numpy.double - python examples

Here are the examples of the python api numpy.double taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.

145 Examples 7

3 View Complete Implementation : test_ndgriddata.py
Copyright MIT License
Author : jgagneastro
    def test_complex_2d(self):
        x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
                     dtype=np.double)
        y = np.arange(x.shape[0], dtype=np.double)
        y = y - 2j*y[::-1]

        xi = x[:,None,:] + np.array([0,0,0])[None,:,None]

        for method in ('nearest', 'linear', 'cubic'):
            for rescale in (True, False):
                msg = repr((method, rescale))
                yi = griddata(x, y, xi, method=method, rescale=rescale)

                astert_equal(yi.shape, (5, 3), err_msg=msg)
                astert_allclose(yi, np.tile(y[:,None], (1, 3)),
                                atol=1e-14, err_msg=msg)

3 View Complete Implementation : test_interpnd.py
Copyright MIT License
Author : jgagneastro
    def test_tri_input(self):
        # Test at single points
        x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
                     dtype=np.double)
        y = np.arange(x.shape[0], dtype=np.double)
        y = y - 3j*y

        tri = qhull.Delaunay(x)
        yi = interpnd.LinearNDInterpolator(tri, y)(x)
        astert_almost_equal(y, yi)

3 View Complete Implementation : test_interpnd.py
Copyright MIT License
Author : jgagneastro
    def test_tri_input(self):
        # Test at single points
        x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
                     dtype=np.double)
        y = np.arange(x.shape[0], dtype=np.double)
        y = y - 3j*y

        tri = qhull.Delaunay(x)
        yi = interpnd.CloughTocher2DInterpolator(tri, y)(x)
        astert_almost_equal(y, yi)

3 View Complete Implementation : test_interpnd.py
Copyright MIT License
Author : jgagneastro
    def test_tripoints_input_rescale(self):
        # Test at single points
        x = np.array([(0,0), (-5,-5), (-5,5), (5, 5), (2.5, 3)],
                     dtype=np.double)
        y = np.arange(x.shape[0], dtype=np.double)
        y = y - 3j*y

        tri = qhull.Delaunay(x)
        yi = interpnd.LinearNDInterpolator(tri.points, y)(x)
        yi_rescale = interpnd.LinearNDInterpolator(tri.points, y,
                rescale=True)(x)
        astert_almost_equal(yi, yi_rescale)

3 View Complete Implementation : test_interpnd.py
Copyright MIT License
Author : jgagneastro
    def test_tri_input_rescale(self):
        # Test at single points
        x = np.array([(0,0), (-5,-5), (-5,5), (5, 5), (2.5, 3)],
                     dtype=np.double)
        y = np.arange(x.shape[0], dtype=np.double)
        y = y - 3j*y

        tri = qhull.Delaunay(x)
        match = ("Rescaling is not supported when pasting a "
                 "Delaunay triangulation as ``points``.")
        with pytest.raises(ValueError, match=match):
            interpnd.LinearNDInterpolator(tri, y, rescale=True)(x)

3 View Complete Implementation : test_ndgriddata.py
Copyright MIT License
Author : jgagneastro
    def test_alternative_call(self):
        x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
                     dtype=np.double)
        y = (np.arange(x.shape[0], dtype=np.double)[:,None]
             + np.array([0,1])[None,:])

        for method in ('nearest', 'linear', 'cubic'):
            for rescale in (True, False):
                msg = repr((method, rescale))
                yi = griddata((x[:,0], x[:,1]), y, (x[:,0], x[:,1]), method=method,
                              rescale=rescale)
                astert_allclose(y, yi, atol=1e-14, err_msg=msg)

3 View Complete Implementation : test_ndgriddata.py
Copyright MIT License
Author : jgagneastro
    def test_multipoint_2d(self):
        x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
                     dtype=np.double)
        y = np.arange(x.shape[0], dtype=np.double)

        xi = x[:,None,:] + np.array([0,0,0])[None,:,None]

        for method in ('nearest', 'linear', 'cubic'):
            for rescale in (True, False):
                msg = repr((method, rescale))
                yi = griddata(x, y, xi, method=method, rescale=rescale)

                astert_equal(yi.shape, (5, 3), err_msg=msg)
                astert_allclose(yi, np.tile(y[:,None], (1, 3)),
                                atol=1e-14, err_msg=msg)

3 View Complete Implementation : gen_fftw_ref.py
Copyright MIT License
Author : jgagneastro
def gen_data(dt):
    arrays = {}

    if dt == np.double:
        pg = './fftw_double'
    elif dt == np.float32:
        pg = './fftw_single'
    else:
        raise ValueError("unknown: %s" % dt)
    # Generate test data using FFTW for reference
    for type in [1, 2, 3, 4, 5, 6, 7, 8]:
        arrays[type] = {}
        for sz in SZ:
            a = Popen([pg, str(type), str(sz)], stdout=PIPE, stderr=STDOUT)
            st = [i.strip() for i in a.stdout.readlines()]
            arrays[type][sz] = np.fromstring(",".join(st), sep=',', dtype=dt)

    return arrays

3 View Complete Implementation : test_ndgriddata.py
Copyright MIT License
Author : jgagneastro
    def test_multivalue_2d(self):
        x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
                     dtype=np.double)
        y = (np.arange(x.shape[0], dtype=np.double)[:,None]
             + np.array([0,1])[None,:])

        for method in ('nearest', 'linear', 'cubic'):
            for rescale in (True, False):
                msg = repr((method, rescale))
                yi = griddata(x, y, x, method=method, rescale=rescale)
                astert_allclose(y, yi, atol=1e-14, err_msg=msg)

3 View Complete Implementation : test_interpnd.py
Copyright MIT License
Author : jgagneastro
    def test_square_rescale(self):
        # Test barycentric interpolation on a rectangle with rescaling
        # agaings the same implementation without rescaling

        points = np.array([(0,0), (0,100), (10,100), (10,0)], dtype=np.double)
        values = np.array([1., 2., -3., 5.], dtype=np.double)

        xx, yy = np.broadcast_arrays(np.linspace(0, 10, 14)[:,None],
                                     np.linspace(0, 100, 14)[None,:])
        xx = xx.ravel()
        yy = yy.ravel()
        xi = np.array([xx, yy]).T.copy()
        zi = interpnd.LinearNDInterpolator(points, values)(xi)
        zi_rescaled = interpnd.LinearNDInterpolator(points, values,
                rescale=True)(xi)

        astert_almost_equal(zi, zi_rescaled)