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"""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 ForkingServer and ThreadingServer 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.
""" |
# (c) 2014 NAME <info@webtrein.nl>
# https://github.com/timraasveld/ansible-string-split-filter/
# (c) 2014 NAME <drybjed@gmail.com>
# http://debops.org/
# License: CC0 1.0 Universal
#
# Statement of Purpose
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|
"""
============
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``.
""" |
"""
Signal Processing Tools
=======================
Convolution:
convolve:
N-dimensional convolution.
correlate:
N-dimensional correlation.
fftconvolve:
N-dimensional convolution using the FFT.
convolve2d:
2-dimensional convolution (more options).
correlate2d:
2-dimensional correlation (more options).
sepfir2d:
Convolve with a 2-D separable FIR filter.
B-splines:
bspline:
B-spline basis function of order n.
gauss_spline:
Gaussian approximation to the B-spline basis function.
cspline1d:
Coefficients for 1-D cubic (3rd order) B-spline.
qspline1d:
Coefficients for 1-D quadratic (2nd order) B-spline.
cspline2d:
Coefficients for 2-D cubic (3rd order) B-spline.
qspline2d:
Coefficients for 2-D quadratic (2nd order) B-spline.
spline_filter:
Smoothing spline (cubic) filtering of a rank-2 array.
Filtering:
order_filter:
N-dimensional order filter.
medfilt:
N-dimensional median filter.
medfilt2:
2-dimensional median filter (faster).
wiener:
N-dimensional wiener filter.
symiirorder1:
2nd-order IIR filter (cascade of first-order systems).
symiirorder2:
4th-order IIR filter (cascade of second-order systems).
lfilter:
1-dimensional FIR and IIR digital linear filtering.
lfiltic:
Construct initial conditions for `lfilter`.
deconvolve:
1-d deconvolution using lfilter.
hilbert:
Compute the analytic signal of a 1-d signal.
get_window:
Create FIR window.
decimate:
Downsample a signal.
detrend:
Remove linear and/or constant trends from data.
resample:
Resample using Fourier method.
Filter design:
bilinear:
Return a digital filter from an analog filter using the bilinear transform.
firwin:
Windowed FIR filter design, with frequency response defined as pass and stop bands.
firwin2:
Windowed FIR filter design, with arbitrary frequency response.
freqs:
Analog filter frequency response.
freqz:
Digital filter frequency response.
iirdesign:
IIR filter design given bands and gains.
iirfilter:
IIR filter design given order and critical frequencies.
invres:
Inverse partial fraction expansion.
kaiser_beta:
Compute the Kaiser parameter beta, given the desired FIR filter attenuation.
kaiser_atten:
Compute the attenuation of a Kaiser FIR filter, given the number of taps
and the transition width at discontinuities in the frequency response.
kaiserord:
Design a Kaiser window to limit ripple and width of transition region.
remez:
Optimal FIR filter design.
residue:
Partial fraction expansion of b(s) / a(s).
residuez:
Partial fraction expansion of b(z) / a(z).
unique_roots:
Unique roots and their multiplicities.
Matlab-style IIR filter design:
butter (buttord):
Butterworth
cheby1 (cheb1ord):
Chebyshev Type I
cheby2 (cheb2ord):
Chebyshev Type II
ellip (ellipord):
Elliptic (Cauer)
bessel:
Bessel (no order selection available -- try butterod)
Linear Systems:
lti:
linear time invariant system object.
lsim:
continuous-time simulation of output to linear system.
lsim2:
like lsim, but `scipy.integrate.odeint` is used.
impulse:
impulse response of linear, time-invariant (LTI) system.
impulse2:
like impulse, but `scipy.integrate.odeint` is used.
step:
step response of continous-time LTI system.
step2:
like step, but `scipy.integrate.odeint` is used.
LTI Representations:
tf2zpk:
transfer function to zero-pole-gain.
zpk2tf:
zero-pole-gain to transfer function.
tf2ss:
transfer function to state-space.
ss2tf:
state-pace to transfer function.
zpk2ss:
zero-pole-gain to state-space.
ss2zpk:
state-space to pole-zero-gain.
Waveforms:
sawtooth:
Periodic sawtooth
square:
Square wave
gausspulse:
Gaussian modulated sinusoid
chirp:
Frequency swept cosine signal, with several frequency functions.
sweep_poly:
Frequency swept cosine signal; frequency is arbitrary polynomial.
Window functions:
get_window:
Return a window of a given length and type.
barthann:
Bartlett-Hann window
bartlett:
Bartlett window
blackman:
Blackman window
blackmanharris:
Minimum 4-term Blackman-Harris window
bohman:
Bohman window
boxcar:
Boxcar window
chebwin:
Dolph-Chebyshev window
flattop:
Flat top window
gaussian:
Gaussian window
general_gaussian:
Generalized Gaussian window
hamming:
Hamming window
hann:
Hann window
kaiser:
Kaiser window
nuttall:
Nuttall's minimum 4-term Blackman-Harris window
parzen:
Parzen window
slepian:
Slepian window
triang:
Triangular window
Wavelets:
daub:
return low-pass
qmf:
return quadrature mirror filter from low-pass
cascade:
compute scaling function and wavelet from coefficients
morlet:
Complex Morlet wavelet.
""" |
"""
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)
""" |
# Test 64-bit COMPARE AND BRANCH in cases where the sheer number of
# instructions causes some branches to be out of range.
# RUN: python %s | llc -mtriple=s390x-linux-gnu | FileCheck %s
# Construct:
#
# before0:
# conditional branch to after0
# ...
# beforeN:
# conditional branch to after0
# main:
# 0xffcc bytes, from MVIY instructions
# conditional branch to main
# after0:
# ...
# conditional branch to main
# afterN:
#
# Each conditional branch sequence occupies 12 bytes if it uses a short
# branch and 16 if it uses a long one. The ones before "main:" have to
# take the branch length into account, which is 6 for short branches,
# so the final (0x34 - 6) / 12 == 3 blocks can use short branches.
# The ones after "main:" do not, so the first 0x34 / 12 == 4 blocks
# can use short branches. The conservative algorithm we use makes
# one of the forward branches unnecessarily long, as noted in the
# check output below.
#
# CHECK: lgb [[REG:%r[0-5]]], 0(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL:\.L[^ ]*]]
# CHECK: lgb [[REG:%r[0-5]]], 1(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 2(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 3(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 4(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# ...as mentioned above, the next one could be a CGRJE instead...
# CHECK: lgb [[REG:%r[0-5]]], 5(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 6(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 7(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# ...main goes here...
# CHECK: lgb [[REG:%r[0-5]]], 25(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL:\.L[^ ]*]]
# CHECK: lgb [[REG:%r[0-5]]], 26(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 27(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 28(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 29(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 30(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 31(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 32(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
|
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
# adapted from http://www.cl.cam.ac.uk/~mgk25/ucs/wcwidth.c
# -thepaul
# This is an implementation of wcwidth() and wcswidth() (defined in
# IEEE Std 1002.1-2001) for Unicode.
#
# http://www.opengroup.org/onlinepubs/007904975/functions/wcwidth.html
# http://www.opengroup.org/onlinepubs/007904975/functions/wcswidth.html
#
# In fixed-width output devices, Latin characters all occupy a single
# "cell" position of equal width, whereas ideographic CJK characters
# occupy two such cells. Interoperability between terminal-line
# applications and (teletype-style) character terminals using the
# UTF-8 encoding requires agreement on which character should advance
# the cursor by how many cell positions. No established formal
# standards exist at present on which Unicode character shall occupy
# how many cell positions on character terminals. These routines are
# a first attempt of defining such behavior based on simple rules
# applied to data provided by the Unicode Consortium.
#
# For some graphical characters, the Unicode standard explicitly
# defines a character-cell width via the definition of the East Asian
# FullWidth (F), Wide (W), Half-width (H), and Narrow (Na) classes.
# In all these cases, there is no ambiguity about which width a
# terminal shall use. For characters in the East Asian Ambiguous (A)
# class, the width choice depends purely on a preference of backward
# compatibility with either historic CJK or Western practice.
# Choosing single-width for these characters is easy to justify as
# the appropriate long-term solution, as the CJK practice of
# displaying these characters as double-width comes from historic
# implementation simplicity (8-bit encoded characters were displayed
# single-width and 16-bit ones double-width, even for Greek,
# Cyrillic, etc.) and not any typographic considerations.
#
# Much less clear is the choice of width for the Not East Asian
# (Neutral) class. Existing practice does not dictate a width for any
# of these characters. It would nevertheless make sense
# typographically to allocate two character cells to characters such
# as for instance EM SPACE or VOLUME INTEGRAL, which cannot be
# represented adequately with a single-width glyph. The following
# routines at present merely assign a single-cell width to all
# neutral characters, in the interest of simplicity. This is not
# entirely satisfactory and should be reconsidered before
# establishing a formal standard in this area. At the moment, the
# decision which Not East Asian (Neutral) characters should be
# represented by double-width glyphs cannot yet be answered by
# applying a simple rule from the Unicode database content. Setting
# up a proper standard for the behavior of UTF-8 character terminals
# will require a careful analysis not only of each Unicode character,
# but also of each presentation form, something the author of these
# routines has avoided to do so far.
#
# http://www.unicode.org/unicode/reports/tr11/
#
# NAME -- 2007-05-26 (Unicode 5.0)
#
# Permission to use, copy, modify, and distribute this software
# for any purpose and without fee is hereby granted. The author
# disclaims all warranties with regard to this software.
#
# Latest C version: http://www.cl.cam.ac.uk/~mgk25/ucs/wcwidth.c
# auxiliary function for binary search in interval table
|
# Test 64-bit COMPARE LOGICAL AND BRANCH in cases where the sheer number of
# instructions causes some branches to be out of range.
# RUN: python %s | llc -mtriple=s390x-linux-gnu | FileCheck %s
# Construct:
#
# before0:
# conditional branch to after0
# ...
# beforeN:
# conditional branch to after0
# main:
# 0xffcc bytes, from MVIY instructions
# conditional branch to main
# after0:
# ...
# conditional branch to main
# afterN:
#
# Each conditional branch sequence occupies 12 bytes if it uses a short
# branch and 16 if it uses a long one. The ones before "main:" have to
# take the branch length into account, which is 6 for short branches,
# so the final (0x34 - 6) / 12 == 3 blocks can use short branches.
# The ones after "main:" do not, so the first 0x34 / 12 == 4 blocks
# can use short branches. The conservative algorithm we use makes
# one of the forward branches unnecessarily long, as noted in the
# check output below.
#
# CHECK: lgb [[REG:%r[0-5]]], 0(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL:\.L[^ ]*]]
# CHECK: lgb [[REG:%r[0-5]]], 1(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 2(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 3(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 4(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# ...as mentioned above, the next one could be a CLGRJL instead...
# CHECK: lgb [[REG:%r[0-5]]], 5(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 6(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 7(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL]]
# ...main goes here...
# CHECK: lgb [[REG:%r[0-5]]], 25(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL:\.L[^ ]*]]
# CHECK: lgb [[REG:%r[0-5]]], 26(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 27(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 28(%r3)
# CHECK: clgrjl %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 29(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 30(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 31(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 32(%r3)
# CHECK: clgr %r4, [[REG]]
# CHECK: jgl [[LABEL]]
|
"""
# 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!
""" |
"""
Artifactor
Artifactor is used to collect artifacts from a number of different plugins and put them into
one place. Artifactor works around a series of events and is geared towards unit testing, though
it is extensible and customizable enough that it can be used for a variety of purposes.
The main guts of Artifactor is around the plugins. Before Artifactor can do anything it must have
a configured plugin. This plugin is then configured to bind certain functions inside itself
to certain events. When Artifactor is triggered to handle a certain event, it will tell the plugin
that that particular event has happened and the plugin will respond accordingly.
In addition to the plugins, Artifactor can also run certain callback functions before and after
the hook function itself. These are call pre and post hook callbacks. Artifactor allows multiple
pre and post hook callbacks to be defined per event, but does not guarantee the order that they
are executed in.
To allow data to be passed to and from hooks, Artifactor has the idea of global and event local
values. The global values persist in the Artifactor instance for its lifetime, but the event local
values are destroyed at the end of each event.
Let's take the example of using the unit testing suite py.test as an example for Artifactor.
Suppose we have a number of tests that run as part of a test suite and we wish to store a text
file that holds the time the test was run and its result. This information is required to reside
in a folder that is relevant to the test itself. This type of job is what Artifactor was designed
for.
To begin with, we need to create a plugin for Artifactor. Consider the following piece of code::
from artifactor import ArtifactorBasePlugin
import time
class Test(ArtifactorBasePlugin):
def plugin_initialize(self):
self.register_plugin_hook('start_test', self.start_test)
self.register_plugin_hook('finish_test', self.finish_test)
def start_test(self, test_name, test_location, artifact_path):
filename = artifact_path + "-" + self.ident + ".log"
with open(filename, "w") as f:
f.write(test_name + "\n")
f.write(str(time.time()) + "\n")
def finish_test(self, test_name, artifact_path, test_result):
filename = artifact_path + "-" + self.ident + ".log"
with open(filename, "w+") as f:
f.write(test_result)
This is a typical plugin in Artifactor, it consists of 2 things. The first item is
the special function called ``plugin_initialize()``. This is important
and is equivilent to the ``__init__()`` that would usually be found in a class definition.
Artifactor calls ``plugin_initialize()`` for each plugin as it loads it.
Inside this section we register the hook functions to their associated events. Each event
can only have a single function associated with it. Event names are able to be freely assigned
so you can customize plugins to work to specific events for your use case.
The ``register_plugin_hook()`` takes an event name as a string and a function to callback when
that event is experienced.
Next we have the hook functions themselves, ``start_test()`` and ``finish_test()``. These
have arguments in their prototypes and these arguments are supplied by Artifactor and are
created either as arguments to the ``fire_hook()`` function, which is responsible for actually
telling Artifactor that an even has occured, or they are created in the pre hook script.
Artifactor uses the global and local values referenced earlier to store these argument values.
When a pre, post or hook callback finishes, it has the opportunity to supply updates to both
the global and local values dictionaries. In doing this, a pre-hook script can prepare data,
which will could be stored in the locals dictionary and then passed to the actual plugin hook
as a keyword argument. local values override global values.
We need to look at an example of this, but first we must configure artifactor and the plugin::
log_dir: /home/me/artiout
per_run: run #test, run, None
overwrite: True
artifacts:
test:
enabled: True
plugin: test
Here we have defined a ``log_dir`` which will be the root of all of our artifacts. We have asked
Artifactor to group the artifacts by run, which means that it will try to create a directory
under the ``log_dir`` which indicates which test "run" this was. We can also specify a value of
"test" here, which will move the test run identifying folder up to the leaf in the tree.
The ``log_dir`` and contents of the config are stored in global values as ``log_dir`` and
``artifactor_config`` respectively. These are the only two global values which are setup by
Artifactor.
This data is then passed to artifactor as a dict, we will assume a variable name of ``config`` here.
Let's consider how we would run this test
art = artifactor.artifactor
art.set_config(config)
art.register_plugin(test.Test, "test")
artifactor.initialize()
a.fire_hook('start_session', run_id=2235)
a.fire_hook('start_test', test_name="my_test", test_location="tests/mytest.py")
a.fire_hook('finish_test', test_name="my_test", test_location="tests/mytest.py",
test_result="FAILED")
a.fire_hook('finish_session')
The art.register_plugin is used to bind a plugin name to a class definition. Notice in the config
section earlier, we have a ``plugin: test`` field. This name ``test`` is what Artifactor will
look for when trying to find the appropriate plugin. When we register the plugin with the
``register_plugin`` function, we take the ``test.Test`` class and essentially give it the name
``test`` so that the names will tie up and the plugin will be used.
Notice that we have sent some information to along with the request to fire the hook. Ignoring the
``start_session`` event for a minute, the ``start_test`` event sends a ``test_name`` and a
``test_location``. However, the ``start_test`` hook also required an argument called
``argument_path``. This is not supplied by the hook, and isn't setup as a global value, so how does
it get there?
Inside Artifactor, by default, a pre_hook callback called ``start_test()`` is bound to the
``start_test`` event. This callback returns a local values update which includes ``artifact_path``.
This is how the artifact_path is returned. This hook can be removed, by running a
``unregister_hook_callback`` with the name of the hook callback.
""" |
# #!/usr/bin/env python3
# # -*- coding: utf-8 -*-
#
# __author__ = 'zhangxianqiang'
#
# import asyncio, logging
#
# import aiomysql
#
# def log(sql, args=()):
# logging.info('SQL: %s' % sql)
#
# @asyncio.coroutine
# def creat_pool(loop, **kw):
# logging.info('creat database connection pool...')
# global __pool
# __pool = yield from aiomysql.create_pool(
# host=kw.get('host', 'localhost'),
# port=kw.get('port', 3306),
# user=kw['user'],
# db=kw['db'],
# charset=kw.get('charset', 'utf8'),
# autocommit=kw.get('autocommit', True),
# maxsize=kw.get('maxsize', 10),
# minsize=kw.get('minsize', 1),
# loop=loop
# )
#
# # Select
# @asyncio.coroutine
# def select(sql, args, size=None):
# log(sql, args)
# global __pool
# with (yield from __pool) as conn: # with 可以检测异常 语法更加好看一点.
# cur = yield from conn.cursor(aiomysql.DictCursor)
# yield from cur.execute(sql.replace('?', '%s'), args or ())
# if size:
# rs = yield from cur.fetchmany(size)
# else:
# rs = yield from cur.fetchall()
# yield from cur.close()
# logging.info('rows returned: %s' % len(rs))
# return rs
#
#
# # Insert, Update, Delete
# @asyncio.coroutine
# def execute(sql, args):
# log(sql)
# with (yield from __pool) as conn:
# try:
# cur = yield from conn.cursor()
# yield from cur.execute(sql.replace('?', '%s'), args)
# affected = cur.rowcount
# yield from cur.close()
# except BaseException as e:
# raise
# return affected
#
#
# class Model(dict, metaclass=ModelMetaclass):
# def __init__(self, **kw):
# super(Model, self).__init__(**kw)
#
# def __getattr__(self, key):
# try:
# return self[key]
# except KeyError:
# raise AttributeError(r"'Model' object has no attribute '%s'" % key)
#
# def __setattr__(self, key, value):
# self[key] = value
#
# def getValue(self, key):
# return getattr(self, key, None)
#
# def getValueOrDefault(self, key):
# value = getattr(self, key, None)
# if value is None:
# field = self.__mappings__[key]
# if field.default is not None:
# value = field.default() if callable(field.default) else field.default
# logging.debug('using default value for %s: %s' % (key, str(value)))
# setattr(self, key, value)
# return value
#
#
# class Field(object):
#
# def __init__(self, name, column_type, primary_key, default):
# self.name = name
# self.column_type = column_type
# self.primary_key = primary_key
# self.default = default
#
# def __str__(self):
# return '<%s, %s:%s>' % (self.__class__.__name__, self.column_type, self.name)
#
#
# class StringField(Field):
#
# def __init__(self, name=None, primary_key=False, default=None, ddl='varchar(100)'):
# super().__init__(name, ddl, primary_key, default)
#
#
# class ModelMetaclass(type):
#
# def __new__(cls, name, bases, attrs):
# # 排除Model类本身:
# if name=='Model':
# return type.__new__(cls, name, bases, attrs)
# # 获取table名称:
# tableName = attrs.get('__table__', None) or name
# logging.info('found model: %s (table: %s)' % (name, tableName))
# # 获取所有的Field和主键名:
# mappings = dict()
# fields = []
# primaryKey = None
# for k, v in attrs.items():
# if isinstance(v, Field):
# logging.info(' found mapping: %s ==> %s' % (k, v))
# mappings[k] = v
# if v.primary_key:
# # 找到主键:
# if primaryKey:
# raise RuntimeError('Duplicate primary key for field: %s' % k)
# primaryKey = k
# else:
# fields.append(k)
# if not primaryKey:
# raise RuntimeError('Primary key not found.')
# for k in mappings.keys():
# attrs.pop(k)
# escaped_fields = list(map(lambda f: '`%s`' % f, fields))
# attrs['__mappings__'] = mappings # 保存属性和列的映射关系
# attrs['__table__'] = tableName
# attrs['__primary_key__'] = primaryKey # 主键属性名
# attrs['__fields__'] = fields # 除主键外的属性名
# # 构造默认的SELECT, INSERT, UPDATE和DELETE语句:
# attrs['__select__'] = 'select `%s`, %s from `%s`' % (primaryKey, ', '.join(escaped_fields), tableName)
# attrs['__insert__'] = 'insert into `%s` (%s, `%s`) values (%s)' % (tableName, ', '.join(escaped_fields), primaryKey, create_args_string(len(escaped_fields) + 1))
# attrs['__update__'] = 'update `%s` set %s where `%s`=?' % (tableName, ', '.join(map(lambda f: '`%s`=?' % (mappings.get(f).name or f), fields)), primaryKey)
# attrs['__delete__'] = 'delete from `%s` where `%s`=?' % (tableName, primaryKey)
# return type.__new__(cls, name, bases, attrs)
#
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
|
"""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.
""" |
# -*- encoding: utf-8 -*-
# back ported from CPython 3
# A. HISTORY OF THE SOFTWARE
# ==========================
#
# Python was created in the early 1990s by NAME at Stichting
# Mathematisch Centrum (CWI, see http://www.cwi.nl) in the Netherlands
# as a successor of a language called ABC. NAME remains Python's
# principal author, although it includes many contributions from others.
#
# In 1995, NAME continued his work on Python at the Corporation for
# National Research Initiatives (CNRI, see http://www.cnri.reston.va.us)
# in Reston, Virginia where he released several versions of the
# software.
#
# In May 2000, NAME and the Python core development team moved to
# BeOpen.com to form the BeOpen PythonLabs team. In October of the same
# year, the PythonLabs team moved to Digital Creations (now Zope
# Corporation, see http://www.zope.com). In 2001, the Python Software
# Foundation (PSF, see http://www.python.org/psf/) was formed, a
# non-profit organization created specifically to own Python-related
# Intellectual Property. Zope Corporation is a sponsoring member of
# the PSF.
#
# All Python releases are Open Source (see http://www.opensource.org for
# the Open Source Definition). Historically, most, but not all, Python
# releases have also been GPL-compatible; the table below summarizes
# the various releases.
#
# Release Derived Year Owner GPL-
# from compatible? (1)
#
# 0.9.0 thru 1.2 1991-1995 CWI yes
# 1.3 thru 1.5.2 1.2 1995-1999 CNRI yes
# 1.6 1.5.2 2000 CNRI no
# 2.0 1.6 2000 BeOpen.com no
# 1.6.1 1.6 2001 CNRI yes (2)
# 2.1 2.0+1.6.1 2001 PSF no
# 2.0.1 2.0+1.6.1 2001 PSF yes
# 2.1.1 2.1+2.0.1 2001 PSF yes
# 2.2 2.1.1 2001 PSF yes
# 2.1.2 2.1.1 2002 PSF yes
# 2.1.3 2.1.2 2002 PSF yes
# 2.2.1 2.2 2002 PSF yes
# 2.2.2 2.2.1 2002 PSF yes
# 2.2.3 2.2.2 2003 PSF yes
# 2.3 2.2.2 2002-2003 PSF yes
# 2.3.1 2.3 2002-2003 PSF yes
# 2.3.2 2.3.1 2002-2003 PSF yes
# 2.3.3 2.3.2 2002-2003 PSF yes
# 2.3.4 2.3.3 2004 PSF yes
# 2.3.5 2.3.4 2005 PSF yes
# 2.4 2.3 2004 PSF yes
# 2.4.1 2.4 2005 PSF yes
# 2.4.2 2.4.1 2005 PSF yes
# 2.4.3 2.4.2 2006 PSF yes
# 2.4.4 2.4.3 2006 PSF yes
# 2.5 2.4 2006 PSF yes
# 2.5.1 2.5 2007 PSF yes
# 2.5.2 2.5.1 2008 PSF yes
# 2.5.3 2.5.2 2008 PSF yes
# 2.6 2.5 2008 PSF yes
# 2.6.1 2.6 2008 PSF yes
# 2.6.2 2.6.1 2009 PSF yes
# 2.6.3 2.6.2 2009 PSF yes
# 2.6.4 2.6.3 2009 PSF yes
# 2.6.5 2.6.4 2010 PSF yes
# 2.7 2.6 2010 PSF yes
#
# Footnotes:
#
# (1) GPL-compatible doesn't mean that we're distributing Python under
# the GPL. All Python licenses, unlike the GPL, let you distribute
# a modified version without making your changes open source. The
# GPL-compatible licenses make it possible to combine Python with
# other software that is released under the GPL; the others don't.
#
# (2) According to NAME 1.6.1 is not GPL-compatible,
# because its license has a choice of law clause. According to
# CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1
# is "not incompatible" with the GPL.
#
# Thanks to the many outside volunteers who have worked under NAME's
# direction to make these releases possible.
#
#
# B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
# ===============================================================
#
# PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
# --------------------------------------------
#
# 1. This LICENSE AGREEMENT is between the Python Software Foundation
# ("PSF"), and the Individual or Organization ("Licensee") accessing and
# otherwise using this software ("Python") in source or binary form and
# its associated documentation.
#
# 2. Subject to the terms and conditions of this License Agreement, PSF hereby
# grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
# analyze, test, perform and/or display publicly, prepare derivative works,
# distribute, and otherwise use Python alone or in any derivative version,
# provided, however, that PSF's License Agreement and PSF's notice of copyright,
# i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
# 2011, 2012, 2013 Python Software Foundation; All Rights Reserved" are retained
# in Python alone or in any derivative version prepared by Licensee.
#
# 3. In the event Licensee prepares a derivative work that is based on
# or incorporates Python or any part thereof, and wants to make
# the derivative work available to others as provided herein, then
# Licensee hereby agrees to include in any such work a brief summary of
# the changes made to Python.
#
# 4. PSF is making Python available to Licensee on an "AS IS"
# basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
# IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
# INFRINGE ANY THIRD PARTY RIGHTS.
#
# 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
# FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
# A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
# OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
#
# 6. This License Agreement will automatically terminate upon a material
# breach of its terms and conditions.
#
# 7. Nothing in this License Agreement shall be deemed to create any
# relationship of agency, partnership, or joint venture between PSF and
# Licensee. This License Agreement does not grant permission to use PSF
# trademarks or trade name in a trademark sense to endorse or promote
# products or services of Licensee, or any third party.
#
# 8. By copying, installing or otherwise using Python, Licensee
# agrees to be bound by the terms and conditions of this License
# Agreement.
#
#
# BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0
# -------------------------------------------
#
# BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1
#
# 1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an
# office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the
# Individual or Organization ("Licensee") accessing and otherwise using
# this software in source or binary form and its associated
# documentation ("the Software").
#
# 2. Subject to the terms and conditions of this BeOpen Python License
# Agreement, BeOpen hereby grants Licensee a non-exclusive,
# royalty-free, world-wide license to reproduce, analyze, test, perform
# and/or display publicly, prepare derivative works, distribute, and
# otherwise use the Software alone or in any derivative version,
# provided, however, that the BeOpen Python License is retained in the
# Software, alone or in any derivative version prepared by Licensee.
#
# 3. BeOpen is making the Software available to Licensee on an "AS IS"
# basis. BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
# IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND
# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT
# INFRINGE ANY THIRD PARTY RIGHTS.
#
# 4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE
# SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS
# AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY
# DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
#
# 5. This License Agreement will automatically terminate upon a material
# breach of its terms and conditions.
#
# 6. This License Agreement shall be governed by and interpreted in all
# respects by the law of the State of California, excluding conflict of
# law provisions. Nothing in this License Agreement shall be deemed to
# create any relationship of agency, partnership, or joint venture
# between BeOpen and Licensee. This License Agreement does not grant
# permission to use BeOpen trademarks or trade names in a trademark
# sense to endorse or promote products or services of Licensee, or any
# third party. As an exception, the "BeOpen Python" logos available at
# http://www.pythonlabs.com/logos.html may be used according to the
# permissions granted on that web page.
#
# 7. By copying, installing or otherwise using the software, Licensee
# agrees to be bound by the terms and conditions of this License
# Agreement.
#
#
# CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1
# ---------------------------------------
#
# 1. This LICENSE AGREEMENT is between the Corporation for National
# Research Initiatives, having an office at 1895 Preston White Drive,
# Reston, VA 20191 ("CNRI"), and the Individual or Organization
# ("Licensee") accessing and otherwise using Python 1.6.1 software in
# source or binary form and its associated documentation.
#
# 2. Subject to the terms and conditions of this License Agreement, CNRI
# hereby grants Licensee a nonexclusive, royalty-free, world-wide
# license to reproduce, analyze, test, perform and/or display publicly,
# prepare derivative works, distribute, and otherwise use Python 1.6.1
# alone or in any derivative version, provided, however, that CNRI's
# License Agreement and CNRI's notice of copyright, i.e., "Copyright (c)
# 1995-2001 Corporation for National Research Initiatives; All Rights
# Reserved" are retained in Python 1.6.1 alone or in any derivative
# version prepared by Licensee. Alternately, in lieu of CNRI's License
# Agreement, Licensee may substitute the following text (omitting the
# quotes): "Python 1.6.1 is made available subject to the terms and
# conditions in CNRI's License Agreement. This Agreement together with
# Python 1.6.1 may be located on the Internet using the following
# unique, persistent identifier (known as a handle): 1895.22/1013. This
# Agreement may also be obtained from a proxy server on the Internet
# using the following URL: http://hdl.handle.net/1895.22/1013".
#
# 3. In the event Licensee prepares a derivative work that is based on
# or incorporates Python 1.6.1 or any part thereof, and wants to make
# the derivative work available to others as provided herein, then
# Licensee hereby agrees to include in any such work a brief summary of
# the changes made to Python 1.6.1.
#
# 4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS"
# basis. CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
# IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND
# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT
# INFRINGE ANY THIRD PARTY RIGHTS.
#
# 5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
# 1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
# A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1,
# OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
#
# 6. This License Agreement will automatically terminate upon a material
# breach of its terms and conditions.
#
# 7. This License Agreement shall be governed by the federal
# intellectual property law of the United States, including without
# limitation the federal copyright law, and, to the extent such
# U.S. federal law does not apply, by the law of the Commonwealth of
# Virginia, excluding Virginia's conflict of law provisions.
# Notwithstanding the foregoing, with regard to derivative works based
# on Python 1.6.1 that incorporate non-separable material that was
# previously distributed under the GNU General Public License (GPL), the
# law of the Commonwealth of Virginia shall govern this License
# Agreement only as to issues arising under or with respect to
# Paragraphs 4, 5, and 7 of this License Agreement. Nothing in this
# License Agreement shall be deemed to create any relationship of
# agency, partnership, or joint venture between CNRI and Licensee. This
# License Agreement does not grant permission to use CNRI trademarks or
# trade name in a trademark sense to endorse or promote products or
# services of Licensee, or any third party.
#
# 8. By clicking on the "ACCEPT" button where indicated, or by copying,
# installing or otherwise using Python 1.6.1, Licensee agrees to be
# bound by the terms and conditions of this License Agreement.
#
# ACCEPT
#
#
# CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2
# --------------------------------------------------
#
# Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam,
# The Netherlands. All rights reserved.
#
# Permission to use, copy, modify, and distribute this software and its
# documentation for any purpose and without fee is hereby granted,
# provided that the above copyright notice appear in all copies and that
# both that copyright notice and this permission notice appear in
# supporting documentation, and that the name of Stichting Mathematisch
# Centrum or CWI not be used in advertising or publicity pertaining to
# distribution of the software without specific, written prior
# permission.
#
# STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
# THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
# FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM 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.
|
"""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.
""" |
"""
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 to create a solution in their 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``.
Format Version 2.0
------------------
The version 1.0 format only allowed the array header to have a total size of
65535 bytes. This can be exceeded by structured arrays with a large number of
columns. The version 2.0 format extends the header size to 4 GiB.
`numpy.save` will automatically save in 2.0 format if the data requires it,
else it will always use the more compatible 1.0 format.
The description of the fourth element of the header therefore has become:
"The next 4 bytes form a little-endian unsigned int: the length of the header
data HEADER_LEN."
Notes
-----
The ``.npy`` format, including reasons for creating it and a comparison of
alternatives, is described fully in the "npy-format" NEP.
""" |
# GUI Application automation and testing library
# Copyright (C) 2006 NAME 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; either version 2.1
# of the License, or (at your option) any later version.
#
# This library 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 Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the
# Free Software Foundation, Inc.,
# 59 Temple Place,
# Suite 330,
# Boston, MA 02111-1307 USA
# #----------------------------------------------------------------
# def GetContextMenu(self):
# rect = self.Rectangle
#
# # set the position of the context menu to be 2 pixels in from
# # the control edge
# pos = c_long ((rect.top+ 2 << 16) | (rect.left + 2))
#
# # get the top window before trying to bring up a context menu
# oldTopWin = FindWindow(0, 0)
#
# # send the message but time-out after 10 mili seconds
# res = DWORD()
# SendMessageTimeout (
# self.handle,
# WM_CONTEXTMENU,
# self.handle,
# pos,
# 0,
# 100, # time out in miliseconds
# byref(res)) # result
#
# # get the top window
# popMenuWin = FindWindow(0, 0)
#
# # if no context menu has opened try right clicking the control
## if oldTopWin == popMenuWin:
## SendMessageTimeout (
## self.handle,
## WM_RBUTTONDOWN,
## 0,
## pos,
## 0,
## 100, # time out in miliseconds
## byref(res)) # result
##
## SendMessageTimeout (
## self.handle,
## WM_RBUTTONUP,
## 2,
## pos,
## 0,
## 100, # time out in miliseconds
## byref(res)) # result
##
## # wait another .1 of a second to allow it to display
## import time
## time.sleep(.1)
##
## # get the top window
## popMenuWin = FindWindow(0, 0)
#
#
# # if we still haven't opened a popup menu
# if oldTopWin == popMenuWin:
# return
#
#
# # get the MenuBar info from the PopupWindow which will
# # give you the Menu Handle for the menu itself
# mbi = MENUBARINFO()
# mbi.cbSize = sizeof(MENUBARINFO)
# ret = GetMenuBarInfo(popMenuWin, OBJID_CLIENT, 0, byref(mbi))
#
# if ret:
# GetMenuItems(mbi.hMenu)
# self.properties["ContextMenu"] = GetMenuItems(mbi.hMenu)
#
#
# # make sure the popup goes away!
# self.handle.SendMessage (WM_CANCELMODE, 0, 0)
# SendMessage (popMenuWin, WM_CANCELMODE, 0, 0)
#
# # if it's still open - then close it.
# if IsWindowVisible(popMenuWin):
# SendMessage (popMenuWin, WM_CLOSE, 0, 0)
# #SendMessage (popMenuWin, WM_DESTROY, 0, 0)
# #SendMessage (popMenuWin, WM_NCDESTROY , 0, 0)
##====================================================================
#def RemoveNonCurrentTabControls(dialog, childWindows):
#
# # find out if there is a tab control and get it if there is one
# tab = None
# for child in childWindows:
# if child.Class == "SysTabControl32":
# tab = child
# break
#
#
# # make a copy of childWindows
# firstTabChildren = list(childWindows)
# if tab:
#
# firstTabHandle = 0
#
# # get the parent of the tab control
# tabParent = GetParent(tab.handle)
#
# # find the control with that hwnd
# tabParent = [c for c in childWindows if \
# c.handle == tabParent][0]
#
# # get the index of the parent
# parentIdx = childWindows.index(tabParent) + 1
#
# passedFirstTab = False
# for child in childWindows[parentIdx:]:
#
# # if the current control is a dialog
# if child.Class == "#32770":
#
# # if this is the first tab
# if not passedFirstTab:
# # then just skip it
# passedFirstTab = True
# firstTabHandle = child.handle
# else:
# # Ok so this is NOT the first tab
# # remove the dialog control itself
# try:
# firstTabChildren.remove(child)
# print "Removing(a): ", child.IsVisible, IsWindowChildOf(firstTabHandle, child.handle)
# except ValueError:
# pass
#
# # then remove all the children of that dialog
# for x in GetChildWindows(child.handle):
# try:
# firstTabChildren.remove(x)
# print "Removing(b): ", child.IsVisible, IsWindowChildOf(firstTabHandle, x)
# except ValueError:
# pass
#
#
# return firstTabChildren
##====================================================================
#class Window(object):
# #----------------------------------------------------------------
# def __init__(self, hwndOrProps):
#
# self.ref = None
#
# # if the argument passed in is a Handle
# if isinstance(hwndOrProps, HwndWrapper):
#
# # wrap the handle
# self.handle = hwndOrProps
#
# # Get the properties from this handle
# self.properties = self.handle.GetProperties()
#
# else:
# self.properties = XMLHelpers.ControlFromXML(hwndOrProps)
#
#
# #----------------------------------------------------------------
# def __getattr__(self, name):
# if name in self.properties:
# return self.properties[name]
# else:
# raise AttributeError("'%s' has no attribute '%s'"% \
# (self.__class__.__name__, name))
#
# #----------------------------------------------------------------
# def GetTitle(self):
# return self.Titles[0]
# Title = property(GetTitle)
#
# #----------------------------------------------------------------
# def GetRectangle(self):
# return self.Rectangles[0]
# Rectangle = property(GetRectangle)
#
# #----------------------------------------------------------------
# def GetFont(self):
# return self.Fonts[0]
#
# #----------------------------------------------------------------
# def SetFont(self, font):
# self.Fonts[0] = font
#
# Font = property(GetFont, SetFont)
#
# #----------------------------------------------------------------
# def Parent(self):
# # do a preliminary construction to a Window
# parent = self.handle.Parent()
#
# # reconstruct it to the correct type
# return WindowClassRegistry().GetClass(parent.Class())(parent.handle)#.hwnd)
#
# #----------------------------------------------------------------
# def Style(self, flag = None):
# style = self.properties['Style']
# if flag:
# return style & flag == flag
# else:
# return style
#
# #----------------------------------------------------------------
# def ExStyle(self, flag = None):
# exstyle = self.properties['ExStyle']
# if flag:
# return exstyle & flag == flag
# else:
# return exstyle
#
# #----------------------------------------------------------------
# def __cmp__(self, other):
# return cmp(self.handle, other.handle)
#
# #----------------------------------------------------------------
# def __hash__(self):
# return hash(self.handle)
#
# #----------------------------------------------------------------
## def __str__(self):
## return "%8d %-15s\t'%s'" % (self.handle,
## "'%s'"% self.FriendlyClassName,
## self.Title)
#
#
##====================================================================
#class DialogWindow(Window):
# #----------------------------------------------------------------
# def __init__(self, hwndOrXML):
#
# self.children = []
#
# # if the argument passed in is a window hanle
# if isinstance(hwndOrXML, (int, long)):
# # read the properties for the dialog itself
# # Get the dialog Rectangle first - to get the control offset
#
# if not IsWindow(hwndOrXML):
# raise "The window handle passed is not valid"
#
# Window.__init__(self, hwndOrXML)
#
#
# else:
# dialogElemReached = False
# for ctrlElem in hwndOrXML.findall("CONTROL"):
#
# # if this is the first time through the dialog
# if not dialogElemReached:
# # initialise the Dialog itself
# Window.__init__(self, ctrlElem)
# dialogElemReached = True
#
# # otherwise contruct each control normally
# else:
# # get the class for the control with that window class
# Klass = WindowClassRegistry().GetClass(ctrlElem.attrib["Class"])
#
# # construct the object and append it
# self.children.append(Klass(ctrlElem))
#
# self.children.insert(0, self)
#
#
# #----------------------------------------------------------------
# def AllControls(self):
# return self.children
#
#
#
# #----------------------------------------------------------------
# def AddReference(self, ref):
#
#
# #print "x"*20, ref.AllControls()
# if len(self.AllControls()) != len(ref.AllControls()):
# print len(self.AllControls()), len(ref.AllControls())
# raise "Numbers of controls on ref. dialog does not match Loc. dialog"
#
#
# allIDsMatched = True
# allClassesMatched = True
# for idx, ctrl in enumerate(self.AllControls()):
# refCtrl = ref.AllControls()[idx]
# ctrl.ref = refCtrl
#
# if ctrl.ControlID != refCtrl.ControlID:
# allIDsMatched = False
#
# if ctrl.Class != refCtrl.Class:
# allClassesMatched = False
#
# toRet = 1
#
# allIDsSameFlag = 2
# allClassesSameFlag = 4
#
# if allIDsMatched:
# toRet += allIDsSameFlag
#
# if allClassesMatched:
# toRet += allClassesSameFlag
#
# return toRet
##====================================================================
#def DefaultWindowHwndReader(hwnd, dialogRect):
#
# ctrl = HwndWrapper(hwnd)
#
# return ctrl.GetProperties()
#
# if dialogRect:
# # offset it's rect depending on it's parents
# rect.left -= dialogRect.left
# rect.top -= dialogRect.top
# rect.right -= dialogRect.left
# rect.bottom -= dialogRect.top
##====================================================================
#def GetClass(hwnd):
# # get the className
# className = (c_wchar * 257)()
# GetClassName (hwnd, byref(className), 256)
# return className.value
#
#
##====================================================================
#def GetTitle(hwnd):
# # get the title
# bufferSize = SendMessage (hwnd, WM_GETTEXTLENGTH, 0, 0)
# title = (c_wchar * bufferSize)()
#
# if bufferSize:
# bufferSize += 1
# SendMessage (hwnd, WM_GETTEXT, bufferSize, title)
#
#
# return title.value
#
#
##====================================================================
#def GetChildWindows(dialog):
#
# # this will be filled in the callback function
# childWindows = []
#
# # callback function for EnumChildWindows
# def enumChildProc(hWnd, LPARAM):
# win = Window(hWnd)
#
# # construct an instance of the appropriate type
# win = WindowClassRegistry().GetClass(win.Class)(hWnd)
#
# # append it to our list
# childWindows.append(win)
#
# # return true to keep going
# return True
#
#
# # define the child proc type
# EnumChildProc = WINFUNCTYPE(c_int, HWND, LPARAM)
# proc = EnumChildProc(enumChildProc)
#
# # loop over all the children (callback called for each)
# EnumChildWindows(dialog.hwnd, proc, 0)
#
# return childWindows
#
##====================================================================
#def IsWindowChildOf(parent, child):
## try:
## parentHwnd = parent.handle
## except:
## parentHwnd = parent
#
# childHwnd = child
#
# while True:
# curParentTest = GetParent(childHwnd)
#
#
# # the current parent matches
# if curParentTest == parentHwnd:
# return True
#
# # we reached the very top of the heirarchy so no more parents
# if curParentTest == 0:
# return False
#
# # the next child is the current parent
# childHwnd = curParentTest
#
# =====================================================
# DEAD XML STUFF CODE
# =====================================================
#
# props['ClientRect'] = ParseRect(ctrl.find("CLIENTRECT"))
#
# props['Rectangle'] = ParseRect(ctrl.find("RECTANGLE"))
#
# props['Font'] = ParseLogFont(ctrl.find("FONT"))
#
# props['Titles'] = ParseTitles(ctrl.find("TITLES"))
#
# for key, item in ctrl.attrib.items():
# props[key] = item
##-----------------------------------------------------------------------------
#def StructToXML(struct, structElem):
# "Convert a ctypes Structure to an ElementTree"
#
# for propName in struct._fields_:
# propName = propName[0]
# itemVal = getattr(struct, propName)
#
# # convert number to string
# if isinstance(itemVal, (int, long)):
# propName += "_LONG"
# itemVal = unicode(itemVal)
#
# structElem.set(propName, EscapeSpecials(itemVal))
#
#
#
#
##====================================================================
#def XMLToMenuItems(element):
# items = []
#
# for item in element:
# itemProp = {}
#
# itemProp["ID"] = int(item.attrib["ID_LONG"])
# itemProp["State"] = int(item.attrib["State_LONG"])
# itemProp["Type"] = int(item.attrib["Type_LONG"])
# itemProp["Text"] = item.attrib["Text"]
#
# #print itemProp
# subMenu = item.find("MENUITEMS")
# if subMenu:
# itemProp["MenuItems"] = XMLToMenuItems(subMenu)
#
# items.append(itemProp)
# return items
#
#
##====================================================================
#def ListToXML(listItems, itemName, element):
#
# for i, string in enumerate(listItems):
#
# element.set("%s%05d"%(itemName, i), EscapeSpecials(string))
#
#
#
##====================================================================
#def XMLToList(element):
# items = []
# for subItem in element:
# items.append(PropFromXML(subItem))
#
##====================================================================
#def PropFromXML(element):
#
# for propName in PropParsers:
# if element.tag == propName.upper():
#
# ToXMLFunc, FromXMLFunc = PropParsers[element.tag.upper()]
#
# return FromXMLFunc(element)
#
# raise "Unknown Element Type : %s"% element.tag
#
##====================================================================
#def PropToXML(parentElement, name, value, ):
# print "=" *20, name, value
#
# ToXMLFunc, FromXMLFunc = PropParsers[element.tag.upper()]
#
# return FromXMLFunc(element)
#
#
#
#
#PropParsers = {
# "Font" : (StructToXML, XMLToFont),
# "Rectangle" : (StructToXML, XMLToRect),
# "ClientRects" : (ListToXML, XMLToRect),
# "Titles" : (TitlesToXML, XMLToTitles),
# "Fonts" : (ListToXML, XMLToList),
# #"Rectangles" : (ListToXML, XMLToList),
# #"" : XMLToMenuItems,
# #"" : XMLToMenuItems,
#
#
#}
#
# USED TO BE NEEDED IN THE XML OUTPUT FUNCTION
# # format the output xml a little
# xml = open(fileName, "rb").read()
#
# import re
# tags = re.compile("""
# (
# <[^/>]+> # An opening tag
# )|
# (
# </[^>]+> # A closing tag
# )|
# (
# <[^>]+/> # an empty element
# )
#
# """, re.VERBOSE)
#
# f = open(fileName, "wb")
# indent = 0
# indentText = " "
# for x in tags.finditer(xml):
#
# # closing tag
# if x.group(2):
# indent -= 1
# f.write(indentText*indent + x.group(2) + "\r\n")
#
# # if the element may have attributes
# else:
# if x.group(1):
# text = x.group(1)
# if x.group(3):
# text = x.group(3)
#
# f.write(indentText*indent + text + "\r\n")
#
##
## Trying to indent the attributes each on a single line
## but it is more complicated then it first looks :-(
##
# items = text.split()
#
#
# f.write(indentText*indent + items[0] + "\r\n")
# indent += 1
# for i in items[1:]:
# f.write(indentText*indent + i + "\r\n")
#
# indent -= 1
#
# # opening tag
# if x.group(1):
# indent += 1
#
# f.close()
##====================================================================
## specializes XMLToStruct for Fonts
#def XMLToFont(element):
# font = LOGFONTW()
# #print element.attrib
# XMLToStruct(element, font)
#
# return font
#
##====================================================================
## specializes XMLToStruct for Rects
#def XMLToRect(element):
# rect = RECT()
#
# XMLToStruct(element, rect)
# return rect
#
##====================================================================
#def TitlesToXML(titles, titleElem):
# for i, string in enumerate(titles):
#
# titleElem.set("s%05d"%i, EscapeSpecials(string))
#
#
##====================================================================
# sys.exit()
#
# # SendText playing around!! - not required
# SetForegroundWindow(handle)
# SendText("here is some test text")
#
#
# # some SendText testing
# text = sys.argv[2]
# import os.path
# if os.path.exists(text):
# text = open(text, "rb").read().decode('utf-16')
#
# print `text`
#
# #SendText("--%s--"%text)
# for c in dialog.AllChildren():
# print "(%6d) %s - '%s'"% (c.handle,c.Class, c.Title)
# if c.Class == "Edit":
# #SetActiveWindow (c.handle)
# SetForegroundWindow(c.handle)
# #SetFocus(c.handle)
# #EnableWindow(c.handle, True)
# SendText("--%s--"%text)
#
#
#
#
# get all the windows involved for this control
#windows.extend(windows[0].Children())
