From 21e1b4c697531878a8dfd061e1e5221827b7b518 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Mon, 27 May 2024 04:23:47 -0700 Subject: [PATCH] Generated from sources --- ...GROReadBench.time_create_GRO_universe.json | 2 +- .../GRO.GROReadBench.time_parse_GRO_file.json | 2 +- ...ROReadBench.time_read_GRO_coordinates.json | 2 +- ...ethods.AtomGroupAttrsBench.time_angle.json | 2 +- ...ds.AtomGroupAttrsBench.time_atomcount.json | 2 +- ...ethods.AtomGroupAttrsBench.time_atoms.json | 2 +- ...methods.AtomGroupAttrsBench.time_bond.json | 2 +- ...ods.AtomGroupAttrsBench.time_dihedral.json | 2 +- ...thods.AtomGroupAttrsBench.time_forces.json | 2 +- ...ods.AtomGroupAttrsBench.time_improper.json | 2 +- ...hods.AtomGroupAttrsBench.time_indices.json | 2 +- ...ds.AtomGroupAttrsBench.time_positions.json | 2 +- ...AtomGroupAttrsBench.time_residuecount.json | 2 +- ...ods.AtomGroupAttrsBench.time_residues.json | 2 +- ...AtomGroupAttrsBench.time_segmentcount.json | 2 +- ...ods.AtomGroupAttrsBench.time_segments.json | 2 +- ...g_methods.AtomGroupAttrsBench.time_ts.json | 2 +- ...thods.AtomGroupAttrsBench.time_unique.json | 2 +- ...ods.AtomGroupAttrsBench.time_velocity.json | 2 +- ...tomGroupMethodsBench.time_bbox_no_pbc.json | 2 +- ...s.AtomGroupMethodsBench.time_bbox_pbc.json | 2 +- ...GroupMethodsBench.time_bsphere_no_pbc.json | 2 +- ...tomGroupMethodsBench.time_bsphere_pbc.json | 2 +- ...mGroupMethodsBench.time_center_no_pbc.json | 2 +- ...AtomGroupMethodsBench.time_center_pbc.json | 2 +- ...roupMethodsBench.time_centroid_no_pbc.json | 2 +- ...omGroupMethodsBench.time_centroid_pbc.json | 2 +- ...tomGroupMethodsBench.time_concatenate.json | 2 +- ...AtomGroupMethodsBench.time_difference.json | 2 +- ...ds.AtomGroupMethodsBench.time_groupby.json | 2 +- ...tomGroupMethodsBench.time_guess_bonds.json | 2 +- ...omGroupMethodsBench.time_intersection.json | 2 +- ...oupMethodsBench.time_is_strict_subset.json | 2 +- ...pMethodsBench.time_is_strict_superset.json | 2 +- ...AtomGroupMethodsBench.time_isdisjoint.json | 2 +- ...s.AtomGroupMethodsBench.time_issubset.json | 2 +- ...AtomGroupMethodsBench.time_issuperset.json | 2 +- ...mGroupMethodsBench.time_pack_into_box.json | 2 +- ...ods.AtomGroupMethodsBench.time_rotate.json | 2 +- ...s.AtomGroupMethodsBench.time_rotateby.json | 2 +- ...hods.AtomGroupMethodsBench.time_split.json | 2 +- ...s.AtomGroupMethodsBench.time_subtract.json | 2 +- ...ethodsBench.time_symmetric_difference.json | 2 +- ....AtomGroupMethodsBench.time_transform.json | 2 +- ....AtomGroupMethodsBench.time_translate.json | 2 +- ...hods.AtomGroupMethodsBench.time_union.json | 2 +- ...thods.AtomGroupMethodsBench.time_wrap.json | 2 +- ...mpoundSplitting.time_center_compounds.json | 2 +- ...entCaching.time_find_cached_fragments.json | 2 +- ...s.FragmentCaching.time_find_fragments.json | 2 +- ...s.FragmentFinding.time_find_fragments.json | 2 +- ...distances.DistancesBench.time_between.json | 2 +- ...ances.DistancesBench.time_calc_angles.json | 2 +- ...tances.DistancesBench.time_calc_bonds.json | 2 +- ...es.DistancesBench.time_calc_dihedrals.json | 2 +- ...es.DistancesBench.time_contact_matrix.json | 2 +- ...ancesBench.time_contact_matrix_sparse.json | 2 +- ...is.distances.DistancesBench.time_dist.json | 2 +- ...nces.DistancesBench.time_dist_offsets.json | 2 +- ...es.DistancesBench.time_distance_array.json | 2 +- ...nch.time_distance_array_pre_allocated.json | 2 +- ...stancesBench.time_self_distance_array.json | 2 +- ...ime_self_distance_array_pre_allocated.json | 2 +- ...aflet.LeafletBench.time_leafletfinder.json | 2 +- ...letOptimizeBench.time_optimize_cutoff.json | 2 +- ...t_coord_axesBench.time_get_coord_axes.json | 2 +- ...t_msd_matrixBench.time_get_msd_matrix.json | 2 +- ...c_funcBench.time_get_path_metric_func.json | 2 +- ...PSA_metricBench.time_discrete_frechet.json | 2 +- ...is.psa.PSA_metricBench.time_hausdorff.json | 2 +- ...sa.PSA_metricBench.time_hausdorff_avg.json | 2 +- ..._metricBench.time_hausdorff_neighbors.json | 2 +- ...a.PSA_metricBench.time_hausdorff_wavg.json | 2 +- ...lysis.psa.PSA_sqnormBench.time_sqnorm.json | 2 +- ...ysis.rdf.SimpleRdfBench.time_interrdf.json | 2 +- .../analysis.rms.RmsdTrajBench.time_RMSD.json | 2 +- .../analysis.rms.RmsfTrajBench.time_RMSF.json | 2 +- ...analysis.rms.SimpleRmsBench.time_rmsd.json | 2 +- .../import.ImportBench.time_import.json | 2 +- ...ectionBench.time_geometric_selections.json | 2 +- ...SelectionBench.time_simple_selections.json | 2 +- .../ram-16GB/scipy/six-null/summary.json | 2 +- .../topology.BondsBench.time_bonds.json | 2 +- ...gy.TopologyGuessBench.time_guessbonds.json | 2 +- ..._reader.TrajReaderCreation.time_reads.json | 2 +- ...ader.TrajReaderIteration.time_strides.json | 2 +- ...GROReadBench.time_create_GRO_universe.json | 2 +- .../GRO.GROReadBench.time_parse_GRO_file.json | 2 +- ...ROReadBench.time_read_GRO_coordinates.json | 2 +- ...ethods.AtomGroupAttrsBench.time_angle.json | 2 +- ...ds.AtomGroupAttrsBench.time_atomcount.json | 2 +- ...ethods.AtomGroupAttrsBench.time_atoms.json | 2 +- ...methods.AtomGroupAttrsBench.time_bond.json | 2 +- ...ods.AtomGroupAttrsBench.time_dihedral.json | 2 +- ...thods.AtomGroupAttrsBench.time_forces.json | 2 +- ...ods.AtomGroupAttrsBench.time_improper.json | 2 +- ...hods.AtomGroupAttrsBench.time_indices.json | 2 +- ...ds.AtomGroupAttrsBench.time_positions.json | 2 +- ...AtomGroupAttrsBench.time_residuecount.json | 2 +- ...ods.AtomGroupAttrsBench.time_residues.json | 2 +- ...AtomGroupAttrsBench.time_segmentcount.json | 2 +- ...ods.AtomGroupAttrsBench.time_segments.json | 2 +- ...g_methods.AtomGroupAttrsBench.time_ts.json | 2 +- ...thods.AtomGroupAttrsBench.time_unique.json | 2 +- ...ods.AtomGroupAttrsBench.time_velocity.json | 2 +- ...tomGroupMethodsBench.time_bbox_no_pbc.json | 2 +- ...s.AtomGroupMethodsBench.time_bbox_pbc.json | 2 +- ...GroupMethodsBench.time_bsphere_no_pbc.json | 2 +- ...tomGroupMethodsBench.time_bsphere_pbc.json | 2 +- ...mGroupMethodsBench.time_center_no_pbc.json | 2 +- ...AtomGroupMethodsBench.time_center_pbc.json | 2 +- ...roupMethodsBench.time_centroid_no_pbc.json | 2 +- ...omGroupMethodsBench.time_centroid_pbc.json | 2 +- ...tomGroupMethodsBench.time_concatenate.json | 2 +- ...AtomGroupMethodsBench.time_difference.json | 2 +- ...ds.AtomGroupMethodsBench.time_groupby.json | 2 +- ...tomGroupMethodsBench.time_guess_bonds.json | 2 +- ...omGroupMethodsBench.time_intersection.json | 2 +- ...oupMethodsBench.time_is_strict_subset.json | 2 +- ...pMethodsBench.time_is_strict_superset.json | 2 +- ...AtomGroupMethodsBench.time_isdisjoint.json | 2 +- ...s.AtomGroupMethodsBench.time_issubset.json | 2 +- ...AtomGroupMethodsBench.time_issuperset.json | 2 +- ...mGroupMethodsBench.time_pack_into_box.json | 2 +- ...ods.AtomGroupMethodsBench.time_rotate.json | 2 +- ...s.AtomGroupMethodsBench.time_rotateby.json | 2 +- ...hods.AtomGroupMethodsBench.time_split.json | 2 +- ...s.AtomGroupMethodsBench.time_subtract.json | 2 +- ...ethodsBench.time_symmetric_difference.json | 2 +- ....AtomGroupMethodsBench.time_transform.json | 2 +- ....AtomGroupMethodsBench.time_translate.json | 2 +- ...hods.AtomGroupMethodsBench.time_union.json | 2 +- ...thods.AtomGroupMethodsBench.time_wrap.json | 2 +- ...mpoundSplitting.time_center_compounds.json | 2 +- ...entCaching.time_find_cached_fragments.json | 2 +- ...s.FragmentCaching.time_find_fragments.json | 2 +- ...s.FragmentFinding.time_find_fragments.json | 2 +- ...distances.DistancesBench.time_between.json | 2 +- ...ances.DistancesBench.time_calc_angles.json | 2 +- ...tances.DistancesBench.time_calc_bonds.json | 2 +- ...es.DistancesBench.time_calc_dihedrals.json | 2 +- ...es.DistancesBench.time_contact_matrix.json | 2 +- ...ancesBench.time_contact_matrix_sparse.json | 2 +- ...is.distances.DistancesBench.time_dist.json | 2 +- ...nces.DistancesBench.time_dist_offsets.json | 2 +- ...es.DistancesBench.time_distance_array.json | 2 +- ...nch.time_distance_array_pre_allocated.json | 2 +- ...stancesBench.time_self_distance_array.json | 2 +- ...ime_self_distance_array_pre_allocated.json | 2 +- ...aflet.LeafletBench.time_leafletfinder.json | 2 +- ...letOptimizeBench.time_optimize_cutoff.json | 2 +- ...t_coord_axesBench.time_get_coord_axes.json | 2 +- ...t_msd_matrixBench.time_get_msd_matrix.json | 2 +- ...c_funcBench.time_get_path_metric_func.json | 2 +- ...PSA_metricBench.time_discrete_frechet.json | 2 +- ...is.psa.PSA_metricBench.time_hausdorff.json | 2 +- ...sa.PSA_metricBench.time_hausdorff_avg.json | 2 +- ..._metricBench.time_hausdorff_neighbors.json | 2 +- ...a.PSA_metricBench.time_hausdorff_wavg.json | 2 +- ...lysis.psa.PSA_sqnormBench.time_sqnorm.json | 2 +- ...ysis.rdf.SimpleRdfBench.time_interrdf.json | 2 +- .../analysis.rms.RmsdTrajBench.time_RMSD.json | 2 +- .../analysis.rms.RmsfTrajBench.time_RMSF.json | 2 +- ...analysis.rms.SimpleRmsBench.time_rmsd.json | 2 +- .../import.ImportBench.time_import.json | 2 +- ...ectionBench.time_geometric_selections.json | 2 +- ...SelectionBench.time_simple_selections.json | 2 +- .../topology.BondsBench.time_bonds.json | 2 +- ...gy.TopologyGuessBench.time_guessbonds.json | 2 +- ..._reader.TrajReaderCreation.time_reads.json | 2 +- ...ader.TrajReaderIteration.time_strides.json | 2 +- index.json | 2 +- info.json | 2 +- regressions.json | 2 +- regressions.xml | 152 +++++++++--------- 175 files changed, 253 insertions(+), 247 deletions(-) diff --git a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/GRO.GROReadBench.time_create_GRO_universe.json b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/GRO.GROReadBench.time_create_GRO_universe.json index 09396247b..b8ea642ba 100644 --- a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/GRO.GROReadBench.time_create_GRO_universe.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/GRO.GROReadBench.time_create_GRO_universe.json @@ -1 +1 @@ -[[18300, 0.36582623299909756], [18370, 0.36252890701871365], [18387, 0.4055814364692196], [18407, 0.3702876310562715], [18409, 0.3635533070191741], [18432, 0.3668798605212942], [18459, 0.3648711759597063], [18487, 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3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_atomcount.json b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_atomcount.json index 597afc773..1550d1dd3 100644 --- a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_atomcount.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 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a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_bond.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_bond.json @@ -1 +1 @@ -[[18300, [3.043480134843666e-06, 3.073441239247118e-06, 3.0775246585609095e-06, 3.07681959741333e-06]], [18370, [3.109130380983029e-06, 3.1054068156248433e-06, 3.0974477671578826e-06, 3.0975235795107944e-06]], [18387, [3.0844341312563375e-06, 3.0516320950760168e-06, 3.0420251018403487e-06, 3.052655643183187e-06]], [18407, [3.1108121819249015e-06, 3.133371913597196e-06, 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3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_forces.json index 21eba2673..e6c19eeb6 100644 --- a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_forces.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_forces.json @@ -1 +1 @@ -[[18300, [1.6989614222601829e-06, 1.6760137395455541e-06, 1.6882059740257024e-06, 1.68946108704701e-06]], [18370, [1.6961014993927058e-06, 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3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_indices.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_indices.json @@ -1 +1 @@ -[[18300, [2.0309706322457948e-07, 2.0449353094170592e-07, 2.0384894012387956e-07, 2.0569811547385208e-07]], [18370, [2.0560480726251032e-07, 2.0591193658766506e-07, 2.0542469752210898e-07, 2.0787415472352996e-07]], [18387, [2.0357534549385939e-07, 2.033492512880225e-07, 2.0311246967656972e-07, 2.0424278569688108e-07]], [18407, [2.1993775415574493e-07, 2.2169331167461083e-07, 2.196460760976902e-07, 2.246090517143455e-07]], [18409, [2.042140664227188e-07, 2.056335350027881e-07, 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3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_positions.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_positions.json @@ -1 +1 @@ -[[18300, [1.7341608089094902e-06, 2.9664825279299957e-06, 1.4380373185401707e-05, 0.00012677837067338473]], [18370, [1.7384802243068887e-06, 2.9263104750976394e-06, 1.402055336670442e-05, 0.0001233426363406969]], [18387, [1.778260815059085e-06, 2.952472246049539e-06, 1.4083361035509537e-05, 0.00012308938214241455]], [18407, [1.7831012252089203e-06, 2.959430663844929e-06, 1.4044568312681274e-05, 0.00012335947775725452]], [18409, [1.8116169540374071e-06, 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a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_residuecount.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_residuecount.json @@ -1 +1 @@ -[[18300, [7.461525018566732e-06, 7.775480707128565e-06, 1.0949738959574358e-05, 4.08400412150118e-05]], [18370, [6.6565552646702625e-06, 6.958375909835896e-06, 9.708816129698506e-06, 3.950159226809813e-05]], [18387, [6.9851419422488245e-06, 7.3681404206056085e-06, 1.0238388436324393e-05, 3.974038926470611e-05]], [18407, [6.678007713774239e-06, 6.9713780781390466e-06, 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a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_residues.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_residues.json @@ -1 +1 @@ -[[18300, [7.082961974132836e-06, 7.411486055640366e-06, 1.0357960685161338e-05, 3.9995321058891576e-05]], [18370, [6.329404460313348e-06, 6.6083304373503626e-06, 9.332569546279803e-06, 3.868781415552929e-05]], [18387, [6.439200289035798e-06, 6.7295161597366e-06, 9.873671275042602e-06, 3.93613641474234e-05]], [18407, [6.428357969591583e-06, 6.6420678714556355e-06, 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b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_ts.json index 3e9b402bb..f1392d1fa 100644 --- a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_ts.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_ts.json @@ -1 +1 @@ -[[18300, [1.840894040182833e-05, 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a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_unique.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_unique.json @@ -1 +1 @@ -[[18300, [2.9030694930956224e-06, 2.9071222938971984e-06, 2.9150380713505105e-06, 2.9147728734339276e-06]], [18370, [2.9236390658053784e-06, 2.9458947178942896e-06, 2.970093274367971e-06, 2.9711667045638304e-06]], [18387, [2.9460178350801673e-06, 2.9227659658834565e-06, 2.9708849289817834e-06, 2.9582798457409765e-06]], [18407, [3.008793723076964e-06, 2.9876525525910342e-06, 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3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupMethodsBench.time_centroid_no_pbc.json index 27f7cf960..f0344be93 100644 --- a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupMethodsBench.time_centroid_no_pbc.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupMethodsBench.time_centroid_no_pbc.json @@ -1 +1 @@ -[[18300, [1.9863976528237497e-05, 2.3187100454472387e-05, 5.2431352599631255e-05, 0.0003289687580295971]], [18370, [1.8791090850987713e-05, 2.1798239097135737e-05, 5.156306203864857e-05, 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3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupMethodsBench.time_centroid_pbc.json b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupMethodsBench.time_centroid_pbc.json index db0234803..5f335d815 100644 --- a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupMethodsBench.time_centroid_pbc.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 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E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupMethodsBench.time_split.json b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupMethodsBench.time_split.json index 7afe4b524..c08a0d226 100644 --- a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupMethodsBench.time_split.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 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index ffa8878d3..16da2991b 100644 --- a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupMethodsBench.time_wrap.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/ag_methods.AtomGroupMethodsBench.time_wrap.json @@ -1 +1 @@ -[[18300, [5.236644874236024e-05, 5.5112908220118175e-05, 8.509195781348703e-05, 0.00035889620097198833]], [18370, [5.253301212741506e-05, 5.581056965248925e-05, 8.654647990624653e-05, 0.00035811793059110637]], [18387, [5.4160588342934995e-05, 5.808190716257722e-05, 8.797317480904953e-05, 0.0003632988004634778]], [18407, 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3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/analysis.distances.DistancesBench.time_calc_bonds.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/analysis.distances.DistancesBench.time_calc_bonds.json @@ -1 +1 @@ -[[18300, [3.7420488096728934e-06, 4.377596220767585e-06, 9.5741031691432e-06, 5.8445395630370967e-05]], [18370, [3.7143547676078413e-06, 4.4286329002651626e-06, 9.473410353221318e-06, 5.842559350863657e-05]], [18387, [3.717700904530261e-06, 4.391323820968384e-06, 9.428670905022457e-06, 5.825210828334093e-05]], [18407, [3.93500391203576e-06, 4.665495899288008e-06, 9.71133291590993e-06, 5.915905527078389e-05]], [18409, [3.835978799210804e-06, 4.453885907406676e-06, 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at end of file diff --git a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/analysis.distances.DistancesBench.time_distance_array_pre_allocated.json b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/analysis.distances.DistancesBench.time_distance_array_pre_allocated.json index a2808a896..39d4681e4 100644 --- a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 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a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/analysis.psa.PSA_get_coord_axesBench.time_get_coord_axes.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/analysis.psa.PSA_get_coord_axesBench.time_get_coord_axes.json @@ -1 +1 @@ -[[18300, [3.5903340193050276e-07, 3.594495705933251e-07, 3.597929017705214e-07, 3.602842505170256e-07, 3.6399986173375124e-07, 3.597753477790766e-07, 3.778952416697436e-07, 3.7677132259390846e-07, 3.766060172411683e-07]], [18370, [3.6084144470741914e-07, 3.6292475616322353e-07, 3.631683248795096e-07, 3.612526850684165e-07, 3.6076171008204183e-07, 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diff --git a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/analysis.psa.PSA_metricBench.time_hausdorff_neighbors.json b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/analysis.psa.PSA_metricBench.time_hausdorff_neighbors.json index 4ce8cc45e..5c6d0995a 100644 --- a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/analysis.psa.PSA_metricBench.time_hausdorff_neighbors.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/analysis.psa.PSA_metricBench.time_hausdorff_neighbors.json @@ -1 +1 @@ -[[18300, [9.483289207449301e-05, 0.00010576638401335463, 0.00012276442961164696, 0.0011715229451915044, 0.0021723318030126395, 0.0035638551732214787, 0.003263930746470578, 0.007302754238480702, 0.012699490005616099]], [18370, [9.082437246146365e-05, 0.00010367379496421884, 0.00011774910804392262, 0.001122424055615233, 0.002109104907140136, 0.00350616949920853, 0.0031616968917660415, 0.00715894321911037, 0.012667691567912698]], [18387, [9.894825931076171e-05, 0.00011418653783671883, 0.00012842572447503123, 0.0012055958828164472, 0.0022129768040031195, 0.0036170731764286757, 0.0033272572327405214, 0.007340775511693209, 0.012823537923395634]], [18407, 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3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/traj_reader.TrajReaderCreation.time_reads.json index 677b3fc5c..4b27fe020 100644 --- a/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/traj_reader.TrajReaderCreation.time_reads.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.11/ram-16GB/scipy/six-null/traj_reader.TrajReaderCreation.time_reads.json @@ -1 +1 @@ -[[18300, [0.0057825037656584755, 0.018206682987511158, 0.00014419907549306852, 0.0003929274637441806]], [18370, [0.0057591841905377805, 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1704139752000, "20847": 1704240218000, "20851": 1704319053000, "20878": 1704836160000, "20883": 1704991848000, "20940": 1705530480000, "20950": 1705576431000, "20959": 1705788311000, "20978": 1706097384000, "21008": 1706645843000, "21015": 1706651360000, "21044": 1707062668000, "21045": 1707062717000, "21072": 1707743962000, "21119": 1708669165000, "21124": 1708673666000, "21158": 1708797985000, "21184": 1709582288000, "21186": 1709584578000, "21198": 1710089542000, "21202": 1710090427000, "21210": 1710193651000, "21260": 1711408213000, "21316": 1711657260000, "21325": 1711725922000, "21329": 1711740375000, "21334": 1711746802000, "21336": 1711748394000, "21353": 1711878779000, "21357": 1711941055000, "21375": 1712012663000, "21429": 1713708970000, "21431": 1713709337000, "21434": 1713767253000, "21436": 1713768580000, "21452": 1714330364000, "21453": 1714330587000, "21498": 1715173256000, "21525": 1716509748000}, "params": {"Cython": ["", "0.27"], "MDAnalysisTests": ["", null], "arch": ["x86_64"], "cpu": ["Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz", "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz"], "machine": ["c3potato", "gp160"], "mock": ["", null], "nose": ["", null], "numpy": [""], "os": ["Linux 3.16.0-4-amd64", "Linux 4.10.0-35-generic"], "psutil": ["", null], "pytest": ["", null], "python": ["2.7", "3.11", "3.6", "3.8", "3.9"], "ram": ["128GB", "16GB"], "scipy": [""], "six": ["", null], "mdanalysistests": ["", null], "branch": ["develop"]}, "graph_param_list": [{"Cython": "", "MDAnalysisTests": "", "arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "mock": "", "nose": "", "numpy": "", "os": "Linux 3.16.0-4-amd64", "psutil": "", "pytest": "", "python": "2.7", "ram": "16GB", "scipy": "", "six": "", "branch": "develop", "mdanalysistests": null}, {"Cython": "0.27", "arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "mdanalysistests": "", "numpy": "", "os": "Linux 3.16.0-4-amd64", "python": "2.7", "ram": "16GB", "scipy": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "os": "Linux 3.16.0-4-amd64", "ram": "16GB", "python": "3.11", "Cython": "", "numpy": "", "scipy": "", "mdanalysistests": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "os": "Linux 3.16.0-4-amd64", "ram": "16GB", "python": "3.8", "Cython": "", "numpy": "", "scipy": "", "mdanalysistests": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}, {"Cython": "", "arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "mdanalysistests": "", "numpy": "", "os": "Linux 3.16.0-4-amd64", "python": "2.7", "ram": "16GB", "scipy": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "os": "Linux 3.16.0-4-amd64", "ram": "16GB", "python": "3.6", "Cython": "", "mdanalysistests": "", "numpy": "", "scipy": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "os": "Linux 3.16.0-4-amd64", "ram": "16GB", "python": "3.9", "Cython": "", "numpy": "", "scipy": "", "mdanalysistests": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}], "benchmarks": {"GRO.GROReadBench.time_create_GRO_universe": {"code": "class GROReadBench:\n def time_create_GRO_universe(self):\n \"\"\"Time to create MDA Universe of GRO\"\"\"\n u = mda.Universe(GRO)", "min_run_count": 2, "name": "GRO.GROReadBench.time_create_GRO_universe", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "85e9386a1154d866631322425b43e682903a28a89efb74527110189dcbd24a4a", "warmup_time": -1}, "GRO.GROReadBench.time_parse_GRO_file": {"code": "class GROReadBench:\n def time_parse_GRO_file(self):\n \"\"\"Time to create topology from GRO file\"\"\"\n p = GROParser(GRO)\n top = p.parse()", "min_run_count": 2, "name": "GRO.GROReadBench.time_parse_GRO_file", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7d0bec284a3cc88727c887e20b4d7a7a6fb3dc8b9a3fed98a8bee728f5920227", "warmup_time": -1}, "GRO.GROReadBench.time_read_GRO_coordinates": {"code": "class GROReadBench:\n def time_read_GRO_coordinates(self):\n \"\"\"Benchmark reading of standard testsuite GRO file.