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"""
========
Glossary
========
.. glossary::
along an axis
Axes are defined for arrays with more than one dimension. A
2-dimensional array has two corresponding axes: the first running
vertically downwards across rows (axis 0), and the second running
horizontally across columns (axis 1).
Many operation can take place along one of these axes. For example,
we can sum each row of an array, in which case we operate along
columns, or axis 1::
>>> x = np.arange(12).reshape((3,4))
>>> x
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.sum(axis=1)
array([ 6, 22, 38])
array
A homogeneous container of numerical elements. Each element in the
array occupies a fixed amount of memory (hence homogeneous), and
can be a numerical element of a single type (such as float, int
or complex) or a combination (such as ``(float, int, float)``). Each
array has an associated data-type (or ``dtype``), which describes
the numerical type of its elements::
>>> x = np.array([1, 2, 3], float)
>>> x
array([ 1., 2., 3.])
>>> x.dtype # floating point number, 64 bits of memory per element
dtype('float64')
# More complicated data type: each array element is a combination of
# and integer and a floating point number
>>> np.array([(1, 2.0), (3, 4.0)], dtype=[('x', int), ('y', float)])
array([(1, 2.0), (3, 4.0)],
dtype=[('x', '<i4'), ('y', '<f8')])
Fast element-wise operations, called `ufuncs`_, operate on arrays.
array_like
Any sequence that can be interpreted as an ndarray. This includes
nested lists, tuples, scalars and existing arrays.
attribute
A property of an object that can be accessed using ``obj.attribute``,
e.g., ``shape`` is an attribute of an array::
>>> x = np.array([1, 2, 3])
>>> x.shape
(3,)
BLAS
`Basic Linear Algebra Subprograms <http://en.wikipedia.org/wiki/BLAS>`_
broadcast
NumPy can do operations on arrays whose shapes are mismatched::
>>> x = np.array([1, 2])
>>> y = np.array([[3], [4]])
>>> x
array([1, 2])
>>> y
array([[3],
[4]])
>>> x + y
array([[4, 5],
[5, 6]])
See `doc.broadcasting`_ for more information.
C order
See `row-major`
column-major
A way to represent items in a N-dimensional array in the 1-dimensional
computer memory. In column-major order, the leftmost index "varies the
fastest": for example the array::
[[1, 2, 3],
[4, 5, 6]]
is represented in the column-major order as::
[1, 4, 2, 5, 3, 6]
Column-major order is also known as the Fortran order, as the Fortran
programming language uses it.
decorator
An operator that transforms a function. For example, a ``log``
decorator may be defined to print debugging information upon
function execution::
>>> def log(f):
... def new_logging_func(*args, **kwargs):
... print "Logging call with parameters:", args, kwargs
... return f(*args, **kwargs)
...
... return new_logging_func
Now, when we define a function, we can "decorate" it using ``log``::
>>> @log
... def add(a, b):
... return a + b
Calling ``add`` then yields:
>>> add(1, 2)
Logging call with parameters: (1, 2) {}
3
dictionary
Resembling a language dictionary, which provides a mapping between
words and descriptions thereof, a Python dictionary is a mapping
between two objects::
>>> x = {1: 'one', 'two': [1, 2]}
Here, `x` is a dictionary mapping keys to values, in this case
the integer 1 to the string "one", and the string "two" to
the list ``[1, 2]``. The values may be accessed using their
corresponding keys::
>>> x[1]
'one'
>>> x['two']
[1, 2]
Note that dictionaries are not stored in any specific order. Also,
most mutable (see *immutable* below) objects, such as lists, may not
be used as keys.
For more information on dictionaries, read the
`Python tutorial <http://docs.python.org/tut>`_.
Fortran order
See `column-major`
flattened
Collapsed to a one-dimensional array. See `ndarray.flatten`_ for details.
immutable
An object that cannot be modified after execution is called
immutable. Two common examples are strings and tuples.
instance
A class definition gives the blueprint for constructing an object::
>>> class House(object):
... wall_colour = 'white'
Yet, we have to *build* a house before it exists::
>>> h = House() # build a house
Now, ``h`` is called a ``House`` instance. An instance is therefore
a specific realisation of a class.
iterable
A sequence that allows "walking" (iterating) over items, typically
using a loop such as::
>>> x = [1, 2, 3]
>>> [item**2 for item in x]
[1, 4, 9]
It is often used in combintion with ``enumerate``::
>>> keys = ['a','b','c']
>>> for n, k in enumerate(keys):
... print "Key %d: %s" % (n, k)
...
Key 0: a
Key 1: b
Key 2: c
list
A Python container that can hold any number of objects or items.
The items do not have to be of the same type, and can even be
lists themselves::
>>> x = [2, 2.0, "two", [2, 2.0]]
The list `x` contains 4 items, each which can be accessed individually::
>>> x[2] # the string 'two'
'two'
>>> x[3] # a list, containing an integer 2 and a float 2.0
[2, 2.0]
It is also possible to select more than one item at a time,
using *slicing*::
>>> x[0:2] # or, equivalently, x[:2]
[2, 2.0]
In code, arrays are often conveniently expressed as nested lists::
>>> np.array([[1, 2], [3, 4]])
array([[1, 2],
[3, 4]])
For more information, read the section on lists in the `Python
tutorial <http://docs.python.org/tut>`_. For a mapping
type (key-value), see *dictionary*.
mask
A boolean array, used to select only certain elements for an operation::
>>> x = np.arange(5)
>>> x
array([0, 1, 2, 3, 4])
>>> mask = (x > 2)
>>> mask
array([False, False, False, True, True], dtype=bool)
>>> x[mask] = -1
>>> x
array([ 0, 1, 2, -1, -1])
masked array
Array that suppressed values indicated by a mask::
>>> x = np.ma.masked_array([np.nan, 2, np.nan], [True, False, True])
>>> x
masked_array(data = [-- 2.0 --],
mask = [ True False True],
fill_value = 1e+20)
<BLANKLINE>
>>> x + [1, 2, 3]
masked_array(data = [-- 4.0 --],
mask = [ True False True],
fill_value = 1e+20)
<BLANKLINE>
Masked arrays are often used when operating on arrays containing
missing or invalid entries.
matrix
A 2-dimensional ndarray that preserves its two-dimensional nature
throughout operations. It has certain special operations, such as ``*``
(matrix multiplication) and ``**`` (matrix power), defined::
>>> x = np.mat([[1, 2], [3, 4]])
>>> x
matrix([[1, 2],
[3, 4]])
>>> x**2
matrix([[ 7, 10],
[15, 22]])
method
A function associated with an object. For example, each ndarray has a
method called ``repeat``::
>>> x = np.array([1, 2, 3])
>>> x.repeat(2)
array([1, 1, 2, 2, 3, 3])
ndarray
See *array*.
reference
If ``a`` is a reference to ``b``, then ``(a is b) == True``. Therefore,
``a`` and ``b`` are different names for the same Python object.
row-major
A way to represent items in a N-dimensional array in the 1-dimensional
computer memory. In row-major order, the rightmost index "varies
the fastest": for example the array::
[[1, 2, 3],
[4, 5, 6]]
is represented in the row-major order as::
[1, 2, 3, 4, 5, 6]
Row-major order is also known as the C order, as the C programming
language uses it. New Numpy arrays are by default in row-major order.
self
Often seen in method signatures, ``self`` refers to the instance
of the associated class. For example:
>>> class Paintbrush(object):
... color = 'blue'
...
... def paint(self):
... print "Painting the city %s!" % self.color
...
>>> p = Paintbrush()
>>> p.color = 'red'
>>> p.paint() # self refers to 'p'
Painting the city red!
slice
Used to select only certain elements from a sequence::
>>> x = range(5)
>>> x
[0, 1, 2, 3, 4]
>>> x[1:3] # slice from 1 to 3 (excluding 3 itself)
[1, 2]
>>> x[1:5:2] # slice from 1 to 5, but skipping every second element
[1, 3]
>>> x[::-1] # slice a sequence in reverse
[4, 3, 2, 1, 0]
Arrays may have more than one dimension, each which can be sliced
individually::
>>> x = np.array([[1, 2], [3, 4]])
>>> x
array([[1, 2],
[3, 4]])
>>> x[:, 1]
array([2, 4])
tuple
A sequence that may contain a variable number of types of any
kind. A tuple is immutable, i.e., once constructed it cannot be
changed. Similar to a list, it can be indexed and sliced::
>>> x = (1, 'one', [1, 2])
>>> x
(1, 'one', [1, 2])
>>> x[0]
1
>>> x[:2]
(1, 'one')
A useful concept is "tuple unpacking", which allows variables to
be assigned to the contents of a tuple::
>>> x, y = (1, 2)
>>> x, y = 1, 2
This is often used when a function returns multiple values:
>>> def return_many():
... return 1, 'alpha', None
>>> a, b, c = return_many()
>>> a, b, c
(1, 'alpha', None)
>>> a
1
>>> b
'alpha'
ufunc
Universal function. A fast element-wise array operation. Examples include
``add``, ``sin`` and ``logical_or``.
view
An array that does not own its data, but refers to another array's
data instead. For example, we may create a view that only shows
every second element of another array::
>>> x = np.arange(5)
>>> x
array([0, 1, 2, 3, 4])
>>> y = x[::2]
>>> y
array([0, 2, 4])
>>> x[0] = 3 # changing x changes y as well, since y is a view on x
>>> y
array([3, 2, 4])
wrapper
Python is a high-level (highly abstracted, or English-like) language.
This abstraction comes at a price in execution speed, and sometimes
it becomes necessary to use lower level languages to do fast
computations. A wrapper is code that provides a bridge between
high and the low level languages, allowing, e.g., Python to execute
code written in C or Fortran.
Examples include ctypes, SWIG and Cython (which wraps C and C++)
and f2py (which wraps Fortran).
""" |
"""
Limits
======
Implemented according to the PhD thesis
http://www.cybertester.com/data/gruntz.pdf, which contains very thorough
descriptions of the algorithm including many examples. We summarize here
the gist of it.
All functions are sorted according to how rapidly varying they are at
infinity using the following rules. Any two functions f and g can be
compared using the properties of L:
L=lim log|f(x)| / log|g(x)| (for x -> oo)
We define >, < ~ according to::
1. f > g .... L=+-oo
we say that:
- f is greater than any power of g
- f is more rapidly varying than g
- f goes to infinity/zero faster than g
2. f < g .... L=0
we say that:
- f is lower than any power of g
3. f ~ g .... L!=0, +-oo
we say that:
- both f and g are bounded from above and below by suitable integral
powers of the other
Examples
========
::
2 < x < exp(x) < exp(x**2) < exp(exp(x))
2 ~ 3 ~ -5
x ~ x**2 ~ x**3 ~ 1/x ~ x**m ~ -x
exp(x) ~ exp(-x) ~ exp(2x) ~ exp(x)**2 ~ exp(x+exp(-x))
f ~ 1/f
So we can divide all the functions into comparability classes (x and x^2
belong to one class, exp(x) and exp(-x) belong to some other class). In
principle, we could compare any two functions, but in our algorithm, we
don't compare anything below the class 2~3~-5 (for example log(x) is
below this), so we set 2~3~-5 as the lowest comparability class.
Given the function f, we find the list of most rapidly varying (mrv set)
subexpressions of it. This list belongs to the same comparability class.
Let's say it is {exp(x), exp(2x)}. Using the rule f ~ 1/f we find an
element "w" (either from the list or a new one) from the same
comparability class which goes to zero at infinity. In our example we
set w=exp(-x) (but we could also set w=exp(-2x) or w=exp(-3x) ...). We
rewrite the mrv set using w, in our case {1/w, 1/w^2}, and substitute it
into f. Then we expand f into a series in w::
f = c0*w^e0 + c1*w^e1 + ... + O(w^en), where e0<e1<...<en, c0!=0
but for x->oo, lim f = lim c0*w^e0, because all the other terms go to zero,
because w goes to zero faster than the ci and ei. So::
for e0>0, lim f = 0
for e0<0, lim f = +-oo (the sign depends on the sign of c0)
for e0=0, lim f = lim c0
We need to recursively compute limits at several places of the algorithm, but
as is shown in the PhD thesis, it always finishes.
Important functions from the implementation:
compare(a, b, x) compares "a" and "b" by computing the limit L.
mrv(e, x) returns list of most rapidly varying (mrv) subexpressions of "e"
rewrite(e, Omega, x, wsym) rewrites "e" in terms of w
leadterm(f, x) returns the lowest power term in the series of f
mrv_leadterm(e, x) returns the lead term (c0, e0) for e
limitinf(e, x) computes lim e (for x->oo)
limit(e, z, z0) computes any limit by converting it to the case x->oo
All the functions are really simple and straightforward except
rewrite(), which is the most difficult/complex part of the algorithm.
When the algorithm fails, the bugs are usually in the series expansion
(i.e. in SymPy) or in rewrite.
This code is almost exact rewrite of the Maple code inside the Gruntz
thesis.
Debugging
---------
Because the gruntz algorithm is highly recursive, it's difficult to
figure out what went wrong inside a debugger. Instead, turn on nice
debug prints by defining the environment variable SYMPY_DEBUG. For
example:
[user@localhost]: SYMPY_DEBUG=True ./bin/isympy
In [1]: limit(sin(x)/x, x, 0)
limitinf(_x*sin(1/_x), _x) = 1
+-mrv_leadterm(_x*sin(1/_x), _x) = (1, 0)
| +-mrv(_x*sin(1/_x), _x) = set([_x])
| | +-mrv(_x, _x) = set([_x])
| | +-mrv(sin(1/_x), _x) = set([_x])
| | +-mrv(1/_x, _x) = set([_x])
| | +-mrv(_x, _x) = set([_x])
| +-mrv_leadterm(exp(_x)*sin(exp(-_x)), _x, set([exp(_x)])) = (1, 0)
| +-rewrite(exp(_x)*sin(exp(-_x)), set([exp(_x)]), _x, _w) = (1/_w*sin(_w), -_x)
| +-sign(_x, _x) = 1
| +-mrv_leadterm(1, _x) = (1, 0)
+-sign(0, _x) = 0
+-limitinf(1, _x) = 1
And check manually which line is wrong. Then go to the source code and
debug this function to figure out the exact problem.
""" |
"""
# ggame
The simple cross-platform sprite and game platform for Brython Server (Pygame, Tkinter to follow?).
Ggame stands for a couple of things: "good game" (of course!) and also "git game" or "github game"
because it is designed to operate with [Brython Server](http://runpython.com) in concert with
Github as a backend file store.
Ggame is **not** intended to be a full-featured gaming API, with every bell and whistle. Ggame is
designed primarily as a tool for teaching computer programming, recognizing that the ability
to create engaging and interactive games is a powerful motivator for many progamming students.
Accordingly, any functional or performance enhancements that *can* be reasonably implemented
by the user are left as an exercise.
## Functionality Goals
The ggame library is intended to be trivially easy to use. For example:
from ggame import App, ImageAsset, Sprite
# Create a displayed object at 100,100 using an image asset
Sprite(ImageAsset("ggame/bunny.png"), (100,100))
# Create the app, with a 500x500 pixel stage
app = App(500,500)
# Run the app
app.run()
## Overview
There are three major components to the `ggame` system: Assets, Sprites and the App.
### Assets
Asset objects (i.e. `ggame.ImageAsset`, etc.) typically represent separate files that
are provided by the "art department". These might be background images, user interface
images, or images that represent objects in the game. In addition, `ggame.SoundAsset`
is used to represent sound files (`.wav` or `.mp3` format) that can be played in the
game.
Ggame also extends the asset concept to include graphics that are generated dynamically
at run-time, such as geometrical objects, e.g. rectangles, lines, etc.
### Sprites
All of the visual aspects of the game are represented by instances of `ggame.Sprite` or
subclasses of it.
### App
Every ggame application must create a single instance of the `ggame.App` class (or
a sub-class of it). Creating an instance of the `ggame.App` class will initiate
creation of a pop-up window on your browser. Executing the app's `run` method will
begin the process of refreshing the visual assets on the screen.
### Events
No game is complete without a player and players produce events. Your code handles user
input by registering to receive keyboard and mouse events using `ggame.App.listenKeyEvent` and
`ggame.App.listenMouseEvent` methods.
## Execution Environment
Ggame is designed to be executed in a web browser using [Brython](http://brython.info/),
[Pixi.js](http://www.pixijs.com/) and [Buzz](http://buzz.jaysalvat.com/). The easiest
way to do this is by executing from [runpython](http://runpython.com), with source
code residing on [github](http://github.com).
When using [runpython](http://runpython.com), you will have to configure your browser
to allow popup windows.
To use Ggame in your own application, you will minimally need to create a folder called
`ggame` in your project. Within `ggame`, copy the `ggame.py`, `sysdeps.py` and
`__init__.py` files from the [ggame project](https://github.com/BrythonServer/ggame).
### Include Ggame as a Git Subtree
From the same directory as your own python sources (note: you must have an existing git
repository with committed files in order for the following to work properly),
execute the following terminal commands:
git remote add -f ggame https://github.com/BrythonServer/ggame.git
git merge -s ours --no-commit ggame/master
mkdir ggame
git read-tree --prefix=ggame/ -u ggame/master
git commit -m "Merge ggame project as our subdirectory"
If you want to pull in updates from ggame in the future:
git pull -s subtree ggame master
You can see an example of how a ggame subtree is used by examining the
[Brython Server Spacewar](https://github.com/BrythonServer/Spacewar) repo on Github.
## Geometry
When referring to screen coordinates, note that the x-axis of the computer screen
is *horizontal* with the zero position on the left hand side of the screen. The
y-axis is *vertical* with the zero position at the **top** of the screen.
Increasing positive y-coordinates correspond to the downward direction on the
computer screen. Note that this is **different** from the way you may have learned
about x and y coordinates in math class!
""" |
# Check the basic discovery process, including a sub-suite.
#
# RUN: %{lit} %{inputs}/discovery \
# RUN: -j 1 --debug --show-tests --show-suites \
# RUN: -v > %t.out 2> %t.err
# RUN: FileCheck --check-prefix=CHECK-BASIC-OUT < %t.out %s
# RUN: FileCheck --check-prefix=CHECK-BASIC-ERR < %t.err %s
#
# CHECK-BASIC-ERR: loading suite config '{{.*}}/discovery/lit.cfg'
# CHECK-BASIC-ERR: loading local config '{{.*}}/discovery/subdir/lit.local.cfg'
# CHECK-BASIC-ERR: loading suite config '{{.*}}/discovery/subsuite/lit.cfg'
#
# CHECK-BASIC-OUT: -- Test Suites --
# CHECK-BASIC-OUT: sub-suite - 2 tests
# CHECK-BASIC-OUT: Source Root: {{.*/discovery/subsuite$}}
# CHECK-BASIC-OUT: Exec Root : {{.*/discovery/subsuite$}}
# CHECK-BASIC-OUT: top-level-suite - 3 tests
# CHECK-BASIC-OUT: Source Root: {{.*/discovery$}}
# CHECK-BASIC-OUT: Exec Root : {{.*/discovery$}}
#
# CHECK-BASIC-OUT: -- Available Tests --
# CHECK-BASIC-OUT: sub-suite :: test-one
# CHECK-BASIC-OUT: sub-suite :: test-two
# CHECK-BASIC-OUT: top-level-suite :: subdir/test-three
# CHECK-BASIC-OUT: top-level-suite :: test-one
# CHECK-BASIC-OUT: top-level-suite :: test-two
# Check discovery when exact test names are given.
#
# RUN: %{lit} \
# RUN: %{inputs}/discovery/subdir/test-three.py \
# RUN: %{inputs}/discovery/subsuite/test-one.txt \
# RUN: -j 1 --show-tests --show-suites -v > %t.out
# RUN: FileCheck --check-prefix=CHECK-EXACT-TEST < %t.out %s
#
# CHECK-EXACT-TEST: -- Available Tests --
# CHECK-EXACT-TEST: sub-suite :: test-one
# CHECK-EXACT-TEST: top-level-suite :: subdir/test-three
# Check discovery when using an exec path.
#
# RUN: %{lit} %{inputs}/exec-discovery \
# RUN: -j 1 --debug --show-tests --show-suites \
# RUN: -v > %t.out 2> %t.err
# RUN: FileCheck --check-prefix=CHECK-ASEXEC-OUT < %t.out %s
# RUN: FileCheck --check-prefix=CHECK-ASEXEC-ERR < %t.err %s
#
# CHECK-ASEXEC-ERR: loading suite config '{{.*}}/exec-discovery/lit.site.cfg'
# CHECK-ASEXEC-ERR: load_config from '{{.*}}/discovery/lit.cfg'
# CHECK-ASEXEC-ERR: loaded config '{{.*}}/discovery/lit.cfg'
# CHECK-ASEXEC-ERR: loaded config '{{.*}}/exec-discovery/lit.site.cfg'
# CHECK-ASEXEC-ERR: loading local config '{{.*}}/discovery/subdir/lit.local.cfg'
# CHECK-ASEXEC-ERR: loading suite config '{{.*}}/discovery/subsuite/lit.cfg'
#
# CHECK-ASEXEC-OUT: -- Test Suites --
# CHECK-ASEXEC-OUT: sub-suite - 2 tests
# CHECK-ASEXEC-OUT: Source Root: {{.*/discovery/subsuite$}}
# CHECK-ASEXEC-OUT: Exec Root : {{.*/discovery/subsuite$}}
# CHECK-ASEXEC-OUT: top-level-suite - 3 tests
# CHECK-ASEXEC-OUT: Source Root: {{.*/discovery$}}
# CHECK-ASEXEC-OUT: Exec Root : {{.*/exec-discovery$}}
#
# CHECK-ASEXEC-OUT: -- Available Tests --
# CHECK-ASEXEC-OUT: sub-suite :: test-one
# CHECK-ASEXEC-OUT: sub-suite :: test-two
# CHECK-ASEXEC-OUT: top-level-suite :: subdir/test-three
# CHECK-ASEXEC-OUT: top-level-suite :: test-one
# CHECK-ASEXEC-OUT: top-level-suite :: test-two
# Check discovery when exact test names are given.
#
# FIXME: Note that using a path into a subsuite doesn't work correctly here.
#
# RUN: %{lit} \
# RUN: %{inputs}/exec-discovery/subdir/test-three.py \
# RUN: -j 1 --show-tests --show-suites -v > %t.out
# RUN: FileCheck --check-prefix=CHECK-ASEXEC-EXACT-TEST < %t.out %s
#
# CHECK-ASEXEC-EXACT-TEST: -- Available Tests --
# CHECK-ASEXEC-EXACT-TEST: top-level-suite :: subdir/test-three
# Check that we don't recurse infinitely when loading an site specific test
# suite located inside the test source root.
#
# RUN: %{lit} \
# RUN: %{inputs}/exec-discovery-in-tree/obj/ \
# RUN: -j 1 --show-tests --show-suites -v > %t.out
# RUN: FileCheck --check-prefix=CHECK-ASEXEC-INTREE < %t.out %s
#
# CHECK-ASEXEC-INTREE: exec-discovery-in-tree-suite - 1 tests
# CHECK-ASEXEC-INTREE-NEXT: Source Root: {{.*/exec-discovery-in-tree$}}
# CHECK-ASEXEC-INTREE-NEXT: Exec Root : {{.*/exec-discovery-in-tree/obj$}}
# CHECK-ASEXEC-INTREE-NEXT: -- Available Tests --
# CHECK-ASEXEC-INTREE-NEXT: exec-discovery-in-tree-suite :: test-one
|
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# Copyright (c), NAME <michael.dehaan@gmail.com>, 2012-2013
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# grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
# analyze, test, perform and/or display publicly, prepare derivative works,
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# Agreement.
|
"""
===================
Universal Functions
===================
Ufuncs are, generally speaking, mathematical functions or operations that are
applied element-by-element to the contents of an array. That is, the result
in each output array element only depends on the value in the corresponding
input array (or arrays) and on no other array elements. NumPy comes with a
large suite of ufuncs, and scipy extends that suite substantially. The simplest
example is the addition operator: ::
>>> np.array([0,2,3,4]) + np.array([1,1,-1,2])
array([1, 3, 2, 6])
The unfunc module lists all the available ufuncs in numpy. Documentation on
the specific ufuncs may be found in those modules. This documentation is
intended to address the more general aspects of unfuncs common to most of
them. All of the ufuncs that make use of Python operators (e.g., +, -, etc.)
have equivalent functions defined (e.g. add() for +)
Type coercion
=============
What happens when a binary operator (e.g., +,-,\\*,/, etc) deals with arrays of
two different types? What is the type of the result? Typically, the result is
the higher of the two types. For example: ::
float32 + float64 -> float64
int8 + int32 -> int32
int16 + float32 -> float32
float32 + complex64 -> complex64
There are some less obvious cases generally involving mixes of types
(e.g. uints, ints and floats) where equal bit sizes for each are not
capable of saving all the information in a different type of equivalent
bit size. Some examples are int32 vs float32 or uint32 vs int32.
Generally, the result is the higher type of larger size than both
(if available). So: ::
int32 + float32 -> float64
uint32 + int32 -> int64
Finally, the type coercion behavior when expressions involve Python
scalars is different than that seen for arrays. Since Python has a
limited number of types, combining a Python int with a dtype=np.int8
array does not coerce to the higher type but instead, the type of the
array prevails. So the rules for Python scalars combined with arrays is
that the result will be that of the array equivalent the Python scalar
if the Python scalar is of a higher 'kind' than the array (e.g., float
vs. int), otherwise the resultant type will be that of the array.
For example: ::
Python int + int8 -> int8
Python float + int8 -> float64
ufunc methods
=============
Binary ufuncs support 4 methods.
**.reduce(arr)** applies the binary operator to elements of the array in
sequence. For example: ::
>>> np.add.reduce(np.arange(10)) # adds all elements of array
45
For multidimensional arrays, the first dimension is reduced by default: ::
>>> np.add.reduce(np.arange(10).reshape(2,5))
array([ 5, 7, 9, 11, 13])
The axis keyword can be used to specify different axes to reduce: ::
>>> np.add.reduce(np.arange(10).reshape(2,5),axis=1)
array([10, 35])
**.accumulate(arr)** applies the binary operator and generates an an
equivalently shaped array that includes the accumulated amount for each
element of the array. A couple examples: ::
>>> np.add.accumulate(np.arange(10))
array([ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45])
>>> np.multiply.accumulate(np.arange(1,9))
array([ 1, 2, 6, 24, 120, 720, 5040, 40320])
The behavior for multidimensional arrays is the same as for .reduce(),
as is the use of the axis keyword).
**.reduceat(arr,indices)** allows one to apply reduce to selected parts
of an array. It is a difficult method to understand. See the documentation
at:
**.outer(arr1,arr2)** generates an outer operation on the two arrays arr1 and
arr2. It will work on multidimensional arrays (the shape of the result is
the concatenation of the two input shapes.: ::
>>> np.multiply.outer(np.arange(3),np.arange(4))
array([[0, 0, 0, 0],
[0, 1, 2, 3],
[0, 2, 4, 6]])
Output arguments
================
All ufuncs accept an optional output array. The array must be of the expected
output shape. Beware that if the type of the output array is of a different
(and lower) type than the output result, the results may be silently truncated
or otherwise corrupted in the downcast to the lower type. This usage is useful
when one wants to avoid creating large temporary arrays and instead allows one
to reuse the same array memory repeatedly (at the expense of not being able to
use more convenient operator notation in expressions). Note that when the
output argument is used, the ufunc still returns a reference to the result.
>>> x = np.arange(2)
>>> np.add(np.arange(2),np.arange(2.),x)
array([0, 2])
>>> x
array([0, 2])
and & or as ufuncs
==================
Invariably people try to use the python 'and' and 'or' as logical operators
(and quite understandably). But these operators do not behave as normal
operators since Python treats these quite differently. They cannot be
overloaded with array equivalents. Thus using 'and' or 'or' with an array
results in an error. There are two alternatives:
1) use the ufunc functions logical_and() and logical_or().
2) use the bitwise operators & and \\|. The drawback of these is that if
the arguments to these operators are not boolean arrays, the result is
likely incorrect. On the other hand, most usages of logical_and and
logical_or are with boolean arrays. As long as one is careful, this is
a convenient way to apply these operators.
""" |
"""
The react module provides functionality for Reactive Programming (RP) and
Functional Reactive Programming (FRP).
It is a bit difficult to explain what FRP really is. This is because
every implementation has its own take on it, and because it requires a
bit of a paradigm shift compared to classic event-driven programming.
FRP does not have to be difficult and we think our implementation of
``flexx.react`` is relatively easy to use. This brief guide takes you
through some of the FRP aspects using code examples.
What is FRP
-----------
(Don't worry if the next two paragraphs sound complicated;
things should start to make sense when we explain thing using code.)
*Where event-driven programming is about reacting to things that happen,
RP is about staying up to date with changing signals.*
In RP the different components in an application communicate via streams
of data. In other words, components keep track of (and react to) the
*signal values* of other components. All signals (except source/input
signals) have one or more upstream signals, and can combine and or
modify these to produce a new signal value. The value of each signal
is *cached*, so that the operations applied to the signal values only
have to be performed when any upstream signal has changed. When a signal
changes its value, it will *notify* its downstream signals, so that
everything stays up-to-date.
In ``flexx.react`` signals are addressed using a string. This may seem
unusual at first, but it allows easy binding for signals on classes,
allow signal loops, and has other advantages that we'll discuss when
we talk about dynamism.
Signals
-------
A signal can be created by decorating a function. In RP-speak, the
function is "lifted" to a signal:
.. code-block:: py
# The function greet() is used to react to signal "name"
@react.connect('name')
def greet(n):
print('hello %!' % n)
The example above looks quite similar to how some event-drive applications
allow binding callbacks to events. There are, however, a few differences:
a) The greet function has now become a signal object, which has an output
of its own (although the output is None in this case, because the
function does not return a value, more on that below); b) The function
(which we'd call the "callback" in an event driven system) does not
accept an event object, but a value that corresponds to the upstream
signal value.
One other advantage of a RP system is that signals can *connect to
multiple upsteam signals*:
.. code-block:: py
@react.connect('first_name', 'last_name')
def greet(first, last):
print('hello %s %s!' % (first, last)
This is a feature that saves a lot of overhead. For any "callback" that
you define, you specify *exactly* what input signals there are, and it will
always be up to date. Doing that in an event-driven system quickly results
in a spaghetti of callbacks and boilerplate to keep track of state.
The function of a signal gets called directly when any of the
upstream signals (or the upstream-upstream signals) change. The return value
of the function represents the output signal value, which can also be None.
When the return value is ``undefined`` (from ``react.undefined`` or
``pyscript.undefined``), the value is ignored and the signal maintains
its current value.
Source and input signals
------------------------
Signals must start somewhere. The *source signal* has a ``_set()`` method
that the programmer can use to set the value of the signal:
.. code-block:: py
@react.source
def name(n):
return n
The function for this source signal is very simple. You usually want
to do some input checking and/or normalization here. Especialy if the input
comes from the user, as is the case with the input signal.
The *input signal* is a source signal that can be called with an argument
to set its value:
.. code-block:: py
@react.input
def name(n='john NAME
if not isinstance(n, str):
raise ValueError('Name must be a string')
return n.capitalized()
# And later ...
name('jane NAME can also see how the default value of the function argument can be
used to specify the initial signal value.
Source and input signals generally do not have upstream signals, but
they can have them.
A complete example
------------------
.. code-block:: py
@react.input
def first_name(s='john'):
return str(s)
@react.input
def last_name(s='NAME
return str(s)
@react.connect('first_name', 'last_name')
def full_name(first, 'last'):
return '%s %s' % (first, last)
@react.connect('full_name')
def greet(name):
print('hello %s!' % name)
Lazy signals
------------
In contrast to normal signals, a *lazy signal* does not update immediately
when the upstream signals changes. It is updated automatically (lazily)
whenever its value is queried. Note that this has little effect when
there is a normal signal downstream.
Lazy signals can be convenient in a situation where values changes rapidly,
while the current value is only needed sparingly. To create, use the
``lazy()`` decorator:
.. code-block:: py
@react.lazy('first_name', 'last_name')
def full_name(first, last):
return '%s %s' % (first, last)
Caching
-------
.. code-block:: py
@react.input
def data_select(id):
return str(id)
@react.input
def data_clean(clean):
return bool(clean)
@react.connect('data_select')
def data(id):
open_connection(id)
return get_data_from_the_web() # this may take a while
@react.connect('data', 'data_clean')
def show_data(data, clean):
if clean:
data = clean_func(data)
plotter.show(data)
This hypothetical example shows how caching helps keep apps efficient.
The ``data`` signal will only update when the ``data_select`` changes.
When ``data_clean`` is changes, the ``show_data`` signal updates, but
it will use the cached value of the data.
The HasSignals class
--------------------
It is often convenient to create classes that have signals. To do so,
inherit from the ``HasSignals`` class:
.. code-block:: py
class Person(react.HasSignals):
def __init__(self, father):
assert isinstance(father, Person)
self.father = father
react.HasSignals.__init__(self)
@react.input
def first_name(s):
return s
@react.connect('father.last_name')
def last_name(s):
return s
@react.connect('first_name', 'last_name')
de greet(first, last):
print('hello %s %s!' % (first, last))
The above example show how you can directly refer to signals on the
object using their name, and even use dot notation to address the signal
of an attribute of the object.
It also shows that the signal functions do not have a ``self`` argument.
They do not have to, but they can if they needs access to the instance.
Dynamism
--------
With dynamism, you can refer to signals of signals, and have the signal
connections be made automatically. Let's modify the last example a bit:
.. code-block:: py
class Person(react.HasSignals):
def __init__(self, father):
self.father(father)
react.HasSignals.__init__(self)
@react.input
def father(f):
assert isinstance(f, Person)
return f
@react.connect('father.last_name')
def last_name(s):
return s
...
In this case, the last name of the father will change when either the father
changes, or the father changes its name. Dynamism also supports star notation:
.. code-block:: py
class Person(react.HasSignals):
@react.input
def children(cc):
assert isinstance(cc, tuple)
assert all([isinstance(c, Person) for c in cc])
return cc
@react.connect('children.*')
def child_names(*names):
return ', '.join(name)
Signal history
--------------
The signal object provides a bit more information than only its value.
The most notable is the value of the signal before the last change.
.. code-block:: py
class Person(react.HasSignals):
@react.connect('first_name'):
def react_to_name_change(self, new_name):
old_name = self.first_name.last_value
new_name = self.first_name.value # == new_name
The signal value also holds information on value update times, but this
is currently private. We'll have to see if this is reliable and
convenient enough to make it public.
Functional RP
-------------
The "F" in FRP stands for functional. Currently, there is limited
support for that, for example:
.. code-block:: py
filter = lambda x: x>0
@react.connect(react.filter(filter, 'number'))
def show_positive_numbers(v):
print(v)
This functionality is to be extended in the future.
Some things just are events
---------------------------
Many things can be described as changing signal values. Even
"left_mouse_down" works pretty well. However, some things really *are*
events, like key presses and timers. How to handle these is still
something we'd need to work out ...
""" |
#
# XML-RPC CLIENT LIBRARY
# $Id$
#
# an XML-RPC client interface for Python.
#
# the marshalling and response parser code can also be used to
# implement XML-RPC servers.
#
# Notes:
# this version is designed to work with Python 2.1 or newer.
#
# History:
# 1999-01-14 fl Created
# 1999-01-15 fl Changed dateTime to use localtime
# 1999-01-16 fl Added Binary/base64 element, default to RPC2 service
# 1999-01-19 fl Fixed array data element (from Skip Montanaro)
# 1999-01-21 fl Fixed dateTime constructor, etc.
# 1999-02-02 fl Added fault handling, handle empty sequences, etc.
# 1999-02-10 fl Fixed problem with empty responses (from Skip Montanaro)
# 1999-06-20 fl Speed improvements, pluggable parsers/transports (0.9.8)
# 2000-11-28 fl Changed boolean to check the truth value of its argument
# 2001-02-24 fl Added encoding/Unicode/SafeTransport patches
# 2001-02-26 fl Added compare support to wrappers (0.9.9/1.0b1)
# 2001-03-28 fl Make sure response tuple is a singleton
# 2001-03-29 fl Don't require empty params element (from NAME 2001-06-10 fl Folded in _xmlrpclib accelerator support (1.0b2)
# 2001-08-20 fl Base xmlrpclib.Error on built-in Exception (from NAME 2001-09-03 fl Allow Transport subclass to override getparser
# 2001-09-10 fl Lazy import of urllib, cgi, xmllib (20x import speedup)
# 2001-10-01 fl Remove containers from memo cache when done with them
# 2001-10-01 fl Use faster escape method (80% dumps speedup)
# 2001-10-02 fl More dumps microtuning
# 2001-10-04 fl Make sure import expat gets a parser (from NAME 2001-10-10 sm Allow long ints to be passed as ints if they don't overflow
# 2001-10-17 sm Test for int and long overflow (allows use on 64-bit systems)
# 2001-11-12 fl Use repr() to marshal doubles (from NAME 2002-03-17 fl Avoid buffered read when possible (from NAME 2002-04-07 fl Added pythondoc comments
# 2002-04-16 fl Added __str__ methods to datetime/binary wrappers
# 2002-05-15 fl Added error constants (from NAME 2002-06-27 fl Merged with Python CVS version
# 2002-10-22 fl Added basic authentication (based on code from NAME 2003-01-22 sm Add support for the bool type
# 2003-02-27 gvr Remove apply calls
# 2003-04-24 sm Use cStringIO if available
# 2003-04-25 ak Add support for nil
# 2003-06-15 gn Add support for time.struct_time
# 2003-07-12 gp Correct marshalling of Faults
# 2003-10-31 mvl Add multicall support
# 2004-08-20 mvl Bump minimum supported Python version to 2.1
# 2014-12-02 ch/doko Add workaround for gzip bomb vulnerability
#
# Copyright (c) 1999-2002 by Secret Labs AB.
# Copyright (c) 1999-2002 by NAME Lundh.
#
# EMAIL http://www.pythonware.com
#
# --------------------------------------------------------------------
# The XML-RPC client interface is
#
# Copyright (c) 1999-2002 by Secret Labs AB
# Copyright (c) 1999-2002 by NAME Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
|
#
# XML-RPC CLIENT LIBRARY
# $Id$
#
# an XML-RPC client interface for Python.
#
# the marshalling and response parser code can also be used to
# implement XML-RPC servers.
#
# Notes:
# this version is designed to work with Python 2.1 or newer.
#
# History:
# 1999-01-14 fl Created
# 1999-01-15 fl Changed dateTime to use localtime
# 1999-01-16 fl Added Binary/base64 element, default to RPC2 service
# 1999-01-19 fl Fixed array data element (from Skip Montanaro)
# 1999-01-21 fl Fixed dateTime constructor, etc.
# 1999-02-02 fl Added fault handling, handle empty sequences, etc.
# 1999-02-10 fl Fixed problem with empty responses (from Skip Montanaro)
# 1999-06-20 fl Speed improvements, pluggable parsers/transports (0.9.8)
# 2000-11-28 fl Changed boolean to check the truth value of its argument
# 2001-02-24 fl Added encoding/Unicode/SafeTransport patches
# 2001-02-26 fl Added compare support to wrappers (0.9.9/1.0b1)
# 2001-03-28 fl Make sure response tuple is a singleton
# 2001-03-29 fl Don't require empty params element (from NAME 2001-06-10 fl Folded in _xmlrpclib accelerator support (1.0b2)
# 2001-08-20 fl Base xmlrpclib.Error on built-in Exception (from NAME 2001-09-03 fl Allow Transport subclass to override getparser
# 2001-09-10 fl Lazy import of urllib, cgi, xmllib (20x import speedup)
# 2001-10-01 fl Remove containers from memo cache when done with them
# 2001-10-01 fl Use faster escape method (80% dumps speedup)
# 2001-10-02 fl More dumps microtuning
# 2001-10-04 fl Make sure import expat gets a parser (from NAME 2001-10-10 sm Allow long ints to be passed as ints if they don't overflow
# 2001-10-17 sm Test for int and long overflow (allows use on 64-bit systems)
# 2001-11-12 fl Use repr() to marshal doubles (from NAME 2002-03-17 fl Avoid buffered read when possible (from NAME 2002-04-07 fl Added pythondoc comments
# 2002-04-16 fl Added __str__ methods to datetime/binary wrappers
# 2002-05-15 fl Added error constants (from NAME 2002-06-27 fl Merged with Python CVS version
# 2002-10-22 fl Added basic authentication (based on code from NAME 2003-01-22 sm Add support for the bool type
# 2003-02-27 gvr Remove apply calls
# 2003-04-24 sm Use cStringIO if available
# 2003-04-25 ak Add support for nil
# 2003-06-15 gn Add support for time.struct_time
# 2003-07-12 gp Correct marshalling of Faults
# 2003-10-31 mvl Add multicall support
# 2004-08-20 mvl Bump minimum supported Python version to 2.1
#
# Copyright (c) 1999-2002 by Secret Labs AB.
# Copyright (c) 1999-2002 by NAME EMAIL http://www.pythonware.com
#
# --------------------------------------------------------------------
# The XML-RPC client interface is
#
# Copyright (c) 1999-2002 by Secret Labs AB
# Copyright (c) 1999-2002 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
#
# things to look into some day:
# TODO: sort out True/False/boolean issues for Python 2.3
|
#########
# Notes #
#########
#
# Archive.org won't accept wget user agent string---it can be anything else
#
# When fetching source files from arxiv.org:
# 0408420 gives most recent
# 0408420vN gives version N
# 0408420vN if N > number of versions gives most recent version
# (the behavior is same for old and new archive identifiers)
#
# The conventions for storing latex source archives files are taken
# from those used in the bulk data made available by arxiv.org via
# Amazon.com's S3 service. This makes it possible to download a bunch
# of source files, untar them, and start working on the data without
# pre-processing the files in any way.
#
# Information here: http://arxiv.org/help/bulk_data_s3
#
# Command to bulk download arxiv sources to the current directory
# s3cmd sync --add-header="x-amz-request-payer: requester" s3://arxiv/src/ .
#
# The entire archive is divided into sub-directories by YYMM. Files
# in each directory can be pdf files or gzip files. The gunzipped
# file can be any number of things: latex source, a tar file,
# postscript, html, plain text, (and more?). It might have been nice
# if the pdf files were gzipped so that _everything_ could be handled
# the same way (gunzip, figure out what it is, act appropriately), but
# the arxiv.org people have decided not to do that. It's true that
# PDF files are already compressed, so gzipping them is kind of a
# waste.
#
# However, when you download a paper via
# http://arxiv.org/e-print/identifier, you don't know if the file will
# be a PDF or a gzip file, and the resulting file has no extension.
# Therefore when fetching papers (from the arxiv.org RSS feed, for
# instance), the code below looks at the file type and puts the
# appropriate extension so that it goes into the data directory just
# like the bulk data provided by arxiv.org via S3.
#
# Thus when you ask for the name of the data file associated with an
# arxiv id, you may want it without an extension (if it was downloaded
# via wget and you want to put the appropriate extension on), or you
# may want it _with_ the extension (in which case the code must look
# to see if it's a pdf or gz file, and give you the correct filename).
#
# Note that it's necessary to call 'file' on the gunzipped file.
# Calling 'file' on the gzipped file allows you to figure out what's
# inside most of the time, but sometimes the results are hilariously
# wrong. All of these contain valid latex:
# 1211.0074.gz: Minix filesystem, V2, 51878 zones
# cond-mat0701210.gz: gzip compressed data, was "/data/new/0022/0022418/src/with",
# math0701257.gz: x86 boot sector, code offset 0x8b
#
# Even calling file on the gunzipped file is bizarrly incorrect
# sometimes -- files are listed as C++, plain text (when they're
# latex), or even 'data'. As long as the output of 'file' has the
# word 'text' in it, I treat it as latex.
#
# The problem with files not being recognized as latex after
# gunzipping apparently has to do with text encodings. Here are a few
# examples of arxiv id's listed as 'data' after gunzipping.
# 1303.5083 1401.5069 1401.5758 1401.5838 1401.6077 1401.6577
# 1401.7056 1402.0695 1402.0700 1402.1495 1402.1968 1402.5880
#
# All of the above is for file v5.04 on OS X 10.9.2 Mavaricks. This
# version of file dates from about 2010 and I'm writing this in 2014.
# Going to the latest version of file v5.17 results in improvements in
# the file type determination for about 300 of the ~3000 files in the
# entire archive of 1 million papers, so, it's not really worth it.
#
# In any case, using file v5.17 installed from MacPorts results in
# messages like:
# ERROR: line 163: regex error 17, (illegal byte sequence)
# This is evidently another text encoding problem and it is discussed here:
# https://trac.macports.org/ticket/38771
# A workaround is to set the shell variables:
# export LC_CTYPE=C
# export LANG=C
#
# There's other weird stuff in the files provided by arxiv.org, like
# PDF files that 'file' reports to be strange things. All of these
# are viewable via Emacs DocView and Apple Preview, although when you
# look at the bytes they do indeed look weird (not like normal PDF
# files).
# ./0003/cond-mat0003162.pdf: data
# ./0304/cond-mat0304406.pdf: MacBinary III data with surprising version number
# ./0404/physics0404071.pdf: data
# ./9803/cond-mat9803359.pdf: data
# ./9805/cond-mat9805146.pdf: data
# ./0402/cs0402023.pdf: POSIX tar archive (GNU)
# ./1204/1204.0257.pdf: data
# ./1204/1204.0258.pdf: data
#
# It takes ~5.5 min to just gunzip everything and untar everything via
# the shell for one month's sumbissions. takes ~10 min to do it via
# python code. I find this acceptable.
#
|
# # -*- coding: utf-8 -*-
# ##############################################################################
# #
# # OpenERP, Open Source Management Solution
# # Copyright (C) 2004-2010 Tiny SPRL (<http://tiny.be>).
# #
# # This program is free software: you can redistribute it and/or modify
# # it under the terms of the GNU Affero General Public License as
# # published by the Free Software Foundation, either version 3 of the
# # License, or (at your option) any later version.
# #
# # This program is distributed in the hope that it will be useful,
# # but WITHOUT ANY WARRANTY; without even the implied warranty of
# # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# # GNU Affero General Public License for more details.
# #
# # You should have received a copy of the GNU Affero General Public License
# # along with this program. If not, see <http://www.gnu.org/licenses/>.
# #
# ##############################################################################
#
# import time
#
# from openerp.osv import fields, osv
# from openerp.tools.translate import _
#
#
# class account_invoice_debit(osv.TransientModel):
#
# """Debits Note from Invoice"""
#
# _name = "account.invoice.debit"
# _description = "Invoice Debit Note"
# _columns = {
# 'date': fields.date('Operation date',
# help='This date will be used as the invoice date '
# 'for Refund Invoice and Period will be '
# 'chosen accordingly!'),
# 'period': fields.many2one('account.period', 'Force period'),
# 'journal_id': fields.many2one('account.journal',
# 'Refund Journal',
# help='You can select here the journal '
# 'to use for the refund invoice '
# 'that will be created. If you '
# 'leave that field empty, it will '
# 'use the same journal as the '
# 'current invoice.'),
# 'description': fields.char('Description', size=128, required=True),
# 'comment': fields.text('Comment', required=True),
# }
#
# def _get_journal(self, cr, uid, context=None):
# obj_journal = self.pool.get('account.journal')
# user_obj = self.pool.get('res.users')
# if context is None:
# context = {}
# inv_type = context.get('type', 'out_invoice')
# company_id = user_obj.browse(
# cr, uid, uid, context=context).company_id.id
# type = (inv_type == 'out_invoice') and 'sale_refund' or \
# (inv_type == 'out_refund') and 'sale' or \
# (inv_type == 'in_invoice') and 'purchase_refund' or \
# (inv_type == 'in_refund') and 'purchase'
# journal = obj_journal.search(cr, uid, [('type', '=', type), (
# 'company_id', '=', company_id)], limit=1, context=context)
# return journal and journal[0] or False
#
# _defaults = {
# 'date': lambda *a: time.strftime('%Y-%m-%d'),
# 'journal_id': _get_journal,
# }
#
# def fields_view_get(self, cr, uid, view_id=None, view_type=False,
# context=None, toolbar=False, submenu=False):
# if context is None:
# context = {}
# journal_obj = self.pool.get('account.journal')
# res = super(account_invoice_debit, self).fields_view_get(
# cr, uid, view_id=view_id, view_type=view_type, context=context,
# toolbar=toolbar, submenu=submenu)
# # Debit note only from customer o purchase invoice
# # type = context.get('journal_type', 'sale_refund')
# type = context.get('journal_type', 'sale')
# if type in ('sale', 'sale_refund'):
# type = 'sale'
# else:
# type = 'purchase'
# for field in res['fields']:
# if field == 'journal_id':
# journal_select = journal_obj._name_search(cr, uid, '', [(
# 'type', '=', type)], context=context, limit=None,
# name_get_uid=1)
# res['fields'][field]['selection'] = journal_select
# return res
#
# def _get_period(self, cr, uid, context={}):
# """
# Return default account period value
# """
# account_period_obj = self.pool.get('account.period')
# ids = account_period_obj.find(cr, uid, context=context)
# period_id = False
# if ids:
# period_id = ids[0]
# return period_id
#
# def _get_orig(self, cr, uid, inv, ref, context={}):
# """
# Return default origin value
# """
# nro_ref = ref
# if inv.type == 'out_invoice':
# nro_ref = inv.number
# orig = _('INV:') + (nro_ref or '') + _('- DATE:') + (
# inv.date_invoice or '') + (' TOTAL:' + str(inv.amount_total) or '')
# return orig
#
# def compute_debit(self, cr, uid, ids, context=None):
# """
# @param cr: the current row, from the database cursor,
# @param uid: the current user’s ID for security checks,
# @param ids: the account invoice refund’s ID or list of IDs
#
# """
# inv_obj = self.pool.get('account.invoice')
# mod_obj = self.pool.get('ir.model.data')
# act_obj = self.pool.get('ir.actions.act_window')
# inv_tax_obj = self.pool.get('account.invoice.tax')
# inv_line_obj = self.pool.get('account.invoice.line')
# res_users_obj = self.pool.get('res.users')
# if context is None:
# context = {}
#
# for form in self.browse(cr, uid, ids, context=context):
# created_inv = []
# date = False
# period = False
# description = False
# company = res_users_obj.browse(
# cr, uid, uid, context=context).company_id
# journal_id = form.journal_id.id
# for inv in inv_obj.browse(cr, uid, context.get('active_ids'),
# context=context):
# if inv.state in ['draft', 'proforma2', 'cancel']:
# raise osv.except_osv(_('Error !'), _(
# 'Can not create a debit note from '
# 'draft/proforma/cancel invoice.'))
# if inv.reconciled and mode in ('cancel', 'modify'):
# raise osv.except_osv(_('Error!'), _(
# 'Cannot %s invoice which is already reconciled, '
# 'invoice should be unreconciled first. You can only '
# 'refund this invoice.') % (mode))
# if form.period.id:
# period = form.period.id
# else:
# # Take period from the current date
# # period = inv.period_id and inv.period_id.id or False
# period = self._get_period(cr, uid, context)
#
# if not journal_id:
# journal_id = inv.journal_id.id
#
# if form.date:
# date = form.date
# if not form.period.id:
# cr.execute("select name from ir_model_fields \
# where model = 'account.period' \
# and name = 'company_id'")
# result_query = cr.fetchone()
# if result_query:
# # in multi company mode
# cr.execute("""select p.id from account_fiscalyear \
# y, account_period p where y.id=p.fiscalyear_id \
# and date(%s) between p.date_start AND \
# p.date_stop and y.company_id = %s limit 1""",
# (date, company.id,))
# else:
# # in mono company mode
# cr.execute("""SELECT id
# from account_period where date(%s)
# between date_start AND date_stop \
# limit 1 """, (date,))
# res = cr.fetchone()
# if res:
# period = res[0]
# else:
# date = inv.date_invoice
# if form.description:
# description = form.description
# else:
# description = inv.name
#
# if not period:
# raise osv.except_osv(_('Insufficient Data!'),
# _('No period found on the invoice.'))
#
# # we get original data of invoice to create a new invoice that
# # is the copy of the original
# invoice = inv_obj.read(cr, uid, [inv.id],
# ['name', 'type', 'number', 'reference',
# 'comment', 'date_due', 'partner_id',
# 'partner_insite', 'partner_contact',
# 'partner_ref', 'payment_term',
# 'account_id', 'currency_id',
# 'invoice_line', 'tax_line',
# 'journal_id', 'period_id'],
# context=context)
# invoice = invoice[0]
# del invoice['id']
# invoice_lines = inv_line_obj.browse(
# cr, uid, invoice['invoice_line'], context=context)
# invoice_lines = inv_obj._refund_cleanup_lines(
# cr, uid, invoice_lines, context=context)
# tax_lines = inv_tax_obj.browse(
# cr, uid, invoice['tax_line'], context=context)
# tax_lines = inv_obj._refund_cleanup_lines(
# cr, uid, tax_lines, context=context)
# # Add origin, parent and comment values
# orig = self._get_orig(cr, uid, inv, invoice[
# 'reference'], context)
# invoice.update({
# 'type': inv.type == 'in_invoice' and 'in_refund' or
# inv.type == 'out_invoice' and 'out_refund',
# 'date_invoice': date,
# 'state': 'draft',
# 'number': False,
# 'invoice_line': invoice_lines,
# 'tax_line': tax_lines,
# 'period_id': period,
# 'parent_id': inv.id,
# 'name': description,
# 'origin': orig,
# 'comment': form['comment']
# })
# # take the id part of the tuple returned for many2one fields
# for field in ('partner_id', 'account_id', 'currency_id',
# 'payment_term', 'journal_id'):
# invoice[field] = invoice[field] and invoice[field][0]
# # create the new invoice
# inv_id = inv_obj.create(cr, uid, invoice, {})
# # we compute due date
# if inv.payment_term.id:
# data = inv_obj.onchange_payment_term_date_invoice(
# cr, uid, [inv_id], inv.payment_term.id, date)
# if 'value' in data and data['value']:
# inv_obj.write(cr, uid, [inv_id], data['value'])
# created_inv.append(inv_id)
# # we get the view id
# xml_id = (inv.type == 'out_refund') and 'action_invoice_tree1' or \
# (inv.type == 'in_refund') and 'action_invoice_tree2' or \
# (inv.type == 'out_invoice') and 'action_invoice_tree3' or \
# (inv.type == 'in_invoice') and 'action_invoice_tree4'
# # we get the model
# result = mod_obj.get_object_reference(cr, uid, 'account', xml_id)
# id = result and result[1] or False
# # we read the act window
# result = act_obj.read(cr, uid, id, context=context)
# # we add the new invoices into domain list
# invoice_domain = eval(result['domain'])
# invoice_domain.append(('id', 'in', created_inv))
# result['domain'] = invoice_domain
# return result
#
# def invoice_debit(self, cr, uid, ids, context=None):
# return self.compute_debit(cr, uid, ids, context=context)
#
#
# # vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:
|
"""Drag-and-drop support for Tkinter.
This is very preliminary. I currently only support dnd *within* one
application, between different windows (or within the same window).
I an trying to make this as generic as possible -- not dependent on
the use of a particular widget or icon type, etc. I also hope that
this will work with Pmw.
To enable an object to be dragged, you must create an event binding
for it that starts the drag-and-drop process. Typically, you should
bind <ButtonPress> to a callback function that you write. The function
should call Tkdnd.dnd_start(source, event), where 'source' is the
object to be dragged, and 'event' is the event that invoked the call
(the argument to your callback function). Even though this is a class
instantiation, the returned instance should not be stored -- it will
be kept alive automatically for the duration of the drag-and-drop.
When a drag-and-drop is already in process for the Tk interpreter, the
call is *ignored*; this normally averts starting multiple simultaneous
dnd processes, e.g. because different button callbacks all
dnd_start().
The object is *not* necessarily a widget -- it can be any
application-specific object that is meaningful to potential
drag-and-drop targets.
Potential drag-and-drop targets are discovered as follows. Whenever
the mouse moves, and at the start and end of a drag-and-drop move, the
Tk widget directly under the mouse is inspected. This is the target
widget (not to be confused with the target object, yet to be
determined). If there is no target widget, there is no dnd target
object. If there is a target widget, and it has an attribute
dnd_accept, this should be a function (or any callable object). The
function is called as dnd_accept(source, event), where 'source' is the
object being dragged (the object passed to dnd_start() above), and
'event' is the most recent event object (generally a <Motion> event;
it can also be <ButtonPress> or <ButtonRelease>). If the dnd_accept()
function returns something other than None, this is the new dnd target
object. If dnd_accept() returns None, or if the target widget has no
dnd_accept attribute, the target widget's parent is considered as the
target widget, and the search for a target object is repeated from
there. If necessary, the search is repeated all the way up to the
root widget. If none of the target widgets can produce a target
object, there is no target object (the target object is None).
The target object thus produced, if any, is called the new target
object. It is compared with the old target object (or None, if there
was no old target widget). There are several cases ('source' is the
source object, and 'event' is the most recent event object):
- Both the old and new target objects are None. Nothing happens.
- The old and new target objects are the same object. Its method
dnd_motion(source, event) is called.
- The old target object was None, and the new target object is not
None. The new target object's method dnd_enter(source, event) is
called.
- The new target object is None, and the old target object is not
None. The old target object's method dnd_leave(source, event) is
called.
- The old and new target objects differ and neither is None. The old
target object's method dnd_leave(source, event), and then the new
target object's method dnd_enter(source, event) is called.
Once this is done, the new target object replaces the old one, and the
Tk mainloop proceeds. The return value of the methods mentioned above
is ignored; if they raise an exception, the normal exception handling
mechanisms take over.
The drag-and-drop processes can end in two ways: a final target object
is selected, or no final target object is selected. When a final
target object is selected, it will always have been notified of the
potential drop by a call to its dnd_enter() method, as described
above, and possibly one or more calls to its dnd_motion() method; its
dnd_leave() method has not been called since the last call to
dnd_enter(). The target is notified of the drop by a call to its
method dnd_commit(source, event).
If no final target object is selected, and there was an old target
object, its dnd_leave(source, event) method is called to complete the
dnd sequence.
Finally, the source object is notified that the drag-and-drop process
is over, by a call to source.dnd_end(target, event), specifying either
the selected target object, or None if no target object was selected.
