From 6410d9a61dea949d13ca2213bc6fd15a314ffe2c Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Tue, 18 Oct 2022 16:36:36 +0200 Subject: [PATCH 01/18] added pybamm interpolant for 3D --- examples/scripts/quick_test.py | 34 +++++++++++++++++++++++++ pybamm/expression_tree/interpolant.py | 36 +++++++++++++++++++++++++++ 2 files changed, 70 insertions(+) create mode 100644 examples/scripts/quick_test.py diff --git a/examples/scripts/quick_test.py b/examples/scripts/quick_test.py new file mode 100644 index 0000000000..e9a89166e9 --- /dev/null +++ b/examples/scripts/quick_test.py @@ -0,0 +1,34 @@ +from scipy.interpolate import RegularGridInterpolator +import numpy as np + +import pybamm + + +def f(x, y, z): + return 2 * x**3 + 3 * y**2 - z + + +x = np.linspace(1, 4, 11) +y = np.linspace(4, 7, 22) +z = np.linspace(7, 9, 33) +xg, yg, zg = np.meshgrid(x, y, z, indexing="ij", sparse=True) +data = f(xg, yg, zg) + +interp = RegularGridInterpolator((x, y, z), data) + +pts = np.array([[2.1, 6.2, 8.3], [3.3, 5.2, 7.1]]) + +interp(pts) + + +var1 = pybamm.StateVector(slice(0, 1)) +var2 = pybamm.StateVector(slice(1, 2)) +var3 = pybamm.StateVector(slice(2, 3)) + +x_in = (x, y, z) +interp = pybamm.Interpolant(x_in, data, (var1, var2, var3), interpolator="linear") + +eval = interp.evaluate(y=np.array([1, 4, 7])) + + +print(eval) diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index b59dc43e67..c92ee64751 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -71,6 +71,26 @@ def __init__( "len(x2) should equal y=shape[0], " f"but x2.shape={x2.shape} and y.shape={y.shape}" ) + elif isinstance(x, (tuple, list)) and len(x) == 3: + x1, x2, x3 = x + if y.ndim != 3: + raise ValueError("y should be three-dimensional if len(x)=3") + + if x1.shape[0] != y.shape[0]: + raise ValueError( + "len(x1) should equal y=shape[0], " + f"but x1.shape={x1.shape} and y.shape={y.shape}" + ) + if x2 is not None and x2.shape[0] != y.shape[1]: + raise ValueError( + "len(x2) should equal y=shape[1], " + f"but x2.shape={x2.shape} and y.shape={y.shape}" + ) + if x3 is not None and x3.shape[0] != y.shape[2]: + raise ValueError( + "len(x3) should equal y=shape[2], " + f"but x3.shape={x3.shape} and y.shape={y.shape}" + ) else: if isinstance(x, (tuple, list)): x1 = x[0] @@ -129,6 +149,16 @@ def __init__( interpolating_function = interpolate.interp2d( x1, x2, y, kind=interpolator ) + elif len(x) == 3: + self.dimension = 3 + if interpolator != "linear": + raise ValueError( + "interpolator should be 'linear' if x is three-dimensional" + ) + else: + interpolating_function = interpolate.RegularGridInterpolator( + (x1, x2, x3), y, method="linear" + ) else: raise ValueError("Invalid dimension of x: {0}".format(len(x))) @@ -199,5 +229,11 @@ def _function_evaluate(self, evaluated_children): else: # raise ValueError("Invalid children dimension: {0}".format(res.ndim)) return res[:, np.newaxis] + elif self.dimension == 3: + res = self.function(np.transpose(children_eval_flat)) + if res.ndim > 1: + return np.diagonal(res)[:, np.newaxis] + else: + return res[:, np.newaxis] else: # pragma: no cover raise ValueError("Invalid dimension: {0}".format(self.dimension)) From 724022603c4c852af5b3ec32b3584c4043014ff9 Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Tue, 18 Oct 2022 16:47:16 +0200 Subject: [PATCH 02/18] added test for 3d interpolant --- examples/scripts/quick_test.py | 5 +++- pybamm/expression_tree/interpolant.py | 2 +- .../test_expression_tree/test_interpolant.py | 23 +++++++++++++++++++ 3 files changed, 28 insertions(+), 2 deletions(-) diff --git a/examples/scripts/quick_test.py b/examples/scripts/quick_test.py index e9a89166e9..340ab8a294 100644 --- a/examples/scripts/quick_test.py +++ b/examples/scripts/quick_test.py @@ -30,5 +30,8 @@ def f(x, y, z): eval = interp.evaluate(y=np.array([1, 4, 7])) - print(eval) + + + +model = pybamm.BaseModel() diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index c92ee64751..33606ce1ed 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -157,7 +157,7 @@ def __init__( ) else: interpolating_function = interpolate.RegularGridInterpolator( - (x1, x2, x3), y, method="linear" + (x1, x2, x3), y, method="linear", bounds_error=False ) else: raise ValueError("Invalid dimension of x: {0}".format(len(x))) diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py index 327149923f..dd9e9de26a 100644 --- a/tests/unit/test_expression_tree/test_interpolant.py +++ b/tests/unit/test_expression_tree/test_interpolant.py @@ -97,6 +97,29 @@ def test_interpolation_2_x_2d_y(self): interp.evaluate(y=np.array([0, 0])), 0, decimal=3 ) + def test_interpolation_3d(self): + def f(x, y, z): + return 2 * x**3 + 3 * y**2 - z + + x = np.linspace(1, 4, 11) + y = np.linspace(4, 7, 22) + z = np.linspace(7, 9, 33) + xg, yg, zg = np.meshgrid(x, y, z, indexing="ij", sparse=True) + data = f(xg, yg, zg) + + var1 = pybamm.StateVector(slice(0, 1)) + var2 = pybamm.StateVector(slice(1, 2)) + var3 = pybamm.StateVector(slice(2, 3)) + + x_in = (x, y, z) + interp = pybamm.Interpolant( + x_in, data, (var1, var2, var3), interpolator="linear" + ) + + value = interp.evaluate(y=np.array([1, 4, 7])) + np.testing.assert_equal(value, f(1, 4, 7)) + + def test_name(self): a = pybamm.Symbol("a") x = np.linspace(0, 1, 200) From 014022d8e93a4a1c58b021976d27a61a20a0f2e8 Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Tue, 18 Oct 2022 17:39:24 +0200 Subject: [PATCH 03/18] casadi interpolation working for simple example --- .../Tutorial 3 - Basic plotting.ipynb | 13 ++++--- ...rial 6 - Managing simulation outputs.ipynb | 9 +++-- examples/scripts/quick_test.py | 34 ++++++++++++------- .../operations/convert_to_casadi.py | 4 +++ 4 files changed, 42 insertions(+), 18 deletions(-) diff --git a/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb b/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb index 1384c9dfa8..c2bcd156ba 100644 --- a/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 3 - Basic plotting.ipynb @@ -961,7 +961,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1000,7 +1000,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1072,7 +1072,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.9.13 ('python39-pybamm')", "language": "python", "name": "python3" }, @@ -1086,7 +1086,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "7dc94e087d5e42ea54b14035c48a0a59093d5180e7f512a1db8f70eb4b99d01e" + } } }, "nbformat": 4, diff --git a/examples/notebooks/Getting Started/Tutorial 6 - Managing simulation outputs.ipynb b/examples/notebooks/Getting Started/Tutorial 6 - Managing simulation outputs.ipynb index d0a7617c70..38f521d602 100644 --- a/examples/notebooks/Getting Started/Tutorial 6 - Managing simulation outputs.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 6 - Managing simulation outputs.ipynb @@ -436,7 +436,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.13 ('python39-pybamm')", "language": "python", "name": "python3" }, @@ -450,7 +450,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.9.13" }, "toc": { "base_numbering": 1, @@ -464,6 +464,11 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "7dc94e087d5e42ea54b14035c48a0a59093d5180e7f512a1db8f70eb4b99d01e" + } } }, "nbformat": 4, diff --git a/examples/scripts/quick_test.py b/examples/scripts/quick_test.py index 340ab8a294..8188b296b7 100644 --- a/examples/scripts/quick_test.py +++ b/examples/scripts/quick_test.py @@ -1,4 +1,3 @@ -from scipy.interpolate import RegularGridInterpolator import numpy as np import pybamm @@ -14,24 +13,35 @@ def f(x, y, z): xg, yg, zg = np.meshgrid(x, y, z, indexing="ij", sparse=True) data = f(xg, yg, zg) -interp = RegularGridInterpolator((x, y, z), data) +x_in = (x, y, z) -pts = np.array([[2.1, 6.2, 8.3], [3.3, 5.2, 7.1]]) +model = pybamm.BaseModel() -interp(pts) +a = pybamm.Variable("a") +b = pybamm.Variable("b") +c = pybamm.Variable("c") +d = pybamm.Variable("d") +interp = pybamm.Interpolant(x_in, data, (a, b, c), interpolator="linear") -var1 = pybamm.StateVector(slice(0, 1)) -var2 = pybamm.StateVector(slice(1, 2)) -var3 = pybamm.StateVector(slice(2, 3)) +model.rhs = {a: 0, b: 0, c: 0, d: interp} # add to model +model.initial_conditions = { + a: pybamm.Scalar(1), + b: pybamm.Scalar(4), + c: pybamm.Scalar(7), + d: pybamm.Scalar(0), +} -x_in = (x, y, z) -interp = pybamm.Interpolant(x_in, data, (var1, var2, var3), interpolator="linear") +model.variables = { + "Something": interp, +} -eval = interp.evaluate(y=np.array([1, 4, 7])) +sim = pybamm.Simulation(model) -print(eval) +t_eval = np.linspace(0, 1, 100) +sim.solve(t_eval) +something = sim.solution["Something"] -model = pybamm.BaseModel() +print("hi") diff --git a/pybamm/expression_tree/operations/convert_to_casadi.py b/pybamm/expression_tree/operations/convert_to_casadi.py index 1531ac06bb..d855898d60 100644 --- a/pybamm/expression_tree/operations/convert_to_casadi.py +++ b/pybamm/expression_tree/operations/convert_to_casadi.py @@ -158,6 +158,10 @@ def _convert(self, symbol, t, y, y_dot, inputs): ) res = LUT(casadi.hcat(converted_children).T).T return res + elif len(converted_children) == 3: + LUT = casadi.interpolant("LUT", solver, symbol.x, symbol.y.ravel()) + res = LUT(casadi.hcat(converted_children).T).T + return res else: # pragma: no cover raise ValueError( "Invalid converted_children count: {0}".format( From f20f2caaa84e775e115f548ab5c1d568295ca7b9 Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Tue, 18 Oct 2022 18:18:18 +0200 Subject: [PATCH 04/18] figuring out how to test convert to casasi --- examples/scripts/quick_test2.py | 33 ++++++++++++++++ .../test_operations/test_convert_to_casadi.py | 38 ++++++++++++++++--- 2 files changed, 66 insertions(+), 5 deletions(-) create mode 100644 examples/scripts/quick_test2.py diff --git a/examples/scripts/quick_test2.py b/examples/scripts/quick_test2.py new file mode 100644 index 0000000000..0e18393230 --- /dev/null +++ b/examples/scripts/quick_test2.py @@ -0,0 +1,33 @@ +import pybamm +import numpy as np +import casadi + + +def f(x, y, z): + return 2 * x**3 + 3 * y**2 - z + + +x = np.linspace(1, 4, 11) +y = np.linspace(4, 7, 22) +z = np.linspace(7, 9, 33) +xg, yg, zg = np.meshgrid(x, y, z, indexing="ij", sparse=True) +data = f(xg, yg, zg) + +var1 = pybamm.StateVector(slice(0, 1)) +var2 = pybamm.StateVector(slice(1, 2)) +var3 = pybamm.StateVector(slice(2, 3)) + +x_in = (x, y, z) +interp = pybamm.Interpolant(x_in, data, (var1, var2, var3), interpolator="linear") + +casadi_y = casadi.MX.sym("y", 3) +interp_casadi = interp.to_casadi(y=casadi_y) + +casadi_f = casadi.Function("f", [casadi_y], [interp_casadi]) +y_test = np.array([1, 4, 7]) + +casadi_sol = casadi_f(y_test) + + +print("hi") +# casadi_f = casadi.Function("f", [casadi_y], [interp_casadi]) diff --git a/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py b/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py index 0286c5efd2..9185efe1c3 100644 --- a/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py +++ b/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py @@ -194,16 +194,17 @@ def test_interpolation(self): interp_casadi = interp.to_casadi(y=casadi_y) # error for converted children count - y3 = ( + y4 = ( + pybamm.StateVector(slice(0, 1)), pybamm.StateVector(slice(0, 