHyCKSnho3DveJvffesLrm5Zfhggvg229hwoSw/v2cc8J0TQoHrsmkQBeR9FevXrhH7KOPhitg77orLH186ik47jgYOhTeeSfVVSacAl1EMkuTJnDFFWFp5AsvhC2PX38deveGn/408s1KMoQCXUQyU506YUpm7lx4+ulwAdNjj0GXLnDrrbHtWplmFOgiktnMwgqYZcvg7rvDFa+//nVYDfPJJ6muLq4U6CJSO9SrB2PHhpuJDBkCs2aFrQb+8Y9UVxY3CnQRqV3atoVXX4U77wzr2C+6CK67Luw1n+YU6CJS+2RlwS9/Ge732rZtWOY4cuTud4tMEwp0Eam9evQI69S7dw9LHs88M6xjT1MKdBGp3dq0gdmzIS8vXJw0dOjOe8akEQW6iEizZmFe/dRT4c03YdSotLy6VIEuIgJhOeMTT4SVL088EdaqpxkFuohIhcaN4fnnw02vr78eXnop1RXtFQW6iEhV7dvD//1fWAnzk5+EOyulCQW6iEh1gwfDX/8KmzfDj38MJSWprigmUQPdzLLNrMjMCs1snpkNqdY+0sxWlLcXmllO4soVEUmSyy4LWwYsXQoPPpjqamISywh9C5Dv7oOB84BbIvR50N0Hlx+r41mgiEhKmIULjurXhxtvhK+/TnVFUUUNdHcvc/eK3zeygcURul1oZnPM7GYz0zSOiGSGjh3DFaXr18Of/pTqaqKKKXzNLMfM5gCvAs9Wa34e6AYUAIcBI6K81mgzm29m89evX78PJYuIJNG4cdCiBdxxB6xYkepq9iimQHf31e6eB+QCd1dr2+jupe5eCjwBRLzXXZX+97l7H3fv07Jly32tW0QkOQ46CMaPDxt5XX99qqvZo1g+FG1Q5elm4Jtq7U2rPD0B+DAulYmI1BSXXgpHHglTpoS9X2qoWEboPcxslpnNIEyvXGVmvczs2vL2a83sLTObCzQH7k9UsSIiKVG3Ltx2W/j65ptTW8semKdwv4I+ffr4/PnzU/b+IiIxcw+7M37wAaxcCa1bp6QMM1vg7hGntrUiRUQkFmbhZhhlZeHepDWQAl1EJFYXXBC2BJg0qUbuxqhAFxGJVevWMHw4vPdeuNtRDaNAFxHZGxddFB4nTUptHREo0EVE9saZZ4a16Y8/Dtu2pbqanSjQRUT2RsOGcO658NVX8OKLqa5mJwp0EZG9VTHt8sgjKS2jOgW6iMjeGjAAOnUKN5Vety7V1eygQBcR2VsVa9JLSsJ2ADWEAl1EZF+cdVZ4fP311NZRhQJdRGRfdOsWttWdNStcPVoDKNBFRPaFGQwaFFa7vPdeqqsBFOgiIvsuPz88zpqV2jrKKdBFRPaVAl1EJEMcfTRkZ8PMmTVisy4FuojIvqpTB/Ly4IsvYPnyVFejQBcR2S81aNpFgS4isj8U6CIiGaJ377BhlwJdRCTN1a8f9nb55BP47LOUlqJAFxHZXwUF4THFo3QFuojI/qoh8+hRA93Mss2syMwKzWyemQ3ZTb/fm1nq1+2IiCRbv35Qr17KA71uDH22APnuXmJmHYEpQN+qHcysFdA5AfWJiNR8DRtCbi7MnQtr10KrVikpI+oI3d3L3L2k/Gk2sDhCtxuBP8XyhmY22szmm9n89evXx16piEhNNmhQeCwqSlkJMc2hm1mOmc0BXgWerdbWCWjs7pGCfhfufp+793H3Pi1bttzrgkVEaqSjjw6PKdx5MaZAd/fV7p4H5AJ3V2u+Cbg5znWJiKSXbt3C4/vvp6yEWD4UbVDl6Wbgm2pdOgL3mNkrwKFmdlcc6xMRSQ+dO4c90lMY6LF8KNrDzO4ASsv7X2VmvYBh7j7B3QdUdDSz5e5+ZWJKFRGpwRo2hA4d4IMPwh2MspK/KjxqoLv7AiA/QtOiCH2PiENNIiLpqVs3+M9/YOVKOOywpL+9LiwSEYmXFM+jK9BFROKla9fwqEAXEUlzGqGLiGQIBbqISIZo1ixc9q9AFxHJAN26wYYNkIKtTRToIiLxlMJpFwW6iEg8KdBFRDKEAl1EJEMo0EVEMkTr1tCkiQJdRCTtmYVR+sqVsGVLUt9agS4iEm8V0y4ffJDUt1Wgi4jEW4rm0RXoIiLxpkAXEckQCnQRkQzRoQPUr69AFxFJe3XrhnuMLl8OxcVJe1sFuohIInTtCqWlsGJF0t5SgS4ikght24bH1auT9pYKdBGRRGjTJjyuWpW0t4wa6GaWbWZFZlZoZvPMbEi19vPMbI6ZzTKzqWaWnbhyRUTSRE5OeKxhI/QtQL67DwbOA26p1v6Mu+e5ez7wDvDT+JYoIpKGKgI9iSP0utE6uHsZUFb+NBtYXK296ke4jYBlcatORCRdVUy51LAROmaWY2ZzgFeBZyO0jzKzJcAgogS6mY02s/lmNn99Cm7RJCKSFK1bh8ckBrq5e+ydzdoDhe7efjft1wEt3P26WF6vT58+Pn/+/JjfX0QkrbRqBVlZ8PnncXtJM1vg7n0itcXyoWiDKk83A99Uaz+gytNNwHf7UKOISObJyYG1a2H79qS8XSxTLj3KV7DMAJ4HrjKzXmZ2bXn7teUrYAqBk4A7E1OqiEiayckB97iO0Pcklg9FFwD5EZoWlbffDNwc37JERDJA1Q9G27VL+NvpwiIRkURJ8lp0BbqISKIk+WpRBbqISKJohC4ikiE0QhcRyRAaoYuIZIjsbGjcWCN0EZGM0KYNrFkT1qMnmAJdRCSRcnJg2zbYsCHhb6VAFxFJpCR+MKpAFxFJpCR+MKpAFxFJJI3QRUQyhEboIiIZIom3olOgi4gkUhJvRadAFxFJpJYtoV49BbqISNrLygr3F9WUi4hIBsjJga+/hi1bEvo2CnQRkURL0koXBbqISKIl6YNRBbqISKIlaemiAl1EJNE0QhcRyRA1ZQ7dzLLNrMjMCs1snpkNqdZ+nZm9ZWZzzWyimVniyhURSUNJ2s8llhH6FiDf3QcD5wG3VGt/1t37uftxQCvghPiWKCKS5lq3Do8JHqHXjdbB3cuAsvKn2cDiau0fV3m6DSiJW3UiIpmgfv1wxWgNGKFjZjlmNgd4FXh2N30KgEOBWVFea7SZzTez+evXr9/bekVE0lPr1rB2LZSWJuwtYgp0d1/t7nlALnB39XYz60mYijnPfc83znP3+9y9j7v3admy5b7ULCKSflq2DPcV3bQpYW8Ry4eiDao83Qx8U639COAhQph/Gd/yREQyRIsW4TGBMxOxjNB7mNksM5sBPA9cZWa9zOza8vY7gabApPKVMKcmplQRkTRWEehfJm7cG8uHoguA/AhNi8rbT4tzTSIimScJga4Li0REkqHiM0MFuohImqshc+giIrK/NOUiIpIhFOgiIhlCc+giIhni4IPDo+bQRUTSXP36kJ2tEbqISEZo0UKBLiKSEVq2hG++gW3bEvLyCnQRkWRJ8EoXBbqISLIo0EVEMoQCXUQkQ1SsRU/Q0kUFuohIsmiELiKSIRToIiIZQlMuIiIZQiN0EZEMoUAXEckQTZtCnToKdBGRtJeVFXZd1By6iEgGqNigyz3uL61AFxFJphYtYPv2sElXnNWN1sHMsoFXgGLgQOB6d3+tSvtA4F6gE3CEu6+Ke5URtG7dOuL3zzjjDP7+978DcM899/Df//3fEfu98sor9OzZE4Du3buzcePGXfrk5eXx5JNPAvDEE09wzTXXRHytyZMnc/zxxwOQn5/P8uXLd+nTrVs3Xnst/LW9+uqrjBw5MuJrTZw4kf/6r/8C4Ic//CHz5s3bpU+rVq1YuHAhAAsWLOD000+P+Fo33XQTo0ePBmDUqFG8/PLLu/SpW7cun332GQArVqzguOOOi/haV155Jb/5zW8AuPbaa5k8eXLEfh988AHZ2dls3ryZrl27RuwzYsQIJkyYAMAtt9zCXXfdFbHf3Llz6dChAwDt2rWjpKRklz4nn3wyDz74IAD33XcfN910U8TX+uc//0nv3r0BOOaYY1i7du0ufXJzc3nuuecAePrpp7niiisivtYjjzzC8OHDARgyZAjvv//+Ln2OOOIIZs2aBcCMGTMYMWJExNe6/fbbOe+88wA455xzmDNnzi59mjVrxrJlywBYvHgxJ510UsTXGjduHGPHjgXg5z//OS+88ELEfmvWrAFg1apV5ObmRuxz2WWXccMNN+x43Ycffjhiv6VLl9K8eXO2bt3K4YcfHrHPueeeyx133AHAbbfdxm233Rax38yZM+nUqRMAHTt25Pvvv9+lz7Bhw5g0aRIADz300I4aq3v22Wfp168fAH379mX16tW79Dn22GOZOnUqAM8//zy/+MUvIr7WAw88wCmnnALAiSeeyJIlS3bp0759e4qKigCYPXs25557bsTXuvXWW7mg6s2is7Mj9ttXUQMd2ALku3uJmXUEpgB9q7QvAwYAU+NamYhIJurXD4qLwSzuL22+F/M4ZtYLuMLdR0VoKwQuiDZCN7PRwGiAdu3a9f7000/3pl4RkVrNzBa4e59IbTHNoZtZjpnNAV4Fnt2fYtz9Pnfv4+59WlZcNSUiIvstpkB399XungfkAncntiQREdkXUQPdzBpUeboZiP9HsyIist9iGaH3MLNZZjYDeB64ysx6mdm1AGbW2cymA0cDj5tZ5I+KRUQkoaKucnH3BUB+hKZF5e0fAUPjW5aIiOwtXVgkIpIhFOgiIhlCgS4ikiH26sKiuL+52XpgX68sagEkZg/Kmqm2nS/onGuD2na+sP/nfJi7R7yIJ6WBvj/MbP7urpbKRLXtfEHnXBvUtvOFxJ6zplxERDKEAl1EJEOkc6Dfl+oCkqy2nS/onGuD2na+kMBzTts5dBER2Vk6j9BFRKQKBbqISIZQoIuIZAgFuohIhqjxgW5mI82syMzmmtmx1doOMLPJZja7/PGAVNUZT1HO+Toze6u8baJZAm5MmAJ7OucqfX5vZrvegTsNRTtfM/u1mU03s0IzOyEVNcZblH/XHcu36S40sxlm1iZVdcaLmU0zs/VmtsudrBOWXe5eYw+gGfAOUB/oAMyp1v5z4Mbyr38L/DzVNSfhnDtV+fpJYEiqa070OZf3aQU8DixPdb1J+BmfDPwx1XUm+Zz/AlxU/vVI4M+prjkO59ym/FxuiNCWkOyq6SP0XGC2uxe7+wqgSbU7KBUAU8u//mf583S3x3N294+r9N0GlCS7wASI9nMGuBH4U/JLS4ho53sOcICZvWZmj5rZQakpM66infMyoGn5182AdUmuL+7cfdUemhOSXTU90A8GNlZ5vglovpv26m3pKto5A2BmBcChwKzklJVQezxnM+sENHb3xUmuK1Gi/YxbA2XuPgR4C7g+eaUlTLRzng6MMbPFhNHrA8krLSUSkl01PdC/ovJ/bYCDyr8Xqb16W7qKds6YWU/gFuA8L/+dLc1FO+ebgJuTWE+ixfLv+pXyr18BeianrISKds5/JkxN9CT8vP+YtMpSIyHZVdMD/S0gz8zqmVk7YIu7b6vSPhM4pfzrU8qfp7s9nrOZHQE8RAjzTNl2NNrPuSNwj5m9AhxqZnelpMr4iXa+hUDFbnx9gEz4IDjaORuVW8quIzN+296ThGRXjb/038wuBi4BHPglYc54mLtPMLOGhHBrA6wCfubu36es2DiJcs5Tga6E8wWY4O4vpqbS+NnTOVfrt9zdj0hBiXEV5WfcALgfaAtsBy509y9SVmycRDnn7sC95d+rB4xx96UpKzYOzOx+YCDQAFhK+M0jodlV4wNdRERiU9OnXEREJEYKdBGRDKFAFxHJEAp0EZEMoUAXEckQCnQRkQyhQBcRyRD/H32p7bq9Ef9/AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -744,7 +230,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can solve with fast mode up to this time to understand why the model is failing"
+    "We can solve with fast mode up to close to this time to understand why the model is failing"
    ]
   },
   {
@@ -755,12 +241,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "714714a935754bfc86ebe456a08cec95",
+       "model_id": "f1f9ab302c97445e87a127e80c53b52b",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=4050.0, step=40.5), Output()), _dom_classes=…"
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=4049.0, step=40.49), Output()), _dom_classes…"
       ]
      },
      "metadata": {},
@@ -768,7 +254,7 @@
     }
    ],
    "source": [
-    "fast_sol = sim.solve([0,4050], solver=fast_solver, inputs={\"Crate\": 1})\n",
+    "fast_sol = sim.solve([0,4049], solver=fast_solver, inputs={\"Crate\": 1})\n",
     "fast_sol.plot([\n",
     "    \"Minimum negative particle surface concentration\",\n",
     "    \"Electrolyte concentration [mol.m-3]\",\n",
@@ -800,14 +286,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 0.00674273 and h = 5.67561e-38, the corrector convergence failed repeatedly or with |h| = hmin.\n",
-      "At t = 0.00174969, , mxstep steps taken before reaching tout.\n",
-      "At t = 0.00174969, , mxstep steps taken before reaching tout.\n"
+      "At t = 0.00668001, , mxstep steps taken before reaching tout.\n",
+      "At t = 0.00174981, , mxstep steps taken before reaching tout.\n",
+      "At t = 0.00174981, , mxstep steps taken before reaching tout.\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -839,7 +325,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -891,12 +377,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With dt_max=10, took 958.496 ms (integration time: 866.262 ms)\n",
-      "With dt_max=20, took 949.896 ms (integration time: 860.214 ms)\n",
-      "With dt_max=100, took 566.709 ms (integration time: 495.021 ms)\n",
-      "With dt_max=1000, took 185.358 ms (integration time: 125.604 ms)\n",
-      "With dt_max=3700, took 144.072 ms (integration time: 93.765 ms)\n",
-      "With 'fast' mode, took 101.695 ms (integration time: 80.549 ms)\n"
+      "With dt_max=10, took 1.177 s (integration time: 1.064 s)\n",
+      "With dt_max=20, took 1.173 s (integration time: 1.063 s)\n",
+      "With dt_max=100, took 733.209 ms (integration time: 639.751 ms)\n",
+      "With dt_max=1000, took 160.866 ms (integration time: 109.546 ms)\n",
+      "With dt_max=3700, took 137.295 ms (integration time: 84.103 ms)\n",
+      "With 'fast' mode, took 98.616 ms (integration time: 78.933 ms)\n"
      ]
     }
    ],
@@ -941,1571 +427,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With dt_max=10, took 850.456 ms (integration time: 705.442 ms)\n",
-      "With dt_max=20, took 944.833 ms (integration time: 780.262 ms)\n",
-      "With dt_max=100, took 588.487 ms (integration time: 467.672 ms)\n",
-      "With dt_max=1000, took 208.493 ms (integration time: 117.439 ms)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "At t = 0.0203346, , mxstep steps taken before reaching tout.\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n"
+      "With dt_max=10, took 1.079 s (integration time: 908.498 ms)\n",
+      "With dt_max=20, took 1.012 s (integration time: 840.830 ms)\n",
+      "With dt_max=100, took 628.936 ms (integration time: 508.004 ms)\n",
+      "With dt_max=1000, took 235.521 ms (integration time: 133.730 ms)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n"
+      "At t = 0.0202946 and h = 4.01795e-28, the corrector convergence failed repeatedly or with |h| = hmin.\n",
+      "At t = 0.0202946 and h = 4.01795e-28, the corrector convergence failed repeatedly or with |h| = hmin.\n",
+      "At t = 0.0202946 and h = 3.77863e-28, the corrector convergence failed repeatedly or with |h| = hmin."
      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "At t = 0.0203346, , mxstep steps taken before reaching tout.\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n"
-     ]
-    },
-    {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n"
+      "With dt_max=3600, took 967.320 ms (integration time: 81.977 ms)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "At t = 0.0203346, , mxstep steps taken before reaching tout.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "With dt_max=3600, took 3.836 s (integration time: 73.770 ms)\n"
+      "\n"
      ]
     }
    ],
@@ -2558,7 +506,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  2.851 s\n"
+      "Took  3.194 s\n"
      ]
     }
    ],
@@ -2592,7 +540,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2623,12 +571,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  1.269 s\n"
+      "Took  1.314 s\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2672,370 +620,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-     ]
-    },
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-     "text": [
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
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-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n",
-      "At t = 0.055843, , mxstep steps taken before reaching tout.\n"
+      "At t = 0.0556187 and h = 1.41541e-28, the corrector convergence failed repeatedly or with |h| = hmin.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  1.758 s\n"
+      "Took  1.145 s\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3079,12 +676,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  651.855 ms\n"
+      "Took  628.190 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3263,7 +860,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x14e3cb9a0>"
+       "<matplotlib.legend.Legend at 0x150729fa0>"
       ]
      },
      "execution_count": 19,
@@ -3310,16 +907,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Exact: 373.526 us\n",
-      "Smooth, k=5: 356.346 us\n",
-      "Smooth, k=10: 304.471 us\n",
-      "Smooth, k=100: 335.816 us\n"
+      "Exact: 366.813 us\n",
+      "Smooth, k=5: 348.585 us\n",
+      "Smooth, k=10: 279.639 us\n",
+      "Smooth, k=100: 325.853 us\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1e56c6b459ea48e6a8f7f9304b3be2b3",
+       "model_id": "1b459eb21aec4a1d87d63e17e4e1db20",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3427,6 +1024,7 @@
       "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
       "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
       "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
+      "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
       "\n"
      ]
     }
@@ -3434,13 +1032,6 @@
    "source": [
     "pybamm.print_citations()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/notebooks/submodel_cracking_DFN_or_SPM,.ipynb b/examples/notebooks/submodel_cracking_DFN_or_SPM.ipynb
similarity index 76%
rename from examples/notebooks/submodel_cracking_DFN_or_SPM,.ipynb
rename to examples/notebooks/submodel_cracking_DFN_or_SPM.ipynb
index d5fafb55bb..b50d7c9ab7 100644
--- a/examples/notebooks/submodel_cracking_DFN_or_SPM,.ipynb
+++ b/examples/notebooks/submodel_cracking_DFN_or_SPM.ipynb
@@ -14,10 +14,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1.1 is available.\n",
+      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1.2 is available.\n",
       "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
@@ -47,10 +47,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# model = pybamm.lithium_ion.SPMe(\n",
-    "# model = pybamm.lithium_ion.SPM(\n",
     "model = pybamm.lithium_ion.DFN(\n",
-    "    options = {\"particle mechanics\": \"swelling and cracking\"}\n",
+    "    options = {\n",
+    "        \"particle\": \"Fickian diffusion\",  \n",
+    "        \"particle mechanics\": \"swelling and cracking\", # other options are \"none\", \"swelling only\"\n",
+    "    }\n",
     ")"
    ]
   },
@@ -87,18 +88,16 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
+      "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…",
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c3b1f934d8e34942a66d8771c0d09d17",
        "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
-      ]
+       "version_minor": 0,
+       "model_id": "653353cedea84635a916a217d9826e3c"
+      }
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -126,18 +125,16 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
+      "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=3600.0, step=10.0), Output()), _dom_classes=…",
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "232ab4b5fb4b49d6832cc709518ad61a",
        "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=3600.0, step=10.0), Output()), _dom_classes=…"
-      ]
+       "version_minor": 0,
+       "model_id": "a5ed452d11ce494d83f30efec6b6abb4"
+      }
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -193,18 +190,16 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
+      "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…",
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f42ca205ce824664917d70d8ff35bebf",
        "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
-      ]
+       "version_minor": 0,
+       "model_id": "dbe2842d65ad4cbd9d0efcaf97a50b57"
+      }
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -234,16 +229,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n",
-      "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
-      "[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n",
-      "[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
-      "[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-      "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
-      "\n"
+      "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n\n"
      ]
     }
    ],
@@ -286,4 +275,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/submodel_loss_of_active_materials.ipynb b/examples/notebooks/submodel_loss_of_active_materials.ipynb
index 4eb3b0367c..3014636425 100644
--- a/examples/notebooks/submodel_loss_of_active_materials.ipynb
+++ b/examples/notebooks/submodel_loss_of_active_materials.ipynb
@@ -17,7 +17,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1.1 is available.\n",
+      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1 is available.\n",
       "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
@@ -59,9 +59,9 @@
     "    model, \n",
     "    experiment = experiment,\n",
     "    parameter_values = param,\n",
-    "    solver = pybamm.CasadiSolver(dt_max=100),\n",
+    "    solver = pybamm.CasadiSolver(\"fast with events\")\n",
     ")\n",
-    "solution = sim1.solve()"
+    "solution = sim1.solve(calc_esoh=False)"
    ]
   },
   {
@@ -83,7 +83,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1296x288 with 3 Axes>"
       ]
@@ -127,7 +127,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x288 with 2 Axes>"
       ]
@@ -160,7 +160,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x288 with 2 Axes>"
       ]
@@ -201,18 +201,18 @@
     "    model, \n",
     "    experiment=experiment,\n",
     "    parameter_values=param,\n",
-    "    solver=pybamm.CasadiSolver(dt_max=100),\n",
+    "    solver=pybamm.CasadiSolver(\"fast with events\"),\n",
     ")\n",
-    "solution2 = sim2.solve()\n",
+    "solution2 = sim2.solve(calc_esoh=False)\n",
     "param.update({\"Positive electrode LAM constant propotional term\": k3})\n",
     "param.update({\"Negative electrode LAM constant propotional term\": k3})\n",
     "sim3 = pybamm.Simulation(\n",
     "    model, \n",
     "    experiment=experiment,\n",
     "    parameter_values=param,\n",
-    "    solver=pybamm.CasadiSolver(dt_max=100),\n",
+    "    solver=pybamm.CasadiSolver(\"fast with events\"),\n",
     ")\n",
-    "solution3 = sim3.solve()"
+    "solution3 = sim3.solve(calc_esoh=False)"
    ]
   },
   {
@@ -222,7 +222,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x288 with 2 Axes>"
       ]
@@ -266,7 +266,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x288 with 2 Axes>"
       ]
@@ -311,7 +311,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1296x360 with 3 Axes>"
       ]
@@ -396,6 +396,13 @@
    "source": [
     "pybamm.print_citations()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/scripts/calendar_ageing.py b/examples/scripts/calendar_ageing.py
index 8dad96b3cf..5d385053bc 100644
--- a/examples/scripts/calendar_ageing.py
+++ b/examples/scripts/calendar_ageing.py
@@ -47,15 +47,8 @@
         "X-averaged total negative electrode SEI thickness",
         "X-averaged negative electrode SEI concentration [mol.m-3]",
         "Loss of lithium to negative electrode SEI [mol]",
-        [
-            "Negative electrode SEI interfacial current density [A.m-2]",
-            "Negative electrode interfacial current density [A.m-2]",
-        ],
-        [
-            "X-averaged negative electrode SEI interfacial current density [A.m-2]",
-            "X-averaged negative electrode interfacial current density [A.m-2]",
-        ],
         "Sum of x-averaged negative electrode interfacial current densities",
-        "X-averaged electrolyte concentration",
+        "Loss of lithium inventory [%]",
+        ["Total lithium lost [mol]", "Loss of lithium to negative electrode SEI [mol]"],
     ],
 )
diff --git a/examples/scripts/conservation_lithium.py b/examples/scripts/conservation_lithium.py
index 8840edb88b..fbf931a014 100644
--- a/examples/scripts/conservation_lithium.py
+++ b/examples/scripts/conservation_lithium.py
@@ -25,10 +25,9 @@
 solution = sim.solution
 
