Event Location:
CIDA
"Sausiripaya" 123 Wijerama Mawatha
Colombo, Western, Sri Lanka
Abstract:
Every new release of SQL Server brings a whole load of new features that an administrator can add to their arsenal of efficiency. SQL Server 2014/2016 has introduced many new features. In this session we will be learning quite a few of the new features of SQL Server 2014/2016. Here is the glimpse of the features we will cover in this session. • live plans for long-running queries • transaction durability and its impact on queries • new cardinality estimate for optimal performance • In-memory OLTP optimization of superior query performance • columnstore indexes and performance tuning • Query Store • Temporal Tables. This 75 minutes will be the most productive time for any DBA and Developer who wants to quickly jumpstart with SQL Server 2014 and its new features.
Speaker(s): Pinal Dave,
Abstract:
Have you used Power BI for a real-time dashboard? Have you looped through source files in your Power Query and applied custom functions on them? Have you changed your data model to help Power QA to respond intelligently? Have you heard of structured columns in Power Query? Have you used Calculated Tables in DAX? Have you used Power BI Embedded? Have you done many other amazing things with Power BI? In this session you can expect many live demos and to be impressed by the extraordinary things you can do with Power BI. You will learn tips and tricks to empower your Power BI solution. Tips and tricks that you learn in this session helps you to make your Power BI solution more scale-able, robust, and with high performance.
Speaker(s): Reza Rad,
Abstract:
The R server 2016 provides many functionalities for database developers. It helps us to get more insight out of data by applying descriptive, predictive, and prescriptive analysis on data which located in SQL server 2016. In this session, first I will provide some descriptions on the descriptive, predictive, and prescriptive analysis. Then I will discuss what algorithm we can use to reach these three analysis using R. Finally, I will show a couple of demos on each of these type of analysis and will present how to write them in SQL Server 2016. the process of writing R code in SQL Server 2016 will be presented and the best practice for doing that also explained.
Speaker(s): Leila Etaati,
Abstract:
In order to ascertain the abilities of cloud computing platform, let us overview what is available offered on Microsoft Azure. Microsoft Azure has the ability to move, store and analyze data within the cloud. It is essential to evaluate multiple opportunities and options with Microsoft Azure data insights. In this session let us talk about strategies on data storage, data partitioning and availability options with Azure. A tour on how best these Azure components can help you achieve success for your Big Data platform.
Speaker(s): Satya Jayanty,
Abstract:
Cortana Analytics is a suite of fully managed Business Intelligence (BI), Big Data, and Advanced Analytics service offerings from Microsoft in the Azure Cloud. A raw data point comes from a user input or from an IoT sensor gets the real value through these services. Session discusses about the features and the abilities of Cortana Intelligence Suite.
Speaker(s): Haritha Thilakarathne,
Abstract:
Prepare to be amazed with what you can achieve using Power Query in this demo-heavy session. Power Query is the data extraction, transformation, and mash up tool. It can be accessed through Excel or from Power BI Desktop. There are numerous data transformation features that can be used to solve real-world data preparation challenges. We will use customer case studies to go through scenarios of preparing data for modelling, and visualization. You will learn how to use features of Power Query such as generators, custom functions, and lots of built-in functions to solve those scenarios. You will also learn some data preparation techniques through Excel itself.
Speaker(s): Reza Rad,
Abstract:
The columnstore index is the standard for storing and querying large data warehousing fact tables. It uses column-based data storage and query processing to achieve up to 10x query performance gains in your data warehouse over traditional row-oriented storage, and up to 10x data compression over the uncompressed data size. Beginning with SQL Server 2016, columnstore indexes enable operational analytics, the ability to run performant real-time analytics on a transactional workload. In this session, you will learn all about columnstore indexes and how to start taking advantage of this technology to achieve true real-time analytics.
Speaker(s): Rushabh Mehta,
Abstract:
Being able to monitor, diagnose and recover from Database Corruption is a critical skill for any SQL Server DBA. In this session we will walk through the techniques required to detect and repair various forms of Corruption Using a number of example corrupted database we will explore how to determine and fix corruption while avoiding some of the common mistakes. Warning: Hex Editors and DBCC Page dumps will be involved!
Speaker(s): Patrick Flynn,
Abstract:
A panel discussion on SQL Server, Business Intelligence and Analytics topics featuring all the speakers of SQLSaturday Sri Lanka. Ask them the questions that you want! The ultimate finale to a day of learning awesomeness! The best way to summarize what you learnt throughout the day!
Speaker(s): Dinesh Priyankara,
Abstract:
The Query Store feature is a game changer for how SQL Server professionals troubleshoot query performance. It provides the ability to natively capture baseline data within SQL Server. Using the performance metrics the Query Store records, we can decide which Execution Plan SQL Server should use when executing a specific query. During this session we will take a thorough look at the Query Store, its architecture, the build-in reporting, DMVs and the performance impact of enabling the Query Store. No matter if you are a DBA or developer, the Query Store has information that can help you
Speaker(s): Dharmendra Keshari,
Abstract:
Neural Network is common thus complex technique in Data Analytics. Azure Machine Learning and SQL Server Data Mining has the option of using Neural Network for different purposes.
Speaker(s): Dinesh Asanka,
Abstract:
The heart of almost all Business Intelligence solutions; Relational Data Warehouse, has been servicing us for decades making the implementation is complete and success. It was always an On-Premises database, but now it is available as a Database-as-a-Service with Microsoft Azure, called Azure SQL Data Warehouse. It is a MPP cloud-based, scale-out relational database that supports workloads from few hundred gigabytes to petabytes of data and it addresses all modern requirements in data warehousing. Let’s talk about traditional On-Premises data warehouse little bit and then discuss about Azure SQL Data Warehouse and implementation of it.
Speaker(s): Dinesh Priyankara,
Abstract:
Azure offers a range of data management technologies and options with the promise of no maintenance overhead. In this session, we will learn about the 2 flavors of SQL Server data platform available in Azure and deep dive into how you can start taking advantage of Azure SQL DB to complement your on-premise data management solutions and how you can build a strategy for moving to the cloud.
Speaker(s): Rushabh Mehta,
Abstract:
Azure Cosmos DB is an evolution of Azure Document DB. It's the industry first globally distributed database service, allow you to scale storage and throughput across many geographical regions with promising low latency. Cosmos DB supports for multi model database, different types of database engines like key-value, document, graph and multi API support like SQL, Azure Table Storage, Gremlin, MongoDB. Let's see in action with a demo, how you can transfer your complex data into a simpler format with Document API and scale it in different regions with specific storage and throughput.
Speaker(s): Hansamali Gamage,
Abstract:
Internet of Things (IoT) is one of the emerging technology which transforms many different industries. IoT often produce large amount of data which is analysed in different levels. Data Analytics and Business Intelligence take a different landscape in IoT. We use different tools and techniques with the power of Cloud to solve the challenges and provide solutions. This session will provide you the information you required to get started with the IoT data analytics with the real case driven scenario.
Speaker(s): Vijayakumar Thurupathan,
Abstract:
This session talks about the newly introduced built-in JSON support of SQL Server. Session will compare the JSON support offered with that of the XML support, comparing and contrasting them on different aspects. Also will walkthrough the new functions introduced with regards to JSON support, performance aspects and real world scenarios on how to get the best out of this feature.
Speaker(s): Shamil Sadique,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: http://lk.linkedin.com/in/dineshpriyankara/
Contact: http://dinesql.blogspot.com/
Dinesh is an experienced professional and database enthusiast with skills in database management systems and business intelligence. Possessing over 18 years of experience on data related technologies, he does training, consulting, and is a top contributor to the local SQL Server community. In recognition of his contributions and expertise he has demonstrated, he was awarded the Microsoft Most Valuable Professional (MVP) award for SQL Server for 13 consecutive years since 2006. He is a Subject Matter Expert on SQL Server exams and has contributed for SQL Server 2012, 2016, 2017 and Azure Data Engineering exam development and Job Task Analysis (JTA). Dinesh is the Founder/Principal Architect of dinesQL (Pvt) Ltd (http://dinesql.com). In his
LinkedIn: https://lk.linkedin.com/in/dineshasanka
Contact: http://htttp://dbfriend.blogspot.com
Dinesh Asanka MVP for more than 8 years and has experience over 15 years with SQL Server different versions. He has spoken in Sri Lanka as well as out side conferences. Regular writer at SQL-Server-Performance.com and co-authored couple of books on SQL Server. Visiting Lecturer at Sri Lanka Institute of Information Technology (SLIIT), Sri Lanka on subjects such as Database Administration, Big Data Analytics, Data Security, Data warehousing amp; Data Mining at B.Sc and M.Sc level. Currently, reading for Mphil in the areas of data warehouse and fuzzy technology at University of Moratuwa, Sri Lanka.
LinkedIn: http://lk.linkedin.com/in/dineshpriyankara/
Contact: http://dinesql.blogspot.com/
Dinesh is an experienced professional and database enthusiast with skills in database management systems and business intelligence. Possessing over 18 years of experience on data related technologies, he does training, consulting, and is a top contributor to the local SQL Server community. In recognition of his contributions and expertise he has demonstrated, he was awarded the Microsoft Most Valuable Professional (MVP) award for SQL Server for 13 consecutive years since 2006. He is a Subject Matter Expert on SQL Server exams and has contributed for SQL Server 2012, 2016, 2017 and Azure Data Engineering exam development and Job Task Analysis (JTA). Dinesh is the Founder/Principal Architect of dinesQL (Pvt) Ltd (http://dinesql.com). In his
LinkedIn: http://www.linkedin.com/in/pinaldave
Contact: https://blog.sqlauthority.com
Pinal Dave is a SQL Server Performance Tuning Expert and an independent consultant. He has authored 12 SQL Server database books, 30 Pluralsight courses and has written over 5000 articles on the database technology on his blog at a https://blog.sqlauthority.com. Along with 16+ years of hands-on experience, he holds a Masters of Science degree and a number of database certifications.
LinkedIn: https://sg.linkedin.com/in/dharmendra-keshari-a7043398
Contact: http://dharmendrakeshari.com/
Dharmendra Keshari has strong and in-depth experience in Database Management. Currently, working as Senior Database Administrator at Micron Technology in Singapore and handling one of the most critical databases which are being used by Singapore Public Hospitals, national specialty centres and polyclinics. Dharmendra is an avid, regular mentor on social media, helping several enthusiast in solving their problems related to Database management. he is co-leader and regular speaker at Singapore SQL Server Pass Chapter (User Group). He has done certification for “Microsoft SQL Server#174; 2008, Implementation” and “Maintenance amp; Administering Microsoft SQL Server 2012 Database”.
LinkedIn: https://lk.linkedin.com/in/shamilsaleem
Shamil has over 8 years of experience working in the field of Database Administration with variety of database technologies including Microsoft SQL Server, Oracle, MySQL and MongoDB. Currently he is working as a Senior Production DBA (Oracle/MySQL) at Pearson Lanka (Pvt) Ltd. He is a Microsoft Certified Solutions Expert (MCSE) in SQL Server 2012 and an Oracle Database 11g Administrator Certified Associate (OCA). He is also certified in ITIL® Foundation Level, while holding a Bachelor’s Degree in Management Information Systems from the University College Dublin - National University of Ireland, Dublin.
LinkedIn: https://www.linkedin.com/in/rushabhbmehta
Contact: http://blogs.solidq.com/RushabhMehta
Rushabh Mehta is a Business Intelligence Mentor with SolidQ and the Managing Director for Solid Quality India Pvt. Ltd. He served as PASS President for 2010/2011 term. Rushabh has worked with MS Data Platform since 1998 and has designed and developed BI systems since 2001. He has been the lead architect for many large-scale and complex BI solutions for clients such as Publix, Raymond James, Jackson Hewitt, US Dept. of Defense, and Microsoft. He is an MVP with Microsoft, Damp;B amp; Melissa Data
LinkedIn: https://uk.linkedin.com/sqlmaster
Contact: http://www.sqlserver-qa.net
My experience surrounded with high focus on the data platform with a track record of defining strategy, designing and delivering digital transformation migrations with major enhancements to current working methods. Worked in a capacity of Head of Data Engineering, Enterprise Data Architect and Solutions Key career accomplishment recognition as an industry expert technical excellence from the Microsoft as Data Platform Most Valuable Professional (MVP) recognition since the year 2006 (13 years as MVP and counting), which recognizes exceptional technical community leaders worldwide who actively deliver, present share their extraordinary contributions, high quality, and real-world expertise. Twitter pod (http://twitter.com/sqlmast
LinkedIn: https://nz.linkedin.com/pub/leila-etaati/36/46/b03
Contact: http://blog.radacad.com
Leila Etaati is the First Microsoft AI MVP in New Zealand and Australia, as well as a Data Platform MVP. She has a Ph.D. from the University of Auckland and is the co-director and data scientist in RADACAD company with many clients around the world. Additionally, she is the co-organizer of Microsoft Business Intelligence and Power BI Use group in Auckland with more than 1300 members. Leila has also co-organized 3 main conferences in Auckland: SQL Saturday Auckland (from 2015), Difinity (from 2017) and Global AI Bootcamp. She is a data scientist, BI consultant, trainer, and speaker. She is a well-known International speaker to many conferences such as MS Ignite, SQL PASS, Data Platform Summit, SQL Saturday, Power BI World Tour and in Europe, USA, Asia, Australia, and NZ. She has over 10 years’ experience with databases and software systems. Leila is an active Technical Microsoft AI blogger in RADACAD.
Contact: http://sqllensman.com/blog/
Patrick Flynn is a Senior Database Administrator with 10 years experience in large 24 x 7 enterprise environments. He is a certified master for SQL Server and a regular attendee of the PASS Summit. Has presented at SQLSaturday events in Sydney, Melbourne, Brisbane and Auckland, Edmonton and Calgary, Oslo and Cambridge
LinkedIn: http://nz.linkedin.com/in/rezarad/
Contact: http://radacad.com/blog
Reza Rad is a Microsoft Regional Director, MVP, author, trainer, speaker and consultant. He is a Microsoft Data Platform MVP for nine years from 2011. Reza wrote many books on Power BI subjects including the Power BI from Rookie to Rock Star, and Pro Power BI Architecture. Reza is the invited speaker at the world's well-known conferences such as Microsoft Ignite, Microsoft Business Applications Summit, Ignite Tour, PASS Summit, and many other conferences. Reza is an active blogger and writes regularly about Power BI at https://radacad.com.
LinkedIn: https://www.linkedin.com/in/rushabhbmehta
Contact: http://blogs.solidq.com/RushabhMehta
Rushabh Mehta is a Business Intelligence Mentor with SolidQ and the Managing Director for Solid Quality India Pvt. Ltd. He served as PASS President for 2010/2011 term. Rushabh has worked with MS Data Platform since 1998 and has designed and developed BI systems since 2001. He has been the lead architect for many large-scale and complex BI solutions for clients such as Publix, Raymond James, Jackson Hewitt, US Dept. of Defense, and Microsoft. He is an MVP with Microsoft, Damp;B amp; Melissa Data
LinkedIn: https://www.linkedin.com/in/hthilakarathne/
Contact: https://naadispeaks.wordpress.com/
Haritha Thilakarathne is a MVP in Artificial Intelligence from Sri Lanka now based in Melbourne. He has hands on experience with Azure Machine Learning, Microsoft Bot Framework, Microsoft Cognitive Services and developing intelligent applications. Haritha is a research scholar at La Trobe University reading for his PhD on Deep Learning with the collaboration of Australian Institute of Sport.
LinkedIn: http://lk.linkedin.com/in/thuru/
Contact: http://www.thuru.net
Thuru is a versatile technical person who has diversified experience in many areas of Information Technology for 6 years. Cloud computing, security, software development, business intelligence and databases are his most preferred technology areas. He is a frequent speaker in Sri Lanka .NET forum and Sri Lanka SQL Server Universe user groups. Thuru is the founder of Colombo Cloud Meet Up where he often talks about various cloud computing topics. Also he is a Microsoft MVP for Microsoft Azure.
LinkedIn: https://www.linkedin.com/in/hansamali-gamage-37777a50/
Contact: http://hansamaligamage.blogspot.com/
Hansamali Gamage is an experienced professional and Microsoft MVP in Visual Studio and Technologies from Sri Lanka. She possesses 5+ years of experience in. NET stack and Azure related services. She is a frequent speaker in SL .NET Forum, SL Data community and global SQLSaturday events and Data camps. She is a tech enthusiasts and an award winning writer on Microsoft Tech Net forum.
LinkedIn: http://nz.linkedin.com/in/rezarad/
Contact: http://radacad.com/blog
Reza Rad is a Microsoft Regional Director, MVP, author, trainer, speaker and consultant. He is a Microsoft Data Platform MVP for nine years from 2011. Reza wrote many books on Power BI subjects including the Power BI from Rookie to Rock Star, and Pro Power BI Architecture. Reza is the invited speaker at the world's well-known conferences such as Microsoft Ignite, Microsoft Business Applications Summit, Ignite Tour, PASS Summit, and many other conferences. Reza is an active blogger and writes regularly about Power BI at https://radacad.com.
The following is a list of sponsors that helped fund the event.


