Exemplo n.º 1
0
    def test_x2_same_as_x1(self):
        """
        x2 same as x1
        """
        # old size
        m = 6

        # new size
        n = 6

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(0., 1., n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.diff(x_old)
        y_old_sd = np.sqrt(y_old)

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new)
        assert_allclose(y_new, y_old)

        y_new, y_new_sd = rebin.rebin(x_old, y_old, x_new, y1_sd=y_old_sd)
        assert_allclose(y_new, y_old)
        assert_allclose(y_new_sd, y_old_sd)
Exemplo n.º 2
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    def test_x2_surrounds_x1_2(self):
        """
        x2 has some bins that span several x1 bins
        Also tests uncertainty propagation. Values calculated using
        original jhykes piecewise constant code
        """
        # old size
        m = 10

        # new size
        n = 3

        # bin edges
        x_old = np.linspace(0.0, 1.0, m + 1)
        x_new = np.linspace(-0.1, 1.2, n + 1)

        # some arbitrary distribution
        y_old = 1.0 + np.sin(x_old[:-1] * np.pi) / np.diff(x_old)

        # with uncertainties
        np.random.seed(1)
        y_old_sd = 0.1 * y_old * uniform((m, ))

        # rebin
        y_new, y_new_sd = rebin.rebin(x_old, y_old, x_new, y1_sd=y_old_sd)

        # compute answer here to check rebin
        y_new_here = np.array([14.99807911, 44.14135692, 13.99807911])
        y_new_here_sd = np.array(
            [5.381524308729351, 12.73174109312833, 5.345145324353735])

        assert_allclose(y_new, y_new_here)
        assert_allclose(y_new_sd, y_new_here_sd)
        assert_allclose(y_new.sum(), y_old.sum())
Exemplo n.º 3
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    def test_x2_surrounds_x1(self):
        """
        x2 range surrounds x1 range
        """
        # old size
        m = 2

        # new size
        n = 3

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(-0.1, 1.2, n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.diff(x_old)

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new)

        # compute answer here to check rebin
        y_old_ave = y_old / np.diff(x_old)
        y_new_here = [y_old_ave[0] * (x_new[1] - 0.),
                      y_old_ave[0] * (x_old[1] - x_new[1]) +
                      y_old_ave[1] * (x_new[2] - x_old[1]),
                      y_old_ave[1] * (x_old[-1] - x_new[-2])]

        assert_allclose(y_new, y_new_here)
        assert_allclose(y_new.sum(), y_old.sum())
Exemplo n.º 4
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    def test_x2_right_overlap_x1_with_constant_distribution(self):
        """
        x2 domain overlaps x1 domain from the right
        """
        # old size
        m = 20

        # new size
        n = 30

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(0.95, 1.05, n + 1)

        # constant spline
        mms_spline = BoundedUnivariateSpline([0, .1, .2, 1],
                                             [1, 1, 1, 1], s=0.)

        y_old = np.array([mms_spline.integral(x_old[i],
                                              x_old[i + 1])
                          for i in range(m)])

        y_new_mms = np.array([mms_spline.integral(x_new[i],
                                                  x_new[i + 1])
                              for i in range(n)])

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new, interp_kind=3)
        assert_allclose(y_new, y_new_mms, rtol=1e-6, atol=1e-4)
Exemplo n.º 5
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    def test_x2_in_x1_2(self):
        """
        x2 has a couple of bins, each of which span more than one original bin
        """
        # old size
        m = 10

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.array([0.25, 0.55, 0.75])

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.ediff1d(x_old)

        y_old = unp.uarray(y_old, 0.1 * y_old * uniform((m,)))

        # rebin
        y_new, y_new_sd = rebin.rebin(x_old,
                                      unp.nominal_values(y_old),
                                      x_new,
                                      y1_sd=unp.std_devs(y_old))

        # compute answer here to check rebin
        y_new_here = unp.uarray(np.zeros(2), np.zeros(2))
        y_new_here[0] = 0.5 * y_old[2] + y_old[3] + y_old[4] + 0.5 * y_old[5]
        y_new_here[1] = 0.5 * y_old[5] + y_old[6] + 0.5 * y_old[7]

        assert_allclose(y_new,
                        unp.nominal_values(y_new_here))

        # mean or nominal value comparison
        assert_allclose(y_new_sd,
                        unp.std_devs(y_new_here))
Exemplo n.º 6
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    def test_x2_in_x1(self):
        """
        x2 only has one bin, and it is surrounded by x1 range
        """
        # old size
        m = 4

        # new size
        n = 1

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(0.3, 0.65, n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.diff(x_old)

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new)