The source object can use this to implement the commit action; this is
sometimes simpler than to do it in the target's dnd_commit(). The
target's dnd_commit() method could then simply be aliased to
dnd_leave().
At any time during a dnd sequence, the application can cancel the
sequence by calling the cancel() method on the object returned by
dnd_start(). This will call dnd_leave() if a target is currently
active; it will never call dnd_commit().
""" |
"""
This page is in the table of contents.
The obj.py script is an import translator plugin to get a carving from an obj file.
An example obj file is box.obj in the models folder.
An import plugin is a script in the interpret_plugins folder which has the function getCarving. It is meant to be run from the interpret tool. To ensure that the plugin works on platforms which do not handle file capitalization properly, give the plugin a lower case name.
The getCarving function takes the file name of an obj file and returns the carving.
From wikipedia, OBJ (or .OBJ) is a geometry definition file format first developed by Wavefront Technologies for its Advanced Visualizer animation package:
http://en.wikipedia.org/wiki/Obj
The Object File specification is at:
http://local.wasp.uwa.edu.au/~pbourke/dataformats/obj/
An excellent link page about obj files is at:
http://people.sc.fsu.edu/~burkardt/data/obj/obj.html
This example gets a carving for the obj file box.obj. This example is run in a terminal in the folder which contains box.obj and obj.py.
> python
Python 2.5.1 (r251:54863, Sep 22 2007, 01:43:31)
[GCC 4.2.1 (SUSE Linux)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import obj
>>> obj.getCarving()
[-62.0579, -41.4791, 0.0, 58.8424, -41.4791, 0.0, -62.0579, 22.1865, 0.0, 58.8424, 22.1865, 0.0,
-62.0579, -41.4791, 39.8714, 58.8424, -41.4791, 39.8714, -62.0579, 22.1865, 39.8714, 58.8424, 22.1865, 39.8714]
[0 [0, 10] [0, 2], 1 [0, 1] [0, 3], 2 [0, 8] [2, 3], 3 [1, 6] [1, 3], 4 [1, 4] [0, 1], 5 [2, 5] [4, 5], 6 [2, 3] [4, 7], 7 [2, 7] [5, 7],
8 [3, 9] [6, 7], 9 [3, 11] [4, 6], 10 [4, 5] [0, 5], 11 [4, 7] [1, 5], 12 [5, 10] [0, 4], 13 [6, 7] [1, 7], 14 [6, 9] [3, 7],
15 [8, 9] [3, 6], 16 [8, 11] [2, 6], 17 [10, 11] [2, 4]]
[0 [0, 1, 2] [0, 2, 3], 1 [3, 1, 4] [3, 1, 0], 2 [5, 6, 7] [4, 5, 7], 3 [8, 6, 9] [7, 6, 4], 4 [4, 10, 11] [0, 1, 5], 5 [5, 10, 12] [5, 4, 0],
6 [3, 13, 14] [1, 3, 7], 7 [7, 13, 11] [7, 5, 1], 8 [2, 15, 16] [3, 2, 6], 9 [8, 15, 14] [6, 7, 3], 10 [0, 17, 12] [2, 0, 4],
11 [9, 17, 16] [4, 6, 2]][11.6000003815, 10.6837882996, 7.80209827423
""" |
#
# ElementTree
# $Id: ElementTree.py 2326 2005-03-17 07:45:21Z USERNAME $
#
# light-weight XML support for Python 1.5.2 and later.
#
# history:
# 2001-10-20 fl created (from various sources)
# 2001-11-01 fl return root from parse method
# 2002-02-16 fl sort attributes in lexical order
# 2002-04-06 fl TreeBuilder refactoring, added PythonDoc markup
# 2002-05-01 fl finished TreeBuilder refactoring
# 2002-07-14 fl added basic namespace support to ElementTree.write
# 2002-07-25 fl added QName attribute support
# 2002-10-20 fl fixed encoding in write
# 2002-11-24 fl changed default encoding to ascii; fixed attribute encoding
# 2002-11-27 fl accept file objects or file names for parse/write
# 2002-12-04 fl moved XMLTreeBuilder back to this module
# 2003-01-11 fl fixed entity encoding glitch for us-ascii
# 2003-02-13 fl added XML literal factory
# 2003-02-21 fl added ProcessingInstruction/PI factory
# 2003-05-11 fl added tostring/fromstring helpers
# 2003-05-26 fl added ElementPath support
# 2003-07-05 fl added makeelement factory method
# 2003-07-28 fl added more well-known namespace prefixes
# 2003-08-15 fl fixed typo in ElementTree.findtext (Thomas NAME 2003-09-04 fl fall back on emulator if ElementPath is not installed
# 2003-10-31 fl markup updates
# 2003-11-15 fl fixed nested namespace bug
# 2004-03-28 fl added XMLID helper
# 2004-06-02 fl added default support to findtext
# 2004-06-08 fl fixed encoding of non-ascii element/attribute names
# 2004-08-23 fl take advantage of post-2.1 expat features
# 2005-02-01 fl added iterparse implementation
# 2005-03-02 fl fixed iterparse support for pre-2.2 versions
#
# Copyright (c) 1999-2005 by NAME All rights reserved.
#
# USERNAME@pythonware.com
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2005 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
|
"""
=====================================================
Optimization and root finding (:mod:`scipy.optimize`)
=====================================================
.. currentmodule:: scipy.optimize
Optimization
============
Local Optimization
------------------
.. autosummary::
:toctree: generated/
minimize - Unified interface for minimizers of multivariate functions
minimize_scalar - Unified interface for minimizers of univariate functions
OptimizeResult - The optimization result returned by some optimizers
The `minimize` function supports the following methods:
.. toctree::
optimize.minimize-neldermead
optimize.minimize-powell
optimize.minimize-cg
optimize.minimize-bfgs
optimize.minimize-newtoncg
optimize.minimize-lbfgsb
optimize.minimize-tnc
optimize.minimize-cobyla
optimize.minimize-slsqp
optimize.minimize-dogleg
optimize.minimize-trustncg
The `minimize_scalar` function supports the following methods:
.. toctree::
optimize.minimize_scalar-brent
optimize.minimize_scalar-bounded
optimize.minimize_scalar-golden
The specific optimization method interfaces below in this subsection are
not recommended for use in new scripts; all of these methods are accessible
via a newer, more consistent interface provided by the functions above.
General-purpose multivariate methods:
.. autosummary::
:toctree: generated/
fmin - Nelder-Mead Simplex algorithm
fmin_powell - Powell's (modified) level set method
fmin_cg - Non-linear (Polak-Ribiere) conjugate gradient algorithm
fmin_bfgs - Quasi-Newton method (Broydon-Fletcher-Goldfarb-Shanno)
fmin_ncg - Line-search Newton Conjugate Gradient
Constrained multivariate methods:
.. autosummary::
:toctree: generated/
fmin_l_bfgs_b - Zhu, Byrd, and Nocedal's constrained optimizer
fmin_tnc - Truncated Newton code
fmin_cobyla - Constrained optimization by linear approximation
fmin_slsqp - Minimization using sequential least-squares programming
differential_evolution - stochastic minimization using differential evolution
Univariate (scalar) minimization methods:
.. autosummary::
:toctree: generated/
fminbound - Bounded minimization of a scalar function
brent - 1-D function minimization using Brent method
golden - 1-D function minimization using Golden Section method
Equation (Local) Minimizers
---------------------------
.. autosummary::
:toctree: generated/
leastsq - Minimize the sum of squares of M equations in N unknowns
nnls - Linear least-squares problem with non-negativity constraint
Global Optimization
-------------------
.. autosummary::
:toctree: generated/
basinhopping - Basinhopping stochastic optimizer
brute - Brute force searching optimizer
differential_evolution - stochastic minimization using differential evolution
Rosenbrock function
-------------------
.. autosummary::
:toctree: generated/
rosen - The Rosenbrock function.
rosen_der - The derivative of the Rosenbrock function.
rosen_hess - The Hessian matrix of the Rosenbrock function.
rosen_hess_prod - Product of the Rosenbrock Hessian with a vector.
Fitting
=======
.. autosummary::
:toctree: generated/
curve_fit -- Fit curve to a set of points
Root finding
============
Scalar functions
----------------
.. autosummary::
:toctree: generated/
brentq - quadratic interpolation Brent method
brenth - Brent method, modified by Harris with hyperbolic extrapolation
ridder - Ridder's method
bisect - Bisection method
newton - Secant method or Newton's method
Fixed point finding:
.. autosummary::
:toctree: generated/
fixed_point - Single-variable fixed-point solver
Multidimensional
----------------
General nonlinear solvers:
.. autosummary::
:toctree: generated/
root - Unified interface for nonlinear solvers of multivariate functions
fsolve - Non-linear multi-variable equation solver
broyden1 - Broyden's first method
broyden2 - Broyden's second method
The `root` function supports the following methods:
.. toctree::
optimize.root-hybr
optimize.root-lm
optimize.root-broyden1
optimize.root-broyden2
optimize.root-anderson
optimize.root-linearmixing
optimize.root-diagbroyden
optimize.root-excitingmixing
optimize.root-krylov
optimize.root-dfsane
Large-scale nonlinear solvers:
.. autosummary::
:toctree: generated/
newton_krylov
anderson
Simple iterations:
.. autosummary::
:toctree: generated/
excitingmixing
linearmixing
diagbroyden
:mod:`Additional information on the nonlinear solvers <scipy.optimize.nonlin>`
Linear Programming
==================
Simplex Algorithm:
.. autosummary::
:toctree: generated/
linprog -- Linear programming using the simplex algorithm
The `linprog` function supports the following methods:
.. toctree::
optimize.linprog-simplex
Utilities
=========
.. autosummary::
:toctree: generated/
approx_fprime - Approximate the gradient of a scalar function
bracket - Bracket a minimum, given two starting points
check_grad - Check the supplied derivative using finite differences
line_search - Return a step that satisfies the strong Wolfe conditions
show_options - Show specific options optimization solvers
LbfgsInvHessProduct - Linear operator for L-BFGS approximate inverse Hessian
""" |
"""This module tests SyntaxErrors.
Here's an example of the sort of thing that is tested.
>>> def f(x):
... global x
Traceback (most recent call last):
SyntaxError: name 'x' is parameter and global
The tests are all raise SyntaxErrors. They were created by checking
each C call that raises SyntaxError. There are several modules that
raise these exceptions-- ast.c, compile.c, future.c, pythonrun.c, and
symtable.c.
The parser itself outlaws a lot of invalid syntax. None of these
errors are tested here at the moment. We should add some tests; since
there are infinitely many programs with invalid syntax, we would need
to be judicious in selecting some.
The compiler generates a synthetic module name for code executed by
doctest. Since all the code comes from the same module, a suffix like
[1] is appended to the module name, As a consequence, changing the
order of tests in this module means renumbering all the errors after
it. (Maybe we should enable the ellipsis option for these tests.)
In ast.c, syntax errors are raised by calling ast_error().
Errors from set_context():
>>> obj.None = 1
Traceback (most recent call last):
SyntaxError: invalid syntax
>>> None = 1
Traceback (most recent call last):
SyntaxError: assignment to keyword
It's a syntax error to assign to the empty tuple. Why isn't it an
error to assign to the empty list? It will always raise some error at
runtime.
>>> () = 1
Traceback (most recent call last):
SyntaxError: can't assign to ()
>>> f() = 1
Traceback (most recent call last):
SyntaxError: can't assign to function call
>>> del f()
Traceback (most recent call last):
SyntaxError: can't delete function call
>>> a + 1 = 2
Traceback (most recent call last):
SyntaxError: can't assign to operator
>>> (x for x in x) = 1
Traceback (most recent call last):
SyntaxError: can't assign to generator expression
>>> 1 = 1
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> "abc" = 1
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> b"" = 1
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> `1` = 1
Traceback (most recent call last):
SyntaxError: invalid syntax
If the left-hand side of an assignment is a list or tuple, an illegal
expression inside that contain should still cause a syntax error.
This test just checks a couple of cases rather than enumerating all of
them.
>>> (a, "b", c) = (1, 2, 3)
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> [a, b, c + 1] = [1, 2, 3]
Traceback (most recent call last):
SyntaxError: can't assign to operator
>>> a if 1 else b = 1
Traceback (most recent call last):
SyntaxError: can't assign to conditional expression
From compiler_complex_args():
>>> def f(None=1):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
From ast_for_arguments():
>>> def f(x, y=1, z):
... pass
Traceback (most recent call last):
SyntaxError: non-default argument follows default argument
>>> def f(x, None):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
>>> def f(*None):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
>>> def f(**None):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
From ast_for_funcdef():
>>> def None(x):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
From ast_for_call():
>>> def f(it, *varargs):
... return list(it)
>>> L = range(10)
>>> f(x for x in L)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> f(x for x in L, 1)
Traceback (most recent call last):
SyntaxError: Generator expression must be parenthesized if not sole argument
>>> f((x for x in L), 1)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> f(i0, i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11,
... i12, i13, i14, i15, i16, i17, i18, i19, i20, i21, i22,
... i23, i24, i25, i26, i27, i28, i29, i30, i31, i32, i33,
... i34, i35, i36, i37, i38, i39, i40, i41, i42, i43, i44,
... i45, i46, i47, i48, i49, i50, i51, i52, i53, i54, i55,
... i56, i57, i58, i59, i60, i61, i62, i63, i64, i65, i66,
... i67, i68, i69, i70, i71, i72, i73, i74, i75, i76, i77,
... i78, i79, i80, i81, i82, i83, i84, i85, i86, i87, i88,
... i89, i90, i91, i92, i93, i94, i95, i96, i97, i98, i99,
... i100, i101, i102, i103, i104, i105, i106, i107, i108,
... i109, i110, i111, i112, i113, i114, i115, i116, i117,
... i118, i119, i120, i121, i122, i123, i124, i125, i126,
... i127, i128, i129, i130, i131, i132, i133, i134, i135,
... i136, i137, i138, i139, i140, i141, i142, i143, i144,
... i145, i146, i147, i148, i149, i150, i151, i152, i153,
... i154, i155, i156, i157, i158, i159, i160, i161, i162,
... i163, i164, i165, i166, i167, i168, i169, i170, i171,
... i172, i173, i174, i175, i176, i177, i178, i179, i180,
... i181, i182, i183, i184, i185, i186, i187, i188, i189,
... i190, i191, i192, i193, i194, i195, i196, i197, i198,
... i199, i200, i201, i202, i203, i204, i205, i206, i207,
... i208, i209, i210, i211, i212, i213, i214, i215, i216,
... i217, i218, i219, i220, i221, i222, i223, i224, i225,
... i226, i227, i228, i229, i230, i231, i232, i233, i234,
... i235, i236, i237, i238, i239, i240, i241, i242, i243,
... i244, i245, i246, i247, i248, i249, i250, i251, i252,
... i253, i254, i255)
Traceback (most recent call last):
SyntaxError: more than 255 arguments
The actual error cases counts positional arguments, keyword arguments,
and generator expression arguments separately. This test combines the
three.
>>> f(i0, i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11,
... i12, i13, i14, i15, i16, i17, i18, i19, i20, i21, i22,
... i23, i24, i25, i26, i27, i28, i29, i30, i31, i32, i33,
... i34, i35, i36, i37, i38, i39, i40, i41, i42, i43, i44,
... i45, i46, i47, i48, i49, i50, i51, i52, i53, i54, i55,
... i56, i57, i58, i59, i60, i61, i62, i63, i64, i65, i66,
... i67, i68, i69, i70, i71, i72, i73, i74, i75, i76, i77,
... i78, i79, i80, i81, i82, i83, i84, i85, i86, i87, i88,
... i89, i90, i91, i92, i93, i94, i95, i96, i97, i98, i99,
... i100, i101, i102, i103, i104, i105, i106, i107, i108,
... i109, i110, i111, i112, i113, i114, i115, i116, i117,
... i118, i119, i120, i121, i122, i123, i124, i125, i126,
... i127, i128, i129, i130, i131, i132, i133, i134, i135,
... i136, i137, i138, i139, i140, i141, i142, i143, i144,
... i145, i146, i147, i148, i149, i150, i151, i152, i153,
... i154, i155, i156, i157, i158, i159, i160, i161, i162,
... i163, i164, i165, i166, i167, i168, i169, i170, i171,
... i172, i173, i174, i175, i176, i177, i178, i179, i180,
... i181, i182, i183, i184, i185, i186, i187, i188, i189,
... i190, i191, i192, i193, i194, i195, i196, i197, i198,
... i199, i200, i201, i202, i203, i204, i205, i206, i207,
... i208, i209, i210, i211, i212, i213, i214, i215, i216,
... i217, i218, i219, i220, i221, i222, i223, i224, i225,
... i226, i227, i228, i229, i230, i231, i232, i233, i234,
... i235, i236, i237, i238, i239, i240, i241, i242, i243,
... (x for x in i244), i245, i246, i247, i248, i249, i250, i251,
... i252=1, i253=1, i254=1, i255=1)
Traceback (most recent call last):
SyntaxError: more than 255 arguments
>>> f(lambda x: x[0] = 3)
Traceback (most recent call last):
SyntaxError: lambda cannot contain assignment
The grammar accepts any test (basically, any expression) in the
keyword slot of a call site. Test a few different options.
>>> f(x()=2)
Traceback (most recent call last):
SyntaxError: keyword can't be an expression
>>> f(a or b=1)
Traceback (most recent call last):
SyntaxError: keyword can't be an expression
>>> f(x.y=1)
Traceback (most recent call last):
SyntaxError: keyword can't be an expression
More set_context():
>>> (x for x in x) += 1
Traceback (most recent call last):
SyntaxError: can't assign to generator expression
>>> None += 1
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> f() += 1
Traceback (most recent call last):
SyntaxError: can't assign to function call
Test continue in finally in weird combinations.
continue in for loop under finally should be ok.
>>> def test():
... try:
... pass
... finally:
... for abc in range(10):
... continue
... print(abc)
>>> test()
9
Start simple, a continue in a finally should not be allowed.
>>> def test():
... for abc in range(10):
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
This is essentially a continue in a finally which should not be allowed.
>>> def test():
... for abc in range(10):
... try:
... pass
... finally:
... try:
... continue
... except:
... pass
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try:
... pass
... finally:
... try:
... continue
... finally:
... pass
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try: pass
... finally:
... try:
... pass
... except:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
There is one test for a break that is not in a loop. The compiler
uses a single data structure to keep track of try-finally and loops,
so we need to be sure that a break is actually inside a loop. If it
isn't, there should be a syntax error.
>>> try:
... print(1)
... break
... print(2)
... finally:
... print(3)
Traceback (most recent call last):
...
SyntaxError: 'break' outside loop
This should probably raise a better error than a SystemError (or none at all).
In 2.5 there was a missing exception and an assert was triggered in a debug
build. The number of blocks must be greater than CO_MAXBLOCKS. SF #1565514
>>> while 1:
... while 2:
... while 3:
... while 4:
... while 5:
... while 6:
... while 8:
... while 9:
... while 10:
... while 11:
... while 12:
... while 13:
... while 14:
... while 15:
... while 16:
... while 17:
... while 18:
... while 19:
... while 20:
... while 21:
... while 22:
... break
Traceback (most recent call last):
...
SystemError: too many statically nested blocks
Misuse of the nonlocal statement can lead to a few unique syntax errors.
>>> def f(x):
... nonlocal x
Traceback (most recent call last):
...
SyntaxError: name 'x' is parameter and nonlocal
>>> def f():
... global x
... nonlocal x
Traceback (most recent call last):
...
SyntaxError: name 'x' is nonlocal and global
>>> def f():
... nonlocal x
Traceback (most recent call last):
...
SyntaxError: no binding for nonlocal 'x' found
From SF bug #1705365
>>> nonlocal x
Traceback (most recent call last):
...
SyntaxError: nonlocal declaration not allowed at module level
TODO(jhylton): Figure out how to test SyntaxWarning with doctest.
## >>> def f(x):
## ... def f():
## ... print(x)
## ... nonlocal x
## Traceback (most recent call last):
## ...
## SyntaxWarning: name 'x' is assigned to before nonlocal declaration
## >>> def f():
## ... x = 1
## ... nonlocal x
## Traceback (most recent call last):
## ...
## SyntaxWarning: name 'x' is assigned to before nonlocal declaration
This tests assignment-context; there was a bug in Python 2.5 where compiling
a complex 'if' (one with 'elif') would fail to notice an invalid suite,
leading to spurious errors.
>>> if 1:
... x() = 1
... elif 1:
... pass
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... x() = 1
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... x() = 1
... elif 1:
... pass
... else:
... pass
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... x() = 1
... else:
... pass
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... pass
... else:
... x() = 1
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
Make sure that the old "raise X, Y[, Z]" form is gone:
>>> raise X, Y
Traceback (most recent call last):
...
SyntaxError: invalid syntax
>>> raise X, Y, Z
Traceback (most recent call last):
...
SyntaxError: invalid syntax
>>> f(a=23, a=234)
Traceback (most recent call last):
...
SyntaxError: keyword argument repeated
>>> del ()
Traceback (most recent call last):
SyntaxError: can't delete ()
>>> {1, 2, 3} = 42
Traceback (most recent call last):
SyntaxError: can't assign to literal
Corner-cases that used to fail to raise the correct error:
>>> def f(*, x=lambda __debug__:0): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> def f(*args:(lambda __debug__:0)): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> def f(**kwargs:(lambda __debug__:0)): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> with (lambda *:0): pass
Traceback (most recent call last):
SyntaxError: named arguments must follow bare *
Corner-cases that used to crash:
>>> def f(**__debug__): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> def f(*xx, __debug__): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
""" |
"""
Discrete Fourier Transform (:mod:`numpy.fft`)
=============================================
.. currentmodule:: numpy.fft
Standard FFTs
-------------
.. autosummary::
:toctree: generated/
fft Discrete Fourier transform.
ifft Inverse discrete Fourier transform.
fft2 Discrete Fourier transform in two dimensions.
ifft2 Inverse discrete Fourier transform in two dimensions.
fftn Discrete Fourier transform in N-dimensions.
ifftn Inverse discrete Fourier transform in N dimensions.
Real FFTs
---------
.. autosummary::
:toctree: generated/
rfft Real discrete Fourier transform.
irfft Inverse real discrete Fourier transform.
rfft2 Real discrete Fourier transform in two dimensions.
irfft2 Inverse real discrete Fourier transform in two dimensions.
rfftn Real discrete Fourier transform in N dimensions.
irfftn Inverse real discrete Fourier transform in N dimensions.
Hermitian FFTs
--------------
.. autosummary::
:toctree: generated/
hfft Hermitian discrete Fourier transform.
ihfft Inverse Hermitian discrete Fourier transform.
Helper routines
---------------
.. autosummary::
:toctree: generated/
fftfreq Discrete Fourier Transform sample frequencies.
rfftfreq DFT sample frequencies (for usage with rfft, irfft).
fftshift Shift zero-frequency component to center of spectrum.
ifftshift Inverse of fftshift.
Background information
----------------------
Fourier analysis is fundamentally a method for expressing a function as a
sum of periodic components, and for recovering the function from those
components. When both the function and its Fourier transform are
replaced with discretized counterparts, it is called the discrete Fourier
transform (DFT). The DFT has become a mainstay of numerical computing in
part because of a very fast algorithm for computing it, called the Fast
Fourier Transform (FFT), which was known to Gauss (1805) and was brought
to light in its current form by NAME and NAME [CT]_. Press et al. [NR]_
provide an accessible introduction to Fourier analysis and its
applications.
Because the discrete Fourier transform separates its input into
components that contribute at discrete frequencies, it has a great number
of applications in digital signal processing, e.g., for filtering, and in
this context the discretized input to the transform is customarily
referred to as a *signal*, which exists in the *time domain*. The output
is called a *spectrum* or *transform* and exists in the *frequency
domain*.
Implementation details
----------------------
There are many ways to define the DFT, varying in the sign of the
exponent, normalization, etc. In this implementation, the DFT is defined
as
.. math::
A_k = \\sum_{m=0}^{n-1} a_m \\exp\\left\\{-2\\pi i{mk \\over n}\\right\\}
\\qquad k = 0,\\ldots,n-1.
The DFT is in general defined for complex inputs and outputs, and a
single-frequency component at linear frequency :math:`f` is
represented by a complex exponential
:math:`a_m = \\exp\\{2\\pi i\\,f m\\Delta t\\}`, where :math:`\\Delta t`
is the sampling interval.
The values in the result follow so-called "standard" order: If ``A =
fft(a, n)``, then ``A[0]`` contains the zero-frequency term (the sum of
the signal), which is always purely real for real inputs. Then ``A[1:n/2]``
contains the positive-frequency terms, and ``A[n/2+1:]`` contains the
negative-frequency terms, in order of decreasingly negative frequency.
For an even number of input points, ``A[n/2]`` represents both positive and
negative Nyquist frequency, and is also purely real for real input. For
an odd number of input points, ``A[(n-1)/2]`` contains the largest positive
frequency, while ``A[(n+1)/2]`` contains the largest negative frequency.
The routine ``np.fft.fftfreq(n)`` returns an array giving the frequencies
of corresponding elements in the output. The routine
``np.fft.fftshift(A)`` shifts transforms and their frequencies to put the
zero-frequency components in the middle, and ``np.fft.ifftshift(A)`` undoes
that shift.
When the input `a` is a time-domain signal and ``A = fft(a)``, ``np.abs(A)``
is its amplitude spectrum and ``np.abs(A)**2`` is its power spectrum.
The phase spectrum is obtained by ``np.angle(A)``.
The inverse DFT is defined as
.. math::
a_m = \\frac{1}{n}\\sum_{k=0}^{n-1}A_k\\exp\\left\\{2\\pi i{mk\\over n}\\right\\}
\\qquad m = 0,\\ldots,n-1.
It differs from the forward transform by the sign of the exponential
argument and the default normalization by :math:`1/n`.
Normalization
-------------
The default normalization has the direct transforms unscaled and the inverse
transforms are scaled by :math:`1/n`. It is possible to obtain unitary
transforms by setting the keyword argument ``norm`` to ``"ortho"`` (default is
`None`) so that both direct and inverse transforms will be scaled by
:math:`1/\\sqrt{n}`.
Real and Hermitian transforms
-----------------------------
When the input is purely real, its transform is Hermitian, i.e., the
component at frequency :math:`f_k` is the complex conjugate of the
component at frequency :math:`-f_k`, which means that for real
inputs there is no information in the negative frequency components that
is not already available from the positive frequency components.
The family of `rfft` functions is
designed to operate on real inputs, and exploits this symmetry by
computing only the positive frequency components, up to and including the
Nyquist frequency. Thus, ``n`` input points produce ``n/2+1`` complex
output points. The inverses of this family assumes the same symmetry of
its input, and for an output of ``n`` points uses ``n/2+1`` input points.
Correspondingly, when the spectrum is purely real, the signal is
Hermitian. The `hfft` family of functions exploits this symmetry by
using ``n/2+1`` complex points in the input (time) domain for ``n`` real
points in the frequency domain.
In higher dimensions, FFTs are used, e.g., for image analysis and
filtering. The computational efficiency of the FFT means that it can
also be a faster way to compute large convolutions, using the property
that a convolution in the time domain is equivalent to a point-by-point
multiplication in the frequency domain.
Higher dimensions
-----------------
In two dimensions, the DFT is defined as
.. math::
A_{kl} = \\sum_{m=0}^{M-1} \\sum_{n=0}^{N-1}
a_{mn}\\exp\\left\\{-2\\pi i \\left({mk\\over M}+{nl\\over N}\\right)\\right\\}
\\qquad k = 0, \\ldots, M-1;\\quad l = 0, \\ldots, N-1,
which extends in the obvious way to higher dimensions, and the inverses
in higher dimensions also extend in the same way.
References
----------
.. [CT] NAME, NAME and John W. NAME, 1965, "An algorithm for the
machine calculation of complex Fourier series," *Math. Comput.*
19: 297-301.
.. [NR] NAME NAME NAME and NAME
2007, *Numerical Recipes: The Art of Scientific Computing*, ch.
12-13. Cambridge Univ. Press, Cambridge, UK.
Examples
--------
For examples, see the various functions.
""" |
"""
==========================================
Statistical functions (:mod:`scipy.stats`)
==========================================
.. module:: scipy.stats
This module contains a large number of probability distributions as
well as a growing library of statistical functions.
Each univariate distribution is an instance of a subclass of `rv_continuous`
(`rv_discrete` for discrete distributions):
.. autosummary::
:toctree: generated/
rv_continuous
rv_discrete
rv_histogram
Continuous distributions
========================
.. autosummary::
:toctree: generated/
alpha -- Alpha
anglit -- Anglit
arcsine -- Arcsine
argus -- Argus
beta -- Beta
betaprime -- Beta Prime
bradford -- Bradford
burr -- Burr (Type III)
burr12 -- Burr (Type XII)
cauchy -- Cauchy
chi -- Chi
chi2 -- Chi-squared
cosine -- Cosine
crystalball -- Crystalball
dgamma -- Double Gamma
dweibull -- Double Weibull
erlang -- Erlang
expon -- Exponential
exponnorm -- Exponentially Modified Normal
exponweib -- Exponentiated Weibull
exponpow -- Exponential Power
f -- F (Snecdor F)
fatiguelife -- Fatigue Life (Birnbaum-Saunders)
fisk -- Fisk
foldcauchy -- Folded Cauchy
foldnorm -- Folded Normal
frechet_r -- Deprecated. Alias for weibull_min
frechet_l -- Deprecated. Alias for weibull_max
genlogistic -- Generalized Logistic
gennorm -- Generalized normal
genpareto -- Generalized Pareto
genexpon -- Generalized Exponential
genextreme -- Generalized Extreme Value
gausshyper -- Gauss Hypergeometric
gamma -- Gamma
gengamma -- Generalized gamma
genhalflogistic -- Generalized Half Logistic
gilbrat -- Gilbrat
gompertz -- Gompertz (Truncated Gumbel)
gumbel_r -- Right Sided Gumbel, Log-Weibull, Fisher-Tippett, Extreme Value Type I
gumbel_l -- Left Sided Gumbel, etc.
halfcauchy -- Half Cauchy
halflogistic -- Half Logistic
halfnorm -- Half Normal
halfgennorm -- Generalized Half Normal
hypsecant -- Hyperbolic Secant
invgamma -- Inverse Gamma
invgauss -- Inverse Gaussian
invweibull -- Inverse Weibull
johnsonsb -- NAME
johnsonsu -- NAME
kappa4 -- Kappa 4 parameter
kappa3 -- Kappa 3 parameter
ksone -- Kolmogorov-Smirnov one-sided (no stats)
kstwobign -- Kolmogorov-Smirnov two-sided test for Large N (no stats)
laplace -- Laplace
levy -- Levy
levy_l
levy_stable
logistic -- Logistic
loggamma -- Log-Gamma
loglaplace -- Log-Laplace (Log Double Exponential)
lognorm -- Log-Normal
lomax -- Lomax (Pareto of the second kind)
maxwell -- Maxwell
mielke -- Mielke's Beta-Kappa
nakagami -- Nakagami
ncx2 -- Non-central chi-squared
ncf -- Non-central F
nct -- Non-central Student's T
norm -- Normal (Gaussian)
pareto -- Pareto
pearson3 -- NAME type III
powerlaw -- Power-function
powerlognorm -- Power log normal
powernorm -- Power normal
rdist -- R-distribution
reciprocal -- Reciprocal
rayleigh -- Rayleigh
rice -- Rice
recipinvgauss -- Reciprocal Inverse Gaussian
semicircular -- Semicircular
skewnorm -- Skew normal
t -- Student's T
trapz -- Trapezoidal
triang -- Triangular
truncexpon -- Truncated Exponential
truncnorm -- Truncated Normal
tukeylambda -- Tukey-Lambda
uniform -- Uniform
vonmises -- Von-Mises (Circular)
vonmises_line -- Von-Mises (Line)
wald -- Wald
weibull_min -- Minimum Weibull (see Frechet)
weibull_max -- Maximum Weibull (see Frechet)
wrapcauchy -- Wrapped Cauchy
Multivariate distributions
==========================
.. autosummary::
:toctree: generated/
multivariate_normal -- Multivariate normal distribution
matrix_normal -- Matrix normal distribution
dirichlet -- Dirichlet
wishart -- Wishart
invwishart -- Inverse Wishart
multinomial -- Multinomial distribution
special_ortho_group -- SO(N) group
ortho_group -- O(N) group
unitary_group -- U(N) gropu
random_correlation -- random correlation matrices
Discrete distributions
======================
.. autosummary::
:toctree: generated/
bernoulli -- Bernoulli
binom -- Binomial
boltzmann -- Boltzmann (Truncated Discrete Exponential)
dlaplace -- Discrete Laplacian
geom -- Geometric
hypergeom -- Hypergeometric
logser -- Logarithmic (Log-Series, Series)
nbinom -- Negative Binomial
planck -- Planck (Discrete Exponential)
poisson -- Poisson
randint -- Discrete Uniform
skellam -- Skellam
zipf -- Zipf
Statistical functions
=====================
Several of these functions have a similar version in scipy.stats.mstats
which work for masked arrays.
.. autosummary::
:toctree: generated/
describe -- Descriptive statistics
gmean -- Geometric mean
hmean -- Harmonic mean
kurtosis -- Fisher or NAME kurtosis
kurtosistest --
mode -- Modal value
moment -- Central moment
normaltest --
skew -- Skewness
skewtest --
kstat --
kstatvar --
tmean -- Truncated arithmetic mean
tvar -- Truncated variance
tmin --
tmax --
tstd --
tsem --
variation -- Coefficient of variation
find_repeats
trim_mean
.. autosummary::
:toctree: generated/
cumfreq
itemfreq
percentileofscore
scoreatpercentile
relfreq
.. autosummary::
:toctree: generated/
binned_statistic -- Compute a binned statistic for a set of data.
binned_statistic_2d -- Compute a 2-D binned statistic for a set of data.
binned_statistic_dd -- Compute a d-D binned statistic for a set of data.
.. autosummary::
:toctree: generated/
obrientransform
bayes_mvs
mvsdist
sem
zmap
zscore
iqr
.. autosummary::
:toctree: generated/
sigmaclip
trimboth
trim1
.. autosummary::
:toctree: generated/
f_oneway
pearsonr
spearmanr
pointbiserialr
kendalltau
weightedtau
linregress
theilslopes
.. autosummary::
:toctree: generated/
ttest_1samp
ttest_ind
ttest_ind_from_stats
ttest_rel
kstest
chisquare
power_divergence
ks_2samp
mannwhitneyu
tiecorrect
rankdata
ranksums
wilcoxon
kruskal
friedmanchisquare
combine_pvalues
jarque_bera
.. autosummary::
:toctree: generated/
ansari
bartlett
levene
shapiro
anderson
anderson_ksamp
binom_test
fligner
median_test
mood
.. autosummary::
:toctree: generated/
boxcox
boxcox_normmax
boxcox_llf
entropy
.. autosummary::
:toctree: generated/
wasserstein_distance
energy_distance
Circular statistical functions
==============================
.. autosummary::
:toctree: generated/
circmean
circvar
circstd
Contingency table functions
===========================
.. autosummary::
:toctree: generated/
chi2_contingency
contingency.expected_freq
contingency.margins
fisher_exact
Plot-tests
==========
.. autosummary::
:toctree: generated/
ppcc_max
ppcc_plot
probplot
boxcox_normplot
Masked statistics functions
===========================
.. toctree::
stats.mstats
Univariate and multivariate kernel density estimation (:mod:`scipy.stats.kde`)
==============================================================================
.. autosummary::
:toctree: generated/
gaussian_kde
For many more stat related functions install the software R and the
interface package rpy.
""" |
#
# ElementTree
# $Id: ElementTree.py 2326 2005-03-17 07:45:21Z USERNAME $
#
# light-weight XML support for Python 1.5.2 and later.
#
# history:
# 2001-10-20 fl created (from various sources)
# 2001-11-01 fl return root from parse method
# 2002-02-16 fl sort attributes in lexical order
# 2002-04-06 fl TreeBuilder refactoring, added PythonDoc markup
# 2002-05-01 fl finished TreeBuilder refactoring
# 2002-07-14 fl added basic namespace support to ElementTree.write
# 2002-07-25 fl added QName attribute support
# 2002-10-20 fl fixed encoding in write
# 2002-11-24 fl changed default encoding to ascii; fixed attribute encoding
# 2002-11-27 fl accept file objects or file names for parse/write
# 2002-12-04 fl moved XMLTreeBuilder back to this module
# 2003-01-11 fl fixed entity encoding glitch for us-ascii
# 2003-02-13 fl added XML literal factory
# 2003-02-21 fl added ProcessingInstruction/PI factory
# 2003-05-11 fl added tostring/fromstring helpers
# 2003-05-26 fl added ElementPath support
# 2003-07-05 fl added makeelement factory method
# 2003-07-28 fl added more well-known namespace prefixes
# 2003-08-15 fl fixed typo in ElementTree.findtext (Thomas NAME 2003-09-04 fl fall back on emulator if ElementPath is not installed
# 2003-10-31 fl markup updates
# 2003-11-15 fl fixed nested namespace bug
# 2004-03-28 fl added XMLID helper
# 2004-06-02 fl added default support to findtext
# 2004-06-08 fl fixed encoding of non-ascii element/attribute names
# 2004-08-23 fl take advantage of post-2.1 expat features
# 2005-02-01 fl added iterparse implementation
# 2005-03-02 fl fixed iterparse support for pre-2.2 versions
#
# Copyright (c) 1999-2005 by NAME All rights reserved.
#
# USERNAME@pythonware.com
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
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# Copyright (c) 1999-2005 by NAME By obtaining, using, and/or copying this software and/or its
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# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
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|
"""
========================================
Special functions (:mod:`scipy.special`)
========================================
.. module:: scipy.special
Nearly all of the functions below are universal functions and follow
broadcasting and automatic array-looping rules. Exceptions are noted.
Error handling
==============
Errors are handled by returning nans, or other appropriate values.
Some of the special function routines will emit warnings when an error
occurs. By default this is disabled. To enable such messages use
``errprint(1)``, and to disable such messages use ``errprint(0)``.
Example:
>>> print scipy.special.bdtr(-1,10,0.3)
>>> scipy.special.errprint(1)
>>> print scipy.special.bdtr(-1,10,0.3)
.. autosummary::
:toctree: generated/
errprint
SpecialFunctionWarning -- Warning that can be issued with ``errprint(True)``
Available functions
===================
Airy functions
--------------
.. autosummary::
:toctree: generated/
airy -- Airy functions and their derivatives.
airye -- Exponentially scaled Airy functions
ai_zeros -- [+]Zeros of Airy functions Ai(x) and Ai'(x)
bi_zeros -- [+]Zeros of Airy functions Bi(x) and Bi'(x)
itairy --
Elliptic Functions and Integrals
--------------------------------
.. autosummary::
:toctree: generated/
ellipj -- Jacobian elliptic functions
ellipk -- Complete elliptic integral of the first kind.
ellipkm1 -- ellipkm1(x) == ellipk(1 - x)
ellipkinc -- Incomplete elliptic integral of the first kind.
ellipe -- Complete elliptic integral of the second kind.
ellipeinc -- Incomplete elliptic integral of the second kind.
Bessel Functions
----------------
.. autosummary::
:toctree: generated/
jv -- Bessel function of real-valued order and complex argument.
jn -- Alias for jv
jve -- Exponentially scaled Bessel function.
yn -- Bessel function of second kind (integer order).
yv -- Bessel function of the second kind (real-valued order).
yve -- Exponentially scaled Bessel function of the second kind.
kn -- Modified Bessel function of the second kind (integer order).
kv -- Modified Bessel function of the second kind (real order).
kve -- Exponentially scaled modified Bessel function of the second kind.
iv -- Modified Bessel function.
ive -- Exponentially scaled modified Bessel function.
hankel1 -- Hankel function of the first kind.
hankel1e -- Exponentially scaled Hankel function of the first kind.
hankel2 -- Hankel function of the second kind.
hankel2e -- Exponentially scaled Hankel function of the second kind.
The following is not an universal function:
.. autosummary::
:toctree: generated/
lmbda -- [+]Sequence of lambda functions with arbitrary order v.
Zeros of Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^
These are not universal functions:
.. autosummary::
:toctree: generated/
jnjnp_zeros -- [+]Zeros of integer-order Bessel functions and derivatives sorted in order.
jnyn_zeros -- [+]Zeros of integer-order Bessel functions and derivatives as separate arrays.
jn_zeros -- [+]Zeros of Jn(x)
jnp_zeros -- [+]Zeros of Jn'(x)
yn_zeros -- [+]Zeros of Yn(x)
ynp_zeros -- [+]Zeros of Yn'(x)
y0_zeros -- [+]Complex zeros: Y0(z0)=0 and values of Y0'(z0)
y1_zeros -- [+]Complex zeros: Y1(z1)=0 and values of Y1'(z1)
y1p_zeros -- [+]Complex zeros of Y1'(z1')=0 and values of Y1(z1')
Faster versions of common Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:toctree: generated/
j0 -- Bessel function of order 0.
j1 -- Bessel function of order 1.
y0 -- Bessel function of second kind of order 0.
y1 -- Bessel function of second kind of order 1.
i0 -- Modified Bessel function of order 0.
i0e -- Exponentially scaled modified Bessel function of order 0.
i1 -- Modified Bessel function of order 1.
i1e -- Exponentially scaled modified Bessel function of order 1.
k0 -- Modified Bessel function of the second kind of order 0.
k0e -- Exponentially scaled modified Bessel function of the second kind of order 0.
k1 -- Modified Bessel function of the second kind of order 1.
k1e -- Exponentially scaled modified Bessel function of the second kind of order 1.
Integrals of Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:toctree: generated/
itj0y0 -- Basic integrals of j0 and y0 from 0 to x.
it2j0y0 -- Integrals of (1-j0(t))/t from 0 to x and y0(t)/t from x to inf.
iti0k0 -- Basic integrals of i0 and k0 from 0 to x.
it2i0k0 -- Integrals of (i0(t)-1)/t from 0 to x and k0(t)/t from x to inf.
besselpoly -- Integral of a Bessel function: Jv(2* a* x) * x[+]lambda from x=0 to 1.
Derivatives of Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:toctree: generated/
jvp -- Nth derivative of Jv(v,z)
yvp -- Nth derivative of Yv(v,z)
kvp -- Nth derivative of Kv(v,z)
ivp -- Nth derivative of Iv(v,z)
h1vp -- Nth derivative of H1v(v,z)
h2vp -- Nth derivative of H2v(v,z)
Spherical Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^
These are not universal functions:
.. autosummary::
:toctree: generated/
sph_jn -- [+]Sequence of spherical Bessel functions, jn(z)
sph_yn -- [+]Sequence of spherical Bessel functions, yn(z)
sph_jnyn -- [+]Sequence of spherical Bessel functions, jn(z) and yn(z)
sph_in -- [+]Sequence of spherical Bessel functions, in(z)
sph_kn -- [+]Sequence of spherical Bessel functions, kn(z)
sph_inkn -- [+]Sequence of spherical Bessel functions, in(z) and kn(z)
Riccati-Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^
These are not universal functions:
.. autosummary::
:toctree: generated/
riccati_jn -- [+]Sequence of Ricatti-Bessel functions of first kind.
riccati_yn -- [+]Sequence of Ricatti-Bessel functions of second kind.
Struve Functions
----------------
.. autosummary::
:toctree: generated/
struve -- Struve function --- Hv(x)
modstruve -- Modified Struve function --- Lv(x)
itstruve0 -- Integral of H0(t) from 0 to x
it2struve0 -- Integral of H0(t)/t from x to Inf.
itmodstruve0 -- Integral of L0(t) from 0 to x.
Raw Statistical Functions
-------------------------
.. seealso:: :mod:`scipy.stats`: Friendly versions of these functions.
.. autosummary::
:toctree: generated/
bdtr -- Sum of terms 0 through k of the binomial pdf.
bdtrc -- Sum of terms k+1 through n of the binomial pdf.
bdtri -- Inverse of bdtr
bdtrik --
bdtrin --
btdtr -- Integral from 0 to x of beta pdf.
btdtri -- Quantiles of beta distribution
btdtria --
btdtrib --
fdtr -- Integral from 0 to x of F pdf.
fdtrc -- Integral from x to infinity under F pdf.
fdtri -- Inverse of fdtrc
fdtridfd --
gdtr -- Integral from 0 to x of gamma pdf.
gdtrc -- Integral from x to infinity under gamma pdf.
gdtria -- Inverse with respect to `a` of gdtr.
gdtrib -- Inverse with respect to `b` of gdtr.
gdtrix -- Inverse with respect to `x` of gdtr.
nbdtr -- Sum of terms 0 through k of the negative binomial pdf.
nbdtrc -- Sum of terms k+1 to infinity under negative binomial pdf.
nbdtri -- Inverse of nbdtr
nbdtrik --
nbdtrin --
ncfdtr -- CDF of non-central t distribution.
ncfdtridfd -- Find degrees of freedom (denominator) of noncentral F distribution.
ncfdtridfn -- Find degrees of freedom (numerator) of noncentral F distribution.
ncfdtri -- Inverse CDF of noncentral F distribution.
ncfdtrinc -- Find noncentrality parameter of noncentral F distribution.
nctdtr -- CDF of noncentral t distribution.
nctdtridf -- Find degrees of freedom of noncentral t distribution.
nctdtrit -- Inverse CDF of noncentral t distribution.
nctdtrinc -- Find noncentrality parameter of noncentral t distribution.
nrdtrimn -- Find mean of normal distribution from cdf and std.
nrdtrisd -- Find std of normal distribution from cdf and mean.
pdtr -- Sum of terms 0 through k of the Poisson pdf.
pdtrc -- Sum of terms k+1 to infinity of the Poisson pdf.
pdtri -- Inverse of pdtr
pdtrik --
stdtr -- Integral from -infinity to t of the Student-t pdf.
stdtridf --
stdtrit --
chdtr -- Integral from 0 to x of the Chi-square pdf.
chdtrc -- Integral from x to infnity of Chi-square pdf.
chdtri -- Inverse of chdtrc.
chdtriv --
ndtr -- Integral from -infinity to x of standard normal pdf
log_ndtr -- Logarithm of integral from -infinity to x of standard normal pdf
ndtri -- Inverse of ndtr (quantiles)
chndtr --
chndtridf --
chndtrinc --
chndtrix --
smirnov -- Kolmogorov-Smirnov complementary CDF for one-sided test statistic (Dn+ or Dn-)
smirnovi -- Inverse of smirnov.
kolmogorov -- The complementary CDF of the (scaled) two-sided test statistic (Kn*) valid for large n.
kolmogi -- Inverse of kolmogorov
tklmbda -- Tukey-Lambda CDF
logit --
expit --
boxcox -- Compute the Box-Cox transformation.
boxcox1p -- Compute the Box-Cox transformation of 1 + x.
inv_boxcox -- Compute the inverse of the Box-Cox tranformation.
inv_boxcox1p -- Compute the inverse of the Box-Cox transformation of 1 + x.
Information Theory Functions
----------------------------
.. autosummary::
:toctree: generated/
entr -- entr(x) = -x*log(x)
rel_entr -- rel_entr(x, y) = x*log(x/y)
kl_div -- kl_div(x, y) = x*log(x/y) - x + y
huber -- Huber loss function.
pseudo_huber -- Pseudo-Huber loss function.
Gamma and Related Functions
---------------------------
.. autosummary::
:toctree: generated/
gamma -- Gamma function.
gammaln -- Log of the absolute value of the gamma function.
gammasgn -- Sign of the gamma function.
gammainc -- Incomplete gamma integral.
gammaincinv -- Inverse of gammainc.
gammaincc -- Complemented incomplete gamma integral.
gammainccinv -- Inverse of gammaincc.
beta -- Beta function.
betaln -- Log of the absolute value of the beta function.
betainc -- Incomplete beta integral.
betaincinv -- Inverse of betainc.
psi -- Logarithmic derivative of the gamma function.
rgamma -- One divided by the gamma function.
polygamma -- Nth derivative of psi function.
multigammaln -- Log of the multivariate gamma.
digamma -- Digamma function (derivative of the logarithm of gamma).
poch -- The Pochhammer symbol (rising factorial).
Error Function and Fresnel Integrals
------------------------------------
.. autosummary::
:toctree: generated/
erf -- Error function.
erfc -- Complemented error function (1- erf(x))
erfcx -- Scaled complemented error function exp(x**2)*erfc(x)
erfi -- Imaginary error function, -i erf(i x)
erfinv -- Inverse of error function
erfcinv -- Inverse of erfc
wofz -- Fadeeva function.
dawsn -- Dawson's integral.
fresnel -- Fresnel sine and cosine integrals.
fresnel_zeros -- Complex zeros of both Fresnel integrals
modfresnelp -- Modified Fresnel integrals F_+(x) and K_+(x)
modfresnelm -- Modified Fresnel integrals F_-(x) and K_-(x)
These are not universal functions:
.. autosummary::
:toctree: generated/
erf_zeros -- [+]Complex zeros of erf(z)
fresnelc_zeros -- [+]Complex zeros of Fresnel cosine integrals
fresnels_zeros -- [+]Complex zeros of Fresnel sine integrals
Legendre Functions
------------------
.. autosummary::
:toctree: generated/
lpmv -- Associated Legendre Function of arbitrary non-negative degree v.
sph_harm -- Spherical Harmonics (complex-valued) Y^m_n(theta,phi)
These are not universal functions:
.. autosummary::
:toctree: generated/
clpmn -- [+]Associated Legendre Function of the first kind for complex arguments.
lpn -- [+]Legendre Functions (polynomials) of the first kind
lqn -- [+]Legendre Functions of the second kind.
lpmn -- [+]Associated Legendre Function of the first kind for real arguments.
lqmn -- [+]Associated Legendre Function of the second kind.
Ellipsoidal Harmonics
---------------------
.. autosummary::
:toctree: generated/
ellip_harm -- Ellipsoidal harmonic E
ellip_harm_2 -- Ellipsoidal harmonic F
ellip_normal -- Ellipsoidal normalization constant
Orthogonal polynomials
----------------------
The following functions evaluate values of orthogonal polynomials:
.. autosummary::
:toctree: generated/
assoc_laguerre
eval_legendre
eval_chebyt
eval_chebyu
eval_chebyc
eval_chebys
eval_jacobi
eval_laguerre
eval_genlaguerre
eval_hermite
eval_hermitenorm
eval_gegenbauer
eval_sh_legendre
eval_sh_chebyt
eval_sh_chebyu
eval_sh_jacobi
The functions below, in turn, return the polynomial coefficients in
:class:`~.orthopoly1d` objects, which function similarly as :ref:`numpy.poly1d`.
The :class:`~.orthopoly1d` class also has an attribute ``weights`` which returns
the roots, weights, and total weights for the appropriate form of Gaussian
quadrature. These are returned in an ``n x 3`` array with roots in the first
column, weights in the second column, and total weights in the final column.
Note that :class:`~.orthopoly1d` objects are converted to ``poly1d`` when doing
arithmetic, and lose information of the original orthogonal polynomial.
.. autosummary::
:toctree: generated/
legendre -- [+]Legendre polynomial P_n(x) (lpn -- for function).
chebyt -- [+]Chebyshev polynomial T_n(x)
chebyu -- [+]Chebyshev polynomial U_n(x)
chebyc -- [+]Chebyshev polynomial C_n(x)
chebys -- [+]Chebyshev polynomial S_n(x)
jacobi -- [+]Jacobi polynomial P^(alpha,beta)_n(x)
laguerre -- [+]Laguerre polynomial, L_n(x)
genlaguerre -- [+]Generalized (Associated) Laguerre polynomial, L^alpha_n(x)
hermite -- [+]Hermite polynomial H_n(x)
hermitenorm -- [+]Normalized Hermite polynomial, He_n(x)
gegenbauer -- [+]Gegenbauer (Ultraspherical) polynomials, C^(alpha)_n(x)
sh_legendre -- [+]shifted Legendre polynomial, P*_n(x)
sh_chebyt -- [+]shifted Chebyshev polynomial, T*_n(x)
sh_chebyu -- [+]shifted Chebyshev polynomial, U*_n(x)
sh_jacobi -- [+]shifted Jacobi polynomial, J*_n(x) = G^(p,q)_n(x)
.. warning::
Computing values of high-order polynomials (around ``order > 20``) using
polynomial coefficients is numerically unstable. To evaluate polynomial
values, the ``eval_*`` functions should be used instead.
Roots and weights for orthogonal polynomials
.. autosummary::
:toctree: generated/
c_roots
cg_roots
h_roots
he_roots
j_roots
js_roots
l_roots
la_roots
p_roots
ps_roots
s_roots
t_roots
ts_roots
u_roots
us_roots
Hypergeometric Functions
------------------------
.. autosummary::
:toctree: generated/
hyp2f1 -- Gauss hypergeometric function (2F1)
hyp1f1 -- Confluent hypergeometric function (1F1)
hyperu -- Confluent hypergeometric function (U)
hyp0f1 -- Confluent hypergeometric limit function (0F1)
hyp2f0 -- Hypergeometric function (2F0)
hyp1f2 -- Hypergeometric function (1F2)
hyp3f0 -- Hypergeometric function (3F0)
Parabolic Cylinder Functions
----------------------------
.. autosummary::
:toctree: generated/
pbdv -- Parabolic cylinder function Dv(x) and derivative.
pbvv -- Parabolic cylinder function Vv(x) and derivative.
pbwa -- Parabolic cylinder function W(a,x) and derivative.
These are not universal functions:
.. autosummary::
:toctree: generated/
pbdv_seq -- [+]Sequence of parabolic cylinder functions Dv(x)
pbvv_seq -- [+]Sequence of parabolic cylinder functions Vv(x)
pbdn_seq -- [+]Sequence of parabolic cylinder functions Dn(z), complex z
Mathieu and Related Functions
-----------------------------
.. autosummary::
:toctree: generated/
mathieu_a -- Characteristic values for even solution (ce_m)
mathieu_b -- Characteristic values for odd solution (se_m)
These are not universal functions:
.. autosummary::
:toctree: generated/
mathieu_even_coef -- [+]sequence of expansion coefficients for even solution
mathieu_odd_coef -- [+]sequence of expansion coefficients for odd solution
The following return both function and first derivative:
.. autosummary::
:toctree: generated/
mathieu_cem -- Even Mathieu function
mathieu_sem -- Odd Mathieu function
mathieu_modcem1 -- Even modified Mathieu function of the first kind
mathieu_modcem2 -- Even modified Mathieu function of the second kind
mathieu_modsem1 -- Odd modified Mathieu function of the first kind
mathieu_modsem2 -- Odd modified Mathieu function of the second kind
Spheroidal Wave Functions
-------------------------
.. autosummary::
:toctree: generated/
pro_ang1 -- Prolate spheroidal angular function of the first kind
pro_rad1 -- Prolate spheroidal radial function of the first kind
pro_rad2 -- Prolate spheroidal radial function of the second kind
obl_ang1 -- Oblate spheroidal angular function of the first kind
obl_rad1 -- Oblate spheroidal radial function of the first kind
obl_rad2 -- Oblate spheroidal radial function of the second kind
pro_cv -- Compute characteristic value for prolate functions
obl_cv -- Compute characteristic value for oblate functions
pro_cv_seq -- Compute sequence of prolate characteristic values
obl_cv_seq -- Compute sequence of oblate characteristic values
The following functions require pre-computed characteristic value:
.. autosummary::
:toctree: generated/
pro_ang1_cv -- Prolate spheroidal angular function of the first kind
pro_rad1_cv -- Prolate spheroidal radial function of the first kind
pro_rad2_cv -- Prolate spheroidal radial function of the second kind
obl_ang1_cv -- Oblate spheroidal angular function of the first kind
obl_rad1_cv -- Oblate spheroidal radial function of the first kind
obl_rad2_cv -- Oblate spheroidal radial function of the second kind
Kelvin Functions
----------------
.. autosummary::
:toctree: generated/
kelvin -- All Kelvin functions (order 0) and derivatives.
kelvin_zeros -- [+]Zeros of All Kelvin functions (order 0) and derivatives
ber -- Kelvin function ber x
bei -- Kelvin function bei x
berp -- Derivative of Kelvin function ber x
beip -- Derivative of Kelvin function bei x
ker -- Kelvin function ker x
kei -- Kelvin function kei x
kerp -- Derivative of Kelvin function ker x
keip -- Derivative of Kelvin function kei x
These are not universal functions:
.. autosummary::
:toctree: generated/
ber_zeros -- [+]Zeros of Kelvin function bei x
bei_zeros -- [+]Zeros of Kelvin function ber x
berp_zeros -- [+]Zeros of derivative of Kelvin function ber x
beip_zeros -- [+]Zeros of derivative of Kelvin function bei x
ker_zeros -- [+]Zeros of Kelvin function kei x
kei_zeros -- [+]Zeros of Kelvin function ker x
kerp_zeros -- [+]Zeros of derivative of Kelvin function ker x
keip_zeros -- [+]Zeros of derivative of Kelvin function kei x
Combinatorics
-------------
.. autosummary::
:toctree: generated/
comb -- [+]Combinations of N things taken k at a time, "N choose k"
perm -- [+]Permutations of N things taken k at a time, "k-permutations of N"
Other Special Functions
-----------------------
.. autosummary::
:toctree: generated/
agm -- Arithmetic-Geometric Mean
bernoulli -- Bernoulli numbers
binom -- Binomial coefficient.
diric -- Dirichlet function (periodic sinc)
euler -- Euler numbers
expn -- Exponential integral.
exp1 -- Exponential integral of order 1 (for complex argument)
expi -- Another exponential integral -- Ei(x)
factorial -- The factorial function, n! = special.gamma(n+1)
factorial2 -- Double factorial, (n!)!
factorialk -- [+](...((n!)!)!...)! where there are k '!'
shichi -- Hyperbolic sine and cosine integrals.
sici -- Integral of the sinc and "cosinc" functions.
spence -- Dilogarithm integral.
lambertw -- Lambert W function
zeta -- Riemann zeta function of two arguments.
zetac -- Standard Riemann zeta function minus 1.
