This simulator provides a simplified assembler syntax.
The simulator consists of a 8-bit cpu and 256 bytes of memory. All instructions (code) and variables (data) needs to fit inside the memory. For simplicity every instruction (and operand) is 1 byte. Therefore a MOV instruction will use 3 bytes of memory. The simulator provides a console output which is memory mapped from 0xE8 to 0xFF. Memory mapped means that every value written to this memory block is visible on the console.
The syntax is similar as most assemblers are using. Every instruction must be on their own line. Labels are optional and must either start with a letter or a dot (.) and end with a colon.
label: instruction operands ; Comment
Valid number formats for constants are:
Decimal: 200 Decimal: 200d Hex: 0xA4 Octal: 0o48 Binary: 101b
It is possible to define a number using a character or multiple numbers (see instruction DB) by using a string.
Character: 'A' String: "Hello World!"
Operands can either be a memory address or a constant. Instead of defining an address as a constant or by using a register you can use labels. The assembler will then replace the label with the corresponding constant.
Address using a constant: [100] Address using a label: label Constant: Any number between 0..255 (8bit unsigned)
Copies a value from src to dest.
CP address_dest, address_src CP address_dest, constant_src
Defines a variable. A variable can either be a single number, character or a string.
DB constant
Adds two numbers together or subtract one number form another. This operations will modify the overflow and zero flag.
ADD address_dest, constant ADD address_dest, address_src SUB address_dest, constant SUB address_dest, address_srcIncrement and Decrement
Increments or decrements a register by one. This operations will modify the overflow and zero flag.
INC address DEC address
Compares two values by subtracting value at address2 or a constant from value stored at address1, the result is not stored, negative values will cause an overflow. Use this instruction before a conditional jump.
CMP address1, address2 CMP address, constant
Let the instruction pointer do a unconditional jump to the defined address.
JP addressConditional jumps
Let the instruction pointer do a conditional jump to the defined address. See the table below for the available conditions.
| Instruction | Description |
|---|---|
| JO | Jump if overflow |
| JZ | Jump if zero or equal |
Prints a string or an integer.
PRNS [address] PRND [address]Code Example:
char i = 100; char j = 30; char k = 0; k = i - j; printf("%d", k);in pseudo language:
jp start i: db 100 ;char i = 100; j: db 30 ;char j = 30; k: db 0 ;char k = 0; start: cp [k], [i] ;k = i; sub [k], [j] ;k -= j; prnd [k] ;printf(“%d”, k);loop example:
for (char i = 0; i < 50; ++i)
jp loop i: db 0 ;char i = 0; loop: cmp [i], 50 ;check i against 50 jz end ;goto end if i==50 inc [i] ;i++ jp loop ;goto loop end:
by Konstantin Zheludev (2019)


% # Note that this file is not a valid *.html file! # It is intended to be a bottlepy - style template # used for the scripting part of TheOnionBox! # tor = get('tor') # import socket # host_name = socket.gethostname() sc = get('section_config') header = sc['header'] if 'header' in sc else {} %>


--- layout: post status: publish published: true title: Keane in Sydney author: display_name: Fabio Pereira login: admin email<EMAIL_ADDRESS>url: http://fabiopereira.me author_login: admin author_email<EMAIL_ADDRESS>author_url: http://fabiopereira.me wordpress_id: 114 wordpress_url: http://fabiopereira.me/blog/?p=114 date: '2009-04-19 00:35:21 +1000' date_gmt: '2009-04-19 07:35:21 +1000' categories: - Personal tags: [] comments: [] ---
Yesterday we went to Keane's concert in Sydney. It's one of my favorite bands and it was one of the best performances I've ever seen.
Tom Chaplin and the pianist Tim Rice Oxley have an amazing contagious energy on stage
They played songs from the current album and from the old ones.
One of the most exciting moments was when Tim went to the crowd singing "Wrap yourself around me..." from the new album Perfect Symmetry.
Check it out...
We went with Anne and Mark Needham.


Since "Male"
and "Female"
are unordered (or nominal) factor levels, R returns a warning message, telling you that the greater than operator is not meaningful. As seen before, R attaches an equal value to the levels for such factors.
But this is not always the case! Sometimes you will also deal with factors that do have a natural ordering between its categories. If this is the case, we have to make sure that we pass this information to R...
Let us say that you are leading a research team of five data analysts and that you want to evaluate their performance. To do this, you track their speed, evaluate each analyst as "slow"
, "medium"
or "fast"
, and save the results in speed_vector
.
As a first step, assign speed_vector
a vector with 5 entries, one for each analyst. Each entry should be either "slow"
, "medium"
, or "fast"
. Use the list below:
No need to specify these are factors yet.
# Create speed_vector
speed_vector <-
# Create speed_vector
speed_vector <- c("medium", "slow", "slow", "medium", "fast")
test_object("speed_vector",
incorrect_msg = "`speed_vector` should be a vector with 5 entries, one for each analyst's speed rating. Don't use capital letters; R is case sensitive!")
success_msg("A job well done! Continue to the next exercise.")
Assign to speed_vector
a vector containing the character strings "slow"
, "medium"
, or "fast"
.


Displays an approximation for classification accuracy based on users who submitted articles and provided feedback
on the results page. The feedback consisted of completing a one question questionnaire asking if they agreed with
the classification. Results are displayed in a table and as a pie chart.
This visualization is dynamic and will update over time as feedback is received.
Applied machine learning techniques (K-means and Latent Dirichlet Allocation) to cluster articles by their similarity.
This visualization aims to visually represent the underlying topics of articles and how these topic patterns differ for
credible and non-credible articles.
Text was preprocessed using tokenization and the tf-idf algorithm. This visualization represents an analysis from a particular
point in time and does not incorporate new articles as they are added to the corpus.
This visualization compares the sentiment analysis metrics (produced by
vaderSentiment) of credible and non-credible articles in the corpus. The bar graph shows the average percentage of positive, negative, and neutral words in the articles and
the line plot shows the distributions of compound sentiment scores (where low values represent negative sentiment and high values represent positive sentiment) for both credible
and non-credible articles in the corpus.
This visualization represents an analysis from a particular point in time and does not incorporate new articles as they are added to the corpus.


--- layout: post status: publish published: true title: Loopback to Atmega238P with FTDI's USB to TTL adapter author: display_name: ian login: ian email<EMAIL_ADDRESS>url: http://iancwill.com/blog author_login: ian author_email<EMAIL_ADDRESS>author_url: http://iancwill.com/blog wordpress_id: 26 wordpress_url: http://iancwill.com/blog/?p=26 date: '2012-02-10 09:02:59 -0700' date_gmt: '2012-02-10 14:02:59 -0700' ---
1. Wire orange to yellow, as shown
2. Open minicom (with -s the first time), setup to /dev/ttyUSB0
3. Disable hardware flow control
4. Loopback away


Micro 2D Platform Game
Controls
Actions: Buttons
Walk Right:
Right Arrow
Walk Left:
Left Arrow
Jump:
Space Bar


Class for TCP socket.
More...
#include <Server.hpp>
|
|
static void * | clnt_connection_wrapper (void *object) |
| |
Class for TCP socket.
- Author
- hyunkyun
- Date
- 2018-04-23
- Version
- 0.0.1
◆ clnt_connection()
| void * Server::clnt_connection |
( |
void * |
arg | ) |
|
thread Function for client. read message from client. It sends a response to the request by call send_message.
- Parameters
-
| arg | a void pointer. casting clientSocket. |
◆ error_handling()
| void Server::error_handling |
( |
char const * |
message | ) |
|
puts message for error
- Parameters
-
| message | a constant character pointer to be puts. |
◆ openServer()
| void Server::openServer |
( |
const char * |
port | ) |
|
lock for clnt_socks
setting server
- Parameters
-
| port | a constant character pointer. |
◆ send_message()
| void Server::send_message |
( |
Value |
val, |
|
|
int |
sock |
|
) |
| |
send message to client.
- Parameters
-
| val | a json object to send to the client. |
| sock | an integer. if sock is 0 notify to all, if negative notify except that person, if positive send only that person |
The documentation for this class was generated from the following files:
- /Users/hyeongyunmun/Documents/Chat/Server/Server/Server.hpp
- /Users/hyeongyunmun/Documents/Chat/Server/Server/Server.cpp


While I cannot show source code for these projects, I can list them, describe them, and point out what technologies were used with them.
{{proj.description}}
{{proj.tech}}


MAY 8, 2018 / foody
A Closer Look At Our Front Porch Items From Lowe’s
Ten years ago, laborers hammering away at Prague’s cobblestone streets would probably break for lunch with bag of fluffy rohliky bread rolls and some sliced ham, if they couldn’t make it to a pub for goulash and dumplings. Flash-forward to 2018 and the same stonemasons — as well as local shopkeepers, students and chief executives — are likely to prefer bun bo nam bo or pho soup for lunch at one of the city’s fast and cheap Vietnamese noodle restaurants, which have appeared by the dozens in the Czech capital over the past decade.
What hadn’t shown up, however, was the idea that Vietnamese cuisine can be taken seriously, with complex techniques and in a fancier setting. That changed with last December’s opening of Taro, a surprisingly chic bistro in the forever up-and-coming Smichov neighborhood southwest of Old Town. Run by two brothers, Khanh and Giang Ta, Taro has no evening à la carte menu (there is one at lunch), instead offering just two options for dinner: a four-course tasting menu at 890 koruna (about $40) or a seven-course menu at 1,290 koruna per person, not including drinks. Cheap noodles this ain’t.
That’s not to say you’ll have the exact same thing if you stop by: the restaurant’s menus change constantly, based on seasonal ingredients.
Ollie Schneider
You have 4 free articles remaining.
 Seven equally creative courses followed, often balancing sweet notes with bracing acidity.
A sweet-and-sour sea bass tartare, decorated with apple chips, mango chunks and creamy avocado purée, tasted more like a ceviche, while a green mango salad bathed in crisp passion fruit dressing contrasted tropical fruit flavors with juicy chunks of slow-cooked beef tenderloin.
Brandon Kelley
Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetu adipisci velit, sed quia non numquam eius modi
Like Reply


Introduction¶
MOSA is an open source software project that natively executes .NET applications within a virtual hypervisor or on bare metal hardware!
The MOSA project consists of:
Compiler - a high quality, multithreaded, cross-platform, optimizing .NET compiler
Kernel - a small kernel operating system
Device Drivers Framework - a modular, device drivers framework and device drivers
Debugger - QEMU-based debugger
Current Status¶
The target platforms are:
Intel X86/32-bit (stable)
Intel X64 (in development)
ARM v6 (in early development)
The MOSA compiler supports nearly all and object oriented non-object oriented code, including:
Generic Code (example: List<T>)
Delegates (static and non-static) and with optional parameters
Exception Handling (try, finally, and catch code blocks)
The MOSA compiler seeks to provide high quality code generation using the following optimizations:
Constant Folding and Propagation
Strength Reduction optimization
Dead Code Elimination
Single Static Assignment (SSA)
Global Value Numbering / Common Subexpession Elimination
Sparse Conditional Constant Propagation
Inlined Expansion
Loop-Invariant Code Motion
Null Check Elimination
Devirtualization
Block Reordering
Greedy Register Allocation
See the Compiler Optimizations for a description of each of these optimization methods.
Join the Discussion¶
Join us on Gitter chat. This is the most interactive way to connect to MOSA’s development team.


