Conformity
问题描述 :
Frosh commencing their studies at Waterloo have diverse interests, as evidenced by their desire to take various combinations of courses from among those available.
University administrators are uncomfortable with this situation, and therefore wish to offer a conformity prize to frosh who choose one of the most popular combinations of courses. How many frosh will win the prize?
输入:
The input consists of several test cases followed by a line containing 0. Each test case begins with an integer 1 ≤ n ≤ 10000, the number of frosh. For each frosh, a line follows containing the course numbers of five distinct courses selected by the frosh. Each course number is an integer between 100 and 499.
The popularity of a combination is the number of frosh selecting exactly the same combination of courses. A combination of courses is considered most popular if no other combination has higher popularity.
输出:
For each line of input, you should output a single line giving the total number of students taking some combination of courses that is most popular.
样例输入:
3 100 101 102 103 488 100 200 300 101 102 103 102 101 488 100 3 200 202 204 206 208 123 234 345 456 321 100 200 300 400 444 0
样例输出:
2 3
解题代码:
//* @author<EMAIL_ADDRESS>import java.io.BufferedReader; import java.io.IOException; import java.io.InputStream; import java.io.InputStreamReader; import java.io.Reader; import java.util.Arrays; import java.util.Collections; import java.util.HashMap; import java.util.Map; import java.util.Set; public class Main { /** * @param args * @throws IOException * @throws NumberFormatException */ public static void main(String[] args) throws NumberFormatException, IOException { // TODO Auto-generated method stub BufferedReader br = new BufferedReader(new InputStreamReader(System.in)); while (true) { Map< String, Integer> map = new HashMap< String, Integer>(); int max = 0; int n = Integer.parseInt(br.readLine()); if (n == 0) break; for (int i = 0; i < n; i++) { String s = br.readLine(); String[] array = s.split(" "); Arrays.sort(array); String key = new String(); for (int j = 0; j < array.length; j++) { key += array[j]; } // System.out.println("key:"+key); if (map.get(key) == null) map.put(key, 1); else { // System.out.println("here"); map.put(key, map.get(key) + 1); } if (max < map.get(key)) max = map.get(key); } int result = 0; Setset = map.keySet(); for (String s : set) { if (max == map.get(s)) result += max; } System.out.println(result); } } }
分析的很详细！赞一个
nice!!!
wt[n - 1]没错，因为下标是[0...n-1]
<EMAIL_ADDRESS>这道题这里的解法最坏情况似乎应该是指数的。回溯的时候
O(n) = O(n-1) + O(n-2) + ….
O(n-1) = O(n-2) + O(n-3)+ …
O(n) – O(n-1) = O(n-1)
O(n) = 2O(n-1)
无需用转移方程也可以解决，哈哈
对，就是把这32位当成一个长度为32的数
辛苦了，顶
Thanks for taking the time to examine this, I really feel strongly about it and love studying a lot more on this topic. If possible, as you acquire experience
前排支持
这张页面太松散拉，重新编辑一下，弄紧凑一点。
不错，赞一个！
红绯鱼：
牛逼牛逼
为什么不直接用STL排序呢？


Ancelotti among the contenders for Arsenal following Emery sacking
by John DugganCarlo Ancelotti is believed to be one of the names Arsenal are considering as their new head coach after Unai Emery was fired.
The Telegraph are reporting that the Italian is one of the contenders to replace the Spaniard, who was shown the door after a run of poor results, culminating in Arsenal's worst run of form since 1992.
Swede Freddie Ljungberg, a popular member of the 'Invincibles' team back in the noughties, is to take charge of team affairs as interim head coach until further notice.
The decision as to who will replace Emery permanently will be made by head of football Raul Sanllehi and technical director Edu, who will make a recommendation to the owners, the Kroenke family.
Ancelotti won the European Cup as a player with AC Milan before managing the Rossoneri to Champions League titles in 2003 and 2007.
Ancelotti has tasted success in London before, when he guided Chelsea to the Premier League and FA Cup double in 2010.
He then brought Real Madrid to the European summit in 2014.
Ancelotti is currently in charge of Serie A side Napoli, who have struggled of late. His contract runs out at the end of the season.
Other names being linked with the position at the Emirates Stadium are ex-Tottenham boss Mauricio Pochettino, Leicester's Brendan Rodgers, Wolves manager Nuno Espirito Santo and Bournemouth's Eddie Howe.


Where Do I Get a Bird?
I still remember when pet stores sold puppies and kittens that lived piled up on top of each other in glass cages for everybody to see. And while yes, a pile of puppies may sound like one of the cutest sights you’ll ever see, it’s actually pretty sad and disturbing. One of my old roommates once brought home a kitten from a pet store and the poor thing was obviously not right in the head. So I wasn’t at all surprised when that kind of thing got banned. But I got my childhood budgies at a pet store, and I got my snake there, as well as the rat and fish that I had for a while between those two. I wanted to know if the animals they are still allowed sell in stores are healthy and happy or if they are more akin to the victims of Cruella De Vil. I found this article by the Avian Welfare Resource Center that basically says there’s still a lot of grey in that area.
I did go check out all the pet stores in Victoria anyway, to see what their birds looked like. I was extremely disappointed with what I found, even with the stores that were really highly rated online. I’m not going to name names here because that was a year ago and things may have changed, but I wasn’t about to buy a bird, or anything else, from any one of them.
I also scouted Craigslist and Usedvictoria quite a bit. There were always a few budgies listed for sale, but since I don’t have a car it was too impractical for me to go to check any of them out.
The SPCA puts pictures and descriptions of all the animals that they are currently looking for homes for on their website. At the time they had these three blue male baby budgies named Bipity, Bopity, and Boo. According to their adoption guidelines , the SPCA will only let birds go to people who can give them photo evidence that they have a cage all set up and ready for them when they get home. That made sense to me so I took the first big step towards getting a budgie and bought a cage, seed, cuttlebones, and a few toys, so that I would be ready to bring home a baby bird when I found the right one.
The one place I hadn’t scouted yet was Petsmart because they’re an hour bus ride from my house. However I did visit their website pretty much daily to fantasy shop for toys. They have a bit of good information in their “Bird Education Center” too. Especially this checklist that everyone should consider carefully before deciding to bring a bird home with them.


