| I l@ve RuBoard |
15.5 Grail: A Python-Based Web BrowserI briefly mentioned the Grail browser near the start of Chapter 10. Many of Python's Internet tools date back to and reuse the work that went into Grail, a full-blown Internet web browser that:
As mentioned earlier, Grail was something of a proof-of-concept for using Python to code large-scale Internet applications. It implements all the usual Internet protocols and works much like common browsers such as Netscape and Internet Explorer. Grail pages are implemented with the Tk text widgets that we met in the GUI part of this book. More interestingly, the Grail browser allows applets to be written in Python. Grail applets are simply bits of Python code that live on a server but are run on a client. If an HTML document references a Python class and file that live on a server machine, Grail automatically downloads the Python code over a socket and runs it on the client machine, passing it information about the browser's user interface. The downloaded Python code may use the passed-in browser context information to customize the user interface, add new kinds of widgets to it, and perform arbitrary client-side processing on the local machine. Roughly speaking, Python applets in Grail serve the same purposes as Java applets in common Internet browsers: they perform client-side tasks that are too complex or slow to implement with other technologies such as server-side CGI scripts and generated HTML. 15.5.1 A Simple Grail Applet ExampleWriting Grail applets is remarkably straightforward. In fact, applets are really just Python/Tkinter programs; with a few exceptions, they don't need to "know" about Grail at all. Let's look at a short example; the code in Example 15-6 simply adds a button to the browser, which changes its appearance each time it's pressed (its bitmap is reconfigured in the button callback handler). There are two components to this page definition: an HTML file and the Python applet code it references. As usual, the grail.html HTML file that follows describes how to format the web page when the HTML's URL address is selected in a browser. But here, the APP tag also specifies a Python applet (class) to be run by the browser. By default, the Python module is assumed to have the same name as the class and must be stored in the same location (URL directory) as the HTML file that references it. Additional APP tag options can override the applet's default location. Example 15-6. PP2E\Internet\Other\grail.html <TITLE>Grail Applet Test Page</TITLE>
Test an Applet Here!
 Click this button! <APP CLASS=Question>  The applet file referenced by the HTML is a Python script that adds widgets to the Tkinter-based Grail browser. Applets are simply classes in Python modules. When the APP tag is encountered in the HTML, the Grail browser downloads the Question.py source code module (Example 15-7) and makes an instance of its Question class, passing in the browser widget as the master (parent). The master is the hook that lets applets attach new widgets to the browser itself; applets extend the GUI constructed by the HTML in this way. Example 15-7. PP2E\Internet\Other\Question.py# Python applet file: Question.py # in the same location (URL) as the html file # that references it; adds widgets to browser; from Tkinter import * class Question: # run by grail? def __init__(self, parent): # parent=browser self.button = Button(parent, bitmap='question', command=self.action) self.button.pack( ) def action(self): if self.button['bitmap'] == 'question': self.button.config(bitmap='questhead') else: self.button.config(bitmap='question') if __name__ == '__main__': root = Tk( ) # run standalone? button = Question(root) # parent=Tk: top-level root.mainloop( ) Notice that nothing in this class is Grail- or Internet-specific; in fact, it can be run (and tested) standalone as a Python/Tkinter program. Figure 15-3 is what it looks like if run standalone on Windows (with a Tk application root object as the master); when run by Grail (with the browser/page object as the master), the button appears as part of the web page instead. Either way, its bitmap changes on each press. Figure 15-3. Running a Grail applet standaloneIn effect, Grail applets are simply Python modules that are linked into HTML pages by using the APP tag. The Grail browser downloads the source code identified by an APP tag and runs it locally on the client during the process of creating the new page. New widgets added to the page (like the button here) may run Python callbacks on the client later, when activated by the user. Applets interact with the user by creating one or more arbitrary Tk widgets. Of course, the previous example is artificial; but notice that the button's callback handler could do anything we can program in Python: updating persistent information, popping up new user interaction dialogs, calling C extensions, etc. However, by working in concert with Python's restricted execution mode (discussed later) applets can be prevented from performing potentially unsafe operations, like opening local files and talking over sockets. Figure 15-4 shows a screen shot of Grail in action, hinting at what's possible with Python code downloaded to and run on a client. It shows the animated "game of life" demo; everything you see here is implemented using Python and the Tkinter GUI interface. To run the demo, you need to install Python with the Tk extension and download the Grail browser to run locally on your machine or copy it off the CD. Then point your browser to a URL where any Grail demo lives. Figure 15-4. A Grail applet demoHaving said all that, I should add that Grail is no longer formally maintained, and is now used primarily for research purposes (Guido never intended for Grail to put Netscape or Microsoft out of business). You can still get it for free (find it at http://www.python.org) and use it for surfing the Web or experimenting with alternative web browser concepts, but it is not the active project it was a few years ago. If you want to code web browser applets in Python, the more common approach today is to use the JPython system described previously to compile your scripts into Java applet bytecode files, and use Java libraries for your scripts' user-interface portions. Embedding Python code in HTML with the Active Scripting extension described later in this chapter is yet another way to integrate client-side code.
|
| I l@ve RuBoard |


120th Seng Kut Snem Festival Organized in Shillong
STAFF CORRESPONDENT
SHILLONG: The call to enliven life with righteous living and earning was the cornerstone of the 120th Seng kut snem festival of the Khasi indigenous faith. It was on this day that 16 men organized the Khasi-Pnar population from being swamped by the western evangelist faiths.
On this auspicious occasion where the Khasi-Pnar faithfuls gathered to usher in the rejuvenation of the traditional faith, the Syiem (Chief) of Hima Khyrim, Dr Balajied Syiem categorically stated that outside forces in all formats are working to demoralize the beliefs and practices. In the course of his speech, he said, “Some 200 years ago, they had brought about light in the form of education but in the process they brought darkness by fooling our indigenous people in our own domain.”
Stressing that “no intimidation will deter us from respecting the faith that we have followed till date”, Balajied Syiem urged the present Khasi-Pnar generation of the community’s age-old sermons “Kamai ia ka Hok” (Righteous living) and “Tip Kur Kha” (Respect your paternal relations and know your own clan).
Syiem added that “the people following the indigenous faith are under certain stress due to the intervention of forces that has even touched the core of governance within the traditional society.”
The cultural secretary of the Seng Khasi/Sen Raij, Antonio Lakiang said that “the indigeneous faith is under trying times with outside forces creeping in.” He reminded the gathering that they should “remain firm in their beliefs and take note of the rightful teachings of our forefathers. Be firm in righteous living and righteous earning,” Lakiang appealed to the masses while reminding them of how the indigeneous faith of the Khasis have stood the onslaught of time.
The Vice-Chancellor of North Eastern Hill University (NEHU), SK Srivastava, in his brief speech, spoke about the indigenous Khasi teachings on mankind and the mutual respect shown to nature.
Earlier during the day, thousands of people marched from Madan Iew Rynhep to Madan Weiking to commemorate the historical event. The rich indigeneous Khasi-Pnar culture was also displayed on the occasion.
Also Read: Seng Kut Snem celebrated in Shillong
Also Watch: AGP leadership has not been vocal against the CAB: Prafulla Kumar Mahanta


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CloudCoin Consortium Membership
Membership in the CloudCoin Consortium allows you to have a say in the direction and future of CloudCoins.
Non-Voting Members
Membership Requirements for Non-Voting Members
Becoming a Non-Voting member is easy and allows you to have all the benefits of membership including inside newsletters, attending meetings and conferences designed for members and the opportunity of becoming a Voting Member.
To apply for Non-Voting membership you must:
- Open a free encrypted email account at ProtonMail.com
- Purchase or earn some CloudCoins to show that you are part of the community.
- Gather your contact information along with a statement of interest and a declaration that you will not use CloudCoins for the purpose of activity considered as unpreferable such as rape, slavery, murder, theft, child abuse, pedophilia, suppression of natural rights, etc.
- Submit an application to<EMAIL_ADDRESS>along with a donation of Authentic CloudCoins. Please specify that you are seeking "Non-Voting" Membership.
- After you become a Non-Voting Member, you will need to re-register every year by mailing a donation of authentic CloudCoins to<EMAIL_ADDRESS>Voting Members
Register in 2017 and become a Founding Member. Founding members get to determine their own poll tax and get to decide what the poll tax will be in the future. Later, registration as a Voting Member may have additional requirements including waiting for five years as a Non-Voting Member! Become a Voting Member now and be part of history!
Voting members get to participate in the election of representatives who govern CloudCoin and elect its President. Voting Members can run for office and get elected as representatives.
Advisory elections are scheduled for December 2019 and full elections on December 2021.
To apply for Non-Voting membership you must:
- First be a Non-Voting Member
Send scans or pictures of the following documents: (Note that you can obscure or cover-up any information that is irrelevant)
- Submit proof that you are married.
- Submit proof that you own Property (land or house)
- Submit proof that you have had a child or children
- A criminal Background Check on Yourself for the place that you live showing that you have not committed crimes that have victims.
- Submit proof that you subscribe to medical service, medical insurance or medical savings.
- Submit proof that you have a retirement savings.
- Submit proof that you contribute or have contributed to you children's education.
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- Submit proof that you have security service or own weapons for self-defense.
- Submit proof that you are not on welfare or dependent on someone else's income (spouses excluded).
- Create a statement that expresses your support for Libertarian thinking and opposition to Communism and Socialism.
- Declare the Primary Language that you speak.
- Send all these to<EMAIL_ADDRESS>requesting "Voting Membership" and include a poll tax / donation of Authentic CloudCoins. Only tax payers are allowed to vote!
- After submission you will be given a test on the principles of philosophy. The test will cover the subject matter of three works: John Locke (Two Treatises of Government), Stephan Molynuex (Universally Preferable Behavior) and Chaffee ( The Philosopher's Way). You may take this test more than once.