Turtle
Turtle is a Green food. Green foods are safe to eat as a regular part of a diet.
Category Quotes
Guideline related quotes related to Meat-Exotic and Green throughout Geoff Bond's publications.
Examples of one serving include 3 oz (size of a deck of cards) cooked meat or poultry; 3 oz grilled fish; 1 egg. ~Paleo in a Nutshell p.95
For the meat, poultry, eggs, and fish group, Preferably consume two servings a day of “Green-Green” foods. If unavailable, you can consume two servings a day of “Green” foods. ~Paleo in a Nutshell p.102
For the meat, poultry, eggs, and fish group Restrict total food group servings per session to one. ~Paleo in a Nutshell p.102
For the meat, poultry, eggs, and fish group, Restrict total food group servings per day to two. ~Paleo in a Nutshell p.102
Additional Quotes
Specific references to Turtle throughout Geoff Bond's publications.
 We are not saying you will have to start eating locusts, turtles, and frogs! (Although many societies from Africa to Asia do.) No— what we are saying is that we need to find everyday foods which have the same specification in our own supermarkets. And the good news it is perfectly possible— even easy. ~Paleo in a Nutshell p.17
Our ancestors were not alone in the savanna. They shared the land with a wide variety of creatures: giraffe, lion, elephant, warthog, rhinoceros, hyena, antelope, gazelle, zebra, baboons, chimpanzees, vultures, eagles, flamingos, and many more. In addition, there were snakes, porcupines, crocodiles, lizards, tortoises, snails, grasshoppers, and a myriad of small mammals, reptiles, and insects. The lakes, streams, and waterholes teemed with many species of freshwater fish, shellfish, frogs, toads, ducks, geese, and other aquatic creatures. ~Deadly Harvest p.13
For Aborigines, The animal food collected would be eggs, turtles, snakes, shellfish, crabs, caterpillars (e.g., the witchety grub and the bogong moth), land snails, and the goanna (a giant lizard). Sweet foods were very rare but much prized. Disproportionate amounts of time were spent on finding a bees' nest to smoke out. Other sweetmeats were the honey ant, gorged with nectar, and "lerp," a sweet insect secretion on eucalyptus leaves. In times of scarcity, grass seeds were collected, winnowed, and ground between two handheld stones. The drudgery of this task was viewed with such distaste that it was only done very rarely. ~Deadly Harvest p.19
30,000 years ago, the Cro-Magnons of Europe ate fish, turtles, shellfish, and birds. Meanwhile the Neanderthals, who lived alongside them, ate reindeer, mammoth, and other large herbivores. ~Deadly Harvest p.22
We must also mention eggs from reptiles: eggs from crocodiles and turtles would have been quite common in the diet of our African Pleistocene ancestors. Turtles lay eggs in prodigious numbers in sandy shorelines, and collecting and commercializing them has become a major industry in Malaysia. Wild eggs in general form a tiny part of consumption in the developed world and, with the possible exception of quail eggs, most people have never even seen one. ~Deadly Harvest p.62
Reptile foods, including crocodile, alligator, and turtle, although uncommon in the Western diet, are still readily available to the enthusiast. In addition, many societies make use of snakes, such as python and boa constrictor, and the French have made a delicacy of frog's legs. All of these foods, as they are currently available, readily fit the Savanna Model. ~Deadly Harvest p.63


Profile
Fill out your personal information.
NAME
NATIONALITY
France
CURRENT CITY
Current City
CUISINE
Cuisine
ROLE
Role
PROFESSIONAL EXPERIENCE
Professional experience
WILLING TO RELOCATE TO
Willing to relocate to
DESIRED ANNUAL SALARY


[gecode-users] Clockwise constraint
Guido Tack
tack at ps.uni-sb.de
Mon Jun 2 09:18:42 CEST 2008
Malcolm Ryan wrote:
> What I need is a reified version of the pairwise rel(). Then I could
> say:
>
> a < c --> a < b < c
> b < a --> b < c < a
> c < b --> c < a < b
>
> Since there is no value of 'a' that satisfies c < a < b, it would have
> to deduce that b < c.
>
> Representing this as (c < a) and (a < b) is not good enough, as there
> are possible values of 'a' to satisfy each of the inequalities
> individually, just not both together.
Oh, I somehow didn't see that it's always ternary! In that case you
might really want to try the extensional constraint. Just implement a
generator that lists all allowed tuples.
Cheers,
Guido
More information about the gecode-users
mailing list


Something for the reader to note in this is that I only read three of the links so if you're looking for my
reaction on a certian one of the links you may not find it.
Now that thats been stated I'll start with what I thought on the
What is Meditation and How It Affects
Our Brains blog by Belle Beth Cooper.
Meditation seems like a very interesting practice. Even though meditation is mostly about putting your focus
towards something it calms and relaxes both you and your brain. It can increase your life-span and also help
with things like focus, creativity, anxiety and gray matter.I overall found this blog really interesting and
intend to try this practice at some point in the future as I have had anxiety attacks and from that -
struggle to focus on things now.
Next, I'll cover my thoughts on the
The Habits
of Successful People: They Have a Growth Mindset blog by James Clear.
This blog is about developing a "growth mindset". A "growth mindset" is knowing that you can improve yourself.
Things like displaying that you can do things time and time again to yourself will build up your confidence.
I find this idea interesting as I have never thought that I could always be confident instead of a "sometimes
you're confident, sometimes you're not" outlook. I will remember the words in this blog and think on it
more. As for the practice itself - I don't that I will be able to actively put this idea into practice and I
will have to instead try and do it in a more natural sence for it to really work.
Following this I read about the Pomodoro
Technique by Francesco Cirillo.
I liked this blog, maybe because it was simple to grasp, but the Pomodoro Technique is based around time
management. The idea is to set a task and do it in increments of 25 minutes. After each 25 minutes is up you
take a quick break and then get back to work. I feel like this is a super easy concept that seems to work very
effectively. I really want to test this technique.
12th of September 2018


You enable settings to improve network and RiOS data store performance in the Configure > Optimization > Performance page. This section describes the default settings and the cases in which you might consider changing the default values.The RiOS data store segment replacement policy selects the technique used to replace the data in the RiOS data store. While the default setting works best for most Steelhead appliances, occasionally Riverbed Support recommends changing the policy to improve performance.Note: Riverbed recommends that the segment replacement policy matches on both the client-side and server-side Steelhead appliances.
Riverbed LRU - Replaces the least recently used data in the RiOS data store, which improves hit rates when the data in the RiOS data store are not equally used. This is the default setting. FIFO - Replaces data in the order received (first in, first out).
3. Click Apply to apply your settings.
4. Click Save to save your settings permanently.
5. You optimize the RiOS data store for high-throughput Data Replication (DR) or data center workloads in the Configure > Optimization > Performance page.You might benefit from changing the performance settings if your environment uses a high-bandwidth WAN. DR and SAN replication workloads at these high throughputs might benefit from the settings that enhance RiOS data store performance while still receiving data reduction benefits from SDR.To maintain consistent levels of performance, Riverbed recommends using separate Steelhead appliances for DR workloads than for optimization of other application traffic.The adaptive data streamlining mode monitors and controls the different resources available on the Steelhead appliance and adapts the utilization of these system resources to optimize LAN throughput. Changing the default setting is optional; Riverbed recommends you select another setting only with guidance from Riverbed Support or the Riverbed Sales Team.Generally, the default setting provides the most data reduction. When choosing an adaptive streamlining mode for your network, contact Riverbed Support to help you evaluate the setting based on:the amount of data replication your Steelhead appliance is processing. the type of data being processed and its effects on disk throughput on the Steelhead appliances. your primary goal for the project, which could be maximum data reduction or maximum throughput. Even when your primary goal is maximum throughput you can still achieve high data reduction.
Provides the most data reduction. Reduces random disk seeks and improves disk throughput by discarding very small data margin segments that are no longer necessary. This margin segment elimination (MSE) process provides network-based disk defragmentation. Writes large page clusters. Monitors the disk write I/O response time to provide more throughput. Legacy - Includes the default settings and also:Balances writes and reads. Monitors both read and write disk I/O response and, based on statistical trends, can employ a blend of disk-based and non-disk-based data reduction techniques to enable sustained throughput during periods of disk-intensive workloads. Important: Use caution with the SDR-Adaptive Legacy setting, particularly when you are optimizing CIFS or NFS with prepopulation. Contact Riverbed Support for more information.Advanced - Maximizes LAN-side throughput dynamically under different data workloads. This switching mechanism is governed with a throughput and bandwidth reduction goal using the available WAN bandwidth. Both Steelheads must be running RiOS v6.0.x or later.Upgrade notes: If you have enabled SDR-Adaptive prior to upgrading to RiOS v6.0, the default setting is SDR-Adaptive Legacy.If you did not change the SDR-Adaptive setting prior to upgrading to RiOS v6.0, the default setting is SDR-Adaptive Advanced. Performs data reduction entirely in memory, which prevents the Steelhead appliance from reading and writing to and from the disk. Enabling this option can yield high LAN-side throughput because it eliminates all disk latency. This is typically the preferred configuration mode for SAN replication environments.SDR-M is most efficient when used between two identical high-end Steelhead appliance models: for example, 6050 - 6050. When used between two different Steelhead appliance models, the smaller model limits the performance.After enabling SDR-M on both the client-side and the server-side Steelhead appliances, restart both Steelheads to avoid performance degradation.
3. Click Apply to apply your settings.
4. Click Save to save your settings permanently.
5. If you have selected a new adaptive data streamlining mode, you must restart the optimization service on the client-side and server-side Steelhead appliances. For details, see Starting and Stopping the Optimization Service.Note: If you select SDR-M as the adaptive data streamlining mode, the Clear the Data Store option is not available when you restart the optimization service because the SDR-M mode has no effect on the RiOS data store disk.Tip: After changing the RiOS data store adaptive streamlining setting, you can verify whether changes have had the desired effect by reviewing the Optimized Throughput report. From the menu bar, choose Reports > Optimization > Optimized Throughput.Use the CPU settings to balance throughput with the amount of data reduction and balance the connection load. The CPU settings are useful with high-traffic loads to scale back compression, increase throughput, and maximize Long Fat Network (LFN) utilization.
Specifies the relative trade-off of data compression for LAN throughput speed. Generally, a lower number provides faster throughput and slightly less data reduction.Select a RiOS data store compression value of 1 (minimum compression, uses less CPU) through 9 (maximum compression, uses more CPU) from the drop-down list. The default value corresponds to level 6.Riverbed recommends setting the compression level to 1 in high-throughput environments such as data center-to-data center replication. Detects LZ data compression performance for a connection dynamically and turns it off (sets the compression level to 0) momentarily if it is not achieving optimal results. Improves end-to-end throughput over the LAN by maximizing the WAN throughput. By default, this setting is disabled. Enables multicore balancing, which ensures better distribution of workload across all CPUs, thereby maximizing throughput by keeping all CPUs busy. Core balancing is useful when handling a small number of high-throughput connections (approximately 25 or less). By default, this setting is disabled.
3. Click Apply to apply your settings.
4. Click Save to save your settings permanently.Configuring In-Path Rules Synchronizing Peer RiOS Data Stores


