comments
stringlengths 2
31.4k
|
|---|
#
# 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
|
"""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 <EMAIL>
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.
""" |
# Test 32-bit COMPARE LOGICAL IMMEDIATE 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:
# 0xffc6 bytes, from MVIY instructions
# conditional branch to main
# after0:
# ...
# conditional branch to main
# afterN:
#
# Each conditional branch sequence occupies 14 bytes if it uses a short
# branch and 20 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 (0x3a - 6) / 14 == 3 blocks can use short branches.
# The ones after "main:" do not, so the first 0x3a / 14 == 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: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clfi [[REG]], 50
# CHECK: jgl [[LABEL:\.L[^ ]*]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clfi [[REG]], 51
# CHECK: jgl [[LABEL]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clfi [[REG]], 52
# CHECK: jgl [[LABEL]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clfi [[REG]], 53
# CHECK: jgl [[LABEL]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clfi [[REG]], 54
# CHECK: jgl [[LABEL]]
# ...as mentioned above, the next one could be a CLIJL instead...
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clfi [[REG]], 55
# CHECK: jgl [[LABEL]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clijl [[REG]], 56, [[LABEL]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clijl [[REG]], 57, [[LABEL]]
# ...main goes here...
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clijl [[REG]], 100, [[LABEL:\.L[^ ]*]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clijl [[REG]], 101, [[LABEL]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clijl [[REG]], 102, [[LABEL]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clijl [[REG]], 103, [[LABEL]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clfi [[REG]], 104
# CHECK: jgl [[LABEL]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clfi [[REG]], 105
# CHECK: jgl [[LABEL]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clfi [[REG]], 106
# CHECK: jgl [[LABEL]]
# CHECK: l [[REG:%r[0-5]]], 0(%r3)
# CHECK: s [[REG]], 0(%r4)
# CHECK: clfi [[REG]], 107
# CHECK: jgl [[LABEL]]
|
# -*- encoding: utf-8 -*-
##############################################################################
#
# Copyright (c) 2009 Veritos - NAME - www.veritos.nl
#
# WARNING: This program as such is intended to be used by professional
# programmers who take the whole responsability of assessing all potential
# consequences resulting from its eventual inadequacies and bugs.
# End users who are looking for a ready-to-use solution with commercial
# garantees and support are strongly adviced to contract a Free Software
# Service Company like Veritos.
#
# This program is Free Software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#
##############################################################################
#
# Deze module werkt in OpenERP 5.0.0 (en waarschijnlijk hoger).
# Deze module werkt niet in OpenERP versie 4 en lager.
#
# Status 1.0 - getest op OpenERP 5.0.3
#
# Versie IP_ADDRESS
# account.account.type
# Basis gelegd voor alle account type
#
# account.account.template
# Basis gelegd met alle benodigde grootboekrekeningen welke via een menu-
# structuur gelinkt zijn aan rubrieken 1 t/m 9.
# De grootboekrekeningen gelinkt aan de account.account.type
# Deze links moeten nog eens goed nagelopen worden.
#
# account.chart.template
# Basis gelegd voor het koppelen van rekeningen aan debiteuren, crediteuren,
# bank, inkoop en verkoop boeken en de BTW configuratie.
#
# Versie IP_ADDRESS
# account.tax.code.template
# Basis gelegd voor de BTW configuratie (structuur)
# Heb als basis het BTW aangifte formulier gebruikt. Of dit werkt?
#
# account.tax.template
# De BTW rekeningen aangemaakt en deze gekoppeld aan de betreffende
# grootboekrekeningen
#
# Versie IP_ADDRESS
# Opschonen van de code en verwijderen van niet gebruikte componenten.
# Versie IP_ADDRESS
# Aanpassen a_expense van 3000 -> 7000
# record id='btw_code_5b' op negatieve waarde gezet
# Versie IP_ADDRESS
# BTW rekeningen hebben typeaanduiding gekregen t.b.v. purchase of sale
# Versie IP_ADDRESS
# Opschonen van module.
# Versie IP_ADDRESS
# Opschonen van module.
# Versie IP_ADDRESS
# Foutje in l10n_nl_wizard.xml gecorrigeerd waardoor de module niet volledig installeerde.
# Versie IP_ADDRESS
# Account Receivable en Payable goed gedefinieerd.
# Versie IP_ADDRESS
# Alle user_type_xxx velden goed gedefinieerd.
# Specifieke bouw en garage gerelateerde grootboeken verwijderd om een standaard module te creeeren.
# Deze module kan dan als basis worden gebruikt voor specifieke doelgroep modules te creeeren.
# Versie IP_ADDRESS
# Correctie van rekening 7010 (stond dubbel met 7014 waardoor installatie verkeerd ging)
# versie IP_ADDRESS
# Correctie op diverse rekening types van user_type_asset -> user_type_liability en user_type_equity
# versie IP_ADDRESS
# Kleine correctie op BTW te vorderen hoog, id was hetzelfde voor beide, waardoor hoog werd overschreven door # overig. Verduidelijking van omschrijvingen in belastingcodes t.b.v. aangifte overzicht.
# versie IP_ADDRESS
# BTW omschrijvingen aangepast, zodat rapporten er beter uitzien. 2a en 5b e.d. verwijderd en enkele omschrijvingen toegevoegd.
# versie IP_ADDRESS - Switch to English
# Added properties_stock_xxx accounts for correct stock valuation, changed 7000-accounts from type cash to type expense
# Changed naming of 7020 and 7030 to Kostprijs omzet xxxx
|
"""
=============================
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
""" |
"""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.
""" |
"""
Wrappers to BLAS library
========================
fblas -- wrappers for Fortran [*] BLAS routines
cblas -- wrappers for ATLAS BLAS routines
get_blas_funcs -- query for wrapper functions.
[*] If ATLAS libraries are available then Fortran routines
actually use ATLAS routines and should perform equally
well to ATLAS routines.
Module fblas
++++++++++++
In the following all function names are shown without type prefixes.
Level 1 routines
----------------
c,s = rotg(a,b)
param = rotmg(d1,d2,x1,y1)
x,y = rot(x,y,c,s,n=(len(x)-offx)/abs(incx),offx=0,incx=1,offy=0,incy=1,overwrite_x=0,overwrite_y=0)
x,y = rotm(x,y,param,n=(len(x)-offx)/abs(incx),offx=0,incx=1,offy=0,incy=1,overwrite_x=0,overwrite_y=0)
x,y = swap(x,y,n=(len(x)-offx)/abs(incx),offx=0,incx=1,offy=0,incy=1)
x = scal(a,x,n=(len(x)-offx)/abs(incx),offx=0,incx=1)
y = copy(x,y,n=(len(x)-offx)/abs(incx),offx=0,incx=1,offy=0,incy=1)
y = axpy(x,y,n=(len(x)-offx)/abs(incx),a=1.0,offx=0,incx=1,offy=0,incy=1)
xy = dot(x,y,n=(len(x)-offx)/abs(incx),offx=0,incx=1,offy=0,incy=1)
xy = dotu(x,y,n=(len(x)-offx)/abs(incx),offx=0,incx=1,offy=0,incy=1)
xy = dotc(x,y,n=(len(x)-offx)/abs(incx),offx=0,incx=1,offy=0,incy=1)
n2 = nrm2(x,n=(len(x)-offx)/abs(incx),offx=0,incx=1)
s = asum(x,n=(len(x)-offx)/abs(incx),offx=0,incx=1)
k = amax(x,n=(len(x)-offx)/abs(incx),offx=0,incx=1)
Prefixes:
rotg,swap,copy,axpy: s,d,c,z
amax: is,id,ic,iz
asum,nrm2: s,d,sc,dz
scal: s,d,c,z,sc,dz
rotm,rotmg,dot: s,d
dotu,dotc: c,z
rot: s,d,cs,zd
Level 2 routines
----------------
y = gemv(alpha,a,x,beta=0.0,y=,offx=0,incx=1,offy=0,incy=1,trans=0,overwrite_y=0)
y = symv(alpha,a,x,beta=0.0,y=,offx=0,incx=1,offy=0,incy=1,lower=0,overwrite_y=0)
y = hemv(alpha,a,x,beta=(0.0, 0.0),y=,offx=0,incx=1,offy=0,incy=1,lower=0,overwrite_y=0)
x = trmv(a,x,offx=0,incx=1,lower=0,trans=0,unitdiag=0,overwrite_x=0)
a = ger(alpha,x,y,incx=1,incy=1,a=0.0,overwrite_x=1,overwrite_y=1,overwrite_a=0)
a = ger{u|c}(alpha,x,y,incx=1,incy=1,a=(0.0,0.0),overwrite_x=1,overwrite_y=1,overwrite_a=0)
Prefixes:
gemv, trmv: s,d,c,z
symv,ger: s,d
hemv,geru,gerc: c,z
Level 3 routines
----------------
c = gemm(alpha,a,b,beta=0.0,c=,trans_a=0,trans_b=0,overwrite_c=0)
Prefixes:
gemm: s,d,c,z
Module cblas
++++++++++++
In the following all function names are shown without type prefixes.
Level 1 routines
----------------
z = axpy(x,y,n=len(x)/abs(incx),a=1.0,incx=1,incy=incx,overwrite_y=0)
Prefixes:
axpy: s,d,c,z
""" |
# -*-cod
# 定义函数
# 作用：
# 最大化代码重用
# 最小化代码冗余
# def read_book():
# print('拿到一本书')
# print('看书')
# print('收起')
#
#
# def learning(name, course, start_section, end_section):
# print('{}报名课程：《{}》'.format(name, course))
# print('从第{}章学习至第{}章'.format(start_section, end_section))
# print('{}学习完毕'.format(name))
#
# # 调用函数
# learning('Tom', 'Python入门', 1, 3)
# 查找
# def intersect(seq1, seq2):
# result = []
# for x in seq1:
# if x in seq2:
# result.append(x)
# return result
#
# seq1 = [1, 3, 4, 5, 6]
# seq2 = [1, 2, 4, 5, 7]
# print(intersect(seq1, seq2))
# 全局变量与局部变量
# x = 50 # 全局变量
#
# def func():
# global x # 引用全局变量
# x = 99
# return x
#
# print('全局x：', x)
# print('局部x：', func())
# print('全局x：', x)
# 嵌套函数与外部变量
# def func():
# x = 100
# def nested():
# nonlocal x # 外部变量
# x = 99
# print(x)
# print(x)
# nested()
# print(x)
#
# func()
# 自定义函数
# def len():
# print('自定义函数')
#
# len() # 已被覆盖
# 参数传递
# 不可变类型 int float tuple 传递副本，函数内操作不影响原始值
# 可变类型 传递地址引用，函数内操作可能会影响原始值
# int(不可变)
#
# x = 50
# def change_int(x):
# x += 10
#
# print(x)
# change_int(x)
# print(x)
# list（可变）
#
# l=['123','456','789']
#
# def change_list(l):
# l[0]='333'
#
# print(l)
# change_list(l)
# print(l)
# 拷贝了就不会影响
# l=['123','456','789']
#
# def change_list(l):
# l[0]='333'
#
# print(l)
# change_list(l.copy())
# print(l)
# string（不可变）
#
# s = 'haha'
# def change_str(s):
# s='heihei'
# change_str(s)
# print(s)
# 参数匹配
# 名称匹配
# 关键字匹配
# def func(a, b, c):
# print(a, b, c)
#
# func(c=3, b=2, a=1)
# 默认参数
# def func(a, b=2, c=3):
# print(a, b, c)
#
# func(1) # 调用省略传值
# 多个参数 一个*传tuple
# def avg(score1, score2):
# return (score1 + score2) / 2
#
# def avg(score1, score2, score3):
# return (score1 + score2 + score3) / 3
#
# def avg(*scores):
# return sum(scores) / len(scores)
#
# result = avg(98.2, 88.1, 70, 65)
# print(result)
# 多个参数 两个*传dict
# def dispaly(**employee):
# print(employee)
#
# # 定义字典表两种方式
# d = {'name': 'Jerry', 'age': 22, 'job': 'dev'}
# d2 = dict(name='Jerry', age=22, job='dev')
#
# dispaly(name='Tom', age='22', job='dev') # 构造函数形式
# dispaly(**d) # 直接传dict
|
#!/usr/bin/env python
# coding: utf-8
# <h1>Table of Contents<span class="tocSkip"></span></h1>
# <div class="toc"><ul class="toc-item"><li><span><a href="#TP-2---Programmation-pour-la-préparation-à-l'agrégation-maths-option-info" data-toc-modified-id="TP-2---Programmation-pour-la-préparation-à-l'agrégation-maths-option-info-1"><span class="toc-item-num">1 </span>TP 2 - Programmation pour la préparation à l'agrégation maths option info</a></span></li><li><span><a href="#Listes" data-toc-modified-id="Listes-2"><span class="toc-item-num">2 </span>Listes</a></span><ul class="toc-item"><li><span><a href="#Exercice-1-:-taille" data-toc-modified-id="Exercice-1-:-taille-2.1"><span class="toc-item-num">2.1 </span>Exercice 1 : <code>taille</code></a></span></li><li><span><a href="#Exercice-2-:-concat" data-toc-modified-id="Exercice-2-:-concat-2.2"><span class="toc-item-num">2.2 </span>Exercice 2 : <code>concat</code></a></span></li><li><span><a href="#Exercice-3-:-appartient" data-toc-modified-id="Exercice-3-:-appartient-2.3"><span class="toc-item-num">2.3 </span>Exercice 3 : <code>appartient</code></a></span></li><li><span><a href="#Exercice-4-:-miroir" data-toc-modified-id="Exercice-4-:-miroir-2.4"><span class="toc-item-num">2.4 </span>Exercice 4 : <code>miroir</code></a></span></li><li><span><a href="#Exercice-5-:-alterne" data-toc-modified-id="Exercice-5-:-alterne-2.5"><span class="toc-item-num">2.5 </span>Exercice 5 : <code>alterne</code></a></span></li><li><span><a href="#Exercice-6-:-nb_occurrences" data-toc-modified-id="Exercice-6-:-nb_occurrences-2.6"><span class="toc-item-num">2.6 </span>Exercice 6 : <code>nb_occurrences</code></a></span></li><li><span><a href="#Exercice-7-:-pairs" data-toc-modified-id="Exercice-7-:-pairs-2.7"><span class="toc-item-num">2.7 </span>Exercice 7 : <code>pairs</code></a></span></li><li><span><a href="#Exercice-8-:-range" data-toc-modified-id="Exercice-8-:-range-2.8"><span class="toc-item-num">2.8 </span>Exercice 8 : <code>range</code></a></span></li><li><span><a href="#Exercice-9-:-premiers" data-toc-modified-id="Exercice-9-:-premiers-2.9"><span class="toc-item-num">2.9 </span>Exercice 9 : <code>premiers</code></a></span></li></ul></li><li><span><a href="#Listes-simplement-chaînée-(manuellement-définies)" data-toc-modified-id="Listes-simplement-chaînée-(manuellement-définies)-3"><span class="toc-item-num">3 </span>Listes simplement chaînée (manuellement définies)</a></span><ul class="toc-item"><li><span><a href="#La-classe-ListeChainee" data-toc-modified-id="La-classe-ListeChainee-3.1"><span class="toc-item-num">3.1 </span>La classe <code>ListeChainee</code></a></span></li><li><span><a href="#Exercice-1-:-taille" data-toc-modified-id="Exercice-1-:-taille-3.2"><span class="toc-item-num">3.2 </span>Exercice 1 : <code>taille</code></a></span></li><li><span><a href="#Exercice-2-:-concat" data-toc-modified-id="Exercice-2-:-concat-3.3"><span class="toc-item-num">3.3 </span>Exercice 2 : <code>concat</code></a></span></li><li><span><a href="#Exercice-3-:-appartient" data-toc-modified-id="Exercice-3-:-appartient-3.4"><span class="toc-item-num">3.4 </span>Exercice 3 : <code>appartient</code></a></span></li><li><span><a href="#Exercice-4-:-miroir" data-toc-modified-id="Exercice-4-:-miroir-3.5"><span class="toc-item-num">3.5 </span>Exercice 4 : <code>miroir</code></a></span></li><li><span><a href="#Exercice-5-:-alterne" data-toc-modified-id="Exercice-5-:-alterne-3.6"><span class="toc-item-num">3.6 </span>Exercice 5 : <code>alterne</code></a></span></li><li><span><a href="#Exercice-6-:-nb_occurrences" data-toc-modified-id="Exercice-6-:-nb_occurrences-3.7"><span class="toc-item-num">3.7 </span>Exercice 6 : <code>nb_occurrences</code></a></span></li><li><span><a href="#Exercice-7-:-pairs" data-toc-modified-id="Exercice-7-:-pairs-3.8"><span class="toc-item-num">3.8 </span>Exercice 7 : <code>pairs</code></a></span></li><li><span><a href="#Exercice-8-:-range" data-toc-modified-id="Exercice-8-:-range-3.9"><span class="toc-item-num">3.9 </span>Exercice 8 : <code>range</code></a></span></li><li><span><a href="#Exercice-9-:-premiers" data-toc-modified-id="Exercice-9-:-premiers-3.10"><span class="toc-item-num">3.10 </span>Exercice 9 : <code>premiers</code></a></span></li></ul></li><li><span><a href="#Quelques-tris-par-comparaison" data-toc-modified-id="Quelques-tris-par-comparaison-4"><span class="toc-item-num">4 </span>Quelques tris par comparaison</a></span><ul class="toc-item"><li><span><a href="#Exercice-10-:-Tri-insertion" data-toc-modified-id="Exercice-10-:-Tri-insertion-4.1"><span class="toc-item-num">4.1 </span>Exercice 10 : Tri insertion</a></span></li><li><span><a href="#Exercice-11-:-Tri-insertion-générique" data-toc-modified-id="Exercice-11-:-Tri-insertion-générique-4.2"><span class="toc-item-num">4.2 </span>Exercice 11 : Tri insertion générique</a></span></li><li><span><a href="#Exercice-12-:-Tri-selection" data-toc-modified-id="Exercice-12-:-Tri-selection-4.3"><span class="toc-item-num">4.3 </span>Exercice 12 : Tri selection</a></span></li><li><span><a href="#Exercices-13,-14,-15-:-Tri-fusion" data-toc-modified-id="Exercices-13,-14,-15-:-Tri-fusion-4.4"><span class="toc-item-num">4.4 </span>Exercices 13, 14, 15 : Tri fusion</a></span></li><li><span><a href="#Comparaisons" data-toc-modified-id="Comparaisons-4.5"><span class="toc-item-num">4.5 </span>Comparaisons</a></span></li></ul></li><li><span><a href="#Listes-:-l'ordre-supérieur" data-toc-modified-id="Listes-:-l'ordre-supérieur-5"><span class="toc-item-num">5 </span>Listes : l'ordre supérieur</a></span><ul class="toc-item"><li><span><a href="#Exercice-16-:-applique" data-toc-modified-id="Exercice-16-:-applique-5.1"><span class="toc-item-num">5.1 </span>Exercice 16 : <code>applique</code></a></span></li><li><span><a href="#Exercice-17" data-toc-modified-id="Exercice-17-5.2"><span class="toc-item-num">5.2 </span>Exercice 17</a></span></li><li><span><a href="#Exercice-18-:-itere" data-toc-modified-id="Exercice-18-:-itere-5.3"><span class="toc-item-num">5.3 </span>Exercice 18 : <code>itere</code></a></span></li><li><span><a href="#Exercice-19" data-toc-modified-id="Exercice-19-5.4"><span class="toc-item-num">5.4 </span>Exercice 19</a></span></li><li><span><a href="#Exercice-20-:-qqsoit-et-ilexiste" data-toc-modified-id="Exercice-20-:-qqsoit-et-ilexiste-5.5"><span class="toc-item-num">5.5 </span>Exercice 20 : <code>qqsoit</code> et <code>ilexiste</code></a></span></li><li><span><a href="#Exercice-21-:-appartient-version-2" data-toc-modified-id="Exercice-21-:-appartient-version-2-5.6"><span class="toc-item-num">5.6 </span>Exercice 21 : <code>appartient</code> version 2</a></span></li><li><span><a href="#Exercice-22-:-filtre" data-toc-modified-id="Exercice-22-:-filtre-5.7"><span class="toc-item-num">5.7 </span>Exercice 22 : <code>filtre</code></a></span></li><li><span><a href="#Exercice-23" data-toc-modified-id="Exercice-23-5.8"><span class="toc-item-num">5.8 </span>Exercice 23</a></span></li><li><span><a href="#Exercice-24-:-reduit" data-toc-modified-id="Exercice-24-:-reduit-5.9"><span class="toc-item-num">5.9 </span>Exercice 24 : <code>reduit</code></a></span></li><li><span><a href="#Exercice-25-:-somme,-produit" data-toc-modified-id="Exercice-25-:-somme,-produit-5.10"><span class="toc-item-num">5.10 </span>Exercice 25 : <code>somme</code>, <code>produit</code></a></span></li><li><span><a href="#Exercice-26-:-miroir-version-2" data-toc-modified-id="Exercice-26-:-miroir-version-2-5.11"><span class="toc-item-num">5.11 </span>Exercice 26 : <code>miroir</code> version 2</a></span></li></ul></li><li><span><a href="#Arbres" data-toc-modified-id="Arbres-6"><span class="toc-item-num">6 </span>Arbres</a></span><ul class="toc-item"><li><span><a href="#Exercice-27" data-toc-modified-id="Exercice-27-6.1"><span class="toc-item-num">6.1 </span>Exercice 27</a></span></li><li><span><a href="#Exercice-28" data-toc-modified-id="Exercice-28-6.2"><span class="toc-item-num">6.2 </span>Exercice 28</a></span></li><li><span><a href="#Exercice-29" data-toc-modified-id="Exercice-29-6.3"><span class="toc-item-num">6.3 </span>Exercice 29</a></span></li><li><span><a href="#Exercice-30" data-toc-modified-id="Exercice-30-6.4"><span class="toc-item-num">6.4 </span>Exercice 30</a></span></li></ul></li><li><span><a href="#Parcours-d'arbres-binaires" data-toc-modified-id="Parcours-d'arbres-binaires-7"><span class="toc-item-num">7 </span>Parcours d'arbres binaires</a></span><ul class="toc-item"><li><span><a href="#Exercice-31" data-toc-modified-id="Exercice-31-7.1"><span class="toc-item-num">7.1 </span>Exercice 31</a></span></li><li><span><a href="#Exercice-32-:-Parcours-naifs-(complexité-quadratique)" data-toc-modified-id="Exercice-32-:-Parcours-naifs-(complexité-quadratique)-7.2"><span class="toc-item-num">7.2 </span>Exercice 32 : Parcours naifs (complexité quadratique)</a></span></li><li><span><a href="#Exercice-33-:-Parcours-linéaires" data-toc-modified-id="Exercice-33-:-Parcours-linéaires-7.3"><span class="toc-item-num">7.3 </span>Exercice 33 : Parcours linéaires</a></span></li><li><span><a href="#Exercice-34-:-parcours-en-largeur-et-en-profondeur" data-toc-modified-id="Exercice-34-:-parcours-en-largeur-et-en-profondeur-7.4"><span class="toc-item-num">7.4 </span>Exercice 34 : parcours en largeur et en profondeur</a></span></li><li><span><a href="#Exercice-35-et-fin" data-toc-modified-id="Exercice-35-et-fin-7.5"><span class="toc-item-num">7.5 </span>Exercice 35 et fin</a></span><ul class="toc-item"><li><span><a href="#Reconstruction-depuis-le-parcours-prefixe" data-toc-modified-id="Reconstruction-depuis-le-parcours-prefixe-7.5.1"><span class="toc-item-num">7.5.1 </span>Reconstruction depuis le parcours prefixe</a></span></li><li><span><a href="#Reconstruction-depuis-le-parcours-en-largeur" data-toc-modified-id="Reconstruction-depuis-le-parcours-en-largeur-7.5.2"><span class="toc-item-num">7.5.2 </span>Reconstruction depuis le parcours en largeur</a></span></li></ul></li></ul></li><li><span><a href="#Conclusion" data-toc-modified-id="Conclusion-8"><span class="toc-item-num">8 </span>Conclusion</a></span></li></ul></div>
# # TP 2 - Programmation pour la préparation à l'agrégation maths option info
# - En Python, version 3.
# In[4]:
|
# -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
# 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.
|
"""
This module contains generic generator functions for traversing tree
(and DAG) structures. It is agnostic to the underlying data structure
and implementation of the tree object. It does this through dependency
injection of the tree's accessor functions: get_parents and
get_children.
The following depth-first traversal methods are implemented:
* Pre-order: Parent yielded before children; child with multiple
parents is yielded when first encountered.
Example use cases (when DAGs are *not* supported):
1. User access. If computing a user's access to a node relies
on the user's access to the node's parents, access to the
parent has to be computed before access to the child can
be determined. To support access chains, a user's access on
a node is actually an accumulation of accesses down from the
root node through the ancestor chain to the actual node.
2. Field value percolated down. If a value for a field is
dependent on a combination of the child's and the parent's
value, the parent's value should be computed before that of
the child's. Similar to "User access", the value would be
percolated down through the entire ancestor chain.
Example: Start Date is
max(node's start date, start date of each ancestor)
This takes the most restrictive value.
3. Depth. When computing the depth of a tree, since a child's
depth value is 1 + the parent's depth value, the parent's
value should be computed before the child's.
4. Fast Subtree Deletion. If the tree is to be pruned during
traversal, an entire subtree can be deleted, without
traversing the children, as soon as the parent is determined
to be deleted.
* Topological: Parent yielded before children; child with multiple
parents yielded only after all its parents are visited.
Example use cases (when DAGs *are* supported):
1. User access. Similar to pre-order, except a user's access
is now determined by taking a *union* of the percolated
access value from each of the node's parents combined with
its own access.
2. Field value percolated down. Similar to pre-order, except the
value for a node is calculated from the array of
percolated values from each of its parents combined
with its own.
Example: Start Date is
max(node's start date, min(max(ancestry of each parent))
This takes the most permissive from all ancestry chains.
3. Depth. Similar to pre-order, except the depth of a node will
be 1 + the minimum (or the maximum depending on semantics)
of the depth of all its parents.
4. Deletion. Deletion of subtrees are not as fast as they are
for pre-order since a node can be accessed through multiple
parents.
* Post-order: Children yielded before its parents.
Example use cases:
1. Counting. When each node wants to count the number of nodes
within its sub-structure, the count for each child has to be
calculated before its parents, since a parent's value
depends on its children.
2. Map function (when order doesn't matter). If a function
needs to be evaluated for each node in a DAG and the order
that the nodes are iterated doesn't matter, then use
post-order since it is faster than topological for DAGs.
3. Field value percolated up. If a value for a field is based
on the value from it's children, the children's values need
to be computed before their parents.
Example: Minimum Due Date of all nodes within the
sub-structure.
Note: In-order traversal is not implemented as of yet. We can do so
if/when needed.
Optimization once DAGs are not supported:
Supporting Directed Acyclic Graphs (DAGs) requires us to use
topological sort, which has the following negative performance
implications:
* For a simple tree, we can immediately skip over traversing
descendants, once it is determined that a parent is not to be yielded
(based on the return value from the 'filter_func' function). However,
since we support DAGs, we cannot simply skip over descendants since
they may still be accessible through a different ancestry chain and
need to be revisited once all their parents are visited.
* For topological sort, we need the get_parents accessor function in
order to determine whether all of a node's parents have been visited.
This means the underlying implementation of the graph needs to have
an efficient way to get a node's parents, perhaps with back pointers
to each node's parents. This requires additional storage space, which
could be eliminated if DAGs are not supported.
""" |
"""
=================
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.
""" |
"""
Numerical python functions written for compatability with matlab(TM)
commands with the same names.
Matlab(TM) compatible functions
-------------------------------
:func:`cohere`
Coherence (normalized cross spectral density)
:func:`csd`
Cross spectral density uing Welch's average periodogram
:func:`detrend`
Remove the mean or best fit line from an array
:func:`find`
Return the indices where some condition is true;
numpy.nonzero is similar but more general.
:func:`griddata`
interpolate irregularly distributed data to a
regular grid.
:func:`prctile`
find the percentiles of a sequence
:func:`prepca`
Principal Component Analysis
:func:`psd`
Power spectral density uing Welch's average periodogram
:func:`rk4`
A 4th order runge kutta integrator for 1D or ND systems
:func:`specgram`
Spectrogram (power spectral density over segments of time)
Miscellaneous functions
-------------------------
Functions that don't exist in matlab(TM), but are useful anyway:
:meth:`cohere_pairs`
Coherence over all pairs. This is not a matlab function, but we
compute coherence a lot in my lab, and we compute it for a lot of
pairs. This function is optimized to do this efficiently by
caching the direct FFTs.
:meth:`rk4`
A 4th order Runge-Kutta ODE integrator in case you ever find
yourself stranded without scipy (and the far superior
scipy.integrate tools)
record array helper functions
-------------------------------
A collection of helper methods for numpyrecord arrays
.. _htmlonly::
See :ref:`misc-examples-index`
:meth:`rec2txt`
pretty print a record array
:meth:`rec2csv`
store record array in CSV file
:meth:`csv2rec`
import record array from CSV file with type inspection
:meth:`rec_append_fields`
adds field(s)/array(s) to record array
:meth:`rec_drop_fields`
drop fields from record array
:meth:`rec_join`
join two record arrays on sequence of fields
:meth:`rec_groupby`
summarize data by groups (similar to SQL GROUP BY)
:meth:`rec_summarize`
helper code to filter rec array fields into new fields
For the rec viewer functions(e rec2csv), there are a bunch of Format
objects you can pass into the functions that will do things like color
negative values red, set percent formatting and scaling, etc.
Example usage::
r = csv2rec('somefile.csv', checkrows=0)
formatd = dict(
weight = FormatFloat(2),
change = FormatPercent(2),
cost = FormatThousands(2),
)
rec2excel(r, 'test.xls', formatd=formatd)
rec2csv(r, 'test.csv', formatd=formatd)
scroll = rec2gtk(r, formatd=formatd)
win = gtk.Window()
win.set_size_request(600,800)
win.add(scroll)
win.show_all()
gtk.main()
Deprecated functions
---------------------
The following are deprecated; please import directly from numpy (with
care--function signatures may differ):
:meth:`conv`
convolution (numpy.convolve)
:meth:`corrcoef`
The matrix of correlation coefficients
:meth:`hist`
Histogram (numpy.histogram)
:meth:`linspace`
Linear spaced array from min to max
:meth:`load`
load ASCII file - use numpy.loadtxt
:meth:`meshgrid`
Make a 2D grid from 2 1 arrays (numpy.meshgrid)
:meth:`polyfit`
least squares best polynomial fit of x to y (numpy.polyfit)
:meth:`polyval`
evaluate a vector for a vector of polynomial coeffs (numpy.polyval)
:meth:`save`
save ASCII file - use numpy.savetxt
:meth:`trapz`
trapeziodal integration (trapz(x,y) -> numpy.trapz(y,x))
:meth:`vander`
the Vandermonde matrix (numpy.vander)
""" |
"""
===============
Array Internals
===============
Internal organization of numpy arrays
=====================================
It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book "Guide to NumPy".
NumPy arrays consist of two major components, the raw array data (from now on,
referred to as the data buffer), and the information about the raw array data.
The data buffer is typically what people think of as arrays in C or Fortran,
a contiguous (and fixed) block of memory containing fixed sized data items.
NumPy also contains a significant set of data that describes how to interpret
the data in the data buffer. This extra information contains (among other things):
1) The basic data element's size in bytes
2) The start of the data within the data buffer (an offset relative to the
beginning of the data buffer).
3) The number of dimensions and the size of each dimension
4) The separation between elements for each dimension (the 'stride'). This
does not have to be a multiple of the element size
5) The byte order of the data (which may not be the native byte order)
6) Whether the buffer is read-only
7) Information (via the dtype object) about the interpretation of the basic
data element. The basic data element may be as simple as a int or a float,
or it may be a compound object (e.g., struct-like), a fixed character field,
or Python object pointers.
8) Whether the array is to interpreted as C-order or Fortran-order.
This arrangement allow for very flexible use of arrays. One thing that it allows
is simple changes of the metadata to change the interpretation of the array buffer.
Changing the byteorder of the array is a simple change involving no rearrangement
of the data. The shape of the array can be changed very easily without changing
anything in the data buffer or any data copying at all
Among other things that are made possible is one can create a new array metadata
object that uses the same data buffer
to create a new view of that data buffer that has a different interpretation
of the buffer (e.g., different shape, offset, byte order, strides, etc) but
shares the same data bytes. Many operations in numpy do just this such as
slices. Other operations, such as transpose, don't move data elements
around in the array, but rather change the information about the shape and strides so that the indexing of the array changes, but the data in the doesn't move.
Typically these new versions of the array metadata but the same data buffer are
new 'views' into the data buffer. There is a different ndarray object, but it
uses the same data buffer. This is why it is necessary to force copies through
use of the .copy() method if one really wants to make a new and independent
copy of the data buffer.
New views into arrays mean the object reference counts for the data buffer
increase. Simply doing away with the original array object will not remove the
data buffer if other views of it still exist.
Multidimensional Array Indexing Order Issues
============================================
What is the right way to index
multi-dimensional arrays? Before you jump to conclusions about the one and
true way to index multi-dimensional arrays, it pays to understand why this is
a confusing issue. This section will try to explain in detail how numpy
indexing works and why we adopt the convention we do for images, and when it
may be appropriate to adopt other conventions.
The first thing to understand is
that there are two conflicting conventions for indexing 2-dimensional arrays.
Matrix notation uses the first index to indicate which row is being selected and
the second index to indicate which column is selected. This is opposite the
geometrically oriented-convention for images where people generally think the
first index represents x position (i.e., column) and the second represents y
position (i.e., row). This alone is the source of much confusion;
matrix-oriented users and image-oriented users expect two different things with
regard to indexing.
The second issue to understand is how indices correspond
to the order the array is stored in memory. In Fortran the first index is the
most rapidly varying index when moving through the elements of a two
dimensional array as it is stored in memory. If you adopt the matrix
convention for indexing, then this means the matrix is stored one column at a
time (since the first index moves to the next row as it changes). Thus Fortran
is considered a Column-major language. C has just the opposite convention. In
C, the last index changes most rapidly as one moves through the array as
stored in memory. Thus C is a Row-major language. The matrix is stored by
rows. Note that in both cases it presumes that the matrix convention for
indexing is being used, i.e., for both Fortran and C, the first index is the
row. Note this convention implies that the indexing convention is invariant
and that the data order changes to keep that so.
But that's not the only way
to look at it. Suppose one has large two-dimensional arrays (images or
matrices) stored in data files. Suppose the data are stored by rows rather than
by columns. If we are to preserve our index convention (whether matrix or
image) that means that depending on the language we use, we may be forced to
reorder the data if it is read into memory to preserve our indexing
convention. For example if we read row-ordered data into memory without
reordering, it will match the matrix indexing convention for C, but not for
Fortran. Conversely, it will match the image indexing convention for Fortran,
but not for C. For C, if one is using data stored in row order, and one wants
to preserve the image index convention, the data must be reordered when
reading into memory.
In the end, which you do for Fortran or C depends on
which is more important, not reordering data or preserving the indexing
convention. For large images, reordering data is potentially expensive, and
often the indexing convention is inverted to avoid that.
The situation with
numpy makes this issue yet more complicated. The internal machinery of numpy
arrays is flexible enough to accept any ordering of indices. One can simply
reorder indices by manipulating the internal stride information for arrays
without reordering the data at all. NumPy will know how to map the new index
order to the data without moving the data.
So if this is true, why not choose
the index order that matches what you most expect? In particular, why not define
row-ordered images to use the image convention? (This is sometimes referred
to as the Fortran convention vs the C convention, thus the 'C' and 'FORTRAN'
order options for array ordering in numpy.) The drawback of doing this is
potential performance penalties. It's common to access the data sequentially,
either implicitly in array operations or explicitly by looping over rows of an
image. When that is done, then the data will be accessed in non-optimal order.
As the first index is incremented, what is actually happening is that elements
spaced far apart in memory are being sequentially accessed, with usually poor
memory access speeds. For example, for a two dimensional image 'im' defined so
that im[0, 10] represents the value at x=0, y=10. To be consistent with usual
Python behavior then im[0] would represent a column at x=0. Yet that data
would be spread over the whole array since the data are stored in row order.
Despite the flexibility of numpy's indexing, it can't really paper over the fact
basic operations are rendered inefficient because of data order or that getting
contiguous subarrays is still awkward (e.g., im[:,0] for the first row, vs
im[0]), thus one can't use an idiom such as for row in im; for col in im does
work, but doesn't yield contiguous column data.
As it turns out, numpy is
smart enough when dealing with ufuncs to determine which index is the most
rapidly varying one in memory and uses that for the innermost loop. Thus for
ufuncs there is no large intrinsic advantage to either approach in most cases.
On the other hand, use of .flat with an FORTRAN ordered array will lead to
non-optimal memory access as adjacent elements in the flattened array (iterator,
actually) are not contiguous in memory.
Indeed, the fact is that Python
indexing on lists and other sequences naturally leads to an outside-to inside
ordering (the first index gets the largest grouping, the next the next largest,
and the last gets the smallest element). Since image data are normally stored
by rows, this corresponds to position within rows being the last item indexed.
If you do want to use Fortran ordering realize that
there are two approaches to consider: 1) accept that the first index is just not
the most rapidly changing in memory and have all your I/O routines reorder
your data when going from memory to disk or visa versa, or use numpy's
mechanism for mapping the first index to the most rapidly varying data. We
recommend the former if possible. The disadvantage of the latter is that many
of numpy's functions will yield arrays without Fortran ordering unless you are
careful to use the 'order' keyword. Doing this would be highly inconvenient.
Otherwise we recommend simply learning to reverse the usual order of indices
when accessing elements of an array. Granted, it goes against the grain, but
it is more in line with Python semantics and the natural order of the data.
""" |
# -*- encoding: 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 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.
|
"""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.
""" |
"""
TestCmd.py: a testing framework for commands and scripts.
The TestCmd module provides a framework for portable automated testing
of executable commands and scripts (in any language, not just Python),
especially commands and scripts that require file system interaction.
In addition to running tests and evaluating conditions, the TestCmd
module manages and cleans up one or more temporary workspace
directories, and provides methods for creating files and directories in
those workspace directories from in-line data, here-documents), allowing
tests to be completely self-contained.
A TestCmd environment object is created via the usual invocation:
import TestCmd
test = TestCmd.TestCmd()
There are a bunch of keyword arguments available at instantiation:
test = TestCmd.TestCmd(description = 'string',
program = 'program_or_script_to_test',
interpreter = 'script_interpreter',
workdir = 'prefix',
subdir = 'subdir',
verbose = Boolean,
match = default_match_function,
diff = default_diff_function,
combine = Boolean)
There are a bunch of methods that let you do different things:
test.verbose_set(1)
test.description_set('string')
test.program_set('program_or_script_to_test')
test.interpreter_set('script_interpreter')
test.interpreter_set(['script_interpreter', 'arg'])
test.workdir_set('prefix')
test.workdir_set('')
test.workpath('file')
test.workpath('subdir', 'file')
test.subdir('subdir', ...)
test.rmdir('subdir', ...)
test.write('file', "contents\n")
test.write(['subdir', 'file'], "contents\n")
test.read('file')
test.read(['subdir', 'file'])
test.read('file', mode)
test.read(['subdir', 'file'], mode)
test.writable('dir', 1)
test.writable('dir', None)
test.preserve(condition, ...)
test.cleanup(condition)
test.command_args(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program')
test.run(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
chdir = 'directory_to_chdir_to',
stdin = 'input to feed to the program\n')
universal_newlines = True)
p = test.start(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
universal_newlines = None)
test.finish(self, p)
test.pass_test()
test.pass_test(condition)
test.pass_test(condition, function)
test.fail_test()
test.fail_test(condition)
test.fail_test(condition, function)
test.fail_test(condition, function, skip)
test.no_result()
test.no_result(condition)
test.no_result(condition, function)
test.no_result(condition, function, skip)
test.stdout()
test.stdout(run)
test.stderr()
test.stderr(run)
test.symlink(target, link)
test.banner(string)
test.banner(string, width)
test.diff(actual, expected)
test.match(actual, expected)
test.match_exact("actual 1\nactual 2\n", "expected 1\nexpected 2\n")
test.match_exact(["actual 1\n", "actual 2\n"],
["expected 1\n", "expected 2\n"])
test.match_re("actual 1\nactual 2\n", regex_string)
test.match_re(["actual 1\n", "actual 2\n"], list_of_regexes)
test.match_re_dotall("actual 1\nactual 2\n", regex_string)
test.match_re_dotall(["actual 1\n", "actual 2\n"], list_of_regexes)
test.tempdir()
test.tempdir('temporary-directory')
test.sleep()
test.sleep(seconds)
test.where_is('foo')
test.where_is('foo', 'PATH1:PATH2')
test.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
test.unlink('file')
test.unlink('subdir', 'file')
The TestCmd module provides pass_test(), fail_test(), and no_result()
unbound functions that report test results for use with the Aegis change
management system. These methods terminate the test immediately,
reporting PASSED, FAILED, or NO RESULT respectively, and exiting with
status 0 (success), 1 or 2 respectively. This allows for a distinction
between an actual failed test and a test that could not be properly
evaluated because of an external condition (such as a full file system
or incorrect permissions).
import TestCmd
TestCmd.pass_test()
TestCmd.pass_test(condition)
TestCmd.pass_test(condition, function)
TestCmd.fail_test()
TestCmd.fail_test(condition)
TestCmd.fail_test(condition, function)
TestCmd.fail_test(condition, function, skip)
TestCmd.no_result()
TestCmd.no_result(condition)
TestCmd.no_result(condition, function)
TestCmd.no_result(condition, function, skip)
The TestCmd module also provides unbound functions that handle matching
in the same way as the match_*() methods described above.