#
#
# styles = {
# "WS_DISABLED" : 134217728, # Variable c_long
# "WS_BORDER" : 8388608, # Variable c_long
# "WS_TABSTOP" : 65536, # Variable c_long << adds min, max, buttons
# "WS_MINIMIZE" : 536870912, # Variable c_long
# "WS_DLGFRAME" : 4194304, # Variable c_long
# "WS_VISIBLE" : 268435456, # Variable c_long
# "WS_OVERLAPPED" : 0, # Variable c_long
# "WS_CHILD" : 1073741824, # Variable c_long
# "WS_CAPTION" : 12582912, # Variable c_long
# "WS_POPUPWINDOW" : 2156396544L, # Variable c_ulong
# "WS_HSCROLL" : 1048576, # Variable c_long
# "WS_THICKFRAME" : 262144, # Variable c_long << takes about 2 pixes off length
# #"WS_SIZEBOX" : WS_THICKFRAME, # alias
# "WS_OVERLAPPEDWINDOW" : 13565952, # Variable c_long << turns off sysmenu!
# #"WS_TILEDWINDOW" : WS_OVERLAPPEDWINDOW, # alias
# "WS_GROUP" : 131072, # Variable c_long << adds both minimize and maximize boxes
# "WS_VSCROLL" : 2097152, # Variable c_long
# "WS_MAXIMIZEBOX" : 65536, # Variable c_long << adds both minimize and maximize boxes
# "WS_MAXIMIZE" : 16777216, # Variable c_long
# "WS_SYSMENU" : 524288, # Variable c_long << adds/removes close box
# "WS_POPUP" : 2147483648L, # Variable c_ulong
# "WS_MINIMIZEBOX" : 131072, # Variable c_long << adds both minimize and maximize boxes
# "WS_CLIPCHILDREN" : 33554432, # Variable c_long
# #"WS_ICONIC" : WS_MINIMIZE, # alias
# "WS_CLIPSIBLINGS" : 67108864, # Variable c_long
# #"WS_TILED" : WS_OVERLAPPED, # alias
# "WS_CHILDWINDOW" : 1073741824, # Variable c_long
#
# }
#
# exstyles = {
# "WS_EX_TOOLWINDOW" : 128, # Variable c_long << small font
# "WS_EX_MDICHILD" : 64, # Variable c_long
# "WS_EX_WINDOWEDGE" : 256, # Variable c_long
# "WS_EX_RIGHT" : 4096, # Variable c_long
# "WS_EX_NOPARENTNOTIFY" : 4, # Variable c_long
# "WS_EX_ACCEPTFILES" : 16, # Variable c_long
# "WS_EX_LEFTSCROLLBAR" : 16384, # Variable c_long
# "WS_EX_OVERLAPPEDWINDOW" : 768, # Variable c_long
# "WS_EX_DLGMODALFRAME" : 1, # Variable c_long << adds Icon
# "WS_EX_TRANSPARENT" : 32, # Variable c_long
# "WS_EX_STATICEDGE" : 131072, # Variable c_long
# "WS_EX_TOPMOST" : 8, # Variable c_long
# "WS_EX_LTRREADING" : 0, # Variable c_long
# "WS_EX_RIGHTSCROLLBAR" : 0, # Variable c_long
# "WS_EX_APPWINDOW" : 262144, # Variable c_long
# "WS_EX_CONTROLPARENT" : 65536, # Variable c_long
# "WS_EX_LEFT" : 0, # Variable c_long
# "WS_EX_PALETTEWINDOW" : 392, # Variable c_long << small font
# "WS_EX_CONTEXTHELP" : 1024, # Variable c_long << adds a CH button
# "WS_EX_CLIENTEDGE" : 512, # Variable c_long
# "WS_EX_RTLREADING" : 8192, # Variable c_long
# }
#
#
#
# for s in styles:
# if dialog.Style(styles[s]):
# print "%30s\t0x%-8x"% (s, styles[s])
#
# print "-"*20
# for s in exstyles:
# if dialog.ExStyle(exstyles[s]):
# print "%30s\t0x%-8x"% (s, exstyles[s])
# print dialog.Font().lfHeight, dialog.Font().lfWidth, dialog.Font().lfFaceName
# print "STyle 0x%08x EXStyle 0x%08x" % (dialog.Style(), dialog.ExStyle())
# print "please type the style to set/unset"
# typed = ""
# while typed.lower() != "x":
# typed = raw_input()
#
# if typed in exstyles:
# old = dlg.ExStyle()
# new = dlg.ExStyle() ^ exstyles[typed]
# SetWindowLong(dlg.handle, GWL_EXSTYLE, c_long(new))
# print "%0x %0x %0x"% (old, new, exstyles[typed])
# SetWindowLong(dlg.handle, GWL_STYLE, dlg.Style() ^268435456)
# SetWindowLong(dlg.handle, GWL_STYLE, dlg.Style() ^268435456)
# SendMessage(dlg.handle, WM_PAINT, 0, 0)
# SetForegroundWindow(dlg.handle)
#
#
# if typed in styles:
# old = dlg.Style()
# new = dlg.Style() ^ styles[typed]
# SetWindowLong(dlg.handle, GWL_STYLE, c_long(new))
# print "%0x %0x %0x"% (old, new, styles[typed])
# SetWindowLong(dlg.handle, GWL_STYLE, dlg.Style() ^268435456)
# SetWindowLong(dlg.handle, GWL_STYLE, dlg.Style() ^268435456)
# SendMessage(dlg.handle, WM_PAINT, 0, 0)
# SetForegroundWindow(dlg.handle)
#dialog = ParentWindow(dlg.handle)
#
#
##====================================================================
#from SendInput import TypeKeys, PressKey, LiftKey, TypeKey, VK_MENU, \
# VK_SHIFT, VK_BACK, VK_DOWN, VK_LEFT
#
#
#def SendText(text):
#
# # write the text passed in
# TypeKeys(text)
#
# # press shift
# PressKey(VK_SHIFT)
# # lowercase 'a'
# #import pm
# #pm.set_trace()
# toType = (VK_LEFT,) * 13
# TypeKeys(toType)
#
# # unpress shift
# LiftKey(VK_SHIFT)
#
#
# PressKey(VK_MENU)
# TypeKey('F')
# LiftKey(VK_MENU)
#
#
# TypeKeys((VK_DOWN,)*4)
#
#
#====================================================================
#class Menuitem(object):
# def __init__(self, item):
# for attr in item.keys():
# self.__dict__["_%s_"%attr] = item[attr]
#
# self.__dict__.setdefault("_MenuItems_", [])
#
# def __getattr__(self, key):
# return getattr(MenuWrapper(self._MenuItems_), key)
#
#
#
##====================================================================
#class MenuWrapper(object):
# def __init__(self, items):
# # clean up the existing menuItem attributes
# # and set them
# self.__items = items
#
# self.__texts = [item['Text'] for item in self.__items]
#
#
# def __getattr__(self, key):
#
# item = find_best_match(key, self._texts_, self.__items)
#
# return item
#
#
##====================================================================
#def MenuSelect(ctrl, menupath, menu_items):
#
# id = FindMenu(menupath, menu_items)
#
# #print ctrl['MenuItems']
# APIFuncs.PostMessage(ctrl.handle, win32defines.WM_COMMAND, id, 0)
#
#
#
#
##====================================================================
#class Dialog2(controls.HwndWrapper.HwndWrapper):
# #----------------------------------------------------------------
# def __init__(self, title = None, class_ = None, timeout = 1, handle = None):
#
# if not handle:
#
# handle = FindDialog(title, testClass = class_)
# waited = 0
# while not handle and waited <= timeout:
# time.sleep(.1)
# handle = FindDialog(title, testClass = class_)
# waited += .1
#
# if not handle:
# raise WindowNotFound("Window not found")
#
# super(Dialog2, self).__init__(handle)
#
# self.controls = [self, ]
# self.controls.extend(self.Children)
#
# controlactions.add_actions(self)
#
# #self._build_control_id_map()
#
# self.ctrl_texts = [ctrl.Text or ctrl.FriendlyClassName for ctrl in self.controls]
#
# # we need to handle controls where the default text is not that interesting e.g.
# # edit boxes
#
#
# #----------------------------------------------------------------
# def __getattr__(self, to_find):
#
# waited = 0
# while waited <= 1:
# try:
# #if "Dialog" in self.ctrl_texts:
# # print "&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&"
# # print self.ctrl_texts, self.Class, self.Children
# ctrl = find_best_match(to_find, self.ctrl_texts, self.controls)
# return controlactions.add_actions(ctrl)
# except WindowNotFound:
# waited += .1
#
# print self
# print "failed to find %s in %s" % (to_find, self.ctrl_texts)
#
# raise
#
#
# #----------------------------------------------------------------
# def MenuSelect(self, path):
#
#
# item_id = FindMenu(self.MenuItems, path)
# #menu_items = MenuWrapper(self.MenuItems)
#
# #item_id = FindMenu(menu_items, path)
#
# #print ctrl['MenuItems']
# self.PostMessage(win32defines.WM_COMMAND, item_id)
#
#
#
#
##====================================================================
#def TestNotepad():
#
# try:
# notepad = Dialog2(title = "^.*Notepad.*", class_ = "Notepad")
# except WindowNotFound:
# os.system("start notepad")
# time.sleep(.1)
# notepad = Dialog2(title = "^.*Notepad.*", class_ = "Notepad")
#
#
# #print notepad.handle
#
## notepad.SendKeys("testing")
## notepad.edit.SendKeys("Here is so\\nme tëÿext%H")
## notepad.SendKeys("{DOWN}{ENTER}")
## #notepad.SendKeys("a")
##
## # need to get active window!!
## notepad.SendKeys("{ESC}")
## notepad.SendKeys("%E")
# if "1" in sys.argv:
# # Select that menu item
# notepad.MenuSelect("File->Page Setup")
#
# # find the dialog
# page_setup = Dialog2(title = "Page Setup")
#
# edit = page_setup.Edit1
# edit.TypeKeys("{HOME}+{END}{BKSP}23")
#
#
# page_setup.Combo1.Select(5)
# time.sleep(1)
#
# page_setup.Combo1.Select("Tabloid")
# time.sleep(1)
#
# # click the printer button
# page_setup.Printer.Click()
#
# dlg = Dialog2("^Page Setup").Properties.Click()
#
# Dialog2(".*Document Properties").Advanced.Click()
#
# Dialog2(".*Advanced Options").Cancel.Click()
#
# Dialog2(".*Document Properties").cancel.Click()
#
# Dialog2("^Page Setup").cancel.Click()
#
# # dialog doesn't go away because 23 that we typed is 'wrong'
# Dialog2(title = "^Page Setup").ok.Click()
#
# # this is teh message box
# Dialog2(title = "^Page Setup").ok.Click()
#
# # dialog doesn't go away because 23 that we typed is 'wrong'
# Dialog2(title = "^Page Setup").cancel.Click()
#
# if "2" in sys.argv:
# # Select that menu item
# notepad.MenuSelect("Format->Font")
#
# font_dlg = Dialog2(title = "^Font$")
#
# font_dlg.combobox2.Select(3)
# time.sleep(2)
#
# Dialog2(title = "^Font$").OK.Click()
#
# if "3" in sys.argv:
# notepad.Edit1.Select(1,4)
# time.sleep(2)
#
# print notepad.edit1.SelectionIndices
#
# if "4" in sys.argv:
#
# raise "NotWorkingYet"
# edit = notepad.Edit1
# print edit.Rectangle
#
# edit.PressMouse(coords = (0,0))
# edit.MoveMouse(coords = (400, 400))
# edit.ReleaseMouse(coords = (400,400))
#
# if "5" in sys.argv:
#
# edit = notepad.Edit1
#
# edit.DoubleClick(coords = (1290,1290))
#
#
#
##====================================================================
#def test():
#
# # some some normal dailogs
# if 1:
# testStrings = ["Combo", "Combo2", "ComboBox", "blah blah", "test" ,"hex" ,"matchwhole" ,"regularExp" ,"wrapsearch" ,"wrap" ,"inalldocs" ,"extend_sel" ,"Find next" ,"markall" ,"up" ,"down" ,"direct" ,"conds"]
# else:
# testStrings = ["blah blah", "first", "from", "from0", "from001", "from2", "from0000003", "from3", "insensitive", "delduplicate", "charCodeOrder"]
#
#
# item_texts = [ctrl.Text or ctrl.FriendlyClassName for ctrl in ctrls]
#
# missedMatches = []
# for test in testStrings:
#
# try:
# ctrl = find_best_match(test, item_texts, ctrls)
# print "%15s %15s %-20s %s"% (test, ctrl.FriendlyClassName, `ctrl.Text[:20]`, str(ctrl.Rectangle))
# except IndexError, e:
# missedMatches.append(test)
#
# if missedMatches:
# print "\nNo Matches for: " + ", ".join(missedMatches)
#
#
#
#if __name__ == "__main__":
# TestNotepad()
#print "\n\nMenuTesting"
#missedMatches = []
#try:
# print MenuWrapper(ctrls[0].MenuItems).File.PageSetup._Text_
#except IndexError, e:
# missedMatches.append(test)
#
#if missedMatches:
# print "\nNo Matches for: " + ", ".join(missedMatches)
#
#
#for ctrl in ctrls:
# print CtrlAccessName(ctrl)
#
# if ctrl.Class in ('ComboBox'):
#
#
# candidates = []
# # find controls that are to it's left
# for ctrl2 in ctrls:
# # if this ctrl has a top or bottom between
# # other ctrl top and bottom
#
# if \
# (((ctrl2.Rectangle.top >= ctrl.Rectangle.top and \
# ctrl2.Rectangle.top < ctrl.Rectangle.bottom) or \
# (ctrl2.Rectangle.bottom > ctrl.Rectangle.top and \
# ctrl2.Rectangle.bottom <= ctrl.Rectangle.bottom)) and\
# ctrl2.Rectangle.left < ctrl.Rectangle.left) \
# or \
# (((ctrl2.Rectangle.right >= ctrl.Rectangle.left and \
# ctrl2.Rectangle.right < ctrl.Rectangle.bottom) or \
# (ctrl2.Rectangle.bottom > ctrl.Rectangle.top and \
# ctrl2.Rectangle.bottom <= ctrl.Rectangle.bottom)) and\
# ctrl2.Rectangle.left < ctrl.Rectangle.left) \
# :
#
#
#
#
#
# candidates.append(ctrl2)
#
#
#
# #for candidate in cadidates:
# # print "%18s - 20%s" % (candidate.Class, "'%s'"%candidate.Title), CtrlAccessName(candidate)
#
#
#
# #if ctrl2.Rectangle.top >= ctrl.Rectangle.top <= ctrl2.Rectangle.bottom or \
# # ctrl2.Rectangle.bottom >= ctrl.Rectangle.top
#
#
# #if ctrl2.Rectangle.top
#
#
#
##import pprint
##pprint.pprint(ctrls)
#
# how should we read in the XML file
# NOT USING MS Components (requirement on machine)
# maybe using built in XML
# maybe using elementtree
# others?
|
"""
Test models for the multilingual library.
# Note: the to_str() calls in all the tests are here only to make it
# easier to test both pre-unicode and current Django.
>>> from testproject.utils import to_str
# make sure the settings are right
>>> from multilingual.languages import LANGUAGES
>>> LANGUAGES
[['en', 'English'], ['pl', 'Polish'], ['zh-cn', 'Simplified Chinese']]
>>> from multilingual import set_default_language
>>> from django.db.models import Q
>>> set_default_language(1)
### Check the table names
>>> Category._meta.translation_model._meta.db_table
'category_language'
>>> Article._meta.translation_model._meta.db_table
'articles_article_translation'
### Create the test data
# Check both assigning via the proxy properties and set_* functions
>>> c = Category()
>>> c.name_en = 'category 1'
>>> c.name_pl = 'kategoria 1'
>>> c.save()
>>> c = Category()
>>> c.set_name('category 2', 'en')
>>> c.set_name('kategoria 2', 'pl')
>>> c.save()
### See if the test data was saved correctly
### Note: first object comes from the initial fixture.
>>> c = Category.objects.all().order_by('id')[1]
>>> to_str((c.name, c.get_name(1), c.get_name(2)))
('category 1', 'category 1', 'kategoria 1')
>>> c = Category.objects.all().order_by('id')[2]
>>> to_str((c.name, c.get_name(1), c.get_name(2)))
('category 2', 'category 2', 'kategoria 2')
### Check translation changes.
### Make sure the name and description properties obey
### set_default_language.
>>> c = Category.objects.all().order_by('id')[1]
# set language: pl
>>> set_default_language(2)
>>> to_str((c.name, c.get_name(1), c.get_name(2)))
('kategoria 1', 'category 1', 'kategoria 1')
>>> c.name = 'kat 1'
>>> to_str((c.name, c.get_name(1), c.get_name(2)))
('kat 1', 'category 1', 'kat 1')
# set language: en
>>> set_default_language('en')
>>> c.name = 'cat 1'
>>> to_str((c.name, c.get_name(1), c.get_name(2)))
('cat 1', 'cat 1', 'kat 1')
>>> c.save()
# Read the entire Category objects from the DB again to see if
# everything was saved correctly.
>>> c = Category.objects.all().order_by('id')[1]
>>> to_str((c.name, c.get_name('en'), c.get_name('pl')))
('cat 1', 'cat 1', 'kat 1')
>>> c = Category.objects.all().order_by('id')[2]
>>> to_str((c.name, c.get_name('en'), c.get_name('pl')))
('category 2', 'category 2', 'kategoria 2')
### Check ordering
>>> set_default_language(1)
>>> to_str([c.name for c in Category.objects.all().order_by('name_en')])
['Fixture category', 'cat 1', 'category 2']
### Check ordering
# start with renaming one of the categories so that the order actually
# depends on the default language
>>> set_default_language(1)
>>> c = Category.objects.get(name='cat 1')
>>> c.name = 'zzz cat 1'
>>> c.save()
>>> to_str([c.name for c in Category.objects.all().order_by('name_en')])
['Fixture category', 'category 2', 'zzz cat 1']
>>> to_str([c.name for c in Category.objects.all().order_by('name')])
['Fixture category', 'category 2', 'zzz cat 1']
>>> to_str([c.name for c in Category.objects.all().order_by('-name')])
['zzz cat 1', 'category 2', 'Fixture category']
>>> set_default_language(2)
>>> to_str([c.name for c in Category.objects.all().order_by('name')])
['Fixture kategoria', 'kat 1', 'kategoria 2']
>>> to_str([c.name for c in Category.objects.all().order_by('-name')])
['kategoria 2', 'kat 1', 'Fixture kategoria']
### Check filtering
# Check for filtering defined by Q objects as well. This is a recent
# improvement: the translation fields are being handled by an
# extension of lookup_inner instead of overridden
# QuerySet._filter_or_exclude
>>> set_default_language('en')
>>> to_str([c.name for c in Category.objects.all().filter(name__contains='2')])
['category 2']
>>> set_default_language('en')
>>> to_str([c.name for c in Category.objects.all().filter(Q(name__contains='2'))])
['category 2']
>>> set_default_language(1)
>>> to_str([c.name for c in
... Category.objects.all().filter(Q(name__contains='2')|Q(name_pl__contains='kat'))])
['Fixture category', 'zzz cat 1', 'category 2']
>>> set_default_language(1)
>>> to_str([c.name for c in Category.objects.all().filter(name_en__contains='2')])
['category 2']
>>> set_default_language(1)
>>> to_str([c.name for c in Category.objects.all().filter(Q(name_pl__contains='kat'))])
['Fixture category', 'zzz cat 1', 'category 2']
>>> set_default_language('pl')
>>> to_str([c.name for c in Category.objects.all().filter(name__contains='k')])
['Fixture kategoria', 'kat 1', 'kategoria 2']
>>> set_default_language('pl')
>>> to_str([c.name for c in Category.objects.all().filter(Q(name__contains='kategoria'))])
['Fixture kategoria', 'kategoria 2']
### Check specifying query set language
>>> c_en = Category.objects.all().for_language('en')
>>> c_pl = Category.objects.all().for_language(2) # both ID and code work here
>>> to_str(c_en.get(name__contains='1').name)
'zzz cat 1'
>>> to_str(c_pl.get(name__contains='1').name)
'kat 1'
>>> to_str([c.name for c in c_en.order_by('name')])
['Fixture category', 'category 2', 'zzz cat 1']
>>> to_str([c.name for c in c_pl.order_by('-name')])
['kategoria 2', 'kat 1', 'Fixture kategoria']
>>> c = c_en.get(id=2)
>>> c.name = 'test'
>>> to_str((c.name, c.name_en, c.name_pl))
('test', 'test', 'kat 1')
>>> c = c_pl.get(id=2)
>>> c.name = 'test'
>>> to_str((c.name, c.name_en, c.name_pl))
('test', 'zzz cat 1', 'test')
### Check filtering spanning more than one model
>>> set_default_language(1)
>>> cat_1 = Category.objects.get(name='zzz cat 1')
>>> cat_2 = Category.objects.get(name='category 2')
>>> a = Article(category=cat_1)
>>> a.set_title('article 1', 1)
>>> a.set_title('artykul 1', 2)
>>> a.set_contents('contents 1', 1)
>>> a.set_contents('zawartosc 1', 1)
>>> a.save()
>>> a = Article(category=cat_2)
>>> a.set_title('article 2', 1)
>>> a.set_title('artykul 2', 2)
>>> a.set_contents('contents 2', 1)
>>> a.set_contents('zawartosc 2', 1)
>>> a.save()
>>> to_str([a.title for a in Article.objects.filter(category=cat_1)])
['article 1']
>>> to_str([a.title for a in Article.objects.filter(category__name=cat_1.name)])
['article 1']
>>> to_str([a.title for a in Article.objects.filter(Q(category__name=cat_1.name)|Q(category__name_pl__contains='2')).order_by('-title')])
['article 2', 'article 1']
### Test the creation of new objects using keywords passed to the
### constructor
>>> set_default_language(2)
>>> c_n = Category.objects.create(name_en='new category', name_pl='nowa kategoria')
>>> to_str((c_n.name, c_n.name_en, c_n.name_pl))
('nowa kategoria', 'new category', 'nowa kategoria')
>>> c_n.save()
>>> c_n2 = Category.objects.get(name_en='new category')
>>> to_str((c_n2.name, c_n2.name_en, c_n2.name_pl))
('nowa kategoria', 'new category', 'nowa kategoria')
>>> set_default_language(2)
>>> c_n3 = Category.objects.create(name='nowa kategoria 2')
>>> to_str((c_n3.name, c_n3.name_en, c_n3.name_pl))
('nowa kategoria 2', None, 'nowa kategoria 2')
########################################
###### Check if the admin behaviour for categories with incomplete translations
>>> from django.contrib.auth.models import User
>>> User.objects.create_superuser('test', 'test_email', 'test_password') and None
>>> from django.test.client import Client
>>> c = Client()
>>> c.login(username='test', password='test_password')
True
# create a category with only 2 translations, skipping the
# first language
>>> resp = c.post('/admin/articles/category/add/',
... {'creator': 1,
... 'translations-TOTAL_FORMS': '3',
... 'translations-INITIAL_FORMS': '0',
... 'translations-0-language_id': '1',
... 'translations-1-language_id': '2',
... 'translations-2-language_id': '3',
... 'translations-1-name': 'pl name',
... 'translations-2-name': 'zh-cn name',
... })
>>> resp.status_code
302
>>> cat = Category.objects.order_by('-id')[0]
>>> cat.name_en
>>> cat.name_pl
u'pl name'
>>> cat.name_zh_cn
u'zh-cn name'
>>> cat.translations.count()
2
""" |
"""Stuff to parse Sun and NeXT audio files.
An audio file consists of a header followed by the data. The structure
of the header is as follows.
+---------------+
| magic word |
+---------------+
| header size |
+---------------+
| data size |
+---------------+
| encoding |
+---------------+
| sample rate |
+---------------+
| # of channels |
+---------------+
| info |
| |
+---------------+
The magic word consists of the 4 characters '.snd'. Apart from the
info field, all header fields are 4 bytes in size. They are all
32-bit unsigned integers encoded in big-endian byte order.
The header size really gives the start of the data.
The data size is the physical size of the data. From the other
parameters the number of frames can be calculated.
The encoding gives the way in which audio samples are encoded.
Possible values are listed below.
The info field currently consists of an ASCII string giving a
human-readable description of the audio file. The info field is
padded with NUL bytes to the header size.
Usage.
Reading audio files:
f = sunau.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().
When the setpos() and rewind() methods are 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' or 'ULAW')
getcompname() -- returns human-readable version of
compression type ('not compressed' matches 'NONE')
getparams() -- returns a tuple consisting of all of the
above in the above order
getmarkers() -- returns None (for compatibility with the
aifc module)
getmark(id) -- raises an error since the mark does not
exist (for compatibility with the aifc module)
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() and the position given to setpos()
are 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 audio files:
f = sunau.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:
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
tell() -- return current position in output file
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.
The close() method is called automatically when the class instance
is destroyed.
""" |
"""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.
""" |
"""
=====================================
Structured Arrays (and Record Arrays)
=====================================
Introduction
============
Numpy provides powerful capabilities to create arrays of structs or records.
These arrays permit one to manipulate the data by the structs or by fields of
the struct. A simple example will show what is meant.: ::
>>> x = np.zeros((2,),dtype=('i4,f4,a10'))
>>> x[:] = [(1,2.,'Hello'),(2,3.,"World")]
>>> x
array([(1, 2.0, 'Hello'), (2, 3.0, 'World')],
dtype=[('f0', '>i4'), ('f1', '>f4'), ('f2', '|S10')])
Here we have created a one-dimensional array of length 2. Each element of
this array is a record that contains three items, a 32-bit integer, a 32-bit
float, and a string of length 10 or less. If we index this array at the second
position we get the second record: ::
>>> x[1]
(2,3.,"World")
Conveniently, one can access any field of the array by indexing using the
string that names that field. In this case the fields have received the
default names 'f0', 'f1' and 'f2'. ::
>>> y = x['f1']
>>> y
array([ 2., 3.], dtype=float32)
>>> y[:] = 2*y
>>> y
array([ 4., 6.], dtype=float32)
>>> x
array([(1, 4.0, 'Hello'), (2, 6.0, 'World')],
dtype=[('f0', '>i4'), ('f1', '>f4'), ('f2', '|S10')])
In these examples, y is a simple float array consisting of the 2nd field
in the record. But, rather than being a copy of the data in the structured
array, it is a view, i.e., it shares exactly the same memory locations.
Thus, when we updated this array by doubling its values, the structured
array shows the corresponding values as doubled as well. Likewise, if one
changes the record, the field view also changes: ::
>>> x[1] = (-1,-1.,"Master")
>>> x
array([(1, 4.0, 'Hello'), (-1, -1.0, 'Master')],
dtype=[('f0', '>i4'), ('f1', '>f4'), ('f2', '|S10')])
>>> y
array([ 4., -1.], dtype=float32)
Defining Structured Arrays
==========================
One defines a structured array through the dtype object. There are
**several** alternative ways to define the fields of a record. Some of
these variants provide backward compatibility with Numeric, numarray, or
another module, and should not be used except for such purposes. These
will be so noted. One specifies record structure in
one of four alternative ways, using an argument (as supplied to a dtype
function keyword or a dtype object constructor itself). This
argument must be one of the following: 1) string, 2) tuple, 3) list, or
4) dictionary. Each of these is briefly described below.
1) String argument (as used in the above examples).
In this case, the constructor expects a comma-separated list of type
specifiers, optionally with extra shape information.
The type specifiers can take 4 different forms: ::
a) b1, i1, i2, i4, i8, u1, u2, u4, u8, f2, f4, f8, c8, c16, a<n>
(representing bytes, ints, unsigned ints, floats, complex and
fixed length strings of specified byte lengths)
b) int8,...,uint8,...,float16, float32, float64, complex64, complex128
(this time with bit sizes)
c) older Numeric/numarray type specifications (e.g. Float32).
Don't use these in new code!
d) Single character type specifiers (e.g H for unsigned short ints).
Avoid using these unless you must. Details can be found in the
Numpy book
These different styles can be mixed within the same string (but why would you
want to do that?). Furthermore, each type specifier can be prefixed
with a repetition number, or a shape. In these cases an array
element is created, i.e., an array within a record. That array
is still referred to as a single field. An example: ::
>>> x = np.zeros(3, dtype='3int8, float32, (2,3)float64')
>>> x
array([([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),
([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),
([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])],
dtype=[('f0', '|i1', 3), ('f1', '>f4'), ('f2', '>f8', (2, 3))])
By using strings to define the record structure, it precludes being
able to name the fields in the original definition. The names can
be changed as shown later, however.
2) Tuple argument: The only relevant tuple case that applies to record
structures is when a structure is mapped to an existing data type. This
is done by pairing in a tuple, the existing data type with a matching
dtype definition (using any of the variants being described here). As
an example (using a definition using a list, so see 3) for further
details): ::
>>> x = np.zeros(3, dtype=('i4',[('r','u1'), ('g','u1'), ('b','u1'), ('a','u1')]))
>>> x
array([0, 0, 0])
>>> x['r']
array([0, 0, 0], dtype=uint8)
In this case, an array is produced that looks and acts like a simple int32 array,
but also has definitions for fields that use only one byte of the int32 (a bit
like Fortran equivalencing).
3) List argument: In this case the record structure is defined with a list of
tuples. Each tuple has 2 or 3 elements specifying: 1) The name of the field
('' is permitted), 2) the type of the field, and 3) the shape (optional).
For example::
>>> x = np.zeros(3, dtype=[('x','f4'),('y',np.float32),('value','f4',(2,2))])
>>> x
array([(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]]),
(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]]),
(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]])],
dtype=[('x', '>f4'), ('y', '>f4'), ('value', '>f4', (2, 2))])
4) Dictionary argument: two different forms are permitted. The first consists
of a dictionary with two required keys ('names' and 'formats'), each having an
equal sized list of values. The format list contains any type/shape specifier
allowed in other contexts. The names must be strings. There are two optional
keys: 'offsets' and 'titles'. Each must be a correspondingly matching list to
the required two where offsets contain integer offsets for each field, and
titles are objects containing metadata for each field (these do not have
to be strings), where the value of None is permitted. As an example: ::
>>> x = np.zeros(3, dtype={'names':['col1', 'col2'], 'formats':['i4','f4']})
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[('col1', '>i4'), ('col2', '>f4')])
The other dictionary form permitted is a dictionary of name keys with tuple
values specifying type, offset, and an optional title. ::
>>> x = np.zeros(3, dtype={'col1':('i1',0,'title 1'), 'col2':('f4',1,'title 2')})
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[(('title 1', 'col1'), '|i1'), (('title 2', 'col2'), '>f4')])
Accessing and modifying field names
===================================
The field names are an attribute of the dtype object defining the record structure.
For the last example: ::
>>> x.dtype.names
('col1', 'col2')
>>> x.dtype.names = ('x', 'y')
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[(('title 1', 'x'), '|i1'), (('title 2', 'y'), '>f4')])
>>> x.dtype.names = ('x', 'y', 'z') # wrong number of names
<type 'exceptions.ValueError'>: must replace all names at once with a sequence of length 2
Accessing field titles
====================================
The field titles provide a standard place to put associated info for fields.
They do not have to be strings. ::
>>> x.dtype.fields['x'][2]
'title 1'
Accessing multiple fields at once
====================================
You can access multiple fields at once using a list of field names: ::
>>> x = np.array([(1.5,2.5,(1.0,2.0)),(3.,4.,(4.,5.)),(1.,3.,(2.,6.))],
dtype=[('x','f4'),('y',np.float32),('value','f4',(2,2))])
Notice that `x` is created with a list of tuples. ::
>>> x[['x','y']]
array([(1.5, 2.5), (3.0, 4.0), (1.0, 3.0)],
dtype=[('x', '<f4'), ('y', '<f4')])
>>> x[['x','value']]
array([(1.5, [[1.0, 2.0], [1.0, 2.0]]), (3.0, [[4.0, 5.0], [4.0, 5.0]]),
(1.0, [[2.0, 6.0], [2.0, 6.0]])],
dtype=[('x', '<f4'), ('value', '<f4', (2, 2))])
The fields are returned in the order they are asked for.::
>>> x[['y','x']]
array([(2.5, 1.5), (4.0, 3.0), (3.0, 1.0)],
dtype=[('y', '<f4'), ('x', '<f4')])
Filling structured arrays
=========================
Structured arrays can be filled by field or row by row. ::
>>> arr = np.zeros((5,), dtype=[('var1','f8'),('var2','f8')])
>>> arr['var1'] = np.arange(5)
If you fill it in row by row, it takes a take a tuple
(but not a list or array!)::
>>> arr[0] = (10,20)
>>> arr
array([(10.0, 20.0), (1.0, 0.0), (2.0, 0.0), (3.0, 0.0), (4.0, 0.0)],
dtype=[('var1', '<f8'), ('var2', '<f8')])
More information
====================================
You can find some more information on recarrays and structured arrays
(including the difference between the two) `here
<http://www.scipy.org/Cookbook/Recarray>`_.
""" |
"""
=============
Miscellaneous
=============
IEEE 754 Floating Point Special Values
--------------------------------------
Special values defined in numpy: nan, inf,
NaNs can be used as a poor-man's mask (if you don't care what the
original value was)
Note: cannot use equality to test NaNs. E.g.: ::
>>> myarr = np.array([1., 0., np.nan, 3.])
>>> np.where(myarr == np.nan)
>>> np.nan == np.nan # is always False! Use special numpy functions instead.
False
>>> myarr[myarr == np.nan] = 0. # doesn't work
>>> myarr
array([ 1., 0., NaN, 3.])
>>> myarr[np.isnan(myarr)] = 0. # use this instead find
>>> myarr
array([ 1., 0., 0., 3.])
Other related special value functions: ::
isinf(): True if value is inf
isfinite(): True if not nan or inf
nan_to_num(): Map nan to 0, inf to max float, -inf to min float
The following corresponds to the usual functions except that nans are excluded
from the results: ::
nansum()
nanmax()
nanmin()
nanargmax()
nanargmin()
>>> x = np.arange(10.)
>>> x[3] = np.nan
>>> x.sum()
nan
>>> np.nansum(x)
42.0
How numpy handles numerical exceptions
--------------------------------------
The default is to ``'warn'`` for ``invalid``, ``divide``, and ``overflow``
and ``'ignore'`` for ``underflow``. But this can be changed, and it can be
set individually for different kinds of exceptions. The different behaviors
are:
- 'ignore' : Take no action when the exception occurs.
- 'warn' : Print a `RuntimeWarning` (via the Python `warnings` module).
- 'raise' : Raise a `FloatingPointError`.
- 'call' : Call a function specified using the `seterrcall` function.
- 'print' : Print a warning directly to ``stdout``.
- 'log' : Record error in a Log object specified by `seterrcall`.
These behaviors can be set for all kinds of errors or specific ones:
- all : apply to all numeric exceptions
- invalid : when NaNs are generated
- divide : divide by zero (for integers as well!)
- overflow : floating point overflows
- underflow : floating point underflows
Note that integer divide-by-zero is handled by the same machinery.
These behaviors are set on a per-thread basis.
Examples
--------
::
>>> oldsettings = np.seterr(all='warn')
>>> np.zeros(5,dtype=np.float32)/0.
invalid value encountered in divide
>>> j = np.seterr(under='ignore')
>>> np.array([1.e-100])**10
>>> j = np.seterr(invalid='raise')
>>> np.sqrt(np.array([-1.]))
FloatingPointError: invalid value encountered in sqrt
>>> def errorhandler(errstr, errflag):
... print "saw stupid error!"
>>> np.seterrcall(errorhandler)
<function err_handler at 0x...>
>>> j = np.seterr(all='call')
>>> np.zeros(5, dtype=np.int32)/0
FloatingPointError: invalid value encountered in divide
saw stupid error!
>>> j = np.seterr(**oldsettings) # restore previous
... # error-handling settings
Interfacing to C
----------------
Only a survey of the choices. Little detail on how each works.
1) Bare metal, wrap your own C-code manually.
- Plusses:
- Efficient
- No dependencies on other tools
- Minuses:
- Lots of learning overhead:
- need to learn basics of Python C API
- need to learn basics of numpy C API
- need to learn how to handle reference counting and love it.
- Reference counting often difficult to get right.
- getting it wrong leads to memory leaks, and worse, segfaults
- API will change for Python 3.0!
2) Cython
- Plusses:
- avoid learning C API's
- no dealing with reference counting
- can code in pseudo python and generate C code
- can also interface to existing C code
- should shield you from changes to Python C api
- has become the de-facto standard within the scientific Python community
- fast indexing support for arrays
- Minuses:
- Can write code in non-standard form which may become obsolete
- Not as flexible as manual wrapping
4) ctypes
- Plusses:
- part of Python standard library
- good for interfacing to existing sharable libraries, particularly
Windows DLLs
- avoids API/reference counting issues
- good numpy support: arrays have all these in their ctypes
attribute: ::
a.ctypes.data a.ctypes.get_strides
a.ctypes.data_as a.ctypes.shape
a.ctypes.get_as_parameter a.ctypes.shape_as
a.ctypes.get_data a.ctypes.strides
a.ctypes.get_shape a.ctypes.strides_as
- Minuses:
- can't use for writing code to be turned into C extensions, only a wrapper
tool.
