diff --git a/graphs/Cython-0.27/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-2.7/ram-16GB/scipy/six-null/selections.GeoSelectionBench.time_geometric_selections.json b/graphs/Cython-0.27/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-2.7/ram-16GB/scipy/six-null/selections.GeoSelectionBench.time_geometric_selections.json deleted file mode 100644 index e54c8bf8f..000000000 --- a/graphs/Cython-0.27/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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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_atoms.json index e2514717f..3c8f9e2b8 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_atoms.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_atoms.json @@ -1 +1 @@ -[[18299, [1.7940328895805604e-07, 1.797379027751829e-07, 1.8018506674078354e-07, 1.7979543342841795e-07]], [18369, [1.794865410492225e-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_dihedral.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_dihedral.json @@ -1 +1 @@ -[[18299, [3.0462020524111794e-06, 3.0461483340801705e-06, 3.0377862687425075e-06, 3.052257434455934e-06]], [18369, [3.0946586811744275e-06, 3.115901523011012e-06, 3.0917821070799876e-06, 3.079380029768586e-06]], [18386, [3.061253102704025e-06, 3.053955084321897e-06, 3.0649820501074218e-06, 3.064525140340975e-06]], [18406, [3.1241418483619055e-06, 3.128217560336242e-06, 3.1163074412200883e-06, 3.131414278831384e-06]], [18408, [3.0526897094074113e-06, 3.0760062062394076e-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_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 @@ -[[18299, [1.6989614222601829e-06, 1.6760137395455541e-06, 1.6882059740257024e-06, 1.68946108704701e-06]], [18369, [1.6961014993927058e-06, 1.7020074178944072e-06, 1.707555282955165e-06, 1.701924866704089e-06]], [18386, [1.6814104942998258e-06, 1.6894991658912361e-06, 1.700558372316363e-06, 1.7064492948617334e-06]], [18406, [1.7491114864931069e-06, 1.7455526010353615e-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_improper.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_improper.json index ad586744b..f204e534e 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_improper.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_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 index 5df00fd67..123427015 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_indices.json +++ 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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.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 index cc59e3665..7a3d2197e 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_residuecount.json +++ b/graphs/Cython/MDAnalysisTests-null/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 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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_residues.json index bbb03232d..21a9205fc 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_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 @@ -[[18299, [7.082961974132836e-06, 7.411486055640366e-06, 1.0357960685161338e-05, 3.9995321058891576e-05]], [18369, [6.329404460313348e-06, 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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/ag_methods.AtomGroupAttrsBench.time_velocity.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_velocity.json index e93b07424..a37a3ac7b 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_velocity.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_velocity.json @@ -1 +1 @@ -[[18299, [1.732782208196981e-06, 1.7192061270016393e-06, 1.7240138251189166e-06, 1.7190111650670496e-06]], [18369, [1.7313522228842789e-06, 1.7367795055841136e-06, 1.748429081264844e-06, 1.7555362506124202e-06]], [18386, [1.711020831621452e-06, 1.6971418671390982e-06, 1.726271089384514e-06, 1.693077867651881e-06]], [18406, [1.7856505749773358e-06, 1.756163771423725e-06, 1.7627816097272608e-06, 1.7580114193422313e-06]], [18408, [1.723473646798623e-06, 1.7243245982121083e-06, 1.7294913650230118e-06, 1.7236122654413798e-06]], [18431, [1.7030541967785594e-06, 1.7143459560448522e-06, 1.7486934263262123e-06, 1.7181045628588782e-06]], [18458, [1.7615905892573818e-06, 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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_bsphere_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_bsphere_no_pbc.json index 84321a23c..7fafa76cd 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_bsphere_no_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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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_symmetric_difference.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_symmetric_difference.json index 82b4c5717..71662aece 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_symmetric_difference.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/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 @@ -[[18299, [3.7420488096728934e-06, 4.377596220767585e-06, 9.5741031691432e-06, 5.8445395630370967e-05]], [18369, [3.7143547676078413e-06, 4.4286329002651626e-06, 9.473410353221318e-06, 5.842559350863657e-05]], [18386, [3.717700904530261e-06, 4.391323820968384e-06, 9.428670905022457e-06, 5.825210828334093e-05]], [18406, [3.93500391203576e-06, 4.665495899288008e-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.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.json index 2787b9f64..6e318870c 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.distances.DistancesBench.time_distance_array.json 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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_self_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_self_distance_array_pre_allocated.json @@ -1 +1 @@ -[[18299, [3.3434065477679207e-06, 2.28426614436709e-05, 0.001997279837572326, 0.2135287499986589]], [18369, [3.424364878674814e-06, 2.3017060267051312e-05, 0.0020257194992154837, 0.2134621754521504]], [18386, [3.3655286576349015e-06, 2.304245638730293e-05, 0.0020119494874961674, 0.21280828199815005]], [18406, [3.4937850675782207e-06, 2.3251524806630202e-05, 0.002050931352035453, 0.21367226401343942]], [18408, 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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.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 @@ -[[18299, [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]], [18369, [3.6084144470741914e-07, 3.6292475616322353e-07, 3.631683248795096e-07, 3.612526850684165e-07, 3.6076171008204183e-07, 3.6931973129621907e-07, 3.863558849907847e-07, 3.801393758087378e-07, 3.7654962992445843e-07]], [18386, 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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_path_metric_funcBench.time_get_path_metric_func.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_path_metric_funcBench.time_get_path_metric_func.json index dfbc859cf..821c638e3 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-3.9/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-3.9/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_atomcount.json index 412620f7f..f2783646d 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-3.9/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_atomcount.json +++ 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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-3.9/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-3.9/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_residuecount.json index ca26c83fb..401e728ed 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-3.9/ram-16GB/scipy/six-null/ag_methods.AtomGroupAttrsBench.time_residuecount.json +++ 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--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-3.9/ram-16GB/scipy/six-null/analysis.distances.DistancesBench.time_distance_array.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-3.9/ram-16GB/scipy/six-null/analysis.distances.DistancesBench.time_distance_array.json index 6d8e6e73c..a244632f2 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-3.9/ram-16GB/scipy/six-null/analysis.distances.DistancesBench.time_distance_array.json +++ 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[20549, 3.6145792741824196e-06], [20598, 3.552096807182921e-06], [20647, 3.6193411963375144e-06]] \ No newline at end of file diff --git a/graphs/summary/ag_methods.AtomGroupAttrsBench.time_atomcount.json b/graphs/summary/ag_methods.AtomGroupAttrsBench.time_atomcount.json index 71c3821aa..ac23ccaf8 100644 --- a/graphs/summary/ag_methods.AtomGroupAttrsBench.time_atomcount.json +++ b/graphs/summary/ag_methods.AtomGroupAttrsBench.time_atomcount.json @@ -1 +1 @@ -[[1849, 6.496551086640185e-07], [1898, 6.489854194752739e-07], [1947, 6.48314123738157e-07], [1996, 6.507144540090655e-07], [2045, 6.526513222751193e-07], [2094, 6.48895492965062e-07], [2143, 6.503590374129217e-07], [2192, 6.541049641621858e-07], [2241, 6.532755811402655e-07], [2290, 6.519116620484668e-07], [2339, 6.525488193528845e-07], [2388, 6.538468348628255e-07], [2486, 6.498508275177314e-07], [2535, 6.542483128434537e-07], [2584, 6.514723403301246e-07], [2633, 6.511571246384005e-07], [2682, 6.554830738788398e-07], [2731, 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1693938516000, "20298": 1694495382000, "20324": 1694722654000, "20341": 1694814422000, "20343": 1694835712000, "20358": 1695106886000, "20368": 1695240617000, "20372": 1695292081000, "20373": 1695292120000, "20384": 1695400867000, "20386": 1695401857000, "20454": 1696946134000, "20457": 1697078683000, "20458": 1697078728000, "20459": 1697078792000, "20469": 1697391364000, "20480": 1698404415000, "20488": 1698428844000, "20496": 1698694861000, "20499": 1698775295000, "20516": 1699129404000, "20534": 1699176277000, "20537": 1699182336000, "20548": 1699259466000, "20552": 1699014347000, "20553": 1699014939000, "20559": 1699351607000, "20563": 1699440505000, "20581": 1699708548000, "20623": 1701595226000, "20629": 1701817765000}, "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 # set core flags for PBC accounting\n MDAnalysis.core.flags['use_periodic_selections'] = periodic_selection[0]\n MDAnalysis.core.flags['use_KDTree_routines'] = periodic_selection[1]\n \n if hasattr(MDAnalysis.Universe, 'select_atoms'):\n self.u.select_atoms(selection_string, updating=dynamic_selection)\n else:\n self.u.selectAtoms(selection_string, updating=dynamic_selection)\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": "b1d29e5a574daee7de2e1c37536258165a3371b550cd29fc2f41c55e796001e1", "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": 18297, "package-2.4.0b1": 18295, "package-2.4.1": 18308, "package-2.4.2": 18400, "package-2.4.3": 19184, "package-2.5.0": 19480, "package-2.6.0": 20001, "package-2.6.1": 20150, "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": 18293, "release-2.4.1": 18305, "release-2.4.2": 18398, "release-2.4.3": 19182, "release-2.5.0": 19477, "release-2.6.0": 20001, "release-2.6.1": 20141, "testdata-0-7-4": 609}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]} \ No newline at end of file +{"project": "mdanalysis", "project_url": "https://www.mdanalysis.org/", "show_commit_url": "https://github.com/MDAnalysis/mdanalysis/commit/", "hash_length": 8, "revision_to_hash": {"82": 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"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": 18297, "package-2.4.0b1": 18295, "package-2.4.1": 18308, "package-2.4.2": 18400, "package-2.4.3": 19184, "package-2.5.0": 19480, "package-2.6.0": 20001, "package-2.6.1": 20150, "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": 18293, "release-2.4.1": 18305, "release-2.4.2": 18398, "release-2.4.3": 19182, "release-2.5.0": 19477, "release-2.6.0": 20001, "release-2.6.1": 20141, "testdata-0-7-4": 609}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]} \ No newline at end of file diff --git a/info.json b/info.json index 45ba6e490..7e3eb7e0f 100644 --- a/info.json +++ b/info.json @@ -1,4 +1,4 @@ { "asv-version": "0.5.1", - "timestamp": 1701864457563 + "timestamp": 1702304795649 } \ No newline at end of file diff --git a/regressions.json b/regressions.json index 3f1a80235..4ba07b7c6 100644 --- a/regressions.json +++ b/regressions.json @@ -1 +1 @@ -{"regressions": [["ag_methods.AtomGroupAttrsBench.time_velocity(10)", "graphs/Cython/MDAnalysisTests/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests-null/mock/nose/numpy/os-Linux 3.16.0-4-amd64/psutil/pytest/python-2.7/ram-16GB/scipy/six/ag_methods.AtomGroupAttrsBench.time_velocity.json", {"python": "2.7"}, 0, 3.181547273938277e-06, 3.0106218058384354e-06, [[4793, 4884, 3.0106218058384354e-06, 3.181547273938277e-06]]], ["ag_methods.AtomGroupAttrsBench.time_velocity(100)", "graphs/Cython/MDAnalysisTests/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests-null/mock/nose/numpy/os-Linux 3.16.0-4-amd64/psutil/pytest/python-2.7/ram-16GB/scipy/six/ag_methods.AtomGroupAttrsBench.time_velocity.json", {"python": "2.7"}, 1, 3.1747576722978874e-06, 2.991487109905019e-06, [[4793, 4884, 2.991487109905019e-06, 3.1747576722978874e-06]]], ["ag_methods.AtomGroupAttrsBench.time_velocity(1000)", "graphs/Cython/MDAnalysisTests/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests-null/mock/nose/numpy/os-Linux 3.16.0-4-amd64/psutil/pytest/python-2.7/ram-16GB/scipy/six/ag_methods.AtomGroupAttrsBench.time_velocity.json", {"python": "2.7"}, 2, 3.179631382118977e-06, 2.9836729558041063e-06, [[4793, 4884, 2.9836729558041063e-06, 3.179631382118977e-06]]], ["ag_methods.AtomGroupAttrsBench.time_velocity(10000)", "graphs/Cython/MDAnalysisTests/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests-null/mock/nose/numpy/os-Linux 3.16.0-4-amd64/psutil/pytest/python-2.7/ram-16GB/scipy/six/ag_methods.AtomGroupAttrsBench.time_velocity.json", {"python": "2.7"}, 3, 3.1825369115706137e-06, 3.022147750959123e-06, [[4793, 4884, 3.022147750959123e-06, 3.1825369115706137e-06]]], ["ag_methods.AtomGroupAttrsBench.time_residuecount(10)", "graphs/Cython/MDAnalysisTests/arch-x86_64/branch-develop/cpu-Intel(R) Xeon(R) CPU E3-1226 v3 @ 3.30GHz/machine-c3potato/mdanalysistests-null/mock/nose/numpy/os-Linux 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regressions2023-12-03T04:45:04Ztag:mdanalysis.asv,2023-12-03:/a4d58bcf51be1e392dcdfdf4859b3a927512b4613ecc956290c80286c2880cde8.25% ag_methods.AtomGroupAttrsBench.time_atoms(100)2023-12-03T04:45:04Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_atoms?python=3.11&p-num_atoms=100&commits=a474dd3d15dacfe2d25085fd5756be7e5874b8e0">8.25% regression</a> on 2023-12-03 02:20:26 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/a474dd3d15dacfe2d25085fd5756be7e5874b8e0">a474dd3d</a>.