#include <dirent.h> struct dirent *readdir(DIR *dirp); int readdir_r(DIR *dirp, struct dirent *entry, struct dirent **result);
Feature Test Macro Requirements for glibc (see feature_test_macros(7)):
readdir_r():
On Linux, the dirent structure is defined as follows:
struct dirent { ino_t d_ino; /* inode number */ off_t d_off; /* not an offset; see NOTES */ unsigned short d_reclen; /* length of this record */ unsigned char d_type; /* type of file; not supported by all filesystem types */ char d_name[256]; /* filename */ };
The only fields in the dirent structure that are mandated by POSIX.1 are: d_name[], of unspecified size, with at most NAME_MAX characters preceding the terminating null byte ('\0'); and (as an XSI extension) d_ino. The other fields are unstandardized, and not present on all systems; see NOTES below for some further details.
The data returned by readdir() may be overwritten by subsequent calls to readdir() for the same directory stream.
The readdir_r() function is a reentrant version of readdir(). It reads the next directory entry from the directory stream dirp, and returns it in the caller-allocated buffer pointed to by entry. (See NOTES for information on allocating this buffer.) A pointer to the returned item is placed in *result; if the end of the directory stream was encountered, then NULL is instead returned in *result.
The readdir_r() function returns 0 on success. On error, it returns a positive error number (listed under ERRORS). If the end of the directory stream is reached, readdir_r() returns 0, and returns NULL in *result.
The readdir_r() function is thread-safe.
The value returned in d_off is the same as would be returned by calling telldir(3) at the current position in the directory stream. Be aware that despite its type and name, the d_off field is seldom any kind of directory offset on modern filesystems. Applications should treat this field as an opaque value, making no assumptions about its contents; see also telldir(3).
Other than Linux, the d_type field is available mainly only on BSD systems. This field makes it possible to avoid the expense of calling lstat(2) if further actions depend on the type of the file. If the _BSD_SOURCE feature test macro is defined, then glibc defines the following macro constants for the value returned in d_type:
If the file type could not be determined, the value DT_UNKNOWN is returned in d_type.
Currently, only some filesystems (among them: Btrfs, ext2, ext3, and ext4) have full support for returning the file type in d_type. All applications must properly handle a return of DT_UNKNOWN.
Since POSIX.1 does not specify the size of the d_name field, and other nonstandard fields may precede that field within the dirent structure, portable applications that use readdir_r() should allocate the buffer whose address is passed in entry as follows:
name_max = pathconf(dirpath, _PC_NAME_MAX); if (name_max == -1) /* Limit not defined, or error */ name_max = 255; /* Take a guess */ len = offsetof(struct dirent, d_name) + name_max + 1; entryp = malloc(len);(POSIX.1 requires that d_name is the last field in a struct dirent.)


Pirate (ARRR) is a cryptocurrency that solves the issue of financial privacy on the blockchain through forced anonymous transactions. Protect your wallet and transactions with Pirate’s completely anonymous network. With forced private transactions, your privacy cannot be compromised by another users’ activity on the network. If you wish to learn more, please join the official Pirate Discord at https://pirate.black/discord/ or message FishyGuts#5085, who will be happy to help out.
100 messages
Before 05-Dec-19 12:00 AM
Talo28-Sep-18 01:03 PM
@ComputerGenie 👴👴👴 is to expensiv :) 720kmd i can offer 400kmd
ComputerGenie 👴👴👴28-Sep-18 01:03 PM
is anyone buying?
I have less than 10k, atm; so, "yes, they are"
@Talo When the cost of goods sold increases, so does the price to the consumer
Talo28-Sep-18 01:07 PM
@ComputerGenie 👴👴👴 i know but i only have 400 kmd left
ComputerGenie 👴👴👴28-Sep-18 01:08 PM
then you might could get 4-5k(edited)
zatJUM28-Sep-18 01:08 PM
@ComputerGenie 👴👴👴 Don't know why "they" are buying "your" PIRATEs and not mine.
crumb28-Sep-18 01:09 PM
Genies' got more street cred
0x0328-Sep-18 01:09 PM
original gangster true piratecoin
scnr
ComputerGenie 👴👴👴28-Sep-18 01:09 PM
Because I'm a "known" miner and people know you less? Dunno. Markets and we traders are irrational by nature
zatJUM28-Sep-18 01:09 PM
with an escrow it's no problem!
jl777c28-Sep-18 01:09 PM
I am buying now at 0.08, but only for large lots
zatJUM28-Sep-18 01:10 PM
I'm not desperate
jl777c28-Sep-18 01:10 PM
0.1 if for 1 million
zatJUM28-Sep-18 01:10 PM
I don't have 1 million to sell
jorian28-Sep-18 01:10 PM
large lots being > 100k?
zatJUM28-Sep-18 01:10 PM
I don't have 1 million
jl777c28-Sep-18 01:12 PM
i can do 100k
ComputerGenie 👴👴👴28-Sep-18 01:12 PM
with an escrow it's no problem!
That could be 1/2 of your problem; escrow, while adding trust, is considered a pain in the ass to most people
zatJUM28-Sep-18 01:13 PM
ok
When I baught my first PIRATE to Shossain everything went well, without any escrow
jl777c28-Sep-18 01:14 PM
just be careful who you OTC with.
if they are brand new, then beware
the newbies would send first to the established
zatJUM28-Sep-18 01:15 PM
for sure
Talo28-Sep-18 01:15 PM
If any of you are interested I offer 400KMD for 8000Pirate
rrrj28828-Sep-18 01:16 PM
Just buy from @jl777c . I guess that's what most ppl do
jl777c28-Sep-18 01:16 PM
I am only buying now
rrrj28828-Sep-18 01:16 PM
Hahahah
Nabob28-Sep-18 01:16 PM
wow, wish i should hold pirates some more time, sold my first trade @ 0.002 (edited)
ComputerGenie 👴👴👴28-Sep-18 01:17 PM
@Nabob how much you selling for 0.002 KMD each?
xRobeSx28-Sep-18 01:18 PM
already DM'd him hah
jl777c28-Sep-18 01:18 PM
when 20Msols is the low hashrate... and PIRATE mining is saturating NH and is the third equihash coin by hashrate
in the first week after it became production ready
before any formal marketing
Nabob28-Sep-18 01:18 PM
@ComputerGenie 👴👴👴
jl777c28-Sep-18 01:18 PM
i decide to only buy for now
Alright28-Sep-18 01:19 PM
@jl777c wts 20k @ .0777, interested?
jl777c28-Sep-18 01:20 PM
ok
Alright28-Sep-18 01:20 PM
couple minutes
house28-Sep-18 01:21 PM
Whatch out James ... it's a trap !  Do you even know why?
Flexatron28-Sep-18 01:36 PM
James offered 1 million at 0.1...damn
0x0328-Sep-18 01:36 PM
some people seem to be intimidated by PIRATE
Flexatron28-Sep-18 01:36 PM
That's like 16 btc
jl777c28-Sep-18 01:37 PM
XMR thread looks like it is a PIRATE thread
jorian28-Sep-18 01:38 PM
so @jl777c i'm curious why a trusted setup is not for a cypherpunk. is it really that bad?
0x0328-Sep-18 01:38 PM
PIRATE thread is the new SPIDERMAN thread
jl777c28-Sep-18 01:39 PM
it gives XMR hope that zcash is compromised
of course there is no evidence that all ceremony participants colluded
and unless all of them did, there is zero problems with the parameters
but they keep repeating how it isnt impossible for it to have happened, so that is basically the same as it happened
plz ignore that XMR had an infinite coin emission bug in actuality
so I could claim there is yet another bug in XMR that is worse than if the zcash params are compromised by all ceremony participants being in collusion
they cant disprove my claim
therefore it must be true, right?
The same people spouting that nonsense are saying everything I do is a scam
xRobeSx28-Sep-18 01:43 PM
yea, clearly they're dumb. We've been hearing the "jl777 is a scam" for what 5 years now?
jl777c28-Sep-18 01:43 PM
yes
house28-Sep-18 01:43 PM
Lack of Ability to disprove /= true(edited)
xRobeSx28-Sep-18 01:43 PM
you've scammed me for sure just kidding of course ha(edited)
you get plausible deniability with XMR, as in "I wasnt the one out of 10 that signed that tx"
with PIRATE there is nothing to deny as there is no indication who did the tx, who got the tx, nor how much it was for
this is why XMR peoples are very concerned about PIRATE
being mandatory privacy was their one edge over zcash
now that is gone
house28-Sep-18 01:46 PM
Newton was a scam too I believe (at first)
jl777c28-Sep-18 01:47 PM
WTB PIRATE 0.08 to 0.1, depending on volume. the more volume, the higher the price
2
house28-Sep-18 01:48 PM
Damn
[curls into fetal position](edited)
1
mentortechies28-Sep-18 01:49 PM
Hey guys... What the heck?
I am really good with techs and it is hard to follow the information on how to swap 15 KMD for pirate.
Nothing is clear in that twitter post.
jl777c28-Sep-18 01:49 PM
#deleted-channel
bushybro28-Sep-18 01:50 PM
This is awesome and crazy at the same time
Oszy28-Sep-18 01:53 PM
@mentortechies download agama, get z address, send kmd, post tx in #deleted-channel wait for $Pirate(edited)
mentortechies28-Sep-18 01:54 PM
Yes, in those instruction you forgot upgrade Agama to latest version if you installed it 5 days ago and you need the version from 3 days ago so you can find Pirate in the list
I am good with tech and was completely lost... you should get clearer instructions.
Oszy28-Sep-18 01:54 PM
yes you need the latest that got released 3 days ago
mentortechies28-Sep-18 01:54 PM
Also, it did not say to join discord in the twitter post. It said telegram.
Oszy28-Sep-18 01:55 PM
There are a lot twitter posts the one i saw said discord
Desktop App for SuperNET DAPPs. Contribute to KomodoPlatform/Agama development by creating an account on GitHub.
mentortechies28-Sep-18 02:01 PM
Also, where is pirate trading? BarterDEX?
Also, BarterDEX is awesome.
Oszy28-Sep-18 02:02 PM
no can't do zAddress on there
Nabob28-Sep-18 02:03 PM
right here, only OTC trades(edited)
Oszy28-Sep-18 02:03 PM
it's trading here
Nabob28-Sep-18 02:04 PM
Price goes around 0.08 to 0.1
jl777cToday at 9:47 PM
WTB PIRATE 0.08 to 0.1, depending on volume. the more volume, the higher the price
wookie28-Sep-18 02:04 PM
@webworker01 not sure why, the pirate that I managed to mine early yesterday, has not shown up in my wallet. It's been almost 24 hours and it's still not here.