        # compute answer here to check rebin
        y_old_ave = y_old / np.diff(x_old)
        y_new_here = (y_old_ave[1] * (x_old[2] - x_new[0]) +
                      y_old_ave[2] * (x_new[1] - x_old[2]))

        assert_allclose(y_new, y_new_here)
Exemplo n.º 7
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    def test_x1_surrounds_x2_with_constant_distribution(self):
        """
        x1 domain surrounds x2
        """
        # old size
        m = 20

        # new size
        n = 30

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(0.05, 0.26, n + 1)

        # constant spline
        mms_spline = BoundedUnivariateSpline([0, .1, .2, 1],
                                             [1, 1, 1, 1], s=0.)

        y_old = np.array([mms_spline.integral(x_old[i],
                                               x_old[i + 1])
                          for i in range(m)])

        y_new_mms = np.array([mms_spline.integral(x_new[i],
                                                  x_new[i + 1])
                              for i in range(n)])

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new, interp_kind=3)

        assert_allclose(y_new, y_new_mms)
Exemplo n.º 8
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    def test_x2_in_x1(self):
        """
        x2 only has one bin, and it is surrounded by x1 range
        """
        # old size
        m = 4

        # new size
        n = 1

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(0.3, 0.65, n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.ediff1d(x_old)

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new,
                            interp_kind='piecewise_constant')

        # compute answer here to check rebin
        y_old_ave  = y_old / np.ediff1d(x_old)
        y_new_here = (y_old_ave[1] * (x_old[2] - x_new[0])
                      + y_old_ave[2] * (x_new[1] - x_old[2]) )

        assert_allclose(y_new, y_new_here)
Exemplo n.º 9
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    def test_x2_surrounds_x1(self):
        """
        x2 range surrounds x1 range
        """
        # old size
        m = 2

        # new size
        n = 3

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(-0.1, 1.2, n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.ediff1d(x_old)

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new,
                            interp_kind='piecewise_constant')

        # compute answer here to check rebin
        y_old_ave  = y_old / np.ediff1d(x_old)
        y_new_here = [y_old_ave[0] * (x_new[1] - 0.),
                      y_old_ave[0] * (x_old[1]- x_new[1])
                      + y_old_ave[1] * (x_new[2] - x_old[1]),
                      y_old_ave[1] * (x_old[-1] - x_new[-2])]

        assert_allclose(y_new, y_new_here)
        assert_allclose(y_new.sum(), y_old.sum())
Exemplo n.º 10
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    def test_GH344(self):
        x_old = np.array([1.5, 2.5, 3.5, 4.5, 5.5, 6.5])
        y_old = np.array([10, 10, 10, 10, 10])
        x_new = np.array([1.7, 2.27332857, 2.84665714, 3.41998571,
                          3.99331429, 4.56664286])
        y_new = rebin.rebin(x_old, y_old, x_new)
        assert_allclose(y_new,
                        [5.7332857] * 5)

        # with uncertainties
        y_old = np.array([11., 12., 13., 14., 15.])

        y_old = unp.uarray(y_old, 0.1 * y_old)

        # rebin
        y_new, y_new_sd = rebin.rebin(x_old,
                                      unp.nominal_values(y_old),
                                      x_new,
                                      y1_sd=unp.std_devs(y_old))

        # compute answer here to check rebin
        y_old_ave = y_old / np.diff(x_old)
        y_new_here = np.array(
            [y_old_ave[0] * (x_new[1] - x_new[0]),

             y_old_ave[0] * (x_old[1] - x_new[1]) +
             y_old_ave[1] * (x_new[2] - x_old[1]),

             y_old_ave[1] * (x_new[3] - x_new[2]),

             y_old_ave[1] * (x_old[2] - x_new[3]) +
             y_old_ave[2] * (x_new[4] - x_old[2]),

             y_old_ave[3] * (x_new[5] - x_old[3]) +
             y_old_ave[2] * (x_old[3] - x_new[4])])

        # mean or nominal value comparison
        assert_allclose(y_new,
                        unp.nominal_values(y_new_here))

        # mean or nominal value comparison
        assert_allclose(y_new_sd,
                        unp.std_devs(y_new_here))
Exemplo n.º 11
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    def test_x2_surrounds_x1_sine_spline(self):
        """
        x2 range is completely above x1 range
        using a random vector to build spline
        """
        # old size
        m = 5

        # new size
        n = 6

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.array([-.3, -.09, 0.11, 0.14, 0.2, 0.28, 0.73])

        subbins = np.array([-.3, -.09, 0., 0.11, 0.14, 0.2, 0.28, 0.4, 0.6,
                            0.73])

        y_old = 1. + np.sin(x_old[:-1] * np.pi)

        # compute spline ----------------------------------
        x_mids = x_old[:-1] + 0.5 * np.ediff1d(x_old)
        xx = np.hstack([x_old[0], x_mids, x_old[-1]])
        yy = np.hstack([y_old[0], y_old, y_old[-1]])

