Import Shared Library Report |
Thursday, 2 May 2019 16:12:53
Your generated files are installed in the following folder:
C:\Program Files\National Instruments\LabVIEW 2018\user.lib\liblsl64
Parsing header file warnings:
No errors/warnings occurred when parsing the header file.
The following errors/warnings occurred when generating the wrapper VIs for this shared library.
VI Not Executable
The VI is not executable because of one of the following reasons:
1. The shared library or a dependent file is not installed. To make the VI executable, you must install the shared library and all support files on the computer on which you run the VI.
2. A required custom control might be empty or cannot be found. To make the VI executable, update the custom control manually.
3. The VI contains a parameter with an unsupported data type.


Afia Schwarzenegger shows off her newly acquired gigantic boob
by RASHADControversial Ghanaian media personality, Afia Schwarzenegger has shared new photo on Instagram and she is glowing like a delicate flower
Not only has her beauty captivated her followers, Afia Schwarzenegger’s newly acquired boobs has mesmerized her fans.
Afia Schwar who was not known to have such an impressive boobs; firm and standing proudly like a military man is now flaunting such boobs.
Already fans are questioning how she came about with such boobs with some even alleging she could be a product of Obengfo.
In any case, what matters most is looks stunning and most men will dream of sipping from her round boobs…lol
SEE ALSO: Wild Video of Big Booty Slay Queen Moesha Boduong chilling with Wizkid in Dubai pops up (Watch)


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Hello everyone I'm Yasmin Adaileh I'm 23 years old
I had born in 1996 at the end of the year so most of
my friend started the follow age while I'm just befor
two month I have started it.
I had studied
accounting but befor it I was gain the university with
language major then I have change the major because I
love accounting.
after gradutation I didn't find a
job but I have a three month training in Land
Transportation commision it was unuseful because it is
a public sector.
My new goal is to success this course Inshalla.
So as I said my major is accounting that is mean I don't have any background of software development, This is a challange and the bigest challange I have take it in my whole live. I will work hard to gain this challange and become a talent developer.
- top activity to do:
- walking
- talking
- shopping
- meet friends
- set with family
- sleeping
- play vedio game
- set in the roof and take a fresh air
- studying
- have a break from every thing
- learning subject:
- learned HTML
- learned CSS
- try to understand JS


{% extends 'base.html'%} {% import 'macros.html' as macro%} {% block styles%} {{ super() }} {% endblock %} {% block content%}
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Generation is a javascript function with arguments:
x and
y are coordinates of the current cell.
n is the number of neighbours, from 0 to 8.
age is the age of cell; age > 0 means the cell is alive, otherwise dead
You can use following functions:
- get(x, y) - returns the age of a cell at (x, y)
The function should return new age for the cell.
Color is a javascript function with arguments:
age is the age of cell; age > 0 means the cell is alive, otherwise dead
You can use following functions:
- blend(a, b, v) - blends colors a and b; v is a value from 0 to 1 which lets you specify how much of each color to mix
The function should return new age for the cell.


Raspberry Pi is a single-board computer roughly the size of a deck of cards. It’s equipped with an ARM processor that runs Linux. USB ports let you attach a keyboard and mouse. Video is supported via HDMI and RCA. Storage is handled by SD cards. There’s an Ethernet port.
You can buy a Raspberry Pi for $35.00.
Will it Run Ruby?
When my Raspberry Pi arrived, I was curious: Will it run Ruby? How about Rails? This article describes my experience with RVM, Ruby, and Rails on a Raspberry Pi, along with the “gotchas” I encountered along the way.
Why Raspberry Pi?
Raspberry Pi was created by a group of UK educators and engineers with a concern: Inexpensive hobbyist computers like the old Commodore 64 no longer exist. Game consoles continue to get better, but there is no replacement for the Commodore. As a result, young people who might become developers get introduced to the profession as gamers or users of Word or Excel. They begin as consumers, not creators. Sad news, because creators shape the world we all enjoy.
Creators get stronger when they have tools for learning, play, and 10,000 hours of practice. Enter Raspberry Pi. It’s a tiny, inexpensive computer. It runs open source software so it is highly accessible to curious minds that are eager to learn, practice, and play.
Yes, It Will Run Ruby
Here’s a screenshot from my Raspberry Pi with the Midori web browser, RVM, Ruby 1.9.3-p327, Rails 3.2.9, Vim 7.3, and other tools. In this shot, I’m using the Pi’s HDMI output to drive a 1080p monitor.
Yes, this tiny, $35 device will drive a monitor at full 1080p HD.
Getting Started
Here’s what you need to get Ruby running on your Raspberry Pi.
- 1 Raspberry Pi with 512MB RAM or more. I bought mine from Newark/element14.
- 1 Monitor that accepts HDMI or RCA video input.
- 1 HDMI or RCA cable, depending on your monitor.
- 1 USB keyboard
- 1 USB mouse
- 1 8GB (or larger) SD card. The instructions recommend 4GB, but I suggest at least 8GB if you expect to run Rails. You’ll need room for gems, git, etc.
- 1 Ethernet cable w/RJ-45 ends.
- 1 high-speed Internet connection.
- A separate computer capable of writing an SD card image, or an SD card pre-configured for Raspberry Pi.
The Pi makers have tested the device with SD cards as large as 32GB, even though 4GB is recommended. Personally, I prefer the larger cards because they allow more room for experiments.
Prepping the SD Card
The Pi’s operating system boots from the SD card. There are several methods for prepping the SD card with the Raspberry Pi system. The easiest: Buy a Pi with a pre-configured SD card.
My Pi arrived before the pre-configured SD card. I’m a little bit impatient when it comes to new gadgets, so I decided to prep an old SD card of my own.
Note: If you’re reading this article, you already know the standard disclaimer about how technology changes rapidly therefore this procedure could be wrong by the time you read this. I’ve included links to references so you can check for updates on your own. You know the risks. Please backup everything that needs it.
References:
- eLinux SD Card Setup. Methods for putting your preferred image on the SD card. I chose the “Copying an image to the SD card in Mac OS X (mostly graphical interface)” method.
- Raspberry Pi Official Downloads. Several SD card images, and a beginners wiki.
Prepping an SD card takes a long time. In my case, it took 23 minutes from the time I executed the SD write command ($ sudo dd...
) to the completion of the process. It was a little disconcerting because the system didn’t do anything during that time. No feedback whatsoever. Sounds like an opportunity for a pull request!
Starting the System
To start your Raspberry Pi system:
- Plug the SD card, USB keyboard & mouse, Ethernet cable, and video cable (HDMI or RCA) into their corresponding sockets.
- Plug in the USB power adapter.
There is no power switch. The device is turned on/off by adding or removing the power cable.
Linux will boot in text mode. When the system is done booting, you will be prompted for a username and password. Here are the default credentials:
raspberrypi login: pi
Password: raspberry
Several seconds later, you will be greeted with the $ prompt. You can continue to use the Pi in text mode, or you can start the X Window GUI with:
1
|
|
Ruby with RVM
I am a big fan of Ruby Version Manager (RVM). I learn best when I break things. RVM’s handling of Ruby versions and gemsets enables me to recover rapidly and well.
This command will install the latest stable versions of RVM and Ruby:
1
|
|
It took over an hour for Ruby to compile on my Pi. The read/write speed of my SD card (Class 4) could have been a factor. I have a faster (Class 10) SD card on order.
Installing Rails
First, make sure you’re using the version of Ruby you want to use.
1
|
|
Next, create a gemset and tell RVM that you want to use it.
1 2 3 |
|
And then install Rails as you would on any other ‘nix-based machine.
1
|
|
Gotchas
The installation process was relatively smooth. Still, here are a few gotchas I encountered with the Pi:
- I already mentioned this, but it’s worth repeating: It took 23 minutes to write the SD card, and there was no feedback along the way. This wasn’t a big deal since I had been pre-warned by one of the wikis.
- apt-get needed an update before I could install git.
$ sudo apt-get update
did the trick. - The Pi will do absolutely nothing without a properly configured SD card. You know how a PC will partially boot (to CMOS) even without a hard drive? Not so with the Pi.
- The micro-USB power port requires 700mA or more of current. Most micro-USB power adapters deliver 500mA or less. Raspberry Pi will only boot if the micro-USB provides sufficient amperage. Only one of my several micro-USB adapters met this requirement.
- A 4GB SD card is far too small if you want to do something useful with the Pi. My first Rails installation failed due to size limitations because I was using a 4GB card. Go with 8GB or larger.
- I’m not 100% sure, but I think that a faster SD card will make a difference especially during the compilation of the Ruby interpreter. This is only a guess at this point. I don’t yet know where the bottlenecks are.
Overall, the gotchas were minor.
For Screenshots, Try Scrot
To take screenshots of the Raspberry Pi desktop, I used Scrot (SCReenshOT). Here’s how to install Scrot:
1
|
|
After you install Scrot, this command will take a shot of the entire desktop and drop it into a file called desktop.png
in your home directory.
1
|
|
To pause five seconds before taking the screenshot:
1
|
|
Analog TV
Don’t laugh: I still have an old analog TV in my living room. It’s twelve years old and it still works. Since Raspberry Pi comes with multiple video outupts, analog TVs are useful again.
Here’s Raspberry Pi running with my old analog TV as a monitor. Reminds me of the Commodore 64 days!
Raspberry Pi will work with a wide range of monitors, from HDMI to ancient analog. That makes the Pi accessible to a large audience of hobbyists and developers.
Conclusion
Raspberry Pi will never replace my primary machine because it’s too slow. But it is certainly fast enough for learning. It meets the goals set forth by the engineering team.
I am excited by the learning opportunities offered by Raspberry Pi. To the Raspberry Pi Foundation: Thank you for an impressive device. I wish you much success.
Update: You might also be interested in BeagleBone Black Running Ubuntu.


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145 Having said all that, the mix block takes the colors it is given as inputs and converts them into what we hope are typical paint reﬂectance spectra that would look like those colors, and then mixes those spectra and converts back to RGB. But unlike the other two kinds of mixing, in this case we can’t say that these colors are “the right answer”; what would happen with real paints depends on their chemical composition and how they’re mixed. There are three more mixing options, but these three are the ones that correspond to real-world color mixing. The mix block will accept any number of colors, and will mix them in equal proportion. If (for any kind of mixing) you want more of one color than another, use the color at weight block to make a “weighted color”: This mixes four parts red paint to one part green paint. All colors in a mixture can be weighted: (Thanks to Scott Burns for his help in understanding paint mixing, along with David Briggs’s tutorial. Remaining mistakes are bh’s.) tl;dr For normal people, Snap ! provides three simple, one-dimensional scales: crayons for speciﬁc interesting colors, color numbers for a continuum of high-contrast colors with a range of hues and shading, and fair hues for a continuum without shading. For color nerds, it provides three-dimensional color spaces RGB, HSL, HSV, and fair-hue variants of the latter two. We recommend “fair HSL” for zeroing in on a desired color. Color numbers, no grays. All color numbers. Just grays. Fair hues. Crayons, no grays.