[PART XIII.] CAMPAIGN FINANCE
A. General Provisions
[§11-301] Purpose. The purpose of this part is to provide transparency in the campaign finance process. Any ambiguity in the provisions of this part shall be construed to support transparency. [L 2010, c 211, pt of §2]


You can create an invisible anchor point for the tour step you want to center.
disablePageScrolling
option in step config
You can toggle CSS class which disables main content element scrolling when tour starts/ends.
TourService
:
no-scroll
CSS class to your global stylesheet
TourService
to start the UI tour:


Getting Things Done
by David Allen – A very simple and efficient time-management method.
The 5 Choices
by Kory Kogon, Adam Merrill, Leena Rinne – An all around productivity
methodology dealing with prioritization, scheduling, technology and
energy management.
Tools and Apps
File Management
Box – Very good cloud storage solution
for business customers.
WorkFlowy – Web-based outlining
application for nested bulletin lists.
Task Management
Airtable – Mix of spreadsheet and
database to help organize work.
Amazing Marvin – Marvin
incorporates principles from behavioral psychology to help beat
procrastination, feel in control and finish todo list.
Any.do – Simple interface, packed with
features, currently the favorite to-do list manager at
Lifehacker.
GitHub Projects
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management process to your code.
Hitask – Easy Project and Task
Management for Teams.
KanbanFlow – Kanban method task
board with Pomodoro technique.
Microsoft To Do –
Microsoft cloud-based task management application based on Wunderlist
technology.
OmniFocus – A Getting
Things Done based task manager for Mac OS X and iOS.
Pomolectron
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Remember the Milk – Great
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Taskade – Simple and collaborative
task lists for teams.
Taskwarrior – An open source
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Timing – Automatic time and
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Tinido – A beautiful and simple task
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Todo.txt – Tired of having complicated
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Todoist – Powerful and cross-platform
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CLI and Taskwarrior.
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Task Automation
AutoIt – Small tool for
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purposes.
Awesome AutoHotKey
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Tasker – Android application
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Password Manager
LastPass – A freemium password
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Knowledge Management
Obie.ai – AI platform that brings all
knowledge and documentation into one spot.
Miscellaneous
ShareX – Screen capture, file
sharing and productivity tool.


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Dashboard
menu
notifications
Logout
SmartRobinS
dashboard
Dashboard
delete
Trashbin
location_on
Map
Dashboard
I am a very simple card. I am good at containing small bits of information. I am convenient because I require little markup to use effectively. I am similar to what is called a panel in other frameworks.


|
LiquidMenu
1.5.1
Menu creation Arduino library for LCDs, wraps LiquidCrystal.
|
Represents a screen shown on the display. More...
#include <LiquidMenu.h>
Public Member Functions | |
Constructors | |
| LiquidScreen () | |
| The main constructor. More... | |
| LiquidScreen (LiquidLine &liquidLine) | |
| Constructor for 1 LiquidLine object. More... | |
| LiquidScreen (LiquidLine &liquidLine1, LiquidLine &liquidLine2) | |
| Constructor for 2 LiquidLine object. More... | |
| LiquidScreen (LiquidLine &liquidLine1, LiquidLine &liquidLine2, LiquidLine &liquidLine3) | |
| Constructor for 3 LiquidLine object. More... | |
| LiquidScreen (LiquidLine &liquidLine1, LiquidLine &liquidLine2, LiquidLine &liquidLine3, LiquidLine &liquidLine4) | |
| Constructor for 4 LiquidLine object. More... | |
Public methods | |
| bool | add_line (LiquidLine &liquidLine) |
| Adds a LiquidLine object to the screen. More... | |
| bool | set_focusPosition (Position position) |
| Sets the focus position for the whole screen at once. More... | |
| void | set_displayLineCount (uint8_t lineCount) |
| Specifies the line size of the display (required for scrolling). More... | |
| void | hide (bool hide) |
| Hides the screen. More... | |
Friends | |
| class | LiquidMenu |
Represents a screen shown on the display.
A screen is made up of LiquidLine objects. It holds pointers to them and calls their functions when it is active. It also knows on which line the focus is. This classes' objects go into a LiquidMenu object which controls them. The public methods are for configuration only.
| LiquidScreen::LiquidScreen | ( | ) |
The main constructor.
This is the main constructor that gets called every time.
|
explicit |
Constructor for 1 LiquidLine object.
| &liquidLine | - pointer to a LiquidLine object |
| LiquidScreen::LiquidScreen | ( | LiquidLine & | liquidLine1, |
| LiquidLine & | liquidLine2 | ||
| ) |
Constructor for 2 LiquidLine object.
| &liquidLine1 | - pointer to a LiquidLine object |
| &liquidLine2 | - pointer to a LiquidLine object |
| LiquidScreen::LiquidScreen | ( | LiquidLine & | liquidLine1, |
| LiquidLine & | liquidLine2, | ||
| LiquidLine & | liquidLine3 | ||
| ) |
Constructor for 3 LiquidLine object.
| &liquidLine1 | - pointer to a LiquidLine object |
| &liquidLine2 | - pointer to a LiquidLine object |
| &liquidLine3 | - pointer to a LiquidLine object |
| LiquidScreen::LiquidScreen | ( | LiquidLine & | liquidLine1, |
| LiquidLine & | liquidLine2, | ||
| LiquidLine & | liquidLine3, | ||
| LiquidLine & | liquidLine4 | ||
| ) |
Constructor for 4 LiquidLine object.
| &liquidLine1 | - pointer to a LiquidLine object |
| &liquidLine2 | - pointer to a LiquidLine object |
| &liquidLine3 | - pointer to a LiquidLine object |
| &liquidLine4 | - pointer to a LiquidLine object |
| bool LiquidScreen::add_line | ( | LiquidLine & | liquidLine | ) |
Adds a LiquidLine object to the screen.
| &liquidLine | - pointer to a LiquidLine object |
MAX_LINES
. The default is 8.| void LiquidScreen::hide | ( | bool | hide | ) |
Hides the screen.
Hiding a screen means that it will be skipped when cycling the screens.
| hide | - true for hiding and false for unhiding |
change_screen
methods.| void LiquidScreen::set_displayLineCount | ( | uint8_t | lineCount | ) |
Specifies the line size of the display (required for scrolling).
This is required when you want to add more lines (LiquidLine objects) to a screen (LiquidScreen object) than the display's line size. The lines will be scrolled.
| lineCount | - the line size of the display |
| bool LiquidScreen::set_focusPosition | ( | Position | position | ) |
Sets the focus position for the whole screen at once.
The valid positions are LEFT
and RIGHT
. CUSTOM
is not valid for this function because it needs individual column and row for every line.
| position | - LEFT or RIGHT |
Position
is enum class. Use Position::(member)
when specifying the position.