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#include "uthash.h"
This document is geared towards C programmers of UNIX systems. Since you're reading this, chances are that you know a hash is used for looking up items using a key. In scripting languages like Perl, hashes are used all the time. In C, hashes don't exist in the language itself. This software provides a hash for C structures.
This software supports four basic operations on hashes.
add
find
delete
iterate/sort
Add, find and delete are normally constant-time operations. This is influenced by your key domain and the hash function.
This hash aims to be minimalistic and efficient. It's around 500 lines of C. It inlines automatically because it's implemented as macros. It's fast as long as the hash function is properly chosen to suit your keys. This is easy; see Appendix A: Choosing the hash function.
No, it's just a single header file: uthash.h. All you need to do is copy the header file into your project, and:
#include "uthash.h"
You may then utilize the hash macros as explained below.
This software has been tested on Linux, Solaris, OpenBSD, Mac OSX and Cygwin. It probably runs on other Unix or Unix-like platforms too.
This software is made available under the BSD license. It is free and open source.
Please follow the link to download on the uthash website.
You can email the author at<EMAIL_ADDRESS>if you have questions not answered by this document.
A hash is comprised of structures. Each structure represents a key-value association. One or more fields act as the key; the structure itself is the value.
#include "uthash.h" struct my_struct { int id; /* key */ char name[10]; UT_hash_handle hh; /* makes this structure hashable */ };
There are no restrictions on the data type or name of the key field(s).
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Note
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Just remember that, as with any hash, the keys have to be unique. |
The UT_hash_handle field must be present in your structure in order to hash it. It is used for the internal bookkeeping that makes the hash work. It does not require initialization. It can be named anything, but you can simplify matters by naming it hh. This allows you to use the easier "convenience" macros to add, find and delete items.
This section introduces the HASH_ADD, HASH_FIND, HASH_DELETE, and HASH_SORT macros. This section demonstrates the macros by example. For a more succinct listing, see Appendix F: Macro Reference.
Your hash must be declared as a NULL-initialized pointer to your structure. Then allocate and initialize your structure and call HASH_ADD. (Here we use the convenience macro HASH_ADD_INT, which is for keys of type int.)
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Important
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The hash must be initialized to NULL! |
struct my_struct *users = NULL; /* important! initialize to NULL */ int add_user(int user_id, char *name) { struct my_struct *s; s = malloc(sizeof(struct my_struct)); s->id = user_id; strcpy(s->name, name); HASH_ADD_INT( users, id, s ); /* id: name of key field */ }
The second parameter to HASH_ADD_INT is the name of the key field. Here, this is id.
Once an item has been added to the hash, do not change the value of its key. Instead, delete the item from the hash, change the key, and then re-add it.
|
Important
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Key uniqueness
Your application must enforce key uniqueness. In other words, do not add the same key to the hash more than once. If you do, hash behavior will be undefined. |
You can test whether a key already exists in the hash using HASH_FIND.
To look up an item in a hash, you must have its key. Then call HASH_FIND. (Here we use the convenience macro HASH_FIND_INT for keys of type int).
struct my_struct *find_user(int user_id) { struct my_struct *s; HASH_FIND_INT( users, &user_id, s ); /* s: output pointer */ return s; }
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Note
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The middle argument is a pointer to the key. You can't pass a literal key value to HASH_FIND. Instead assign the literal value to a variable, and pass a pointer to the variable. |
Here, s is the output variable of HASH_FIND_INT. It's a pointer to the sought item, or NULL if the key wasn't found in the hash.
To delete an item from a hash, you must have a pointer to the item.
void delete_user(struct my_struct *user) { HASH_DEL( users, user); /* user: pointer to deletee */ }
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Note
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Deleting an item from a hash just removes it— it does not free it. |
You can loop over the items in the hash by starting from the beginning and following the hh.next pointer.
void print_users() { struct my_struct *s; for(s=users; s != NULL; s=s->hh.next) { printf("user id %d: name %s\n", s->id, s->name); } }
There is also an hh.prev pointer you could use to iterate backwards through the hash, starting from any known item.
The items in the hash are, by default, traversed in the order they were added ("insertion order") when you follow the hh.next pointer. But you can sort the items into a new order using HASH_SORT. E.g.,
HASH_SORT( users, name_sort );
The second argument is a pointer to a comparison function. It must accept two arguments which are pointers to two items to compare. Its return value should be less than zero, zero, or greater than zero, if the first item sorts before, equal to, or after the second item, respectively. (Just like strcmp).
int name_sort(struct my_struct *a, struct my_struct *b) { return strcmp(a->name,b->name); } int id_sort(struct my_struct *a, struct my_struct *b) { return (a->id - b->id); } void sort_by_name() { HASH_SORT(users, name_sort); } void sort_by_id() { HASH_SORT(users, id_sort); }
When the items in the hash are sorted, the first item may also change position; so in the example above, users may point to a different item after calling HASH_SORT.
We'll repeat all the code and embellish it with a main() function to form a working example.
If this code was placed in a file called example.c in the same directory as uthash.h, it could be compiled and run like this:
cc -o example example.c ./example
Follow the prompts to try the program, and type Ctrl-C when done.
#include <stdio.h> /* gets */ #include <stdlib.h> /* atoi, malloc */ #include <string.h> /* strcpy */ #include "uthash.h" struct my_struct { int id; /* key */ char name[10]; UT_hash_handle hh; /* makes this structure hashable */ }; struct my_struct *users = NULL; int add_user(int user_id, char *name) { struct my_struct *s; s = malloc(sizeof(struct my_struct)); s->id = user_id; strcpy(s->name, name); HASH_ADD_INT( users, id, s ); /* id: name of key field */ } struct my_struct *find_user(int user_id) { struct my_struct *s; HASH_FIND_INT( users, &user_id, s ); /* s: output pointer */ return s; } void delete_user(struct my_struct *user) { HASH_DEL( users, user); /* user: pointer to deletee */ } void print_users() { struct my_struct *s; for(s=users; s != NULL; s=s->hh.next) { printf("user id %d: name %s\n", s->id, s->name); } } int name_sort(struct my_struct *a, struct my_struct *b) { return strcmp(a->name,b->name); } int id_sort(struct my_struct *a, struct my_struct *b) { return (a->id - b->id); } void sort_by_name() { HASH_SORT(users, name_sort); } void sort_by_id() { HASH_SORT(users, id_sort); } int main(int argc, char *argv[]) { char in[10]; int id=1; struct my_struct *s; while (1) { printf("1. add user\n"); printf("2. find user\n"); printf("3. delete user\n"); printf("4. sort items by name\n"); printf("5. sort items by id\n"); printf("6. print users\n"); gets(in); switch(atoi(in)) { case 1: printf("name?\n"); add_user(id++, gets(in)); break; case 2: printf("id?\n"); s = find_user(atoi(gets(in))); printf("user: %s\n", s ? s->name : "unknown"); break; case 3: printf("id?\n"); s = find_user(atoi(gets(in))); if (s) delete_user(s); else printf("id unknown\n"); break; case 4: sort_by_name(); break; case 5: sort_by_id(); break; case 6: print_users(); break; } } }
This program is included in the distribution in tests/example.c. You can run make example in that directory to compile it easily.
This is almost the same as using integer keys except the macros are named HASH_ADD_STR and HASH_FIND_STR.
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Note
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char[ ] vs. char*
The string is within the structure in this example— name is a char[10] field. If instead our structure merely pointed to the key (i.e., name was declared char *), we'd use HASH_ADD_KEYPTR, described in Appendix F. |
#include <string.h> /* strcpy */ #include <stdlib.h> /* malloc */ #include <stdio.h> /* printf */ #include "uthash.h" struct my_struct { char name[10]; /* key */ int id; UT_hash_handle hh; /* makes this structure hashable */ }; int main(int argc, char *argv[]) { char **n, *names[] = { "joe", "bob", "betty", NULL }; struct my_struct *s, *users = NULL; int i=0; for (n = names; *n != NULL; n++) { s = malloc(sizeof(struct my_struct)); strcpy(s->name, *n); s->id = i++; HASH_ADD_STR( users, name, s ); } HASH_FIND_STR( users, "betty", s); if (s) printf("betty's id is %d\n", s->id); }
This example is included in the distribution in tests/test15.c. It prints:
betty's id is 2
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Note
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Remember, don't change an item's key after adding it to the hash. (Instead, delete the item from the hash, change the key and then re-add it to the hash). |
Your key field can have any data type, and it can even be an aggregate of multiple contiguous fields.
The generalized version of the macros (e.g., HASH_ADD) must be used. In addition to the usual arguments, the generalized macros require the name of the UT_hash_handle field, and the key length.
#include <stdlib.h> /* malloc */ #include <stddef.h> /* offsetof */ #include <stdio.h> /* printf */ #include <string.h> /* memset */ #include "uthash.h" struct my_event { struct timeval tv; /* key is aggregate of this field */ char event_code; /* and this field. */ int user_id; UT_hash_handle hh; /* makes this structure hashable */ }; int main(int argc, char *argv[]) { struct my_event *e, ev, *events = NULL; int i, keylen; keylen = offsetof(struct my_event, event_code) + sizeof(char) - offsetof(struct my_event, tv); for(i = 0; i < 10; i++) { e = malloc(sizeof(struct my_event)); memset(e,0,sizeof(struct my_event)); e->tv.tv_sec = i * (60*60*24*365); /* i years (sec)*/ e->tv.tv_usec = 0; e->event_code = 'a'+(i%2); /* meaningless */ e->user_id = i; HASH_ADD( hh, events, tv, keylen, e); } /* look for one specific event */ memset(&ev,0,sizeof(struct my_event)); ev.tv.tv_sec = 5 * (60*60*24*365); ev.tv.tv_usec = 0; ev.event_code = 'b'; HASH_FIND( hh, events, &ev.tv, keylen, e ); if (e) printf("found: user %d, unix time %ld\n", e->user_id, e->tv.tv_sec); }
This example is included in the distribution in tests/test16.c.
A structure can be in multiple hashes simultaneously. For example, you might want to hash a structure on both an ID field and on a unique username.
struct my_struct { int id; /* key 1 */ char username[10]; /* key 2 */ UT_hash_handle hh1,hh2; /* makes this structure hashable */ };
In the example above, the structure can now be added to two separate hashes; in one hash, id is its key, while in the other hash, username is the key. (There is no requirement that the two hashes have different key fields. They could both use the same key, such as id).
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Note
|
You need to have a separate UT_hash_handle for each hash that your structure will participate in simultaneously. These are hh1 and hh2 in this example. |