<br> - New value: 195ns, old value: 180ns.<br> - Latest value: 195ns (8.25% worse than best value 180ns).tag:mdanalysis.asv,2023-09-11:/b4374e3f10f12978149df6fbe0ad1bb7fa62f708d8d8af4e1704e2ff7685aa8716.57% analysis.psa.PSA_sqnormBench.time_sqnorm(4, 1000, 0)2023-09-12T04:09:52Z<a href="index.html#analysis.psa.PSA_sqnormBench.time_sqnorm?python=3.11&p-num_cols=4&p-num_rows=1000&p-axis=0&commits=16647cb4934950d01c7a67b70c99858da48a9237">16.57% regression</a> on 2023-09-11 22:09:42 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/16647cb4934950d01c7a67b70c99858da48a9237">16647cb4</a>.<br> - New value: 24.3μs, old value: 20.9μs.<br> - Latest value: 24.3μs (16.57% worse than best value 20.9μs).tag:mdanalysis.asv,2023-09-04:/5b0d7003323b128ba687f6caab7165c8d20385d816c285a557a0deeaecf7fdb510.54% 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">10.54% regression</a> on 2023-09-04 13:47:17 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/28532012363342c36dcdfa7d497252be90ff261e">28532012</a>.<br> +tag:mdanalysis.asv,1970-01-01:/b117d33cc616f4b447052bd76edb5124af2e1e9262b15ffadbde650353d04495Airspeed Velocitymdanalysis performance regressions2023-12-11T04:57:50Ztag:mdanalysis.asv,2023-12-11:/090ba83773e3d5f794c072dd649b71dec8c2af8cc0a354bea2be0ad55c81faa418.31% analysis.distances.DistancesBench.time_contact_matrix_sparse(1000)2023-12-11T04:57:50Z<a href="index.html#analysis.distances.DistancesBench.time_contact_matrix_sparse?python=3.9&p-num_atoms=1000&commits=51e45beb8f78a218289da2b6e672599ee49c981a">18.31% regression</a> on 2023-12-11 00:19:25 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/51e45beb8f78a218289da2b6e672599ee49c981a">51e45beb</a>.<br> + New value: 3.38s, old value: 2.86s.<br> + Latest value: 3.38s (18.31% worse than best value 2.86s).tag:mdanalysis.asv,2023-09-15:/a9f56b9b04492f42d575ed196e198945fa059b496eaf90ec85a0c24aed1378e8-0.24% analysis.psa.PSA_sqnormBench.time_sqnorm(4, 10000, 0)2023-09-16T04:09:37Z<a href="index.html#analysis.psa.PSA_sqnormBench.time_sqnorm?python=3.11&p-num_cols=4&p-num_rows=10000&p-axis=0&commits=d41d42b2bfd55d2114a7095041ef8d71dc452291">-0.24% regression</a> on 2023-09-15 20:41:52 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/d41d42b2bfd55d2114a7095041ef8d71dc452291">d41d42b2</a>.<br> + New value: 160μs, old value: 161μs.<br> + Latest value: 197μs (23.75% worse than best value 159μs).tag:mdanalysis.asv,2023-09-11:/b4374e3f10f12978149df6fbe0ad1bb7fa62f708d8d8af4e1704e2ff7685aa8717.66% analysis.psa.PSA_sqnormBench.time_sqnorm(4, 1000, 0)2023-09-12T04:09:52Z<a href="index.html#analysis.psa.PSA_sqnormBench.time_sqnorm?python=3.11&p-num_cols=4&p-num_rows=1000&p-axis=0&commits=16647cb4934950d01c7a67b70c99858da48a9237">17.66% regression</a> on 2023-09-11 22:09:42 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/16647cb4934950d01c7a67b70c99858da48a9237">16647cb4</a>.<br> + New value: 24.5μs, old value: 20.9μs.<br> + Latest value: 24.5μs (17.66% worse than best value 20.9μs).tag:mdanalysis.asv,2023-09-04:/5b0d7003323b128ba687f6caab7165c8d20385d816c285a557a0deeaecf7fdb510.69% 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">10.69% 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: 46.9μs, old value: 42.4μs.<br> - Latest value: 46.9μs (10.54% worse than best value 42.4μs).tag:mdanalysis.asv,2023-09-04:/662752af510b97d4ce5597ed3685dd5cd84d47a872f09de9b6a29bb2efeb0e4712.06% 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">12.06% 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: 46.9μs (10.69% worse than best value 42.4μs).tag:mdanalysis.asv,2023-09-04:/662752af510b97d4ce5597ed3685dd5cd84d47a872f09de9b6a29bb2efeb0e4712.06% 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">12.06% 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: 47.3μs, old value: 42.2μs.<br> Latest value: 47.3μs (12.06% worse than best value 42.2μs).tag:mdanalysis.asv,2023-09-04:/9364da5a860ed497853f46086eaac4e4299375b38a7bef692bce24c1d9fcb4d35.74% analysis.rms.SimpleRmsBench.time_rmsd(500, None, True, 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=True&p-superposition=True&commits=28532012363342c36dcdfa7d497252be90ff261e">5.74% 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: 74.0μs, old value: 70.0μs.<br> @@ -13,9 +15,9 @@ New value: 33.0μs, old value: 30.9μs.<br> Latest value: 33.0μs (6.87% worse than best value 30.9μs).tag:mdanalysis.asv,2023-08-16:/6e837040f4b4e5efe7703a5b9fa98598304aae92487276685907abff53e604b55.57% ag_methods.AtomGroupMethodsBench.time_subtract(100)2023-08-17T05:39:35Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_subtract?python=3.11&p-num_atoms=100&commits=acfd1222a94965a4b0a31c3d6870c5c5252829fd">5.57% 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: 33.4μs, old value: 31.6μs.<br> - Latest value: 33.4μs (5.57% worse than best value 31.6μs).tag:mdanalysis.asv,2023-08-16:/c432cd6af0bfe57846b0f5e59976f347a93cd49c00f978cf373225158d20e6465.63% 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">5.63% 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: 41.5μs, old value: 39.3μs.<br> - Latest value: 41.5μs (5.63% worse than best value 39.3μs).tag:mdanalysis.asv,2023-08-15:/635f9193b3c63a0807aa773f206ab24ac936879587b3ecad97d63a62640ce89e5.65% 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">5.65% 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: 33.4μs (5.57% worse than best value 31.6μs).tag:mdanalysis.asv,2023-08-16:/c432cd6af0bfe57846b0f5e59976f347a93cd49c00f978cf373225158d20e6465.82% 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">5.82% 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: 41.6μs, old value: 39.3μs.<br> + Latest value: 41.6μs (5.82% worse than best value 39.3μs).tag:mdanalysis.asv,2023-08-15:/635f9193b3c63a0807aa773f206ab24ac936879587b3ecad97d63a62640ce89e5.65% 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">5.65% 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 (5.65% worse than best value 60.1μs).tag:mdanalysis.asv,2023-06-26:/13216b1078a7b13bff5ad5a75cd73da52e97cf3ba7c2a06bc4674321e9095a1d5.26% analysis.psa.PSA_get_msd_matrixBench.time_get_msd_matrix(1000, 50)2023-06-27T03:32:14Z<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=b943ca09cc0174b69656fa249abf8729bef25142">5.26% regression</a> on 2023-06-26 14:09:02 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/b943ca09cc0174b69656fa249abf8729bef25142">b943ca09</a>.<br> New value: 633ms, old value: 601ms.<br> @@ -291,35 +293,7 @@ New value: 49.0ms, old value: 13.1ms.<br> Latest value: 49.0ms (275.09% worse than best value 13.1ms).tag:mdanalysis.asv,2018-03-21:/a8a1c971626153c92ba1e08aec659825863d348a90933904f46b19e06e53b5525.60% selections.SimpleSelectionBench.time_simple_selections('nucleic')2020-06-15T13:09:12Z<a href="index.html#selections.SimpleSelectionBench.time_simple_selections?python=2.7&p-selection_string=%27nucleic%27&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">5.60% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> New value: 15.5ms, old value: 14.7ms.<br> - Latest value: 15.8ms (60.93% worse than best value 9.81ms).tag:mdanalysis.asv,2018-03-21:/6fd816c1e1f0175bbf8d8d9375f86eed365c4bd736b35942430b38265171b6ca411.64% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', True, [True, False])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">411.64% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 51.0ms, old value: 9.98ms.<br> - Latest value: 53.6ms (437.43% worse than best value 9.98ms).tag:mdanalysis.asv,2018-03-21:/73d582471e63f35325ec78855f422dce57d4c8ac99f25bb64305b9c33d08d6d2413.18% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', False, [True, False])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">413.18% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 50.8ms, old value: 9.89ms.<br> - Latest value: 53.6ms (442.20% worse than best value 9.89ms).tag:mdanalysis.asv,2018-03-21:/ff40fd74f2286f0c4defa1bf491ee986de269c042276ac0e2e9e40134a456d25414.16% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', True, [True, False])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">414.16% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 50.9ms, old value: 9.91ms.<br> - Latest value: 53.5ms (440.27% worse than best value 9.91ms).tag:mdanalysis.asv,2018-03-21:/a63c8e526b0334d601160007ddfd4040e129f5b188a2eb9612a53b39373e09b3415.96% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', False, [True, False])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">415.96% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 51.1ms, old value: 9.90ms.<br> - Latest value: 53.6ms (441.63% worse than best value 9.90ms).tag:mdanalysis.asv,2018-03-21:/ee56ff33278f13808ad81645c04e745193251f0945de3a03edf3d51d52643a3b337.61% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', True, [False, True])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">337.61% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 48.9ms, old value: 11.2ms.<br> - Latest value: 48.9ms (337.61% worse than best value 11.2ms).tag:mdanalysis.asv,2018-03-21:/31f7502d94ede39f529f9674230e858e33968fd30dd3cc148dc404b97dac8553283.23% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', True, [True, False])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">283.23% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 52.1ms, old value: 13.6ms.<br> - Latest value: 52.3ms (284.20% worse than best value 13.6ms).tag:mdanalysis.asv,2018-03-21:/5759f24a37ea04a9f557b0c61150a4fe12244c2e98698f7fb96a170134887581331.66% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', False, [False, True])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">331.66% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 48.1ms, old value: 11.1ms.<br> - Latest value: 48.8ms (337.44% worse than best value 11.1ms).tag:mdanalysis.asv,2018-03-21:/1244d008381e189aa60d1cbd3fea51c2fb4ad56cc2da0c1990c896094bddbfd1273.34% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', False, [True, False])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">273.34% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 50.8ms, old value: 13.6ms.<br> - Latest value: 52.1ms (282.86% worse than best value 13.6ms).tag:mdanalysis.asv,2018-03-21:/aed40c4f050d71aa1c82f7319bef822404c21534c5d364d06b564ffafe1f83ee350.04% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', True, [False, True])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">350.04% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 48.7ms, old value: 10.8ms.<br> - Latest value: 48.7ms (350.04% worse than best value 10.8ms).tag:mdanalysis.asv,2018-03-21:/124fab91a66e818731c4c47cf2a048c2ddb84b6fc71b2e0e391fdb5620eb0ec4293.85% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', True, [True, False])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">293.85% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 52.0ms, old value: 13.2ms.<br> - Latest value: 52.0ms (293.85% worse than best value 13.2ms).tag:mdanalysis.asv,2018-03-21:/44bc43a5c392b1da26dd785e7006c0548b539dd9d5059c2bb51d61125a07675b345.43% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', False, [False, True])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">345.43% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 48.3ms, old value: 10.8ms.<br> - Latest value: 48.5ms (347.22% worse than best value 10.8ms).tag:mdanalysis.asv,2018-03-21:/172bfb59b314107eb783153b1bbc0c4ceec683f2a3f5247dbd7ccc5e646abd09295.27% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', False, [True, False])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">295.27% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 52.0ms, old value: 13.2ms.<br> - Latest value: 52.0ms (295.27% worse than best value 13.2ms).tag:mdanalysis.asv,2018-03-21:/6ee24bd5e4aeb3df68711c2b311f0aa8f31e5dd5790d53989fec30d5f8a7177c115.09% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', True, [True, False])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">115.09% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 3.71ms, old value: 1.72ms.<br> - Latest value: 6.35ms (268.23% worse than best value 1.72ms).tag:mdanalysis.asv,2018-03-21:/5c06a03a08550bdfb747d0c6b8b61eaa31d24afbc7c06bcd72547559934df060112.48% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', False, [True, False])2020-06-15T13:07:43Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">112.48% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> - New value: 3.68ms, old value: 1.73ms.<br> - Latest value: 6.30ms (264.28% worse than best value 1.73ms).tag:mdanalysis.asv,2018-03-21:/889f0732edf0b3707bac9b06906d24cb9f3e7eed521bbbbd67fa4fda6447deef6.42% analysis.rdf.SimpleRdfBench.time_interrdf(20, [0, 5], 100)2020-06-15T12:56:27Z<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=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">6.42% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> + Latest value: 15.8ms (60.93% worse than best value 9.81ms).tag:mdanalysis.asv,2018-03-21:/889f0732edf0b3707bac9b06906d24cb9f3e7eed521bbbbd67fa4fda6447deef6.42% analysis.rdf.SimpleRdfBench.time_interrdf(20, [0, 5], 100)2020-06-15T12:56:27Z<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=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">6.42% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> New value: 29.6ms, old value: 27.8ms.