\"\"\"\n GROReader(GRO)", "min_run_count": 2, "name": "GRO.GROReadBench.time_read_GRO_coordinates", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "987e7a4b2431ce8645e1a4d7c1135f76c9b35e5eb29b0f6818860f96531be147", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_angle": {"code": "class AtomGroupAttrsBench:\n def time_angle(self, num_atoms):\n \"\"\"Benchmark simple angle\n calculation. Requires ag\n with three atoms.\n \"\"\"\n self.ag[:3].angle\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_angle", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "a83a2ce99c0673f57ac3eac16b49a9db026ae64b6fcaa4389bf47eed59124dbb", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_atomcount": {"code": "class AtomGroupAttrsBench:\n def time_atomcount(self, num_atoms):\n \"\"\"Benchmark counting of atoms in\n atomgroup.\n \"\"\"\n self.ag.n_atoms\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_atomcount", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "d31f8341e31a7bc198133f258c72536b54ec904f97ac4f28a767f9970856818a", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_atoms": {"code": "class AtomGroupAttrsBench:\n def time_atoms(self, num_atoms):\n \"\"\"Benchmark returning of identical\n atomgroup.\n \"\"\"\n self.ag.atoms\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_atoms", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "6cd2f945901dc324d1dabcb388bc3fe0c3b5977d49056e5d7a7859c40e2c9af8", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_bond": {"code": "class AtomGroupAttrsBench:\n def time_bond(self, num_atoms):\n \"\"\"Benchmark Bond object creation.\n Requires ag of size 2.\n \"\"\"\n self.ag[:2].bond\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_bond", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3507b8355b5b01697e086a646c559412d09fde51fc7139f4524f7cb58aa7ae67", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_dihedral": {"code": "class AtomGroupAttrsBench:\n def time_dihedral(self, num_atoms):\n \"\"\"Benchmark Dihedral object\n creation time. Requires ag of\n size 4.\n \"\"\"\n self.ag[:4].dihedral\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_dihedral", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "5775f1a2f7666febcc47f94b77702bd75a2a7ccd7c9044265c965dbad779a351", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_forces": {"code": "class AtomGroupAttrsBench:\n def time_forces(self, num_atoms):\n \"\"\"Benchmark atomgroup force\n calculation.\n \"\"\"\n try:\n self.ag.forces\n except NoDataError:\n pass\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_forces", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "0186fc73e76b8e3f8cb39e7e11f7c6e9837d800b2379b14591432ee9e710d69e", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_improper": {"code": "class AtomGroupAttrsBench:\n def time_improper(self, num_atoms):\n \"\"\"Benchmark improper dihedral\n calculation. Requires ag of size\n 4.\n \"\"\"\n self.ag[:4].improper\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_improper", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "d7ba217b21841225ff1df0b473d66794db6b2c92c56bbb3cb04c042fb43e967c", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_indices": {"code": "class AtomGroupAttrsBench:\n def time_indices(self, num_atoms):\n \"\"\"Benchmark atom index calculation.\n \"\"\"\n self.ag.ix\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_indices", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "8a27cd7f019ca3f35aa37d112e72f6b0311c37dee14a33adff3dffd8b6ee2602", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_positions": {"code": "class AtomGroupAttrsBench:\n def time_positions(self, num_atoms):\n \"\"\"Benchmark returning the positions\n of the atoms in the group.\n \"\"\"\n self.ag.positions\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_positions", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7f538e5ccf7c68b715cb9ba1f0a85764faa5e09216ba83be3b88804f26504dcd", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_residuecount": {"code": "class AtomGroupAttrsBench:\n def time_residuecount(self, num_atoms):\n \"\"\"Benchmark counting of residues in\n atomgroup.\n \"\"\"\n self.ag.n_residues\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_residuecount", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "6ba8023af1d40da84022b2eba1e494b20eb80d1257fa5beabc2206f8d380b237", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_residues": {"code": "class AtomGroupAttrsBench:\n def time_residues(self, num_atoms):\n \"\"\"Benchmark creation of the ResidueGroup\n from the AtomGroup.\n \"\"\"\n self.ag.residues\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_residues", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "72655f2bf91db254ada1b26646da826894a223384a4691f99b06b291107b938e", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_segmentcount": {"code": "class AtomGroupAttrsBench:\n def time_segmentcount(self, num_atoms):\n \"\"\"Benchmark counting of segments in\n atomgroup.\n \"\"\"\n self.ag.n_segments\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_segmentcount", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "ea9ccc59b78a3786c8ed142a014c476162b2d37437c8f93a4b05644dd851aede", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_segments": {"code": "class AtomGroupAttrsBench:\n def time_segments(self, num_atoms):\n \"\"\"Benchmark determination of sorted\n SegmentGroup from AtomGroup.\n \"\"\"\n self.ag.segments\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_segments", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "ec21432839b2caac695337844d8b3feff546e74083cc61d846692523160bcf70", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_ts": {"code": "class AtomGroupAttrsBench:\n def time_ts(self, num_atoms):\n \"\"\"Benchmark returning of a timestep\n instance from atomgroup.\n \"\"\"\n self.ag.ts\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_ts", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "0aadba3b29a020c1edbc55253049c8f9dc252759321e51f110ca895d99ce52a6", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_unique": {"code": "class AtomGroupAttrsBench:\n def time_unique(self, num_atoms):\n \"\"\"Benchmark determination of unique\n elements in atomgroup.\n \"\"\"\n self.ag.unique\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_unique", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "1c2345530d78545ab7011e17420325ec0f2b12a472ade667a7253fd672a06f70", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_velocity": {"code": "class AtomGroupAttrsBench:\n def time_velocity(self, num_atoms):\n \"\"\"Benchmark atomgroup velocity\n values return.\n \"\"\"\n try:\n self.ag.velocities\n except NoDataError:\n pass\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_velocity", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3841e8cfb0518bdc257c0479f3844b8bdf80ee1e2bf7b2245ba4ba95533e2ce8", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_bbox_no_pbc": {"code": "class AtomGroupMethodsBench:\n def time_bbox_no_pbc(self, num_atoms):\n \"\"\"Benchmark bounding box calculation\n with pbc inactive.\n \"\"\"\n self.ag.bbox(pbc=False)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_bbox_no_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "f4d3c92e8dc5efa1a09726e3c1dea49453d5625ee6952475869546919575a0ed", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_bbox_pbc": {"code": "class AtomGroupMethodsBench:\n def time_bbox_pbc(self, num_atoms):\n \"\"\"Benchmark bounding box calculation\n with pbc active.\n \"\"\"\n self.ag.bbox(pbc=True)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_bbox_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "02deed74348541e19fd7811f9aa232bd53410903de3a1432d7cc090a3184198e", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_bsphere_no_pbc": {"code": "class AtomGroupMethodsBench:\n def time_bsphere_no_pbc(self, num_atoms):\n \"\"\"Benchmark bounding sphere calculation\n with pbc inactive.\n \"\"\"\n self.ag.bsphere(pbc=False)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_bsphere_no_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7f229e23767623d3052357b0d2e36620742990cbec01fa9df982997a64d0b558", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_bsphere_pbc": {"code": "class AtomGroupMethodsBench:\n def time_bsphere_pbc(self, num_atoms):\n \"\"\"Benchmark bounding sphere calculation\n with pbc active.\n \"\"\"\n self.ag.bsphere(pbc=True)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_bsphere_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "669f068c60e5023f28ea98225386244efdd0907ea6895c590088cfd170bef050", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_center_no_pbc": {"code": "class AtomGroupMethodsBench:\n def time_center_no_pbc(self, num_atoms):\n \"\"\"Benchmark center calculation with\n pbc inactive.\n \"\"\"\n self.ag.center(weights=self.weights,\n pbc=False)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_center_no_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "5499bbde1d376804c4c410b7cac45ebc709dc4065d374891ccaca4c67b497d8d", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_center_pbc": {"code": "class AtomGroupMethodsBench:\n def time_center_pbc(self, num_atoms):\n \"\"\"Benchmark center calculation with\n pbc active.\n \"\"\"\n self.ag.center(weights=self.weights,\n pbc=True)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_center_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "9e281b5399aef60e0a823b2a228dee94c0e31bd95d8806c1e15b861cf23d83b6", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_centroid_no_pbc": {"code": "class AtomGroupMethodsBench:\n def time_centroid_no_pbc(self, num_atoms):\n \"\"\"Benchmark centroid calculation with\n pbc inactive.\n \"\"\"\n self.ag.centroid(pbc=False)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_centroid_no_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "56366be992afa8468328495085d4bd7f270fe821af6b4412e097e071a5841aba", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_centroid_pbc": {"code": "class AtomGroupMethodsBench:\n def time_centroid_pbc(self, num_atoms):\n \"\"\"Benchmark centroid calculation with\n pbc active.\n \"\"\"\n self.ag.centroid(pbc=True)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_centroid_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7ce43af0540e7c4b35689a263dfc228d0a4a2d2d92727d2d44b1e397c32a8726", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_concatenate": {"code": "class AtomGroupMethodsBench:\n def time_concatenate(self, num_atoms):\n \"\"\"Benchmark atomgroup concatenation.\n \"\"\"\n self.ag.concatenate(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_concatenate", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "d5249c87dfce2c91bde201106b2e4198849051c93364564de86ce872159c1428", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_difference": {"code": "class AtomGroupMethodsBench:\n def time_difference(self, num_atoms):\n \"\"\"Benchmark atomgroup difference\n operation.\n \"\"\"\n self.ag.difference(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_difference", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "56ce3f2f2c4257dee720e7b07315ce2595afc66137f7324b405ebfb060e65dd3", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_groupby": {"code": "class AtomGroupMethodsBench:\n def time_groupby(self, num_atoms):\n \"\"\"Benchmark atomgroup groupby\n operation.\n \"\"\"\n self.ag.groupby('resnames')\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_groupby", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "e42dfd364b2726e568511c4eae084feead8bd62e3ac3d2046ca6c0ae2a061363", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_guess_bonds": {"code": "class AtomGroupMethodsBench:\n def time_guess_bonds(self, num_atoms):\n \"\"\"Benchmark atomgroup bond guessing\n with artificially-seeded vdw values.\n \"\"\"\n self.ag.guess_bonds(self.vdwradii)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_guess_bonds", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "b22111561b487e88b079412e82b48de4d70d39b0c868ec64305cebdcdf3ecd0b", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_intersection": {"code": "class AtomGroupMethodsBench:\n def time_intersection(self, num_atoms):\n \"\"\"Benchmark ag intersection.\n \"\"\"\n self.ag.intersection(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_intersection", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "af81d67c8ba6789db6632156d5ee37d16c1d7145bc51d97faf62f5a44e71a216", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_is_strict_subset": {"code": "class AtomGroupMethodsBench:\n def time_is_strict_subset(self, num_atoms):\n \"\"\"Benchmark ag strict subset operation.\n \"\"\"\n self.ag.is_strict_subset(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_is_strict_subset", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "4dff2b0102568196662f408ec7682e479b1f28f3c0811e1d09928f67803ff1fd", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_is_strict_superset": {"code": "class AtomGroupMethodsBench:\n def time_is_strict_superset(self, num_atoms):\n \"\"\"Benchmark ag strict superset operation.\n \"\"\"\n self.ag.is_strict_superset(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_is_strict_superset", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "2c81f4e274971df7fb627c5c8e82a821572d65175a56f86824b9a5d644170653", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_isdisjoint": {"code": "class AtomGroupMethodsBench:\n def time_isdisjoint(self, num_atoms):\n \"\"\"Benchmark disjoint operation between\n atomgroups.\n \"\"\"\n self.ag.isdisjoint(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_isdisjoint", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "9573a798d7d80b64066ae7114626b83b763312b43c4435eac392b8c0d3fb8a08", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_issubset": {"code": "class AtomGroupMethodsBench:\n def time_issubset(self, num_atoms):\n \"\"\"Benchmark subset operation between\n atomgroups.\n \"\"\"\n self.ag.issubset(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_issubset", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "f6bd03304591ed1af9f0654198f07148f408505a8ba8d1ec24c4613d5980686b", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_issuperset": {"code": "class AtomGroupMethodsBench:\n def time_issuperset(self, num_atoms):\n \"\"\"Benchmark superset operation between\n atomgroups.\n \"\"\"\n self.ag.issuperset(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_issuperset", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "da0c4343c5544f68e3ee19e45eb427df5f51ad6fdf3d3a1380d864a310e832f1", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_pack_into_box": {"code": "class AtomGroupMethodsBench:\n def time_pack_into_box(self, num_atoms):\n \"\"\"Benchmark shifting atoms of ag\n into primary unit cell, using\n default parameters.\n \"\"\"\n self.ag.pack_into_box()\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_pack_into_box", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "54f8934b8fe61ba797a7909743b108e39de31f183b1e1ae485cf0f6ce32462c7", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_rotate": {"code": "class AtomGroupMethodsBench:\n def time_rotate(self, num_atoms):\n \"\"\"Benchmark simple rotation operation\n on atomgroup.\n \"\"\"\n self.ag.rotate(self.rot_matrix)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_rotate", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "6e1749c80e708a81dc9b1c9e930f935e6c602721ffd2c8aaef3124f78895e3fc", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_rotateby": {"code": "class AtomGroupMethodsBench:\n def time_rotateby(self, num_atoms):\n \"\"\"Benchmark rotation by an angle\n of the ag coordinates.\n \"\"\"\n self.ag.rotateby(angle=45,\n axis=[1,0,0])\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_rotateby", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3a363526876a2804ba8aa888ffe51737d852a4f374b4afcea48089e448dd31cb", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_split": {"code": "class AtomGroupMethodsBench:\n def time_split(self, num_atoms):\n \"\"\"Benchmark ag splitting into\n multiple ags based on a simple\n criterion.\n \"\"\"\n self.ag.split('residue')\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_split", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "416f626f3c7231a69b7aeff75fba459fe50c0154712be099617e3bbb6e5916d4", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_subtract": {"code": "class AtomGroupMethodsBench:\n def time_subtract(self, num_atoms):\n \"\"\"Benchmark ag subtraction.\n \"\"\"\n self.ag.subtract(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_subtract", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "a850d565fa6aecc39128e9d35a85496508288f8b8989d9fdb21f77796d838786", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_symmetric_difference": {"code": "class AtomGroupMethodsBench:\n def time_symmetric_difference(self, num_atoms):\n \"\"\"Benchmark ag symmetric difference\n operation.\n \"\"\"\n self.ag.symmetric_difference(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_symmetric_difference", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "0f5ae06c5f76555fb6a63e41f85fd32373e10d49a309803a4b06be75e05ec0a9", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_transform": {"code": "class AtomGroupMethodsBench:\n def time_transform(self, num_atoms):\n \"\"\"Benchmark application of transformation\n matrix to atomgroup.\n \"\"\"\n self.ag.transform(self.trans)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_transform", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "6dc5031197d6b02edba0f8d4bd19cec798be5c5b740547575f7cade39a38b391", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_translate": {"code": "class AtomGroupMethodsBench:\n def time_translate(self, num_atoms):\n \"\"\"Benchmark the application of a\n translation vector to the ag\n coordinates.\n \"\"\"\n self.ag.translate([0,0.5,1])\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_translate", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "97a8195e6e8cd22f3b5cfa3a9f162d518b497d64e2cd734e91212b74e9620d3a", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_union": {"code": "class AtomGroupMethodsBench:\n def time_union(self, num_atoms):\n \"\"\"Benchmark union operation\n on atomgroups.\n \"\"\"\n self.ag.union(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_union", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3e19a73408a52eca4f12d1df1f083093bf51de80adc8a78f3448451658ae113b", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_wrap": {"code": "class AtomGroupMethodsBench:\n def time_wrap(self, num_atoms):\n \"\"\"Benchmark wrap() operation on\n atomgroup with default params.\n \"\"\"\n self.ag.wrap()\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_wrap", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "e17b327697d267cf044412cc544be3b2e309edad29b10d0ab5af64a7b847d269", "warmup_time": -1}, "ag_methods.CompoundSplitting.time_center_compounds": {"code": "class CompoundSplitting:\n def time_center_compounds(self, *args):\n self.u.atoms.center(None, compound='residues')\n\n def setup(self, n_atoms, n_compounds, homogeneous, contiguous):\n rg = np.random.Generator(np.random.MT19937(3000))\n \n # some parameter screening for nonsensical combinations.