1)), pybamm.StateVector(slice(0, 1)), pybamm.StateVector(slice(0, 1)), ) - x3_ = [np.linspace(0, 1) for _ in range(3)] - x3 = np.column_stack(x3_) - data3 = 2 * x3 # np.tile(2 * x3, (10, 1)).T + x4_ = [np.linspace(0, 1) for _ in range(4)] + x4 = np.column_stack(x4_) + data4 = 2 * x4 # np.tile(2 * x3, (10, 1)).T with self.assertRaisesRegex(ValueError, "Invalid dimension of x"): - interp = pybamm.Interpolant(x3_, data3, y3, interpolator="linear") + interp = pybamm.Interpolant(x4_, data4, y4, interpolator="linear") interp_casadi = interp.to_casadi(y=casadi_y) def test_interpolation_2d(self): @@ -246,6 +247,33 @@ def test_interpolation_2d(self): interp = pybamm.Interpolant(x_, Y, y, interpolator="pchip") interp_casadi = interp.to_casadi(y=casadi_y) + def test_interpolation_3d(self): + def f(x, y, z): + return 2 * x**3 + 3 * y**2 - z + + x = np.linspace(1, 4, 11) + y = np.linspace(4, 7, 22) + z = np.linspace(7, 9, 33) + xg, yg, zg = np.meshgrid(x, y, z, indexing="ij", sparse=True) + data = f(xg, yg, zg) + + var1 = pybamm.StateVector(slice(0, 1)) + var2 = pybamm.StateVector(slice(1, 2)) + var3 = pybamm.StateVector(slice(2, 3)) + + x_in = (x, y, z) + interp = pybamm.Interpolant( + x_in, data, (var1, var2, var3), interpolator="linear" + ) + + casadi_y = casadi.MX.sym("y", 3) + interp_casadi = interp.to_casadi(y=casadi_y) + + y_test = np.array([1, 4, 7]) + # casadi_f = casadi.Function("f", [casadi_y], [interp_casadi]) + + np.testing.assert_array_equal(f(*y_test), interp_casadi(y=y_test)) + def test_concatenations(self): y = np.linspace(0, 1, 10)[:, np.newaxis] a = pybamm.Vector(y) From 657c77905822588528f0547136b42937c115f20a Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Wed, 19 Oct 2022 09:16:01 +0200 Subject: [PATCH 05/18] added test for converting to casadi --- examples/scripts/quick_test2.py | 53 ++++++++++++++++++- .../operations/convert_to_casadi.py | 6 +-- .../test_expression_tree/test_interpolant.py | 5 +- .../test_operations/test_convert_to_casadi.py | 11 ++-- 4 files changed, 63 insertions(+), 12 deletions(-) diff --git a/examples/scripts/quick_test2.py b/examples/scripts/quick_test2.py index 0e18393230..5f2e1257cf 100644 --- a/examples/scripts/quick_test2.py +++ b/examples/scripts/quick_test2.py @@ -7,7 +7,7 @@ def f(x, y, z): return 2 * x**3 + 3 * y**2 - z -x = np.linspace(1, 4, 11) +x = np.arange(1, 4.1, 0.1) y = np.linspace(4, 7, 22) z = np.linspace(7, 9, 33) xg, yg, zg = np.meshgrid(x, y, z, indexing="ij", sparse=True) @@ -24,10 +24,61 @@ def f(x, y, z): interp_casadi = interp.to_casadi(y=casadi_y) casadi_f = casadi.Function("f", [casadi_y], [interp_casadi]) + + y_test = np.array([1, 4, 7]) +casadi_sol = casadi_f(y_test) +pybamm_sol = interp.evaluate(y=y_test) +real_sol = f(*y_test) +print(casadi_sol, pybamm_sol, real_sol) + +y_test = np.array([2, 4, 7]) +casadi_sol = casadi_f(y_test) +pybamm_sol = interp.evaluate(y=y_test) +real_sol = f(*y_test) +print(casadi_sol, pybamm_sol, real_sol) + +y_test = np.array([1, 5, 7]) +casadi_sol = casadi_f(y_test) +pybamm_sol = interp.evaluate(y=y_test) +real_sol = f(*y_test) +print(casadi_sol, pybamm_sol, real_sol) +y_test = np.array([1, 4, 8]) casadi_sol = casadi_f(y_test) +pybamm_sol = interp.evaluate(y=y_test) +real_sol = f(*y_test) +print(casadi_sol, pybamm_sol, real_sol) + +xg, yg, zg = np.meshgrid(x, y, z, indexing="ij") +y_eval = np.stack([xg.flatten(), yg.flatten(), zg.flatten()], axis=-1) + +pybamm_sol = interp.evaluate(y=y_eval) + + +x_ = [np.linspace(0, 1), np.linspace(0, 1)] + +X = list(np.meshgrid(*x_)) + +x = np.column_stack([el.reshape(-1, 1) for el in X]) +y = (pybamm.StateVector(slice(0, 2)), pybamm.StateVector(slice(0, 2))) +casadi_y = casadi.MX.sym("y", 2) +# linear +y_test = np.array([0.4, 0.6]) +Y = (2 * x).sum(axis=1).reshape(*[len(el) for el in x_]) +for interpolator in ["linear"]: + interp = pybamm.Interpolant(x_, Y, y, interpolator=interpolator) + interp_casadi = interp.to_casadi(y=casadi_y) + f = casadi.Function("f", [casadi_y], [interp_casadi]) +# square +y = (pybamm.StateVector(slice(0, 1)), pybamm.StateVector(slice(0, 1))) +Y = (x**2).sum(axis=1).reshape(*[len(el) for el in x_]) +interp = pybamm.Interpolant(x_, Y, y, interpolator="linear") +interp_casadi = interp.to_casadi(y=casadi_y) +f = casadi.Function("f", [casadi_y], [interp_casadi]) +pybamm_sol = interp.evaluate(y=y_test) +casadi_sol = f(y_test) print("hi") # casadi_f = casadi.Function("f", [casadi_y], [interp_casadi]) diff --git a/pybamm/expression_tree/operations/convert_to_casadi.py b/pybamm/expression_tree/operations/convert_to_casadi.py index d855898d60..661771a123 100644 --- a/pybamm/expression_tree/operations/convert_to_casadi.py +++ b/pybamm/expression_tree/operations/convert_to_casadi.py @@ -152,16 +152,12 @@ def _convert(self, symbol, t, y, y_dot, inputs): return casadi.interpolant( "LUT", solver, symbol.x, symbol.y.flatten() )(*converted_children) - elif len(converted_children) == 2: + elif len(converted_children) in [2, 3]: LUT = casadi.interpolant( "LUT", solver, symbol.x, symbol.y.ravel(order="F") ) res = LUT(casadi.hcat(converted_children).T).T return res - elif len(converted_children) == 3: - LUT = casadi.interpolant("LUT", solver, symbol.x, symbol.y.ravel()) - res = LUT(casadi.hcat(converted_children).T).T - return res else: # pragma: no cover raise ValueError( "Invalid converted_children count: {0}".format( diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py index dd9e9de26a..272ad66825 100644 --- a/tests/unit/test_expression_tree/test_interpolant.py +++ b/tests/unit/test_expression_tree/test_interpolant.py @@ -116,9 +116,8 @@ def f(x, y, z): x_in, data, (var1, var2, var3), interpolator="linear" ) - value = interp.evaluate(y=np.array([1, 4, 7])) - np.testing.assert_equal(value, f(1, 4, 7)) - + value = interp.evaluate(y=np.array([1, 5, 8])) + np.testing.assert_equal(value, f(1, 5, 8)) def test_name(self): a = pybamm.Symbol("a") diff --git a/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py b/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py index 9185efe1c3..3029ce8476 100644 --- a/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py +++ b/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py @@ -268,11 +268,16 @@ def f(x, y, z): casadi_y = casadi.MX.sym("y", 3) interp_casadi = interp.to_casadi(y=casadi_y) + casadi_f = casadi.Function("f", [casadi_y], [interp_casadi]) - y_test = np.array([1, 4, 7]) - # casadi_f = casadi.Function("f", [casadi_y], [interp_casadi]) + y_test = np.array([1, 5, 8]) - np.testing.assert_array_equal(f(*y_test), interp_casadi(y=y_test)) + casadi_sol = casadi_f(y_test) + true_value = f(1, 5, 8) + + self.assertIsInstance(casadi_sol, casadi.DM) + + np.testing.assert_equal(true_value, casadi_sol.__float__()) def test_concatenations(self): y = np.linspace(0, 1, 10)[:, np.newaxis] From 52c28cbc9ac1aaf82e2cb4695859514ef8316e85 Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Wed, 19 Oct 2022 09:36:43 +0200 Subject: [PATCH 06/18] added minimal working example of 3d interpolation --- examples/scripts/minimal_interp3d_example.py | 72 +++++++++++++++++ examples/scripts/quick_test.py | 47 ----------- examples/scripts/quick_test2.py | 84 -------------------- 3 files changed, 72 insertions(+), 131 deletions(-) create mode 100644 examples/scripts/minimal_interp3d_example.py delete mode 100644 examples/scripts/quick_test.py delete mode 100644 examples/scripts/quick_test2.py diff --git a/examples/scripts/minimal_interp3d_example.py b/examples/scripts/minimal_interp3d_example.py new file mode 100644 index 0000000000..c9ef5a2cd5 --- /dev/null +++ b/examples/scripts/minimal_interp3d_example.py @@ -0,0 +1,72 @@ +import numpy as np + +import pybamm +import matplotlib.pyplot as plt + + +def f(x, y, z): + return 2 * x**3 + 3 * y**2 - z + + +x = np.linspace(1, 4, 100) +y = np.linspace(4, 7, 105) +z = np.linspace(7, 9, 110) +xg, yg, zg = np.meshgrid(x, y, z, indexing="ij", sparse=True) +data = f(xg, yg, zg) + +x_in = (x, y, z) + +model = pybamm.BaseModel() + +a = pybamm.Variable("a") +b = pybamm.Variable("b") +c = pybamm.Variable("c") +d = pybamm.Variable("d") + +interp = pybamm.Interpolant(x_in, data, (a, b, c), interpolator="linear") + +model.rhs = {a: 3, b: 3, c: 2, d: interp} # add to model +model.initial_conditions = { + a: pybamm.Scalar(1), + b: pybamm.Scalar(4), + c: pybamm.Scalar(7), + d: pybamm.Scalar(0), +} + +model.variables = { + "Something": interp, + "a": a, + "b": b, + "c": c, + "d": d, +} + +# solver = pybamm.CasadiSolver() +sim = pybamm.Simulation(model) + +t_eval = np.linspace(0, 1, 100) +sim.solve(t_eval) + +a_eval = sim.solution["a"](t_eval) +b_eval = sim.solution["b"](t_eval) +c_eval = sim.solution["c"](t_eval) +d_eval = sim.solution["d"](t_eval) +something = sim.solution["Something"](t_eval) + +difference = something - f(a_eval, b_eval, c_eval) + +fig, ax = plt.subplots(2, 1, figsize=(10, 5), sharex=True) + +ax[0].plot(t_eval, f(a_eval, b_eval, c_eval), label="Original") +ax[0].plot(t_eval, something, label="Interpolated") +ax[0].set_ylabel("Value") +ax[0].legend() + +ax[1].plot(t_eval, np.abs(f(a_eval, b_eval, c_eval) - something), label="Original") +ax[1].set_ylabel("Difference") + +ax[-1].set_xlabel("Time [s]") +for a in ax: + a.grid() + +plt.show() diff --git a/examples/scripts/quick_test.py b/examples/scripts/quick_test.py deleted file mode 100644 index 8188b296b7..0000000000 --- a/examples/scripts/quick_test.py +++ /dev/null @@ -1,47 +0,0 @@ -import numpy as np - -import pybamm - - -def f(x, y, z): - return 2 * x**3 + 3 * y**2 - z - - -x = np.linspace(1, 4, 11) -y = np.linspace(4, 7, 22) -z = np.linspace(7, 9, 33) -xg, yg, zg = np.meshgrid(x, y, z, indexing="ij", sparse=True) -data = f(xg, yg, zg) - -x_in = (x, y, z) - -model = pybamm.BaseModel() - -a = pybamm.Variable("a") -b = pybamm.Variable("b") -c = pybamm.Variable("c") -d = pybamm.Variable("d") - -interp = pybamm.Interpolant(x_in, data, (a, b, c), interpolator="linear") - -model.rhs = {a: 0, b: 0, c: 0, d: interp} # add to model -model.initial_conditions = { - a: pybamm.Scalar(1), - b: pybamm.Scalar(4), - c: pybamm.Scalar(7), - d: pybamm.Scalar(0), -} - -model.variables = { - "Something": interp, -} - -sim = pybamm.Simulation(model) - -t_eval = np.linspace(0, 1, 100) -sim.solve(t_eval) - -something = sim.solution["Something"] - - -print("hi") diff --git a/examples/scripts/quick_test2.py b/examples/scripts/quick_test2.py deleted file mode 100644 index 5f2e1257cf..0000000000 --- a/examples/scripts/quick_test2.py +++ /dev/null @@ -1,84 +0,0 @@ -import pybamm -import numpy as np -import casadi - - -def f(x, y, z): - return 2 * x**3 + 3 * y**2 - z - - -x = np.arange(1, 4.1, 0.1) -y = np.linspace(4, 7, 22) -z = np.linspace(7, 9, 33) -xg, yg, zg = np.meshgrid(x, y, z, indexing="ij", sparse=True) -data = f(xg, yg, zg) - -var1 = pybamm.StateVector(slice(0, 1)) -var2 = pybamm.StateVector(slice(1, 2)) -var3 = pybamm.StateVector(slice(2, 3)) - -x_in = (x, y, z) -interp = pybamm.Interpolant(x_in, data, (var1, var2, var3), interpolator="linear") - -casadi_y = casadi.MX.sym("y", 3) -interp_casadi = interp.to_casadi(y=casadi_y) - -casadi_f = casadi.Function("f", [casadi_y], [interp_casadi]) - - -y_test = np.array([1, 4, 