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670 lines
11 KiB
670 lines
11 KiB
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after ex_open
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I/O word size 8
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after ex_get_init_ext(exoid, &par), error = 0
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database parameters:
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title = 'This is a test'
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num_dim = 3
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num_assembly = 0
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num_blobs = 3
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num_nodes = 0
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num_edge = 0
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num_face = 0
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num_elem = 0
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num_elem_blk = 0
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num_node_sets = 0
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num_side_sets = 0
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after ex_get_blob(exoid, &blobs[i]), error = 0
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Blob named 'Tempus' has id 100. It contains 10 entries.
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after ex_get_blob(exoid, &blobs[i]), error = 0
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Blob named 'IOSS' has id 200. It contains 20 entries.
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after ex_get_blob(exoid, &blobs[i]), error = 0
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Blob named 'Solver' has id 300. It contains 15 entries.
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after ex_get_blobs(exoid, blb), error = 0
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Blob named 'Tempus' has id 100. It contains 10 entries.
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Blob named 'IOSS' has id 200. It contains 20 entries.
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Blob named 'Solver' has id 300. It contains 15 entries.
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Blob named 'Tempus' with id 100. It contains 2 attributes:
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Name: 'Scale', Type = 6, Value Count = 1
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1.5
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Name: 'Units', Type = 4, Value Count = 4
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1 0 0 -1
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Blob named 'IOSS' with id 200. It contains 1 attributes:
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Name: 'Offset', Type = 6, Value Count = 3
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1.1 2.2 3.3
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Blob named 'Solver' with id 300. It contains 2 attributes:
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Name: 'Dimension', Type = 2, Value Count = 7
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l e n g t h
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Name: 'Offset', Type = 6, Value Count = 3
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1.1 2.2 3.3
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GLOBAL contains 1 attributes:
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Name: 'SOLID_MODEL', Type = 2, Value Count = 24
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after ex_get_reduction_variable_param(exoid, EX_BLOB, &num_red_vars), error = 0
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after ex_get_variable_param(exoid, EX_BLOB, &num_vars), error = 0
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after ex_get_reduction_variable_names(exoid, EX_BLOB, num_red_vars, var_names), error = 0
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There are 4 blob reduction variables; their names are :
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'Momentum_X'
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'Momentum_Y'
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'Momentum_Z'
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'Kinetic_Energy'
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after ex_get_variable_names(exoid, EX_BLOB, num_vars, var_names), error = 0
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There are 3 blob variables; their names are :
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'X'
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'XDOT'
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'XDDOT'
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There are 4 time steps in the database.
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after ex_get_time(exoid, i + 1, &time_value), error = 0
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Time at step 1 is 0.010000.
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after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
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Values for Blob 100 at step 1: 0.020000 0.030000 0.040000 0.050000
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.020
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1.020
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2.020
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3.020
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4.020
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5.020
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6.020
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7.020
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8.020
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9.020
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.030
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1.030
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2.030
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3.030
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4.030
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5.030
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6.030
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7.030
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8.030
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9.030
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.040
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1.040
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2.040
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3.040
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4.040
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5.040
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6.040
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7.040
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8.040
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9.040
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after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
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Values for Blob 200 at step 1: 1.020000 1.030000 1.040000 1.050000
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.030
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1.030
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2.030
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3.030
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4.030
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5.030
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6.030
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7.030
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8.030
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9.030
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10.030
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11.030
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12.030
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13.030
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14.030
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15.030
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16.030
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17.030
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18.030
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19.030
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.040
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1.040
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2.040
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3.040
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4.040
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5.040
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6.040
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7.040
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8.040
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9.040
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10.040
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11.040
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12.040
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13.040
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14.040
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15.040
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16.040
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17.040
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18.040
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19.040
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.050
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1.050
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2.050
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3.050
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4.050
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5.050
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6.050
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7.050
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8.050
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9.050
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10.050
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11.050
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12.050
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13.050
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14.050
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15.050
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16.050
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17.050
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18.050
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19.050
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after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
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Values for Blob 300 at step 1: 2.020000 2.030000 2.040000 2.050000
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.040
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1.040
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2.040
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3.040
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4.040
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5.040
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6.040
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7.040
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8.040
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9.040
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10.040
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11.040
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12.040
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13.040
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14.040
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.050
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1.050
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2.050
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3.050
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4.050
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5.050
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6.050
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7.050
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8.050
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9.050
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10.050
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11.050
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12.050
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13.050
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14.050
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.060
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1.060
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2.060
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3.060
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4.060
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5.060
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6.060
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7.060
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8.060
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9.060
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10.060
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11.060
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12.060
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13.060
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14.060
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after ex_get_time(exoid, i + 1, &time_value), error = 0
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Time at step 2 is 0.020000.