<br> Latest value: 29.6ms (16.44% worse than best value 25.4ms).tag:mdanalysis.asv,2018-03-21:/3ffa800af7498e40db39ce4ab6a6f17dfe6ab5ecec0913031ff821341a7480a112.87% analysis.rdf.SimpleRdfBench.time_interrdf(20, [0, 15], 100)2020-06-15T12:56:27Z<a href="index.html#analysis.rdf.SimpleRdfBench.time_interrdf?python=2.7&p-nbins=20&p-range_val=%5B0%2C+15%5D&p-natoms=100&commits=41c86ed12af3da2f2ed72e6073f14185b394fedb-74defb7fb168145e58388cc67af811559cf23921">12.87% regression</a> on 2018-03-21 15:16:55 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/41c86ed12af3da2f2ed72e6073f14185b394fedb...74defb7fb168145e58388cc67af811559cf23921">41c86ed1...74defb7f</a>.<br> New value: 31.5ms, old value: 27.9ms.<br> @@ -745,35 +719,29 @@ New value: 115ms, old value: 109ms.<br> Latest value: 115ms (11944113.02% worse than best value 966ns).tag:mdanalysis.asv,2016-12-23:/9ea55e70c244d064924ddddf6fcdd2cbc9aa67d89f44c4acb992cb8036a43e1d2.24% import.ImportBench.time_import2020-02-27T17:42:35Z<a href="index.html#import.ImportBench.time_import?python=2.7&commits=c6050648f526436cbe4fee944e2d0828eb9daa60">2.24% regression</a> on 2016-12-23 11:33:53 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/c6050648f526436cbe4fee944e2d0828eb9daa60">c6050648</a>.<br> New value: 561ns, old value: 549ns.<br> - Latest value: 2.69μs (391.17% worse than best value 549ns).tag:mdanalysis.asv,2017-01-03:/542a7be1acec073cb203e334c6fe4cdc7ab619451ff170f06c11386d83908e104.34% selections.GeoSelectionBench.time_geometric_selections('prop z >= 5.0', True, [False, True])2020-02-27T17:33:55Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27prop+z+%3E%3D+5.0%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=bf378bf72fbfe1211e41355d3abd328504b39806-58200c876b381a006b42285c44e762defc332241">4.34% regression</a> on 2017-01-03 03:39:32 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/bf378bf72fbfe1211e41355d3abd328504b39806...58200c876b381a006b42285c44e762defc332241">bf378bf7...58200c87</a>.<br> - New value: 1.10ms, old value: 1.05ms.<br> - Latest value: 1.11ms (5.86% worse than best value 1.05ms).tag:mdanalysis.asv,2017-01-03:/3346daf4e96a7dcd7daa85e8c48c205974d12838cd90b0a9301423d2a8a30c8a7.12% selections.GeoSelectionBench.time_geometric_selections('prop abs z <= 5.0', True, [True, False])2020-02-27T17:33:55Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27prop+abs+z+%3C%3D+5.0%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=bf378bf72fbfe1211e41355d3abd328504b39806-58200c876b381a006b42285c44e762defc332241">7.12% regression</a> on 2017-01-03 03:39:32 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/bf378bf72fbfe1211e41355d3abd328504b39806...58200c876b381a006b42285c44e762defc332241">bf378bf7...58200c87</a>.<br> - New value: 767μs, old value: 716μs.<br> - Latest value: 789μs (10.25% worse than best value 716μs).tag:mdanalysis.asv,2017-01-12:/f94f54217ab0ae0236c03b37648f389ef25def8099ce96aea2ea919ee59497a54.40% selections.GeoSelectionBench.time_geometric_selections('prop abs z <= 5.0', True, [False, True])2020-02-27T17:12:14Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27prop+abs+z+%3C%3D+5.0%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=5838d5f482b43ea3ed1c7d4400e4dbdc712fd8b0-39c29e5154d316f41ef0f47910cb04c6c7c0ba1f">4.40% regression</a> on 2017-01-12 14:58:34 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/5838d5f482b43ea3ed1c7d4400e4dbdc712fd8b0...39c29e5154d316f41ef0f47910cb04c6c7c0ba1f">5838d5f4...39c29e51</a>.<br> - New value: 766μs, old value: 733μs.<br> - Latest value: 786μs (7.17% worse than best value 733μs).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,2017-01-19:/d2e74003c9ee6f6404f31508061dccd334869e7b81f9e59c1188db0340a62dd12.05% ag_methods.AtomGroupMethodsBench.time_bsphere_no_pbc(10000)2020-02-27T16:50:36Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_bsphere_no_pbc?python=2.7&p-num_atoms=10000&commits=9ef4db2baf18de6ea5f4ee7b493458a9be5463bf-9ded575784673baefc181161f80bfb91b5f7c2be">2.05% regression</a> on 2017-01-19 09:39:51 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ef4db2baf18de6ea5f4ee7b493458a9be5463bf...9ded575784673baefc181161f80bfb91b5f7c2be">9ef4db2b...9ded5757</a>.<br> + Latest value: 2.69μs (391.17% worse than best value 549ns).tag:mdanalysis.asv,2017-01-19:/d2e74003c9ee6f6404f31508061dccd334869e7b81f9e59c1188db0340a62dd12.05% ag_methods.AtomGroupMethodsBench.time_bsphere_no_pbc(10000)2020-02-27T16:50:36Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_bsphere_no_pbc?python=2.7&p-num_atoms=10000&commits=9ef4db2baf18de6ea5f4ee7b493458a9be5463bf-9ded575784673baefc181161f80bfb91b5f7c2be">2.05% regression</a> on 2017-01-19 09:39:51 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ef4db2baf18de6ea5f4ee7b493458a9be5463bf...9ded575784673baefc181161f80bfb91b5f7c2be">9ef4db2b...9ded5757</a>.<br> New value: 607μs, old value: 595μs.<br> - Latest value: 631μs (10.78% worse than best value 570μs).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> + 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:/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> + New value: 4.58μs, old value: 3.86μs.<br> + Latest value: 7.67μs (98.49% worse than best value 3.86μs).tag:mdanalysis.asv,2017-01-26:/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:/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:/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:/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:/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,2017-01-26:/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: 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> New value: 128ms, old value: 115ms.<br> - Latest value: 185ms (25626867.94% worse than best value 722ns).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: 185ms (25626867.94% worse than best value 722ns).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> @@ -785,17 +753,17 @@ New value: 3.18μs, old value: 2.98μs.<br> Latest value: 3.18μs (6.57% worse than best value 2.98μs).tag:mdanalysis.asv,2017-03-15:/39e3c5373f12298d69cc64e90ece6d9068797497778d8611af7944b31c4f4a655.31% ag_methods.AtomGroupAttrsBench.time_velocity(10000)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_velocity?python=2.7&p-num_atoms=10000&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">5.31% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> New value: 3.18μs, old value: 3.02μs.<br> - Latest value: 3.18μs (5.31% worse than best value 3.02μs).tag:mdanalysis.asv,2017-03-15:/8944dcda28fe45c9041d838d22723909c608169b72044cf89cf59f64f91a793d6.40% ag_methods.AtomGroupAttrsBench.time_forces(10)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_forces?python=2.7&p-num_atoms=10&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">6.40% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> + Latest value: 3.18μs (5.31% worse than best value 3.02μs).tag:mdanalysis.asv,2017-03-15:/0365c77a3c0d966338652c429803a9de10f151566548da9a308f9c4ab9fa2de813.45% ag_methods.AtomGroupAttrsBench.time_positions(10)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_positions?python=2.7&p-num_atoms=10&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">13.45% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> + New value: 2.77μs, old value: 2.44μs.<br> + Latest value: 2.77μs (13.45% worse than best value 2.44μs).tag:mdanalysis.asv,2017-03-15:/94b127e6e61b5a97a9d9fa837cf9ff761f463fbacda15563d58fb8d8286855649.68% ag_methods.AtomGroupAttrsBench.time_positions(100)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_positions?python=2.7&p-num_atoms=100&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">9.68% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> + New value: 3.92μs, old value: 3.58μs.<br> + Latest value: 3.92μs (9.68% worse than best value 3.58μs).tag:mdanalysis.asv,2017-03-15:/8944dcda28fe45c9041d838d22723909c608169b72044cf89cf59f64f91a793d6.40% ag_methods.AtomGroupAttrsBench.time_forces(10)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_forces?python=2.7&p-num_atoms=10&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">6.40% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> New value: 3.12μs, old value: 2.94μs.<br> Latest value: 3.18μs (8.39% worse than best value 2.94μs).tag:mdanalysis.asv,2017-03-15:/31e3b820b64abe59526defa618be71a3cfa3c1102a81685e6b53d7413f8fa68a6.25% ag_methods.AtomGroupAttrsBench.time_forces(1000)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_forces?python=2.7&p-num_atoms=1000&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">6.25% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> New value: 3.13μs, old value: 2.95μs.<br> Latest value: 3.13μs (6.25% worse than best value 2.95μs).tag:mdanalysis.asv,2017-03-15:/cbed044d436525998a57aadf8b1bbfa4c2ec163bb493a659e9cc4eae2a5049ea5.75% ag_methods.AtomGroupAttrsBench.time_forces(10000)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_forces?python=2.7&p-num_atoms=10000&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">5.75% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> New value: 3.12μs, old value: 2.95μs.<br> - Latest value: 3.16μs (7.15% worse than best value 2.95μs).tag:mdanalysis.asv,2017-03-15:/0365c77a3c0d966338652c429803a9de10f151566548da9a308f9c4ab9fa2de813.45% ag_methods.AtomGroupAttrsBench.time_positions(10)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_positions?python=2.7&p-num_atoms=10&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">13.45% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> - New value: 2.77μs, old value: 2.44μs.<br> - Latest value: 2.77μs (13.45% worse than best value 2.44μs).tag:mdanalysis.asv,2017-03-15:/94b127e6e61b5a97a9d9fa837cf9ff761f463fbacda15563d58fb8d8286855649.68% ag_methods.AtomGroupAttrsBench.time_positions(100)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_positions?python=2.7&p-num_atoms=100&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">9.68% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> - New value: 3.92μs, old value: 3.58μs.<br> - Latest value: 3.92μs (9.68% worse than best value 3.58μs).tag:mdanalysis.asv,2017-03-15:/714ebe267b2f0c318bd867e5112e1e29ffd824ba27adaa54af743d8adc13c47d4.91% ag_methods.AtomGroupAttrsBench.time_velocity(100)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_velocity?python=2.7&p-num_atoms=100&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">4.91% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> + Latest value: 3.16μs (7.15% worse than best value 2.95μs).tag:mdanalysis.asv,2017-03-15:/714ebe267b2f0c318bd867e5112e1e29ffd824ba27adaa54af743d8adc13c47d4.91% ag_methods.AtomGroupAttrsBench.time_velocity(100)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_velocity?python=2.7&p-num_atoms=100&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">4.91% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> New value: 3.19μs, old value: 3.04μs.<br> Latest value: 3.24μs (6.44% worse than best value 3.04μs).tag:mdanalysis.asv,2017-03-15:/4be357777768c3a4c00fa5b91db53f408b86591fdfb4f7b9c867172207b051bd5.29% ag_methods.AtomGroupAttrsBench.time_velocity(1000)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_velocity?python=2.7&p-num_atoms=1000&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">5.29% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> New value: 3.22μs, old value: 3.06μs.<br> @@ -803,31 +771,29 @@ New value: 3.21μs, old value: 3.06μs.<br> Latest value: 3.21μs (5.00% worse than best value 3.06μs).tag:mdanalysis.asv,2017-03-15:/ae915cf4045897cd5b8462e576ab2bbcefc084556cf3ec2a003febaaa76b006521.12% ag_methods.AtomGroupMethodsBench.time_split(100)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_split?python=2.7&p-num_atoms=100&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">21.12% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> New value: 36.5μs, old value: 30.1μs.<br> - Latest value: 50.0μs (66.18% worse than best value 30.1μs).tag:mdanalysis.asv,2017-03-15:/8944dcda28fe45c9041d838d22723909c608169b72044cf89cf59f64f91a793d5.96% ag_methods.AtomGroupAttrsBench.time_forces(10)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_forces?python=2.7&p-num_atoms=10&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">5.96% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> - New value: 3.18μs, old value: 3.00μs.<br> - Latest value: 3.22μs (7.36% worse than best value 3.00μs).tag:mdanalysis.asv,2017-03-15:/cbed044d436525998a57aadf8b1bbfa4c2ec163bb493a659e9cc4eae2a5049ea5.80% ag_methods.AtomGroupAttrsBench.time_forces(10000)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_forces?python=2.7&p-num_atoms=10000&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">5.80% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> - New value: 3.17μs, old value: 3.00μs.<br> - Latest value: 3.20μs (6.68% worse than best value 3.00μs).tag:mdanalysis.asv,2017-03-15:/0365c77a3c0d966338652c429803a9de10f151566548da9a308f9c4ab9fa2de812.25% ag_methods.AtomGroupAttrsBench.time_positions(10)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_positions?python=2.7&p-num_atoms=10&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">12.25% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> + Latest value: 50.0μs (66.18% worse than best value 30.1μs).tag:mdanalysis.asv,2017-03-15:/0365c77a3c0d966338652c429803a9de10f151566548da9a308f9c4ab9fa2de812.25% ag_methods.AtomGroupAttrsBench.time_positions(10)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_positions?python=2.7&p-num_atoms=10&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">12.25% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> New value: 2.77μs, old value: 2.47μs.