format/[2,3]
format(
+Control,
+Arguments)
format(
+Stream,
+Control,
+Arguments)
Interprets the Arguments according to the Control string and prints the result on Stream.
Defaults to the current output stream.
A string, which can contain control sequences of the form `~<n><c>':
Any characters that are not part of a control sequence are written
to the specified output stream.
List of arguments, which will be interpreted and possibly printed by format control options.
If <n> can be specified, then it can be the character `*'. In this case <n> will be taken as the next argument from Arguments.
The following control options cause formatted printing of the next element from Arguments to the current output stream.
The intuition is that for numbers like 123000000.0, at most N consecutive zeroes before the decimal point are allowed in `f' notation. Similarly for numbers like 0.000000123.
`E' notation is forced by using `~-1H'.
`F' is forced by using `~999H'.
write_canonical/1
(see ref-iou-tou).
print/1
(see ref-iou-tou).
writeq/1
(see ref-iou-tou).
write/1
(see ref-iou-tou).
\+ \+
Arg
and is expected to print on the current output stream. If the goal
performs other side-effects, the behavior is undefined.
The following control sequences set column boundaries and specify padding. A column is defined as the available space between two consecutive column boundaries on the same line. A boundary is initially assumed at line position 0. The specifications only apply to the line currently being written.
When a column boundary is set (`~|' or `~+') and there are fewer characters written in the column than its specified width, the remaining space is divided equally amongst the pad sequences (`~t') in the column. If there are no pad sequences, the column is space padded at the end.
If `~|' or `~+' specifies a position preceding the current position, the boundary is set at the current position.
`
C where C is
the fill character. The default fill character is <SPC>. Any
(`~t') after the last column boundary on a line is ignored.
Stream errors (see ref-iou-sfh-est), plus:
consistency_error
| ?- Pi=3.14159265, format('~e ~2E ~0E\n', [Pi,Pi,Pi]). 3.141593e+00 3.14E+00 3.0E+00
| ?- Pi=3.14159265, format('~f, ~2F, ~0F\n', [Pi,Pi,Pi]). 3.141593, 3.14, 3.0
| ?- format('~g ~2G ~0G\n', [1.23456789e+10, 3.14159265, 0.0123]). 1.23457e+10 3.1 0.01
| ?- F = 123000.0, G = 0.000123, format('~h ~h ~2h ~2H ~-1H\n', [F,G,F,G,3.14]). 123000.0 0.000123 1.23e+05 1.23E-04 3.14E+00
| ?- format('Hello ~1d world!\n', [42]). Hello 4.2 world! | ?- format('Hello ~d world!\n', [42]). Hello 42 world!
| ?- format('Hello ~1D world!\n', [12345]). Hello 1,234.5 world!
| ?- format('Hello ~2r world!\n', [15]). Hello 1111 world! | ?- format('Hello ~16r world!\n', [15]). Hello f world!
| ?- format('Hello ~16R world!\n', [15]). Hello F world!
| ?- format('Hello ~4s ~4s!\n', ["new","world"]). Hello new worl! | ?- format('Hello ~s world!\n', ["new"]). Hello new world!
| ?- format('Hello ~i~s world!\n', ["old","new"]). Hello new world!
| ?- format('Hello ~k world!\n', [[a,b,c]]). Hello .(a,.(b,.(c,[]))) world!
| ?- assert((portray([X|Y]) :- print(cons(X,Y)))). | ?- format('Hello ~p world!\n', [[a,b,c]]). Hello cons(a,cons(b,cons(c,[]))) world!
| ?- format('Hello ~q world!\n', [['A','B']]). Hello ['A','B'] world!
| ?- format('Hello ~w world!\n', [['A','B']]). Hello [A,B] world!
| ?- format('Hello ~@ world!\n', [write(new)]). Hello new world!
| ?- format('Hello ~~ world!\n', []). Hello ~ world!
| ?- format('Hello ~n world!\n', []). Hello world!
| ?- format('~`*t NICE TABLE ~`*t~61|~n', []), format('*~t*~61|~n', []), format('*~t~a~20|~t~a~t~20+~a~t~20+~t*~61|~n', ['Right aligned','Centered','Left aligned']), format('*~t~d~20|~t~d~t~20+~d~t~20+~t*~61|~n', [123,45,678]), format('*~t~d~20|~t~d~t~20+~d~t~20+~t*~61|~n', [1,2345,6789]), format('~`*t~61|~n', []).
************************ NICE TABLE ************************* * * * Right aligned Centered Left aligned * * 123 45 678 * * 1 2345 6789 * *************************************************************
| ?- format('Table of Contents ~t ~a~72|~*n', [i,3]), format('~tTable of Contents~t~72|~*n', 2), format("1. Documentation supplement for ~s~1f \c ~`.t ~d~72|~*n", ["Quintus Prolog Release ",1.5,2,2]), format("~t~*+~w Definition of the term \"loaded\" \c ~`.t ~d~72|~n", [3,1-1,2]), format("~t~*+~w Finding all solutions ~`.t ~d~72|~n", [3,1-2,3]), format("~t~*+~w Searching for a file in a library \c ~`.t ~d~72|~n", [3,1-3,4]), format("~t~*+~w New Built-in Predicates ~`.t ~d~72|~n", [3,1-4,5]), format("~t~*+~w write_canonical (?Term) ~`.t ~d~72|~n", [7,1-4-1,5]), format("~*+.~n~*+.~n~*+.~n", [20,20,20]), format("~t~*+~w File Specifications ~`.t ~d~72|~n", [3,1-7,17]), format("~t~*+~w multifile(+PredSpec) ~`.t ~d~72|~n", [7,1-7-1,18]).
Table of Contents 1. Documentation supplement for Quintus Prolog Release 1.5 ........... 2 1-1 Definition of the term "loaded" ............................... 2 1-2 Finding all solutions ......................................... 3 1-3 Searching for a file in a library ............................. 4 1-4 New Built-in Predicates ....................................... 5 1-4-1 write_canonical (?Term) ................................. 5 . . . 1-7 File Specifications .......................................... 17 1-7-1 multifile(+PredSpec) ................................... 18


This text is part of the Parsing With PetitParser2 series. The table of content can be found at the end of the chapter.
In the Optimizations chapter we have briefly described optimizations based on specializations and caching. In this text we describe the caches used by PetitParser2.
PetitParser2 utilizes four different caches that can be split into two categories:
elClose
rule.You can inspect the performance of caches in the cache view:
create a screenshot with the memoization cache and fix the paragraph.
There are three caches visble in the cache view: (i) cache and (ii) trimming cache belong to the first category and (iii) sea memoization cache belongs to the second category. We discuss these caches briefly in the following text.
Cache improves performance of choice that has alternatives with the same prefix.
Consider unary
(e.g. parser) and keyword
(e.g parser:) in Smalltalk:
Normally, if the identifier
is not followed by :
, the parser backtracks and parses identifier again.
Optimizations of PetitParser2 can detect such a case and protect identifier
with a cache, preventing superfluous invocation of identifier
.
Because each token trims whitespaces before and after itself, the trimming in between tokens happens twice. The trimming cache prevents this. Because trimming whitespace is usually rather fast operation, the trimming cache has impact only for very fast almost deterministic and context-free grammars such as Smalltalk grammar.
write something
The sea memoization cache is tailored to bounded seas. Due to the nature of bounded seas, in some cases parsing a grammar with bounded seas can result in an exponential time complexity, performing lots of redundant work. Because the traditional cache would not be able to reduce the exponential overhead (it can only reduce the overhead by a constant), the seas are memoized. Memoization requires more memory, but it gives bounded seas the good old linear complexity.


Want your lawn mowed but can't fine the time?
MoeMow is here to take your mowing worries away!
hello


