WIP: add asv benchmarks

benchmarks/asv.conf.json

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+{
+    // The version of the config file format.  Do not change, unless
+    // you know what you are doing.
+    "version": 1,
+
+    // The name of the project being benchmarked
+    "project": "sparr",
+
+    // The project's homepage
+    "project_url": "http://project-homepage.org/",
+
+    // The URL or local path of the source code repository for the
+    // project being benchmarked
+    "repo": "..",
+
+    // List of branches to benchmark. If not provided, defaults to "master"
+    // (for git) or "tip" (for mercurial).
+    // "branches": ["master"], // for git
+    // "branches": ["tip"],    // for mercurial
+
+    // The DVCS being used.  If not set, it will be automatically
+    // determined from "repo" by looking at the protocol in the URL
+    // (if remote), or by looking for special directories, such as
+    // ".git" (if local).
+    // "dvcs": "git",
+
+    // The tool to use to create environments.  May be "conda",
+    // "virtualenv" or other value depending on the plugins in use.
+    // If missing or the empty string, the tool will be automatically
+    // determined by looking for tools on the PATH environment
+    // variable.
+    "environment_type": "virtualenv",
+
+    // the base URL to show a commit for the project.
+    // "show_commit_url": "http://github.com/owner/project/commit/",
+
+    // The Pythons you'd like to test against.  If not provided, defaults
+    // to the current version of Python used to run `asv`.
+    // "pythons": ["2.7", "3.3"],
+
+    // The matrix of dependencies to test.  Each key 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).
+    //
+     "matrix": {
+         "numpy": ["1.10.4"],
+         "Cython": ["0.23"],
+         "scipy": ["0.17.0"]
+     },
+
+    // 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'.
+    //
+    // "exclude": [
+    //     {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows
+    //     {"environment_type": "conda", "six": null}, // don't run without six on conda
+    // ],
+    //
+    // "include": [
+    //     // additional env for python2.7
+    //     {"python": "2.7", "numpy": "1.8"},
+    //     // additional env if run on windows+conda
+    //     {"platform": "win32", "environment_type": "conda", "python": "2.7", "libpython": ""},
+    // ],
+
+    // The directory (relative to the current directory) that benchmarks are
+    // stored in.  If not provided, defaults to "benchmarks"
+    // "benchmark_dir": "benchmarks",
+
+    // The directory (relative to the current directory) to cache the Python
+    // environments in.  If not provided, defaults to "env"
+    // "env_dir": "env",
+
+    // The directory (relative to the current directory) that raw benchmark
+    // results are stored in.  If not provided, defaults to "results".
+    // "results_dir": "results",
+
+    // The directory (relative to the current directory) that the html tree
+    // should be written to.  If not provided, defaults to "html".
+    // "html_dir": "html",
+
+    // The number of characters to retain in the commit hashes.
+    // "hash_length": 8,
+
+    // `asv` will cache wheels of the recent builds in each
+    // environment, making them faster to install next time.  This is
+    // number of builds to keep, per environment.
+     "wheel_cache_size": 10
+
+    // 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
+    // }
+}

benchmarks/benchmarks/__init__.py

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+

benchmarks/benchmarks/benchmarks.py

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+# Write the benchmarking functions here.
+# See "Writing benchmarks" in the asv docs for more information.
+
+from sparr import MapArray as M
+from scipy.sparse import dok_matrix
+
+try:
+    # https://github.com/scipy/scipy/pull/6004/
+    from scipy.sparse import fast_lil_matrix
+except:
+    pass
+
+try:
+    from pysparse.sparse import spmatrix
+except:
+    pass
+
+try:
+    xrange
+except NameError:
+    # python 3
+    xrange = range
+
+
+def map_poisson2d(n, func_name):
+    n2 = n*n
+    if func_name == "map_array":
+        L = M(shape=(n2, n2))
+    elif func_name == "dok_matrix":
+        L = dok_matrix((n2, n2))
+    elif func_name == "ll_mat":
+        L = spmatrix.ll_mat(n2, n2)
+    elif func_name == "fast_lil":
+        L = fast_lil_matrix((n2, n2))
+    for i in xrange(n):
+        for j in xrange(n):
+            k = i + n*j
+            L[k,k] = 4
+            if i > 0:
+               L[k,k-1] = -1
+            if i < n-1:
+               L[k,k+1] = -1
+            if j > 0:
+               L[k,k-n] = -1
+            if j < n-1:
+               L[k,k+n] = -1
+    return L
+
+
+class BenchPoisson2D(object):
+
+    params = ([10, 100, 300], ['map_array', 'll_mat', 'fast_lil', 'dok_matrix'])
+
+    def time_poisson2d(self, n, func_name):
+        xxx = map_poisson2d(n, func_name)
+    time_poisson2d.param_names = ['n', 'class']
+    time_poisson2d.timeout = 120.0
+
+    def peakmem_poisson2d(self, n, func_name):
+        xxx = map_poisson2d(n, func_name)
+    peakmem_poisson2d.param_names = ['n', 'class']
+    peakmem_poisson2d.timeout = 120.0
+
+    def mem_poisson2d(self, n, func_name):
+        xxx = map_poisson2d(n, func_name)
+    mem_poisson2d.param_names = ['n', 'class']
+    mem_poisson2d.timeout = 120.0
+
+#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_iterkeys(self):
+#        for key in self.d.iterkeys():
+#            pass
+
+#    def time_range(self):
+#        d = self.d
+#        for key in range(500):
+#            x = d[key]
+
+#    def time_xrange(self):
+#        d = self.d
+#        for key in xrange(500):
+#            x = d[key]
+
+
+#class MemSuite:
+#    def mem_list(self):
+#        return [0] * 256