Clues: A. Everyone starts to understand value in active levying of deposits spread between banks (8) B. Sporty car picking up snippets of ABC radio station perhaps (11) C. Ultimately ironic, they being seen like ugliest ducklings! (7) D. This crap: "Do it! Otherwise..." (12) E. Deer is killed as source of insulation (6) F. Mostly Muslim people at home among peaceful animists (6) G. More energy used by kitchen utensil (7) H. Drops of blood sugar ... unhealthy possibilities involved! (5) I. India damaged having heartless Modi as despot (3 4) J. Dull month after its vacation (6) K. Ms Harris embraces quiet capital (7) L. Judge one of a pair which may be described as child-bearing? (8) M. Pointlessly controlling us, army politics! (8 4) N. How one might feel at sea, under storm... not right? (9) O. In naughty mood, reset clocks in cars (9) P. They treat the young ones, upsetting staid priest (11) Q. As unqualified doctor, lacking a couple of letters at end of name? (3) R. Rereading earlier code on machine, process it linking emended sources (9) S. Votes tossed into furnace (5) T. Leaders of torturous regime escaped with a definite fortune (8) U. Country seized by thug and army (6) V. Callings keeping Lisa from callings? (9) W. Careful to avoid a mocking (3) X. Anesthetic gas, deluxe: no nasties included (5) Y. They end with things often said about 'real' distances (8) Z. He shows his stripes in kamikaze brashness (5)


No DMs, please
Please avoid sending me direct messages (DMs) to ask me questions. (Especially technical questions.)
If you can ask me a question in a public space, please ask it
there.
If you can’t or aren’t sure whether you can ask it in public, feel free
to DM me (but know that my first response might be to request that you
re-ask it in a public space).
Some suggestions for public spaces to ask questions in include:
- the
#discuss
channel on the Sorbet Slack or the Sorbet Twitter account (for Sorbet-related questions), - GitHub Issues and GitHub Discussions where available
- Public work channels (if you and I are co-workers)
The exception: if you have questions for me relating to my employer, or are curious to know whether I’m open to new roles elsewhere, please do email or DM me, as I do not prefer answering these questions in public.
Whatever you do, do not let the first message be merely “Hello” or “👋” because that will ding me immediately, and then I’ll impatiently await the actual question while watching a “user is typing” indicator.
Why?
Questions are excellent moments to teach and share knowledge. When asked in public spaces, a couple good things happen:
Other people get to see the question.
Sometimes other people learn more simply from seeing the questions other people are asking. Seeing other peoples’ questions helps them expand their horizons into new territories.
Other people get to see the answer.
Chances are, you’re neither the first nor the last person to ask me your question. By asking it in the open, it makes it easier for other people to find answers to their questions in the future.
My teammates get a chance to learn from my answers.
Part of my job is to help those around me grow. One way I can do this is by sharing my knowledge with them. When they get to see how I answer questions, it helps share my knowledge with my teammates and equip them to help answer similar questions in the future.
You get answers faster in expectation.
I check all notifications at the same speed. Whether you send me a DM or mention me in a Slack channel, I’ll respond to those notifications at the same speed. For example, if I’m on vacation, I’ll have all notifications paused, and you may not hear back from me for days or weeks. Asking the question in public increases your odds that someone else who can answer your question sees it quickly.
Exceptions
Of course, feel free to send me DMs for situations like these:
If you have something personal or confidential to say. This includes if you have individual feedback to give to me. (Thanks in advance for your feedback.)
You’re not comfortable asking a question in a public space. (If this is the case, please say so, otherwise my first response will likely be to request that you re-ask your question in public.)
If you don’t know of a good public space to ask it. For example, the Sorbet Slack is a good place to ask me Sorbet questions, but is not a good place to ask me questions about other projects. In these cases feel free to contact me directly (but know that if there is a good public forum to ask the question, I may redirect you there first before answering).
To share jokes or memes 😉 (especially those that you think might be misinterpreted if shared with an overly-wide audience).


| column_collection_functions {SparkR} | R Documentation |
Collection functions defined for Column
.
array_contains(x, value) array_distinct(x) array_except(x, y) array_intersect(x, y) array_join(x, delimiter, ...) array_max(x) array_min(x) array_position(x, value) array_remove(x, value) array_repeat(x, count) array_sort(x) arrays_overlap(x, y) array_union(x, y) arrays_zip(x, ...) concat(x, ...) element_at(x, extraction) explode(x) explode_outer(x) flatten(x) from_json(x, schema, ...) map_from_arrays(x, y) map_keys(x) map_values(x) posexplode(x) posexplode_outer(x) reverse(x) shuffle(x) size(x) slice(x, start, length) sort_array(x, asc = TRUE) to_json(x, ...) ## S4 method for signature 'Column' reverse(x) ## S4 method for signature 'Column' to_json(x, ...) ## S4 method for signature 'Column' concat(x, ...) ## S4 method for signature 'Column,characterOrstructType' from_json(x, schema, as.json.array = FALSE, ...)   Note the difference in the following methods:
|
value |
A value to compute on.
|
y |
Column to compute on. |
delimiter |
a character string that is used to concatenate the elements of column. |
... |
additional argument(s). In |
count |
a Column or constant determining the number of repetitions. |
extraction |
index to check for in array or key to check for in map |
schema |
a structType object to use as the schema to use when parsing the JSON string. Since Spark 2.3, the DDL-formatted string is also supported for the schema. |
start |
the starting index |
length |
the length of the slice |
asc |
a logical flag indicating the sorting order. TRUE, sorting is in ascending order. FALSE, sorting is in descending order. |
as.json.array |
indicating if input string is JSON array of objects or a single object. |
nullReplacement |
an optional character string that is used to replace the Null values. |
reverse
: Returns a reversed string or an array with reverse order of elements.
to_json
: Converts a column containing a structType
, a mapType
or an arrayType
into a Column of JSON string.
Resolving the Column can fail if an unsupported type is encountered.
concat
: Concatenates multiple input columns together into a single column.
The function works with strings, binary and compatible array columns.
from_json
: Parses a column containing a JSON string into a Column of structType
with the specified schema
or array of structType
if as.json.array
is set
to TRUE
. If the string is unparseable, the Column will contain the value NA.
array_contains
: Returns null if the array is null, true if the array contains
the value, and false otherwise.
array_distinct
: Removes duplicate values from the array.
array_except
: Returns an array of the elements in the first array but not in the second
array, without duplicates. The order of elements in the result is not determined.
array_intersect
: Returns an array of the elements in the intersection of the given two
arrays, without duplicates.
array_join
: Concatenates the elements of column using the delimiter.
Null values are replaced with nullReplacement if set, otherwise they are ignored.
array_max
: Returns the maximum value of the array.
array_min
: Returns the minimum value of the array.
array_position
: Locates the position of the first occurrence of the given value
in the given array. Returns NA if either of the arguments are NA.
Note: The position is not zero based, but 1 based index. Returns 0 if the given
value could not be found in the array.
array_remove
: Removes all elements that equal to element from the given array.
array_repeat
: Creates an array containing x
repeated the number of times
given by count
.
array_sort
: Sorts the input array in ascending order. The elements of the input array
must be orderable. NA elements will be placed at the end of the returned array.
arrays_overlap
: Returns true if the input arrays have at least one non-null element in
common. If not and both arrays are non-empty and any of them contains a null, it returns null.
It returns false otherwise.
array_union
: Returns an array of the elements in the union of the given two arrays,
without duplicates.
arrays_zip
: Returns a merged array of structs in which the N-th struct contains all N-th
values of input arrays.
shuffle
: Returns a random permutation of the given array.
flatten
: Creates a single array from an array of arrays.
If a structure of nested arrays is deeper than two levels, only one level of nesting is removed.
map_from_arrays
: Creates a new map column. The array in the first column is used for
keys. The array in the second column is used for values. All elements in the array for key
should not be null.
map_keys
: Returns an unordered array containing the keys of the map.
map_values
: Returns an unordered array containing the values of the map.
element_at
: Returns element of array at given index in extraction
if
x
is array. Returns value for the given key in extraction
if x
is map.
Note: The position is not zero based, but 1 based index.
explode
: Creates a new row for each element in the given array or map column.
size
: Returns length of array or map.
slice
: Returns an array containing all the elements in x from the index start
(array indices start at 1, or from the end if start is negative) with the specified length.
sort_array
: Sorts the input array in ascending or descending order according to
the natural ordering of the array elements. NA elements will be placed at the beginning of
the returned array in ascending order or at the end of the returned array in descending order.
posexplode
: Creates a new row for each element with position in the given array
or map column.
explode
: Creates a new row for each element in the given array or map column.
Unlike explode
, if the array/map is null
or empty
then null
is produced.
posexplode_outer
: Creates a new row for each element with position in the given
array or map column. Unlike posexplode
, if the array/map is null
or empty
then the row (null
, null
) is produced.
reverse since 1.5.0
to_json since 2.2.0
concat since 1.5.0
from_json since 2.2.0
array_contains since 1.6.0
array_distinct since 2.4.0
array_except since 2.4.0
array_intersect since 2.4.0
array_join since 2.4.0
array_max since 2.4.0
array_min since 2.4.0
array_position since 2.4.0
array_remove since 2.4.0
array_repeat since 2.4.0
array_sort since 2.4.0
arrays_overlap since 2.4.0
array_union since 2.4.0
arrays_zip since 2.4.0
shuffle since 2.4.0
flatten since 2.4.0
map_from_arrays since 2.4.0
map_keys since 2.3.0
map_values since 2.3.0
element_at since 2.4.0
explode since 1.5.0
size since 1.5.0
slice since 2.4.0
sort_array since 1.6.0
posexplode since 2.1.0
explode_outer since 2.3.0
posexplode_outer since 2.3.0
## Not run:
##D # Dataframe used throughout this doc
##D df <- createDataFrame(cbind(model = rownames(mtcars), mtcars))
##D tmp <- mutate(df, v1 = create_array(df$mpg, df$cyl, df$hp))
##D head(select(tmp, array_contains(tmp$v1, 21), size(tmp$v1), shuffle(tmp$v1)))
##D head(select(tmp, array_max(tmp$v1), array_min(tmp$v1), array_distinct(tmp$v1)))
##D head(select(tmp, array_position(tmp$v1, 21), array_repeat(df$mpg, 3), array_sort(tmp$v1)))
##D head(select(tmp, flatten(tmp$v1), reverse(tmp$v1), array_remove(tmp$v1, 21)))
##D tmp2 <- mutate(tmp, v2 = explode(tmp$v1))
##D head(tmp2)
##D head(select(tmp, posexplode(tmp$v1)))
##D head(select(tmp, slice(tmp$v1, 2L, 2L)))
##D head(select(tmp, sort_array(tmp$v1)))
##D head(select(tmp, sort_array(tmp$v1, asc = FALSE)))
##D tmp3 <- mutate(df, v3 = create_map(df$model, df$cyl))
##D head(select(tmp3, map_keys(tmp3$v3), map_values(tmp3$v3)))
##D head(select(tmp3, element_at(tmp3$v3, "Valiant")))
##D tmp4 <- mutate(df, v4 = create_array(df$mpg, df$cyl), v5 = create_array(df$cyl, df$hp))
##D head(select(tmp4, concat(tmp4$v4, tmp4$v5), arrays_overlap(tmp4$v4, tmp4$v5)))
##D head(select(tmp4, array_except(tmp4$v4, tmp4$v5), array_intersect(tmp4$v4, tmp4$v5)))
##D head(select(tmp4, array_union(tmp4$v4, tmp4$v5)))
##D head(select(tmp4, arrays_zip(tmp4$v4, tmp4$v5), map_from_arrays(tmp4$v4, tmp4$v5)))
##D head(select(tmp, concat(df$mpg, df$cyl, df$hp)))
##D tmp5 <- mutate(df, v6 = create_array(df$model, df$model))
##D head(select(tmp5, array_join(tmp5$v6, "#"), array_join(tmp5$v6, "#", "NULL")))
## End(Not run)
## Not run:
##D # Converts a struct into a JSON object
##D df2 <- sql("SELECT named_struct('date', cast('2000-01-01' as date)) as d")
##D select(df2, to_json(df2$d, dateFormat = 'dd/MM/yyyy'))
##D
##D # Converts an array of structs into a JSON array
##D df2 <- sql("SELECT array(named_struct('name', 'Bob'), named_struct('name', 'Alice')) as people")
##D df2 <- mutate(df2, people_json = to_json(df2$people))
##D
##D # Converts a map into a JSON object
##D df2 <- sql("SELECT map('name', 'Bob')) as people")
##D df2 <- mutate(df2, people_json = to_json(df2$people))
##D
##D # Converts an array of maps into a JSON array
##D df2 <- sql("SELECT array(map('name', 'Bob'), map('name', 'Alice')) as people")
##D df2 <- mutate(df2, people_json = to_json(df2$people))
## End(Not run)
## Not run:
##D df2 <- sql("SELECT named_struct('date', cast('2000-01-01' as date)) as d")
##D df2 <- mutate(df2, d2 = to_json(df2$d, dateFormat = 'dd/MM/yyyy'))
##D schema <- structType(structField("date", "string"))
##D head(select(df2, from_json(df2$d2, schema, dateFormat = 'dd/MM/yyyy')))
##D df2 <- sql("SELECT named_struct('name', 'Bob') as people")
##D df2 <- mutate(df2, people_json = to_json(df2$people))
##D schema <- structType(structField("name", "string"))
##D head(select(df2, from_json(df2$people_json, schema)))
##D head(select(df2, from_json(df2$people_json, "name STRING")))
## End(Not run)
## Not run:
##D df2 <- createDataFrame(data.frame(
##D id = c(1, 2, 3), text = c("a,b,c", NA, "d,e")
##D ))
##D
##D head(select(df2, df2$id, explode_outer(split_string(df2$text, ","))))
##D head(select(df2, df2$id, posexplode_outer(split_string(df2$text, ","))))
## End(Not run)