\n if n_compounds > n_atoms:\n raise NotImplementedError\n \n if n_compounds == 1 and not (homogeneous and contiguous):\n raise NotImplementedError\n \n if n_compounds == n_atoms:\n if not (homogeneous and contiguous):\n raise NotImplementedError\n compound_indices = np.arange(n_compounds)\n elif homogeneous:\n ats_per_compound, remainder = divmod(n_atoms, n_compounds)\n if remainder:\n raise NotImplementedError\n compound_indices = np.tile(np.arange(n_compounds),\n (ats_per_compound, 1)).T.ravel()\n else:\n compound_indices = np.sort(np.floor(rg.random(n_atoms)\n * n_compounds).astype(np.int))\n \n unique_indices = np.unique(compound_indices)\n if len(unique_indices) != n_compounds:\n raise RuntimeError\n \n if not contiguous:\n rg.shuffle(compound_indices)\n \n self.u = MDAnalysis.Universe.empty(n_atoms,\n n_residues=n_compounds,\n n_segments=1,\n atom_resindex=compound_indices,\n trajectory=True)\n self.u.atoms.positions = rg.random((n_atoms, 3),\n dtype=np.float32) * 100\n self.u.dimensions = [50, 50, 50, 90, 90, 90]", "min_run_count": 2, "name": "ag_methods.CompoundSplitting.time_center_compounds", "number": 0, "param_names": ["param1", "param2", "param3", "param4"], "params": [["100", "10000", "1000000"], ["1", "10", "100"], ["True", "False"], ["True", "False"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "1a2731a96f06d85a503ae24d79262851ff5c7c0755d17baf7819456d54075f17", "warmup_time": -1}, "ag_methods.FragmentCaching.time_find_cached_fragments": {"code": "class FragmentCaching:\n def time_find_cached_fragments(self, universe_type):\n frags = self.u.atoms.fragments\n\n def setup(self, universe_type):\n super(FragmentCaching, self).setup(universe_type)\n frags = self.u.atoms.fragments # Priming the cache", "min_run_count": 2, "name": "ag_methods.FragmentCaching.time_find_cached_fragments", "number": 0, "param_names": ["universe_type"], "params": [["'large_fragment_small_solvents'", "'large_fragment'", "'polymer_chains'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "e10a712eaf0f8c9a2489bc4f2dbc9677be29528aa3314aa5bcb814b05e4c5cd8", "warmup_time": -1}, "ag_methods.FragmentCaching.time_find_fragments": {"code": "class FragmentFinding:\n def time_find_fragments(self, universe_type):\n frags = self.u.atoms.fragments\n\nclass FragmentCaching:\n def setup(self, universe_type):\n super(FragmentCaching, self).setup(universe_type)\n frags = self.u.atoms.fragments # Priming the cache", "min_run_count": 2, "name": "ag_methods.FragmentCaching.time_find_fragments", "number": 0, "param_names": ["universe_type"], "params": [["'large_fragment_small_solvents'", "'large_fragment'", "'polymer_chains'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "9ee4ba9d30022cba05eab57475bff54164b9f4ffc8a37286663ae8befdd78225", "warmup_time": -1}, "ag_methods.FragmentFinding.time_find_fragments": {"code": "class FragmentFinding:\n def time_find_fragments(self, universe_type):\n frags = self.u.atoms.fragments\n\n def setup(self, universe_type):\n if universe_type == 'large_fragment_small_solvents':\n univ = (TPR, XTC)\n elif universe_type == 'large_fragment':\n univ = (PSF, DCD)\n else:\n univ = (TRZ_psf, TRZ)\n self.u = MDAnalysis.Universe(*univ)", "min_run_count": 2, "name": "ag_methods.FragmentFinding.time_find_fragments", "number": 0, "param_names": ["universe_type"], "params": [["'large_fragment_small_solvents'", "'large_fragment'", "'polymer_chains'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7629f34633e0ba4daad90ab0adc33965e3f05cd82382570a2941d46085851b39", "warmup_time": -1}, "analysis.distances.DistancesBench.time_between": {"code": "class DistancesBench:\n def time_between(self, num_atoms):\n \"\"\"Benchmark determination of subgroup\n of atomgroup that is within a specific\n distance of two other atomgroups.\n \"\"\"\n distances.between(\n group=self.ag3, A=self.ag1, B=self.ag2, distance=15.0\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_between", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "06923b19f8f155c8ef51227176b95f08c0b87f7f7f81ff6a72eaebe8b2927bca", "warmup_time": -1}, "analysis.distances.DistancesBench.time_calc_angles": {"code": "class DistancesBench:\n def time_calc_angles(self, num_atoms):\n \"\"\"Benchmark calculation of angles between\n atoms in three atomgroups.\n \"\"\"\n mda.lib.distances.calc_angles(\n self.coords_1, self.coords_2, self.coords_3\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_calc_angles", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3bf2d6564e727c2553e682fe2f6290e199915ad8f0ea5bd8d0dc8e6a35c8c193", "warmup_time": -1}, "analysis.distances.DistancesBench.time_calc_bonds": {"code": "class DistancesBench:\n def time_calc_bonds(self, num_atoms):\n \"\"\"Benchmark calculation of bonds between\n atoms in two atomgroups.\n \"\"\"\n mda.lib.distances.calc_bonds(self.coords_1, self.coords_2)\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_calc_bonds", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "c82f8a3b8b73aa472c1b332c89f7225cdc60f0a35fc6f526d818427e0c0f4260", "warmup_time": -1}, "analysis.distances.DistancesBench.time_calc_dihedrals": {"code": "class DistancesBench:\n def time_calc_dihedrals(self, num_atoms):\n \"\"\"Benchmark calculation of dihedrals between\n atoms in four atomgroups.\n \"\"\"\n mda.lib.distances.calc_dihedrals(\n self.coords_1, self.coords_2, self.coords_3, self.coords_4\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_calc_dihedrals", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "8e267b78a21ada5eaa0968be8444c971d3b72cdb1f245bdc8bdb0a8b41b66c65", "warmup_time": -1}, "analysis.distances.DistancesBench.time_contact_matrix": {"code": "class DistancesBench:\n def time_contact_matrix(self, num_atoms):\n \"\"\"Benchmark calculation of contacts within\n a single numpy array using the default arguments\n to contact_matrix.\n \"\"\"\n distances.contact_matrix(\n coord=self.coords_1, cutoff=15.0, returntype=\"numpy\", box=None\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_contact_matrix", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "c91c137bba8896a5e5f082a89a5f74a4250cd550276f5c0dd7e67099a3e4ce27", "warmup_time": -1}, "analysis.distances.DistancesBench.time_contact_matrix_sparse": {"code": "class DistancesBench:\n def time_contact_matrix_sparse(self, num_atoms):\n \"\"\"Benchmark calculation of contacts within\n a single numpy array using the slower reduced\n memory implementation of contact_matrix intended\n for larger systems.\n \"\"\"\n distances.contact_matrix(\n coord=self.coords_1, cutoff=15.0, returntype=\"sparse\", box=None\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_contact_matrix_sparse", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "90afc393d0d08334fd0a603ceb4c1a30e1d495c974d7fbf3e902b3d87afa19d1", "warmup_time": -1}, "analysis.distances.DistancesBench.time_dist": {"code": "class DistancesBench:\n def time_dist(self, num_atoms):\n \"\"\"Benchmark calculation of distances between\n atoms in two atomgroups with no offsets\n to resids.\n \"\"\"\n distances.dist(A=self.ag1, B=self.ag2, offset=0)\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_dist", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3663b232cc72f4cdece318f342aa8287da2493eca63495991d9421aab0dbb4a9", "warmup_time": -1}, "analysis.distances.DistancesBench.time_dist_offsets": {"code": "class DistancesBench:\n def time_dist_offsets(self, num_atoms):\n \"\"\"Benchmark calculation of distances between\n atoms in two atomgroups with offsets\n to resids.\n \"\"\"\n distances.dist(A=self.ag1, B=self.ag2, offset=20)\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_dist_offsets", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "87ed1892f89d366f02c57b9aa83c6fa277a3f9bec85750e08ef2ba0c5a385709", "warmup_time": -1}, "analysis.distances.DistancesBench.time_distance_array": {"code": "class DistancesBench:\n def time_distance_array(self, num_atoms):\n \"\"\"Benchmark calculation of all distances\n between two numpy arrays of coordinates,\n using default arguments to distance_array.\n \"\"\"\n distances.distance_array(\n reference=self.coords_1,\n configuration=self.coords_2,\n box=None,\n result=None,\n backend=\"serial\",\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_distance_array", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "423bb13d31eacb2b6e5d3f261552b10be8a2e7388ff95d71dc782c0c8731a23b", "warmup_time": -1}, "analysis.distances.DistancesBench.time_distance_array_pre_allocated": {"code": "class DistancesBench:\n def time_distance_array_pre_allocated(self, num_atoms):\n \"\"\"Benchmark calculation of all distances\n between two numpy arrays of coordinates,\n using distance_array with a preallocated\n result array.\n \"\"\"\n distances.distance_array(\n reference=self.coords_1,\n configuration=self.coords_2,\n box=None,\n result=self.allocated_array_2D,\n backend=\"serial\",\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_distance_array_pre_allocated", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "46dcac9910323321beb5396d4e413bb5975d6fa512b22bf0df6bcd873672d43e", "warmup_time": -1}, "analysis.distances.DistancesBench.time_self_distance_array": {"code": "class DistancesBench:\n def time_self_distance_array(self, num_atoms):\n \"\"\"Benchmark calculation of all distances\n within a single numpy array of coordinates\n using default parameters to self_distance_array.\n \"\"\"\n distances.self_distance_array(\n reference=self.coords_1, box=None, result=None, backend=\"serial\"\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_self_distance_array", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "5c8f51df8cbca15ce193bc4c18e14d5772ad7e53fc9f565cdc096b24308e6f3f", "warmup_time": -1}, "analysis.distances.DistancesBench.time_self_distance_array_pre_allocated": {"code": "class DistancesBench:\n def time_self_distance_array_pre_allocated(self, num_atoms):\n \"\"\"Benchmark calculation of all distances\n within a single numpy array of coordinates\n using self_distance_array with preallocated\n result array.\n \"\"\"\n distances.self_distance_array(\n reference=self.coords_1,\n box=None,\n result=self.allocated_array_1D,\n backend=\"serial\",\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_self_distance_array_pre_allocated", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "216345f414efc996a92de0ab4f74763fe20124827e2a57adf0e9bcc93ea05530", "warmup_time": -1}, "analysis.leaflet.LeafletBench.time_leafletfinder": {"code": "class LeafletBench:\n def time_leafletfinder(self, cutoff, sparse, pbc):\n \"\"\"Benchmark LeafletFinder for test lipid\n membrane system.\n \"\"\"\n leaflet.LeafletFinder(universe=self.u,\n select=self.headgroup_sel,\n cutoff=cutoff,\n pbc=pbc,\n sparse=sparse)\n\n def setup(self, cutoff, sparse, pbc):\n self.u = MDAnalysis.Universe(Martini_membrane_gro)\n self.headgroup_sel = 'name PO4'", "min_run_count": 2, "name": "analysis.leaflet.LeafletBench.time_leafletfinder", "number": 0, "param_names": ["cutoff", "sparse", "pbc"], "params": [["7.0", "15.0", "23.0"], ["None", "True", "False"], ["True", "False"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "2458c4484b875134228107f4bce8daa33aeb344a704e3af71edcbb03b58fc057", "warmup_time": -1}, "analysis.leaflet.LeafletOptimizeBench.time_optimize_cutoff": {"code": "class LeafletOptimizeBench:\n def time_optimize_cutoff(self, sparse, pbc):\n \"\"\"Benchmark optimize_cutoff for test lipid\n membrane system using default network distance\n range.\n \"\"\"\n leaflet.optimize_cutoff(universe=self.u,\n select=self.headgroup_sel,\n pbc=pbc,\n sparse=sparse)\n\n def setup(self, sparse, pbc):\n self.u = MDAnalysis.Universe(Martini_membrane_gro)\n self.headgroup_sel = 'name PO4'", "min_run_count": 2, "name": "analysis.leaflet.LeafletOptimizeBench.time_optimize_cutoff", "number": 0, "param_names": ["sparse", "pbc"], "params": [["None", "True", "False"], ["True", "False"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "73a3a56d78b0454ad8df949ea3929d5b1281bea8bc6afb57e61589b4d99c688c", "warmup_time": -1}, "analysis.psa.PSA_get_coord_axesBench.time_get_coord_axes": {"code": "class PSA_get_coord_axesBench:\n def time_get_coord_axes(self, time_steps, n_atoms):\n \"\"\"Benchmark get_coord_axes in psa module\n \"\"\"\n psa.get_coord_axes(path=self.path)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n # only using condensed path input\n # data structure for now\n self.path = np.random.rand(time_steps,\n n_atoms * 3)", "min_run_count": 2, "name": "analysis.psa.PSA_get_coord_axesBench.time_get_coord_axes", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "1000"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "86cb5086b8445a130e30b1506114ef9279166f177f3b89ad2641253d58117f93", "warmup_time": -1}, "analysis.psa.PSA_get_msd_matrixBench.time_get_msd_matrix": {"code": "class PSA_get_msd_matrixBench:\n def time_get_msd_matrix(self, time_steps, n_atoms):\n \"\"\"Benchmark for get_msd_matrix in psa module\n \"\"\"\n # only default argument for axis is benchmarked\n psa.get_msd_matrix(P=self.P,\n Q=self.Q,\n axis=None)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_get_msd_matrixBench.time_get_msd_matrix", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "1000"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "9dcdda75f655ab20762a5a9c171d804a3f908f3b76043f3fb66e1c5e471aa2c0", "warmup_time": -1}, "analysis.psa.PSA_get_path_metric_funcBench.time_get_path_metric_func": {"code": "class PSA_get_path_metric_funcBench:\n def time_get_path_metric_func(self, path_metric):\n \"\"\"Benchmark for get_path_metric_func in psa\n module\n \"\"\"\n psa.get_path_metric_func(name=path_metric)", "min_run_count": 2, "name": "analysis.psa.PSA_get_path_metric_funcBench.time_get_path_metric_func", "number": 0, "param_names": ["path_metric"], "params": [["'hausdorff'", "'weighted_average_hausdorff'", "'average_hausdorff'", "'hausdorff_neighbors'", "'discrete_frechet'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "fc71d48ec5e111b5f38e7d741ab5f63fc3c8615325be6916878225ada0d3e828", "warmup_time": -1}, "analysis.psa.PSA_metricBench.time_discrete_frechet": {"code": "class PSA_metricBench:\n def time_discrete_frechet(self, time_steps, n_atoms):\n \"\"\"Benchmark for discrete_frechet() in psa module.\n \"\"\"\n psa.discrete_frechet(P=self.P,\n Q=self.Q)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_metricBench.time_discrete_frechet", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "200"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3bb3d861752ef9ed6f77ac94cad9ed2e42c56d8df988a838892b313d1104b7cb", "warmup_time": -1}, "analysis.psa.PSA_metricBench.time_hausdorff": {"code": "class PSA_metricBench:\n def time_hausdorff(self, time_steps, n_atoms):\n \"\"\"Benchmark for hausdorff() in psa module.\n \"\"\"\n psa.hausdorff(P=self.P,\n Q=self.Q)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_metricBench.time_hausdorff", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "200"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "e0c90539a3b87665619c0c5923a412b1457f5cf08564b891b2ea25345fc720fc", "warmup_time": -1}, "analysis.psa.PSA_metricBench.time_hausdorff_avg": {"code": "class PSA_metricBench:\n def time_hausdorff_avg(self, time_steps, n_atoms):\n \"\"\"Benchmark for hausdorff_avg() in psa module.\n \"\"\"\n psa.hausdorff_avg(P=self.P,\n Q=self.Q)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_metricBench.time_hausdorff_avg", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "200"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "bda335edfdd48be1e40d51ba67bd57127dc22570ed81c79b25c9c34e98073cad", "warmup_time": -1}, "analysis.psa.PSA_metricBench.time_hausdorff_neighbors": {"code": "class PSA_metricBench:\n def time_hausdorff_neighbors(self, time_steps, n_atoms):\n \"\"\"Benchmark for hausdorff_neighbors() in psa module.\n \"\"\"\n psa.hausdorff_neighbors(P=self.P,\n Q=self.Q)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_metricBench.time_hausdorff_neighbors", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "200"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "77321a9a92915cf788b65037e075327c72374c4f6dd8a105749c502756260a7a", "warmup_time": -1}, "analysis.psa.PSA_metricBench.time_hausdorff_wavg": {"code": "class PSA_metricBench:\n def time_hausdorff_wavg(self, time_steps, n_atoms):\n \"\"\"Benchmark for hausdorff_wavg() in psa module.\n \"\"\"\n psa.hausdorff_wavg(P=self.P,\n Q=self.Q)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_metricBench.time_hausdorff_wavg", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "200"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "162c1711b4953d5045859022df8b1477ad41b166cf80dc31e0b961c87761ed81", "warmup_time": -1}, "analysis.psa.PSA_sqnormBench.time_sqnorm": {"code": "class PSA_sqnormBench:\n def time_sqnorm(self, num_cols, num_rows, axis):\n \"\"\"Benchmark sqnorm in psa module\n \"\"\"\n psa.sqnorm(v=self.v, axis=axis)\n\n def setup(self, num_cols, num_rows, axis):\n np.random.seed(170089)\n self.v = np.random.rand(num_rows, num_cols)", "min_run_count": 2, "name": "analysis.psa.PSA_sqnormBench.time_sqnorm", "number": 0, "param_names": ["num_cols", "num_rows", "axis"], "params": [["2", "3", "4"], ["100", "1000", "10000"], ["None", "0", "1", "-1"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "5360f9d9650b651e056f22f98fd02bda2307b2f2eabb3fbe7c1c4b0dffe5f70b", "warmup_time": -1}, "analysis.rdf.SimpleRdfBench.time_interrdf": {"code": "class SimpleRdfBench:\n def time_interrdf(self, nbins, range_val, natoms):\n \"\"\"Benchmark a full trajectory parse\n by MDAnalysis.analysis.rdf.InterRDF\n \"\"\"\n self.rdf.run()\n\n def setup(self, nbins, range_val, natoms):\n \n self.sel_str = 'name OW'\n \n self.u = MDAnalysis.Universe(TPR, XTC)\n \n try:\n self.sel = self.u.select_atoms(self.sel_str)[:natoms]\n except AttributeError:\n self.sel = self.u.selectAtoms(self.sel_str)[:natoms]\n \n # do not include initialization of the\n # InterRDF object in the benchmark itself\n \n self.rdf = InterRDF(g1=self.sel,\n g2=self.sel,\n nbins=nbins,\n range=range_val)", "min_run_count": 2, "name": "analysis.rdf.SimpleRdfBench.time_interrdf", "number": 0, "param_names": ["nbins", "range_val", "natoms"], "params": [["20", "75", "200"], ["[0, 5]", "[0, 15]", "[0, 20]"], ["1", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "834273d73c592c18978de979c278573fc8d7997972d1e690ed8efbbeadf8ca08", "warmup_time": -1}, "analysis.rms.RmsdTrajBench.time_RMSD": {"code": "class RmsdTrajBench:\n def time_RMSD(self, select, weights):\n \"\"\"Benchmark RMSD.run() method, which parses\n over the entire trajectory.\n \"\"\"\n self.RMSD_inst.run()\n\n def setup(self, select, weights):\n self.u = MDAnalysis.Universe(PSF, DCD)\n self.RMSD_inst = rms.RMSD(atomgroup=self.u,\n reference=None,\n select=select,\n weights=weights)", "min_run_count": 2, "name": "analysis.rms.RmsdTrajBench.time_RMSD", "number": 0, "param_names": ["select", "weights"], "params": [["'all'", "'backbone'"], ["None", "'mass'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "64836ac01572a5047b1dd99fff0059e19db814e8d869fba0f667e8ce391dab94", "warmup_time": -1}, "analysis.rms.RmsfTrajBench.time_RMSF": {"code": "class RmsfTrajBench:\n def time_RMSF(self, n_atoms, step, weights):\n \"\"\"Benchmark RMSF.run() method, which parses\n over the entire trajectory.\n \"\"\"\n self.RMSF_inst.run(step=step)\n\n def setup(self, n_atoms, step, weights):\n self.u = MDAnalysis.Universe(PSF, DCD)\n self.ag = self.u.atoms[:n_atoms]\n self.RMSF_inst = rms.RMSF(atomgroup=self.ag,\n weights=weights)", "min_run_count": 2, "name": "analysis.rms.RmsfTrajBench.time_RMSF", "number": 0, "param_names": ["n_atoms", "step", "weights"], "params": [["100", "500", "2000"], ["None", "3"], ["None", "'mass'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "99e0776f38969e49506709a4f7297c981a8d81c0febde80cbbc90d2f0f163b4c", "warmup_time": -1}, "analysis.rms.SimpleRmsBench.time_rmsd": {"code": "class SimpleRmsBench:\n def time_rmsd(self, num_atoms, weights, center, superposition):\n \"\"\"Benchmark rmsd function using a setup similar to\n its docstring example code along with several possible\n permutations of parameters.\n \"\"\"\n rms.rmsd(a=self.A,\n b=self.B,\n weights=weights,\n center=center,\n superposition=superposition)\n\n def setup(self, num_atoms, weights, center, superposition):\n # mimic rmsd docstring example code\n self.u = MDAnalysis.Universe(PSF, DCD)\n # ag.positions is the new syntax\n # but older commit hashes will need to use\n # ag.coordinates()\n try:\n self.A = self.u.atoms.positions.copy()[:num_atoms]\n self.u.trajectory[-1]\n self.B = self.u.atoms.positions.copy()[:num_atoms]\n except:\n self.A = self.u.atoms.positions.copy()[:num_atoms]\n self.u.trajectory[-1]\n self.B = self.u.atoms.positions.copy()[:num_atoms]", "min_run_count": 2, "name": "analysis.rms.SimpleRmsBench.time_rmsd", "number": 0, "param_names": ["num_atoms", "weights", "center", "superposition"], "params": [["100", "500", "2000"], ["None", "[1.0, 0.5]"], ["False", "True"], ["False", "True"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "bda0e78ff13a61dce070b3eddef389263aef31ccdd42ddb3a643bfacf8264b02", "warmup_time": -1}, "import.ImportBench.time_import": {"code": "class ImportBench:\n def time_import(self):\n \"\"\"Benchmark time needed to import MDAnalysis\n \"\"\"\n import MDAnalysis as mda\n pass", "min_run_count": 2, "name": "import.ImportBench.time_import", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "61f437034b6040b9faf8e324049e743318ff56255c98cd868a547bac5c256124", "warmup_time": -1}, "selections.GeoSelectionBench.time_geometric_selections": {"code": "class GeoSelectionBench:\n def time_geometric_selections(self,\n selection_string,\n dynamic_selection,\n periodic_selection):\n \n # TODO: Do we need a kwarg similar to old `use_KDTree_routines`\n # flag? We used to benchmark that.\n self.u.select_atoms(selection_string,\n updating=dynamic_selection,\n periodic=periodic_selection[0],\n )\n\n def setup(self,\n selection_string,\n dynamic_selection,\n periodic_selection):\n self.u = MDAnalysis.Universe(GRO)", "min_run_count": 2, "name": "selections.GeoSelectionBench.time_geometric_selections", "number": 0, "param_names": ["selection_string", "dynamic_selection", "periodic_selection"], "params": [["'around 5.0 resid 1'", "'sphlayer 2.4 6.0 (protein)'", "'sphzone 6.0 (protein)'", "'cylayer 5 10 10 -8 protein'", "'cyzone 15 4 -8 protein'", "'point 5.0 5.0 5.0 3.5'", "'prop z >= 5.0'", "'prop abs z <= 5.0'"], ["True", "False"], ["[False, True]", "[True, False]"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3e43b2b234945b3a318c65087db5dda171f737beaec83199d0461b3ea3b0d93e", "warmup_time": -1}, "selections.SimpleSelectionBench.time_simple_selections": {"code": "class SimpleSelectionBench:\n def time_simple_selections(self, selection_string):\n \"\"\"Benchmark simple selections on the protein-based\n standard test GRO file.