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after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
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Values for Blob 100 at step 2: 0.040000 0.060000 0.080000 0.100000
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.040
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1.040
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2.040
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3.040
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4.040
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5.040
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6.040
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7.040
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8.040
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9.040
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.060
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1.060
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2.060
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3.060
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4.060
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5.060
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6.060
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7.060
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8.060
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9.060
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.080
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1.080
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2.080
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3.080
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4.080
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5.080
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6.080
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7.080
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8.080
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9.080
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after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
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Values for Blob 200 at step 2: 1.040000 1.060000 1.080000 1.100000
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.050
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1.050
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2.050
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3.050
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4.050
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5.050
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6.050
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7.050
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8.050
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9.050
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10.050
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11.050
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12.050
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13.050
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14.050
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15.050
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16.050
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17.050
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18.050
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19.050
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.070
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1.070
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2.070
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3.070
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4.070
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5.070
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6.070
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7.070
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8.070
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9.070
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10.070
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11.070
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12.070
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13.070
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14.070
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15.070
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16.070
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17.070
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18.070
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19.070
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.090
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1.090
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2.090
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3.090
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4.090
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5.090
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6.090
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7.090
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8.090
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9.090
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10.090
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11.090
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12.090
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13.090
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14.090
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15.090
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16.090
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17.090
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18.090
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19.090
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after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
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Values for Blob 300 at step 2: 2.040000 2.060000 2.080000 2.100000
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.060
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1.060
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2.060
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3.060
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4.060
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5.060
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6.060
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7.060
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8.060
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9.060
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10.060
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11.060
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12.060
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13.060
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14.060
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.080
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1.080
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2.080
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3.080
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4.080
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5.080
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6.080
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7.080
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8.080
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9.080
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10.080
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11.080
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12.080
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13.080
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14.080
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.100
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1.100
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2.100
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3.100
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4.100
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5.100
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6.100
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7.100
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8.100
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9.100
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10.100
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11.100
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12.100
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13.100
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14.100
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after ex_get_time(exoid, i + 1, &time_value), error = 0
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Time at step 3 is 0.030000.
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after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
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Values for Blob 100 at step 3: 0.060000 0.090000 0.120000 0.150000
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.060
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1.060
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2.060
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3.060
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4.060
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5.060
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6.060
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7.060
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8.060
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9.060
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.090
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1.090
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2.090
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3.090
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4.090
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5.090
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6.090
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7.090
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8.090
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9.090
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.120
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1.120
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2.120
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3.120
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4.120
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5.120
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6.120
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7.120
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8.120
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9.120
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after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
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Values for Blob 200 at step 3: 1.060000 1.090000 1.120000 1.150000
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.070
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1.070
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2.070
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3.070
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4.070
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5.070
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6.070
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7.070
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8.070
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9.070
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10.070
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11.070
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12.070
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13.070
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14.070
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15.070
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16.070
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17.070
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18.070
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19.070
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.100
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1.100
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2.100
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3.100
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4.100
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5.100
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6.100
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7.100
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8.100
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9.100
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10.100
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11.100
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12.100
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13.100
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14.100
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15.100
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16.100
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17.100
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18.100
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19.100
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.130
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1.130
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2.130
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3.130
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4.130
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5.130
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6.130
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7.130
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8.130
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9.130
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10.130
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11.130
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12.130
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13.130
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14.130
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15.130
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16.130
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17.130
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18.130
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19.130
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after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
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Values for Blob 300 at step 3: 2.060000 2.090000 2.120000 2.150000
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.080
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1.080
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2.080
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3.080
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4.080
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5.080
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6.080
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7.080
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8.080
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9.080
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10.080
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11.080
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12.080
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13.080
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14.080
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.110
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1.110
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2.110
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3.110
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4.110
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5.110
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6.110
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7.110
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8.110
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9.110
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10.110
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11.110
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12.110
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13.110
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14.110
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.140
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1.140
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2.140
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3.140
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4.140
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5.140
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6.140
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7.140
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8.140
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9.140
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10.140
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11.140
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12.140
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13.140
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14.140
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after ex_get_time(exoid, i + 1, &time_value), error = 0
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Time at step 4 is 0.040000.