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_exact)
test = TestCmd.TestCmd(match = TestCmd.match_re)
test = TestCmd.TestCmd(match = TestCmd.match_re_dotall)
The TestCmd module provides unbound functions that can be used for the
"diff" argument to TestCmd.TestCmd instantiation:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_re,
diff = TestCmd.diff_re)
test = TestCmd.TestCmd(diff = TestCmd.simple_diff)
The "diff" argument can also be used with standard difflib functions:
import difflib
test = TestCmd.TestCmd(diff = difflib.context_diff)
test = TestCmd.TestCmd(diff = difflib.unified_diff)
Lastly, the where_is() method also exists in an unbound function
version.
import TestCmd
TestCmd.where_is('foo')
TestCmd.where_is('foo', 'PATH1:PATH2')
TestCmd.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
""" |
"""
============
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 Platform integer (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)
========== =========================================================
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`` and
that ``float`` is ``np.float``. 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.
""" |
"""
========================
Broadcasting over arrays
========================
The term broadcasting describes how numpy treats arrays with different
shapes during arithmetic operations. Subject to certain constraints,
the smaller array is "broadcast" across the larger array so that they
have compatible shapes. Broadcasting provides a means of vectorizing
array operations so that looping occurs in C instead of Python. It does
this without making needless copies of data and usually leads to
efficient algorithm implementations. There are, however, cases where
broadcasting is a bad idea because it leads to inefficient use of memory
that slows computation.
NumPy operations are usually done on pairs of arrays on an
element-by-element basis. In the simplest case, the two arrays must
have exactly the same shape, as in the following example:
>>> a = np.array([1.0, 2.0, 3.0])
>>> b = np.array([2.0, 2.0, 2.0])
>>> a * b
array([ 2., 4., 6.])
NumPy's broadcasting rule relaxes this constraint when the arrays'
shapes meet certain constraints. The simplest broadcasting example occurs
when an array and a scalar value are combined in an operation:
>>> a = np.array([1.0, 2.0, 3.0])
>>> b = 2.0
>>> a * b
array([ 2., 4., 6.])
The result is equivalent to the previous example where ``b`` was an array.
We can think of the scalar ``b`` being *stretched* during the arithmetic
operation into an array with the same shape as ``a``. The new elements in
``b`` are simply copies of the original scalar. The stretching analogy is
only conceptual. NumPy is smart enough to use the original scalar value
without actually making copies, so that broadcasting operations are as
memory and computationally efficient as possible.
The code in the second example is more efficient than that in the first
because broadcasting moves less memory around during the multiplication
(``b`` is a scalar rather than an array).
General Broadcasting Rules
==========================
When operating on two arrays, NumPy compares their shapes element-wise.
It starts with the trailing dimensions, and works its way forward. Two
dimensions are compatible when
1) they are equal, or
2) one of them is 1
If these conditions are not met, a
``ValueError: frames are not aligned`` exception is thrown, indicating that
the arrays have incompatible shapes. The size of the resulting array
is the maximum size along each dimension of the input arrays.
Arrays do not need to have the same *number* of dimensions. For example,
if you have a ``256x256x3`` array of RGB values, and you want to scale
each color in the image by a different value, you can multiply the image
by a one-dimensional array with 3 values. Lining up the sizes of the
trailing axes of these arrays according to the broadcast rules, shows that
they are compatible::
Image (3d array): 256 x 256 x 3
Scale (1d array): 3
Result (3d array): 256 x 256 x 3
When either of the dimensions compared is one, the other is
used. In other words, dimensions with size 1 are stretched or "copied"
to match the other.
In the following example, both the ``A`` and ``B`` arrays have axes with
length one that are expanded to a larger size during the broadcast
operation::
A (4d array): 8 x 1 x 6 x 1
B (3d array): 7 x 1 x 5
Result (4d array): 8 x 7 x 6 x 5
Here are some more examples::
A (2d array): 5 x 4
B (1d array): 1
Result (2d array): 5 x 4
A (2d array): 5 x 4
B (1d array): 4
Result (2d array): 5 x 4
A (3d array): 15 x 3 x 5
B (3d array): 15 x 1 x 5
Result (3d array): 15 x 3 x 5
A (3d array): 15 x 3 x 5
B (2d array): 3 x 5
Result (3d array): 15 x 3 x 5
A (3d array): 15 x 3 x 5
B (2d array): 3 x 1
Result (3d array): 15 x 3 x 5
Here are examples of shapes that do not broadcast::
A (1d array): 3
B (1d array): 4 # trailing dimensions do not match
A (2d array): 2 x 1
B (3d array): 8 x 4 x 3 # second from last dimensions mismatched
An example of broadcasting in practice::
>>> x = np.arange(4)
>>> xx = x.reshape(4,1)
>>> y = np.ones(5)
>>> z = np.ones((3,4))
>>> x.shape
(4,)
>>> y.shape
(5,)
>>> x + y
<type 'exceptions.ValueError'>: shape mismatch: objects cannot be broadcast to a single shape
>>> xx.shape
(4, 1)
>>> y.shape
(5,)
>>> (xx + y).shape
(4, 5)
>>> xx + y
array([[ 1., 1., 1., 1., 1.],
[ 2., 2., 2., 2., 2.],
[ 3., 3., 3., 3., 3.],
[ 4., 4., 4., 4., 4.]])
>>> x.shape
(4,)
>>> z.shape
(3, 4)
>>> (x + z).shape
(3, 4)
>>> x + z
array([[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.]])
Broadcasting provides a convenient way of taking the outer product (or
any other outer operation) of two arrays. The following example shows an
outer addition operation of two 1-d arrays::
>>> a = np.array([0.0, 10.0, 20.0, 30.0])
>>> b = np.array([1.0, 2.0, 3.0])
>>> a[:, np.newaxis] + b
array([[ 1., 2., 3.],
[ 11., 12., 13.],
[ 21., 22., 23.],
[ 31., 32., 33.]])
Here the ``newaxis`` index operator inserts a new axis into ``a``,
making it a two-dimensional ``4x1`` array. Combining the ``4x1`` array
with ``b``, which has shape ``(3,)``, yields a ``4x3`` array.
See `this article <http://wiki.scipy.org/EricsBroadcastingDoc>`_
for illustrations of broadcasting concepts.
""" |
"""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.
""" |
"""
.. function:: xmlparse([root:None, strict:1, namespace:False, xmlprototype], query:None)
Parses an input xml stream. It starts parsing when it finds a root tag. A provided XML prototype fragment is used to create an schema, mapping from xml to a relational table.
If multiple values are found for the same tag in the input stream, then all values are returned separated with a tab (use tab-set operators to process them).
If no XML prototype is provided, then a jdict of the data is returned. If no *root* tag is provided, then the output is a raw feed of {path:data} pairs without any row aggregation.
Rootless mode is usefull when trying to find what *root* tag to use.
Is a *root* tag is provided then each returned row, contains a jdict of all the paths found below the specified *root* tag.
:XML prototype:
XML prototype may be:
- a fragment of XML which will be matched with the input data.
- a jpack.
- a jdict.
If a the characters **"*"** or **"$"** are provided as a value of any of these prototypes, then a full XML subtree of a path will be returned in the resulting data.
:'namespace' or 'ns' option:
Include namespace information in the returned jdicts.
:'fast' option (default 0):
Read input data in bulk. For some XML input files (having lots of small line lengths), it can speed up XML processing by up to 30%. The downside of this option, is that when an error
occurs no last line information is returned, so use this option only when you are sure that the XML input is well formed.
- fast:0 (default), parses the input stream in a conservative line by line way
- fast:1 ,is the same as fast:0, but it doesn't return *Last line* information in the case of an error
- fast:2 ,in this mode XMLPARSER doesn't convert HTML entities and doesn't skip "<?xml version=..." lines
:'strict' option:
- strict:2 ,if a failure occurs, the current transaction will be cancelled. Additionally if a tag isn't found in the xml prototype it will be regarded as failure.
- strict:1 (default), if a failure occurs, the current transaction will be cancelled. Undeclared tags aren't regarded as failure.
- strict:0 , returns all data that succesfully parses. The difference with strict 1, is that strict 0 tries to restart the xml-parsing after the failures and doesn't fail the transaction.
- strict:-1 , returns all input lines in which the xml parser finds a problem. In essence this works as a negative xml parser.
For strict modes 0 and -1, the fast:0 mode is enforced.
:Returned table schema:
Column names are named according to the schema of the provided xml prototype.
Examples:
>>> table1('''
... '<a><b>row1val1</b><b>row1val1b</b><b>row1val1c</b></a>'
... '<a>'
... '<b>'
... 'row2val1</b><c><d>row2val</d></c>'
... '</a>'
... ''')
>>> sql("select * from (xmlparse select * from table1)") # doctest: +NORMALIZE_WHITESPACE
C1
-------------------
{"a/b":"row1val1"}
{"a/b":"row1val1b"}
{"a/b":"row1val1c"}
{"a/b":"row2val1"}
{"a/c/d":"row2val"}
>>> sql("select jgroupunion(jdictkeys(c1)) from (xmlparse select * from table1)") # doctest: +NORMALIZE_WHITESPACE
jgroupunion(jdictkeys(c1))
--------------------------
["a/b","a/c/d"]
>>> sql('''select * from (xmlparse '["a/b","a/c/d"]' select * from table1)''') # doctest: +NORMALIZE_WHITESPACE
b | c_d
--------------------------------------
row1val1 row1val1b row1val1c |
row2val1 | row2val
>>> sql("select * from (xmlparse '<a><b>val1</b><b>val1</b><c><d>val2</d></c></a>' select * from table1)") # doctest: +NORMALIZE_WHITESPACE
b | b1 | c_d
----------------------------------------
row1val1 | row1val1b row1val1c |
row2val1 | | row2val
>>> sql("select * from (xmlparse root:a '<t><a><b>val1</b><c><d>val2</d></c></a></t>' select * from table1)") # doctest: +NORMALIZE_WHITESPACE
b | c_d
--------------------------------------
row1val1 row1val1b row1val1c |
row2val1 | row2val
>>> table2('''
... '<a b="attrval1"><b>row1val1</b></a>'
... '<a>'
... '<b>'
... 'row2val1</b><c>asdf<d>row2val</d></c>'
... '</a>'
... ''')
>>> sql("select * from (xmlparse '<a b=\\"v\\"><b>v</b><c><d>v</d></c></a>' select * from table2)")
b | b1 | c_d
-----------------------------
attrval1 | row1val1 |
| row2val1 | row2val
>>> sql('''select * from (xmlparse '["a/@/b","a/b","a/c/d"]' select * from table2)''')
b | b1 | c_d
-----------------------------
attrval1 | row1val1 |
| row2val1 | row2val
>>> sql('''select * from (xmlparse '{"a/b":[1,2] ,"a/c/d":1}' select * from table2)''')
b | b1 | c_d
-----------------------
row1val1 | |
row2val1 | | row2val
>>> sql('''select * from (xmlparse '{"a/b":[1,2] ,"a/c":[1,"*"]}' select * from table2)''')
b | b1 | c | c_$
-----------------------------------------
row1val1 | | |
row2val1 | | asdf | asdf<d>row2val</d>
>>> sql('''select * from (xmlparse '["a/b", "a/c", "a/c/*"]' select * from table2)''')
b | c | c_$
------------------------------------
row1val1 | |
row2val1 | asdf | asdf<d>row2val</d>
>>> sql('''select * from (xmlparse root:a '{"a/b":"", "a":"*"}' select * from table2)''') # doctest: +NORMALIZE_WHITESPACE
b | $
------------------------------------------------------
row1val1 | <b>row1val1</b>
row2val1 |
<b>
row2val1</b><c>asdf<d>row2val</d></c>
>>> sql("select * from (xmlparse '<a><b>v</b><c>*</c></a>' select * from table2)")
b | c_$
-----------------------------
row1val1 |
row2val1 | asdf<d>row2val</d>
>>> sql("select * from (xmlparse root:a select * from table2)")
C1
-------------------------------------------------
{"a/@/b":"attrval1","a/b":"row1val1"}
{"a/b":"row2val1","a/c/d":"row2val","a/c":"asdf"}
>>> table2('''
... '<a b="attrval1"><b>row1val1</b></a>'
... '<a>'
... '</b>'
... 'row2val1</b><c><d>row2val</d></c>'
... '</a>'
... ''')
>>> sql("select * from (xmlparse strict:0 '<a b=\\"v\\"><b>v</b><c><d>v</d></c></a>' select * from table2)")
b | b1 | c_d
-------------------------
attrval1 | row1val1 |
>>> table3('''
... '<a><b>row1val1</b></a>'
... '<a>'
... '<b np="np">'
... 'row2val1</b><c><d>row2val</d></c>'
... '</a>'
... ''')
>>> sql("select * from (xmlparse strict:2 '<a><b>val1</b><c><d>val2</d></c></a>' select * from table3)") #doctest:+ELLIPSIS +NORMALIZE_WHITESPACE
Traceback (most recent call last):
...
OperatorError: Madis SQLError:
Operator XMLPARSE: Undeclared path in XML-prototype was found in the input data. The path is:
/b/@/np
The data to insert into path was:
np
Last input line was:
<b np="np">
<BLANKLINE>
>>> table4('''
... '<a><b>row1val1</b></a>'
... '<a><b>row1val2</b</a>'
... '<a><b np="np">row1val1</b></a>'
... '<a><b>row1val3/b></a>'
... '<a><b>row1val4</b></a>'
... ''')
>>> sql("select * from (xmlparse strict:-1 '<a><b>val1</b><c><d>val2</d></c></a>' select * from table4)")
C1
----------------------
<a><b>row1val2</b</a>
<a><b>row1val3/b></a>
>>> table5('''
... '<a><b><a><b>row1val1</b></a></b></a>'
... '<a><b>row2val1</b></a>'
... '<a><b>row3val1</b></a>'
... '<a><b>row4val1</b><c>row4val2</c>'
... '</a>'
... ''')
>>> sql('''select * from (xmlparse '["a/b", "a/c"]' select * from table5)''')
b | c
-------------------
row1val1 |
row2val1 |
row3val1 |
row4val1 | row4val2
""" |
"""
This page is in the table of contents.
Carve is the most important plugin to define for your printer.
It carves a shape into svg slice layers. It also sets the layer thickness and perimeter width for the rest of the tool chain.
The carve manual page is at:
http://fabmetheus.crsndoo.com/wiki/index.php/Skeinforge_Carve
On the Arcol Blog a method of deriving the layer thickness is posted. That article "Machine Calibrating" is at:
http://blog.arcol.hu/?p=157
==Settings==
===Add Layer Template to SVG===
Default is on.
When selected, the layer template will be added to the svg output, which adds javascript control boxes. So 'Add Layer Template to SVG' should be selected when the svg will be viewed in a browser.
When off, no controls will be added, the svg output will only include the fabrication paths. So 'Add Layer Template to SVG' should be deselected when the svg will be used by other software, like Inkscape.
===Extra Decimal Places===
Default is two.
Defines the number of extra decimal places export will output compared to the number of decimal places in the layer thickness. The higher the 'Extra Decimal Places', the more significant figures the output numbers will have.
===Import Coarseness===
Default is one.
When a triangle mesh has holes in it, the triangle mesh slicer switches over to a slow algorithm that spans gaps in the mesh. The higher the 'Import Coarseness' setting, the wider the gaps in the mesh it will span. An import coarseness of one means it will span gaps of the perimeter width.
===Layer Thickness===
Default is 0.4 mm.
Defines the the thickness of the layers skeinforge will cut your object into, in the z direction. This is the most important carve setting, many values in the toolchain are derived from the layer thickness.
For a 0.5 mm nozzle usable values are 0.3 mm to 0.5 mm. Note; if you are using thinner layers make sure to adjust the extrusion speed as well.
===Layers===
Carve slices from bottom to top. To get a single layer, set the "Layers From" to zero and the "Layers To" to one. The 'Layers From' until 'Layers To' range is a python slice.
====Layers From====
Default is zero.
Defines the index of the bottom layer that will be carved. If the 'Layers From' is the default zero, the carving will start from the lowest layer. If the 'Layers From' index is negative, then the carving will start from the 'Layers From' index below the top layer.
For example if your object is 5 mm tall and your layer thicknes is 1 mm if you set layers from to 3 you will ignore the first 3 mm and start from 3 mm.
====Layers To====
Default is a huge number, which will be limited to the highest index layer.
Defines the index of the top layer that will be carved. If the 'Layers To' index is a huge number like the default, the carving will go to the top of the model. If the 'Layers To' index is negative, then the carving will go to the 'Layers To' index below the top layer.
This is the same as layers from, only it defines when to end the generation of gcode.
===Mesh Type===
Default is 'Correct Mesh'.
====Correct Mesh====
When selected, the mesh will be accurately carved, and if a hole is found, carve will switch over to the algorithm that spans gaps.
====Unproven Mesh====
When selected, carve will use the gap spanning algorithm from the start. The problem with the gap spanning algothm is that it will span gaps, even if there is not actually a gap in the model.
===Perimeter Width over Thickness===
Default is 1.8.
Defines the ratio of the extrusion perimeter width to the layer thickness. This parameter tells skeinforge how wide the perimeter wall is expected to be in relation to the layer thickness. Default value of 1.8 for the default layer thickness of 0.4 states that a single filament perimeter wall should be 0.4 mm * 1.8 = 0.72 mm wide. The higher the value the more the perimeter will be inset. A ratio of one means the extrusion is a circle, the default ratio of 1.8 means the extrusion is a wide oval.
This is an important value because if you are calibrating your machine you need to ensure that the speed of the head and the extrusion rate in combination produce a wall that is 'Layer Thickness' * 'Perimeter Width over Thickness' wide. To start with 'Perimeter Width over Thickness' is probably best left at the default of 1.8 and the extrusion rate adjusted to give the correct calculated wall thickness.
Adjustment is in the 'Speed' section with 'Feed Rate' controlling speed of the head in X & Y and 'Flow Rate' controlling the extrusion rate. Initially it is probably easier to start adjusting the flow rate only a little at a time until you get a single filament of the correct width. If you change too many parameters at once you can get in a right mess.
===SVG Viewer===
Default is webbrowser.
If the 'SVG Viewer' is set to the default 'webbrowser', the scalable vector graphics file will be sent to the default browser to be opened. If the 'SVG Viewer' is set to a program name, the scalable vector graphics file will be sent to that program to be opened.
==Examples==
The following examples carve the file Screw Holder Bottom.stl. The examples are run in a terminal in the folder which contains Screw Holder Bottom.stl and carve.py.
> python carve.py
This brings up the carve dialog.
> python carve.py Screw Holder Bottom.stl
The carve tool is parsing the file:
Screw Holder Bottom.stl
..
The carve tool has created the file:
.. Screw Holder Bottom_carve.svg
""" |
"""This module tests SyntaxErrors.
Here's an example of the sort of thing that is tested.
>>> def f(x):
... global x
Traceback (most recent call last):
SyntaxError: name 'x' is parameter and global
The tests are all raise SyntaxErrors. They were created by checking
each C call that raises SyntaxError. There are several modules that
raise these exceptions-- ast.c, compile.c, future.c, pythonrun.c, and
symtable.c.
The parser itself outlaws a lot of invalid syntax. None of these
errors are tested here at the moment. We should add some tests; since
there are infinitely many programs with invalid syntax, we would need
to be judicious in selecting some.
The compiler generates a synthetic module name for code executed by
doctest. Since all the code comes from the same module, a suffix like
[1] is appended to the module name, As a consequence, changing the
order of tests in this module means renumbering all the errors after
it. (Maybe we should enable the ellipsis option for these tests.)
In ast.c, syntax errors are raised by calling ast_error().
Errors from set_context():
>>> obj.None = 1
Traceback (most recent call last):
SyntaxError: invalid syntax
>>> None = 1
Traceback (most recent call last):
SyntaxError: can't assign to keyword
It's a syntax error to assign to the empty tuple. Why isn't it an
error to assign to the empty list? It will always raise some error at
runtime.
>>> () = 1
Traceback (most recent call last):
SyntaxError: can't assign to ()
>>> f() = 1
Traceback (most recent call last):
SyntaxError: can't assign to function call
>>> del f()
Traceback (most recent call last):
SyntaxError: can't delete function call
>>> a + 1 = 2
Traceback (most recent call last):
SyntaxError: can't assign to operator
>>> (x for x in x) = 1
Traceback (most recent call last):
SyntaxError: can't assign to generator expression
>>> 1 = 1
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> "abc" = 1
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> b"" = 1
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> `1` = 1
Traceback (most recent call last):
SyntaxError: invalid syntax
If the left-hand side of an assignment is a list or tuple, an illegal
expression inside that contain should still cause a syntax error.
This test just checks a couple of cases rather than enumerating all of
them.
>>> (a, "b", c) = (1, 2, 3)
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> [a, b, c + 1] = [1, 2, 3]
Traceback (most recent call last):
SyntaxError: can't assign to operator
>>> a if 1 else b = 1
Traceback (most recent call last):
SyntaxError: can't assign to conditional expression
From compiler_complex_args():
>>> def f(None=1):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
From ast_for_arguments():
>>> def f(x, y=1, z):
... pass
Traceback (most recent call last):
SyntaxError: non-default argument follows default argument
>>> def f(x, None):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
>>> def f(*None):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
>>> def f(**None):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
From ast_for_funcdef():
>>> def None(x):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
From ast_for_call():
>>> def f(it, *varargs):
... return list(it)
>>> L = range(10)
>>> f(x for x in L)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> f(x for x in L, 1)
Traceback (most recent call last):
SyntaxError: Generator expression must be parenthesized if not sole argument
>>> f((x for x in L), 1)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> f(i0, i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11,
... i12, i13, i14, i15, i16, i17, i18, i19, i20, i21, i22,
... i23, i24, i25, i26, i27, i28, i29, i30, i31, i32, i33,
... i34, i35, i36, i37, i38, i39, i40, i41, i42, i43, i44,
... i45, i46, i47, i48, i49, i50, i51, i52, i53, i54, i55,
... i56, i57, i58, i59, i60, i61, i62, i63, i64, i65, i66,
... i67, i68, i69, i70, i71, i72, i73, i74, i75, i76, i77,
... i78, i79, i80, i81, i82, i83, i84, i85, i86, i87, i88,
... i89, i90, i91, i92, i93, i94, i95, i96, i97, i98, i99,
... i100, i101, i102, i103, i104, i105, i106, i107, i108,
... i109, i110, i111, i112, i113, i114, i115, i116, i117,
... i118, i119, i120, i121, i122, i123, i124, i125, i126,
... i127, i128, i129, i130, i131, i132, i133, i134, i135,
... i136, i137, i138, i139, i140, i141, i142, i143, i144,
... i145, i146, i147, i148, i149, i150, i151, i152, i153,
... i154, i155, i156, i157, i158, i159, i160, i161, i162,
... i163, i164, i165, i166, i167, i168, i169, i170, i171,
... i172, i173, i174, i175, i176, i177, i178, i179, i180,
... i181, i182, i183, i184, i185, i186, i187, i188, i189,
... i190, i191, i192, i193, i194, i195, i196, i197, i198,
... i199, i200, i201, i202, i203, i204, i205, i206, i207,
... i208, i209, i210, i211, i212, i213, i214, i215, i216,
... i217, i218, i219, i220, i221, i222, i223, i224, i225,
... i226, i227, i228, i229, i230, i231, i232, i233, i234,
... i235, i236, i237, i238, i239, i240, i241, i242, i243,
... i244, i245, i246, i247, i248, i249, i250, i251, i252,
... i253, i254, i255)
Traceback (most recent call last):
SyntaxError: more than 255 arguments
The actual error cases counts positional arguments, keyword arguments,
and generator expression arguments separately. This test combines the
three.
>>> f(i0, i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11,
... i12, i13, i14, i15, i16, i17, i18, i19, i20, i21, i22,
... i23, i24, i25, i26, i27, i28, i29, i30, i31, i32, i33,
... i34, i35, i36, i37, i38, i39, i40, i41, i42, i43, i44,
... i45, i46, i47, i48, i49, i50, i51, i52, i53, i54, i55,
... i56, i57, i58, i59, i60, i61, i62, i63, i64, i65, i66,
... i67, i68, i69, i70, i71, i72, i73, i74, i75, i76, i77,
... i78, i79, i80, i81, i82, i83, i84, i85, i86, i87, i88,
... i89, i90, i91, i92, i93, i94, i95, i96, i97, i98, i99,
... i100, i101, i102, i103, i104, i105, i106, i107, i108,
... i109, i110, i111, i112, i113, i114, i115, i116, i117,
... i118, i119, i120, i121, i122, i123, i124, i125, i126,
... i127, i128, i129, i130, i131, i132, i133, i134, i135,
... i136, i137, i138, i139, i140, i141, i142, i143, i144,
... i145, i146, i147, i148, i149, i150, i151, i152, i153,
... i154, i155, i156, i157, i158, i159, i160, i161, i162,
... i163, i164, i165, i166, i167, i168, i169, i170, i171,
... i172, i173, i174, i175, i176, i177, i178, i179, i180,
... i181, i182, i183, i184, i185, i186, i187, i188, i189,
... i190, i191, i192, i193, i194, i195, i196, i197, i198,
... i199, i200, i201, i202, i203, i204, i205, i206, i207,
... i208, i209, i210, i211, i212, i213, i214, i215, i216,
... i217, i218, i219, i220, i221, i222, i223, i224, i225,
... i226, i227, i228, i229, i230, i231, i232, i233, i234,
... i235, i236, i237, i238, i239, i240, i241, i242, i243,
... (x for x in i244), i245, i246, i247, i248, i249, i250, i251,
... i252=1, i253=1, i254=1, i255=1)
Traceback (most recent call last):
SyntaxError: more than 255 arguments
>>> f(lambda x: x[0] = 3)
Traceback (most recent call last):
SyntaxError: lambda cannot contain assignment
The grammar accepts any test (basically, any expression) in the
keyword slot of a call site. Test a few different options.
>>> f(x()=2)
Traceback (most recent call last):
SyntaxError: keyword can't be an expression
>>> f(a or b=1)
Traceback (most recent call last):
SyntaxError: keyword can't be an expression
>>> f(x.y=1)
Traceback (most recent call last):
SyntaxError: keyword can't be an expression
More set_context():
>>> (x for x in x) += 1
Traceback (most recent call last):
SyntaxError: can't assign to generator expression
>>> None += 1
Traceback (most recent call last):
SyntaxError: can't assign to keyword
>>> f() += 1
Traceback (most recent call last):
SyntaxError: can't assign to function call
Test continue in finally in weird combinations.
continue in for loop under finally should be ok.
>>> def test():
... try:
... pass
... finally:
... for abc in range(10):
... continue
... print(abc)
>>> test()
9
Start simple, a continue in a finally should not be allowed.
>>> def test():
... for abc in range(10):
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
This is essentially a continue in a finally which should not be allowed.
>>> def test():
... for abc in range(10):
... try:
... pass
... finally:
... try:
... continue
... except:
... pass
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try:
... pass
... finally:
... try:
... continue
... finally:
... pass
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try: pass
... finally:
... try:
... pass
... except:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
There is one test for a break that is not in a loop. The compiler
uses a single data structure to keep track of try-finally and loops,
so we need to be sure that a break is actually inside a loop. If it
isn't, there should be a syntax error.
>>> try:
... print(1)
... break
... print(2)
... finally:
... print(3)
Traceback (most recent call last):
...
SyntaxError: 'break' outside loop
This should probably raise a better error than a SystemError (or none at all).
In 2.5 there was a missing exception and an assert was triggered in a debug
build. The number of blocks must be greater than CO_MAXBLOCKS. SF #1565514
>>> while 1:
... while 2:
... while 3:
... while 4:
... while 5:
... while 6:
... while 8:
... while 9:
... while 10:
... while 11:
... while 12:
... while 13:
... while 14:
... while 15:
... while 16:
... while 17:
... while 18:
... while 19:
... while 20:
... while 21:
... while 22:
... break
Traceback (most recent call last):
...
SystemError: too many statically nested blocks
Misuse of the nonlocal statement can lead to a few unique syntax errors.
>>> def f(x):
... nonlocal x
Traceback (most recent call last):
...
SyntaxError: name 'x' is parameter and nonlocal
>>> def f():
... global x
... nonlocal x
Traceback (most recent call last):
...
SyntaxError: name 'x' is nonlocal and global
>>> def f():
... nonlocal x
Traceback (most recent call last):
...
SyntaxError: no binding for nonlocal 'x' found
From SF bug #1705365
>>> nonlocal x
Traceback (most recent call last):
...
SyntaxError: nonlocal declaration not allowed at module level
TODO(jhylton): Figure out how to test SyntaxWarning with doctest.
## >>> def f(x):
## ... def f():
## ... print(x)
## ... nonlocal x
## Traceback (most recent call last):
## ...
## SyntaxWarning: name 'x' is assigned to before nonlocal declaration
## >>> def f():
## ... x = 1
## ... nonlocal x
## Traceback (most recent call last):
## ...
## SyntaxWarning: name 'x' is assigned to before nonlocal declaration
This tests assignment-context; there was a bug in Python 2.5 where compiling
a complex 'if' (one with 'elif') would fail to notice an invalid suite,
leading to spurious errors.
>>> if 1:
... x() = 1
... elif 1:
... pass
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... x() = 1
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... x() = 1
... elif 1:
... pass
... else:
... pass
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... x() = 1
... else:
... pass
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... pass
... else:
... x() = 1
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
Make sure that the old "raise X, Y[, Z]" form is gone:
>>> raise X, Y
Traceback (most recent call last):
...
SyntaxError: invalid syntax
>>> raise X, Y, Z
Traceback (most recent call last):
...
SyntaxError: invalid syntax
>>> f(a=23, a=234)
Traceback (most recent call last):
...
SyntaxError: keyword argument repeated
>>> del ()
Traceback (most recent call last):
SyntaxError: can't delete ()
>>> {1, 2, 3} = 42
Traceback (most recent call last):
SyntaxError: can't assign to literal
Corner-cases that used to fail to raise the correct error:
>>> def f(*, x=lambda __debug__:0): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> def f(*args:(lambda __debug__:0)): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> def f(**kwargs:(lambda __debug__:0)): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> with (lambda *:0): pass
Traceback (most recent call last):
SyntaxError: named arguments must follow bare *
Corner-cases that used to crash:
>>> def f(**__debug__): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> def f(*xx, __debug__): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
""" |
"""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().
""" |
# $Id$
# based upon
# piddle.py -- Plug In Drawing, Does Little Else
# Copyright (C) 1999 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 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
# Progress Reports...
# JJS, 2/10/99: as discussed, I've removed the Shape classes and moved
# the drawing methods into the Canvas class. Numerous other changes
# as discussed by email as well.
# JJS, 2/11/99: removed Canvas default access functions; added fontHeight
# etc. functions; fixed numerous typos; added drawRect and drawRoundRect
# (how could I forget those?). Added StateSaver utility class.
# 2/11/99 (later): minor fixes.
# JJS, 2/12/99: removed scaling/sizing references. Changed event handler
# mechanism per Magnus's idea. Changed drawCurve into a fillable
# drawing function (needs default implementation). Removed edgeList
# from drawPolygon. Added drawFigure. Changed drawLines to draw
# a set of disconnected lines (of uniform color and width).
# 2/12/99 (later): added HexColor function and WWW color constants.
# Fixed bug in StateSaver. Changed params to drawArc.
# JJS, 2/17/99: added operator methods to Color; added default implementation
# of drawRoundRect in terms of Line, Rect, and Arc.
# JJS, 2/18/99: added isInteractive and canUpdate methods to Canvas.
# JJS, 2/19/99: added drawImage method; added angle parameter to drawString.
# JJS, 3/01/99: nailed down drawFigure interface (and added needed constants).
# JJS, 3/08/99: added arcPoints and curvePoints methods; added default
# implementations for drawRect, drawRoundRect, drawArc, drawCurve,
# drawEllipse, and drawFigure (!), mostly thanks to Magnus.
# JJS, 3/09/99: added 'closed' parameter to drawPolygon, drawCurve, and
# drawFigure. Made use of this in several default implementations.
# JJS, 3/11/99: added 'onKey' callback and associated constants; also added
# Canvas.setInfoLine(s) method.
# JJS, 3/12/99: typo in drawFigure.__doc__ corrected (thanks to NAME JJS, 3/19/99: fixed bug in drawArc (also thanks to NAME JJS, 5/30/99: fixed bug in arcPoints.
# JJS, 6/10/99: added __repr__ method to Font.
# JJS, 6/22/99: added additional WWW colors thanks to NAME JJS, 6/29/99: added inch and cm units
# JJS, 6/30/99: added size & name parameters to Canvas.__init__
# JJS, 9/21/99: fixed bug in arcPoints
# JJS, 9/29/99: added drawMultiLineStrings, updated fontHeight with new definition
# JJS, 10/21/99: made Color immutable; fixed bugs in default fontHeight,
# drawMultiLineString
|
#
# XML-RPC CLIENT LIBRARY
# $Id: xmlrpclib.py 41594 2005-12-04 19:11:17Z 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
|
"""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 <EMAIL>
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
===============
SciPy 2-D sparse matrix package.
Original code by NAME and extended by NAME NAME and NAME 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 lil_matrix (recommended) or
dok_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.
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.todense(), 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).
Package Contents
================
Modules
-------
.. autosummary::
:toctree: generated/
base - Base class for sparse matrices
bsr - Compressed Block Sparse Row matrix format
compressed - Sparse matrix base class using compressed storage
construct - Functions to construct sparse matrices
coo - A sparse matrix in COOrdinate or 'triplet' format
csc - Compressed Sparse Column matrix format
csgraph - Compressed Sparse graph algorithms
csr - Compressed Sparse Row matrix format
data - Base class for sparse matrice with a .data attribute
dia - Sparse DIAgonal format
dok - Dictionary Of Keys based matrix
extract - Functions to extract parts of sparse matrices
lil - LInked List sparse matrix class
linalg -
sparsetools - A collection of routines for sparse matrix operations
spfuncs - Functions that operate on sparse matrices
sputils - Utility functions for sparse matrix module
Classes
-------
.. autosummary::
:toctree: generated/
SparseEfficiencyWarning -
SparseWarning -
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
---------
.. autosummary::
:toctree: generated/
bmat - Build a sparse matrix from sparse sub-blocks
cs_graph_components -
eye - Sparse MxN matrix whose k-th diagonal is all ones
find -
hstack - Stack sparse matrices horizontally (column wise)
identity - Identity matrix in sparse format
issparse -
isspmatrix -
isspmatrix_bsr -
isspmatrix_coo -
isspmatrix_csc -
isspmatrix_csr -
isspmatrix_dia -
isspmatrix_dok -
isspmatrix_lil -
kron - kronecker product of two sparse matrices
kronsum - kronecker sum of sparse matrices
lil_diags - Generate a lil_matrix with the given diagonals
lil_eye - RxC lil_matrix whose k-th diagonal set to one
rand - Random values in a given shape
spdiags - Return a sparse matrix from diagonals
tril - Lower triangular portion of a matrix in sparse format
triu - Upper triangular portion of a matrix in sparse format
vstack - Stack sparse matrices vertically (row wise)
""" |
"""Drag-and-drop support for Tkinter.
This is very preliminary. I currently only support dnd *within* one
application, between different windows (or within the same window).
I an trying to make this as generic as possible -- not dependent on
the use of a particular widget or icon type, etc. I also hope that
this will work with Pmw.
To enable an object to be dragged, you must create an event binding
for it that starts the drag-and-drop process. Typically, you should
bind <ButtonPress> to a callback function that you write. The function
should call Tkdnd.dnd_start(source, event), where 'source' is the
object to be dragged, and 'event' is the event that invoked the call
(the argument to your callback function). Even though this is a class
instantiation, the returned instance should not be stored -- it will
be kept alive automatically for the duration of the drag-and-drop.
When a drag-and-drop is already in process for the Tk interpreter, the
call is *ignored*; this normally averts starting multiple simultaneous
dnd processes, e.g. because different button callbacks all
dnd_start().
The object is *not* necessarily a widget -- it can be any
application-specific object that is meaningful to potential
drag-and-drop targets.
Potential drag-and-drop targets are discovered as follows. Whenever
the mouse moves, and at the start and end of a drag-and-drop move, the
Tk widget directly under the mouse is inspected. This is the target
widget (not to be confused with the target object, yet to be
determined). If there is no target widget, there is no dnd target
object. If there is a target widget, and it has an attribute
dnd_accept, this should be a function (or any callable object). The
function is called as dnd_accept(source, event), where 'source' is the
object being dragged (the object passed to dnd_start() above), and
'event' is the most recent event object (generally a <Motion> event;
it can also be <ButtonPress> or <ButtonRelease>). If the dnd_accept()
function returns something other than None, this is the new dnd target
object. If dnd_accept() returns None, or if the target widget has no
dnd_accept attribute, the target widget's parent is considered as the
target widget, and the search for a target object is repeated from
there. If necessary, the search is repeated all the way up to the
root widget. If none of the target widgets can produce a target
object, there is no target object (the target object is None).
The target object thus produced, if any, is called the new target
object. It is compared with the old target object (or None, if there
was no old target widget). There are several cases ('source' is the
source object, and 'event' is the most recent event object):
- Both the old and new target objects are None. Nothing happens.
- The old and new target objects are the same object. Its method
dnd_motion(source, event) is called.
- The old target object was None, and the new target object is not
None. The new target object's method dnd_enter(source, event) is
called.
- The new target object is None, and the old target object is not
None. The old target object's method dnd_leave(source, event) is
called.
- The old and new target objects differ and neither is None. The old
target object's method dnd_leave(source, event), and then the new
target object's method dnd_enter(source, event) is called.
Once this is done, the new target object replaces the old one, and the
Tk mainloop proceeds. The return value of the methods mentioned above
is ignored; if they raise an exception, the normal exception handling
mechanisms take over.
The drag-and-drop processes can end in two ways: a final target object
is selected, or no final target object is selected. When a final
target object is selected, it will always have been notified of the
potential drop by a call to its dnd_enter() method, as described
above, and possibly one or more calls to its dnd_motion() method; its
dnd_leave() method has not been called since the last call to
dnd_enter(). The target is notified of the drop by a call to its
method dnd_commit(source, event).
If no final target object is selected, and there was an old target
object, its dnd_leave(source, event) method is called to complete the
dnd sequence.
Finally, the source object is notified that the drag-and-drop process
is over, by a call to source.dnd_end(target, event), specifying either
the selected target object, or None if no target object was selected.
The source object can use this to implement the commit action; this is
sometimes simpler than to do it in the target's dnd_commit(). The
target's dnd_commit() method could then simply be aliased to
dnd_leave().
At any time during a dnd sequence, the application can cancel the
sequence by calling the cancel() method on the object returned by
dnd_start(). This will call dnd_leave() if a target is currently
active; it will never call dnd_commit().
""" |
#!/usr/bin/env python
#
# This is special grunt-webfont verion of eotlitetool.py.
# https://github.com/sapegin/grunt-webfont
#
# Changes:
# * Output option now works.
# * Compatible with Python 3.
#
# ***** 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 <EMAIL>
#
# 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
|
"""
=================================================
Orthogonal distance regression (:mod:`scipy.odr`)
=================================================
.. currentmodule:: scipy.odr
Package Content
===============
.. autosummary::
:toctree: generated/
Data -- The data to fit.
RealData -- Data with weights as actual std. dev.s and/or covariances.
Model -- Stores information about the function to be fit.
ODR -- Gathers all info & manages the main fitting routine.
Output -- Result from the fit.
odr -- Low-level function for ODR.
OdrWarning -- Warning about potential problems when running ODR
OdrError -- Error exception.
OdrStop -- Stop exception.
odr_error -- Same as OdrError (for backwards compatibility)
odr_stop -- Same as OdrStop (for backwards compatibility)
Prebuilt models:
.. autosummary::
:toctree: generated/
polynomial
.. data:: exponential
.. data:: multilinear
.. data:: unilinear
.. data:: quadratic
.. data:: polynomial
Usage information
=================
Introduction
------------
Why Orthogonal Distance Regression (ODR)? Sometimes one has
measurement errors in the explanatory (a.k.a., "independent")
variable(s), not just the response (a.k.a., "dependent") variable(s).
Ordinary Least Squares (OLS) fitting procedures treat the data for
explanatory variables as fixed, i.e., not subject to error of any kind.
Furthermore, OLS procedures require that the response variables be an
explicit function of the explanatory variables; sometimes making the
equation explicit is impractical and/or introduces errors. ODR can
handle both of these cases with ease, and can even reduce to the OLS
case if that is sufficient for the problem.
ODRPACK is a FORTRAN-77 library for performing ODR with possibly
non-linear fitting functions. It uses a modified trust-region
Levenberg-Marquardt-type algorithm [1]_ to estimate the function
parameters. The fitting functions are provided by Python functions
operating on NumPy arrays. The required derivatives may be provided
by Python functions as well, or may be estimated numerically. ODRPACK
can do explicit or implicit ODR fits, or it can do OLS. Input and
output variables may be multi-dimensional. Weights can be provided to
account for different variances of the observations, and even
covariances between dimensions of the variables.