In this assignment you will install software that will be used during the course. This software includes a programming text editor and a PHP and MySQL Environment. Also as part of this assignment you will learn how to take screenshots and make them into images. The deliverables this the assignment are two peer-graded images of your running software and completing an autograded assignment.
If you follow the instructions carefully and look at the handouts and watch the podcasts, this assignment should be pretty straightforward and take only a few hours.
Note: As you install on Windows, you may be asked for permission to open ports or to allow an application through the firewall - say 'yes' to these dialog boxes if they are popping up when you first install or first start your PHP environment.
If you get stuck on this assignment - feel free to get assistance from anyone who can help. All we want is that you have successfully installed the software and can use it on your system. This is not intended as a difficult or challenging assignment.
There are several sources of information so you can do the assignment:
When you go into CTools -> Lessons -> "Autograded: Your First Database" you will be given a set of SQL statements that are unique to you that you are to run in your PHPMyAdmin.
Following the instructions, use PHPMyAdmin to make a database, and make the table and then run the INSERT statements and take a screen shot. Then run the SELECT statement and enter the data you get from that SELECT statement into the auto-graded portion of the assignment.
The first screen shot for the peer-graded portion of the assignment shows the Browse tab in phpMyAdmin showing the data you inserted.
The second screen shot in the peer-graded portion is to verify that display_errors is On in your PHP configuration:


|
Elektra
0.8.14
|
intensively (except for file header).
This document provides an introduction in how the source code of libelektra is organized and how and where to add functionality.
Make sure to read DESIGN together with this document.
After you downloaded and unpacked Elektra you see untypically many folders. The reason is that Elektra project consists of many activities.
The most important are:
libelektra is the ANSI/ISO C-Core which does interacts between the user and the plugins.
The plugins have all kinds of dependencies. It is the responsibility of the plugins to find and check them using CMake. The same guidelines apply for all code in the repository including the plugins.
libloader
is responsible for loading the backend modules. It works for various operating systems by using libltdl
, but has an optimized code for static linking and win32.
kdb is the commandline-tool to access and initialize the Elektra database.
It is only allowed to break a guideline if there is a good reason for it. When doing so, document the fact either in the commit message, or as comment next to the code.
Of course all rules of good software engineering apply, like to use good names, make software testable and reusable. The purpose of the guidelines is to have a consistent style, not to teach programming.
If you see broken guidelines do not hesitate to fix them. At least put a TODO marker to make the places visible.
If you see inconsistency do not hesitate to talk about it with the intent to add a new rule here.
See DESIGN document too, they complement each other.
/**/
with doxygen style for functions.//
for single line statements about the code in the next line.*
are next to the variable names.const
as much as possible.static
methods if they should not be externally visible..c
, Header files .h
.elektra
for internal usage. External API either starts with ks
, key
or kdb
.Example: src/libelektra/kdb.c
.cpp
, Header files .hpp
.static
, but anonymous namespaces.Example: src/bindings/cpp/include/kdb.hpp
doxygen
is used to document the API and to build the html and pdf output. We support also the import of markdown pages, but a minimum version of 1.8.8 of Doxygen is required for this feature (Anyways you can find the API Doc online). Links between markdown files will be converted with the Markdown Link Converter. Markdown pages are used in the pdf, therefore watch your characters and provide a proper encoding!
@
to start doxygen tags\copydetails
, @copybrief
and @copydetails
intensively (except for file header).Files should start with:
/** * @file * * @brief <short statement about the content of the file> * * @copyright BSD License (see doc/COPYING or http://www.libelektra.org) */
Note:


Similar Words to Happy:
-Adored
-Alive
-Appreciated
-Cheerful
-Ecstatic
Similar Words To Ready To Learn:
-Motivated
-Uplifted
-Driven
-Centered
-Assertive
Similar Words to Excited:
-Ecstatic
-Passionate
-Appreciated
-Cheerful
-Ecstatic
Similar Words to Focused:
-Targeted
-Centered
-Organized
-Listening
-Concentration
Similar Words to Calm:
-Tranquil
-Quiet
-Still
-Peaceful
-Collected
Similar Words to Okay:
-Melancholy
-So-So
-Alright
-Fine
-Neutral
Similar Words to Bored:
-Yawn
-Uninterested
-Inpatient
-Dull
-Underwhelmed
Similar Words to Worried:
-Cautious
-Anxious
-Afraid
-Uncomfortable
-Sick
Similar Words to Frustrated:
-Pestered
-Rebellious
-Unaccepted
-Confused
-Puzzled
Similar Words to Angry:
-Bitter
-Furious
-Outraged
-Spiteful
-Tearful
Similar Words to Out Of Control:
-Hurt
-Attacked
-Helpless
-Exhausted
-Overwhelmed


Vacation
Roulette
Find a vacation
leaving
from
returning
???
$
$$
$$$
???
Beach
Disney
Gambling
Historic
Mountains
National Parks
Outdoors
Romantic
Shopping
Skiing
Theme Park
???
Near
Fair
Far
Take Me Away
DFW
Mar 3
Tuesday
LGA
Apr 4
Saturday
Starting at
$248


DND 4E Character Sheet maker
Home
Projects
DND Charsheet
Blogs
Gaming
Design
[REDACTED]
Contact
Mythos
Character Name:
Level:
Age:
Race:
Dragonborn
Dwarf
Eladrin
Elf
Half-Elf
Halfling
Human
Tiefling
Class:
Cleric
Fighter
Paladin
Ranger
Rogue
Warlock
Warlord
Wizard
Standard (3d6-arranged)
Static (3d6-ordered)
PointBuy (Base 8, 25 points)
Array (16-14-13-12-11-10)


