| Opcode/Instruction | Op/En | 64/32-bit Mode | CPUID Feature Flag | Description |
| 0F 16 /r MOVLHPS xmm1, xmm2 | RM | V/V | SSE | Move two packed single-precision floatingpoint values from low quadword of xmm2 to high quadword of xmm1. |
| VEX.NDS.128.0F.WIG 16 /r VMOVLHPS xmm1, xmm2, xmm3 | RVM | V/V | AVX | Merge two packed single-precision floatingpoint values from low quadword of xmm3 and low quadword of xmm2. |
| Op/En | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| RM | ModRM:reg (w) | ModRM:r/m (r) | NA | NA |
| RVM | ModRM:reg (w) | VEX.vvvv (r) | ModRM:r/m (r) | NA |
This instruction cannot be used for memory to register moves. 128-bit two-argument form: Moves two packed single-precision floating-point values from the low quadword of the second XMM argument (second operand) to the high quadword of the first XMM register (first argument). The low quadword of the destination operand is left unchanged. The upper 128 bits of the corresponding YMM destination register are unmodified. 128-bit three-argument form Moves two packed single-precision floating-point values from the low quadword of the third XMM argument (third operand) to the high quadword of the destination (first operand). Copies the low quadword from the second XMM argument (second operand) to the low quadword of the destination (first operand). The upper 128-bits of the destination YMM register are zeroed. If VMOVLHPS is encoded with VEX.L= 1, an attempt to execute the instruction encoded with VEX.L= 1 will cause an #UD exception.
In 64-bit mode, use of the REX.R prefix permits this instruction to access additional registers (XMM8-XMM15).
MOVLHPS (128-bit two-argument form) DEST[63:0] (Unmodified) DEST[127:64] ← SRC[63:0] DEST[VLMAX-1:128] (Unmodified) VMOVLHPS (128-bit three-argument form) DEST[63:0] ← SRC1[63:0] DEST[127:64] ← SRC2[63:0] DEST[VLMAX-1:128] ← 0
| MOVHLPS: | __m128 _mm_movelh_ps(__m128 a, __m128 b) |
None.
See Exceptions Type 7; additionally
| #UD | If VEX.L= 1. |


In Terminal
- type sqlite3 to work in a database
- type .exit to exit
- type .schema to view the schema
- type .help for help
Data Types
- Integer, a positive or negative whole number
- Text, a text string
- Date, the date formatted as YYYY-MM-DD for the year, month, and day
- Real, a decimal value
Clauses
Commands. They are written in all caps, and perform specific task in SQL
- Ex: CREATE TABLE celebs (id INTEGER, name TEXT, age INTEGER);
- INSERT INTO celebs (id, name, age) VALUES (1, 'Justin Bieber', 21); - Inserts a row
- SELECT * FROM celebs; - lets you view the entire table
- Replace * with the column name to only see that column
- You can query more than one column by separating the names with a comma.
- DISTINCT genre - queries the unique items in that column
- UPDATE celebs
- SET age = 22 WHERE id = 1;
- ALTER TABLE celebs ADD COLUMN twitter_handle TEXT;
- DELETE FROM celebs WHERE twitter_handle IS NULL; - will delete all rows where the condition is met, in this case null in the twitter_handle column
- SELECT * FROM movies WHERE name LIKE 'Se_en'; - LIKE is an operator used with the WHERE clause to find a certain bit of information. LIKE is not case sensitive. (_) is a wildcard that allows you to search for a word with any character in the _ place.
- SELECT * FROM movies WHERE name LIKE '%man%'; - % is another operator. When surrounding a word searches for items containing those characters
- SELECT * FROM movies WHERE name LIKE 'a%'; - When % is at the end, it search for items starting with those characters. %a would search for items ending with a.
- SELECT * FROM movies WHERE name BETWEEN 'A' AND 'J'; - BETWEEN is an operator that queries all items that start with the first character and up to, but not including the second character.
- SELECT * FROM movies
WHERE year BETWEEN 1990 AND 2000
AND genre = 'comedy'; - AND is used to add another condition when querying
- SELECT * FROM movies
WHERE genre = 'comedy'
OR year < 1980; - OR is used to include more than one conditions in a query
- SELECT * FROM movies
ORDER BY imdb_rating DESC; - ORDER BY lets you order your content by DESC for descending or ASC for ascending
- SELECT * FROM movies
ORDER BY imdb_rating ASC
LIMIT 3; - LIMIT returns only that many queries
Operators
Commonly used when using the WHERE clause
- = equals
- != not equals
- > greater than
- < less than
- >= greater than or equal to
- <= less than or equal to


Automatic audit fields
instead of setting the audit values manually on each and every save or update operation, you can take advantage of a DBContext
instance where you will instantiate the context by providing it with the details of the current user performing the operations once, and then use the db context to perform all subsequesnt save/update operations so that all the audit fields will be set on the entities automatically.
Enable audit fields
simply add a property of type ModifiedBy
to the entity class where you'd like to enable audit fields. The ModifiedBy
type is provided by the library. It can be inherited and other properties can be added to it as you please.
public class Book : Entity
{
public string Title { get; set; }
public ModifiedBy ModifiedBy { get; set; }
}
Instantiate a DBContext
instantiate a context by providing it a ModifiedBy
instance with the current user's details filled in.
var currentUser = new ModifiedBy
{
UserID = "xxxxxxxxxxxx",
UserName = "Kip Jennings"
};
var db = new DBContext(modifiedBy: currentUser);
Perform entity operations
in order for the auto audit fields to work, you must use the db context to perform the operations instead of the DB
static methods like you'd typically use.
var book = new Book { Title = "test book" };
await db.SaveAsync(book);
await db.Update<Book>()
.MatchID(book.ID)
.Modify(b => b.Title, "updated title")
.ExecuteAsync();
doing so will result in the following document in mongodb:
{
"_id" : ObjectId("xxxxxxxxxxxx"),
"Title" : "updated title", //this will initially be 'test book'
"ModifiedBy" : {
"UserID" : "xxxxxxxxxxxx",
"UserName" : "Kip Jennings"
}
}
Get/set audit values
it is also possible to instantiate a DBContext
without supplying a ModifiedBy
to the constructor and set or get it like so:
var dbContext = new DBContext();
db.ModifiedBy = new ModifiedBy
{
UserID = "xxxxxxxxxxxx",
UserName = "Kip Jennings"
};
var currentUser = db.ModifiedBy;
Transaction support
a transaction with audit field support can be performed with the DBContext like so:
var db = new DBContext(modifiedBy: currentUser);
using (db.Transaction())
{
await db.SaveAsync(book);
await db.CommitAsync();
}
or it can be performed with a Transaction
instance like so:
using (var TN = DB.Transaction(modifiedBy: currentUser))
{
await TN.SaveAsync(book);
await TN.CommitAsync();
}
Note
please refer to the transactions page for a detailed explanation of how transactions work.


This class is used to generate the Struts Validator Form as well as the
This class is used to generate Spring Validation rules
as well as the Hibernate mapping file.
User() -
Constructor for class br.emprestes.sitas.model.User
User(String) -
Constructor for class br.emprestes.sitas.model.User