A condition
is a boolean
combination of test
elements, each
of which defines a condition on the vertex (or
link). Condition test
elements can
be combined with the
and
,
or
, and
not
elements using
disjunctive normal form.
<condition>
Attributes None.
Children
test |
member of a required set | A requirement for the corresponding vertex’s or edge’s attribute values |
and |
member of a required set | Requires all of the included tests be satisfied |
or |
member of a required set | Requires one of the included tests be satisfied |
not |
member of a required set | Requires that the specified test not be satisfied |
Content Model
(or | and | not | test)
<condition> <and> <test> <operator>eq</operator> <attribute-name>objtype</attribute-name> <value>person</value> </test> <not> <test> <operator>exists</operator> <attribute-name>cluster-coeff</attribute-name> </test> </not> </and> </condition>


--- tags: - 基础算法-模拟 grammar_cjkRuby: true catalog: true layout: post header-img: "img/header/P25.jpg" preview-img: "/img/preview/P25.jpg" ---
标签：模拟，平面几何
Have you ever tried to explain to thecoordinator, why it is eight hours to the contest and not a single problem hasbeen prepared yet? Misha had. And this time he has a really strong excuse: hefaced a space-time paradox! Space and time replaced each other.
The entire universe turned into an enormousclock face with three hands — hour, minute, and second. Time froze, andclocks now show the timeh hours, m minutes, s seconds.
Last time Misha talked with the coordinatoratt1 o'clock, so now he stands on the number t1on the clock face. The contest should be ready byt2 o'clock.In the terms of paradox it means that Misha has to go to numbert2somehow. Note that he doesn't have to move forward only: in these circumstancestime has no direction.
Clock hands are very long, and Misha cannotget round them. He also cannot step over as it leads to the collapse ofspace-time. That is, if hour clock points 12 and Misha stands at 11 then hecannot move to 1 along the top arc. He has to follow all the way round theclock center (of course, if there are no other hands on his way).
Given the hands' positions,t1,and t2, find if Misha can prepare the contest on time (orshould we say on space?). That is, find if he can move fromt1to t2 by the clock face.
Input
Five integers h,m, s,t1, t2 (1 ≤ h ≤ 12, 0 ≤ m, s ≤ 59, 1 ≤ t1, t2 ≤ 12,t1 ≠ t2).
Misha's position and the target time do notcoincide with the position of any hand.
Output
Print "YES" (quotes for clarity),if Misha can prepare the contest on time, and "NO" otherwise.
You can print each character either upper-or lowercase ("YeS" and "yes" are valid when the answer is"YES").
Examples
Input
12 30 45 3 11
Output
NO
Input
12 0 1 12 1
Output
YES
Input
3 47 0 4 9
Output
YES
Note
The three examples are shown on thepictures below from left to right. The starting position of Misha is shown withgreen, the ending position is shown with pink. Note that the positions of thehands on the pictures are not exact, but are close to the exact and the answeris the same.
题意：给定一个时刻，将其视为钟面，指针将其划分为许多区域，再读入两个时刻，问这两个时刻是否在同一个区域内
直接模拟就好了，注意下精度的问题
Code
#include<bits/stdc++.h> #define rep(i,a,b) for(int i=a;i<=b;i++) #define dep(i,a,b) for(int i=a;i>=b;i--) #define mem(x,num) memset(x,num,sizeof x) using namespace std; double h,m,s,t1,t2; bool first,second; int main() { first=second=true; cin>>h>>m>>s>>t1>>t2; if(h==12)h=0; if(t1==12)t1=0; if(t2==12)t2=0; h=h*30+m/60+s/3600; m=(m+s/60)*6; s=s*6; t1*=30,t2*=30; //cout<<h<<endl<<m<<endl<<s<<endl<<t1<<endl<<t2<<endl; if(t1>t2)swap(t1,t2); if(h>t1&&h<t2)first=false; if(m>t1&&m<t2)first=false; if(s>t1&&s<t2)first=false; if(h>t2&&h<=360)second=false; if(m>t2&&m<=360)second=false; if(s>t2&&s<=360)second=false; if(h<t1&&h>=0)second=false; if(m<t1&&m>=0)second=false; if(s<t1&&m>=0)second=false; if(first||second)cout<<"YES\n"; else cout<<"NO\n"; return 0; }


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Home
Reference > A Syntax > <qualified>
prev
next
Syntax
Description
There are two kinds of qualified expressions
-
Dot.
This form is used to select a named component
of the value of the expression before the dot.
-
Subscript/construct/call.
This form is used to select a numbered component
of the value of the expression before the parenthesis,
to construct an object,
or to call a function.
The semantics of qualified expression in the simple case is controlled by the type
of initial expression. It is also possible to select an alternate semantics
by specifying a type or view following a sharp.
Semantics
The semantics of dot qualified expression is given by
by the parsing transforms specified below and the semantics of
the Dot function.
The semantics of subscript/construct/call qualified expression depends upon the
type of value from evaluating the initial expression.
-
Function.
If the value has type func, then that function is called
passing the values of the expressions inside the parenthesis as parameters.
-
Type Constructor.
If the value has type type, then a new object of that
type is created. If defined, the sys:Init function for that object is then called.
For example, if T is a type then
T(a,b,c)
creates a new object, X of type T and if HasDot(T,"sys:Init") then calls
X.sys:Init(a,b,c)
-
View Constructor.
The view sys:Make function is called. For example if V is a view, then
V(a,b,c)
produces
V.sys:Make(a,b,c)
By convention the result should be in view V. So here
V(a,b,c)~V
-
Subscript.
Otherwise, the sys:Subscript function for the value is called.
For example,
X(i,j)
results in
X.sys:Subscript(i,j)
In all except the subscript case, the sharp and expression after the parenthesis are not permitted.
For subscript, the following example
X(i,j)#V
results in
X.sys:Subscript#V(i,j)
Parsing
When parsed subscript/call qualified expressions are represented by
xcom:call nodes.
When parsed, a dot qualified expressions are represented
by xdom:call nodes whose kind is set to "dot".
For example, each dot expression in the first column and the call expression
in the second column are represented by identical xdom:call nodes,
except the first has kind "dot" and
the second has kind "normal".
| a.b |
Dot(a,"b") |
| a.b#c |
Dot(a,"b",c) |
| a.b[L] |
Dot[L](a,"b") |
| a.b[L]#c |
Dot[L](a,"b",c) |
Note that the Dot function is more general that the dot qualified expression,
because the selected name can be a computed value rather than a constant.


"
This page serves as a collection of the assignments in Colt Steele's "The Web Developer's Bootcamp" on Udemy up to, but not including, the Javascript section.
I deviated slightly, but this is just a simple website with a picture and a list.
Check it out!In this exercise, we create a table with borders of some Pokemon characters. Still very simple.
Check it out!This was a practice exercise working with different elements, classes, and IDs.
Check it out!This is a completely non-functional tic-tac-toe board made to illustrate the CSS Grid system.
Check it out!This is a series of images taken from www.unsplash.com and placed in a CSS grid.
Check it out!This is a blog with some Pirate Ipsum inserted to demonstrate different CSS techniques.
Check it out!This image gallery was created using Bootstrap 3.
Check it out!This exercise demonstrates more capabilities of Bootstrap 3 before moving on to Bootstrap 4.
Check it out!This is a collection of images of different patterns with a jumbotron with a background image.
Check it out!This is a recreation of a home page of an instragram store using Bootstrap 4.
Check it out!