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Juice Shop is an intentionally insecure webapp written in Node.js, Express and AngularJS. It contains over 30 challenges of varying difficulty tracked on a score board.
Juice Shop is now an official OWASP Tool Project
Complete UI translations available for several languages
One-click-Cloud-deployment without hacking restrictions*
*Written confirmation of Heroku tech support: Everything except DDoS attacks is okay!
Automatic provisioning of a Juice Shop VirtualBox VM
You can now use your Google account to log in
Solved challenges are announced as push notifications
Restore your hacking progress - like a kid of the 80's
Juice Shop now sports 30+7 tracked challenges!
The official companion guide for the aspiring hacker!
Timeline? When it's done!
5 dedicated episodes hacking all challenges (up to that date)
All you need for a romantic hacker-movie night!
I18N is managed via CrowdIn, but GitHub PRs also work fine
Our contribution guideline will help you send PRs in no-time
Licensed under the MIT license.
Created with reveal.js - The HTML Presentation Framework


|
Spring Security Framework | |||||||||
| PREV NEXT | FRAMES NO FRAMES |
| Packages that use SpringSecurityFilter | |
|---|---|
| org.springframework.security.concurrent | Concurrent session control and registration classes. |
| org.springframework.security.context | Provides a "request context". |
| org.springframework.security.providers.anonymous | Allows you to secure every invocation (especially useful for web request URI security) by always having either an actual principal or an anonymous principal authenticated. |
| org.springframework.security.securechannel | Classes that ensure web requests are received over required transport channels. |
| org.springframework.security.ui | Authentication processing mechanisms, which respond to the submission of authentication credentials using various protocols (eg BASIC, CAS, form login etc). |
| org.springframework.security.ui.basicauth | Authenticates HTTP BASIC authentication requests. |
| org.springframework.security.ui.cas | Authenticates standard web browser users via JA-SIG Central Authentication Service (CAS). |
| org.springframework.security.ui.logout | |
| org.springframework.security.ui.ntlm | |
| org.springframework.security.ui.openid | Authenticates standard web browser users via OpenID. |
| org.springframework.security.ui.preauth | |
| org.springframework.security.ui.preauth.header | |
| org.springframework.security.ui.preauth.j2ee | |
| org.springframework.security.ui.preauth.websphere | |
| org.springframework.security.ui.preauth.x509 | |
| org.springframework.security.ui.rememberme | Support for remembering a user between different web sessions. |
| org.springframework.security.ui.switchuser | Provides HTTP-based "switch user" (su) capabilities. |
| org.springframework.security.ui.webapp | Authenticates users via HTTP properties, headers and session. |
| org.springframework.security.wrapper | Populates a Servlet request with a new Spring Security compliant
HttpServletRequestWrapper . |
| Uses of SpringSecurityFilter in org.springframework.security.concurrent |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.concurrent | |
|---|---|
class |
ConcurrentSessionFilter
Filter required by concurrent session handling package. |
| Uses of SpringSecurityFilter in org.springframework.security.context |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.context | |
|---|---|
class |
HttpSessionContextIntegrationFilter
Populates the SecurityContextHolder with information obtained from
the HttpSession . |
| Uses of SpringSecurityFilter in org.springframework.security.providers.anonymous |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.providers.anonymous | |
|---|---|
class |
AnonymousProcessingFilter
Detects if there is no Authentication object in the SecurityContextHolder , and
populates it with one if needed. |
| Uses of SpringSecurityFilter in org.springframework.security.securechannel |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.securechannel | |
|---|---|
class |
ChannelProcessingFilter
Ensures a web request is delivered over the required channel. |
| Uses of SpringSecurityFilter in org.springframework.security.ui |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui | |
|---|---|
class |
AbstractProcessingFilter
Abstract processor of browser-based HTTP-based authentication requests. |
class |
ExceptionTranslationFilter
Handles any AccessDeniedException and AuthenticationException thrown within the
filter chain. |
class |
SessionFixationProtectionFilter
Detects that a user has been authenticated since the start of the request and starts a new session. |
| Uses of SpringSecurityFilter in org.springframework.security.ui.basicauth |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.basicauth | |
|---|---|
class |
BasicProcessingFilter
Processes a HTTP request's BASIC authorization headers, putting the result into the SecurityContextHolder . |
| Uses of SpringSecurityFilter in org.springframework.security.ui.cas |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.cas | |
|---|---|
class |
CasProcessingFilter
Processes a CAS service ticket. |
| Uses of SpringSecurityFilter in org.springframework.security.ui.logout |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.logout | |
|---|---|
class |
LogoutFilter
Logs a principal out. |
| Uses of SpringSecurityFilter in org.springframework.security.ui.ntlm |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.ntlm | |
|---|---|
class |
NtlmProcessingFilter
A clean-room implementation for Spring Security of an NTLM HTTP filter leveraging the JCIFS library. |
| Uses of SpringSecurityFilter in org.springframework.security.ui.openid |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.openid | |
|---|---|
class |
OpenIDAuthenticationProcessingFilter
|
| Uses of SpringSecurityFilter in org.springframework.security.ui.preauth |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.preauth | |
|---|---|
class |
AbstractPreAuthenticatedProcessingFilter
Base class for processing filters that handle pre-authenticated authentication requests. |
| Uses of SpringSecurityFilter in org.springframework.security.ui.preauth.header |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.preauth.header | |
|---|---|
class |
RequestHeaderPreAuthenticatedProcessingFilter
A simple pre-authenticated filter which obtains the username from a request header, for use with systems such as CA Siteminder. |
| Uses of SpringSecurityFilter in org.springframework.security.ui.preauth.j2ee |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.preauth.j2ee | |
|---|---|
class |
J2eePreAuthenticatedProcessingFilter
This AbstractPreAuthenticatedProcessingFilter implementation is based on the J2EE container-based authentication mechanism. |
| Uses of SpringSecurityFilter in org.springframework.security.ui.preauth.websphere |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.preauth.websphere | |
|---|---|
class |
WebSpherePreAuthenticatedProcessingFilter
This AbstractPreAuthenticatedProcessingFilter implementation is based on WebSphere authentication. |
| Uses of SpringSecurityFilter in org.springframework.security.ui.preauth.x509 |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.preauth.x509 | |
|---|---|
class |
X509PreAuthenticatedProcessingFilter
|
| Uses of SpringSecurityFilter in org.springframework.security.ui.rememberme |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.rememberme | |
|---|---|
class |
RememberMeProcessingFilter
Detects if there is no Authentication object in the SecurityContext , and populates it
with a remember-me authentication token if a RememberMeServices
implementation so requests. |
| Uses of SpringSecurityFilter in org.springframework.security.ui.switchuser |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.switchuser | |
|---|---|
class |
SwitchUserProcessingFilter
Switch User processing filter responsible for user context switching. |
| Uses of SpringSecurityFilter in org.springframework.security.ui.webapp |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.ui.webapp | |
|---|---|
class |
AuthenticationProcessingFilter
Processes an authentication form. |
class |
DefaultLoginPageGeneratingFilter
For internal use with namespace configuration in the case where a user doesn't configure a login page. |
| Uses of SpringSecurityFilter in org.springframework.security.wrapper |
|---|
| Subclasses of SpringSecurityFilter in org.springframework.security.wrapper | |
|---|---|
class |
SecurityContextHolderAwareRequestFilter
A Filter which populates the ServletRequest with a new request wrapper. |
|
Spring Security Framework | |||||||||
| PREV NEXT | FRAMES NO FRAMES |