        # build spline
        spl = splrep(xx, yy)

        area_old = np.array([splint(x_old[i], x_old[i + 1], spl)
                             for i in range(m)])

        # computing subbin areas
        area_subbins = np.zeros((subbins.size - 1,))
        for i in range(area_subbins.size):
            a, b = subbins[i: i + 2]
            a = max([a, x_old[0]])
            b = min([b, x_old[-1]])
            if b > a:
                area_subbins[i] = splint(a, b, spl)

        # summing subbin contributions in y_new_ref
        y_new_ref = np.zeros((x_new.size - 1,))
        y_new_ref[1] = y_old[0] * area_subbins[2] / area_old[0]
        y_new_ref[2] = y_old[0] * area_subbins[3] / area_old[0]
        y_new_ref[3] = y_old[0] * area_subbins[4] / area_old[0]
        y_new_ref[4] = y_old[1] * area_subbins[5] / area_old[1]

        y_new_ref[5]  = y_old[1] * area_subbins[6] / area_old[1]
        y_new_ref[5] += y_old[2] * area_subbins[7] / area_old[2]
        y_new_ref[5] += y_old[3] * area_subbins[8] / area_old[3]

        # call rebin function
        y_new = rebin.rebin(x_old, y_old, x_new, interp_kind=3)

        assert_allclose(y_new, y_new_ref)
Exemplo n.º 12
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    def test_y1_uncertainties(self):
        """
        x2 range surrounds x1 range, y1 has uncertainties
        """
        # old size
        m = 2

        # new size
        n = 3

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(-0.1, 1.2, n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.ediff1d(x_old)

        # with uncertainties
        y_old = unp.uarray(y_old, 0.1 * y_old * uniform((m,)))

        # rebin
        y_new, y_new_sd = rebin.rebin(x_old,
                                      unp.nominal_values(y_old),
                                      x_new,
                                      y1_sd=unp.std_devs(y_old))

        # compute answer here to check rebin
        y_old_ave  = y_old / np.ediff1d(x_old)
        y_new_here = np.array(
                     [y_old_ave[0] * (x_new[1] - 0.),
                      y_old_ave[0] * (x_old[1] - x_new[1])
                      + y_old_ave[1]*(x_new[2] - x_old[1]),
                      y_old_ave[1] * (x_old[-1] - x_new[-2])])


        # mean or nominal value comparison
        assert_allclose(y_new,
                        unp.nominal_values(y_new_here))

        # mean or nominal value comparison
        assert_allclose(y_new_sd,
                        unp.std_devs(y_new_here))

        assert_allclose(y_new.sum(),
                        unp.nominal_values(y_new_here).sum())
Exemplo n.º 13
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    def test_y1_uncertainties(self):
        """
        x2 range surrounds x1 range, y1 has uncertainties
        """
        # old size
        m = 2

        # new size
        n = 3

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(-0.1, 1.2, n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.diff(x_old)

        # with uncertainties
        y_old = unp.uarray(y_old, 0.1 * y_old * uniform((m,)))

        # rebin
        y_new, y_new_sd = rebin.rebin(x_old,
                                      unp.nominal_values(y_old),
                                      x_new,
                                      y1_sd=unp.std_devs(y_old))

        # compute answer here to check rebin
        y_old_ave = y_old / np.diff(x_old)
        y_new_here = np.array(
            [y_old_ave[0] * (x_new[1] - 0.),
             y_old_ave[0] * (x_old[1] - x_new[1]) +
             y_old_ave[1] * (x_new[2] - x_old[1]),
             y_old_ave[1] * (x_old[-1] - x_new[-2])])

        # mean or nominal value comparison
        assert_allclose(y_new,
                        unp.nominal_values(y_new_here))

        # mean or nominal value comparison
        assert_allclose(y_new_sd,
                        unp.std_devs(y_new_here))

        assert_allclose(y_new.sum(),
                        unp.nominal_values(y_new_here).sum())
Exemplo n.º 14
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    def test_x2_same_as_x1(self):
        """
        x2 same as x1
        """
        # old size
        m = 6

        # new size
        n = 6

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(0., 1., n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.ediff1d(x_old)

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new)

        assert_allclose(y_new, y_old)
Exemplo n.º 15
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    def test_x2_lower_than_x1(self):
        """
        x2 range is completely lower than x1 range
        """
        # old size
        m = 2

        # new size
        n = 3

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(-0.2, -0.0, n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.ediff1d(x_old)

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new)

        assert_allclose(y_new, [0., 0., 0.])
        assert_allclose(y_new.sum(), 0.)
Exemplo n.º 16
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    def test_x2_lower_than_x1(self):
        """
        x2 range is completely lower than x1 range
        """
        # old size
        m = 2