UDISKS
Section: udisks (1)Updated: April 2008
Index Return to Main Contents
NAME
udisks - udisks command line toolSYNOPSIS
- udisks [OPTIONS]
DESCRIPTION
udisks
OPTIONS
--dump
- Dump all information about all devices. Please include this when filing bugs.
--enumerate
- Lists all object paths of devices know to the daemon.
--enumerate-device-files
- Lists all device files of devices know to the daemon.
--monitor
- Connect to the daemon and print a line every time an event happens.
--monitor-detail
- Like --monitor but prints the full details of the device whenever an event happens.
--show-info device_file
- Shows information about device_file.
--inhibit-polling device_file [-- program arg ...]
- Inhibits polling on device_file. If no program is given, polling is inhibited until Ctrl+C is pressed. Otherwise the program is spawned and the polling is only inhibited until the program terminates.
--poll-for-media device_file
- Polls device_file to perform media detection.
--inhibit-all-polling [-- program arg ...]
- Inhibits polling on all devices. If no program is given, polling is inhibited until Ctrl+C is pressed. Otherwise the program is spawned and the polling is only inhibited until the program terminates.
--mount device_file [--mount-fstype fstype] [--mount-options options]
- Mounts the device represented by device_file using the file system fstype and a comma-separated list of options.
--unmount device_file [--unmount-options options]
- Unmounts the device represented by device_file using a comma-separated list of options.
--detach device_file [--detach-options options]
- Detaches (e.g. powering down the physical port the device is connected to) the device represented by device_file using a comma-separated list of options.
--ata-smart-refresh device_file [--ata-smart-wakeup]
- Refreshes ATA SMART data for device_file. If the disk is sleeping it will not be woken up unless --ata-smart-wakeup is passed. will be used.
--ata-smart-refresh device_file --ata-smart-simulate blob
- Read ATA SMART data from blob for device_file. This can only be done by the super user and is typically used for testing that clients relying on the ATA SMART data provided by udisks behave correctly. The libatasmart git repository at m[blue]http://git.0pointer.de/?p=libatasmart.git;a=summarym[] contains a collection of blobs from failing disks.
--inhibit [-- program arg ...]
-
Inhibits clients from invoking methods on the daemon that require authorization (all methods will return the
org.freedesktop.UDisks.Error.Inhibited
error) if the caller is not the super user. This is typically used by OS installers and other programs that expects full control of the system, specifically to avoid automounting devices. Only the super user can do this.
If no program is given, the daemon is inhibited until Ctrl+C is pressed. Otherwise the program is spawned and the daemon is only inhibited until the program terminates.
--set-spindown device_file --spindown-timeout seconds [-- program arg ...]
-
Configures disk spindown timeout on
device_file
to
seconds. See
the section called lqSPINNING DOWN DISKSrq
for important information before using this option.
If no program is given, the spindown time will be used until Ctrl+C is pressed. Otherwise the program is spawned and the the spindown timeout will only be used until the program terminates.
--set-spindown-all --spindown-timeout seconds [-- program arg ...]
-
Configures disk spindown timeout on all disks that can be spun down to
seconds. See
the section called lqSPINNING DOWN DISKSrq
for important information before using this option.
If no program is given, the spindown time will be used until Ctrl+C is pressed. Otherwise the program is spawned and the the spindown timeout will only be used until the program terminates.
--help
- Show help options.
SPINNING DOWN DISKS
Caution should be exercised when configuring disk spin down timeouts.
Note that every time a disk is spun down, the lqstart-stop-countrq ATA SMART attribute will increase by 1 and most disks are only good for a limited number (typically 50,000 but it varies by manufacturer and model). In addition, the drive may take as long as 30 seconds to respond to subsequent disk access - most drives are typically faster, see the lqspin-up-timerq ATA SMART attribute.
On the other hand, cautious use (e.g. using conservative timeouts) of the ability to spin down disks, can be a good way to trade power consumption (typically 8 vs 1 Watts for 3.5" drives) and heat emission for a slightly higher latency.
AUTHOR
Written by David Zeuthen<EMAIL_ADDRESS>with a lot of help from many others.
BUGS
Please send bug reports to either the distribution bug tracker or the upstream bug tracker at m[blue]http://bugs.freedesktop.org/enter_bug.cgi?product=udisksm[].
SEE ALSO
udisks-daemon(8), udisks(7),
Index
This document was created by man2html, using the manual pages.
Time: 05:29:11 GMT, December 24, 2015


Huatulco (Bahias de Huatulco) is a resort town located at the southern end of Mexico’s Pacific Coast, south of Acapulco. It sits at the foot of the Sierra Madre del Sur mountains on a series of nine bays, surrounded by quiet, unspoiled tropical beauty.
It is a very useful location to wait out strong winds or to clear in or out of Mexico.
Location 15° 45.97'N, 96° 07.2'W.
The Marina now checks you in and out if you are arriving / departing to/from a Mexican Port. Thus saving you a trip to the Port Captain.
To obtain a Temporary Import Permit if this is your first Mexican port, go to the Banjercito Bank. Cruisers report the paperwork is pretty straightforward.
The Marina however cannot assist with zarpes or clearance out of the country. After obtaining proof that your marina bill is paid, you must go to the Port Captain in Santa Cruz, then to the bank, then to Immigration, then to Customs (with perhaps another bank visit). Customs makes arrangements to inspect your boat. You will need to start all this several days in advance of your anticipated weather window for the Tehuantepec if you are going south.
The Port Captain and Customs are only open 09:00-17:00 on weekdays. Immigration office closes at 13:00.
Last updated January 2013.
The marina here is a Fonatur marina and reported to be very friendly. It is a 30 minute walk from the town centre (25 pesos for a taxi).
The anchorage to the south west, at Santa Cruz, has room for several transient boats to the east of the cruise ship dock. The anchorage is limited by coral, the channel to the darsena, and panga moooring bouys. It is possible to use the sea wall behind the tour boat dock to secure the dinghy. If going into the town, if will be cheaper to walk a couple of blocks away from the cruise ship berths before picking up a taxi.
The town, has a supermarket, also hardware stores and some chandlery/fishing supplies.
Last updated June 2012.


Cart
Item Number
Quantity
Bike Item Number:
Quantity:
Add to cart
Buy cart
Bike Lookup
Item Number:
Lookup Bike
Item Number
Name
Description
Category
Inv
Price


Mason, ate my Tivo remote. I could have killed him! My wife intervened. We went a week without watching TV. We've both been busy with parenting, work, and summer trip planning. I finally broke down and started looking for a replacement Tivo remote. You can get them online for 25-35 dollars. On an impulse buy at "Best Buy" I purchased the Harmony 676 universal remote. It totally rocks! Its the first universal remote that I've owned that has worked flawlessly. Read about the
Harmony 676 remote. The remote is not cheap. It cost $200 at Best Buy, but I've since found it at numerous locations (walmart.com for $119 and Amazon.com for $115) cheaper. I've ordered another one from Wal Mart and plan on returning the other one to Best Buy.


Greta Thunberg to take part in Portuguese parliamentary session
Climate change activist Greta Thunberg has accepted an invitation to take part in a Portuguese parliamentary session in the coming weeks when she passes through Portugal en-route to the Madrid Climate Summit.
The Swedish teen will participate in the work group at the invitation of the Environment Committee at the end of this month or beginning of next month. She is current crossing the Atlantic in a catamaran, nearing the Azores


