{% extends 'templates/nhs_content_layout.html' %} {% block content %} Diabetes pages Understanding your diabetes type 2 diagnosis Food and keeping active for people with diabetes type 2 Health problems caused by diabetes type 2 Check if you have diabetes type 2 {% endblock %}


Raygun sending error to our server
Uses
raygun4js
library to send caught JavaScript errors to our server
error-receiver
.


Rabbit Kingdom
问题描述 :
Long long ago, there was an ancient rabbit kingdom in the forest. Every rabbit in this kingdom was not cute but totally pugnacious, so the kingdom was in chaos in season and out of season.
n rabbits were numbered form 1 to n. All rabbits’ weight is an integer. For some unknown reason, two rabbits would fight each other if and only if their weight is NOT co-prime.
Now the king had arranged the n rabbits in a line ordered by their numbers. The king planned to send some rabbits into prison. He wanted to know that, if he sent all rabbits between the i-th one and the j-th one(including the i-th one and the j-th one) into prison, how many rabbits in the prison would not fight with others.
Please note that a rabbit would not fight with himself.
n rabbits were numbered form 1 to n. All rabbits’ weight is an integer. For some unknown reason, two rabbits would fight each other if and only if their weight is NOT co-prime.
Now the king had arranged the n rabbits in a line ordered by their numbers. The king planned to send some rabbits into prison. He wanted to know that, if he sent all rabbits between the i-th one and the j-th one(including the i-th one and the j-th one) into prison, how many rabbits in the prison would not fight with others.
Please note that a rabbit would not fight with himself.
输入:
The input consists of several test cases.
The first line of each test case contains two integer n, m, indicating the number of rabbits and the queries.
The following line contains n integers, and the i-th integer Wi indicates the weight of the i-th rabbit.
Then m lines follow. Each line represents a query. It contains two integers L and R, meaning the king wanted to ask about the situation that if he sent all rabbits from the L-th one to the R-th one into prison.
(1 <= n, m, Wi <= 200000, 1 <= L <= R <= n)
The input ends with n = 0 and m = 0.
The first line of each test case contains two integer n, m, indicating the number of rabbits and the queries.
The following line contains n integers, and the i-th integer Wi indicates the weight of the i-th rabbit.
Then m lines follow. Each line represents a query. It contains two integers L and R, meaning the king wanted to ask about the situation that if he sent all rabbits from the L-th one to the R-th one into prison.
(1 <= n, m, Wi <= 200000, 1 <= L <= R <= n)
The input ends with n = 0 and m = 0.
输出:
The input consists of several test cases.
The first line of each test case contains two integer n, m, indicating the number of rabbits and the queries.
The following line contains n integers, and the i-th integer Wi indicates the weight of the i-th rabbit.
Then m lines follow. Each line represents a query. It contains two integers L and R, meaning the king wanted to ask about the situation that if he sent all rabbits from the L-th one to the R-th one into prison.
(1 <= n, m, Wi <= 200000, 1 <= L <= R <= n)
The input ends with n = 0 and m = 0.
The first line of each test case contains two integer n, m, indicating the number of rabbits and the queries.
The following line contains n integers, and the i-th integer Wi indicates the weight of the i-th rabbit.
Then m lines follow. Each line represents a query. It contains two integers L and R, meaning the king wanted to ask about the situation that if he sent all rabbits from the L-th one to the R-th one into prison.
(1 <= n, m, Wi <= 200000, 1 <= L <= R <= n)
The input ends with n = 0 and m = 0.
样例输入:
3 2 2 1 4 1 2 1 3 6 4 3 6 1 2 5 3 1 3 4 6 4 4 2 6 0 0
样例输出:
2 1 1 3 1 2HintIn the second case, the answer of the 4-th query is 2, because only 1 and 5 is co-prime with other numbers in the interval [2,6] .
题意：给N个数，有M个查询，问区间[L,R]之间有多少个数与这个区间内的其他数都互质。
易得，一个区间内的数的个数减去，与其他数不互质的数即可——即离当前数i左边最近的不互质的数的位置（设为L[i]）和右边最近的不互质的数的位置（设为R[i]）有一个在区间[L,R]内。那么问题就变成统计：1.区间[L,R]中有多少个数的L[i]或R[i]在区间[L,R]内。2.多少个数的L[i]且R[i]在区间[L,R]内。对于每个询问，答案就是区间内的数减去1的结果，再加上2的结果。
2的结果其实就是询问有多少个区间[L[i],R[i]]完全在给定区间[L,R]内。其实1也可以转化为相同的问题，即区间[L[i],i]或[R[i],i]，是否在给定区间内。
对于问有多少个区间是在给定的区间内，可以直接离线搞。
#include <iostream> #include <stdio.h> #include <math.h> #include <string.h> #include <algorithm> #include <stdlib.h> #include <string> #include <queue> #include <map> #include <vector> #include <set> #define maxn 1000010 #define oo<PHONE_NUMBER> #define clearAll(a) memset(a,0,sizeof(a)) #define sq(a) ((a)*(a)) #define LL(x) (x<<1) #define RR(x) (x<<1|1) #define MID(a,b) (a+((b-a)>>1)) using namespace std; typedef long long ll; const int N=200015; struct Range { int st,ed,ind; Range(){} Range(int st,int ed,int ind) : st(st),ed(ed),ind(ind) {} bool operator<(const Range &B)const { return ed<B.ed; } }; int n,m,mx; int A[N],L[N],R[N],pos[N]; int ans[3][N],num[N]; vector<Range>rab[3],range; void fun(bool flag) { if(flag==0) { for(int i=0;i<=mx;i++) pos[i]=0; for(int i=1;i<=n;i++) { int tmp=A[i]; for(int j=2;j*j<=tmp;j++) { if(tmp%j) continue; L[i]=max(L[i],pos[j]); pos[j]=i; while(tmp%j==0) tmp/=j; } if(tmp!=1) { L[i]=max(L[i],pos[tmp]); pos[tmp]=i; } } } else { for(int i=0;i<=mx;i++) pos[i]=n+1; for(int i=n;i>=1;i--) { int tmp=A[i]; for(int j=2;j*j<=tmp;j++) { if(tmp%j) continue; R[i]=min(R[i],pos[j]); pos[j]=i; while(tmp%j==0) tmp/=j; } if(tmp!=1) { R[i]=min(R[i],pos[tmp]); pos[tmp]=i; } } } } struct BIT { int T[N]; void clear() { for(int i=1;i<=n+10;i++) T[i]=0; } int lowbit(int x) { return x&(-x); } void updata(int pos) { if(pos==0||pos==n+1) return; for(int i=pos;i<=n;i+=lowbit(i)) T[i]++; } int query(int st,int ed) { int ans=0; for(int i=ed;i>=1;i-=lowbit(i)) ans+=T[i]; for(int i=st-1;i>=1;i-=lowbit(i)) ans-=T[i]; return ans; } }seg; void getAns(int key) { seg.clear(); int ind=0; for(int i=0;i<m;i++) { int st=range[i].st; int ed=range[i].ed; int id=range[i].ind; while(ind<n&&rab[key][ind].ed<=ed) { seg.updata(rab[key][ind].st); ind++; } ans[key][id]+=seg.query(st,ed); } } int main() { while(scanf("%d%d",&n,&m)!=EOF) { if(n==0&&m==0) break; range.clear(); mx=0; rab[0].clear(); rab[1].clear(); rab[2].clear(); for(int i=0;i<N;i++) { L[i]=0; R[i]=n+1; ans[0][i]=ans[1][i]=ans[2][i]=0; } for(int i=1;i<=n;i++) { scanf("%d",&A[i]); mx=max(mx,A[i]); } for(int i=0;i<m;i++) { int x,y; scanf("%d%d",&x,&y); num[i]=y-x+1; range.push_back(Range(x,y,i)); } fun(0); fun(1); for(int i=1;i<=n;i++) { rab[0].push_back(Range(L[i],i,0)); rab[1].push_back(Range(i,R[i],0)); rab[2].push_back(Range(L[i],R[i],0)); } sort(range.begin(),range.end()); sort(rab[0].begin(),rab[0].end()); sort(rab[1].begin(),rab[1].end()); sort(rab[2].begin(),rab[2].end()); getAns(0); getAns(1); getAns(2); for(int i=0;i<m;i++) printf("%d\n",num[i]-ans[0][i]-ans[1][i]+ans[2][i]); } return 0; }
参考：http://blog.csdn.net/shiqi_614/article/details/15419815
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为何只有六分钟？？？？
你永远没有办法去评价一个人，因为你没有度过他的人生
你永远没有办法去评价一个人，因为你没有度过他的人生
你永远没有办法去评价一个人，因为你没有度过他的人生
你永远没有办法去评价一个人，因为你没有度过他的人生
可有视频为证？
可有视频为证？
可有视频为证？
我想知道的8尾呢 呵呵 真期待 12 啊
我想知道的8尾呢 呵呵 真期待 12 啊
我想知道的8尾呢 呵呵 真期待 12 啊
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！
对对对！最喜欢索伦了！


⚠ Documentation is version-specific: This page was generated for ScottPlot 4.1.4-beta
Additional documentation and more version-specific cookbooks are on the ScottPlot Website
Function plots are defined by a function (not X/Y data points) so the curve is continuous and can be zoomed in and out infinitely
var plt = new ScottPlot.Plot(600, 400);
// Functions are defined as delegates with an input and output
var func1 = new Func((x) => Math.Sin(x) * Math.Sin(x / 2));
var func2 = new Func((x) => Math.Sin(x) * Math.Sin(x / 3));
var func3 = new Func((x) => Math.Cos(x) * Math.Sin(x / 5));
// Add functions to the plot
plt.AddFunction(func1, lineWidth: 2);
plt.AddFunction(func2, lineWidth: 2, lineStyle: LineStyle.Dot);
plt.AddFunction(func3, lineWidth: 2, lineStyle: LineStyle.Dash);
plt.SaveFig("function_quickstart.png");


Gracenote provides track-based mood data that allow users to generate playlists based on the mood they want to listen to. When the user selects a mood, the application provides a playlist of music that corresponds to the selected mood.
An important advantage of MoodTrack-level perceptual descriptor of a piece of music, using emotional terminology that a typical listener might use to describe the audio track; includes hierarchical categories of increasing granularity. See Sonic Attributes. is that it is track-specific and independent of other track metadata. This enables you to create intuitive user interfaces that can create mood-based playlists across different genres, eras, or artist types, and so on. The user can also filter the playlist using one or more of these other attributes to refine the playlist.
For details on implementing Mood features, see Implementing Mood