7]) -casadi_sol = casadi_f(y_test) -pybamm_sol = interp.evaluate(y=y_test) -real_sol = f(*y_test) -print(casadi_sol, pybamm_sol, real_sol) - -y_test = np.array([2, 4, 7]) -casadi_sol = casadi_f(y_test) -pybamm_sol = interp.evaluate(y=y_test) -real_sol = f(*y_test) -print(casadi_sol, pybamm_sol, real_sol) - -y_test = np.array([1, 5, 7]) -casadi_sol = casadi_f(y_test) -pybamm_sol = interp.evaluate(y=y_test) -real_sol = f(*y_test) -print(casadi_sol, pybamm_sol, real_sol) - -y_test = np.array([1, 4, 8]) -casadi_sol = casadi_f(y_test) -pybamm_sol = interp.evaluate(y=y_test) -real_sol = f(*y_test) -print(casadi_sol, pybamm_sol, real_sol) - -xg, yg, zg = np.meshgrid(x, y, z, indexing="ij") -y_eval = np.stack([xg.flatten(), yg.flatten(), zg.flatten()], axis=-1) - -pybamm_sol = interp.evaluate(y=y_eval) - - -x_ = [np.linspace(0, 1), np.linspace(0, 1)] - -X = list(np.meshgrid(*x_)) - -x = np.column_stack([el.reshape(-1, 1) for el in X]) -y = (pybamm.StateVector(slice(0, 2)), pybamm.StateVector(slice(0, 2))) -casadi_y = casadi.MX.sym("y", 2) -# linear -y_test = np.array([0.4, 0.6]) -Y = (2 * x).sum(axis=1).reshape(*[len(el) for el in x_]) -for interpolator in ["linear"]: - interp = pybamm.Interpolant(x_, Y, y, interpolator=interpolator) - interp_casadi = interp.to_casadi(y=casadi_y) - f = casadi.Function("f", [casadi_y], [interp_casadi]) -# square -y = (pybamm.StateVector(slice(0, 1)), pybamm.StateVector(slice(0, 1))) -Y = (x**2).sum(axis=1).reshape(*[len(el) for el in x_]) -interp = pybamm.Interpolant(x_, Y, y, interpolator="linear") -interp_casadi = interp.to_casadi(y=casadi_y) -f = casadi.Function("f", [casadi_y], [interp_casadi]) - -pybamm_sol = interp.evaluate(y=y_test) -casadi_sol = f(y_test) - -print("hi") -# casadi_f = casadi.Function("f", [casadi_y], [interp_casadi]) From 1fabb269824d3acba2200232170fa20befd644c6 Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Wed, 19 Oct 2022 09:48:59 +0200 Subject: [PATCH 07/18] updated docstring and added extrapolate option --- pybamm/expression_tree/interpolant.py | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index 33606ce1ed..442c14e343 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -10,14 +10,18 @@ class Interpolant(pybamm.Function): """ - Interpolate data in 1D. + Interpolate data in 1D, 2D, or 3D. Interpolation in 3D required the input data to be + on a regular grid. Parameters ---------- x : iterable of :class:`numpy.ndarray` - 1-D array(s) of real values defining the data point coordinates. + The data point coordinates. If 1-D, then this is an array(s) of real values. If, + 2D or 3D interpolation, then this is to ba a tuple of 1D arrays (one for each + dimension) which together define the coordinates of the points. y : :class:`numpy.ndarray` - The values of the function to interpolate at the data points. + The values of the function to interpolate at the data points. In 2D and 3D, this + should be a matrix of two and three dimensions respectively. children : iterable of :class:`pybamm.Symbol` Node(s) to use when evaluating the interpolant. Each child corresponds to an entry of x @@ -26,7 +30,7 @@ class Interpolant(pybamm.Function): function" is given. interpolator : str, optional Which interpolator to use. Can be "linear", "cubic", or "pchip". Default is - "linear". + "linear". For 3D interpolation, only "linear" is currently supported. 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. @@ -151,13 +155,23 @@ def __init__( ) elif len(x) == 3: self.dimension = 3 + + if extrapolate: + fill_value = None + else: + fill_value = np.nan + if interpolator != "linear": raise ValueError( "interpolator should be 'linear' if x is three-dimensional" ) else: interpolating_function = interpolate.RegularGridInterpolator( - (x1, x2, x3), y, method="linear", bounds_error=False + (x1, x2, x3), + y, + method="linear", + bounds_error=False, + fill_value=fill_value, ) else: raise ValueError("Invalid dimension of x: {0}".format(len(x))) From b44e4dbfa3b787606ca628d817385c49f08d5b7b Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Wed, 19 Oct 2022 09:55:27 +0200 Subject: [PATCH 08/18] rebuilt docs --- pybamm/expression_tree/interpolant.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index 442c14e343..e3fd184f95 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -10,8 +10,8 @@ class Interpolant(pybamm.Function): """ - Interpolate data in 1D, 2D, or 3D. Interpolation in 3D required the input data to be - on a regular grid. + Interpolate data in 1D, 2D, or 3D. Interpolation in 3D requires the input data to be + on a regular grid (as per scipy.interpolate.RegularGridInterpolator). Parameters ---------- @@ -34,6 +34,8 @@ class Interpolant(pybamm.Function): 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. + Generally, it is best to set this to be False for 3D interpolation due to + the higher potential for errors in extrapolation. **Extends**: :class:`pybamm.Function` """ From ac59fd6f105cdd0d17dc20aa57a18d438e5644eb Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Wed, 19 Oct 2022 10:26:06 +0200 Subject: [PATCH 09/18] updated changelog --- CHANGELOG.