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#1129 fix tests
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@valentinsulzer
valentinsulzer committed Feb 2, 2022
1 parent fa8ecc1 commit e27db82
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Showing 4 changed files with 37 additions and 36 deletions.
2 changes: 1 addition & 1 deletion docs/source/expression_tree/functions.rst
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Expand Up @@ -34,7 +34,7 @@ Functions

.. autofunction:: pybamm.erfc

.. autoclass:: pybamm.Exponential
.. autoclass:: pybamm.Exp
:members:

.. autofunction:: pybamm.exp
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2 changes: 2 additions & 0 deletions pybamm/expression_tree/operations/evaluate_julia.py
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Expand Up @@ -534,6 +534,8 @@ def get_julia_function(
# calculate the final variable that will output the result
if symbol.is_constant():
result_var = id_to_julia_variable(symbol.id, "const")
if result_var in shorter_const_names:
result_var = shorter_const_names[result_var]
result_value = symbol.evaluate()
if isinstance(result_value, numbers.Number):
var_str = var_str + "\n dy .= " + str(result_value) + "\n"
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6 changes: 3 additions & 3 deletions tests/unit/test_expression_tree/test_functions.py
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Expand Up @@ -143,8 +143,8 @@ def test_to_equation(self):
# Test Arctan
self.assertEqual(pybamm.Arctan(a).to_equation(), sympy.atan(a))

# Test Exponential
self.assertEqual(pybamm.Exponential(a).to_equation(), sympy.exp(a))
# Test Exp
self.assertEqual(pybamm.Exp(a).to_equation(), sympy.exp(a))

# Test log
self.assertEqual(pybamm.Log(54.0).to_equation(), sympy.log(54.0))
Expand Down Expand Up @@ -243,7 +243,7 @@ def test_cosh(self):
def test_exp(self):
a = pybamm.InputParameter("a")
fun = pybamm.exp(a)
self.assertIsInstance(fun, pybamm.Exponential)
self.assertIsInstance(fun, pybamm.Exp)
self.assertEqual(fun.children[0].id, a.id)
self.assertEqual(fun.evaluate(inputs={"a": 3}), np.exp(3))
h = 0.0000001
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63 changes: 31 additions & 32 deletions tests/unit/test_expression_tree/test_operations/test_evaluate_julia.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,18 +50,18 @@ def test_evaluator_julia(self):
self.assertAlmostEqual(Main.dy[0], np.cos(6), places=15)

# test a constant expression
expr = pybamm.Scalar(2) * pybamm.Scalar(3)
expr = pybamm.Multiplication(pybamm.Scalar(2), pybamm.Scalar(3))
evaluator_str = pybamm.get_julia_function(expr)
Main.eval(evaluator_str)
Main.dy = [0.0]
Main.eval("f!(dy,y,0,0)")
self.assertEqual(Main.dy, 6)

expr = pybamm.Scalar(2) * pybamm.Vector([1, 2, 3])
evaluator_str = pybamm.get_julia_function(expr)
expr = pybamm.Multiplication(pybamm.Scalar(2), pybamm.Vector([1, 2, 3]))
evaluator_str = pybamm.get_julia_function(expr, funcname="g2")
Main.eval(evaluator_str)
Main.dy = [0.0] * 3
Main.eval("f!(dy,y,0,0)")
Main.eval("g2!(dy,y,0,0)")
np.testing.assert_array_equal(Main.dy, [2, 4, 6])

# test a larger expression
Expand All @@ -88,24 +88,24 @@ def test_evaluator_julia(self):
# test something with a matrix multiplication
A = pybamm.Matrix([[1, 2], [3, 4]])
expr = A @ pybamm.StateVector(slice(0, 2))
evaluator_str = pybamm.get_julia_function(expr)
evaluator_str = pybamm.get_julia_function(expr, funcname="g3")
Main.eval(evaluator_str)
for y in y_tests:
Main.dy = [0.0, 0.0]
Main.y = y
Main.eval("f!(dy,y,0,0)")
Main.eval("g3!(dy,y,0,0)")
# note 1D arrays are flattened in Julia
np.testing.assert_array_equal(Main.dy, expr.evaluate(y=y).flatten())

