Coverage for /usr/lib/python3/dist-packages/numpy/__init__.py : 90%

""" 

NumPy 

===== 

Provides 

1. An array object of arbitrary homogeneous items 

2. Fast mathematical operations over arrays 

3. Linear Algebra, Fourier Transforms, Random Number Generation 

How to use the documentation 

---------------------------- 

Documentation is available in two forms: docstrings provided 

with the code, and a loose standing reference guide, available from 

`the NumPy homepage <https://www.scipy.org>`_. 

We recommend exploring the docstrings using 

`IPython <https://ipython.org>`_, an advanced Python shell with 

TAB-completion and introspection capabilities. See below for further 

instructions. 

The docstring examples assume that `numpy` has been imported as `np`:: 

>>> import numpy as np 

Code snippets are indicated by three greater-than signs:: 

>>> x = 42 

>>> x = x + 1 

Use the built-in ``help`` function to view a function's docstring:: 

>>> help(np.sort) 

... # doctest: +SKIP 

For some objects, ``np.info(obj)`` may provide additional help. This is 

particularly true if you see the line "Help on ufunc object:" at the top 

of the help() page. Ufuncs are implemented in C, not Python, for speed. 

The native Python help() does not know how to view their help, but our 

np.info() function does. 

To search for documents containing a keyword, do:: 

>>> np.lookfor('keyword') 

... # doctest: +SKIP 

General-purpose documents like a glossary and help on the basic concepts 

of numpy are available under the ``doc`` sub-module:: 

>>> from numpy import doc 

>>> help(doc) 

... # doctest: +SKIP 

Available subpackages 

--------------------- 

doc 

Topical documentation on broadcasting, indexing, etc. 

lib 

Basic functions used by several sub-packages. 

random 

Core Random Tools 

linalg 

Core Linear Algebra Tools 

fft 

Core FFT routines 

polynomial 

Polynomial tools 

testing 

NumPy testing tools 

f2py 

Fortran to Python Interface Generator. 

distutils 

Enhancements to distutils with support for 

Fortran compilers support and more. 

Utilities 

--------- 

test 

Run numpy unittests 

show_config 

Show numpy build configuration 

dual 

Overwrite certain functions with high-performance Scipy tools 

matlib 

Make everything matrices. 

__version__ 

NumPy version string 

Viewing documentation using IPython 

----------------------------------- 

Start IPython with the NumPy profile (``ipython -p numpy``), which will 

import `numpy` under the alias `np`. Then, use the ``cpaste`` command to 

paste examples into the shell. To see which functions are available in 

`numpy`, type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use 

``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow 

down the list. To view the docstring for a function, use 

``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view 

the source code). 

Copies vs. in-place operation 

----------------------------- 

Most of the functions in `numpy` return a copy of the array argument 

(e.g., `np.sort`). In-place versions of these functions are often 

available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``. 

Exceptions to this rule are documented. 

""" 

from __future__ import division, absolute_import, print_function 

import sys 

import warnings 

from ._globals import ModuleDeprecationWarning, VisibleDeprecationWarning 

from ._globals import _NoValue 

# We first need to detect if we're being called as part of the numpy setup 

# procedure itself in a reliable manner. 

try: 

__NUMPY_SETUP__ 

except NameError: 

__NUMPY_SETUP__ = False 

if __NUMPY_SETUP__: 

sys.stderr.write('Running from numpy source directory.\n') 

else: 

try: 

from numpy.__config__ import show as show_config 

except ImportError: 

msg = """Error importing numpy: you should not try to import numpy from 

its source directory; please exit the numpy source tree, and relaunch 

your python interpreter from there.""" 

raise ImportError(msg) 

from .version import git_revision as __git_revision__ 

from .version import version as __version__ 

__all__ = ['ModuleDeprecationWarning', 

'VisibleDeprecationWarning'] 

# Allow distributors to run custom init code 

from . import _distributor_init 

from . import core 

from .core import * 

from . import compat 

from . import lib 

from .lib import * 

from . import linalg 

from . import fft 

from . import polynomial 

from . import random 

from . import ctypeslib 

from . import ma 

from . import matrixlib as _mat 

from .matrixlib import * 

from .compat import long 

# Make these accessible from numpy name-space 

# but not imported in from numpy import * 

if sys.version_info[0] >= 3: 

from builtins import bool, int, float, complex, object, str 

unicode = str 

else: 

from __builtin__ import bool, int, float, complex, object, unicode, str 

from .core import round, abs, max, min 

# now that numpy modules are imported, can initialize limits 

core.getlimits._register_known_types() 

__all__.extend(['__version__', 'show_config']) 

__all__.extend(core.__all__) 

__all__.extend(_mat.__all__) 

__all__.extend(lib.__all__) 

__all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma']) 

# Filter out Cython harmless warnings 

warnings.filterwarnings("ignore", message="numpy.dtype size changed") 

warnings.filterwarnings("ignore", message="numpy.ufunc size changed") 

warnings.filterwarnings("ignore", message="numpy.ndarray size changed") 

# oldnumeric and numarray were removed in 1.9. In case some packages import 

# but do not use them, we define them here for backward compatibility. 

oldnumeric = 'removed' 

numarray = 'removed' 

# We don't actually use this ourselves anymore, but I'm not 100% sure that 

# no-one else in the world is using it (though I hope not) 

from .testing import Tester 

# Pytest testing 

from numpy._pytesttester import PytestTester 

test = PytestTester(__name__) 

del PytestTester 

def _sanity_check(): 

""" 

Quick sanity checks for common bugs caused by environment. 

There are some cases e.g. with wrong BLAS ABI that cause wrong 

results under specific runtime conditions that are not necessarily 

achieved during test suite runs, and it is useful to catch those early. 

See https://github.com/numpy/numpy/issues/8577 and other 

similar bug reports. 

""" 

try: 

x = ones(2, dtype=float32) 

if not abs(x.dot(x) - 2.0) < 1e-5: 

raise AssertionError() 

except AssertionError: 

msg = ("The current Numpy installation ({!r}) fails to " 

"pass simple sanity checks. This can be caused for example " 

"by incorrect BLAS library being linked in, or by mixing " 

"package managers (pip, conda, apt, ...). Search closed " 

"numpy issues for similar problems.") 

raise RuntimeError(msg.format(__file__)) 

_sanity_check() 

del _sanity_check