Convenience Functions
---------------------
.. autosummary::
:toctree: generated/
cbrt -- Cube root.
exp10 -- 10 raised to the x power.
exp2 -- 2 raised to the x power.
radian -- radian angle given degrees, minutes, and seconds.
cosdg -- cosine of the angle given in degrees.
sindg -- sine of the angle given in degrees.
tandg -- tangent of the angle given in degrees.
cotdg -- cotangent of the angle given in degrees.
log1p -- log(1+x)
expm1 -- exp(x)-1
cosm1 -- cos(x)-1
round -- round the argument to the nearest integer. If argument ends in 0.5 exactly, pick the nearest even integer.
xlogy -- x*log(y)
xlog1py -- x*log1p(y)
exprel -- (exp(x)-1)/x
sinc -- sin(x)/x
.. [+] in the description indicates a function which is not a universal
.. function and does not follow broadcasting and automatic
.. array-looping rules.
""" |
"""
Mongo-based Experiment driver and worker client
===============================================
Components involved:
- mongo
e.g. mongod ...
- driver
e.g. hyperopt-mongo-search mongo://address bandit_json bandit_algo_json
- worker
e.g. hyperopt-mongo-worker --loop mongo://address
Mongo
=====
Mongo (daemon process mongod) is used for IPC between the driver and worker.
Configure it as you like, so that hyperopt-mongo-search can communicate with it.
I think there is some support in this file for an ssh+mongo connection type.
The experiment uses the following collections for IPC:
* jobs - documents of a standard form used to store suggested trials and their
results. These documents have keys:
* spec : subdocument returned by bandit_algo.suggest
* exp_key: an identifier of which driver suggested this trial
* cmd: a tuple (protocol, ...) identifying bandit.evaluate
* state: 0, 1, 2, 3 for job state (new, running, ok, fail)
* owner: None for new jobs, (hostname, pid) for started jobs
* book_time: time a job was reserved
* refresh_time: last time the process running the job checked in
* result: the subdocument returned by bandit.evaluate
* error: for jobs of state 3, a reason for failure.
* logs: a dict of sequences of strings received by ctrl object
* info: info messages
* warn: warning messages
* error: error messages
* fs - a gridfs storage collection (used for pickling)
* drivers - documents describing drivers. These are used to prevent two drivers
from using the same exp_key simultaneously, and to attach saved states.
* exp_key
* workdir: [optional] path where workers should chdir to
Attachments:
* pkl: [optional] saved state of experiment class
* bandit_args_kwargs: [optional] pickled (clsname, args, kwargs) to
reconstruct bandit in worker processes
The MongoJobs, MongoExperiment, and CtrlObj classes as well as the main_worker
method form the abstraction barrier around this database layout.
Driver
======
A driver directs an experiment, by calling a bandit_algo to suggest trial
points, and queuing them in mongo so that a worker can evaluate that trial
point.
The hyperopt-mongo-search script creates a single MongoExperiment instance, and
calls its run() method.
Saving and Resuming
-------------------
The command
"hyperopt-mongo-search bandit algo"
creates a new experiment or resumes an existing experiment.
The command
"hyperopt-mongo-search --exp-key=<EXPKEY>"
can only resume an existing experiment.
The command
"hyperopt-mongo-search --clear-existing bandit algo"
can only create a new experiment, and potentially deletes an existing one.
The command
"hyperopt-mongo-search --clear-existing --exp-key=EXPKEY bandit algo"
can only create a new experiment, and potentially deletes an existing one.
By default, MongoExperiment.run will try to save itself before returning. It
does so by pickling itself to a file called 'exp_key' in the fs collection.
Resuming means unpickling that file and calling run again.
The MongoExperiment instance itself is minimal (a key, a bandit, a bandit algo,
a workdir, a poll interval). The only stateful element is the bandit algo. The
difference between resume and start is in the handling of the bandit algo.
Worker
======
A worker looks up a job in a mongo database, maps that job document to a
runnable python object, calls that object, and writes the return value back to
the database.
A worker *reserves* a job by atomically identifying a document in the jobs
collection whose owner is None and whose state is 0, and setting the state to
1. If it fails to identify such a job, it loops with a random sleep interval
of a few seconds and polls the database.
If hyperopt-mongo-worker is called with a --loop argument then it goes back to
the database after finishing a job to identify and perform another one.
CtrlObj
-------
The worker allocates a CtrlObj and passes it to bandit.evaluate in addition to
the subdocument found at job['spec']. A bandit can use ctrl.info, ctrl.warn,
ctrl.error and so on like logger methods, and those messages will be written
to the mongo database (to job['logs']). They are not written synchronously
though, they are written when the bandit.evaluate function calls
ctrl.checkpoint().
Ctrl.checkpoint does several things:
* flushes logging messages to the database
* updates the refresh_time
* optionally updates the result subdocument
The main_worker routine calls Ctrl.checkpoint(rval) once after the
bandit.evalute function has returned before setting the state to 2 or 3 to
finalize the job in the database.
""" |
"""Stuff to parse AIFF-C and AIFF files.
Unless explicitly stated otherwise, the description below is true
both for AIFF-C files and AIFF files.
An AIFF-C file has the following structure.
+-----------------+
| FORM |
+-----------------+
| <size> |
+----+------------+
| | AIFC |
| +------------+
| | <chunks> |
| | . |
| | . |
| | . |
+----+------------+
An AIFF file has the string "AIFF" instead of "AIFC".
A chunk consists of an identifier (4 bytes) followed by a size (4 bytes,
big endian order), followed by the data. The size field does not include
the size of the 8 byte header.
The following chunk types are recognized.
FVER
<version number of AIFF-C defining document> (AIFF-C only).
MARK
<# of markers> (2 bytes)
list of markers:
<marker ID> (2 bytes, must be > 0)
<position> (4 bytes)
<marker name> ("pstring")
COMM
<# of channels> (2 bytes)
<# of sound frames> (4 bytes)
<size of the samples> (2 bytes)
<sampling frequency> (10 bytes, IEEE 80-bit extended
floating point)
in AIFF-C files only:
<compression type> (4 bytes)
<human-readable version of compression type> ("pstring")
SSND
<offset> (4 bytes, not used by this program)
<blocksize> (4 bytes, not used by this program)
<sound data>
A pstring consists of 1 byte length, a string of characters, and 0 or 1
byte pad to make the total length even.
Usage.
Reading AIFF files:
f = aifc.open(file, 'r')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods read(), seek(), and close().
In some types of audio files, if the setpos() method is not used,
the seek() method is not necessary.
This returns an instance of a class with the following public methods:
getnchannels() -- returns number of audio channels (1 for
mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getcomptype() -- returns compression type ('NONE' for AIFF files)
getcompname() -- returns human-readable version of
compression type ('not compressed' for AIFF files)
getparams() -- returns a tuple consisting of all of the
above in the above order
getmarkers() -- get the list of marks in the audio file or None
if there are no marks
getmark(id) -- get mark with the specified id (raises an error
if the mark does not exist)
readframes(n) -- returns at most n frames of audio
rewind() -- rewind to the beginning of the audio stream
setpos(pos) -- seek to the specified position
tell() -- return the current position
close() -- close the instance (make it unusable)
The position returned by tell(), the position given to setpos() and
the position of marks are all compatible and have nothing to do with
the actual position in the file.
The close() method is called automatically when the class instance
is destroyed.
Writing AIFF files:
f = aifc.open(file, 'w')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods write(), tell(), seek(), and
close().
This returns an instance of a class with the following public methods:
aiff() -- create an AIFF file (AIFF-C default)
aifc() -- create an AIFF-C file
setnchannels(n) -- set the number of channels
setsampwidth(n) -- set the sample width
setframerate(n) -- set the frame rate
setnframes(n) -- set the number of frames
setcomptype(type, name)
-- set the compression type and the
human-readable compression type
setparams(tuple)
-- set all parameters at once
setmark(id, pos, name)
-- add specified mark to the list of marks
tell() -- return current position in output file (useful
in combination with setmark())
writeframesraw(data)
-- write audio frames without pathing up the
file header
writeframes(data)
-- write audio frames and patch up the file header
close() -- patch up the file header and close the
output file
You should set the parameters before the first writeframesraw or
writeframes. The total number of frames does not need to be set,
but when it is set to the correct value, the header does not have to
be patched up.
It is best to first set all parameters, perhaps possibly the
compression type, and then write audio frames using writeframesraw.
When all frames have been written, either call writeframes('') or
close() to patch up the sizes in the header.
Marks can be added anytime. If there are any marks, ypu must call
close() after all frames have been written.
The close() method is called automatically when the class instance
is destroyed.
When a file is opened with the extension '.aiff', an AIFF file is
written, otherwise an AIFF-C file is written. This default can be
changed by calling aiff() or aifc() before the first writeframes or
writeframesraw.
""" |
# FAILED ATTEMPT AT REFORMING MOLECULESTUB
# Perhaps I'll come back to this later...
#from grendel import type_checking_enabled, sanity_checking_enabled
#from grendel.chemistry.atom import Atom
#from grendel.gmath import magnitude, angle_between_vectors
#from grendel.gmath.matrix import Matrix
#from grendel.util.decorators import with_flexible_arguments, typechecked, IterableOf
#from grendel.util.exceptions import ChemistryError
#from grendel.util.overloading import overloaded, OverloadedFunctionCallError
#from grendel.util.strings import indented
#from grendel.util.units import strip_units, DistanceUnit, AngularUnit, Radians, Degrees, Angstroms, isunit
## Immutable "friend class" of Molecule
#class MoleculeStub(object):
# """
# Immutable "friend" class of `Molecule`, used for hashing.
# """
#
# ####################
# # Class Attributes #
# ####################
#
# eq_precision = 8
# same_internal_tol = {AngularUnit: 0.0001*Degrees, DistanceUnit: 1e-7*Angstroms}
#
# ##############
# # Attributes #
# ##############
#
# multiplicity = None
# """ The multiplicity of the electronic state of the molecule.
# (i.e. 2S+1 where S is the total spin). Defaults to singlet. """
#
# charge = None
# """ The charge on the molecule. Defaults to neutral. """
#
# reoriented_matrix = None
#
# ######################
# # Private Attributes #
# ######################
#
# _hash = None
# _cartesian_representation = None
# _cartesian_units = None
# _internal_representation = None
# _from_molecule = None
# _xyz = None
# _element_list = None
#
# ##################
# # Initialization #
# ##################
#
# @overloaded
# def __init__(self, *args, **kwargs):
# raise OverloadedFunctionCallError
#
# @__init__.overload_with(
# atoms=IterableOf('Atom'),
# )
# def __init__(self,
# atoms,
# **kwargs):
# self.__init__(
# [(atom.element, atom.isotope) for atom in atoms],
# Matrix([atom.position for atom in atoms]),
# **kwargs)
#
# @__init__.overload_with(
# cartesian_units=isunit,
# charge=(int, None),
# multiplicity=(int, None)
# )
# def __init__(self,
# elements_and_isotopes,
# xyz,
# cartesian_units=DistanceUnit.default,
# charge=None,
# multiplicity=None):
# self._cartesian_units = cartesian_units
# self.charge = charge if charge is not None else Molecule.default_charge
# self.multiplicity = multiplicity if multiplicity is not None else Molecule.default_multiplicity
# self._xyz = xyz
# self._element_list = elements_and_isotopes
# # TODO strip units
# tmpmol = Molecule(
# [Atom(el, iso, pos) for (el, iso), pos in zip(self._element_list, self._xyz.iter_rows)],
# charge=self.charge,
# multiplicity=self.multiplicity
# )
# self.reoriented_matrix = tmpmol.reoriented().xyz
#
# ###################
# # Special Methods #
# ###################
#
# def __hash__(self):
# if self._hash is not None:
# return self._hash
# self._hash = MoleculeDict.hash_for(self)
# return self._hash
#
# def __eq__(self, other):
# if isinstance(other, MoleculeStub):
# if [a.isotope for a in self] != [a.isotope for a in other]:
# return False
# elif (self.multiplicity, self.charge) != (other.multiplicity, other.charge):
# return False
# else:
# reoriented = self.reoriented_matrix * self._cartesian_units.to(DistanceUnit.default)
# rounded = [round(v, MoleculeStub.eq_precision) for v in reoriented.ravel()]
# other_oriented = other.reoriented_matrix * other._cartesian_units.to(DistanceUnit.default)
# other_rounded = [round(v, MoleculeStub.eq_precision) for v in other_oriented.ravel()]
# return rounded == other_rounded
# else:
# return NotImplemented
#
#
# ###########
# # Methods #
# ###########
#
# # TODO document this!
# # TODO class variables for default tolerances
# def is_valid_stub_for(self, other, cart_tol=None, internal_tol=None, ang_tol=None):
# """
# """
# # cart_tol is used for comparison between cartesian positions
# cart_tol = cart_tol or 1e-8*Angstroms
# # internal_tol should be unitless, since the difference between internal coordinates could have multiple
# # units, and we're taking the magnitude across these units
# internal_tol = internal_tol or MoleculeStub.same_internal_tol
# # ang_tol is used when comparing cartesian geometries. If the angle between two corresponding atoms
# # in self and other differs from the angle between the first two corresponding atoms in self and other
# # by more than ang_tol, we assume they do not have the same geometry and thus return False
# ang_tol = ang_tol or 1e-5*Degrees
# #--------------------------------------------------------------------------------#
# if type_checking_enabled:
# if not isinstance(other, Molecule):
# raise TypeError
# if isinstance(other, MoleculeStub):
# raise TypeError
# #--------------------------------------------------------------------------------#
# if self.multiplicity != other.multiplicity:
# return False
# elif self.charge != other.charge:
# return False
# else:
# if len(self._element_list) != other.natoms:
# for num, (element, isotope) in enumerate(self._element_list):
# if (other[num].element, other[num].isotope) != (element, isotope):
# return False
# if other.natoms <= 1:
# # if we have 1 or 0 atoms and we've gotten this far, we have a match
# return True
# #--------------------------------------------------------------------------------#
# # no failures yet, so we have to compare geometries
# # if self has an internal_representation, use it
# if self._internal_representation is not None:
# diff = self._internal_representation.values - self._internal_representation.values_for_molecule(other)
# if any(abs(d) > internal_tol[c.units.genre].in_units(c.units) for d, c in zip(diff, self._internal_representation)):
# return False
# return True
# # if mol has an internal representation, use it:
# elif other.internal_representation is not None:
# diff = other.internal_representation.values - other.internal_representation.values_for_molecule(self)
# if any(abs(d) > internal_tol[c.units.genre].in_units(c.units) for d, c in zip(diff, other.internal_representation)):
# return False
# return True
# else:
# # They're both fully cartesian. This could take a while...
# # We should first try to short-circuit as many ways as possible
# #----------------------------------------#
# # first strip units and store stripped versions to speed up the rest of the work
# # strip the units off of ang_tol
# ang_tol = strip_units(ang_tol, Radians)
# # strip the units off of cart_tol
# cart_tol = strip_units(cart_tol, self._cartesian_units)
# # make a list of positions with stripped units, since we'll use it up to three times
# stripped = [strip_units(atom, self._cartesian_units) for atom in (self if self.is_centered() else self.recentered()) ]
# other_stripped = [strip_units(atom, self._cartesian_units) for atom in (other if other.is_centered() else other.recentered())]
# #----------------------------------------#
# # Try to short-circuit negatively by looking for an inconsistancy in the angles
# # between pairs of corresponding atoms
# # If the first atom is at the origin, use the second one
# offset = 1
# if stripped[0].is_zero():
# if magnitude(stripped[0] - other_stripped[0]) > cart_tol:
# return False
# else:
# if sanity_checking_enabled and stripped[1].is_zero():
# raise ChemistryError, "FrozenMolecule:\n{}\nhas two atoms on top of each other.".format(indented(str(self)))
# if other_stripped[1].is_zero():
# return False
# else:
# offset = 2
# first_ang = angle_between_vectors(self.atoms[1].pos, other.atoms[1].pos)
# else:
# if other_stripped[0].is_zero():
# return False
# else:
# first_ang = angle_between_vectors(self.atoms[0].pos, other.atoms[0].pos)
# for apos, opos in zip(stripped[offset:], other_stripped[offset:]):
# if apos.is_zero():
# if magnitude(apos - opos) > cart_tol:
# return False
# elif opos.is_zero():
# # Try again, since Tensor.zero_cutoff could smaller than cart_tol, causing a false zero
# if magnitude(apos - opos) > cart_tol:
# return False
# else:
# ang = angle_between_vectors(apos, opos)
# if abs(ang - first_ang) > ang_tol:
# return False
# # Also, the magnitude of the distance from the center of mass should be the same:
# if abs(apos.magnitude() - opos.magnitude()) > cart_tol:
# return False
# #----------------------------------------#
# # Try to short-circuit positively:
# exact_match = True
# for apos, opos in zip(stripped, other_stripped):
# if magnitude(apos - opos) > cart_tol:
# exact_match = False
# break
# if exact_match:
# return True
# exact_match = True
# # Check negative version
# for apos, opos in zip(stripped, other_stripped):
# if magnitude(apos + opos) > cart_tol:
# exact_match = False
# break
# if exact_match:
# return True
# #----------------------------------------#
# # We can't short-circuit, so this is the only means we have left
# # It's far more expensive than the rest, but it always works.
# return self.has_same_geometry(other, cart_tol)
#
#
######################
## Dependent Imports #
######################
#
#from grendel.chemistry.molecule_dict import MoleculeDict
#from grendel.chemistry.molecule import Molecule
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
## GNU GENERAL PUBLIC LICENSE
## Version 2, June 1991
## Copyright (C) 1989, 1991 Free Software Foundation, Inc.
## 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
## Everyone is permitted to copy and distribute verbatim copies
## of this license document, but changing it is not allowed.
## Preamble
## The licenses for most software are designed to take away your
##freedom to share and change it. By contrast, the GNU General Public
##License is intended to guarantee your freedom to share and change free
##software--to make sure the software is free for all its users. This
##General Public License applies to most of the Free Software
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##using it. (Some other Free Software Foundation software is covered by
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##your programs, too.
## When we speak of free software, we are referring to freedom, not
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## For example, if you distribute copies of such a program, whether
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##identifiable sections of that work are not derived from the Program,
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## 7. If, as a consequence of a court judgment or allegation of patent
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|
# from trex import redis
#
# from .mixins import REDIS_HOST, REDIS_PORT
#
#
# class TestRedisConnections(unittest.TestCase):
# _KEYS = ['trex:testwatch1', 'trex:testwatch2']
#
# @defer.inlineCallbacks
# def setUp(self):
# self.connections = []
# self.db = yield self._getRedisConnection()
# yield self.db.delete(self._KEYS)
#
# @defer.inlineCallbacks
# def tearDown(self):
# for connection in self.connections:
# l = [connection.delete(k) for k in self._KEYS]
# yield defer.DeferredList(l)
# yield connection.disconnect()
#
# def _db_connected(self, connection):
# self.connections.append(connection)
# return connection
#
# def _getRedisConnection(self, host=REDIS_HOST, port=REDIS_PORT, db=0):
# return redis.Connection(
# host, port, dbid=db, reconnect=False).addCallback(
# self._db_connected)
#
# def _check_watcherror(self, response, shouldError=False):
# if shouldError:
# self.assertIsInstance(response, Failure)
# self.assertIsInstance(response.value, redis.WatchError)
# else:
# self.assertNotIsInstance(response, Failure)
#
# @defer.inlineCallbacks
# def testRedisWatchFail(self):
# db1 = yield self._getRedisConnection()
# yield self.db.set(self._KEYS[0], 'foo')
# t = yield self.db.multi(self._KEYS[0])
# self.assertIsInstance(t, redis.RedisProtocol)
# yield t.set(self._KEYS[1], 'bar')
# # This should trigger a failure
# yield db1.set(self._KEYS[0], 'bar1')
# yield t.commit().addBoth(self._check_watcherror, shouldError=True)
#
# @defer.inlineCallbacks
# def testRedisWatchSucceed(self):
# yield self.db.set(self._KEYS[0], 'foo')
# t = yield self.db.multi(self._KEYS[0])
# self.assertIsInstance(t, redis.RedisProtocol)
# yield t.set(self._KEYS[0], 'bar')
# yield t.commit().addBoth(self._check_watcherror, shouldError=False)
#
# @defer.inlineCallbacks
# def testRedisMultiNoArgs(self):
# yield self.db.set(self._KEYS[0], 'foo')
# t = yield self.db.multi()
# self.assertIsInstance(t, redis.RedisProtocol)
# yield t.set(self._KEYS[1], 'bar')
# yield t.commit().addBoth(self._check_watcherror, shouldError=False)
#
# @defer.inlineCallbacks
# def testRedisWithBulkCommands_transactions(self):
# t = yield self.db.watch(self._KEYS)
# yield t.mget(self._KEYS)
# t = yield t.multi()
# yield t.commit()
# self.assertEqual(0, t.transactions)
# self.assertFalse(t.inTransaction)
#
# @defer.inlineCallbacks
# def testRedisWithBulkCommands_inTransaction(self):
# t = yield self.db.watch(self._KEYS)
# yield t.mget(self._KEYS)
# self.assertTrue(t.inTransaction)
# yield t.unwatch()
#
# @defer.inlineCallbacks
# def testRedisWithBulkCommands_mget(self):
# yield self.db.set(self._KEYS[0], "foo")
# yield self.db.set(self._KEYS[1], "bar")
#
# m0 = yield self.db.mget(self._KEYS)
# t = yield self.db.watch(self._KEYS)
# m1 = yield t.mget(self._KEYS)
# t = yield t.multi()
# yield t.mget(self._KEYS)
# (m2,) = yield t.commit()
#
# self.assertEqual(["foo", "bar"], m0)
# self.assertEqual(m0, m1)
# self.assertEqual(m0, m2)
#
# @defer.inlineCallbacks
# def testRedisWithBulkCommands_hgetall(self):
# yield self.db.hset(self._KEYS[0], "foo", "bar")
# yield self.db.hset(self._KEYS[0], "bar", "foo")
#
# h0 = yield self.db.hgetall(self._KEYS[0])
# t = yield self.db.watch(self._KEYS[0])
# h1 = yield t.hgetall(self._KEYS[0])
# t = yield t.multi()
# yield t.hgetall(self._KEYS[0])
# (h2,) = yield t.commit()
#
# self.assertEqual({"foo": "bar", "bar": "foo"}, h0)
# self.assertEqual(h0, h1)
# self.assertEqual(h0, h2)
|
"""
============
Array basics
============
Array types and conversions between types
=========================================
NumPy supports a much greater variety of numerical types than Python does.
This section shows which are available, and how to modify an array's data-type.
========== ==========================================================
Data type Description
========== ==========================================================
bool_ Boolean (True or False) stored as a byte
int_ Default integer type (same as C ``long``; normally either
``int64`` or ``int32``)
intc Identical to C ``int`` (normally ``int32`` or ``int64``)
intp Integer used for indexing (same as C ``ssize_t``; normally
either ``int32`` or ``int64``)
int8 Byte (-128 to 127)
int16 Integer (-32768 to 32767)
int32 Integer (-2147483648 to 2147483647)
int64 Integer (-9223372036854775808 to 9223372036854775807)
uint8 Unsigned integer (0 to 255)
uint16 Unsigned integer (0 to 65535)
uint32 Unsigned integer (0 to 4294967295)
uint64 Unsigned integer (0 to 18446744073709551615)
float_ Shorthand for ``float64``.
float16 Half precision float: sign bit, 5 bits exponent,
10 bits mantissa
float32 Single precision float: sign bit, 8 bits exponent,
23 bits mantissa
float64 Double precision float: sign bit, 11 bits exponent,
52 bits mantissa
complex_ Shorthand for ``complex128``.
complex64 Complex number, represented by two 32-bit floats (real
and imaginary components)
complex128 Complex number, represented by two 64-bit floats (real
and imaginary components)
========== ==========================================================
Additionally to ``intc`` the platform dependent C integer types ``short``,
``long``, ``longlong`` and their unsigned versions are defined.
NumPy numerical types are instances of ``dtype`` (data-type) objects, each
having unique characteristics. Once you have imported NumPy using
::
>>> import numpy as np
the dtypes are available as ``np.bool_``, ``np.float32``, etc.
Advanced types, not listed in the table above, are explored in
section :ref:`structured_arrays`.
There are 5 basic numerical types representing booleans (bool), integers (int),
unsigned integers (uint) floating point (float) and complex. Those with numbers
in their name indicate the bitsize of the type (i.e. how many bits are needed
to represent a single value in memory). Some types, such as ``int`` and
``intp``, have differing bitsizes, dependent on the platforms (e.g. 32-bit
vs. 64-bit machines). This should be taken into account when interfacing
with low-level code (such as C or Fortran) where the raw memory is addressed.
Data-types can be used as functions to convert python numbers to array scalars
(see the array scalar section for an explanation), python sequences of numbers
to arrays of that type, or as arguments to the dtype keyword that many numpy
functions or methods accept. Some examples::
>>> import numpy as np
>>> x = np.float32(1.0)
>>> x
1.0
>>> y = np.int_([1,2,4])
>>> y
array([1, 2, 4])
>>> z = np.arange(3, dtype=np.uint8)
>>> z
array([0, 1, 2], dtype=uint8)
Array types can also be referred to by character codes, mostly to retain
backward compatibility with older packages such as Numeric. Some
documentation may still refer to these, for example::
>>> np.array([1, 2, 3], dtype='f')
array([ 1., 2., 3.], dtype=float32)
We recommend using dtype objects instead.
To convert the type of an array, use the .astype() method (preferred) or
the type itself as a function. For example: ::
>>> z.astype(float) #doctest: +NORMALIZE_WHITESPACE
array([ 0., 1., 2.])
>>> np.int8(z)
array([0, 1, 2], dtype=int8)
Note that, above, we use the *Python* float object as a dtype. NumPy knows
that ``int`` refers to ``np.int_``, ``bool`` means ``np.bool_``,
that ``float`` is ``np.float_`` and ``complex`` is ``np.complex_``.
The other data-types do not have Python equivalents.
To determine the type of an array, look at the dtype attribute::
>>> z.dtype
dtype('uint8')
dtype objects also contain information about the type, such as its bit-width
and its byte-order. The data type can also be used indirectly to query
properties of the type, such as whether it is an integer::
>>> d = np.dtype(int)
>>> d
dtype('int32')
>>> np.issubdtype(d, int)
True
>>> np.issubdtype(d, float)
False
Array Scalars
=============
NumPy generally returns elements of arrays as array scalars (a scalar
with an associated dtype). Array scalars differ from Python scalars, but
for the most part they can be used interchangeably (the primary
exception is for versions of Python older than v2.x, where integer array
scalars cannot act as indices for lists and tuples). There are some
exceptions, such as when code requires very specific attributes of a scalar
or when it checks specifically whether a value is a Python scalar. Generally,
problems are easily fixed by explicitly converting array scalars
to Python scalars, using the corresponding Python type function
(e.g., ``int``, ``float``, ``complex``, ``str``, ``unicode``).
The primary advantage of using array scalars is that
they preserve the array type (Python may not have a matching scalar type
available, e.g. ``int16``). Therefore, the use of array scalars ensures
identical behaviour between arrays and scalars, irrespective of whether the
value is inside an array or not. NumPy scalars also have many of the same
methods arrays do.
Extended Precision
==================
Python's floating-point numbers are usually 64-bit floating-point numbers,
nearly equivalent to ``np.float64``. In some unusual situations it may be
useful to use floating-point numbers with more precision. Whether this
is possible in numpy depends on the hardware and on the development
environment: specifically, x86 machines provide hardware floating-point
with 80-bit precision, and while most C compilers provide this as their
``long double`` type, MSVC (standard for Windows builds) makes
``long double`` identical to ``double`` (64 bits). NumPy makes the
compiler's ``long double`` available as ``np.longdouble`` (and
``np.clongdouble`` for the complex numbers). You can find out what your
numpy provides with``np.finfo(np.longdouble)``.
NumPy does not provide a dtype with more precision than C
``long double``s; in particular, the 128-bit IEEE quad precision
data type (FORTRAN's ``REAL*16``) is not available.
For efficient memory alignment, ``np.longdouble`` is usually stored
padded with zero bits, either to 96 or 128 bits. Which is more efficient
depends on hardware and development environment; typically on 32-bit
systems they are padded to 96 bits, while on 64-bit systems they are
typically padded to 128 bits. ``np.longdouble`` is padded to the system
default; ``np.float96`` and ``np.float128`` are provided for users who
want specific padding. In spite of the names, ``np.float96`` and
``np.float128`` provide only as much precision as ``np.longdouble``,
that is, 80 bits on most x86 machines and 64 bits in standard
Windows builds.
Be warned that even if ``np.longdouble`` offers more precision than
python ``float``, it is easy to lose that extra precision, since
python often forces values to pass through ``float``. For example,
the ``%`` formatting operator requires its arguments to be converted
to standard python types, and it is therefore impossible to preserve
extended precision even if many decimal places are requested. It can
be useful to test your code with the value
``1 + np.finfo(np.longdouble).eps``.
""" |
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
"""
HTTP Exception
--------------
This module processes Python exceptions that relate to HTTP exceptions
by defining a set of exceptions, all subclasses of HTTPException.
Each exception, in addition to being a Python exception that can be
raised and caught, is also a WSGI application and ``webob.Response``
object.
This module defines exceptions according to RFC 2068 [1]_ : codes with
100-300 are not really errors; 400's are client errors, and 500's are
server errors. According to the WSGI specification [2]_ , the application
can call ``start_response`` more then once only under two conditions:
(a) the response has not yet been sent, or (b) if the second and
subsequent invocations of ``start_response`` have a valid ``exc_info``
argument obtained from ``sys.exc_info()``. The WSGI specification then
requires the server or gateway to handle the case where content has been
sent and then an exception was encountered.
Exception
HTTPException
HTTPOk
* 200 - HTTPOk
* 201 - HTTPCreated
* 202 - HTTPAccepted
* 203 - HTTPNonAuthoritativeInformation
* 204 - HTTPNoContent
* 205 - HTTPResetContent
* 206 - HTTPPartialContent
HTTPRedirection
* 300 - HTTPMultipleChoices
* 301 - HTTPMovedPermanently
* 302 - HTTPFound
* 303 - HTTPSeeOther
* 304 - HTTPNotModified
* 305 - HTTPUseProxy
* 306 - Unused (not implemented, obviously)
* 307 - HTTPTemporaryRedirect
HTTPError
HTTPClientError
* 400 - HTTPBadRequest
* 401 - HTTPUnauthorized
* 402 - HTTPPaymentRequired
* 403 - HTTPForbidden
* 404 - HTTPNotFound
* 405 - HTTPMethodNotAllowed
* 406 - HTTPNotAcceptable
* 407 - HTTPProxyAuthenticationRequired
* 408 - HTTPRequestTimeout
* 409 - HTTPConflict
* 410 - HTTPGone
* 411 - HTTPLengthRequired
* 412 - HTTPPreconditionFailed
* 413 - HTTPRequestEntityTooLarge
* 414 - HTTPRequestURITooLong
* 415 - HTTPUnsupportedMediaType
* 416 - HTTPRequestRangeNotSatisfiable
* 417 - HTTPExpectationFailed
HTTPServerError
* 500 - HTTPInternalServerError
* 501 - HTTPNotImplemented
* 502 - HTTPBadGateway
* 503 - HTTPServiceUnavailable
* 504 - HTTPGatewayTimeout
* 505 - HTTPVersionNotSupported
Subclass usage notes:
---------------------
The HTTPException class is complicated by 4 factors:
1. The content given to the exception may either be plain-text or
as html-text.
2. The template may want to have string-substitutions taken from
the current ``environ`` or values from incoming headers. This
is especially troublesome due to case sensitivity.
3. The final output may either be text/plain or text/html
mime-type as requested by the client application.
4. Each exception has a default explanation, but those who
raise exceptions may want to provide additional detail.
Subclass attributes and call parameters are designed to provide an easier path
through the complications.
Attributes:
``code``
the HTTP status code for the exception
``title``
remainder of the status line (stuff after the code)
``explanation``
a plain-text explanation of the error message that is
not subject to environment or header substitutions;
it is accessible in the template via %(explanation)s
``detail``
a plain-text message customization that is not subject
to environment or header substitutions; accessible in
the template via %(detail)s
``body_template``
a content fragment (in HTML) used for environment and
header substitution; the default template includes both
the explanation and further detail provided in the
message
Parameters:
``detail``
a plain-text override of the default ``detail``
``headers``
a list of (k,v) header pairs
``comment``
a plain-text additional information which is
usually stripped/hidden for end-users
``body_template``
a string.Template object containing a content fragment in HTML
that frames the explanation and further detail
To override the template (which is HTML content) or the plain-text
explanation, one must subclass the given exception; or customize it
after it has been created. This particular breakdown of a message
into explanation, detail and template allows both the creation of
plain-text and html messages for various clients as well as
error-free substitution of environment variables and headers.
The subclasses of :class:`~_HTTPMove`
(:class:`~HTTPMultipleChoices`, :class:`~HTTPMovedPermanently`,
:class:`~HTTPFound`, :class:`~HTTPSeeOther`, :class:`~HTTPUseProxy` and
:class:`~HTTPTemporaryRedirect`) are redirections that require a ``Location``
field. Reflecting this, these subclasses have two additional keyword arguments:
``location`` and ``add_slash``.
Parameters:
``location``
to set the location immediately
``add_slash``
set to True to redirect to the same URL as the request, except with a
``/`` appended
Relative URLs in the location will be resolved to absolute.
References:
.. [1] http://www.python.org/peps/pep-0333.html#error-handling
.. [2] http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html#sec10.5
""" |
#
# XML-RPC CLIENT LIBRARY
# $Id$
#
# an XML-RPC client interface for Python.
#
# the marshalling and response parser code can also be used to
# implement XML-RPC servers.
#
# Notes:
# this version is designed to work with Python 2.1 or newer.
#
# History:
# 1999-01-14 fl Created
# 1999-01-15 fl Changed dateTime to use localtime
# 1999-01-16 fl Added Binary/base64 element, default to RPC2 service
# 1999-01-19 fl Fixed array data element (from Skip Montanaro)
# 1999-01-21 fl Fixed dateTime constructor, etc.
# 1999-02-02 fl Added fault handling, handle empty sequences, etc.
# 1999-02-10 fl Fixed problem with empty responses (from Skip Montanaro)
# 1999-06-20 fl Speed improvements, pluggable parsers/transports (0.9.8)
# 2000-11-28 fl Changed boolean to check the truth value of its argument
# 2001-02-24 fl Added encoding/Unicode/SafeTransport patches
# 2001-02-26 fl Added compare support to wrappers (0.9.9/1.0b1)
# 2001-03-28 fl Make sure response tuple is a singleton
# 2001-03-29 fl Don't require empty params element (from NAME 2001-06-10 fl Folded in _xmlrpclib accelerator support (1.0b2)
# 2001-08-20 fl Base xmlrpclib.Error on built-in Exception (from NAME 2001-09-03 fl Allow Transport subclass to override getparser
# 2001-09-10 fl Lazy import of urllib, cgi, xmllib (20x import speedup)
# 2001-10-01 fl Remove containers from memo cache when done with them
# 2001-10-01 fl Use faster escape method (80% dumps speedup)
# 2001-10-02 fl More dumps microtuning
# 2001-10-04 fl Make sure import expat gets a parser (from NAME 2001-10-10 sm Allow long ints to be passed as ints if they don't overflow
# 2001-10-17 sm Test for int and long overflow (allows use on 64-bit systems)
# 2001-11-12 fl Use repr() to marshal doubles (from NAME 2002-03-17 fl Avoid buffered read when possible (from NAME 2002-04-07 fl Added pythondoc comments
# 2002-04-16 fl Added __str__ methods to datetime/binary wrappers
# 2002-05-15 fl Added error constants (from NAME 2002-06-27 fl Merged with Python CVS version
# 2002-10-22 fl Added basic authentication (based on code from NAME 2003-01-22 sm Add support for the bool type
# 2003-02-27 gvr Remove apply calls
# 2003-04-24 sm Use cStringIO if available
# 2003-04-25 ak Add support for nil
# 2003-06-15 gn Add support for time.struct_time
# 2003-07-12 gp Correct marshalling of Faults
# 2003-10-31 mvl Add multicall support
# 2004-08-20 mvl Bump minimum supported Python version to 2.1
#
# Copyright (c) 1999-2002 by Secret Labs AB.
# Copyright (c) 1999-2002 by NAME EMAIL http://www.pythonware.com
#
# --------------------------------------------------------------------
# The XML-RPC client interface is
#
# Copyright (c) 1999-2002 by Secret Labs AB
# Copyright (c) 1999-2002 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
#
# things to look into some day:
# TODO: sort out True/False/boolean issues for Python 2.3
|
#!/usr/bin/env vpython
#
# [VPYTHON:BEGIN]
# wheel: <
# name: "infra/python/wheels/google-auth-py2_py3"
# version: "version:1.2.1"
# >
#
# wheel: <
# name: "infra/python/wheels/pyasn1-py2_py3"
# version: "version:0.4.5"
# >
#
# wheel: <
# name: "infra/python/wheels/pyasn1_modules-py2_py3"
# version: "version:0.2.4"
# >
#
# wheel: <
# name: "infra/python/wheels/six"
# version: "version:1.10.0"
# >
#
# wheel: <
# name: "infra/python/wheels/cachetools-py2_py3"
# version: "version:2.0.1"
# >
# wheel: <
# name: "infra/python/wheels/rsa-py2_py3"
# version: "version:4.0"
# >
#
# wheel: <
# name: "infra/python/wheels/requests"
# version: "version:2.13.0"
# >
#
# wheel: <
# name: "infra/python/wheels/google-api-python-client-py2_py3"
# version: "version:1.6.2"
# >
#
# wheel: <
# name: "infra/python/wheels/httplib2-py2_py3"
# version: "version:0.12.1"
# >
#
# wheel: <
# name: "infra/python/wheels/oauth2client-py2_py3"
# version: "version:3.0.0"
# >
#
# wheel: <
# name: "infra/python/wheels/uritemplate-py2_py3"
# version: "version:3.0.0"
# >
#
# wheel: <
# name: "infra/python/wheels/google-auth-oauthlib-py2_py3"
# version: "version:0.3.0"
# >
#
# wheel: <
# name: "infra/python/wheels/requests-oauthlib-py2_py3"
# version: "version:1.2.0"
# >
#
# wheel: <
# name: "infra/python/wheels/oauthlib-py2_py3"
# version: "version:3.0.1"
# >
#
# wheel: <
# name: "infra/python/wheels/google-auth-httplib2-py2_py3"
# version: "version:0.0.3"
# >
# [VPYTHON:END]
#
# Copyright 2019 The ANGLE Project Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
#
# generate_deqp_stats.py:
# Checks output of deqp testers and generates stats using the GDocs API
#
# prerequirements:
# https://devsite.googleplex.com/sheets/api/quickstart/python
# Follow the quickstart guide.
#
# usage: generate_deqp_stats.py [-h] [--auth_path [AUTH_PATH]] [--spreadsheet [SPREADSHEET]]
# [--verbosity [VERBOSITY]]
#
# optional arguments:
# -h, --help show this help message and exit
# --auth_path [AUTH_PATH]
# path to directory containing authorization data (credentials.json and
# token.pickle). [default=<home>/.auth]
# --spreadsheet [SPREADSHEET]
# ID of the spreadsheet to write stats to. [default
# ='1D6Yh7dAPP-aYLbX3HHQD8WubJV9XPuxvkKowmn2qhIw']
# --verbosity [VERBOSITY]
# Verbosity of output. Valid options are [DEBUG, INFO, WARNING, ERROR].
# [default=INFO]
|
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True, default_section='DEFAULT',
interpolation=<unset>, converters=<unset>):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
When `default_section' is given, the name of the special section is
named accordingly. By default it is called ``"DEFAULT"`` but this can
be customized to point to any other valid section name. Its current
value can be retrieved using the ``parser_instance.default_section``
attribute and may be modified at runtime.
When `interpolation` is given, it should be an Interpolation subclass
instance. It will be used as the handler for option value
pre-processing when using getters. RawConfigParser objects don't do
any sort of interpolation, whereas ConfigParser uses an instance of
BasicInterpolation. The library also provides a ``zc.buildbot``
inspired ExtendedInterpolation implementation.
When `converters` is given, it should be a dictionary where each key
represents the name of a type converter and each value is a callable
implementing the conversion from string to the desired datatype. Every
converter gets its corresponding get*() method on the parser object and
section proxies.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the iterable of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
#
# ElementTree
# $Id: ElementTree.py 2326 2005-03-17 07:45:21Z USERNAME $
#
# light-weight XML support for Python 1.5.2 and later.
#
# history:
# 2001-10-20 fl created (from various sources)
# 2001-11-01 fl return root from parse method
# 2002-02-16 fl sort attributes in lexical order
# 2002-04-06 fl TreeBuilder refactoring, added PythonDoc markup
# 2002-05-01 fl finished TreeBuilder refactoring
# 2002-07-14 fl added basic namespace support to ElementTree.write
# 2002-07-25 fl added QName attribute support
# 2002-10-20 fl fixed encoding in write
# 2002-11-24 fl changed default encoding to ascii; fixed attribute encoding
# 2002-11-27 fl accept file objects or file names for parse/write
# 2002-12-04 fl moved XMLTreeBuilder back to this module
# 2003-01-11 fl fixed entity encoding glitch for us-ascii
# 2003-02-13 fl added XML literal factory
# 2003-02-21 fl added ProcessingInstruction/PI factory
# 2003-05-11 fl added tostring/fromstring helpers
# 2003-05-26 fl added ElementPath support
# 2003-07-05 fl added makeelement factory method
# 2003-07-28 fl added more well-known namespace prefixes
# 2003-08-15 fl fixed typo in ElementTree.findtext (Thomas NAME 2003-09-04 fl fall back on emulator if ElementPath is not installed
# 2003-10-31 fl markup updates
# 2003-11-15 fl fixed nested namespace bug
# 2004-03-28 fl added XMLID helper
# 2004-06-02 fl added default support to findtext
# 2004-06-08 fl fixed encoding of non-ascii element/attribute names
# 2004-08-23 fl take advantage of post-2.1 expat features
# 2005-02-01 fl added iterparse implementation
# 2005-03-02 fl fixed iterparse support for pre-2.2 versions
#
# Copyright (c) 1999-2005 by NAME All rights reserved.
#
# USERNAME@pythonware.com
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2005 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
# Licensed to PSF under a Contributor Agreement.
# See http://www.python.org/2.4/license for licensing details.
|
"""
Define a simple format for saving numpy arrays to disk with the full
information about them.
The ``.npy`` format is the standard binary file format in NumPy for
persisting a *single* arbitrary NumPy array on disk. The format stores all
of the shape and dtype information necessary to reconstruct the array
correctly even on another machine with a different architecture.
The format is designed to be as simple as possible while achieving
its limited goals.
The ``.npz`` format is the standard format for persisting *multiple* NumPy
arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``
files, one for each array.
Capabilities
------------
- Can represent all NumPy arrays including nested record arrays and
object arrays.
- Represents the data in its native binary form.
- Supports Fortran-contiguous arrays directly.
- Stores all of the necessary information to reconstruct the array
including shape and dtype on a machine of a different
architecture. Both little-endian and big-endian arrays are
supported, and a file with little-endian numbers will yield
a little-endian array on any machine reading the file. The
types are described in terms of their actual sizes. For example,
if a machine with a 64-bit C "long int" writes out an array with
"long ints", a reading machine with 32-bit C "long ints" will yield
an array with 64-bit integers.
- Is straightforward to reverse engineer. Datasets often live longer than
the programs that created them. A competent developer should be
able create a solution in his preferred programming language to
read most ``.npy`` files that he has been given without much
documentation.
- Allows memory-mapping of the data. See `open_memmep`.
- Can be read from a filelike stream object instead of an actual file.
- Stores object arrays, i.e. arrays containing elements that are arbitrary
Python objects. Files with object arrays are not to be mmapable, but
can be read and written to disk.
Limitations
-----------
- Arbitrary subclasses of numpy.ndarray are not completely preserved.
Subclasses will be accepted for writing, but only the array data will
be written out. A regular numpy.ndarray object will be created
upon reading the file.
.. warning::
Due to limitations in the interpretation of structured dtypes, dtypes
with fields with empty names will have the names replaced by 'f0', 'f1',
etc. Such arrays will not round-trip through the format entirely
accurately. The data is intact; only the field names will differ. We are
working on a fix for this. This fix will not require a change in the
file format. The arrays with such structures can still be saved and
restored, and the correct dtype may be restored by using the
``loadedarray.view(correct_dtype)`` method.
File extensions
---------------
We recommend using the ``.npy`` and ``.npz`` extensions for files saved
in this format. This is by no means a requirement; applications may wish
to use these file formats but use an extension specific to the
application. In the absence of an obvious alternative, however,
we suggest using ``.npy`` and ``.npz``.
Version numbering
-----------------
The version numbering of these formats is independent of NumPy version
numbering. If the format is upgraded, the code in `numpy.io` will still
be able to read and write Version 1.0 files.
Format Version 1.0
------------------
The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.
The next 1 byte is an unsigned byte: the major version number of the file
format, e.g. ``\\x01``.
The next 1 byte is an unsigned byte: the minor version number of the file
format, e.g. ``\\x00``. Note: the version of the file format is not tied
to the version of the numpy package.
The next 2 bytes form a little-endian unsigned short int: the length of
the header data HEADER_LEN.
The next HEADER_LEN bytes form the header data describing the array's
format. It is an ASCII string which contains a Python literal expression
of a dictionary. It is terminated by a newline (``\\n``) and padded with
spaces (``\\x20``) to make the total length of
``magic string + 4 + HEADER_LEN`` be evenly divisible by 16 for alignment
purposes.
The dictionary contains three keys:
"descr" : dtype.descr
An object that can be passed as an argument to the `numpy.dtype`
constructor to create the array's dtype.
"fortran_order" : bool
Whether the array data is Fortran-contiguous or not. Since
Fortran-contiguous arrays are a common form of non-C-contiguity,
we allow them to be written directly to disk for efficiency.
"shape" : tuple of int
The shape of the array.
For repeatability and readability, the dictionary keys are sorted in
alphabetic order. This is for convenience only. A writer SHOULD implement
this if possible. A reader MUST NOT depend on this.
Following the header comes the array data. If the dtype contains Python
objects (i.e. ``dtype.hasobject is True``), then the data is a Python
pickle of the array. Otherwise the data is the contiguous (either C-
or Fortran-, depending on ``fortran_order``) bytes of the array.
Consumers can figure out the number of bytes by multiplying the number
of elements given by the shape (noting that ``shape=()`` means there is
1 element) by ``dtype.itemsize``.
Notes
-----
The ``.npy`` format, including reasons for creating it and a comparison of
alternatives, is described fully in the "npy-format" NEP.
""" |
# -*- encoding: utf-8 -*-
##############################################################################
#
# Copyright (c) 2009 Veritos - NAME - www.veritos.nl
#
# WARNING: This program as such is intended to be used by professional
# programmers who take the whole responsability of assessing all potential
# consequences resulting from its eventual inadequacies and bugs.
# End users who are looking for a ready-to-use solution with commercial
# garantees and support are strongly adviced to contract a Free Software
# Service Company like Veritos.
#
# This program is Free Software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#
##############################################################################
#
# Deze module werkt in OpenERP 5.0.0 (en waarschijnlijk hoger).
# Deze module werkt niet in OpenERP versie 4 en lager.
#
# Status 1.0 - getest op OpenERP 5.0.3
#
# Versie IP_ADDRESS
# account.account.type
# Basis gelegd voor alle account type
#
# account.account.template
# Basis gelegd met alle benodigde grootboekrekeningen welke via een menu-
# structuur gelinkt zijn aan rubrieken 1 t/m 9.
# De grootboekrekeningen gelinkt aan de account.account.type
# Deze links moeten nog eens goed nagelopen worden.
#
# account.chart.template
# Basis gelegd voor het koppelen van rekeningen aan debiteuren, crediteuren,
# bank, inkoop en verkoop boeken en de BTW configuratie.
#
# Versie 5.0.0.2
# account.tax.code.template
# Basis gelegd voor de BTW configuratie (structuur)
# Heb als basis het BTW aangifte formulier gebruikt. Of dit werkt?
#
# account.tax.template
# De BTW rekeningen aangemaakt en deze gekoppeld aan de betreffende
# grootboekrekeningen
#
# Versie 5.0.0.3
# Opschonen van de code en verwijderen van niet gebruikte componenten.
# Versie 5.0.0.4
# Aanpassen a_expense van 3000 -> 7000
# record id='btw_code_5b' op negatieve waarde gezet
# Versie 5.0.0.5
# BTW rekeningen hebben typeaanduiding gekregen t.b.v. purchase of sale
# Versie 5.0.0.6
# Opschonen van module.
# Versie 5.0.0.7
# Opschonen van module.
# Versie 5.0.0.8
# Foutje in l10n_nl_wizard.xml gecorrigeerd waardoor de module niet volledig installeerde.
# Versie 5.0.0.9
# Account Receivable en Payable goed gedefinieerd.
# Versie 5.0.1.0
# Alle user_type_xxx velden goed gedefinieerd.
# Specifieke bouw en garage gerelateerde grootboeken verwijderd om een standaard module te creeeren.
# Deze module kan dan als basis worden gebruikt voor specifieke doelgroep modules te creeeren.
# Versie 5.0.1.1
# Correctie van rekening 7010 (stond dubbel met 7014 waardoor installatie verkeerd ging)
# versie 5.0.1.2
# Correctie op diverse rekening types van user_type_asset -> user_type_liability en user_type_equity
# versie 5.0.1.3
# Kleine correctie op BTW te vorderen hoog, id was hetzelfde voor beide, waardoor hoog werd overschreven door # overig. Verduidelijking van omschrijvingen in belastingcodes t.b.v. aangifte overzicht.
# versie 5.0.1.4
# BTW omschrijvingen aangepast, zodat rapporten er beter uitzien. 2a en 5b e.d. verwijderd en enkele omschrijvingen toegevoegd.
# versie 5.0.1.5 - Switch to English
# Added properties_stock_xxx accounts for correct stock valuation, changed 7000-accounts from type cash to type expense
# Changed naming of 7020 and 7030 to Kostprijs omzet xxxx
|
"""
This module contains dsolve() and different helper functions that it
uses.
dsolve() solves ordinary differential equations. See the docstring on
the various functions for their uses. Note that partial differential
equations support is in pde.py. Note that ode_hint() functions have
docstrings describing their various methods, but they are intended for
internal use. Use dsolve(ode, func, hint=hint) to solve an ode using a
specific hint. See also the docstring on dsolve().
**Functions in this module**
These are the user functions in this module:
- dsolve() - Solves ODEs.
- classify_ode() - Classifies ODEs into possible hints for dsolve().
- checkodesol() - Checks if an equation is the solution to an ODE.
- ode_order() - Returns the order (degree) of an ODE.
- homogeneous_order() - Returns the homogeneous order of an
expression.
These are the non-solver helper functions that are for internal use.
The user should use the various options to dsolve() to obtain the
functionality provided by these functions:
- odesimp() - Does all forms of ODE simplification.
- ode_sol_simplicity() - A key function for comparing solutions by
simplicity.
- constantsimp() - Simplifies arbitrary constants.
- constant_renumber() - Renumber arbitrary constants
- _handle_Integral() - Evaluate unevaluated Integrals.
See also the docstrings of these functions.
**Solving methods currently implemented**
The following methods are implemented for solving ordinary differential
equations. See the docstrings of the various ode_hint() functions for
more information on each (run help(ode)):
- 1st order separable differential equations
- 1st order differential equations whose coefficients or dx and dy
are functions homogeneous of the same order.
- 1st order exact differential equations.
- 1st order linear differential equations
- 1st order Bernoulli differential equations.
- 2nd order Liouville differential equations.
- nth order linear homogeneous differential equation with constant
coefficients.
- nth order linear inhomogeneous differential equation with constant
coefficients using the method of undetermined coefficients.
- nth order linear inhomogeneous differential equation with constant
coefficients using the method of variation of parameters.
**Philosophy behind this module**
This module is designed to make it easy to add new ODE solving methods
without having to mess with the solving code for other methods. The
idea is that there is a classify_ode() function, which takes in an ODE
and tells you what hints, if any, will solve the ODE. It does this
without attempting to solve the ODE, so it is fast. Each solving method
is a hint, and it has its own function, named ode_hint. That function
takes in the ODE and any match expression gathered by classify_ode and
returns a solved result. If this result has any integrals in it, the
ode_hint function will return an unevaluated Integral class. dsolve(),
which is the user wrapper function around all of this, will then call
odesimp() on the result, which, among other things, will attempt to
solve the equation for the dependent variable (the function we are
solving for), simplify the arbitrary constants in the expression, and
evaluate any integrals, if the hint allows it.
**How to add new solution methods**
If you have an ODE that you want dsolve() to be able to solve, try to
avoid adding special case code here. Instead, try finding a general
method that will solve your ODE, as well as others. This way, the ode
module will become more robust, and unhindered by special case hacks.
WolphramAlpha and Maple's DETools[odeadvisor] function are two resources
you can use to classify a specific ODE. It is also better for a method
to work with an nth order ODE instead of only with specific orders, if
possible.
To add a new method, there are a few things that you need to do. First,
you need a hint name for your method. Try to name your hint so that it
is unambiguous with all other methods, including ones that may not be
implemented yet. If your method uses integrals, also include a
"hint_Integral" hint. If there is more than one way to solve ODEs with
your method, include a hint for each one, as well as a "hint_best" hint.
Your ode_hint_best() function should choose the best using min with
ode_sol_simplicity as the key argument. See
ode_1st_homogeneous_coeff_best(), for example. The function that uses
your method will be called ode_hint(), so the hint must only use
characters that are allowed in a Python function name (alphanumeric
characters and the underscore '_' character). Include a function for
every hint, except for "_Integral" hints (dsolve() takes care of those
automatically). Hint names should be all lowercase, unless a word is
commonly capitalized (such as Integral or Bernoulli). If you have a hint
that you do not want to run with "all_Integral" that doesn't have an
"_Integral" counterpart (such as a best hint that would defeat the
purpose of "all_Integral"), you will need to remove it manually in the
dsolve() code. See also the classify_ode() docstring for guidelines on
writing a hint name.
Determine *in general* how the solutions returned by your method
compare with other methods that can potentially solve the same ODEs.
Then, put your hints in the allhints tuple in the order that they should
be called. The ordering of this tuple determines which hints are
default. Note that exceptions are ok, because it is easy for the user to
choose individual hints with dsolve(). In general, "_Integral" variants
should go at the end of the list, and "_best" variants should go before
the various hints they apply to. For example, the
"undetermined_coefficients" hint comes before the
"variation_of_parameters" hint because, even though variation of
parameters is more general than undetermined coefficients, undetermined
coefficients generally returns cleaner results for the ODEs that it can
solve than variation of parameters does, and it does not require
integration, so it is much faster.
Next, you need to have a match expression or a function that matches the
type of the ODE, which you should put in classify_ode() (if the match
function is more than just a few lines, like
_undetermined_coefficients_match(), it should go outside of
classify_ode()). It should match the ODE without solving for it as much
as possible, so that classify_ode() remains fast and is not hindered by
bugs in solving code. Be sure to consider corner cases. For example, if
your solution method involves dividing by something, make sure you
exclude the case where that division will be 0.
In most cases, the matching of the ODE will also give you the various
parts that you need to solve it. You should put that in a dictionary
(.match() will do this for you), and add that as matching_hints['hint']
= matchdict in the relevant part of classify_ode. classify_ode will
then send this to dsolve(), which will send it to your function as the
match argument. Your function should be named ode_hint(eq, func, order,
match). If you need to send more information, put it in the match
dictionary. For example, if you had to substitute in a dummy variable
in classify_ode to match the ODE, you will need to pass it to your
function using the match dict to access it. You can access the
independent variable using func.args[0], and the dependent variable (the
function you are trying to solve for) as func.func. If, while trying to
solve the ODE, you find that you cannot, raise NotImplementedError.
dsolve() will catch this error with the "all" meta-hint, rather than
causing the whole routine to fail.
Add a docstring to your function that describes the method employed.
Like with anything else in SymPy, you will need to add a doctest to the
docstring, in addition to real tests in test_ode.py. Try to maintain
consistency with the other hint functions' docstrings. Add your method
to the list at the top of this docstring. Also, add your method to
ode.txt in the docs/src directory, so that the Sphinx docs will pull its
docstring into the main SymPy documentation. Be sure to make the Sphinx
documentation by running "make html" from within the doc directory to
verify that the docstring formats correctly.
If your solution method involves integrating, use C.Integral() instead
of integrate(). This allows the user to bypass hard/slow integration by
using the "_Integral" variant of your hint. In most cases, calling
.doit() will integrate your solution. If this is not the case, you will
need to write special code in _handle_Integral(). Arbitrary constants
should be symbols named C1, C2, and so on. All solution methods should
return an equality instance. If you need an arbitrary number of
arbitrary constants, you can use constants =
numbered_symbols(prefix='C', function=Symbol, start=1). If it is
possible to solve for the dependent function in a general way, do so.
Otherwise, do as best as you can, but do not call solve in your
ode_hint() function. odesimp() will attempt to solve the solution for
you, so you do not need to do that. Lastly, if your ODE has a common
simplification that can be applied to your solutions, you can add a
special case in odesimp() for it. For example, solutions returned from
the "1st_homogeneous_coeff" hints often have many log() terms, so
odesimp() calls logcombine() on them (it also helps to write the
arbitrary constant as log(C1) instead of C1 in this case). Also
consider common ways that you can rearrange your solution to have
constantsimp() take better advantage of it. It is better to put
simplification in odesimp() than in your method, because it can then be
turned off with the simplify flag in dsolve(). If you have any
extraneous simplification in your function, be sure to only run it using
"if match.get('simplify', True):", especially if it can be slow or if it
can reduce the domain of the solution.
Finally, as with every contribution to SymPy, your method will need to
be tested. Add a test for each method in test_ode.py. Follow the
conventions there, i.e., test the solver using dsolve(eq, f(x),
hint=your_hint), and also test the solution using checkodesol (you can
put these in a separate tests and skip/XFAIL if it runs too slow/doesn't
work). Be sure to call your hint specifically in dsolve, that way the
test won't be broken simply by the introduction of another matching
hint. If your method works for higher order (>1) ODEs, you will need to
run sol = constant_renumber(sol, 'C', 1, order), for each solution, where
order is the order of the ODE. This is because constant_renumber renumbers
the arbitrary constants by printing order, which is platform dependent.