The `scipy.odr` package offers an object-oriented interface to
ODRPACK, in addition to the low-level `odr` function.
Additional background information about ODRPACK can be found in the
`ODRPACK User's Guide
<https://docs.scipy.org/doc/external/odrpack_guide.pdf>`_, reading
which is recommended.
Basic usage
-----------
1. Define the function you want to fit against.::
def f(B, x):
'''Linear function y = m*x + b'''
# B is a vector of the parameters.
# x is an array of the current x values.
# x is in the same format as the x passed to Data or RealData.
#
# Return an array in the same format as y passed to Data or RealData.
return B[0]*x + B[1]
2. Create a Model.::
linear = Model(f)
3. Create a Data or RealData instance.::
mydata = Data(x, y, wd=1./power(sx,2), we=1./power(sy,2))
or, when the actual covariances are known::
mydata = RealData(x, y, sx=sx, sy=sy)
4. Instantiate ODR with your data, model and initial parameter estimate.::
myodr = ODR(mydata, linear, beta0=[1., 2.])
5. Run the fit.::
myoutput = myodr.run()
6. Examine output.::
myoutput.pprint()
References
----------
.. [1] NAME and NAME "Orthogonal Distance Regression,"
in "Statistical analysis of measurement error models and
applications: proceedings of the AMS-IMS-SIAM joint summer research
conference held June 10-16, 1989," Contemporary Mathematics,
vol. 112, pg. 186, 1990.
""" |
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
#!/usr/bin/env python
# -*- 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 *
# * *
# ***************************************************************************/
|
"""
=============================
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``.
""" |
#!/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 *
# * *
# ***************************************************************************/
# This file contains global definitions of program names. The plain names are
# the usually lower-case program or package names. The display names are
# properly capitalized for use in human-readable sentences.
|
# class FitHarnessOld(object):
# def __init__(harn, train_loader, vali_loader, test_loader):
# harn.train_loader = train_loader
# harn.vali_loader = vali_loader
# harn.test_loader = test_loader
# harn.criterion = ContrastiveLoss(margin=1.0)
# harn.model = Siamese()
# harn.lr_scheduler = LRSchedule.exp
# harn.use_cuda = False
# # harn.model = torch.nn.DataParallel(model, device_ids=[0, 1]).cuda()
# harn.config = {
# 'maxIterations': 10000,
# 'displayInterval': 1,
# 'vail_displayInterval': 1,
# 'model_dir': '.',
# 'margin': 1.0,
# }
# harn.lr = 0.001
# harn.epoch = 0
# def log(harn, msg):
# print(msg)
# def log_value(harn, key, value, n_iter):
# print('{}={} @ {}'.format(key, value, n_iter))
# def load_snapshot(harn, load_path):
# snapshot = torch.load(load_path)
# # loadModelState(model, snapshot)
# harn.model.load_state_dict(snapshot['state_dict'])
# harn.epoch = snapshot['epoch'] + 1
# harn.log('Model loaded from {}'.format(load_path))
# def run(harn):
# # optimizer = harn.config.optimizer(model.parameters(), lr=lr)
# # harn.optimizer = torch.optim.SGD(harn.model.parameters(), lr=harn.lr)
# harn.optimizer = torch.optim.Adam(harn.model.parameters(), lr=harn.lr)
# # train loop
# # configure("runs/afrl", flush_secs=2)
# while True:
# harn.train_epoch()
# harn.validation_epoch()
# harn.save_snapshot()
# # check for termination
# if harn.epoch > harn.config['maxIterations']:
# harn.log('Maximum harn.epoch reached, terminating ...')
# break
# harn.epoch += 1
# def train_epoch(harn):
# ave_metrics = {
# 'loss': 0,
# 'accuracy': 0,
# 'pos_dist': 0,
# 'neg_dist': 0,
# }
# # change learning rate
# harn.optimizer, harn.lr = harn.lr_scheduler(harn.optimizer, harn.epoch, harn.lr, 2)
# # train batch
# for batch_idx, (data0, data1, target) in enumerate(harn.train_loader):
# target = target.type(torch.FloatTensor)
# if harn.use_cuda:
# data0, data1, target = data0.cuda(), data1.cuda(), target.cuda()
# data0, data1, target = Variable(data0), Variable(data1), Variable(target)
# input_batch = (data0, data1, target)
# print('Begin batch {}'.format(batch_idx))
# t_cur_metrics = harn.train_batch(input_batch)
# for k, v in t_cur_metrics.items():
# ave_metrics[k] += v
# # display training info
# if (batch_idx + 1) % harn.config['displayInterval'] == 0:
# for k in ave_metrics.keys():
# ave_metrics[k] /= harn.config['displayInterval']
# n_train = len(harn.train_loader)
# harn.log('Epoch {0}: {1} / {2} | lr:{3} - tloss:{4:.5f} acc:{5:.2f} | sdis:{6:.3f} ddis:{7:.3f}'.format(
# harn.epoch, batch_idx, n_train, harn.lr,
# ave_metrics['loss'], ave_metrics['accuracy'],
# ave_metrics['pos_dist'], ave_metrics['neg_dist']))
# iter_idx = harn.epoch * n_train + batch_idx
# for key, value in ave_metrics.items():
# harn.log_value('train ' + key, value, iter_idx)
# # diagnoseGradients(model.parameters())
# for k in ave_metrics.keys():
# ave_metrics[k] = 0
# def validation_epoch(harn):
# ave_metrics = {
# 'loss': 0,
# 'accuracy': 0,
# 'pos_dist': 0,
# 'neg_dist': 0,
# }
# final_metrics = ave_metrics.copy()
# for vali_idx, (t_data0, t_data1, t_target) in enumerate(harn.vali_loader):
# t_target = t_target.type(torch.FloatTensor)
# if harn.use_cuda:
# t_data0, t_data1, t_target = t_data0.cuda(), t_data1.cuda(), t_target.cuda()
# t_data0, t_data1, t_target = Variable(t_data0), Variable(t_data1), Variable(t_target)
# input_batch = (t_data0, t_data1, t_target)
# print('Begin batch {}'.format(vali_idx))
# v_cur_metrics = harn.validation_batch(input_batch)
# for k, v in v_cur_metrics.items():
# ave_metrics[k] += v
# final_metrics[k] += v
# if (vali_idx + 1) % harn.config['vail_displayInterval'] == 0:
# for k in ave_metrics.keys():
# ave_metrics[k] /= harn.config['displayInterval']
# harn.log('Epoch {0}: {1} / {2} | vloss:{3:.5f} acc:{4:.2f} | sdis:{5:.3f} ddis:{6:.3f}'.format(
# harn.epoch, vali_idx, len(harn.vali_loader),
# ave_metrics['loss'], ave_metrics['accuracy'],
# ave_metrics['pos_dist'], ave_metrics['neg_dist']))
# for k in ave_metrics.keys():
# ave_metrics[k] = 0
# for k in final_metrics.keys():
# final_metrics[k] /= len(harn.vali_loader)
# harn.log('Epoch {0}: final vloss:{1:.5f} acc:{2:.2f} | sdis:{3:.3f} ddis:{4:.3f}'.format(
# harn.epoch, final_metrics['loss'], final_metrics['accuracy'],
# final_metrics['pos_dist'], final_metrics['neg_dist']))
# iter_idx = harn.epoch * len(harn.vali_loader) + vali_idx
# for key, value in final_metrics.items():
# harn.log_value('validation ' + key, value, iter_idx)
# def save_snapshot(harn):
# # save snapshot
# save_path = join(harn.config['model_dir'], 'snapshot_epoch_{}.pt'.format(harn.epoch))
# # torch.save(checkpoint(model, harn.epoch), save_path)
# harn.log('Snapshot saved to {}'.format(save_path))
# def train_batch(harn, input_batch):
# harn.model.train(True)
# input1, input2, label = input_batch
# output0, output1, output = harn.model(input1, input2)
# t_metrics = harn._measure_metrics(output0, output1, label, harn.config['margin'])
# # loss = harn.criterion(output, label)
# loss = harn.criterion(output0, output1, label)
# harn.optimizer.zero_grad()
# loss.backward()
# harn.optimizer.step()
# # loss = loss / input1.size()[0]
# loss_sum = loss.data.sum()
# inf = float("inf")
# if loss_sum == inf or loss_sum == -inf:
# harn.log("WARNING: received an inf loss, setting loss value to 0")
# loss_value = 0
# else:
# loss_value = loss.data[0]
# t_metrics['loss'] = loss_value
# return t_metrics
# def validation_batch(harn, input_batch):
# harn.model.train(False)
# input1, input2, label = input_batch
# output0, output1, output = harn.model(input1, input2)
# v_metrics = harn._measure_metrics(output0, output1, label, harn.config['margin'])
# # loss = harn.criterion(output, label)
# loss = harn.criterion(output0, output1, label)
# # loss = loss / input1.size()[0]
# loss_sum = loss.data.sum()
# inf = float("inf")
# if loss_sum == inf or loss_sum == -inf:
# harn.log("WARNING: received an inf loss, setting loss value to 0")
# loss_value = 0
# else:
# loss_value = loss.data[0]
# v_metrics['loss'] = loss_value
# return v_metrics
# def _measure_metrics(harn, output0, output1, label, margin):
# diff = torch.abs(output0 - output1)
# l21 = torch.sqrt(torch.pow(diff, 2).sum(dim=1))
# label_tensor = torch.from_numpy(label.data.cpu().numpy())
# l21_tensor = torch.from_numpy(l21.data.cpu().numpy())
# # Distance
# is_pos = torch.ByteTensor()
# POS_LABEL = 1
# NEG_LABEL = 0
# torch.eq(label_tensor, POS_LABEL, out=is_pos) # y==1
# pos_dist = 0 if len(l21_tensor[is_pos]) == 0 else l21_tensor[is_pos].mean()
# neg_dist = 0 if len(l21_tensor[~is_pos]) == 0 else l21_tensor[~is_pos].mean()
# # print('same dis : diff dis {} : {}'.format(l21_tensor[is_pos == 0].mean(), l21_tensor[is_pos].mean()))
# # accuracy
# pred_pos_flags = torch.ByteTensor()
# torch.le(l21_tensor, margin, out=pred_pos_flags) # y==1's idx
# cur_score = torch.FloatTensor(label.size(0))
# cur_score.fill_(NEG_LABEL)
# cur_score[pred_pos_flags] = POS_LABEL
# accuracy = torch.eq(cur_score, label_tensor).sum() / label_tensor.size(0)
# metrics = {
# 'accuracy': accuracy,
# 'pos_dist': pos_dist,
# 'neg_dist': neg_dist,
# }
# return metrics
|
"""
Binary serialization
NPY format
==========
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 of
``len(magic string) + 2 + len(length) + HEADER_LEN`` be evenly divisible
by 64 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."
Format Version 3.0
------------------
This version replaces the ASCII string (which in practice was latin1) with
a utf8-encoded string, so supports structured types with any unicode field
names.
Notes
-----
The ``.npy`` format, including motivation for creating it and a comparison of
alternatives, is described in the `"npy-format" NEP
<https://www.numpy.org/neps/nep-0001-npy-format.html>`_, however details have
evolved with time and this document is more current.
""" |
# 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 <EMAIL>, 2012-2013
# Copyright (c), NAME <EMAIL>, 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
# --------------------------------------------
#
# 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, 2014 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.
|
"""
Overview
--------
This module implements the Multiple Imputation through Chained
Equations (MICE) approach to handling missing data in statistical data
analyses. The approach has the following steps:
0. Impute each missing value with the mean of the observed values of
the same variable.
1. For each variable in the data set with missing values (termed the
'focus variable'), do the following:
1a. Fit an 'imputation model', which is a regression model for the
focus variable, regressed on the observed and (current) imputed values
of some or all of the other variables.
1b. Impute the missing values for the focus variable. Currently this
imputation must use the 'predictive mean matching' (pmm) procedure.
2. Once all variables have been imputed, fit the 'analysis model' to
the data set.
3. Repeat steps 1-2 multiple times and combine the results using a
'combining rule' to produce point estimates of all parameters in the
analysis model and standard errors for them.
The imputations for each variable are based on an imputation model
that is specified via a model class and a formula for the regression
relationship. The default model is OLS, with a formula specifying
main effects for all other variables.
The MICE procedure can be used in one of two ways:
* If the goal is only to produce imputed data sets, the MICEData class
can be used to wrap a data frame, providing facilities for doing the
imputation. Summary plots are available for assessing the performance
of the imputation.
* If the imputed data sets are to be used to fit an additional
'analysis model', a MICE instance can be used. After specifying the
MICE instance and running it, the results are combined using the
`combine` method. Results and various summary plots are then
available.
Terminology
-----------
The primary goal of the analysis is usually to fit and perform
inference using an 'analysis model'. If an analysis model is not
specified, then imputed datasets are produced for later use.
The MICE procedure involves a family of imputation models. There is
one imputation model for each variable with missing values. An
imputation model may be conditioned on all or a subset of the
remaining variables, using main effects, transformations,
interactions, etc. as desired.
A 'perturbation method' is a method for setting the parameter estimate
in an imputation model. The 'gaussian' perturbation method first fits
the model (usually using maximum likelihood, but it could use any
statsmodels fit procedure), then sets the parameter vector equal to a
draw from the Gaussian approximation to the sampling distribution for
the fit. The 'bootstrap' perturbation method sets the parameter
vector equal to a fitted parameter vector obtained when fitting the
conditional model to a bootstrapped version of the data set.
Class structure
---------------
There are two main classes in the module:
* 'MICEData' wraps a Pandas dataframe, incorporating information about
the imputation model for each variable with missing values. It can
be used to produce multiply imputed data sets that are to be further
processed or distributed to other researchers. A number of plotting
procedures are provided to visualize the imputation results and
missing data patterns. The `history_func` hook allows any features
of interest of the imputed data sets to be saved for further
analysis.
* 'MICE' takes both a 'MICEData' object and an analysis model
specification. It runs the multiple imputation, fits the analysis
models, and combines the results to produce a `MICEResults` object.
The summary method of this results object can be used to see the key
estimands and inferential quantities..
Notes
-----
By default, to conserve memory 'MICEData' saves very little
information from one iteration to the next. The data set passed by
the user is copied on entry, but then is over-written each time new
imputations are produced. If using 'MICE', the fitted
analysis models and results are saved. MICEData includes a
`history_callback` hook that allows arbitrary information from the
intermediate datasets to be saved for future use.
References
----------
JL NAME 'Multiple Imputation: A Primer', Stat Methods Med Res,
1999.
TE Raghunathan et al.: 'A Multivariate Technique for Multiply
Imputing Missing Values Using a Sequence of Regression Models', Survey
Methodology, 2001.
SAS Institute: 'Predictive Mean Matching Method for Monotone Missing
Data', SAS 9.2 User's Guide, 2014.
A NAME et al.: 'Multiple Imputation with Diagnostics (mi) in R:
Opening Windows into the Black Box', Journal of Statistical Software,
2009.
""" |
"""
========
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).
""" |
"""
=============================
Byteswapping and byte order
=============================
Introduction to byte ordering and ndarrays
==========================================
The ``ndarray`` is an object that provide a python array interface to data
in memory.
It often happens that the memory that you want to view with an array is
not of the same byte ordering as the computer on which you are running
Python.
For example, I might be working on a computer with a little-endian CPU -
such as an Intel Pentium, but I have loaded some data from a file
written by a computer that is big-endian. Let's say I have loaded 4
bytes from a file written by a Sun (big-endian) computer. I know that
these 4 bytes represent two 16-bit integers. On a big-endian machine, a
two-byte integer is stored with the Most Significant Byte (MSB) first,
and then the Least Significant Byte (LSB). Thus the bytes are, in memory order:
#. MSB integer 1
#. LSB integer 1
#. MSB integer 2
#. LSB integer 2
Let's say the two integers were in fact 1 and 770. Because 770 = 256 *
3 + 2, the 4 bytes in memory would contain respectively: 0, 1, 3, 2.
The bytes I have loaded from the file would have these contents:
>>> big_end_str = chr(0) + chr(1) + chr(3) + chr(2)
>>> big_end_str
'\\x00\\x01\\x03\\x02'
We might want to use an ``ndarray`` to access these integers. In that
case, we can create an array around this memory, and tell numpy that
there are two integers, and that they are 16 bit and big-endian:
>>> import numpy as np
>>> big_end_arr = np.ndarray(shape=(2,),dtype='>i2', buffer=big_end_str)
>>> big_end_arr[0]
1
>>> big_end_arr[1]
770
Note the array ``dtype`` above of ``>i2``. The ``>`` means 'big-endian'
(``<`` is little-endian) and ``i2`` means 'signed 2-byte integer'. For
example, if our data represented a single unsigned 4-byte little-endian
integer, the dtype string would be ``<u4``.
In fact, why don't we try that?
>>> little_end_u4 = np.ndarray(shape=(1,),dtype='<u4', buffer=big_end_str)
>>> little_end_u4[0] == 1 * 256**1 + 3 * 256**2 + 2 * 256**3
True
Returning to our ``big_end_arr`` - in this case our underlying data is
big-endian (data endianness) and we've set the dtype to match (the dtype
is also big-endian). However, sometimes you need to flip these around.
.. warning::
Scalars currently do not include byte order information, so extracting
a scalar from an array will return an integer in native byte order.
Hence:
>>> big_end_arr[0].dtype.byteorder == little_end_u4[0].dtype.byteorder
True
Changing byte ordering
======================
As you can imagine from the introduction, there are two ways you can
affect the relationship between the byte ordering of the array and the
underlying memory it is looking at:
* Change the byte-ordering information in the array dtype so that it
interprets the underlying data as being in a different byte order.
This is the role of ``arr.newbyteorder()``
* Change the byte-ordering of the underlying data, leaving the dtype
interpretation as it was. This is what ``arr.byteswap()`` does.
The common situations in which you need to change byte ordering are:
#. Your data and dtype endianess don't match, and you want to change
the dtype so that it matches the data.
#. Your data and dtype endianess don't match, and you want to swap the
data so that they match the dtype
#. Your data and dtype endianess match, but you want the data swapped
and the dtype to reflect this
Data and dtype endianness don't match, change dtype to match data
-----------------------------------------------------------------
We make something where they don't match:
>>> wrong_end_dtype_arr = np.ndarray(shape=(2,),dtype='<i2', buffer=big_end_str)
>>> wrong_end_dtype_arr[0]
256
The obvious fix for this situation is to change the dtype so it gives
the correct endianness:
>>> fixed_end_dtype_arr = wrong_end_dtype_arr.newbyteorder()
>>> fixed_end_dtype_arr[0]
1
Note the array has not changed in memory:
>>> fixed_end_dtype_arr.tobytes() == big_end_str
True
Data and type endianness don't match, change data to match dtype
----------------------------------------------------------------
You might want to do this if you need the data in memory to be a certain
ordering. For example you might be writing the memory out to a file
that needs a certain byte ordering.
>>> fixed_end_mem_arr = wrong_end_dtype_arr.byteswap()
>>> fixed_end_mem_arr[0]
1
Now the array *has* changed in memory:
>>> fixed_end_mem_arr.tobytes() == big_end_str
False
Data and dtype endianness match, swap data and dtype
----------------------------------------------------
You may have a correctly specified array dtype, but you need the array
to have the opposite byte order in memory, and you want the dtype to
match so the array values make sense. In this case you just do both of
the previous operations:
>>> swapped_end_arr = big_end_arr.byteswap().newbyteorder()
>>> swapped_end_arr[0]
1
>>> swapped_end_arr.tobytes() == big_end_str
False
An easier way of casting the data to a specific dtype and byte ordering
can be achieved with the ndarray astype method:
>>> swapped_end_arr = big_end_arr.astype('<i2')
>>> swapped_end_arr[0]
1
>>> swapped_end_arr.tobytes() == big_end_str
False
""" |
"""
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).
""" |
"""The tests for the MQTT light platform.
Configuration for RGB Version with brightness:
light:
platform: mqtt
name: "Office Light RGB"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
brightness_state_topic: "office/rgb1/brightness/status"
brightness_command_topic: "office/rgb1/brightness/set"
rgb_state_topic: "office/rgb1/rgb/status"
rgb_command_topic: "office/rgb1/rgb/set"
qos: 0
payload_on: "on"
payload_off: "off"
Configuration for XY Version with brightness:
light:
platform: mqtt
name: "Office Light XY"
state_topic: "office/xy1/light/status"
command_topic: "office/xy1/light/switch"
brightness_state_topic: "office/xy1/brightness/status"
brightness_command_topic: "office/xy1/brightness/set"
xy_state_topic: "office/xy1/xy/status"
xy_command_topic: "office/xy1/xy/set"
qos: 0
payload_on: "on"
payload_off: "off"
config without RGB:
light:
platform: mqtt
name: "Office Light"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
brightness_state_topic: "office/rgb1/brightness/status"
brightness_command_topic: "office/rgb1/brightness/set"
qos: 0
payload_on: "on"
payload_off: "off"
config without RGB and brightness:
light:
platform: mqtt
name: "Office Light"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
qos: 0
payload_on: "on"
payload_off: "off"
config for RGB Version with brightness and scale:
light:
platform: mqtt
name: "Office Light RGB"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
brightness_state_topic: "office/rgb1/brightness/status"
brightness_command_topic: "office/rgb1/brightness/set"
brightness_scale: 99
rgb_state_topic: "office/rgb1/rgb/status"
rgb_command_topic: "office/rgb1/rgb/set"
rgb_scale: 99
qos: 0
payload_on: "on"
payload_off: "off"
config with brightness and color temp
light:
platform: mqtt
name: "Office Light Color Temp"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
brightness_state_topic: "office/rgb1/brightness/status"
brightness_command_topic: "office/rgb1/brightness/set"
brightness_scale: 99
color_temp_state_topic: "office/rgb1/color_temp/status"
color_temp_command_topic: "office/rgb1/color_temp/set"
qos: 0
payload_on: "on"
payload_off: "off"
config with brightness and effect
light:
platform: mqtt
name: "Office Light Color Temp"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
brightness_state_topic: "office/rgb1/brightness/status"
brightness_command_topic: "office/rgb1/brightness/set"
brightness_scale: 99
effect_state_topic: "office/rgb1/effect/status"
effect_command_topic: "office/rgb1/effect/set"
effect_list:
- rainbow
- colorloop
qos: 0
payload_on: "on"
payload_off: "off"
config for RGB Version with white value and scale:
light:
platform: mqtt
name: "Office Light RGB"
state_topic: "office/rgb1/light/status"
command_topic: "office/rgb1/light/switch"
white_value_state_topic: "office/rgb1/white_value/status"
white_value_command_topic: "office/rgb1/white_value/set"
white_value_scale: 99
rgb_state_topic: "office/rgb1/rgb/status"
rgb_command_topic: "office/rgb1/rgb/set"
rgb_scale: 99
qos: 0
payload_on: "on"
payload_off: "off"
""" |
# -*- 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.
|
"""Stuff to parse AIFF-C and AIFF files.
Unless explicitly stated otherwise, the description below is true
both for AIFF-C files and AIFF files.
An AIFF-C file has the following structure.
+-----------------+
| FORM |
+-----------------+
| <size> |
+----+------------+
| | AIFC |
| +------------+
| | <chunks> |
| | . |
| | . |
| | . |
+----+------------+
An AIFF file has the string "AIFF" instead of "AIFC".
A chunk consists of an identifier (4 bytes) followed by a size (4 bytes,
big endian order), followed by the data. The size field does not include
the size of the 8 byte header.
The following chunk types are recognized.
FVER
<version number of AIFF-C defining document> (AIFF-C only).
MARK
<# of markers> (2 bytes)
list of markers:
<marker ID> (2 bytes, must be > 0)
<position> (4 bytes)
<marker name> ("pstring")
COMM
<# of channels> (2 bytes)
<# of sound frames> (4 bytes)
<size of the samples> (2 bytes)
<sampling frequency> (10 bytes, IEEE 80-bit extended
floating point)
in AIFF-C files only:
<compression type> (4 bytes)
<human-readable version of compression type> ("pstring")
SSND
<offset> (4 bytes, not used by this program)
<blocksize> (4 bytes, not used by this program)
<sound data>
A pstring consists of 1 byte length, a string of characters, and 0 or 1
byte pad to make the total length even.
Usage.
Reading AIFF files:
f = aifc.open(file, 'r')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods read(), seek(), and close().
In some types of audio files, if the setpos() method is not used,
the seek() method is not necessary.
This returns an instance of a class with the following public methods:
getnchannels() -- returns number of audio channels (1 for
mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getcomptype() -- returns compression type ('NONE' for AIFF files)
getcompname() -- returns human-readable version of
compression type ('not compressed' for AIFF files)
getparams() -- returns a namedtuple consisting of all of the
above in the above order
getmarkers() -- get the list of marks in the audio file or None
if there are no marks
getmark(id) -- get mark with the specified id (raises an error
if the mark does not exist)
readframes(n) -- returns at most n frames of audio
rewind() -- rewind to the beginning of the audio stream
setpos(pos) -- seek to the specified position
tell() -- return the current position
close() -- close the instance (make it unusable)
The position returned by tell(), the position given to setpos() and
the position of marks are all compatible and have nothing to do with
the actual position in the file.
The close() method is called automatically when the class instance
is destroyed.
Writing AIFF files:
f = aifc.open(file, 'w')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods write(), tell(), seek(), and
close().
This returns an instance of a class with the following public methods:
aiff() -- create an AIFF file (AIFF-C default)
aifc() -- create an AIFF-C file
setnchannels(n) -- set the number of channels
setsampwidth(n) -- set the sample width
setframerate(n) -- set the frame rate
setnframes(n) -- set the number of frames
setcomptype(type, name)
-- set the compression type and the
human-readable compression type
setparams(tuple)
-- set all parameters at once
setmark(id, pos, name)
-- add specified mark to the list of marks
tell() -- return current position in output file (useful
in combination with setmark())
writeframesraw(data)
-- write audio frames without pathing up the
file header
writeframes(data)
-- write audio frames and patch up the file header
close() -- patch up the file header and close the
output file
You should set the parameters before the first writeframesraw or
writeframes. The total number of frames does not need to be set,
but when it is set to the correct value, the header does not have to
be patched up.
It is best to first set all parameters, perhaps possibly the
compression type, and then write audio frames using writeframesraw.
When all frames have been written, either call writeframes('') or
close() to patch up the sizes in the header.
Marks can be added anytime. If there are any marks, you must call
close() after all frames have been written.
The close() method is called automatically when the class instance
is destroyed.
When a file is opened with the extension '.aiff', an AIFF file is
written, otherwise an AIFF-C file is written. This default can be
changed by calling aiff() or aifc() before the first writeframes or
writeframesraw.
""" |
# 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
|
#
# 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
|
#
# tested on | Windows native | Linux cross-compilation
# ------------------------+-------------------+---------------------------
# MSVS C++ 2010 Express | WORKS | n/a
# Mingw-w64 | WORKS | WORKS
# Mingw-w32 | WORKS | WORKS
# MinGW | WORKS | untested
#
#####
# Notes about MSVS C++ :
#
# - MSVC2010-Express compiles to 32bits only.
#
#####
# Notes about Mingw-w64 and Mingw-w32 under Windows :
#
# - both can be installed using the official installer :
# http://mingw-w64.sourceforge.net/download.php#mingw-builds
#
# - if you want to compile both 32bits and 64bits, don't forget to
# run the installer twice to install them both.
#
# - install them into a path that does not contain spaces
# ( example : "C:/Mingw-w32", "C:/Mingw-w64" )
#
# - if you want to compile faster using the "-j" option, don't forget
# to install the appropriate version of the Pywin32 python extension
# available from : http://sourceforge.net/projects/pywin32/files/
#
# - before running scons, you must add into the environment path
# the path to the "/bin" directory of the Mingw version you want
# to use :
#
# set PATH=C:/Mingw-w32/bin;%PATH%
#
# - then, scons should be able to detect gcc.
# - Mingw-w32 only compiles 32bits.
# - Mingw-w64 only compiles 64bits.
#
# - it is possible to add them both at the same time into the PATH env,
# if you also define the MINGW32_PREFIX and MINGW64_PREFIX environment
# variables.
# For instance, you could store that set of commands into a .bat script
# that you would run just before scons :
#
# set PATH=C:\mingw-w32\bin;%PATH%
# set PATH=C:\mingw-w64\bin;%PATH%
# set MINGW32_PREFIX=C:\mingw-w32\bin\
# set MINGW64_PREFIX=C:\mingw-w64\bin\
#
#####
# Notes about Mingw, Mingw-w64 and Mingw-w32 under Linux :
#
# - default toolchain prefixes are :
# "i586-mingw32msvc-" for MinGW
# "i686-w64-mingw32-" for Mingw-w32
# "x86_64-w64-mingw32-" for Mingw-w64
#
# - if both MinGW and Mingw-w32 are installed on your system
# Mingw-w32 should take the priority over MinGW.
#
# - it is possible to manually override prefixes by defining
# the MINGW32_PREFIX and MINGW64_PREFIX environment variables.
#
#####
# Notes about Mingw under Windows :
#
# - this is the MinGW version from http://mingw.org/
# - install it into a path that does not contain spaces
# ( example : "C:/MinGW" )
# - several DirectX headers might be missing. You can copy them into
# the C:/MinGW/include" directory from this page :
# https://code.google.com/p/mingw-lib/source/browse/trunk/working/avcodec_to_widget_5/directx_include/
# - before running scons, add the path to the "/bin" directory :
# set PATH=C:/MinGW/bin;%PATH%
# - scons should be able to detect gcc.
#
#####
# TODO :
#
# - finish to cleanup this script to remove all the remains of previous hacks and workarounds
# - make it work with the Windows7 SDK that is supposed to enable 64bits compilation for MSVC2010-Express
# - confirm it works well with other Visual Studio versions.
# - update the wiki about the pywin32 extension required for the "-j" option under Windows.
# - update the wiki to document MINGW32_PREFIX and MINGW64_PREFIX
#
|
"""
===============
Array Internals
===============
Internal organization of numpy arrays
=====================================
It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book "Guide to Numpy".
Numpy arrays consist of two major components, the raw array data (from now on,
referred to as the data buffer), and the information about the raw array data.
The data buffer is typically what people think of as arrays in C or Fortran,
a contiguous (and fixed) block of memory containing fixed sized data items.
Numpy also contains a significant set of data that describes how to interpret
the data in the data buffer. This extra information contains (among other things):
1) The basic data element's size in bytes
2) The start of the data within the data buffer (an offset relative to the
beginning of the data buffer).
3) The number of dimensions and the size of each dimension
4) The separation between elements for each dimension (the 'stride'). This
does not have to be a multiple of the element size
5) The byte order of the data (which may not be the native byte order)
6) Whether the buffer is read-only
7) Information (via the dtype object) about the interpretation of the basic
data element. The basic data element may be as simple as a int or a float,
or it may be a compound object (e.g., struct-like), a fixed character field,
or Python object pointers.
8) Whether the array is to interpreted as C-order or Fortran-order.
This arrangement allow for very flexible use of arrays. One thing that it allows
is simple changes of the metadata to change the interpretation of the array buffer.
Changing the byteorder of the array is a simple change involving no rearrangement
of the data. The shape of the array can be changed very easily without changing
anything in the data buffer or any data copying at all
Among other things that are made possible is one can create a new array metadata
object that uses the same data buffer
to create a new view of that data buffer that has a different interpretation
of the buffer (e.g., different shape, offset, byte order, strides, etc) but
shares the same data bytes. Many operations in numpy do just this such as
slices. Other operations, such as transpose, don't move data elements
around in the array, but rather change the information about the shape and strides so that the indexing of the array changes, but the data in the doesn't move.
Typically these new versions of the array metadata but the same data buffer are
new 'views' into the data buffer. There is a different ndarray object, but it
uses the same data buffer. This is why it is necessary to force copies through
use of the .copy() method if one really wants to make a new and independent
copy of the data buffer.
New views into arrays mean the the object reference counts for the data buffer
increase. Simply doing away with the original array object will not remove the
data buffer if other views of it still exist.
Multidimensional Array Indexing Order Issues
============================================
What is the right way to index
multi-dimensional arrays? Before you jump to conclusions about the one and
true way to index multi-dimensional arrays, it pays to understand why this is
a confusing issue. This section will try to explain in detail how numpy
indexing works and why we adopt the convention we do for images, and when it
may be appropriate to adopt other conventions.
The first thing to understand is
that there are two conflicting conventions for indexing 2-dimensional arrays.
Matrix notation uses the first index to indicate which row is being selected and
the second index to indicate which column is selected. This is opposite the
geometrically oriented-convention for images where people generally think the
first index represents x position (i.e., column) and the second represents y
position (i.e., row). This alone is the source of much confusion;
matrix-oriented users and image-oriented users expect two different things with
regard to indexing.
The second issue to understand is how indices correspond
to the order the array is stored in memory. In Fortran the first index is the
most rapidly varying index when moving through the elements of a two
dimensional array as it is stored in memory. If you adopt the matrix
convention for indexing, then this means the matrix is stored one column at a
time (since the first index moves to the next row as it changes). Thus Fortran
is considered a Column-major language. C has just the opposite convention. In
C, the last index changes most rapidly as one moves through the array as
stored in memory. Thus C is a Row-major language. The matrix is stored by
rows. Note that in both cases it presumes that the matrix convention for
indexing is being used, i.e., for both Fortran and C, the first index is the
row. Note this convention implies that the indexing convention is invariant
and that the data order changes to keep that so.
But that's not the only way
to look at it. Suppose one has large two-dimensional arrays (images or
matrices) stored in data files. Suppose the data are stored by rows rather than
by columns. If we are to preserve our index convention (whether matrix or
image) that means that depending on the language we use, we may be forced to
reorder the data if it is read into memory to preserve our indexing
convention. For example if we read row-ordered data into memory without
reordering, it will match the matrix indexing convention for C, but not for
Fortran. Conversely, it will match the image indexing convention for Fortran,
but not for C. For C, if one is using data stored in row order, and one wants
to preserve the image index convention, the data must be reordered when
reading into memory.
In the end, which you do for Fortran or C depends on
which is more important, not reordering data or preserving the indexing
convention. For large images, reordering data is potentially expensive, and
often the indexing convention is inverted to avoid that.
The situation with
numpy makes this issue yet more complicated. The internal machinery of numpy
arrays is flexible enough to accept any ordering of indices. One can simply
reorder indices by manipulating the internal stride information for arrays
without reordering the data at all. Numpy will know how to map the new index
order to the data without moving the data.
So if this is true, why not choose
the index order that matches what you most expect? In particular, why not define
row-ordered images to use the image convention? (This is sometimes referred
to as the Fortran convention vs the C convention, thus the 'C' and 'FORTRAN'
order options for array ordering in numpy.) The drawback of doing this is
potential performance penalties. It's common to access the data sequentially,
either implicitly in array operations or explicitly by looping over rows of an
image. When that is done, then the data will be accessed in non-optimal order.
As the first index is incremented, what is actually happening is that elements
spaced far apart in memory are being sequentially accessed, with usually poor
memory access speeds. For example, for a two dimensional image 'im' defined so
that im[0, 10] represents the value at x=0, y=10. To be consistent with usual
Python behavior then im[0] would represent a column at x=0. Yet that data
would be spread over the whole array since the data are stored in row order.
Despite the flexibility of numpy's indexing, it can't really paper over the fact
basic operations are rendered inefficient because of data order or that getting
contiguous subarrays is still awkward (e.g., im[:,0] for the first row, vs
im[0]), thus one can't use an idiom such as for row in im; for col in im does
work, but doesn't yield contiguous column data.
As it turns out, numpy is
smart enough when dealing with ufuncs to determine which index is the most
rapidly varying one in memory and uses that for the innermost loop. Thus for
ufuncs there is no large intrinsic advantage to either approach in most cases.
On the other hand, use of .flat with an FORTRAN ordered array will lead to
non-optimal memory access as adjacent elements in the flattened array (iterator,
actually) are not contiguous in memory.
Indeed, the fact is that Python
indexing on lists and other sequences naturally leads to an outside-to inside
ordering (the first index gets the largest grouping, the next the next largest,
and the last gets the smallest element). Since image data are normally stored
by rows, this corresponds to position within rows being the last item indexed.
If you do want to use Fortran ordering realize that
there are two approaches to consider: 1) accept that the first index is just not
the most rapidly changing in memory and have all your I/O routines reorder
your data when going from memory to disk or visa versa, or use numpy's
mechanism for mapping the first index to the most rapidly varying data. We
recommend the former if possible. The disadvantage of the latter is that many
of numpy's functions will yield arrays without Fortran ordering unless you are
careful to use the 'order' keyword. Doing this would be highly inconvenient.
Otherwise we recommend simply learning to reverse the usual order of indices
when accessing elements of an array. Granted, it goes against the grain, but
it is more in line with Python semantics and the natural order of the data.
""" |
# -*- 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.
|
"""==============
Array indexing
==============
Array indexing refers to any use of the square brackets ([]) to index
array values. There are many options to indexing, which give numpy
indexing great power, but with power comes some complexity and the
potential for confusion. This section is just an overview of the
various options and issues related to indexing. Aside from single
element indexing, the details on most of these options are to be
found in related sections.
Assignment vs referencing
=========================
Most of the following examples show the use of indexing when
referencing data in an array. The examples work just as well
when assigning to an array. See the section at the end for
specific examples and explanations on how assignments work.
Single element indexing
=======================
Single element indexing for a 1-D array is what one expects. It work
exactly like that for other standard Python sequences. It is 0-based,
and accepts negative indices for indexing from the end of the array. ::
>>> x = np.arange(10)
>>> x[2]
2
>>> x[-2]
8
Unlike lists and tuples, numpy arrays support multidimensional indexing
for multidimensional arrays. That means that it is not necessary to
separate each dimension's index into its own set of square brackets. ::
>>> x.shape = (2,5) # now x is 2-dimensional
>>> x[1,3]
8
>>> x[1,-1]
9
Note that if one indexes a multidimensional array with fewer indices
than dimensions, one gets a subdimensional array. For example: ::
>>> x[0]
array([0, 1, 2, 3, 4])
That is, each index specified selects the array corresponding to the
rest of the dimensions selected. In the above example, choosing 0
means that the remaining dimension of length 5 is being left unspecified,
and that what is returned is an array of that dimensionality and size.
It must be noted that the returned array is not a copy of the original,
but points to the same values in memory as does the original array.
In this case, the 1-D array at the first position (0) is returned.
So using a single index on the returned array, results in a single
element being returned. That is: ::
>>> x[0][2]
2
So note that ``x[0,2] = x[0][2]`` though the second case is more
inefficient as a new temporary array is created after the first index
that is subsequently indexed by 2.
Note to those used to IDL or Fortran memory order as it relates to
indexing. NumPy uses C-order indexing. That means that the last
index usually represents the most rapidly changing memory location,
unlike Fortran or IDL, where the first index represents the most
rapidly changing location in memory. This difference represents a
great potential for confusion.
Other indexing options
======================
It is possible to slice and stride arrays to extract arrays of the
same number of dimensions, but of different sizes than the original.
The slicing and striding works exactly the same way it does for lists
and tuples except that they can be applied to multiple dimensions as
well. A few examples illustrates best: ::
>>> x = np.arange(10)
>>> x[2:5]
array([2, 3, 4])
>>> x[:-7]
array([0, 1, 2])
>>> x[1:7:2]
array([1, 3, 5])
>>> y = np.arange(35).reshape(5,7)
>>> y[1:5:2,::3]
array([[ 7, 10, 13],
[21, 24, 27]])
Note that slices of arrays do not copy the internal array data but
also produce new views of the original data.
It is possible to index arrays with other arrays for the purposes of
selecting lists of values out of arrays into new arrays. There are
two different ways of accomplishing this. One uses one or more arrays
of index values. The other involves giving a boolean array of the proper
shape to indicate the values to be selected. Index arrays are a very
powerful tool that allow one to avoid looping over individual elements in
arrays and thus greatly improve performance.
It is possible to use special features to effectively increase the
number of dimensions in an array through indexing so the resulting
array aquires the shape needed for use in an expression or with a
specific function.
Index arrays
============
NumPy arrays may be indexed with other arrays (or any other sequence-
like object that can be converted to an array, such as lists, with the
exception of tuples; see the end of this document for why this is). The
use of index arrays ranges from simple, straightforward cases to
complex, hard-to-understand cases. For all cases of index arrays, what
is returned is a copy of the original data, not a view as one gets for
slices.
Index arrays must be of integer type. Each value in the array indicates
which value in the array to use in place of the index. To illustrate: ::
>>> x = np.arange(10,1,-1)
>>> x
array([10, 9, 8, 7, 6, 5, 4, 3, 2])
>>> x[np.array([3, 3, 1, 8])]
array([7, 7, 9, 2])
The index array consisting of the values 3, 3, 1 and 8 correspondingly
create an array of length 4 (same as the index array) where each index
is replaced by the value the index array has in the array being indexed.