 t = solution["Time [s]"].entries
-Ne = solution["Total concentration in electrolyte [mol]"].entries
 Np = solution["Total lithium in positive electrode [mol]"].entries
 Nn = solution["Total lithium in negative electrode [mol]"].entries
-Ntot = Np + Nn + Ne
+Ntot = solution["Total lithium [mol]"].entries
 
 fig, ax = plt.subplots(1, 2, figsize=(12, 5))
 
diff --git a/examples/scripts/cycling_ageing_yang.py b/examples/scripts/cycling_ageing_yang.py
index 326ed2170c..14bae0cc1a 100644
--- a/examples/scripts/cycling_ageing_yang.py
+++ b/examples/scripts/cycling_ageing_yang.py
@@ -1,5 +1,8 @@
 import pybamm as pb
 
+# Note: the Yang model is still in active development and results do not
+# match with those reported in the paper
+
 pb.set_logging_level("NOTICE")
 model = pb.lithium_ion.Yang2017()
 
@@ -27,18 +30,18 @@
             "Rest for 30 minutes",
             "Discharge at 1 C until 2.8 V",
         ),
-        # (
-        #     "Charge at 1 C until 4.2 V",
-        #     "Hold at 4.2 V until C/20",
-        #     "Rest for 30 minutes",
-        #     "Discharge at 2 C until 2.8 V",
-        # ),
-        # (
-        #     "Charge at 1 C until 4.2 V",
-        #     "Hold at 4.2 V until C/20",
-        #     "Rest for 30 minutes",
-        #     "Discharge at 3 C until 2.8 V",
-        # ),
+        (
+            "Charge at 1 C until 4.2 V",
+            "Hold at 4.2 V until C/20",
+            "Rest for 30 minutes",
+            "Discharge at 2 C until 2.8 V",
+        ),
+        (
+            "Charge at 1 C until 4.2 V",
+            "Hold at 4.2 V until C/20",
+            "Rest for 30 minutes",
+            "Discharge at 3 C until 2.8 V",
+        ),
     ]
 )
 sim = pb.Simulation(model, experiment=experiment)
@@ -56,6 +59,9 @@
         "X-averaged negative electrode porosity",
         "Negative electrode SEI interfacial current density [A.m-2]",
         "X-averaged total negative electrode SEI thickness [m]",
-        "Loss of lithium to negative electrode SEI [mol]",
+        [
+            "Total lithium lost [mol]",
+            "Loss of lithium to negative electrode SEI [mol]",
+        ],
     ]
 )
diff --git a/examples/scripts/experimental_protocols/cccv.py b/examples/scripts/experimental_protocols/cccv.py
index 9ce33479a9..6851438cf4 100644
--- a/examples/scripts/experimental_protocols/cccv.py
+++ b/examples/scripts/experimental_protocols/cccv.py
@@ -20,7 +20,7 @@
 model = pybamm.lithium_ion.DFN()
 
 sim = pybamm.Simulation(
-    model, experiment=experiment, solver=pybamm.CasadiSolver("safe")
+    model, experiment=experiment, solver=pybamm.CasadiSolver("fast with events")
 )
 sim.solve()
 
diff --git a/pybamm/CITATIONS.txt b/pybamm/CITATIONS.txt
index 61ccc186d4..b8bc61f349 100644
--- a/pybamm/CITATIONS.txt
+++ b/pybamm/CITATIONS.txt
@@ -113,6 +113,16 @@
   doi = {10.1149/1945-7111/aba5d1},
 }
 
+@article{Mohtat2019,
+  title={Towards better estimability of electrode-specific state of health: Decoding the cell expansion},
+  author={Mohtat, Peyman and Lee, Suhak and Siegel, Jason B and Stefanopoulou, Anna G},
+  journal={Journal of Power Sources},
+  volume={427},
+  pages={101--111},
+  year={2019},
+  publisher={Elsevier}
+}
+
 @article{Malengier2018,
   year  = {2018},
   month = {feb},
diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 05e66cf5d0..cdf440fced 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -210,7 +210,7 @@ def version(formatted=False):
 #
 # Solver classes
 #
-from .solvers.solution import Solution
+from .solvers.solution import Solution, make_cycle_solution
 from .solvers.processed_variable import ProcessedVariable
 from .solvers.processed_symbolic_variable import ProcessedSymbolicVariable
 from .solvers.base_solver import BaseSolver
diff --git a/pybamm/experiments/experiment.py b/pybamm/experiments/experiment.py
index 724d913565..676690231e 100644
--- a/pybamm/experiments/experiment.py
+++ b/pybamm/experiments/experiment.py
@@ -48,6 +48,8 @@ class Experiment:
     period : string, optional
         Period (1/frequency) at which to record outputs. Default is 1 minute. Can be
         overwritten by individual operating conditions.
+    termination : list, optional
+        List of conditions under which to terminate the experiment. Default is None.
     use_simulation_setup_type : str
         Whether to use the "new" (default) or "old" simulation set-up type. "new" is
         faster at simulating individual steps but has higher set-up overhead
@@ -61,6 +63,7 @@ def __init__(
         operating_conditions,
         parameters=None,
         period="1 minute",
+        termination=None,
         use_simulation_setup_type="new",
         drive_cycles={},
     ):
@@ -105,6 +108,7 @@ def __init__(
         else:
             raise TypeError("experimental parameters should be a dictionary")
 
+        self.termination = self.read_termination(termination)
         self.use_simulation_setup_type = use_simulation_setup_type
 
     def __str__(self):
@@ -181,8 +185,9 @@ def read_string(self, cond, drive_cycles):
                 # e.g. for 3 hours
                 idx = cond_list.index("for")
                 end_time = self.convert_time_to_seconds(cond_list[idx + 1 :])
-                ext_drive_cycle = self.extend_drive_cycle(drive_cycles[cond_list[1]],
-                                                          end_time)
+                ext_drive_cycle = self.extend_drive_cycle(
+                    drive_cycles[cond_list[1]], end_time
+                )
                 # Drive cycle as numpy array
                 dc_data = ext_drive_cycle
                 # Find the type of drive cycle ("A", "V", or "W")
@@ -244,8 +249,9 @@ def extend_drive_cycle(self, drive_cycle, end_time):
         while loop_end_time <= end_time:
             # Extend the drive cycle until the drive cycle time
             # becomes greater than specified end time
-            temp_time.append(np.append(temp_time[i - 1],
-                                       temp_time[0] + temp_time[i - 1][-1] + 1))
+            temp_time.append(
+                np.append(temp_time[i - 1], temp_time[0] + temp_time[i - 1][-1] + 1)
+            )
             loop_end_time = temp_time[i][-1]
             i += 1
         time = temp_time[-1]
@@ -363,3 +369,38 @@ def convert_time_to_seconds(self, time_and_units):
                 )
             )
         return time_in_seconds
+
+    def read_termination(self, termination):
+        """
+        Read the termination reason. If this condition is hit, the experiment will stop.
+        """
+        if termination is None:
+            return {}
+        elif isinstance(termination, str):
+            termination = [termination]
+
+        termination_dict = {}
+        for term in termination:
+            term_list = term.split()
+            if term_list[-1] == "capacity":
+                end_discharge = "".join(term_list[:-1])
+                if end_discharge.endswith("%"):
+                    end_discharge_percent = end_discharge.split("%")[0]
+                    termination_dict["capacity"] = (float(end_discharge_percent), "%")
+                elif end_discharge.endswith("Ah"):
+                    end_discharge_Ah = end_discharge.split("Ah")[0]
+                    termination_dict["capacity"] = (float(end_discharge_Ah), "Ah")
+                elif end_discharge.endswith("A.h"):
+                    end_discharge_Ah = end_discharge.split("A.h")[0]
+                    termination_dict["capacity"] = (float(end_discharge_Ah), "Ah")
+                else:
+                    raise ValueError(
+                        "Capacity termination must be given in the form "
+                        "'80%', '4Ah', or '4A.h'"
+                    )
+            else:
+                raise ValueError(
+                    "Only capacity can be provided as a termination reason, "
+                    "e.g. '80% capacity' or '4 Ah capacity'"
+                )
+        return termination_dict
diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py
index 5b289198c4..e9ac8152ca 100644
--- a/pybamm/expression_tree/concatenations.py
+++ b/pybamm/expression_tree/concatenations.py
@@ -363,8 +363,8 @@ def intersect(s1, s2):
     if len(all_intersects) == 0:
         return ""
     intersect = max(all_intersects, key=len)
-    # lstrip removes leading white space
-    return intersect.lstrip()
+    # remove leading and trailing white space
+    return intersect.lstrip().rstrip()
 
 
 def simplified_concatenation(*children):
diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py
index af2e566bea..029531298e 100644
--- a/pybamm/expression_tree/interpolant.py
+++ b/pybamm/expression_tree/interpolant.py
@@ -23,9 +23,7 @@ class Interpolant(pybamm.Function):
         Name of the interpolant. Default is None, in which case the name "interpolating
         function" is given.
     interpolator : str, optional
-        Which interpolator to use ("pchip" or "cubic spline"). Note that whichever
-        interpolator is used must be differentiable (for ``Interpolator._diff``).
-        Default is "cubic spline". Note that "pchip" may give slow results.
+        Which interpolator to use ("linear", "pchip", or "cubic spline").
     extrapolate : bool, optional
         Whether to extrapolate for points that are outside of the parametrisation
         range, or return NaN (following default behaviour from scipy). Default is True.
@@ -106,10 +104,8 @@ def __init__(
         else:
             raise ValueError("interpolator '{}' not recognised".format(interpolator))
         # Set name
-        if name is not None and not name.startswith("interpolating function"):
-            name = "interpolating function ({})".format(name)
-        else:
-            name = "interpolating function"
+        if name is None:
+            name = "interpolating_function"
         self.x = x
         self.y = y
         self.entries_string = entries_string
@@ -140,7 +136,9 @@ def entries_string(self, value):
     def set_id(self):
         """ See :meth:`pybamm.Symbol.set_id()`. """
         self._id = hash(
-            (self.__class__, self.name, self.entries_string) + tuple(self.domain)
+            (self.__class__, self.name, self.entries_string)
+            + tuple([child.id for child in self.children])
+            + tuple(self.domain)
         )
 