Navigation2¶
1. Overview¶
The ROS 2 Navigation System is the control system that enables a robot to autonomously reach a goal state, such as a specific position and orientation relative to a specific map. Given a current pose, a map, and a goal, such as a destination pose, the navigation system generates a plan to reach the goal, and outputs commands to autonomously drive the robot, respecting any safety constraints and avoiding obstacles encountered along the way.
2. Running the demo¶
The nav2_bringup
package is an example bringup system for
navigation2 applications.
Note
- We recommend doing this on a Ubuntu 18.04 installation. We have build issues on 16.04. (see https://github.com/ros-planning/navigation2/issues/353)
- This stack and ROS2 are still in heavy development and there are some bugs and stability issues being worked on, so please do not try this on a robot without taking heavy safety precautions. THE ROBOT MAY CRASH!
- It is recommended to start with simulation using Gazebo before proceeding to run on a physical robot
2.1 Launch Navigation2 in simulation with Gazebo¶
Pre-requisites:¶
- Gazebo installed on the system
- gazebo_ros_pkgs for ROS2 installed on the system
- A Gazebo world for simulating the robot (see Gazebo tutorials)
- A map of that world saved to a map.pgm and map.yaml (see ROS Navigation tutorials)
Terminal 1: Launch Gazebo
Example: See turtlebot3_gazebo models for details.
source /opt/robot_devkit/robot_devkit_setup.bash
export GAZEBO_MODEL_PATH=$GAZEBO_MODEL_PATH:~/robot_devkit/rdk_ws/turtlebot3_ws/src/turtlebot3/turtlebot3_simulations/turtlebot3_gazebo/models/
gazebo --verbose -s libgazebo_ros_init.so $HOME/robot_devkit/rdk_ws/turtlebot3_ws/src/navigation2/navigation2/nav2_system_tests/worlds/turtlebot3_ros2_demo.world
view result at rviz2 and gazebo
Terminal 2: Launch your robot specific transforms
source /opt/robot_devkit/robot_devkit_setup.bash
export TURTLEBOT3_MODEL=waffle
ros2 launch turtlebot3_bringup turtlebot3_state_publisher.launch.py use_sim_time:=True
Example: See turtlebot3_gazebo for details.
Terminal 3: Launch navigation2
source /opt/robot_devkit/robot_devkit_setup.bash
# Launch the nav2 system
ros2 launch nav2_bringup nav2_bringup_launch.py use_sim_time:=True autostart:=True \
map:=$HOME/robot_devkit/rdk_ws/turtlebot3_ws/src/navigation2/navigation2/nav2_system_tests/maps/map_circular.yaml
Terminal 4: Run RViz with navigation2 config file
source /opt/robot_devkit/robot_devkit_setup.bash
ros2 run rviz2 rviz2 -d $(ros2 pkg prefix nav2_bringup)/share/nav2_bringup/launch/nav2_default_view.rviz
Note
In RViz:
- You should see the map
- Localize the robot using “2D Pose Estimate” button.
- Make sure all transforms from odom are present. (odom->base_link->base_scan)
- Send the robot a goal using “Navigation2 Goal” button. Note: this uses a ROS2 Action to send the goal, and a pop-up window will appear on your screen with a ‘cancel’ button if you wish to cancel
To view the robot model in RViz:
- Add “RobotModel”, set “Description Source” with “File”, set “Description File” with the name of the urdf file for your robot (example: turtlebot3_burger.urdf)”
2.2 Launch Navigation2 on a Robot¶
Pre-requisites:¶
- Run SLAM or Cartographer with tele-op to drive the robot and generate a map of an area for testing first. The directions below assume this has already been done. If not, it can be done in ROS1 before beginning to install our code.
- Publish all the transforms from your robot from base_link to base_scan
Terminal 1: Launch the code using this launch file and your map.yaml
ros2 launch nav2_bringup nav2_bringup_launch.py map:=<full/path/to/map.yaml> map_type:=occupancy
Terminal 2: Run RVIZ
ros2 run rviz2 rviz2 -d $(ros2 pkg prefix nav2_bringup)/share/nav2_bringup/launch/nav2_default_view.rviz
- In RVIZ2:
- Make sure all transforms from odom are present. (odom->base_link->base_scan)
- Localize the robot using “2D Pose Estimate” button.
- Send the robot a goal using “2D Nav Goal” button.
3. Known issues¶
- This stack and ROS2 are still in heavy development and there are some bugs and stability issues being worked on, so please do not try this on a robot without taking heavy safety precautions. THE ROBOT MAY CRASH!
- For a current list of known issues, see https://github.com/ros-planning/navigation2/issues.
4. ToDo¶
- Add additional maps and examples.
- Add instructions for running navigation2 with SLAM.


- How do I change hostname and/or IP address and/or listening port of my Gluu Server?
- How do I set my port to something other than 443?
- How do I customize the IDP to ask for Email instead of Username for login?
- How do I add additional roles to oxTrust (Gluu's web UI) and/or change permissions set for existing ones?
- How do I properly reboot my Gluu Server?
- How do I perform matinenace on my Gluu Server?
How do I change hostname and/or IP address and/or listening port of my Gluu Server?
There is no easy way to change any of those once your instance is already deployed. At very least it would require to modify a lot of settings stored in LDAP configuration entries, in Apache/Tomcat configuration, and perhaps in custom authentication scripts' sources too (if you plan to use one). If you need to change an IP address, we recommend doing a fresh install on a new VM.
Aside from using static public ip address, you could opt to use some static ip address from private range of your internal network, and to employ some sort of reverse proxy, or load balancer, or simple port forwarding on another internet-facing device that will forward connections to your instance in that network. When it's set up this way you can freely change your public ip address on this internet-facing intermediary device (just will need to make sure your DNS records will be updated accordingly each time).
How do I set my port to something other than 443?
Ports other then 443 are not really supported. Port 443 is the Apache Web Server. But you'd have to update all the metadata too, like the config data in https://hostname/.well-known/openid-configuration
and also the SAML metadata. And even then you still might face bugs.
Recommendation: use a virtual ethernet interface and a different IP address on your server rather than trying to update the port to a different port.
How do I customize the IDP to ask for Email instead of Username for login?
In oxTrust navigate to the Manage Authentication tab within the Configuration section. By default the Primary Key and Local Key are set to uid
. Set those values to mail
and now your Gluu Server will expect email as the identifier instead of username.
Now you will want to update your IDP login page to display Email Address
as the requested identifier. In order to do that you need to modify the login.xhtml
file, which is located in /opt/tomcat/webapps/oxauth/
. Insert Email Address
as the value for outputLabel
; this snippet is under the dialog
class. See the screenshot below.
How do I add additional roles to oxTrust (Gluu's web UI) and/or change permissions set for existing ones?
To accomplish something like that, you would need to implement new dynamic rules in Jboss Seam, and then implement those rules in the UI, as current "manager" and "user" roles are defined within security.drl
and hard-coded in OxTrust. During login, it checks for the manager group's membership, and adds the role to the web context. If you'll still opt to change this framework, we won't be able to provide you any help regarding this currently.
oxTrust was designed to be a tool for administrators. There are some basic user features, but we don't really encourage usage of oxTrust as a user facing application.
A better approach might be to write a standalone application that calls the SCIM API's or even the LDAP API's with just the data that you want to expose, then make that new application an Openid Connect Relying Party (so authentication of users attempting to use it could be handled by your Gluu instance).
How do I properly reboot my Gluu Server?
Follow these instructions to properly reboot your Gluu Server:
- Stop Gluu Server container first:
a) For CentOS 6.x / RHEL6.x / Ubuntu 14.04:service gluu-server-2.x.x stop
b) For CentOS 7.x / RHEL7.x:/sbin/gluu-server-2.x.x stop
- Reboot the server
- After return, check if Gluu Server container started or not:
a) For CentOS 6.x / RHEL6.x / Ubuntu 14.04:gluu-server-2.x.x status
b) For CentOS 7.x / RHEL7.x:/sbin/gluu-server-2.x.x status
- If the Gluu Server didn't start, start it:
a) For CentOS 6.x / RHEL6.x / Ubuntu 14.04:service gluu-server-2.x.x start
b) For CentOS 7.x / RHEL7.x:/sbin/gluu-server-2.x.x start
- Wait 10 mins while the server is prepared. Check the login from your web browser.
How do I perform matinenace on my Gluu Server?
Sometimes it's required to push system updates (not an OS upgrade) in your Gluu Server VM. Here are a few steps to get you started:
- Backup your whole VM.
- Login to your Gluu Server VM.
- Become root.
- Login to the Gluu Server container using the following command: service gluu-server-x.x.x login
(for RHEL/CentOS 7 users: /sbin/gluu-server-x.x.x login
)
- Push updates inside the container using the following command: yum update
(for RPM based systems) or apt-get update
(for DEB based systems).
- Exit the container.
- Stop Gluu-Server container using the following command: service gluu-server-x.x.x stop
(for RHEL/CentOS 7users: /sbin/gluu-server-x.x.x stop
).
- Perform yum update
or apt-get update
in the host system.
- Do a soft reboot.
- After the VM returns from a succesful reboot, check the system status:
- See if your Gluu Server container started by running the following command: service gluu-server-x.x.x status
.
- Login to the container.
- See if there is any issue in the wrapper.log
file or in the idp-process.log
file (if you are using SAML).
- Wait for 10 minutes.
- Check Gluu Server Web UI.