History of Changes
- Introduction and explanation of symbols
- Version 1.2 (2019-10-3)
- Version 1.1 (2012-12-28)
- Version 1.0 (2010-05-05)
- Version 1.0 RC (2008-04-02)
- Version 1.0 beta (2007-11-08)
- Version 0.9 (2007-05-17)
- Version 0.8 (2007-03-28)
- Version 0.7 (2007-02-14)
- Version 0.6 (2007-01-08)
- Version 0.5dev (2006-11-16)
- All Committers
Introduction and explanation of symbols
Changes are sorted by "type" and then chronologically with the most recent at the top. These symbols denote the various action types:=add, =fix, =update
Version 1.2 (2019-10-3)
Changes to the Code Base
- Switched Maven groupId to org.scriptella Committed by ejboy.
- Upgraded Janino driver to 3.1.0 Committed by ejboy.
Contributors to this release
We thank the following people for their contributions to this release.
This is a list of all people who participated as committers:
Fyodor Kupolov (ejboy).
Version 1.1 (2012-12-28)
Changes to the Code Base
- JEXL 2.0 Support Committed by ejboy. See Issue 12161.
- Flexible parsing/formatting rules for text/csv drivers Committed by ejboy. See Issue 52167.
- StringIndexOutOfBoundsException when parsing SQL statements with ternary expressions Committed by ejboy. See Issue 52891.
- JDBC: Add support for queries in batch-mode Committed by ejboy. See Issue 14268.
- Detect missing query.next call Committed by ejboy. See Issue 12257.
- Add support for batch execution. Committed by ejboy. See Issue 7157.
- Detect missing query.next call in queries. Committed by ejboy. See Issue 12257.
Contributors to this release
We thank the following people for their contributions to this release.
This is a list of all people who participated as committers:
Fyodor Kupolov (ejboy).
Version 1.0 (2010-05-05)
Changes to the Code Base
- CUBRID Database driver Committed by ejboy. See Issue 11778.
- nostat command line option to suppress statistics collecting. Committed by ejboy. See Issue 8700.
- Fixed ArrayIndexOutOfBounds thrown when lazy-initialization fails for connection Committed by ejboy.
- Wrong alias for PostgreSQL for auto driver Committed by volk. See Issue 7338.
- db2 driver is not automatically resolved Committed by ejboy. See Issue 10987.
- Cannot load class when scriptella is packaged in an EAR file in JBoss Committed by ejboy. See Issue 10660.
- Cannot Write to HTTP URL Committed by ejboy. See Issue 7390.
- Use null_string to expand $variable when it has null value. CSV, text driver Committed by ejboy. See Issue 8756.
- Fix FindBugs issues Committed by ejboy. See Issue 9990.
- Upgrade dependencies: hsqldb <IP_ADDRESS>, velocity 1.6.2, janino 2.5.15, h2 1.1.116 Committed by ejboy. See Issue 7074.
Contributors to this release
We thank the following people for their contributions to this release.
This is a list of all people who participated as committers:
Fyodor Kupolov (ejboy), Kirill Volgin (volk).
Version 1.0 RC (2008-04-02)
Changes to the Code Base
- odbc alias for JDBC-ODBC bridge driver Committed by ejboy. See Issue 7252.
- Support for SQLSheet JDBC driver to work with Excel files. Committed by volk. See Issue 7234.
- IBM AS/400 Driver Committed by volk. See Issue 7227.
- etl.text utility methods to format strings Committed by ejboy. See Issue 7048.
- Execute multiple ETL files in Spring Committed by ejboy. See Issue 7003.
- Add "exclude=true/false" attribute for dialect tag. Committed by ejboy. See Issue 6924.
- Error working whith blobs Committed by ejboy. See Issue 6132.
- scriptella.execution.EtlExecutorException: Java heap space -java.lang.OutOfMemoryError: Committed by ejboy. See Issue 5658.
- NullpointerException in JEXL expressions if running in multiple threads Committed by ejboy. See Issue 6773.
- Inserting NULL values into a DB2 database Committed by ejboy. See Issue 6913.
- Make driver attribute optional with default value of "auto" Committed by ejboy. See Issue 7160.
- Replace opencsv with a built-in implementation Committed by ejboy. See Issue 7098.
- Simplified onerror regex matching for message and type attributes Committed by ejboy. See Issue 5627.
- Use column label in addition to column name for JDBC metadata Committed by ejboy. See Issue 6713.
Contributors to this release
We thank the following people for their contributions to this release.
This is a list of all people who participated as committers:
Fyodor Kupolov (ejboy), Kirill Volgin (volk).
Version 1.0 beta (2007-11-08)
Changes to the Code Base
- Driver for running external Scriptella ETL files Committed by ejboy. See Issue 6086.
- Add "autocommit.size" JDBC connection parameter. Committed by ejboy. See Issue 6256.
- "autocommit" connection parameter Committed by ejboy. See Issue 5845.
- Impossible to import NULL with CSV driver Committed by ejboy. See Issue 5760.
- JdbcException throwing during driver loading Committed by volk. See Issue 5698.
- Upgrade to Velocity 1.5 Committed by ejboy. See Issue 6294.
- [Compatibility] Upgrade to Janino 2.5.11 Committed by ejboy. See Issue 6293.
- Several minor bugs were fixed, cosmetic fixes for code, site and documentation. Committed by ejboy.
Contributors to this release
We thank the following people for their contributions to this release.
This is a list of all people who participated as committers:
Fyodor Kupolov (ejboy), Kirill Volgin (volk).
Version 0.9 (2007-05-17)
Changes to Build
- Since this release JDK 6 is required to build Scriptella Committed by ejboy.
Changes to the Code Base
- Auto-discovery for JDBC drivers Committed by ejboy. See Issue 3672.
- Support for JSR 223 (Scripting for the Java Platform) Committed by ejboy. See Issue 5007.
- Add ?{textfile 'filename/url' } to upload CLOBs from text files. Committed by ejboy. See Issue 5037.
- Database migration template Committed by ejboy. See Issue 5253.
- SpringDriverTest failed in coverage mode Committed by ejboy. See Issue 5260.
- Fix for LDAP when using search.scope and search.basedn parameters Committed by ejboy. See Issue 5390.
- Simplified dialect names matching Committed by ejboy. See Issue 5252.
- Support for bind variables/expressions in a href attribute of include element Committed by ejboy. See Issue 5473.
- skip_lines connection parameter for CSV and Text driver Committed by ejboy. See Issue 5600.
Contributors to this release
We thank the following people for their contributions to this release.
This is a list of all people who participated as committers:
Fyodor Kupolov (ejboy).
Version 0.8 (2007-03-28)
Changes to Project Administration
- Upgrade to Janino 2.5.6 Committed by ejboy. See Issue 5033.
Changes to Build
- Remove unused commons-logging classes from scriptella.jar Committed by ejboy. See Issue 5036.
Changes to the Code Base
- Add a feature to work with dates in ${ ... } JEXL expressions Committed by ejboy. See Issue 5024.
- Runnable and Callable façade for EtlExecutor Committed by ejboy. See Issue 5009.
- Mail provider Committed by ejboy. See Issue 5007.
- NULL not handled for CLOBs and BLOBs Committed by ejboy. See Issue 5016.
- Fix Spring context propagation Committed by ejboy. See Issue 4648.
- Send output to the console if no output file URL was specified for Velocity and Text driver Committed by ejboy. See Issue 5031.
- Automatically convert windows DRIVE:/ paths to file:/DRIVE:/ URL Committed by ejboy. See Issue 5029.
Contributors to this release
We thank the following people for their contributions to this release.
This is a list of all people who participated as committers:
Fyodor Kupolov (ejboy).
Version 0.7 (2007-02-14)
Changes to Build
- Include scriptella-src-ide.zip in a binary distribution Committed by ejboy. See Issue 4386.
Changes to the Code Base
- JEXL driver Committed by ejboy. See Issue 3792.
- XML XPath provider Committed by ejboy. See Issue 4308.
- XML example Committed by ejboy. See Issue 4338.
- Compile error on JDK 6 Committed by ejboy. See Issue 4291.
- Do not produce bug report if no driver on classpath Committed by ejboy,volk. See Issue 4668.
- Improved examples, ODBC example was not working. Committed by ejboy.
- Make "throughput" parameter in JMX mbean more accurate Committed by ejboy. See Issue 4397.
- Various performance optimizations. Committed by ejboy.
Contributors to this release
We thank the following people for their contributions to this release.
This is a list of all people who participated as committers:
Fyodor Kupolov (ejboy), (ejboy,volk).
Version 0.6 (2007-01-08)
Changes to the Code Base
- Text Provider Committed by ejboy. See Issue 3903.
- PostgreSQL Driver Committed by volk. See Issue 3976.
- DB2 Driver Committed by volk. See Issue 3981.
- Lucene search engine driver Committed by volk. See Issue 3987.
- Rollback ETL on Ctrl-C Committed by ejboy. See Issue 4061.
- transaction.isolation property for JDBC connection Committed by ejboy. See Issue 4062.
- Add JMX mbean for monitoring Committed by ejboy. See Issue 4034.
- Improved test code coverage Committed by ejboy.
Contributors to this release
We thank the following people for their contributions to this release.
This is a list of all people who participated as committers:
Fyodor Kupolov (ejboy), Kirill Volgin (volk).
Version 0.5dev (2006-11-16)
Changes to Project Administration
- Supported libraries upgraded to Janino 2.5.1, H2 1.0, HSQLDB <IP_ADDRESS>, opencsv 1.7 Committed by ejboy.
Changes to the Code Base
- Log SQLWarnings Committed by ejboy. See Issue 3621.
- Configurable sql delimiters for JDBC Bridge Committed by ejboy. See Issue 3622.
- add lazy-init connection attribute Committed by ejboy. See Issue 3655.
- Oracle driver Committed by volk. See Issue 3657.
- Sybase driver Committed by volk. See Issue 3690.
- Resultset Data types conversion (including BLOB/CLOB) Committed by ejboy. See Issue 3740.
- Support for Oracle Hints Committed by ejboy. See Issue 3742.
- MySQL driver Committed by volk. See Issue 3793.
- scriptella.bat not woking if no JAVA_HOME Committed by ejboy. See Issue 3653.
- Illegal handling of -- comments Committed by ejboy. See Issue 3653.
- Performance improvements Committed by ejboy.
- Improved code coverage Committed by ejboy.
Contributors to this release
We thank the following people for their contributions to this release.
This is a list of all people who participated as committers:
Fyodor Kupolov (ejboy), Kirill Volgin (volk).
All Committers
This is a list of all people who have ever participated as committers on this project.
- Fyodor Kupolov (ejboy)
- Kirill Volgin (volk)


As a white middle-class male I haven't had much experience with any type of negative stereotypes and am priviledged to not expect to have to prove any of them wrong. As a student that was interesting in psycology since high school, I know that priming someone with negative stereotypes can significantly impact the way that they preform on tests and even can be triggered by simply asking the student their race with out any negative information associated.
A lot of people seem to be concerned with the "don't offend anyone" culture that seems to becoming popular as of late and I feel stuck in a sensible middle. I think it's important to be inclusive to all people who you interact with until they prove to you that they aren't worth including. I tend to try and be aware of various social situations and be as nice and inclusive as possible but I usually don't try and be an activist about most things, probably due to my propensity to stay out of akward situations. I'm not going to be the one who is picketing on the side walk but if some is being disadvantaged or made fun of in a mean way I will be sure to say something.
I don't want to be mean or overt in the way that I point things out though. I think quietly pointing someone to a better way of life or way of acting is always the way to go about it. "Yeah, I wouldn't use that word" or "Eh that's not true/nice to say" is always going to be better than yelling at someone for being wrong.