struct my_struct *users_by_id = NULL, *users_by_name = NULL, *s; int i; char *name; s = malloc(sizeof(struct my_struct)); s->id = 1; strcpy(s->username, "thanson"); HASH_ADD(hh1, users_by_id, id, sizeof(int), s); HASH_ADD(hh2, users_by_name, username, strlen(s->username), s); /* lookup user by ID */ i=1; HASH_FIND(hh1, users_by_id, &i, sizeof(int), s); if (s) printf("found id %d: %s\n", i, s->username); /* lookup user by username */ name = "thanson"; HASH_FIND(hh2, users_by_name, name, strlen(name), s); if (s) printf("found user %s: %d\n", name, s->id);
When participating in two or more hashes simultaneously, always use the same key field with the same hash handle. E.g., in this example, hh1 is always used with the id key, and hh2 is always used with the username key.
You can use this hash in a threaded program. But, the locking is up to you. You must protect the hash against concurrent modification. For every hash that you create in a threaded program, you should create a lock to protect it.
Concurrent hash lookups (HASH_FIND) are permitted, but modifications (HASH_ADD, HASH_DEL, and HASH_SORT) cannot be concurrent. Therefore, you can use a read/write lock which provides concurrent-read/exclusive-write semantics. Or you can use a mutex-style lock to prevent any concurrent access to the hash, but this is less efficient.
Internally this software uses a particular hash function, such as Bernstein's hash, to transform a key to a bucket number.
#define HASH_BER(key,keylen,num_bkts,bkt,i,j,k) \ bkt = 0; \ while (keylen--) bkt = (bkt * 33) + *key++; \ bkt &= (num_bkts-1);
Several such hash functions are built-in. The default is Jenkin's hash, but your key domain may get better distribution with another function. You can use a specific hash function by compiling your program with -DHASH_FUNCTION=HASH_xyz where xyz is one of the symbolic names listed below. E.g.,
cc -DHASH_FUNCTION=HASH_OAT -o program program.c
| Symbol | Name |
|---|---|
| JEN | Jenkins (default) |
| BER | Bernstein |
| SAX | Shift-Add-Xor |
| OAT | One-at-a-time |
| FNV | Fowler/Noll/Vo |
| JSW | Julienne Walker |
You can easily determine the best hash function for your key domain. To do so, you'll need to run your program once in a data-collection pass, and then run the collected data through an included analysis utility.
First you must build the analysis utility. From the top-level directory,
cd tests/ make
We'll use test14.c to demonstrate the data-collection and analysis steps (using sh syntax):
cc -DHASH_EMIT_KEYS=3 -I../src -o test14 test14.c ./test14 3>test14.keys ./keystats test14.keys
The numeric value of HASH_EMIT_KEYS is a file descriptor. Any file descriptor that your program doesn't use for its own purposes can be used instead of 3.
|
Note
|
The data-collection mode enabled by -DHASH_EMIT_KEYS=x should not be used in production code. |
The result of running the keystats command should look like this:
fcn hash_q #items #buckets #dups flags add_usec find_usec --- ---------- ---------- ---------- ---------- ---------- ---------- ---------- BER 1.000000 1219 512 0 ok 1206 550 FNV 1.000000 1219 512 0 ok 1734 660 JEN 1.000000 1219 512 0 ok 2008 845 OAT 0.998897 1219 512 0 ok 1941 833 SAX 0.997514 1219 1024 0 ok 2217 674 JSW 0.994652 1219 512 0 ok 1369 608
Usually, you should just pick the first hash function that is listed. Here, this is BER. This is the function that provides the most even distribution for your keys. If several have the same hash_q, then choose the fastest one according to the find_usec column.
|
Tip
|
Sometimes you might pick the fastest one even though its hash_q isn't the best. This is ok, particularly if its substantially faster (ideally hash functions have negligible performance overhead but in reality they can vary). |
symbolic name of hash function
a number between 0 and 1 that measures how evenly the items are distributed among the buckets (1 is best)
the number of keys that were read in from the emitted key file
the number of buckets in the hash after all the keys were added
the number of duplicate keys encounted in the emitted key file. Duplicates keys are filtered out to maintain key uniqueness. (Duplicates are normal. For example, if the application adds an item to a hash, deletes it, then re-adds it, the key is written twice to the emitted file.)
this is either ok, or noexpand if the expansion inhibited flag is set, described in Appendix C. It is not recommended to use a hash function that has the noexpand flag set.
the clock time in microseconds required to add all the keys to a hash
the clock time in microseconds required to look up every key in the hash
The hash_q statistic measures how evenly the keys are distributed among the buckets. It is a value between 0 (worst) and 1 (best). You can access it in this way, if you're interested in observing how well your hash keys hold up to the ideal distribution.
/* using the example "users" hash from previous listings */ void print_hashq() { if (users) printf("hash_q: %f\n", users->hh.tbl->hash_q); }
The hash_q statistic is updated whenever bucket expansion occurs (see Appendix C).
A low value of hash_q may lead to reduced lookup (HASH_FIND) performance. You can change the hash function to one that distributes your keys more evenly; see Appendix A: Choosing the hash function.
Internally this hash manages the number of buckets, with the goal of having enough buckets so that each one contains only small number of items.
This hash attempts to keep fewer than 10 items in each bucket. When an item is added that would cause a bucket to exceed this number, the number of buckets in the hash is doubled and the items are redistributed.
|
Note
|
Bucket expansion occurs automatically and invisibly as needed. There is no need for the application to know when it occurs. |
There is a hook for this event, primarily for uthash development.
#include "uthash.h" #undef uthash_expand_fyi #define uthash_expand_fyi(tbl) printf("expanded to %d buckets\n", tbl->num_buckets) ...
During bucket expansion, if the number of items in a bucket exceeds the ideal number, then the expansion threshold for that bucket will be increased (instead of being 10, it will be a multiple of 10). This prevents excessive bucket expansion for hash functions that tend to over-utilize a few buckets yet have good distribution overall.
Some key domains may not yield a good bucket distribution using the default (or manually-specified) hash function.
If this occurs, statistic hash_q reflects the uneven distribution by tending closer to 0 than 1. This means that items are piling up in some buckets while other buckets are under-utilized. Bucket expansion then takes place as this hash tries to keep a bounded number of items in each bucket.
However, if items continue to be distributed unevenly after repeated bucket expansion, there is a point at which this hash in effect says, "This key domain is really incompatible with this hash function. I could spend all day expanding the buckets but it isn't helping to spread out the items in the hash, so I'm going to cut my losses and stop expanding the buckets."
The decision to inhibit expansion is made by a heuristic rule: if two consecutive bucket expansions yield hash_q values below 0.5, then the bucket expansion inhibited flag is set for this hash.
Because this condition would cause HASH_FIND lookups to exhibit worse than constant-time performance, your application can choose to be made aware if it occurs. (Usually you don't need to worry about this, if you used the keystats utility during development to select a good hash for your keys).
#include "uthash.h" #undef uthash_noexpand_fyi #define uthash_noexpand_fyi printf("warning: bucket expansion inhibited\n"); ...
Once set, the bucket expansion inhibited flag remains in effect as long as the hash has items in it.
By default this hash implementation uses malloc and free to manage memory. If your application uses its own custom allocator, this hash can use them too.
#include "uthash.h" /* undefine the defaults */ #undef uthash_bkt_malloc #undef uthash_bkt_free #undef uthash_tbl_malloc #undef uthash_tbl_free /* re-define, specifying alternate functions */ #define uthash_bkt_malloc(sz) my_malloc(sz) /* for UT_hash_bucket */ #define uthash_bkt_free(ptr) my_free(ptr) #define uthash_tbl_malloc(sz) my_malloc(sz) /* for UT_hash_table */ #define uthash_tbl_free(ptr) my_free(ptr) ...
If memory allocation fails (i.e., the malloc function returned NULL), the default behavior is to terminate the process by calling exit(-1). This can be modified by re-defining the uthash_fatal macro.
#undef uthash_fatal #define uthash_fatal(msg) my_fatal_function(msg);
The fatal function should terminate the process; uthash does not support "returning a failure" if memory cannot be allocated.
The UT_hash_handle data structure includes next, prev, hh_next and hh_prev fields. The former two fields determine the "application" ordering (that is, iteration order that corresponds to the order the items were added). The latter two fields determine the "bucket" order. These link the UT_hash_handles together in a doubly-linked list that defines a bucket.
If a program that uses this hash is compiled with -DHASH_DEBUG=1, a special internal consistency-checking mode is activated. In this mode, the integrity of the whole hash is checked following every add or delete operation.
|
Note
|
This is for debugging the uthash software only, not for use in production code. |
In this mode, any internal errors in the hash data structure will cause a message to be printed to stderr and the program to exit.
the hash is walked in its entirety twice: once in bucket order and a second time in application order
the total number of items encountered in both walks is checked against the stored number
during the walk in bucket order, each item's hh_prev pointer is compared for equality with the last visited item
during the walk in application order, each item's prev pointer is compared for equality with the last visited item
In the tests/ directory, running make debug will run all the tests in this mode.
These macros add, find, delete and sort the items in a hash.
| macro | arguments |
|---|---|
| HASH_ADD | (hh_name, head, keyfield_name, key_len, item_ptr) |
| HASH_ADD_KEYPTR | (hh_name, head, key_ptr, key_len, item_ptr) |
| HASH_FIND | (hh_name, head, key_ptr, key_len, item_ptr) |
| HASH_DELETE | (hh_name, head, item_ptr) |
| HASH_SORT | (head, cmp) |
|
Note
|
HASH_ADD_KEYPTR is used when the structure contains a pointer to the key, rather than the key itself. |
The convenience macros do the same thing as the generalized macros, but require fewer arguments. They have key and naming restrictions (see below).
| macro | arguments |
|---|---|
| HASH_ADD_INT | (head, keyfield_name, item_ptr) |
| HASH_FIND_INT | (head, key_ptr, item_ptr) |
| HASH_ADD_STR | (head, keyfield_name, item_ptr) |
| HASH_FIND_STR | (head, key_ptr, item_ptr) |
| HASH_DEL | (head, item_ptr) |
In order to use the convenience macros,
the structure's UT_hash_handle field must be named hh, and
the key field must be of type int or char[]
HASH_DEL does not require the second condition.
name of the UT_hash_handle field in the structure. Conventionally called hh.
the structure pointer variable which acts as the "head" of the hash. So named because it points to the first item which is added to the hash.
the name of the key field in the structure. (In the case of a multi-field key, this is the first field of the key). If you're new to macros, it might seem strange to pass the name of a field as a parameter. See note.
the length of the key field in bytes. E.g. for an integer key, this is sizeof(int), while for a string key it's strlen(key). (For a multi-field key, see the notes in this guide on calculating key length).
for HASH_FIND, this is a pointer to the key to look up in the hash (since it's a pointer, you can't directly pass a literal value here). For HASH_ADD_KEYPTR, this is the address of the key of the item being added.
pointer to the structure being added, deleted or looked up. This is an input parameter for HASH_ADD and HASH_DELETE macros, and an output parameter for HASH_FIND.
pointer to comparison function which accepts two arguments (pointers to items to compare) and returns an int specifying whether the first item should sort before, equal to, or after the second item (like strcmp).