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contemplate the canon
The games below are apocryphal and should be approached with caution
perform the ritual


Ocean [ag52], ocean, in Crystal Sea
Last updated Turn #73
Ocean [ag52], ocean, in Crystal Sea
------------------------------------------------------------------------
24: Fast Sail [ht2u], ship, 3 hulls, 1 rowing port, 9 sails, arrived from
24: Ocean [af52], owner:
24: Stewart [k0u], Pen, with 12 sailors
24: Hungry looking birds circle overhead.
25: Fast Sail [ht2u], ship, 3 hulls, 1 rowing port, 9 sails, departed for
25: Ocean [ag51].
30: The fog has cleared.
30: It is no longer windy.
Routes leaving Ocean:
North, to Ocean [af52], 6 days
East, to Ocean [ag53], 6 days
South, to Ocean [ah52], 6 days
West, to Ocean [ag51], 6 days


The user interface, in the industrial design field of human–machine interaction, is the space where interactions between humans and machines occur. The goal of this interaction is effective operation and control of the machine on the user's end, and feedback from the machine, which aids the operator in making operational decisions. Examples of this broad concept of user interfaces include the interactive aspects of computer operating systems, hand tools, heavy machinery operator controls, and process controls. The design considerations applicable when creating user interfaces are related to or involve such disciplines as ergonomics and psychology.
User Experience (UX) involves a person's behaviors, attitudes, and emotions about using a particular product, system or service. User Experience includes the practical, experiential, affective, meaningful and valuable aspects of human-computer interaction and product ownership. Additionally, it includes a person’s perceptions of system aspects such as utility, ease of use and efficiency. User Experience may be considered subjective in nature to the degree that it is about individual perception and thought with respect to the system. User Experience is dynamic as it is constantly modified over time due to changing usage circumstances and changes to individual systems as well as the wider usage context in which they can be found.
Interactive Design is defined as a user-oriented field of study that focuses on meaningful communication of media through cyclical and collaborative processes between people and technology. Successful interactive designs have simple, clearly defined goals, a strong purpose and intuitive screen interface.
In computer science, functional programming is a programming paradigm, a style of building the structure and elements of computer programs, that treats computation as the evaluation of mathematical functions and avoids changing state and mutable data. It is a declarative programming paradigm, which means programming is done with expressions. In functional code, the output value of a function depends only on the arguments that are input to the function, so calling a function f twice with the same value for an argument x will produce the same result f(x) both times. Eliminating side effects, i.e. changes in state that do not depend on the function inputs, can make it much easier to understand and predict the behavior of a program, which is one of the key motivations for the development of functional programming.


Features that have a checkmark are complete and available for download in the CI build.
These are the changes to each version that has been released on the official Visual Studio extension gallery.


Two juveniles among 16 men found in lorry container
Update: The 16 people found in a trailer on a ferry bound for Rosslare included two juveniles, Labour party leader Brendan Howlin has said.
They have now been taken into the care of the HSE while the 14 adults have been transferred to a reception centre.
There is a working assumption that the people are Kurds, but it is uncertain if they are Iraqi or Iranian Kurds, Mr Howlin told RTÉ radio’s Today with Séan O’Rourke show.
The Wexford TD called for tighter scrutiny on all fronts and said there is a need to ensure that proper checks are carried out in ports and that they are “more thorough and vigorous.”
He added that yesterday’s incident brought back memories of the tragedy in Drinagh, Co. Wexford, in 2001 when the bodies of eight people, including four children, were found in a trailer.
The possibility of a tragedy remains and could happen at any time, he said.
On the same programme John Martin, policy manager for Northern Ireland with the UK Road Haulage Association said that the discovery of the 16 on board the ferry highlighted the need for greater checks at ports.
It also reinforced the concern that the Republic and Northern Ireland could become a backdoor into the UK, he said. It was unfair that at present the onus is on operators and drivers to thwart illegal immigrants. Many had to purchase expensive high tech security measures.
Kevin Hyland, the United Kingdom’s first Independent Anti-Slavery Commissioner called for intervention to happen “way back upstream”.
Earlier: Sixteen males found in lorry container in Wexford believed to be Kurdish
It is believed the 16 men found in the back of a lorry on board a Cherbourg to Rosslare ferry yesterday are Kurdish.
They have spent the night in a reception centre in Dublin.
They spent up to 12 hours in the trailer of an articulated tractor unit before being discovered at Rosslare port yesterday afternoon.
They had travelled on a Stena ferry from Cherbourg before staff at the port found them.
Superintendent Jim Doyle said all 16 are in excellent health.
Mr Doyle said: "I understand at this time that all the males are adult and Middle-Eastern, however gardaí have yet to confirm if one male is a juvenile.
"That particular lorry, tractor and trailer are owned by a local Irish-based company, the owners are cooperating fully with the investigation.
"The driver is a male, believed to be in his 50s, he is also a long-time employee of the company and he is also assisting us with our inquiries."
Pippa Woolnough, from the Immigrant Council of Ireland, said the incident shows how desperate people are to flee their homeland.
READ MORE
‘Asylum seekers will continue to risk their lives’: Irish Refugee Council call for 'real' action
Ms Woolnough said: "It is very upsetting to hear that people are feeling that their only option is to put their lives in danger to travel either away from danger or persecution or they have been forced or coerced into this situation.
"So I am very glad to hear the Taoiseach saying that he is taking a humanitarian approach first."
READ MORE
A slice of history: Ever wondered why there is no Kurdish nation?


easy way: brew install jmeter --with-plugins
or download apache-Jmeter.zip
test plan ->thread group -> recording controller
workbench -> HTTP(S) Test Script Recorder
firefox: advanced -> network -> manual proxy config


Welcome to GitKA, a game changing program on KA to help people trying to collab in small groups to systematically collab. This website tries to recreate the ease of collabing on GitHub but may not be as good due to restrictions on servers on KA.
This program was created for Primavera......
You might want to read through the guides first before starting out
GitKA
The Guide
Welcome to the guides !!
Here you will be learning some basic concepts regarding GitKA
What is GitKA ?
GitKA is a program that tries to make the collabing on KA easier
How to use it ?
In GitKA there are a few things to remember... only the owner of the program can control changes being made to the file. So to request the owner to make changes, you need to submit a pull request in the appropriate thread in the T&T. To learn more make a spin-off of this project and then see the below questions. If you are starting a collab then spin-of of this program and then complete the basic variables form in the first script tag ( about line 427).
What is a pull request ?
When you want to change something in the files the owner has posted one must submit a pull request. The owner can either accept or deny your request.
Can unkown users change stuff by posting in the thread ?
No. Only users mentioned in the memberKaid variable can request changes and only the owner can accept, deny or add files
How can the owner delete a file
He or she must delete the comment which added the file :)
Can I make someone else the owner ?
Yes ! GitKA is very flexible. You can give another person the right to add or delete files by putting their Kaid in the ownerKaid variable instead of yours. The only thing is the you cannot make two people the owners. [This is for security concerns].
How many people can I make members of the collab ?
As many as you want :P
notifications
Repo name:
Add a file
File name
Time Posted
README.MD
edit
No README file added
code
File Name:
arrow_back
arrow_back
Pull Request:
What is a pull request ?
When you want to change some code but cannot because you aren't the author... You must submit a pull request and wait for the owner to approve


