idpr =eye(p-r) c = empty([0,r])# starting pointco = empty([0, p-r])# will hold orth-compl.idrcount =0forrowinrange(p):# (must be ones() instead of 1 because of 2d-requirementiflstsq( m[row,:], ones(1) )[2] ==0oridrcount >= r: c = r_[ c, zeros(r) ] co ...
defpaddingAnswers(answerSheet1, blankSheet1):numRowsA, numColsA, numBandsA, dataTypeA = ipcv.dimensions(answerSheet1) numRowsB, numColsB, numBandsB, dataTypeB = ipcv.dimensions(blankSheet1)printnumRowsB, numColsBifnumBandsA ==3: answerSheet = cv2.cvtColor(answerSheet1, cv.CV_BGR2GRA...
In the former case, their mean value can be used for the removal of co-registration errors. In the latter case, an error-related vector field can be derived by interpolations with kriging, radial basis functions or other techniques (Burrough & McDonnell, 1998). The co-registration error ...
{in_barrier_polyline_features}文档来自于网络搜索Kriging_3d{cell_size}{search_radius}{out_variance_prediction_raster}文档来自于网络搜索NaturalNeighbor_3d{cell_size}文档来自于网络搜索Spline_3d{cell_size}{REGULARIZED|TENSION}{weight}{number_points}文档来自于网络搜索TopoToRaster_3d{cell_size}{extent}{...
The Interpolate Points tool uses empirical Bayesian kriging to perform the interpolation. interpolated_rf = features.analyze_patterns.interpolate_points( rainfall, field='RAINFALL') {"cost": 0.119} Let us create another map of Tamil Nadu state and render the output from Interpolate Points tool map...
开发者ID:jfleroux,项目名称:vacumm,代码行数:31,代码来源:test_regrid_kriging_regrid.py 示例4: code_file_name ▲点赞 2▼ """Test :func:`~vacumm.misc.grid.misc.coord2slice`"""fromvcmqimportcode_file_name, P, os, N, create_lon,code_file_namefromvacumm.misc.gridimportcoord2slice, create...
开发者ID:Kenneth-T-Moore,项目名称:OpenMDAO-Framework,代码行数:31,代码来源:kriging_surrogate.py 示例4: dfa ▲点赞 2▼ defdfa(x, ave=None, l=None):x = np.array(x)ifaveisNone: ave = np.mean(x) y = np.cumsum(x) y -= aveiflisNone: ...
def_exec_vector(self, a, bd, mask):"""Solves the kriging system as a vectorized operation. This method can take a lot of memory for large grids and/or large datasets."""npt = bd.shape[0] n = self.X_ADJUSTED.shape[0] zero_index =Nonezero_value =Falsea_inv = scipy.linalg.inv(...