""" Test fitting of Fermi-Dirac distributions."""

import unittest
import numpy as np
import matplotlib.pyplot as plt
from qtt.algorithms.functions import FermiLinear, linear_function
from qtt.algorithms.fitting import initFermiLinear, _estimate_fermi_model_center_amplitude, fitFermiLinear


class TestFitting(unittest.TestCase):

    def test_initial_estimate_fermi_linear(self, fig=100):
        expected_parameters = [0.01000295, 0.51806569, -4.88800525, 0.12838861, 0.25382811]
        x_data = np.arange(-20, 10, 0.1)
        y_data = FermiLinear(x_data, *expected_parameters)
        y_data += 0.005 * np.random.rand(y_data.size)

        linear_part, fermi_part = initFermiLinear(x_data, y_data, fig=fig)

        ylin = linear_function(x_data, *linear_part)
        yr = y_data - ylin

        cc, A = _estimate_fermi_model_center_amplitude(x_data, yr, fig=fig)
        np.testing.assert_almost_equal(cc, expected_parameters[2], decimal=1)
        np.testing.assert_almost_equal(A, expected_parameters[3], decimal=1)
        self.assertTrue(fermi_part is not None)
        plt.close('all')

    def test_fit_fermi_linear(self, fig=100, verbose=1):
        expected_parameters = [0.01000295, 0.51806569, -4.88800525, 0.12838861, 0.25382811]
        x_data = np.arange(-20, 10, 0.1)
        y_data = FermiLinear(x_data, *expected_parameters)
        y_data += 0.005 * np.random.rand(y_data.size)

        actual_parameters, _ = fitFermiLinear(x_data, y_data, verbose=verbose, fig=fig, use_lmfit=False)
        absolute_difference_parameters = np.abs(actual_parameters - expected_parameters)

        y_data_fitted = FermiLinear(x_data, *actual_parameters)
        max_difference = np.max(np.abs(y_data_fitted - y_data))

        if verbose:
            print('expected: %s' % expected_parameters)
            print('fitted:   %s' % actual_parameters)
            print('temperature: %.2f' % (actual_parameters[-1]))
            print('max diff parameters: %.2f' % (absolute_difference_parameters.max()))
            print('max diff values: %.4f' % (max_difference.max()))

        self.assertTrue(np.all(max_difference < 1.0e-2))
        self.assertTrue(np.all(absolute_difference_parameters < 0.6))

        try:
            import lmfit
            have_lmfit = True
        except ImportError:
            have_lmfit = False

        if have_lmfit:
            self.assertIsNotNone(lmfit.__file__)
            actual_parameters, _ = fitFermiLinear(x_data, y_data, verbose=1, fig=fig, use_lmfit=True)
            absolute_difference_parameters = np.abs(actual_parameters - expected_parameters)
            self.assertTrue(np.all(absolute_difference_parameters < 0.1))

        # test with data from F006
        x_data = np.array([-20., -19.9, -19.8, -19.7, -19.6, -19.5, -19.4, -19.3, -19.2,
                           -19.1, -19., -18.9, -18.8, -18.7, -18.6, -18.5, -18.4, -18.3,
                           -18.2, -18.1, -18., -17.9, -17.8, -17.7, -17.6, -17.5, -17.4,
                           -17.3, -17.2, -17.1, -17., -16.9, -16.8, -16.7, -16.6, -16.5,
                           -16.4, -16.3, -16.2, -16.1, -16., -15.9, -15.8, -15.7, -15.6,
                           -15.5, -15.4, -15.3, -15.2, -15.1, -15., -14.9, -14.8, -14.7,
                           -14.6, -14.5, -14.4, -14.3, -14.2, -14.1])
        y_data = np.array([0.03055045, 0.0311075, 0.03098561, 0.03033496, 0.03006341,
                           0.03072266, 0.03183486, 0.03170599, 0.03199145, 0.03224666,
                           0.03164276, 0.03156053, 0.03133487, 0.03184649, 0.03224385,
                           0.03207413, 0.03196082, 0.03229934, 0.03158735, 0.03120681,
                           0.03119833, 0.03220412, 0.03185901, 0.03124884, 0.03129008,
                           0.0314923, 0.0315841, 0.0313667, 0.03115382, 0.03069049,
                           0.03058055, 0.02923863, 0.02789339, 0.02437544, 0.01896179,
                           0.01776424, 0.01175409, 0.01074043, 0.00950811, 0.0074723,
                           0.0060949, 0.00575982, 0.00501728, 0.00490061, 0.00465821,
                           0.00440039, 0.00434098, 0.00429608, 0.00421024, 0.0042945,
                           0.0042552, 0.00433429, 0.00440945, 0.00446915, 0.00446351,
                           0.00439317, 0.00447768, 0.0044295, 0.00450926, 0.0045605])