     def _function_new_copy(self, children):
diff --git a/pybamm/input/parameters/lead_acid/experiments/1C_discharge_from_full/parameters.csv b/pybamm/input/parameters/lead_acid/experiments/1C_discharge_from_full/parameters.csv
index 046e2386c8..83f1871487 100644
--- a/pybamm/input/parameters/lead_acid/experiments/1C_discharge_from_full/parameters.csv
+++ b/pybamm/input/parameters/lead_acid/experiments/1C_discharge_from_full/parameters.csv
@@ -16,8 +16,8 @@ Edge heat transfer coefficient [W.m-2.K-1],0.3,,
 # Electrical
 Number of electrodes connected in parallel to make a cell,8,Manufacturer,
 Number of cells connected in series to make a battery,6,Manufacturer,
-Lower voltage cut-off [V],1.73,(just under) 10.5V across 6-cell battery,
-Upper voltage cut-off [V],2.44,(just over) 14.5V across 6-cell battery,
+Lower voltage cut-off [V],1.75,10.5V across 6-cell battery,
+Upper voltage cut-off [V],2.42,14.5V across 6-cell battery,
 ,,,
 # Initial conditions
 Initial State of Charge,1,-,
diff --git a/pybamm/input/parameters/lithium_ion/experiments/1C_charge_from_empty_Mohtat2020/parameters.csv b/pybamm/input/parameters/lithium_ion/experiments/1C_charge_from_empty_Mohtat2020/parameters.csv
index d1c0a2367c..7ec34bd29f 100644
--- a/pybamm/input/parameters/lithium_ion/experiments/1C_charge_from_empty_Mohtat2020/parameters.csv
+++ b/pybamm/input/parameters/lithium_ion/experiments/1C_charge_from_empty_Mohtat2020/parameters.csv
@@ -14,8 +14,8 @@ Ambient temperature [K], 298.15,,
 # Electrical
 Number of electrodes connected in parallel to make a cell,1,,
 Number of cells connected in series to make a battery,1,,
-Lower voltage cut-off [V],2.5,,
-Upper voltage cut-off [V],4.25,,just above 4.2
+Lower voltage cut-off [V],2.8,,
+Upper voltage cut-off [V],4.2,,
 ,,,
 # Initial conditions
 Initial concentration in negative electrode [mol.m-3],48.8682,Peyman MPM, x0 (0.0017) * Csmax_n
diff --git a/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Chen2020/parameters.csv b/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Chen2020/parameters.csv
index e9dd8657c3..15bc6f541e 100644
--- a/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Chen2020/parameters.csv
+++ b/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Chen2020/parameters.csv
@@ -10,7 +10,7 @@ Ambient temperature [K],298.15,,
 Number of electrodes connected in parallel to make a cell,1,,
 Number of cells connected in series to make a battery,1,,
 Lower voltage cut-off [V],2.5,,
-Upper voltage cut-off [V],4.4,,
+Upper voltage cut-off [V],4.2,,
 ,,,
 # Initial conditions
 Initial concentration in negative electrode [mol.m-3],29866,Chen 2020,
diff --git a/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Kim2011/parameters.csv b/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Kim2011/parameters.csv
index 41be8dc122..150c0eaf83 100644
--- a/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Kim2011/parameters.csv
+++ b/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Kim2011/parameters.csv
@@ -16,7 +16,7 @@ Ambient temperature [K],298.15,,
 Number of electrodes connected in parallel to make a cell,1,,
 Number of cells connected in series to make a battery,1,,
 Lower voltage cut-off [V],2.7,,
-Upper voltage cut-off [V],4.5,,
+Upper voltage cut-off [V],4.2,,
 ,,,
 # Initial conditions
 Initial concentration in negative electrode [mol.m-3],18081,0.63*2.84E4,
diff --git a/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Marquis2019/parameters.csv b/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Marquis2019/parameters.csv
index abe562c72b..7c0a2cc3cf 100644
--- a/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Marquis2019/parameters.csv
+++ b/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Marquis2019/parameters.csv
@@ -15,7 +15,7 @@ Total heat transfer coefficient [W.m-2.K-1],10,,
 Number of electrodes connected in parallel to make a cell,1,,
 Number of cells connected in series to make a battery,1,,
 Lower voltage cut-off [V],3.105,,
-Upper voltage cut-off [V],4.2,,
+Upper voltage cut-off [V],4.1,,
 ,,,
 # Initial conditions
 Initial concentration in negative electrode [mol.m-3],19986.609595075,Scott Moura FastDFN,
diff --git a/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Ramadass2004/parameters.csv b/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Ramadass2004/parameters.csv
index a8f6d93a75..2ce1f5d908 100644
--- a/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Ramadass2004/parameters.csv
+++ b/pybamm/input/parameters/lithium_ion/experiments/1C_discharge_from_full_Ramadass2004/parameters.csv
@@ -13,8 +13,8 @@ Edge heat transfer coefficient [W.m-2.K-1],0.3,,
 # Electrical
 Number of electrodes connected in parallel to make a cell,1,,
 Number of cells connected in series to make a battery,1,,
-Lower voltage cut-off [V],2,,
-Upper voltage cut-off [V],4.3,,
+Lower voltage cut-off [V],2.8,,
+Upper voltage cut-off [V],4.2,,
 ,,,
 # Initial conditions
 Initial concentration in negative electrode [mol.m-3],22610.7,Ramadass 2002,
diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Ai2020/graphite_ocp_Enertech_Ai2020_function.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Ai2020/graphite_ocp_Enertech_Ai2020_function.py
index ffbda91789..bef966074e 100644
--- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Ai2020/graphite_ocp_Enertech_Ai2020_function.py
+++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Ai2020/graphite_ocp_Enertech_Ai2020_function.py
@@ -2,14 +2,14 @@ def graphite_ocp_Enertech_Ai2020_function(sto):
     """
     Graphite  Open Circuit Potential (OCP) as a a function of the
     stochiometry. The fit is taken from the Enertech cell [1], which is only accurate
-       for 0.0065 < sto < 0.84.
+    for 0.0065 < sto < 0.84.
 
-        References
-        ----------
-        .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020).
-        Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in
-        Lithium-Ion Pouch Cells. Journal of The Electrochemical Society, 167(1),
-        013512. DOI: 10.1149/2.0122001JES
+    References
+    ----------
+    .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020).
+    Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in
+    Lithium-Ion Pouch Cells. Journal of The Electrochemical Society, 167(1),
+    013512. DOI: 10.1149/2.0122001JES
 
     Parameters
     ----------
diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_UMBL_Mohtat2020/graphite_ocp_PeymanMPM.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_UMBL_Mohtat2020/graphite_ocp_PeymanMPM.py
index 739d1a168c..d63fffe337 100644
--- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_UMBL_Mohtat2020/graphite_ocp_PeymanMPM.py
+++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_UMBL_Mohtat2020/graphite_ocp_PeymanMPM.py
@@ -13,7 +13,7 @@ def graphite_ocp_PeymanMPM(sto):
 
     u_eq = (
         0.063
-        + 0.8 * pybamm.exp(-75 * (sto + 0.007))
+        + 0.8 * pybamm.exp(-75 * (sto + 0.001))
         - 0.0120 * pybamm.tanh((sto - 0.127) / 0.016)
         - 0.0118 * pybamm.tanh((sto - 0.155) / 0.016)
         - 0.0035 * pybamm.tanh((sto - 0.220) / 0.020)
diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/NMC_UMBL_Mohtat2020/NMC_ocp_PeymanMPM.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/NMC_UMBL_Mohtat2020/NMC_ocp_PeymanMPM.py
index b2eb8f54fd..22348a2a9d 100644
--- a/pybamm/input/parameters/lithium_ion/positive_electrodes/NMC_UMBL_Mohtat2020/NMC_ocp_PeymanMPM.py
+++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/NMC_UMBL_Mohtat2020/NMC_ocp_PeymanMPM.py
@@ -20,11 +20,11 @@ def NMC_ocp_PeymanMPM(sto):
     u_eq = (
         4.3452
         - 1.6518 * sto
-        + 1.6225 * sto ** 2
-        - 2.0843 * sto ** 3
-        + 3.5146 * sto ** 4
-        - 2.2166 * sto ** 5
-        - 0.5623 * 10 ** (-4) * pybamm.exp(109.451 * sto - 100.006)
+        + 1.6225 * (sto ** 2)
+        - 2.0843 * (sto ** 3)
+        + 3.5146 * (sto ** 4)
+        - 2.2166 * (sto ** 5)
+        - 0.5623e-4 * pybamm.exp(109.451 * sto - 100.006)
     )
 
     return u_eq
diff --git a/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv b/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv
index ca4c8ceb28..3cee296e21 100644
--- a/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv
+++ b/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv
@@ -6,7 +6,7 @@ Inner SEI reaction proportion,0.5,,
 Inner SEI partial molar volume [m3.mol-1],9.585e-5, Safari paper,
 Outer SEI partial molar volume [m3.mol-1],9.585e-5, Safari paper,
 SEI reaction exchange current density [A.m-2],1.5E-7, Guess,
-SEI resistivity [Ohm.m],5E6, Safari paper,
+SEI resistivity [Ohm.m],2e5, Safari paper,
 Outer SEI solvent diffusivity [m2.s-1],2.5E-22, Single paper,
 Bulk solvent concentration [mol.m-3],2.636E3, Ploehn paper,
 Ratio of inner and outer SEI exchange current densities,1, Assume same,
@@ -25,4 +25,3 @@ SEI open-circuit potential [V], 0.4, Safari paper,
 # Reaction-driven LAM example,,,
 Negative electrode reaction-driven LAM factor [m3.mol-1],0,,
 Positive electrode reaction-driven LAM factor [m3.mol-1],0,,
-
diff --git a/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv b/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv
index 9157f6b795..0adc9cb90e 100644
--- a/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv
+++ b/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv
@@ -6,7 +6,7 @@ Inner SEI reaction proportion,0.5,,
 Inner SEI partial molar volume [m3.mol-1],9.585e-5, Safari 2009,
 Outer SEI partial molar volume [m3.mol-1],9.585e-5, Safari 2009,
 SEI reaction exchange current density [A.m-2],1.5e-6, Ramadass 2004,
-SEI resistivity [Ohm.m],5e6, Safari 2009,
+SEI resistivity [Ohm.m],2e5, Safari 2009,
 Outer SEI solvent diffusivity [m2.s-1],2.5E-22, Single paper,
 Bulk solvent concentration [mol.m-3],2.636E3, Ploehn paper,
 Ratio of inner and outer SEI exchange current densities,1, Assume same,
diff --git a/pybamm/input/parameters/lithium_ion/seis/yang2017_sei/parameters.csv b/pybamm/input/parameters/lithium_ion/seis/yang2017_sei/parameters.csv
index 1cecd23ea2..a5936b42eb 100644
--- a/pybamm/input/parameters/lithium_ion/seis/yang2017_sei/parameters.csv
+++ b/pybamm/input/parameters/lithium_ion/seis/yang2017_sei/parameters.csv
@@ -4,7 +4,7 @@ Name [units],Value,Reference,Notes
 # SEI properties,,,
 Inner SEI partial molar volume [m3.mol-1],9.586E-05, Safari paper, 0.162/1690
 Outer SEI partial molar volume [m3.mol-1],9.586E-05, Safari paper, 0.162/1690
-SEI resistivity [Ohm.m],2.00E+06, Safari paper (1/5e-6),
+SEI resistivity [Ohm.m],2e5, Safari paper (1/5e-6),
 Initial inner SEI thickness [m],0,,
 Initial outer SEI thickness [m],5E-09,,
 EC initial concentration in electrolyte [mol.m-3],4.541E+03, Safari paper,
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index f7e49591c8..2a0b140f0c 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -402,11 +402,17 @@ def set_initial_conditions_from(self, solution, inplace=True):
         if inplace is True:
             model = self
         else:
-            model = self.new_copy()
+            model = self.new_empty_copy()
+            model.rhs = self.rhs.copy()
+            model.algebraic = self.algebraic.copy()
+            model.initial_conditions = self.initial_conditions.copy()
+            model.boundary_conditions = self.boundary_conditions.copy()
+            model.variables = self.variables.copy()
+            model.events = self.events.copy()
 
         if isinstance(solution, pybamm.Solution):
             solution = solution.last_state
-        for var, equation in model.initial_conditions.items():
+        for var in self.initial_conditions:
             if isinstance(var, pybamm.Variable):
                 try:
                     final_state = solution[var.name]
@@ -419,7 +425,9 @@ def set_initial_conditions_from(self, solution, inplace=True):
                     )
                 if isinstance(solution, pybamm.Solution):
                     final_state = final_state.data
-                if final_state.ndim == 1:
+                if final_state.ndim == 0:
+                    final_state_eval = np.array([final_state])
+                elif final_state.ndim == 1:
                     final_state_eval = final_state[-1:]
                 elif final_state.ndim == 2:
                     final_state_eval = final_state[:, -1]
@@ -458,11 +466,11 @@ def set_initial_conditions_from(self, solution, inplace=True):
 
         # Also update the concatenated initial conditions if the model is already
         # discretised
-        if model.is_discretised:
+        if self.is_discretised:
             # Unpack slices for sorting
-            y_slices = {var.id: slce for var, slce in model.y_slices.items()}
+            y_slices = {var.id: slce for var, slce in self.y_slices.items()}
             slices = []
-            for symbol in model.initial_conditions.keys():
+            for symbol in self.initial_conditions.keys():
                 if isinstance(symbol, pybamm.Concatenation):
                     # must append the slice for the whole concatenation, so that
                     # equations get sorted correctly
@@ -937,8 +945,11 @@ def default_spatial_methods(self):
 
     @property
     def default_solver(self):
-        """Return default solver based on whether model is ODE model or DAE model."""
-        return pybamm.CasadiSolver(mode="safe")
+        """Return default solver based on whether model is ODE/DAE or algebraic"""
+        if len(self.rhs) == 0 and len(self.algebraic) != 0:
+            return pybamm.CasadiAlgebraicSolver()
+        else:
+            return pybamm.CasadiSolver(mode="safe")
 
 
 # helper functions for finding symbols
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index dc7098fb64..88fcfb3cef 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -682,6 +682,9 @@ def build_model(self):
         pybamm.logger.debug("Setting SoC variables ({})".format(self.name))
         self.set_soc_variables()
 
+        pybamm.logger.debug("Setting degradation variables ({})".format(self.name))
+        self.set_degradation_variables()
+
         # Massive hack for consistent delta_phi = phi_s - phi_e with SPMe
         # This needs to be corrected
         if isinstance(self, pybamm.lithium_ion.SPMe):
@@ -967,7 +970,7 @@ def set_voltage_variables(self):
         # mode)
         # A tolerance of 1 is sufficiently small since the dimensionless voltage is
         # scaled with the thermal voltage (0.025V) and hence has a range of around 60
-        tol = 1
+        tol = 5
         self.events.append(
             pybamm.Event(
                 "Minimum voltage switch",
@@ -987,6 +990,13 @@ def set_voltage_variables(self):
         I_dim = self.variables["Current [A]"]
         self.variables.update({"Terminal power [W]": I_dim * V_dim})
 
+    def set_degradation_variables(self):
+        """
+        Set variables that quantify degradation.
+        This function is overriden by the base battery models
+        """
+        pass
+
     def set_soc_variables(self):
         """
         Set variables relating to the state of charge.
diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py
index 1de3c2b091..5722bd42f2 100644
--- a/pybamm/models/full_battery_models/lithium_ion/__init__.py
+++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py
@@ -2,6 +2,7 @@
 # Root of the lithium-ion models module.
 #
 from .base_lithium_ion_model import BaseModel
+from .electrode_soh import ElectrodeSOH
 from .spm import SPM
 from .spme import SPMe
 from .dfn import DFN
diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
index faf82febb2..8cd771c944 100644
--- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
+++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
@@ -46,6 +46,70 @@ def set_standard_output_variables(self):
             }
         )
 
+    def set_degradation_variables(self):
+        """ Sets variables that quantify degradation (LAM, LLI, etc) """
+        param = self.param
+
+        # LAM
+        C_n = self.variables["Negative electrode capacity [A.h]"]
+        C_p = self.variables["Positive electrode capacity [A.h]"]
+
+        LAM_ne = (1 - C_n / param.C_n_init) * 100
+        LAM_pe = (1 - C_p / param.C_p_init) * 100
+
+        # LLI
+        n_Li_e = self.variables["Total lithium in electrolyte [mol]"]
+        n_Li_p = self.variables["Total lithium in positive electrode [mol]"]
+        n_Li_n = self.variables["Total lithium in negative electrode [mol]"]
+        n_Li_particles = n_Li_n + n_Li_p
+        n_Li = n_Li_particles + n_Li_e
+
+        # LLI is usually defined based only on the percentage lithium lost from
+        # particles
+        LLI = (1 - n_Li_particles / param.n_Li_particles_init) * 100
+        LLI_tot = (1 - n_Li / param.n_Li_init) * 100
+
+        self.variables.update(
+            {
+                "LAM_ne [%]": LAM_ne,
+                "LAM_pe [%]": LAM_pe,
+                "LLI [%]": LLI,
+                "Loss of active material in negative electrode [%]": LAM_ne,
+                "Loss of active material in positive electrode [%]": LAM_pe,
+                "Loss of lithium inventory [%]": LLI,
+                "Loss of lithium inventory, including electrolyte [%]": LLI_tot,
+                # Total lithium
+                "Total lithium [mol]": n_Li,
+                "Total lithium in particles [mol]": n_Li_particles,
+                # Lithium lost
+                "Total lithium lost [mol]": param.n_Li_init - n_Li,
+                "Total lithium lost from particles [mol]": param.n_Li_particles_init
+                - n_Li_particles,
+                "Total lithium lost from electrolyte [mol]": param.n_Li_e_init - n_Li_e,
+            }
+        )
+
+        # Lithium lost to side reactions
+        # Different way of measuring LLI but should give same value
+        LLI_sei_n = self.variables["Loss of lithium to negative electrode SEI [mol]"]
+        LLI_sei_p = self.variables["Loss of lithium to positive electrode SEI [mol]"]
+        LLI_pl_n = self.variables[
+            "Loss of lithium to negative electrode lithium plating [mol]"
+        ]
+        LLI_pl_p = self.variables[
+            "Loss of lithium to positive electrode lithium plating [mol]"
+        ]
+
+        LLI_reactions = LLI_sei_n + LLI_sei_p + LLI_pl_n + LLI_pl_p
+        self.variables.update(
+            {
+                "Total lithium lost to side reactions [mol]": LLI_reactions,
+                "Total capacity lost to side reactions [A.h]": LLI_reactions
+                * param.F
+                / 3600,
+            }
+        )
+
     def set_sei_submodel(self):
 