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We’ve already seen that hackers often coin jargon by overgeneralizing grammatical rules. This is one aspect of a more general fondness for form-versus-content language jokes that shows up particularly in hackish writing. One correspondent reports that he consistently misspells ‘wrong’ as ‘worng’. Others have been known to criticize glitches in Jargon File drafts by observing (in the mode of Douglas Hofstadter) “This sentence no verb”, or “Too repetetetive”, or “Bad speling”, or “Incorrectspa cing.” Similarly, intentional spoonerisms are often made of phrases relating to confusion or things that are confusing; ‘dain bramage’ for ‘brain damage’ is perhaps the most common (similarly, a hacker would be likely to write “Excuse me, I’m cixelsyd today”, rather than “I’m dyslexic today”). This sort of thing is quite common and is enjoyed by all concerned.
Hackers tend to use quotes as balanced delimiters like parentheses, much to the dismay of American editors. Thus, if “Jim is going” is a phrase, and so are “Bill runs” and “Spock groks”, then hackers generally prefer to write: “Jim is going”, “Bill runs”, and “Spock groks”. This is incorrect according to standard American usage (which would put the continuation commas and the final period inside the string quotes); however, it is counter-intuitive to hackers to mutilate literal strings with characters that don’t belong in them. Given the sorts of examples that can come up in discussions of programming, American-style quoting can even be grossly misleading. When communicating command lines or small pieces of code, extra characters can be a real pain in the neck.
Consider, for example, a sentence in a *vi* tutorial that looks like this:
Then delete a line from the file by typing “dd”.
Standard usage would make this
Then delete a line from the file by typing “dd.”
but that would be very bad — because the reader would be prone to type the string d-d-dot, and it happens that in vi(1), dot repeats the last command accepted. The net result would be to delete two lines!
The Jargon File follows hackish usage throughout.
Interestingly, a similar style is now preferred practice in Great Britain, though the older style (which became established for typographical reasons having to do with the aesthetics of comma and quotes in typeset text) is still accepted there. Hart’s Rules and the Oxford Dictionary for Writers and Editors call the hacker-like style ‘new’ or ‘logical’ quoting. This returns British English to the style many other languages (including Spanish, French, Italian, Catalan, and German) have been using all along.
Another hacker habit is a tendency to distinguish between ‘scare’ quotes and ‘speech’ quotes; that is, to use British-style single quotes for marking and reserve American-style double quotes for actual reports of speech or text included from elsewhere. Interestingly, some authorities describe this as correct general usage, but mainstream American English has gone to using double-quotes indiscriminately enough that hacker usage appears marked [and, in fact, I thought this was a personal quirk of mine until I checked with Usenet —ESR] One further permutation that is definitely not standard is a hackish tendency to do marking quotes by using apostrophes (single quotes) in pairs; that is, ’like this’. This is modelled on string and character literal syntax in some programming languages (reinforced by the fact that many character-only terminals display the apostrophe in typewriter style, as a vertical single quote).
One quirk that shows up frequently in the *email* style of Unix hackers in particular is a tendency for some things that are normally all-lowercase (including usernames and the names of commands and C routines) to remain uncapitalized even when they occur at the beginning of sentences. It is clear that, for many hackers, the case of such identifiers becomes a part of their internal representation (the ‘spelling’) and cannot be overridden without mental effort (an appropriate reflex because Unix and C both distinguish cases and confusing them can lead to *lossage*). A way of escaping this dilemma is simply to avoid using these constructions at the beginning of sentences.
There seems to be a meta-rule behind these nonstandard hackerisms to the effect that precision of expression is more important than conformance to traditional rules; where the latter create ambiguity or lose information they can be discarded without a second thought. It is notable in this respect that other hackish inventions (for example, in vocabulary) also tend to carry very precise shades of meaning even when constructed to appear slangy and loose. In fact, to a hacker, the contrast between ‘loose’ form and ‘tight’ content in jargon is a substantial part of its humor!
Hackers have also developed a number of punctuation and emphasis conventions adapted to single-font all-ASCII communications links, and these are occasionally carried over into written documents even when normal means of font changes, underlining, and the like are available.
One of these is that TEXT IN ALL CAPS IS INTERPRETED AS ‘LOUD’, and this becomes such an ingrained synesthetic reflex that a person who goes to caps-lock while in *talk mode* may be asked to “stop shouting, please, you’re hurting my ears!”.
Also, it is common to use bracketing with unusual characters to signify emphasis. The asterisk is most common, as in “What the *hell*?” even though this interferes with the common use of the asterisk suffix as a footnote mark. The underscore is also common, suggesting underlining (this is particularly common with book titles; for example, “It is often alleged that Joe Haldeman wrote _The_Forever_War_ as a rebuttal to Robert Heinlein’s earlier novel of the future military, _Starship_Troopers_.”). Other forms exemplified by “=hell=”, “\hell/”, or “/hell/” are occasionally seen (it’s claimed that in the last example the first slash pushes the letters over to the right to make them italic, and the second keeps them from falling over). On FidoNet, you might see #bright# and ^dark^ text, which was actually interpreted by some reader software. Finally, words may also be emphasized L I K E T H I S, or by a series of carets (^) under them on the next line of the text.
There is a semantic difference between *emphasis like this* (which emphasizes the phrase as a whole), and *emphasis* *like* *this* (which suggests the writer speaking very slowly and distinctly, as if to a very young child or a mentally impaired person). Bracketing a word with the ‘*’ character may also indicate that the writer wishes readers to consider that an action is taking place or that a sound is being made. Examples: *bang*, *hic*, *ring*, *grin*, *kick*, *stomp*, *mumble*.
One might also see the above sound effects as <bang>, <hic>, <ring>, <grin>, <kick>, <stomp>, <mumble>. This use of angle brackets to mark their contents originally derives from conventions used in *BNF*, but since about 1993 it has been reinforced by the HTML markup used on the World Wide Web.
Angle-bracket enclosure is also used to indicate that a term stands for some *random* member of a larger class (this is straight from *BNF*). Examples like the following are common:
So this <ethnic> walks into a bar one day...
There is also an accepted convention for ‘writing under erasure’; the text>
Be nice to this fool^H^H^H^Hgentleman, he’s visiting from corporate HQ.
reads roughly as “Be nice to this fool, er, gentleman...”, with irony emphasized. The digraph ^H is often used as a print representation for a backspace, and was actually very visible on old-style printing terminals. As the text was being composed the characters would be echoed and printed immediately, and when a correction was made the backspace keystrokes would be echoed with the string ‘^H’. Of course, the final composed text would have no trace of the backspace characters (or the original erroneous text).
Accidental writing under erasure occurs when using the Unix talk program to chat interactively to another user. On a PC-style keyboard most users instinctively press the backspace key to delete mistakes, but this may not achieve the desired effect, and merely displays a ^H symbol. The user typically presses backspace a few times before their brain realises the problem — especially likely if the user is a touch-typist — and since each character is transmitted as soon as it is typed, Freudian slips and other inadvertent admissions are (barring network delays) clearly visible for the other user to see.
Deliberate use of ^H for writing under erasure parallels (and may have been influenced by) the ironic use of ‘slashouts’ in science-fiction fanzines.
A related habit uses editor commands to signify corrections to previous text. This custom faded in email as more mailers got good editing capabilities, only to take on new life on IRCs and other line-based chat systems.
The s/Erik/Eric/ says “change Erik to Eric in the preceding”. This syntax is borrowed from the Unix editing tools ed and sed, but is widely recognized by non-Unix hackers as well.
In a formula, * signifies multiplication but two asterisks in a row are a shorthand for exponentiation (this derives from FORTRAN, and is also used in Ada). Thus, one might write 2 ** 8 = 256.
Another notation for exponentiation one sees more frequently uses the caret (^, ASCII 1011110); one might write instead 2^8 = 256. This goes all the way back to Algol-60, which used the archaic ASCII ‘up-arrow’ that later became the caret; this was picked up by Kemeny and Kurtz’s original BASIC, which in turn influenced the design of the bc(1) and dc(1) Unix tools, which have probably done most to reinforce the convention on Usenet. (TeX math mode also uses ^ for exponention.) The notation is mildly confusing to C programmers, because ^ means bitwise exclusive-or in C. Despite this, it was favored 3:1 over ** in a late-1990 snapshot of Usenet. It is used consistently in this lexicon.
In on-line exchanges, hackers tend to use decimal forms or improper fractions (‘3.5’ or ‘7/2’) rather than ‘typewriter style’ mixed fractions (‘3-1/2’). The major motive here is probably that the former are more readable in a monospaced font, together with a desire to avoid the risk that the latter might be read as ‘three minus one-half’. The decimal form is definitely preferred for fractions with a terminating decimal representation; there may be some cultural influence here from the high status of scientific notation.
Another on-line convention, used especially for very large or very small numbers, is taken from C (which derived it from FORTRAN). This is a form of ‘scientific notation’ using ‘e’ to replace ‘*10^’; for example, one year is about 3e7 (that is, 3 × 10 7) seconds long.
The tilde (~) is commonly used in a quantifying sense of ‘approximately’; that is, ~50 means ‘about fifty’.
On Usenet and in the *MUD* world, common C boolean, logical, and relational operators such as |, &, ||, &&, !, ==, !=, >, <, >=, and <= are often combined with English. The Pascal not-equals, <>, is also recognized, and occasionally one sees /= for not-equals (from Ada, Common Lisp, and Fortran 90). The use of prefix ‘!’ as a loose synonym for ‘not-’ or ‘no-’ is particularly common; thus, ‘!clue’ is read ‘no-clue’ or ‘clueless’.
A related practice borrows syntax from preferred programming languages to express ideas in a natural-language text. For example, one might see the following:
In the above, the #ifdef/#endif pair is a conditional compilation syntax from C; here, it implies that the text between (which is a *flame*) should be evaluated only if you have turned on (or defined on) the switch FLAME. The #include at the end is C for “include standard disclaimer here”; the ‘standard disclaimer’ is understood to read, roughly, “These are my personal opinions and not to be construed as the official position of my employer.”
The top section in the example, with < at the left margin, is an example of an inclusion convention we’ll discuss below.
More recently, following on the huge popularity of the World Wide Web, pseudo-HTML markup has become popular for similar purposes:
You’ll even see this with an HTML-style attribute modifier:
Another recent (late 1990s) construction now common on Usenet seems to be borrowed from Unix shell syntax or Perl. It consists of using a dollar sign before an uppercased form of a word or acronym to suggest any *random* member of the class indicated by the word. Thus: ‘$PHB’ means “any random member of the class ‘Pointy-Haired Boss’”.
Hackers also mix letters and numbers more freely than in mainstream usage. In particular, it is good hackish style to write a digit sequence where you intend the reader to understand the text string that names that number in English. So, hackers prefer to write ‘1970s’ rather than ‘nineteen-seventies’ or ‘1970’s’ (the latter looks like a possessive).
It should also be noted that hackers exhibit much less reluctance to use multiply-nested parentheses than is normal in English. Part of this is almost certainly due to influence from LISP (which uses deeply nested parentheses (like this (see?)) in its syntax a lot), but it has also been suggested that a more basic hacker trait of enjoying playing with complexity and pushing systems to their limits is in operation.
Finally, it is worth mentioning that many studies of on-line communication have shown that electronic links have a de-inhibiting effect on people. Deprived of the body-language cues through which emotional state is expressed, people tend to forget everything about other parties except what is presented over that ASCII link. This has both good and bad effects. A good one is that it encourages honesty and tends to break down hierarchical authority relationships; a bad one is that it may encourage depersonalization and gratuitous rudeness. Perhaps in response to this, experienced netters often display a sort of conscious formal politesse in their writing that has passed out of fashion in other spoken and written media (for example, the phrase “Well said, sir!” is not uncommon).
Many introverted hackers who are next to inarticulate in person communicate with considerable fluency over the net, perhaps precisely because they can forget on an unconscious level that they are dealing with people and thus don’t feel stressed and anxious as they would face to face.
Though it is considered gauche to publicly criticize posters for poor spelling or grammar, the network places a premium on literacy and clarity of expression. It may well be that future historians of literature will see in it a revival of the great tradition of personal letters as art.