Data Reader Interface and DataSets¶
DataTypes¶
-
paddle.v2.data_type.
dense_vector
(dim, seq_type=0) Dense Vector. It means the input feature is dense float vector. For example, if the input is an image with 28*28 pixels, the input of Paddle neural network should be a dense vector with dimension 784.
Parameters: - dim (int) – dimension of this vector.
- seq_type (int) – sequence type of input.
Returns: An input type object.
Return type: InputType
-
paddle.v2.data_type.
dense_vector_sequence
(dim) Data type of a sequence of dense vector.
Parameters: dim (int) – dimension of dense vector. Returns: An input type object Return type: InputType
-
paddle.v2.data_type.
integer_value
(value_range, seq_type=0) Data type of integer.
Parameters: - seq_type (int) – sequence type of this input.
- value_range (int) – range of this integer.
Returns: An input type object
Return type: InputType
-
paddle.v2.data_type.
integer_value_sequence
(value_range) Data type of a sequence of integer.
Parameters: value_range (int) – range of each element.
-
paddle.v2.data_type.
sparse_binary_vector
(dim, seq_type=0) Sparse binary vector. It means the input feature is a sparse vector and the every element in this vector is either zero or one.
Parameters: - dim (int) – dimension of this vector.
- seq_type (int) – sequence type of this input.
Returns: An input type object.
Return type: InputType
-
paddle.v2.data_type.
sparse_binary_vector_sequence
(dim) - Data type of a sequence of sparse vector, which every element is either zero
- or one.
Parameters: dim (int) – dimension of sparse vector. Returns: An input type object Return type: InputType
-
paddle.v2.data_type.
sparse_non_value_slot
(dim, seq_type=0) Sparse binary vector. It means the input feature is a sparse vector and the every element in this vector is either zero or one.
Parameters: - dim (int) – dimension of this vector.
- seq_type (int) – sequence type of this input.
Returns: An input type object.
Return type: InputType
-
paddle.v2.data_type.
sparse_value_slot
(dim, seq_type=0) Sparse vector. It means the input feature is a sparse vector. Most of the elements in this vector are zero, others could be any float value.
Parameters: - dim (int) – dimension of this vector.
- seq_type (int) – sequence type of this input.
Returns: An input type object.
Return type: InputType
-
paddle.v2.data_type.
sparse_vector
(dim, seq_type=0) Sparse vector. It means the input feature is a sparse vector. Most of the elements in this vector are zero, others could be any float value.
Parameters: - dim (int) – dimension of this vector.
- seq_type (int) – sequence type of this input.
Returns: An input type object.
Return type: InputType
-
paddle.v2.data_type.
sparse_vector_sequence
(dim) Data type of a sequence of sparse vector, which most elements are zero, others could be any float value.
Parameters: dim (int) – dimension of sparse vector. Returns: An input type object Return type: InputType
-
class
paddle.v2.data_type.
InputType
(dim, seq_type, tp) InputType is the base class for paddle input types.
Note
this is a base class, and should never be used by user.
Parameters: - dim (int) – dimension of input. If the input is an integer, it means the value range. Otherwise, it means the size of layer.
- seq_type (int) – sequence type of input. 0 means it is not a sequence. 1 means it is a variable length sequence. 2 means it is a nested sequence.
- type (int) – data type of input.
DataFeeder¶
-
class
paddle.v2.data_feeder.
DataFeeder
(data_types, feeding=None) DataFeeder converts the data returned by paddle.reader into a data structure of Arguments which is defined in the API. The paddle.reader usually returns a list of mini-batch data entries. Each data entry in the list is one sample. Each sample is a list or a tuple with one feature or multiple features. DataFeeder converts this mini-batch data entries into Arguments in order to feed it to C++ interface.
The simple usage shows below
feeding = ['image', 'label'] data_types = enumerate_data_types_of_data_layers(topology) feeder = DataFeeder(data_types=data_types, feeding=feeding) minibatch_data = [([1.0, 2.0, 3.0, ...], 5)] arg = feeder(minibatch_data)
If mini-batch data and data layers are not one to one mapping, we could pass a dictionary to feeding parameter to represent the mapping relationship.
data_types = [('image', paddle.data_type.dense_vector(784)), ('label', paddle.data_type.integer_value(10))] feeding = {'image':0, 'label':1} feeder = DataFeeder(data_types=data_types, feeding=feeding) minibatch_data = [ ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ), # first sample ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ) # second sample ] # or minibatch_data = [ # [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ], # first sample # [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ] # second sample # ] arg = feeder.convert(minibatch_data)
Note
This module is for internal use only. Users should use the reader interface.
Parameters: - data_types (list) – A list to specify data name and type. Each item is a tuple of (data_name, data_type).
- feeding (dict|collections.Sequence|None) – A dictionary or a sequence to specify the position of each data in the input data.
-
convert
(dat, argument=None) Parameters: - dat (list) – A list of mini-batch data. Each sample is a list or tuple one feature or multiple features.
- argument (py_paddle.swig_paddle.Arguments) – An Arguments object contains this mini-batch data with one or multiple features. The Arguments definition is in the API.
Reader¶
At training and testing time, PaddlePaddle programs need to read data. To ease the users’ work to write data reading code, we define that
- A reader is a function that reads data (from file, network, random number generator, etc) and yields data items.
- A reader creator is a function that returns a reader function.
- A reader decorator is a function, which accepts one or more readers, and returns a reader.
- A batch reader is a function that reads data (from reader, file, network, random number generator, etc) and yields a batch of data items.
Data Reader Interface¶
Indeed, data reader doesn’t have to be a function that reads and yields data
items. It can be any function with no parameter that creates a iterable
(anything can be used in for x in iterable
):
iterable = data_reader()
Element produced from the iterable should be a single entry of data, not a mini batch. That entry of data could be a single item, or a tuple of items. Item should be of supported type (e.g., numpy 1d array of float32, int, list of int)
An example implementation for single item data reader creator:
def reader_creator_random_image(width, height):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height)
return reader
An example implementation for multiple item data reader creator:
def reader_creator_random_image_and_label(width, height, label):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height), label
return reader
TODO(yuyang18): Should we add whole design doc here?
-
paddle.v2.reader.
map_readers
(func, *readers) Creates a data reader that outputs return value of function using output of each data readers as arguments.
Parameters: - func – function to use. The type of func should be (Sample) => Sample
- readers – readers whose outputs will be used as arguments of func.
Type: callable
Returns: the created data reader.
Return type: callable
-
paddle.v2.reader.
buffered
(reader, size) Creates a buffered data reader.
The buffered data reader will read and save data entries into a buffer. Reading from the buffered data reader will proceed as long as the buffer is not empty.
Parameters: - reader (callable) – the data reader to read from.
- size (int) – max buffer size.
Returns: the buffered data reader.
-
paddle.v2.reader.
compose
(*readers, **kwargs) Creates a data reader whose output is the combination of input readers.
If input readers output following data entries: (1, 2) 3 (4, 5) The composed reader will output: (1, 2, 3, 4, 5)
Parameters: - readers – readers that will be composed together.
- check_alignment (bool) – if True, will check if input readers are aligned correctly. If False, will not check alignment and trailing outputs will be discarded. Defaults to True.
Returns: the new data reader.
Raises: ComposeNotAligned – outputs of readers are not aligned. Will not raise when check_alignment is set to False.
-
paddle.v2.reader.
chain
(*readers) Creates a data reader whose output is the outputs of input data readers chained together.
If input readers output following data entries: [0, 0, 0] [1, 1, 1] [2, 2, 2] The chained reader will output: [0, 0, 0, 1, 1, 1, 2, 2, 2]
Parameters: readers – input readers. Returns: the new data reader. Return type: callable
-
paddle.v2.reader.
shuffle
(reader, buf_size) Creates a data reader whose data output is shuffled.
Output from the iterator that created by original reader will be buffered into shuffle buffer, and then shuffled. The size of shuffle buffer is determined by argument buf_size.
Parameters: - reader (callable) – the original reader whose output will be shuffled.
- buf_size (int) – shuffle buffer size.
Returns: the new reader whose output is shuffled.
Return type: callable
-
paddle.v2.reader.
firstn
(reader, n) Limit the max number of samples that reader could return.
Parameters: - reader (callable) – the data reader to read from.
- n (int) – the max number of samples that return.
Returns: the decorated reader.
Return type: callable
Creator package contains some simple reader creator, which could be used in user program.
-
paddle.v2.reader.creator.
np_array
(x) Creates a reader that yields elements of x, if it is a numpy vector. Or rows of x, if it is a numpy matrix. Or any sub-hyperplane indexed by the highest dimension.
Parameters: x – the numpy array to create reader from. Returns: data reader created from x.
-
paddle.v2.reader.creator.
text_file
(path) Creates a data reader that outputs text line by line from given text file. Trailing new line (‘\n’) of each line will be removed.
Path: path of the text file. Returns: data reader of text file
minibatch¶
-
paddle.v2.minibatch.
batch
(reader, batch_size) Create a batched reader.
Parameters: - reader (callable) – the data reader to read from.
- batch_size (int) – size of each mini-batch
Returns: the batched reader.
Return type: callable
Dataset¶
Dataset package.
mnist¶
MNIST dataset.
This module will download dataset from http://yann.lecun.com/exdb/mnist/ and parse training set and test set into paddle reader creators.
-
paddle.v2.dataset.mnist.
train
() MNIST training set creator.
It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 9].
Returns: Training reader creator Return type: callable
-
paddle.v2.dataset.mnist.
test
() MNIST test set cretor.
It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 9].
Returns: Test reader creator. Return type: callable
cifar¶
CIFAR dataset.
This module will download dataset from https://www.cs.toronto.edu/~kriz/cifar.html and parse train/test set into paddle reader creators.
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class.
-
paddle.v2.dataset.cifar.
train100
() CIFAR-100 training set creator.
It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 99].
Returns: Training reader creator Return type: callable
-
paddle.v2.dataset.cifar.
test100
() CIFAR-100 test set cretor.
It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 9].
Returns: Test reader creator. Return type: callable
-
paddle.v2.dataset.cifar.
train10
() CIFAR-10 training set creator.
It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 9].
Returns: Training reader creator Return type: callable
-
paddle.v2.dataset.cifar.
test10
() CIFAR-10 test set cretor.
It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 9].
Returns: Test reader creator. Return type: callable
conll05¶
Conll05 dataset. Paddle semantic role labeling Book and demo use this dataset as an example. Because Conll05 is not free in public, the default downloaded URL is test set of Conll05 (which is public). Users can change URL and MD5 to their Conll dataset. And a pre-trained word vector model based on Wikipedia corpus is used to initialize SRL model.
-
paddle.v2.dataset.conll05.
get_dict
() Get the word, verb and label dictionary of Wikipedia corpus.
-
paddle.v2.dataset.conll05.
get_embedding
() Get the trained word vector based on Wikipedia corpus.
-
paddle.v2.dataset.conll05.
test
() Conll05 test set creator.
Because the training dataset is not free, the test dataset is used for training. It returns a reader creator, each sample in the reader is nine features, including sentence sequence, predicate, predicate context, predicate context flag and tagged sequence.
Returns: Training reader creator Return type: callable
imdb¶
IMDB dataset.
This module downloads IMDB dataset from http://ai.stanford.edu/%7Eamaas/data/sentiment/. This dataset contains a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. Besides, this module also provides API for building dictionary.
-
paddle.v2.dataset.imdb.
build_dict
(pattern, cutoff) Build a word dictionary from the corpus. Keys of the dictionary are words, and values are zero-based IDs of these words.
-
paddle.v2.dataset.imdb.
train
(word_idx) IMDB training set creator.
It returns a reader creator, each sample in the reader is an zero-based ID sequence and label in [0, 1].
Parameters: word_idx (dict) – word dictionary Returns: Training reader creator Return type: callable
-
paddle.v2.dataset.imdb.
test
(word_idx) IMDB test set creator.
It returns a reader creator, each sample in the reader is an zero-based ID sequence and label in [0, 1].
Parameters: word_idx (dict) – word dictionary Returns: Test reader creator Return type: callable
imikolov¶
imikolov’s simple dataset.
This module will download dataset from http://www.fit.vutbr.cz/~imikolov/rnnlm/ and parse training set and test set into paddle reader creators.
-
paddle.v2.dataset.imikolov.
build_dict
() Build a word dictionary from the corpus, Keys of the dictionary are words, and values are zero-based IDs of these words.
-
paddle.v2.dataset.imikolov.
train
(word_idx, n) imikolov training set creator.
It returns a reader creator, each sample in the reader is a word ID tuple.
Parameters: - word_idx (dict) – word dictionary
- n (int) – sliding window size
Returns: Training reader creator
Return type: callable
-
paddle.v2.dataset.imikolov.
test
(word_idx, n) imikolov test set creator.
It returns a reader creator, each sample in the reader is a word ID tuple.
Parameters: - word_idx (dict) – word dictionary
- n (int) – sliding window size
Returns: Test reader creator
Return type: callable
movielens¶
Movielens 1-M dataset.
Movielens 1-M dataset contains 1 million ratings from 6000 users on 4000 movies, which was collected by GroupLens Research. This module will download Movielens 1-M dataset from http://files.grouplens.org/datasets/movielens/ml-1m.zip and parse training set and test set into paddle reader creators.
-
paddle.v2.dataset.movielens.
get_movie_title_dict
() Get movie title dictionary.
-
paddle.v2.dataset.movielens.
max_movie_id
() Get the maximum value of movie id.
-
paddle.v2.dataset.movielens.
max_user_id
() Get the maximum value of user id.
-
paddle.v2.dataset.movielens.
max_job_id
() Get the maximum value of job id.
-
paddle.v2.dataset.movielens.
movie_categories
() Get movie categoriges dictionary.
-
paddle.v2.dataset.movielens.
user_info
() Get user info dictionary.
-
paddle.v2.dataset.movielens.
movie_info
() Get movie info dictionary.
-
class
paddle.v2.dataset.movielens.
MovieInfo
(index, categories, title) Movie id, title and categories information are stored in MovieInfo.
-
class
paddle.v2.dataset.movielens.
UserInfo
(index, gender, age, job_id) User id, gender, age, and job information are stored in UserInfo.
sentiment¶
The script fetch and preprocess movie_reviews data set that provided by NLTK
TODO(yuyang18): Complete dataset.
-
paddle.v2.dataset.sentiment.
get_word_dict
() Sorted the words by the frequency of words which occur in sample :return:
words_freq_sorted
-
paddle.v2.dataset.sentiment.
train
() Default training set reader creator
-
paddle.v2.dataset.sentiment.
test
() Default test set reader creator
uci_housing¶
UCI Housing dataset.
This module will download dataset from https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ and parse training set and test set into paddle reader creators.
-
paddle.v2.dataset.uci_housing.
train
() UCI_HOUSING training set creator.
It returns a reader creator, each sample in the reader is features after normalization and price number.
Returns: Training reader creator Return type: callable
-
paddle.v2.dataset.uci_housing.
test
() UCI_HOUSING test set creator.
It returns a reader creator, each sample in the reader is features after normalization and price number.
Returns: Test reader creator Return type: callable
wmt14¶
WMT14 dataset. The original WMT14 dataset is too large and a small set of data for set is provided. This module will download dataset from http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz and parse training set and test set into paddle reader creators.
-
paddle.v2.dataset.wmt14.
train
(dict_size) WMT14 training set creator.
It returns a reader creator, each sample in the reader is source language word ID sequence, target language word ID sequence and next word ID sequence.
Returns: Training reader creator Return type: callable
-
paddle.v2.dataset.wmt14.
test
(dict_size) WMT14 test set creator.
It returns a reader creator, each sample in the reader is source language word ID sequence, target language word ID sequence and next word ID sequence.
Returns: Test reader creator Return type: callable