'Vampires' a.k.a. John Carpenter's 'Vampires' (1998)
A movie review by Walter Frith
Member of the 'Internet Movie Critics Association' http://www.geocities.com/Hollywood/Studio/5713/index.html
and
Member of the 'Online Film Critics Society' http://ofcs.org/ofcs/
John Carpenter's vision of the undead is very lively in his latest film entitled 'Vampires'. The film is based on the novel 'Vampire$' by John Steakley and is written for the screen by Don Jakoby. In the ads for the film, it is labeled John Carpenter's 'Vampires'. It is admirable for a director to want his name on the head of a film because after all, he IS supposed to be proud of his work but what puzzled about the film was Carpenter's lack of restraint which is filled with an unusual amount of gore for a John Carpenter film. For a vampire flick, the film looks heavily influenced by 1996's 'From Dusk Till Dawn' which had enough wild bloody gore to fill a dozen movies.
James Woods, who likes to make up many of his own 'in your face' remarks in improvised dialogue, stars as a vampire killer, raised and trained by the Catholic church to destroy vampires moving around all parts of the globe. The film tries to cast off stereotypes that have carried vampires throughout film history. Woods says that crosses don't work and other stereotypes are cast off as well. There is a new breed of vampire on the loose that can walk around in the daylight.
Traveling through New Mexico as the film opens, Woods and his gang of vampire killers include an army of "soldiers" and Daniel Baldwin plays Woods' right hand man and Gregory Sierra plays a priest re-cruited by the church to aid Woods on his hunt to destroy the unholy beasts. Thomas Ian Griffith plays Valek, the First Vampire who has lived for 600 years and who has been the influence for all vampires created throughout history. No vampire film I've ever seen has taken on such a powerful presence of the villain's profile as this film does. Valek and his army of the undead are after a relic in the form of a black cross that will give them special power. The vampires in this film are extremely hard to kill and Valek is practically invincible.
One night, at a victory party of sorts after Woods and his army destroy a nest belonging to Valek, Valek rises to literally crash the party and kill almost all the vampire slayers. Valek sinks his fangs into a young prostitute at the party played by Sheryl Lee and her transformation will not take place right away and Baldwin sort of falls for her and tries to protect her from the transformation.
Two other characters are key to the film's outcome and wild but predictable climax. Maximilian Schell plays a high ranking Cardinal in the church who oversees Woods' assignment and Tim Guinee plays a priest who is rather timid in his initial work with Woods.
'Vampires' uses an over-the-top approach to win over the audience and its timing to open at Halloween is no doubt a good marketing strategy but the film comes up short. Its problem is that it's static and suffers from a one track mind in its approach to the subject matter. Francis Ford Coppola's vision of 'Bram Stoker's Dracula' in 1992 was stunning and captured an enormous amount of erotica contained in the novel. Even Robert Rodriguez's 'From Dusk Till Dawn' used its first half as a crime drama and took a bizarre turn in the second half. A young person's vampire flick in 1985 called 'Fright Night' had Chris Sarandon as a ghoulish type of vampire who wore designer clothes and romped about with an obnoxious personality.
'Vampires' becomes very tiresome after its first 45 minutes and it eventually runs out of steam and its lack of good character development is further complicated by repetitive scenes of visual gore and Carpenter has always been a visual director whose talents, like Brian De Palma and Peter Hyams, might have been better served on weekly formula television shows or the world of music videos because these directors and their lack of sub text are below average at best.
OUT OF 5 > * *
Visit FILM FOLLOW-UP by Walter Frith http://www.cgocable.net/~wfrith/movies.htm
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
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I am an Undergraduate at the Department of Computer Science and Engineering in the Faculty of Engineering of University of Moratuwa- Sri Lanka. I have obtained a Diploma in Management Accounting at CIMA (Charted Institute of Management Accounts). Also I have been awarded with University Colours for Sports. My objective is to be a talented Computer Science Engineer in the future and serve my motherland; Sri Lanka.
I am responsible for planing, developing and implementing effective marketing communication and for making customer focused decisions. I am also the software tester who checks the software against the requirements.


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Ultimate Go: Service with Kubernetes 4.1
24 June 2023
I attended recently a golang
programming training. Our instructor is a respected Golang developer himself William Kennedy who has trained thousands of developers worldwide in Go.
The class I attended teaches how to build production-level services in Go leveraging the power of Kubernetes. From the beginning, you will pair program with the Bill Kennedy walking through the design philosophies and guidelines for building services in Go. With each new feature that is added to the service, you will learn how to deploy to and manage the Kubernetes environment used to run the service.
I will outline here my install experience for the starter service. I installed it on my Mac during our training but this time, this is my Linux experience.
Install brew
Dependecy installation scripts from makefile
are calling brew
so it's the first dependency to install. On Arch Linux, I tried the aur
version of brew-git but it's installing its own ruby dependencies instead of using the available so I have to remove it.
$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
==> Checking for `sudo` access (which may request your password)...
==> This script will install:
/home/linuxbrew/.linuxbrew/bin/brew
/home/linuxbrew/.linuxbrew/share/doc/homebrew
/home/linuxbrew/.linuxbrew/share/man/man1/brew.1
/home/linuxbrew/.linuxbrew/share/zsh/site-functions/_brew
/home/linuxbrew/.linuxbrew/etc/bash_completion.d/brew
/home/linuxbrew/.linuxbrew/Homebrew
==> The following new directories will be created:
/home/linuxbrew/.linuxbrew/bin
/home/linuxbrew/.linuxbrew/etc
/home/linuxbrew/.linuxbrew/include
/home/linuxbrew/.linuxbrew/lib
/home/linuxbrew/.linuxbrew/sbin
/home/linuxbrew/.linuxbrew/share
/home/linuxbrew/.linuxbrew/var
/home/linuxbrew/.linuxbrew/opt
/home/linuxbrew/.linuxbrew/share/zsh
/home/linuxbrew/.linuxbrew/share/zsh/site-functions
/home/linuxbrew/.linuxbrew/var/homebrew
/home/linuxbrew/.linuxbrew/var/homebrew/linked
/home/linuxbrew/.linuxbrew/Cellar
/home/linuxbrew/.linuxbrew/Caskroom
/home/linuxbrew/.linuxbrew/Frameworks
...
==> Installation successful!
==> Next steps:
$ (echo; echo 'eval "$(/home/linuxbrew/.linuxbrew/bin/brew shellenv)"') >> /home/$USER/.zprofile
$ eval "$(/home/linuxbrew/.linuxbrew/bin/brew shellenv)"
$ sudo pacman -S base-devel
warning: base-devel-1-1 is up to date -- reinstalling
resolving dependencies...
looking for conflicting packages...
Packages (1) base-devel-1-1
...
(1/1) reinstalling base-devel [###############################################] 100%
$ brew install gcc
...
==> Installing gcc
==> Pouring gcc--13.1.0.x86_64_linux.bottle.tar.gz
==> Creating the GCC specs file: /home/linuxbrew/.linuxbrew/Cellar/gcc/13.1.0/bin/../lib/gcc/current/gcc/x86_64-pc-linux-gnu/13/specs
🍺 /home/linuxbrew/.linuxbrew/Cellar/gcc/13.1.0: 1,668 files, 320.2MB
==> Running `brew cleanup gcc`...
Install tooling and dependencies
$ make dev-brew-common
$ make dev-docker
$ make dev-gotooling
$ sudo curl -fL https://app.getambassador.io/download/tel2oss/releases/download/v2.14.0/telepresence-linux-amd64 -o /usr/local/bin/telepresence
$ sudo chmod a+x /usr/local/bin/telepresence
Verify you have properly installed the dependencies
$ make test
CGO_ENABLED=0 go test -count=1 ./...
...
ok github.com/ardanlabs/service/app/services/sales-api/tests/v1 5.634s
ok github.com/ardanlabs/service/business/core/product 6.418s
ok github.com/ardanlabs/service/business/core/user 5.633s
ok github.com/ardanlabs/service/business/core/usersummary 5.176s
ok github.com/ardanlabs/service/business/web/auth 0.108s
ok github.com/ardanlabs/service/foundation/vault 3.117s
ok github.com/ardanlabs/service/foundation/worker 0.303s
Start the project
$ make dev-up
kind create cluster \
--image kindest/node:v1.27.3 \
--name ardan-starter-cluster \
--config zarf/k8s/dev/kind-config.yaml
Creating cluster "ardan-starter-cluster" ...
✓ Ensuring node image (kindest/node:v1.27.3) 🖼
✓ Preparing nodes 📦
✓ Writing configuration 📜
✓ Starting control-plane 🕹
✓ Installing CNI 🔌
✓ Installing StorageClass 💾
Set kubectl context to "kind-ardan-starter-cluster"
You can now use your cluster with:
kubectl cluster-info --context kind-ardan-starter-cluster
Thanks for using kind! 😊
kubectl wait --timeout=120s --namespace=local-path-storage --for=condition=Available deployment/local-path-provisioner
deployment.apps/local-path-provisioner condition met
kind load docker-image datawire/ambassador-telepresence-manager:2.14.1 --name ardan-starter-cluster
...
telepresence --context=kind-ardan-starter-cluster helm install
Launching Telepresence User Daemon
Traffic Manager installed successfully
telepresence --context=kind-ardan-starter-cluster connect
Launching Telepresence Root Daemon
Need root privileges to run: /usr/local/bin/telepresence daemon-foreground /home/$USER/.cache/telepresence/logs /home/$USER/.config/telepresence
[sudo] password for $USER:
Connected to context kind-ardan-starter-cluster (https://<IP_ADDRESS>:38665)
$ make dev-update-apply
docker build \
-f zarf/docker/dockerfile.service \
-t ardanlabs/service/sales-api:0.0.1 \
--build-arg BUILD_REF=0.0.1 \
--build-arg BUILD_DATE=`date -u +"%Y-%m-%dT%H:%M:%SZ"` \
.
[+] Building 30.7s (18/18) FINISHED
...
docker build \
-f zarf/docker/dockerfile.metrics \
-t ardanlabs/service/sales-api-metrics:0.0.1 \
--build-arg BUILD_REF=0.0.1 \
--build-arg BUILD_DATE=`date -u +"%Y-%m-%dT%H:%M:%SZ"` \
.
[+] Building 11.5s (14/14) FINISHED


