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, "name")
interp = pybamm.Interpolant(x, x, a)
self.assertEqual(interp.name, "interpolating_function")
def test_interpolation(self):
x = np.linspace(0, 1)
y = pybamm.StateVector(slice(0, 2))
casadi_y = casadi.MX.sym("y", 2)
# linear
y_test = np.array([0.4, 0.6])
for interpolator in ["linear", "pchip", "cubic spline"]:
interp = pybamm.Interpolant(x, 2 * x, y, interpolator=interpolator)
interp_casadi = interp.to_casadi(y=casadi_y)
f = casadi.Function("f", [casadi_y], [interp_casadi])
np.testing.assert_array_almost_equal(interp.evaluate(y=y_test),
f(y_test))
# square
y = pybamm.StateVector(slice(0, 1))
for interpolator in ["pchip", "cubic spline"]:
interp = pybamm.Interpolant(x, x**2, y, interpolator=interpolator)
interp_casadi = interp.to_casadi(y=casadi_y)
f = casadi.Function("f", [casadi_y], [interp_casadi])
np.testing.assert_array_almost_equal(interp.evaluate(y=y_test),
f(y_test))
# len(x)=1 but y is 2d
y = pybamm.StateVector(slice(0, 1))
casadi_y = casadi.MX.sym("y", 1)
data = np.tile(2 * x, (10, 1)).T
y_test = np.array([0.4])
for interpolator in ["linear", "pchip", "cubic spline"]:
interp = pybamm.Interpolant(x, data, y, interpolator=interpolator)
interp_casadi = interp.to_casadi(y=casadi_y)
f = casadi.Function("f", [casadi_y], [interp_casadi])
np.testing.assert_array_almost_equal(interp.evaluate(y=y_test),
f(y_test))
def test_errors(self):
with self.assertRaisesRegex(ValueError,
"data should have exactly two columns"):
pybamm.Interpolant(np.ones(10), None)
with self.assertRaisesRegex(ValueError,
"interpolator 'bla' not recognised"):
pybamm.Interpolant(np.ones((10, 2)), None, interpolator="bla")
def test_interpolation(self):
x = np.linspace(0, 1)[:, np.newaxis]
y = pybamm.StateVector(slice(0, 2))
# linear
linear = np.hstack([x, 2 * x])
for interpolator in ["pchip", "cubic spline"]:
interp = pybamm.Interpolant(linear, y, interpolator=interpolator)
np.testing.assert_array_almost_equal(
interp.evaluate(y=np.array([0.397, 1.5]))[:, 0],
np.array([0.794, 3]))
# square
square = np.hstack([x, x**2])
y = pybamm.StateVector(slice(0, 1))
for interpolator in ["pchip", "cubic spline"]:
interp = pybamm.Interpolant(square, y, interpolator=interpolator)
np.testing.assert_array_almost_equal(
interp.evaluate(y=np.array([0.397]))[:, 0],
np.array([0.397**2]))
# with extrapolation set to False
for interpolator in ["pchip", "cubic spline"]:
interp = pybamm.Interpolant(square,
y,
interpolator=interpolator,
extrapolate=False)
np.testing.assert_array_equal(
interp.evaluate(y=np.array([2]))[:, 0], np.array([np.nan]))
def test_process_interpolant(self):
x = np.linspace(0, 10)[:, np.newaxis]
data = np.hstack([x, 2 * x])
parameter_values = pybamm.ParameterValues(
{"a": 3.01, "Times two": ("times two", data)}
)
a = pybamm.Parameter("a")
func = pybamm.FunctionParameter("Times two", {"a": a})
processed_func = parameter_values.process_symbol(func)
self.assertIsInstance(processed_func, pybamm.Interpolant)
self.assertEqual(processed_func.evaluate(), 6.02)
# process differentiated function parameter
diff_func = func.diff(a)
processed_diff_func = parameter_values.process_symbol(diff_func)
self.assertEqual(processed_diff_func.evaluate(), 2)
# interpolant defined up front
interp2 = pybamm.Interpolant(data[:, 0], data[:, 1], a)
