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#1477 put back algebraic solver sens tests
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@martinjrobins
martinjrobins committed Aug 18, 2021
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153 changes: 153 additions & 0 deletions tests/unit/test_solvers/test_casadi_algebraic_solver.py
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import pybamm
import unittest
import numpy as np
from scipy.optimize import least_squares
import tests


class TestCasadiAlgebraicSolver(unittest.TestCase):
Expand Down Expand Up @@ -172,6 +174,157 @@ def test_solve_with_input(self):
np.testing.assert_array_equal(solution.y, -7)


class TestCasadiAlgebraicSolverSensitivity(unittest.TestCase):
def test_solve_with_symbolic_input(self):
# Simple system: a single algebraic equation
var = pybamm.Variable("var")
model = pybamm.BaseModel()
model.algebraic = {var: var + pybamm.InputParameter("param")}
model.initial_conditions = {var: 2}
model.variables = {"var": var}

# create discretisation
disc = pybamm.Discretisation()
disc.process_model(model)

# Solve
solver = pybamm.CasadiAlgebraicSolver()
solution = solver.solve(model, [0])
np.testing.assert_array_equal(solution["var"].value({"param": 7}), -7)
np.testing.assert_array_equal(solution["var"].value({"param": 3}), -3)
np.testing.assert_array_equal(solution["var"].sensitivity({"param": 3}), -1)

def test_least_squares_fit(self):
# Simple system: a single algebraic equation
var = pybamm.Variable("var", domain="negative electrode")
model = pybamm.BaseModel()
p = pybamm.InputParameter("p")
q = pybamm.InputParameter("q")
model.algebraic = {var: (var - p)}
model.initial_conditions = {var: 3}
model.variables = {"objective": (var - q) ** 2 + (p - 3) ** 2}

# create discretisation
disc = tests.get_discretisation_for_testing()
disc.process_model(model)

# Solve
solver = pybamm.CasadiAlgebraicSolver()
solution = solver.solve(model, [0])
sol_var = solution["objective"]

def objective(x):
return sol_var.value({"p": x[0], "q": x[1]}).full().flatten()

# without jacobian
lsq_sol = least_squares(objective, [2, 2], method="lm")
np.testing.assert_array_almost_equal(lsq_sol.x, [3, 3], decimal=3)

def jac(x):
return sol_var.sensitivity({"p": x[0], "q": x[1]})

# with jacobian
lsq_sol = least_squares(objective, [2, 2], jac=jac, method="lm")
np.testing.assert_array_almost_equal(lsq_sol.x, [3, 3], decimal=3)

def test_solve_with_symbolic_input_1D_scalar_input(self):
var = pybamm.Variable("var", "negative electrode")
model = pybamm.BaseModel()
param = pybamm.InputParameter("param")
model.algebraic = {var: var + param}
model.initial_conditions = {var: 2}
model.variables = {"var": var}

# create discretisation
disc = tests.get_discretisation_for_testing()
disc.process_model(model)

# Solve - scalar input
solver = pybamm.CasadiAlgebraicSolver()
solution = solver.solve(model, [0])
np.testing.assert_array_equal(solution["var"].value({"param": 7}), -7)
np.testing.assert_array_equal(solution["var"].value({"param": 3}), -3)
np.testing.assert_array_equal(solution["var"].sensitivity({"param": 3}), -1)

def test_solve_with_symbolic_input_1D_vector_input(self):
var = pybamm.Variable("var", "negative electrode")
model = pybamm.BaseModel()
param = pybamm.InputParameter("param", "negative electrode")
model.algebraic = {var: var + param}
model.initial_conditions = {var: 2}
model.variables = {"var": var}

# create discretisation
disc = tests.get_discretisation_for_testing()
disc.process_model(model)

# Solve - scalar input
solver = pybamm.CasadiAlgebraicSolver()
solution = solver.solve(model, [0])
n = disc.mesh["negative electrode"].npts

solver = pybamm.CasadiAlgebraicSolver()
solution = solver.solve(model, [0])
p = np.linspace(0, 1, n)[:, np.newaxis]
np.testing.assert_array_almost_equal(
solution["var"].value({"param": 3 * np.ones(n)}), -3
)
np.testing.assert_array_almost_equal(
solution["var"].value({"param": 2 * p}), -2 * p
)
np.testing.assert_array_almost_equal(
solution["var"].sensitivity({"param": 3 * np.ones(n)}), -np.eye(40)
)
np.testing.assert_array_almost_equal(
solution["var"].sensitivity({"param": p}), -np.eye(40)
)

def test_solve_with_symbolic_input_in_initial_conditions(self):
# Simple system: a single algebraic equation
var = pybamm.Variable("var")
model = pybamm.BaseModel()
model.algebraic = {var: var + 2}
model.initial_conditions = {var: pybamm.InputParameter("param")}
model.variables = {"var": var}

# create discretisation
disc = pybamm.Discretisation()
disc.process_model(model)

# Solve
solver = pybamm.CasadiAlgebraicSolver()
solution = solver.solve(model, [0])
np.testing.assert_array_equal(solution["var"].value({"param": 7}), -2)
np.testing.assert_array_equal(solution["var"].value({"param": 3}), -2)
np.testing.assert_array_equal(solution["var"].sensitivity({"param": 3}), 0)

def test_least_squares_fit_input_in_initial_conditions(self):
# Simple system: a single algebraic equation
var = pybamm.Variable("var", domain="negative electrode")
model = pybamm.BaseModel()
p = pybamm.InputParameter("p")
q = pybamm.InputParameter("q")
model.algebraic = {var: (var - p)}
model.initial_conditions = {var: p}
model.variables = {"objective": (var - q) ** 2 + (p - 3) ** 2}

# create discretisation
disc = tests.get_discretisation_for_testing()
disc.process_model(model)

# Solve
solver = pybamm.CasadiAlgebraicSolver()
solution = solver.solve(model, [0])
sol_var = solution["objective"]

def objective(x):
return sol_var.value({"p": x[0], "q": x[1]}).full().flatten()

# without jacobian
lsq_sol = least_squares(objective, [2, 2], method="lm")
np.testing.assert_array_almost_equal(lsq_sol.x, [3, 3], decimal=3)


if __name__ == "__main__":
print("Add -v for more debug output")
import sys
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