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### This script reports process metrics to ganglia.
###
### Notes:
### This script exposes values for CPU and memory utilization
### for running processes. You can retrieve the process ID from
### either providing a pidfile or an awk regular expression.
### Using a pidfile is the most efficient and direct method.
###
### When using a regular expression, keep in mind that there is
### a chance for a false positive. This script will help to avoid
### these by only returning parent processes. This means that
### the results are limited to processes where ppid = 1.
###
### This script also comes with the ability to test your regular
### expressions via command line arguments "-t".
###
### Testing:
### -- This is a correct examples of how to monitor apache.
###
### $ python procstat.py -p httpd -v '/var/run/httpd.pid' -t
### Testing httpd: /var/run/httpd.pid
### Processes in this group:
### PID, ARGS
### 11058 /usr/sbin/httpd
### 8817 /usr/sbin/httpd
### 9000 /usr/sbin/httpd
### 9001 /usr/sbin/httpd
###
### waiting 2 seconds
### procstat_httpd_mem: 202076 KB [The total memory utilization]
### procstat_httpd_cpu: 0.3 percent [The total percent CPU utilization]
###
### -- This example shows a regex that returns no processes with a
### ppid of 1.
###
### $ python procstat.py -p test -v 'wrong' -t
### Testing test: wrong
### failed getting pgid: no process returned
### ps -Ao pid,ppid,pgid,args | awk 'wrong && $2 == 1 && !/awk/ && !/procstat\.py/ {print $0}'
###
### -- This example shows a regex that returns more than one process
### with a ppid of 1.
###
### $ python procstat.py -p test -v '/mingetty/' -t
### Testing test: /mingetty/
### failed getting pgid: more than 1 result returned
### ps -Ao pid,ppid,pgid,args | awk '/mingetty/ && $2 == 1 && !/awk/ && !/procstat\.py/ {print $0}'
### 7313 1 7313 /sbin/mingetty tty1
### 7314 1 7314 /sbin/mingetty tty2
### 7315 1 7315 /sbin/mingetty tty3
### 7316 1 7316 /sbin/mingetty tty4
### 7317 1 7317 /sbin/mingetty tty5
### 7318 1 7318 /sbin/mingetty tty6
###
### Command Line Example:
### $ python procstat.py -p httpd,opennms,splunk,splunk-web \
### -v '/var/run/httpd.pid','/opt/opennms/logs/daemon/opennms.pid','/splunkd.*start/','/twistd.*SplunkWeb/'
###
### procstat_httpd_mem: 202068 KB [The total memory utilization]
### procstat_splunk_mem: 497848 KB [The total memory utilization]
### procstat_splunk-web_mem: 32636 KB [The total memory utilization]
### procstat_opennms_mem: 623112 KB [The total memory utilization]
### procstat_httpd_cpu: 0.3 percent [The total percent CPU utilization]
### procstat_splunk_cpu: 0.6 percent [The total percent CPU utilization]
### procstat_splunk-web_cpu: 0.1 percent [The total percent CPU utilization]
### procstat_opennms_cpu: 7.1 percent [The total percent CPU utilization]
###
### Example Values:
### httpd: /var/run/httpd.pid or \/usr\/sbin\/httpd
### mysqld: /var/run/mysqld/mysqld.pid or /\/usr\/bin\/mysqld_safe/
### postgresql: /var/run/postmaster.[port].pid or /\/usr\/bin\/postmaster.*[port]/
### splunk: /splunkd.*start/
### splunk-web: /twistd.*SplunkWeb/
### opennms: /opt/opennms/logs/daemon/opennms.pid or java.*Dopennms
### netflow: /java.*NetFlow/
### postfix: /var/spool/postfix/pid/master.pid or /\/usr\/libexec\/postfix\/master/
###
### Error Tests:
### python procstat.py -p test-more,test-none,test-pidfail -v '/java/','/javaw/','java.pid' -t
###
### Changelog:
### v1.0.1 - 2010-07-23
### * Initial version
###
### v1.1.0 - 2010-07-28
### * Modified the process regex search to find the parent
### process and then find all processes with the same process
### group ID (pgid). "ps" is only used for regex searching on
### the initial lookup for the parent pid (ppid). Now all
### subsequent calls use /proc/[pid]/stat for CPU jiffies, and
### /proc/[pid]/statm for memory rss.
### * Added testing switch "-t" to help troubleshoot a regex
### * Added display switches "-s" and "-m" to format the output
### of /proc/[pid]/stat and /proc/[pid]/statm
###
### Copyright NAME 2010
### License to use, modify, and distribute under the GPL
### http://www.gnu.org/licenses/gpl.txt
|
"""==============
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
only 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 (number of index elements,
size of row).
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])
>>> 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.nonzero()
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
""" |
# -*- 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.
|
"""
========
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 combination 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*.
record array
An `ndarray`_ with `structured data type`_ which has been subclassed as
np.recarray and whose dtype is of type np.record, making the
fields of its data type to be accessible by attribute.
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])
structured data type
A data type composed of other datatypes
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).
""" |
"""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.
""" |
# 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.
|
"""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
# (c) 2013, NAME <EMAIL>
#
# This file is part of Ansible.
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
#
#
# Author: NAME <EMAIL>
#
# Description:
# This module queries local or remote Docker daemons and generates
# inventory information.
#
# This plugin does not support targeting of specific hosts using the --host
# flag. Instead, it queries the Docker API for each container, running
# or not, and returns this data all once.
#
# The plugin returns the following custom attributes on Docker containers:
# docker_args
# docker_config
# docker_created
# docker_driver
# docker_exec_driver
# docker_host_config
# docker_hostname_path
# docker_hosts_path
# docker_id
# docker_image
# docker_name
# docker_network_settings
# docker_path
# docker_resolv_conf_path
# docker_state
# docker_volumes
# docker_volumes_rw
#
# Requirements:
# The docker-py module: https://github.com/dotcloud/docker-py
#
# Notes:
# A config file can be used to configure this inventory module, and there
# are several environment variables that can be set to modify the behavior
# of the plugin at runtime:
# DOCKER_CONFIG_FILE
# DOCKER_HOST
# DOCKER_VERSION
# DOCKER_TIMEOUT
# DOCKER_PRIVATE_SSH_PORT
# DOCKER_DEFAULT_IP
#
# Environment Variables:
# environment variable: DOCKER_CONFIG_FILE
# description:
# - A path to a Docker inventory hosts/defaults file in YAML format
# - A sample file has been provided, colocated with the inventory
# file called 'docker.yml'
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_HOST
# description:
# - The socket on which to connect to a Docker daemon API
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_VERSION
# description:
# - Version of the Docker API to use
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_TIMEOUT
# description:
# - Timeout in seconds for connections to Docker daemon API
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_PRIVATE_SSH_PORT
# description:
# - The private port (container port) on which SSH is listening
# for connections
# default: 22
# required: false
# environment variable: DOCKER_DEFAULT_IP
# description:
# - This environment variable overrides the container SSH connection
# IP address (aka, 'ansible_ssh_host')
#
# This option allows one to override the ansible_ssh_host whenever
# Docker has exercised its default behavior of binding private ports
# to all interfaces of the Docker host. This behavior, when dealing
# with remote Docker hosts, does not allow Ansible to determine
# a proper host IP address on which to connect via SSH to containers.
# By default, this inventory module assumes all IP_ADDRESS-exposed
# ports to be bound to localhost:<port>. To override this
# behavior, for example, to bind a container's SSH port to the public
# interface of its host, one must manually set this IP.
#
# It is preferable to begin to launch Docker containers with
# ports exposed on publicly accessible IP addresses, particularly
# if the containers are to be targeted by Ansible for remote
# configuration, not accessible via localhost SSH connections.
#
# Docker containers can be explicitly exposed on IP addresses by
# a) starting the daemon with the --ip argument
# b) running containers with the -P/--publish ip::containerPort
# argument
# default: IP_ADDRESS if port exposed on IP_ADDRESS by Docker
# required: false
#
# Examples:
# Use the config file:
# DOCKER_CONFIG_FILE=./docker.yml docker.py --list
#
# Connect to docker instance on localhost port 4243
# DOCKER_HOST=tcp://localhost:4243 docker.py --list
#
# Any container's ssh port exposed on IP_ADDRESS will mapped to
# another IP address (where Ansible will attempt to connect via SSH)
# DOCKER_DEFAULT_IP=IP_ADDRESS docker.py --list
|
"""
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).
""" |
"""Drag-and-drop support for Tkinter.
This is very preliminary. I currently only support dnd *within* one
application, between different windows (or within the same window).
I am trying to make this as generic as possible -- not dependent on
the use of a particular widget or icon type, etc. I also hope that
this will work with Pmw.
To enable an object to be dragged, you must create an event binding
for it that starts the drag-and-drop process. Typically, you should
bind <ButtonPress> to a callback function that you write. The function
should call Tkdnd.dnd_start(source, event), where 'source' is the
object to be dragged, and 'event' is the event that invoked the call
(the argument to your callback function). Even though this is a class
instantiation, the returned instance should not be stored -- it will
be kept alive automatically for the duration of the drag-and-drop.
When a drag-and-drop is already in process for the Tk interpreter, the
call is *ignored*; this normally averts starting multiple simultaneous
dnd processes, e.g. because different button callbacks all
dnd_start().
The object is *not* necessarily a widget -- it can be any
application-specific object that is meaningful to potential
drag-and-drop targets.
Potential drag-and-drop targets are discovered as follows. Whenever
the mouse moves, and at the start and end of a drag-and-drop move, the
Tk widget directly under the mouse is inspected. This is the target
widget (not to be confused with the target object, yet to be
determined). If there is no target widget, there is no dnd target
object. If there is a target widget, and it has an attribute
dnd_accept, this should be a function (or any callable object). The
function is called as dnd_accept(source, event), where 'source' is the
object being dragged (the object passed to dnd_start() above), and
'event' is the most recent event object (generally a <Motion> event;
it can also be <ButtonPress> or <ButtonRelease>). If the dnd_accept()
function returns something other than None, this is the new dnd target
object. If dnd_accept() returns None, or if the target widget has no
dnd_accept attribute, the target widget's parent is considered as the
target widget, and the search for a target object is repeated from
there. If necessary, the search is repeated all the way up to the
root widget. If none of the target widgets can produce a target
object, there is no target object (the target object is None).
The target object thus produced, if any, is called the new target
object. It is compared with the old target object (or None, if there
was no old target widget). There are several cases ('source' is the
source object, and 'event' is the most recent event object):
- Both the old and new target objects are None. Nothing happens.
- The old and new target objects are the same object. Its method
dnd_motion(source, event) is called.
- The old target object was None, and the new target object is not
None. The new target object's method dnd_enter(source, event) is
called.
- The new target object is None, and the old target object is not
None. The old target object's method dnd_leave(source, event) is
called.
- The old and new target objects differ and neither is None. The old
target object's method dnd_leave(source, event), and then the new
target object's method dnd_enter(source, event) is called.
Once this is done, the new target object replaces the old one, and the
Tk mainloop proceeds. The return value of the methods mentioned above
is ignored; if they raise an exception, the normal exception handling
mechanisms take over.
The drag-and-drop processes can end in two ways: a final target object
is selected, or no final target object is selected. When a final
target object is selected, it will always have been notified of the
potential drop by a call to its dnd_enter() method, as described
above, and possibly one or more calls to its dnd_motion() method; its
dnd_leave() method has not been called since the last call to
dnd_enter(). The target is notified of the drop by a call to its
method dnd_commit(source, event).
If no final target object is selected, and there was an old target
object, its dnd_leave(source, event) method is called to complete the
dnd sequence.
Finally, the source object is notified that the drag-and-drop process
is over, by a call to source.dnd_end(target, event), specifying either
the selected target object, or None if no target object was selected.
The source object can use this to implement the commit action; this is
sometimes simpler than to do it in the target's dnd_commit(). The
target's dnd_commit() method could then simply be aliased to
dnd_leave().
At any time during a dnd sequence, the application can cancel the
sequence by calling the cancel() method on the object returned by
dnd_start(). This will call dnd_leave() if a target is currently
active; it will never call dnd_commit().
""" |
"""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.
""" |
"""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.
""" |
#
# ElementTree
# $Id: ElementTree.py 2326 2005-03-17 07:45:21Z USERNAME $
#
# light-weight XML support for Python 1.5.2 and later.
#
# history:
# 2001-10-20 fl created (from various sources)
# 2001-11-01 fl return root from parse method
# 2002-02-16 fl sort attributes in lexical order
# 2002-04-06 fl TreeBuilder refactoring, added PythonDoc markup
# 2002-05-01 fl finished TreeBuilder refactoring
# 2002-07-14 fl added basic namespace support to ElementTree.write
# 2002-07-25 fl added QName attribute support
# 2002-10-20 fl fixed encoding in write
# 2002-11-24 fl changed default encoding to ascii; fixed attribute encoding
# 2002-11-27 fl accept file objects or file names for parse/write
# 2002-12-04 fl moved XMLTreeBuilder back to this module
# 2003-01-11 fl fixed entity encoding glitch for us-ascii
# 2003-02-13 fl added XML literal factory
# 2003-02-21 fl added ProcessingInstruction/PI factory
# 2003-05-11 fl added tostring/fromstring helpers
# 2003-05-26 fl added ElementPath support
# 2003-07-05 fl added makeelement factory method
# 2003-07-28 fl added more well-known namespace prefixes
# 2003-08-15 fl fixed typo in ElementTree.findtext (Thomas NAME 2003-09-04 fl fall back on emulator if ElementPath is not installed
# 2003-10-31 fl markup updates
# 2003-11-15 fl fixed nested namespace bug
# 2004-03-28 fl added XMLID helper
# 2004-06-02 fl added default support to findtext
# 2004-06-08 fl fixed encoding of non-ascii element/attribute names
# 2004-08-23 fl take advantage of post-2.1 expat features
# 2005-02-01 fl added iterparse implementation
# 2005-03-02 fl fixed iterparse support for pre-2.2 versions
#
# Copyright (c) 1999-2005 by NAME All rights reserved.
#
# EMAIL
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2005 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
# Licensed to PSF under a Contributor Agreement.
# See http://www.python.org/2.4/license for licensing details.
|
# -*- coding: utf-8 -*-
# routers are dictionaries of URL routing parameters.
#
# For each request, the effective router is:
# the built-in default base router (shown below),
# updated by the BASE router in routes.py routers,
# updated by the app-specific router in routes.py routers (if any),
# updated by the app-specific router from applications/app/routes.py routers (if any)
#
#
# Router members:
#
# default_application: default application name
# applications: list of all recognized applications, or 'ALL' to use all currently installed applications
# Names in applications are always treated as an application names when they appear first in an incoming URL.
# Set applications=None to disable the removal of application names from outgoing URLs.
# domains: optional dict mapping domain names to application names
# The domain name can include a port number: domain.com:8080
# The application name can include a controller: appx/ctlrx
# or a controller and a function: appx/ctlrx/fcnx
# Example:
# domains = { "domain.com" : "app",
# "x.domain.com" : "appx",
# },
# path_prefix: a path fragment that is prefixed to all outgoing URLs and stripped from all incoming URLs
#
# Note: default_application, applications, domains & path_prefix are permitted only in the BASE router,
# and domain makes sense only in an application-specific router.
# The remaining members can appear in the BASE router (as defaults for all applications)
# or in application-specific routers.
#
# default_controller: name of default controller
# default_function: name of default function (in all controllers) or dictionary of default functions
# by controller
# controllers: list of valid controllers in selected app
# or "DEFAULT" to use all controllers in the selected app plus 'static'
# or None to disable controller-name removal.
# Names in controllers are always treated as controller names when they appear in an incoming URL after
# the (optional) application and language names.
# functions: list of valid functions in the default controller (default None) or dictionary of valid
# functions by controller.
# If present, the default function name will be omitted when the controller is the default controller
# and the first arg does not create an ambiguity.
# languages: list of all supported languages
# Names in languages are always treated as language names when they appear in an incoming URL after
# the (optional) application name.
# default_language
# The language code (for example: en, it-it) optionally appears in the URL following
# the application (which may be omitted). For incoming URLs, the code is copied to
# request.uri_language; for outgoing URLs it is taken from request.uri_language.
# If languages=None, language support is disabled.
# The default_language, if any, is omitted from the URL.
# To use the incoming language in your application, add this line to one of your models files:
# if request.uri_language: T.force(request.uri_language)
# root_static: list of static files accessed from root (by default, favicon.ico & robots.txt)
# (mapped to the default application's static/ directory)
# Each default (including domain-mapped) application has its own root-static files.
# domain: the domain that maps to this application (alternative to using domains in the BASE router)
# exclusive_domain: If True (default is False), an exception is raised if an attempt is made to generate
# an outgoing URL with a different application without providing an explicit host.
# map_hyphen: If True (default is False), hyphens in incoming /a/c/f fields are converted
# to underscores, and back to hyphens in outgoing URLs.
# Language, args and the query string are not affected.
# map_static: By default (None), the default application is not stripped from static URLs.
# Set map_static=True to override this policy.
# Set map_static=False to map lang/static/file to static/lang/file
# acfe_match: regex for valid application, controller, function, extension /a/c/f.e
# file_match: regex for valid subpath (used for static file paths)
# if file_match does not contain '/', it is uses to validate each element of a static file subpath,
# rather than the entire subpath.
# args_match: regex for valid args
# This validation provides a measure of security.
# If it is changed, the application perform its own validation.
#
#
# The built-in default router supplies default values (undefined members are None):
#
# default_router = dict(
# default_application = 'init',
# applications = 'ALL',
# default_controller = 'default',
# controllers = 'DEFAULT',
# default_function = 'index',
# functions = None,
# default_language = None,
# languages = None,
# root_static = ['favicon.ico', 'robots.txt'],
# map_static = None,
# domains = None,
# map_hyphen = False,
# acfe_match = r'\w+$', # legal app/ctlr/fcn/ext
# file_match = r'([-+=@$%\w]|(?<=[-+=@$%\w])[./])*$', # legal static subpath
# args_match = r'([\w@ -]|(?<=[\w@ -])[.=])*$', # legal arg in args
# )
#
# See rewrite.map_url_in() and rewrite.map_url_out() for implementation details.
# This simple router set overrides only the default application name,
# but provides full rewrite functionality.
|
# (c) 2013, NAME <EMAIL> red hat, inc
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
# take a list of files and (optionally) a list of paths
# return the first existing file found in the paths
# [file1, file2, file3], [path1, path2, path3]
# search order is:
# path1/file1
# path1/file2
# path1/file3
# path2/file1
# path2/file2
# path2/file3
# path3/file1
# path3/file2
# path3/file3
# first file found with os.path.exists() is returned
# no file matches raises ansibleerror
# EXAMPLES
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: foo ${inventory_hostname} bar
# paths: /tmp/production /tmp/staging
# that will look for files in this order:
# /tmp/production/foo
# ${inventory_hostname}
# bar
# /tmp/staging/foo
# ${inventory_hostname}
# bar
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: /some/place/foo ${inventory_hostname} /some/place/else
# that will look for files in this order:
# /some/place/foo
# $relative_path/${inventory_hostname}
# /some/place/else
# example - including tasks:
# tasks:
# - include: $item
# with_first_found:
# - files: generic
# paths: tasks/staging tasks/production
# this will include the tasks in the file generic where it is found first (staging or production)
# example simple file lists
#tasks:
#- name: first found file
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname} foo
# example skipping if no matched files
# First_found also offers the ability to control whether or not failing
# to find a file returns an error or not
#
#- name: first found file - or skip
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname}
# skip: true
# example a role with default configuration and configuration per host
# you can set multiple terms with their own files and paths to look through.
# consider a role that sets some configuration per host falling back on a default config.
#
#- name: some configuration template
# template: src={{ item }} dest=/etc/file.cfg mode=0444 owner=root group=root
# with_first_found:
# - files:
# - ${inventory_hostname}/etc/file.cfg
# paths:
# - ../../../templates.overwrites
# - ../../../templates
# - files:
# - etc/file.cfg
# paths:
# - templates
# the above will return an empty list if the files cannot be found at all
# if skip is unspecificed or if it is set to false then it will return a list
# error which can be caught bye ignore_errors: true for that action.
# finally - if you want you can use it, in place to replace first_available_file:
# you simply cannot use the - files, path or skip options. simply replace
# first_available_file with with_first_found and leave the file listing in place
#
#
# - name: with_first_found like first_available_file
# action: copy src=$item dest=/tmp/faftest
# with_first_found:
# - ../files/foo
# - ../files/bar
# - ../files/baz
# ignore_errors: true
|
"""
[08-21-2015] Challenge #228 [Hard] Golomb Rulers
https://www.reddit.com/r/dailyprogrammer/comments/3hsgr0/08212015_challenge_228_hard_golomb_rulers/
# Description
A typical ruler has many evenly spaced markings. For instance a standard 12” ruler has 13 marks along its edge, each
spaced 1” apart. This is great, and allows the measurement all (integer) values of length between 1” and 12”.
However, a standard ruler is grossly inefficient. For example, the distance of length 1” can be measured multiple ways
on this ruler: 0 to 1, 1 to 2, 2 to 3, etc.
A mathematician named NAME had an idea about making rulers more efficient, and rulers of this type are
named after him. A Golomb ruler comprises a series of marks such that no two pairs of marks are the same distance
apart. Below is an example. This ruler has markings that allow all integer distances between 1-6 units to be measured.
Not only that, but each distance can be measured in only way way.
0 1 4 6
+-+-----+----+
You can see how you can measure every integer distance between 1 and 6:
0 1 4 6
+-+-----+----+
1 +-+
2 +----+
3 +-----+
4 +-------+
5 +----------+
6 +------------+
Golomb rulers are described by their **order**, which is the number of marks on their edge. The example above is an
order 4 ruler. The length of a Golomb ruler is the distance between the outer two marks and, obviously, represents the
longest distance it can measure. The above example has a length of 6.
There is no requirement that a Golomb ruler measures all distances up to their length – the only requirement is that
each distance is only measured in one way. However, if a ruler does measure all distances, it is classified as a
*perfect* Golomb ruler. The above example is a perfect Golumb ruler. Finally, a Golomb ruler is described as *optimal*
if no shorter ruler of the same order exists.
Today's challenge is to determine where to place the marks on an optimal (but not necessarily perfect) Golomb ruler
when given its order.
# Input Description
You'll be given a single integer on a line representing the optimal Golomb ruler order. Examples:
3
5
# Output Description
Your program should emit the length of the optimal Golomb ruler and the placement of the marks. Note that some have
multiple solutions, so any or all of the solutions can be yielded. Examples:
3 3 0 1 3
5 11 0 1 4 9 11
0 2 7 8 11
Here you can see that we have two solutions for a Golomb ruler of order five and length 11.
# Challenge Input
8
7
10
20
26
# Challenge Output
Beware the word wrap!
8 34 0 1 4 9 15 22 32 34
7 25 0 1 4 10 18 23 25
0 1 7 11 20 23 25
0 1 11 16 19 23 25
0 2 3 10 16 21 25
0 2 7 13 21 22 25
10 55 0 1 6 10 23 26 34 41 53 55
20 283 0 1 8 11 68 77 94 116 121 156 158 179 194 208 212 228 240 253 259 283
26 492 0 1 33 83 104 110 124 163 185 200 203 249 251 258 314 318 343 356 386 430 440 456 464 475 487 492
""" |
"""
# ggame
The simple cross-platform sprite and game platform for Brython Server (Pygame, Tkinter to follow?).
Ggame stands for a couple of things: "good game" (of course!) and also "git game" or "github game"
because it is designed to operate with [Brython Server](http://runpython.com) in concert with
Github as a backend file store.
Ggame is **not** intended to be a full-featured gaming API, with every bell and whistle. Ggame is
designed primarily as a tool for teaching computer programming, recognizing that the ability
to create engaging and interactive games is a powerful motivator for many progamming students.
Accordingly, any functional or performance enhancements that *can* be reasonably implemented
by the user are left as an exercise.
## Functionality Goals
The ggame library is intended to be trivially easy to use. For example:
from ggame import App, ImageAsset, Sprite
# Create a displayed object at 100,100 using an image asset
Sprite(ImageAsset("ggame/bunny.png"), (100,100))
# Create the app, with a 500x500 pixel stage
app = App(500,500)
# Run the app
app.run()
## Overview
There are three major components to the `ggame` system: Assets, Sprites and the App.
### Assets
Asset objects (i.e. `ggame.ImageAsset`, etc.) typically represent separate files that
are provided by the "art department". These might be background images, user interface
images, or images that represent objects in the game. In addition, `ggame.SoundAsset`
is used to represent sound files (`.wav` or `.mp3` format) that can be played in the
game.
Ggame also extends the asset concept to include graphics that are generated dynamically
at run-time, such as geometrical objects, e.g. rectangles, lines, etc.
### Sprites
All of the visual aspects of the game are represented by instances of `ggame.Sprite` or
subclasses of it.
### App
Every ggame application must create a single instance of the `ggame.App` class (or
a sub-class of it). Creating an instance of the `ggame.App` class will initiate
creation of a pop-up window on your browser. Executing the app's `run` method will
begin the process of refreshing the visual assets on the screen.
### Events
No game is complete without a player and players produce events. Your code handles user
input by registering to receive keyboard and mouse events using `ggame.App.listenKeyEvent` and
`ggame.App.listenMouseEvent` methods.
## Execution Environment
Ggame is designed to be executed in a web browser using [Brython](http://brython.info/),
[Pixi.js](http://www.pixijs.com/) and [Buzz](http://buzz.jaysalvat.com/). The easiest
way to do this is by executing from [runpython](http://runpython.com), with source
code residing on [github](http://github.com).
When using [runpython](http://runpython.com), you will have to configure your browser
to allow popup windows.
To use Ggame in your own application, you will minimally need to create a folder called
`ggame` in your project. Within `ggame`, copy the `ggame.py`, `sysdeps.py` and
`__init__.py` files from the [ggame project](https://github.com/BrythonServer/ggame).
### Include Ggame as a Git Subtree
From the same directory as your own python sources (note: you must have an existing git
repository with committed files in order for the following to work properly),
execute the following terminal commands:
git remote add -f ggame https://github.com/BrythonServer/ggame.git
git merge -s ours --no-commit ggame/master
mkdir ggame
git read-tree --prefix=ggame/ -u ggame/master
git commit -m "Merge ggame project as our subdirectory"
If you want to pull in updates from ggame in the future:
git pull -s subtree ggame master
You can see an example of how a ggame subtree is used by examining the
[Brython Server Spacewar](https://github.com/BrythonServer/Spacewar) repo on Github.
## Geometry
When referring to screen coordinates, note that the x-axis of the computer screen
is *horizontal* with the zero position on the left hand side of the screen. The
y-axis is *vertical* with the zero position at the **top** of the screen.
Increasing positive y-coordinates correspond to the downward direction on the
computer screen. Note that this is **different** from the way you may have learned
about x and y coordinates in math class!
""" |
#--------Gof (порождающие паттерны) Builder-----------------------------------------------------------------------------
#
# class Building:
# def make_basement(self,basement):
# pass
# def make_walls(self,walls):
# pass
# def make_roof(self,roof):
# pass
#
# class Sky_scriber(Building):
# def __init__(self):
# self.basement = None
# self.walls = None
# self.roof = None
#
# def make_basement(self, basement):
# self.basement = basement
#
# def make_walls(self, walls):
# self.walls = walls
#
# def make_roof(self,roof):
# self.roof = roof
#
# class Cottage(Building):
# def __init__(self):
# self.basement = None
# self.walls = None
# self.roof = None
#
# def make_basement(self, basement):
# self.basement = basement
#
# def make_walls(self, walls):
# self.walls = walls
#
# def make_roof(self, roof):
# self.roof = roof
#
# class Foreman:
# def __init__(self,builder):
# self.builder = builder
#
# def build(self):
# self.builder.build_basement()
# self.builder.build_walls()
# self.builder.build_roof()
#
#
# class Builder:
# def __init__(self):
# self.building = None
# def get_building(self):
# return self.building
#
# def build_basement(self):
# pass
#
# def build_walls(self):
# pass
#
# def build_roof(self):
# pass
#
#
# class Sky_scriber_builder(Builder):
# def __init__(self):
# Builder.__init__(self)
# self.building = Sky_scriber()
#
# def build_basement(self):
# self.building.make_basement("basement")
#
# def build_walls(self):
# self.building.make_walls("walls")
#
# def build_roof(self):
# self.building.make_roof("roof")
#
#
# class Cottage_builder(Builder):
# def __init__(self):
# Builder.__init__(self)
# self.building = Sky_scriber()
#
# def build_basement(self):
# self.building.make_basement("basement")
#
# def build_walls(self):
# self.building.make_walls("walls")
#
# def build_roof(self):
# self.building.make_roof("roof")
#
# def main():
# cottage_builder = Cottage_builder()
# foreman = Foreman(cottage_builder)
# foreman.build()
# cottage = cottage_builder.get_building()
#
# main()
# ----------------------------------------------------------------------------------------------------------------------
#
# ----------(Анти) Паттерн Singleton------------------------------------------------------------------------------------
# class Singleton():
# instance = None
# def __new__(cls, *args, **kwargs):
# if Singleton.instance == None:
# Singleton.instance = object.__new__(cls)
# return Singleton.instance
# def __init__(self,a):
# self.a = a
#
# a1 = Singleton(10)
# a2 = Singleton(39)
# print(a1.a)
# print(a2.a)
# print(a1==a2)
# ----------------------------------------------------------------------------------------------------------------------
#
# -------Flyweight приспособленец---------------------------------------------------------------------------------------
#
# class Character_flyweight():
# def __init__(self,character):
# self.character = character
#
# class Factory():
# def __init__(self):
# self.map = {}
# def instance_character(self,char):
# if self.map.get(char)!= None:
# return self.map.get(char)
# else:
# c = Character_flyweight(char)
# self.map[char] = c
# return c
# factory = Factory()
#
# def convert_to_list(word):
# lis = []
# for char in word:
# lis.append(factory.instance_character(char))
# return lis
#
# lis_word = convert_to_list("abbaaa").
# print(lis_word)
# ----------------------------------------------------------------------------------------------------------------------
#----------------Proxy pattern-------реализация кеша--------------------------------------------------------------------
# class Operation:
# def operation(self,a,b):
# return a + b
#
# class Proxy_operation():
# def __init__(self):
# self.operation = Operation
# self.cache = []
#
# def operation(self,a,b):
# for tup in self.cache:
# if tup[0] == a and tup[1] == b:
# return tup[2]
#
#
# res = self.operation.operation(a,b)
# self.cache.append((a,b,res))
# return res
# ----------------------------------------------------------------------------------------------------------------------
# =========работа с Super()===================================
# class A:
# def __init__(self,a):
# self.a = a
#
# class B:
# def __init__(self,b):
# A.__init__(self,a)
# self.b = b
# ------------------------
#
# class B(A):
# def __init__(self,a,b):
# super(B,self).__init__(a)
# self.b = b
# pb = B(10,20)
# print(pb)
|
#!/usr/bin/env python
# (c) 2013, NAME <EMAIL>
#
# This file is part of Ansible.
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
#
#
# Author: NAME <EMAIL>
#
# Description:
# This module queries local or remote Docker daemons and generates
# inventory information.
#
# This plugin does not support targeting of specific hosts using the --host
# flag. Instead, it queries the Docker API for each container, running
# or not, and returns this data all once.
#
# The plugin returns the following custom attributes on Docker containers:
# docker_args
# docker_config
# docker_created
# docker_driver
# docker_exec_driver
# docker_host_config
# docker_hostname_path
# docker_hosts_path
# docker_id
# docker_image
# docker_name
# docker_network_settings
# docker_path
# docker_resolv_conf_path
# docker_state
# docker_volumes
# docker_volumes_rw
#
# Requirements:
# The docker-py module: https://github.com/dotcloud/docker-py
#
# Notes:
# A config file can be used to configure this inventory module, and there
# are several environment variables that can be set to modify the behavior
# of the plugin at runtime:
# DOCKER_CONFIG_FILE
# DOCKER_HOST
# DOCKER_VERSION
# DOCKER_TIMEOUT
# DOCKER_PRIVATE_SSH_PORT
# DOCKER_DEFAULT_IP
#
# Environment Variables:
# environment variable: DOCKER_CONFIG_FILE
# description:
# - A path to a Docker inventory hosts/defaults file in YAML format
# - A sample file has been provided, colocated with the inventory
# file called 'docker.yml'
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_HOST
# description:
# - The socket on which to connect to a Docker daemon API
# required: false
# default: Uses docker.docker.Client constructor defaults
# environment variable: DOCKER_VERSION
# description:
# - Version of the Docker API to use
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_TIMEOUT
# description:
# - Timeout in seconds for connections to Docker daemon API
# default: Uses docker.docker.Client constructor defaults
# required: false
# environment variable: DOCKER_PRIVATE_SSH_PORT
# description:
# - The private port (container port) on which SSH is listening
# for connections
# default: 22
# required: false
# environment variable: DOCKER_DEFAULT_IP
# description:
# - This environment variable overrides the container SSH connection
# IP address (aka, 'ansible_ssh_host')
#
# This option allows one to override the ansible_ssh_host whenever
# Docker has exercised its default behavior of binding private ports
# to all interfaces of the Docker host. This behavior, when dealing
# with remote Docker hosts, does not allow Ansible to determine
# a proper host IP address on which to connect via SSH to containers.
# By default, this inventory module assumes all IP_ADDRESS-exposed
# ports to be bound to localhost:<port>. To override this
# behavior, for example, to bind a container's SSH port to the public
# interface of its host, one must manually set this IP.
#
# It is preferable to begin to launch Docker containers with
# ports exposed on publicly accessible IP addresses, particularly
# if the containers are to be targeted by Ansible for remote
# configuration, not accessible via localhost SSH connections.
#
# Docker containers can be explicitly exposed on IP addresses by
# a) starting the daemon with the --ip argument
# b) running containers with the -P/--publish ip::containerPort
# argument
# default: IP_ADDRESS if port exposed on IP_ADDRESS by Docker
# required: false
#
# Examples:
# Use the config file:
# DOCKER_CONFIG_FILE=./docker.yml docker.py --list
#
# Connect to docker instance on localhost port 4243
# DOCKER_HOST=tcp://localhost:4243 docker.py --list
#
# Any container's ssh port exposed on IP_ADDRESS will mapped to
# another IP address (where Ansible will attempt to connect via SSH)
# DOCKER_DEFAULT_IP=IP_ADDRESS docker.py --list
|
"""
==============
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 structured 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).
""" |
"""
Discrete Fourier Transform (:mod:`numpy.fft`)
=============================================
.. currentmodule:: numpy.fft
Standard FFTs
-------------
.. autosummary::
:toctree: generated/
fft Discrete Fourier transform.
ifft Inverse discrete Fourier transform.
fft2 Discrete Fourier transform in two dimensions.
ifft2 Inverse discrete Fourier transform in two dimensions.
fftn Discrete Fourier transform in N-dimensions.
ifftn Inverse discrete Fourier transform in N dimensions.
Real FFTs
---------
.. autosummary::
:toctree: generated/
rfft Real discrete Fourier transform.
irfft Inverse real discrete Fourier transform.
rfft2 Real discrete Fourier transform in two dimensions.
irfft2 Inverse real discrete Fourier transform in two dimensions.
rfftn Real discrete Fourier transform in N dimensions.
irfftn Inverse real discrete Fourier transform in N dimensions.
Hermitian FFTs
--------------
.. autosummary::
:toctree: generated/
hfft Hermitian discrete Fourier transform.
ihfft Inverse Hermitian discrete Fourier transform.
Helper routines
---------------
.. autosummary::
:toctree: generated/
fftfreq Discrete Fourier Transform sample frequencies.
rfftfreq DFT sample frequencies (for usage with rfft, irfft).
fftshift Shift zero-frequency component to center of spectrum.
ifftshift Inverse of fftshift.
Background information
----------------------
Fourier analysis is fundamentally a method for expressing a function as a
sum of periodic components, and for recovering the function from those
components. When both the function and its Fourier transform are
replaced with discretized counterparts, it is called the discrete Fourier
transform (DFT). The DFT has become a mainstay of numerical computing in
part because of a very fast algorithm for computing it, called the Fast
Fourier Transform (FFT), which was known to Gauss (1805) and was brought
to light in its current form by NAME and NAME [CT]_. Press et al. [NR]_
provide an accessible introduction to Fourier analysis and its
applications.
Because the discrete Fourier transform separates its input into
components that contribute at discrete frequencies, it has a great number
of applications in digital signal processing, e.g., for filtering, and in
this context the discretized input to the transform is customarily
referred to as a *signal*, which exists in the *time domain*. The output
is called a *spectrum* or *transform* and exists in the *frequency
domain*.
Implementation details
----------------------
There are many ways to define the DFT, varying in the sign of the
exponent, normalization, etc. In this implementation, the DFT is defined
as
.. math::
A_k = \\sum_{m=0}^{n-1} a_m \\exp\\left\\{-2\\pi i{mk \\over n}\\right\\}
\\qquad k = 0,\\ldots,n-1.
The DFT is in general defined for complex inputs and outputs, and a
single-frequency component at linear frequency :math:`f` is
represented by a complex exponential
:math:`a_m = \\exp\\{2\\pi i\\,f m\\Delta t\\}`, where :math:`\\Delta t`
is the sampling interval.
The values in the result follow so-called "standard" order: If ``A =
fft(a, n)``, then ``A[0]`` contains the zero-frequency term (the sum of
the signal), which is always purely real for real inputs. Then ``A[1:n/2]``
contains the positive-frequency terms, and ``A[n/2+1:]`` contains the
negative-frequency terms, in order of decreasingly negative frequency.
For an even number of input points, ``A[n/2]`` represents both positive and
negative Nyquist frequency, and is also purely real for real input. For
an odd number of input points, ``A[(n-1)/2]`` contains the largest positive
frequency, while ``A[(n+1)/2]`` contains the largest negative frequency.
The routine ``np.fft.fftfreq(n)`` returns an array giving the frequencies
of corresponding elements in the output. The routine
``np.fft.fftshift(A)`` shifts transforms and their frequencies to put the
zero-frequency components in the middle, and ``np.fft.ifftshift(A)`` undoes
that shift.
When the input `a` is a time-domain signal and ``A = fft(a)``, ``np.abs(A)``
is its amplitude spectrum and ``np.abs(A)**2`` is its power spectrum.
The phase spectrum is obtained by ``np.angle(A)``.