Pacman, now with ghosts.
Minimax, Expectimax,
Evaluation.
In this project, you will design agents for the classic version of Pacman, including ghosts. Along the way, you will implement both minimax and expectimax search and try your hand at evaluation function design.
The code base has not changed much from the previous project, but please start with a fresh installation, rather than intermingling files from project 1. You can, however, use your search.py
and searchAgents.py
in any way you want.
The code for this project contains the following files, available as a zip archive.
multiAgents.py |
Where all of your multi-agent search agents will reside. |
pacman.py
| The main file that runs Pacman games. This file also describes a Pacman GameState type, which you will use extensively in this project |
game.py |
The logic behind how the Pacman world works. This file describes several supporting types like AgentState, Agent, Direction, and Grid. |
util.py |
Useful data structures for implementing search algorithms. |
graphicsDisplay.py |
Graphics for Pacman |
graphicsUtils.py |
Support for Pacman graphics |
textDisplay.py |
ASCII graphics for Pacman |
ghostAgents.py |
Agents to control ghosts |
keyboardAgents.py |
Keyboard interfaces to control Pacman |
layout.py |
Code for reading layout files and storing their contents |
What to submit: You will fill in portions of multiAgents.py
during the assignment. You should submit this file with your code and comments. You may also submit supporting files (like search.py
, etc.) that you use in your code. Please do not change the other files in this distribution or submit any of our original files other than multiAgents.py
. Directions for submitting are on the course website; this assignment is submitted with the command submit p2
.
Evaluation: Your code will be autograded for technical correctness. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder. However, the correctness of your implementation -- not the autograder's judgements -- will be the final judge of your score. If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work.
Academic Dishonesty: We will be checking your code against other submissions in the class for logical redundancy. If you copy someone else's code and submit it with minor changes, we will know. These cheat detectors are quite hard to fool, so please don't try. We trust you all to submit your own work only; please don't let us down. If you do, we will pursue the strongest consequences available to us.
Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff for help. Office hours, section, and the newsgroup are there for your support; please use them. If you can't make our office hours, let us know and we will schedule more. We want these projects to be rewarding and instructional, not frustrating and demoralizing. But, we don't know when or how to help unless you ask.
Newsgroup: Post your questions (but not project solutions) on the newsgroup. Please be careful not to post spoilers to the newsgroup.
First, play a game of classic Pacman:
python pacman.pyNow, run the provided
ReflexAgent
in multiAgents.py
:
python pacman.py -p ReflexAgentNote that it plays quite poorly even on simple layouts:
python pacman.py -p ReflexAgent -l testClassicInspect its code (in
multiAgents.py
) and make sure you understand what it's doing.
Question 1 (3 points) Improve the ReflexAgent
in multiAgents.py
to play respectably. The provided reflex agent code provides some helpful examples of methods that query the GameState
for information. A capable reflex agent will have to consider both food locations and ghost locations to perform well. Your agent should easily and reliably clear the testClassic
layout:
python pacman.py -p ReflexAgent -l testClassicTry out your reflex agent on the default
mediumClassic
layout with one ghost or two (and animation off to speed up the display):
python pacman.py --frameTime 0 -p ReflexAgent -k 1
python pacman.py --frameTime 0 -p ReflexAgent -k 2How does your agent fare? It will likely often die with 2 ghosts on the default board, unless your evaluation function is quite good.
Note: you can never have more ghosts than the layout permits.
Note: As features, try the reciprocal of important values (such as distance to food) rather than just the values themselves.
Note: The evaluation function you're writing is evaluating state-action pairs; in later parts of the project, you'll be evaluating states.
Options: Default ghosts are random; you can also play for fun with slightly smarter directional ghosts using -g DirectionalGhost
. If the randomness is preventing you from telling whether your agent is improving, you can use -f
to run with a fixed random seed (same random choices every game). You can also play multiple games in a row with -n
. Turn off graphics with -q
to run lots of games quickly.
The autograder will check that your agent can rapidly clear the
openClassic
layout ten times without dying more than twice or thrashing around infinitely (i.e. repeatedly moving back and forth between two positions, making no progress).
python pacman.py -p ReflexAgent -l openClassic -n 10 -q
Don't spend too much time on this question, though, as the meat of the project lies ahead.
Question 2 (5 points) Now you will write an adversarial search agent in the provided MinimaxAgent
class stub in multiAgents.py
. Your minimax agent should work with any number of ghosts, so you'll have to write an algorithm that is slightly more general than what appears in the textbook.
In particular, your minimax tree will have multiple min layers (one for each ghost) for every max layer.
Your code should also expand the game tree to an arbitrary depth. Score the leaves of your minimax tree with the supplied self.evaluationFunction
, which defaults to scoreEvaluationFunction
.
MinimaxAgent
extends MultiAgentAgent
, which gives access to self.depth
and self.evaluationFunction
. Make sure your minimax code makes reference to these two variables where appropriate as these variables are populated in response to command line options.
Important: A single search ply is considered to be one Pacman move and all the ghosts' responses, so depth 2 search will involve Pacman and each ghost moving two times.
Hints and Observations
self.evaluationFunction
). You shouldn't change this function, but recognize that now we're evaluating *states* rather than actions, as we were for the reflex agent. Look-ahead agents evaluate future states whereas reflex agents evaluate actions from the current state.minimaxClassic
layout are 9, 8, 7, -492 for depths 1, 2, 3 and 4 respectively. Note that your minimax agent will often win (15/20 games for us) despite the dire prediction of depth 4 minimax.
python pacman.py -p MinimaxAgent -l minimaxClassic -a depth=4
Directions.STOP
action from Pacman's list of possible actions. Depth 2 should be pretty quick, but depth 3 or 4 will be slow. Don't worry, the next question will speed up the search somewhat.
GameStates
, either passed in to getAction
or generated via GameState.generateSuccessor
. In this project, you will not be abstracting to simplified states.
openClassic
and mediumClassic
(the default), you'll find Pacman to be good at not dying, but quite bad at winning. He'll often thrash around without making progress. He might even thrash around right next to a dot without eating it because he doesn't know where he'd go after eating that dot. Don't worry if you see this behavior, question 5 will clean up all of these issues.
python pacman.py -p MinimaxAgent -l trappedClassic -a depth=3Make sure you understand why Pacman rushes the closest ghost in this case.
Question 3 (3 points) Make a new agent that uses alpha-beta pruning to more efficiently explore the minimax tree, in AlphaBetaAgent
. Again, your algorithm will be slightly more general than the pseudo-code in the textbook, so part of the challenge is to extend the alpha-beta pruning logic appropriately to multiple minimizer agents.
You should see a speed-up (perhaps depth 3 alpha-beta will run as fast as depth 2 minimax). Ideally, depth 3 on smallClassic
should run in just a few seconds per move or faster.
python pacman.py -p AlphaBetaAgent -a depth=3 -l smallClassic
The AlphaBetaAgent
minimax values should be identical to the MinimaxAgent
minimax values, although the actions it selects can vary because of different tie-breaking behavior. Again, the minimax values of the initial state in the minimaxClassic
layout are 9, 8, 7 and -492 for depths 1, 2, 3 and 4 respectively.
Question 4 (3 points)
Random ghosts are of course not optimal minimax agents, and so modeling them with minimax search may not be appropriate. Fill in ExpectimaxAgent
, where your agent
agent will no longer take the min over all ghost actions, but the expectation according to your agent's model of how the ghosts
act. To simplify your code, assume you will only be running against RandomGhost
ghosts, which choose amongst their
getLegalAction
s uniformly at random.
You should now observe a more cavalier approach in close quarters with ghosts. In particular, if Pacman perceives that he could be trapped but might escape to grab a few more pieces of food, he'll at least try. Investigate the results of these two scenarios:
python pacman.py -p AlphaBetaAgent -l trappedClassic -a depth=3 -q -n 10
python pacman.py -p ExpectimaxAgent -l trappedClassic -a depth=3 -q -n 10You should find that your
ExpectimaxAgent
wins about half the time, while your AlphaBetaAgent
always loses. Make sure you understand why the behavior here differs from the minimax case.
Question 5 (6 points) Write a better evaluation function for pacman in the provided function
betterEvaluationFunction
. The evaluation function should evaluate states, rather than actions like your reflex agent evaluation function did. You may use any tools at your disposal for evaluation, including your search code from the last project. With depth 2 search, your evaluation function should clear the smallClassic
layout with two random ghosts more than half the time for full credit and still run at a reasonable rate.
python pacman.py -l smallClassic -p ExpectimaxAgent -a evalFn=better -q -n 10
Document your evaluation function! We're very curious about what great ideas you have, so don't be shy. We reserve the right to reward bonus points for clever solutions and show demonstrations in class.
Hints and Observations
Mini Contest (3 points extra credit) Pacman's been doing well so far, but things are about to get a bit more challenging. This time, we'll pit Pacman against smarter foes in a trickier maze. In particular, the ghosts will actively chase Pacman instead of wandering around randomly, and the maze features more twists and dead-ends, but also extra pellets to give Pacman a fighting chance. You're free to have Pacman use any search procedure, search depth, and evaluation function you like. The only limit is that games can last a maximum of 3 minutes (with graphics off), so be sure to use your computation wisely. We'll run the contest with the following command:
python pacman.py -l contestClassic -p ContestAgent -g DirectionalGhost -q -n 10
The three teams with the highest score (details: we run 10 games, games longer than 3 minutes get score 0, lowest and highest 2 scores discarded, the rest averaged) will receive 3, 2, and 1 extra credit points respectively and can look on with pride as their Pacman agents are shown off in class. Be sure to document what your agent is doing, as we may award additional extra credit to creative solutions even if they're not in the top 3.
Project 2 is done. Go Pacman!


Figure 1: The Actions menu
By selecting items in the Actions menu, the user can perform
any of the three stages involved in the analysis:
| Extrapolate | Extrapolates the scattering curve to q = 0 and q = infinity |
| Transform | Calculates the appropriate transform of the extrapolated data |
| Extract Parameters | Interprets the 1-D correlation function; parameters describing the volume fraction profile are extracted automatically |
These processes, and selection of the type of analysis to be performed, use input parameters which can be edited by selecting
the Parameters item from the Edit
menu.


Christmas delivery nightmare: More than half of online shoppers suffered delivery issues last year
by Michael SearlesMore than half of people suffered a delivery disaster during last year’s festive period after ordering online, according to new research from Which.
Of the 2,071 people survey, 54 per cent said they had encountered problems with the delivery of their packages.
The issues ranged from being left outside in the rain to not turning up at all.
Read more: Festive cheer for UK high street as shoppers plan to spend more
One in five people said at least one of their deliveries had failed to arrive on time, while a quarter revealed they had ordered a parcel that never turned up.
One respondent was unable to leave university for the Christmas break on time due to waiting for a package, while another had a delivery left with an unauthorised neighbour who was then away for two weeks.
Parcels were frequently left in unauthorised places too.
One person told Which the “courier put the parcel through the window to the downstairs bathroom and it literally ended up in the toilet”, while another said their parcels were constantly left at the back of the house where they got “wet and damaged”.
Around nine per cent of respondents said they had been left dishonest notes from couriers claiming they weren’t in, when they were.
A further 29 per cent of respondents said deliveries arrived earlier than expected, which in some cases meant the recipient wasn’t home.
Despite consumer’s rights meaning retailers have a responsibility to ensure online orders are delivered to customers – and are liable if it doesn’t – just 31 per cent complained about the delivery to their retailer.
Read more: Definitive guide to the best Christmas sandwiches of 2019
Adam French, Which consumer rights expert, said: “Under the Consumer Rights Act, online retailers have a duty to ensure online orders are delivered within a reasonable time and in good condition, so if your delivery hasn’t gone to plan, don’t suffer in silence and speak to your retailer to resolve the issue.
“But be aware if you give permission for your delivery to be left in a specified safe place or received by a nominated neighbour and something goes wrong, you will still be considered to have received the delivery. Think very carefully about those options when you’re making a purchase”


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15-Aug-18 08:08 PM
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[dscrdeql] <NWhiskeyTango> If someone already stated that, im sorry...just busy and behind on a lot of stuff
[dscrdeql] <ghost> good night
[dscrdeql] <NWhiskeyTango> Night ghost
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[dscrdeql] <NWhiskeyTango> Gm
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The Florida Gators
The Utah Utes. They are my home town team, they havent allways been the style pinical they are today. But in recent years they have come out as some of the coldest players in the game right now. The red on red goes nice with the white and black compliments. The cold weather allows for some icy handwarmder and loose fitted long sleeves. A DB's dream to play for Jay Hill. They are sponsered by Under Aurmor and get all the latest in gear.  The stadium is nice, but compared to some of the bigger schools it is a little undersized. Good coaching and playing makes up for it. And the sauce left on the feild is second to none.