Getting started with Simpletools\Cassandra
Simpletools\Cassandra has been designed to provide you with all benefits of Apache Cassandra, but with simplified and flexible PHP methods.
Requirements
Simpletools\Cassandra requires PHP version >= 5.3.0
Simpletools\Cassandra requires Cassandra PHP Extension version >= 1.3.2
Setup
Simpletools\Cassandra can be used as a standalone class and we have included a majority of examples with this approch in mind. However, if you are using Simpletools\Mvc alongside, our examples can be simplified. Click here to view the Simpletools\Cassandra snippets for usage inside a model.
Installation
To see how to install \Simpletools framework please click here.
Configuration
To enable Simpletools\Cassandra, you need to define your cluster settings:
use Simpletools\Db\Cassandra\Client;
Client::cluster([
'default' => true,
'name' => 'CLUSTER_NAME',
'hosts' => ['<IP_ADDRESS>'],
'username' => 'USERNAME',
'password' => 'PASSWORD,
'port' => 'PORT',
'keyspace' => 'KEYSPACE'
]);
\Simpletools\Db\Cassandra\Query
Simpletools\Db\Cassandra\Query is an easy to use component allowing you to easily interact with your Cassandra database.
SELECT
To select you first need to create a new instance of \Simpletools\Db\Cassandra\Query:
use Simpletools\Db\Cassandra\Query;
$query = (new Query('TABLE'))
->client(new Client('CLIENT')) // optional - if this isn't set, it will use the default (set in the cluster config)
->keyspace('KEYSPACE') // optional - if this isn't set, it will use the default (set in the cluster config)
To retrieve only a set of columns, simply use the columns() method:
$query->columns('COLUMN_1','COLUMN_2','COLUMN_3'); // example 1
$query->columns([
'*',
'COLUMN_1' => 'json | json:object | json:array',
'COLUMN_2' => function($value){
return json_decode($value)
}
]); // example 2
To include a WHERE statement to your query use (all columns in the WHERE statement MUST be included in the PRIMARY index):
$query->where('COLUMN_1','VALUE_1');
You can then add an AND by using:
$query->also('COLUMN_2', 'IN', ['VALUE_2','VALUE_3','VALUE_4']);
To include a SORT BY statement to your query use (all columns in the SORT BY statement MUST be included in the PRIMARY index):
$query->sort('COLUMN_1','VALUE_1');
To include a GROUP BY statement to your query use (all columns in the GROUP BY statement MUST be included in the PRIMARY index):
$query->group('COLUMN_1','VALUE_1');
To view the generated query while developing or to store in logs use:
echo $query->getQuery();
/* Output: SELECT COLUMN_1, COLUMN_2 FROM KEYSPACE.TABLE WHERE COLUMN_1 = VALUE_1 AND COLUMN_2 IN ('VALUE_2','VALUE_3','VALUE_4') */
Once you are ready, you can then run the query with the following methods:
$query->fetch(); // fetch the first row
$query->fetchAll(); // fetch all rows (array of objects)
echo $query->length(); // returns the amount of results (int)
PLEASE NOE: The fetchAll() method is not needed if you loop on the $query variable. The plugin is smart enough to know that you would like to fetch all results at this point. An example of this is below.
A complete example of a SELECT query would look like (to catch insert errors, we suggest adding a try catch block around this example):
use Simpletools\Db\Cassandra\Query;
$query = (new Query('TABLE'))
->client(new Client('CLIENT')) // optional - if this isn't set, it will use the default (set in the cluster config)
->keyspace('KEYSPACE') // optional - if this isn't set, it will use the default (set in the cluster config)
->columns('COLUMN_1','COLUMN_2','COLUMN_3')
->where('COLUMN_1','VALUE_1')
->also('COLUMN_2','IN',['VALUE_2','VALUE_3','VALUE_4']);
echo "Query: " . $query->getQuery();
/* Output: SELECT COLUMN_1,COLUMN_2 FROM KEYSPACE.TABLE WHERE COLUMN_1 = VALUE_1 AND COLUMN_2 IN ('VALUE_2','VALUE_3','VALUE_4') */
if ($query->length())
foreach ($query as $row)
echo json_encode($row) . "\n";
/* Output: {"COLUMN_1":"VALUE_1","COLUMN_2":"VALUE_2"} */
/* Output: {"COLUMN_1":"VALUE_1","COLUMN_2":"VALUE_3"} */
/* Output: {"COLUMN_1":"VALUE_1","COLUMN_2":"VALUE_4"} */
INSERT
To insert you first need to create a new instance of \Simpletools\Db\Cassandra\Query:
use Simpletools\Db\Cassandra\Query;
$query = (new Query('TABLE'))
->client(new Client('CLIENT')) // optional - if this isn't set, it will use the default (set in the cluster config)
->keyspace('KEYSPACE') // optional - if this isn't set, it will use the default (set in the cluster config)
Use the set() method to specify the data for the insert:
$query->set([
'COLUMN_1'=>'VALUE_1',
'COLUMN_2'=>'VALUE_2',
'COLUMN_3'=>'VALUE_3'
]);
If you only want to insert, if the row doesn't already exist, use insertIgnore() instead of set():
$query->insertIgnore([
'COLUMN_1'=>'VALUE_1',
'COLUMN_2'=>'VALUE_2',
'COLUMN_3'=>'VALUE_3'
]);
To use Cassandra's built in expiry feature, simply use expires():
$query->expires(10); // specify expiry time in seconds
To view the generated query while developing or to store in logs use:
echo $query->getQuery();
/* Output: INSERT INTO KEYSPACE.TABLE (COLUMN_1,COLUMN_2,COLUMN_3) VALUES("VALUE_1","VALUE_2","VALUE_3") */
Once you are ready, you can then run the query with run():
$query->run();
A complete example of a INSERT query would look like (to catch insert errors, we suggest adding a try catch block around this example):
use Simpletools\Db\Cassandra\Query;
$query = (new Query('TABLE'))
->client(new Client('CLIENT')) // optional - if this isn't set, it will use the default (set in the cluster config)
->keyspace('KEYSPACE') // optional - if this isn't set, it will use the default (set in the cluster config)
->set([
'COLUMN_1'=>'VALUE_1',
'COLUMN_2'=>'VALUE_2',
'COLUMN_3'=>'VALUE_3'
])
->run();
UPDATE
To update you first need to create a new instance of \Simpletools\Db\Cassandra\Query:
use Simpletools\Db\Cassandra\Query;
$query = (new Query('TABLE'))
->client(new Client('CLIENT')) // optional - if this isn't set, it will use the default (set in the cluster config)
->keyspace('KEYSPACE') // optional - if this isn't set, it will use the default (set in the cluster config)
Use the update() method to specify columns/data to update:
$query->update([
'COLUMN_1'=>'VALUE_1',
'COLUMN_3'=>'VALUE_3'
]);
To view the generated query while developing or to store in logs use:
echo $query->getQuery();
/* Output: INSERT INTO KEYSPACE.TABLE (COLUMN_1,COLUMN_2,COLUMN_3) VALUES("VALUE_1","VALUE_2","VALUE_3") */
Once you are ready, you can then run the query with run():
$query->run();
A complete example of a UPDATE query would look like (to catch insert errors, we suggest adding a try catch block around this example):
use Simpletools\Db\Cassandra\Query;
$query = (new Query('TABLE'))
->client(new Client('CLIENT')) // optional - if this isn't set, it will use the default (set in the cluster config)
->keyspace('KEYSPACE') // optional - if this isn't set, it will use the default (set in the cluster config)
->update([
'COLUMN_1' => 'VALUE_1',
'COLUMN_3' => 'VALUE_3'
])
->run();
DELETE
To update you first need to create a new instance of \Simpletools\Db\Cassandra\Query:
use Simpletools\Db\Cassandra\Query;
$query = (new Query('TABLE'))
->client(new Client('CLIENT')) // optional - if this isn't set, it will use the default (set in the cluster config)
->keyspace('KEYSPACE') // optional - if this isn't set, it will use the default (set in the cluster config)
Use the delete() method to specify columns/data to update:
$query->delete('COLUMN_1', 'VALUE_1');
To view the generated query while developing or to store in logs use:
echo $query->getQuery();
/* Output: DELETE FROM KEYSPACE.TABLE WHERE COLUMN_1='VALUE_1' */
Once you are ready, you can then run the query with run():
$query->run();
A complete example of a DELETE query would look like (to catch insert errors, we suggest adding a try catch block around this example):
use Simpletools\Db\Cassandra\Query;
$query = (new Query('TABLE'))
->client(new Client('CLIENT')) // optional - if this isn't set, it will use the default (set in the cluster config)
->keyspace('KEYSPACE') // optional - if this isn't set, it will use the default (set in the cluster config)
->delete('COLUMN_1', 'VALUE_1')
->run();
\Simpletools\Db\Cassandra\Query for \Simpletools\Mvc
Great news! If you are using Simpletools\Mvc alongside Simpletools\Db\Cassandra, our \Query examples located above can be simplified for usage inside models.
If you are using \Query inside a \Mvc\Model, you simply need to extend your model class:
use Simpletools\Db\Cassandra\Model;
/*
* OPTIONAL SETTINGS:
* these can be changed on a model basis
* if these arn't defined here, default cluster settings will be used
*/
const CLUSTER = "CLUSTER_NAME";
const KEYSPACE = "KEYSPACE_NAME";
class Example extends Model
...
SELECT
An example SELECT statement would be ("TABLE_NAME" should be replaced with the name of the table that you want to query):
$query = $this->TABLE_NAME('COLUMN_1','COLUMN_2','COLUMN_3')
->where('COLUMN','VALUE');
$result = $query->fetchAll();
...
INSERT
An example INSERT statement would be ("TABLE_NAME" should be replaced with the name of the table that you want to query):
$this->TABLE_NAME()
->set([
'COLUMN_1' => 'VALUE_1',
'COLUMN_2' => 'VALUE_2',
'COLUMN_3' => 'VALUE_3',
])
->run();
UPDATE
An example UPDATE statement would be ("TABLE_NAME" should be replaced with the name of the table that you want to query):
$this->TABLE_NAME()
->update([
'COLUMN_1' => 'VALUE_1',
'COLUMN_3' => 'VALUE_3'
])
->run();
DELETE
An example DELETE statement would be ("TABLE_NAME" should be replaced with the name of the table that you want to query):
$this->TABLE_NAME()
->delete('COLUMN','VALUE')
->run();
\Simpletools\Db\Cassandra\Type\Uuid
Simpletools\Db\Cassandra\Type\Uuid can be used to generate and prepare UUID's for use in queries.
PLEASE NOTE: query examples below, assume you are using \Simpletools\Mvc and the code is being used inside of a \Simpletools\Mvc\Model. However, the \Uuid plugin can be used in the same way, when using the plugin as a standalone class.
To generate a new UUID, you first need to create a new instance of \Simpletools\Db\Cassandra\Type\Uuid:
use Simpletools\Db\Cassandra\Type\Uuid;
$uuid = new Uuid());
echo $uuid;
/* Output: 971aa1e4-8395-11e9-bc42-526af7764f64 */
The generated UUID can then be used in an INSERT query:
$this->TABLE_NAME()
->set([
'COLUMN_1' => $uuid,
'COLUMN_2' => 'VALUE_2',
'COLUMN_3' => 'VALUE_3',
])
->run();
\Simpletools\Db\Cassandra\Type\AutoIncrement
Simpletools\Db\Cassandra\Type\AutoIncrement can be used to generate and maintain auto increment id's for use in queries. Unfortunately, there is no built in Cassandra method to do this for you. Therefore, we have put together a plugin to handle this for you.
Setup
To start using \Simpletools\Db\Cassandra\Type\AutoIncrement, you first need to setup a table in cassandra. This table is used to store the counters. This can be done very easily by using the following built-in setup() method. This only needs to be run once, and should be removed from your code, after the tables have been created for you.
use Simpletools\Db\Cassandra\Type\AutoIncrement;
AutoIncrement::settings([
'cluster' => 'CLUSTER_NAME',
'keyspace' => 'KEYSPACE',
'table' => 'counter_table'
]);
AutoIncrement::setup();
Usage
PLEASE NOTE: \AutoIncrement returns id's as BigInt's. Therefore, you need to ensure that the column data type in Cassandra is set to "BIGINT". Alternatively, you can cast the returned id, to a standard integer, before you use it in \Query.
use Simpletools\Db\Cassandra\Type\AutoIncrement;
AutoIncrement::settings([
'cluster' => 'CLUSTER_NAME',
'keyspace' => 'KEYSPACE',
'table' => 'counter_table'
]);
$id = new AutoIncrement('COUNTER_NAME'));
echo $id->value();
/* Output: 1 */
PLEASE NOTE: query examples below, assume you are using \Simpletools\Mvc and the code is being used inside of a \Simpletools\Mvc\Model. However, the \AutoIncrement plugin can be used in the same way, when using the plugin as a standalone class.
The generated id can then be used in an INSERT query:
$this->TABLE_NAME()
->set([
'COLUMN_1' => $id,
'COLUMN_2' => 'VALUE_2',
'COLUMN_3' => 'VALUE_3',
])
->run();
\Simpletools\Db\Cassandra\Batch
Simpletools\Db\Cassandra\Batch can be used to combine multiple write queries, to be run in order. If one or more queries in the batch are invalid or fail to run, none of the queries will be run. This is useful in cases where you need to ensure a query has been run successfully, before going on the run the following query.
An example batch query would be:
use Simpletools\Db\Cassandra\Query;
$batch = new Simpletools\Db\Cassandra\Batch();
$b = $batch
->add(
(new Query('user'))->set([
'COLUMN_1' => 'VALUE_1',
'COLUMN_2' => 'VALUE_2'
])
)
->add(
(new Query('user'))->delete('COLUMN_1','VALUE_1')
);
echo $b->getQuery();
OR
$b->run();