Try to test every corner case of your solver, including a range of
orders if it is a nth order solver, but if your solver is slow, auch as
if it involves hard integration, try to keep the test run time down.
Feel free to refactor existing hints to avoid duplicating code or
creating inconsistencies. If you can show that your method exactly
duplicates an existing method, including in the simplicity and speed of
obtaining the solutions, then you can remove the old, less general
method. The existing code is tested extensively in test_ode.py, so if
anything is broken, one of those tests will surely fail.
""" |
"""
The event module provides a simple system for properties and events,
to let different components of an application react to each-other and
to user input.
In short:
* The :class:`HasEvents <flexx.event.HasEvents>` class provides objects
that have properties and can emit events.
* There are three decorators to create :func:`properties <flexx.event.prop>`,
:func:`readonlies <flexx.event.readonly>` and
:func:`emitters <flexx.event.emitter>`.
* There is a decorator to :func:`connect <flexx.event.connect>` a method
to an event.
Event
-----
An event is something that has occurred at a certain moment in time,
such as the mouse being pressed down or a property changing its value.
In this framework events are represented with dictionary objects that
provide information about the event (such as what button was pressed,
or the old and new value of a property). A custom :class:`Dict <flexx.event.Dict>`
class is used that inherits from ``dict`` but allows attribute access,
e.g. ``ev.button`` as an alternative to ``ev['button']``.
The HasEvents class
-------------------
The :class:`HasEvents <flexx.event.HasEvents>` class provides a base
class for objects that have properties and/or emit events. E.g. a
``flexx.ui.Widget`` inherits from ``flexx.app.Model``, which inherits
from ``flexx.event.HasEvents``.
Events are emitted using the :func:`emit() <flexx.event.HasEvents.emit>`
method, which accepts a name for the type of the event, and optionally a dict,
e.g. ``emitter.emit('mouse_down', dict(button=1, x=103, y=211))``.
The HasEvents object will add two attributes to the event: ``source``,
a reference to the HasEvents object itself, and ``type``, a string
indicating the type of the event.
As a user, you generally do not need to emit events explicitly; events are
automatically emitted, e.g. when setting a property.
Handler
-------
A handler is an object that can handle events. Handlers can be created
using the :func:`connect <flexx.event.connect>` decorator:
.. code-block:: python
from flexx import event
class MyObject(event.HasEvents):
@event.connect('foo')
def handle_foo(self, *events):
print(events)
ob = MyObject()
ob.emit('foo', dict(value=42)) # will invoke handle_foo()
This example demonstrates a few concepts. Firstly, the handler is
connected via a *connection-string* that specifies the type of the
event; in this case the handler is connected to the event-type "foo"
of the object. This connection-string can also be a path, e.g.
"sub.subsub.event_type". This allows for some powerful mechanics, as
discussed in the section on dynamism.
One can also see that the handler function accepts ``*events`` argument.
This is because handlers can be passed zero or more events. If a handler
is called manually (e.g. ``ob.handle_foo()``) it will have zero events.
When called by the event system, it will have at least 1 event. When
e.g. a property is set twice, the handler function is called
just once, with multiple events, in the next event loop iteration. It
is up to the programmer to determine whether only one action is
required, or whether all events need processing. In the latter case,
just use ``for ev in events: ...``.
In most cases, you will connect to events that are known beforehand,
like those they correspond to properties, readonlies and emitters.
If you connect to an event that is not known (as in the example above)
it might be a typo and Flexx will display a warning. Use `'!foo'` as a
connection string (i.e. prepend an exclamation mark) to suppress such
warnings.
Another useful feature of the event system is that a handler can connect to
multiple events at once:
.. code-block:: python
class MyObject(event.HasEvents):
@event.connect('foo', 'bar')
def handle_foo_and_bar(self, *events):
print(events)
To create a handler from a normal function, use the
:func:`HasEvents.connect() <flexx.event.HasEvents.connect>` method:
.. code-block:: python
h = event.HasEvents()
# Using a decorator
@h.connect('foo', 'bar')
def handle_func1(self, *events):
print(events)
# Explicit notation
def handle_func2(self, *events):
print(events)
h.connect(handle_func2, 'foo', 'bar')
Event emitters
--------------
Apart from using :func:`emit() <flexx.event.HasEvents.emit>` there are
certain attributes of ``HasEvents`` instances that generate events.
Properties
==========
Settable properties can be created easiliy using the
:func:`prop <flexx.event.prop>` decorator:
.. code-block:: python
class MyObject(event.HasEvents):
@event.prop
def foo(self, v=0):
''' This is a float indicating bla bla ...
'''
return float(v)
The function that is decorated is essentially the setter function, and
should have one argument (the new value for the property), which can
have a default value (representing the initial value). The function
body is used to validate and normalize the provided input. In this case
the input is simply cast to a float. The docstring of the function will
be the docstring of the property (e.g. for Sphynx docs).
An alternative initial value for a property can be provided upon instantiation:
.. code-block:: python
m = MyObject(foo=3)
Readonly
========
Readonly properties are created with the
:func:`readonly <flexx.event.readonly>` decorator. The value of a
readonly property can be set internally using the
:func:`_set_prop() <flexx.event.HasEvents._set_prop>` method:.
.. code-block:: python
class MyObject(event.HasEvents):
@event.readonly
def foo(self, v=0):
''' This is a float indicating bla.
'''
return float(v)
def _somewhere(self):
self._set_prop('foo', 42)
Emitter
=======
Emitter attributes make it easy to generate events, and function as a
placeholder to document events on a class. They are created with the
:func:`emitter <flexx.event.emitter>` decorator.
.. code-block:: python
class MyObject(event.HasEvents):
@event.emitter
def mouse_down(self, js_event):
''' Event emitted when the mouse is pressed down.
'''
return dict(button=js_event.button)
Emitters can have any number of arguments and should return a dictionary,
which will get emitted as an event, with the event type matching the name
of the emitter.
Labels
------
Labels are a feature that makes it possible to infuence the order by
which event handlers are called, and provide a means to disconnect
specific (groups of) handlers. The label is part of the connection
string: 'foo.bar:label'.
.. code-block:: python
class MyObject(event.HasEvents):
@event.connect('foo')
def given_foo_handler(*events):
...
@event.connect('foo:aa')
def my_foo_handler(*events):
# This one is called first: 'aa' < 'given_f...'
...
When an event is emitted, the event is added to the pending events of
the handlers in the order of a key, which is the label if present, and
otherwise the name of the handler. Note that this does not guarantee
the order in case a handler has multiple connections: a handler can be
scheduled to handle its events due to another event, and a handler
always handles all its pending events at once.
The label can also be used in the
:func:`disconnect() <flexx.event.HasEvents.disconnect>` method:
.. code-block:: python
@h.connect('foo:mylabel')
def handle_foo(*events):
...
...
h.disconnect('foo:mylabel') # don't need reference to handle_foo
Dynamism
--------
Dynamism is a concept that allows one to connect to events for which
the source can change. For the following example, assume that ``Node``
is a ``HasEvents`` subclass that has properties ``parent`` and
``children``.
.. code-block:: python
main = Node()
main.parent = Node()
main.children = Node(), Node()
@main.connect('parent.foo')
def parent_foo_handler(*events):
...
@main.connect('children*.foo')
def children_foo_handler(*events):
...
The ``parent_foo_handler`` gets invoked when the "foo" event gets
emitted on the parent of main. Similarly, the ``children_foo_handler``
gets invoked when any of the children emits its "foo" event. Note that
in some cases you might also want to connect to changes of the ``parent``
or ``children`` property itself.
The event system automatically reconnects handlers when necessary. This
concept makes it very easy to connect to the right events without the
need for a lot of boilerplate code.
Note that the above example would also work if ``parent`` would be a
regular attribute instead of a property, but the handler would not be
automatically reconnected when it changed.
Patterns
--------
This event system is quite flexible and designed to cover the needs
of a variety of event/messaging mechanisms. This section discusses
how this system relates to some common patterns, and how these can be
implemented.
Observer pattern
================
The idea of the observer pattern is that observers keep track (the state
of) of an object, and that object is agnostic about what it's tracked by.
For example, in a music player, instead of writing code to update the
window-title inside the function that starts a song, there would be a
concept of a "current song", and the window would listen for changes to
the current song to update the title when it changes.
In ``flexx.event``, a ``HasEvents`` object keeps track of its observers
(handlers) and notifies them when there are changes. In our music player
example, there would be a property "current_song", and a handler to
take action when it changes.
As is common in the observer pattern, the handlers keep track of the
handlers that they observe. Therefore both handlers and ``HasEvents``
objects have a ``dispose()`` method for cleaning up.
Signals and slots
=================
The Qt GUI toolkit makes use of a mechanism called "signals and slots" as
an easy way to connect different components of an application. In
``flexx.event`` signals translate to readonly properties, and slots to
the handlers that connect to them.
Overloadable event handlers
===========================
In Qt, the "event system" consists of methods that handles an event, which
can be overloaded in subclasses to handle an event differently. In
``flexx.event``, handlers can similarly be re-implemented in subclasses,
and these can call the original handler using ``super()`` if needed.
Publish-subscribe pattern
==========================
In pub-sub, publishers generate messages identified by a 'topic', and
subscribers can subscribe to such topics. There can be zero or more publishers
and zero or more subscribers to any topic.
In ``flexx.event`` a `HasEvents` object can play the role of a broker.
Publishers can simply emit events. The event type represents the message
topic. Subscribers are represented by handlers.
""" |
#!/usr/bin/env python
# coding: utf-8
# ---
# syncID: e6ccf19a4b454ca594388eeaa88ebe12
# title: "Calculate Vegetation Biomass from LiDAR Data in Python"
# description: "Learn to calculate the biomass of standing vegetation using a canopy height model data product."
# dateCreated: 2017-06-21
# authors: NAME contributors: NAME estimatedTime: 1 hour
# packagesLibraries: numpy, gdal, matplotlib, matplotlib.pyplot, os
# topics: lidar,remote-sensing
# languagesTool: python
# dataProduct: DP1.10098.001, DP3.30015.001,
# code1: https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/tutorials/Python/Lidar/lidar-biomass/calc-biomass_py/calc-biomass_py.ipynb
# tutorialSeries: intro-lidar-py-series
# urlTitle: calc-biomass-py
# ---
# <div id="ds-objectives" markdown="1">
#
# In this tutorial, we will calculate the biomass for a section of the SJER site. We
# will be using the Canopy Height Model discrete LiDAR data product as well as NEON
# field data on vegetation data. This tutorial will calculate Biomass for individual
# trees in the forest.
#
# ### Objectives
# After completing this tutorial, you will be able to:
#
# * Learn how to apply a guassian smoothing fernal for high-frequency spatial filtering
# * Apply a watershed segmentation algorithm for delineating tree crowns
# * Calculate biomass predictor variables from a CHM
# * Setup training data for Biomass predictions
# * Apply a Random Forest machine learning approach to calculate biomass
#
#
# ### Install Python Packages
#
# * **numpy**
# * **gdal**
# * **matplotlib**
# * **matplotlib.pyplot**
# * **os**
#
#
# ### Download Data
#
# If you have already downloaded the data set for the Data Institute, you have the
# data for this tutorial within the SJER directory. If you would like to just
# download the data for this tutorial use the following link.
#
# <a href="https://neondata.sharefile.com/d-s58db39240bf49ac8" class="link--button link--arrow">
# Download the Biomass Calculation teaching data subset</a>
#
# </div>
# In this tutorial, we will calculate the biomass for a section of the SJER site. We
# will be using the Canopy Height Model discrete LiDAR data product as well as NEON
# field data on vegetation data. This tutorial will calculate Biomass for individual
# trees in the forest.
#
# The calculation of biomass consists of four primary steps:
#
# 1. Delineating individual tree crowns
# 2. Calculating predictor variables for all individuals
# 3. Collecting training data
# 4. Applying a regression model to estiamte biomass from predictors
#
# In this tutorial we will use a watershed segmentation algorithm for delineating
# tree crowns (step 1) and and a Random Forest (RF) machine learning algorithm for
# relating the predictor variables to biomass (part 4). The predictor variables were
# selected following suggestions by Gleason et al. (2012) and biomass estimates were
# determined from DBH (diamter at breast height) measurements following relationships
# given in Jenkins et al. (2003).
#
# ## Get Started
#
# First, we need to specify the directory where we will find and save the data needed for this tutorial. You will need to change this line to suit your local machine. I have decided to save my data in the following directory:
# In[1]:
|
"""
Basic functions used by several sub-packages and
useful to have in the main name-space.
Type Handling
-------------
================ ===================
iscomplexobj Test for complex object, scalar result
isrealobj Test for real object, scalar result
iscomplex Test for complex elements, array result
isreal Test for real elements, array result
imag Imaginary part
real Real part
real_if_close Turns complex number with tiny imaginary part to real
isneginf Tests for negative infinity, array result
isposinf Tests for positive infinity, array result
isnan Tests for nans, array result
isinf Tests for infinity, array result
isfinite Tests for finite numbers, array result
isscalar True if argument is a scalar
nan_to_num Replaces NaN's with 0 and infinities with large numbers
cast Dictionary of functions to force cast to each type
common_type Determine the minimum common type code for a group
of arrays
mintypecode Return minimal allowed common typecode.
================ ===================
Index Tricks
------------
================ ===================
mgrid Method which allows easy construction of N-d
'mesh-grids'
``r_`` Append and construct arrays: turns slice objects into
ranges and concatenates them, for 2d arrays appends rows.
index_exp Konrad Hinsen's index_expression class instance which
can be useful for building complicated slicing syntax.
================ ===================
Useful Functions
----------------
================ ===================
select Extension of where to multiple conditions and choices
extract Extract 1d array from flattened array according to mask
insert Insert 1d array of values into Nd array according to mask
linspace Evenly spaced samples in linear space
logspace Evenly spaced samples in logarithmic space
fix Round x to nearest integer towards zero
mod Modulo mod(x,y) = x % y except keeps sign of y
amax Array maximum along axis
amin Array minimum along axis
ptp Array max-min along axis
cumsum Cumulative sum along axis
prod Product of elements along axis
cumprod Cumluative product along axis
diff Discrete differences along axis
angle Returns angle of complex argument
unwrap Unwrap phase along given axis (1-d algorithm)
sort_complex Sort a complex-array (based on real, then imaginary)
trim_zeros Trim the leading and trailing zeros from 1D array.
vectorize A class that wraps a Python function taking scalar
arguments into a generalized function which can handle
arrays of arguments using the broadcast rules of
numerix Python.
================ ===================
Shape Manipulation
------------------
================ ===================
squeeze Return a with length-one dimensions removed.
atleast_1d Force arrays to be > 1D
atleast_2d Force arrays to be > 2D
atleast_3d Force arrays to be > 3D
vstack Stack arrays vertically (row on row)
hstack Stack arrays horizontally (column on column)
column_stack Stack 1D arrays as columns into 2D array
dstack Stack arrays depthwise (along third dimension)
split Divide array into a list of sub-arrays
hsplit Split into columns
vsplit Split into rows
dsplit Split along third dimension
================ ===================
Matrix (2D Array) Manipulations
-------------------------------
================ ===================
fliplr 2D array with columns flipped
flipud 2D array with rows flipped
rot90 Rotate a 2D array a multiple of 90 degrees
eye Return a 2D array with ones down a given diagonal
diag Construct a 2D array from a vector, or return a given
diagonal from a 2D array.
mat Construct a Matrix
bmat Build a Matrix from blocks
================ ===================
Polynomials
-----------
================ ===================
poly1d A one-dimensional polynomial class
poly Return polynomial coefficients from roots
roots Find roots of polynomial given coefficients
polyint Integrate polynomial
polyder Differentiate polynomial
polyadd Add polynomials
polysub Substract polynomials
polymul Multiply polynomials
polydiv Divide polynomials
polyval Evaluate polynomial at given argument
================ ===================
Import Tricks
-------------
================ ===================
ppimport Postpone module import until trying to use it
ppimport_attr Postpone module import until trying to use its attribute
ppresolve Import postponed module and return it.
================ ===================
Machine Arithmetics
-------------------
================ ===================
machar_single Single precision floating point arithmetic parameters
machar_double Double precision floating point arithmetic parameters
================ ===================
Threading Tricks
----------------
================ ===================
ParallelExec Execute commands in parallel thread.
================ ===================
1D Array Set Operations
-----------------------
Set operations for 1D numeric arrays based on sort() function.
================ ===================
ediff1d Array difference (auxiliary function).
unique Unique elements of an array.
intersect1d Intersection of 1D arrays with unique elements.
setxor1d Set exclusive-or of 1D arrays with unique elements.
in1d Test whether elements in a 1D array are also present in
another array.
union1d Union of 1D arrays with unique elements.
setdiff1d Set difference of 1D arrays with unique elements.
================ ===================
""" |
"""
Numerical python functions written for compatability with matlab(TM)
commands with the same names.
Matlab(TM) compatible functions
-------------------------------
:func:`cohere`
Coherence (normalized cross spectral density)
:func:`csd`
Cross spectral density uing Welch's average periodogram
:func:`detrend`
Remove the mean or best fit line from an array
:func:`find`
Return the indices where some condition is true;
numpy.nonzero is similar but more general.
:func:`griddata`
interpolate irregularly distributed data to a
regular grid.
:func:`prctile`
find the percentiles of a sequence
:func:`prepca`
Principal Component Analysis
:func:`psd`
Power spectral density uing Welch's average periodogram
:func:`rk4`
A 4th order runge kutta integrator for 1D or ND systems
:func:`specgram`
Spectrogram (power spectral density over segments of time)
Miscellaneous functions
-------------------------
Functions that don't exist in matlab(TM), but are useful anyway:
:meth:`cohere_pairs`
Coherence over all pairs. This is not a matlab function, but we
compute coherence a lot in my lab, and we compute it for a lot of
pairs. This function is optimized to do this efficiently by
caching the direct FFTs.
:meth:`rk4`
A 4th order Runge-Kutta ODE integrator in case you ever find
yourself stranded without scipy (and the far superior
scipy.integrate tools)
record array helper functions
-------------------------------
A collection of helper methods for numpyrecord arrays
.. _htmlonly::
See :ref:`misc-examples-index`
:meth:`rec2txt`
pretty print a record array
:meth:`rec2csv`
store record array in CSV file
:meth:`csv2rec`
import record array from CSV file with type inspection
:meth:`rec_append_fields`
adds field(s)/array(s) to record array
:meth:`rec_drop_fields`
drop fields from record array
:meth:`rec_join`
join two record arrays on sequence of fields
:meth:`rec_groupby`
summarize data by groups (similar to SQL GROUP BY)
:meth:`rec_summarize`
helper code to filter rec array fields into new fields
For the rec viewer functions(e rec2csv), there are a bunch of Format
objects you can pass into the functions that will do things like color
negative values red, set percent formatting and scaling, etc.
Example usage::
r = csv2rec('somefile.csv', checkrows=0)
formatd = dict(
weight = FormatFloat(2),
change = FormatPercent(2),
cost = FormatThousands(2),
)
rec2excel(r, 'test.xls', formatd=formatd)
rec2csv(r, 'test.csv', formatd=formatd)
scroll = rec2gtk(r, formatd=formatd)
win = gtk.Window()
win.set_size_request(600,800)
win.add(scroll)
win.show_all()
gtk.main()
Deprecated functions
---------------------
The following are deprecated; please import directly from numpy (with
care--function signatures may differ):
:meth:`conv`
convolution (numpy.convolve)
:meth:`corrcoef`
The matrix of correlation coefficients
:meth:`hist`
Histogram (numpy.histogram)
:meth:`linspace`
Linear spaced array from min to max
:meth:`load`
load ASCII file - use numpy.loadtxt
:meth:`meshgrid`
Make a 2D grid from 2 1 arrays (numpy.meshgrid)
:meth:`polyfit`
least squares best polynomial fit of x to y (numpy.polyfit)
:meth:`polyval`
evaluate a vector for a vector of polynomial coeffs (numpy.polyval)
:meth:`save`
save ASCII file - use numpy.savetxt
:meth:`trapz`
trapeziodal integration (trapz(x,y) -> numpy.trapz(y,x))
:meth:`vander`
the Vandermonde matrix (numpy.vander)
""" |
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to read all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use select() to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
- Standard framework for select-based multiplexing
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <lkcl@samba.org>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
"""
=================================================
Orthogonal distance regression (:mod:`scipy.odr`)
=================================================
.. currentmodule:: scipy.odr
Package Content
===============
.. autosummary::
:toctree: generated/
Data -- The data to fit.
RealData -- Data with weights as actual std. dev.s and/or covariances.
Model -- Stores information about the function to be fit.
ODR -- Gathers all info & manages the main fitting routine.
Output -- Result from the fit.
odr -- Low-level function for ODR.
OdrWarning -- Warning about potential problems when running ODR
OdrError -- Error exception.
OdrStop -- Stop exception.
Prebuilt models:
.. autosummary::
polynomial
.. data:: exponential
.. data:: multilinear
.. data:: unilinear
.. data:: quadratic
.. data:: polynomial
Usage information
=================
Introduction
------------
Why Orthogonal Distance Regression (ODR)? Sometimes one has
measurement errors in the explanatory (a.k.a., "independent")
variable(s), not just the response (a.k.a., "dependent") variable(s).
Ordinary Least Squares (OLS) fitting procedures treat the data for
explanatory variables as fixed, i.e., not subject to error of any kind.
Furthermore, OLS procedures require that the response variables be an
explicit function of the explanatory variables; sometimes making the
equation explicit is impractical and/or introduces errors. ODR can
handle both of these cases with ease, and can even reduce to the OLS
case if that is sufficient for the problem.
ODRPACK is a FORTRAN-77 library for performing ODR with possibly
non-linear fitting functions. It uses a modified trust-region
Levenberg-Marquardt-type algorithm [1]_ to estimate the function
parameters. The fitting functions are provided by Python functions
operating on NumPy arrays. The required derivatives may be provided
by Python functions as well, or may be estimated numerically. ODRPACK
can do explicit or implicit ODR fits, or it can do OLS. Input and
output variables may be multi-dimensional. Weights can be provided to
account for different variances of the observations, and even
covariances between dimensions of the variables.
The `scipy.odr` package offers an object-oriented interface to
ODRPACK, in addition to the low-level `odr` function.
Additional background information about ODRPACK can be found in the
`ODRPACK User's Guide
<https://docs.scipy.org/doc/external/odrpack_guide.pdf>`_, reading
which is recommended.
Basic usage
-----------
1. Define the function you want to fit against.::
def f(B, x):
'''Linear function y = m*x + b'''
# B is a vector of the parameters.
# x is an array of the current x values.
# x is in the same format as the x passed to Data or RealData.
#
# Return an array in the same format as y passed to Data or RealData.
return B[0]*x + B[1]
2. Create a Model.::
linear = Model(f)
3. Create a Data or RealData instance.::
mydata = Data(x, y, wd=1./power(sx,2), we=1./power(sy,2))
or, when the actual covariances are known::
mydata = RealData(x, y, sx=sx, sy=sy)
4. Instantiate ODR with your data, model and initial parameter estimate.::
myodr = ODR(mydata, linear, beta0=[1., 2.])
5. Run the fit.::
myoutput = myodr.run()
6. Examine output.::
myoutput.pprint()
References
----------
.. [1] NAME and NAME "Orthogonal Distance Regression,"
in "Statistical analysis of measurement error models and
applications: proceedings of the AMS-IMS-SIAM joint summer research
conference held June 10-16, 1989," Contemporary Mathematics,
vol. 112, pg. 186, 1990.
""" |
""" Copyright 2012, July 31
Written by NAME (Cheer) NAME EMAIL product in a grid
Problem 11
In the 20 X 20 grid below, four numbers
along a diagonal line have been marked in red.
08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48
The product of these numbers is 26 63 78 14 = 1788696.
What is the greatest product of four adjacent numbers in any direction
(up, down, left, right, or diagonally) in the 20 X 20 grid?
""" |
#Process:
#1. Get a list of original sdf files
#2. Use chopRDKit02.py generates fragments and list of files with total atom number, carbon atom number, nitrogen and oxygen atom number
#3. Form lists of by atom numbers
#4. Run rmRedLinker03.py or rmRedRigid01.py on different lists generated by step 3. Remove redundancy of linkers and rigids.
#5. Remove temp file and dir.
#main-script:
# - eMolFrag.py
#sub-scripts used:
# - loader.py
# - chopRDKit02.py,
# - combineLinkers01.py
# - rmRedRigid01.py,
# - rmRedLinker03.py,
# - mol-ali-04.py.
#Usage: Read README file for detailed information.
# 1. Configure path: python ConfigurePath.py # Path only need to be set before the first run if no changes to the paths.
# 2. /Path_to_Python/python /Path_to_scripts/eMolFrag.py /Path_to_input_directory/ /Path_to_output_directory/ Number-Of-Cores
#Args:
# - /Path to Python/ ... Use python to run the script
# - /Path to scripts/echop.py ... The directory of scripts and the name of the entrance to the software
# - /Path to input directory/ ... The path to the input directory, in which is the input molecules in *.mol2 format
# - /Path to output directory/ ... The path to the output directory, in which is the output files
# - Number-Of-Cores ... Number of processings to be used in the run
#Update Log:
#This script is written by NAME Created 01/17/2016 - Chop
# Modification 01/17/2016 - Remove bug
# Modification 01/18/2016 - Reconnect linkers
# Modification 01/21/2016 - Remove redundancy
# Modification 02/29/2016 - Remove bug
# Modification 03/16/2016 - Remove bug
# Modification 03/17/2016 - Remove bug
# Modification 03/24/2016 - Remove bug
# Modification 03/25/2016 - Change each step to functions
# Modification 04/03/2016 - Remove bug
# Modification 04/06/2016 - Reduce temp output files
# Modification 04/06/2016 - Remove bug
# Modification 04/06/2016 - Start parallel with chop
# Modification 04/17/2016 - Improve efficiency
# Modification 04/18/2016 - Remove bug
# Modification 05/24/2016 - Start parallel with remove redundancy
# Modification 06/14/2016 - Add parallel option as arg
# Modification 06/14/2016 - Remove bug
# Modification 06/29/2016 - Remove bug
# Modification 07/08/2016 - Change similarity criteria of rigids from 0.95 to 0.97
# Modification 07/11/2016 - Improve efficiency
# Modification 07/18/2016 - Pack up, format.
# Modification 07/19/2016 - Solve python 2.x/3.x compatibility problem.
# Modification 07/20/2016 - Solve python 2.x/3.x compatibility problem.
# Modification 07/21/2016 - Solve python 2.x/3.x compatibility problem.
# Modification 07/22/2016 - Solve python 2.x/3.x compatibility problem.
# Modification 07/22/2016 - Modify README file
# Last revision 09/13/2016 - Solve output path conflict problem.
|
# Databricks notebook source
# MAGIC %md
# MAGIC ScaDaMaLe Course [site](https://lamastex.github.io/scalable-data-science/sds/3/x/) and [book](https://lamastex.github.io/ScaDaMaLe/index.html)
# MAGIC
# MAGIC This is a 2019-2021 augmentation and update of [Adam NAME initial notebooks.
# MAGIC
# MAGIC _Thanks to [Christian NAME and [William NAME for their contributions towards making these materials Spark 3.0.1 and Python 3+ compliant._
# COMMAND ----------
# MAGIC %md
# MAGIC # Convolutional Neural Networks
# MAGIC ## aka CNN, ConvNet
# COMMAND ----------
# MAGIC %md
# MAGIC As a baseline, let's start a lab running with what we already know.
# MAGIC
# MAGIC We'll take our deep feed-forward multilayer perceptron network, with ReLU activations and reasonable initializations, and apply it to learning the MNIST digits.
# MAGIC
# MAGIC The main part of the code looks like the following (full code you can run is in the next cell):
# MAGIC
# MAGIC ```
# MAGIC # imports, setup, load data sets
# MAGIC
# MAGIC model = Sequential()
# MAGIC model.add(Dense(20, input_dim=784, kernel_initializer='normal', activation='relu'))
# MAGIC model.add(Dense(15, kernel_initializer='normal', activation='relu'))
# MAGIC model.add(Dense(10, kernel_initializer='normal', activation='softmax'))
# MAGIC model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['categorical_accuracy'])
# MAGIC
# MAGIC categorical_labels = to_categorical(y_train, num_classes=10)
# MAGIC
# MAGIC history = model.fit(X_train, categorical_labels, epochs=100, batch_size=100)
# MAGIC
# MAGIC # print metrics, plot errors
# MAGIC ```
# MAGIC
# MAGIC Note the changes, which are largely about building a classifier instead of a regression model:
# MAGIC * Output layer has one neuron per category, with softmax activation
# MAGIC * __Loss function is cross-entropy loss__
# MAGIC * Accuracy metric is categorical accuracy
# COMMAND ----------
# MAGIC %md
# MAGIC Let's hold pointers into wikipedia for these new concepts.
# COMMAND ----------
# MAGIC %scala
# MAGIC //This allows easy embedding of publicly available information into any other notebook
# MAGIC //Example usage:
# MAGIC // displayHTML(frameIt("https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation#Topics_in_LDA",250))
# MAGIC def frameIt( u:String, h:Int ) : String = {
# MAGIC """<iframe
# MAGIC src=""""+ u+""""
# MAGIC width="95%" height="""" + h + """"
# MAGIC sandbox>
# MAGIC <p>
# MAGIC <a href="http://spark.apache.org/docs/latest/index.html">
# MAGIC Fallback link for browsers that, unlikely, don't support frames
# MAGIC </a>
# MAGIC </p>
# MAGIC </iframe>"""
# MAGIC }
# MAGIC displayHTML(frameIt("https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression",500))
# COMMAND ----------
# MAGIC %scala
# MAGIC displayHTML(frameIt("https://en.wikipedia.org/wiki/Softmax_function",380))
# COMMAND ----------
# MAGIC %md
# MAGIC The following is from: [https://www.quora.com/How-does-Keras-calculate-accuracy](https://www.quora.com/How-does-Keras-calculate-accuracy).
# MAGIC
# MAGIC **Categorical accuracy:**
# MAGIC
# MAGIC ```%python
# MAGIC def categorical_accuracy(y_true, y_pred):
# MAGIC return K.cast(K.equal(K.argmax(y_true, axis=-1),
# MAGIC K.argmax(y_pred, axis=-1)),
# MAGIC K.floatx())
# MAGIC ```
# MAGIC
# MAGIC > `K.argmax(y_true)` takes the highest value to be the prediction and matches against the comparative set.
# COMMAND ----------
# MAGIC %md
# MAGIC Watch (1:39)
# MAGIC * [](https://www.youtube.com/watch?v=tRsSi_sqXjI)
# MAGIC
# MAGIC Watch (1:54)
# MAGIC * [](https://www.youtube.com/watch?v=x449QQDhMDE)
# COMMAND ----------
|
"""
=================
Django S3 storage
=================
Usage
=====
Settings
--------
``DEFAULT_FILE_STORAGE``
~~~~~~~~~~~~~~~~~~~~~~~~
This setting store the path to the S3 storage class, the first part correspond
to the filepath and the second the name of the class, if you've got
``example.com`` in your ``PYTHONPATH`` and store your storage file in
``example.com/libs/storages/S3Storage.py``, the resulting setting will be::
DEFAULT_FILE_STORAGE = 'libs.storages.S3Storage.S3Storage'
If you keep the same filename as in repository, it should always end with
``S3Storage.S3Storage``.
``AWS_ACCESS_KEY_ID``
~~~~~~~~~~~~~~~~~~~~~
Your Amazon Web Services access key, as a string.
``AWS_SECRET_ACCESS_KEY``
~~~~~~~~~~~~~~~~~~~~~~~~~
Your Amazon Web Services secret access key, as a string.
``AWS_STORAGE_BUCKET_NAME``
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Your Amazon Web Services storage bucket name, as a string.
``AWS_CALLING_FORMAT``
~~~~~~~~~~~~~~~~~~~~~~
The way you'd like to call the Amazon Web Services API, for instance if you
prefer subdomains::
from S3 import CallingFormat
AWS_CALLING_FORMAT = CallingFormat.SUBDOMAIN
``AWS_HEADERS`` (optionnal)
~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you'd like to set headers sent with each file of the storage::
# see http://developer.yahoo.com/performance/rules.html#expires
AWS_HEADERS = {
'Expires': 'Thu, 15 Apr 2010 20:00:00 GMT',
'Cache-Control': 'max-age=86400',
}
Fields
------
Once you're done, ``default_storage`` will be the S3 storage::
>>> from django.core.files.storage import default_storage
>>> print default_storage.__class__
<class 'backends.S3Storage.S3Storage'>
This way, if you define a new ``FileField``, it will use the S3 storage::
>>> from django.db import models
>>> class Resume(models.Model):
... pdf = models.FileField(upload_to='pdfs')
... photos = models.ImageField(upload_to='photos')
...
>>> resume = Resume()
>>> print resume.pdf.storage
<backends.S3Storage.S3Storage object at ...>
Tests
=====
Initialization::
>>> from django.core.files.storage import default_storage
>>> from django.core.files.base import ContentFile
>>> from django.core.cache import cache
>>> from models import MyStorage
Storage
-------
Standard file access options are available, and work as expected::
>>> default_storage.exists('storage_test')
False
>>> file = default_storage.open('storage_test', 'w')
>>> file.write('storage contents')
>>> file.close()
>>> default_storage.exists('storage_test')
True
>>> file = default_storage.open('storage_test', 'r')
>>> file.read()
'storage contents'
>>> file.close()
>>> default_storage.delete('storage_test')
>>> default_storage.exists('storage_test')
False
Model
-----
An object without a file has limited functionality::
>>> obj1 = MyStorage()
>>> obj1.normal
<FieldFile: None>
>>> obj1.normal.size
Traceback (most recent call last):
...
ValueError: The 'normal' attribute has no file associated with it.
Saving a file enables full functionality::
>>> obj1.normal.save('django_test.txt', ContentFile('content'))
>>> obj1.normal
<FieldFile: tests/django_test.txt>
>>> obj1.normal.size
7
>>> obj1.normal.read()
'content'
Files can be read in a little at a time, if necessary::
>>> obj1.normal.open()
>>> obj1.normal.read(3)
'con'
>>> obj1.normal.read()
'tent'
>>> '-'.join(obj1.normal.chunks(chunk_size=2))
'co-nt-en-t'
Save another file with the same name::
>>> obj2 = MyStorage()
>>> obj2.normal.save('django_test.txt', ContentFile('more content'))
>>> obj2.normal
<FieldFile: tests/django_test_.txt>
>>> obj2.normal.size
12
Push the objects into the cache to make sure they pickle properly::
>>> cache.set('obj1', obj1)
>>> cache.set('obj2', obj2)
>>> cache.get('obj2').normal
<FieldFile: tests/django_test_.txt>
Deleting an object deletes the file it uses, if there are no other objects
still using that file::
>>> obj2.delete()
>>> obj2.normal.save('django_test.txt', ContentFile('more content'))
>>> obj2.normal
<FieldFile: tests/django_test_.txt>
Default values allow an object to access a single file::
>>> obj3 = MyStorage.objects.create()
>>> obj3.default
<FieldFile: tests/default.txt>
>>> obj3.default.read()
'default content'
But it shouldn't be deleted, even if there are no more objects using it::
>>> obj3.delete()
>>> obj3 = MyStorage()
>>> obj3.default.read()
'default content'
Verify the fix for #5655, making sure the directory is only determined once::
>>> obj4 = MyStorage()
>>> obj4.random.save('random_file', ContentFile('random content'))
>>> obj4.random
<FieldFile: .../random_file>
Clean up the temporary files::
>>> obj1.normal.delete()
>>> obj2.normal.delete()
>>> obj3.default.delete()
>>> obj4.random.delete()
""" |
# -*- coding: utf-8 -*-
# This file is part of ranger, the console file manager.
# This configuration file is licensed under the same terms as ranger.
# ===================================================================
#
# NOTE: If you copied this file to /etc/ranger/commands_full.py or
# ~/.config/ranger/commands_full.py, then it will NOT be loaded by ranger,
# and only serve as a reference.
#
# ===================================================================
# This file contains ranger's commands.
# It's all in python; lines beginning with # are comments.
#
# Note that additional commands are automatically generated from the methods
# of the class ranger.core.actions.Actions.
#
# You can customize commands in the files /etc/ranger/commands.py (system-wide)
# and ~/.config/ranger/commands.py (per user).
# They have the same syntax as this file. In fact, you can just copy this
# file to ~/.config/ranger/commands_full.py with
# `ranger --copy-config=commands_full' and make your modifications, don't
# forget to rename it to commands.py. You can also use
# `ranger --copy-config=commands' to copy a short sample commands.py that
# has everything you need to get started.
# But make sure you update your configs when you update ranger.
#
# ===================================================================
# Every class defined here which is a subclass of `Command' will be used as a
# command in ranger. Several methods are defined to interface with ranger:
# execute(): called when the command is executed.
# cancel(): called when closing the console.
# tab(tabnum): called when <TAB> is pressed.
# quick(): called after each keypress.
#
# tab() argument tabnum is 1 for <TAB> and -1 for <S-TAB> by default
#
# The return values for tab() can be either:
# None: There is no tab completion
# A string: Change the console to this string
# A list/tuple/generator: cycle through every item in it
#
# The return value for quick() can be:
# False: Nothing happens
# True: Execute the command afterwards
#
# The return value for execute() and cancel() doesn't matter.
#
# ===================================================================
# Commands have certain attributes and methods that facilitate parsing of
# the arguments:
#
# self.line: The whole line that was written in the console.
# self.args: A list of all (space-separated) arguments to the command.
# self.quantifier: If this command was mapped to the key "X" and
# the user pressed 6X, self.quantifier will be 6.
# self.arg(n): The n-th argument, or an empty string if it doesn't exist.
# self.rest(n): The n-th argument plus everything that followed. For example,
# if the command was "search foo bar a b c", rest(2) will be "bar a b c"
# self.start(n): Anything before the n-th argument. For example, if the
# command was "search foo bar a b c", start(2) will be "search foo"
#
# ===================================================================
# And this is a little reference for common ranger functions and objects:
#
# self.fm: A reference to the "fm" object which contains most information
# about ranger.
# self.fm.notify(string): Print the given string on the screen.
# self.fm.notify(string, bad=True): Print the given string in RED.
# self.fm.reload_cwd(): Reload the current working directory.
# self.fm.thisdir: The current working directory. (A File object.)
# self.fm.thisfile: The current file. (A File object too.)
# self.fm.thistab.get_selection(): A list of all selected files.
# self.fm.execute_console(string): Execute the string as a ranger command.
# self.fm.open_console(string): Open the console with the given string
# already typed in for you.
# self.fm.move(direction): Moves the cursor in the given direction, which
# can be something like down=3, up=5, right=1, left=1, to=6, ...
#
# File objects (for example self.fm.thisfile) have these useful attributes and
# methods:
#
# tfile.path: The path to the file.
# tfile.basename: The base name only.
# tfile.load_content(): Force a loading of the directories content (which
# obviously works with directories only)
# tfile.is_directory: True/False depending on whether it's a directory.
#
# For advanced commands it is unavoidable to dive a bit into the source code
# of ranger.
# ===================================================================
|
#!/usr/bin/python2
# SPDX-License-Identifier: GPL-2.0
# exported-sql-viewer.py: view data from sql database
# Copyright (c) 2014-2018, Intel Corporation.
# To use this script you will need to have exported data using either the
# export-to-sqlite.py or the export-to-postgresql.py script. Refer to those
# scripts for details.
#
# Following on from the example in the export scripts, a
# call-graph can be displayed for the pt_example database like this:
#
# python tools/perf/scripts/python/exported-sql-viewer.py pt_example
#
# Note that for PostgreSQL, this script supports connecting to remote databases
# by setting hostname, port, username, password, and dbname e.g.
#
# python tools/perf/scripts/python/exported-sql-viewer.py "hostname=myhost username=myuser password=mypassword dbname=pt_example"
#
# The result is a GUI window with a tree representing a context-sensitive
# call-graph. Expanding a couple of levels of the tree and adjusting column
# widths to suit will display something like:
#
# Call Graph: pt_example
# Call Path Object Count Time(ns) Time(%) Branch Count Branch Count(%)
# v- ls
# v- 2638:2638
# v- _start ld-2.19.so 1 10074071 100.0 211135 100.0
# |- unknown unknown 1 13198 0.1 1 0.0
# >- _dl_start ld-2.19.so 1 1400980 13.9 19637 9.3
# >- _d_linit_internal ld-2.19.so 1 448152 4.4 11094 5.3
# v-__libc_start_main@plt ls 1 8211741 81.5 180397 85.4
# >- _dl_fixup ld-2.19.so 1 7607 0.1 108 0.1
# >- __cxa_atexit libc-2.19.so 1 11737 0.1 10 0.0
# >- __libc_csu_init ls 1 10354 0.1 10 0.0
# |- _setjmp libc-2.19.so 1 0 0.0 4 0.0
# v- main ls 1 8182043 99.6 180254 99.9
#
# Points to note:
# The top level is a command name (comm)
# The next level is a thread (pid:tid)
# Subsequent levels are functions
# 'Count' is the number of calls
# 'Time' is the elapsed time until the function returns
# Percentages are relative to the level above
# 'Branch Count' is the total number of branches for that function and all
# functions that it calls
# There is also a "All branches" report, which displays branches and
# possibly disassembly. However, presently, the only supported disassembler is
# Intel XED, and additionally the object code must be present in perf build ID
# cache. To use Intel XED, libxed.so must be present. To build and install
# libxed.so:
# git clone https://github.com/intelxed/mbuild.git mbuild
# git clone https://github.com/intelxed/xed
# cd xed
# ./mfile.py --share
# sudo ./mfile.py --prefix=/usr/local install
# sudo ldconfig
#
# Example report:
#
# Time CPU Command PID TID Branch Type In Tx Branch
# 8107675239590 2 ls 22011 22011 return from interrupt No ffffffff86a00a67 native_irq_return_iret ([kernel]) -> 7fab593ea260 _start (ld-2.19.so)
# 7fab593ea260 48 89 e7 mov %rsp, %rdi
# 8107675239899 2 ls 22011 22011 hardware interrupt No 7fab593ea260 _start (ld-2.19.so) -> ffffffff86a012e0 page_fault ([kernel])
# 8107675241900 2 ls 22011 22011 return from interrupt No ffffffff86a00a67 native_irq_return_iret ([kernel]) -> 7fab593ea260 _start (ld-2.19.so)
# 7fab593ea260 48 89 e7 mov %rsp, %rdi
# 7fab593ea263 e8 c8 06 00 00 callq 0x7fab593ea930
# 8107675241900 2 ls 22011 22011 call No 7fab593ea263 _start+0x3 (ld-2.19.so) -> 7fab593ea930 _dl_start (ld-2.19.so)
# 7fab593ea930 55 pushq %rbp
# 7fab593ea931 48 89 e5 mov %rsp, %rbp
# 7fab593ea934 41 57 pushq %r15
# 7fab593ea936 41 56 pushq %r14
# 7fab593ea938 41 55 pushq %r13
# 7fab593ea93a 41 54 pushq %r12
# 7fab593ea93c 53 pushq %rbx
# 7fab593ea93d 48 89 fb mov %rdi, %rbx
# 7fab593ea940 48 83 ec 68 sub $0x68, %rsp
# 7fab593ea944 0f 31 rdtsc
# 7fab593ea946 48 c1 e2 20 shl $0x20, %rdx
# 7fab593ea94a 89 c0 mov %eax, %eax
# 7fab593ea94c 48 09 c2 or %rax, %rdx
# 7fab593ea94f 48 8b 05 1a 15 22 00 movq 0x22151a(%rip), %rax
# 8107675242232 2 ls 22011 22011 hardware interrupt No 7fab593ea94f _dl_start+0x1f (ld-2.19.so) -> ffffffff86a012e0 page_fault ([kernel])
# 8107675242900 2 ls 22011 22011 return from interrupt No ffffffff86a00a67 native_irq_return_iret ([kernel]) -> 7fab593ea94f _dl_start+0x1f (ld-2.19.so)
# 7fab593ea94f 48 8b 05 1a 15 22 00 movq 0x22151a(%rip), %rax
# 7fab593ea956 48 89 15 3b 13 22 00 movq %rdx, 0x22133b(%rip)
# 8107675243232 2 ls 22011 22011 hardware interrupt No 7fab593ea956 _dl_start+0x26 (ld-2.19.so) -> ffffffff86a012e0 page_fault ([kernel])
|
"""
========================
Broadcasting over arrays
========================
The term broadcasting describes how numpy treats arrays with different
shapes during arithmetic operations. Subject to certain constraints,
the smaller array is "broadcast" across the larger array so that they
have compatible shapes. Broadcasting provides a means of vectorizing
array operations so that looping occurs in C instead of Python. It does
this without making needless copies of data and usually leads to
efficient algorithm implementations. There are, however, cases where
broadcasting is a bad idea because it leads to inefficient use of memory
that slows computation.
NumPy operations are usually done on pairs of arrays on an
element-by-element basis. In the simplest case, the two arrays must
have exactly the same shape, as in the following example:
>>> a = np.array([1.0, 2.0, 3.0])
>>> b = np.array([2.0, 2.0, 2.0])
>>> a * b
array([ 2., 4., 6.])
NumPy's broadcasting rule relaxes this constraint when the arrays'
shapes meet certain constraints. The simplest broadcasting example occurs
when an array and a scalar value are combined in an operation:
>>> a = np.array([1.0, 2.0, 3.0])
>>> b = 2.0
>>> a * b
array([ 2., 4., 6.])
The result is equivalent to the previous example where ``b`` was an array.
We can think of the scalar ``b`` being *stretched* during the arithmetic
operation into an array with the same shape as ``a``. The new elements in
``b`` are simply copies of the original scalar. The stretching analogy is
only conceptual. NumPy is smart enough to use the original scalar value
without actually making copies, so that broadcasting operations are as
memory and computationally efficient as possible.
The code in the second example is more efficient than that in the first
because broadcasting moves less memory around during the multiplication
(``b`` is a scalar rather than an array).
General Broadcasting Rules
==========================
When operating on two arrays, NumPy compares their shapes element-wise.
It starts with the trailing dimensions, and works its way forward. Two
dimensions are compatible when
1) they are equal, or
2) one of them is 1
If these conditions are not met, a
``ValueError: frames are not aligned`` exception is thrown, indicating that
the arrays have incompatible shapes. The size of the resulting array
is the maximum size along each dimension of the input arrays.
Arrays do not need to have the same *number* of dimensions. For example,
if you have a ``256x256x3`` array of RGB values, and you want to scale
each color in the image by a different value, you can multiply the image
by a one-dimensional array with 3 values. Lining up the sizes of the
trailing axes of these arrays according to the broadcast rules, shows that
they are compatible::
Image (3d array): 256 x 256 x 3
Scale (1d array): 3
Result (3d array): 256 x 256 x 3
When either of the dimensions compared is one, the other is
used. In other words, dimensions with size 1 are stretched or "copied"
to match the other.
In the following example, both the ``A`` and ``B`` arrays have axes with
length one that are expanded to a larger size during the broadcast
operation::
A (4d array): 8 x 1 x 6 x 1
B (3d array): 7 x 1 x 5
Result (4d array): 8 x 7 x 6 x 5
Here are some more examples::
A (2d array): 5 x 4
B (1d array): 1
Result (2d array): 5 x 4
A (2d array): 5 x 4
B (1d array): 4
Result (2d array): 5 x 4
A (3d array): 15 x 3 x 5
B (3d array): 15 x 1 x 5
Result (3d array): 15 x 3 x 5
A (3d array): 15 x 3 x 5
B (2d array): 3 x 5
Result (3d array): 15 x 3 x 5
A (3d array): 15 x 3 x 5
B (2d array): 3 x 1
Result (3d array): 15 x 3 x 5
Here are examples of shapes that do not broadcast::
A (1d array): 3
B (1d array): 4 # trailing dimensions do not match
A (2d array): 2 x 1
B (3d array): 8 x 4 x 3 # second from last dimensions mismatched
An example of broadcasting in practice::
>>> x = np.arange(4)
>>> xx = x.reshape(4,1)
>>> y = np.ones(5)
>>> z = np.ones((3,4))
>>> x.shape
(4,)
>>> y.shape
(5,)
>>> x + y
<type 'exceptions.ValueError'>: shape mismatch: objects cannot be broadcast to a single shape
>>> xx.shape
(4, 1)
>>> y.shape
(5,)
>>> (xx + y).shape
(4, 5)
>>> xx + y
array([[ 1., 1., 1., 1., 1.],
[ 2., 2., 2., 2., 2.],
[ 3., 3., 3., 3., 3.],
[ 4., 4., 4., 4., 4.]])
>>> x.shape
(4,)
>>> z.shape
(3, 4)
>>> (x + z).shape
(3, 4)
>>> x + z
array([[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.]])
Broadcasting provides a convenient way of taking the outer product (or
any other outer operation) of two arrays. The following example shows an
outer addition operation of two 1-d arrays::
>>> a = np.array([0.0, 10.0, 20.0, 30.0])
>>> b = np.array([1.0, 2.0, 3.0])
>>> a[:, np.newaxis] + b
array([[ 1., 2., 3.],
[ 11., 12., 13.],
[ 21., 22., 23.],
[ 31., 32., 33.]])
Here the ``newaxis`` index operator inserts a new axis into ``a``,
making it a two-dimensional ``4x1`` array. Combining the ``4x1`` array
with ``b``, which has shape ``(3,)``, yields a ``4x3`` array.
See `this article <http://wiki.scipy.org/EricsBroadcastingDoc>`_
for illustrations of broadcasting concepts.
""" |
"""Doctest for method/function calls.
We're going the use these types for extra testing
>>> from UserList import UserList
>>> from UserDict import UserDict
We're defining four helper functions
>>> def e(a,b):
... print a, b
>>> def f(*a, **k):
... print a, test_support.sortdict(k)
>>> def g(x, *y, **z):
... print x, y, test_support.sortdict(z)
>>> def h(j=1, a=2, h=3):
... print j, a, h
Argument list examples
>>> f()
() {}
>>> f(1)
(1,) {}
>>> f(1, 2)
(1, 2) {}
>>> f(1, 2, 3)
(1, 2, 3) {}
>>> f(1, 2, 3, *(4, 5))
(1, 2, 3, 4, 5) {}
>>> f(1, 2, 3, *[4, 5])
(1, 2, 3, 4, 5) {}
>>> f(1, 2, 3, *UserList([4, 5]))
(1, 2, 3, 4, 5) {}
Here we add keyword arguments
>>> f(1, 2, 3, **{'a':4, 'b':5})
(1, 2, 3) {'a': 4, 'b': 5}
>>> f(1, 2, 3, *[4, 5], **{'a':6, 'b':7})
(1, 2, 3, 4, 5) {'a': 6, 'b': 7}
>>> f(1, 2, 3, x=4, y=5, *(6, 7), **{'a':8, 'b': 9})
(1, 2, 3, 6, 7) {'a': 8, 'b': 9, 'x': 4, 'y': 5}
>>> f(1, 2, 3, **UserDict(a=4, b=5))
(1, 2, 3) {'a': 4, 'b': 5}
>>> f(1, 2, 3, *(4, 5), **UserDict(a=6, b=7))
(1, 2, 3, 4, 5) {'a': 6, 'b': 7}
>>> f(1, 2, 3, x=4, y=5, *(6, 7), **UserDict(a=8, b=9))
(1, 2, 3, 6, 7) {'a': 8, 'b': 9, 'x': 4, 'y': 5}
Examples with invalid arguments (TypeErrors). We're also testing the function
names in the exception messages.
Verify clearing of SF bug #733667
>>> e(c=4)
Traceback (most recent call last):
...
TypeError: e() got an unexpected keyword argument 'c'
>>> g()
Traceback (most recent call last):
...
TypeError: g() takes at least 1 argument (0 given)
>>> g(*())
Traceback (most recent call last):
...
TypeError: g() takes at least 1 argument (0 given)
>>> g(*(), **{})
Traceback (most recent call last):
...
TypeError: g() takes at least 1 argument (0 given)
>>> g(1)
1 () {}
>>> g(1, 2)
1 (2,) {}
>>> g(1, 2, 3)
1 (2, 3) {}
>>> g(1, 2, 3, *(4, 5))
1 (2, 3, 4, 5) {}
>>> class Nothing: pass
...
>>> g(*Nothing())
Traceback (most recent call last):
...
TypeError: g() argument after * must be a sequence, not instance
>>> class Nothing:
... def __len__(self): return 5
...
>>> g(*Nothing())
Traceback (most recent call last):
...
TypeError: g() argument after * must be a sequence, not instance
>>> class Nothing():
... def __len__(self): return 5
... def __getitem__(self, i):
... if i<3: return i
... else: raise IndexError(i)
...
>>> g(*Nothing())
0 (1, 2) {}
>>> class Nothing:
... def __init__(self): self.c = 0
... def __iter__(self): return self
... def next(self):
... if self.c == 4:
... raise StopIteration
... c = self.c
... self.c += 1
... return c
...
>>> g(*Nothing())
0 (1, 2, 3) {}
Make sure that the function doesn't stomp the dictionary
>>> d = {'a': 1, 'b': 2, 'c': 3}
>>> d2 = d.copy()
>>> g(1, d=4, **d)
1 () {'a': 1, 'b': 2, 'c': 3, 'd': 4}
>>> d == d2
True
What about willful misconduct?
>>> def saboteur(**kw):
... kw['x'] = 'm'
... return kw
>>> d = {}
>>> kw = saboteur(a=1, **d)
>>> d
{}
>>> g(1, 2, 3, **{'x': 4, 'y': 5})
Traceback (most recent call last):
...
TypeError: g() got multiple values for keyword argument 'x'
>>> f(**{1:2})
Traceback (most recent call last):
...
TypeError: f() keywords must be strings
>>> h(**{'e': 2})
Traceback (most recent call last):
...
TypeError: h() got an unexpected keyword argument 'e'
>>> h(*h)
Traceback (most recent call last):
...
TypeError: h() argument after * must be a sequence, not function
>>> dir(*h)
Traceback (most recent call last):
...
TypeError: dir() argument after * must be a sequence, not function
>>> None(*h)
Traceback (most recent call last):
...
TypeError: NoneType object argument after * must be a sequence, \
not function
>>> h(**h)
Traceback (most recent call last):
...
TypeError: h() argument after ** must be a mapping, not function
>>> dir(**h)
Traceback (most recent call last):
...
TypeError: dir() argument after ** must be a mapping, not function
>>> None(**h)
Traceback (most recent call last):
...
TypeError: NoneType object argument after ** must be a mapping, \
not function
>>> dir(b=1, **{'b': 1})
Traceback (most recent call last):
...
TypeError: dir() got multiple values for keyword argument 'b'
Another helper function
>>> def f2(*a, **b):
... return a, b
>>> d = {}
>>> for i in xrange(512):
... key = 'k%d' % i
... d[key] = i
>>> a, b = f2(1, *(2,3), **d)
>>> len(a), len(b), b == d
(3, 512, True)
>>> class Foo:
... def method(self, arg1, arg2):
... return arg1+arg2
>>> x = Foo()
>>> Foo.method(*(x, 1, 2))
3
>>> Foo.method(x, *(1, 2))
3
>>> Foo.method(*(1, 2, 3))
Traceback (most recent call last):
...
TypeError: unbound method method() must be called with Foo instance as \
first argument (got int instance instead)
>>> Foo.method(1, *[2, 3])
Traceback (most recent call last):
...
TypeError: unbound method method() must be called with Foo instance as \
first argument (got int instance instead)
A PyCFunction that takes only positional parameters shoud allow an
empty keyword dictionary to pass without a complaint, but raise a
TypeError if te dictionary is not empty
>>> try:
... silence = id(1, *{})
... True
... except:
... False
True
>>> id(1, **{'foo': 1})
Traceback (most recent call last):
...
TypeError: id() takes no keyword arguments
""" |
#!/usr/bin/env python
##
## @file rewrite_pydoc.py
## @brief Convert libSBML Python doc file to something readable as docstrings
## @author NAME Purpose:
##
## The comments in the libSBML source code use Doxygen mark-up; this
## content is read by Doxygen and Javadoc, in combination with other
## scripts, to produce the libSBML API documentation in the libSBML "docs"
## directory. When creating the Python language interface, SWIG takes the
## comments and inserts them as documentation strings in the Python code,
## which is good from the perspective of being an easy way to provide help
## for the Python interface classes and methods, but bad from the
## perspective that it is full of Doxygen markup and not suitable for
## direct reading by humans.
##
## This program converts the Doxygen-based documentation strings by
## rewriting them to plain text. This plain text can then be included in
## the final Python bindings for libSBML, so that users can use the Python
## interactive help system to view the documentation.
##
## This program is not a general converter; it is designed specifically to
## work with the way that we generate the libSBML python bindings.
## However, it should not be too difficult to adapt to other similar
## software projects.
##
## The main hardwired assumptions are the following:
##
## * The input file to rewrite_pydoc.py is the output produced by our
## ../../swig/swigdoc.py, which produces documentation definitions for
## swig. These have the form shown in the following example:
##
## %feature("docstring") SBMLReader::SBMLReader "
## Creates a new SBMLReader and returns it.
##
## The libSBML SBMLReader objects offer methods for reading SBML in
## XML form from files and text strings.
## ";
##
## The output of rewrite_pydoc.py is another .i file in which all Doxygen
## tags have been translated and the docstring contents have been
## reformatted for use in the python plain-text interactive help system.
##
## * In our process for producing the libSBML Python bindings, we take the
## output of rewrite_pydoc.py and include it in the input to swig. This
## is done via an %include command in the ../local.i file. The
## consequence is that swig reads these %feature commands, and uses them
## when it produces a file named "libsbml.py" containing the Python code
## for the libSBML interface. The objects and methods in "libsbml.py"
## contain Python-style "docstrings" that are a combination of we defined
## in the .i file and what swig itself constructs. (In particular, swig
## adds documentation about the method signatures, because the methods
## are interfaces to native code and Python introspection cannot reveal
## the data types of the parameters.)
##
## * The Doxygen markup understood by rewrite_pydoc.py is not the complete
## set of all possible Doxygen tags. We don't use all possible Doxygen
## tags in the libSBML documentation, and so this program only looks for
## the ones we have been using.
##
##
## Special features:
##
## * When looking for @htmlinclude files, it first checks to see if a
## version of the file with a .txt extension exists in the same location
## where it finds the .html file. If the .txt eversion exists, it
## includes that instead of the .html file. (This allows hand-formatted
## text files to be used, which is useful for files containing tables,
## because the Python HTML parser library doesn't handle tables.)
##
## * When expanding @image directives, it looks for a file with the
## extension .txt in the same directory where it finds the .jpg file. If
## the .txt version exists, it includes that; if it doesn't exist, it
## does not include anything. (Since the docstrings are plain-text, no
## other action seems sensible in this context.)
##
##
## <!--------------------------------------------------------------------------
## This file is part of libSBML. Please visit http://sbml.org for more
## information about SBML, and the latest version of libSBML.
##
## Copyright (C) 2009-2013 jointly by the following organizations:
## 1. California Institute of Technology, Pasadena, CA, USA
## 2. EMBL European Bioinformatics Institute (EBML-EBI), Hinxton, UK
##
## Copyright (C) 2006-2008 by the California Institute of Technology,
## Pasadena, CA, USA
##
## Copyright (C) 2002-2005 jointly by the following organizations:
## 1. California Institute of Technology, Pasadena, CA, USA
## 2. Japan Science and Technology Agency, Japan
##
## This library is free software; you can redistribute it and/or modify it
## under the terms of the GNU Lesser General Public License as published by
## the Free Software Foundation. A copy of the license agreement is provided
## in the file named "LICENSE.txt" included with this software distribution
## and also available online as http://sbml.org/software/libsbml/license.html
## ------------------------------------------------------------------------ -->
|
"""
========================
Broadcasting over arrays
========================
The term broadcasting describes how numpy treats arrays with different
shapes during arithmetic operations. Subject to certain constraints,
the smaller array is "broadcast" across the larger array so that they
have compatible shapes. Broadcasting provides a means of vectorizing
array operations so that looping occurs in C instead of Python. It does
this without making needless copies of data and usually leads to
efficient algorithm implementations. There are, however, cases where
broadcasting is a bad idea because it leads to inefficient use of memory
that slows computation.
