Graduate student in geography and environmental studies at Northeastern Illinois University. Undergraduate degree in environmental studies from Lake Forest College. Currently work at an after school program where we teach the students about environmental processes. This is why environmental education is a passion of mine.
<EMAIL_ADDRESS>GIS 1
GIS for natural systems management
GIS 3
Project 1
This was a map I created as part of a final project in GIS 1. The overall purpose was to get a better understanding of the best neighborhood to stay in within New York City based on a set of requirements and preferences.
My requirements included low crime areas and access to public transportation.
My preferences included neighborhoods with side walk cafes, access to water front parks, access to outdoor recreation, and being near Times Square.
By assigning a weight to each of my requirements and preferences and using a formula to perform a final calculation, I concluded that Central Harlem is the best neighborhood to stay in while visiting New York City.
Project 2
Map 1
Map 2
These are two maps I created in GIS for natural systems management in which I compared current and future suitable habitats for gray wolves as global warming continues to evolve.
I designed two different models using ModelBuilder in Arc which depicted current expected potential gray wolf habitats and potential future gray wolf habitats based on 2099 projections.
The models were generated on data sets consisting of temperature ranges, landcovers, and rainfall patterns based on suitable habitats. I used the intersect tool within my models to uncover where suitable habitats for this specie is located based on a set of conditions. After I ran both of these models I was able to evaluate how their habitats would change.
Map 1 illustrates the range for current gray wolf habitats throughout the United States.
Map 2 displays how the location of gray wolf habitats will change based on 2099 temperature, landcover, and rainfall projections.
Future projections show how gray wolf habitats will decrease as the climate continues to warm. Their habitats are predicted to move to the Northwest region of the United States.
Project 3
I created this map in GIS 1 with two other individuals in which we designed a project in order to help the Spring Valley Nature Center with creating a usable map that has a real world application for continuous use.
We began with creating a orthogonal map image of the Center. Then used a base layer to map trees and benches.
The conservation director asked us to map all of the memorial trees throughout the nature center which were all oaks. We used the Mapit app in order to obtain the coordinate points for our final map.
We took the entire map, excel file, and overlay to create the .kml file which can be used to make any future changes.
Project 4
QGIS Map
My QGIS map represents critical habitat ecoregions of the most endangered species in the United States. I used feature blending in this program to map the location of five critical endangered habitats which consisted of the bighorn sheep, white river spinedace, Mississippi gopher frog, long-eared bat, and green sea turtle.
The bighorn sheep critical habitats are located in the Sierran Steppe-Mixed Forest-Coniferous Forest-Alpine Meadow, California Dry Steppe, American Semi-Desert and Desert, and Intermountain Semi-Desert and Desert Provinces.
The white river spinedace can be found in both the Intermountain Semi-Desert and Desert and Nevada-Utah Mountains-Semi-Desert-Coniferous Forest-Alpine Meadow Provinces.
The Mississippi gopher frog is located throughout the Outer Coastal Plain Mixed Forest, Southeastern Mixed Forest, and Lower Mississippi Riverine Forest Provinces.
The long-eared bat inhabits the Central Appalachian Broadleaf Forest-Coniferous Forest-Meadow and Eastern Broadleaf Forest (Oceanic) Provinces.
Lastly, the green sea turtle critical habitats are in the Puerto Rico Province.
The purpose of this project was to not only learn a new mapping feature in QGIS but to illustrate the importance of these ecoregions to species at risk of extinction. The feature blending of the layers shows the areas or ecoregions which require conservation management actions to protect these species.
Project 5
Python Project
My Python Script
The overall goal of this project was to generate a python script tool to batch process a data set. In addition, it gave me the opportunity to develop my GIS scripting and programming skills.
As indicated from my script above, I created a file geodatabase with a user-definable name to store all the preprocessed shapefiles. Then I had to change the projections of all the shapefiles to the same user-definable coordinate system. Lastly, I clipped all of the projected shapefiles using a user-definable clipping feature and stored all of my output features to the newly created geodatabase.
The image above displays each of the user-definable parameters and their data type which I set up in order for this script tool to batch process the data.
This final image shows the dialog box with each of the required parameters set up to run the script tool. After each parameter is filled in with the necessary inputs and data, the tool can be run and the data will be clipped which will then be stored to the geodatabase.
Data Collection, Laboratory Techniques, Tree Identification, Field Methods, Data Analysis/Documentation, Analytical Researcher.
Knowledge in GIS Geographic Information Systems, Google Earth, and Python.Highly interested in geography and environmental studies. This area of study is important in order to approach questions and solutions to environmental issues. Some of my other interests include National Parks, wilderness areas, education, environmental planning and policy, art, camping, hiking, traveling, animals, music, computers, trees, and rocks.


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If you want to power up your learning I'd suggest you take a look at this awesome book on f# #webdev https://t.co/OHRddtuCPK
— FSharpTV (@AwesomeFSharp) June 13, 2016
@tamizhvendan has a nice blog https://t.co/1MmamNNGw2. He's also author of "F# Applied" on web with F#+Suave https://t.co/bhJYPZRI0r
— Don Syme (@dsyme) June 13, 2016
F# Applied is a very good book. Teaches how to build Suave framework from ground up.
— Vagif Abilov (@ooobject) June 16, 2016
I bought this @tamizhvendan book about @SuaveIO few days ago and must say it is totally kick ass! #goodjob #fsharphttps://t.co/xuKRZbSKc0
— Roman Provazník (@Dzoukr) June 16, 2016
I've just started reading @tamizhvendan book about @SuaveIO and F#. Really nice to start with you own implementation of #suave. #recommend
— Vicenç García-Altés (@vgaltes) June 14, 2016
I find F# Applied very well written. Thanks @tamizhvendan for a great job! (and a ;) for the @SuaveIO team)
— Lay (@laygr) August 17, 2016


Embeditor is a super easy way to embed a fancy text/code editor onto any webpage.
How do I use it? Just add this to your HTML page:
<iframe src="http://embeditor.org/"></iframe>
That's it! To make it bigger/smaller, use CSS to resize. The editor will take up 100% height/width of the iframe.
This intro message will appear unless a 'mode' is specified in the iframe src's querystring (i.e. ?mode=text). Valid modes are 'text', 'html', 'css', and 'javascript'.
More details, API info, and examples can be found @ http://derek.github.com/embeditor/


The Current State of the Hyperbook [show]
The hyperbook is an unfinished hypertext document that
was meant to be what one would read to learn TLA+ and
PlusCal. The
TLA+ Video Course
now serves as an introduction to TLA+, and I'm recommending
Hillel Wayne's Web Site and Book
as an introduction to PlusCal.
I have stopped working on the hyperbook and don't know what to
do with it. (I would welcome suggestions.)
The first 6 chapters of the hyperbook cover approximately the same material as the Video Course, but in greater depth and with different examples. It also teaches the use of PlusCal. Chapters 7 through 9 provide more realistic examples. The hyperbook's TLA+ Proof Track is probably the best introduction to writing TLA+ proofs and checking them with the TLAPS prover. The rest of this web page describes the state of the hyperbook at the time of the last release, in 2015. The Hyperbook [show]
This is the start of a hypertext "book" containing two tutorials:
Principles of Concurrent Computing and Specification of
Concurrent Systems.
The tutorials are two tracks that share much text--especially at the
beginning.
Both tutorials are based
on TLA+.
The Principles track, which I hope will eventually be suitable
for an undergraduate course on concurrent computing, will mainly use
the
PlusCal algorithm language
rather than
TLA+ for describing algorithms. There is also a TLA+
Proof track that explains how to use the TLAPS
proof system.
You will probably read the hyperbook with Adobe Reader, but Foxit Reader also works. Other readers should also work, but it seems that Preview, the default pdf reader on the Mac, does not. I welcome comments and suggestions on the form, style, and contents. Also, please tell me if anything doesn't work as you think it should. My email address is on my home page.
The hyperbook consists of a collection of pdf files; the root file is
If you are just starting to read this hyperbook, click here.This leads you to the introduction About This Hyperbook. Read it. You will then know what to do next. News [show]
I am still rewriting some parts of the hyperbook, so some material may
be in an inconsistent state. Read the About This Hyperbook
page for more information. (Click on "If you are just starting..."
from the start page or on the "?" in the left margin from most other
pages.)
The LaTeX sources for the hyperbook have been released. See below. The Source Files [show]
The LaTeX source files for the hyperbook are now available for
download.
One reason for this is to encourage you to contribute your own
material to the hyperbook.
(Contact me before starting if you want that material to appear
in the official release of the hyperbook.)
You could also write your own hypertext documents using the
commands that I created for the purpose.
Reading the source files will allow you to figure out how to do
it. The file hypertlabook.sty and the packages it imports
define the relevant commands.
The source files can be downloaded
by
clicking
here.
Extracting the files from thie zip file creates a folder (directory)
Versions [show]
I hope to post new versions of the hyperbook continually.
So, far I haven't been very good at doing that, but I hope to do
better. Major changes will be announced on
the
TLA+ Google Group.
However, I may post a new version and change the last modified
date of this page whenever something you should read has changed--even
if it's just the correction of a minor typo.
Downloading and unzipping a file is easy, so check this page
regularly.
|
|||