<br> Latest value: 2.77μs (12.25% worse than best value 2.47μs).tag:mdanalysis.asv,2017-03-15:/94b127e6e61b5a97a9d9fa837cf9ff761f463fbacda15563d58fb8d8286855648.02% ag_methods.AtomGroupAttrsBench.time_positions(100)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_positions?python=2.7&p-num_atoms=100&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">8.02% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> New value: 3.96μs, old value: 3.66μs.<br> - Latest value: 3.97μs (8.48% worse than best value 3.66μs).tag:mdanalysis.asv,2017-04-10:/abeabb1776c3453438cc896698bd5aed990074659490337a92b9ade380a4b3596.04% selections.SimpleSelectionBench.time_simple_selections('resnum 1:10')2020-02-27T15:28:03Z<a href="index.html#selections.SimpleSelectionBench.time_simple_selections?python=2.7&p-selection_string=%27resnum+1%3A10%27&commits=2521d78b7bf4b480fc993ede0bcc365177a702f9">6.04% regression</a> on 2017-04-10 15:03:20 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/2521d78b7bf4b480fc993ede0bcc365177a702f9">2521d78b</a>.<br> + Latest value: 3.97μs (8.48% worse than best value 3.66μs).tag:mdanalysis.asv,2017-03-15:/8944dcda28fe45c9041d838d22723909c608169b72044cf89cf59f64f91a793d5.96% ag_methods.AtomGroupAttrsBench.time_forces(10)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_forces?python=2.7&p-num_atoms=10&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">5.96% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> + New value: 3.18μs, old value: 3.00μs.<br> + Latest value: 3.22μs (7.36% worse than best value 3.00μs).tag:mdanalysis.asv,2017-03-15:/cbed044d436525998a57aadf8b1bbfa4c2ec163bb493a659e9cc4eae2a5049ea5.80% ag_methods.AtomGroupAttrsBench.time_forces(10000)2020-02-27T15:49:48Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_forces?python=2.7&p-num_atoms=10000&commits=4d0f3e3127c1a6ade53c04bf7f93d916cebcef39-1b165be7a06b7c6505a4430dcca98503c7102e5a">5.80% regression</a> on 2017-03-15 16:25:43 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/4d0f3e3127c1a6ade53c04bf7f93d916cebcef39...1b165be7a06b7c6505a4430dcca98503c7102e5a">4d0f3e31...1b165be7</a>.<br> + New value: 3.17μs, old value: 3.00μs.<br> + Latest value: 3.20μs (6.68% worse than best value 3.00μs).tag:mdanalysis.asv,2017-04-10:/abeabb1776c3453438cc896698bd5aed990074659490337a92b9ade380a4b3596.04% selections.SimpleSelectionBench.time_simple_selections('resnum 1:10')2020-02-27T15:28:03Z<a href="index.html#selections.SimpleSelectionBench.time_simple_selections?python=2.7&p-selection_string=%27resnum+1%3A10%27&commits=2521d78b7bf4b480fc993ede0bcc365177a702f9">6.04% regression</a> on 2017-04-10 15:03:20 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/2521d78b7bf4b480fc993ede0bcc365177a702f9">2521d78b</a>.<br> New value: 187μs, old value: 177μs.<br> Latest value: 227μs (32.08% worse than best value 172μs).tag:mdanalysis.asv,2017-04-17:/30fc6b1cae21bd23da20212217725fedc5d7a43555a1edc41775184d7de7ecd110.19% selections.SimpleSelectionBench.time_simple_selections('bynum 0:10')2020-02-27T15:28:03Z<a href="index.html#selections.SimpleSelectionBench.time_simple_selections?python=2.7&p-selection_string=%27bynum+0%3A10%27&commits=2521d78b7bf4b480fc993ede0bcc365177a702f9-ca30efa938a0123b546429ec295e8d6a151bb8fd">10.19% regression</a> on 2017-04-17 14:41:37 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/2521d78b7bf4b480fc993ede0bcc365177a702f9...ca30efa938a0123b546429ec295e8d6a151bb8fd">2521d78b...ca30efa9</a>.<br> New value: 118μs, old value: 107μs.<br> - Latest value: 119μs (15.31% worse than best value 103μs).tag:mdanalysis.asv,2017-04-10:/e67a9440acce24c44a1125d71b47f15db0b682d4e0d7905930c38ca5ea5c67dd28.02% ag_methods.AtomGroupMethodsBench.time_groupby(10)2020-02-27T15:28:03Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_groupby?python=2.7&p-num_atoms=10&commits=2521d78b7bf4b480fc993ede0bcc365177a702f9">28.02% regression</a> on 2017-04-10 15:03:20 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/2521d78b7bf4b480fc993ede0bcc365177a702f9">2521d78b</a>.<br> - New value: 12.5μs, old value: 9.73μs.<br> - Latest value: 16.7μs (71.60% worse than best value 9.73μs).tag:mdanalysis.asv,2017-04-17:/566be6eb6d8f935be59666d074a7f95fc9ecafe6c6b5e8c6133de55928ec4e9d8.56% selections.SimpleSelectionBench.time_simple_selections('resnum 1:10')2020-02-27T15:28:03Z<a href="index.html#selections.SimpleSelectionBench.time_simple_selections?python=2.7&p-selection_string=%27resnum+1%3A10%27&commits=2521d78b7bf4b480fc993ede0bcc365177a702f9-ca30efa938a0123b546429ec295e8d6a151bb8fd">8.56% regression</a> on 2017-04-17 14:41:37 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/2521d78b7bf4b480fc993ede0bcc365177a702f9...ca30efa938a0123b546429ec295e8d6a151bb8fd">2521d78b...ca30efa9</a>.<br> + Latest value: 119μs (15.31% worse than best value 103μs).tag:mdanalysis.asv,2017-04-17:/566be6eb6d8f935be59666d074a7f95fc9ecafe6c6b5e8c6133de55928ec4e9d8.56% selections.SimpleSelectionBench.time_simple_selections('resnum 1:10')2020-02-27T15:28:03Z<a href="index.html#selections.SimpleSelectionBench.time_simple_selections?python=2.7&p-selection_string=%27resnum+1%3A10%27&commits=2521d78b7bf4b480fc993ede0bcc365177a702f9-ca30efa938a0123b546429ec295e8d6a151bb8fd">8.56% regression</a> on 2017-04-17 14:41:37 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/2521d78b7bf4b480fc993ede0bcc365177a702f9...ca30efa938a0123b546429ec295e8d6a151bb8fd">2521d78b...ca30efa9</a>.<br> New value: 188μs, old value: 173μs.<br> Latest value: 226μs (30.61% worse than best value 173μs).tag:mdanalysis.asv,2017-04-17:/30fc6b1cae21bd23da20212217725fedc5d7a43555a1edc41775184d7de7ecd111.71% selections.SimpleSelectionBench.time_simple_selections('bynum 0:10')2020-02-27T15:28:03Z<a href="index.html#selections.SimpleSelectionBench.time_simple_selections?python=2.7&p-selection_string=%27bynum+0%3A10%27&commits=2521d78b7bf4b480fc993ede0bcc365177a702f9-ca30efa938a0123b546429ec295e8d6a151bb8fd">11.71% regression</a> on 2017-04-17 14:41:37 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/2521d78b7bf4b480fc993ede0bcc365177a702f9...ca30efa938a0123b546429ec295e8d6a151bb8fd">2521d78b...ca30efa9</a>.<br> New value: 118μs, old value: 105μs.<br> - Latest value: 118μs (11.81% worse than best value 105μs).tag:mdanalysis.asv,2017-05-07:/2a28007cdf356fc0e7ddc93f3ce1c6696b5c1c0dc321aa9f7259e488febb2e9724.17% ag_methods.FragmentFinding.time_find_fragments('large_fragment')2020-02-27T15:11:41Z<a href="index.html#ag_methods.FragmentFinding.time_find_fragments?python=2.7&p-universe_type=%27large_fragment%27&commits=d5667ae9b52c1f14d9cc0fe3cb77799c9a5635c2">24.17% regression</a> on 2017-05-07 12:42:39 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/d5667ae9b52c1f14d9cc0fe3cb77799c9a5635c2">d5667ae9</a>.<br> + Latest value: 118μs (11.81% worse than best value 105μs).tag:mdanalysis.asv,2017-04-10:/e67a9440acce24c44a1125d71b47f15db0b682d4e0d7905930c38ca5ea5c67dd28.02% ag_methods.AtomGroupMethodsBench.time_groupby(10)2020-02-27T15:28:03Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_groupby?python=2.7&p-num_atoms=10&commits=2521d78b7bf4b480fc993ede0bcc365177a702f9">28.02% regression</a> on 2017-04-10 15:03:20 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/2521d78b7bf4b480fc993ede0bcc365177a702f9">2521d78b</a>.<br> + New value: 12.5μs, old value: 9.73μs.<br> + Latest value: 16.7μs (71.60% worse than best value 9.73μs).tag:mdanalysis.asv,2017-05-07:/2a28007cdf356fc0e7ddc93f3ce1c6696b5c1c0dc321aa9f7259e488febb2e9724.17% ag_methods.FragmentFinding.time_find_fragments('large_fragment')2020-02-27T15:11:41Z<a href="index.html#ag_methods.FragmentFinding.time_find_fragments?python=2.7&p-universe_type=%27large_fragment%27&commits=d5667ae9b52c1f14d9cc0fe3cb77799c9a5635c2">24.17% regression</a> on 2017-05-07 12:42:39 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/d5667ae9b52c1f14d9cc0fe3cb77799c9a5635c2">d5667ae9</a>.<br> New value: 159ms, old value: 128ms.<br> Latest value: 185ms (25626867.94% worse than best value 722ns).tag:mdanalysis.asv,2017-06-13:/71970aa3535b55d39c0ac21736f10a4828b9b51874eb0509e848cece9a4bd81416.63% ag_methods.FragmentFinding.time_find_fragments('large_fragment')2020-02-27T14:41:13Z<a href="index.html#ag_methods.FragmentFinding.time_find_fragments?python=2.7&p-universe_type=%27large_fragment%27&commits=ebe2b84e64456aa7ef9d665a281f096f4dbfbb00">16.63% regression</a> on 2017-06-13 17:16:12 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/ebe2b84e64456aa7ef9d665a281f096f4dbfbb00">ebe2b84e</a>.<br> New value: 185ms, old value: 159ms.<br> - Latest value: 185ms (25626867.94% worse than best value 722ns).tag:mdanalysis.asv,2017-08-01:/6e978f54fb50245d0b4dcf5df735fcaf6ee94dff3748632d4885c9599d2371ab4.70% selections.GeoSelectionBench.time_geometric_selections('prop abs z <= 5.0', True, [False, True])2020-02-27T14:00:59Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27prop+abs+z+%3C%3D+5.0%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=e0bc3034042411ecfb9eb123e5bb207c593eba28-d8af6c76ec65bff71a8638d9c0bed500f7855c82">4.70% regression</a> on 2017-08-01 17:20:59 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/e0bc3034042411ecfb9eb123e5bb207c593eba28...d8af6c76ec65bff71a8638d9c0bed500f7855c82">e0bc3034...d8af6c76</a>.<br> - New value: 791μs, old value: 755μs.<br> - Latest value: 819μs (8.52% worse than best value 755μs).tag:mdanalysis.asv,2017-12-07:/8fffc681d38f9bd8d51be8152d66d67bb62cd12fce2bc5649c71859901745a2b28.80% 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">28.80% 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: 185ms (25626867.94% worse than best value 722ns).tag:mdanalysis.asv,2017-12-07:/8fffc681d38f9bd8d51be8152d66d67bb62cd12fce2bc5649c71859901745a2b28.80% 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">28.80% 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: 879ns, old value: 683ns.<br> Latest value: 879ns (36.20% worse than best value 645ns).tag:mdanalysis.asv,2017-12-07:/88f238968feaa9c701d99a61195944e56f79fd10946a85e1104dc814e790337228.88% 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">28.88% 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: 877ns, old value: 680ns.<br> @@ -835,13 +801,19 @@ 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:/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: 886ns (36.16% worse than best value 651ns).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:/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:/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> @@ -849,7 +821,15 @@ New value: 339μs, old value: 314μs.<br> Latest value: 346μs (22.99% worse than best value 281μs).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:/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: 13.2μs (1302.07% worse than best value 941ns).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:/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:/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:/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> @@ -857,17 +837,7 @@ 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:/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:/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:/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:/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:/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> + 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> @@ -875,29 +845,7 @@ 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:/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> - 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:/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:/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:/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:/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> + Latest value: 13.2μs (20.78% worse than best value 11.0μs).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> @@ -905,7 +853,25 @@ 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,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: 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:/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:/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:/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:/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,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> @@ -999,45 +965,7 @@ New value: 5.00ms, old value: 4.71ms.<br> Latest value: 5.24ms (218.53% worse than best value 1.64ms).tag:mdanalysis.asv,2018-09-21:/c1d82545a6f8a2e9ba7222499e2e7d32cd5ac8acf8217d969fcc9f6e2d5b44fb5.33% analysis.rms.RmsfTrajBench.time_RMSF(2000, 3, None)2019-05-10T15:23:21Z<a href="index.html#analysis.rms.RmsfTrajBench.time_RMSF?python=2.7&p-n_atoms=2000&p-step=3&p-weights=None&commits=69730f4a5f19e3bed621a70de5f0b89cd69b0b6b-8918d42d1da0c653f41295fb7a131d7c04d46578">5.33% regression</a> on 2018-09-21 11:15:51 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/69730f4a5f19e3bed621a70de5f0b89cd69b0b6b...8918d42d1da0c653f41295fb7a131d7c04d46578">69730f4a...8918d42d</a>.<br> New value: 7.22ms, old value: 6.85ms.<br> - Latest value: 7.22ms (95.91% worse than best value 3.68ms).tag:mdanalysis.asv,2018-10-08:/60f9a8deb4f0a88f23493103c63765d3bbbb54194f6930a3f757d3c50a4568ea0.66% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', True, [True, False])2019-05-10T02:11:25Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=97db6d1742e3d16dbadf37f01f064821b6f66351">0.66% regression</a> on 2018-10-08 17:13:27 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/97db6d1742e3d16dbadf37f01f064821b6f66351">97db6d17</a>.<br> - New value: 51.3ms, old value: 50.9ms.<br> - Latest value: 53.5ms (440.27% worse than best value 9.91ms).tag:mdanalysis.asv,2019-02-11:/266f064ac7c230b499e03e27f6967948d9ee4c9f2b9d96758f9badabe114b66a72.04% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', True, [True, False])2019-05-09T03:17:22Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=3.6&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=5d2969eef3ef68dce2bd1e7b08db6ff0c0c6715e-0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">72.04% regression</a> on 2019-02-11 12:03:46 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/5d2969eef3ef68dce2bd1e7b08db6ff0c0c6715e...0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">5d2969ee...0fef1a38</a>.