Initial Claims Weaker, But Still Not At Cautionary Levels
by New Deal DemocratSummary
- No recession is going to begin unless and until layoffs increase, and the lack of any such increase has been the best argument that no recession is imminent.
- Barring additional poor government policies, the long leading indicators strongly suggest that the threat of a recession will end by about mid-year next year.
- Unless initial claims start to be reported in the 230s, and continuing claims continue to trend higher, no interim recession will be signaled.
I've been monitoring initial jobless claims closely for the past several months to see if there are any signs of a slowdown turning into something worse. Simply put, no recession is going to begin unless and until layoffs increase, and the lack of any such increase has been the best argument that no recession is imminent.
My two thresholds for initial claims are:
1. If the four-week average on claims is more than 10% above its expansion low.
2. If the YoY% change in the monthly average turns higher.
I've also added a threshold for the less leading, but also much less volatile four-week average of continuing claims at 5% higher YoY.
This week's reading of 227,000, the second such reading in a row, is certainly weak compared with the past four months. As a result, the four-week moving average of claims, at 221,000, is 9.7% above the lowest reading of this expansion:
On a YoY% change basis, the four-week average is very slightly, as in 0.1%, above its level one year ago:
For the first three weeks of November, the average is 221,667 vs. 224,500 for the entire month of November last year, or less by -1.3%:
Although these readings are all weak, they remain positive. Neither threshold for a cautionary recession signal has been met.
Meanwhile, the less volatile four-week average of continuing claims is 1.8% above where it was a year ago:
This certainly is cautionary and is consistent with a significant slowdown. But there have been similar readings in 1967, 1985-6, three times in the 1990s, and briefly in 2003 and 2005, all without a recession following. So the threshold for continuing claims being a negative has not been met either.
Barring additional poor government policies - i.e., if the economy is left to its own devices - the long leading indicators strongly suggest that the threat of a recession will end by about mid-year next year. Unless initial claims start to be reported in the 230s, and continuing claims continue to trend higher, into the 1.770 million range (by mid-December, after which the YoY comparisons for continuing claims get much easier), no interim recession will be signaled.
Editor's Note: The summary bullets for this article were chosen by Seeking Alpha editors.


Nature Hab
© 2021 by Matthew Newbold, all rights reserved.
Thank you for stopping by! We appreciate your enthusiasm for outdoor inhabitants!
or
Bugs
Filler Text
Plants


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HTMLis an abbreviation for a programming language.
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Desert King
问题描述 :
After days of study, he finally figured his plan out. He wanted the average cost of each mile of the channels to be minimized. In other words, the ratio of the overall cost of the channels to the total length must be minimized. He just needs to build the necessary channels to bring water to all the villages, which means there will be only one way to connect each village to the capital.
His engineers surveyed the country and recorded the position and altitude of each village. All the channels must go straight between two villages and be built horizontally. Since every two villages are at different altitudes, they concluded that each channel between two villages needed a vertical water lifter, which can lift water up or let water flow down. The length of the channel is the horizontal distance between the two villages. The cost of the channel is the height of the lifter. You should notice that each village is at a different altitude, and different channels can’t share a lifter. Channels can intersect safely and no three villages are on the same line.
As King David’s prime scientist and programmer, you are asked to find out the best solution to build the channels.
输入:
输出:
样例输入:
4 0 0 0 0 1 1 1 1 2 1 0 3 0
样例输出:
1.000
解题代码:
//* @author: import java.util.*; import java.math.*; import java.io.FileReader; class Node{ int x,y,z; } public class Main { static int []mark=new int[1010]; static double []dis=new double[1010]; static int []from=new int [1010]; static double sumup,sumdown; static double [][]up=new double [1010][1010]; static double [][]down=new double [1010][1010]; static double [][]map=new double [1010][1010]; static double dist(double x1,double y1,double x2,double y2) { return Math.sqrt((double)(x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)); } static void prim(int n) { Arrays.fill(mark, 0); mark[1]=1;dis[1]=0; sumup=sumdown=0; for (int i=2;i<=n;i++) { dis[i]=map[1][i]; from[i]=1; } for (int i=1;i< n;i++) { double min=1e100; int sit=0; for (int j=1;j<=n;j++) if (mark[j]==0&&dis[j]< min) { min=dis[j]; sit=j; } mark[sit]=1; sumup+=up[sit][from[sit]]; sumdown+=down[sit][from[sit]]; for (int j=1;j<=n;j++) if (mark[j]==0&&dis[j]>map[sit][j]) { dis[j]=map[sit][j]; from[j]=sit; } } } public static void main(String[] args) throws Exception{ Scanner in=new Scanner(System.in); Node []a=new Node[1010]; while (true) { int n=in.nextInt(); if (n==0) break; for (int i=1;i<=n;i++) { a[i]=new Node(); a[i].x=in.nextInt(); a[i].y=in.nextInt(); a[i].z=in.nextInt(); } double mid=30; for (int i=1;i<=n;i++) for (int j=i+1;j<=n;j++) { up[i][j]=up[j][i]=Math.abs(a[i].z-a[j].z); down[i][j]=down[j][i]=dist(a[i].x,a[i].y,a[j].x,a[j].y); } double lastmid=30; while (true) { for (int i=1;i<=n;i++) for (int j=i+1;j<=n;j++) { map[j][i]=map[i][j]=up[i][j]-mid*down[i][j]; } prim(n); mid=sumup/sumdown; if (Math.abs(mid-lastmid)< 0.001) break; lastmid=mid; } System.out.printf("%.3f\n",mid); } } }
程序员会算法，等于一个古惑仔带了zhi抢啊
helpful！！ mark
嗯，尽量吧。题目太多了，很多解题报告是用程序自动发布的，所以就只有代码了
不对吧 你算的an是前面说有的项的和
这配图…..
额，不对应吧
很好的思路，可以借鉴
赞一个
We do not harbor that I couldn’t remove other individuals’ problems whenever this arrived at.
这是研发类的题还是算法类？
yeah, This is very tricky. If you use Cpp,you need more code
居然看懂了
第23行：
hash = -1是否应该改成hash[s ] = -1
因为是要把从字符串s的start位到当前位在hash中重置
修改提交后能accept，但是不修改居然也能accept
对于第一题，我的直觉告诉我换不换都一样，求指教


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Editing {{.Title}}
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Check it out in [FrontPage] article
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I was born and raised in Portland, Oregon. After graduating from Lincoln High School, I played a year of Division III baseball at Pacific University in Forest Grove, OR. A few years (and schools) later, I graduated from Portland State with a degree in Communication Studies.
While going to school back home in Portland, I began coaching baseball at my alma mater and discovered a passion for coaching/teaching. I turned that passion into my first job after graduation working as a Special Eduaction Paraeducator. It was an incredibly rewarding experience to be part of a team that ensured that special education students received a more equitable and inclusive high school education. After five years as a para and nine years coaching baseball, I was ready for a new challenge. That is how I now find myself as a student at Epicodus.
I currently live in SE Portland with my fiance and our two cats and two dogs.
Most of my hobbies and interests are sports-related. I really enjoy staying physically active through playing sports. I don't love just working out or just running so being on a basketball or softball team or having a race to train for really helps me stay in shape. That has been significantly more difficult during the pandemic, unfortunately. On the bright side, while working from home I had much more time and energy to take my dogs on long walks around the neighborhood.
I also love attending live sporting events. I have season tickets to the Portland Thorns and am a huge women's soccer fan in general. I was lucky enough to be able to travel to France for the 2019 Women's World Cup. It was absolutely incredible. I love going to Portland Timbers games as well and try to get to as many games as I can during a season. My first sporting love though was the Portland Trail Blazers. I remember going to games as a young kid at the Memorial Colliseum before the Rose Garden was built.
My top 5 favorite games I've seen in person:
My other hobbies and interests include:


2,400kg
Hull sailing weight,
(less than a
Range Rover)
The fixed wing-sail
24 metres high
103 sq metres
52mph
top speed
20m
10m
6 crew
0
Sail height similar to length of an Airbus 320 wing
Headsail (Jib)
Trampoline
Crossbeams
Hulls
Crew
Rudders
15
metres
8.48
metres
Daggerboards/foils
2,400kg
Hull sailing weight,
(less than a Range Rover)
The fixed wing-sail
24 metres high
103 sq metres
52mph
top speed
20m
6 crew
10m
0
Sail height similar to length of an Airbus 320 wing
Headsail (Jib)
Trampoline
Crossbeams
Crew
Hulls
15
metres
Rudders
8.48
metres
Daggerboards/foils
The fixed wing-sail
24 metres high
103 sq metres
2,400kg
Hull sailing weight,
(less than a Range Rover)
52mph
top speed
20m
6 crew
10m
0
Sail height similar to length of an Airbus 320 wing
Trampoline
Headsail (Jib)
Crossbeams
Crew
Hulls
15
metres
Rudders
8.48
metres
Daggerboards/foils
2,400kg
Hull sailing weight,
(less than a
Range Rover)
The fixed wing-sail
24 metres high
103 sq metres
52mph
top speed
20m
10m
6 crew
0
Sail height similar to length of an Airbus 320 wing
Headsail (Jib)
Trampoline
Crossbeams
Hulls
Crew
Rudders
15
metres
8.48
metres
Daggerboards/foils
The fixed wing-sail
24 metres high
103 sq metres
2,400kg
Hull sailing weight,
(less than a Range Rover)
52mph
top speed
20m
6 crew
10m
0
Sail height similar to length of an Airbus 320 wing
Trampoline
Headsail (Jib)
Crossbeams
Crew
Hulls
15
metres
Rudders
8.48
metres
Daggerboards/foils