\n \"\"\"\n if hasattr(MDAnalysis.Universe, 'select_atoms'):\n self.u.select_atoms(selection_string)\n else:\n self.u.selectAtoms(selection_string)\n\n def setup(self, selection_string):\n self.u = MDAnalysis.Universe(GRO)", "min_run_count": 2, "name": "selections.SimpleSelectionBench.time_simple_selections", "number": 0, "param_names": ["selection_string"], "params": [["'protein'", "'backbone'", "'nucleic'", "'nucleicbackbone'", "'resid 1:10'", "'resnum 1:10'", "'resname LYS'", "'name CA'", "'bynum 0:10'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3d7336e8388a8daf7d76bc3faec4ee662f2ddce17529d2b322ec539be91758c3", "warmup_time": -1}, "topology.BondsBench.time_bonds": {"code": "class BondsBench:\n def time_bonds(self, num_bonds):\n \"\"\"Benchmark for calculating bonds\"\"\"\n self.u.bonds\n\n def setup(self, num_bonds):\n self.u = MDAnalysis.Universe.empty(2*num_bonds)\n bonds = np.arange(2*num_bonds).reshape(num_bonds, 2)\n self.u.add_bonds(bonds)", "min_run_count": 2, "name": "topology.BondsBench.time_bonds", "number": 0, "param_names": ["num_bonds"], "params": [["1000", "10000", "100000", "1000000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "53f46341b02a43024f5c677dcdfef05390e63757bfb308dc62104475abb08dc9", "warmup_time": -1}, "topology.TopologyGuessBench.time_guessbonds": {"code": "class TopologyGuessBench:\n def time_guessbonds(self, num_atoms):\n \"\"\"Benchmark for guessing bonds\"\"\"\n guessers.guess_bonds(self.ag, self.ag.positions,\n box=self.ag.dimensions,\n vdwradii=self.vdwradii)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}", "min_run_count": 2, "name": "topology.TopologyGuessBench.time_guessbonds", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "c8b85b9228a93b83692b65e61c7a9fdd017ca22d7702f18034d1a5f2c068bcb7", "warmup_time": -1}, "traj_reader.TrajReaderCreation.time_reads": {"code": "class TrajReaderCreation:\n def time_reads(self, traj_format):\n \"\"\"Simple benchmark for reading traj file formats\n from our standard test files.\n \"\"\"\n self.traj_reader(self.traj_file)\n\n def setup(self, traj_format):\n self.traj_dict = traj_dict\n self.traj_file, self.traj_reader = self.traj_dict[traj_format]", "min_run_count": 2, "name": "traj_reader.TrajReaderCreation.time_reads", "number": 0, "param_names": ["traj_format"], "params": [["'XTC'", "'TRR'", "'DCD'", "'NCDF'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "4b030c7c2af116b5eaaec4c77913ed39dda95d0f355f11be9ee99403dfa71f93", "warmup_time": -1}, "traj_reader.TrajReaderIteration.time_strides": {"code": "class TrajReaderIteration:\n def time_strides(self, traj_format):\n \"\"\"Benchmark striding over full trajectory\n test files for each format.\n \"\"\"\n for ts in self.reader_object:\n pass\n\n def setup(self, traj_format):\n self.traj_dict = traj_dict\n self.traj_file, self.traj_reader = self.traj_dict[traj_format]\n self.reader_object = self.traj_reader(self.traj_file)", "min_run_count": 2, "name": "traj_reader.TrajReaderIteration.time_strides", "number": 0, "param_names": ["traj_format"], "params": [["'XTC'", "'TRR'", "'DCD'", "'NCDF'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "d2890f87c636b5336c33c4ede5c97f90f26b52ea890f4514459dee27de3b92fc", "warmup_time": -1}}, "machines": {"gp160": {"arch": "x86_64", "cpu": "Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz", "machine": "gp160", "os": "Linux 4.10.0-35-generic", "ram": "128GB", "version": 1}, "c3potato": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "os": "Linux 3.16.0-4-amd64", "ram": "16GB", "version": 1}}, "tags": {"RotamerConvolveMD-1.0": 1133, "data-0-7-4": 605, "package-2.1.0": 15591, "package-2.2.0": 16644, "package-2.3.0": 17472, "package-2.4.0": 18298, "package-2.4.0b1": 18296, "package-2.4.1": 18309, "package-2.4.2": 18401, "package-2.4.3": 19188, "package-2.5.0": 19486, "package-2.6.0": 20008, "package-2.6.1": 20157, "package-2.7.0": 20792, "pre-release-0-7-0-rc3": 350, "pre-sub-trr": 964, "prerelease-0-6-0-rc2": 152, "prerelease-0-6-rc1": 139, "prerelease-0.8.0-pre-1": 1046, "rel-0-5-0": 82, "release-0-5-1": 104, "release-0-6-0": 154, "release-0-6-1": 170, "release-0-6-2": 205, "release-0-6-3": 256, "release-0-6-4": 283, "release-0-6-4-1": 289, "release-0-7-0": 381, "release-0-7-1": 429, "release-0-7-2": 483, "release-0-7-3": 554, "release-0-7-4": 604, "release-0.10.0": 1482, "release-0.11.0": 1834, "release-0.12.0": 1979, "release-0.12.1": 2011, "release-0.13.0": 2515, "release-0.14.0": 2925, "release-0.15.0": 3265, "release-0.16.0": 5063, "release-0.16.1": 5323, "release-0.16.2": 5542, "release-0.17.0": 6680, "release-0.18.0": 7138, "release-0.19.0": 8272, "release-0.19.1": 8344, "release-0.19.2": 8351, "release-0.20.0": 9175, "release-0.20.1": 9194, "release-0.7.5": 680, "release-0.7.5.1": 698, "release-0.7.6": 810, "release-0.8.0": 1142, "release-0.8.0rc2": 1096, "release-0.8.0rc3": 1105, "release-0.8.0rc4": 1122, "release-0.8.1": 1188, "release-0.8.1rc1": 1163, "release-0.9.0": 1337, "release-0.9.1": 1357, "release-0.9.2": 1389, "release-1.0.0": 11186, "release-1.0.1": 13177, "release-1.1.0": 13996, "release-1.1.1": 14043, "release-2.0.0": 14943, "release-2.0.0-beta": 14620, "release-2.1.0": 15589, "release-2.2.0": 16641, "release-2.3.0": 17472, "release-2.4.0": 18294, 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1698428844000, "20501": 1698694861000, "20504": 1698775295000, "20521": 1699129404000, "20539": 1699176277000, "20542": 1699182336000, "20553": 1699259466000, "20557": 1699014347000, "20558": 1699014939000, "20564": 1699351607000, "20568": 1699440505000, "20585": 1699708548000, "20618": 1701595226000, "20621": 1701817765000, "20646": 1702279165000, "20652": 1702319363000, "20655": 1702345284000, "20662": 1702606235000, "20668": 1702671939000, "20685": 1702745963000, "20686": 1702746067000, "20688": 1702746519000, "20693": 1702805701000, "20702": 1702835318000, "20750": 1703116999000, "20756": 1703149858000, "20763": 1703228973000, "20771": 1703345713000, "20778": 1703424420000, "20785": 1703613580000, "20789": 1703619619000, "20792": 1703622753000, "20794": 1703633462000, "20798": 1703635433000, "20802": 1703635776000, "20842": 1704139752000, "20847": 1704240218000, "20851": 1704319053000, "20878": 1704836160000, "20883": 1704991848000, "20940": 1705530480000, "20950": 1705576431000, "20959": 1705788311000, "20978": 1706097384000, "21008": 1706645843000, "21015": 1706651360000, "21044": 1707062668000, "21045": 1707062717000, "21072": 1707743962000, "21119": 1708669165000, "21124": 1708673666000, "21158": 1708797985000, "21184": 1709582288000, "21186": 1709584578000, "21198": 1710089542000, "21202": 1710090427000, "21210": 1710193651000, "21260": 1711408213000, "21316": 1711657260000, "21325": 1711725922000, "21329": 1711740375000, "21334": 1711746802000, "21336": 1711748394000, "21353": 1711878779000, "21357": 1711941055000, "21375": 1712012663000, "21429": 1713708970000, "21431": 1713709337000, "21434": 1713767253000, "21436": 1713768580000, "21452": 1714330364000, "21453": 1714330587000, "21497": 1715173256000, "21521": 1716509748000, "21524": 1716763690000}, "params": {"Cython": ["", "0.27"], "MDAnalysisTests": ["", null], "arch": ["x86_64"], "cpu": ["Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz", "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz"], "machine": ["c3potato", "gp160"], "mock": ["", null], "nose": ["", null], "numpy": [""], "os": ["Linux 3.16.0-4-amd64", "Linux 4.10.0-35-generic"], "psutil": ["", null], "pytest": ["", null], "python": ["2.7", "3.11", "3.6", "3.8", "3.9"], "ram": ["128GB", "16GB"], "scipy": [""], "six": ["", null], "mdanalysistests": ["", null], "branch": ["develop"]}, "graph_param_list": [{"Cython": "", "MDAnalysisTests": "", "arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "mock": "", "nose": "", "numpy": "", "os": "Linux 3.16.0-4-amd64", "psutil": "", "pytest": "", "python": "2.7", "ram": "16GB", "scipy": "", "six": "", "branch": "develop", "mdanalysistests": null}, {"Cython": "0.27", "arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "mdanalysistests": "", "numpy": "", "os": "Linux 3.16.0-4-amd64", "python": "2.7", "ram": "16GB", "scipy": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "os": "Linux 3.16.0-4-amd64", "ram": "16GB", "python": "3.11", "Cython": "", "numpy": "", "scipy": "", "mdanalysistests": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "os": "Linux 3.16.0-4-amd64", "ram": "16GB", "python": "3.8", "Cython": "", "numpy": "", "scipy": "", "mdanalysistests": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}, {"Cython": "", "arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "mdanalysistests": "", "numpy": "", "os": "Linux 3.16.0-4-amd64", "python": "2.7", "ram": "16GB", "scipy": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "os": "Linux 3.16.0-4-amd64", "ram": "16GB", "python": "3.6", "Cython": "", "mdanalysistests": "", "numpy": "", "scipy": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "os": "Linux 3.16.0-4-amd64", "ram": "16GB", "python": "3.9", "Cython": "", "numpy": "", "scipy": "", "mdanalysistests": "", "branch": "develop", "MDAnalysisTests": null, "mock": null, "nose": null, "psutil": null, "pytest": null, "six": null}], "benchmarks": {"GRO.GROReadBench.time_create_GRO_universe": {"code": "class GROReadBench:\n def time_create_GRO_universe(self):\n \"\"\"Time to create MDA Universe of GRO\"\"\"\n u = mda.Universe(GRO)", "min_run_count": 2, "name": "GRO.GROReadBench.time_create_GRO_universe", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "85e9386a1154d866631322425b43e682903a28a89efb74527110189dcbd24a4a", "warmup_time": -1}, "GRO.GROReadBench.time_parse_GRO_file": {"code": "class GROReadBench:\n def time_parse_GRO_file(self):\n \"\"\"Time to create topology from GRO file\"\"\"\n p = GROParser(GRO)\n top = p.parse()", "min_run_count": 2, "name": "GRO.GROReadBench.time_parse_GRO_file", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7d0bec284a3cc88727c887e20b4d7a7a6fb3dc8b9a3fed98a8bee728f5920227", "warmup_time": -1}, "GRO.GROReadBench.time_read_GRO_coordinates": {"code": "class GROReadBench:\n def time_read_GRO_coordinates(self):\n \"\"\"Benchmark reading of standard testsuite GRO file.\"\"\"\n GROReader(GRO)", "min_run_count": 2, "name": "GRO.GROReadBench.time_read_GRO_coordinates", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "987e7a4b2431ce8645e1a4d7c1135f76c9b35e5eb29b0f6818860f96531be147", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_angle": {"code": "class AtomGroupAttrsBench:\n def time_angle(self, num_atoms):\n \"\"\"Benchmark simple angle\n calculation. Requires ag\n with three atoms.\n \"\"\"\n self.ag[:3].angle\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_angle", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "a83a2ce99c0673f57ac3eac16b49a9db026ae64b6fcaa4389bf47eed59124dbb", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_atomcount": {"code": "class AtomGroupAttrsBench:\n def time_atomcount(self, num_atoms):\n \"\"\"Benchmark counting of atoms in\n atomgroup.\n \"\"\"\n self.ag.n_atoms\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_atomcount", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "d31f8341e31a7bc198133f258c72536b54ec904f97ac4f28a767f9970856818a", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_atoms": {"code": "class AtomGroupAttrsBench:\n def time_atoms(self, num_atoms):\n \"\"\"Benchmark returning of identical\n atomgroup.\n \"\"\"\n self.ag.atoms\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_atoms", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "6cd2f945901dc324d1dabcb388bc3fe0c3b5977d49056e5d7a7859c40e2c9af8", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_bond": {"code": "class AtomGroupAttrsBench:\n def time_bond(self, num_atoms):\n \"\"\"Benchmark Bond object creation.\n Requires ag of size 2.\n \"\"\"\n self.ag[:2].bond\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_bond", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3507b8355b5b01697e086a646c559412d09fde51fc7139f4524f7cb58aa7ae67", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_dihedral": {"code": "class AtomGroupAttrsBench:\n def time_dihedral(self, num_atoms):\n \"\"\"Benchmark Dihedral object\n creation time. Requires ag of\n size 4.\n \"\"\"\n self.ag[:4].dihedral\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_dihedral", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "5775f1a2f7666febcc47f94b77702bd75a2a7ccd7c9044265c965dbad779a351", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_forces": {"code": "class AtomGroupAttrsBench:\n def time_forces(self, num_atoms):\n \"\"\"Benchmark atomgroup force\n calculation.\n \"\"\"\n try:\n self.ag.forces\n except NoDataError:\n pass\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_forces", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "0186fc73e76b8e3f8cb39e7e11f7c6e9837d800b2379b14591432ee9e710d69e", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_improper": {"code": "class AtomGroupAttrsBench:\n def time_improper(self, num_atoms):\n \"\"\"Benchmark improper dihedral\n calculation. Requires ag of size\n 4.\n \"\"\"\n self.ag[:4].improper\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_improper", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "d7ba217b21841225ff1df0b473d66794db6b2c92c56bbb3cb04c042fb43e967c", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_indices": {"code": "class AtomGroupAttrsBench:\n def time_indices(self, num_atoms):\n \"\"\"Benchmark atom index calculation.\n \"\"\"\n self.ag.ix\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_indices", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "8a27cd7f019ca3f35aa37d112e72f6b0311c37dee14a33adff3dffd8b6ee2602", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_positions": {"code": "class AtomGroupAttrsBench:\n def time_positions(self, num_atoms):\n \"\"\"Benchmark returning the positions\n of the atoms in the group.\n \"\"\"\n self.ag.positions\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_positions", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7f538e5ccf7c68b715cb9ba1f0a85764faa5e09216ba83be3b88804f26504dcd", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_residuecount": {"code": "class AtomGroupAttrsBench:\n def time_residuecount(self, num_atoms):\n \"\"\"Benchmark counting of residues in\n atomgroup.\n \"\"\"\n self.ag.n_residues\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_residuecount", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "6ba8023af1d40da84022b2eba1e494b20eb80d1257fa5beabc2206f8d380b237", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_residues": {"code": "class AtomGroupAttrsBench:\n def time_residues(self, num_atoms):\n \"\"\"Benchmark creation of the ResidueGroup\n from the AtomGroup.\n \"\"\"\n self.ag.residues\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_residues", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "72655f2bf91db254ada1b26646da826894a223384a4691f99b06b291107b938e", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_segmentcount": {"code": "class AtomGroupAttrsBench:\n def time_segmentcount(self, num_atoms):\n \"\"\"Benchmark counting of segments in\n atomgroup.\n \"\"\"\n self.ag.n_segments\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_segmentcount", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "ea9ccc59b78a3786c8ed142a014c476162b2d37437c8f93a4b05644dd851aede", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_segments": {"code": "class AtomGroupAttrsBench:\n def time_segments(self, num_atoms):\n \"\"\"Benchmark determination of sorted\n SegmentGroup from AtomGroup.\n \"\"\"\n self.ag.segments\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_segments", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "ec21432839b2caac695337844d8b3feff546e74083cc61d846692523160bcf70", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_ts": {"code": "class AtomGroupAttrsBench:\n def time_ts(self, num_atoms):\n \"\"\"Benchmark returning of a timestep\n instance from atomgroup.\n \"\"\"\n self.ag.ts\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_ts", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "0aadba3b29a020c1edbc55253049c8f9dc252759321e51f110ca895d99ce52a6", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_unique": {"code": "class AtomGroupAttrsBench:\n def time_unique(self, num_atoms):\n \"\"\"Benchmark determination of unique\n elements in atomgroup.\n \"\"\"\n self.ag.unique\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_unique", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "1c2345530d78545ab7011e17420325ec0f2b12a472ade667a7253fd672a06f70", "warmup_time": -1}, "ag_methods.AtomGroupAttrsBench.time_velocity": {"code": "class AtomGroupAttrsBench:\n def time_velocity(self, num_atoms):\n \"\"\"Benchmark atomgroup velocity\n values return.\n \"\"\"\n try:\n self.ag.velocities\n except NoDataError:\n pass\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]", "min_run_count": 2, "name": "ag_methods.AtomGroupAttrsBench.time_velocity", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3841e8cfb0518bdc257c0479f3844b8bdf80ee1e2bf7b2245ba4ba95533e2ce8", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_bbox_no_pbc": {"code": "class AtomGroupMethodsBench:\n def time_bbox_no_pbc(self, num_atoms):\n \"\"\"Benchmark bounding box calculation\n with pbc inactive.\n \"\"\"\n self.ag.bbox(pbc=False)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_bbox_no_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "f4d3c92e8dc5efa1a09726e3c1dea49453d5625ee6952475869546919575a0ed", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_bbox_pbc": {"code": "class AtomGroupMethodsBench:\n def time_bbox_pbc(self, num_atoms):\n \"\"\"Benchmark bounding box calculation\n with pbc active.\n \"\"\"\n self.ag.bbox(pbc=True)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_bbox_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "02deed74348541e19fd7811f9aa232bd53410903de3a1432d7cc090a3184198e", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_bsphere_no_pbc": {"code": "class AtomGroupMethodsBench:\n def time_bsphere_no_pbc(self, num_atoms):\n \"\"\"Benchmark bounding sphere calculation\n with pbc inactive.\n \"\"\"\n self.ag.bsphere(pbc=False)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_bsphere_no_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7f229e23767623d3052357b0d2e36620742990cbec01fa9df982997a64d0b558", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_bsphere_pbc": {"code": "class AtomGroupMethodsBench:\n def time_bsphere_pbc(self, num_atoms):\n \"\"\"Benchmark bounding sphere calculation\n with pbc active.\n \"\"\"\n self.ag.bsphere(pbc=True)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_bsphere_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "669f068c60e5023f28ea98225386244efdd0907ea6895c590088cfd170bef050", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_center_no_pbc": {"code": "class AtomGroupMethodsBench:\n def time_center_no_pbc(self, num_atoms):\n \"\"\"Benchmark center calculation with\n pbc inactive.\n \"\"\"\n self.ag.center(weights=self.weights,\n pbc=False)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_center_no_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "5499bbde1d376804c4c410b7cac45ebc709dc4065d374891ccaca4c67b497d8d", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_center_pbc": {"code": "class AtomGroupMethodsBench:\n def time_center_pbc(self, num_atoms):\n \"\"\"Benchmark center calculation with\n pbc active.\n \"\"\"\n self.ag.center(weights=self.weights,\n pbc=True)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_center_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "9e281b5399aef60e0a823b2a228dee94c0e31bd95d8806c1e15b861cf23d83b6", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_centroid_no_pbc": {"code": "class AtomGroupMethodsBench:\n def time_centroid_no_pbc(self, num_atoms):\n \"\"\"Benchmark centroid calculation with\n pbc inactive.\n \"\"\"\n self.ag.centroid(pbc=False)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_centroid_no_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "56366be992afa8468328495085d4bd7f270fe821af6b4412e097e071a5841aba", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_centroid_pbc": {"code": "class AtomGroupMethodsBench:\n def time_centroid_pbc(self, num_atoms):\n \"\"\"Benchmark centroid calculation with\n pbc active.\n \"\"\"\n self.ag.centroid(pbc=True)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_centroid_pbc", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7ce43af0540e7c4b35689a263dfc228d0a4a2d2d92727d2d44b1e397c32a8726", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_concatenate": {"code": "class AtomGroupMethodsBench:\n def time_concatenate(self, num_atoms):\n \"\"\"Benchmark atomgroup concatenation.\n \"\"\"\n self.ag.concatenate(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_concatenate", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "d5249c87dfce2c91bde201106b2e4198849051c93364564de86ce872159c1428", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_difference": {"code": "class AtomGroupMethodsBench:\n def time_difference(self, num_atoms):\n \"\"\"Benchmark atomgroup difference\n operation.\n \"\"\"\n self.ag.difference(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_difference", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "56ce3f2f2c4257dee720e7b07315ce2595afc66137f7324b405ebfb060e65dd3", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_groupby": {"code": "class AtomGroupMethodsBench:\n def time_groupby(self, num_atoms):\n \"\"\"Benchmark atomgroup groupby\n operation.\n \"\"\"\n self.ag.groupby('resnames')\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_groupby", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "e42dfd364b2726e568511c4eae084feead8bd62e3ac3d2046ca6c0ae2a061363", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_guess_bonds": {"code": "class AtomGroupMethodsBench:\n def time_guess_bonds(self, num_atoms):\n \"\"\"Benchmark atomgroup bond guessing\n with artificially-seeded vdw values.\n \"\"\"\n self.ag.guess_bonds(self.vdwradii)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_guess_bonds", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "b22111561b487e88b079412e82b48de4d70d39b0c868ec64305cebdcdf3ecd0b", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_intersection": {"code": "class AtomGroupMethodsBench:\n def time_intersection(self, num_atoms):\n \"\"\"Benchmark ag intersection.\n \"\"\"\n self.ag.intersection(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_intersection", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "af81d67c8ba6789db6632156d5ee37d16c1d7145bc51d97faf62f5a44e71a216", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_is_strict_subset": {"code": "class AtomGroupMethodsBench:\n def time_is_strict_subset(self, num_atoms):\n \"\"\"Benchmark ag strict subset operation.