        figx = fig if fig is None else fig + 100
        linear_part, fermi_part = initFermiLinear(x_data, y_data, fig=figx)
        np.testing.assert_almost_equal(linear_part, [0, 0], decimal=1)
        np.testing.assert_almost_equal(fermi_part, [-16.7, 0.02755, 0.01731], decimal=2)
        plt.close('all')

b25ea403d7bda769c62c37fe451afd0971d595f6
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class TestFitting(unittest.TestCase):

    def test_initial_estimate_fermi_linear(self, fig=100):
        expected_parameters = [0.01000295, 0.51806569, -4.88800525, 
            0.12838861, 0.25382811]
        x_data = np.arange(-20, 10, 0.1)
        y_data = FermiLinear(x_data, *expected_parameters)
        y_data += 0.005 * np.random.rand(y_data.size)
        linear_part, fermi_part = initFermiLinear(x_data, y_data, fig=fig)
        ylin = linear_function(x_data, *linear_part)
        yr = y_data - ylin
        cc, A = _estimate_fermi_model_center_amplitude(x_data, yr, fig=fig)
        np.testing.assert_almost_equal(cc, expected_parameters[2], decimal=1)
        np.testing.assert_almost_equal(A, expected_parameters[3], decimal=1)
        self.assertTrue(fermi_part is not None)
        plt.close('all')
    <mask token>

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class TestFitting(unittest.TestCase):

    def test_initial_estimate_fermi_linear(self, fig=100):
        expected_parameters = [0.01000295, 0.51806569, -4.88800525, 
            0.12838861, 0.25382811]
        x_data = np.arange(-20, 10, 0.1)
        y_data = FermiLinear(x_data, *expected_parameters)
        y_data += 0.005 * np.random.rand(y_data.size)
        linear_part, fermi_part = initFermiLinear(x_data, y_data, fig=fig)
        ylin = linear_function(x_data, *linear_part)
        yr = y_data - ylin
        cc, A = _estimate_fermi_model_center_amplitude(x_data, yr, fig=fig)
        np.testing.assert_almost_equal(cc, expected_parameters[2], decimal=1)
        np.testing.assert_almost_equal(A, expected_parameters[3], decimal=1)
        self.assertTrue(fermi_part is not None)
        plt.close('all')