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Channel
public class Channel : NSObject, Codable
Base representation of some wingu trigger configured in portal. All detailed triggers like Geofence
or Beacon
will inherit from this class.
Detailed information about triggers can be found in class specific for every type. This class contain parameters related to wingu portal or time of discovery.
Note: All delegates method will always return
Channel
or collection ofChannel
even if you monitor only for one type.
However object of this class without subclass cannot be used for anything inside wingu SDK. Initialization should be avoided and all method will map to one of the supported subclass.
Pack
collection for multilanguage is not supported with current version of the SDK. It will be returned always with end-user device language whenever your configured content on wingu portal supports it.
-
Unique identifier
Declaration
Swift
public var uID: String?
-
Name of the
Channel
. This shouldn’t be confused withDeck.name
.Declaration
Swift
public var name: String?
-
Depends on trigger and SDK configuration this may not exist in case of trigger discovery. Avoid forcing.
Declaration
Swift
public var content: Content?
-
Location of channel depends of trigger. Some of the triggers are without location or location may be outdated.
Declaration
Swift
public var location: Location?
-
Declaration
Swift
public required init(from decoder: Decoder) throws
-
Declaration
Swift
public func encode(to encoder: Encoder) throws
-
Encode
Channel
object withJSONEncoder
Declaration
Swift
public func encode() -> Data?
Return Value
encoded Data. If this value is
nil
check your logs.
-
Update channel content by forcing manual update.
Note: Channels will be update automatically with a time specify
BeaconScanner
.
This method is optional and will override those settings.Declaration
Swift
public func update(_ completion:@escaping(_ success: Bool) -> Void)
Parameters
completion
success of failure of request