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{% extends "kali.html" %} {% block title %}Carthage Login{% endblock %} {% block extra_style %} {{block.super}} {% endblock %} {% block content_head %}
Please use your Carthage username to log in. Do not include @carthage.edu as part of your username.
{% if form.errors or errors %}
Please enter a correct username and password.
Both fields are case-sensitive.
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as part of your username.
NOTE: Your password expires every 6 months,
so if you have not used your Carthage account in over
6 months, you will have to
change your password
.
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{% endif %}Do you want to log out and log in as a new user?
{% endif %} {% endblock %} {% block extra_content %} {% if user.is_anonymous %}If your password does not work, you can reset it at password.carthage.edu .
If you do not have a Carthage user account, please contact the Help Desk at<PHONE_NUMBER>. If you are a new entering student you may also contact the Admission Office at<PHONE_NUMBER>.
{% endif %} {% endblock %}


{% extends "main.html" %} {% block content %}
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com.innowhere.jnieasy.core.mem
Interfaces
DLLManager
DynamicLibrary
Fetch
JNIEasyLibrary
LockedRegistry
NativeAddress
NativeBuffer
NativeBufferIterator
NativeManager
NativeStateManager
UnFetch


Pen City [aa0d], port city, in Penhome [ar46], in Oceania
Last updated Turn #143
Pen City [aa0d], port city, in Penhome [ar46], in Oceania
------------------------------------------------------------------------
1: Apollo [b5e], Imperial Empire, with 11 workers, six sailors, 63 lt.
1: xbowmen, exited Pleasure Sloop [v5n], accompanied by:
1: Lutra [a4w], Imperial Empire, with 12 paladins, 24 pikemen
1: Lungor [y4r], Imperial Empire
1: Hera [g3f], Imperial Empire, with five soldiers, 34 pikemen
1: Thor [r4g], Imperial Empire, with 38 pikemen
1: Eos [a2t], Imperial Empire, with ten workers, 48 lt. xbowmen
1: Khaki Blazer [w4j], Imperial Empire, with eight soldiers,
1: six pikemen, 31 lt. foot
1: Banian [e7n], Imperial Empire, duke, the High Priest of the Church
1: of Eres [151], with two soldiers, ten angels
1: Winstin [o7y], Mandor Confederacy, prisoner
1: Black Blazer [s8u], Mandor Confederacy, prisoner
1: Pink Blazer [n8e], Mandor Confederacy, prisoner
1: Baltor [v5s], Mandor Confederacy, prisoner
1: Merbit [e2m], Mandor Confederacy, exited Pen City [aa0d].
1: Apollo [b5e] attacks Merbit [e2m]!
1:
1: Apollo [b5e], Imperial Empire, behind 3, with 11 workers,
1: six sailors, 63 lt. xbowmen, accompanied by:
1: Lutra [a4w], Imperial Empire, with 12 paladins, 24 pikemen
1: Lungor [y4r], Imperial Empire, behind 9
1: Hera [g3f], Imperial Empire, with five soldiers, 34 pikemen
1: Thor [r4g], Imperial Empire, with 38 pikemen
1: Eos [a2t], Imperial Empire, behind 9, with ten workers, 48 lt.
1: xbowmen
1: Khaki Blazer [w4j], Imperial Empire, with eight soldiers,
1: six pikemen, 31 lt. foot
1: Banian [e7n], Imperial Empire, duke, the High Priest of the
1: Church of Eres [151], with two soldiers, ten angels
1:
1: Merbit [e2m], Mandor Confederacy, behind 5
1:
1: Initial missile round for the defense (Merbit [e2m]):
1: Zero missile hits!
1:
1: Combat round: one
1: Missile phase:
1: Apollo's forces hit with 22 missiles.
1: Merbit [e2m] suffers a hit!
1:
1: Apollo [b5e] is victorious!
1:
1: Merbit [e2m] was taken prisoner.
1:
1: Dorothy [v1b] bought one old book [fy8m] from Pen City [aa0d] for
1: 975 gold.
1: Dorothy [v1b] bought one pitch [261] from Pen City [aa0d] for
1: 47 gold.
2: Dorothy [v1b], Pen, with one worker, 94 sailors, entered Pleasure
2: Sloop [v5n].
2: Trade Wind [w7c] has sunk!
2: Trade Wind [w7c] has sunk!
2: Pen City [aa0d] bought 50 fish [87] from Apollo [b5e] for 200 gold.
4: Apollo [b5e], Imperial Empire, with 11 workers, six sailors, 63 lt.
4: xbowmen, exited Pen City [aa0d], accompanied by:
4: Lutra [a4w], Imperial Empire, with 12 paladins, 24 pikemen
4: Lungor [y4r], Imperial Empire
4: Hera [g3f], Imperial Empire, with five soldiers, 34 pikemen
4: Thor [r4g], Imperial Empire, with 38 pikemen
4: Eos [a2t], Imperial Empire, with ten workers, 48 lt. xbowmen
4: Khaki Blazer [w4j], Imperial Empire, with eight soldiers,
4: six pikemen, 31 lt. foot
4: Banian [e7n], Imperial Empire, duke, the High Priest of the Church
4: of Eres [151], with two soldiers, ten angels
4: Winstin [o7y], Mandor Confederacy, prisoner
4: Black Blazer [s8u], Mandor Confederacy, prisoner
4: Pink Blazer [n8e], Mandor Confederacy, prisoner
4: Baltor [v5s], Mandor Confederacy, prisoner
4: Merbit [e2m], Mandor Confederacy, prisoner
6: Apollo [b5e], Imperial Empire, with 11 workers, six sailors, 63 lt.
6: xbowmen, arrived from Penhome [ar46], accompanied by:
6: Lutra [a4w], Imperial Empire, with 12 paladins, 24 pikemen
6: Lungor [y4r], Imperial Empire
6: Hera [g3f], Imperial Empire, with five soldiers, 34 pikemen
6: Thor [r4g], Imperial Empire, with 38 pikemen
6: Eos [a2t], Imperial Empire, with ten workers, 48 lt. xbowmen
6: Khaki Blazer [w4j], Imperial Empire, with eight soldiers,
6: six pikemen, 31 lt. foot
6: Banian [e7n], Imperial Empire, duke, the High Priest of the Church
6: of Eres [151], with two soldiers, ten angels
6: Winstin [o7y], Mandor Confederacy, prisoner
6: Black Blazer [s8u], Mandor Confederacy, prisoner
6: Pink Blazer [n8e], Mandor Confederacy, prisoner
6: Baltor [v5s], Mandor Confederacy, prisoner
6: Merbit [e2m], Mandor Confederacy, prisoner
8: Apollo [b5e], Imperial Empire, with 11 workers, six sailors, 63 lt.
8: xbowmen, entered Pleasure Sloop [v5n], accompanied by:
8: Lutra [a4w], Imperial Empire, with 12 paladins, 24 pikemen
8: Lungor [y4r], Imperial Empire
8: Hera [g3f], Imperial Empire, with five soldiers, 34 pikemen
8: Thor [r4g], Imperial Empire, with 38 pikemen
8: Eos [a2t], Imperial Empire, with ten workers, 48 lt. xbowmen
8: Khaki Blazer [w4j], Imperial Empire, with eight soldiers,
8: six pikemen, 31 lt. foot
8: Banian [e7n], Imperial Empire, duke, the High Priest of the Church
8: of Eres [151], with two soldiers, ten angels
8: Winstin [o7y], Mandor Confederacy, prisoner
8: Black Blazer [s8u], Mandor Confederacy, prisoner
8: Pink Blazer [n8e], Mandor Confederacy, prisoner
8: Baltor [v5s], Mandor Confederacy, prisoner
8: Merbit [e2m], Mandor Confederacy, prisoner
9: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails,
9: departed for Ocean [ar45].
Routes leaving Pen City:
North, to Ocean [aq46], The Inner Sea, 1 day
East, to Ocean [ar47], The Inner Sea, 1 day
South, to Ocean [as46], The Inner Sea, 1 day
West, to Ocean [ar45], The Inner Sea, 1 day
Out, forest, to Penhome [ar46], 1 day
Skills taught here:
Trading [130], Shipcraft [120], Forestry [128]
Market report:
trade who price qty wt/ea item
----- --- ----- --- ----- ----
buy aa0d 113 12 1 nazgul tail [319]
buy aa0d 9 9 5 clay pots [95]
buy aa0d 5 6 1 savage ears [318]
buy aa0d 5 11 10 centaur hides [320]
buy aa0d 4 100 2 fish [87]
sell aa0d 5 25 10 fish oil [97]
sell aa0d 12 25 30 wood [77]
sell aa0d 47 10 100 pitch [261]
sell aa0d 500 1 5 Manual of Forestry [ii0x]