The IAAF Takes a Giant Step Backwards
(This editorial appeared in the Analytical Distance Runner for Week 940
and was written by Ken Young)
The IAAF has made their decision on keeping world road records. Two of the four criteria that were detailed in the IAAF press release (of a total of eight criteria) are good or at least reasonable. These are the requirements that the course have been measured by an "A" or "B" IAAF/AIMS approved measurer and that the decrease in elevation shall not exceed 1 m/km. The doping control criterion has a caveat that "no new world bests will be ratified from 1 January unless the athlete has had a doping control on the date of the race." The 1 January refers to the 01 Jan 2003 starting date for the recognition of world records. Clearly, this is intended to "grandfather" in a bunch of "records" that do not meet this standard. Eventually, one would assume that all of the grandfathered "records" would be surpassed by performances that adhere to this criterion but drug-aided marks have a tendency to persist for decades. Performances that lie six standard deviations out on the tail of the distribution, i.e., much faster than any other peformances at that distance (and for that sex) will tend to remain on such lists and cast doubt on the overall veracity of the process. In addition, drug-aided performances may still go undetected despite this criterion, and not be uncovered until the performances have been "institutionalized", i.e., not to be removed. If an anti-doping requirement is to be effected, then it should apply to performances prior to 01 Jan 2003. Otherwise, there is a double standard. Score this as a step backwards. The definition of a "record-quality" course includes, "The start and finish points on a course, measured along a straight line between them, shall not be further apart than 50% of the race distance." This has been discussed at some length in the previous issue of ADR and again in this issue. The real question is "Why has the IAAF adopted this 50% S/F rule?" Apparently one member of the committee (council) that made this decision insisted that courses with up to a 50% separation between the start and finish be allowed. It turns out that two of the performances on the list proposed for acceptance as "world records" were set in this person's country by nationals of his country. If a 30% rule were adopted, neither of these marks would have qualified. This serves to demonstrate that the basis for the IAAF road record criteria is political rather than technical. Score this as a big step backwards. A glaring omission is that there is no provision for a validation. The USA has 20 years of experience with its validation process and we have learned that a number of things can produce a short course, no matter how expert the measurer may be. Altho the course itself may be accurate, the validation serves to determine whether or not the race was run as the course was certified. Were the start and finish lines (and any turn-arounds) in the correct locations? Were all coned restrictions adhered to? Announcing a record and then having to make a retraction puts the race administration and to some extent, the NGB, in an embarrassing situation. One can see why a politician would oppose such a validation process. However, had the USA followed such a "no validations needed" policy, our records list would be full of garbage "records" as witnessed by the (minority) number of courses that have failed their validation. The validation of a course should be conducted by a person not connected with the race organization and, in the case of world records, should not be a citizen of the country whose course is being validated nor of the nation the would-be record-holder is from. Validations should also be done by measurers who maintain high technical standards. Score this as a giant step backwards. The IAAF has decreed that women's records can be set in mixed races. While this is the present policy in the USA, this is NOT the direction that we should be going in. It seems evident that one motivating factor in having the elite women start before the elite men/masses start was in anticipation that the IAAF would restrict world records to races in which one could be certain that the elite women were not paced by men,as evidenced by a number of previous statements issued by the IAAF That this is a good idea for several reasons has been clearly demonstrated by the London Marathon and this year, by the New York City Marathon. It definitely enhances one's appreciation of the (women's) race when it is not buried in the men's race. A positive step in this direction by the IAAF would have induced many more marathons (and other races) to take this step. This in turn, would have made it feasible for NGB's to rule likewise. This combination would have all but assured that the elite women would commonly have their own race (and glory). As it stands, marathons in particular are discouraged from having an early start for the elite women since a mixed race with male pacing would be more likely to produce a record. The IAAF really dropped the ball on this one, score it as a fumble, loss of yardage, and a turn-over. Examination of the list of performances meeting all of the criteria reveals that intermediate times may be used for IAAF record purposes. This is a bad idea for a number of reasons. First, the care taken to lay out intermediate distances is rarely at the same level as that taken to lay out the start => finish distance. Often, intermediate distances are "laid in" with a single measurement. For an intermediate distance to qualify for "record quality" it should be subject to ALL of the same requirements that the overall course is subject to, including two measurements (within 0.08%), complete documentation, and location marks (PK-nails, etc) in the same way the finish line is marked. Second, a special validation is needed to assure that the intermediate times were indeed taken at the proper location. This is much more difficult than with a finish line which is generally well photographed and well documented. As a minimum, photographs showing the runner passing the timer with sufficient ground detail to ascertain the exact timing location (to within a meter or two) should be required. All too often, intermediate timers are not positioned properly due to time limitations during the conduct of the race. In addition, there is rarely anything more than a sign and a person timing to indicate the intermediate "finish", quite unlike a "real" finish line, making it much more difficult to validate the "finish" for an intermediate distance. Third, what about runners who are deprived of a "record" simply because the race organization did not provide timing at the corresponding intermediate point? For example, Naoko Takahashi is listed with "WR's" for 25 km and 30 km for intermediate times from the Berlin Marathon. The pace for those two "WR's" is 3:18 per km (for both). However, Paula Radcliffe averaged 3:15 per km for the entire marathon at Chicago, making it a virtual certainty that, had those intermediate times been taken, she would be credited with the 25K and 30K records. Fourth, what about runners who run negative splits (like Khalid Khannouchi)? Perhaps race organizers could document an intermediate time at 12.195 km and use the time differential from that point to the finish for a 30 km record. That has just as much validity as using a time from the start to an intermediate point. Fifth, allowing say 30 km records to be set during a marathon, greatly discourages runners from racing the 30 km distance. Likewise, allowing 20 km records to be set during a half marathon discourages race promoters from holding 20 km races. In case you hadn't noticed, the road racing scene is evolving to a future in which there are 5K, 10K, half marathon, and marathon races and little else. Look at the variety of different distances that runners had to choose from in the 1960's and 1970's. You will find the 25km distance commonly contested by the top runners. Today, only two significant 25 km races are left of the dozens that were held in the 1970's. Today, 30 km races are as scarce as hen's teeth (well, not quite but almost). Why have records at distances such as 20 km and 30 km if they are dominated by intermediate times made enroute to longer distances and there are virtually no races being held at these distances? Wouldn't it make better sense to restrict records to actual race distances? Sixth, what if a pacer goes through 30 km and "claims" a 30 km record but drops out shortly thereafter? There is a precedent that maintains a runner must finish the stated distance of the race in order for intermediate times to count. Is the race a 30 km race or is it a marathon? You can't have it both ways. Score a step backwards for this one. Ekiden records??? They have to be kidding. This is another blatant political decision. How many countries actually hold ekiden races? I'll warrant that there are more countries holding 10 mile races than those holding ekidens and there are MANY more ten mile races than ekidens. What standards are there for ekidens? Are the lengths of each leg specified as well as the number of legs? The very few ekidens that have been held outside the Orient have, for the most part, been one-time affairs and have not proved popular. Give us a break. Score a big step backwards for this one. This would have been a perfect opportunity to institute the use of fractional seconds in road record keeping, following the ADR practice of using tenths. However, this would be too much to expect from an organization that seems intent on trashing the credibility of road racing as a legitimate sport, on par with track and field. The standards for record-keeping must be determined in a method as free from political considerations as possible. Back in the very early 1980's, I was given the task of setting up the rules whereby road records would be kept for the USA. Bob Hersh, chairman of the TAC Records Committee, called me on the phone and out-lined what he wanted me to do in an hour long conversation. I had no connection with a major race and was not actively involved with TAC except on matters related to course certification. I had started an organization known as the National Running Data Center with the purpose of gathering, organizing, and publishing records and rankings lists for the USA. In order to provide meaningful lists, I had to consider course accuracies and the effects of drop and wind on performance. Having a sizeable database of race results, I was able to come up with what I felt to be reasonable requirements for recognizing road records. These included four main components: (1) a certified course, (2) post-race validation, (3) drop not exceeding 2 m/km, and (4) S/F separation not exceeding 10%. These were adopted by the USA in 1983 and were modified in 1988, relaxing the S/F separation to 30% and tightening the drop requirement to 1 m/km. The 1988 modifications were based on a more rigorous examination than the original rules were based on. The choice of record distances was based on commonly run distances (in the USA). The choice of the 8 km distance rather than 5 miles was made so that either distance could qualify for a record. Until the 8 km distance was made a record distance, there were relatively few 8 km races in the USA. Now there are roughly equal numbers of 8K and 5M races. These standards have stood the test of time quite well and the present USA road record list (now maintained by the RRIC) is generally recognized as very reliable. Now the IAAF comes along, 20 years later, and tries to re-invent the wheel. Rather than adopt what has already been shown to work, the process is thrown to the politicians and you see what devolves. This latest decision by the IAAF does not promote the sport of road racing and if anything, degrades the record-keeping process to the level of political in-fighting. Overall, the IAAF has taken a giant step backwards. NOTE: ADR will continue to maintain its present standards, without regard to what the IAAF proclaims as world records. The objective is to compile meaningful and reliable statistical lists.