| Score Table | ||
| Won | Lost | Tied |
| 0 | 0 | 0 |
Is a zero-sum hand game usually played between two people, in which each player simultaneously forms one of three shapes with an outstretched hand. These shapes are "rock" (a simple fist), "paper" (a flat hand), and "scissors" (a fist with the index and middle fingers together forming a V). The game has only three possible outcomes other than a tie: a player who decides to play rock will beat another player who has chosen scissors ("rock crushes scissors") but will lose to one who has played paper ("paper covers rock"); a play of paper will lose to a play of scissors ("scissors cut paper"). If both players throw the same shape, the game is tied and is usually immediately replayed to break the tie.
Rock is a naturally occurring solid aggregate of one or more minerals or mineraloids. For example, the common rock granite is a combination of the quartz, feldspar and biotite minerals. The Earth's outer solid layer, the lithosphere, is made of rock.
Paper is a thin material produced by pressing together moist fibres of cellulose pulp derived from wood, rags or grasses, and drying them into flexible sheets. It is a versatile material with many uses, including writing, printing, packaging, cleaning, and a number of industrial and construction processes.


2 Samuel 22:40
For thou hast girded me with strength to battle: them that rose up against me hast thou subdued under me.


Before Change
// We only document __init__.py files... except for these special cases.
// Use the unix full pathname from the root of the checked out repo
explicit_wanted_files = [
"spynnaker/gsyn_tools.py",
"spynnaker/spike_checker.py",
"spynnaker/plot_utils.py",
"spynnaker/pyNN/abstract_spinnaker_common.py",
"spynnaker/pyNN/exceptions.py",
"spynnaker/pyNN/spynnaker_simulator_interface.py",
"spynnaker/pyNN/spynnaker_external_device_plugin_manager.py",
"spynnaker/pyNN/models/abstract_pynn_model.py",
"spynnaker/pyNN/models/projection.py",
"spynnaker/pyNN/models/defaults.py",
"spynnaker/pyNN/models/recorder.py",
"spynnaker/pyNN/models/neuron/key_space_tracker.py",
"spynnaker/pyNN/models/neuron/synaptic_matrices.py",
"spynnaker/pyNN/models/neuron/master_pop_table.py",
"spynnaker/pyNN/models/neuron/synaptic_matrix.py",
"spynnaker/pyNN/models/neuron/synapse_io.py",
"spynnaker/pyNN/models/neuron/synaptic_matrix_app.py",
"spynnaker/pyNN/models/neuron/plasticity/stdp/common.py",
"spynnaker/pyNN/models/spike_source/spike_source_array_vertex.py",
"spynnaker/pyNN/models/spike_source/spike_source_poisson_vertex.py",
"spynnaker/pyNN/models/spike_source/spike_source_poisson_machine_vertex.py",
"spynnaker/pyNN/models/common/recording_utils.py",
"spynnaker/pyNN/utilities/bit_field_utilities.py",
"spynnaker/pyNN/utilities/spynnaker_failed_state.py",
"spynnaker/pyNN/utilities/constants.py",
"spynnaker/pyNN/utilities/data_cache.py",
"spynnaker/pyNN/utilities/extracted_data.py",
"spynnaker/pyNN/utilities/fake_HBP_Portal_machine_provider.py",
"spynnaker/pyNN/utilities/running_stats.py",
"spynnaker/pyNN/utilities/utility_calls.py",
"spynnaker/pyNN/utilities/struct.py",
"spynnaker/pyNN/utilities/variable_cache.py",
"spynnaker8/spynnaker8_simulator_interface.py",
"spynnaker8/spynnaker_plotting.py",
"spynnaker8/utilities/neo_convertor.py",
"spynnaker8/utilities/neo_compare.py"]
options = ["-o", output_dir, ".",
// Exclude test and setup code
"p8_integration_tests/*", "unittests/*", "setup.py"]
options.extend(filtered_files("spynnaker", explicit_wanted_files))
options.extend(filtered_files("spynnaker8", explicit_wanted_files))
apidoc.main(options)
After Change
fl not in ("index.rst", "modules.rst")):
os.remove(fl)
os.chdir("../..") // WARNING! RELATIVE FILENAMES CHANGE MEANING HERE!
apidoc.main([
"-o", _output_dir, ".",
// Exclude test and setup code
"p8_integration_tests/*", "unittests/*", "setup.py",
*filtered_files("spynnaker", _unfiltered_files),
*filtered_files("spynnaker8", _unfiltered_files)])


This is supposed to be title. This is supposed to be title.This is supposed to be title.This is supposed to be title.This is supposed to be title.
Description
This font should occupy line width equal to 9 sms. This font should occupy line width equal to 9 sms. This font should occupy line width equal to 9 sms. This font should occupy line width equal to 9 sms. This font should occupy line width equal to 9 sms. This font should occupy line width equal to 9 sms. This font should occupy line width equal to 9 sms.
Information
| Status | Open |
| Severity | Open |
| Priority | Open |
| Resolution | Open |
| Team | Open |
| Board | Open |
Comments
* This is comment a sample comment
* This is comment a sample comment
* This is comment a sample comment
* This is comment a sample comment
This font should occopy line width equal to 3 sms. This font should occopy line width equal to 3 sms. This font should occopy line width equal to 3 sms. This font should occopy line width equal to 3 sms. This font should occopy line width equal to 3 sms. This font should occopy line width equal to 3 sms.
this is watchers list


Films I've Watched In January 2016
Ordered as per my memory!
- V for Vendetta
- Cartel Land
- Howl
- Silence of The Lambs
- Sicario
- Star Wars 7
- Legend
- Mad Max: Fury Road
Silence of the Lambs easily comes out top and I’ll be the millionth person to say that the acting and story is captivating. If you’ve not seen it, put it high on your list of things to watch.
Mad Max is the most over rated. Despite the various internet list raving about it the non-stop action and lack of real character development just didn’t click with me. The fact that they ended up turning around on the road just to go back again felt like a bit of a cop out.
Legend was another underwhelming occurrence. I felt like it was trying to appeal to the separate male and female audiences in tandem and not really connecting with either. Whilst the technical effect of having Tom Hardy play both roles was interesting, it wasn’t enough to salvage the story. In the end it was the worst of both a love story and a gangster film.
Similarly, Sicario started off as a cop/military film before throwing that all out the window to become a revenge film. The first half is intense and lets you develop a real distrust of the authority figures, but this eventually fizzles out as the film moves onto the revenge part before ending with either questions for the audience to think about themselves but they actually feel like plot holes. Still, it peaked my interest in the Mexican Cartel story, hence I gave Cartel Land a watch. It’s an alright documentary, but less about the Cartels and more about the resistance against them.
My only trip to the cinema in January was to beat the crowds and finally see the behemoth that is the new Star Wars. It does everything right but it doesn’t push any boundaries. The more I think of it, the more I realise that it is basically a HD remake of A New Hope. But still, I look forward to number 8 and see whether that is a bit more adventurous.
Finally, Howl, your classic lower budget horror film. It ticks the boxes and provides enough core and creature shots to leave you feeling satisfied. Watch it as an appetiser to Dog Soldiers.