         # negative electrode SEI
diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
new file mode 100644
index 0000000000..d93f4658c6
--- /dev/null
+++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
@@ -0,0 +1,95 @@
+#
+# A model to calculate electrode-specific SOH
+#
+import pybamm
+import numpy as np
+
+
+class ElectrodeSOH(pybamm.BaseModel):
+    """Model to calculate electrode-specific SOH, from [1]_.
+
+    .. math::
+        n_{Li} &= \\frac{3600}{F}(y_{100}C_p + x_{100}C_n),
+        \\
+        V_{max} &= U_p(y_{100}) - U_n(x_{100}),
+        \\
+        V_{min} &= U_p(y_{0}) - U_n(x_{0})
+        \\
+        x_0 &= x_{100} - \\frac{C}{C_n},
+        \\
+        y_0 &= y_{100} + \\frac{C}{C_p}.
+
+    References
+    ----------
+    .. [1] Mohtat, P., Lee, S., Siegel, J. B., & Stefanopoulou, A. G. (2019). Towards
+    better estimability of electrode-specific state of health: Decoding the cell
+    expansion. Journal of Power Sources, 427, 101-111.
+
+    **Extends:** :class:`pybamm.BaseModel`
+    """
+
+    def __init__(self, name="Electrode-specific SOH model"):
+        pybamm.citations.register("Mohtat2019")
+        super().__init__(name)
+        param = pybamm.LithiumIonParameters()
+
+        Un = param.U_n_dimensional
+        Up = param.U_p_dimensional
+        T_ref = param.T_ref
+
+        x_100 = pybamm.Variable("x_100", bounds=(0, 1))
+        C = pybamm.Variable("C", bounds=(0, np.inf))
+
+        V_max = pybamm.InputParameter("V_max")
+        V_min = pybamm.InputParameter("V_min")
+        C_n = pybamm.InputParameter("C_n")
+        C_p = pybamm.InputParameter("C_p")
+        n_Li = pybamm.InputParameter("n_Li")
+
+        y_100 = (n_Li * param.F / 3600 - x_100 * C_n) / C_p
+        x_0 = x_100 - C / C_n
+        y_0 = y_100 + C / C_p
+
+        self.algebraic = {
+            x_100: Up(y_100, T_ref) - Un(x_100, T_ref) - V_max,
+            C: Up(y_0, T_ref) - Un(x_0, T_ref) - V_min,
+        }
+
+        # initial guess must be chosen such that 0 < x_0, y_0, x_100, y_100 < 1
+        # First guess for x_100
+        x_100_init = 0.85
+        # Make sure x_0 = x_100 - C/C_n > 0
+        C_init = param.Q
+        C_init = pybamm.minimum(C_n * x_100_init - 0.1, C_init)
+        # Make sure y_100 > 0
+        # x_100_init = pybamm.minimum(n_Li * param.F / 3600 / C_n - 0.01, x_100_init)
+        self.initial_conditions = {
+            x_100: x_100_init,
+            C: C_init,
+        }
+
+        self.variables = {
+            "x_100": x_100,
+            "y_100": y_100,
+            "C": C,
+            "x_0": x_0,
+            "y_0": y_0,
+            "Un(x_100)": Un(x_100, T_ref),
+            "Un(x_0)": Un(x_0, T_ref),
+            "Up(y_100)": Up(y_100, T_ref),
+            "Up(y_0)": Up(y_0, T_ref),
+            "Up(y_100) - Un(x_100)": Up(y_100, T_ref) - Un(x_100, T_ref),
+            "Up(y_0) - Un(x_0)": Up(y_0, T_ref) - Un(x_0, T_ref),
+            "n_Li_100": 3600 / param.F * (y_100 * C_p + x_100 * C_n),
+            "n_Li_0": 3600 / param.F * (y_0 * C_p + x_0 * C_n),
+            "n_Li": n_Li,
+            "C_n": C_n,
+            "C_p": C_p,
+            "C_n * (x_100 - x_0)": C_n * (x_100 - x_0),
+            "C_p * (y_100 - y_0)": C_p * (y_0 - y_100),
+        }
+
+    @property
+    def default_solver(self):
+        # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings
+        return pybamm.AlgebraicSolver()
diff --git a/pybamm/models/submodels/active_material/base_active_material.py b/pybamm/models/submodels/active_material/base_active_material.py
index baeb692906..1c94af7e60 100644
--- a/pybamm/models/submodels/active_material/base_active_material.py
+++ b/pybamm/models/submodels/active_material/base_active_material.py
@@ -23,6 +23,7 @@ def __init__(self, param, domain, options):
         super().__init__(param, domain, options=options)
 
     def _get_standard_active_material_variables(self, eps_solid):
+        param = self.param
         eps_solid_av = pybamm.x_average(eps_solid)
 
         variables = {
@@ -53,6 +54,17 @@ def _get_standard_active_material_variables(self, eps_solid):
             return variables
 
         else:
+            # Update electrode capacity variables
+            if self.domain == "Negative":
+                L = param.L_n
+                c_s_max = param.c_n_max
+            elif self.domain == "Positive":
+                L = param.L_p
+                c_s_max = param.c_p_max
+
+            C = eps_solid_av * L * param.A_cc * c_s_max * param.F / 3600
+            variables.update({self.domain + " electrode capacity [A.h]": C})
+
             if self.domain == "Negative":
                 x = pybamm.standard_spatial_vars.x_n
                 R = self.param.R_n(x)
diff --git a/pybamm/models/submodels/electrolyte_diffusion/base_electrolyte_diffusion.py b/pybamm/models/submodels/electrolyte_diffusion/base_electrolyte_diffusion.py
index f40124d0f1..0983339d77 100644
--- a/pybamm/models/submodels/electrolyte_diffusion/base_electrolyte_diffusion.py
+++ b/pybamm/models/submodels/electrolyte_diffusion/base_electrolyte_diffusion.py
@@ -132,18 +132,16 @@ def _get_total_concentration_electrolyte(self, c_e, epsilon):
         Returns
         -------
         variables : dict
-            The "Total concentration in electrolyte [mol]" variable.
+            The "Total lithium in electrolyte [mol]" variable.
         """
 
         c_e_typ = self.param.c_e_typ
         L_x = self.param.L_x
         A = self.param.A_cc
 
-        c_e_total = pybamm.x_average(epsilon * c_e)
+        c_e_av = pybamm.x_average(epsilon * c_e)
 
-        variables = {
-            "Total concentration in electrolyte [mol]": c_e_typ * L_x * A * c_e_total
-        }
+        variables = {"Total lithium in electrolyte [mol]": c_e_typ * L_x * A * c_e_av}
 
         return variables
 
diff --git a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
index 5f0dd09b43..c6a1781b91 100644
--- a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
@@ -62,11 +62,14 @@ def get_coupled_variables(self, variables):
         ]
         j0_plating = param.j0_plating(c_e_n, c_plated_Li, T)
         phi_ref = param.U_n_ref / param.potential_scale
-        eta_plating = -(delta_phi + phi_ref + eta_sei)
+
+        eta_stripping = delta_phi + phi_ref + eta_sei
+        eta_plating = -eta_stripping
         prefactor = 1 / (2 * (1 + self.param.Theta * T))
         # j_stripping is always negative, because there is no stripping, only plating
         j_stripping = -j0_plating * pybamm.exp(prefactor * eta_plating)
 
+        variables.update(self._get_standard_overpotential_variables(eta_stripping))
         variables.update(self._get_standard_reaction_variables(j_stripping))
 
         # Update whole cell variables, which also updates the "sum of" variables
diff --git a/pybamm/models/submodels/interface/lithium_plating/no_plating.py b/pybamm/models/submodels/interface/lithium_plating/no_plating.py
index 8a7394701e..a8f07179da 100644
--- a/pybamm/models/submodels/interface/lithium_plating/no_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/no_plating.py
@@ -30,6 +30,12 @@ def get_fundamental_variables(self):
         return variables
 
     def get_coupled_variables(self, variables):
+        zero = pybamm.FullBroadcast(
+            pybamm.Scalar(0), self.domain.lower() + " electrode", "current collector"
+        )
+        variables.update(self._get_standard_overpotential_variables(zero))
+        variables.update(self._get_standard_reaction_variables(zero))
+
         # Update whole cell variables, which also updates the "sum of" variables
         if (
             "Negative electrode lithium plating interfacial current density"
diff --git a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
index 0463120abc..5cc4fbe644 100644
--- a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
@@ -70,6 +70,7 @@ def get_coupled_variables(self, variables):
             prefactor * eta_stripping
         ) - j0_plating * pybamm.exp(prefactor * eta_plating)
 
+        variables.update(self._get_standard_overpotential_variables(eta_stripping))
         variables.update(self._get_standard_reaction_variables(j_stripping))
 
         # Update whole cell variables, which also updates the "sum of" variables
diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py
index 62ff8cc441..23f9864d5a 100644
--- a/pybamm/models/submodels/interface/sei/base_sei.py
+++ b/pybamm/models/submodels/interface/sei/base_sei.py
@@ -163,6 +163,9 @@ def _get_standard_concentration_variables(self, variables):
                 + domain
                 + " SEI concentration [mol.m-3]": n_SEI_av * n_scale,
                 "Loss of lithium to " + domain + " SEI [mol]": Q_sei * n_scale,
+                "Loss of capacity to "
+                + domain
+                + " SEI [A.h]": Q_sei * n_scale * self.param.F / 3600,
             }
         )
 
diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
index 6dc760ab60..bac7795efd 100644
--- a/pybamm/models/submodels/particle/base_particle.py
+++ b/pybamm/models/submodels/particle/base_particle.py
@@ -114,7 +114,8 @@ def _get_total_concentration_variables(self, variables):
             "R-averaged " + self.domain.lower() + " particle concentration"
         ]
         eps_s = variables[self.domain + " electrode active material volume fraction"]
-        c_s_vol_av = pybamm.x_average(eps_s * c_s_rav)
+        eps_s_av = pybamm.x_average(eps_s)
+        c_s_vol_av = pybamm.x_average(eps_s * c_s_rav) / eps_s_av
         if self.domain == "Negative":
             c_scale = self.param.c_n_max
             L = self.param.L_n
@@ -125,13 +126,14 @@ def _get_total_concentration_variables(self, variables):
 
         variables.update(
             {
+                self.domain + " electrode SOC": c_s_vol_av,
                 self.domain + " electrode volume-averaged concentration": c_s_vol_av,
                 self.domain
                 + " electrode "
                 + "volume-averaged concentration [mol.m-3]": c_s_vol_av * c_scale,
                 "Total lithium in "
                 + self.domain.lower()
-                + " electrode [mol]": c_s_vol_av * c_scale * L * A,
+                + " electrode [mol]": c_s_vol_av * eps_s_av * c_scale * L * A,
             }
         )
         return variables
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 359b6c06b8..4e8530e27a 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -273,6 +273,37 @@ def _set_dimensional_parameters(self):
         )
         # intermediate variable  [K*m^3/mol]
 
+        # Electrode capacities
+        x_n = pybamm.SpatialVariable(
+            "x_n", domain=["negative electrode"], coord_sys="cartesian"
+        )
+        x_p = pybamm.SpatialVariable(
+            "x_p", domain=["positive electrode"], coord_sys="cartesian"
+        )
+
+        eps_s_n_av = pybamm.x_average(self.epsilon_s_n(x_n))
+        eps_s_p_av = pybamm.x_average(self.epsilon_s_p(x_p))
+        self.C_n_init = eps_s_n_av * self.L_n * self.A_cc * self.c_n_max * self.F / 3600
+        self.C_p_init = eps_s_p_av * self.L_p * self.A_cc * self.c_p_max * self.F / 3600
+
+        # Total lithium
+        eps = pybamm.Concatenation(self.epsilon_n, self.epsilon_s, self.epsilon_p)
+
+        c_e_av = pybamm.x_average(eps) * self.c_e_typ
+        self.n_Li_e_init = c_e_av * self.L_x * self.A_cc
+
+        eps_s_n = self.epsilon_s_n(x_n)
+        c_n = self.c_n_init(x_n)
+        c_n_av = pybamm.x_average(eps_s_n * c_n)
+        self.n_Li_n_init = c_n_av * self.c_n_max * self.L_n * self.A_cc
+
+        eps_s_p = self.epsilon_s_p(x_p)
+        c_p = self.c_p_init(x_p)
+        c_p_av = pybamm.x_average(eps_s_p * c_p)
+        self.n_Li_p_init = c_p_av * self.c_p_max * self.L_p * self.A_cc
+
+        self.n_Li_particles_init = self.n_Li_n_init + self.n_Li_p_init
+        self.n_Li_init = self.n_Li_particles_init + self.n_Li_e_init
         # loss of active material parameters
         self.m_LAM_n = pybamm.Parameter(
             "Negative electrode LAM constant exponential term"
@@ -392,7 +423,7 @@ def U_n_dimensional(self, sto, T):
         # add a term to ensure that the OCP goes to infinity at 0 and -infinity at 1
         # this will not affect the OCP for most values of sto
         # see #1435
-        u_ref -= 1e-6 * pybamm.log(sto / (1 - sto))
+        u_ref = u_ref + 1e-6 * (1 / sto + 1 / (sto - 1))
         return u_ref + (T - self.T_ref) * self.dUdT_n_dimensional(sto)
 
     def U_p_dimensional(self, sto, T):
@@ -402,7 +433,7 @@ def U_p_dimensional(self, sto, T):
         # add a term to ensure that the OCP goes to infinity at 0 and -infinity at 1
         # this will not affect the OCP for most values of sto
         # see #1435
-        u_ref -= 1e-6 * pybamm.log(sto / (1 - sto))
+        u_ref = u_ref + 1e-6 * (1 / sto + 1 / (sto - 1))
         return u_ref + (T - self.T_ref) * self.dUdT_p_dimensional(sto)
 
     def dUdT_n_dimensional(self, sto):
diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py
index 20b19ec25d..95e52160a3 100644
--- a/pybamm/parameters/parameter_sets.py
+++ b/pybamm/parameters/parameter_sets.py
@@ -175,6 +175,7 @@
     "electrolyte": "LiPF6_Mohtat2020",
     "experiment": "1C_charge_from_empty_Mohtat2020",
     "sei": "example",
+    "lithium plating": "yang2017_Li_plating",
     "citation": "Mohtat2020",
 }
 
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 5cb0011366..52f4224388 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -587,7 +587,6 @@ def process_symbol(self, symbol):
             Symbol with Parameter instances replaced by Value
 
         """
-
         try:
             return self._processed_symbols[symbol.id]
         except KeyError:
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 99bf226c77..e5ff1ed883 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -19,19 +19,15 @@ def __getitem__(self, i):
 def ax_min(data):
     """Calculate appropriate minimum axis value for plotting"""
     data_min = np.nanmin(data)
-    if data_min <= 0:
-        return 1.04 * data_min
-    else:
-        return 0.96 * data_min
+    data_max = np.nanmax(data)
+    return data_max - 1.05 * (data_max - data_min)
 
 
 def ax_max(data):
     """Calculate appropriate maximum axis value for plotting"""
+    data_min = np.nanmin(data)
     data_max = np.nanmax(data)
-    if data_max <= 0:
-        return 0.96 * data_max
-    else:
-        return 1.04 * data_max
+    return data_min + 1.05 * (data_max - data_min)
 
 
 def split_long_string(title, max_words=None):
@@ -503,7 +499,7 @@ def plot(self, t, dynamic=False):
                         (self.plots[key][i][j],) = ax.plot(
                             full_t / self.time_scaling_factor,
                             variable(full_t, warn=False),
-                            # color=self.colors[i],
+                            color=self.colors[i],
                             linestyle=linestyle,
                         )
                         variable_handles.append(self.plots[key][0][j])
@@ -539,7 +535,7 @@ def plot(self, t, dynamic=False):
                         (self.plots[key][i][j],) = ax.plot(
                             self.first_dimensional_spatial_variable[key],
                             variable(t_in_seconds, **spatial_vars, warn=False),
-                            # color=self.colors[i],
+                            color=self.colors[i],
                             linestyle=linestyle,
                             zorder=10,
                         )
@@ -656,24 +652,26 @@ def dynamic_plot(self, testing=False, step=None):
             step = step or self.max_t / 100
             widgets.interact(
                 lambda t: self.plot(t, dynamic=False),
-                t=widgets.FloatSlider(min=0, max=self.max_t, step=step, value=0),
+                t=widgets.FloatSlider(
+                    min=self.min_t, max=self.max_t, step=step, value=self.min_t
+                ),
                 continuous_update=False,
             )
         else:
             import matplotlib.pyplot as plt
             from matplotlib.widgets import Slider
 
