|
| 1 | +from .common import Benchmark |
| 2 | + |
| 3 | +import numpy as np |
| 4 | + |
| 5 | + |
| 6 | +class ClipFloat(Benchmark): |
| 7 | + param_names = ["dtype", "size"] |
| 8 | + params = [ |
| 9 | + [np.float32, np.float64, np.longdouble], |
| 10 | + [100, 100_000] |
| 11 | + ] |
| 12 | + |
| 13 | + def setup(self, dtype, size): |
| 14 | + rng = np.random.default_rng() |
| 15 | + self.array = rng.random(size=size).astype(dtype) |
| 16 | + self.dataout = np.full_like(self.array, 0.5) |
| 17 | + |
| 18 | + def time_clip(self, dtype, size): |
| 19 | + np.clip(self.array, 0.125, 0.875, self.dataout) |
| 20 | + |
| 21 | + |
| 22 | +class ClipInteger(Benchmark): |
| 23 | + param_names = ["dtype", "size"] |
| 24 | + params = [ |
| 25 | + [np.int32, np.int64], |
| 26 | + [100, 100_000] |
| 27 | + ] |
| 28 | + |
| 29 | + def setup(self, dtype, size): |
| 30 | + rng = np.random.default_rng() |
| 31 | + self.array = rng.integers(256, size=size, dtype=dtype) |
| 32 | + self.dataout = np.full_like(self.array, 128) |
| 33 | + |
| 34 | + def time_clip(self, dtype, size): |
| 35 | + np.clip(self.array, 32, 224, self.dataout) |
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