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index a3659b4742..1dde45a59b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,8 @@ # [Unreleased](https://github.com/pybamm-team/PyBaMM/) +## Features +- Added three-dimensional interpolation [#2380](https://github.com/pybamm-team/PyBaMM/pull/2380) + ## Bug fixes - For simulations with events that cause the simulation to stop early, the sensitivities could be evaluated incorrectly to zero ([#2337](https://github.com/pybamm-team/PyBaMM/pull/2337)) From 9a805e9d1becf1cf4e8b8e8fc0b45b6c2dd5a3a8 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 19 Oct 2022 08:27:17 +0000 Subject: [PATCH 10/18] style: pre-commit fixes --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 1dde45a59b..901960e9c7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,7 @@ # [Unreleased](https://github.com/pybamm-team/PyBaMM/) ## Features + - Added three-dimensional interpolation [#2380](https://github.com/pybamm-team/PyBaMM/pull/2380) ## Bug fixes From c3cea62e49da9418cfd5969b26597502040a8e2c Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Mon, 24 Oct 2022 11:33:19 +0200 Subject: [PATCH 11/18] added coverage --- .../test_expression_tree/test_interpolant.py | 43 ++++++++++++++++++- 1 file changed, 42 insertions(+), 1 deletion(-) diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py index 272ad66825..0f7522f6dd 100644 --- a/tests/unit/test_expression_tree/test_interpolant.py +++ b/tests/unit/test_expression_tree/test_interpolant.py @@ -97,7 +97,7 @@ def test_interpolation_2_x_2d_y(self): interp.evaluate(y=np.array([0, 0])), 0, decimal=3 ) - def test_interpolation_3d(self): + def test_interpolation_3_x(self): def f(x, y, z): return 2 * x**3 + 3 * y**2 - z @@ -119,6 +119,47 @@ def f(x, y, z): value = interp.evaluate(y=np.array([1, 5, 8])) np.testing.assert_equal(value, f(1, 5, 8)) + value = interp.evaluate(y=np.array([[1, 1, 1], [5, 4, 4], [8, 7, 7]])) + np.testing.assert_array_equal( + value, np.array([[f(1, 5, 8)], [f(1, 4, 7)], [f(1, 4, 7)]]) + ) + + # Test raising error if data is not 3D + data_4d = np.zeros((11, 22, 33, 5)) + with self.assertRaisesRegex(ValueError, "y should be three-dimensional"): + interp = pybamm.Interpolant( + x_in, data_4d, (var1, var2, var3), interpolator="linear" + ) + + # Test raising error if wrong shapes + with self.assertRaisesRegex(ValueError, "x1.shape"): + interp = pybamm.Interpolant( + x_in, np.zeros((12, 22, 33)), (var1, var2, var3), interpolator="linear" + ) + + with self.assertRaisesRegex(ValueError, "x2.shape"): + interp = pybamm.Interpolant( + x_in, np.zeros((11, 23, 33)), (var1, var2, var3), interpolator="linear" + ) + + with self.assertRaisesRegex(ValueError, "x3.shape"): + interp = pybamm.Interpolant( + x_in, np.zeros((11, 22, 34)), (var1, var2, var3), interpolator="linear" + ) + + # Raise error if not linear + with self.assertRaisesRegex(ValueError, "interpolator should be 'linear'"): + interp = pybamm.Interpolant( + x_in, data, (var1, var2, var3), interpolator="cubic" + ) + + # Check returns nan if extrapolate set to False + interp = pybamm.Interpolant( + x_in, data, (var1, var2, var3), interpolator="linear", extrapolate=False + ) + value = interp.evaluate(y=np.array([0, 0, 0])) + np.testing.assert_equal(value, np.nan) + def test_name(self): a = pybamm.Symbol("a") x = np.linspace(0, 1, 200) From 1cc7aa9477ee4d1778b4e0612df699e1c6cdb7c6 Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Mon, 24 Oct 2022 14:56:29 +0200 Subject: [PATCH 12/18] improve coverage --- pybamm/expression_tree/interpolant.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index e3fd184f95..abf2ed97a0 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -243,13 +243,9 @@ def _function_evaluate(self, evaluated_children): if res.ndim > 1: return np.diagonal(res)[:, np.newaxis] else: - # raise ValueError("Invalid children dimension: {0}".format(res.ndim)) return res[:, np.newaxis] elif self.dimension == 3: res = self.function(np.transpose(children_eval_flat)) - if res.ndim > 1: - return np.diagonal(res)[:, np.newaxis] - else: - return res[:, np.newaxis] + return res[:, np.newaxis] else: # pragma: no cover raise ValueError("Invalid dimension: {0}".format(self.dimension)) From 0f621990ca8ab7d7b33b2d324a90ad7b528cd3a4 Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Mon, 24 Oct 2022 15:33:20 +0200 Subject: [PATCH 13/18] allow for cubic interpolation --- pybamm/expression_tree/interpolant.py | 11 +++++++---- tests/unit/test_expression_tree/test_interpolant.py | 11 +++++++++-- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index abf2ed97a0..a8cbd8dc39 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -30,7 +30,8 @@ class Interpolant(pybamm.Function): function" is given. interpolator : str, optional Which interpolator to use. Can be "linear", "cubic", or "pchip". Default is - "linear". For 3D interpolation, only "linear" is currently supported. + "linear". For 3D interpolation, only "linear" an "cubic" are currently + supported. 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. @@ -163,15 +164,17 @@ def __init__( else: fill_value = np.nan - if interpolator != "linear": + possible_interpolators = ["linear", "cubic"] + if interpolator not in possible_interpolators: raise ValueError( - "interpolator should be 'linear' if x is three-dimensional" + """interpolator should be 'linear' or 'cubic' + for 3D interpolation""" ) else: interpolating_function = interpolate.RegularGridInterpolator( (x1, x2, x3), y, - method="linear", + method=interpolator, bounds_error=False, fill_value=fill_value, ) diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py index 0f7522f6dd..d35b166780 100644 --- a/tests/unit/test_expression_tree/test_interpolant.py +++ b/tests/unit/test_expression_tree/test_interpolant.py @@ -124,6 +124,13 @@ def f(x, y, z): value, np.array([[f(1, 5, 8)], [f(1, 4, 7)], [f(1, 4, 7)]]) ) + # check also works for cubic + interp = pybamm.Interpolant( + x_in, data, (var1, var2, var3), interpolator="cubic" + ) + value = interp.evaluate(y=np.array([1, 5, 8])) + np.testing.assert_equal(value, f(1, 5, 8)) + # Test raising error if data is not 3D data_4d = np.zeros((11, 22, 33, 5)) with self.assertRaisesRegex(ValueError, "y should be three-dimensional"): @@ -148,9 +155,9 @@ def f(x, y, z): ) # Raise error if not linear - with self.assertRaisesRegex(ValueError, "interpolator should be 'linear'"): + with self.assertRaisesRegex(ValueError, "interpolator should be 'linear' or 'cubic'"): interp = pybamm.Interpolant( - x_in, data, (var1, var2, var3), interpolator="cubic" + x_in, data, (var1, var2, var3), interpolator="pchip" ) # Check returns nan if extrapolate set to False From 35f5d64d21a7cf2662cd53469307a6c4eb6ca01e Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Mon, 24 Oct 2022 15:36:29 +0200 Subject: [PATCH 14/18] fixed flake8 --- tests/unit/test_expression_tree/test_interpolant.