Negative values are permitted and work as they do with single indices
or slices: ::
>>> x[np.array([3,3,-3,8])]
array([7, 7, 4, 2])
It is an error to have index values out of bounds: ::
>>> x[np.array([3, 3, 20, 8])]
<type 'exceptions.IndexError'>: index 20 out of bounds 0<=index<9
Generally speaking, what is returned when index arrays are used is
an array with the same shape as the index array, but with the type
and values of the array being indexed. As an example, we can use a
multidimensional index array instead: ::
>>> x[np.array([[1,1],[2,3]])]
array([[9, 9],
[8, 7]])
Indexing Multi-dimensional arrays
=================================
Things become more complex when multidimensional arrays are indexed,
particularly with multidimensional index arrays. These tend to be
more unusual uses, but they are permitted, and they are useful for some
problems. We'll start with the simplest multidimensional case (using
the array y from the previous examples): ::
>>> y[np.array([0,2,4]), np.array([0,1,2])]
array([ 0, 15, 30])
In this case, if the index arrays have a matching shape, and there is
an index array for each dimension of the array being indexed, the
resultant array has the same shape as the index arrays, and the values
correspond to the index set for each position in the index arrays. In
this example, the first index value is 0 for both index arrays, and
thus the first value of the resultant array is y[0,0]. The next value
is y[2,1], and the last is y[4,2].
If the index arrays do not have the same shape, there is an attempt to
broadcast them to the same shape. If they cannot be broadcast to the
same shape, an exception is raised: ::
>>> y[np.array([0,2,4]), np.array([0,1])]
<type 'exceptions.ValueError'>: shape mismatch: objects cannot be
broadcast to a single shape
The broadcasting mechanism permits index arrays to be combined with
scalars for other indices. The effect is that the scalar value is used
for all the corresponding values of the index arrays: ::
>>> y[np.array([0,2,4]), 1]
array([ 1, 15, 29])
Jumping to the next level of complexity, it is possible to only
partially index an array with index arrays. It takes a bit of thought
to understand what happens in such cases. For example if we just use
one index array with y: ::
>>> y[np.array([0,2,4])]
array([[ 0, 1, 2, 3, 4, 5, 6],
[14, 15, 16, 17, 18, 19, 20],
[28, 29, 30, 31, 32, 33, 34]])
What results is the construction of a new array where each value of
the index array selects one row from the array being indexed and the
resultant array has the resulting shape (size of row, number index
elements).
An example of where this may be useful is for a color lookup table
where we want to map the values of an image into RGB triples for
display. The lookup table could have a shape (nlookup, 3). Indexing
such an array with an image with shape (ny, nx) with dtype=np.uint8
(or any integer type so long as values are with the bounds of the
lookup table) will result in an array of shape (ny, nx, 3) where a
triple of RGB values is associated with each pixel location.
In general, the shape of the resultant array will be the concatenation
of the shape of the index array (or the shape that all the index arrays
were broadcast to) with the shape of any unused dimensions (those not
indexed) in the array being indexed.
Boolean or "mask" index arrays
==============================
Boolean arrays used as indices are treated in a different manner
entirely than index arrays. Boolean arrays must be of the same shape
as the initial dimensions of the array being indexed. In the
most straightforward case, the boolean array has the same shape: ::
>>> b = y>20
>>> y[b]
array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])
Unlike in the case of integer index arrays, in the boolean case, the
result is a 1-D array containing all the elements in the indexed array
corresponding to all the true elements in the boolean array. The
elements in the indexed array are always iterated and returned in
:term:`row-major` (C-style) order. The result is also identical to
``y[np.nonzero(b)]``. As with index arrays, what is returned is a copy
of the data, not a view as one gets with slices.
The result will be multidimensional if y has more dimensions than b.
For example: ::
>>> b[:,5] # use a 1-D boolean whose first dim agrees with the first dim of y
array([False, False, False, True, True], dtype=bool)
>>> y[b[:,5]]
array([[21, 22, 23, 24, 25, 26, 27],
[28, 29, 30, 31, 32, 33, 34]])
Here the 4th and 5th rows are selected from the indexed array and
combined to make a 2-D array.
In general, when the boolean array has fewer dimensions than the array
being indexed, this is equivalent to y[b, ...], which means
y is indexed by b followed by as many : as are needed to fill
out the rank of y.
Thus the shape of the result is one dimension containing the number
of True elements of the boolean array, followed by the remaining
dimensions of the array being indexed.
For example, using a 2-D boolean array of shape (2,3)
with four True elements to select rows from a 3-D array of shape
(2,3,5) results in a 2-D result of shape (4,5): ::
>>> x = np.arange(30).reshape(2,3,5)
>>> x
array([[[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]],
[[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]]])
>>> b = np.array([[True, True, False], [False, True, True]])
>>> x[b]
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]])
For further details, consult the numpy reference documentation on array indexing.
Combining index arrays with slices
==================================
Index arrays may be combined with slices. For example: ::
>>> y[np.array([0,2,4]),1:3]
array([[ 1, 2],
[15, 16],
[29, 30]])
In effect, the slice is converted to an index array
np.array([[1,2]]) (shape (1,2)) that is broadcast with the index array
to produce a resultant array of shape (3,2).
Likewise, slicing can be combined with broadcasted boolean indices: ::
>>> y[b[:,5],1:3]
array([[22, 23],
[29, 30]])
Structural indexing tools
=========================
To facilitate easy matching of array shapes with expressions and in
assignments, the np.newaxis object can be used within array indices
to add new dimensions with a size of 1. For example: ::
>>> y.shape
(5, 7)
>>> y[:,np.newaxis,:].shape
(5, 1, 7)
Note that there are no new elements in the array, just that the
dimensionality is increased. This can be handy to combine two
arrays in a way that otherwise would require explicitly reshaping
operations. For example: ::
>>> x = np.arange(5)
>>> x[:,np.newaxis] + x[np.newaxis,:]
array([[0, 1, 2, 3, 4],
[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8]])
The ellipsis syntax maybe used to indicate selecting in full any
remaining unspecified dimensions. For example: ::
>>> z = np.arange(81).reshape(3,3,3,3)
>>> z[1,...,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
This is equivalent to: ::
>>> z[1,:,:,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
Assigning values to indexed arrays
==================================
As mentioned, one can select a subset of an array to assign to using
a single index, slices, and index and mask arrays. The value being
assigned to the indexed array must be shape consistent (the same shape
or broadcastable to the shape the index produces). For example, it is
permitted to assign a constant to a slice: ::
>>> x = np.arange(10)
>>> x[2:7] = 1
or an array of the right size: ::
>>> x[2:7] = np.arange(5)
Note that assignments may result in changes if assigning
higher types to lower types (like floats to ints) or even
exceptions (assigning complex to floats or ints): ::
>>> x[1] = 1.2
>>> x[1]
1
>>> x[1] = 1.2j
<type 'exceptions.TypeError'>: can't convert complex to long; use
long(abs(z))
Unlike some of the references (such as array and mask indices)
assignments are always made to the original data in the array
(indeed, nothing else would make sense!). Note though, that some
actions may not work as one may naively expect. This particular
example is often surprising to people: ::
>>> x = np.arange(0, 50, 10)
>>> x
array([ 0, 10, 20, 30, 40])
>>> x[np.array([1, 1, 3, 1])] += 1
>>> x
array([ 0, 11, 20, 31, 40])
Where people expect that the 1st location will be incremented by 3.
In fact, it will only be incremented by 1. The reason is because
a new array is extracted from the original (as a temporary) containing
the values at 1, 1, 3, 1, then the value 1 is added to the temporary,
and then the temporary is assigned back to the original array. Thus
the value of the array at x[1]+1 is assigned to x[1] three times,
rather than being incremented 3 times.
Dealing with variable numbers of indices within programs
========================================================
The index syntax is very powerful but limiting when dealing with
a variable number of indices. For example, if you want to write
a function that can handle arguments with various numbers of
dimensions without having to write special case code for each
number of possible dimensions, how can that be done? If one
supplies to the index a tuple, the tuple will be interpreted
as a list of indices. For example (using the previous definition
for the array z): ::
>>> indices = (1,1,1,1)
>>> z[indices]
40
So one can use code to construct tuples of any number of indices
and then use these within an index.
Slices can be specified within programs by using the slice() function
in Python. For example: ::
>>> indices = (1,1,1,slice(0,2)) # same as [1,1,1,0:2]
>>> z[indices]
array([39, 40])
Likewise, ellipsis can be specified by code by using the Ellipsis
object: ::
>>> indices = (1, Ellipsis, 1) # same as [1,...,1]
>>> z[indices]
array([[28, 31, 34],
[37, 40, 43],
[46, 49, 52]])
For this reason it is possible to use the output from the np.where()
function directly as an index since it always returns a tuple of index
arrays.
Because the special treatment of tuples, they are not automatically
converted to an array as a list would be. As an example: ::
>>> z[[1,1,1,1]] # produces a large array
array([[[[27, 28, 29],
[30, 31, 32], ...
>>> z[(1,1,1,1)] # returns a single value
40
""" |
# 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 <EMAIL>, 2012-2013
# Copyright (c), NAME <EMAIL>, 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
# --------------------------------------------
#
# 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, 2014 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.
|
"""
******************************
Self-organizing maps (``som``)
******************************
.. index:: self-organizing map (SOM)
.. index::
single: projection; self-organizing map (SOM)
`Self-organizing map <http://en.wikipedia.org/wiki/Self-organizing_map>`_
(SOM) is an unsupervised learning algorithm that infers low,
typically two-dimensional discretized representation of the input
space, called a map. The map preserves topological properties of the
input space, such that the cells that are close in the map include data
instances that are similar to each other.
=================================
Inference of Self-Organizing Maps
=================================
The main class for inference of self-organizing maps is :obj:`SOMLearner`.
The class initializes the topology of the map and returns an inference
objects which, given the data, performs the optimization of the map::
import Orange
som = Orange.projection.som.SOMLearner(map_shape=(8, 8),
initialize=Orange.projection.som.InitializeRandom)
data = Orange.data.table("iris.tab")
map = som(data)
.. autoclass:: SOMLearner
:members:
.. autoclass:: SOMMap
:members:
Topology
--------
.. autodata:: HexagonalTopology
.. autodata:: RectangularTopology
Map initialization
------------------
.. autodata:: InitializeLinear
.. autodata:: InitializeRandom
Node neighbourhood
------------------
.. autodata:: NeighbourhoodGaussian
.. autodata:: NeighbourhoodBubble
.. autodata:: NeighbourhoodEpanechicov
=============================================
Supervised Learning with Self-Organizing Maps
=============================================
Supervised learning requires class-labeled data. For training,
class information is first added to data instances as a regular
feature by extending the feature vectors accordingly. Next, the
map is trained, and the training data projected to nodes. Each
node then classifies to the majority class. The dimensions
corresponding to the class features are then removed from the
prototype vector of each node in the map. For classification,
the data instance is projected to the best matching cell, returning
the associated class.
An example of the code that trains and then classifies on the same
data set is::
import Orange
import random
learner = Orange.projection.som.SOMSupervisedLearner(map_shape=(4, 4))
data = Orange.data.Table("iris.tab")
classifier = learner(data)
random.seed(50)
for d in random.sample(data, 5):
print "%-15s originally %-15s" % (classifier(d), d.getclass())
.. autoclass:: SOMSupervisedLearner
:members:
==================
Supporting Classes
==================
The actual map optimization algorithm is implemented by :class:`Solver`
class which is used by both the :class:`SOMLearner` and the
:class:`SOMSupervisedLearner`.
.. autoclass:: Solver
:members:
Class :obj:`Map` stores the self-organizing map composed of :obj:`Node`
objects. The code below (:download:`som-node.py <code/som-node.py>`)
shows an example how to access the information stored in the node of the
map:
.. literalinclude:: code/som-node.py
:lines: 7-
.. autoclass:: Map
:members:
.. autoclass:: Node
:members:
========
Examples
========
The following code (:download:`som-mapping.py <code/som-mapping.py>`)
infers self-organizing map from Iris data set. The map is rather small,
and consists of only 9 cells. We optimize the network, and then report
how many data instances were mapped into each cell. The second part
of the code reports on data instances from one of the corner cells:
.. literalinclude:: code/som-mapping.py
:lines: 7-
The output of this code is::
Node Instances
(0, 0) 31
(0, 1) 7
(0, 2) 0
(1, 0) 24
(1, 1) 7
(1, 2) 50
(2, 0) 10
(2, 1) 21
(2, 2) 0
Data instances in cell (0, 1):
[6.9, 3.1, 4.9, 1.5, 'Iris-versicolor']
[6.7, 3.0, 5.0, 1.7, 'Iris-versicolor']
[6.3, 2.9, 5.6, 1.8, 'Iris-virginica']
[6.5, 3.2, 5.1, 2.0, 'Iris-virginica']
[6.4, 2.7, 5.3, 1.9, 'Iris-virginica']
[6.1, 2.6, 5.6, 1.4, 'Iris-virginica']
[6.5, 3.0, 5.2, 2.0, 'Iris-virginica']
""" |
#!/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 <EMAIL>
#
# 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
|
"""Test module for the noddy examples
Noddy 1:
>>> import noddy
>>> n1 = noddy.Noddy()
>>> n2 = noddy.Noddy()
>>> del n1
>>> del n2
Noddy 2
>>> import noddy2
>>> n1 = noddy2.Noddy('jim', 'fulton', 42)
>>> n1.first
'jim'
>>> n1.last
'NAME n1.number
42
>>> n1.name()
'jim NAME n1.first = 'will'
>>> n1.name()
'will NAME n1.last = 'NAME n1.name()
'will NAME del n1.first
>>> n1.name()
Traceback (most recent call last):
...
AttributeError: first
>>> n1.first
Traceback (most recent call last):
...
AttributeError: first
>>> n1.first = 'drew'
>>> n1.first
'drew'
>>> del n1.number
Traceback (most recent call last):
...
TypeError: can't delete numeric/char attribute
>>> n1.number=2
>>> n1.number
2
>>> n1.first = 42
>>> n1.name()
'42 NAME n2 = noddy2.Noddy()
>>> n2.name()
' '
>>> n2.first
''
>>> n2.last
''
>>> del n2.first
>>> n2.first
Traceback (most recent call last):
...
AttributeError: first
>>> n2.first
Traceback (most recent call last):
...
AttributeError: first
>>> n2.name()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
AttributeError: first
>>> n2.number
0
>>> n3 = noddy2.Noddy('jim', 'fulton', 'waaa')
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: an integer is required
>>> del n1
>>> del n2
Noddy 3
>>> import noddy3
>>> n1 = noddy3.Noddy('jim', 'fulton', 42)
>>> n1 = noddy3.Noddy('jim', 'fulton', 42)
>>> n1.name()
'jim NAME del n1.first
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: Cannot delete the first attribute
>>> n1.first = 42
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: The first attribute value must be a string
>>> n1.first = 'will'
>>> n1.name()
'will NAME n2 = noddy3.Noddy()
>>> n2 = noddy3.Noddy()
>>> n2 = noddy3.Noddy()
>>> n3 = noddy3.Noddy('jim', 'fulton', 'waaa')
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: an integer is required
>>> del n1
>>> del n2
Noddy 4
>>> import noddy4
>>> n1 = noddy4.Noddy('jim', 'fulton', 42)
>>> n1.first
'jim'
>>> n1.last
'NAME n1.number
42
>>> n1.name()
'jim NAME n1.first = 'will'
>>> n1.name()
'will NAME n1.last = 'NAME n1.name()
'will NAME del n1.first
>>> n1.name()
Traceback (most recent call last):
...
AttributeError: first
>>> n1.first
Traceback (most recent call last):
...
AttributeError: first
>>> n1.first = 'drew'
>>> n1.first
'drew'
>>> del n1.number
Traceback (most recent call last):
...
TypeError: can't delete numeric/char attribute
>>> n1.number=2
>>> n1.number
2
>>> n1.first = 42
>>> n1.name()
'42 NAME n2 = noddy4.Noddy()
>>> n2 = noddy4.Noddy()
>>> n2 = noddy4.Noddy()
>>> n2 = noddy4.Noddy()
>>> n2.name()
' '
>>> n2.first
''
>>> n2.last
''
>>> del n2.first
>>> n2.first
Traceback (most recent call last):
...
AttributeError: first
>>> n2.first
Traceback (most recent call last):
...
AttributeError: first
>>> n2.name()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
AttributeError: first
>>> n2.number
0
>>> n3 = noddy4.Noddy('jim', 'fulton', 'waaa')
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: an integer is required
Test cyclic gc(?)
>>> import gc
>>> gc.disable()
>>> x = []
>>> l = [x]
>>> n2.first = l
>>> n2.first
[[]]
>>> l.append(n2)
>>> del l
>>> del n1
>>> del n2
>>> sys.getrefcount(x)
3
>>> ignore = gc.collect()
>>> sys.getrefcount(x)
2
>>> gc.enable()
""" |
"""
Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG)
LOBPCG is a preconditioned eigensolver for large symmetric positive definite
(SPD) generalized eigenproblems.
Call the function lobpcg - see help for lobpcg.lobpcg. See also lobpcg.as2d,
which can be used in the preconditioner (example below)
Acknowledgements
----------------
lobpcg.py code was written by NAME Many thanks belong to NAME the author of the algorithm, for lots of advice and support.
Examples
--------
>>> # Solve A x = lambda B x with constraints and preconditioning.
>>> n = 100
>>> vals = [nm.arange( n, dtype = nm.float64 ) + 1]
>>> # Matrix A.
>>> operatorA = spdiags( vals, 0, n, n )
>>> # Matrix B
>>> operatorB = nm.eye( n, n )
>>> # Constraints.
>>> Y = nm.eye( n, 3 )
>>> # Initial guess for eigenvectors, should have linearly independent
>>> # columns. Column dimension = number of requested eigenvalues.
>>> X = sc.rand( n, 3 )
>>> # Preconditioner - inverse of A.
>>> ivals = [1./vals[0]]
>>> def precond( x ):
invA = spdiags( ivals, 0, n, n )
y = invA * x
if sp.issparse( y ):
y = y.toarray()
return as2d( y )
>>>
>>> # Alternative way of providing the same preconditioner.
>>> #precond = spdiags( ivals, 0, n, n )
>>>
>>> tt = time.clock()
>>> eigs, vecs = lobpcg( X, operatorA, operatorB, blockVectorY = Y,
>>> operatorT = precond,
>>> residualTolerance = 1e-4, maxIterations = 40,
>>> largest = False, verbosityLevel = 1 )
>>> print 'solution time:', time.clock() - tt
>>> print eigs
Notes
-----
In the following ``n`` denotes the matrix size and ``m`` the number
of required eigenvalues (smallest or largest).
The LOBPCG code internally solves eigenproblems of the size 3``m`` on every
iteration by calling the "standard" dense eigensolver, so if ``m`` is not
small enough compared to ``n``, it does not make sense to call the LOBPCG
code, but rather one should use the "standard" eigensolver, e.g. scipy or symeig
function in this case. If one calls the LOBPCG algorithm for 5``m``>``n``,
it will most likely break internally, so the code tries to call the standard
function instead.
It is not that n should be large for the LOBPCG to work, but rather the
ratio ``n``/``m`` should be large. It you call the LOBPCG code with ``m``=1
and ``n``=10, it should work, though ``n`` is small. The method is intended
for extremely large ``n``/``m``, see e.g., reference [28] in
http://arxiv.org/abs/0705.2626
The convergence speed depends basically on two factors:
1. How well relatively separated the seeking eigenvalues are from the rest of
the eigenvalues. One can try to vary ``m`` to make this better.
2. How well conditioned the problem is. This can be changed by using proper
preconditioning. For example, a rod vibration test problem (under tests
directory) is ill-conditioned for large ``n``, so convergence will be
slow, unless efficient preconditioning is used. For this specific problem,
a good simple preconditioner function would be a linear solve for A, which
is easy to code since A is tridiagonal.
References
----------
A. NAME Toward the Optimal Preconditioned Eigensolver: Locally Optimal
Block Preconditioned Conjugate Gradient Method. SIAM Journal on Scientific
Computing 23 (2001), no. 2,
pp. 517-541. http://dx.doi.org/10.1137/S1064827500366124
A. NAME NAME NAME and NAME Block Locally
Optimal Preconditioned Eigenvalue Xolvers (BLOPEX) in hypre and PETSc
(2007). http://arxiv.org/abs/0705.2626
A. NAME C and MATLAB implementations:
http://www-math.cudenver.edu/~aknyazev/software/BLOPEX/
""" |
"""
=======================================
Signal processing (:mod:`scipy.signal`)
=======================================
Convolution
===========
.. autosummary::
:toctree: generated/
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.
choose_conv_method -- Chooses faster of FFT and direct convolution methods.
B-splines
=========
.. autosummary::
:toctree: generated/
bspline -- B-spline basis function of order n.
cubic -- B-spline basis function of order 3.
quadratic -- B-spline basis function of order 2.
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.
cspline1d_eval -- Evaluate a cubic spline at the given points.
qspline1d_eval -- Evaluate a quadratic spline at the given points.
spline_filter -- Smoothing spline (cubic) filtering of a rank-2 array.
Filtering
=========
.. autosummary::
:toctree: generated/
order_filter -- N-dimensional order filter.
medfilt -- N-dimensional median filter.
medfilt2d -- 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`.
lfilter_zi -- Compute an initial state zi for the lfilter function that
-- corresponds to the steady state of the step response.
filtfilt -- A forward-backward filter.
savgol_filter -- Filter a signal using the Savitzky-Golay filter.
deconvolve -- 1-d deconvolution using lfilter.
sosfilt -- 1-dimensional IIR digital linear filtering using
-- a second-order sections filter representation.
sosfilt_zi -- Compute an initial state zi for the sosfilt function that
-- corresponds to the steady state of the step response.
sosfiltfilt -- A forward-backward filter for second-order sections.
hilbert -- Compute 1-D analytic signal, using the Hilbert transform.
hilbert2 -- Compute 2-D analytic signal, using the Hilbert transform.
decimate -- Downsample a signal.
detrend -- Remove linear and/or constant trends from data.
resample -- Resample using Fourier method.
resample_poly -- Resample using polyphase filtering method.
upfirdn -- Upsample, apply FIR filter, downsample.
Filter design
=============
.. autosummary::
:toctree: generated/
bilinear -- Digital filter from an analog filter using
-- the bilinear transform.
findfreqs -- Find array of frequencies for computing filter response.
firls -- FIR filter design using least-squares error minimization.
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.
sosfreqz -- Digital filter frequency response for SOS format filter.
group_delay -- Digital filter group delay.
iirdesign -- IIR filter design given bands and gains.
iirfilter -- IIR filter design given order and critical frequencies.
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.
kaiser_beta -- Compute the Kaiser parameter beta, given the desired
-- FIR filter attenuation.
kaiserord -- Design a Kaiser window to limit ripple and width of
-- transition region.
savgol_coeffs -- Compute the FIR filter coefficients for a Savitzky-Golay
-- filter.
remez -- Optimal FIR filter design.
unique_roots -- Unique roots and their multiplicities.
residue -- Partial fraction expansion of b(s) / a(s).
residuez -- Partial fraction expansion of b(z) / a(z).
invres -- Inverse partial fraction expansion for analog filter.
invresz -- Inverse partial fraction expansion for digital filter.
BadCoefficients -- Warning on badly conditioned filter coefficients
Lower-level filter design functions:
.. autosummary::
:toctree: generated/
abcd_normalize -- Check state-space matrices and ensure they are rank-2.
band_stop_obj -- Band Stop Objective Function for order minimization.
besselap -- Return (z,p,k) for analog prototype of Bessel filter.
buttap -- Return (z,p,k) for analog prototype of Butterworth filter.
cheb1ap -- Return (z,p,k) for type I Chebyshev filter.
cheb2ap -- Return (z,p,k) for type II Chebyshev filter.
cmplx_sort -- Sort roots based on magnitude.
ellipap -- Return (z,p,k) for analog prototype of elliptic filter.
lp2bp -- Transform a lowpass filter prototype to a bandpass filter.
lp2bs -- Transform a lowpass filter prototype to a bandstop filter.
lp2hp -- Transform a lowpass filter prototype to a highpass filter.
lp2lp -- Transform a lowpass filter prototype to a lowpass filter.
normalize -- Normalize polynomial representation of a transfer function.
Matlab-style IIR filter design
==============================
.. autosummary::
:toctree: generated/
butter -- Butterworth
buttord
cheby1 -- Chebyshev Type I
cheb1ord
cheby2 -- Chebyshev Type II
cheb2ord
ellip -- Elliptic (Cauer)
ellipord
bessel -- Bessel (no order selection available -- try butterod)
Continuous-Time Linear Systems
==============================
.. autosummary::
:toctree: generated/
lti -- Continuous-time linear time invariant system base class.
StateSpace -- Linear time invariant system in state space form.
TransferFunction -- Linear time invariant system in transfer function form.
ZerosPolesGain -- Linear time invariant system in zeros, poles, gain form.
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.
freqresp -- frequency response of a continuous-time LTI system.
bode -- Bode magnitude and phase data (continuous-time LTI).
Discrete-Time Linear Systems
============================
.. autosummary::
:toctree: generated/
dlti -- Discrete-time linear time invariant system base class.
StateSpace -- Linear time invariant system in state space form.
TransferFunction -- Linear time invariant system in transfer function form.
ZerosPolesGain -- Linear time invariant system in zeros, poles, gain form.
dlsim -- simulation of output to a discrete-time linear system.
dimpulse -- impulse response of a discrete-time LTI system.
dstep -- step response of a discrete-time LTI system.
dfreqresp -- frequency response of a discrete-time LTI system.
dbode -- Bode magnitude and phase data (discrete-time LTI).
LTI Representations
===================
.. autosummary::
:toctree: generated/
tf2zpk -- transfer function to zero-pole-gain.
tf2sos -- transfer function to second-order sections.
tf2ss -- transfer function to state-space.
zpk2tf -- zero-pole-gain to transfer function.
zpk2sos -- zero-pole-gain to second-order sections.
zpk2ss -- zero-pole-gain to state-space.
ss2tf -- state-pace to transfer function.
ss2zpk -- state-space to pole-zero-gain.
sos2zpk -- second-order sections to zero-pole-gain.
sos2tf -- second-order sections to transfer function.
cont2discrete -- continuous-time to discrete-time LTI conversion.
place_poles -- pole placement.
Waveforms
=========
.. autosummary::
:toctree: generated/
chirp -- Frequency swept cosine signal, with several freq functions.
gausspulse -- Gaussian modulated sinusoid
max_len_seq -- Maximum length sequence
sawtooth -- Periodic sawtooth
square -- Square wave
sweep_poly -- Frequency swept cosine signal; freq is arbitrary polynomial
Window functions
================
.. autosummary::
:toctree: generated/
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
cosine -- Cosine window
exponential -- Exponential window
flattop -- Flat top window
gaussian -- Gaussian window
general_gaussian -- Generalized Gaussian window
hamming -- Hamming window
hann -- Hann window
hanning -- Hann window
kaiser -- Kaiser window
nuttall -- Nuttall's minimum 4-term Blackman-Harris window
parzen -- Parzen window
slepian -- Slepian window
triang -- Triangular window
tukey -- Tukey window
Wavelets
========
.. autosummary::
:toctree: generated/
cascade -- compute scaling function and wavelet from coefficients
daub -- return low-pass
morlet -- Complex Morlet wavelet.
qmf -- return quadrature mirror filter from low-pass
ricker -- return ricker wavelet
cwt -- perform continuous wavelet transform
Peak finding
============
.. autosummary::
:toctree: generated/
find_peaks_cwt -- Attempt to find the peaks in the given 1-D array
argrelmin -- Calculate the relative minima of data
argrelmax -- Calculate the relative maxima of data
argrelextrema -- Calculate the relative extrema of data
Spectral Analysis
=================
.. autosummary::
:toctree: generated/
periodogram -- Compute a (modified) periodogram
welch -- Compute a periodogram using Welch's method
csd -- Compute the cross spectral density, using Welch's method
coherence -- Compute the magnitude squared coherence, using Welch's method
spectrogram -- Compute the spectrogram
lombscargle -- Computes the Lomb-Scargle periodogram
vectorstrength -- Computes the vector strength
""" |
# 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 <EMAIL>, 2012-2013
# Copyright (c), NAME <EMAIL>, 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
# --------------------------------------------
#
# 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, 2014 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.
|
""" Concatenate datasets of :mod:`time series data<pySPACE.resources.dataset_defs.time_series>`
This operation requires test data with no splits.
The result of this operation concatenates the datasets of the input.
For instance, if the input consists of the three datasets "A",
"B", "C", the result will contain only one dataset "All".
.. note:: Each dataset can only be used once for concatenation!
Specification file Parameters
++++++++++++++++++++++++++++++
type
----
This parameter has to be set to **concatenate**.
(*obligatory, concatenate*)
name_pattern
------------
The name
of the result dataset can be specified within *name_pattern*.
The first time series object of every concatenated set will contain a 'new_set'
flag in the specs to allow later reconstruction of the individual sets.
(*optional, default:'"%(dataset_name)s"[:-1]+"_All"'*)
dataset_constraints
----------------------
Optionally, constraints can be passed to the operation that specify which
datasets are concatenated. For instance, the constraint
'"%(dataset_name1)s".strip("}{").split("}{")[1:] ==
"%(dataset_name2)s".strip("}{").split("}{")[1:]' would cause
that only datasets are combined, that were created by the same processing
with the same parametrization.
.. todo:: Document the definition of dataset1 and is dataset2!
change_time
-----------
If *change_time* is True, the appended time series objects get a new, artificial
start and end time, to ensure that the time is unique for further investigations.
(*optional, default: False*)
Exemplary Call
++++++++++++++
A typical operation specification file might look like this
.. code-block:: yaml
type: concatenate
name_pattern: '"%(dataset_name)s"[:-1]'
change_time: False
input_path: "operation_results/2009_8_13_15_8_57"
dataset_constraints:
# Combine only datasets that have been created using the same parameterization
- '"%(dataset_name1)s".strip("}{").split("}{")[1:] == "%(dataset_name2)s".strip("}{").split("}{")[1:]'
Example dataset_constraints
++++++++++++++++++++++++++++++
:Combine only datasets that have been created using the same parameterization:
``- '"%(dataset_name1)s".strip("}{").split("}{")[1:] == "%(dataset_name2)s".strip("}{").split("}{")[1:]'``
Application Examples
++++++++++++++++++++
Run123 versus Run45
-------------------
The following example concatenates Runs 1, 2 and 3 from within the same Session
of the same Subject to a joint "Run123". The similar is done for "Run45".
.. code-block:: yaml
type: concatenate
input_path: "prewindowed/BRIO_Oddball_5subjects_0_1000ms_Preprocessed"
change_time: False
name_pattern: '"%(dataset_name)s"[:-1] + ("123" if "%(dataset_name)s"[-1:] in ["1","2","3"] else "45")'
dataset_constraints:
- '"%(dataset_name1)s".strip("}{").split("_")[0] == "%(dataset_name2)s".strip("}{").split("_")[0]'
- '"%(dataset_name1)s".strip("}{").split("_")[1] == "%(dataset_name2)s".strip("}{").split("_")[1]'
- '(("%(dataset_name1)s".strip("}{").split("_")[2] == "Run1") and ("%(dataset_name2)s".strip("}{").split("_")[2] == "Run2" or "%(dataset_name2)s".strip("}{").split("_")[2] == "Run3")) or ("%(dataset_name1)s".strip("}{").split("_")[2] == "Run4" and "%(dataset_name2)s".strip("}{").split("_")[2] == "Run5")'
In the following shuffle example, the Runs called "Run123"
will be used for training, and the runs called "Run45" from the same subject
and session will be used for test:
.. code-block:: yaml
type: shuffle
input_path: "prewindowed/BRIO_Oddball_5subjects_0_1000ms_Preprocessed_Run123_Run45"
change_time: False
dataset_constraints:
- '"%(dataset_name1)s".strip("}{").split("_")[0] == "%(dataset_name2)s".strip("}{").split("_")[0]'
- '"%(dataset_name1)s".strip("}{").split("_")[1] == "%(dataset_name2)s".strip("}{").split("_")[1]'
- '"%(dataset_name1)s".strip("}{").split("_")[2] == "Run123"'
- '"%(dataset_name2)s".strip("}{").split("_")[2] == "Run45"'
For the usage o the shuffle operation refer to :mod:`pySPACE.missions.operations.shuffle`.
.. note::
Problems in connection with the
:class:`~pySPACE.missions.nodes.source.time_series_source.TimeSeries2TimeSeriesSourceNode`
can also occur as described in the
:mod:`~pySPACE.missions.operations.merge` module.
:Author: NAME (EMAIL)
:Input: :mod:`pySPACE.pySPACE.resources.dataset_defs.time_series`
""" |
#"""Ops for graph construction."""
#from __future__ import print_function
#from __future__ import division
#from __future__ import unicode_literals
#
#import sys
#import traceback
#import tensorflow as tf
#from keras import backend as K
#
#def cosine_distances(test, support):
# """Computes pairwise cosine distances between provided tensors
#
# Parameters
# ----------
# test: tf.Tensor
# Of shape (n_test, n_feat)
# support: tf.Tensor
# Of shape (n_support, n_feat)
#
# Returns
# -------
# tf.Tensor:
# Of shape (n_test, n_support)
# """
# rnorm_test = tf.rsqrt(tf.reduce_sum(tf.square(test), 1,
# keep_dims=True)) + K.epsilon()
# rnorm_support = tf.rsqrt(tf.reduce_sum(tf.square(support), 1,
# keep_dims=True)) + K.epsilon()
# test_normalized = test * rnorm_test
# support_normalized = support * rnorm_support
#
# # Transpose for mul
# support_normalized_t = tf.transpose(support_normalized, perm=[1,0])
# g = tf.matmul(test_normalized, support_normalized_t) # Gram matrix
# return g
#
#def euclidean_distance(test, support, max_dist_sq=20):
# """Computes pairwise euclidean distances between provided tensors
#
# TODO(rbharath): BROKEN! THIS DOESN'T WORK!
#
# Parameters
# ----------
# test: tf.Tensor
# Of shape (n_test, n_feat)
# support: tf.Tensor
# Of shape (n_support, n_feat)
# max_dist_sq: float, optional
# Maximum pairwise distance allowed.
#
# Returns
# -------
# tf.Tensor:
# Of shape (n_test, n_support)
# """
# test = tf.expand_dims(test, 1)
# support = tf.expand_dims(support, 0)
# g = -tf.maximum(tf.reduce_sum(tf.square(test - support), 2), max_dist_sq)
# return g
#
#def add_bias(tensor, init=None, name=None):
# """Add a bias term to a tensor.
#
# Parameters
# ----------
# tensor: tf.Tensor
# Variable tensor.
# init: float
# Bias initializer. Defaults to zero.
# name: str
# Name for this op. Defaults to tensor.op.name.
#
# Returns
# -------
# tf.Tensor
# A biased tensor with the same shape as the input tensor.
# """
# if init is None:
# init = tf.zeros([tensor.get_shape()[-1].value])
# with tf.name_scope(name, tensor.op.name, [tensor]):
# b = tf.Variable(init, name='b')
# return tf.nn.bias_add(tensor, b)
#
#
#def dropout(tensor, dropout_prob, training=True, training_only=True):
# """Random dropout.
#
# This implementation supports "always-on" dropout (training_only=False), which
# can be used to calculate model uncertainty. See Gal and NAME http://arxiv.org/abs/1506.02142.
#
# NOTE(user): To simplify the implementation, I have chosen not to reverse
# the scaling that occurs in tf.nn.dropout when using dropout during
# inference. This shouldn't be an issue since the activations will be scaled
# by the same constant in both training and inference. This means that there
# are no training-time differences between networks that use dropout during
# inference and those that do not.
#
# Parameters
# ----------
# tensor: tf.Tensor
# Input tensor.
# dropout_prob: float
# Float giving dropout probability for weights (NOT keep probability).
# training_only: bool
# Boolean. If True (standard dropout), apply dropout only
# during training. If False, apply dropout during inference as well.
#
# Returns
# -------
# tf.Tensor:
# A tensor with the same shape as the input tensor.
# """
# if not dropout_prob:
# return tensor # do nothing
# keep_prob = 1.0 - dropout_prob
# if training or not training_only:
# tensor = tf.nn.dropout(tensor, keep_prob)
# return tensor
#
#
#def fully_connected_layer(tensor, size=None, weight_init=None, bias_init=None,
# name=None):
# """Fully connected layer.
#
# Parameters
# ----------
# tensor: tf.Tensor
# Input tensor.
# size: int
# Number of output nodes for this layer.
# weight_init: float
# Weight initializer.
# bias_init: float
# Bias initializer.
# name: str
# Name for this op. Defaults to 'fully_connected'.
#
# Returns
# -------
# tf.Tensor:
# A new tensor representing the output of the fully connected layer.
#
# Raises
# ------
# ValueError
# If input tensor is not 2D.
# """
# if len(tensor.get_shape()) != 2:
# raise ValueError('Dense layer input must be 2D, not %dD'
# % len(tensor.get_shape()))
# if weight_init is None:
# num_features = tensor.get_shape()[-1].value
# weight_init = tf.truncated_normal([num_features, size], stddev=0.01)
# if bias_init is None:
# bias_init = tf.zeros([size])
#
# with tf.name_scope(name, 'fully_connected', [tensor]):
# w = tf.Variable(weight_init, name='w', dtype=tf.float32)
# b = tf.Variable(bias_init, name='b', dtype=tf.float32)
# return tf.nn.xw_plus_b(tensor, w, b)
#
#def weight_decay(penalty_type, penalty):
# """Add weight decay.
#
# Args:
# model: TensorflowGraph.
#
# Returns:
# A scalar tensor containing the weight decay cost.
#
# Raises:
# NotImplementedError: If an unsupported penalty type is requested.
# """
# variables = []
# # exclude bias variables
# for v in tf.trainable_variables():
# if v.get_shape().ndims == 2:
# variables.append(v)
#
# with tf.name_scope('weight_decay'):
# if penalty_type == 'l1':
# cost = tf.add_n([tf.reduce_sum(tf.abs(v)) for v in variables])
# elif penalty_type == 'l2':
# cost = tf.add_n([tf.nn.l2_loss(v) for v in variables])
# else:
# raise NotImplementedError('Unsupported penalty_type %s' % penalty_type)
# cost *= penalty
# tf.scalar_summary('Weight Decay Cost', cost)
# return cost
#
#
#def multitask_logits(features, num_tasks, num_classes=2, weight_init=None,
# bias_init=None, dropout_prob=None, name=None):
# """Create a logit tensor for each classification task.
#
# Args:
# features: A 2D tensor with dimensions batch_size x num_features.
# num_tasks: Number of classification tasks.
# num_classes: Number of classes for each task.
# weight_init: Weight initializer.
# bias_init: Bias initializer.
# dropout_prob: Float giving dropout probability for weights (NOT keep
# probability).
# name: Name for this op. Defaults to 'multitask_logits'.
#
# Returns:
# A list of logit tensors; one for each classification task.
# """
# logits_list = []
# with tf.name_scope('multitask_logits'):
# for task_idx in range(num_tasks):
# with tf.name_scope(name,
# ('task' + str(task_idx).zfill(len(str(num_tasks)))), [features]):
# logits_list.append(
# logits(features, num_classes, weight_init=weight_init,
# bias_init=bias_init, dropout_prob=dropout_prob))
# return logits_list
#
#
#def logits(features, num_classes=2, weight_init=None, bias_init=None,
# dropout_prob=None, name=None):
# """Create a logits tensor for a single classification task.
#
# You almost certainly don't want dropout on there -- it's like randomly setting
# the (unscaled) probability of a target class to 0.5.
#
# Args:
# features: A 2D tensor with dimensions batch_size x num_features.
# num_classes: Number of classes for each task.
# weight_init: Weight initializer.
# bias_init: Bias initializer.
# dropout_prob: Float giving dropout probability for weights (NOT keep
# probability).
# name: Name for this op.
#
# Returns:
# A logits tensor with shape batch_size x num_classes.
# """
# with tf.name_scope(name, 'logits', [features]) as name:
# return dropout(
# fully_connected_layer(features, num_classes, weight_init=weight_init,
# bias_init=bias_init, name=name),
# dropout_prob)
#
#
#def softmax_N(tensor, name=None):
# """Apply softmax across last dimension of a tensor.
#
# Args:
# tensor: Input tensor.
# name: Name for this op. If None, defaults to 'softmax_N'.
#
# Returns:
# A tensor with softmax-normalized values on the last dimension.
# """
# with tf.name_scope(name, 'softmax_N', [tensor]):
# exp_tensor = tf.exp(tensor)
# reduction_indices = [tensor.get_shape().ndims - 1]
# return tf.div(exp_tensor,
# tf.reduce_sum(exp_tensor,
# reduction_indices=reduction_indices,
# keep_dims=True))
#
#def optimizer(optimizer="adam", learning_rate=.001, momentum=.9):
