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| -rw-r--r-- | benchmarks/benchmarks/bench_itemselection.py | 45 |
1 files changed, 45 insertions, 0 deletions
diff --git a/benchmarks/benchmarks/bench_itemselection.py b/benchmarks/benchmarks/bench_itemselection.py new file mode 100644 index 000000000..27fc49e30 --- /dev/null +++ b/benchmarks/benchmarks/bench_itemselection.py @@ -0,0 +1,45 @@ +from __future__ import absolute_import, division, print_function + +from .common import Benchmark, TYPES1 + +import numpy as np + + +class Take(Benchmark): + params = [ + [(1000, 1), (1000, 2), (2, 1000, 1), (1000, 3)], + ["raise", "wrap", "clip"], + TYPES1] + param_names = ["shape", "mode", "dtype"] + + def setup(self, shape, mode, dtype): + self.arr = np.ones(shape, dtype) + self.indices = np.arange(1000) + + def time_contiguous(self, shape, mode, dtype): + self.arr.take(self.indices, axis=-2, mode=mode) + + +class PutMask(Benchmark): + params = [ + [True, False], + TYPES1] + param_names = ["values_is_scalar", "dtype"] + + def setup(self, values_is_scalar, dtype): + if values_is_scalar: + self.vals = np.array(1., dtype=dtype) + else: + self.vals = np.ones(1000, dtype=dtype) + + self.arr = np.ones(1000, dtype=dtype) + + self.dense_mask = np.ones(1000, dtype="bool") + self.sparse_mask = np.zeros(1000, dtype="bool") + + def time_dense(self, values_is_scalar, dtype): + np.putmask(self.arr, self.dense_mask, self.vals) + + def time_sparse(self, values_is_scalar, dtype): + np.putmask(self.arr, self.sparse_mask, self.vals) + |