-            # create an initial plot at time 0
-            self.plot(0, dynamic=True)
+            # create an initial plot at time self.min_t
+            self.plot(self.min_t, dynamic=True)
 
             axcolor = "lightgoldenrodyellow"
             ax_slider = plt.axes([0.315, 0.02, 0.37, 0.03], facecolor=axcolor)
             self.slider = Slider(
                 ax_slider,
                 "Time [{}]".format(self.time_unit),
-                0,
+                self.min_t,
                 self.max_t,
-                valinit=0,
+                valinit=self.min_t,
                 color="#1f77b4",
             )
             self.slider.on_changed(self.slider_update)
@@ -703,13 +701,12 @@ def slider_update(self, t):
                             warn=False,
                         )
                         plot[i][j].set_ydata(var)
-                        var_min = min(var_min, np.nanmin(var))
-                        var_max = max(var_max, np.nanmax(var))
+                        var_min = min(var_min, ax_min(var))
+                        var_max = max(var_max, ax_max(var))
                 # update boundaries between subdomains
                 y_min, y_max = self.axis_limits[key][2:]
                 if y_min is None and y_max is None:
-                    y_min, y_max = ax_min(var_min), ax_max(var_max)
-                    ax.set_ylim(y_min, y_max)
+                    ax.set_ylim(var_min, var_max)
             elif self.variables[key][0][0].dimensions == 2:
                 # 2D plot: plot as a function of x and y at time t
                 # Read dictionary of spatial variables
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 034b3bd67c..f1a84313de 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -418,7 +418,8 @@ def set_up_model_for_experiment_new(self, model):
                         pybamm.Event(
                             "Voltage cut-off [V] [experiment]",
                             new_model.variables["Terminal voltage [V]"]
-                            - op_inputs["Voltage cut-off [V]"] / model.param.n_cells,
+                            - pybamm.InputParameter("Voltage cut-off [V]")
+                            / model.param.n_cells,
                         )
                     )
 
@@ -542,6 +543,7 @@ def solve(
         solver=None,
         check_model=True,
         save_at_cycles=None,
+        calc_esoh=True,
         starting_solution=None,
         **kwargs,
     ):
@@ -575,7 +577,11 @@ def solve(
         save_at_cycles : int or list of ints, optional
             Which cycles to save the full sub-solutions for. If None, all cycles are
             saved. If int, every multiple of save_at_cycles is saved. If list, every
-            cycle in the list is saved.
+            cycle in the list is saved. The first cycle (cycle 1) is always saved.
+        calc_esoh : bool, optional
+            Whether to include eSOH variables in the summary variables. If `False`
+            then only summary variables that do not require the eSOH calculation
+            are calculated. Default is True.
         starting_solution : :class:`pybamm.Solution`
             The solution to start stepping from. If None (default), then self._solution
             is used. Must be None if not using an experiment.
@@ -674,13 +680,27 @@ def solve(
 
             if starting_solution is None:
                 starting_solution_cycles = []
+                starting_solution_summary_variables = []
             else:
                 starting_solution_cycles = starting_solution.cycles.copy()
+                starting_solution_summary_variables = (
+                    starting_solution.all_summary_variables.copy()
+                )
 
             cycle_offset = len(starting_solution_cycles)
             all_cycle_solutions = starting_solution_cycles
+            all_summary_variables = starting_solution_summary_variables
             current_solution = starting_solution
 
+            # Set up eSOH model (for summary variables)
+            if calc_esoh is True:
+                esoh_model = pybamm.lithium_ion.ElectrodeSOH()
+                esoh_sim = pybamm.Simulation(
+                    esoh_model, parameter_values=self.parameter_values
+                )
+            else:
+                esoh_sim = None
+
             idx = 0
             num_cycles = len(self.experiment.cycle_lengths)
             feasible = True  # simulation will stop if experiment is infeasible
@@ -694,6 +714,23 @@ def solve(
                 steps = []
                 cycle_solution = None
 
+                # Decide whether we should save this cycle
+                save_this_cycle = (
+                    # always save cycle 1
+                    cycle_num == 1
+                    # None: save all cycles
+                    or save_at_cycles is None
+                    # list: save all cycles in the list
+                    or (
+                        isinstance(save_at_cycles, list)
+                        and cycle_num + cycle_offset in save_at_cycles
+                    )
+                    # int: save all multiples
+                    or (
+                        isinstance(save_at_cycles, int)
+                        and (cycle_num + cycle_offset) % save_at_cycles == 0
+                    )
+                )
                 for step_num in range(1, cycle_length + 1):
                     exp_inputs = self._experiment_inputs[idx]
                     dt = self._experiment_times[idx]
@@ -725,9 +762,9 @@ def solve(
 
                     # Only allow events specified by experiment
                     if not (
-                        cycle_solution is None
-                        or cycle_solution.termination == "final time"
-                        or "[experiment]" in cycle_solution.termination
+                        step_solution is None
+                        or step_solution.termination == "final time"
+                        or "[experiment]" in step_solution.termination
                     ):
                         feasible = False
                         break
@@ -739,7 +776,7 @@ def solve(
                 if feasible is False:
                     pybamm.logger.warning(
                         "\n\n\tExperiment is infeasible: '{}' ".format(
-                            cycle_solution.termination
+                            step_solution.termination
                         )
                         + "was triggered during '{}'. ".format(
                             self.experiment.operating_conditions_strings[idx]
@@ -751,13 +788,51 @@ def solve(
                     )
                     break
 
+                if save_this_cycle:
+                    self._solution = self._solution + cycle_solution
+
                 # At the final step of the inner loop we save the cycle
-                self._solution = self.solution + cycle_solution
-                cycle_solution.steps = steps
+                cycle_solution, cycle_summary_variables = pybamm.make_cycle_solution(
+                    steps,
+                    esoh_sim,
+                    save_this_cycle=save_this_cycle,
+                )
                 all_cycle_solutions.append(cycle_solution)
+                all_summary_variables.append(cycle_summary_variables)
+
+                # Calculate capacity_start using the first cycle
+                if cycle_num == 1:
+                    if "capacity" in self.experiment.termination:
+                        # Note capacity_start could be defined as
+                        # self.parameter_values["Nominal cell capacity [A.h]"] instead
+                        capacity_start = all_summary_variables[0]["Capacity [A.h]"]
+                        value, typ = self.experiment.termination["capacity"]
+                        if typ == "Ah":
+                            capacity_stop = value
+                        elif typ == "%":
+                            capacity_stop = value / 100 * capacity_start
+                    else:
+                        capacity_stop = None
+
+                if capacity_stop is not None:
+                    capacity_now = cycle_summary_variables["Capacity [A.h]"]
+                    if np.isnan(capacity_now) or capacity_now > capacity_stop:
+                        pybamm.logger.notice(
+                            f"Capacity is now {capacity_now:.3f} Ah "
+                            f"(originally {capacity_start:.3f} Ah, "
+                            f"will stop at {capacity_stop:.3f} Ah)"
+                        )
+                    else:
+                        pybamm.logger.notice(
+                            "Stopping experiment since capacity "
+                            f"({capacity_now:.3f} Ah) "
+                            f"is below stopping capacity ({capacity_stop:.3f} Ah)."
+                        )
+                        break
 
-            if self.solution is not None:
+            if self.solution is not None and len(all_cycle_solutions) > 0:
                 self.solution.cycles = all_cycle_solutions
+                self.solution.set_summary_variables(all_summary_variables)
 
             pybamm.logger.notice(
                 "Finish experiment simulation, took {}".format(timer.time())
diff --git a/pybamm/solvers/algebraic_solver.py b/pybamm/solvers/algebraic_solver.py
index 837c40c400..b097f26886 100644
--- a/pybamm/solvers/algebraic_solver.py
+++ b/pybamm/solvers/algebraic_solver.py
@@ -129,92 +129,101 @@ def jac_fn(y_alg):
                 y_alg[:, idx] = y0_alg
             # Otherwise calculate new y0
             else:
-                # Methods which use least-squares are specified as either "lsq", which
-                # uses the default method, or with "lsq__methodname"
-                if self.method.startswith("lsq"):
-
-                    if self.method == "lsq":
-                        method = "trf"
-                    else:
-                        method = self.method[5:]
-                    if jac_fn is None:
-                        jac_fn = "2-point"
-                    timer.reset()
-                    sol = optimize.least_squares(
-                        root_fun,
-                        y0_alg,
-                        method=method,
-                        ftol=self.tol,
-                        jac=jac_fn,
-                        bounds=model.bounds,
-                        **self.extra_options,
-                    )
-                    integration_time += timer.time()
-                # Methods which use minimize are specified as either "minimize", which
-                # uses the default method, or with "minimize__methodname"
-                elif self.method.startswith("minimize"):
-                    # Adapt the root function for minimize
-                    def root_norm(y):
-                        return np.sum(root_fun(y) ** 2)
-
-                    if jac_fn is None:
-                        jac_norm = None
-                    else:
-
-                        def jac_norm(y):
-                            return np.sum(2 * root_fun(y) * jac_fn(y), 0)
-
-                    if self.method == "minimize":
-                        method = None
+                itr = 0
+                maxiter = 2
+                success = False
+                while not success:
+                    # Methods which use least-squares are specified as either "lsq",
+                    # which uses the default method, or with "lsq__methodname"
+                    if self.method.startswith("lsq"):
+
+                        if self.method == "lsq":
+                            method = "trf"
+                        else:
+                            method = self.method[5:]
+                        if jac_fn is None:
+                            jac_fn = "2-point"
+                        timer.reset()
+                        sol = optimize.least_squares(
+                            root_fun,
+                            y0_alg,
+                            method=method,
+                            ftol=self.tol,
+                            jac=jac_fn,
+                            bounds=model.bounds,
+                            **self.extra_options,
+                        )
+                        integration_time += timer.time()
+                    # Methods which use minimize are specified as either "minimize",
+                    # which uses the default method, or with "minimize__methodname"
+                    elif self.method.startswith("minimize"):
+                        # Adapt the root function for minimize
+                        def root_norm(y):
+                            return np.sum(root_fun(y) ** 2)
+
+                        if jac_fn is None:
+                            jac_norm = None
+                        else:
+
+                            def jac_norm(y):
+                                return np.sum(2 * root_fun(y) * jac_fn(y), 0)
+
+                        if self.method == "minimize":
+                            method = None
+                        else:
+                            method = self.method[10:]
+                        extra_options = self.extra_options
+                        if np.any(model.bounds[0] != -np.inf) or np.any(
+                            model.bounds[1] != np.inf
+                        ):
+                            bounds = [
+                                (lb, ub)
+                                for lb, ub in zip(model.bounds[0], model.bounds[1])
+                            ]
+                            extra_options["bounds"] = bounds
+                        timer.reset()
+                        sol = optimize.minimize(
+                            root_norm,
+                            y0_alg,
+                            method=method,
+                            tol=self.tol,
+                            jac=jac_norm,
+                            **extra_options,
+                        )
+                        integration_time += timer.time()
                     else:
-                        method = self.method[10:]
-                    extra_options = self.extra_options
-                    if np.any(model.bounds[0] != -np.inf) or np.any(
-                        model.bounds[1] != np.inf
-                    ):
-                        bounds = [
-                            (lb, ub) for lb, ub in zip(model.bounds[0], model.bounds[1])
-                        ]
-                        extra_options["bounds"] = bounds
-                    timer.reset()
-                    sol = optimize.minimize(
-                        root_norm,
-                        y0_alg,
-                        method=method,
-                        tol=self.tol,
-                        jac=jac_norm,
-                        **extra_options,
-                    )
-                    integration_time += timer.time()
-                else:
-                    timer.reset()
-                    sol = optimize.root(
-                        root_fun,
-                        y0_alg,
-                        method=self.method,
-                        tol=self.tol,
-                        jac=jac_fn,
-                        options=self.extra_options,
-                    )
-                    integration_time += timer.time()
-
-                if sol.success and np.all(abs(sol.fun) < self.tol):
-                    # update initial guess for the next iteration
-                    y0_alg = sol.x
-                    # update solution array
-                    y_alg[:, idx] = y0_alg
-                elif not sol.success:
-                    raise pybamm.SolverError(
-                        "Could not find acceptable solution: {}".format(sol.message)
-                    )
-                else:
-                    raise pybamm.SolverError(
-                        "Could not find acceptable solution: solver terminated "
-                        "successfully, but maximum solution error "
-                        "({}) above tolerance ({})".format(
-                            np.max(abs(sol.fun)), self.tol
+                        timer.reset()
+                        sol = optimize.root(
+                            root_fun,
+                            y0_alg,
+                            method=self.method,
+                            tol=self.tol,
+                            jac=jac_fn,
+                            options=self.extra_options,
                         )
-                    )
+                        integration_time += timer.time()
+
+                    if sol.success and np.all(abs(sol.fun) < self.tol):
+                        # update initial guess for the next iteration
+                        y0_alg = sol.x
+                        # update solution array
+                        y_alg[:, idx] = y0_alg
+                        success = True
+                    elif not sol.success:
+                        raise pybamm.SolverError(
+                            "Could not find acceptable solution: {}".format(sol.message)
+                        )
+                    else:
+                        y0_alg = sol.x
+                        if itr > maxiter:
+                            raise pybamm.SolverError(
+                                "Could not find acceptable solution: solver terminated "
+                                "successfully, but maximum solution error "
+                                "({}) above tolerance ({})".format(
+                                    np.max(abs(sol.fun)), self.tol
+                                )
+                            )
+                    itr += 1
 
         # Concatenate differential part
         y_diff = np.r_[[y0_diff] * len(t_eval)].T
diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py
index 4af9f9726b..5bf6c183a1 100644
--- a/pybamm/solvers/base_solver.py
+++ b/pybamm/solvers/base_solver.py
@@ -123,7 +123,7 @@ def copy(self):
         new_solver.models_set_up = {}
         return new_solver
 
-    def set_up(self, model, inputs=None, t_eval=None):
+    def set_up(self, model, inputs=None, t_eval=None, ics_only=False):
         """Unpack model, perform checks, and calculate jacobian.
 
         Parameters
@@ -279,69 +279,6 @@ def report(string):
                 jac_call = None
             return func, func_call, jac_call
 
-        # Check for heaviside and modulo functions in rhs and algebraic and add
-        # discontinuity events if these exist.
-        # Note: only checks for the case of t < X, t <= X, X < t, or X <= t, but also
-        # accounts for the fact that t might be dimensional
-        # Only do this for DAE models as ODE models can deal with discontinuities fine
-        if len(model.algebraic) > 0:
-            for symbol in itertools.chain(
-                model.concatenated_rhs.pre_order(),
-                model.concatenated_algebraic.pre_order(),
-            ):
-                if isinstance(symbol, pybamm.Heaviside):
-                    found_t = False
-                    # Dimensionless
-                    if symbol.right.id == pybamm.t.id:
-                        expr = symbol.left
-                        found_t = True
-                    elif symbol.left.id == pybamm.t.id:
-                        expr = symbol.right
-                        found_t = True
-                    # Dimensional
-                    elif symbol.right.id == (pybamm.t * model.timescale_eval).id:
-                        expr = symbol.left.new_copy() / symbol.right.right.new_copy()
-                        found_t = True
-                    elif symbol.left.id == (pybamm.t * model.timescale_eval).id:
-                        expr = symbol.right.new_copy() / symbol.left.right.new_copy()
-                        found_t = True
-
-                    # Update the events if the heaviside function depended on t
-                    if found_t:
-                        model.events.append(
-                            pybamm.Event(
-                                str(symbol),
-                                expr.new_copy(),
-                                pybamm.EventType.DISCONTINUITY,
-                            )
-                        )
-                elif isinstance(symbol, pybamm.Modulo):
-                    found_t = False
-                    # Dimensionless
-                    if symbol.left.id == pybamm.t.id:
-                        expr = symbol.right
-                        found_t = True
-                    # Dimensional
-                    elif symbol.left.id == (pybamm.t * model.timescale_eval).id:
-                        expr = symbol.right.new_copy() / symbol.left.right.new_copy()
-                        found_t = True
-
-                    # Update the events if the modulo function depended on t
-                    if found_t:
-                        if t_eval is None:
-                            N_events = 200
-                        else:
-                            N_events = t_eval[-1] // expr.value
-
-                        for i in np.arange(N_events):
-                            model.events.append(
-                                pybamm.Event(
-                                    str(symbol),
-                                    expr.new_copy() * pybamm.Scalar(i + 1),
-                                    pybamm.EventType.DISCONTINUITY,
-                                )
-                            )
-
         # Process initial conditions
         initial_conditions = process(
             model.concatenated_initial_conditions,
@@ -349,107 +286,175 @@ def report(string):
             use_jacobian=False,
         )[0]
         init_eval = InitialConditions(initial_conditions, model)
-
-        # Process rhs, algebraic and event expressions
-        rhs, rhs_eval, jac_rhs = process(model.concatenated_rhs, "RHS")
-        algebraic, algebraic_eval, jac_algebraic = process(
-            model.concatenated_algebraic, "algebraic"
-        )
-
-        # Calculate initial conditions
+        model.init_eval = init_eval
         model.y0 = init_eval(inputs)
 