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py index d35b166780..b247a300b3 100644 --- a/tests/unit/test_expression_tree/test_interpolant.py +++ b/tests/unit/test_expression_tree/test_interpolant.py @@ -155,7 +155,9 @@ def f(x, y, z): ) # Raise error if not linear - with self.assertRaisesRegex(ValueError, "interpolator should be 'linear' or 'cubic'"): + with self.assertRaisesRegex( + ValueError, "interpolator should be 'linear' or 'cubic'" + ): interp = pybamm.Interpolant( x_in, data, (var1, var2, var3), interpolator="pchip" ) From 8740cf68dc6cca03d453f39b8765545ce404c6d8 Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Mon, 24 Oct 2022 17:21:21 +0200 Subject: [PATCH 15/18] cope with testing for shape --- pybamm/expression_tree/interpolant.py | 17 +++++++++++++++++ .../test_expression_tree/test_interpolant.py | 6 ++++++ 2 files changed, 23 insertions(+) diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index a8cbd8dc39..581ef8d598 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -252,3 +252,20 @@ def _function_evaluate(self, evaluated_children): return res[:, np.newaxis] else: # pragma: no cover raise ValueError("Invalid dimension: {0}".format(self.dimension)) + + def _evaluate_for_shape(self): + """ + Default behaviour: has same shape as all child + See :meth:`pybamm.Symbol.evaluate_for_shape()` + """ + evaluated_children = [child.evaluate_for_shape() for child in self.children] + + # RegularGridInterpolator cannot accept nan values so run the + # interpolation with the average values the interpolation range + if self.dimension == 3: + new_evaluated_children = [] + for child, interp_range in zip(evaluated_children, self.function.grid): + new_evaluated_children.append(np.ones_like(child) * interp_range.mean()) + return self._function_evaluate(new_evaluated_children) * np.nan + else: + return self._function_evaluate(evaluated_children) diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py index b247a300b3..c821884dc3 100644 --- a/tests/unit/test_expression_tree/test_interpolant.py +++ b/tests/unit/test_expression_tree/test_interpolant.py @@ -169,6 +169,12 @@ def f(x, y, z): value = interp.evaluate(y=np.array([0, 0, 0])) np.testing.assert_equal(value, np.nan) + # Check testing for shape works + interp = pybamm.Interpolant( + x_in, data, (var1, var2, var3), interpolator="cubic" + ) + interp.test_shape() + def test_name(self): a = pybamm.Symbol("a") x = np.linspace(0, 1, 200) From 689d69ccfd47354d175f89fb4bf52c1ff7fd18e7 Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Mon, 24 Oct 2022 20:17:08 +0200 Subject: [PATCH 16/18] #2380 hacky solution to deal with nans inconsistent children --- pybamm/expression_tree/interpolant.py | 71 ++++++++++++++++++++------- 1 file changed, 54 insertions(+), 17 deletions(-) diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index 581ef8d598..c1e1176be7 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -248,24 +248,61 @@ def _function_evaluate(self, evaluated_children): else: return res[:, np.newaxis] elif self.dimension == 3: - res = self.function(np.transpose(children_eval_flat)) - return res[:, np.newaxis] + + # If the children are scalars, we need to add a dimension + shapes = [] + for child in evaluated_children: + if isinstance(child, float): + shapes.append(()) + else: + shapes.append(child.shape) + shapes = set(shapes) + shapes.discard(()) + + if len(shapes) > 1: + raise ValueError( + "All children must have the same shape for 3D interpolation" + ) + + if shapes == {}: + shape = (1,) + else: + shape = shapes.pop() + new_evaluated_children = [] + for child in evaluated_children: + + if isinstance(child, float): + new_evaluated_children.append(np.reshape(child, shape)) + elif child.shape == shape: + new_evaluated_children.append(child) + else: + new_evaluated_children.append(np.reshape(child, shape)) + + # return nans if there are any within the children + nans = np.isnan(new_evaluated_children) + if np.any(nans): + nan_children = [] + for child, interp_range in zip( + new_evaluated_children, self.function.grid + ): + nan_children.append(np.ones_like(child) * interp_range.mean()) + return self.function(np.transpose(nan_children)) * np.nan + else: + res = self.function(np.transpose(new_evaluated_children)) + return res[:, np.newaxis] + else: # pragma: no cover raise ValueError("Invalid dimension: {0}".format(self.dimension)) - def _evaluate_for_shape(self): - """ - Default behaviour: has same shape as all child - See :meth:`pybamm.Symbol.evaluate_for_shape()` - """ - evaluated_children = [child.evaluate_for_shape() for child in self.children] + # def _evaluate_for_shape(self): + # """ + # Default behaviour: has same shape as all child + # See :meth:`pybamm.Symbol.evaluate_for_shape()` + # """ + # evaluated_children = [child.evaluate_for_shape() for child in