Python numpy.fromfunction() Examples
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Example #1
Source File: graph_util.py From nasbench with Apache License 2.0 | 6 votes |
def permute_graph(graph, label, permutation): """Permutes the graph and labels based on permutation. Args: graph: np.ndarray adjacency matrix. label: list of labels of same length as graph dimensions. permutation: a permutation list of ints of same length as graph dimensions. Returns: np.ndarray where vertex permutation[v] is vertex v from the original graph """ # vertex permutation[v] in new graph is vertex v in the old graph forward_perm = zip(permutation, list(range(len(permutation)))) inverse_perm = [x[1] for x in sorted(forward_perm)] edge_fn = lambda x, y: graph[inverse_perm[x], inverse_perm[y]] == 1 new_matrix = np.fromfunction(np.vectorize(edge_fn), (len(label), len(label)), dtype=np.int8) new_label = [label[inverse_perm[i]] for i in range(len(label))] return new_matrix, new_label
Example #2
Source File: from_data.py From chainer with MIT License | 6 votes |
def fromfunction(function, shape, **kwargs): """ Constructs an array by executing a function over each coordinate. This is currently equivalent to :func:`numpy.fromfunction` wrapped by :func:`chainerx.array`, given the device argument. Note: Keywords other than ``dtype`` and ``device`` are passed to ```function```. .. seealso:: :func:`numpy.fromfunction` """ dtype = kwargs.pop('dtype', float) device = kwargs.pop('device', None) return chainerx.array( numpy.fromfunction( function, shape, dtype=dtype, **kwargs), device=device) # TODO(hvy): Optimize with pre-allocated memory using count for non-native # devices.
Example #3
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 6 votes |
def test_nearest_masked_swath_target(self): """Test that a masked array works as a target.""" data = np.fromfunction(lambda y, x: y * x, (50, 10)) lons = np.fromfunction(lambda y, x: 3 + x, (50, 10)) lats = np.fromfunction(lambda y, x: 75 - y, (50, 10)) mask = np.ones_like(lons, dtype=np.bool) mask[::2, ::2] = False swath_def = geometry.SwathDefinition( lons=np.ma.masked_array(lons, mask=mask), lats=np.ma.masked_array(lats, mask=False) ) res = kd_tree.resample_nearest(swath_def, data.ravel(), swath_def, 50000, segments=3) cross_sum = res.sum() # expected = 12716 # if masks aren't respected expected = 12000 self.assertEqual(cross_sum, expected)
Example #4
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 6 votes |
def test_gauss_multi(self): data = np.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100)) lons = np.fromfunction( lambda y, x: 3 + (10.0 / 100) * x, (5000, 100)) lats = np.fromfunction( lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) data_multi = np.column_stack((data.ravel(), data.ravel(), data.ravel())) with catch_warnings(UserWarning) as w: res = kd_tree.resample_gauss(swath_def, data_multi, self.area_def, 50000, [25000, 15000, 10000], segments=1) self.assertFalse(len(w) != 1) self.assertFalse(('Possible more' not in str(w[0].message))) cross_sum = res.sum() expected = 1461.8429990248171 self.assertAlmostEqual(cross_sum, expected)
Example #5
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 6 votes |
def test_gauss_multi_mp(self): data = np.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100)) lons = np.fromfunction( lambda y, x: 3 + (10.0 / 100) * x, (5000, 100)) lats = np.fromfunction( lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) data_multi = np.column_stack((data.ravel(), data.ravel(), data.ravel())) with catch_warnings(UserWarning) as w: res = kd_tree.resample_gauss(swath_def, data_multi, self.area_def, 50000, [ 25000, 15000, 10000], nprocs=2, segments=1) self.assertFalse(len(w) != 1) self.assertFalse(('Possible more' not in str(w[0].message))) cross_sum = res.sum() expected = 1461.8429990248171 self.assertAlmostEqual(cross_sum, expected)
Example #6
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 6 votes |