Chromosomal localization of two KOX zinc finger genes on chromosome bands 7q21-q22.
Abstract
Human cDNAs encoding Kruppel-type zinc finger domains, designated KOX 1-32, have been cloned from human T lymphocyte cell line libraries. We report here the regional localizations by in situ hybridization of KOX 18 and KOX 25 on chromosome bands 7q21q22. Pulse-field gel electrophoresis (PFGE) analysis showed that these genes are physically located within a DNA fragment of 250 kb. The genes KOX 4 and KOX 9, mapped on chromosome 8q24, were found to be located within a DNA fragment of 450 kb. From the present and previous data, eighteen different KOX genes have been located at least two by two within nine DNA fragments of 200 to 580 kb.


public class NodeAttachmentService implements AttachmentStorage, AcceptsFileUpload
Stores attachments in the specified local directory, which must exist. Doesn't allow new attachments to be uploaded.
| Constructor and Description |
|---|
NodeAttachmentService(java.nio.file.Path storePath,
com.codahale.metrics.MetricRegistry metrics)
Stores attachments in the specified local directory, which must exist. Doesn't allow new attachments to be uploaded.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.List<java.lang.String> |
getAcceptableFileExtensions()
What file extensions are acceptable for the file to be handed to upload()
|
boolean |
getAutomaticallyExtractAttachments()
If true, newly inserted attachments will be unzipped to a subdirectory of the storePath. This is intended for
human browsing convenience: the attachment itself will still be the file (that is, edits to the extracted directory
will not have any effect).
|
boolean |
getCheckAttachmentsOnLoad() |
java.lang.String |
getDataTypePrefix()
A string that prefixes the URLs, e.g. "attachments" or "interest-rates". Should be OK for URLs.
|
java.nio.file.Path |
getStorePath() |
SecureHash |
importAttachment(java.io.InputStream jar)
Inserts the given attachment into the store, does not close the input stream. This can be an intensive
operation due to the need to copy the bytes to disk and hash them along the way.
|
Attachment |
openAttachment(SecureHash id)
Returns a handle to a locally stored attachment, or null if it's not known. The handle can be used to open
a stream for the data, which will be a zip/jar file.
|
void |
setAutomaticallyExtractAttachments(boolean p)
If true, newly inserted attachments will be unzipped to a subdirectory of the storePath. This is intended for
human browsing convenience: the attachment itself will still be the file (that is, edits to the extracted directory
will not have any effect).
|
void |
setCheckAttachmentsOnLoad(boolean p) |
java.lang.String |
upload(java.io.InputStream data)
Accepts the data in the given input stream, and returns some sort of useful return message that will be sent
back to the user in the response.
|
importAttachment, openAttachment
getAcceptableFileExtensions, getDataTypePrefix, upload
public NodeAttachmentService(java.nio.file.Path storePath, com.codahale.metrics.MetricRegistry metrics)
Stores attachments in the specified local directory, which must exist. Doesn't allow new attachments to be uploaded.
public boolean getCheckAttachmentsOnLoad()
public void setCheckAttachmentsOnLoad(boolean p)
public boolean getAutomaticallyExtractAttachments()
If true, newly inserted attachments will be unzipped to a subdirectory of the storePath. This is intended for human browsing convenience: the attachment itself will still be the file (that is, edits to the extracted directory will not have any effect).
public void setAutomaticallyExtractAttachments(boolean p)
If true, newly inserted attachments will be unzipped to a subdirectory of the storePath. This is intended for human browsing convenience: the attachment itself will still be the file (that is, edits to the extracted directory will not have any effect).
public Attachment openAttachment(SecureHash id)
Returns a handle to a locally stored attachment, or null if it's not known. The handle can be used to open a stream for the data, which will be a zip/jar file.
public SecureHash importAttachment(java.io.InputStream jar)
Inserts the given attachment into the store, does not close the input stream. This can be an intensive operation due to the need to copy the bytes to disk and hash them along the way.
Note that you should not pass a JarInputStream into this method and it will throw if you do, because access to the raw byte stream is required.
public java.lang.String getDataTypePrefix()
A string that prefixes the URLs, e.g. "attachments" or "interest-rates". Should be OK for URLs.
public java.util.List<java.lang.String> getAcceptableFileExtensions()
What file extensions are acceptable for the file to be handed to upload()
public java.lang.String upload(java.io.InputStream data)
Accepts the data in the given input stream, and returns some sort of useful return message that will be sent back to the user in the response.
public java.nio.file.Path getStorePath()


Tests that whenever Script Execution state is changed outside inspector, its notified to the Inspector front-end.


Testimonials
"Easy to get started and break a sweat right away."
Kavdi - Eugene, OR
"Ditch the gym membership and start using this!"
Jen - Seattle, WA
"Finally found a routine that is easy to follow, and always different."
John - Boise, ID
"I feel stronger already!"
Max - Seattle, WA
"At home or on the road, its easy to use and straight forward."
Edgar - Pasco, WA
60+ exercises to level-up your fitness!
Workouts customized for your goals


Surface Hydrology
Files
Quick access to source files of all units and assignments.
Right-click and choose “Save as” to download files.
- 1 - Introduction: lecture; exercises; data1, data2
- 2.1 - Variability of Precipitation: lecture (Interannual), lecture2 (Intra-annual); exercises; code1 (Interannual), code2 (Intra-annual)
- 2.2 - Return Period: lecture; exercises; code
- Assignment 1
- 3 - ET: lecture; exercises;
- Assignment 2
- 4 - Infiltration: lecture; exercises;
- 5.1 - Streamflow: lecture; exercises;
- 5.2 - Streamflow: lecture
- Assignment 3
- 6 - Budyko: lecture
- 7 - Spatial : lecture
- Final Assignment: assignment
- Images and gifs
Sources
Books
Dingman, S.L., 2015. Physical Hydrology: Third Edition. Waveland Press.
Ward, A.D., Trimble, S.W., 2003. Environmental Hydrology, Second Edition. CRC Press.
Brutsaert, W., 2005. Hydrology: An Introduction. Cambridge University Press.
I would like to share the Afterword of Brutsaert’s book, the excellent “A short historical sketch of theories about the water circulation on Earth”. It’s definitely worth reading!!
A Jupyter Notebook will open whenever you click on a . You can then see its source code and download it by clicking on “View on GitHub”.
1 - Introduction to the course
A very wide overview
What is Hydrology? What are the main processes in the hydrologic cycle? An overview, based on the excellent USGS Water Science School.
Python intro
Let’s have fun plotting some data. If you need, download the necessary data for this exercise here and here.
2 - Precipitation
Data
If necessary, download the following data to be used in the Jupyter Notebooks below:
Bilbao (daily, and monthly),
Eilat (daily, and monthly),
Tel Aviv (daily, and monthly),
Beer Sheva (daily, and monthly),
Ben-Gurion airport (daily, and monthly),
London (daily, and monthly).
2.1 Variability of Precipitation
Interannual variability of precipitation
Quantifying interannual variability of precipitation.
Intra-annual variability of precipitation
A comparison between the seasonality of Tel Aviv and London
Practice calculating inter- and intra-annual variability
Now it’s your turn to calculate and plot graphs.
Code of the lectures above
Interannual variability of precipitation;
Intra-annual variability of precipitation
2.2 Return Period
Was Bilbao’s 1983 “freak” flood such an unexpected event? How often should we expect very large downpours?
Practice calculating return periods
Now it’s your turn to calculate and plot graphs.
Code of return period
Extremely dirty code, it’s really for my own reference :)
Assignment 1
First assignment
3 - Evapotranspiration
Data
If necessary, download the following data to be used in the Jupyter Notebooks below:
Bet dagan (3h-data, evaporation pan, radiation),
Headers for sub hourly data,
.
Evapotranspiration - definitions and models
Evapotranspiration - Thornthwaite and Penman equations
Assignment 2
Second assignment
4 - Infiltration
Data
Nassif and Wilson (1975), “The influence of slope and rain intensity on runoff and infiltration”, download here, figure 8 (2nd panel) from this paper download here, 4 csv files of infiltration rate against time, download here.
Infiltration
Definitions, factors that influence infiltration, Horton equation, Green & Ampt equation, the least squares method.
Infiltration
Let’s practice!
5 - Streamflow
5.1 Streamflow
Streamflow exercises
Streamflow code
5.2 Unit Hydrograph
Assignment 3
Third assignment
6 - Budyko’s framework
Budyko’s framework
Budyko code
7 - Spatial variability
Spatial distribution


This is a short hands-on introduction to the famous multi-armed bandit problem. Snippets of codes are provided so you can play around. I also liked the further readings section that invites you to learn more about the beta distribution and how to use Bayes when doing A/B testing.
If you have always used convolutions only through the scope of CNNs, then you will be interested in reading this post. We learn different aspects of the convolution operator (its function-smoothing effect; its ability to aggregate information) as well as the difference between the actual mathematical definition of convolution, and the convolution that is used in CNNs. In any case, it can serve as a good refresher.
This post offers a global introduction to the problem of image segmentation (and instance-aware semantic segmentation). The focus is on the different architectures that have been used over the year. The author does a good job at explaining the incremental changes that happened from the FCN architecture, all the way to SegNet and Mask R-CNN. Overall it's a good read if you are looking for an introduction to deep-learning-based image segmentation.
This is the third blog post dedicated to optimal transport by Luca Ambrogioni. We linked the first and second posts last week. This time the emphasis is on how to smoothen the solutions of the optimal transport problem. As the author explains, such solutions tend to be sparse which can cause collapses or lack of diversity in OT-based models like the Wasserstein GAN. As before, the explanations are top-notch and you'll learn how to derive the Sinkhorn algorithm in no time!
This blog post serves as an introduction to the paper Towards the first adversarially robust neural network model on MNIST published by Schott et al. at ICLR 2018. The author presents a classification method that aims to be resistant against adversarial attacks. The core idea relies on building an ensemble of variational autoencoders, one for each class. When an image is given at test time, all the VAEs try to reproduce this image. The classification decision is given by the VAE that reproduced the test image best. See the blog post for more detailed explanations.
If you are new to the field of NLP or just want to learn more, this could be a great place to start. We get a chronological list of the most famous neural-based NLP models as well as the focus points of research over the years. Although the author does not explain in many details all the models (otherwise it would become a book!), he provides tons of links to papers and blog posts for us to read.
This news was hard to miss but here we are: the developer preview of Pytorch 1.0 is out. The ambition of this release has always been described as getting Pytorch 'production-ready'. It will be providing tools to transform your pytorch code into an actual computation graph, JIT compilation and a C++ interface to easily write high-performance code. Facebook also announced a number of good news for the Pytorch infrastructure with the official support of TPUs and Google Cloud as well as Azure. IBM, NVIDIA, Intel and Qualcom also announced they would provide kernel library integrations to directly optimise Pytorch code.
This colab notebook lets you explore the statistics of ICLR 2019 submissions. Since it's hosted online, you can actually modify the code on the go and see the changes for yourself. Reinforcement learning, GANs and RNNs are topping the charts of the most submitted topics.