--- layout: post esbma_id: 1457 date: 2003-05-13 12:00:00 keywords: cornsharp title: CornSharp source code? ---
Heh. Phillip Pearson gives CornSharp a try, and wants to know if I will release source code. I've had several other requests for the source as well. In principle, I have no real objection to giving the source away. I have no intention of trying to make money with this app. However, there may be practical obstacles.
The primary problem is that CornSharp uses one commercial third party component (the SSH feature), and I'm thinking about using another one (a better WYSIWYG editor). I might be able to separate those pieces into plugins and release the remainder. This is only likely to happen if I get around to it. I have a day job, so CornSharp gets my attention in randomly scheduled bursts.
I do plan to continue improving CornSharp, because I use it every day, and it's pretty darn lame.


This is a simple node.js server,
just for serving static files.
Comes handy in quick prototyping projects, etc
...


// TODO(ar): move to different subdomain, currently not working on gortc.io
On start, ice‑client (your browser) fetches ice‑configuration from server gortc/web
(which handles both STUN with gortc/stun and
HTTP),
creates new RTCPeerConnection and gathers ice‑candidates via binding requests to server.
Server saves received packet as stun.Message
before responding with binding response.
Then ice‑client creates ice‑offer and sends it to web server.
Server parses ice‑offer as stun.Session
and every ice‑candidate in it
as ice.Candidate
with
gortc/ice. For server‑reflexive candidates
server dumps
saved stun.Message
.
Use go get -u gortc.io/stun/cmd/stun-decode
to decode raw STUN messages.
// TODO(ar): visualise client-server interaction


Web Page Input and Output
Referencing HTML Elements
Greetings
Hello Eugene!
Hello LCC!
Hello CIT!
Accessing Element Attributes
JavaScript tutorials // Getting a reference to a value attribute
Name:
Event Handlers
This paragraph has an onmouseover event
Using a button to get input
Enter your name:
Hello:
Displaying Output
Student's name:


Unlimited number of items
and binding to native input
Value:
Limited to one item
Limited to three items
Demo data source: Yahoo
geo.places


Projects on this site were conveived and designed by students on the MABD course in the Department of Typography and Graphic Communication, University of Reading. The projects were completed during the Publishing Platforms spring module in 2017, taught by Lozana Rossenova and Ruth Blacksell.
Site design & development by Lozana Rossenova.
Site concept is based on Rhizome's Narrative Archives platform.


--- layout: default ---
Go beyond a simple turn-key website and make your business stand out with exceptional photography, branding, design and interactivity. Our services come a la carte or as a package carefully designed to meet the goals strategically planned out for your organization’s long term growth.
We take a comprehensive look at our clients' business goals and develop marketing strategies that will ignite a growing audience.
We uphold the belief that high-quality design aesthetics make marketing more successful, and never settle for mediocre visual storytelling.
We value the power of technological innovation to improve workflow, streamline tasks, and strengthen output.
Our client partnerships are designed for long-term results. We strive to develop relationships with our clients, and become a part of their brand's evolution.
“Boosted overall operations and efficiency.”


#include <par_ui/par_utils.h>
Go to the source code of this file.
Functions |
| void | configure_homing (par_trajectory_planning::commands &cmd) |
| | Helper function for configuring the homing operation.
|
| void | configure_PTP_motion (ros::ServiceClient &coord_client, par_kinematics::coord &coords, par_trajectory_planning::commands &cmd) |
| | Helper function to configure PTP motions.
|
| void | configure_single_motion (par_trajectory_planning::commands &cmd) |
| | Helper function to configure single motions.
|
| int | menu () |
| | Simple menu for guiding the user through the application.
|
| void | pick_configuration_file (const boost::filesystem::path &directory, std::string &file) |
| | Helper function for selecting a configuration file.
|
| void | push_angles (ros::ServiceClient &coord_client, par_kinematics::coord &coords, par_trajectory_planning::commands &cmd) |
| | Helper function to initialize xyz vector with coordinates.
|
Function Documentation
| void configure_homing |
( |
par_trajectory_planning::commands & |
cmd |
) |
|
Helper function for configuring the homing operation.
Function for configuring homing parameters.
- Parameters:
-
| cmd | Object with instructions for the motion. |
Definition at line 147 of file par_utils.cpp.
| void configure_PTP_motion |
( |
ros::ServiceClient & |
coord_client, |
|
|
par_kinematics::coord & |
coords, |
|
|
par_trajectory_planning::commands & |
cmd | |
|
) |
| | |
Helper function to configure PTP motions.
Function for configuring PTP motions.
- Parameters:
-
| coord_client | Object for requesting coordinates. |
| coords | Object with coordinates from the kinematic model. |
| cmd | Object with instructions for the motion. |
Definition at line 81 of file par_utils.cpp.
| void configure_single_motion |
( |
par_trajectory_planning::commands & |
cmd |
) |
|
Helper function to configure single motions.
Function configuring a single motion.
- Parameters:
-
| cmd | Object with instructions for the motion. |
Definition at line 34 of file par_utils.cpp.
Simple menu for guiding the user through the application.
The menu presented to the user.
- Returns:
- User's menu choice.
Definition at line 173 of file par_utils.cpp.
| void pick_configuration_file |
( |
const boost::filesystem::path & |
directory, |
|
|
std::string & |
file | |
|
) |
| | |
Helper function for selecting a configuration file.
Function for choosing a configuration file.
- Parameters:
-
| directory | Contains base path for configuration files. |
| file | the selected file. |
Definition at line 121 of file par_utils.cpp.
| void push_angles |
( |
ros::ServiceClient & |
coord_client, |
|
|
par_kinematics::coord & |
coords, |
|
|
par_trajectory_planning::commands & |
cmd | |
|
) |
| | |
Helper function to initialize xyz vector with coordinates.
Function that asks kinematic model for motor angles.
Author: W. van Teijlingen<EMAIL_ADDRESS>- Parameters:
-
| coord_client | Object for requesting coordinates. |
| coords | Object with coordinates from the kinematic model. |
| cmd | Object with instructions for the motion. |
Definition at line 13 of file par_utils.cpp.


Repaint test for REGRESSION (r41203): Facebook friend suggestions disappear on update. Make sure when a layer switches
from being self-painting to non-self-painting that a layout happens to fix up the floating objects lists. You should see a 100x100 green square below.
If you see any red, the test has failed.


{% extends "base.html" %} {% block body %}
Study the talks from the General Conference of the Church of Jesus Christ of Latter-Day Saints. This will generate a plan with a talk to study each day. The plan will have all of the talks from the most recent conference and then fill the rest of the days with older talks chosen at random. Choose how many times you wish to study the most recent conference talks.
{% endblock %}


What number do you want to count to?
What number do you want to count by to get to that number?
Press me!


Rejuvenating your business with our user and design centric scalable solutions.
Why us
Automation
We try to have as little manual process as possible since we believe in spending time to build great things that users love
instead of wasting time in doing monotonous tasks that just kills time.
Latest Technologies
Our teams are constantly adapting the latest technologies. With the latest technologies and frameworks, we deliver speed
and quality at scale.
Customer Collaboration
We keep customer satisfaction as our centre goal and continuously strive to achieve it by building long-lasting relations
with our customers.
Solutions rather than just services
Our team of experts is here to find solutions to all your technology related troubles and provide you with the end to end
solutions which increases your business outcome and growth.
Quality Engineering with speed
We ensure high test coverage, good practices, extensibility and maintenance while developing your software. While doing all
this we make it our priority to deliver in time.
Long lasting Endurance
We are always ready to go an extra mile to make sure that we build the best possible product for you.
Services
Development
We provide development in Mobile, Web, and cloud services. We assure a bug-free product that complies with all your requirements
and follows all the industry standards.
Infrastructure
Engineering
We provide Infrastructure services that help your Development Operations to achieve higher efficiency, faster time to market
and better quality of software builds.
UI/UX
Design
We provide UI/UX design solutions that are based on market research. Which enhances the user experience and enables brand
loyalty.
IT Staff
Augmentation
Need a technical skill set for a short span of time to get the job done? We are here for you. You can lease our tech team
for any duration of time.
We start with the Project Requirements Document which consists of all the conditions and tasks which are mentioned by the
client that must be completed to ensure the completion of the project. It will further include class
diagrams and high-level architecture of the project.
Planning & Estimation
Once we freeze the requirements, we use Jira for planning and controlling our development cycle. Every requirement will be
assigned a user story and every user story will be assigned with one or multiple tasks. Time estimations
will be made and any kind of deviations will be recorded.
Designing & Development
We initiate with mockups and design and then start hacking. Each and every small PR is reviewed and the following things
are checked- it’s up to the standard we need, covers all edge cases, has proper documentation, there
are no hacks and the code does not create technical debts. Before the code review, we have CI that
runs linting checks, integration tests, unit tests and functional tests in parallel.
Testing & Delivery
Lastly, we check for code coverage with test coverage to make sure it’s 80% at least. Once all the check passes it goes to
the staging server. Every PR is deployed in an environment that is close to production. After this
based on the test metrics decided in the task, it goes to Q/A. Once approved by Q/A, we merge to master.
On merge to master our CircleCI build triggers again and deploys to production.


{% block meta_title %}Choose your department · FireCARES{% endblock %}
{% include 'firestation/_favicon.html' %}
{% include "firestation/_firecares_style.html" %}
{% include 'google_analytics.html' %}
{% include 'firestation/_mobile_navbar.html' %}
{% include 'firestation/_mobile_search.html' %}
You're almost there!  After an administrator verifies
your information, you'll be granted administrative access to this department.
{% endblock %}
{% include 'firestation/_partners.html' %}
{% include 'firestation/_footer.html' %}
{% include "firestation/_firecares_scripts.html" %}


Before Change
def create_model(trial):
// We optimize the numbers of layers and their units.
n_layers = int(trial.suggest_uniform("n_layers", 1, 4))
layers = []
for i in range(n_layers):
n_units = int(trial.suggest_loguniform("n_units_l{}".format(i), 4, 128))
After Change
def create_model(trial):
// We optimize the numbers of layers and their units.
n_layers = trial.suggest_int("n_layers", 1, 3)
layers = []
for i in range(n_layers):
n_units = int(trial.suggest_loguniform("n_units_l{}".format(i), 4, 128))


2 overrides of PerformAction
Microsoft.PowerShell.Security (2)
security\SignatureCommands.cs (2)
280
protected override Signature
PerformAction
(string filePath)
437
protected override Signature
PerformAction
(string filePath)
1 reference to PerformAction
Microsoft.PowerShell.Security (1)
security\SignatureCommands.cs (1)
208
if ((Signature =
PerformAction
(resolvedFilePath)) != null)


Step one: Grab your gear.
- Grab a piece of paper
- Grab a notebook (If you find a little one it's much better)
- Grab a pencil (We all make mistakes and we want to be able to correct them.)
Step two: Create the goal statement
- Write on the top of the peace of paper "One day, one week, one month without (Language you think you can't stop talking for a day)"
- Below that write at least 5 reasons of why you want to accomplish this goal. (This is REALLY important)
- Below that write a detailed plan that you will be following for the next 30 days
- Create a little calendar in the bottom (This will be used to track your "Not speaking English" progress)
- Fold it in half about 4 times until it's about the size of a card.
- Write in it "Once in the morning and twice in the night"
- Create a second calendar (This will be used to track the times you read your goal)
- (This is a little nice plus that will help you get through those weekends you failed to follow the plan) Next to the calendars I wrote "Failure is part of success, keep it up!"
Step three: Support team
You WILL need someone to hold you accountable. Here are some examples on how to do this:
- Tell everybody around you that you “forgot” how to speak (Language)
- Ask them to tell you when they hear you speaking (Language)
- Make the challenge with another exchange student. (You can learn from eachother)
Step Four: The notebook
The plan you'll follow is up to you, but I HIGHLY recommend the use of this simple yet powerful strategy:
- Find out how to say "How do you say _____ in (Language you want to learn)"
- Write down the sentence or word you just discovered in the notebook.
After knowing this sentence everything else will be a matter of curiosity, and consistency. In the next page I'll show you how to put it all together.
Click "Next step" to put it all together