5) SWIG (automatic wrapper generator)
- Plusses:
- around a long time
- multiple scripting language support
- C++ support
- Good for wrapping large (many functions) existing C libraries
- Minuses:
- generates lots of code between Python and the C code
- can cause performance problems that are nearly impossible to optimize
out
- interface files can be hard to write
- doesn't necessarily avoid reference counting issues or needing to know
API's
7) scipy.weave
- Plusses:
- can turn many numpy expressions into C code
- dynamic compiling and loading of generated C code
- can embed pure C code in Python module and have weave extract, generate
interfaces and compile, etc.
- Minuses:
- Future very uncertain: it's the only part of Scipy not ported to Python 3
and is effectively deprecated in favor of Cython.
8) Psyco
- Plusses:
- Turns pure python into efficient machine code through jit-like
optimizations
- very fast when it optimizes well
- Minuses:
- Only on intel (windows?)
- Doesn't do much for numpy?
Interfacing to Fortran:
-----------------------
The clear choice to wrap Fortran code is
`f2py <http://docs.scipy.org/doc/numpy-dev/f2py/>`_.
Pyfort is an older alternative, but not supported any longer.
Fwrap is a newer project that looked promising but isn't being developed any
longer.
Interfacing to C++:
-------------------
1) Cython
2) CXX
3) Boost.python
4) SWIG
5) SIP (used mainly in PyQT)
""" |
"""
Convert an RDF graph into an image for displaying in the notebook, via GraphViz
It has two parts:
- conversion from rdf into dot language. Code based in rdflib.utils.rdf2dot
- rendering of the dot graph into an image. Code based on
ipython-hierarchymagic, which in turn bases it from Sphinx
See https://github.com/tkf/ipython-hierarchymagic
License for RDFLIB
------------------
Copyright (c) 2002-2015, RDFLib Team
See CONTRIBUTORS and http://github.com/RDFLib/rdflib
All rights reserved.
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 NAME 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.
License for ipython-hierarchymagic
----------------------------------
ipython-hierarchymagic is licensed under the term of the Simplified
BSD License (BSD 2-clause license), as follows:
Copyright (c) 2012 NAME rights reserved.
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.
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.
License for Sphinx
------------------
`run_dot` function and `HierarchyMagic._class_name` method in this
extension heavily based on Sphinx code `sphinx.ext.graphviz.render_dot`
and `InheritanceGraph.class_name`.
Copyright notice for Sphinx can be found below.
Copyright (c) 2007-2011 by the Sphinx team (see AUTHORS file).
All rights reserved.
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.
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.
""" |
#
# 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.
# --------------------------------------------------------------------
|
#
# 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.
# --------------------------------------------------------------------
|
"""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 local and global (<doctest test.test_syntax[0]>, line 1)
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):
File "<doctest test.test_syntax[1]>", line 1
SyntaxError: cannot assign to None
>>> None = 1
Traceback (most recent call last):
File "<doctest test.test_syntax[2]>", line 1
SyntaxError: cannot assign to None
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):
File "<doctest test.test_syntax[3]>", line 1
SyntaxError: can't assign to ()
>>> f() = 1
Traceback (most recent call last):
File "<doctest test.test_syntax[4]>", line 1
SyntaxError: can't assign to function call
>>> del f()
Traceback (most recent call last):
File "<doctest test.test_syntax[5]>", line 1
SyntaxError: can't delete function call
>>> a + 1 = 2
Traceback (most recent call last):
File "<doctest test.test_syntax[6]>", line 1
SyntaxError: can't assign to operator
>>> (x for x in x) = 1
Traceback (most recent call last):
File "<doctest test.test_syntax[7]>", line 1
SyntaxError: can't assign to generator expression
>>> 1 = 1
Traceback (most recent call last):
File "<doctest test.test_syntax[8]>", line 1
SyntaxError: can't assign to literal
>>> "abc" = 1
Traceback (most recent call last):
File "<doctest test.test_syntax[8]>", line 1
SyntaxError: can't assign to literal
>>> `1` = 1
Traceback (most recent call last):
File "<doctest test.test_syntax[10]>", line 1
SyntaxError: can't assign to repr
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):
File "<doctest test.test_syntax[11]>", line 1
SyntaxError: can't assign to literal
>>> [a, b, c + 1] = [1, 2, 3]
Traceback (most recent call last):
File "<doctest test.test_syntax[12]>", line 1
SyntaxError: can't assign to operator
>>> a if 1 else b = 1
Traceback (most recent call last):
File "<doctest test.test_syntax[13]>", line 1
SyntaxError: can't assign to conditional expression
From compiler_complex_args():
>>> def f(None=1):
... pass
Traceback (most recent call last):
File "<doctest test.test_syntax[14]>", line 1
SyntaxError: cannot assign to None
From ast_for_arguments():
>>> def f(x, y=1, z):
... pass
Traceback (most recent call last):
File "<doctest test.test_syntax[15]>", line 1
SyntaxError: non-default argument follows default argument
>>> def f(x, None):
... pass
Traceback (most recent call last):
File "<doctest test.test_syntax[16]>", line 1
SyntaxError: cannot assign to None
>>> def f(*None):
... pass
Traceback (most recent call last):
File "<doctest test.test_syntax[17]>", line 1
SyntaxError: cannot assign to None
>>> def f(**None):
... pass
Traceback (most recent call last):
File "<doctest test.test_syntax[18]>", line 1
SyntaxError: cannot assign to None
From ast_for_funcdef():
>>> def None(x):
... pass
Traceback (most recent call last):
File "<doctest test.test_syntax[19]>", line 1
SyntaxError: cannot assign to None
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):
File "<doctest test.test_syntax[23]>", line 1
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):
File "<doctest test.test_syntax[25]>", line 1
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):
File "<doctest test.test_syntax[26]>", line 1
SyntaxError: more than 255 arguments
>>> f(lambda x: x[0] = 3)
Traceback (most recent call last):
File "<doctest test.test_syntax[27]>", line 1
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):
File "<doctest test.test_syntax[28]>", line 1
SyntaxError: keyword can't be an expression
>>> f(a or b=1)
Traceback (most recent call last):
File "<doctest test.test_syntax[29]>", line 1
SyntaxError: keyword can't be an expression
>>> f(x.y=1)
Traceback (most recent call last):
File "<doctest test.test_syntax[30]>", line 1
SyntaxError: keyword can't be an expression
More set_context():
>>> (x for x in x) += 1
Traceback (most recent call last):
File "<doctest test.test_syntax[31]>", line 1
SyntaxError: can't assign to generator expression
>>> None += 1
Traceback (most recent call last):
File "<doctest test.test_syntax[32]>", line 1
SyntaxError: cannot assign to None
>>> f() += 1
Traceback (most recent call last):
File "<doctest test.test_syntax[33]>", line 1
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):
...
File "<doctest test.test_syntax[36]>", line 6
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):
...
File "<doctest test.test_syntax[37]>", line 6
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
File "<doctest test.test_syntax[38]>", line 5
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
File "<doctest test.test_syntax[39]>", line 6
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try:
... pass
... finally:
... try:
... continue
... finally:
... pass
Traceback (most recent call last):
...
File "<doctest test.test_syntax[40]>", line 7
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try: pass
... finally:
... try:
... pass
... except:
... continue
Traceback (most recent call last):
...
File "<doctest test.test_syntax[41]>", line 8
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):
...
File "<doctest test.test_syntax[42]>", line 3
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
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):
...
File "<doctest test.test_syntax[44]>", line 2
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... x() = 1
Traceback (most recent call last):
...
File "<doctest test.test_syntax[45]>", line 4
SyntaxError: can't assign to function call
>>> if 1:
... x() = 1
... elif 1:
... pass
... else:
... pass
Traceback (most recent call last):
...
File "<doctest test.test_syntax[46]>", line 2
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... x() = 1
... else:
... pass
Traceback (most recent call last):
...
File "<doctest test.test_syntax[47]>", line 4
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... pass
... else:
... x() = 1
Traceback (most recent call last):
...
File "<doctest test.test_syntax[48]>", line 6
SyntaxError: can't assign to function call
>>> f(a=23, a=234)
Traceback (most recent call last):
...
File "<doctest test.test_syntax[49]>", line 1
SyntaxError: keyword argument repeated
>>> del ()
Traceback (most recent call last):
...
File "<doctest test.test_syntax[50]>", line 1
SyntaxError: can't delete ()
>>> {1, 2, 3} = 42
Traceback (most recent call last):
...
File "<doctest test.test_syntax[50]>", line 1
SyntaxError: can't assign to literal
Corner-case that used to crash:
>>> def f(*xx, **__debug__): pass
Traceback (most recent call last):
SyntaxError: cannot assign to __debug__
""" |
#
# ElementTree
# $Id$
#
# 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.
#
# EMAIL 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.
# --------------------------------------------------------------------
|
"""
=============================
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``.
""" |
"""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.
""" |
"""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().
""" |
# (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
|
#
# XML-RPC CLIENT LIBRARY
# $Id: xmlrpclib.py 65467 2008-08-04 00:50:11Z USERNAME $
#
# 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
|
"""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.
""" |
"""subprocess - Subprocesses with accessible I/O streams
This module allows you to spawn processes, connect to their
input/output/error pipes, and obtain their return codes. This module
intends to replace several other, older modules and functions, like:
os.system
os.spawn*
os.popen*
popen2.*
commands.*
Information about how the subprocess module can be used to replace these
modules and functions can be found below.
Using the subprocess module
===========================
This module defines one class called Popen:
class Popen(args, bufsize=0, executable=None,
stdin=None, stdout=None, stderr=None,
preexec_fn=None, close_fds=False, shell=False,
cwd=None, env=None, universal_newlines=False,
startupinfo=None, creationflags=0):
Arguments are:
args should be a string, or a sequence of program arguments. The
program to execute is normally the first item in the args sequence or
string, but can be explicitly set by using the executable argument.
On UNIX, with shell=False (default): In this case, the Popen class
uses os.execvp() to execute the child program. args should normally
be a sequence. A string will be treated as a sequence with the string
as the only item (the program to execute).
On UNIX, with shell=True: If args is a string, it specifies the
command string to execute through the shell. If args is a sequence,
the first item specifies the command string, and any additional items
will be treated as additional shell arguments.
On Windows: the Popen class uses CreateProcess() to execute the child
program, which operates on strings. If args is a sequence, it will be
converted to a string using the list2cmdline method. Please note that
not all MS Windows applications interpret the command line the same
way: The list2cmdline is designed for applications using the same
rules as the MS C runtime.
bufsize, if given, has the same meaning as the corresponding argument
to the built-in open() function: 0 means unbuffered, 1 means line
buffered, any other positive value means use a buffer of
(approximately) that size. A negative bufsize means to use the system
default, which usually means fully buffered. The default value for
bufsize is 0 (unbuffered).
stdin, stdout and stderr specify the executed programs' standard
input, standard output and standard error file handles, respectively.
Valid values are PIPE, an existing file descriptor (a positive
integer), an existing file object, and None. PIPE indicates that a
new pipe to the child should be created. With None, no redirection
will occur; the child's file handles will be inherited from the
parent. Additionally, stderr can be STDOUT, which indicates that the
stderr data from the applications should be captured into the same
file handle as for stdout.
If preexec_fn is set to a callable object, this object will be called
in the child process just before the child is executed.
If close_fds is true, all file descriptors except 0, 1 and 2 will be
closed before the child process is executed.
if shell is true, the specified command will be executed through the
shell.
If cwd is not None, the current directory will be changed to cwd
before the child is executed.
If env is not None, it defines the environment variables for the new
process.
If universal_newlines is true, the file objects stdout and stderr are
opened as a text files, but lines may be terminated by any of '\n',
the Unix end-of-line convention, '\r', the Macintosh convention or
'\r\n', the Windows convention. All of these external representations
are seen as '\n' by the Python program. Note: This feature is only
available if Python is built with universal newline support (the
default). Also, the newlines attribute of the file objects stdout,
stdin and stderr are not updated by the communicate() method.
The startupinfo and creationflags, if given, will be passed to the
underlying CreateProcess() function. They can specify things such as
appearance of the main window and priority for the new process.
(Windows only)
This module also defines some shortcut functions:
call(*popenargs, **kwargs):
Run command with arguments. Wait for command to complete, then
return the returncode attribute.
The arguments are the same as for the Popen constructor. Example:
retcode = call(["ls", "-l"])
check_call(*popenargs, **kwargs):
Run command with arguments. Wait for command to complete. If the
exit code was zero then return, otherwise raise
CalledProcessError. The CalledProcessError object will have the
return code in the returncode attribute.
The arguments are the same as for the Popen constructor. Example:
check_call(["ls", "-l"])
check_output(*popenargs, **kwargs):
Run command with arguments and return its output as a byte string.
If the exit code was non-zero it raises a CalledProcessError. The
CalledProcessError object will have the return code in the returncode
attribute and output in the output attribute.
The arguments are the same as for the Popen constructor. Example:
output = check_output(["ls", "-l", "/dev/null"])
Exceptions
----------
Exceptions raised in the child process, before the new program has
started to execute, will be re-raised in the parent. Additionally,
the exception object will have one extra attribute called
'child_traceback', which is a string containing traceback information
from the childs point of view.
The most common exception raised is OSError. This occurs, for
example, when trying to execute a non-existent file. Applications
should prepare for OSErrors.
A ValueError will be raised if Popen is called with invalid arguments.
check_call() and check_output() will raise CalledProcessError, if the
called process returns a non-zero return code.
Security
--------
Unlike some other popen functions, this implementation will never call
/bin/sh implicitly. This means that all characters, including shell
metacharacters, can safely be passed to child processes.
Popen objects
=============
Instances of the Popen class have the following methods:
poll()
Check if child process has terminated. Returns returncode
attribute.
wait()
Wait for child process to terminate. Returns returncode attribute.
communicate(input=None)
Interact with process: Send data to stdin. Read data from stdout
and stderr, until end-of-file is reached. Wait for process to
terminate. The optional input argument should be a string to be
sent to the child process, or None, if no data should be sent to
the child.
communicate() returns a tuple (stdout, stderr).
Note: The data read is buffered in memory, so do not use this
method if the data size is large or unlimited.
The following attributes are also available:
stdin
If the stdin argument is PIPE, this attribute is a file object
that provides input to the child process. Otherwise, it is None.
stdout
If the stdout argument is PIPE, this attribute is a file object
that provides output from the child process. Otherwise, it is
None.
stderr
If the stderr argument is PIPE, this attribute is file object that
provides error output from the child process. Otherwise, it is
None.
pid
The process ID of the child process.
returncode
The child return code. A None value indicates that the process
hasn't terminated yet. A negative value -N indicates that the
child was terminated by signal N (UNIX only).
Replacing older functions with the subprocess module
====================================================
In this section, "a ==> b" means that b can be used as a replacement
for a.
Note: All functions in this section fail (more or less) silently if
the executed program cannot be found; this module raises an OSError
exception.
In the following examples, we assume that the subprocess module is
imported with "from subprocess import *".
Replacing /bin/sh shell backquote
---------------------------------
output=`mycmd myarg`
==>
output = Popen(["mycmd", "myarg"], stdout=PIPE).communicate()[0]
Replacing shell pipe line
-------------------------
output=`dmesg | grep hda`
==>
p1 = Popen(["dmesg"], stdout=PIPE)
p2 = Popen(["grep", "hda"], stdin=p1.stdout, stdout=PIPE)
output = p2.communicate()[0]
Replacing os.system()
---------------------
sts = os.system("mycmd" + " myarg")
==>
p = Popen("mycmd" + " myarg", shell=True)
pid, sts = os.waitpid(p.pid, 0)
Note:
* Calling the program through the shell is usually not required.
* It's easier to look at the returncode attribute than the
exitstatus.
A more real-world example would look like this:
try:
retcode = call("mycmd" + " myarg", shell=True)
if retcode < 0:
print >>sys.stderr, "Child was terminated by signal", -retcode
else:
print >>sys.stderr, "Child returned", retcode
except OSError, e:
print >>sys.stderr, "Execution failed:", e
Replacing os.spawn*
-------------------
P_NOWAIT example:
pid = os.spawnlp(os.P_NOWAIT, "/bin/mycmd", "mycmd", "myarg")
==>
pid = Popen(["/bin/mycmd", "myarg"]).pid
P_WAIT example:
retcode = os.spawnlp(os.P_WAIT, "/bin/mycmd", "mycmd", "myarg")
==>
retcode = call(["/bin/mycmd", "myarg"])
Vector example:
os.spawnvp(os.P_NOWAIT, path, args)
==>
Popen([path] + args[1:])
Environment example:
os.spawnlpe(os.P_NOWAIT, "/bin/mycmd", "mycmd", "myarg", env)
==>
Popen(["/bin/mycmd", "myarg"], env={"PATH": "/usr/bin"})
Replacing os.popen*
-------------------
pipe = os.popen("cmd", mode='r', bufsize)
==>
pipe = Popen("cmd", shell=True, bufsize=bufsize, stdout=PIPE).stdout
pipe = os.popen("cmd", mode='w', bufsize)
==>
pipe = Popen("cmd", shell=True, bufsize=bufsize, stdin=PIPE).stdin
(child_stdin, child_stdout) = os.popen2("cmd", mode, bufsize)
==>
p = Popen("cmd", shell=True, bufsize=bufsize,
stdin=PIPE, stdout=PIPE, close_fds=True)
(child_stdin, child_stdout) = (p.stdin, p.stdout)
(child_stdin,
child_stdout,
child_stderr) = os.popen3("cmd", mode, bufsize)
==>
p = Popen("cmd", shell=True, bufsize=bufsize,
stdin=PIPE, stdout=PIPE, stderr=PIPE, close_fds=True)
(child_stdin,
child_stdout,
child_stderr) = (p.stdin, p.stdout, p.stderr)
(child_stdin, child_stdout_and_stderr) = os.popen4("cmd", mode,
bufsize)
==>
p = Popen("cmd", shell=True, bufsize=bufsize,
stdin=PIPE, stdout=PIPE, stderr=STDOUT, close_fds=True)
(child_stdin, child_stdout_and_stderr) = (p.stdin, p.stdout)
On Unix, os.popen2, os.popen3 and os.popen4 also accept a sequence as
the command to execute, in which case arguments will be passed
directly to the program without shell intervention. This usage can be
replaced as follows:
(child_stdin, child_stdout) = os.popen2(["/bin/ls", "-l"], mode,
bufsize)
==>
p = Popen(["/bin/ls", "-l"], bufsize=bufsize, stdin=PIPE, stdout=PIPE)
(child_stdin, child_stdout) = (p.stdin, p.stdout)
Return code handling translates as follows:
pipe = os.popen("cmd", 'w')
...
rc = pipe.close()
if rc is not None and rc % 256:
print "There were some errors"
==>
process = Popen("cmd", 'w', shell=True, stdin=PIPE)
...
process.stdin.close()
if process.wait() != 0:
print "There were some errors"
Replacing popen2.*
------------------
(child_stdout, child_stdin) = popen2.popen2("somestring", bufsize, mode)
==>
p = Popen(["somestring"], shell=True, bufsize=bufsize
stdin=PIPE, stdout=PIPE, close_fds=True)
(child_stdout, child_stdin) = (p.stdout, p.stdin)
On Unix, popen2 also accepts a sequence as the command to execute, in
which case arguments will be passed directly to the program without
shell intervention. This usage can be replaced as follows:
(child_stdout, child_stdin) = popen2.popen2(["mycmd", "myarg"], bufsize,
mode)
==>
p = Popen(["mycmd", "myarg"], bufsize=bufsize,
stdin=PIPE, stdout=PIPE, close_fds=True)
(child_stdout, child_stdin) = (p.stdout, p.stdin)
The popen2.Popen3 and popen2.Popen4 basically works as subprocess.Popen,
except that:
* subprocess.Popen raises an exception if the execution fails
* the capturestderr argument is replaced with the stderr argument.
* stdin=PIPE and stdout=PIPE must be specified.
* popen2 closes all filedescriptors by default, but you have to specify
close_fds=True with subprocess.Popen.
""" |
# -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
# SKR03
# =====
# Dieses Modul bietet Ihnen einen deutschen Kontenplan basierend auf dem SKR03.
# Gemäss der aktuellen Einstellungen ist die Firma nicht Umsatzsteuerpflichtig.
# Diese Grundeinstellung ist sehr einfach zu ändern und bedarf in der Regel
# grundsätzlich eine initiale Zuweisung von Steuerkonten zu Produkten und / oder
# Sachkonten oder zu Partnern.
# Die Umsatzsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten bei den Produktstammdaten hinterlegt werden (in Abhängigkeit der
# Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter Finanzbuchhaltung
# (Kategorie: Umsatzsteuer).
# Die Vorsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten ebenso bei den Produktstammdaten hinterlegt werden (in Abhängigkeit
# der Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter
# Finanzbuchhaltung (Kategorie: Vorsteuer).
# Die Zuordnung der Steuern für Ein- und Ausfuhren aus EU Ländern, sowie auch
# für den Ein- und Verkauf aus und in Drittländer sollten beim Partner
# (Lieferant/Kunde)hinterlegt werden (in Anhängigkeit vom Herkunftsland
# des Lieferanten/Kunden). Die Zuordnung beim Kunden ist 'höherwertig' als
# die Zuordnung bei Produkten und überschreibt diese im Einzelfall.
#
# Zur Vereinfachung der Steuerausweise und Buchung bei Auslandsgeschäften
# erlaubt Odoo ein generelles Mapping von Steuerausweis und Steuerkonten
# (z.B. Zuordnung 'Umsatzsteuer 19%' zu 'steuerfreie Einfuhren aus der EU')
# zwecks Zuordnung dieses Mappings zum ausländischen Partner (Kunde/Lieferant).
# Die Rechnungsbuchung beim Einkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Vorsteuer Steuermessbetrag (z.B. Vorsteuer
# Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Vorsteuern' (z.B. Vorsteuer
# 19%). Durch multidimensionale Hierachien können verschiedene Positionen
# zusammengefasst werden und dann in Form eines Reports ausgegeben werden.
#
# Die Rechnungsbuchung beim Verkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Umsatzsteuer Steuermessbetrag
# (z.B. Umsatzsteuer Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Umsatzsteuer'
# (z.B. Umsatzsteuer 19%). Durch multidimensionale Hierachien können
# verschiedene Positionen zusammengefasst werden.
# Die zugewiesenen Steuerausweise können auf Ebene der einzelnen
# Rechnung (Eingangs- und Ausgangsrechnung) nachvollzogen werden,
# und dort gegebenenfalls angepasst werden.
# Rechnungsgutschriften führen zu einer Korrektur (Gegenposition)
# der Steuerbuchung, in Form einer spiegelbildlichen Buchung.
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# ***********************IMPORTANT NMAP LICENSE TERMS************************
# * *
# * The Nmap Security Scanner is (C) 1996-2013 Insecure.Com LLC. Nmap is *
# * also a registered trademark of Insecure.Com LLC. This program is free *
# * software; you may redistribute and/or modify it under the terms of the *
# * GNU General Public License as published by the Free Software *
# * Foundation; Version 2 ("GPL"), BUT ONLY WITH ALL OF THE CLARIFICATIONS *
# * AND EXCEPTIONS DESCRIBED HEREIN. This guarantees your right to use, *
# * modify, and redistribute this software under certain conditions. If *
# * you wish to embed Nmap technology into proprietary software, we sell *
# * alternative licenses (contact EMAIL Dozens of software *
# * vendors already license Nmap technology such as host discovery, port *
# * scanning, OS detection, version detection, and the Nmap Scripting *
# * Engine. *
# * *
# * Note that the GPL places important restrictions on "derivative works", *
# * yet it does not provide a detailed definition of that term. To avoid *
# * misunderstandings, we interpret that term as broadly as copyright law *
# * allows. For example, we consider an application to constitute a *
# * derivative work for the purpose of this license if it does any of the *
# * following with any software or content covered by this license *
# * ("Covered Software"): *
# * *
# * o Integrates source code from Covered Software. *
# * *
# * o Reads or includes copyrighted data files, such as Nmap's nmap-os-db *
# * or nmap-service-probes. *
# * *
# * o Is designed specifically to execute Covered Software and parse the *
# * results (as opposed to typical shell or execution-menu apps, which will *
# * execute anything you tell them to). *
# * *
# * o Includes Covered Software in a proprietary executable installer. The *
# * installers produced by InstallShield are an example of this. Including *
# * Nmap with other software in compressed or archival form does not *
# * trigger this provision, provided appropriate open source decompression *
# * or de-archiving software is widely available for no charge. For the *
# * purposes of this license, an installer is considered to include Covered *
# * Software even if it actually retrieves a copy of Covered Software from *
# * another source during runtime (such as by downloading it from the *
# * Internet). *
# * *
# * o Links (statically or dynamically) to a library which does any of the *
# * above. *
# * *
# * o Executes a helper program, module, or script to do any of the above. *
# * *
# * This list is not exclusive, but is meant to clarify our interpretation *
# * of derived works with some common examples. Other people may interpret *
# * the plain GPL differently, so we consider this a special exception to *
# * the GPL that we apply to Covered Software. Works which meet any of *
# * these conditions must conform to all of the terms of this license, *
# * particularly including the GPL Section 3 requirements of providing *
# * source code and allowing free redistribution of the work as a whole. *
# * *
# * As another special exception to the GPL terms, Insecure.Com LLC grants *
# * permission to link the code of this program with any version of the *
# * OpenSSL library which is distributed under a license identical to that *
# * listed in the included docs/licenses/OpenSSL.txt file, and distribute *
# * linked combinations including the two. *
# * *
# * Any redistribution of Covered Software, including any derived works, *
# * must obey and carry forward all of the terms of this license, including *
# * obeying all GPL rules and restrictions. For example, source code of *
# * the whole work must be provided and free redistribution must be *
# * allowed. All GPL references to "this License", are to be treated as *
# * including the terms and conditions of this license text as well. *
# * *
# * Because this license imposes special exceptions to the GPL, Covered *
# * Work may not be combined (even as part of a larger work) with plain GPL *
# * software. The terms, conditions, and exceptions of this license must *
# * be included as well. This license is incompatible with some other open *
# * source licenses as well. In some cases we can relicense portions of *
# * Nmap or grant special permissions to use it in other open source *
# * software. Please contact EMAIL with any such requests. *
# * Similarly, we don't incorporate incompatible open source software into *
# * Covered Software without special permission from the copyright holders. *
# * *
# * If you have any questions about the licensing restrictions on using *
# * Nmap in other works, are happy to help. As mentioned above, we also *
# * offer alternative license to integrate Nmap into proprietary *
# * applications and appliances. These contracts have been sold to dozens *
# * of software vendors, and generally include a perpetual license as well *
# * as providing for priority support and updates. They also fund the *
# * continued development of Nmap. Please email EMAIL for further *
# * information. *
# * *
# * If you have received a written license agreement or contract for *
# * Covered Software stating terms other than these, you may choose to use *
# * and redistribute Covered Software under those terms instead of these. *
# * *
# * Source is provided to this software because we believe users have a *
# * right to know exactly what a program is going to do before they run it. *
# * This also allows you to audit the software for security holes (none *
# * have been found so far). *
# * *
# * Source code also allows you to port Nmap to new platforms, fix bugs, *
# * and add new features. You are highly encouraged to send your changes *
# * to the EMAIL mailing list for possible incorporation into the *
# * main distribution. By sending these changes to Fyodor or one of the *
# * Insecure.Org development mailing lists, or checking them into the Nmap *
# * source code repository, it is understood (unless you specify otherwise) *
# * that you are offering the Nmap Project (Insecure.Com LLC) the *
# * unlimited, non-exclusive right to reuse, modify, and relicense the *
# * code. Nmap will always be available Open Source, but this is important *
# * because the inability to relicense code has caused devastating problems *
# * for other Free Software projects (such as KDE and NASM). We also *
# * occasionally relicense the code to third parties as discussed above. *
# * If you wish to specify special license conditions of your *
# * contributions, just say so when you send them. *
# * *
# * 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 Nmap *
# * license file for more details (it's in a COPYING file included with *
# * Nmap, and also available from https://svn.nmap.org/nmap/COPYING *
# * *
# ***************************************************************************/
|
"""
--- Day 10: Knot Hash ---
You come across some programs that are trying to implement a software emulation of a hash based on knot-tying. The hash these programs are implementing isn't very strong, but you decide to help them anyway. You make a mental note to remind the Elves later not to invent their own cryptographic functions.
This hash function simulates tying a knot in a circle of string with 256 marks on it. Based on the input to be hashed, the function repeatedly selects a span of string, brings the ends together, and gives the span a half-twist to reverse the order of the marks within it. After doing this many times, the order of the marks is used to build the resulting hash.
4--5 pinch 4 5 4 1
/ \ 5,0,1 / \/ \ twist / \ / \
3 0 --> 3 0 --> 3 X 0
\ / \ /\ / \ / \ /
2--1 2 1 2 5
To achieve this, begin with a list of numbers from 0 to 255, a current position which begins at 0 (the first element in the list), a skip size (which starts at 0), and a sequence of lengths (your puzzle input). Then, for each length:
Reverse the order of that length of elements in the list, starting with the element at the current position.
Move the current position forward by that length plus the skip size.
Increase the skip size by one.
The list is circular; if the current position and the length try to reverse elements beyond the end of the list, the operation reverses using as many extra elements as it needs from the front of the list. If the current position moves past the end of the list, it wraps around to the front. Lengths larger than the size of the list are invalid.
Here's an example using a smaller list:
Suppose we instead only had a circular list containing five elements, 0, 1, 2, 3, 4, and were given input lengths of 3, 4, 1, 5.
The list begins as [0] 1 2 3 4 (where square brackets indicate the current position).
The first length, 3, selects ([0] 1 2) 3 4 (where parentheses indicate the sublist to be reversed).
After reversing that section (0 1 2 into 2 1 0), we get ([2] 1 0) 3 4.
Then, the current position moves forward by the length, 3, plus the skip size, 0: 2 1 0 [3] 4. Finally, the skip size increases to 1.
The second length, 4, selects a section which wraps: 2 1) 0 ([3] 4.
The sublist 3 4 2 1 is reversed to form 1 2 4 3: 4 3) 0 ([1] 2.
The current position moves forward by the length plus the skip size, a total of 5, causing it not to move because it wraps around: 4 3 0 [1] 2. The skip size increases to 2.
The third length, 1, selects a sublist of a single element, and so reversing it has no effect.
The current position moves forward by the length (1) plus the skip size (2): 4 [3] 0 1 2. The skip size increases to 3.
The fourth length, 5, selects every element starting with the second: 4) ([3] 0 1 2. Reversing this sublist (3 0 1 2 4 into 4 2 1 0 3) produces: 3) ([4] 2 1 0.
Finally, the current position moves forward by 8: 3 4 2 1 [0]. The skip size increases to 4.
In this example, the first two numbers in the list end up being 3 and 4; to check the process, you can multiply them together to produce 12.
However, you should instead use the standard list size of 256 (with values 0 to 255) and the sequence of lengths in your puzzle input. Once this process is complete, what is the result of multiplying the first two numbers in the list?
--- Part Two ---
The logic you've constructed forms a single round of the Knot Hash algorithm; running the full thing requires many of these rounds. Some input and output processing is also required.
First, from now on, your input should be taken not as a list of numbers, but as a string of bytes instead. Unless otherwise specified, convert characters to bytes using their ASCII codes. This will allow you to handle arbitrary ASCII strings, and it also ensures that your input lengths are never larger than 255. For example, if you are given 1,2,3, you should convert it to the ASCII codes for each character: 49,44,50,44,51.
Once you have determined the sequence of lengths to use, add the following lengths to the end of the sequence: 17, 31, 73, 47, 23. For example, if you are given 1,2,3, your final sequence of lengths should be 49,44,50,44,51,17,31,73,47,23 (the ASCII codes from the input string combined with the standard length suffix values).
Second, instead of merely running one round like you did above, run a total of 64 rounds, using the same length sequence in each round. The current position and skip size should be preserved between rounds. For example, if the previous example was your first round, you would start your second round with the same length sequence (3, 4, 1, 5, 17, 31, 73, 47, 23, now assuming they came from ASCII codes and include the suffix), but start with the previous round's current position (4) and skip size (4).
Once the rounds are complete, you will be left with the numbers from 0 to 255 in some order, called the sparse hash. Your next task is to reduce these to a list of only 16 numbers called the dense hash. To do this, use numeric bitwise XOR to combine each consecutive block of 16 numbers in the sparse hash (there are 16 such blocks in a list of 256 numbers). So, the first element in the dense hash is the first sixteen elements of the sparse hash XOR'd together, the second element in the dense hash is the second sixteen elements of the sparse hash XOR'd together, etc.
For example, if the first sixteen elements of your sparse hash are as shown below, and the XOR operator is ^, you would calculate the first output number like this:
65 ^ 27 ^ 9 ^ 1 ^ 4 ^ 3 ^ 40 ^ 50 ^ 91 ^ 7 ^ 6 ^ 0 ^ 2 ^ 5 ^ 68 ^ 22 = 64
Perform this operation on each of the sixteen blocks of sixteen numbers in your sparse hash to determine the sixteen numbers in your dense hash.
Finally, the standard way to represent a Knot Hash is as a single hexadecimal string; the final output is the dense hash in hexadecimal notation. Because each number in your dense hash will be between 0 and 255 (inclusive), always represent each number as two hexadecimal digits (including a leading zero as necessary). So, if your first three numbers are 64, 7, 255, they correspond to the hexadecimal numbers 40, 07, ff, and so the first six characters of the hash would be 4007ff. Because every Knot Hash is sixteen such numbers, the hexadecimal representation is always 32 hexadecimal digits (0-f) long.
Here are some example hashes:
The empty string becomes a2582a3a0e66e6e86e3812dcb672a272.
AoC 2017 becomes 33efeb34ea91902bb2f59c9920caa6cd.
1,2,3 becomes 3efbe78a8d82f29979031a4aa0b16a9d.
1,2,4 becomes 63960835bcdc130f0b66d7ff4f6a5a8e.
Treating your puzzle input as a string of ASCII characters, what is the Knot Hash of your puzzle input? Ignore any leading or trailing whitespace you might encounter.
""" |
"""
Simple config
=============
Although CherryPy uses the :mod:`Python logging module <logging>`, it does so
behind the scenes so that simple logging is simple, but complicated logging
is still possible. "Simple" logging means that you can log to the screen
(i.e. console/stdout) or to a file, and that you can easily have separate
error and access log files.
Here are the simplified logging settings. You use these by adding lines to
your config file or dict. You should set these at either the global level or
per application (see next), but generally not both.
* ``log.screen``: Set this to True to have both "error" and "access" messages
printed to stdout.
* ``log.access_file``: Set this to an absolute filename where you want
"access" messages written.
* ``log.error_file``: Set this to an absolute filename where you want "error"
messages written.
Many events are automatically logged; to log your own application events, call
:func:`cherrypy.log`.
Architecture
============
Separate scopes
---------------
CherryPy provides log managers at both the global and application layers.
This means you can have one set of logging rules for your entire site,
and another set of rules specific to each application. The global log
manager is found at :func:`cherrypy.log`, and the log manager for each
application is found at :attr:`app.log<cherrypy._cptree.Application.log>`.
If you're inside a request, the latter is reachable from
``cherrypy.request.app.log``; if you're outside a request, you'll have to
obtain a reference to the ``app``: either the return value of
:func:`tree.mount()<cherrypy._cptree.Tree.mount>` or, if you used
:func:`quickstart()<cherrypy.quickstart>` instead, via
``cherrypy.tree.apps['/']``.
By default, the global logs are named "cherrypy.error" and "cherrypy.access",
and the application logs are named "cherrypy.error.2378745" and
"cherrypy.access.2378745" (the number is the id of the Application object).
This means that the application logs "bubble up" to the site logs, so if your
application has no log handlers, the site-level handlers will still log the
messages.
Errors vs. Access
-----------------
Each log manager handles both "access" messages (one per HTTP request) and
"error" messages (everything else). Note that the "error" log is not just for
errors! The format of access messages is highly formalized, but the error log
isn't--it receives messages from a variety of sources (including full error
tracebacks, if enabled).
If you are logging the access log and error log to the same source, then there
is a possibility that a specially crafted error message may replicate an access
log message as described in CWE-117. In this case it is the application
developer's responsibility to manually escape data before using CherryPy's log()
functionality, or they may create an application that is vulnerable to CWE-117.
This would be achieved by using a custom handler escape any special characters,
and attached as described below.
Custom Handlers
===============
The simple settings above work by manipulating Python's standard :mod:`logging`
module. So when you need something more complex, the full power of the standard
module is yours to exploit. You can borrow or create custom handlers, formats,
filters, and much more. Here's an example that skips the standard FileHandler
and uses a RotatingFileHandler instead:
::
#python
log = app.log
# Remove the default FileHandlers if present.
log.error_file = ""
log.access_file = ""
maxBytes = getattr(log, "rot_maxBytes", 10000000)
backupCount = getattr(log, "rot_backupCount", 1000)
# Make a new RotatingFileHandler for the error log.
fname = getattr(log, "rot_error_file", "error.log")
h = handlers.RotatingFileHandler(fname, 'a', maxBytes, backupCount)
h.setLevel(DEBUG)
h.setFormatter(_cplogging.logfmt)
log.error_log.addHandler(h)
# Make a new RotatingFileHandler for the access log.
fname = getattr(log, "rot_access_file", "access.log")
h = handlers.RotatingFileHandler(fname, 'a', maxBytes, backupCount)
h.setLevel(DEBUG)
h.setFormatter(_cplogging.logfmt)
log.access_log.addHandler(h)
The ``rot_*`` attributes are pulled straight from the application log object.
Since "log.*" config entries simply set attributes on the log object, you can
add custom attributes to your heart's content. Note that these handlers are
used ''instead'' of the default, simple handlers outlined above (so don't set
the "log.error_file" config entry, for example).
""" |
"""Discussion of bloom constants for bup:
There are four basic things to consider when building a bloom filter:
The size, in bits, of the filter
The capacity, in entries, of the filter
The probability of a false positive that is tolerable
The number of bits readily available to use for addressing filter bits
There is one major tunable that is not directly related to the above:
k: the number of bits set in the filter per entry
Here's a wall of numbers showing the relationship between k; the ratio between
the filter size in bits and the entries in the filter; and pfalse_positive:
mn|k=3 |k=4 |k=5 |k=6 |k=7 |k=8 |k=9 |k=10 |k=11
8|3.05794|2.39687|2.16792|2.15771|2.29297|2.54917|2.92244|3.41909|4.05091
9|2.27780|1.65770|1.40703|1.32721|1.34892|1.44631|1.61138|1.84491|2.15259
10|1.74106|1.18133|0.94309|0.84362|0.81937|0.84555|0.91270|1.01859|1.16495
11|1.36005|0.86373|0.65018|0.55222|0.51259|0.50864|0.53098|0.57616|0.64387
12|1.08231|0.64568|0.45945|0.37108|0.32939|0.31424|0.31695|0.33387|0.36380
13|0.87517|0.49210|0.33183|0.25527|0.21689|0.19897|0.19384|0.19804|0.21013
14|0.71759|0.38147|0.24433|0.17934|0.14601|0.12887|0.12127|0.12012|0.12399
15|0.59562|0.30019|0.18303|0.12840|0.10028|0.08523|0.07749|0.07440|0.07468
16|0.49977|0.23941|0.13925|0.09351|0.07015|0.05745|0.05049|0.04700|0.04587
17|0.42340|0.19323|0.10742|0.06916|0.04990|0.03941|0.03350|0.03024|0.02870
18|0.36181|0.15765|0.08392|0.05188|0.03604|0.02748|0.02260|0.01980|0.01827
19|0.31160|0.12989|0.06632|0.03942|0.02640|0.01945|0.01549|0.01317|0.01182
20|0.27026|0.10797|0.05296|0.03031|0.01959|0.01396|0.01077|0.00889|0.00777
21|0.23591|0.09048|0.04269|0.02356|0.01471|0.01014|0.00759|0.00609|0.00518
22|0.20714|0.07639|0.03473|0.01850|0.01117|0.00746|0.00542|0.00423|0.00350
23|0.18287|0.06493|0.02847|0.01466|0.00856|0.00555|0.00392|0.00297|0.00240
24|0.16224|0.05554|0.02352|0.01171|0.00663|0.00417|0.00286|0.00211|0.00166
25|0.14459|0.04779|0.01957|0.00944|0.00518|0.00316|0.00211|0.00152|0.00116
26|0.12942|0.04135|0.01639|0.00766|0.00408|0.00242|0.00157|0.00110|0.00082
27|0.11629|0.03595|0.01381|0.00626|0.00324|0.00187|0.00118|0.00081|0.00059
28|0.10489|0.03141|0.01170|0.00515|0.00259|0.00146|0.00090|0.00060|0.00043
29|0.09492|0.02756|0.00996|0.00426|0.00209|0.00114|0.00069|0.00045|0.00031
30|0.08618|0.02428|0.00853|0.00355|0.00169|0.00090|0.00053|0.00034|0.00023
31|0.07848|0.02147|0.00733|0.00297|0.00138|0.00072|0.00041|0.00025|0.00017
32|0.07167|0.01906|0.00633|0.00250|0.00113|0.00057|0.00032|0.00019|0.00013
Here's a table showing available repository size for a given pfalse_positive
and three values of k (assuming we only use the 160 bit SHA1 for addressing the
filter and 8192bytes per object):
pfalse|obj k=4 |cap k=4 |obj k=5 |cap k=5 |obj k=6 |cap k=6
2.500%|139333497228|1038.11 TiB|558711157|4262.63 GiB|13815755|105.41 GiB
1.000%|104489450934| 778.50 TiB|436090254|3327.10 GiB|11077519| 84.51 GiB
0.125%| 57254889824| 426.58 TiB|261732190|1996.86 GiB| 7063017| 55.89 GiB
This eliminates pretty neatly any k>6 as long as we use the raw SHA for
addressing.
filter size scales linearly with repository size for a given k and pfalse.
Here's a table of filter sizes for a 1 TiB repository:
pfalse| k=3 | k=4 | k=5 | k=6
2.500%| 138.78 MiB | 126.26 MiB | 123.00 MiB | 123.37 MiB
1.000%| 197.83 MiB | 168.36 MiB | 157.58 MiB | 153.87 MiB
0.125%| 421.14 MiB | 307.26 MiB | 262.56 MiB | 241.32 MiB
For bup:
* We want the bloom filter to fit in memory; if it doesn't, the k pagefaults
per lookup will be worse than the two required for midx.