Class: Mongoid::Association::Many
- Extended by:
- Forwardable
- Includes:
- Enumerable
- Defined in:
- lib/mongoid/association/many.rb
Overview
This is the superclass for all many to one and many to many association proxies.
Direct Known Subclasses
Instance Attribute Summary
Attributes inherited from Proxy
#_association, #_base, #_target
Instance Method Summary collapse
-
#blank? ⇒ true, false
Is the association empty?.
-
#create(attributes = nil, type = nil, &block) ⇒ Document
Creates a new document on the references many association.
-
#create!(attributes = nil, type = nil, &block) ⇒ Document
Creates a new document on the references many association.
-
#find_or_create_by(attrs = {}, type = nil, &block) ⇒ Document
Find the first document given the conditions, or creates a new document with the conditions that were supplied.
-
#find_or_create_by!(attrs = {}, type = nil, &block) ⇒ Document
Find the first document given the conditions, or creates a new document with the conditions that were supplied.
-
#find_or_initialize_by(attrs = {}, type = nil, &block) ⇒ Document
Find the first
Document
given the conditions, or instantiates a new document with the conditions that were supplied. -
#nil? ⇒ false
This proxy can never be nil.
-
#respond_to?(name, include_private = false) ⇒ true, false
Since method_missing is overridden we should override this as well.
-
#scoped ⇒ Criteria
This is public access to the association's criteria.
-
#serializable_hash(options = {}) ⇒ Hash
Gets the document as a serializable hash, used by ActiveModel's JSON and XML serializers.
-
#unscoped ⇒ Criteria
Get a criteria for the embedded documents without the default scoping applied.
Methods inherited from Proxy
apply_ordering, #extend_proxies, #init, #klass, #reset_unloaded, #substitutable
Methods included from Marshalable
Dynamic Method Handling
This class handles dynamic methods through the method_missing method in the class Mongoid::Association::Proxy
Instance Method Details
#blank? ⇒ true, false
Is the association empty?
24 25 26 |
# File 'lib/mongoid/association/many.rb', line 24 def blank? !any? end |
#create(attributes = nil, type = nil, &block) ⇒ Document
Creates a new document on the references many association. This will save the document if the parent has been persisted.
41 42 43 44 45 46 47 48 49 |
# File 'lib/mongoid/association/many.rb', line 41 def create(attributes = nil, type = nil, &block) if attributes.is_a?(::Array) attributes.map { |attrs| create(attrs, type, &block) } else doc = build(attributes, type, &block) _base.persisted? ? doc.save : raise_unsaved(doc) doc end end |
#create!(attributes = nil, type = nil, &block) ⇒ Document
Creates a new document on the references many association. This will save the document if the parent has been persisted and will raise an error if validation fails.
66 67 68 69 70 71 72 73 74 |
# File 'lib/mongoid/association/many.rb', line 66 def create!(attributes = nil, type = nil, &block) if attributes.is_a?(::Array) attributes.map { |attrs| create!(attrs, type, &block) } else doc = build(attributes, type, &block) _base.persisted? ? doc.save! : raise_unsaved(doc) doc end end |
#find_or_create_by(attrs = {}, type = nil, &block) ⇒ Document
Find the first document given the conditions, or creates a new document with the conditions that were supplied.
@param [ Hash ] attrs The attributes to search or create with.
@param [ Class ] type The optional type of document to create.
86 87 88 |
# File 'lib/mongoid/association/many.rb', line 86 def find_or_create_by(attrs = {}, type = nil, &block) find_or(:create, attrs, type, &block) end |
#find_or_create_by!(attrs = {}, type = nil, &block) ⇒ Document
Find the first document given the conditions, or creates a new document with the conditions that were supplied. This will raise an error if validation fails.
102 103 104 |
# File 'lib/mongoid/association/many.rb', line 102 def find_or_create_by!(attrs = {}, type = nil, &block) find_or(:create!, attrs, type, &block) end |
#find_or_initialize_by(attrs = {}, type = nil, &block) ⇒ Document
Find the first Document
given the conditions, or instantiates a new document with the conditions that were supplied
116 117 118 |
# File 'lib/mongoid/association/many.rb', line 116 def find_or_initialize_by(attrs = {}, type = nil, &block) find_or(:build, attrs, type, &block) end |
#nil? ⇒ false
This proxy can never be nil.
128 129 130 |
# File 'lib/mongoid/association/many.rb', line 128 def nil? false end |
#respond_to?(name, include_private = false) ⇒ true, false
Since method_missing is overridden we should override this as well.
143 144 145 146 |
# File 'lib/mongoid/association/many.rb', line 143 def respond_to?(name, include_private = false) [].respond_to?(name, include_private) || klass.respond_to?(name, include_private) || super end |
#scoped ⇒ Criteria
This is public access to the association's criteria.
156 157 158 |
# File 'lib/mongoid/association/many.rb', line 156 def scoped criteria end |
#serializable_hash(options = {}) ⇒ Hash
Gets the document as a serializable hash, used by ActiveModel's JSON and XML serializers. This override is just to be able to pass the :include and :except options to get associations in the hash.
176 177 178 |
# File 'lib/mongoid/association/many.rb', line 176 def serializable_hash( = {}) _target.map { |document| document.serializable_hash() } end |
#unscoped ⇒ Criteria
Get a criteria for the embedded documents without the default scoping applied.
189 190 191 |
# File 'lib/mongoid/association/many.rb', line 189 def unscoped criteria.unscoped end |


Declaration: This blog article is totaly not my original article. I just reproduce it from colah’s blog – Understanding LSTM Networks, since it is really an excellent article of explanation of LSTM and I am afraid that I may lose the link of this article or the author may change her blog address. So I put this article into my own blog… Moreover, the Chinese version of this article, translated by Not_GOD, are available here.
Recurrent Neural Networks
Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.
Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones.
Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist. In the above diagram, a chunk of neural network, $A$, looks at some input $x_{t}$ and outputs a value $h_{t}$. A loop allows information to be passed from one step of the network to the next.
These loops make recurrent neural networks seem kind of mysterious. However, if you think a bit more, it turns out that they aren’t all that different than a normal neural network. A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor. Consider what happens if we unroll the loop: This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists. They’re the natural architecture of neural network to use for such data.
And they certainly are used! In the last few years, there have been incredible success applying RNNs to a variety of problems: speech recognition, language modeling, translation, image captioning… The list goes on. I’ll leave discussion of the amazing feats one can achieve with RNNs to Andrej Karpathy’s excellent blog post, The Unreasonable Effectiveness of Recurrent Neural Networks. But they really are pretty amazing.
Essential to these successes is the use of “LSTMs”, a very special kind of recurrent neural network which works, for many tasks, much much better than the standard version. Almost all exciting results based on recurrent neural networks are achieved with them. It’s these LSTMs that this essay will explore.
The Problem of Long-Term Dependencies
One of the appeals of RNNs is the idea that they might be able to connect previous information to the present task, such as using previous video frames might inform the understanding of the present frame. If RNNs could do this, they’d be extremely useful. But can they? It depends.
Sometimes, we only need to look at recent information to perform the present task. For example, consider a language model trying to predict the next word based on the previous ones. If we are trying to predict the last word in “the clouds are in the sky”, we don’t need any further context – it’s pretty obvious the next word is going to be sky. In such cases, where the gap between the relevant information and the place that it’s needed is small, RNNs can learn to use the past information. But there are also cases where we need more context. Consider trying to predict the last word in the text “I grew up in France… I speak fluent French”. Recent information suggests that the next word is probably the name of a language, but if we want to narrow down which language, we need the context of France, from further back. It’s entirely possible for the gap between the relevant information and the point where it is needed to become very large.
Unfortunately, as that gap grows, RNNs become unable to learn to connect the information. In theory, RNNs are absolutely capable of handling such “long-term dependencies”. A human could carefully pick parameters for them to solve toy problems of this form. Sadly, in practice, RNNs don’t seem to be able to learn them. The problem was explored in depth by Hochreiter (1991) [German] and Bengio, et al. (1994), who found some pretty fundamental reasons why it might be difficult.
Thankfully, LSTMs don’t have this problem!
LSTM Networks
Long Short Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. They were introduced by Hochreiter & Schmidhuber (1997), and were refined and popularized by many people in following work. (In addition to the original authors, a lot of people contributed to the modern LSTM. A non-comprehensive list is: Felix Gers, Fred Cummins, Santiago Fernandez, Justin Bayer, Daan Wierstra, Julian Togelius, Faustino Gomez, Matteo Gagliolo, and Alex Graves) They work tremendously well on a large variety of problems, and are now widely used.
LSTMs are explicitly designed to avoid the long-term dependency problem. Remembering information for long periods of time is practically their default behavior, not something they struggle to learn!
All recurrent neural networks have the form of a chain of repeating modules of neural network. In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer. LSTMs also have this chain like structure, but the repeating module has a different structure. Instead of having a single neural network layer, there are four, interacting in a very special way. Don’t worry about the details of what’s going on. We’ll walk through the LSTM diagram step by step later. For now, let’s just try to get comfortable with the notation we’ll be using. In the above diagram, each line carries an entire vector, from the output of one node to the inputs of others. The pink circles represent pointwise operations, like vector addition, while the yellow boxes are learned neural network layers. Lines merging denote concatenation, while a line forking denote its content being copied and the copies going to different locations.
The Core Idea Behind LSTMs
The key to LSTMs is the cell state, the horizontal line running through the top of the diagram.
The cell state is kind of like a conveyor belt. It runs straight down the entire chain, with only some minor linear interactions. It’s very easy for information to just flow along it unchanged. The LSTM does have the ability to remove or add information to the cell state, carefully regulated by structures called gates.
Gates are a way to optionally let information through. They are composed out of a sigmoid neural net layer and a pointwise multiplication operation. The sigmoid layer outputs numbers between zero and one, describing how much of each component should be let through. A value of zero means “let nothing through”, while a value of one means “let everything through”!
An LSTM has three of these gates, to protect and control the cell state.
Step-by-Step LSTM Walk Through
The first step in our LSTM is to decide what information we’re going to throw away from the cell state. This decision is made by a sigmoid layer called the “forget gate layer”. It looks at $h_{t-1}$ and $x_{t}$, and outputs a number between $0$ and $1$ for each number in the cell state $C_{t-1}$. A $1$ represents “completely keep this” while a $0$ represents “completely get rid of this”.
Let’s go back to our example of a language model trying to predict the next word based on all the previous ones. In such a problem, the cell state might include the gender of the present subject, so that the correct pronouns can be used. When we see a new subject, we want to forget the gender of the old subject. The next step is to decide what new information we’re going to store in the cell state. This has two parts. First, a sigmoid layer called the “input gate layer” decides which values we’ll update. Next, a tanh layer creates a vector of new candidate values, $\tilde{C}_{t}$, that could be added to the state. In the next step, we’ll combine these two to create an update to the state.
In the example of our language model, we’d want to add the gender of the new subject to the cell state, to replace the old one we’re forgetting. It’s now time to update the old cell state, $C_{t-1}$, into the new cell state $C_{t}$. The previous steps already decided what to do, we just need to actually do it.
We multiply the old state by $f_{t}$, forgetting the things we decided to forget earlier. Then we add $i_{t}*\tilde{C}_{t}$. This is the new candidate values, scaled by how much we decided to update each state value.
In the case of the language model, this is where we’d actually drop the information about the old subject’s gender and add the new information, as we decided in the previous steps. Finally, we need to decide what we’re going to output. This output will be based on our cell state, but will be a filtered version. First, we run a sigmoid layer which decides what parts of the cell state we’re going to output. Then, we put the cell state through $tanh$ (to push the values to be between $-1$ and $1$) and multiply it by the output of the sigmoid gate, so that we only output the parts we decided to.
For the language model example, since it just saw a subject, it might want to output information relevant to a verb, in case that’s what is coming next. For example, it might output whether the subject is singular or plural, so that we know what form a verb should be conjugated into if that’s what follows next.
Variants on Long Short Term Memory
What I’ve described so far is a pretty normal LSTM. But not all LSTMs are the same as the above. In fact, it seems like almost every paper involving LSTMs uses a slightly different version. The differences are minor, but it’s worth mentioning some of them.
One popular LSTM variant, introduced by Gers & Schmidhuber (2000), is adding “peephole connections”. This means that we let the gate layers look at the cell state. The above diagram adds peepholes to all the gates, but many papers will give some peepholes and not others.
Another variation is to use coupled forget and input gates. Instead of separately deciding what to forget and what we should add new information to, we make those decisions together. We only forget when we’re going to input something in its place. We only input new values to the state when we forget something older. A slightly more dramatic variation on the LSTM is the Gated Recurrent Unit, or GRU, introduced by Cho, et al. (2014). It combines the forget and input gates into a single “update gate”. It also merges the cell state and hidden state, and makes some other changes. The resulting model is simpler than standard LSTM models, and has been growing increasingly popular. These are only a few of the most notable LSTM variants. There are lots of others, like Depth Gated RNNs by Yao, et al. (2015). There’s also some completely different approach to tackling long-term dependencies, like Clockwork RNNs by Koutnik, et al. (2014).
Which of these variants is best? Do the differences matter? Greff, et al. (2015) do a nice comparison of popular variants, finding that they’re all about the same. Jozefowicz, et al. (2015) tested more than ten thousand RNN architectures, finding some that worked better than LSTMs on certain tasks.
Conclusion
Earlier, I mentioned the remarkable results people are achieving with RNNs. Essentially all of these are achieved using LSTMs. They really work a lot better for most tasks!
Written down as a set of equations, LSTMs look pretty intimidating. Hopefully, walking through them step by step in this essay has made them a bit more approachable.
LSTMs were a big step in what we can accomplish with RNNs. It’s natural to wonder: is there another big step? A common opinion among researchers is: “Yes! There is a next step and it’s attention!” The idea is to let every step of an RNN pick information to look at from some larger collection of information. For example, if you are using an RNN to create a caption describing an image, it might pick a part of the image to look at for every word it outputs. In fact, Xu, et al. (2015) do exactly this – it might be a fun starting point if you want to explore attention! There’s been a number of really exciting results using attention, and it seems like a lot more are around the corner…
Attention isn’t the only exciting thread in RNN research. For example, Grid LSTMs by Kalchbrenner, et al. (2015) seem extremely promising. Work using RNNs in generative models – such as Gregor, et al. (2015), Chung, et al. (2015), or Bayer & Osendorfer (2015) – also seems very interesting. The last few years have been an exciting time for recurrent neural networks, and the coming ones promise to only be more so!
Acknowledgments
I’m grateful to a number of people for helping me better understand LSTMs, commenting on the visualizations, and providing feedback on this post.
I’m very grateful to my colleagues at Google for their helpful feedback, especially Oriol Vinyals, Greg Corrado, Jon Shlens, Luke Vilnis, and Ilya Sutskever. I’m also thankful to many other friends and colleagues for taking the time to help me, including Dario Amodei, and Jacob Steinhardt. I’m especially thankful to Kyunghyun Cho for extremely thoughtful correspondence about my diagrams.
Before this post, I practiced explaining LSTMs during two seminar series I taught on neural networks. Thanks to everyone who participated in those for their patience with me, and for their feedback.
Reference & More LSTM Information
- colah’s blog – Understanding LSTM Networks
- Grid-LSTM Slides
- Grid Long Short-Term Memory
- Learning to Forget: Continual Prediction with LSTM
- LSTM in Recurrent Neural Networks
- Supervised Sequence Labelling with Recurrent Neural Networks
- Long Short-Term Memory
- Unsupervised Learning of Video Representations using LSTMs
- Unsupervised Learning of Video Representations using LSTMs – GitHub