{% comment %} Client scores. Building an array of the global score pour each client family. {% endcomment %} {% assign client-scores-families = "" | split: '' %} {% assign client-scores-y = "" | split: '' %} {% assign client-scores-a = "" | split: '' %} {% assign client-scores-n = "" | split: '' %} {% assign client-scores-u = "" | split: '' %} {% comment %} 1. We go through each existing feature. {% endcomment %} {% for feature in site.features %} {% assign feature-stats = feature.stats %} {% comment %} 2. We go through each family of the current feature. {% endcomment %} {% for family in feature.stats %} {% assign family-key = family | first %} {% assign family-values = feature.stats[family-key] %} {% assign client-index = 0 %} {% comment %} 3. We go through each platform of the current family and keep track of the support value for the most recent version. {% endcomment %} {% for platform in family-values %} {% assign platform-key = platform | first %} {% assign platform-values = platform | last %} {% comment %} 4. We add the family:platform to the `client-scores-families` array if it doesn't exist yet. {% endcomment %} {% assign family-platform-key = '' | split: '' %} {% assign family-platform-key = family-platform-key | push: family-key %} {% assign family-platform-key = family-platform-key | push: platform-key %} {% assign family-platform-key = family-platform-key | join: ':' %} {% unless client-scores-families contains family-platform-key %} {% assign client-scores-families = client-scores-families | push: family-platform-key %} {% assign client-scores-y = client-scores-y | push: 0 %} {% assign client-scores-a = client-scores-a | push: 0 %} {% assign client-scores-n = client-scores-n | push: 0 %} {% assign client-scores-u = client-scores-u | push: 0 %} {% assign client-index = client-scores-families.size | minus: 1 %} {% else %} {% comment %} If the family already exists, we get its index in the `client-scores-families` array. {% endcomment %} {% for client-scores-family in client-scores-families %} {% if client-scores-family == family-platform-key %} {% assign client-index = forloop.index | minus: 1 %} {% endif %} {% endfor %} {% endunless %} {% comment %} 5. We pick the latest version for the current platform. And we add a point to its corresponding value in `client-scores-x-value` value. {% endcomment %} {% for version in platform-values %} {% if forloop.last == true %} {% assign version-key = version | first %} {% assign version-values = version | last | split: ' ' | first %} {% case version-values %} {% when 'y' %} {% comment %} Because liquid doesn't allow to change an array's value (like `a[b]=c`), we need to create a new temporary array and reassign all values. Yikes. {% endcomment %} {% assign new-client-scores-y = '' | split:'' %} {% for client-scores-y-value in client-scores-y %} {% assign i = forloop.index | minus:1 %} {% assign new-value = client-scores-y[i] %} {% if i == client-index %} {% assign new-value = client-scores-y[client-index] | plus:1 %} {% endif %} {% assign new-client-scores-y = new-client-scores-y | push:new-value %} {% endfor %} {% assign client-scores-y = new-client-scores-y %} {% when 'a' %} {% assign new-client-scores-a = '' | split:'' %} {% for client-scores-a-value in client-scores-a %} {% assign i = forloop.index | minus:1 %} {% assign new-value = client-scores-a[i] %} {% if i == client-index %} {% assign new-value = client-scores-a[client-index] | plus:1 %} {% endif %} {% assign new-client-scores-a = new-client-scores-a | push:new-value %} {% endfor %} {% assign client-scores-a = new-client-scores-a %} {% when 'n' %} {% assign new-client-scores-n = '' | split:'' %} {% for client-scores-n-value in client-scores-n %} {% assign i = forloop.index | minus:1 %} {% assign new-value = client-scores-n[i] %} {% if i == client-index %} {% assign new-value = client-scores-n[client-index] | plus:1 %} {% endif %} {% assign new-client-scores-n = new-client-scores-n | push:new-value %} {% endfor %} {% assign client-scores-n = new-client-scores-n %} {% when 'u' %} {% assign new-client-scores-u = '' | split:'' %} {% for client-scores-u-value in client-scores-u %} {% assign i = forloop.index | minus:1 %} {% assign new-value = client-scores-u[i] %} {% if i == client-index %} {% assign new-value = client-scores-u[client-index] | plus:1 %} {% endif %} {% assign new-client-scores-u = new-client-scores-u | push:new-value %} {% endfor %} {% assign client-scores-u = new-client-scores-u %} {% endcase %} {% endif %} {% endfor %} {% endfor %} {% endfor %} {% endfor %} {% comment %} 6. We sort all our scores based on the ones with the most `y` values. {% endcomment %} {% assign client-scores-sorted = "" | split: '' %} {% for client in client-scores-families %} {% assign i = forloop.index | minus:1 %} {% assign client-scores-sorted-y = client-scores-y[i] | prepend: '0000' | slice: -4, 4 %} {% assign client-scores-sorted-a = client-scores-a[i] | prepend: '0000' | slice: -4, 4 %} {% assign client-scores-sorted-n = client-scores-n[i] | prepend: '0000' | slice: -4, 4 %} {% assign client-scores-sorted-u = client-scores-u[i] | prepend: '0000' | slice: -4, 4 %} {% assign client-sorted-string = client-scores-sorted-y | append:'|' | append:client-scores-sorted-a | append:'|' | append:client-scores-sorted-n | append:'|' | append:client-scores-sorted-u | append:'|' | append:client-scores-families[i] %} {% assign client-scores-sorted = client-scores-sorted | push: client-sorted-string %} {% endfor %} {% assign client-scores-sorted = client-scores-sorted | sort | reverse %} {% assign client-scores-families = "" | split: '' %} {% assign client-scores-y = "" | split: '' %} {% assign client-scores-a = "" | split: '' %} {% assign client-scores-n = "" | split: '' %} {% assign client-scores-u = "" | split: '' %} {% for client in client-scores-sorted %} {% assign client-sorted-array = client | split: '|' %} {% assign client-scores-y = client-scores-y | push: client-sorted-array[0] %} {% assign client-scores-a = client-scores-a | push: client-sorted-array[1] %} {% assign client-scores-n = client-scores-n | push: client-sorted-array[2] %} {% assign client-scores-u = client-scores-u | push: client-sorted-array[3] %} {% assign client-scores-families = client-scores-families | push: client-sorted-array[4] %} {% endfor %} {% comment %} 7. We display the data. {% endcomment %}


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Card name of the generated card. (Optional: max 30 characters. English/ASCII characters only)
Card type of the generated card. (Optional)
Card subtype of the generated card, e.g. Creature type(s) for Creatures, Aura for Enchantments, Equipment for Artifacts. (Optional: max 30 characters. English/ASCII characters only)
Mana cost of the generated card. (Optional: use the traditional format, e.g. 1GW or 3U. Put hybrid mana symbols in brackets, e.g. 1{GW}{GW} or 3{2U})
(This app is currently disabled due to a backend issue. Sorry! :( )
Tutorial Example {1}{U}{U} (uncommon)SorceryDraw cards equal to the number of cards target opponent discarded this turn.


In Brief -- by Zachary McGhee
RULES OF ENGAGEMENT [Friedkin] -- Boy, did they screw up reel one! Really a mess from minute one to forty-five, an introduction as thought provoking as colored toilet paper, the movie takes a sudden turn for interesting once the courtroom battle begins, Tommy Lee Jones and Samuel L. Jackson kick into high gear, and those pathetic Saving Private Ryan-wannabe battle scenes cease fire. It certainly feels like something downloaded from the creative mind of one Tom Clancy, with A Few Good Men tossed in for good measure. C+
Sent via Deja.com http://www.deja.com/ Before you buy.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
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June 2020 - Present
Datagrokr Analytics
Software Developer
Dec 2019 - June 2020
Software Developer Intern
Feb 2018 - Nov 2019
Ergode IT Services Pvt Ltd.


I think we all try to live up to these values in some shape or form. I would answer 3 for most of the values. One value, Quality, is important for the type of work you want to do and represent yourself. Knowledge is one topic that someone recently asked for advice on. Other exercise to help with feeling down is physical exercise.


IsoGene: Order-Restricted Inference for Microarray Experiments
Offers framework for testing for monotonic relationship between gene expression and doses in a microarray experiment. Several testing procedures including the global likelihood-ratio test (Bartholomew, 1961), Williams (1971, 1972), Marcus (1976), M (Hu et al. 2005) and the modified M (Lin et al. 2007) are used to test for the monotonic trend in gene expression with respect to doses. BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2004) FDR controlling procedures are applied to adjust the raw p-values obtained from the permutations.
| Version: |
1.0-24 |
| Depends: |
R (≥ 2.10), Iso |
| Imports: |
xtable, Biobase, affy, ff (≥ 2.0.0), tcltk |
| Published: |
2015-07-31 |
| Author: |
Dan Lin, Setia Pramana, Tobias Verbeke and Ziv Shkedy |
| Maintainer: |
Rudradev Sengupta <rudradev.sengupta at uhasselt.be> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
IsoGene results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=IsoGene
to link to this page.


php
programming
phpstorm
debug
xdebug
zend debugger
php
My first article title
Debugging with PHPStorm and XDebug
My second article title
My third article title


Invalid flags are not handled. Add a *)
case.
#!/bin/sh
while getopts "vr" f
do
case "$f" in
v) echo "verbose" ;;
r) echo "recursive" ;;
esac
done
#!/bin/sh
while getopts "vr" f
do
case "$f" in
v) echo "verbose" ;;
r) echo "recursive" ;;
*) echo "usage: $0 [-v] [-r]" >&2
exit 1 ;;
esac
done
The case
statement handling getopts
arguments does not have a default branch to handle unknown flags.
When a flag is not recognized, such as if passing -Z
to the example code, getopts
will set the variable to a literal question mark ?
. This should be handled along with all the valid flags, usually by printing a usage message and exiting with failure.
Using a \?)
or ?)
case will also match invalid flags, but*)
would additionally match things like the empty string if the variable name was misspelled.
If your script's logic handles unrecognized flags in another way, e.g. after the case
statement, you can ignore this warning.


4.30.2016
Check out this sunburst plot I made in D3 which displays the relative freqeuncies of play sequences which ended in a scoring play during the 2011/2012 NBA Season. View the source code for this visualization on bl.ocks.org here.
03.25.2016
A D3 scatterplot showing the average offensive and average defensive pace of play for every team in the league (NBA 2015/2016 season). Is anyone surprised that the Warriors lead the league in offensive pace? Didn’t think so.
03.11.2016
A regression model which predicts whether an NFL play will be a passing play or a rushing play based on time, down, distance to first down, offensive and defensive score, and the actual teams that are playing offense and defense. Use three letter abbreviations for NFL teams such as ‘ATL’ for Atlanta or ‘NE’ for New England. The model was trained using play by play data from 2002 to 2012 (10 seasons). If you're interested in the machine learning you can read more in a paper written by my colleagues and I here.
[edit] The server running the prediction model has been suspended but please feel free to contact me if you're interested in trying it.


Vendor Name:
GILLES GAIGNERY
Reference Number:
4500217233
Contract Date:
2009-23-09
Description of Work:
Training consultants
Contract Period:
2009-23-09 to 2010-08-01
Delivery Date:
2010-08-01
Original Contract Value:
$0.00
Current Contract Value:
$18,900.00


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Hooks a function as part of a listener. Functions annotated with this will be called during event firing.
annotation class SubscribeEvent


1927
6 the first coca cola radio program, "vivian the coca-cola girl," debuts.
1927
6 the first coca cola radio program, "vivian the coca-cola girl," debuts.
1927
6 the first coca cola radio program, "vivian the coca-cola girl," debuts.
1927
6 the first coca cola radio program, "vivian the coca-cola girl," debuts.
1927
6 the first coca cola radio program, "vivian the coca-cola girl," debuts.
1927
6 the first coca cola radio program, "vivian the coca-cola girl," debuts.