processed_interp2 = parameter_values.process_symbol(interp2)
self.assertEqual(processed_interp2.evaluate(), 6.02)
data3 = np.hstack([x, 3 * x])
interp3 = pybamm.Interpolant(data3[:, 0], data3[:, 1], a)
processed_interp3 = parameter_values.process_symbol(interp3)
self.assertEqual(processed_interp3.evaluate(), 9.03)
def get_interp_fun(variable_name, domain):
"""
Create a :class:`pybamm.Function` object using the variable, to allow plotting with
:class:`pybamm.QuickPlot` (interpolate in space to match edges, and then create
function to interpolate in time)
"""
variable = comsol_variables[variable_name]
if domain == ["negative electrode"]:
comsol_x = comsol_variables["x_n"]
elif domain == ["positive electrode"]:
comsol_x = comsol_variables["x_p"]
elif domain == whole_cell:
comsol_x = comsol_variables["x"]
# Make sure to use dimensional space
pybamm_x = mesh.combine_submeshes(*domain).nodes * L_x
variable = interp.interp1d(comsol_x, variable, axis=0)(pybamm_x)
fun = pybamm.Interpolant(
comsol_t,
variable.T,
pybamm.t * pybamm_model.timescale.evaluate(),
)
fun.domain = domain
fun.mesh = mesh.combine_submeshes(*domain)
fun.secondary_mesh = None
return fun
def test_processing(self):
x = np.linspace(0, 1)[:, np.newaxis]
y = pybamm.StateVector(slice(0, 2))
linear = np.hstack([x, 2 * x])
interp = pybamm.Interpolant(linear, y)
self.assertEqual(interp.id, interp.new_copy().id)
self.assertEqual(interp.id, interp.simplify().id)
def _function_new_copy(self, children):
""" See :meth:`Function._function_new_copy()` """
return pybamm.Interpolant(self.data,
*children,
name=self.name,
interpolator=self.interpolator,
extrapolate=self.extrapolate,
entries_string=self.entries_string)
def test_errors(self):
with self.assertRaisesRegex(ValueError, "x1"):
pybamm.Interpolant(np.ones(10), np.ones(11), pybamm.Symbol("a"))
with self.assertRaisesRegex(ValueError, "x2"):
pybamm.Interpolant((np.ones(10), np.ones(11)), np.ones((10, 12)),
pybamm.Symbol("a"))
with self.assertRaisesRegex(ValueError, "y should"):
pybamm.Interpolant((np.ones(10), np.ones(11)), np.ones(10),
pybamm.Symbol("a"))
with self.assertRaisesRegex(ValueError,
"interpolator 'bla' not recognised"):
pybamm.Interpolant(np.ones(10),
np.ones(10),
pybamm.Symbol("a"),
interpolator="bla")
with self.assertRaisesRegex(ValueError, "child should have size 1"):
pybamm.Interpolant(np.ones(10), np.ones((10, 11)),
pybamm.StateVector(slice(0, 2)))
with self.assertRaisesRegex(ValueError, "should equal"):
pybamm.Interpolant((np.ones(10), np.ones(12)), np.ones((10, 12)),
pybamm.Symbol("a"))
with self.assertRaisesRegex(ValueError,
"interpolator should be 'linear'"):
pybamm.Interpolant(
(np.ones(10), np.ones(12)),
np.ones((10, 12)),
(pybamm.Symbol("a"), pybamm.Symbol("b")),
interpolator="cubic spline",
)
def test_interpolation_1_x_2d_y(self):
x = np.linspace(0, 1, 200)
y = np.tile(2 * x, (10, 1)).T
var = pybamm.StateVector(slice(0, 1))
# linear
for interpolator in ["linear", "pchip", "cubic spline"]:
interp = pybamm.Interpolant(x, y, var, interpolator=interpolator)
np.testing.assert_array_almost_equal(
interp.evaluate(y=np.array([0.397])), 0.794 * np.ones((10, 1)))
def test_interpolation_2_x_2d_y(self):
x = (np.arange(-5.01, 5.01, 0.05), np.arange(-5.01, 5.01, 0.05))
