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# Write the benchmarking functions here.
# See "Writing benchmarks" in the asv docs for more information.
import os.path as osp
import numpy as np
from tempfile import TemporaryDirectory
import h5py
class TimeSuite:
"""
An example benchmark that times the performance of various kinds
of iterating over dictionaries in Python.
"""
def setup(self):
self._td = TemporaryDirectory()
path = osp.join(self._td.name, 'test.h5')
with h5py.File(path, 'w') as f:
f['a'] = np.arange(100000)
self.f = h5py.File(path, 'r')
def teardown(self):
self.f.close()
self._td.cleanup()
def time_many_small_reads(self):
ds = self.f['a']
for i in range(10000):
arr = ds[i * 10:(i + 1) * 10]
class WritingTimeSuite:
"""Based on example in GitHub issue 492:
https://github.com/h5py/h5py/issues/492
"""
def setup(self):
self._td = TemporaryDirectory()
path = osp.join(self._td.name, 'test.h5')
self.f = h5py.File(path, 'w')
self.shape = shape = (128, 1024, 512)
self.f.create_dataset(
'a', shape=shape, dtype=np.float32, chunks=(1, shape[1], 64)
)
def teardown(self):
self.f.close()
self._td.cleanup()
def time_write_index_last_axis(self):
ds = self.f['a']
data = np.zeros(self.shape[:2])
for i in range(self.shape[2]):
ds[..., i] = data
def time_write_slice_last_axis(self):
ds = self.f['a']
data = np.zeros(self.shape[:2])
for i in range(self.shape[2]):
ds[..., i:i+1] = data[..., np.newaxis]
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