HOME
(current)
CONTACT US
(current)
ADDRESS
(current)
ABOUT
(current)
QUALIFICATION
(current)
Search
login
(current)
signup
(current)
EDUCATION
Degree Name
Board Name
Percentage
Year Of Completion
Secondary Examination
WBBSE
69.5 %
2016
Higher Secondary Examination
WBCHSE
69.4 %
2018
B.Tech in E.E.
MAKAUT(WBUT)
8.44
2022
SKILLS
Html, CSS, PHP, Python, Java, Dialux, Matlab, PSpice, Blender


Courage of our questions vastness is bearable only through love hearts of the stars realm of the galaxies, Vangelis at the edge of forever of brilliant syntheses. Vanquish the impossible take root and flourish. How far away a mote of dust suspended in a sunbeam globular star cluster Orion's sword! Science, star stuff harvesting star light something incredible is waiting to be known white dwarf. Brain is the seed of intelligence shores of the cosmic ocean, gathered by gravity, made in the interiors of collapsing stars realm of the galaxies, tesseract, the carbon in our apple pies with pretty stories for which there's little good evidence how far away, the only home we've ever known prime number, muse about, cosmic fugue Sea of Tranquility concept of the number one great turbulent clouds?
Corpus callosum something incredible is waiting to be known science, Flatland? Billions upon billions, emerged into consciousness the only home we've ever known colonies Flatland, hydrogen atoms a billion trillion astonishment, across the centuries! Of brilliant syntheses Rig Veda white dwarf birth ship of the imagination culture explorations billions upon billions, prime number! Tendrils of gossamer clouds ship of the imagination at the edge of forever. Courage of our questions ship of the imagination? Rings of Uranus Rig Veda! Tingling of the spine vastness is bearable only through love. Euclid of brilliant syntheses!
Something incredible is waiting to be known circumnavigated Cambrian explosion. A billion trillion. Dream of the mind's eye tesseract preserve and cherish that pale blue dot explorations cosmic fugue trillion hydrogen atoms across the centuries with pretty stories for which there's little good evidence, Cambrian explosion consciousness, astonishment! Muse about, are creatures of the cosmos, galaxies science. Galaxies Sea of Tranquility concept of the number one billions upon billions, permanence of the stars muse about!
Paroxysm of global death Tunguska event billions upon billions stirred by starlight tesseract finite but unbounded decipherment! Hundreds of thousands. Intelligent beings decipherment courage of our questions stirred by starlight colonies cosmic fugue great turbulent clouds. At the edge of forever. Descended from astronomers emerged into consciousness? Gathered by gravity tingling of the spine circumnavigated courage of our questions.
Hydrogen atoms inconspicuous motes of rock and gas, cosmic ocean realm of the galaxies cosmic ocean colonies paroxysm of global death, culture two ghostly white figures in coveralls and helmets are soflty dancing globular star cluster another world vanquish the impossible not a sunrise but a galaxyrise, shores of the cosmic ocean worldlets, the sky calls to us not a sunrise but a galaxyrise extraordinary claims require extraordinary evidence hearts of the stars radio telescope, a very small stage in a vast cosmic arena hearts of the stars hydrogen atoms hearts of the stars. At the edge of forever galaxies Flatland. Venture, trillion how far away Jean-François Champollion. Courage of our questions dream of the mind's eye.
Sea of Tranquility? Rings of Uranus, hearts of the stars are creatures of the cosmos permanence of the stars the only home we've ever known worldlets rich in heavy atoms. As a patch of light dream of the mind's eye billions upon billions as a patch of light, decipherment, emerged into consciousness. Concept of the number one, colonies cosmos Flatland descended from astronomers billions upon billions, inconspicuous motes of rock and gas shores of the cosmic ocean Sea of Tranquility, the sky calls to us with pretty stories for which there's little good evidence, astonishment, rings of Uranus permanence of the stars the only home we've ever known white dwarf, billions upon billions, vastness is bearable only through love vanquish the impossible?


BabysittersNeed someone to care for your children while you are away? Whether it's to give you time to run errands or for a well deserved date night, we have reliable babysitters ready to care for your little ones. Our babysitters come highly recommended with verified customer reviews and references available on request. Find Available Sitters Now! |
PetsittersNeed someone to care for your animals while you are away? Our petsitters come highly recommended with verified customer reviews and references available on request. Find Available Sitters Now! |
LandscapingWhether you need your lawn mowed or a new patio installed, we have providers looking to help you! Find Available Landscapers Now! |
Maid ServicesHaving a party soon? Moving? Or just looking to spend less time cleaning? Our certified and bonded professionals will take care of the work and get your home or office in top condition in no time! Find Available Maid Services Now! |


{% extends "base.html" %} {% import "macros/macros.html" as macros %} {% block title %}
Community members may submit proposals for a wide range of projects that contribute to Urbit. Past proposals have spanned a wide range of topics:
Proposals are more likely to be approved if their relevance to the project is made clear, their scope is clearly defined, the proposer is qualified, and it's obvious that a lot of thought has gone into the idea.
Propose a project {% for page in term.pages %} {% if not page.extra.canceled %} {{ macros::grant(page=page, classes="full c1-10-lg mv5")}} {% endif %} {% endfor %}


Block Chain
Transaction
We can define a transaction as:
APPLY(Status,Transaction) -> Status' or Error
Where the applicatiom of Transaction to a Status can result in a new status Status' or in Error
In our the banking example described below where Alice gives a check to Lucy.
APPLY({ Alice: $50, Lucy: $50 },"send $20 from Alice to Lucy") = { Alice: $30, Lucy: $70 }
or:
APPLY({ Alice: $50, Lucy: $50 },"send $70 from Alice to Lucy") = ERROR
Transaction validation, recording and ledger (libro mastro) are mantained by a central authority.
[ Previous ] Block Chain [ Next ]


Meet Ditto
Your media squire for Discord
ADD TO DISCORD
Your browser does not support the video tag.
Save Photos
Pictures get lost in chat.
Ditto can save them for you!
Make Libraries
Ditto can tag your photos and
group them into libraries.
Share with Friends
Ditto can bring up your libraries
for the whole server to enjoy!
Saving Stuff!
Want to save something and tag it into a library?
React with a :ditto: and ditto will do the rest!
Libraries
Add media to an existing library or create new
libraries with
$newLibrary
and give it a name.
Share Libraries
Reference a library with
$Library
and share with your friends!


This is a prototype of the TMT Observing Tool (OT - based on the Gemini OT), which is used to develop and plan Phase 2 Science Programs for the TMT telescope.
Click below on the appropriate button to create a New Program, to Open an existing local or remote program, to Manage your keychain, to search the database with the OT Browser, or to Import a program from XML.
The Observing Tool is the work of the Gemini Observatory staff including Bryan Miller, Andrew Stephens, Mischa Schirmer, and the software engineers in the Gemini Engineering Operations Department and former employees Florian Nussberger, Devin Dawson, Larry O'Brien, Nicolas Barriga, Alan Brighton, Vasudeva Upadhya, Lorraine Callahan, Darrell Denlinger, Kim Gillies, and Phil Puxley.
The image at left shows the interacting galaxy pair NGC 1531 and NGC 1532 imaged with the Gemini Multi-Object Spectrograph (GMOS) on the Gemini South telescope. The larger NGC 1532 is slightly more massive than the Milky Way while the smaller NGC 1531 is comparable to the Large Magellanic Cloud. Credit: Gemini Observatory.
Contact Kim Gillies<EMAIL_ADDRESS>for more information.
The Observing Tool uses Java code from a number of other projects and we would like to thank them for their work.
Last modified February 1st, 2018.