def test_gauss_multi_mp_segments(self): data = np.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100)) lons = np.fromfunction( lambda y, x: 3 + (10.0 / 100) * x, (5000, 100)) lats = np.fromfunction( lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) data_multi = np.column_stack((data.ravel(), data.ravel(), data.ravel())) with catch_warnings(UserWarning) as w: res = kd_tree.resample_gauss(swath_def, data_multi, self.area_def, 50000, [ 25000, 15000, 10000], nprocs=2, segments=1) self.assertFalse(len(w) != 1) self.assertFalse('Possible more' not in str(w[0].message)) cross_sum = res.sum() expected = 1461.8429990248171 self.assertAlmostEqual(cross_sum, expected)
Example #7
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 6 votes |
def test_masked_gauss(self): data = np.ones((50, 10)) data[:, 5:] = 2 lons = np.fromfunction(lambda y, x: 3 + x, (50, 10)) lats = np.fromfunction(lambda y, x: 75 - y, (50, 10)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) mask = np.ones((50, 10)) mask[:, :5] = 0 masked_data = np.ma.array(data, mask=mask) res = kd_tree.resample_gauss(swath_def, masked_data.ravel(), self.area_def, 50000, 25000, segments=1) expected_mask = np.fromfile(os.path.join(os.path.dirname(__file__), 'test_files', 'mask_test_mask.dat'), sep=' ').reshape((800, 800)) expected_data = np.fromfile(os.path.join(os.path.dirname(__file__), 'test_files', 'mask_test_data.dat'), sep=' ').reshape((800, 800)) expected = expected_data.sum() cross_sum = res.data.sum() self.assertTrue(np.array_equal(expected_mask, res.mask)) self.assertAlmostEqual(cross_sum, expected, places=3)
Example #8
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 6 votes |
def test_masked_full(self): data = np.ones((50, 10)) data[:, 5:] = 2 mask = np.ones((50, 10)) mask[:, :5] = 0 masked_data = np.ma.array(data, mask=mask) lons = np.fromfunction(lambda y, x: 3 + x, (50, 10)) lats = np.fromfunction(lambda y, x: 75 - y, (50, 10)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) res = kd_tree.resample_nearest(swath_def, masked_data.ravel( ), self.area_def, 50000, fill_value=None, segments=1) expected_fill_mask = np.fromfile(os.path.join(os.path.dirname(__file__), 'test_files', 'mask_test_full_fill.dat'), sep=' ').reshape((800, 800)) fill_mask = res.mask self.assertTrue(np.array_equal(fill_mask, expected_fill_mask))
Example #9
Source File: ROI.py From tf-pose with Apache License 2.0 | 6 votes |
def getArrayRegion(self, arr, img=None, axes=(0, 1), **kwds): """ Return the result of ROI.getArrayRegion() masked by the elliptical shape of the ROI. Regions outside the ellipse are set to 0. """ # Note: we could use the same method as used by PolyLineROI, but this # implementation produces a nicer mask. arr = ROI.getArrayRegion(self, arr, img, axes, **kwds) if arr is None or arr.shape[axes[0]] == 0 or arr.shape[axes[1]] == 0: return arr w = arr.shape[axes[0]] h = arr.shape[axes[1]] ## generate an ellipsoidal mask mask = np.fromfunction(lambda x,y: (((x+0.5)/(w/2.)-1)**2+ ((y+0.5)/(h/2.)-1)**2)**0.5 < 1, (w, h)) # reshape to match array axes if axes[0] > axes[1]: mask = mask.T shape = [(n if i in axes else 1) for i,n in enumerate(arr.shape)] mask = mask.reshape(shape) return arr * mask
Example #10
Source File: graph_util.py From eval-nas with MIT License | 6 votes |
def permute_graph(graph, label, permutation): """Permutes the graph and labels based on permutation. Args: graph: np.ndarray adjacency matrix. label: list of labels of same length as graph dimensions. permutation: a permutation list of ints of same length as graph dimensions. Returns: np.ndarray where vertex permutation[v] is vertex v from the original graph """ # vertex permutation[v] in new graph is vertex v in the old graph forward_perm = zip(permutation, list(range(len(permutation)))) inverse_perm = [x[1] for x in sorted(forward_perm)] edge_fn = lambda x, y: graph[inverse_perm[x], inverse_perm[y]] == 1 new_matrix = np.fromfunction(np.vectorize(edge_fn), (len(label), len(label)), dtype=np.int8) new_label = [label[inverse_perm[i]] for i in range(len(label))] return new_matrix, new_label
Example #11