Fossil Threads in the Web of Life
What's 75 million years old and brand spanking new? A teenage Utahceratops! Come to the Saroyan, armed with your best dinosaur roar, when Scott Sampson, Research Curator at the Utah Museum of Natural History, steps to the podium. Sampson's research has focused on the ecology and evolution of late Cretaceous dinosaurs and he has conducted fieldwork in a number of countries in Africa.
Scott Sampson is a Canadian-born paleontologist who received his Ph.D. in zoology from the University of Toronto. His doctoral work focused on two new species of ceratopsids, or horned dinosaurs, from the Late Cretaceous of Montana, as well as the growth and function of certopsid horns and frills.
Following graduation in 1993, Sampson spent a year working at the American Museum of Natual History in New York City, followed by five years as assistant professor of anatomy at the New York College of Osteopathic Medicine on Long Island. He arrived at the University of Utah accepting a dual position as assistant professor in the Department of Geology and Geophysics and curator of vertebrate paleontology at the Utah Museum of Natural History. His research interests largely revolve around the phylogenetics, functional morphology, and evolution of Late Cretaceous dinosaurs.
In addition to his museum and laboratory-based studies, Sampson has conducted paleontological work in Zimbabwe, South Africa, and Madagascar, as well as the United States and Canada. He was also the on-the-air host for the Discovery Channel's Dinosaur Planet and recently completed a book, Dinosaur Odyssey: Fossil Threads in the Web of Life, which is one of the most comprehensive surveys of dinosaurs and their worlds to date.


This is a small virtual machine, used by Pico Labs to host node pico engines.
You may use pico engines on this site, at your own risk. Any picos you create may be removed without notice.
We strongly suggest that you set up your own pico engine. It's easy and free.


Dear {{ adminuser.username }},
{{user.username}} has just registered in Club Manager.
Please login to Club Manager and edit their profile:-
> Tick the "Registered User" box
Once payment confirmation has been received
> tick the "Active Member" box
> Specify the "Member Expires" date (often this is 31 December of the current year)
Sincerely,
The Club Manager 2020 Team


El Salvador questions child molestation ruling
by EndPlaySAN SALVADOR, El Salvador (AP) - Church and civic groups in El Salvador are expressing outrage after a court reduced a sexual abuse charge against a judge to a misdemeanor.
At issue is the case of a judge who allegedly touched a 10-year-old girl's genitals through her clothes on a street in San Salvador in February.
Prosecutors charged him with sexual abuse, which could carry a prison sentence of 8 to 12 years.
However, justices of the First Criminal Tribunal ruled that brief touching of a clothed person in a public space constituted a violation of public decency statutes, punishable by a fine.
Legislators said Thursday they are preparing to change the law to specify such touching of a child would be subject to prison terms similar to sex abuse.


--- faq: - question: Who is this website for? answer: Jekyll is a simple, blog-aware, static site generator. It takes a template directory containing raw text files in various formats, runs it through a converter (like Markdown) and our Liquid renderer, and spits out a complete, ready-to-publish static website suitable for serving with your favorite web server. - question: How to submit a support request? answer: The front matter is where Jekyll starts to get really cool. Any file that contains a YAML front matter block will be processed by Jekyll as a special file. The front matter must be the first thing in the file and must take the form of valid YAML set between triple-dashed lines - question: How to track your support ticket? answer: A static file is a file that does not contain any YAML front matter. These include images, PDFs, and other un-rendered content. ---


PLANET SICK-BOY: http://www.sick-boy.com "We Put the SIN in Cinema"
Director Katya Bankowsky began filming Shadow Boxers, a documentary about woman fighters, in 1995 when, after 68 years of being an all-male event, the prestigious New York Golden Gloves tournament finally allowed women to compete. Much to the astonishment of the male-dominated sport, the women turned boxing on its ear by proving girls can be just as ferocious as boys in the ring.
Boxers concentrates on a former kickboxing champion from Holland named Lucia Rijker. She describes the sport as addictive and all-consuming, telling stories about other fighters who abandoned their men and their children to pursue success between the ropes. Beautiful and well spoken (at least for now), we see Rijker gain both confidence and credibility with each opponent she faces.
There is another fighter who gets some quality screen time toward the beginning of Boxers. Jill `Flyin' Lion' Matthews, a diminutive, smart-mouthed (at least for now) boxer who turns pro after winning her division of the Golden Gloves, facetiously asks if her makeup is messed up when she steps out of the ring. When a reporter asks how she prepares for her fights, she quickly snaps back, `With a manicure and a pedicure.'
But the real draw to Boxers is the way Bankowsky pieces together the boxing scenes. They're elegant and hypnotic, playing out in slow motion over a hip-hop soundtrack. Bankowsky also switches between color stock and grainy black-and-white film, which only adds to the film's dream-like quality.
Bankowsky also edited and produced Boxers, which starts to drag a bit toward the end, when the film isn't concentrating on boxing. The only other knock I can give the film is the repetitive score provided by DJ Zoël, a former MTV Latino VJ. Boxers isn't a first-round knockout, but it's probably a second-round TKO. Even if you dislike the sport, you'll still find the film enjoyable and entertaining.
1:12 – Not Rated but contains some bloody boxing scenes and adult language
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


| Home | Trees | Indices | Help |
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object --+ | StructureBuilder
Builder class for the mmLib.Structure object hierarchy. StructureBuilder must be subclassed with a working parse_format() method to implement a working builder.
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Inherited from |
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Inherited from |
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x.__init__(...) initializes x; see x.__class__.__doc__ for signature
|
This methods needs to be reimplemented in a functional subclass. This function is called with the file object (or any other object passed in to build a Structure from) to begin the reading process. This is usually used to open the source file. |
Called after the read_start method. Does nothing currently, but may be used in the future. |
This method needs to be reimplemented in a functional subclass. The subclassed read_atoms method should call load_atom once for every atom in the structure, and should not call any other load_* methods. |
Called repeatedly by the implementation of read_atoms to load all the data for a single atom. The data is contained in the atm_map argument, and is not well documented at this point. Look at this function and you'll figure it out. |
Runs the name service on all atoms needing to be named. This is a complicated function which corrects most commonly found errors and omissions from PDB files. |
This method needs to be reimplemented in a functional subclass. The subclassed read_metadata method should call the various load_* methods to set non-atom coordinate data for the Structure. |
Call by the implementation of load_metadata to load bond information on the structure. The keys of the bond map are a 2-tuple of the bonded Atom instances, and the value is a dictionary containing information on the type of bond, which may also be a symmetry operator. [bond_map] keys: (atm1, atm2) values: bond_data_map(s) [bond_data_map] bond_type -> text description of bond type: covalent, salt bridge, hydrogen, cispeptide atm1_symop -> symmetry operation (if any) to be applied to atm1 atm2_symop -> same as above, for atom 2 The symmetry operations themselves are a 3x4 array of floating point values composed of the 3x3 rotation matrix and the 3x1 translation. |
The argument helix_list is a list of Python dictionaries with information to build build AlphaHelix objects into the Structure. The dictionary has attributes: helix_id: The ID of the helix chain_id: The chain_id where the helix is located frag_id1: The start fragment_id of the helix frag_id2: The end fragment_id of the helix helix_class: The PDB helix class number detaisl: Text commont about the helix |
The argument beta_sheet_list is a list of Python dictionaries with information to build build BetaSheet objects into the Structure. The dictionary has attributes: sheet_id: ID of the sheet num_strands: total number of strands in the beta sheet strand_list: list of dictionaries describing the strand with the following attributes: chain_id1/frag_id1: chain_id and fragment_id of inital residue in the strand chain_id2/frag_id2: chain_id and fragment_id of end residue in the strand sense: the sense of the strand with respect to the previous strand, either the string parallel or anti_parallel reg_chain_id, reg_frag_id, reg_atom: registration atom in current strand reg_prev_chain_id, reg_prev_frag_id, reg_prev_atom: registration atom in previous strand |
This method needs to be reimplemented in a functional subclass. The subclassed read_end method can be used for any clean up from the file loading process you need, or may be left unimplemented. |
Called for final cleanup after structure source reading is done. Currently, this method does nothing but may be used in future versions. |
| Home | Trees | Indices | Help |
|
|---|
| Generated by Epydoc 3.0.1 on Mon Jun 27 14:43:16 2011 | http://epydoc.sourceforge.net |


--- ---
A message describing the rejection
Indicates if the transition was redirected.
When a transition is redirected, the rejection type will be RejectType.SUPERSEDED and this flag will be true.
The type of the rejection.
This value is an number representing the type of transition rejection. If using Typescript, this is a Typescript enum.
2
)3
)4
)5
)6
)Returns a Rejection due to aborted transition
Returns a Rejection due to aborted transition
Returns a Rejection due to ignored transition
Returns a Rejection due to invalid transition
Returns true if the obj is a rejected promise created from the asPromise
factory
Returns a Rejection
Normalizes a value as a Rejection. If the value is already a Rejection, returns it. Otherwise, wraps and returns the value as a Rejection (Rejection type: ERROR).
detail
if it is already a Rejection
, else returns an ERROR Rejection.
Returns a Rejection due to redirected transition
Returns a Rejection due to transition superseded
Generated using TypeDoc
A detail object
This value varies based on the mechanism for rejecting the transition. For example, if an error was thrown from a hook, the
detail
will be theError
object. If a hook returned a rejected promise, thedetail
will be the rejected value.