NumPy operations are usually done on pairs of arrays on an
element-by-element basis. In the simplest case, the two arrays must
have exactly the same shape, as in the following example:
>>> a = np.array([1.0, 2.0, 3.0])
>>> b = np.array([2.0, 2.0, 2.0])
>>> a * b
array([ 2., 4., 6.])
NumPy's broadcasting rule relaxes this constraint when the arrays'
shapes meet certain constraints. The simplest broadcasting example occurs
when an array and a scalar value are combined in an operation:
>>> a = np.array([1.0, 2.0, 3.0])
>>> b = 2.0
>>> a * b
array([ 2., 4., 6.])
The result is equivalent to the previous example where ``b`` was an array.
We can think of the scalar ``b`` being *stretched* during the arithmetic
operation into an array with the same shape as ``a``. The new elements in
``b`` are simply copies of the original scalar. The stretching analogy is
only conceptual. NumPy is smart enough to use the original scalar value
without actually making copies, so that broadcasting operations are as
memory and computationally efficient as possible.
The code in the second example is more efficient than that in the first
because broadcasting moves less memory around during the multiplication
(``b`` is a scalar rather than an array).
General Broadcasting Rules
==========================
When operating on two arrays, NumPy compares their shapes element-wise.
It starts with the trailing dimensions, and works its way forward. Two
dimensions are compatible when
1) they are equal, or
2) one of them is 1
If these conditions are not met, a
``ValueError: frames are not aligned`` exception is thrown, indicating that
the arrays have incompatible shapes. The size of the resulting array
is the maximum size along each dimension of the input arrays.
Arrays do not need to have the same *number* of dimensions. For example,
if you have a ``256x256x3`` array of RGB values, and you want to scale
each color in the image by a different value, you can multiply the image
by a one-dimensional array with 3 values. Lining up the sizes of the
trailing axes of these arrays according to the broadcast rules, shows that
they are compatible::
Image (3d array): 256 x 256 x 3
Scale (1d array): 3
Result (3d array): 256 x 256 x 3
When either of the dimensions compared is one, the other is
used. In other words, dimensions with size 1 are stretched or "copied"
to match the other.
In the following example, both the ``A`` and ``B`` arrays have axes with
length one that are expanded to a larger size during the broadcast
operation::
A (4d array): 8 x 1 x 6 x 1
B (3d array): 7 x 1 x 5
Result (4d array): 8 x 7 x 6 x 5
Here are some more examples::
A (2d array): 5 x 4
B (1d array): 1
Result (2d array): 5 x 4
A (2d array): 5 x 4
B (1d array): 4
Result (2d array): 5 x 4
A (3d array): 15 x 3 x 5
B (3d array): 15 x 1 x 5
Result (3d array): 15 x 3 x 5
A (3d array): 15 x 3 x 5
B (2d array): 3 x 5
Result (3d array): 15 x 3 x 5
A (3d array): 15 x 3 x 5
B (2d array): 3 x 1
Result (3d array): 15 x 3 x 5
Here are examples of shapes that do not broadcast::
A (1d array): 3
B (1d array): 4 # trailing dimensions do not match
A (2d array): 2 x 1
B (3d array): 8 x 4 x 3 # second from last dimensions mismatched
An example of broadcasting in practice::
>>> x = np.arange(4)
>>> xx = x.reshape(4,1)
>>> y = np.ones(5)
>>> z = np.ones((3,4))
>>> x.shape
(4,)
>>> y.shape
(5,)
>>> x + y
<type 'exceptions.ValueError'>: shape mismatch: objects cannot be broadcast to a single shape
>>> xx.shape
(4, 1)
>>> y.shape
(5,)
>>> (xx + y).shape
(4, 5)
>>> xx + y
array([[ 1., 1., 1., 1., 1.],
[ 2., 2., 2., 2., 2.],
[ 3., 3., 3., 3., 3.],
[ 4., 4., 4., 4., 4.]])
>>> x.shape
(4,)
>>> z.shape
(3, 4)
>>> (x + z).shape
(3, 4)
>>> x + z
array([[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.]])
Broadcasting provides a convenient way of taking the outer product (or
any other outer operation) of two arrays. The following example shows an
outer addition operation of two 1-d arrays::
>>> a = np.array([0.0, 10.0, 20.0, 30.0])
>>> b = np.array([1.0, 2.0, 3.0])
>>> a[:, np.newaxis] + b
array([[ 1., 2., 3.],
[ 11., 12., 13.],
[ 21., 22., 23.],
[ 31., 32., 33.]])
Here the ``newaxis`` index operator inserts a new axis into ``a``,
making it a two-dimensional ``4x1`` array. Combining the ``4x1`` array
with ``b``, which has shape ``(3,)``, yields a ``4x3`` array.
See `this article <http://wiki.scipy.org/EricsBroadcastingDoc>`_
for illustrations of broadcasting concepts.
""" |
"""
Introduction
============
SqlSoup provides a convenient way to access existing database tables without
having to declare table or mapper classes ahead of time. It is built on top of
the SQLAlchemy ORM and provides a super-minimalistic interface to an existing
database.
SqlSoup effectively provides a coarse grained, alternative interface to
working with the SQLAlchemy ORM, providing a "self configuring" interface
for extremely rudimental operations. It's somewhat akin to a
"super novice mode" version of the ORM. While SqlSoup can be very handy,
users are strongly encouraged to use the full ORM for non-trivial applications.
Suppose we have a database with users, books, and loans tables
(corresponding to the PyWebOff dataset, if you're curious).
Creating a SqlSoup gateway is just like creating an SQLAlchemy
engine::
>>> from sqlalchemy.ext.sqlsoup import SqlSoup
>>> db = SqlSoup('sqlite:///:memory:')
or, you can re-use an existing engine::
>>> db = SqlSoup(engine)
You can optionally specify a schema within the database for your
SqlSoup::
>>> db.schema = myschemaname
Loading objects
===============
Loading objects is as easy as this::
>>> users = db.users.all()
>>> users.sort()
>>> users
[MappedUsers(name=u'Joe NAME MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',password=u'basepair',classname=None,admin=1)]
Of course, letting the database do the sort is better::
>>> db.users.order_by(db.users.name).all()
[MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',password=u'basepair',classname=None,admin=1), MappedUsers(name=u'Joe NAME access is intuitive::
>>> users[0].email
u'student@example.edu'
Of course, you don't want to load all users very often. Let's add a
WHERE clause. Let's also switch the order_by to DESC while we're at
it::
>>> from sqlalchemy import or_, and_, desc
>>> where = or_(db.users.name=='Bhargan Basepair', db.users.email=='student@example.edu')
>>> db.users.filter(where).order_by(desc(db.users.name)).all()
[MappedUsers(name=u'Joe NAME MappedUsers(name=u'Bhargan NAME can also use .first() (to retrieve only the first object from a query) or
.one() (like .first when you expect exactly one user -- it will raise an
exception if more were returned)::
>>> db.users.filter(db.users.name=='Bhargan NAME
MappedUsers(name=u'Bhargan NAME name is the primary key, this is equivalent to
>>> db.users.get('Bhargan NAME
MappedUsers(name=u'Bhargan NAME is also equivalent to
>>> db.users.filter_by(name='Bhargan NAME
MappedUsers(name=u'Bhargan NAME is like filter, but takes kwargs instead of full clause expressions.
This makes it more concise for simple queries like this, but you can't do
complex queries like the or\_ above or non-equality based comparisons this way.
Full query documentation
------------------------
Get, filter, filter_by, order_by, limit, and the rest of the
query methods are explained in detail in :ref:`ormtutorial_querying`.
Modifying objects
=================
Modifying objects is intuitive::
>>> user = _
>>> user.email = 'basepair+nospam@example.edu'
>>> db.commit()
(SqlSoup leverages the sophisticated SQLAlchemy unit-of-work code, so
multiple updates to a single object will be turned into a single
``UPDATE`` statement when you commit.)
To finish covering the basics, let's insert a new loan, then delete
it::
>>> book_id = db.books.filter_by(title='Regional Variation in Moss').first().id
>>> db.loans.insert(book_id=book_id, user_name=user.name)
MappedLoans(book_id=2,user_name=u'Bhargan NAME
>>> loan = db.loans.filter_by(book_id=2, user_name='Bhargan NAME
>>> db.delete(loan)
>>> db.commit()
You can also delete rows that have not been loaded as objects. Let's
do our insert/delete cycle once more, this time using the loans
table's delete method. (For SQLAlchemy experts: note that no flush()
call is required since this delete acts at the SQL level, not at the
Mapper level.) The same where-clause construction rules apply here as
to the select methods.
::
>>> db.loans.insert(book_id=book_id, user_name=user.name)
MappedLoans(book_id=2,user_name=u'Bhargan NAME
>>> db.loans.delete(db.loans.book_id==2)
You can similarly update multiple rows at once. This will change the
book_id to 1 in all loans whose book_id is 2::
>>> db.loans.update(db.loans.book_id==2, book_id=1)
>>> db.loans.filter_by(book_id=1).all()
[MappedLoans(book_id=1,user_name=u'Joe NAME 7, 12, 0, 0))]
Joins
=====
Occasionally, you will want to pull out a lot of data from related
tables all at once. In this situation, it is far more efficient to
have the database perform the necessary join. (Here we do not have *a
lot of data* but hopefully the concept is still clear.) SQLAlchemy is
smart enough to recognize that loans has a foreign key to users, and
uses that as the join condition automatically.
::
>>> join1 = db.join(db.users, db.loans, isouter=True)
>>> join1.filter_by(name='Joe NAME
[MappedJoin(name=u'Joe NAME0,book_id=1,user_name=u'Joe NAME 7, 12, 0, 0))]
If you're unfortunate enough to be using MySQL with the default MyISAM
storage engine, you'll have to specify the join condition manually,
since MyISAM does not store foreign keys. Here's the same join again,
with the join condition explicitly specified::
>>> db.join(db.users, db.loans, db.users.name==db.loans.user_name, isouter=True)
<class 'sqlalchemy.ext.sqlsoup.MappedJoin'>
You can compose arbitrarily complex joins by combining Join objects
with tables or other joins. Here we combine our first join with the
books table::
>>> join2 = db.join(join1, db.books)
>>> join2.all()
[MappedJoin(name=u'Joe NAME0,book_id=1,user_name=u'Joe NAME 7, 12, 0, 0),id=1,title=u'Mustards I Have Known',published_year=u'1989',authors=u'Jones')]
If you join tables that have an identical column name, wrap your join
with `with_labels`, to disambiguate columns with their table name
(.c is short for .columns)::
>>> db.with_labels(join1).c.keys()
[u'users_name', u'users_email', u'users_password', u'users_classname', u'users_admin', u'loans_book_id', u'loans_user_name', u'loans_loan_date']
You can also join directly to a labeled object::
>>> labeled_loans = db.with_labels(db.loans)
>>> db.join(db.users, labeled_loans, isouter=True).c.keys()
[u'name', u'email', u'password', u'classname', u'admin', u'loans_book_id', u'loans_user_name', u'loans_loan_date']
Relationships
=============
You can define relationships on SqlSoup classes:
>>> db.users.relate('loans', db.loans)
These can then be used like a normal SA property:
>>> db.users.get('Joe Student').loans
[MappedLoans(book_id=1,user_name=u'Joe NAME 7, 12, 0, 0))]
>>> db.users.filter(~db.users.loans.any()).all()
[MappedUsers(name=u'Bhargan NAME can take any options that the relationship function accepts in normal mapper definition:
>>> del db._cache['users']
>>> db.users.relate('loans', db.loans, order_by=db.loans.loan_date, cascade='all, delete-orphan')
Advanced Use
============
Sessions, Transations and Application Integration
-------------------------------------------------
**Note:** please read and understand this section thoroughly before using SqlSoup in any web application.
SqlSoup uses a ScopedSession to provide thread-local sessions. You
can get a reference to the current one like this::
>>> session = db.session
The default session is available at the module level in SQLSoup, via::
>>> from sqlalchemy.ext.sqlsoup import Session
The configuration of this session is ``autoflush=True``, ``autocommit=False``.
This means when you work with the SqlSoup object, you need to call ``db.commit()``
in order to have changes persisted. You may also call ``db.rollback()`` to
roll things back.
Since the SqlSoup object's Session automatically enters into a transaction as soon
as it's used, it is *essential* that you call ``commit()`` or ``rollback()``
on it when the work within a thread completes. This means all the guidelines
for web application integration at :ref:`session_lifespan` must be followed.
The SqlSoup object can have any session or scoped session configured onto it.
This is of key importance when integrating with existing code or frameworks
such as Pylons. If your application already has a ``Session`` configured,
pass it to your SqlSoup object::
>>> from myapplication import Session
>>> db = SqlSoup(session=Session)
If the ``Session`` is configured with ``autocommit=True``, use ``flush()``
instead of ``commit()`` to persist changes - in this case, the ``Session``
closes out its transaction immediately and no external management is needed. ``rollback()`` is also not available. Configuring a new SQLSoup object in "autocommit" mode looks like::
>>> from sqlalchemy.orm import scoped_session, sessionmaker
>>> db = SqlSoup('sqlite://', session=scoped_session(sessionmaker(autoflush=False, expire_on_commit=False, autocommit=True)))
Mapping arbitrary Selectables
-----------------------------
SqlSoup can map any SQLAlchemy ``Selectable`` with the map
method. Let's map a ``Select`` object that uses an aggregate function;
we'll use the SQLAlchemy ``Table`` that SqlSoup introspected as the
basis. (Since we're not mapping to a simple table or join, we need to
tell SQLAlchemy how to find the *primary key* which just needs to be
unique within the select, and not necessarily correspond to a *real*
PK in the database.)
::
>>> from sqlalchemy import select, func
>>> b = db.books._table
>>> s = select([b.c.published_year, func.count('*').label('n')], from_obj=[b], group_by=[b.c.published_year])
>>> s = s.alias('years_with_count')
>>> years_with_count = db.map(s, primary_key=[s.c.published_year])
>>> years_with_count.filter_by(published_year='1989').all()
[MappedBooks(published_year=u'1989',n=1)]
Obviously if we just wanted to get a list of counts associated with
book years once, raw SQL is going to be less work. The advantage of
mapping a Select is reusability, both standalone and in Joins. (And if
you go to full SQLAlchemy, you can perform mappings like this directly
to your object models.)
An easy way to save mapped selectables like this is to just hang them on
your db object::
>>> db.years_with_count = years_with_count
Python is flexible like that!
Raw SQL
-------
SqlSoup works fine with SQLAlchemy's text construct, described in :ref:`sqlexpression_text`.
You can also execute textual SQL directly using the `execute()` method,
which corresponds to the `execute()` method on the underlying `Session`.
Expressions here are expressed like ``text()`` constructs, using named parameters
with colons::
>>> rp = db.execute('select name, email from users where name like :name order by name', name='%Bhargan%')
>>> for name, email in rp.fetchall(): print name, email
Bhargan Basepair EMAIL you can get at the current transaction's connection using `connection()`. This is the
raw connection object which can accept any sort of SQL expression or raw SQL string passed to the database::
>>> conn = db.connection()
>>> conn.execute("'select name, email from users where name like ? order by name'", '%Bhargan%')
Dynamic table names
-------------------
You can load a table whose name is specified at runtime with the entity() method:
>>> tablename = 'loans'
>>> db.entity(tablename) == db.loans
True
entity() also takes an optional schema argument. If none is specified, the
default schema is used.
""" |
"""
A class for converting a PySB model to a set of ordinary differential
equations for integration in MATLAB.
Note that for use in MATLAB, the name of the ``.m`` file must match the name of
the exported MATLAB class (e.g., ``robertson.m`` for the example below).
For information on how to use the model exporters, see the documentation
for :py:mod:`pysb.export`.
Output for the Robertson example model
======================================
Information on the form and usage of the generated MATLAB class is contained in
the documentation for the MATLAB model, as shown in the following example for
``pysb.examples.robertson``::
classdef robertson
% A simple three-species chemical kinetics system known as "Robertson's
% example", as presented in:
%
% NAME The solution of a set of reaction rate equations, in Numerical
% Analysis: An Introduction, NAME ed., Academic Press, 1966, pp. 178-182.
%
% A class implementing the ordinary differential equations
% for the robertson model.
%
% Save as robertson.m.
%
% Generated by pysb.export.matlab.MatlabExporter.
%
% Properties
% ----------
% observables : struct
% A struct containing the names of the observables from the
% PySB model as field names. Each field in the struct
% maps the observable name to a matrix with two rows:
% the first row specifies the indices of the species
% associated with the observable, and the second row
% specifies the coefficients associated with the species.
% For any given timecourse of model species resulting from
% integration, the timecourse for an observable can be
% retrieved using the get_observable method, described
% below.
%
% parameters : struct
% A struct containing the names of the parameters from the
% PySB model as field names. The nominal values are set by
% the constructor and their values can be overriden
% explicitly once an instance has been created.
%
% Methods
% -------
% robertson.odes(tspan, y0)
% The right-hand side function for the ODEs of the model,
% for use with MATLAB ODE solvers (see Examples).
%
% robertson.get_initial_values()
% Returns a vector of initial values for all species,
% specified in the order that they occur in the original
% PySB model (i.e., in the order found in model.species).
% Non-zero initial conditions are specified using the
% named parameters included as properties of the instance.
% Hence initial conditions other than the defaults can be
% used by assigning a value to the named parameter and then
% calling this method. The vector returned by the method
% is used for integration by passing it to the MATLAB
% solver as the y0 argument.
%
% robertson.get_observables(y)
% Given a matrix of timecourses for all model species
% (i.e., resulting from an integration of the model),
% get the trajectories corresponding to the observables.
% Timecourses are returned as a struct which can be
% indexed by observable name.
%
% Examples
% --------
% Example integration using default initial and parameter
% values:
%
% >> m = robertson();
% >> tspan = [0 100];
% >> [t y] = ode15s(@m.odes, tspan, m.get_initial_values());
%
% Retrieving the observables:
%
% >> y_obs = m.get_observables(y)
%
properties
observables
parameters
end
methods
function self = robertson()
% Assign default parameter values
self.parameters = struct( ...
'k1', 0.040000000000000001, ...
'k2', 30000000, ...
'k3', 10000, ...
'A_0', 1, ...
'B_0', 0, ...
'C_0', 0);
% Define species indices (first row) and coefficients
% (second row) of named observables
self.observables = struct( ...
'A_total', [1; 1], ...
'B_total', [2; 1], ...
'C_total', [3; 1]);
end
function initial_values = get_initial_values(self)
% Return the vector of initial conditions for all
% species based on the values of the parameters
% as currently defined in the instance.
initial_values = zeros(1,3);
initial_values(1) = self.parameters.A_0; % A()
initial_values(2) = self.parameters.B_0; % B()
initial_values(3) = self.parameters.C_0; % C()
end
function y = odes(self, tspan, y0)
% Right hand side function for the ODEs
% Shorthand for the struct of model parameters
p = self.parameters;
% A();
y(1,1) = -p.k1*y0(1) + p.k3*y0(2)*y0(3);
% B();
y(2,1) = p.k1*y0(1) - p.k2*power(y0(2), 2) - p.k3*y0(2)*y0(3);
% C();
y(3,1) = p.k2*power(y0(2), 2);
end
function y_obs = get_observables(self, y)
% Retrieve the trajectories for the model observables
% from a matrix of the trajectories of all model
% species.
% Initialize the struct of observable timecourses
% that we will return
y_obs = struct();
% Iterate over the observables;
observable_names = fieldnames(self.observables);
for i = 1:numel(observable_names)
obs_matrix = self.observables.(observable_names{i});
species = obs_matrix(1, :);
coefficients = obs_matrix(2, :);
y_obs.(observable_names{i}) = ...
y(:, species) * coefficients';
end
end
end
end
""" |
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to reqd all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use select() to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
- Standard framework for select-based multiplexing
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <lkcl@samba.org>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
"""ctypes-based OpenGL wrapper for Python
This is the PyOpenGL 3.x tree, it attempts to provide
a largely compatible API for code written with the
PyOpenGL 2.x series using the ctypes foreign function
interface system.
Configuration Variables:
There are a few configuration variables in this top-level
module. Applications should be the only code that tweaks
these variables, mid-level libraries should not take it
upon themselves to disable/enable features at this level.
The implication there is that your library code should be
able to work with any of the valid configurations available
with these sets of flags.
Further, once any entry point has been loaded, the variables
can no longer be updated. The OpenGL._confligflags module
imports the variables from this location, and once that
import occurs the flags should no longer be changed.
ERROR_CHECKING -- if set to a False value before
importing any OpenGL.* libraries will completely
disable error-checking. This can dramatically
improve performance, but makes debugging far
harder.
This is intended to be turned off *only* in a
production environment where you *know* that
your code is entirely free of situations where you
use exception-handling to handle error conditions,
i.e. where you are explicitly checking for errors
everywhere they can occur in your code.
Default: True
ERROR_LOGGING -- If True, then wrap array-handler
functions with error-logging operations so that all exceptions
will be reported to log objects in OpenGL.logs, note that
this means you will get lots of error logging whenever you
have code that tests by trying something and catching an
error, this is intended to be turned on only during
development so that you can see why something is failing.
Errors are normally logged to the OpenGL.errors logger.
Only triggers if ERROR_CHECKING is True
Default: False
ERROR_ON_COPY -- if set to a True value before
importing the numpy/lists support modules, will
cause array operations to raise
OpenGL.error.CopyError if the operation
would cause a data-copy in order to make the
passed data-type match the target data-type.
This effectively disables all list/tuple array
support, as they are inherently copy-based.
This feature allows for optimisation of your
application. It should only be enabled during
testing stages to prevent raising errors on
recoverable conditions at run-time.
Default: False
CONTEXT_CHECKING -- if set to True, PyOpenGL will wrap
*every* GL and GLU call with a check to see if there
is a valid context. If there is no valid context
then will throw OpenGL.errors.NoContext. This is an
*extremely* slow check and is not enabled by default,
intended to be enabled in order to track down (wrong)
code that uses GL/GLU entry points before the context
has been initialized (something later Linux GLs are
very picky about).
Default: False
STORE_POINTERS -- if set to True, PyOpenGL array operations
will attempt to store references to pointers which are
being passed in order to prevent memory-access failures
if the pointed-to-object goes out of scope. This
behaviour is primarily intended to allow temporary arrays
to be created without causing memory errors, thus it is
trading off performance for safety.
To use this flag effectively, you will want to first set
ERROR_ON_COPY to True and eliminate all cases where you
are copying arrays. Copied arrays *will* segfault your
application deep within the GL if you disable this feature!
Once you have eliminated all copying of arrays in your
application, you will further need to be sure that all
arrays which are passed to the GL are stored for at least
the time period for which they are active in the GL. That
is, you must be sure that your array objects live at least
until they are no longer bound in the GL. This is something
you need to confirm by thinking about your application's
structure.
When you are sure your arrays won't cause seg-faults, you
can set STORE_POINTERS=False in your application and enjoy
a (slight) speed up.
Note: this flag is *only* observed when ERROR_ON_COPY == True,
as a safety measure to prevent pointless segfaults
Default: True
WARN_ON_FORMAT_UNAVAILABLE -- If True, generates
logging-module warn-level events when a FormatHandler
plugin is not loadable (with traceback).
Default: False
FULL_LOGGING -- If True, then wrap functions with
logging operations which reports each call along with its
arguments to the OpenGL.calltrace logger at the INFO
level. This is *extremely* slow. You should *not* enable
this in production code!
You will need to have a logging configuration (e.g.
logging.basicConfig()
) call in your top-level script to see the results of the
logging.
Default: False
ALLOW_NUMPY_SCALARS -- if True, we will wrap
all GLint/GLfloat calls conversions with wrappers
that allow for passing numpy scalar values.
Note that this is experimental, *not* reliable,
and very slow!
Note that byte/char types are not wrapped.
Default: False
UNSIGNED_BYTE_IMAGES_AS_STRING -- if True, we will return
GL_UNSIGNED_BYTE image-data as strings, instead of arrays
for glReadPixels and glGetTexImage
Default: True
FORWARD_COMPATIBLE_ONLY -- only include OpenGL 3.1 compatible
entry points. Note that this will generally break most
PyOpenGL code that hasn't been explicitly made "legacy free"
via a significant rewrite.
Default: False
SIZE_1_ARRAY_UNPACK -- if True, unpack size-1 arrays to be
scalar values, as done in PyOpenGL 1.5 -> 3.0.0, that is,
if a glGenList( 1 ) is done, return a uint rather than
an array of uints.
Default: True
USE_ACCELERATE -- if True, attempt to use the OpenGL_accelerate
package to provide Cython-coded accelerators for core wrapping
operations.
Default: True
MODULE_ANNOTATIONS -- if True, attempt to annotate alternates() and
constants to track in which module they are defined (only useful
for the documentation-generation passes, really).
Default: False
""" |
"""
=============================
Byteswapping and byte order
=============================
Introduction to byte ordering and ndarrays
==========================================
The ``ndarray`` is an object that provide a python array interface to data
in memory.
It often happens that the memory that you want to view with an array is
not of the same byte ordering as the computer on which you are running
Python.
For example, I might be working on a computer with a little-endian CPU -
such as an Intel Pentium, but I have loaded some data from a file
written by a computer that is big-endian. Let's say I have loaded 4
bytes from a file written by a Sun (big-endian) computer. I know that
these 4 bytes represent two 16-bit integers. On a big-endian machine, a
two-byte integer is stored with the Most Significant Byte (MSB) first,
and then the Least Significant Byte (LSB). Thus the bytes are, in memory order:
#. MSB integer 1
#. LSB integer 1
#. MSB integer 2
#. LSB integer 2
Let's say the two integers were in fact 1 and 770. Because 770 = 256 *
3 + 2, the 4 bytes in memory would contain respectively: 0, 1, 3, 2.
The bytes I have loaded from the file would have these contents:
>>> big_end_str = chr(0) + chr(1) + chr(3) + chr(2)
>>> big_end_str
'\\x00\\x01\\x03\\x02'
We might want to use an ``ndarray`` to access these integers. In that
case, we can create an array around this memory, and tell numpy that
there are two integers, and that they are 16 bit and big-endian:
>>> import numpy as np
>>> big_end_arr = np.ndarray(shape=(2,),dtype='>i2', buffer=big_end_str)
>>> big_end_arr[0]
1
>>> big_end_arr[1]
770
Note the array ``dtype`` above of ``>i2``. The ``>`` means 'big-endian'
(``<`` is little-endian) and ``i2`` means 'signed 2-byte integer'. For
example, if our data represented a single unsigned 4-byte little-endian
integer, the dtype string would be ``<u4``.
In fact, why don't we try that?
>>> little_end_u4 = np.ndarray(shape=(1,),dtype='<u4', buffer=big_end_str)
>>> little_end_u4[0] == 1 * 256**1 + 3 * 256**2 + 2 * 256**3
True
Returning to our ``big_end_arr`` - in this case our underlying data is
big-endian (data endianness) and we've set the dtype to match (the dtype
is also big-endian). However, sometimes you need to flip these around.
.. warning::
Scalars currently do not include byte order information, so extracting
a scalar from an array will return an integer in native byte order.
Hence:
>>> big_end_arr[0].dtype.byteorder == little_end_u4[0].dtype.byteorder
True
Changing byte ordering
======================
As you can imagine from the introduction, there are two ways you can
affect the relationship between the byte ordering of the array and the
underlying memory it is looking at:
* Change the byte-ordering information in the array dtype so that it
interprets the underlying data as being in a different byte order.
This is the role of ``arr.newbyteorder()``
* Change the byte-ordering of the underlying data, leaving the dtype
interpretation as it was. This is what ``arr.byteswap()`` does.
The common situations in which you need to change byte ordering are:
#. Your data and dtype endianess don't match, and you want to change
the dtype so that it matches the data.
#. Your data and dtype endianess don't match, and you want to swap the
data so that they match the dtype
#. Your data and dtype endianess match, but you want the data swapped
and the dtype to reflect this
Data and dtype endianness don't match, change dtype to match data
-----------------------------------------------------------------
We make something where they don't match:
>>> wrong_end_dtype_arr = np.ndarray(shape=(2,),dtype='<i2', buffer=big_end_str)
>>> wrong_end_dtype_arr[0]
256
The obvious fix for this situation is to change the dtype so it gives
the correct endianness:
>>> fixed_end_dtype_arr = wrong_end_dtype_arr.newbyteorder()
>>> fixed_end_dtype_arr[0]
1
Note the array has not changed in memory:
>>> fixed_end_dtype_arr.tobytes() == big_end_str
True
Data and type endianness don't match, change data to match dtype
----------------------------------------------------------------
You might want to do this if you need the data in memory to be a certain
ordering. For example you might be writing the memory out to a file
that needs a certain byte ordering.
>>> fixed_end_mem_arr = wrong_end_dtype_arr.byteswap()
>>> fixed_end_mem_arr[0]
1
Now the array *has* changed in memory:
>>> fixed_end_mem_arr.tobytes() == big_end_str
False
Data and dtype endianness match, swap data and dtype
----------------------------------------------------
You may have a correctly specified array dtype, but you need the array
to have the opposite byte order in memory, and you want the dtype to
match so the array values make sense. In this case you just do both of
the previous operations:
>>> swapped_end_arr = big_end_arr.byteswap().newbyteorder()
>>> swapped_end_arr[0]
1
>>> swapped_end_arr.tobytes() == big_end_str
False
An easier way of casting the data to a specific dtype and byte ordering
can be achieved with the ndarray astype method:
>>> swapped_end_arr = big_end_arr.astype('<i2')
>>> swapped_end_arr[0]
1
>>> swapped_end_arr.tobytes() == big_end_str
False
""" |
"""Audio Processor. Takes live audio and generates MIDI information from it.
Here are the configuration options, and they go on and on. Some of
them are optimistic that I'll come back later and add other options, but
don't count on it. Probably the most important thing to wrap your head
around is that you set a frame size for audio capture (say, 512), and each
audio processor uses stores some multiple of that before it does anything.
A multiple of "4" means the audio processor waits until 2048 bytes (4x512)
have arrived before doing anything. The hop size (which aubio uses as a
window for its work) is then a multiple of that. Most of the time it seems
like the hop size should be exactly half of the window size, so you'd put
.5 in that configuration option. If the window size and hop size should be
the same (which worked best for me for beat detection), enter 1 as the
configuration option.
On the MIDI side, nearly everything is about what controller, note, or sysex
message should be used to output information. Presumably you'll just need
one or the other for each audio processor. It really depends on what is going
to be on the receiving end of this information how you set this up. The
defaults use some safe options--manufacturer is the MIDI "for educational use"
prefix, and the controllers are not officially mapped to anything. Notes are
only (potentially) used for pitches, but it should be clear how you could
modify the code to use notes for anything else as well.
Usage:
audioprocessor.py [options]
Options:
-h --help Show this screen.
Quits after.
--listsounddevices List available sound devices.
Quits after.
--listmidiports List MIDI ports.
Quits after.
--writeinifile Write options to ini file, as specified by
inifile option. If the file already present,
a backup is made of original.
--inifile=FILE Name of options settings file.
[default: soundtomidi.ini]
--inputdevice=DEVICE ID of the sound input device. System default
audio input device will be used if not
specified.
[default: default]
--channels=CHANNELS Number of channels to capture.
[default: 1]
--samplerate=SAMPLERATE Capture rate for audio samples.
[default: 44100]
--framesize=FRAMESIZE Size of each frame captured.
[default: 512]
--stdout=STDOUT Echo message to standard out.
[default: False]
--stdoutformat=STDOUTFORMAT Format for standard out messages. Options are
"verbose", "bytes", "bin" or "hex".
[default: verbose]
--midiout=MIDIOUT Send MIDI messages?
[default: True]
--outport=MIDIOUTPORT Name of the MIDI output port. If left as
default, uses first MIDI port found.
[default: default]
--outchannel=OUTCHANNEL Number of the MIDI channel to send messages on.
Valid numbers 1-16.
[default: 14]
--sysexmanf=MANF Manufacturer prefix code for sysex messages.
Int or hex values, separarated by space.
[default: 0x7D]
--gettempo=TEMPO Get the tempo of the audio.
[default: True]
--talg=TALG Aubio algorithm for determining the tempo.
[default: default]
--tframemult=TFRAMEMULT Number of frames to use in calculation.
[default: 1]
--thopmult=THOPMULT Hop size, as percent of FRAMEMULT.
[default: .5]
--taverage=TAVERAGE Number of BPM values to average.
[default: 1]
--tcount=TCOUNT Number of BPM averages to be stored before
the most common one is sent as a message.
[default: 1]
--tcontrolnum=TCONTROLNUM Controller number to send BPM messages.
If "None", no control messages will be sent.
[default: 14]
--tcontroltype=TCONTROLTYPE How to encode the BPM value for control.
"minus60" sends BPM value minus 60.
EG: 60 BPM = 0 value,
120 BPM = 60 value,
187 BPM = 127 value.
[default: minus60]
--tsysexnum=TSYSEXNUM Prefix to send prior to BPM in
sysex messages.
If "None", no sysex messages will be sent.
[default: 0x0B]
--tsysextype=TSYSEXTYPE How to encode the BPM value for sysex.
"minus60" sends BPM value minus 60.
(See --tsysexcontroltype)
"twobytes" takes the BPM value to
the tenth (EG, 128.1), multiplies it
by 10 (1281), then spreads this
across two 7 bit values (0x10 0x01)
[default: twobytes]
--getbeats=BEATS Get the beats of the audio.
[default: True]
--balg=BALG Aubio algorithm to use for the beat.
[default: default]
--bframemult=BFRAMEMULT Number of frames to use in calculation.
[default: 1]
--bhopmult=BHOPMULT Hop size, as percent of FRAMEMULT.
[default: 1]
--bcontrolnum=BCONTROLNUM Controller number to send beat messages.
If "None", no control messages will be sent.
[default: 15]
--bsysexnum=BSYSEXNUM Prefix to send prior to beat number
in sysex messages.
If "None", no sysex messages will be sent.
[default: 0x1B]
--bvaltype=BVALTYPE Type of value to send with beat
controller or sysex message.
Any arbitrary number 0-127, or a
comma separated looping listing of
values to send. For example,
"0,1,2,3" will send "0" for the first
beat, "3" for fourth beat, then back to
"0" for the next one.
[default: 0,1,2,3,4,5,6,7]
--bclock=BCLOCK Send 24 clock ticks messages after each beat.
(Not yet implemented)
[default: False]
--getrms=RMS Get the RMS.
[default: True]
--rframemult=FFRAMEMULT Number of frames to use in calculation.
[default: 4]
--rhopmult=FHOPMULT Hop size, as percent of FRAMEMULT.
[default: 1]
--rcontrolnum=FRMSNUM Controller number to send RMS messages.
If "None", no frequency strength sysex messages
will be sent.
[default: 20]
--rsysexnum=FSYSEXNUM Prefix to send prior to RMS values
If "None", no RMS sysex messages will be sent.
[default: 0x1F]
--rgraceful=FRMSGRACEFUL Gracefully let go of RMS peaks.
EG: one frame peaks at 100, followed by a drop
to 20. Instead of immediately reflecting the
new value, this rule sets a cut-off for the
drop to the chosen percent. The higher the
percent, the slower the decline. New high peaks
reset this graceful fade and it starts again.
Set to 0.0 to turn off.
[default: .5]
--getfrequencies=FREQS Get the strength of filtered frequencies.
[default: True]
--falg=FALG Aubio algorithm to use for determining
the strength of the frequencies.
[default: default]
--fframemult=FFRAMEMULT Number of frames to use in calculation.
[default: 4]
--fhopmult=FHOPMULT Hop size, as percent of FRAMEMULT.
[default: 1]
--fcount=FCOUNT Number of frequency values to hold
before taking any action. Maximum value
of set will be sent.
[default: 2]
--fbuckets=FBUCKETS Filter bands to use for use for dividing up
frequencies. Comma separated list of values
plus a low and high end barrier value.
See Aubio docs "filterbanks" for more details.
Shortcuts "octave" and "third-octave" shortcut
for standard octave or 1/3 octave bands.
[default: third-octave]
--fsysexnum=FSYSEXNUM Prefix to send prior to frequency strength
values.
If "None", no sysex messages will be sent.
[default: 0x0F]
--fgraceful=FGRACEFUL Gracefully let go of frequency peaks.
EG: a frame peaks at 100, followed by
a drop to 20. Instead of immediately reflecting
the new value, this rule sets a cut-off for the
drop to the chosen percent. The higher the
percent, the slower the decline. New high peaks
reset this graceful fade and it starts again.
Set to 0.0 to turn off.
[default: .8]
--getpitch=PITCHES Get the fundamental pitch of the audio.
[default: True]
--palg=PALG Aubio algorithm to use for pitch of the audio.
[default: yin]
--pframemult=PFRAMEMULT Number of frames to use in calculation.
[default: 2]
--phopmult=PHOPMULT Hop size, as percent of FRAMEMULT.
[default: .5]
--ptolerance=PTOLERANCE Required confidence level for a pitch.
[default: 0.5]
--pcount=PCOUNT Number of pitch averages to be stored before
the most common one is sent as a message.
[default: 8]
--plowcutoff=PLOWCUTOFF Lowest pitch to consider.
[default: 0]
--phighcutoff=PHIGHCUTOFF Highest pitch to consider.
[default: 127]
--pfoldoctaves=PFOLDOCTAVES Return just 12 note values instead of the
possible 128.
[default: False]
--pnumoffset=PNUMOFFSET Used only with the above option. Shifts the "C"
value to somewhere else, and each note above
that. Middle C is "60", which is the default.
[default: 60]
--pnoteon=PNOTEON Send note on messages for the audio pitch.
[default: True]
--pnoteoff=PNOTEOFF Send note off messages when a new audio
pitch doesn't match previous pitch.
[default: True]
--pcontrolnum=PCONTROLNUM Controller number to send pitches.
If "None", no control messages will be sent.
[default: 21]
--psysexnum=PSYSEXNUM Prefix to send prior to sending note value.
If "None", no sysex messages will be sent.
[default: 0x09]
""" |
# -*- coding: utf-8 -*-
# -- Dual Licence ----------------------------------------------------------
############################################################################
# GPL License #
# #
# This file is a SCons (http://www.scons.org/) builder #
# Copyright (c) 2012-14, NAME <philipp.kraus@flashpixx.de> #
# This program is free software: you can redistribute it and/or modify #
# it under the terms of the GNU General Public License as #
# published by the Free Software Foundation, either version 3 of the #
# License, or (at your option) any later version. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with this program. If not, see <http://www.gnu.org/licenses/>. #
############################################################################
# --------------------------------------------------------------------------
############################################################################
# BSD 3-Clause License #
# #
# This file is a SCons (http://www.scons.org/) builder #
# Copyright (c) 2012-14, NAME <philipp.kraus@flashpixx.de> #
# All rights reserved. #
# #
# Redistribution and use in source and binary forms, with or without #
# modification, are permitted provided that the following conditions are #
# met: #
# #
# 1. Redistributions of source code must retain the above copyright #
# notice, this list of conditions and the following disclaimer. #
# #
# 2. Redistributions in binary form must reproduce the above copyright #
# notice, this list of conditions and the following disclaimer in the #
# documentation and/or other materials provided with the distribution. #
# #
# 3. Neither the name of the copyright holder nor the names of its #
# contributors may be used to endorse or promote products derived from #
# this software without specific prior written permission. #
# #
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS #
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT #
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A #
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT #
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, #
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED #
# TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR #
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF #
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING #
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS #
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #
############################################################################
# The Unpack Builder can be used for unpacking archives (eg Zip, TGZ, BZ, ... ).
# The emitter of the Builder reads the archive data and creates a returning file list
# the builder extract the archive. The environment variable stores a dictionary "UNPACK"
# for set different extractions (subdict "EXTRACTOR"):
# {
# PRIORITY => a value for setting the extractor order (lower numbers = extractor is used earlier)
# SUFFIX => defines a list with file suffixes, which should be handled with this extractor
# EXTRACTSUFFIX => suffix of the extract command
# EXTRACTFLAGS => a string parameter for the RUN command for extracting the data
# EXTRACTCMD => full extract command of the builder
# RUN => the main program which will be started (if the parameter is empty, the extractor will be ignored)
# LISTCMD => the listing command for the emitter
# LISTFLAGS => the string options for the RUN command for showing a list of files
# LISTSUFFIX => suffix of the list command
# LISTEXTRACTOR => a optional Python function, that is called on each output line of the
# LISTCMD for extracting file & dir names, the function need two parameters (first line number,
# second line content) and must return a string with the file / dir path (other value types
# will be ignored)
# }
# Other options in the UNPACK dictionary are:
# STOPONEMPTYFILE => bool variable for stoping if the file has empty size (default True)
# VIWEXTRACTOUTPUT => shows the output messages of the extraction command (default False)
# EXTRACTDIR => path in that the data will be extracted (default #)
#
# The file which is handled by the first suffix match of the extractor, the extractor list can be append for other files.
# The order of the extractor dictionary creates the listing & extractor command eg file extension .tar.gz should be
# before .gz, because the tar.gz is extract in one shoot.
#
# Under *nix system these tools are supported: tar, bzip2, gzip, unzip
# Under Windows only 7-Zip (http://www.7-zip.org/) is supported
|
"""
Matplotlib provides sophisticated date plotting capabilities, standing on the
shoulders of python :mod:`datetime`, the add-on modules :mod:`pytz` and
:mod:`dateutil`. :class:`datetime` objects are converted to floating point
numbers which represent time in days since 0001-01-01 UTC, plus 1. For
example, 0001-01-01, 06:00 is 1.25, not 0.25. The helper functions
:func:`date2num`, :func:`num2date` and :func:`drange` are used to facilitate
easy conversion to and from :mod:`datetime` and numeric ranges.
.. note::
Like Python's datetime, mpl uses the Gregorian calendar for all
conversions between dates and floating point numbers. This practice
is not universal, and calendar differences can cause confusing
differences between what Python and mpl give as the number of days
since 0001-01-01 and what other software and databases yield. For
example, the US Naval Observatory uses a calendar that switches
from Julian to Gregorian in October, 1582. Hence, using their
calculator, the number of days between 0001-01-01 and 2006-04-01 is
732403, whereas using the Gregorian calendar via the datetime
module we find::
In [31]:date(2006,4,1).toordinal() - date(1,1,1).toordinal()
Out[31]:732401
A wide range of specific and general purpose date tick locators and
formatters are provided in this module. See
:mod:`matplotlib.ticker` for general information on tick locators
and formatters. These are described below.
All the matplotlib date converters, tickers and formatters are
timezone aware, and the default timezone is given by the timezone
parameter in your :file:`matplotlibrc` file. If you leave out a
:class:`tz` timezone instance, the default from your rc file will be
assumed. If you want to use a custom time zone, pass a
:class:`pytz.timezone` instance with the tz keyword argument to
:func:`num2date`, :func:`plot_date`, and any custom date tickers or
locators you create. See `pytz <http://pythonhosted.org/pytz/>`_ for
information on :mod:`pytz` and timezone handling.
The `dateutil module <https://dateutil.readthedocs.io/en/stable/>`_ provides
additional code to handle date ticking, making it easy to place ticks
on any kinds of dates. See examples below.
Date tickers
------------
Most of the date tickers can locate single or multiple values. For
example::
# import constants for the days of the week
from matplotlib.dates import MO, TU, WE, TH, FR, SA, SU
# tick on mondays every week
loc = WeekdayLocator(byweekday=MO, tz=tz)
# tick on mondays and saturdays
loc = WeekdayLocator(byweekday=(MO, SA))
In addition, most of the constructors take an interval argument::
# tick on mondays every second week
loc = WeekdayLocator(byweekday=MO, interval=2)
The rrule locator allows completely general date ticking::
# tick every 5th easter
rule = rrulewrapper(YEARLY, byeaster=1, interval=5)
loc = RRuleLocator(rule)
Here are all the date tickers:
* :class:`MinuteLocator`: locate minutes
* :class:`HourLocator`: locate hours
* :class:`DayLocator`: locate specifed days of the month
* :class:`WeekdayLocator`: Locate days of the week, e.g., MO, TU
* :class:`MonthLocator`: locate months, e.g., 7 for july
* :class:`YearLocator`: locate years that are multiples of base
* :class:`RRuleLocator`: locate using a
:class:`matplotlib.dates.rrulewrapper`. The
:class:`rrulewrapper` is a simple wrapper around a
:class:`dateutil.rrule` (`dateutil
<https://dateutil.readthedocs.io/en/stable/>`_) which allow almost
arbitrary date tick specifications. See `rrule example
<../examples/pylab_examples/date_demo_rrule.html>`_.
* :class:`AutoDateLocator`: On autoscale, this class picks the best
:class:`MultipleDateLocator` to set the view limits and the tick
locations.
Date formatters
---------------
Here all all the date formatters:
* :class:`AutoDateFormatter`: attempts to figure out the best format
to use. This is most useful when used with the :class:`AutoDateLocator`.
* :class:`DateFormatter`: use :func:`strftime` format strings
* :class:`IndexDateFormatter`: date plots with implicit *x*
indexing.
""" |
"""
Filters
-------
Filters are an interesting and somewhat challenging part of the code base. They are used for
two different purposes:
- To figure out which nodes in the xml hierarchy to start rendering from. These are called
'finder filters' or 'content filters'. This is done before rendering starts.
- To figure out which nodes under a selected nodes in the xml hierarchy should be rendered. These
are called 'render filters'. This is done during the render process with a test in the
DoxygenToRstRendererFactory.
General Implementation
~~~~~~~~~~~~~~~~~~~~~~
Filters are essential just tests to see if a node matches certain parameters that are needed to
decide whether or not to include it in some output.
As these filters are declared once and then used on multiple nodes, we model them as object
hierarchies that encapsulate the required test and take a node (with its context) and return True or
False.
If you wanted a test which figures out if a node has the node_type 'memberdef' you might create the
following object hierarchy:
node_is_memberdef = InFilter(AttributeAccessor(Node(), 'node_type'), ['memberdef'])
This reads from the inside out, as get the node, then get the node_type attribute from it, and see
if the value of the attribute is in the list ['memberdef'].
The Node() is called a 'Selector'. Parent() is also a selector. It means given the current context,
work with the parent of the current node rather than the node itself. This allows you to frame tests
in terms of a node's parent as well as the node which helps when we want nodes with particular
parents and not others.
The AttributeAccessor() is called an 'Accessor'. It wraps up an attempt to access a particular
attribute on the selected node. There are quite a few different specific accessors but they can
mostly be generalised with the AttributeAccessor. This code has evolved over time and initially the
implementation involved specific accessor classes (which are still used in large parts of it.)
The InFilter() is unsurprisingly called a 'Filter'. There are lots of different filters. Filters
either act on the results of Accessors or on the results of other Filters and they always return
True or False. The AndFilter and the OrFilter can be used to combine the outputs of other Filters
with logical 'and' and 'or' operations.
You can build up some pretty complex expressions with this level of freedom as you
might imagine. The complexity is unfortunate but necessary as the nature of filtering the xml is
quite complex.
Finder Filters
~~~~~~~~~~~~~~
The implementation of the filters can change a little depending on how they are called. Finder
filters are called from the breathe.finder.doxygen.index and breathe.finder.doxygen.compound files.
They are called like this:
# Descend down the hierarchy
# ...
if filter_.allow(node_stack):
matches.append(self.data_object)
# Keep on descending
# ...
This means that the result of the filter does not stop us descending down the hierarchy and testing
more nodes. This simplifies the filters as they only have to return true for the exact nodes they
are interested in and they don't have to worry about allowing the iteration down the hierarchy to
continue for nodes which don't match.
An example of a finder filter is:
AndFilter(
InFilter(NodeTypeAccessor(Node()), ["compound"]),
InFilter(KindAccessor(Node()), ["group"]),
InFilter(NameAccessor(Node()), ["mygroup"])
)
This says, return True for all the nodes of node_type 'compound' with 'kind' set to 'group' which
have the name 'mygroup'. It returns false for everything else, but when a node matching this is
found then it is added to the matches list by the code above.
It is therefore relatively easy to write finder filters. If you have two separate node filters like
the one above and you want to match on both of them then you can do:
OrFilter(
node_filter_1,
node_filter_2
)
To combine them.
Content Filters
~~~~~~~~~~~~~~~
Content filters are harder than the finder filters as they are responsible for halting the iteration
down the hierarchy if they return false. This means that if you're interested in memberdef nodes
with a particular attribute then you have to check for that but also include a clause which allows
all other non-memberdef nodes to pass through as you don't want to interrupt them.
This means you end up with filters like this:
OrFilter(
AndFilter(
InFilter(NodeTypeAccessor(Node()), ["compound"]),
InFilter(KindAccessor(Node()), ["group"]),
InFilter(NameAccessor(Node()), ["mygroup"])
),
NotFilter(
AndFilter(
InFilter(NodeTypeAccessor(Node()), ["compound"]),
InFilter(KindAccessor(Node()), ["group"]),
)
)
)
Which is to say that we want to let through a compound, with kind group, with name 'mygroup' but
we're also happy if the node is **not** a compund with kind group. Really we just don't want to let
through any compounds with kind group with name other than 'mygroup'. As such, we can rephrase this
as:
NotFilter(
AndFilter(
InFilter(NodeTypeAccessor(Node()), ["compound"]),
InFilter(KindAccessor(Node()), ["group"]),
NotFilter(InFilter(NameAccessor(Node()), ["mygroup"]))
)
)
Using logical manipulation we can rewrite this as:
OrFilter(
NotFilter(InFilter(NodeTypeAccessor(Node()), ["compound"])),
NotFilter(InFilter(KindAccessor(Node()), ["group"])),
InFilter(NameAccessor(Node()), ["mygroup"])
)
We reads: allow if it isn't a compound, or if it is a compound but doesn't have a 'kind' of 'group',
but if it is a compound and has a 'kind' of 'group then only allow it if it is named 'mygroup'.
Helper Syntax
~~~~~~~~~~~~~
Some of these filter declarations get a little awkward to read and write. They are not laid out in
manner which reads smoothly. Additional helper methods and operator overloads have been introduced
to help with this.
AttributeAccessor objects are created in property methods on the Selector classes so:
node.kind
Where node has been declared as a Node() instance. Results in:
AttributeAccessor(Node(), 'kind')
The '==' and '!=' operators on the Accessors have been overloaded to return the appropriate filters
so that:
node.kind == 'group'
Results in:
InFilter(AttributeAccessor(Node(), 'kind'), ['kind'])
We also override the binary 'and' (&), 'or' (|) and 'not' (~) operators in Python to apply
AndFilters, OrFilters and NotFilters respectively. We have to override the binary operators as they
actual 'and', 'or' and 'not' operators cannot be overridden. So:
(node.node_type == 'compound') & (node.name == 'mygroup')
Translates to:
AndFilter(
InFilter(NodeTypeAccessor(Node()), ["compound"])),
InFilter(NameAccessor(Node()), ["mygroup"])
)
Where the former is hopefully more readable without sacrificing too much to the abstract magic of
operator overloads.
Operator Precedences & Extra Parenthesis
''''''''''''''''''''''''''''''''''''''''
As the binary operators have a lower operator precedence than '==' and '!=' and some other operators
we have to include additional parenthesis in the expressions to group them as we want. So instead of
writing:
node.node_type == 'compound' & node.name == 'mygroup'
We have to write:
(node.node_type == 'compound') & (node.name == 'mygroup')
""" |
"""
============
Array basics
============
Array types and conversions between types
=========================================
NumPy supports a much greater variety of numerical types than Python does.
This section shows which are available, and how to modify an array's data-type.
========== ==========================================================
Data type Description
========== ==========================================================
bool_ Boolean (True or False) stored as a byte
int_ Default integer type (same as C ``long``; normally either
``int64`` or ``int32``)
intc Identical to C ``int`` (normally ``int32`` or ``int64``)
intp Integer used for indexing (same as C ``ssize_t``; normally
either ``int32`` or ``int64``)
int8 Byte (-128 to 127)
int16 Integer (-32768 to 32767)
int32 Integer (-2147483648 to 2147483647)
int64 Integer (-9223372036854775808 to 9223372036854775807)
uint8 Unsigned integer (0 to 255)
uint16 Unsigned integer (0 to 65535)
uint32 Unsigned integer (0 to 4294967295)
uint64 Unsigned integer (0 to 18446744073709551615)
float_ Shorthand for ``float64``.
float16 Half precision float: sign bit, 5 bits exponent,
10 bits mantissa
float32 Single precision float: sign bit, 8 bits exponent,
23 bits mantissa
float64 Double precision float: sign bit, 11 bits exponent,
52 bits mantissa
complex_ Shorthand for ``complex128``.
complex64 Complex number, represented by two 32-bit floats (real
and imaginary components)
complex128 Complex number, represented by two 64-bit floats (real
and imaginary components)
========== ==========================================================
Additionally to ``intc`` the platform dependent C integer types ``short``,
``long``, ``longlong`` and their unsigned versions are defined.
NumPy numerical types are instances of ``dtype`` (data-type) objects, each
having unique characteristics. Once you have imported NumPy using
::
>>> import numpy as np
the dtypes are available as ``np.bool_``, ``np.float32``, etc.
Advanced types, not listed in the table above, are explored in
section :ref:`structured_arrays`.
There are 5 basic numerical types representing booleans (bool), integers (int),
unsigned integers (uint) floating point (float) and complex. Those with numbers
in their name indicate the bitsize of the type (i.e. how many bits are needed
to represent a single value in memory). Some types, such as ``int`` and
``intp``, have differing bitsizes, dependent on the platforms (e.g. 32-bit
vs. 64-bit machines). This should be taken into account when interfacing
with low-level code (such as C or Fortran) where the raw memory is addressed.
Data-types can be used as functions to convert python numbers to array scalars
(see the array scalar section for an explanation), python sequences of numbers
to arrays of that type, or as arguments to the dtype keyword that many numpy
functions or methods accept. Some examples::
>>> import numpy as np
>>> x = np.float32(1.0)
>>> x
1.0
>>> y = np.int_([1,2,4])
>>> y
array([1, 2, 4])
>>> z = np.arange(3, dtype=np.uint8)
>>> z
array([0, 1, 2], dtype=uint8)
Array types can also be referred to by character codes, mostly to retain
backward compatibility with older packages such as Numeric. Some
documentation may still refer to these, for example::
>>> np.array([1, 2, 3], dtype='f')
array([ 1., 2., 3.], dtype=float32)
We recommend using dtype objects instead.
To convert the type of an array, use the .astype() method (preferred) or
the type itself as a function. For example: ::
>>> z.astype(float) #doctest: +NORMALIZE_WHITESPACE
array([ 0., 1., 2.])
>>> np.int8(z)
array([0, 1, 2], dtype=int8)
Note that, above, we use the *Python* float object as a dtype. NumPy knows
that ``int`` refers to ``np.int_``, ``bool`` means ``np.bool_``,
that ``float`` is ``np.float_`` and ``complex`` is ``np.complex_``.
The other data-types do not have Python equivalents.
To determine the type of an array, look at the dtype attribute::
>>> z.dtype
dtype('uint8')
dtype objects also contain information about the type, such as its bit-width
and its byte-order. The data type can also be used indirectly to query
properties of the type, such as whether it is an integer::
>>> d = np.dtype(int)
>>> d
dtype('int32')
>>> np.issubdtype(d, int)
True
>>> np.issubdtype(d, float)
False
Array Scalars
=============
NumPy generally returns elements of arrays as array scalars (a scalar
with an associated dtype). Array scalars differ from Python scalars, but
for the most part they can be used interchangeably (the primary
exception is for versions of Python older than v2.x, where integer array
scalars cannot act as indices for lists and tuples). There are some
exceptions, such as when code requires very specific attributes of a scalar
or when it checks specifically whether a value is a Python scalar. Generally,
problems are easily fixed by explicitly converting array scalars
to Python scalars, using the corresponding Python type function
(e.g., ``int``, ``float``, ``complex``, ``str``, ``unicode``).
The primary advantage of using array scalars is that
they preserve the array type (Python may not have a matching scalar type
available, e.g. ``int16``). Therefore, the use of array scalars ensures
identical behaviour between arrays and scalars, irrespective of whether the
value is inside an array or not. NumPy scalars also have many of the same
methods arrays do.