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Short identifier for this ruling in the form of a question |
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Primary source for this ruling from a dev (facebook rules group) |
|
Secondary source for public access (imgur picture of facebook post) |
|
Name of developer that wrote the ruling |
|
yyyy-mm-dd |
|
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|
The full text content of the ruling
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played for
against
Find Pytkovic
Player Name
Match Date:
Team Name
home
Team Name
away
Match Date:
Team Name
home
Team Name
away


Reports any attempt to assign a java.lang.Date or
java.lang.Calendar field from a method parameter.
Since Date or Calendar are often
treated as immutable values but are actually mutable, assigning to such a field from a method parameter may
result in an object having its state modified unexpectedly. While occasionally useful for performance
reasons, this construct is inherently bug-prone.
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Run Player One Export Block Import Blocks(XML)
Run Player Two Export Block Import Blocks(XML)
Battle!


Ayush Dhanai
A 7th semester day scholar at Graphic Era Hill University, Dehradun, pursuing Bachelor's in Technology in Computer Science and Engineering.
Interested in inventing new gadgets and software by using technologies like Machine Learning and the Internet Of Things.
Akhil Tiwari
I am pursuing B.Tech in computer science and engineering from Graphic Era Hill University, Dehradun. I am interested in working in new and emerging technologies like AI, Blockchain, and IoT. I am a self-motivated, creative and curious person who loves to solve problems and build cool stuff.


Watch: BJP politician removed from international summit
by News DeskAn Indian politician, Vijay Jolly, was removed by security officials at the second Asia Pacific Summit emerged on Tuesday after he rudely interrupted an address by National Assembly Deputy Speaker Qasim Sure.
Jolly is associated with the ruling right-wing Bharatiya Janata Party (BJP).
Suri was raising the plight of Kashmiris in his address, when the Indian politician got up and interrupted him.
In the video, Jolly can be seen gesturing to Suri and saying: “I voice my protest…Jammu and Kashmir is not a part of this summit…How can he say this. This is wrong, he cannot say this is a human rights violation”.
A voice in the crowd replies to him saying, “No let him speak”.
Jolly was then forcibly removed by security from the event.
The video soon began doing the rounds on Twitter with users lambasting the Indian official over trying to prevent Suri from raising the Kashmir issue at the summit.
Over 860 delegates from 40 countries attended the second edition of the intercontinental summit.
This year’s theme is addressing critical challenges: peace, reconciliation, interdependence, mutual prosperity and universal value.
This is not the first time an Indian politician has embarrassed New Delhi on the international stage. Earlier on November 17, former Indian military general Maj Gen (R) SP Sinha screamed that “Maut ke badle maut [death for death], balatkar ke badle balatkar [rape for rape] during a high-decibel debate on the Kashmiri Pandit exodus on TV 9 Bharatvarsh.


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Hours: {{ '{0:,}'.format((member.csgo_info.total_time_played / 3600) | round(0)) }}
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Kills for weapon type:
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Check user profile visibility.
You can check the user profile at steam.


Training Deep Neural Networks with Synthetic Data using LGSVL Simulator
This documentation describes the whole pipeline for training 3D object detection deep networks with synthetic data collected from LGSVL Simulator.
In this project, we are going to use LGSVL Simulator Python APIs to randomly generate multiple scenes including environments and cars and to collect numerous synthetic data in KITTI format. With this data, we will train a state-of-the-art neural network using open-source implementation for KITTI object detection and evaluate its performance. Finally, we are going to deploy a final model and perform a real-time detection using ROS.
This project is mostly based on a state-of-the-art neural network using open-source implementations with some modifications: SECOND V1.5 for KITTI object detection
Table of Contents
- Prerequisites
- Setup
- Getting Started
- How to Collect Data
- Collecting Synthetic Data
- Preprocessing Data
- Config
- How to Train SECOND Model
- Data Visualization
- Training Deep Neural Network
- How to Deploy a Model
- Real-time Detection with ROS
- References
Prerequisites
- Docker CE
- NVIDIA Docker
- NVIDIA graphics card (required for training/inference with GPU)
Setup
Installing Docker
Please refer to Lane Following Model to install Docker CE and NVIDIA Docker.
Pulling Docker Image
Docker image is provided to be used alongside this repository. The docker image is available here.
To pull the image use the following command:
docker pull lgsvl/second-ros
What's inside Docker Image
- Ubuntu 16.04
- CUDA 9.0
- cuDNN <IP_ADDRESS>
- Python 3.6
- PyTorch 1.0.0
- SpConv 1.0
- ROS Kinetic
Cloning the Repository
This repository includes a submodule for SECOND network. To make sure that the submodule is also cloned use the following command:
git clone --recurse-submodules https://github.com/lgsvl/second-ros.git
Getting Started
We have provided a pretrained model located in model/hybrid_v4/voxelnet-817104.tckpt
and a sample ROSBAG with LiDAR point clouds. Before collecting your own data and starting to train a new model, you can run following command to start a demo:
docker-compose up second-ros
After a few seconds the pretrained model should be loaded and detect NPC vehicles from the ROSBAG. Rviz visualization window will open up as below:
To get into the container:
docker-compose run second-ros bash
How to Collect Data
Collecting Synthetic Data in KITTI Format
We use LGSVL Simulator's Python APIs for collecting synthetic data. We will use the script named kitti_parser.py
, and the simulator must be up and running before we start the script.
The script will first load a SanFrancisco
scene and spawn the ego vehicle with some NPC vehicles around in random positions in the scene. For each frame, it will save a camera image as a PNG file and LiDAR point clouds as a binary along with labels for NPCs captured by LiDAR. We parse the ground truth data into KITTI format.
To run the script and start collecting data:
python kitti_parser.py --start-index 0 --num-data 100 --dataset kitti --training
The script takes 4 arguments as below: - start-index: Starting index of kitti filename - num-data: Number of data points to collect - dataset: Name of dataset - training: Whether it's training dataset or testing
You can also get the sensor information for camera and LiDAR such as a camera projection matirx or a transformation matrix between camera and LiDAR. We use this information for projecting 3D bounding boxes into 2D bounding boxes since KITTI requires 3D and 2D bounding boxes as well.
You can take this code as an example for collecting ground truth data in KITTI format. You are very welcome to write your own script to collect data in your own way in any other formats (e.g., NuScenes).
Preprocessing Data
kitti_parser.py
will create a directory structure under /root/data
as below and save synthetic data into it:
└── YOUR_DATASET
├── training # Training set
| ├── image_2
| ├── calib
| ├── velodyne
| ├── velodyne_reduced
| └── label_2
└── testing # Validation set
├── image_2
├── calib
├── velodyne
├── velodyne_reduced
└── label_2
For using SECOND network and its visualization tool, we first need to prepare pickles as below:
- To create KITTI image infos:
python second.pytorch/second/create_data.py create_kitti_info_file --data_path=KITTI_DATASET_ROOT
- To create reduced point cloud:
python second.pytorch/second/create_data.py create_reduced_point_cloud --data_path=KITTI_DATASET_ROOT
- To create ground truth database:
python second.pytorch/second/create_data.py create_groundtruth_database --data_path=KITTI_DATASET_ROOT
Config
Make sure that you have correct paths for your data in a config file (e.g., /root/second.pytorch/second/configs/car.fhd.config
)
train_input_reader: {
...
database_sampler {
database_info_path: "/root/data/kitti/training/kitti_dbinfos_train.pkl"
...
}
kitti_info_path: "/root/data/kitti/training/kitti_infos_train.pkl"
kitti_root_path: "/root/data/kitti"
}
...
eval_input_reader: {
...
kitti_info_path: "/root/data/kitti/testing/kitti_infos_val.pkl"
kitti_root_path: "/root/data/kitti"
}
How to Train SECOND Network
Data Visualization
It's best practice to verify your collected data before training a model. You can visualize your data in a web viewer and check LiDAR point clouds, camera images, and bounding boxes. You can also try your model and test a model inference.
To launch the visualization tool:
- In Docker container, run
python ./second.pytorch/second/kittiviewer/backend.py main
- In your host machine, run
cd ./second.pytorch/second/kittiviewer/frontend && python -m http.server
- Open your browser and enter your frontend URL (e.g., http://<IP_ADDRESS>:8000)
- Put backend URL into backend (e.g., http://<IP_ADDRESS>:16666)
- Put kitti root path into rootPath (e.g., /root/data/kitti)
- Put kitti info path into infoPath (e.g., /root/data/kitti/training/kitti_infos_train.pkl)
- Click load to load your data
- Put a data index into a blue box at the bottom center of a screen and press Enter
For inference step:
- Put model checkpoint path into checkpointPath (e.g., /root/model/hybrid_v4/voxelnet-817104.tckpt)
- Put model config path into configPath (e.g., /root/model/hybrid_v4/pipeline.config)
- Click buildNet to build your model
- Click inference to detect objects
Training Deep Neural Network
After collecting enough amount of data, you can start training your own model using SECOND network. Please cd into second.pytorch/second
and run below commands.
To train your own model:
python ./pytorch/train.py train --config_path=./configs/car.fhd.config --model_dir=/path/to/model_dir
To evaluate a model:
python ./pytorch/train.py evaluate --config_path=./configs/car.fhd.config --model_dir=/path/to/model_dir --measure_time=True --batch_size=1
We provide a pretrained model located in
model/hybrid_v4/voxelnet-817104.tckpt
.
How to Deploy a Model
Real-time Detection with ROS
We implemented a ROS node catkin_ws/src/second_ros/scripts/second_ros.py
to deploy a trained model and perform a real-time 3D object detection using LiDAR point clouds. You can launch this node either as a standalone ROS node or using the provided launch file.
To launch as a stansalone ROS node:
rosrun second_ros second_ros.py
To launch with a launch file:
roslaunch second_ros second_ros
The launch file will run the second-ros
node, play a sample rosbag with LiDAR point clouds, and open up rviz for visualizations.
The SECOND ROS node subscribes to a topic named /kitti/velo/pointcloud
for LiDAR point cloud, pre-process the point cloud data, feed into the deployed model for prediction, and finally publish detected 3D bounding boxes into a topic /detections
as a bounding box array. In our machine, the performance was about 20 Hz with 64 channels LiDAR.