-        casadi_terminate_events = []
-        terminate_events_eval = []
-        interpolant_extrapolation_events_eval = []
-        discontinuity_events_eval = []
-        for n, event in enumerate(model.events):
-            if event.event_type == pybamm.EventType.DISCONTINUITY:
-                # discontinuity events are evaluated before the solver is called,
-                # so don't need to process them
-                discontinuity_events_eval.append(event)
-            elif event.event_type == pybamm.EventType.SWITCH:
-                if (
-                    isinstance(self, pybamm.CasadiSolver)
-                    and self.mode == "fast with events"
-                    and model.algebraic != {}
+        if not ics_only:
+            # Check for heaviside and modulo functions in rhs and algebraic and add
+            # discontinuity events if these exist.
+            # Note: only checks for the case of t < X, t <= X, X < t, or X <= t,
+            # but also accounts for the fact that t might be dimensional
+            # Only do this for DAE models as ODE models can deal with discontinuities
+            # fine
+            if len(model.algebraic) > 0:
+                for symbol in itertools.chain(
+                    model.concatenated_rhs.pre_order(),
+                    model.concatenated_algebraic.pre_order(),
                 ):
-                    # Save some events to casadi_terminate_events for the 'fast with
-                    # events' mode of the casadi solver
-                    # We only need to do this if the model is a DAE model
-                    # see #1082
-                    k = 20
-                    init_sign = float(
-                        np.sign(event.evaluate(0, model.y0.full(), inputs=inputs))
+                    if isinstance(symbol, pybamm.Heaviside):
+                        found_t = False
+                        # Dimensionless
+                        if symbol.right.id == pybamm.t.id:
+                            expr = symbol.left
+                            found_t = True
+                        elif symbol.left.id == pybamm.t.id:
+                            expr = symbol.right
+                            found_t = True
+                        # Dimensional
+                        elif symbol.right.id == (pybamm.t * model.timescale_eval).id:
+                            expr = (
+                                symbol.left.new_copy() / symbol.right.right.new_copy()
+                            )
+                            found_t = True
+                        elif symbol.left.id == (pybamm.t * model.timescale_eval).id:
+                            expr = (
+                                symbol.right.new_copy() / symbol.left.right.new_copy()
+                            )
+                            found_t = True
+
+                        # Update the events if the heaviside function depended on t
+                        if found_t:
+                            model.events.append(
+                                pybamm.Event(
+                                    str(symbol),
+                                    expr.new_copy(),
+                                    pybamm.EventType.DISCONTINUITY,
+                                )
+                            )
+                    elif isinstance(symbol, pybamm.Modulo):
+                        found_t = False
+                        # Dimensionless
+                        if symbol.left.id == pybamm.t.id:
+                            expr = symbol.right
+                            found_t = True
+                        # Dimensional
+                        elif symbol.left.id == (pybamm.t * model.timescale_eval).id:
+                            expr = (
+                                symbol.right.new_copy() / symbol.left.right.new_copy()
+                            )
+                            found_t = True
+
+                        # Update the events if the modulo function depended on t
+                        if found_t:
+                            if t_eval is None:
+                                N_events = 200
+                            else:
+                                N_events = t_eval[-1] // expr.value
+
+                            for i in np.arange(N_events):
+                                model.events.append(
+                                    pybamm.Event(
+                                        str(symbol),
+                                        expr.new_copy() * pybamm.Scalar(i + 1),
+                                        pybamm.EventType.DISCONTINUITY,
+                                    )
+                                )
+
+            # Process rhs, algebraic and event expressions
+            rhs, rhs_eval, jac_rhs = process(model.concatenated_rhs, "RHS")
+            algebraic, algebraic_eval, jac_algebraic = process(
+                model.concatenated_algebraic, "algebraic"
+            )
+            casadi_terminate_events = []
+            terminate_events_eval = []
+            interpolant_extrapolation_events_eval = []
+            discontinuity_events_eval = []
+            for n, event in enumerate(model.events):
+                if event.event_type == pybamm.EventType.DISCONTINUITY:
+                    # discontinuity events are evaluated before the solver is called,
+                    # so don't need to process them
+                    discontinuity_events_eval.append(event)
+                elif event.event_type == pybamm.EventType.SWITCH:
+                    if (
+                        isinstance(self, pybamm.CasadiSolver)
+                        and self.mode == "fast with events"
+                        and model.algebraic != {}
+                    ):
+                        # Save some events to casadi_terminate_events for the 'fast with
+                        # events' mode of the casadi solver
+                        # We only need to do this if the model is a DAE model
+                        # see #1082
+                        k = 20
+                        init_sign = float(
+                            np.sign(event.evaluate(0, model.y0.full(), inputs=inputs))
+                        )
+                        # We create a sigmoid for each event which will multiply the
+                        # rhs. Doing * 2 - 1 ensures that when the event is crossed,
+                        # the sigmoid is zero. Hence the rhs is zero and the solution
+                        # stays constant for the rest of the simulation period
+                        # We can then cut off the part after the event was crossed
+                        event_sigmoid = (
+                            pybamm.sigmoid(0, init_sign * event.expression, k) * 2 - 1
+                        )
+                        event_casadi = process(
+                            event_sigmoid, f"event_{n}", use_jacobian=False
+                        )[0]
+                        # use the actual casadi object as this will go into the rhs
+                        casadi_terminate_events.append(event_casadi)
+                else:
+                    # use the function call
+                    event_eval = process(
+                        event.expression, f"event_{n}", use_jacobian=False
+                    )[1]
+                    if event.event_type == pybamm.EventType.TERMINATION:
+                        terminate_events_eval.append(event_eval)
+                    elif event.event_type == pybamm.EventType.INTERPOLANT_EXTRAPOLATION:
+                        interpolant_extrapolation_events_eval.append(event_eval)
+
+            # Add the solver attributes
+            model.rhs_eval = rhs_eval
+            model.algebraic_eval = algebraic_eval
+            model.jac_algebraic_eval = jac_algebraic
+            model.casadi_terminate_events = casadi_terminate_events
+            model.terminate_events_eval = terminate_events_eval
+            model.discontinuity_events_eval = discontinuity_events_eval
+            model.interpolant_extrapolation_events_eval = (
+                interpolant_extrapolation_events_eval
+            )
+
+            # Save CasADi functions for the CasADi solver
+            # Note: when we pass to casadi the ode part of the problem must be in
+            # explicit form so we pre-multiply by the inverse of the mass matrix
+            if isinstance(self.root_method, pybamm.CasadiAlgebraicSolver) or isinstance(
+                self, (pybamm.CasadiSolver, pybamm.CasadiAlgebraicSolver)
+            ):
+                # can use DAE solver to solve model with algebraic equations only
+                if len(model.rhs) > 0:
+                    mass_matrix_inv = casadi.MX(model.mass_matrix_inv.entries)
+                    explicit_rhs = mass_matrix_inv @ rhs(
+                        t_casadi, y_casadi, p_casadi_stacked
                     )
-                    # We create a sigmoid for each event which will multiply the
-                    # rhs. Doing * 2 - 1 ensures that when the event is crossed,
-                    # the sigmoid is zero. Hence the rhs is zero and the solution
-                    # stays constant for the rest of the simulation period
-                    # We can then cut off the part after the event was crossed
-                    event_sigmoid = (
-                        pybamm.sigmoid(0, init_sign * event.expression, k) * 2 - 1
+                    model.casadi_rhs = casadi.Function(
+                        "rhs", [t_casadi, y_casadi, p_casadi_stacked], [explicit_rhs]
                     )
-                    event_casadi = process(
-                        event_sigmoid, f"event_{n}", use_jacobian=False
-                    )[0]
-                    # use the actual casadi object as this will go into the rhs
-                    casadi_terminate_events.append(event_casadi)
+                model.casadi_algebraic = algebraic
+            if len(model.rhs) == 0:
+                # No rhs equations: residuals is algebraic only
+                model.residuals_eval = Residuals(algebraic, "residuals", model)
+                model.jacobian_eval = jac_algebraic
+            elif len(model.algebraic) == 0:
+                # No algebraic equations: residuals is rhs only
+                model.residuals_eval = Residuals(rhs, "residuals", model)
+                model.jacobian_eval = jac_rhs
+            # Calculate consistent initial conditions for the algebraic equations
             else:
-                # use the function call
-                event_eval = process(
-                    event.expression, f"event_{n}", use_jacobian=False
-                )[1]
-                if event.event_type == pybamm.EventType.TERMINATION:
-                    terminate_events_eval.append(event_eval)
-                elif event.event_type == pybamm.EventType.INTERPOLANT_EXTRAPOLATION:
-                    interpolant_extrapolation_events_eval.append(event_eval)
-
-        # Add the solver attributes
-        model.init_eval = init_eval
-        model.rhs_eval = rhs_eval
-        model.algebraic_eval = algebraic_eval
-        model.jac_algebraic_eval = jac_algebraic
-        model.casadi_terminate_events = casadi_terminate_events
-        model.terminate_events_eval = terminate_events_eval
-        model.discontinuity_events_eval = discontinuity_events_eval
-        model.interpolant_extrapolation_events_eval = (
-            interpolant_extrapolation_events_eval
-        )
-
-        # Save CasADi functions for the CasADi solver
-        # Note: when we pass to casadi the ode part of the problem must be in explicit
-        # form so we pre-multiply by the inverse of the mass matrix
-        if isinstance(self.root_method, pybamm.CasadiAlgebraicSolver) or isinstance(
-            self, (pybamm.CasadiSolver, pybamm.CasadiAlgebraicSolver)
-        ):
-            # can use DAE solver to solve model with algebraic equations only
-            if len(model.rhs) > 0:
-                mass_matrix_inv = casadi.MX(model.mass_matrix_inv.entries)
-                explicit_rhs = mass_matrix_inv @ rhs(
-                    t_casadi, y_casadi, p_casadi_stacked
-                )
-                model.casadi_rhs = casadi.Function(
-                    "rhs", [t_casadi, y_casadi, p_casadi_stacked], [explicit_rhs]
+                all_states = pybamm.NumpyConcatenation(
+                    model.concatenated_rhs, model.concatenated_algebraic
                 )
-            model.casadi_algebraic = algebraic
-        if len(model.rhs) == 0:
-            # No rhs equations: residuals is algebraic only
-            model.residuals_eval = Residuals(algebraic, "residuals", model)
-            model.jacobian_eval = jac_algebraic
-        elif len(model.algebraic) == 0:
-            # No algebraic equations: residuals is rhs only
-            model.residuals_eval = Residuals(rhs, "residuals", model)
-            model.jacobian_eval = jac_rhs
-        # Calculate consistent initial conditions for the algebraic equations
-        else:
-            all_states = pybamm.NumpyConcatenation(
-                model.concatenated_rhs, model.concatenated_algebraic
-            )
-            # Process again, uses caching so should be quick
-            residuals_eval, jacobian_eval = process(all_states, "residuals")[1:]
-            model.residuals_eval = residuals_eval
-            model.jacobian_eval = jacobian_eval
+                # Process again, uses caching so should be quick
+                residuals_eval, jacobian_eval = process(all_states, "residuals")[1:]
+                model.residuals_eval = residuals_eval
+                model.jacobian_eval = jacobian_eval
 
         pybamm.logger.info("Finish solver set-up")
 
@@ -650,9 +655,7 @@ def solve(
             # Check that initial conditions have not been updated
             if ics_set_up.id != model.concatenated_initial_conditions.id:
                 # If the new initial conditions are different, set up again
-                # Doing the whole setup again might be slow, but no need to prematurely
-                # optimize this
-                self.set_up(model, ext_and_inputs_list[0], t_eval)
+                self.set_up(model, ext_and_inputs_list[0], t_eval, ics_only=True)
                 self.models_set_up[model][
                     "initial conditions"
                 ] = model.concatenated_initial_conditions
@@ -984,7 +987,7 @@ def step(
                 model.y0 = old_solution.all_ys[-1][:, -1]
             else:
                 model.y0 = (
-                    model.set_initial_conditions_from(old_solution)
+                    model.set_initial_conditions_from(old_solution, inplace=False)
                     .concatenated_initial_conditions.evaluate(0, inputs=ext_and_inputs)
                     .flatten()
                 )
diff --git a/pybamm/solvers/casadi_algebraic_solver.py b/pybamm/solvers/casadi_algebraic_solver.py
index ef87e0968c..739c519aa2 100644
--- a/pybamm/solvers/casadi_algebraic_solver.py
+++ b/pybamm/solvers/casadi_algebraic_solver.py
@@ -116,7 +116,9 @@ def _integrate(self, model, t_eval, inputs_dict=None):
                             event.event_type
                             == pybamm.EventType.INTERPOLANT_EXTRAPOLATION
                             and (
-                                event.expression.evaluate(0, y0.full(), inputs=inputs)
+                                event.expression.evaluate(
+                                    0, y0.full(), inputs=inputs_dict
+                                )
                                 < self.extrap_tol
                             )
                         ):
diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py
index 7023d921c6..9d240750f8 100644
--- a/pybamm/solvers/solution.py
+++ b/pybamm/solvers/solution.py
@@ -104,6 +104,12 @@ def __init__(
         # Add self as sub-solution for compatibility with ProcessedVariable
         self._sub_solutions = [self]
 
+        # initialize empty cycles
+        self._cycles = []
+
+        # Initialize empty summary variables
+        self._summary_variables = None
+
         # Solution now uses CasADi
         pybamm.citations.register("Andersson2019")
 
@@ -185,6 +191,35 @@ def termination(self, value):
         """Updates the reason for termination"""
         self._termination = value
 
+    @property
+    def first_state(self):
+        """
+        A Solution object that only contains the first state. This is faster to evaluate
+        than the full solution when only the first state is needed (e.g. to initialize
+        a model with the solution)
+        """
+        try:
+            return self._first_state
+        except AttributeError:
+            new_sol = Solution(
+                self.all_ts[0][:1],
+                self.all_ys[0][:, :1],
+                self.all_models[:1],
+                self.all_inputs[:1],
+                None,
+                None,
+                "success",
+            )
+            new_sol._all_inputs_casadi = self.all_inputs_casadi[:1]
+            new_sol._sub_solutions = self.sub_solutions[:1]
+
+            new_sol.solve_time = 0
+            new_sol.integration_time = 0
+            new_sol.set_up_time = 0
+
+            self._first_state = new_sol
+            return self._first_state
+
     @property
     def last_state(self):
         """
@@ -205,7 +240,7 @@ def last_state(self):
                 self.termination,
             )
             new_sol._all_inputs_casadi = self.all_inputs_casadi[-1:]
-            new_sol._sub_solutions = self.sub_solutions
+            new_sol._sub_solutions = self.sub_solutions[-1:]
 
             new_sol.solve_time = 0
             new_sol.integration_time = 0
@@ -218,6 +253,30 @@ def last_state(self):
     def total_time(self):
         return self.set_up_time + self.solve_time
 
+    @property
+    def cycles(self):
+        return self._cycles
+
+    @cycles.setter
+    def cycles(self, cycles):
+        self._cycles = cycles
+
+    @property
+    def summary_variables(self):
+        return self._summary_variables
+
+    def set_summary_variables(self, all_summary_variables):
+        summary_variables = {var: [] for var in all_summary_variables[0]}
+        for sum_vars in all_summary_variables:
+            for name, value in sum_vars.items():
+                summary_variables[name].append(value)
+
+        summary_variables["Cycle number"] = range(1, len(all_summary_variables) + 1)
+        self.all_summary_variables = all_summary_variables
+        self._summary_variables = pybamm.FuzzyDict(
+            {name: np.array(value) for name, value in summary_variables.items()}
+        )
+
     def update(self, variables):
         """Add ProcessedVariables to the dictionary of variables in the solution"""
         # Convert single entry to list
@@ -485,7 +544,8 @@ def __add__(self, other):
 
     def __radd__(self, other):
         """
-        Function to deal with the case `None + Solution` (returns `Solution`)
+        Right-side adding with special handling for the case None + Solution (returns
+        Solution)
         """
         if other is None:
             return self.copy()
@@ -495,7 +555,7 @@ def __radd__(self, other):
             )
 
     def copy(self):
-        new_sol = Solution(
+        new_sol = self.__class__(
             self.all_ts,
             self.all_ys,
             self.all_models,
@@ -512,3 +572,178 @@ def copy(self):
         new_sol.set_up_time = self.set_up_time
 
