129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
import numpy
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import pathfinder.model as model
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import scipy.optimize
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import pytest
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def test_dotdipolemodel_repr():
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mod = model.DotDipoleModel((), 1)
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assert repr(mod) == "DotDipoleModel([], 1)"
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def test_dotdipolemodel_m():
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mod = model.DotDipoleModel([model.DotMeasurement(1, (0, 0, 0)), model.DotMeasurement(2, (0, 0, 0))], 1)
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assert mod.m == 2
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def test_dotdipolemodel_cost():
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mod = model.DotDipoleModel([model.DotMeasurement(1, (0, 0, 1)), model.DotMeasurement(2, (1, 0, 0))], 1)
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costs = mod.costs()
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jac = mod.jac()
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pt_to_test = numpy.array((1, 2, 3, 4, 5, 6))
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expected_cost = [-1.05408565512728256, -2.05293155269909457]
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expected_jacobian = [
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[
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-0.007460090362383803, -0.009325112952979752, -0.009325112952979752,
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0.007968732887091788, 0.00856214916591777, 0.006697126575321822
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], [
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-0.00512240832571883, -0.008537347209531383, -0.01024481665143766,
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0.005098015905120168, 0.007927536694564856, 0.008488562368334061
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]
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]
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numpy.testing.assert_allclose(costs(pt_to_test), expected_cost, err_msg="Costs aren't as expected.")
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numpy.testing.assert_allclose(jac(pt_to_test), expected_jacobian, err_msg="Jacobian aren't as expected.")
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def print_result(msg, result):
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print(msg)
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print(f"\tResult: {result.x}")
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print(f"\tSuccess: {result.success}. {result.message}")
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try:
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print(f"\tFunc evals: {result.nfev}")
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except AttributeError:
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pass
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try:
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print(f"\tJacb evals: {result.njev}")
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except AttributeError:
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pass
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@pytest.mark.skip(reason="old")
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def test_dot_dipole_model_jac():
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v1 = -0.05547767706400186526225414
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v2 = -0.06018573388098888319642888
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v3 = -0.06364032191901859480476888
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v4 = -0.06488383879243851188402150
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v5 = -0.06297148063759813929659130
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v6 = -0.05735489606460216
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v7 = -0.07237320672886623
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v8 = -0.1082531754730548
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c1 = model.DotMeasurement(v1, [0, 0, 1])
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c2 = model.DotMeasurement(v2, [0, 0, 2])
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c3 = model.DotMeasurement(v3, [0, 0, 3])
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c4 = model.DotMeasurement(v4, [0, 0, 4])
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c5 = model.DotMeasurement(v5, [0, 0, 5])
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c6 = model.DotMeasurement(v6, [0, 0, 6])
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c7 = model.DotMeasurement(v7, [1, 1, 7])
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c8 = model.DotMeasurement(v8, [1, 2, 3])
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mod = model.DotDipoleModel([c1, c2, c3, c4, c5, c6], 1)
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res = scipy.optimize.least_squares(mod.costs(), numpy.array([1, 2, 3, 4, 5, 6]), jac=mod.jac(), ftol=1e-12)
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print_result("6 dots, least sq", res)
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mod2 = model.DotDipoleModel([c1, c2, c3, c4, c5, c6, c7, c8], 1)
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res2 = scipy.optimize.least_squares(mod2.costs(), numpy.array([0, 0, 0, 0, 0, 0]), jac=mod2.jac(), ftol=1e-12, gtol=3e-16)
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print_result("7 dots, least squares", res2)
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print(mod2.costs()(res2.x))
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print(mod2.costs()(numpy.array([1, 3, 5, 5, 6, 7])))
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@pytest.mark.skip(reason="bad test")
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def test_dot_2dipoles_model_jac():
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dots_12andone = [
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model.DotMeasurement(-0.1978319326584865, [-4, -1, 0]),
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model.DotMeasurement(-0.1273171638293727, [-4, -5, 0]),
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model.DotMeasurement(-0.05545025617224288, [-4, -9, 0]),
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model.DotMeasurement(-0.4960209997369774, [-1, -1, 0]),
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model.DotMeasurement(-0.1763373289754278, [-1, -5, 0]),
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model.DotMeasurement(-0.04946346672578462, [-1, -9, 0]),
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model.DotMeasurement(-0.5633156098386561, [1, -1, 0]),
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model.DotMeasurement(-0.113765134433174, [1, -5, 0]),
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model.DotMeasurement(-0.0294893572499722, [1, -9, 0]),
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model.DotMeasurement(0.7794941616360612, [4, -1, 0]),
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model.DotMeasurement(0.1110683086477768, [4, -5, 0]),
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model.DotMeasurement(0.01183272220840589, [4, -9, 0]),
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model.DotMeasurement(-0.1096485462119833, [1, 1, 1]),
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model.DotMeasurement(-0.3925851888077783, [1, 1, -1])
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]
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# dots = [
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# model.Dot(-0.1978319326584865, [-4, -1, 0]),
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# model.Dot(-0.1273171638293727, [-4, -5, 0]),
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# model.Dot(-0.05545025617224288, [-4, -9, 0]),
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# model.Dot(-0.4960209997369774, [-1, -1, 0]),
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# model.Dot(-0.1763373289754278, [-1, -5, 0]),
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# model.Dot(-0.04946346672578462, [-1, -9, 0]),
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# model.Dot(-0.5633156098386561, [1, -1, 0]),
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# model.Dot(-0.113765134433174, [1, -5, 0]),
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# model.Dot(-0.0294893572499722, [1, -9, 0]),
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# model.Dot(0.7794941616360612, [4, -1, 0]),
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# model.Dot(0.1110683086477768, [4, -5, 0]),
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# model.Dot(0.01183272220840589, [4, -9, 0]),
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# model.Dot(-0.0261092728062841, [-4, -13, 0]),
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# model.Dot(-0.02035559593904894, [-1, -13, 0]),
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# model.Dot(-0.01296894715810522, [1, -13, 0]),
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# model.Dot(0.0003306001626435171, [4, -13, 0]),
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# model.Dot(0.2612817068810759, [7, -1, 0]),
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# model.Dot(0.1203841445911355, [7, -5, 0]),
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# model.Dot(0.03425933872931543, [7, -9, 0]),
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# model.Dot(0.01068547688208644, [7, -13, 0]),
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# ]
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mod = model.DotDipoleModel(dots_12andone, 2)
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res = scipy.optimize.least_squares(mod.costs(), numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), jac=mod.jac(), ftol=1e-12)
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print_result("6 dots, least sq", res)
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print(mod.costs()(res.x))
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for val in res.x:
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print(val)
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