xx, yy = np.meshgrid(x[0], x[1])
z = np.sin(xx**2 + yy**2)
var1 = pybamm.StateVector(slice(0, 1))
var2 = pybamm.StateVector(slice(1, 2))
# linear
interp = pybamm.Interpolant(x, z, (var1, var2), interpolator="linear")
np.testing.assert_array_almost_equal(
interp.evaluate(y=np.array([0, 0])), 0, decimal=3)
def test_interpolation(self):
x = np.linspace(0, 1)[:, np.newaxis]
y = pybamm.StateVector(slice(0, 2))
casadi_y = casadi.MX.sym("y", 2)
# linear
linear = np.hstack([x, 2 * x])
y_test = np.array([0.4, 0.6])
for interpolator in ["pchip", "cubic spline"]:
interp = pybamm.Interpolant(linear, y, interpolator=interpolator)
interp_casadi = interp.to_casadi(y=casadi_y)
f = casadi.Function("f", [casadi_y], [interp_casadi])
np.testing.assert_array_almost_equal(interp.evaluate(y=y_test), f(y_test))
# square
square = np.hstack([x, x ** 2])
y = pybamm.StateVector(slice(0, 1))
for interpolator in ["pchip", "cubic spline"]:
interp = pybamm.Interpolant(square, y, interpolator=interpolator)
interp_casadi = interp.to_casadi(y=casadi_y)
f = casadi.Function("f", [casadi_y], [interp_casadi])
np.testing.assert_array_almost_equal(interp.evaluate(y=y_test), f(y_test))
def test_diff(self):
x = np.linspace(0, 1)[:, np.newaxis]
y = pybamm.StateVector(slice(0, 2))
# linear (derivative should be 2)
linear = np.hstack([x, 2 * x])
for interpolator in ["pchip", "cubic spline"]:
interp_diff = pybamm.Interpolant(linear,
y,
interpolator=interpolator).diff(y)
np.testing.assert_array_almost_equal(
interp_diff.evaluate(y=np.array([0.397, 1.5]))[:, 0],
np.array([2, 2]))
# square (derivative should be 2*x)
square = np.hstack([x, x**2])
for interpolator in ["pchip", "cubic spline"]:
interp_diff = pybamm.Interpolant(square,
y,
interpolator=interpolator).diff(y)
np.testing.assert_array_almost_equal(
interp_diff.evaluate(y=np.array([0.397, 0.806]))[:, 0],
np.array([0.794, 1.612]),
decimal=3,
)
def test_diff(self):
x = np.linspace(0, 1, 200)
y = pybamm.StateVector(slice(0, 2))
# linear (derivative should be 2)
# linear interpolator cannot be differentiated
for interpolator in ["pchip", "cubic spline"]:
interp_diff = pybamm.Interpolant(x,
2 * x,
y,
interpolator=interpolator).diff(y)
np.testing.assert_array_almost_equal(
interp_diff.evaluate(y=np.array([0.397, 1.5]))[:, 0],
np.array([2, 2]))
# square (derivative should be 2*x)
for interpolator in ["pchip", "cubic spline"]:
interp_diff = pybamm.Interpolant(x,
x**2,
y,
interpolator=interpolator).diff(y)
np.testing.assert_array_almost_equal(
interp_diff.evaluate(y=np.array([0.397, 0.806]))[:, 0],
np.array([0.794, 1.612]),
decimal=3,
)
def test_drive_cycle_interpolant(self):
model = pybamm.lithium_ion.SPM()
param = model.default_parameter_values
# Import drive cycle from file
drive_cycle = pd.read_csv(
pybamm.get_parameters_filepath(
os.path.join("input", "drive_cycles", "US06.csv")
),
comment="#",
skip_blank_lines=True,
header=None,
).to_numpy()
timescale = param.evaluate(model.timescale)
current_interpolant = pybamm.Interpolant(
drive_cycle[:, 0], drive_cycle[:, 1], timescale * pybamm.t
)
param["Current function [A]"] = current_interpolant
time_data = drive_cycle[:, 0]
sim = pybamm.Simulation(model, parameter_values=param)
# check solution is returned at the times in the data
sim.solve()