* We want the pfalse_positive to be low enough that the cost of sometimes
faulting on the midx doesn't overcome the benefit of the bloom filter.
* We have readily available 160 bits for addressing the filter.
* We want to be able to have a single bloom address entire repositories of
reasonable size.
Based on these parameters, a combination of k=4 and k=5 provides the behavior
that bup needs. As such, I've implemented bloom addressing, adding and
checking functions in C for these two values. Because k=5 requires less space
and gives better overall pfalse_positive performance, it is preferred if a
table with k=5 can represent the repository.
None of this tells us what max_pfalse_positive to choose.
Brandon NAME <lostlogic@lostlogicx.com> 2011-02-04
""" |
"""
Simple config
=============
Although CherryPy uses the :mod:`Python logging module <logging>`, it does so
behind the scenes so that simple logging is simple, but complicated logging
is still possible. "Simple" logging means that you can log to the screen
(i.e. console/stdout) or to a file, and that you can easily have separate
error and access log files.
Here are the simplified logging settings. You use these by adding lines to
your config file or dict. You should set these at either the global level or
per application (see next), but generally not both.
* ``log.screen``: Set this to True to have both "error" and "access" messages
printed to stdout.
* ``log.access_file``: Set this to an absolute filename where you want
"access" messages written.
* ``log.error_file``: Set this to an absolute filename where you want "error"
messages written.
Many events are automatically logged; to log your own application events, call
:func:`cherrypy.log`.
Architecture
============
Separate scopes
---------------
CherryPy provides log managers at both the global and application layers.
This means you can have one set of logging rules for your entire site,
and another set of rules specific to each application. The global log
manager is found at :func:`cherrypy.log`, and the log manager for each
application is found at :attr:`app.log<cherrypy._cptree.Application.log>`.
If you're inside a request, the latter is reachable from
``cherrypy.request.app.log``; if you're outside a request, you'll have to obtain
a reference to the ``app``: either the return value of
:func:`tree.mount()<cherrypy._cptree.Tree.mount>` or, if you used
:func:`quickstart()<cherrypy.quickstart>` instead, via ``cherrypy.tree.apps['/']``.
By default, the global logs are named "cherrypy.error" and "cherrypy.access",
and the application logs are named "cherrypy.error.2378745" and
"cherrypy.access.2378745" (the number is the id of the Application object).
This means that the application logs "bubble up" to the site logs, so if your
application has no log handlers, the site-level handlers will still log the
messages.
Errors vs. Access
-----------------
Each log manager handles both "access" messages (one per HTTP request) and
"error" messages (everything else). Note that the "error" log is not just for
errors! The format of access messages is highly formalized, but the error log
isn't--it receives messages from a variety of sources (including full error
tracebacks, if enabled).
Custom Handlers
===============
The simple settings above work by manipulating Python's standard :mod:`logging`
module. So when you need something more complex, the full power of the standard
module is yours to exploit. You can borrow or create custom handlers, formats,
filters, and much more. Here's an example that skips the standard FileHandler
and uses a RotatingFileHandler instead:
::
#python
log = app.log
# Remove the default FileHandlers if present.
log.error_file = ""
log.access_file = ""
maxBytes = getattr(log, "rot_maxBytes", 10000000)
backupCount = getattr(log, "rot_backupCount", 1000)
# Make a new RotatingFileHandler for the error log.
fname = getattr(log, "rot_error_file", "error.log")
h = handlers.RotatingFileHandler(fname, 'a', maxBytes, backupCount)
h.setLevel(DEBUG)
h.setFormatter(_cplogging.logfmt)
log.error_log.addHandler(h)
# Make a new RotatingFileHandler for the access log.
fname = getattr(log, "rot_access_file", "access.log")
h = handlers.RotatingFileHandler(fname, 'a', maxBytes, backupCount)
h.setLevel(DEBUG)
h.setFormatter(_cplogging.logfmt)
log.access_log.addHandler(h)
The ``rot_*`` attributes are pulled straight from the application log object.
Since "log.*" config entries simply set attributes on the log object, you can
add custom attributes to your heart's content. Note that these handlers are
used ''instead'' of the default, simple handlers outlined above (so don't set
the "log.error_file" config entry, for example).
""" |
"""
=============
Miscellaneous
=============
IEEE 754 Floating Point Special Values:
-----------------------------------------------
Special values defined in numpy: nan, inf,
NaNs can be used as a poor-man's mask (if you don't care what the
original value was)
Note: cannot use equality to test NaNs. E.g.: ::
>>> myarr = np.array([1., 0., np.nan, 3.])
>>> np.where(myarr == np.nan)
>>> np.nan == np.nan # is always False! Use special numpy functions instead.
False
>>> myarr[myarr == np.nan] = 0. # doesn't work
>>> myarr
array([ 1., 0., NaN, 3.])
>>> myarr[np.isnan(myarr)] = 0. # use this instead find
>>> myarr
array([ 1., 0., 0., 3.])
Other related special value functions: ::
isinf(): True if value is inf
isfinite(): True if not nan or inf
nan_to_num(): Map nan to 0, inf to max float, -inf to min float
The following corresponds to the usual functions except that nans are excluded
from the results: ::
nansum()
nanmax()
nanmin()
nanargmax()
nanargmin()
>>> x = np.arange(10.)
>>> x[3] = np.nan
>>> x.sum()
nan
>>> np.nansum(x)
42.0
How numpy handles numerical exceptions:
------------------------------------------
The default is to ``'warn'`` for ``invalid``, ``divide``, and ``overflow``
and ``'ignore'`` for ``underflow``. But this can be changed, and it can be
set individually for different kinds of exceptions. The different behaviors
are:
- 'ignore' : Take no action when the exception occurs.
- 'warn' : Print a `RuntimeWarning` (via the Python `warnings` module).
- 'raise' : Raise a `FloatingPointError`.
- 'call' : Call a function specified using the `seterrcall` function.
- 'print' : Print a warning directly to ``stdout``.
- 'log' : Record error in a Log object specified by `seterrcall`.
These behaviors can be set for all kinds of errors or specific ones:
- all : apply to all numeric exceptions
- invalid : when NaNs are generated
- divide : divide by zero (for integers as well!)
- overflow : floating point overflows
- underflow : floating point underflows
Note that integer divide-by-zero is handled by the same machinery.
These behaviors are set on a per-thread basis.
Examples:
------------
::
>>> oldsettings = np.seterr(all='warn')
>>> np.zeros(5,dtype=np.float32)/0.
invalid value encountered in divide
>>> j = np.seterr(under='ignore')
>>> np.array([1.e-100])**10
>>> j = np.seterr(invalid='raise')
>>> np.sqrt(np.array([-1.]))
FloatingPointError: invalid value encountered in sqrt
>>> def errorhandler(errstr, errflag):
... print "saw stupid error!"
>>> np.seterrcall(errorhandler)
<function err_handler at 0x...>
>>> j = np.seterr(all='call')
>>> np.zeros(5, dtype=np.int32)/0
FloatingPointError: invalid value encountered in divide
saw stupid error!
>>> j = np.seterr(**oldsettings) # restore previous
... # error-handling settings
Interfacing to C:
-----------------
Only a survey of the choices. Little detail on how each works.
1) Bare metal, wrap your own C-code manually.
- Plusses:
- Efficient
- No dependencies on other tools
- Minuses:
- Lots of learning overhead:
- need to learn basics of Python C API
- need to learn basics of numpy C API
- need to learn how to handle reference counting and love it.
- Reference counting often difficult to get right.
- getting it wrong leads to memory leaks, and worse, segfaults
- API will change for Python 3.0!
2) pyrex
- Plusses:
- avoid learning C API's
- no dealing with reference counting
- can code in psuedo python and generate C code
- can also interface to existing C code
- should shield you from changes to Python C api
- become pretty popular within Python community
- Minuses:
- Can write code in non-standard form which may become obsolete
- Not as flexible as manual wrapping
- Maintainers not easily adaptable to new features
Thus:
3) cython - fork of pyrex to allow needed features for SAGE
- being considered as the standard scipy/numpy wrapping tool
- fast indexing support for arrays
4) ctypes
- Plusses:
- part of Python standard library
- good for interfacing to existing sharable libraries, particularly
Windows DLLs
- avoids API/reference counting issues
- good numpy support: arrays have all these in their ctypes
attribute: ::
a.ctypes.data a.ctypes.get_strides
a.ctypes.data_as a.ctypes.shape
a.ctypes.get_as_parameter a.ctypes.shape_as
a.ctypes.get_data a.ctypes.strides
a.ctypes.get_shape a.ctypes.strides_as
- Minuses:
- can't use for writing code to be turned into C extensions, only a wrapper
tool.
5) SWIG (automatic wrapper generator)
- Plusses:
- around a long time
- multiple scripting language support
- C++ support
- Good for wrapping large (many functions) existing C libraries
- Minuses:
- generates lots of code between Python and the C code
- can cause performance problems that are nearly impossible to optimize
out
- interface files can be hard to write
- doesn't necessarily avoid reference counting issues or needing to know
API's
7) Weave
- Plusses:
- Phenomenal tool
- can turn many numpy expressions into C code
- dynamic compiling and loading of generated C code
- can embed pure C code in Python module and have weave extract, generate
interfaces and compile, etc.
- Minuses:
- Future uncertain--lacks a champion
8) Psyco
- Plusses:
- Turns pure python into efficient machine code through jit-like
optimizations
- very fast when it optimizes well
- Minuses:
- Only on intel (windows?)
- Doesn't do much for numpy?
Interfacing to Fortran:
-----------------------
Fortran: Clear choice is f2py. (Pyfort is an older alternative, but not
supported any longer)
Interfacing to C++:
-------------------
1) CXX
2) Boost.python
3) SWIG
4) Sage has used cython to wrap C++ (not pretty, but it can be done)
5) SIP (used mainly in PyQT)
""" |
"""
Linear mixed effects models are regression models for dependent data.
They can be used to estimate regression relationships involving both
means and variances.
These models are also known as multilevel linear models, and
hierarchical linear models.
The MixedLM class fits linear mixed effects models to data, and
provides support for some common post-estimation tasks. This is a
group-based implementation that is most efficient for models in which
the data can be partitioned into independent groups. Some models with
crossed effects can be handled by specifying a model with a single
group.
The data are partitioned into disjoint groups. The probability model
for group i is:
Y = X*beta + Z*gamma + epsilon
where
* n_i is the number of observations in group i
* Y is a n_i dimensional response vector (called endog in MixedLM)
* X is a n_i x k_fe dimensional design matrix for the fixed effects
(called exog in MixedLM)
* beta is a k_fe-dimensional vector of fixed effects parameters
(called fe_params in MixedLM)
* Z is a design matrix for the random effects with n_i rows (called
exog_re in MixedLM). The number of columns in Z can vary by group
as discussed below.
* gamma is a random vector with mean 0. The covariance matrix for the
first `k_re` elements of `gamma` (called cov_re in MixedLM) is
common to all groups. The remaining elements of `gamma` are
variance components as discussed in more detail below. Each group
receives its own independent realization of gamma.
* epsilon is a n_i dimensional vector of iid normal
errors with mean 0 and variance sigma^2; the epsilon
values are independent both within and between groups
Y, X and Z must be entirely observed. beta, Psi, and sigma^2 are
estimated using ML or REML estimation, and gamma and epsilon are
random so define the probability model.
The marginal mean structure is E[Y | X, Z] = X*beta. If only the mean
structure is of interest, GEE is an alternative to using linear mixed
models.
Two types of random effects are supported. Standard random effects
are correlated with each other in arbitrary ways. Every group has the
same number (`k_re`) of standard random effects, with the same joint
distribution (but with independent realizations across the groups).
Variance components are uncorrelated with each other, and with the
standard random effects. Each variance component has mean zero, and
all realizations of a given variance component have the same variance
parameter. The number of realized variance components per variance
parameter can differ across the groups.
The primary reference for the implementation details is:
MJ NAME NAME (1988). "Newton Raphson and EM algorithms for
linear mixed effects models for repeated measures data". Journal of
the American Statistical Association. Volume 83, Issue 404, pages
1014-1022.
See also this more recent document:
http://econ.ucsb.edu/~doug/245a/Papers/Mixed%20Effects%20Implement.pdf
All the likelihood, gradient, and Hessian calculations closely follow
Lindstrom and Bates 1988, adapted to support variance components.
The following two documents are written more from the perspective of
users:
http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf
http://lme4.r-forge.r-project.org/slides/2009-07-07-Rennes/3Longitudinal-4.pdf
Notation:
* `cov_re` is the random effects covariance matrix (referred to above
as Psi) and `scale` is the (scalar) error variance. For a single
group, the marginal covariance matrix of endog given exog is scale*I
+ Z * cov_re * Z', where Z is the design matrix for the random
effects in one group.
* `vcomp` is a vector of variance parameters. The length of `vcomp`
is determined by the number of keys in either the `exog_vc` argument
to ``MixedLM``, or the `vc_formula` argument when using formulas to
fit a model.
Notes:
1. Three different parameterizations are used in different places.
The regression slopes (usually called `fe_params`) are identical in
all three parameterizations, but the variance parameters differ. The
parameterizations are:
* The "user parameterization" in which cov(endog) = scale*I + Z *
cov_re * Z', as described above. This is the main parameterization
visible to the user.
* The "profile parameterization" in which cov(endog) = I +
Z * cov_re1 * Z'. This is the parameterization of the profile
likelihood that is maximized to produce parameter estimates.
(see Lindstrom and Bates for details). The "user" cov_re is
equal to the "profile" cov_re1 times the scale.
* The "square root parameterization" in which we work with the Cholesky
factor of cov_re1 instead of cov_re directly. This is hidden from the
user.
All three parameterizations can be packed into a vector by
(optionally) concatenating `fe_params` together with the lower
triangle or Cholesky square root of the dependence structure, followed
by the variance parameters for the variance components. The are
stored as square roots if (and only if) the random effects covariance
matrix is stored as its Cholesky factor. Note that when unpacking, it
is important to either square or reflect the dependence structure
depending on which parameterization is being used.
Two score methods are implemented. One takes the score with respect
to the elements of the random effects covariance matrix (used for
inference once the MLE is reached), and the other takes the score with
respect to the parameters of the Cholesky square root of the random
effects covariance matrix (used for optimization).
The numerical optimization uses GLS to avoid explicitly optimizing
over the fixed effects parameters. The likelihood that is optimized
is profiled over both the scale parameter (a scalar) and the fixed
effects parameters (if any). As a result of this profiling, it is
difficult and unnecessary to calculate the Hessian of the profiled log
likelihood function, so that calculation is not implemented here.
Therefore, optimization methods requiring the Hessian matrix such as
the Newton-Raphson algorithm cannot be used for model fitting.
""" |
"""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.
""" |
"""
=====================================
Sparse matrices (:mod:`scipy.sparse`)
=====================================
.. currentmodule:: scipy.sparse
SciPy 2-D sparse matrix package for numeric data.
Contents
========
Sparse matrix classes
---------------------
.. autosummary::
:toctree: generated/
bsr_matrix - Block Sparse Row matrix
coo_matrix - A sparse matrix in COOrdinate format
csc_matrix - Compressed Sparse Column matrix
csr_matrix - Compressed Sparse Row matrix
dia_matrix - Sparse matrix with DIAgonal storage
dok_matrix - Dictionary Of Keys based sparse matrix
lil_matrix - Row-based linked list sparse matrix
Functions
---------
Building sparse matrices:
.. autosummary::
:toctree: generated/
eye - Sparse MxN matrix whose k-th diagonal is all ones
identity - Identity matrix in sparse format
kron - kronecker product of two sparse matrices
kronsum - kronecker sum of sparse matrices
diags - Return a sparse matrix from diagonals
spdiags - Return a sparse matrix from diagonals
block_diag - Build a block diagonal sparse matrix
tril - Lower triangular portion of a matrix in sparse format
triu - Upper triangular portion of a matrix in sparse format
bmat - Build a sparse matrix from sparse sub-blocks
hstack - Stack sparse matrices horizontally (column wise)
vstack - Stack sparse matrices vertically (row wise)
rand - Random values in a given shape
Sparse matrix tools:
.. autosummary::
:toctree: generated/
find
Identifying sparse matrices:
.. autosummary::
:toctree: generated/
issparse
isspmatrix
isspmatrix_csc
isspmatrix_csr
isspmatrix_bsr
isspmatrix_lil
isspmatrix_dok
isspmatrix_coo
isspmatrix_dia
Submodules
----------
.. autosummary::
:toctree: generated/
csgraph - Compressed sparse graph routines
linalg - sparse linear algebra routines
Exceptions
----------
.. autosummary::
:toctree: generated/
SparseEfficiencyWarning
SparseWarning
Usage information
=================
There are seven available sparse matrix types:
1. csc_matrix: Compressed Sparse Column format
2. csr_matrix: Compressed Sparse Row format
3. bsr_matrix: Block Sparse Row format
4. lil_matrix: List of Lists format
5. dok_matrix: Dictionary of Keys format
6. coo_matrix: COOrdinate format (aka IJV, triplet format)
7. dia_matrix: DIAgonal format
To construct a matrix efficiently, use either dok_matrix or lil_matrix.
The lil_matrix class supports basic slicing and fancy
indexing with a similar syntax to NumPy arrays. As illustrated below,
the COO format may also be used to efficiently construct matrices.
To perform manipulations such as multiplication or inversion, first
convert the matrix to either CSC or CSR format. The lil_matrix format is
row-based, so conversion to CSR is efficient, whereas conversion to CSC
is less so.
All conversions among the CSR, CSC, and COO formats are efficient,
linear-time operations.
Matrix vector product
---------------------
To do a vector product between a sparse matrix and a vector simply use
the matrix `dot` method, as described in its docstring:
>>> import numpy as np
>>> from scipy.sparse import csr_matrix
>>> A = csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
>>> v = np.array([1, 0, -1])
>>> A.dot(v)
array([ 1, -3, -1], dtype=int64)
.. warning:: As of NumPy 1.7, `np.dot` is not aware of sparse matrices,
therefore using it will result on unexpected results or errors.
The corresponding dense array should be obtained first instead:
>>> np.dot(A.toarray(), v)
array([ 1, -3, -1], dtype=int64)
but then all the performance advantages would be lost.
The CSR format is specially suitable for fast matrix vector products.
Example 1
---------
Construct a 1000x1000 lil_matrix and add some values to it:
>>> from scipy.sparse import lil_matrix
>>> from scipy.sparse.linalg import spsolve
>>> from numpy.linalg import solve, norm
>>> from numpy.random import rand
>>> A = lil_matrix((1000, 1000))
>>> A[0, :100] = rand(100)
>>> A[1, 100:200] = A[0, :100]
>>> A.setdiag(rand(1000))
Now convert it to CSR format and solve A x = b for x:
>>> A = A.tocsr()
>>> b = rand(1000)
>>> x = spsolve(A, b)
Convert it to a dense matrix and solve, and check that the result
is the same:
>>> x_ = solve(A.toarray(), b)
Now we can compute norm of the error with:
>>> err = norm(x-x_)
>>> err < 1e-10
True
It should be small :)
Example 2
---------
Construct a matrix in COO format:
>>> from scipy import sparse
>>> from numpy import array
>>> I = array([0,3,1,0])
>>> J = array([0,3,1,2])
>>> V = array([4,5,7,9])
>>> A = sparse.coo_matrix((V,(I,J)),shape=(4,4))
Notice that the indices do not need to be sorted.
Duplicate (i,j) entries are summed when converting to CSR or CSC.
>>> I = array([0,0,1,3,1,0,0])
>>> J = array([0,2,1,3,1,0,0])
>>> V = array([1,1,1,1,1,1,1])
>>> B = sparse.coo_matrix((V,(I,J)),shape=(4,4)).tocsr()
This is useful for constructing finite-element stiffness and mass matrices.
Further Details
---------------
CSR column indices are not necessarily sorted. Likewise for CSC row
indices. Use the .sorted_indices() and .sort_indices() methods when
sorted indices are required (e.g. when passing data to other libraries).
""" |
"""
Low-level LAPACK functions (:mod:`scipy.linalg.lapack`)
=======================================================
This module contains low-level functions from the LAPACK library.
The `*gegv` family of routines have been removed from LAPACK 3.6.0
and have been deprecated in SciPy 0.17.0. They will be removed in
a future release.
.. versionadded:: 0.12.0
.. warning::
These functions do little to no error checking.
It is possible to cause crashes by mis-using them,
so prefer using the higher-level routines in `scipy.linalg`.
Finding functions
-----------------
.. autosummary::
get_lapack_funcs
All functions
-------------
.. autosummary::
:toctree: generated/
sgbsv
dgbsv
cgbsv
zgbsv
sgbtrf
dgbtrf
cgbtrf
zgbtrf
sgbtrs
dgbtrs
cgbtrs
zgbtrs
sgebal
dgebal
cgebal
zgebal
sgees
dgees
cgees
zgees
sgeev
dgeev
cgeev
zgeev
sgeev_lwork
dgeev_lwork
cgeev_lwork
zgeev_lwork
sgegv
dgegv
cgegv
zgegv
sgehrd
dgehrd
cgehrd
zgehrd
sgehrd_lwork
dgehrd_lwork
cgehrd_lwork
zgehrd_lwork
sgelss
dgelss
cgelss
zgelss
sgelss_lwork
dgelss_lwork
cgelss_lwork
zgelss_lwork
sgelsd
dgelsd
cgelsd
zgelsd
sgelsd_lwork
dgelsd_lwork
cgelsd_lwork
zgelsd_lwork
sgelsy
dgelsy
cgelsy
zgelsy
sgelsy_lwork
dgelsy_lwork
cgelsy_lwork
zgelsy_lwork
sgeqp3
dgeqp3
cgeqp3
zgeqp3
sgeqrf
dgeqrf
cgeqrf
zgeqrf
sgerqf
dgerqf
cgerqf
zgerqf
sgesdd
dgesdd
cgesdd
zgesdd
sgesdd_lwork
dgesdd_lwork
cgesdd_lwork
zgesdd_lwork
sgesvd
dgesvd
cgesvd
zgesvd
sgesvd_lwork
dgesvd_lwork
cgesvd_lwork
zgesvd_lwork
sgesv
dgesv
cgesv
zgesv
sgesvx
dgesvx
cgesvx
zgesvx
sgecon
dgecon
cgecon
zgecon
ssysv
dsysv
csysv
zsysv
ssysv_lwork
dsysv_lwork
csysv_lwork
zsysv_lwork
ssysvx
dsysvx
csysvx
zsysvx
ssysvx_lwork
dsysvx_lwork
csysvx_lwork
zsysvx_lwork
chesv
zhesv
chesv_lwork
zhesv_lwork
chesvx
zhesvx
chesvx_lwork
zhesvx_lwork
sgetrf
dgetrf
cgetrf
zgetrf
sgetri
dgetri
cgetri
zgetri
sgetri_lwork
dgetri_lwork
cgetri_lwork
zgetri_lwork
sgetrs
dgetrs
cgetrs
zgetrs
sgges
dgges
cgges
zgges
sggev
dggev
cggev
zggev
chbevd
zhbevd
chbevx
zhbevx
cheev
zheev
cheevd
zheevd
cheevr
zheevr
chegv
zhegv
chegvd
zhegvd
chegvx
zhegvx
slarf
dlarf
clarf
zlarf
slarfg
dlarfg
clarfg
zlarfg
slartg
dlartg
clartg
zlartg
slasd4
dlasd4
slaswp
dlaswp
claswp
zlaswp
slauum
dlauum
clauum
zlauum
spbsv
dpbsv
cpbsv
zpbsv
spbtrf
dpbtrf
cpbtrf
zpbtrf
spbtrs
dpbtrs
cpbtrs
zpbtrs
sposv
dposv
cposv
zposv
sposvx
dposvx
cposvx
zposvx
spocon
dpocon
cpocon
zpocon
spotrf
dpotrf
cpotrf
zpotrf
spotri
dpotri
cpotri
zpotri
spotrs
dpotrs
cpotrs
zpotrs
crot
zrot
strsyl
dtrsyl
ctrsyl
ztrsyl
strtri
dtrtri
ctrtri
ztrtri
strtrs
dtrtrs
ctrtrs
ztrtrs
cunghr
zunghr
cungqr
zungqr
cungrq
zungrq
cunmqr
zunmqr
sgtsv
dgtsv
cgtsv
zgtsv
sptsv
dptsv
cptsv
zptsv
slamch
dlamch
sorghr
dorghr
sorgqr
dorgqr
sorgrq
dorgrq
sormqr
dormqr
ssbev
dsbev
ssbevd
dsbevd
ssbevx
dsbevx
ssyev
dsyev
ssyevd
dsyevd
ssyevr
dsyevr
ssygv
dsygv
ssygvd
dsygvd
ssygvx
dsygvx
slange
dlange
clange
zlange
ilaver
""" |
#!/usr/bin/env python
# infill_generator.py
#
# Generate hatch fills for the closed paths (polygons) in the currently
# selected document elements. If no elements are selected, then all the
# polygons throughout the document are hatched. The fill rule is an odd/even
# rule: odd numbered intersections (1, 3, 5, etc.) are a hatch line entering
# a polygon while even numbered intersections (2, 4, 6, etc.) are the same
# hatch line exiting the polygon.
#
# This extension first decomposes the selected <path>, <rect>, <line>,
# <polyline>, <polygon>, <circle>, and <ellipse> elements into individual
# moveto and lineto coordinates. These coordinates are then used to build vertex lists.
# Only the vertex lists corresponding to polygons (closed paths) are
# kept. Note that a single graphical element may be composed of several
# subpaths, each subpath potentially a polygon.
#
# Once the lists of all the vertices are built, potential hatch lines are
# "projected" through the bounding box containing all of the vertices.
# For each potential hatch line, all intersections with all the polygon
# edges are determined. These intersections are stored as decimal fractions
# indicating where along the length of the hatch line the intersection
# occurs. These values will always be in the range [0, 1]. A value of 0
# indicates that the intersection is at the start of the hatch line, a value
# of 0.5 midway, and a value of 1 at the end of the hatch line.
#
# For a given hatch line, all the fractional values are sorted and any
# duplicates removed. Duplicates occur, for instance, when the hatch
# line passes through a polygon vertex and thus intersects two edges
# segments of the polygon: the end of one edge and the start of
# another.
#
# Once sorted and duplicates removed, an odd/even rule is applied to
# determine which segments of the potential hatch line are within
# polygons. These segments found to be within polygons are then saved
# and become the hatch fill lines which will be drawn.
#
# With each saved hatch fill line, information about which SVG graphical
# element it is within is saved. This way, the hatch fill lines can
# later be grouped with the element they are associated with. This makes
# it possible to manipulate the two -- graphical element and hatch lines --
# as a single object within Inkscape.
#
# Note: we also save the transformation matrix for each graphical element.
# That way, when we group the hatch fills with the element they are
# filling, we can invert the transformation. That is, in order to compute
# the hatch fills, we first have had apply ALL applicable transforms to
# all the graphical elements. We need to do that so that we know where in
# the drawing each of the graphical elements are relative to one another.
# However, this means that the hatch lines have been computed in a setting
# where no further transforms are needed. If we then put these hatch lines
# into the same groups as the elements being hatched in the ORIGINAL
# drawing, then the hatch lines will have transforms applied again. So,
# once we compute the hatch lines, we need to invert the transforms of
# the group they will be placed in and apply this inverse transform to the
# hatch lines. Hence the need to save the transform matrix for every
# graphical element.
# Written by NAME for the Eggbot Project
# dan dot newman at mtbaldy dot us
# Last updated 28 November 2010
# 15 October 2010
# Updated by NAME 6/14/2012
# Added tolerance parameter
# Update by NAME, 6/20/2012
# Add min span/gap width
# 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., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
|
"""
=====================================
Sparse matrices (:mod:`scipy.sparse`)
=====================================
.. currentmodule:: scipy.sparse
SciPy 2-D sparse matrix package for numeric data.
Contents
========
Sparse matrix classes
---------------------
.. autosummary::
:toctree: generated/
bsr_matrix - Block Sparse Row matrix
coo_matrix - A sparse matrix in COOrdinate format
csc_matrix - Compressed Sparse Column matrix
csr_matrix - Compressed Sparse Row matrix
dia_matrix - Sparse matrix with DIAgonal storage
dok_matrix - Dictionary Of Keys based sparse matrix
lil_matrix - Row-based linked list sparse matrix
Functions
---------
Building sparse matrices:
.. autosummary::
:toctree: generated/
eye - Sparse MxN matrix whose k-th diagonal is all ones
identity - Identity matrix in sparse format
kron - kronecker product of two sparse matrices
kronsum - kronecker sum of sparse matrices
diags - Return a sparse matrix from diagonals
spdiags - Return a sparse matrix from diagonals
block_diag - Build a block diagonal sparse matrix
tril - Lower triangular portion of a matrix in sparse format
triu - Upper triangular portion of a matrix in sparse format
bmat - Build a sparse matrix from sparse sub-blocks
hstack - Stack sparse matrices horizontally (column wise)
vstack - Stack sparse matrices vertically (row wise)
rand - Random values in a given shape
Sparse matrix tools:
.. autosummary::
:toctree: generated/
find
Identifying sparse matrices:
.. autosummary::
:toctree: generated/
issparse
isspmatrix
isspmatrix_csc
isspmatrix_csr
isspmatrix_bsr
isspmatrix_lil
isspmatrix_dok
isspmatrix_coo
isspmatrix_dia
Submodules
----------
.. autosummary::
:toctree: generated/
csgraph - Compressed sparse graph routines
linalg - sparse linear algebra routines
Exceptions
----------
.. autosummary::
:toctree: generated/
SparseEfficiencyWarning
SparseWarning
Usage information
=================
There are seven available sparse matrix types:
1. csc_matrix: Compressed Sparse Column format
2. csr_matrix: Compressed Sparse Row format
3. bsr_matrix: Block Sparse Row format
4. lil_matrix: List of Lists format
5. dok_matrix: Dictionary of Keys format
6. coo_matrix: COOrdinate format (aka IJV, triplet format)
7. dia_matrix: DIAgonal format
To construct a matrix efficiently, use either dok_matrix or lil_matrix.
The lil_matrix class supports basic slicing and fancy
indexing with a similar syntax to NumPy arrays. As illustrated below,
the COO format may also be used to efficiently construct matrices.
To perform manipulations such as multiplication or inversion, first
convert the matrix to either CSC or CSR format. The lil_matrix format is
row-based, so conversion to CSR is efficient, whereas conversion to CSC
is less so.
All conversions among the CSR, CSC, and COO formats are efficient,
linear-time operations.
Matrix vector product
---------------------
To do a vector product between a sparse matrix and a vector simply use
the matrix `dot` method, as described in its docstring:
>>> import numpy as np
>>> from scipy.sparse import csr_matrix
>>> A = csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
>>> v = np.array([1, 0, -1])
>>> A.dot(v)
array([ 1, -3, -1], dtype=int64)
.. warning:: As of NumPy 1.7, `np.dot` is not aware of sparse matrices,
therefore using it will result on unexpected results or errors.
The corresponding dense array should be obtained first instead:
>>> np.dot(A.toarray(), v)
array([ 1, -3, -1], dtype=int64)
but then all the performance advantages would be lost.
The CSR format is specially suitable for fast matrix vector products.
Example 1
---------
Construct a 1000x1000 lil_matrix and add some values to it:
>>> from scipy.sparse import lil_matrix
>>> from scipy.sparse.linalg import spsolve
>>> from numpy.linalg import solve, norm
>>> from numpy.random import rand
>>> A = lil_matrix((1000, 1000))
>>> A[0, :100] = rand(100)
>>> A[1, 100:200] = A[0, :100]
>>> A.setdiag(rand(1000))
Now convert it to CSR format and solve A x = b for x:
>>> A = A.tocsr()
>>> b = rand(1000)
>>> x = spsolve(A, b)
Convert it to a dense matrix and solve, and check that the result
is the same:
>>> x_ = solve(A.toarray(), b)
Now we can compute norm of the error with:
>>> err = norm(x-x_)
>>> err < 1e-10
True
It should be small :)
Example 2
---------
Construct a matrix in COO format:
>>> from scipy import sparse
>>> from numpy import array
>>> I = array([0,3,1,0])
>>> J = array([0,3,1,2])
>>> V = array([4,5,7,9])
>>> A = sparse.coo_matrix((V,(I,J)),shape=(4,4))
Notice that the indices do not need to be sorted.
Duplicate (i,j) entries are summed when converting to CSR or CSC.
>>> I = array([0,0,1,3,1,0,0])
>>> J = array([0,2,1,3,1,0,0])
>>> V = array([1,1,1,1,1,1,1])
>>> B = sparse.coo_matrix((V,(I,J)),shape=(4,4)).tocsr()
This is useful for constructing finite-element stiffness and mass matrices.
Further Details
---------------
CSR column indices are not necessarily sorted. Likewise for CSC row
indices. Use the .sorted_indices() and .sort_indices() methods when
sorted indices are required (e.g. when passing data to other libraries).
""" |
"""
========
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).
""" |
#!/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 python3
# # NOT USED FOR NOW (WE USE FTP INSTEAD OF SFTP)
#
# """Define a class that deal with the low level sftp manager."""
#
# from socket import timeout
# import paramiko
#
#
# class SFTPError(Exception):
# def __init__(self, message):
# self.message = message
# def __str__(self):
# return repr('SFTP error: ' + self.message)
#
#
# class SFTPManager(object):
# """Class to connect to a sftp server with SSH using paramiko.
#
# :param host: hostname server
# :type host: str
# :param user: username to use for connection
# :type user: str
# :param PASSWORD: PASSWORD to use for connection
# :type PASSWORD: str
#
# """
# def __init__(self, host, user='', PASSWORD='', port=2222):
# self.host = host
# self.user = user
# self.PASSWORD = PASSWORD
# self.port = port
#
# self.transport = None
# self.sftp = None
#
# def connection(self):
# """Connect to server."""
# try:
# self.transport = paramiko.Transport((self.host, self.port))
# self.transport.connect(username=self.user, PASSWORD=self.PASSWORD)
# self.sftp = paramiko.SFTPClient.from_transport(self.transport)
# except timeout:
# raise SFTPError('Timeout error')
# except Exception as error:
# raise SFTPError('connect; ' + str(error))
#
# def disconnect(self):
# """Close connection to sftp server."""
# self.sftp.close()
# self.transport.close()
# self.sftp = None
# self.transport = None
#
# def cd(self, path):
# """Set the current directory on the server.
#
# :param path: path to set
# :type path: str
#
# """
# try:
# self.sftp.chdir(path)
# except Exception as error:
# raise SFTPError('cd; {} {}'.format(path, str(error)))
#
# def mkdir(self, dirname):
# """Create directory."""
# try:
# self.sftp.mkdir(dirname)
# except Exception as error:
# raise SFTPError('mkdir; ' + str(error))
#
# def listdir(self, path='.'):
# """Return a list containing the names of the entries in the given path."""
# try:
# result = self.sftp.listdir(path)
# except Exception as error:
# raise SFTPError('lisdir; ' + str(error))
# else:
# return result
#
# def listdir_attr(self, path='.'):
# """List the given path.
#
# Return the names and other informations about entries.
#
# """
# try:
# result = self.sftp.listdir_attr(path)
# except Exception as error:
# raise SFTPError('lisdir attrs; ' + str(error))
# else:
# return result
#
# def put(self, local_filename, server_filename):
# """Upload a file into server.
#
# :param local_filename: local filename to upload
# :type local_filename: str
# :param server_filename: server filename to upload
# :type server_filename: str
# :return: ok or error message
#
# """
# try:
# self.sftp.put(local_filename, server_filename)
# except Exception as error:
# raise SFTPError('upload; ' + str(error))
#
# def get(self, local_filename, server_filename):
# """Download a file from server.
#
# :param local_filename: local filename to create
# :type local_filename: str
# :param server_filename: server filename to download
# :type server_filename: str
# :return: server ok or error message
#
# """
# try:
# self.sftp.get(server_filename, local_filename)
# except Exception as error:
# raise SFTPError('download; ' + str(error))
#
# def countfiles(self, path='.'):
# """Count the file in the given path.
#
# :param path: path to count
# :type path: str
# :return: number of files
#
# """
# nb_files = int()
# infos = self.listdir_attr(path)
# for info in infos:
# if '.' in info.filename:
# nb_files += 1
# else:
# nb_files += self.countfiles(path + '/' + info.filename)
# return nb_files
|
"""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 local and global (<doctest test.test_syntax[0]>, line 1)
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():
TODO(jhylton): "assignment to None" is inconsistent with other messages
>>> obj.None = 1
Traceback (most recent call last):
SyntaxError: assignment to None (<doctest test.test_syntax[1]>, line 1)
>>> None = 1
Traceback (most recent call last):
SyntaxError: assignment to None (<doctest test.test_syntax[2]>, line 1)
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 () (<doctest test.test_syntax[3]>, line 1)
>>> f() = 1
Traceback (most recent call last):
SyntaxError: can't assign to function call (<doctest test.test_syntax[4]>, line 1)
>>> del f()
Traceback (most recent call last):
SyntaxError: can't delete function call (<doctest test.test_syntax[5]>, line 1)
>>> a + 1 = 2
Traceback (most recent call last):
SyntaxError: can't assign to operator (<doctest test.test_syntax[6]>, line 1)
>>> (x for x in x) = 1
Traceback (most recent call last):
SyntaxError: can't assign to generator expression (<doctest test.test_syntax[7]>, line 1)
>>> 1 = 1
Traceback (most recent call last):
SyntaxError: can't assign to literal (<doctest test.test_syntax[8]>, line 1)
>>> "abc" = 1
Traceback (most recent call last):
SyntaxError: can't assign to literal (<doctest test.test_syntax[9]>, line 1)
>>> `1` = 1
Traceback (most recent call last):
SyntaxError: can't assign to repr (<doctest test.test_syntax[10]>, line 1)
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 (<doctest test.test_syntax[11]>, line 1)
>>> [a, b, c + 1] = [1, 2, 3]
Traceback (most recent call last):
SyntaxError: can't assign to operator (<doctest test.test_syntax[12]>, line 1)
>>> a if 1 else b = 1
Traceback (most recent call last):
SyntaxError: can't assign to conditional expression (<doctest test.test_syntax[13]>, line 1)
From compiler_complex_args():
>>> def f(None=1):
... pass
Traceback (most recent call last):
SyntaxError: assignment to None (<doctest test.test_syntax[14]>, line 1)
From ast_for_arguments():
>>> def f(x, y=1, z):
... pass
Traceback (most recent call last):
SyntaxError: non-default argument follows default argument (<doctest test.test_syntax[15]>, line 1)
>>> def f(x, None):
... pass
Traceback (most recent call last):
SyntaxError: assignment to None (<doctest test.test_syntax[16]>, line 1)
>>> def f(*None):
... pass
Traceback (most recent call last):
SyntaxError: assignment to None (<doctest test.test_syntax[17]>, line 1)
>>> def f(**None):
... pass
Traceback (most recent call last):
SyntaxError: assignment to None (<doctest test.test_syntax[18]>, line 1)
From ast_for_funcdef():
>>> def None(x):
... pass
Traceback (most recent call last):
SyntaxError: assignment to None (<doctest test.test_syntax[19]>, line 1)
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 (<doctest test.test_syntax[23]>, line 1)
>>> 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 (<doctest test.test_syntax[25]>, line 1)
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 (<doctest test.test_syntax[26]>, line 1)
>>> f(lambda x: x[0] = 3)
Traceback (most recent call last):
SyntaxError: lambda cannot contain assignment (<doctest test.test_syntax[27]>, line 1)
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 (<doctest test.test_syntax[28]>, line 1)
>>> f(a or b=1)
Traceback (most recent call last):
SyntaxError: keyword can't be an expression (<doctest test.test_syntax[29]>, line 1)
>>> f(x.y=1)
Traceback (most recent call last):
SyntaxError: keyword can't be an expression (<doctest test.test_syntax[30]>, line 1)
From ast_for_expr_stmt():
>>> (x for x in x) += 1
Traceback (most recent call last):
SyntaxError: augmented assignment to generator expression not possible (<doctest test.test_syntax[31]>, line 1)
>>> None += 1
Traceback (most recent call last):
SyntaxError: assignment to None (<doctest test.test_syntax[32]>, line 1)
>>> f() += 1
Traceback (most recent call last):
SyntaxError: illegal expression for augmented assignment (<doctest test.test_syntax[33]>, line 1)
Test continue in finally in weird combinations.
continue in for loop under finally shouuld 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 (<doctest test.test_syntax[36]>, line 6)
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 (<doctest test.test_syntax[37]>, line 7)
>>> def foo():
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause (<doctest test.test_syntax[38]>, line 5)
>>> def foo():
... for a in ():
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause (<doctest test.test_syntax[39]>, line 6)
>>> def foo():
... for a in ():
... try:
... pass
... finally:
... try:
... continue
... finally:
... pass
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause (<doctest test.test_syntax[40]>, line 7)
>>> def foo():
... for a in ():
... try: pass
... finally:
... try:
... pass
... except:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause (<doctest test.test_syntax[41]>, line 8)
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 (<doctest test.test_syntax[42]>, line 3)
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
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 (<doctest test.test_syntax[44]>, line 2)
>>> if 1:
... pass
... elif 1:
... x() = 1
Traceback (most recent call last):
...
SyntaxError: can't assign to function call (<doctest test.test_syntax[45]>, line 4)
>>> if 1:
... x() = 1
... elif 1:
... pass
... else:
... pass
Traceback (most recent call last):
...
SyntaxError: can't assign to function call (<doctest test.test_syntax[46]>, line 2)
>>> if 1:
... pass
... elif 1:
... x() = 1
... else:
... pass
Traceback (most recent call last):
...
SyntaxError: can't assign to function call (<doctest test.test_syntax[47]>, line 4)
>>> if 1:
... pass
... elif 1:
... pass
... else:
... x() = 1
Traceback (most recent call last):
...
SyntaxError: can't assign to function call (<doctest test.test_syntax[48]>, line 6)
>>> f(a=23, a=234)
Traceback (most recent call last):
...
SyntaxError: keyword argument repeated (<doctest test.test_syntax[49]>, line 1)
""" |
"""
Simple config
=============
Although CherryPy uses the :mod:`Python logging module <logging>`, it does so
behind the scenes so that simple logging is simple, but complicated logging
is still possible. "Simple" logging means that you can log to the screen
(i.e. console/stdout) or to a file, and that you can easily have separate
error and access log files.