Extended Precision
==================
Python's floating-point numbers are usually 64-bit floating-point numbers,
nearly equivalent to ``np.float64``. In some unusual situations it may be
useful to use floating-point numbers with more precision. Whether this
is possible in numpy depends on the hardware and on the development
environment: specifically, x86 machines provide hardware floating-point
with 80-bit precision, and while most C compilers provide this as their
``long double`` type, MSVC (standard for Windows builds) makes
``long double`` identical to ``double`` (64 bits). NumPy makes the
compiler's ``long double`` available as ``np.longdouble`` (and
``np.clongdouble`` for the complex numbers). You can find out what your
numpy provides with``np.finfo(np.longdouble)``.
NumPy does not provide a dtype with more precision than C
``long double``s; in particular, the 128-bit IEEE quad precision
data type (FORTRAN's ``REAL*16``) is not available.
For efficient memory alignment, ``np.longdouble`` is usually stored
padded with zero bits, either to 96 or 128 bits. Which is more efficient
depends on hardware and development environment; typically on 32-bit
systems they are padded to 96 bits, while on 64-bit systems they are
typically padded to 128 bits. ``np.longdouble`` is padded to the system
default; ``np.float96`` and ``np.float128`` are provided for users who
want specific padding. In spite of the names, ``np.float96`` and
``np.float128`` provide only as much precision as ``np.longdouble``,
that is, 80 bits on most x86 machines and 64 bits in standard
Windows builds.
Be warned that even if ``np.longdouble`` offers more precision than
python ``float``, it is easy to lose that extra precision, since
python often forces values to pass through ``float``. For example,
the ``%`` formatting operator requires its arguments to be converted
to standard python types, and it is therefore impossible to preserve
extended precision even if many decimal places are requested. It can
be useful to test your code with the value
``1 + np.finfo(np.longdouble).eps``.
""" |
# Copyright 2017 NAME - t-h-i-n-x.net
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Changelog
#
# 1.0 2017/01/03 Initial release
#
# Config parameters
#
# - slave 8 bit Value of the I2C slave address for the chip.
# Defaults to 0x40. Possible values are from 0x40 to 0x4F.
# - shunt Float Value of the shunt resistor in Ohms. Default is 0.1.
# - vrange Integer Vrange value of the chip. Valid values are 16 or 32.
# Default is 32.
# - gaindiv Integer Gain divider (PGA) value of the chip. Valid values
# are from (1, 2, 4 , 8). Default is 8.
# - mode Integer Value of the chip mode. Possible values are from
# 0x0 to 0x7. Default is 0x7.
# - badc Integer Value of the voltage bus ADC settings. Possible
# values are from 0x0 to 0xF. Default is 0x3.
# - sadc Integer Value of the shunt voltage ADC settings. Possible
# values are from 0x0 to 0xF. Default is 0x3.
# - vmax Float Value of the desired vmax value for automatic
# calibration. Default is None. This parameter will
# only be used of imax is also not None.
# - imax Float Value of the desired imax value for automatic
# calibration. Default is None. If imax is given,
# the values for vrange, gaindiv and currentLSB will be
# ignored and calculated instead. If imax is higher than
# possible, then the highest possible value will be
# used instead and overflow may occur.
# - currentLSB Float Value of the current LSB to use. Default is None.
# If you mistrust the automatic calibration you can
# set the current LSB manual with this parameter. If
# used, make sure to manual set the desired gaindiv also.
# - bus String Name of the I2C bus
#
# Usage remarks
#
# - The default values of this driver are valid for a 32 V Bus range, a maximum
# possible current of 3.2 A and a current resolution of around 98 microAmperes/Bit.
# If you are fine with this you can just use those defaults.
# - If you want to have some more configuration while keeping it still simple you
# can provide parameters for vmax and imax and the driver will do its best to
# automatically calculate vrange, gaindiv and calibration with a very good resolution.
# - If you prefer complete manual setup you should set vrange, gaindiv, currentLSB and
# optional fine-tuned calibration (in this order).
# - Setting the calibration register via setCalibration() is to be used for the final
# calibration as explained in the chip spec for the final fine tuning. It must not
# be used for the currentLSB setting as this is calculated automatically by this
# driver based on the values of shunt and gaindiv.
# - This driver implements an automatical calibration feature calibrate(vmax, imax)
# that can be used during device creation and also at runtime. The value for vmax
# is used to set vrange within the allowed limits. The value for imax is used to
# set gaindiv so that the maximal desired current can be measured at the highest
# possible resolution for current LSB. If the desired imax is higher than the
# possible imax based on the value of shunt, then the maximum possible imax will
# be used. You get the choosen values via the response of the calibrate(...) call.
# In this case, sending a higher current through the shunt will result in overflow
# which will generate a debugging message (only when reading the bus voltage).
# - If values for vmax and imax are given at device creation they will override the
# init values for vrange and gaindiv as those will be ignored then and calculated via
# the automatic calibration feature instead.
# - All chip parameters with the exception of shunt can be changed at runtime. If
# an updated parameter has an influence on the currentLSB and/or calibration value,
# then this/these will be re-calculated automatically and the calibration register
# will be set also. If you use setCalibration() for final fine-tuning you have to
# repeat that step again if automatic calibration has taken place.
# - Updating of the mode value at runtime allows triggered conversions and power-down
# of the chip.
# - If you are unsure about the calculated values set debugging to "True" and look at
# the debugging messages as they will notify you about all resulting values. Or
# call getConfiguration() to see all values.
# - If you encounter overflow (getting the overflow error) try to increase the
# gaindiv value or reduce the shunt value (please as real hardware change).
#
# Implementation remarks
#
# - This driver is implemented based on the specs from Intel.
# - The default value for the shunt resistor of 0.1 Ohms is appropriate for the
# breakout board from Adafruit for this chip (Adafruit PRODUCT ID: 904).
# - The parameter value for shunt can't be changed at runtime after device
# creation because it is very unlikely to modify the shunt resistor during operation
# of the chip. Please provide the correct value via the config options or at
# device creation if the default value does not suit your hardware setup.
# - This driver uses floating point calculation and takes no care about integer
# only arithmetics. For that reason, the mathematical lowest possible LSB value is
# calculated automatically and used for best resolution with the exception when you
# manual set your own current LSB value.
# - If you want to override/select the current LSB value manual you can do that
# via config parameter or at runtime. In this case make sure to use the correct
# corresponding gaindiv value otherwise the value readings will be wrong.
# - If for some reason (e.g. an impropriate setting of the currentLSB) the value
# of the calibration register would be out of its allowed bounds it will be set
# to zero so that all current and power readings will also be zero to avoid wrong
# measurements until the calibration register is set again to an allowed range.
# - This driver does not use the shunt adc register as this value is not needed
# for operation if the calibration register is used.
#
|
"""
========================================
Special functions (:mod:`scipy.special`)
========================================
.. module:: scipy.special
Nearly all of the functions below are universal functions and follow
broadcasting and automatic array-looping rules. Exceptions are
noted.
.. seealso::
`scipy.special.cython_special` -- Typed Cython versions of special functions
Error handling
==============
Errors are handled by returning NaNs or other appropriate values.
Some of the special function routines can emit warnings when an error
occurs. By default this is disabled; to enable it use `errprint`.
.. autosummary::
:toctree: generated/
errprint -- Set or return the error printing flag for special functions.
SpecialFunctionWarning -- Warning that can be issued with ``errprint(True)``
Available functions
===================
Airy functions
--------------
.. autosummary::
:toctree: generated/
airy -- Airy functions and their derivatives.
airye -- Exponentially scaled Airy functions and their derivatives.
ai_zeros -- [+]Compute `nt` zeros and values of the Airy function Ai and its derivative.
bi_zeros -- [+]Compute `nt` zeros and values of the Airy function Bi and its derivative.
itairy -- Integrals of Airy functions
Elliptic Functions and Integrals
--------------------------------
.. autosummary::
:toctree: generated/
ellipj -- Jacobian elliptic functions
ellipk -- Complete elliptic integral of the first kind.
ellipkm1 -- Complete elliptic integral of the first kind around `m` = 1
ellipkinc -- Incomplete elliptic integral of the first kind
ellipe -- Complete elliptic integral of the second kind
ellipeinc -- Incomplete elliptic integral of the second kind
Bessel Functions
----------------
.. autosummary::
:toctree: generated/
jv -- Bessel function of the first kind of real order and complex argument.
jn -- Bessel function of the first kind of real order and complex argument
jve -- Exponentially scaled Bessel function of order `v`.
yn -- Bessel function of the second kind of integer order and real argument.
yv -- Bessel function of the second kind of real order and complex argument.
yve -- Exponentially scaled Bessel function of the second kind of real order.
kn -- Modified Bessel function of the second kind of integer order `n`
kv -- Modified Bessel function of the second kind of real order `v`
kve -- Exponentially scaled modified Bessel function of the second kind.
iv -- Modified Bessel function of the first kind of real order.
ive -- Exponentially scaled modified Bessel function of the first kind
hankel1 -- Hankel function of the first kind
hankel1e -- Exponentially scaled Hankel function of the first kind
hankel2 -- Hankel function of the second kind
hankel2e -- Exponentially scaled Hankel function of the second kind
The following is not an universal function:
.. autosummary::
:toctree: generated/
lmbda -- [+]Jahnke-Emden Lambda function, Lambdav(x).
Zeros of Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^
These are not universal functions:
.. autosummary::
:toctree: generated/
jnjnp_zeros -- [+]Compute zeros of integer-order Bessel functions Jn and Jn'.
jnyn_zeros -- [+]Compute nt zeros of Bessel functions Jn(x), Jn'(x), Yn(x), and Yn'(x).
jn_zeros -- [+]Compute zeros of integer-order Bessel function Jn(x).
jnp_zeros -- [+]Compute zeros of integer-order Bessel function derivative Jn'(x).
yn_zeros -- [+]Compute zeros of integer-order Bessel function Yn(x).
ynp_zeros -- [+]Compute zeros of integer-order Bessel function derivative Yn'(x).
y0_zeros -- [+]Compute nt zeros of Bessel function Y0(z), and derivative at each zero.
y1_zeros -- [+]Compute nt zeros of Bessel function Y1(z), and derivative at each zero.
y1p_zeros -- [+]Compute nt zeros of Bessel derivative Y1'(z), and value at each zero.
Faster versions of common Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:toctree: generated/
j0 -- Bessel function of the first kind of order 0.
j1 -- Bessel function of the first kind of order 1.
y0 -- Bessel function of the second kind of order 0.
y1 -- Bessel function of the second kind of order 1.
i0 -- Modified Bessel function of order 0.
i0e -- Exponentially scaled modified Bessel function of order 0.
i1 -- Modified Bessel function of order 1.
i1e -- Exponentially scaled modified Bessel function of order 1.
k0 -- Modified Bessel function of the second kind of order 0, :math:`K_0`.
k0e -- Exponentially scaled modified Bessel function K of order 0
k1 -- Modified Bessel function of the second kind of order 1, :math:`K_1(x)`.
k1e -- Exponentially scaled modified Bessel function K of order 1
Integrals of Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:toctree: generated/
itj0y0 -- Integrals of Bessel functions of order 0
it2j0y0 -- Integrals related to Bessel functions of order 0
iti0k0 -- Integrals of modified Bessel functions of order 0
it2i0k0 -- Integrals related to modified Bessel functions of order 0
besselpoly -- [+]Weighted integral of a Bessel function.
Derivatives of Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:toctree: generated/
jvp -- Compute nth derivative of Bessel function Jv(z) with respect to `z`.
yvp -- Compute nth derivative of Bessel function Yv(z) with respect to `z`.
kvp -- Compute nth derivative of real-order modified Bessel function Kv(z)
ivp -- Compute nth derivative of modified Bessel function Iv(z) with respect to `z`.
h1vp -- Compute nth derivative of Hankel function H1v(z) with respect to `z`.
h2vp -- Compute nth derivative of Hankel function H2v(z) with respect to `z`.
Spherical Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:toctree: generated/
spherical_jn -- Spherical Bessel function of the first kind or its derivative.
spherical_yn -- Spherical Bessel function of the second kind or its derivative.
spherical_in -- Modified spherical Bessel function of the first kind or its derivative.
spherical_kn -- Modified spherical Bessel function of the second kind or its derivative.
Riccati-Bessel Functions
^^^^^^^^^^^^^^^^^^^^^^^^
These are not universal functions:
.. autosummary::
:toctree: generated/
riccati_jn -- [+]Compute Ricatti-Bessel function of the first kind and its derivative.
riccati_yn -- [+]Compute Ricatti-Bessel function of the second kind and its derivative.
Struve Functions
----------------
.. autosummary::
:toctree: generated/
struve -- Struve function.
modstruve -- Modified Struve function.
itstruve0 -- Integral of the Struve function of order 0.
it2struve0 -- Integral related to the Struve function of order 0.
itmodstruve0 -- Integral of the modified Struve function of order 0.
Raw Statistical Functions
-------------------------
.. seealso:: :mod:`scipy.stats`: Friendly versions of these functions.
.. autosummary::
:toctree: generated/
bdtr -- Binomial distribution cumulative distribution function.
bdtrc -- Binomial distribution survival function.
bdtri -- Inverse function to `bdtr` with respect to `p`.
bdtrik -- Inverse function to `bdtr` with respect to `k`.
bdtrin -- Inverse function to `bdtr` with respect to `n`.
btdtr -- Cumulative density function of the beta distribution.
btdtri -- The `p`-th quantile of the beta distribution.
btdtria -- Inverse of `btdtr` with respect to `a`.
btdtrib -- btdtria(a, p, x)
fdtr -- F cumulative distribution function.
fdtrc -- F survival function.
fdtri -- The `p`-th quantile of the F-distribution.
fdtridfd -- Inverse to `fdtr` vs dfd
gdtr -- Gamma distribution cumulative density function.
gdtrc -- Gamma distribution survival function.
gdtria -- Inverse of `gdtr` vs a.
gdtrib -- Inverse of `gdtr` vs b.
gdtrix -- Inverse of `gdtr` vs x.
nbdtr -- Negative binomial cumulative distribution function.
nbdtrc -- Negative binomial survival function.
nbdtri -- Inverse of `nbdtr` vs `p`.
nbdtrik -- Inverse of `nbdtr` vs `k`.
nbdtrin -- Inverse of `nbdtr` vs `n`.
ncfdtr -- Cumulative distribution function of the non-central F distribution.
ncfdtridfd -- Calculate degrees of freedom (denominator) for the noncentral F-distribution.
ncfdtridfn -- Calculate degrees of freedom (numerator) for the noncentral F-distribution.
ncfdtri -- Inverse cumulative distribution function of the non-central F distribution.
ncfdtrinc -- Calculate non-centrality parameter for non-central F distribution.
nctdtr -- Cumulative distribution function of the non-central `t` distribution.
nctdtridf -- Calculate degrees of freedom for non-central t distribution.
nctdtrit -- Inverse cumulative distribution function of the non-central t distribution.
nctdtrinc -- Calculate non-centrality parameter for non-central t distribution.
nrdtrimn -- Calculate mean of normal distribution given other params.
nrdtrisd -- Calculate standard deviation of normal distribution given other params.
pdtr -- Poisson cumulative distribution function
pdtrc -- Poisson survival function
pdtri -- Inverse to `pdtr` vs m
pdtrik -- Inverse to `pdtr` vs k
stdtr -- Student t distribution cumulative density function
stdtridf -- Inverse of `stdtr` vs df
stdtrit -- Inverse of `stdtr` vs `t`
chdtr -- Chi square cumulative distribution function
chdtrc -- Chi square survival function
chdtri -- Inverse to `chdtrc`
chdtriv -- Inverse to `chdtr` vs `v`
ndtr -- Gaussian cumulative distribution function.
log_ndtr -- Logarithm of Gaussian cumulative distribution function.
ndtri -- Inverse of `ndtr` vs x
chndtr -- Non-central chi square cumulative distribution function
chndtridf -- Inverse to `chndtr` vs `df`
chndtrinc -- Inverse to `chndtr` vs `nc`
chndtrix -- Inverse to `chndtr` vs `x`
smirnov -- Kolmogorov-Smirnov complementary cumulative distribution function
smirnovi -- Inverse to `smirnov`
kolmogorov -- Complementary cumulative distribution function of Kolmogorov distribution
kolmogi -- Inverse function to kolmogorov
tklmbda -- Tukey-Lambda cumulative distribution function
logit -- Logit ufunc for ndarrays.
expit -- Expit ufunc for ndarrays.
boxcox -- Compute the Box-Cox transformation.
boxcox1p -- Compute the Box-Cox transformation of 1 + `x`.
inv_boxcox -- Compute the inverse of the Box-Cox transformation.
inv_boxcox1p -- Compute the inverse of the Box-Cox transformation.
Information Theory Functions
----------------------------
.. autosummary::
:toctree: generated/
entr -- Elementwise function for computing entropy.
rel_entr -- Elementwise function for computing relative entropy.
kl_div -- Elementwise function for computing Kullback-Leibler divergence.
huber -- Huber loss function.
pseudo_huber -- Pseudo-Huber loss function.
Gamma and Related Functions
---------------------------
.. autosummary::
:toctree: generated/
gamma -- Gamma function.
gammaln -- Logarithm of the absolute value of the Gamma function for real inputs.
loggamma -- Principal branch of the logarithm of the Gamma function.
gammasgn -- Sign of the gamma function.
gammainc -- Regularized lower incomplete gamma function.
gammaincinv -- Inverse to `gammainc`
gammaincc -- Regularized upper incomplete gamma function.
gammainccinv -- Inverse to `gammaincc`
beta -- Beta function.
betaln -- Natural logarithm of absolute value of beta function.
betainc -- Incomplete beta integral.
betaincinv -- Inverse function to beta integral.
psi -- The digamma function.
rgamma -- Gamma function inverted
polygamma -- Polygamma function n.
multigammaln -- Returns the log of multivariate gamma, also sometimes called the generalized gamma.
digamma -- psi(x[, out])
poch -- Rising factorial (z)_m
Error Function and Fresnel Integrals
------------------------------------
.. autosummary::
:toctree: generated/
erf -- Returns the error function of complex argument.
erfc -- Complementary error function, ``1 - erf(x)``.
erfcx -- Scaled complementary error function, ``exp(x**2) * erfc(x)``.
erfi -- Imaginary error function, ``-i erf(i z)``.
erfinv -- Inverse function for erf.
erfcinv -- Inverse function for erfc.
wofz -- Faddeeva function
dawsn -- Dawson's integral.
fresnel -- Fresnel sin and cos integrals
fresnel_zeros -- Compute nt complex zeros of sine and cosine Fresnel integrals S(z) and C(z).
modfresnelp -- Modified Fresnel positive integrals
modfresnelm -- Modified Fresnel negative integrals
These are not universal functions:
.. autosummary::
:toctree: generated/
erf_zeros -- [+]Compute nt complex zeros of error function erf(z).
fresnelc_zeros -- [+]Compute nt complex zeros of cosine Fresnel integral C(z).
fresnels_zeros -- [+]Compute nt complex zeros of sine Fresnel integral S(z).
Legendre Functions
------------------
.. autosummary::
:toctree: generated/
lpmv -- Associated Legendre function of integer order and real degree.
sph_harm -- Compute spherical harmonics.
These are not universal functions:
.. autosummary::
:toctree: generated/
clpmn -- [+]Associated Legendre function of the first kind for complex arguments.
lpn -- [+]Legendre function of the first kind.
lqn -- [+]Legendre function of the second kind.
lpmn -- [+]Sequence of associated Legendre functions of the first kind.
lqmn -- [+]Sequence of associated Legendre functions of the second kind.
Ellipsoidal Harmonics
---------------------
.. autosummary::
:toctree: generated/
ellip_harm -- Ellipsoidal harmonic functions E^p_n(l)
ellip_harm_2 -- Ellipsoidal harmonic functions F^p_n(l)
ellip_normal -- Ellipsoidal harmonic normalization constants gamma^p_n
Orthogonal polynomials
----------------------
The following functions evaluate values of orthogonal polynomials:
.. autosummary::
:toctree: generated/
assoc_laguerre -- Compute the generalized (associated) Laguerre polynomial of degree n and order k.
eval_legendre -- Evaluate Legendre polynomial at a point.
eval_chebyt -- Evaluate Chebyshev polynomial of the first kind at a point.
eval_chebyu -- Evaluate Chebyshev polynomial of the second kind at a point.
eval_chebyc -- Evaluate Chebyshev polynomial of the first kind on [-2, 2] at a point.
eval_chebys -- Evaluate Chebyshev polynomial of the second kind on [-2, 2] at a point.
eval_jacobi -- Evaluate Jacobi polynomial at a point.
eval_laguerre -- Evaluate Laguerre polynomial at a point.
eval_genlaguerre -- Evaluate generalized Laguerre polynomial at a point.
eval_hermite -- Evaluate physicist's Hermite polynomial at a point.
eval_hermitenorm -- Evaluate probabilist's (normalized) Hermite polynomial at a point.
eval_gegenbauer -- Evaluate Gegenbauer polynomial at a point.
eval_sh_legendre -- Evaluate shifted Legendre polynomial at a point.
eval_sh_chebyt -- Evaluate shifted Chebyshev polynomial of the first kind at a point.
eval_sh_chebyu -- Evaluate shifted Chebyshev polynomial of the second kind at a point.
eval_sh_jacobi -- Evaluate shifted Jacobi polynomial at a point.
The following functions compute roots and quadrature weights for
orthogonal polynomials:
.. autosummary::
:toctree: generated/
roots_legendre -- Gauss-Legendre quadrature.
roots_chebyt -- Gauss-Chebyshev (first kind) quadrature.
roots_chebyu -- Gauss-Chebyshev (second kind) quadrature.
roots_chebyc -- Gauss-Chebyshev (first kind) quadrature.
roots_chebys -- Gauss-Chebyshev (second kind) quadrature.
roots_jacobi -- Gauss-Jacobi quadrature.
roots_laguerre -- Gauss-Laguerre quadrature.
roots_genlaguerre -- Gauss-generalized Laguerre quadrature.
roots_hermite -- Gauss-Hermite (physicst's) quadrature.
roots_hermitenorm -- Gauss-Hermite (statistician's) quadrature.
roots_gegenbauer -- Gauss-Gegenbauer quadrature.
roots_sh_legendre -- Gauss-Legendre (shifted) quadrature.
roots_sh_chebyt -- Gauss-Chebyshev (first kind, shifted) quadrature.
roots_sh_chebyu -- Gauss-Chebyshev (second kind, shifted) quadrature.
roots_sh_jacobi -- Gauss-Jacobi (shifted) quadrature.
The functions below, in turn, return the polynomial coefficients in
:class:`~.orthopoly1d` objects, which function similarly as :ref:`numpy.poly1d`.
The :class:`~.orthopoly1d` class also has an attribute ``weights`` which returns
the roots, weights, and total weights for the appropriate form of Gaussian
quadrature. These are returned in an ``n x 3`` array with roots in the first
column, weights in the second column, and total weights in the final column.
Note that :class:`~.orthopoly1d` objects are converted to ``poly1d`` when doing
arithmetic, and lose information of the original orthogonal polynomial.
.. autosummary::
:toctree: generated/
legendre -- [+]Legendre polynomial.
chebyt -- [+]Chebyshev polynomial of the first kind.
chebyu -- [+]Chebyshev polynomial of the second kind.
chebyc -- [+]Chebyshev polynomial of the first kind on :math:`[-2, 2]`.
chebys -- [+]Chebyshev polynomial of the second kind on :math:`[-2, 2]`.
jacobi -- [+]Jacobi polynomial.
laguerre -- [+]Laguerre polynomial.
genlaguerre -- [+]Generalized (associated) Laguerre polynomial.
hermite -- [+]Physicist's Hermite polynomial.
hermitenorm -- [+]Normalized (probabilist's) Hermite polynomial.
gegenbauer -- [+]Gegenbauer (ultraspherical) polynomial.
sh_legendre -- [+]Shifted Legendre polynomial.
sh_chebyt -- [+]Shifted Chebyshev polynomial of the first kind.
sh_chebyu -- [+]Shifted Chebyshev polynomial of the second kind.
sh_jacobi -- [+]Shifted Jacobi polynomial.
.. warning::
Computing values of high-order polynomials (around ``order > 20``) using
polynomial coefficients is numerically unstable. To evaluate polynomial
values, the ``eval_*`` functions should be used instead.
Hypergeometric Functions
------------------------
.. autosummary::
:toctree: generated/
hyp2f1 -- Gauss hypergeometric function 2F1(a, b; c; z).
hyp1f1 -- Confluent hypergeometric function 1F1(a, b; x)
hyperu -- Confluent hypergeometric function U(a, b, x) of the second kind
hyp0f1 -- Confluent hypergeometric limit function 0F1.
hyp2f0 -- Hypergeometric function 2F0 in y and an error estimate
hyp1f2 -- Hypergeometric function 1F2 and error estimate
hyp3f0 -- Hypergeometric function 3F0 in y and an error estimate
Parabolic Cylinder Functions
----------------------------
.. autosummary::
:toctree: generated/
pbdv -- Parabolic cylinder function D
pbvv -- Parabolic cylinder function V
pbwa -- Parabolic cylinder function W
These are not universal functions:
.. autosummary::
:toctree: generated/
pbdv_seq -- [+]Parabolic cylinder functions Dv(x) and derivatives.
pbvv_seq -- [+]Parabolic cylinder functions Vv(x) and derivatives.
pbdn_seq -- [+]Parabolic cylinder functions Dn(z) and derivatives.
Mathieu and Related Functions
-----------------------------
.. autosummary::
:toctree: generated/
mathieu_a -- Characteristic value of even Mathieu functions
mathieu_b -- Characteristic value of odd Mathieu functions
These are not universal functions:
.. autosummary::
:toctree: generated/
mathieu_even_coef -- [+]Fourier coefficients for even Mathieu and modified Mathieu functions.
mathieu_odd_coef -- [+]Fourier coefficients for even Mathieu and modified Mathieu functions.
The following return both function and first derivative:
.. autosummary::
:toctree: generated/
mathieu_cem -- Even Mathieu function and its derivative
mathieu_sem -- Odd Mathieu function and its derivative
mathieu_modcem1 -- Even modified Mathieu function of the first kind and its derivative
mathieu_modcem2 -- Even modified Mathieu function of the second kind and its derivative
mathieu_modsem1 -- Odd modified Mathieu function of the first kind and its derivative
mathieu_modsem2 -- Odd modified Mathieu function of the second kind and its derivative
Spheroidal Wave Functions
-------------------------
.. autosummary::
:toctree: generated/
pro_ang1 -- Prolate spheroidal angular function of the first kind and its derivative
pro_rad1 -- Prolate spheroidal radial function of the first kind and its derivative
pro_rad2 -- Prolate spheroidal radial function of the secon kind and its derivative
obl_ang1 -- Oblate spheroidal angular function of the first kind and its derivative
obl_rad1 -- Oblate spheroidal radial function of the first kind and its derivative
obl_rad2 -- Oblate spheroidal radial function of the second kind and its derivative.
pro_cv -- Characteristic value of prolate spheroidal function
obl_cv -- Characteristic value of oblate spheroidal function
pro_cv_seq -- Characteristic values for prolate spheroidal wave functions.
obl_cv_seq -- Characteristic values for oblate spheroidal wave functions.
The following functions require pre-computed characteristic value:
.. autosummary::
:toctree: generated/
pro_ang1_cv -- Prolate spheroidal angular function pro_ang1 for precomputed characteristic value
pro_rad1_cv -- Prolate spheroidal radial function pro_rad1 for precomputed characteristic value
pro_rad2_cv -- Prolate spheroidal radial function pro_rad2 for precomputed characteristic value
obl_ang1_cv -- Oblate spheroidal angular function obl_ang1 for precomputed characteristic value
obl_rad1_cv -- Oblate spheroidal radial function obl_rad1 for precomputed characteristic value
obl_rad2_cv -- Oblate spheroidal radial function obl_rad2 for precomputed characteristic value
Kelvin Functions
----------------
.. autosummary::
:toctree: generated/
kelvin -- Kelvin functions as complex numbers
kelvin_zeros -- [+]Compute nt zeros of all Kelvin functions.
ber -- Kelvin function ber.
bei -- Kelvin function bei
berp -- Derivative of the Kelvin function `ber`
beip -- Derivative of the Kelvin function `bei`
ker -- Kelvin function ker
kei -- Kelvin function ker
kerp -- Derivative of the Kelvin function ker
keip -- Derivative of the Kelvin function kei
These are not universal functions:
.. autosummary::
:toctree: generated/
ber_zeros -- [+]Compute nt zeros of the Kelvin function ber(x).
bei_zeros -- [+]Compute nt zeros of the Kelvin function bei(x).
berp_zeros -- [+]Compute nt zeros of the Kelvin function ber'(x).
beip_zeros -- [+]Compute nt zeros of the Kelvin function bei'(x).
ker_zeros -- [+]Compute nt zeros of the Kelvin function ker(x).
kei_zeros -- [+]Compute nt zeros of the Kelvin function kei(x).
kerp_zeros -- [+]Compute nt zeros of the Kelvin function ker'(x).
keip_zeros -- [+]Compute nt zeros of the Kelvin function kei'(x).
Combinatorics
-------------
.. autosummary::
:toctree: generated/
comb -- [+]The number of combinations of N things taken k at a time.
perm -- [+]Permutations of N things taken k at a time, i.e., k-permutations of N.
Lambert W and Related Functions
-------------------------------
.. autosummary::
:toctree: generated/
lambertw -- Lambert W function.
wrightomega -- Wright Omega function.
Other Special Functions
-----------------------
.. autosummary::
:toctree: generated/
agm -- Arithmetic, Geometric Mean.
bernoulli -- Bernoulli numbers B0..Bn (inclusive).
binom -- Binomial coefficient
diric -- Periodic sinc function, also called the Dirichlet function.
euler -- Euler numbers E0..En (inclusive).
expn -- Exponential integral E_n
exp1 -- Exponential integral E_1 of complex argument z
expi -- Exponential integral Ei
factorial -- The factorial of a number or array of numbers.
factorial2 -- Double factorial.
factorialk -- [+]Multifactorial of n of order k, n(!!...!).
shichi -- Hyperbolic sine and cosine integrals.
sici -- Sine and cosine integrals.
spence -- Spence's function, also known as the dilogarithm.
zeta -- Riemann zeta function.
zetac -- Riemann zeta function minus 1.
Convenience Functions
---------------------
.. autosummary::
:toctree: generated/
cbrt -- Cube root of `x`
exp10 -- 10**x
exp2 -- 2**x
radian -- Convert from degrees to radians
cosdg -- Cosine of the angle `x` given in degrees.
sindg -- Sine of angle given in degrees
tandg -- Tangent of angle x given in degrees.
cotdg -- Cotangent of the angle `x` given in degrees.
log1p -- Calculates log(1+x) for use when `x` is near zero
expm1 -- exp(x) - 1 for use when `x` is near zero.
cosm1 -- cos(x) - 1 for use when `x` is near zero.
round -- Round to nearest integer
xlogy -- Compute ``x*log(y)`` so that the result is 0 if ``x = 0``.
xlog1py -- Compute ``x*log1p(y)`` so that the result is 0 if ``x = 0``.
logsumexp -- Compute the log of the sum of exponentials of input elements.
exprel -- Relative error exponential, (exp(x)-1)/x, for use when `x` is near zero.
sinc -- Return the sinc function.
.. [+] in the description indicates a function which is not a universal
.. function and does not follow broadcasting and automatic
.. array-looping rules.
""" |
"""Stuff to parse AIFF-C and AIFF files.
Unless explicitly stated otherwise, the description below is true
both for AIFF-C files and AIFF files.
An AIFF-C file has the following structure.
+-----------------+
| FORM |
+-----------------+
| <size> |
+----+------------+
| | AIFC |
| +------------+
| | <chunks> |
| | . |
| | . |
| | . |
+----+------------+
An AIFF file has the string "AIFF" instead of "AIFC".
A chunk consists of an identifier (4 bytes) followed by a size (4 bytes,
big endian order), followed by the data. The size field does not include
the size of the 8 byte header.
The following chunk types are recognized.
FVER
<version number of AIFF-C defining document> (AIFF-C only).
MARK
<# of markers> (2 bytes)
list of markers:
<marker ID> (2 bytes, must be > 0)
<position> (4 bytes)
<marker name> ("pstring")
COMM
<# of channels> (2 bytes)
<# of sound frames> (4 bytes)
<size of the samples> (2 bytes)
<sampling frequency> (10 bytes, IEEE 80-bit extended
floating point)
in AIFF-C files only:
<compression type> (4 bytes)
<human-readable version of compression type> ("pstring")
SSND
<offset> (4 bytes, not used by this program)
<blocksize> (4 bytes, not used by this program)
<sound data>
A pstring consists of 1 byte length, a string of characters, and 0 or 1
byte pad to make the total length even.
Usage.
Reading AIFF files:
f = aifc.open(file, 'r')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods read(), seek(), and close().
In some types of audio files, if the setpos() method is not used,
the seek() method is not necessary.
This returns an instance of a class with the following public methods:
getnchannels() -- returns number of audio channels (1 for
mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getcomptype() -- returns compression type ('NONE' for AIFF files)
getcompname() -- returns human-readable version of
compression type ('not compressed' for AIFF files)
getparams() -- returns a namedtuple consisting of all of the
above in the above order
getmarkers() -- get the list of marks in the audio file or None
if there are no marks
getmark(id) -- get mark with the specified id (raises an error
if the mark does not exist)
readframes(n) -- returns at most n frames of audio
rewind() -- rewind to the beginning of the audio stream
setpos(pos) -- seek to the specified position
tell() -- return the current position
close() -- close the instance (make it unusable)
The position returned by tell(), the position given to setpos() and
the position of marks are all compatible and have nothing to do with
the actual position in the file.
The close() method is called automatically when the class instance
is destroyed.
Writing AIFF files:
f = aifc.open(file, 'w')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods write(), tell(), seek(), and
close().
This returns an instance of a class with the following public methods:
aiff() -- create an AIFF file (AIFF-C default)
aifc() -- create an AIFF-C file
setnchannels(n) -- set the number of channels
setsampwidth(n) -- set the sample width
setframerate(n) -- set the frame rate
setnframes(n) -- set the number of frames
setcomptype(type, name)
-- set the compression type and the
human-readable compression type
setparams(tuple)
-- set all parameters at once
setmark(id, pos, name)
-- add specified mark to the list of marks
tell() -- return current position in output file (useful
in combination with setmark())
writeframesraw(data)
-- write audio frames without pathing up the
file header
writeframes(data)
-- write audio frames and patch up the file header
close() -- patch up the file header and close the
output file
You should set the parameters before the first writeframesraw or
writeframes. The total number of frames does not need to be set,
but when it is set to the correct value, the header does not have to
be patched up.
It is best to first set all parameters, perhaps possibly the
compression type, and then write audio frames using writeframesraw.
When all frames have been written, either call writeframes('') or
close() to patch up the sizes in the header.
Marks can be added anytime. If there are any marks, you must call
close() after all frames have been written.
The close() method is called automatically when the class instance
is destroyed.
When a file is opened with the extension '.aiff', an AIFF file is
written, otherwise an AIFF-C file is written. This default can be
changed by calling aiff() or aifc() before the first writeframes or
writeframesraw.
""" |
"""
Objects for dealing with Chebyshev series.
This module provides a number of objects (mostly functions) useful for
dealing with Chebyshev series, including a `Chebyshev` class that
encapsulates the usual arithmetic operations. (General information
on how this module represents and works with such polynomials is in the
docstring for its "parent" sub-package, `numpy.polynomial`).
Constants
---------
- `chebdomain` -- Chebyshev series default domain, [-1,1].
- `chebzero` -- (Coefficients of the) Chebyshev series that evaluates
identically to 0.
- `chebone` -- (Coefficients of the) Chebyshev series that evaluates
identically to 1.
- `chebx` -- (Coefficients of the) Chebyshev series for the identity map,
``f(x) = x``.
Arithmetic
----------
- `chebadd` -- add two Chebyshev series.
- `chebsub` -- subtract one Chebyshev series from another.
- `chebmul` -- multiply two Chebyshev series.
- `chebdiv` -- divide one Chebyshev series by another.
- `chebpow` -- raise a Chebyshev series to an positive integer power
- `chebval` -- evaluate a Chebyshev series at given points.
- `chebval2d` -- evaluate a 2D Chebyshev series at given points.
- `chebval3d` -- evaluate a 3D Chebyshev series at given points.
- `chebgrid2d` -- evaluate a 2D Chebyshev series on a Cartesian product.
- `chebgrid3d` -- evaluate a 3D Chebyshev series on a Cartesian product.
Calculus
--------
- `chebder` -- differentiate a Chebyshev series.
- `chebint` -- integrate a Chebyshev series.
Misc Functions
--------------
- `chebfromroots` -- create a Chebyshev series with specified roots.
- `chebroots` -- find the roots of a Chebyshev series.
- `chebvander` -- Vandermonde-like matrix for Chebyshev polynomials.
- `chebvander2d` -- Vandermonde-like matrix for 2D power series.
- `chebvander3d` -- Vandermonde-like matrix for 3D power series.
- `chebgauss` -- Gauss-Chebyshev quadrature, points and weights.
- `chebweight` -- Chebyshev weight function.
- `chebcompanion` -- symmetrized companion matrix in Chebyshev form.
- `chebfit` -- least-squares fit returning a Chebyshev series.
- `chebpts1` -- Chebyshev points of the first kind.
- `chebpts2` -- Chebyshev points of the second kind.
- `chebtrim` -- trim leading coefficients from a Chebyshev series.
- `chebline` -- Chebyshev series representing given straight line.
- `cheb2poly` -- convert a Chebyshev series to a polynomial.
- `poly2cheb` -- convert a polynomial to a Chebyshev series.
Classes
-------
- `Chebyshev` -- A Chebyshev series class.
See also
--------
`numpy.polynomial`
Notes
-----
The implementations of multiplication, division, integration, and
differentiation use the algebraic identities [1]_:
.. math ::
T_n(x) = \\frac{z^n + z^{-n}}{2} \\\\
z\\frac{dx}{dz} = \\frac{z - z^{-1}}{2}.
where
.. math :: x = \\frac{z + z^{-1}}{2}.
These identities allow a Chebyshev series to be expressed as a finite,
symmetric Laurent series. In this module, this sort of Laurent series
is referred to as a "z-series."
References
----------
.. [1] NAME et al., "Combinatorial Trigonometry with Chebyshev
Polynomials," *Journal of Statistical Planning and Inference 14*, 2008
(preprint: http://www.math.hmc.edu/~benjamin/papers/CombTrig.pdf, pg. 4)
""" |
"""
=============================
Subclassing ndarray in python
=============================
Credits
-------
This page is based with thanks on the wiki page on subclassing by NAME - http://www.scipy.org/Subclasses.
Introduction
------------
Subclassing ndarray is relatively simple, but it has some complications
compared to other Python objects. On this page we explain the machinery
that allows you to subclass ndarray, and the implications for
implementing a subclass.
ndarrays and object creation
============================
Subclassing ndarray is complicated by the fact that new instances of
ndarray classes can come about in three different ways. These are:
#. Explicit constructor call - as in ``MySubClass(params)``. This is
the usual route to Python instance creation.
#. View casting - casting an existing ndarray as a given subclass
#. New from template - creating a new instance from a template
instance. Examples include returning slices from a subclassed array,
creating return types from ufuncs, and copying arrays. See
:ref:`new-from-template` for more details
The last two are characteristics of ndarrays - in order to support
things like array slicing. The complications of subclassing ndarray are
due to the mechanisms numpy has to support these latter two routes of
instance creation.
.. _view-casting:
View casting
------------
*View casting* is the standard ndarray mechanism by which you take an
ndarray of any subclass, and return a view of the array as another
(specified) subclass:
>>> import numpy as np
>>> # create a completely useless ndarray subclass
>>> class C(np.ndarray): pass
>>> # create a standard ndarray
>>> arr = np.zeros((3,))
>>> # take a view of it, as our useless subclass
>>> c_arr = arr.view(C)
>>> type(c_arr)
<class 'C'>
.. _new-from-template:
Creating new from template
--------------------------
New instances of an ndarray subclass can also come about by a very
similar mechanism to :ref:`view-casting`, when numpy finds it needs to
create a new instance from a template instance. The most obvious place
this has to happen is when you are taking slices of subclassed arrays.
For example:
>>> v = c_arr[1:]
>>> type(v) # the view is of type 'C'
<class 'C'>
>>> v is c_arr # but it's a new instance
False
The slice is a *view* onto the original ``c_arr`` data. So, when we
take a view from the ndarray, we return a new ndarray, of the same
class, that points to the data in the original.
There are other points in the use of ndarrays where we need such views,
such as copying arrays (``c_arr.copy()``), creating ufunc output arrays
(see also :ref:`array-wrap`), and reducing methods (like
``c_arr.mean()``.
Relationship of view casting and new-from-template
--------------------------------------------------
These paths both use the same machinery. We make the distinction here,
because they result in different input to your methods. Specifically,
:ref:`view-casting` means you have created a new instance of your array
type from any potential subclass of ndarray. :ref:`new-from-template`
means you have created a new instance of your class from a pre-existing
instance, allowing you - for example - to copy across attributes that
are particular to your subclass.
Implications for subclassing
----------------------------
If we subclass ndarray, we need to deal not only with explicit
construction of our array type, but also :ref:`view-casting` or
:ref:`new-from-template`. Numpy has the machinery to do this, and this
machinery that makes subclassing slightly non-standard.
There are two aspects to the machinery that ndarray uses to support
views and new-from-template in subclasses.
The first is the use of the ``ndarray.__new__`` method for the main work
of object initialization, rather then the more usual ``__init__``
method. The second is the use of the ``__array_finalize__`` method to
allow subclasses to clean up after the creation of views and new
instances from templates.
A brief Python primer on ``__new__`` and ``__init__``
=====================================================
``__new__`` is a standard Python method, and, if present, is called
before ``__init__`` when we create a class instance. See the `python
__new__ documentation
<http://docs.python.org/reference/datamodel.html#object.__new__>`_ for more detail.
For example, consider the following Python code:
.. testcode::
class C(object):
def __new__(cls, *args):
print 'Cls in __new__:', cls
print 'Args in __new__:', args
return object.__new__(cls, *args)
def __init__(self, *args):
print 'type(self) in __init__:', type(self)
print 'Args in __init__:', args
meaning that we get:
>>> c = C('hello')
Cls in __new__: <class 'C'>
Args in __new__: ('hello',)
type(self) in __init__: <class 'C'>
Args in __init__: ('hello',)
When we call ``C('hello')``, the ``__new__`` method gets its own class
as first argument, and the passed argument, which is the string
``'hello'``. After python calls ``__new__``, it usually (see below)
calls our ``__init__`` method, with the output of ``__new__`` as the
first argument (now a class instance), and the passed arguments
following.
As you can see, the object can be initialized in the ``__new__``
method or the ``__init__`` method, or both, and in fact ndarray does
not have an ``__init__`` method, because all the initialization is
done in the ``__new__`` method.
Why use ``__new__`` rather than just the usual ``__init__``? Because
in some cases, as for ndarray, we want to be able to return an object
of some other class. Consider the following:
.. testcode::
class D(C):
def __new__(cls, *args):
print 'D cls is:', cls
print 'D args in __new__:', args
return C.__new__(C, *args)
def __init__(self, *args):
# we never get here
print 'In D __init__'
meaning that:
>>> obj = D('hello')
D cls is: <class 'D'>
D args in __new__: ('hello',)
Cls in __new__: <class 'C'>
Args in __new__: ('hello',)
>>> type(obj)
<class 'C'>
The definition of ``C`` is the same as before, but for ``D``, the
``__new__`` method returns an instance of class ``C`` rather than
``D``. Note that the ``__init__`` method of ``D`` does not get
called. In general, when the ``__new__`` method returns an object of
class other than the class in which it is defined, the ``__init__``
method of that class is not called.
This is how subclasses of the ndarray class are able to return views
that preserve the class type. When taking a view, the standard
ndarray machinery creates the new ndarray object with something
like::
obj = ndarray.__new__(subtype, shape, ...
where ``subdtype`` is the subclass. Thus the returned view is of the
same class as the subclass, rather than being of class ``ndarray``.
That solves the problem of returning views of the same type, but now
we have a new problem. The machinery of ndarray can set the class
this way, in its standard methods for taking views, but the ndarray
``__new__`` method knows nothing of what we have done in our own
``__new__`` method in order to set attributes, and so on. (Aside -
why not call ``obj = subdtype.__new__(...`` then? Because we may not
have a ``__new__`` method with the same call signature).
The role of ``__array_finalize__``
==================================
``__array_finalize__`` is the mechanism that numpy provides to allow
subclasses to handle the various ways that new instances get created.
Remember that subclass instances can come about in these three ways:
#. explicit constructor call (``obj = MySubClass(params)``). This will
call the usual sequence of ``MySubClass.__new__`` then (if it exists)
``MySubClass.__init__``.
#. :ref:`view-casting`
#. :ref:`new-from-template`
Our ``MySubClass.__new__`` method only gets called in the case of the
explicit constructor call, so we can't rely on ``MySubClass.__new__`` or
``MySubClass.__init__`` to deal with the view casting and
new-from-template. It turns out that ``MySubClass.__array_finalize__``
*does* get called for all three methods of object creation, so this is
where our object creation housekeeping usually goes.
* For the explicit constructor call, our subclass will need to create a
new ndarray instance of its own class. In practice this means that
we, the authors of the code, will need to make a call to
``ndarray.__new__(MySubClass,...)``, or do view casting of an existing
array (see below)
* For view casting and new-from-template, the equivalent of
``ndarray.__new__(MySubClass,...`` is called, at the C level.
The arguments that ``__array_finalize__`` recieves differ for the three
methods of instance creation above.
The following code allows us to look at the call sequences and arguments:
.. testcode::
import numpy as np
class C(np.ndarray):
def __new__(cls, *args, **kwargs):
print 'In __new__ with class %s' % cls
return np.ndarray.__new__(cls, *args, **kwargs)
def __init__(self, *args, **kwargs):
# in practice you probably will not need or want an __init__
# method for your subclass
print 'In __init__ with class %s' % self.__class__
def __array_finalize__(self, obj):
print 'In array_finalize:'
print ' self type is %s' % type(self)
print ' obj type is %s' % type(obj)
Now:
>>> # Explicit constructor
>>> c = C((10,))
In __new__ with class <class 'C'>
In array_finalize:
self type is <class 'C'>
obj type is <type 'NoneType'>
In __init__ with class <class 'C'>
>>> # View casting
>>> a = np.arange(10)
>>> cast_a = a.view(C)
In array_finalize:
self type is <class 'C'>
obj type is <type 'numpy.ndarray'>
>>> # Slicing (example of new-from-template)
>>> cv = c[:1]
In array_finalize:
self type is <class 'C'>
obj type is <class 'C'>
The signature of ``__array_finalize__`` is::
def __array_finalize__(self, obj):
``ndarray.__new__`` passes ``__array_finalize__`` the new object, of our
own class (``self``) as well as the object from which the view has been
taken (``obj``). As you can see from the output above, the ``self`` is
always a newly created instance of our subclass, and the type of ``obj``
differs for the three instance creation methods:
* When called from the explicit constructor, ``obj`` is ``None``
* When called from view casting, ``obj`` can be an instance of any
subclass of ndarray, including our own.
* When called in new-from-template, ``obj`` is another instance of our
own subclass, that we might use to update the new ``self`` instance.
Because ``__array_finalize__`` is the only method that always sees new
instances being created, it is the sensible place to fill in instance
defaults for new object attributes, among other tasks.
This may be clearer with an example.
Simple example - adding an extra attribute to ndarray
-----------------------------------------------------
.. testcode::
import numpy as np
class InfoArray(np.ndarray):
def __new__(subtype, shape, dtype=float, buffer=None, offset=0,
strides=None, order=None, info=None):
# Create the ndarray instance of our type, given the usual
# ndarray input arguments. This will call the standard
# ndarray constructor, but return an object of our type.
# It also triggers a call to InfoArray.__array_finalize__
obj = np.ndarray.__new__(subtype, shape, dtype, buffer, offset, strides,
order)
# set the new 'info' attribute to the value passed
obj.info = info
# Finally, we must return the newly created object:
return obj
def __array_finalize__(self, obj):
# ``self`` is a new object resulting from
# ndarray.__new__(InfoArray, ...), therefore it only has
# attributes that the ndarray.__new__ constructor gave it -
# i.e. those of a standard ndarray.
#
# We could have got to the ndarray.__new__ call in 3 ways:
# From an explicit constructor - e.g. InfoArray():
# obj is None
# (we're in the middle of the InfoArray.__new__
# constructor, and self.info will be set when we return to
# InfoArray.__new__)
if obj is None: return
# From view casting - e.g arr.view(InfoArray):
# obj is arr
# (type(obj) can be InfoArray)
# From new-from-template - e.g infoarr[:3]
# type(obj) is InfoArray
#
# Note that it is here, rather than in the __new__ method,
# that we set the default value for 'info', because this
# method sees all creation of default objects - with the
# InfoArray.__new__ constructor, but also with
# arr.view(InfoArray).
self.info = getattr(obj, 'info', None)
# We do not need to return anything
Using the object looks like this:
>>> obj = InfoArray(shape=(3,)) # explicit constructor
>>> type(obj)
<class 'InfoArray'>
>>> obj.info is None
True
>>> obj = InfoArray(shape=(3,), info='information')
>>> obj.info
'information'
>>> v = obj[1:] # new-from-template - here - slicing
>>> type(v)
<class 'InfoArray'>
>>> v.info
'information'
>>> arr = np.arange(10)
>>> cast_arr = arr.view(InfoArray) # view casting
>>> type(cast_arr)
<class 'InfoArray'>
>>> cast_arr.info is None
True
This class isn't very useful, because it has the same constructor as the
bare ndarray object, including passing in buffers and shapes and so on.
We would probably prefer the constructor to be able to take an already
formed ndarray from the usual numpy calls to ``np.array`` and return an
object.
Slightly more realistic example - attribute added to existing array
-------------------------------------------------------------------
Here is a class that takes a standard ndarray that already exists, casts
as our type, and adds an extra attribute.
.. testcode::
import numpy as np
class RealisticInfoArray(np.ndarray):
def __new__(cls, input_array, info=None):
# Input array is an already formed ndarray instance
# We first cast to be our class type
obj = np.asarray(input_array).view(cls)
# add the new attribute to the created instance
obj.info = info
# Finally, we must return the newly created object:
return obj
def __array_finalize__(self, obj):
# see InfoArray.__array_finalize__ for comments
if obj is None: return
self.info = getattr(obj, 'info', None)
So:
>>> arr = np.arange(5)
>>> obj = RealisticInfoArray(arr, info='information')
>>> type(obj)
<class 'RealisticInfoArray'>
>>> obj.info
'information'
>>> v = obj[1:]
>>> type(v)
<class 'RealisticInfoArray'>
>>> v.info
'information'
.. _array-wrap:
``__array_wrap__`` for ufuncs
-------------------------------------------------------
``__array_wrap__`` gets called at the end of numpy ufuncs and other numpy
functions, to allow a subclass to set the type of the return value
and update attributes and metadata. Let's show how this works with an example.
First we make the same subclass as above, but with a different name and
some print statements:
.. testcode::
import numpy as np
class MySubClass(np.ndarray):
def __new__(cls, input_array, info=None):
obj = np.asarray(input_array).view(cls)
obj.info = info
return obj
def __array_finalize__(self, obj):
print 'In __array_finalize__:'
print ' self is %s' % repr(self)
print ' obj is %s' % repr(obj)
if obj is None: return
self.info = getattr(obj, 'info', None)
def __array_wrap__(self, out_arr, context=None):
print 'In __array_wrap__:'
print ' self is %s' % repr(self)
print ' arr is %s' % repr(out_arr)
# then just call the parent
return np.ndarray.__array_wrap__(self, out_arr, context)
We run a ufunc on an instance of our new array:
>>> obj = MySubClass(np.arange(5), info='spam')
In __array_finalize__:
self is MySubClass([0, 1, 2, 3, 4])
obj is array([0, 1, 2, 3, 4])
>>> arr2 = np.arange(5)+1
>>> ret = np.add(arr2, obj)
In __array_wrap__:
self is MySubClass([0, 1, 2, 3, 4])
arr is array([1, 3, 5, 7, 9])
In __array_finalize__:
self is MySubClass([1, 3, 5, 7, 9])
obj is MySubClass([0, 1, 2, 3, 4])
>>> ret
MySubClass([1, 3, 5, 7, 9])
>>> ret.info
'spam'
Note that the ufunc (``np.add``) has called the ``__array_wrap__`` method of the
input with the highest ``__array_priority__`` value, in this case
``MySubClass.__array_wrap__``, with arguments ``self`` as ``obj``, and
``out_arr`` as the (ndarray) result of the addition. In turn, the
default ``__array_wrap__`` (``ndarray.__array_wrap__``) has cast the
result to class ``MySubClass``, and called ``__array_finalize__`` -
hence the copying of the ``info`` attribute. This has all happened at the C level.
But, we could do anything we wanted:
.. testcode::
class SillySubClass(np.ndarray):
def __array_wrap__(self, arr, context=None):
return 'I lost your data'
>>> arr1 = np.arange(5)
>>> obj = arr1.view(SillySubClass)
>>> arr2 = np.arange(5)
>>> ret = np.multiply(obj, arr2)
>>> ret
'I lost your data'
So, by defining a specific ``__array_wrap__`` method for our subclass,
we can tweak the output from ufuncs. The ``__array_wrap__`` method
requires ``self``, then an argument - which is the result of the ufunc -
and an optional parameter *context*. This parameter is returned by some
ufuncs as a 3-element tuple: (name of the ufunc, argument of the ufunc,
domain of the ufunc). ``__array_wrap__`` should return an instance of
its containing class. See the masked array subclass for an
implementation.
In addition to ``__array_wrap__``, which is called on the way out of the
ufunc, there is also an ``__array_prepare__`` method which is called on
the way into the ufunc, after the output arrays are created but before any
computation has been performed. The default implementation does nothing
but pass through the array. ``__array_prepare__`` should not attempt to
access the array data or resize the array, it is intended for setting the
output array type, updating attributes and metadata, and performing any
checks based on the input that may be desired before computation begins.
Like ``__array_wrap__``, ``__array_prepare__`` must return an ndarray or
subclass thereof or raise an error.
Extra gotchas - custom ``__del__`` methods and ndarray.base
-----------------------------------------------------------
One of the problems that ndarray solves is keeping track of memory
ownership of ndarrays and their views. Consider the case where we have
created an ndarray, ``arr`` and have taken a slice with ``v = arr[1:]``.
The two objects are looking at the same memory. Numpy keeps track of
where the data came from for a particular array or view, with the
``base`` attribute:
>>> # A normal ndarray, that owns its own data
>>> arr = np.zeros((4,))
>>> # In this case, base is None
>>> arr.base is None
True
>>> # We take a view
>>> v1 = arr[1:]
>>> # base now points to the array that it derived from
>>> v1.base is arr
True
>>> # Take a view of a view
>>> v2 = v1[1:]
>>> # base points to the view it derived from
>>> v2.base is v1
True
In general, if the array owns its own memory, as for ``arr`` in this
case, then ``arr.base`` will be None - there are some exceptions to this
- see the numpy book for more details.
The ``base`` attribute is useful in being able to tell whether we have
a view or the original array. This in turn can be useful if we need
to know whether or not to do some specific cleanup when the subclassed
array is deleted. For example, we may only want to do the cleanup if
the original array is deleted, but not the views. For an example of
how this can work, have a look at the ``memmap`` class in
``numpy.core``.
""" |
"""Drag-and-drop support for Tkinter.
This is very preliminary. I currently only support dnd *within* one
application, between different windows (or within the same window).
I an trying to make this as generic as possible -- not dependent on
the use of a particular widget or icon type, etc. I also hope that
this will work with Pmw.
To enable an object to be dragged, you must create an event binding
for it that starts the drag-and-drop process. Typically, you should
bind <ButtonPress> to a callback function that you write. The function
should call Tkdnd.dnd_start(source, event), where 'source' is the
object to be dragged, and 'event' is the event that invoked the call
(the argument to your callback function). Even though this is a class
instantiation, the returned instance should not be stored -- it will
be kept alive automatically for the duration of the drag-and-drop.
When a drag-and-drop is already in process for the Tk interpreter, the
call is *ignored*; this normally averts starting multiple simultaneous
dnd processes, e.g. because different button callbacks all
dnd_start().
The object is *not* necessarily a widget -- it can be any
application-specific object that is meaningful to potential
drag-and-drop targets.
Potential drag-and-drop targets are discovered as follows. Whenever
the mouse moves, and at the start and end of a drag-and-drop move, the
Tk widget directly under the mouse is inspected. This is the target
widget (not to be confused with the target object, yet to be
determined). If there is no target widget, there is no dnd target
object. If there is a target widget, and it has an attribute
dnd_accept, this should be a function (or any callable object). The
function is called as dnd_accept(source, event), where 'source' is the
object being dragged (the object passed to dnd_start() above), and
'event' is the most recent event object (generally a <Motion> event;
it can also be <ButtonPress> or <ButtonRelease>). If the dnd_accept()
function returns something other than None, this is the new dnd target
object. If dnd_accept() returns None, or if the target widget has no
dnd_accept attribute, the target widget's parent is considered as the
target widget, and the search for a target object is repeated from
there. If necessary, the search is repeated all the way up to the
root widget. If none of the target widgets can produce a target
object, there is no target object (the target object is None).
The target object thus produced, if any, is called the new target
object. It is compared with the old target object (or None, if there
was no old target widget). There are several cases ('source' is the
source object, and 'event' is the most recent event object):
- Both the old and new target objects are None. Nothing happens.
- The old and new target objects are the same object. Its method
dnd_motion(source, event) is called.
- The old target object was None, and the new target object is not
None. The new target object's method dnd_enter(source, event) is
called.
- The new target object is None, and the old target object is not
None. The old target object's method dnd_leave(source, event) is
called.
- The old and new target objects differ and neither is None. The old
target object's method dnd_leave(source, event), and then the new
target object's method dnd_enter(source, event) is called.
Once this is done, the new target object replaces the old one, and the
Tk mainloop proceeds. The return value of the methods mentioned above
is ignored; if they raise an exception, the normal exception handling
mechanisms take over.