{% extends "layout.html" %} {% block title %}Index{% endblock %} {% block head %} {{ super() }} {% endblock %}} {% block page_header %}MSUCOM Class of 2024 Qbank{% endblock %} {% block page_header_p %} {% endblock %} {% block content %}
This is a repository for practice exams made by peers. Please register in order to view and add your own exams. It is just a formality, I thought it would be interesting to eventually add functionality for a user profile that tracks exams you have taken and your scores in them. For right now, it just keeps outsiders form stealing our medical knowledge.Have fun!
If you encounter bugs, email me.
Working on case-problem format.
Ability to post images and draw on them to make visual examinations
Predictive multiple choive, that can populate exams with other user's multiple choice questions.
Design the exam screen to more closely simulate examplify, complete with flagging, highlighting, timers, and terribly laid-out toolsets.
Loop in tools for a note-share.

OAUTH with MSU login.
Added swipe left/right functionality to practice exams
Improved overall design of site, especially for mobile users.
Changed password storage to a more secure variant. Login times will be a bit longer, which is good.
Fixed some regex on quiz adding that should parse input better.
Password reset and automailer functionality live.
Added message flashing.
Fixed some bugs that were causing internal errors with database mismatches.
Added a tip jar.


...and Cora just figured out how to unlock the iPod. Sigh. Sometimes the kid's aptitude with electronics is a little too much.
1/12/2011, 5:45:00 PM
Favs: 0
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link--- layout: tweet ---
...and Cora just figured out how to unlock the iPod. Sigh. Sometimes the kid's aptitude with electronics is a little too much.
1/12/2011, 5:45:00 PM
Favs: 0
Retweets: 0
link


Be sure to disconnect all power sources before cleaning up, and remember to follow appropriate lab procedures. While it may be tempting to simply throw away the gel and buffer, certain solutions popular in labs can be carcinogenic and must be disposed of properly. Your instructor will provide appropriate containers to do so. Afterwards, wash all glass materials with cool water, making sure to wear gloves. Place them on a rack to dry and wipe down the surfaces you used with a mild cleaning solution.
Congratulations, you have completed the gel electrophoresis simulation! Continue onwards for a brief quiz on what you have learned.


Congratulations, you have successfully navigated to my GitHub Pages Portfolio Assignment. Here you will find a bit of personal information and some interesting links. Feel free to explore and click on anything clickable.
Video Bio
This project consists of implementing a button that generates pacmen with random movements.
This project consists of two eyes that follow the mouse position on the screen.
This project consists of an animated marker that shows the bus stops between MIT and Harvard.


We're sorry but Arkavidia 5.0 site doesn't work properly without JavaScript enabled. Please enable it to continue.