\n \"\"\"\n self.ag.is_strict_subset(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_is_strict_subset", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "4dff2b0102568196662f408ec7682e479b1f28f3c0811e1d09928f67803ff1fd", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_is_strict_superset": {"code": "class AtomGroupMethodsBench:\n def time_is_strict_superset(self, num_atoms):\n \"\"\"Benchmark ag strict superset operation.\n \"\"\"\n self.ag.is_strict_superset(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_is_strict_superset", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "2c81f4e274971df7fb627c5c8e82a821572d65175a56f86824b9a5d644170653", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_isdisjoint": {"code": "class AtomGroupMethodsBench:\n def time_isdisjoint(self, num_atoms):\n \"\"\"Benchmark disjoint operation between\n atomgroups.\n \"\"\"\n self.ag.isdisjoint(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_isdisjoint", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "9573a798d7d80b64066ae7114626b83b763312b43c4435eac392b8c0d3fb8a08", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_issubset": {"code": "class AtomGroupMethodsBench:\n def time_issubset(self, num_atoms):\n \"\"\"Benchmark subset operation between\n atomgroups.\n \"\"\"\n self.ag.issubset(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_issubset", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "f6bd03304591ed1af9f0654198f07148f408505a8ba8d1ec24c4613d5980686b", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_issuperset": {"code": "class AtomGroupMethodsBench:\n def time_issuperset(self, num_atoms):\n \"\"\"Benchmark superset operation between\n atomgroups.\n \"\"\"\n self.ag.issuperset(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_issuperset", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "da0c4343c5544f68e3ee19e45eb427df5f51ad6fdf3d3a1380d864a310e832f1", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_pack_into_box": {"code": "class AtomGroupMethodsBench:\n def time_pack_into_box(self, num_atoms):\n \"\"\"Benchmark shifting atoms of ag\n into primary unit cell, using\n default parameters.\n \"\"\"\n self.ag.pack_into_box()\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_pack_into_box", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "54f8934b8fe61ba797a7909743b108e39de31f183b1e1ae485cf0f6ce32462c7", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_rotate": {"code": "class AtomGroupMethodsBench:\n def time_rotate(self, num_atoms):\n \"\"\"Benchmark simple rotation operation\n on atomgroup.\n \"\"\"\n self.ag.rotate(self.rot_matrix)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_rotate", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "6e1749c80e708a81dc9b1c9e930f935e6c602721ffd2c8aaef3124f78895e3fc", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_rotateby": {"code": "class AtomGroupMethodsBench:\n def time_rotateby(self, num_atoms):\n \"\"\"Benchmark rotation by an angle\n of the ag coordinates.\n \"\"\"\n self.ag.rotateby(angle=45,\n axis=[1,0,0])\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_rotateby", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3a363526876a2804ba8aa888ffe51737d852a4f374b4afcea48089e448dd31cb", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_split": {"code": "class AtomGroupMethodsBench:\n def time_split(self, num_atoms):\n \"\"\"Benchmark ag splitting into\n multiple ags based on a simple\n criterion.\n \"\"\"\n self.ag.split('residue')\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_split", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "416f626f3c7231a69b7aeff75fba459fe50c0154712be099617e3bbb6e5916d4", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_subtract": {"code": "class AtomGroupMethodsBench:\n def time_subtract(self, num_atoms):\n \"\"\"Benchmark ag subtraction.\n \"\"\"\n self.ag.subtract(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_subtract", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "a850d565fa6aecc39128e9d35a85496508288f8b8989d9fdb21f77796d838786", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_symmetric_difference": {"code": "class AtomGroupMethodsBench:\n def time_symmetric_difference(self, num_atoms):\n \"\"\"Benchmark ag symmetric difference\n operation.\n \"\"\"\n self.ag.symmetric_difference(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_symmetric_difference", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "0f5ae06c5f76555fb6a63e41f85fd32373e10d49a309803a4b06be75e05ec0a9", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_transform": {"code": "class AtomGroupMethodsBench:\n def time_transform(self, num_atoms):\n \"\"\"Benchmark application of transformation\n matrix to atomgroup.\n \"\"\"\n self.ag.transform(self.trans)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_transform", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "6dc5031197d6b02edba0f8d4bd19cec798be5c5b740547575f7cade39a38b391", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_translate": {"code": "class AtomGroupMethodsBench:\n def time_translate(self, num_atoms):\n \"\"\"Benchmark the application of a\n translation vector to the ag\n coordinates.\n \"\"\"\n self.ag.translate([0,0.5,1])\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_translate", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "97a8195e6e8cd22f3b5cfa3a9f162d518b497d64e2cd734e91212b74e9620d3a", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_union": {"code": "class AtomGroupMethodsBench:\n def time_union(self, num_atoms):\n \"\"\"Benchmark union operation\n on atomgroups.\n \"\"\"\n self.ag.union(self.ag)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_union", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3e19a73408a52eca4f12d1df1f083093bf51de80adc8a78f3448451658ae113b", "warmup_time": -1}, "ag_methods.AtomGroupMethodsBench.time_wrap": {"code": "class AtomGroupMethodsBench:\n def time_wrap(self, num_atoms):\n \"\"\"Benchmark wrap() operation on\n atomgroup with default params.\n \"\"\"\n self.ag.wrap()\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.weights = np.ones(num_atoms)\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}\n self.rot_matrix = np.ones((3,3))\n self.trans = np.ones((4,4))", "min_run_count": 2, "name": "ag_methods.AtomGroupMethodsBench.time_wrap", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "e17b327697d267cf044412cc544be3b2e309edad29b10d0ab5af64a7b847d269", "warmup_time": -1}, "ag_methods.CompoundSplitting.time_center_compounds": {"code": "class CompoundSplitting:\n def time_center_compounds(self, *args):\n self.u.atoms.center(None, compound='residues')\n\n def setup(self, n_atoms, n_compounds, homogeneous, contiguous):\n rg = np.random.Generator(np.random.MT19937(3000))\n \n # some parameter screening for nonsensical combinations.\n if n_compounds > n_atoms:\n raise NotImplementedError\n \n if n_compounds == 1 and not (homogeneous and contiguous):\n raise NotImplementedError\n \n if n_compounds == n_atoms:\n if not (homogeneous and contiguous):\n raise NotImplementedError\n compound_indices = np.arange(n_compounds)\n elif homogeneous:\n ats_per_compound, remainder = divmod(n_atoms, n_compounds)\n if remainder:\n raise NotImplementedError\n compound_indices = np.tile(np.arange(n_compounds),\n (ats_per_compound, 1)).T.ravel()\n else:\n compound_indices = np.sort(np.floor(rg.random(n_atoms)\n * n_compounds).astype(np.int))\n \n unique_indices = np.unique(compound_indices)\n if len(unique_indices) != n_compounds:\n raise RuntimeError\n \n if not contiguous:\n rg.shuffle(compound_indices)\n \n self.u = MDAnalysis.Universe.empty(n_atoms,\n n_residues=n_compounds,\n n_segments=1,\n atom_resindex=compound_indices,\n trajectory=True)\n self.u.atoms.positions = rg.random((n_atoms, 3),\n dtype=np.float32) * 100\n self.u.dimensions = [50, 50, 50, 90, 90, 90]", "min_run_count": 2, "name": "ag_methods.CompoundSplitting.time_center_compounds", "number": 0, "param_names": ["param1", "param2", "param3", "param4"], "params": [["100", "10000", "1000000"], ["1", "10", "100"], ["True", "False"], ["True", "False"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "1a2731a96f06d85a503ae24d79262851ff5c7c0755d17baf7819456d54075f17", "warmup_time": -1}, "ag_methods.FragmentCaching.time_find_cached_fragments": {"code": "class FragmentCaching:\n def time_find_cached_fragments(self, universe_type):\n frags = self.u.atoms.fragments\n\n def setup(self, universe_type):\n super(FragmentCaching, self).setup(universe_type)\n frags = self.u.atoms.fragments # Priming the cache", "min_run_count": 2, "name": "ag_methods.FragmentCaching.time_find_cached_fragments", "number": 0, "param_names": ["universe_type"], "params": [["'large_fragment_small_solvents'", "'large_fragment'", "'polymer_chains'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "e10a712eaf0f8c9a2489bc4f2dbc9677be29528aa3314aa5bcb814b05e4c5cd8", "warmup_time": -1}, "ag_methods.FragmentCaching.time_find_fragments": {"code": "class FragmentFinding:\n def time_find_fragments(self, universe_type):\n frags = self.u.atoms.fragments\n\nclass FragmentCaching:\n def setup(self, universe_type):\n super(FragmentCaching, self).setup(universe_type)\n frags = self.u.atoms.fragments # Priming the cache", "min_run_count": 2, "name": "ag_methods.FragmentCaching.time_find_fragments", "number": 0, "param_names": ["universe_type"], "params": [["'large_fragment_small_solvents'", "'large_fragment'", "'polymer_chains'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "9ee4ba9d30022cba05eab57475bff54164b9f4ffc8a37286663ae8befdd78225", "warmup_time": -1}, "ag_methods.FragmentFinding.time_find_fragments": {"code": "class FragmentFinding:\n def time_find_fragments(self, universe_type):\n frags = self.u.atoms.fragments\n\n def setup(self, universe_type):\n if universe_type == 'large_fragment_small_solvents':\n univ = (TPR, XTC)\n elif universe_type == 'large_fragment':\n univ = (PSF, DCD)\n else:\n univ = (TRZ_psf, TRZ)\n self.u = MDAnalysis.Universe(*univ)", "min_run_count": 2, "name": "ag_methods.FragmentFinding.time_find_fragments", "number": 0, "param_names": ["universe_type"], "params": [["'large_fragment_small_solvents'", "'large_fragment'", "'polymer_chains'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7629f34633e0ba4daad90ab0adc33965e3f05cd82382570a2941d46085851b39", "warmup_time": -1}, "analysis.distances.DistancesBench.time_between": {"code": "class DistancesBench:\n def time_between(self, num_atoms):\n \"\"\"Benchmark determination of subgroup\n of atomgroup that is within a specific\n distance of two other atomgroups.\n \"\"\"\n distances.between(\n group=self.ag3, A=self.ag1, B=self.ag2, distance=15.0\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_between", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "06923b19f8f155c8ef51227176b95f08c0b87f7f7f81ff6a72eaebe8b2927bca", "warmup_time": -1}, "analysis.distances.DistancesBench.time_calc_angles": {"code": "class DistancesBench:\n def time_calc_angles(self, num_atoms):\n \"\"\"Benchmark calculation of angles between\n atoms in three atomgroups.\n \"\"\"\n mda.lib.distances.calc_angles(\n self.coords_1, self.coords_2, self.coords_3\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_calc_angles", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3bf2d6564e727c2553e682fe2f6290e199915ad8f0ea5bd8d0dc8e6a35c8c193", "warmup_time": -1}, "analysis.distances.DistancesBench.time_calc_bonds": {"code": "class DistancesBench:\n def time_calc_bonds(self, num_atoms):\n \"\"\"Benchmark calculation of bonds between\n atoms in two atomgroups.\n \"\"\"\n mda.lib.distances.calc_bonds(self.coords_1, self.coords_2)\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_calc_bonds", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "c82f8a3b8b73aa472c1b332c89f7225cdc60f0a35fc6f526d818427e0c0f4260", "warmup_time": -1}, "analysis.distances.DistancesBench.time_calc_dihedrals": {"code": "class DistancesBench:\n def time_calc_dihedrals(self, num_atoms):\n \"\"\"Benchmark calculation of dihedrals between\n atoms in four atomgroups.\n \"\"\"\n mda.lib.distances.calc_dihedrals(\n self.coords_1, self.coords_2, self.coords_3, self.coords_4\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_calc_dihedrals", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "8e267b78a21ada5eaa0968be8444c971d3b72cdb1f245bdc8bdb0a8b41b66c65", "warmup_time": -1}, "analysis.distances.DistancesBench.time_contact_matrix": {"code": "class DistancesBench:\n def time_contact_matrix(self, num_atoms):\n \"\"\"Benchmark calculation of contacts within\n a single numpy array using the default arguments\n to contact_matrix.\n \"\"\"\n distances.contact_matrix(\n coord=self.coords_1, cutoff=15.0, returntype=\"numpy\", box=None\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_contact_matrix", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "c91c137bba8896a5e5f082a89a5f74a4250cd550276f5c0dd7e67099a3e4ce27", "warmup_time": -1}, "analysis.distances.DistancesBench.time_contact_matrix_sparse": {"code": "class DistancesBench:\n def time_contact_matrix_sparse(self, num_atoms):\n \"\"\"Benchmark calculation of contacts within\n a single numpy array using the slower reduced\n memory implementation of contact_matrix intended\n for larger systems.\n \"\"\"\n distances.contact_matrix(\n coord=self.coords_1, cutoff=15.0, returntype=\"sparse\", box=None\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_contact_matrix_sparse", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "90afc393d0d08334fd0a603ceb4c1a30e1d495c974d7fbf3e902b3d87afa19d1", "warmup_time": -1}, "analysis.distances.DistancesBench.time_dist": {"code": "class DistancesBench:\n def time_dist(self, num_atoms):\n \"\"\"Benchmark calculation of distances between\n atoms in two atomgroups with no offsets\n to resids.\n \"\"\"\n distances.dist(A=self.ag1, B=self.ag2, offset=0)\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_dist", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3663b232cc72f4cdece318f342aa8287da2493eca63495991d9421aab0dbb4a9", "warmup_time": -1}, "analysis.distances.DistancesBench.time_dist_offsets": {"code": "class DistancesBench:\n def time_dist_offsets(self, num_atoms):\n \"\"\"Benchmark calculation of distances between\n atoms in two atomgroups with offsets\n to resids.\n \"\"\"\n distances.dist(A=self.ag1, B=self.ag2, offset=20)\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_dist_offsets", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "87ed1892f89d366f02c57b9aa83c6fa277a3f9bec85750e08ef2ba0c5a385709", "warmup_time": -1}, "analysis.distances.DistancesBench.time_distance_array": {"code": "class DistancesBench:\n def time_distance_array(self, num_atoms):\n \"\"\"Benchmark calculation of all distances\n between two numpy arrays of coordinates,\n using default arguments to distance_array.\n \"\"\"\n distances.distance_array(\n reference=self.coords_1,\n configuration=self.coords_2,\n box=None,\n result=None,\n backend=\"serial\",\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_distance_array", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "423bb13d31eacb2b6e5d3f261552b10be8a2e7388ff95d71dc782c0c8731a23b", "warmup_time": -1}, "analysis.distances.DistancesBench.time_distance_array_pre_allocated": {"code": "class DistancesBench:\n def time_distance_array_pre_allocated(self, num_atoms):\n \"\"\"Benchmark calculation of all distances\n between two numpy arrays of coordinates,\n using distance_array with a preallocated\n result array.\n \"\"\"\n distances.distance_array(\n reference=self.coords_1,\n configuration=self.coords_2,\n box=None,\n result=self.allocated_array_2D,\n backend=\"serial\",\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_distance_array_pre_allocated", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "46dcac9910323321beb5396d4e413bb5975d6fa512b22bf0df6bcd873672d43e", "warmup_time": -1}, "analysis.distances.DistancesBench.time_self_distance_array": {"code": "class DistancesBench:\n def time_self_distance_array(self, num_atoms):\n \"\"\"Benchmark calculation of all distances\n within a single numpy array of coordinates\n using default parameters to self_distance_array.\n \"\"\"\n distances.self_distance_array(\n reference=self.coords_1, box=None, result=None, backend=\"serial\"\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_self_distance_array", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "5c8f51df8cbca15ce193bc4c18e14d5772ad7e53fc9f565cdc096b24308e6f3f", "warmup_time": -1}, "analysis.distances.DistancesBench.time_self_distance_array_pre_allocated": {"code": "class DistancesBench:\n def time_self_distance_array_pre_allocated(self, num_atoms):\n \"\"\"Benchmark calculation of all distances\n within a single numpy array of coordinates\n using self_distance_array with preallocated\n result array.\n \"\"\"\n distances.self_distance_array(\n reference=self.coords_1,\n box=None,\n result=self.allocated_array_1D,\n backend=\"serial\",\n )\n\n def setup(self, num_atoms):\n np.random.seed(17809)\n self.coords_1 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(9008716)\n self.coords_2 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(15809)\n self.coords_3 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n np.random.seed(871600)\n self.coords_4 = np.random.random_sample((num_atoms, 3)).astype(\n np.float32\n )\n \n self.allocated_array_2D = np.empty(\n (num_atoms, num_atoms), dtype=np.float64\n )\n self.array_shape_1D = int(num_atoms * (num_atoms - 1) / 2.0)\n self.allocated_array_1D = np.empty(\n self.array_shape_1D, dtype=np.float64\n )\n self.u = mda.Universe(GRO)\n self.ag1 = self.u.atoms[:num_atoms]\n self.ag2 = self.u.atoms[num_atoms: 2 * num_atoms]\n self.ag3 = self.u.atoms[-num_atoms:]", "min_run_count": 2, "name": "analysis.distances.DistancesBench.time_self_distance_array_pre_allocated", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "216345f414efc996a92de0ab4f74763fe20124827e2a57adf0e9bcc93ea05530", "warmup_time": -1}, "analysis.leaflet.LeafletBench.time_leafletfinder": {"code": "class LeafletBench:\n def time_leafletfinder(self, cutoff, sparse, pbc):\n \"\"\"Benchmark LeafletFinder for test lipid\n membrane system.\n \"\"\"\n leaflet.LeafletFinder(universe=self.u,\n select=self.headgroup_sel,\n cutoff=cutoff,\n pbc=pbc,\n sparse=sparse)\n\n def setup(self, cutoff, sparse, pbc):\n self.u = MDAnalysis.Universe(Martini_membrane_gro)\n self.headgroup_sel = 'name PO4'", "min_run_count": 2, "name": "analysis.leaflet.LeafletBench.time_leafletfinder", "number": 0, "param_names": ["cutoff", "sparse", "pbc"], "params": [["7.0", "15.0", "23.0"], ["None", "True", "False"], ["True", "False"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "2458c4484b875134228107f4bce8daa33aeb344a704e3af71edcbb03b58fc057", "warmup_time": -1}, "analysis.leaflet.LeafletOptimizeBench.time_optimize_cutoff": {"code": "class LeafletOptimizeBench:\n def time_optimize_cutoff(self, sparse, pbc):\n \"\"\"Benchmark optimize_cutoff for test lipid\n membrane system using default network distance\n range.\n \"\"\"\n leaflet.optimize_cutoff(universe=self.u,\n select=self.headgroup_sel,\n pbc=pbc,\n sparse=sparse)\n\n def setup(self, sparse, pbc):\n self.u = MDAnalysis.Universe(Martini_membrane_gro)\n self.headgroup_sel = 'name PO4'", "min_run_count": 2, "name": "analysis.leaflet.LeafletOptimizeBench.time_optimize_cutoff", "number": 0, "param_names": ["sparse", "pbc"], "params": [["None", "True", "False"], ["True", "False"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "73a3a56d78b0454ad8df949ea3929d5b1281bea8bc6afb57e61589b4d99c688c", "warmup_time": -1}, "analysis.psa.PSA_get_coord_axesBench.time_get_coord_axes": {"code": "class PSA_get_coord_axesBench:\n def time_get_coord_axes(self, time_steps, n_atoms):\n \"\"\"Benchmark get_coord_axes in psa module\n \"\"\"\n psa.get_coord_axes(path=self.path)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n # only using condensed path input\n # data structure for now\n self.path = np.random.rand(time_steps,\n n_atoms * 3)", "min_run_count": 2, "name": "analysis.psa.PSA_get_coord_axesBench.time_get_coord_axes", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "1000"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "86cb5086b8445a130e30b1506114ef9279166f177f3b89ad2641253d58117f93", "warmup_time": -1}, "analysis.psa.PSA_get_msd_matrixBench.time_get_msd_matrix": {"code": "class PSA_get_msd_matrixBench:\n def time_get_msd_matrix(self, time_steps, n_atoms):\n \"\"\"Benchmark for get_msd_matrix in psa module\n \"\"\"\n # only default argument for axis is benchmarked\n psa.get_msd_matrix(P=self.P,\n Q=self.Q,\n axis=None)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_get_msd_matrixBench.time_get_msd_matrix", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "1000"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "9dcdda75f655ab20762a5a9c171d804a3f908f3b76043f3fb66e1c5e471aa2c0", "warmup_time": -1}, "analysis.psa.PSA_get_path_metric_funcBench.time_get_path_metric_func": {"code": "class PSA_get_path_metric_funcBench:\n def time_get_path_metric_func(self, path_metric):\n \"\"\"Benchmark for get_path_metric_func in psa\n module\n \"\"\"\n psa.get_path_metric_func(name=path_metric)", "min_run_count": 2, "name": "analysis.psa.PSA_get_path_metric_funcBench.time_get_path_metric_func", "number": 0, "param_names": ["path_metric"], "params": [["'hausdorff'", "'weighted_average_hausdorff'", "'average_hausdorff'", "'hausdorff_neighbors'", "'discrete_frechet'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "fc71d48ec5e111b5f38e7d741ab5f63fc3c8615325be6916878225ada0d3e828", "warmup_time": -1}, "analysis.psa.PSA_metricBench.time_discrete_frechet": {"code": "class PSA_metricBench:\n def time_discrete_frechet(self, time_steps, n_atoms):\n \"\"\"Benchmark for discrete_frechet() in psa module.\n \"\"\"\n psa.discrete_frechet(P=self.P,\n Q=self.Q)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_metricBench.time_discrete_frechet", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "200"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3bb3d861752ef9ed6f77ac94cad9ed2e42c56d8df988a838892b313d1104b7cb", "warmup_time": -1}, "analysis.psa.PSA_metricBench.time_hausdorff": {"code": "class PSA_metricBench:\n def time_hausdorff(self, time_steps, n_atoms):\n \"\"\"Benchmark for hausdorff() in psa module.