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Changes in SysIdentPy¶
v0.1.7¶
CONTRIBUTORS¶
wilsonrljr
CHANGES¶
The update v0.1.7 has been released with major changes and additional features. There are several API modifications and you will need to change your code to have the new (and upcoming) features. All modifications are meant to make future expansion easier.
On the user’s side, the changes are not that disruptive, but in the background there are many changes that allowed the inclusion of new features and bug fixes that would be complex to solve without the changes. Check the documentation page
Many classes were basically rebuild it from scratch, so I suggest to look at the new examples of how to use the new version.
I will present the main updates below in order to highlight features and usability and then all API changes will be reported.
- MAJOR: NARX models with Fourier basis function Issue63, Issue64
The user can choose which basis they want by importing it from sysidentpy.basis_function. Check the notebooks with examples of how to use it.
Polynomial and Fourier are supported for now. New basis functions will be added in next releases.
- MAJOR: NAR models Issue58
It was already possible to build Polynomial NAR models, but with some hacks. Now the user just need to pass model_type=”NAR” to build NAR models.
The user doesn’t need to pass a vector of zeros as input anymore.
Works for any model structure selection algorithm (FROLS, AOLS, MetaMSS)
- Major: NFIR models Issue59
NFIR models are models where the output depends only on past inputs. It was already possible to build Polynomial NFIR models, but with a lot of code on the user’s side (much more than NAR, btw). Now the user just need to pass model_type=”NFIR” to build NFIR models.
Works for any model structure selection algorithm (FROLS, AOLS, MetaMSS)
- Major: Select the order for the residues lags to use in Extended Least Squares - elag
The user can select the maximum lag of the residues to be used in the Extended Least Squares algorithm. In previous versions sysidentpy used a predefined subset of residual lags.
The degree of the lags follows the degree of the basis function
- Major: Residual analysis methods Issue60
There are now specific functions to calculate the autocorrelation of the residuals and cross-correlation for the analysis of the residuals. In previous versions the calculation was limited to just two inputs, for example, limiting user usability.
- Major: Plotting methods Issue61
The plotting functions are now separated from the models objects, so there are more flexibility regarding what to plot.
Residual plots were separated from the forecast plot
- API Change: sysidentpy.polynomial_basis.PolynomialNarmax is deprecated. Use sysidentpy.model_structure_selection.FROLS instead. Issue64
Now the user doesn’t need to pass the number of inputs as a parameter.
Added the elag parameter for unbiased_estimator. Now the user can define the number of lags of the residues for parameter estimation using the Extended Least Squares algorithm.
model_type parameter: now the user can select the model type to be built. The options are “NARMAX”, “NAR” and “NFIR”. “NARMAX” is the default. If you want to build a NAR model without any “hack”, just set model_type=”NAR”. The same for “NFIR” models.
- API Change: sysidentpy.polynomial_basis.MetaMSS is deprecated. Use sysidentpy.model_structure_selection.MetaMSS instead. Issue64
Now the user doesn’t need to pass the number of inputs as a parameter.
Added the elag parameter for unbiased_estimator. Now the user can define the number of lags of the residues for parameter estimation using the Extended Least Squares algorithm.
API Change: sysidentpy.polynomial_basis.AOLS is deprecated. Use sysidentpy.model_structure_selection.AOLS instead. Issue64
API Change: sysidentpy.polynomial_basis.SimulatePolynomialNarmax is deprecated. Use sysidentpy.simulation.SimulateNARMAX instead.
- API Change: Introducing sysidentpy.basis_function. Because NARMAX models can be built on different basis function, a new module is added to make easier to implement new basis functions in future updates Issue64.
Each basis function class must have a fit and predict method to be used in training and prediction respectively.
- API Change: unbiased_estimator method moved to Estimators class.
added elag option
change the build_information_matrix method to build_output_matrix
- API Change (new): sysidentpy.narmax_base
This is the new base for building NARMAX models. The classes have been rewritten to make it easier to expand functionality.
- API Change (new): sysidentpy.narmax_base.GenerateRegressors
create_narmax_code: Creates the base coding that allows representation for the NARMAX, NAR, and NFIR models.
regressor_space: Creates the encoding representation for the NARMAX, NAR, and NFIR models.
- API Change (new): sysidentpy.narmax_base.ModelInformation
_get_index_from_regressor_code: Get the index of the model code representation in regressor space.
_list_output_regressor_code: Create a flattened array of output regressors.
_list_input_regressor_code: Create a flattened array of input regressors.
_get_lag_from_regressor_code: Get the maximum lag from array of regressors.
_get_max_lag_from_model_code: the name says it all.
_get_max_lag: Get the maximum lag from ylag and xlag.
- API Change (new): sysidentpy.narmax_base.InformationMatrix
_create_lagged_X: Create a lagged matrix of inputs without combinations.
_create_lagged_y: Create a lagged matrix of the output without combinations.
build_output_matrix: Build the information matrix of output values.
build_input_matrix: Build the information matrix of input values.
build_input_output_matrix: Build the information matrix of input and output values.
- API Change (new): sysidentpy.narmax_base.ModelPrediction
predict: base method for prediction. Support infinity_steps ahead, one-step ahead and n-steps ahead prediction and any basis function.
_one_step_ahead_prediction: Perform the 1-step-ahead prediction for any basis function.
_n_step_ahead_prediction: Perform the n-step-ahead prediction for polynomial basis.
_model_prediction: Perform the infinity-step-ahead prediction for polynomial basis.
_narmax_predict: wrapper for NARMAX and NAR models.
_nfir_predict: wrapper for NFIR models.
_basis_function_predict: Perform the infinity-step-ahead prediction for basis functions other than polynomial.
basis_function_n_step_prediction: Perform the n-step-ahead prediction for basis functions other than polynomial.
- API Change (new): sysidentpy.model_structure_selection.FROLS Issue62, Issue64
Based on the old sysidentpy.polynomial_basis.PolynomialNARMAX. The class has been rebuilt with new functions and optimized code.
Enforcing keyword-only arguments. This is an effort to promote clear and non-ambiguous use of the library.
Add support for new basis functions.
The user can choose the residual lags.
No need to pass the number of inputs anymore.
Improved docstring.
Fixed minor grammatical and spelling mistakes.
New prediction method.
many under the hood changes.
- API Change (new): sysidentpy.model_structure_selection.MetaMSS Issue64
Based on the old sysidentpy.polynomial_basis.MetaMSS. The class has been rebuilt with new functions and optimized code.
Enforcing keyword-only arguments. This is an effort to promote clear and non-ambiguous use of the library.
The user can choose the residual lags.
Extended Least Squares support.
Add support for new basis functions.
No need to pass the number of inputs anymore.
Improved docstring.
Fixed minor grammatical and spelling mistakes.
New prediction method.
many under the hood changes.
- API Change (new): sysidentpy.model_structure_selection.AOLS Issue64
Based on the old sysidentpy.polynomial_basis.AOLS. The class has been rebuilt with new functions and optimized code.
Enforcing keyword-only arguments. This is an effort to promote clear and non-ambiguous use of the library.
Add support for new basis functions.
No need to pass the number of inputs anymore.
Improved docstring.
Change “l” parameter to “L”.
Fixed minor grammatical and spelling mistakes.
New prediction method.
many under the hood changes.
- API Change (new): sysidentpy.simulation.SimulateNARMAX
Based on the old sysidentpy.polynomial_basis.SimulatePolynomialNarmax. The class has been rebuilt with new functions and optimized code.
Fix the Extended Least Squares support.
Fix n-steps ahead prediction and 1-step ahead prediction.
Enforcing keyword-only arguments. This is an effort to promote clear and non-ambiguous use of the library.
The user can choose the residual lags.
Improved docstring.
Fixed minor grammatical and spelling mistakes.
New prediction method.
Do not inherit from the structure selection algorithm anymore, only from narmax_base. Avoid circular import and other issues.
many under the hood changes.
- API Change (new): sysidentpy.residues
compute_residues_autocorrelation: the name says it all.
calculate_residues: get the residues from y and yhat.
get_unnormalized_e_acf: compute the unnormalized autocorrelation of the residues.
compute_cross_correlation: compute cross correlation between two signals.
_input_ccf
_normalized_correlation: compute the normalized correlation between two signals.
- API Change (new): sysidentpy.utils.plotting
plot_results: plot the forecast
plot_residues_correlation: the name says it all.
- API Change (new): sysidentpy.utils.display_results
results: return the model regressors, estimated parameter and ERR index of the fitted model in a table.
- DOC: Air passenger benchmark
Added notebook with Air passenger forecasting benchmark.
We compare SysIdentPy against prophet, neuralprophet, autoarima, tbats and many more.
- DOC: Load forecasting benchmark
Added notebook with load forecasting benchmark.
- DOC: PV forecasting benchmark
Added notebook with PV forecasting benchmark.
- DOC: Presenting main functionality
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Multiple Inputs usage
Example rewritten following the new api
Fixed minor grammatical and spelling mistakes.
- DOC: Information Criteria - Examples
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Important notes and examples of how to use Extended Least Squares
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Setting specific lags
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Parameter Estimation
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Using the Meta-Model Structure Selection (MetaMSS) algorithm for building Polynomial NARX models
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Using the Accelerated Orthogonal Least-Squares algorithm for building Polynomial NARX models
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Example: F-16 Ground Vibration Test benchmark
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Building NARX Neural Network using Sysidentpy
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Building NARX models using general estimators
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Simulate a Predefined Model
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: System Identification Using Adaptive Filters
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Identification of an electromechanical system
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
Example rewritten following the new api.
Fixed minor grammatical and spelling mistakes.
- DOC: Introduction to NARMAX models
Fixed grammatical and spelling mistakes.
v0.1.6¶
CONTRIBUTORS¶
wilsonrljr
CHANGES¶
- MAJOR: Meta-Model Structure Selection Algorithm (Meta-MSS).
A new method for build NARMAX models based on metaheuristics. The algorithm uses a Binary hybrid Particle Swarm Optimization and Gravitational Search Algorithm with a new cost function to build parsimonious models.
New class for the BPSOGSA algorithm. New algorithms can be adapted in the Meta-MSS framework.
Future updates will add NARX models for classification and multiobjective model structure selection.
- MAJOR: Accelerated Orthogonal Least-Squares algorithm.
Added the new class AOLS to build NARX models using the Accelerated Orthogonal Least-Squares algorithm.
At the best of my knowledge, this is the first time this algorithm is used in the NARMAX framework. The tests I’ve made are promising, but use it with caution until the results are formalized into a research paper.
Added notebook with a simple example of how to use MetaMSS and a simple model comparison of the Electromechanical system.
Added notebook with a simple example of how to use AOLS
- Added ModelInformation class. This class have methods to return model information such as max_lag of a model code.
added _list_output_regressor_code
added _list_input_regressor_code
added _get_lag_from_regressor_code
added _get_max_lag_from_model_code
- Minor performance improvement: added the argument “predefined_regressors” in build_information_matrix function on base.py
to improve the performance of the Simulation method.
Pytorch is now an optional dependency. Use pip install sysidentpy[‘full’]
Fix code format issues.
Fixed minor grammatical and spelling mistakes.
Fix issues related to html on Jupyter notebooks examples on documentation.
Updated Readme with examples of how to use.
Improved descriptions and comments in methods.
- metaheuristics.bpsogsa (detailed description on code docstring)
added evaluate_objective_function
added optimize
added generate_random_population
added mass_calculation
added calculate_gravitational_constant
added calculate_acceleration
added update_velocity_position
FIX issue #52
v0.1.5¶
CONTRIBUTORS¶
wilsonrljr
CHANGES¶
- MAJOR: n-steps-ahead prediction.
- Now you can define the numbers of steps ahead in the predict function.
Only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.
- MAJOR: Simulating predefined models.
Added the new class SimulatePolynomialNarmax to handle the simulation of known model structures.
Now you can simulate predefined models by just passing the model structure codification. Check the notebook examples.
Added 4 new notebooks in the example section.
Added iterative notebooks. Now you can run the notebooks in Jupyter notebook section of the documentation in Colab.
Fix code format issues.
Added new tests for SimulatePolynomialNarmax and generate_data.
Started changes related to numpy 1.19.4 update. There are still some Deprecation warnings that will be fixed in next update.
Fix issues related to html on Jupyter notebooks examples on documentation.
Updated Readme with examples of how to use.
v0.1.4¶
CONTRIBUTORS¶
wilsonrljr
CHANGES¶
- MAJOR: Introducing NARX Neural Network in SysIdentPy.
Now you can build NARX Neural Network on SysIdentPy.
This feature is built on top of Pytorch. See the docs for more details and examples of how to use.
- MAJOR: Introducing general estimators in SysIdentPy.
Now you are able to use any estimator that have Fit/Predict methods (estimators from Sklearn and Catboost, for example) and build NARX models based on those estimators.
We use the core functions of SysIdentPy and keep the Fit/Predict approach from those estimators to keep the process easy to use.
More estimators are coming soon like XGboost.
Added notebooks to show how to build NARX neural Network.
Added notebooks to show how to build NARX models using general estimators.
Changed the default parameters of the plot_results function.
NOTE: We will keeping improving the Polynomial NARX models (new model structure selection algorithms and multiobjective identification
is on our roadmap). These recent modifications will allow us to introduce new NARX models like PWARX models very soon.
New template for the documentation site.
Fix issues related to html on Jupyter notebooks examples on documentation.
Updated Readme with examples of how to use.
v0.1.3¶
CONTRIBUTORS¶
wilsonrljr
renard162
CHANGES¶
Fixed a bug concerning the xlag and ylag in multiple input scenarios.
Refactored predict function. Improved performance up to 87% depending on the number of regressors.
You can set lags with different size for each input.
Added a new function to get the max value of xlag and ylag. Work with int, list, nested lists.
Fixed tests for information criteria.
Added SysIdentPy logo.
Refactored code of all classes following PEP 8 guidelines to improve readability.
Added Citation information on Readme.
Changes on information Criteria tests.
Added workflow to run the tests when merge branch into master.
Added new site domain.
Updated docs.