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Attitude That Pay Forever
Hunger For Learning
90%
Selling
40%
Making Relationship
90%
Spirituality
30%
Going To The Extra Mile
75%


Window:
Join Room
Open Room
Draw Enable
Draw Disable
Erace Dicome
Enter Room Id
Auto Open Or Join Room
...
Tools:
Drop Multiple DCM Files Here
or
AR Based P2P Collabrative Medical Image Manipulation


Task
问题描述 :
But life is not so easy sometimes. For example, here is a recipe for making pizza dough:
1.Mix the yeast with warm water, wait for 5 to 10 minutes.
2.Mix the the remaining ingredients 7 to 9 minutes.
3.Mix the yeast and the remaining ingredients together for 10 to 15 minutes.
4.Wait 90 to 120 minutes for the dough to rise.
5.Punch the dough and let it rest for 10 to 15 minutes.
6.Roll the dough.
In this case, tasks 1 and 2 may be scheduled after the first minute (we always spend the first minute to read the recipe and come up with a plan). The earliest task 3 may be started is at 8 minutes, and task 4 may start at 18 minutes after the start, and so on. This recipe is relatively simple, but if some tasks have many dependent tasks then scheduling can become unmanageable. Sometimes, the recipe may in fact be impossible to execute. For example, consider the following abstract recipe:
1.task 1
2.after task 1 but within 2 minutes of it, do task 2
3.at least 3 minutes after task 2 but within 2 minutes of task 1, do task 3
In this problem, you are given a number of tasks. Some tasks are related to another based on their starting times. You are asked to assign a starting time to each task to satisfy all constraints if possible, or report that no valid schedule is possible.
输入:
task i starts at least A minutes later than task j
task i starts within A minutes of the starting time of task j
where i and j are the task numbers of two different tasks (1 ≤ i, j ≤ n), and A is a non-negative integer (A ≤ 150). The first form states that task i must start at least A minutes later than the start time of task j. The second form states that task i must start no earlier than task j, and within A minutes of the starting time of task j. There may be multiple constraints involving the same pair of tasks. Note that at least and within include the end points (i.e. if task 1 starts at 1 minute and task 2 starts at 4 minutes, then task 2 starts at least 3 minutes later than task 1, and within 3 minutes of the starting time of task 1).
The input is terminated by n = 0.
输出:
task i starts at least A minutes later than task j
task i starts within A minutes of the starting time of task j
where i and j are the task numbers of two different tasks (1 ≤ i, j ≤ n), and A is a non-negative integer (A ≤ 150). The first form states that task i must start at least A minutes later than the start time of task j. The second form states that task i must start no earlier than task j, and within A minutes of the starting time of task j. There may be multiple constraints involving the same pair of tasks. Note that at least and within include the end points (i.e. if task 1 starts at 1 minute and task 2 starts at 4 minutes, then task 2 starts at least 3 minutes later than task 1, and within 3 minutes of the starting time of task 1).
The input is terminated by n = 0.
样例输入:
6 10 task 3 starts at least 5 minutes later than task 1 task 3 starts within 10 minutes of the starting time of task 1 task 3 starts at least 7 minutes later than task 2 task 3 starts within 9 minutes of the starting time of task 2 task 4 starts at least 10 minutes later than task 3 task 4 starts within 15 minutes of the starting time of task 3 task 5 starts at least 90 minutes later than task 4 task 5 starts within 120 minutes of the starting time of task 4 task 6 starts at least 10 minutes later than task 5 task 6 starts within 15 minutes of the starting time of task 5 3 4 task 2 starts at least 0 minutes later than task 1 task 2 starts within 2 minutes of the starting time of task 1 task 3 starts at least 3 minutes later than task 2 task 3 starts within 2 minutes of the starting time of task 1 0
样例输出:
3 1 8 18 108 118 Impossible.
这是一道查分约束方程的题。难点在于如何建图，task i starts at least A minutes later than task j得到的方程是：tj – ti <= -Ａ，task i starts within A minutes of the starting time of task j得到的方程是： tj – ti <= 0和ti – tj <= A，然后建图，最后用bellman_ford来判，注意输出时，为防止时间有负数或超出1000000，每个数要减去最小的数并加1。
程序代码：
#include<iostream>
#include<cstdio>
#include<string>
#include<cctype>
#include<sstream>
using namespace std;
#define MAXN 110
#define INF (1 << 30)
int d[MAXN], u[MAXN * MAXN], v[MAXN * MAXN], w[MAXN * MAXN];
int e, n, vMin, num[4];
int ballman_ford()
{
for(int i = 0; i <= n; d[i++] = INF);
d[0] = 0;
for(int i = 0; i < n; i++){
for(int j = 0; j < e; j++){
if(d[v[j]] > d[u[j]] + w[j])
d[v[j]] = d[u[j]] + w[j];
}
}
for(int i = 0; i < e; i++)
if(d[v[i]] > d[u[i]] + w[i])
return 0;
vMin = (1 << 30);
for(int i = 1; i <= n; i++)
if(vMin > d[i]) vMin = d[i];
return 1;
}
int main()
{
string input, s;
int i, j, k, l, m, flag;
//freopen("input.txt", "r", stdin);
while(scanf("%d", &n)){
if(!n) break;
scanf("%d", &m);
cin.ignore();
e = 0;
for(i = 0; i < m; i++){
getline(cin, input);
istringstream in (input);
//cout<<input<<endl;
num[0] = num[1] = num[2] = 0;
for(j = 0, l = 0; in>>s; j++){
if(isdigit(s[0])){
for(k = 0; k < s.size(); k++)
num[l] = num[l] * 10 + s[k] - '0';
l++;
}
if(j == 3){
if(s == "at") flag = 1;
else flag = 0;
}
}
if(flag){
u[e] = num[0]; v[e] = num[2]; w[e++] = -num[1];
}else {
u[e] = num[0]; v[e] = num[2]; w[e++] = 0;
u[e] = num[2]; v[e] = num[0]; w[e++] = num[1];
}
}
for(i = 1; i <= n; e++, i++){
u[e] = 0; v[e] = i; w[e] = 0;
if(ballman_ford()){
for(i = 1; i <= n; i++)
printf("%d%c", d[i] - vMin + 1, i == n ? '/n' : ' ');
}else printf("Impossible./n");
}
return 0;
}版权声明：本文为博主原创文章，未经博主允许不得转载。
参考：http://blog.csdn.net/zhaofukai/article/details/6279471
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我的订单号是1130126020840470 2013-01-26 12:11下的单，什么时候能发货呀!很期待呀！
我的订单号是1130126020840470 2013-01-26 12:11下的单，什么时候能发货呀!很期待呀！
我的订单号是1130126020840470 2013-01-26 12:11下的单，什么时候能发货呀!很期待呀！
我的订单号是1130126020840470 2013-01-26 12:11下的单，什么时候能发货呀!很期待呀！
第一种方法怎么会是O(log(m+n))呢？没有二分的话直接遍历应该就是O(m+n)吧？
有一点问题。。后面动态规划的程序中
int dp[n+1][W+1];
会报错 提示表达式必须含有常量值。该怎么修改呢。。
还真是，i7的cpu跑了3秒
这一题的代码这样提交时错误的~少考虑了很多情况
对，还是这种实现比较好理解。虽然效率有点低
居然看懂了
全是智力分析的题
嗯，是写错了，文中以更改。MAX ( L(X[0..m-2], Y[0..n-1]), L(X[0..m-1], Y[0..n-2])
额楼主能否在发布代码的同时对解题思路做个讲解呢？这样大家在学习的时候就方便多了。
厉害啊，我遇见数学问题就头疼！
看见的晚了，只能提交一次唉
大致看了下，算法的时间复杂度没有很本性的提升，算不上高效。
有意思，有难度