\n \"\"\"\n psa.hausdorff(P=self.P,\n Q=self.Q)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_metricBench.time_hausdorff", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "200"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "e0c90539a3b87665619c0c5923a412b1457f5cf08564b891b2ea25345fc720fc", "warmup_time": -1}, "analysis.psa.PSA_metricBench.time_hausdorff_avg": {"code": "class PSA_metricBench:\n def time_hausdorff_avg(self, time_steps, n_atoms):\n \"\"\"Benchmark for hausdorff_avg() in psa module.\n \"\"\"\n psa.hausdorff_avg(P=self.P,\n Q=self.Q)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_metricBench.time_hausdorff_avg", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "200"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "bda335edfdd48be1e40d51ba67bd57127dc22570ed81c79b25c9c34e98073cad", "warmup_time": -1}, "analysis.psa.PSA_metricBench.time_hausdorff_neighbors": {"code": "class PSA_metricBench:\n def time_hausdorff_neighbors(self, time_steps, n_atoms):\n \"\"\"Benchmark for hausdorff_neighbors() in psa module.\n \"\"\"\n psa.hausdorff_neighbors(P=self.P,\n Q=self.Q)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_metricBench.time_hausdorff_neighbors", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "200"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "77321a9a92915cf788b65037e075327c72374c4f6dd8a105749c502756260a7a", "warmup_time": -1}, "analysis.psa.PSA_metricBench.time_hausdorff_wavg": {"code": "class PSA_metricBench:\n def time_hausdorff_wavg(self, time_steps, n_atoms):\n \"\"\"Benchmark for hausdorff_wavg() in psa module.\n \"\"\"\n psa.hausdorff_wavg(P=self.P,\n Q=self.Q)\n\n def setup(self, time_steps, n_atoms):\n np.random.seed(170089)\n self.P = np.random.rand(time_steps,\n n_atoms,\n 3)\n np.random.seed(971132)\n self.Q = np.random.rand(time_steps,\n n_atoms,\n 3)", "min_run_count": 2, "name": "analysis.psa.PSA_metricBench.time_hausdorff_wavg", "number": 0, "param_names": ["time_steps", "n_atoms"], "params": [["10", "100", "200"], ["5", "25", "50"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "162c1711b4953d5045859022df8b1477ad41b166cf80dc31e0b961c87761ed81", "warmup_time": -1}, "analysis.psa.PSA_sqnormBench.time_sqnorm": {"code": "class PSA_sqnormBench:\n def time_sqnorm(self, num_cols, num_rows, axis):\n \"\"\"Benchmark sqnorm in psa module\n \"\"\"\n psa.sqnorm(v=self.v, axis=axis)\n\n def setup(self, num_cols, num_rows, axis):\n np.random.seed(170089)\n self.v = np.random.rand(num_rows, num_cols)", "min_run_count": 2, "name": "analysis.psa.PSA_sqnormBench.time_sqnorm", "number": 0, "param_names": ["num_cols", "num_rows", "axis"], "params": [["2", "3", "4"], ["100", "1000", "10000"], ["None", "0", "1", "-1"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "5360f9d9650b651e056f22f98fd02bda2307b2f2eabb3fbe7c1c4b0dffe5f70b", "warmup_time": -1}, "analysis.rdf.SimpleRdfBench.time_interrdf": {"code": "class SimpleRdfBench:\n def time_interrdf(self, nbins, range_val, natoms):\n \"\"\"Benchmark a full trajectory parse\n by MDAnalysis.analysis.rdf.InterRDF\n \"\"\"\n self.rdf.run()\n\n def setup(self, nbins, range_val, natoms):\n \n self.sel_str = 'name OW'\n \n self.u = MDAnalysis.Universe(TPR, XTC)\n \n try:\n self.sel = self.u.select_atoms(self.sel_str)[:natoms]\n except AttributeError:\n self.sel = self.u.selectAtoms(self.sel_str)[:natoms]\n \n # do not include initialization of the\n # InterRDF object in the benchmark itself\n \n self.rdf = InterRDF(g1=self.sel,\n g2=self.sel,\n nbins=nbins,\n range=range_val)", "min_run_count": 2, "name": "analysis.rdf.SimpleRdfBench.time_interrdf", "number": 0, "param_names": ["nbins", "range_val", "natoms"], "params": [["20", "75", "200"], ["[0, 5]", "[0, 15]", "[0, 20]"], ["1", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "834273d73c592c18978de979c278573fc8d7997972d1e690ed8efbbeadf8ca08", "warmup_time": -1}, "analysis.rms.RmsdTrajBench.time_RMSD": {"code": "class RmsdTrajBench:\n def time_RMSD(self, select, weights):\n \"\"\"Benchmark RMSD.run() method, which parses\n over the entire trajectory.\n \"\"\"\n self.RMSD_inst.run()\n\n def setup(self, select, weights):\n self.u = MDAnalysis.Universe(PSF, DCD)\n self.RMSD_inst = rms.RMSD(atomgroup=self.u,\n reference=None,\n select=select,\n weights=weights)", "min_run_count": 2, "name": "analysis.rms.RmsdTrajBench.time_RMSD", "number": 0, "param_names": ["select", "weights"], "params": [["'all'", "'backbone'"], ["None", "'mass'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "64836ac01572a5047b1dd99fff0059e19db814e8d869fba0f667e8ce391dab94", "warmup_time": -1}, "analysis.rms.RmsfTrajBench.time_RMSF": {"code": "class RmsfTrajBench:\n def time_RMSF(self, n_atoms, step, weights):\n \"\"\"Benchmark RMSF.run() method, which parses\n over the entire trajectory.\n \"\"\"\n self.RMSF_inst.run(step=step)\n\n def setup(self, n_atoms, step, weights):\n self.u = MDAnalysis.Universe(PSF, DCD)\n self.ag = self.u.atoms[:n_atoms]\n self.RMSF_inst = rms.RMSF(atomgroup=self.ag,\n weights=weights)", "min_run_count": 2, "name": "analysis.rms.RmsfTrajBench.time_RMSF", "number": 0, "param_names": ["n_atoms", "step", "weights"], "params": [["100", "500", "2000"], ["None", "3"], ["None", "'mass'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "99e0776f38969e49506709a4f7297c981a8d81c0febde80cbbc90d2f0f163b4c", "warmup_time": -1}, "analysis.rms.SimpleRmsBench.time_rmsd": {"code": "class SimpleRmsBench:\n def time_rmsd(self, num_atoms, weights, center, superposition):\n \"\"\"Benchmark rmsd function using a setup similar to\n its docstring example code along with several possible\n permutations of parameters.\n \"\"\"\n rms.rmsd(a=self.A,\n b=self.B,\n weights=weights,\n center=center,\n superposition=superposition)\n\n def setup(self, num_atoms, weights, center, superposition):\n # mimic rmsd docstring example code\n self.u = MDAnalysis.Universe(PSF, DCD)\n # ag.positions is the new syntax\n # but older commit hashes will need to use\n # ag.coordinates()\n try:\n self.A = self.u.atoms.positions.copy()[:num_atoms]\n self.u.trajectory[-1]\n self.B = self.u.atoms.positions.copy()[:num_atoms]\n except:\n self.A = self.u.atoms.positions.copy()[:num_atoms]\n self.u.trajectory[-1]\n self.B = self.u.atoms.positions.copy()[:num_atoms]", "min_run_count": 2, "name": "analysis.rms.SimpleRmsBench.time_rmsd", "number": 0, "param_names": ["num_atoms", "weights", "center", "superposition"], "params": [["100", "500", "2000"], ["None", "[1.0, 0.5]"], ["False", "True"], ["False", "True"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "bda0e78ff13a61dce070b3eddef389263aef31ccdd42ddb3a643bfacf8264b02", "warmup_time": -1}, "import.ImportBench.time_import": {"code": "class ImportBench:\n def time_import(self):\n \"\"\"Benchmark time needed to import MDAnalysis\n \"\"\"\n import MDAnalysis as mda\n pass", "min_run_count": 2, "name": "import.ImportBench.time_import", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "61f437034b6040b9faf8e324049e743318ff56255c98cd868a547bac5c256124", "warmup_time": -1}, "selections.GeoSelectionBench.time_geometric_selections": {"code": "class GeoSelectionBench:\n def time_geometric_selections(self,\n selection_string,\n dynamic_selection,\n periodic_selection):\n \n # TODO: Do we need a kwarg similar to old `use_KDTree_routines`\n # flag? We used to benchmark that.\n self.u.select_atoms(selection_string,\n updating=dynamic_selection,\n periodic=periodic_selection[0],\n )\n\n def setup(self,\n selection_string,\n dynamic_selection,\n periodic_selection):\n self.u = MDAnalysis.Universe(GRO)", "min_run_count": 2, "name": "selections.GeoSelectionBench.time_geometric_selections", "number": 0, "param_names": ["selection_string", "dynamic_selection", "periodic_selection"], "params": [["'around 5.0 resid 1'", "'sphlayer 2.4 6.0 (protein)'", "'sphzone 6.0 (protein)'", "'cylayer 5 10 10 -8 protein'", "'cyzone 15 4 -8 protein'", "'point 5.0 5.0 5.0 3.5'", "'prop z >= 5.0'", "'prop abs z <= 5.0'"], ["True", "False"], ["[False, True]", "[True, False]"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3e43b2b234945b3a318c65087db5dda171f737beaec83199d0461b3ea3b0d93e", "warmup_time": -1}, "selections.SimpleSelectionBench.time_simple_selections": {"code": "class SimpleSelectionBench:\n def time_simple_selections(self, selection_string):\n \"\"\"Benchmark simple selections on the protein-based\n standard test GRO file.\n \"\"\"\n if hasattr(MDAnalysis.Universe, 'select_atoms'):\n self.u.select_atoms(selection_string)\n else:\n self.u.selectAtoms(selection_string)\n\n def setup(self, selection_string):\n self.u = MDAnalysis.Universe(GRO)", "min_run_count": 2, "name": "selections.SimpleSelectionBench.time_simple_selections", "number": 0, "param_names": ["selection_string"], "params": [["'protein'", "'backbone'", "'nucleic'", "'nucleicbackbone'", "'resid 1:10'", "'resnum 1:10'", "'resname LYS'", "'name CA'", "'bynum 0:10'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3d7336e8388a8daf7d76bc3faec4ee662f2ddce17529d2b322ec539be91758c3", "warmup_time": -1}, "topology.BondsBench.time_bonds": {"code": "class BondsBench:\n def time_bonds(self, num_bonds):\n \"\"\"Benchmark for calculating bonds\"\"\"\n self.u.bonds\n\n def setup(self, num_bonds):\n self.u = MDAnalysis.Universe.empty(2*num_bonds)\n bonds = np.arange(2*num_bonds).reshape(num_bonds, 2)\n self.u.add_bonds(bonds)", "min_run_count": 2, "name": "topology.BondsBench.time_bonds", "number": 0, "param_names": ["num_bonds"], "params": [["1000", "10000", "100000", "1000000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "53f46341b02a43024f5c677dcdfef05390e63757bfb308dc62104475abb08dc9", "warmup_time": -1}, "topology.TopologyGuessBench.time_guessbonds": {"code": "class TopologyGuessBench:\n def time_guessbonds(self, num_atoms):\n \"\"\"Benchmark for guessing bonds\"\"\"\n guessers.guess_bonds(self.ag, self.ag.positions,\n box=self.ag.dimensions,\n vdwradii=self.vdwradii)\n\n def setup(self, num_atoms):\n self.u = MDAnalysis.Universe(GRO)\n self.ag = self.u.atoms[:num_atoms]\n self.vdwradii = {'H':1.0,\n 'C':1.0,\n 'N':1.0,\n 'O':1.0,\n 'DUMMY':1.0}", "min_run_count": 2, "name": "topology.TopologyGuessBench.time_guessbonds", "number": 0, "param_names": ["num_atoms"], "params": [["10", "100", "1000", "10000"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "c8b85b9228a93b83692b65e61c7a9fdd017ca22d7702f18034d1a5f2c068bcb7", "warmup_time": -1}, "traj_reader.TrajReaderCreation.time_reads": {"code": "class TrajReaderCreation:\n def time_reads(self, traj_format):\n \"\"\"Simple benchmark for reading traj file formats\n from our standard test files.\n \"\"\"\n self.traj_reader(self.traj_file)\n\n def setup(self, traj_format):\n self.traj_dict = traj_dict\n self.traj_file, self.traj_reader = self.traj_dict[traj_format]", "min_run_count": 2, "name": "traj_reader.TrajReaderCreation.time_reads", "number": 0, "param_names": ["traj_format"], "params": [["'XTC'", "'TRR'", "'DCD'", "'NCDF'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "4b030c7c2af116b5eaaec4c77913ed39dda95d0f355f11be9ee99403dfa71f93", "warmup_time": -1}, "traj_reader.TrajReaderIteration.time_strides": {"code": "class TrajReaderIteration:\n def time_strides(self, traj_format):\n \"\"\"Benchmark striding over full trajectory\n test files for each format.\n \"\"\"\n for ts in self.reader_object:\n pass\n\n def setup(self, traj_format):\n self.traj_dict = traj_dict\n self.traj_file, self.traj_reader = self.traj_dict[traj_format]\n self.reader_object = self.traj_reader(self.traj_file)", "min_run_count": 2, "name": "traj_reader.TrajReaderIteration.time_strides", "number": 0, "param_names": ["traj_format"], "params": [["'XTC'", "'TRR'", "'DCD'", "'NCDF'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "d2890f87c636b5336c33c4ede5c97f90f26b52ea890f4514459dee27de3b92fc", "warmup_time": -1}}, "machines": {"gp160": {"arch": "x86_64", "cpu": "Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz", "machine": "gp160", "os": "Linux 4.10.0-35-generic", "ram": "128GB", "version": 1}, "c3potato": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz", "machine": "c3potato", "os": "Linux 3.16.0-4-amd64", "ram": "16GB", "version": 1}}, "tags": {"RotamerConvolveMD-1.0": 1133, "data-0-7-4": 605, "package-2.1.0": 15591, "package-2.2.0": 16644, "package-2.3.0": 17472, "package-2.4.0": 18298, "package-2.4.0b1": 18296, "package-2.4.1": 18309, "package-2.4.2": 18401, "package-2.4.3": 19188, "package-2.5.0": 19486, "package-2.6.0": 20008, "package-2.6.1": 20157, "package-2.7.0": 20792, "pre-release-0-7-0-rc3": 350, "pre-sub-trr": 964, "prerelease-0-6-0-rc2": 152, "prerelease-0-6-rc1": 139, "prerelease-0.8.0-pre-1": 1046, "rel-0-5-0": 82, "release-0-5-1": 104, "release-0-6-0": 154, "release-0-6-1": 170, "release-0-6-2": 205, "release-0-6-3": 256, "release-0-6-4": 283, "release-0-6-4-1": 289, "release-0-7-0": 381, "release-0-7-1": 429, "release-0-7-2": 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3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.8/ram-16GB/scipy/six-null/analysis.distances.DistancesBench.time_between.json", {"python": "3.8"}, 0, 0.0002370185040413065, 0.00021699489075310374, [[null, 17555, 0.00021699489075310374, 0.0002370185040413065]]], ["analysis.distances.DistancesBench.time_contact_matrix(10)", "graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.8/ram-16GB/scipy/six-null/analysis.distances.DistancesBench.time_contact_matrix.json", {"python": "3.8"}, 0, 9.858083937598287e-05, 9.224701578877553e-05, [[null, 17187, 9.224701578877553e-05, 9.858083937598287e-05]]], ["analysis.distances.DistancesBench.time_calc_dihedrals(1000)", "graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.9/ram-16GB/scipy/six-null/analysis.distances.DistancesBench.time_calc_dihedrals.json", {"python": "3.9"}, 2, 6.346838721047246e-05, 6.007094059016679e-05, [[19741, 20006, 6.007094059016679e-05, 6.346838721047246e-05]]], ["analysis.rdf.SimpleRdfBench.time_interrdf(75, [0, 5], 10000)", "graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.9/ram-16GB/scipy/six-null/analysis.rdf.SimpleRdfBench.time_interrdf.json", {"python": "3.9"}, 15, 4.447386829182506, 3.935694976709783, [[null, 21202, 3.935694976709783, 4.447386829182506]]], ["analysis.rdf.SimpleRdfBench.time_interrdf(75, [0, 15], 1000)", "graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests/mock-null/nose-null/numpy/os-Linux 3.16.0-4-amd64/psutil-null/pytest-null/python-3.9/ram-16GB/scipy/six-null/analysis.rdf.SimpleRdfBench.time_interrdf.json", {"python": "3.9"}, 18, 1.2471143016591668, 1.1413855263963342, [[null, 21202, 1.1413855263963342, 1.2471143016591668]]]]} \ No newline at end of file diff --git a/regressions.xml b/regressions.xml index d6002dd26..3bc6decd0 100644 --- a/regressions.xml +++ b/regressions.xml @@ -1,5 +1,11 @@ -tag:mdanalysis.asv,1970-01-01:/b117d33cc616f4b447052bd76edb5124af2e1e9262b15ffadbde650353d04495Airspeed Velocitymdanalysis performance regressions2024-03-11T04:10:47Ztag:mdanalysis.asv,2024-03-10:/a39b30a755b3441b7b014ba97fde22327f5b68395608ae181d5b8e6111106f8813.00% analysis.rdf.SimpleRdfBench.time_interrdf(75, [0, 5], 10000)2024-03-11T04:10:47Z<a href="index.html#analysis.rdf.SimpleRdfBench.time_interrdf?python=3.9&p-nbins=75&p-range_val=%5B0%2C+5%5D&p-natoms=10000&commits=9a2cd43fccd4426f91b195ea9902e5b78a6c2e3b">13.00% regression</a> on 2024-03-10 10:07:07 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/9a2cd43fccd4426f91b195ea9902e5b78a6c2e3b">9a2cd43f</a>.<br> +tag:mdanalysis.asv,1970-01-01:/b117d33cc616f4b447052bd76edb5124af2e1e9262b15ffadbde650353d04495Airspeed Velocitymdanalysis performance regressions2024-05-27T03:59:43Ztag:mdanalysis.asv,2024-05-26:/f651486445689662aa2feaacc7876170142d684a863b5fd6481c066f5b5c656426.85% ag_methods.AtomGroupAttrsBench.time_indices(1000)2024-05-27T03:59:43Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_indices?python=3.11&p-num_atoms=1000&commits=81062f370305226592afdbc9a2b60f671b2652c5">26.85% regression</a> on 2024-05-26 15:48:10 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/81062f370305226592afdbc9a2b60f671b2652c5">81062f37</a>.<br> + New value: 258ns, old value: 204ns.<br> + Latest value: 258ns (26.94% worse than best value 203ns).tag:mdanalysis.asv,2024-05-26:/0980a07a3ac9fc037d04d88f8e8a19cae5e3f1b85063cd7837ec68ca984e51cd22.12% ag_methods.AtomGroupAttrsBench.time_atoms(1000)2024-05-27T03:58:53Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atoms?python=3.11&p-num_atoms=1000&commits=81062f370305226592afdbc9a2b60f671b2652c5">22.12% regression</a> on 2024-05-26 15:48:10 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/81062f370305226592afdbc9a2b60f671b2652c5">81062f37</a>.<br> + New value: 220ns, old value: 180ns.<br> + Latest value: 220ns (22.12% worse than best value 180ns).tag:mdanalysis.asv,2024-05-26:/26d285b9adb827e7bfd53994b1ec483f8cf07e7e855a5543538606d3c464d93d17.40% ag_methods.AtomGroupAttrsBench.time_atomcount(1000)2024-05-27T03:58:43Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atomcount?python=3.11&p-num_atoms=1000&commits=81062f370305226592afdbc9a2b60f671b2652c5">17.40% regression</a> on 2024-05-26 15:48:10 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/81062f370305226592afdbc9a2b60f671b2652c5">81062f37</a>.<br> + New value: 461ns, old value: 393ns.<br> + Latest value: 461ns (17.54% worse than best value 392ns).tag:mdanalysis.asv,2024-03-10:/a39b30a755b3441b7b014ba97fde22327f5b68395608ae181d5b8e6111106f8813.00% analysis.rdf.SimpleRdfBench.time_interrdf(75, [0, 5], 10000)2024-03-11T04:10:47Z<a href="index.html#analysis.rdf.SimpleRdfBench.time_interrdf?python=3.9&p-nbins=75&p-range_val=%5B0%2C+5%5D&p-natoms=10000&commits=9a2cd43fccd4426f91b195ea9902e5b78a6c2e3b">13.00% regression</a> on 2024-03-10 10:07:07 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/9a2cd43fccd4426f91b195ea9902e5b78a6c2e3b">9a2cd43f</a>.<br> New value: 4.45s, old value: 3.94s.<br> Latest value: 4.45s (13.00% worse than best value 3.94s).tag:mdanalysis.asv,2024-03-10:/bfa8982a304dca41e8971ae7d27e316e262cb3ad09fd02668aa5f0efa8b1880f9.26% analysis.rdf.SimpleRdfBench.time_interrdf(75, [0, 15], 1000)2024-03-11T04:10:47Z<a href="index.html#analysis.rdf.SimpleRdfBench.time_interrdf?python=3.9&p-nbins=75&p-range_val=%5B0%2C+15%5D&p-natoms=1000&commits=9a2cd43fccd4426f91b195ea9902e5b78a6c2e3b">9.26% regression</a> on 2024-03-10 10:07:07 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/9a2cd43fccd4426f91b195ea9902e5b78a6c2e3b">9a2cd43f</a>.<br> New value: 1.25s, old value: 1.14s.<br> @@ -11,9 +17,9 @@ New value: 920μs, old value: 858μs.<br> Latest value: 920μs (7.20% worse than best value 858μs).tag:mdanalysis.asv,2024-01-09:/c21fcf6764a6a10936fe8d0e51cdc0722bd9de6b447a3f6a12ee2ae7ab5182145.50% analysis.psa.PSA_get_msd_matrixBench.time_get_msd_matrix(1000, 50)2024-01-10T04:10:06Z<a href="index.html#analysis.psa.PSA_get_msd_matrixBench.time_get_msd_matrix?python=3.11&p-time_steps=1000&p-n_atoms=50&commits=8ff8acc3bd940d0c5c39d9e49c9aba5c33007fd1">5.50% regression</a> on 2024-01-09 14:36:00 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8ff8acc3bd940d0c5c39d9e49c9aba5c33007fd1">8ff8acc3</a>.<br> New value: 632ms, old value: 599ms.<br> - Latest value: 632ms (5.50% worse than best value 599ms).tag:mdanalysis.asv,2023-09-04:/5b0d7003323b128ba687f6caab7165c8d20385d816c285a557a0deeaecf7fdb510.91% analysis.rms.SimpleRmsBench.time_rmsd(100, None, False, True)2023-09-05T04:18:01Z<a href="index.html#analysis.rms.SimpleRmsBench.time_rmsd?python=3.11&p-num_atoms=100&p-weights=None&p-center=False&p-superposition=True&commits=28532012363342c36dcdfa7d497252be90ff261e">10.91% regression</a> on 2023-09-04 13:47:17 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/28532012363342c36dcdfa7d497252be90ff261e">28532012</a>.<br> - New value: 47.0μs, old value: 42.4μs.<br> - Latest value: 47.0μs (10.91% worse than best value 42.4μs).tag:mdanalysis.asv,2023-09-04:/662752af510b97d4ce5597ed3685dd5cd84d47a872f09de9b6a29bb2efeb0e4711.46% analysis.rms.SimpleRmsBench.time_rmsd(100, None, True, True)2023-09-05T04:18:01Z<a href="index.html#analysis.rms.SimpleRmsBench.time_rmsd?python=3.11&p-num_atoms=100&p-weights=None&p-center=True&p-superposition=True&commits=28532012363342c36dcdfa7d497252be90ff261e">11.46% regression</a> on 2023-09-04 13:47:17 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/28532012363342c36dcdfa7d497252be90ff261e">28532012</a>.<br> + Latest value: 632ms (5.50% worse than best value 599ms).tag:mdanalysis.asv,2023-09-04:/5b0d7003323b128ba687f6caab7165c8d20385d816c285a557a0deeaecf7fdb510.99% analysis.rms.SimpleRmsBench.time_rmsd(100, None, False, True)2023-09-05T04:18:01Z<a href="index.html#analysis.rms.SimpleRmsBench.time_rmsd?python=3.11&p-num_atoms=100&p-weights=None&p-center=False&p-superposition=True&commits=28532012363342c36dcdfa7d497252be90ff261e">10.99% regression</a> on 2023-09-04 13:47:17 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/28532012363342c36dcdfa7d497252be90ff261e">28532012</a>.<br> + New value: 47.1μs, old value: 42.4μs.<br> + Latest value: 47.1μs (10.99% worse than best value 42.4μs).tag:mdanalysis.asv,2023-09-04:/662752af510b97d4ce5597ed3685dd5cd84d47a872f09de9b6a29bb2efeb0e4711.46% analysis.rms.SimpleRmsBench.time_rmsd(100, None, True, True)2023-09-05T04:18:01Z<a href="index.html#analysis.rms.SimpleRmsBench.time_rmsd?python=3.11&p-num_atoms=100&p-weights=None&p-center=True&p-superposition=True&commits=28532012363342c36dcdfa7d497252be90ff261e">11.46% regression</a> on 2023-09-04 13:47:17 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/28532012363342c36dcdfa7d497252be90ff261e">28532012</a>.<br> New value: 47.1μs, old value: 42.2μs.<br> Latest value: 47.1μs (11.46% worse than best value 42.2μs).tag:mdanalysis.asv,2023-09-04:/e24d953165796237602e8179682213884621ac049a352ed989be3298eeb6cc4a5.94% analysis.rms.SimpleRmsBench.time_rmsd(500, None, False, True)2023-09-05T04:18:01Z<a href="index.html#analysis.rms.SimpleRmsBench.time_rmsd?python=3.11&p-num_atoms=500&p-weights=None&p-center=False&p-superposition=True&commits=28532012363342c36dcdfa7d497252be90ff261e">5.94% regression</a> on 2023-09-04 13:47:17 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/28532012363342c36dcdfa7d497252be90ff261e">28532012</a>.<br> New value: 74.6μs, old value: 70.5μs.<br> @@ -21,11 +27,11 @@ New value: 74.9μs, old value: 70.0μs.