Module: filters.rank
¶
|
Auto-level image using local histogram. |
Return greyscale local autolevel of an image. |
|
|
Local bottom-hat of an image. |
|
Enhance contrast of an image. |
Enhance contrast of an image. |
|
|
Local entropy. |
|
Equalize image using local histogram. |
|
Return local geometric mean of an image. |
|
Return local gradient of an image (i.e. |
Return local gradient of an image (i.e. |
|
|
Majority filter assign to each pixel the most occuring value within its neighborhood. |
|
Return local maximum of an image. |
|
Return local mean of an image. |
|
Apply a flat kernel bilateral filter. |
|
Return local mean of an image. |
|
Return local median of an image. |
|
Return local minimum of an image. |
|
Return local mode of an image. |
|
Noise feature. |
|
Local Otsu’s threshold value for each pixel. |
|
Return local percentile of an image. |
|
Return the local number (population) of pixels. |
|
Return the local number (population) of pixels. |
|
Return the local number (population) of pixels. |
|
Return image subtracted from its local mean. |
Return image subtracted from its local mean. |
|
|
Return the local sum of pixels. |
|
Apply a flat kernel bilateral filter. |
|
Return the local sum of pixels. |
|
Local threshold of an image. |
Local threshold of an image. |
|
|
Local top-hat of an image. |
Normalized sliding window histogram |
autolevel¶
-
skimage.filters.rank.
autolevel
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Auto-level image using local histogram.
This filter locally stretches the histogram of greyvalues to cover the entire range of values from “white” to “black”.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import autolevel >>> img = data.camera() >>> auto = autolevel(img, disk(5))
Examples using skimage.filters.rank.autolevel
¶
autolevel_percentile¶
-
skimage.filters.rank.
autolevel_percentile
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)[source]¶ Return greyscale local autolevel of an image.
This filter locally stretches the histogram of greyvalues to cover the entire range of values from “white” to “black”.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- p0, p1float in [0, …, 1]
Define the [p0, p1] percentile interval to be considered for computing the value.
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples using skimage.filters.rank.autolevel_percentile
¶
bottomhat¶
-
skimage.filters.rank.
bottomhat
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Local bottom-hat of an image.
This filter computes the morphological closing of the image and then subtracts the result from the original image.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- mask2-D array
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import bottomhat >>> img = data.camera() >>> out = bottomhat(img, disk(5))
enhance_contrast¶
-
skimage.filters.rank.
enhance_contrast
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Enhance contrast of an image.
This replaces each pixel by the local maximum if the pixel greyvalue is closer to the local maximum than the local minimum. Otherwise it is replaced by the local minimum.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
The result of the local enhance_contrast.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import enhance_contrast >>> img = data.camera() >>> out = enhance_contrast(img, disk(5))
Examples using skimage.filters.rank.enhance_contrast
¶
enhance_contrast_percentile¶
-
skimage.filters.rank.
enhance_contrast_percentile
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)[source]¶ Enhance contrast of an image.
This replaces each pixel by the local maximum if the pixel greyvalue is closer to the local maximum than the local minimum. Otherwise it is replaced by the local minimum.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- p0, p1float in [0, …, 1]
Define the [p0, p1] percentile interval to be considered for computing the value.
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples using skimage.filters.rank.enhance_contrast_percentile
¶
entropy¶
-
skimage.filters.rank.
entropy
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Local entropy.
The entropy is computed using base 2 logarithm i.e. the filter returns the minimum number of bits needed to encode the local greylevel distribution.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- outndarray (double)
Output image.
References
Examples
>>> from skimage import data >>> from skimage.filters.rank import entropy >>> from skimage.morphology import disk >>> img = data.camera() >>> ent = entropy(img, disk(5))
equalize¶
-
skimage.filters.rank.
equalize
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False)[source]¶ Equalize image using local histogram.
- Parameters
- image(N, M[,P]) ndarray (uint8, uint16)
Input image.
- selemndarray
The neighborhood expressed as an ndarray of 1’s and 0’s.
- out(N, M[,P]) array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_y, shift_zint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out(N, M[,P]) ndarray (same dtype as input image)
Output image.
Examples
>>> from skimage import data >>> from skimage.morphology import disk, ball >>> from skimage.filters.rank import equalize >>> import numpy as np >>> img = data.camera() >>> equ = equalize(img, disk(5)) >>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8) >>> equ_vol = equalize(volume, ball(5))
geometric_mean¶
-
skimage.filters.rank.
geometric_mean
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Return local geometric mean of an image.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
References
- 1
Gonzalez, R. C. and Wood, R. E. “Digital Image Processing (3rd Edition).” Prentice-Hall Inc, 2006.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import mean >>> img = data.camera() >>> avg = geometric_mean(img, disk(5))
gradient¶
-
skimage.filters.rank.
gradient
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Return local gradient of an image (i.e. local maximum - local minimum).
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import gradient >>> img = data.camera() >>> out = gradient(img, disk(5))
gradient_percentile¶
-
skimage.filters.rank.
gradient_percentile
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)[source]¶ Return local gradient of an image (i.e. local maximum - local minimum).
Only greyvalues between percentiles [p0, p1] are considered in the filter.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- p0, p1float in [0, …, 1]
Define the [p0, p1] percentile interval to be considered for computing the value.
- Returns
- out2-D array (same dtype as input image)
Output image.
majority¶
-
skimage.filters.rank.
majority
(image, selem, *, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Majority filter assign to each pixel the most occuring value within its neighborhood.
- Parameters
- imagendarray
Image array (uint8, uint16 array).
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- outndarray
If None, a new array will be allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage import data >>> from skimage.filters.rank import majority >>> from skimage.morphology import disk >>> img = data.camera() >>> maj_img = majority(img, disk(5))
maximum¶
-
skimage.filters.rank.
maximum
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Return local maximum of an image.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
See also
Notes
The lower algorithm complexity makes
skimage.filters.rank.maximum
more efficient for larger images and structuring elements.Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import maximum >>> img = data.camera() >>> out = maximum(img, disk(5))
Examples using skimage.filters.rank.maximum
¶
mean¶
-
skimage.filters.rank.
mean
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Return local mean of an image.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import mean >>> img = data.camera() >>> avg = mean(img, disk(5))
mean_bilateral¶
-
skimage.filters.rank.
mean_bilateral
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10)[source]¶ Apply a flat kernel bilateral filter.
This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity.
Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element.
Radiometric similarity is defined by the greylevel interval [g-s0, g+s1] where g is the current pixel greylevel.
Only pixels belonging to the structuring element and having a greylevel inside this interval are averaged.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- s0, s1int
Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.
- Returns
- out2-D array (same dtype as input image)
Output image.
See also
denoise_bilateral
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import mean_bilateral >>> img = data.camera().astype(np.uint16) >>> bilat_img = mean_bilateral(img, disk(20), s0=10,s1=10)
mean_percentile¶
-
skimage.filters.rank.
mean_percentile
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)[source]¶ Return local mean of an image.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- p0, p1float in [0, …, 1]
Define the [p0, p1] percentile interval to be considered for computing the value.
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples using skimage.filters.rank.mean_percentile
¶
median¶
-
skimage.filters.rank.
median
(image, selem=None, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Return local median of an image.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array, optional
The neighborhood expressed as a 2-D array of 1’s and 0’s. If None, a full square of size 3 is used.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
See also
skimage.filters.median
Implementation of a median filtering which handles images with floating precision.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import median >>> img = data.camera() >>> med = median(img, disk(5))
minimum¶
-
skimage.filters.rank.
minimum
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Return local minimum of an image.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
See also
Notes
The lower algorithm complexity makes
skimage.filters.rank.minimum
more efficient for larger images and structuring elements.Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import minimum >>> img = data.camera() >>> out = minimum(img, disk(5))
Examples using skimage.filters.rank.minimum
¶
modal¶
-
skimage.filters.rank.
modal
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Return local mode of an image.
The mode is the value that appears most often in the local histogram.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import modal >>> img = data.camera() >>> out = modal(img, disk(5))
noise_filter¶
-
skimage.filters.rank.
noise_filter
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Noise feature.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
References
- 1
N. Hashimoto et al. Referenceless image quality evaluation for whole slide imaging. J Pathol Inform 2012;3:9.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import noise_filter >>> img = data.camera() >>> out = noise_filter(img, disk(5))
otsu¶
-
skimage.filters.rank.
otsu
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Local Otsu’s threshold value for each pixel.
- Parameters
- imagendarray
Image array (uint8 array).
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- outndarray
If None, a new array will be allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
References
Examples
>>> from skimage import data >>> from skimage.filters.rank import otsu >>> from skimage.morphology import disk >>> img = data.camera() >>> local_otsu = otsu(img, disk(5)) >>> thresh_image = img >= local_otsu
percentile¶
-
skimage.filters.rank.
percentile
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0)[source]¶ Return local percentile of an image.
Returns the value of the p0 lower percentile of the local greyvalue distribution.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- p0float in [0, …, 1]
Set the percentile value.
- Returns
- out2-D array (same dtype as input image)
Output image.
pop¶
-
skimage.filters.rank.
pop
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Return the local number (population) of pixels.
The number of pixels is defined as the number of pixels which are included in the structuring element and the mask.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage.morphology import square >>> import skimage.filters.rank as rank >>> img = 255 * np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> rank.pop(img, square(3)) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [4, 6, 6, 6, 4]], dtype=uint8)
pop_bilateral¶
-
skimage.filters.rank.
pop_bilateral
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10)[source]¶ Return the local number (population) of pixels.
The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. Additionally pixels must have a greylevel inside the interval [g-s0, g+s1] where g is the greyvalue of the center pixel.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- s0, s1int
Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage.morphology import square >>> import skimage.filters.rank as rank >>> img = 255 * np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) >>> rank.pop_bilateral(img, square(3), s0=10, s1=10) array([[3, 4, 3, 4, 3], [4, 4, 6, 4, 4], [3, 6, 9, 6, 3], [4, 4, 6, 4, 4], [3, 4, 3, 4, 3]], dtype=uint16)
pop_percentile¶
-
skimage.filters.rank.
pop_percentile
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)[source]¶ Return the local number (population) of pixels.
The number of pixels is defined as the number of pixels which are included in the structuring element and the mask.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- p0, p1float in [0, …, 1]
Define the [p0, p1] percentile interval to be considered for computing the value.
- Returns
- out2-D array (same dtype as input image)
Output image.
subtract_mean¶
-
skimage.filters.rank.
subtract_mean
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Return image subtracted from its local mean.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import subtract_mean >>> img = data.camera() >>> out = subtract_mean(img, disk(5))
subtract_mean_percentile¶
-
skimage.filters.rank.
subtract_mean_percentile
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)[source]¶ Return image subtracted from its local mean.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- p0, p1float in [0, …, 1]
Define the [p0, p1] percentile interval to be considered for computing the value.
- Returns
- out2-D array (same dtype as input image)
Output image.
sum¶
-
skimage.filters.rank.
sum
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Return the local sum of pixels.
Note that the sum may overflow depending on the data type of the input array.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage.morphology import square >>> import skimage.filters.rank as rank >>> img = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> rank.sum(img, square(3)) array([[1, 2, 3, 2, 1], [2, 4, 6, 4, 2], [3, 6, 9, 6, 3], [2, 4, 6, 4, 2], [1, 2, 3, 2, 1]], dtype=uint8)
sum_bilateral¶
-
skimage.filters.rank.
sum_bilateral
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10)[source]¶ Apply a flat kernel bilateral filter.
This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity.
Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element (selem).
Radiometric similarity is defined by the greylevel interval [g-s0, g+s1] where g is the current pixel greylevel.
Only pixels belonging to the structuring element AND having a greylevel inside this interval are summed.
Note that the sum may overflow depending on the data type of the input array.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- s0, s1int
Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.
- Returns
- out2-D array (same dtype as input image)
Output image.
See also
denoise_bilateral
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import sum_bilateral >>> img = data.camera().astype(np.uint16) >>> bilat_img = sum_bilateral(img, disk(10), s0=10, s1=10)
sum_percentile¶
-
skimage.filters.rank.
sum_percentile
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)[source]¶ Return the local sum of pixels.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
Note that the sum may overflow depending on the data type of the input array.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- p0, p1float in [0, …, 1]
Define the [p0, p1] percentile interval to be considered for computing the value.
- Returns
- out2-D array (same dtype as input image)
Output image.
threshold¶
-
skimage.filters.rank.
threshold
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Local threshold of an image.
The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage.morphology import square >>> from skimage.filters.rank import threshold >>> img = 255 * np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> threshold(img, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=uint8)
threshold_percentile¶
-
skimage.filters.rank.
threshold_percentile
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0)[source]¶ Local threshold of an image.
The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- p0float in [0, …, 1]
Set the percentile value.
- Returns
- out2-D array (same dtype as input image)
Output image.
tophat¶
-
skimage.filters.rank.
tophat
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶ Local top-hat of an image.
This filter computes the morphological opening of the image and then subtracts the result from the original image.
- Parameters
- image2-D array (uint8, uint16)
Input image.
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array (same dtype as input)
If None, a new array is allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- Returns
- out2-D array (same dtype as input image)
Output image.
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import tophat >>> img = data.camera() >>> out = tophat(img, disk(5))
windowed_histogram¶
-
skimage.filters.rank.
windowed_histogram
(image, selem, out=None, mask=None, shift_x=False, shift_y=False, n_bins=None)[source]¶ Normalized sliding window histogram
- Parameters
- imagendarray
Image array (uint8 array).
- selem2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- outndarray
If None, a new array will be allocated.
- maskndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
- shift_x, shift_yint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
- n_binsint or None
The number of histogram bins. Will default to
image.max() + 1
if None is passed.
- Returns
- out3-D array with float dtype of dimensions (H,W,N), where (H,W) are
the dimensions of the input image and N is n_bins or
image.max() + 1
if no value is provided as a parameter. Effectively, each pixel is a N-D feature vector that is the histogram. The sum of the elements in the feature vector will be 1, unless no pixels in the window were covered by both selem and mask, in which case all elements will be 0.
Examples
>>> from skimage import data >>> from skimage.filters.rank import windowed_histogram >>> from skimage.morphology import disk >>> img = data.camera() >>> hist_img = windowed_histogram(img, disk(5))