tau = sim.model.timescale.evaluate()
np.testing.assert_array_almost_equal(sim.solution.t, time_data / tau)
# check warning raised if the largest gap in t_eval is bigger than the
# smallest gap in the data
with self.assertWarns(pybamm.SolverWarning):
sim.solve(t_eval=np.linspace(0, 1, 100))
# check warning raised if t_eval doesnt contain time_data , but has a finer
# resolution (can still solve, but good for users to know they dont have
# the solution returned at the data points)
with self.assertWarns(pybamm.SolverWarning):
sim.solve(t_eval=np.linspace(0, time_data[-1], 800))
os.chdir(pybamm.__path__[0] + "/..")
pybamm.set_logging_level("INFO")
# load model and update parameters so the input current is the US06 drive cycle
model = pybamm.lithium_ion.SPMe({"thermal": "lumped"})
param = model.default_parameter_values
# import drive cycle from file
drive_cycle = pd.read_csv("pybamm/input/drive_cycles/US06.csv",
comment="#",
header=None).to_numpy()
# create interpolant
timescale = param.evaluate(model.timescale)
current_interpolant = pybamm.Interpolant(drive_cycle[:, 0], drive_cycle[:, 1],
timescale * pybamm.t)
# set drive cycle
param["Current function [A]"] = current_interpolant
# create and run simulation using the CasadiSolver in "fast" mode, remembering to
# pass in the updated parameters
sim = pybamm.Simulation(model,
parameter_values=param,
solver=pybamm.CasadiSolver(mode="fast"))
sim.solve()
sim.plot([
"Negative particle surface concentration [mol.m-3]",
"Electrolyte concentration [mol.m-3]",
"Positive particle surface concentration [mol.m-3]",
"Current [A]",
pybamm.set_logging_level("INFO")
# load model and update parameters so the input current is the US06 drive cycle
model = pybamm.lithium_ion.SPMe({"thermal": "lumped"})
param = model.default_parameter_values
# import drive cycle from file
drive_cycle = pd.read_csv(
"pybamm/input/drive_cycles/US06.csv", comment="#", header=None
).to_numpy()
# create interpolant
timescale = param.evaluate(model.timescale)
current_interpolant = pybamm.Interpolant(drive_cycle, timescale * pybamm.t)
# set drive cycle
param["Current function [A]"] = current_interpolant
# create and run simulation using the CasadiSolver in "fast" mode, remembering to
# pass in the updated parameters
sim = pybamm.Simulation(
model, parameter_values=param, solver=pybamm.CasadiSolver(mode="fast")
)
sim.solve()
sim.plot(
[
"Negative particle surface concentration [mol.m-3]",
"Electrolyte concentration [mol.m-3]",
def test_name(self):
a = pybamm.Symbol("a")
x = np.linspace(0, 1)[:, np.newaxis]
interp = pybamm.Interpolant(np.hstack([x, x]), a, "name")
self.assertEqual(interp.name, "interpolating function (name)")
def _process_symbol(self, symbol):
""" See :meth:`ParameterValues.process_symbol()`. """
if isinstance(symbol, pybamm.Parameter):
value = self[symbol.name]
# Scalar inherits name (for updating parameters) and domain (for Broadcast)
return pybamm.Scalar(value, name=symbol.name, domain=symbol.domain)
elif isinstance(symbol, pybamm.FunctionParameter):
new_children = [self.process_symbol(child) for child in symbol.children]
function_name = self[symbol.name]
# if current setter, process any parameters that are symbols and
# store the evaluated symbol in the parameters_eval dict
if isinstance(function_name, pybamm.GetCurrent):