    def test_fit_fermi_linear(self, fig=100, verbose=1):
        expected_parameters = [0.01000295, 0.51806569, -4.88800525, 
            0.12838861, 0.25382811]
        x_data = np.arange(-20, 10, 0.1)
        y_data = FermiLinear(x_data, *expected_parameters)
        y_data += 0.005 * np.random.rand(y_data.size)
        actual_parameters, _ = fitFermiLinear(x_data, y_data, verbose=
            verbose, fig=fig, use_lmfit=False)
        absolute_difference_parameters = np.abs(actual_parameters -
            expected_parameters)
        y_data_fitted = FermiLinear(x_data, *actual_parameters)
        max_difference = np.max(np.abs(y_data_fitted - y_data))
        if verbose:
            print('expected: %s' % expected_parameters)
            print('fitted:   %s' % actual_parameters)
            print('temperature: %.2f' % actual_parameters[-1])
            print('max diff parameters: %.2f' %
                absolute_difference_parameters.max())
            print('max diff values: %.4f' % max_difference.max())
        self.assertTrue(np.all(max_difference < 0.01))
        self.assertTrue(np.all(absolute_difference_parameters < 0.6))
        try:
            import lmfit
            have_lmfit = True
        except ImportError:
            have_lmfit = False
        if have_lmfit:
            self.assertIsNotNone(lmfit.__file__)
            actual_parameters, _ = fitFermiLinear(x_data, y_data, verbose=1,
                fig=fig, use_lmfit=True)
            absolute_difference_parameters = np.abs(actual_parameters -
                expected_parameters)
            self.assertTrue(np.all(absolute_difference_parameters < 0.1))
        x_data = np.array([-20.0, -19.9, -19.8, -19.7, -19.6, -19.5, -19.4,
            -19.3, -19.2, -19.1, -19.0, -18.9, -18.8, -18.7, -18.6, -18.5, 
            -18.4, -18.3, -18.2, -18.1, -18.0, -17.9, -17.8, -17.7, -17.6, 
            -17.5, -17.4, -17.3, -17.2, -17.1, -17.0, -16.9, -16.8, -16.7, 
            -16.6, -16.5, -16.4, -16.3, -16.2, -16.1, -16.0, -15.9, -15.8, 
            -15.7, -15.6, -15.5, -15.4, -15.3, -15.2, -15.1, -15.0, -14.9, 
            -14.8, -14.7, -14.6, -14.5, -14.4, -14.3, -14.2, -14.1])
        y_data = np.array([0.03055045, 0.0311075, 0.03098561, 0.03033496, 
            0.03006341, 0.03072266, 0.03183486, 0.03170599, 0.03199145, 
            0.03224666, 0.03164276, 0.03156053, 0.03133487, 0.03184649, 
            0.03224385, 0.03207413, 0.03196082, 0.03229934, 0.03158735, 
            0.03120681, 0.03119833, 0.03220412, 0.03185901, 0.03124884, 
            0.03129008, 0.0314923, 0.0315841, 0.0313667, 0.03115382, 
            0.03069049, 0.03058055, 0.02923863, 0.02789339, 0.02437544, 
            0.01896179, 0.01776424, 0.01175409, 0.01074043, 0.00950811, 
            0.0074723, 0.0060949, 0.00575982, 0.00501728, 0.00490061, 
            0.00465821, 0.00440039, 0.00434098, 0.00429608, 0.00421024, 
            0.0042945, 0.0042552, 0.00433429, 0.00440945, 0.00446915, 
            0.00446351, 0.00439317, 0.00447768, 0.0044295, 0.00450926, 
            0.0045605])
        figx = fig if fig is None else fig + 100
        linear_part, fermi_part = initFermiLinear(x_data, y_data, fig=figx)
        np.testing.assert_almost_equal(linear_part, [0, 0], decimal=1)
        np.testing.assert_almost_equal(fermi_part, [-16.7, 0.02755, 0.01731
            ], decimal=2)
        plt.close('all')

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import unittest
import numpy as np
import matplotlib.pyplot as plt
from qtt.algorithms.functions import FermiLinear, linear_function
from qtt.algorithms.fitting import initFermiLinear, _estimate_fermi_model_center_amplitude, fitFermiLinear


class TestFitting(unittest.TestCase):

    def test_initial_estimate_fermi_linear(self, fig=100):
        expected_parameters = [0.01000295, 0.51806569, -4.88800525, 
            0.12838861, 0.25382811]
        x_data = np.arange(-20, 10, 0.1)
        y_data = FermiLinear(x_data, *expected_parameters)
        y_data += 0.005 * np.random.rand(y_data.size)
        linear_part, fermi_part = initFermiLinear(x_data, y_data, fig=fig)
        ylin = linear_function(x_data, *linear_part)
        yr = y_data - ylin
        cc, A = _estimate_fermi_model_center_amplitude(x_data, yr, fig=fig)
        np.testing.assert_almost_equal(cc, expected_parameters[2], decimal=1)
        np.testing.assert_almost_equal(A, expected_parameters[3], decimal=1)
        self.assertTrue(fermi_part is not None)
        plt.close('all')