Here are the simplified logging settings. You use these by adding lines to
your config file or dict. You should set these at either the global level or
per application (see next), but generally not both.
* ``log.screen``: Set this to True to have both "error" and "access" messages
printed to stdout.
* ``log.access_file``: Set this to an absolute filename where you want
"access" messages written.
* ``log.error_file``: Set this to an absolute filename where you want "error"
messages written.
Many events are automatically logged; to log your own application events, call
:func:`cherrypy.log`.
Architecture
============
Separate scopes
---------------
CherryPy provides log managers at both the global and application layers.
This means you can have one set of logging rules for your entire site,
and another set of rules specific to each application. The global log
manager is found at :func:`cherrypy.log`, and the log manager for each
application is found at :attr:`app.log<cherrypy._cptree.Application.log>`.
If you're inside a request, the latter is reachable from
``cherrypy.request.app.log``; if you're outside a request, you'll have to
obtain a reference to the ``app``: either the return value of
:func:`tree.mount()<cherrypy._cptree.Tree.mount>` or, if you used
:func:`quickstart()<cherrypy.quickstart>` instead, via
``cherrypy.tree.apps['/']``.
By default, the global logs are named "cherrypy.error" and "cherrypy.access",
and the application logs are named "cherrypy.error.2378745" and
"cherrypy.access.2378745" (the number is the id of the Application object).
This means that the application logs "bubble up" to the site logs, so if your
application has no log handlers, the site-level handlers will still log the
messages.
Errors vs. Access
-----------------
Each log manager handles both "access" messages (one per HTTP request) and
"error" messages (everything else). Note that the "error" log is not just for
errors! The format of access messages is highly formalized, but the error log
isn't--it receives messages from a variety of sources (including full error
tracebacks, if enabled).
If you are logging the access log and error log to the same source, then there
is a possibility that a specially crafted error message may replicate an access
log message as described in CWE-117. In this case it is the application
developer's responsibility to manually escape data before using CherryPy's log()
functionality, or they may create an application that is vulnerable to CWE-117.
This would be achieved by using a custom handler escape any special characters,
and attached as described below.
Custom Handlers
===============
The simple settings above work by manipulating Python's standard :mod:`logging`
module. So when you need something more complex, the full power of the standard
module is yours to exploit. You can borrow or create custom handlers, formats,
filters, and much more. Here's an example that skips the standard FileHandler
and uses a RotatingFileHandler instead:
::
#python
log = app.log
# Remove the default FileHandlers if present.
log.error_file = ""
log.access_file = ""
maxBytes = getattr(log, "rot_maxBytes", 10000000)
backupCount = getattr(log, "rot_backupCount", 1000)
# Make a new RotatingFileHandler for the error log.
fname = getattr(log, "rot_error_file", "error.log")
h = handlers.RotatingFileHandler(fname, 'a', maxBytes, backupCount)
h.setLevel(DEBUG)
h.setFormatter(_cplogging.logfmt)
log.error_log.addHandler(h)
# Make a new RotatingFileHandler for the access log.
fname = getattr(log, "rot_access_file", "access.log")
h = handlers.RotatingFileHandler(fname, 'a', maxBytes, backupCount)
h.setLevel(DEBUG)
h.setFormatter(_cplogging.logfmt)
log.access_log.addHandler(h)
The ``rot_*`` attributes are pulled straight from the application log object.
Since "log.*" config entries simply set attributes on the log object, you can
add custom attributes to your heart's content. Note that these handlers are
used ''instead'' of the default, simple handlers outlined above (so don't set
the "log.error_file" config entry, for example).
""" |
# # Written for Theano 0.6 and 0.7, needs some changes for more recent
# # versions of Theano.
#
#
# #### Libraries
# # Standard library
# import cPickle
# import gzip
#
# # Third-party libraries
# import numpy as np
# import theano
# import theano.tensor as T
# from theano.tensor.nnet import conv
# from theano.tensor.nnet import softmax
# from theano.tensor import shared_randomstreams
# from theano.tensor.signal import downsample
#
# # Activation functions for neurons
# def linear(z): return z
# def ReLU(z): return T.maximum(0.0, z)
# from theano.tensor.nnet import sigmoid
# from theano.tensor import tanh
#
#
# #### Constants
# GPU = True
# if GPU:
# print "Trying to run under a GPU. If this is not desired, then modify "+\
# "network3.py\nto set the GPU flag to False."
# try: theano.config.device = 'gpu'
# except: pass # it's already set
# theano.config.floatX = 'float32'
# else:
# print "Running with a CPU. If this is not desired, then the modify "+\
# "network3.py to set\nthe GPU flag to True."
#
# #### Load the MNIST data
# def load_data_shared(filename="../data/mnist.pkl.gz"):
# f = gzip.open(filename, 'rb')
# training_data, validation_data, test_data = cPickle.load(f)
# f.close()
# def shared(data):
# """Place the data into shared variables. This allows Theano to copy
# the data to the GPU, if one is available.
# """
# shared_x = theano.shared(
# np.asarray(data[0], dtype=theano.config.floatX), borrow=True)
# shared_y = theano.shared(
# np.asarray(data[1], dtype=theano.config.floatX), borrow=True)
# return shared_x, T.cast(shared_y, "int32")
# return [shared(training_data), shared(validation_data), shared(test_data)]
#
# #### Main class used to construct and train networks
# class Network(object):
#
# def __init__(self, layers, mini_batch_size):
# """Takes a list of `layers`, describing the network architecture, and
# a value for the `mini_batch_size` to be used during training
# by stochastic gradient descent.
# """
# self.layers = layers
# self.mini_batch_size = mini_batch_size
# self.params = [param for layer in self.layers for param in layer.params]
# self.x = T.matrix("x")
# self.y = T.ivector("y")
# init_layer = self.layers[0]
# init_layer.set_inpt(self.x, self.x, self.mini_batch_size)
# for j in xrange(1, len(self.layers)):
# prev_layer, layer = self.layers[j-1], self.layers[j]
# layer.set_inpt(
# prev_layer.output, prev_layer.output_dropout, self.mini_batch_size)
# self.output = self.layers[-1].output
# self.output_dropout = self.layers[-1].output_dropout
#
# def SGD(self, training_data, epochs, mini_batch_size, eta,
# validation_data, test_data, lmbda=0.0):
# """Train the network using mini-batch stochastic gradient descent."""
# training_x, training_y = training_data
# validation_x, validation_y = validation_data
# test_x, test_y = test_data
#
# # compute number of minibatches for training, validation and testing
# num_training_batches = size(training_data)/mini_batch_size
# num_validation_batches = size(validation_data)/mini_batch_size
# num_test_batches = size(test_data)/mini_batch_size
#
# # define the (regularized) cost function, symbolic gradients, and updates
# l2_norm_squared = sum([(layer.w**2).sum() for layer in self.layers])
# cost = self.layers[-1].cost(self)+\
# 0.5*lmbda*l2_norm_squared/num_training_batches
# grads = T.grad(cost, self.params)
# updates = [(param, param-eta*grad)
# for param, grad in zip(self.params, grads)]
#
# # define functions to train a mini-batch, and to compute the
# # accuracy in validation and test mini-batches.
# i = T.lscalar() # mini-batch index
# train_mb = theano.function(
# [i], cost, updates=updates,
# givens={
# self.x:
# training_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
# self.y:
# training_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
# })
# validate_mb_accuracy = theano.function(
# [i], self.layers[-1].accuracy(self.y),
# givens={
# self.x:
# validation_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
# self.y:
# validation_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
# })
# test_mb_accuracy = theano.function(
# [i], self.layers[-1].accuracy(self.y),
# givens={
# self.x:
# test_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
# self.y:
# test_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
# })
# self.test_mb_predictions = theano.function(
# [i], self.layers[-1].y_out,
# givens={
# self.x:
# test_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
# })
# # Do the actual training
# best_validation_accuracy = 0.0
# for epoch in xrange(epochs):
# for minibatch_index in xrange(num_training_batches):
# iteration = num_training_batches*epoch+minibatch_index
# if iteration % 1000 == 0:
# print("Training mini-batch number {0}".format(iteration))
# cost_ij = train_mb(minibatch_index)
# if (iteration+1) % num_training_batches == 0:
# validation_accuracy = np.mean(
# [validate_mb_accuracy(j) for j in xrange(num_validation_batches)])
# print("Epoch {0}: validation accuracy {1:.2%}".format(
# epoch, validation_accuracy))
# if validation_accuracy >= best_validation_accuracy:
# print("This is the best validation accuracy to date.")
# best_validation_accuracy = validation_accuracy
# best_iteration = iteration
# if test_data:
# test_accuracy = np.mean(
# [test_mb_accuracy(j) for j in xrange(num_test_batches)])
# print('The corresponding test accuracy is {0:.2%}'.format(
# test_accuracy))
# print("Finished training network.")
# print("Best validation accuracy of {0:.2%} obtained at iteration {1}".format(
# best_validation_accuracy, best_iteration))
# print("Corresponding test accuracy of {0:.2%}".format(test_accuracy))
#
# #### Define layer types
#
# class ConvPoolLayer(object):
# Used to create a combination of a convolutional and a max-pooling
# layer. A more sophisticated implementation would separate the
# two, but for our purposes we'll always use them together, and it
# simplifies the code, so it makes sense to combine them.
#
#
# def __init__(self, filter_shape, image_shape, poolsize=(2, 2),
# activation_fn=sigmoid):
# `filter_shape` is a tuple of length 4, whose entries are the number
# of filters, the number of input feature maps, the filter height, and the
# filter width.
# `image_shape` is a tuple of length 4, whose entries are the
# mini-batch size, the number of input feature maps, the image
# height, and the image width.
# `poolsize` is a tuple of length 2, whose entries are the y and
# x pooling sizes.
#
# self.filter_shape = filter_shape
# self.image_shape = image_shape
# self.poolsize = poolsize
# self.activation_fn=activation_fn
# # initialize weights and biases
# n_out = (filter_shape[0]*np.prod(filter_shape[2:])/np.prod(poolsize))
# self.w = theano.shared(
# np.asarray(
# np.random.normal(loc=0, scale=np.sqrt(1.0/n_out), size=filter_shape),
# dtype=theano.config.floatX),
# borrow=True)
# self.b = theano.shared(
# np.asarray(
# np.random.normal(loc=0, scale=1.0, size=(filter_shape[0],)),
# dtype=theano.config.floatX),
# borrow=True)
# self.params = [self.w, self.b]
#
# def set_inpt(self, inpt, inpt_dropout, mini_batch_size):
# self.inpt = inpt.reshape(self.image_shape)
# conv_out = conv.conv2d(
# input=self.inpt, filters=self.w, filter_shape=self.filter_shape,
# image_shape=self.image_shape)
# pooled_out = downsample.max_pool_2d(
# input=conv_out, ds=self.poolsize, ignore_border=True)
# self.output = self.activation_fn(
# pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
# self.output_dropout = self.output # no dropout in the convolutional layers
#
# class FullyConnectedLayer(object):
#
# def __init__(self, n_in, n_out, activation_fn=sigmoid, p_dropout=0.0):
# self.n_in = n_in
# self.n_out = n_out
# self.activation_fn = activation_fn
# self.p_dropout = p_dropout
# # Initialize weights and biases
# self.w = theano.shared(
# np.asarray(
# np.random.normal(
# loc=0.0, scale=np.sqrt(1.0/n_out), size=(n_in, n_out)),
# dtype=theano.config.floatX),
# name='w', borrow=True)
# self.b = theano.shared(
# np.asarray(np.random.normal(loc=0.0, scale=1.0, size=(n_out,)),
# dtype=theano.config.floatX),
# name='b', borrow=True)
# self.params = [self.w, self.b]
#
# def set_inpt(self, inpt, inpt_dropout, mini_batch_size):
# self.inpt = inpt.reshape((mini_batch_size, self.n_in))
# self.output = self.activation_fn(
# (1-self.p_dropout)*T.dot(self.inpt, self.w) + self.b)
# self.y_out = T.argmax(self.output, axis=1)
# self.inpt_dropout = dropout_layer(
# inpt_dropout.reshape((mini_batch_size, self.n_in)), self.p_dropout)
# self.output_dropout = self.activation_fn(
# T.dot(self.inpt_dropout, self.w) + self.b)
#
# def accuracy(self, y):
# "Return the accuracy for the mini-batch."
# return T.mean(T.eq(y, self.y_out))
#
# class SoftmaxLayer(object):
#
# def __init__(self, n_in, n_out, p_dropout=0.0):
# self.n_in = n_in
# self.n_out = n_out
# self.p_dropout = p_dropout
# # Initialize weights and biases
# self.w = theano.shared(
# np.zeros((n_in, n_out), dtype=theano.config.floatX),
# name='w', borrow=True)
# self.b = theano.shared(
# np.zeros((n_out,), dtype=theano.config.floatX),
# name='b', borrow=True)
# self.params = [self.w, self.b]
#
# def set_inpt(self, inpt, inpt_dropout, mini_batch_size):
# self.inpt = inpt.reshape((mini_batch_size, self.n_in))
# self.output = softmax((1-self.p_dropout)*T.dot(self.inpt, self.w) + self.b)
# self.y_out = T.argmax(self.output, axis=1)
# self.inpt_dropout = dropout_layer(
# inpt_dropout.reshape((mini_batch_size, self.n_in)), self.p_dropout)
# self.output_dropout = softmax(T.dot(self.inpt_dropout, self.w) + self.b)
#
# def cost(self, net):
# "Return the log-likelihood cost."
# return -T.mean(T.log(self.output_dropout)[T.arange(net.y.shape[0]), net.y])
#
# def accuracy(self, y):
# "Return the accuracy for the mini-batch."
# return T.mean(T.eq(y, self.y_out))
#
#
# #### Miscellanea
# def size(data):
# "Return the size of the dataset `data`."
# return data[0].get_value(borrow=True).shape[0]
#
# def dropout_layer(layer, p_dropout):
# srng = shared_randomstreams.RandomStreams(
# np.random.RandomState(0).randint(999999))
# mask = srng.binomial(n=1, p=1-p_dropout, size=layer.shape)
|
"""
Components/Banner
=================
.. seealso::
`Material Design spec, Banner <https://material.io/components/banners>`_
.. rubric:: A banner displays a prominent message and related optional actions.
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/banner.png
:align: center
Usage
=====
.. code-block:: python
from kivy.lang import Builder
from kivy.factory import Factory
from kivymd.app import MDApp
Builder.load_string('''
<ExampleBanner@Screen>
MDBanner:
id: banner
text: ["One line string text example without actions."]
# The widget that is under the banner.
# It will be shifted down to the height of the banner.
over_widget: screen
vertical_pad: toolbar.height
MDToolbar:
id: toolbar
title: "Example Banners"
elevation: 10
pos_hint: {'top': 1}
BoxLayout:
id: screen
orientation: "vertical"
size_hint_y: None
height: Window.height - toolbar.height
OneLineListItem:
text: "Banner without actions"
on_release: banner.show()
Widget:
''')
class Test(MDApp):
def build(self):
return Factory.ExampleBanner()
Test().run()
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/banner-example-1.gif
:align: center
.. rubric:: Banner type.
By default, the banner is of the type ``'one-line'``:
.. code-block:: kv
MDBanner:
text: ["One line string text example without actions."]
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/banner-one-line.png
:align: center
To use a two-line banner, specify the ``'two-line'`` :attr:`MDBanner.type` for the banner
and pass the list of two lines to the :attr:`MDBanner.text` parameter:
.. code-block:: kv
MDBanner:
type: "two-line"
text:
["One line string text example without actions.", "This is the second line of the banner message."]
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/banner-two-line.png
:align: center
Similarly, create a three-line banner:
.. code-block:: kv
MDBanner:
type: "three-line"
text:
["One line string text example without actions.", "This is the second line of the banner message.", "and this is the third line of the banner message."]
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/banner-three-line.png
:align: center
To add buttons to any type of banner,
use the :attr:`MDBanner.left_action` and :attr:`MDBanner.right_action` parameters,
which should take a list ['Button name', function]:
.. code-block:: kv
MDBanner:
text: ["One line string text example without actions."]
left_action: ["CANCEL", lambda x: None]
Or two buttons:
.. code-block:: kv
MDBanner:
text: ["One line string text example without actions."]
left_action: ["CANCEL", lambda x: None]
right_action: ["CLOSE", lambda x: None]
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/banner-actions.png
:align: center
If you want to use the icon on the left in the banner,
add the prefix `'-icon'` to the banner type:
.. code-block:: kv
MDBanner:
type: "one-line-icon"
icon: f"{images_path}/kivymd.png"
text: ["One line string text example without actions."]
.. image:: https://github.com/HeaTTheatR/KivyMD-data/raw/master/gallery/kivymddoc/banner-icon.png
:align: center
.. Note:: `See full example <https://github.com/kivymd/KivyMD/wiki/Components-Banner>`_
""" |
"""
Low-level LAPACK functions
==========================
This module contains low-level functions from the LAPACK library.
.. versionadded:: 0.12.0
.. warning::
These functions do little to no error checking.
It is possible to cause crashes by mis-using them,
so prefer using the higher-level routines in `scipy.linalg`.
Finding functions
=================
.. autosummary::
get_lapack_funcs
All functions
=============
.. autosummary::
:toctree: generated/
sgbsv
dgbsv
cgbsv
zgbsv
sgbtrf
dgbtrf
cgbtrf
zgbtrf
sgbtrs
dgbtrs
cgbtrs
zgbtrs
sgebal
dgebal
cgebal
zgebal
sgees
dgees
cgees
zgees
sgeev
dgeev
cgeev
zgeev
sgeev_lwork
dgeev_lwork
cgeev_lwork
zgeev_lwork
sgegv
dgegv
cgegv
zgegv
sgehrd
dgehrd
cgehrd
zgehrd
sgehrd_lwork
dgehrd_lwork
cgehrd_lwork
zgehrd_lwork
sgelss
dgelss
cgelss
zgelss
sgelss_lwork
dgelss_lwork
cgelss_lwork
zgelss_lwork
sgelsd
dgelsd
cgelsd
zgelsd
sgelsd_lwork
dgelsd_lwork
cgelsd_lwork
zgelsd_lwork
sgelsy
dgelsy
cgelsy
zgelsy
sgelsy_lwork
dgelsy_lwork
cgelsy_lwork
zgelsy_lwork
sgeqp3
dgeqp3
cgeqp3
zgeqp3
sgeqrf
dgeqrf
cgeqrf
zgeqrf
sgerqf
dgerqf
cgerqf
zgerqf
sgesdd
dgesdd
cgesdd
zgesdd
sgesdd_lwork
dgesdd_lwork
cgesdd_lwork
zgesdd_lwork
sgesv
dgesv
cgesv
zgesv
sgetrf
dgetrf
cgetrf
zgetrf
sgetri
dgetri
cgetri
zgetri
sgetri_lwork
dgetri_lwork
cgetri_lwork
zgetri_lwork
sgetrs
dgetrs
cgetrs
zgetrs
sgges
dgges
cgges
zgges
sggev
dggev
cggev
zggev
chbevd
zhbevd
chbevx
zhbevx
cheev
zheev
cheevd
zheevd
cheevr
zheevr
chegv
zhegv
chegvd
zhegvd
chegvx
zhegvx
slarf
dlarf
clarf
zlarf
slarfg
dlarfg
clarfg
zlarfg
slartg
dlartg
clartg
zlartg
dlasd4
slasd4
slaswp
dlaswp
claswp
zlaswp
slauum
dlauum
clauum
zlauum
spbsv
dpbsv
cpbsv
zpbsv
spbtrf
dpbtrf
cpbtrf
zpbtrf
spbtrs
dpbtrs
cpbtrs
zpbtrs
sposv
dposv
cposv
zposv
spotrf
dpotrf
cpotrf
zpotrf
spotri
dpotri
cpotri
zpotri
spotrs
dpotrs
cpotrs
zpotrs
crot
zrot
strsyl
dtrsyl
ctrsyl
ztrsyl
strtri
dtrtri
ctrtri
ztrtri
strtrs
dtrtrs
ctrtrs
ztrtrs
cunghr
zunghr
cungqr
zungqr
cungrq
zungrq
cunmqr
zunmqr
sgtsv
dgtsv
cgtsv
zgtsv
sptsv
dptsv
cptsv
zptsv
slamch
dlamch
sorghr
dorghr
sorgqr
dorgqr
sorgrq
dorgrq
sormqr
dormqr
ssbev
dsbev
ssbevd
dsbevd
ssbevx
dsbevx
ssyev
dsyev
ssyevd
dsyevd
ssyevr
dsyevr
ssygv
dsygv
ssygvd
dsygvd
ssygvx
dsygvx
slange
dlange
clange
zlange
""" |
"""
[2016-03-23] Challenge #259 [Intermediate] Mahjong Hands
https://www.reddit.com/r/dailyprogrammer/comments/4bmdwz/20160323_challenge_259_intermediate_mahjong_hands/
# Description
You are the biggest, baddest mahjong player around. Your enemies tremble at your presence on the battlefield, and you
can barely walk ten steps before a fan begs you for an autograph.
However, you have a dark secret that would ruin you if it ever came to light. You're terrible at determining whether a
hand is a winning hand. For now, you've been able to bluff and bluster your way, but you know that one day you won't be
able to get away with it.
As such, you've decided to write a program to assist you!
## Further Details
Mahjong (not to be confused with [mahjong solitaire](http://en.wikipedia.org/wiki/Mahjong_solitaire)) is a game where
hands are composed from combinations of tiles. There are a number of variants of mahjong, but for this challenge, we
will consider a simplified variant of Japanese Mahjong which is also known as NAME Basic Version
There are three suits in this variant, "Bamboo", "Circle" and "Character". Every tile that belongs to these suits has a
value that ranges from 1 - 9.
To complete a hand, tiles are organised into groups. If every tile in a hand belongs to a single group (and each tile
can only be used once), the hand is a winning hand.
For now, we shall consider the groups "Pair", "Set" and "Sequence". They are composed as follows:
Pair - Two tiles with the same suit and value
Set - Three tiles with the same suit and value
Sequence - Three tiles with the same suit, and which increment in value, such as "Circle 2, Circle 3, Circle 4". There
is no value wrapping so "Circle 9, Circle 1, Circle 2" would not be considered valid.
A hand is composed of 14 tiles.
## Bonus 1 - Adding Quads
There is actually a fourth group called a "Quad". It is just like a pair and a set, except it is composed of four tiles.
What makes this group special is that a hand containing quads will actually have a hand larger than 14, 1 for every
quad. This is fine, as long as there is *1, and only 1 pair*.
## Bonus 2 - Adding Honour Tiles
In addition to the tiles belonging to the three suits, there are 7 additional tiles. These tiles have no value, and are
collectively known as "honour" tiles.
As they have no value, they cannot be members of a sequence. Furthermore, they can only be part of a set or pair with
tiles that are exactly the same. For example, "Red Dragon, Red Dragon, Red Dragon" would be a valid set, but "Red
Dragon, Green Dragon, Red Dragon" would not.
These additional tiles are:
* Green Dragon
* Red Dragon
* White Dragon
* North Wind
* East Wind
* South Wind
* West Wind
## Bonus 3 - Seven Pairs
There are a number of special hands that are an exception to the above rules. One such hand is "Seven Pairs". As the
name suggests, it is a hand composed of seven pairs.
# Formal Inputs & Outputs
## Input description
### Basic
You will be provided with N on a single line, followed by N lines of the following format:
<tile suit>,<value>
### Bonus 2
In addition, the lines may be of the format:
<honour tile>
## Output description
You should output whether the hand is a winning hand or not.
# Sample Inputs and Outputs
## Sample Input (Standard)
14
Circle,4
Circle,5
Circle,6
Bamboo,1
Bamboo,2
Bamboo,3
Character,2
Character,2
Character,2
Circle,1
Circle,1
Bamboo,7
Bamboo,8
Bamboo,9
## Sample Output (Standard)
Winning hand
## Sample Input (Standard)
14
Circle,4
Bamboo,1
Circle,5
Bamboo,2
Character,2
Bamboo,3
Character,2
Circle,6
Character,2
Circle,1
Bamboo,8
Circle,1
Bamboo,7
Bamboo,9
## Sample Output (Standard)
Winning hand
## Sample Input (Standard)
14
Circle,4
Circle,5
Circle,6
Circle,4
Circle,5
Circle,6
Circle,1
Circle,1
Bamboo,7
Bamboo,8
Bamboo,9
Circle,4
Circle,5
Circle,6
## Sample Output (Standard)
Winning hand
## Sample Input (Bonus 1)
15
Circle,4
Circle,5
Circle,6
Bamboo,1
Bamboo,2
Bamboo,3
Character,2
Character,2
Character,2
Character,2
Circle,1
Circle,1
Bamboo,7
Bamboo,8
Bamboo,9
## Sample Output (Bonus 1)
Winning hand
## Sample Input (Bonus 1)
16
Circle,4
Circle,5
Circle,6
Bamboo,1
Bamboo,2
Bamboo,3
Character,2
Character,2
Character,2
Character,2
Circle,1
Circle,1
Circle,1
Bamboo,7
Bamboo,8
Bamboo,9
## Sample Output (Bonus 1)
Not a winning hand
## Sample Input (Bonus 2)
14
Circle,4
Circle,5
Circle,6
Bamboo,1
Bamboo,2
Bamboo,3
Red Dragon
Red Dragon
Red Dragon
Circle,1
Circle,1
Bamboo,7
Bamboo,8
Bamboo,9
## Sample Output (Bonus 2)
Winning hand
## Sample Input (Bonus 2)
14
Circle,4
Circle,5
Circle,6
Bamboo,1
Bamboo,2
Bamboo,3
Red Dragon
Green Dragon
White Dragon
Circle,1
Circle,1
Bamboo,7
Bamboo,8
Bamboo,9
## Sample Output (Bonus 2)
Not a winning hand
## Sample Input (Bonus 3)
14
Circle,4
Circle,4
Character,5
Character,5
Bamboo,5
Bamboo,5
Circle,5
Circle,5
Circle,7
Circle,7
Circle,9
Circle,9
Circle,9
Circle,9
## Sample Output (Bonus 3)
Winning hand
# Notes
None of the bonus components depend on each other, and can be implemented in any order. The test cases do not presume
completion of earlier bonus components. The order is just the recommended implementation order.
Many thanks to Redditor /u/oketa for this submission to /r/dailyprogrammer_ideas. If you have any ideas, please submit
them there!
""" |
"""
=============
Miscellaneous
=============
IEEE 754 Floating Point Special Values
--------------------------------------
Special values defined in numpy: nan, inf,
NaNs can be used as a poor-man's mask (if you don't care what the
original value was)
Note: cannot use equality to test NaNs. E.g.: ::
>>> myarr = np.array([1., 0., np.nan, 3.])
>>> np.where(myarr == np.nan)
>>> np.nan == np.nan # is always False! Use special numpy functions instead.
False
>>> myarr[myarr == np.nan] = 0. # doesn't work
>>> myarr
array([ 1., 0., NaN, 3.])
>>> myarr[np.isnan(myarr)] = 0. # use this instead find
>>> myarr
array([ 1., 0., 0., 3.])
Other related special value functions: ::
isinf(): True if value is inf
isfinite(): True if not nan or inf
nan_to_num(): Map nan to 0, inf to max float, -inf to min float
The following corresponds to the usual functions except that nans are excluded
from the results: ::
nansum()
nanmax()
nanmin()
nanargmax()
nanargmin()
>>> x = np.arange(10.)
>>> x[3] = np.nan
>>> x.sum()
nan
>>> np.nansum(x)
42.0
How numpy handles numerical exceptions
--------------------------------------
The default is to ``'warn'`` for ``invalid``, ``divide``, and ``overflow``
and ``'ignore'`` for ``underflow``. But this can be changed, and it can be
set individually for different kinds of exceptions. The different behaviors
are:
- 'ignore' : Take no action when the exception occurs.
- 'warn' : Print a `RuntimeWarning` (via the Python `warnings` module).
- 'raise' : Raise a `FloatingPointError`.
- 'call' : Call a function specified using the `seterrcall` function.
- 'print' : Print a warning directly to ``stdout``.
- 'log' : Record error in a Log object specified by `seterrcall`.
These behaviors can be set for all kinds of errors or specific ones:
- all : apply to all numeric exceptions
- invalid : when NaNs are generated
- divide : divide by zero (for integers as well!)
- overflow : floating point overflows
- underflow : floating point underflows
Note that integer divide-by-zero is handled by the same machinery.
These behaviors are set on a per-thread basis.
Examples
--------
::
>>> oldsettings = np.seterr(all='warn')
>>> np.zeros(5,dtype=np.float32)/0.
invalid value encountered in divide
>>> j = np.seterr(under='ignore')
>>> np.array([1.e-100])**10
>>> j = np.seterr(invalid='raise')
>>> np.sqrt(np.array([-1.]))
FloatingPointError: invalid value encountered in sqrt
>>> def errorhandler(errstr, errflag):
... print "saw stupid error!"
>>> np.seterrcall(errorhandler)
<function err_handler at 0x...>
>>> j = np.seterr(all='call')
>>> np.zeros(5, dtype=np.int32)/0
FloatingPointError: invalid value encountered in divide
saw stupid error!
>>> j = np.seterr(**oldsettings) # restore previous
... # error-handling settings
Interfacing to C
----------------
Only a survey of the choices. Little detail on how each works.
1) Bare metal, wrap your own C-code manually.
- Plusses:
- Efficient
- No dependencies on other tools
- Minuses:
- Lots of learning overhead:
- need to learn basics of Python C API
- need to learn basics of numpy C API
- need to learn how to handle reference counting and love it.
- Reference counting often difficult to get right.
- getting it wrong leads to memory leaks, and worse, segfaults
- API will change for Python 3.0!
2) Cython
- Plusses:
- avoid learning C API's
- no dealing with reference counting
- can code in pseudo python and generate C code
- can also interface to existing C code
- should shield you from changes to Python C api
- has become the de-facto standard within the scientific Python community
- fast indexing support for arrays
- Minuses:
- Can write code in non-standard form which may become obsolete
- Not as flexible as manual wrapping
4) ctypes
- Plusses:
- part of Python standard library
- good for interfacing to existing sharable libraries, particularly
Windows DLLs
- avoids API/reference counting issues
- good numpy support: arrays have all these in their ctypes
attribute: ::
a.ctypes.data a.ctypes.get_strides
a.ctypes.data_as a.ctypes.shape
a.ctypes.get_as_parameter a.ctypes.shape_as
a.ctypes.get_data a.ctypes.strides
a.ctypes.get_shape a.ctypes.strides_as
- Minuses:
- can't use for writing code to be turned into C extensions, only a wrapper
tool.
5) SWIG (automatic wrapper generator)
- Plusses:
- around a long time
- multiple scripting language support
- C++ support
- Good for wrapping large (many functions) existing C libraries
- Minuses:
- generates lots of code between Python and the C code
- can cause performance problems that are nearly impossible to optimize
out
- interface files can be hard to write
- doesn't necessarily avoid reference counting issues or needing to know
API's
7) scipy.weave
- Plusses:
- can turn many numpy expressions into C code
- dynamic compiling and loading of generated C code
- can embed pure C code in Python module and have weave extract, generate
interfaces and compile, etc.
- Minuses:
- Future very uncertain: it's the only part of Scipy not ported to Python 3
and is effectively deprecated in favor of Cython.
8) Psyco
- Plusses:
- Turns pure python into efficient machine code through jit-like
optimizations
- very fast when it optimizes well
- Minuses:
- Only on intel (windows?)
- Doesn't do much for numpy?
Interfacing to Fortran:
-----------------------
The clear choice to wrap Fortran code is
`f2py <http://docs.scipy.org/doc/numpy-dev/f2py/>`_.
Pyfort is an older alternative, but not supported any longer.
Fwrap is a newer project that looked promising but isn't being developed any
longer.
Interfacing to C++:
-------------------
1) Cython
2) CXX
3) Boost.python
4) SWIG
5) SIP (used mainly in PyQT)
""" |
"""
This is a procedural interface to the matplotlib object-oriented
plotting library.
The following plotting commands are provided; the majority have
Matlab(TM) analogs and similar argument.
_Plotting commands
acorr - plot the autocorrelation function
annotate - annotate something in the figure
arrow - add an arrow to the axes
axes - Create a new axes
axhline - draw a horizontal line across axes
axvline - draw a vertical line across axes
axhspan - draw a horizontal bar across axes
axvspan - draw a vertical bar across axes
axis - Set or return the current axis limits
bar - make a bar chart
barh - a horizontal bar chart
broken_barh - a set of horizontal bars with gaps
box - set the axes frame on/off state
boxplot - make a box and whisker plot
cla - clear current axes
clabel - label a contour plot
clf - clear a figure window
clim - adjust the color limits of the current image
close - close a figure window
colorbar - add a colorbar to the current figure
cohere - make a plot of coherence
contour - make a contour plot
contourf - make a filled contour plot
csd - make a plot of cross spectral density
delaxes - delete an axes from the current figure
draw - Force a redraw of the current figure
errorbar - make an errorbar graph
figlegend - make legend on the figure rather than the axes
figimage - make a figure image
figtext - add text in figure coords
figure - create or change active figure
fill - make filled polygons
findobj - recursively find all objects matching some criteria
gca - return the current axes
gcf - return the current figure
gci - get the current image, or None
getp - get a handle graphics property
grid - set whether gridding is on
hist - make a histogram
hold - set the axes hold state
ioff - turn interaction mode off
ion - turn interaction mode on
isinteractive - return True if interaction mode is on
imread - load image file into array
imshow - plot image data
ishold - return the hold state of the current axes
legend - make an axes legend
loglog - a log log plot
matshow - display a matrix in a new figure preserving aspect
pcolor - make a pseudocolor plot
pcolormesh - make a pseudocolor plot using a quadrilateral mesh
pie - make a pie chart
plot - make a line plot
plot_date - plot dates
plotfile - plot column data from an ASCII tab/space/comma delimited file
pie - pie charts
polar - make a polar plot on a PolarAxes
psd - make a plot of power spectral density
quiver - make a direction field (arrows) plot
rc - control the default params
rgrids - customize the radial grids and labels for polar
savefig - save the current figure
scatter - make a scatter plot
setp - set a handle graphics property
semilogx - log x axis
semilogy - log y axis
show - show the figures
specgram - a spectrogram plot
spy - plot sparsity pattern using markers or image
stem - make a stem plot
subplot - make a subplot (numrows, numcols, axesnum)
subplots_adjust - change the params controlling the subplot positions of current figure
subplot_tool - launch the subplot configuration tool
suptitle - add a figure title
table - add a table to the plot
text - add some text at location x,y to the current axes
thetagrids - customize the radial theta grids and labels for polar
title - add a title to the current axes
xcorr - plot the autocorrelation function of x and y
xlim - set/get the xlimits
ylim - set/get the ylimits
xticks - set/get the xticks
yticks - set/get the yticks
xlabel - add an xlabel to the current axes
ylabel - add a ylabel to the current axes
autumn - set the default colormap to autumn
bone - set the default colormap to bone
cool - set the default colormap to cool
copper - set the default colormap to copper
flag - set the default colormap to flag
gray - set the default colormap to gray
hot - set the default colormap to hot
hsv - set the default colormap to hsv
jet - set the default colormap to jet
pink - set the default colormap to pink
prism - set the default colormap to prism
spring - set the default colormap to spring
summer - set the default colormap to summer
winter - set the default colormap to winter
spectral - set the default colormap to spectral
_Event handling
connect - register an event handler
disconnect - remove a connected event handler
_Matrix commands
cumprod - the cumulative product along a dimension
cumsum - the cumulative sum along a dimension
detrend - remove the mean or besdt fit line from an array
diag - the k-th diagonal of matrix
diff - the n-th differnce of an array
eig - the eigenvalues and eigen vectors of v
eye - a matrix where the k-th diagonal is ones, else zero
find - return the indices where a condition is nonzero
fliplr - flip the rows of a matrix up/down
flipud - flip the columns of a matrix left/right
linspace - a linear spaced vector of N values from min to max inclusive
logspace - a log spaced vector of N values from min to max inclusive
meshgrid - repeat x and y to make regular matrices
ones - an array of ones
rand - an array from the uniform distribution [0,1]
randn - an array from the normal distribution
rot90 - rotate matrix k*90 degress counterclockwise
squeeze - squeeze an array removing any dimensions of length 1
tri - a triangular matrix
tril - a lower triangular matrix
triu - an upper triangular matrix
vander - the Vandermonde matrix of vector x
svd - singular value decomposition
zeros - a matrix of zeros
_Probability
levypdf - The levy probability density function from the char. func.
normpdf - The Gaussian probability density function
rand - random numbers from the uniform distribution
randn - random numbers from the normal distribution
_Statistics
corrcoef - correlation coefficient
cov - covariance matrix
amax - the maximum along dimension m
mean - the mean along dimension m
median - the median along dimension m
amin - the minimum along dimension m
norm - the norm of vector x
prod - the product along dimension m
ptp - the max-min along dimension m
std - the standard deviation along dimension m
asum - the sum along dimension m
_Time series analysis
bartlett - M-point Bartlett window
blackman - M-point Blackman window
cohere - the coherence using average periodiogram
csd - the cross spectral density using average periodiogram
fft - the fast Fourier transform of vector x
hamming - M-point Hamming window
hanning - M-point Hanning window
hist - compute the histogram of x
kaiser - M length Kaiser window
psd - the power spectral density using average periodiogram
sinc - the sinc function of array x
_Dates
date2num - convert python datetimes to numeric representation
drange - create an array of numbers for date plots
num2date - convert numeric type (float days since 0001) to datetime
_Other
angle - the angle of a complex array
griddata - interpolate irregularly distributed data to a regular grid
load - load ASCII data into array
polyfit - fit x, y to an n-th order polynomial
polyval - evaluate an n-th order polynomial
roots - the roots of the polynomial coefficients in p
save - save an array to an ASCII file
trapz - trapezoidal integration
__end
""" |
"""
=============================
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 undelying 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 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
""" |
"""
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 mean 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 normalization by :math:`1/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.
""" |
"""
========================
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 larger of the two is
used. In other words, the smaller of two axes is 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://www.scipy.org/EricsBroadcastingDoc>`_
for illustrations of broadcasting concepts.
""" |
"""
=================
Structured Arrays
=================
Introduction
============
NumPy provides powerful capabilities to create arrays of structured datatype.
These arrays permit one to manipulate the data by named fields. A simple
example will show what is meant.: ::
>>> x = np.array([(1,2.,'Hello'), (2,3.,"World")],
... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')])
>>> x
array([(1, 2.0, 'Hello'), (2, 3.0, 'World')],
dtype=[('foo', '>i4'), ('bar', '>f4'), ('baz', '|S10')])
Here we have created a one-dimensional array of length 2. Each element of
this array is a structure that contains three items, a 32-bit integer, a 32-bit
float, and a string of length 10 or less. If we index this array at the second
position we get the second structure: ::
>>> x[1]
(2,3.,"World")
Conveniently, one can access any field of the array by indexing using the
string that names that field. ::
>>> y = x['bar']
>>> y
array([ 2., 3.], dtype=float32)
>>> y[:] = 2*y
>>> y
array([ 4., 6.], dtype=float32)
>>> x
array([(1, 4.0, 'Hello'), (2, 6.0, 'World')],
dtype=[('foo', '>i4'), ('bar', '>f4'), ('baz', '|S10')])
In these examples, y is a simple float array consisting of the 2nd field
in the structured type. But, rather than being a copy of the data in the structured
array, it is a view, i.e., it shares exactly the same memory locations.
Thus, when we updated this array by doubling its values, the structured
array shows the corresponding values as doubled as well. Likewise, if one
changes the structured array, the field view also changes: ::
>>> x[1] = (-1,-1.,"Master")
>>> x
array([(1, 4.0, 'Hello'), (-1, -1.0, 'Master')],
dtype=[('foo', '>i4'), ('bar', '>f4'), ('baz', '|S10')])
>>> y
array([ 4., -1.], dtype=float32)
Defining Structured Arrays
==========================
One defines a structured array through the dtype object. There are
**several** alternative ways to define the fields of a record. Some of
these variants provide backward compatibility with Numeric, numarray, or
another module, and should not be used except for such purposes. These
will be so noted. One specifies record structure in
one of four alternative ways, using an argument (as supplied to a dtype
function keyword or a dtype object constructor itself). This
argument must be one of the following: 1) string, 2) tuple, 3) list, or
4) dictionary. Each of these is briefly described below.
1) String argument.
In this case, the constructor expects a comma-separated list of type
specifiers, optionally with extra shape information. The fields are
given the default names 'f0', 'f1', 'f2' and so on.
The type specifiers can take 4 different forms: ::
a) b1, i1, i2, i4, i8, u1, u2, u4, u8, f2, f4, f8, c8, c16, a<n>
(representing bytes, ints, unsigned ints, floats, complex and
fixed length strings of specified byte lengths)
b) int8,...,uint8,...,float16, float32, float64, complex64, complex128
(this time with bit sizes)
c) older Numeric/numarray type specifications (e.g. Float32).
Don't use these in new code!
d) Single character type specifiers (e.g H for unsigned short ints).
Avoid using these unless you must. Details can be found in the
NumPy book
These different styles can be mixed within the same string (but why would you
want to do that?). Furthermore, each type specifier can be prefixed
with a repetition number, or a shape. In these cases an array
element is created, i.e., an array within a record. That array
is still referred to as a single field. An example: ::
>>> x = np.zeros(3, dtype='3int8, float32, (2,3)float64')
>>> x
array([([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),
([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),
([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])],
dtype=[('f0', '|i1', 3), ('f1', '>f4'), ('f2', '>f8', (2, 3))])
By using strings to define the record structure, it precludes being
able to name the fields in the original definition. The names can
be changed as shown later, however.
2) Tuple argument: The only relevant tuple case that applies to record
structures is when a structure is mapped to an existing data type. This
is done by pairing in a tuple, the existing data type with a matching
dtype definition (using any of the variants being described here). As
an example (using a definition using a list, so see 3) for further
details): ::
>>> x = np.zeros(3, dtype=('i4',[('r','u1'), ('g','u1'), ('b','u1'), ('a','u1')]))
>>> x
array([0, 0, 0])
>>> x['r']
array([0, 0, 0], dtype=uint8)
In this case, an array is produced that looks and acts like a simple int32 array,
but also has definitions for fields that use only one byte of the int32 (a bit
like Fortran equivalencing).
3) List argument: In this case the record structure is defined with a list of
tuples. Each tuple has 2 or 3 elements specifying: 1) The name of the field
('' is permitted), 2) the type of the field, and 3) the shape (optional).