M3GNet¶
M3GNet is a new materials graph neural network architecture that incorporates 3-body interactions. A key difference with prior materials graph implementations such as MEGNet is the addition of the coordinates for atoms and the 3×3 lattice matrix in crystals, which are necessary for obtaining tensorial quantities such as forces and stresses via auto-differentiation.
As a framework, M3GNet has diverse applications, including:
Interatomic potential development. With the same training data, M3GNet performs similarly to state-of-the-art machine learning interatomic potentials (ML-IAPs). However, a key feature of a graph representation is its flexibility to scale to diverse chemical spaces. One of the key accomplishments of M3GNet is the development of a universal IAP that can work across the entire periodic table of the elements by training on relaxations performed in the Materials Project.
Surrogate models for property predictions. Like the previous MEGNet architecture, M3GNet can be used to develop surrogate models for property predictions, achieving in many cases accuracies that better or similar to other state-of-the-art ML models.
For detailed performance benchmarks, please refer to the publication in the References section. The API documentation is available via the Github Page.
Table of Contents¶
System requirements
Installation
Change Log
Usage
Model training
Matterverse
API docs
Datasets
References
System requirements¶
Inferences using the pre-trained models can be ran on any standard computer. For model training, the GPU memory needs to be > 18 Gb for a batch size of 32 using the crystal training data. In our work, we used a single RTX 3090 GPU for model training.
Installation¶
M3GNet can be installed via pip:
pip install m3gnet
You can also directly download the source from Github and install from source.
Apple Silicon Installation¶
Apple Silicon (M1, M1 Pro, M1 Max, M1 Ultra) has extremely powerful ML capabilities, but special steps are needed for the installation of tensorflow and other dependencies. Here are the recommended installation steps.
Ensure that you already have XCode and CLI installed.
Install Miniconda or Anaconda.
Create a Python 3.9 environment.
conda create --name m3gnet python=3.9 conda activate m3gnet
First install tensorflow and its dependencies for Apple Silicon.
conda install -c apple tensorflow-deps pip install tensorflow-macos
If you wish, you can install
tensorflow-metal
, which helps speed up training. If you encounter strange tensorflow errors, you should uninstalltensorflow-metal
and see if it fixes the errors first.pip install tensorflow-metal
Install m3gnet but ignore dependencies (otherwise, pip will look for tensorflow).
pip install --no-deps m3gnet
Install other dependencies like pymatgen, etc. manually.
pip install protobuf==3.20.0 pymatgen ase cython
Once you are done, you can try running
pytest m3gnet
to see if all tests pass.
Change Log¶
See change log
Usage¶
Structure relaxation¶
A M3Gnet universal potential for the periodic table has been developed using data from Materials Project relaxations since 2012. This universal potential can be used to perform structural relaxation of any arbitrary crystal as follows.
import warnings
from m3gnet.models import Relaxer
from pymatgen.core import Lattice, Structure
for category in (UserWarning, DeprecationWarning):
warnings.filterwarnings("ignore", category=category, module="tensorflow")
# Init a Mo structure with stretched lattice (DFT lattice constant ~ 3.168)
mo = Structure(Lattice.cubic(3.3), ["Mo", "Mo"], [[0., 0., 0.], [0.5, 0.5, 0.5]])
relaxer = Relaxer() # This loads the default pre-trained model
relax_results = relaxer.relax(mo, verbose=True)
final_structure = relax_results['final_structure']
final_energy_per_atom = float(relax_results['trajectory'].energies[-1] / len(mo))
print(f"Relaxed lattice parameter is {final_structure.lattice.abc[0]:.3f} Å")
print(f"Final energy is {final_energy_per_atom:.3f} eV/atom")
The output is as follows:
Relaxed lattice parameter is 3.169 Å
Final energy is -10.859 eV/atom
The initial lattice parameter of 3.3 Å was successfully relaxed to 3.169 Å, close to the DFT value of 3.168 Å. The final energy -10.859 eV/atom is also close to Materials Project DFT value of -10.8456 eV/atom.
The relaxation takes less than 20 seconds on a single laptop.
The table below provides more comprehensive benchmarks for cubic crystals based on exp data on Wikipedia and MP DFT data. The Jupyter notebook is in the examples folder. This benchmark is limited to cubic crystals for ease of comparison since there is only one lattice parameter. Of course, M3GNet is not limited to cubic systems (see LiFePO4 example).
Material |
Crystal structure |
a (Å) |
MP a (Å) |
M3GNet a (Å) |
% error vs Expt |
% error vs MP |
|---|---|---|---|---|---|---|
Ac |
FCC |
5.31 |
5.66226 |
5.6646 |
6.68% |
0.04% |
Ag |
FCC |
4.079 |
4.16055 |
4.16702 |
2.16% |
0.16% |
Al |
FCC |
4.046 |
4.03893 |
4.04108 |
-0.12% |
0.05% |
AlAs |
Zinc blende (FCC) |
5.6605 |
5.73376 |
5.73027 |
1.23% |
-0.06% |
AlP |
Zinc blende (FCC) |
5.451 |
5.50711 |
5.50346 |
0.96% |
-0.07% |
AlSb |
Zinc blende (FCC) |
6.1355 |
6.23376 |
6.22817 |
1.51% |
-0.09% |
Ar |
FCC |
5.26 |
5.64077 |
5.62745 |
6.99% |
-0.24% |
Au |
FCC |
4.065 |
4.17129 |
4.17431 |
2.69% |
0.07% |
BN |
Zinc blende (FCC) |
3.615 |
3.626 |
3.62485 |
0.27% |
-0.03% |
BP |
Zinc blende (FCC) |
4.538 |
4.54682 |
4.54711 |
0.20% |
0.01% |
Ba |
BCC |
5.02 |
5.0303 |
5.03454 |
0.29% |
0.08% |
C (diamond) |
Diamond (FCC) |
3.567 |
3.57371 |
3.5718 |
0.13% |
-0.05% |
Ca |
FCC |
5.58 |
5.50737 |
5.52597 |
-0.97% |
0.34% |
CaVO3 |
Cubic perovskite |
3.767 |
3.83041 |
3.83451 |
1.79% |
0.11% |
CdS |
Zinc blende (FCC) |
5.832 |
5.94083 |
5.9419 |
1.88% |
0.02% |
CdSe |
Zinc blende (FCC) |
6.05 |
6.21283 |
6.20987 |
2.64% |
-0.05% |
CdTe |
Zinc blende (FCC) |
6.482 |
6.62905 |
6.62619 |
2.22% |
-0.04% |
Ce |
FCC |
5.16 |
4.72044 |
4.71921 |
-8.54% |
-0.03% |
Cr |
BCC |
2.88 |
2.87403 |
2.84993 |
-1.04% |
-0.84% |
CrN |
Halite |
4.149 |
- |
4.16068 |
0.28% |
- |
Cs |
BCC |
6.05 |
6.11004 |
5.27123 |
-12.87% |
-13.73% |
CsCl |
Caesium chloride |
4.123 |
4.20906 |
4.20308 |
1.94% |
-0.14% |
CsF |
Halite |
6.02 |
6.11801 |
6.1265 |
1.77% |
0.14% |
CsI |
Caesium chloride |
4.567 |
4.66521 |
4.90767 |
7.46% |
5.20% |
Cu |
FCC |
3.597 |
3.62126 |
3.61199 |
0.42% |
-0.26% |
Eu |
BCC |
4.61 |
4.63903 |
4.34783 |
-5.69% |
-6.28% |
EuTiO3 |
Cubic perovskite |
7.81 |
3.96119 |
3.92943 |
-49.69% |
-0.80% |
Fe |
BCC |
2.856 |
2.84005 |
2.85237 |
-0.13% |
0.43% |
GaAs |
Zinc blende (FCC) |
5.653 |
5.75018 |
5.75055 |
1.73% |
0.01% |
GaP |
Zinc blende (FCC) |
5.4505 |
5.5063 |
5.5054 |
1.01% |
-0.02% |
GaSb |
Zinc blende (FCC) |
6.0959 |
6.21906 |
6.21939 |
2.03% |
0.01% |
Ge |
Diamond (FCC) |
5.658 |
5.76286 |
5.7698 |
1.98% |
0.12% |
HfC0.99 |
Halite |
4.64 |
4.65131 |
4.65023 |
0.22% |
-0.02% |
HfN |
Halite |
4.392 |
4.53774 |
4.53838 |
3.33% |
0.01% |
InAs |
Zinc blende (FCC) |
6.0583 |
6.18148 |
6.25374 |
3.23% |
1.17% |
InP |
Zinc blende (FCC) |
5.869 |
5.95673 |
5.9679 |
1.69% |
0.19% |
InSb |
Zinc blende (FCC) |
6.479 |
6.63322 |
6.63863 |
2.46% |
0.08% |
Ir |
FCC |
3.84 |
3.87573 |
3.87716 |
0.97% |
0.04% |
K |
BCC |
5.23 |
5.26212 |
5.4993 |
5.15% |
4.51% |
KBr |
Halite |
6.6 |
6.70308 |
6.70797 |
1.64% |
0.07% |
KCl |
Halite |
6.29 |
6.38359 |
6.39634 |
1.69% |
0.20% |
KF |
Halite |
5.34 |
5.42398 |
5.41971 |
1.49% |
-0.08% |
KI |
Halite |
7.07 |
7.18534 |
7.18309 |
1.60% |
-0.03% |
KTaO3 |
Cubic perovskite |
3.9885 |
4.03084 |
4.03265 |
1.11% |
0.05% |
Kr |
FCC |
5.72 |
6.49646 |
6.25924 |
9.43% |
-3.65% |
Li |
BCC |
3.49 |
3.42682 |
3.41891 |
-2.04% |
-0.23% |
LiBr |
Halite |
5.5 |
5.51343 |
5.51076 |
0.20% |
-0.05% |
LiCl |
Halite |
5.14 |
5.15275 |
5.14745 |
0.15% |
-0.10% |
LiF |
Halite |
4.03 |
4.08343 |
4.08531 |
1.37% |
0.05% |
LiI |
Halite |
6.01 |
6.0257 |
6.02709 |
0.28% |
0.02% |
MgO |
Halite (FCC) |
4.212 |
4.25648 |
4.2567 |
1.06% |
0.01% |
Mo |
BCC |
3.142 |
3.16762 |
3.16937 |
0.87% |
0.06% |
Na |
BCC |
4.23 |
4.17262 |
4.19684 |
-0.78% |
0.58% |
NaBr |
Halite |
5.97 |
6.0276 |
6.01922 |
0.82% |
-0.14% |
NaCl |
Halite |
5.64 |
5.69169 |
5.69497 |
0.97% |
0.06% |
NaF |
Halite |
4.63 |
4.69625 |
4.69553 |
1.42% |
-0.02% |
NaI |
Halite |
6.47 |
6.532 |
6.52739 |
0.89% |
-0.07% |
Nb |
BCC |
3.3008 |
3.32052 |
3.32221 |
0.65% |
0.05% |
NbN |
Halite |
4.392 |
4.45247 |
4.45474 |
1.43% |
0.05% |
Ne |
FCC |
4.43 |
4.30383 |
6.95744 |
57.05% |
61.66% |
Ni |
FCC |
3.499 |
3.5058 |
3.5086 |
0.27% |
0.08% |
Pb |
FCC |
4.92 |
5.05053 |
5.02849 |
2.21% |
-0.44% |
PbS |
Halite (FCC) |
5.9362 |
6.00645 |
6.01752 |
1.37% |
0.18% |
PbTe |
Halite (FCC) |
6.462 |
6.56567 |
6.56111 |
1.53% |
-0.07% |
Pd |
FCC |
3.859 |
3.95707 |
3.95466 |
2.48% |
-0.06% |
Pt |
FCC |
3.912 |
3.97677 |
3.97714 |
1.67% |
0.01% |
Rb |
BCC |
5.59 |
5.64416 |
5.63235 |
0.76% |
-0.21% |
RbBr |
Halite |
6.89 |
7.02793 |
6.98219 |
1.34% |
-0.65% |
RbCl |
Halite |
6.59 |
6.69873 |
6.67994 |
1.36% |
-0.28% |
RbF |
Halite |
5.65 |
5.73892 |
5.76843 |
2.10% |
0.51% |
RbI |
Halite |
7.35 |
7.48785 |
7.61756 |
3.64% |
1.73% |
Rh |
FCC |
3.8 |
3.8439 |
3.84935 |
1.30% |
0.14% |
ScN |
Halite |
4.52 |
4.51831 |
4.51797 |
-0.04% |
-0.01% |
Si |
Diamond (FCC) |
5.43102 |
5.46873 |
5.45002 |
0.35% |
-0.34% |
Sr |
FCC |
6.08 |
6.02253 |
6.04449 |
-0.58% |
0.36% |
SrTiO3 |
Cubic perovskite |
3.98805 |
3.94513 |
3.94481 |
-1.08% |
-0.01% |
SrVO3 |
Cubic perovskite |
3.838 |
3.90089 |
3.90604 |
1.77% |
0.13% |
Ta |
BCC |
3.3058 |
3.32229 |
3.31741 |
0.35% |
-0.15% |
TaC0.99 |
Halite |
4.456 |
4.48208 |
4.48225 |
0.59% |
0.00% |
Th |
FCC |
5.08 |
5.04122 |
5.04483 |
-0.69% |
0.07% |
TiC |
Halite |
4.328 |
4.33565 |
4.33493 |
0.16% |
-0.02% |
TiN |
Halite |
4.249 |
4.25353 |
4.25254 |
0.08% |
-0.02% |
V |
BCC |
3.0399 |
2.99254 |
2.99346 |
-1.53% |
0.03% |
VC0.97 |
Halite |
4.166 |
4.16195 |
4.16476 |
-0.03% |
0.07% |
VN |
Halite |
4.136 |
4.12493 |
4.1281 |
-0.19% |
0.08% |
W |
BCC |
3.155 |
3.18741 |
3.18826 |
1.05% |
0.03% |
Xe |
FCC |
6.2 |
6.66148 |
7.06991 |
14.03% |
6.13% |
Yb |
FCC |
5.49 |
5.44925 |
5.45807 |
-0.58% |
0.16% |
ZnO |
Halite (FCC) |
4.58 |
4.33888 |
4.33424 |
-5.37% |
-0.11% |
ZnS |
Zinc blende (FCC) |
5.42 |
5.45027 |
5.45297 |
0.61% |
0.05% |
ZrC0.97 |
Halite |
4.698 |
4.72434 |
4.72451 |
0.56% |
0.00% |
ZrN |
Halite |
4.577 |
4.61762 |
4.61602 |
0.85% |
-0.03% |
From the table, it can be observed that almost all M3GNet-relaxed cubic lattice constants are within 1% of the DFT values. The only major errors are with EuTiO3, iodides (RbI and CsI) and the noble gases. It is quite likely the Wikipedia value for EuTiO3 is wrong by a factor of 2 and the lower than expected accuracy on iodides and noble gases may be due to the paucity of data in these chemical systems. It should be noted that M3GNet is expected to reproduce the MP DFT value and not the experimental values, which are only provided as an additional point of reference.
All relaxations take less than 1s on a M1 Max Mac.
CLI tool¶
A simple CLI tool has been written. Right now, it supports just doing structure relaxations with M3GNet, which is immediately useful for quick testing of the capabilities of M3GNet itself. More features will be developed in future if there is user interest. Examples below.
m3g relax --infile Li2O.cif # Outputs to stdout the relaxed structure.
m3g relax --infile Li2O.cif --outfile Li2O_relaxed.cif # Outputs to a file the relaxed structure.
Molecular dynamics¶
Similarly, the universal IAP can be used to perform molecular dynamics (MD) simulations as well.
from pymatgen.core import Structure, Lattice
from m3gnet.models import MolecularDynamics
# Init a Mo structure with stretched lattice (DFT lattice constant ~ 3.168)
mo = Structure(Lattice.cubic(3.3),
["Mo", "Mo"], [[0., 0., 0.], [0.5, 0.5, 0.5]])
md = MolecularDynamics(
atoms=mo,
temperature=1000, # 1000 K
ensemble='nvt', # NVT ensemble
timestep=1, # 1fs,
trajectory="mo.traj", # save trajectory to mo.traj
logfile="mo.log", # log file for MD
loginterval=100, # interval for record the log
)
md.run(steps=1000)
After the run, mo.log
contains thermodynamic information similar to the following:
Time[ps] Etot[eV] Epot[eV] Ekin[eV] T[K]
0.0000 -21.3307 -21.3307 0.0000 0.0
0.1000 -21.3307 -21.3307 0.0000 0.0
0.2000 -21.2441 -21.3087 0.0645 249.7
0.3000 -21.0466 -21.2358 0.1891 731.6
0.4000 -20.9702 -21.1149 0.1447 559.6
0.5000 -20.9380 -21.1093 0.1713 662.6
0.6000 -20.9176 -21.1376 0.2200 850.9
0.7000 -20.9016 -21.1789 0.2773 1072.8
0.8000 -20.8804 -21.1638 0.2835 1096.4
0.9000 -20.8770 -21.0695 0.1925 744.5
1.0000 -20.8908 -21.0772 0.1864 721.2
The MD run takes less than 1 minute.
Model training¶
You can also train your own IAP using the PotentialTrainer
in m3gnet.trainers
. The training dataset can include:
structures, a list of pymatgen Structures
energies, a list of energy floats with unit eV.
forces, a list of nx3 force matrix with unit eV/Å, where n is the number of atom in each structure. n does not need to be the same for all structures.
stresses, a list of 3x3 stress matrices with unit GPa (optional)
For stresses, we use the convention that compressive stress gives negative values. Stresses obtained from VASP calculations (default unit is kBar) should be multiplied by -0.1 to work directly with the model.
We use validation dataset to select the stopping epoch number. The dataset has similar format as the training dataset.
A minimal example of model training is shown below.
from m3gnet.models import M3GNet, Potential
from m3gnet.trainers import PotentialTrainer
import tensorflow as tf
m3gnet = M3GNet(is_intensive=False)
potential = Potential(model=m3gnet)
trainer = PotentialTrainer(
potential=potential, optimizer=tf.keras.optimizers.Adam(1e-3)
)
trainer.train(
structures,
energies,
forces,
stresses,
validation_graphs_or_structures=val_structures,
val_energies=val_energies,
val_forces=val_forces,
val_stresses=val_stresses,
epochs=100,
fit_per_element_offset=True,
save_checkpoint=False,
)
Matterverse¶
As an example of the power of M3GNet for materials discovery, we have created a database of yet-to-be-synthesized materials called matterverse.ai. At the time of writing, matterverse.ai has 31 million structures, of which more than 1 million are predicted to be potentially stable. The initial candidate list was generated via combinatorial isovalent ionic substitutions based on the common oxidation states of non-noble-gas elements on 5,283 binary, ternary and quaternary structural prototypes in the 2019 version of the ICSD database.
API docs¶
The API docs are available here.
Datasets¶
The training data used to develop the universal M3GNet IAP is MPF.2021.2.8
and is hosted on
figshare with DOI 10.6084/m9.figshare.19470599
.
Reference¶
Please cite the following work:
Chi Chen, and Shyue Ping Ong. "A Universal Graph Deep Learning Interatomic Potential for the Periodic Table."
arXiv preprint [arXiv:2202.02450](https://arxiv.org/abs/2202.02450) (2022).
Acknowledgements¶
This work was primarily supported by the Materials Project, funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract no. DE-AC02-05-CH11231: Materials Project program KC23MP. This work used the Expanse supercomputing cluster at the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562.