--- layout: successCriteria title: "Section Headings" successCriteria: "2-4-10" successCriteriaTitle: "Section Headings" technology: "alt1" ---
2.4.10 Section Headings: Section headings organize the words and images on a webpage. (Level AAA)
Note 1: "Headings" include titles and subtitles.
Note 2: This success criterion covers words and images, but not user interface components such as form fields, menus and checkboxes. Success Criterion 4.1.2 covers those.


Enumeration members
NewUserRegisteredMail
NewUserRegisteredMail: = "NewUserRegisteredMail"
NotificationMail
NotificationMail: = "NotificationMail"
NotificationMailItem
NotificationMailItem: = "NotificationMailItem"
ShareMail
ShareMail: = "ShareMail"
ShareNotFoundPage
ShareNotFoundPage: = "ShareNotFoundPage"
SharePage
SharePage: = "SharePage"
UserEmailConflictSolvedMail
UserEmailConflictSolvedMail: = "UserEmailConflictSolvedMail"
UserInvitationMail
UserInvitationMail: = "UserInvitationMail"
UserLockedMail
UserLockedMail: = "UserLockedMail"
UserReviewedMail
UserReviewedMail: = "UserReviewedMail"
UserTriggeredDeactivationRequestMail
UserTriggeredDeactivationRequestMail: = "UserTriggeredDeactivationRequestMail"
UserUnlockedMail
UserUnlockedMail: = "UserUnlockedMail"


Chapter 2
Hello {{ message }}
Enter your name below
Chapter 3
v-show
You must send a message for help!
Send word to allies for help!
{{ $data }}
Chapter 4
v-for
ranging v-for: v-for is "$value in $array"
(opposite of php)
v-for is for loop. Multiplication table of 4:
{{ i-1 }} times 4 equal {{ (i-1) * 4 }}


Rani Sentana Friend of the Bride Maid of Honor
Amanda Bytheway Sister of the Bride Bridesmaid
Ruthanna Daines Sister of the Bride Bridesmaid
Zahra Garrett Childhood friend of the Bride Bridesmaid
Molly & Anna Bytheway Nieces of the Bride Flower Girls
Tim Doyle Childhood friend of the Groom Best Man
Michael New Brother of the Groom Groomsman
Eric Brelsford College friend of the Groom Groomsman
Ben Flasher Friend of the Groom Groomsman
Nate Hall Brother of the Bride Groomsman


Storage Peripherals
Storage
Storage refers to holding information somewhere. RAM, Random Access Memory, is short-term
memory. It is volatile memory because the memory is automatically "erased" when the power is turned
interrupted. The RAM memory is located inside the computer case on the motherboard. A motherboard is
not the keyboard. The keyboard is what you type with. A motherboard holds RAM memory, electronic circuits
and other computer parts including the CPU. ROM , Read-Only-Memory, is not volatile meaning the memory is
still there when power is interrupted or turned off. When the computer is turned back on again, ROM memory
is still in storage on the internal hard disk.
| ROLL | NAME | |||||||||
| SEMESTER-I | SEMESTER-II | TOTAL | ||||||||
| S11 | S12 | S13 | S21 | S22 | S23 | |||||
| 01 | S BASU | 33 | 44 | 47 | 41 | 36 | 40 | 241 | ||
| 02 | R JAIN | ABSENT | 40 | 30 | 40 | 110 | ||||
| 03 | D MUKHERJEE | 34 | 32 | 34 | 42 | 29 | 31 | 202 | ||
| 04 | G FRANCIS | 32 | 42 | 20 | ABSENT | 94 | ||||
| 05 | B SHAH | 22 | 33 | 44 | 41 | 32 | 21 | 193 | ||
| 06 | P GUHA | 34 | 41 | 34 | 31 | 26 | 31 | 197 |


Carbon penalties
Sir, – I completely agree with Brian Falter (Letters, November 21st) about the environmental impact of international football.
Sadly, judging by the Irish Times article, “Slovakia v Ireland: Cheap routes, ticket info and the price of a pint”, our breath is being wasted. Nowhere in this article does it mention the serious carbon footprint from flying.
Can I suggest The Irish Times might in future call attention to the environmental impact of options when recommending ways of getting to international sporting events?
I am sure the best fans in the world will be only too delighted to see the climate crisis not having to be decided on penalties for once. – Yours, etc,
ULTAN Ó BROIN,
Florence, Italy.


ZoomCharts FacetChart integration example with MongoDB and PHP
In this example we are showing you how ZoomCharts can fetch data interactively from MongoDB database through PHP middleware. See the source code here.
Click on each of the bar to drilldown. ZoomCharts then connects to MongoDB database to fetch requested data.
Please visit FacetChart documentation for more information.


A selection of my work
Utilizing HTML, CSS and Bootstrap in order to style a vacation website for the lovely state of Colorado. Key topics included utilizing Bootstrap components, as well as learning the power of tag-classes!
Check the code!
GitHubUtilizing HTML and CSS in order to show & tell about my cat, Hoot!
Check the code!
GitHubUtilizing HTML and CSS in order to share Led Zeppelin with the world!
Check the code!
GitHub© 2016 Miles Hewitson Productions


The scene graph nodes to use for this grabbable's appearance. Either this and volume or modelUri must be specified
If this canDropIntoContainers is true, the grabbable will drop itself into containers when appropriate. Otherwise it will return to its scene graph-specified transform when it is dropped.
Use this pose for the grabbing hand, and position the grabbable at the "grab point" of that pose.
Use this transform when this grabbable is being grabbed.
If this prop is true the grabbable will ignore all rotation other than yaw from its parent so that it is always upright.
Controls how child nodes are hidden. If showChildren is set to ShowGrabbableChildren.Always or if the grabbable has no children, this prop is ignored.
The unique-to-the-gadget id to use to link up this item with its counterparts in remote gadgets. This must be specified if the grabbable is using the NetworkedItem style.
Color to apply to the grab handle.
Uniform scale to apply to the grab handle.
The model to use for the grab handle of this grabbable. Either this prop or appearance and volume must be specified.
Called when the grabbable is dropped.
Called when the grabbable is grabbed.
If remoteInterfaceLocks is provided, the callback provided with this property is called whenever a message comes in from a remote instance of this gadget. If this gadget is the master, any events received from this callback will be from a remote gadget. If this gadget is remote, any events received from this callback will be from the master.
If this property is defined, the gadget will be shared with remote users that are in the same room as the owner. Any remote instance of this gadget will be started with provided initial interface locks.
Tells the standard grabbable when to show its children.
If this prop is specified, the user will be presented with a delete option when they activate the A menu while this gadget is highlighted. If a callback is provided, the callback will be called when the user selects delete. If true is provided, the gadget will be closed when the user selects delete.
Defines when the grabbable can be grabbed, and whether or not it counts as the topmost grabbable in the gadget.
The intersection volume to use in conjunction with appearance to determine when the user is intersecting the grabbable.
Generated using TypeDoc
Props for AvStandardGrabbable


A5 Typebook
$80
216mm L x 155mm W x 25mm H8.5" L x 6.1" W x 0.98" H
256 lined smartpages100gsmHard, casebound bonded leather coverElastic closureRibbon page marker