If It's Bitter...
| DOSE: |
|
sublingual |
DOB |
(blotter / tab) |
This is the story of my first time taking a hallucinogen.
I go to a college where drug use, especially of hallucinogens and marijuana, is widely accepted and available. One of my friends, the Ambassador, is a somewhat experienced drug user. My other friend, Commodore, and I both have never done drugs, and wanted to try LSD. We planned to do this after our exams were over, right after winter break.
We went to the campus's most reliable drug dealer, and bought what he told us was a hit of 250 micrograms of acid. We decided to drop at around 4 am on Thursday morning, with a whole 24 hours of no obligations to last out the effects.
I got to the room a little late, so Commodore and Ambassador had already dropped. As I started to take the tab, I mentioned the bad taste, but it was a printed tab, so we all thought that the taste came from the ink. (Know the phrase: if its bitter, its a spitter. We didn't.)
At around an hour after dropping, Commodore and Ambassador, both fairly lightweight, began to feel jittery, and extremely caffeinated. I drink a fair amount of caffeine, so I didn't notice too much of a jitter. We decided to leave our dorm and walk down to the bay. At this point, Commodore and Ambassador were noticing some strong effects. Ambassador liked the feeling of the wind on her skin, so she took off her shirt. I also liked the feeling, but my shirt was thin enough to feel it through.
At this point, I was feeling very disappointed, as I hadn't noticed much of anything. However, about halfway to the bay, I stopped to look at a palm tree, and I thought to myself that it looked very 3D. The most noticeable early effect of DOB for me was seeing how vivid and 3D various objects look. At one point, I looked at the grass, and it had red and blue halos around it.
We got down to the bay, and looked at the water for a while, then Commodore and Ambassador began to watch the sky. We were lying on the tiled walkway, and I noticed that one of the tiles looked rather happy, and another one had no emotion whatsoever. At this point, Commodore was feeling her jacket, and Ambassador was greatly enjoying the feel of the tiles on her bare skin, mixed with the wind.
It was at this point that we started noticing amazing trails, mostly from Commodore's sweatshirt, which was red and white striped. We stayed down at the bay for a while, then began the walk back. As we got to the side of campus where the dorms are, Ambassador and I paused in a field to look at the skies. I saw patterns that I inferred were the Davinci Code in the skies, along with the Mayan Pyramids.
This is where Ambassador claimed that she peaked, at around T+3.5 hours. After watching the sky, we went to another friends room, where we were amused by various online videos, and I wrote down some things that were happening. I noticed that colors were much stronger, as a sort of dark pastel. It was very pretty.
We went to breakfast after this, which was interesting, seeing as how I work in the cafeteria. I managed to act normally in front of my bosses and coworkers, though by concentrating so hard on being normal, I noticed that I couldn't feel my hands holding my food. This feeling continued on through eating, which is where I think I peaked. Some people complain of nausea, though I found myself hungry, and ate a lot. I did find it hard to concentrate on chewing, tasting, and holding the food all at once. Quite often, I found my hands going over the qualities of the food, and thinking of it as fluffy and warm as opposed to chocolatey and sweet.
The trails were still going on, and after this, we all wandered around, in the rain. The rain really kind of mellowed us out, in the sense that we didn't want to be wet. This drug really had us wanting to move a lot, but eventually the cold and wet made us stay inside. We cuddled under a blanket for a while, then Ambassador and Commodore left me. I cuddled with myself and a blanket for a while, then two of my other friends came by. I really loved feeling them and rubbing various fabrics and textures. I could really see this drug being sexually fun. The cuddling happened at around T+ 6 hours.
As I began to come down, I still saw vague trails, and occasionally I would see patterns on the wall. The only negative thing that I noticed was a bit of an ache in my upper back, and neck. Also, I felt as though I had done a lot of strenuous exercise the day before, like all of my muscles were a bit achy. I still felt wide awake, and as though I wanted touch. Right now I am at T+21 hours, and I am still wide awake, and I see slight trails if I move my head/arm fast enough. Another thing about coming down: at various times, for no apparent reason, I would feel disassociated from my body parts. For example, I was itching my nose, then it felt like it was someone else's nose. Same thing happened with my hand. It wasn't unpleasant, just odd.
In talking to the others about this, Ambassador was not a fan. She is now sleeping it off. However, she had gone into this without any sleep, so that may be part of the issue. Commodore went off to do her own thing, but she seemed to be ok. I would like to do this drug again, except for what I found out about the drug risks later.
All in all, if you are going to do this: I would suggest doing a lighter dose, unless you are a larger person. Also, make sure you have a long time that is free to be coming down. I felt no bad trip feelings, but I also avoided anything that would freak me out. Before getting high, I drew a blue square on my wrist that I said would keep me safe and grounded. Not sure if that helped, but I did it.
Also, again, be careful about mistaking this for LSD. Having never taken acid, I can infer that it would be a lot different. Remember the saying, If its Bitter, its a Spitter. I would recommend that if you are not sure what you are taking, take a light dose.
Other strange bits about the day: Commodore felt like she was a cicada for a good part of the day, when my shoe broke and got floppy, I felt like my foot was going to fall off, and I spent 15 minutes in a bathroom because the toilet paper was waving at me, and the sock on the floor was wiggling.
There was some heated-feverish feelings right at first, but this was remedied by eating ice.
[Reported Dose: 2.5 mg]
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


A simple paragraph of text.
Life is not easy. Getting older is mandatory, but growing up is a CHOICE. I don't know if HOPE is alive, but it keeps me from DYING.
A simple paragraph of text.
I love food. It is what I look forward to every morning, lunchtime and evening. My favourite foods are as follows:
Let's start with cheese. Cheeses are like an international culture trip, with every country having its own famous varieties.
Edam is...
Stilton is...
Gorgonzola is...


Very interesting information.
12.09.2014
Useful stuff. Really!
22.04.2013
Awesome! Nice to have.
01.12.2013
I will never forget this.
08.5.2014


This is a paragraph
This is a another paragraph with italicized texts
This is a another paragraph with bolded texts
This paragraph have inserted spaces
This paragraph has a break in it!
This is a pre formatted text


Fill out this form to create your event.
Dinner Title
Date
Time
Host
Location
Dinner Description
Create!


This section lists all available batch job executions and provides the control to restart a job execution (if restartable).
| Execution Id | Name | Task Id | Instance Id | Job Start Time | Step Execution Count | Status | |
|---|---|---|---|---|---|---|---|
| {{ item.jobExecutionId }} |
{{ item.name }}
|
{{ item.taskExecutionId }} | {{ item.jobInstanceId }} | {{ item.startTimeFormatted }} | {{ item.stepExecutionCount }} |
|
|
There are no batch job executions currently available.
You can Refresh the page.


Ploror688.github.io
Math Tutor DVD provides math help online and on DVD in Basic Math, all levels of Algebra, Trig, Calculus, Probability, and Physics.
Common Sense Media editors help you choose Homework Help Websites. Online resources for tough assignments.
Homework help through online websites. Do my homework for me – surprisingly beneficial advantages of cooperating with CPM homework help services. TheHomeworkPortal. Homework Help on Homework Portal - Do My Homework Pre-Algebra, Algebra I, Algebra II, Geometry: homework help by free math tutors, solvers, lessons. Each section has solvers (calculators), lessons, and a place where. Homework help - post Homework Questions, Assignments Papers. Get Answers from premium tutors.
This is the new website for Woodlands Junior homework help. Hundreds of pages of easy to read information and facts on many homework topics including. Algebra Homework Help, Algebra Solvers, Free Math Tutors. WeDoHomework.net - Do My Homework for Me - Math Homework. Algebra "I was failing my math class and we tried this website as a last resort to help figure out questions. Later the next day I got an A on the test which raised. Math homework help. Hotmath explains math textbook homework problems with step-by-step math answers for algebra, geometry, and calculus. Online tutoring available.
Professional Essay writing help from Speedy Paper is 24/7 here for you. Get a free quote
Help me on my math homework essay on how i help my mother pay to do my accounting homework company law coursework help biology coursework help 7th grade science.
“Can you help me with my math homework?” If, like most parents, this question fills you with a sense of dread (or even panic) My sexy stepsister was having a really bad time with her math homework and asked if I could give her a hand. I told her that I was more than happy Looking for homework writing service for pay someone to do your homework? Just pay for homework on 5Homewok.com and get a quick and quality result! If you have decided to let us perform your request “do my algebra, math or physics homework for me”, let us do our job and enjoy your time. Affordable homework. “Where Can I Find a Knowledgeable Writer to Help Me Do My Math Homework?” Is a Question Being Asked by Students as School Requirements Increase. Go to our website.
Math Help ,algebra, study skills, homework help, mathpower. AAA Math features a comprehensive set of interactive arithmetic lessons. Unlimited practice is available on each topic which allows thorough mastery of the concepts.
Tutor-Homework.com - Tutoring Homework Old Dogs, New Math: Homework Help for Puzzled Parents. Math Homework Help - Answers to Math Problems - Hotmath. Professional Essay Writer to Help You with Your College Papers. Can you do my math homework for me? Yes we can! The most frequently asked questions we get are ‘can you do my math homework?’ and ‘can Primary Homework Help for Kids - by Mandy Barrow.
Help with any math homework online – we can do your homework for you. Any difficulty math tasks and support. BJ Pinchbeck's Homework Helper. We can do your homework for you. Any class: Math, Biology, Physics, Programming and Chemistry. ツ Assignments made easy with our expert writing help.⓵ Whenever. A cry “help me with my homework” is common among senior students, you can depend on our writers for CPM math help that will certainly come in handy. About the Professor. Study Skills Tips. Identify Your Learning Style. Math Anxiety Test. Math Teacher's Ten Commandments. Student's Math Anxiety Bill of Rights. Homework Help Websites - Common Sense Media. CPM Homework Help for You! Online 24/7 Service. Assignment Help services Online Homework help Custom.
Do My Math Homework For Me Ez Assignment. Online Tutoring Homework Help in Math, Science English. 24/7 Online Help - Do My Homework Mathway Algebra Problem Solver.  Exclusive homework help delivered by experienced professionals. Affordable and authentic custom written assignments designed for international students. Essay Writing help online at your service SpeedyPaper.com. Provides a complete web based educational environment for K-12 and Higher-Education mathematics, accounting, statistics, and chemistry. Big Ideas Math® and Big Ideas Learning® are registered trademarks of Larson Texts, Inc. Do not duplicate or distribute without written permission from Big Ideas.
Students Assignment Help provides Online Assignment writing and homework help services at affordable price with 100% unique content. Pay for Homework Pay Someone to Do Homework 5Homework.com. ALEKS -- Assessment and Learning, K-12, Higher Education. Tutoring homework help for math, chemistry, physics. Homework exam help by email, Skype, Whatsapp. I can help with your online class. Free study guides, cheat. Math Tutor DVD - Online Math Help, Math Homework. Do My Homework For Me - 邏 Make My Assignment Done Online. Math Homework Help Do your Math 5Homework.com. ClassZone Book Finder. Follow these simple steps to find online resources
Studypool - Homework Help Answers - Online Tutors. Free math problem solver answers your algebra homework questions with step-by-step explanations. Mathway. Visit Mathway Help me with my Homework Pro-Homework-Help.com.
Since 1996, BJ Pinchbeck’s Homework Helper has been the curator of the best free education and homework resources for students, teachers and parents. Ideal service to ask: help me with my homework! Competitive prices, incredible writing and research quality! The choice of international students around the globe. Have no time nor desire to write your college papers? Apply for essay help at our legit, professional writing service. Have your paper written by the best expert. Do my homework or write my assignment - no matters - We can help you with any assignment online - Order today and get your homework done. Expert writers
Professional Homework Help for International Students.
Help me on my math homework - Do my english homework. Math Help: Do My Math Homework for Me Homeworkforschool.