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_dtype(self): lons = np.fromfunction(lambda y, x: 3 + x, (50, 10)) lats = np.fromfunction(lambda y, x: 75 - y, (50, 10)) grid_def = geometry.GridDefinition(lons, lats) lons = np.asarray(lons, dtype='f4') lats = np.asarray(lats, dtype='f4') swath_def = geometry.SwathDefinition(lons=lons, lats=lats) valid_input_index, valid_output_index, index_array, distance_array = \ kd_tree.get_neighbour_info(swath_def, grid_def, 50000, neighbours=1, segments=1)
Example #12
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_masked_nearest_1d(self): data = np.ones((800, 800)) data[:400, :] = 2 lons = np.fromfunction(lambda x: 3 + x / 100., (500,)) lats = np.fromfunction(lambda x: 75 - x / 10., (500,)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) mask = np.ones((800, 800)) mask[400:, :] = 0 masked_data = np.ma.array(data, mask=mask) res = kd_tree.resample_nearest(self.area_def, masked_data.ravel(), swath_def, 50000, segments=1) self.assertEqual(res.mask.sum(), 112)
Example #13
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_custom_multi(self): def wf1(dist): return 1 - dist / 100000.0 def wf2(dist): return 1 def wf3(dist): return np.cos(dist) ** 2 data = np.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100)) lons = np.fromfunction( lambda y, x: 3 + (10.0 / 100) * x, (5000, 100)) lats = np.fromfunction( lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) data_multi = np.column_stack((data.ravel(), data.ravel(), data.ravel())) with catch_warnings(UserWarning) as w: res = kd_tree.resample_custom(swath_def, data_multi, self.area_def, 50000, [wf1, wf2, wf3], segments=1) self.assertFalse(len(w) != 1) self.assertFalse('Possible more' not in str(w[0].message)) cross_sum = res.sum() expected = 1461.8428378742638 self.assertAlmostEqual(cross_sum, expected)
Example #14
Source File: test_data_reduce.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_reduce_north_pole(self): """Test reducing around the poles.""" from pyresample import utils area_id = 'ease_sh' description = 'Antarctic EASE grid' proj_id = 'ease_sh' projection = '+proj=laea +lat_0=-90 +lon_0=0 +a=6371228.0 +units=m' x_size = 425 y_size = 425 area_extent = (-5326849.0625, -5326849.0625, 5326849.0625, 5326849.0625) area_def = utils.get_area_def(area_id, description, proj_id, projection, x_size, y_size, area_extent) grid_lons, grid_lats = area_def.get_lonlats() area_id = 'ease_sh' description = 'Antarctic EASE grid' proj_id = 'ease_sh' projection = '+proj=laea +lat_0=-90 +lon_0=0 +a=6371228.0 +units=m' x_size = 1000 y_size = 1000 area_extent = (-532684.0625, -532684.0625, 532684.0625, 532684.0625) smaller_area_def = utils.get_area_def(area_id, description, proj_id, projection, x_size, y_size, area_extent) data = np.fromfunction(lambda y, x: (y + x), (1000, 1000)) lons, lats = smaller_area_def.get_lonlats() lons, lats, data = swath_from_lonlat_grid(grid_lons, grid_lats, lons, lats, data, 7000) cross_sum = data.sum() expected = 999000000.0 self.assertAlmostEqual(cross_sum, expected)
Example #15
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_custom(self): def wf(dist): return 1 - dist / 100000.0 data = np.fromfunction(lambda y, x: (y + x) * 10 ** -5, (5000, 100)) lons = np.fromfunction( lambda y, x: 3 + (10.0 / 100) * x, (5000, 100)) lats = np.fromfunction( lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) with catch_warnings(UserWarning) as w: res = kd_tree.resample_custom(swath_def, data.ravel(), self.area_def, 50000, wf, segments=1) # PyProj proj/CRS and "more than 8 neighbours" are warned about self.assertFalse(len(w) > 2) neighbour_warn = False for warn in w: if 'Possible more' in str(warn.message): neighbour_warn = True break self.assertTrue(neighbour_warn) if len(w) == 2: proj_crs_warn = False for warn in w: if 'important projection information' in str(warn.message): proj_crs_warn = True break self.assertTrue(proj_crs_warn) cross_sum = res.sum() expected = 4872.8100347930776 self.assertAlmostEqual(cross_sum, expected)
Example #16