The key to feeling good about yourself isn’t as simple as erasing your worries with a concussion, as Renee Bennett (Amy Schumer) does in I Feel Pretty. In my experience, having self-confidence is a never-ending internal struggle with my doubts. Even though I always push through—eventually landing my current job,1 for instance—there are always more concerns just around the corner. So, though the film’s message that beauty is in the eye of the beholder is geared towards women, I found it to be quite moving.
Before her accident, Renee was never happy with how she looked—exercise bikes couldn’t hold her weight and guys would pass over her to hit on other women. All of that changes after she hits her head during spin class. Suddenly, Renee looks in the mirror and is pleased with what she sees. With a newfound, almost cartoony enthusiasm rivaling that of SpongeBob SquarePants, not only does Renee manage to snag a boyfriend named Ethan (Rory Scovel), she also climbs the ranks of cosmetics firm Lily LeClaire. (She goes from doing website maintenance in a basement office to giving a presentation of an upcoming product line to top executives). Given how challenging suppressing insecurities can be, it’s gratifying to see Renee do so with such ease.
With a sharp contrast in performance before and after the accident, Amy Schumer makes Renee’s transformation believable. Her hushed murmur during in an early scene in which Renee asks for shoes in size double wide clearly conveys Renee’s insecurities, while the loud, brashly confident tone she adapts for later scenes demonstrates her newfound confidence. After the accident, Renee checks herself out in the mirror and resoundly proclaims how great her breasts and butt look with little regard for what Tasha (Sasheer Zamata), the cyclist instructor, will think of her.
If you’re worried that watching Schumer channel that much positive energy for almost two hours will get annoying, rest assured that it rarely does. Outside of a couple cringey scenes where Renee’s new personality gets on the nerves of her best friends Vivian (Aidy Bryant) and Jane (Busy Philipps), the film’s focus is, thankfully, kept on her positive attributes. It’s hard to be irritated at Renee when she encourages an introverted Ethan to be more outgoing or works to ensure that LeClaire’s new products are better suited for the common woman.
As much as I connect with I Feel Pretty, I wish the filmmakers trusted me to infer the film’s message through watching Renee bounce from one accomplishment to another. Instead, the ending shows her explaining the lesson while presenting LeClaire’s product line. Spelling it out makes me feel condescended to—episodes of children’s shows such as Sesame Street and Arthur conclude the same way.
Even with a heavy-handed moral, I Feel Pretty serves as a powerful reminder of what’s important. Amidst concerns such as finding the right suit for work or building muscle at the gym, it’s easy to forget that what truly matters isn’t how I look, but how I feel.
- See my review of Monsters University for greater detail↩