for param, sym in function_name.parameters.items():
if isinstance(sym, pybamm.Symbol):
new_sym = self.process_symbol(sym)
function_name.parameters[param] = new_sym
function_name.parameters_eval[param] = new_sym.evaluate()
# If loading data, need to update interpolant with
# evaluated parameters
if isinstance(function_name, pybamm.GetCurrentData):
function_name.interpolate()
# Create Function or Interpolant objec
if isinstance(function_name, tuple):
# If function_name is a tuple then it should be (name, data) and we need
# to create an Interpolant
name, data = function_name
function = pybamm.Interpolant(data, *new_children, name=name)
else:
# otherwise create standard function
function = pybamm.Function(function_name, *new_children)
# Differentiate if necessary
if symbol.diff_variable is None:
return function
else:
# return differentiated function
new_diff_variable = self.process_symbol(symbol.diff_variable)
return function.diff(new_diff_variable)
elif isinstance(symbol, pybamm.BinaryOperator):
# process children
new_left = self.process_symbol(symbol.left)
new_right = self.process_symbol(symbol.right)
# make new symbol, ensure domain remains the same
new_symbol = symbol.__class__(new_left, new_right)
new_symbol.domain = symbol.domain
return new_symbol
# Unary operators
elif isinstance(symbol, pybamm.UnaryOperator):
new_child = self.process_symbol(symbol.child)
new_symbol = symbol._unary_new_copy(new_child)
# ensure domain remains the same
new_symbol.domain = symbol.domain
return new_symbol
# Functions
elif isinstance(symbol, pybamm.Function):
new_children = [self.process_symbol(child) for child in symbol.children]
return symbol._function_new_copy(new_children)
# Concatenations
elif isinstance(symbol, pybamm.Concatenation):
new_children = [self.process_symbol(child) for child in symbol.children]
return symbol._concatenation_new_copy(new_children)
else:
# Backup option: return new copy of the object
try:
return symbol.new_copy()
except NotImplementedError:
raise NotImplementedError(
"Cannot process parameters for symbol of type '{}'".format(
type(symbol)
)
)
fun.domain = domain
fun.mesh = mesh.combine_submeshes(*domain)
fun.secondary_mesh = None
return fun
comsol_c_n_surf = get_interp_fun("c_n_surf", ["negative electrode"])
comsol_c_e = get_interp_fun("c_e", whole_cell)
comsol_c_p_surf = get_interp_fun("c_p_surf", ["positive electrode"])
comsol_phi_n = get_interp_fun("phi_n", ["negative electrode"])
comsol_phi_e = get_interp_fun("phi_e", whole_cell)
comsol_phi_p = get_interp_fun("phi_p", ["positive electrode"])
comsol_voltage = pybamm.Interpolant(
comsol_t,
comsol_variables["voltage"],
pybamm.t * pybamm_model.timescale.evaluate(),
)
comsol_voltage.mesh = None
comsol_voltage.secondary_mesh = None
# Create comsol model with dictionary of Matrix variables
comsol_model = pybamm.lithium_ion.BaseModel()
comsol_model.variables = {
"Negative particle surface concentration [mol.m-3]": comsol_c_n_surf,
"Electrolyte concentration [mol.m-3]": comsol_c_e,
"Positive particle surface concentration [mol.m-3]": comsol_c_p_surf,
"Current [A]": pybamm_model.variables["Current [A]"],
"Negative electrode potential [V]": comsol_phi_n,
"Electrolyte potential [V]": comsol_phi_e,