Department for Transport homepage
Social media use
Information about departmental social media policies.
DfT Twitter policy
The department’s twitter channel @transportgovuk is managed by the digital engagement team, on behalf of colleagues across the department.
We do not use automation tools eg RSS feeds to generate tweets.
If you follow us, you can expect between 2 to 10 tweets a day.
Tweets will include alerts about:
- announcements
- press releases
- new content published on our website
- new content uploaded to other digital channels eg YouTube
- marketing campaign messages
- invitations to consult
- information from our ministerial team about what they’re doing
- occasional live coverage of events
Following
If you follow us on Twitter we may follow you back. Being followed by us does not imply endorsement of any kind.
Availability
We will update and monitor our Twitter account during office hours, Monday to Friday. Twitter may occasionally be unavailable and we accept no responsibility for lack of service due to Twitter downtime.
@replies and direct messages
We welcome feedback, ideas and engagement from all our followers, and endeavour to join the conversation where possible. However, we are not able to reply individually to all the messages we receive via Twitter.
The digital engagement team read all @replies and direct messages and ensures that any emerging themes or helpful suggestions are passed to the relevant colleagues in the department.
We cannot engage on issues of party politics or answer questions which break the rules of our general comments policy.
The usual ways of contacting us for official correspondence are detailed in the contact us section of our website.
THINK! road safety twitter policy
The THINK! road safety channel @THINKgovuk is managed by the THINK! campaigns team.
We do not use automation tools eg RSS feeds to generate tweets.
If you follow us, you can expect between 2 to 10 tweets a day.
Tweets will include alerts about:
- THINK! campaigns
- road safety information
- THINK! press releases
- new content published on THINK! channels
- events relevant to road safety
- selected road safety news from stakeholders
Following
If you follow us on Twitter we may follow you back. Being followed by us does not imply endorsement of any kind.
Availability
We will update and monitor our Twitter account during office hours, Monday to Friday. Twitter may occasionally be unavailable and we accept no responsibility for lack of service due to Twitter downtime.
@replies and direct messages
We welcome feedback, ideas and engagement from all our followers, and endeavour to join the conversation where possible. However, we are not able to reply individually to all the messages we receive via Twitter.
The campaigns team read all @replies and direct messages and ensures that any emerging themes or helpful suggestions are passed to the relevant colleagues in the team.
We cannot engage on issues of party politics or answer questions which break the rules of our general comments policy.
Contact the campaigns team outside of Twitter if necessary.


{% extends 'base.html' %} {% block title%} PIT Transcriptor {% endblock %} {% block content%} PIT Transcriptor is a tool designed to create transcriptions of dev-centric audio content. {% for message in get_flashed_messages() %} {{message}} {% endfor %} Upload a file and get started! Only $0.08 per minute (while in alpha!) {% include 'upload_form.html' %} {% endblock %}
PIT Transcriptor is a tool designed to create transcriptions of dev-centric audio content.


I want to digress here. When the API docs are thin (say after a new ArchivesSpace release) you’ll really benefit from experimenting.
Read what’s there and guess at the next step.
curl -H "X-ArchivesSpace-Session: $SESSION" \
http://localhost:8089/agents/people
I get back an error
{
"error":{
"page":[
"Parameter required but no value provided"
],
"id_set":[
"Parameter required but no value provided"
],
"all_ids":[
"Parameter required but no value provided"
]
}
}
And it lets us know the options expected. We will use “all_ids=true” next.


| Figure 1 |
Pictures of (A) the venomous coral snake ( M. fulvius) (phot ... |
| Figure 2 |
Study area. Blue represents range of the eastern coral snake ... |
| Figure 3 |
Habitat suitability for M. fulvius throughout the Southeast ... |
| Figure 4 |
Habitat suitability for M. fulvius in the near future (2050) ... |
| Figure 5 |
Multivariate environmental similarity surfaces (MESS) analys ... |
| Figure 6 |
Change in suitable habitat from current conditions (1950–200 ... |


Yesterday I was working on active_admin. This is the first time I am using active admin in any of my project, I find it pretty awesome. If you never used it then just it a try. So while building dashboard for the active admin. I felt of the need to something like application_model ;) so that the place where I used modules as application model in ruby on rails
I was implementing highcharts in my application and I want to calculate Daily counts in last 30 days for most of the models likes users, products etc
Before
So I need this method for lots of models.
def self.daily_records_counts_array_in_past_n_days(range = 30.days)
counts = self.group('Date(created_at)').count
(range.ago.to_date..Date.today).map {|date| counts[date] || }
# A method which will return an array of daily new records of users (ie signups) in last 30 days (unless specified)
end
Now Same functionality i need for product model and other models too. So instead of copy pasting this method in all the model, we can create a module and include that module in required models.
After
In lib directory, create a directory ie modules (or name it whatever you want to.)
Now create a file ie act_as_countable.rb there and paste this code.
module ActAsCountable
def self.included(base)
base.extend(ClassMethods)
end
module ClassMethods
# define all the class methods here.
# this method returns total records array in last 30 days. Something like: [10, 40, 70, 90....]
def total_records_counts_array_in_past_n_days(range = 30.days)
total_records = []
counts = self.group('Date(created_at)').count
(30.days.ago.to_date..Date.today).each do |day|
total_records.push(total_records.sum (counts[day] || ))
end
total_records
end
# this method returns total daily news records array in last 30 days. Something like: [10, 2, 40, 15....]
def daily_records_counts_array_in_past_n_days(range = 30.days)
counts = self.group('Date(created_at)').count
(range.ago.to_date..Date.today).map {|date| counts[date] || }
end
end
end
Now In Models, where you need these methods. Just include them by:
include ActAsCountable
Once includes you can call these methods as normal class methods like.
User.total_records_counts_array_in_past_n_days
Product.total_records_counts_array_in_past_n_days
User.daily_records_counts_array_in_past_n_days(15.days)
Product.daily_records_counts_array_in_past_n_days(15.days)
If you want to define instance methods in you module. Just define them in the same module like this.
module ActAsCountable
def self.included(base)
base.extend(ClassMethods)
end
module ClassMethods
def total_records_counts_array_in_past_n_days(range = 30.days)
total_records = []
counts = self.group('Date(created_at)').count
(30.days.ago.to_date..Date.today).each do |day|
total_records.push(total_records.sum (counts[day] || ))
end
total_records
end
def daily_records_counts_array_in_past_n_days(range = 30.days)
counts = self.group('Date(created_at)').count
(range.ago.to_date..Date.today).map {|date| counts[date] || }
end
end
def my_instance_method
# this is my sample blank instance method
end
end
Note: You need to add load these modules via specifying the path in application_controller.rb. Also you need to restart your application.
config.autoload_paths = Dir["#{config.root}/lib/modules/**"]
You can use module even to refactor the code. For example. Generally my user model has lot of methods related to fetch data from facebook, once user signs up. So create a module for those instance methods.


CURVELENGTH return length of a curve (a list of points) Compute the length of a curve given as a list of following points. L = curveLength(X, Y); L = curveLength(POINTS); POINTS should be a [NxD] array, with N being the numbe of points and D the dimension of the points. PT = curveLength(..., TYPE); Specifies if the last point is connected to the first one. TYPE can be either 'closed' or 'open'. TODO : specify norm (euclidian, taxi, ...). Example: Compute the perimeter of a circle with radius 1 curveLength(circleAsPolygon([0 0 1], 500), 'closed') -> return 6.2831 See also: polylines2d, curveCentroid --------- author : David Legland INRA - TPV URPOI - BIA IMASTE created the 14/06/2004


BlenderNEURON Class Reference¶
-
class
blenderneuron.commnode.
CommNode
(server_end, on_client_connected=None, on_server_setup=None, coverage=False)[source]¶ -
-
read_client_address_file
()[source]¶ Read from a file in tmp folder that contains the address of the other node’s XML-RCP server :return: The address of the other end’s XML-RCP server
-
save_server_address_file
()[source]¶ Create a file in tmp folder that contains the address of this node’s XML-RCP server :return: Nothing
-
server_types
= {'Blender': 'NEURON', 'Control-Blender': 'Blender', 'Control-NEURON': 'NEURON', 'NEURON': 'Blender', 'Package': 'Blender'}¶
-