The inverse DFT is defined as
.. math::
a_m = \\frac{1}{n}\\sum_{k=0}^{n-1}A_k\\exp\\left\\{2\\pi i{mk\\over n}\\right\\}
\\qquad m = 0,\\ldots,n-1.
It differs from the forward transform by the sign of the exponential
argument and the default normalization by :math:`1/n`.
Normalization
-------------
The default normalization has the direct transforms unscaled and the inverse
transforms are scaled by :math:`1/n`. It is possible to obtain unitary
transforms by setting the keyword argument ``norm`` to ``"ortho"`` (default is
`None`) so that both direct and inverse transforms will be scaled by
:math:`1/\\sqrt{n}`.
Real and Hermitian transforms
-----------------------------
When the input is purely real, its transform is Hermitian, i.e., the
component at frequency :math:`f_k` is the complex conjugate of the
component at frequency :math:`-f_k`, which means that for real
inputs there is no information in the negative frequency components that
is not already available from the positive frequency components.
The family of `rfft` functions is
designed to operate on real inputs, and exploits this symmetry by
computing only the positive frequency components, up to and including the
Nyquist frequency. Thus, ``n`` input points produce ``n/2+1`` complex
output points. The inverses of this family assumes the same symmetry of
its input, and for an output of ``n`` points uses ``n/2+1`` input points.
Correspondingly, when the spectrum is purely real, the signal is
Hermitian. The `hfft` family of functions exploits this symmetry by
using ``n/2+1`` complex points in the input (time) domain for ``n`` real
points in the frequency domain.
In higher dimensions, FFTs are used, e.g., for image analysis and
filtering. The computational efficiency of the FFT means that it can
also be a faster way to compute large convolutions, using the property
that a convolution in the time domain is equivalent to a point-by-point
multiplication in the frequency domain.
Higher dimensions
-----------------
In two dimensions, the DFT is defined as
.. math::
A_{kl} = \\sum_{m=0}^{M-1} \\sum_{n=0}^{N-1}
a_{mn}\\exp\\left\\{-2\\pi i \\left({mk\\over M}+{nl\\over N}\\right)\\right\\}
\\qquad k = 0, \\ldots, M-1;\\quad l = 0, \\ldots, N-1,
which extends in the obvious way to higher dimensions, and the inverses
in higher dimensions also extend in the same way.
References
----------
.. [CT] NAME, NAME and John W. NAME, 1965, "An algorithm for the
machine calculation of complex Fourier series," *Math. Comput.*
19: 297-301.
.. [NR] NAME NAME NAME and NAME
2007, *Numerical Recipes: The Art of Scientific Computing*, ch.
12-13. Cambridge Univ. Press, Cambridge, UK.
Examples
--------
For examples, see the various functions.
""" |
"""
Airy Functions
--------------
* airy -- Airy functions and their derivatives.
* airye -- Exponentially scaled Airy functions
* ai_zeros -- [+]Zeros of Airy functions Ai(x) and Ai'(x)
* bi_zeros -- [+]Zeros of Airy functions Bi(x) and Bi'(x)
Elliptic Functions and Integrals
--------------------------------
* ellipj -- Jacobian elliptic functions
* ellipk -- Complete elliptic integral of the first kind.
* ellipkinc -- Incomplete elliptic integral of the first kind.
* ellipe -- Complete elliptic integral of the second kind.
* ellipeinc -- Incomplete elliptic integral of the second kind.
Bessel Functions
----------------
* jn -- Bessel function of integer order and real argument.
* jv -- Bessel function of real-valued order and complex argument.
* jve -- Exponentially scaled Bessel function.
* yn -- Bessel function of second kind (integer order).
* yv -- Bessel function of the second kind (real-valued order).
* yve -- Exponentially scaled Bessel function of the second kind.
* kn -- Modified Bessel function of the second kind (integer order).
* kv -- Modified Bessel function of the second kind (real order).
* kve -- Exponentially scaled modified Bessel function of the second kind.
* iv -- Modified Bessel function.
* ive -- Exponentially scaled modified Bessel function.
* hankel1 -- Hankel function of the first kind.
* hankel1e -- Exponentially scaled Hankel function of the first kind.
* hankel2 -- Hankel function of the second kind.
* hankel2e -- Exponentially scaled Hankel function of the second kind.
* lmbda -- [+]Sequence of lambda functions with arbitrary order v.
Zeros of Bessel Functions
.........................
* jnjnp_zeros -- [+]Zeros of integer-order Bessel functions and derivatives sorted in order.
* jnyn_zeros -- [+]Zeros of integer-order Bessel functions and derivatives as separate arrays.
* jn_zeros -- [+]Zeros of Jn(x)
* jnp_zeros -- [+]Zeros of Jn'(x)
* yn_zeros -- [+]Zeros of Yn(x)
* ynp_zeros -- [+]Zeros of Yn'(x)
* y0_zeros -- [+]Complex zeros: Y0(z0)=0 and values of Y0'(z0)
* y1_zeros -- [+]Complex zeros: Y1(z1)=0 and values of Y1'(z1)
* y1p_zeros -- [+]Complex zeros of Y1'(z1')=0 and values of Y1(z1')
Faster versions of common Bessel Functions
..........................................
* j0 -- Bessel function of order 0.
* j1 -- Bessel function of order 1.
* y0 -- Bessel function of second kind of order 0.
* y1 -- Bessel function of second kind of order 1.
* i0 -- Modified Bessel function of order 0.
* i0e -- Exponentially scaled modified Bessel function of order 0.
* i1 -- Modified Bessel function of order 1.
* i1e -- Exponentially scaled modified Bessel function of order 1.
* k0 -- Modified Bessel function of the second kind of order 0.
* k0e -- Exponentially scaled modified Bessel function of the second kind of order 0.
* k1 -- Modified Bessel function of the second kind of order 1.
* k1e -- Exponentially scaled modified Bessel function of the second kind of order 1.
Integrals of Bessel Functions
.............................
* itj0y0 -- Basic integrals of j0 and y0 from 0 to x.
* it2j0y0 -- Integrals of (1-j0(t))/t from 0 to x and y0(t)/t from x to inf.
* iti0k0 -- Basic integrals of i0 and k0 from 0 to x.
* it2i0k0 -- Integrals of (i0(t)-1)/t from 0 to x and k0(t)/t from x to inf.
* besselpoly -- Integral of a bessel function: Jv(2* a* x) * x[+]lambda from x=0 to 1.
Derivatives of Bessel Functions
...............................
* jvp -- Nth derivative of Jv(v,z)
* yvp -- Nth derivative of Yv(v,z)
* kvp -- Nth derivative of Kv(v,z)
* ivp -- Nth derivative of Iv(v,z)
* h1vp -- Nth derivative of H1v(v,z)
* h2vp -- Nth derivative of H2v(v,z)
Spherical Bessel Functions
..........................
* sph_jn -- [+]Sequence of spherical Bessel functions, jn(z)
* sph_yn -- [+]Sequence of spherical Bessel functions, yn(z)
* sph_jnyn -- [+]Sequence of spherical Bessel functions, jn(z) and yn(z)
* sph_in -- [+]Sequence of spherical Bessel functions, in(z)
* sph_kn -- [+]Sequence of spherical Bessel functions, kn(z)
* sph_inkn -- [+]Sequence of spherical Bessel functions, in(z) and kn(z)
Ricatti-Bessel Functions
........................
* riccati_jn -- [+]Sequence of Ricatti-Bessel functions of first kind.
* riccati_yn -- [+]Sequence of Ricatti-Bessel functions of second kind.
Struve Functions
----------------
* struve -- Struve function --- Hv(x)
* modstruve -- Modified struve function --- Lv(x)
* itstruve0 -- Integral of H0(t) from 0 to x
* it2struve0 -- Integral of H0(t)/t from x to Inf.
* itmodstruve0 -- Integral of L0(t) from 0 to x.
Raw Statistical Functions (Friendly versions in scipy.stats)
------------------------------------------------------------
* bdtr -- Sum of terms 0 through k of of the binomial pdf.
* bdtrc -- Sum of terms k+1 through n of the binomial pdf.
* bdtri -- Inverse of bdtr
* btdtr -- Integral from 0 to x of beta pdf.
* btdtri -- Quantiles of beta distribution
* fdtr -- Integral from 0 to x of F pdf.
* fdtrc -- Integral from x to infinity under F pdf.
* fdtri -- Inverse of fdtrc
* gdtr -- Integral from 0 to x of gamma pdf.
* gdtrc -- Integral from x to infinity under gamma pdf.
* gdtria --
* gdtrib --
* gdtrix --
* nbdtr -- Sum of terms 0 through k of the negative binomial pdf.
* nbdtrc -- Sum of terms k+1 to infinity under negative binomial pdf.
* nbdtri -- Inverse of nbdtr
* pdtr -- Sum of terms 0 through k of the Poisson pdf.
* pdtrc -- Sum of terms k+1 to infinity of the Poisson pdf.
* pdtri -- Inverse of pdtr
* stdtr -- Integral from -infinity to t of the Student-t pdf.
* stdtridf --
* stdtrit --
* chdtr -- Integral from 0 to x of the Chi-square pdf.
* chdtrc -- Integral from x to infnity of Chi-square pdf.
* chdtri -- Inverse of chdtrc.
* ndtr -- Integral from -infinity to x of standard normal pdf
* ndtri -- Inverse of ndtr (quantiles)
* smirnov -- Kolmogorov-Smirnov complementary CDF for one-sided test statistic (Dn+ or Dn-)
* smirnovi -- Inverse of smirnov.
* kolmogorov -- The complementary CDF of the (scaled) two-sided test statistic (Kn*) valid for large n.
* kolmogi -- Inverse of kolmogorov
* tklmbda -- Tukey-Lambda CDF
Gamma and Related Functions
---------------------------
* gamma -- Gamma function.
* gammaln -- Log of the absolute value of the gamma function.
* gammainc -- Incomplete gamma integral.
* gammaincinv -- Inverse of gammainc.
* gammaincc -- Complemented incomplete gamma integral.
* gammainccinv -- Inverse of gammaincc.
* beta -- Beta function.
* betaln -- Log of the absolute value of the beta function.
* betainc -- Incomplete beta integral.
* betaincinv -- Inverse of betainc.
* psi(digamma) -- Logarithmic derivative of the gamma function.
* rgamma -- One divided by the gamma function.
* polygamma -- Nth derivative of psi function.
Error Function and Fresnel Integrals
------------------------------------
* erf -- Error function.
* erfc -- Complemented error function (1- erf(x))
* erfinv -- Inverse of error function
* erfcinv -- Inverse of erfc
* erf_zeros -- [+]Complex zeros of erf(z)
* fresnel -- Fresnel sine and cosine integrals.
* fresnel_zeros -- Complex zeros of both Fresnel integrals
* fresnelc_zeros -- [+]Complex zeros of fresnel cosine integrals
* fresnels_zeros -- [+]Complex zeros of fresnel sine integrals
* modfresnelp -- Modified Fresnel integrals F_+(x) and K_+(x)
* modfresnelm -- Modified Fresnel integrals F_-(x) and K_-(x)
Legendre Functions
------------------
* lpn -- [+]Legendre Functions (polynomials) of the first kind
* lqn -- [+]Legendre Functions of the second kind.
* lpmn -- [+]Associated Legendre Function of the first kind.
* lqmn -- [+]Associated Legendre Function of the second kind.
* lpmv -- Associated Legendre Function of arbitrary non-negative degree v.
* sph_harm -- Spherical Harmonics (complex-valued) Y^m_n(theta,phi)
Orthogonal polynomials --- 15 types
These functions all return a polynomial class which can then be
evaluated: vals = chebyt(n)(x)
This class also has an attribute 'weights' which
return the roots, weights, and total weights for the appropriate
form of Gaussian quadrature. These are returned in an n x 3 array with roots
in the first column, weights in the second column, and total weights in the final
column
* legendre -- [+]Legendre polynomial P_n(x) (lpn -- for function).
* chebyt -- [+]Chebyshev polynomial T_n(x)
* chebyu -- [+]Chebyshev polynomial U_n(x)
* chebyc -- [+]Chebyshev polynomial C_n(x)
* chebys -- [+]Chebyshev polynomial S_n(x)
* jacobi -- [+]Jacobi polynomial P^(alpha,beta)_n(x)
* laguerre -- [+]Laguerre polynomial, L_n(x)
* genlaguerre -- [+]Generalized (Associated) Laguerre polynomial, L^alpha_n(x)
* hermite -- [+]Hermite polynomial H_n(x)
* hermitenorm -- [+]Normalized Hermite polynomial, He_n(x)
* gegenbauer -- [+]Gegenbauer (Ultraspherical) polynomials, C^(alpha)_n(x)
* sh_legendre -- [+]shifted Legendre polynomial, P*_n(x)
* sh_chebyt -- [+]shifted Chebyshev polynomial, T*_n(x)
* sh_chebyu -- [+]shifted Chebyshev polynomial, U*_n(x)
* sh_jacobi -- [+]shifted Jacobi polynomial, J*_n(x) = G^(p,q)_n(x)
HyperGeometric Functions
------------------------
* hyp2f1 -- Gauss hypergeometric function (2F1)
* hyp1f1 -- Confluent hypergeometric function (1F1)
* hyperu -- Confluent hypergeometric function (U)
* hyp0f1 -- Confluent hypergeometric limit function (0F1)
* hyp2f0 -- Hypergeometric function (2F0)
* hyp1f2 -- Hypergeometric function (1F2)
* hyp3f0 -- Hypergeometric function (3F0)
Parabolic Cylinder Functions
----------------------------
* pbdv -- Parabolic cylinder function Dv(x) and derivative.
* pbvv -- Parabolic cylinder function Vv(x) and derivative.
* pbwa -- Parabolic cylinder function W(a,x) and derivative.
* pbdv_seq -- [+]Sequence of parabolic cylinder functions Dv(x)
* pbvv_seq -- [+]Sequence of parabolic cylinder functions Vv(x)
* pbdn_seq -- [+]Sequence of parabolic cylinder functions Dn(z), complex z
mathieu and Related Functions (and derivatives)
-----------------------------------------------
* mathieu_a -- Characteristic values for even solution (ce_m)
* mathieu_b -- Characteristic values for odd solution (se_m)
* mathieu_even_coef -- [+]sequence of expansion coefficients for even solution
* mathieu_odd_coef -- [+]sequence of expansion coefficients for odd solution
**All the following return both function and first derivative**
* mathieu_cem -- Even mathieu function
* mathieu_sem -- Odd mathieu function
* mathieu_modcem1 -- Even modified mathieu function of the first kind
* mathieu_modcem2 -- Even modified mathieu function of the second kind
* mathieu_modsem1 -- Odd modified mathieu function of the first kind
* mathieu_modsem2 -- Odd modified mathieu function of the second kind
Spheroidal Wave Functions
-------------------------
* pro_ang1 -- Prolate spheroidal angular function of the first kind
* pro_rad1 -- Prolate spheroidal radial function of the first kind
* pro_rad2 -- Prolate spheroidal radial function of the second kind
* obl_ang1 -- Oblate spheroidal angluar function of the first kind
* obl_rad1 -- Oblate spheroidal radial function of the first kind
* obl_rad2 -- Oblate spheroidal radial function of the second kind
* pro_cv -- Compute characteristic value for prolate functions
* obl_cv -- Compute characteristic value for oblate functions
* pro_cv_seq -- Compute sequence of prolate characteristic values
* obl_cv_seq -- Compute sequence of oblate characteristic values
**The following functions require pre-computed characteristic values**
* pro_ang1_cv -- Prolate spheroidal angular function of the first kind
* pro_rad1_cv -- Prolate spheroidal radial function of the first kind
* pro_rad2_cv -- Prolate spheroidal radial function of the second kind
* obl_ang1_cv -- Oblate spheroidal angluar function of the first kind
* obl_rad1_cv -- Oblate spheroidal radial function of the first kind
* obl_rad2_cv -- Oblate spheroidal radial function of the second kind
Kelvin Functions
----------------
* kelvin -- All Kelvin functions (order 0) and derivatives.
* kelvin_zeros -- [+]Zeros of All Kelvin functions (order 0) and derivatives
* ber -- Kelvin function ber x
* bei -- Kelvin function bei x
* berp -- Derivative of Kelvin function ber x
* beip -- Derivative of Kelvin function bei x
* ker -- Kelvin function ker x
* kei -- Kelvin function kei x
* kerp -- Derivative of Kelvin function ker x
* keip -- Derivative of Kelvin function kei x
* ber_zeros -- [+]Zeros of Kelvin function bei x
* bei_zeros -- [+]Zeros of Kelvin function ber x
* berp_zeros -- [+]Zeros of derivative of Kelvin function ber x
* beip_zeros -- [+]Zeros of derivative of Kelvin function bei x
* ker_zeros -- [+]Zeros of Kelvin function kei x
* kei_zeros -- [+]Zeros of Kelvin function ker x
* kerp_zeros -- [+]Zeros of derivative of Kelvin function ker x
* keip_zeros -- [+]Zeros of derivative of Kelvin function kei x
Other Special Functions
-----------------------
* expn -- Exponential integral.
* exp1 -- Exponential integral of order 1 (for complex argument)
* expi -- Another exponential integral -- Ei(x)
* wofz -- Fadeeva function.
* dawsn -- Dawson's integral.
* shichi -- Hyperbolic sine and cosine integrals.
* sici -- Integral of the sinc and "cosinc" functions.
* spence -- Dilogarithm integral.
* zeta -- Riemann zeta function of two arguments.
* zetac -- 1.0 - standard Riemann zeta function.
Convenience Functions
---------------------
* cbrt -- Cube root.
* exp10 -- 10 raised to the x power.
* exp2 -- 2 raised to the x power.
* radian -- radian angle given degrees, minutes, and seconds.
* cosdg -- cosine of the angle given in degrees.
* sindg -- sine of the angle given in degrees.
* tandg -- tangent of the angle given in degrees.
* cotdg -- cotangent of the angle given in degrees.
* log1p -- log(1+x)
* expm1 -- exp(x)-1
* cosm1 -- cos(x)-1
* round -- round the argument to the nearest integer. If argument ends in 0.5 exactly, pick the nearest even integer.
-------
[+] in the description indicates a function which is not a universal
function and does not follow broadcasting and automatic
array-looping rules.
Error handling
--------------
Errors are handled by returning nans, or other appropriate values.
Some of the special function routines will print an error message
when an error occurs. By default this printing
is disabled. To enable such messages use errprint(1)
To disable such messages use errprint(0).
Example:
>>> print scipy.special.bdtr(-1,10,0.3)
>>> scipy.special.errprint(1)
>>> print scipy.special.bdtr(-1,10,0.3)
""" |
"""
This is a procedural interface to the matplotlib object-oriented
plotting library.
The following plotting commands are provided; the majority have
MATLAB |reg| [*]_ analogs and similar arguments.
.. |reg| unicode:: 0xAE
_Plotting commands
acorr - plot the autocorrelation function
annotate - annotate something in the figure
arrow - add an arrow to the axes
axes - Create a new axes
axhline - draw a horizontal line across axes
axvline - draw a vertical line across axes
axhspan - draw a horizontal bar across axes
axvspan - draw a vertical bar across axes
axis - Set or return the current axis limits
autoscale - turn axis autoscaling on or off, and apply it
bar - make a bar chart
barh - a horizontal bar chart
broken_barh - a set of horizontal bars with gaps
box - set the axes frame on/off state
boxplot - make a box and whisker plot
violinplot - make a violin plot
cla - clear current axes
clabel - label a contour plot
clf - clear a figure window
clim - adjust the color limits of the current image
close - close a figure window
colorbar - add a colorbar to the current figure
cohere - make a plot of coherence
contour - make a contour plot
contourf - make a filled contour plot
csd - make a plot of cross spectral density
delaxes - delete an axes from the current figure
draw - Force a redraw of the current figure
errorbar - make an errorbar graph
figlegend - make legend on the figure rather than the axes
figimage - make a figure image
figtext - add text in figure coords
figure - create or change active figure
fill - make filled polygons
findobj - recursively find all objects matching some criteria
gca - return the current axes
gcf - return the current figure
gci - get the current image, or None
getp - get a graphics property
grid - set whether gridding is on
hist - make a histogram
hold - set the axes hold state
ioff - turn interaction mode off
ion - turn interaction mode on
isinteractive - return True if interaction mode is on
imread - load image file into array
imsave - save array as an image file
imshow - plot image data
ishold - return the hold state of the current axes
legend - make an axes legend
locator_params - adjust parameters used in locating axis ticks
loglog - a log log plot
matshow - display a matrix in a new figure preserving aspect
margins - set margins used in autoscaling
pause - pause for a specified interval
pcolor - make a pseudocolor plot
pcolormesh - make a pseudocolor plot using a quadrilateral mesh
pie - make a pie chart
plot - make a line plot
plot_date - plot dates
plotfile - plot column data from an ASCII tab/space/comma delimited file
pie - pie charts
polar - make a polar plot on a PolarAxes
psd - make a plot of power spectral density
quiver - make a direction field (arrows) plot
rc - control the default params
rgrids - customize the radial grids and labels for polar
savefig - save the current figure
scatter - make a scatter plot
setp - set a graphics property
semilogx - log x axis
semilogy - log y axis
show - show the figures
specgram - a spectrogram plot
spy - plot sparsity pattern using markers or image
stem - make a stem plot
subplot - make one subplot (numrows, numcols, axesnum)
subplots - make a figure with a set of (numrows, numcols) subplots
subplots_adjust - change the params controlling the subplot positions of current figure
subplot_tool - launch the subplot configuration tool
suptitle - add a figure title
table - add a table to the plot
text - add some text at location x,y to the current axes
thetagrids - customize the radial theta grids and labels for polar
tick_params - control the appearance of ticks and tick labels
ticklabel_format - control the format of tick labels
title - add a title to the current axes
tricontour - make a contour plot on a triangular grid
tricontourf - make a filled contour plot on a triangular grid
tripcolor - make a pseudocolor plot on a triangular grid
triplot - plot a triangular grid
xcorr - plot the autocorrelation function of x and y
xlim - set/get the xlimits
ylim - set/get the ylimits
xticks - set/get the xticks
yticks - set/get the yticks
xlabel - add an xlabel to the current axes
ylabel - add a ylabel to the current axes
autumn - set the default colormap to autumn
bone - set the default colormap to bone
cool - set the default colormap to cool
copper - set the default colormap to copper
flag - set the default colormap to flag
gray - set the default colormap to gray
hot - set the default colormap to hot
hsv - set the default colormap to hsv
jet - set the default colormap to jet
pink - set the default colormap to pink
prism - set the default colormap to prism
spring - set the default colormap to spring
summer - set the default colormap to summer
winter - set the default colormap to winter
spectral - set the default colormap to spectral
_Event handling
connect - register an event handler
disconnect - remove a connected event handler
_Matrix commands
cumprod - the cumulative product along a dimension
cumsum - the cumulative sum along a dimension
detrend - remove the mean or besdt fit line from an array
diag - the k-th diagonal of matrix
diff - the n-th differnce of an array
eig - the eigenvalues and eigen vectors of v
eye - a matrix where the k-th diagonal is ones, else zero
find - return the indices where a condition is nonzero
fliplr - flip the rows of a matrix up/down
flipud - flip the columns of a matrix left/right
linspace - a linear spaced vector of N values from min to max inclusive
logspace - a log spaced vector of N values from min to max inclusive
meshgrid - repeat x and y to make regular matrices
ones - an array of ones
rand - an array from the uniform distribution [0,1]
randn - an array from the normal distribution
rot90 - rotate matrix k*90 degress counterclockwise
squeeze - squeeze an array removing any dimensions of length 1
tri - a triangular matrix
tril - a lower triangular matrix
triu - an upper triangular matrix
vander - the Vandermonde matrix of vector x
svd - singular value decomposition
zeros - a matrix of zeros
_Probability
normpdf - The Gaussian probability density function
rand - random numbers from the uniform distribution
randn - random numbers from the normal distribution
_Statistics
amax - the maximum along dimension m
amin - the minimum along dimension m
corrcoef - correlation coefficient
cov - covariance matrix
mean - the mean along dimension m
median - the median along dimension m
norm - the norm of vector x
prod - the product along dimension m
ptp - the max-min along dimension m
std - the standard deviation along dimension m
asum - the sum along dimension m
ksdensity - the kernel density estimate
_Time series analysis
bartlett - M-point Bartlett window
blackman - M-point Blackman window
cohere - the coherence using average periodiogram
csd - the cross spectral density using average periodiogram
fft - the fast Fourier transform of vector x
hamming - M-point Hamming window
hanning - M-point Hanning window
hist - compute the histogram of x
kaiser - M length Kaiser window
psd - the power spectral density using average periodiogram
sinc - the sinc function of array x
_Dates
date2num - convert python datetimes to numeric representation
drange - create an array of numbers for date plots
num2date - convert numeric type (float days since 0001) to datetime
_Other
angle - the angle of a complex array
griddata - interpolate irregularly distributed data to a regular grid
load - Deprecated--please use loadtxt.
loadtxt - load ASCII data into array.
polyfit - fit x, y to an n-th order polynomial
polyval - evaluate an n-th order polynomial
roots - the roots of the polynomial coefficients in p
save - Deprecated--please use savetxt.
savetxt - save an array to an ASCII file.
trapz - trapezoidal integration
__end
.. [*] MATLAB is a registered trademark of The MathWorks, Inc.
""" |
# 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
|
#!/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 *
# * *
# ***************************************************************************/
|
"""
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')
""" |
# """
# B splines
#
#
# somewhere we should reuse coeffs here... so that a next fit looks if is has the same number of knots, and if yes, uses the previous fit as starting value.
#
# """
#
# import sys
#
# from pycs.gen import *
#
# import numpy as np
# import math
# import matplotlib.pyplot as plt
# import scipy.optimize as spopt
# import scipy.interpolate as spip
#
#
#
# def fitcubbspline(x, y, yerr, t, cini=None, verbose=True):
# """
# This is "my own" cubic B-spline fitting method, using leastsq from scipy.
# I know, this looks like a very naive idea from somebody who has not a clue what a spline is...
# But... recall that we want to
# 1) control the positions of the knots (probably on a fixed grid etc)
# 2) have an irregular sampling of points (and perhaps also of knots)
# 3) IMPORTANT : and that it may well be that we have several "points" to fit for one single JD
# (this last point kills sp.interpolate.splrep as well as probably all direct methods, inverse filtering etc) !
#
# x y : the data
# t : the x-positions of the knots, WITH "prefix" and "suffix" knots !
# cini : initial coeffs for the knots t. If None, we start from zero.
#
#
# We use the notation "t, c, k" from scipy.interpolate.splev etc : t the knots, c the corresponding coeffs, k the degree
# """
#
#
# k = 3 # cubic spline
# if cini == None:
# if verbose :
# print "Spline fit : starting from 0.0"
# cini = np.zeros(len(t)) # initial coeffs = 0.0
# else:
# # we check that they are compatible :
# if len(cini) != len(t):
# raise RuntimeError, "Spline fit : cini has the wrong length"
#
# leastsqres = spopt.leastsq(splinefiterrfunc, cini, args=(t, k, x, y, yerr), full_output=1)
# # this should be faster without the full output...
#
# if verbose:
# print "Spline fit : %i function calls" % leastsqres[2]['nfev']
#
# fittedc = leastsqres[0]
# tck = (t, fittedc, k)
#
# return tck
#
#
# def splinefiterrfunc(c, t, k, x, y, yerr):
# """
# Auxiliary function for the fit.
# Give me a spline (c,t,k) and some points (x, y, yerr) and I return the vector of differences.
# """
# tck = (t, c, k)
# interpy = spip.splev(x, tck, der=0)
# return (y - interpy)/yerr
#
#
#
# def knots(x, sheme = "test1"):
# """
# Give me the x coords of some point, and I give you some knots according to a given sheme...
#
# stupid : for testing purposes
#
# test1 : I want to get the knots selected from a "fixed" absolute grid, JD = 0, n, 2n, 3n ...
#
# We add the extra coeffictients at both sides of t. See
# http://mathworld.wolfram.com/B-Spline.html
#
# """
# # In this first step, we add the "interior" knots (i.e. containing the extremal knots, but not repeating them).
#
# if sheme == "stupid" :
# t = np.linspace(x[0], x[-1], 10)
#
# if sheme == "test1" :
# n = 10 # n = 6 -> so the grid is 0, 6, 12, ... ie equally spaced by 6
# first = x[0] - (x[0] % n) # this is nearly an int, but no need to convert to int().
# t = np.arange(first, x[-1] + n, n)
#
# # We add some extra coefficients at both sides of t
# prefix = np.ones(3)*t[0]
# suffix = np.ones(3)*t[-1]
# fullt = np.concatenate([prefix, t, suffix])
#
# return fullt
#
#
# def cbsp(lcs, splitgap=60, oldtcks=None, verbose=True, plot=True):
# """
# First try to get a cubic B-spline fit working, simultaneously for n lightcurves, and return a chi2 like something.
# Give me a list of lightcurves and I return you a value for chi2, using a specified spline fit etc
#
# oldtcks : if not "None" but a list of tcks, we will try to start the fit of the spline coeffs using these ...
# This typically works if the number of knots has not changed, i.e. when we optimize microlensing...
#
# """
#
# jdslist = []
# magslist = []
# magerrslist = []
# for thislc in lcs:
# jdslist.append(thislc.getjds()[thislc.mask])
# magslist.append(thislc.getmags()[thislc.mask])
# magerrslist.append(thislc.magerrs[thislc.mask])
#
# mergedjds = np.concatenate(jdslist)
# mergedmags = np.concatenate(magslist)
# mergedmagerrs = np.concatenate(magerrslist)
#
# # Now the sorting ...
# sortedindices = np.argsort(mergedjds)
# sortedjds = mergedjds[sortedindices]
# sortedmags = mergedmags[sortedindices]
# sortedmagerrs = mergedmagerrs[sortedindices]
#
# # We need to find the overlapping regions ?
# # For a first try, let's split this as usual :
# first = sortedjds[:-1]
# second = sortedjds[1:]
# gapindices = np.where(second - first > splitgap)[0] + 1
# # make one big vector of all the indices :
# indices = np.arange(len(sortedjds))
# # split it according to the gaps :
# indexlist = np.hsplit(indices, gapindices)
# if verbose:
# print "We have %i splines." % len(indexlist)
#
# if oldtcks == None:
# # Then we do not have previous splines to start from
# oldtcks = [None] * len(indexlist)
#
# tcks = [] # we will append here the splines from the individual spline fits, not only for plotting, also to pass them to the next call !
# chi2s = [] # the resulting chi2s
# ns = [] # the number of points for that spline
#
# for indexes, oldtck in zip(indexlist, oldtcks): # i.e. for each "season" aka "group" of points
# jds = sortedjds[indexes]
# mags = sortedmags[indexes]
# magerrs = sortedmagerrs[indexes]
#
# t = knots(jds, sheme="test1")
# if (oldtck != None) and (len(t) == len(oldtck[0])) : # Then we should be able to reuse this...
# tck = fitcubbspline(jds, mags, magerrs, t, cini=oldtck[1], verbose=False)
# else:
# tck = fitcubbspline(jds, mags, magerrs, t, verbose=False)
#
# #if verbose:
# # for (t, c) in zip(tck[0], tck[1]):
# # print "t = %8.3f -> c = %8.3f" % (t, c)
#
# tcks.append(tck)
#
# diffs = (mags - spip.splev(jds, tck, der=0))/magerrs
# chi2 = np.sum(diffs * diffs)
#
# if verbose:
# print "chi2 : %8.3f for %i points" % (chi2, len(jds))
#
# chi2s.append(chi2)
# ns.append(len(jds))
#
# totchi2 = np.sum(np.array(chi2s))
# totn = np.sum(np.array(ns))
# chi2n = totchi2/float(totn)
# if verbose:
# print "tot : %8.3f for %i points" % (totchi2, totn)
# print "chi2n: %8.3f" % (chi2n)
#
#
#
# if plot:
# plt.figure(figsize=(12,8)) # sets figure size
# axes = plt.gca()
#
# # The points
# plt.errorbar(sortedjds, sortedmags, sortedmagerrs, fmt=".", color="red", ecolor="#BBBBBB")
#
# # The groups
# for groupindexes in indexlist:
# plt.axvline(sortedjds[groupindexes[0]], color = "#AAAAAA", dashes = (5,5))
# plt.axvline(sortedjds[groupindexes[-1]], color = "#AAAAAA", dashes = (5,5))
#
# # The spline
# for (tck, indexes) in zip(tcks, indexlist):
# xnew = np.linspace(sortedjds[indexes][0], sortedjds[indexes][-1], 1000)
# ynew = spip.splev(xnew,tck,der=0)
#
# plt.plot(xnew, ynew, color="blue")
#
# for knot in tck[0]:
# plt.axvline(knot, color = "#0000AA", dashes = (2,2))
#
#
# # Splines may get huge, so we want to limit the axis ranges :
# axes.set_ylim((min(sortedmags) - 0.1, max(sortedmags) + 0.1))
#
#
# # Something for astronomers only : we invert the y axis direction !
# axes.set_ylim(axes.get_ylim()[::-1])
#
# # And we make a title for that combination of lightcurves :
# #plt.title("Lightcurves", fontsize=20)
# plt.xlabel("Days", fontsize=16)
# plt.ylabel("Magnitude", fontsize=16)
# plt.title("Spline", fontsize=16)
#
# plt.xlim([2340, 5000])
#
# plt.show()
#
#
# return {'chi2':totchi2, 'n':totn, 'chi2n':chi2n, 'tcks':tcks}
#
#
# #def test(lcs, splitgap=60, usemask=True, verbose=True, plot=True):
# # """
# # First try to get a cubic B-spline fit working, simultaneously for n lightcurves, and return a chi2 like something.
# # Give me a list of lightcurves and I return you a value for chi2, using a specified spline fit etc
# #
# #
# # """
# #
# # jdslist = []
# # magslist = []
# # magerrslist = []
# # for thislc in lcs:
# # jdslist.append(thislc.getjds()[thislc.mask])
# # magslist.append(thislc.getmags()[thislc.mask])
# # magerrslist.append(thislc.magerrs[thislc.mask])
# #
# # mergedjds = np.concatenate(jdslist)
# # mergedmags = np.concatenate(magslist)
# # mergedmagerrs = np.concatenate(magerrslist)
# #
# # # Now the sorting ...
# # sortedindices = np.argsort(mergedjds)
# # sortedjds = mergedjds[sortedindices]
# # sortedmags = mergedmags[sortedindices]
# # sortedmagerrs = mergedmagerrs[sortedindices]
# #
# # # We need to find the overlapping regions ?
# # # For a first try, let's split this as usual :
# # first = sortedjds[:-1]
# # second = sortedjds[1:]
# # gapindices = np.where(second - first > splitgap)[0] + 1
# # # make one big vector of all the indices :
# # indices = np.arange(len(sortedjds))
# # # split it according to the gaps :
# # indexlist = np.hsplit(indices, gapindices)
# # if verbose:
# # print "We have %i splines." % len(indexlist)
# #
# #
# # tcks = [] # we will append here the splines from the individual spline fits (for plotting ...)
# # chi2s = [] # the resulting chi2s
# # ns = [] # the number of points for that spline
# #
# # for indexes in indexlist: # i.e. for each "season" aka "group" of points
# # jds = sortedjds[indexes]
# # mags = sortedmags[indexes]
# # magerrs = sortedmagerrs[indexes]
# #
# # #t = knots(jds, sheme="test1")
# # #tck = fitcubbspline(jds, mags, magerrs, t, verbose=False)
# #
# # tck = spip.splrep(jds, mags, w=(1.0/magerrs))
# # print tck
# # #maspline = spip.UnivariateSpline(jds, mags, w=magerrs, k=3)
# #
# # #tck = [maspline.get_knots(), maspline.get_coeffs(), 3]
# # #print len(maspline.get_knots())
# #
# # #if verbose:
# # # for (t, c) in zip(tck[0], tck[1]):
# # # print "t = %8.3f -> c = %8.3f" % (t, c)
# #
# # tcks.append(tck)
# #
# # diffs = (mags - spip.splev(jds, tck, der=0))/magerrs
# # chi2 = np.sum(diffs * diffs)
# #
# # if verbose:
# # print "chi2 : %8.3f for %i points" % (chi2, len(jds))
# #
# # chi2s.append(chi2)
# # ns.append(len(jds))
# #
# # totchi2 = np.sum(np.array(chi2s))
# # totn = np.sum(np.array(ns))
# # chi2n = totchi2/float(totn)
# # if verbose:
# # print "tot : %8.3f for %i points" % (totchi2, totn)
# # print "chi2n: %8.3f" % (chi2n)
# #
# #
# #
# # if plot:
# # plt.figure(figsize=(12,8)) # sets figure size
# # axes = plt.gca()
# #
# # # The points
# # plt.errorbar(sortedjds, sortedmags, sortedmagerrs, fmt=".", color="red", ecolor="#BBBBBB")
# #
# # # The groups
# # for groupindexes in indexlist:
# # plt.axvline(sortedjds[groupindexes[0]], color = "#AAAAAA", dashes = (5,5))
# # plt.axvline(sortedjds[groupindexes[-1]], color = "#AAAAAA", dashes = (5,5))
# #
# # # The spline
# # for (tck, indexes) in zip(tcks, indexlist):
# # xnew = np.linspace(sortedjds[indexes][0], sortedjds[indexes][-1], 1000)
# # ynew = spip.splev(xnew,tck,der=0)
# #
# # plt.plot(xnew, ynew, color="blue")
# #
# # for knot in tck[0]:
# # plt.axvline(knot, color = "#0000AA", dashes = (2,2))
# #
# #
# # # Splines may get huge, so we want to limit the axis ranges :
# # axes.set_ylim((min(sortedmags) - 0.1, max(sortedmags) + 0.1))
# #
# #
# # # Something for astronomers only : we invert the y axis direction !
# # axes.set_ylim(axes.get_ylim()[::-1])
# #
# # # And we make a title for that combination of lightcurves :
# # #plt.title("Lightcurves", fontsize=20)
# # plt.xlabel("Days", fontsize=16)
# # plt.ylabel("Magnitude", fontsize=16)
# # plt.title("Spline", fontsize=16)
# #
# # plt.xlim([2340, 5000])
# #
# # plt.show()
# #
# #
# # #return {'chi2':totchi2, 'n':totn, 'chi2n':chi2n}
# #
# #
# #def one(lcs, verbose=True, plot=True):
# # """
# # Trying to build one big spline over all the groups...
# #
# # """
# #
# #
# # jdslist = []
# # magslist = []
# # magerrslist = []
# # for thislc in lcs:
# # jdslist.append(thislc.getjds()[thislc.mask])
# # magslist.append(thislc.getmags()[thislc.mask])
# # magerrslist.append(thislc.magerrs[thislc.mask])
# #
# # mergedjds = np.concatenate(jdslist)
# # mergedmags = np.concatenate(magslist)
# # mergedmagerrs = np.concatenate(magerrslist)
# #
# # # Now the sorting ...
# # sortedindices = np.argsort(mergedjds)
# # sortedjds = mergedjds[sortedindices]
# # sortedmags = mergedmags[sortedindices]
# # sortedmagerrs = mergedmagerrs[sortedindices]
# #
# # jds = sortedjds
# # mags = sortedmags
# # magerrs = sortedmagerrs
# #
# # #t = np.linspace(jds[0], jds[-1], 10) # the knots
# # t = np.arange(int(math.floor(jds[0])), int(math.ceil(jds[-1])), 30)
# #
# # tck = fitcubbspline(jds, mags, magerrs, t, verbose=True)
# #
# # if plot:
# # plt.figure(figsize=(12,8)) # sets figure size
# # axes = plt.gca()
# #
# # # The points
# # plt.errorbar(sortedjds, sortedmags, sortedmagerrs, fmt=".", color="red", ecolor="#BBBBBB")
# #
# # # The spline
# #
# # xnew = np.linspace(sortedjds[0], sortedjds[-1], 1000)
# # ynew = spip.splev(xnew,tck,der=0)
# #
# # plt.plot(xnew, ynew, color="blue")
# #
# # for knot in tck[0]:
# # plt.axvline(knot, color = "#0000AA", dashes = (2,2))
# #
# # # Something for astronomers only : we invert the y axis direction !
# # axes.set_ylim(axes.get_ylim()[::-1])
# #
# # # And we make a title for that combination of lightcurves :
# # #plt.title("Lightcurves", fontsize=20)
# # plt.xlabel("Days", fontsize=16)
# # plt.ylabel("Magnitude", fontsize=16)
# # plt.title("Spline", fontsize=16)
# #
# # plt.xlim([2340, 5000])
# #
# # plt.show()
# #
# #
# # diffs = (mags - spip.splev(jds, tck, der=0))/magerrs
# # chi2 = np.sum(diffs * diffs)
# #
# # if verbose:
# # print "Chi2 : %8.3f" % chi2
# #
# #
# # return chi2
# #
# #
|
#
# 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.
# --------------------------------------------------------------------
|
"""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.