The following code shows one way to read an infrared remote control device (the sort used in TVs and stereo systems).
Here pin 1 to gpio7 (P1-26) via a 4K7 resistor, pin 2 to ground
(P1-14), and pin 3 to 5V (P1-2).
#include <stdio.h>
#include <pigpio.h>
#define IR_PIN 7
#define OUTSIDE_CODE 0
#define INSIDE_CODE 1
#define MIN_MESSAGE_GAP 3000
#define MAX_MESSAGE_END 3000
#define MAX_TRANSITIONS 500
/*
using the FNV-1a
hash
from
http://isthe.com/chongo/tech/comp/fnv/#FNV-param
*/
#define FNV_PRIME_32 16777619
#define FNV_BASIS_32<PHONE_NUMBER>U
static volatile uint32_t ir_hash = 0;
typedef struct
{
int state;
int count;
int level;
uint16_t micros[MAX_TRANSITIONS];
} decode_t;
/* forward declarations */
void alert(int gpio, int level, uint32_t
tick);
uint32_t getHash(decode_t * decode);
void updateState(decode_t * decode, int
level, uint32_t micros);
int main(int argc, char * argv[])
{
if (gpioInitialise()<0)
{
return 1 ;
}
/* IR pin as input */
gpioSetMode(IR_PIN, PI_INPUT);
/* 5ms max gap after last pulse */
gpioSetWatchdog(IR_PIN, 5);
/* monitor IR level changes */
gpioSetAlertFunc(IR_PIN, alert);
while (1)
{
if (ir_hash)
{
/* non-zero means
new decode */
printf("ir code is
%u\ ", ir_hash);
ir_hash = 0;
}
gpioDelay(100000); /* check remote
10 times per second */
}
gpioTerminate();
}
void alert(int gpio, int level, uint32_t tick)
{
static int inited = 0;
static decode_t activeHigh, activeLow;
static uint32_t lastTick;
uint32_t diffTick;
if (!inited)
{
inited = 1;
activeHigh.state = OUTSIDE_CODE;
activeHigh.level = PI_LOW;
activeLow.state =
OUTSIDE_CODE; activeLow.level = PI_HIGH;
lastTick = tick;
return;
}
diffTick = tick - lastTick;
if (level != PI_TIMEOUT) lastTick = tick;
updateState(&activeHigh, level, diffTick);
updateState(&activeLow, level, diffTick);
}
void updateState(decode_t * decode, int level, uint32_t micros)
{
/*
We are dealing with active high as
well as active low
remotes. Abstract the common
functionality.
*/
if (decode->state == OUTSIDE_CODE)
{
if (level == decode->level)
{
if (micros >
MIN_MESSAGE_GAP)
{
decode->state = INSIDE_CODE;
decode->count = 0;
}
}
}
else
{
if (micros > MAX_MESSAGE_END)
{
/* end of message
*/
/* ignore if last
code not consumed */
if (!ir_hash)
ir_hash = getHash(decode);
decode->state =
OUTSIDE_CODE;
}
else
{
if
(decode->count < (MAX_TRANSITIONS-1))
{
if (level != PI_TIMEOUT)
decode->micros[decode->count++] = micros;
}
}
}
}
int compare(unsigned int oldval, unsigned int newval)
{
if (newval < (oldval
* 0.75)) {return 1;}
else if (oldval < (newval * 0.75)) {return 2;}
else
{return 4;}
}
uint32_t getHash(decode_t * decode)
{
/* use FNV-1a */
uint32_t hash;
int i, value;
if (decode->count < 6) {return 0;}
hash = FNV_BASIS_32;
for (i=0; i<(decode->count-2); i++)
{
value =
compare(decode->micros[i], decode->micros[i+2]);
hash = hash ^ value;
hash = (hash * FNV_PRIME_32);
}
return hash;
}
cc -o ir_remote ir_remote.c -lpigpio -lrt
-lpthread
sudo ./ir_remote
A hash code is formed from the level transitions detected during a remote key press. This is likely to be unique over multiple remotes and keys.
While the program is running you can capture the waveform using the notification feature built in to pigpio. Issue the following commands on the Pi.
pigs no
pig2vcd </dev/pigpio0 >ir.vcd &
pigs nb 0 0x80 # set bits for gpios 7 (0x80)
Press a few different remotes and keys. Then enter
pigs nc 0
The file ir.vcd will contain the captured waveform, which can be viewed using GTKWave.
Overview
Remote A typical waveform
Remote B typical waveform


|
ESP32 HTTPS Server
|
The ResourceParameters provide access to the parameters passed in the URI. More...
#include <ResourceParameters.hpp>
Public Member Functions | |
| bool | isQueryParameterSet (std::string const &name) |
| Checks whether a specific HTTPS query parameter is set. More... | |
| bool | getQueryParameter (std::string const &name, std::string &value) |
| Returns an HTTP query parameter. More... | |
| std::vector< std::pair< std::string, std::string > >::iterator | beginQueryParameters () |
| Provides iterator access to the query parameters. More... | |
| std::vector< std::pair< std::string, std::string > >::iterator | endQueryParameters () |
| Counterpart to beginQueryParameters() for iterating over query parameters. | |
| size_t | getQueryParameterCount (bool unique=false) |
| Returns the number of query parameters. More... | |
| bool | getPathParameter (size_t const idx, std::string &value) |
| Checks for the existence of a path parameter and returns it as string. More... | |
| std::string | getPathParameter (size_t const idx) |
| Directly returns a path parameter. More... | |
Protected Member Functions | |
| void | setQueryParameter (std::string const &name, std::string const &value) |
| void | resetPathParameters () |
| void | setPathParameter (size_t idx, std::string const &val) |
Friends | |
| class | ResourceResolver |
The ResourceParameters provide access to the parameters passed in the URI.
There are two types of parameters: Path parameters and query parameters.
Path parameters are the values that fill the asterisk placeholders in the route definition of a ResourceNode.
Query parameters are the key-value pairs after a question mark which can be added to each request, either by specifying them manually or as result of submitting an HTML form with a GET as method property.
| std::vector< std::pair< std::string, std::string > >::iterator httpsserver::ResourceParameters::beginQueryParameters | ( | ) |
Provides iterator access to the query parameters.
Query parameters are key-value pairs that are appended to the URI after a question mark.
If you want just a specific parameter, have a look at getQueryParameter()
The iterator will yield pairs of std::string, of which the first value specifies the query parameter key and the second value corresponds to the query parameters value. If the entry is value-less, the second value will be the empty string.
If the same key is used multiple times in the query, the iterator will yield it multiple times, once for each occurence with the specific value.
| bool httpsserver::ResourceParameters::getPathParameter | ( | size_t const | idx, |
| std::string & | value | ||
| ) |
Checks for the existence of a path parameter and returns it as string.
Path parameters are defined by an asterisk as placeholder when specifying the path of the ResourceNode and addressed by an index starting at 0 for the first parameter.
For values of idx that have no matching placeholder, value is left unchanged and the method will return false.
| idx | Defines the index of the parameter to return, starting with 0. |
| value | The value is written into this parameter. |
| std::string httpsserver::ResourceParameters::getPathParameter | ( | size_t const | idx | ) |
Directly returns a path parameter.
Path parameters are defined by an asterisk as placeholder when specifying the path of the ResourceNode and addressed by an index starting at 0 for the first parameter.
This method will return the parameter specified by the index. The caller is responsible to assure that the index exists. Otherwise, an empty string will be returned.
| idx | Defines the index of the parameter to return, starting with 0. |
| bool httpsserver::ResourceParameters::getQueryParameter | ( | std::string const & | name, |
| std::string & | value | ||
| ) |
Returns an HTTP query parameter.
Query parameters are key-value pairs that are appended to the URI after a question mark.
The name parameter specifies the name of the query parameter to retrieve. If it is set, the value is written to the value parameter and true is returned. If the parameter does not exist, value is left unchanged and false is returned. If the parameter is used without a value, an empty string is written to value and true is returned.
| name | The name of the parameter to retrieve. If the parameter exists multiple times, the first occurence is used for the value. Use beginQueryParameters() to retrieve all values. |
| value | The target to write the value to, if the parameter exists. |
| size_t httpsserver::ResourceParameters::getQueryParameterCount | ( | bool | unique = false | ) |
Returns the number of query parameters.
Query parameters are key-value pairs that are appended to the URI after a question mark.
| unique | If true, return the number of unique keys (using the same key multiple times is counted only once). False by default, as checking for uniqueness is not efficient. |
| bool httpsserver::ResourceParameters::isQueryParameterSet | ( | std::string const & | name | ) |
Checks whether a specific HTTPS query parameter is set.
Query parameters are key-value pairs that are appended to the URI after a question mark.
If the key exists (either as a value-less parameter or with a value), the function returns true.
| name | The parameter to check |


{% extends "sharkdata_doc_base.html" %} {% block title %}SHARKdata - Documentation{% endblock%} {% block header %}Documentation{% endblock%} {% block doc-content %}
SHARKdata is a web service with a REST API to be used for accessing marine environmental monitoring data from the seas surrounding Sweden. SHARKdata also have a web site for documentation and administration where it is possible to access some parts of the data directly.
SHARKdata is build on the concept of datasets. A dataset is a collection of data with a unique identifier and connected metadata that describes the dataset. Is should be possible to assign a version stamp to a dataset. Metadata can, for example, be used by data catalogues like ECDS.
Requests via the REST API are made by URL:s, for example https://sharkdata.smhi.se/datasets/table.txt to get a text table containing all available datasets.
The response format will vary depending on client needs. Text and JSON formats are always available. Text files can always be opened in standard text editors or spreadsheet programs. JSON is easy to use if the client software is written in JavaScript or Python, but parsers are available for other languages as well. Other formats like KML, XML, WFS, etc. will be developed in cooperation with clients when needed.
SHARKdata is based on modules and new modules will be developed over time. The module Resources contains administrative data. It can contain files for header translations, screening of data, taxonomic information, etc.
SHARKdata is currently under development and only a limited number of datasets and response formats are available for test.
There is no full API description yet, but these examples can be a start. Click on the links below to test the API.
SHARKdata is written in Python 3 and based on the Django web framework. All code developed in the project is open source and published under the MIT license. The code repository can be found here: https://github.com/sharkdata/sharkdata. Please contact us by email to<EMAIL_ADDRESS>if you are developing a client application and want to be notified when we are planning changes to the API.
{% endblock doc-content%}


Name
HPL_sum Combine (sum) two buffers.
Synopsis
#include "hpl.h"
void
HPL_sum(
const int
N
,
const void *
IN
,
void *
INOUT
,
const HPL_T_TYPE
DTYPE
);
Description
HPL_sum
combines (sum) two buffers.
Arguments
N (input) const int
On entry, N specifies the length of the buffers to be
combined. N must be at least zero.
IN (input) const void *
On entry, IN points to the input-only buffer to be combined.
INOUT (input/output) void *
On entry, INOUT points to the input-output buffer to be
combined. On exit, the entries of this array contains the
combined results.
DTYPE (input) const HPL_T_TYPE
On entry, DTYPE specifies the type of the buffers operands.
See Also
HPL_broadcast,
HPL_reduce,
HPL_all_reduce,
HPL_barrier,
HPL_min,
HPL_max,
HPL_sum.


