Test various parallel algorithms.
(rtData, rtData2, grad, grad2, total_npts)
| 44 | print(text, res) |
| 45 | |
| 46 | def testArrays(rtData, rtData2, grad, grad2, total_npts): |
| 47 | " Test various parallel algorithms." |
| 48 | if rank == 0: |
| 49 | print('-----------------------') |
| 50 | PRINT( "SUM ones:", algs.sum(rtData / rtData) - total_npts ) |
| 51 | |
| 52 | PRINT( "SUM sin:", (algs.sum(algs.sin(rtData) + 1) - numpy.sum(numpy.sin(rtData2) + 1)) / numpy.sum(numpy.sin(rtData2) + 1) ) |
| 53 | |
| 54 | PRINT( "rtData min:", algs.min(rtData) - numpy.min(rtData2) ) |
| 55 | PRINT( "rtData max:", algs.max(rtData) - numpy.max(rtData2) ) |
| 56 | PRINT( "rtData sum:", (algs.sum(rtData) - numpy.sum(rtData2)) / (2*numpy.sum(rtData2)) ) |
| 57 | PRINT( "rtData mean:", (algs.mean(rtData) - numpy.mean(rtData2)) / (2*numpy.mean(rtData2)) ) |
| 58 | PRINT( "rtData var:", (algs.var(rtData) - numpy.var(rtData2)) / numpy.var(rtData2) ) |
| 59 | PRINT( "rtData std:", (algs.std(rtData) - numpy.std(rtData2)) / numpy.std(rtData2) ) |
| 60 | |
| 61 | PRINT( "grad min:", algs.min(grad) - numpy.min(grad2) ) |
| 62 | PRINT( "grad max:", algs.max(grad) - numpy.max(grad2) ) |
| 63 | PRINT( "grad min 0:", algs.min(grad, 0) - numpy.min(grad2, 0) ) |
| 64 | PRINT( "grad max 0:", algs.max(grad, 0) - numpy.max(grad2, 0) ) |
| 65 | PRINT( "grad min 1:", algs.sum(algs.min(grad, 1)) - numpy.sum(numpy.min(grad2, 1)) ) |
| 66 | PRINT( "grad max 1:", algs.sum(algs.max(grad, 1)) - numpy.sum(numpy.max(grad2, 1)) ) |
| 67 | PRINT( "grad sum 1:", algs.sum(algs.sum(grad, 1)) - numpy.sum(numpy.sum(grad2, 1)) ) |
| 68 | PRINT( "grad var:", (algs.var(grad) - numpy.var(grad2)) / numpy.var(grad2) ) |
| 69 | PRINT( "grad var 0:", (algs.var(grad, 0) - numpy.var(grad2, 0)) / numpy.var(grad2, 0) ) |
| 70 | |
| 71 | w = vtkRTAnalyticSource() |
| 72 | # Update with ghost level because gradient needs it |