""" |
"""
=================
Django S3 storage
=================
Usage
=====
Settings
--------
``DEFAULT_FILE_STORAGE``
~~~~~~~~~~~~~~~~~~~~~~~~
This setting store the path to the S3 storage class, the first part correspond
to the filepath and the second the name of the class, if you've got
``example.com`` in your ``PYTHONPATH`` and store your storage file in
``example.com/libs/storages/S3Storage.py``, the resulting setting will be::
DEFAULT_FILE_STORAGE = 'libs.storages.S3Storage.S3Storage'
If you keep the same filename as in repository, it should always end with
``S3Storage.S3Storage``.
``AWS_ACCESS_KEY_ID``
~~~~~~~~~~~~~~~~~~~~~
Your Amazon Web Services access key, as a string.
``AWS_SECRET_ACCESS_KEY``
~~~~~~~~~~~~~~~~~~~~~~~~~
Your Amazon Web Services secret access key, as a string.
``AWS_STORAGE_BUCKET_NAME``
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Your Amazon Web Services storage bucket name, as a string.
``AWS_CALLING_FORMAT``
~~~~~~~~~~~~~~~~~~~~~~
The way you'd like to call the Amazon Web Services API, for instance if you
prefer subdomains::
from S3 import CallingFormat
AWS_CALLING_FORMAT = CallingFormat.SUBDOMAIN
``AWS_HEADERS`` (optionnal)
~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you'd like to set headers sent with each file of the storage::
# see http://developer.yahoo.com/performance/rules.html#expires
AWS_HEADERS = {
'Expires': 'Thu, 15 Apr 2010 20:00:00 GMT',
'Cache-Control': 'max-age=86400',
}
Fields
------
Once you're done, ``default_storage`` will be the S3 storage::
>>> from django.core.files.storage import default_storage
>>> print default_storage.__class__
<class 'backends.S3Storage.S3Storage'>
This way, if you define a new ``FileField``, it will use the S3 storage::
>>> from django.db import models
>>> class Resume(models.Model):
... pdf = models.FileField(upload_to='pdfs')
... photos = models.ImageField(upload_to='photos')
...
>>> resume = Resume()
>>> print resume.pdf.storage
<backends.S3Storage.S3Storage object at ...>
Tests
=====
Initialization::
>>> from django.core.files.storage import default_storage
>>> from django.core.files.base import ContentFile
>>> from django.core.cache import cache
>>> from models import MyStorage
Storage
-------
Standard file access options are available, and work as expected::
>>> default_storage.exists('storage_test')
False
>>> file = default_storage.open('storage_test', 'w')
>>> file.write('storage contents')
>>> file.close()
>>> default_storage.exists('storage_test')
True
>>> file = default_storage.open('storage_test', 'r')
>>> file.read()
'storage contents'
>>> file.close()
>>> default_storage.delete('storage_test')
>>> default_storage.exists('storage_test')
False
Model
-----
An object without a file has limited functionality::
>>> obj1 = MyStorage()
>>> obj1.normal
<FieldFile: None>
>>> obj1.normal.size
Traceback (most recent call last):
...
ValueError: The 'normal' attribute has no file associated with it.
Saving a file enables full functionality::
>>> obj1.normal.save('django_test.txt', ContentFile('content'))
>>> obj1.normal
<FieldFile: tests/django_test.txt>
>>> obj1.normal.size
7
>>> obj1.normal.read()
'content'
Files can be read in a little at a time, if necessary::
>>> obj1.normal.open()
>>> obj1.normal.read(3)
'con'
>>> obj1.normal.read()
'tent'
>>> '-'.join(obj1.normal.chunks(chunk_size=2))
'co-nt-en-t'
Save another file with the same name::
>>> obj2 = MyStorage()
>>> obj2.normal.save('django_test.txt', ContentFile('more content'))
>>> obj2.normal
<FieldFile: tests/django_test_.txt>
>>> obj2.normal.size
12
Push the objects into the cache to make sure they pickle properly::
>>> cache.set('obj1', obj1)
>>> cache.set('obj2', obj2)
>>> cache.get('obj2').normal
<FieldFile: tests/django_test_.txt>
Deleting an object deletes the file it uses, if there are no other objects
still using that file::
>>> obj2.delete()
>>> obj2.normal.save('django_test.txt', ContentFile('more content'))
>>> obj2.normal
<FieldFile: tests/django_test_.txt>
Default values allow an object to access a single file::
>>> obj3 = MyStorage.objects.create()
>>> obj3.default
<FieldFile: tests/default.txt>
>>> obj3.default.read()
'default content'
But it shouldn't be deleted, even if there are no more objects using it::
>>> obj3.delete()
>>> obj3 = MyStorage()
>>> obj3.default.read()
'default content'
Verify the fix for #5655, making sure the directory is only determined once::
>>> obj4 = MyStorage()
>>> obj4.random.save('random_file', ContentFile('random content'))
>>> obj4.random
<FieldFile: .../random_file>
Clean up the temporary files::
>>> obj1.normal.delete()
>>> obj2.normal.delete()
>>> obj3.default.delete()
>>> obj4.random.delete()
""" |
"""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().
""" |
"""
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 Subtract polynomials
polymul Multiply polynomials
polydiv Divide polynomials
polyval Evaluate polynomial at given argument
================ ===================
Iterators
---------
================ ===================
Arrayterator A buffered iterator for big arrays.
================ ===================
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.
================ ===================
Array Set Operations
-----------------------
Set operations for numeric arrays based on sort() function.
================ ===================
unique Unique elements of an array.
isin Test whether each element of an ND array is present
anywhere within a second array.
ediff1d Array difference (auxiliary function).
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.
================ ===================
""" |
"""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().
""" |
"""
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 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 remaining dimension of lenth 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 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 unusal uses, but theyare permitted, and they are useful for some
problems. We'll start with thesimplest 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 resulant 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 array being indexed, or broadcastable to the same shape. 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])
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. As
with index arrays, what is returned is a copy of the data, not a view
as one gets with slices.
With broadcasting, multidimensional arrays may be the result. For
example: ::
>>> b[:,5] # use a 1-D boolean that broadcasts with 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.
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
""" |
"""The ``restler`` package is a simple and flexible serialization to JSON and XML of App Engine Models and Queries.
A Simple Example
----------------
First, we'll need to import some appengine and
restler package classes and functions.
>>> from google.appengine.ext import db
>>> from restler.serializers import ModelStrategy, to_json, to_xml, SKIP
To help with our examples, let's create a simple ``db.Model`` class that we'll later serialize.
>>> class Person(db.Model):
... first_name = db.StringProperty()
... last_name = db.StringProperty()
... ssn = db.StringProperty()
Next, we'll create an instance of the Person class.
>>> jean = Person(first_name="Jeanne", last_name="d'Arc", ssn="N/A")
Now, let's try serializing it:
>>> to_json(jean)
'{"first_name": "Jeanne", "last_name": "d\'Arc", "ssn": "N/A"}'
How about to XML?
>>> to_xml(jean)
"<result><person><first_name>Jeanne</first_name><last_name>d'Arc</last_name><ssn>N/A</ssn></person></result>"
Include/Exclude Fields
----------------------
Perfect, that's exactly what we wanted. *Almost...* An SSN is
rather sensitive information that we really shouldn't expose.
To keep SSN from being serialized, we have to introduce
``restler.serializers.ModelStrategy`` which describes how to
serialize a ``google.ext.db.Model`` i.e. which properties to
serialize.
To only serialize ``first_name`` and ``last_name`` we'd create a ModelStrategy
as follows:
>>> person_strategy = ModelStrategy(Person).include("first_name", "last_name")
>>> to_json(jean, person_strategy)
'{"first_name": "Jeanne", "last_name": "d\'Arc"}'
If we supply a ModelStrategy without including any fields, we'll get an
empty json object.
>>> person_strategy = ModelStrategy(Person)
>>> to_json(jean, person_strategy)
'{}'
If our Person model defined a lot of properties, it might be tedious to add
all of the fields. It would be nice to declare that we want *all* properties
of the Person model *except* for SSN. And, in fact, we can do that as follows:
>>> person_strategy = ModelStrategy(Person, include_all_fields=True).exclude("ssn")
>>> to_json(jean, person_strategy)
'{"first_name": "Jeanne", "last_name": "d\'Arc"}'
To summarize, ``Restler`` will serialize all properties of a Model unless there is
a ``ModelStrategy`` that defines which properties are to be serialized.
Renaming Fields
---------------
What if we wanted to use ``family_name`` instead of ``last_name`` and ``given_name``
instead of ``first_name``? We do that as follows:
>>> person_strategy = ModelStrategy(Person).include("ssn", given_name="first_name", family_name="last_name")
>>> to_json(jean, person_strategy)
'{"family_name": "d\'Arc", "ssn": "N/A", "given_name": "Jeanne"}'
Derived Fields
--------------
What if we wanted one field called ``full_name`` instead of the individual ``first_name``
and ``last_name`` properties? We'd do that by creating a ``callable`` (generally a function)
that will be called with an instance of the model and an optional context (any object).
>>> def full_name_func(obj):
... return obj.first_name + ' ' + obj.last_name
>>> person_strategy = ModelStrategy(Person).include(full_name=full_name_func)
>>> to_json(jean, person_strategy)
'{"full_name": "Jeanne NAME let's assume we want to include the SSN field only if it looks like a real SSN. We'll
do that by *overriding* the default ``ssn`` property that returns a special ``SKIP`` object
that will tell the serializer to not include the field in the json output.
>>> def ssn_func(obj):
... if len(obj.ssn) and obj.ssn[0].isdigit():
... return obj.ssn
... return SKIP
**NOTE:** restler won't allow you to hide an exposed field by *just* redefining it. You must
explicitly ``override`` it. Here, since we're explicitly saying to ``include_all_fields``
we need to ``override`` ``ssn``
>>> person_strategy = ModelStrategy(Person, include_all_fields=True).override(ssn=ssn_func)
So here we see that the ``ssn`` field is skipped for Jean.
>>> to_json(jean, person_strategy)
'{"first_name": "Jeanne", "last_name": "d\'Arc"}'
But in Kurt's json, ssn is included:
>>> to_json(Person(first_name="Kurt", last_name="Cobain", ssn="536-90-4399"), person_strategy)
'{"first_name": "Kurt", "last_name": "Cobain", "ssn": "536-90-4399"}'
Context Objects
---------------
Sometimes it's important to change how serialization is done based on some state of the system.
For example, perhaps we want to display ``ssn`` only if the user is logged in. We'd do that
by passing in a ``context`` object (usually a ``dictionary``) which will be passed to each
``callable`` that takes two parameters (the first parameter being the model instance). Let's redo
the above example using a ``context`` object.
>>> def ssn_func(obj, context):
... if context.has_key('is_logged_in') and bool(context['is_logged_in']):
... return obj.ssn
... return SKIP
>>> person_strategy = ModelStrategy(Person, include_all_fields=True).override(ssn=ssn_func)
>>> to_json(Person(first_name="Kurt", last_name="Cobain", ssn="536-90-4399"), person_strategy, dict(is_logged_in=True))
'{"first_name": "Kurt", "last_name": "Cobain", "ssn": "536-90-4399"}'
>>> to_json(Person(first_name="Kurt", last_name="Cobain", ssn="536-90-4399"), person_strategy, dict(is_logged_in=False))
'{"first_name": "Kurt", "last_name": "Cobain"}'
Serialization Strategies
------------------------
Most of the time we're not dealing with just one model but rather a collection of models
that we want to serialize in a consistent manner -- most likely for a specific ``version`` of
an API. The container is called a ``SerializationStrategy``. You don't normally instantiate
a ``SerializationStrategy``. Rather, you combine two or more ``ModelStrategy`` instances together
and the result is a ``SerializationStrategy``
Here's an example:
>>> class Address(db.Model):
... street1 = db.StringProperty()
... street2 = db.StringProperty()
... city = db.StringProperty()
... state = db.StringProperty()
... zip = db.StringProperty()
>>> ser_strategy = ModelStrategy(Person, include_all_fields=True) + ModelStrategy(Address, include_all_fields=True)
>>> addr = Address(street1="4422 Colfax Ave.", city="Minneapolis", state="MN", zip="55407")
>>> to_json([jean, addr], ser_strategy)
'[{"first_name": "Jeanne", "last_name": "d\'Arc", "ssn": "N/A"}, {"city": "Minneapolis", "street2": null, "state": "MN", "zip": "55407", "street1": "4422 Colfax Ave."}]'
Here's an example of how you might version an API
>>> v1_person_strategy = ModelStrategy(Person, include_all_fields=True)
>>> v1_address_strategy = ModelStrategy(Address).include("street1", "city", "state", "zip")
>>> v2_person_strategy = v1_person_strategy.exclude("ssn") # shouldn't include ssn
>>> v2_address_strategy = v1_address_strategy.include("street2") # forgot street2
>>> v1_strategy = v1_person_strategy + v1_address_strategy
>>> v2_strategy = v2_person_strategy + v2_address_strategy
>>> to_json([jean, addr], v1_strategy)
'[{"first_name": "Jeanne", "last_name": "d\'Arc", "ssn": "N/A"}, {"street1": "4422 Colfax Ave.", "state": "MN", "zip": "55407", "city": "Minneapolis"}]'
>>> to_json([jean, addr], v2_strategy)
'[{"first_name": "Jeanne", "last_name": "d\'Arc"}, {"street1": "4422 Colfax Ave.", "state": "MN", "street2": null, "zip": "55407", "city": "Minneapolis"}]'
""" |
"""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.
""" |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright (C) 2009-2014:
# 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
#
|
"""
=============================
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
""" |
"""
=============================
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
""" |
"""
========
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*.
record array
An `ndarray`_ with `structured data type`_ which has been subclassed as
np.recarray and whose dtype is of type np.record, making the
fields of its data type to be accessible by attribute.
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])
structured data type
A data type composed of other datatypes
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).
""" |
# Copyright 2011 NAME Copyright 2008 (C) Nicira, Inc.
#
# This file is part of POX.
#
# POX is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# POX 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 POX. If not, see <http://www.gnu.org/licenses/>.
# This file is derived from the packet library in NOX, which was
# developed by Nicira, Inc.
#======================================================================
#
# DNS Message Format
#
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | ID |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# |QR| Opcode |AA|TC|RD|RA|Z |AD|CD| RCODE |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Questions |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Answerrs |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Authority RRs |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Additional RRs |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Questions ... |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Answer RRs ... |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Authority RRs.. |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Additional RRs. |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
#
# Question format:
#
# 1 1 1 1 1 1
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | |
# / QNAME /
# / /
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | QTYPE |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | QCLASS |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
#
#
#
# All RRs have the following format:
# 1 1 1 1 1 1
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | |
# / /
# / NAME /
# | |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | TYPE |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | CLASS |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | TTL |
# | |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | RDLENGTH |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--|
# / RDATA /
# / /
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
#
#
# TODO:
# SOA data
# CNAME data
# MX data
#======================================================================
|
#
# 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
|
"""
pigpio is a Python module for the Raspberry which talks to
the pigpio daemon to allow control of the general purpose
input outputs (gpios).
[http://abyz.co.uk/rpi/pigpio/python.html]
*Features*
o the pigpio Python module can run on Windows, Macs, or Linux
o controls one or more Pi's
o independent PWM on any of gpios 0-31 simultaneously
o independent servo pulses on any of gpios 0-31 simultaneously
o callbacks when any of gpios 0-31 change state
o creating and transmitting precisely timed waveforms
o reading/writing gpios and setting their modes
o wrappers for I2C, SPI, and serial links
o creating and running scripts on the pigpio daemon
*gpios*
ALL gpios are identified by their Broadcom number.
*Notes*
Transmitted waveforms are accurate to a microsecond.
Callback level changes are time-stamped and will be
accurate to within a few microseconds.
*Settings*
A number of settings are determined when the pigpio daemon is started.
o the sample rate (1, 2, 4, 5, 8, or 10 us, default 5 us).
o the set of gpios which may be updated (generally written to). The
default set is those available on the Pi board revision.
o the available PWM frequencies (see [*set_PWM_frequency*]).
*Exceptions*
By default a fatal exception is raised if you pass an invalid
argument to a pigpio function.
If you wish to handle the returned status yourself you should set
pigpio.exceptions to False.
You may prefer to check the returned status in only a few parts
of your code. In that case do the following.
...
pigpio.exceptions = False
# Code where you want to test the error status.
pigpio.exceptions = True
...
*Usage*
This module uses the services of the C pigpio library. pigpio
must be running on the Pi(s) whose gpios are to be manipulated.
The normal way to start pigpio is as a daemon (during system
start).
sudo pigpiod
Your Python program must import pigpio and create one or more
instances of the pigpio.pi class. This class gives access to
a specified Pi's gpios.
...
pi1 = pigpio.pi() # pi1 accesses the local Pi's gpios
pi2 = pigpio.pi('tom') # pi2 accesses tom's gpios
pi3 = pigpio.pi('dick') # pi3 accesses NAME gpios
pi1.write(4, 0) # set local Pi's gpio 4 low
pi2.write(4, 1) # set tom's gpio 4 to high
pi3.read(4) # get level of NAME gpio 4
...
The later example code snippets assume that pi is an instance of
the pigpio.pi class.
OVERVIEW
Essential
pigpio.pi Initialise Pi connection
stop Stop a Pi connection
Beginner
set_mode Set a gpio mode
get_mode Get a gpio mode
set_pull_up_down Set/clear gpio pull up/down resistor
read Read a gpio
write Write a gpio
set_PWM_dutycycle Start/stop PWM pulses on a gpio
get_PWM_dutycycle Get PWM dutycycle set on a gpio
set_servo_pulsewidth Start/Stop servo pulses on a gpio
get_servo_pulsewidth Get servo pulsewidth set on a gpio
callback Create gpio level change callback
wait_for_edge Wait for gpio level change
Intermediate
gpio_trigger Send a trigger pulse to a gpio
set_watchdog Set a watchdog on a gpio
set_PWM_range Configure PWM range of a gpio
get_PWM_range Get configured PWM range of a gpio
set_PWM_frequency Set PWM frequency of a gpio
get_PWM_frequency Get PWM frequency of a gpio
read_bank_1 Read all bank 1 gpios
read_bank_2 Read all bank 2 gpios
clear_bank_1 Clear selected gpios in bank 1
clear_bank_2 Clear selected gpios in bank 2
set_bank_1 Set selected gpios in bank 1
set_bank_2 Set selected gpios in bank 2
Advanced
get_PWM_real_range Get underlying PWM range for a gpio
notify_open Request a notification handle
notify_begin Start notifications for selected gpios
notify_pause Pause notifications
notify_close Close a notification
bb_serial_read_open Open a gpio for bit bang serial reads
bb_serial_read Read bit bang serial data from a gpio
bb_serial_read_close Close a gpio for bit bang serial reads
bb_serial_invert Invert serial logic (1 invert, 0 normal)
hardware_clock Start hardware clock on supported gpios
hardware_PWM Start hardware PWM on supported gpios
Scripts
store_script Store a script
run_script Run a stored script
script_status Get script status and parameters
stop_script Stop a running script
delete_script Delete a stored script
Waves
wave_clear Deletes all waveforms
wave_add_new Starts a new waveform
wave_add_generic Adds a series of pulses to the waveform
wave_add_serial Adds serial data to the waveform
wave_create Creates a waveform from added data
wave_delete Deletes one or more waveforms
wave_send_once Transmits a waveform once
wave_send_repeat Transmits a waveform repeatedly
wave_chain Transmits a chain of waveforms
wave_tx_busy Checks to see if a waveform has ended
wave_tx_stop Aborts the current waveform
wave_get_micros Length in microseconds of the current waveform
wave_get_max_micros Absolute maximum allowed micros
wave_get_pulses Length in pulses of the current waveform
wave_get_max_pulses Absolute maximum allowed pulses
wave_get_cbs Length in cbs of the current waveform
wave_get_max_cbs Absolute maximum allowed cbs
I2C
i2c_open Opens an I2C device
i2c_close Closes an I2C device
i2c_write_quick SMBus write quick
i2c_write_byte SMBus write byte
i2c_read_byte SMBus read byte
i2c_write_byte_data SMBus write byte data
i2c_write_word_data SMBus write word data
i2c_read_byte_data SMBus read byte data
i2c_read_word_data SMBus read word data
i2c_process_call SMBus process call
i2c_write_block_data SMBus write block data
i2c_read_block_data SMBus read block data
i2c_block_process_call SMBus block process call
i2c_read_i2c_block_data SMBus read I2C block data
i2c_write_i2c_block_data SMBus write I2C block data
i2c_read_device Reads the raw I2C device
i2c_write_device Writes the raw I2C device
i2c_zip Performs multiple I2C transactions
bb_i2c_open Opens gpios for bit banging I2C
bb_i2c_close Closes gpios for bit banging I2C
bb_i2c_zip Performs multiple bit banged I2C transactions
SPI
spi_open Opens a SPI device
spi_close Closes a SPI device
spi_read Reads bytes from a SPI device
spi_write Writes bytes to a SPI device
spi_xfer Transfers bytes with a SPI device
Serial
serial_open Opens a serial device (/dev/tty*)
serial_close Closes a serial device
serial_read Reads bytes from a serial device
serial_read_byte Reads a byte from a serial device
serial_write Writes bytes to a serial device
serial_write_byte Writes a byte to a serial device
serial_data_available Returns number of bytes ready to be read
CUSTOM
custom_1 User custom function 1
custom_2 User custom function 2
Utility
get_current_tick Get current tick (microseconds)
get_hardware_revision Get hardware revision
get_pigpio_version Get the pigpio version
pigpio.error_text Gets error text from error number
pigpio.tickDiff Returns difference between two ticks
""" |
#
# ElementTree
# $Id: ElementTree.py 2326 2005-03-17 07:45:21Z USERNAME $
#
# light-weight XML support for Python 1.5.2 and later.
#
# history:
# 2001-10-20 fl created (from various sources)
# 2001-11-01 fl return root from parse method
# 2002-02-16 fl sort attributes in lexical order
# 2002-04-06 fl TreeBuilder refactoring, added PythonDoc markup
# 2002-05-01 fl finished TreeBuilder refactoring
# 2002-07-14 fl added basic namespace support to ElementTree.write
# 2002-07-25 fl added QName attribute support
# 2002-10-20 fl fixed encoding in write
# 2002-11-24 fl changed default encoding to ascii; fixed attribute encoding
# 2002-11-27 fl accept file objects or file names for parse/write
# 2002-12-04 fl moved XMLTreeBuilder back to this module
# 2003-01-11 fl fixed entity encoding glitch for us-ascii
# 2003-02-13 fl added XML literal factory
# 2003-02-21 fl added ProcessingInstruction/PI factory
# 2003-05-11 fl added tostring/fromstring helpers
# 2003-05-26 fl added ElementPath support
# 2003-07-05 fl added makeelement factory method
# 2003-07-28 fl added more well-known namespace prefixes
# 2003-08-15 fl fixed typo in ElementTree.findtext (Thomas NAME 2003-09-04 fl fall back on emulator if ElementPath is not installed
# 2003-10-31 fl markup updates
# 2003-11-15 fl fixed nested namespace bug
# 2004-03-28 fl added XMLID helper
# 2004-06-02 fl added default support to findtext
# 2004-06-08 fl fixed encoding of non-ascii element/attribute names
# 2004-08-23 fl take advantage of post-2.1 expat features
# 2005-02-01 fl added iterparse implementation
# 2005-03-02 fl fixed iterparse support for pre-2.2 versions
#
# Copyright (c) 1999-2005 by NAME All rights reserved.
#
# EMAIL
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2005 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
# Licensed to PSF under a Contributor Agreement.
# See http://www.python.org/2.4/license for licensing details.
|
"""
The :class:`.CDNClient` class provides a simple API for downloading Steam content from SteamPipe
Initializing :class:`.CDNClient` requires a logged in :class:`.SteamClient` instance
.. code:: python
mysteam = SteamClient()
...
mycdn = CDNClient(mysteam)
Getting depot manifests for an app
.. code:: python
>>> mycdn.get_manifests(570)
[<CDNDepotManifest('Dota 2 Content', app_id=570, depot_id=373301, gid=6397590570861788404, creation_time='2019-06-29 16:03:11')>,
<CDNDepotManifest('Dota 2 Content 2', app_id=570, depot_id=381451, gid=5769691971272474272, creation_time='2019-06-29 00:19:02')>,
<CDNDepotManifest('Dota 2 Content 3', app_id=570, depot_id=381452, gid=3194393866044592918, creation_time='2019-06-27 00:05:38')>,
<CDNDepotManifest('Dota 2 Content 4', app_id=570, depot_id=381453, gid=8005824150061180163, creation_time='2019-06-08 07:49:57')>,
<CDNDepotManifest('Dota 2 Content 5', app_id=570, depot_id=381454, gid=9003299908441378336, creation_time='2019-06-26 18:56:19')>,
<CDNDepotManifest('Dota 2 Content 6', app_id=570, depot_id=381455, gid=8000458746487720619, creation_time='2019-06-29 00:19:43')>,
<CDNDepotManifest('Dota 2 Win32', app_id=570, depot_id=373302, gid=3561463682334619841, creation_time='2019-06-29 00:16:28')>,
<CDNDepotManifest('Dota 2 Win64', app_id=570, depot_id=373303, gid=6464064782313084040, creation_time='2019-06-29 00:16:43')>,
<CDNDepotManifest('Dota 2 Mac', app_id=570, depot_id=373304, gid=5979018571482579541, creation_time='2019-06-29 00:16:59')>,
<CDNDepotManifest('Dota 2 English', app_id=570, depot_id=373305, gid=4435851250675935801, creation_time='2015-06-01 20:15:37')>,
<CDNDepotManifest('Dota 2 Linux', app_id=570, depot_id=373306, gid=4859464855297921815, creation_time='2019-06-29 00:17:25')>,
<CDNDepotManifest('Dota 2 Korean', app_id=570, depot_id=373308, gid=8598853793233320583, creation_time='2019-03-05 17:16:49')>,
<CDNDepotManifest('Dota 2 Simplified Chinese', app_id=570, depot_id=373309, gid=6975893321745168138, creation_time='2019-06-25 21:40:37')>,
<CDNDepotManifest('Dota 2 Russian', app_id=570, depot_id=381456, gid=5425063725991897591, creation_time='2019-03-05 17:19:53')>,
<CDNDepotManifest('Dota 2 Workshop tools', app_id=570, depot_id=381450, gid=8629205096668418087, creation_time='2019-06-29 16:04:18')>,
<CDNDepotManifest('Dota 2 OpenGL Windows', app_id=570, depot_id=401531, gid=6502316736107281444, creation_time='2019-06-07 19:04:08')>,
<CDNDepotManifest('Dota 2 Vulkan Common', app_id=570, depot_id=401535, gid=6405492872419215600, creation_time='2019-06-07 19:04:11')>,
<CDNDepotManifest('Dota 2 Vulkan Win64', app_id=570, depot_id=401536, gid=3821288251412129608, creation_time='2019-06-25 21:42:29')>,
<CDNDepotManifest('Dota 2 Vulkan Linux64', app_id=570, depot_id=401537, gid=3144805829218032316, creation_time='2019-06-17 16:54:43')>,
<CDNDepotManifest('Dota 2 VR', app_id=570, depot_id=313255, gid=706332602567268673, creation_time='2017-10-04 18:52:14')>,
<CDNDepotManifest('Dota 2 Vulkan Mac', app_id=570, depot_id=401538, gid=2223235822414824351, creation_time='2019-06-11 19:37:19')>]
>>> mycdn.get_manifests(570, filter_func=lambda depot_id, info: 'Dota 2 Content' in info['name'])
[<CDNDepotManifest('Dota 2 Content', app_id=570, depot_id=373301, gid=6397590570861788404, creation_time='2019-06-29 16:03:11')>,
<CDNDepotManifest('Dota 2 Content 2', app_id=570, depot_id=381451, gid=5769691971272474272, creation_time='2019-06-29 00:19:02')>,
<CDNDepotManifest('Dota 2 Content 3', app_id=570, depot_id=381452, gid=3194393866044592918, creation_time='2019-06-27 00:05:38')>,
<CDNDepotManifest('Dota 2 Content 4', app_id=570, depot_id=381453, gid=8005824150061180163, creation_time='2019-06-08 07:49:57')>,
<CDNDepotManifest('Dota 2 Content 5', app_id=570, depot_id=381454, gid=9003299908441378336, creation_time='2019-06-26 18:56:19')>,
<CDNDepotManifest('Dota 2 Content 6', app_id=570, depot_id=381455, gid=8000458746487720619, creation_time='2019-06-29 00:19:43')>]
Listing files
.. code:: python
>>> file_list = mycdn.iter_files(570)
>>> list(file_list)[:10]
[<CDNDepotFile(570, 373301, 6397590570861788404, 'game\\dota_addons\\dungeon\\particles\\test_particle\\generic_attack_crit_blur_rope.vpcf_c', 2134)>,
<CDNDepotFile(570, 373301, 6397590570861788404, 'game\\dota_addons\\dungeon\\materials\\blends\\mud_brick_normal_psd_5cc4fe8b.vtex_c', 351444)>,
<CDNDepotFile(570, 373301, 6397590570861788404, 'game\\dota_addons\\hero_demo\\scripts\\vscripts\\la_spawn_enemy_at_target.lua', 1230)>,
<CDNDepotFile(570, 373301, 6397590570861788404, 'game\\dota_addons\\winter_2018\\particles\\dark_moon\\darkmoon_last_hit_effect_damage_flash_b.vpcf_c', 1386)>,
<CDNDepotFile(570, 373301, 6397590570861788404, 'game\\dota_addons\\dungeon\\scripts\\vscripts\\abilities\\siltbreaker_line_wave.lua', 3305)>,
<CDNDepotFile(570, 373301, 6397590570861788404, 'game\\dota_addons\\dungeon\\materials\\models\\heroes\\broodmother\\broodmother_body_poison.vmat_c', 10888)>,
<CDNDepotFile(570, 373301, 6397590570861788404, 'game\\dota\\resource\\cursor\\workshop\\sltv_shaker_cursor_pack\\cursor_spell_default.ani', 4362)>,
<CDNDepotFile(570, 373301, 6397590570861788404, 'game\\dota_addons\\overthrow\\panorama\\images\\custom_game\\team_icons\\team_icon_tiger_01_png.vtex_c', 18340)>,
<CDNDepotFile(570, 373301, 6397590570861788404, 'game\\dota\\resource\\cursor\\valve\\ti7\\cursor_attack_illegal.bmp', 4152)>,
<CDNDepotFile(570, 373301, 6397590570861788404, 'game\\dota_addons\\winter_2018\\models\\creeps\\ice_biome\\undeadtusk\\undead_tuskskeleton01.vmdl_c', 13516)>
Reading a file directly from SteamPipe
.. code:: python
>>> file_list = mycdn.iter_files(570, r'game\dota\gameinfo.gi')
>>> myfile = next(file_list)
<CDNDepotFile(570, 373301, 6397590570861788404, 'game\\dota\\gameinfo.gi', 6808)>
>>> print(myfile.read(80).decode('utf-8'))
"GameInfo"
{
game "Dota 2"
title "Dota 2"
gamelogo 1
type multiplayer_only
...
""" |
"""Exception classes for CherryPy.
CherryPy provides (and uses) exceptions for declaring that the HTTP response
should be a status other than the default "200 OK". You can ``raise`` them like
normal Python exceptions. You can also call them and they will raise
themselves; this means you can set an
:class:`HTTPError<cherrypy._cperror.HTTPError>`
or :class:`HTTPRedirect<cherrypy._cperror.HTTPRedirect>` as the
:attr:`request.handler<cherrypy._cprequest.Request.handler>`.
.. _redirectingpost:
Redirecting POST
================
When you GET a resource and are redirected by the server to another Location,
there's generally no problem since GET is both a "safe method" (there should
be no side-effects) and an "idempotent method" (multiple calls are no different
than a single call).
POST, however, is neither safe nor idempotent--if you
charge a credit card, you don't want to be charged twice by a redirect!
For this reason, *none* of the 3xx responses permit a user-agent (browser) to
resubmit a POST on redirection without first confirming the action with the
user:
===== ================================= ===========
300 Multiple Choices Confirm with the user
301 Moved Permanently Confirm with the user
302 Found (Object moved temporarily) Confirm with the user
303 See Other GET the new URI; no confirmation
304 Not modified for conditional GET only;
POST should not raise this error
305 Use Proxy Confirm with the user
307 Temporary Redirect Confirm with the user
308 Permanent Redirect No confirmation
===== ================================= ===========
However, browsers have historically implemented these restrictions poorly;
in particular, many browsers do not force the user to confirm 301, 302
or 307 when redirecting POST. For this reason, CherryPy defaults to 303,
which most user-agents appear to have implemented correctly. Therefore, if
you raise HTTPRedirect for a POST request, the user-agent will most likely
attempt to GET the new URI (without asking for confirmation from the user).
We realize this is confusing for developers, but it's the safest thing we
could do. You are of course free to raise ``HTTPRedirect(uri, status=302)``
or any other 3xx status if you know what you're doing, but given the
environment, we couldn't let any of those be the default.
Custom Error Handling
=====================
.. image:: /refman/cperrors.gif
Anticipated HTTP responses
--------------------------
The 'error_page' config namespace can be used to provide custom HTML output for
expected responses (like 404 Not Found). Supply a filename from which the
output will be read. The contents will be interpolated with the values
%(status)s, %(message)s, %(traceback)s, and %(version)s using plain old Python
`string formatting
<http://docs.python.org/2/library/stdtypes.html#string-formatting-operations>`_.
::
_cp_config = {
'error_page.404': os.path.join(localDir, "static/index.html")
}
Beginning in version 3.1, you may also provide a function or other callable as
an error_page entry. It will be passed the same status, message, traceback and
version arguments that are interpolated into templates::
def error_page_402(status, message, traceback, version):
return "Error %s - Well, I'm very sorry but you haven't paid!" % status
cherrypy.config.update({'error_page.402': error_page_402})
Also in 3.1, in addition to the numbered error codes, you may also supply
"error_page.default" to handle all codes which do not have their own error_page
entry.
Unanticipated errors
--------------------
CherryPy also has a generic error handling mechanism: whenever an unanticipated
error occurs in your code, it will call
:func:`Request.error_response<cherrypy._cprequest.Request.error_response>` to
set the response status, headers, and body. By default, this is the same
output as
:class:`HTTPError(500) <cherrypy._cperror.HTTPError>`. If you want to provide
some other behavior, you generally replace "request.error_response".
Here is some sample code that shows how to display a custom error message and
send an e-mail containing the error::
from cherrypy import _cperror
def handle_error():
cherrypy.response.status = 500
cherrypy.response.body = [
"<html><body>Sorry, an error occurred</body></html>"
]
sendMail('EMAIL',
'Error in your web app',
_cperror.format_exc())
@cherrypy.config(**{'request.error_response': handle_error})
class Root:
pass
Note that you have to explicitly set
:attr:`response.body <cherrypy._cprequest.Response.body>`
and not simply return an error message as a result.