#Import libraries
import re
import requests
import pandas as pd
from .constants import OMIM_MAPPING_URL, URL
[docs]def get_data(url):
"""
A function to get data from url ('http://www.uniprot.org/docs/mimtosp.txt')
and parse it to a list of lists contains OMIM IDs, genes or proteins names
and uniprot IDs.
"""
# get data from url as text and save it in 'txt'
resp = requests.get(url)
txt = resp.text
#txt=open("mimtosp.txt","r").readlines()
final_text=[] # a list to have final list that returned by the function.
start_reading_bol = False # a semaphore to allow or deny reading lines.
prev_list=[] # a list to save the previous line if it has OMIM id.
for line in txt.split('\n'): # split the text by 'new line' and loop.
start_reading_bol = start_reading(line, start_reading_bol) # open or close the semaphore.
if start_reading_bol: # if the semaphore is opened
# get each line as a list after filtering it
text_list = list(filter(filter_text,re.split(" | |'|(|)", re.sub('[:,(,),\n]','',line))))
length_list=len(text_list) # get the lenght of the list
"""
if the line contains OMIM id and have more than one protein
then create a new record for each protein
"""
if not length_list % 2==0 and length_list>3:
final_text.append([text_list[0],text_list[1],text_list[2]])
for i in range(3,length_list,2):
new_text_list=[text_list[0],text_list[i],text_list[i+1]]
final_text.append(new_text_list)
elif length_list%2 == 0:
"""
if the line doesn't contain OMIM id, the it related to the previous line
"""
for i in range(0,length_list,2):
new_text_list=[prev_list[0],text_list[i],text_list[i+1]]
final_text.append(new_text_list)
else:
"""
if the OMIM id has only one protein
"""
final_text.append(text_list)
if not length_list % 2==0:
"""
if the line has OMIM id, then save it as a list to use it when we have new line without OMIM id
"""
prev_list=text_list
omim_pheno = get_omim_phenotype_ids()
s1 = set([entry[0] for entry in final_text[1:-1]])
return [entry for entry in final_text[1:-1] if entry[0] in omim_pheno]
"""
A function uses to filter lists
"""
def filter_text(text):
remove = [",","",None]
if text in remove:
return False
else:
return True
"""
Controling the semaphore
"""
[docs]def start_reading(line, reading_bool):
"""This function is checking the start of the lines"""
if line.startswith("_"):
return True
elif line.startswith("-"):
return False
else:
return reading_bool
[docs]def get_omim_phenotype_ids():
"""This function will return a list of OMIM Ids that link to phenotypes"""
mapping = pd.read_csv(OMIM_MAPPING_URL, sep="\t", header=4)
mapping = mapping.loc[mapping["MIM Entry Type (see FAQ 1.3 at https://omim.org/help/faq)"].isin(["phenotype", "predominantly phenotypes"])]
return set(mapping["# MIM Number"].apply(str))