    def test_fit_fermi_linear(self, fig=100, verbose=1):
        expected_parameters = [0.01000295, 0.51806569, -4.88800525, 
            0.12838861, 0.25382811]
        x_data = np.arange(-20, 10, 0.1)
        y_data = FermiLinear(x_data, *expected_parameters)
        y_data += 0.005 * np.random.rand(y_data.size)
        actual_parameters, _ = fitFermiLinear(x_data, y_data, verbose=
            verbose, fig=fig, use_lmfit=False)
        absolute_difference_parameters = np.abs(actual_parameters -
            expected_parameters)
        y_data_fitted = FermiLinear(x_data, *actual_parameters)
        max_difference = np.max(np.abs(y_data_fitted - y_data))
        if verbose:
            print('expected: %s' % expected_parameters)
            print('fitted:   %s' % actual_parameters)
            print('temperature: %.2f' % actual_parameters[-1])
            print('max diff parameters: %.2f' %
                absolute_difference_parameters.max())
            print('max diff values: %.4f' % max_difference.max())
        self.assertTrue(np.all(max_difference < 0.01))
        self.assertTrue(np.all(absolute_difference_parameters < 0.6))
        try:
            import lmfit
            have_lmfit = True
        except ImportError:
            have_lmfit = False
        if have_lmfit:
            self.assertIsNotNone(lmfit.__file__)
            actual_parameters, _ = fitFermiLinear(x_data, y_data, verbose=1,
                fig=fig, use_lmfit=True)
            absolute_difference_parameters = np.abs(actual_parameters -
                expected_parameters)
            self.assertTrue(np.all(absolute_difference_parameters < 0.1))
        x_data = np.array([-20.0, -19.9, -19.8, -19.7, -19.6, -19.5, -19.4,
            -19.3, -19.2, -19.1, -19.0, -18.9, -18.8, -18.7, -18.6, -18.5, 
            -18.4, -18.3, -18.2, -18.1, -18.0, -17.9, -17.8, -17.7, -17.6, 
            -17.5, -17.4, -17.3, -17.2, -17.1, -17.0, -16.9, -16.8, -16.7, 
            -16.6, -16.5, -16.4, -16.3, -16.2, -16.1, -16.0, -15.9, -15.8, 
            -15.7, -15.6, -15.5, -15.4, -15.3, -15.2, -15.1, -15.0, -14.9, 
            -14.8, -14.7, -14.6, -14.5, -14.4, -14.3, -14.2, -14.1])
        y_data = np.array([0.03055045, 0.0311075, 0.03098561, 0.03033496, 
            0.03006341, 0.03072266, 0.03183486, 0.03170599, 0.03199145, 
            0.03224666, 0.03164276, 0.03156053, 0.03133487, 0.03184649, 
            0.03224385, 0.03207413, 0.03196082, 0.03229934, 0.03158735, 
            0.03120681, 0.03119833, 0.03220412, 0.03185901, 0.03124884, 
            0.03129008, 0.0314923, 0.0315841, 0.0313667, 0.03115382, 
            0.03069049, 0.03058055, 0.02923863, 0.02789339, 0.02437544, 
            0.01896179, 0.01776424, 0.01175409, 0.01074043, 0.00950811, 
            0.0074723, 0.0060949, 0.00575982, 0.00501728, 0.00490061, 
            0.00465821, 0.00440039, 0.00434098, 0.00429608, 0.00421024, 
            0.0042945, 0.0042552, 0.00433429, 0.00440945, 0.00446915, 
            0.00446351, 0.00439317, 0.00447768, 0.0044295, 0.00450926, 
            0.0045605])
        figx = fig if fig is None else fig + 100
        linear_part, fermi_part = initFermiLinear(x_data, y_data, fig=figx)
        np.testing.assert_almost_equal(linear_part, [0, 0], decimal=1)
        np.testing.assert_almost_equal(fermi_part, [-16.7, 0.02755, 0.01731
            ], decimal=2)
        plt.close('all')

""" Test fitting of Fermi-Dirac distributions."""

import unittest
import numpy as np
import matplotlib.pyplot as plt
from qtt.algorithms.functions import FermiLinear, linear_function
from qtt.algorithms.fitting import initFermiLinear, _estimate_fermi_model_center_amplitude, fitFermiLinear


class TestFitting(unittest.TestCase):

    def test_initial_estimate_fermi_linear(self, fig=100):
        expected_parameters = [0.01000295, 0.51806569, -4.88800525, 0.12838861, 0.25382811]
        x_data = np.arange(-20, 10, 0.1)
        y_data = FermiLinear(x_data, *expected_parameters)
        y_data += 0.005 * np.random.rand(y_data.size)

        linear_part, fermi_part = initFermiLinear(x_data, y_data, fig=fig)