| View source on GitHub |
LinearRegressor
An estimator for TensorFlow Linear regression problems.
Inherits From: Estimator
Train a linear regression model to predict label value given observation of feature values.
categorical_column_a = categorical_column_with_hash_bucket(...) categorical_column_b = categorical_column_with_hash_bucket(...) categorical_feature_a_x_categorical_feature_b = crossed_column(...) # Estimator using the default optimizer. estimator = LinearRegressor( feature_columns=[categorical_column_a, categorical_feature_a_x_categorical_feature_b]) # Or estimator using the FTRL optimizer with regularization. estimator = LinearRegressor( feature_columns=[categorical_column_a, categorical_feature_a_x_categorical_feature_b], optimizer=tf.keras.optimizers.Ftrl( learning_rate=0.1, l1_regularization_strength=0.001 )) # Or estimator using an optimizer with a learning rate decay. estimator = LinearRegressor( feature_columns=[categorical_column_a, categorical_feature_a_x_categorical_feature_b], optimizer=lambda: tf.keras.optimizers.Ftrl( learning_rate=tf.exponential_decay( learning_rate=0.1, global_step=tf.get_global_step(), decay_steps=10000, decay_rate=0.96)) # Or estimator with warm-starting from a previous checkpoint. estimator = LinearRegressor( feature_columns=[categorical_column_a, categorical_feature_a_x_categorical_feature_b], warm_start_from="/path/to/checkpoint/dir") # Input builders def input_fn_train: # Returns tf.data.Dataset of (x, y) tuple where y represents label's class # index. pass def input_fn_eval: # Returns tf.data.Dataset of (x, y) tuple where y represents label's class # index. pass def input_fn_predict: # Returns tf.data.Dataset of (x, None) tuple. pass estimator.train(input_fn=input_fn_train) metrics = estimator.evaluate(input_fn=input_fn_eval) predictions = estimator.predict(input_fn=input_fn_predict)
Input of train
and evaluate
should have following features,
otherwise there will be a KeyError:
weight_column
is not None
, a feature with key=weight_column
whose
value is a Tensor
.column
in feature_columns
:column
is a SparseColumn
, a feature with key=column.name
whose value
is a SparseTensor
.column
is a WeightedSparseColumn
, two features: the first with
key
the id column name, the second with key
the weight column name.
Both features' value
must be a SparseTensor
.column
is a RealValuedColumn
, a feature with key=column.name
whose value
is a Tensor
.Loss is calculated by using mean squared error.
Estimators can be used while eager execution is enabled. Note that input_fn
and all hooks are executed inside a graph context, so they have to be written
to be compatible with graph mode. Note that input_fn
code using tf.data
generally works in both graph and eager modes.
__init__
__init__( feature_columns, model_dir=None, label_dimension=1, weight_column=None, optimizer='Ftrl', config=None, partitioner=None, warm_start_from=None, loss_reduction=losses.Reduction.SUM, sparse_combiner='sum' )
Constructs an Estimator
instance.
See estimators for more information.
To warm-start an Estimator
:
estimator = tf.estimator.DNNClassifier( feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb], hidden_units=[1024, 512, 256], warm_start_from="/path/to/checkpoint/dir")
For more details on warm-start configuration, see
tf.estimator.WarmStartSettings
.
model_fn
: Model function. Follows the signature:
Args:
features
: This is the first item returned from the input_fn
passed to train
, evaluate
, and predict
. This should be a
single tf.Tensor
or dict
of same.labels
: This is the second item returned from the input_fn
passed to train
, evaluate
, and predict
. This should be a
single tf.Tensor
or dict
of same (for multi-head models).
If mode is tf.estimator.ModeKeys.PREDICT
, labels=None
will
be passed. If the model_fn
's signature does not accept
mode
, the model_fn
must still be able to handle
labels=None
.mode
: Optional. Specifies if this is training, evaluation or
prediction. See tf.estimator.ModeKeys
.params
: Optional dict
of hyperparameters. Will receive what
is passed to Estimator in params
parameter. This allows
to configure Estimators from hyper parameter tuning.config
: Optional estimator.RunConfig
object. Will receive what
is passed to Estimator as its config
parameter, or a default
value. Allows setting up things in your model_fn
based on
configuration such as num_ps_replicas
, or model_dir
.Returns:
tf.estimator.EstimatorSpec
model_dir
: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into an estimator to
continue training a previously saved model. If PathLike
object, the
path will be resolved. If None
, the model_dir in config
will be used
if set. If both are set, they must be same. If both are None
, a
temporary directory will be used.
config
: estimator.RunConfig
configuration object.params
: dict
of hyper parameters that will be passed into model_fn
.
Keys are names of parameters, values are basic python types.warm_start_from
: Optional string filepath to a checkpoint or SavedModel to
warm-start from, or a tf.estimator.WarmStartSettings
object to fully configure warm-starting.If None, only TRAINABLE variables are warm-started. If the string filepath is provided instead of a tf.estimator.WarmStartSettings, then all variables are warm-started, and it is assumed that vocabularies and tf.Tensor names are unchanged.
ValueError
: parameters of model_fn
don't match params
.ValueError
: if this is called via a subclass and if that class overrides
a member of Estimator
.config
model_dir
model_fn
Returns the model_fn
which is bound to self.params
.
The model_fn
with following signature:
def model_fn(features, labels, mode, config)
params
eval_dir
eval_dir(name=None)
Shows the directory name where evaluation metrics are dumped.
name
: Name of the evaluation if user needs to run multiple evaluations on
different data sets, such as on training data vs test data. Metrics for
different evaluations are saved in separate folders, and appear
separately in tensorboard.A string which is the path of directory contains evaluation metrics.
evaluate
evaluate( input_fn, steps=None, hooks=None, checkpoint_path=None, name=None )
Evaluates the model given evaluation data input_fn
.
For each step, calls input_fn
, which returns one batch of data.
Evaluates until:
- steps
batches are processed, or
- input_fn
raises an end-of-input exception (tf.errors.OutOfRangeError
or
StopIteration
).
input_fn
: A function that constructs the input data for evaluation. See
Premade Estimators
for more information. The
function should construct and return one of the following: * A
tf.data.Dataset
object: Outputs of Dataset
object must be a tuple
(features, labels)
with same constraints as below. * A tuple
(features, labels)
: Where features
is a tf.Tensor
or a dictionary
of string feature name to Tensor
and labels
is a Tensor
or a
dictionary of string label name to Tensor
. Both features
and
labels
are consumed by model_fn
. They should satisfy the expectation
of model_fn
from inputs.steps
: Number of steps for which to evaluate model. If None
, evaluates
until input_fn
raises an end-of-input exception.hooks
: List of tf.train.SessionRunHook
subclass instances. Used for
callbacks inside the evaluation call.checkpoint_path
: Path of a specific checkpoint to evaluate. If None
, the
latest checkpoint in model_dir
is used. If there are no checkpoints
in model_dir
, evaluation is run with newly initialized Variables
instead of ones restored from checkpoint.name
: Name of the evaluation if user needs to run multiple evaluations on
different data sets, such as on training data vs test data. Metrics for
different evaluations are saved in separate folders, and appear
separately in tensorboard.A dict containing the evaluation metrics specified in model_fn
keyed by
name, as well as an entry global_step
which contains the value of the
global step for which this evaluation was performed. For canned
estimators, the dict contains the loss
(mean loss per mini-batch) and
the average_loss
(mean loss per sample). Canned classifiers also return
the accuracy
. Canned regressors also return the label/mean
and the
prediction/mean
.
ValueError
: If steps <= 0
.experimental_export_all_saved_models
experimental_export_all_saved_models( export_dir_base, input_receiver_fn_map, assets_extra=None, as_text=False, checkpoint_path=None )
Exports a SavedModel
with tf.MetaGraphDefs
for each requested mode.
For each mode passed in via the input_receiver_fn_map
,
this method builds a new graph by calling the input_receiver_fn
to obtain
feature and label Tensor
s. Next, this method calls the Estimator
's
model_fn
in the passed mode to generate the model graph based on
those features and labels, and restores the given checkpoint
(or, lacking that, the most recent checkpoint) into the graph.
Only one of the modes is used for saving variables to the SavedModel
(order of preference: tf.estimator.ModeKeys.TRAIN
,
tf.estimator.ModeKeys.EVAL
, then
tf.estimator.ModeKeys.PREDICT
), such that up to three
tf.MetaGraphDefs
are saved with a single set of variables in a single
SavedModel
directory.
For the variables and tf.MetaGraphDefs
, a timestamped export directory
below
export_dir_base
, and writes a SavedModel
into it containing
the tf.MetaGraphDef
for the given mode and its associated signatures.
For prediction, the exported MetaGraphDef
will provide one SignatureDef
for each element of the export_outputs
dict returned from the model_fn
,
named using the same keys. One of these keys is always
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
,
indicating which
signature will be served when a serving request does not specify one.
For each signature, the outputs are provided by the corresponding
tf.estimator.export.ExportOutput
s, and the inputs are always the input
receivers provided by
the serving_input_receiver_fn
.
For training and evaluation, the train_op
is stored in an extra
collection,
and loss, metrics, and predictions are included in a SignatureDef
for the
mode in question.
Extra assets may be written into the SavedModel
via the assets_extra
argument. This should be a dict, where each key gives a destination path
(including the filename) relative to the assets.extra directory. The
corresponding value gives the full path of the source file to be copied.
For example, the simple case of copying a single file without renaming it
is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}
.
export_dir_base
: A string containing a directory in which to create
timestamped subdirectories containing exported SavedModel
s.input_receiver_fn_map
: dict of tf.estimator.ModeKeys
to
input_receiver_fn
mappings, where the input_receiver_fn
is a
function that takes no arguments and returns the appropriate subclass of
InputReceiver
.assets_extra
: A dict specifying how to populate the assets.extra directory
within the exported SavedModel
, or None
if no extra assets are
needed.as_text
: whether to write the SavedModel
proto in text format.checkpoint_path
: The checkpoint path to export. If None
(the default),
the most recent checkpoint found within the model directory is chosen.The string path to the exported directory.
ValueError
: if any input_receiver_fn
is None
, no export_outputs
are provided, or no checkpoint can be found.export_saved_model
export_saved_model( export_dir_base, serving_input_receiver_fn, assets_extra=None, as_text=False, checkpoint_path=None, experimental_mode=ModeKeys.PREDICT )
Exports inference graph as a SavedModel
into the given dir.
For a detailed guide, see Using SavedModel with Estimators.
This method builds a new graph by first calling the
serving_input_receiver_fn
to obtain feature Tensor
s, and then calling
this Estimator
's model_fn
to generate the model graph based on those
features. It restores the given checkpoint (or, lacking that, the most
recent checkpoint) into this graph in a fresh session. Finally it creates
a timestamped export directory below the given export_dir_base
, and writes
a SavedModel
into it containing a single tf.MetaGraphDef
saved from this
session.
The exported MetaGraphDef
will provide one SignatureDef
for each
element of the export_outputs
dict returned from the model_fn
, named
using
the same keys. One of these keys is always
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
,
indicating which
signature will be served when a serving request does not specify one.
For each signature, the outputs are provided by the corresponding
tf.estimator.export.ExportOutput
s, and the inputs are always the input
receivers provided by
the serving_input_receiver_fn
.
Extra assets may be written into the SavedModel
via the assets_extra
argument. This should be a dict, where each key gives a destination path
(including the filename) relative to the assets.extra directory. The
corresponding value gives the full path of the source file to be copied.
For example, the simple case of copying a single file without renaming it
is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}
.
The experimental_mode parameter can be used to export a single
train/eval/predict graph as a SavedModel
.
See experimental_export_all_saved_models
for full docs.
export_dir_base
: A string containing a directory in which to create
timestamped subdirectories containing exported SavedModel
s.serving_input_receiver_fn
: A function that takes no argument and returns a
tf.estimator.export.ServingInputReceiver
or
tf.estimator.export.TensorServingInputReceiver
.assets_extra
: A dict specifying how to populate the assets.extra directory
within the exported SavedModel
, or None
if no extra assets are
needed.as_text
: whether to write the SavedModel
proto in text format.checkpoint_path
: The checkpoint path to export. If None
(the default),
the most recent checkpoint found within the model directory is chosen.experimental_mode
: tf.estimator.ModeKeys
value indicating with mode
will be exported. Note that this feature is experimental.The string path to the exported directory.
ValueError
: if no serving_input_receiver_fn
is provided, no
export_outputs
are provided, or no checkpoint can be found.export_savedmodel
export_savedmodel( export_dir_base, serving_input_receiver_fn, assets_extra=None, as_text=False, checkpoint_path=None, strip_default_attrs=False )
Exports inference graph as a SavedModel
into the given dir. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
This function has been renamed, use export_saved_model
instead.
For a detailed guide, see Using SavedModel with Estimators.
This method builds a new graph by first calling the
serving_input_receiver_fn
to obtain feature Tensor
s, and then calling
this Estimator
's model_fn
to generate the model graph based on those
features. It restores the given checkpoint (or, lacking that, the most
recent checkpoint) into this graph in a fresh session. Finally it creates
a timestamped export directory below the given export_dir_base
, and writes
a SavedModel
into it containing a single tf.MetaGraphDef
saved from this
session.
The exported MetaGraphDef
will provide one SignatureDef
for each
element of the export_outputs
dict returned from the model_fn
, named
using
the same keys. One of these keys is always
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
,
indicating which
signature will be served when a serving request does not specify one.
For each signature, the outputs are provided by the corresponding
tf.estimator.export.ExportOutput
s, and the inputs are always the input
receivers provided by
the serving_input_receiver_fn
.
Extra assets may be written into the SavedModel
via the assets_extra
argument. This should be a dict, where each key gives a destination path
(including the filename) relative to the assets.extra directory. The
corresponding value gives the full path of the source file to be copied.
For example, the simple case of copying a single file without renaming it
is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}
.
export_dir_base
: A string containing a directory in which to create
timestamped subdirectories containing exported SavedModel
s.serving_input_receiver_fn
: A function that takes no argument and returns a
tf.estimator.export.ServingInputReceiver
or
tf.estimator.export.TensorServingInputReceiver
.assets_extra
: A dict specifying how to populate the assets.extra directory
within the exported SavedModel
, or None
if no extra assets are
needed.as_text
: whether to write the SavedModel
proto in text format.checkpoint_path
: The checkpoint path to export. If None
(the default),
the most recent checkpoint found within the model directory is chosen.strip_default_attrs
: Boolean. If True
, default-valued attributes will be
removed from the NodeDef
s. For a detailed guide, see Stripping
Default-Valued Attributes.The string path to the exported directory.
ValueError
: if no serving_input_receiver_fn
is provided, no
export_outputs
are provided, or no checkpoint can be found.get_variable_names
get_variable_names()
Returns list of all variable names in this model.
List of names.
ValueError
: If the Estimator
has not produced a checkpoint yet.get_variable_value
get_variable_value(name)
Returns value of the variable given by name.
name
: string or a list of string, name of the tensor.Numpy array - value of the tensor.
ValueError
: If the Estimator
has not produced a checkpoint yet.latest_checkpoint
latest_checkpoint()
Finds the filename of the latest saved checkpoint file in model_dir
.
The full path to the latest checkpoint or None
if no checkpoint was
found.
predict
predict( input_fn, predict_keys=None, hooks=None, checkpoint_path=None, yield_single_examples=True )
Yields predictions for given features.
Please note that interleaving two predict outputs does not work. See: issue/20506
input_fn
: A function that constructs the features. Prediction continues
until input_fn
raises an end-of-input exception
(tf.errors.OutOfRangeError
or StopIteration
).
See Premade Estimators
for more information. The function should construct and return one of
the following:
tf.data.Dataset
object: Outputs of Dataset
object must have
same constraints as below.tf.Tensor
or a dictionary of string feature name to
Tensor
. features are consumed by model_fn
. They should satisfy
the expectation of model_fn
from inputs.predict_keys
: list of str
, name of the keys to predict. It is used if
the tf.estimator.EstimatorSpec.predictions
is a dict
. If
predict_keys
is used then rest of the predictions will be filtered
from the dictionary. If None
, returns all.
hooks
: List of tf.train.SessionRunHook
subclass instances. Used for
callbacks inside the prediction call.checkpoint_path
: Path of a specific checkpoint to predict. If None
, the
latest checkpoint in model_dir
is used. If there are no checkpoints
in model_dir
, prediction is run with newly initialized Variables
instead of ones restored from checkpoint.yield_single_examples
: If False
, yields the whole batch as returned by
the model_fn
instead of decomposing the batch into individual
elements. This is useful if model_fn
returns some tensors whose first
dimension is not equal to the batch size.Evaluated values of predictions
tensors.
ValueError
: If batch length of predictions is not the same and
yield_single_examples
is True
.ValueError
: If there is a conflict between predict_keys
and
predictions
. For example if predict_keys
is not None
but
tf.estimator.EstimatorSpec.predictions
is not a dict
.train
train( input_fn, hooks=None, steps=None, max_steps=None, saving_listeners=None )
Trains a model given training data input_fn
.
input_fn
: A function that provides input data for training as minibatches.
See Premade Estimators
for more information. The function should construct and return one of
the following:tf.data.Dataset
object: Outputs of Dataset
object must be
a tuple (features, labels)
with same constraints as below.(features, labels)
: Where features
is a tf.Tensor
or
a dictionary of string feature name to Tensor
and labels
is a
Tensor
or a dictionary of string label name to Tensor
. Both
features
and labels
are consumed by model_fn
. They should
satisfy the expectation of model_fn
from inputs.hooks
: List of tf.train.SessionRunHook
subclass instances. Used for
callbacks inside the training loop.steps
: Number of steps for which to train the model. If None
, train
forever or train until input_fn
generates the tf.errors.OutOfRange
error or StopIteration
exception. steps
works incrementally. If you
call two times train(steps=10)
then training occurs in total 20 steps.
If OutOfRange
or StopIteration
occurs in the middle, training stops
before 20 steps. If you don't want to have incremental behavior please
set max_steps
instead. If set, max_steps
must be None
.max_steps
: Number of total steps for which to train model. If None
,
train forever or train until input_fn
generates the
tf.errors.OutOfRange
error or StopIteration
exception. If set,
steps
must be None
. If OutOfRange
or StopIteration
occurs in the
middle, training stops before max_steps
steps. Two calls to
train(steps=100)
means 200 training iterations. On the other hand, two
calls to train(max_steps=100)
means that the second call will not do
any iteration since first call did all 100 steps.saving_listeners
: list of CheckpointSaverListener
objects. Used for
callbacks that run immediately before or after checkpoint savings.self
, for chaining.
ValueError
: If both steps
and max_steps
are not None
.ValueError
: If either steps
or max_steps <= 0
.