lclpy.plot package¶
Submodules¶
lclpy.plot.plot module¶
-
lclpy.plot.plot.
iterations_plot
(data, title='Iterations - evaluation value plot.', width=20, height=10)¶ Plots the data in an iterations-value plot.
- Parameters
data (collections.namedtuple) – The data tuple one gets by performing a localsearch algorithm.
title (str, optional) –
The title of the plot.
Default is ‘Iterations - evaluation value plot.’.
width (int, optional) – The width of the plot.
height (int, optional) – The height of the plot.
-
lclpy.plot.plot.
plot
(data, title='Time - evaluation value plot.', width=20, height=10)¶ Plots the data in a time-value plot.
- Parameters
data (collections.namedtuple) – The data tuple one gets by performing a localsearch algorithm.
title (str, optional) –
The title of the plot.
Default is ‘Time - evaluation value plot.’.
width (int, optional) – The width of the plot.
height (int, optional) – The height of the plot.
-
lclpy.plot.plot.
plot_single_stat
(benchmark_single_stat, title='Single stat table', algorithm_names=None, problem_names=None, width=10, height=None)¶ Plots a single statistic of a benchmark.
- Parameters
benchmark_single_stat (numpy.ndarray) – A single 2D array that is the result from a function in “lclpy.benchmark.statistics”.
algorithm_names (list of str or tuple of str, optional) – Must be the same length as the algorithm list given to the original benchmark. The indices of a name corresponds with the indices of the algorithms given to the original benchmark.
problem_names (list of str or tuple of str, optional) – Must be the same length as the problem list given to the original benchmark. The indices of a name corresponds with the indices of the problems given to the original benchmark.
width (float, optional) – The width of the table in inches. Default is 10.
height (float, optional) – The height of the table in inches. The default scales with the amount of items in the table.
Module contents¶
Contains functions to plot the results of localsearch algorithms.


Design Challenge 1
The first design challenge we did was to make a structure with the materials provided (Straws, string and
tape) that could hold a cup and the weight we were putting on it by adding pennies until it came into contact with the ground.
Our team won the challenge out of all the participants thanks to the structural advantages that triangles offer.
Everyone put a lot of effort into the project, I think that I was able to help by giving a base idea of making triangles to increase the strength of the structure.
Design Challenge 2
In the second design challenge we had to make a ball roll the greatest distance possible but it had to finish within
a defined area. To do this we used the materials given to make a long path that we were sustaining with our arms
and we used the mats to try to guide the ball to the are that we had filled with things that stopped the movement
of the ball before it left the defined area.
Our team won the challenge (again!) out of all the participants because we were really risky but were able to pull off
what we had practiced at a long distance. This time everyone had a different role in the challenge, I was encharge of letting the ball
roll and making sure that it would be aligned.


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Welcome to Since 1891, the home for alumni and friends of The Brown Daily Herald. Meet up with old friends, connect with The Herald’s many successful alums, see The Herald’s major current major initiatives and the latest news from 195 Angell, and get in touch with current students!


{% extends "base.html" %} {% set active_page = "logged-in" %} {% from "security/_macros.html" import render_field_with_errors, render_field, render_field_errors %} {% from 'global/macros/login_form_macros.html' import email with context %} {% from 'global/macros/login_form_macros.html' import password with context %} {% from 'global/macros/login_form_macros.html' import text with context %} {% from 'global/macros/form_macros.html' import checkbox with context %} {% block content %}


In the last post we know ShareX is a very powerful screen capturing tool. Read more introduction about ShareX at this link:
TechReview #2 - Ultimate screen capture weapon, ShareX.
Previously when I use the default screen capturing tool in Windows, Snipping Tool to capture screen, often I will have to edit the picture with Microsoft Paint to make simple adjustment such like adding arrows, text, or brackets.
The flow of overall process was
Start Snipping Tool
-> Open capture mode
-> Capure screen
-> Save as picture
-> Look for picture location
-> Edit with Paint
-> Save and done
Using ShareX, the process will be
Start ShareX in capture mode Hotkey
-> Capture screen
-> Edit with ShareX
-> Save and done
I’ll show you the basic screen capturing process and the awesome edit feature.
The Walk-Through
Ignite ShareX region capture mode by Ctrl + Print Screen
. The selection will form a dotted-line region on objects, click once to capture whatever inside the bracket or you can just click and drag over the screen for custom capture.
The captured image will be generated in the file list, we are using the 1.png
in this example . Right-click on the image and select Edit image, this will start the built-in editor.
There is a top bar with a series of editing tools like drawing rectangle and circle, text, arrow, blur effect, crop image and so on.
Drawing Circle and Rectangle
is a very basic feature of most editors. Default color is red which is suitable for emphasizing in an image. You can draw the form and later change the shape and size as well the as the position.
Drawing Steps
is useful in introducing procedure by adding numbering at random places.Drawing Text
and Drawing speech balloon
can be used to adding explanation or note.
Both of the results can be altered as desired after first creation.
Drawing Cursor
enabling user to add mouse cursor in any place. This trick could be used in a tutorial image to tell the reader where to click on the corresponding image.
Effect Blur
and Effect Pixelate
are really useful in hiding sensitive information in an image which could be the car number plate, home address, name of a person, face of a person and contact number etc.
Crop Image
is a vital tool in simple image editing tool to get rid with the unwanted portion of the photo or you might just want to crop to better ratio.
Tips: Beware this is a dangerous tool here as the image will be cropped the moment you release the click as you can see in the GIF. And there will be no “Undo” option to go back after the crop which I think this can be the next improvement.
Image
allows user to flip and rotate an image. Horizontally and vertically flipping, 90 and 180 degrees rotating. Also, image and canvas size can be altered using this tool too.
Drawing Image
is one of the more advanced features to allow a user to duplicate selected portion and drag it around to cover the other part of a photo. In the GIF, I managed to cover up the image preview with the alpha background. This to me is really convenient as I won’t need to access the Gimp or Photoshop to achieve the same effect.
Conclusion
The edit function is a killer feature for me to be a big fan of ShareX. It tremendously simplifies the process of creating a meaningful screenshot in no time.
Posted on Utopian.io - Rewarding Open Source Contributors
This page is synchronized from the post: ‘Start edit screenshot with ShareX powerful editing tool ‘