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Loth Robotics was established in March 2019 by Sakıp Sabancı Anatolian High School students as an FRC team. In 2021, we decided to grow our team to become our school's robotics club. Today there are 46 members, 2 mentors and an administrative team consisting of a leader, 4 vice leaders and 2 general coordinators in our team.
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Affirmation and Stereotype Threat
Date: Feb 05, 2015
Happiest, The Proudest, or the Most Satisfied.
My happiest, prodest and most satisfied moment? Wow, that's not an easy question. Although answer should be fairly easy. I've recently got married, went to Europe for honeymoon, move to California. There has been several life changing events (mostly positive), I can't put those moments as prodest or most satisfied ones. I would say perhaps the day I passed Project Management Certification test or the day I got a big promotion are more qualified answers to the question. I guess I cherish the most values that related to personal achievement, personal development, competence and perhaps influence.
And trust me, I am trying very hard to live up to those values.
Why those values are important TO ME?
I think genuine happiness and empathy towards others and self comes from confidence. Perhaps the reason is because I am not very confident person and I want to change that. I am constantly trying to increase my competency level so I can be more confident. It's not long ago I realized that that confidence is what I am lacking of. I think new skill, knowledge and development makes me feel good because that makes me feel confident and that is important to me.
Self awareness is also very important value. I am sure understanding of my weakness will help me to meditate difficult times.
When I am feeling down..
It has been ups and downs since I started 'phase 0' with DBC. Some weeks were easier than others, some challenges took hours of struggle.
When I feel down, here is what I do. I try to understand 'why'. I try to be logical (and that's one of my best skill and the worst). That works out so far but sometimes I just leave my desk and drink a couple of beer and that helps me too. I guess as much as I care of values like 'achievement, development and competency', I have relatively high expectation on me and that becomes little heavy on my shoulder from time to time.
The other day, at about noon I was walking with my dog, I started wonder why I wasn't happy with any of jobs I had. I wondered why I failed to be a prominent banker, manager or whatever professions I once dreamed. I wonder what brought me here, now. Despite the fact that I worked hard and tried to be best at given circumstances, I became no one. Certainly not a banker and not a manager with a window office.
New Dream
And then I realized how lucky I am having a second and third and fourth chances and revalue what matters the most and people I love. I really do not care of being a banker or having a window office any more. Those dreams were there for me to go forward at that time but no good for now. Now I have a new dream that I am working on. I have the best supporters - my family and friends and more importantly, better 'ME'. Suddenly I realized that I am in California and it's 21 degree in Feb and wonder what it would like if I am in a window office in Ottawa(-25 today). I don't think I would be happy anyway.
Happiest, The Proudest, or the Most Satisfied.
My happiest, prodest and most satisfied moment? Wow, that's not an easy question. Although answer should be fairly easy. I've recently got married, went to Europe for honeymoon, move to California. There has been several life changing events (mostly positive), I can't put those moments as prodest or most satisfied ones. I would say perhaps the day I passed Project Management Certification test or the day I got a big promotion are more qualified answers to the question. I guess I cherish the most values that related to personal achievement, personal development, competence and perhaps influence.
And trust me, I am trying very hard to live up to those values.
Why those values are important TO ME?
I think genuine happiness and empathy towards others and self comes from confidence. Perhaps the reason is because I am not very confident person and I want to change that. I am constantly trying to increase my competency level so I can be more confident. It's not long ago I realized that that confidence is what I am lacking of. I think new skill, knowledge and development makes me feel good because that makes me feel confident and that is important to me.
Self awareness is also very important value. I am sure understanding of my weakness will help me to meditate difficult times.
When I am feeling down..
It has been ups and downs since I started 'phase 0' with DBC. Some weeks were easier than others, some challenges took hours of struggle.
When I feel down, here is what I do. I try to understand 'why'. I try to be logical (and that's one of my best skill and the worst). That works out so far but sometimes I just leave my desk and drink a couple of beer and that helps me too. I guess as much as I care of values like 'achievement, development and competency', I have relatively high expectation on me and that becomes little heavy on my shoulder from time to time.
The other day, at about noon I was walking with my dog, I started wonder why I wasn't happy with any of jobs I had. I wondered why I failed to be a prominent banker, manager or whatever professions I once dreamed. I wonder what brought me here, now. Despite the fact that I worked hard and tried to be best at given circumstances, I became no one. Certainly not a banker and not a manager with a window office.
New Dream
And then I realized how lucky I am having a second and third and fourth chances and revalue what matters the most and people I love. I really do not care of being a banker or having a window office any more. Those dreams were there for me to go forward at that time but no good for now. Now I have a new dream that I am working on. I have the best supporters - my family and friends and more importantly, better 'ME'. Suddenly I realized that I am in California and it's 21 degree in Feb and wonder what it would like if I am in a window office in Ottawa(-25 today). I don't think I would be happy anyway.


syscall.h File Reference
Nachos system call interface. More...
#include "copyright.h"
Go to the source code of this file.
|
Classes |
| struct | Nachos_Time |
| | Defines the Nachos basic time unit. More...
|
Defines |
| #define | SC_Halt 0 |
| #define | SC_Exit 1 |
| #define | SC_Exec 2 |
| #define | SC_Join 3 |
| #define | SC_Create 4 |
| #define | SC_Open 5 |
| #define | SC_Read 6 |
| #define | SC_Write 7 |
| #define | SC_Seek 8 |
| #define | SC_Close 9 |
| #define | SC_NewThread 10 |
| #define | SC_Yield 11 |
| #define | SC_PError 12 |
| #define | SC_P 13 |
| #define | SC_V 14 |
| #define | SC_SemCreate 15 |
| #define | SC_SemDestroy 16 |
| #define | SC_LockCreate 17 |
| #define | SC_LockDestroy 18 |
| #define | SC_LockAcquire 19 |
| #define | SC_LockRelease 20 |
| #define | SC_CondCreate 21 |
| #define | SC_CondDestroy 22 |
| #define | SC_CondWait 23 |
| #define | SC_CondSignal 24 |
| #define | SC_CondBroadcast 25 |
| #define | SC_TtySend 26 |
| #define | SC_TtyReceive 27 |
| #define | SC_Mkdir 28 |
| #define | SC_Rmdir 29 |
| #define | SC_Remove 32 |
| #define | SC_FSList 33 |
| #define | SC_SysTime 34 |
| #define | ConsoleInput 0 |
| #define | ConsoleOutput 1 |
Typedefs |
| typedef int | ThreadId |
| typedef int | OpenFileId |
| typedef int | SemId |
| typedef int | LockId |
| typedef int | CondId |
Functions |
| void | Halt () |
| void | SysTime (Nachos_Time *t) |
| void | Exit (int status) |
| ThreadId | Exec (char *name) |
| ThreadId | NewThread (char *debugName, int func, int arg) |
| int | Join (ThreadId id) |
| void | Yield () |
| void | PError (char *mess) |
| int | Create (char *name, int size) |
| OpenFileId | Open (char *name) |
| int | Write (char *buffer, int size, OpenFileId id) |
| int | Read (char *buffer, int size, OpenFileId id) |
| int | Seek (int offset, OpenFileId id) |
| int | Close (OpenFileId id) |
| int | Remove (char *name) |
| int | Mkdir (char *name) |
| int | Rmdir (char *name) |
| void | FSList () |
| SemId | SemCreate (char *debug_name, int count) |
| int | SemDestroy (SemId sema) |
| int | V (SemId sema) |
| int | P (SemId sema) |
| LockId | LockCreate (char *debugName) |
| int | LockDestroy (LockId id) |
| int | LockAcquire (LockId id) |
| int | LockRelease (LockId id) |
| CondId | CondCreate (char *debugName) |
| int | CondDestroy (CondId id) |
| int | CondWait (CondId cond) |
| int | CondSignal (CondId cond) |
| int | CondBroadcast (CondId cond) |
| int | TtySend (char *mess) |
| int | TtyReceive (char *mess, int length) |
Detailed Description
Nachos system call interface.
These are Nachos kernel operations that can be invoked from user programs, by trapping to the kernel via the "syscall" instruction.
This file is included by user programs and by the Nachos kernel.
Each of these is invoked by a user program by simply calling the procedure; an assembly language stub stuffs the system call code into a register, and traps to the kernel. The kernel procedures are then invoked in the Nachos kernel, after appropriate error checking, from the system call entry point in exception.cc.
Define Documentation
| #define SC_CondBroadcast 25 |
|
| #define SC_CondDestroy 22 |
|
| #define SC_LockAcquire 19 |
|
| #define SC_LockDestroy 18 |
|
| #define SC_LockRelease 20 |
|
Typedef Documentation
Function Documentation
|
|
Close a file. Abort on error.
|
| int CondBroadcast |
( |
CondId |
cond |
) |
|
|
| CondId CondCreate |
( |
char * |
debugName |
) |
|
|
| int CondSignal |
( |
CondId |
cond |
) |
|
|
| int Create |
( |
char * |
name, |
|
|
int |
size |
|
) |
|
|
|
|
Quit without dropping core.
|
| LockId LockCreate |
( |
char * |
debugName |
) |
|
|
| int Mkdir |
( |
char * |
name |
) |
|
|
| ThreadId NewThread |
( |
char * |
debugName, |
|
|
int |
func, |
|
|
int |
arg |
|
) |
|
|
| void PError |
( |
char * |
mess |
) |
|
|
|
|
Print the last error message with the personalized one "mess" |
| int Read |
( |
char * |
buffer, |
|
|
int |
size, |
|
|
OpenFileId |
id |
|
) |
|
|
| int Remove |
( |
char * |
name |
) |
|
|
| int Rmdir |
( |
char * |
name |
) |
|
|
| SemId SemCreate |
( |
char * |
debug_name, |
|
|
int |
count |
|
) |
|
|
| int SemDestroy |
( |
SemId |
sema |
) |
|
|
| int TtyReceive |
( |
char * |
mess, |
|
|
int |
length |
|
) |
|
|
| int TtySend |
( |
char * |
mess |
) |
|
|
| int Write |
( |
char * |
buffer, |
|
|
int |
size, |
|
|
OpenFileId |
id |
|
) |
|
|
Generated on Mon Oct 17 19:44:17 2005 for Nachos by
1.4.4


