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| author | Rohit Goswami <rog32@hi.is> | 2023-01-29 13:32:27 +0000 |
|---|---|---|
| committer | Rohit Goswami <rog32@hi.is> | 2023-03-04 15:42:57 +0000 |
| commit | 354121222ca6981c1429f026e8d1977d59c4b56c (patch) | |
| tree | ace2347733dec1481f2908e37679073f9986d87a /benchmarks | |
| parent | 6d43684fe6588d134582bb8a9988f226f31bd023 (diff) | |
| download | numpy-354121222ca6981c1429f026e8d1977d59c4b56c.tar.gz | |
BUG,BENCH: Ensure np.nans in np.unique benchmarks
Diffstat (limited to 'benchmarks')
| -rw-r--r-- | benchmarks/benchmarks/bench_lib.py | 4 |
1 files changed, 3 insertions, 1 deletions
diff --git a/benchmarks/benchmarks/bench_lib.py b/benchmarks/benchmarks/bench_lib.py index b64f8ab17..89b8d923a 100644 --- a/benchmarks/benchmarks/bench_lib.py +++ b/benchmarks/benchmarks/bench_lib.py @@ -132,7 +132,9 @@ class Unique(Benchmark): # produce a randomly shuffled array with the # approximate desired percentage np.nan content base_array = np.random.uniform(size=array_size) - base_array[base_array < percent_nans / 100.] = np.nan + n_nan = int(percent_nans * array_size) + nan_indices = np.random.randint(array_size, size=n_nan) + base_array[nan_indices] = np.nan self.arr = base_array def time_unique(self, array_size, percent_nans): |