# """Create model optimizer.
#
# Parameters
# ----------
# optimizer: str, optional
# Name of optimizer
# learning_rate: float, optional
# Learning rate for algorithm
# momentum: float, optional
# Momentum rate
#
# Returns
# -------
# A training Optimizer.
#
# Raises:
# NotImplementedError: If an unsupported optimizer is requested.
# """
# # TODO(user): gradient clipping (see Minimize)
# if optimizer == 'adagrad':
# train_op = tf.train.AdagradOptimizer(learning_rate)
# elif optimizer == 'adam':
# train_op = tf.train.AdamOptimizer(learning_rate)
# elif optimizer == 'momentum':
# train_op = tf.train.MomentumOptimizer(learning_rate,
# momentum)
# elif optimizer == 'rmsprop':
# train_op = tf.train.RMSPropOptimizer(learning_rate,
# momentum)
# elif optimizer == 'sgd':
# train_op = tf.train.GradientDescentOptimizer(learning_rate)
# else:
# raise NotImplementedError('Unsupported optimizer %s' % optimizer)
# return train_op
|
#
# ElementTree
# $Id: ElementTree.py 65372 2008-08-01 19:11:22Z USERNAME $
#
# light-weight XML support for Python 2.2 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
# 2004-09-03 fl made Element class visible; removed factory
# 2005-02-01 fl added iterparse implementation
# 2005-03-02 fl fixed iterparse support for pre-2.2 versions
# 2005-11-12 fl added tostringlist/fromstringlist helpers
# 2006-07-05 fl merged in selected changes from the 1.3 sandbox
# 2006-07-05 fl removed support for 2.1 and earlier
# 2007-06-21 fl added deprecation/future warnings
# 2007-08-25 fl added doctype hook, added parser version attribute etc
# 2007-08-26 fl added new serializer code (better namespace handling, etc)
# 2007-08-27 fl warn for broken /tag searches on tree level
# 2007-09-02 fl added html/text methods to serializer (experimental)
# 2007-09-05 fl added method argument to tostring/tostringlist
# 2007-09-06 fl improved error handling
#
# Copyright (c) 1999-2007 by NAME All rights reserved.
#
# EMAIL 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.
# --------------------------------------------------------------------
|
# -*- coding: utf-8 -*-
# This file is part of ranger, the console file manager.
# This configuration file is licensed under the same terms as ranger.
# ===================================================================
#
# NOTE: If you copied this file to ~/.config/ranger/commands_full.py,
# then it will NOT be loaded by ranger, and only serve as a reference.
#
# ===================================================================
# This file contains ranger's commands.
# It's all in python; lines beginning with # are comments.
#
# Note that additional commands are automatically generated from the methods
# of the class ranger.core.actions.Actions.
#
# You can customize commands in the file ~/.config/ranger/commands.py.
# It has the same syntax as this file. In fact, you can just copy this
# file there with `ranger --copy-config=commands' and make your modifications.
# But make sure you update your configs when you update ranger.
#
# ===================================================================
# Every class defined here which is a subclass of `Command' will be used as a
# command in ranger. Several methods are defined to interface with ranger:
# execute(): called when the command is executed.
# cancel(): called when closing the console.
# tab(tabnum): called when <TAB> is pressed.
# quick(): called after each keypress.
#
# tab() argument tabnum is 1 for <TAB> and -1 for <S-TAB> by default
#
# The return values for tab() can be either:
# None: There is no tab completion
# A string: Change the console to this string
# A list/tuple/generator: cycle through every item in it
#
# The return value for quick() can be:
# False: Nothing happens
# True: Execute the command afterwards
#
# The return value for execute() and cancel() doesn't matter.
#
# ===================================================================
# Commands have certain attributes and methods that facilitate parsing of
# the arguments:
#
# self.line: The whole line that was written in the console.
# self.args: A list of all (space-separated) arguments to the command.
# self.quantifier: If this command was mapped to the key "X" and
# the user pressed 6X, self.quantifier will be 6.
# self.arg(n): The n-th argument, or an empty string if it doesn't exist.
# self.rest(n): The n-th argument plus everything that followed. For example,
# if the command was "search foo bar a b c", rest(2) will be "bar a b c"
# self.start(n): Anything before the n-th argument. For example, if the
# command was "search foo bar a b c", start(2) will be "search foo"
#
# ===================================================================
# And this is a little reference for common ranger functions and objects:
#
# self.fm: A reference to the "fm" object which contains most information
# about ranger.
# self.fm.notify(string): Print the given string on the screen.
# self.fm.notify(string, bad=True): Print the given string in RED.
# self.fm.reload_cwd(): Reload the current working directory.
# self.fm.thisdir: The current working directory. (A File object.)
# self.fm.thisfile: The current file. (A File object too.)
# self.fm.thistab.get_selection(): A list of all selected files.
# self.fm.execute_console(string): Execute the string as a ranger command.
# self.fm.open_console(string): Open the console with the given string
# already typed in for you.
# self.fm.move(direction): Moves the cursor in the given direction, which
# can be something like down=3, up=5, right=1, left=1, to=6, ...
#
# File objects (for example self.fm.thisfile) have these useful attributes and
# methods:
#
# cf.path: The path to the file.
# cf.basename: The base name only.
# cf.load_content(): Force a loading of the directories content (which
# obviously works with directories only)
# cf.is_directory: True/False depending on whether it's a directory.
#
# For advanced commands it is unavoidable to dive a bit into the source code
# of ranger.
# ===================================================================
|
"""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 <EMAIL>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
# #!/usr/bin/env python
#
# """
# @package ion.agents.platform.test.test_platform_agent_with_rsn
# @file ion/agents/platform/test/test_platform_agent_with_rsn.py
# @author NAME @brief Test cases for platform agent interacting with RSN
# """
#
# __author__ = 'Carlos NAME __license__ = 'Apache 2.0'
#
# # The following can be prefixed with PLAT_NETWORK=single to exercise the tests
# # with a single platform (with no sub-platforms). Otherwise a small network is
# # used. See HelperTestMixin.
# # bin/nosetests -sv --nologcapture ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_resource_monitoring
# # bin/nosetests -sv --nologcapture ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_capabilities
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_some_state_transitions
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_get_set_resources
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_some_commands
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_resource_monitoring
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_resource_monitoring_recent
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_external_event_dispatch
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_connect_disconnect_instrument
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_check_sync
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_execute_resource
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_resource_states
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_lost_connection_and_reconnect
# # bin/nosetests -sv ion/agents/platform/test/test_platform_agent_with_rsn.py:TestPlatformAgent.test_alerts
# #
#
#
# from ion.agents.platform.test.base_test_platform_agent_with_rsn import BaseIntTestPlatform
# from pyon.public import log, CFG
#
# from pyon.util.containers import get_ion_ts
#
# from interface.objects import AgentCommand
# from interface.objects import CapabilityType
# from interface.objects import AgentCapability
#
# from interface.objects import StreamAlertType, AggregateStatusType
#
# from pyon.core.exception import Conflict
#
# from pyon.event.event import EventSubscriber
#
# from ion.agents.platform.platform_agent import PlatformAgentState
# from ion.agents.platform.platform_agent import PlatformAgentEvent
# from ion.agents.platform.responses import NormalResponse
# from ion.agents.platform.rsn.rsn_platform_driver import RSNPlatformDriverState
# from ion.agents.platform.rsn.rsn_platform_driver import RSNPlatformDriverEvent
#
# from ion.services.dm.utility.granule.record_dictionary import RecordDictionaryTool
# from pyon.public import IonObject
# from pyon.util.containers import current_time_millis
# from ion.agents.platform.util import ntp_2_ion_ts
#
# from gevent import sleep
# from gevent.event import AsyncResult
# from mock import patch
# from pyon.public import CFG
# import unittest
# import os
#
# @patch.dict(CFG, {'endpoint': {'receive': {'timeout': 180}}})
# @unittest.skipIf((not os.getenv('PYCC_MODE', False)) and os.getenv('CEI_LAUNCH_TEST', False), 'Skip until tests support launch port agent configurations.')
# class TestPlatformAgent(BaseIntTestPlatform):
#
# def _create_network_and_start_root_platform(self, clean_up=None):
# """
# Call this at the beginning of each test. We need to make sure that
# the patched timeout is in effect for the actions performed here.
#
# @note this used to be done in setUp, but the patch.dict mechanism does
# *not* take effect in setUp!
#
# An addCleanup function is added to reset/shutdown the network and stop the
# root platform. Should avoid leaked processes/greenlet upon failing tests
# (except perhaps if they happen during the launch of the root platform).
#
# @param clean_up Not None to override default pre-cleanUp calls.
# """
# self._set_receive_timeout()
#
# self.p_root = None
#
# # NOTE The tests expect to use values set up by HelperTestMixin for
# # the following networks (see ion/agents/platform/test/helper.py)
# if self.PLATFORM_ID == 'Node1D':
# #self.p_root = self._create_small_hierarchy()
# instr_keys = ["SBE37_SIM_01", ]
# self.p_root = self._set_up_small_hierarchy_with_some_instruments(instr_keys)
#
# elif self.PLATFORM_ID == 'LJ01D':
# self.p_root = self._create_single_platform()
#
# else:
# self.fail("self.PLATFORM_ID expected to be one of: 'Node1D', 'LJ01D'")
#
# self._start_platform(self.p_root)
#
# def done():
# if self.p_root:
# try:
# if clean_up:
# clean_up()
# else:
# # default "done" sequence for most tests
# try:
# self._go_inactive()
# self._reset()
# finally: # attempt shutdown anyway
# self._shutdown()
# finally:
# self._stop_platform(self.p_root)
# self.p_root = None
# self.addCleanup(done)
#
# def _connect_instrument(self):
# #
# # TODO more realistic settings for the connection
# #
# port_id = self.PORT_ID
# instrument_id = self.INSTRUMENT_ID
# instrument_attributes = self.INSTRUMENT_ATTRIBUTES_AND_VALUES
#
# kwargs = dict(
# port_id = port_id,
# instrument_id = instrument_id,
# attributes = instrument_attributes
# )
# result = self._execute_resource(RSNPlatformDriverEvent.CONNECT_INSTRUMENT, **kwargs)
# log.info("CONNECT_INSTRUMENT = %s", result)
# self.assertIsInstance(result, dict)
# self.assertIn(port_id, result)
# self.assertIsInstance(result[port_id], dict)
# returned_attrs = self._verify_valid_instrument_id(instrument_id, result[port_id])
# if isinstance(returned_attrs, dict):
# for attrName in instrument_attributes:
# self.assertIn(attrName, returned_attrs)
#
# def _disconnect_instrument(self):
# # TODO real settings and corresp verification
#
# port_id = self.PORT_ID
# instrument_id = self.INSTRUMENT_ID
#
# kwargs = dict(
# port_id = port_id,
# instrument_id = instrument_id
# )
# result = self._execute_resource(RSNPlatformDriverEvent.DISCONNECT_INSTRUMENT, **kwargs)
# log.info("DISCONNECT_INSTRUMENT = %s", result)
# self.assertIsInstance(result, dict)
# self.assertIn(port_id, result)
# self.assertIsInstance(result[port_id], dict)
# self.assertIn(instrument_id, result[port_id])
# self._verify_instrument_disconnected(instrument_id, result[port_id][instrument_id])
#
# def _turn_on_port(self):
# # TODO real settings and corresp verification
#
# port_id = self.PORT_ID
#
# kwargs = dict(
# port_id = port_id
# )
# result = self._execute_resource(RSNPlatformDriverEvent.TURN_ON_PORT, **kwargs)
# log.info("TURN_ON_PORT = %s", result)
# self.assertIsInstance(result, dict)
# self.assertTrue(port_id in result)
# self.assertEquals(result[port_id], NormalResponse.PORT_TURNED_ON)
#
# def _turn_off_port(self):
# # TODO real settings and corresp verification
#
# port_id = self.PORT_ID
#
# kwargs = dict(
# port_id = port_id
# )
# result = self._execute_resource(RSNPlatformDriverEvent.TURN_OFF_PORT, **kwargs)
# log.info("TURN_OFF_PORT = %s", result)
# self.assertIsInstance(result, dict)
# self.assertTrue(port_id in result)
# self.assertEquals(result[port_id], NormalResponse.PORT_TURNED_OFF)
#
# def _get_resource(self):
# """
# Gets platform attribute values/
# """
# attrNames = self.ATTR_NAMES
# #
# # OOIION-631: use get_ion_ts() as a basis for using system time, which is
# # a string.
# #
# cur_time = get_ion_ts()
# from_time = str(int(cur_time) - 50000) # a 50-sec time window
# attrs = [(attr_id, from_time) for attr_id in attrNames]
# kwargs = dict(attrs=attrs)
# cmd = AgentCommand(command=PlatformAgentEvent.GET_RESOURCE, kwargs=kwargs)
# retval = self._execute_agent(cmd)
# attr_values = retval.result
# self.assertIsInstance(attr_values, dict)
# for attr_name in attrNames:
# self._verify_valid_attribute_id(attr_name, attr_values)
#
# def _set_resource(self):
# attrNames = self.ATTR_NAMES
# writ_attrNames = self.WRITABLE_ATTR_NAMES
#
# # do valid settings:
#
# # TODO more realistic value depending on attribute's type
# attrs = [(attrName, self.VALID_ATTR_VALUE) for attrName in attrNames]
# log.info("%r: setting attributes=%s", self.PLATFORM_ID, attrs)
# kwargs = dict(attrs=attrs)
# cmd = AgentCommand(command=PlatformAgentEvent.SET_RESOURCE, kwargs=kwargs)
# retval = self._execute_agent(cmd)
# attr_values = retval.result
# self.assertIsInstance(attr_values, dict)
# for attrName in attrNames:
# if attrName in writ_attrNames:
# self._verify_valid_attribute_id(attrName, attr_values)
# else:
# self._verify_not_writable_attribute_id(attrName, attr_values)
#
# # try invalid settings:
#
# # set invalid values to writable attributes:
# attrs = [(attrName, self.INVALID_ATTR_VALUE) for attrName in writ_attrNames]
# log.info("%r: setting attributes=%s", self.PLATFORM_ID, attrs)
# kwargs = dict(attrs=attrs)
# cmd = AgentCommand(command=PlatformAgentEvent.SET_RESOURCE, kwargs=kwargs)
# retval = self._execute_agent(cmd)
# attr_values = retval.result
# self.assertIsInstance(attr_values, dict)
# for attrName in writ_attrNames:
# self._verify_attribute_value_out_of_range(attrName, attr_values)
#
# def _get_subplatform_ids(self):
# kwargs = dict(subplatform_ids=None)
# cmd = AgentCommand(command=PlatformAgentEvent.GET_RESOURCE, kwargs=kwargs)
# retval = self._execute_agent(cmd)
# subplatform_ids = retval.result
# self.assertIsInstance(subplatform_ids, (list, tuple))
# return subplatform_ids
#
# def test_capabilities(self):
# self._create_network_and_start_root_platform()
#
# agt_cmds_all = [
# PlatformAgentEvent.INITIALIZE,
# PlatformAgentEvent.RESET,
# PlatformAgentEvent.SHUTDOWN,
# PlatformAgentEvent.GO_ACTIVE,
# PlatformAgentEvent.GO_INACTIVE,
# PlatformAgentEvent.RUN,
#
# PlatformAgentEvent.CLEAR,
# PlatformAgentEvent.PAUSE,
# PlatformAgentEvent.RESUME,
# #PlatformAgentEvent.GET_RESOURCE_CAPABILITIES,
# #PlatformAgentEvent.PING_RESOURCE,
# #PlatformAgentEvent.GET_RESOURCE,
# #PlatformAgentEvent.SET_RESOURCE,
# #PlatformAgentEvent.EXECUTE_RESOURCE,
# #PlatformAgentEvent.GET_RESOURCE_STATE,
#
# PlatformAgentEvent.START_MONITORING,
# PlatformAgentEvent.STOP_MONITORING,
#
# PlatformAgentEvent.RUN_MISSION,
# PlatformAgentEvent.ABORT_MISSION,
# PlatformAgentEvent.KILL_MISSION,
# ]
#
# def sort_caps(caps_list):
# agt_cmds = []
# agt_pars = []
# res_cmds = []
# res_iface = []
# res_pars = []
#
# if len(caps_list)>0 and isinstance(caps_list[0], AgentCapability):
# agt_cmds = [x.name for x in caps_list if x.cap_type==CapabilityType.AGT_CMD]
# agt_pars = [x.name for x in caps_list if x.cap_type==CapabilityType.AGT_PAR]
# res_cmds = [x.name for x in caps_list if x.cap_type==CapabilityType.RES_CMD]
# res_iface = [x.name for x in caps_list if x.cap_type==CapabilityType.RES_IFACE]
# res_pars = [x.name for x in caps_list if x.cap_type==CapabilityType.RES_PAR]
#
# elif len(caps_list)>0 and isinstance(caps_list[0], dict):
# agt_cmds = [x['name'] for x in caps_list if x['cap_type']==CapabilityType.AGT_CMD]
# agt_pars = [x['name'] for x in caps_list if x['cap_type']==CapabilityType.AGT_PAR]
# res_cmds = [x['name'] for x in caps_list if x['cap_type']==CapabilityType.RES_CMD]
# res_iface = [x['name'] for x in caps_list if x['cap_type']==CapabilityType.RES_IFACE]
# res_pars = [x['name'] for x in caps_list if x['cap_type']==CapabilityType.RES_PAR]
#
# state = self._pa_client.get_agent_state()
# log.debug("sort_caps: in agent state=%s\n"
# "agt_cmds => %s\n"
# "agt_pars => %s\n"
# "res_cmds => %s\n"
# "res_iface => %s\n"
# "res_pars => %s\n",
# state, agt_cmds, agt_pars, res_cmds, res_iface, res_pars)
#
# return agt_cmds, agt_pars, res_cmds, res_iface, res_pars
#
# def verify_schema(caps_list):
#
# dd_list = ['display_name','description']
# ddt_list = ['display_name','description','type']
# ddvt_list = ['display_name','description','visibility','type']
# ddak_list = ['display_name','description','args','kwargs']
# kkvt_res_list = ['display_name', 'description', 'visibility',
# 'type, monitor_cycle_seconds', 'precision',
# 'min_val', 'max_val', 'units', 'group']
# stream_list = ['tdb', 'tdbtdb']
#
# for x in caps_list:
# if isinstance(x,dict):
# x.pop('type_')
# x = IonObject('AgentCapability', **x)
#
# try:
# if x.cap_type == CapabilityType.AGT_CMD:
# if x['name'] == 'example':
# pass
# keys = x.schema.keys()
# for y in ddak_list:
# self.assertIn(y, keys)
#
# elif x.cap_type == CapabilityType.AGT_PAR:
# if x.name != 'example':
# keys = x.schema.keys()
# for y in ddvt_list:
# self.assertIn(y, keys)
#
# elif x.cap_type == CapabilityType.RES_CMD:
# keys = x.schema.keys()
# for y in ddak_list:
# self.assertIn(y, keys)
#
# elif x.cap_type == CapabilityType.RES_IFACE:
# pass
#
# elif x.cap_type == CapabilityType.RES_PAR:
# pass
# #keys = x.schema.keys()
# #for y in kkvt_res_list:
# # self.assertIn(y, keys)
#
# elif x.cap_type == CapabilityType.AGT_STATES:
# for (k,v) in x.schema.iteritems():
# keys = v.keys()
# for y in dd_list:
# self.assertIn(y, keys)
#
# elif x.cap_type == CapabilityType.ALERT_DEFS:
# for (k,v) in x.schema.iteritems():
# keys = v.keys()
# for y in ddt_list:
# self.assertIn(y, keys)
#
# elif x.cap_type == CapabilityType.AGT_CMD_ARGS:
# pass
# """
# for (k,v) in x.schema.iteritems():
# keys = v.keys()
# for y in ddt_list:
# self.assertIn(y, keys)
# """
#
# elif x.cap_type == CapabilityType.AGT_STREAMS:
# pass
# #keys = x.schema.keys()
# #for y in stream_list:
# # self.assertIn(y, keys)
#
# except Exception:
# print '### ERROR verifying schema for'
# print x['name']
# raise
#
# agt_pars_all = [
# 'example',
# 'child_agg_status',
# 'alerts',
# 'aggstatus',
# 'rollup_status',
# ]
# res_pars_all = []
# res_cmds_all = [
# RSNPlatformDriverEvent.CONNECT_INSTRUMENT,
# RSNPlatformDriverEvent.DISCONNECT_INSTRUMENT,
# RSNPlatformDriverEvent.TURN_ON_PORT,
# RSNPlatformDriverEvent.TURN_OFF_PORT,
# # RSNPlatformDriverEvent.CHECK_SYNC #OOIION-1623 Remove until Check Sync requirements fully defined
# ]
#
# ##################################################################
# # UNINITIALIZED
# ##################################################################
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
#
# # Get exposed capabilities in current state.
# retval = self._pa_client.get_capabilities()
#
# # Validate capabilities for state UNINITIALIZED.
# agt_cmds, agt_pars, res_cmds, res_iface, res_pars = sort_caps(retval)
#
# agt_cmds_uninitialized = [
# PlatformAgentEvent.INITIALIZE,
# PlatformAgentEvent.SHUTDOWN,
# ]
# self.assertItemsEqual(agt_cmds, agt_cmds_uninitialized)
# self.assertItemsEqual(agt_pars, agt_pars_all)
# self.assertItemsEqual(res_cmds, [])
# self.assertItemsEqual(res_pars, [])
#
# # Get exposed capabilities in all states.
# retval = self._pa_client.get_capabilities(current_state=False)
#
# # Validate all capabilities as read from state UNINITIALIZED.
# agt_cmds, agt_pars, res_cmds, res_iface, res_pars = sort_caps(retval)
#
# self.assertItemsEqual(agt_cmds, agt_cmds_all)
# self.assertItemsEqual(agt_pars, agt_pars_all)
# self.assertItemsEqual(res_cmds, [])
# #self.assertItemsEqual(res_pars, [])
#
# verify_schema(retval)
#
# ##################################################################
# # INACTIVE
# ##################################################################
# self._initialize()
#
# # Get exposed capabilities in current state.
# retval = self._pa_client.get_capabilities()
#
# # Validate capabilities for state INACTIVE.
# agt_cmds, agt_pars, res_cmds, res_iface, res_pars = sort_caps(retval)
#
# agt_cmds_inactive = [
# PlatformAgentEvent.RESET,
# PlatformAgentEvent.SHUTDOWN,
# PlatformAgentEvent.GO_ACTIVE,
# #PlatformAgentEvent.PING_RESOURCE,
# #PlatformAgentEvent.GET_RESOURCE_CAPABILITIES,
# #PlatformAgentEvent.GET_RESOURCE_STATE,
# ]
#
# self.assertItemsEqual(agt_cmds, agt_cmds_inactive)
# self.assertItemsEqual(agt_pars, agt_pars_all)
# self.assertItemsEqual(res_cmds, [])
# #self.assertItemsEqual(res_pars, [])
#
# # Get exposed capabilities in all states.
# retval = self._pa_client.get_capabilities(False)
#
# # Validate all capabilities as read from state INACTIVE.
# agt_cmds, agt_pars, res_cmds, res_iface, res_pars = sort_caps(retval)
#
# self.assertItemsEqual(agt_cmds, agt_cmds_all)
# self.assertItemsEqual(agt_pars, agt_pars_all)
# self.assertItemsEqual(res_cmds, [])
# #self.assertItemsEqual(res_pars, [])
#
# verify_schema(retval)
#
# print '############### resource params'
# for x in res_pars:
# print str(x)
#
# ##################################################################
# # IDLE
# ##################################################################
# self._go_active()
#
# # Get exposed capabilities in current state.
# retval = self._pa_client.get_capabilities()
#
# # Validate capabilities for state IDLE.
# agt_cmds, agt_pars, res_cmds, res_iface, res_pars = sort_caps(retval)
#
# agt_cmds_idle = [
# PlatformAgentEvent.RESET,
# PlatformAgentEvent.SHUTDOWN,
# PlatformAgentEvent.GO_INACTIVE,
# PlatformAgentEvent.RUN,
# #PlatformAgentEvent.PING_RESOURCE,
# #PlatformAgentEvent.GET_RESOURCE_CAPABILITIES,
# #PlatformAgentEvent.GET_RESOURCE_STATE,
# ]
#
# self.assertItemsEqual(agt_cmds, agt_cmds_idle)
# self.assertItemsEqual(agt_pars, agt_pars_all)
# self.assertItemsEqual(res_cmds, [])
# #self.assertItemsEqual(res_pars, [])
#
# # Get exposed capabilities in all states as read from IDLE.
# retval = self._pa_client.get_capabilities(False)
#
# # Validate all capabilities as read from state IDLE.
# agt_cmds, agt_pars, res_cmds, res_iface, res_pars = sort_caps(retval)
#
# self.assertItemsEqual(agt_cmds, agt_cmds_all)
# self.assertItemsEqual(agt_pars, agt_pars_all)
# self.assertItemsEqual(res_cmds, [])
# #self.assertItemsEqual(res_pars, [])
#
# verify_schema(retval)
#
# ##################################################################
# # COMMAND
# ##################################################################
# self._run()
#
# # Get exposed capabilities in current state.
# retval = self._pa_client.get_capabilities()
#
# # Validate capabilities of state COMMAND
# agt_cmds, agt_pars, res_cmds, res_iface, res_pars = sort_caps(retval)
#
# agt_cmds_command = [
# PlatformAgentEvent.GO_INACTIVE,
# PlatformAgentEvent.RESET,
# PlatformAgentEvent.SHUTDOWN,
# PlatformAgentEvent.PAUSE,
# PlatformAgentEvent.CLEAR,
#
# #PlatformAgentEvent.GET_RESOURCE_CAPABILITIES,
# #PlatformAgentEvent.PING_RESOURCE,
# #PlatformAgentEvent.GET_RESOURCE,
# #PlatformAgentEvent.SET_RESOURCE,
# #PlatformAgentEvent.EXECUTE_RESOURCE,
# #PlatformAgentEvent.GET_RESOURCE_STATE,
#
# PlatformAgentEvent.START_MONITORING,
#
# PlatformAgentEvent.RUN_MISSION,
# ]
#
# self.assertItemsEqual(agt_cmds, agt_cmds_command)
# self.assertItemsEqual(agt_pars, agt_pars_all)
# self.assertItemsEqual(res_cmds, res_cmds_all)
# #self.assertItemsEqual(res_pars, res_pars_all)
#
# verify_schema(retval)
#
# ##################################################################
# # STOPPED
# ##################################################################
# self._pause()
#
# # Get exposed capabilities in current state.
# retval = self._pa_client.get_capabilities()
#
# # Validate capabilities of state STOPPED
# agt_cmds, agt_pars, res_cmds, res_iface, res_pars = sort_caps(retval)
#
# agt_cmds_stopped = [
# PlatformAgentEvent.RESUME,
# PlatformAgentEvent.CLEAR,
# PlatformAgentEvent.GO_INACTIVE,
# PlatformAgentEvent.RESET,
# PlatformAgentEvent.SHUTDOWN,
# #PlatformAgentEvent.PING_RESOURCE,
# #PlatformAgentEvent.GET_RESOURCE_CAPABILITIES,
# #PlatformAgentEvent.GET_RESOURCE_STATE,
# ]
#
# self.assertItemsEqual(agt_cmds, agt_cmds_stopped)
# self.assertItemsEqual(agt_pars, agt_pars_all)
# self.assertItemsEqual(res_cmds, [])
# #self.assertItemsEqual(res_pars, res_pars_all)
#
# verify_schema(retval)
#
# # back to COMMAND:
# self._resume()
#
# ##################################################################
# # MONITORING
# ##################################################################
# self._start_resource_monitoring()
#
# # Get exposed capabilities in current state.
# retval = self._pa_client.get_capabilities()
#
# # Validate capabilities of state MONITORING
# agt_cmds, agt_pars, res_cmds, res_iface, res_pars = sort_caps(retval)
#
# agt_cmds_monitoring = [
# PlatformAgentEvent.RESET,
# PlatformAgentEvent.SHUTDOWN,
#
# #PlatformAgentEvent.GET_RESOURCE_CAPABILITIES,
# #PlatformAgentEvent.PING_RESOURCE,
# #PlatformAgentEvent.GET_RESOURCE,
# #PlatformAgentEvent.SET_RESOURCE,
# #PlatformAgentEvent.EXECUTE_RESOURCE,
# #PlatformAgentEvent.GET_RESOURCE_STATE,
#
# PlatformAgentEvent.STOP_MONITORING,
#
# PlatformAgentEvent.RUN_MISSION,
#
# ]
#
# self.assertItemsEqual(agt_cmds, agt_cmds_monitoring)
# self.assertItemsEqual(agt_pars, agt_pars_all)
# self.assertItemsEqual(res_cmds, res_cmds_all)
# #self.assertItemsEqual(res_pars, res_pars_all)
#
# verify_schema(retval)
#
# # return to COMMAND state:
# self._stop_resource_monitoring()
#
#
# ###################
# # ALL CAPABILITIES
# ###################
#
# # Get exposed capabilities in all states as read from state COMMAND.
# retval = self._pa_client.get_capabilities(False)
#
# # Validate all capabilities as read from state COMMAND
# agt_cmds, agt_pars, res_cmds, res_iface, res_pars = sort_caps(retval)
#
# self.assertItemsEqual(agt_cmds, agt_cmds_all)
# self.assertItemsEqual(agt_pars, agt_pars_all)
# self.assertItemsEqual(res_cmds, res_cmds_all)
# #self.assertItemsEqual(res_pars, res_pars_all)
#
# verify_schema(retval)
#
# def test_some_state_transitions(self):
# self._create_network_and_start_root_platform(self._shutdown)
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
#
# self._initialize() # -> INACTIVE
# self._reset() # -> UNINITIALIZED
#
# self._initialize() # -> INACTIVE
# self._go_active() # -> IDLE
# self._reset() # -> UNINITIALIZED """
#
# self._initialize() # -> INACTIVE
# self._go_active() # -> IDLE
# self._run() # -> COMMAND
# self._pause() # -> STOPPED
# self._resume() # -> COMMAND
# self._clear() # -> IDLE
# self._reset() # -> UNINITIALIZED
#
# def test_get_set_resources(self):
# self._create_network_and_start_root_platform()
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
# self._ping_agent()
#
# self._initialize()
# self._go_active()
# self._run()
#
# self._get_resource()
# self._set_resource()
#
# def test_some_commands(self):
# self._create_network_and_start_root_platform()
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
# self._ping_agent()
#
# self._initialize()
# self._go_active()
# self._run()
#
# self._ping_agent()
# self._ping_resource()
#
# self._get_metadata()
# self._get_subplatform_ids()
#
# ports = self._get_ports()
# for port_id in ports:
# self._get_connected_instruments(port_id)
#
# def test_resource_monitoring(self):
# #
# # Basic test for resource monitoring: starts monitoring, waits for
# # a sample to be published, and stops resource monitoring.
# #
# self._create_network_and_start_root_platform()
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
# self._ping_agent()
#
# self._initialize()
# self._go_active()
# self._run()
#
# self._start_resource_monitoring()
# try:
# self._wait_for_a_data_sample()
# finally:
# self._stop_resource_monitoring()
#
# def test_resource_monitoring_recent(self):
# #
# # https://jira.oceanobservatories.org/tasks/browse/OOIION-1372
# #
# # Verifies that the requests for attribute values are always for
# # the most recent ones, meaning that the retrieved values should *not*
# # be older than a small multiple of the nominal monitoring rate, even
# # after a long period in non-monitoring state.
# # See ResourceMonitor._retrieve_attribute_values
# #
#
# # start this test as in test_resource_monitoring()
# self.test_resource_monitoring()
# # which completes right after stopping monitoring. We want that initial
# # start/stop-monitoring phase to make this test more comprehensive.
# self._assert_state(PlatformAgentState.COMMAND)
#
# # now, the rest of this test does the following:
# # - pick an attribute to use as a basis for the time parameters to
# # be used in the test
# # - wait for a while in the current non-monitoring mode
# # - re-enable monitoring
# # - wait for a sample to be published
# # - verify that new received data sample is "recent"
# # - stop monitoring
#
# # first, use an attribute (from the root platform being tested) with
# # a minimal monitoring rate, since that attribute should be reported
# # in a first sample received after re-enabling the monitoring.
# attr = None
# for attr_id, plat_attr in self._platform_attributes[self.PLATFORM_ID].iteritems():
# if attr is None or \
# float(plat_attr['monitor_cycle_seconds']) < float(attr['monitor_cycle_seconds']):
# attr = plat_attr
#
# self.assertIsNotNone(attr,
# "some attribute expected to be defined for %r to "
# "actually proceed with this test" % self.PLATFORM_ID)
#
# attr_id = attr['attr_id']
# monitor_cycle_seconds = attr['monitor_cycle_seconds']
# log.info("test_resource_monitoring_recent: using attr_id=%r: monitor_cycle_seconds=%s",
# attr_id, monitor_cycle_seconds)
#
# # sleep for twice the interval defining "recent":
# from ion.agents.platform.resource_monitor import _MULT_INTERVAL
# time_to_sleep = 2 * (_MULT_INTERVAL * monitor_cycle_seconds)
# log.info("test_resource_monitoring_recent: sleeping for %s secs "
# "before resuming monitoring", time_to_sleep)
# sleep(time_to_sleep)
#
# # reset the variables associated with the _wait_for_a_data_sample call below:
# self._samples_received = []
# self._async_data_result = AsyncResult()
#
# #################################################
# # re-start monitoring and wait for new sample:
# log.info("test_resource_monitoring_recent: re-starting monitoring")
# self._start_resource_monitoring(recursion=False)
# # should also work with recursion to children but set recursion=False
# # to avoid wasting the extra time in this test.
#
# try:
# self._wait_for_a_data_sample()
#
# # get current time here (right after receiving sample) for comparison below:
# curr_time_millis = current_time_millis()
#
# # verify that the timestamp of the received sample is not too old.
# # For this, use the minimum of the reported timestamps:
# rdt = RecordDictionaryTool.load_from_granule(self._samples_received[0])
# log.trace("test_resource_monitoring_recent: rdt:\n%s", rdt.pretty_print())
# temporal_parameter_name = rdt.temporal_parameter
# times = rdt[temporal_parameter_name]
# log.trace("test_resource_monitoring_recent: times:\n%s", self._pp.pformat(times))
#
# # minimum reported timestamp (note the NTP -> ION_time conversion):
# min_reported_time_ntp = min(times)
# min_reported_time_millis = float(ntp_2_ion_ts(min_reported_time_ntp))
# log.info("test_resource_monitoring_recent: sample received, min_reported_time_millis=%s",
# int(min_reported_time_millis))
#
# # finally verify that it is actually not older than the small multiple
# # of monitor_cycle_seconds plus some additional tolerance (which is
# # arbitrarily set here to 10 secs):
# lower_limit_millis = \
# curr_time_millis - 1000 * (_MULT_INTERVAL * monitor_cycle_seconds + 10)
#
# self.assertGreaterEqual(
# min_reported_time_millis, lower_limit_millis,
# "min_reported_time_millis=%s must be >= %s. Diff=%s millis" % (
# min_reported_time_millis, lower_limit_millis,
# min_reported_time_millis - lower_limit_millis))
#
# finally:
# self._stop_resource_monitoring(recursion=False)
#
# def test_external_event_dispatch(self):
# self._create_network_and_start_root_platform()
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
# self._ping_agent()
#
# self._initialize()
#
# # according to process_oms_event() (in service_gateway_service.py)
# # https://github.com/ooici/coi-services/blob/999c4315259082a9e50d6f4f96f8dd606073fda8/ion/services/coi/service_gateway_service.py#L339-370