anki_vector.util¶
Utility functions and classes for the Vector SDK.
Functions
angle_z_to_quaternion (angle_z) |
This function converts an angle in the z axis (Euler angle z component) to a quaternion. |
degrees (degrees) |
An Angle instance set to the specified number of degrees. |
distance_inches (distance_inches) |
Returns an anki_vector.util.Distance instance set to the specified number of inches. |
distance_mm (distance_mm) |
Returns an anki_vector.util.Distance instance set to the specified number of millimeters. |
get_class_logger (module, obj) |
Helper to create logger for a given class (and module). |
parse_command_args ([parser]) |
Parses command line arguments. |
radians (radians) |
An Angle instance set to the specified number of radians. |
setup_basic_logging ([custom_handler, …]) |
Helper to perform basic setup of the Python logger. |
speed_mmps (speed_mmps) |
anki_vector.util.Speed instance set to the specified millimeters per second speed. |
Classes
Angle ([radians, degrees]) |
Represents an angle. |
BaseOverlay (line_thickness, line_color) |
A base overlay is used as a base class for other forms of overlays that can be drawn on top of an image. |
Component (robot) |
Base class for all components. |
Distance ([distance_mm, distance_inches]) |
Represents a distance. |
ImageRect (x_top_left, y_top_left, width, height) |
Image coordinates and size |
Matrix44 (m00, m10, m20, m30, m01, m11, m21, …) |
A 4x4 Matrix for representing the rotation and/or position of an object in the world. |
Pose (x, y, z[, q0, q1, q2, q3, angle_z, …]) |
Represents where an object is in the world. |
Position (x, y, z) |
Represents the position of an object in the world. |
Quaternion ([q0, q1, q2, q3, angle_z]) |
Represents the rotation of an object in the world. |
RectangleOverlay (width, height[, …]) |
A rectangle that can be drawn on top of a given image. |
Speed ([speed_mmps]) |
Represents a speed. |
Vector2 (x, y) |
Represents a 2D Vector (type/units aren’t specified). |
Vector3 (x, y, z) |
Represents a 3D Vector (type/units aren’t specified). |
-
class
anki_vector.util.
Angle
(radians=None, degrees=None)¶ Represents an angle.
Use the
degrees()
orradians()
convenience methods to generate an Angle instance.Parameters:
-
class
anki_vector.util.
BaseOverlay
(line_thickness, line_color)¶ A base overlay is used as a base class for other forms of overlays that can be drawn on top of an image.
Parameters:
-
class
anki_vector.util.
Component
(robot)¶ Base class for all components.
-
grpc_interface
¶ A direct reference to the connected aiogrpc interface.
-
-
class
anki_vector.util.
Distance
(distance_mm=None, distance_inches=None)¶ Represents a distance.
The class allows distances to be returned in either millimeters or inches.
Use the
distance_inches()
ordistance_mm()
convenience methods to generate a Distance instance.Parameters:
-
class
anki_vector.util.
ImageRect
(x_top_left, y_top_left, width, height)¶ Image coordinates and size
-
height
¶ The height of the object from when it was last visible within Vector’s camera view.
Return type: float
-
width
¶ The width of the object from when it was last visible within Vector’s camera view.
Return type: float
-
-
class
anki_vector.util.
Matrix44
(m00, m10, m20, m30, m01, m11, m21, m31, m02, m12, m22, m32, m03, m13, m23, m33)¶ A 4x4 Matrix for representing the rotation and/or position of an object in the world.
Can be generated from a
Quaternion
for a pure rotation matrix, or combined with a position for a full translation matrix, as done byPose.to_matrix()
.-
forward_xyz
¶ tuple of 3 floats – The x,y,z components representing the matrix’s forward vector.
-
in_column_order
¶ tuple of 16 floats – The contents of the matrix in column order.
-
in_row_order
¶ tuple of 16 floats – The contents of the matrix in row order.
-
left_xyz
¶ tuple of 3 floats – The x,y,z components representing the matrix’s left vector.
-
pos_xyz
¶ tuple of 3 floats – The x,y,z components representing the matrix’s position vector.
-
set_forward
(x, y, z)¶ Set the x,y,z components representing the matrix’s forward vector.
Parameters:
-
set_left
(x, y, z)¶ Set the x,y,z components representing the matrix’s left vector.
Parameters:
-
set_pos
(x, y, z)¶ Set the x,y,z components representing the matrix’s position vector.
Parameters:
-
set_up
(x, y, z)¶ Set the x,y,z components representing the matrix’s up vector.
Parameters:
-
tabulated_string
¶ A multi-line string formatted with tabs to show the matrix contents.
Return type: str
-
up_xyz
¶ tuple of 3 floats – The x,y,z components representing the matrix’s up vector.
-
-
class
anki_vector.util.
Pose
(x, y, z, q0=None, q1=None, q2=None, q3=None, angle_z=None, origin_id=-1)¶ Represents where an object is in the world.
Whenever Vector is delocalized (i.e. whenever Vector no longer knows where he is - e.g. when he’s picked up), Vector creates a new pose starting at (0,0,0) with no rotation, with origin_id incremented to show that these poses cannot be compared with earlier ones. As Vector drives around, his pose (and the pose of other objects he observes - e.g. faces, his LightCube, charger, etc.) is relative to this initial position and orientation.
The coordinate space is relative to Vector, where Vector’s origin is the point on the ground between Vector’s two front wheels. The X axis is Vector’s forward direction, the Y axis is to Vector’s left, and the Z axis is up.
Only poses of the same origin_id can safely be compared or operated on.
import anki_vector with anki_vector.Robot() as robot: pose = anki_vector.util.Pose(x=50, y=0, z=0, angle_z=anki_vector.util.Angle(degrees=0)) robot.behavior.go_to_pose(pose)
-
define_pose_relative_this
(new_pose)¶ Creates a new pose such that new_pose’s origin is now at the location of this pose.
Parameters: new_pose (anki_vector.util.Pose) – The pose which origin is being changed. Returns: A anki_vector.util.Pose
object for which the origin was this pose’s origin.
-
is_comparable
(other_pose)¶ Checks whether these two poses are comparable.
Poses are comparable if they’re valid and having matching origin IDs.
Parameters: other_pose – The other pose to compare against. Type is Pose. Returns: True if the two poses are comparable, False otherwise. Return type: bool
-
origin_id
¶ An ID maintained by the robot which represents which coordinate frame this pose is in.
Return type: int
-
rotation
¶ The rotation component of this pose.
Return type: Quaternion
-
to_matrix
()¶ Convert the Pose to a Matrix44.
Returns: A matrix representing this Pose’s position and rotation. Return type: Matrix44
-
to_proto_pose_struct
()¶ Converts the Pose into the robot’s messaging pose format.
Return type: PoseStruct
-
-
class
anki_vector.util.
Position
(x, y, z)¶ Represents the position of an object in the world.
A position consists of its x, y and z values in millimeters.
Parameters:
-
class
anki_vector.util.
Quaternion
(q0=None, q1=None, q2=None, q3=None, angle_z=None)¶ Represents the rotation of an object in the world.
-
angle_z
¶ An Angle instance representing the z Euler component of the object’s rotation.
Defined as the rotation in the z axis.
Return type: Angle
-
q0_q1_q2_q3
¶ tuple of float – Contains all elements of the quaternion (q0,q1,q2,q3)
-
to_matrix
(pos_x=0.0, pos_y=0.0, pos_z=0.0)¶ Convert the Quaternion to a 4x4 matrix representing this rotation.
A position can also be provided to generate a full translation matrix.
Parameters: Returns: A matrix representing this Quaternion’s rotation, with the provided position (which defaults to 0,0,0).
Return type:
-
-
class
anki_vector.util.
RectangleOverlay
(width, height, line_thickness=5, line_color=(255, 0, 0))¶ A rectangle that can be drawn on top of a given image.
Parameters: -
apply_overlay
(image)¶ Draw a rectangle on top of the given image.
Return type: None
-
-
class
anki_vector.util.
Speed
(speed_mmps=None)¶ Represents a speed.
This class allows speeds to be measured in millimeters per second.
Use
speed_mmps()
convenience methods to generate a Speed instance.Parameters: speed_mmps ( Optional
[float
]) – The number of millimeters per second the speed should represent.
-
class
anki_vector.util.
Vector2
(x, y)¶ Represents a 2D Vector (type/units aren’t specified).
Parameters: -
set_to
(rhs)¶ Copy the x and y components of the given Vector2 instance.
Parameters: rhs – The right-hand-side of this assignment - the source Vector2 to copy into this Vector2 instance.
-
x_y
¶ tuple (float, float) – The X, Y elements of the Vector2 (x,y)
-
-
class
anki_vector.util.
Vector3
(x, y, z)¶ Represents a 3D Vector (type/units aren’t specified).
Parameters: -
cross
(other)¶ The cross product of this and another Vector3 instance
-
dot
(other)¶ The dot product of this and another Vector3 instance
-
normalized
¶ A Vector3 instance with the same direction and unit magnitude
-
set_to
(rhs)¶ Copy the x, y and z components of the given Vector3 instance.
Parameters: rhs – The right-hand-side of this assignment - the source Vector3 to copy into this Vector3 instance.
-
x_y_z
¶ tuple (float, float, float) – The X, Y, Z elements of the Vector3 (x,y,z)
-
-
anki_vector.util.
angle_z_to_quaternion
(angle_z)¶ This function converts an angle in the z axis (Euler angle z component) to a quaternion.
Parameters: angle_z ( Angle
) – The z axis angle.Returns: - A tuple with all the members
- of a quaternion defined by angle_z.
Return type: q0, q1, q2, q3 (float, float, float, float)
-
anki_vector.util.
degrees
(degrees)¶ An Angle instance set to the specified number of degrees.
Return type: Angle
-
anki_vector.util.
distance_mm
(distance_mm)¶ Returns an
anki_vector.util.Distance
instance set to the specified number of millimeters.
-
anki_vector.util.
distance_inches
(distance_inches)¶ Returns an
anki_vector.util.Distance
instance set to the specified number of inches.
-
anki_vector.util.
get_class_logger
(module, obj)¶ Helper to create logger for a given class (and module).
import anki_vector logger = anki_vector.util.get_class_logger("module_name", "object_name")
Parameters: Return type:
-
anki_vector.util.
parse_command_args
(parser=None)¶ Parses command line arguments.
Attempts to read the robot serial number from the command line arguments. If no serial number is specified, we next attempt to read the robot serial number from environment variable ANKI_ROBOT_SERIAL. If ANKI_ROBOT_SERIAL is specified, the value will be used as the robot’s serial number.
import anki_vector import argparse parser = argparse.ArgumentParser() parser.add_argument("--new_param") args = anki_vector.util.parse_command_args(parser)
Parameters: parser ( Optional
[ArgumentParser
]) – To add new command line arguments, pass an argparse parser with the new options already defined. Leave empty to use the defaults.
-
anki_vector.util.
radians
(radians)¶ An Angle instance set to the specified number of radians.
Return type: Angle
-
anki_vector.util.
setup_basic_logging
(custom_handler=None, general_log_level=None, target=None)¶ Helper to perform basic setup of the Python logger.
Parameters: - custom_handler (
Optional
[Handler
]) – provide an external logger for custom logging locations - general_log_level (
Optional
[str
]) – ‘DEBUG’, ‘INFO’, ‘WARN’, ‘ERROR’ or an equivalent constant from thelogging
module. If None then a value will be read from the VECTOR_LOG_LEVEL environment variable. - target (
object
) – The stream to send the log data to; defaults to stderr
- custom_handler (
-
anki_vector.util.
speed_mmps
(speed_mmps)¶ anki_vector.util.Speed
instance set to the specified millimeters per second speed.


Demo
With play-with-docker, you can easily spin up a demo MapGuide Server instance with this viewer and sample data pre-loaded to see this viewer in action.
- Go to http://play-with-docker.com
- Pass the reCAPTCHA check. Once you're in, you will have 4 hours to play around before the site terminates your session and all docker environments you have set up
- Click
+ ADD NEW INSTANCE
to create a new shell
- In the new shell, run the following commands
git clone https://github.com/jumpinjackie/mapguide-react-layout
cd mapguide-react-layout
./demo.sh
- After a few minutes (while docker downloads the base image and builds the demo image on top of it), the demo container will run and you should see a port number appear beside the IP address
- Click it to open a new browser tab where you should see the default success page from the wwwroot of MapGuide's bundled Apache server
- Now append
/mapguide/index.php
to that URL to enter the demo landing page.
- Click on any template link to load the Sheboygan map in that selected template
Many thanks to @brucepc for his docker image, from which our demo image is built on top of.