""" |
# Configuration file for jupyter-notebook.
#------------------------------------------------------------------------------
# Application(SingletonConfigurable) configuration
#------------------------------------------------------------------------------
## This is an application.
## The date format used by logging formatters for %(asctime)s
#c.Application.log_datefmt = '%Y-%m-%d %H:%M:%S'
## The Logging format template
#c.Application.log_format = '[%(name)s]%(highlevel)s %(message)s'
## Set the log level by value or name.
#c.Application.log_level = 30
#------------------------------------------------------------------------------
# JupyterApp(Application) configuration
#------------------------------------------------------------------------------
## Base class for Jupyter applications
## Answer yes to any prompts.
#c.JupyterApp.answer_yes = False
## Full path of a config file.
#c.JupyterApp.config_file = ''
## Specify a config file to load.
#c.JupyterApp.config_file_name = ''
## Generate default config file.
#c.JupyterApp.generate_config = False
#------------------------------------------------------------------------------
# NotebookApp(JupyterApp) configuration
#------------------------------------------------------------------------------
## Set the Access-Control-Allow-Credentials: true header
#c.NotebookApp.allow_credentials = False
## Set the Access-Control-Allow-Origin header
#
# Use '*' to allow any origin to access your server.
#
# Takes precedence over allow_origin_pat.
#c.NotebookApp.allow_origin = ''
## Use a regular expression for the Access-Control-Allow-Origin header
#
# Requests from an origin matching the expression will get replies with:
#
# Access-Control-Allow-Origin: origin
#
# where `origin` is the origin of the request.
#
# Ignored if allow_origin is set.
#c.NotebookApp.allow_origin_pat = ''
## DEPRECATED use base_url
#c.NotebookApp.base_project_url = '/'
## The base URL for the notebook server.
#
# Leading and trailing slashes can be omitted, and will automatically be added.
#c.NotebookApp.base_url = '/'
## Specify what command to use to invoke a web browser when opening the notebook.
# If not specified, the default browser will be determined by the `webbrowser`
# standard library module, which allows setting of the BROWSER environment
# variable to override it.
#c.NotebookApp.browser = ''
## The full path to an SSL/TLS certificate file.
#c.NotebookApp.certfile = ''
## The full path to a certificate authority certificate for SSL/TLS client
# authentication.
#c.NotebookApp.client_ca = ''
## The config manager class to use
#c.NotebookApp.config_manager_class = 'notebook.services.config.manager.ConfigManager'
## The notebook manager class to use.
#c.NotebookApp.contents_manager_class = 'notebook.services.contents.filemanager.FileContentsManager'
## Extra keyword arguments to pass to `set_secure_cookie`. See tornado's
# set_secure_cookie docs for details.
#c.NotebookApp.cookie_options = {}
## The random bytes used to secure cookies. By default this is a new random
# number every time you start the Notebook. Set it to a value in a config file
# to enable logins to persist across server sessions.
#
# Note: Cookie secrets should be kept private, do not share config files with
# cookie_secret stored in plaintext (you can read the value from a file).
#c.NotebookApp.cookie_secret = b''
## The file where the cookie secret is stored.
#c.NotebookApp.cookie_secret_file = ''
## The default URL to redirect to from `/`
#c.NotebookApp.default_url = '/tree'
## Disable cross-site-request-forgery protection
#
# Jupyter notebook 4.3.1 introduces protection from cross-site request
# forgeries, requiring API requests to either:
#
# - originate from pages served by this server (validated with XSRF cookie and
# token), or - authenticate with a token
#
# Some anonymous compute resources still desire the ability to run code,
# completely without authentication. These services can disable all
# authentication and security checks, with the full knowledge of what that
# implies.
#c.NotebookApp.disable_check_xsrf = False
## Whether to enable MathJax for typesetting math/TeX
#
# MathJax is the javascript library Jupyter uses to render math/LaTeX. It is
# very large, so you may want to disable it if you have a slow internet
# connection, or for offline use of the notebook.
#
# When disabled, equations etc. will appear as their untransformed TeX source.
#c.NotebookApp.enable_mathjax = True
## extra paths to look for Javascript notebook extensions
#c.NotebookApp.extra_nbextensions_path = []
## Extra paths to search for serving static files.
#
# This allows adding javascript/css to be available from the notebook server
# machine, or overriding individual files in the IPython
#c.NotebookApp.extra_static_paths = []
## Extra paths to search for serving jinja templates.
#
# Can be used to override templates from notebook.templates.
#c.NotebookApp.extra_template_paths = []
##
#c.NotebookApp.file_to_run = ''
## Use minified JS file or not, mainly use during dev to avoid JS recompilation
#c.NotebookApp.ignore_minified_js = False
## (bytes/sec) Maximum rate at which messages can be sent on iopub before they
# are limited.
#c.NotebookApp.iopub_data_rate_limit = 0
## (msg/sec) Maximum rate at which messages can be sent on iopub before they are
# limited.
#c.NotebookApp.iopub_msg_rate_limit = 0
## The IP address the notebook server will listen on.
#c.NotebookApp.ip = 'localhost'
## Supply extra arguments that will be passed to Jinja environment.
#c.NotebookApp.jinja_environment_options = {}
## Extra variables to supply to jinja templates when rendering.
#c.NotebookApp.jinja_template_vars = {}
## The kernel manager class to use.
#c.NotebookApp.kernel_manager_class = 'notebook.services.kernels.kernelmanager.MappingKernelManager'
## The kernel spec manager class to use. Should be a subclass of
# `jupyter_client.kernelspec.KernelSpecManager`.
#
# The Api of KernelSpecManager is provisional and might change without warning
# between this version of Jupyter and the next stable one.
#c.NotebookApp.kernel_spec_manager_class = 'jupyter_client.kernelspec.KernelSpecManager'
## The full path to a private key file for usage with SSL/TLS.
#c.NotebookApp.keyfile = ''
## The login handler class to use.
#c.NotebookApp.login_handler_class = 'notebook.auth.login.LoginHandler'
## The logout handler class to use.
#c.NotebookApp.logout_handler_class = 'notebook.auth.logout.LogoutHandler'
## A custom url for MathJax.js. Should be in the form of a case-sensitive url to
# MathJax, for example: /static/components/MathJax/MathJax.js
#c.NotebookApp.mathjax_url = ''
## Dict of Python modules to load as notebook server extensions.Entry values can
# be used to enable and disable the loading ofthe extensions. The extensions
# will be loaded in alphabetical order.
#c.NotebookApp.nbserver_extensions = {}
## The directory to use for notebooks and kernels.
#c.NotebookApp.notebook_dir = ''
## Whether to open in a browser after starting. The specific browser used is
# platform dependent and determined by the python standard library `webbrowser`
# module, unless it is overridden using the --browser (NotebookApp.browser)
# configuration option.
#c.NotebookApp.open_browser = True
## Hashed password to use for web authentication.
#
# To generate, type in a python/IPython shell:
#
# from notebook.auth import passwd; passwd()
#
# The string should be of the form type:salt:hashed-password.
#c.NotebookApp.password = ''
## The port the notebook server will listen on.
#c.NotebookApp.port = 8888
## The number of additional ports to try if the specified port is not available.
#c.NotebookApp.port_retries = 50
## DISABLED: use %pylab or %matplotlib in the notebook to enable matplotlib.
#c.NotebookApp.pylab = 'disabled'
## (sec) Time window used to check the message and data rate limits.
#c.NotebookApp.rate_limit_window = 1.0
## Reraise exceptions encountered loading server extensions?
#c.NotebookApp.reraise_server_extension_failures = False
## DEPRECATED use the nbserver_extensions dict instead
#c.NotebookApp.server_extensions = []
## The session manager class to use.
#c.NotebookApp.session_manager_class = 'notebook.services.sessions.sessionmanager.SessionManager'
## Supply SSL options for the tornado HTTPServer. See the tornado docs for
# details.
#c.NotebookApp.ssl_options = {}
## Token used for authenticating first-time connections to the server.
#
# When no password is enabled, the default is to generate a new, random token.
#
# Setting to an empty string disables authentication altogether, which is NOT
# RECOMMENDED.
#c.NotebookApp.token = '<generated>'
## Supply overrides for the tornado.web.Application that the Jupyter notebook
# uses.
#c.NotebookApp.tornado_settings = {}
## Whether to trust or not X-Scheme/X-Forwarded-Proto and X-Real-Ip/X-Forwarded-
# For headerssent by the upstream reverse proxy. Necessary if the proxy handles
# SSL
#c.NotebookApp.trust_xheaders = False
## DEPRECATED, use tornado_settings
#c.NotebookApp.webapp_settings = {}
## The base URL for websockets, if it differs from the HTTP server (hint: it
# almost certainly doesn't).
#
# Should be in the form of an HTTP origin: ws[s]://hostname[:port]
#c.NotebookApp.websocket_url = ''
#------------------------------------------------------------------------------
# ConnectionFileMixin(LoggingConfigurable) configuration
#------------------------------------------------------------------------------
## Mixin for configurable classes that work with connection files
## JSON file in which to store connection info [default: kernel-<pid>.json]
#
# This file will contain the IP, ports, and authentication key needed to connect
# clients to this kernel. By default, this file will be created in the security
# dir of the current profile, but can be specified by absolute path.
#c.ConnectionFileMixin.connection_file = ''
## set the control (ROUTER) port [default: random]
#c.ConnectionFileMixin.control_port = 0
## set the heartbeat port [default: random]
#c.ConnectionFileMixin.hb_port = 0
## set the iopub (PUB) port [default: random]
#c.ConnectionFileMixin.iopub_port = 0
## Set the kernel's IP address [default localhost]. If the IP address is
# something other than localhost, then Consoles on other machines will be able
# to connect to the Kernel, so be careful!
#c.ConnectionFileMixin.ip = ''
## set the shell (ROUTER) port [default: random]
#c.ConnectionFileMixin.shell_port = 0
## set the stdin (ROUTER) port [default: random]
#c.ConnectionFileMixin.stdin_port = 0
##
#c.ConnectionFileMixin.transport = 'tcp'
#------------------------------------------------------------------------------
# KernelManager(ConnectionFileMixin) configuration
#------------------------------------------------------------------------------
## Manages a single kernel in a subprocess on this host.
#
# This version starts kernels with Popen.
## Should we autorestart the kernel if it dies.
#c.KernelManager.autorestart = True
## DEPRECATED: Use kernel_name instead.
#
# The Popen Command to launch the kernel. Override this if you have a custom
# kernel. If kernel_cmd is specified in a configuration file, Jupyter does not
# pass any arguments to the kernel, because it cannot make any assumptions about
# the arguments that the kernel understands. In particular, this means that the
# kernel does not receive the option --debug if it given on the Jupyter command
# line.
#c.KernelManager.kernel_cmd = []
#------------------------------------------------------------------------------
# Session(Configurable) configuration
#------------------------------------------------------------------------------
## Object for handling serialization and sending of messages.
#
# The Session object handles building messages and sending them with ZMQ sockets
# or ZMQStream objects. Objects can communicate with each other over the
# network via Session objects, and only need to work with the dict-based IPython
# message spec. The Session will handle serialization/deserialization, security,
# and metadata.
#
# Sessions support configurable serialization via packer/unpacker traits, and
# signing with HMAC digests via the key/keyfile traits.
#
# Parameters ----------
#
# debug : bool
# whether to trigger extra debugging statements
# packer/unpacker : str : 'json', 'pickle' or import_string
# importstrings for methods to serialize message parts. If just
# 'json' or 'pickle', predefined JSON and pickle packers will be used.
# Otherwise, the entire importstring must be used.
#
# The functions must accept at least valid JSON input, and output *bytes*.
#
# For example, to use msgpack:
# packer = 'msgpack.packb', unpacker='msgpack.unpackb'
# pack/unpack : callables
# You can also set the pack/unpack callables for serialization directly.
# session : bytes
# the ID of this Session object. The default is to generate a new UUID.
# username : USERNAME username added to message headers. The default is to ask the OS.
# key : bytes
# The key used to initialize an HMAC signature. If unset, messages
# will not be signed or checked.
# keyfile : filepath
# The file containing a key. If this is set, `key` will be initialized
# to the contents of the file.
## Threshold (in bytes) beyond which an object's buffer should be extracted to
# avoid pickling.
#c.Session.buffer_threshold = 1024
## Whether to check PID to protect against calls after fork.
#
# This check can be disabled if fork-safety is handled elsewhere.
#c.Session.check_pid = True
## Threshold (in bytes) beyond which a buffer should be sent without copying.
#c.Session.copy_threshold = 65536
## Debug output in the Session
#c.Session.debug = False
## The maximum number of digests to remember.
#
# The digest history will be culled when it exceeds this value.
#c.Session.digest_history_size = 65536
## The maximum number of items for a container to be introspected for custom
# serialization. Containers larger than this are pickled outright.
#c.Session.item_threshold = 64
## execution key, for signing messages.
#c.Session.key = b''
## path to file containing execution key.
#c.Session.keyfile = ''
## Metadata dictionary, which serves as the default top-level metadata dict for
# each message.
#c.Session.metadata = {}
## The name of the packer for serializing messages. Should be one of 'json',
# 'pickle', or an import name for a custom callable serializer.
#c.Session.packer = 'json'
## The UUID identifying this session.
#c.Session.session = ''
## The digest scheme used to construct the message signatures. Must have the form
# 'hmac-HASH'.
#c.Session.signature_scheme = 'hmac-sha256'
## The name of the unpacker for unserializing messages. Only used with custom
# functions for `packer`.
#c.Session.unpacker = 'json'
## Username for the Session. Default is your system username.
#c.Session.username = 'vishnu'
#------------------------------------------------------------------------------
# MultiKernelManager(LoggingConfigurable) configuration
#------------------------------------------------------------------------------
## A class for managing multiple kernels.
## The name of the default kernel to start
#c.MultiKernelManager.default_kernel_name = 'python3'
## The kernel manager class. This is configurable to allow subclassing of the
# KernelManager for customized behavior.
#c.MultiKernelManager.kernel_manager_class = 'jupyter_client.ioloop.IOLoopKernelManager'
#------------------------------------------------------------------------------
# MappingKernelManager(MultiKernelManager) configuration
#------------------------------------------------------------------------------
## A KernelManager that handles notebook mapping and HTTP error handling
##
#c.MappingKernelManager.root_dir = ''
#------------------------------------------------------------------------------
# ContentsManager(LoggingConfigurable) configuration
#------------------------------------------------------------------------------
## Base class for serving files and directories.
#
# This serves any text or binary file, as well as directories, with special
# handling for JSON notebook documents.
#
# Most APIs take a path argument, which is always an API-style unicode path, and
# always refers to a directory.
#
# - unicode, not url-escaped
# - '/'-separated
# - leading and trailing '/' will be stripped
# - if unspecified, path defaults to '',
# indicating the root path.
##
#c.ContentsManager.checkpoints = None
##
#c.ContentsManager.checkpoints_class = 'notebook.services.contents.checkpoints.Checkpoints'
##
#c.ContentsManager.checkpoints_kwargs = {}
## Glob patterns to hide in file and directory listings.
#c.ContentsManager.hide_globs = ['__pycache__', '*.pyc', '*.pyo', '.DS_Store', '*.so', '*.dylib', '*~']
## Python callable or importstring thereof
#
# To be called on a contents model prior to save.
#
# This can be used to process the structure, such as removing notebook outputs
# or other side effects that should not be saved.
#
# It will be called as (all arguments passed by keyword)::
#
# hook(path=path, model=model, contents_manager=self)
#
# - model: the model to be saved. Includes file contents.
# Modifying this dict will affect the file that is stored.
# - path: the API path of the save destination
# - contents_manager: this ContentsManager instance
#c.ContentsManager.pre_save_hook = None
## The base name used when creating untitled directories.
#c.ContentsManager.untitled_directory = 'Untitled Folder'
## The base name used when creating untitled files.
#c.ContentsManager.untitled_file = 'untitled'
## The base name used when creating untitled notebooks.
#c.ContentsManager.untitled_notebook = 'Untitled'
#------------------------------------------------------------------------------
# FileManagerMixin(Configurable) configuration
#------------------------------------------------------------------------------
## Mixin for ContentsAPI classes that interact with the filesystem.
#
# Provides facilities for reading, writing, and copying both notebooks and
# generic files.
#
# Shared by FileContentsManager and FileCheckpoints.
#
# Note ---- Classes using this mixin must provide the following attributes:
#
# root_dir : USERNAME A directory against against which API-style paths are to be resolved.
#
# log : logging.Logger
## By default notebooks are saved on disk on a temporary file and then if
# succefully written, it replaces the old ones. This procedure, namely
# 'atomic_writing', causes some bugs on file system whitout operation order
# enforcement (like some networked fs). If set to False, the new notebook is
# written directly on the old one which could fail (eg: full filesystem or quota
# )
#c.FileManagerMixin.use_atomic_writing = True
#------------------------------------------------------------------------------
# FileContentsManager(FileManagerMixin,ContentsManager) configuration
#------------------------------------------------------------------------------
## Python callable or importstring thereof
#
# to be called on the path of a file just saved.
#
# This can be used to process the file on disk, such as converting the notebook
# to a script or HTML via nbconvert.
#
# It will be called as (all arguments passed by keyword)::
#
# hook(os_path=os_path, model=model, contents_manager=instance)
#
# - path: the filesystem path to the file just written - model: the model
# representing the file - contents_manager: this ContentsManager instance
#c.FileContentsManager.post_save_hook = None
##
#c.FileContentsManager.root_dir = ''
## DEPRECATED, use post_save_hook. Will be removed in Notebook 5.0
#c.FileContentsManager.save_script = False
#------------------------------------------------------------------------------
# NotebookNotary(LoggingConfigurable) configuration
#------------------------------------------------------------------------------
## A class for computing and verifying notebook signatures.
## The hashing algorithm used to sign notebooks.
#c.NotebookNotary.algorithm = 'sha256'
## The sqlite file in which to store notebook signatures. By default, this will
# be in your Jupyter data directory. You can set it to ':memory:' to disable
# sqlite writing to the filesystem.
#c.NotebookNotary.db_file = ''
## The secret key with which notebooks are signed.
#c.NotebookNotary.secret = b''
## The file where the secret key is stored.
#c.NotebookNotary.secret_file = ''
## A callable returning the storage backend for notebook signatures. The default
# uses an SQLite database.
#c.NotebookNotary.store_factory = traitlets.Undefined
#------------------------------------------------------------------------------
# KernelSpecManager(LoggingConfigurable) configuration
#------------------------------------------------------------------------------
## If there is no Python kernelspec registered and the IPython kernel is
# available, ensure it is added to the spec list.
#c.KernelSpecManager.ensure_native_kernel = True
## The kernel spec class. This is configurable to allow subclassing of the
# KernelSpecManager for customized behavior.
#c.KernelSpecManager.kernel_spec_class = 'jupyter_client.kernelspec.KernelSpec'
## Whitelist of allowed kernel names.
#
# By default, all installed kernels are allowed.
#c.KernelSpecManager.whitelist = set()
|
"""
Define a simple format for saving numpy arrays to disk with the full
information about them.
The ``.npy`` format is the standard binary file format in NumPy for
persisting a *single* arbitrary NumPy array on disk. The format stores all
of the shape and dtype information necessary to reconstruct the array
correctly even on another machine with a different architecture.
The format is designed to be as simple as possible while achieving
its limited goals.
The ``.npz`` format is the standard format for persisting *multiple* NumPy
arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``
files, one for each array.
Capabilities
------------
- Can represent all NumPy arrays including nested record arrays and
object arrays.
- Represents the data in its native binary form.
- Supports Fortran-contiguous arrays directly.
- Stores all of the necessary information to reconstruct the array
including shape and dtype on a machine of a different
architecture. Both little-endian and big-endian arrays are
supported, and a file with little-endian numbers will yield
a little-endian array on any machine reading the file. The
types are described in terms of their actual sizes. For example,
if a machine with a 64-bit C "long int" writes out an array with
"long ints", a reading machine with 32-bit C "long ints" will yield
an array with 64-bit integers.
- Is straightforward to reverse engineer. Datasets often live longer than
the programs that created them. A competent developer should be
able create a solution in his preferred programming language to
read most ``.npy`` files that he has been given without much
documentation.
- Allows memory-mapping of the data. See `open_memmep`.
- Can be read from a filelike stream object instead of an actual file.
- Stores object arrays, i.e. arrays containing elements that are arbitrary
Python objects. Files with object arrays are not to be mmapable, but
can be read and written to disk.
Limitations
-----------
- Arbitrary subclasses of numpy.ndarray are not completely preserved.
Subclasses will be accepted for writing, but only the array data will
be written out. A regular numpy.ndarray object will be created
upon reading the file.
.. warning::
Due to limitations in the interpretation of structured dtypes, dtypes
with fields with empty names will have the names replaced by 'f0', 'f1',
etc. Such arrays will not round-trip through the format entirely
accurately. The data is intact; only the field names will differ. We are
working on a fix for this. This fix will not require a change in the
file format. The arrays with such structures can still be saved and
restored, and the correct dtype may be restored by using the
``loadedarray.view(correct_dtype)`` method.
File extensions
---------------
We recommend using the ``.npy`` and ``.npz`` extensions for files saved
in this format. This is by no means a requirement; applications may wish
to use these file formats but use an extension specific to the
application. In the absence of an obvious alternative, however,
we suggest using ``.npy`` and ``.npz``.
Version numbering
-----------------
The version numbering of these formats is independent of NumPy version
numbering. If the format is upgraded, the code in `numpy.io` will still
be able to read and write Version 1.0 files.
Format Version 1.0
------------------
The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.
The next 1 byte is an unsigned byte: the major version number of the file
format, e.g. ``\\x01``.
The next 1 byte is an unsigned byte: the minor version number of the file
format, e.g. ``\\x00``. Note: the version of the file format is not tied
to the version of the numpy package.
The next 2 bytes form a little-endian unsigned short int: the length of
the header data HEADER_LEN.
The next HEADER_LEN bytes form the header data describing the array's
format. It is an ASCII string which contains a Python literal expression
of a dictionary. It is terminated by a newline (``\\n``) and padded with
spaces (``\\x20``) to make the total length of
``magic string + 4 + HEADER_LEN`` be evenly divisible by 16 for alignment
purposes.
The dictionary contains three keys:
"descr" : dtype.descr
An object that can be passed as an argument to the `numpy.dtype`
constructor to create the array's dtype.
"fortran_order" : bool
Whether the array data is Fortran-contiguous or not. Since
Fortran-contiguous arrays are a common form of non-C-contiguity,
we allow them to be written directly to disk for efficiency.
"shape" : tuple of int
The shape of the array.
For repeatability and readability, the dictionary keys are sorted in
alphabetic order. This is for convenience only. A writer SHOULD implement
this if possible. A reader MUST NOT depend on this.
Following the header comes the array data. If the dtype contains Python
objects (i.e. ``dtype.hasobject is True``), then the data is a Python
pickle of the array. Otherwise the data is the contiguous (either C-
or Fortran-, depending on ``fortran_order``) bytes of the array.
Consumers can figure out the number of bytes by multiplying the number
of elements given by the shape (noting that ``shape=()`` means there is
1 element) by ``dtype.itemsize``.
Notes
-----
The ``.npy`` format, including reasons for creating it and a comparison of
alternatives, is described fully in the "npy-format" NEP.
""" |
"""
Define a simple format for saving numpy arrays to disk with the full
information about them.
The ``.npy`` format is the standard binary file format in NumPy for
persisting a *single* arbitrary NumPy array on disk. The format stores all
of the shape and dtype information necessary to reconstruct the array
correctly even on another machine with a different architecture.
The format is designed to be as simple as possible while achieving
its limited goals.
The ``.npz`` format is the standard format for persisting *multiple* NumPy
arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``
files, one for each array.
Capabilities
------------
- Can represent all NumPy arrays including nested record arrays and
object arrays.
- Represents the data in its native binary form.
- Supports Fortran-contiguous arrays directly.
- Stores all of the necessary information to reconstruct the array
including shape and dtype on a machine of a different
architecture. Both little-endian and big-endian arrays are
supported, and a file with little-endian numbers will yield
a little-endian array on any machine reading the file. The
types are described in terms of their actual sizes. For example,
if a machine with a 64-bit C "long int" writes out an array with
"long ints", a reading machine with 32-bit C "long ints" will yield
an array with 64-bit integers.
- Is straightforward to reverse engineer. Datasets often live longer than
the programs that created them. A competent developer should be
able to create a solution in their preferred programming language to
read most ``.npy`` files that he has been given without much
documentation.
- Allows memory-mapping of the data. See `open_memmep`.
- Can be read from a filelike stream object instead of an actual file.
- Stores object arrays, i.e. arrays containing elements that are arbitrary
Python objects. Files with object arrays are not to be mmapable, but
can be read and written to disk.
Limitations
-----------
- Arbitrary subclasses of numpy.ndarray are not completely preserved.
Subclasses will be accepted for writing, but only the array data will
be written out. A regular numpy.ndarray object will be created
upon reading the file.
.. warning::
Due to limitations in the interpretation of structured dtypes, dtypes
with fields with empty names will have the names replaced by 'f0', 'f1',
etc. Such arrays will not round-trip through the format entirely
accurately. The data is intact; only the field names will differ. We are
working on a fix for this. This fix will not require a change in the
file format. The arrays with such structures can still be saved and
restored, and the correct dtype may be restored by using the
``loadedarray.view(correct_dtype)`` method.
File extensions
---------------
We recommend using the ``.npy`` and ``.npz`` extensions for files saved
in this format. This is by no means a requirement; applications may wish
to use these file formats but use an extension specific to the
application. In the absence of an obvious alternative, however,
we suggest using ``.npy`` and ``.npz``.
Version numbering
-----------------
The version numbering of these formats is independent of NumPy version
numbering. If the format is upgraded, the code in `numpy.io` will still
be able to read and write Version 1.0 files.
Format Version 1.0
------------------
The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.
The next 1 byte is an unsigned byte: the major version number of the file
format, e.g. ``\\x01``.
The next 1 byte is an unsigned byte: the minor version number of the file
format, e.g. ``\\x00``. Note: the version of the file format is not tied
to the version of the numpy package.
The next 2 bytes form a little-endian unsigned short int: the length of
the header data HEADER_LEN.
The next HEADER_LEN bytes form the header data describing the array's
format. It is an ASCII string which contains a Python literal expression
of a dictionary. It is terminated by a newline (``\\n``) and padded with
spaces (``\\x20``) to make the total length of
``magic string + 4 + HEADER_LEN`` be evenly divisible by 16 for alignment
purposes.
The dictionary contains three keys:
"descr" : dtype.descr
An object that can be passed as an argument to the `numpy.dtype`
constructor to create the array's dtype.
"fortran_order" : bool
Whether the array data is Fortran-contiguous or not. Since
Fortran-contiguous arrays are a common form of non-C-contiguity,
we allow them to be written directly to disk for efficiency.
"shape" : tuple of int
The shape of the array.
For repeatability and readability, the dictionary keys are sorted in
alphabetic order. This is for convenience only. A writer SHOULD implement
this if possible. A reader MUST NOT depend on this.
Following the header comes the array data. If the dtype contains Python
objects (i.e. ``dtype.hasobject is True``), then the data is a Python
pickle of the array. Otherwise the data is the contiguous (either C-
or Fortran-, depending on ``fortran_order``) bytes of the array.
Consumers can figure out the number of bytes by multiplying the number
of elements given by the shape (noting that ``shape=()`` means there is
1 element) by ``dtype.itemsize``.
Format Version 2.0
------------------
The version 1.0 format only allowed the array header to have a total size of
65535 bytes. This can be exceeded by structured arrays with a large number of
columns. The version 2.0 format extends the header size to 4 GiB.
`numpy.save` will automatically save in 2.0 format if the data requires it,
else it will always use the more compatible 1.0 format.
The description of the fourth element of the header therefore has become:
"The next 4 bytes form a little-endian unsigned int: the length of the header
data HEADER_LEN."
Notes
-----
The ``.npy`` format, including reasons for creating it and a comparison of
alternatives, is described fully in the "npy-format" NEP.
""" |
"""
HTTP Exception
--------------
This module processes Python exceptions that relate to HTTP exceptions
by defining a set of exceptions, all subclasses of HTTPException.
Each exception, in addition to being a Python exception that can be
raised and caught, is also a WSGI application and ``webob.Response``
object.
This module defines exceptions according to RFC 2068 [1]_ : codes with
100-300 are not really errors; 400's are client errors, and 500's are
server errors. According to the WSGI specification [2]_ , the application
can call ``start_response`` more then once only under two conditions:
(a) the response has not yet been sent, or (b) if the second and
subsequent invocations of ``start_response`` have a valid ``exc_info``
argument obtained from ``sys.exc_info()``. The WSGI specification then
requires the server or gateway to handle the case where content has been
sent and then an exception was encountered.
Exception
HTTPException
HTTPOk
* 200 - HTTPOk
* 201 - HTTPCreated
* 202 - HTTPAccepted
* 203 - HTTPNonAuthoritativeInformation
* 204 - HTTPNoContent
* 205 - HTTPResetContent
* 206 - HTTPPartialContent
HTTPRedirection
* 300 - HTTPMultipleChoices
* 301 - HTTPMovedPermanently
* 302 - HTTPFound
* 303 - HTTPSeeOther
* 304 - HTTPNotModified
* 305 - HTTPUseProxy
* 306 - Unused (not implemented, obviously)
* 307 - HTTPTemporaryRedirect
HTTPError
HTTPClientError
* 400 - HTTPBadRequest
* 401 - HTTPUnauthorized
* 402 - HTTPPaymentRequired
* 403 - HTTPForbidden
* 404 - HTTPNotFound
* 405 - HTTPMethodNotAllowed
* 406 - HTTPNotAcceptable
* 407 - HTTPProxyAuthenticationRequired
* 408 - HTTPRequestTimeout
* 409 - HTTPConflict
* 410 - HTTPGone
* 411 - HTTPLengthRequired
* 412 - HTTPPreconditionFailed
* 413 - HTTPRequestEntityTooLarge
* 414 - HTTPRequestURITooLong
* 415 - HTTPUnsupportedMediaType
* 416 - HTTPRequestRangeNotSatisfiable
* 417 - HTTPExpectationFailed
HTTPServerError
* 500 - HTTPInternalServerError
* 501 - HTTPNotImplemented
* 502 - HTTPBadGateway
* 503 - HTTPServiceUnavailable
* 504 - HTTPGatewayTimeout
* 505 - HTTPVersionNotSupported
Subclass usage notes:
---------------------
The HTTPException class is complicated by 4 factors:
1. The content given to the exception may either be plain-text or
as html-text.
2. The template may want to have string-substitutions taken from
the current ``environ`` or values from incoming headers. This
is especially troublesome due to case sensitivity.
3. The final output may either be text/plain or text/html
mime-type as requested by the client application.
4. Each exception has a default explanation, but those who
raise exceptions may want to provide additional detail.
Subclass attributes and call parameters are designed to provide an easier path
through the complications.
Attributes:
``code``
the HTTP status code for the exception
``title``
remainder of the status line (stuff after the code)
``explanation``
a plain-text explanation of the error message that is
not subject to environment or header substitutions;
it is accessible in the template via %(explanation)s
``detail``
a plain-text message customization that is not subject
to environment or header substitutions; accessible in
the template via %(detail)s
``body_template``
a content fragment (in HTML) used for environment and
header substitution; the default template includes both
the explanation and further detail provided in the
message
Parameters:
``detail``
a plain-text override of the default ``detail``
``headers``
a list of (k,v) header pairs
``comment``
a plain-text additional information which is
usually stripped/hidden for end-users
``body_template``
a string.Template object containing a content fragment in HTML
that frames the explanation and further detail
To override the template (which is HTML content) or the plain-text
explanation, one must subclass the given exception; or customize it
after it has been created. This particular breakdown of a message
into explanation, detail and template allows both the creation of
plain-text and html messages for various clients as well as
error-free substitution of environment variables and headers.
The subclasses of :class:`~_HTTPMove`
(:class:`~HTTPMultipleChoices`, :class:`~HTTPMovedPermanently`,
:class:`~HTTPFound`, :class:`~HTTPSeeOther`, :class:`~HTTPUseProxy` and
:class:`~HTTPTemporaryRedirect`) are redirections that require a ``Location``
field. Reflecting this, these subclasses have two additional keyword arguments:
``location`` and ``add_slash``.
Parameters:
``location``
to set the location immediately
``add_slash``
set to True to redirect to the same URL as the request, except with a
``/`` appended
Relative URLs in the location will be resolved to absolute.
References:
.. [1] http://www.python.org/peps/pep-0333.html#error-handling
.. [2] http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html#sec10.5
""" |
"""CPStats, a package for collecting and reporting on program statistics.
Overview
========
Statistics about program operation are an invaluable monitoring and debugging
tool. Unfortunately, the gathering and reporting of these critical values is
usually ad-hoc. This package aims to add a centralized place for gathering
statistical performance data, a structure for recording that data which
provides for extrapolation of that data into more useful information,
and a method of serving that data to both human investigators and
monitoring software. Let's examine each of those in more detail.
Data Gathering
--------------
Just as Python's `logging` module provides a common importable for gathering
and sending messages, performance statistics would benefit from a similar
common mechanism, and one that does *not* require each package which wishes
to collect stats to import a third-party module. Therefore, we choose to
re-use the `logging` module by adding a `statistics` object to it.
That `logging.statistics` object is a nested dict. It is not a custom class,
because that would:
1. require libraries and applications to import a third-party module in
order to participate
2. inhibit innovation in extrapolation approaches and in reporting tools, and
3. be slow.
There are, however, some specifications regarding the structure of the dict.::
{
+----"SQLAlchemy": {
| "Inserts": 4389745,
| "Inserts per Second":
| lambda s: s["Inserts"] / (time() - s["Start"]),
| C +---"Table Statistics": {
| o | "widgets": {-----------+
N | l | "Rows": 1.3M, | Record
a | l | "Inserts": 400, |
m | e | },---------------------+
e | c | "froobles": {
s | t | "Rows": 7845,
p | i | "Inserts": 0,
a | o | },
c | n +---},
e | "Slow Queries":
| [{"Query": "SELECT * FROM widgets;",
| "Processing Time": 47.840923343,
| },
| ],
+----},
}
The `logging.statistics` dict has four levels. The topmost level is nothing
more than a set of names to introduce modularity, usually along the lines of
package names. If the SQLAlchemy project wanted to participate, for example,
it might populate the item `logging.statistics['SQLAlchemy']`, whose value
would be a second-layer dict we call a "namespace". Namespaces help multiple
packages to avoid collisions over key names, and make reports easier to read,
to boot. The maintainers of SQLAlchemy should feel free to use more than one
namespace if needed (such as 'SQLAlchemy ORM'). Note that there are no case
or other syntax constraints on the namespace names; they should be chosen
to be maximally readable by humans (neither too short nor too long).
Each namespace, then, is a dict of named statistical values, such as
'Requests/sec' or 'Uptime'. You should choose names which will look
good on a report: spaces and capitalization are just fine.
In addition to scalars, values in a namespace MAY be a (third-layer)
dict, or a list, called a "collection". For example, the CherryPy
:class:`StatsTool` keeps track of what each request is doing (or has most
recently done) in a 'Requests' collection, where each key is a thread ID; each
value in the subdict MUST be a fourth dict (whew!) of statistical data about
each thread. We call each subdict in the collection a "record". Similarly,
the :class:`StatsTool` also keeps a list of slow queries, where each record
contains data about each slow query, in order.
Values in a namespace or record may also be functions, which brings us to:
Extrapolation
-------------
The collection of statistical data needs to be fast, as close to unnoticeable
as possible to the host program. That requires us to minimize I/O, for example,
but in Python it also means we need to minimize function calls. So when you
are designing your namespace and record values, try to insert the most basic
scalar values you already have on hand.
When it comes time to report on the gathered data, however, we usually have
much more freedom in what we can calculate. Therefore, whenever reporting
tools (like the provided :class:`StatsPage` CherryPy class) fetch the contents
of `logging.statistics` for reporting, they first call
`extrapolate_statistics` (passing the whole `statistics` dict as the only
argument). This makes a deep copy of the statistics dict so that the
reporting tool can both iterate over it and even change it without harming
the original. But it also expands any functions in the dict by calling them.
For example, you might have a 'Current Time' entry in the namespace with the
value "lambda scope: time.time()". The "scope" parameter is the current
namespace dict (or record, if we're currently expanding one of those
instead), allowing you access to existing static entries. If you're truly
evil, you can even modify more than one entry at a time.
However, don't try to calculate an entry and then use its value in further
extrapolations; the order in which the functions are called is not guaranteed.
This can lead to a certain amount of duplicated work (or a redesign of your
schema), but that's better than complicating the spec.
After the whole thing has been extrapolated, it's time for:
Reporting
---------
The :class:`StatsPage` class grabs the `logging.statistics` dict, extrapolates
it all, and then transforms it to HTML for easy viewing. Each namespace gets
its own header and attribute table, plus an extra table for each collection.
This is NOT part of the statistics specification; other tools can format how
they like.
You can control which columns are output and how they are formatted by updating
StatsPage.formatting, which is a dict that mirrors the keys and nesting of
`logging.statistics`. The difference is that, instead of data values, it has
formatting values. Use None for a given key to indicate to the StatsPage that a
given column should not be output. Use a string with formatting
(such as '%.3f') to interpolate the value(s), or use a callable (such as
lambda v: v.isoformat()) for more advanced formatting. Any entry which is not
mentioned in the formatting dict is output unchanged.
Monitoring
----------
Although the HTML output takes pains to assign unique id's to each <td> with
statistical data, you're probably better off fetching /cpstats/data, which
outputs the whole (extrapolated) `logging.statistics` dict in JSON format.
That is probably easier to parse, and doesn't have any formatting controls,
so you get the "original" data in a consistently-serialized format.
Note: there's no treatment yet for datetime objects. Try time.time() instead
for now if you can. Nagios will probably thank you.
Turning Collection Off
----------------------
It is recommended each namespace have an "Enabled" item which, if False,
stops collection (but not reporting) of statistical data. Applications
SHOULD provide controls to pause and resume collection by setting these
entries to False or True, if present.
Usage
=====
To collect statistics on CherryPy applications::
from cherrypy.lib import cpstats
appconfig['/']['tools.cpstats.on'] = True
To collect statistics on your own code::
import logging
# Initialize the repository
if not hasattr(logging, 'statistics'): logging.statistics = {}
# Initialize my namespace
mystats = logging.statistics.setdefault('My Stuff', {})
# Initialize my namespace's scalars and collections
mystats.update({
'Enabled': True,
'Start Time': time.time(),
'Important Events': 0,
'Events/Second': lambda s: (
(s['Important Events'] / (time.time() - s['Start Time']))),
})
...
for event in events:
...
# Collect stats
if mystats.get('Enabled', False):
mystats['Important Events'] += 1
To report statistics::
root.cpstats = cpstats.StatsPage()
To format statistics reports::
See 'Reporting', above.
""" |
"""
==================================
Constants (:mod:`scipy.constants`)
==================================
.. currentmodule:: scipy.constants
Physical and mathematical constants and units.