         return new_sol
+
+
+def make_cycle_solution(step_solutions, esoh_sim=None, save_this_cycle=True):
+    """
+    Function to create a Solution for an entire cycle, and associated summary variables
+
+    Parameters
+    ----------
+    step_solutions : list of :class:`Solution`
+        Step solutions that form the entire cycle
+    esoh_sim : :class:`pybamm.Simulation`, optional
+        A simulation, whose model should be a :class:`pybamm.lithium_ion.ElectrodeSOH`
+        model, which is used to calculate some of the summary variables. If `None`
+        (default) then only summary variables that do not require the eSOH calculation
+        are calculated. See [1] for more details on eSOH variables.
+    save_this_cycle : bool, optional
+        Whether to save the entire cycle variables or just the summary variables.
+        Default True
+
+    Returns
+    -------
+    cycle_solution : :class:`pybamm.Solution` or None
+        The Solution object for this cycle, or None (if save_this_cycle is False)
+    cycle_summary_variables : dict
+        Dictionary of summary variables for this cycle
+
+    References
+    ----------
+    .. [1] Mohtat, P., Lee, S., Siegel, J. B., & Stefanopoulou, A. G. (2019). Towards
+    better estimability of electrode-specific state of health: Decoding the cell
+    expansion. Journal of Power Sources, 427, 101-111.
+
+    """
+    sum_sols = step_solutions[0].copy()
+    for step_solution in step_solutions[1:]:
+        sum_sols = sum_sols + step_solution
+
+    cycle_solution = Solution(
+        sum_sols.all_ts,
+        sum_sols.all_ys,
+        sum_sols.all_models,
+        sum_sols.all_inputs,
+        sum_sols.t_event,
+        sum_sols.y_event,
+        sum_sols.termination,
+    )
+    cycle_solution._all_inputs_casadi = sum_sols.all_inputs_casadi
+    cycle_solution._sub_solutions = sum_sols.sub_solutions
+
+    cycle_solution.solve_time = sum_sols.solve_time
+    cycle_solution.integration_time = sum_sols.integration_time
+    cycle_solution.set_up_time = sum_sols.set_up_time
+
+    cycle_solution.steps = step_solutions
+
+    cycle_summary_variables = get_cycle_summary_variables(cycle_solution, esoh_sim)
+
+    if save_this_cycle:
+        cycle_solution.cycle_summary_variables = cycle_summary_variables
+    else:
+        cycle_solution = None
+
+    return cycle_solution, cycle_summary_variables
+
+
+def get_cycle_summary_variables(cycle_solution, esoh_sim):
+    Q = cycle_solution["Discharge capacity [A.h]"].data
+    min_Q = np.min(Q)
+    max_Q = np.max(Q)
+
+    cycle_summary_variables = pybamm.FuzzyDict(
+        {
+            "Minimum measured discharge capacity [A.h]": min_Q,
+            "Maximum measured discharge capacity [A.h]": max_Q,
+            "Measured capacity [A.h]": max_Q - min_Q,
+        }
+    )
+
+    degradation_variables = [
+        "Negative electrode capacity [A.h]",
+        "Positive electrode capacity [A.h]",
+        # LAM, LLI
+        "Loss of active material in negative electrode [%]",
+        "Loss of active material in positive electrode [%]",
+        "Loss of lithium inventory [%]",
+        "Loss of lithium inventory, including electrolyte [%]",
+        # Total lithium
+        "Total lithium [mol]",
+        "Total lithium in electrolyte [mol]",
+        "Total lithium in positive electrode [mol]",
+        "Total lithium in negative electrode [mol]",
+        "Total lithium in particles [mol]",
+        # Lithium lost
+        "Total lithium lost [mol]",
+        "Total lithium lost from particles [mol]",
+        "Total lithium lost from electrolyte [mol]",
+        "Loss of lithium to negative electrode SEI [mol]",
+        "Loss of lithium to positive electrode SEI [mol]",
+        "Loss of lithium to negative electrode lithium plating [mol]",
+        "Loss of lithium to positive electrode lithium plating [mol]",
+        "Loss of capacity to negative electrode SEI [A.h]",
+        "Loss of capacity to positive electrode SEI [A.h]",
+        "Loss of capacity to negative electrode lithium plating [A.h]",
+        "Loss of capacity to positive electrode lithium plating [A.h]",
+        "Total lithium lost to side reactions [mol]",
+        "Total capacity lost to side reactions [A.h]",
+        # Resistance
+        "Local ECM resistance [Ohm]",
+    ]
+    first_state = cycle_solution.first_state
+    last_state = cycle_solution.last_state
+    for var in degradation_variables:
+        data_first = first_state[var].data
+        data_last = last_state[var].data
+        cycle_summary_variables[var] = data_last[0]
+        var_lowercase = var[0].lower() + var[1:]
+        cycle_summary_variables["Change in " + var_lowercase] = (
+            data_last[0] - data_first[0]
+        )
+
+    if esoh_sim is not None:
+        V_min = esoh_sim.parameter_values["Lower voltage cut-off [V]"]
+        V_max = esoh_sim.parameter_values["Upper voltage cut-off [V]"]
+        C_n = last_state["Negative electrode capacity [A.h]"].data[0]
+        C_p = last_state["Positive electrode capacity [A.h]"].data[0]
+        n_Li = last_state["Total lithium in particles [mol]"].data[0]
+        if esoh_sim.solution is not None:
+            # initialize with previous solution if it is available
+            esoh_sim.built_model.set_initial_conditions_from(esoh_sim.solution)
+            solver = None
+        else:
+            x_100_init = np.max(cycle_solution["Negative electrode SOC"].data)
+            # make sure x_0 > 0
+            C_init = np.minimum(0.95 * (C_n * x_100_init), max_Q - min_Q)
+
+            # Solve the esoh model and add outputs to the summary variables
+            # use CasadiAlgebraicSolver if there are interpolants
+            if isinstance(
+                esoh_sim.parameter_values["Negative electrode OCP [V]"], tuple
+            ) or isinstance(
+                esoh_sim.parameter_values["Positive electrode OCP [V]"], tuple
+            ):
+                solver = pybamm.CasadiAlgebraicSolver()
+                # Choose x_100_init so as not to violate the interpolation limits
+                y_100_min = np.min(
+                    esoh_sim.parameter_values["Positive electrode OCP [V]"][1][:, 0]
+                )
+                x_100_max = (
+                    n_Li * pybamm.constants.F.value / 3600 - y_100_min * C_p
+                ) / C_n
+                x_100_init = np.minimum(x_100_init, 0.99 * x_100_max)
+            else:
+                solver = None
+            # Update initial conditions using the cycle solution
+            esoh_sim.build()
+            esoh_sim.built_model.set_initial_conditions_from(
+                {"x_100": x_100_init, "C": C_init}
+            )
+        esoh_sol = esoh_sim.solve(
+            [0],
+            inputs={
+                "V_min": V_min,
+                "V_max": V_max,
+                "C_n": C_n,
+                "C_p": C_p,
+                "n_Li": n_Li,
+            },
+            solver=solver,
+        )
+        for var in esoh_sim.built_model.variables:
+            cycle_summary_variables[var] = esoh_sol[var].data[0]
+
+        cycle_summary_variables["Capacity [A.h]"] = cycle_summary_variables["C"]
+
+    return cycle_summary_variables
diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py
index 166bee579d..1179ac1cbe 100644
--- a/tests/integration/test_models/standard_output_tests.py
+++ b/tests/integration/test_models/standard_output_tests.py
@@ -52,6 +52,7 @@ def test_all(self, skip_first_timestep=False):
 
         if self.chemistry == "Lithium-ion":
             self.run_test_class(ParticleConcentrationTests)
+            self.run_test_class(DegradationTests)
 
         if self.model.options["convection"] != "none":
             self.run_test_class(VelocityTests)
@@ -418,7 +419,7 @@ def __init__(self, model, param, disc, solution, operating_condition):
         self.c_e_n_av = solution["X-averaged negative electrolyte concentration"]
         self.c_e_s_av = solution["X-averaged separator electrolyte concentration"]
         self.c_e_p_av = solution["X-averaged positive electrolyte concentration"]
-        self.c_e_tot = solution["Total concentration in electrolyte [mol]"]
+        self.c_e_tot = solution["Total lithium in electrolyte [mol]"]
 
         self.N_e_hat = solution["Electrolyte flux"]
         # self.N_e_hat = solution["Reduced cation flux"]
@@ -724,3 +725,33 @@ def test_all(self):
         self.test_velocity_boundaries()
         self.test_vertical_velocity()
         self.test_velocity_vs_current()
+
+
+class DegradationTests(BaseOutputTest):
+    def __init__(self, model, param, disc, solution, operating_condition):
+        super().__init__(model, param, disc, solution, operating_condition)
+
+        self.LAM_ne = solution["Loss of active material in negative electrode [%]"]
+        self.LAM_pe = solution["Loss of active material in positive electrode [%]"]
+        self.LLI = solution["Loss of lithium inventory [%]"]
+        self.n_Li_lost = solution["Total lithium lost [mol]"]
+        self.n_Li_lost_rxn = solution["Total lithium lost to side reactions [mol]"]
+
+    def test_degradation_modes(self):
+        """Test degradation modes are between 0 and 100%"""
+        np.testing.assert_array_less(-1e-3, self.LLI(self.t))
+        np.testing.assert_array_less(-1e-3, self.LAM_ne(self.t))
+        np.testing.assert_array_less(-1e-3, self.LAM_pe(self.t))
+        np.testing.assert_array_less(self.LLI(self.t), 100)
+        np.testing.assert_array_less(self.LAM_ne(self.t), 100)
+        np.testing.assert_array_less(self.LAM_pe(self.t), 100)
+
+    def test_lithium_lost(self):
+        """Test the two ways of measuring lithium lost give the same value"""
+        np.testing.assert_array_almost_equal(
+            self.n_Li_lost(self.t), self.n_Li_lost_rxn(self.t), decimal=3
+        )
+
+    def test_all(self):
+        self.test_degradation_modes()
+        self.test_lithium_lost()
diff --git a/tests/integration/test_models/test_submodels/test_external_circuit/test_function_control.py b/tests/integration/test_models/test_submodels/test_external_circuit/test_function_control.py
index 29d388a3c4..c3a2bc243a 100644
--- a/tests/integration/test_models/test_submodels/test_external_circuit/test_function_control.py
+++ b/tests/integration/test_models/test_submodels/test_external_circuit/test_function_control.py
@@ -55,7 +55,7 @@ def constant_current(variables):
     def test_constant_voltage(self):
         def constant_voltage(variables):
             V = variables["Terminal voltage [V]"]
-            return V - 4.1
+            return V - 4.08
 
         # load models
         models = [
@@ -66,8 +66,8 @@ def constant_voltage(variables):
         # load parameter values and process models and geometry
         params = [model.default_parameter_values for model in models]
 
-        # First model: 4.1V charge
-        params[0].update({"Voltage function [V]": 4.1}, check_already_exists=False)
+        # First model: 4.08V charge
+        params[0].update({"Voltage function [V]": 4.08}, check_already_exists=False)
 
         # set parameters and discretise models
         var = pybamm.standard_spatial_vars
diff --git a/tests/unit/test_citations.py b/tests/unit/test_citations.py
index f43f50f3ed..3dce857540 100644
--- a/tests/unit/test_citations.py
+++ b/tests/unit/test_citations.py
@@ -173,6 +173,14 @@ def test_reniers_2019(self):
         pybamm.active_material.ReactionDriven(None, None, None, True)
         self.assertIn("Reniers2019", citations._papers_to_cite)
 
+    def test_mohtat_2019(self):
+        citations = pybamm.citations
+
+        citations._reset()
+        self.assertNotIn("Mohtat2019", citations._papers_to_cite)
+        pybamm.lithium_ion.ElectrodeSOH()
+        self.assertIn("Mohtat2019", citations._papers_to_cite)
+
     def test_parameter_citations(self):
         citations = pybamm.citations
 
diff --git a/tests/unit/test_experiments/test_experiment.py b/tests/unit/test_experiments/test_experiment.py
index 4a39436fe2..df88b4a937 100644
--- a/tests/unit/test_experiments/test_experiment.py
+++ b/tests/unit/test_experiments/test_experiment.py
@@ -38,19 +38,18 @@ def test_read_strings(self):
                 "Run US06 (V) for 5 minutes",
                 "Run US06 (W) for 0.5 hours",
             ],
-            {"test": "test"}, drive_cycles={"US06": drive_cycle},
+            {"test": "test"},
+            drive_cycles={"US06": drive_cycle},
             period="20 seconds",
         )
 
         # Calculation for operating conditions of drive cycle
         time_0 = drive_cycle[:, 0][-1]
         period_0 = numpy.min(numpy.diff(drive_cycle[:, 0]))
-        drive_cycle_1 = experiment.extend_drive_cycle(
-            drive_cycle, end_time=300)
+        drive_cycle_1 = experiment.extend_drive_cycle(drive_cycle, end_time=300)
         time_1 = drive_cycle_1[:, 0][-1]
         period_1 = numpy.min(numpy.diff(drive_cycle_1[:, 0]))
-        drive_cycle_2 = experiment.extend_drive_cycle(
-            drive_cycle, end_time=1800)
+        drive_cycle_2 = experiment.extend_drive_cycle(drive_cycle, end_time=1800)
         time_2 = drive_cycle_2[:, 0][-1]
         period_2 = numpy.min(numpy.diff(drive_cycle_2[:, 0]))
 
@@ -74,18 +73,20 @@ def test_read_strings(self):
         )
         # Check drive cycle operating conditions
         self.assertTrue(
-            ((experiment.operating_conditions[-3][0] == drive_cycle).all() & (
-                experiment.operating_conditions[-3][1] == "A") & (
-                experiment.operating_conditions[-3][2] == time_0).all() & (
-                experiment.operating_conditions[-3][3] == period_0).all() & (
-                experiment.operating_conditions[-2][0] == drive_cycle_1).all() & (
-                experiment.operating_conditions[-2][1] == "V") & (
-                experiment.operating_conditions[-2][2] == time_1).all() & (
-                experiment.operating_conditions[-2][3] == period_1).all() & (
-                experiment.operating_conditions[-1][0] == drive_cycle_2).all() & (
-                experiment.operating_conditions[-1][1] == "W") & (
-                experiment.operating_conditions[-1][2] == time_2).all() & (
-                experiment.operating_conditions[-1][3] == period_2).all())
+            (
+                (experiment.operating_conditions[-3][0] == drive_cycle).all()
+                & (experiment.operating_conditions[-3][1] == "A")
+                & (experiment.operating_conditions[-3][2] == time_0).all()
+                & (experiment.operating_conditions[-3][3] == period_0).all()
+                & (experiment.operating_conditions[-2][0] == drive_cycle_1).all()
+                & (experiment.operating_conditions[-2][1] == "V")
+                & (experiment.operating_conditions[-2][2] == time_1).all()
+                & (experiment.operating_conditions[-2][3] == period_1).all()
+                & (experiment.operating_conditions[-1][0] == drive_cycle_2).all()
+                & (experiment.operating_conditions[-1][1] == "W")
+                & (experiment.operating_conditions[-1][2] == time_2).all()
+                & (experiment.operating_conditions[-1][3] == period_2).all()
+            )
         )
         self.assertEqual(
             experiment.events,
@@ -170,21 +171,13 @@ def test_bad_strings(self):
             pybamm.Experiment(["Discharge at 1 A at 2 hours"])
         with self.assertRaisesRegex(ValueError, "Instruction must be"):
             pybamm.Experiment(["Run at 1 A for 2 hours"])
-        with self.assertRaisesRegex(
-            ValueError, "Type of drive cycle must be"
-        ):
+        with self.assertRaisesRegex(ValueError, "Type of drive cycle must be"):
             pybamm.Experiment(["Run US06 for 2 hours"])
-        with self.assertRaisesRegex(
-            ValueError, "Instruction must be"
-        ):
+        with self.assertRaisesRegex(ValueError, "Instruction must be"):
             pybamm.Experiment(["Run at at 1 A for 2 hours"])
-        with self.assertRaisesRegex(
-            ValueError, "Instruction must be"
-        ):
+        with self.assertRaisesRegex(ValueError, "Instruction must be"):
             pybamm.Experiment(["Play at 1 A for 2 hours"])
-        with self.assertRaisesRegex(
-            ValueError, "Instruction"
-        ):
+        with self.assertRaisesRegex(ValueError, "Instruction"):
             pybamm.Experiment(["Cell Charge at 1 A for 2 hours"])
         with self.assertRaisesRegex(ValueError, "units must be"):
             pybamm.Experiment(["Discharge at 1 B for 2 hours"])
@@ -195,6 +188,41 @@ def test_bad_strings(self):
         ):
             pybamm.Experiment([], "not a dictionary")
 
+    def test_termination(self):
+        experiment = pybamm.Experiment(["Discharge at 1 C for 20 seconds"])
+        self.assertEqual(experiment.termination, {})
+
+        experiment = pybamm.Experiment(
+            ["Discharge at 1 C for 20 seconds"], termination="80.7% capacity"
+        )
+        self.assertEqual(experiment.termination, {"capacity": (80.7, "%")})
+        experiment = pybamm.Experiment(
+            ["Discharge at 1 C for 20 seconds"], termination="80.7 % capacity"
+        )
+        self.assertEqual(experiment.termination, {"capacity": (80.7, "%")})
+
+        experiment = pybamm.Experiment(
+            ["Discharge at 1 C for 20 seconds"], termination="4.1Ah capacity"
+        )
+        self.assertEqual(experiment.termination, {"capacity": (4.1, "Ah")})
+        experiment = pybamm.Experiment(
+            ["Discharge at 1 C for 20 seconds"], termination="4.1 A.h capacity"
+        )
+        self.assertEqual(experiment.termination, {"capacity": (4.1, "Ah")})
+
+        with self.assertRaisesRegex(ValueError, "Only capacity"):
+            experiment = pybamm.Experiment(
+                ["Discharge at 1 C for 20 seconds"], termination="bla bla capacity bla"
+            )
+        with self.assertRaisesRegex(ValueError, "Only capacity"):
+            experiment = pybamm.Experiment(
+                ["Discharge at 1 C for 20 seconds"], termination="4 A.h something else"
+            )
+        with self.assertRaisesRegex(ValueError, "Capacity termination"):
+            experiment = pybamm.Experiment(
+                ["Discharge at 1 C for 20 seconds"], termination="1 capacity"
+            )
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py
index 78898798a8..9362241919 100644
--- a/tests/unit/test_experiments/test_simulation_with_experiment.py
+++ b/tests/unit/test_experiments/test_simulation_with_experiment.py
@@ -107,6 +107,7 @@ def test_run_experiment(self):
         self.assertGreater(sol2.t[-1], sol.t[-1])
         self.assertEqual(sol2.cycles[0], sol.cycles[0])
         self.assertEqual(len(sol2.cycles), 2)
+        # Check starting solution is unchanged
         self.assertEqual(len(sol.cycles), 1)
 
         # save
@@ -118,17 +119,19 @@ def test_run_experiment(self):
     def test_run_experiment_old_setup_type(self):
         experiment = pybamm.Experiment(
             [
-                "Discharge at C/20 for 1 hour",
-                "Charge at 1 A until 4.1 V",
-                "Hold at 4.1 V until C/2",
-                "Discharge at 2 W for 1 hour",
+                (
+                    "Discharge at C/20 for 1 hour",
+                    "Charge at 1 A until 4.1 V",
+                    "Hold at 4.1 V until C/2",
+                    "Discharge at 2 W for 1 hour",
+                ),
             ],
             use_simulation_setup_type="old",
         )
         model = pybamm.lithium_ion.SPM()
         sim = pybamm.Simulation(model, experiment=experiment)
-        sim.solve(solver=pybamm.CasadiSolver())
-        self.assertEqual(sim._solution.termination, "final time")
+        solution1 = sim.solve(solver=pybamm.CasadiSolver())
+        self.assertEqual(solution1.termination, "final time")
 
     def test_run_experiment_breaks_early(self):
         experiment = pybamm.Experiment(["Discharge at 2 C for 1 hour"])
@@ -141,6 +144,84 @@ def test_run_experiment_breaks_early(self):
         pybamm.set_logging_level("WARNING")
         self.assertEqual(sim._solution, None)
 
