189 lines
7.4 KiB
Python
189 lines
7.4 KiB
Python
import numpy as np
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from numpy.testing import assert_equal, assert_array_equal, assert_allclose
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from pytest import raises as assert_raises
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from scipy.interpolate import griddata, NearestNDInterpolator
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class TestGriddata(object):
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def test_fill_value(self):
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x = [(0,0), (0,1), (1,0)]
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y = [1, 2, 3]
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yi = griddata(x, y, [(1,1), (1,2), (0,0)], fill_value=-1)
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assert_array_equal(yi, [-1., -1, 1])
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yi = griddata(x, y, [(1,1), (1,2), (0,0)])
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assert_array_equal(yi, [np.nan, np.nan, 1])
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def test_alternative_call(self):
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x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
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dtype=np.double)
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y = (np.arange(x.shape[0], dtype=np.double)[:,None]
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+ np.array([0,1])[None,:])
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for method in ('nearest', 'linear', 'cubic'):
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for rescale in (True, False):
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msg = repr((method, rescale))
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yi = griddata((x[:,0], x[:,1]), y, (x[:,0], x[:,1]), method=method,
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rescale=rescale)
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assert_allclose(y, yi, atol=1e-14, err_msg=msg)
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def test_multivalue_2d(self):
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x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
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dtype=np.double)
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y = (np.arange(x.shape[0], dtype=np.double)[:,None]
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+ np.array([0,1])[None,:])
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for method in ('nearest', 'linear', 'cubic'):
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for rescale in (True, False):
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msg = repr((method, rescale))
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yi = griddata(x, y, x, method=method, rescale=rescale)
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assert_allclose(y, yi, atol=1e-14, err_msg=msg)
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def test_multipoint_2d(self):
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x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
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dtype=np.double)
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y = np.arange(x.shape[0], dtype=np.double)
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xi = x[:,None,:] + np.array([0,0,0])[None,:,None]
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for method in ('nearest', 'linear', 'cubic'):
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for rescale in (True, False):
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msg = repr((method, rescale))
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yi = griddata(x, y, xi, method=method, rescale=rescale)
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assert_equal(yi.shape, (5, 3), err_msg=msg)
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assert_allclose(yi, np.tile(y[:,None], (1, 3)),
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atol=1e-14, err_msg=msg)
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def test_complex_2d(self):
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x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
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dtype=np.double)
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y = np.arange(x.shape[0], dtype=np.double)
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y = y - 2j*y[::-1]
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xi = x[:,None,:] + np.array([0,0,0])[None,:,None]
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for method in ('nearest', 'linear', 'cubic'):
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for rescale in (True, False):
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msg = repr((method, rescale))
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yi = griddata(x, y, xi, method=method, rescale=rescale)
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assert_equal(yi.shape, (5, 3), err_msg=msg)
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assert_allclose(yi, np.tile(y[:,None], (1, 3)),
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atol=1e-14, err_msg=msg)
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def test_1d(self):
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x = np.array([1, 2.5, 3, 4.5, 5, 6])
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y = np.array([1, 2, 0, 3.9, 2, 1])
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for method in ('nearest', 'linear', 'cubic'):
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assert_allclose(griddata(x, y, x, method=method), y,
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err_msg=method, atol=1e-14)
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assert_allclose(griddata(x.reshape(6, 1), y, x, method=method), y,
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err_msg=method, atol=1e-14)
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assert_allclose(griddata((x,), y, (x,), method=method), y,
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err_msg=method, atol=1e-14)
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def test_1d_borders(self):
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# Test for nearest neighbor case with xi outside
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# the range of the values.
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x = np.array([1, 2.5, 3, 4.5, 5, 6])
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y = np.array([1, 2, 0, 3.9, 2, 1])
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xi = np.array([0.9, 6.5])
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yi_should = np.array([1.0, 1.0])
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method = 'nearest'
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assert_allclose(griddata(x, y, xi,
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method=method), yi_should,
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err_msg=method,
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atol=1e-14)
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assert_allclose(griddata(x.reshape(6, 1), y, xi,
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method=method), yi_should,
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err_msg=method,
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atol=1e-14)
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assert_allclose(griddata((x, ), y, (xi, ),
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method=method), yi_should,
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err_msg=method,
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atol=1e-14)
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def test_1d_unsorted(self):
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x = np.array([2.5, 1, 4.5, 5, 6, 3])
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y = np.array([1, 2, 0, 3.9, 2, 1])
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for method in ('nearest', 'linear', 'cubic'):
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assert_allclose(griddata(x, y, x, method=method), y,
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err_msg=method, atol=1e-10)
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assert_allclose(griddata(x.reshape(6, 1), y, x, method=method), y,
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err_msg=method, atol=1e-10)
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assert_allclose(griddata((x,), y, (x,), method=method), y,
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err_msg=method, atol=1e-10)
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def test_square_rescale_manual(self):
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points = np.array([(0,0), (0,100), (10,100), (10,0), (1, 5)], dtype=np.double)
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points_rescaled = np.array([(0,0), (0,1), (1,1), (1,0), (0.1, 0.05)], dtype=np.double)
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values = np.array([1., 2., -3., 5., 9.], dtype=np.double)
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xx, yy = np.broadcast_arrays(np.linspace(0, 10, 14)[:,None],
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np.linspace(0, 100, 14)[None,:])
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xx = xx.ravel()
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yy = yy.ravel()
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xi = np.array([xx, yy]).T.copy()
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for method in ('nearest', 'linear', 'cubic'):
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msg = method
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zi = griddata(points_rescaled, values, xi/np.array([10, 100.]),
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method=method)
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zi_rescaled = griddata(points, values, xi, method=method,
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rescale=True)
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assert_allclose(zi, zi_rescaled, err_msg=msg,
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atol=1e-12)
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def test_xi_1d(self):
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# Check that 1-D xi is interpreted as a coordinate
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x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
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dtype=np.double)
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y = np.arange(x.shape[0], dtype=np.double)
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y = y - 2j*y[::-1]
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xi = np.array([0.5, 0.5])
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for method in ('nearest', 'linear', 'cubic'):
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p1 = griddata(x, y, xi, method=method)
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p2 = griddata(x, y, xi[None,:], method=method)
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assert_allclose(p1, p2, err_msg=method)
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xi1 = np.array([0.5])
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xi3 = np.array([0.5, 0.5, 0.5])
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assert_raises(ValueError, griddata, x, y, xi1,
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method=method)
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assert_raises(ValueError, griddata, x, y, xi3,
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method=method)
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def test_nearest_options():
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# smoke test that NearestNDInterpolator accept cKDTree options
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npts, nd = 4, 3
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x = np.arange(npts*nd).reshape((npts, nd))
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y = np.arange(npts)
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nndi = NearestNDInterpolator(x, y)
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opts = {'balanced_tree': False, 'compact_nodes': False}
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nndi_o = NearestNDInterpolator(x, y, tree_options=opts)
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assert_allclose(nndi(x), nndi_o(x), atol=1e-14)
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def test_nearest_list_argument():
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nd = np.array([[0, 0, 0, 0, 1, 0, 1],
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[0, 0, 0, 0, 0, 1, 1],
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[0, 0, 0, 0, 1, 1, 2]])
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d = nd[:, 3:]
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# z is np.array
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NI = NearestNDInterpolator((d[0], d[1]), d[2])
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assert_array_equal(NI([0.1, 0.9], [0.1, 0.9]), [0, 2])
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# z is list
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NI = NearestNDInterpolator((d[0], d[1]), list(d[2]))
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assert_array_equal(NI([0.1, 0.9], [0.1, 0.9]), [0, 2])
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