Mathematical constants
======================
============ =================================================================
``pi`` Pi
``golden`` Golden ratio
============ =================================================================
Physical constants
==================
============= =================================================================
``c`` speed of light in vacuum
``mu_0`` the magnetic constant :math:`\mu_0`
``epsilon_0`` the electric constant (vacuum permittivity), :math:`\epsilon_0`
``h`` the Planck constant :math:`h`
``hbar`` :math:`\hbar = h/(2\pi)`
``G`` Newtonian constant of gravitation
``g`` standard acceleration of gravity
``e`` elementary charge
``R`` molar gas constant
``alpha`` fine-structure constant
``N_A`` Avogadro constant
``k`` Boltzmann constant
``sigma`` Stefan-Boltzmann constant :math:`\sigma`
``Wien`` Wien displacement law constant
``Rydberg`` Rydberg constant
``m_e`` electron mass
``m_p`` proton mass
``m_n`` neutron mass
============= =================================================================
Constants database
------------------
In addition to the above variables, :mod:`scipy.constants` also contains the
2010 CODATA recommended values [CODATA2010]_ database containing more physical
constants.
.. autosummary::
:toctree: generated/
value -- Value in physical_constants indexed by key
unit -- Unit in physical_constants indexed by key
precision -- Relative precision in physical_constants indexed by key
find -- Return list of physical_constant keys with a given string
ConstantWarning -- Constant sought not in newest CODATA data set
.. data:: physical_constants
Dictionary of physical constants, of the format
``physical_constants[name] = (value, unit, uncertainty)``.
Available constants:
====================================================================== ====
%(constant_names)s
====================================================================== ====
Units
=====
SI prefixes
-----------
============ =================================================================
``yotta`` :math:`10^{24}`
``zetta`` :math:`10^{21}`
``exa`` :math:`10^{18}`
``peta`` :math:`10^{15}`
``tera`` :math:`10^{12}`
``giga`` :math:`10^{9}`
``mega`` :math:`10^{6}`
``kilo`` :math:`10^{3}`
``hecto`` :math:`10^{2}`
``deka`` :math:`10^{1}`
``deci`` :math:`10^{-1}`
``centi`` :math:`10^{-2}`
``milli`` :math:`10^{-3}`
``micro`` :math:`10^{-6}`
``nano`` :math:`10^{-9}`
``pico`` :math:`10^{-12}`
``femto`` :math:`10^{-15}`
``atto`` :math:`10^{-18}`
``zepto`` :math:`10^{-21}`
============ =================================================================
Binary prefixes
---------------
============ =================================================================
``kibi`` :math:`2^{10}`
``mebi`` :math:`2^{20}`
``gibi`` :math:`2^{30}`
``tebi`` :math:`2^{40}`
``pebi`` :math:`2^{50}`
``exbi`` :math:`2^{60}`
``zebi`` :math:`2^{70}`
``yobi`` :math:`2^{80}`
============ =================================================================
Weight
------
================= ============================================================
``gram`` :math:`10^{-3}` kg
``metric_ton`` :math:`10^{3}` kg
``grain`` one grain in kg
``lb`` one pound (avoirdupous) in kg
``oz`` one ounce in kg
``stone`` one stone in kg
``grain`` one grain in kg
``long_ton`` one long ton in kg
``short_ton`` one short ton in kg
``troy_ounce`` one Troy ounce in kg
``troy_pound`` one Troy pound in kg
``carat`` one carat in kg
``m_u`` atomic mass constant (in kg)
================= ============================================================
Angle
-----
================= ============================================================
``degree`` degree in radians
``arcmin`` arc minute in radians
``arcsec`` arc second in radians
================= ============================================================
Time
----
================= ============================================================
``minute`` one minute in seconds
``hour`` one hour in seconds
``day`` one day in seconds
``week`` one week in seconds
``year`` one year (365 days) in seconds
``Julian_year`` one Julian year (365.25 days) in seconds
================= ============================================================
Length
------
================= ============================================================
``inch`` one inch in meters
``foot`` one foot in meters
``yard`` one yard in meters
``mile`` one mile in meters
``mil`` one mil in meters
``pt`` one point in meters
``survey_foot`` one survey foot in meters
``survey_mile`` one survey mile in meters
``nautical_mile`` one nautical mile in meters
``fermi`` one Fermi in meters
``angstrom`` one Angstrom in meters
``micron`` one micron in meters
``au`` one astronomical unit in meters
``light_year`` one light year in meters
``parsec`` one parsec in meters
================= ============================================================
Pressure
--------
================= ============================================================
``atm`` standard atmosphere in pascals
``bar`` one bar in pascals
``torr`` one torr (mmHg) in pascals
``psi`` one psi in pascals
================= ============================================================
Area
----
================= ============================================================
``hectare`` one hectare in square meters
``acre`` one acre in square meters
================= ============================================================
Volume
------
=================== ========================================================
``liter`` one liter in cubic meters
``gallon`` one gallon (US) in cubic meters
``gallon_imp`` one gallon (UK) in cubic meters
``fluid_ounce`` one fluid ounce (US) in cubic meters
``fluid_ounce_imp`` one fluid ounce (UK) in cubic meters
``bbl`` one barrel in cubic meters
=================== ========================================================
Speed
-----
================= ==========================================================
``kmh`` kilometers per hour in meters per second
``mph`` miles per hour in meters per second
``mach`` one Mach (approx., at 15 C, 1 atm) in meters per second
``knot`` one knot in meters per second
================= ==========================================================
Temperature
-----------
===================== =======================================================
``zero_Celsius`` zero of Celsius scale in Kelvin
``degree_Fahrenheit`` one Fahrenheit (only differences) in Kelvins
===================== =======================================================
.. autosummary::
:toctree: generated/
C2K
K2C
F2C
C2F
F2K
K2F
Energy
------
==================== =======================================================
``eV`` one electron volt in Joules
``calorie`` one calorie (thermochemical) in Joules
``calorie_IT`` one calorie (International Steam Table calorie, 1956) in Joules
``erg`` one erg in Joules
``Btu`` one British thermal unit (International Steam Table) in Joules
``Btu_th`` one British thermal unit (thermochemical) in Joules
``ton_TNT`` one ton of TNT in Joules
==================== =======================================================
Power
-----
==================== =======================================================
``hp`` one horsepower in watts
==================== =======================================================
Force
-----
==================== =======================================================
``dyn`` one dyne in newtons
``lbf`` one pound force in newtons
``kgf`` one kilogram force in newtons
==================== =======================================================
Optics
------
.. autosummary::
:toctree: generated/
lambda2nu
nu2lambda
References
==========
.. [CODATA2010] CODATA Recommended Values of the Fundamental
Physical Constants 2010.
http://physics.nist.gov/cuu/Constants/index.html
""" |
"""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.
""" |
# (c) 2013, NAME <EMAIL> red hat, inc
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
# take a list of files and (optionally) a list of paths
# return the first existing file found in the paths
# [file1, file2, file3], [path1, path2, path3]
# search order is:
# path1/file1
# path1/file2
# path1/file3
# path2/file1
# path2/file2
# path2/file3
# path3/file1
# path3/file2
# path3/file3
# first file found with os.path.exists() is returned
# no file matches raises ansibleerror
# EXAMPLES
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: foo ${inventory_hostname} bar
# paths: /tmp/production /tmp/staging
# that will look for files in this order:
# /tmp/production/foo
# ${inventory_hostname}
# bar
# /tmp/staging/foo
# ${inventory_hostname}
# bar
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: /some/place/foo ${inventory_hostname} /some/place/else
# that will look for files in this order:
# /some/place/foo
# $relative_path/${inventory_hostname}
# /some/place/else
# example - including tasks:
# tasks:
# - include: $item
# with_first_found:
# - files: generic
# paths: tasks/staging tasks/production
# this will include the tasks in the file generic where it is found first (staging or production)
# example simple file lists
#tasks:
#- name: first found file
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname} foo
# example skipping if no matched files
# First_found also offers the ability to control whether or not failing
# to find a file returns an error or not
#
#- name: first found file - or skip
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname}
# skip: true
# example a role with default configuration and configuration per host
# you can set multiple terms with their own files and paths to look through.
# consider a role that sets some configuration per host falling back on a default config.
#
#- name: some configuration template
# template: src={{ item }} dest=/etc/file.cfg mode=0444 owner=root group=root
# with_first_found:
# - files:
# - ${inventory_hostname}/etc/file.cfg
# paths:
# - ../../../templates.overwrites
# - ../../../templates
# - files:
# - etc/file.cfg
# paths:
# - templates
# the above will return an empty list if the files cannot be found at all
# if skip is unspecificed or if it is set to false then it will return a list
# error which can be caught bye ignore_errors: true for that action.
# finally - if you want you can use it, in place to replace first_available_file:
# you simply cannot use the - files, path or skip options. simply replace
# first_available_file with with_first_found and leave the file listing in place
#
#
# - name: with_first_found like first_available_file
# action: copy src=$item dest=/tmp/faftest
# with_first_found:
# - ../files/foo
# - ../files/bar
# - ../files/baz
# ignore_errors: true
|
"""
=============================
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__`` receives 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``.
Subclassing and Downstream Compatibility
----------------------------------------
When sub-classing ``ndarray`` or creating duck-types that mimic the ``ndarray``
interface, it is your responsibility to decide how aligned your APIs will be
with those of numpy. For convenience, many numpy functions that have a corresponding
``ndarray`` method (e.g., ``sum``, ``mean``, ``take``, ``reshape``) work by checking
if the first argument to a function has a method of the same name. If it exists, the
method is called instead of coercing the arguments to a numpy array.
For example, if you want your sub-class or duck-type to be compatible with
numpy's ``sum`` function, the method signature for this object's ``sum`` method
should be the following:
.. testcode::
def sum(self, axis=None, dtype=None, out=None, keepdims=False):
...
This is the exact same method signature for ``np.sum``, so now if a user calls
``np.sum`` on this object, numpy will call the object's own ``sum`` method and
pass in these arguments enumerated above in the signature, and no errors will
be raised because the signatures are completely compatible with each other.
If, however, you decide to deviate from this signature and do something like this:
.. testcode::
def sum(self, axis=None, dtype=None):
...
This object is no longer compatible with ``np.sum`` because if you call ``np.sum``,
it will pass in unexpected arguments ``out`` and ``keepdims``, causing a TypeError
to be raised.
If you wish to maintain compatibility with numpy and its subsequent versions (which
might add new keyword arguments) but do not want to surface all of numpy's arguments,
your function's signature should accept ``**kwargs``. For example:
.. testcode::
def sum(self, axis=None, dtype=None, **unused_kwargs):
...
This object is now compatible with ``np.sum`` again because any extraneous arguments
(i.e. keywords that are not ``axis`` or ``dtype``) will be hidden away in the
``**unused_kwargs`` parameter.
""" |
"""
mGameController 0.5
GitHub Page: https://github.com/thedixieflatline/mGameController
mGameController an app for the game Assetto Corsa.
Provides the ability to get control inputs from game devices in Assetto Corsa
App developed by NAME submit bugs or requests to the Assetto Corsa forum
http://www.assettocorsa.net/forum/index.php
You will need to use the Pygame I have supplied in the source as it is compiled to run in Python 3.3
To activate copy mGameController folder to C:\Program Files (x86)\Steam\steamapps\common\assettocorsa\apps\python
This app is more of a tutorial on how to do this. I have developed this for something I am working on in my own Assetto Corsa apps.
I started out to try to find a way to get keyboard an mouse events going with my apps
Pygame does support this through event but because it is based on SDL the event do not fire if there is not a render window running which I do not want to pop up when the game is going
But I found that becasue game devices are separate becasue they are usb they have an independent connection to the event queue they can run windowless
I did most of the code below trying to work out the issues and decide which way to use the features
I thought other app developers would appreciate knowing how to do this and perhaps even use the technique.
I will continue to develop this into a more generic class to handle a lot of this so developers can just plug and go on more easily integrate it into their own work if they wish.
If there is a lot of interest I might even look at adding in serial device support in the future
This code is setup to be a demo and also I have combined some logic together rather than split it all up to make it easier to read
Also some of the processing in the main loop could be moved out and some values stored on start up and not updated every frame
Again this was for readability and to make it easier to follow as well as to show beginners how to handle the inputs and how to scan for and work changes to capabilities of using different devices
I have commented throughout but the best place to start the tutorial from is the acUpdate function near the bottom
First read the comments in acUpdate that explain the 2 ways to get inputs and the pros and cons and features of each method
Then go and look at the 2 different class descriptions. Start with class GameController and then read class DisplayClass
Take note of what happens when each class is initialized and the secondary init of DisplayClass
DisplayClass also contains the 2 functions that run the program and do all of the work.
Here I have put the Pygame object method calls for convenience but you can check out the full docs here
Pygame docs online here http://www.pygame.org/docs/
Possible joystick event types: JOYAXISMOTION JOYBALLMOTION JOYBUTTONDOWN JOYBUTTONUP JOYHATMOTION
JOYAXISMOTION joy, axis, value
JOYBALLMOTION joy, ball, rel
JOYHATMOTION joy, hat, value
JOYBUTTONUP joy, button
JOYBUTTONDOWN joy, button
Top level joystick class methods
pygame.joystick.init — Initialize the joystick module.
pygame.joystick.quit — Uninitialize the joystick module.
pygame.joystick.get_init — Returns True if the joystick module is initialized.
pygame.joystick.get_count — Returns the number of joysticks.
joystick object instance methods
pygame.joystick.Joystick.init — initialize the Joystick
pygame.joystick.Joystick.quit — uninitialize the Joystick
pygame.joystick.Joystick.get_init — check if the Joystick is initialized
pygame.joystick.Joystick.get_id — get the Joystick ID
pygame.joystick.Joystick.get_name — get the Joystick system name
pygame.joystick.Joystick.get_numaxes — get the number of axes on a Joystick
pygame.joystick.Joystick.get_axis — get the current position of an axis
pygame.joystick.Joystick.get_numballs — get the number of trackballs on a Joystick
pygame.joystick.Joystick.get_ball — get the relative position of a trackball
pygame.joystick.Joystick.get_numbuttons — get the number of buttons on a Joystick
pygame.joystick.Joystick.get_button — get the current button state
pygame.joystick.Joystick.get_numhats — get the number of hat controls on a Joystick
pygame.joystick.Joystick.get_hat — get the position of a joystick hat
""" |
"""Stuff to parse Sun and NeXT audio files.
An audio file consists of a header followed by the data. The structure
of the header is as follows.
+---------------+
| magic word |
+---------------+
| header size |
+---------------+
| data size |
+---------------+
| encoding |
+---------------+
| sample rate |
+---------------+
| # of channels |
+---------------+
| info |
| |
+---------------+
The magic word consists of the 4 characters '.snd'. Apart from the
info field, all header fields are 4 bytes in size. They are all
32-bit unsigned integers encoded in big-endian byte order.
The header size really gives the start of the data.
The data size is the physical size of the data. From the other
parameters the number of frames can be calculated.
The encoding gives the way in which audio samples are encoded.
Possible values are listed below.
The info field currently consists of an ASCII string giving a
human-readable description of the audio file. The info field is
padded with NUL bytes to the header size.
Usage.
Reading audio files:
f = sunau.open(file, 'r')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods read(), seek(), and close().
When the setpos() and rewind() methods are not used, the seek()
method is not necessary.
This returns an instance of a class with the following public methods:
getnchannels() -- returns number of audio channels (1 for
mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getcomptype() -- returns compression type ('NONE' or 'ULAW')
getcompname() -- returns human-readable version of
compression type ('not compressed' matches 'NONE')
getparams() -- returns a tuple consisting of all of the
above in the above order
getmarkers() -- returns None (for compatibility with the
aifc module)
getmark(id) -- raises an error since the mark does not
exist (for compatibility with the aifc module)
readframes(n) -- returns at most n frames of audio
rewind() -- rewind to the beginning of the audio stream
setpos(pos) -- seek to the specified position
tell() -- return the current position
close() -- close the instance (make it unusable)
The position returned by tell() and the position given to setpos()
are compatible and have nothing to do with the actual position in the
file.
The close() method is called automatically when the class instance
is destroyed.
Writing audio files:
f = sunau.open(file, 'w')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods write(), tell(), seek(), and
close().
This returns an instance of a class with the following public methods:
setnchannels(n) -- set the number of channels
setsampwidth(n) -- set the sample width
setframerate(n) -- set the frame rate
setnframes(n) -- set the number of frames
setcomptype(type, name)
-- set the compression type and the
human-readable compression type
setparams(tuple)-- set all parameters at once
tell() -- return current position in output file
writeframesraw(data)
-- write audio frames without pathing up the
file header
writeframes(data)
-- write audio frames and patch up the file header
close() -- patch up the file header and close the
output file
You should set the parameters before the first writeframesraw or
writeframes. The total number of frames does not need to be set,
but when it is set to the correct value, the header does not have to
be patched up.
It is best to first set all parameters, perhaps possibly the
compression type, and then write audio frames using writeframesraw.
When all frames have been written, either call writeframes('') or
close() to patch up the sizes in the header.
The close() method is called automatically when the class instance
is destroyed.
""" |
"""
Linear algebra
--------------
Linear equations
................
Basic linear algebra is implemented; you can for example solve the linear
equation system::
x + 2*y = -10
3*x + 4*y = 10
using ``lu_solve``::
>>> A = matrix([[1, 2], [3, 4]])
>>> b = matrix([-10, 10])
>>> x = lu_solve(A, b)
>>> x
matrix(
[['30.0'],
['-20.0']])
If you don't trust the result, use ``residual`` to calculate the residual ||A*x-b||::
>>> residual(A, x, b)
matrix(
[['3.46944695195361e-18'],
['3.46944695195361e-18']])
>>> str(eps)
'2.22044604925031e-16'
As you can see, the solution is quite accurate. The error is caused by the
inaccuracy of the internal floating point arithmetic. Though, it's even smaller
than the current machine epsilon, which basically means you can trust the
result.
If you need more speed, use NumPy. Or choose a faster data type using the
keyword ``force_type``::
>>> lu_solve(A, b, force_type=float)
matrix(
[[29.999999999999996],
[-19.999999999999996]])
``lu_solve`` accepts overdetermined systems. It is usually not possible to solve
such systems, so the residual is minimized instead. Internally this is done
using Cholesky decomposition to compute a least squares approximation. This means
that that ``lu_solve`` will square the errors. If you can't afford this, use
``qr_solve`` instead. It is twice as slow but more accurate, and it calculates
the residual automatically.
Matrix factorization
....................
The function ``lu`` computes an explicit LU factorization of a matrix::
>>> P, L, U = lu(matrix([[0,2,3],[4,5,6],[7,8,9]]))
>>> print P
[0.0 0.0 1.0]
[1.0 0.0 0.0]
[0.0 1.0 0.0]
>>> print L
[ 1.0 0.0 0.0]
[ 0.0 1.0 0.0]
[0.571428571428571 0.214285714285714 1.0]
>>> print U
[7.0 8.0 9.0]
[0.0 2.0 3.0]
[0.0 0.0 0.214285714285714]
>>> print P.T*L*U
[0.0 2.0 3.0]
[4.0 5.0 6.0]
[7.0 8.0 9.0]
Interval matrices
-----------------
Matrices may contain interval elements. This allows one to perform
basic linear algebra operations such as matrix multiplication
and equation solving with rigorous error bounds::
>>> a = matrix([['0.1','0.3','1.0'],
... ['7.1','5.5','4.8'],
... ['3.2','4.4','5.6']], force_type=mpi)
>>>
>>> b = matrix(['4','0.6','0.5'], force_type=mpi)
>>> c = lu_solve(a, b)
>>> c
matrix(
[[[5.2582327113062393041, 5.2582327113062749951]],
[[-13.155049396267856583, -13.155049396267821167]],
[[7.4206915477497212555, 7.4206915477497310922]]])
>>> print a*c
[ [3.9999999999999866773, 4.0000000000000133227]]
[[0.59999999999972430942, 0.60000000000027142733]]
[[0.49999999999982236432, 0.50000000000018474111]]
""" |
# #!/usr/bin/env python-sirius
# """BSMP class tests."""
# import unittest
# from unittest.mock import Mock
#
# from siriuspy.util import check_public_interface_namespace
#
#
# class _TestBSMP(TestCase):
# """Test BSMP class."""
#
# api = (
# 'ID_device',
# 'variables',
# 'functions',
# 'parse_stream',
# )
#
# def test_api(self):
# """Test API."""
# self.assertTrue(check_public_interface_namespace(BSMP, TestBSMP.api))
#
# def test_init(self):
# """Test parameters are initialized correctly."""
# id_device = 0
# variables = {'var1': 'val1', 'var2': 'val2'}
# functions = {'func1': print, 'func2': BSMP.parse_stream}
#
# bsmp = BSMP(id_device, variables, functions)
#
# self.assertEqual(bsmp.ID_device, 0)
# self.assertEqual(bsmp.variables, variables)
# self.assertEqual(bsmp.functions, functions)
#
# def test_parse_stream_small_stream(self):
# """Test ValueError is raised when stream is too small."""
# with self.assertRaises(ValueError):
# BSMP.parse_stream(['\x30', '\x00', '\x25', '\x00'])
#
# def test_parse_stream_no_checksum(self):
# """Test ValueError is raised when checksum fails."""
# stream = ['\x30', '\x00', '\x25', '\x00', '\x00', '\x00']
# with self.assertRaises(ValueError):
# BSMP.parse_stream(stream)
#
# def test_parse_stream(self):
# """Test stream is correctly parsed."""
# stream = ['\x00', '\x0A', '\x02', '\x00', '\x00', '\x00']
# stream = StreamChecksum.includeChecksum(stream)
# id_receiver, id_cmd, load_size, load = BSMP.parse_stream(stream)
# # print(id_receiver, id_cmd, load_size, load)
# self.assertEqual(id_receiver, '\x00')
# self.assertEqual(id_cmd, 10)
# self.assertEqual(load_size, 2)
# self.assertEqual(load, ['\x00', '\x00'])
#
#
# class _TestBSMPQuery(TestCase):
# """Test BSMPQuery class."""
#
# api = (
# 'slaves',
# 'add_slave',
# 'cmd_0x00',
# 'cmd_0x10',
# 'cmd_0x12',
# 'cmd_0x30',
# 'cmd_0x32',
# 'cmd_0x50'
# )
#
# def setUp(self):
# """Common setup for all tests."""
# self.variables = {'var1': 'val1', 'var2': 'val2'}
# self.functions = {'func1': print, 'func2': BSMP.parse_stream}
# self.slaves = list()
# for i in range(3):
# mock = Mock()
# id_device = PropertyMock(return_value=i+1)
# type(mock).ID_device = id_device
# self.slaves.append(mock)
#
# self.bsmp = BSMPQuery(self.variables, self.functions, self.slaves)
#
# def test_api(self):
# """Test API."""
# self.assertTrue(
# check_public_interface_namespace(BSMPQuery, TestBSMPQuery.api))
#
# def test_init(self):
# """Test initial values passed in the constructor."""
# self.assertEqual(self.bsmp.ID_device, 0)
# self.assertEqual(self.bsmp.variables, self.variables)
# self.assertEqual(self.bsmp.functions, self.functions)
#
# def test_cmd_0x00(self):
# """Test query is called with correct parameters."""
# self.bsmp.cmd_0x00(1)
# self.bsmp.slaves[1].query.assert_called_with(0x00, ID_receiver=1)
#
# def test_cmd_0x10(self):
# """Test query is called with correct parameters."""
# self.bsmp.cmd_0x10(1, 2)
# self.bsmp.slaves[1].query.assert_called_with(
# 0x10, ID_receiver=1, ID_variable=2)
#
# def test_cmd_0x12(self):
# """Test query is called with correct parameters."""
# self.bsmp.cmd_0x12(2, 1)
# self.bsmp.slaves[2].query.assert_called_with(
# 0x12, ID_receiver=2, ID_group=1)
#
# def test_cmd_0x30(self):
# """Test query is called with correct parameters."""
# self.bsmp.cmd_0x30(3, 4, 2)
# self.bsmp.slaves[3].query.assert_called_with(
# 0x30, ID_receiver=3, ID_group=4, IDs_variable=2)
#
# def test_cmd_0x30_exc(self):
# """Test query is called with correct parameters."""
# with self.assertRaises(ValueError):
# self.bsmp.cmd_0x30(3, 2, 2)
#
# def test_cmd_0x32(self):
# """Test query is called with correct parameters."""
# self.bsmp.cmd_0x32(3)
# self.bsmp.slaves[3].query.assert_called_with(0x32, ID_receiver=3)
#
# def test_cmd_0x50(self):
# """Test query is called with correct parameters."""
# self.bsmp.cmd_0x50(ID_receiver=1)
# self.bsmp.slaves[1].query.assert_called_with(0x50, ID_receiver=1)
#
#
# class _TestBSMPResponse(TestCase):
# """Test BSMPResponse class."""
#
# api = (
# 'query',
# 'create_group_of_variables',
# 'remove_groups',
# 'cmd_0x01',
# 'cmd_0x11',
# 'cmd_0x13',
# 'cmd_0x51'
# )
#
# def test_api(self):
# """Test API."""
# self.assertTrue(
# check_public_interface_namespace(
# BSMPResponse, TestBSMPResponse.api))
#
# def test_query_2_resp(self):
# """Test mapping from query to response is correct."""
# self.assertEqual(BSMPResponse._query2resp[0x00], 'cmd_0x01')
# self.assertEqual(BSMPResponse._query2resp[0x10], 'cmd_0x11')
# self.assertEqual(BSMPResponse._query2resp[0x12], 'cmd_0x13')
# self.assertEqual(BSMPResponse._query2resp[0x30], 'create_group_of_variables')
# self.assertEqual(BSMPResponse._query2resp[0x32], 'remove_groups')
# self.assertEqual(BSMPResponse._query2resp[0x50], 'cmd_0x51')
|
"""
==============
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 remaining dimension of lenth 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 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 unusal uses, but theyare permitted, and they are useful for some
problems. We'll start with thesimplest 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 resulant 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 array being indexed, or broadcastable to the same shape. 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])
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. As
with index arrays, what is returned is a copy of the data, not a view
as one gets with slices.
With broadcasting, multidimensional arrays may be the result. For
example: ::
>>> b[:,5] # use a 1-D boolean that broadcasts with 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.
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
""" |
# -*- 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
|
#!/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/env python
# txt2tags - generic text conversion tool
# http://txt2tags.sf.net
#
# Copyright 2001, 2002, 2003, 2004, 2005, 2006 NAME 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, version 2.
#
# 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 have received a copy of the GNU General Public License along
# with this program, on the COPYING file.
#
########################################################################
#
# BORING CODE EXPLANATION AHEAD
#
# Just read if you wish to understand how the txt2tags code works
#
########################################################################
#
# Version 2.0 was a complete rewrite for the program 'core'.
#
# Now the code that [1] parses the marked text is separated from the
# code that [2] insert the target tags.
#
# [1] made by: def convert()
# [2] made by: class BlockMaster
#
# The structures of the marked text are identified and its contents are
# extracted into a data holder (Python lists and dictionaries).
#
# When parsing the source file, the blocks (para, lists, quote, table)
# are opened with BlockMaster, right when found. Then its contents,
# which spans on several lines, are feeded into a special holder on the
# BlockMaster instance. Just when the block is closed, the target tags
# are inserted for the full block as a whole, in one pass. This way, we
# have a better control on blocks. Much better than the previous line by
# line approach.
#
# In other words, whenever inside a block, the parser *holds* the tag
# insertion process, waiting until the full block is read. That was
# needed primary to close paragraphs for the new XHTML target, but
# proved to be a very good adding, improving many other processing.
#
# -------------------------------------------------------------------
#
# There is also a brand new code for the Configuration schema, 100%
# rewritten. There are new classes, all self documented: CommandLine,
# SourceDocument, ConfigMaster and ConfigLines. In short, a new RAW
# Config format was created, and all kind of configuration is first
# converted to this format, and then a generic method parses it.
#
# The init processing was changed also, and now the functions which
# gets informations about the input files are: get_infiles_config(),
# process_source_file() and convert_this_files()
#
# Other parts are untouched, and remains the same as in v1.7, as the
# marks regexes, target Headers and target Tags & Rules.
#
########################################################################
# Now I think the code is nice, easier to read and understand
#XXX Python coding warning
# Avoid common mistakes:
# - do NOT use newlist=list instead newlist=list[:]
# - do NOT use newdic=dic instead newdic=dic.copy()
# - do NOT use dic[key] instead dic.get(key)
# - do NOT use del dic[key] without has_key() before
#XXX Smart Image Align don't work if the image is a link
# Can't fix that because the image is expanded together with the
# link, at the linkbank filling moment. Only the image is passed
# to parse_images(), not the full line, so it is always 'middle'.
#XXX Paragraph separation not valid inside Quote
# Quote will not have <p></p> inside, instead will close and open
# again the <blockquote>. This really sux in CSS, when defining a
# different background color. Still don't know how to fix it.
#XXX TODO (maybe)
# New mark or macro which expands to an anchor full title.
# It is necessary to parse the full document in this order:
# DONE 1st scan: HEAD: get all settings, including %!includeconf
# DONE 2nd scan: BODY: expand includes & apply %!preproc
# 3rd scan: BODY: read titles and compose TOC info
# 4th scan: BODY: full parsing, expanding [#anchor] 1st
# Steps 2 and 3 can be made together, with no tag adding.
# Two complete body scans will be *slow*, don't know if it worths.
# One solution may be add the titles as postproc rules
##############################################################################
# User config (1=ON, 0=OFF)
|
# (c) 2013, NAME <EMAIL> red hat, inc
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
# take a list of files and (optionally) a list of paths
# return the first existing file found in the paths
# [file1, file2, file3], [path1, path2, path3]
# search order is:
# path1/file1
# path1/file2
# path1/file3
# path2/file1
# path2/file2
# path2/file3
# path3/file1
# path3/file2
# path3/file3
# first file found with os.path.exists() is returned
# no file matches raises ansibleerror
# EXAMPLES
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: foo ${inventory_hostname} bar
# paths: /tmp/production /tmp/staging
# that will look for files in this order:
# /tmp/production/foo
# ${inventory_hostname}
# bar
# /tmp/staging/foo
# ${inventory_hostname}
# bar
# - name: copy first existing file found to /some/file
# action: copy src=$item dest=/some/file
# with_first_found:
# - files: /some/place/foo ${inventory_hostname} /some/place/else
# that will look for files in this order:
# /some/place/foo
# $relative_path/${inventory_hostname}
# /some/place/else
# example - including tasks:
# tasks:
# - include: $item
# with_first_found:
# - files: generic
# paths: tasks/staging tasks/production
# this will include the tasks in the file generic where it is found first (staging or production)
# example simple file lists
#tasks:
#- name: first found file
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname} foo
# example skipping if no matched files
# First_found also offers the ability to control whether or not failing
# to find a file returns an error or not
#
#- name: first found file - or skip
# action: copy src=$item dest=/etc/file.cfg
# with_first_found:
# - files: foo.${inventory_hostname}
# skip: true
# example a role with default configuration and configuration per host
# you can set multiple terms with their own files and paths to look through.
# consider a role that sets some configuration per host falling back on a default config.
#
#- name: some configuration template
# template: src={{ item }} dest=/etc/file.cfg mode=0444 owner=root group=root
# with_first_found:
# - files:
# - ${inventory_hostname}/etc/file.cfg
# paths:
# - ../../../templates.overwrites
# - ../../../templates
# - files:
# - etc/file.cfg
# paths:
# - templates
# the above will return an empty list if the files cannot be found at all
# if skip is unspecificed or if it is set to false then it will return a list
# error which can be caught bye ignore_errors: true for that action.
# finally - if you want you can use it, in place to replace first_available_file:
# you simply cannot use the - files, path or skip options. simply replace
# first_available_file with with_first_found and leave the file listing in place
#
#
# - name: with_first_found like first_available_file
# action: copy src=$item dest=/tmp/faftest
# with_first_found:
# - ../files/foo
# - ../files/bar
# - ../files/baz
# ignore_errors: true
|
#
# ElementTree
# $Id: ElementTree.py 2326 2005-03-17 07:45:21Z USERNAME $
#
# light-weight XML support for Python 1.5.2 and later.
#
# history:
# 2001-10-20 fl created (from various sources)
# 2001-11-01 fl return root from parse method
# 2002-02-16 fl sort attributes in lexical order
# 2002-04-06 fl TreeBuilder refactoring, added PythonDoc markup
# 2002-05-01 fl finished TreeBuilder refactoring
# 2002-07-14 fl added basic namespace support to ElementTree.write
# 2002-07-25 fl added QName attribute support
# 2002-10-20 fl fixed encoding in write
# 2002-11-24 fl changed default encoding to ascii; fixed attribute encoding
# 2002-11-27 fl accept file objects or file names for parse/write
# 2002-12-04 fl moved XMLTreeBuilder back to this module
# 2003-01-11 fl fixed entity encoding glitch for us-ascii
# 2003-02-13 fl added XML literal factory
# 2003-02-21 fl added ProcessingInstruction/PI factory
# 2003-05-11 fl added tostring/fromstring helpers
# 2003-05-26 fl added ElementPath support
# 2003-07-05 fl added makeelement factory method
# 2003-07-28 fl added more well-known namespace prefixes
# 2003-08-15 fl fixed typo in ElementTree.findtext (Thomas NAME 2003-09-04 fl fall back on emulator if ElementPath is not installed
# 2003-10-31 fl markup updates
# 2003-11-15 fl fixed nested namespace bug
# 2004-03-28 fl added XMLID helper
# 2004-06-02 fl added default support to findtext
# 2004-06-08 fl fixed encoding of non-ascii element/attribute names
# 2004-08-23 fl take advantage of post-2.1 expat features
# 2005-02-01 fl added iterparse implementation
# 2005-03-02 fl fixed iterparse support for pre-2.2 versions
# 2012-06-29 EMAIL Made all classes new-style
# 2012-07-02 EMAIL Include dist. ElementPath
# 2013-02-27 EMAIL renamed module files, kept namespace.
#
# Copyright (c) 1999-2005 by NAME All rights reserved.
#
# EMAIL
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2005 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
# Licensed to PSF under a Contributor Agreement.
# See http://www.python.org/2.4/license for licensing details.
|
"""
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.
""" |
"""Exception classes for CherryPy.
CherryPy provides (and uses) exceptions for declaring that the HTTP response
should be a status other than the default "200 OK". You can ``raise`` them like
normal Python exceptions. You can also call them and they will raise
themselves; this means you can set an
:class:`HTTPError<cherrypy._cperror.HTTPError>`
or :class:`HTTPRedirect<cherrypy._cperror.HTTPRedirect>` as the
:attr:`request.handler<cherrypy._cprequest.Request.handler>`.
.. _redirectingpost:
Redirecting POST
================
When you GET a resource and are redirected by the server to another Location,
there's generally no problem since GET is both a "safe method" (there should
be no side-effects) and an "idempotent method" (multiple calls are no different
than a single call).
POST, however, is neither safe nor idempotent--if you
charge a credit card, you don't want to be charged twice by a redirect!
For this reason, *none* of the 3xx responses permit a user-agent (browser) to
resubmit a POST on redirection without first confirming the action with the
user:
===== ================================= ===========
300 Multiple Choices Confirm with the user
301 Moved Permanently Confirm with the user
302 Found (Object moved temporarily) Confirm with the user
303 See Other GET the new URI--no confirmation
304 Not modified (for conditional GET only--POST should not raise this error)
305 Use Proxy Confirm with the user
307 Temporary Redirect Confirm with the user
===== ================================= ===========
However, browsers have historically implemented these restrictions poorly;
in particular, many browsers do not force the user to confirm 301, 302
or 307 when redirecting POST. For this reason, CherryPy defaults to 303,
which most user-agents appear to have implemented correctly. Therefore, if
you raise HTTPRedirect for a POST request, the user-agent will most likely
attempt to GET the new URI (without asking for confirmation from the user).
We realize this is confusing for developers, but it's the safest thing we
could do. You are of course free to raise ``HTTPRedirect(uri, status=302)``
or any other 3xx status if you know what you're doing, but given the
environment, we couldn't let any of those be the default.
Custom Error Handling
=====================
.. image:: /refman/cperrors.gif
Anticipated HTTP responses
--------------------------
The 'error_page' config namespace can be used to provide custom HTML output for
expected responses (like 404 Not Found). Supply a filename from which the
output will be read. The contents will be interpolated with the values
%(status)s, %(message)s, %(traceback)s, and %(version)s using plain old Python
`string formatting <http://docs.python.org/2/library/stdtypes.html#string-formatting-operations>`_.
::
_cp_config = {
'error_page.404': os.path.join(localDir, "static/index.html")
}
Beginning in version 3.1, you may also provide a function or other callable as
an error_page entry. It will be passed the same status, message, traceback and
version arguments that are interpolated into templates::
def error_page_402(status, message, traceback, version):
return "Error %s - Well, I'm very sorry but you haven't paid!" % status
cherrypy.config.update({'error_page.402': error_page_402})
Also in 3.1, in addition to the numbered error codes, you may also supply
"error_page.default" to handle all codes which do not have their own error_page
entry.
Unanticipated errors
--------------------
CherryPy also has a generic error handling mechanism: whenever an unanticipated
error occurs in your code, it will call
:func:`Request.error_response<cherrypy._cprequest.Request.error_response>` to
set the response status, headers, and body. By default, this is the same
output as
:class:`HTTPError(500) <cherrypy._cperror.HTTPError>`. If you want to provide
some other behavior, you generally replace "request.error_response".
Here is some sample code that shows how to display a custom error message and
send an e-mail containing the error::
from cherrypy import _cperror
def handle_error():
cherrypy.response.status = 500
cherrypy.response.body = [
"<html><body>Sorry, an error occured</body></html>"
]
sendMail('EMAIL',
'Error in your web app',
_cperror.format_exc())
@cherrypy.config(**{'request.error_response': handle_error})
class Root:
pass
Note that you have to explicitly set
:attr:`response.body <cherrypy._cprequest.Response.body>`
and not simply return an error message as a result.
""" |
"""
=======================================
Signal processing (:mod:`scipy.signal`)
=======================================
.. module:: 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.
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.
zpk2tf -- zero-pole-gain to transfer function.
tf2ss -- transfer function to state-space.
ss2tf -- state-pace to transfer function.
zpk2ss -- zero-pole-gain to state-space.
ss2zpk -- state-space to pole-zero-gain.
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
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
""" |
"""
Signal Processing Tools
=======================
Convolution:
convolve:
N-dimensional convolution.
correlate:
N-dimensional correlation.
fftconvolve:
N-dimensional convolution using the FFT.
convolve2d:
2-dimensional convolution (more options).
correlate2d:
2-dimensional correlation (more options).
sepfir2d:
Convolve with a 2-D separable FIR filter.
