## File: PKG-INFO

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bottleneck 1.2.1+ds1-1
 `123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144` ``````Metadata-Version: 1.1 Name: Bottleneck Version: 1.2.1 Summary: Fast NumPy array functions written in C Home-page: https://github.com/kwgoodman/bottleneck Author: Keith Goodman Author-email: bottle-neck@googlegroups.com License: Simplified BSD Download-URL: http://pypi.python.org/pypi/Bottleneck Description: Bottleneck is a collection of fast NumPy array functions written in C. Let's give it a try. Create a NumPy array:: >>> import numpy as np >>> a = np.array([1, 2, np.nan, 4, 5]) Find the nanmean:: >>> import bottleneck as bn >>> bn.nanmean(a) 3.0 Moving window mean:: >>> bn.move_mean(a, window=2, min_count=1) array([ 1. , 1.5, 2. , 4. , 4.5]) Benchmark ========= Bottleneck comes with a benchmark suite:: >>> bn.bench() Bottleneck performance benchmark Bottleneck 1.3.0.dev0; Numpy 1.12.1 Speed is NumPy time divided by Bottleneck time NaN means approx one-fifth NaNs; float64 used no NaN no NaN NaN no NaN NaN (100,) (1000,1000)(1000,1000)(1000,1000)(1000,1000) axis=0 axis=0 axis=0 axis=1 axis=1 nansum 67.3 0.3 0.7 2.5 2.4 nanmean 194.8 1.9 2.1 3.4 3.1 nanstd 241.5 1.6 2.1 2.7 2.6 nanvar 229.7 1.7 2.1 2.7 2.5 nanmin 34.1 0.7 1.1 0.8 2.6 nanmax 45.6 0.7 2.7 1.0 3.7 median 111.0 1.3 5.6 1.0 4.8 nanmedian 108.8 5.9 6.7 5.6 6.7 ss 16.3 1.1 1.2 1.6 1.6 nanargmin 89.2 2.9 5.1 2.2 5.6 nanargmax 107.4 3.0 5.4 2.2 5.8 anynan 19.4 0.3 35.0 0.5 29.9 allnan 39.9 146.6 128.3 115.8 75.6 rankdata 55.0 2.6 2.3 2.9 2.8 nanrankdata 59.8 2.8 2.2 3.2 2.5 partition 4.4 1.2 1.6 1.0 1.4 argpartition 3.5 1.1 1.4 1.1 1.6 replace 17.7 1.4 1.4 1.3 1.4 push 3440.0 7.8 9.5 20.0 15.5 move_sum 4743.0 75.7 156.1 195.4 211.1 move_mean 8760.9 116.2 167.4 252.1 258.8 move_std 8979.9 96.1 196.3 144.0 256.3 move_var 11216.8 127.3 243.9 225.9 321.4 move_min 2245.3 20.6 36.7 23.2 42.1 move_max 2223.7 20.5 37.2 24.1 42.4 move_argmin 3664.0 48.2 73.3 40.2 83.9 move_argmax 3916.9 42.0 75.4 41.5 81.2 move_median 2023.3 166.8 173.7 153.8 154.3 move_rank 1208.5 1.9 1.9 2.5 2.8 You can also run a detailed benchmark for a single function using, for example, the command:: >>> bn.bench_detailed("move_median", fraction_nan=0.3) Only arrays with data type (dtype) int32, int64, float32, and float64 are accelerated. All other dtypes result in calls to slower, unaccelerated functions. In the rare case of a byte-swapped input array (e.g. a big-endian array on a little-endian operating system) the function will not be accelerated regardless of dtype. Where ===== =================== ======================================================== download https://pypi.python.org/pypi/Bottleneck docs http://berkeleyanalytics.com/bottleneck code https://github.com/kwgoodman/bottleneck mailing list https://groups.google.com/group/bottle-neck =================== ======================================================== License ======= Bottleneck is distributed under a Simplified BSD license. See the LICENSE file for details. Install ======= Requirements: ======================== ==================================================== Bottleneck Python 2.7, 3.5, 3.6; NumPy 1.12.1 Compile gcc, clang, MinGW or MSVC Unit tests nose ======================== ==================================================== To install Bottleneck on GNU/Linux, Mac OS X, et al.:: \$ sudo python setup.py install To install bottleneck on Windows, first install MinGW and add it to your system path. Then install Bottleneck with the commands:: python setup.py install --compiler=mingw32 Alternatively, you can use the Windows binaries created by Christoph Gohlke: http://www.lfd.uci.edu/~gohlke/pythonlibs/#bottleneck Unit tests ========== After you have installed Bottleneck, run the suite of unit tests:: >>> import bottleneck as bn >>> bn.test() Ran 169 tests in 57.205s OK Platform: OS Independent Classifier: Development Status :: 4 - Beta Classifier: Environment :: Console Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: BSD License Classifier: Operating System :: OS Independent Classifier: Programming Language :: C Classifier: Programming Language :: Python Classifier: Programming Language :: Python :: 3 Classifier: Topic :: Scientific/Engineering Requires: numpy ``````