The drag-and-drop processes can end in two ways: a final target object
is selected, or no final target object is selected. When a final
target object is selected, it will always have been notified of the
potential drop by a call to its dnd_enter() method, as described
above, and possibly one or more calls to its dnd_motion() method; its
dnd_leave() method has not been called since the last call to
dnd_enter(). The target is notified of the drop by a call to its
method dnd_commit(source, event).
If no final target object is selected, and there was an old target
object, its dnd_leave(source, event) method is called to complete the
dnd sequence.
Finally, the source object is notified that the drag-and-drop process
is over, by a call to source.dnd_end(target, event), specifying either
the selected target object, or None if no target object was selected.
The source object can use this to implement the commit action; this is
sometimes simpler than to do it in the target's dnd_commit(). The
target's dnd_commit() method could then simply be aliased to
dnd_leave().
At any time during a dnd sequence, the application can cancel the
sequence by calling the cancel() method on the object returned by
dnd_start(). This will call dnd_leave() if a target is currently
active; it will never call dnd_commit().
""" |
#! /usr/bin/python
# Multistage table builder
# (c) NAME 2008
##############################################################################
# This script was submitted to the PCRE project by NAME as part of
# the upgrading of Unicode property support. The new code speeds up property
# matching many times. The script is for the use of PCRE maintainers, to
# generate the pcre_ucd.c file that contains a digested form of the Unicode
# data tables.
#
# The script has now been upgraded to Python 3 for PCRE2, and should be run in
# the maint subdirectory, using the command
#
# [python3] ./MultiStage2.py >../src/pcre2_ucd.c
#
# It requires four Unicode data tables, DerivedGeneralCategory.txt,
# GraphemeBreakProperty.txt, Scripts.txt, and CaseFolding.txt, to be in the
# Unicode.tables subdirectory. The first of these is found in the "extracted"
# subdirectory of the Unicode database (UCD) on the Unicode web site; the
# second is in the "auxiliary" subdirectory; the other two are directly in the
# UCD directory.
#
# Minor modifications made to this script:
# Added #! line at start
# Removed tabs
# Made it work with Python 2.4 by rewriting two statements that needed 2.5
# Consequent code tidy
# Adjusted data file names to take from the Unicode.tables directory
# Adjusted global table names by prefixing _pcre_.
# Commented out stuff relating to the casefolding table, which isn't used;
# removed completely in 2012.
# Corrected size calculation
# Add #ifndef SUPPORT_UCP to use dummy tables when no UCP support is needed.
# Update for PCRE2: name changes, and SUPPORT_UCP is abolished.
#
# Major modifications made to this script:
# Added code to add a grapheme break property field to records.
#
# Added code to search for sets of more than two characters that must match
# each other caselessly. A new table is output containing these sets, and
# offsets into the table are added to the main output records. This new
# code scans CaseFolding.txt instead of UnicodeData.txt.
#
# Update for Python3:
# . Processed with 2to3, but that didn't fix everything
# . Changed string.strip to str.strip
# . Added encoding='utf-8' to the open() call
# . Inserted 'int' before blocksize/ELEMS_PER_LINE because an int is
# required and the result of the division is a float
#
# The main tables generated by this script are used by macros defined in
# pcre2_internal.h. They look up Unicode character properties using short
# sequences of code that contains no branches, which makes for greater speed.
#
# Conceptually, there is a table of records (of type ucd_record), containing a
# script number, character type, grapheme break type, offset to caseless
# matching set, and offset to the character's other case for every character.
# However, a real table covering all Unicode characters would be far too big.
# It can be efficiently compressed by observing that many characters have the
# same record, and many blocks of characters (taking 128 characters in a block)
# have the same set of records as other blocks. This leads to a 2-stage lookup
# process.
#
# This script constructs four tables. The ucd_caseless_sets table contains
# lists of characters that all match each other caselessly. Each list is
# in order, and is terminated by NOTACHAR (0xffffffff), which is larger than
# any valid character. The first list is empty; this is used for characters
# that are not part of any list.
#
# The ucd_records table contains one instance of every unique record that is
# required. The ucd_stage1 table is indexed by a character's block number, and
# yields what is in effect a "virtual" block number. The ucd_stage2 table is a
# table of "virtual" blocks; each block is indexed by the offset of a character
# within its own block, and the result is the offset of the required record.
#
# Example: lowercase "a" (U+0061) is in block 0
# lookup 0 in stage1 table yields 0
# lookup 97 in the first table in stage2 yields 16
# record 17 is { 33, 5, 11, 0, -32 }
# 33 = ucp_Latin => Latin script
# 5 = ucp_Ll => Lower case letter
# 11 = ucp_gbOther => Grapheme break property "Other"
# 0 => not part of a caseless set
# -32 => Other case is U+0041
#
# Almost all lowercase latin characters resolve to the same record. One or two
# are different because they are part of a multi-character caseless set (for
# example, k, K and the Kelvin symbol are such a set).
#
# Example: hiragana letter A (U+3042) is in block 96 (0x60)
# lookup 96 in stage1 table yields 88
# lookup 66 in the 88th table in stage2 yields 467
# record 470 is { 26, 7, 11, 0, 0 }
# 26 = ucp_Hiragana => Hiragana script
# 7 = ucp_Lo => Other letter
# 11 = ucp_gbOther => Grapheme break property "Other"
# 0 => not part of a caseless set
# 0 => No other case
#
# In these examples, no other blocks resolve to the same "virtual" block, as it
# happens, but plenty of other blocks do share "virtual" blocks.
#
# There is a fourth table, maintained by hand, which translates from the
# individual character types such as ucp_Cc to the general types like ucp_C.
#
# NAME 03 July 2008
#
# 01-March-2010: Updated list of scripts for Unicode 5.2.0
# 30-April-2011: Updated list of scripts for Unicode 6.0.0
# July-2012: Updated list of scripts for Unicode 6.1.0
# 20-August-2012: Added scan of GraphemeBreakProperty.txt and added a new
# field in the record to hold the value. Luckily, the
# structure had a hole in it, so the resulting table is
# not much bigger than before.
# 18-September-2012: Added code for multiple caseless sets. This uses the
# final hole in the structure.
# 30-September-2012: Added RegionalIndicator break property from Unicode 6.2.0
# 13-May-2014: Updated for PCRE2
# 03-June-2014: Updated for Python 3
# 20-June-2014: Updated for Unicode 7.0.0
# 12-August-2014: Updated to put Unicode version into the file
##############################################################################
|
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to reqd all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use select() to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
- Standard framework for select-based multiplexing
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <lkcl@samba.org>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
# -*- coding: utf-8 -*-
################################################################################
# header_operations expanded v.1.0.1 #
################################################################################
# TABLE OF CONTENTS
################################################################################
#
# [ Z00 ] Introduction and Credits.
# [ Z01 ] Operation Modifiers.
# [ Z02 ] Flow Control.
# [ Z03 ] Mathematical Operations.
# [ Z04 ] Script/Trigger Parameters and Results.
# [ Z05 ] Keyboard and Mouse Input.
# [ Z06 ] World Map.
# [ Z07 ] Game Settings.
# [ Z08 ] Factions.
# [ Z09 ] Parties and Party Templates.
# [ Z10 ] Troops.
# [ Z11 ] Quests.
# [ Z12 ] Items.
# [ Z13 ] Sounds and Music Tracks.
# [ Z14 ] Positions.
# [ Z15 ] Game Notes.
# [ Z16 ] Tableaus and Heraldics.
# [ Z17 ] String Operations.
# [ Z18 ] Output And Messages.
# [ Z19 ] Game Control: Screens, Menus, Dialogs and Encounters.
# [ Z20 ] Scenes and Missions.
# [ Z21 ] Scene Props and Prop Instances.
# [ Z22 ] Agents and Teams.
# [ Z23 ] Presentations.
# [ Z24 ] Multiplayer And Networking.
# [ Z25 ] Remaining Esoteric Stuff.
# [ Z26 ] Hardcoded Compiler-Related Code.
#
################################################################################
################################################################################
# [ Z00 ] INTRODUCTION AND CREDITS
################################################################################
# Everyone who has ever tried to mod Mount&Blade games knows perfectly well,
# that the documentation for it's Module System is severely lacking. Warband
# Module System, while introducing many new and useful operations, did not
# improve considerably in the way of documentation. What's worse, a number of
# outright errors and inconsistencies appeared between what was documented in
# the comments to the header_operations.py file (which was the root source of
# all Warband scripting documentation, whether you like it or not), and what
# was actually implemented in the game engine.
# Sooner or later someone was bound to dedicate some time and effort to fix
# this problem by properly documenting the file. It just so happened that I
# was the first person crazy enough to accept the challenge.
# I have tried to make this file a self-sufficient source of information on
# every operation that the Warband scripting engine knows of. Naturally I
# failed - there are still many operations for which there is simply not
# enough information, or operations with effects that have not yet been
# thoroughly tested and confirmed. But as far as I know, there is currently
# no other reference more exhaustive than this. I tried to make the file
# useful to both seasoned scripters and complete newbies, and to a certain
# degree this file can even serve as a tutorial into Warband scripting -
# though it still won't replace the wealth of tutorials produced by the
# Warband modding community.
# I really hope you will find it useful as well.
# NAME AKA NAME Jan 18th, 2012.
# And the credits.
# First of all, I should credit Taleworlds for the creation of this game and
# it's Module System. Without them, I wouldn't be able to work on this file
# so even though I'm often sceptical about their programming style and quality
# of their code, they still did a damn good job delivering this game to all
# of us.
# And then I should credit many members from the Warband modding community
# who have shared their knowledge and helped me clear out many uncertainties
# and inconsistencies. Special credits (in no particular order) go to
# cmpxchg8b, Caba'drin, SonKidd, MadVader, dunde, Ikaguia, MadocComadrin,
# Cjkjvfnby, shokkueibu.
################################################################################
# [ Z01 ] OPERATION MODIFIERS
################################################################################
|
"""
[6/25/2014] Challenge #168 [Intermediate] Block Count, Length & Area
https://www.reddit.com/r/dailyprogrammer/comments/291x9h/6252014_challenge_168_intermediate_block_count/
#Description:
In construction there comes a need to compute the length and area of a jobsite. The areas and lengths computed are used
by estimators
to price out the cost to build that jobsite. If for example a jobsite was a building with a parking lot and had
concrete walkways and some nice
pavers and landscaping it would be good to know the areas of all these and some lengths (for concrete curbs, landscape
headerboard, etc)
So for today's challenge we are going to automate the tedious process of calculating the length and area of aerial
plans or photos.
#ASCII Photo:
To keep this within our scope we have converted the plans into an ASCII picture. We have scaled the plans so 1
character is a square
with dimensions of 10 ft x 10 ft.
The photo is case sensitive. so a "O" and "o" are 2 different blocks of areas to compute.
#Blocks Counts, Lengths and Areas:
Some shorthand to follow:
* SF = square feet
* LF = linear feet
If you have the following picture.
####
OOOO
####
mmmm
* # has a block count of 2. we have 2 areas not joined made up of #
* O and m have a block count of 1. they only have 1 areas each made up of their ASCII character.
* O has 4 blocks. Each block is 100 SF and so you have 400 SF of O.
* O has a circumference length of that 1 block count of 100 LF.
* m also has 4 blocks so there is 400 SF of m and circumference length of 100 LF
* # has 2 block counts each of 4. So # has a total area of 800 SF and a total circumference length of 200 LF.
Pay close attention to how "#" was handled. It was seen as being 2 areas made up of # but the final length and area
adds them together even thou they not together. It recognizes the two areas by having a block count of 2 (2 non-joined
areas made up of "#" characters) while the others only have a block count of 1.
#Input:
Your input is a 2-D ASCII picture. The ASCII characters used are any non-whitespace characters.
##Example:
####
@@oo
o*@!
****
#Output:
You give a Length and Area report of all the blocks.
##Example: (using the example input)
Block Count, Length & Area Report
=================================
#: Total SF (400), Total Circumference LF (100) - Found 1 block
@: Total SF (300), Total Circumference LF (100) - Found 2 blocks
o: Total SF (300), Total Circumference LF (100) - Found 2 blocks
*: Total SF (500), Total Circumference LF (120) - Found 1 block
!: Total SF (100), Total Circumference LF (40) - Found 1 block
#Easy Mode (optional):
Remove the need to compute the block count. Just focus on area and circumference length.
#Challenge Input:
So we have a "B" building. It has a "D" driveway. "O" and "o" landscaping. "c" concrete walks. "p" pavers. "V" & "v"
valley gutters. @ and T tree planting.
Finally we have # as Asphalt Paving.
ooooooooooooooooooooooDDDDDooooooooooooooooooooooooooooo
ooooooooooooooooooooooDDDDDooooooooooooooooooooooooooooo
ooo##################o#####o#########################ooo
o@o##################o#####o#########################ooo
ooo##################o#####o#########################oTo
o@o##################################################ooo
ooo##################################################oTo
o@o############ccccccccccccccccccccccc###############ooo
pppppppppppppppcOOOOOOOOOOOOOOOOOOOOOc###############oTo
o@o############cOBBBBBBBBBBBBBBBBBBBOc###############ooo
ooo####V#######cOBBBBBBBBBBBBBBBBBBBOc###############oTo
o@o####V#######cOBBBBBBBBBBBBBBBBBBBOc###############ooo
ooo####V#######cOBBBBBBBBBBBBBBBBBBBOcpppppppppppppppppp
o@o####V#######cOBBBBBBBBBBBBBBBBBBBOc###############ooo
ooo####V#######cOBBBBBBBBBBBBBBBBBBBOc######v########oTo
o@o####V#######cOBBBBBBBBBBBBBBBBBBBOc######v########ooo
ooo####V#######cOOOOOOOOOOOOOOOOOOOOOc######v########oTo
o@o####V#######ccccccccccccccccccccccc######v########ooo
ooo####V#######ppppppppppppppppppppppp######v########oTo
o@o############ppppppppppppppppppppppp###############ooo
oooooooooooooooooooooooooooooooooooooooooooooooooooooooo
oooooooooooooooooooooooooooooooooooooooooooooooooooooooo
#FAQ:
Diagonals do not connect. The small example shows this. The @ areas are 2 blocks and not 1 because of the Diagonal.
""" |
"""
Priority Queue with Binary Heaps:
---------------------------------
Introduction:
------------
A priority queue acts like a queue in that you can dequeue and item by
removing it from the front. However, in a priority queue the logical order
of items inside the queue is determined by their priority. The highest priority
items are at the front of the queue and the lowest priority items are at the
back.
The classic way to implement a priority queue is using a data structure
called a binary heap. A binary heap will allow us to both enqueue and dequeue
items in O(logn).
The binary heap is interesting to study because when we diagram the heap it
looks a lot like a tree, but when we implement it we only use a single list
as an internal representation.
The binary heap has two common variations: the 'min heap,' in which the
smallest key is always at the front, and the 'max heap,' in which the largest
key is always at the front.
In this section, we will implement the min heap.
Basic Operations List:
---------------------
BinaryHeap(): creates a new, empty binary heap.
insert(k): adds a new item to the heap.
findMin(): returns the item with the minimum key value, leaving item in the heap.
delMin(): returns the item with the minimum key value, removing the item from the list.
isEmpty(): returns true if the heap is empty, false otherwise.
size(): returns the number of items in the heap.
buildHeap(list): builds a new heap from a list of keys.
The Structure Property:
-----------------------
In order to make our heap work effeciently, we will take advantage of the
logarithmic nature of the tree to represent our heap.
In order to guarantee logarithmic performance, we must keep our tree balanced.
A balanced binary tree has roughly the same number of nodes in the left and right
subtrees of the root.
In our heap implemention we keep the tree balanced by creating a 'complete
binary tree.'
A complete binary tree is a tree in which each level has all of its nodes.
The exception to this is the bottom of the tree, which we fill in from left to
right.
Interesting Property:
---------------------
Another interesting property of a complete tree is that we can represent it
using a single list. We do not need to use nodes and references or even lists
of lists.
Because the tree is complete, the left child of the parent (at position p) is
is the node that is found a position 2p in the list. Similarly, the right child
of the parent is at position 2p+1 in the list.
The Heap Order Property:
------------------------
The 'heap order property' is as follows: In a heap, for every node x with
parent p, the key in p is smaller than or equal to the key in x.
Heap Operations:
----------------
We will begin our implemention of a binary heap with the constructor.
Since the entire binary heap can be represented by a single list, all the constructor
will do is initialize the list and an attribute currentSize to keep track of the
current size of the heap.
You will notice that an empty binary heap has a single zero as the first element
of heapList and that this zero is not used, but is there so that a simple
integer can be used in later methods.
Insert method:
--------------
The next method we will implement in 'insert.'
The easiest, and most efficient, way to add an item to a list is to simply
append the item to the end of the list. The good news about appending is that
it guarantees that we will maintain the complete tree property.
The bad news is that we will very likely violate the heap structure property.
However, it is possible to write a method that will allow us to regain the heap
structure property by comparing the newly added items with its parent.
If the newly added item is less than its parent, then we can swap the item
with its parent.
Notice that when we percolate an item up, we are restoring the heap property
between the newly added item and the parent. We are also perserving the heap
property for any siblings.
delMin method notes:
--------------------
Since the heap property requires that the root of the tree be the smallest
item in the tree, finding the minimum item is easy. The hard part of delMin
is restoring full compliance with the heap structure and heap order properties
after the root has been removed.
We can restore our heap in two steps.
1. We will restore the root item by taking the last item in the list and moving
it to the root position.
2. We will restore the heap order property by pushng the new root node down
the tree to its proper position.
In order to maintain the heap order property, all we need to do is swap the
root with its smallest child less than the root. After the initial swap, we may
repeat the swapping process with a node and its children until the node is swapped
into a position on the tree where it is already less than both children.
The code for percolating a node down the tree is found in the 'percDown' and
'minChild' methods.
buildHeap method:
-----------------
To finish our discussion of binary heaps, we will look at a method to build
an entire heap from a list of keys.
If we start with an entire list then we can build the whole heap in O(n)
operations.
We will start from the middle of the list. Although we start out in the middle
of the tree and work our way back towards the root, the percDown method enusres that
the largest child is always down the tree. Beacuse it is a complete binary tree,
any nodes past the halfway point will be leaves and therefore have no children.
""" |
"""
Project Euler Problem 8
=======================
Find the greatest product of thirteen consecutive digits in the 1000-digit
number.
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
""" |
"""
Objects for dealing with Chebyshev series.
This module provides a number of objects (mostly functions) useful for
dealing with Chebyshev series, including a `Chebyshev` class that
encapsulates the usual arithmetic operations. (General information
on how this module represents and works with such polynomials is in the
docstring for its "parent" sub-package, `numpy.polynomial`).
Constants
---------
- `chebdomain` -- Chebyshev series default domain, [-1,1].
- `chebzero` -- (Coefficients of the) Chebyshev series that evaluates
identically to 0.
- `chebone` -- (Coefficients of the) Chebyshev series that evaluates
identically to 1.
- `chebx` -- (Coefficients of the) Chebyshev series for the identity map,
``f(x) = x``.
Arithmetic
----------
- `chebadd` -- add two Chebyshev series.
- `chebsub` -- subtract one Chebyshev series from another.
- `chebmul` -- multiply two Chebyshev series.
- `chebdiv` -- divide one Chebyshev series by another.
- `chebpow` -- raise a Chebyshev series to an positive integer power
- `chebval` -- evaluate a Chebyshev series at given points.
- `chebval2d` -- evaluate a 2D Chebyshev series at given points.
- `chebval3d` -- evaluate a 3D Chebyshev series at given points.
- `chebgrid2d` -- evaluate a 2D Chebyshev series on a Cartesian product.
- `chebgrid3d` -- evaluate a 3D Chebyshev series on a Cartesian product.
Calculus
--------
- `chebder` -- differentiate a Chebyshev series.
- `chebint` -- integrate a Chebyshev series.
Misc Functions
--------------
- `chebfromroots` -- create a Chebyshev series with specified roots.
- `chebroots` -- find the roots of a Chebyshev series.
- `chebvander` -- Vandermonde-like matrix for Chebyshev polynomials.
- `chebvander2d` -- Vandermonde-like matrix for 2D power series.
- `chebvander3d` -- Vandermonde-like matrix for 3D power series.
- `chebgauss` -- Gauss-Chebyshev quadrature, points and weights.
- `chebweight` -- Chebyshev weight function.
- `chebcompanion` -- symmetrized companion matrix in Chebyshev form.
- `chebfit` -- least-squares fit returning a Chebyshev series.
- `chebpts1` -- Chebyshev points of the first kind.
- `chebpts2` -- Chebyshev points of the second kind.
- `chebtrim` -- trim leading coefficients from a Chebyshev series.
- `chebline` -- Chebyshev series representing given straight line.
- `cheb2poly` -- convert a Chebyshev series to a polynomial.
- `poly2cheb` -- convert a polynomial to a Chebyshev series.
Classes
-------
- `Chebyshev` -- A Chebyshev series class.
See also
--------
`numpy.polynomial`
Notes
-----
The implementations of multiplication, division, integration, and
differentiation use the algebraic identities [1]_:
.. math ::
T_n(x) = \\frac{z^n + z^{-n}}{2} \\\\
z\\frac{dx}{dz} = \\frac{z - z^{-1}}{2}.
where
.. math :: x = \\frac{z + z^{-1}}{2}.
These identities allow a Chebyshev series to be expressed as a finite,
symmetric Laurent series. In this module, this sort of Laurent series
is referred to as a "z-series."
References
----------
.. [1] NAME et al., "Combinatorial Trigonometry with Chebyshev
Polynomials," *Journal of Statistical Planning and Inference 14*, 2008
(preprint: http://www.math.hmc.edu/~benjamin/papers/CombTrig.pdf, pg. 4)
""" |
"""
Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG)
LOBPCG is a preconditioned eigensolver for large symmetric positive definite
(SPD) generalized eigenproblems.
Call the function lobpcg - see help for lobpcg.lobpcg. See also lobpcg.as2d,
which can be used in the preconditioner (example below)
Acknowledgements
----------------
lobpcg.py code was written by NAME Many thanks belong to NAME the author of the algorithm, for lots of advice and support.
Examples
--------
>>> # Solve A x = lambda B x with constraints and preconditioning.
>>> n = 100
>>> vals = [nm.arange( n, dtype = nm.float64 ) + 1]
>>> # Matrix A.
>>> operatorA = spdiags( vals, 0, n, n )
>>> # Matrix B
>>> operatorB = nm.eye( n, n )
>>> # Constraints.
>>> Y = nm.eye( n, 3 )
>>> # Initial guess for eigenvectors, should have linearly independent
>>> # columns. Column dimension = number of requested eigenvalues.
>>> X = sc.rand( n, 3 )
>>> # Preconditioner - inverse of A.
>>> ivals = [1./vals[0]]
>>> def precond( x ):
invA = spdiags( ivals, 0, n, n )
y = invA * x
if sp.issparse( y ):
y = y.toarray()
return as2d( y )
>>>
>>> # Alternative way of providing the same preconditioner.
>>> #precond = spdiags( ivals, 0, n, n )
>>>
>>> tt = time.clock()
>>> eigs, vecs = lobpcg( X, operatorA, operatorB, blockVectorY = Y,
>>> operatorT = precond,
>>> residualTolerance = 1e-4, maxIterations = 40,
>>> largest = False, verbosityLevel = 1 )
>>> print 'solution time:', time.clock() - tt
>>> print eigs
Notes
-----
In the following ``n`` denotes the matrix size and ``m`` the number
of required eigenvalues (smallest or largest).
The LOBPCG code internally solves eigenproblems of the size 3``m`` on every
iteration by calling the "standard" dense eigensolver, so if ``m`` is not
small enough compared to ``n``, it does not make sense to call the LOBPCG
code, but rather one should use the "standard" eigensolver, e.g. scipy or symeig
function in this case. If one calls the LOBPCG algorithm for 5``m``>``n``,
it will most likely break internally, so the code tries to call the standard
function instead.
It is not that n should be large for the LOBPCG to work, but rather the
ratio ``n``/``m`` should be large. It you call the LOBPCG code with ``m``=1
and ``n``=10, it should work, though ``n`` is small. The method is intended
for extremely large ``n``/``m``, see e.g., reference [28] in
http://arxiv.org/abs/0705.2626
The convergence speed depends basically on two factors:
1. How well relatively separated the seeking eigenvalues are from the rest of
the eigenvalues. One can try to vary ``m`` to make this better.
2. How well conditioned the problem is. This can be changed by using proper
preconditioning. For example, a rod vibration test problem (under tests
directory) is ill-conditioned for large ``n``, so convergence will be
slow, unless efficient preconditioning is used. For this specific problem,
a good simple preconditioner function would be a linear solve for A, which
is easy to code since A is tridiagonal.
References
----------
A. NAME Toward the Optimal Preconditioned Eigensolver: Locally Optimal
Block Preconditioned Conjugate Gradient Method. SIAM Journal on Scientific
Computing 23 (2001), no. 2,
pp. 517-541. http://dx.doi.org/10.1137/S1064827500366124
A. NAME NAME NAME and NAME Block Locally
Optimal Preconditioned Eigenvalue Xolvers (BLOPEX) in hypre and PETSc
(2007). http://arxiv.org/abs/0705.2626
A. NAME C and MATLAB implementations:
http://www-math.cudenver.edu/~aknyazev/software/BLOPEX/
""" |
"""Stuff to parse AIFF-C and AIFF files.
Unless explicitly stated otherwise, the description below is true
both for AIFF-C files and AIFF files.
An AIFF-C file has the following structure.
+-----------------+
| FORM |
+-----------------+
| <size> |
+----+------------+
| | AIFC |
| +------------+
| | <chunks> |
| | . |
| | . |
| | . |
+----+------------+
An AIFF file has the string "AIFF" instead of "AIFC".
A chunk consists of an identifier (4 bytes) followed by a size (4 bytes,
big endian order), followed by the data. The size field does not include
the size of the 8 byte header.
The following chunk types are recognized.
FVER
<version number of AIFF-C defining document> (AIFF-C only).
MARK
<# of markers> (2 bytes)
list of markers:
<marker ID> (2 bytes, must be > 0)
<position> (4 bytes)
<marker name> ("pstring")
COMM
<# of channels> (2 bytes)
<# of sound frames> (4 bytes)
<size of the samples> (2 bytes)
<sampling frequency> (10 bytes, IEEE 80-bit extended
floating point)
in AIFF-C files only:
<compression type> (4 bytes)
<human-readable version of compression type> ("pstring")
SSND
<offset> (4 bytes, not used by this program)
<blocksize> (4 bytes, not used by this program)
<sound data>
A pstring consists of 1 byte length, a string of characters, and 0 or 1
byte pad to make the total length even.
Usage.
Reading AIFF files:
f = aifc.open(file, 'r')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods read(), seek(), and close().
In some types of audio files, if the setpos() method is not used,
the seek() method is not necessary.
This returns an instance of a class with the following public methods:
getnchannels() -- returns number of audio channels (1 for
mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getcomptype() -- returns compression type ('NONE' for AIFF files)
getcompname() -- returns human-readable version of
compression type ('not compressed' for AIFF files)
getparams() -- returns a tuple consisting of all of the
above in the above order
getmarkers() -- get the list of marks in the audio file or None
if there are no marks
getmark(id) -- get mark with the specified id (raises an error
if the mark does not exist)
readframes(n) -- returns at most n frames of audio
rewind() -- rewind to the beginning of the audio stream
setpos(pos) -- seek to the specified position
tell() -- return the current position
close() -- close the instance (make it unusable)
The position returned by tell(), the position given to setpos() and
the position of marks are all compatible and have nothing to do with
the actual position in the file.
The close() method is called automatically when the class instance
is destroyed.
Writing AIFF files:
f = aifc.open(file, 'w')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods write(), tell(), seek(), and
close().
This returns an instance of a class with the following public methods:
aiff() -- create an AIFF file (AIFF-C default)
aifc() -- create an AIFF-C file
setnchannels(n) -- set the number of channels
setsampwidth(n) -- set the sample width
setframerate(n) -- set the frame rate
setnframes(n) -- set the number of frames
setcomptype(type, name)
-- set the compression type and the
human-readable compression type
setparams(tuple)
-- set all parameters at once
setmark(id, pos, name)
-- add specified mark to the list of marks
tell() -- return current position in output file (useful
in combination with setmark())
writeframesraw(data)
-- write audio frames without pathing up the
file header
writeframes(data)
-- write audio frames and patch up the file header
close() -- patch up the file header and close the
output file
You should set the parameters before the first writeframesraw or
writeframes. The total number of frames does not need to be set,
but when it is set to the correct value, the header does not have to
be patched up.
It is best to first set all parameters, perhaps possibly the
compression type, and then write audio frames using writeframesraw.
When all frames have been written, either call writeframes('') or
close() to patch up the sizes in the header.
Marks can be added anytime. If there are any marks, ypu must call
close() after all frames have been written.
The close() method is called automatically when the class instance
is destroyed.
When a file is opened with the extension '.aiff', an AIFF file is
written, otherwise an AIFF-C file is written. This default can be
changed by calling aiff() or aifc() before the first writeframes or
writeframesraw.
""" |
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
#!/usr/bin/env python
# (c) 2013, NAME <paul.durivage@gmail.com>
#
# This file is part of Ansible.
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
#
#
# Author: NAME <paul.durivage@gmail.com>
#
# Description:
# This module queries local or remote Docker daemons and generates
# inventory information.
#
# This plugin does not support targeting of specific hosts using the --host
# flag. Instead, it queries the Docker API for each container, running
# or not, and returns this data all once.
#
# The plugin returns the following custom attributes on Docker containers:
# docker_args
# docker_config
# docker_created
# docker_driver
# docker_exec_driver
# docker_host_config
# docker_hostname_path
# docker_hosts_path
# docker_id
# docker_image
# docker_name
# docker_network_settings
# docker_path
# docker_resolv_conf_path
# docker_state
# docker_volumes
# docker_volumes_rw
#
# Requirements:
# The docker-py module: https://github.com/dotcloud/docker-py
#
# Notes:
# A config file can be used to configure this inventory module, and there
# are several environment variables that can be set to modify the behavior
# of the plugin at runtime:
# DOCKER_CONFIG_FILE
# DOCKER_HOST
# DOCKER_VERSION
# DOCKER_TIMEOUT
# DOCKER_PRIVATE_SSH_PORT
# DOCKER_DEFAULT_IP
#
# Environment Variables:
# environment variable: DOCKER_CONFIG_FILE
# description:
# - A path to a Docker inventory hosts/defaults file in YAML format
# - A sample file has been provided, colocated with the inventory
# file called 'docker.yml'
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_HOST
# description:
# - The socket on which to connect to a Docker daemon API
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_VERSION
# description:
# - Version of the Docker API to use
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_TIMEOUT
# description:
# - Timeout in seconds for connections to Docker daemon API
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_PRIVATE_SSH_PORT
# description:
# - The private port (container port) on which SSH is listening
# for connections
# default: 22
# required: false
# environment variable: DOCKER_DEFAULT_IP
# description:
# - This environment variable overrides the container SSH connection
# IP address (aka, 'ansible_ssh_host')
#
# This option allows one to override the ansible_ssh_host whenever
# Docker has exercised its default behavior of binding private ports
# to all interfaces of the Docker host. This behavior, when dealing
# with remote Docker hosts, does not allow Ansible to determine
# a proper host IP address on which to connect via SSH to containers.
# By default, this inventory module assumes all IP_ADDRESS-exposed
# ports to be bound to localhost:<port>. To override this
# behavior, for example, to bind a container's SSH port to the public
# interface of its host, one must manually set this IP.
#
# It is preferable to begin to launch Docker containers with
# ports exposed on publicly accessible IP addresses, particularly
# if the containers are to be targeted by Ansible for remote
# configuration, not accessible via localhost SSH connections.
#
# Docker containers can be explicitly exposed on IP addresses by
# a) starting the daemon with the --ip argument
# b) running containers with the -P/--publish ip::containerPort
# argument
# default: IP_ADDRESS if port exposed on IP_ADDRESS by Docker
# required: false
#
# Examples:
# Use the config file:
# DOCKER_CONFIG_FILE=./docker.yml docker.py --list
#
# Connect to docker instance on localhost port 4243
# DOCKER_HOST=tcp://localhost:4243 docker.py --list
#
# Any container's ssh port exposed on IP_ADDRESS will mapped to
# another IP address (where Ansible will attempt to connect via SSH)
# DOCKER_DEFAULT_IP=1.2.3.4 docker.py --list
|
"""Drag-and-drop support for Tkinter.
This is very preliminary. I currently only support dnd *within* one
application, between different windows (or within the same window).
I am trying to make this as generic as possible -- not dependent on
the use of a particular widget or icon type, etc. I also hope that
this will work with Pmw.
To enable an object to be dragged, you must create an event binding
for it that starts the drag-and-drop process. Typically, you should
bind <ButtonPress> to a callback function that you write. The function
should call Tkdnd.dnd_start(source, event), where 'source' is the
object to be dragged, and 'event' is the event that invoked the call
(the argument to your callback function). Even though this is a class
instantiation, the returned instance should not be stored -- it will
be kept alive automatically for the duration of the drag-and-drop.
When a drag-and-drop is already in process for the Tk interpreter, the
call is *ignored*; this normally averts starting multiple simultaneous
dnd processes, e.g. because different button callbacks all
dnd_start().
The object is *not* necessarily a widget -- it can be any
application-specific object that is meaningful to potential
drag-and-drop targets.
Potential drag-and-drop targets are discovered as follows. Whenever
the mouse moves, and at the start and end of a drag-and-drop move, the
Tk widget directly under the mouse is inspected. This is the target
widget (not to be confused with the target object, yet to be
determined). If there is no target widget, there is no dnd target
object. If there is a target widget, and it has an attribute
dnd_accept, this should be a function (or any callable object). The
function is called as dnd_accept(source, event), where 'source' is the
object being dragged (the object passed to dnd_start() above), and
'event' is the most recent event object (generally a <Motion> event;
it can also be <ButtonPress> or <ButtonRelease>). If the dnd_accept()
function returns something other than None, this is the new dnd target
object. If dnd_accept() returns None, or if the target widget has no
dnd_accept attribute, the target widget's parent is considered as the
target widget, and the search for a target object is repeated from
there. If necessary, the search is repeated all the way up to the
root widget. If none of the target widgets can produce a target
object, there is no target object (the target object is None).
The target object thus produced, if any, is called the new target
object. It is compared with the old target object (or None, if there
was no old target widget). There are several cases ('source' is the
source object, and 'event' is the most recent event object):
- Both the old and new target objects are None. Nothing happens.
- The old and new target objects are the same object. Its method
dnd_motion(source, event) is called.
- The old target object was None, and the new target object is not
None. The new target object's method dnd_enter(source, event) is
called.
- The new target object is None, and the old target object is not
None. The old target object's method dnd_leave(source, event) is
called.
- The old and new target objects differ and neither is None. The old
target object's method dnd_leave(source, event), and then the new
target object's method dnd_enter(source, event) is called.
Once this is done, the new target object replaces the old one, and the
Tk mainloop proceeds. The return value of the methods mentioned above
is ignored; if they raise an exception, the normal exception handling
mechanisms take over.
The drag-and-drop processes can end in two ways: a final target object
is selected, or no final target object is selected. When a final
target object is selected, it will always have been notified of the
potential drop by a call to its dnd_enter() method, as described
above, and possibly one or more calls to its dnd_motion() method; its
dnd_leave() method has not been called since the last call to
dnd_enter(). The target is notified of the drop by a call to its
method dnd_commit(source, event).
If no final target object is selected, and there was an old target
object, its dnd_leave(source, event) method is called to complete the
dnd sequence.
Finally, the source object is notified that the drag-and-drop process
is over, by a call to source.dnd_end(target, event), specifying either
the selected target object, or None if no target object was selected.
The source object can use this to implement the commit action; this is
sometimes simpler than to do it in the target's dnd_commit(). The
target's dnd_commit() method could then simply be aliased to
dnd_leave().
At any time during a dnd sequence, the application can cancel the
sequence by calling the cancel() method on the object returned by
dnd_start(). This will call dnd_leave() if a target is currently
active; it will never call dnd_commit().
""" |
#!/usr/bin/env python
# Copyright (c) 2015 ARM Limited
# All rights reserved
#
# The license below extends only to copyright in the software and shall
# not be construed as granting a license to any other intellectual
# property including but not limited to intellectual property relating
# to a hardware implementation of the functionality of the software
# licensed hereunder. You may use the software subject to the license
# terms below provided that you ensure that this notice is replicated
# unmodified and in its entirety in all distributions of the software,
# modified or unmodified, in source code or in binary form.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met: redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer;
# redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution;
# neither the name of the copyright holders nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Copyright 2008 Google Inc. All rights reserved.
# http://code.google.com/p/protobuf/
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Authors: NAME This script is used to dump ASCII traces of the instruction dependency
# graph to protobuf format.
#
# The ASCII trace format uses one line per instruction with the format
# instruction sequence number, (optional) pc, (optional) weight, type,
# (optional) flags, (optional) physical addr, (optional) size, comp delay,
# (repeated) order dependencies comma-separated, and (repeated) register
# dependencies comma-separated.
#
# examples:
# seq_num,[pc],[weight,]type,[p_addr,size,flags,]comp_delay:[rob_dep]:
# [reg_dep]
# 1,35652,1,COMP,8500::
# 2,35656,1,COMP,0:,1:
# 3,35660,1,LOAD,1748752,4,74,500:,2:
# 4,35660,1,COMP,0:,3:
# 5,35664,1,COMP,3000::,4
# 6,35666,1,STORE,1748752,4,74,1000:,3:,4,5
# 7,35666,1,COMP,3000::,4
# 8,35670,1,STORE,1748748,4,74,0:,6,3:,7
# 9,35670,1,COMP,500::,7
|
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to reqd all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use select() to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
- Standard framework for select-based multiplexing
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <lkcl@samba.org>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
#!/usr/bin/python
#Algoriths Project Part 1a
#Omar NAME 441
#Dr. NAME NOTES
#Python's built in pow function uses Binary Exponentiation and reducing modulo n to compute modular
#exponentiation. This is the same algorithm as MODULAR-EXPONENTIATION(a,b,n) as used in the text
#For large number mutliplication Python uses Karatsuba's method as discusssed in class
#Encrypted using modulus of 2048 bits
#Message Encrypted with Private Key =
#549335432742725778252187541104443188156944438806863457411666058499398272260706426139538267238120336092084632198514701950566203930065985324580534295693425367212921830205866755643739579288731322322946366466576799796974416100601383412159359169170613839877922173796152893918170136479717941167924064476336789776106984955596378941959676443995574307557232184168653454435294749983774161045180981596162964832360087083009219442813368249004389009182055455524458934480504555947413171214222377987666294266525295763559510397442092718659910879958017424466509571661222667744582625838716048450963735149873220637697801126262181088272
#n = 2372112898706524098783243835606671423055801883554227254030743710505202283932667011668956139382911768876035660572032080308562219037288900124052316286309512108625859836958747947762092799677854295671866288119481685786760570903533545560435541052326183788082279075073373227880942687435505490994525413101260845901748238215480998501123816262694263026377952163660645333809073068011604416987281948409408692393376191358516220341631487894075618891499412550098438456600441042870219500840853342452184082591601805986792948794525871595912715813197678328912976549353915846570322821639411967156886422360861220109970600152445030560129
#public key e = KEY883919633554596704012915783900570809149483856078010145425692545878452812725561415102822918517227924598205956910940350062144643427460974258169951841328548095289498955467345087157904399185646775059360160689508306113707875539862799501027047474838298216312008836598256088581250099042957573530717659415412893768343977899980494510094815770699761034869232518446869348437561961594909995056962983992121384916099020899755884457999313029602625570516932900789485878260172195900227111449085645227576679740196755445527867666825244974372425673866849078226602801561771006724501838806746943672716086807419555183315337s
|
"""
==========================================
Statistical functions (:mod:`scipy.stats`)
==========================================
.. module:: scipy.stats
This module contains a large number of probability distributions as
well as a growing library of statistical functions.
Each univariate distribution is an instance of a subclass of `rv_continuous`
(`rv_discrete` for discrete distributions):
.. autosummary::
:toctree: generated/
rv_continuous
rv_discrete
Continuous distributions
========================
.. autosummary::
:toctree: generated/
alpha -- Alpha
anglit -- Anglit
arcsine -- Arcsine
beta -- Beta
betaprime -- Beta Prime
bradford -- Bradford
burr -- Burr
cauchy -- Cauchy
chi -- Chi
chi2 -- Chi-squared
cosine -- Cosine
dgamma -- Double Gamma
dweibull -- Double Weibull
erlang -- Erlang
expon -- Exponential
exponnorm -- Exponentially Modified Normal
exponweib -- Exponentiated Weibull
exponpow -- Exponential Power
f -- F (Snecdor F)
fatiguelife -- Fatigue Life (Birnbaum-Saunders)
fisk -- Fisk
foldcauchy -- Folded Cauchy
foldnorm -- Folded Normal
frechet_r -- Frechet Right Sided, Extreme Value Type II (Extreme LB) or weibull_min
frechet_l -- Frechet Left Sided, Weibull_max
genlogistic -- Generalized Logistic
gennorm -- Generalized normal
genpareto -- Generalized Pareto
genexpon -- Generalized Exponential
genextreme -- Generalized Extreme Value
gausshyper -- Gauss Hypergeometric
gamma -- Gamma
gengamma -- Generalized gamma
genhalflogistic -- Generalized Half Logistic
gilbrat -- Gilbrat
gompertz -- Gompertz (Truncated Gumbel)
gumbel_r -- Right Sided Gumbel, Log-Weibull, Fisher-Tippett, Extreme Value Type I
gumbel_l -- Left Sided Gumbel, etc.
halfcauchy -- Half Cauchy
halflogistic -- Half Logistic
halfnorm -- Half Normal
halfgennorm -- Generalized Half Normal
hypsecant -- Hyperbolic Secant
invgamma -- Inverse Gamma
invgauss -- Inverse Gaussian
invweibull -- Inverse Weibull
johnsonsb -- NAME
johnsonsu -- NAME
ksone -- Kolmogorov-Smirnov one-sided (no stats)
kstwobign -- Kolmogorov-Smirnov two-sided test for Large N (no stats)
laplace -- Laplace
levy -- Levy
levy_l
levy_stable
logistic -- Logistic
loggamma -- Log-Gamma
loglaplace -- Log-Laplace (Log Double Exponential)
lognorm -- Log-Normal
lomax -- Lomax (Pareto of the second kind)
maxwell -- Maxwell
mielke -- Mielke's Beta-Kappa
nakagami -- Nakagami
ncx2 -- Non-central chi-squared
ncf -- Non-central F
nct -- Non-central Student's T
norm -- Normal (Gaussian)
pareto -- Pareto
pearson3 -- Pearson type III
powerlaw -- Power-function
powerlognorm -- Power log normal
powernorm -- Power normal
rdist -- R-distribution
reciprocal -- Reciprocal
rayleigh -- Rayleigh
rice -- Rice
recipinvgauss -- Reciprocal Inverse Gaussian
semicircular -- Semicircular
t -- Student's T
triang -- Triangular
truncexpon -- Truncated Exponential
truncnorm -- Truncated Normal
tukeylambda -- Tukey-Lambda
uniform -- Uniform
vonmises -- Von-Mises (Circular)
vonmises_line -- Von-Mises (Line)
wald -- Wald
weibull_min -- Minimum Weibull (see Frechet)
weibull_max -- Maximum Weibull (see Frechet)
wrapcauchy -- Wrapped Cauchy
Multivariate distributions
==========================
.. autosummary::
:toctree: generated/
multivariate_normal -- Multivariate normal distribution
dirichlet -- Dirichlet
wishart -- Wishart
invwishart -- Inverse Wishart
Discrete distributions
======================
.. autosummary::
:toctree: generated/
bernoulli -- Bernoulli
binom -- Binomial
boltzmann -- Boltzmann (Truncated Discrete Exponential)
dlaplace -- Discrete Laplacian
geom -- Geometric
hypergeom -- Hypergeometric
logser -- Logarithmic (Log-Series, Series)
nbinom -- Negative Binomial
planck -- Planck (Discrete Exponential)
poisson -- Poisson
randint -- Discrete Uniform
skellam -- Skellam
zipf -- Zipf
Statistical functions
=====================
Several of these functions have a similar version in scipy.stats.mstats
which work for masked arrays.
.. autosummary::
:toctree: generated/
describe -- Descriptive statistics
gmean -- Geometric mean
hmean -- Harmonic mean
kurtosis -- Fisher or Pearson kurtosis
kurtosistest --
mode -- Modal value
moment -- Central moment
normaltest --
skew -- Skewness
skewtest --
kstat --
kstatvar --
tmean -- Truncated arithmetic mean
tvar -- Truncated variance
tmin --
tmax --
tstd --
tsem --
nanmean -- Mean, ignoring NaN values
nanstd -- Standard deviation, ignoring NaN values
nanmedian -- Median, ignoring NaN values
variation -- Coefficient of variation
find_repeats
trim_mean
.. autosummary::
:toctree: generated/
cumfreq
histogram2
histogram
itemfreq
percentileofscore
scoreatpercentile
relfreq
.. autosummary::
:toctree: generated/
binned_statistic -- Compute a binned statistic for a set of data.
binned_statistic_2d -- Compute a 2-D binned statistic for a set of data.
binned_statistic_dd -- Compute a d-D binned statistic for a set of data.
.. autosummary::
:toctree: generated/
obrientransform
signaltonoise
bayes_mvs
mvsdist
sem
zmap
zscore
.. autosummary::
:toctree: generated/
sigmaclip
threshold
trimboth
trim1
.. autosummary::
:toctree: generated/
f_oneway
pearsonr
spearmanr
pointbiserialr
kendalltau
linregress
theilslopes
f_value
.. autosummary::
:toctree: generated/
ttest_1samp
ttest_ind
ttest_ind_from_stats
ttest_rel
kstest
chisquare
power_divergence
ks_2samp
NAME
tiecorrect
rankdata
ranksums
wilcoxon
kruskal
friedmanchisquare
combine_pvalues
ss
square_of_sums
jarque_bera
.. autosummary::
:toctree: generated/
ansari
bartlett
levene
shapiro
anderson
anderson_ksamp
binom_test
fligner
median_test
mood
.. autosummary::
:toctree: generated/
boxcox
boxcox_normmax
boxcox_llf
entropy
.. autosummary::
:toctree: generated/
chisqprob
betai
Circular statistical functions
==============================
.. autosummary::
:toctree: generated/
circmean
circvar
circstd
Contingency table functions
===========================
.. autosummary::
:toctree: generated/
chi2_contingency
contingency.expected_freq
contingency.margins
fisher_exact
Plot-tests
==========
.. autosummary::
:toctree: generated/
ppcc_max
ppcc_plot
probplot
boxcox_normplot
Masked statistics functions
===========================
.. toctree::
stats.mstats
Univariate and multivariate kernel density estimation (:mod:`scipy.stats.kde`)
==============================================================================
.. autosummary::
:toctree: generated/
gaussian_kde
For many more stat related functions install the software R and the
interface package rpy.
""" |
#!/usr/bin/env python
# Try to determine how much RAM is currently being used per program.
# Note per _program_, not per process. So for example this script
# will report RAM used by all httpd process together. In detail it reports:
# sum(private RAM for program processes) + sum(Shared RAM for program processes)
# The shared RAM is problematic to calculate, and this script automatically
# selects the most accurate method available for your kernel.
# Licence: LGPLv2
# Author: EMAIL Source: http://www.pixelbeat.org/scripts/ps_mem.py
# V1.0 06 Jul 2005 Initial release
# V1.1 11 Aug 2006 root permission required for accuracy
# V1.2 08 Nov 2006 Add total to output
# Use KiB,MiB,... for units rather than K,M,...
# V1.3 22 Nov 2006 Ignore shared col from /proc/$pid/statm for
# 2.6 kernels up to and including 2.6.9.
# There it represented the total file backed extent
# V1.4 23 Nov 2006 Remove total from output as it's meaningless
# (the shared values overlap with other programs).
# Display the shared column. This extra info is
# useful, especially as it overlaps between programs.
# V1.5 26 Mar 2007 Remove redundant recursion from human()
# V1.6 05 Jun 2007 Also report number of processes with a given name.
# Patch from EMAIL V1.7 20 Sep 2007 Use PSS from /proc/$pid/smaps if available, which
# fixes some over-estimation and allows totalling.
# Enumerate the PIDs directly rather than using ps,
# which fixes the possible race between reading
# RSS with ps, and shared memory with this program.
# Also we can show non truncated command names.
# V1.8 28 Sep 2007 More accurate matching for stats in /proc/$pid/smaps
# as otherwise could match libraries causing a crash.
# Patch from EMAIL V1.9 20 Feb 2008 Fix invalid values reported when PSS is available.
# Reported by NAME <arvidjaar@mail.ru>
# V3.3 24 Jun 2014
# http://github.com/pixelb/scripts/commits/master/scripts/ps_mem.py
# Notes:
#
# All interpreted programs where the interpreter is started
# by the shell or with env, will be merged to the interpreter
# (as that's what's given to exec). For e.g. all python programs
# starting with "#!/usr/bin/env python" will be grouped under python.
# You can change this by using the full command line but that will
# have the undesirable affect of splitting up programs started with
# differing parameters (for e.g. mingetty tty[1-6]).
#
# For 2.6 kernels up to and including 2.6.13 and later 2.4 redhat kernels
# (rmap vm without smaps) it can not be accurately determined how many pages
# are shared between processes in general or within a program in our case:
# http://lkml.org/lkml/2005/7/6/250
# A warning is printed if overestimation is possible.
# In addition for 2.6 kernels up to 2.6.9 inclusive, the shared
# value in /proc/$pid/statm is the total file-backed extent of a process.
# We ignore that, introducing more overestimation, again printing a warning.
# Since kernel 2.6.23-rc8-mm1 PSS is available in smaps, which allows
# us to calculate a more accurate value for the total RAM used by programs.
#
# Programs that use CLONE_VM without CLONE_THREAD are discounted by assuming
# they're the only programs that have the same /proc/$PID/smaps file for
# each instance. This will fail if there are multiple real instances of a
# program that then use CLONE_VM without CLONE_THREAD, or if a clone changes
# its memory map while we're checksumming each /proc/$PID/smaps.
#
# I don't take account of memory allocated for a program
# by other programs. For e.g. memory used in the X server for
# a program could be determined, but is not.
#
# FreeBSD is supported if linprocfs is mounted at /compat/linux/proc/
# FreeBSD 8.0 supports up to a level of Linux 2.6.16
|
{'handleType': '0', 'rotate': (99.5, 137.5, 137.5, 137.5), 'baseSize_s': 0.1600000560283661, 'af2': 1.0, 'pruneRatio': 0.75, 'radiusTweak': (1.0, 1.0, 1.0, 1.0), 'pruneWidthPeak': 0.5, 'boneStep': (1, 1, 1, 1), 'nrings': 0, 'leafScale': 0.4000000059604645, 'makeMesh': False, 'baseSize': 0.30000001192092896, 'lengthV': (0.0, 0.10000000149011612, 0.0, 0.0), 'shapeS': '10', 'pruneBase': 0.11999999731779099, 'af3': 4.0, 'loopFrames': 0, 'horzLeaves': True, 'curveRes': (8, 5, 3, 1), 'minRadius': 0.001500000013038516, 'leafDist': '6', 'rotateV': (15.0, 0.0, 0.0, 0.0), 'bevel': True, 'curveBack': (0.0, 0.0, 0.0, 0.0), 'leafScaleV': 0.15000000596046448, 'prunePowerHigh': 0.5, 'rootFlare': 1.0, 'prune': False, 'branches': (0, 55, 10, 1), 'taperCrown': 0.5, 'useArm': False, 'splitBias': 0.5499999523162842, 'segSplits': (0.10000000149011612, 0.5, 0.20000000298023224, 0.0), 'resU': 4, 'useParentAngle': True, 'ratio': 0.014999999664723873, 'taper': (1.0, 1.0, 1.0, 1.0), 'length': (0.800000011920929, 0.6000000238418579, 0.5, 0.10000000149011612), 'scale0': 1.0, 'scaleV': 2.0, 'leafRotate': 137.5, 'shape': '7', 'scaleV0': 0.10000000149011612, 'leaves': 150, 'scale': 5.0, 'leafShape': 'hex', 'prunePowerLow': 0.0010000000474974513, 'splitAngle': (18.0, 18.0, 22.0, 0.0), 'seed': 0, 'showLeaves': True, 'downAngle': (0.0, 26.209999084472656, 52.55999755859375, 30.0), 'leafDownAngle': 30.0, 'autoTaper': True, 'rMode': 'rotate', 'leafScaleX': 0.20000000298023224, 'leafScaleT': 0.10000000149011612, 'gust': 1.0, 'armAnim': False, 'wind': 1.0, 'leafRotateV': 15.0, 'baseSplits': 3, 'attractOut': (0.0, 0.800000011920929, 0.0, 0.0), 'armLevels': 2, 'leafAnim': False, 'ratioPower': 1.2000000476837158, 'splitHeight': 0.20000000298023224, 'splitByLen': True, 'af1': 1.0, 'branchDist': 1.5, 'closeTip': False, 'previewArm': False, 'attractUp': (3.5, -1.899843692779541, 0.0, 0.0), 'bevelRes': 1, 'pruneWidth': 0.3400000035762787, 'gustF': 0.07500000298023224, 'leafangle': -12.0, 'curveV': (20.0, 50.0, 75.0, 0.0), 'useOldDownAngle': True, 'leafDownAngleV': -10.0, 'frameRate': 1.0, 'splitAngleV': (5.0, 5.0, 5.0, 0.0), 'levels': 2, 'downAngleV': (0.0, 10.0, 10.0, 10.0), 'customShape': (0.5, 1.0, 0.30000001192092896, 0.5), 'curve': (0.0, -15.0, 0.0, 0.0)} |
#
# XML-RPC CLIENT LIBRARY
# $Id$
#
# an XML-RPC client interface for Python.
#
# the marshalling and response parser code can also be used to
# implement XML-RPC servers.
#
# Notes:
# this version is designed to work with Python 2.1 or newer.
#
# History:
# 1999-01-14 fl Created
# 1999-01-15 fl Changed dateTime to use localtime
# 1999-01-16 fl Added Binary/base64 element, default to RPC2 service
# 1999-01-19 fl Fixed array data element (from Skip Montanaro)
# 1999-01-21 fl Fixed dateTime constructor, etc.
# 1999-02-02 fl Added fault handling, handle empty sequences, etc.
# 1999-02-10 fl Fixed problem with empty responses (from Skip Montanaro)
# 1999-06-20 fl Speed improvements, pluggable parsers/transports (0.9.8)
# 2000-11-28 fl Changed boolean to check the truth value of its argument
# 2001-02-24 fl Added encoding/Unicode/SafeTransport patches
# 2001-02-26 fl Added compare support to wrappers (0.9.9/1.0b1)
# 2001-03-28 fl Make sure response tuple is a singleton
# 2001-03-29 fl Don't require empty params element (from NAME 2001-06-10 fl Folded in _xmlrpclib accelerator support (1.0b2)
# 2001-08-20 fl Base xmlrpclib.Error on built-in Exception (from NAME 2001-09-03 fl Allow Transport subclass to override getparser
# 2001-09-10 fl Lazy import of urllib, cgi, xmllib (20x import speedup)
# 2001-10-01 fl Remove containers from memo cache when done with them
# 2001-10-01 fl Use faster escape method (80% dumps speedup)
# 2001-10-02 fl More dumps microtuning
# 2001-10-04 fl Make sure import expat gets a parser (from NAME 2001-10-10 sm Allow long ints to be passed as ints if they don't overflow
# 2001-10-17 sm Test for int and long overflow (allows use on 64-bit systems)
# 2001-11-12 fl Use repr() to marshal doubles (from NAME 2002-03-17 fl Avoid buffered read when possible (from NAME 2002-04-07 fl Added pythondoc comments
# 2002-04-16 fl Added __str__ methods to datetime/binary wrappers
# 2002-05-15 fl Added error constants (from NAME 2002-06-27 fl Merged with Python CVS version
# 2002-10-22 fl Added basic authentication (based on code from NAME 2003-01-22 sm Add support for the bool type
# 2003-02-27 gvr Remove apply calls
# 2003-04-24 sm Use cStringIO if available
# 2003-04-25 ak Add support for nil
# 2003-06-15 gn Add support for time.struct_time
# 2003-07-12 gp Correct marshalling of Faults
# 2003-10-31 mvl Add multicall support
# 2004-08-20 mvl Bump minimum supported Python version to 2.1
# 2014-12-02 ch/doko Add workaround for gzip bomb vulnerability
#
# Copyright (c) 1999-2002 by Secret Labs AB.
# Copyright (c) 1999-2002 by NAME Lundh.
#
# EMAIL http://www.pythonware.com
#
# --------------------------------------------------------------------
# The XML-RPC client interface is
#
# Copyright (c) 1999-2002 by Secret Labs AB
# Copyright (c) 1999-2002 by NAME Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
|
"""
===================
Universal Functions
===================
Ufuncs are, generally speaking, mathematical functions or operations that are
applied element-by-element to the contents of an array. That is, the result
in each output array element only depends on the value in the corresponding
input array (or arrays) and on no other array elements. NumPy comes with a
large suite of ufuncs, and scipy extends that suite substantially. The simplest
example is the addition operator: ::
>>> np.array([0,2,3,4]) + np.array([1,1,-1,2])
array([1, 3, 2, 6])
The unfunc module lists all the available ufuncs in numpy. Documentation on
the specific ufuncs may be found in those modules. This documentation is
intended to address the more general aspects of unfuncs common to most of
them. All of the ufuncs that make use of Python operators (e.g., +, -, etc.)
have equivalent functions defined (e.g. add() for +)
Type coercion
=============
What happens when a binary operator (e.g., +,-,\\*,/, etc) deals with arrays of
two different types? What is the type of the result? Typically, the result is
the higher of the two types. For example: ::
float32 + float64 -> float64
int8 + int32 -> int32
int16 + float32 -> float32
float32 + complex64 -> complex64
There are some less obvious cases generally involving mixes of types
(e.g. uints, ints and floats) where equal bit sizes for each are not
capable of saving all the information in a different type of equivalent
bit size. Some examples are int32 vs float32 or uint32 vs int32.