self.children] - # RegularGridInterpolator cannot accept nan values so run the - # interpolation with the average values the interpolation range - if self.dimension == 3: - new_evaluated_children = [] - for child, interp_range in zip(evaluated_children, self.function.grid): - new_evaluated_children.append(np.ones_like(child) * interp_range.mean()) - return self._function_evaluate(new_evaluated_children) * np.nan - else: - return self._function_evaluate(evaluated_children) + # # RegularGridInterpolator cannot accept nan values so run the + # # interpolation with the average values the interpolation range + # if self.dimension == 3: + # else: + # return self._function_evaluate(evaluated_children) From 731567df1a2f5b6084ff08d28808eb2df0899175 Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Mon, 24 Oct 2022 20:39:18 +0200 Subject: [PATCH 17/18] #2380 added tests for 3D interpolation --- pybamm/expression_tree/interpolant.py | 23 ++++--------------- .../test_expression_tree/test_interpolant.py | 11 ++++++++- 2 files changed, 15 insertions(+), 19 deletions(-) diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index c1e1176be7..387976157d 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -252,7 +252,7 @@ def _function_evaluate(self, evaluated_children): # If the children are scalars, we need to add a dimension shapes = [] for child in evaluated_children: - if isinstance(child, float): + if isinstance(child, (float, int)): shapes.append(()) else: shapes.append(child.shape) @@ -271,12 +271,12 @@ def _function_evaluate(self, evaluated_children): new_evaluated_children = [] for child in evaluated_children: - if isinstance(child, float): - new_evaluated_children.append(np.reshape(child, shape)) + if isinstance(child, (float, int)): + new_evaluated_children.append(np.reshape(child, shape).flatten()) elif child.shape == shape: - new_evaluated_children.append(child) + new_evaluated_children.append(child.flatten()) else: - new_evaluated_children.append(np.reshape(child, shape)) + new_evaluated_children.append(np.reshape(child, shape).flatten()) # return nans if there are any within the children nans = np.isnan(new_evaluated_children) @@ -293,16 +293,3 @@ def _function_evaluate(self, evaluated_children): else: # pragma: no cover raise ValueError("Invalid dimension: {0}".format(self.dimension)) - - # def _evaluate_for_shape(self): - # """ - # Default behaviour: has same shape as all child - # See :meth:`pybamm.Symbol.evaluate_for_shape()` - # """ - # evaluated_children = [child.evaluate_for_shape() for child in self.children] - - # # RegularGridInterpolator cannot accept nan values so run the - # # interpolation with the average values the interpolation range - # if self.dimension == 3: - # else: - # return self._function_evaluate(evaluated_children) diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py index c821884dc3..d286cba60b 100644 --- a/tests/unit/test_expression_tree/test_interpolant.py +++ b/tests/unit/test_expression_tree/test_interpolant.py @@ -169,12 +169,21 @@ def f(x, y, z): value = interp.evaluate(y=np.array([0, 0, 0])) np.testing.assert_equal(value, np.nan) - # Check testing for shape works + # Check testing for shape works (i.e. using nans) interp = pybamm.Interpolant( x_in, data, (var1, var2, var3), interpolator="cubic" ) interp.test_shape() + # test with inconsistent children shapes + # (this can occur is one child is a scaler and the others + # are vaiables) + evaluated_children = [np.array([[1]]), 4, np.array([[7]])] + value = interp._function_evaluate(evaluated_children) + + evaluated_children = [np.array([[1]]), np.ones(()) * 4, np.array([[7]])] + value = interp._function_evaluate(evaluated_children) + def test_name(self): a = pybamm.Symbol("a") x = np.linspace(0, 1, 200) From 1f1258d37f8373886590cec8a03a8df175110173 Mon Sep 17 00:00:00 2001 From: Scott Marquis Date: Mon, 24 Oct 2022 21:43:45 +0200 Subject: [PATCH 18/18] #2380 improve coverege --- pybamm/expression_tree/interpolant.py | 6 ++---- tests/unit/test_expression_tree/test_interpolant.py | 9 +++++++++ 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index 387976157d..fdea90b306 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -264,16 +264,14 @@ def _function_evaluate(self, evaluated_children): "All children must have the same shape for 3D interpolation" ) - if shapes == {}: + if len(shapes) == 0: shape = (1,) else: shape = shapes.pop() new_evaluated_children = [] for child in evaluated_children: - if isinstance(child, (float, int)): - new_evaluated_children.append(np.reshape(child, shape).flatten()) - elif child.shape == shape: + if hasattr(child, "shape") and child.shape == shape: new_evaluated_children.append(child.flatten()) else: new_evaluated_children.append(np.reshape(child, shape).flatten()) diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py index d286cba60b..64bb3b0590 100644 --- a/tests/unit/test_expression_tree/test_interpolant.py +++ b/tests/unit/test_expression_tree/test_interpolant.py @@ -184,6 +184,15 @@ def f(x, y, z): evaluated_children = [np.array([[1]]), np.ones(()) * 4, np.array([[7]])] value = interp._function_evaluate(evaluated_children) + # Test evaluation fails with different child shapes + with self.assertRaisesRegex(ValueError, "All children must"): + evaluated_children = [np.array([[1, 1]]), np.ones(()) * 4, np.array([[7]])] + value = interp._function_evaluate(evaluated_children) + + # Test runs when all children are scalsrs + evaluated_children = [1, 4, 7] + value = interp._function_evaluate(evaluated_children) + def test_name(self): a = pybamm.Symbol("a") x = np.linspace(0, 1, 200)