37.77
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President and
Chief
Intellicorp
Intelligence
this is a note
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public


Uses of Emojis
Design research conducted on Teenagers and millennium people to figure out current scope trend and future of using emojis in textual conversations
Design research conducted on Teenagers and millennium people to figure out current scope trend and future of using emojis in textual conversations


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Active ingredients
(in each gelcap)
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Pyrilamine maleate 15 mg
Purpose
Pain reliever
Stimulant
Diuretic
Uses
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Ask a doctor before use if you have
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| Package Group |
Package Artifact |
Package Version |
Remote Licenses |
Local Licenses |
| org.keycloak |
keycloak-adapter-core |
12.0.3 |
|
|
| org.keycloak |
keycloak-adapter-spi |
12.0.3 |
|
|
| org.keycloak |
keycloak-authz-client |
12.0.3 |
|
|
| org.keycloak |
keycloak-common |
12.0.3 |
|
|
| org.keycloak |
keycloak-core |
12.0.3 |
|
|
| org.keycloak |
keycloak-jboss-adapter-core |
12.0.3 |
|
|
| org.keycloak |
keycloak-undertow-adapter |
12.0.3 |
|
|
| org.keycloak |
keycloak-undertow-adapter-spi |
12.0.3 |
|
|
| org.keycloak |
keycloak-wildfly-adapter |
12.0.3 |
|
|
| org.keycloak |
keycloak-wildfly-elytron-oidc-adapter |
12.0.3 |
|
|
| org.keycloak |
keycloak-wildfly-subsystem |
12.0.3 |
|
|