B-splines:
bspline:
B-spline basis function of order n.
gauss_spline:
Gaussian approximation to the B-spline basis function.
cspline1d:
Coefficients for 1-D cubic (3rd order) B-spline.
qspline1d:
Coefficients for 1-D quadratic (2nd order) B-spline.
cspline2d:
Coefficients for 2-D cubic (3rd order) B-spline.
qspline2d:
Coefficients for 2-D quadratic (2nd order) B-spline.
spline_filter:
Smoothing spline (cubic) filtering of a rank-2 array.
Filtering:
order_filter:
N-dimensional order filter.
medfilt:
N-dimensional median filter.
medfilt2:
2-dimensional median filter (faster).
wiener:
N-dimensional wiener filter.
symiirorder1:
2nd-order IIR filter (cascade of first-order systems).
symiirorder2:
4th-order IIR filter (cascade of second-order systems).
lfilter:
1-dimensional FIR and IIR digital linear filtering.
lfiltic:
Construct initial conditions for `lfilter`.
deconvolve:
1-d deconvolution using lfilter.
hilbert:
Compute the analytic signal of a 1-d signal.
get_window:
Create FIR window.
decimate:
Downsample a signal.
detrend:
Remove linear and/or constant trends from data.
resample:
Resample using Fourier method.
Filter design:
bilinear:
Return a digital filter from an analog filter using the bilinear transform.
firwin:
Windowed FIR filter design, with frequency response defined as pass and stop bands.
firwin2:
Windowed FIR filter design, with arbitrary frequency response.
freqs:
Analog filter frequency response.
freqz:
Digital filter frequency response.
iirdesign:
IIR filter design given bands and gains.
iirfilter:
IIR filter design given order and critical frequencies.
invres:
Inverse partial fraction expansion.
kaiser_beta:
Compute the Kaiser parameter beta, given the desired FIR filter attenuation.
kaiser_atten:
Compute the attenuation of a Kaiser FIR filter, given the number of taps
and the transition width at discontinuities in the frequency response.
kaiserord:
Design a Kaiser window to limit ripple and width of transition region.
remez:
Optimal FIR filter design.
residue:
Partial fraction expansion of b(s) / a(s).
residuez:
Partial fraction expansion of b(z) / a(z).
unique_roots:
Unique roots and their multiplicities.
Matlab-style IIR filter design:
butter (buttord):
Butterworth
cheby1 (cheb1ord):
Chebyshev Type I
cheby2 (cheb2ord):
Chebyshev Type II
ellip (ellipord):
Elliptic (Cauer)
bessel:
Bessel (no order selection available -- try butterod)
Linear Systems:
lti:
linear time invariant system object.
lsim:
continuous-time simulation of output to linear system.
lsim2:
like lsim, but `scipy.integrate.odeint` is used.
impulse:
impulse response of linear, time-invariant (LTI) system.
impulse2:
like impulse, but `scipy.integrate.odeint` is used.
step:
step response of continous-time LTI system.
step2:
like step, but `scipy.integrate.odeint` is used.
LTI Representations:
tf2zpk:
transfer function to zero-pole-gain.
zpk2tf:
zero-pole-gain to transfer function.
tf2ss:
transfer function to state-space.
ss2tf:
state-pace to transfer function.
zpk2ss:
zero-pole-gain to state-space.
ss2zpk:
state-space to pole-zero-gain.
Waveforms:
sawtooth:
Periodic sawtooth
square:
Square wave
gausspulse:
Gaussian modulated sinusoid
chirp:
Frequency swept cosine signal, with several frequency functions.
sweep_poly:
Frequency swept cosine signal; frequency is arbitrary polynomial.
Window functions:
get_window:
Return a window of a given length and type.
barthann:
Bartlett-Hann window
bartlett:
Bartlett window
blackman:
Blackman window
blackmanharris:
Minimum 4-term Blackman-Harris window
bohman:
Bohman window
boxcar:
Boxcar window
chebwin:
Dolph-Chebyshev window
flattop:
Flat top window
gaussian:
Gaussian window
general_gaussian:
Generalized Gaussian window
hamming:
Hamming window
hann:
Hann window
kaiser:
Kaiser window
nuttall:
Nuttall's minimum 4-term Blackman-Harris window
parzen:
Parzen window
slepian:
Slepian window
triang:
Triangular window
Wavelets:
daub:
return low-pass
qmf:
return quadrature mirror filter from low-pass
cascade:
compute scaling function and wavelet from coefficients
morlet:
Complex Morlet wavelet.
""" |
"""
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')
""" |
# import pyglet
# import sys
# from random import randint
#
#
# class Chip8(pyglet.window.Window):
#
# def __init__(self):
#
# # General use registers
# self.vx = 0x00
# self.vy = 0x00
# self.vz = 0x00
# self.v0 = 0x00
#
# # Register used as a flag
# self.vf = 0x00
#
# # Used for memory addresses, we only use its 12 lowest bits
# self.I = 0x00
#
# self.key_inputs = [0] * 16
# self.display_buffer = [0] * 64 * 32
#
# self.memory = [0] * 4096
#
# self.sound_timer = 0
# self.delay_timer = 0
#
# self.index = 0
# self.pc = 0
#
# self.stack = []
#
# self.fonts = [0xF0, 0x90, 0x90, 0x90, 0xF0, # 0
# 0x20, 0x60, 0x20, 0x20, 0x70, # 1
# 0xF0, 0x10, 0xF0, 0x80, 0xF0, # 2
# 0xF0, 0x10, 0xF0, 0x10, 0xF0, # 3
# 0x90, 0x90, 0xF0, 0x10, 0x10, # 4
# 0xF0, 0x80, 0xF0, 0x10, 0xF0, # 5
# 0xF0, 0x80, 0xF0, 0x90, 0xF0, # 6
# 0xF0, 0x10, 0x20, 0x40, 0x40, # 7
# 0xF0, 0x90, 0xF0, 0x90, 0xF0, # 8
# 0xF0, 0x90, 0xF0, 0x10, 0xF0, # 9
# 0xF0, 0x90, 0xF0, 0x90, 0x90, # A
# 0xE0, 0x90, 0xE0, 0x90, 0xE0, # B
# 0xF0, 0x80, 0x80, 0x80, 0xF0, # C
# 0xE0, 0x90, 0x90, 0x90, 0xE0, # D
# 0xF0, 0x80, 0xF0, 0x80, 0xF0, # E
# 0xF0, 0x80, 0xF0, 0x80, 0x80 # F
# ]
#
# def main(self):
# self.initialize()
# self.load_rom(sys.argv[1])
#
# while not self.has_exit:
# self.dispatch_events()
# self.cycle()
# self.draw()
#
# def initialize(self):
# # That should clear pyglet screen
# self.clear()
#
# # General use registers
# self.vx = 0x00 # 8bit register
# self.vy = 0x00 # 8bit register
# self.vz = 0x00 # 8bit register
# self.v0 = 0x00 # 8bit register
#
# # Register used as a flag
# self.vf = 0x00
#
# # Used for memory addresses, we only use its 12 lowest bits
# self.I = 0x00 # 16bit register
#
# # RAM memory 4KB
# self.memory = [0]*4096 # max 4096
#
# # Screen
# self.display_buffer = [0]*64*32 # 64*32
#
# self.stack = []
# self.key_inputs = [0]*16
# self.opcode = 0
# self.index = 0
#
# self.delay_timer = 0
# self.sound_timer = 0
#
# self.pc = 0x200
#
# i = 0
# while i < 80:
# # load 80-char font set
# self.memory[i] = self.fonts[i]
# i += 1
#
# def load_rom(self, rom_path):
# data = open(rom_path, 'rb').read()
# for i, part in enumerate(data):
# self.memory[0x200 + i] = part
# print(hex(part))
#
# def draw(self):
# print("draw")
#
# def cycle(self):
# # Extract opcode from memory
# self.opcode = self.memory[self.pc]
#
# # Extract the parameters
# self.vx = (self.opcode & 0x0f00) >> 8
# self.vy = (self.opcode & 0x00f0) >> 4
#
# # Execute opcode
# try:
# self.funcmap[self.opcode]()
# except:
# print("Unknown instruction: %X" % self.opcode)
#
# # Increment program counter
# self.pc += 2
#
# # Decrement timers
# if self.delay_timer > 0:
# self.delay_timer -= 1
#
# if self.sound_timer > 0:
# self.sound_timer -= 1
#
# if self.sound_timer == 0:
# print("Beep")
#
# # Opcode implementations
# # 00E0 - CLS
# def clear_screen(self):
# # Clear the display.
# self.screen = [0] * 64 * 32
# self.clear()
#
# # 00EE - RET
# def return_from_subroutine(self):
# # The interpreter sets the program counter to the address at the top of the stack, then subtracts 1 from the stack pointer.
# self.pc = self.stack.pop()
#
# #1nnn - JP addr
# def jump_to_location(self):
#
# #The interpreter sets the program counter to nnn.
# jump_address = self.opcode & 0x0fff
#
# self.pc = jump_address
#
# #2nnn - CALL addr
# def call_subroutine(self):
#
# #The interpreter increments the stack pointer, then puts the current PC on the top of the stack. The PC is then set to nnn.
# self.stack.append(self.pc)
#
# jump_address = self.opcode & 0x0fff
#
# self.pc = jump_address
#
# #3xkk - SE Vx, byte
# def skip_instruction_if_vx_equal_kk(self):
#
# #The interpreter compares register Vx to kk, and if they are equal, increments the program counter by 2.
# kk = self.opcode & 0x00ff
#
# if(self.vx == kk):
#
# self.pc += 2
#
# #4xkk - SNE Vx, byte
# def skip_instruction_if_vx_notequal_kk(self):
#
# #The interpreter compares register Vx to kk, and if they are not equal, increments the program counter by 2.
# kk = self.opcode & 0x00ff
#
# if(self.vx != kk):
#
# self.pc += 2
#
# #5xy0 - SE Vx, Vy
# def skip_instruction_if_vx_equal_vy(self):
#
# #The interpreter compares register Vx to register Vy, and if they are equal, increments the program counter by 2.
# if(self.vx == self.vy):
#
# self.pc += 2
#
# #6xkk - LD Vx, byte
# def set_vx(self):
#
# #The interpreter puts the value kk into register Vx.
# kk = self.opcode & 0x00ff
#
# self.vx = kk
#
# #7xkk - ADD Vx, byte
# def increment_vx_kk_units(self):
#
# #Adds the value kk to the value of register Vx, then stores the result in Vx.
# kk = self.opcode & 0x00ff
#
# self.vx += kk
#
# #8xy0 - LD Vx, Vy
# def set_vx_equal_vy(self):
#
# #Stores the value of register Vy in register Vx.
# self.vx = self.vy
#
#
# #8xy1 - OR Vx, Vy
# def set_vx_bitwise_or_vx_vy(self):
#
# #Performs a bitwise OR on the values of Vx and Vy, then stores the result in Vx. A bitwise OR compares the corrseponding bits from two values, and if either bit is 1, then the same bit in the result is also 1. Otherwise, it is 0.
# self.vx = self.vx | self.vy
#
#
# #8xy2 - AND Vx, Vy
# def set_vx_bitwise_and_vx_vy(self):
#
# #Performs a bitwise AND on the values of Vx and Vy, then stores the result in Vx. A bitwise AND compares the corrseponding bits from two values, and if both bits are 1, then the same bit in the result is also 1. Otherwise, it is 0.
# self.vx = self.vx & self.vy
#
#
# #8xy3 - XOR Vx, Vy
# def set_vx_bitwise_xor_vx_vy(self):
#
# #Performs a bitwise exclusive OR on the values of Vx and Vy, then stores the result in Vx. An exclusive OR compares the corrseponding bits from two values, and if the bits are not both the same, then the corresponding bit in the result is set to 1. Otherwise, it is 0.
# self.vx = self.vx ^ self.vy
#
# #8xy4 - ADD Vx, Vy
# def set_vx_sum_vx_vy(self):
#
# #The values of Vx and Vy are added together. If the result is greater than 8 bits (i.e., > 255,) VF is set to 1, otherwise 0. Only the lowest 8 bits of the result are kept, and stored in Vx.
#
# #Set the parity bit
# if(self.vx > 0xff):
# self.vf = 1
#
# else:
# self.vf = 0
#
# #We just keep the lowest 8 bits
# self.vx = self.vx + self.vy
# self.vx &= 0x00ff
#
# #8xy5 - SUB Vx, Vy
# def set_vx_substraction_vx_vy(self):
#
# #If Vx > Vy, then VF is set to 1, otherwise 0. Then Vy is subtracted from Vx, and the results stored in Vx.
# if(self.vx > self.vy):
# self.vf = 1
#
# else:
# self.vf = 0
#
# self.vx -= self.vy
#
# #8xy6 - SHR Vx {, Vy}
# def set_vx_vx_divided_by_two(self):
#
# #If the least-significant bit of Vx is 1, then VF is set to 1, otherwise 0. Then Vx is divided by 2.
# self.vf = self.vx & 0x0001
#
# #Divide by 2
# self.vx = self.vx >> 1
#
# #8xy7 - SUBN Vx, Vy
# def set_vx_substraction_vy_vx(self):
#
# #If Vy > Vx, then VF is set to 1, otherwise 0. Then Vx is subtracted from Vy, and the results stored in Vx.
# if(self.vy > self.vx):
# self.vf = 1
#
# else:
# self.vf = 0
#
# self.vx = self.vy - self.vx
#
# #8xyE - SHL Vx {, Vy}
# def set_vx_vx_multiplied_by_two(self):
#
# #If the most-significant bit of Vx is 1, then VF is set to 1, otherwise to 0. Then Vx is multiplied by 2.
# most_significant_bit = self.vx & 0x8000 >> 15
#
# if(most_significant_bit == 1):
# self.vf = 1
#
# else:
# self.vf = 0
#
# self.vf = self.vf << 1
#
#
# #9xy0 - SNE Vx, Vy
# def skip_instruction_if_vx_not_equal_vy(self):
#
# #The values of Vx and Vy are compared, and if they are not equal, the program counter is increased by 2.
# if(self.vx == self.vy):
#
# self.pc += 2
#
# #Annn - LD I, addr
# def set_I_to_nnn(self):
#
# #The value of register I is set to nnn.
# self.I = self.opcode & 0x0fff
#
# #Bnnn - JP V0, addr
# def jump_to_nnn_sum_v0(self):
#
# #The program counter is set to nnn plus the value of V0.
# self.pc = self.opcode & 0x0fff + self.v0
#
# #Cxkk - RND Vx, byte
# def set_vx_random_and_kk(self):
#
# #The interpreter generates a random number from 0 to 255, which is then ANDed with the value kk.
# #The results are stored in Vx.
# self.vx = (randint() % 255) & (self.opcode & 0x00ff)
#
#
# #Dxyn - DRW Vx, Vy, nibble
# def draw_sprite(self):
#
# #The interpreter reads n bytes from memory, starting at the address stored in I.
# #These bytes are then displayed as sprites on screen at coordinates (Vx, Vy).
# #Sprites are XORed onto the existing screen. If this causes any pixels to be erased,
# #VF is set to 1, otherwise it is set to 0. If the sprite is positioned so part of it is
# #outside the coordinates of the display, it wraps around to the opposite side of the screen.
#
# bytes_to_read = int(self.opcode & 0x00f)
#
# #Read the bytes
# for offset in range(bytes_to_read):
#
# mask = 0x80
#
# byte_to_draw = self.memory[self.I + offset]
#
# for index in range(8):
#
# xpos = (self.vx + index) % 64
# ypos = (self.vy + offset) % 32
#
# previous_pixel_value = self.screen[xpos][ypos]
#
# self.screen[xpos][ypos] ^= (byte_to_draw & mask) >> (8 - index)
#
# new_pixel_value = self.screen[xpos][ypos]
#
# #Move the mask
# mask = mask >> 1
#
# #A pixel has been erased
# if(previous_pixel_value == 1 and new_pixel_value == 0):
# self.vf = 1
#
# else:
# self.vf = 0
#
#
# #Ex9E - SKP Vx
# def skip_instruction_if_key_with_value_vx_pressed(self):
#
# #Checks the keyboard, and if the key corresponding to the value of Vx is currently in the down position, PC is increased by 2.
# if(self.key_inputs[self.vx] == 1):
# self.pc +=2
#
#
# #ExA1 - SKNP Vx
# def skip_instruction_if_key_with_value_vx_not_pressed(self):
#
# #Checks the keyboard, and if the key corresponding to the value of Vx is currently in the up position, PC is increased by 2.
# if(self.key_inputs[self.vx] != 1):
# self.pc +=2
#
# #Fx07 - LD Vx, DT
# def set_vx_to_delay_timer(self):
#
# #The value of DT is placed into Vx.
# self.vx = self.delay_timer
#
#
# #Fx0A - LD Vx, K
# def wait_for_keypress(self):
#
# #All execution stops until a key is pressed, then the value of that key is stored in Vx.
# pressed = -1
#
# while(pressed == -1):
# for key_index, key_value in zip(range(16), self.key_inputs):
#
# if(key_value == 1):
#
# pressed = key_index
#
# self.vx = pressed
#
# # Fx15 - LD DT, Vx
# def set_delay_timer_to_vx(self):
#
# # DT is set equal to the value of Vx.
# self.delay_timer = self.vx
#
#
# # Fx18 - LD ST, Vx
# def set_sound_timer_to_vx(self):
#
# # ST is set equal to the value of Vx.
# self.sound_timer = self.vx
#
# # Fx1E - ADD I, Vx
# def set_I_I_sum_vx(self):
#
# # The values of I and Vx are added, and the results are stored in I.
# self.I += self.vx
#
#
# # Fx29 - LD F, Vx
# def set_I_to_sprite_location_of_value_of_vx(self):
#
# # The value of I is set to the location for the hexadecimal sprite corresponding to the value of Vx.
# self.I = int(self.vx) * 5
#
# # Fx33 - LD B, Vx
# def store_number_in_memory(self):
#
# # The interpreter takes the decimal value of Vx, and places the hundreds digit in memory at location in I,
# # the tens digit at location I+1, and the ones digit at location I+2.
# number_to_store = str(int(self.vx))
#
# for index, char in zip(range(3), number_to_store):
#
# self.memory[self.I + index] = hex(int(char))
#
# # Fx55 - LD [I], Vx
# def store_registers_in_memory(self):
#
# # The interpreter copies the values of registers V0 through Vx into memory, starting at the address in I.
#
# # TODO: Refactor
# self.memory[self.I] = self.vx
# self.memory[self.I + 1] = self.vy
# self.memory[self.I + 2] = self.vz
# self.memory[self.I + 3] = self.v0
#
# # Fx65 - LD Vx, [I]
# def load_registers_from_memory(self):
#
# # The interpreter reads values from memory starting at location I into registers V0 through Vx.
#
# #TODO: Refactor
# self.vx = self.memory[self.I]
# self.vy = self.memory[self.I + 1]
# self.vz = self.memory[self.I + 2]
# self.v0 = self.memory[self.I + 3]
#
# if __name__ == "__main__":
# chip8 = Chip8()
|
#!/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 *
# * *
# ***************************************************************************/
|
"""
HTTP Exception
--------------
This module processes Python exceptions that relate to HTTP exceptions
by defining a set of exceptions, all subclasses of HTTPException.
Each exception, in addition to being a Python exception that can be
raised and caught, is also a WSGI application and ``webob.Response``
object.
This module defines exceptions according to RFC 2068 [1]_ : codes with
100-300 are not really errors; 400's are client errors, and 500's are
server errors. According to the WSGI specification [2]_ , the application
can call ``start_response`` more then once only under two conditions:
(a) the response has not yet been sent, or (b) if the second and
subsequent invocations of ``start_response`` have a valid ``exc_info``
argument obtained from ``sys.exc_info()``. The WSGI specification then
requires the server or gateway to handle the case where content has been
sent and then an exception was encountered.
Exception
HTTPException
HTTPOk
* 200 - HTTPOk
* 201 - HTTPCreated
* 202 - HTTPAccepted
* 203 - HTTPNonAuthoritativeInformation
* 204 - HTTPNoContent
* 205 - HTTPResetContent
* 206 - HTTPPartialContent
HTTPRedirection
* 300 - HTTPMultipleChoices
* 301 - HTTPMovedPermanently
* 302 - HTTPFound
* 303 - HTTPSeeOther
* 304 - HTTPNotModified
* 305 - HTTPUseProxy
* 306 - Unused (not implemented, obviously)
* 307 - HTTPTemporaryRedirect
HTTPError
HTTPClientError
* 400 - HTTPBadRequest
* 401 - HTTPUnauthorized
* 402 - HTTPPaymentRequired
* 403 - HTTPForbidden
* 404 - HTTPNotFound
* 405 - HTTPMethodNotAllowed
* 406 - HTTPNotAcceptable
* 407 - HTTPProxyAuthenticationRequired
* 408 - HTTPRequestTimeout
* 409 - HTTPConflict
* 410 - HTTPGone
* 411 - HTTPLengthRequired
* 412 - HTTPPreconditionFailed
* 413 - HTTPRequestEntityTooLarge
* 414 - HTTPRequestURITooLong
* 415 - HTTPUnsupportedMediaType
* 416 - HTTPRequestRangeNotSatisfiable
* 417 - HTTPExpectationFailed
* 428 - HTTPPreconditionRequired
* 429 - HTTPTooManyRequests
* 431 - HTTPRequestHeaderFieldsTooLarge
HTTPServerError
* 500 - HTTPInternalServerError
* 501 - HTTPNotImplemented
* 502 - HTTPBadGateway
* 503 - HTTPServiceUnavailable
* 504 - HTTPGatewayTimeout
* 505 - HTTPVersionNotSupported
* 511 - HTTPNetworkAuthenticationRequired
Subclass usage notes:
---------------------
The HTTPException class is complicated by 4 factors:
1. The content given to the exception may either be plain-text or
as html-text.
2. The template may want to have string-substitutions taken from
the current ``environ`` or values from incoming headers. This
is especially troublesome due to case sensitivity.
3. The final output may either be text/plain or text/html
mime-type as requested by the client application.
4. Each exception has a default explanation, but those who
raise exceptions may want to provide additional detail.
Subclass attributes and call parameters are designed to provide an easier path
through the complications.
Attributes:
``code``
the HTTP status code for the exception
``title``
remainder of the status line (stuff after the code)
``explanation``
a plain-text explanation of the error message that is
not subject to environment or header substitutions;
it is accessible in the template via %(explanation)s
``detail``
a plain-text message customization that is not subject
to environment or header substitutions; accessible in
the template via %(detail)s
``body_template``
a content fragment (in HTML) used for environment and
header substitution; the default template includes both
the explanation and further detail provided in the
message
Parameters:
``detail``
a plain-text override of the default ``detail``
``headers``
a list of (k,v) header pairs
``comment``
a plain-text additional information which is
usually stripped/hidden for end-users
``body_template``
a string.Template object containing a content fragment in HTML
that frames the explanation and further detail
To override the template (which is HTML content) or the plain-text
explanation, one must subclass the given exception; or customize it
after it has been created. This particular breakdown of a message
into explanation, detail and template allows both the creation of
plain-text and html messages for various clients as well as
error-free substitution of environment variables and headers.
The subclasses of :class:`~_HTTPMove`
(:class:`~HTTPMultipleChoices`, :class:`~HTTPMovedPermanently`,
:class:`~HTTPFound`, :class:`~HTTPSeeOther`, :class:`~HTTPUseProxy` and
:class:`~HTTPTemporaryRedirect`) are redirections that require a ``Location``
field. Reflecting this, these subclasses have two additional keyword arguments:
``location`` and ``add_slash``.
Parameters:
``location``
to set the location immediately
``add_slash``
set to True to redirect to the same URL as the request, except with a
``/`` appended
Relative URLs in the location will be resolved to absolute.
References:
.. [1] http://www.python.org/peps/pep-0333.html#error-handling
.. [2] http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html#sec10.5
""" |
"""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.
""" |
#!/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 page is in the table of contents.
Cool is a script to cool the shape.
The cool manual page is at:
http://www.bitsfrombytes.com/wiki/index.php?title=Skeinforge_Cool
Allan NAME aka The Masked Retriever's has written the "Skeinforge Quicktip: Cool" at:
http://blog.thingiverse.com/2009/07/28/skeinforge-quicktip-cool/
==Operation==
The default 'Activate Cool' checkbox is on. When it is on, the functions described below will work, when it is off, the functions will not be called.
==Settings==
===Cool Type===
Default is 'Orbit', because many extruders do not operate properly at very slow flow rates.
====Orbit====
When selected, cool will add orbits with the extruder off to give the layer time to cool, so that the next layer is not extruded on a molten base. The orbits will be around the largest island on that layer.
====Slow Down====
When selected, cool will slow down the extruder so that it will take the minimum layer time to extrude the layer.
===Maximum Cool===
Default is 2 Celcius.
If it takes less time to extrude the layer than the minimum layer time, then cool will lower the temperature by the 'Maximum Cool' setting times the layer time over the minimum layer time.
===Minimum Layer Time===
Default is 60 seconds.
Defines the minimum amount of time the extruder will spend on a layer, this is an important setting.
===Minimum Orbital Radius===
Default is 10 millimeters.
When the orbit cool type is selected, if the area of the largest island is as large as the square of the "Minimum Orbital Radius" then the orbits will be just within the island. If the island is smaller, then the orbits will be in a square of the "Minimum Orbital Radius" around the center of the island.
===Turn Fan On at Beginning===
Default is on.
When selected, cool will turn the fan on at the beginning of the fabrication.
===Turn Fan On at Ending===
Default is on.
When selected, cool will turn the fan off at the ending of the fabrication.
==Alterations==
Cool looks for alteration files in the alterations folder in the .skeinforge folder in the home directory. Cool does not care if the text file names are capitalized, but some file systems do not handle file name cases properly, so to be on the safe side you should give them lower case names. If it doesn't find the file it then looks in the alterations folder in the skeinforge_plugins folder. If it doesn't find anything there it looks in the craft_plugins folder. The cool start and end text idea is from:
http://makerhahn.blogspot.com/2008/10/yay-minimug.html
===cool_start.gcode===
Cool will add cool_start.gcode to the start of the orbits if it exists.
===cool_end.gcode===
After it has added the orbits, it will add the file cool_end.gcode if it exists.
==Examples==
The following examples cool the file Screw Holder Bottom.stl. The examples are run in a terminal in the folder which contains Screw Holder Bottom.stl and cool.py.
> python cool.py
This brings up the cool dialog.
> python cool.py Screw Holder Bottom.stl
The cool tool is parsing the file:
Screw Holder Bottom.stl
..
The cool tool has created the file:
.. Screw Holder Bottom_cool.gcode
> python
Python 2.5.1 (r251:54863, Sep 22 2007, 01:43:31)
[GCC 4.2.1 (SUSE Linux)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import cool
>>> cool.main()
This brings up the cool dialog.
>>> cool.writeOutput('Screw Holder Bottom.stl')
The cool tool is parsing the file:
Screw Holder Bottom.stl
..
The cool tool has created the file:
.. Screw Holder Bottom_cool.gcode
""" |
# Copyright 2011,2012 NAME Copyright 2008 (C) Nicira, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at:
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is derived from the packet library in NOX, which was
# developed by Nicira, Inc.
#======================================================================
#
# DNS Message Format
#
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | ID |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# |QR| Opcode |AA|TC|RD|RA|Z |AD|CD| RCODE |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Questions |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Answerrs |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Authority RRs |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Total Additional RRs |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Questions ... |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Answer RRs ... |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Authority RRs.. |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | Additional RRs. |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
#
# Question format:
#
# 1 1 1 1 1 1
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | |
# / QNAME /
# / /
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | QTYPE |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | QCLASS |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
#
#
#
# All RRs have the following format:
# 1 1 1 1 1 1
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | |
# / /
# / NAME /
# | |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | TYPE |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | CLASS |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | TTL |
# | |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# | RDLENGTH |
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--|
# / RDATA /
# / /
# +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
#
#
#======================================================================
# TODO:
# SOA data
# General cleaup/rewrite (code is/has gotten pretty bad)
|
"""
=============================
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
# ***** 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
|
"""
Project Euler Problem 8: https://projecteuler.net/problem=8
Largest product in a series
The four adjacent digits in the 1000-digit number that have the greatest
product are 9 × 9 × 8 × 9 = 5832.
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
Find the thirteen adjacent digits in the 1000-digit number that have the
greatest product. What is the value of this product?
""" |
"""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.
""" |
"""CPStats, a package for collecting and reporting on program statistics.
Overview
========
Statistics about program operation are an invaluable monitoring and debugging
tool. Unfortunately, the gathering and reporting of these critical values is
usually ad-hoc. This package aims to add a centralized place for gathering
statistical performance data, a structure for recording that data which
provides for extrapolation of that data into more useful information,
and a method of serving that data to both human investigators and
monitoring software. Let's examine each of those in more detail.
Data Gathering
--------------
Just as Python's `logging` module provides a common importable for gathering
and sending messages, performance statistics would benefit from a similar
common mechanism, and one that does *not* require each package which wishes
to collect stats to import a third-party module. Therefore, we choose to
re-use the `logging` module by adding a `statistics` object to it.
That `logging.statistics` object is a nested dict. It is not a custom class,
because that would:
1. require libraries and applications to import a third-party module in
order to participate
2. inhibit innovation in extrapolation approaches and in reporting tools, and
3. be slow.
There are, however, some specifications regarding the structure of the dict.::
{
+----"SQLAlchemy": {
| "Inserts": 4389745,
| "Inserts per Second":
| lambda s: s["Inserts"] / (time() - s["Start"]),
| C +---"Table Statistics": {
| o | "widgets": {-----------+
N | l | "Rows": 1.3M, | Record
a | l | "Inserts": 400, |
m | e | },---------------------+
e | c | "froobles": {
s | t | "Rows": 7845,
p | i | "Inserts": 0,
a | o | },
c | n +---},
e | "Slow Queries":
| [{"Query": "SELECT * FROM widgets;",
| "Processing Time": 47.840923343,
| },
| ],
+----},
}
The `logging.statistics` dict has four levels. The topmost level is nothing
more than a set of names to introduce modularity, usually along the lines of
package names. If the SQLAlchemy project wanted to participate, for example,
it might populate the item `logging.statistics['SQLAlchemy']`, whose value
would be a second-layer dict we call a "namespace". Namespaces help multiple
packages to avoid collisions over key names, and make reports easier to read,
to boot. The maintainers of SQLAlchemy should feel free to use more than one
namespace if needed (such as 'SQLAlchemy ORM'). Note that there are no case
or other syntax constraints on the namespace names; they should be chosen
to be maximally readable by humans (neither too short nor too long).
Each namespace, then, is a dict of named statistical values, such as
'Requests/sec' or 'Uptime'. You should choose names which will look
good on a report: spaces and capitalization are just fine.
In addition to scalars, values in a namespace MAY be a (third-layer)
dict, or a list, called a "collection". For example, the CherryPy
:class:`StatsTool` keeps track of what each request is doing (or has most
recently done) in a 'Requests' collection, where each key is a thread ID; each
value in the subdict MUST be a fourth dict (whew!) of statistical data about
each thread. We call each subdict in the collection a "record". Similarly,
the :class:`StatsTool` also keeps a list of slow queries, where each record
contains data about each slow query, in order.
Values in a namespace or record may also be functions, which brings us to:
Extrapolation
-------------
The collection of statistical data needs to be fast, as close to unnoticeable
as possible to the host program. That requires us to minimize I/O, for example,
but in Python it also means we need to minimize function calls. So when you
are designing your namespace and record values, try to insert the most basic
scalar values you already have on hand.
When it comes time to report on the gathered data, however, we usually have
much more freedom in what we can calculate. Therefore, whenever reporting
tools (like the provided :class:`StatsPage` CherryPy class) fetch the contents
of `logging.statistics` for reporting, they first call
`extrapolate_statistics` (passing the whole `statistics` dict as the only
argument). This makes a deep copy of the statistics dict so that the
reporting tool can both iterate over it and even change it without harming
the original. But it also expands any functions in the dict by calling them.
For example, you might have a 'Current Time' entry in the namespace with the
value "lambda scope: time.time()". The "scope" parameter is the current
namespace dict (or record, if we're currently expanding one of those
instead), allowing you access to existing static entries. If you're truly
evil, you can even modify more than one entry at a time.
However, don't try to calculate an entry and then use its value in further
extrapolations; the order in which the functions are called is not guaranteed.
This can lead to a certain amount of duplicated work (or a redesign of your
schema), but that's better than complicating the spec.
After the whole thing has been extrapolated, it's time for:
Reporting
---------
The :class:`StatsPage` class grabs the `logging.statistics` dict, extrapolates
it all, and then transforms it to HTML for easy viewing. Each namespace gets
its own header and attribute table, plus an extra table for each collection.
This is NOT part of the statistics specification; other tools can format how
they like.
You can control which columns are output and how they are formatted by updating
StatsPage.formatting, which is a dict that mirrors the keys and nesting of
`logging.statistics`. The difference is that, instead of data values, it has
formatting values. Use None for a given key to indicate to the StatsPage that a
given column should not be output. Use a string with formatting
(such as '%.3f') to interpolate the value(s), or use a callable (such as
lambda v: v.isoformat()) for more advanced formatting. Any entry which is not
mentioned in the formatting dict is output unchanged.
Monitoring
----------
Although the HTML output takes pains to assign unique id's to each <td> with
statistical data, you're probably better off fetching /cpstats/data, which
outputs the whole (extrapolated) `logging.statistics` dict in JSON format.
That is probably easier to parse, and doesn't have any formatting controls,
so you get the "original" data in a consistently-serialized format.
Note: there's no treatment yet for datetime objects. Try time.time() instead
for now if you can. Nagios will probably thank you.
Turning Collection Off
----------------------
It is recommended each namespace have an "Enabled" item which, if False,
stops collection (but not reporting) of statistical data. Applications
SHOULD provide controls to pause and resume collection by setting these
entries to False or True, if present.
Usage
=====
To collect statistics on CherryPy applications::
from cherrypy.lib import cpstats
appconfig['/']['tools.cpstats.on'] = True
To collect statistics on your own code::
import logging
# Initialize the repository
if not hasattr(logging, 'statistics'): logging.statistics = {}
# Initialize my namespace
mystats = logging.statistics.setdefault('My Stuff', {})
# Initialize my namespace's scalars and collections
mystats.update({
'Enabled': True,
'Start Time': time.time(),
'Important Events': 0,
'Events/Second': lambda s: (
(s['Important Events'] / (time.time() - s['Start Time']))),
})
...
for event in events:
...
# Collect stats
if mystats.get('Enabled', False):
mystats['Important Events'] += 1
To report statistics::
root.cpstats = cpstats.StatsPage()
To format statistics reports::
See 'Reporting', above.
""" |
"""
This page is in the table of contents.
Skeiniso is an analyze viewer to display a gcode file in an isometric view.
The skeiniso manual page is at:
http://fabmetheus.crsndoo.com/wiki/index.php/Skeinforge_Skeiniso
==Operation==
The default 'Activate Skeiniso' checkbox is off. When it is on, the functions described below will work when called from the skeinforge toolchain, when it is off, the functions will not be called from the toolchain. The functions will still be called, whether or not the 'Activate Skeiniso' checkbox is on, when skeiniso is run directly.
Skeiniso requires skeinforge comments in the gcode file to distinguish the loops and edges. If the comments are deleted, all threads will be displayed as generic threads. To get the penultimate file of the tool chain, just before export deletes the comments, select 'Save Penultimate Gcode' in export, and open the gcode file with the suffix '_penultimate.gcode' with skeiniso.
The viewer is simple, the viewpoint can only be moved in a sphere around the center of the model by changing the viewpoint latitude and longitude. Different regions of the model can be hidden by setting the width of the thread to zero. The alternating bands act as contour bands and their brightness and width can be changed.
==Settings==
===Animation===
====Animation Line Quickening====
Default is one.
The quickness of the tool animation over the quickness of the actual tool.
====Animation Slide Show Rate====
Default is two layers per second.
The rate, in layers per second, at which the layer changes when the soar or dive button is pressed..
===Axis Rulings===
Default is on.
When selected, rulings will be drawn on the axis lines.
===Banding===
====Band Height====
Default is five layers.
Defines the height of the band in layers, a pair of bands is twice that height.
====Bottom Band Brightness====
Default is 0.7.
Defines the ratio of the brightness of the bottom band over the brightness of the top band. The higher it is the brighter the bottom band will be.
====Bottom Layer Brightness====
Default is one.
Defines the ratio of the brightness of the bottom layer over the brightness of the top layer. With a low bottom layer brightness ratio the bottom of the model will be darker than the top of the model, as if it was being illuminated by a light just above the top.
====Bright Band Start====
Default choice is 'From the Top'.
The button group that determines where the bright band starts from.
=====From the Bottom=====
When selected, the bright bands will start from the bottom.
=====From the Top=====
When selected, the bright bands will start from the top.
===Draw Arrows===
Default is on.
When selected, arrows will be drawn at the end of each line segment.
===Export Menu===
When the submenu in the export menu item in the file menu is clicked, an export canvas dialog will be displayed, which can export the canvas to a file.
===Go Around Extruder Off Travel===
Default is off.
When selected, the display will include the travel when the extruder is off, which means it will include the nozzle wipe path if any.
===Layers===
====Layer====
Default is zero.
On the display window, the Up button increases the 'Layer' by one, and the Down button decreases the layer by one. When the layer displayed in the layer spin box is changed then <Return> is hit, the layer shown will be set to the spin box, to a mimimum of zero and to a maximum of the highest index layer.The Soar button increases the layer at the 'Animation Slide Show Rate', and the Dive (double left arrow button beside the layer field) button decreases the layer at the slide show rate.
====Layer Extra Span====
Default is a huge number.
The viewer will draw the layers in the range including the 'Layer' index and the 'Layer' index plus the 'Layer Extra Span'. If the 'Layer Extra Span' is negative, the layers viewed will start at the 'Layer' index, plus the 'Layer Extra Span', and go up to and include the 'Layer' index. If the 'Layer Extra Span' is zero, only the 'Layer' index layer will be displayed. If the 'Layer Extra Span' is positive, the layers viewed will start at the 'Layer' index, and go up to and include the 'Layer' index plus the 'Layer Extra Span'.
===Line===
Default is zero.