<br> Latest value: 74.9μs (7.03% worse than best value 70.0μs).tag:mdanalysis.asv,2023-08-16:/4c49742b7165aec1a21aea66b9f97d4692774e9c04c443a15321e385323619807.60% ag_methods.AtomGroupMethodsBench.time_subtract(10)2023-08-17T05:39:35Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_subtract?python=3.11&p-num_atoms=10&commits=acfd1222a94965a4b0a31c3d6870c5c5252829fd">7.60% regression</a> on 2023-08-16 07:05:33 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/acfd1222a94965a4b0a31c3d6870c5c5252829fd">acfd1222</a>.<br> New value: 33.3μs, old value: 30.9μs.<br> - Latest value: 33.3μs (7.60% worse than best value 30.9μs).tag:mdanalysis.asv,2023-08-16:/c432cd6af0bfe57846b0f5e59976f347a93cd49c00f978cf373225158d20e6466.70% ag_methods.AtomGroupMethodsBench.time_subtract(1000)2023-08-17T05:39:35Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_subtract?python=3.11&p-num_atoms=1000&commits=acfd1222a94965a4b0a31c3d6870c5c5252829fd">6.70% regression</a> on 2023-08-16 07:05:33 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/acfd1222a94965a4b0a31c3d6870c5c5252829fd">acfd1222</a>.<br> - New value: 41.9μs, old value: 39.3μs.<br> - Latest value: 41.9μs (6.70% worse than best value 39.3μs).tag:mdanalysis.asv,2023-08-16:/a9a04b9044628faa4dccf2ed80b2b06130cc41e69c5f606615f5a54415b753e37.52% ag_methods.AtomGroupMethodsBench.time_subtract(100)2023-08-17T04:51:16Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_subtract?python=3.11&p-num_atoms=100&commits=9032eb035e969b98e193134cb80050c489fd134f">7.52% regression</a> on 2023-08-16 21:36:20 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/9032eb035e969b98e193134cb80050c489fd134f">9032eb03</a>.<br> - New value: 34.0μs, old value: 31.6μs.<br> - Latest value: 34.0μs (7.52% worse than best value 31.6μs).tag:mdanalysis.asv,2023-08-15:/635f9193b3c63a0807aa773f206ab24ac936879587b3ecad97d63a62640ce89e5.66% analysis.distances.DistancesBench.time_calc_dihedrals(1000)2023-08-16T09:38:50Z<a href="index.html#analysis.distances.DistancesBench.time_calc_dihedrals?python=3.9&p-num_atoms=1000&commits=03447ea4183c561138faa8548f14e8f9f6358e26-fa0fb82690e4daa5c0a81189ef448b81b43f355b">5.66% regression</a> on 2023-08-15 08:41:17 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/03447ea4183c561138faa8548f14e8f9f6358e26...fa0fb82690e4daa5c0a81189ef448b81b43f355b">03447ea4...fa0fb826</a>.<br> + Latest value: 33.3μs (7.60% worse than best value 30.9μs).tag:mdanalysis.asv,2023-08-16:/c432cd6af0bfe57846b0f5e59976f347a93cd49c00f978cf373225158d20e6466.88% ag_methods.AtomGroupMethodsBench.time_subtract(1000)2023-08-17T05:39:35Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_subtract?python=3.11&p-num_atoms=1000&commits=acfd1222a94965a4b0a31c3d6870c5c5252829fd">6.88% regression</a> on 2023-08-16 07:05:33 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/acfd1222a94965a4b0a31c3d6870c5c5252829fd">acfd1222</a>.<br> + New value: 42.0μs, old value: 39.3μs.<br> + Latest value: 42.0μs (6.88% worse than best value 39.3μs).tag:mdanalysis.asv,2023-08-16:/a9a04b9044628faa4dccf2ed80b2b06130cc41e69c5f606615f5a54415b753e37.63% ag_methods.AtomGroupMethodsBench.time_subtract(100)2023-08-17T04:51:16Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_subtract?python=3.11&p-num_atoms=100&commits=9032eb035e969b98e193134cb80050c489fd134f">7.63% regression</a> on 2023-08-16 21:36:20 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/9032eb035e969b98e193134cb80050c489fd134f">9032eb03</a>.<br> + New value: 34.1μs, old value: 31.6μs.<br> + Latest value: 34.1μs (7.63% worse than best value 31.6μs).tag:mdanalysis.asv,2023-08-15:/635f9193b3c63a0807aa773f206ab24ac936879587b3ecad97d63a62640ce89e5.66% analysis.distances.DistancesBench.time_calc_dihedrals(1000)2023-08-16T09:38:50Z<a href="index.html#analysis.distances.DistancesBench.time_calc_dihedrals?python=3.9&p-num_atoms=1000&commits=03447ea4183c561138faa8548f14e8f9f6358e26-fa0fb82690e4daa5c0a81189ef448b81b43f355b">5.66% regression</a> on 2023-08-15 08:41:17 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/03447ea4183c561138faa8548f14e8f9f6358e26...fa0fb82690e4daa5c0a81189ef448b81b43f355b">03447ea4...fa0fb826</a>.<br> New value: 63.5μs, old value: 60.1μs.<br> Latest value: 63.5μs (5.66% worse than best value 60.1μs).tag:mdanalysis.asv,2023-01-23:/40b5cf4249e9a8bdfe91126e82a509d7f909eabaa83bc89a04334109540e648014.99% ag_methods.FragmentFinding.time_find_fragments('large_fragment')2023-01-24T04:49:53Z<a href="index.html#ag_methods.FragmentFinding.time_find_fragments?python=3.8&p-universe_type=%27large_fragment%27&commits=d73523f93c53338d8365dac98ffc1615c097d3be">14.99% regression</a> on 2023-01-23 21:03:08 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/d73523f93c53338d8365dac98ffc1615c097d3be">d73523f9</a>.<br> New value: 259ms, old value: 226ms.<br> @@ -729,25 +735,25 @@ New value: 607μs, old value: 595μs.<br> Latest value: 631μs (10.78% worse than best value 570μs).tag:mdanalysis.asv,2017-01-18:/3fd8bc76efca88119c166345a96f19df4cfe67e3f0f5750ba8a7e77dc35f4cb60.63% analysis.rdf.SimpleRdfBench.time_interrdf(20, [0, 5], 100)2020-02-27T16:50:36Z<a href="index.html#analysis.rdf.SimpleRdfBench.time_interrdf?python=2.7&p-nbins=20&p-range_val=%5B0%2C+5%5D&p-natoms=100&commits=9ef4db2baf18de6ea5f4ee7b493458a9be5463bf">0.63% regression</a> on 2017-01-18 05:04:24 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/9ef4db2baf18de6ea5f4ee7b493458a9be5463bf">9ef4db2b</a>.<br> New value: 28.4ms, old value: 28.2ms.<br> - Latest value: 29.7ms (16.36% worse than best value 25.5ms).tag:mdanalysis.asv,2017-01-26:/801069e19022134ed50deac5d115d1127f8ebeb6bb05d40a50436f1b434194b7411.72% import.ImportBench.time_import2020-02-27T16:41:58Z<a href="index.html#import.ImportBench.time_import?python=2.7&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">411.72% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> - New value: 2.90μs, old value: 567ns.<br> - Latest value: 2.90μs (411.72% worse than best value 567ns).tag:mdanalysis.asv,2017-01-26:/67fdd8304fdd6379e18928d83a8463079fef30411641be34758d39760518cefe9.10% ag_methods.AtomGroupMethodsBench.time_guess_bonds(10)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_guess_bonds?python=2.7&p-num_atoms=10&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">9.10% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> + Latest value: 29.7ms (16.36% worse than best value 25.5ms).tag:mdanalysis.asv,2017-01-26:/67fdd8304fdd6379e18928d83a8463079fef30411641be34758d39760518cefe9.10% ag_methods.AtomGroupMethodsBench.time_guess_bonds(10)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_guess_bonds?python=2.7&p-num_atoms=10&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">9.10% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> New value: 3.32ms, old value: 3.04ms.<br> - Latest value: 4.21ms (968.63% worse than best value 394μs).tag:mdanalysis.asv,2017-01-26:/e7c61532728470c824b8d97ab074e0f7dca8401a9e535662d4fdc8235fea786f8.16% ag_methods.AtomGroupMethodsBench.time_split(10)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_split?python=2.7&p-num_atoms=10&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">8.16% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> - New value: 14.3μs, old value: 13.2μs.<br> - Latest value: 17.0μs (28.59% worse than best value 13.2μs).tag:mdanalysis.asv,2017-01-26:/295771294a052f6cd3483c12ebbc910a5000a56f637bcd60cc4c9a9737b7a80210.95% ag_methods.AtomGroupMethodsBench.time_split(1000)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_split?python=2.7&p-num_atoms=1000&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">10.95% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> - New value: 382μs, old value: 345μs.<br> - Latest value: 597μs (73.30% worse than best value 345μs).tag:mdanalysis.asv,2017-01-26:/79734f785007a168bf8dd866d384aff66db6bb891df80878a9ed4dd3cae655209.09% ag_methods.AtomGroupMethodsBench.time_split(10000)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_split?python=2.7&p-num_atoms=10000&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">9.09% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> - New value: 19.1ms, old value: 17.6ms.<br> - Latest value: 25.6ms (161.62% worse than best value 9.78ms).tag:mdanalysis.asv,2017-01-26:/38f08b1223e7f7757206c70d820b70ac2bcbcebede22b877ced44a643375553011.50% ag_methods.FragmentFinding.time_find_fragments('large_fragment')2020-02-27T16:41:58Z<a href="index.html#ag_methods.FragmentFinding.time_find_fragments?python=2.7&p-universe_type=%27large_fragment%27&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">11.50% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> + Latest value: 4.21ms (968.63% worse than best value 394μs).tag:mdanalysis.asv,2017-01-26:/38f08b1223e7f7757206c70d820b70ac2bcbcebede22b877ced44a643375553011.50% ag_methods.FragmentFinding.time_find_fragments('large_fragment')2020-02-27T16:41:58Z<a href="index.html#ag_methods.FragmentFinding.time_find_fragments?python=2.7&p-universe_type=%27large_fragment%27&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">11.50% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> New value: 128ms, old value: 115ms.<br> Latest value: 185ms (25626867.94% worse than best value 722ns).tag:mdanalysis.asv,2017-01-26:/640de2a4187e16b45dbc3355df77ca79a474eddb812aeb2a80f1a1fa104611f426.07% ag_methods.AtomGroupAttrsBench.time_bond(10)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_bond?python=2.7&p-num_atoms=10&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">26.07% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> New value: 4.85μs, old value: 3.85μs.<br> Latest value: 7.57μs (96.55% worse than best value 3.85μs).tag:mdanalysis.asv,2017-01-26:/5c486b6502abfaacee917d483637894ab4fada0e5539279ff348473c4798e3e423.54% ag_methods.AtomGroupAttrsBench.time_bond(10000)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_bond?python=2.7&p-num_atoms=10000&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">23.54% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> New value: 4.78μs, old value: 3.87μs.<br> - Latest value: 8.03μs (107.59% worse than best value 3.87μs).tag:mdanalysis.asv,2017-01-26:/6b514e2d83c94f857641dc56ff147a9a6524606bd6c2f1714ce247d287534f9f18.66% ag_methods.AtomGroupAttrsBench.time_improper(10000)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_improper?python=2.7&p-num_atoms=10000&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">18.66% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> + Latest value: 8.03μs (107.59% worse than best value 3.87μs).tag:mdanalysis.asv,2017-01-26:/e7c61532728470c824b8d97ab074e0f7dca8401a9e535662d4fdc8235fea786f8.16% ag_methods.AtomGroupMethodsBench.time_split(10)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_split?python=2.7&p-num_atoms=10&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">8.16% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> + New value: 14.3μs, old value: 13.2μs.<br> + Latest value: 17.0μs (28.59% worse than best value 13.2μs).tag:mdanalysis.asv,2017-01-26:/295771294a052f6cd3483c12ebbc910a5000a56f637bcd60cc4c9a9737b7a80210.95% ag_methods.AtomGroupMethodsBench.time_split(1000)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_split?python=2.7&p-num_atoms=1000&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">10.95% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> + New value: 382μs, old value: 345μs.<br> + Latest value: 597μs (73.30% worse than best value 345μs).tag:mdanalysis.asv,2017-01-26:/79734f785007a168bf8dd866d384aff66db6bb891df80878a9ed4dd3cae655209.09% ag_methods.AtomGroupMethodsBench.time_split(10000)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_split?python=2.7&p-num_atoms=10000&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">9.09% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> + New value: 19.1ms, old value: 17.6ms.<br> + Latest value: 25.6ms (161.62% worse than best value 9.78ms).tag:mdanalysis.asv,2017-01-26:/6b514e2d83c94f857641dc56ff147a9a6524606bd6c2f1714ce247d287534f9f18.66% ag_methods.AtomGroupAttrsBench.time_improper(10000)2020-02-27T16:41:58Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_improper?python=2.7&p-num_atoms=10000&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">18.66% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> New value: 4.58μs, old value: 3.86μs.<br> - Latest value: 7.67μs (98.49% worse than best value 3.86μs).tag:mdanalysis.asv,2017-02-25:/5e50dd73cf2d33c838f39cd621cb034432dfb9bc220231fce0f2807c9fe5c8785.65% ag_methods.AtomGroupAttrsBench.time_forces(100)2020-02-27T15:58:30Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_forces?python=2.7&p-num_atoms=100&commits=803d8134b994a225a0337c9be652d14f307cc365-4d0f3e3127c1a6ade53c04bf7f93d916cebcef39">5.65% regression</a> on 2017-02-25 08:07:46 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/803d8134b994a225a0337c9be652d14f307cc365...4d0f3e3127c1a6ade53c04bf7f93d916cebcef39">803d8134...4d0f3e31</a>.<br> + Latest value: 7.67μs (98.49% worse than best value 3.86μs).tag:mdanalysis.asv,2017-01-26:/801069e19022134ed50deac5d115d1127f8ebeb6bb05d40a50436f1b434194b7411.72% import.ImportBench.time_import2020-02-27T16:41:58Z<a href="index.html#import.ImportBench.time_import?python=2.7&commits=ebacb4f2b7a18a43ea41aebb640dae066c19e90f-dad6b32f29d89cd91688588426a256d0495b2a6a">411.72% regression</a> on 2017-01-26 06:31:27 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/ebacb4f2b7a18a43ea41aebb640dae066c19e90f...dad6b32f29d89cd91688588426a256d0495b2a6a">ebacb4f2...dad6b32f</a>.<br> + New value: 2.90μs, old value: 567ns.<br> + Latest value: 2.90μs (411.72% worse than best value 567ns).tag:mdanalysis.asv,2017-02-25:/5e50dd73cf2d33c838f39cd621cb034432dfb9bc220231fce0f2807c9fe5c8785.65% ag_methods.AtomGroupAttrsBench.time_forces(100)2020-02-27T15:58:30Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_forces?python=2.7&p-num_atoms=100&commits=803d8134b994a225a0337c9be652d14f307cc365-4d0f3e3127c1a6ade53c04bf7f93d916cebcef39">5.65% regression</a> on 2017-02-25 08:07:46 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/803d8134b994a225a0337c9be652d14f307cc365...4d0f3e3127c1a6ade53c04bf7f93d916cebcef39">803d8134...4d0f3e31</a>.<br> New value: 3.17μs, old value: 3.00μs.<br> Latest value: 3.19μs (6.12% worse than best value 3.00μs).tag:mdanalysis.asv,2017-02-25:/3b81476c2af87ca77289ae696711fc1e38b6f2b1310bfeec6f71d7073387001e5.67% ag_methods.AtomGroupAttrsBench.time_forces(1000)2020-02-27T15:58:30Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_forces?python=2.7&p-num_atoms=1000&commits=803d8134b994a225a0337c9be652d14f307cc365-4d0f3e3127c1a6ade53c04bf7f93d916cebcef39">5.67% regression</a> on 2017-02-25 08:07:46 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/803d8134b994a225a0337c9be652d14f307cc365...4d0f3e3127c1a6ade53c04bf7f93d916cebcef39">803d8134...4d0f3e31</a>.<br> New value: 3.18μs, old value: 3.01μs.<br> @@ -807,25 +813,59 @@ New value: 887ns, old value: 686ns.<br> Latest value: 887ns (37.45% worse than best value 645ns).tag:mdanalysis.asv,2017-12-07:/92dc0aa7873f63a5d0fa58dc07d8fb9b25f79e52dae6baadaa60965dc0c12b0528.85% ag_methods.AtomGroupAttrsBench.time_atomcount(10000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atomcount?python=2.7&p-num_atoms=10000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">28.85% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 886ns, old value: 687ns.<br> - Latest value: 886ns (36.16% worse than best value 651ns).tag:mdanalysis.asv,2017-12-07:/196ac26d3da23784428e2c2a919e69e3acbfe056d8075af49ddf966de1fcb58f6.20% ag_methods.AtomGroupAttrsBench.time_segmentcount(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_segmentcount?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">6.20% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + Latest value: 886ns (36.16% worse than best value 651ns).tag:mdanalysis.asv,2017-12-07:/4d33fc45833741999855f47a700dd677a31274b262b8e667d624c3b890e3dead49.42% ag_methods.AtomGroupMethodsBench.time_concatenate(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_concatenate?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">49.42% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 5.75μs, old value: 3.85μs.<br> + Latest value: 5.86μs (52.29% worse than best value 3.85μs).tag:mdanalysis.asv,2017-12-07:/67619b69593a377b94a7dd21ff838669936fe97e280ccfe9f1a07a55780dad1249.16% ag_methods.AtomGroupMethodsBench.time_concatenate(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_concatenate?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">49.16% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 6.08μs, old value: 4.08μs.<br> + Latest value: 6.11μs (49.85% worse than best value 4.08μs).tag:mdanalysis.asv,2017-12-07:/0c26950f19d50217e9581e0d6a8533dd7b9a0c7d7c6ab80ed3aff0282d23bb7f41.81% ag_methods.AtomGroupMethodsBench.time_concatenate(1000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_concatenate?python=2.7&p-num_atoms=1000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">41.81% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 6.66μs, old value: 4.70μs.<br> + Latest value: 6.66μs (41.81% worse than best value 4.70μs).tag:mdanalysis.asv,2017-12-07:/5f07df4a4d8ab3bce9cb53a85c1b4c40e590d4834319b0a284ce540e70a2c32918.43% ag_methods.AtomGroupMethodsBench.time_concatenate(10000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_concatenate?python=2.7&p-num_atoms=10000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">18.43% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 13.0μs, old value: 11.0μs.<br> + Latest value: 13.2μs (20.78% worse than best value 11.0μs).tag:mdanalysis.asv,2017-12-07:/196ac26d3da23784428e2c2a919e69e3acbfe056d8075af49ddf966de1fcb58f6.20% ag_methods.AtomGroupAttrsBench.time_segmentcount(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_segmentcount?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">6.20% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 13.6μs, old value: 12.8μs.<br> - Latest value: 13.6μs (1074.35% worse than best value 1.16μs).tag:mdanalysis.asv,2017-12-07:/f15b04be825b22311d50bcaf860d9f5d2cdcaae7adcb378b3c797bd88f3ad5a99.92% ag_methods.AtomGroupMethodsBench.time_symmetric_difference(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_symmetric_difference?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">9.92% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 19.1μs, old value: 17.4μs.<br> - Latest value: 20.1μs (15.80% worse than best value 17.4μs).tag:mdanalysis.asv,2017-12-07:/4bf46e9f719a17fa2273d80caa079e330cbe3dbb96dd750d1c0343c95f9e842310.92% ag_methods.AtomGroupMethodsBench.time_symmetric_difference(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_symmetric_difference?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">10.92% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 21.6μs, old value: 19.5μs.<br> - Latest value: 23.0μs (17.90% worse than best value 19.5μs).tag:mdanalysis.asv,2017-12-07:/feccdd57a4338fa9efb10bee1ce86ec44a22cc306fdebc68bb9a97cc93c23a9a2.83% ag_methods.AtomGroupMethodsBench.time_symmetric_difference(1000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_symmetric_difference?python=2.7&p-num_atoms=1000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">2.83% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 86.0μs, old value: 83.6μs.<br> - Latest value: 88.6μs (5.93% worse than best value 83.6μs).tag:mdanalysis.asv,2017-12-07:/570267a59e5af7a7b1dbdccb91b67e0e8d38a95eb5ee619fcfea6e5a91e826647.81% ag_methods.AtomGroupAttrsBench.time_residues(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_residues?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">7.81% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 12.5μs, old value: 11.6μs.<br> - Latest value: 12.5μs (2540.79% worse than best value 474ns).tag:mdanalysis.asv,2017-12-07:/8f89d43787d2423ec7ea5b355af58ea47531a9196ac1687817a1f5a2b623c96215.96% ag_methods.AtomGroupMethodsBench.time_isdisjoint(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_isdisjoint?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">15.96% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + Latest value: 13.6μs (1074.35% worse than best value 1.16μs).tag:mdanalysis.asv,2017-12-07:/8fffc681d38f9bd8d51be8152d66d67bb62cd12fce2bc5649c71859901745a2b27.89% ag_methods.AtomGroupAttrsBench.time_atomcount(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atomcount?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">27.89% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 875ns, old value: 684ns.<br> + Latest value: 875ns (35.11% worse than best value 648ns).tag:mdanalysis.asv,2017-12-07:/88f238968feaa9c701d99a61195944e56f79fd10946a85e1104dc814e790337226.01% ag_methods.AtomGroupAttrsBench.time_atomcount(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atomcount?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">26.01% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 863ns, old value: 685ns.<br> + Latest value: 874ns (34.83% worse than best value 649ns).tag:mdanalysis.asv,2017-12-07:/86c501b6c0b2c2470d5f85bad3020e7952e127f7fb6d7b9752bb5ed99bb2008227.27% ag_methods.AtomGroupAttrsBench.time_atomcount(1000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atomcount?python=2.7&p-num_atoms=1000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">27.27% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 878ns, old value: 690ns.<br> + Latest value: 878ns (34.69% worse than best value 652ns).tag:mdanalysis.asv,2017-12-07:/92dc0aa7873f63a5d0fa58dc07d8fb9b25f79e52dae6baadaa60965dc0c12b0527.72% ag_methods.AtomGroupAttrsBench.time_atomcount(10000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atomcount?python=2.7&p-num_atoms=10000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">27.72% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 881ns, old value: 690ns.<br> + Latest value: 881ns (34.64% worse than best value 655ns).tag:mdanalysis.asv,2017-12-07:/a735dd24bc899f5e03b59e94ad31337ffb28bc27261c8e9d5cf2174e7c8297d314.25% ag_methods.AtomGroupMethodsBench.time_intersection(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_intersection?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">14.25% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 17.2μs, old value: 15.1μs.