| void AirportHandler::addAirplane |
( |
Airplane * |
airplane | ) |
|
| void AirportHandler::addAirport |
( |
Airport * |
airport | ) |
|
| ESuccess AirportHandler::addXmlData |
( |
const string & |
fileName | ) |
|
Add airplanes, airports and runways from an given xml file. Preconditions: propperlyInitialised()
- Parameters
-
| bool AirportHandler::airplaneExists |
( |
const string & |
callsign | ) |
|
Check if an airplane exists Preconditions: propperlyInitialised()
- Parameters
-
- Returns
- bool
| bool AirportHandler::airportEmpty |
( |
Airport * |
airport | ) |
|
Check if an airport is empty (no airplanes in or around the airport) Preconditions: propperlyInitialised()
- Parameters
-
- Returns
- bool
| bool AirportHandler::airportExists |
( |
const string & |
iata | ) |
|
Check if an airport already exists. Preconditions: propperlyInitialised()
- Parameters
-
- Returns
- bool
| void AirportHandler::fileOutput |
( |
const string & |
fileName = "AirportInfo.txt" | ) |
|
Outputs a file, with the info of the airplanes and airports, to the working directory Preconditions: propperlyInitialised()
| const vector< Airplane * > & AirportHandler::getAirplanes |
( |
| ) |
const |
Returns a vector with all the saved airplanes Preconditions: propperlyInitialised()
- Returns
- airplanes
| Airport * AirportHandler::getAirport |
( |
const string & |
name | ) |
|
Get the pointer to the airportwith the given name. Preconditions: propperlyInitialised()
- Parameters
-
- Returns
- Airport*
| const vector< Airport * > & AirportHandler::getAirports |
( |
| ) |
const |
Returns a vector with the saved airports Preconditions: propperlyInitialised()
- Returns
- airports
| string AirportHandler::getInfo |
( |
| ) |
|
Get a string with the info of all planes and airports Preconditions: propperlyInitialised()
- Returns
- info
| void AirportHandler::GraphicalAirport2D |
( |
const string & |
iata | ) |
|
Outputs a file with the given airports iata as file name, containing a 2D graphical representation of the Airport in it's current state Preconditions: airportExists(string AirportIata); Preconditions: propperlyInitialised()
- Parameters
-
| void AirportHandler::GraphicalAirport3D |
( |
const string & |
iata | ) |
|
Outputs an ini file containg the data to be read in a graphical engine to represent the airport in a 3D environement Preconditions: propperlyInitialised()
- Parameters
-
| bool AirportHandler::properlyInitialised |
( |
| ) |
|
Check if the Airporthandler was propperly initiallized and isn't NULL
- Returns
- bool
| void AirportHandler::removeAirplane |
( |
const string & |
number | ) |
|
Removes an airplane from the saved Airplanes Preconditions: AirportHandler::airplaneExists(callsign) Preconditions: propperlyInitialised() Postconditions: !AirportHandler::airplaneExists(callsign)
- Parameters
-
| void AirportHandler::removeAirport |
( |
const string & |
iata | ) |
|
Removes an airport from the saved airports Preconditions: AirportHandler::airportExists(iata) Preconditions: propperlyInitialised() Postcondition: !AirportHandler::airportExists(iata)
- Parameters
-
| void AirportHandler::runSimulation |
( |
const string & |
name | ) |
|
Run a simultaion of an airport Preconditions: !AirportHandler::getAirports().empty() && !AirportHandler::getAirplanes().empty() && !AirportHandler::getAirport(iata)->getRunways().empty() Preconditions: propperlyInitialised()
- Parameters
-
| name | of the airport to run a simulation on |
| void AirportHandler::setAirplanes |
( |
const vector< Airplane * > & |
airplanes | ) |
|
Dets the saved planes to the given vector of planes. Note that if you already had other airplanes saved, they will dissapear from this handler and might cause memory leaks if not deleted properly. It is adviced to only use this function to initialize the vector of airplanes. Precondition: AirportHandler::validAirplanes(Airplanes); Preconditions: propperlyInitialised() Postconditions: AirportHandler::getAirplanes() == Airplanes
- Parameters
-
| void AirportHandler::setAirports |
( |
const vector< Airport * > & |
airports | ) |
|
Sets the saved airports to the given vector of airports. Note that if you already had other airports saved, they will dissapear from this handler and might cause memory leaks if not deleted properly. It is adviced to only use this function to initialize the vector of airports. Precondition: validAirports(vector<Airport*>) Preconditions: propperlyInitialised() Postcondition: Airports == AirportHandler::getAirports()
- Parameters
-
| void AirportHandler::sortPlanesBySquawk |
( |
| ) |
|
Sorts the airplanes vector by squawk codes Preconditions: propperlyInitialised()
| string AirportHandler::timeToString |
( |
double |
passedTimeUnits | ) |
|
' Changes a double to it's corresponding time in string format (hh:mm) Time is relative to the starting time of the simulation (default = 12:00) Preconditions: propperlyInitialised()
- Parameters
-
- Returns
- string
| bool AirportHandler::validAirplane |
( |
Airplane * |
airplane | ) |
|
Check if an airplane is valid to add to the system (no duplicate or faulty initialised) Preconditions: propperlyInitialised()
- Parameters
-
- Returns
- bool
| bool AirportHandler::validAirplanes |
( |
vector< Airplane * > |
airplanes | ) |
|
Check if the vector of airplanes is valid (no duplicates or faulty initialised airplanes) Preconditions: propperlyInitialised()
- Parameters
-
- Returns
- bool
| bool AirportHandler::validAirport |
( |
Airport * |
airport | ) |
|
Check if an airport is valid to add to the system (no duplicate or faulty initialised) Preconditions: propperlyInitialised()
- Parameters
-
- Returns
- bool
| bool AirportHandler::validAirports |
( |
vector< Airport * > |
airports | ) |
|
Check if the vector of airports is valid (no duplicates or faulty initialised airports) Preconditions: propperlyInitialised()
- Parameters
-
- Returns
- bool
| bool AirportHandler::validFileName |
( |
const string & |
name | ) |
|
Check if a filename is valid Preconditions: propperlyInitialised()
- Parameters
-
- Returns
- bool
The documentation for this class was generated from the following files:


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Source code on GitHub
Original concept by J.E. Murphy
Chrome extension by Eugene Cheung


Beano
A Pino inspired logger for the browser
Open the browser console to see the results of running the logger!
Install
import logger from 'beano';
logger.info({groupByMessage: true}, 'look at me!')


@teddycleps @cryptoaddict86 @Chainzillaio This better not be FatChocolateChipCookie tokens and be the real thing!
..But if you absolutely positively have to have an independent cookie chain backed by BTC hashpower:
/komodod -ac_name=FatChocolateChipCookie -ac_supply=(how...


@* * This template is called from the `index` template. This template * handles the rendering of the page header and body tags. It takes * two arguments, a `String` for the title of the page and an `Html` * object to insert into the body of the page. *@ @(connected: String)(content: Html)
@* Here's where we render the page title `String`. *@


A simple solution to bring prostheses to anybody anywhere
As part of the 2020 student challenge from Eurotech our team of students, Aakil, Anna, Mihail and Konstantin, sought out a frugal solution to bring prosthesesto anybody in developing countries on a low budget and a donation basis.


Triggered when a controller is changed.
on( "change", ( event, controller ) => {} )
an event object
a controller itself
Triggered when a controller gets hidden. If a controller is not visible when event handlers are set, event handlers are invoked immediately.
on( "hide", ( event, name ) => {} )
an event object
a controller name
Triggered when an active page is changed. If an active page exists when event handlers are set, event handlers are invoked immediately.
on( "page", ( event, newPageName, oldPageName ) => {} )
an event object
a new active page name or a null
an old active page name or a null
Triggered when a controller gets shown. If a controller is visible when event handlers are set, event handlers are invoked immediately.
on( "show", ( event, name ) => {} )
an event object
a controller name
Triggered when a controller is initialized, or when a controller is changed and its fields are initialized. If a controller is initialized when event handlers are set, event handlers are invoked immediately.
on( "value", ( event, controller ) => {} )
an event object
a controller itself
Iterates over fields of this controller, calling the iteratee for each field. The iteratee is bound to the thisArg and invoked with three arguments: field, name and this controller. The iteratee may exit iteration early by explicitly returning false.
the function called per iteration
the this binding of the iteratee
this
Returns the active page. If the active page does not exist, returns null.
the active page
Returns the component of the specified name. If the component of the specified name does not exist, returns null.
a component name
the component of the specified name
Returns the mapping from the component factory name to the wcardinal.controller.ComponentFactory. The returned mapping is a read-only.
the mapping from the component name to the wcardinal.controller.ComponentFactory
Returns the component factory of the specified name. If the component factory of the specified name does not exist, returns null.
a component factory name
the component factory of the specified name
Returns the mapping from the component name to the wcardinal.controller.Component. The returned mapping is a read-only.
the mapping from the component name to the wcardinal.controller.Component
Returns the name.
the name
Returns the page of the specified name. If the page of the specified name does not exist. returns null.
a page name
the page of the specified name
Returns the mapping from the page factory name to the wcardinal.controller.PageFactory. The returned mapping is a read-only.
the mapping from the page name to the wcardinal.controller.PageFactory
Returns the page factory of the specified name. If the page factory of the specified name does not exist, returns null.
a page factory name
the page factory of the specified name
Returns the mapping from the page name to the wcardinal.controller.Page. The returned mapping is a read-only.
the mapping from the pagen name to the wcardinal.controller.Page.
Returns the parent.
the parent
Returns the popup of the specified name. If the popup of the specified name does not exist, returns null.
a popup name
the popup of the specified name
Returns the mapping from the popup factory name to the wcardinal.controller.PopupFactory. The returned mapping is a read-only.
the mapping from the popup name to the wcardinal.controller.PopupFactory
Returns the popup factory of the specified name. If the popup factory of the specified name does not exist, returns null.
a popup factory name
the popup factory of the specified name
Returns the mapping from the popup name to the wcardinal.controller.Popup. The returned mapping is a read-only.
the mapping from the popup name to the wcardinal.controller.Popup
Makes this controller invisible.
this
Returns true if this controller is invisible.
true if this controller is invisible
Returns true if this is historical.
true if this is historical
Returns true if this controller is locked.
true if this controller is locked
Returns true if this is non-null.
true if this is non-null
Returns true if this is read-only.
true if this is read-only
Returns true if this controller is visible.
true if this controller is visible
Locks this controller.
this
Unregister the event handlers of the specified events.
off( "success" );
// Unregister all the handlers of the events with the namespace `sample`.
off( ".sample" );
// Unregister all the handlers of the `success` event with the namespace `sample`.
off( "success.sample" );
// Multiple event types
off( "success fail" );
space-separated event types
the event handler to be unregistered
this
Unregister the event handlers of the specified events.
off( "success" );
// Unregister all the handlers of the events with the namespace `sample`.
off( ".sample" );
// Unregister all the handlers of the `success` event with the namespace `sample`.
off( "success.sample" );
// Multiple event types
off( "success fail" );
event target
space-separated event types
the event handler to be unregistered
this
Unregisters the event handler for the unregistration events of the specified events.
onoff("eventname", handler);
space-separated event names
an event handler
this
Unregisters the event handlers for the registration events of the specified events.
offon("eventname", handler);
space-separated event types
an event handler
this
Registers the specified event handler for the specified events. When the handler is invoked, the this keyword is a reference to the this class. This method supports space-separated event types and namespces.
on( "success", function(){} );
// `success` event with a namespace `sample`
on( "success.sample", function(){} );
// Multiple namespaces
on( "success.ns1.ns2.ns3", function(){} );
// Multiple event types
on( "success fail", function(){} );
// Event target
on( eventTarget, "success", function(){} );
space-separated event types
the event handler to be registered
this
Registers the specified event handler for the specified events. When the handler is invoked, the this keyword is a reference to the this class. This method supports space-separated event types and namespces.
on( "success", function(){} );
// `success` event with a namespace `sample`
on( "success.sample", function(){} );
// Multiple namespaces
on( "success.ns1.ns2.ns3", function(){} );
// Multiple event types
on( "success fail", function(){} );
// Event target
on( eventTarget, "success", function(){} );
event target or event types
space-separated event types
the event handler to be registered
this
Registers the specified event handler for the specified events. The specified event handler is invoked at most once. When the handler is invoked, the this keyword is a reference to the this class. This method supports space-separated event types and namespces.
one( "success", function(){} );
// `success` event with a namespace `sample`
one( "success.sample", function(){} );
// Multiple namespaces
one( "success.ns1.ns2.ns3", function(){} );
// Multiple event types
one( "success fail", function(){} );
space-separated event types
The event handler to be registered
this
Registers the specified event handler for the specified events. The specified event handler is invoked at most once. When the handler is invoked, the this keyword is a reference to the this class. This method supports space-separated event types and namespces.
one( "success", function(){} );
// `success` event with a namespace `sample`
one( "success.sample", function(){} );
// Multiple namespaces
one( "success.ns1.ns2.ns3", function(){} );
// Multiple event types
one( "success fail", function(){} );
event target
space-separated event types
The event handler to be registered
this
Registers the event handler for the unregistration events of the specified events. The handler is invoked with Connection and a boolean when the event handlers of the specified events are unregistered. The latter boolean gets true if this is the last handler of the event identified by Connection.type.
onoff("eventname", function( connection, isLast ){
// DO SOMETHING HERE
});
space-separated event names
an event handler
this
Registers the event handler for the registration events of the specified events. The handler is invoked with Connection and a boolean when the event handlers of the specified events are registered. The latter boolean gets true if this is the first handler of the event identified by Connection.type.
onon("eventname", function( connection, isFirst ){
if( some-condition ) {
connection.trigger( this, null, [ first-event-parameter, second-event-parameter, ... ] );
}
});
space-separated event types
an event handler
this
Invokes all the handlers of the specified event.
retrigger( originalEvent, "success" );
// With two parameters true and 156
retrigger( originalEvent, "success", [true, 156] );
// Namespaced event type
retrigger( originalEvent, "success.sample" );
// Multiple event types
retrigger( originalEvent, "success fail" );
the event object
space-separated event types
the returned values of the invoked handlers
Invokes all the handlers of the specified event.
retrigger( originalEvent, "success" );
// With two parameters true and 156
retrigger( originalEvent, "success", [true, 156] );
// Namespaced event type
retrigger( originalEvent, "success.sample" );
// Multiple event types
retrigger( originalEvent, "success fail" );
event target
the event object
space-separated event types
event parameters
the returned values of the invoked handlers
Makes this controller visible.
this
Returns the JSON object representing this controller.
the JSON object representing this controller
Returns the string representing this controller.
the string representing this controller
Invokes all the handlers of the specified event.
trigger( "success" );
// With two parameters true and 156
trigger( "success", [true, 156] );
// Namespaced event type
trigger( "success.sample" );
// Multiple event types
trigger( "success fail" );
space-separated event types
event parameters
the returned values of the invoked handlers
Invokes all the handlers of the specified event.
trigger( "success" );
// With two parameters true and 156
trigger( "success", [true, 156] );
// Namespaced event type
trigger( "success.sample" );
// Multiple event types
trigger( "success fail" );
Event target
space-separated event types
event parameters
the returned values of the invoked handlers
Unlock this controller.
this
Extends the specified target with the Connectable methods.
non-null target to be extended
the specified target
Generated using TypeDoc
Represents a component.