<br> - New value: 6.50ms, old value: 3.78ms.<br> - Latest value: 6.50ms (72.04% worse than best value 3.78ms).tag:mdanalysis.asv,2019-02-11:/fe8d86bd78ecc87e7acb8cdbd2596a91e1d09b15046e50029735c87c7ed8b59f72.93% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', False, [True, False])2019-05-09T03:17:22Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=3.6&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=5d2969eef3ef68dce2bd1e7b08db6ff0c0c6715e-0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">72.93% regression</a> on 2019-02-11 12:03:46 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/5d2969eef3ef68dce2bd1e7b08db6ff0c0c6715e...0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">5d2969ee...0fef1a38</a>.<br> - New value: 6.44ms, old value: 3.72ms.<br> - Latest value: 6.44ms (72.93% worse than best value 3.72ms).tag:mdanalysis.asv,2019-02-07:/078b90af7242efadc8d40cf1fc2164c9dadd4ae5e1f85ef05b7f0542687210d15.04% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', True, [True, False])2019-05-09T01:42:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=3d7396798e2bfc392157d313ddcbd3cfa99b3a5c">5.04% regression</a> on 2019-02-07 18:49:14 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/3d7396798e2bfc392157d313ddcbd3cfa99b3a5c">3d739679</a>.<br> - New value: 53.6ms, old value: 51.0ms.<br> - Latest value: 53.6ms (437.43% worse than best value 9.98ms).tag:mdanalysis.asv,2019-02-07:/5945c017863bf00152d60c56ab53d8cb42553badc53de204b79ed59ff437411d5.65% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', False, [True, False])2019-05-09T01:42:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=3d7396798e2bfc392157d313ddcbd3cfa99b3a5c">5.65% regression</a> on 2019-02-07 18:49:14 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/3d7396798e2bfc392157d313ddcbd3cfa99b3a5c">3d739679</a>.<br> - New value: 53.6ms, old value: 50.8ms.<br> - Latest value: 53.6ms (442.20% worse than best value 9.89ms).tag:mdanalysis.asv,2019-02-11:/7ec309be4fb92dfbfe17d32f1efd340854e31c2d053ca16465150415480ac4156.04% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', True, [True, False])2019-05-09T01:04:55Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">6.04% regression</a> on 2019-02-11 12:03:46 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">0fef1a38</a>.<br> - New value: 53.8ms, old value: 50.7ms.<br> - Latest value: 53.8ms (446.04% worse than best value 9.86ms).tag:mdanalysis.asv,2019-02-11:/fe5600bf8153721ba778c23c58718af76925ad4b5800629cfeaabc74bcf37fd75.03% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', False, [True, False])2019-05-09T01:04:55Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">5.03% regression</a> on 2019-02-11 12:03:46 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">0fef1a38</a>.<br> - New value: 53.4ms, old value: 50.9ms.<br> - Latest value: 53.4ms (440.40% worse than best value 9.89ms).tag:mdanalysis.asv,2019-02-11:/8bd6ae219584b918c3c9f133fa283dcd7f2123b60292afc2c06022a7a65c751d5.80% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', True, [True, False])2019-05-09T01:04:55Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">5.80% regression</a> on 2019-02-11 12:03:46 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">0fef1a38</a>.<br> - New value: 53.7ms, old value: 50.7ms.<br> - Latest value: 53.7ms (443.03% worse than best value 9.88ms).tag:mdanalysis.asv,2019-02-11:/e357d180cd15efada5ea75d93fa2ba4793676ea2a965c8865e4f0737624577115.68% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', False, [True, False])2019-05-09T01:04:55Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">5.68% regression</a> on 2019-02-11 12:03:46 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">0fef1a38</a>.<br> - New value: 53.6ms, old value: 50.7ms.<br> - Latest value: 53.6ms (443.41% worse than best value 9.87ms).tag:mdanalysis.asv,2019-02-11:/bbcc084412b17c9f3ee75576b5272868721c9512a72b214303534b48ade4649582.36% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', True, [True, False])2019-05-09T01:04:55Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">82.36% regression</a> on 2019-02-11 12:03:46 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">0fef1a38</a>.<br> - New value: 6.45ms, old value: 3.54ms.<br> - Latest value: 6.50ms (309.44% worse than best value 1.59ms).tag:mdanalysis.asv,2019-02-11:/0ca4ffcb78c33702f114b82cc723f7fdd60324f5549d2a0b0b8281f44d86a02c83.32% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', False, [True, False])2019-05-09T01:04:55Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">83.32% regression</a> on 2019-02-11 12:03:46 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">0fef1a38</a>.<br> - New value: 6.41ms, old value: 3.49ms.<br> - Latest value: 6.44ms (301.41% worse than best value 1.61ms).tag:mdanalysis.asv,2019-02-11:/bbcc084412b17c9f3ee75576b5272868721c9512a72b214303534b48ade4649567.21% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', True, [True, False])2019-05-09T01:04:55Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">67.21% regression</a> on 2019-02-11 12:03:46 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">0fef1a38</a>.<br> - New value: 6.35ms, old value: 3.80ms.<br> - Latest value: 6.35ms (268.23% worse than best value 1.72ms).tag:mdanalysis.asv,2019-02-11:/0ca4ffcb78c33702f114b82cc723f7fdd60324f5549d2a0b0b8281f44d86a02c67.94% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', False, [True, False])2019-05-09T01:04:55Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">67.94% regression</a> on 2019-02-11 12:03:46 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/0fef1a38143f4bfcb74dd44374d915b31e8b5a6c">0fef1a38</a>.<br> - New value: 6.30ms, old value: 3.75ms.<br> - Latest value: 6.30ms (264.28% worse than best value 1.73ms).tag:mdanalysis.asv,2019-02-14:/236a6075381c6f9e1cae452b87d16280659a282ee0af8128d3a608b25731d7038.25% selections.GeoSelectionBench.time_geometric_selections('around 5.0 resid 1', True, [False, True])2019-05-08T22:55:05Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27around+5.0+resid+1%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=77d8f7c4ca776938bfe1f627e3791c87cf55ece3">8.25% regression</a> on 2019-02-14 13:06:04 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/77d8f7c4ca776938bfe1f627e3791c87cf55ece3">77d8f7c4</a>.<br> - New value: 7.40ms, old value: 6.83ms.<br> - Latest value: 7.40ms (13.13% worse than best value 6.54ms).tag:mdanalysis.asv,2019-02-14:/ebfe4ae0112bcbd2960bf6ef4e3fa929feb298dd4429b7edb42c4a4472354e158.81% selections.GeoSelectionBench.time_geometric_selections('around 5.0 resid 1', False, [False, True])2019-05-08T22:55:05Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27around+5.0+resid+1%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=77d8f7c4ca776938bfe1f627e3791c87cf55ece3">8.81% regression</a> on 2019-02-14 13:06:04 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/77d8f7c4ca776938bfe1f627e3791c87cf55ece3">77d8f7c4</a>.<br> - New value: 7.36ms, old value: 6.76ms.<br> - Latest value: 7.36ms (13.35% worse than best value 6.49ms).tag:mdanalysis.asv,2019-02-14:/236a6075381c6f9e1cae452b87d16280659a282ee0af8128d3a608b25731d7038.61% selections.GeoSelectionBench.time_geometric_selections('around 5.0 resid 1', True, [False, True])2019-05-08T22:55:05Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27around+5.0+resid+1%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=77d8f7c4ca776938bfe1f627e3791c87cf55ece3">8.61% regression</a> on 2019-02-14 13:06:04 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/77d8f7c4ca776938bfe1f627e3791c87cf55ece3">77d8f7c4</a>.<br> - New value: 7.40ms, old value: 6.81ms.<br> - Latest value: 7.40ms (8.61% worse than best value 6.81ms).tag:mdanalysis.asv,2019-02-14:/ebfe4ae0112bcbd2960bf6ef4e3fa929feb298dd4429b7edb42c4a4472354e158.53% selections.GeoSelectionBench.time_geometric_selections('around 5.0 resid 1', False, [False, True])2019-05-08T22:55:05Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27around+5.0+resid+1%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=77d8f7c4ca776938bfe1f627e3791c87cf55ece3">8.53% regression</a> on 2019-02-14 13:06:04 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/77d8f7c4ca776938bfe1f627e3791c87cf55ece3">77d8f7c4</a>.<br> - New value: 7.39ms, old value: 6.81ms.<br> - Latest value: 7.39ms (13.93% worse than best value 6.48ms).tag:mdanalysis.asv,2019-02-14:/236a6075381c6f9e1cae452b87d16280659a282ee0af8128d3a608b25731d7036.16% selections.GeoSelectionBench.time_geometric_selections('around 5.0 resid 1', True, [False, True])2019-05-08T22:55:05Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=3.6&p-selection_string=%27around+5.0+resid+1%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=77d8f7c4ca776938bfe1f627e3791c87cf55ece3">6.16% regression</a> on 2019-02-14 13:06:04 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/77d8f7c4ca776938bfe1f627e3791c87cf55ece3">77d8f7c4</a>.<br> - New value: 10.8ms, old value: 10.1ms.<br> - Latest value: 10.8ms (6.16% worse than best value 10.1ms).tag:mdanalysis.asv,2019-02-14:/ebfe4ae0112bcbd2960bf6ef4e3fa929feb298dd4429b7edb42c4a4472354e156.46% selections.GeoSelectionBench.time_geometric_selections('around 5.0 resid 1', False, [False, True])2019-05-08T22:55:05Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=3.6&p-selection_string=%27around+5.0+resid+1%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=77d8f7c4ca776938bfe1f627e3791c87cf55ece3">6.46% regression</a> on 2019-02-14 13:06:04 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/77d8f7c4ca776938bfe1f627e3791c87cf55ece3">77d8f7c4</a>.<br> - New value: 10.7ms, old value: 10.1ms.<br> - Latest value: 10.7ms (6.46% worse than best value 10.1ms).tag:mdanalysis.asv,2016-04-13:/8cccc4faa54b73d31643c506ba774737770b4f8810dad7ac5f02ef39df42723113.09% ag_methods.AtomGroupMethodsBench.time_transform(10)2018-08-14T23:51:27Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_transform?python=2.7&p-num_atoms=10&commits=78331d5435a5287322c04203aeeecab4edb1d9fc-96c927262daeb21d00c33b9112e5be283f45b78a">13.09% regression</a> on 2016-04-13 10:45:10 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/78331d5435a5287322c04203aeeecab4edb1d9fc...96c927262daeb21d00c33b9112e5be283f45b78a">78331d54...96c92726</a>.<br> + Latest value: 7.22ms (95.91% worse than best value 3.68ms).tag:mdanalysis.asv,2016-04-13:/8cccc4faa54b73d31643c506ba774737770b4f8810dad7ac5f02ef39df42723113.09% ag_methods.AtomGroupMethodsBench.time_transform(10)2018-08-14T23:51:27Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_transform?python=2.7&p-num_atoms=10&commits=78331d5435a5287322c04203aeeecab4edb1d9fc-96c927262daeb21d00c33b9112e5be283f45b78a">13.09% regression</a> on 2016-04-13 10:45:10 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/78331d5435a5287322c04203aeeecab4edb1d9fc...96c927262daeb21d00c33b9112e5be283f45b78a">78331d54...96c92726</a>.<br> New value: 14.5μs, old value: 12.8μs.<br> Latest value: 22.5μs (75.34% worse than best value 12.8μs).tag:mdanalysis.asv,2016-04-13:/c3b7c44ccc206ea0c9591d80279cb8460938eb9844910d1ecbae99ee693e75e65.77% ag_methods.AtomGroupMethodsBench.time_transform(100)2018-08-14T23:51:27Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_transform?python=2.7&p-num_atoms=100&commits=78331d5435a5287322c04203aeeecab4edb1d9fc-96c927262daeb21d00c33b9112e5be283f45b78a">5.77% regression</a> on 2016-04-13 10:45:10 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/78331d5435a5287322c04203aeeecab4edb1d9fc...96c927262daeb21d00c33b9112e5be283f45b78a">78331d54...96c92726</a>.<br> New value: 22.3μs, old value: 21.1μs.<br> @@ -1079,11 +1007,7 @@ New value: 509ns, old value: 422ns.<br> Latest value: 514ns (21.79% worse than best value 422ns).tag:mdanalysis.asv,2015-09-21:/22eac4acddb7f316d22c24573a8b5a4a73e1e82dcd530b0f928ff0af27b6945021.45% analysis.psa.PSA_get_path_metric_funcBench.time_get_path_metric_func('discrete_frechet')2018-07-19T14:50:26Z<a href="index.html#analysis.psa.PSA_get_path_metric_funcBench.time_get_path_metric_func?python=2.7&p-path_metric=%27discrete_frechet%27&commits=58484f51a3f7145d93a350762ed7f4199e1c62bf">21.45% regression</a> on 2015-09-21 16:06:06 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/58484f51a3f7145d93a350762ed7f4199e1c62bf">58484f51</a>.<br> New value: 511ns, old value: 421ns.<br> - Latest value: 517ns (22.71% worse than best value 421ns).tag:mdanalysis.asv,2015-11-03:/a98cb82a036e2f22222077472c381a91fae2bd14d5b76511f0d13ea1d50d0445232.61% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', True, [False, True])2018-07-18T22:54:56Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=35fb1b0fa959354258f7d135443cdc067bb320bb">232.61% regression</a> on 2015-11-03 11:41:53 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/35fb1b0fa959354258f7d135443cdc067bb320bb">35fb1b0f</a>.<br> - New value: 54.3ms, old value: 16.3ms.<br> - Latest value: 54.3ms (232.61% worse than best value 16.3ms).tag:mdanalysis.asv,2015-11-03:/6d4a8617555ed8429c83a0f67641a03979d355bc612379a1f8643c6efbf6a496233.42% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', False, [False, True])2018-07-18T22:54:56Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=35fb1b0fa959354258f7d135443cdc067bb320bb">233.42% regression</a> on 2015-11-03 11:41:53 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/35fb1b0fa959354258f7d135443cdc067bb320bb">35fb1b0f</a>.<br> - New value: 54.3ms, old value: 16.3ms.