Salary is incredibly important to all of us. In the most basic sense, the majority of people would not do their job if they were not paid for it.
But it's importance goes deeper than that. We often seek to make decisions that best optimize our future salary -- whether it be the cities we choose to live in or the majors we choose to study.
The first step to predicting salary is to do some preliminary exploration of our dataset.
It's interesting to note that the data seems to be a very specific slice of the population.
Every person in the dataset has at least a Bachelor's degree, with roughly 40% having a higher degree.
It's interesting to note the locations of people in the dataset. The plot below shows that the distribution is very much skewed to the major cities -- which questions whether analysis of this dataset is applicable outside the major cities.
The plot below shows the distribution of ages. We see that it seems to be the case that as one ages -- there is a larger likelihood of making more than 60k.
It's also interesting to consider how the features interact with each other -- as well as with the class label (below/above 60k). The feature correlation matrix below answers that question. Note that you can click on the image to see it in an interactive view
There are some obvious correlations in the feature matrix (like between the different types of job labels and major labels) but there are some interesting ones as well.
It seems that there is a high correlation between the province and the location that a person studied -- which means that it's typical for people to work in the same province as they study.
Given some of the feature correlations, my intuition told me that some manual feature selection might help improve my model's accuracy -- however it did not seem to help.
I also attempted to add an additional feature which attempted to guess whether someone was working in their field of study -- however no change was observed in my model's accuracy.
I attempted numerous machine learning strategies -- such as model averaging, stacking and blending. The most succesful approach was to train a single Gradient Boosted Tree Classifier -- with parameters fine-tund with cross-validation.
I used One Hot Encoding for the categorical data, in order to remove any notion of ordinality, and automatic feature selection that removed all features lower than the median feature importance.
With 10-fold cross-validation, my model had an accuracy of 0.76570 (+/- 0.00243) and an Area Under Curve (using prediction probability) of 0.83931.
The resulting feature importances are shown below. The values represent how much a feature contributes to the resulting classification.
The most important feature is the number of hours worked in a week -- a fairly obvious feature choice. The second most important feature is the type of job one works in.
While one's job on it's own is definitely not enough to inform of us of their salary, it might be interesting to see the effect that it has.
Here, I select some random partition of the data. I keep all of the features constant except for the feature corresponding to the job one works in. I then observe how many people would be making above $60k out of the group of people for every single type of job.
It's interesting to note that a lot of the results are fairly uniform. This suggests that while one's job is an important factor -- it's not enough to change it without changing anything else (such as major or age).
Nonetheless we see some interesting information. If any random person moved to a technical field -- they'd have about a 40% larger chance of making above $60k. Public adminstration and the financial sector also tend to lead to higher salaries.
On the flipside, education tends to have significantly lower salaries.
This dataset is definitely not well distributed or large enough to draw any large general conclusions. However it seems to be the case that if you work long hours, are in a good field (hopefully a Technical field) -- you will most likely make a decent amount of money.
But hey, if you don't do any of these things, no need to worry -- my model only has 77% accuracy after all.


Comments
1) p.3344v: 'Bushman rice' changes into creatures with wings that Lloyd does not know the proper name of, 2) This story is found in Book II-37
Contributors
Date
13 October 1873
Categories
Celestial bodies and aeroscopy
Keywords
stars (Altair star and 'Bushman rice') , stars (Altair star called ||xo hai by the |xam) , stars (and 'Bushman rice') , stars (and rain) , stars (and seasons) , stars (|xam names of) , stars (what the 'Bushman rice' does when the ||xo hai stars come out) , Bushman rice (what the 'Bushman rice' does when the ||xo hai stars come out) , Bushman rice (ant larvae) , Bushman rice ('Bushman rice' star) , Bushman rice (and stars) , Bushman rice (and seasons) , Bushman rice (and rain) , Bushman rice (its maggots or young) , Bushman rice (a description of) , Bushman rice (the doings of) , ant larvae (or 'Bushman rice') , ant larvae (what the 'Bushman rice' does when the ||xo hai stars come out) , names (of stars) , names (the Altair star called ||xo hai by the |xam) , seasons (and stars) , seasons (what the 'Bushman rice' does when the ||xo hai stars come out) , rain (and stars) , rain (and what the 'Bushman rice' does when the ||xo hai stars come out)
Story Pages
3344-3347


Parents who blended their names together for daughter's surname brutally blasted by mums and dads
Parents Courtney and Laura decided they wanted to give their little girl Lyla a combination of both of their surnames, Cassar and Sheldon, to give her the last name 'Casseldon'
by James Rodger, Luke Matthews, https://www.facebook.com/jamesrodgerjournalistTwo parents have been branded "peak stupid" after giving their daughter a "blended" surname.
The mum and dad's decision to combine their last names to create their daughter's surname has been slammed.
But, remarkably, not everybody has been left open-mouthed by their decision.
In fact, some others think the moniker is "lovely" - yes really.
Parents Courtney and Laura decided they wanted to give their little girl Lyla a combination of both of their surnames, Cassar and Sheldon, to give her the last name 'Casseldon', reports Mirror .
Explaining how they love the modern moniker, Courtney said: "I like the idea of our daughter having a part of both of us," as reported by the Daily Telegraph .
“With hyphens (double barrel surnames) I find a lot of kids end up choosing one name because it is easier.
“We played around with a few last names and found one that we like and sounds like a last name.”
The move split opinion online, which varied from those who thought it was 'lovely' to others who said it was 'ridiculous'.
One said: “This is peak stupid.”
Another replied: “Family trees are going to get much harder in the future."
A third defended the decision, arguing: "It's the parents right to name their children anything they like."


I am currently (as of July 2019) a college student going onto my Senior year at the University of Washington. Previous to this boot camp, I had no background in coding nor did I have any desire to do so. However, as a Biology major, although the things that I were learning were interesting, a career in medicine seemed to not fit me. This is how I came upon this class, and hopefully I will not only do well, but more importantly enjoy the process. Coming from a family with two computer science majors, I have a lot of not only moral support, but also the help to succeed.


Gold Coast bus crash leaves four in hospital
by Matt McDonaldUPDATE @ 4.30PM | Four people have been taken to hospital after a crash involving a bus and two cars at Arundel.
The bus crashed into the back of a car in the westbound lanes of Brisbane Rd just before 3.30pm.
The impact forced the car to smash into the rear of another car in front.
Paramedics, including Critical Care and High Acuity Response teams assessed 15 people at the scene.
11 of those did not need any further treatment.
Two females in their 30s, a man in his 30s and a man in his 70s were also transported to the Gold Coast University Hospital in a stable condition.
It’s understood their injuries are minor.
The accident has now been cleared.
EARLIER @ 4.00PM | At least 15 people are being assessed by paramedics after a crash involving a bus at Arundel.
The bus collided with two cars on Brisbane Rd just before 3.30pm.
Five Queensland Ambulance Service crews are currently on scene.
It’s unclear at this stage what injuries have been suffered.
The crash has caused extensive delays on Brisbane Rd.
Motorists are urged to avoid the area.
More to come.