stratified_plume_model.outer_main¶
-
stratified_plume_model.
outer_main
(yi, yo, particles, profile, p, neighbor, delta_z)[source]¶ Manage the integration of the outer plume segments
Calculates the outer plume solution, creates the appropriate neighbor interpolation object, and returns the complete solution
- Parameters
- yiInnerPlume object
Object containing the inner plume state space and methods to extract the state space variables.
- yoOuterPlume object
Object containing the outer plume state space and methods to extract the state space variables.
- particleslist of dispersed_phases.PlumeParticle objects
List of dispersed_phases.PlumeParticle objects containing the dispersed phase local conditions and behavior.
- profileambient.Profile object
The ambient CTD object used by the simulation.
- pModelParams object
Object containing the fixed model parameters for the stratified plume model.
- neighborscipy.interpolate.interp1d object
Container holding the latest solution for the inner plume state space.
- delta_zfloat
Maximum step size to use in the simulation (m). The ODE solver in calculate is set up with adaptive step size integration, so in theory this value determines the largest step size in the output data, but not the numerical stability of the calculation.
- Returns
- zndarray
Vector of elevations where the outer plume solution is obtained (m).
- yndarray
Matrix of outer plume state space solutions. Each row corresponds to a depth in z.
- neighborscipy.interpolate.interp1d object
An updated neighbor interpolation object with the outer plume solution ready to use with integration of the inner plume.
Notes
There can be many outer plume segments, each associated with a localized, intense area of detrainment from the inner plume. Each of these outer plume segments generally stop at a level of neutral buoyancy before reaching the next segment, so they will each need to have an independent integration. Thus, this function contains an iteration loop that terminates when the plume has been integrated from the top to the bottom of the inner plume.
Once an outer plume segment stops at a level of neutral buoyancy, this function searches for the next outer plume by collecting detrained fluid over a length of inner plume equal to nwidths times the half-width and attempts to start an integration. If the negative buoyancy of that fluid is inadequate to overcome the upward drag of the inner plume, then the outer plume is said to be “not viable,” and the algorithm attemps to do this again with the next nwidths of detrained water. Once the outer plume segment becomes viable, those initial conditions are passed to the smp.calculate function, and the outer plume is integrated to neutral buoyancy. This succession of steps repeats until the bottom of the inner plume is reached.
When dissolution in the inner plume is large enough that the detained fluid is heavier than ambient (e.g., enriched by CO2 such that the solution is not dilute), then outer plume segments can tend to overlap. In this case, also, the lowest outer plume segment may descend beyond the starting point of the inner plume. This function assumes that the bottom of the CTD cast indicates the sea floor; hence, integration always stops at the sea bottom.


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--- title: Add math to your R with LaTeX author: Nellie Ponarul date: '2020-10-16' slug: add-math-to-your-r-with-latex categories: - R tags: - latex - R subtitle: '' summary: '' authors: ["nponarul"] lastmod: '2020-10-16T08:30:13-04:00' featured: no image: caption: '' focal_point: '' preview_only: no projects: [] ---
You may want to incorporate math functions into your R markdown output, whether that be just a set of math questions, math symbols, or add equations to plots. LaTeX is a typesetting language for typing math questions, which is super powerful and fairly easy to use. If you’re new to LateX, I highly recommend checking out the following resources:
In order to use LaTeX in Rmarkdown, you’ll need to have it installed on your computer first: Go here to download
We can do inline equations surrounded by single “$” characters, or do a block equation surrounded by “$$”.
I can display an equation like so \(Y = \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_3\), or using a block:
\[ Y = \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_3 \]
We can also add LaTeX to plots, using the latex2exp
package. The TeX
function from this package renders text with inline LaTeX equations.
library(ggplot2)
ggplot(data = mtcars, aes(x = mpg, y = disp)) +
geom_point() +
xlab("Miles per US Gallon")+
ylab(latex2exp::TeX("Displacement ($in.^3$)")) +
ggtitle("Example of LaTeX in a Plot (mtcars)") +
theme_bw()