        ylin = linear_function(x_data, *linear_part)
        yr = y_data - ylin

        cc, A = _estimate_fermi_model_center_amplitude(x_data, yr, fig=fig)
        np.testing.assert_almost_equal(cc, expected_parameters[2], decimal=1)
        np.testing.assert_almost_equal(A, expected_parameters[3], decimal=1)
        self.assertTrue(fermi_part is not None)
        plt.close('all')

    def test_fit_fermi_linear(self, fig=100, verbose=1):
        expected_parameters = [0.01000295, 0.51806569, -4.88800525, 0.12838861, 0.25382811]
        x_data = np.arange(-20, 10, 0.1)
        y_data = FermiLinear(x_data, *expected_parameters)
        y_data += 0.005 * np.random.rand(y_data.size)

        actual_parameters, _ = fitFermiLinear(x_data, y_data, verbose=verbose, fig=fig, use_lmfit=False)
        absolute_difference_parameters = np.abs(actual_parameters - expected_parameters)

        y_data_fitted = FermiLinear(x_data, *actual_parameters)
        max_difference = np.max(np.abs(y_data_fitted - y_data))

        if verbose:
            print('expected: %s' % expected_parameters)
            print('fitted:   %s' % actual_parameters)
            print('temperature: %.2f' % (actual_parameters[-1]))
            print('max diff parameters: %.2f' % (absolute_difference_parameters.max()))
            print('max diff values: %.4f' % (max_difference.max()))

        self.assertTrue(np.all(max_difference < 1.0e-2))
        self.assertTrue(np.all(absolute_difference_parameters < 0.6))

        try:
            import lmfit
            have_lmfit = True
        except ImportError:
            have_lmfit = False

        if have_lmfit:
            self.assertIsNotNone(lmfit.__file__)
            actual_parameters, _ = fitFermiLinear(x_data, y_data, verbose=1, fig=fig, use_lmfit=True)
            absolute_difference_parameters = np.abs(actual_parameters - expected_parameters)
            self.assertTrue(np.all(absolute_difference_parameters < 0.1))

        # test with data from F006
        x_data = np.array([-20., -19.9, -19.8, -19.7, -19.6, -19.5, -19.4, -19.3, -19.2,
                           -19.1, -19., -18.9, -18.8, -18.7, -18.6, -18.5, -18.4, -18.3,
                           -18.2, -18.1, -18., -17.9, -17.8, -17.7, -17.6, -17.5, -17.4,
                           -17.3, -17.2, -17.1, -17., -16.9, -16.8, -16.7, -16.6, -16.5,
                           -16.4, -16.3, -16.2, -16.1, -16., -15.9, -15.8, -15.7, -15.6,
                           -15.5, -15.4, -15.3, -15.2, -15.1, -15., -14.9, -14.8, -14.7,
                           -14.6, -14.5, -14.4, -14.3, -14.2, -14.1])
        y_data = np.array([0.03055045, 0.0311075, 0.03098561, 0.03033496, 0.03006341,
                           0.03072266, 0.03183486, 0.03170599, 0.03199145, 0.03224666,
                           0.03164276, 0.03156053, 0.03133487, 0.03184649, 0.03224385,
                           0.03207413, 0.03196082, 0.03229934, 0.03158735, 0.03120681,
                           0.03119833, 0.03220412, 0.03185901, 0.03124884, 0.03129008,
                           0.0314923, 0.0315841, 0.0313667, 0.03115382, 0.03069049,
                           0.03058055, 0.02923863, 0.02789339, 0.02437544, 0.01896179,
                           0.01776424, 0.01175409, 0.01074043, 0.00950811, 0.0074723,
                           0.0060949, 0.00575982, 0.00501728, 0.00490061, 0.00465821,
                           0.00440039, 0.00434098, 0.00429608, 0.00421024, 0.0042945,
                           0.0042552, 0.00433429, 0.00440945, 0.00446915, 0.00446351,
                           0.00439317, 0.00447768, 0.0044295, 0.00450926, 0.0045605])

        figx = fig if fig is None else fig + 100
        linear_part, fermi_part = initFermiLinear(x_data, y_data, fig=figx)
        np.testing.assert_almost_equal(linear_part, [0, 0], decimal=1)
        np.testing.assert_almost_equal(fermi_part, [-16.7, 0.02755, 0.01731], decimal=2)
        plt.close('all')