The index of the selected line on the layer that is highlighted when the 'Display Line' mouse tool is chosen. The line spin box up button increases the 'Line' by one. If the line index of the layer goes over the index of the last line, the layer index will be increased by one and the new line index will be zero. The down button decreases the line index by one. If the line index goes below the index of the first line, the layer index will be decreased by one and the new line index will be at the last line. When the line displayed in the line field is changed then <Return> is hit, the line shown will be set to the line field, to a mimimum of zero and to a maximum of the highest index line. The Soar button increases the line at the speed at which the extruder would move, times the 'Animation Line Quickening' ratio, and the Dive (double left arrow button beside the line field) button decreases the line at the animation line quickening ratio.
===Mouse Mode===
Default is 'Display Line'.
The mouse tool can be changed from the 'Mouse Mode' menu button or picture button. The mouse tools listen to the arrow keys when the canvas has the focus. Clicking in the canvas gives the canvas the focus, and when the canvas has the focus a thick black border is drawn around the canvas.
====Display Line====
The 'Display Line' tool will display the highlight the selected line, and display the file line count, counting from one, and the gcode line itself. When the 'Display Line' tool is active, clicking the canvas will select the closest line to the mouse click.
====Viewpoint Move====
The 'Viewpoint Move' tool will move the viewpoint in the xy plane when the mouse is clicked and dragged on the canvas.
====Viewpoint Rotate====
The 'Viewpoint Rotate' tool will rotate the viewpoint around the origin, when the mouse is clicked and dragged on the canvas, or the arrow keys have been used and <Return> is pressed. The viewpoint can also be moved by dragging the mouse. The viewpoint latitude will be increased when the mouse is dragged from the center towards the edge. The viewpoint longitude will be changed by the amount around the center the mouse is dragged. This is not very intuitive, but I don't know how to do this the intuitive way and I have other stuff to develop. If the shift key is pressed; if the latitude is changed more than the longitude, only the latitude will be changed, if the longitude is changed more only the longitude will be changed.
===Number of Fill Layers===
====Number of Fill Bottom Layers====
Default is one.
The "Number of Fill Bottom Layers" is the number of layers at the bottom which will be colored olive.
===Number of Fill Top Layers===
Default is one.
The "Number of Fill Top Layers" is the number of layers at the top which will be colored blue.
===Scale===
Default is ten.
The scale setting is the scale of the image in pixels per millimeter, the higher the number, the greater the size of the display.
The zoom in mouse tool will zoom in the display at the point where the mouse was clicked, increasing the scale by a factor of two. The zoom out tool will zoom out the display at the point where the mouse was clicked, decreasing the scale by a factor of two.
===Screen Inset===
====Screen Horizontal Inset====
Default is one hundred.
The "Screen Horizontal Inset" determines how much the canvas will be inset in the horizontal direction from the edge of screen, the higher the number the more it will be inset and the smaller it will be.
====Screen Vertical Inset====
Default is two hundred and twenty.
The "Screen Vertical Inset" determines how much the canvas will be inset in the vertical direction from the edge of screen, the higher the number the more it will be inset and the smaller it will be..
===Viewpoint===
====Viewpoint Latitude====
Default is fifteen degrees.
The "Viewpoint Latitude" is the latitude of the viewpoint, a latitude of zero is the top pole giving a top view, a latitude of ninety gives a side view and a latitude of 180 gives a bottom view.
====Viewpoint Longitude====
Default is 210 degrees.
The "Viewpoint Longitude" is the longitude of the viewpoint.
===Width===
The width of each type of thread and of each axis can be changed. If the width is set to zero, the thread will not be visible.
====Width of Axis Negative Side====
Default is two.
Defines the width of the negative side of the axis.
====Width of Axis Positive Side====
Default is six.
Defines the width of the positive side of the axis.
====Width of Infill Thread====
Default is one.
The "Width of Infill Thread" sets the width of the green extrusion threads, those threads which are not loops and not part of the raft.
====Width of Fill Bottom Thread====
Default is two.
The "Width of Fill Bottom Thread" sets the width of the olive extrusion threads at the bottom of the model.
====Width of Fill Top Thread====
Default is two.
The "Width of Fill Top Thread" sets the width of the blue extrusion threads at the top of the model.
====Width of Loop Thread====
Default is three.
The "Width of Loop Thread" sets the width of the yellow loop threads, which are not edges.
====Width of Perimeter Inside Thread====
Default is eight.
The "Width of Perimeter Inside Thread" sets the width of the orange inside edge threads.
====Width of Perimeter Outside Thread====
Default is eight.
The "Width of Perimeter Outside Thread" sets the width of the red outside edge threads.
====Width of Raft Thread====
Default is one.
The "Width of Raft Thread" sets the width of the brown raft threads.
====Width of Selection Thread====
Default is six.
The "Width of Selection Thread" sets the width of the selected line.
====Width of Travel Thread====
Default is zero.
The "Width of Travel Thread" sets the width of the grey extruder off travel threads.
==Icons==
The dive, soar and zoom icons are from Mark James' soarSilk icon set 1.3 at:
http://www.famfamfam.com/lab/icons/silk/
==Gcodes==
An explanation of the gcodes is at:
http://reprap.org/bin/view/Main/Arduino_GCode_Interpreter
and at:
http://reprap.org/bin/view/Main/MCodeReference
A gode example is at:
http://forums.reprap.org/file.php?12,file=565
==Examples==
Below are examples of skeiniso being used. These examples are run in a terminal in the folder which contains Screw Holder_penultimate.gcode and skeiniso.py.
> python skeiniso.py
This brings up the skeiniso dialog.
> python skeiniso.py Screw Holder_penultimate.gcode
This brings up the skeiniso viewer to view the gcode file.
""" |
#!/usr/bin/env python
#
# ##############################################################################
# ##############################################################################
#
# SickGear Process Media extension for NZBGet
# ===========================================
#
# If NZBGet v17+ is installed on the same system as SickGear then as a local install,
#
# 1) Add the location of this extension to NZBGet Settings/PATHS/ScriptDir
#
# 2) Navigate to any named TV category at Settings/Categories, click "Choose" Category.Extensions then Apply SickGear-NG
#
# This is the best set up to automatically get script updates from SickGear
#
# #############
#
# NZBGet version 16 and earlier are no longer supported, please upgrade
#
# ############
#
# Notes:
# Debian doesn't have pip, _if_ requests is needed, try "apt install python-requests"
# -----
# Enjoy
#
# ##############################################################################
# ##############################################################################
#
# Copyright (C) 2016 SickGear Developers
#
# 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.
#
##############################################################################
### NZBGET QUEUE/POST-PROCESSING SCRIPT ###
### QUEUE EVENTS: NZB_ADDED, NZB_DELETED, URL_COMPLETED, NZB_MARKED ###
# Send "Process Media" requests to SickGear
#
# Process Media extension version: 2.6.
# <!--
# For more info and updates please visit forum topic at
# -->
# <span style="display:block;position:absolute;right:20px;top:105px;width:138px;height:74px;background:url(https://raw.githubusercontent.com/SickGear/SickGear/master/gui/slick/images/sickgear.png)"></span>
# <span id="steps-btn" style="display:inline-block;margin-top:10px;padding:5px 10px;cursor:pointer" class="label label-important" onclick="var steps$ = $('#setup-steps'), isShown=-1 !== $('#steps-btn').html().search('View'), ngVersion = parseInt(/^\d+/.exec(Options.option('version'))[0], 10); $('#ng-version').html('v'+ngVersion); (16 < ngVersion) && $('#sgng-newer').show() || $('#sgng-older').show() && $('#sgng-step2').hide(); !isShown ? steps$.hide() && $(this).html('View setup guide') && $(this).removeClass('label-info') && $(this).addClass('label-important'): steps$.show() && $(this).html('Hide setup guide') && $(this).removeClass('label-important') && $(this).addClass('label-info'); return !1;">View setup guide</span>
# <span id="setup-steps" style="display:none;color:#666">
# <span style="display:block;padding:7px 4px;margin-top:3px;background-color:#efefef;border:1px solid #ccc;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px">
# <span style="width:1em;float:left;padding:3px 0 0 3px">
# <span class="label label-important">1</span>
# </span>
# <span style="display:block;margin-left:1.75em;padding:3px 3px 3px 0">
# <span id="sgng-newer" style="display:none">
# With this <span style="font-weight:bold">NZBGet <span id="ng-version"></span></span> installed on the same system as SickGear,
# add the location of this extension to NZBGet Settings/PATHS/ScriptDir
# </span>
# <span id="sgng-older" style="display:none">
# <!-- if python <a href="https://pypi.python.org/pypi/requests" title="requests library page" target="_blank">requests library</a>
# is not installed, then <strong style="font-weight:bold;color:#128D12 !important">sg_base_path</strong> must be set -->
# NZBGet 17.0 or later required, please upgrade.
# </span>
# </span>
# </span>
# <span id="sgng-step2">
# <span style="display:block;padding:7px 4px;margin-top:3px;background-color:#efefef;border:1px solid #ccc;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px">
# <span style="width:1em;float:left;padding:3px 0 0 3px">
# <span class="label label-important">2</span>
# </span>
# <span style="display:block;margin-left:1.75em;padding-left: 0">
# For a TV Category at NZBGet Settings/CATEGORIES, click <span class="btn" style="vertical-align:text-bottom;padding:1px 5px 0;line-height:16px">Choose</span>, enable "<span style="color:#222">SickGear-NG</span>", apply, save all changes, and reload NZBGet
# </span>
# </span> <!-- /sgng-step2 -->
# </span>
# </span> <!-- /setup-steps -->
##############################################################################
### OPTIONS ###
#
#Test connection@Test SickGear connection
#
# <!-- commented out as no longer supported
# <span class="label label-info">
# Optional</span>
# SickGear <span style="font-weight:bold;color:#128D12 !important">base installation path</span>.
# use where NZBGet v16 or older is installed on the same system as SickGear, and no python requests library is installed
# (use "pip list" to check installed modules)
# #sg_base_path=
# -->
# <span class="label label-info">
# Optional</span>
# SickGear server ipaddress [default:IP_ADDRESS aka localhost].
# change if SickGear is not installed on the same localhost as NZBGet
#sg_host=localhost
# <span class="label label-info">
# Optional</span>
# SickGear HTTP Port [default:8081] (1025-65535).
#sg_port=8081
# <span class="label label-info">
# Optional</span>
# SickGear Username.
#sg_username=
# <span class="label label-info">
# Optional</span>
# SickGear Password.
#sg_password=
# <span class="label label-info">
# Optional</span>
# SickGear has SSL enabled [default:No] (yes, no).
#sg_ssl=no
# <span class="label label-warning">
# Advanced use</span>
# SickGear Web Root.
# change if using a custom SickGear web_root setting (e.g. for a reverse proxy)
#sg_web_root=
# <span class="label label-info">
# Optional</span>
# Print more logging messages [default:No] (yes, no).
# For debugging or if you need to report a bug.
#sg_verbose=no
### NZBGET QUEUE/POST-PROCESSING SCRIPT ###
##############################################################################
|
# -*- coding: utf-8 -*-
# This file is part of ranger, the console file manager.
# This configuration file is licensed under the same terms as ranger.
# ===================================================================
#
# NOTE: If you copied this file to /etc/ranger/commands_full.py or
# ~/.config/ranger/commands_full.py, then it will NOT be loaded by ranger,
# and only serve as a reference.
#
# ===================================================================
# This file contains ranger's commands.
# It's all in python; lines beginning with # are comments.
#
# Note that additional commands are automatically generated from the methods
# of the class ranger.core.actions.Actions.
#
# You can customize commands in the files /etc/ranger/commands.py (system-wide)
# and ~/.config/ranger/commands.py (per user).
# They have the same syntax as this file. In fact, you can just copy this
# file to ~/.config/ranger/commands_full.py with
# `ranger --copy-config=commands_full' and make your modifications, don't
# forget to rename it to commands.py. You can also use
# `ranger --copy-config=commands' to copy a short sample commands.py that
# has everything you need to get started.
# But make sure you update your configs when you update ranger.
#
# ===================================================================
# Every class defined here which is a subclass of `Command' will be used as a
# command in ranger. Several methods are defined to interface with ranger:
# execute(): called when the command is executed.
# cancel(): called when closing the console.
# tab(tabnum): called when <TAB> is pressed.
# quick(): called after each keypress.
#
# tab() argument tabnum is 1 for <TAB> and -1 for <S-TAB> by default
#
# The return values for tab() can be either:
# None: There is no tab completion
# A string: Change the console to this string
# A list/tuple/generator: cycle through every item in it
#
# The return value for quick() can be:
# False: Nothing happens
# True: Execute the command afterwards
#
# The return value for execute() and cancel() doesn't matter.
#
# ===================================================================
# Commands have certain attributes and methods that facilitate parsing of
# the arguments:
#
# self.line: The whole line that was written in the console.
# self.args: A list of all (space-separated) arguments to the command.
# self.quantifier: If this command was mapped to the key "X" and
# the user pressed 6X, self.quantifier will be 6.
# self.arg(n): The n-th argument, or an empty string if it doesn't exist.
# self.rest(n): The n-th argument plus everything that followed. For example,
# if the command was "search foo bar a b c", rest(2) will be "bar a b c"
# self.start(n): Anything before the n-th argument. For example, if the
# command was "search foo bar a b c", start(2) will be "search foo"
#
# ===================================================================
# And this is a little reference for common ranger functions and objects:
#
# self.fm: A reference to the "fm" object which contains most information
# about ranger.
# self.fm.notify(string): Print the given string on the screen.
# self.fm.notify(string, bad=True): Print the given string in RED.
# self.fm.reload_cwd(): Reload the current working directory.
# self.fm.thisdir: The current working directory. (A File object.)
# self.fm.thisfile: The current file. (A File object too.)
# self.fm.thistab.get_selection(): A list of all selected files.
# self.fm.execute_console(string): Execute the string as a ranger command.
# self.fm.open_console(string): Open the console with the given string
# already typed in for you.
# self.fm.move(direction): Moves the cursor in the given direction, which
# can be something like down=3, up=5, right=1, left=1, to=6, ...
#
# File objects (for example self.fm.thisfile) have these useful attributes and
# methods:
#
# tfile.path: The path to the file.
# tfile.basename: The base name only.
# tfile.load_content(): Force a loading of the directories content (which
# obviously works with directories only)
# tfile.is_directory: True/False depending on whether it's a directory.
#
# For advanced commands it is unavoidable to dive a bit into the source code
# of ranger.
# ===================================================================
|
"""
Discrete Fourier Transform (:mod:`numpy.fft`)
=============================================
.. currentmodule:: numpy.fft
The SciPy module `scipy.fft` is a more comprehensive superset
of ``numpy.fft``, which includes only a basic set of routines.
Standard FFTs
-------------
.. autosummary::
:toctree: generated/
fft Discrete Fourier transform.
ifft Inverse discrete Fourier transform.
fft2 Discrete Fourier transform in two dimensions.
ifft2 Inverse discrete Fourier transform in two dimensions.
fftn Discrete Fourier transform in N-dimensions.
ifftn Inverse discrete Fourier transform in N dimensions.
Real FFTs
---------
.. autosummary::
:toctree: generated/
rfft Real discrete Fourier transform.
irfft Inverse real discrete Fourier transform.
rfft2 Real discrete Fourier transform in two dimensions.
irfft2 Inverse real discrete Fourier transform in two dimensions.
rfftn Real discrete Fourier transform in N dimensions.
irfftn Inverse real discrete Fourier transform in N dimensions.
Hermitian FFTs
--------------
.. autosummary::
:toctree: generated/
hfft Hermitian discrete Fourier transform.
ihfft Inverse Hermitian discrete Fourier transform.
Helper routines
---------------
.. autosummary::
:toctree: generated/
fftfreq Discrete Fourier Transform sample frequencies.
rfftfreq DFT sample frequencies (for usage with rfft, irfft).
fftshift Shift zero-frequency component to center of spectrum.
ifftshift Inverse of fftshift.
Background information
----------------------
Fourier analysis is fundamentally a method for expressing a function as a
sum of periodic components, and for recovering the function from those
components. When both the function and its Fourier transform are
replaced with discretized counterparts, it is called the discrete Fourier
transform (DFT). The DFT has become a mainstay of numerical computing in
part because of a very fast algorithm for computing it, called the Fast
Fourier Transform (FFT), which was known to Gauss (1805) and was brought
to light in its current form by NAME NAME [CT]_. Press et al. [NR]_
provide an accessible introduction to Fourier analysis and its
applications.
Because the discrete Fourier transform separates its input into
components that contribute at discrete frequencies, it has a great number
of applications in digital signal processing, e.g., for filtering, and in
this context the discretized input to the transform is customarily
referred to as a *signal*, which exists in the *time domain*. The output
is called a *spectrum* or *transform* and exists in the *frequency
domain*.
Implementation details
----------------------
There are many ways to define the DFT, varying in the sign of the
exponent, normalization, etc. In this implementation, the DFT is defined
as
.. math::
A_k = \\sum_{m=0}^{n-1} a_m \\exp\\left\\{-2\\pi i{mk \\over n}\\right\\}
\\qquad k = 0,\\ldots,n-1.
The DFT is in general defined for complex inputs and outputs, and a
single-frequency component at linear frequency :math:`f` is
represented by a complex exponential
:math:`a_m = \\exp\\{2\\pi i\\,f m\\Delta t\\}`, where :math:`\\Delta t`
is the sampling interval.
The values in the result follow so-called "standard" order: If ``A =
fft(a, n)``, then ``A[0]`` contains the zero-frequency term (the sum of
the signal), which is always purely real for real inputs. Then ``A[1:n/2]``
contains the positive-frequency terms, and ``A[n/2+1:]`` contains the
negative-frequency terms, in order of decreasingly negative frequency.
For an even number of input points, ``A[n/2]`` represents both positive and
negative Nyquist frequency, and is also purely real for real input. For
an odd number of input points, ``A[(n-1)/2]`` contains the largest positive
frequency, while ``A[(n+1)/2]`` contains the largest negative frequency.
The routine ``np.fft.fftfreq(n)`` returns an array giving the frequencies
of corresponding elements in the output. The routine
``np.fft.fftshift(A)`` shifts transforms and their frequencies to put the
zero-frequency components in the middle, and ``np.fft.ifftshift(A)`` undoes
that shift.
When the input `a` is a time-domain signal and ``A = fft(a)``, ``np.abs(A)``
is its amplitude spectrum and ``np.abs(A)**2`` is its power spectrum.
The phase spectrum is obtained by ``np.angle(A)``.
The inverse DFT is defined as
.. math::
a_m = \\frac{1}{n}\\sum_{k=0}^{n-1}A_k\\exp\\left\\{2\\pi i{mk\\over n}\\right\\}
\\qquad m = 0,\\ldots,n-1.
It differs from the forward transform by the sign of the exponential
argument and the default normalization by :math:`1/n`.
Type Promotion
--------------
`numpy.fft` promotes ``float32`` and ``complex64`` arrays to ``float64`` and
``complex128`` arrays respectively. For an FFT implementation that does not
promote input arrays, see `scipy.fftpack`.
Normalization
-------------
The argument ``norm`` indicates which direction of the pair of direct/inverse
transforms is scaled and with what normalization factor.
The default normalization (``"backward"``) has the direct (forward) transforms
unscaled and the inverse (backward) transforms scaled by :math:`1/n`. It is
possible to obtain unitary transforms by setting the keyword argument ``norm``
to ``"ortho"`` so that both direct and inverse transforms are scaled by
:math:`1/\\sqrt{n}`. Finally, setting the keyword argument ``norm`` to
``"forward"`` has the direct transforms scaled by :math:`1/n` and the inverse
transforms unscaled (i.e. exactly opposite to the default ``"backward"``).
`None` is an alias of the default option ``"backward"`` for backward
compatibility.
Real and Hermitian transforms
-----------------------------
When the input is purely real, its transform is Hermitian, i.e., the
component at frequency :math:`f_k` is the complex conjugate of the
component at frequency :math:`-f_k`, which means that for real
inputs there is no information in the negative frequency components that
is not already available from the positive frequency components.
The family of `rfft` functions is
designed to operate on real inputs, and exploits this symmetry by
computing only the positive frequency components, up to and including the
Nyquist frequency. Thus, ``n`` input points produce ``n/2+1`` complex
output points. The inverses of this family assumes the same symmetry of
its input, and for an output of ``n`` points uses ``n/2+1`` input points.
Correspondingly, when the spectrum is purely real, the signal is
Hermitian. The `hfft` family of functions exploits this symmetry by
using ``n/2+1`` complex points in the input (time) domain for ``n`` real
points in the frequency domain.
In higher dimensions, FFTs are used, e.g., for image analysis and
filtering. The computational efficiency of the FFT means that it can
also be a faster way to compute large convolutions, using the property
that a convolution in the time domain is equivalent to a point-by-point
multiplication in the frequency domain.
Higher dimensions
-----------------
In two dimensions, the DFT is defined as
.. math::
A_{kl} = \\sum_{m=0}^{M-1} \\sum_{n=0}^{N-1}
a_{mn}\\exp\\left\\{-2\\pi i \\left({mk\\over M}+{nl\\over N}\\right)\\right\\}
\\qquad k = 0, \\ldots, M-1;\\quad l = 0, \\ldots, N-1,
which extends in the obvious way to higher dimensions, and the inverses
in higher dimensions also extend in the same way.
References
----------
.. [CT] NAME NAME and John W. NAME, 1965, "An algorithm for the
machine calculation of complex Fourier series," *Math. Comput.*
19: 297-301.
.. [NR] NAME NAME NAME and NAME
2007, *Numerical Recipes: The Art of Scientific Computing*, ch.
12-13. Cambridge Univ. Press, Cambridge, UK.
Examples
--------
For examples, see the various functions.
""" |
"""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.
""" |
"""
##################################
### Model and Moderation Tests ###
##################################
>>> import datetime
>>> from threadedcomments.models import FreeThreadedComment, ThreadedComment, TestModel
>>> from threadedcomments.models import MARKDOWN, TEXTILE, REST, PLAINTEXT
>>> from django.contrib.auth.models import User
>>> from django.contrib.contenttypes.models import ContentType
>>> from threadedcomments.moderation import moderator, CommentModerator
>>> from django.core import mail
>>> topic = TestModel.objects.create(name = "Test")
>>> user = User.objects.create_user('user', 'EMAIL', password='password')
>>> user2 = User.objects.create_user('user2', 'EMAIL', password='password')
#######################
### ThreadedComment ###
#######################
>>> comment1 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS',
... comment = 'This is fun! This is very fun!',
... )
>>> comment2 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS',
... comment = 'This is stupid! I hate it!',
... )
>>> comment3 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS', parent = comment2,
... comment = 'I agree, the first comment was wrong and you are right!',
... )
>>> comment4 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS',
... comment = 'What are we talking about?',
... )
>>> comment5 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS', parent = comment3,
... comment = "I'm a fanboy!",
... )
>>> comment6 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS', parent = comment1,
... comment = "What are you talking about?",
... )
>>> class Moderator1(CommentModerator):
... enable_field = 'is_public'
... auto_close_field = 'date'
... close_after = 15
>>> moderator.register(TestModel, Moderator1)
>>> comment7 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS',
... comment = "Post moderator addition. Does it still work?",
... )
>>> topic.is_public = False
>>> topic.save()
>>> comment8 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS', parent = comment7,
... comment = "This should not appear, due to enable_field",
... )
>>> moderator.unregister(TestModel)
>>> comment9 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS',
... comment = "This should appear again, due to unregistration",
... )
>>> len(mail.outbox)
0
>>> class Moderator2(CommentModerator):
... enable_field = 'is_public'
... auto_close_field = 'date'
... close_after = 15
... akismet = False
... email_notification = True
>>> moderator.register(TestModel, Moderator2)
>>> comment10 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS',
... comment = "This should not appear again, due to registration with a new manager.",
... )
>>> topic.is_public = True
>>> topic.save()
>>> comment11 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS', parent = comment1,
... comment = "This should appear again.",
... )
>>> len(mail.outbox)
1
>>> mail.outbox = []
>>> topic.date = topic.date - datetime.timedelta(days = 20)
>>> topic.save()
>>> comment12 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS', parent = comment7,
... comment = "This shouldn't appear, due to close_after=15.",
... )
>>> topic.date = topic.date + datetime.timedelta(days = 20)
>>> topic.save()
>>> moderator.unregister(TestModel)
>>> class Moderator3(CommentModerator):
... max_comment_length = 10
>>> moderator.register(TestModel, Moderator3)
>>> comment13 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS', parent = comment7,
... comment = "This shouldn't appear because it has more than 10 chars.",
... )
>>> comment14 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS', parent = comment7,
... comment = "<10chars",
... )
>>> moderator.unregister(TestModel)
>>> class Moderator4(CommentModerator):
... allowed_markup = [REST,]
>>> moderator.register(TestModel, Moderator4)
>>> comment15 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS', parent = comment7,
... comment = "INVALID Markup. Should not show up.", markup=TEXTILE
... )
>>> comment16 = ThreadedComment.objects.create_for_object(
... topic, user = USERNAME ip_address = 'IP_ADDRESS', parent = comment7,
... comment = "VALID Markup. Should show up.", markup=REST
... )
>>> moderator.unregister(TestModel)
>>> tree = ThreadedComment.public.get_tree(topic)
>>> for comment in tree:
... print "%s %s" % (" " * comment.depth, comment.comment)
This is fun! This is very fun!
What are you talking about?
This should appear again.
This is stupid! I hate it!
I agree, the first comment was wrong and you are right!
I'm a fanboy!
What are we talking about?
Post moderator addition. Does it still work?
<10chars
VALID Markup. Should show up.
This should appear again, due to unregistration
>>> tree = ThreadedComment.objects.get_tree(topic)
>>> for comment in tree:
... print "%s %s" % (" " * comment.depth, comment.comment)
This is fun! This is very fun!
What are you talking about?
This should appear again.
This is stupid! I hate it!
I agree, the first comment was wrong and you are right!
I'm a fanboy!
What are we talking about?
Post moderator addition. Does it still work?
This shouldn't appear because it has more than 10 chars.
<10chars
VALID Markup. Should show up.
This should appear again, due to unregistration
>>> tree = ThreadedComment.objects.get_tree(topic, root=comment2)
>>> for comment in tree:
... print "%s %s" % (" " * comment.depth, comment.comment)
This is stupid! I hate it!
I agree, the first comment was wrong and you are right!
I'm a fanboy!
>>> tree = ThreadedComment.objects.get_tree(topic, root=comment2.id)
>>> for comment in tree:
... print "%s %s" % (" " * comment.depth, comment.comment)
This is stupid! I hate it!
I agree, the first comment was wrong and you are right!
I'm a fanboy!
>>>
###########################
### FreeThreadedComment ###
###########################
>>> fcomment1 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS',
... comment = 'This is fun! This is very fun!',
... )
>>> fcomment2 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS',
... comment = 'This is stupid! I hate it!',
... )
>>> fcomment3 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment2,
... comment = 'I agree, the first comment was wrong and you are right!',
... )
>>> fcomment4 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS',
... website="http://www.eflorenzano.com/", email="EMAIL",
... comment = 'What are we talking about?',
... )
>>> fcomment5 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment3,
... comment = "I'm a fanboy!",
... )
>>> fcomment6 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment1,
... comment = "What are you talking about?",
... )
>>> moderator.register(TestModel, Moderator1)
>>> fcomment7 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS',
... comment = "Post moderator addition. Does it still work?",
... )
>>> topic.is_public = False
>>> topic.save()
>>> fcomment8 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment7,
... comment = "This should not appear, due to enable_field",
... )
>>> moderator.unregister(TestModel)
>>> fcomment9 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS',
... comment = "This should appear again, due to unregistration",
... )
>>> len(mail.outbox)
0
>>> moderator.register(TestModel, Moderator2)
>>> fcomment10 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS',
... comment = "This should not appear again, due to registration with a new manager.",
... )
>>> topic.is_public = True
>>> topic.save()
>>> fcomment11 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment1,
... comment = "This should appear again.",
... )
>>> len(mail.outbox)
1
>>> mail.outbox = []
>>> topic.date = topic.date - datetime.timedelta(days = 20)
>>> topic.save()
>>> fcomment12 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment7,
... comment = "This shouldn't appear, due to close_after=15.",
... )
>>> topic.date = topic.date + datetime.timedelta(days = 20)
>>> topic.save()
>>> moderator.unregister(TestModel)
>>> moderator.register(TestModel, Moderator3)
>>> fcomment13 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment7,
... comment = "This shouldn't appear because it has more than 10 chars.",
... )
>>> fcomment14 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment7,
... comment = "<10chars",
... )
>>> moderator.unregister(TestModel)
>>> class Moderator5(CommentModerator):
... allowed_markup = [REST,]
... max_depth = 3
>>> moderator.register(TestModel, Moderator5)
>>> fcomment15 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment7,
... comment = "INVALID Markup. Should not show up.", markup=TEXTILE
... )
>>> fcomment16 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = None,
... comment = "VALID Markup. Should show up.", markup=REST
... )
>>> fcomment17 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment16,
... comment = "Building Depth...Should Show Up.", markup=REST
... )
>>> fcomment18 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment17,
... comment = "More Depth...Should Show Up.", markup=REST
... )
>>> fcomment19 = FreeThreadedComment.objects.create_for_object(
... topic, name = "Eric", ip_address = 'IP_ADDRESS', parent = fcomment18,
... comment = "Too Deep..Should NOT Show UP", markup=REST
... )
>>> moderator.unregister(TestModel)
>>> tree = FreeThreadedComment.public.get_tree(topic)
>>> for comment in tree:
... print "%s %s" % (" " * comment.depth, comment.comment)
This is fun! This is very fun!
What are you talking about?
This should appear again.
This is stupid! I hate it!
I agree, the first comment was wrong and you are right!
I'm a fanboy!
What are we talking about?
Post moderator addition. Does it still work?
<10chars
This should appear again, due to unregistration
VALID Markup. Should show up.
Building Depth...Should Show Up.
More Depth...Should Show Up.
>>> tree = FreeThreadedComment.objects.get_tree(topic)
>>> for comment in tree:
... print "%s %s" % (" " * comment.depth, comment.comment)
This is fun! This is very fun!
What are you talking about?
This should appear again.
This is stupid! I hate it!
I agree, the first comment was wrong and you are right!
I'm a fanboy!
What are we talking about?
Post moderator addition. Does it still work?
This shouldn't appear because it has more than 10 chars.
<10chars
This should appear again, due to unregistration
VALID Markup. Should show up.
Building Depth...Should Show Up.
More Depth...Should Show Up.
>>> tree = FreeThreadedComment.objects.get_tree(topic, root=comment2)
>>> for comment in tree:
... print "%s %s" % (" " * comment.depth, comment.comment)
This is stupid! I hate it!
I agree, the first comment was wrong and you are right!
I'm a fanboy!
>>> tree = FreeThreadedComment.objects.get_tree(topic, root=comment2.id)
>>> for comment in tree:
... print "%s %s" % (" " * comment.depth, comment.comment)
This is stupid! I hate it!
I agree, the first comment was wrong and you are right!
I'm a fanboy!
>>>
############################
### Views and URLs Tests ###
############################
>>> from django.core.urlresolvers import reverse
>>> from django.test.client import Client
>>> from django.utils.simplejson import loads
>>> from xml.dom.minidom import parseString
>>> topic = TestModel.objects.create(name = "Test2")
>>> old_topic = topic
>>> content_type = ContentType.objects.get_for_model(topic)
>>>
#######################################
### FreeThreadedComments URLs Tests ###
#######################################
>>> c = Client()
>>> url = reverse('tc_free_comment',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id}
... )
>>> response = c.post(url, {'comment' : 'test1', 'name' : 'eric', 'website' : 'http://www.eflorenzano.com/', 'email' : 'EMAIL', 'next' : '/'})
>>> FreeThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'website': u'http://www.eflorenzano.com/', 'comment': u'test1', 'name': u'eric', 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'is_public': True, 'ip_address': None, 'email': u'EMAIL', 'is_approved': False}
# Testing Preview
>>> response = c.post(url, {'comment' : 'test1', 'name' : 'eric', 'website' : 'http://www.eflorenzano.com/', 'email' : 'EMAIL', 'next' : '/', 'preview' : 'True'})
>>> len(response.content) > 0
True
# Testing Edit
>>> latest = FreeThreadedComment.objects.latest('date_submitted')
>>> url = reverse('tc_free_comment_edit', kwargs={'edit_id' : latest.pk})
>>> response = c.post(url, {'comment' : 'test1_edited', 'name' : 'eric', 'website' : 'http://www.eflorenzano.com/', 'email' : 'EMAIL', 'next' : '/'})
>>> FreeThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'website': u'http://www.eflorenzano.com/', 'comment': u'test1_edited', 'name': u'eric', 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'is_public': True, 'ip_address': None, 'email': u'EMAIL', 'is_approved': False}
>>> latest.save()
# Testing Edit With Preview
>>> response = c.post(url, {'comment' : 'test1_edited', 'name' : 'eric', 'website' : 'http://www.eflorenzano.com/', 'email' : 'EMAIL', 'next' : '/', 'preview' : 'True'})
>>> FreeThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'website': u'http://www.eflorenzano.com/', 'comment': u'test1', 'name': u'eric', 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'is_public': True, 'ip_address': None, 'email': u'EMAIL', 'is_approved': False}
>>> len(response.content) > 0
True
>>> url = reverse('tc_free_comment_ajax',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id,
... 'ajax' : 'json'}
... )
>>> response = c.post(url, {'comment' : 'test2', 'name' : 'eric', 'website' : 'http://www.eflorenzano.com/', 'email' : 'EMAIL'})
>>> tmp = loads(response.content)
>>> FreeThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'website': u'http://www.eflorenzano.com/', 'comment': u'test2', 'name': u'eric', 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'is_public': True, 'ip_address': None, 'email': u'EMAIL', 'is_approved': False}
# Testing Edit AJAX JSON
>>> latest = FreeThreadedComment.objects.latest('date_submitted')
>>> url = reverse('tc_free_comment_edit_ajax',
... kwargs={'edit_id': latest.pk, 'ajax' : 'json'})
>>> response = c.post(url, {'comment' : 'test2_edited', 'name' : 'eric', 'website' : 'http://www.eflorenzano.com/', 'email' : 'EMAIL'})
>>> tmp = loads(response.content)
>>> FreeThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'website': u'http://www.eflorenzano.com/', 'comment': u'test2_edited', 'name': u'eric', 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'is_public': True, 'ip_address': None, 'email': u'EMAIL', 'is_approved': False}
>>> latest.save()
# Testing Edit AJAX XML
>>> url = reverse('tc_free_comment_edit_ajax',
... kwargs={'edit_id': latest.pk, 'ajax' : 'xml'})
>>> response = c.post(url, {'comment' : 'test2_edited', 'name' : 'eric', 'website' : 'http://www.eflorenzano.com/', 'email' : 'EMAIL'})
>>> tmp = parseString(response.content)
>>> FreeThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'website': u'http://www.eflorenzano.com/', 'comment': u'test2_edited', 'name': u'eric', 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'is_public': True, 'ip_address': None, 'email': u'EMAIL', 'is_approved': False}
>>> latest.save()
>>> url = reverse('tc_free_comment_ajax',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id,
... 'ajax' : 'xml'}
... )
>>> response = c.post(url, {'comment' : 'test3', 'name' : 'eric', 'website' : 'http://www.eflorenzano.com/', 'email' : 'EMAIL', 'next' : '/'})
>>> tmp = parseString(response.content)
>>> FreeThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'website': u'http://www.eflorenzano.com/', 'comment': u'test3', 'name': u'eric', 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'is_public': True, 'ip_address': None, 'email': u'EMAIL', 'is_approved': False}
>>> parent = FreeThreadedComment.objects.latest('date_submitted')
>>> url = reverse('tc_free_comment_parent',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id,
... 'parent_id' : parent.id}
... )
>>> response = c.post(url, {'comment' : 'test4', 'name' : 'eric', 'website' : 'http://www.eflorenzano.com/', 'email' : 'EMAIL', 'next' : '/'})
>>> FreeThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'website': u'http://www.eflorenzano.com/', 'comment': u'test4', 'name': u'eric', 'parent': <FreeThreadedComment: test3>, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'is_public': True, 'ip_address': None, 'email': u'EMAIL', 'is_approved': False}
>>> url = reverse('tc_free_comment_parent_ajax',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id,
... 'parent_id' : parent.id, 'ajax' : 'json'}
... )
>>> response = c.post(url, {'comment' : 'test5', 'name' : 'eric', 'website' : 'http://www.eflorenzano.com/', 'email' : 'EMAIL'})
>>> tmp = loads(response.content)
>>> FreeThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'website': u'http://www.eflorenzano.com/', 'comment': u'test5', 'name': u'eric', 'parent': <FreeThreadedComment: test3>, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'is_public': True, 'ip_address': None, 'email': u'EMAIL', 'is_approved': False}
>>> url = reverse('tc_free_comment_parent_ajax',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id,
... 'parent_id' : parent.id, 'ajax' : 'xml'}
... )
>>> response = c.post(url, {'comment' : 'test6', 'name' : 'eric', 'website' : 'http://www.eflorenzano.com/', 'email' : 'EMAIL'})
>>> tmp = parseString(response.content)
>>> FreeThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'website': u'http://www.eflorenzano.com/', 'comment': u'test6', 'name': u'eric', 'parent': <FreeThreadedComment: test3>, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'is_public': True, 'ip_address': None, 'email': u'EMAIL', 'is_approved': False}
###################################
### ThreadedComments URLs Tests ###
###################################
>>> u = User.objects.create_user('testuser', 'EMAIL', password='password')
>>> u.is_active = True
>>> u.save()
>>> c.login(username='testuser', password='password')
True
>>> url = reverse('tc_comment',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id}
... )
>>> response = c.post(url, {'comment' : 'test7', 'next' : '/'})
>>> ThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'comment': u'test7', 'is_approved': False, 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'user': <User: USERNAME 'is_public': True, 'ip_address': None}
# Testing Preview
>>> response = c.post(url, {'comment' : 'test7', 'next' : '/', 'preview' : 'True'})
>>> len(response.content) > 0
True
# Testing Edit
>>> latest = ThreadedComment.objects.latest('date_submitted')
>>> url = reverse('tc_comment_edit', kwargs={'edit_id' : latest.pk})
>>> response = c.post(url, {'comment' : 'test7_edited', 'next' : '/'})
>>> ThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'comment': u'test7_edited', 'is_approved': False, 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'user': <User: USERNAME 'is_public': True, 'ip_address': None}
>>> latest.save()
# Testing Edit With Preview
>>> response = c.post(url, {'comment' : 'test7_edited', 'next' : '/', 'preview' : 'True'})
>>> len(response.content) > 0
True
>>> ThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'comment': u'test7', 'is_approved': False, 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'user': <User: USERNAME 'is_public': True, 'ip_address': None}
>>> url = reverse('tc_comment_ajax',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id,
... 'ajax' : 'json'}
... )
>>> response = c.post(url, {'comment' : 'test8'})
>>> tmp = loads(response.content)
>>> ThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'comment': u'test8', 'is_approved': False, 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'user': <User: USERNAME 'is_public': True, 'ip_address': None}
# Testing Edit AJAX JSON
>>> latest = ThreadedComment.objects.latest('date_submitted')
>>> url = reverse('tc_comment_edit_ajax', kwargs={'edit_id': latest.pk, 'ajax' : 'json'})
>>> response = c.post(url, {'comment' : 'test8_edited'})
>>> tmp = loads(response.content)
>>> ThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'comment': u'test8_edited', 'is_approved': False, 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'user': <User: USERNAME 'is_public': True, 'ip_address': None}
>>> latest.save()
# Testing Edit AJAX XML
>>> url = reverse('tc_comment_edit_ajax', kwargs={'edit_id': latest.pk, 'ajax' : 'xml'})
>>> response = c.post(url, {'comment' : 'test8_edited'})
>>> tmp = parseString(response.content)
>>> ThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'comment': u'test8_edited', 'is_approved': False, 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'user': <User: USERNAME 'is_public': True, 'ip_address': None}
>>> latest.save()
>>> url = reverse('tc_comment_ajax',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id,
... 'ajax' : 'xml'}
... )
>>> response = c.post(url, {'comment' : 'test9'})
>>> tmp = parseString(response.content)
>>> ThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'comment': u'test9', 'is_approved': False, 'parent': None, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'user': <User: USERNAME 'is_public': True, 'ip_address': None}
>>> parent = ThreadedComment.objects.latest('date_submitted')
>>> url = reverse('tc_comment_parent',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id,
... 'parent_id' : parent.id}
... )
>>> response = c.post(url, {'comment' : 'test10', 'next' : '/'})
>>> ThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'comment': u'test10', 'is_approved': False, 'parent': <ThreadedComment: test9>, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'user': <User: USERNAME 'is_public': True, 'ip_address': None}
>>> url = reverse('tc_comment_parent_ajax',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id,
... 'parent_id' : parent.id, 'ajax' : 'json'}
... )
>>> response = c.post(url, {'comment' : 'test11'})
>>> tmp = loads(response.content)
>>> ThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'comment': u'test11', 'is_approved': False, 'parent': <ThreadedComment: test9>, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'user': <User: USERNAME 'is_public': True, 'ip_address': None}
>>> url = reverse('tc_comment_parent_ajax',
... kwargs={'content_type': content_type.id, 'object_id' : topic.id,
... 'parent_id' : parent.id, 'ajax' : 'xml'}
... )
>>> response = c.post(url, {'comment' : 'test12'})
>>> tmp = parseString(response.content)
>>> ThreadedComment.objects.latest('date_submitted').get_base_data(show_dates=False)
{'comment': u'test12', 'is_approved': False, 'parent': <ThreadedComment: test9>, 'markup': u'plaintext', 'content_object': <TestModel: TestModel object>, 'user': <User: USERNAME 'is_public': True, 'ip_address': None}
>>>
######################
### DELETION Tests ###
######################
###########################
### FreeThreadedComment ###
###########################
>>> latest = FreeThreadedComment.objects.latest('date_submitted')
>>> latest_id = latest.pk
>>> non_used_user = User.objects.create_user('user999', 'EMAIL', password='password2')
>>> latest.user = non_used_user
>>> latest.save()
>>> url = reverse('tc_free_comment_delete',
... kwargs={'object_id':latest_id})
>>> response = c.post(url, {'next' : '/'})
>>> response['Location'].split('?')[-1] == 'next=/freecomment/%d/delete/' % latest_id
True
>>> u.is_superuser = True
>>> u.save()
>>> response = c.post(url, {'next' : '/'})
>>> response['Location']
'http://testserver/'
>>> FreeThreadedComment.objects.get(id=latest_id)
Traceback (most recent call last):
...
