initial benchmark framework to visualise regression

.gitignore

@@ -39,3 +39,7 @@ nosetests.xml
 
 # PyCharm
 .idea
+
+# asv
+.asv/
+benchmarks/*.pyc

CONTRIBUTE

@@ -249,6 +249,32 @@ cannot re-upload another with the same version number
 updating just the metadata is possible: `[python setup.py] make pypimeta`
 
 
+UPDATING GH-PAGES
+-----------------
+
+The most important file is README.rst, which sould always be kept up-to-date
+and in sync with the in-line source documentation. This will affect all of the
+following:
+
+- The [main repository site](https://github.com/tqdm/tqdm) which automatically
+  serves the latest README.rst as well as links to all of github's features.
+  This is the preferred online referral link for tqdm.
+- The [PyPi mirror](https://pypi.python.org/pypi/tqdm) which automatically
+  serves the latest release built from README.rst as well as links to past
+  releases.
+- Many external web crawlers.
+
+
+Additionally (less maintained), there exists:
+
+- A [wiki](https://github.com/tqdm/tqdm/wiki) which is publicly editable.
+- The [gh-pages project](https://tqdm.github.io/tqdm/) which is built from the
+  [gh-pages branch](https://github.com/tqdm/tqdm/tree/gh-pages), which is
+  built using [asv](https://github.com/spacetelescope/asv/).
+- The [gh-pages root](https://tqdm.github.io/) which is built from a separate
+  outdated [github.io repo](https://github.com/tqdm/tqdm.github.io).
+
+
 QUICK DEV SUMMARY
 -----------------
 

asv.conf.json

@@ -0,0 +1,28 @@
+{
+    "version": 1,
+    "project": "tqdm",
+    "project_url": "https://github.com/tqdm/tqdm/",
+    "repo": ".",
+    "environment_type": "virtualenv",
+    "show_commit_url": "http://github.com/tqdm/tqdm/commit/",
+    // "pythons": ["2.7", "3.3"],
+    // "matrix": {
+    //     "numpy": ["1.6", "1.7"],
+    //     "six": ["", null],        // test with and without six installed
+    //     "pip+emcee": [""],   // emcee is only available for install with pip.
+    // },
+    // "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": ""},
+    // ],
+    "benchmark_dir": "benchmarks",
+    "env_dir": ".asv/env",
+    "results_dir": ".asv/results",
+    "html_dir": ".asv/html",
+}

benchmarks/benchmarks.py

@@ -0,0 +1,25 @@
+# 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):
+        from tqdm import tqdm
+        self.tqdm = tqdm
+        try:
+            self.iterable = xrange(int(6e6))
+        except:
+            self.iterable = range(int(6e6))
+
+    def time_tqdm(self):
+        [0 for _ in self.tqdm(self.iterable)]
+
+
+class MemSuite:
+    # def mem_list(self):
+    #     return [0] * 256
+    pass