back
Developed based on work of
Evan Wallace
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Introduction to Information Technology
https://edu.gcfglobal.org/en/computerbasics/about-this-tutorial/content/
Problem Solving With Computer
1.1 Introduction;
There are number of problems occurs in our daily life. These problems should be solved
carefully to get the desired solution. Sometimes, we may have to calculate simple interest, we
may have to prepare mark sheet, salary sheet and may have to do numerous sophisticated
calculations.
 We
have to give instructions to the computer to do certain specific tasks. So, there should be
software and the software is the collection of programs, where programs are the set of
instructions which are given by the users. The software is written to solve a particular
problem that takes some input from users, process it and give the meaningful result as output.
If the instructions are not correct then the computer gives wrong result.
Just writing code is not sufficient to solve a problem. Program must be planned before coding
in any computer language available. There are many activities to be done before and after
writing code for the software development.
Stages while solving a problem using computer:
1. Problem Analysis
2. Algorithm Development
3. Flowcharting
4. Coding
5. Compilation and Execution
6. Debugging and Testing
7. Documentation
1.1 Problem analysis
Problem analysis is the process of becoming familiar with the problem. We need to analyze
and understand it well before solving a problem. The user’s requirements cannot be fulfilled
without clear understanding of his/her problem in depth. Inadequate identification of problem
may cause program less useful and insufficient. In this phase, we determine the requirements
like variables, functions, etc. to solve the problem. That means we gather the required
resources to solve the problem defined in the problem definition phase. We also determine
the bounds of the solution. For example: Banking solution, Hospital Medical study etc.
1.2 Algorithms and Flowchart
Algorithm:
An algorithm is a finite set of steps defining the solution of a particular problem. An
algorithm is expressed in pseudo code - something resembling C language or Pascal, but with
some statements in English rather than within the programming language. Developing an
efficient algorithm requires lot of practice and skill. It must be noted that an efficient
algorithm is one which is capable of giving the solution to the problem by using minimum
resources of the system such as memory and processor’s time. Algorithm is a language
independent, well structured and detailed. It will enable the programmer to translate into a
computer program using any high-level language.
Features of Algorithm:
Following features should be present in an algorithm:
1. Proper understanding of the problem
2. Use of procedures/functions to emphasize modularity
3. Choice of variable names
4. Documentation of the program
Criteria to be following by an algorithm:
The following is the criteria to be followed by an algorithm:
Input: There should be zero or more values which are to be supplied.
Output: At least one result is to be produced.
Definiteness: Each step must be clear and unambiguous.
Finiteness: If we trace the steps of an algorithm, then for all cases, the algorithm must
terminate after a finite number of steps.
Effectiveness: Each step must be sufficiently basic that a person using only paper and
pencil can in principle carry it out. In addition, not only each step is definite, it must
also be feasible.
Example 1: Algorithm to compute and display the sum of two numbers.
1. Start
2. Read two numbers a and b
3. Calculate the sum of a and b and store it in sum.
4. Display the value of sum.
5. Stop
Example 2: Algorithm to computer the average of three numbers.
1. Start
2. Read three numbers a, b and c
3. Calculate the sum of a, b and c then divide the sum by 3 and store it in average.
4. Display the value of average
5. Stop
Example 3: Algorithm to calculate the factorial of a given number.
1. Start
2. Read the number n
3. Initialize i=1, fact=1
4. Is i = = n?
if yes, goto step 7
if no, go to step 5
5. fact = fact * i
6. i = i + 1, go to step 4
7. Print fact
8. Stop
Download the pdf file to get all the unit wise notes.
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ABSOLUTE POWER A film review by Chuck Dowling Copyright 1997 Chuck Dowling
Absolute Power (1997) * out of ***** - Cast:Clint Eastwood, Gene Hackman, Ed Harris, Scott Glenn, Judy Davis, E.G. Marshall, Laura Linney. Writer:William Goldman. Director:Clint Eastwood.
In 1992, Clint Eastwood won a Best Director Oscar for his excellent western "Unforgiven". The next year he followed that up by directing the equally excellent "A Perfect World". In 1995 he received great acclaim for directing "The Bridges of Madison County". Something has happened to Clint Eastwood. Something bad.
"Absolute Power" is an absolute mess. The film opens with two very pointless scenes, and then dives into the story. It seems that Eastwood is a burglar and one night he decides to break into the house of a millionaire (Marshall). While in the vault of the house, a drunken couple enters the room. Eastwood is able to hide in the vault, and luckily it has a two-way mirror so that he can see everything going on. As it turns out, the two people are the millionaire's wife, and the President of the United States (Hackman). As they play around with each other, the film keeps cutting to Eastwood every three seconds so he can look disgusted. We must see this reaction 75 times during the scene. The President then decides he's going to slap the girl around, and then turns into a raving psychotic. He tries to kill the girl (this is the President of the United States folks) and during the struggle the secret service breaks into the room and kills the girl. Eastwood now must decide whether or not to run or to try and bring down the President of the United States.
Eastwood is also supposed to be a "master of disguises". His brilliant disguises consist of a hat and jacket, sometimes a fake mustache. Amazingly, even though he just looks like Clint Eastwood with a hat on, he is able to fool people who are just 10 feet away.
Eastwood has used the same composer for his films since the mid-80s, a person by the name of Lennie Niehaus, who is absolutely terrible. However, in "Unforgiven", "A Perfect World", and here again in "Absolute Power" there's about 5 minutes of really good score. Each and every time, Clint Eastwood has composed that score himself. So why can't he do the entire film? He obviously knows what good score is. Why he lets Niehaus compose a boring score for each movie he makes is beyond me.
There is one decent scene in the film. It's when the cop assigned to the case (Harris) goes to talk to Eastwood about his possible connection with the crime. It's a very simple scene, it's just two men talking. But the two stars seem to play off of each other well and I was hoping for more interaction between their two characters.
The resolution of the events in the film is both unsatisfying and unclear. But the main problem with the whole premise is that the villain of the film is the President of the United States. It's absurd. Another problem is that the President, being an insane psychopath, should be the main focus of the film. He's not.  I hope Hackman got paid a lot of money for his cameo appearance. It's also equally absurd that we are supposed to be rooting for Eastwood, who is also a criminal. But I guess since he's not a violent criminal then that makes it ok.
-- Chuck Dowling Visit Chuck's Movie Reviews at http://users.southeast.net/~chuckd21/ Over 1,600 movies rated and/or reviewed! Movie news, box office reports, film related links, and reader's polls and reviews.
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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to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


ACM will host recurring meetings on Thursdays at 5:15 p.m. in Gardner Hall Room 208.
Debbie overlooks ACM, you can always contact her if you want to join.
Let a current ACM member know you want to be invited to the ACM group on Canvas.
Enter event here


Kevin Campbell reckons Moise Kean and Marco Silva need a heart-to-heart
by Tom ProcterFormer Everton striker Kevin Campbell believes Marco Silva needs to pull Moise Kean for a heart-to-heart to help get him firing at Goodison Park.
The Italian teenager is under a bit of pressure at the moment having failed to score for the club in 11 appearances.
Kean joined the club in a £27.5 million move from Juventus (BBC Sport) in the summer and arrived on Merseyside with a big reputation.
But so far he’s found the going tough, and the manager clearly doesn’t have full trust in him just yet, with Richarlison and Dominic Calvert-Lewin getting more game time.
In the eyes of Campbell, struggling Kean could do with an arm around the shoulder to take some of the pressure off.
He told the Liverpool Echo: “Maybe it’s time for a heart-to-heart between Kean and the manager.
“At the end of the day it’s down to the manager, and I think the support might be wearing a bit thin with Marco Silva as opposed to Moise Kean.

TBR’s view – Kean needs trust
It is important for Silva to ensure Kean knows he trusts him by persisting with him in his correct position.
Silva has not made life easy for him so far – starting him on the right-wing against Watfordrecently and taking him off at half-time must have been a tough evening for him.
He is a fantastic prospect and needs the love from his manager.
He’ll get that and plenty of patience from the fans, but Silva has to show he believes in the starlet.
Have something to tell us about this article?
Let us know


Addition (+)
Subtraction (-)
Multiplication (x)
Division (/)
The Tie Fighters are attacking! Choose a math function to practice and destroy the attackers with your correct answers!
Fire!
Quit
Congratulations! You destroyed all of the tie fighters! Click to continue...
The tie fighters have prevailed...Click to continue and return to the base...


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
inputs = tf.placeholder(dtype=tf.float32, shape=(None, image_width, image_height, image_channels))
zz = tf.placeholder(dtype=tf.float32, shape=(None, z_dim))
learn_rate = tf.placeholder(dtype=tf.float32)
return inputs, zz, learn_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False, alpha = 0.01):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
c1 = tf.layers.conv2d(images, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
c1 = tf.maximum(alpha*c1, c1)
c2 = tf.layers.conv2d(c1, 256, 5, strides=2, padding='same')
c2 = tf.layers.batch_normalization(c2, training=True)
c2 = tf.maximum(alpha*c2, c2)
c3 = tf.layers.conv2d(c2, 512, 5, strides=2, padding='same')
c3 = tf.layers.batch_normalization(c3, training=True)
c3 = tf.maximum(alpha*c3, c3)
flat = tf.reshape(c3, (-1, 4*4*512))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True, alpha=0.01):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
with tf.variable_scope('generator', reuse = (not is_train)):
x1 = tf.layers.dense(z, 7*7*512)
x1 = tf.reshape(x1, (-1, 7, 7, 512))
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(alpha * x1, x1)
x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha * x2, x2)
x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=2, padding='same')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha * x2, x2)
logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides=2, padding='same')
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning__rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning__rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning__rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, print_every=10, show_every=100):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# saver = tf.train.Saver()
# sample_z = np.random.uniform(-1, 1, size=(72, z_dim))
steps = 0
losses = []
input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
d_loss, g_loss = model_loss(input_real, input_z, len(data_image_mode))
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# TODO: Train Model
batch_images = batch_images*2
steps += 1
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
_ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
if steps % print_every == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
if steps % show_every == 0:
show_generator_output(sess, 25, input_z, len(data_image_mode), data_image_mode)
# #saver.save(sess, './checkpoints/facegenerator.ckpt')
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 128
learning_rate = 0.005
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 128
learning_rate = 0.005
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Adwoa Ohenewaa adjudged Face of NAFAC 2019
by Millicent Tamakloe/Abigail Yadago, GNAKoforidua, Nov 14, GNA - Adwoa Ohenewaa, a 25 year old graduate nurse, has been adjudged Face of National Festival of Arts and Culture (NAFAC) 2019, in a keenly contested competition at the ongoing NAFAC in Kofordua.
Out of the 29 contestants representing all 16 regions in the country, Miss Ohenewaa who represented the Western North Region, emerged as the overall winner and received a cash price of GH¢1,000.00, a two- year ambassadorial contract with the Ministry of Culture, Tourism and Arts, a citation and assorted gifts.
Benedicta Oforiwa Boateng, Eastern Region representative who was the first runner-up and first in the talent hunt section, received an amount of Gh¢700.00, a supporting ambassadorial role contract and assorted gifts whiles Matilda Nana Yaa Agyei, Ashanti Region representative and second runner up took home Gh¢500.00 and assorted gifts.
The show held at the Centre for National Culture Auditorium in Koforidua, last night, witnessed thrilling cultural and traditional performances by contestants and top Ghanaian musicians such as Sherifa Gunu.
The night full of glamour, elegance, beauty and tradition was organized by the National Commission for Culture under the Ministry of Tourism, Arts and Culture, to search for a queen who would promote the Ghanaian culture for job creation.
Sherifa Gunu advised the contestants that in whatever they found themselves doing, they should do it well and try to promote the Ghanaian culture wherever they went to give a good representation of Ghana.
GNA


