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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,159 @@ | ||
| from __future__ import division, absolute_import, print_function | ||
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| import MDAnalysis | ||
| import numpy as np | ||
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| try: | ||
| from MDAnalysis.analysis import psa | ||
| except: | ||
| pass | ||
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| class PSA_sqnormBench(object): | ||
| """Benchmarks for MDAnalysis.analysis.psa. | ||
| sqnorm | ||
| """ | ||
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| params = ([2,3,4], | ||
| [100,1000,10000], | ||
| [None, 0, 1, -1]) | ||
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| # num_cols is equivalent to dimensions | ||
| # num_rows is equivalent to i.e., num atoms | ||
| param_names = ['num_cols', | ||
| 'num_rows', | ||
| 'axis'] | ||
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| def setup(self, num_cols, num_rows, axis): | ||
| np.random.seed(170089) | ||
| self.v = np.random.rand(num_rows, num_cols) | ||
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| def time_sqnorm(self, num_cols, num_rows, axis): | ||
| """Benchmark sqnorm in psa module | ||
| """ | ||
| psa.sqnorm(v=self.v, axis=axis) | ||
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| class PSA_get_msd_matrixBench(object): | ||
| """Benchmarks for MDAnalysis.analysis.psa. | ||
| get_msd_matrix | ||
| """ | ||
|
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| params = ([10,100,1000], | ||
| [5,25,50]) | ||
|
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| # since the function is defined to work with | ||
| # 3N dimension data sets, we will restrict | ||
| # benchmarks to that dimensionality | ||
| param_names = ['time_steps', | ||
| 'n_atoms'] | ||
|
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| def setup(self, time_steps, n_atoms): | ||
| np.random.seed(170089) | ||
| self.P = np.random.rand(time_steps, | ||
| n_atoms, | ||
| 3) | ||
| np.random.seed(971132) | ||
| self.Q = np.random.rand(time_steps, | ||
| n_atoms, | ||
| 3) | ||
|
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| def time_get_msd_matrix(self, time_steps, n_atoms): | ||
| """Benchmark for get_msd_matrix in psa module | ||
| """ | ||
| # only default argument for axis is benchmarked | ||
| psa.get_msd_matrix(P=self.P, | ||
| Q=self.Q, | ||
| axis=None) | ||
|
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| class PSA_get_coord_axesBench(object): | ||
| """Benchmarks for MDAnalysis.analysis.psa. | ||
| get_coord_axes | ||
| """ | ||
|
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| params = ([10,100,1000], | ||
| [5, 25, 50]) | ||
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| param_names = ['time_steps', | ||
| 'n_atoms'] | ||
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| def setup(self, time_steps, n_atoms): | ||
| np.random.seed(170089) | ||
| # only using condensed path input | ||
| # data structure for now | ||
| self.path = np.random.rand(time_steps, | ||
| n_atoms * 3) | ||
|
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| def time_get_coord_axes(self, time_steps, n_atoms): | ||
| """Benchmark get_coord_axes in psa module | ||
| """ | ||
| psa.get_coord_axes(path=self.path) | ||
|
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| class PSA_get_path_metric_funcBench(object): | ||
| """Benchmark for MDAnalysis.analysis.psa. | ||
| get_path_metric_func | ||
| """ | ||
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| params = (['hausdorff', | ||
| 'weighted_average_hausdorff', | ||
| 'average_hausdorff', | ||
| 'hausdorff_neighbors', | ||
| 'discrete_frechet']) | ||
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| param_names = ['path_metric'] | ||
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| def time_get_path_metric_func(self, path_metric): | ||
| """Benchmark for get_path_metric_func in psa | ||
| module | ||
| """ | ||
| psa.get_path_metric_func(name=path_metric) | ||
|
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| class PSA_metricBench(object): | ||
| """Benchmarks for the various path metric calculations | ||
| in the psa module. | ||
| """ | ||
|
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| params = ([10,100,200], | ||
| [5,25,50]) | ||
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| param_names = ['time_steps', | ||
| 'n_atoms'] | ||
|
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| def setup(self, time_steps, n_atoms): | ||
| np.random.seed(170089) | ||
| self.P = np.random.rand(time_steps, | ||
| n_atoms, | ||
| 3) | ||
| np.random.seed(971132) | ||
| self.Q = np.random.rand(time_steps, | ||
| n_atoms, | ||
| 3) | ||
|
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| def time_hausdorff(self, time_steps, n_atoms): | ||
| """Benchmark for hausdorff() in psa module. | ||
| """ | ||
| psa.hausdorff(P=self.P, | ||
| Q=self.Q) | ||
|
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| def time_hausdorff_wavg(self, time_steps, n_atoms): | ||
| """Benchmark for hausdorff_wavg() in psa module. | ||
| """ | ||
| psa.hausdorff_wavg(P=self.P, | ||
| Q=self.Q) | ||
|
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| def time_hausdorff_avg(self, time_steps, n_atoms): | ||
| """Benchmark for hausdorff_avg() in psa module. | ||
| """ | ||
| psa.hausdorff_avg(P=self.P, | ||
| Q=self.Q) | ||
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| def time_hausdorff_neighbors(self, time_steps, n_atoms): | ||
| """Benchmark for hausdorff_neighbors() in psa module. | ||
| """ | ||
| psa.hausdorff_neighbors(P=self.P, | ||
| Q=self.Q) | ||
|
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| def time_discrete_frechet(self, time_steps, n_atoms): | ||
| """Benchmark for discrete_frechet() in psa module. | ||
| """ | ||
| psa.discrete_frechet(P=self.P, | ||
| Q=self.Q) |
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| Original file line number | Diff line number | Diff line change |
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@@ -78,7 +78,6 @@ | |
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| from .. import core | ||
| from . import distances | ||
| from .. import selections | ||
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| class LeafletFinder(object): | ||
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