        # new size
        n = 3

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(-0.2, -0.0, n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.diff(x_old)

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new)

        assert_allclose(y_new, [0., 0., 0.])
        assert_allclose(y_new.sum(), 0.)
Exemplo n.º 17
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    def test_x2_above_x1(self):
        """
        x2 range is completely above x1 range
        """
        # old size
        m = 20

        # new size
        n = 30

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(1.2, 10., n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.diff(x_old)

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new)

        assert_allclose(y_new, np.zeros((n,)))
        assert_allclose(y_new.sum(), 0.)
Exemplo n.º 18
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    def test_x2_above_x1(self):
        """
        x2 range is completely above x1 range
        """
        # old size
        m = 20

        # new size
        n = 30

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(1.2, 10., n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.ediff1d(x_old)

        # rebin
        y_new = rebin.rebin(x_old, y_old, x_new)

        assert_allclose(y_new, np.zeros((n,)))
        assert_allclose(y_new.sum(), 0.)
Exemplo n.º 19
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    def test_x2_surrounds_x1_2(self):
        """
        x2 has some bins that span several x1 bins
        Also tests uncertainty propagation. Values calculated using
        original jhykes piecewise constant code
        """
        # old size
        m = 10

        # new size
        n = 3

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.linspace(-0.1, 1.2, n + 1)

        # some arbitrary distribution
        y_old = 1. + np.sin(x_old[:-1] * np.pi) / np.ediff1d(x_old)

        # with uncertainties
        np.random.seed(1)
        y_old_sd = 0.1 * y_old * uniform((m,))

        # rebin
        y_new, y_new_sd = rebin.rebin(x_old,
                                      y_old,
                                      x_new,
                                      y1_sd=y_old_sd)

        # compute answer here to check rebin
        y_new_here = np.array([14.99807911, 44.14135692, 13.99807911])
        y_new_here_sd = np.array([5.381524308729351,
                                  12.73174109312833,
                                  5.345145324353735])

        assert_allclose(y_new, y_new_here)
        assert_allclose(y_new_sd, y_new_here_sd)
        assert_allclose(y_new.sum(), y_old.sum())
Exemplo n.º 20
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    def test_y1_uncertainties_spline_with_constant_distribution(self):
        """

        """
        # old size
        m = 5

        # new size
        n = 6

        # bin edges
        x_old = np.linspace(0., 1., m + 1)
        x_new = np.array([-.3, -.09, 0.11, 0.14, 0.2, 0.28, 0.73])

        subbins = np.array([-.3, -.09, 0., 0.11, 0.14, 0.2, 0.28, 0.4, 0.6,
                            0.73])

        y_old = 1. + np.sin(x_old[:-1] * np.pi)

        # compute spline ----------------------------------
        x_mids = x_old[:-1] + 0.5 * np.ediff1d(x_old)
        xx = np.hstack([x_old[0], x_mids, x_old[-1]])
        yy = np.hstack([y_old[0], y_old, y_old[-1]])

        # build spline
        spl = splrep(xx, yy)

        area_old = np.array([splint(x_old[i],x_old[i+1], spl)
                             for i in range(m)])

        # with uncertainties
        y_old = unp.uarray(y_old, 0.1 * y_old * uniform((m,)))

        # computing subbin areas
        area_subbins = np.zeros((subbins.size - 1,))
        for i in range(area_subbins.size):
            a, b = subbins[i: i + 2]
            a = max([a, x_old[0]])
            b = min([b, x_old[-1]])
            if b > a:
                area_subbins[i] = splint(a, b, spl)

        # summing subbin contributions in y_new_ref
        a = np.zeros((x_new.size - 1,))
        y_new_ref = unp.uarray(a, a)
        y_new_ref[1] = y_old[0] * area_subbins[2] / area_old[0]
        y_new_ref[2] = y_old[0] * area_subbins[3] / area_old[0]
        y_new_ref[3] = y_old[0] * area_subbins[4] / area_old[0]
        y_new_ref[4] = y_old[1] * area_subbins[5] / area_old[1]

        y_new_ref[5]  = y_old[1] * area_subbins[6] / area_old[1]
        y_new_ref[5] += y_old[2] * area_subbins[7] / area_old[2]
        y_new_ref[5] += y_old[3] * area_subbins[8] / area_old[3]

        # call rebin function
        y_new = rebin.rebin(x_old, y_old, x_new, interp_kind=3)

        # mean or nominal value comparison
        assert_allclose(unp.nominal_values(y_new),
                        unp.nominal_values(y_new_ref))

        # mean or nominal value comparison
        assert_allclose(unp.std_devs(y_new),
                        unp.std_devs(y_new_ref))