Generally, the result is the higher type of larger size than both
(if available). So: ::
int32 + float32 -> float64
uint32 + int32 -> int64
Finally, the type coercion behavior when expressions involve Python
scalars is different than that seen for arrays. Since Python has a
limited number of types, combining a Python int with a dtype=np.int8
array does not coerce to the higher type but instead, the type of the
array prevails. So the rules for Python scalars combined with arrays is
that the result will be that of the array equivalent the Python scalar
if the Python scalar is of a higher 'kind' than the array (e.g., float
vs. int), otherwise the resultant type will be that of the array.
For example: ::
Python int + int8 -> int8
Python float + int8 -> float64
ufunc methods
=============
Binary ufuncs support 4 methods.
**.reduce(arr)** applies the binary operator to elements of the array in
sequence. For example: ::
>>> np.add.reduce(np.arange(10)) # adds all elements of array
45
For multidimensional arrays, the first dimension is reduced by default: ::
>>> np.add.reduce(np.arange(10).reshape(2,5))
array([ 5, 7, 9, 11, 13])
The axis keyword can be used to specify different axes to reduce: ::
>>> np.add.reduce(np.arange(10).reshape(2,5),axis=1)
array([10, 35])
**.accumulate(arr)** applies the binary operator and generates an an
equivalently shaped array that includes the accumulated amount for each
element of the array. A couple examples: ::
>>> np.add.accumulate(np.arange(10))
array([ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45])
>>> np.multiply.accumulate(np.arange(1,9))
array([ 1, 2, 6, 24, 120, 720, 5040, 40320])
The behavior for multidimensional arrays is the same as for .reduce(),
as is the use of the axis keyword).
**.reduceat(arr,indices)** allows one to apply reduce to selected parts
of an array. It is a difficult method to understand. See the documentation
at:
**.outer(arr1,arr2)** generates an outer operation on the two arrays arr1 and
arr2. It will work on multidimensional arrays (the shape of the result is
the concatenation of the two input shapes.: ::
>>> np.multiply.outer(np.arange(3),np.arange(4))
array([[0, 0, 0, 0],
[0, 1, 2, 3],
[0, 2, 4, 6]])
Output arguments
================
All ufuncs accept an optional output array. The array must be of the expected
output shape. Beware that if the type of the output array is of a different
(and lower) type than the output result, the results may be silently truncated
or otherwise corrupted in the downcast to the lower type. This usage is useful
when one wants to avoid creating large temporary arrays and instead allows one
to reuse the same array memory repeatedly (at the expense of not being able to
use more convenient operator notation in expressions). Note that when the
output argument is used, the ufunc still returns a reference to the result.
>>> x = np.arange(2)
>>> np.add(np.arange(2),np.arange(2.),x)
array([0, 2])
>>> x
array([0, 2])
and & or as ufuncs
==================
Invariably people try to use the python 'and' and 'or' as logical operators
(and quite understandably). But these operators do not behave as normal
operators since Python treats these quite differently. They cannot be
overloaded with array equivalents. Thus using 'and' or 'or' with an array
results in an error. There are two alternatives:
1) use the ufunc functions logical_and() and logical_or().
2) use the bitwise operators & and \\|. The drawback of these is that if
the arguments to these operators are not boolean arrays, the result is
likely incorrect. On the other hand, most usages of logical_and and
logical_or are with boolean arrays. As long as one is careful, this is
a convenient way to apply these operators.
""" |
# (c) 2013, NAME <skvidal@fedoraproject.org> red hat, inc
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
# take a list of files and (optionally) a list of paths
# return the first existing file found in the paths
# [file1, file2, file3], [path1, path2, path3]
# search order is:
# path1/file1
# path1/file2
# path1/file3
# path2/file1
# path2/file2
# path2/file3
# path3/file1
# path3/file2
# path3/file3
# first file found with os.path.exists() is returned
# no file matches raises ansibleerror
# EXAMPLES
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: foo ${inventory_hostname} bar
# paths: /tmp/production /tmp/staging
# that will look for files in this order:
# /tmp/production/foo
# ${inventory_hostname}
# bar
# /tmp/staging/foo
# ${inventory_hostname}
# bar
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: /some/place/foo ${inventory_hostname} /some/place/else
# that will look for files in this order:
# /some/place/foo
# $relative_path/${inventory_hostname}
# /some/place/else
# example - including tasks:
# tasks:
# - include: $item
# with_first_found:
# - files: generic
# paths: tasks/staging tasks/production
# this will include the tasks in the file generic where it is found first (staging or production)
# example simple file lists
#tasks:
#- name: first found file
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname} foo
# example skipping if no matched files
# First_found also offers the ability to control whether or not failing
# to find a file returns an error or not
#
#- name: first found file - or skip
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname}
# skip: true
# example a role with default configuration and configuration per host
# you can set multiple terms with their own files and paths to look through.
# consider a role that sets some configuration per host falling back on a default config.
#
#- name: some configuration template
# template: src={{ item }} dest=/etc/file.cfg mode=0444 owner=root group=root
# with_first_found:
# - files:
# - ${inventory_hostname}/etc/file.cfg
# paths:
# - ../../../templates.overwrites
# - ../../../templates
# - files:
# - etc/file.cfg
# paths:
# - templates
# the above will return an empty list if the files cannot be found at all
# if skip is unspecificed or if it is set to false then it will return a list
# error which can be caught bye ignore_errors: true for that action.
# finally - if you want you can use it, in place to replace first_available_file:
# you simply cannot use the - files, path or skip options. simply replace
# first_available_file with with_first_found and leave the file listing in place
#
#
# - name: with_first_found like first_available_file
# action: copy src=$item dest=/tmp/faftest
# with_first_found:
# - ../files/foo
# - ../files/bar
# - ../files/baz
# ignore_errors: true
|
"""
TestCmd.py: a testing framework for commands and scripts.
The TestCmd module provides a framework for portable automated testing
of executable commands and scripts (in any language, not just Python),
especially commands and scripts that require file system interaction.
In addition to running tests and evaluating conditions, the TestCmd
module manages and cleans up one or more temporary workspace
directories, and provides methods for creating files and directories in
those workspace directories from in-line data, here-documents), allowing
tests to be completely self-contained.
A TestCmd environment object is created via the usual invocation:
import TestCmd
test = TestCmd.TestCmd()
There are a bunch of keyword arguments available at instantiation:
test = TestCmd.TestCmd(description = 'string',
program = 'program_or_script_to_test',
interpreter = 'script_interpreter',
workdir = 'prefix',
subdir = 'subdir',
verbose = Boolean,
match = default_match_function,
diff = default_diff_function,
combine = Boolean)
There are a bunch of methods that let you do different things:
test.verbose_set(1)
test.description_set('string')
test.program_set('program_or_script_to_test')
test.interpreter_set('script_interpreter')
test.interpreter_set(['script_interpreter', 'arg'])
test.workdir_set('prefix')
test.workdir_set('')
test.workpath('file')
test.workpath('subdir', 'file')
test.subdir('subdir', ...)
test.rmdir('subdir', ...)
test.write('file', "contents\n")
test.write(['subdir', 'file'], "contents\n")
test.read('file')
test.read(['subdir', 'file'])
test.read('file', mode)
test.read(['subdir', 'file'], mode)
test.writable('dir', 1)
test.writable('dir', None)
test.preserve(condition, ...)
test.cleanup(condition)
test.command_args(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program')
test.run(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
chdir = 'directory_to_chdir_to',
stdin = 'input to feed to the program\n')
universal_newlines = True)
p = test.start(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
universal_newlines = None)
test.finish(self, p)
test.pass_test()
test.pass_test(condition)
test.pass_test(condition, function)
test.fail_test()
test.fail_test(condition)
test.fail_test(condition, function)
test.fail_test(condition, function, skip)
test.no_result()
test.no_result(condition)
test.no_result(condition, function)
test.no_result(condition, function, skip)
test.stdout()
test.stdout(run)
test.stderr()
test.stderr(run)
test.symlink(target, link)
test.banner(string)
test.banner(string, width)
test.diff(actual, expected)
test.match(actual, expected)
test.match_exact("actual 1\nactual 2\n", "expected 1\nexpected 2\n")
test.match_exact(["actual 1\n", "actual 2\n"],
["expected 1\n", "expected 2\n"])
test.match_re("actual 1\nactual 2\n", regex_string)
test.match_re(["actual 1\n", "actual 2\n"], list_of_regexes)
test.match_re_dotall("actual 1\nactual 2\n", regex_string)
test.match_re_dotall(["actual 1\n", "actual 2\n"], list_of_regexes)
test.tempdir()
test.tempdir('temporary-directory')
test.sleep()
test.sleep(seconds)
test.where_is('foo')
test.where_is('foo', 'PATH1:PATH2')
test.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
test.unlink('file')
test.unlink('subdir', 'file')
The TestCmd module provides pass_test(), fail_test(), and no_result()
unbound functions that report test results for use with the Aegis change
management system. These methods terminate the test immediately,
reporting PASSED, FAILED, or NO RESULT respectively, and exiting with
status 0 (success), 1 or 2 respectively. This allows for a distinction
between an actual failed test and a test that could not be properly
evaluated because of an external condition (such as a full file system
or incorrect permissions).
import TestCmd
TestCmd.pass_test()
TestCmd.pass_test(condition)
TestCmd.pass_test(condition, function)
TestCmd.fail_test()
TestCmd.fail_test(condition)
TestCmd.fail_test(condition, function)
TestCmd.fail_test(condition, function, skip)
TestCmd.no_result()
TestCmd.no_result(condition)
TestCmd.no_result(condition, function)
TestCmd.no_result(condition, function, skip)
The TestCmd module also provides unbound functions that handle matching
in the same way as the match_*() methods described above.
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_exact)
test = TestCmd.TestCmd(match = TestCmd.match_re)
test = TestCmd.TestCmd(match = TestCmd.match_re_dotall)
The TestCmd module provides unbound functions that can be used for the
"diff" argument to TestCmd.TestCmd instantiation:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_re,
diff = TestCmd.diff_re)
test = TestCmd.TestCmd(diff = TestCmd.simple_diff)
The "diff" argument can also be used with standard difflib functions:
import difflib
test = TestCmd.TestCmd(diff = difflib.context_diff)
test = TestCmd.TestCmd(diff = difflib.unified_diff)
Lastly, the where_is() method also exists in an unbound function
version.
import TestCmd
TestCmd.where_is('foo')
TestCmd.where_is('foo', 'PATH1:PATH2')
TestCmd.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
""" |
"""
TestCmd.py: a testing framework for commands and scripts.
The TestCmd module provides a framework for portable automated testing
of executable commands and scripts (in any language, not just Python),
especially commands and scripts that require file system interaction.
In addition to running tests and evaluating conditions, the TestCmd
module manages and cleans up one or more temporary workspace
directories, and provides methods for creating files and directories in
those workspace directories from in-line data, here-documents), allowing
tests to be completely self-contained.
A TestCmd environment object is created via the usual invocation:
import TestCmd
test = TestCmd.TestCmd()
There are a bunch of keyword arguments available at instantiation:
test = TestCmd.TestCmd(description = 'string',
program = 'program_or_script_to_test',
interpreter = 'script_interpreter',
workdir = 'prefix',
subdir = 'subdir',
verbose = Boolean,
match = default_match_function,
diff = default_diff_function,
combine = Boolean)
There are a bunch of methods that let you do different things:
test.verbose_set(1)
test.description_set('string')
test.program_set('program_or_script_to_test')
test.interpreter_set('script_interpreter')
test.interpreter_set(['script_interpreter', 'arg'])
test.workdir_set('prefix')
test.workdir_set('')
test.workpath('file')
test.workpath('subdir', 'file')
test.subdir('subdir', ...)
test.rmdir('subdir', ...)
test.write('file', "contents\n")
test.write(['subdir', 'file'], "contents\n")
test.read('file')
test.read(['subdir', 'file'])
test.read('file', mode)
test.read(['subdir', 'file'], mode)
test.writable('dir', 1)
test.writable('dir', None)
test.preserve(condition, ...)
test.cleanup(condition)
test.command_args(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program')
test.run(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
chdir = 'directory_to_chdir_to',
stdin = 'input to feed to the program\n')
universal_newlines = True)
p = test.start(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
universal_newlines = None)
test.finish(self, p)
test.pass_test()
test.pass_test(condition)
test.pass_test(condition, function)
test.fail_test()
test.fail_test(condition)
test.fail_test(condition, function)
test.fail_test(condition, function, skip)
test.no_result()
test.no_result(condition)
test.no_result(condition, function)
test.no_result(condition, function, skip)
test.stdout()
test.stdout(run)
test.stderr()
test.stderr(run)
test.symlink(target, link)
test.banner(string)
test.banner(string, width)
test.diff(actual, expected)
test.match(actual, expected)
test.match_exact("actual 1\nactual 2\n", "expected 1\nexpected 2\n")
test.match_exact(["actual 1\n", "actual 2\n"],
["expected 1\n", "expected 2\n"])
test.match_re("actual 1\nactual 2\n", regex_string)
test.match_re(["actual 1\n", "actual 2\n"], list_of_regexes)
test.match_re_dotall("actual 1\nactual 2\n", regex_string)
test.match_re_dotall(["actual 1\n", "actual 2\n"], list_of_regexes)
test.tempdir()
test.tempdir('temporary-directory')
test.sleep()
test.sleep(seconds)
test.where_is('foo')
test.where_is('foo', 'PATH1:PATH2')
test.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
test.unlink('file')
test.unlink('subdir', 'file')
The TestCmd module provides pass_test(), fail_test(), and no_result()
unbound functions that report test results for use with the Aegis change
management system. These methods terminate the test immediately,
reporting PASSED, FAILED, or NO RESULT respectively, and exiting with
status 0 (success), 1 or 2 respectively. This allows for a distinction
between an actual failed test and a test that could not be properly
evaluated because of an external condition (such as a full file system
or incorrect permissions).
import TestCmd
TestCmd.pass_test()
TestCmd.pass_test(condition)
TestCmd.pass_test(condition, function)
TestCmd.fail_test()
TestCmd.fail_test(condition)
TestCmd.fail_test(condition, function)
TestCmd.fail_test(condition, function, skip)
TestCmd.no_result()
TestCmd.no_result(condition)
TestCmd.no_result(condition, function)
TestCmd.no_result(condition, function, skip)
The TestCmd module also provides unbound functions that handle matching
in the same way as the match_*() methods described above.
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_exact)
test = TestCmd.TestCmd(match = TestCmd.match_re)
test = TestCmd.TestCmd(match = TestCmd.match_re_dotall)
The TestCmd module provides unbound functions that can be used for the
"diff" argument to TestCmd.TestCmd instantiation:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_re,
diff = TestCmd.diff_re)
test = TestCmd.TestCmd(diff = TestCmd.simple_diff)
The "diff" argument can also be used with standard difflib functions:
import difflib
test = TestCmd.TestCmd(diff = difflib.context_diff)
test = TestCmd.TestCmd(diff = difflib.unified_diff)
Lastly, the where_is() method also exists in an unbound function
version.
import TestCmd
TestCmd.where_is('foo')
TestCmd.where_is('foo', 'PATH1:PATH2')
TestCmd.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
""" |
"""
Basic functions used by several sub-packages and
useful to have in the main name-space.
Type Handling
-------------
================ ===================
iscomplexobj Test for complex object, scalar result
isrealobj Test for real object, scalar result
iscomplex Test for complex elements, array result
isreal Test for real elements, array result
imag Imaginary part
real Real part
real_if_close Turns complex number with tiny imaginary part to real
isneginf Tests for negative infinity, array result
isposinf Tests for positive infinity, array result
isnan Tests for nans, array result
isinf Tests for infinity, array result
isfinite Tests for finite numbers, array result
isscalar True if argument is a scalar
nan_to_num Replaces NaN's with 0 and infinities with large numbers
cast Dictionary of functions to force cast to each type
common_type Determine the minimum common type code for a group
of arrays
mintypecode Return minimal allowed common typecode.
================ ===================
Index Tricks
------------
================ ===================
mgrid Method which allows easy construction of N-d
'mesh-grids'
``r_`` Append and construct arrays: turns slice objects into
ranges and concatenates them, for 2d arrays appends rows.
index_exp Konrad Hinsen's index_expression class instance which
can be useful for building complicated slicing syntax.
================ ===================
Useful Functions
----------------
================ ===================
select Extension of where to multiple conditions and choices
extract Extract 1d array from flattened array according to mask
insert Insert 1d array of values into Nd array according to mask
linspace Evenly spaced samples in linear space
logspace Evenly spaced samples in logarithmic space
fix Round x to nearest integer towards zero
mod Modulo mod(x,y) = x % y except keeps sign of y
amax Array maximum along axis
amin Array minimum along axis
ptp Array max-min along axis
cumsum Cumulative sum along axis
prod Product of elements along axis
cumprod Cumluative product along axis
diff Discrete differences along axis
angle Returns angle of complex argument
unwrap Unwrap phase along given axis (1-d algorithm)
sort_complex Sort a complex-array (based on real, then imaginary)
trim_zeros Trim the leading and trailing zeros from 1D array.
vectorize A class that wraps a Python function taking scalar
arguments into a generalized function which can handle
arrays of arguments using the broadcast rules of
numerix Python.
================ ===================
Shape Manipulation
------------------
================ ===================
squeeze Return a with length-one dimensions removed.
atleast_1d Force arrays to be > 1D
atleast_2d Force arrays to be > 2D
atleast_3d Force arrays to be > 3D
vstack Stack arrays vertically (row on row)
hstack Stack arrays horizontally (column on column)
column_stack Stack 1D arrays as columns into 2D array
dstack Stack arrays depthwise (along third dimension)
stack Stack arrays along a new axis
split Divide array into a list of sub-arrays
hsplit Split into columns
vsplit Split into rows
dsplit Split along third dimension
================ ===================
Matrix (2D Array) Manipulations
-------------------------------
================ ===================
fliplr 2D array with columns flipped
flipud 2D array with rows flipped
rot90 Rotate a 2D array a multiple of 90 degrees
eye Return a 2D array with ones down a given diagonal
diag Construct a 2D array from a vector, or return a given
diagonal from a 2D array.
mat Construct a Matrix
bmat Build a Matrix from blocks
================ ===================
Polynomials
-----------
================ ===================
poly1d A one-dimensional polynomial class
poly Return polynomial coefficients from roots
roots Find roots of polynomial given coefficients
polyint Integrate polynomial
polyder Differentiate polynomial
polyadd Add polynomials
polysub Substract polynomials
polymul Multiply polynomials
polydiv Divide polynomials
polyval Evaluate polynomial at given argument
================ ===================
Import Tricks
-------------
================ ===================
ppimport Postpone module import until trying to use it
ppimport_attr Postpone module import until trying to use its attribute
ppresolve Import postponed module and return it.
================ ===================
Machine Arithmetics
-------------------
================ ===================
machar_single Single precision floating point arithmetic parameters
machar_double Double precision floating point arithmetic parameters
================ ===================
Threading Tricks
----------------
================ ===================
ParallelExec Execute commands in parallel thread.
================ ===================
1D Array Set Operations
-----------------------
Set operations for 1D numeric arrays based on sort() function.
================ ===================
ediff1d Array difference (auxiliary function).
unique Unique elements of an array.
intersect1d Intersection of 1D arrays with unique elements.
setxor1d Set exclusive-or of 1D arrays with unique elements.
in1d Test whether elements in a 1D array are also present in
another array.
union1d Union of 1D arrays with unique elements.
setdiff1d Set difference of 1D arrays with unique elements.
================ ===================
""" |
"""
Python Builtins
---------------
Most buildin functions (that make sense in JS) are automatically
translated to JavaScript: isinstance, issubclass, callable, hasattr,
getattr, setattr, delattr, print, len, max, min, chr, ord, dict, list,
tuple, range, pow, sum, round, int, float, str, bool, abs, divmod, all,
any, enumerate, zip, reversed, sorted, filter, map.
.. pyscript_example::
# "self" is replaced with "this"
self.foo
# Printing just works
print('some test')
print(a, b, c, sep='-')
# Getting the length of a string or array
len(foo)
# Rounding and abs
round(foo) # round to nearest integer
int(foo) # round towards 0 as in Python
abs(foo)
# min and max
min(foo)
min(a, b, c)
max(foo)
max(a, b, c)
# divmod
a, b = divmod(100, 7) # -> 14, 2
# Aggregation
sum(foo)
all(foo)
any(foo)
# Turning things into numbers, bools and strings
str(s)
float(x)
bool(y)
int(z) # this rounds towards zero like in Python
chr(65) # -> 'A'
ord('A') # -> 65
# Turning things into lists and dicts
dict([['foo', 1], ['bar', 2]]) # -> {'foo': 1, 'bar': 2}
list('abc') # -> ['a', 'b', 'c']
dict(other_dict) # make a copy
list(other_list) # make copy
The isinstance function (and friends)
-------------------------------------
The ``isinstance()`` function works for all JS primitive types, but also
for user-defined classes.
.. pyscript_example::
# Basic types
isinstance(3, float) # in JS there are no ints
isinstance('', str)
isinstance([], list)
isinstance({}, dict)
isinstance(foo, types.FunctionType)
# Can also use JS strings
isinstance(3, 'number')
isinstance('', 'string')
isinstance([], 'array')
isinstance({}, 'object')
isinstance(foo, 'function')
# You can use it on your own types too ...
isinstance(x, MyClass)
isinstance(x, 'MyClass') # equivalent
isinstance(x, 'Object') # also yields true (subclass of Object)
# issubclass works too
issubclass(Foo, Bar)
# As well as callable
callable(foo)
hasattr, getattr, setattr and delattr
-------------------------------------
.. pyscript_example::
a = {'foo': 1, 'bar': 2}
hasattr(a, 'foo') # -> True
hasattr(a, 'fooo') # -> False
hasattr(null, 'foo') # -> False
getattr(a, 'foo') # -> 1
getattr(a, 'fooo') # -> raise AttributeError
getattr(a, 'fooo', 3) # -> 3
getattr(null, 'foo', 3) # -> 3
setattr(a, 'foo', 2)
delattr(a, 'foo')
Creating sequences
------------------
.. pyscript_example::
range(10)
range(2, 10, 2)
range(100, 0, -1)
reversed(foo)
sorted(foo)
enumerate(foo)
zip(foo, bar)
filter(func, foo)
map(func, foo)
List methods
------------
.. pyscript_example::
# Call a.append() if it exists, otherwise a.push()
a.append(x)
# Similar for remove()
a.remove(x)
Dict methods
------------
.. pyscript_example::
a = {'foo': 3}
a['foo']
a.get('foo', 0)
a.get('foo')
a.keys()
Str methods
-----------
.. pyscript_example::
"foobar".startswith('foo')
Additional sugar
----------------
.. pyscript_example::
# Get time (number of seconds since epoch)
print(time.time())
# High resolution timer (as in time.perf_counter on Python 3)
t0 = time.perf_counter()
do_something()
t1 = time.perf_counter()
print('this took me', t1-t0, 'seconds')
""" |
"""
FASTA/QUAL format (:mod:`skbio.io.format.fasta`)
================================================
.. currentmodule:: skbio.io.format.fasta
The FASTA file format (``fasta``) stores biological (i.e., nucleotide or
protein) sequences in a simple plain text format that is both human-readable
and easy to parse. The file format was first introduced and used in the FASTA
software package [1]_. Additional descriptions of the file format can be found
in [2]_ and [3]_.
An example of a FASTA-formatted file containing two DNA sequences::
>seq1 db-accession-149855
CGATGTCGATCGATCGATCGATCAG
>seq2 db-accession-34989
CATCGATCGATCGATGCATGCATGCATG
The QUAL file format is an additional format related to FASTA. A FASTA file is
sometimes accompanied by a QUAL file, particuarly when the FASTA file contains
sequences generated on a high-throughput sequencing instrument. QUAL files
store a Phred quality score (nonnegative integer) for each base in a sequence
stored in FASTA format (see [4]_ for more details). scikit-bio supports reading
and writing FASTA (and optionally QUAL) file formats.
Format Support
--------------
**Has Sniffer: Yes**
+------+------+---------------------------------------------------------------+
|Reader|Writer| Object Class |
+======+======+===============================================================+
|Yes |Yes |generator of :mod:`skbio.sequence.Sequence` objects |
+------+------+---------------------------------------------------------------+
|Yes |Yes |:mod:`skbio.alignment.SequenceCollection` |
+------+------+---------------------------------------------------------------+
|Yes |Yes |:mod:`skbio.alignment.Alignment` |
+------+------+---------------------------------------------------------------+
|Yes |Yes |:mod:`skbio.sequence.Sequence` |
+------+------+---------------------------------------------------------------+
|Yes |Yes |:mod:`skbio.sequence.DNA` |
+------+------+---------------------------------------------------------------+
|Yes |Yes |:mod:`skbio.sequence.RNA` |
+------+------+---------------------------------------------------------------+
|Yes |Yes |:mod:`skbio.sequence.Protein` |
+------+------+---------------------------------------------------------------+
.. note:: All readers and writers support an optional QUAL file via the
``qual`` parameter. If one is provided, quality scores will be read/written
in addition to FASTA sequence data.
Format Specification
--------------------
The following sections define the FASTA and QUAL file formats in detail.
FASTA Format
^^^^^^^^^^^^
A FASTA file contains one or more biological sequences. The sequences are
stored sequentially, with a *record* for each sequence (also referred to as a
*FASTA record*). Each *record* consists of a single-line *header* (sometimes
referred to as a *defline*, *label*, *description*, or *comment*) followed by
the sequence data, optionally split over multiple lines.
.. note:: Blank or whitespace-only lines are only allowed at the beginning of
the file, between FASTA records, or at the end of the file. A blank or
whitespace-only line after the header line, within the sequence (for FASTA
files), or within quality scores (for QUAL files) will raise an error.
scikit-bio will ignore leading and trailing whitespace characters on each
line while reading.
.. note:: scikit-bio does not currently support legacy FASTA format (i.e.,
headers/comments denoted with a semicolon). The format supported by
scikit-bio (described below in detail) most closely resembles the
description given in NCBI's BLAST documentation [3]_. See [2]_ for more
details on legacy FASTA format. If you would like legacy FASTA format
support added to scikit-bio, please consider submitting a feature request on
the
`scikit-bio issue tracker <https://github.com/biocore/scikit-bio/issues>`_
(pull requests are also welcome!).
Sequence Header
~~~~~~~~~~~~~~~
Each sequence header consists of a single line beginning with a greater-than
(``>``) symbol. Immediately following this is a sequence identifier (ID) and
description separated by one or more whitespace characters. The sequence ID and
description are stored in the sequence `metadata` attribute, under the `'id'`
and `'description'` keys, repectively. Both are optional. Each will be
represented as the empty string (``''``) in `metadata` if it is not present
in the header.
A sequence ID consists of a single *word*: all characters after the greater-
than symbol and before the first whitespace character (if any) are taken as the
sequence ID. Unique sequence IDs are not strictly enforced by the FASTA format
itself. A single standardized ID format is similarly not enforced by the FASTA
format, though it is often common to use a unique library accession number for
a sequence ID (e.g., NCBI's FASTA defline format [5]_).
.. note:: scikit-bio will enforce sequence ID uniqueness depending on the type
of object that the FASTA file is read into. For example, reading a FASTA
file as a generator of ``Sequence`` objects will not enforce
unique IDs since it simply yields each sequence it finds in the FASTA file.
However, if the FASTA file is read into a ``SequenceCollection`` object, ID
uniqueness will be enforced because that is a requirement of a
``SequenceCollection``.
If a description is present, it is taken as the remaining characters that
follow the sequence ID and initial whitespace(s). The description is considered
additional information about the sequence (e.g., comments about the source of
the sequence or the molecule that it encodes).
For example, consider the following header::
>seq1 db-accession-149855
``seq1`` is the sequence ID and ``db-accession-149855`` is the sequence
description.
.. note:: scikit-bio's readers will remove all leading and trailing whitespace
from the description. If a header line begins with whitespace following the
``>``, the ID is assumed to be missing and the remainder of the line is
taken as the description.
Sequence Data
~~~~~~~~~~~~~
Biological sequence data follows the header, and can be split over multiple
lines. The sequence data (i.e., nucleotides or amino acids) are stored using
the standard IUPAC lexicon (single-letter codes).
.. note:: scikit-bio supports both upper and lower case characters.
This functionality depends on the type of object the data is
being read into. For ``Sequence``
objects, sciki-bio doesn't care about the case. However, for other object
types, such as :class:`skbio.sequence.DNA`, :class:`skbio.sequence.RNA`,
and :class:`skbio.sequence.Protein`, the `lowercase` parameter
must be used to control case functionality. Refer to the documentation for
the constructors for details.
.. note:: Both ``-`` and ``.`` are supported as gap characters. See
:mod:`skbio.sequence` for more details on how scikit-bio interprets
sequence data in its in-memory objects.
Validation is performed for all scikit-bio objects which support it. This
consists of all objects which enforce usage of IUPAC characters. If any
invalid IUPAC characters are found in the sequence while reading from the
FASTA file, an exception is raised.
QUAL Format
^^^^^^^^^^^
A QUAL file contains quality scores for one or more biological sequences stored
in a corresponding FASTA file. QUAL format is very similar to FASTA format: it
stores records sequentially, with each record beginning with a header line
containing a sequence ID and description. The same rules apply to QUAL headers
as FASTA headers (see the above sections for details). scikit-bio processes
FASTA and QUAL headers in exactly the same way.
Quality scores are automatically stored in the object's `positional_metadata`
attribute, under the `'quality'` column.
Instead of storing biological sequence data in each record, a QUAL file stores
a Phred quality score for each base in the corresponding sequence. Quality
scores are represented as nonnegative integers separated by whitespace
(typically a single space or newline), and can span multiple lines.
.. note:: When reading FASTA and QUAL files, scikit-bio requires records to be
in the same order in both files (i.e., each FASTA and QUAL record must have
the same ID and description after being parsed). In addition to having the
same order, the number of FASTA records must match the number of QUAL
records (i.e., missing or additonal records are not allowed). scikit-bio
also requires that the number of quality scores match the number of bases in
the corresponding sequence.
When writing FASTA and QUAL files, scikit-bio will maintain the same
ordering of records in both files (i.e., using the same ID and description
in both records) to support future reading.
Format Parameters
-----------------
The following parameters are available to change how FASTA/QUAL files are read
or written in scikit-bio.
QUAL File Parameter (Readers and Writers)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The ``qual`` parameter is available to all FASTA format readers and writers. It
can be any file-like type supported by scikit-bio's I/O registry (e.g., file
handle, file path, etc.). If ``qual`` is provided when reading, quality scores
will be included in each in-memory ``Sequence`` object, in addition
to sequence data stored in the FASTA file. When writing, quality scores will be
written in QUAL format in addition to the sequence data being written in FASTA
format.
Reader-specific Parameters
^^^^^^^^^^^^^^^^^^^^^^^^^^
The available reader parameters differ depending on which reader is used.
Generator, SequenceCollection, and Alignment Reader Parameters
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The ``constructor`` parameter can be used with the ``Sequence``
generator, ``SequenceCollection``, and ``Alignment`` FASTA readers.
``constructor`` specifies the in-memory type of each sequence that is parsed,
and defaults to ``Sequence``. ``constructor`` should be a subclass of
``Sequence``. For example, if you know that the FASTA file you're
reading contains protein sequences, you would pass
``constructor=Protein`` to the reader call.
.. note:: The FASTA sniffer will not attempt to guess the ``constructor``
parameter, so it will always default to ``Sequence`` if another
type is not provided to the reader.
Sequence Reader Parameters
~~~~~~~~~~~~~~~~~~~~~~~~~~
The ``seq_num`` parameter can be used with the ``Sequence``,
``DNA``, ``RNA``, and ``Protein`` FASTA readers. ``seq_num`` specifies which
sequence to read from the FASTA file (and optional QUAL file), and defaults to
1 (i.e., such that the first sequence is read). For example, to read the 50th
sequence from a FASTA file, you would pass ``seq_num=50`` to the reader call.
Writer-specific Parameters
^^^^^^^^^^^^^^^^^^^^^^^^^^
The following parameters are available to all FASTA format writers:
- ``id_whitespace_replacement``: string to replace **each** whitespace
character in a sequence ID. This parameter is useful for cases where an
in-memory sequence ID contains whitespace, which would result in an on-disk
representation that would not be read back into memory as the same ID (since
IDs in FASTA format cannot contain whitespace). Defaults to ``_``. If
``None``, no whitespace replacement is performed and IDs are written as they
are stored in memory (this has the potential to create an invalid
FASTA-formatted file; see note below). This parameter also applies to a QUAL
file if one is provided.
- ``description_newline_replacement``: string to replace **each** newline
character in a sequence description. Since a FASTA header must be a single
line, newlines are not allowed in sequence descriptions and must be replaced
in order to write a valid FASTA file. Defaults to a single space. If
``None``, no newline replacement is performed and descriptions are written as
they are stored in memory (this has the potential to create an invalid
FASTA-formatted file; see note below). This parameter also applies to a QUAL
file if one is provided.
- ``max_width``: integer specifying the maximum line width (i.e., number of
characters) for sequence data and/or quality scores. If a sequence or its
quality scores are longer than ``max_width``, it will be split across
multiple lines, each with a maximum width of ``max_width``. Note that there
are some caveats when splitting quality scores. A single quality score will
*never* be split across multiple lines, otherwise it would become two
different quality scores when read again. Thus, splitting only occurs
*between* quality scores. This makes it possible to have a single long
quality score written on its own line that exceeds ``max_width``. For
example, the quality score ``12345`` would not be split across multiple lines
even if ``max_width=3``. Thus, a 5-character line would be written. Default
behavior is to not split sequence data or quality scores across multiple
lines.
- ``lowercase``: String or boolean array. If a string, it is treated as a key
into the positional metadata of the object. If a boolean array, it
indicates characters to write in lowercase. Characters in the sequence
corresponding to `True` values will be written in lowercase. The boolean
array must be the same length as the sequence.
.. note:: The FASTA format writers will have noticeably better runtime
performance if ``id_whitespace_replacement`` and/or
``description_newline_replacement`` are set to ``None`` so that whitespace
replacement is not performed during writing. However, this can potentially
create invalid FASTA files, especially if there are newline characters in
the IDs or descriptions. For IDs with whitespace, this can also affect how
the IDs are read into memory in a subsequent read operation. For example, if
an in-memory sequence ID is ``'seq 1'`` and
``id_whitespace_replacement=None``, reading the FASTA file back into memory
would result in an ID of ``'seq'``, and ``'1'`` would be part of the
sequence description.
Examples
--------
Reading and Writing FASTA Files
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Suppose we have the following FASTA file with five equal-length sequences
(example modified from [6]_)::
>seq1 Turkey
AAGCTNGGGCATTTCAGGGTGAGCCCGGGCAATACAGGGTAT
>seq2 Salmo gair
AAGCCTTGGCAGTGCAGGGTGAGCCGTGG
CCGGGCACGGTAT
>seq3 H. Sapiens
ACCGGTTGGCCGTTCAGGGTACAGGTTGGCCGTTCAGGGTAA
>seq4 Chimp
AAACCCTTGCCG
TTACGCTTAAAC
CGAGGCCGGGAC
ACTCAT
>seq5 Gorilla
AAACCCTTGCCGGTACGCTTAAACCATTGCCGGTACGCTTAA
.. note:: Original copyright notice for the above example file:
*(c) Copyright 1986-2008 by The University of Washington. Written by NAME
Felsenstein. Permission is granted to copy this document provided that no
fee is charged for it and that this copyright notice is not removed.*
Note that the sequences are not required to be of equal length in order for the
file to be a valid FASTA file (this depends on the object that you're reading
the file into). Also note that some of the sequences occur on a single line,
while others are split across multiple lines.
Let's define this file in-memory as a ``StringIO``, though this could be a real
file path, file handle, or anything that's supported by scikit-bio's I/O
registry in practice:
>>> fl = [u">seq1 Turkey\\n",
... u"AAGCTNGGGCATTTCAGGGTGAGCCCGGGCAATACAGGGTAT\\n",
... u">seq2 Salmo gair\\n",
... u"AAGCCTTGGCAGTGCAGGGTGAGCCGTGG\\n",
... u"CCGGGCACGGTAT\\n",
... u">seq3 H. Sapiens\\n",
... u"ACCGGTTGGCCGTTCAGGGTACAGGTTGGCCGTTCAGGGTAA\\n",
... u">seq4 Chimp\\n",
... u"AAACCCTTGCCG\\n",
... u"TTACGCTTAAAC\\n",
... u"CGAGGCCGGGAC\\n",
... u"ACTCAT\\n",
... u">seq5 Gorilla\\n",
... u"AAACCCTTGCCGGTACGCTTAAACCATTGCCGGTACGCTTAA\\n"]
Let's read the FASTA file into a ``SequenceCollection``:
>>> from skbio import SequenceCollection
>>> sc = SequenceCollection.read(fl)
>>> sc.sequence_lengths()
[42, 42, 42, 42, 42]
>>> sc.ids()
[u'seq1', u'seq2', u'seq3', u'seq4', u'seq5']
We see that all 5 sequences have 42 characters, and that each of the sequence
IDs were successfully read into memory.
Since these sequences are of equal length (presumably because they've been
aligned), let's load the FASTA file into an ``Alignment`` object, which is a
more appropriate data structure:
>>> from skbio import Alignment
>>> aln = Alignment.read(fl)
>>> aln.sequence_length()
42
Note that we were able to read the FASTA file into two different data
structures (``SequenceCollection`` and ``Alignment``) using the exact same
``read`` method call (and underlying reading/parsing logic). Also note that we
didn't specify a file format in the ``read`` call. The FASTA sniffer detected
the correct file format for us!
Let's inspect the type of sequences stored in the ``Alignment``:
>>> aln[0]
Sequence
------------------------------------------------
Metadata:
u'description': u'Turkey'
u'id': u'seq1'
Stats:
length: 42
------------------------------------------------
0 AAGCTNGGGC ATTTCAGGGT GAGCCCGGGC AATACAGGGT AT
By default, sequences are loaded as ``Sequence`` objects. We can
change the type of sequence via the ``constructor`` parameter:
>>> from skbio import DNA
>>> aln = Alignment.read(fl, constructor=DNA)
>>> aln[0] # doctest: +NORMALIZE_WHITESPACE
DNA
------------------------------------------------
Metadata:
u'description': u'Turkey'
u'id': u'seq1'
Stats:
length: 42
has gaps: False
has degenerates: True
has non-degenerates: True
GC-content: 54.76%
------------------------------------------------
0 AAGCTNGGGC ATTTCAGGGT GAGCCCGGGC AATACAGGGT AT
We now have an ``Alignment`` of ``DNA`` objects instead of
``Sequence`` objects.
To write the alignment in FASTA format:
>>> from io import StringIO
>>> with StringIO() as fh:
... print(aln.write(fh).getvalue())
>seq1 Turkey
AAGCTNGGGCATTTCAGGGTGAGCCCGGGCAATACAGGGTAT
>seq2 Salmo gair
AAGCCTTGGCAGTGCAGGGTGAGCCGTGGCCGGGCACGGTAT
>seq3 H. Sapiens
ACCGGTTGGCCGTTCAGGGTACAGGTTGGCCGTTCAGGGTAA
>seq4 Chimp
AAACCCTTGCCGTTACGCTTAAACCGAGGCCGGGACACTCAT
>seq5 Gorilla
AAACCCTTGCCGGTACGCTTAAACCATTGCCGGTACGCTTAA
<BLANKLINE>
Both ``SequenceCollection`` and ``Alignment`` load all of the sequences from
the FASTA file into memory at once. If the FASTA file is large (which is often
the case), this may be infeasible if you don't have enough memory. To work
around this issue, you can stream the sequences using scikit-bio's
generator-based FASTA reader and writer. The generator-based reader yields
``Sequence`` objects (or subclasses if ``constructor`` is supplied)
one at a time, instead of loading all sequences into memory. For example, let's
use the generator-based reader to process a single sequence at a time in a
``for`` loop:
>>> import skbio.io
>>> for seq in skbio.io.read(fl, format='fasta'):
... seq
... print('')
Sequence
------------------------------------------------
Metadata:
u'description': u'Turkey'
u'id': u'seq1'
Stats:
length: 42
------------------------------------------------
0 AAGCTNGGGC ATTTCAGGGT GAGCCCGGGC AATACAGGGT AT
<BLANKLINE>
Sequence
------------------------------------------------
Metadata:
u'description': u'Salmo gair'
u'id': u'seq2'
Stats:
length: 42
------------------------------------------------
0 AAGCCTTGGC AGTGCAGGGT GAGCCGTGGC CGGGCACGGT AT
<BLANKLINE>
Sequence
------------------------------------------------
Metadata:
u'description': u'H. Sapiens'
u'id': u'seq3'
Stats:
length: 42
------------------------------------------------
0 ACCGGTTGGC CGTTCAGGGT ACAGGTTGGC CGTTCAGGGT AA
<BLANKLINE>
Sequence
------------------------------------------------
Metadata:
u'description': u'Chimp'
u'id': u'seq4'
Stats:
length: 42
------------------------------------------------
0 AAACCCTTGC CGTTACGCTT AAACCGAGGC CGGGACACTC AT
<BLANKLINE>
Sequence
------------------------------------------------
Metadata:
u'description': u'Gorilla'
u'id': u'seq5'
Stats:
length: 42
------------------------------------------------
0 AAACCCTTGC CGGTACGCTT AAACCATTGC CGGTACGCTT AA
<BLANKLINE>
A single sequence can also be read into a ``Sequence`` (or subclass):
>>> from skbio import Sequence
>>> seq = Sequence.read(fl)
>>> seq
Sequence
------------------------------------------------
Metadata:
u'description': u'Turkey'
u'id': u'seq1'
Stats:
length: 42
------------------------------------------------
0 AAGCTNGGGC ATTTCAGGGT GAGCCCGGGC AATACAGGGT AT
By default, the first sequence in the FASTA file is read. This can be
controlled with ``seq_num``. For example, to read the fifth sequence:
>>> seq = Sequence.read(fl, seq_num=5)
>>> seq
Sequence
------------------------------------------------
Metadata:
u'description': u'Gorilla'
u'id': u'seq5'
Stats:
length: 42
------------------------------------------------
0 AAACCCTTGC CGGTACGCTT AAACCATTGC CGGTACGCTT AA
We can use the same API to read the fifth sequence into a ``DNA``:
>>> dna_seq = DNA.read(fl, seq_num=5)
>>> dna_seq
DNA
------------------------------------------------
Metadata:
u'description': u'Gorilla'
u'id': u'seq5'
Stats:
length: 42
has gaps: False
has degenerates: False
has non-degenerates: True
GC-content: 50.00%
------------------------------------------------
0 AAACCCTTGC CGGTACGCTT AAACCATTGC CGGTACGCTT AA
Individual sequence objects can also be written in FASTA format:
>>> with StringIO() as fh:
... print(dna_seq.write(fh).getvalue())
>seq5 Gorilla
AAACCCTTGCCGGTACGCTTAAACCATTGCCGGTACGCTTAA
<BLANKLINE>
Reading and Writing FASTA/QUAL Files
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In addition to reading and writing standalone FASTA files, scikit-bio also
supports reading and writing FASTA and QUAL files together. Suppose we have the
following FASTA file::
>seq1 db-accession-149855
CGATGTC
>seq2 db-accession-34989
CATCG
Also suppose we have the following QUAL file::
>seq1 db-accession-149855
40 39 39 4
50 1 100
>seq2 db-accession-34989
3 3 10 42 80
>>> fasta_fl = [
... u">seq1 db-accession-149855\\n",
... u"CGATGTC\\n",
... u">seq2 db-accession-34989\\n",
... u"CATCG\\n"]
>>> qual_fl = [
... u">seq1 db-accession-149855\\n",
... u"40 39 39 4\\n",
... u"50 1 100\\n",
... u">seq2 db-accession-34989\\n",
... u"3 3 10 42 80\\n"]
To read in a single ``Sequence`` at a time, we can use the
generator-based reader as we did above, providing both FASTA and QUAL files:
>>> for seq in skbio.io.read(fasta_fl, qual=qual_fl, format='fasta'):
... seq
... print('')
Sequence
------------------------------------------
Metadata:
u'description': u'db-accession-149855'
u'id': u'seq1'
Positional metadata:
u'quality': <dtype: uint8>
Stats:
length: 7
------------------------------------------
0 CGATGTC
<BLANKLINE>
Sequence
-----------------------------------------
Metadata:
u'description': u'db-accession-34989'
u'id': u'seq2'
Positional metadata:
u'quality': <dtype: uint8>
Stats:
length: 5
-----------------------------------------
0 CATCG
<BLANKLINE>
Note that the sequence objects have quality scores stored as positional
metadata since we provided a QUAL file. The other FASTA readers operate in a
similar manner.
Now let's load the sequences and their quality scores into a
``SequenceCollection``:
>>> sc = SequenceCollection.read(fasta_fl, qual=qual_fl)
>>> sc
<SequenceCollection: n=2; mean +/- std length=6.00 +/- 1.00>
To write the sequence data and quality scores in the ``SequenceCollection`` to
FASTA and QUAL files, respectively, we run:
>>> new_fasta_fh = StringIO()
>>> new_qual_fh = StringIO()
>>> _ = sc.write(new_fasta_fh, qual=new_qual_fh)
>>> print(new_fasta_fh.getvalue())
>seq1 db-accession-149855
CGATGTC
>seq2 db-accession-34989
CATCG
<BLANKLINE>
>>> print(new_qual_fh.getvalue())
>seq1 db-accession-149855
40 39 39 4 50 1 100
>seq2 db-accession-34989
3 3 10 42 80
<BLANKLINE>
>>> new_fasta_fh.close()
>>> new_qual_fh.close()
References
----------
.. [1] NAME NAME (1985). "Rapid and sensitive protein similarity
searches". Science 227 (4693): 1435-41.
.. [2] http://en.wikipedia.org/wiki/FASTA_format
.. [3] http://blast.ncbi.nlm.nih.gov/blastcgihelp.shtml
.. [4] https://www.broadinstitute.org/crd/wiki/index.php/Qual
.. [5] NAME The BLAST Sequence Analysis Tool. 2002 Oct 9
[Updated 2003 Aug 13]. In: NAME J, NAME J, editors. The NCBI Handbook
[Internet]. Bethesda (MD): National Center for Biotechnology Information
(US); 2002-. Chapter 16. Available from:
http://www.ncbi.nlm.nih.gov/books/NBK21097/
.. [6] http://evolution.genetics.washington.edu/phylip/doc/sequence.html
""" |
#!/usr/bin/env python
# (c) 2013, NAME <paul.durivage@gmail.com>
#
# This file is part of Ansible.
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
#
#
# Author: NAME <paul.durivage@gmail.com>
#
# Description:
# This module queries local or remote Docker daemons and generates
# inventory information.
#
# This plugin does not support targeting of specific hosts using the --host
# flag. Instead, it queries the Docker API for each container, running
# or not, and returns this data all once.
#
# The plugin returns the following custom attributes on Docker containers:
# docker_args
# docker_config
# docker_created
# docker_driver
# docker_exec_driver
# docker_host_config
# docker_hostname_path
# docker_hosts_path
# docker_id
# docker_image
# docker_name
# docker_network_settings
# docker_path
# docker_resolv_conf_path
# docker_state
# docker_volumes
# docker_volumes_rw
#
# Requirements:
# The docker-py module: https://github.com/dotcloud/docker-py
#
# Notes:
# A config file can be used to configure this inventory module, and there
# are several environment variables that can be set to modify the behavior
# of the plugin at runtime:
# DOCKER_CONFIG_FILE
# DOCKER_HOST
# DOCKER_VERSION
# DOCKER_TIMEOUT
# DOCKER_PRIVATE_SSH_PORT
# DOCKER_DEFAULT_IP
#
# Environment Variables:
# environment variable: DOCKER_CONFIG_FILE
# description:
# - A path to a Docker inventory hosts/defaults file in YAML format
# - A sample file has been provided, colocated with the inventory
# file called 'docker.yml'
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_HOST
# description:
# - The socket on which to connect to a Docker daemon API
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_VERSION
# description:
# - Version of the Docker API to use
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_TIMEOUT
# description:
# - Timeout in seconds for connections to Docker daemon API
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_PRIVATE_SSH_PORT
# description:
# - The private port (container port) on which SSH is listening
# for connections
# default: 22
# required: false
# environment variable: DOCKER_DEFAULT_IP
# description:
# - This environment variable overrides the container SSH connection
# IP address (aka, 'ansible_ssh_host')
#
# This option allows one to override the ansible_ssh_host whenever
# Docker has exercised its default behavior of binding private ports
# to all interfaces of the Docker host. This behavior, when dealing
# with remote Docker hosts, does not allow Ansible to determine
# a proper host IP address on which to connect via SSH to containers.
# By default, this inventory module assumes all IP_ADDRESS-exposed
# ports to be bound to localhost:<port>. To override this
# behavior, for example, to bind a container's SSH port to the public
# interface of its host, one must manually set this IP.
#
# It is preferable to begin to launch Docker containers with
# ports exposed on publicly accessible IP addresses, particularly
# if the containers are to be targeted by Ansible for remote
# configuration, not accessible via localhost SSH connections.
#
# Docker containers can be explicitly exposed on IP addresses by
# a) starting the daemon with the --ip argument
# b) running containers with the -P/--publish ip::containerPort
# argument
# default: IP_ADDRESS if port exposed on IP_ADDRESS by Docker
# required: false
#
# Examples:
# Use the config file:
# DOCKER_CONFIG_FILE=./docker.yml docker.py --list
#
# Connect to docker instance on localhost port 4243
# DOCKER_HOST=tcp://localhost:4243 docker.py --list
#
# Any container's ssh port exposed on IP_ADDRESS will mapped to
# another IP address (where Ansible will attempt to connect via SSH)
# DOCKER_DEFAULT_IP=1.2.3.4 docker.py --list
|
#!/usr/bin/env python
# ***** BEGIN LICENSE BLOCK *****
# Version: MPL 1.1/GPL 2.0/LGPL 2.1
#
# The contents of this file are subject to the Mozilla Public License Version
# 1.1 (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
# http://www.mozilla.org/MPL/
#
# Software distributed under the License is distributed on an "AS IS" basis,
# WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
# for the specific language governing rights and limitations under the
# License.
#
# The Original Code is font utility code.
#
# The Initial Developer of the Original Code is Mozilla Corporation.
# Portions created by the Initial Developer are Copyright (C) 2009
# the Initial Developer. All Rights Reserved.
#
# Contributor(s):
# NAME <jdaggett@mozilla.com>
#
# Alternatively, the contents of this file may be used under the terms of
# either the GNU General Public License Version 2 or later (the "GPL"), or
# the GNU Lesser General Public License Version 2.1 or later (the "LGPL"),
# in which case the provisions of the GPL or the LGPL are applicable instead
# of those above. If you wish to allow use of your version of this file only
# under the terms of either the GPL or the LGPL, and not to allow others to
# use your version of this file under the terms of the MPL, indicate your
# decision by deleting the provisions above and replace them with the notice
# and other provisions required by the GPL or the LGPL. If you do not delete
# the provisions above, a recipient may use your version of this file under
# the terms of any one of the MPL, the GPL or the LGPL.
#
# ***** END LICENSE BLOCK ***** */
# eotlitetool.py - create EOT version of OpenType font for use with IE
#
# Usage: eotlitetool.py [-o output-filename] font1 [font2 ...]
#
# OpenType file structure
# http://www.microsoft.com/typography/otspec/otff.htm
#
# Types:
#
# BYTE 8-bit unsigned integer.
# CHAR 8-bit signed integer.
# USHORT 16-bit unsigned integer.
# SHORT 16-bit signed integer.
# ULONG 32-bit unsigned integer.
# Fixed 32-bit signed fixed-point number (16.16)
# LONGDATETIME Date represented in number of seconds since 12:00 midnight, January 1, 1904. The value is represented as a signed 64-bit integer.
#
# SFNT Header
#
# Fixed sfnt version // 0x00010000 for version 1.0.
# USHORT numTables // Number of tables.
# USHORT searchRange // (Maximum power of 2 <= numTables) x 16.
# USHORT entrySelector // Log2(maximum power of 2 <= numTables).
# USHORT rangeShift // NumTables x 16-searchRange.
#
# Table Directory
#
# ULONG tag // 4-byte identifier.
# ULONG checkSum // CheckSum for this table.
# ULONG offset // Offset from beginning of TrueType font file.
# ULONG length // Length of this table.
#
# OS/2 Table (Version 4)
#
# USHORT version // 0x0004
# SHORT xAvgCharWidth
# USHORT usWeightClass
# USHORT usWidthClass
# USHORT fsType
# SHORT ySubscriptXSize
# SHORT ySubscriptYSize
# SHORT ySubscriptXOffset
# SHORT ySubscriptYOffset
# SHORT ySuperscriptXSize
# SHORT ySuperscriptYSize
# SHORT ySuperscriptXOffset
# SHORT ySuperscriptYOffset
# SHORT yStrikeoutSize
# SHORT yStrikeoutPosition
# SHORT sFamilyClass
# BYTE panose[10]
# ULONG ulUnicodeRange1 // Bits 0-31
# ULONG ulUnicodeRange2 // Bits 32-63
# ULONG ulUnicodeRange3 // Bits 64-95
# ULONG ulUnicodeRange4 // Bits 96-127
# CHAR achVendID[4]
# USHORT fsSelection
# USHORT usFirstCharIndex
# USHORT usLastCharIndex
# SHORT sTypoAscender
# SHORT sTypoDescender
# SHORT sTypoLineGap
# USHORT usWinAscent
# USHORT usWinDescent
# ULONG ulCodePageRange1 // Bits 0-31
# ULONG ulCodePageRange2 // Bits 32-63
# SHORT sxHeight
# SHORT sCapHeight
# USHORT usDefaultChar
# USHORT usBreakChar
# USHORT usMaxContext
#
#
# The Naming Table is organized as follows:
#
# [name table header]
# [name records]
# [string data]
#
# Name Table Header
#
# USHORT format // Format selector (=0).
# USHORT count // Number of name records.
# USHORT stringOffset // Offset to start of string storage (from start of table).
#
# Name Record
#
# USHORT platformID // Platform ID.
# USHORT encodingID // Platform-specific encoding ID.
# USHORT languageID // Language ID.
# USHORT nameID // Name ID.
# USHORT length // String length (in bytes).
# USHORT offset // String offset from start of storage area (in bytes).
#
# head Table
#
# Fixed tableVersion // Table version number 0x00010000 for version 1.0.
# Fixed fontRevision // Set by font manufacturer.
# ULONG checkSumAdjustment // To compute: set it to 0, sum the entire font as ULONG, then store 0xB1B0AFBA - sum.
# ULONG magicNumber // Set to 0x5F0F3CF5.
# USHORT flags
# USHORT unitsPerEm // Valid range is from 16 to 16384. This value should be a power of 2 for fonts that have TrueType outlines.
# LONGDATETIME created // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# LONGDATETIME modified // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# SHORT xMin // For all glyph bounding boxes.
# SHORT yMin
# SHORT xMax
# SHORT yMax
# USHORT macStyle
# USHORT lowestRecPPEM // Smallest readable size in pixels.
# SHORT fontDirectionHint
# SHORT indexToLocFormat // 0 for short offsets, 1 for long.
# SHORT glyphDataFormat // 0 for current format.
#
#
#
# Embedded OpenType (EOT) file format
# http://www.w3.org/Submission/EOT/
#
# EOT version 0x00020001
#
# An EOT font consists of a header with the original OpenType font
# appended at the end. Most of the data in the EOT header is simply a
# copy of data from specific tables within the font data. The exceptions
# are the 'Flags' field and the root string name field. The root string
# is a set of names indicating domains for which the font data can be
# used. A null root string implies the font data can be used anywhere.
# The EOT header is in little-endian byte order but the font data remains
# in big-endian order as specified by the OpenType spec.
#
# Overall structure:
#
# [EOT header]
# [EOT name records]
# [font data]
#
# EOT header
#
# ULONG eotSize // Total structure length in bytes (including string and font data)
# ULONG fontDataSize // Length of the OpenType font (FontData) in bytes
# ULONG version // Version number of this format - 0x00020001
# ULONG flags // Processing Flags (0 == no special processing)
# BYTE fontPANOSE[10] // OS/2 Table panose
# BYTE charset // DEFAULT_CHARSET (0x01)
# BYTE italic // 0x01 if ITALIC in OS/2 Table fsSelection is set, 0 otherwise
# ULONG weight // OS/2 Table usWeightClass
# USHORT fsType // OS/2 Table fsType (specifies embedding permission flags)
# USHORT magicNumber // Magic number for EOT file - 0x504C.
# ULONG unicodeRange1 // OS/2 Table ulUnicodeRange1
# ULONG unicodeRange2 // OS/2 Table ulUnicodeRange2
# ULONG unicodeRange3 // OS/2 Table ulUnicodeRange3
# ULONG unicodeRange4 // OS/2 Table ulUnicodeRange4
# ULONG codePageRange1 // OS/2 Table ulCodePageRange1
# ULONG codePageRange2 // OS/2 Table ulCodePageRange2
# ULONG checkSumAdjustment // head Table CheckSumAdjustment
# ULONG reserved[4] // Reserved - must be 0
# USHORT padding1 // Padding - must be 0
#
# EOT name records
#
# USHORT FamilyNameSize // Font family name size in bytes
# BYTE FamilyName[FamilyNameSize] // Font family name (name ID = 1), little-endian UTF-16
# USHORT Padding2 // Padding - must be 0
#
# USHORT StyleNameSize // Style name size in bytes
# BYTE StyleName[StyleNameSize] // Style name (name ID = 2), little-endian UTF-16
# USHORT Padding3 // Padding - must be 0
#
# USHORT VersionNameSize // Version name size in bytes
# bytes VersionName[VersionNameSize] // Version name (name ID = 5), little-endian UTF-16
# USHORT Padding4 // Padding - must be 0
#
# USHORT FullNameSize // Full name size in bytes
# BYTE FullName[FullNameSize] // Full name (name ID = 4), little-endian UTF-16
# USHORT Padding5 // Padding - must be 0
#
# USHORT RootStringSize // Root string size in bytes
# BYTE RootString[RootStringSize] // Root string, little-endian UTF-16
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