Part one of this series shows how to install Ubuntu on the BeagleBone Black. We used a MacBook Pro to burn an Ubuntu disk image onto an SD card, and then we booted a BeagleBone Black from the image we created.
This post, part two, covers the installation of the Ubuntu GUI, Ruby, and Rails. All of the commands in this article are run on the BeagleBone Black.
Node.js bonus: Rails requires a JavaScript runtime, and Node.js offered the quickest way to fulfill this requirement. Details appear within.
Gotcha: Partition Size
At the end of part one, we successfully booted the Bone with the Ubuntu command line interface. Now it’s time to add the GUI. Two things to know about the Ubuntu GUI installation…
- The partition we created on the SD card (in part one) is too small to hold the Ubuntu GUI. Solution: The SD card has some extra room, and we can re-size the partition.
- We can install the Ubuntu GUI with a single line command. But it takes an hour for the Bone to download, compile, and install all of the GUI libraries.
If we try to install the GUI with insufficient space, the installation will fail midway and we will have wasted half an hour. So it’s best to re-size first.
Doing Our Own Re-Sizing?
Why didn’t the partition have the right size when we originally downloaded the image file? Good question.
My guess: The image file was designed to be as small as possible in order to minimize download time. That makes sense. I plan to test this theory by creating a new image with a larger partition size and a pre-installed GUI. If the test is successful, results will be posted on this blog. If the mission fails, then the secretary will disavow any knowledge…
How to Re-Size the Partition
df
is the Unix ‘disk free’ command. It shows used/available disk space.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
|
ll
is one of the useful aliases built into Ubuntu’s .bashrc
file. We can use it to examine the partitions on the SD card.
1 2 3 4 5 6 7 8 9 10 11 12 |
|
fdisk
will resize the partition we’re after. root
priviledges are required to modify the partition table, so we’ll use sudo
. The standard sudo disclaimer applies.
A few useful fdisk
commands:
p
prints the partition table.d
deletes a partition.n
creates a new partition.w
write new information to the partition table.q
quits fdisk.
Here’s the plan: In order to increase the size of /dev/mmcblk0p2
, we will delete it, and then re-create it with a larger size. Note the use of the d
and n
commands in the following sequence. When fdisk
asks us for the new partition number, type, first sector, and last sector, we will choose the defaults. Choosing the defaults will use all of the available space on the SD card.
Your numbers will vary depending on the size and configuration of your SD card, but the general procedure will be the same.
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|
The warning/error message at the end just means that we need reboot the Bone before the partition table takes effect.
Reboot the BeagleBone Black, holding down the boot switch (closest to the USB port) to ensure that we boot to the system on the SD card instead of the eMMC.
After the Bone reboots, run resize2fs
to expand the partition created above.
1 2 3 4 5 6 7 8 |
|
And now we have enough room to install the Ubuntu GUI.
Installing the Ubuntu GUI
Update (Feb2014): In the comments below, Jonathan Chan describes how he updated the desktop installation files before installing the Ubuntu desktop. To update the desktop installation files, grab the latest list of packages (update
) and then upgrade
the packages that need it.
1 2 3 4 5 6 7 |
|
After the partition on the SD card has been resized, this command will intall the GUI. The command is short, but the process takes about an hour:
1
|
|
When installation and compilation are complete, Ubuntu will return to the command prompt. Here’s how to reboot:
1
|
|
Several minutes later, the Ubuntu 12 GUI will appear. Congratulations! Login using the default credentials (login: ubuntu, pw: ubuntu) to get to the Ubuntu desktop.
Gotcha: Running RVM
I prefer to use the Ruby Version Manager (RVM) for bouncing between Ruby versions. RVM misbehaved, initially. Then I learned something special about RVM’s interaction with Ubuntu. From the RVM support pages…
For RVM to work properly, you have to set the ‘Run command as login shell’ checkbox on the Title and Command tab inside of gnome-terminal’s Settings page.
This image shows the checkbox mentioned in the quote. It’s inside of the lower red rectangle.
Details are in the RVM support article Integrating RVM with gnome-terminal.
Gotcha: Ruby Installation
Ruby installation initially failed (speculation) because the Bone went into power save mode during the installation process. Ruby installed successfully on the second try when I kept the machine awake by moving the mouse. This power save hypothesis has not been thoroughly tested.
Gotcha: Rails Installation
Rails needs a JavaScript runtime in order to function. Here are two ways to meet this requirement:
- Install Node.js, or
- Install therubyracer gem.
Installing therubyracer
did not work for me. But Node.js installed quickly, like so:
1
|
|
Now that we have a JavaScript runtime, Rails runs successfully.
Side note: If you want to see what a Node.js-equipped Bone can do, check out OpenROV.
Conclusion
Bottom line: Ruby, Rails, and Ubuntu can run successfully on BeagleBone Black, with Node.js as a bonus.
The BeagleBone Black is a cool option for running Ubuntu. Yes, it runs slower than a typical laptop. But it runs, and at $45 each we can build a cluster of Bones if we’re concerned about performance!
What an amazing bundle of technology.
You might also be interested in Ruby on Raspberry Pi.


Albany Home Page
Albany is the main demonstration application of the AgileComponets strategy. It is a PDE code that strives to be built almost entirely from functionality in reusable libraries (such as Trilinos/STK/Dakota). Albany plays a large role in demonstrating and maturing functionality of new libraries, and also in the interfaces and interoperability between these libraries. It also serves to expose gaps in our coverage of capabilities and interface. Another aspect of the project is to serve as a template for writing new applications against Trilinos/STK/Dakota. It uses CMake, CTest, and git, and grabs almost all configuration information from installed Trilinos. Albany was granted OpenSource license to share with collaborators.Distinguishing Capabilities
The highlight of Albany is the PDE assembly. The template-based generic programming approach allows developers to just program for residual equations, and all manner of derivatives and polynomial propogations get automatically computed with no development effort. This approach uses Phalanx for rapid and flexible addition of physics, which works closely with Sacado and Stokhos for automatic propagation of derivatives and UQ. Intrepid2 and Shards packages are used for the local discretization. A second strength of Albany is the demonstration of transformational analysis algorithms. Albany demonstrates the clean use of all Solver/Analysis tools in Trilinosi (through Piro, which was developed in Albany) including NOX, LOCA, Tempus and ROL. On any problem we not only get a solution, but can also get sensitivities, run optimization problems, and perform uncertainty quantification. All of these approaches can access all of the linear solver options in Trilinos that are exposed by the Stratimikos layer. The third main strength is the early adoption of STK, the sierra toolkit libraries. This includes the mesh database, IO, and will be growing soon to include mesh changes for stk_rebalance and stk_adapt.Physics Sets: what PDEs
Albany strives for a bit of a paradigm shift, where a code is defined more by its analysis capabilities and data structure choices, which are difficult to change, and less by the physcics set. Much of Albany was developed in FY08-10 for solving simple heat transfer and poisson equations. With nonlinear source terms, this was adequate for developing and verifying all the hooks for analysis algorithms from sensitivity analysis to UQ. Recently, Navier-Stokes equations have been added to the general Albany physics set. In FY11, two new projects were funded to develop application codes on Albany. These physics sets are developed in the Albany code, yet are distinct in many ways as well: C++ namespace, project teams, and funding sources.LCM: Laboratory for Computational Mechanics
The first application project build on Albany is the LCM, or Laboratory for Computational Mechanics [Ostien-PI, Salinger, Mota, Foulk, Littlewood]. This project is creating an OpenSource computational mechanics RandD code, with a particular emphasis on fracture and failure models. The infrastructure in Albany, which is aimed at rapid development of new physics with automated generation of analytic derivatives and sensitivities, is well suited for trying out new discretizations and new material models. The links to Dakota and handling of model parameter are set up to perform calibration and UQ studies. LCM serves as a research tool that can be shared with academics. Successful research ideas and code will be migrated to the production mechanics applications of Adagio and Presto. Albany can link against the LAME material library, and we are looking at ways of getting derivative info through it for more robust solution algorithms.QCAD: Quantum Device Design
The other new application code built on Albany is the QCAD quantum dot design LDRD (Schrodinger-Poisson) [Muller-PI, Gao, Nielsen, Salinger]. A poisson solve for classical representation of charge distribution is coupled to a Schrodinger region for quantum effects.Embedded UQ
Albany is also serving as a main development and demonstration environment for embedded UQ research [Phipps-PI, Wildey]. By leveraging the templated fill environment, polynomial representations can be directly propagated through the physics assembly. Many issues with data structures, parallelization, and linear algebra are being addressed.Further development
The new applications have exposed many weaknesses in the Albany code, and more importantly, some gaps in the aggregate Agile Components infrastructure. For instance, an initial implementation in Trilinos of time integrators has been developed in responses to the needs of the transient dynamics problem. Future development includes: load balancing and uniform mesh refinement, transitioning to Tpetra and a templated software stack, being a testbed for embedded UQ methods, early adoption of architecture-aware PDE assembly kernels, and eventually a full error estimation and adaptivity capability. Albany is making the transition from a demonstration prototype to a research code. It still seriously lacks full boundary condition support, any multi-physics capability, post-processing, and documentation (all the hard stuff). The sister code Drekar [Pawlowski, Cyr] has developed the infrastructure for multi-physics applications with varying physics and discretizations, which Albany does not support.


The top Quake score in a single game. {{ player.name }}'s best score achieved in {{ player.qscoreMap.name | MapNameFilter }}.


3555 Farnam Street
Omaha, NE 68131
Official Home Page
Updated July 1, 2014
Updated August 10, 2015
Updated February 28, 2015>
(A commemorative book first sold at the 2015 Annual Meeting and now for sale on eBay.)
If you have any comments about our WEB page, you can either write us at the address shown above or e-mail us at<EMAIL_ADDRESS>However, due to the limited number of personnel in our corporate office, we are unable to provide a direct response.


Ocean [af74], ocean, in Crystal Sea
Last updated Turn #118
Ocean [af74], ocean, in Crystal Sea
------------------------------------------------------------------------
3: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails, arrived
3: from Quickrock [ad1c], owner:
3: Dorothy [v1b], Pen, with 39 peasants, 30 soldiers, 94 sailors,
3: accompanied by:
3: Cindy [z1c], Pen, with 14 peasants, one worker, 40 soldiers,
3: two skirmishers
3: Banian [e7n], Imperial Empire, knight, the High Priest of the
3: Church of Eres [151], with 16 soldiers, ten angels
3: Drummond [x8q], Imperial Empire, with 30 soldiers
3: Roland [o6t], Imperial Empire, Bishop of the Church of
3: Anteus [152], with 34 soldiers, five angels
3: Fortuis [h1m], Imperial Empire, with 20 soldiers
4: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails,
4: departed for Endless Plains [af75].
Routes leaving Ocean:
North, to Ocean [ae74], 6 days
East, plain, to Endless Plains [af75], Imperia, 2 days
South, city, to Quickrock [ad1c], Imperia, 1 day
South, plain, to Endless Plains [ag74], Imperia, impassable
Garrison [fv0k], garrison, Imperial Empire, with ten soldiers
West, to Ocean [af73], 6 days
It is raining.