+    def test_run_experiment_termination(self):
+        # with percent
+        experiment = pybamm.Experiment(
+            [
+                (
+                    "Discharge at 1C until 3V",
+                    "Charge at 1C until 4.2 V",
+                    "Hold at 4.2V until C/10",
+                ),
+            ]
+            * 10,
+            termination="99% capacity",
+        )
+        model = pybamm.lithium_ion.SPM({"SEI": "ec reaction limited"})
+        param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Chen2020)
+        param["SEI kinetic rate constant [m.s-1]"] = 1e-14
+        sim = pybamm.Simulation(model, experiment=experiment, parameter_values=param)
+        sol = sim.solve(solver=pybamm.CasadiSolver())
+        C = sol.summary_variables["Capacity [A.h]"]
+        np.testing.assert_array_less(np.diff(C), 0)
+        # all but the last value should be above the termination condition
+        np.testing.assert_array_less(0.99 * C[0], C[:-1])
+
+        # with Ah value
+        experiment = pybamm.Experiment(
+            [
+                (
+                    "Discharge at 1C until 3V",
+                    "Charge at 1C until 4.2 V",
+                    "Hold at 4.2V until C/10",
+                ),
+            ]
+            * 10,
+            termination="5.04Ah capacity",
+        )
+        model = pybamm.lithium_ion.SPM({"SEI": "ec reaction limited"})
+        param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Chen2020)
+        param["SEI kinetic rate constant [m.s-1]"] = 1e-14
+        sim = pybamm.Simulation(model, experiment=experiment, parameter_values=param)
+        sol = sim.solve(solver=pybamm.CasadiSolver())
+        # all but the last value should be above the termination condition
+        np.testing.assert_array_less(5.04, C[:-1])
+
+    def test_save_at_cycles(self):
+        experiment = pybamm.Experiment(
+            [
+                (
+                    "Discharge at 1C until 3.3V",
+                    "Charge at 1C until 4.1 V",
+                    "Hold at 4.1V until C/10",
+                ),
+            ]
+            * 10,
+        )
+        model = pybamm.lithium_ion.SPM()
+        sim = pybamm.Simulation(model, experiment=experiment)
+        sol = sim.solve(
+            solver=pybamm.CasadiSolver("fast with events"), save_at_cycles=2
+        )
+        # Solution saves "None" for the cycles that are not saved
+        for cycle_num in [2, 4, 6, 8]:
+            self.assertIsNone(sol.cycles[cycle_num])
+        for cycle_num in [0, 1, 3, 5, 7, 9]:
+            self.assertIsNotNone(sol.cycles[cycle_num])
+        # Summary variables are not None
+        self.assertIsNotNone(sol.summary_variables["Capacity [A.h]"])
+
+        sol = sim.solve(
+            solver=pybamm.CasadiSolver("fast with events"), save_at_cycles=[3, 4, 5, 9]
+        )
+        # Note offset by 1 (0th cycle is cycle 1)
+        for cycle_num in [1, 5, 6, 7, 9]:
+            self.assertIsNone(sol.cycles[cycle_num])
+        for cycle_num in [0, 2, 3, 4, 8]:
+            self.assertIsNotNone(sol.cycles[cycle_num])
+        # Summary variables are not None
+        self.assertIsNotNone(sol.summary_variables["Capacity [A.h]"])
+
     def test_inputs(self):
         experiment = pybamm.Experiment(
             ["Discharge at C/2 for 1 hour", "Rest for 1 hour"]
diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py
index 02076633d7..2a9d0679a3 100644
--- a/tests/unit/test_expression_tree/test_interpolant.py
+++ b/tests/unit/test_expression_tree/test_interpolant.py
@@ -92,7 +92,9 @@ def test_name(self):
         a = pybamm.Symbol("a")
         x = np.linspace(0, 1, 200)
         interp = pybamm.Interpolant(x, x, a, "name")
-        self.assertEqual(interp.name, "interpolating function (name)")
+        self.assertEqual(interp.name, "name")
+        interp = pybamm.Interpolant(x, x, a)
+        self.assertEqual(interp.name, "interpolating_function")
 
     def test_diff(self):
         x = np.linspace(0, 1, 200)
diff --git a/tests/unit/test_models/test_base_model.py b/tests/unit/test_models/test_base_model.py
index 9910c52c17..9ad1ce6cc4 100644
--- a/tests/unit/test_models/test_base_model.py
+++ b/tests/unit/test_models/test_base_model.py
@@ -909,15 +909,15 @@ def test_set_initial_condition_errors(self):
         ):
             model.set_initial_conditions_from({"var": np.ones((5, 6, 7, 8))})
 
-        var_concat_neg = pybamm.Variable("var_concat_neg", domain="negative electrode")
-        var_concat_sep = pybamm.Variable("var_concat_sep", domain="separator")
+        var_concat_neg = pybamm.Variable("var concat neg", domain="negative electrode")
+        var_concat_sep = pybamm.Variable("var concat sep", domain="separator")
         var_concat = pybamm.concatenation(var_concat_neg, var_concat_sep)
         model.algebraic = {var_concat: -var_concat}
         model.initial_conditions = {var_concat: 1}
         with self.assertRaisesRegex(
             NotImplementedError, "Variable in concatenation must be 1D"
         ):
-            model.set_initial_conditions_from({"var_concat_neg": np.ones((5, 6, 7))})
+            model.set_initial_conditions_from({"var concat neg": np.ones((5, 6, 7))})
 
         # Inconsistent model and variable names
         model = pybamm.BaseModel()
@@ -935,50 +935,6 @@ def test_set_initial_condition_errors(self):
             model.set_initial_conditions_from({"wrong var": 2})
 
 
-class TestStandardBatteryBaseModel(unittest.TestCase):
-    def test_default_solver(self):
-        model = pybamm.BaseBatteryModel()
-        self.assertIsInstance(model.default_solver, pybamm.CasadiSolver)
-
-        # check that default_solver gives you a new solver, not an internal object
-        solver = model.default_solver
-        solver = pybamm.BaseModel()
-        self.assertIsInstance(model.default_solver, pybamm.CasadiSolver)
-        self.assertIsInstance(solver, pybamm.BaseModel)
-
-        # check that adding algebraic variables gives DAE solver
-        a = pybamm.Variable("a")
-        model.algebraic = {a: a - 1}
-        self.assertIsInstance(
-            model.default_solver, (pybamm.IDAKLUSolver, pybamm.CasadiSolver)
-        )
-
-        # Check that turning off jacobian gives casadi solver
-        model.use_jacobian = False
-        self.assertIsInstance(model.default_solver, pybamm.CasadiSolver)
-
-    def test_default_parameters(self):
-        # check parameters are read in ok
-        model = pybamm.BaseBatteryModel()
-        self.assertEqual(
-            model.default_parameter_values["Reference temperature [K]"], 298.15
-        )
-
-        # change path and try again
-
-        cwd = os.getcwd()
-        os.chdir("..")
-        model = pybamm.BaseBatteryModel()
-        self.assertEqual(
-            model.default_parameter_values["Reference temperature [K]"], 298.15
-        )
-        os.chdir(cwd)
-
-    def test_timescale(self):
-        model = pybamm.BaseModel()
-        self.assertEqual(model.timescale.evaluate(), 1)
-
-
 if __name__ == "__main__":
     print("Add -v for more debug output")
     import sys
diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
index 5fa27e96be..3f4f7ce25b 100644
--- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
+++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
@@ -5,6 +5,7 @@
 import pybamm
 import unittest
 import io
+import os
 from contextlib import redirect_stdout
 
 OPTIONS_DICT = {
@@ -263,6 +264,42 @@ def test_get_coupled_variables(self):
         with self.assertRaisesRegex(pybamm.ModelError, "Missing variable"):
             model.build_model()
 
+    def test_default_solver(self):
+        model = pybamm.BaseBatteryModel()
+        self.assertIsInstance(model.default_solver, pybamm.CasadiSolver)
+
+        # check that default_solver gives you a new solver, not an internal object
+        solver = model.default_solver
+        solver = pybamm.BaseModel()
+        self.assertIsInstance(model.default_solver, pybamm.CasadiSolver)
+        self.assertIsInstance(solver, pybamm.BaseModel)
+
+        # check that adding algebraic variables gives algebraic solver
+        a = pybamm.Variable("a")
+        model.algebraic = {a: a - 1}
+        self.assertIsInstance(model.default_solver, pybamm.CasadiAlgebraicSolver)
+
+    def test_default_parameters(self):
+        # check parameters are read in ok
+        model = pybamm.BaseBatteryModel()
+        self.assertEqual(
+            model.default_parameter_values["Reference temperature [K]"], 298.15
+        )
+
+        # change path and try again
+
+        cwd = os.getcwd()
+        os.chdir("..")
+        model = pybamm.BaseBatteryModel()
+        self.assertEqual(
+            model.default_parameter_values["Reference temperature [K]"], 298.15
+        )
+        os.chdir(cwd)
+
+    def test_timescale(self):
+        model = pybamm.BaseModel()
+        self.assertEqual(model.timescale.evaluate(), 1)
+
 
 class TestOptions(unittest.TestCase):
     def test_print_options(self):
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
new file mode 100644
index 0000000000..5ec8ffd64d
--- /dev/null
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
@@ -0,0 +1,48 @@
+#
+# Tests for the lithium-ion electrode-specific SOH model
+#
+import pybamm
+import unittest
+
+
+class TestElectrodeSOH(unittest.TestCase):
+    def test_known_solution(self):
+        model = pybamm.lithium_ion.ElectrodeSOH()
+
+        param = pybamm.LithiumIonParameters()
+        parameter_values = pybamm.ParameterValues(
+            chemistry=pybamm.parameter_sets.Marquis2019
+        )
+        sim = pybamm.Simulation(model, parameter_values=parameter_values)
+
+        V_min = 3
+        V_max = 4.2
+        C_n = parameter_values.evaluate(param.C_n_init)
+        C_p = parameter_values.evaluate(param.C_p_init)
+        n_Li = parameter_values.evaluate(param.n_Li_particles_init)
+
+        # Solve the model and check outputs
+        sol = sim.solve(
+            [0],
+            inputs={
+                "V_min": V_min,
+                "V_max": V_max,
+                "C_n": C_n,
+                "C_p": C_p,
+                "n_Li": n_Li,
+            },
+        )
+        self.assertAlmostEqual(sol["Up(y_100) - Un(x_100)"].data[0], V_max, places=5)
+        self.assertAlmostEqual(sol["Up(y_0) - Un(x_0)"].data[0], V_min, places=5)
+        self.assertAlmostEqual(sol["n_Li_100"].data[0], n_Li, places=5)
+        self.assertAlmostEqual(sol["n_Li_0"].data[0], n_Li, places=5)
+
+
+if __name__ == "__main__":
+    print("Add -v for more debug output")
+    import sys
+
+    if "-v" in sys.argv:
+        debug = True
+    pybamm.settings.debug_mode = True
+    unittest.main()
diff --git a/tests/unit/test_models/test_submodels/test_interface/test_inverse_kinetics/test_inverse_butler_volmer.py b/tests/unit/test_models/test_submodels/test_interface/test_inverse_kinetics/test_inverse_butler_volmer.py
index ff6f2ac3d1..fcb300cf42 100644
--- a/tests/unit/test_models/test_submodels/test_interface/test_inverse_kinetics/test_inverse_butler_volmer.py
+++ b/tests/unit/test_models/test_submodels/test_interface/test_inverse_kinetics/test_inverse_butler_volmer.py
@@ -11,7 +11,7 @@ class TestBaseModel(unittest.TestCase):
     def test_public_functions(self):
         param = pybamm.LithiumIonParameters()
 
-        a = pybamm.Scalar(0)
+        a = pybamm.Scalar(0.5)
         variables = {
             "Negative electrode open circuit potential": a,
             "Negative particle surface concentration": a,
diff --git a/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_butler_volmer.py b/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_butler_volmer.py
index d1f3f53304..03db13b94d 100644
--- a/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_butler_volmer.py
+++ b/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_butler_volmer.py
@@ -12,12 +12,12 @@ def test_public_functions(self):
         param = pybamm.LithiumIonParameters()
 
         a_n = pybamm.FullBroadcast(
-            pybamm.Scalar(0), ["negative electrode"], "current collector"
+            pybamm.Scalar(0.5), ["negative electrode"], "current collector"
         )
         a_p = pybamm.FullBroadcast(
-            pybamm.Scalar(0), ["positive electrode"], "current collector"
+            pybamm.Scalar(0.5), ["positive electrode"], "current collector"
         )
-        a = pybamm.Scalar(0)
+        a = pybamm.Scalar(0.5)
         variables = {
             "Current collector current density": a,
             "Negative electrode potential": a_n,
diff --git a/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_tafel.py b/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_tafel.py
index 9dadf64e88..ae0e96d3df 100644
--- a/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_tafel.py
+++ b/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_tafel.py
@@ -12,12 +12,12 @@ def test_public_function(self):
         param = pybamm.LithiumIonParameters()
 
         a_n = pybamm.FullBroadcast(
-            pybamm.Scalar(0), ["negative electrode"], "current collector"
+            pybamm.Scalar(0.5), ["negative electrode"], "current collector"
         )
         a_p = pybamm.FullBroadcast(
-            pybamm.Scalar(0), ["positive electrode"], "current collector"
+            pybamm.Scalar(0.5), ["positive electrode"], "current collector"
         )
-        a = pybamm.Scalar(0)
+        a = pybamm.Scalar(0.5)
         variables = {
             "Current collector current density": a,
             "Negative electrode potential": a_n,
diff --git a/tests/unit/test_models/test_submodels/test_interface/test_lead_acid.py b/tests/unit/test_models/test_submodels/test_interface/test_lead_acid.py
index 7a23a2ac52..2cc0268dfc 100644
--- a/tests/unit/test_models/test_submodels/test_interface/test_lead_acid.py
+++ b/tests/unit/test_models/test_submodels/test_interface/test_lead_acid.py
@@ -12,12 +12,12 @@ def test_public_functions(self):
         param = pybamm.LeadAcidParameters()
 
         a_n = pybamm.FullBroadcast(
-            pybamm.Scalar(0), ["negative electrode"], "current collector"
+            pybamm.Scalar(0.5), ["negative electrode"], "current collector"
         )
         a_p = pybamm.FullBroadcast(
-            pybamm.Scalar(0), ["positive electrode"], "current collector"
+            pybamm.Scalar(0.5), ["positive electrode"], "current collector"
         )
-        a = pybamm.Scalar(0)
+        a = pybamm.Scalar(0.5)
         variables = {
             "Current collector current density": a,
             "Negative electrode potential": a_n,
diff --git a/tests/unit/test_models/test_submodels/test_interface/test_lithium_ion.py b/tests/unit/test_models/test_submodels/test_interface/test_lithium_ion.py
index baa1f9e0a5..a34ac6aecf 100644
--- a/tests/unit/test_models/test_submodels/test_interface/test_lithium_ion.py
+++ b/tests/unit/test_models/test_submodels/test_interface/test_lithium_ion.py
@@ -12,12 +12,12 @@ def test_public_functions(self):
         param = pybamm.LithiumIonParameters()
 
         a_n = pybamm.FullBroadcast(
-            pybamm.Scalar(0), ["negative electrode"], "current collector"
+            pybamm.Scalar(0.5), ["negative electrode"], "current collector"
         )
         a_p = pybamm.FullBroadcast(
-            pybamm.Scalar(0), ["positive electrode"], "current collector"
+            pybamm.Scalar(0.5), ["positive electrode"], "current collector"
         )
-        a = pybamm.Scalar(0)
+        a = pybamm.Scalar(0.5)
         variables = {
             "Current collector current density": a,
             "Negative electrode potential": a_n,
diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index ca7b0e9526..1b1976d184 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -183,24 +183,38 @@ def test_step(self):
         dt = 0.001
         model = pybamm.lithium_ion.SPM()
         sim = pybamm.Simulation(model)
+
         sim.step(dt)  # 1 step stores first two points
         tau = sim.model.timescale.evaluate()
         self.assertEqual(sim.solution.t.size, 2)
         self.assertEqual(sim.solution.y.full()[0, :].size, 2)
         self.assertEqual(sim.solution.t[0], 0)
         self.assertEqual(sim.solution.t[1], dt / tau)
+        saved_sol = sim.solution
+
         sim.step(dt)  # automatically append the next step
         self.assertEqual(sim.solution.t.size, 3)
         self.assertEqual(sim.solution.y.full()[0, :].size, 3)
         self.assertEqual(sim.solution.t[0], 0)
         self.assertEqual(sim.solution.t[1], dt / tau)
         self.assertEqual(sim.solution.t[2], 2 * dt / tau)
+
         sim.step(dt, save=False)  # now only store the two end step points
         self.assertEqual(sim.solution.t.size, 2)
         self.assertEqual(sim.solution.y.full()[0, :].size, 2)
         self.assertEqual(sim.solution.t[0], 2 * dt / tau)
         self.assertEqual(sim.solution.t[1], 3 * dt / tau)
 
+        # Start from saved solution
+        sim.step(
+            dt, starting_solution=saved_sol
+        )  # now only store the two end step points
+        self.assertEqual(sim.solution.t.size, 3)
+        self.assertEqual(sim.solution.y.full()[0, :].size, 3)
+        self.assertEqual(sim.solution.t[0], 0)
+        self.assertEqual(sim.solution.t[1], dt / tau)
+        self.assertEqual(sim.solution.t[2], 2 * dt / tau)
+
     def test_solve_with_inputs(self):
         model = pybamm.lithium_ion.SPM()
         param = model.default_parameter_values