DoesNotExist: FreeThreadedComment matching query does not exist.
>>> latest.save()
>>> response = c.get(url, {'next' : '/'})
>>> len(response.content) > 0
True
>>> FreeThreadedComment.objects.get(id=latest_id) != None
True
>>> u.is_superuser = False
>>> u.save()
#######################
### ThreadedComment ###
#######################
>>> latest = ThreadedComment.objects.latest('date_submitted')
>>> latest_id = latest.pk
>>> latest.user = non_used_user
>>> latest.save()
>>> url = reverse('tc_comment_delete',
... kwargs={'object_id':latest_id})
>>> response = c.post(url, {'next' : '/'})
>>> response['Location'].split('?')[-1]
'next=/comment/18/delete/'
>>> u.is_superuser = True
>>> u.save()
>>> response = c.post(url, {'next' : '/'})
>>> response['Location']
'http://testserver/'
>>> ThreadedComment.objects.get(id=latest_id)
Traceback (most recent call last):
...
DoesNotExist: ThreadedComment matching query does not exist.
>>> latest.save()
>>> response = c.get(url, {'next' : '/'})
>>> len(response.content) > 0
True
>>> ThreadedComment.objects.get(id=latest_id) != None
True
#########################
### Templatetag Tests ###
#########################
>>> from django.template import Context, Template
>>> from threadedcomments.templatetags import threadedcommentstags as tags
>>> topic = TestModel.objects.create(name = "Test3")
>>> c = Context({'topic' : topic, 'old_topic' : old_topic, 'parent' : comment9})
>>> Template('{% load threadedcommentstags %}{% get_comment_url topic %}').render(c)
u'/comment/10/3/'
>>> Template('{% load threadedcommentstags %}{% get_comment_url topic parent %}').render(c)
u'/comment/10/3/8/'
>>> Template('{% load threadedcommentstags %}{% get_comment_url_json topic %}').render(c)
u'/comment/10/3/json/'
>>> Template('{% load threadedcommentstags %}{% get_comment_url_xml topic %}').render(c)
u'/comment/10/3/xml/'
>>> Template('{% load threadedcommentstags %}{% get_comment_url_json topic parent %}').render(c)
u'/comment/10/3/8/json/'
>>> Template('{% load threadedcommentstags %}{% get_comment_url_xml topic parent %}').render(c)
u'/comment/10/3/8/xml/'
>>> Template('{% load threadedcommentstags %}{% get_comment_count for old_topic as count %}{{ count }}').render(c)
u'6'
>>> Template('{% load threadedcommentstags %}{% get_latest_comments 2 as comments %}{{ comments }}').render(c)
u'[<ThreadedComment: test12>, <ThreadedComment: test11>]'
>>> Template('{% load threadedcommentstags %}{% get_threaded_comment_form as form %}{{ form }}').render(c)
u'<tr><th><label for="id_comment">comment:</label></th><td><textarea id="id_comment" rows="10" cols="40" name="comment"></textarea></td></tr>\\n<tr><th><label for="id_markup">Markup:</label></th><td><select name="markup" id="id_markup">\\n<option value="">---------</option>\\n<option value="1">markdown</option>\\n<option value="2">textile</option>\\n<option value="3">restructuredtext</option>\\n<option value="5" selected="selected">plaintext</option>\\n</select></td></tr>'
>>> c = Context({'topic' : topic, 'old_topic' : old_topic, 'parent' : FreeThreadedComment.objects.latest('date_submitted')})
>>> Template('{% load threadedcommentstags %}{% get_free_comment_url topic %}').render(c)
u'/freecomment/10/3/'
>>> Template('{% load threadedcommentstags %}{% get_free_comment_url topic parent %}').render(c)
u'/freecomment/10/3/20/'
>>> Template('{% load threadedcommentstags %}{% get_free_comment_url_json topic %}').render(c)
u'/freecomment/10/3/json/'
>>> Template('{% load threadedcommentstags %}{% get_free_comment_url_xml topic %}').render(c)
u'/freecomment/10/3/xml/'
>>> Template('{% load threadedcommentstags %}{% get_free_comment_url_json topic parent %}').render(c)
u'/freecomment/10/3/20/json/'
>>> Template('{% load threadedcommentstags %}{% get_free_comment_url_xml topic parent %}').render(c)
u'/freecomment/10/3/20/xml/'
>>> Template('{% load threadedcommentstags %}{% get_free_comment_count for old_topic as count %}{{ count }}').render(c)
u'6'
>>> Template('{% load threadedcommentstags %}{% get_latest_free_comments 2 as comments %}{{ comments }}').render(c)
u'[<FreeThreadedComment: test6>, <FreeThreadedComment: test5>]'
>>> Template('{% load threadedcommentstags %}{% get_free_threaded_comment_form as form %}{{ form }}').render(c)
u'<tr><th><label for="id_name">Name:</label></th><td><input id="id_name" type="text" name="name" maxlength="128" /></td></tr>\\n<tr><th><label for="id_website">Site:</label></th><td><input id="id_website" type="text" name="website" maxlength="200" /></td></tr>\\n<tr><th><label for="id_email">E-mail address:</label></th><td><input id="id_email" type="text" name="email" maxlength="75" /></td></tr>\\n<tr><th><label for="id_comment">comment:</label></th><td><textarea id="id_comment" rows="10" cols="40" name="comment"></textarea></td></tr>\\n<tr><th><label for="id_markup">Markup:</label></th><td><select name="markup" id="id_markup">\\n<option value="">---------</option>\\n<option value="1">markdown</option>\\n<option value="2">textile</option>\\n<option value="3">restructuredtext</option>\\n<option value="5" selected="selected">plaintext</option>\\n</select></td></tr>'
>>> c = Context({'topic' : old_topic, 'parent' : FreeThreadedComment.objects.latest('date_submitted'), 'user':user})
>>> Template('{% load threadedcommentstags %}{% get_free_threaded_comment_tree for topic as tree %}[{% for item in tree %}({{ item.depth }}){{ item.comment }},{% endfor %}]').render(c)
u'[(0)test1,(0)test2,(0)test3,(1)test4,(1)test5,(1)test6,]'
>>> Template('{% load threadedcommentstags %}{% get_free_threaded_comment_tree for topic 17 as tree %}[{% for item in tree %}({{ item.depth }}){{ item.comment }},{% endfor %}]').render(c)
u'[(0)test3,(1)test4,(1)test5,(1)test6,]'
>>> Template('{% load threadedcommentstags %}{% get_threaded_comment_tree for topic as tree %}[{% for item in tree %}({{ item.depth }}){{ item.comment }},{% endfor %}]').render(c)
u'[(0)test7,(0)test8,(0)test9,(1)test10,(1)test11,(1)test12,]'
>>> Template('{% load threadedcommentstags %}{% get_threaded_comment_tree for topic 15 as tree %}[{% for item in tree %}({{ item.depth }}){{ item.comment }},{% endfor %}]').render(c)
u'[(0)test9,(1)test10,(1)test11,(1)test12,]'
>>> Template('{% load threadedcommentstags %}{% get_user_comments for user as comments %}{{ comments }}').render(c)
u'[<ThreadedComment: VALID Markup. Should show up.>, <ThreadedComment: <10chars>, <ThreadedComment: This shouldn't appear because it has more than 10 ...>, <ThreadedComment: This should appear again.>, <ThreadedComment: This should appear again, due to unregistration>, <ThreadedComment: Post moderator addition. Does it still work?>, <ThreadedComment: What are you talking about?>, <ThreadedComment: I'm a fanboy!>, <ThreadedComment: What are we talking about?>, <ThreadedComment: I agree, the first comment was wrong and you are r...>, <ThreadedComment: This is stupid! I hate it!>, <ThreadedComment: This is fun! This is very fun!>]'
>>> Template('{% load threadedcommentstags %}{% get_user_comment_count for user as comment_count %}{{ comment_count }}').render(c)
u'12'
>>> markdown_txt = '''
... A First Level Header
... ====================
...
... A Second Level Header
... ---------------------
...
... Now is the time for all good men to come to
... the aid of their country. This is just a
... regular paragraph.
...
... The quick brown fox jumped over the lazy
... dog's back.
...
... ### Header 3
...
... > This is a blockquote.
... >
... > This is the second paragraph in the blockquote.
... >
... > ## This is an H2 in a blockquote
... '''
>>> comment_markdown = ThreadedComment.objects.create_for_object(
... old_topic, user = USERNAME ip_address = 'IP_ADDRESS', markup = MARKDOWN,
... comment = markdown_txt,
... )
>>> c = Context({'comment' : comment_markdown})
>>> Template("{% load threadedcommentstags %}{% auto_transform_markup comment %}").render(c).replace('\\n', '')
u"<h1>...
>>> textile_txt = '''
... h2{color:green}. This is a title
...
... h3. This is a subhead
...
... p{color:red}. This is some text of dubious character. Isn't the use of "quotes" just lazy ... writing -- and theft of 'intellectual property' besides? I think the time has come to see a block quote.
...
... bq[fr]. This is a block quote. I'll admit it's not the most exciting block quote ever devised.
...
... Simple list:
...
... #{color:blue} one
... # two
... # three
...
... Multi-level list:
...
... # one
... ## aye
... ## bee
... ## see
... # two
... ## x
... ## y
... # three
...
... Mixed list:
...
... * Point one
... * Point two
... ## Step 1
... ## Step 2
... ## Step 3
... * Point three
... ** Sub point 1
... ** Sub point 2
...
...
... Well, that went well. How about we insert an <a href="/" title="watch out">old-fashioned ... hypertext link</a>? Will the quote marks in the tags get messed up? No!
...
... "This is a link (optional title)":http://www.textism.com
...
... table{border:1px solid black}.
... |_. this|_. is|_. a|_. header|
... <{background:gray}. |\2. this is|{background:red;width:200px}. a|^<>{height:200px}. row|
... |this|<>{padding:10px}. is|^. another|(bob#bob). row|
...
... An image:
...
... !/common/textist.gif(optional alt text)!
...
... # Librarians rule
... # Yes they do
... # But you knew that
...
... Some more text of dubious character. Here is a noisome string of CAPITAL letters. Here is ... something we want to _emphasize_.
... That was a linebreak. And something to indicate *strength*. Of course I could use <em>my ... own HTML tags</em> if I <strong>felt</strong> like it.
...
... h3. Coding
...
... This <code>is some code, "isn't it"</code>. Watch those quote marks! Now for some preformatted text:
...
... <pre>
... <code>
... $text = str_replace("<p>%::%</p>","",$text);
... $text = str_replace("%::%</p>","",$text);
... $text = str_replace("%::%","",$text);
...
... </code>
... </pre>
...
... This isn't code.
...
...
... So you see, my friends:
...
... * The time is now
... * The time is not later
... * The time is not yesterday
... * We must act
... '''
>>> comment_textile = ThreadedComment.objects.create_for_object(
... old_topic, user = USERNAME ip_address = 'IP_ADDRESS', markup = TEXTILE,
... comment = textile_txt,
... )
>>> c = Context({'comment' : comment_textile})
>>> Template("{% load threadedcommentstags %}{% auto_transform_markup comment %}").render(c)
u'<h2 style="color:green;">This is a title</h2>\\n\\n<h3>This is a subhead</h3>\\n\\n<p style="color:red;">This is some text of dubious character. Isn’t the use of “quotes” just lazy… writing—and theft of ‘intellectual property’ besides? I think the time has come to see a block quote.</p>\\n\\n<blockquote lang="fr">\\n<p>This is a block quote. I’ll admit it’s not the most exciting block quote ever devised.</p>\\n</blockquote>\\n\\n<p>Simple list:</p>\\n\\n<ol>\\n<li style="color:blue;">one</li>\\n<li>two</li>\\n<li>three</li>\\n</ol>\\n\\n<p>Multi-level list:</p>\\n\\n<ol>\\n<li>one\\n<ol>\\n<li>aye</li>\\n<li>bee</li>\\n<li>see</li>\\n</ol>\\n</li>\\n<li>two\\n<ol>\\n<li>x</li>\\n<li>y</li>\\n</ol>\\n</li>\\n<li>three</li>\\n</ol>\\n\\n<p>Mixed list:</p>\\n\\n<ul>\\n<li>Point one</li>\\n<li>Point two<br />\\n## Step 1<br />\\n## Step 2<br />\\n## Step 3</li>\\n<li>Point three\\n<ul>\\n<li>Sub point 1</li>\\n<li>Sub point 2</li>\\n</ul>\\n</li>\\n</ul>\\n\\n<p>Well, that went well. How about we insert an <a href="/" title="watch out">old-fashioned… hypertext link</a>? Will the quote marks in the tags get messed up? No!</p>\\n\\n<p><a href="http://www.textism.com" title="optional title">This is a link</a></p>\\n\\n<table style="border:1px solid black;">\\n<tr>\\n<th>this</th>\\n<th>is</th>\\n<th>a</th>\\n<th>header</th>\\n</tr>\\n<tr style="background:gray;" align="left">\\n<td>\\x02. this is</td>\\n<td style="background:red;width:200px;">a</td>\\n<td style="height:200px;" align="justify" valign="top">row</td>\\n</tr>\\n<tr>\\n<td>this</td>\\n<td style="padding:10px;" align="justify">is</td>\\n<td valign="top">another</td>\\n<td class="bob" id="bob">row</td>\\n</tr>\\n</table>\\n\\n<p>An image:</p>\\n\\n<p><img src="/common/textist.gif" title="optional alt text" alt="optional alt text" /></p>\\n\\n<ol>\\n<li>Librarians rule</li>\\n<li>Yes they do</li>\\n<li>But you knew that</li>\\n</ol>\\n\\n<p>Some more text of dubious character. Here is a noisome string of <span class="caps">CAPITAL</span> letters. Here is… something we want to <em>emphasize</em>. <br />\\nThat was a linebreak. And something to indicate <strong>strength</strong>. Of course I could use <em>my… own <span class="caps">HTML</span> tags</em> if I <strong>felt</strong> like it.</p>\\n\\n<h3>Coding</h3>\\n\\n<p>This <code>is some code, “isn’t it”</code>. Watch those quote marks! Now for some preformatted text:</p>\\n\\n<pre>\\n<code>\\n $text = str_replace("<p>%::%</p>","",$text);\\n $text = str_replace("%::%</p>","",$text);\\n $text = str_replace("%::%","",$text);\\n\\n</code>\\n</pre>\\n\\n<p>This isn’t code.</p>\\n\\n<p>So you see, my friends:</p>\\n\\n<ul>\\n<li>The time is now</li>\\n<li>The time is not later</li>\\n<li>The time is not yesterday</li>\\n<li>We must act</li>\\n</ul>'
>>> rest_txt = '''
... FooBar Header
... =============
... reStructuredText is **nice**. It has its own webpage_.
...
... A table:
...
... ===== ===== ======
... Inputs Output
... ------------ ------
... A B A or B
... ===== ===== ======
... False False False
... True False True
... False True True
... True True True
... ===== ===== ======
...
... RST TracLinks
... -------------
...
... See also ticket `#42`::.
...
... .. _webpage: http://docutils.sourceforge.net/rst.html
... '''
>>> comment_rest = ThreadedComment.objects.create_for_object(
... old_topic, user = USERNAME ip_address = 'IP_ADDRESS', markup = REST,
... comment = rest_txt,
... )
>>> c = Context({'comment' : comment_rest})
>>> Template("{% load threadedcommentstags %}{% auto_transform_markup comment %}").render(c)
u'<p>reStructuredText is...
>>> comment_plaintext = ThreadedComment.objects.create_for_object(
... old_topic, user = USERNAME ip_address = 'IP_ADDRESS', markup = PLAINTEXT,
... comment = '<b>This is Funny</b>',
... )
>>> c = Context({'comment' : comment_plaintext})
>>> Template("{% load threadedcommentstags %}{% auto_transform_markup comment %}").render(c)
u'<b>This is Funny</b>'
>>> comment_plaintext = ThreadedComment.objects.create_for_object(
... old_topic, user = USERNAME ip_address = 'IP_ADDRESS', markup = PLAINTEXT,
... comment = '<b>This is Funny</b>',
... )
>>> c = Context({'comment' : comment_plaintext})
>>> Template("{% load threadedcommentstags %}{% auto_transform_markup comment as abc %}{{ abc }}").render(c)
u'<b>This is Funny</b>'
>>>
##################################
### Gravatar Templatetag Tests ###
##################################
>>> c = Context({'email' : "EMAIL", 'rating' : "G", 'size' : 30, 'default': 'img:blank'})
>>> Template('{% load gravatar %}{% get_gravatar_url for email %}').render(c)
u'http://www.gravatar.com/avatar.php?gravatar_id=04d6b8e8d3c68899ac88eb8623392150&rating=R&size=80&default=http%3A%2F%2Fsite.gravatar.com%2Fimages%2Fcommon%2Ftop%2Flogo.gif'
>>> Template('{% load gravatar %}{% get_gravatar_url for email as var %}Var: {{ var }}').render(c)
u'Var: http://www.gravatar.com/avatar.php?gravatar_id=04d6b8e8d3c68899ac88eb8623392150&rating=R&size=80&default=http%3A%2F%2Fsite.gravatar.com%2Fimages%2Fcommon%2Ftop%2Flogo.gif'
>>> Template('{% load gravatar %}{% get_gravatar_url for email size 30 rating "G" default img:blank as var %}Var: {{ var }}').render(c)
u'Var: http://www.gravatar.com/avatar.php?gravatar_id=04d6b8e8d3c68899ac88eb8623392150&rating=G&size=30&default=img%3Ablank'
>>> Template('{% load gravatar %}{% get_gravatar_url for email size size rating rating default default as var %}Var: {{ var }}').render(c)
u'Var: http://www.gravatar.com/avatar.php?gravatar_id=04d6b8e8d3c68899ac88eb8623392150&rating=G&size=30&default=img%3Ablank'
>>> Template('{% load gravatar %}{{ email|gravatar }}').render(c)
u'http://www.gravatar.com/avatar.php?gravatar_id=04d6b8e8d3c68899ac88eb8623392150&rating=R&size=80&default=http%3A%2F%2Fsite.gravatar.com%2Fimages%2Fcommon%2Ftop%2Flogo.gif'
""" |
"""
This page is in the table of contents.
Stretch is very important Skeinforge plugin that allows you to partially compensate for the fact that extruded holes are smaller then they should be. It stretches the threads to partially compensate for filament shrinkage when extruded.
The stretch manual page is at:
http://fabmetheus.crsndoo.com/wiki/index.php/Skeinforge_Stretch
Extruded holes are smaller than the model because while printing an arc the head is depositing filament on both sides of the arc but in the inside of the arc you actually need less material then on the outside of the arc. You can read more about this on the RepRap ArcCompensation page:
http://reprap.org/bin/view/Main/ArcCompensation
In general, stretch will widen holes and push corners out. In practice the filament contraction will not be identical to the algorithm, so even once the optimal parameters are determined, the stretch script will not be able to eliminate the inaccuracies caused by contraction, but it should reduce them.
All the defaults assume that the thread sequence choice setting in fill is the perimeter being extruded first, then the loops, then the infill. If the thread sequence choice is different, the optimal thread parameters will also be different. In general, if the infill is extruded first, the infill would have to be stretched more so that even after the filament shrinkage, it would still be long enough to connect to the loop or perimeter.
Holes should be made with the correct area for their radius. In other words, for example if your modeling program approximates a hole of radius one (area = pi) by making a square with the points at [(1,0), (0,1), (-1,0), (0,-1)] (area = 2), the radius should be increased by sqrt(pi/2). This can be done in fabmetheus xml by writing:
radiusAreal='True'
in the attributes of the object or any parent of that object. In other modeling programs, you'll have to this manually or make a script. If area compensation is not done, then changing the stretch parameters to over compensate for too small hole areas will lead to incorrect compensation in other shapes.
==Operation==
The default 'Activate Stretch' checkbox is off. When it is on, the functions described below will work, when it is off, the functions will not be called.
==Settings==
===Loop Stretch Over Perimeter Width===
Default is 0.1.
Defines the ratio of the maximum amount the loop aka inner shell threads will be stretched compared to the perimeter width, in general this value should be the same as the 'Perimeter Outside Stretch Over Perimeter Width' setting.
===Path Stretch Over Perimeter Width===
Default is zero.
Defines the ratio of the maximum amount the threads which are not loops, like the infill threads, will be stretched compared to the perimeter width.
===Perimeter===
====Perimeter Inside Stretch Over Perimeter Width====
Default is 0.32.
Defines the ratio of the maximum amount the inside perimeter thread will be stretched compared to the perimeter width, this is the most important setting in stretch. The higher the value the more it will stretch the perimeter and the wider holes will be. If the value is too small, the holes could be drilled out after fabrication, if the value is too high, the holes would be too wide and the part would have to junked.
====Perimeter Outside Stretch Over Perimeter Width====
Default is 0.1.
Defines the ratio of the maximum amount the outside perimeter thread will be stretched compared to the perimeter width, in general this value should be around a third of the 'Perimeter Inside Stretch Over Perimeter Width' setting.
===Stretch from Distance over Perimeter Width===
Default is two.
The stretch algorithm works by checking at each turning point on the extrusion path what the direction of the thread is at a distance of 'Stretch from Distance over Perimeter Width' times the perimeter width, on both sides, and moves the thread in the opposite direction. So it takes the current turning-point, goes "Stretch from Distance over Perimeter Width" * "Perimeter Width" ahead, reads the direction at that point. Then it goes the same distance in back in time, reads the direction at that other point. It then moves the thread in the opposite direction, away from the center of the arc formed by these 2 points+directions.
The magnitude of the stretch increases with:
the amount that the direction of the two threads is similar and
by the '..Stretch Over Perimeter Width' ratio.
==Examples==
The following examples stretch the file Screw Holder Bottom.stl. The examples are run in a terminal in the folder which contains Screw Holder Bottom.stl and stretch.py.
> python stretch.py
This brings up the stretch dialog.
> python stretch.py Screw Holder Bottom.stl
The stretch tool is parsing the file:
Screw Holder Bottom.stl
..
The stretch tool has created the file:
.. Screw Holder Bottom_stretch.gcode
""" |
"""==============
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 unusal uses, but theyare permitted, and they are useful for some
problems. We'll start with thesimplest 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 resulant 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
""" |
"""
Wrappers to LAPACK library
==========================
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)
""" |
"""
=============
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)
""" |
"""
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
================ ===================
Iterators
---------
================ ===================
Arrayterator A buffered iterator for big arrays.
================ ===================
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.
================ ===================
""" |
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#-------------------------------------------------------------------------------
# Name: camera_gui.py
# Purpose: This file supports quality assurance for sensor frames.
#
# Author: NAME Created: 18.09.2014
# Copyright: (c) GrafR 2014
# Licence: Apache 2.0
#-------------------------------------------------------------------------------
#!/usr/bin/env python
|
"""
Statistical Functions
=====================
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_continous.
For each given name the following methods are available. See docstring
for rv_continuous for more information
:rvs:
random variates with the distribution
:pdf:
probability density function
:cdf:
cumulative distribution function
:sf:
survival function (1.0 - cdf)
:ppf:
percent-point function (inverse of cdf)
:isf:
inverse survival function
:stats:
mean, variance, and optionally skew and kurtosis
Calling the instance as a function returns a frozen pdf whose shape,
location, and scale parameters are fixed.
Distributions
---------------
The distributions available with the above methods are:
Continuous (Total == 81 distributions)
---------------------------------------
.. autosummary::
:toctree: generated/
norm Normal (Gaussian)
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-Sanders)
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
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
invnorm Inverse Normal
invgauss Inverse Gaussian
invweibull Inverse Weibull
USERNAME NAME
USERNAME 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
gilbrat Gilbrat
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
pareto Pareto
powerlaw Power-function
powerlognorm Power log normal
powernorm Power normal
rdist R distribution
reciprocal Reciprocal
rayleigh NAME
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
=============== ==============================================================
Discrete (Total == 10 distributions)
==============================================================================
binom Binomial
bernoulli Bernoulli
nbinom Negative Binomial
geom Geometric
hypergeom Hypergeometric
logser Logarithmic (Log-Series, Series)
poisson Poisson
planck Planck (Discrete Exponential)
boltzmann Boltzmann (Truncated Discrete Exponential)
randint Discrete Uniform
zipf Zipf
dlaplace Discrete Laplacian
=============== ==============================================================
Statistical Functions (adapted from NAME ==============================================================
gmean Geometric mean
hmean Harmonic mean
mean Arithmetic mean
cmedian Computed median
median Median
mode Modal value
tmean Truncated arithmetic mean
tvar Truncated variance
tmin _
tmax _
tstd _
tsem _
moment Central moment
variation Coefficient of variation
skew Skewness
kurtosis Fisher or Pearson kurtosis
describe Descriptive statistics
skewtest _
kurtosistest _
normaltest _
================= ==============================================================
================= ==============================================================
itemfreq _
scoreatpercentile _
percentileofscore _
histogram2 _
histogram _
cumfreq _
relfreq _
================= ==============================================================
================= ==============================================================
obrientransform _
signaltonoise _
bayes_mvs _
sem _
zmap _
================= ==============================================================
================= ==============================================================
threshold _
trimboth _
trim1 _
================= ==============================================================
================= ==============================================================
f_oneway _
paired _
pearsonr _
spearmanr _
pointbiserialr _
kendalltau _
linregress _
================= ==============================================================
================= ==============================================================
ttest_1samp _
ttest_ind _
ttest_rel _
kstest _
chisquare _
ks_2samp _
meanwhitneyu _
tiecorrect _
ranksums _
wilcoxon _
kruskal _
friedmanchisquare _
================= ==============================================================
================= ==============================================================
ansari _
bartlett _
levene _
shapiro _
anderson _
binom_test _
fligner _
mood _
oneway _
================= ==============================================================
================= ==============================================================
glm _
================= ==============================================================
================= ==============================================================
Plot-tests
================================================================================
probplot _
ppcc_max _
ppcc_plot _
================= ==============================================================
For many more stat related functions install the software R and the
interface package rpy.
""" |
# """
# ..
# >>> from djangorestframework.parsers import FormParser
# >>> from djangorestframework.compat import RequestFactory
# >>> from djangorestframework.views import View
# >>> from StringIO import StringIO
# >>> from urllib import urlencode
# >>> req = RequestFactory().get('/')
# >>> some_view = View()
# >>> some_view.request = req # Make as if this request had been dispatched
#
# FormParser
# ============
#
# Data flatening
# ----------------
#
# Here is some example data, which would eventually be sent along with a post request :
#
# >>> inpt = urlencode([
# ... ('key1', 'bla1'),
# ... ('key2', 'blo1'), ('key2', 'blo2'),
# ... ])
#
# Default behaviour for :class:`parsers.FormParser`, is to return a single value for each parameter :
#
# >>> (data, files) = FormParser(some_view).parse(StringIO(inpt))
# >>> data == {'key1': 'bla1', 'key2': 'blo1'}
# True
#
# However, you can customize this behaviour by subclassing :class:`parsers.FormParser`, and overriding :meth:`parsers.FormParser.is_a_list` :
#
# >>> class MyFormParser(FormParser):
# ...
# ... def is_a_list(self, key, val_list):
# ... return len(val_list) > 1
#
# This new parser only flattens the lists of parameters that contain a single value.
#
# >>> (data, files) = MyFormParser(some_view).parse(StringIO(inpt))
# >>> data == {'key1': 'bla1', 'key2': ['blo1', 'blo2']}
# True
#
# .. note:: The same functionality is available for :class:`parsers.MultiPartParser`.
#
# Submitting an empty list
# --------------------------
#
# When submitting an empty select multiple, like this one ::
#
# <select multiple="multiple" name="key2"></select>
#
# The browsers usually strip the parameter completely. A hack to avoid this, and therefore being able to submit an empty select multiple, is to submit a value that tells the server that the list is empty ::
#
# <select multiple="multiple" name="key2"><option value="_empty"></select>
#
# :class:`parsers.FormParser` provides the server-side implementation for this hack. Considering the following posted data :
#
# >>> inpt = urlencode([
# ... ('key1', 'blo1'), ('key1', '_empty'),
# ... ('key2', '_empty'),
# ... ])
#
# :class:`parsers.FormParser` strips the values ``_empty`` from all the lists.
#
# >>> (data, files) = MyFormParser(some_view).parse(StringIO(inpt))
# >>> data == {'key1': 'blo1'}
# True
#
# Oh ... but wait a second, the parameter ``key2`` isn't even supposed to be a list, so the parser just stripped it.
#
# >>> class MyFormParser(FormParser):
# ...
# ... def is_a_list(self, key, val_list):
# ... return key == 'key2'
# ...
# >>> (data, files) = MyFormParser(some_view).parse(StringIO(inpt))
# >>> data == {'key1': 'blo1', 'key2': []}
# True
#
# Better like that. Note that you can configure something else than ``_empty`` for the empty value by setting :attr:`parsers.FormParser.EMPTY_VALUE`.
# """
# import httplib, mimetypes
# from tempfile import TemporaryFile
# from django.test import TestCase
# from djangorestframework.compat import RequestFactory
# from djangorestframework.parsers import MultiPartParser
# from djangorestframework.views import View
# from StringIO import StringIO
#
# def encode_multipart_formdata(fields, files):
# """For testing multipart parser.
# fields is a sequence of (name, value) elements for regular form fields.
# files is a sequence of (name, filename, value) elements for data to be uploaded as files
# Return (content_type, body)."""
# BOUNDARY = '----------ThIs_Is_tHe_bouNdaRY_$'
# CRLF = '\r\n'
# L = []
# for (key, value) in fields:
# L.append('--' + BOUNDARY)
# L.append('Content-Disposition: form-data; name="%s"' % key)
# L.append('')
# L.append(value)
# for (key, filename, value) in files:
# L.append('--' + BOUNDARY)
# L.append('Content-Disposition: form-data; name="%s"; filename="%s"' % (key, filename))
# L.append('Content-Type: %s' % get_content_type(filename))
# L.append('')
# L.append(value)
# L.append('--' + BOUNDARY + '--')
# L.append('')
# body = CRLF.join(L)
# content_type = 'multipart/form-data; boundary=%s' % BOUNDARY
# return content_type, body
#
# def get_content_type(filename):
# return mimetypes.guess_type(filename)[0] or 'application/octet-stream'
#
#class TestMultiPartParser(TestCase):
# def setUp(self):
# self.req = RequestFactory()
# self.content_type, self.body = encode_multipart_formdata([('key1', 'val1'), ('key1', 'val2')],
# [('file1', 'pic.jpg', 'blablabla'), ('file1', 't.txt', 'blobloblo')])
#
# def test_multipartparser(self):
# """Ensure that MultiPartParser can parse multipart/form-data that contains a mix of several files and parameters."""
# post_req = RequestFactory().post('/', self.body, content_type=self.content_type)
# view = View()
# view.request = post_req
# (data, files) = MultiPartParser(view).parse(StringIO(self.body))
# self.assertEqual(data['key1'], 'val1')
# self.assertEqual(files['file1'].read(), 'blablabla')
|
# 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
# --------------------------------------------
#
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# ("PSF"), and the Individual or Organization ("Licensee") accessing and
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# Agreement.
|
"""
>>> from scipy import array, matrix
>>> from pybrain.auxiliary.pca import makeCentered
>>> data = array([[2.5, 2.4],
... [0.5, 0.7],
... [2.2, 2.9],
... [1.9, 2.2],
... [3.1, 3.0],
... [2.3, 2.7],
... [2.0, 1.6],
... [1.0, 1.1],
... [1.5, 1.6],
... [1.1, 0.9]])
>>> makeCentered(data)
array([[ 0.69, 0.49],
[-1.31, -1.21],
[ 0.39, 0.99],
[ 0.09, 0.29],
[ 1.29, 1.09],
[ 0.49, 0.79],
[ 0.19, -0.31],
[-0.81, -0.81],
[-0.31, -0.31],
[-0.71, -1.01]])
Tests for regular PCA
---------------------
>>> from pybrain.auxiliary.pca import pca, reduceDim
>>> pca(data, 1)
array([[-0.6778734 , -0.73517866]])
>>> reduceDim(data, 1)
matrix([[-0.82797019],
[ 1.77758033],
[-0.99219749],
[-0.27421042],
[-1.67580142],
[-0.9129491 ],
[ 0.09910944],
[ 1.14457216],
[ 0.43804614],
[ 1.22382056]])
>>> reduceDim(data, 2)
matrix([[-0.82797019, -0.17511531],
[ 1.77758033, 0.14285723],
[-0.99219749, 0.38437499],
[-0.27421042, 0.13041721],
[-1.67580142, -0.20949846],
[-0.9129491 , 0.17528244],
[ 0.09910944, -0.3498247 ],
[ 1.14457216, 0.04641726],
[ 0.43804614, 0.01776463],
[ 1.22382056, -0.16267529]])
>>> data2 = matrix([
... [2.4, 2.5],
... [0.7, 0.5],
... [2.9, 2.2],
... [2.2, 1.9],
... [3.0, 3.1],
... [2.7, 2.3],
... [1.6, 2.0],
... [1.1, 1.0],
... [1.6, 1.5],
... [0.9, 1.1]])
>>> reduceDim(data2, 2)
matrix([[ 0.17511531, 0.82797019],
[-0.14285723, -1.77758033],
[-0.38437499, 0.99219749],
[-0.13041721, 0.27421042],
[ 0.20949846, 1.67580142],
[-0.17528244, 0.9129491 ],
[ 0.3498247 , -0.09910944],
[-0.04641726, -1.14457216],
[-0.01776463, -0.43804614],
[ 0.16267529, -1.22382056]])
>>> data3 = matrix([
... [7.0, 4.0, 3.0],
... [4.0, 1.0, 8.0],
... [6.0, 3.0, 5.0],
... [8.0, 6.0, 1.0],
... [8.0, 5.0, 7.0],
... [7.0, 2.0, 9.0],
... [5.0, 3.0, 3.0],
... [9.0, 5.0, 8.0],
... [7.0, 4.0, 5.0],
... [8.0, 2.0, 2.0]])
>>> reduceDim(data3, 1)
matrix([[-2.15142276],
[ 3.80418259],
[ 0.15321328],
[-4.7065185 ],
[ 1.29375788],
[ 4.0993133 ],
[-1.62582148],
[ 2.11448986],
[-0.2348172 ],
[-2.74637697]])
Tests for probabilistic PCA
---------------------------
>>> from pybrain.auxiliary.pca import pPca
>>> pc = pPca(data, 1)
>>> x, y = pc[0, 0], pc[0, 1]
>>> x / y
0.92...
""" |
"""
========
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).
""" |