def _process_symbol(self, symbol):
""" See :meth:`ParameterValues.process_symbol()`. """
if isinstance(symbol, pybamm.Parameter):
value = self[symbol.name]
if isinstance(value, numbers.Number):
# Scalar inherits name (for updating parameters) and domain (for
# Broadcast)
return pybamm.Scalar(value,
name=symbol.name,
domain=symbol.domain)
elif isinstance(value, pybamm.Symbol):
new_value = self.process_symbol(value)
new_value.domain = symbol.domain
return new_value
else:
raise TypeError("Cannot process parameter '{}'".format(value))
elif isinstance(symbol, pybamm.FunctionParameter):
new_children = []
for child in symbol.children:
if symbol.diff_variable is not None and any(
x.id == symbol.diff_variable.id
for x in child.pre_order()):
# Wrap with NotConstant to avoid simplification,
# which would stop symbolic diff from working properly
new_child = pybamm.NotConstant(child.new_copy())
new_children.append(self.process_symbol(new_child))
else:
new_children.append(self.process_symbol(child))
function_name = self[symbol.name]
# Create Function or Interpolant or Scalar object
if isinstance(function_name, tuple):
# If function_name is a tuple then it should be (name, data) and we need
# to create an Interpolant
name, data = function_name
function = pybamm.Interpolant(data[:, 0],
data[:, 1],
*new_children,
name=name)
# Define event to catch extrapolation. In these events the sign is
# important: it should be positive inside of the range and negative
# outside of it
self.parameter_events.append(
pybamm.Event(
"Interpolant {} lower bound".format(name),
pybamm.min(new_children[0] - min(data[:, 0])),
pybamm.EventType.INTERPOLANT_EXTRAPOLATION,
))
self.parameter_events.append(
pybamm.Event(
"Interpolant {} upper bound".format(name),
pybamm.min(max(data[:, 0]) - new_children[0]),
pybamm.EventType.INTERPOLANT_EXTRAPOLATION,
))
elif isinstance(function_name, numbers.Number):
# If the "function" is provided is actually a scalar, return a Scalar
# object instead of throwing an error.
# Also use ones_like so that we get the right shapes
function = pybamm.Scalar(
function_name,
name=symbol.name) * pybamm.ones_like(*new_children)
elif (isinstance(function_name, pybamm.Symbol)
and function_name.evaluates_to_number()):
# If the "function" provided is a pybamm scalar-like, use ones_like to
# get the right shape
# This also catches input parameters
function = function_name * pybamm.ones_like(*new_children)
elif callable(function_name):
# otherwise evaluate the function to create a new PyBaMM object
function = function_name(*new_children)
elif isinstance(function_name, pybamm.Interpolant):
function = function_name
else:
raise TypeError(
"Parameter provided for '{}' ".format(symbol.name) +
"is of the wrong type (should either be scalar-like or callable)"
)
# Differentiate if necessary
if symbol.diff_variable is None:
function_out = function
else:
# return differentiated function
new_diff_variable = self.process_symbol(symbol.diff_variable)
function_out = function.diff(new_diff_variable)
# Convert possible float output to a pybamm scalar
if isinstance(function_out, numbers.Number):
return pybamm.Scalar(function_out)
# Process again just to be sure
return self.process_symbol(function_out)
elif isinstance(symbol, pybamm.BinaryOperator):