<br> + Latest value: 18.0μs (19.63% worse than best value 15.1μs).tag:mdanalysis.asv,2017-12-07:/726b0652d831481aa8b07631c276f6e71984214ec31d72ee5c6eb1079a71a03712.55% ag_methods.AtomGroupMethodsBench.time_intersection(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_intersection?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">12.55% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 20.5μs, old value: 18.2μs.<br> + Latest value: 20.5μs (12.55% worse than best value 18.2μs).tag:mdanalysis.asv,2017-12-05:/634e658893a1a54bb0599c74265c3e67fda545d3127ae3de0ef87cd7ca9fd2503.51% ag_methods.AtomGroupMethodsBench.time_intersection(1000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_intersection?python=2.7&p-num_atoms=1000&commits=9b074d53ce84f40de11c9599349d75cdfc12fa8c-1e11a67c39e35e3fd4775a6ac759440eb0f8cabc">3.51% regression</a> on 2017-12-05 12:30:24 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9b074d53ce84f40de11c9599349d75cdfc12fa8c...1e11a67c39e35e3fd4775a6ac759440eb0f8cabc">9b074d53...1e11a67c</a>.<br> + New value: 92.0μs, old value: 88.8μs.<br> + Latest value: 93.7μs (5.51% worse than best value 88.8μs).tag:mdanalysis.asv,2017-12-07:/a407549bfa1d2e2b8620ba26434e7622c9f21f6332004063e471f5a0d0f716a79.76% ag_methods.AtomGroupMethodsBench.time_guess_bonds(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_guess_bonds?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">9.76% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 4.16ms, old value: 3.79ms.<br> + Latest value: 4.21ms (968.63% worse than best value 394μs).tag:mdanalysis.asv,2017-12-07:/8f89d43787d2423ec7ea5b355af58ea47531a9196ac1687817a1f5a2b623c96215.96% ag_methods.AtomGroupMethodsBench.time_isdisjoint(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_isdisjoint?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">15.96% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 17.9μs, old value: 15.4μs.<br> Latest value: 18.6μs (20.65% worse than best value 15.4μs).tag:mdanalysis.asv,2017-12-07:/1e7377c3dd91bba7097c6c1c702b05b74ee21b7d6252188d77b7c9732ff9b1c313.02% ag_methods.AtomGroupMethodsBench.time_isdisjoint(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_isdisjoint?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">13.02% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 21.1μs, old value: 18.7μs.<br> Latest value: 21.8μs (16.53% worse than best value 18.7μs).tag:mdanalysis.asv,2017-12-07:/1b4d5e61d1c0e5182f9f85a4d81f503f5a474b512c798e3942d3cd9144019f343.83% ag_methods.AtomGroupMethodsBench.time_isdisjoint(1000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_isdisjoint?python=2.7&p-num_atoms=1000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">3.83% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 93.4μs, old value: 90.0μs.<br> - Latest value: 94.6μs (5.15% worse than best value 90.0μs).tag:mdanalysis.asv,2017-12-07:/a407549bfa1d2e2b8620ba26434e7622c9f21f6332004063e471f5a0d0f716a79.76% ag_methods.AtomGroupMethodsBench.time_guess_bonds(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_guess_bonds?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">9.76% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 4.16ms, old value: 3.79ms.<br> - Latest value: 4.21ms (968.63% worse than best value 394μs).tag:mdanalysis.asv,2017-12-07:/e37cb4e6e5aae107bf0dcdfea325b35c399ada9fa71f8d2f78b675d18b157cc88.20% ag_methods.AtomGroupMethodsBench.time_split(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_split?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">8.20% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + Latest value: 94.6μs (5.15% worse than best value 90.0μs).tag:mdanalysis.asv,2017-12-07:/0b58bd636c52ac64b940b69f55effabd33541e5698ba754ca194df9a9ea8fb3424.81% ag_methods.AtomGroupMethodsBench.time_union(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_union?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">24.81% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 11.7μs, old value: 9.38μs.<br> + Latest value: 11.7μs (24.81% worse than best value 9.38μs).tag:mdanalysis.asv,2017-12-07:/32a539e733604b54f49cccfe16077c7a348a22ba494c1807644c543f95b8092817.26% ag_methods.AtomGroupMethodsBench.time_union(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_union?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">17.26% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 13.8μs, old value: 11.7μs.<br> + Latest value: 14.3μs (21.89% worse than best value 11.7μs).tag:mdanalysis.asv,2017-12-07:/feeb6f660c64a03ab309aa6258d81edc3c5ca9ef1d02d2459021b8356cf1b4c74.00% ag_methods.AtomGroupAttrsBench.time_segments(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_segments?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">4.00% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 12.8μs, old value: 12.3μs.<br> + Latest value: 13.2μs (2162.81% worse than best value 584ns).tag:mdanalysis.asv,2017-12-07:/570267a59e5af7a7b1dbdccb91b67e0e8d38a95eb5ee619fcfea6e5a91e826647.81% ag_methods.AtomGroupAttrsBench.time_residues(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_residues?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">7.81% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 12.5μs, old value: 11.6μs.<br> + Latest value: 12.5μs (2540.79% worse than best value 474ns).tag:mdanalysis.asv,2017-12-07:/f15b04be825b22311d50bcaf860d9f5d2cdcaae7adcb378b3c797bd88f3ad5a99.92% ag_methods.AtomGroupMethodsBench.time_symmetric_difference(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_symmetric_difference?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">9.92% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 19.1μs, old value: 17.4μs.<br> + Latest value: 20.1μs (15.80% worse than best value 17.4μs).tag:mdanalysis.asv,2017-12-07:/4bf46e9f719a17fa2273d80caa079e330cbe3dbb96dd750d1c0343c95f9e842310.92% ag_methods.AtomGroupMethodsBench.time_symmetric_difference(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_symmetric_difference?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">10.92% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 21.6μs, old value: 19.5μs.<br> + Latest value: 23.0μs (17.90% worse than best value 19.5μs).tag:mdanalysis.asv,2017-12-07:/feccdd57a4338fa9efb10bee1ce86ec44a22cc306fdebc68bb9a97cc93c23a9a2.83% ag_methods.AtomGroupMethodsBench.time_symmetric_difference(1000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_symmetric_difference?python=2.7&p-num_atoms=1000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">2.83% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 86.0μs, old value: 83.6μs.<br> + Latest value: 88.6μs (5.93% worse than best value 83.6μs).tag:mdanalysis.asv,2017-12-07:/12502a841499f83354a63d33566474375447bfc82e68ecd912ffec2614e6881d24.24% ag_methods.AtomGroupAttrsBench.time_bond(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_bond?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">24.24% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 7.57μs, old value: 6.09μs.<br> + Latest value: 7.57μs (96.55% worse than best value 3.85μs).tag:mdanalysis.asv,2017-12-07:/f7b3a2c68beb70ece563a4d8e7d426903b0429730f2b4b39e0d10f239f6a447425.98% ag_methods.AtomGroupAttrsBench.time_bond(10000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_bond?python=2.7&p-num_atoms=10000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">25.98% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 7.64μs, old value: 6.06μs.<br> + Latest value: 8.03μs (107.59% worse than best value 3.87μs).tag:mdanalysis.asv,2017-12-07:/63b2e3a9abad632bd5285ee2e1ada983660f0804a0d570dc0db582a611bf7fd311.42% ag_methods.AtomGroupMethodsBench.time_difference(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_difference?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">11.42% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 27.4μs, old value: 24.6μs.<br> + Latest value: 27.4μs (11.42% worse than best value 24.6μs).tag:mdanalysis.asv,2017-12-07:/e37cb4e6e5aae107bf0dcdfea325b35c399ada9fa71f8d2f78b675d18b157cc88.20% ag_methods.AtomGroupMethodsBench.time_split(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_split?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">8.20% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 17.0μs, old value: 15.7μs.<br> Latest value: 17.0μs (28.59% worse than best value 13.2μs).tag:mdanalysis.asv,2017-12-07:/e3b4956f9b79643ed41f033fe150e5bd908985088e1476dee23e0cefea85359917.83% ag_methods.AtomGroupMethodsBench.time_split(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_split?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">17.83% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 49.5μs, old value: 42.0μs.<br> @@ -833,51 +873,17 @@ New value: 597μs, old value: 488μs.<br> Latest value: 597μs (73.30% worse than best value 345μs).tag:mdanalysis.asv,2017-12-07:/9c57b0381f2d1c1db97ecb4f2e1ff50042f59f319bfdf1ed1fa60e4c94f66efd11.81% ag_methods.AtomGroupMethodsBench.time_split(10000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_split?python=2.7&p-num_atoms=10000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">11.81% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 24.8ms, old value: 22.2ms.<br> - Latest value: 25.6ms (161.62% worse than best value 9.78ms).tag:mdanalysis.asv,2017-12-07:/4d33fc45833741999855f47a700dd677a31274b262b8e667d624c3b890e3dead49.42% ag_methods.AtomGroupMethodsBench.time_concatenate(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_concatenate?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">49.42% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 5.75μs, old value: 3.85μs.<br> - Latest value: 5.86μs (52.29% worse than best value 3.85μs).tag:mdanalysis.asv,2017-12-07:/67619b69593a377b94a7dd21ff838669936fe97e280ccfe9f1a07a55780dad1249.16% ag_methods.AtomGroupMethodsBench.time_concatenate(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_concatenate?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">49.16% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 6.08μs, old value: 4.08μs.<br> - Latest value: 6.11μs (49.85% worse than best value 4.08μs).tag:mdanalysis.asv,2017-12-07:/0c26950f19d50217e9581e0d6a8533dd7b9a0c7d7c6ab80ed3aff0282d23bb7f41.81% ag_methods.AtomGroupMethodsBench.time_concatenate(1000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_concatenate?python=2.7&p-num_atoms=1000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">41.81% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 6.66μs, old value: 4.70μs.<br> - Latest value: 6.66μs (41.81% worse than best value 4.70μs).tag:mdanalysis.asv,2017-12-07:/5f07df4a4d8ab3bce9cb53a85c1b4c40e590d4834319b0a284ce540e70a2c32918.43% ag_methods.AtomGroupMethodsBench.time_concatenate(10000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_concatenate?python=2.7&p-num_atoms=10000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">18.43% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 13.0μs, old value: 11.0μs.<br> - Latest value: 13.2μs (20.78% worse than best value 11.0μs).tag:mdanalysis.asv,2017-12-07:/12502a841499f83354a63d33566474375447bfc82e68ecd912ffec2614e6881d24.24% ag_methods.AtomGroupAttrsBench.time_bond(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_bond?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">24.24% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 7.57μs, old value: 6.09μs.<br> - Latest value: 7.57μs (96.55% worse than best value 3.85μs).tag:mdanalysis.asv,2017-12-07:/f7b3a2c68beb70ece563a4d8e7d426903b0429730f2b4b39e0d10f239f6a447425.98% ag_methods.AtomGroupAttrsBench.time_bond(10000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_bond?python=2.7&p-num_atoms=10000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">25.98% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 7.64μs, old value: 6.06μs.<br> - Latest value: 8.03μs (107.59% worse than best value 3.87μs).tag:mdanalysis.asv,2017-12-07:/f3291da4eb49e3f43624c9828e402f68a1eb9f76aa2cdd5b9d145cebd64517eb25.82% ag_methods.AtomGroupAttrsBench.time_improper(10000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_improper?python=2.7&p-num_atoms=10000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">25.82% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + Latest value: 25.6ms (161.62% worse than best value 9.78ms).tag:mdanalysis.asv,2017-12-07:/f3291da4eb49e3f43624c9828e402f68a1eb9f76aa2cdd5b9d145cebd64517eb25.82% ag_methods.AtomGroupAttrsBench.time_improper(10000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_improper?python=2.7&p-num_atoms=10000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">25.82% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 7.67μs, old value: 6.09μs.<br> - Latest value: 7.67μs (98.49% worse than best value 3.86μs).tag:mdanalysis.asv,2017-12-07:/feeb6f660c64a03ab309aa6258d81edc3c5ca9ef1d02d2459021b8356cf1b4c74.00% ag_methods.AtomGroupAttrsBench.time_segments(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_segments?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">4.00% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 12.8μs, old value: 12.3μs.<br> - Latest value: 13.2μs (2162.81% worse than best value 584ns).tag:mdanalysis.asv,2017-12-07:/a735dd24bc899f5e03b59e94ad31337ffb28bc27261c8e9d5cf2174e7c8297d314.25% ag_methods.AtomGroupMethodsBench.time_intersection(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_intersection?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">14.25% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 17.2μs, old value: 15.1μs.<br> - Latest value: 18.0μs (19.63% worse than best value 15.1μs).tag:mdanalysis.asv,2017-12-07:/726b0652d831481aa8b07631c276f6e71984214ec31d72ee5c6eb1079a71a03712.55% ag_methods.AtomGroupMethodsBench.time_intersection(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_intersection?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">12.55% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 20.5μs, old value: 18.2μs.<br> - Latest value: 20.5μs (12.55% worse than best value 18.2μs).tag:mdanalysis.asv,2017-12-05:/634e658893a1a54bb0599c74265c3e67fda545d3127ae3de0ef87cd7ca9fd2503.51% ag_methods.AtomGroupMethodsBench.time_intersection(1000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_intersection?python=2.7&p-num_atoms=1000&commits=9b074d53ce84f40de11c9599349d75cdfc12fa8c-1e11a67c39e35e3fd4775a6ac759440eb0f8cabc">3.51% regression</a> on 2017-12-05 12:30:24 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9b074d53ce84f40de11c9599349d75cdfc12fa8c...1e11a67c39e35e3fd4775a6ac759440eb0f8cabc">9b074d53...1e11a67c</a>.<br> - New value: 92.0μs, old value: 88.8μs.<br> - Latest value: 93.7μs (5.51% worse than best value 88.8μs).tag:mdanalysis.asv,2017-12-07:/023f9a0aeeccb1e4bd8890c511a193e2aea3b2594d38568a98dd849c8390b22e18.10% ag_methods.AtomGroupMethodsBench.time_groupby(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_groupby?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">18.10% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + Latest value: 7.67μs (98.49% worse than best value 3.86μs).tag:mdanalysis.asv,2017-12-07:/2bcaf5f59960c3cc07d5f13cbf5533780162edc3a82f53264c778e040781e78d6.22% ag_methods.AtomGroupAttrsBench.time_residuecount(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_residuecount?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">6.22% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> + New value: 13.1μs, old value: 12.3μs.<br> + Latest value: 13.2μs (1302.07% worse than best value 941ns).tag:mdanalysis.asv,2017-12-07:/023f9a0aeeccb1e4bd8890c511a193e2aea3b2594d38568a98dd849c8390b22e18.10% ag_methods.AtomGroupMethodsBench.time_groupby(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_groupby?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">18.10% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 14.7μs, old value: 12.5μs.<br> Latest value: 16.7μs (71.60% worse than best value 9.73μs).tag:mdanalysis.asv,2017-12-07:/73f5cadff5ba87d294bb225d2b1d60dd0fb4798de08fe88226cb7ae5f8ac82e620.63% ag_methods.AtomGroupMethodsBench.time_groupby(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_groupby?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">20.63% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 49.2μs, old value: 40.8μs.<br> Latest value: 49.2μs (48.05% worse than best value 33.2μs).tag:mdanalysis.asv,2017-12-07:/cead8e28f5f7fd3b9124744872f8e7fa83af0b531aeb2948997e260765f9cc448.15% ag_methods.AtomGroupMethodsBench.time_groupby(1000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_groupby?python=2.7&p-num_atoms=1000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">8.15% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> New value: 339μs, old value: 314μs.<br> - Latest value: 346μs (22.99% worse than best value 281μs).tag:mdanalysis.asv,2017-12-07:/63b2e3a9abad632bd5285ee2e1ada983660f0804a0d570dc0db582a611bf7fd311.42% ag_methods.AtomGroupMethodsBench.time_difference(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_difference?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">11.42% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 27.4μs, old value: 24.6μs.<br> - Latest value: 27.4μs (11.42% worse than best value 24.6μs).tag:mdanalysis.asv,2017-12-07:/0b58bd636c52ac64b940b69f55effabd33541e5698ba754ca194df9a9ea8fb3424.81% ag_methods.AtomGroupMethodsBench.time_union(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_union?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">24.81% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 11.7μs, old value: 9.38μs.<br> - Latest value: 11.7μs (24.81% worse than best value 9.38μs).tag:mdanalysis.asv,2017-12-07:/32a539e733604b54f49cccfe16077c7a348a22ba494c1807644c543f95b8092817.26% ag_methods.AtomGroupMethodsBench.time_union(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_union?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">17.26% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 13.8μs, old value: 11.7μs.<br> - Latest value: 14.3μs (21.89% worse than best value 11.7μs).tag:mdanalysis.asv,2017-12-07:/8fffc681d38f9bd8d51be8152d66d67bb62cd12fce2bc5649c71859901745a2b27.89% ag_methods.AtomGroupAttrsBench.time_atomcount(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atomcount?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">27.89% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 875ns, old value: 684ns.<br> - Latest value: 875ns (35.11% worse than best value 648ns).tag:mdanalysis.asv,2017-12-07:/88f238968feaa9c701d99a61195944e56f79fd10946a85e1104dc814e790337226.01% ag_methods.AtomGroupAttrsBench.time_atomcount(100)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atomcount?python=2.7&p-num_atoms=100&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">26.01% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 863ns, old value: 685ns.<br> - Latest value: 874ns (34.83% worse than best value 649ns).tag:mdanalysis.asv,2017-12-07:/86c501b6c0b2c2470d5f85bad3020e7952e127f7fb6d7b9752bb5ed99bb2008227.27% ag_methods.AtomGroupAttrsBench.time_atomcount(1000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atomcount?python=2.7&p-num_atoms=1000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">27.27% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 878ns, old value: 690ns.<br> - Latest value: 878ns (34.69% worse than best value 652ns).tag:mdanalysis.asv,2017-12-07:/92dc0aa7873f63a5d0fa58dc07d8fb9b25f79e52dae6baadaa60965dc0c12b0527.72% ag_methods.AtomGroupAttrsBench.time_atomcount(10000)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atomcount?python=2.7&p-num_atoms=10000&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">27.72% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 881ns, old value: 690ns.<br> - Latest value: 881ns (34.64% worse than best value 655ns).tag:mdanalysis.asv,2017-12-07:/2bcaf5f59960c3cc07d5f13cbf5533780162edc3a82f53264c778e040781e78d6.22% ag_methods.AtomGroupAttrsBench.time_residuecount(10)2020-02-27T11:28:18Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_residuecount?python=2.7&p-num_atoms=10&commits=1e11a67c39e35e3fd4775a6ac759440eb0f8cabc-54d5bab1b2aa27b7f914078cc3e5a892e79ef479">6.22% regression</a> on 2017-12-07 07:20:11 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/1e11a67c39e35e3fd4775a6ac759440eb0f8cabc...54d5bab1b2aa27b7f914078cc3e5a892e79ef479">1e11a67c...54d5bab1</a>.<br> - New value: 13.1μs, old value: 12.3μs.<br> - Latest value: 13.2μs (1302.07% worse than best value 941ns).tag:mdanalysis.asv,2018-08-20:/ff1c243ad3931732758e3cf7a923f4c6b096dde880ee55e6b4a2b3866707de565.53% analysis.rdf.SimpleRdfBench.time_interrdf(20, [0, 20], 1000)2020-02-26T14:41:04Z<a href="index.html#analysis.rdf.SimpleRdfBench.time_interrdf?python=2.7&p-nbins=20&p-range_val=%5B0%2C+20%5D&p-natoms=1000&commits=57d77d80a3236e9503473e2609ece2bdb59bc07e-d73b653f19e8446bbb9de51bb41d71f78d148d30">5.53% regression</a> on 2018-08-20 15:17:07 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/57d77d80a3236e9503473e2609ece2bdb59bc07e...d73b653f19e8446bbb9de51bb41d71f78d148d30">57d77d80...d73b653f</a>.<br> + Latest value: 346μs (22.99% worse than best value 281μs).tag:mdanalysis.asv,2018-08-20:/ff1c243ad3931732758e3cf7a923f4c6b096dde880ee55e6b4a2b3866707de565.53% analysis.rdf.SimpleRdfBench.time_interrdf(20, [0, 20], 1000)2020-02-26T14:41:04Z<a href="index.html#analysis.rdf.SimpleRdfBench.time_interrdf?python=2.7&p-nbins=20&p-range_val=%5B0%2C+20%5D&p-natoms=1000&commits=57d77d80a3236e9503473e2609ece2bdb59bc07e-d73b653f19e8446bbb9de51bb41d71f78d148d30">5.53% regression</a> on 2018-08-20 15:17:07 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/57d77d80a3236e9503473e2609ece2bdb59bc07e...d73b653f19e8446bbb9de51bb41d71f78d148d30">57d77d80...d73b653f</a>.<br> New value: 665ms, old value: 630ms.<br> Latest value: 1.17s (584.75% worse than best value 171ms).tag:mdanalysis.asv,2018-08-20:/8e2f91dc8696f0a166b3f72ea6611f08290543a8d31dca2398df40694b933db45.33% analysis.rdf.SimpleRdfBench.time_interrdf(75, [0, 20], 1000)2020-02-26T14:41:04Z<a href="index.html#analysis.rdf.SimpleRdfBench.time_interrdf?python=2.7&p-nbins=75&p-range_val=%5B0%2C+20%5D&p-natoms=1000&commits=57d77d80a3236e9503473e2609ece2bdb59bc07e-d73b653f19e8446bbb9de51bb41d71f78d148d30">5.33% regression</a> on 2018-08-20 15:17:07 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/57d77d80a3236e9503473e2609ece2bdb59bc07e...d73b653f19e8446bbb9de51bb41d71f78d148d30">57d77d80...d73b653f</a>.<br> New value: 664ms, old value: 630ms.<br>