| Opcode | Instruction | Op/En | 64-Bit Mode | Compat/Leg Mode | Description |
|---|---|---|---|---|---|
| AE | SCAS m8 | ZO | Valid | Valid | Compare AL with byte at ES:(E)DI or RDI, then set status flags.* |
| AF | SCAS m16 | ZO | Valid | Valid | Compare AX with word at ES:(E)DI or RDI, then set status flags.* |
| AF | SCAS m32 | ZO | Valid | Valid | Compare EAX with doubleword at ES(E)DI or RDI then set status flags.* |
| REX.W + AF | SCAS m64 | ZO | Valid | N.E. | Compare RAX with quadword at RDI or EDI then set status flags. |
| AE | SCASB | ZO | Valid | Valid | Compare AL with byte at ES:(E)DI or RDI then set status flags.* |
| AF | SCASW | ZO | Valid | Valid | Compare AX with word at ES:(E)DI or RDI then set status flags.* |
| AF | SCASD | ZO | Valid | Valid | Compare EAX with doubleword at ES:(E)DI or RDI then set status flags.* |
| REX.W + AF | SCASQ | ZO | Valid | N.E. | Compare RAX with quadword at RDI or EDI then set status flags. |
* In64-bitmode,only64-bit(RDI)and32-bit(EDI)addresssizesaresupported.Innon-64-bitmode,only32-bit(EDI)and16-bit(DI) address sizes are supported.
| Op/En | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| ZO | NA | NA | NA | NA |
In non-64-bit modes and in default 64-bit mode: this instruction compares a byte, word, doubleword or quadword specified using a memory operand with the value in AL, AX, or EAX. It then sets status flags in EFLAGS recording the results. The memory operand address is read from ES:(E)DI register (depending on the address-size attribute of the instruction and the current operational mode). Note that ES cannot be overridden with a segment override prefix.
At the assembly-code level, two forms of this instruction are allowed. The explicit-operand form and the no-operands form. The explicit-operand form (specified using the SCAS mnemonic) allows a memory operand to be specified explicitly. The memory operand must be a symbol that indicates the size and location of the operand value. The register operand is then automatically selected to match the size of the memory operand (AL register for byte comparisons, AX for word comparisons, EAX for doubleword comparisons). The explicit-operand form is provided to allow documentation. Note that the documentation provided by this form can be misleading. That is, the memory operand symbol must specify the correct type (size) of the operand (byte, word, or doubleword) but it does not have to specify the correct location. The location is always specified by ES:(E)DI.
The no-operands form of the instruction uses a short form of SCAS. Again, ES:(E)DI is assumed to be the memory operand and AL, AX, or EAX is assumed to be the register operand. The size of operands is selected by the mnemonic: SCASB (byte comparison), SCASW (word comparison), or SCASD (doubleword comparison).
After the comparison, the (E)DI register is incremented or decremented automatically according to the setting of the DF flag in the EFLAGS register. If the DF flag is 0, the (E)DI register is incremented; if the DF flag is 1, the (E)DI register is decremented. The register is incremented or decremented by 1 for byte operations, by 2 for word operations, and by 4 for doubleword operations.
SCAS, SCASB, SCASW, SCASD, and SCASQ can be preceded by the REP prefix for block comparisons of ECX bytes, words, doublewords, or quadwords. Often, however, these instructions will be used in a LOOP construct that takes
some action based on the setting of status flags. See “REP/REPE/REPZ /REPNE/REPNZ—Repeat String Operation Prefix” in this chapter for a description of the REP prefix.
In 64-bit mode, the instruction’s default address size is 64-bits, 32-bit address size is supported using the prefix 67H. Using a REX prefix in the form of REX.W promotes operation on doubleword operand to 64 bits. The 64-bit no-operand mnemonic is SCASQ. Address of the memory operand is specified in either RDI or EDI, and AL/AX/EAX/RAX may be used as the register operand. After a comparison, the destination register is incremented or decremented by the current operand size (depending on the value of the DF flag). See the summary chart at the beginning of this section for encoding data and limits.
Non-64-bit Mode: IF (Byte comparison) THEN temp ← AL − SRC; SetStatusFlags(temp); THEN IF DF = 0 THEN (E)DI ← (E)DI + 1; ELSE (E)DI ← (E)DI – 1; FI; ELSE IF (Word comparison) THEN temp ← AX − SRC; SetStatusFlags(temp); IF DF = 0 THEN (E)DI ← (E)DI + 2; ELSE (E)DI ← (E)DI – 2; FI; FI; ELSE IF (Doubleword comparison) THEN temp ← EAX – SRC; SetStatusFlags(temp); IF DF = 0 THEN (E)DI ← (E)DI + 4; ELSE (E)DI ← (E)DI – 4; FI; FI; FI; 64-bit Mode: IF (Byte cmparison) THEN temp ← AL − SRC; SetStatusFlags(temp); THEN IF DF = 0 THEN (R|E)DI ← (R|E)DI + 1; ELSE (R|E)DI ← (R|E)DI – 1; FI; ELSE IF (Word comparison) THEN temp ← AX − SRC; SetStatusFlags(temp); IF DF = 0 THEN (R|E)DI ← (R|E)DI + 2; ELSE (R|E)DI ← (R|E)DI – 2; FI; FI; ELSE IF (Doubleword comparison) THEN temp ← EAX – SRC; SetStatusFlags(temp); IF DF = 0 THEN (R|E)DI ← (R|E)DI + 4; ELSE (R|E)DI ← (R|E)DI – 4; FI; FI; ELSE IF (Quadword comparison using REX.W ) THEN temp ← RAX − SRC; SetStatusFlags(temp); IF DF = 0 THEN (R|E)DI ← (R|E)DI + 8; ELSE (R|E)DI ← (R|E)DI – 8; FI; FI; F
The OF, SF, ZF, AF, PF, and CF flags are set according to the temporary result of the comparison.
| #GP(0) | If a memory operand effective address is outside the limit of the ES segment. |
| If the ES register contains a NULL segment selector. | |
| If an illegal memory operand effective address in the ES segment is given. | |
| #PF(fault-code) | If a page fault occurs. |
| #AC(0) | If alignment checking is enabled and an unaligned memory reference is made while the current privilege level is 3. |
| #UD | If the LOCK prefix is used. |
| #GP | If a memory operand effective address is outside the CS, DS, ES, FS, or GS segment limit. |
| #SS | If a memory operand effective address is outside the SS segment limit. |
| #UD | If the LOCK prefix is used. |
| #GP(0) | If a memory operand effective address is outside the CS, DS, ES, FS, or GS segment limit. |
| #SS(0) | If a memory operand effective address is outside the SS segment limit. |
| #PF(fault-code) | If a page fault occurs. |
| #AC(0) | If alignment checking is enabled and an unaligned memory reference is made. |
| #UD | If the LOCK prefix is used. |
Same exceptions as in protected mode.
| #GP(0) | If the memory address is in a non-canonical form. |
| #PF(fault-code) | If a page fault occurs. |
| #AC(0) | If alignment checking is enabled and an unaligned memory reference is made while the current privilege level is 3. |
| #UD | If the LOCK prefix is used. |


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pf
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title: 'A Short Introduction to PF: A C++ Library for Particle Filtering' tags:
name: Department of Statistics, University of Virginia, PO Box 400135, Charlottesvilla, VA 22904, USA index: 1 date: 14 July 2020
The PF
library provides class and function templates that offer fast implementations for a variety of particle filtering algorithms. Each of these algorithms are useful for a wide range of time series models.
In this library, each available particle filtering algorithm is provided as an abstract base class template. Once the data analyst has a specific state-space or hidden Markov model in mind, she will pick which type(s) of particle filtering algorithm(s) to associate with that model by including the appropriate header file. For each model-particle filter pair, she will write a class template for her model that inherits from the particle filter's base class template.
The details of each algorithm are abstracted away, and each algorithm's class template's required functions are pure virtual methods, meaning that the data analyst will not be able to omit any function that is required by the algorithm.
This is by no means the first C++
library to be offered that provides particle filter implementations. Other options include LibBi
[], Biips
[], SMCTC
[:2009:JSSOBK:v30i06] and Nimble
[:10.1080/10618600.2016.1172487]. The goals of these software packages are different, though–users of these libraries write their models in a scripting language, and that model file gets parsed into C++
code. This library is designed for users that prefer to work in C++
directly.
State-space models describe a partially-observed Markov chain–they possess a hidden/latent variable at each time point, in addition to an observed variable. Filtering" is defined as obtaining the distributions of each unobserved state/code random variable, conditioning on all of the observed information up to that point in time.</tt>Particle filters" are a class of algorithms that approximate this sequence of distributions with weighted samples (termed particles). Filtering is a useful tool for a variety of applications in a variety of fields, but it should also be mentioned that they can be used for real-time forecasting with only minor modifications, and they are critical component of more advanced parameter estimation algorithms.
Unfortunately, it takes time and effort to implement different particle filters well, and this is true for two reasons. First, the mathematical notation used to describe them can be complex. The second reason is that, even if they are correctly implemented, they can be quite slow, limiting the number of tasks that they would be feasible for. This library attempts to provide speed and abstraction to mitigate these two difficulties.
Additionally, this software is designed in an object-oriented manner. This allows for individual particle filters to be used in isolation, and it also facilitates the implementation of more complicated algorithms that update many particle filters through many iterations in a stateful way, possibly in parallel. For this second class of algorithms, there are usually two necessary loops: the
outer" loop that loops over time, and the</tt>inner" loop that iterates over each distinct particle filter. Without an object-oriented design, there would be a third loop, which loops over all particle samples in each particle filter. Some examples of opportunities include particle filters with parallelized resampling schemes [@1453776,1309.2918], particle Markov chain Monte Carlo algorithms [], importance sampling "squared" [], and the particle swarm algorithm [].
Finally, this library is "header-only." As a result, building your
C++
project is as simple as possible. The only required steps are #include
-ing relevant headers, and pointing the compiler at the include/pf/
directory. This directory stores all necessary code, although there there are unit tests and examples provided as well.
A fully-worked example is provided along with this software available at https://github.com/tbrown122387/pf. This example considers modeling a financial time series with a simple stochastic volatility model [] with three parameters: $, $ and $$. For this model, the observable rate of return $y_t$ is normally distributed after conditioning on the contemporaneous state random variable $x_t$. The mean parameter of this normal distribution will remain fixed at $0$. However, the scale of this distribution will vary with the evolving $x_t$. When $x_t$ is relatively high, the returns will have a high conditional variance and be "volatile." When $x_t$ is low, the returns will be much less volatile.
The observation equation is {eqnarray} y_t = e^{x_t/2} z_t {eqnarray} where $z_t


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NLPCraft 0.8.0 brings about several important bug fixes, improvements and enhancements.
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South Carolina mom's 'extreme indifference to human life' caused toddler son's death, warrant says
by Michelle Ewing, Cox Media Group National Content DeskDILLON, S.C. -
Authorities in South Carolina have arrested a Dillon mother in connection with her toddler son's death.
According to WBTW and WMBF, 33-year-old Okedia Colvin's "extreme indifference to human life" caused the Nov. 7 death of the 2-year-old boy, whose feeding tube had stopped working properly, an arrest warrant said. Colvin never sought medical assistance for the child after the malfunction, investigators alleged.
Colvin, who was jailed Friday on a charge of homicide by child abuse/neglect, remains in the Dillon County Detention Center, WBTW reported.
The South Carolina Law Enforcement Division is investigating the incident and likely will release more details Monday, a spokesman told WMBF.
© 2019 Cox Media Group.


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