<br> - Latest value: 54.3ms (233.42% worse than best value 16.3ms).tag:mdanalysis.asv,2015-11-23:/c1e34152847a60031a2362775bd8e86b050d86d1d998edfca31a2092acb30dfa5.45% GRO.GROReadBench.time_parse_GRO_file2018-07-18T15:45:06Z<a href="index.html#GRO.GROReadBench.time_parse_GRO_file?python=2.7&commits=0c3aa90cf0a748642a9aab5e50562ca1b9fedc18">5.45% regression</a> on 2015-11-23 12:35:18 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/0c3aa90cf0a748642a9aab5e50562ca1b9fedc18">0c3aa90c</a>.<br> + Latest value: 517ns (22.71% worse than best value 421ns).tag:mdanalysis.asv,2015-11-23:/c1e34152847a60031a2362775bd8e86b050d86d1d998edfca31a2092acb30dfa5.45% GRO.GROReadBench.time_parse_GRO_file2018-07-18T15:45:06Z<a href="index.html#GRO.GROReadBench.time_parse_GRO_file?python=2.7&commits=0c3aa90cf0a748642a9aab5e50562ca1b9fedc18">5.45% regression</a> on 2015-11-23 12:35:18 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/0c3aa90cf0a748642a9aab5e50562ca1b9fedc18">0c3aa90c</a>.<br> New value: 167ms, old value: 159ms.<br> Latest value: 333ms (109.67% worse than best value 159ms).tag:mdanalysis.asv,2015-11-23:/c1e34152847a60031a2362775bd8e86b050d86d1d998edfca31a2092acb30dfa5.39% GRO.GROReadBench.time_parse_GRO_file2018-07-18T15:45:06Z<a href="index.html#GRO.GROReadBench.time_parse_GRO_file?python=2.7&commits=0c3aa90cf0a748642a9aab5e50562ca1b9fedc18">5.39% regression</a> on 2015-11-23 12:35:18 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/0c3aa90cf0a748642a9aab5e50562ca1b9fedc18">0c3aa90c</a>.<br> New value: 168ms, old value: 159ms.<br> @@ -1157,9 +1081,7 @@ New value: 166ms, old value: 64.4ms.<br> Latest value: 166ms (157.87% worse than best value 64.4ms).tag:mdanalysis.asv,2016-11-07:/ff34772d4c0650718b9ff45f9001c72bd631bcb3ae80153ddcdd320a6cce68967.89% GRO.GROReadBench.time_parse_GRO_file2018-07-15T14:01:05Z<a href="index.html#GRO.GROReadBench.time_parse_GRO_file?python=2.7&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">7.89% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> New value: 181ms, old value: 167ms.<br> - Latest value: 333ms (109.67% worse than best value 159ms).tag:mdanalysis.asv,2017-01-10:/6fe74ad9856b936c30dabab68da6e8f0bcdda86e6a213243d2f432a5dd0187496.80% selections.GeoSelectionBench.time_geometric_selections('prop abs z <= 5.0', True, [True, False])2018-07-15T09:06:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27prop+abs+z+%3C%3D+5.0%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=cd2f1aaa4ec840db8b78290e8c57e2801c9abb03-5838d5f482b43ea3ed1c7d4400e4dbdc712fd8b0">6.80% regression</a> on 2017-01-10 07:57:58 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/cd2f1aaa4ec840db8b78290e8c57e2801c9abb03...5838d5f482b43ea3ed1c7d4400e4dbdc712fd8b0">cd2f1aaa...5838d5f4</a>.<br> - New value: 783μs, old value: 733μs.<br> - Latest value: 783μs (6.80% worse than best value 733μs).tag:mdanalysis.asv,2017-05-07:/f281a9d96613d95421a485c782845049d32391c146d83069659845e17bbe390011.71% ag_methods.AtomGroupAttrsBench.time_segments(100)2018-07-15T01:03:31Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_segments?python=2.7&p-num_atoms=100&commits=d5667ae9b52c1f14d9cc0fe3cb77799c9a5635c2">11.71% regression</a> on 2017-05-07 12:42:39 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/d5667ae9b52c1f14d9cc0fe3cb77799c9a5635c2">d5667ae9</a>.<br> + Latest value: 333ms (109.67% worse than best value 159ms).tag:mdanalysis.asv,2017-05-07:/f281a9d96613d95421a485c782845049d32391c146d83069659845e17bbe390011.71% ag_methods.AtomGroupAttrsBench.time_segments(100)2018-07-15T01:03:31Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_segments?python=2.7&p-num_atoms=100&commits=d5667ae9b52c1f14d9cc0fe3cb77799c9a5635c2">11.71% regression</a> on 2017-05-07 12:42:39 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/d5667ae9b52c1f14d9cc0fe3cb77799c9a5635c2">d5667ae9</a>.<br> New value: 12.5μs, old value: 11.2μs.<br> Latest value: 12.5μs (2044.34% worse than best value 582ns).tag:mdanalysis.asv,2017-05-07:/4d272ebd8ad9ee7caed97709ff9395779e58645492b4af0a36817a2a02a89a3c19.37% ag_methods.AtomGroupAttrsBench.time_segments(10)2018-07-15T01:03:31Z<a href="index.html#ag_methods.AtomGroupAttrsBench.time_segments?python=2.7&p-num_atoms=10&commits=d5667ae9b52c1f14d9cc0fe3cb77799c9a5635c2">19.37% regression</a> on 2017-05-07 12:42:39 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/d5667ae9b52c1f14d9cc0fe3cb77799c9a5635c2">d5667ae9</a>.<br> New value: 12.3μs, old value: 10.3μs.<br> @@ -1219,31 +1141,7 @@ New value: 46.0ms, old value: 25.3ms.<br> Latest value: 48.0ms (545.05% worse than best value 7.44ms).tag:mdanalysis.asv,2017-11-23:/91be3efce94f38e8d5f493f7e8a8e9e2946782315bcb9cc761210c902fe8b04f76.03% selections.SimpleSelectionBench.time_simple_selections('backbone')2018-07-14T13:05:38Z<a href="index.html#selections.SimpleSelectionBench.time_simple_selections?python=2.7&p-selection_string=%27backbone%27&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">76.03% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> New value: 49.2ms, old value: 28.0ms.<br> - Latest value: 49.8ms (281.66% worse than best value 13.0ms).tag:mdanalysis.asv,2017-11-23:/de24c07ef150ac7a0be1912d36b8aa00f9659603f60c1b0918924e30f3b0b85372.86% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', True, [True, False])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">72.86% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 50.7ms, old value: 29.3ms.<br> - Latest value: 53.8ms (446.04% worse than best value 9.86ms).tag:mdanalysis.asv,2017-11-23:/69a97d80ce4054ed3b8a906ee287e8c781da443aa54b940a2acc82f1cc3b75b372.95% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', False, [True, False])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">72.95% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 50.6ms, old value: 29.3ms.<br> - Latest value: 53.4ms (440.40% worse than best value 9.89ms).tag:mdanalysis.asv,2017-11-23:/3860c21e9b58c928f25a2b7dae819727120f2eef68154c5d10424db7abefe5e576.69% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', True, [True, False])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">76.69% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 50.7ms, old value: 28.7ms.<br> - Latest value: 53.7ms (443.03% worse than best value 9.88ms).tag:mdanalysis.asv,2017-11-23:/c42fdce3f74de1061476a3f8545b88aaa3c5752f9e6fffdef0464c499a9e3a1076.48% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', False, [True, False])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">76.48% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 50.7ms, old value: 28.7ms.<br> - Latest value: 53.6ms (443.41% worse than best value 9.87ms).tag:mdanalysis.asv,2017-11-23:/ef787843aa41882d2e56ac6672c42e1a9fad6632d791d15e5f367b6bb846711a81.28% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', True, [False, True])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">81.28% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 48.7ms, old value: 26.9ms.<br> - Latest value: 48.7ms (340.56% worse than best value 11.1ms).tag:mdanalysis.asv,2017-11-23:/5380d2e3bc287a7c895e256bd08f76555761dd45b230cb32fc11dce8b918555773.12% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', True, [True, False])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">73.12% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 50.6ms, old value: 29.2ms.<br> - Latest value: 52.7ms (288.44% worse than best value 13.6ms).tag:mdanalysis.asv,2017-11-23:/62323bfffd683a7a25eded7230663e1df28d97d7f5720b0b7e51c7178011ced180.99% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', False, [False, True])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">80.99% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 48.7ms, old value: 26.9ms.<br> - Latest value: 50.6ms (353.56% worse than best value 11.2ms).tag:mdanalysis.asv,2017-11-23:/c3a256f3c99b520e66648f8a1d2af0cb60579971c0bf093721bf1b4ef1a62a9d73.37% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', False, [True, False])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">73.37% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 50.7ms, old value: 29.2ms.<br> - Latest value: 52.2ms (283.52% worse than best value 13.6ms).tag:mdanalysis.asv,2017-11-23:/a9fb8abf2db724550d760cdbdb6632d2e14b1af0da8a1fe7f4ec4b80b6ec4be281.15% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', True, [False, True])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">81.15% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 48.4ms, old value: 26.7ms.<br> - Latest value: 48.4ms (348.12% worse than best value 10.8ms).tag:mdanalysis.asv,2017-11-23:/16510557b4d773c393f15054cf8fcca646735fa44ee928b480aaba1232ade39c74.61% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', True, [True, False])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">74.61% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 50.8ms, old value: 29.1ms.<br> - Latest value: 51.9ms (291.92% worse than best value 13.2ms).tag:mdanalysis.asv,2017-11-23:/e538a368dce0e2e846e619d1feea0df8e7673fe26ac87b736b8ffdf846c889d180.22% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', False, [False, True])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">80.22% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 48.4ms, old value: 26.9ms.<br> - Latest value: 48.4ms (346.61% worse than best value 10.8ms).tag:mdanalysis.asv,2017-11-23:/73d8f0ec9ac88c06a2d5896552ae7c282009b0ccd1e9d6f29da454ea5d0fcc3d73.85% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', False, [True, False])2018-07-14T13:04:17Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=9ba1ab964920acfc986d8e264f78c965e062e9d0-9b074d53ce84f40de11c9599349d75cdfc12fa8c">73.85% regression</a> on 2017-11-23 14:27:53 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/9ba1ab964920acfc986d8e264f78c965e062e9d0...9b074d53ce84f40de11c9599349d75cdfc12fa8c">9ba1ab96...9b074d53</a>.<br> - New value: 50.5ms, old value: 29.1ms.<br> - Latest value: 52.1ms (292.66% worse than best value 13.3ms).tag:mdanalysis.asv,2018-01-21:/bf0325a55f7f749cfa78e09b64150aa80257ae1207a32185d615bacdeacddb160.80% traj_reader.TrajReaderCreation.time_reads('NCDF')2018-07-14T11:18:21Z<a href="index.html#traj_reader.TrajReaderCreation.time_reads?python=2.7&p-traj_format=%27NCDF%27&commits=a35ce016fb8301d40cd6505171907ea0af9cd584-b837af1dc9d2b4dee2a5c6ea61639c369a011014">0.80% regression</a> on 2018-01-21 16:53:34 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/a35ce016fb8301d40cd6505171907ea0af9cd584...b837af1dc9d2b4dee2a5c6ea61639c369a011014">a35ce016...b837af1d</a>.<br> + Latest value: 49.8ms (281.66% worse than best value 13.0ms).tag:mdanalysis.asv,2018-01-21:/bf0325a55f7f749cfa78e09b64150aa80257ae1207a32185d615bacdeacddb160.80% traj_reader.TrajReaderCreation.time_reads('NCDF')2018-07-14T11:18:21Z<a href="index.html#traj_reader.TrajReaderCreation.time_reads?python=2.7&p-traj_format=%27NCDF%27&commits=a35ce016fb8301d40cd6505171907ea0af9cd584-b837af1dc9d2b4dee2a5c6ea61639c369a011014">0.80% regression</a> on 2018-01-21 16:53:34 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/a35ce016fb8301d40cd6505171907ea0af9cd584...b837af1dc9d2b4dee2a5c6ea61639c369a011014">a35ce016...b837af1d</a>.<br> New value: 584μs, old value: 579μs.<br> Latest value: 611μs (5.53% worse than best value 579μs).tag:mdanalysis.asv,2018-01-21:/2ba878751bff4ea80480e12e386b935b60bdf7d12fa79a8464150d7abb922d665.58% selections.SimpleSelectionBench.time_simple_selections('resnum 1:10')2018-07-14T11:17:58Z<a href="index.html#selections.SimpleSelectionBench.time_simple_selections?python=2.7&p-selection_string=%27resnum+1%3A10%27&commits=a35ce016fb8301d40cd6505171907ea0af9cd584-b837af1dc9d2b4dee2a5c6ea61639c369a011014">5.58% regression</a> on 2018-01-21 16:53:34 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/a35ce016fb8301d40cd6505171907ea0af9cd584...b837af1dc9d2b4dee2a5c6ea61639c369a011014">a35ce016...b837af1d</a>.<br> New value: 207μs, old value: 196μs.<br> @@ -1291,43 +1189,7 @@ New value: 46.4ms, old value: 19.2ms.<br> Latest value: 46.4ms (558.50% worse than best value 7.05ms).tag:mdanalysis.asv,2018-06-12:/9b7cabd833d0b73aab3eef0d22cbdecc590583364c1db1d22518044317f31c29128.50% selections.SimpleSelectionBench.time_simple_selections('backbone')2018-06-29T19:51:56Z<a href="index.html#selections.SimpleSelectionBench.time_simple_selections?python=2.7&p-selection_string=%27backbone%27&commits=8883f906fd4d921d050573a1ead297f2624ae008">128.50% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> New value: 49.3ms, old value: 21.6ms.<br> - Latest value: 49.3ms (294.06% worse than best value 12.5ms).tag:mdanalysis.asv,2018-06-12:/5b1edde99412a13d41fa47f7ac4fec0107c9247e966d21720ab98515234132627.37% selections.GeoSelectionBench.time_geometric_selections('around 5.0 resid 1', True, [True, False])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27around+5.0+resid+1%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">7.37% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 49.0ms, old value: 45.6ms.<br> - Latest value: 49.0ms (521.46% worse than best value 7.89ms).tag:mdanalysis.asv,2018-06-12:/52208c90ed2458fbcccfcea64b8fb8c0ed5f633614782206643017fad555f2a16.82% selections.GeoSelectionBench.time_geometric_selections('around 5.0 resid 1', False, [True, False])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27around+5.0+resid+1%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">6.82% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 49.0ms, old value: 45.8ms.<br> - Latest value: 49.0ms (362.60% worse than best value 10.6ms).tag:mdanalysis.asv,2018-06-12:/e2b798203a72428137749661deefe51dc28ed79f4deb0c91935d4f6b216f282e37.26% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', True, [False, True])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">37.26% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 101ms, old value: 73.7ms.<br> - Latest value: 101ms (58.15% worse than best value 63.9ms).tag:mdanalysis.asv,2018-06-12:/05511d76882f3162ab4e0de72b5542fe5a578dcd9f17f682f21af0a54a1e8d72124.75% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', True, [True, False])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">124.75% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 50.9ms, old value: 22.6ms.