| Total net profit | -10.74 | Gross profit | 60.63 | Gross loss | -71.37 |
| Profit factor | 0.85 | Expected payoff | -0.31 | ||
| Absolute drawdown | -24.69 | Maximal drawdown (%) | 55.98 (178.91%) | ||
| Total trades | 35 | Profit trades (% of total) | 5 (14.29%) | Loss trades (% of total) | 30 (85.71%) |
| Long positions (won %) | 18 (16.67%) | Short positions (won %) | 17 (11.76%) | ||
| Largest | profit trade | 17.08 | loss trade | -2.65 | |
| Average | profit trade | 12.13 | loss trade | -2.38 | |
| Minimum | profit trade | 5.53 | loss trade | -2.19 | |
| Maximum | take profit in pips | 175.9 | stop loss in pips | -27.2 | |
| Average | take profit in pips | 174.6 | stop loss in pips | -24.4 | |
| Minimum | take profit in pips | 171.8 | stop loss in pips | -23.1 | |
| Maximum | Reward-Risk | 7.6 | Duration in minutes | 11443 | |
| Average | Reward-Risk | 7.1 | Duration in minutes | 1128.4 | |
| Minimum | Reward-Risk | 6.3 | Duration in minutes | 1 | |
| Maximum | consecutive wins (profit in money) | 2.0 (33.9) | consecutive losses (loss in money) | 20.0 (-47.12) | |
| Average | consecutive wins | 1.0 | consecutive losses | 7.0 |
| Total net profit | 9.95 | Gross profit | 45.99 | Gross loss | -36.04 |
| Profit factor | 1.28 | Expected payoff | 0.55 | ||
| Absolute drawdown | -2.61 | Maximal drawdown (%) | 33.43 (106.84%) | ||
| Total trades | 18 | Profit trades (% of total) | 3 (16.67%) | Loss trades (% of total) | 15 (83.33%) |
| Largest | profit trade | 17.08 | loss trade | -2.61 | |
| Average | profit trade | 15.33 | loss trade | -2.4 | |
| Minimum | profit trade | 12.09 | loss trade | -2.29 | |
| Maximum | take profit in pips | 175.1 | stop loss in pips | -26.8 | |
| Average | take profit in pips | 174.5 | stop loss in pips | -24.5 | |
| Minimum | take profit in pips | 172.2 | stop loss in pips | -23.9 | |
| Maximum | Reward-Risk | 7.3 | Duration in minutes | 11443.0 | |
| Average | Reward-Risk | 7.1 | Duration in minutes | 1463.1 | |
| Minimum | Reward-Risk | 6.4 | Duration in minutes | 25.0 | |
| Maximum | consecutive wins (profit in money) | 2.0 (33.9) | consecutive losses (loss in money) | 14.0 (-33.43) | |
| Average | consecutive wins | 2.0 | consecutive losses | 7.0 |
| Total net profit | -20.69 | Gross profit | 14.64 | Gross loss | -35.33 |
| Profit factor | 0.41 | Expected payoff | -1.22 | ||
| Absolute drawdown | -28.09 | Maximal drawdown (%) | 25.73 (-1090.25%) | ||
| Total trades | 17 | Profit trades (% of total) | 2 (11.76%) | Loss trades (% of total) | 15 (88.24%) |
| Largest | profit trade | 9.11 | loss trade | -2.65 | |
| Average | profit trade | 7.32 | loss trade | -2.36 | |
| Minimum | profit trade | 5.53 | loss trade | -2.19 | |
| Maximum | take profit in pips | 175.9 | stop loss in pips | -27.2 | |
| Average | take profit in pips | 174.7 | stop loss in pips | -24.3 | |
| Minimum | take profit in pips | 171.8 | stop loss in pips | -23.1 | |
| Maximum | Reward-Risk | 7.6 | Duration in minutes | 6412.0 | |
| Average | Reward-Risk | 7.2 | Duration in minutes | 773.9 | |
| Minimum | Reward-Risk | Duration in minutes | 1.0 | ||
| Maximum | consecutive wins (profit in money) | 1.0 (5.53) | consecutive losses (loss in money) | 12.0 (-28.09) | |
| Average | consecutive wins | 1.0 | consecutive losses | 6.0 |


Find Out More About Julian Dean
The links below will take you to some other websites where you can find out more about Julian and his cycling career:
The links below will take you to some other websites where you can find out more about Julian and his cycling career:


I am a student of Computer Science at Politechnika Krakowska. I have a great interest in mathematics and new technologies. I started my adventure with programming three years ago. My first program was in C++ where I learned the basics of algorithms, then I was passionate about C# and Java. Currently, I am developing my skills in backend technologies and databases. Take a look at my projects!


Provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. In addition to its primary test, it also provides functions for simulation-based power analysis, reading in common data set formats, and visualizing results. Temporarily contains an edited version of function hr.mcp() from package 'wild1', written by Glen Sargeant. For tutorial see: http://evolve.zoo.ox.ac.uk/Evolve/Perspectev.html.
| Version: | 1.1 |
| Depends: | R (≥ 2.10), ggplot2, foreach, ape, doParallel, parallel, grDevices, stats, methods |
| Imports: | mapproj, boot, sp |
| Published: | 2015-08-09 |
| Author: | Kenneth B. Hoehn [aut, cre], Glen A. Sargeant [ctb] |
| Maintainer: | Kenneth B. Hoehn <perspectev at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Citation: | perspectev citation info |
| CRAN checks: | perspectev results |
| Reference manual: | perspectev.pdf |
| Package source: | perspectev_1.1.tar.gz |
| Windows binaries: | r-devel: perspectev_1.1.zip, r-release: perspectev_1.1.zip, r-oldrel: perspectev_1.1.zip |
| OS X binaries: | r-release: perspectev_1.1.tgz, r-oldrel: perspectev_1.1.tgz |
| Old sources: | perspectev archive |
Please use the canonical form https://CRAN.R-project.org/package=perspectev to link to this page.


This is a real-time communications help desk sample application. It provides both a customer and an agent perspective. To try this out you will need someone to play the role of both the customer and the agent. You can try this solo by opening two different browsers windows. You can also test this with multiple customers and agents. Enjoy!
Click here to enter this sample application as a customer. As a customer, simply hit the helpdesk button at the top right corner of the page.
Click here to enter this sample application as an agent. As an agent, you will need to enter a name to register and then join the available queues.


--- layout: documentation title: FuzzyHashStructureType ---
The FuzzyHashStructureType is used for characterizing the internal components of a cryptograhic fuzzy hash algorithmic calculation.
| Field Name | Type | Description |
|---|---|---|
| Block_Size0..1 | IntegerObjectPropertyType |
The Block_Size field is optional and specifies the calculated block size for this fuzzy hash calculation. |
| Block_Hash0..1 | FuzzyHashBlockType |
The Block_Hash field is optional and enables specification of the elemental components utilized for a fuzzy hash calculation on the hashed object utilizing Block_Size to calculate trigger points. |


The old part of this town is a very attractive medieval fishing port. It has now expanded into a large modern city on the landward side and is Iocated just south of the promontory of Gargano.
Unfortunately however, it now appears this port is closed to leisure boats (see comment at bottom of page - September 2015).
Position 41°19.9'N, 16°17.5'E.
The Harbourmaster and Customs have their offices and the base of the western mole.
The Police office is in the town.
Shelter here is very good, and whilst there are restrictions as to where leisure vessels can go (see comment at bottom of page), it is still possible to anchor here with some shelter.
Last updated September 2015.


{% load helper_tags_team %}
Dear Customer,
Please find attached freight invoices for {{ number_of_shipment }} shipment(s){% if pod_files %}, along with Proofs of Delivery.{% else %}.{% endif %}
| Invoice No. | Invoice Date | Invoice Amount | {% if credit_period > 0 %}Due Date | {% endif %}
|---|---|---|---|
| {{ invoice.invoice_number }} | {{ invoice.invoice_date }} | {{ invoice.invoice_amount }} | {% if credit_period > 0 %}{{ invoice.due_date }} | {% endif %}
* Remarks
{% endif %}Terms & Conditions:
Thanks and Regards
Team Aaho
Trans IQ Technologies Pvt Ltd
{#


Abdisa Tuffa
About
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Connect with Me