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_nearest_from_sample(self): data = np.fromfunction(lambda y, x: y * x, (50, 10)) lons = np.fromfunction(lambda y, x: 3 + x, (50, 10)) lats = np.fromfunction(lambda y, x: 75 - y, (50, 10)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) valid_input_index, valid_output_index, index_array, distance_array = \ kd_tree.get_neighbour_info(swath_def, self.area_def, 50000, neighbours=1, segments=1) res = kd_tree.get_sample_from_neighbour_info('nn', (800, 800), data.ravel(), valid_input_index, valid_output_index, index_array) cross_sum = res.sum() expected = 15874591.0 self.assertEqual(cross_sum, expected)
Example #17
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_masked_fill_float(self): data = np.ones((50, 10)) lons = np.fromfunction(lambda y, x: 3 + x, (50, 10)) lats = np.fromfunction(lambda y, x: 75 - y, (50, 10)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) res = kd_tree.resample_nearest(swath_def, data.ravel(), self.area_def, 50000, fill_value=None, segments=1) expected_fill_mask = np.fromfile(os.path.join(os.path.dirname(__file__), 'test_files', 'mask_test_fill_value.dat'), sep=' ').reshape((800, 800)) fill_mask = res.mask self.assertTrue(np.array_equal(fill_mask, expected_fill_mask))
Example #18
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_nearest_empty_masked(self): data = np.fromfunction(lambda y, x: y * x, (50, 10)) lons = np.fromfunction(lambda y, x: 165 + x, (50, 10)) lats = np.fromfunction(lambda y, x: 75 - y, (50, 10)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) res = kd_tree.resample_nearest(swath_def, data.ravel(), self.area_def, 50000, segments=1, fill_value=None) cross_sum = res.mask.sum() expected = res.size self.assertTrue(cross_sum == expected)
Example #19
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_nearest_empty_multi_masked(self): data = np.fromfunction(lambda y, x: y * x, (50, 10)) lons = np.fromfunction(lambda y, x: 165 + x, (50, 10)) lats = np.fromfunction(lambda y, x: 75 - y, (50, 10)) data_multi = np.column_stack((data.ravel(), data.ravel(), data.ravel())) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) res = kd_tree.resample_nearest(swath_def, data_multi, self.area_def, 50000, segments=1, fill_value=None) self.assertEqual(res.shape, (800, 800, 3))
Example #20
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_gauss_multi_mp_segments_empty(self): data = np.fromfunction(lambda y, x: (y + x) * 10 ** -6, (5000, 100)) lons = np.fromfunction( lambda y, x: 165 + (10.0 / 100) * x, (5000, 100)) lats = np.fromfunction( lambda y, x: 75 - (50.0 / 5000) * y, (5000, 100)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) data_multi = np.column_stack((data.ravel(), data.ravel(), data.ravel())) res = kd_tree.resample_gauss(swath_def, data_multi, self.area_def, 50000, [ 25000, 15000, 10000], nprocs=2, segments=1) cross_sum = res.sum() self.assertTrue(cross_sum == 0)
Example #21
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_nearest_empty(self): data = np.fromfunction(lambda y, x: y * x, (50, 10)) lons = np.fromfunction(lambda y, x: 165 + x, (50, 10)) lats = np.fromfunction(lambda y, x: 75 - y, (50, 10)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) res = kd_tree.resample_nearest(swath_def, data.ravel(), self.area_def, 50000, segments=1) cross_sum = res.sum() expected = 0 self.assertEqual(cross_sum, expected)
Example #22
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_nearest_1d(self): data = np.fromfunction(lambda x, y: x * y, (800, 800)) lons = np.fromfunction(lambda x: 3 + x / 100., (500,)) lats = np.fromfunction(lambda x: 75 - x / 10., (500,)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) res = kd_tree.resample_nearest(self.area_def, data.ravel(), swath_def, 50000, segments=1) cross_sum = res.sum() expected = 35821299.0 self.assertEqual(res.shape, (500,)) self.assertEqual(cross_sum, expected)
Example #23
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_nearest(self): data = np.fromfunction(lambda y, x: y * x, (50, 10)) lons = np.fromfunction(lambda y, x: 3 + x, (50, 10)) lats = np.fromfunction(lambda y, x: 75 - y, (50, 10)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) res = kd_tree.resample_nearest(swath_def, data.ravel(), self.area_def, 50000, segments=1) cross_sum = res.sum() expected = 15874591.0 self.assertEqual(cross_sum, expected)