| Opcode/Instruction | Op / En | 64/32 bit Mode Support | CPUID Feature Flag | Description |
|---|---|---|---|---|
| VEX.128.66.0F3A.W0 1D /r ib VCVTPS2PH xmm1/m64, xmm2, imm8 | A | V/V | F16C | Convert four packed single-precision floating-point values in xmm2 to packed half-precision (16-bit) floating-point values in xmm1/m64. Imm8 provides rounding controls. |
| VEX.256.66.0F3A.W0 1D /r ib VCVTPS2PH xmm1/m128, ymm2, imm8 | A | V/V | F16C | Convert eight packed single-precision floating-point values in ymm2 to packed half-precision (16-bit) floating-point values in xmm1/m128. Imm8 provides rounding controls. |
| EVEX.128.66.0F3A.W0 1D /r ib VCVTPS2PH xmm1/m64 {k1}{z}, xmm2, imm8 | B | V/V | AVX512VL AVX512F | Convert four packed single-precision floating-point values in xmm2 to packed half-precision (16-bit) floating-point values in xmm1/m64. Imm8 provides rounding controls. |
| EVEX.256.66.0F3A.W0 1D /r ib VCVTPS2PH xmm1/m128 {k1}{z}, ymm2, imm8 | B | V/V | AVX512VL AVX512F | Convert eight packed single-precision floating-point values in ymm2 to packed half-precision (16-bit) floating-point values in xmm1/m128. Imm8 provides rounding controls. |
| EVEX.512.66.0F3A.W0 1D /r ib VCVTPS2PH ymm1/m256 {k1}{z}, zmm2{sae}, imm8 | B | V/V | AVX512F | Convert sixteen packed single-precision floating-point values in zmm2 to packed half-precision (16-bit) floating-point values in ymm1/m256. Imm8 provides rounding controls. |
| Op/En | Tuple Type | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| A | NA | ModRM:r/m (w) | ModRM:reg (r) | Imm8 | NA |
| B | Half Mem | ModRM:r/m (w) | ModRM:reg (r) | Imm8 | NA |
Convert packed single-precision floating values in the source operand to half-precision (16-bit) floating-point values and store to the destination operand. The rounding mode is specified using the immediate field (imm8).
Underflow results (i.e., tiny results) are converted to denormals. MXCSR.FTZ is ignored. If a source element is denormal relative to the input format with DM masked and at least one of PM or UM unmasked; a SIMD exception will be raised with DE, UE and PE set.
The immediate byte defines several bit fields that control rounding operation. The effect and encoding of the RC field are listed in Table 5-12.
| Bits | Field Name/value | Description | Comment |
|---|---|---|---|
| Imm[1:0] | RC=00B | Round to nearest even | If Imm[2] = 0 |
| RC=01B | Round down | ||
| RC=10B | Round up | ||
| RC=11B | Truncate | ||
| Imm[2] | MS1=0 | Use imm[1:0] for rounding | Ignore MXCSR.RC |
| MS1=1 | Use MXCSR.RC for rounding | ||
| Imm[7:3] | Ignored | Ignored by processor |
VEX.128 version: The source operand is a XMM register. The destination operand is a XMM register or 64-bit memory location. If the destination operand is a register then the upper bits (MAXVL-1:64) of corresponding register are zeroed.
VEX.256 version: The source operand is a YMM register. The destination operand is a XMM register or 128-bit memory location. If the destination operand is a register, the upper bits (MAXVL-1:128) of the corresponding destination register are zeroed.
Note: VEX.vvvv and EVEX.vvvv are reserved (must be 1111b).
EVEX encoded versions: The source operand is a ZMM/YMM/XMM register. The destination operand is a YMM/XMM/XMM (low 64-bits) register or a 256/128/64-bit memory location, conditionally updated with writemask k1. Bits (MAXVL-1:256/128/64) of the corresponding destination register are zeroed.
vCvt_s2h(SRC1[31:0]) { IF Imm[2] = 0 THEN ; using Imm[1:0] for rounding control, see Table 5-12 RETURN Cvt_Single_Precision_To_Half_Precision_FP_Imm(SRC1[31:0]); ELSE ; using MXCSR.RC for rounding control RETURN Cvt_Single_Precision_To_Half_Precision_FP_Mxcsr(SRC1[31:0]); FI; }
(KL, VL) = (4, 128), (8, 256), (16, 512) FOR j←0 TO KL-1 i←j * 16 k←j * 32 IF k1[j] OR *no writemask* THEN DEST[i+15:i]← vCvt_s2h(SRC[k+31:k]) ELSE IF *merging-masking* ; merging-masking THEN *DEST[i+15:i] remains unchanged* ELSE ; zeroing-masking DEST[i+15:i] ← 0 FI FI; ENDFOR DEST[MAXVL-1:VL/2] ← 0
(KL, VL) = (4, 128), (8, 256), (16, 512) FOR j←0 TO KL-1 i←j * 16 k←j * 32 IF k1[j] OR *no writemask* THEN DEST[i+15:i]← vCvt_s2h(SRC[k+31:k]) ELSE *DEST[i+15:i] remains unchanged* ; merging-masking FI; ENDFOR
DEST[15:0] ←vCvt_s2h(SRC1[31:0]); DEST[31:16] ←vCvt_s2h(SRC1[63:32]); DEST[47:32] ←vCvt_s2h(SRC1[95:64]); DEST[63:48] ←vCvt_s2h(SRC1[127:96]); DEST[79:64] ←vCvt_s2h(SRC1[159:128]); DEST[95:80] ←vCvt_s2h(SRC1[191:160]); DEST[111:96] ←vCvt_s2h(SRC1[223:192]); DEST[127:112] ←vCvt_s2h(SRC1[255:224]); DEST[MAXVL-1:128] ← 0
DEST[15:0] ←vCvt_s2h(SRC1[31:0]); DEST[31:16] ←vCvt_s2h(SRC1[63:32]); DEST[47:32] ←vCvt_s2h(SRC1[95:64]); DEST[63:48] ←vCvt_s2h(SRC1[127:96]); DEST[MAXVL-1:64] ← 0
None
VCVTPS2PH __m256i _mm512_cvtps_ph(__m512 a);
VCVTPS2PH __m256i _mm512_mask_cvtps_ph(__m256i s, __mmask16 k,__m512 a);
VCVTPS2PH __m256i _mm512_maskz_cvtps_ph(__mmask16 k,__m512 a);
VCVTPS2PH __m256i _mm512_cvt_roundps_ph(__m512 a, const int imm);
VCVTPS2PH __m256i _mm512_mask_cvt_roundps_ph(__m256i s, __mmask16 k,__m512 a, const int imm);
VCVTPS2PH __m256i _mm512_maskz_cvt_roundps_ph(__mmask16 k,__m512 a, const int imm);
VCVTPS2PH __m128i _mm256_mask_cvtps_ph(__m128i s, __mmask8 k,__m256 a);
VCVTPS2PH __m128i _mm256_maskz_cvtps_ph(__mmask8 k,__m256 a);
VCVTPS2PH __m128i _mm_mask_cvtps_ph(__m128i s, __mmask8 k,__m128 a);
VCVTPS2PH __m128i _mm_maskz_cvtps_ph(__mmask8 k,__m128 a);
VCVTPS2PH __m128i _mm_cvtps_ph ( __m128 m1, const int imm);
VCVTPS2PH __m128i _mm256_cvtps_ph(__m256 m1, const int imm);
Invalid, Underflow, Overflow, Precision, Denormal (if MXCSR.DAZ=0);
VEX-encoded instructions, see Exceptions Type 11 (do not report #AC);
EVEX-encoded instructions, see Exceptions Type E11.
| #UD | If VEX.W=1. |
| #UD | If VEX.vvvv != 1111B or EVEX.vvvv != 1111B. |


More Website Templates @ TemplateMonster.com - July 28, 2014!
Stories
I am so happy to meet Rahul Mehta. Registering in CharmAsh is very effective. It has changed my life for better.
Hope it helps you too!
I always wanted to make a beautiful and polite girl as my girlfriend. Luckily, here I found Mishu, the
girl of my dreams. Thank you so much, CharmAsh.
I was looking for a friend who is really good at sports and with whom I could build my own team. This is
really cool that I have met that guy here :)
I am so excited by registering myself here. Vivek proposed me here. I am so happy.
I am 24 years, and by meeting Vivek, I feel so much love and care. Thanks, CharmAsh!
I met Radhika Roshan at CharmAsh three years ago. I am married now and have two lovely kids.
My life is set. Thanks a lot.
I am really glad to interact with so many people. Many of them are my friends. It's a lovely web-site.
It's really interesting to know about so many different people. Keep it up, CharmAsh !


I'm a software engineer at
Highspot
where we're empowering revenue teams with better content and better customer conversations.


Are You Sure You want to delete this Doctor records
With this allthe records of this doctor will remove from the database


Members
Members
- Search members, manage their data, create new one
Member locks
- Search and deactivate member lockouts


Welcome to FinMind
FinMind is about Financial Data, Visualize and Mining.
Introduce
Stocks are financial products that most people are familiar with. The stock price will be affected by many factors, including central bank interest rates, exchange rates, raw material prices, and international oil prices.
Similarly, other financial products will interact with each other. If we conduct a holistic financial analysis, we can not only look at a single piece of data, but also extract valuable information from it.
The following will introduce our products.
Financial Data
- More than ten kinds financial data. Every day update.
Financial Visualize
- At least five kinds Visualize tools on every data. ( In development )
Financial Mining
- Holistic financial analysis. ( In development )
Getting started
from FinMind.Data import Load
import datetime
date = '2018-12-13'
print('load TaiwanStockInfo')
TaiwanStockInfo = Load.FinData(dataset = 'TaiwanStockInfo')
print( TaiwanStockInfo[:5] )
load TaiwanStockInfo
stock_id stock_name kinds
0 1201 味全 食品工業
1 1203 味王 食品工業
2 1210 大成 食品工業
3 1213 大飲 食品工業
4 1215 卜蜂 食品工業
print('load TaiwanStockPrice {} '.format(TaiwanStockInfo.loc[2,'stock_id']))
TaiwanStockPrice = Load.FinData(
dataset = 'TaiwanStockPrice',
select = TaiwanStockInfo.loc[2,'stock_id'],
#select = ['1210'],
date = date)
print( TaiwanStockPrice[:5] )
load TaiwanStockPrice 1210
Open High Low Close Adj_Close Volume date stock_id
0 32.5 33.20 32.30 33.00 33.00 1668333 2018-12-13 1210
1 33.3 35.05 33.25 33.95 33.95 3916120 2018-12-14 1210
2 34.5 35.50 34.35 34.35 34.35 4543063 2018-12-17 1210
3 34.4 34.85 32.80 32.80 32.80 3726316 2018-12-18 1210
4 33.0 33.40 32.30 32.75 32.75 1887001 2018-12-19 1210
The parameter table
is what dataset been load.
The function FinData
is load taiwan stock prices by selecting
TaiwanStockInfo.loc[2,'stock_id'] and date
>= '2018-12-13',
or you can load some stock id like 1210
.
print('load GovernmentBonds list')
GovernmentBonds_list = Load.FinDataList(dataset = 'GovernmentBonds')
print( GovernmentBonds_list[:5] )
print('load GovernmentBonds {}'.format(GovernmentBonds_list[3]))
GovernmentBonds = Load.FinData(
dataset = 'GovernmentBonds',
select = GovernmentBonds_list[3],
date = date)
print( GovernmentBonds[:5] )
load GovernmentBonds list
['Canada 1-Month', 'Canada 1-Year', 'Canada 10-Year', 'Canada 2-Month', 'Canada 2-Year']
load GovernmentBonds Canada 2-Month
date Price Open High Low ChangePercent bound_type
0 2018-12-13 1.630 1.625 1.630 1.62 0.0000 Canada 2-Month
1 2018-12-14 1.630 1.625 1.630 1.62 0.0000 Canada 2-Month
2 2018-12-17 1.625 1.630 1.635 1.62 -0.0031 Canada 2-Month
3 2018-12-18 1.625 1.630 1.630 1.62 0.0000 Canada 2-Month
4 2018-12-19 1.625 1.620 1.625 1.62 0.0000 Canada 2-Month
The function FinDataList
is load GovernmentBonds_list.
The function FinData
is load GovernmentBonds by selecting Canada 2-Month
and date
>= '2018-12-13'.
more can follow deom.py in my github.