Intro to Miller's programming language¶
In the Miller in 10 minutes page we took a tour of some of Miller's most-used verbs including cat
, head
, tail
, cut
, and sort
. These are analogs of familiar system commands, but empowered by field-name indexing and file-format awareness: the system sort
command only knows about lines and column names like 1,2,3,4
, while mlr sort
knows about CSV/TSV/JSON/etc records, and field names like color,shape,flag,index
.
We also caught a glimpse of Miller's put
and filter
verbs. These two are special since they let you express statements using Miller's programming language. It's a embedded domain-specific language since it's inside Miller: often referred to simply as the Miller DSL.
In the DSL reference page we have a complete reference to Miller's programming language. For now, let's take a quick look at key features -- you can use as few or as many features as you like.
Records and fields¶
Let's keep using the example.csv file:
mlr --c2p put '$cost = $quantity * $rate' example.csv
color shape flag k index quantity rate cost yellow triangle true 1 11 43.6498 9.8870<PHONE_NUMBER> red square true 2 15 79.2778 0.0130 1.0306114 red circle true 3 16 13.8103 2.9010 40.063680299999994 red square false 4 48 77.5542 7.4670<PHONE_NUMBER>999999 purple triangle false 5 51 81.2290 8.5910<PHONE_NUMBER>999999 red square false 6 64 77.1991 9.5310<PHONE_NUMBER>000001 purple triangle false 7 65 80.1405 5.8240 466.738272 yellow circle true 8 73 63.9785 4.2370<PHONE_NUMBER> yellow circle true 9 87 63.5058 8.3350<PHONE_NUMBER>000001 purple square false 10 91 72.3735 8.2430<PHONE_NUMBER>000001
When we type that, a few things are happening:
- We refer to fields in the input data using a dollar sign and then the field name, e.g.
$quantity
. (If a field name contains special characters like a dot or slash, just use curly braces:${field.name}
.) - The expression
$cost = $quantity * $rate
is executed once per record of the data file. Our example.csv has 10 records so this expression was executed 10 times, with the field names$quantity
and$rate
each time bound to the current record's values for those fields. - On the left-hand side we have the new field name
$cost
which didn't come from the input data. Assignments to new variables result in a new field being placed after all the other ones. If we'd assigned to an existing field name, it would have been updated in-place. - The entire expression is surrounded by single quotes (with an adjustment needed on Windows), to get it past the system shell. Inside those, only double quotes have meaning in Miller's programming language.
Multi-line statements, and statements-from-file¶
You can use more than one statement, separating them with semicolons, and optionally putting them on lines of their own:
mlr --c2p put '$cost = $quantity * $rate; $index = $index * 100' example.csv
color shape flag k index quantity rate cost yellow triangle true 1 1100 43.6498 9.8870<PHONE_NUMBER> red square true 2 1500 79.2778 0.0130 1.0306114 red circle true 3 1600 13.8103 2.9010 40.063680299999994 red square false 4 4800 77.5542 7.4670<PHONE_NUMBER>999999 purple triangle false 5 5100 81.2290 8.5910<PHONE_NUMBER>999999 red square false 6 6400 77.1991 9.5310<PHONE_NUMBER>000001 purple triangle false 7 6500 80.1405 5.8240 466.738272 yellow circle true 8 7300 63.9785 4.2370<PHONE_NUMBER> yellow circle true 9 8700 63.5058 8.3350<PHONE_NUMBER>000001 purple square false 10 9100 72.3735 8.2430<PHONE_NUMBER>000001
mlr --c2p put ' $cost = $quantity * $rate; $index *= 100 ' example.csv
color shape flag k index quantity rate cost yellow triangle true 1 1100 43.6498 9.8870<PHONE_NUMBER> red square true 2 1500 79.2778 0.0130 1.0306114 red circle true 3 1600 13.8103 2.9010 40.063680299999994 red square false 4 4800 77.5542 7.4670<PHONE_NUMBER>999999 purple triangle false 5 5100 81.2290 8.5910<PHONE_NUMBER>999999 red square false 6 6400 77.1991 9.5310<PHONE_NUMBER>000001 purple triangle false 7 6500 80.1405 5.8240 466.738272 yellow circle true 8 7300 63.9785 4.2370<PHONE_NUMBER> yellow circle true 9 8700 63.5058 8.3350<PHONE_NUMBER>000001 purple square false 10 9100 72.3735 8.2430<PHONE_NUMBER>000001
One of Miller's key features is the ability to express data-transformation right there at the keyboard, interactively. But if you find yourself using expressions repeatedly, you can put everything between the single quotes into a file and refer to that using put -f
:
cat dsl-example.mlr
$cost = $quantity * $rate; $index *= 100
mlr --c2p put -f dsl-example.mlr example.csv
color shape flag k index quantity rate cost yellow triangle true 1 1100 43.6498 9.8870<PHONE_NUMBER> red square true 2 1500 79.2778 0.0130 1.0306114 red circle true 3 1600 13.8103 2.9010 40.063680299999994 red square false 4 4800 77.5542 7.4670<PHONE_NUMBER>999999 purple triangle false 5 5100 81.2290 8.5910<PHONE_NUMBER>999999 red square false 6 6400 77.1991 9.5310<PHONE_NUMBER>000001 purple triangle false 7 6500 80.1405 5.8240 466.738272 yellow circle true 8 7300 63.9785 4.2370<PHONE_NUMBER> yellow circle true 9 8700 63.5058 8.3350<PHONE_NUMBER>000001 purple square false 10 9100 72.3735 8.2430<PHONE_NUMBER>000001
This becomes particularly important on Windows. Quite a bit of effort was put into making Miller on Windows be able to handle the kinds of single-quoted expressions we're showing here, but if you get syntax-error messages on Windows using examples in this documentation, you can put the parts between single quotes into a file and refer to that using mlr put -f
-- or, use the triple-double-quote trick as described in the Miller on Windows page.
Out-of-stream variables, begin, and end¶
Above we saw that your expression is executed once per record -- if a file has a million records, your expression will be executed a million times, once for each record. But you can mark statements to only be executed once, either before the record stream begins, or after the record stream is ended. If you know about AWK, you might have noticed that Miller's programming language is loosely inspired by it, including the begin
and end
statements.
Above we also saw that names like $quantity
are bound to each record in turn.
To make begin
and end
statements useful, we need somewhere to put things that persist across the duration of the record stream, and a way to emit them. Miller uses out-of-stream variables (or oosvars for short) whose names start with an @
sigil, along with the emit
keyword to write them into the output record stream:
mlr --c2p --from example.csv put 'begin { @sum = 0 } @sum += $quantity; end {emit @sum}'
color shape flag k index quantity rate yellow triangle true 1 11 43.6498 9.8870 red square true 2 15 79.2778 0.0130 red circle true 3 16 13.8103 2.9010 red square false 4 48 77.5542 7.4670 purple triangle false 5 51 81.2290 8.5910 red square false 6 64 77.1991 9.5310 purple triangle false 7 65 80.1405 5.8240 yellow circle true 8 73 63.9785 4.2370 yellow circle true 9 87 63.5058 8.3350 purple square false 10 91 72.3735 8.2430 sum 652.7185
If you want the end-block output to be the only output, and not include the records from the input data, you can use mlr put -q
:
mlr --c2p --from example.csv put -q 'begin { @sum = 0 } @sum += $quantity; end {emit @sum}'
sum 652.7185
mlr --c2j --from example.csv put -q 'begin { @sum = 0 } @sum += $quantity; end {emit @sum}'
[ { "sum": 652.7185 } ]
mlr --c2j --from example.csv put -q ' begin { @count = 0; @sum = 0 } @count += 1; @sum += $quantity; end {emit (@count, @sum)} '
[ { "count": 10, "sum": 652.7185 } ]
We'll see in the documentation for stats1 that there's a lower-keystroking way to get counts and sums of things:
mlr --c2j --from example.csv stats1 -a sum,count -f quantity
[ { "quantity_sum": 652.7185, "quantity_count": 10 } ]
So, take this sum/count example as an indication of the kinds of things you can do using Miller's programming language.
Context variables¶
Also inspired by AWK, the Miller DSL has the following special context variables:
FILENAME
-- the filename the current record came from. Especially useful in things likemlr ... *.csv
.FILENUM
-- similarly, but integer 1,2,3,... rather than filenam.eNF
-- the number of fields in the current record. Note that if you assign$newcolumn = some value
thenNF
will increment.NR
-- starting from 1, counter of how many records processed so far.FNR
-- similar, but resets to 1 at the start of each file.
cat context-example.mlr
$nf = NF; $nr = NR; $fnr = FNR; $filename = FILENAME; $filenum = FILENUM; $newnf = NF;
cat data/a.csv
a,b,c 1,2,3 4,5,6
cat data/b.csv
a,b,c 7,8,9
mlr --c2p put -f context-example.mlr data/a.csv data/b.csv
a b c nf nr fnr filename filenum newnf 1 2 3 3 1 1 data/a.csv 1 8 4 5 6 3 2 2 data/a.csv 1 8 7 8 9 3 3 1 data/b.csv 2 8
Functions and local variables¶
You can define your own functions:
cat factorial-example.mlr
func factorial(n) { if (n <= 1) { return n } else { return n * factorial(n-1) } }
mlr --c2p --from example.csv put -f factorial-example.mlr -e '$fact = factorial(NR)'
color shape flag k index quantity rate fact yellow triangle true 1 11 43.6498 9.8870 1 red square true 2 15 79.2778 0.0130 2 red circle true 3 16 13.8103 2.9010 6 red square false 4 48 77.5542 7.4670 24 purple triangle false 5 51 81.2290 8.5910 120 red square false 6 64 77.1991 9.5310 720 purple triangle false 7 65 80.1405 5.8240 5040 yellow circle true 8 73 63.9785 4.2370 40320 yellow circle true 9 87 63.5058 8.3350 362880 purple square false 10 91 72.3735 8.2430 3628800
Note that here we used the -f
flag to put
to load our function
definition, and also the -e
flag to add another statement on the command
line. (We could have also put $fact = factorial(NR)
inside
factorial-example.mlr
but that would have made that file less flexible for our
future use.)
If-statements, loops, and local variables¶
Suppose you want to only compute sums conditionally -- you can use an if
statement:
cat if-example.mlr
begin { @count_of_red = 0; @sum_of_red = 0 } if ($color == "red") { @count_of_red += 1; @sum_of_red += $quantity; } end { emit (@count_of_red, @sum_of_red) }
mlr --c2p --from example.csv put -q -f if-example.mlr
count_of_red sum_of_red 4<PHONE_NUMBER>9999996
Miller's else-if is spelled elif
.
As we'll see more of in the control-structures reference
page, Miller has a few kinds of
for-loops. In addition to the usual 3-part for (i = 0; i < 10; i += 1)
kind
that many programming languages have, Miller also lets you loop over
maps and arrays. We
haven't encountered maps and arrays yet in this introduction, but for now it
suffices to know that $*
is a special variable holding the current record as
a map:
cat for-example.mlr
for (k, v in $*) { print "KEY IS ". k .  Now we've seen four kinds of variables:
- Record fields like
$shape
- Out-of-stream variables like
@sum
- Local variables like
k
- Built-in context variables like
NF
andNR
If you're curious about scope and extent of local variables, you can read more in the section on variables.
Arithmetic¶
Numbers in Miller's programming language are intended to operate with the principle of least surprise:
- Internally, numbers are either 64-bit signed integers or double-precision floating-point.
- Sums, differences, and products of integers are also integers (so
2*3=6
not6.0
) -- unless the result of the operation would overflow a 64-bit signed integer in which case the result is automatically converted to float. (If you ever want integer-to-integer arithmetic, usex .+ y
,x .* y
, etc.) - Quotients of integers are integers if the division is exact, else floating-point: so
6/2=3
but7/2=3.5
.
You can read more about this in the arithmetic reference.
Absent data¶
In addition to types including string, number (int/float), maps, and arrays, Miller variables can also be absent. This is when a variable never had a value assigned to it. Miller's treatment of absent data is intended to make it easy for you to handle non-homogeneous data. We'll see more in the null-data reference but the basic idea is:
- Adding a number to absent gives the number back. This means you don't have to put
@sum = 0
in yourbegin
blocks. - Any variable which has the absent value is not assigned. This means you don't have to check presence of things from one record to the next.
For example, you can sum up all the $a
values across records without having to check whether they're present or not:
mlr --json cat absent-example.json
[ { "a": 1, "b": 2 }, { "c": 3 }, { "a": 4, "b": 5 } ]
mlr --json put '@sum_of_a += $a; end {emit @sum_of_a}' absent-example.json
[ { "a": 1, "b": 2 }, { "c": 3 }, { "a": 4, "b": 5 }, { "sum_of_a": 5 } ]


Wishlist
Dark Brown Leather Watch
$200.00 $180.00
Dining Chair
$300.00 $220.00
Creative Wooden Stand
$100.00 $80.00
Dark Brown Leather Watch
$200.00 $180.00
Creative Wooden Stand
$200.00 $180.00
NEW COLLECTION /
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LEARN MORE$360.00 $300.00
Hurley Dry-Fit Chino Short. Men's chino short. Outseam Length: 19 Dri-FIT Technology helps keep you dry and comfortable. Made with sweat-wicking fabric. Fitted waist with belt loops. Button waist with zip fly provides a classic look and feel .
Quantity:| SKU: | 12345 |
| Categories: | Fashion, Men, Sunglasses |
| Tags: | Fashion, Men |
| Share on: |


For sterling app functioning you need to authorize. But don't worry, this won't take much time, just 5 seconds – and you can enjoy its great features!
For sterling app functioning you need to authorize. But don't worry, this won't take much time, just 5 seconds – and you can enjoy its great features!
For sterling app functioning you need to authorize. But don't worry, this won't take much time, just 5 seconds – and you can enjoy its great features!


These fonts are distributed subject to the my license, which is just a copy of one from the Berkley Software distribution. They are original work on my part.
| Family | Roman | Italic / Oblique | Bold | Bold Oblique | Black | SmallCaps |
|---|---|---|---|---|---|---|
| Caslon |
||||||
| Monospace |
||||||
| Cupola |
||||||
| Caliban |
These are not a complete implementation of Unicode. Not all of the bitmap fonts have been cleaned up, and the Roman font is more complete than the italic, bold, black or small caps fonts (and its bitmaps more likely to be clean). The postscript fonts are type 0 fonts and as such are not useable in ATM (as far as I know anyway), they do work on the postscript level 2 lazyprinters I've tested them on (both from HP) and on ghostscript.
They do not contain
They now contain Unicode 3 additions for the blocks I have chosen to implement.
The CaslonRoman font contains (other fonts contain fewer):
Reencoding a Type0 postscript font is not easy. If you wish to do it, here are some hints that may help.
These fonts tend to have one large Type1 font called <fontname>Base
(so CaslonItalic.pfa
would contain a font called
CaslonItalicBase
), this contains almost all the glyphs in the
font (some of the fonts will get glyphs out of the standard ZapfDingbats
font that almost all postscript printers have preinstalled, since ZapfDingbats
is part of Unicode). You may reencode this type1 just as you normally would
any other type1. The standard postscript names for unicode characters may
be found at
adobe's
site. The afm file will also show what characters names are found in
each font.
Other people's Unicode fonts
The cupola font does not use the standard postscript names for unicode characters (the mapping between unicode code points and glyphs is correct).


Scott Murphy @hellocreation
...just enough to know it has a lot of potential.
1 client project
1 personal project
and this presentation.
$('.toggleTrigger').click(function() { dosomething(); }); $('.orderTrigger').click(function() { doanotherthing(); }); $('.profileTrigger').click(function() { dosomething(); }); function dosomething() { //console.log('do this work?'); }
var myApp = myApp || {}; myApp.loginWindow = { init: function() { }, openModal: function() { } } myApp.anotherThing = { }
<script src="http://ajax.cdnjs.com/ajax/libs/underscore.js/1.1.4/underscore-min.js"> <script src="http://ajax.cdnjs.com/ajax/libs/backbone.js/0.3.3/backbone-min.js">
<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.4.4/jquery.min.js">
Underscore provides 60-odd functions that support both the usual functional suspects: map, select, invoke — as well as more specialized helpers: function binding, javascript templating, deep equality testing, and so on. It delegates to built-in functions, if present, so modern browsers will use the native implementations of forEach, map, reduce, filter, every, some and indexOf.
Tour = { "name": "Lovely Tour", "price": "15", "pid": 0 }
var Tour = Backbone.Model.extend({ initialize: function(){ alert("Sup! "); } });
// Create a new tour var tour1 = new Tour({ name: "Boring Tour", price: '15'}); var tour1Price = portal.get('price'); tour1.set({ name: "New name for Tour"}); //One great feature of backbone is that it takes care of saving. //It uses jQuery Ajax to do this (so I'm told) tour1.save();
A collection is collection of Models
TourCollection = [ { "name": "Lovely Tour", "price": "15", "pid": 0 }, { "name": "Awesome Tour", "price": "15", "pid": 1 }, ]
TourCollection = Backbone.Collection.extend({ //tie the collection to a model model: Tour, //If you define a comparator, it will be used to maintain //the collection in sorted order. comparator: function(item) { return item.get('pid'); } });
var MyApp = Backbone.Controller.extend({ routes: { "": "root", "showTours": "tourList", }, root: function() { }, tourList: function() { }, });
new TourListView({model:tours});
var TourListView = Backbone.View.extend({ //associate dom element with this view el: $('#tourListView'), events: { "click .contact" : "contactBtnClick", "hover .picture" : "pictureHover", }, render: function() { //show this view }, contactBtnClick: function() {}, pictureHover: function() {} });
Using jasmine - http://tinnedfruit.com/2011/03/03/testing-backbone-apps-with-jasmine-sinon.html


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Communicate between devices in airplane mode... Is that even possible?
Try common colors like "red", "blue", or HEX codes like "#E42D42".
Designed by Mingjie Jiang (@itsmingjie)
Wootton Hack Club 2019-20


I'm just curious as the data seems to be missing the cells 4,8, 5,7 and 5,8 to make the total for the puzzle 388 instead of 405.  -- Cheers. Mark Lawrence.