# async_event_result, events_received = self._start_event_subscriber2(
# count=1,
# event_type="OMSDeviceStatusEvent",
# origin_type='OMS Platform'
# )
#
# self._go_active()
# self._run()
#
# # verify reception of the external event:
# log.info("waiting for external event notification... (timeout=%s)", self._receive_timeout)
# async_event_result.get(timeout=self._receive_timeout)
# self.assertEquals(len(events_received), 1)
# log.info("external events received: (%d): %s", len(events_received), events_received)
#
# def test_connect_disconnect_instrument(self):
# self._create_network_and_start_root_platform()
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
# self._ping_agent()
#
# self._initialize()
# self._go_active()
# self._run()
#
# self._connect_instrument()
# self._turn_on_port()
#
# self._turn_off_port()
# self._disconnect_instrument()
#
# def test_check_sync(self):
# self._create_network_and_start_root_platform()
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
# self._ping_agent()
#
# self._initialize()
# self._go_active()
# self._run()
#
# self._check_sync()
#
# self._connect_instrument()
# self._check_sync()
#
# self._disconnect_instrument()
# self._check_sync()
#
# def test_execute_resource(self):
# self._create_network_and_start_root_platform()
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
#
# self._initialize()
# self._go_active()
# self._run()
#
# self._execute_resource(RSNPlatformDriverEvent.CHECK_SYNC)
#
# def test_resource_states(self):
# self._create_network_and_start_root_platform(self._shutdown)
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
#
# with self.assertRaises(Conflict):
# self._pa_client.get_resource_state()
#
# self._initialize()
#
# self._start_event_subscriber(event_type="ResourceAgentResourceStateEvent",
# count=2)
#
# res_state = self._pa_client.get_resource_state()
# self.assertEqual(res_state, RSNPlatformDriverState.DISCONNECTED)
#
# self._go_active()
#
# res_state = self._pa_client.get_resource_state()
# self.assertEqual(res_state, RSNPlatformDriverState.CONNECTED)
#
# self._run()
#
# res_state = self._pa_client.get_resource_state()
# self.assertEqual(res_state, RSNPlatformDriverState.CONNECTED)
#
# self._go_inactive()
#
# res_state = self._pa_client.get_resource_state()
# self.assertEqual(res_state, RSNPlatformDriverState.DISCONNECTED)
#
# self._reset()
#
# with self.assertRaises(Conflict):
# self._pa_client.get_resource_state()
#
# self._async_event_result.get(timeout=self._receive_timeout)
# self.assertGreaterEqual(len(self._events_received), 2)
#
# def test_lost_connection_and_reconnect(self):
# #
# # Starts up the network and puts the root platform in the MONITORING
# # state; then makes the simulator generate synthetic exceptions for
# # any call, which are handled by the driver as "connection lost"
# # situations; then it verifies the publication of the associated event
# # from the agent, and the LOST_CONNECTION state for the agent.
# # Finally, it instructs the simulator to resume working normally,
# # which should make the reconnect logic in the agent to recover the
# # connection and go back to the state where it was at connection lost.
# #
#
# ######################################################
# # set up network and put root in MONITORING state
#
# self._create_network_and_start_root_platform()
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
# self._initialize()
#
# async_event_result, events_received = self._start_event_subscriber2(
# count=1,
# event_type="ResourceAgentConnectionLostErrorEvent",
# origin=self.p_root.platform_device_id)
#
# self._go_active()
# self._run()
#
# self._start_resource_monitoring()
#
# # let normal activity go on for a while:
# sleep(15)
#
# ######################################################
# # disable simulator to trigger lost connection:
# log.debug("disabling simulator")
# self._simulator_disable()
#
# # verify a ResourceAgentConnectionLostErrorEvent was published:
# async_event_result.get(timeout=self._receive_timeout)
# self.assertEquals(len(events_received), 1)
#
# # verify the platform is now in LOST_CONNECTION:
# self._assert_state(PlatformAgentState.LOST_CONNECTION)
#
# ######################################################
# # reconnect phase
#
# # re-enable simulator so connection is re-established:
# log.debug("re-enabling simulator")
# self._simulator_enable()
#
# # wait for a bit for the reconnection to take effect:
# sleep(15)
#
# # verify the platform is now back in MONITORING
# self._assert_state(PlatformAgentState.MONITORING)
#
# self._stop_resource_monitoring()
#
# def test_alerts(self):
# #
# # Tests alert processing/publication from the platform agent. Both
# # alert definitions passed via configuration and alert definitions
# # passed via the agent's set_agent({'alerts' : alert_defs}) method
# # are tested.
# #
#
# def start_DeviceStatusAlertEvent_subscriber(value_id, sub_type):
# """
# @return async_event_result Use it to wait for the expected event
# """
# event_type = "DeviceStatusAlertEvent"
#
# async_event_result = AsyncResult()
#
# def consume_event(evt, *args, **kwargs):
# log.info('DeviceStatusAlertEvent_subscriber received evt: %s', str(evt))
# if evt.type_ != event_type or \
# evt.value_id != value_id or \
# evt.sub_type != sub_type:
# return
#
# async_event_result.set(evt)
#
# kwargs = dict(event_type=event_type,
# callback=consume_event,
# origin=self.p_root.platform_device_id,
# sub_type=sub_type)
#
# sub = EventSubscriber(**kwargs)
# sub.start()
# log.info("registered DeviceStatusAlertEvent subscriber: %s", kwargs)
#
# self._event_subscribers.append(sub)
# sub._ready_event.wait(timeout=self._receive_timeout)
#
# return async_event_result
#
# # before the creation of the network, set some alert defs for the
# # configuration of the root platform we are testing:
# alerts_for_config = [
# {
# 'name' : 'input_bus_current_warning_interval',
# 'stream_name' : 'parsed',
# 'value_id' : 'input_bus_current',
# 'description' : 'input_bus_current is above normal range.',
# 'alert_type' : StreamAlertType.WARNING,
# 'aggregate_type' : AggregateStatusType.AGGREGATE_DATA,
# 'lower_bound' : None,
# 'lower_rel_op' : None,
# 'upper_bound' : 200.0,
# 'upper_rel_op' : '<',
# 'alert_class' : 'IntervalAlert'
# }]
# self._set_additional_extra_fields_for_platform_configuration(
# self.PLATFORM_ID, {'alerts': alerts_for_config})
#
# self._create_network_and_start_root_platform()
#
# self._assert_state(PlatformAgentState.UNINITIALIZED)
# self._ping_agent()
#
# self._initialize()
#
# # verify we get reported the configured alerts:
# configed_alerts = self._pa_client.get_agent(['alerts'])['alerts']
# self.assertEquals(len(alerts_for_config), len(configed_alerts),
# "must have %d alerts defined from configuration but got %d" % (
# len(alerts_for_config), len(configed_alerts)))
#
# # define some additional alerts:
# # NOTE: see ion/agents/platform/rsn/simulator/oms_values.py for the
# # sinusoidal waveforms that are generated; here we depend on those
# # ranges to indicate the upper_bounds for these alarms; for example,
# # input_voltage fluctuates within -500.0 to +500, so we specify
# # upper_bound = 400.0 to see the alert being published.
# new_alert_defs = [
# {
# 'name' : 'input_voltage_warning_interval',
# 'stream_name' : 'parsed',
# 'value_id' : 'input_voltage',
# 'description' : 'input_voltage is above normal range.',
# 'alert_type' : StreamAlertType.WARNING,
# 'aggregate_type' : AggregateStatusType.AGGREGATE_DATA,
# 'lower_bound' : None,
# 'lower_rel_op' : None,
# 'upper_bound' : 400.0,
# 'upper_rel_op' : '<',
# 'alert_class' : 'IntervalAlert'
# }]
#
# # All the alerts to be set: the configured ones plus the new ones above:
# alert_defs = configed_alerts + new_alert_defs
#
# self._pa_client.set_agent({'alerts' : alert_defs})
#
# retval = self._pa_client.get_agent(['alerts'])['alerts']
# log.debug('alerts: %s', self._pp.pformat(retval))
# self.assertEquals(len(alert_defs), len(retval),
# "must have %d alerts defined here but got %d" % (
# len(alert_defs), len(retval)))
#
# self._go_active()
# self._run()
#
# #################################################################
# # prepare to receive alert publications:
# # note: as the values for the above streams fluctuate we should get
# # both WARNING and ALL_CLEAR events:
#
# # NOTE that the verifications below are for both the configured
# # alerts and the additional alerts set via set_agent.
#
# async_event_result1 = start_DeviceStatusAlertEvent_subscriber(
# value_id="input_voltage",
# sub_type=StreamAlertType._str_map[StreamAlertType.WARNING])
#
# async_event_result2 = start_DeviceStatusAlertEvent_subscriber(
# value_id="input_bus_current",
# sub_type=StreamAlertType._str_map[StreamAlertType.WARNING])
#
# async_event_result3 = start_DeviceStatusAlertEvent_subscriber(
# value_id="input_voltage",
# sub_type=StreamAlertType._str_map[StreamAlertType.ALL_CLEAR])
#
# async_event_result4 = start_DeviceStatusAlertEvent_subscriber(
# value_id="input_bus_current",
# sub_type=StreamAlertType._str_map[StreamAlertType.ALL_CLEAR])
#
# self._start_resource_monitoring()
#
# # wait for the expected DeviceStatusAlertEvent events:
# # (60sec timeout enough for the sine periods associated to the streams)
# async_event_result1.get(timeout=60)
# async_event_result2.get(timeout=60)
# async_event_result3.get(timeout=60)
# async_event_result4.get(timeout=60)
#
# self._stop_resource_monitoring()
|
#!/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 <EMAIL>
#
# 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
|
#!/usr/bin/python
# -*- encoding: utf-8; py-indent-offset: 4 -*-
# +------------------------------------------------------------------+
# | ____ _ _ __ __ _ __ |
# | / ___| |__ ___ ___| | __ | \/ | |/ / |
# | | | | '_ \ / _ \/ __| |/ / | |\/| | ' / |
# | | |___| | | | __/ (__| < | | | | . \ |
# | \____|_| |_|\___|\___|_|\_\___|_| |_|_|\_\ |
# | |
# | Copyright NAME 2013 EMAIL |
# +------------------------------------------------------------------+
#
# This file is part of Check_MK.
# The official homepage is at http://mathias-kettner.de/check_mk.
#
# check_mk 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 in version 2. check_mk is distributed
# in the hope that it will be useful, but WITHOUT ANY WARRANTY; with-
# out even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# PARTICULAR PURPOSE. See the GNU General Public License for more de-
# ails. You should have received a copy of the GNU General Public
# License along with GNU Make; see the file COPYING. If not, write
# to the Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
# Boston, MA 02110-1301 USA.
# .-README---------------------------------------------------------------.
# | ____ _ |
# | | _ \ ___ __ _ __| | _ __ ___ ___ |
# | | |_) / _ \/ _` |/ _` | | '_ ` _ \ / _ \ |
# | | _ < __/ (_| | (_| | | | | | | | __/ |
# | |_| \_\___|\__,_|\__,_| |_| |_| |_|\___| |
# | |
# +----------------------------------------------------------------------+
# | A few words about the implementation details of WATO. |
# `----------------------------------------------------------------------'
# [1] Files and Folders
# WATO organizes hosts in folders. A wato folder is represented by a
# OS directory. If the folder contains host definitions, then in that
# directory a file name "hosts.mk" is kept.
# The directory hierarchy of WATO is rooted at etc/check_mk/conf.d/wato.
# All files in and below that directory are kept by WATO. WATO does not
# touch any other files or directories in conf.d.
# A *path* in WATO means a relative folder path to that directory. The
# root folder has the empty path (""). Folders are separated by slashes.
# Each directory contains a file ".wato" which keeps information needed
# by WATO but not by Check_MK itself.
# [2] Global variables
# Yes. Global variables are bad. But we use them anyway. Please go away
# if you do not like this. Global variables - if properly used - can make
# implementation a lot easier and clearer. Of course we could pack everything
# into a class and use class variables. But what's the difference?
#
# g_folders -> A dictionary of all folders, the key are there paths,
# the values are dictionaries. Keys beginning
# with a period are not persisted. Important keys are:
#
# ".folders" -> List of subfolders. This key is present even for leaf folders.
# ".parent" -> parent folder (not name, but Python reference!). Missing for the root folder
# ".name" -> OS name of the folder
# ".path" -> absolute path of folder
# ".hosts" -> Hosts in that folder. This key is present even if there are no hosts.
# If the hosts in the folder have not been loaded yet, then the key
# is missing.
# "title" -> Title/alias of that folder
# "attributes" -> Attributes to be inherited to subfolders and hosts
# "num_hosts" -> number of hosts in this folder (this is identical to
# to len() of the entry ".hosts" but is persisted for
# performance issues.
# ".total_hosts" -> recursive number of hosts, computed on demand by
# num_hosts_in()
# ".siteid" -> This attribute is mandatory for host objects and optional for folder
# objects. In case of hosts and single WATO setup it is always None.
#
#
# g_folder -> The folder object representing the folder the user is
# currently operating in.
#
# g_root_folder -> The folder object representing the root folder
#
# At the beginning of each page, those three global variables are
# set. All folders are loaded, but only their meta-data, not the
# actual Check_MK files (hosts.mk). WATO is designed for managing
# 100.000 hosts. So operations on all hosts might last a while...
#
# g_configvars -> dictionary of variables in main.mk that can be configured
# via WATO.
#
# g_html_head_open -> True, if the HTML head has already been rendered.
#.
# .-Init-----------------------------------------------------------------.
# | ___ _ _ |
# | |_ _|_ __ (_) |_ |
# | | || '_ \| | __| |
# | | || | | | | |_ |
# | |___|_| |_|_|\__| |
# | |
# +----------------------------------------------------------------------+
# | Importing, Permissions, global variables |
# `----------------------------------------------------------------------'
|
"""
=====================================
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
spmatrix - Sparse matrix base class
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
random - 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).
""" |
# -*- 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.
|
"""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 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 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 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 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.
""" |
"""
=============================
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``.
""" |
"""
=============
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
3) 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.
4) 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
5) 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.
6) 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)
""" |
"""
Types used to represent a full function/module as an Abstract Syntax Tree.
Most types are small, and are merely used as tokens in the AST. A tree diagram
has been included below to illustrate the relationships between the AST types.
AST Type Tree
-------------
::
*Basic*
|--->Assignment
| |--->AugmentedAssignment
| |--->AddAugmentedAssignment
| |--->SubAugmentedAssignment
| |--->MulAugmentedAssignment
| |--->DivAugmentedAssignment
| |--->ModAugmentedAssignment
|
|--->CodeBlock
|
|
|--->Token
| |--->Attribute
| |--->For
| |--->String
| | |--->QuotedString
| | |--->Comment
| |--->Type
| | |--->IntBaseType
| | | |--->_SizedIntType
| | | |--->SignedIntType
| | | |--->UnsignedIntType
| | |--->FloatBaseType
| | |--->FloatType
| | |--->ComplexBaseType
| | |--->ComplexType
| |--->Node
| | |--->Variable
| | | |---> Pointer
| | |--->FunctionPrototype
| | |--->FunctionDefinition
| |--->Element
| |--->Declaration
| |--->While
| |--->Scope
| |--->Stream
| |--->Print
| |--->FunctionCall
| |--->BreakToken
| |--->ContinueToken
| |--->NoneToken
|
|--->Statement
|--->Return
Predefined types
----------------
A number of ``Type`` instances are provided in the ``sympy.codegen.ast`` module
for convenience. Perhaps the two most common ones for code-generation (of numeric
codes) are ``float32`` and ``float64`` (known as single and double precision respectively).
There are also precision generic versions of Types (for which the codeprinters selects the
underlying data type at time of printing): ``real``, ``integer``, ``complex_``, ``bool_``.
The other ``Type`` instances defined are:
- ``intc``: Integer type used by C's "int".
- ``intp``: Integer type used by C's "unsigned".
- ``int8``, ``int16``, ``int32``, ``int64``: n-bit integers.
- ``uint8``, ``uint16``, ``uint32``, ``uint64``: n-bit unsigned integers.
- ``float80``: known as "extended precision" on modern x86/amd64 hardware.
- ``complex64``: Complex number represented by two ``float32`` numbers
- ``complex128``: Complex number represented by two ``float64`` numbers
Using the nodes
---------------
It is possible to construct simple algorithms using the AST nodes. Let's construct a loop applying
Newton's method::
>>> from sympy import symbols, cos
>>> from sympy.codegen.ast import While, Assignment, aug_assign, Print
>>> t, dx, x = symbols('tol delta val')
>>> expr = cos(x) - x**3
>>> whl = While(abs(dx) > t, [
... Assignment(dx, -expr/expr.diff(x)),
... aug_assign(x, '+', dx),
... Print([x])
... ])
>>> from sympy.printing import pycode
>>> py_str = pycode(whl)
>>> print(py_str)
while (abs(delta) > tol):
delta = (val**3 - math.cos(val))/(-3*val**2 - math.sin(val))
val += delta
print(val)
>>> import math
>>> tol, val, delta = 1e-5, 0.5, float('inf')
>>> exec(py_str)
1.1121416371
0.909672693737
0.867263818209
0.865477135298
0.865474033111
>>> print('%3.1g' % (math.cos(val) - val**3))
-3e-11
If we want to generate Fortran code for the same while loop we simple call ``fcode``::
>>> from sympy.printing.fcode import fcode
>>> print(fcode(whl, standard=2003, source_format='free'))
do while (abs(delta) > tol)
delta = (val**3 - cos(val))/(-3*val**2 - sin(val))
val = val + delta
print *, val
end do
There is a function constructing a loop (or a complete function) like this in
:mod:`sympy.codegen.algorithms`.
""" |
"""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 <EMAIL>
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.
""" |
"""
=======================================
Signal processing (:mod:`scipy.signal`)
=======================================
Convolution
===========
.. autosummary::
:toctree: generated/
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
=========
.. autosummary::
:toctree: generated/
bspline -- B-spline basis function of order n.
cubic -- B-spline basis function of order 3.
quadratic -- B-spline basis function of order 2.
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.
cspline1d_eval -- Evaluate a cubic spline at the given points.
qspline1d_eval -- Evaluate a quadratic spline at the given points.
spline_filter -- Smoothing spline (cubic) filtering of a rank-2 array.
Filtering
=========
.. autosummary::
:toctree: generated/
order_filter -- N-dimensional order filter.
medfilt -- N-dimensional median filter.
medfilt2d -- 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`.
lfilter_zi -- Compute an initial state zi for the lfilter function that
-- corresponds to the steady state of the step response.
filtfilt -- A forward-backward filter.
savgol_filter -- Filter a signal using the Savitzky-Golay filter.
deconvolve -- 1-d deconvolution using lfilter.
sosfilt -- 1-dimensional IIR digital linear filtering using
-- a second-order-sections filter representation.
sosfilt_zi -- Compute an initial state zi for the sosfilt function that
-- corresponds to the steady state of the step response.
hilbert -- Compute 1-D analytic signal, using the Hilbert transform.
hilbert2 -- Compute 2-D analytic signal, using the Hilbert transform.
decimate -- Downsample a signal.
detrend -- Remove linear and/or constant trends from data.
resample -- Resample using Fourier method.
Filter design
=============
.. autosummary::
:toctree: generated/
bilinear -- Digital filter from an analog filter using
-- the bilinear transform.
findfreqs -- Find array of frequencies for computing filter response.
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.
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.
kaiser_beta -- Compute the Kaiser parameter beta, given the desired
-- FIR filter attenuation.
kaiserord -- Design a Kaiser window to limit ripple and width of
-- transition region.
savgol_coeffs -- Compute the FIR filter coefficients for a Savitzky-Golay
-- filter.
remez -- Optimal FIR filter design.
unique_roots -- Unique roots and their multiplicities.
residue -- Partial fraction expansion of b(s) / a(s).
residuez -- Partial fraction expansion of b(z) / a(z).
invres -- Inverse partial fraction expansion for analog filter.
invresz -- Inverse partial fraction expansion for digital filter.
Lower-level filter design functions:
.. autosummary::
:toctree: generated/
abcd_normalize -- Check state-space matrices and ensure they are rank-2.
band_stop_obj -- Band Stop Objective Function for order minimization.
besselap -- Return (z,p,k) for analog prototype of Bessel filter.
buttap -- Return (z,p,k) for analog prototype of Butterworth filter.
cheb1ap -- Return (z,p,k) for type I Chebyshev filter.
cheb2ap -- Return (z,p,k) for type II Chebyshev filter.
cmplx_sort -- Sort roots based on magnitude.
ellipap -- Return (z,p,k) for analog prototype of elliptic filter.
lp2bp -- Transform a lowpass filter prototype to a bandpass filter.
lp2bs -- Transform a lowpass filter prototype to a bandstop filter.
lp2hp -- Transform a lowpass filter prototype to a highpass filter.
lp2lp -- Transform a lowpass filter prototype to a lowpass filter.
normalize -- Normalize polynomial representation of a transfer function.
Matlab-style IIR filter design
==============================
.. autosummary::
:toctree: generated/
butter -- Butterworth
buttord
cheby1 -- Chebyshev Type I
cheb1ord
cheby2 -- Chebyshev Type II
cheb2ord
ellip -- Elliptic (Cauer)
ellipord
bessel -- Bessel (no order selection available -- try butterod)
Continuous-Time Linear Systems
==============================
.. autosummary::
:toctree: generated/
freqresp -- frequency response of a continuous-time LTI system.
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.
bode -- Calculate Bode magnitude and phase data.
Discrete-Time Linear Systems
============================
.. autosummary::
:toctree: generated/
dlsim -- simulation of output to a discrete-time linear system.
dimpulse -- impulse response of a discrete-time LTI system.
dstep -- step response of a discrete-time LTI system.
LTI Representations
===================
.. autosummary::
:toctree: generated/
tf2zpk -- transfer function to zero-pole-gain.
tf2sos -- transfer function to second-order sections.
tf2ss -- transfer function to state-space.
zpk2tf -- zero-pole-gain to transfer function.
zpk2sos -- zero-pole-gain to second-order sections.
zpk2ss -- zero-pole-gain to state-space.
ss2tf -- state-pace to transfer function.
ss2zpk -- state-space to pole-zero-gain.
sos2zpk -- second-order-sections to zero-pole-gain.
sos2tf -- second-order-sections to transfer function.
cont2discrete -- continuous-time to discrete-time LTI conversion.
Waveforms
=========
.. autosummary::
:toctree: generated/
chirp -- Frequency swept cosine signal, with several freq functions.
gausspulse -- Gaussian modulated sinusoid
max_len_seq -- Maximum length sequence
sawtooth -- Periodic sawtooth
square -- Square wave
sweep_poly -- Frequency swept cosine signal; freq is arbitrary polynomial
Window functions
================
.. autosummary::
:toctree: generated/
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
cosine -- Cosine window
exponential -- Exponential 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
========
.. autosummary::
:toctree: generated/
cascade -- compute scaling function and wavelet from coefficients
daub -- return low-pass
morlet -- Complex Morlet wavelet.
qmf -- return quadrature mirror filter from low-pass
ricker -- return ricker wavelet
cwt -- perform continuous wavelet transform
Peak finding
============
.. autosummary::
:toctree: generated/
find_peaks_cwt -- Attempt to find the peaks in the given 1-D array
argrelmin -- Calculate the relative minima of data
argrelmax -- Calculate the relative maxima of data
argrelextrema -- Calculate the relative extrema of data
Spectral Analysis
=================
.. autosummary::
:toctree: generated/
periodogram -- Computes a (modified) periodogram
welch -- Compute a periodogram using Welch's method
lombscargle -- Computes the Lomb-Scargle periodogram
vectorstrength -- Computes the vector strength
""" |
"""
SQLAlchemy-Utils provides way of automatically calculating aggregate values of
related models and saving them to parent model.
This solution is inspired by RoR counter cache,
`counter_culture`_ and `stackoverflow reply by NAME times you may have situations where you need to calculate dynamically some
aggregate value for given model. Some simple examples include:
- Number of products in a catalog
- Average rating for movie
- Latest forum post
- Total price of orders for given customer
Now all these aggregates can be elegantly implemented with SQLAlchemy
column_property_ function. However when your data grows calculating these
values on the fly might start to hurt the performance of your application. The
more aggregates you are using the more performance penalty you get.
This module provides way of calculating these values automatically and
efficiently at the time of modification rather than on the fly.
Features
--------
* Automatically updates aggregate columns when aggregated values change
* Supports aggregate values through arbitrary number levels of relations
* Highly optimized: uses single query per transaction per aggregate column
* Aggregated columns can be of any data type and use any selectable scalar
expression
.. _column_property:
http://docs.sqlalchemy.org/en/latest/orm/mapper_config.html#using-column-property
.. _counter_culture: https://github.com/magnusvk/counter_culture
.. _stackoverflow reply by NAME
http://stackoverflow.com/questions/13693872/
Simple aggregates
-----------------
::
from sqlalchemy_utils import aggregated
class Thread(Base):
__tablename__ = 'thread'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.Unicode(255))
@aggregated('comments', sa.Column(sa.Integer))
def comment_count(self):
return sa.func.count('1')
comments = sa.orm.relationship(
'Comment',
backref='thread'
)
class Comment(Base):
__tablename__ = 'comment'
id = sa.Column(sa.Integer, primary_key=True)
content = sa.Column(sa.UnicodeText)
thread_id = sa.Column(sa.Integer, sa.ForeignKey(Thread.id))
thread = Thread(name=u'SQLAlchemy development')
thread.comments.append(Comment(u'Going good!'))
thread.comments.append(Comment(u'Great new features!'))
session.add(thread)
session.commit()
thread.comment_count # 2
Custom aggregate expressions
----------------------------
Aggregate expression can be virtually any SQL expression not just a simple
function taking one parameter. You can try things such as subqueries and
different kinds of functions.
In the following example we have a Catalog of products where each catalog
knows the net worth of its products.
::
from sqlalchemy_utils import aggregated
class Catalog(Base):
__tablename__ = 'catalog'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.Unicode(255))
@aggregated('products', sa.Column(sa.Integer))
def net_worth(self):
return sa.func.sum(Product.price)
products = sa.orm.relationship('Product')
class Product(Base):
__tablename__ = 'product'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.Unicode(255))
price = sa.Column(sa.Numeric)
catalog_id = sa.Column(sa.Integer, sa.ForeignKey(Catalog.id))
Now the net_worth column of Catalog model will be automatically whenever:
* A new product is added to the catalog
* A product is deleted from the catalog
* The price of catalog product is changed
::
from decimal import Decimal
product1 = Product(name='Some product', price=Decimal(1000))
product2 = Product(name='Some other product', price=Decimal(500))
catalog = Catalog(
name=u'My first catalog',
products=[
product1,
product2
]
)
session.add(catalog)
session.commit()
session.refresh(catalog)
catalog.net_worth # 1500
session.delete(product2)
session.commit()
session.refresh(catalog)
catalog.net_worth # 1000
product1.price = 2000
session.commit()
session.refresh(catalog)
catalog.net_worth # 2000
Multiple aggregates per class
-----------------------------
Sometimes you may need to define multiple aggregate values for same class. If
you need to define lots of relationships pointing to same class, remember to
define the relationships as viewonly when possible.
::
from sqlalchemy_utils import aggregated
class Customer(Base):
__tablename__ = 'customer'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.Unicode(255))
@aggregated('orders', sa.Column(sa.Integer))
def orders_sum(self):
return sa.func.sum(Order.price)
@aggregated('invoiced_orders', sa.Column(sa.Integer))
def invoiced_orders_sum(self):
return sa.func.sum(Order.price)
orders = sa.orm.relationship('Order')
invoiced_orders = sa.orm.relationship(
'Order',
primaryjoin=
'sa.and_(Order.customer_id == Customer.id, Order.invoiced)',
viewonly=True
)
class Order(Base):
__tablename__ = 'order'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.Unicode(255))
price = sa.Column(sa.Numeric)
invoiced = sa.Column(sa.Boolean, default=False)
customer_id = sa.Column(sa.Integer, sa.ForeignKey(Customer.id))
Many-to-Many aggregates
-----------------------
Aggregate expressions also support many-to-many relationships. The usual use
scenarios includes things such as:
1. Friend count of a user
2. Group count where given user belongs to
::
user_group = sa.Table('user_group', Base.metadata,
sa.Column('user_id', sa.Integer, sa.ForeignKey('user.id')),
sa.Column('group_id', sa.Integer, sa.ForeignKey('group.id'))
)
class User(Base):
__tablename__ = 'user'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.Unicode(255))
@aggregated('groups', sa.Column(sa.Integer, default=0))
def group_count(self):
return sa.func.count('1')
groups = sa.orm.relationship(
'Group',
backref='users',
secondary=user_group
)
class Group(Base):
__tablename__ = 'group'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.Unicode(255))
user = User(name=u'John NAME
user.groups = [Group(name=u'Group A'), Group(name=u'Group B')]
session.add(user)
session.commit()
session.refresh(user)
user.group_count # 2
Multi-level aggregates
----------------------
Aggregates can span across multiple relationships. In the following example
each Catalog has a net_worth which is the sum of all products in all
categories.
::
from sqlalchemy_utils import aggregated
class Catalog(Base):
__tablename__ = 'catalog'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.Unicode(255))
@aggregated('categories.products', sa.Column(sa.Integer))
def net_worth(self):
return sa.func.sum(Product.price)
categories = sa.orm.relationship('Category')
class Category(Base):
__tablename__ = 'category'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.Unicode(255))
catalog_id = sa.Column(sa.Integer, sa.ForeignKey(Catalog.id))
products = sa.orm.relationship('Product')
class Product(Base):
__tablename__ = 'product'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.Unicode(255))
price = sa.Column(sa.Numeric)
category_id = sa.Column(sa.Integer, sa.ForeignKey(Category.id))
Examples
--------
Average movie rating
^^^^^^^^^^^^^^^^^^^^
::
from sqlalchemy_utils import aggregated
class Movie(Base):
__tablename__ = 'movie'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.Unicode(255))
@aggregated('ratings', sa.Column(sa.Numeric))
def avg_rating(self):
return sa.func.avg(Rating.stars)
ratings = sa.orm.relationship('Rating')
class Rating(Base):
__tablename__ = 'rating'
id = sa.Column(sa.Integer, primary_key=True)
stars = sa.Column(sa.Integer)
movie_id = sa.Column(sa.Integer, sa.ForeignKey(Movie.id))
movie = Movie('Terminator 2')
movie.ratings.append(Rating(stars=5))
movie.ratings.append(Rating(stars=4))
movie.ratings.append(Rating(stars=3))
session.add(movie)
session.commit()
movie.avg_rating # 4
TODO
----
* Special consideration should be given to `deadlocks`_.
.. _deadlocks:
http://mina.naguib.ca/blog/2010/11/22/postgresql-foreign-key-deadlocks.html
""" |
"""
Wrappers to LAPACK library
==========================
NOTE: this module is deprecated -- use scipy.linalg.lapack instead!
flapack -- wrappers for Fortran [*] LAPACK routines
clapack -- wrappers for ATLAS LAPACK routines
calc_lwork -- calculate optimal lwork parameters
get_lapack_funcs -- query for wrapper functions.
[*] If ATLAS libraries are available then Fortran routines
actually use ATLAS routines and should perform equally
well to ATLAS routines.
Module flapack
++++++++++++++
In the following all function names are shown without
type prefix (s,d,c,z). Optimal values for lwork can
be computed using calc_lwork module.
Linear Equations
----------------
Drivers::
lu,piv,x,info = gesv(a,b,overwrite_a=0,overwrite_b=0)
lub,piv,x,info = gbsv(kl,ku,ab,b,overwrite_ab=0,overwrite_b=0)
c,x,info = posv(a,b,lower=0,overwrite_a=0,overwrite_b=0)
Computational routines::
lu,piv,info = getrf(a,overwrite_a=0)
x,info = getrs(lu,piv,b,trans=0,overwrite_b=0)
inv_a,info = getri(lu,piv,lwork=min_lwork,overwrite_lu=0)
c,info = potrf(a,lower=0,clean=1,overwrite_a=0)
x,info = potrs(c,b,lower=0,overwrite_b=0)
inv_a,info = potri(c,lower=0,overwrite_c=0)
inv_c,info = trtri(c,lower=0,unitdiag=0,overwrite_c=0)
Linear Least Squares (LLS) Problems
-----------------------------------
Drivers::
v,x,s,rank,info = gelss(a,b,cond=-1.0,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
Computational routines::
qr,tau,info = geqrf(a,lwork=min_lwork,overwrite_a=0)
q,info = orgqr|ungqr(qr,tau,lwork=min_lwork,overwrite_qr=0,overwrite_tau=1)
Generalized Linear Least Squares (LSE and GLM) Problems
-------------------------------------------------------
Standard Eigenvalue and Singular Value Problems
-----------------------------------------------
Drivers::
w,v,info = syev|heev(a,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0)
w,v,info = syevd|heevd(a,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0)
w,v,info = syevr|heevr(a,compute_v=1,lower=0,vrange=,irange=,atol=-1.0,lwork=min_lwork,overwrite_a=0)
t,sdim,(wr,wi|w),vs,info = gees(select,a,compute_v=1,sort_t=0,lwork=min_lwork,select_extra_args=(),overwrite_a=0)
wr,(wi,vl|w),vr,info = geev(a,compute_vl=1,compute_vr=1,lwork=min_lwork,overwrite_a=0)
u,s,vt,info = gesdd(a,compute_uv=1,lwork=min_lwork,overwrite_a=0)
Computational routines::
ht,tau,info = gehrd(a,lo=0,hi=n-1,lwork=min_lwork,overwrite_a=0)
ba,lo,hi,pivscale,info = gebal(a,scale=0,permute=0,overwrite_a=0)
Generalized Eigenvalue and Singular Value Problems
--------------------------------------------------
Drivers::
w,v,info = sygv|hegv(a,b,itype=1,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
w,v,info = sygvd|hegvd(a,b,itype=1,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
(alphar,alphai|alpha),beta,vl,vr,info = ggev(a,b,compute_vl=1,compute_vr=1,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
Auxiliary routines
------------------
a,info = lauum(c,lower=0,overwrite_c=0)
a = laswp(a,piv,k1=0,k2=len(piv)-1,off=0,inc=1,overwrite_a=0)
Module clapack
++++++++++++++
Linear Equations
----------------
Drivers::
lu,piv,x,info = gesv(a,b,rowmajor=1,overwrite_a=0,overwrite_b=0)
c,x,info = posv(a,b,lower=0,rowmajor=1,overwrite_a=0,overwrite_b=0)
Computational routines::
lu,piv,info = getrf(a,rowmajor=1,overwrite_a=0)
x,info = getrs(lu,piv,b,trans=0,rowmajor=1,overwrite_b=0)
inv_a,info = getri(lu,piv,rowmajor=1,overwrite_lu=0)
c,info = potrf(a,lower=0,clean=1,rowmajor=1,overwrite_a=0)
x,info = potrs(c,b,lower=0,rowmajor=1,overwrite_b=0)
inv_a,info = potri(c,lower=0,rowmajor=1,overwrite_c=0)
inv_c,info = trtri(c,lower=0,unitdiag=0,rowmajor=1,overwrite_c=0)
Auxiliary routines
------------------
a,info = lauum(c,lower=0,rowmajor=1,overwrite_c=0)
Module calc_lwork
+++++++++++++++++
Optimal lwork is maxwrk. Default is minwrk.
minwrk,maxwrk = gehrd(prefix,n,lo=0,hi=n-1)
minwrk,maxwrk = gesdd(prefix,m,n,compute_uv=1)
minwrk,maxwrk = gelss(prefix,m,n,nrhs)
minwrk,maxwrk = getri(prefix,n)
minwrk,maxwrk = geev(prefix,n,compute_vl=1,compute_vr=1)
minwrk,maxwrk = heev(prefix,n,lower=0)
minwrk,maxwrk = syev(prefix,n,lower=0)
minwrk,maxwrk = gees(prefix,n,compute_v=1)
minwrk,maxwrk = geqrf(prefix,m,n)
minwrk,maxwrk = gqr(prefix,m,n)
""" |
"""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().
""" |
"""
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
""" |
"""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, you must call
close() after all frames have been written.
The close() method is called automatically when the class instance
is destroyed.
When a file is opened with the extension '.aiff', an AIFF file is
written, otherwise an AIFF-C file is written. This default can be
changed by calling aiff() or aifc() before the first writeframes or
writeframesraw.
""" |
"""
===============
Array Internals
===============
Internal organization of numpy arrays
=====================================
It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book "Guide to Numpy".
Numpy arrays consist of two major components, the raw array data (from now on,
referred to as the data buffer), and the information about the raw array data.
The data buffer is typically what people think of as arrays in C or Fortran,
a contiguous (and fixed) block of memory containing fixed sized data items.
Numpy also contains a significant set of data that describes how to interpret
the data in the data buffer. This extra information contains (among other things):
1) The basic data element's size in bytes
2) The start of the data within the data buffer (an offset relative to the
beginning of the data buffer).
3) The number of dimensions and the size of each dimension
4) The separation between elements for each dimension (the 'stride'). This
does not have to be a multiple of the element size
5) The byte order of the data (which may not be the native byte order)
6) Whether the buffer is read-only
7) Information (via the dtype object) about the interpretation of the basic
data element. The basic data element may be as simple as a int or a float,
or it may be a compound object (e.g., struct-like), a fixed character field,
or Python object pointers.
8) Whether the array is to interpreted as C-order or Fortran-order.
This arrangement allow for very flexible use of arrays. One thing that it allows
is simple changes of the metadata to change the interpretation of the array buffer.
Changing the byteorder of the array is a simple change involving no rearrangement
of the data. The shape of the array can be changed very easily without changing
anything in the data buffer or any data copying at all
Among other things that are made possible is one can create a new array metadata
object that uses the same data buffer
to create a new view of that data buffer that has a different interpretation
of the buffer (e.g., different shape, offset, byte order, strides, etc) but
shares the same data bytes. Many operations in numpy do just this such as
slices. Other operations, such as transpose, don't move data elements
around in the array, but rather change the information about the shape and strides so that the indexing of the array changes, but the data in the doesn't move.
Typically these new versions of the array metadata but the same data buffer are
new 'views' into the data buffer. There is a different ndarray object, but it
uses the same data buffer. This is why it is necessary to force copies through
use of the .copy() method if one really wants to make a new and independent
copy of the data buffer.
New views into arrays mean the the object reference counts for the data buffer
increase. Simply doing away with the original array object will not remove the
data buffer if other views of it still exist.
Multidimensional Array Indexing Order Issues
============================================
What is the right way to index
multi-dimensional arrays? Before you jump to conclusions about the one and
true way to index multi-dimensional arrays, it pays to understand why this is
a confusing issue. This section will try to explain in detail how numpy
indexing works and why we adopt the convention we do for images, and when it
may be appropriate to adopt other conventions.
The first thing to understand is
that there are two conflicting conventions for indexing 2-dimensional arrays.
Matrix notation uses the first index to indicate which row is being selected and
the second index to indicate which column is selected. This is opposite the
geometrically oriented-convention for images where people generally think the
first index represents x position (i.e., column) and the second represents y
position (i.e., row). This alone is the source of much confusion;
matrix-oriented users and image-oriented users expect two different things with
regard to indexing.
The second issue to understand is how indices correspond
to the order the array is stored in memory. In Fortran the first index is the
most rapidly varying index when moving through the elements of a two
dimensional array as it is stored in memory. If you adopt the matrix
convention for indexing, then this means the matrix is stored one column at a
time (since the first index moves to the next row as it changes). Thus Fortran
is considered a Column-major language. C has just the opposite convention. In
C, the last index changes most rapidly as one moves through the array as
stored in memory. Thus C is a Row-major language. The matrix is stored by
rows. Note that in both cases it presumes that the matrix convention for
indexing is being used, i.e., for both Fortran and C, the first index is the
row. Note this convention implies that the indexing convention is invariant
and that the data order changes to keep that so.
But that's not the only way
to look at it. Suppose one has large two-dimensional arrays (images or
matrices) stored in data files. Suppose the data are stored by rows rather than
by columns. If we are to preserve our index convention (whether matrix or
image) that means that depending on the language we use, we may be forced to
reorder the data if it is read into memory to preserve our indexing
convention. For example if we read row-ordered data into memory without
reordering, it will match the matrix indexing convention for C, but not for
Fortran. Conversely, it will match the image indexing convention for Fortran,
but not for C. For C, if one is using data stored in row order, and one wants
to preserve the image index convention, the data must be reordered when
reading into memory.
In the end, which you do for Fortran or C depends on
which is more important, not reordering data or preserving the indexing
convention. For large images, reordering data is potentially expensive, and
often the indexing convention is inverted to avoid that.
The situation with
numpy makes this issue yet more complicated. The internal machinery of numpy
arrays is flexible enough to accept any ordering of indices. One can simply
reorder indices by manipulating the internal stride information for arrays
without reordering the data at all. Numpy will know how to map the new index
order to the data without moving the data.
So if this is true, why not choose
the index order that matches what you most expect? In particular, why not define
row-ordered images to use the image convention? (This is sometimes referred
to as the Fortran convention vs the C convention, thus the 'C' and 'FORTRAN'
order options for array ordering in numpy.) The drawback of doing this is
potential performance penalties. It's common to access the data sequentially,
either implicitly in array operations or explicitly by looping over rows of an
image. When that is done, then the data will be accessed in non-optimal order.
As the first index is incremented, what is actually happening is that elements
spaced far apart in memory are being sequentially accessed, with usually poor
memory access speeds. For example, for a two dimensional image 'im' defined so
that im[0, 10] represents the value at x=0, y=10. To be consistent with usual
Python behavior then im[0] would represent a column at x=0. Yet that data
would be spread over the whole array since the data are stored in row order.
Despite the flexibility of numpy's indexing, it can't really paper over the fact
basic operations are rendered inefficient because of data order or that getting
contiguous subarrays is still awkward (e.g., im[:,0] for the first row, vs
im[0]), thus one can't use an idiom such as for row in im; for col in im does
work, but doesn't yield contiguous column data.
As it turns out, numpy is
smart enough when dealing with ufuncs to determine which index is the most
rapidly varying one in memory and uses that for the innermost loop. Thus for
ufuncs there is no large intrinsic advantage to either approach in most cases.
On the other hand, use of .flat with an FORTRAN ordered array will lead to
non-optimal memory access as adjacent elements in the flattened array (iterator,
actually) are not contiguous in memory.
Indeed, the fact is that Python
indexing on lists and other sequences naturally leads to an outside-to inside
ordering (the first index gets the largest grouping, the next the next largest,
and the last gets the smallest element). Since image data are normally stored
by rows, this corresponds to position within rows being the last item indexed.
If you do want to use Fortran ordering realize that
there are two approaches to consider: 1) accept that the first index is just not
the most rapidly changing in memory and have all your I/O routines reorder
your data when going from memory to disk or visa versa, or use numpy's
mechanism for mapping the first index to the most rapidly varying data. We
recommend the former if possible. The disadvantage of the latter is that many
of numpy's functions will yield arrays without Fortran ordering unless you are
careful to use the 'order' keyword. Doing this would be highly inconvenient.
Otherwise we recommend simply learning to reverse the usual order of indices
when accessing elements of an array. Granted, it goes against the grain, but
it is more in line with Python semantics and the natural order of the data.