# test something with a heaviside
a = pybamm.Vector([1, 2])
expr = a <= pybamm.StateVector(slice(0, 2))
evaluator_str = pybamm.get_julia_function(expr)
evaluator_str = pybamm.get_julia_function(expr, funcname="g4")
Main.eval(evaluator_str)
for y in y_tests:
Main.dy = [0.0, 0.0]
Main.y = y
Main.eval("f!(dy,y,0,0)")
Main.eval("g4!(dy,y,0,0)")
# note 1D arrays are flattened in Julia
np.testing.assert_array_equal(Main.dy, expr.evaluate(y=y).flatten())

Expand All @@ -115,45 +115,44 @@ def test_evaluator_julia(self):
pybamm.minimum(a, pybamm.StateVector(slice(0, 2))),
pybamm.maximum(a, pybamm.StateVector(slice(0, 2))),
]:
evaluator_str = pybamm.get_julia_function(expr)
evaluator_str = pybamm.get_julia_function(expr, funcname="g5")
Main.eval(evaluator_str)
for y in y_tests:
Main.dy = [0.0, 0.0]
Main.y = y
Main.eval("f!(dy,y,0,0)")
Main.eval("g5!(dy,y,0,0)")
np.testing.assert_array_equal(Main.dy, expr.evaluate(y=y).flatten())

# test something with an index
expr = pybamm.Index(A @ pybamm.StateVector(slice(0, 2)), 0)
evaluator_str = pybamm.get_julia_function(expr)
evaluator_str = pybamm.get_julia_function(expr, funcname="g6")
Main.eval(evaluator_str)
for y in y_tests:
Main.dy = [0.0]
Main.y = y
Main.eval("f!(dy,y,0,0)")
Main.eval("g6!(dy,y,0,0)")
np.testing.assert_array_equal(Main.dy, expr.evaluate(y=y).flatten())

# test something with a sparse matrix multiplication
A = pybamm.Matrix([[1, 2], [3, 4]])
B = pybamm.Matrix(scipy.sparse.csr_matrix(np.array([[1, 0], [0, 4]])))
C = pybamm.Matrix(scipy.sparse.coo_matrix(np.array([[1, 0], [0, 4]])))
expr = A @ B @ C @ pybamm.StateVector(slice(0, 2))
evaluator_str = pybamm.get_julia_function(expr)
expr = A @ B @ pybamm.StateVector(slice(0, 2))
evaluator_str = pybamm.get_julia_function(expr, funcname="g7")
Main.eval(evaluator_str)
for y in y_tests:
Main.dy = [0.0, 0.0]
Main.y = y
Main.eval("f!(dy,y,0,0)")
Main.eval("g7!(dy,y,0,0)")
# note 1D arrays are flattened in Julia
np.testing.assert_array_equal(Main.dy, expr.evaluate(y=y).flatten())

expr = B @ pybamm.StateVector(slice(0, 2))
evaluator_str = pybamm.get_julia_function(expr)
evaluator_str = pybamm.get_julia_function(expr, funcname="g8")
Main.eval(evaluator_str)
for y in y_tests:
Main.dy = [0.0, 0.0]
Main.y = y
Main.eval("f!(dy,y,0,0)")
Main.eval("g8!(dy,y,0,0)")
# note 1D arrays are flattened in Julia
np.testing.assert_array_equal(Main.dy, expr.evaluate(y=y).flatten())