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after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
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Values for Blob 100 at step 4: 0.080000 0.120000 0.160000 0.200000
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after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
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0.080
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1.080
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2.080
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3.080
|
|
4.080
|
|
5.080
|
|
6.080
|
|
7.080
|
|
8.080
|
|
9.080
|
|
after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
|
|
0.120
|
|
1.120
|
|
2.120
|
|
3.120
|
|
4.120
|
|
5.120
|
|
6.120
|
|
7.120
|
|
8.120
|
|
9.120
|
|
after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
|
|
0.160
|
|
1.160
|
|
2.160
|
|
3.160
|
|
4.160
|
|
5.160
|
|
6.160
|
|
7.160
|
|
8.160
|
|
9.160
|
|
after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
|
|
Values for Blob 200 at step 4: 1.080000 1.120000 1.160000 1.200000
|
|
after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
|
|
0.090
|
|
1.090
|
|
2.090
|
|
3.090
|
|
4.090
|
|
5.090
|
|
6.090
|
|
7.090
|
|
8.090
|
|
9.090
|
|
10.090
|
|
11.090
|
|
12.090
|
|
13.090
|
|
14.090
|
|
15.090
|
|
16.090
|
|
17.090
|
|
18.090
|
|
19.090
|
|
after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
|
|
0.130
|
|
1.130
|
|
2.130
|
|
3.130
|
|
4.130
|
|
5.130
|
|
6.130
|
|
7.130
|
|
8.130
|
|
9.130
|
|
10.130
|
|
11.130
|
|
12.130
|
|
13.130
|
|
14.130
|
|
15.130
|
|
16.130
|
|
17.130
|
|
18.130
|
|
19.130
|
|
after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
|
|
0.170
|
|
1.170
|
|
2.170
|
|
3.170
|
|
4.170
|
|
5.170
|
|
6.170
|
|
7.170
|
|
8.170
|
|
9.170
|
|
10.170
|
|
11.170
|
|
12.170
|
|
13.170
|
|
14.170
|
|
15.170
|
|
16.170
|
|
17.170
|
|
18.170
|
|
19.170
|
|
after ex_get_reduction_vars(exoid, i + 1, EX_BLOB, blb[k].id, num_red_vars, var_values), error = 0
|
|
Values for Blob 300 at step 4: 2.080000 2.120000 2.160000 2.200000
|
|
after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
|
|
0.100
|
|
1.100
|
|
2.100
|
|
3.100
|
|
4.100
|
|
5.100
|
|
6.100
|
|
7.100
|
|
8.100
|
|
9.100
|
|
10.100
|
|
11.100
|
|
12.100
|
|
13.100
|
|
14.100
|
|
after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
|
|
0.140
|
|
1.140
|
|
2.140
|
|
3.140
|
|
4.140
|
|
5.140
|
|
6.140
|
|
7.140
|
|
8.140
|
|
9.140
|
|
10.140
|
|
11.140
|
|
12.140
|
|
13.140
|
|
14.140
|
|
after ex_get_var(exoid, i + 1, EX_BLOB, var_idx+1, blobs[k].id, blobs[k].num_entry, vals), error = 0
|
|
0.180
|
|
1.180
|
|
2.180
|
|
3.180
|
|
4.180
|
|
5.180
|
|
6.180
|
|
7.180
|
|
8.180
|
|
9.180
|
|
10.180
|
|
11.180
|
|
12.180
|
|
13.180
|
|
14.180
|
|
after ex_close(exoid), error = 0
|
|
|