""" |
# FIXME: to be fixed... does not work as of today
# import unittest
# from datetime import datetime, timedelta
# from DIRAC.ResourceStatusSystem.Utilities.mock import Mock
# from DIRAC.Core.LCG.GOCDBClient import GOCDBClient
# from DIRAC.Core.LCG.SLSClient import *
# from DIRAC.Core.LCG.SAMResultsClient import *
# from DIRAC.Core.LCG.GGUSTicketsClient import GGUSTicketsClient
# #from DIRAC.ResourceStatusSystem.Utilities.Exceptions import *
# #from DIRAC.ResourceStatusSystem.Utilities.Utils import *
#
# #############################################################################
#
# class ClientsTestCase(unittest.TestCase):
# """ Base class for the clients test cases
# """
# def setUp(self):
#
# from DIRAC.Core.Base.Script import parseCommandLine
# parseCommandLine()
#
# self.mockRSS = Mock()
#
# self.GOCCli = GOCDBClient()
# self.SLSCli = SLSClient()
# self.SAMCli = SAMResultsClient()
# self.GGUSCli = GGUSTicketsClient()
#
# #############################################################################
#
# class GOCDBClientSuccess(ClientsTestCase):
#
# def test__downTimeXMLParsing(self):
# now = datetime.utcnow().replace(microsecond = 0, second = 0)
# tomorrow = datetime.utcnow().replace(microsecond = 0, second = 0) + timedelta(hours = 24)
# inAWeek = datetime.utcnow().replace(microsecond = 0, second = 0) + timedelta(days = 7)
#
# nowLess12h = str( now - timedelta(hours = 12) )[:-3]
# nowPlus8h = str( now + timedelta(hours = 8) )[:-3]
# nowPlus24h = str( now + timedelta(hours = 24) )[:-3]
# nowPlus40h = str( now + timedelta(hours = 40) )[:-3]
# nowPlus50h = str( now + timedelta(hours = 50) )[:-3]
# nowPlus60h = str( now + timedelta(hours = 60) )[:-3]
#
# XML_site_ongoing = '<?xml version="1.0"?>\n<ROOT><DOWNTIME ID="78505456" PRIMARY_KEY="28490G0" CLASSIFICATION="SCHEDULED"><SITENAME>GRISU-ENEA-GRID</SITENAME><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems SITE</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowLess12h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus24h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME></ROOT>\n'
# XML_node_ongoing = '<?xml version="1.0"?>\n<ROOT><DOWNTIME ID="78505455" PRIMARY_KEY="28490G0" CLASSIFICATION="SCHEDULED"><HOSTNAME>egse-cresco.portici.enea.it</HOSTNAME><HOSTED_BY>GRISU-ENEA-GRID</HOSTED_BY><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowLess12h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus24h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME></ROOT>\n'
# XML_nodesite_ongoing = '<?xml version="1.0"?>\n<ROOT><DOWNTIME ID="78505455" PRIMARY_KEY="28490G0" CLASSIFICATION="SCHEDULED"><HOSTNAME>egse-cresco.portici.enea.it</HOSTNAME><HOSTED_BY>GRISU-ENEA-GRID</HOSTED_BY><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowLess12h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus8h+'</FORMATED_END_DATE></DOWNTIME><DOWNTIME ID="78505456" PRIMARY_KEY="28490G0" CLASSIFICATION="SCHEDULED"><SITENAME>GRISU-ENEA-GRID</SITENAME><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems SITE</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowLess12h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus24h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME></ROOT>\n'
#
# XML_site_startingIn8h = '<?xml version="1.0"?>\n<ROOT><DOWNTIME ID="78505456" PRIMARY_KEY="28490G0" CLASSIFICATION="SCHEDULED"><SITENAME>GRISU-ENEA-GRID</SITENAME><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems SITE</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowPlus8h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus24h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME></ROOT>\n'
# XML_node_startingIn8h = '<?xml version="1.0"?>\n<ROOT><DOWNTIME ID="78505455" PRIMARY_KEY="28490G0" CLASSIFICATION="SCHEDULED"><HOSTNAME>egse-cresco.portici.enea.it</HOSTNAME><HOSTED_BY>GRISU-ENEA-GRID</HOSTED_BY><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowPlus8h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus24h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME></ROOT>\n'
#
# XML_site_ongoing_and_site_starting_in_24_hours = '<?xml version="1.0"?>\n<ROOT><DOWNTIME ID="78505456" PRIMARY_KEY="28490G1" CLASSIFICATION="SCHEDULED"><SITENAME>GRISU-ENEA-GRID</SITENAME><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems SITE</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowLess12h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus8h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME><DOWNTIME ID="78505457" PRIMARY_KEY="28490G0" CLASSIFICATION="SCHEDULED"><SITENAME>GRISU-ENEA-GRID</SITENAME><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems SITE 2</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowPlus24h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus40h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME></ROOT>\n'
#
# XML_site_startingIn24h_and_site_startingIn50h = '<?xml version="1.0"?>\n<ROOT><DOWNTIME ID="78505456" PRIMARY_KEY="28490G1" CLASSIFICATION="SCHEDULED"><SITENAME>GRISU-ENEA-GRID</SITENAME><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems SITE</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowPlus24h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus40h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME><DOWNTIME ID="78505457" PRIMARY_KEY="28490G0" CLASSIFICATION="SCHEDULED"><SITENAME>GRISU-ENEA-GRID</SITENAME><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems SITE</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowPlus50h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus60h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME></ROOT>\n'
#
# XML_site_ongoing_and_other_site_starting_in_24_hours = '<?xml version="1.0"?>\n<ROOT><DOWNTIME ID="78505456" PRIMARY_KEY="28490G1" CLASSIFICATION="SCHEDULED"><SITENAME>GRISU-ENEA-GRID</SITENAME><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems SITE</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowLess12h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus8h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME><DOWNTIME ID="78505457" PRIMARY_KEY="28490G0" CLASSIFICATION="SCHEDULED"><SITENAME>CERN-PROD</SITENAME><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems SITE 2</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowPlus24h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus40h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME></ROOT>\n'
# XML_node_ongoing_and_other_node_starting_in_24_hours = '<?xml version="1.0"?>\n<ROOT><DOWNTIME ID="78505456" PRIMARY_KEY="28490G1" CLASSIFICATION="SCHEDULED"><HOSTNAME>egse-cresco.portici.enea.it</HOSTNAME><HOSTED_BY>GRISU-ENEA-GRID</HOSTED_BY><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems RESOURCE</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowLess12h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus8h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME><DOWNTIME ID="78505457" PRIMARY_KEY="28490G0" CLASSIFICATION="SCHEDULED"><HOSTNAME>ce112.cern.ch</HOSTNAME><HOSTED_BY>CERN-PROD</HOSTED_BY><SEVERITY>OUTAGE</SEVERITY><DESCRIPTION>Software problems RESOURCE 2</DESCRIPTION><INSERT_DATE>1276273965</INSERT_DATE><START_DATE>1276360500</START_DATE><END_DATE>1276878660</END_DATE><FORMATED_START_DATE>'+nowPlus24h+'</FORMATED_START_DATE><FORMATED_END_DATE>'+nowPlus40h+'</FORMATED_END_DATE><GOCDB_PORTAL_URL>https://next.gocdb.eu/portal/index.php?Page_Type=View_Object&object_id=18509&grid_id=0</GOCDB_PORTAL_URL></DOWNTIME></ROOT>\n'
#
#
# res = self.GOCCli._downTimeXMLParsing(XML_site_ongoing, 'Site')
# self.assertEqual(res.keys()[0], '28490G0 GRISU-ENEA-GRID')
# self.assertEqual(res['28490G0 GRISU-ENEA-GRID']['SITENAME'], 'GRISU-ENEA-GRID')
#
# res = self.GOCCli._downTimeXMLParsing(XML_node_ongoing, 'Resource')
# self.assertEqual(res.keys()[0], '28490G0 egse-cresco.portici.enea.it')
# self.assertEqual(res['28490G0 egse-cresco.portici.enea.it']['HOSTNAME'], 'egse-cresco.portici.enea.it')
# self.assertEqual(res['28490G0 egse-cresco.portici.enea.it']['HOSTED_BY'], 'GRISU-ENEA-GRID')
#
# res = self.GOCCli._downTimeXMLParsing(XML_site_ongoing, 'Resource')
# self.assertEqual(res, {})
# res = self.GOCCli._downTimeXMLParsing(XML_node_ongoing, 'Site')
# self.assertEqual(res, {})
#
# res = self.GOCCli._downTimeXMLParsing(XML_nodesite_ongoing, 'Site')
# self.assertEquals(len(res), 1)
# self.assertEqual(res.keys()[0], '28490G0 GRISU-ENEA-GRID')
# self.assertEqual(res['28490G0 GRISU-ENEA-GRID']['SITENAME'], 'GRISU-ENEA-GRID')
#
# res = self.GOCCli._downTimeXMLParsing(XML_nodesite_ongoing, 'Resource')
# self.assertEquals(len(res), 1)
# self.assertEqual(res.keys()[0], '28490G0 egse-cresco.portici.enea.it')
# self.assertEqual(res['28490G0 egse-cresco.portici.enea.it']['HOSTNAME'], 'egse-cresco.portici.enea.it')
#
# res = self.GOCCli._downTimeXMLParsing(XML_site_startingIn8h, 'Site', None, now)
# self.assertEqual(res, {})
# res = self.GOCCli._downTimeXMLParsing(XML_node_startingIn8h, 'Resource', None, now)
# self.assertEqual(res, {})
#
# res = self.GOCCli._downTimeXMLParsing(XML_site_ongoing_and_site_starting_in_24_hours, 'Site', None, now)
# self.assertEqual(res.keys()[0], '28490G1 GRISU-ENEA-GRID')
# self.assertEqual(res['28490G1 GRISU-ENEA-GRID']['SITENAME'], 'GRISU-ENEA-GRID')
#
# res = self.GOCCli._downTimeXMLParsing(XML_site_ongoing_and_site_starting_in_24_hours, 'Resource', None, now)
# self.assertEqual(res, {})
# res = self.GOCCli._downTimeXMLParsing(XML_site_startingIn24h_and_site_startingIn50h, 'Site', None, now)
# self.assertEqual(res, {})
#
# res = self.GOCCli._downTimeXMLParsing(XML_site_startingIn24h_and_site_startingIn50h, 'Site', None, tomorrow)
# self.assertEqual(res.keys()[0], '28490G1 GRISU-ENEA-GRID')
# self.assertEqual(res['28490G1 GRISU-ENEA-GRID']['SITENAME'], 'GRISU-ENEA-GRID')
#
# res = self.GOCCli._downTimeXMLParsing(XML_site_ongoing_and_other_site_starting_in_24_hours, 'Site', ['GRISU-ENEA-GRID'])
# self.assertEqual(res.keys()[0], '28490G1 GRISU-ENEA-GRID')
# self.assertEqual(res['28490G1 GRISU-ENEA-GRID']['SITENAME'], 'GRISU-ENEA-GRID')
# res = self.GOCCli._downTimeXMLParsing(XML_site_ongoing_and_other_site_starting_in_24_hours, 'Site', ['GRISU-ENEA-GRID', 'CERN-PROD'])
# self.assert_('28490G1 GRISU-ENEA-GRID' in res.keys())
# self.assert_('28490G0 CERN-PROD' in res.keys())
# self.assertEqual(res['28490G1 GRISU-ENEA-GRID']['SITENAME'], 'GRISU-ENEA-GRID')
# self.assertEqual(res['28490G0 CERN-PROD']['SITENAME'], 'CERN-PROD')
# res = self.GOCCli._downTimeXMLParsing(XML_site_ongoing_and_other_site_starting_in_24_hours, 'Site', 'CERN-PROD')
# self.assertEqual(res.keys()[0], '28490G0 CERN-PROD')
# self.assertEqual(res['28490G0 CERN-PROD']['SITENAME'], 'CERN-PROD')
# res = self.GOCCli._downTimeXMLParsing(XML_site_ongoing_and_other_site_starting_in_24_hours, 'Site', 'CNAF-T1')
# self.assertEqual(res, {})
#
# res = self.GOCCli._downTimeXMLParsing(XML_site_ongoing_and_other_site_starting_in_24_hours, 'Site', ['GRISU-ENEA-GRID', 'CERN-PROD'], now)
# self.assertEqual(res.keys()[0], '28490G1 GRISU-ENEA-GRID')
# self.assertEqual(res['28490G1 GRISU-ENEA-GRID']['SITENAME'], 'GRISU-ENEA-GRID')
# res = self.GOCCli._downTimeXMLParsing(XML_site_ongoing_and_other_site_starting_in_24_hours, 'Site', ['GRISU-ENEA-GRID', 'CERN-PROD'], inAWeek)
# self.assertEqual(res.keys()[0], '28490G0 CERN-PROD')
# self.assertEqual(res['28490G0 CERN-PROD']['SITENAME'], 'CERN-PROD')
#
# res = self.GOCCli._downTimeXMLParsing(XML_node_ongoing_and_other_node_starting_in_24_hours, 'Resource', ['egse-cresco.portici.enea.it'])
# self.assertEqual(res.keys()[0], '28490G1 egse-cresco.portici.enea.it')
# self.assertEqual(res['28490G1 egse-cresco.portici.enea.it']['HOSTNAME'], 'egse-cresco.portici.enea.it')
# res = self.GOCCli._downTimeXMLParsing(XML_node_ongoing_and_other_node_starting_in_24_hours, 'Resource', ['egse-cresco.portici.enea.it', 'ce112.cern.ch'])
# self.assert_('28490G1 egse-cresco.portici.enea.it' in res.keys())
# self.assert_('28490G0 ce112.cern.ch' in res.keys())
# self.assertEqual(res['28490G1 egse-cresco.portici.enea.it']['HOSTNAME'], 'egse-cresco.portici.enea.it')
# self.assertEqual(res['28490G0 ce112.cern.ch']['HOSTNAME'], 'ce112.cern.ch')
# res = self.GOCCli._downTimeXMLParsing(XML_node_ongoing_and_other_node_starting_in_24_hours, 'Resource', 'ce112.cern.ch')
# self.assertEqual(res.keys()[0], '28490G0 ce112.cern.ch')
# self.assertEqual(res['28490G0 ce112.cern.ch']['HOSTNAME'], 'ce112.cern.ch')
# res = self.GOCCli._downTimeXMLParsing(XML_node_ongoing_and_other_node_starting_in_24_hours, 'Resource', 'grid0.fe.infn.it')
# self.assertEqual(res, {})
#
# res = self.GOCCli._downTimeXMLParsing(XML_node_ongoing_and_other_node_starting_in_24_hours, 'Resource', ['egse-cresco.portici.enea.it', 'ce112.cern.ch'], now)
# self.assert_('28490G1 egse-cresco.portici.enea.it' in res.keys())
# self.assertEqual(res['28490G1 egse-cresco.portici.enea.it']['HOSTNAME'], 'egse-cresco.portici.enea.it')
# res = self.GOCCli._downTimeXMLParsing(XML_node_ongoing_and_other_node_starting_in_24_hours, 'Resource', ['egse-cresco.portici.enea.it', 'ce112.cern.ch'], inAWeek)
# self.assertEqual(res.keys()[0], '28490G0 ce112.cern.ch')
# self.assertEqual(res['28490G0 ce112.cern.ch']['HOSTNAME'], 'ce112.cern.ch')
#
#
# def test_getStatus(self):
# for granularity in ('Site', 'Resource'):
# res = self.GOCCli.getStatus(granularity, 'XX')['Value']
# self.assertEqual(res, None)
# res = self.GOCCli.getStatus(granularity, 'XX', datetime.utcnow())['Value']
# self.assertEqual(res, None)
# res = self.GOCCli.getStatus(granularity, 'XX', datetime.utcnow(), 12)['Value']
# self.assertEqual(res, None)
#
# res = self.GOCCli.getStatus('Site', 'pic')['Value']
# self.assertEqual(res, None)
#
# def test_getServiceEndpointInfo(self):
# for granularity in ('hostname', 'sitename', 'roc',
# 'country', 'service_type', 'monitored'):
# res = self.GOCCli.getServiceEndpointInfo(granularity, 'XX')['Value']
# self.assertEqual(res, [])
#
# #############################################################################
#
# class SAMResultsClientSuccess(ClientsTestCase):
#
# def test_getStatus(self):
# res = self.SAMCli.getStatus('Resource', 'grid0.fe.infn.it', 'INFN-FERRARA')['Value']
# self.assertEqual(res, {'SS':'ok'})
# res = self.SAMCli.getStatus('Resource', 'grid0.fe.infn.it', 'INFN-FERRARA', ['ver'])['Value']
# self.assertEqual(res, {'ver':'ok'})
# res = self.SAMCli.getStatus('Resource', 'grid0.fe.infn.it', 'INFN-FERRARA', ['LHCb CE-lhcb-os', 'PilotRole'])['Value']
# self.assertEqual(res, {'PilotRole':'ok', 'LHCb CE-lhcb-os':'ok'})
# res = self.SAMCli.getStatus('Resource', 'grid0.fe.infn.it', 'INFN-FERRARA', ['wrong'])['Value']
# self.assertEqual(res, None)
# res = self.SAMCli.getStatus('Resource', 'grid0.fe.infn.it', 'INFN-FERRARA', ['ver', 'wrong'])['Value']
# self.assertEqual(res, {'ver':'ok'})
# res = self.SAMCli.getStatus('Resource', 'grid0.fe.infn.it', 'INFN-FERRARA')['Value']
# self.assertEqual(res, {'SS':'ok'})
#
# res = self.SAMCli.getStatus('Site', 'INFN-FERRARA')['Value']
# self.assertEqual(res, {'SiteStatus':'ok'})
#
# #############################################################################
#
# #class SAMResultsClientFailure(ClientsTestCase):
# #
# # def test_getStatus(self):
# # self.failUnlessRaises(NoSAMTests, self.SAMCli.getStatus, 'Resource', 'XX', 'INFN-FERRARA')
#
# #############################################################################
#
# class SLSClientSuccess(ClientsTestCase):
#
# def test_getAvailabilityStatus(self):
# res = self.SLSCli.getAvailabilityStatus('RAL-LHCb_FAILOVER')['Value']
# self.assertEqual(res, 100)
#
# def test_getServiceInfo(self):
# res = self.SLSCli.getServiceInfo('CASTORLHCB_LHCBMDST', ["Volume to be recallled GB"])['Value']
# self.assertEqual(res["Volume to be recallled GB"], 0.0)
#
# #############################################################################
#
# #class SLSClientFailure(ClientsTestCase):
# #
# # def test_getStatus(self):
# # self.failUnlessRaises(NoServiceException, self.SLSCli.getAvailabilityStatus, 'XX')
#
# #############################################################################
#
# class GGUSTicketsClientSuccess(ClientsTestCase):
#
# def test_getTicketsList(self):
# res = self.GGUSCli.getTicketsList('INFN-CAGLIARI')['Value']
# self.assertEqual(res[0]['open'], 0)
#
#
# #############################################################################
#
# if __name__ == '__main__':
# suite = unittest.defaultTestLoader.loadTestsFromTestCase(ClientsTestCase)
# suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(GOCDBClientSuccess))
# # suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(GOCDBClient_Failure))
# suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(SAMResultsClientSuccess))
# # suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(SAMResultsClientFailure))
# suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(SLSClientSuccess))
# # suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(SLSClientFailure))
# suite.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(GGUSTicketsClientSuccess))
# testResult = unittest.TextTestRunner(verbosity=2).run(suite)
|
"""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__
""" |
"""
Basic functions used by several sub-packages and
useful to have in the main name-space.
Type Handling
-------------
================ ===================
iscomplexobj Test for complex object, scalar result
isrealobj Test for real object, scalar result
iscomplex Test for complex elements, array result
isreal Test for real elements, array result
imag Imaginary part
real Real part
real_if_close Turns complex number with tiny imaginary part to real
isneginf Tests for negative infinity, array result
isposinf Tests for positive infinity, array result
isnan Tests for nans, array result
isinf Tests for infinity, array result
isfinite Tests for finite numbers, array result
isscalar True if argument is a scalar
nan_to_num Replaces NaN's with 0 and infinities with large numbers
cast Dictionary of functions to force cast to each type
common_type Determine the minimum common type code for a group
of arrays
mintypecode Return minimal allowed common typecode.
================ ===================
Index Tricks
------------
================ ===================
mgrid Method which allows easy construction of N-d
'mesh-grids'
``r_`` Append and construct arrays: turns slice objects into
ranges and concatenates them, for 2d arrays appends rows.
index_exp Konrad Hinsen's index_expression class instance which
can be useful for building complicated slicing syntax.
================ ===================
Useful Functions
----------------
================ ===================
select Extension of where to multiple conditions and choices
extract Extract 1d array from flattened array according to mask
insert Insert 1d array of values into Nd array according to mask
linspace Evenly spaced samples in linear space
logspace Evenly spaced samples in logarithmic space
fix Round x to nearest integer towards zero
mod Modulo mod(x,y) = x % y except keeps sign of y
amax Array maximum along axis
amin Array minimum along axis
ptp Array max-min along axis
cumsum Cumulative sum along axis
prod Product of elements along axis
cumprod Cumluative product along axis
diff Discrete differences along axis
angle Returns angle of complex argument
unwrap Unwrap phase along given axis (1-d algorithm)
sort_complex Sort a complex-array (based on real, then imaginary)
trim_zeros Trim the leading and trailing zeros from 1D array.
vectorize A class that wraps a Python function taking scalar
arguments into a generalized function which can handle
arrays of arguments using the broadcast rules of
numerix Python.
================ ===================
Shape Manipulation
------------------
================ ===================
squeeze Return a with length-one dimensions removed.
atleast_1d Force arrays to be > 1D
atleast_2d Force arrays to be > 2D
atleast_3d Force arrays to be > 3D
vstack Stack arrays vertically (row on row)
hstack Stack arrays horizontally (column on column)
column_stack Stack 1D arrays as columns into 2D array
dstack Stack arrays depthwise (along third dimension)
stack Stack arrays along a new axis
split Divide array into a list of sub-arrays
hsplit Split into columns
vsplit Split into rows
dsplit Split along third dimension
================ ===================
Matrix (2D Array) Manipulations
-------------------------------
================ ===================
fliplr 2D array with columns flipped
flipud 2D array with rows flipped
rot90 Rotate a 2D array a multiple of 90 degrees
eye Return a 2D array with ones down a given diagonal
diag Construct a 2D array from a vector, or return a given
diagonal from a 2D array.
mat Construct a Matrix
bmat Build a Matrix from blocks
================ ===================
Polynomials
-----------
================ ===================
poly1d A one-dimensional polynomial class
poly Return polynomial coefficients from roots
roots Find roots of polynomial given coefficients
polyint Integrate polynomial
polyder Differentiate polynomial
polyadd Add polynomials
polysub Substract polynomials
polymul Multiply polynomials
polydiv Divide polynomials
polyval Evaluate polynomial at given argument
================ ===================
Import Tricks
-------------
================ ===================
ppimport Postpone module import until trying to use it
ppimport_attr Postpone module import until trying to use its attribute
ppresolve Import postponed module and return it.
================ ===================
Machine Arithmetics
-------------------
================ ===================
machar_single Single precision floating point arithmetic parameters
machar_double Double precision floating point arithmetic parameters
================ ===================
Threading Tricks
----------------
================ ===================
ParallelExec Execute commands in parallel thread.
================ ===================
1D Array Set Operations
-----------------------
Set operations for 1D numeric arrays based on sort() function.
================ ===================
ediff1d Array difference (auxiliary function).
unique Unique elements of an array.
intersect1d Intersection of 1D arrays with unique elements.
setxor1d Set exclusive-or of 1D arrays with unique elements.
in1d Test whether elements in a 1D array are also present in
another array.
union1d Union of 1D arrays with unique elements.
setdiff1d Set difference of 1D arrays with unique elements.
================ ===================
""" |
"""
High quality resources
- https://github.com/vinta/awesome-python
- Manage CLI -> http://plumbum.readthedocs.org/en/latest/
- Create tasks -> http://www.pyinvoke.org/
- https://automatetheboringstuff.com/
PEP 8 is the de-facto code style guide for Python.
pip install pep8
$ pep8 optparse.py
optparse.py:69:11: E401 multiple imports on one line
optparse.py:77:1: E302 expected 2 blank lines, found 1
optparse.py:88:5: E301 expected 1 blank line, found 0
optparse.py:222:34: W602 deprecated form of raising exception
optparse.py:347:31: E211 whitespace before '('
The program autopep8 can be used to automatically reformat code in the PEP 8 style.
$ pip install autopep8
Use it to format a file in-place with:
$ autopep8 --in-place optparse.py
With pyvenv
pyvenv myenv
source myenv/bin/activate
python
With virtualenv & virtualenvwrapper
http://www.marinamele.com/2014/07/install-python3-on-mac-os-x-and-use-virtualenv-and-virtualenvwrapper.html
mkvirtualenv --python=/usr/local/bin/python3 myenv
deactivate
workon myenv
Magic Methods (source: http://www.rafekettler.com/magicmethods.html)
Comparison magic methods
Python has a whole slew of magic methods designed to implement intuitive
comparisons between objects using operators, not awkward method calls.
They also provide a way to override the default Python behavior for
comparisons of objects (by reference). Here's the list of those methods
and what they do:
__cmp__(self, other)
__cmp__ is the most basic of the comparison magic methods. It actually
implements behavior for all of the comparison operators (<, ==, !=, etc.),
but it might not do it the way you want (for example, if whether one
instance was equal to another were determined by one criterion and and
whether an instance is greater than another were determined by something
else). __cmp__ should return a negative integer if self < other, zero if
self == other, and positive if self > other. It's usually best to define
each comparison you need rather than define them all at once, but __cmp__
can be a good way to save repetition and improve clarity when you need all
comparisons implemented with similar criteria.
__eq__(self, other)
Defines behavior for the equality operator, ==.
__ne__(self, other)
Defines behavior for the inequality operator, !=.
__lt__(self, other)
Defines behavior for the less-than operator, <.
__gt__(self, other)
Defines behavior for the greater-than operator, >.
__le__(self, other)
Defines behavior for the less-than-or-equal-to operator, <=.
__ge__(self, other)
Defines behavior for the greater-than-or-equal-to operator, >=.
Numeric magic methods
Just like you can create ways for instances of your class to be compared
with comparison operators, you can define behavior for numeric operators.
Buckle your seat belts, folks, there's a lot of these. For organization's
sake, I've split the numeric magic methods into 5 categories: unary
operators, normal arithmetic operators, reflected arithmetic operators
(more on this later), augmented assignment, and type conversions.
Unary operators and functions
Unary operators and functions only have one operand, e.g. negation, absolute value, etc.
__pos__(self)
Implements behavior for unary positive (e.g. +some_object)
__neg__(self)
Implements behavior for negation (e.g. -some_object)
__abs__(self)
Implements behavior for the built in abs() function.
__invert__(self)
Implements behavior for inversion using the ~ operator. For an explanation on what this does, see the Wikipedia article on bitwise operations.
__round__(self, n)
Implements behavior for the built in round() function. n is the number of decimal places to round to.
__floor__(self)
Implements behavior for math.floor(), i.e., rounding down to the nearest integer.
__ceil__(self)
Implements behavior for math.ceil(), i.e., rounding up to the nearest integer.
__trunc__(self)
Implements behavior for math.trunc(), i.e., truncating to an integral.
Normal arithmetic operators
Now, we cover the typical binary operators (and a function or two):
+, -, * and the like.
These are, for the most part, pretty self-explanatory.
__add__(self, other)
Implements addition.
__sub__(self, other)
Implements subtraction.
__mul__(self, other)
Implements multiplication.
__floordiv__(self, other)
Implements integer division using the // operator.
__div__(self, other)
Implements division using the / operator.
__truediv__(self, other)
Implements true division. Note that this only works when from __future__ import division is in effect.
__mod__(self, other)
Implements modulo using the % operator.
__divmod__(self, other)
Implements behavior for long division using the divmod() built in function.
__pow__
Implements behavior for exponents using the ** operator.
__lshift__(self, other)
Implements left bitwise shift using the << operator.
__rshift__(self, other)
Implements right bitwise shift using the >> operator.
__and__(self, other)
Implements bitwise and using the & operator.
__or__(self, other)
Implements bitwise or using the | operator.
__xor__(self, other)
Implements bitwise xor using the ^ operator.
Augmented assignment
Python also has a wide variety of magic methods to allow custom behavior
to be defined for augmented assignment. You're probably already familiar
with augmented assignment, it combines "normal" operators with assignment.
If you still don't know what I'm talking about, here's an example:
x = 5
x += 1 # in other words x = x + 1
Each of these methods should return the value that the variable on the
left hand side should be assigned to (for instance, for a += b,
__iadd__ might return a + b, which would be assigned to a).
Here's the list:
__iadd__(self, other)
Implements addition with assignment.
__isub__(self, other)
Implements subtraction with assignment.
__imul__(self, other)
Implements multiplication with assignment.
__ifloordiv__(self, other)
Implements integer division with assignment using the //= operator.
__idiv__(self, other)
Implements division with assignment using the /= operator.
__itruediv__(self, other)
Implements true division with assignment. Note that this only works
when from __future__ import division is in effect.
__imod__(self, other)
Implements modulo with assignment using the %= operator.
__ipow__
Implements behavior for exponents with assignment using the **= operator.
__ilshift__(self, other)
Implements left bitwise shift with assignment using the <<= operator.
__irshift__(self, other)
Implements right bitwise shift with assignment using the >>= operator.
__iand__(self, other)
Implements bitwise and with assignment using the &= operator.
__ior__(self, other)
Implements bitwise or with assignment using the |= operator.
__ixor__(self, other)
Implements bitwise xor with assignment using the ^= operator.
Type conversion magic methods
Python also has an array of magic methods designed to implement behavior
for built in type conversion functions like float(). Here they are:
__int__(self)
Implements type conversion to int.
__long__(self)
Implements type conversion to long.
__float__(self)
Implements type conversion to float.
__complex__(self)
Implements type conversion to complex.
__oct__(self)
Implements type conversion to octal.
__hex__(self)
Implements type conversion to hexadecimal.
__index__(self)
Implements type conversion to an int when the object is used in a slice
expression. If you define a custom numeric type that might be used in
slicing, you should define __index__.
__trunc__(self)
Called when math.trunc(self) is called. __trunc__ should return the
value of `self truncated to an integral type (usually a long).
__coerce__(self, other)
Method to implement mixed mode arithmetic. __coerce__ should return
None if type conversion is impossible. Otherwise, it should return a
pair (2-tuple) of self and other, manipulated to have the same type.
Representing your Classes
It's often useful to have a string representation of a class. In Python,
there's a few methods that you can implement in your class definition
to customize how built in functions that return representations of your
class behave.
__str__(self)
Defines behavior for when str() is called on an instance of your class.
__repr__(self)
Defines behavior for when repr() is called on an instance of your class.
The major difference between str() and repr() is intended audience.
repr() is intended to produce output that is mostly machine-readable
(in many cases, it could be valid Python code even), whereas str() is
intended to be human-readable.
__unicode__(self)
Defines behavior for when unicode() is called on an instance of your class.
unicode() is like str(), but it returns a unicode string. Be wary: if
a client calls str() on an instance of your class and you've only
defined __unicode__(), it won't work. You should always try to define
__str__() as well in case someone doesn't have the luxury of using unicode.
__format__(self, formatstr)
Defines behavior for when an instance of your class is used in new-style
string formatting. For instance, "Hello, {0:abc}!".format(a) would lead
to the call a.__format__("abc"). This can be useful for defining your
own numerical or string types that you might like to give special
formatting options.
__hash__(self)
Defines behavior for when hash() is called on an instance of your class.
It has to return an integer, and its result is used for quick key
comparison in dictionaries. Note that this usually entails implementing
__eq__ as well. Live by the following rule: a == b implies
hash(a) == hash(b).
__nonzero__(self)
Defines behavior for when bool() is called on an instance of your class.
Should return True or False, depending on whether you would want to
consider the instance to be True or False.
__dir__(self)
Defines behavior for when dir() is called on an instance of your class.
This method should return a list of attributes for the user. Typically,
implementing __dir__ is unnecessary, but it can be vitally important
for interactive use of your classes if you redefine __getattr__ or
__getattribute__ (which you will see in the next section) or are otherwise
dynamically generating attributes.
__sizeof__(self)
Defines behavior for when sys.getsizeof() is called on an instance of
your class. This should return the size of your object, in bytes. This
is generally more useful for Python classes implemented in C extensions,
but it helps to be aware of it.
""" |
""" IPy - class and tools for handling of IPv4 and IPv6 Addresses and Networks.
$HeadURL: http://svn.23.nu/svn/repos/IPy/trunk/IPy.py $
$Id: IPy.py,v 1.1 2007/08/14 09:39:00 cristian Exp $
The IP class allows a comfortable parsing and handling for most
notations in use for IPv4 and IPv6 Addresses and Networks. It was
greatly inspired bei RIPE's Perl module NET::IP's interface but
doesn't share the Implementation. It doesn't share non-CIDR netmasks,
so funky stuff lixe a netmask 0xffffff0f can't be done here.
>>> ip = IP('IP_ADDRESS/30')
>>> for x in ip:
... print x
...
IP_ADDRESS
IP_ADDRESS
IP_ADDRESS
IP_ADDRESS
>>> ip2 = IP('0x7f000000/30')
>>> ip == ip2
1
>>> ip.reverseNames()
['IP_ADDRESS.in-addr.arpa.', 'IP_ADDRESS.in-addr.arpa.', 'IP_ADDRESS.in-addr.arpa.', 'IP_ADDRESS.in-addr.arpa.']
>>> ip.reverseName()
'0-IP_ADDRESS.in-addr.arpa.'
>>> ip.iptype()
'PRIVATE'
It can detect about a dozen different ways of expressing IP addresses
and networks, parse them and distinguish between IPv4 and IPv6 addresses.
>>> IP('IP_ADDRESS/8').version()
4
>>> IP('::1').version()
6
>>> print IP(0x7f000001)
IP_ADDRESS
>>> print IP('0x7f000001')
IP_ADDRESS
>>> print IP('IP_ADDRESS')
IP_ADDRESS
>>> print IP('10')
IP_ADDRESS
>>> print IP('1080:0:0:0:8:800:200C:417A')
IP_ADDRESS
>>> print IP('1080::8:800:200C:417A')
IP_ADDRESS
>>> print IP('::1')
IP_ADDRESS
>>> print IP('::IP_ADDRESS')
IP_ADDRESS3
>>> print IP('IP_ADDRESS/8')
IP_ADDRESS/8
>>> print IP('IP_ADDRESS/IP_ADDRESS')
IP_ADDRESS/8
>>> print IP('IP_ADDRESS-IP_ADDRESS')
IP_ADDRESS/8
Nearly all class methods which return a string have an optional
parameter 'wantprefixlen' which controlles if the prefixlen or netmask
is printed. Per default the prefilen is always shown if the net
contains more than one address.
wantprefixlen == 0 / None don't return anything IP_ADDRESS
wantprefixlen == 1 /prefix IP_ADDRESS/24
wantprefixlen == 2 /netmask IP_ADDRESS/IP_ADDRESS
wantprefixlen == 3 -lastip IP_ADDRESS-IP_ADDRESS
You can also change the defaults on an per-object basis by fiddeling with the class members
NoPrefixForSingleIp
WantPrefixLen
>>> IP('IP_ADDRESS/32').strNormal()
'IP_ADDRESS'
>>> IP('IP_ADDRESS/24').strNormal()
'IP_ADDRESS/24'
>>> IP('IP_ADDRESS/24').strNormal(0)
'IP_ADDRESS'
>>> IP('IP_ADDRESS/24').strNormal(1)
'IP_ADDRESS/24'
>>> IP('IP_ADDRESS/24').strNormal(2)
'IP_ADDRESS/IP_ADDRESS'
>>> IP('IP_ADDRESS/24').strNormal(3)
'IP_ADDRESS-IP_ADDRESS'
>>> ip = IP('IP_ADDRESS')
>>> print ip
IP_ADDRESS
>>> ip.NoPrefixForSingleIp = None
>>> print ip
IP_ADDRESS/32
>>> ip.WantPrefixLen = 3
>>> print ip
IP_ADDRESS-IP_ADDRESS
Further Information might be available at http://c0re.jp/c0de/IPy/
Hacked 2001 by EMAIL
* better comparison (__cmp__ and friends)
* tests for __cmp__
* always write hex values lowercase
* interpret IP_ADDRESS as IP_ADDRESS
* move size in bits into class variables to get rid of some "if self._ipversion ..."
* support for base85 encoding
* support for output of IPv6 encoded IPv4 Addresses
* update address type tables
* first-last notation should be allowed for IPv6
* add IPv6 docstring examples
* check better for negative parameters
* add addition / aggregation
* move reverse name stuff out of the classes and refactor it
* support for aggregation of more than two nets at once
* support for aggregation with "holes"
* support for finding common prefix
* '>>' and '<<' for prefix manipulation
* add our own exceptions instead ValueError all the time
* rename checkPrefix to checkPrefixOk
* add more documentation and doctests
* refactor
""" |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2015-2015: Alignak team, see AUTHORS.txt file for contributors
#
# This file is part of Alignak.
#
# Alignak 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.
#
# Alignak 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 Alignak. If not, see <http://www.gnu.org/licenses/>.
#
#
# This file incorporates work covered by the following copyright and
# permission notice:
#
# Copyright (C) 2009-2014:
# NAME EMAIL NAME EMAIL NAME EMAIL NAME EMAIL NAME EMAIL NAME EMAIL NAME EMAIL NAME EMAIL NAME EMAIL NAME EMAIL NAME EMAIL NAME EMAIL NAME EMAIL This file is part of Shinken.
#
# Shinken 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.
#
# Shinken 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 Shinken. If not, see <http://www.gnu.org/licenses/>.
# Calendar date
# -------------
# '(\d{4})-(\d{2})-(\d{2}) - (\d{4})-(\d{2})-(\d{2}) / (\d+) ([0-9:, -]+)'
# => len = 8 => CALENDAR_DATE
#
# '(\d{4})-(\d{2})-(\d{2}) / (\d+) ([0-9:, -]+)'
# => len = 5 => CALENDAR_DATE
#
# '(\d{4})-(\d{2})-(\d{2}) - (\d{4})-(\d{2})-(\d{2}) ([0-9:, -]+)'
# => len = 7 => CALENDAR_DATE
#
# '(\d{4})-(\d{2})-(\d{2}) ([0-9:, -]+)'
# => len = 4 => CALENDAR_DATE
#
# Month week day
# --------------
# '([a-z]*) (\d+) ([a-z]*) - ([a-z]*) (\d+) ([a-z]*) / (\d+) ([0-9:, -]+)'
# => len = 8 => MONTH WEEK DAY
# e.g.: wednesday 1 january - thursday 2 july / 3
#
# '([a-z]*) (\d+) - ([a-z]*) (\d+) / (\d+) ([0-9:, -]+)' => len = 6
# e.g.: february 1 - march 15 / 3 => MONTH DATE
# e.g.: monday 2 - thusday 3 / 2 => WEEK DAY
# e.g.: day 2 - day 6 / 3 => MONTH DAY
#
# '([a-z]*) (\d+) - (\d+) / (\d+) ([0-9:, -]+)' => len = 6
# e.g.: february 1 - 15 / 3 => MONTH DATE
# e.g.: thursday 2 - 4 => WEEK DAY
# e.g.: day 1 - 4 => MONTH DAY
#
# '([a-z]*) (\d+) ([a-z]*) - ([a-z]*) (\d+) ([a-z]*) ([0-9:, -]+)' => len = 7
# e.g.: wednesday 1 january - thursday 2 july => MONTH WEEK DAY
#
# '([a-z]*) (\d+) - (\d+) ([0-9:, -]+)' => len = 7
# e.g.: thursday 2 - 4 => WEEK DAY
# e.g.: february 1 - 15 / 3 => MONTH DATE
# e.g.: day 1 - 4 => MONTH DAY
#
# '([a-z]*) (\d+) - ([a-z]*) (\d+) ([0-9:, -]+)' => len = 5
# e.g.: february 1 - march 15 => MONTH DATE
# e.g.: monday 2 - thusday 3 => WEEK DAY
# e.g.: day 2 - day 6 => MONTH DAY
#
# '([a-z]*) (\d+) ([0-9:, -]+)' => len = 3
# e.g.: february 3 => MONTH DATE
# e.g.: thursday 2 => WEEK DAY
# e.g.: day 3 => MONTH DAY
#
# '([a-z]*) (\d+) ([a-z]*) ([0-9:, -]+)' => len = 4
# e.g.: thusday 3 february => MONTH WEEK DAY
#
# '([a-z]*) ([0-9:, -]+)' => len = 6
# e.g.: thusday => normal values
#
# Types: CALENDAR_DATE
# MONTH WEEK DAY
# WEEK DAY
# MONTH DATE
# MONTH DAY
#
|
"""
This module processes Python exceptions that relate to HTTP exceptions
by defining a set of exceptions, all subclasses of HTTPException.
Each exception, in addition to being a Python exception that can be
raised and caught, is also a WSGI application and ``webob.Response``
object.
This module defines exceptions according to RFC 2068 [1]_ : codes with
100-300 are not really errors; 400's are client errors, and 500's are
server errors. According to the WSGI specification [2]_ , the application
can call ``start_response`` more then once only under two conditions:
(a) the response has not yet been sent, or (b) if the second and
subsequent invocations of ``start_response`` have a valid ``exc_info``
argument obtained from ``sys.exc_info()``. The WSGI specification then
requires the server or gateway to handle the case where content has been
sent and then an exception was encountered.
Exception
HTTPException
HTTPOk
* 200 - :class:`HTTPOk`
* 201 - :class:`HTTPCreated`
* 202 - :class:`HTTPAccepted`
* 203 - :class:`HTTPNonAuthoritativeInformation`
* 204 - :class:`HTTPNoContent`
* 205 - :class:`HTTPResetContent`
* 206 - :class:`HTTPPartialContent`
HTTPRedirection
* 300 - :class:`HTTPMultipleChoices`
* 301 - :class:`HTTPMovedPermanently`
* 302 - :class:`HTTPFound`
* 303 - :class:`HTTPSeeOther`
* 304 - :class:`HTTPNotModified`
* 305 - :class:`HTTPUseProxy`
* 307 - :class:`HTTPTemporaryRedirect`
* 308 - :class:`HTTPPermanentRedirect`
HTTPError
HTTPClientError
* 400 - :class:`HTTPBadRequest`
* 401 - :class:`HTTPUnauthorized`
* 402 - :class:`HTTPPaymentRequired`
* 403 - :class:`HTTPForbidden`
* 404 - :class:`HTTPNotFound`
* 405 - :class:`HTTPMethodNotAllowed`
* 406 - :class:`HTTPNotAcceptable`
* 407 - :class:`HTTPProxyAuthenticationRequired`
* 408 - :class:`HTTPRequestTimeout`
* 409 - :class:`HTTPConflict`
* 410 - :class:`HTTPGone`
* 411 - :class:`HTTPLengthRequired`
* 412 - :class:`HTTPPreconditionFailed`
* 413 - :class:`HTTPRequestEntityTooLarge`
* 414 - :class:`HTTPRequestURITooLong`
* 415 - :class:`HTTPUnsupportedMediaType`
* 416 - :class:`HTTPRequestRangeNotSatisfiable`
* 417 - :class:`HTTPExpectationFailed`
* 422 - :class:`HTTPUnprocessableEntity`
* 423 - :class:`HTTPLocked`
* 424 - :class:`HTTPFailedDependency`
* 428 - :class:`HTTPPreconditionRequired`
* 429 - :class:`HTTPTooManyRequests`
* 431 - :class:`HTTPRequestHeaderFieldsTooLarge`
* 451 - :class:`HTTPUnavailableForLegalReasons`
HTTPServerError
* 500 - :class:`HTTPInternalServerError`
* 501 - :class:`HTTPNotImplemented`
* 502 - :class:`HTTPBadGateway`
* 503 - :class:`HTTPServiceUnavailable`
* 504 - :class:`HTTPGatewayTimeout`
* 505 - :class:`HTTPVersionNotSupported`
* 511 - :class:`HTTPNetworkAuthenticationRequired`
Usage notes
-----------
The HTTPException class is complicated by 4 factors:
1. The content given to the exception may either be plain-text or
as html-text.
2. The template may want to have string-substitutions taken from
the current ``environ`` or values from incoming headers. This
is especially troublesome due to case sensitivity.
3. The final output may either be text/plain or text/html
mime-type as requested by the client application.
4. Each exception has a default explanation, but those who
raise exceptions may want to provide additional detail.
Subclass attributes and call parameters are designed to provide an easier path
through the complications.
Attributes:
``code``
the HTTP status code for the exception
``title``
remainder of the status line (stuff after the code)
``explanation``
a plain-text explanation of the error message that is
not subject to environment or header substitutions;
it is accessible in the template via %(explanation)s
``detail``
a plain-text message customization that is not subject
to environment or header substitutions; accessible in
the template via %(detail)s
``body_template``
a content fragment (in HTML) used for environment and
header substitution; the default template includes both
the explanation and further detail provided in the
message
Parameters:
``detail``
a plain-text override of the default ``detail``
``headers``
a list of (k,v) header pairs
``comment``
a plain-text additional information which is
usually stripped/hidden for end-users
``body_template``
a string.Template object containing a content fragment in HTML
that frames the explanation and further detail
To override the template (which is HTML content) or the plain-text
explanation, one must subclass the given exception; or customize it
after it has been created. This particular breakdown of a message
into explanation, detail and template allows both the creation of
plain-text and html messages for various clients as well as
error-free substitution of environment variables and headers.
The subclasses of :class:`~_HTTPMove`
(:class:`~HTTPMultipleChoices`, :class:`~HTTPMovedPermanently`,
:class:`~HTTPFound`, :class:`~HTTPSeeOther`, :class:`~HTTPUseProxy` and
:class:`~HTTPTemporaryRedirect`) are redirections that require a ``Location``
field. Reflecting this, these subclasses have two additional keyword arguments:
``location`` and ``add_slash``.
Parameters:
``location``
to set the location immediately
``add_slash``
set to True to redirect to the same URL as the request, except with a
``/`` appended
Relative URLs in the location will be resolved to absolute.