If you are not the original author of this question you must change the id on the first line of the directive. If you add a unit test,
make sure that you add the parameter :autograde: unittest
to the directive. Likewise if you want
to make an autograded question available through the practice tool add :practice: T
as a parameter.


Termsheet
12:33 23-May-2017 - shared with you by {{pro.name}}
Financial Model
12:37 20-May-2017 - shared with you by {{pro.name}}
Thoughts from second meeting
18:13 10-May-2017 - note by you
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Date and Source


--- title: "Omega Group Pay Discrimination" author: "Harsh Tripathi" summary: "A project focussed on Statistical Analysis." tags: - Statistical Analysis date: "2021-09-12T00:00:00Z" image: focal_point: Smart output: html_document: theme: flatly highlight: zenburn number_sections: yes toc: yes toc_float: yes code_folding: show ---
The data we are going to analyze is of employees in Omega Group Plc., a large multinational company. The objective is to find out if the employees are facing gender-based salary discrimination.
#Reading the CSV
omega <- read_csv("omega.csv")
#Skimming the data
skim(omega)
| Name | omega |
| Number of rows | 50 |
| Number of columns | 3 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| gender | 0 | 1 | 4 | 6 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| salary | 0 | 1 | 68717 | 8638.2 | 47033 | 63303.16 | 68847 | 74777.7 | 84576 | ▁▃▇▆▅ |
| experience | 0 | 1 | 14 | 11.9 | 0 | 2.25 | 15 | 20.8 | 44 | ▇▃▅▂▁ |
omega %>%
select(gender, experience, salary) %>%
ggpairs(aes(colour=gender, alpha = 0.3))+
theme_bw()
It can be observed that the dataset contains data of more female employees than male employees. On average, the experience and salary of males is higher than that of females.
Observing the scatterplot between experience and salary by gender closely -
omega %>%
#Scatterplot between salary and experience
ggplot(aes(x=experience, y=salary, col=gender)) +
geom_point() +
#Black and white theme
theme_bw() +
#Putting title and axes labels
labs(title = "Scatterplot between Salary and Experience",
x = "Number of years of work experience",
y = "Salary",
color = "Gender")
We can observe that salary and work experience are positively correlated. As the number of years of work experience increase, salary increases on average. This relation holds true for males as well as females. However, many women executives have experience less than 5 years and several even have zero year. This means Omega just started to include women in their workforce, while experience of male executives are more evenly distributed.
# Summary Statistics of salary by gender
mosaic::favstats (salary ~ gender, data=omega)
## gender min Q1 median Q3 max mean sd n missing
## 1 female 47033 60338 64618 70033 78800 64543 7567 26 0
## 2 male 54768 68331 74675 78568 84576 73239 7463 24 0
We observe that the mean salary for males is higher than that of females by around $8,600. We will construct confidence intervals and conduct a hypothesis test to analyze if this difference is statistically significant.
#Constructing confidence interval for mean salaries of males and females
gender_salary_ci <- omega %>%
#Grouping data by gender
group_by(gender) %>%
summarise(
#Calculating mean and std dev of salary
mean_salary = mean(salary),
sd_salary = sd(salary),
#Count of employees by gender
count = n(),
#Getting t-critical for 95% confidence level
t_critical = qt(0.975, count-1),
#Calculating the standard error and margin of error
se_salary = sd_salary/sqrt(count),
margin_of_error = t_critical * se_salary,
#Calculating confidence interval of mean salary
salary_low = mean_salary - margin_of_error,
salary_high = mean_salary + margin_of_error
)
#Displaying calculated values
print(gender_salary_ci, width = Inf)
## # A tibble: 2 x 9
## gender mean_salary sd_salary count t_critical se_salary margin_of_error
## <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 female 64543. 7567. 26 2.06 1484. 3056.
## 2 male 73239. 7463. 24 2.07 1523. 3151.
## salary_low salary_high
## <dbl> <dbl>
## 1 61486. 67599.
## 2 70088. 76390.
We observe that the 95% confidence intervals for mean salaries of both genders are not overlapping. Hence, we can conclude that there is a significant difference between the population means of salaries of males and females.
We can also run a t-test to analyze if there is a statistically significant difference between the mean salaries of males and females.
Null Hypothesis \(H_0\) : \(\mu_{male} - \mu_{female} = 0\)
Alternative Hypothesis \(H_1\) : \(\mu_{male} - \mu_{female} \neq 0\)
#Hypothesis testing
t.test(salary ~ gender, data= omega)
##
## Welch Two Sample t-test
##
## data: salary by gender
## t = -4, df = 48, p-value = 2e-04
## alternative hypothesis: true difference in means between group female and group male is not equal to 0
## 95 percent confidence interval:
## -12973 -4420
## sample estimates:
## mean in group female mean in group male
## 64543 73239
By conducting this t-test, it can be concluded that the null hypothesis can be rejected at 95% confidence level as the p-value is less than 0.05. We can conclude that there is a significant difference between the population means of salaries of males and females.
However, there is a possibility that this statistically significant difference between the mean salaries of males and females could be attributable to other reasons such as differences in experience. Hence, we compare the relationship between experience and gender.
# Summary Statistics of experience by gender
mosaic::favstats (experience ~ gender, data=omega)
## gender min Q1 median Q3 max mean sd n missing
## 1 female 0 0.25 3.0 14.0 29 7.38 8.51 26 0
## 2 male 1 15.75 19.5 31.2 44 21.12 10.92 24 0
The mean experience for male employees is higher than that for female employees by around 14 years. To analyze if this difference in experience is statistically significant, we will construct confidence intervals and carry out a hypothesis test.
#Constructing confidence interval for mean experience of males and females
experience_ci <- omega %>%
#Grouping data by gender
group_by(gender) %>%
summarise(
#Calculating mean and std dev of experience
mean_experience = mean(experience),
sd_experience = sd(experience),
#Count of employees by gender
count = n(),
#Getting t-critical for 95% confidence level
t_critical = qt(0.975, count-1),
#Calculating the standard error and margin of error
se_experience = sd_experience/sqrt(count),
margin_of_error = t_critical * se_experience,
#Calculating confidence interval of mean experience
experience_low = mean_experience - margin_of_error,
experience_high = mean_experience + margin_of_error
)
#Displaying calculated values
print(experience_ci, width = Inf)
## # A tibble: 2 x 9
## gender mean_experience sd_experience count t_critical se_experience
## <chr> <dbl> <dbl> <int> <dbl> <dbl>
## 1 female 7.38 8.51 26 2.06 1.67
## 2 male 21.1 10.9 24 2.07 2.23
## margin_of_error experience_low experience_high
## <dbl> <dbl> <dbl>
## 1 3.44 3.95 10.8
## 2 4.61 16.5 25.7
We observe that the 95% confidence intervals for mean experience of both genders are not overlapping. Hence, we can conclude that there is a significant difference between the population means of experience of males and females.
We can also run a t-test to analyze if there is a statistically significant difference between the mean experience of males and females.
Null Hypothesis \(H_0\) : \(\mu_{male} - \mu_{female} = 0\)
Alternative Hypothesis \(H_1\) : \(\mu_{male} - \mu_{female} \neq 0\)
#Hypothesis testing using t.test()
t.test(experience ~ gender, data= omega)
##
## Welch Two Sample t-test
##
## data: experience by gender
## t = -5, df = 43, p-value = 1e-05
## alternative hypothesis: true difference in means between group female and group male is not equal to 0
## 95 percent confidence interval:
## -19.35 -8.13
## sample estimates:
## mean in group female mean in group male
## 7.38 21.12
We observe that the mean experience of males is greater than that of females, and the confidence intervals for both genders are not overlapping. The p value of t test is 1e-05. Therefore, we can conclude that the population means of years of experience of males and females are statistically not the same at 95% confidence level.
Now we analyze the correlation between experience and salary.
cor.test(x = omega$salary, y = omega$experience)
##
## Pearson's product-moment correlation
##
## data: x and y
## t = 9, df = 48, p-value = 2e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.676 0.884
## sample estimates:
## cor
## 0.803
The correlation between experience and salary is very high (0.803) and it is statistically significant at 95% confidence level as the p-value is less than 0.05.
The conclusion that there is a statistically significant difference between the mean experience of males and females endangers our previous one about the difference in male and female salaries. The statistically significant difference in salaries of males and females cannot be attributed just to gender, as there is also a significant difference between the mean experience of males and females. A combination of factors could be the reason of the difference between salaries between males and females.


class UEgroup:
This class is used to describe traffic profile and requirements of group of UE which the simulation will run for. It is assumed that all UEs shares the same traffic profile and service requirements, and will be served by the same slice.
| Method | __init__ |
Undocumented |
| Instance Variable | num_usersDL |
Undocumented |
| Instance Variable | num_usersUL |
Undocumented |
| Instance Variable | p_sizeDL |
Undocumented |
| Instance Variable | p_sizeUL |
Undocumented |
| Instance Variable | p_arr_rateDL |
Undocumented |
| Instance Variable | p_arr_rateUL |
Undocumented |
| Instance Variable | sinr_0DL |
Initial sinr value for DL |
| Instance Variable | sinr_0UL |
Initial sinr value for UL |
| Instance Variable | sch |
Intra Slice scheduler algorithm |
| Instance Variable | label |
Slice label |
| Instance Variable | req |
Dictionary with services requirements |
| Instance Variable | mmMd |
Undocumented |
| Instance Variable | lyrs |
Undocumented |
| Instance Variable | gr |
Inter Slice scheduler time granularity |
| Instance Variable | mgr |
Meassurement time granularity |
| Instance Variable | schIn |
Inter Slice scheduler algorithm |
| Instance Variable | usersDL |
Undocumented |
| Instance Variable | flowsDL |
Undocumented |
| Instance Variable | usersUL |
Undocumented |
| Instance Variable | flowsUL |
Undocumented |
| Method | setReq |
This method sets the service requirements depending on the UE group traffic profile and required delay |
| Method | setInitialSINR |
This method sets the initial SINR value |
| Method | initializeUEs |
This method creates the UEs with its traffic flows, and initializes the asociated PEM methods |
| Method | activateSliceScheds |
This method activates PEM methods from the intra Slice schedulers |
| Method | printSliceResults |
This method prints main simulation results on the terminal, gets the considered kpi from the statistic files, and builds kpi plots |
Undocumented
This method sets the service requirements depending on the UE group traffic profile and required delay
This method creates the UEs with its traffic flows, and initializes the asociated PEM methods


When should guh trigger this rule?
What action(s) should guh execute?
When rule enters:
When rule exits: