Benchmarks

asv.conf.json

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+{
+  "version": 1,
+  "project": "acquire-imaging",
+  "project_url": "https://github.com/acquire-project",
+  "repo": "https://github.com/acquire-project/acquire-python.git",
+  "repo_subdir": "",
+  "build_command": [
+    "python -m pip install build",
+    "python -m build",
+    "PIP_NO_BUILD_ISOLATION=false python -mpip wheel --no-deps --no-index -w {build_cache_dir} {build_dir}"
+  ],
+  "branches": ["main"],
+  "dvcs": "git",
+  "environment_type": "virtualenv",
+  "install_timeout": 600,
+  "show_commit_url": "http://github.com/acquire-project/acquire-python/commit/",
+  "pythons": ["3.8", "3.9", "3.10"],
+  "conda_channels": ["conda-forge", "defaults"],
+
+  // The matrix of dependencies to test.  Each key of the "req"
+  // requirements dictionary is the name of a package (in PyPI) and
+  // the values are version numbers.  An empty list or empty string
+  // indicates to just test against the default (latest)
+  // version. null indicates that the package is to not be
+  // installed. If the package to be tested is only available from
+  // PyPi, and the 'environment_type' is conda, then you can preface
+  // the package name by 'pip+', and the package will be installed
+  // via pip (with all the conda available packages installed first,
+  // followed by the pip installed packages).
+  //
+  // The ``@env`` and ``@env_nobuild`` keys contain the matrix of
+  // environment variables to pass to build and benchmark commands.
+  // An environment will be created for every combination of the
+  // cartesian product of the "@env" variables in this matrix.
+  // Variables in "@env_nobuild" will be passed to every environment
+  // during the benchmark phase, but will not trigger creation of
+  // new environments.  A value of ``null`` means that the variable
+  // will not be set for the current combination.
+  //
+  // "matrix": {
+  //     "req": {
+  //         "numpy": ["1.6", "1.7"],
+  //         "six": ["", null],  // test with and without six installed
+  //         "pip+emcee": [""]   // emcee is only available for install with pip.
+  //     },
+  //     "env": {"ENV_VAR_1": ["val1", "val2"]},
+  //     "env_nobuild": {"ENV_VAR_2": ["val3", null]},
+  // },
+
+  // Combinations of libraries/python versions can be excluded/included
+  // from the set to test. Each entry is a dictionary containing additional
+  // key-value pairs to include/exclude.
+  //
+  // An exclude entry excludes entries where all values match. The
+  // values are regexps that should match the whole string.
+  //
+  // An include entry adds an environment. Only the packages listed
+  // are installed. The 'python' key is required. The exclude rules
+  // do not apply to includes.
+  //
+  // In addition to package names, the following keys are available:
+  //
+  // - python
+  //     Python version, as in the *pythons* variable above.
+  // - environment_type
+  //     Environment type, as above.
+  // - sys_platform
+  //     Platform, as in sys.platform. Possible values for the common
+  //     cases: 'linux2', 'win32', 'cygwin', 'darwin'.
+  // - req
+  //     Required packages
+  // - env
+  //     Environment variables
+  // - env_nobuild
+  //     Non-build environment variables
+  //
+  // "exclude": [
+  //     {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows
+  //     {"environment_type": "conda", "req": {"six": null}}, // don't run without six on conda
+  //     {"env": {"ENV_VAR_1": "val2"}}, // skip val2 for ENV_VAR_1
+  // ],
+  //
+  // "include": [
+  //     // additional env for python2.7
+  //     {"python": "2.7", "req": {"numpy": "1.8"}, "env_nobuild": {"FOO": "123"}},
+  //     // additional env if run on windows+conda
+  //     {"platform": "win32", "environment_type": "conda", "python": "2.7", "req": {"libpython": ""}},
+  // ],
+  "benchmark_dir": "benchmarks",
+  "env_dir": ".asv/env",
+  "results_dir": ".asv/results",
+  "html_dir": ".asv/html",
+  "hash_length": 8,
+  "build_cache_size": 2,
+
+  // The commits after which the regression search in `asv publish`
+  // should start looking for regressions. Dictionary whose keys are
+  // regexps matching to benchmark names, and values corresponding to
+  // the commit (exclusive) after which to start looking for
+  // regressions.  The default is to start from the first commit
+  // with results. If the commit is `null`, regression detection is
+  // skipped for the matching benchmark.
+  //
+  // "regressions_first_commits": {
+  //    "some_benchmark": "352cdf",  // Consider regressions only after this commit
+  //    "another_benchmark": null,   // Skip regression detection altogether
+  // },
+
+  // The thresholds for relative change in results, after which `asv
+  // publish` starts reporting regressions. Dictionary of the same
+  // form as in ``regressions_first_commits``, with values
+  // indicating the thresholds.  If multiple entries match, the
+  // maximum is taken. If no entry matches, the default is 5%.
+  //
+  // "regressions_thresholds": {
+  //    "some_benchmark": 0.01,     // Threshold of 1%
+  //    "another_benchmark": 0.5,   // Threshold of 50%
+  // },
+}

benchmarks/benchmarks.py

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+# Write the benchmarking functions here.
+# See "Writing benchmarks" in the asv docs for more information.
+
+
+class TimeSuite:
+    """
+    An example benchmark that times the performance of various kinds
+    of iterating over dictionaries in Python.
+    """
+    def setup(self):
+        self.d = {}
+        for x in range(500):
+            self.d[x] = None
+
+    def time_keys(self):
+        for key in self.d.keys():
+            pass
+
+    def time_values(self):
+        for value in self.d.values():
+            pass
+
+    def time_range(self):
+        d = self.d
+        for key in range(500):
+            d[key]
+
+
+class MemSuite:
+    def mem_list(self):
+        return [0] * 256