<br> - Latest value: 50.9ms (437.08% worse than best value 9.47ms).tag:mdanalysis.asv,2018-06-12:/e19fb44a4c87dd0e955f73df58a88da4e7e3e82b0af94833441dfc8d9295ca2b37.84% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', False, [False, True])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">37.84% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 101ms, old value: 73.5ms.<br> - Latest value: 101ms (58.32% worse than best value 64.0ms).tag:mdanalysis.asv,2018-06-12:/4f360e66d7542fe1558cc0b5f6ef4446a95d155211762564c6099fc0c5bc6bfa124.47% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', False, [True, False])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">124.47% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 50.8ms, old value: 22.6ms.<br> - Latest value: 50.8ms (435.42% worse than best value 9.49ms).tag:mdanalysis.asv,2018-06-12:/efb92e15f46fbcd764cdbc69453cfdd36ee987c42875534abac14411226769cc37.99% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', True, [False, True])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">37.99% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 101ms, old value: 73.5ms.<br> - Latest value: 101ms (58.88% worse than best value 63.9ms).tag:mdanalysis.asv,2018-06-12:/8fa3474f9272b98fad274350ffc89617452610cfb36799bb5123cb96c5cce4bc126.04% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', True, [True, False])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">126.04% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 50.9ms, old value: 22.5ms.<br> - Latest value: 50.9ms (436.55% worse than best value 9.48ms).tag:mdanalysis.asv,2018-06-12:/1111e583b11a7f6db66ba9f95994297cc209cc7c1f00bb0c50ba5abc92935ec336.48% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', False, [False, True])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">36.48% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 101ms, old value: 74.1ms.<br> - Latest value: 101ms (58.20% worse than best value 63.9ms).tag:mdanalysis.asv,2018-06-12:/338f756787f0bbeb981ca4a3b50ed23a9fa3b39ee7f4db8312fba6a32542f2a8125.12% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', False, [True, False])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">125.12% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 51.0ms, old value: 22.6ms.<br> - Latest value: 51.0ms (437.47% worse than best value 9.49ms).tag:mdanalysis.asv,2018-06-12:/4324da20087042407010c10200ebd7077ea8d42851e52758610d8e7c3976eb74139.43% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', True, [False, True])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">139.43% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 48.7ms, old value: 20.3ms.<br> - Latest value: 48.7ms (352.06% worse than best value 10.8ms).tag:mdanalysis.asv,2018-06-12:/20b4457cbb4832caeb61dbb937cb4dbf6d2babbd2a1f757bb2cd7b12de6cc935130.74% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', True, [True, False])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">130.74% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 52.1ms, old value: 22.6ms.<br> - Latest value: 52.1ms (303.66% worse than best value 12.9ms).tag:mdanalysis.asv,2018-06-12:/4c44e2bf48fe51476cd997be23d6b12cb4d4ff33d7956701f3f0ba810b65d993141.63% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', False, [False, True])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">141.63% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 48.9ms, old value: 20.2ms.<br> - Latest value: 48.9ms (358.67% worse than best value 10.7ms).tag:mdanalysis.asv,2018-06-12:/8696ac831341d2bba3e9683617e895b028c35f4a261e2a13537615de69eb086f142.02% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', True, [False, True])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">142.02% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 48.8ms, old value: 20.2ms.<br> - Latest value: 48.8ms (369.10% worse than best value 10.4ms).tag:mdanalysis.asv,2018-06-12:/42a7d2294d8c22ef270b49df74b568ca9a5c078b10688a97deab665fb9c85d81132.44% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', True, [True, False])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">132.44% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 52.0ms, old value: 22.4ms.<br> - Latest value: 52.0ms (307.90% worse than best value 12.7ms).tag:mdanalysis.asv,2018-06-12:/c6ebf4adccac478ecc9422939599fdec984a5fd0a5f9cae4febd9d7c58dd36b5139.46% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', False, [False, True])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">139.46% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 48.4ms, old value: 20.2ms.<br> - Latest value: 48.4ms (366.56% worse than best value 10.4ms).tag:mdanalysis.asv,2018-06-12:/4e39111ebea7207bc0a3a65264392bc01ef412112770b2e83b85d646996f8032130.72% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', False, [True, False])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">130.72% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 51.6ms, old value: 22.4ms.<br> - Latest value: 51.6ms (304.64% worse than best value 12.8ms).tag:mdanalysis.asv,2018-06-12:/8412d3081cd29618244694d41dc895413062ec33e7622922535ed89e58b5f5355.53% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', False, [True, False])2018-06-29T19:50:40Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=8883f906fd4d921d050573a1ead297f2624ae008">5.53% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> - New value: 3.67ms, old value: 3.48ms.<br> - Latest value: 3.67ms (129.34% worse than best value 1.60ms).tag:mdanalysis.asv,2018-06-12:/0eb1505ae98a42d7235ecf8c08b5b34ae1073fb94999bd18739a6baf5dd232876.01% analysis.psa.PSA_metricBench.time_hausdorff_wavg(10, 25)2018-06-29T19:48:20Z<a href="index.html#analysis.psa.PSA_metricBench.time_hausdorff_wavg?python=2.7&p-time_steps=10&p-n_atoms=25&commits=8883f906fd4d921d050573a1ead297f2624ae008">6.01% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> + Latest value: 49.3ms (294.06% worse than best value 12.5ms).tag:mdanalysis.asv,2018-06-12:/0eb1505ae98a42d7235ecf8c08b5b34ae1073fb94999bd18739a6baf5dd232876.01% analysis.psa.PSA_metricBench.time_hausdorff_wavg(10, 25)2018-06-29T19:48:20Z<a href="index.html#analysis.psa.PSA_metricBench.time_hausdorff_wavg?python=2.7&p-time_steps=10&p-n_atoms=25&commits=8883f906fd4d921d050573a1ead297f2624ae008">6.01% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> New value: 90.0μs, old value: 84.9μs.<br> Latest value: 90.0μs (6.01% worse than best value 84.9μs).tag:mdanalysis.asv,2018-06-12:/b631ce8033d53320c71d3ca04b41fcf562ca84e9b9ab436de74799a5675352f47.59% analysis.psa.PSA_metricBench.time_hausdorff_avg(10, 25)2018-06-29T19:47:47Z<a href="index.html#analysis.psa.PSA_metricBench.time_hausdorff_avg?python=2.7&p-time_steps=10&p-n_atoms=25&commits=8883f906fd4d921d050573a1ead297f2624ae008">7.59% regression</a> on 2018-06-12 10:14:31 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/8883f906fd4d921d050573a1ead297f2624ae008">8883f906</a>.<br> New value: 88.6μs, old value: 82.3μs.<br> @@ -1377,61 +1239,7 @@ New value: 28.0ms, old value: 13.0ms.<br> Latest value: 49.8ms (281.66% worse than best value 13.0ms).tag:mdanalysis.asv,2016-11-07:/b751e8852b01ea984e7979e643094f86a20d362cb9391f71e0ee439b6786b2905.59% selections.SimpleSelectionBench.time_simple_selections('nucleic')2018-06-14T19:41:02Z<a href="index.html#selections.SimpleSelectionBench.time_simple_selections?python=2.7&p-selection_string=%27nucleic%27&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">5.59% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> New value: 15.5ms, old value: 14.7ms.<br> - Latest value: 15.6ms (58.09% worse than best value 9.86ms).tag:mdanalysis.asv,2016-11-07:/d1fc9df75ea5505143011973537d2e1d5cd89f3abde5703e216a8ba002a5e08115.22% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', True, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">15.22% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 73.7ms, old value: 63.9ms.<br> - Latest value: 101ms (58.15% worse than best value 63.9ms).tag:mdanalysis.asv,2016-11-07:/1f2c9044bfb1f765fa4d498fadb9517a877f0ae0c00a4579eba0ad7647dc294d115.36% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', True, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">115.36% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 20.4ms, old value: 9.47ms.<br> - Latest value: 50.9ms (437.08% worse than best value 9.47ms).tag:mdanalysis.asv,2016-11-07:/a13596ba86a1d032a6775c65b038f6e50c505ce33091ef1037a0422f5f66991a14.86% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', False, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">14.86% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 73.5ms, old value: 64.0ms.<br> - Latest value: 101ms (58.32% worse than best value 64.0ms).tag:mdanalysis.asv,2016-11-07:/5e88837b4d1e40f4d4d696bd9f10cb5101e91f577e29c5c3f48d4e03766793bb114.95% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', False, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">114.95% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 20.4ms, old value: 9.49ms.<br> - Latest value: 50.8ms (435.42% worse than best value 9.49ms).tag:mdanalysis.asv,2016-11-07:/14a2c79ae49e44e6eb048d053c400d49052bb227dcfb6ac85bce2cc57636d84314.12% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', True, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">14.12% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 73.5ms, old value: 64.4ms.<br> - Latest value: 101ms (58.88% worse than best value 63.9ms).tag:mdanalysis.asv,2016-11-07:/aa0f760602ea6c287cb380cbb23d48ca0cea91378af430843745b78ff0daa60f115.22% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', True, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">115.22% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 20.4ms, old value: 9.48ms.<br> - Latest value: 50.9ms (436.55% worse than best value 9.48ms).tag:mdanalysis.asv,2016-11-07:/2f03ac0d53682a5654ec81410e76e7fdf19c388a18abaf3399434cfe69c91c1a14.81% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', False, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">14.81% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 73.4ms, old value: 63.9ms.<br> - Latest value: 101ms (58.20% worse than best value 63.9ms).tag:mdanalysis.asv,2016-11-07:/2d16c5a10192033a0ac1064607b5a05faff35072d7fd8fb48c983b4c4bd24665114.72% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', False, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">114.72% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 20.4ms, old value: 9.49ms.<br> - Latest value: 51.0ms (437.47% worse than best value 9.49ms).tag:mdanalysis.asv,2016-11-07:/6ff897668ab2dc7fc55ee5d24f830e3feecdf834c6578dc08b811d2d643c3b3188.81% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', True, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">88.81% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 20.3ms, old value: 10.8ms.<br> - Latest value: 48.7ms (352.06% worse than best value 10.8ms).tag:mdanalysis.asv,2016-11-07:/5362b7dbb0e1270548cd4e3759790d6aa5cbace723db027a5af63c7d64de1f5472.48% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', True, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">72.48% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 22.6ms, old value: 13.1ms.<br> - Latest value: 52.1ms (303.66% worse than best value 12.9ms).tag:mdanalysis.asv,2016-11-07:/c32a28ed47d9de32e9f6a7bacc892ec849659e9163a98897428fd5b5a9442dde87.55% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', False, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">87.55% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 20.2ms, old value: 10.8ms.<br> - Latest value: 48.9ms (358.67% worse than best value 10.7ms).tag:mdanalysis.asv,2016-11-07:/71320f53edb6ede55821fc3f1d4620541fe5b151ee5bd031a39af9700661f6f092.17% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', True, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">92.17% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 20.2ms, old value: 10.5ms.<br> - Latest value: 48.8ms (369.10% worse than best value 10.4ms).tag:mdanalysis.asv,2016-11-07:/8458ddd7982c348e5b838e893bba94d5f1e38a2850fc2ac2cbcb1bb2015c842d75.48% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', True, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">75.48% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 22.4ms, old value: 12.7ms.<br> - Latest value: 52.0ms (307.90% worse than best value 12.7ms).tag:mdanalysis.asv,2016-11-07:/54e31154918ccd2404cbe36f768cae0964f54c566b52d0cd1e51d61e42de484294.84% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', False, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94.84% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 20.2ms, old value: 10.4ms.<br> - Latest value: 48.4ms (366.56% worse than best value 10.4ms).tag:mdanalysis.asv,2016-11-07:/56f1a7e8439bae887422c85782374811b379394062663d446d82a0d50042179775.38% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', False, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">75.38% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 22.4ms, old value: 12.8ms.<br> - Latest value: 51.6ms (304.64% worse than best value 12.8ms).tag:mdanalysis.asv,2016-11-07:/1f2c9044bfb1f765fa4d498fadb9517a877f0ae0c00a4579eba0ad7647dc294d174.49% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', True, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">174.49% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 27.1ms, old value: 9.86ms.<br> - Latest value: 53.8ms (446.04% worse than best value 9.86ms).tag:mdanalysis.asv,2016-11-07:/5e88837b4d1e40f4d4d696bd9f10cb5101e91f577e29c5c3f48d4e03766793bb172.08% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', False, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">172.08% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 26.9ms, old value: 9.89ms.