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You can preorder Intel's Kaby Lake-X and Skylake-X CPUs next week
June 14, 2017
Intel formally introduced its Kaby Lake-X and Skylake-X processor lineup at Computex last month, but at the
time it didn't say anything about a specific release date. It turns out that Intel was waiting for E3 to
announce availability. Starting on June 19 (this coming Monday), some of Intel's Core-X series CPUs will be
available to preorder.
Read More
Here's how Microsoft's $500 Xbox One X compares to a PC
June 12, 2017
Several weeks ago Microsoft lifted the covers slightly off its new next-gen console, codenamed Project
Scorpio. Today, the software giant took the full wraps off what is now called the Xbox One X, which launches
this year for $500. Here's the lowdown on how powerful Scorpio is, and how it compares to a gaming PC.
Read More
Buy a GTX 1080 or 1080 Ti, get Destiny 2 free with early beta access
June 10, 2017
Nvidia's got a sweet deal lined up for its next graphics card bundle. If you buy a GTX 1080 or 1080 Ti, you
can snag a free copy of Destiny 2, and get in on the game's PC beta in the process. And it's not just any
old beta access. It's [Early] Access, according to Nvidia. Why is Early in brackets? Who knows! But the
point is, you get Destiny 2 and can try it out before the release date on October 24.
Read More


THE EVENING STAR
A film review by Walter Frith
Copyright 1997 Walter Frith
'The Evening Star' is the long awaited sequel to 1983's 'Terms of Endearment.' Shirley MacLaine returns as Aurora Greenway, the firey, outspoken and no nonsense character we came to admire in the first film. As 'The Evening Star' begins it is 1988, approximately 15 years since the untimely death of Aurora's daughter Emma portrayed in 'Terms of Endearment' by Debra Winger. Aurora's three grandchildren are now fully grown and they all have misguided and disfunctional lives. Throughout the course of the movie there are many sentimental moments on film which echo the tone and feel of the first film as we are re-united with old characters and come to know some new ones. The problem 'The Evening Star' has is the inevitable scrutiny it will come under in being compared to 'Terms of Endearment.' If that picture had never been made then there might be something here but while it is entertaining and contains the realistic look of everyday life, it is a disappointing follow up movie with absolutely nothing new to offer. The most memorable character from 'Terms of Endearment' was Garrett Breedlove played brilliantly by Jack Nicholson and his character is desperately needed and guess what? He has a cameo but it comes at a bad time at about an hour and forty five minutes into this movie which runs two hours and eight minutes. We are fed up with the film by the time he shows up and it's a case of too little too late. There is a ridiculous and silly storyline early in the film which has Aurora carrying on an affair with here therapist which lasts about half of the movie and it bored me silly. It is hard to make a good sequel to a movie that won five Academy Awards including Best Picture of the year. For those who don't want the memory of the first film tarnished stay away from this one but if you long for more of the same you'll get it but only on a superficial and less than ambitious level.
OUT OF 5> * * 1/2
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.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{% if default_age %}
The input rider's age is not valid, using default value of {{age}} instead.
{% else %}
The rider's age is: {{age}}.
{% endif %}
The rider's gender is: {{gender}}.
{% if default_date %}
The input pick up date is not valid, using the default value of 06/01/2016 instead.
{% else %}
The pick up date is: {{date}}.
{% endif%}
The pick up time is: {{hour}} o'clock.
The start station is: {{start_station}}.
{% if default_temperature %}
The input temperature is not valid, using default value of {{temperature}} instead.
{% else %}
The temperature is: {{temperature}}.
{% endif %}
{% if PRCP > 0 %}
It is raining.
{% endif %}
{% if SNOW > 0 %}
It is snowing.
{% endif %}
The start station is marked in blue. The green circles show the centroids of the top {{N}} most likely end-neighborhoods for this Citibike ride. The areas of the green circles are proportional the probabilities.
The top {{N}} most likely end-neighborhoods are:
| Neighborhood | Probability (%) |
|---|---|
| {{ neighborhood['name'] }} | {{ neighborhood['prob']}} |