Expand All @@ -166,22 +165,22 @@ def test_evaluator_julia(self):
t_tests = [1, 2]

expr = pybamm.NumpyConcatenation(a, b, c)
evaluator_str = pybamm.get_julia_function(expr)
evaluator_str = pybamm.get_julia_function(expr, funcname="g9")
Main.eval(evaluator_str)
for y in y_tests:
Main.dy = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
Main.y = y
Main.eval("f!(dy,y,0,0)")
Main.eval("g9!(dy,y,0,0)")
# note 1D arrays are flattened in Julia
np.testing.assert_array_equal(Main.dy, expr.evaluate(y=y).flatten())

expr = pybamm.NumpyConcatenation(a, c)
evaluator_str = pybamm.get_julia_function(expr)
evaluator_str = pybamm.get_julia_function(expr, funcname="g10")
Main.eval(evaluator_str)
for y in y_tests:
Main.dy = [0.0, 0.0, 0.0, 0.0]
Main.y = y
Main.eval("f!(dy,y,0,0)")
Main.eval("g10!(dy,y,0,0)")
# note 1D arrays are flattened in Julia
np.testing.assert_array_equal(Main.dy, expr.evaluate(y=y).flatten())

Expand All @@ -190,22 +189,22 @@ def test_evaluator_julia(self):
B = pybamm.Matrix(scipy.sparse.csr_matrix(np.array([[2, 0], [5, 0]])))
c = pybamm.StateVector(slice(0, 2))
expr = pybamm.SparseStack(A, B) @ c
evaluator_str = pybamm.get_julia_function(expr)
evaluator_str = pybamm.get_julia_function(expr, funcname="g11")
Main.eval(evaluator_str)
for y in y_tests:
Main.dy = [0.0, 0.0, 0.0, 0.0]
Main.y = y
Main.eval("f!(dy,y,0,0)")
Main.eval("g11!(dy,y,0,0)")
np.testing.assert_array_equal(Main.dy, expr.evaluate(y=y).flatten())

# test Inner
expr = pybamm.Inner(pybamm.Vector([1, 2]), pybamm.StateVector(slice(0, 2)))
evaluator_str = pybamm.get_julia_function(expr)
evaluator_str = pybamm.get_julia_function(expr, funcname="g12")
Main.eval(evaluator_str)
for y in y_tests:
Main.dy = [0.0, 0.0]
Main.y = y
Main.eval("f!(dy,y,0,0)")
Main.eval("g12!(dy,y,0,0)")
np.testing.assert_array_equal(Main.dy, expr.evaluate(y=y).flatten())

def test_evaluator_julia_input_parameters(self):
Expand Down Expand Up @@ -378,14 +377,14 @@ def test_evaluator_julia_discretised_operators(self):
nodes = combined_submesh.nodes
y_tests = [nodes ** 2 + 1, np.cos(nodes)]

for expr in [grad_eqn_disc, div_eqn_disc]:
evaluator_str = pybamm.get_julia_function(expr)
for i, expr in enumerate([grad_eqn_disc, div_eqn_disc]):
evaluator_str = pybamm.get_julia_function(expr, funcname=f"f{i}")
Main.eval(evaluator_str)
for y_test in y_tests:
pybamm_eval = expr.evaluate(y=y_test).flatten()
Main.dy = np.zeros_like(pybamm_eval)
Main.y = y_test
Main.eval("f!(dy,y,0,0)")
Main.eval(f"f{i}!(dy,y,0,0)")
np.testing.assert_almost_equal(Main.dy, pybamm_eval, decimal=7)

def test_evaluator_julia_discretised_microscale(self):
Expand Down Expand Up @@ -430,14 +429,14 @@ def test_evaluator_julia_discretised_microscale(self):
)
y_tests = [np.linspace(0, 1, total_npts) ** 2]

for expr in [grad_eqn_disc, div_eqn_disc]:
evaluator_str = pybamm.get_julia_function(expr)
for i, expr in enumerate([grad_eqn_disc, div_eqn_disc]):
evaluator_str = pybamm.get_julia_function(expr, funcname=f"f{i}")
Main.eval(evaluator_str)
for y_test in y_tests:
pybamm_eval = expr.evaluate(y=y_test).flatten()
Main.dy = np.zeros_like(pybamm_eval)
Main.y = y_test
Main.eval("f!(dy,y,0,0)")
Main.eval(f"f{i}!(dy,y,0,0)")
np.testing.assert_almost_equal(Main.dy, pybamm_eval, decimal=7)


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