For example::
>>> x = np.zeros(3, dtype=[('x','f4'),('y',np.float32),('value','f4',(2,2))])
>>> x
array([(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]]),
(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]]),
(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]])],
dtype=[('x', '>f4'), ('y', '>f4'), ('value', '>f4', (2, 2))])
4) Dictionary argument: two different forms are permitted. The first consists
of a dictionary with two required keys ('names' and 'formats'), each having an
equal sized list of values. The format list contains any type/shape specifier
allowed in other contexts. The names must be strings. There are two optional
keys: 'offsets' and 'titles'. Each must be a correspondingly matching list to
the required two where offsets contain integer offsets for each field, and
titles are objects containing metadata for each field (these do not have
to be strings), where the value of None is permitted. As an example: ::
>>> x = np.zeros(3, dtype={'names':['col1', 'col2'], 'formats':['i4','f4']})
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[('col1', '>i4'), ('col2', '>f4')])
The other dictionary form permitted is a dictionary of name keys with tuple
values specifying type, offset, and an optional title. ::
>>> x = np.zeros(3, dtype={'col1':('i1',0,'title 1'), 'col2':('f4',1,'title 2')})
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[(('title 1', 'col1'), '|i1'), (('title 2', 'col2'), '>f4')])
Accessing and modifying field names
===================================
The field names are an attribute of the dtype object defining the structure.
For the last example: ::
>>> x.dtype.names
('col1', 'col2')
>>> x.dtype.names = ('x', 'y')
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[(('title 1', 'x'), '|i1'), (('title 2', 'y'), '>f4')])
>>> x.dtype.names = ('x', 'y', 'z') # wrong number of names
<type 'exceptions.ValueError'>: must replace all names at once with a sequence of length 2
Accessing field titles
====================================
The field titles provide a standard place to put associated info for fields.
They do not have to be strings. ::
>>> x.dtype.fields['x'][2]
'title 1'
Accessing multiple fields at once
====================================
You can access multiple fields at once using a list of field names: ::
>>> x = np.array([(1.5,2.5,(1.0,2.0)),(3.,4.,(4.,5.)),(1.,3.,(2.,6.))],
dtype=[('x','f4'),('y',np.float32),('value','f4',(2,2))])
Notice that `x` is created with a list of tuples. ::
>>> x[['x','y']]
array([(1.5, 2.5), (3.0, 4.0), (1.0, 3.0)],
dtype=[('x', '<f4'), ('y', '<f4')])
>>> x[['x','value']]
array([(1.5, [[1.0, 2.0], [1.0, 2.0]]), (3.0, [[4.0, 5.0], [4.0, 5.0]]),
(1.0, [[2.0, 6.0], [2.0, 6.0]])],
dtype=[('x', '<f4'), ('value', '<f4', (2, 2))])
The fields are returned in the order they are asked for.::
>>> x[['y','x']]
array([(2.5, 1.5), (4.0, 3.0), (3.0, 1.0)],
dtype=[('y', '<f4'), ('x', '<f4')])
Filling structured arrays
=========================
Structured arrays can be filled by field or row by row. ::
>>> arr = np.zeros((5,), dtype=[('var1','f8'),('var2','f8')])
>>> arr['var1'] = np.arange(5)
If you fill it in row by row, it takes a take a tuple
(but not a list or array!)::
>>> arr[0] = (10,20)
>>> arr
array([(10.0, 20.0), (1.0, 0.0), (2.0, 0.0), (3.0, 0.0), (4.0, 0.0)],
dtype=[('var1', '<f8'), ('var2', '<f8')])
Record Arrays
=============
For convenience, numpy provides "record arrays" which allow one to access
fields of structured arrays by attribute rather than by index. Record arrays
are structured arrays wrapped using a subclass of ndarray,
:class:`numpy.recarray`, which allows field access by attribute on the array
object, and record arrays also use a special datatype, :class:`numpy.record`,
which allows field access by attribute on the individual elements of the array.
The simplest way to create a record array is with :func:`numpy.rec.array`: ::
>>> recordarr = np.rec.array([(1,2.,'Hello'),(2,3.,"World")],
... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')])
>>> recordarr.bar
array([ 2., 3.], dtype=float32)
>>> recordarr[1:2]
rec.array([(2, 3.0, 'World')],
dtype=[('foo', '<i4'), ('bar', '<f4'), ('baz', 'S10')])
>>> recordarr[1:2].foo
array([2], dtype=int32)
>>> recordarr.foo[1:2]
array([2], dtype=int32)
>>> recordarr[1].baz
'World'
numpy.rec.array can convert a wide variety of arguments into record arrays,
including normal structured arrays: ::
>>> arr = array([(1,2.,'Hello'),(2,3.,"World")],
... dtype=[('foo', 'i4'), ('bar', 'f4'), ('baz', 'S10')])
>>> recordarr = np.rec.array(arr)
The numpy.rec module provides a number of other convenience functions for
creating record arrays, see :ref:`record array creation routines
<routines.array-creation.rec>`.
A record array representation of a structured array can be obtained using the
appropriate :ref:`view`: ::
>>> arr = np.array([(1,2.,'Hello'),(2,3.,"World")],
... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'a10')])
>>> recordarr = arr.view(dtype=dtype((np.record, arr.dtype)),
... type=np.recarray)
For convenience, viewing an ndarray as type `np.recarray` will automatically
convert to `np.record` datatype, so the dtype can be left out of the view: ::
>>> recordarr = arr.view(np.recarray)
>>> recordarr.dtype
dtype((numpy.record, [('foo', '<i4'), ('bar', '<f4'), ('baz', 'S10')]))
To get back to a plain ndarray both the dtype and type must be reset. The
following view does so, taking into account the unusual case that the
recordarr was not a structured type: ::
>>> arr2 = recordarr.view(recordarr.dtype.fields or recordarr.dtype, np.ndarray)
Record array fields accessed by index or by attribute are returned as a record
array if the field has a structured type but as a plain ndarray otherwise. ::
>>> recordarr = np.rec.array([('Hello', (1,2)),("World", (3,4))],
... dtype=[('foo', 'S6'),('bar', [('A', int), ('B', int)])])
>>> type(recordarr.foo)
<type 'numpy.ndarray'>
>>> type(recordarr.bar)
<class 'numpy.core.records.recarray'>
Note that if a field has the same name as an ndarray attribute, the ndarray
attribute takes precedence. Such fields will be inaccessible by attribute but
may still be accessed by index.
""" |
"""
====================================
Linear algebra (:mod:`scipy.linalg`)
====================================
.. currentmodule:: scipy.linalg
Linear algebra functions.
.. seealso::
`numpy.linalg` for more linear algebra functions. Note that
although `scipy.linalg` imports most of them, identically named
functions from `scipy.linalg` may offer more or slightly differing
functionality.
Basics
======
.. autosummary::
:toctree: generated/
inv - Find the inverse of a square matrix
solve - Solve a linear system of equations
solve_banded - Solve a banded linear system
solveh_banded - Solve a Hermitian or symmetric banded system
solve_circulant - Solve a circulant system
solve_triangular - Solve a triangular matrix
solve_toeplitz - Solve a toeplitz matrix
det - Find the determinant of a square matrix
norm - Matrix and vector norm
lstsq - Solve a linear least-squares problem
pinv - Pseudo-inverse (Moore-Penrose) using lstsq
pinv2 - Pseudo-inverse using svd
pinvh - Pseudo-inverse of hermitian matrix
kron - Kronecker product of two arrays
tril - Construct a lower-triangular matrix from a given matrix
triu - Construct an upper-triangular matrix from a given matrix
orthogonal_procrustes - Solve an orthogonal Procrustes problem
matrix_balance - Balance matrix entries with a similarity transformation
LinAlgError
Eigenvalue Problems
===================
.. autosummary::
:toctree: generated/
eig - Find the eigenvalues and eigenvectors of a square matrix
eigvals - Find just the eigenvalues of a square matrix
eigh - Find the e-vals and e-vectors of a Hermitian or symmetric matrix
eigvalsh - Find just the eigenvalues of a Hermitian or symmetric matrix
eig_banded - Find the eigenvalues and eigenvectors of a banded matrix
eigvals_banded - Find just the eigenvalues of a banded matrix
Decompositions
==============
.. autosummary::
:toctree: generated/
lu - LU decomposition of a matrix
lu_factor - LU decomposition returning unordered matrix and pivots
lu_solve - Solve Ax=b using back substitution with output of lu_factor
svd - Singular value decomposition of a matrix
svdvals - Singular values of a matrix
diagsvd - Construct matrix of singular values from output of svd
orth - Construct orthonormal basis for the range of A using svd
cholesky - Cholesky decomposition of a matrix
cholesky_banded - Cholesky decomp. of a sym. or Hermitian banded matrix
cho_factor - Cholesky decomposition for use in solving a linear system
cho_solve - Solve previously factored linear system
cho_solve_banded - Solve previously factored banded linear system
polar - Compute the polar decomposition.
qr - QR decomposition of a matrix
qr_multiply - QR decomposition and multiplication by Q
qr_update - Rank k QR update
qr_delete - QR downdate on row or column deletion
qr_insert - QR update on row or column insertion
rq - RQ decomposition of a matrix
qz - QZ decomposition of a pair of matrices
ordqz - QZ decomposition of a pair of matrices with reordering
schur - Schur decomposition of a matrix
rsf2csf - Real to complex Schur form
hessenberg - Hessenberg form of a matrix
.. seealso::
`scipy.linalg.interpolative` -- Interpolative matrix decompositions
Matrix Functions
================
.. autosummary::
:toctree: generated/
expm - Matrix exponential
logm - Matrix logarithm
cosm - Matrix cosine
sinm - Matrix sine
tanm - Matrix tangent
coshm - Matrix hyperbolic cosine
sinhm - Matrix hyperbolic sine
tanhm - Matrix hyperbolic tangent
signm - Matrix sign
sqrtm - Matrix square root
funm - Evaluating an arbitrary matrix function
expm_frechet - Frechet derivative of the matrix exponential
expm_cond - Relative condition number of expm in the Frobenius norm
fractional_matrix_power - Fractional matrix power
Matrix Equation Solvers
=======================
.. autosummary::
:toctree: generated/
solve_sylvester - Solve the Sylvester matrix equation
solve_continuous_are - Solve the continuous-time algebraic Riccati equation
solve_discrete_are - Solve the discrete-time algebraic Riccati equation
solve_discrete_lyapunov - Solve the discrete-time Lyapunov equation
solve_lyapunov - Solve the (continous-time) Lyapunov equation
Special Matrices
================
.. autosummary::
:toctree: generated/
block_diag - Construct a block diagonal matrix from submatrices
circulant - Circulant matrix
companion - Companion matrix
dft - Discrete Fourier transform matrix
hadamard - Hadamard matrix of order 2**n
hankel - Hankel matrix
helmert - Helmert matrix
hilbert - Hilbert matrix
invhilbert - Inverse Hilbert matrix
leslie - Leslie matrix
pascal - Pascal matrix
invpascal - Inverse Pascal matrix
toeplitz - Toeplitz matrix
tri - Construct a matrix filled with ones at and below a given diagonal
Low-level routines
==================
.. autosummary::
:toctree: generated/
get_blas_funcs
get_lapack_funcs
find_best_blas_type
.. seealso::
`scipy.linalg.blas` -- Low-level BLAS functions
`scipy.linalg.lapack` -- Low-level LAPACK functions
`scipy.linalg.cython_blas` -- Low-level BLAS functions for Cython
`scipy.linalg.cython_lapack` -- Low-level LAPACK functions for Cython
""" |
#!/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
|
"""
===================
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. Additional ufuncts
available in xxx in scipy. 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. These methods are explained in detail in xxx
(or are they, I don't see anything in the ufunc docstring that is useful?).
**.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.
""" |
# -*- encoding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# Copyright (C) 2004-2009 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/>.
#
##############################################################################
# SKR03
# =====
# Dieses Modul bietet Ihnen einen deutschen Kontenplan basierend auf dem SKR03.
# Gemäss der aktuellen Einstellungen ist die Firma nicht Umsatzsteuerpflichtig.
# Diese Grundeinstellung ist sehr einfach zu ändern und bedarf in der Regel
# grundsätzlich eine initiale Zuweisung von Steuerkonten zu Produkten und / oder
# Sachkonten oder zu Partnern.
# Die Umsatzsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten bei den Produktstammdaten hinterlegt werden (in Abhängigkeit der
# Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter Finanzbuchhaltung
# (Kategorie: Umsatzsteuer).
# Die Vorsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten ebenso bei den Produktstammdaten hinterlegt werden (in Abhängigkeit
# der Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter
# Finanzbuchhaltung (Kategorie: Vorsteuer).
# Die Zuordnung der Steuern für Ein- und Ausfuhren aus EU Ländern, sowie auch
# für den Ein- und Verkauf aus und in Drittländer sollten beim Partner
# (Lieferant/Kunde)hinterlegt werden (in Anhängigkeit vom Herkunftsland
# des Lieferanten/Kunden). Die Zuordnung beim Kunden ist 'höherwertig' als
# die Zuordnung bei Produkten und überschreibt diese im Einzelfall.
#
# Zur Vereinfachung der Steuerausweise und Buchung bei Auslandsgeschäften
# erlaubt OpenERP ein generelles Mapping von Steuerausweis und Steuerkonten
# (z.B. Zuordnung 'Umsatzsteuer 19%' zu 'steuerfreie Einfuhren aus der EU')
# zwecks Zuordnung dieses Mappings zum ausländischen Partner (Kunde/Lieferant).
# Die Rechnungsbuchung beim Einkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Vorsteuer Steuermessbetrag (z.B. Vorsteuer
# Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Vorsteuern' (z.B. Vorsteuer
# 19%). Durch multidimensionale Hierachien können verschiedene Positionen
# zusammengefasst werden und dann in Form eines Reports ausgegeben werden.
#
# Die Rechnungsbuchung beim Verkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Umsatzsteuer Steuermessbetrag
# (z.B. Umsatzsteuer Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Umsatzsteuer'
# (z.B. Umsatzsteuer 19%). Durch multidimensionale Hierachien können
# verschiedene Positionen zusammengefasst werden.
# Die zugewiesenen Steuerausweise können auf Ebene der einzelnen
# Rechnung (Eingangs- und Ausgangsrechnung) nachvollzogen werden,
# und dort gegebenenfalls angepasst werden.
# Rechnungsgutschriften führen zu einer Korrektur (Gegenposition)
# der Steuerbuchung, in Form einer spiegelbildlichen Buchung.
# SKR04
# =====
# Dieses Modul bietet Ihnen einen deutschen Kontenplan basierend auf dem SKR04.
# Gemäss der aktuellen Einstellungen ist die Firma nicht Umsatzsteuerpflichtig,
# d.h. im Standard existiert keine Zuordnung von Produkten und Sachkonten zu
# Steuerschlüsseln.
# Diese Grundeinstellung ist sehr einfach zu ändern und bedarf in der Regel
# grundsätzlich eine initiale Zuweisung von Steuerschlüsseln zu Produkten und / oder
# Sachkonten oder zu Partnern.
# Die Umsatzsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten bei den Produktstammdaten hinterlegt werden (in Abhängigkeit der
# Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter Finanzbuchhaltung
# (Kategorie: Umsatzsteuer).
# Die Vorsteuern (voller Steuersatz, reduzierte Steuer und steuerfrei)
# sollten ebenso bei den Produktstammdaten hinterlegt werden (in Abhängigkeit
# der Steuervorschriften). Die Zuordnung erfolgt auf dem Aktenreiter
# Finanzbuchhaltung (Kategorie: Vorsteuer).
# Die Zuordnung der Steuern für Ein- und Ausfuhren aus EU Ländern, sowie auch
# für den Ein- und Verkauf aus und in Drittländer sollten beim Partner
# (Lieferant/Kunde) hinterlegt werden (in Anhängigkeit vom Herkunftsland
# des Lieferanten/Kunden). Die Zuordnung beim Kunden ist 'höherwertig' als
# die Zuordnung bei Produkten und überschreibt diese im Einzelfall.
#
# Zur Vereinfachung der Steuerausweise und Buchung bei Auslandsgeschäften
# erlaubt OpenERP ein generelles Mapping von Steuerausweis und Steuerkonten
# (z.B. Zuordnung 'Umsatzsteuer 19%' zu 'steuerfreie Einfuhren aus der EU')
# zwecks Zuordnung dieses Mappings zum ausländischen Partner (Kunde/Lieferant).
# Die Rechnungsbuchung beim Einkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Vorsteuer Steuermessbetrag (z.B. Vorsteuer
# Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Vorsteuern' (z.B. Vorsteuer
# 19%). Durch multidimensionale Hierachien können verschiedene Positionen
# zusammengefasst werden und dann in Form eines Reports ausgegeben werden.
#
# Die Rechnungsbuchung beim Verkauf bewirkt folgendes:
# Die Steuerbemessungsgrundlage (exklusive Steuer) wird ausgewiesen bei den
# jeweiligen Kategorien für den Umsatzsteuer Steuermessbetrag
# (z.B. Umsatzsteuer Steuermessbetrag Voller Steuersatz 19%).
# Der Steuerbetrag erscheint unter der Kategorie 'Umsatzsteuer'
# (z.B. Umsatzsteuer 19%). Durch multidimensionale Hierachien können
# verschiedene Positionen zusammengefasst werden.
# Die zugewiesenen Steuerausweise können auf Ebene der einzelnen
# Rechnung (Eingangs- und Ausgangsrechnung) nachvollzogen werden,
# und dort gegebenenfalls angepasst werden.
# Rechnungsgutschriften führen zu einer Korrektur (Gegenposition)
# der Steuerbuchung, in Form einer spiegelbildlichen Buchung.
|
"""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 code is part of Ansible, but is an independent component.
# This particular file snippet, and this file snippet only, is BSD licensed.
# Modules you write using this snippet, which is embedded dynamically by Ansible
# still belong to the author of the module, and may assign their own license
# to the complete work.
#
# Copyright (c), NAME <michael.dehaan@gmail.com>, 2012-2013
# Copyright (c), NAME <tkuratomi@ansible.com>, 2015
# All rights reserved.
#
# 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.
#
# 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 match_hostname function and supporting code is under the terms and
# conditions of the Python Software Foundation License. They were taken from
# the Python3 standard library and adapted for use in Python2. See comments in the
# source for which code precisely is under this License. PSF License text
# follows:
#
# PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
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# otherwise using this software ("Python") in source or binary form and
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# FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
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|
"""
Language and whisper obfuscation system
Evennia contrib - Griatch 2015
This module is intented to be used with an emoting system (such as
contrib/rpsystem.py). It offers the ability to obfuscate spoken words
in the game in various ways:
- Language: The language functionality defines a pseudo-language map
to any number of languages. The string will be obfuscated depending
on a scaling that (most likely) will be input as a weighted average of
the language skill of the speaker and listener.
- Whisper: The whisper functionality will gradually "fade out" a
whisper along as scale 0-1, where the fading is based on gradually
removing sections of the whisper that is (supposedly) easier to
overhear (for example "s" sounds tend to be audible even when no other
meaning can be determined).
Usage:
```python
from evennia.contrib import rplanguages
# need to be done once, here we create the "default" lang
rplanguages.add_language()
say = "This is me talking."
whisper = "This is me whispering.
print rplanguages.obfuscate_language(say, level=0.0)
<<< "This is me talking."
print rplanguages.obfuscate_language(say, level=0.5)
<<< "This is me byngyry."
print rplanguages.obfuscate_language(say, level=1.0)
<<< "Daly ly sy byngyry."
result = rplanguages.obfuscate_whisper(whisper, level=0.0)
<<< "This is me whispering"
result = rplanguages.obfuscate_whisper(whisper, level=0.2)
<<< "This is m- whisp-ring"
result = rplanguages.obfuscate_whisper(whisper, level=0.5)
<<< "---s -s -- ---s------"
result = rplanguages.obfuscate_whisper(whisper, level=0.7)
<<< "---- -- -- ----------"
result = rplanguages.obfuscate_whisper(whisper, level=1.0)
<<< "..."
```
To set up new languages, import and use the `add_language()`
helper method in this module. This allows you to customize the
"feel" of the semi-random language you are creating. Especially
the `word_length_variance` helps vary the length of translated
words compared to the original and can help change the "feel" for
the language you are creating. You can also add your own
dictionary and "fix" random words for a list of input words.
Below is an example of "elvish", using "rounder" vowels and sounds:
```python
phonemes = "oi oh ee ae aa eh ah ao aw ay er ey ow ia ih iy " \
"oy ua uh uw y p b t d f v t dh s z sh zh ch jh k " \
"ng g m n l r w",
vowels = "eaoiuy"
grammar = "v vv vvc vcc vvcc cvvc vccv vvccv vcvccv vcvcvcc vvccvvcc " \
"vcvvccvvc cvcvvcvvcc vcvcvvccvcvv",
word_length_variance = 1
noun_postfix = "'la"
manual_translations = {"the":"y'e", "we":"uyi", "she":"semi", "he":"emi",
"you": "do", 'me':'mi','i':'me', 'be':"hy'e", 'and':'y'}
rplanguages.add_language(key="elvish", phonemes=phonemes, grammar=grammar,
word_length_variance=word_length_variance,
noun_postfix=noun_postfix, vowels=vowels,
manual_translations=manual_translations
auto_translations="my_word_file.txt")
```
This will produce a decicively more "rounded" and "soft" language
than the default one. The few manual_translations also make sure
to make it at least look superficially "reasonable".
The `auto_translations` keyword is useful, this accepts either a
list or a path to a file of words (one per line) to automatically
create fixed translations for according to the grammatical rules.
This allows to quickly build a large corpus of translated words
that never change (if this is desired).
""" |
"""Yahoo Search Web Services
This module implements a set of classes and functions to work with the
Yahoo Search Web Services. All results from these services are properly
formatted XML, and this package facilitates for proper parsing of these
result sets. Some of the features include:
* Extendandable API, with replaceable backend XML parsers, and
I/O interface.
* Type and value checking on search parameters, including
automatic type conversion (when appropriate and possible)
* Flexible return format, including DOM objects, or fully
parsed result objects
You can either instantiate a search object directly, or use the factory
function create_search() in this module (see below). The supported classes
of searches are:
VideoSearch - Video Search
ImageSearch - Image Search
WebSearch - Web Search
NewsSearch - News Search
LocalSearch - Local Search
RelatedSuggestion - Web Search Related Suggestion
SpellingSuggestion - Web Search Spelling Suggestion
TermExtraction - Term Extraction service
ContextSearch - Web Search with a context
The different sub-classes of Search supports different sets of query
parameters. They all require an application ID parameter (app_id). The
following tables describes all other allowed parameters for each of the
supported services:
Web Related Spelling Context Term
----- ------- -------- ------- ------
query [X] [X] [X] [X] [X]
type [X] . . [X]
results [X] [X] . [X]
start [X] . . [X]
format [X] . . [X]
adult_ok [X] . . [X]
similar_ok [X] . . [X]
language [X] . . [X]
country [X] . . [X]
context . . . [X] [X]
Image Video News Local
----- ----- ----- -----
query [X] [X] [X] [X]
type [X] [X] [X] .
results [X] [X] [X] [X]
start [X] [X] [X] [X]
format [X] [X] . .
adult_ok [X] [X] . .
language . . . [X]
country . . . .
sort . . [X] [X]
coloration [X] . . .
radius . . . [X]
street . . . [X]
city . . . [X]
state . . . [X]
zip . . . [X]
location . . . [X]
longitude . . . [X]
latitude . . . [X]
List Folders List URLs
------------ ---------
folder . [X]
yahooid [X] [X]
results [X] [X]
start [X] [X]
Each of these parameter is implemented as an attribute of each
respective class. For example, you can set parameters like:
from yahoo.search.webservices import WebSearch
app_id = "something"
srch = WebSearch(app_id)
srch.query = "Leif NAME
srch.results = 40
or, if you are using the factory function:
from yahoo.search.webservices import create_search
app_id = "something"
srch = create_search("Web", app_id, query="Leif NAME, results=40)
or, the last alternative, a combination of the previous two:
from yahoo.search.webservices import WebSearch
app_id = "something"
srch = WebSearch(app_id, query="Leif NAME, results=40)
To retrieve a certain parameter value, simply access it as any normal
attribute:
print "Searched for ", srch.query
For more information on these parameters, and their allowed values, please
see the official Yahoo Search Services documentation (XXX missing URL?)
Once the webservice object has been created, you can retrieve a parsed
object (typically a DOM object) using the get_results() method:
dom = srch.get_results()
This DOM object contains all results, and can be used as is. For easier
use of the results, you can use the built-in results factory, which will
traverse the entire DOM object, and create a list of results objects.
results = srch.parse_results(dom)
or, by using the implicit call to get_results():
results = srch.parse_results()
The default XML parser and results factories should be adequate for most
users, so use the parse_results() when possible. However, both the XML
parser and the results parser can easily be overriden.
EXAMPLE:
#!/usr/bin/python
import sys
from yahoo.search.webservices import create_search
service = argv[1]
query = " ".join(sys.argv[2:])
app_id = "something"
x = create_search(service, app_id, query=query, results=5)
if x is None:
x = create_search("Web", app_id, query=query, results=5)
dom = srch.get_results()
results = srch.parse_results(dom)
for res in results:
url = res.Url
summary = res['Summary']
print "%s -> %s" (summary, url)
""" |
# In the 20×20 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 the same direction (up, down, left, right, or diagonally) in the 20×20 grid?
#
|
#import unittest
#
#from DIRAC.Core.Base import Script
#Script.parseCommandLine()
#
#from DIRAC.ResourceStatusSystem.Utilities.mock import Mock
#from DIRAC.ResourceStatusSystem.Client.JobsClient import JobsClient
#from DIRAC.ResourceStatusSystem.Client.PilotsClient import PilotsClient
#from DIRAC.ResourceStatusSystem.Client.ResourceStatusClient import ResourceStatusClient
#from DIRAC.ResourceStatusSystem.Client.ResourceManagementClient import ResourceManagementClient
#
#from DIRAC.ResourceStatusSystem.Utilities import CS
#
#ValidRes = CS.getTypedDictRootedAt("GeneralConfig")['Resource']
#ValidStatus = CS.getTypedDictRootedAt("GeneralConfig")['Status']
#
##############################################################################
#
#class ClientsTestCase( unittest.TestCase ):
# """ Base class for the clients test cases
# """
# def setUp( self ):
#
# self.mockRSS = Mock()
#
# self.RSCli = ResourceStatusClient( serviceIn = self.mockRSS )
# self.RMCli = ResourceManagementClient( serviceIn = self.mockRSS )
# self.PilotsCli = PilotsClient()
# self.JobsCli = JobsClient()
#
##############################################################################
#
#class ResourceStatusClientSuccess( ClientsTestCase ):
#
# def test_getPeriods( self ):
# self.mockRSS.getPeriods.return_value = {'OK':True, 'Value':[]}
# for granularity in ValidRes:
# for status in ValidStatus:
# res = self.RSCli.getPeriods( granularity, 'XX', status, 20 )
# self.assertEqual(res['OK'], True)
# self.assertEqual( res['Value'], [] )
#
# def test_getServiceStats( self ):
# self.mockRSS.getServiceStats.return_value = {'OK':True, 'Value':[]}
# res = self.RSCli.getServiceStats( 'Site', '' )
# self.assertEqual( res['Value'], [] )
#
# def test_getResourceStats( self ):
# self.mockRSS.getResourceStats.return_value = {'OK':True, 'Value':[]}
# res = self.RSCli.getResourceStats( 'Site', '' )
# self.assertEqual( res['Value'], [] )
# res = self.RSCli.getResourceStats( 'Service', '' )
# self.assertEqual( res['Value'], [] )
#
# def test_getStorageElementsStats( self ):
# self.mockRSS.getStorageElementsStats.return_value = {'OK':True, 'Value':[]}
# res = self.RSCli.getStorageElementsStats( 'Site', '', "Read" )
# self.assertEqual( res['Value'], [] )
# res = self.RSCli.getStorageElementsStats( 'Resource', '', "Read")
# self.assertEqual( res['Value'], [] )
#
# def test_getMonitoredStatus( self ):
# self.mockRSS.getSitesStatusWeb.return_value = {'OK':True, 'Value': {'Records': [['', '', '', '', 'Active', '']]}}
# self.mockRSS.getServicesStatusWeb.return_value = {'OK':True, 'Value':{'Records': [['', '', '', '', 'Active', '']]}}
# self.mockRSS.getResourcesStatusWeb.return_value = {'OK':True, 'Value':{'Records': [['', '', '', '', '', 'Active', '']]}}
# self.mockRSS.getStorageElementsStatusWeb.return_value = {'OK':True, 'Value':{'Records': [['', '', '', '', 'Active', '']]}}
# for g in ValidRes:
# res = self.RSCli.getMonitoredStatus( g, 'a' )
# self.assertEqual( res['Value'], ['Active'] )
# res = self.RSCli.getMonitoredStatus( g, ['a'] )
# self.assertEqual( res['Value'], ['Active'] )
# res = self.RSCli.getMonitoredStatus( g, ['a', 'b'] )
# self.assertEqual( res['Value'], ['Active', 'Active'] )
#
# def test_getCachedAccountingResult( self ):
# self.mockRSS.getCachedAccountingResult.return_value = {'OK':True, 'Value':[]}
# res = self.RMCli.getCachedAccountingResult( 'XX', 'pippo', 'ZZ' )
# self.assertEqual( res['Value'], [] )
#
# def test_getCachedResult( self ):
# self.mockRSS.getCachedResult.return_value = {'OK':True, 'Value':[]}
# res = self.RMCli.getCachedResult( 'XX', 'pippo', 'ZZ', 1 )
# self.assertEqual( res['Value'], [] )
#
# def test_getCachedIDs( self ):
# self.mockRSS.getCachedIDs.return_value = {'OK':True,
# 'Value':[78805473L, 78805473L, 78805473L, 78805473L]}
# res = self.RMCli.getCachedIDs( 'XX', 'pippo' )
# self.assertEqual( res['Value'], [78805473L, 78805473L, 78805473L, 78805473L] )
#
#
#
##############################################################################
#
#class JobsClientSuccess( ClientsTestCase ):
#
# def test_getJobsSimpleEff( self ):
# WMS_Mock = Mock()
# WMS_Mock.getSiteSummaryWeb.return_value = {'OK': True,
# 'rpcStub': ( ( 'WorkloadManagement/WMSAdministrator',
# {'skipCACheck': True,
# 'delegatedGroup': 'diracAdmin',
# 'delegatedDN': '/DC=ch/DC=cern/OU=Organic Units/OU=Users/CN=fstagni/CN=693025/CN=Federico Stagni', 'timeout': 600} ),
# 'getSiteSummaryWeb', ( {'Site': 'LCG.CERN.ch'}, [], 0, 500 ) ),
# 'Value': {'TotalRecords': 1,
# 'ParameterNames': ['Site', 'GridType', 'Country', 'Tier', 'MaskStatus', 'Received', 'Checking', 'Staging', 'Waiting', 'Matched', 'Running', 'Stalled', 'Done', 'Completed', 'Failed', 'Efficiency', 'Status'],
# 'Extras': {'ru': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'fr': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 12L, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'ch': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 4L, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 1L}, 'nl': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'uk': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'Unknown': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'de': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 1L, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'it': {'Received': 0, 'Staging': 0, 'Checking': 1L, 'Completed': 0, 'Waiting': 2L, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'hu': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'cy': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'bg': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'au': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 10L, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'il': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'br': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'ie': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'pl': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 0, 'Stalled': 0, 'Matched': 0}, 'es': {'Received': 0, 'Staging': 0, 'Checking': 0, 'Completed': 0, 'Waiting': 0, 'Failed': 0, 'Running': 0, 'Done': 2L, 'Stalled': 0, 'Matched': 0}},
# 'Records': [['LCG.CERN.ch', 'LCG', 'ch', 'Tier-1', 'Active', 0, 0, 0, 4L, 1L, 0, 0, 0, 0, 0, '0.0', 'Idle']]}}
# res = self.JobsCli.getJobsSimpleEff( 'XX', RPCWMSAdmin = WMS_Mock )
# self.assertEqual( res, {'LCG.CERN.ch': 'Idle'} )
#
##############################################################################
#
#class PilotsClientSuccess( ClientsTestCase ):
#
## def test_getPilotsStats(self):
## self.mockRSS.getPeriods.return_value = {'OK':True, 'Value':[]}
## for granularity in ValidRes:
## for status in ValidStatus:
## res = self.RSCli.getPeriods(granularity, 'XX', status, 20)
## self.assertEqual(res['Periods'], [])
#
# def test_getPilotsSimpleEff( self ):
# #self.mockRSS.getPilotsSimpleEff.return_value = {'OK':True, 'Value':{'Records': [['', '', 0, 3L, 0, 0, 0, 283L, 66L, 0, 0, 352L, '1.00', '81.25', 'Fair', 'Yes']]}}
#
# WMS_Mock = Mock()
# WMS_Mock.getPilotSummaryWeb.return_value = {'OK': True,
# 'rpcStub': ( ( 'WorkloadManagement/WMSAdministrator',
# {'skipCACheck': True,
# 'delegatedGroup': 'diracAdmin',
# 'delegatedDN': '/DC=ch/DC=cern/OU=Organic Units/OU=Users/CN=fstagni/CN=693025/CN=Federico Stagni', 'timeout': 600} ),
# 'getPilotSummaryWeb', ( {'GridSite': 'LCG.Ferrara.it'}, [], 0, 500 ) ),
# 'Value': {
# 'TotalRecords': 0,
# 'ParameterNames': ['Site', 'CE', 'Submitted', 'Ready', 'Scheduled', 'Waiting', 'Running', 'Done', 'Aborted', 'Done_Empty', 'Aborted_Hour', 'Total', 'PilotsPerJob', 'PilotJobEff', 'Status', 'InMask'],
# 'Extras': {'Scheduled': 0, 'Status': 'Poor', 'Aborted_Hour': 20L, 'Waiting': 59L, 'Submitted': 6L, 'PilotsPerJob': '1.03', 'Ready': 0, 'Running': 0, 'PilotJobEff': '39.34', 'Done': 328L, 'Aborted': 606L, 'Done_Empty': 9L, 'Total': 999L},
# 'Records': []}}
#
# res = self.PilotsCli.getPilotsSimpleEff( 'Site', 'LCG.Ferrara.it', RPCWMSAdmin = WMS_Mock )
# self.assertEqual( res, None )
# res = self.PilotsCli.getPilotsSimpleEff( 'Resource', 'grid0.fe.infn.it', 'LCG.Ferrara.it', RPCWMSAdmin = WMS_Mock )
# self.assertEqual( res, None )
#
##############################################################################
#
#if __name__ == '__main__':
# suite = unittest.defaultTestLoader.loadTestsFromTestCase( ClientsTestCase )
# suite.addTest( unittest.defaultTestLoader.loadTestsFromTestCase( ResourceStatusClientSuccess ) )
# suite.addTest( unittest.defaultTestLoader.loadTestsFromTestCase( JobsClientSuccess ) )
# suite.addTest( unittest.defaultTestLoader.loadTestsFromTestCase( PilotsClientSuccess ) )
# testResult = unittest.TextTestRunner( verbosity = 2 ).run( suite )
|
"""
Artifactor
Artifactor is used to collect artifacts from a number of different plugins and put them into
one place. Artifactor works around a series of events and is geared towards unit testing, though
it is extensible and customizable enough that it can be used for a variety of purposes.
The main guts of Artifactor is around the plugins. Before Artifactor can do anything it must have
a configured plugin. This plugin is then configured to bind certain functions inside itself
to certain events. When Artifactor is triggered to handle a certain event, it will tell the plugin
that that particular event has happened and the plugin will respond accordingly.
In addition to the plugins, Artifactor can also run certain callback functions before and after
the hook function itself. These are call pre and post hook callbacks. Artifactor allows multiple
pre and post hook callbacks to be defined per event, but does not guarantee the order that they
are executed in.
To allow data to be passed to and from hooks, Artifactor has the idea of global and event local
values. The global values persist in the Artifactor instance for its lifetime, but the event local
values are destroyed at the end of each event.
Let's take the example of using the unit testing suite py.test as an example for Artifactor.
Suppose we have a number of tests that run as part of a test suite and we wish to store a text
file that holds the time the test was run and its result. This information is required to reside
in a folder that is relevant to the test itself. This type of job is what Artifactor was designed
for.
To begin with, we need to create a plugin for Artifactor. Consider the following piece of code::
from artifactor import ArtifactorBasePlugin
import time
class Test(ArtifactorBasePlugin):
def plugin_initialize(self):
self.register_plugin_hook('start_test', self.start_test)
self.register_plugin_hook('finish_test', self.finish_test)
def start_test(self, test_name, test_location, artifact_path):
filename = artifact_path + "-" + self.ident + ".log"
with open(filename, "w") as f:
f.write(test_name + "\n")
f.write(str(time.time()) + "\n")
def finish_test(self, test_name, artifact_path, test_result):
filename = artifact_path + "-" + self.ident + ".log"
with open(filename, "w+") as f:
f.write(test_result)
This is a typical plugin in Artifactor, it consists of 2 things. The first item is
the special function called ``plugin_initialize()``. This is important
and is equivilent to the ``__init__()`` that would usually be found in a class definition.
Artifactor calls ``plugin_initialize()`` for each plugin as it loads it.
Inside this section we register the hook functions to their associated events. Each event
can only have a single function associated with it. Event names are able to be freely assigned
so you can customize plugins to work to specific events for your use case.
The ``register_plugin_hook()`` takes an event name as a string and a function to callback when
that event is experienced.
Next we have the hook functions themselves, ``start_test()`` and ``finish_test()``. These
have arguments in their prototypes and these arguments are supplied by Artifactor and are
created either as arguments to the ``fire_hook()`` function, which is responsible for actually
telling Artifactor that an even has occured, or they are created in the pre hook script.
Artifactor uses the global and local values referenced earlier to store these argument values.
When a pre, post or hook callback finishes, it has the opportunity to supply updates to both
the global and local values dictionaries. In doing this, a pre-hook script can prepare data,
which will could be stored in the locals dictionary and then passed to the actual plugin hook
as a keyword argument. local values override global values.
We need to look at an example of this, but first we must configure artifactor and the plugin::
log_dir: /home/me/artiout
per_run: run #test, run, None
overwrite: True
artifacts:
test:
enabled: True
plugin: test
Here we have defined a ``log_dir`` which will be the root of all of our artifacts. We have asked
Artifactor to group the artifacts by run, which means that it will try to create a directory
under the ``log_dir`` which indicates which test "run" this was. We can also specify a value of
"test" here, which will move the test run identifying folder up to the leaf in the tree.
The ``log_dir`` and contents of the config are stored in global values as ``log_dir`` and
``artifactor_config`` respectively. These are the only two global values which are setup by
Artifactor.
This data is then passed to artifactor as a dict, we will assume a variable name of ``config`` here.
Let's consider how we would run this test
art = artifactor.artifactor
art.set_config(config)
art.register_plugin(test.Test, "test")
artifactor.initialize()
a.fire_hook('start_session', run_id=2235)
a.fire_hook('start_test', test_name="my_test", test_location="tests/mytest.py")
a.fire_hook('finish_test', test_name="my_test", test_location="tests/mytest.py",
test_result="FAILED")
a.fire_hook('finish_session')
The art.register_plugin is used to bind a plugin name to a class definition. Notice in the config
section earlier, we have a ``plugin: test`` field. This name ``test`` is what Artifactor will
look for when trying to find the appropriate plugin. When we register the plugin with the
``register_plugin`` function, we take the ``test.Test`` class and essentially give it the name
``test`` so that the names will tie up and the plugin will be used.
Notice that we have sent some information to along with the request to fire the hook. Ignoring the
``start_session`` event for a minute, the ``start_test`` event sends a ``test_name`` and a
``test_location``. However, the ``start_test`` hook also required an argument called
``argument_path``. This is not supplied by the hook, and isn't setup as a global value, so how does
it get there?
Inside Artifactor, by default, a pre_hook callback called ``start_test()`` is bound to the
``start_test`` event. This callback returns a local values update which includes ``artifact_path``.
This is how the artifact_path is returned. This hook can be removed, by running a
``unregister_hook_callback`` with the name of the hook callback.
""" |
"""
Display date and time.
This module allows one or more datetimes to be displayed.
All datetimes share the same format_time but can set their own timezones.
Timezones are defined in the `format` using the TZ name in squiggly brackets eg
`{GMT}`, `{Portugal}`, `{Europe/Paris}`, `{America/Argentina/Buenos_Aires}`.
ISO-3166 two letter country codes eg `{de}` can also be used but if more than
one timezone exists for the country eg `{us}` the first one will be selected.
`{Local}` can be used for the local settings of your computer.
Note: Timezones are case sensitive
A full list of timezones can be found at
https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
Configuration parameters:
block_hours: length of time period for all blocks in hours (default 12)
blocks: a string, where each character represents time period
from the start of a time period.
(default '🕛🕧🕐🕜🕑🕝🕒🕞🕓🕟🕔🕠🕕🕡🕖🕢🕗🕣🕘🕤🕙🕥🕚🕦')
button_change_format: button that switches format used setting to None
disables (default 1)
button_change_time_format: button that switches format_time used. Setting
to None disables (default 2)
button_reset: button that switches display to the first timezone. Setting
to None disables (default 3)
cycle: If more than one display then how many seconds between changing the
display (default 0)
format: defines the timezones displayed. This can be a single string or a
list. If a list is supplied then the formats can be cycled through
using `cycle` or by button click. (default '{Local}')
format_time: format to use for the time, strftime directives such as `%H`
can be used this can be either a string or to allow multiple formats as
a list. The one used can be changed by button click.
*(default ['[{name_unclear} ]%c', '[{name_unclear} ]%x %X',
'[{name_unclear} ]%a %H:%M', '[{name_unclear} ]{icon}'])*
locale: Override the system locale. Examples:
when set to 'fr_FR' %a on Tuesday is 'mar.'.
(default None)
round_to_nearest_block: defines how a block icon is chosen. Examples:
when set to True, '13:14' is '🕐', '13:16' is '🕜' and '13:31' is '🕜';
when set to False, '13:14' is '🕐', '13:16' is '🕐' and '13:31' is '🕜'.