# process children
new_left = self.process_symbol(symbol.left)
new_right = self.process_symbol(symbol.right)
# Special case for averages, which can appear as "integral of a broadcast"
# divided by "integral of a broadcast"
# this construction seems very specific but can appear often when averaging
if (isinstance(symbol, pybamm.Division)
# right is integral(Broadcast(1))
and (isinstance(new_right, pybamm.Integral)
and isinstance(new_right.child, pybamm.Broadcast)
and new_right.child.child.id == pybamm.Scalar(1).id)
# left is integral
and isinstance(new_left, pybamm.Integral)):
# left is integral(Broadcast)
if (isinstance(new_left.child, pybamm.Broadcast)
and new_left.child.child.domain == []):
integrand = new_left.child
if integrand.auxiliary_domains == {}:
return integrand.orphans[0]
else:
domain = integrand.auxiliary_domains["secondary"]
if "tertiary" not in integrand.auxiliary_domains:
return pybamm.PrimaryBroadcast(
integrand.orphans[0], domain)
else:
auxiliary_domains = {
"secondary":
integrand.auxiliary_domains["tertiary"]
}
return pybamm.FullBroadcast(
integrand.orphans[0], domain,
auxiliary_domains)
# left is "integral of concatenation of broadcasts"
elif isinstance(new_left.child, pybamm.Concatenation) and all(
isinstance(child, pybamm.Broadcast)
for child in new_left.child.children):
return self.process_symbol(pybamm.x_average(
new_left.child))
# make new symbol, ensure domain remains the same
new_symbol = symbol._binary_new_copy(new_left, new_right)
new_symbol.domain = symbol.domain
return new_symbol
# Unary operators
elif isinstance(symbol, pybamm.UnaryOperator):
new_child = self.process_symbol(symbol.child)
new_symbol = symbol._unary_new_copy(new_child)
# ensure domain remains the same
new_symbol.domain = symbol.domain
return new_symbol
# Functions
elif isinstance(symbol, pybamm.Function):
new_children = [
self.process_symbol(child) for child in symbol.children
]
return symbol._function_new_copy(new_children)
# Concatenations
elif isinstance(symbol, pybamm.Concatenation):
new_children = [
self.process_symbol(child) for child in symbol.children
]
return symbol._concatenation_new_copy(new_children)
else:
# Backup option: return new copy of the object
try:
return symbol.new_copy()
except NotImplementedError:
raise NotImplementedError(
"Cannot process parameters for symbol of type '{}'".format(
type(symbol)))
def _process_symbol(self, symbol):
""" See :meth:`ParameterValues.process_symbol()`. """
if isinstance(symbol, pybamm.Parameter):
value = self[symbol.name]
if isinstance(value, numbers.Number):
# Scalar inherits name (for updating parameters) and domain (for
# Broadcast)
return pybamm.Scalar(value,
name=symbol.name,
domain=symbol.domain)
elif isinstance(value, pybamm.InputParameter):
value.domain = symbol.domain
return value
elif isinstance(symbol, pybamm.FunctionParameter):
new_children = [
self.process_symbol(child) for child in symbol.children
]
function_name = self[symbol.name]
# Create Function or Interpolant or Scalar object
if isinstance(function_name, tuple):
# If function_name is a tuple then it should be (name, data) and we need
# to create an Interpolant
name, data = function_name
function = pybamm.Interpolant(data, *new_children, name=name)
elif isinstance(function_name, numbers.Number):