<br> - Latest value: 53.4ms (440.40% worse than best value 9.89ms).tag:mdanalysis.asv,2016-11-07:/aa0f760602ea6c287cb380cbb23d48ca0cea91378af430843745b78ff0daa60f172.54% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', True, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">172.54% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 26.9ms, old value: 9.88ms.<br> - Latest value: 53.7ms (443.03% worse than best value 9.88ms).tag:mdanalysis.asv,2016-11-07:/2d16c5a10192033a0ac1064607b5a05faff35072d7fd8fb48c983b4c4bd24665173.24% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', False, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">173.24% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 27.0ms, old value: 9.87ms.<br> - Latest value: 53.6ms (443.41% worse than best value 9.87ms).tag:mdanalysis.asv,2016-11-07:/6ff897668ab2dc7fc55ee5d24f830e3feecdf834c6578dc08b811d2d643c3b31142.48% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', True, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">142.48% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 26.9ms, old value: 11.1ms.<br> - Latest value: 48.7ms (340.56% worse than best value 11.1ms).tag:mdanalysis.asv,2016-11-07:/5362b7dbb0e1270548cd4e3759790d6aa5cbace723db027a5af63c7d64de1f54113.53% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', True, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">113.53% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 29.2ms, old value: 13.7ms.<br> - Latest value: 52.7ms (288.44% worse than best value 13.6ms).tag:mdanalysis.asv,2016-11-07:/c32a28ed47d9de32e9f6a7bacc892ec849659e9163a98897428fd5b5a9442dde141.50% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', False, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">141.50% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 26.9ms, old value: 11.2ms.<br> - Latest value: 50.6ms (353.56% worse than best value 11.2ms).tag:mdanalysis.asv,2016-11-07:/33c93de9ec14cdff4847f1e72a7f111672fbdb6a3cdaa86079ae50589410e4a1114.84% selections.GeoSelectionBench.time_geometric_selections('cylayer 5 10 10 -8 protein', False, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cylayer+5+10+10+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">114.84% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 29.2ms, old value: 13.6ms.<br> - Latest value: 52.2ms (283.52% worse than best value 13.6ms).tag:mdanalysis.asv,2016-11-07:/71320f53edb6ede55821fc3f1d4620541fe5b151ee5bd031a39af9700661f6f0147.37% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', True, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">147.37% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 26.7ms, old value: 10.8ms.<br> - Latest value: 48.4ms (348.12% worse than best value 10.8ms).tag:mdanalysis.asv,2016-11-07:/8458ddd7982c348e5b838e893bba94d5f1e38a2850fc2ac2cbcb1bb2015c842d119.62% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', True, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">119.62% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 29.1ms, old value: 13.2ms.<br> - Latest value: 51.9ms (291.92% worse than best value 13.2ms).tag:mdanalysis.asv,2016-11-07:/54e31154918ccd2404cbe36f768cae0964f54c566b52d0cd1e51d61e42de4842147.82% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', False, [False, True])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BFalse%2C+True%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">147.82% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 26.9ms, old value: 10.8ms.<br> - Latest value: 48.4ms (346.61% worse than best value 10.8ms).tag:mdanalysis.asv,2016-11-07:/56f1a7e8439bae887422c85782374811b379394062663d446d82a0d500421797119.09% selections.GeoSelectionBench.time_geometric_selections('cyzone 15 4 -8 protein', False, [True, False])2018-06-14T19:39:48Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27cyzone+15+4+-8+protein%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">119.09% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> - New value: 29.1ms, old value: 13.3ms.<br> - Latest value: 52.1ms (292.66% worse than best value 13.3ms).tag:mdanalysis.asv,2016-11-07:/54635b69d1604fda9dacecc4e2be821fc0fa8dc8fe09cd52b956b1e6d0fc9de055.05% ag_methods.AtomGroupMethodsBench.time_transform(10)2018-06-14T19:32:33Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_transform?python=2.7&p-num_atoms=10&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">55.05% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> + Latest value: 15.6ms (58.09% worse than best value 9.86ms).tag:mdanalysis.asv,2016-11-07:/54635b69d1604fda9dacecc4e2be821fc0fa8dc8fe09cd52b956b1e6d0fc9de055.05% ag_methods.AtomGroupMethodsBench.time_transform(10)2018-06-14T19:32:33Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_transform?python=2.7&p-num_atoms=10&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">55.05% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> New value: 22.5μs, old value: 14.5μs.<br> Latest value: 22.5μs (75.34% worse than best value 12.8μs).tag:mdanalysis.asv,2016-11-07:/bd307f47c5cbc35a877ef1ab1f1155bca4a3fe20bb47e9355cad79f6bba43f2735.34% ag_methods.AtomGroupMethodsBench.time_transform(100)2018-06-14T19:32:33Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_transform?python=2.7&p-num_atoms=100&commits=94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">35.34% regression</a> on 2016-11-07 04:08:16 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/94f1d75a378677a2ebc6ba4e9f242b5b64cfb277">94f1d75a</a>.<br> New value: 30.2μs, old value: 22.3μs.<br> @@ -1529,35 +1337,7 @@ New value: 43.3μs, old value: 41.9μs.<br> Latest value: 44.3μs (12.68% worse than best value 39.3μs).tag:mdanalysis.asv,2016-11-30:/70484d77134c55d4d243237cb290b2adc61fdf6df16c8a8e3613fb1b540b546d10.68% ag_methods.AtomGroupMethodsBench.time_bsphere_no_pbc(100)2018-06-14T17:45:07Z<a href="index.html#ag_methods.AtomGroupMethodsBench.time_bsphere_no_pbc?python=2.7&p-num_atoms=100&commits=a6a6db3f9147500f05e61bc8a8e1629be46a3505">10.68% regression</a> on 2016-11-30 05:37:41 in commit <a href="https://github.com/MDAnalysis/mdanalysis/commit/a6a6db3f9147500f05e61bc8a8e1629be46a3505">a6a6db3f</a>.<br> New value: 38.2μs, old value: 34.5μs.<br> - Latest value: 38.2μs (10.68% worse than best value 34.5μs).tag:mdanalysis.asv,2017-10-04:/5377e70ee06eefaeac275966576be0d377df72ccd17d3211650eb732b2aed8b6330.78% selections.GeoSelectionBench.time_geometric_selections('around 5.0 resid 1', True, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27around+5.0+resid+1%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">330.78% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 45.6ms, old value: 10.6ms.<br> - Latest value: 49.0ms (521.46% worse than best value 7.89ms).tag:mdanalysis.asv,2017-10-04:/f662afa436c0425a3e886d065c0a79ce05df963331d4275f2723e81ca907b15f333.05% selections.GeoSelectionBench.time_geometric_selections('around 5.0 resid 1', False, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27around+5.0+resid+1%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">333.05% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 45.8ms, old value: 10.6ms.<br> - Latest value: 49.0ms (362.60% worse than best value 10.6ms).tag:mdanalysis.asv,2017-10-04:/07a338c3b936a4747c0fc40b7c6329a99e9dfe081ac8f73b3ce48f023238d05310.96% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', True, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">10.96% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 22.6ms, old value: 20.4ms.<br> - Latest value: 50.9ms (437.08% worse than best value 9.47ms).tag:mdanalysis.asv,2017-10-04:/d25e45e25efe1c6949c97f569555f63e0cddd89d094a0775d6c52423f1aba46a10.96% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', False, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">10.96% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 22.6ms, old value: 20.4ms.<br> - Latest value: 50.8ms (435.42% worse than best value 9.49ms).tag:mdanalysis.asv,2017-10-04:/71ab21f01d22261b08328f653eb6dc0de155a5d6a49256c10917aade4d1039c510.29% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', True, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">10.29% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 22.5ms, old value: 20.4ms.<br> - Latest value: 50.9ms (436.55% worse than best value 9.48ms).tag:mdanalysis.asv,2017-10-04:/f13988f5eb3fe36c29b76d8c91b8839c02c50fe4e054044b71452765a11cf44b11.19% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', False, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">11.19% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 22.6ms, old value: 20.4ms.<br> - Latest value: 51.0ms (437.47% worse than best value 9.49ms).tag:mdanalysis.asv,2017-10-04:/f65c680c62f14ffa26a067cc0ef7bc48c618a8f43f7462e8d60c65d08962c62b117.17% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', True, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">117.17% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 3.54ms, old value: 1.63ms.<br> - Latest value: 3.71ms (132.25% worse than best value 1.60ms).tag:mdanalysis.asv,2017-10-04:/99bc073496f45f489bc50c66a3536debf398c1ed7235d2ce1ebaff92a2f18143117.33% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', False, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">117.33% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 3.48ms, old value: 1.60ms.<br> - Latest value: 3.67ms (129.34% worse than best value 1.60ms).tag:mdanalysis.asv,2017-10-04:/07a338c3b936a4747c0fc40b7c6329a99e9dfe081ac8f73b3ce48f023238d0538.47% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', True, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">8.47% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 29.3ms, old value: 27.1ms.<br> - Latest value: 53.8ms (446.04% worse than best value 9.86ms).tag:mdanalysis.asv,2017-10-04:/d25e45e25efe1c6949c97f569555f63e0cddd89d094a0775d6c52423f1aba46a8.84% selections.GeoSelectionBench.time_geometric_selections('sphlayer 2.4 6.0 (protein)', False, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphlayer+2.4+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">8.84% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 29.3ms, old value: 26.9ms.<br> - Latest value: 53.4ms (440.40% worse than best value 9.89ms).tag:mdanalysis.asv,2017-10-04:/71ab21f01d22261b08328f653eb6dc0de155a5d6a49256c10917aade4d1039c56.58% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', True, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">6.58% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 28.7ms, old value: 26.9ms.<br> - Latest value: 53.7ms (443.03% worse than best value 9.88ms).tag:mdanalysis.asv,2017-10-04:/f13988f5eb3fe36c29b76d8c91b8839c02c50fe4e054044b71452765a11cf44b6.64% selections.GeoSelectionBench.time_geometric_selections('sphzone 6.0 (protein)', False, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27sphzone+6.0+%28protein%29%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">6.64% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 28.7ms, old value: 27.0ms.<br> - Latest value: 53.6ms (443.41% worse than best value 9.87ms).tag:mdanalysis.asv,2017-10-04:/f65c680c62f14ffa26a067cc0ef7bc48c618a8f43f7462e8d60c65d08962c62b116.45% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', True, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=True&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">116.45% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 3.54ms, old value: 1.64ms.<br> - Latest value: 6.50ms (309.44% worse than best value 1.59ms).tag:mdanalysis.asv,2017-10-04:/99bc073496f45f489bc50c66a3536debf398c1ed7235d2ce1ebaff92a2f18143117.70% selections.GeoSelectionBench.time_geometric_selections('point 5.0 5.0 5.0 3.5', False, [True, False])2018-06-13T20:31:26Z<a href="index.html#selections.GeoSelectionBench.time_geometric_selections?python=2.7&p-selection_string=%27point+5.0+5.0+5.0+3.5%27&p-dynamic_selection=False&p-periodic_selection=%5BTrue%2C+False%5D&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">117.70% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> - New value: 3.49ms, old value: 1.61ms.<br> - Latest value: 6.44ms (301.41% worse than best value 1.61ms).tag:mdanalysis.asv,2017-10-04:/0a5edb8f2b5b1937062e686ed5d4b2125a8cf195504fc379235893c208467cd410.03% analysis.rdf.SimpleRdfBench.time_interrdf(20, [0, 15], 100)2018-06-13T20:30:19Z<a href="index.html#analysis.rdf.SimpleRdfBench.time_interrdf?python=2.7&p-nbins=20&p-range_val=%5B0%2C+15%5D&p-natoms=100&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">10.03% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> + Latest value: 38.2μs (10.68% worse than best value 34.5μs).tag:mdanalysis.asv,2017-10-04:/0a5edb8f2b5b1937062e686ed5d4b2125a8cf195504fc379235893c208467cd410.03% analysis.rdf.SimpleRdfBench.time_interrdf(20, [0, 15], 100)2018-06-13T20:30:19Z<a href="index.html#analysis.rdf.SimpleRdfBench.time_interrdf?python=2.7&p-nbins=20&p-range_val=%5B0%2C+15%5D&p-natoms=100&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">10.03% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> New value: 31.4ms, old value: 28.5ms.<br> Latest value: 31.6ms (22.33% worse than best value 25.8ms).tag:mdanalysis.asv,2017-10-04:/a5e492dd40896a0fc7d8833f2395c5a0f0cdc5163f94791ccf008e1e1ac26c5224.55% analysis.rdf.SimpleRdfBench.time_interrdf(20, [0, 15], 1000)2018-06-13T20:30:19Z<a href="index.html#analysis.rdf.SimpleRdfBench.time_interrdf?python=2.7&p-nbins=20&p-range_val=%5B0%2C+15%5D&p-natoms=1000&commits=65a6d6be8b497433ca27cd00fb2d092dbcad9651-d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">24.55% regression</a> on 2017-10-04 23:03:22 in commits <a href="https://github.com/MDAnalysis/mdanalysis/commit/../compare/65a6d6be8b497433ca27cd00fb2d092dbcad9651...d5dec2b6f2be00fac35ee8f9be0106a244d6cc45">65a6d6be...d5dec2b6</a>.<br> New value: 201ms, old value: 162ms.<br>