This documentation describes the hData content payload for the Referral Request Extension.
| Name | Referral Request Extension Description |
| Version Aware URL | |
| Creation Date | 29 March 2012 |
| Summary | This Referral Request hData Extension contains information about referral and consultation requests made for a patient. |
| Author(s) | REST+ Pilot Team |
| Date | Author(s) | Version URL | Major Changes | Comments |
|---|---|---|---|---|
| 29 March 2012 | King, Kling, Gregorowicz, et. al. | First Version | Draft - In Progress |
All hData section documents identified by an XML namespace must be declared in the extensions node of the root document. Extensions are either "mandatory" or "optional." Each section document has an address expressed as a Uniform Resource Identifier (URI). Each URI is registered in the extensions node of the root document. Sections can contain either single data type documents (e.g., XML, JSON, HTML) or URIs referencing other sections. Section document content must be expressed in XML and document types must be declared in the extensions node of the root document.
This is the information to be sent from one healthcare provider to another regarding a specific patient. The referral request may contain specific medical procedures to be performed (accompanied by previously obtained authorizations) and links to relevant clinical information.
| Name | Type | Mandatory/Optional | Notes |
|---|---|---|---|
| id | string | M | Generated unique id for the referral request |
| date | dateTime | M | The date and time the referral request was created |
| disposition | code | O |
Contains the type of response or action that the requesting provider would like from the consulting provider. |
| number | string | O | Contains unique permanent referral number assigned by the originating system. |
| priority | code | O | Contains the urgency of the referral
|
| reason | URL | O | Link to the Condition entry that is the reason for this referral or consultation |
| status | code | O | Contains the status of the referral. |
| requestedProcedure | URL | O | Link to the Procedure entry that is requested by this referral or consultation. |
| reasonDescription | string | M | Contains narrative text describing reason for referral. |
| category | code | O | Contains the location at which the referral will take place.
|
| requestedNumTreatments | integer | O | Contains the requested number of times that the treatment may be administered to the patient without obtaining additional authorization |
| requestedSpecialty | string | O | The requested specialty of the consulting provider |
| requestedConsultingProvider | URL | O | Link to healththcare provider entry for the requested consulting provider |
| requestingProvider | URL | M | Link to the healthcare provider who is requesting the referral or consultation |
| recommendedPlanOfCare | URL | O | Link to a Plan of Care entry |
| authorizationEffectiveDate | interval | O | Contains the effective interval of the authorization |
| authorizationID | string | O | Contains the coverage application's permanent identifier assigned to track the authorization and all related billing documents |
| authorizationReimbursementLimit | quantity | O | Contains the dollar limit for reimbursement specified by the coverage plan for the authorized treatment. |
| authorizationNumberOfTreatments | quantity | O | Contains the number of times that the authorized treatment may be administered to the patient without obtaining additional authorization. |
| authorizationProcessDate | dateTime | O | Contains the date that the authorization originated with the authorizing party |
| insurancePayor | URL | O | Insurance provider information associated with the requested referral |
Referral Request is characterized by:
The XML Schema Definition (XSD) for Referral Request specifies how to formally describe the elements in an XML document.
ReferralRequest.xsd is located at the following URI: https://raw.github.com/ssayer/health-data-standards/develop/greenc32/schemas/rest_plus/referralrequest.xsd
ReferralRequest.xsd uses complex data types defined in the data_types schema at the following URI: https://raw.github.com/ssayer/health-data-standards/develop/greenc32/schemas/rest_plus/data_types.xsd
Referral Request may be represented in XML and JSON format.
Components of the Referral Request Extension Specification have been drawn from authoritative source and reference documents, as follows:


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title: The Upside Of Being Distracted With Film
tags:
- Articles
- film
- Fuji X100
- General Photography
- Panasonic LX5
status: publish
type: post
published: true
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---
Yes, I am distracted with film.  When it comes to gear lust a quick hundred bucks or so usually satisfies the need via flea-bay. Check out the Awesome lens I snagged for TWENTY bucks. Yep - that's like a couple cups of coffee.
[image title="Nikkor135mm_720" size="medium" id="2888" align="none" linkto="viewer" ]
Of course gear lust is constantly quenched via the deliveries of film - a very nice tactical sensation that is absolutely a requirement of making images. The upside to this is I feel no compulsion to continue to search for the "perfect" digital camera. I have my LX5 - I am happy with that for quickie shots. I have a HUGE array of film equipment that dates from the 1930's to the pinnacle of film tech. All of it is a dream in terms of refinement of function and focus on making an image vs. fiddling around with far far too many controls that have nothing to do with the matter at hand.
If I were not this distracted with my film stuff I would definitely be buying the Fuji X100 ASAP. Just so I can discover all the things I don't like about it compared to my Leica M film cameras. At least for the time being I am perfectly content. Speaking of the Fuji, based on all the initial hub-bub I would definitely be getting one if I weren't temporarily off the digital gear treadmill. I does look to be a very pleasant device to use. At least in digital world.
Speaking of cool film images check these out from the 1950's - amazing, simple, beautiful, interesting, no tricks, just strait up photography.
For something a little more modern check this guy I met up with that is a youngster that shoots nothing but film in 35mm and 120 - seriously nice work.
Only 5 more days until there is a lucky winner of the Nikon F3. I sure hope the winner will be gracious enough to share some images with us.
RB