AMAZING PEOPLE ARE MAKING DESIGNS IN A FUN ENVIRONMENT
When you work with us, you are a part of a team of committed collaborators. All aspects of our software development are intergrated, interactive and agile.
Our approach unifies design,development and product management to create exceptional products.
We start each project by understanding your business goals, the user`s and the product requirements.
Then we help translate them into effective and elegant solutions.
Before we sign a contract or write a line of code our team will spend the necessary time needed to understand your product vision
The outcome of this scoping session is a proposal or high-level statement of work.
Based on your requirements, we can provide a complete offering or just the pieces you need.
DESIGN
DEVELOPMENT
PRODUCT MANAGEMENT
Our design practice offers a full range of services including brand strategy, interraction and visual design and user experience through testing. Throughout your project, our designers create and implement visual design and work flows, solicit user feedback and work with you to make sure what gets built is what is needed.
All engineers are fluent in the latest enterprises, mobile and web development technologies. They collaborate with your team to write, and improve code on a daily basis, using proven practices such as test-driven development and pair-programming.
Planning and development is iterative. Because we are constantly coding and testing, the products we build are always ready to go live.This iterative process allows for changes as business requirements evolve.
DELANI STUDIO
2019


@(addressForm: Form[models.Address],emp: models.employees.Employee, employee: models.employees.Employee)
@import helper._
@main("Upadte Address",employee){
Update Address for @emp.getFname @emp.getSname
@* Display error message from flash, if it is set *@
@if(flash.containsKey("error")) {
@flash.get("error")
}
@form(action=routes.HomeController.updateAddressSubmit(emp.getId), 'class -> "form-horizontal", 'role -> "form") {
@* CSRF attack prevention *@
@* This token, sent by the controller, will be used to authenticate the form submission *@
@CSRF.formField
@inputText(addressForm("nr"), '_label -> "House Number", 'class -> "form-control")
@inputText(addressForm("street"), '_label -> "Street", 'class -> "form-control")
@inputText(addressForm("town"), '_label -> "Town", 'class -> "form-control")
@inputText(addressForm("city"), '_label -> "City", 'class -> "form-control")
@inputText(addressForm("eircode"), '_label -> "Eircode", 'class -> "form-control")
} @* end of form *@
} @* end of main *@


| Summary | |
|---|---|
| Building Idle |
{{building}} {{idle}} |
| History |
|---|
|
bjstmngbgr02/Acceptance_test bjstmngbgr02/Acceptance_test bjstmngbgr02/Acceptance_test bjstmngbgr02/Acceptance_test bjstmngbgr02/Acceptance_test bjstmngbgr02/Acceptance_test bjstmngbgr02/Acceptance_test bjstmngbgr02/Acceptance_test bjstmngbgr02/Acceptance_test |


router
util
This module is a dependency of the main ui.router module. Do not include this module as a dependency in your angular app (use ui.router module instead).


---
layout: page
title: Course Evaluations
permalink: /Course-Feedback/
---
Here are the results of the the feedbacks recieved for the course (Analyzed and contrasted with the previous course offering):
Analyzed Feedback Results
Here are the results of the anonymous cousre evaluation conducted by the University:
IUST Evaluation


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import include.{heading, services_sidebar} @import config.ApplicationConfig @import views.html.helper.CSRF @import models.registrationprogress.Section @import views.html.registrationprogress.section @this(main2: include.main2) @( sections: Seq[Section], declarationAvailable: Boolean, businessName: String, serviceNames: Seq[String], canEditPreApplication: Boolean, hasCompleteNominatedOfficer: Boolean = false, nominatedOfficerName: Option[String] = None )(implicit request: Request[_],m:Messages, lang: Lang, appConfig: ApplicationConfig) @main2( title = Messages("progress.title") ) { @* Heading *@
@Messages("progress.lede")
@Messages("progress.saved.automatically")
@if(declarationAvailable) { @Messages("progress.declarationavailable") } else { @Messages("progress.declarationunavailable") }
@if(declarationAvailable) { }


Dear Sir, CONFIDENTIAL BUSINESS ASSISTANCE I am Mr. Mohammed Jaja, Deputy Secretary-General to the Nigerian Football Association parent body of the World Youths Championship, NIGERIAN 99 Local Organising Committee. An affiliate of Federation of International Football Association (FIFA). In the course of our preparation to host the World Youth Soccer Championship Tagged (NIGERIAN 99) two years ago. Huge sum of money running into millions of United States Dollars was budgeted by the then Military Government for the successful organisation of this competition. In the same vein, the FIFA President Mr. Joseph (Sepp)Blaster donated some millions of Dollars for the same project, not to talk of Corporate bodies, Organisations and Football loving individual as the Deputy Secretary to both the Local Organising Committee (LOC) and the Nigerian Football Association (NFA), I and some of my colleagues in sensitive position were able to Over -Invoice most of the contracts, construction and refurbishing of the 8 stadia,Press Conference Rooms,Communication facilities and all other infrastructures used for the competition.The contractors who handled these projects had been paid off. I wish to inform you that the sum of US$18.5 million was realised as a result of Over-Invoicing after the successful competition and the said sum is presently lying in my Organisations (NFA) coded account with the Central Bank of Nigeria,ready for transfer into an overseas account of our choice.It might interest you to know that the New Civilian President of Nigeria has ordered that all Debt owned to foreign contractors should be paid immediately in order to woo more foreign investors into Nigeria. In the light of the above, I has been mandated by my colleagues to seek for an honest and trustworthy foreigner who will assist us in ensuring the successful Transfer of the above sum into his Personal/Companies Account. All necessary modalities/plan is duly worked out accordingly, you the account owner will be entitled to 20% of the Total fund while 5% will be used to settle any expenses that might be incurred by both parties and 75% will before me and my colleagues. You might be surprised and curious on how I got your detail in particular,it is courtesy of the Chamber of Commerce,Mines and Industry. All we require from you is your Co-operation as this Transaction is absolutely risk-free. We kindly request that you handle this transaction with utmost secrecy.We are hoping to develop a good business relationship with you. Your immediate response will be highly appreciated.I will want you to respond or through this email<EMAIL_ADDRESS>because of the confidentiality which this transaction needs. Yours sincerely, MR. Mohammed