# If the "function" is provided is actually a scalar, return a Scalar
# object instead of throwing an error.
# Also use ones_like so that we get the right shapes
function = pybamm.Scalar(
function_name,
name=symbol.name) * pybamm.ones_like(*new_children)
elif isinstance(function_name, pybamm.InputParameter):
# Replace the function with an input parameter
function = function_name
elif (isinstance(function_name, pybamm.Symbol)
and function_name.evaluates_to_number()):
# If the "function" provided is a pybamm scalar-like, use ones_like to
# get the right shape
function = function_name * pybamm.ones_like(*new_children)
elif callable(function_name):
# otherwise evaluate the function to create a new PyBaMM object
function = function_name(*new_children)
else:
raise TypeError(
"Parameter provided for '{}' ".format(symbol.name) +
"is of the wrong type (should either be scalar-like or callable)"
)
# Differentiate if necessary
if symbol.diff_variable is None:
function_out = function
else:
# return differentiated function
new_diff_variable = self.process_symbol(symbol.diff_variable)
function_out = function.diff(new_diff_variable)
# Convert possible float output to a pybamm scalar
if isinstance(function_out, numbers.Number):
return pybamm.Scalar(function_out)
# Process again just to be sure
return self.process_symbol(function_out)
elif isinstance(symbol, pybamm.BinaryOperator):
# process children
new_left = self.process_symbol(symbol.left)
new_right = self.process_symbol(symbol.right)
# make new symbol, ensure domain remains the same
new_symbol = symbol._binary_new_copy(new_left, new_right)
new_symbol.domain = symbol.domain
return new_symbol
# Unary operators
elif isinstance(symbol, pybamm.UnaryOperator):
new_child = self.process_symbol(symbol.child)
new_symbol = symbol._unary_new_copy(new_child)
# ensure domain remains the same
new_symbol.domain = symbol.domain
return new_symbol
# Functions
elif isinstance(symbol, pybamm.Function):
new_children = [
self.process_symbol(child) for child in symbol.children
]
return symbol._function_new_copy(new_children)
# Concatenations
elif isinstance(symbol, pybamm.Concatenation):
new_children = [
self.process_symbol(child) for child in symbol.children
]
return symbol._concatenation_new_copy(new_children)
else:
# Backup option: return new copy of the object
try:
return symbol.new_copy()
except NotImplementedError:
raise NotImplementedError(
"Cannot process parameters for symbol of type '{}'".format(
type(symbol)))
def test_processing(self):
x = np.linspace(0, 1, 200)
y = pybamm.StateVector(slice(0, 2))
interp = pybamm.Interpolant(x, 2 * x, y)
self.assertEqual(interp.id, interp.new_copy().id)
def US06_experiment(reply=False):
model = pybamm.lithium_ion.DFN()
# import drive cycle from file
if reply:
drive_cycle = pd.read_csv("drive_cycle.csv", comment="#", header=None).to_numpy()
elif not reply:
drive_cycle = pd.read_csv("US06.csv", comment="#", header=None).to_numpy()
# create interpolant
param = model.default_parameter_values
timescale = param.evaluate(model.timescale)
current_interpolant = pybamm.Interpolant(
drive_cycle[:, 0], drive_cycle[:, 1], timescale * pybamm.t
)
# set drive cycle
param["Current function [A]"] = current_interpolant
sim_US06_1 = pybamm.Simulation(
model, parameter_values=param, solver=pybamm.CasadiSolver(mode="fast")
)
sol_US06_1 = sim_US06_1.solve()
if reply:
time = foo1.plot_graph(sol_US06_1, sim_US06_1, reply=reply)
return time
solved = False
print("REACHED")
while not solved:
try:
cycle = US06_experiment_cycle()
# cycle = ["Charge at 1 A until 4.1 V", "Hold at 4.1 V until 50 mA"]
experiment = pybamm.Experiment(cycle)
sim_cccv = pybamm.Simulation(model, experiment=experiment)
sol_cccv = sim_cccv.solve()
new_model = model.set_initial_conditions_from(sol_cccv, inplace=False)
sim_US06_2 = pybamm.Simulation(
new_model,
parameter_values=param,
solver=pybamm.CasadiSolver(mode="fast"),
)
sol_US06_2 = sim_US06_2.solve()
# pybamm.dynamic_plot(
# [sol_US06_1, sol_US06_2],
# labels=["Default initial conditions", "Fully charged"],
# )
solution = [sol_US06_1, sol_US06_2]
sim = [sim_US06_1, sim_US06_2]
t = solution[0]["Time [s]"]
final_time = int(t.entries[len(t.entries) - 1])
time = random.randint(0, final_time)
plot = pybamm.QuickPlot(
sim,
labels=["Default initial conditions", "Fully charged"],
time_unit="seconds",
)
plot.plot(time)
plot.fig.savefig("foo.png", dpi=300)
print(sol_US06_1)
solved = True
except:
pass
# print("Exception")
return time, cycle