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#1477 merge in #1100 
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‎CHANGELOG.md

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@@ -160,12 +160,13 @@ This release adds new operators for more complex models, some basic sensitivity
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## Breaking changes
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- Changed sensitivity API. Removed `ProcessedSymbolicVariable`, all sensitivity now handled within the solvers and `ProcessedVariable` ()
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- Renamed `quick_plot_vars` to `output_variables` in `Simulation` to be consistent with `QuickPlot`. Passing `quick_plot_vars` to `Simulation.plot()` has been deprecated and `output_variables` should be passed instead ([#1099](https://github.com/pybamm-team/PyBaMM/pull/1099))
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- The "fast diffusion" particle option has been renamed "uniform profile" ([#1130](https://github.com/pybamm-team/PyBaMM/pull/1130))
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- The modules containing standard parameters are now classes so they can take options
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(e.g. `standard_parameters_lithium_ion` is now `LithiumIonParameters`) ([#1120](https://github.com/pybamm-team/PyBaMM/pull/1120))
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- Renamed `quick_plot_vars` to `output_variables` in `Simulation` to be consistent with `QuickPlot`. Passing `quick_plot_vars` to `Simulation.plot()` has been deprecated and `output_variables` should be passed instead ([#1099](https://github.com/pybamm-team/PyBaMM/pull/1099))
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# [v0.2.3](https://github.com/pybamm-team/PyBaMM/tree/v0.2.3) - 2020-07-01
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This release enables the use of [Google Colab](https://colab.research.google.com/github/pybamm-team/PyBaMM/blob/main/) for running example notebooks, and adds some small new features and bug fixes.

‎docs/source/solvers/processed_variable.rst

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@@ -3,6 +3,3 @@ Post-Process Variables
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.. autoclass:: pybamm.ProcessedVariable
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:members:
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.. autoclass:: pybamm.ProcessedSymbolicVariable
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:members:

‎examples/notebooks/DFN-sensitivity.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Sensitivity analysis for the DFN"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Example showing how to perform sensitivity analysis for the DFN with PyBaMM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install pybamm -q\n",
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"import pybamm\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Load model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = pybamm.lithium_ion.SPMe()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Before performing a sensitivity analysis, we scale the parameters with their reference values. Some parameters are functions of states, but we can evaluate them at appropriate values of the states to obtain a reference value. In this notebook, we choose to study the effect on the voltage of:\n",
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"- negative particle diffusivity (via Ds_n)\n",
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"- positive particle diffusivity (via Ds_p)\n",
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"- electrolyte diffusivity (via D_e)\n",
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"- electrolyte conductivity (via kappa_e)\n",
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"- negative electrode kinetics (via j0_n)\n",
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"- positive electrode kinetics (via j0_p)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"param = model.default_parameter_values\n",
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"# Get reference values for evaluating functions\n",
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"ce_ref = param[\"Typical electrolyte concentration [mol.m-3]\"]\n",
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"csn_ref = param[\"Maximum concentration in negative electrode [mol.m-3]\"]\n",
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"csp_ref = param[\"Maximum concentration in positive electrode [mol.m-3]\"]\n",
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"T_ref = param[\"Reference temperature [K]\"]\n",
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"# Evaluate functions at reference values\n",
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"Dsn_ref = param[\"Negative electrode diffusivity [m2.s-1]\"](0.5, T_ref).evaluate()\n",
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"Dsp_ref = param[\"Positive electrode diffusivity [m2.s-1]\"](0.5, T_ref).evaluate()\n",
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"De_ref = param[\"Electrolyte diffusivity [m2.s-1]\"](ce_ref, T_ref).evaluate()\n",
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"kappae_ref = param[\"Electrolyte conductivity [S.m-1]\"](ce_ref, T_ref).evaluate()\n",
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"j0n_ref = param.evaluate(param[\"Negative electrode exchange-current density [A.m-2]\"](ce_ref, 0.5 * csn_ref, T_ref))\n",
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"j0p_ref = param.evaluate(param[\"Positive electrode exchange-current density [A.m-2]\"](ce_ref, 0.5 * csp_ref, T_ref))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"param[\"Negative electrode diffusivity [m2.s-1]\"] = Dsn_ref * pybamm.InputParameter(\"Dsn\")\n",
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"param[\"Positive electrode diffusivity [m2.s-1]\"] = Dsp_ref * pybamm.InputParameter(\"Dsp\")\n",
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"# param[\"Electrolyte diffusivity [m2.s-1]\"] = De_ref * pybamm.InputParameter(\"D_e\")\n",
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"# param[\"Electrolyte conductivity [S.m-1]\"] = kappae_ref * pybamm.InputParameter(\"kappa_e\")\n",
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"# param[\"Negative electrode exchange-current density [A.m-2]\"] = j0n_ref * pybamm.InputParameter(\"j0n\")\n",
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"# param[\"Positive electrode exchange-current density [A.m-2]\"] = j0p_ref * pybamm.InputParameter(\"j0p\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Create simulation, run and read solution"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"solver = pybamm.CasadiSolver(mode=\"fast\", sensitivity=\"casadi\")\n",
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"sim = pybamm.Simulation(model, parameter_values=param, solver=solver)\n",
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"solution = sim.solve(t_eval=np.linspace(0,3600), inputs={\"Dsn\": 1, \"Dsp\": 1})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.015949444000000312"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"solution.solve_time"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Since we have not specified the parameter values when solving, the resulting solution contains _symbolic_ variables, such as the voltage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 3.73 s, sys: 194 ms, total: 3.92 s\n",
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"Wall time: 3.91 s\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'all': DM(sparse: 1500-by-100, 73500 nnz\n",
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" (30, 0) -> -8.80308e-05\n",
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" (31, 0) -> -9.35556e-05\n",
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" (32, 0) -> -0.000107887\n",
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" ...\n",
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" (1497, 98) -> 0.0170617\n",
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" (1498, 98) -> 0.021474\n",
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" (1499, 98) -> 0.0260439),\n",
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" 'Dsn': DM([00, 00, 00, ..., 0.0170617, 0.021474, 0.0260439]),\n",
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" 'Dsp': DM([00, 00, 00, ..., 00, 00, 00])}"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"%%time\n",
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"solution[\"X-averaged negative particle concentration\"].sensitivity"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"solver = pybamm.CasadiSolver(mode=\"fast\")\n",
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"sim = pybamm.Simulation(model, parameter_values=param, solver=solver)\n",
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"solution = sim.solve(t_eval=np.linspace(0,3600), inputs={\"Dsn\": 1, \"Dsp\": 1})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.005656635999997661"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"solution.solve_time"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<pybamm.solvers.processed_variable.ProcessedVariable at 0x13da41210>"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"V = solution[\"Terminal voltage [V]\"]\n",
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"V"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we can evaluate the voltage at specific values for the input parameters to get both the value"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'all': DM([0, 0.000305216, 0.000323451, 0.000307878, 0.000281315, 0.000252542, 0.000226269, 0.00020523, 0.000191031, 0.000184763, 0.000187613, 0.000201625, 0.000230913, 0.000283916, 0.000377805, 0.000547086, 0.000859691, 0.00144399, 0.00252183, 0.00440237, 0.00728527, 0.0106768, 0.0129031, 0.0123404, 0.00953625, 0.00642767, 0.00416935, 0.00284452, 0.00214823, 0.00179002, 0.00157891, 0.00140318, 0.00120162, 0.000947612, 0.000645371, 0.0003332, 8.76979e-05, 2.15342e-05, 0.000267893, 0.000949333, 0.00213811, 0.00382395, 0.00590563, 0.00821168, 0.0105401, 0.0127, 0.0145411, 0.0159704, 0.0169657, 0.017614]),\n",
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" 'Dsn': DM([0, 0.000305216, 0.000323451, 0.000307878, 0.000281315, 0.000252542, 0.000226269, 0.00020523, 0.000191031, 0.000184763, 0.000187613, 0.000201625, 0.000230913, 0.000283916, 0.000377805, 0.000547086, 0.000859691, 0.00144399, 0.00252183, 0.00440237, 0.00728527, 0.0106768, 0.0129031, 0.0123404, 0.00953625, 0.00642767, 0.00416935, 0.00284452, 0.00214823, 0.00179002, 0.00157891, 0.00140318, 0.00120162, 0.000947612, 0.000645371, 0.0003332, 8.76979e-05, 2.15342e-05, 0.000267893, 0.000949333, 0.00213811, 0.00382395, 0.00590563, 0.00821168, 0.0105401, 0.0127, 0.0145411, 0.0159704, 0.0169657, 0.017614])}"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"V.sensitivity"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"V.value({\"Dsn\": 1, \"Dsp\": 1, \"D_e\": 1, \"kappa_e\": 1, \"j0n\": 1, \"j0p\": 1})"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"and sensitivity"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"sens = V.sensitivity({\"Dsn\": 1, \"Dsp\": 1, \"D_e\": 1, \"kappa_e\": 1, \"j0n\": 1, \"j0p\": 1})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sens"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"ename": "AttributeError",
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"evalue": "'ProcessedVariable' object has no attribute 'symbolic_inputs_dict'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-13-ef5967a63575>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mV\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbolic_inputs_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e-6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mV_down\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mV\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mAttributeError\u001b[0m: 'ProcessedVariable' object has no attribute 'symbolic_inputs_dict'"
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]
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}
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],
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"source": [
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"inputs = {k: 1 for k in V.symbolic_inputs_dict.keys()}\n",
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"h = 1e-6\n",
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"for k in inputs.keys():\n",
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" V_down = V.value(inputs)\n",
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" inputs[k] = 1 + h\n",
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" V_up = V.value(inputs)\n",
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" sens = (V_up - V_down) / h\n",
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" print(sens)\n",
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" inputs[k] = 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"inputs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎pybamm/__init__.py

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#
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from .solvers.solution import Solution
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from .solvers.processed_variable import ProcessedVariable
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from .solvers.processed_symbolic_variable import ProcessedSymbolicVariable
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from .solvers.base_solver import BaseSolver
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from .solvers.dummy_solver import DummySolver
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from .solvers.algebraic_solver import AlgebraicSolver

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