2 writes to Property
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Package Development
- Introduction
- Package Discovery
- Service Providers
- Resources
- Commands
- Public Assets
- Publishing File Groups
Introduction
Packages are the primary way of adding functionality to Laravel. Packages might be anything from a great way to work with dates like Carbon, or an entire BDD testing framework like Behat.
There are different types of packages. Some packages are stand-alone, meaning they work with any PHP framework. Carbon and Behat are examples of stand-alone packages. Any of these packages may be used with Laravel by requesting them in your composer.json
file.
On the other hand, other packages are specifically intended for use with Laravel. These packages may have routes, controllers, views, and configuration specifically intended to enhance a Laravel application. This guide primarily covers the development of those packages that are Laravel specific.
A Note On Facades
When writing a Laravel application, it generally does not matter if you use contracts or facades since both provide essentially equal levels of testability. However, when writing packages, your package will not typically have access to all of Laravel's testing helpers. If you would like to be able to write your package tests as if they existed inside a typical Laravel application, you may use the Orchestral Testbench package.
Package Discovery
In a Laravel application's config/app.php
configuration file, the providers
option defines a list of service providers that should be loaded by Laravel. When someone installs your package, you will typically want your service provider to be included in this list. Instead of requiring users to manually add your service provider to the list, you may define the provider in the extra
section of your package's composer.json
file. In addition to service providers, you may also list any facades you would like to be registered:
"extra": {
"laravel": {
"providers": [
"Barryvdh\\Debugbar\\ServiceProvider"
],
"aliases": {
"Debugbar": "Barryvdh\\Debugbar\\Facade"
}
}
},
Once your package has been configured for discovery, Laravel will automatically register its service providers and facades when it is installed, creating a convenient installation experience for your package's users.
Opting Out Of Package Discovery
If you are the consumer of a package and would like to disable package discovery for a package, you may list the package name in the extra
section of your application's composer.json
file:
"extra": {
"laravel": {
"dont-discover": [
"barryvdh/laravel-debugbar"
]
}
},
You may disable package discovery for all packages using the *
character inside of your application's dont-discover
directive:
"extra": {
"laravel": {
"dont-discover": [
"*"
]
}
},
Service Providers
Service providers are the connection points between your package and Laravel.html. A service provider is responsible for binding things into Laravel's service container and informing Laravel where to load package resources such as views, configuration, and localization files.
A service provider extends the Illuminate\Support\ServiceProvider
class and contains two methods: register
and boot
. The base ServiceProvider
class is located in the illuminate/support
Composer package, which you should add to your own package's dependencies. To learn more about the structure and purpose of service providers, check out their documentation.
Resources
Configuration
Typically, you will need to publish your package's configuration file to the application's own config
directory. This will allow users of your package to easily override your default configuration options. To allow your configuration files to be published, call the publishes
method from the boot
method of your service provider:
/**
* Perform post-registration booting of services.
*
* @return void
*/
public function boot()
{
$this->publishes([
__DIR__.'/path/to/config/courier.php' => config_path('courier.php'),
]);
}
Now, when users of your package execute Laravel's vendor:publish
command, your file will be copied to the specified publish location. Once your configuration has been published, its values may be accessed like any other configuration file:
$value = config('courier.option');
{note} You should not define Closures in your configuration files. They can not be serialized correctly when users execute the
config:cache
Artisan command.
Default Package Configuration
You may also merge your own package configuration file with the application's published copy. This will allow your users to define only the options they actually want to override in the published copy of the configuration. To merge the configurations, use the mergeConfigFrom
method within your service provider's register
method:
/**
* Register bindings in the container.
*
* @return void
*/
public function register()
{
$this->mergeConfigFrom(
__DIR__.'/path/to/config/courier.php', 'courier'
);
}
{note} This method only merges the first level of the configuration array. If your users partially define a multi-dimensional configuration array, the missing options will not be merged.
Routes
If your package contains routes, you may load them using the loadRoutesFrom
method. This method will automatically determine if the application's routes are cached and will not load your routes file if the routes have already been cached:
/**
* Perform post-registration booting of services.
*
* @return void
*/
public function boot()
{
$this->loadRoutesFrom(__DIR__.'/routes.php');
}
Migrations
If your package contains database migrations, you may use the loadMigrationsFrom
method to inform Laravel how to load them. The loadMigrationsFrom
method accepts the path to your package's migrations as its only argument:
/**
* Perform post-registration booting of services.
*
* @return void
*/
public function boot()
{
$this->loadMigrationsFrom(__DIR__.'/path/to/migrations');
}
Once your package's migrations have been registered, they will automatically be run when the php artisan migrate
command is executed. You do not need to export them to the application's main database/migrations
directory.
Translations
If your package contains translation files, you may use the loadTranslationsFrom
method to inform Laravel how to load them. For example, if your package is named courier
, you should add the following to your service provider's boot
method:
/**
* Perform post-registration booting of services.
*
* @return void
*/
public function boot()
{
$this->loadTranslationsFrom(__DIR__.'/path/to/translations', 'courier');
}
Package translations are referenced using the package::file.line
syntax convention. So, you may load the courier
package's welcome
line from the messages
file like so:
echo trans('courier::messages.welcome');
Publishing Translations
If you would like to publish your package's translations to the application's resources/lang/vendor
directory, you may use the service provider's publishes
method. The publishes
method accepts an array of package paths and their desired publish locations. For example, to publish the translation files for the courier
package, you may do the following:
/**
* Perform post-registration booting of services.
*
* @return void
*/
public function boot()
{
$this->loadTranslationsFrom(__DIR__.'/path/to/translations', 'courier');
$this->publishes([
__DIR__.'/path/to/translations' => resource_path('lang/vendor/courier'),
]);
}
Now, when users of your package execute Laravel's vendor:publish
Artisan command, your package's translations will be published to the specified publish location.
Views
To register your package's views with Laravel, you need to tell Laravel where the views are located. You may do this using the service provider's loadViewsFrom
method. The loadViewsFrom
method accepts two arguments: the path to your view templates and your package's name. For example, if your package's name is courier
, you would add the following to your service provider's boot
method:
/**
* Perform post-registration booting of services.
*
* @return void
*/
public function boot()
{
$this->loadViewsFrom(__DIR__.'/path/to/views', 'courier');
}
Package views are referenced using the package::view
syntax convention. So, once your view path is registered in a service provider, you may load the admin
view from the courier
package like so:
Route::get('admin', function () {
return view('courier::admin');
});
Overriding Package Views
When you use the loadViewsFrom
method, Laravel actually registers two locations for your views: the application's resources/views/vendor
directory and the directory you specify. So, using the courier
example, Laravel will first check if a custom version of the view has been provided by the developer in resources/views/vendor/courier
. Then, if the view has not been customized, Laravel will search the package view directory you specified in your call to loadViewsFrom
. This makes it easy for package users to customize / override your package's views.
Publishing Views
If you would like to make your views available for publishing to the application's resources/views/vendor
directory, you may use the service provider's publishes
method. The publishes
method accepts an array of package view paths and their desired publish locations:
/**
* Perform post-registration booting of services.
*
* @return void
*/
public function boot()
{
$this->loadViewsFrom(__DIR__.'/path/to/views', 'courier');
$this->publishes([
__DIR__.'/path/to/views' => resource_path('views/vendor/courier'),
]);
}
Now, when users of your package execute Laravel's vendor:publish
Artisan command, your package's views will be copied to the specified publish location.
Commands
To register your package's Artisan commands with Laravel, you may use the commands
method. This method expects an array of command class names. Once the commands have been registered, you may execute them using the Artisan CLI:
/**
* Bootstrap the application services.
*
* @return void
*/
public function boot()
{
if ($this->app->runningInConsole()) {
$this->commands([
FooCommand::class,
BarCommand::class,
]);
}
}
Public Assets
Your package may have assets such as JavaScript, CSS, and images. To publish these assets to the application's public
directory, use the service provider's publishes
method. In this example, we will also add a public
asset group tag, which may be used to publish groups of related assets:
/**
* Perform post-registration booting of services.
*
* @return void
*/
public function boot()
{
$this->publishes([
__DIR__.'/path/to/assets' => public_path('vendor/courier'),
], 'public');
}
Now, when your package's users execute the vendor:publish
command, your assets will be copied to the specified publish location. Since you will typically need to overwrite the assets every time the package is updated, you may use the --force
flag:
php artisan vendor:publish --tag=public --force
Publishing File Groups
You may want to publish groups of package assets and resources separately. For instance, you might want to allow your users to publish your package's configuration files without being forced to publish your package's assets. You may do this by "tagging" them when calling the publishes
method from a package's service provider. For example, let's use tags to define two publish groups in the boot
method of a package service provider:
/**
* Perform post-registration booting of services.
*
* @return void
*/
public function boot()
{
$this->publishes([
__DIR__.'/../config/package.php' => config_path('package.php')
], 'config');
$this->publishes([
__DIR__.'/../database/migrations/' => database_path('migrations')
], 'migrations');
}
Now your users may publish these groups separately by referencing their tag when executing the vendor:publish
command:
php artisan vendor:publish --tag=config


Lines of code written
Glasses of wine drunk
Projects done
Satisfied Clients
Do every act of your life as if it were your last.
Difficult times always create opportunities for you to experience more love in your life.
I will always choose a lazy person to do a difficult job because he will find an easy way to do it.


Chapter 4. Flocking
Often in video games, nonplayer characters must move in cohesive groups rather than independently. Let's consider some examples. Say you're writing an online role-playing game, and just outside the main town is a meadow of sheep. Your sheep would appear more realistic if they were grazing in a flock rather than walking around aimlessly. Perhaps in this same role-playing game is a flock of birds that prey on the game's human inhabitants. Here again, birds that hunt in flocks rather than independently would seem more realistic and pose the challenge to the player of dealing with somewhat cooperating groups of predators. It's not a huge leap of faith to see that you could apply such flocking behavior to giant ants, bees, rats, or sea creatures as well.
These examples of local fauna moving, grazing, or attacking in herds or flocks might seem like obvious ways in which you can use flocking behavior in games. With that said, you do not need to limit such flocking behavior to fauna and can, in fact, extend it to other nonplayer characters. For example, in a real-time strategy simulation, you can use group movement behavior for nonplayer unit movement. These units can be computer-controlled humans, trolls, orcs, or mechanized vehicles of all sorts. In a combat flight simulation, you can apply such group movement to computer-controlled squadrons of aircraft. In a first-person shooter, computer-controlled enemy or friendly squads can employ such group movement. You even can use variations on basic flocking behavior to simulate crowds of people loitering around a town square, for example.
In all these examples, the idea is to have the nonplayer characters move cohesively with the illusion of having purpose.
At the heart of such group behavior lie basic flocking algorithms such as the one presented by Craig Reynolds in his 1987 SIGGRAPH paper, "Flocks, Herds, and Schools: A Distributed Behavioral Model." You can apply the algorithm Reynolds presented in its original form to simulate flocks of birds, fish, or other creatures, or in modified versions to simulate group movement of units, squads, or air squadrons. In this chapter we're going to take a close look at a basic flocking algorithm and show how you can modify it to handle such situations as obstacle avoidance. For generality, we'll use the term units to refer to the individual entities comprising the group—for example, birds, sheep, aircraft, humans, and so on—throughout the remainder of this chapter.
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