References:
.. [1] http://www.python.org/peps/pep-0333.html#error-handling
.. [2] http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html#sec10.5
""" |
"""
Tick locating and formatting
============================
This module contains classes to support completely configurable tick locating
and formatting. Although the locators know nothing about major or minor
ticks, they are used by the Axis class to support major and minor tick
locating and formatting. Generic tick locators and formatters are provided,
as well as domain specific custom ones..
Default Formatter
-----------------
The default formatter identifies when the x-data being
plotted is a small range on top of a large off set. To
reduce the chances that the ticklabels overlap the ticks
are labeled as deltas from a fixed offset. For example::
ax.plot(np.arange(2000, 2010), range(10))
will have tick of 0-9 with an offset of +2e3. If this
is not desired turn off the use of the offset on the default
formatter::
ax.get_xaxis().get_major_formatter().set_useOffset(False)
set the rcParam ``axes.formatter.useoffset=False`` to turn it off
globally, or set a different formatter.
Tick locating
-------------
The Locator class is the base class for all tick locators. The locators
handle autoscaling of the view limits based on the data limits, and the
choosing of tick locations. A useful semi-automatic tick locator is
MultipleLocator. You initialize this with a base, e.g., 10, and it picks axis
limits and ticks that are multiples of your base.
The Locator subclasses defined here are
:class:`NullLocator`
No ticks
:class:`FixedLocator`
Tick locations are fixed
:class:`IndexLocator`
locator for index plots (e.g., where x = range(len(y)))
:class:`LinearLocator`
evenly spaced ticks from min to max
:class:`LogLocator`
logarithmically ticks from min to max
:class:`MultipleLocator`
ticks and range are a multiple of base;
either integer or float
:class:`OldAutoLocator`
choose a MultipleLocator and dyamically reassign it for
intelligent ticking during navigation
:class:`MaxNLocator`
finds up to a max number of ticks at nice locations
:class:`AutoLocator`
:class:`MaxNLocator` with simple defaults. This is the default
tick locator for most plotting.
:class:`AutoMinorLocator`
locator for minor ticks when the axis is linear and the
major ticks are uniformly spaced. It subdivides the major
tick interval into a specified number of minor intervals,
defaulting to 4 or 5 depending on the major interval.
There are a number of locators specialized for date locations - see
the dates module
You can define your own locator by deriving from Locator. You must
override the __call__ method, which returns a sequence of locations,
and you will probably want to override the autoscale method to set the
view limits from the data limits.
If you want to override the default locator, use one of the above or a
custom locator and pass it to the x or y axis instance. The relevant
methods are::
ax.xaxis.set_major_locator( xmajorLocator )
ax.xaxis.set_minor_locator( xminorLocator )
ax.yaxis.set_major_locator( ymajorLocator )
ax.yaxis.set_minor_locator( yminorLocator )
The default minor locator is the NullLocator, e.g., no minor ticks on by
default.
Tick formatting
---------------
Tick formatting is controlled by classes derived from Formatter. The
formatter operates on a single tick value and returns a string to the
axis.
:class:`NullFormatter`
no labels on the ticks
:class:`IndexFormatter`
set the strings from a list of labels
:class:`FixedFormatter`
set the strings manually for the labels
:class:`FuncFormatter`
user defined function sets the labels
:class:`FormatStrFormatter`
use a sprintf format string
:class:`ScalarFormatter`
default formatter for scalars; autopick the fmt string
:class:`LogFormatter`
formatter for log axes
You can derive your own formatter from the Formatter base class by
simply overriding the ``__call__`` method. The formatter class has access
to the axis view and data limits.
To control the major and minor tick label formats, use one of the
following methods::
ax.xaxis.set_major_formatter( xmajorFormatter )
ax.xaxis.set_minor_formatter( xminorFormatter )
ax.yaxis.set_major_formatter( ymajorFormatter )
ax.yaxis.set_minor_formatter( yminorFormatter )
See :ref:`pylab_examples-major_minor_demo1` for an example of setting
major and minor ticks. See the :mod:`matplotlib.dates` module for
more information and examples of using date locators and formatters.
""" |
# vim: set fileencoding=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 *
# * *
# ***************************************************************************/
|
"""
Writing Plugins
---------------
nose supports plugins for test collection, selection, observation and
reporting. There are two basic rules for plugins:
* Plugin classes should subclass :class:`nose.plugins.Plugin`.
* Plugins may implement any of the methods described in the class
:doc:`IPluginInterface <interface>` in nose.plugins.base. Please note that
this class is for documentary purposes only; plugins may not subclass
IPluginInterface.
Hello World
===========
Here's a basic plugin. It doesn't do much so read on for more ideas or dive
into the :doc:`IPluginInterface <interface>` to see all available hooks.
.. code-block:: python
import logging
import os
from nose.plugins import Plugin
log = logging.getLogger('nose.plugins.helloworld')
class HelloWorld(Plugin):
name = 'helloworld'
def options(self, parser, env=os.environ):
super(HelloWorld, self).options(parser, env=env)
def configure(self, options, conf):
super(HelloWorld, self).configure(options, conf)
if not self.enabled:
return
def finalize(self, result):
log.info('Hello pluginized world!')
Registering
===========
.. Note::
Important note: the following applies only to the default
plugin manager. Other plugin managers may use different means to
locate and load plugins.
For nose to find a plugin, it must be part of a package that uses
setuptools_, and the plugin must be included in the entry points defined
in the setup.py for the package:
.. code-block:: python
setup(name='Some plugin',
# ...
entry_points = {
'nose.plugins.0.10': [
'someplugin = someplugin:SomePlugin'
]
},
# ...
)
Once the package is installed with install or develop, nose will be able
to load the plugin.
.. _setuptools: http://peak.telecommunity.com/DevCenter/setuptools
Registering a plugin without setuptools
=======================================
It is currently possible to register a plugin programmatically by
creating a custom nose runner like this :
.. code-block:: python
import nose
from yourplugin import YourPlugin
if __name__ == '__main__':
nose.main(addplugins=[YourPlugin()])
Defining options
================
All plugins must implement the methods ``options(self, parser, env)``
and ``configure(self, options, conf)``. Subclasses of nose.plugins.Plugin
that want the standard options should call the superclass methods.
nose uses optparse.OptionParser from the standard library to parse
arguments. A plugin's ``options()`` method receives a parser
instance. It's good form for a plugin to use that instance only to add
additional arguments that take only long arguments (--like-this). Most
of nose's built-in arguments get their default value from an environment
variable.
A plugin's ``configure()`` method receives the parsed ``OptionParser`` options
object, as well as the current config object. Plugins should configure their
behavior based on the user-selected settings, and may raise exceptions
if the configured behavior is nonsensical.
Logging
=======
nose uses the logging classes from the standard library. To enable users
to view debug messages easily, plugins should use ``logging.getLogger()`` to
acquire a logger in the ``nose.plugins`` namespace.
Recipes
=======
* Writing a plugin that monitors or controls test result output
Implement any or all of ``addError``, ``addFailure``, etc., to monitor test
results. If you also want to monitor output, implement
``setOutputStream`` and keep a reference to the output stream. If you
want to prevent the builtin ``TextTestResult`` output, implement
``setOutputSteam`` and *return a dummy stream*. The default output will go
to the dummy stream, while you send your desired output to the real stream.
Example: `examples/html_plugin/htmlplug.py`_
* Writing a plugin that handles exceptions
Subclass :doc:`ErrorClassPlugin <errorclasses>`.
Examples: :doc:`nose.plugins.deprecated <deprecated>`,
:doc:`nose.plugins.skip <skip>`
* Writing a plugin that adds detail to error reports
Implement ``formatError`` and/or ``formatFailure``. The error tuple
you return (error class, error message, traceback) will replace the
original error tuple.
Examples: :doc:`nose.plugins.capture <capture>`,
:doc:`nose.plugins.failuredetail <failuredetail>`
* Writing a plugin that loads tests from files other than python modules
Implement ``wantFile`` and ``loadTestsFromFile``. In ``wantFile``,
return True for files that you want to examine for tests. In
``loadTestsFromFile``, for those files, return an iterable
containing TestCases (or yield them as you find them;
``loadTestsFromFile`` may also be a generator).
Example: :doc:`nose.plugins.doctests <doctests>`
* Writing a plugin that prints a report
Implement ``begin`` if you need to perform setup before testing
begins. Implement ``report`` and output your report to the provided stream.
Examples: :doc:`nose.plugins.cover <cover>`, :doc:`nose.plugins.prof <prof>`
* Writing a plugin that selects or rejects tests
Implement any or all ``want*`` methods. Return False to reject the test
candidate, True to accept it -- which means that the test candidate
will pass through the rest of the system, so you must be prepared to
load tests from it if tests can't be loaded by the core loader or
another plugin -- and None if you don't care.
Examples: :doc:`nose.plugins.attrib <attrib>`,
:doc:`nose.plugins.doctests <doctests>`, :doc:`nose.plugins.testid <testid>`
More Examples
=============
See any builtin plugin or example plugin in the examples_ directory in
the nose source distribution. There is a list of third-party plugins
`on jottit`_.
.. _examples/html_plugin/htmlplug.py: http://python-nose.googlecode.com/svn/trunk/examples/html_plugin/htmlplug.py
.. _examples: http://python-nose.googlecode.com/svn/trunk/examples
.. _on jottit: http://nose-plugins.jottit.com/
""" |
"""Trust Region Reflective algorithm for least-squares optimization.
The algorithm is based on ideas from paper [STIR]_. The main idea is to
account for the presence of the bounds by appropriate scaling of the variables (or,
equivalently, changing a trust-region shape). Let's introduce a vector v:
| ub[i] - x[i], if g[i] < 0 and ub[i] < np.inf
v[i] = | x[i] - lb[i], if g[i] > 0 and lb[i] > -np.inf
| 1, otherwise
where g is the gradient of a cost function and lb, ub are the bounds. Its
components are distances to the bounds at which the anti-gradient points (if
this distance is finite). Define a scaling matrix D = diag(v**0.5).
First-order optimality conditions can be stated as
D^2 g(x) = 0.
Meaning that components of the gradient should be zero for strictly interior
variables, and components must point inside the feasible region for variables
on the bound.
Now consider this system of equations as a new optimization problem. If the
point x is strictly interior (not on the bound), then the left-hand side is
differentiable and the Newton step for it satisfies
(D^2 H + diag(g) Jv) p = -D^2 g
where H is the Hessian matrix (or its J^T J approximation in least squares),
Jv is the Jacobian matrix of v with components -1, 1 or 0, such that all
elements of matrix C = diag(g) Jv are non-negative. Introduce the change
of the variables x = D x_h (_h would be "hat" in LaTeX). In the new variables,
we have a Newton step satisfying
B_h p_h = -g_h,
where B_h = D H D + C, g_h = D g. In least squares B_h = J_h^T J_h, where
J_h = J D. Note that J_h and g_h are proper Jacobian and gradient with respect
to "hat" variables. To guarantee global convergence we formulate a
trust-region problem based on the Newton step in the new variables:
0.5 * p_h^T B_h p + g_h^T p_h -> min, ||p_h|| <= Delta
In the original space B = H + D^{-1} C D^{-1}, and the equivalent trust-region
problem is
0.5 * p^T B p + g^T p -> min, ||D^{-1} p|| <= Delta
Here, the meaning of the matrix D becomes more clear: it alters the shape
of a trust-region, such that large steps towards the bounds are not allowed.
In the implementation, the trust-region problem is solved in "hat" space,
but handling of the bounds is done in the original space (see below and read
the code).
The introduction of the matrix D doesn't allow to ignore bounds, the algorithm
must keep iterates strictly feasible (to satisfy aforementioned
differentiability), the parameter theta controls step back from the boundary
(see the code for details).
The algorithm does another important trick. If the trust-region solution
doesn't fit into the bounds, then a reflected (from a firstly encountered
bound) search direction is considered. For motivation and analysis refer to
[STIR]_ paper (and other papers of the authors). In practice, it doesn't need
a lot of justifications, the algorithm simply chooses the best step among
three: a constrained trust-region step, a reflected step and a constrained
Cauchy step (a minimizer along -g_h in "hat" space, or -D^2 g in the original
space).
Another feature is that a trust-region radius control strategy is modified to
account for appearance of the diagonal C matrix (called diag_h in the code).
Note that all described peculiarities are completely gone as we consider
problems without bounds (the algorithm becomes a standard trust-region type
algorithm very similar to ones implemented in MINPACK).
The implementation supports two methods of solving the trust-region problem.
The first, called 'exact', applies SVD on Jacobian and then solves the problem
very accurately using the algorithm described in [JJMore]_. It is not
applicable to large problem. The second, called 'lsmr', uses the 2-D subspace
approach (sometimes called "indefinite dogleg"), where the problem is solved
in a subspace spanned by the gradient and the approximate Gauss-Newton step
found by ``scipy.sparse.linalg.lsmr``. A 2-D trust-region problem is
reformulated as a 4th order algebraic equation and solved very accurately by
``numpy.roots``. The subspace approach allows to solve very large problems
(up to couple of millions of residuals on a regular PC), provided the Jacobian
matrix is sufficiently sparse.
References
----------
.. [STIR] NAME NAME NAME and NAME "A Subspace, Interior,
and Conjugate Gradient Method for Large-Scale Bound-Constrained
Minimization Problems," SIAM Journal on Scientific Computing,
Vol. 21, Number 1, pp 1-23, 1999.
.. [JJMore] NAME NAME "The Levenberg-Marquardt Algorithm: Implementation
and Theory," Numerical Analysis, ed. NAME Lecture
""" |
#!/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 <EMAIL>
#
# 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
|
"""automatically manage newlines in repository files
This extension allows you to manage the type of line endings (CRLF or
LF) that are used in the repository and in the local working
directory. That way you can get CRLF line endings on Windows and LF on
Unix/Mac, thereby letting everybody use their OS native line endings.
The extension reads its configuration from a versioned ``.hgeol``
configuration file found in the root of the working directory. The
``.hgeol`` file use the same syntax as all other Mercurial
configuration files. It uses two sections, ``[patterns]`` and
``[repository]``.
The ``[patterns]`` section specifies how line endings should be
converted between the working directory and the repository. The format is
specified by a file pattern. The first match is used, so put more
specific patterns first. The available line endings are ``LF``,
``CRLF``, and ``BIN``.
Files with the declared format of ``CRLF`` or ``LF`` are always
checked out and stored in the repository in that format and files
declared to be binary (``BIN``) are left unchanged. Additionally,
``native`` is an alias for checking out in the platform's default line
ending: ``LF`` on Unix (including Mac OS X) and ``CRLF`` on
Windows. Note that ``BIN`` (do nothing to line endings) is Mercurial's
default behavior; it is only needed if you need to override a later,
more general pattern.
The optional ``[repository]`` section specifies the line endings to
use for files stored in the repository. It has a single setting,
``native``, which determines the storage line endings for files
declared as ``native`` in the ``[patterns]`` section. It can be set to
``LF`` or ``CRLF``. The default is ``LF``. For example, this means
that on Windows, files configured as ``native`` (``CRLF`` by default)
will be converted to ``LF`` when stored in the repository. Files
declared as ``LF``, ``CRLF``, or ``BIN`` in the ``[patterns]`` section
are always stored as-is in the repository.
Example versioned ``.hgeol`` file::
[patterns]
**.py = native
**.vcproj = CRLF
**.txt = native
Makefile = LF
**.jpg = BIN
[repository]
native = LF
.. note::
The rules will first apply when files are touched in the working
directory, e.g. by updating to null and back to tip to touch all files.
The extension uses an optional ``[eol]`` section read from both the
normal Mercurial configuration files and the ``.hgeol`` file, with the
latter overriding the former. You can use that section to control the
overall behavior. There are three settings:
- ``eol.native`` (default ``os.linesep``) can be set to ``LF`` or
``CRLF`` to override the default interpretation of ``native`` for
checkout. This can be used with :hg:`archive` on Unix, say, to
generate an archive where files have line endings for Windows.
- ``eol.only-consistent`` (default True) can be set to False to make
the extension convert files with inconsistent EOLs. Inconsistent
means that there is both ``CRLF`` and ``LF`` present in the file.
Such files are normally not touched under the assumption that they
have mixed EOLs on purpose.
- ``eol.fix-trailing-newline`` (default False) can be set to True to
ensure that converted files end with a EOL character (either ``\\n``
or ``\\r\\n`` as per the configured patterns).
The extension provides ``cleverencode:`` and ``cleverdecode:`` filters
like the deprecated win32text extension does. This means that you can
disable win32text and enable eol and your filters will still work. You
only need to these filters until you have prepared a ``.hgeol`` file.
The ``win32text.forbid*`` hooks provided by the win32text extension
have been unified into a single hook named ``eol.checkheadshook``. The
hook will lookup the expected line endings from the ``.hgeol`` file,
which means you must migrate to a ``.hgeol`` file first before using
the hook. ``eol.checkheadshook`` only checks heads, intermediate
invalid revisions will be pushed. To forbid them completely, use the
``eol.checkallhook`` hook. These hooks are best used as
``pretxnchangegroup`` hooks.
See :hg:`help patterns` for more information about the glob patterns
used.
""" |
#!/usr/bin/env python
# Copyright (c) 2013 - 2015 ARM Limited
# All rights reserved
#
# The license below extends only to copyright in the software and shall
# not be construed as granting a license to any other intellectual
# property including but not limited to intellectual property relating
# to a hardware implementation of the functionality of the software
# licensed hereunder. You may use the software subject to the license
# terms below provided that you ensure that this notice is replicated
# unmodified and in its entirety in all distributions of the software,
# modified or unmodified, in source code or in binary form.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met: redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer;
# redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution;
# neither the name of the copyright holders nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Copyright 2008 Google Inc. All rights reserved.
# http://code.google.com/p/protobuf/
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Authors: NAME
#
# This script is used to dump protobuf traces of the instruction dependency
# graph to ASCII format.
#
# The ASCII trace format uses one line per instruction with the format
# instruction sequence number, (optional) pc, (optional) weight, type
# (optional) flags, (optional) phys addr, (optional) size, comp delay,
# (repeated) order dependencies comma-separated, and (repeated) register
# dependencies comma-separated.
#
# examples:
# seq_num,[pc],[weight,]type,[p_addr,size,flags,]comp_delay:[rob_dep]:
# [reg_dep]
# 1,35652,1,COMP,8500::
# 2,35656,1,COMP,0:,1:
# 3,35660,1,LOAD,1748752,4,74,500:,2:
# 4,35660,1,COMP,0:,3:
# 5,35664,1,COMP,3000::,4
# 6,35666,1,STORE,1748752,4,74,1000:,3:,4,5
# 7,35666,1,COMP,3000::,4
# 8,35670,1,STORE,1748748,4,74,0:,6,3:,7
# 9,35670,1,COMP,500::,7
|
#!/usr/bin/env python
# SPDX-License-Identifier: GPL-2.0
# exported-sql-viewer.py: view data from sql database
# Copyright (c) 2014-2018, Intel Corporation.
# To use this script you will need to have exported data using either the
# export-to-sqlite.py or the export-to-postgresql.py script. Refer to those
# scripts for details.
#
# Following on from the example in the export scripts, a
# call-graph can be displayed for the pt_example database like this:
#
# python tools/perf/scripts/python/exported-sql-viewer.py pt_example
#
# Note that for PostgreSQL, this script supports connecting to remote databases
# by setting hostname, port, username, password, and dbname e.g.
#
# python tools/perf/scripts/python/exported-sql-viewer.py "hostname=myhost username=myuser password=mypassword dbname=pt_example"
#
# The result is a GUI window with a tree representing a context-sensitive
# call-graph. Expanding a couple of levels of the tree and adjusting column
# widths to suit will display something like:
#
# Call Graph: pt_example
# Call Path Object Count Time(ns) Time(%) Branch Count Branch Count(%)
# v- ls
# v- 2638:2638
# v- _start ld-2.19.so 1 10074071 100.0 211135 100.0
# |- unknown unknown 1 13198 0.1 1 0.0
# >- _dl_start ld-2.19.so 1 1400980 13.9 19637 9.3
# >- _d_linit_internal ld-2.19.so 1 448152 4.4 11094 5.3
# v-__libc_start_main@plt ls 1 8211741 81.5 180397 85.4
# >- _dl_fixup ld-2.19.so 1 7607 0.1 108 0.1
# >- __cxa_atexit libc-2.19.so 1 11737 0.1 10 0.0
# >- __libc_csu_init ls 1 10354 0.1 10 0.0
# |- _setjmp libc-2.19.so 1 0 0.0 4 0.0
# v- main ls 1 8182043 99.6 180254 99.9
#
# Points to note:
# The top level is a command name (comm)
# The next level is a thread (pid:tid)
# Subsequent levels are functions
# 'Count' is the number of calls
# 'Time' is the elapsed time until the function returns
# Percentages are relative to the level above
# 'Branch Count' is the total number of branches for that function and all
# functions that it calls
# There is also a "All branches" report, which displays branches and
# possibly disassembly. However, presently, the only supported disassembler is
# Intel XED, and additionally the object code must be present in perf build ID
# cache. To use Intel XED, libxed.so must be present. To build and install
# libxed.so:
# git clone https://github.com/intelxed/mbuild.git mbuild
# git clone https://github.com/intelxed/xed
# cd xed
# ./mfile.py --share
# sudo ./mfile.py --prefix=/usr/local install
# sudo ldconfig
#
# Example report:
#
# Time CPU Command PID TID Branch Type In Tx Branch
# 8107675239590 2 ls 22011 22011 return from interrupt No ffffffff86a00a67 native_irq_return_iret ([kernel]) -> 7fab593ea260 _start (ld-2.19.so)
# 7fab593ea260 48 89 e7 mov %rsp, %rdi
# 8107675239899 2 ls 22011 22011 hardware interrupt No 7fab593ea260 _start (ld-2.19.so) -> ffffffff86a012e0 page_fault ([kernel])
# 8107675241900 2 ls 22011 22011 return from interrupt No ffffffff86a00a67 native_irq_return_iret ([kernel]) -> 7fab593ea260 _start (ld-2.19.so)
# 7fab593ea260 48 89 e7 mov %rsp, %rdi
# 7fab593ea263 e8 c8 06 00 00 callq 0x7fab593ea930
# 8107675241900 2 ls 22011 22011 call No 7fab593ea263 _start+0x3 (ld-2.19.so) -> 7fab593ea930 _dl_start (ld-2.19.so)
# 7fab593ea930 55 pushq %rbp
# 7fab593ea931 48 89 e5 mov %rsp, %rbp
# 7fab593ea934 41 57 pushq %r15
# 7fab593ea936 41 56 pushq %r14
# 7fab593ea938 41 55 pushq %r13
# 7fab593ea93a 41 54 pushq %r12
# 7fab593ea93c 53 pushq %rbx
# 7fab593ea93d 48 89 fb mov %rdi, %rbx
# 7fab593ea940 48 83 ec 68 sub $0x68, %rsp
# 7fab593ea944 0f 31 rdtsc
# 7fab593ea946 48 c1 e2 20 shl $0x20, %rdx
# 7fab593ea94a 89 c0 mov %eax, %eax
# 7fab593ea94c 48 09 c2 or %rax, %rdx
# 7fab593ea94f 48 8b 05 1a 15 22 00 movq 0x22151a(%rip), %rax
# 8107675242232 2 ls 22011 22011 hardware interrupt No 7fab593ea94f _dl_start+0x1f (ld-2.19.so) -> ffffffff86a012e0 page_fault ([kernel])
# 8107675242900 2 ls 22011 22011 return from interrupt No ffffffff86a00a67 native_irq_return_iret ([kernel]) -> 7fab593ea94f _dl_start+0x1f (ld-2.19.so)
# 7fab593ea94f 48 8b 05 1a 15 22 00 movq 0x22151a(%rip), %rax
# 7fab593ea956 48 89 15 3b 13 22 00 movq %rdx, 0x22133b(%rip)
# 8107675243232 2 ls 22011 22011 hardware interrupt No 7fab593ea956 _dl_start+0x26 (ld-2.19.so) -> ffffffff86a012e0 page_fault ([kernel])
|
#!/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 *
# * *
# ***************************************************************************/
|
"""
==========================================
Statistical functions (:mod:`scipy.stats`)
==========================================
.. module:: scipy.stats
This module contains a large number of probability distributions as
well as a growing library of statistical functions.
Each included distribution is an instance of the class rv_continuous:
For each given name the following methods are available:
.. autosummary::
:toctree: generated/
rv_continuous
rv_continuous.pdf
rv_continuous.logpdf
rv_continuous.cdf
rv_continuous.logcdf
rv_continuous.sf
rv_continuous.logsf
rv_continuous.ppf
rv_continuous.isf
rv_continuous.moment
rv_continuous.stats
rv_continuous.entropy
rv_continuous.fit
rv_continuous.expect
Calling the instance as a function returns a frozen pdf whose shape,
location, and scale parameters are fixed.
Similarly, each discrete distribution is an instance of the class
rv_discrete:
.. autosummary::
:toctree: generated/
rv_discrete
rv_discrete.rvs
rv_discrete.pmf
rv_discrete.logpmf
rv_discrete.cdf
rv_discrete.logcdf
rv_discrete.sf
rv_discrete.logsf
rv_discrete.ppf
rv_discrete.isf
rv_discrete.stats
rv_discrete.moment
rv_discrete.entropy
rv_discrete.expect
Continuous distributions
========================
.. autosummary::
:toctree: generated/
alpha -- Alpha
anglit -- Anglit
arcsine -- Arcsine
beta -- Beta
betaprime -- Beta Prime
bradford -- Bradford
burr -- Burr
cauchy -- Cauchy
chi -- Chi
chi2 -- Chi-squared
cosine -- Cosine
dgamma -- Double Gamma
dweibull -- Double Weibull
erlang -- Erlang
expon -- Exponential
exponweib -- Exponentiated Weibull
exponpow -- Exponential Power
f -- F (Snecdor F)
fatiguelife -- Fatigue Life (Birnbaum-Saunders)
fisk -- Fisk
foldcauchy -- Folded Cauchy
foldnorm -- Folded Normal
frechet_r -- Frechet Right Sided, Extreme Value Type II (Extreme LB) or weibull_min
frechet_l -- Frechet Left Sided, Weibull_max
genlogistic -- Generalized Logistic
genpareto -- Generalized Pareto
genexpon -- Generalized Exponential
genextreme -- Generalized Extreme Value
gausshyper -- Gauss Hypergeometric
gamma -- Gamma
gengamma -- Generalized gamma
genhalflogistic -- Generalized Half Logistic
gilbrat -- Gilbrat
gompertz -- Gompertz (Truncated Gumbel)
gumbel_r -- Right Sided Gumbel, Log-Weibull, Fisher-Tippett, Extreme Value Type I
gumbel_l -- Left Sided Gumbel, etc.
halfcauchy -- Half Cauchy
halflogistic -- Half Logistic
halfnorm -- Half Normal
hypsecant -- Hyperbolic Secant
invgamma -- Inverse Gamma
invgauss -- Inverse Gaussian
invweibull -- Inverse Weibull
johnsonsb -- NAME
johnsonsu -- NAME
ksone -- Kolmogorov-Smirnov one-sided (no stats)
kstwobign -- Kolmogorov-Smirnov two-sided test for Large N (no stats)
laplace -- Laplace
logistic -- Logistic
loggamma -- Log-Gamma
loglaplace -- Log-Laplace (Log Double Exponential)
lognorm -- Log-Normal
lomax -- Lomax (Pareto of the second kind)
maxwell -- Maxwell
mielke -- Mielke's Beta-Kappa
nakagami -- Nakagami
ncx2 -- Non-central chi-squared
ncf -- Non-central F
nct -- Non-central Student's T
norm -- Normal (Gaussian)
pareto -- Pareto
pearson3 -- Pearson type III
powerlaw -- Power-function
powerlognorm -- Power log normal
powernorm -- Power normal
rdist -- R-distribution
reciprocal -- Reciprocal
rayleigh -- Rayleigh
rice -- Rice
recipinvgauss -- Reciprocal Inverse Gaussian
semicircular -- Semicircular
t -- Student's T
triang -- Triangular
truncexpon -- Truncated Exponential
truncnorm -- Truncated Normal
tukeylambda -- Tukey-Lambda
uniform -- Uniform
vonmises -- Von-Mises (Circular)
wald -- Wald
weibull_min -- Minimum Weibull (see Frechet)
weibull_max -- Maximum Weibull (see Frechet)
wrapcauchy -- Wrapped Cauchy
Multivariate distributions
==========================
.. autosummary::
:toctree: generated/
multivariate_normal -- Multivariate normal distribution
dirichlet -- Dirichlet
Discrete distributions
======================
.. autosummary::
:toctree: generated/
bernoulli -- Bernoulli
binom -- Binomial
boltzmann -- Boltzmann (Truncated Discrete Exponential)
dlaplace -- Discrete Laplacian
geom -- Geometric
hypergeom -- Hypergeometric
logser -- Logarithmic (Log-Series, Series)
nbinom -- Negative Binomial
planck -- Planck (Discrete Exponential)
poisson -- Poisson
randint -- Discrete Uniform
skellam -- Skellam
zipf -- Zipf
Statistical functions
=====================
Several of these functions have a similar version in scipy.stats.mstats
which work for masked arrays.
.. autosummary::
:toctree: generated/
describe -- Descriptive statistics
gmean -- Geometric mean
hmean -- Harmonic mean
kurtosis -- Fisher or Pearson kurtosis
kurtosistest --
mode -- Modal value
moment -- Central moment
normaltest --
skew -- Skewness
skewtest --
tmean -- Truncated arithmetic mean
tvar -- Truncated variance
tmin --
tmax --
tstd --
tsem --
nanmean -- Mean, ignoring NaN values
nanstd -- Standard deviation, ignoring NaN values
nanmedian -- Median, ignoring NaN values
variation -- Coefficient of variation
.. autosummary::
:toctree: generated/
cumfreq _
histogram2 _
histogram _
itemfreq _
percentileofscore _
scoreatpercentile _
relfreq _
.. autosummary::
:toctree: generated/
binned_statistic -- Compute a binned statistic for a set of data.
binned_statistic_2d -- Compute a 2-D binned statistic for a set of data.
binned_statistic_dd -- Compute a d-D binned statistic for a set of data.
.. autosummary::
:toctree: generated/
obrientransform
signaltonoise
bayes_mvs
sem
zmap
zscore
.. autosummary::
:toctree: generated/
sigmaclip
threshold
trimboth
trim1
.. autosummary::
:toctree: generated/
f_oneway
pearsonr
spearmanr
pointbiserialr
kendalltau
linregress
theilslopes
.. autosummary::
:toctree: generated/
ttest_1samp
ttest_ind
ttest_rel
kstest
chisquare
power_divergence
ks_2samp
mannwhitneyu
tiecorrect
rankdata
ranksums
wilcoxon
kruskal
friedmanchisquare
combine_pvalues
.. autosummary::
:toctree: generated/
ansari
bartlett
levene
shapiro
anderson
anderson_ksamp
binom_test
fligner
median_test
mood
.. autosummary::
:toctree: generated/
boxcox
boxcox_normmax
boxcox_llf
entropy
Contingency table functions
===========================
.. autosummary::
:toctree: generated/
chi2_contingency
contingency.expected_freq
contingency.margins
fisher_exact
Plot-tests
==========
.. autosummary::
:toctree: generated/
ppcc_max
ppcc_plot
probplot
boxcox_normplot
Masked statistics functions
===========================
.. toctree::
stats.mstats
Univariate and multivariate kernel density estimation (:mod:`scipy.stats.kde`)
==============================================================================
.. autosummary::
:toctree: generated/
gaussian_kde
For many more stat related functions install the software R and the
interface package rpy.
""" |
"""
==============
Array Creation
==============
Introduction
============
There are 5 general mechanisms for creating arrays:
1) Conversion from other Python structures (e.g., lists, tuples)
2) Intrinsic numpy array array creation objects (e.g., arange, ones, zeros,
etc.)
3) Reading arrays from disk, either from standard or custom formats
4) Creating arrays from raw bytes through the use of strings or buffers
5) Use of special library functions (e.g., random)
This section will not cover means of replicating, joining, or otherwise
expanding or mutating existing arrays. Nor will it cover creating object
arrays or record arrays. Both of those are covered in their own sections.
Converting Python array_like Objects to Numpy Arrays
====================================================
In general, numerical data arranged in an array-like structure in Python can
be converted to arrays through the use of the array() function. The most
obvious examples are lists and tuples. See the documentation for array() for
details for its use. Some objects may support the array-protocol and allow
conversion to arrays this way. A simple way to find out if the object can be
converted to a numpy array using array() is simply to try it interactively and
see if it works! (The Python Way).
Examples: ::
>>> x = np.array([2,3,1,0])
>>> x = np.array([2, 3, 1, 0])
>>> x = np.array([[1,2.0],[0,0],(1+1j,3.)]) # note mix of tuple and lists,
and types
>>> x = np.array([[ 1.+0.j, 2.+0.j], [ 0.+0.j, 0.+0.j], [ 1.+1.j, 3.+0.j]])
Intrinsic Numpy Array Creation
==============================
Numpy has built-in functions for creating arrays from scratch:
zeros(shape) will create an array filled with 0 values with the specified
shape. The default dtype is float64.
``>>> np.zeros((2, 3))
array([[ 0., 0., 0.], [ 0., 0., 0.]])``
ones(shape) will create an array filled with 1 values. It is identical to
zeros in all other respects.
arange() will create arrays with regularly incrementing values. Check the
docstring for complete information on the various ways it can be used. A few
examples will be given here: ::
>>> np.arange(10)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.arange(2, 10, dtype=np.float)
array([ 2., 3., 4., 5., 6., 7., 8., 9.])
>>> np.arange(2, 3, 0.1)
array([ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])
Note that there are some subtleties regarding the last usage that the user
should be aware of that are described in the arange docstring.
linspace() will create arrays with a specified number of elements, and
spaced equally between the specified beginning and end values. For
example: ::
>>> np.linspace(1., 4., 6)
array([ 1. , 1.6, 2.2, 2.8, 3.4, 4. ])
The advantage of this creation function is that one can guarantee the
number of elements and the starting and end point, which arange()
generally will not do for arbitrary start, stop, and step values.
indices() will create a set of arrays (stacked as a one-higher dimensioned
array), one per dimension with each representing variation in that dimension.
An example illustrates much better than a verbal description: ::
>>> np.indices((3,3))
array([[[0, 0, 0], [1, 1, 1], [2, 2, 2]], [[0, 1, 2], [0, 1, 2], [0, 1, 2]]])
This is particularly useful for evaluating functions of multiple dimensions on
a regular grid.
Reading Arrays From Disk
========================
This is presumably the most common case of large array creation. The details,
of course, depend greatly on the format of data on disk and so this section
can only give general pointers on how to handle various formats.
Standard Binary Formats
-----------------------
Various fields have standard formats for array data. The following lists the
ones with known python libraries to read them and return numpy arrays (there
may be others for which it is possible to read and convert to numpy arrays so
check the last section as well)
::
HDF5: PyTables
FITS: PyFITS
Examples of formats that cannot be read directly but for which it is not hard to
convert are those formats supported by libraries like PIL (able to read and
write many image formats such as jpg, png, etc).
Common ASCII Formats
------------------------
Comma Separated Value files (CSV) are widely used (and an export and import
option for programs like Excel). There are a number of ways of reading these
files in Python. There are CSV functions in Python and functions in pylab
(part of matplotlib).
More generic ascii files can be read using the io package in scipy.
Custom Binary Formats
---------------------
There are a variety of approaches one can use. If the file has a relatively
simple format then one can write a simple I/O library and use the numpy
fromfile() function and .tofile() method to read and write numpy arrays
directly (mind your byteorder though!) If a good C or C++ library exists that
read the data, one can wrap that library with a variety of techniques though
that certainly is much more work and requires significantly more advanced
knowledge to interface with C or C++.
Use of Special Libraries
------------------------
There are libraries that can be used to generate arrays for special purposes
and it isn't possible to enumerate all of them. The most common uses are use
of the many array generation functions in random that can generate arrays of
random values, and some utility functions to generate special matrices (e.g.
diagonal).
""" |
"""
Module pdoc provides types and functions for accessing the public
documentation of a Python module. This includes modules (and
sub-modules), functions, classes and module, class and instance
variables. Docstrings are taken from modules, functions and classes
using the special `__doc__` attribute. Docstrings for variables are
extracted by examining the module's abstract syntax tree.
The public interface of a module is determined through one of two
ways. If `__all__` is defined in the module, then all identifiers in
that list will be considered public. No other identifiers will be
considered as public. Conversely, if `__all__` is not defined, then
`pdoc` will heuristically determine the public interface. There are
three rules that are applied to each identifier in the module:
1. If the name starts with an underscore, it is **not** public.
2. If the name is defined in a different module, it is **not** public.
3. If the name refers to an immediate sub-module, then it is public.
Once documentation for a module is created with `pdoc.Module`, it
can be output as either HTML or plain text using the covenience
functions `pdoc.html` and `pdoc.text`, or the corresponding methods
`pdoc.Module.html` and `pdoc.Module.text`.
Alternatively, you may run an HTTP server with the `pdoc` script
included with this module.
Compatibility
-------------
`pdoc` has been tested on Python 2.6, 2.7 and 3.3. It seems to work
on all three.
Contributing
------------
`pdoc` [is on GitHub](https://github.com/BurntSushi/pdoc). Pull
requests and bug reports are welcome.
Linking to other identifiers
----------------------------
In your documentation, you may link to other identifiers in
your module or submodules. Linking is automatically done for
you whenever you surround an identifier with a back quote
(grave). The identifier name must be fully qualified. For
example, <code>\`pdoc.Doc.docstring\`</code> is correct while
<code>\`Doc.docstring\`</code> is incorrect.
If the `pdoc` script is used to run an HTTP server, then external
linking to other packages installed is possible. No extra work is
necessary; simply use the fully qualified path. For example,
<code>\`nflvid.slice\`</code> will create a link to the `nflvid.slice`
function, which is **not** a part of `pdoc` at all.
Where does pdoc get documentation from?
---------------------------------------
Broadly speaking, `pdoc` gets everything you see from introspecting the
module. This includes words describing a particular module, class,
function or variable. While `pdoc` does some analysis on the source
code of a module, importing the module itself is necessary to use
Python's introspection features.
In Python, objects like modules, functions, classes and methods have
a special attribute named `__doc__` which contains that object's
*docstring*. The docstring comes from a special placement of a string
in your source code. For example, the following code shows how to
define a function with a docstring and access the contents of that
docstring:
#!python
>>> def test():
... '''This is a docstring.'''
... pass
...
>>> test.__doc__
'This is a docstring.'
Something similar can be done for classes and modules too. For classes,
the docstring should come on the line immediately following `class
...`. For modules, the docstring should start on the first line of
the file. These docstrings are what you see for each module, class,
function and method listed in the documentation produced by `pdoc`.
The above just about covers *standard* uses of docstrings in Python.
`pdoc` extends the above in a few important ways.
### Special docstring conventions used by `pdoc`
**Firstly**, docstrings can be inherited. Consider the following code
sample:
#!python
>>> class A (object):
... def test():
... '''Docstring for A.'''
...
>>> class B (A):
... def test():
... pass
...
>>> print(A.test.__doc__)
Docstring for A.
>>> print(B.test.__doc__)
None
In Python, the docstring for `B.test` is empty, even though one was
defined in `A.test`. If `pdoc` generates documentation for the above
code, then it will automatically attach the docstring for `A.test` to
`B.test` only if `B.test` does not have a docstring. In the default
HTML output, an inherited docstring is grey.
**Secondly**, docstrings can be attached to variables, which includes
module (or global) variables, class variables and instance variables.
Python by itself [does not allow docstrings to be attached to
variables](http://www.python.org/dev/peps/pep-0224). For example:
#!python
variable = "SomeValue"
'''Docstring for variable.'''
The resulting `variable` will have no `__doc__` attribute. To
compensate, `pdoc` will read the source code when it's available to
infer a connection between a variable and a docstring. The connection
is only made when an assignment statement is followed by a docstring.
Something similar is done for instance variables as well. By
convention, instance variables are initialized in a class's `__init__`
method. Therefore, `pdoc` adheres to that convention and looks for
docstrings of variables like so:
#!python
def __init__(self):
self.variable = "SomeValue"
'''Docstring for instance variable.'''
Note that `pdoc` only considers attributes defined on `self` as
instance variables.
Class and instance variables can also have inherited docstrings.
**Thirdly and finally**, docstrings can be overridden with a special
`__pdoc__` dictionary that `pdoc` inspects if it exists. The keys of
`__pdoc__` should be identifiers within the scope of the module. (In
the case of an instance variable `self.variable` for class `A`, its
module identifier would be `A.variable`.) The values of `__pdoc__`
should be docstrings.
This particular feature is useful when there's no feasible way of
attaching a docstring to something. A good example of this is a
[namedtuple](http://goo.gl/akfXJ9):
#!python
__pdoc__ = {}
Table = namedtuple('Table', ['types', 'names', 'rows'])
__pdoc__['Table.types'] = 'Types for each column in the table.'
__pdoc__['Table.names'] = 'The names of each column in the table.'
__pdoc__['Table.rows'] = 'Lists corresponding to each row in the table.'
`pdoc` will then show `Table` as a class with documentation for the
`types`, `names` and `rows` members.
Note that assignments to `__pdoc__` need to placed where they'll be
executed when the module is imported. For example, at the top level
of a module or in the definition of a class.
If `__pdoc__[key] = None`, then `key` will not be included in the
public interface of the module.
License
-------
`pdoc` is in the public domain via the
[UNLICENSE](http://unlicense.org).
""" |
"""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.
""" |