NTFS-3G(8) NTFS-3G(8) NAME ntfs-3g - Third Generation Read/Write NTFS Driver SYNOPSIS ntfs-3g [-o option[,...]] volume mount_point mount -t ntfs-3g [-o option[,...]] volume mount_point lowntfs-3g [-o option[,...]] volume mount_point mount -t lowntfs-3g [-o option[,...]] volume mount_point DESCRIPTION ntfs-3g is an NTFS driver, which can create, remove, rename, move files, directories, hard links, and streams; it can read and write files, including streams, sparse files and transparently compressed files; it can handle special files like symbolic links, devices, and FIFOs; more‐ over it provides standard management of file ownership and permissions, including POSIX ACLs. It comes in two variants ntfs-3g and lowntfs-3g with a few differences mentioned below in relevant options descriptions. The volume to be mounted can be either a block device or an image file. Access Handling and Security By default, files and directories are owned by the effective user and group of the mounting process, and everybody has full read, write, execu‐ tion and directory browsing permissions. You can also assign permissions to a single user by using the uid and/or the gid options together with the umask, or fmask and dmask options. Doing so, Windows users have full access to the files created by ntfs-3g. But, by setting the permissions option, you can benefit from the full ownership and permissions features as defined by POSIX. Moreover, by defining a Windows-to-Linux user mapping, the ownerships and permissions are even applied to Windows users and conversely. If ntfs-3g is set setuid-root then non-root users will be also able to mount volumes. Windows Filename Compatibility NTFS supports several filename namespaces: DOS, Win32 and POSIX. While the ntfs-3g driver handles all of them, it always creates new files in the POSIX namespace for maximum portability and interoperability reasons. This means that filenames are case sensitive and all characters are allowed except '/' and '\0'. This is perfectly legal on Windows, though some application may get confused. The option windows_names may be used to apply Windows restrictions to new file names. Alternate Data Streams (ADS) NTFS stores all data in streams. Every file has exactly one unnamed data stream and can have many named data streams. The size of a file is the size of its unnamed data stream. By default, ntfs-3g will only read the unnamed data stream. By using the options "streams_interface=windows", with the ntfs-3g driver (not possible with lowntfs-3g), you will be able to read any named data streams, simply by specifying the stream's name after a colon. For exam‐ ple: cat some.mp3:artist Named data streams act like normal files, so you can read from them, write to them and even delete them (using rm). You can list all the named data streams a file has by getting the "ntfs.streams.list" extended attribute. OPTIONS Below is a summary of the options that ntfs-3g accepts. uid=value and gid=value Set the owner and the group of files and directories. The values are numerical. The defaults are the uid and gid of the current process. umask=value Set the bitmask of the file and directory permissions that are not present. The value is given in octal. The default value is 0 which means full access to everybody. fmask=value Set the bitmask of the file permissions that are not present. The value is given in octal. The default value is 0 which means full access to everybody. dmask=value Set the bitmask of the directory permissions that are not present. The value is given in octal. The default value is 0 which means full access to everybody. usermapping=file-name Use file file-name as the user mapping file instead of the default .NTFS-3G/UserMapping. If file-name defines a full path, the file must be located on a partition previously mounted. If it defines a relative path, it is interpreted relative to the root of NTFS par‐ tition being mounted. When a user mapping file is defined, the options uid=, gid=, umask=, fmask=, dmask= and silent are ignored. permissions Set standard permissions on created files and use standard access control. This option is set by default when a user mapping file is present. acl Enable setting Posix ACLs on created files and use them for access control. This option is only available on specific builds. It is set by default when a user mapping file is present and the permis‐ sions mount option is not set. inherit When creating a new file, set its initial protections according to inheritance rules defined in parent directory. These rules deviate from Posix specifications, but yield a better Windows compatibil‐ ity. The compression option or a valid user mapping file is required for this option to be effective. ro Mount filesystem read-only. Useful if Windows is hibernated or the NTFS journal file is unclean. locale=value This option can be useful when wanting a language specific locale environment. It is however discouraged as it leads to files with untranslatable chars to not be visible. force This option is obsolete. It has been superseded by the recover and norecover options. recover Recover and try to mount a partition which was not unmounted prop‐ erly by Windows. The Windows logfile is cleared, which may cause inconsistencies. Currently this is the default option. norecover Do not try to mount a partition which was not unmounted properly by Windows. ignore_case (only with lowntfs-3g) Ignore character case when accessing a file (FOO, Foo, foo, etc. designate the same file). All files are displayed with lower case in directory listings. remove_hiberfile Unlike in case of read-only mount, the read-write mount is denied if the NTFS volume is hibernated. One needs either to resume Win‐ dows and shutdown it properly, or use this option which will remove the Windows hibernation file. Please note, this means that the saved Windows session will be completely lost. Use this option under your own responsibility. atime, noatime, relatime The atime option updates inode access time for each access. The noatime option disables inode access time updates which can speed up file operations and prevent sleeping (notebook) disks spinning up too often thus saving energy and disk lifetime. The relatime option is very similar to noatime. It updates inode access times relative to modify or change time. The access time is only updated if the previous access time was earlier than the current modify or change time. Unlike noatime this option doesn't break applications that need to know if a file has been read since the last time it was modified. This is the default behaviour. delay_mtime Delay the updating of file modification time and file change time until the file is closed. This is mainly useful for files which are written to without changing their size, such as databases or file system images mounted as loop. show_sys_files Show the metafiles in directory listings. Otherwise the default behaviour is to hide the metafiles, which are special files used to store the NTFS structure. Please note that even when this option is specified, "$MFT" may not be visible due to a glibc bug. Furthermore, irrespectively of show_sys_files, all files are accessible by name, for example you can always do "ls -l '$UpCase'". hide_hid_files Hide the hidden files and directories in directory listings, the hidden files and directories being the ones whose NTFS attribute have the hidden flag set. The hidden files will not be selected when using wildcards in commands, but all files and directories remain accessible by full name, for example you can always display the Windows trash bin directory by : "ls -ld '$RECYCLE.BIN'". hide_dot_files Set the hidden flag in the NTFS attribute for created files and directories whose first character of the name is a dot. Such files and directories normally do not appear in directory listings, and when the flag is set they do not appear in Windows directory dis‐ plays either. When a file is renamed or linked with a new name, the hidden flag is adjusted to the latest name. windows_names This option prevents files, directories and extended attributes to be created with a name not allowed by windows, either because it contains some not allowed character (which are the nine characters " * / : < > ? \ | and those whose code is less than 0x20) or because the last character is a space or a dot. Existing such files can still be read (and renamed). allow_other This option overrides the security measure restricting file access to the user mounting the filesystem. This option is only allowed to root, but this restriction can be overridden by the 'user_allow_other' option in the /etc/fuse.conf file. max_read=value With this option the maximum size of read operations can be set. The default is infinite. Note that the size of read requests is limited anyway to 32 pages (which is 128kbyte on i386). silent Do nothing, without returning any error, on chmod and chown opera‐ tions, when the permissions option is not set and no user mapping file is defined. This option is on by default. no_def_opts By default ntfs-3g acts as if "silent" (ignore errors on chmod and chown), "allow_other" (allow any user to access files) and "nonempty" (allow mounting on non-empty directories) were set, and "no_def_opts" cancels these default options. streams_interface=value This option controls how the user can access Alternate Data Streams (ADS) or in other words, named data streams. It can be set to, one of none, windows or xattr. If the option is set to none, the user will have no access to the named data streams. If it is set to windows (not possible with lowntfs-3g), then the user can access them just like in Windows (eg. cat file:stream). If it's set to xattr, then the named data streams are mapped to xattrs and user can manipulate them using {get,set}fattr utilities. The default is xattr. user_xattr Same as streams_interface=xattr. efs_raw This option should only be used in backup or restore situation. It changes the apparent size of files and the behavior of read and write operation so that encrypted files can be saved and restored without being decrypted. The user.ntfs.efsinfo extended attribute has also to be saved and restored for the file to be decrypted. compression This option enables creating new transparently compressed files in directories marked for compression. A directory is marked for com‐ pression by setting the bit 11 (value 0x00000800) in its Windows attribute. In such a directory, new files are created compressed and new subdirectories are themselves marked for compression. The option and the flag have no effect on existing files. nocompression This option disables creating new transparently compressed files in directories marked for compression. Existing compressed files can still be read and updated. Currently this is the default option. big_writes This option prevents fuse from splitting write buffers into 4K chunks, enabling big write buffers to be transferred from the application in a single step (up to some system limit, generally 128K bytes). debug Makes ntfs-3g to print a lot of debug output from libntfs-3g and FUSE. no_detach Makes ntfs-3g to not detach from terminal and print some debug output. USER MAPPING NTFS uses specific ids to record the ownership of files instead of the uid and gid used by Linux. As a consequence a mapping between the ids has to be defined for ownerships to be recorded into NTFS and recognized. By default, this mapping is fetched from the file .NTFS-3G/UserMapping located in the NTFS partition. The option usermapping= may be used to define another location. When the option permissions is set and no map‐ ping file is found, a default mapping is used. Each line in the user mapping file defines a mapping. It is organized in three fields separated by colons. The first field identifies a uid, the second field identifies a gid and the third one identifies the corre‐ sponding NTFS id, known as a SID. The uid and the gid are optional and defining both of them for the same SID is not recommended. If no interoperation with Windows is needed, you can use the option per‐ missions to define a standard mapping. Alternately, you may define your own mapping by setting a single default mapping with no uid and gid. In both cases, files created on Linux will appear to Windows as owned by a foreign user, and files created on Windows will appear to Linux as owned by root. Just copy the example below and replace the 9 and 10-digit num‐ bers by any number not greater than<PHONE_NUMBER>. The resulting behavior is the same as the one with the option permission set with no ownership option and no user mapping file available. ::S-1-5-21-3141592653-589793238-462643383-10000 If a strong interoperation with Windows is needed, the mapping has to be defined for each user and group known in both system, and the SIDs used by Windows has to be collected. This will lead to a user mapping file like : john::S-1-5-21-3141592653-589793238-462643383-1008 mary::S-1-5-21-3141592653-589793238-462643383-1009 :smith:S-1-5-21-3141592653-589793238-462643383-513 ::S-1-5-21-3141592653-589793238-462643383-10000 The utility ntfs-3g.usermap may be used to create such a user mapping file. EXAMPLES Mount /dev/sda1 to /mnt/windows: ntfs-3g /dev/sda1 /mnt/windows or mount -t ntfs-3g /dev/sda1 /mnt/windows Mount the ntfs data partition /dev/sda3 to /mnt/data with standard Linux permissions applied : ntfs-3g -o permissions /dev/sda3 /mnt/data or mount -t ntfs-3g -o permissions /dev/sda3 /mnt/data Read-only mount /dev/sda5 to /home/user/mnt and make user with uid 1000 to be the owner of all files: ntfs-3g /dev/sda5 /home/user/mnt -o ro,uid=1000 /etc/fstab entry for the above (the sixth and last field has to be zero to avoid a file system check at boot time) : /dev/sda5 /home/user/mnt ntfs-3g ro,uid=1000 0 0 Unmount /mnt/windows: umount /mnt/windows EXIT CODES To facilitate the use of the ntfs-3g driver in scripts, an exit code is returned to give an indication of the mountability status of a volume. Value 0 means success, and all other ones mean an error. The unique error codes are documented in the ntfs-3g.probe(8) manual page. KNOWN ISSUES Please see http://www.tuxera.com/support/ for common questions and known issues. If you would find a new one in the latest release of the software then please send an email describing it in detail. You can contact the development team on the<EMAIL_ADDRESS>address. AUTHORS ntfs-3g was based on and a major improvement to ntfsmount and libntfs which were written by Yura Pakhuchiy and the Linux-NTFS team. The improvements were made, the ntfs-3g project was initiated and currently led by long time Linux-NTFS team developer Szabolcs Szakacsits (szaka@tuxera.com). THANKS Several people made heroic efforts, often over five or more years which resulted the ntfs-3g driver. Most importantly they are Anton Alta‐ parmakov, Jean-Pierre AndrÃ©, Richard Russon, Szabolcs Szakacsits, Yura Pakhuchiy, Yuval Fledel, and the author of the groundbreaking FUSE filesystem development framework, Miklos Szeredi. SEE ALSO ntfs-3g.probe(8), ntfsprogs(8), attr(5), getfattr(1) ntfs-3g 2012.1.15AR.5 February 2010 NTFS-3G(8)


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