3.0.0-2
The Nxus Core package includes the basic Application framework for building a Nxus app.
You'll probably find the following resources useful background and help in building Nxus applcations.
The full set of Nxus docs is available at http://docs.gonxus.org.
> npm install @nxus/core --save
In your root application, create a new Application instance:
import {Application} from '@nxus/core'
let app = new Application(options)
app.start()
export default app
Nxus is built around the concept of a boot cycle. The application dispatches events in the following order:
| Boot Stage | Description |
|---|---|
init |
indicates the application is starting up and initializing modules. Other modules are not gauranteed to be available at this phase. |
load |
modules are initialized and loading. This is the place to do any internal setup (outside of the constructor). Other modules are not gauranteed to be available at this phase. |
startup |
all modules have been loaded and are available. This is the place to do any setup that requires data/input from other modules (like Storage) |
launch |
the application is launching and all services have been started. Routes are accessible. Use onceAfter('launch') to gaurantee execution after the application has completely launched |
By defaul the Application will look for other Nxus modules in the following order:
package.json
file.namespace
application config option.In order to access module commands, use the Application.get() method.
let router = Application.get('router')
new App(...options)
Available options are:
appDir: the location to use to load the default 'package.json' file.
namespace: any additional namespaces to use to load modules in the node_modules folder. Can be a string or array of strings.
modules: an array of paths to require into the application
debug: Boolean to display debug messages, including startup banner
script: Boolean to indicate the application is a CLI script, silences all logging/output messages except for explicit console.log calls
The Application exposes a core config
object that contains application and module specific configuration values.
Nxus uses the rc library to provide application configuration.
The application configuration can usually be found in a .nxusrc
file in the root folder.
You can override specific confirguation values using command line environment variables, which supports nesting.
nxus_myconfig__value__first=true npm start
will translate into an application config of
console.log(app.config.myconfig) // {value: {first: true}}
The Core Application class.
Extends Dispatcher
(Object)
the configuration options
import {Application} from '@nxus/core'
let app = new Application(options)
app.start()
export default app
ConfigurationManager loads the internal app.config hash using the following order (each overwrites any values of the previous):
config
hash of the project package.json fileThe core Dispatcher class, which implements promisified
Extends EventEmitter
import { Dispatcher } from '@nxus/core'
class MyClass extends Dispatcher {
...
}
The core Module class. This provides a messaging proxy layer between modules and calling code. The main advantage of this proxy class is that missing modules won't cause exceptions in the code.
Modules are accessed through the Application.get() method
Extends Dispatcher
let router = app.get('router')
Producer modules should register themselves with the use() method, and define gather() and respond() handlers:
app.get('router').use(this).gather('route')
app.get('templater').use(this).respond('template')
Consumer modules should get the module they need to use and call provide or request
app.get('router').provide('route', ...)
app.get('templater').request('render', ...)
Modules proxy event names as methods to provide/request, so these are synomymous with above:
app.get('router').route(...)
app.get('templater').render(...)
Default implementations should be indicated by using default() to occur before provide()
Overriding another implementation can use replace() to occur after provide()
app.get('router').default('route', GET', '/', ...)
app.get('router').replace('route', GET', '/', ...) Provide, default, and replace all return a proxy object if called with no arguments, so these are synonymous with above:
app.get('router').default().route('GET', '/', ...)
app.get('router').replace().route('GET', '/', ...)
The PluginManager handles all of the module loading. Load order is as follows:
namespace
config option, and packages in the @nxus
namespace.modules
config option[loadNxusModules description]
([type])
[description]
[type]
:
[description]
[loadAdditionalModules description]
([type])
[description]
([type])
[description]
[type]
:
[description]
The Watcher class monitors the project directory and restarts the application whenever there is a change in files detected. Useful for development.


Carpool your way to a better world
About Savaree
Going somewhere? Want company? Savaree is the answer. Need a ride? Savaree is the answer. Savaree is an open-source ridesharing platform that brings together drivers going a certain route and people who need a ride along that route. Drivers register their route and the approximate time of departure along the route, so potential carpoolers can search for routes that match where they want to go.


 Someone has to be right, this app was made so that you can see what others think about your driving skills.
SCORE - Your score is a combination of several factors. First, if someone posts a negative or positive comment about a maneuver you pulled your score will be adjusted accordingly. Also, for each day that goes by since the first comment was made about your plate you will 5 bonus points.
SEARCH - To search for a plate, just go to the search page and type in the state abbreviation and plate number you are looking for. The results will show the user score and you can scroll through their messages.
LEADER BOARD - To view the Leader board, just click the Leader board button. Once on the page you can filter by City, State or Zip to see what the current top, (or bottom) scores are for your query.
DELETE - You cannot delete messages about your plate, you must own your driving. To delete your user account, simply click on "Help" and then "Account" to navigate to the Delete Account page. Here you can delete your account, click the "Delete Account" button. This will remove the information you entered when you registered.


Navigator Pane
The Navigator pane displays your repository, information models, model versions, model status, and builds in progress in a familiar tree view.
You can expand or collapse a directory by clicking the plus or minus icon in the Navigator pane.
The Navigator pane can also be customized. When the administration client first appears, the Navigator pane is attached to the application window. The pane is docked. If you want to undock the Navigator pane, turning it into a floating window, click the pane and drag it anywhere on the screen. To re-dock the Navigator window, click the window and drag it anywhere on the application window and release the mouse button.


--- layout: default ---
The Estimote Unity plugin allows you to use the Estimote SDK within Unity and deploy to iOS or Android. Whether you are using Estimote beacons or any other iBeacon manufacturer this plugin will work for you (excluding the cloud based estimote features). We do however recommend you use Estimote beacons as they are reliable and very well built.
You can purchase Estimote Unity from the Asset Store.
You can access the latest documentation using the link below.


{% extends "homepage/base.html" %} {% load crispy_forms_tags%} {%load static%} {% block content%}
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| static bool | GetGlobalDefaultDebug () |
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| static void | GlobalDefaultDebugOff () |
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| static void | GlobalDefaultDebugOn () |
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| static void | SetGlobalDefaultDebug (bool debugFlag) |
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| static void | GlobalWarningDisplayOn () |
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| static void | GlobalWarningDisplayOff () |
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| static void | SetGlobalWarningDisplay (bool flag) |
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| static bool | GetGlobalWarningDisplay () |
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| static double | GetGlobalDefaultCoordinateTolerance () |
| | Access the global tolerance to determine congruent spaces. More...
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| static void | SetGlobalDefaultCoordinateTolerance (double) |
| | Access the global tolerance to determine congruent spaces. More...
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| static double | GetGlobalDefaultDirectionTolerance () |
| | Access the global tolerance to determine congruent spaces. More...
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| static void | SetGlobalDefaultDirectionTolerance (double) |
| | Access the global tolerance to determine congruent spaces. More...
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| static bool | SetGlobalDefaultThreader (const std::string &threader) |
| | Set/Get the default threader used for process objects. More...
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| static std::string | GetGlobalDefaultThreader () |
| | Set/Get the default threader used for process objects. More...
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| static void | SetGlobalDefaultNumberOfThreads (unsigned int n) |
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| static unsigned int | GetGlobalDefaultNumberOfThreads () |
| | Set/Get the default threader used for process objects. More...
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| void | CheckImageMatchingDimension (const Image &image1, const Image &image2, const std::string &image2Name) |
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| void | CheckImageMatchingPixelType (const Image &image1, const Image &image2, const std::string &image2Name) |
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| void | CheckImageMatchingSize (const Image &image1, const Image &image2, const std::string &image2Name) |
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| virtual unsigned long | AddITKObserver (const itk::EventObject &, itk::Command *) |
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| virtual itk::ProcessObject * | GetActiveProcess () |
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| virtual void | OnActiveProcessDelete () |
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| virtual void | onCommandDelete (const itk::simple::Command *cmd) noexcept |
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| virtual void | PreUpdate (itk::ProcessObject *p) |
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| virtual void | RemoveITKObserver (EventCommand &e) |
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| | NonCopyable ()=default |
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| | NonCopyable (const NonCopyable &)=delete |
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| NonCopyable & | operator= (const NonCopyable &)=delete |
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| template<class TImageType > |
| static void | FixNonZeroIndex (TImageType *img) |
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| template<class TImageType > |
| static TImageType::ConstPointer | CastImageToITK (const Image &img) |
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| template<class TPixelType , unsigned int VImageDimension, unsigned int VLength, template< typename, unsigned int > class TVector> |
| static Image | CastITKToImage (itk::Image< TVector< TPixelType, VLength >, VImageDimension > *img) |
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| template<class TImageType > |
| static Image | CastITKToImage (TImageType *img) |
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| static const itk::EventObject & | GetITKEventObject (EventEnum e) |
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| template<typename T > |
| static std::ostream & | ToStringHelper (std::ostream &os, const T &v) |
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| static std::ostream & | ToStringHelper (std::ostream &os, const char &v) |
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| static std::ostream & | ToStringHelper (std::ostream &os, const signed char &v) |
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| static std::ostream & | ToStringHelper (std::ostream &os, const unsigned char &v) |
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Segments structures in images based on a user supplied edge potential map.
- IMPORTANT
- The SegmentationLevelSetImageFilter class and the ShapeDetectionLevelSetFunction class contain additional information necessary to gain full understanding of how to use this filter.
- OVERVIEW
- This class is a level set method segmentation filter. An initial contour is propagated outwards (or inwards) until it ''sticks'' to the shape boundaries. This is done by using a level set speed function based on a user supplied edge potential map. This approach for segmentation follows that of Malladi et al (1995).
- INPUTS
- This filter requires two inputs. The first input is a initial level set. The initial level set is a real image which contains the initial contour/surface as the zero level set. For example, a signed distance function from the initial contour/surface is typically used. Note that for this algorithm the initial contour has to be wholly within (or wholly outside) the structure to be segmented.
- The second input is the feature image. For this filter, this is the edge potential map. General characteristics of an edge potential map is that it has values close to zero in regions near the edges and values close to one inside the shape itself. Typically, the edge potential map is compute from the image gradient, for example:
\[ g(I) = 1 / ( 1 + | (\nabla * G)(I)| ) \]
\[ g(I) = \exp^{-|(\nabla * G)(I)|} \]
where \( I \) is image intensity and \( (\nabla * G) \) is the derivative of Gaussian operator.
- See SegmentationLevelSetImageFilter and SparseFieldLevelSetImageFilter for more information on Inputs.
- PARAMETERS
- The PropagationScaling parameter can be used to switch from propagation outwards (POSITIVE scaling parameter) versus propagating inwards (NEGATIVE scaling parameter).
The smoothness of the resulting contour/surface can be adjusted using a combination of PropagationScaling and CurvatureScaling parameters. The larger the CurvatureScaling parameter, the smoother the resulting contour. The CurvatureScaling parameter should be non-negative for proper operation of this algorithm. To follow the implementation in Malladi et al paper, set the PropagtionScaling to \(\pm 1.0\) and CurvatureScaling to \( \epsilon \) .
Note that there is no advection term for this filter. Setting the advection scaling will have no effect.
- OUTPUTS
- The filter outputs a single, scalar, real-valued image. Negative values in the output image represent the inside of the segmented region and positive values in the image represent the outside of the segmented region. The zero crossings of the image correspond to the position of the propagating front.
- See SparseFieldLevelSetImageFilter and SegmentationLevelSetImageFilter for more information.
- REFERENCES
- "Shape Modeling with Front Propagation: A Level Set Approach", R. Malladi, J. A. Sethian and B. C. Vermuri. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol 17, No. 2, pp 158-174, February 1995
- See also
- SegmentationLevelSetImageFilter
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ShapeDetectionLevelSetFunction
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SparseFieldLevelSetImageFilter
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itk::simple::ShapeDetectionLevelSet for the procedural interface
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itk::ShapeDetectionLevelSetImageFilter for the Doxygen on the original ITK class.
Definition at line 96 of file sitkShapeDetectionLevelSetImageFilter.h.