(default True)
Format placeholders:
{icon} a character representing the time from `blocks`
{name} friendly timezone name eg `Buenos Aires`
{name_unclear} friendly timezone name eg `Buenos Aires` but is empty if
only one timezone is provided
{timezone} full timezone name eg `America/Argentina/Buenos_Aires`
{timezone_unclear} full timezone name eg `America/Argentina/Buenos_Aires`
but is empty if only one timezone is provided
Requires:
pytz: cross platform time zone library for python
tzlocal: tzinfo object for the local timezone
Examples:
```
# cycling through London, Warsaw, Tokyo
clock {
cycle = 30
format = ["{Europe/London}", "{Europe/Warsaw}", "{Asia/Tokyo}"]
format_time = "{name} %H:%M"
}
# Show the time and date in New York
clock {
format = "Big Apple {America/New_York}"
format_time = "%Y-%m-%d %H:%M:%S"
}
# wall clocks
clock {
format = "{Asia/Calcutta} {Africa/Nairobi} {Asia/Bangkok}"
format_time = "{name} {icon}"
}
```
@author USERNAME BSD
SAMPLE OUTPUT
{'full_text': 'Sun 15 Jan 2017 23:27:17 GMT'}
london
{'full_text': 'Thursday Feb 23 1:42 AM London'}
""" |
"""CPStats, a package for collecting and reporting on program statistics.
Overview
========
Statistics about program operation are an invaluable monitoring and debugging
tool. Unfortunately, the gathering and reporting of these critical values is
usually ad-hoc. This package aims to add a centralized place for gathering
statistical performance data, a structure for recording that data which
provides for extrapolation of that data into more useful information,
and a method of serving that data to both human investigators and
monitoring software. Let's examine each of those in more detail.
Data Gathering
--------------
Just as Python's `logging` module provides a common importable for gathering
and sending messages, performance statistics would benefit from a similar
common mechanism, and one that does *not* require each package which wishes
to collect stats to import a third-party module. Therefore, we choose to
re-use the `logging` module by adding a `statistics` object to it.
That `logging.statistics` object is a nested dict. It is not a custom class,
because that would:
1. require libraries and applications to import a third-party module in
order to participate
2. inhibit innovation in extrapolation approaches and in reporting tools, and
3. be slow.
There are, however, some specifications regarding the structure of the dict.::
{
+----"SQLAlchemy": {
| "Inserts": 4389745,
| "Inserts per Second":
| lambda s: s["Inserts"] / (time() - s["Start"]),
| C +---"Table Statistics": {
| o | "widgets": {-----------+
N | l | "Rows": 1.3M, | Record
a | l | "Inserts": 400, |
m | e | },---------------------+
e | c | "froobles": {
s | t | "Rows": 7845,
p | i | "Inserts": 0,
a | o | },
c | n +---},
e | "Slow Queries":
| [{"Query": "SELECT * FROM widgets;",
| "Processing Time": 47.840923343,
| },
| ],
+----},
}
The `logging.statistics` dict has four levels. The topmost level is nothing
more than a set of names to introduce modularity, usually along the lines of
package names. If the SQLAlchemy project wanted to participate, for example,
it might populate the item `logging.statistics['SQLAlchemy']`, whose value
would be a second-layer dict we call a "namespace". Namespaces help multiple
packages to avoid collisions over key names, and make reports easier to read,
to boot. The maintainers of SQLAlchemy should feel free to use more than one
namespace if needed (such as 'SQLAlchemy ORM'). Note that there are no case
or other syntax constraints on the namespace names; they should be chosen
to be maximally readable by humans (neither too short nor too long).
Each namespace, then, is a dict of named statistical values, such as
'Requests/sec' or 'Uptime'. You should choose names which will look
good on a report: spaces and capitalization are just fine.
In addition to scalars, values in a namespace MAY be a (third-layer)
dict, or a list, called a "collection". For example, the CherryPy
:class:`StatsTool` keeps track of what each request is doing (or has most
recently done) in a 'Requests' collection, where each key is a thread ID; each
value in the subdict MUST be a fourth dict (whew!) of statistical data about
each thread. We call each subdict in the collection a "record". Similarly,
the :class:`StatsTool` also keeps a list of slow queries, where each record
contains data about each slow query, in order.
Values in a namespace or record may also be functions, which brings us to:
Extrapolation
-------------
The collection of statistical data needs to be fast, as close to unnoticeable
as possible to the host program. That requires us to minimize I/O, for example,
but in Python it also means we need to minimize function calls. So when you
are designing your namespace and record values, try to insert the most basic
scalar values you already have on hand.
When it comes time to report on the gathered data, however, we usually have
much more freedom in what we can calculate. Therefore, whenever reporting
tools (like the provided :class:`StatsPage` CherryPy class) fetch the contents
of `logging.statistics` for reporting, they first call
`extrapolate_statistics` (passing the whole `statistics` dict as the only
argument). This makes a deep copy of the statistics dict so that the
reporting tool can both iterate over it and even change it without harming
the original. But it also expands any functions in the dict by calling them.
For example, you might have a 'Current Time' entry in the namespace with the
value "lambda scope: time.time()". The "scope" parameter is the current
namespace dict (or record, if we're currently expanding one of those
instead), allowing you access to existing static entries. If you're truly
evil, you can even modify more than one entry at a time.
However, don't try to calculate an entry and then use its value in further
extrapolations; the order in which the functions are called is not guaranteed.
This can lead to a certain amount of duplicated work (or a redesign of your
schema), but that's better than complicating the spec.
After the whole thing has been extrapolated, it's time for:
Reporting
---------
The :class:`StatsPage` class grabs the `logging.statistics` dict, extrapolates
it all, and then transforms it to HTML for easy viewing. Each namespace gets
its own header and attribute table, plus an extra table for each collection.
This is NOT part of the statistics specification; other tools can format how
they like.
You can control which columns are output and how they are formatted by updating
StatsPage.formatting, which is a dict that mirrors the keys and nesting of
`logging.statistics`. The difference is that, instead of data values, it has
formatting values. Use None for a given key to indicate to the StatsPage that a
given column should not be output. Use a string with formatting
(such as '%.3f') to interpolate the value(s), or use a callable (such as
lambda v: v.isoformat()) for more advanced formatting. Any entry which is not
mentioned in the formatting dict is output unchanged.
Monitoring
----------
Although the HTML output takes pains to assign unique id's to each <td> with
statistical data, you're probably better off fetching /cpstats/data, which
outputs the whole (extrapolated) `logging.statistics` dict in JSON format.
That is probably easier to parse, and doesn't have any formatting controls,
so you get the "original" data in a consistently-serialized format.
Note: there's no treatment yet for datetime objects. Try time.time() instead
for now if you can. Nagios will probably thank you.
Turning Collection Off
----------------------
It is recommended each namespace have an "Enabled" item which, if False,
stops collection (but not reporting) of statistical data. Applications
SHOULD provide controls to pause and resume collection by setting these
entries to False or True, if present.
Usage
=====
To collect statistics on CherryPy applications::
from cherrypy.lib import cpstats
appconfig['/']['tools.cpstats.on'] = True
To collect statistics on your own code::
import logging
# Initialize the repository
if not hasattr(logging, 'statistics'): logging.statistics = {}
# Initialize my namespace
mystats = logging.statistics.setdefault('My Stuff', {})
# Initialize my namespace's scalars and collections
mystats.update({
'Enabled': True,
'Start Time': time.time(),
'Important Events': 0,
'Events/Second': lambda s: (
(s['Important Events'] / (time.time() - s['Start Time']))),
})
...
for event in events:
...
# Collect stats
if mystats.get('Enabled', False):
mystats['Important Events'] += 1
To report statistics::
root.cpstats = cpstats.StatsPage()
To format statistics reports::
See 'Reporting', above.
""" |
"""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
""" |
# RUN: %{lit} %{inputs}/discovery | FileCheck --check-prefix=CHECK-BASIC %s
# CHECK-BASIC: Testing: 5 tests
# Check that we exit with an error if we do not discover any tests, even with --allow-empty-runs.
#
# RUN: not %{lit} %{inputs}/nonexistent 2>&1 | FileCheck --check-prefix=CHECK-BAD-PATH %s
# RUN: not %{lit} %{inputs}/nonexistent --allow-empty-runs 2>&1 | FileCheck --check-prefix=CHECK-BAD-PATH %s
# CHECK-BAD-PATH: error: did not discover any tests for provided path(s)
# Check that we exit with an error if we filter out all tests, but allow it with --allow-empty-runs.
# Check that we exit with an error if we skip all tests, but allow it with --allow-empty-runs.
#
# RUN: not %{lit} --filter 'nonexistent' %{inputs}/discovery 2>&1 | FileCheck --check-prefixes=CHECK-BAD-FILTER,CHECK-BAD-FILTER-ERROR %s
# RUN: %{lit} --filter 'nonexistent' --allow-empty-runs %{inputs}/discovery 2>&1 | FileCheck --check-prefixes=CHECK-BAD-FILTER,CHECK-BAD-FILTER-ALLOW %s
# RUN: not %{lit} --filter-out '.*' %{inputs}/discovery 2>&1 | FileCheck --check-prefixes=CHECK-BAD-FILTER,CHECK-BAD-FILTER-ERROR %s
# RUN: %{lit} --filter-out '.*' --allow-empty-runs %{inputs}/discovery 2>&1 | FileCheck --check-prefixes=CHECK-BAD-FILTER,CHECK-BAD-FILTER-ALLOW %s
# CHECK-BAD-FILTER: error: filter did not match any tests (of 5 discovered).
# CHECK-BAD-FILTER-ERROR: Use '--allow-empty-runs' to suppress this error.
# CHECK-BAD-FILTER-ALLOW: Suppressing error because '--allow-empty-runs' was specified.
# Check that regex-filtering works, is case-insensitive, and can be configured via env var.
#
# RUN: %{lit} --filter 'o[a-z]e' %{inputs}/discovery | FileCheck --check-prefix=CHECK-FILTER %s
# RUN: %{lit} --filter 'O[A-Z]E' %{inputs}/discovery | FileCheck --check-prefix=CHECK-FILTER %s
# RUN: env LIT_FILTER='o[a-z]e' %{lit} %{inputs}/discovery | FileCheck --check-prefix=CHECK-FILTER %s
# RUN: %{lit} --filter-out 'test-t[a-z]' %{inputs}/discovery | FileCheck --check-prefix=CHECK-FILTER %s
# RUN: %{lit} --filter-out 'test-t[A-Z]' %{inputs}/discovery | FileCheck --check-prefix=CHECK-FILTER %s
# RUN: env LIT_FILTER_OUT='test-t[a-z]' %{lit} %{inputs}/discovery | FileCheck --check-prefix=CHECK-FILTER %s
# CHECK-FILTER: Testing: 2 of 5 tests
# CHECK-FILTER: Excluded: 3
# Check that maximum counts work
#
# RUN: %{lit} --max-tests 3 %{inputs}/discovery | FileCheck --check-prefix=CHECK-MAX %s
# CHECK-MAX: Testing: 3 of 5 tests
# CHECK-MAX: Excluded: 2
# Check that sharding partitions the testsuite in a way that distributes the
# rounding error nicely (i.e. 5/3 => 2 2 1, not 1 1 3 or whatever)
#
# RUN: %{lit} --num-shards 3 --run-shard 1 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD0-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD0-OUT < %t.out %s
# CHECK-SHARD0-ERR: note: Selecting shard 1/3 = size 2/5 = tests #(3*k)+1 = [1, 4]
# CHECK-SHARD0-OUT: Testing: 2 of 5 tests
# CHECK-SHARD0-OUT: Excluded: 3
#
# RUN: %{lit} --num-shards 3 --run-shard 2 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD1-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD1-OUT < %t.out %s
# CHECK-SHARD1-ERR: note: Selecting shard 2/3 = size 2/5 = tests #(3*k)+2 = [2, 5]
# CHECK-SHARD1-OUT: Testing: 2 of 5 tests
#
# RUN: %{lit} --num-shards 3 --run-shard 3 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD2-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD2-OUT < %t.out %s
# CHECK-SHARD2-ERR: note: Selecting shard 3/3 = size 1/5 = tests #(3*k)+3 = [3]
# CHECK-SHARD2-OUT: Testing: 1 of 5 tests
# Check that sharding via env vars works.
#
# RUN: env LIT_NUM_SHARDS=3 LIT_RUN_SHARD=1 %{lit} %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD0-ENV-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD0-ENV-OUT < %t.out %s
# CHECK-SHARD0-ENV-ERR: note: Selecting shard 1/3 = size 2/5 = tests #(3*k)+1 = [1, 4]
# CHECK-SHARD0-ENV-OUT: Testing: 2 of 5 tests
#
# RUN: env LIT_NUM_SHARDS=3 LIT_RUN_SHARD=2 %{lit} %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD1-ENV-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD1-ENV-OUT < %t.out %s
# CHECK-SHARD1-ENV-ERR: note: Selecting shard 2/3 = size 2/5 = tests #(3*k)+2 = [2, 5]
# CHECK-SHARD1-ENV-OUT: Testing: 2 of 5 tests
#
# RUN: env LIT_NUM_SHARDS=3 LIT_RUN_SHARD=3 %{lit} %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD2-ENV-ERR < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD2-ENV-OUT < %t.out %s
# CHECK-SHARD2-ENV-ERR: note: Selecting shard 3/3 = size 1/5 = tests #(3*k)+3 = [3]
# CHECK-SHARD2-ENV-OUT: Testing: 1 of 5 tests
# Check that providing more shards than tests results in 1 test per shard
# until we run out, then 0.
#
# RUN: %{lit} --num-shards 100 --run-shard 2 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD-BIG-ERR1 < %t.err %s
# RUN: FileCheck --check-prefix=CHECK-SHARD-BIG-OUT1 < %t.out %s
# CHECK-SHARD-BIG-ERR1: note: Selecting shard 2/100 = size 1/5 = tests #(100*k)+2 = [2]
# CHECK-SHARD-BIG-OUT1: Testing: 1 of 5 tests
#
# RUN: %{lit} --num-shards 100 --run-shard 6 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD-BIG-ERR2 < %t.err %s
# CHECK-SHARD-BIG-ERR2: note: Selecting shard 6/100 = size 0/5 = tests #(100*k)+6 = []
# CHECK-SHARD-BIG-ERR2: warning: shard does not contain any tests. Consider decreasing the number of shards.
#
# RUN: %{lit} --num-shards 100 --run-shard 50 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD-BIG-ERR3 < %t.err %s
# CHECK-SHARD-BIG-ERR3: note: Selecting shard 50/100 = size 0/5 = tests #(100*k)+50 = []
# CHECK-SHARD-BIG-ERR3: warning: shard does not contain any tests. Consider decreasing the number of shards.
# Check that range constraints are enforced
#
# RUN: not %{lit} --num-shards 0 --run-shard 2 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD-ERR < %t.err %s
# CHECK-SHARD-ERR: error: argument --num-shards: requires positive integer, but found '0'
#
# RUN: not %{lit} --num-shards 3 --run-shard 4 %{inputs}/discovery >%t.out 2>%t.err
# RUN: FileCheck --check-prefix=CHECK-SHARD-ERR2 < %t.err %s
# CHECK-SHARD-ERR2: error: --run-shard must be between 1 and --num-shards (inclusive)
|
"""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.
""" |
# Test 64-bit COMPARE AND BRANCH in cases where the sheer number of
# instructions causes some branches to be out of range.
# RUN: python %s | llc -mtriple=s390x-linux-gnu | FileCheck %s
# Construct:
#
# before0:
# conditional branch to after0
# ...
# beforeN:
# conditional branch to after0
# main:
# 0xffcc bytes, from MVIY instructions
# conditional branch to main
# after0:
# ...
# conditional branch to main
# afterN:
#
# Each conditional branch sequence occupies 12 bytes if it uses a short
# branch and 16 if it uses a long one. The ones before "main:" have to
# take the branch length into account, which is 6 for short branches,
# so the final (0x34 - 6) / 12 == 3 blocks can use short branches.
# The ones after "main:" do not, so the first 0x34 / 12 == 4 blocks
# can use short branches. The conservative algorithm we use makes
# one of the forward branches unnecessarily long, as noted in the
# check output below.
#
# CHECK: lgb [[REG:%r[0-5]]], 0(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL:\.L[^ ]*]]
# CHECK: lgb [[REG:%r[0-5]]], 1(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 2(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 3(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 4(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# ...as mentioned above, the next one could be a CGRJE instead...
# CHECK: lgb [[REG:%r[0-5]]], 5(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 6(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 7(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# ...main goes here...
# CHECK: lgb [[REG:%r[0-5]]], 25(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL:\.L[^ ]*]]
# CHECK: lgb [[REG:%r[0-5]]], 26(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 27(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 28(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 29(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 30(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 31(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 32(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
|
# Sketch - A Python-based interactive drawing program
# Copyright (C) 1996, 1997, 1998 by NAME This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library 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
# Library General Public License for more details.
#
# You should have received a copy of the GNU Library General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
# Functions to manipulate selection info
#
# Representation
#
# The set of currently selected objects in Sketch is represented as a
# list of tuples. Each of the tuples has the form:
#
# (PATH, OBJ)
#
# where OBJ is a selected object and PATH is a tuple of ints describing
# the path through the hierarchy of objects to OBJ, usually starting
# from the document at the top of the hierarchy. Each item in PATH is
# the index of the next object in the path. For example, the second
# object in the first layer has the PATH (0, 1) (indexes start from 0).
#
# This representation serves two purposes:
#
# 1. storing the path to the object allows fast access to the
# parents of the selected object.
#
# 2. it allows sorting the list by path, which results in a list
# with the objects lowest in the stack of objects at the front.
#
# A sorted list is important when changing the stacking order of
# objects, since the indices, i.e. the path elements, may change
# during the operation.
#
# Sorting the list also allows to make sure that each selected
# object is listed exactly once in the list.
#
# This representation, if the list is sorted, is called the _standard
# representation_.
#
# Alternative Representations:
#
# There are several alternative representations that are mainly useful
# in the methods of compound objects that rearrange the children. In
# those methods, the path is usually taken relative to self. Where
# selection info has to be passed to children, to rearrange their
# children, the first component of the path is stripped, so that the
# path is relative to the child.
#
# All of the alternative representations are lists of tuples sorted at
# least by the first item of the tuples.
#
# Tree:
#
# An alternative representation of the selection info is a list of
# tuples of the form:
#
# (INDEX, LIST)
#
# where INDEX is just the first part of the PATH of the standard
# representation and LIST is a list of selection info in standard
# representation but with each PATH stripped of its first component
# which is INDEX. That is, LIST is selection info in standard form
# relative to the compound object given by INDEX.
#
# Tree2:
#
# Just like Tree1, but if LIST would contain just one item with an empty
# PATH (an empty tuple), LIST is replaced by the object.
#
# Sliced Tree:
#
# A variant of Tree2, where consecutive items with an object (i.e.
# something that is no list) are replaced by a tuple `(start, end)'
# where start is the lowest INDEX and end the highest. Consecutive items
# are items where the INDEX parts are consecutive integers.
#
#
# Creating Selection Info:
#
# Selecting objects is done for instance by the GraphicsObject method
# SelectSubobject. In a compound object, when it has determined that a
# certain non compound child obj is to be selected, this method
# constructs a selection info tuple by calling build_info:
#
# info1 = build_info(idx1, obj)
#
# idx is the index of obj in the compound object's list of children.
# info1 will then be just a tuple: ((idx1,) obj) This info is returned
# to the caller, its parent, which is often another compound object.
# This parent then extends the selection info with
#
# info2 = prepend_idx(idx2, info)
#
# This results in a new tuple new_info: ((idx2, idx1), obj). idx2 is, of
# course, the index of the compound object in its parent's list of
# children.
#
# Finally, the document object receives such a selection info tuple from
# one of its layers, prepends that layer's index to the info and puts it
# into the list of selected objects.
#
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# ***********************IMPORTANT NMAP LICENSE TERMS************************
# * *
# * The Nmap Security Scanner is (C) 1996-2013 Insecure.Com LLC. Nmap is *
# * also a registered trademark of Insecure.Com LLC. This program is free *
# * software; you may redistribute and/or modify it under the terms of the *
# * GNU General Public License as published by the Free Software *
# * Foundation; Version 2 ("GPL"), BUT ONLY WITH ALL OF THE CLARIFICATIONS *
# * AND EXCEPTIONS DESCRIBED HEREIN. This guarantees your right to use, *
# * modify, and redistribute this software under certain conditions. If *
# * you wish to embed Nmap technology into proprietary software, we sell *
# * alternative licenses (contact EMAIL Dozens of software *
# * vendors already license Nmap technology such as host discovery, port *
# * scanning, OS detection, version detection, and the Nmap Scripting *
# * Engine. *
# * *
# * Note that the GPL places important restrictions on "derivative works", *
# * yet it does not provide a detailed definition of that term. To avoid *
# * misunderstandings, we interpret that term as broadly as copyright law *
# * allows. For example, we consider an application to constitute a *
# * derivative work for the purpose of this license if it does any of the *
# * following with any software or content covered by this license *
# * ("Covered Software"): *
# * *
# * o Integrates source code from Covered Software. *
# * *
# * o Reads or includes copyrighted data files, such as Nmap's nmap-os-db *
# * or nmap-service-probes. *
# * *
# * o Is designed specifically to execute Covered Software and parse the *
# * results (as opposed to typical shell or execution-menu apps, which will *
# * execute anything you tell them to). *
# * *
# * o Includes Covered Software in a proprietary executable installer. The *
# * installers produced by InstallShield are an example of this. Including *
# * Nmap with other software in compressed or archival form does not *
# * trigger this provision, provided appropriate open source decompression *
# * or de-archiving software is widely available for no charge. For the *
# * purposes of this license, an installer is considered to include Covered *
# * Software even if it actually retrieves a copy of Covered Software from *
# * another source during runtime (such as by downloading it from the *
# * Internet). *
# * *
# * o Links (statically or dynamically) to a library which does any of the *
# * above. *
# * *
# * o Executes a helper program, module, or script to do any of the above. *
# * *
# * This list is not exclusive, but is meant to clarify our interpretation *
# * of derived works with some common examples. Other people may interpret *
# * the plain GPL differently, so we consider this a special exception to *
# * the GPL that we apply to Covered Software. Works which meet any of *
# * these conditions must conform to all of the terms of this license, *
# * particularly including the GPL Section 3 requirements of providing *
# * source code and allowing free redistribution of the work as a whole. *
# * *
# * As another special exception to the GPL terms, Insecure.Com LLC grants *
# * permission to link the code of this program with any version of the *
# * OpenSSL library which is distributed under a license identical to that *
# * listed in the included docs/licenses/OpenSSL.txt file, and distribute *
# * linked combinations including the two. *
# * *
# * Any redistribution of Covered Software, including any derived works, *
# * must obey and carry forward all of the terms of this license, including *
# * obeying all GPL rules and restrictions. For example, source code of *
# * the whole work must be provided and free redistribution must be *
# * allowed. All GPL references to "this License", are to be treated as *
# * including the terms and conditions of this license text as well. *
# * *
# * Because this license imposes special exceptions to the GPL, Covered *
# * Work may not be combined (even as part of a larger work) with plain GPL *
# * software. The terms, conditions, and exceptions of this license must *
# * be included as well. This license is incompatible with some other open *
# * source licenses as well. In some cases we can relicense portions of *
# * Nmap or grant special permissions to use it in other open source *
# * software. Please contact EMAIL with any such requests. *
# * Similarly, we don't incorporate incompatible open source software into *
# * Covered Software without special permission from the copyright holders. *
# * *
# * If you have any questions about the licensing restrictions on using *
# * Nmap in other works, are happy to help. As mentioned above, we also *
# * offer alternative license to integrate Nmap into proprietary *
# * applications and appliances. These contracts have been sold to dozens *
# * of software vendors, and generally include a perpetual license as well *
# * as providing for priority support and updates. They also fund the *
# * continued development of Nmap. Please email EMAIL for further *
# * information. *
# * *
# * If you have received a written license agreement or contract for *
# * Covered Software stating terms other than these, you may choose to use *
# * and redistribute Covered Software under those terms instead of these. *
# * *
# * Source is provided to this software because we believe users have a *
# * right to know exactly what a program is going to do before they run it. *
# * This also allows you to audit the software for security holes (none *
# * have been found so far). *
# * *
# * Source code also allows you to port Nmap to new platforms, fix bugs, *
# * and add new features. You are highly encouraged to send your changes *
# * to the EMAIL mailing list for possible incorporation into the *
# * main distribution. By sending these changes to Fyodor or one of the *
# * Insecure.Org development mailing lists, or checking them into the Nmap *
# * source code repository, it is understood (unless you specify otherwise) *
# * that you are offering the Nmap Project (Insecure.Com LLC) the *
# * unlimited, non-exclusive right to reuse, modify, and relicense the *
# * code. Nmap will always be available Open Source, but this is important *
# * because the inability to relicense code has caused devastating problems *
# * for other Free Software projects (such as KDE and NASM). We also *
# * occasionally relicense the code to third parties as discussed above. *
# * If you wish to specify special license conditions of your *
# * contributions, just say so when you send them. *
# * *
# * 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 Nmap *
# * license file for more details (it's in a COPYING file included with *
# * Nmap, and also available from https://svn.nmap.org/nmap/COPYING *
# * *
# ***************************************************************************/
|
"""
Beta diversity measures (:mod:`skbio.diversity.beta`)
=====================================================
.. currentmodule:: skbio.diversity.beta
This package contains helper functions for working with scipy's pairwise
distance (``pdist``) functions in scikit-bio, and will eventually be expanded
to contain pairwise distance/dissimilarity methods that are not implemented
(or planned to be implemented) in scipy.
The functions in this package currently support applying ``pdist`` functions
to all pairs of samples in a sample by observation count or abundance matrix
and returning an ``skbio.DistanceMatrix`` object. This application is
illustrated below for a few different forms of input.
Functions
---------
.. autosummary::
:toctree: generated/
pw_distances
pw_distances_from_table
Examples
--------
Create a table containing 7 OTUs and 6 samples:
.. plot::
:context:
>>> from skbio.diversity.beta import pw_distances
>>> import numpy as np
>>> data = [[23, 64, 14, 0, 0, 3, 1],
... [0, 3, 35, 42, 0, 12, 1],
... [0, 5, 5, 0, 40, 40, 0],
... [44, 35, 9, 0, 1, 0, 0],
... [0, 2, 8, 0, 35, 45, 1],
... [0, 0, 25, 35, 0, 19, 0]]
>>> ids = list('ABCDEF')
Compute Bray-Curtis distances between all pairs of samples and return a
``DistanceMatrix`` object:
>>> bc_dm = pw_distances(data, ids, "braycurtis")
>>> print(bc_dm)
6x6 distance matrix
IDs:
'A', 'B', 'C', 'D', 'E', 'F'
Data:
[[ 0. 0.78787879 0.86666667 0.30927835 0.85714286 0.81521739]
[ 0.78787879 0. 0.78142077 0.86813187 0.75 0.1627907 ]
[ 0.86666667 0.78142077 0. 0.87709497 0.09392265 0.71597633]
[ 0.30927835 0.86813187 0.87709497 0. 0.87777778 0.89285714]
[ 0.85714286 0.75 0.09392265 0.87777778 0. 0.68235294]
[ 0.81521739 0.1627907 0.71597633 0.89285714 0.68235294 0. ]]
Compute Jaccard distances between all pairs of samples and return a
``DistanceMatrix`` object:
>>> j_dm = pw_distances(data, ids, "jaccard")
>>> print(j_dm)
6x6 distance matrix
IDs:
'A', 'B', 'C', 'D', 'E', 'F'
Data:
[[ 0. 0.83333333 1. 1. 0.83333333 1. ]
[ 0.83333333 0. 1. 1. 0.83333333 1. ]
[ 1. 1. 0. 1. 1. 1. ]
[ 1. 1. 1. 0. 1. 1. ]
[ 0.83333333 0.83333333 1. 1. 0. 1. ]
[ 1. 1. 1. 1. 1. 0. ]]
Determine if the resulting distance matrices are significantly correlated
by computing the Mantel correlation between them. Then determine if the
p-value is significant based on an alpha of 0.05:
>>> from skbio.stats.distance import mantel
>>> r, p_value, n = mantel(j_dm, bc_dm)
>>> print(r)
-0.209362157621
>>> print(p_value < 0.05)
False
Compute PCoA for both distance matrices, and then find the Procrustes
M-squared value that results from comparing the coordinate matrices.
>>> from skbio.stats.ordination import PCoA
>>> bc_pc = PCoA(bc_dm).scores()
>>> j_pc = PCoA(j_dm).scores()
>>> from skbio.stats.spatial import procrustes
>>> print(procrustes(bc_pc.site, j_pc.site)[2])
0.466134984787
All of this only gets interesting in the context of sample metadata, so
let's define some:
>>> import pandas as pd
>>> try:
... # not necessary for normal use
... pd.set_option('show_dimensions', True)
... except KeyError:
... pass
>>> sample_md = {
... 'A': {'body_site': 'gut', 'subject': 's1'},
... 'B': {'body_site': 'skin', 'subject': 's1'},
... 'C': {'body_site': 'tongue', 'subject': 's1'},
... 'D': {'body_site': 'gut', 'subject': 's2'},
... 'E': {'body_site': 'tongue', 'subject': 's2'},
... 'F': {'body_site': 'skin', 'subject': 's2'}}
>>> sample_md = pd.DataFrame.from_dict(sample_md, orient='index')
>>> sample_md
subject body_site
A s1 gut
B s1 skin
C s1 tongue
D s2 gut
E s2 tongue
F s2 skin
<BLANKLINE>
[6 rows x 2 columns]
Now let's plot our PCoA results, coloring each sample by the subject it
was taken from:
>>> fig = bc_pc.plot(sample_md, 'subject',
... axis_labels=('PC 1', 'PC 2', 'PC 3'),
... title='Samples colored by subject', cmap='jet', s=50)
.. plot::
:context:
We don't see any clustering/grouping of samples. If we were to instead color
the samples by the body site they were taken from, we see that the samples
form three separate groups:
>>> import matplotlib.pyplot as plt
>>> plt.close('all') # not necessary for normal use
>>> fig = bc_pc.plot(sample_md, 'body_site',
... axis_labels=('PC 1', 'PC 2', 'PC 3'),
... title='Samples colored by body site', cmap='jet', s=50)
Ordination techniques, such as PCoA, are useful for exploratory analysis. The
next step is to quantify the strength of the grouping/clustering that we see in
ordination plots. There are many statistical methods available to accomplish
this; many operate on distance matrices. Let's use ANOSIM to quantify the
strength of the clustering we see in the ordination plots above, using our
Bray-Curtis distance matrix and sample metadata.
First test the grouping of samples by subject:
>>> from skbio.stats.distance import anosim
>>> results = anosim(bc_dm, sample_md, column='subject', permutations=999)
>>> results['test statistic']
-0.4074074074074075
>>> results['p-value'] < 0.1
False
The negative value of ANOSIM's R statistic indicates anti-clustering and the
p-value is insignificant at an alpha of 0.1.
Now let's test the grouping of samples by body site:
>>> results = anosim(bc_dm, sample_md, column='body_site', permutations=999)
>>> results['test statistic']
1.0
>>> results['p-value'] < 0.1
True
The R statistic of 1.0 indicates strong separation of samples based on body
site. The p-value is significant at an alpha of 0.1.
References
----------
.. [1] http://matplotlib.org/examples/mplot3d/scatter3d_demo.html
""" |
"""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().
""" |
"""Request body processing for CherryPy.
.. versionadded:: 3.2
Application authors have complete control over the parsing of HTTP request
entities. In short,
:attr:`cherrypy.request.body<cherrypy._cprequest.Request.body>`
is now always set to an instance of
:class:`RequestBody<cherrypy._cpreqbody.RequestBody>`,
and *that* class is a subclass of :class:`Entity<cherrypy._cpreqbody.Entity>`.
When an HTTP request includes an entity body, it is often desirable to
provide that information to applications in a form other than the raw bytes.
Different content types demand different approaches. Examples:
* For a GIF file, we want the raw bytes in a stream.
* An HTML form is better parsed into its component fields, and each text field
decoded from bytes to unicode.
* A JSON body should be deserialized into a Python dict or list.
When the request contains a Content-Type header, the media type is used as a
key to look up a value in the
:attr:`request.body.processors<cherrypy._cpreqbody.Entity.processors>` dict.
If the full media
type is not found, then the major type is tried; for example, if no processor
is found for the 'image/jpeg' type, then we look for a processor for the
'image' types altogether. If neither the full type nor the major type has a
matching processor, then a default processor is used
(:func:`default_proc<cherrypy._cpreqbody.Entity.default_proc>`). For most
types, this means no processing is done, and the body is left unread as a
raw byte stream. Processors are configurable in an 'on_start_resource' hook.
Some processors, especially those for the 'text' types, attempt to decode bytes
to unicode. If the Content-Type request header includes a 'charset' parameter,
this is used to decode the entity. Otherwise, one or more default charsets may
be attempted, although this decision is up to each processor. If a processor
successfully decodes an Entity or Part, it should set the
:attr:`charset<cherrypy._cpreqbody.Entity.charset>` attribute
on the Entity or Part to the name of the successful charset, so that
applications can easily re-encode or transcode the value if they wish.
If the Content-Type of the request entity is of major type 'multipart', then
the above parsing process, and possibly a decoding process, is performed for
each part.
For both the full entity and multipart parts, a Content-Disposition header may
be used to fill :attr:`name<cherrypy._cpreqbody.Entity.name>` and
:attr:`filename<cherrypy._cpreqbody.Entity.filename>` attributes on the
request.body or the Part.
.. _custombodyprocessors:
Custom Processors
=================
You can add your own processors for any specific or major MIME type. Simply add
it to the :attr:`processors<cherrypy._cprequest.Entity.processors>` dict in a
hook/tool that runs at ``on_start_resource`` or ``before_request_body``.
Here's the built-in JSON tool for an example::
def json_in(force=True, debug=False):
request = cherrypy.serving.request
def json_processor(entity):
'''Read application/json data into request.json.'''
if not entity.headers.get("Content-Length", ""):
raise cherrypy.HTTPError(411)
body = entity.fp.read()
try:
request.json = json_decode(body)
except ValueError:
raise cherrypy.HTTPError(400, 'Invalid JSON document')
if force:
request.body.processors.clear()
request.body.default_proc = cherrypy.HTTPError(
415, 'Expected an application/json content type')
request.body.processors['application/json'] = json_processor
We begin by defining a new ``json_processor`` function to stick in the
``processors`` dictionary. All processor functions take a single argument,
the ``Entity`` instance they are to process. It will be called whenever a
request is received (for those URI's where the tool is turned on) which
has a ``Content-Type`` of "application/json".
First, it checks for a valid ``Content-Length`` (raising 411 if not valid),
then reads the remaining bytes on the socket. The ``fp`` object knows its
own length, so it won't hang waiting for data that never arrives. It will
return when all data has been read. Then, we decode those bytes using
Python's built-in ``json`` module, and stick the decoded result onto
``request.json`` . If it cannot be decoded, we raise 400.
If the "force" argument is True (the default), the ``Tool`` clears the
``processors`` dict so that request entities of other ``Content-Types``
aren't parsed at all. Since there's no entry for those invalid MIME
types, the ``default_proc`` method of ``cherrypy.request.body`` is
called. But this does nothing by default (usually to provide the page
handler an opportunity to handle it.)
But in our case, we want to raise 415, so we replace
``request.body.default_proc``
with the error (``HTTPError`` instances, when called, raise themselves).
If we were defining a custom processor, we can do so without making a ``Tool``.
Just add the config entry::
request.body.processors = {'application/json': json_processor}
Note that you can only replace the ``processors`` dict wholesale this way,
not update the existing one.
""" |
"""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.
""" |
"""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().
""" |
"""
#Cyclic 4
R.<x1,x2,x3,x4> = QQ[]
polys = [x1+x2+x3+x4,x1*x2+x2*x3+x3*x4+x4*x1,x1*x2*x3+x2*x3*x4+x3*x4*x1+x4*x1*x2]
TropicalPrevariety(polys)
#Should be equivalent (up to homogenization) to:
R.ideal(polys).groebner_fan().tropical_intersection().rays()
#Reduced cyclic 8
R.<y_1,y_2,y_3,y_4,y_5,y_6,y_7> = QQ[]
polys = [1 + y_1 + y_2 + y_3 + y_4 + y_5 + y_6 + y_7,y_1 + y_1*y_2 + y_2*y_3
+ y_3*y_4 + y_4*y_5 + y_5*y_6 + y_6*y_7 + y_7,y_1*y_2 + y_1*y_2*y_3
+ y_2*y_3*y_4 + y_3*y_4*y_5 + y_4*y_5*y_6 + y_5*y_6*y_7
+ y_6*y_7 + y_7*y_1,y_1*y_2*y_3 + y_1*y_2*y_3*y_4 + y_2*y_3*y_4*y_5
+ y_3*y_4*y_5*y_6 + y_4*y_5*y_6*y_7 + y_5*y_6*y_7 + y_6*y_7*y_1
+ y_7*y_1*y_2,y_1*y_2*y_3*y_4 + y_1*y_2*y_3*y_4*y_5 + y_2*y_3*y_4*y_5*y_6
+ y_3*y_4*y_5*y_6*y_7 + y_4*y_5*y_6*y_7 + y_5*y_6*y_7*y_1 + y_6*y_7*y_1*y_2
+ y_7*y_1*y_2*y_3,y_1*y_2*y_3*y_4*y_5 + y_1*y_2*y_3*y_4*y_5*y_6
+ y_2*y_3*y_4*y_5*y_6*y_7 + y_3*y_4*y_5*y_6*y_7 + y_4*y_5*y_6*y_7*y_1
+ y_5*y_6*y_7*y_1*y_2 + y_6*y_7*y_1*y_2*y_3
+ y_7*y_1*y_2*y_3*y_4,y_1*y_2*y_3*y_4*y_5*y_6 + y_1*y_2*y_3*y_4*y_5*y_6*y_7
+ y_2*y_3*y_4*y_5*y_6*y_7+ y_3*y_4*y_5*y_6*y_7*y_1 + y_4*y_5*y_6*y_7*y_1*y_2
+ y_5*y_6*y_7*y_1*y_2*y_3+ y_6*y_7*y_1*y_2*y_3*y_4 + y_7*y_1*y_2*y_3*y_4*y_5]
TropicalPrevariety(polys)
""" |
"""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.
""" |
"""
<Program>
namespace.py
<Started>
September 2009
<Author>
NAME
This is the namespace layer that ensures separation of the namespaces of
untrusted code and our code. It provides a single public function to be
used to setup the context in which untrusted code is exec'd (that is, the
context that is seen as the __builtins__ by the untrusted code).
The general idea is that any function or object that is available between
trusted and untrusted code gets wrapped in a function or object that does
validation when the function or object is used. In general, if user code
is not calling any functions improperly, neither the user code nor our
trusted code should ever notice that the objects and functions they are
dealing with have been wrapped by this namespace layer.
All of our own api functions are wrapped in NamespaceAPIFunctionWrapper
objects whose wrapped_function() method is mapped in to the untrusted
code's context. When called, the wrapped_function() method performs
argument, return value, and exception validation as well as additional
wrapping and unwrapping, as needed, that is specific to the function
that was ultimately being called. If the return value or raised exceptions
are not considered acceptable, a NamespaceViolationError is raised. If the
arguments are not acceptable, a TypeError is raised.
Note that callback functions that are passed from untrusted user code
to trusted code are also wrapped (these are arguments to wrapped API
functions, so we get to wrap them before calling the underlying function).
The reason we wrap these is so that we can intercept calls to the callback
functions and wrap arguments passed to them, making sure that handles
passed as arguments to the callbacks get wrapped before user code sees them.
The function and object wrappers have been defined based on the API as
documented at https://seattle.cs.washington.edu/wiki/RepyLibrary
Example of using this module (this is really the only way to use the module):
import namespace
usercontext = {}
namespace.wrap_and_insert_api_functions(usercontext)
safe.safe_exec(usercode, usercontext)
The above code will result in the dict usercontext being populated with keys
that are the names of the functions available to the untrusted code (such as
'open') and the values are the wrapped versions of the actual functions to be
called (such as 'emulfile.emulated_open').
Note that some functions wrapped by this module lose some python argument
flexibility. Wrapped functions can generally only have keyword args in
situations where the arguments are optional. Using keyword arguments for
required args may not be supported, depending on the implementation of the
specific argument check/wrapping/unwrapping helper functions for that
particular wrapped function. If this becomes a problem, it can be dealt with
by complicating some of the argument checking/wrapping/unwrapping code in
this module to make the checking functions more flexible in how they take
their arguments.
Implementation details:
The majority of the code in this module is made up of helper functions to do
argument checking, etc. for specific wrapped functions.
The most important parts to look at in this module for maintenance and
auditing are the following:
USERCONTEXT_WRAPPER_INFO
The USERCONTEXT_WRAPPER_INFO is a dictionary that defines the API
functions that are wrapped and inserted into the user context when
wrap_and_insert_api_functions() is called.
FILE_OBJECT_WRAPPER_INFO
LOCK_OBJECT_WRAPPER_INFO
TCP_SOCKET_OBJECT_WRAPPER_INFO
TCP_SERVER_SOCKET_OBJECT_WRAPPER_INFO
UDP_SERVER_SOCKET_OBJECT_WRAPPER_INFO
VIRTUAL_NAMESPACE_OBJECT_WRAPPER_INFO
The above four dictionaries define the methods available on the wrapped
objects that are returned by wrapped functions. Additionally, timerhandle
and commhandle objects are wrapped but instances of these do not have any
public methods and so no *_WRAPPER_INFO dictionaries are defined for them.
NamespaceObjectWrapper
NamespaceAPIFunctionWrapper
The above two classes are the only two types of objects that will be
allowed in untrusted code. In fact, instances of NamespaceAPIFunctionWrapper
are never actually allowed in untrusted code. Rather, each function that
is wrapped has a single NamespaceAPIFunctionWrapper instance created
when wrap_and_insert_api_functions() is called and what is actually made
available to the untrusted code is the wrapped_function() method of each
of the corresponding NamespaceAPIFunctionWrapper instances.
NamespaceInternalError
If this error is raised anywhere (along with any other unexpected exceptions),
it should result in termination of the running program (see the except blocks
in NamespaceAPIFunctionWrapper.wrapped_function).
""" |
"""
=================
Structured Arrays
=================
Introduction
============
Numpy provides powerful capabilities to create arrays of structured datatype.
These arrays permit one to manipulate the data by named fields. A simple
example will show what is meant.: ::
>>> x = np.array([(1,2.,'Hello'), (2,3.,"World")],
... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')])
>>> x
array([(1, 2.0, 'Hello'), (2, 3.0, 'World')],
dtype=[('foo', '>i4'), ('bar', '>f4'), ('baz', '|S10')])
Here we have created a one-dimensional array of length 2. Each element of
this array is a structure that contains three items, a 32-bit integer, a 32-bit
float, and a string of length 10 or less. If we index this array at the second
position we get the second structure: ::
>>> x[1]
(2,3.,"World")
Conveniently, one can access any field of the array by indexing using the
string that names that field. ::
>>> y = x['foo']
>>> y
array([ 2., 3.], dtype=float32)
>>> y[:] = 2*y
>>> y
array([ 4., 6.], dtype=float32)
>>> x
array([(1, 4.0, 'Hello'), (2, 6.0, 'World')],
dtype=[('foo', '>i4'), ('bar', '>f4'), ('baz', '|S10')])
In these examples, y is a simple float array consisting of the 2nd field
in the structured type. But, rather than being a copy of the data in the structured
array, it is a view, i.e., it shares exactly the same memory locations.
Thus, when we updated this array by doubling its values, the structured
array shows the corresponding values as doubled as well. Likewise, if one
changes the structured array, the field view also changes: ::
>>> x[1] = (-1,-1.,"Master")
>>> x
array([(1, 4.0, 'Hello'), (-1, -1.0, 'Master')],
dtype=[('foo', '>i4'), ('bar', '>f4'), ('baz', '|S10')])
>>> y
array([ 4., -1.], dtype=float32)
Defining Structured Arrays
==========================
One defines a structured array through the dtype object. There are
**several** alternative ways to define the fields of a record. Some of
these variants provide backward compatibility with Numeric, numarray, or
another module, and should not be used except for such purposes. These
will be so noted. One specifies record structure in
one of four alternative ways, using an argument (as supplied to a dtype
function keyword or a dtype object constructor itself). This
argument must be one of the following: 1) string, 2) tuple, 3) list, or
4) dictionary. Each of these is briefly described below.
1) String argument.
In this case, the constructor expects a comma-separated list of type
specifiers, optionally with extra shape information. The fields are
given the default names 'f0', 'f1', 'f2' and so on.
The type specifiers can take 4 different forms: ::
a) b1, i1, i2, i4, i8, u1, u2, u4, u8, f2, f4, f8, c8, c16, a<n>
(representing bytes, ints, unsigned ints, floats, complex and
fixed length strings of specified byte lengths)
b) int8,...,uint8,...,float16, float32, float64, complex64, complex128
(this time with bit sizes)
c) older Numeric/numarray type specifications (e.g. Float32).
Don't use these in new code!
d) Single character type specifiers (e.g H for unsigned short ints).
Avoid using these unless you must. Details can be found in the
Numpy book
These different styles can be mixed within the same string (but why would you
want to do that?). Furthermore, each type specifier can be prefixed
with a repetition number, or a shape. In these cases an array
element is created, i.e., an array within a record. That array
is still referred to as a single field. An example: ::
>>> x = np.zeros(3, dtype='3int8, float32, (2,3)float64')
>>> x
array([([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),
([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),
([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])],
dtype=[('f0', '|i1', 3), ('f1', '>f4'), ('f2', '>f8', (2, 3))])
By using strings to define the record structure, it precludes being
able to name the fields in the original definition. The names can
be changed as shown later, however.
2) Tuple argument: The only relevant tuple case that applies to record
structures is when a structure is mapped to an existing data type. This
is done by pairing in a tuple, the existing data type with a matching
dtype definition (using any of the variants being described here). As
an example (using a definition using a list, so see 3) for further
details): ::
>>> x = np.zeros(3, dtype=('i4',[('r','u1'), ('g','u1'), ('b','u1'), ('a','u1')]))
>>> x
array([0, 0, 0])
>>> x['r']
array([0, 0, 0], dtype=uint8)
In this case, an array is produced that looks and acts like a simple int32 array,
but also has definitions for fields that use only one byte of the int32 (a bit
like Fortran equivalencing).
3) List argument: In this case the record structure is defined with a list of
tuples. Each tuple has 2 or 3 elements specifying: 1) The name of the field
('' is permitted), 2) the type of the field, and 3) the shape (optional).
For example::
>>> x = np.zeros(3, dtype=[('x','f4'),('y',np.float32),('value','f4',(2,2))])
>>> x
array([(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]]),
(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]]),
(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]])],
dtype=[('x', '>f4'), ('y', '>f4'), ('value', '>f4', (2, 2))])
4) Dictionary argument: two different forms are permitted. The first consists
of a dictionary with two required keys ('names' and 'formats'), each having an
equal sized list of values. The format list contains any type/shape specifier
allowed in other contexts. The names must be strings. There are two optional
keys: 'offsets' and 'titles'. Each must be a correspondingly matching list to
the required two where offsets contain integer offsets for each field, and
titles are objects containing metadata for each field (these do not have
to be strings), where the value of None is permitted. As an example: ::
>>> x = np.zeros(3, dtype={'names':['col1', 'col2'], 'formats':['i4','f4']})
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[('col1', '>i4'), ('col2', '>f4')])
The other dictionary form permitted is a dictionary of name keys with tuple
values specifying type, offset, and an optional title. ::
>>> x = np.zeros(3, dtype={'col1':('i1',0,'title 1'), 'col2':('f4',1,'title 2')})
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[(('title 1', 'col1'), '|i1'), (('title 2', 'col2'), '>f4')])
Accessing and modifying field names
===================================
The field names are an attribute of the dtype object defining the structure.
For the last example: ::
>>> x.dtype.names
('col1', 'col2')
>>> x.dtype.names = ('x', 'y')
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[(('title 1', 'x'), '|i1'), (('title 2', 'y'), '>f4')])
>>> x.dtype.names = ('x', 'y', 'z') # wrong number of names
<type 'exceptions.ValueError'>: must replace all names at once with a sequence of length 2
Accessing field titles
====================================
The field titles provide a standard place to put associated info for fields.
They do not have to be strings. ::
>>> x.dtype.fields['x'][2]
'title 1'
Accessing multiple fields at once
====================================
You can access multiple fields at once using a list of field names: ::
>>> x = np.array([(1.5,2.5,(1.0,2.0)),(3.,4.,(4.,5.)),(1.,3.,(2.,6.))],
dtype=[('x','f4'),('y',np.float32),('value','f4',(2,2))])
Notice that `x` is created with a list of tuples. ::
>>> x[['x','y']]
array([(1.5, 2.5), (3.0, 4.0), (1.0, 3.0)],
dtype=[('x', '<f4'), ('y', '<f4')])
>>> x[['x','value']]
array([(1.5, [[1.0, 2.0], [1.0, 2.0]]), (3.0, [[4.0, 5.0], [4.0, 5.0]]),
(1.0, [[2.0, 6.0], [2.0, 6.0]])],
dtype=[('x', '<f4'), ('value', '<f4', (2, 2))])
The fields are returned in the order they are asked for.::
>>> x[['y','x']]
array([(2.5, 1.5), (4.0, 3.0), (3.0, 1.0)],
dtype=[('y', '<f4'), ('x', '<f4')])
Filling structured arrays
=========================
Structured arrays can be filled by field or row by row. ::
>>> arr = np.zeros((5,), dtype=[('var1','f8'),('var2','f8')])
>>> arr['var1'] = np.arange(5)
If you fill it in row by row, it takes a take a tuple
(but not a list or array!)::
>>> arr[0] = (10,20)
>>> arr
array([(10.0, 20.0), (1.0, 0.0), (2.0, 0.0), (3.0, 0.0), (4.0, 0.0)],
dtype=[('var1', '<f8'), ('var2', '<f8')])
Record Arrays
=============
For convenience, numpy provides "record arrays" which allow one to access
fields of structured arrays by attribute rather than by index. Record arrays
are structured arrays wrapped using a subclass of ndarray,
:class:`numpy.recarray`, which allows field access by attribute on the array
object, and record arrays also use a special datatype, :class:`numpy.record`,
which allows field access by attribute on the individual elements of the array.
The simplest way to create a record array is with :func:`numpy.rec.array`: ::
>>> recordarr = np.rec.array([(1,2.,'Hello'),(2,3.,"World")],
... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')])
>>> recordarr.bar
array([ 2., 3.], dtype=float32)
>>> recordarr[1:2]
rec.array([(2, 3.0, 'World')],
dtype=[('foo', '<i4'), ('bar', '<f4'), ('baz', 'S10')])
>>> recordarr[1:2].foo
array([2], dtype=int32)
>>> recordarr.foo[1:2]
array([2], dtype=int32)
>>> recordarr[1].baz
'World'
numpy.rec.array can convert a wide variety of arguments into record arrays,
including normal structured arrays: ::
>>> arr = array([(1,2.,'Hello'),(2,3.,"World")],
... dtype=[('foo', 'i4'), ('bar', 'f4'), ('baz', 'S10')])
>>> recordarr = np.rec.array(arr)
The numpy.rec module provides a number of other convenience functions for
creating record arrays, see :ref:`record array creation routines
<routines.array-creation.rec>`.
A record array representation of a structured array can be obtained using the
appropriate :ref:`view`: ::
>>> arr = np.array([(1,2.,'Hello'),(2,3.,"World")],
... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'a10')])
>>> recordarr = arr.view(dtype=dtype((np.record, arr.dtype)),
... type=np.recarray)
For convenience, viewing an ndarray as type `np.recarray` will automatically
convert to `np.record` datatype, so the dtype can be left out of the view: ::
>>> recordarr = arr.view(np.recarray)
>>> recordarr.dtype
dtype((numpy.record, [('foo', '<i4'), ('bar', '<f4'), ('baz', 'S10')]))
To get back to a plain ndarray both the dtype and type must be reset. The
following view does so, taking into account the unusual case that the
recordarr was not a structured type: ::
>>> arr2 = recordarr.view(recordarr.dtype.fields or recordarr.dtype, np.ndarray)
Record array fields accessed by index or by attribute are returned as a record
array if the field has a structured type but as a plain ndarray otherwise. ::
>>> recordarr = np.rec.array([('Hello', (1,2)),("World", (3,4))],
... dtype=[('foo', 'S6'),('bar', [('A', int), ('B', int)])])
>>> type(recordarr.foo)
<type 'numpy.ndarray'>
>>> type(recordarr.bar)
<class 'numpy.core.records.recarray'>
Note that if a field has the same name as an ndarray attribute, the ndarray
attribute takes precedence. Such fields will be inaccessible by attribute but
may still be accessed by index.
""" |
#import os
#import mock
#from uuid import uuid4
#from urllib2 import urlopen
#
#from django.test import TestCase
#from django.core.files.base import ContentFile
#from django.conf import settings
#from django.core.files.storage import FileSystemStorage
#
#from boto.s3.key import Key
#
#from storages.backends import s3boto
#
#__all__ = (
# 'SafeJoinTest',
# 'S3BotoStorageTests',
# #'S3BotoStorageFileTests',
#)
#
#class S3BotoTestCase(TestCase):
# @mock.patch('storages.backends.s3boto.S3Connection')
# def setUp(self, S3Connection):
# self.storage = s3boto.S3BotoStorage()
#
#
#class SafeJoinTest(TestCase):
# def test_normal(self):
# path = s3boto.safe_join("", "path/to/somewhere", "other", "path/to/somewhere")
# self.assertEquals(path, "path/to/somewhere/other/path/to/somewhere")
#
# def test_with_dot(self):
# path = s3boto.safe_join("", "path/./somewhere/../other", "..",
# ".", "to/./somewhere")
# self.assertEquals(path, "path/to/somewhere")
#
# def test_base_url(self):
# path = s3boto.safe_join("base_url", "path/to/somewhere")
# self.assertEquals(path, "base_url/path/to/somewhere")
#
# def test_base_url_with_slash(self):
# path = s3boto.safe_join("base_url/", "path/to/somewhere")
# self.assertEquals(path, "base_url/path/to/somewhere")
#
# def test_suspicious_operation(self):
# self.assertRaises(ValueError,
# s3boto.safe_join, "base", "../../../../../../../etc/passwd")
#
#class S3BotoStorageTests(S3BotoTestCase):
#
# def test_storage_save(self):
# """
# Test saving a file
# """
# name = 'test_storage_save.txt'
# content = ContentFile('new content')
# self.storage.save(name, content)
# self.storage.bucket.get_key.assert_called_once_with(name)
#
# key = self.storage.bucket.get_key.return_value
# key.set_metadata.assert_called_with('Content-Type', 'text/plain')
# key.set_contents_from_file.assert_called_with(
# content,
# headers={},
# policy=self.storage.acl,
# reduced_redundancy=self.storage.reduced_redundancy,
# )
#
# def test_storage_save_gzip(self):
# """
# Test saving a file with gzip enabled.
# """
# if not s3boto.IS_GZIPPED: # Gzip not available.
# return
# name = 'test_storage_save.css'
# content = ContentFile("I should be gzip'd")
# self.storage.save(name, content)
# key = self.storage.bucket.get_key.return_value
# key.set_metadata.assert_called_with('Content-Type', 'text/css')
# key.set_contents_from_file.assert_called_with(
# content,
# headers={'Content-Encoding': 'gzip'},
# policy=self.storage.acl,
# reduced_redundancy=self.storage.reduced_redundancy,
# )
#
# def test_compress_content_len(self):
# """
# Test that file returned by _compress_content() is readable.
# """
# if not s3boto.IS_GZIPPED: # Gzip not available.
# return
# content = ContentFile("I should be gzip'd")
# content = self.storage._compress_content(content)
# self.assertTrue(len(content.read()) > 0)
#
# def test_storage_open_write(self):
# """
# Test opening a file in write mode
# """
# name = 'test_open_for_writing.txt'
# content = 'new content'
#
# # Set the ACL header used when creating/writing data.
# self.storage.bucket.connection.provider.acl_header = 'x-amz-acl'
# # Set the mocked key's bucket
# self.storage.bucket.get_key.return_value.bucket = self.storage.bucket
# # Set the name of the mock object
# self.storage.bucket.get_key.return_value.name = name
#
# file = self.storage.open(name, 'w')
# self.storage.bucket.get_key.assert_called_with(name)
#
# file.write(content)
# self.storage.bucket.initiate_multipart_upload.assert_called_with(
# name,
# headers={'x-amz-acl': 'public-read'},
# reduced_redundancy=self.storage.reduced_redundancy,
# )
#
# # Save the internal file before closing
# _file = file.file
# file.close()
# file._multipart.upload_part_from_file.assert_called_with(
# _file, 1, headers=self.storage.headers,
# )
# file._multipart.complete_upload.assert_called_once()
#
# #def test_storage_exists_and_delete(self):
# # # show file does not exist
# # name = self.prefix_path('test_exists.txt')
# # self.assertFalse(self.storage.exists(name))
# #
# # # create the file
# # content = 'new content'
# # file = self.storage.open(name, 'w')
# # file.write(content)
# # file.close()
# #
# # # show file exists
# # self.assertTrue(self.storage.exists(name))
# #
# # # delete the file
# # self.storage.delete(name)
# #
# # # show file does not exist
# # self.assertFalse(self.storage.exists(name))
#
# def test_storage_listdir_base(self):
# file_names = ["some/path/1.txt", "2.txt", "other/path/3.txt", "4.txt"]
#
# self.storage.bucket.list.return_value = []
# for p in file_names:
# key = mock.MagicMock(spec=Key)
# key.name = p
# self.storage.bucket.list.return_value.append(key)
#
# dirs, files = self.storage.listdir("")
#
# self.assertEqual(len(dirs), 2)
# for directory in ["some", "other"]:
# self.assertTrue(directory in dirs,
# """ "%s" not in directory list "%s".""" % (
# directory, dirs))
#
# self.assertEqual(len(files), 2)
# for filename in ["2.txt", "4.txt"]:
# self.assertTrue(filename in files,
# """ "%s" not in file list "%s".""" % (
# filename, files))
#
# def test_storage_listdir_subdir(self):
# file_names = ["some/path/1.txt", "some/2.txt"]
#
# self.storage.bucket.list.return_value = []
# for p in file_names:
# key = mock.MagicMock(spec=Key)
# key.name = p
# self.storage.bucket.list.return_value.append(key)
#
# dirs, files = self.storage.listdir("some/")
# self.assertEqual(len(dirs), 1)
# self.assertTrue('path' in dirs,
# """ "path" not in directory list "%s".""" % (dirs,))
#
# self.assertEqual(len(files), 1)
# self.assertTrue('2.txt' in files,
# """ "2.txt" not in files list "%s".""" % (files,))
#
# #def test_storage_size(self):
# # name = self.prefix_path('test_storage_size.txt')
# # content = 'new content'
# # f = ContentFile(content)
# # self.storage.save(name, f)
# # self.assertEqual(self.storage.size(name), f.size)
# #
# #def test_storage_url(self):
# # name = self.prefix_path('test_storage_size.txt')
# # content = 'new content'
# # f = ContentFile(content)
# # self.storage.save(name, f)
# # self.assertEqual(content, urlopen(self.storage.url(name)).read())
#
##class S3BotoStorageFileTests(S3BotoTestCase):
## def test_multipart_upload(self):
## nparts = 2
## name = self.prefix_path("test_multipart_upload.txt")
## mode = 'w'
## f = s3boto.S3BotoStorageFile(name, mode, self.storage)
## content_length = 1024 * 1024# 1 MB
## content = 'a' * content_length
##
## bytes = 0
## target = f._write_buffer_size * nparts
## while bytes < target:
## f.write(content)
## bytes += content_length
##
## # make the buffer roll over so f._write_counter
## # is incremented
## f.write("finished")
##
## # verify upload was multipart and correctly partitioned
## self.assertEqual(f._write_counter, nparts)
##
## # complete the upload
## f.close()
##
## # verify that the remaining buffered bytes were
## # uploaded when the file was closed.
## self.assertEqual(f._write_counter, nparts+1)
|
"""
.. index:: multidimensional scaling (mds)
.. index::
single: projection; multidimensional scaling (mds)
**********************************
Multidimensional scaling (``mds``)
**********************************
The functionality to perform multidimensional scaling
(http://en.wikipedia.org/wiki/Multidimensional_scaling).
The main class to perform multidimensional scaling is
:class:`Orange.projection.mds.MDS`
.. autoclass:: Orange.projection.mds.MDS
:members:
:exclude-members: Torgerson, get_distance, get_stress, calc_stress, run
.. automethod:: calc_stress(stress_func=SgnRelStress)
.. automethod:: run(iter, stress_func=SgnRelStress, eps=1e-3, progress_callback=None)
Stress functions
================
Stress functions that can be used for MDS have to be implemented as functions
or callable classes:
.. method:: \ __call__(correct, current, weight=1.0)
Compute the stress using the correct and the current distance value (the
:obj:`Orange.projection.mds.MDS.distances` and
:obj:`Orange.projection.mds.MDS.projected_distances` elements).
:param correct: correct (actual) distance between elements, represented by
the two points.
:type correct: float
:param current: current distance between the points in the MDS space.
:type current: float
This module provides the following stress functions:
* :obj:`SgnRelStress`
* :obj:`KruskalStress`
* :obj:`SammonStress`
* :obj:`SgnSammonStress`
Examples
========
MDS Scatterplot
---------------
The following script computes the Euclidean distance between the data
instances and runs MDS. Final coordinates are plotted with matplotlib
(not included with orange, http://matplotlib.sourceforge.net/).
Example (:download:`mds-scatterplot.py <code/mds-scatterplot.py>`)
.. literalinclude:: code/mds-scatterplot.py
:lines: 7-
The script produces a file *mds-scatterplot.py.png*. Color denotes
the class. Iris is a relatively simple data set with respect to
classification; to no surprise we see that MDS finds such instance
placement in 2D where instances of different classes are well separated.
Note that MDS has no knowledge of points' classes.
.. image:: files/mds-scatterplot.png
A more advanced example
-----------------------
The following script performs 10 steps of Smacof optimization before computing
the stress. This is suitable if you have a large dataset and want to save some
time.
Example (:download:`mds-advanced.py <code/mds-advanced.py>`)
.. literalinclude:: code/mds-advanced.py
:lines: 7-
A few representative lines of the output are::
<-0.633911848068, 0.112218663096> [5.1, 3.5, 1.4, 0.2, 'Iris-setosa']
<-0.624193906784, -0.111143872142> [4.9, 3.0, 1.4, 0.2, 'Iris-setosa']
...
<0.265250980854, 0.237793982029> [7.0, 3.2, 4.7, 1.4, 'Iris-versicolor']
<0.208580598235, 0.116296850145> [6.4, 3.2, 4.5, 1.5, 'Iris-versicolor']
...
<0.635814905167, 0.238721415401> [6.3, 3.3, 6.0, 2.5, 'Iris-virginica']
<0.356859534979, -0.175976261497> [5.8, 2.7, 5.1, 1.9, 'Iris-virginica']
...
""" |
# Test 64-bit COMPARE AND BRANCH in cases where the sheer number of
# instructions causes some branches to be out of range.
# RUN: python %s | llc -mtriple=s390x-linux-gnu | FileCheck %s
# Construct:
#
# before0:
# conditional branch to after0
# ...
# beforeN:
# conditional branch to after0
# main:
# 0xffcc bytes, from MVIY instructions
# conditional branch to main
# after0:
# ...
# conditional branch to main
# afterN:
#
# Each conditional branch sequence occupies 12 bytes if it uses a short
# branch and 16 if it uses a long one. The ones before "main:" have to
# take the branch length into account, which is 6 for short branches,
# so the final (0x34 - 6) / 12 == 3 blocks can use short branches.
# The ones after "main:" do not, so the first 0x34 / 12 == 4 blocks
# can use short branches. The conservative algorithm we use makes
# one of the forward branches unnecessarily long, as noted in the
# check output below.
#
# CHECK: lgb [[REG:%r[0-5]]], 0(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL:\.L[^ ]*]]
# CHECK: lgb [[REG:%r[0-5]]], 1(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 2(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 3(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 4(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# ...as mentioned above, the next one could be a CGRJE instead...
# CHECK: lgb [[REG:%r[0-5]]], 5(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 6(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 7(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# ...main goes here...
# CHECK: lgb [[REG:%r[0-5]]], 25(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL:\.L[^ ]*]]
# CHECK: lgb [[REG:%r[0-5]]], 26(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 27(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 28(%r3)
# CHECK: cgrje %r4, [[REG]], [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 29(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 30(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 31(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
# CHECK: lgb [[REG:%r[0-5]]], 32(%r3)
# CHECK: cgr %r4, [[REG]]
# CHECK: jge [[LABEL]]
|
"""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.
""" |
#
# 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
|
"""
This is a procedural interface to the matplotlib object-oriented
plotting library.
The following plotting commands are provided; the majority have
Matlab(TM) analogs and similar argument.
_Plotting commands
acorr - plot the autocorrelation function
annotate - annotate something in the figure
arrow - add an arrow to the axes
axes - Create a new axes
axhline - draw a horizontal line across axes
axvline - draw a vertical line across axes
axhspan - draw a horizontal bar across axes
axvspan - draw a vertical bar across axes
axis - Set or return the current axis limits
bar - make a bar chart
barh - a horizontal bar chart
broken_barh - a set of horizontal bars with gaps
box - set the axes frame on/off state
boxplot - make a box and whisker plot
cla - clear current axes
clabel - label a contour plot
clf - clear a figure window
clim - adjust the color limits of the current image
close - close a figure window
colorbar - add a colorbar to the current figure
cohere - make a plot of coherence
contour - make a contour plot
contourf - make a filled contour plot
csd - make a plot of cross spectral density
delaxes - delete an axes from the current figure
draw - Force a redraw of the current figure
errorbar - make an errorbar graph
figlegend - make legend on the figure rather than the axes
figimage - make a figure image
figtext - add text in figure coords
figure - create or change active figure
fill - make filled polygons
findobj - recursively find all objects matching some criteria
gca - return the current axes
gcf - return the current figure
gci - get the current image, or None
getp - get a handle graphics property
grid - set whether gridding is on
hist - make a histogram
hold - set the axes hold state
ioff - turn interaction mode off
ion - turn interaction mode on
isinteractive - return True if interaction mode is on
imread - load image file into array
imshow - plot image data
ishold - return the hold state of the current axes
legend - make an axes legend
loglog - a log log plot
matshow - display a matrix in a new figure preserving aspect
pcolor - make a pseudocolor plot
pcolormesh - make a pseudocolor plot using a quadrilateral mesh
pie - make a pie chart
plot - make a line plot
plot_date - plot dates
plotfile - plot column data from an ASCII tab/space/comma delimited file
pie - pie charts
polar - make a polar plot on a PolarAxes
psd - make a plot of power spectral density
quiver - make a direction field (arrows) plot
rc - control the default params
rgrids - customize the radial grids and labels for polar
savefig - save the current figure
scatter - make a scatter plot
setp - set a handle graphics property
semilogx - log x axis
semilogy - log y axis
show - show the figures
specgram - a spectrogram plot
spy - plot sparsity pattern using markers or image
stem - make a stem plot
subplot - make a subplot (numrows, numcols, axesnum)
subplots_adjust - change the params controlling the subplot positions of current figure
subplot_tool - launch the subplot configuration tool
suptitle - add a figure title
table - add a table to the plot
text - add some text at location x,y to the current axes
thetagrids - customize the radial theta grids and labels for polar
title - add a title to the current axes
xcorr - plot the autocorrelation function of x and y
xlim - set/get the xlimits
ylim - set/get the ylimits
xticks - set/get the xticks
yticks - set/get the yticks
xlabel - add an xlabel to the current axes
ylabel - add a ylabel to the current axes
autumn - set the default colormap to autumn
bone - set the default colormap to bone
cool - set the default colormap to cool
copper - set the default colormap to copper
flag - set the default colormap to flag
gray - set the default colormap to gray
hot - set the default colormap to hot
hsv - set the default colormap to hsv
jet - set the default colormap to jet
pink - set the default colormap to pink
prism - set the default colormap to prism
spring - set the default colormap to spring
summer - set the default colormap to summer
winter - set the default colormap to winter
spectral - set the default colormap to spectral
_Event handling
connect - register an event handler
disconnect - remove a connected event handler
_Matrix commands
cumprod - the cumulative product along a dimension
cumsum - the cumulative sum along a dimension
detrend - remove the mean or besdt fit line from an array
diag - the k-th diagonal of matrix
diff - the n-th differnce of an array
eig - the eigenvalues and eigen vectors of v
eye - a matrix where the k-th diagonal is ones, else zero
find - return the indices where a condition is nonzero
fliplr - flip the rows of a matrix up/down
flipud - flip the columns of a matrix left/right
linspace - a linear spaced vector of N values from min to max inclusive
logspace - a log spaced vector of N values from min to max inclusive
meshgrid - repeat x and y to make regular matrices
ones - an array of ones
rand - an array from the uniform distribution [0,1]
randn - an array from the normal distribution
rot90 - rotate matrix k*90 degress counterclockwise
squeeze - squeeze an array removing any dimensions of length 1
tri - a triangular matrix
tril - a lower triangular matrix
triu - an upper triangular matrix
vander - the Vandermonde matrix of vector x
svd - singular value decomposition
zeros - a matrix of zeros
_Probability
levypdf - The levy probability density function from the char. func.
normpdf - The Gaussian probability density function
rand - random numbers from the uniform distribution
randn - random numbers from the normal distribution
_Statistics
corrcoef - correlation coefficient
cov - covariance matrix
amax - the maximum along dimension m
mean - the mean along dimension m
median - the median along dimension m
amin - the minimum along dimension m
norm - the norm of vector x
prod - the product along dimension m
ptp - the max-min along dimension m
std - the standard deviation along dimension m
asum - the sum along dimension m
_Time series analysis
bartlett - M-point Bartlett window
blackman - M-point Blackman window
cohere - the coherence using average periodiogram
csd - the cross spectral density using average periodiogram
fft - the fast Fourier transform of vector x
hamming - M-point Hamming window
hanning - M-point Hanning window
hist - compute the histogram of x
kaiser - M length Kaiser window
psd - the power spectral density using average periodiogram
sinc - the sinc function of array x
_Dates
date2num - convert python datetimes to numeric representation
drange - create an array of numbers for date plots
num2date - convert numeric type (float days since 0001) to datetime
_Other
angle - the angle of a complex array
griddata - interpolate irregularly distributed data to a regular grid
load - load ASCII data into array
polyfit - fit x, y to an n-th order polynomial
polyval - evaluate an n-th order polynomial
roots - the roots of the polynomial coefficients in p
save - save an array to an ASCII file
trapz - trapezoidal integration
__end
""" |
"""Exception classes for CherryPy.
CherryPy provides (and uses) exceptions for declaring that the HTTP response
should be a status other than the default "200 OK". You can ``raise`` them like
normal Python exceptions. You can also call them and they will raise themselves;
this means you can set an :class:`HTTPError<cherrypy._cperror.HTTPError>`
or :class:`HTTPRedirect<cherrypy._cperror.HTTPRedirect>` as the
:attr:`request.handler<cherrypy._cprequest.Request.handler>`.
.. _redirectingpost:
Redirecting POST
================
When you GET a resource and are redirected by the server to another Location,
there's generally no problem since GET is both a "safe method" (there should
be no side-effects) and an "idempotent method" (multiple calls are no different
than a single call).
POST, however, is neither safe nor idempotent--if you
charge a credit card, you don't want to be charged twice by a redirect!
For this reason, *none* of the 3xx responses permit a user-agent (browser) to
resubmit a POST on redirection without first confirming the action with the user:
===== ================================= ===========
300 Multiple Choices Confirm with the user
301 Moved Permanently Confirm with the user
302 Found (Object moved temporarily) Confirm with the user
303 See Other GET the new URI--no confirmation
304 Not modified (for conditional GET only--POST should not raise this error)
305 Use Proxy Confirm with the user
307 Temporary Redirect Confirm with the user
===== ================================= ===========
However, browsers have historically implemented these restrictions poorly;
in particular, many browsers do not force the user to confirm 301, 302
or 307 when redirecting POST. For this reason, CherryPy defaults to 303,
which most user-agents appear to have implemented correctly. Therefore, if
you raise HTTPRedirect for a POST request, the user-agent will most likely
attempt to GET the new URI (without asking for confirmation from the user).
We realize this is confusing for developers, but it's the safest thing we
could do. You are of course free to raise ``HTTPRedirect(uri, status=302)``
or any other 3xx status if you know what you're doing, but given the
environment, we couldn't let any of those be the default.
Custom Error Handling
=====================
.. image:: /refman/cperrors.gif
Anticipated HTTP responses
--------------------------
The 'error_page' config namespace can be used to provide custom HTML output for
expected responses (like 404 Not Found). Supply a filename from which the output
will be read. The contents will be interpolated with the values %(status)s,
%(message)s, %(traceback)s, and %(version)s using plain old Python
`string formatting <http://www.python.org/doc/2.6.4/library/stdtypes.html#string-formatting-operations>`_.
::
_cp_config = {'error_page.404': os.path.join(localDir, "static/index.html")}
Beginning in version 3.1, you may also provide a function or other callable as
an error_page entry. It will be passed the same status, message, traceback and
version arguments that are interpolated into templates::
def error_page_402(status, message, traceback, version):
return "Error %s - Well, I'm very sorry but you haven't paid!" % status
cherrypy.config.update({'error_page.402': error_page_402})
Also in 3.1, in addition to the numbered error codes, you may also supply
"error_page.default" to handle all codes which do not have their own error_page entry.
Unanticipated errors
--------------------
CherryPy also has a generic error handling mechanism: whenever an unanticipated
error occurs in your code, it will call
:func:`Request.error_response<cherrypy._cprequest.Request.error_response>` to set
the response status, headers, and body. By default, this is the same output as
:class:`HTTPError(500) <cherrypy._cperror.HTTPError>`. If you want to provide
some other behavior, you generally replace "request.error_response".
Here is some sample code that shows how to display a custom error message and
send an e-mail containing the error::
from cherrypy import _cperror
def handle_error():
cherrypy.response.status = 500
cherrypy.response.body = ["<html><body>Sorry, an error occured</body></html>"]
sendMail('error@domain.com', 'Error in your web app', _cperror.format_exc())
class Root:
_cp_config = {'request.error_response': handle_error}
Note that you have to explicitly set :attr:`response.body <cherrypy._cprequest.Response.body>`
and not simply return an error message as a result.
""" |
#
# ElementTree
# $Id: ElementTree.py 3224 2007-08-27 21:23:39Z 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
# 2006-11-18 fl added parser support for IronPython (ElementIron)
# 2007-08-27 fl fixed newlines in attributes
#
# Copyright (c) 1999-2007 by NAME All rights reserved.
#
# USERNAME@pythonware.com
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2007 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 the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
###########################################################################################
# Implementation of the stochastic depth algorithm described in the paper
#
# NAME et al. "Deep networks with stochastic depth." arXiv preprint arXiv:1603.09382 (2016).
#
# Reference torch implementation can be found at https://github.com/yueatsprograms/Stochastic_Depth
#
# There are some differences in the implementation:
# - A BN->ReLU->Conv is used for skip connection when input and output shapes are different,
# as oppose to a padding layer.
# - The residual block is different: we use BN->ReLU->Conv->BN->ReLU->Conv, as oppose to
# Conv->BN->ReLU->Conv->BN (->ReLU also applied to skip connection).
# - We did not try to match with the same initialization, learning rate scheduling, etc.
#
#--------------------------------------------------------------------------------
# A sample from the running log (We achieved ~9.4% error after 500 epochs, some
# more careful tuning of the hyper parameters and maybe also the arch is needed
# to achieve the reported numbers in the paper):
#
# INFO:root:Epoch[80] Batch [50] Speed: 1020.95 samples/sec Train-accuracy=0.910080
# INFO:root:Epoch[80] Batch [100] Speed: 1013.41 samples/sec Train-accuracy=0.912031
# INFO:root:Epoch[80] Batch [150] Speed: 1035.48 samples/sec Train-accuracy=0.913438
# INFO:root:Epoch[80] Batch [200] Speed: 1045.00 samples/sec Train-accuracy=0.907344
# INFO:root:Epoch[80] Batch [250] Speed: 1055.32 samples/sec Train-accuracy=0.905937
# INFO:root:Epoch[80] Batch [300] Speed: 1071.71 samples/sec Train-accuracy=0.912500
# INFO:root:Epoch[80] Batch [350] Speed: 1033.73 samples/sec Train-accuracy=0.910937
# INFO:root:Epoch[80] Train-accuracy=0.919922
# INFO:root:Epoch[80] Time cost=48.348
# INFO:root:Saved checkpoint to "sd-110-0081.params"
# INFO:root:Epoch[80] Validation-accuracy=0.880142
# ...
# INFO:root:Epoch[115] Batch [50] Speed: 1037.04 samples/sec Train-accuracy=0.937040
# INFO:root:Epoch[115] Batch [100] Speed: 1041.12 samples/sec Train-accuracy=0.934219
# INFO:root:Epoch[115] Batch [150] Speed: 1036.02 samples/sec Train-accuracy=0.933125
# INFO:root:Epoch[115] Batch [200] Speed: 1057.49 samples/sec Train-accuracy=0.938125
# INFO:root:Epoch[115] Batch [250] Speed: 1060.56 samples/sec Train-accuracy=0.933438
# INFO:root:Epoch[115] Batch [300] Speed: 1046.25 samples/sec Train-accuracy=0.935625
# INFO:root:Epoch[115] Batch [350] Speed: 1043.83 samples/sec Train-accuracy=0.927188
# INFO:root:Epoch[115] Train-accuracy=0.938477
# INFO:root:Epoch[115] Time cost=47.815
# INFO:root:Saved checkpoint to "sd-110-0116.params"
# INFO:root:Epoch[115] Validation-accuracy=0.884415
# ...
# INFO:root:Saved checkpoint to "sd-110-0499.params"
# INFO:root:Epoch[498] Validation-accuracy=0.908554
# INFO:root:Epoch[499] Batch [50] Speed: 1068.28 samples/sec Train-accuracy=0.991422
# INFO:root:Epoch[499] Batch [100] Speed: 1053.10 samples/sec Train-accuracy=0.991094
# INFO:root:Epoch[499] Batch [150] Speed: 1042.89 samples/sec Train-accuracy=0.995156
# INFO:root:Epoch[499] Batch [200] Speed: 1066.22 samples/sec Train-accuracy=0.991406
# INFO:root:Epoch[499] Batch [250] Speed: 1050.56 samples/sec Train-accuracy=0.990781
# INFO:root:Epoch[499] Batch [300] Speed: 1032.02 samples/sec Train-accuracy=0.992500
# INFO:root:Epoch[499] Batch [350] Speed: 1062.16 samples/sec Train-accuracy=0.992969
# INFO:root:Epoch[499] Train-accuracy=0.994141
# INFO:root:Epoch[499] Time cost=47.401
# INFO:root:Saved checkpoint to "sd-110-0500.params"
# INFO:root:Epoch[499] Validation-accuracy=0.906050
# ###########################################################################################
|
# This is a sample transform plugin script for bbcrack
# All transform plugin scripts need to be named trans*.py, in the plugins folder
# Each plugin script should add Transform objects.
# First define a new Transform class, inheriting either from Transform_char or
# Transform_string:
# - Transform_char: for transforms that apply to each character/byte
# independently, not depending on the location of the character.
# (example: simple XOR)
# - Transform_string: for all other transforms, that may apply to several
# characters at once, or taking into account the location of the character.
# (example: XOR with increasing key)
# Transform_char is usually much faster because it uses a translation table.
# A class represents a generic transform (obfuscation algorithm), such as XOR
# or XOR+ROL.
# When the class is instantiated as an object, it includes the keys of the
# obfuscation algorithm, specified as parameters. (e.g. "XOR 4F" or "XOR 4F +
# ROL 3")
# For each transform class, you need to implement the following methods/variables:
# - a description and an short name for the transform
# - __init__() to store parameters
# - iter_params() to generate all the possible parameters for bruteforcing
# - transform_char() or transform_string() to apply the transform to a single
# character or to the whole string at once.
# Then do not forget to add to the proper level 1, 2 or 3. (see below after
# class samples)
# If you develop useful plugin scripts and you would like me to reference them,
# or if you think about additional transforms that bbcrack should include,
# please contact me using this form: http://www.decalage.info/contact
# See below for three different examples:
# 1) Transform_char with single parameter
# 2) Transform_char with multiple parameters
# 3) Transform_string
#------------------------------------------------------------------------------
##class Transform_SAMPLE_XOR (Transform_char):
## """
## sample XOR Transform, single parameter
## """
## # Provide a description for the transform, and an id (short name for
## # command line options):
## gen_name = 'SAMPLE XOR with 8 bits static key A. Parameters: A (1-FF).'
## gen_id = 'samplexor'
##
## # the __init__ method must store provided parameters and build the specific
## # name and shortname of the transform with parameters
## def __init__(self, params):
## """
## constructor for the Transform object.
## This method needs to be overloaded for every specific Transform.
## It should set name and shortname according to the provided parameters.
## (for example shortname="xor_17" for a XOR transform with params=17)
## params: single value or tuple of values, parameters for the transformation
## """
## self.params = params
## self.name = "Sample XOR %02X" % params
## # this shortname will be used to save bbcrack and bbtrans results to files
## self.shortname = "samplexor%02X" % params
##
## def transform_char (self, char):
## """
## Method to be overloaded, only for a transform that acts on a character.
## This method should apply the transform to the provided char, using params
## as parameters, and return the transformed data as a character.
## (here character = string of length 1)
##
## NOTE: here the algorithm can be slow, because it will only be used 256
## times to build a translation table.
## """
## # here params is an integer
## return chr(ord(char) ^ self.params)
##
## @staticmethod
## def iter_params ():
## """
## Method to be overloaded.
## This static method should iterate over all possible parameters for the
## transform function, yielding each set of parameters as a single value
## or a tuple of values.
## (for example for a XOR transform, it should yield 1 to 255)
## This method should be used on the Transform class in order to
## instantiate a Transform object with each set of parameters.
## """
## # the XOR key can be 1 to 255 (0 would be identity)
## for key in xrange(1,256):
## yield key
#------------------------------------------------------------------------------
##class Transform_SAMPLE_XOR_ROL (Transform_char):
## """
## Sample XOR+ROL Transform - multiple parameters
## """
## # generic name for the class:
## gen_name = 'XOR with static 8 bits key A, then rotate B bits left. Parameters: A (1-FF), B (1-7).'
## gen_id = 'xor_rol'
##
## def __init__(self, params):
## # Here we assume that params is a tuple with two integers:
## self.params = params
## self.name = "XOR %02X then ROL %d" % params
## self.shortname = "xor%02X_rol%d" % params
##
## def transform_char (self, char):
## # here params is a tuple
## xor_key, rol_bits = self.params
## return chr(rol(ord(char) ^ xor_key, rol_bits))
##
## @staticmethod
## def iter_params ():
## "return (XOR key, ROL bits)"
## # the XOR key can be 1 to 255 (0 would be like ROL)
## for xor_key in xrange(1,256):
## # the ROL bits can be 1 to 7:
## for rol_bits in xrange(1,8):
## # yield a tuple with XOR key and ROL bits:
## yield (xor_key, rol_bits)
#------------------------------------------------------------------------------
##class Transform_SAMPLE_XOR_INC (Transform_string):
## """
## Sample XOR Transform, with incrementing key
## (this kind of transform must be implemented as a Transform_string, because
## it gives different results depending on the location of the character)
## """
## # generic name for the class:
## gen_name = 'XOR with 8 bits key A incrementing after each character. Parameters: A (0-FF).'
## gen_id = 'xor_inc'
##
## def __init__(self, params):
## self.params = params
## self.name = "XOR %02X INC" % params
## self.shortname = "xor%02X_inc" % params
##
## def transform_string (self, data):
## """
## Method to be overloaded, only for a transform that acts on a string
## globally.
## This method should apply the transform to the data string, using params
## as parameters, and return the transformed data as a string.
## (the resulting string does not need to have the same length as data)
## """
## # here params is an integer
## out = ''
## for i in xrange(len(data)):
## xor_key = (self.params + i) & 0xFF
## out += chr(ord(data[i]) ^ xor_key)
## return out
##
## @staticmethod
## def iter_params ():
## # the XOR key can be 0 to 255 (0 is not identity here)
## for xor_key in xrange(0,256):
## yield xor_key
#------------------------------------------------------------------------------
# Second, add it to the proper level:
# - level 1 for fast transform with up to 2000 iterations (e.g. xor, xor+rol)
# - level 2 for slower transforms or more iterations (e.g. xor+add)
# - level 3 for slow or infrequent transforms
##add_transform(Transform_SAMPLE_XOR, level=1)
##add_transform(Transform_SAMPLE_XOR_ROL, level=1)
##add_transform(Transform_SAMPLE_XOR_INC, level=2)
# see bbcrack.py and the Transform classes for more options.
|
#!/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
|