Example #24
Source File: test_kd_tree.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_nearest_mp(self): data = np.fromfunction(lambda y, x: y * x, (50, 10)) lons = np.fromfunction(lambda y, x: 3 + x, (50, 10)) lats = np.fromfunction(lambda y, x: 75 - y, (50, 10)) swath_def = geometry.SwathDefinition(lons=lons, lats=lats) res = kd_tree.resample_nearest(swath_def, data.ravel(), self.area_def, 50000, nprocs=2, segments=1) cross_sum = res.sum() expected = 15874591.0 self.assertEqual(cross_sum, expected)
Example #25
Source File: test_data_reduce.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_area_con_reduce(self): data = np.fromfunction(lambda y, x: (y + x), (1000, 1000)) lons = np.fromfunction( lambda y, x: -180 + (360.0 / 1000) * x, (1000, 1000)) lats = np.fromfunction( lambda y, x: -90 + (180.0 / 1000) * y, (1000, 1000)) grid_lons, grid_lats = self.area_def.get_lonlats() valid_index = get_valid_index_from_lonlat_grid(grid_lons, grid_lats, lons, lats, 7000) data = data[valid_index] cross_sum = data.sum() expected = 20685125.0 self.assertAlmostEqual(cross_sum, expected)
Example #26
Source File: test_data_reduce.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_cartesian_reduce(self): data = np.fromfunction(lambda y, x: (y + x), (1000, 1000)) lons = np.fromfunction( lambda y, x: -180 + (360.0 / 1000) * x, (1000, 1000)) lats = np.fromfunction( lambda y, x: -90 + (180.0 / 1000) * y, (1000, 1000)) grid = self.area_def.get_cartesian_coords() lons, lats, data = swath_from_cartesian_grid(grid, lons, lats, data, 7000) cross_sum = data.sum() expected = 20685125.0 self.assertAlmostEqual(cross_sum, expected)
Example #27
Source File: test_data_reduce.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_reduce_boundary(self): data = np.fromfunction(lambda y, x: (y + x), (1000, 1000)) lons = np.fromfunction( lambda y, x: -180 + (360.0 / 1000) * x, (1000, 1000)) lats = np.fromfunction( lambda y, x: -90 + (180.0 / 1000) * y, (1000, 1000)) boundary_lonlats = self.area_def.get_boundary_lonlats() lons, lats, data = swath_from_lonlat_boundaries(boundary_lonlats[0], boundary_lonlats[1], lons, lats, data, 7000) cross_sum = data.sum() expected = 20685125.0 self.assertAlmostEqual(cross_sum, expected)
Example #28
Source File: test_data_reduce.py From pyresample with GNU Lesser General Public License v3.0 | 5 votes |
def test_reduce(self): data = np.fromfunction(lambda y, x: (y + x), (1000, 1000)) lons = np.fromfunction( lambda y, x: -180 + (360.0 / 1000) * x, (1000, 1000)) lats = np.fromfunction( lambda y, x: -90 + (180.0 / 1000) * y, (1000, 1000)) grid_lons, grid_lats = self.area_def.get_lonlats() lons, lats, data = swath_from_lonlat_grid(grid_lons, grid_lats, lons, lats, data, 7000) cross_sum = data.sum() expected = 20685125.0 self.assertAlmostEqual(cross_sum, expected)
Example #29
Source File: linbasex.py From PyAbel with MIT License | 5 votes |
def _Slices(Beta, legendre_orders, smoothing=0): """Convolve Beta with a Gaussian function of 1/e width smoothing. """ pol = len(legendre_orders) NP = len(Beta[0]) # number of points in 3_d plot. index = range(NP) Beta_convol = np.zeros((pol, NP)) Slice_3D = np.zeros((pol, 2*NP, 2*NP)) # Convolve Beta's with smoothing function if smoothing > 0: # smoothing function Basis_s = np.fromfunction(lambda i: np.exp(-(i - (NP)/2)**2 / (2*smoothing**2))/(smoothing*2.5), (NP,)) for i in range(pol): Beta_convol[i] = np.convolve(Basis_s, Beta[i], mode='same') else: Beta_convol = Beta # Calculate ordered slices: for i in range(pol): Slice_3D[i] = np.fromfunction(lambda k, l: _SL(i, (k-NP), (l-NP), Beta_convol, index, legendre_orders), (2*NP, 2*NP)) # Sum ordered slices up Slice = np.sum(Slice_3D, axis=0) return Slice, Beta_convol
Example #30
Source File: test_dcorrx.py From hyppo with Apache License 2.0 | 5 votes |
def test_distance(self): n = 6 x = np.ones(n) y = np.arange(1, n + 1) distx = np.zeros((n, n)) disty = np.fromfunction(lambda i, j: np.abs(i - j), (n, n)) stat1 = DcorrX(max_lag=1).test(x, y)[0] stat2 = DcorrX(max_lag=1).test(distx, disty)[0] assert_almost_equal(stat1, stat2, decimal=0)