Itanic
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**Itanic**: n.
The Intel Itanium, so called in reference to the legendary disaster that
was the Titanic. This term bubbled up in several places on the Internet
in 1999 when it was beginning to become clear that the Itanium was
turning into the most expensive and protracted flop in the history of
the semiconductor industry.
--------------
+------------------------+----------------------------+------------------------+
| `Prev `__ | `Up <../I.html>`__ | `Next `__ |
+------------------------+----------------------------+------------------------+
| ISP | `Home <../index.html>`__ | ITS |
+------------------------+----------------------------+------------------------+


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Aequitas API¶
src.aequitas.group
¶
-
class
src.aequitas.group.
Group
[source]¶ -
get_crosstabs
(df, score_thresholds=None, model_id=1, attr_cols=None)[source]¶ Creates univariate groups and calculates group metrics.
- Parameters
df – a dataframe containing the following required columns [score, label_value].
score_thresholds – dictionary { ‘rank_abs’:[] , ‘rank_pct’:[], ‘score’:[] }
model_id – the model ID on which to subset the df.
attr_cols – optional, list of names of columns corresponding to group attributes (i.e., gender, age category, race, etc.).
- Returns
A dataframe of group score, label, and error statistics and absolute bias metric values grouped by unique attribute values
-
src.aequitas.bias
¶
-
class
src.aequitas.bias.
Bias
(key_columns=('model_id', 'score_threshold', 'attribute_name'), sample_df=None, non_attr_cols=('score', 'model_id', 'as_of_date', 'entity_id', 'rank_abs', 'rank_pct', 'id', 'label_value'), input_group_metrics=('ppr', 'pprev', 'precision', 'fdr', 'for', 'fpr', 'fnr', 'tpr', 'tnr', 'npv'), fill_divbyzero=None)[source]¶ -
get_disparity_major_group
(df, original_df, key_columns=None, input_group_metrics=None, fill_divbyzero=None, check_significance=None, alpha=0.05, mask_significance=True, label_score_ref='fpr')[source]¶ Calculates disparities between groups for the predefined list of group metrics using the majority group within each attribute as the reference group (denominator).
- Parameters
df – output dataframe of Group class get_crosstabs() method.
original_df – a dataframe of sample features and model results. Includes a required ‘score ‘column and possible ‘label_value’ column.
key_columns – optional, key identifying columns for grouping variables and bias metrics in intermediate joins. Defaults are ‘model_id’, ‘score_threshold’, ‘attribute_name’.
input_group_metrics – optional, columns list corresponding to the group metrics for which we want to calculate disparity values
fill_divbyzero – optional, fill value to use when divided by zero. Default is None.
check_significance – measures for which to determine statistical significance beyond label_value and score. Default is all metrics.
alpha – statistical significance level to use in significance determination. Default is 5e-2 (0.05).
mask_significance – whether to display a T/F mask over calculated p-values from statistical significance determination. Default is True.
label_score_ref – default reference group to use for score and label_value statistical significance calculations.
- Returns
A dataframe with same number of rows as the input (crosstab) with additional disparity metrics columns and ref_group_values for each metric.
-
get_disparity_min_metric
(df, original_df, key_columns=None, input_group_metrics=None, fill_divbyzero=None, check_significance=None, alpha=0.05, mask_significance=True, label_score_ref='fpr')[source]¶ Calculates disparities between groups for the predefined list of group metrics using the group with the minimum value for each absolute bias metric as the reference group (denominator).
- Parameters
df – output dataframe of Group class get_crosstabs() method.
original_df – a dataframe of sample features and model results. Includes a required ‘score ‘column and possible ‘label_value’ column.
key_columns – optional, key identifying columns for grouping variables and bias metrics in intermediate joins. Defaults are ‘model_id’, ‘score_threshold’, ‘attribute_name’.
input_group_metrics – optional, columns list corresponding to the group metrics for which we want to calculate disparity values
fill_divbyzero – optional, fill value to use when divided by zero. Default is None.
check_significance – measures for which to determine statistical significance beyond label_value and score. Default is all metrics.
alpha – statistical significance level to use in significance determination. Default is 5e-2 (0.05).
mask_significance – whether to display a T/F mask over calculated p-values from statistical significance determination. Default is True.
label_score_ref – default reference group to use for score and label_value statistical significance calculations.
- Returns
A dataframe with same number of rows as the input (crosstab) with additional disparity metrics columns and ref_group_values for each metric.
-
get_disparity_predefined_groups
(df, original_df, ref_groups_dict, key_columns=None, input_group_metrics=None, fill_divbyzero=None, check_significance=None, alpha=0.05, mask_significance=True)[source]¶ Calculates disparities between groups for the predefined list of group metrics using a predefined reference group (denominator) value for each attribute.
- Parameters
df – output dataframe of Group class get_crosstabs() method.
original_df – dataframe of sample features and model results. Includes a required ‘score ‘column and possible ‘label_value’ column.
ref_groups_dict – dictionary of format: {‘attribute_name’: ‘attribute_value’, …}
key_columns – optional, key identifying columns for grouping variables and bias metrics in intermediate joins. Defaults are ‘model_id’, ‘score_threshold’, ‘attribute_name’.
input_group_metrics – optional, columns list corresponding to the group metrics for which we want to calculate disparity values
fill_divbyzero – optional, fill value to use when divided by zero. Default is None.
check_significance – measures for which to determine statistical significance beyond label_value and score. Default is all metrics.
alpha – statistical significance level to use in significance determination. Default is 5e-2 (0.05).
mask_significance – whether to display a T/F mask over calculated p-values from statistical significance determination. Default is True.
- Returns
A dataframe with same number of rows as the input (crosstab) with additional disparity metrics columns and ref_group_values for each metric.
-
src.aequitas.fairness
¶
-
class
src.aequitas.fairness.
Fairness
(fair_eval=None, tau=None, fair_measures_depend=None, type_parity_depend=None, high_level_fairness_depend=None)[source]¶ -
get_fairness_measures_supported
(input_df)[source]¶ Determine fairness measures supported based on columns in data frame.
-
get_group_attribute_fairness
(group_value_df, fair_measures_requested=None)[source]¶ Determines whether the minimum value for each fairness measure in fair_measures_requested is ‘False’ across all attribute_values defined by a group attribute_name. If ‘False’ is present, determination for the attribute is False for given fairness measure.
- Parameters
group_value_df – output dataframe of get_group_value_fairness() method
- Returns
A dataframe of fairness measures at the attribute level (no attribute_values)
-
get_group_value_fairness
(bias_df, tau=None, fair_measures_requested=None)[source]¶ Calculates the fairness measures defined in fair_measures_requested dictionary and adds them as columns to the input bias_df.
- Parameters
bias_df – the output dataframe from bias/ disparity calculation methods.
tau – optional, the threshold for fair/ unfair evaluation.
fair_measures_requested – optional, a dictionary containing fairness measures as keys and the corresponding input bias disparity as values.
- Returns
Bias_df dataframe with additional columns for each of the fairness measures defined in the fair_measures dictionary
-
get_overall_fairness
(group_attribute_df)[source]¶ Calculates overall fairness regardless of the group_attributes. Searches for ‘False’ parity determinations across group_attributes and outputs ‘True’ determination if all group_attributes are fair.
- Parameters
group_attribute_df – the output df of the get_group_attributes_fairness
- Returns
A dictionary of overall, unsupervised, and supervised fairness determinations
-
src.aequitas.plotting
¶
-
class
src.aequitas.plotting.
Plot
(key_metrics=('pprev', 'ppr', 'fdr', 'for', 'fpr', 'fnr'), key_disparities=('pprev_disparity', 'ppr_disparity', 'fdr_disparity', 'for_disparity', 'fpr_disparity', 'fnr_disparity'))[source]¶ Plotting object allows for visualization of absolute group bias metrics and relative disparities calculated by Aequitas Group(), Bias(), and Fairness() class instances.
-
plot_disparity
(disparity_table, group_metric, attribute_name, color_mapping=None, ax=None, fig=None, label_dict=None, title=True, highlight_fairness=False, min_group_size=None, significance_alpha=0.05)[source]¶ Create treemap based on a single bias disparity metric across attribute groups.
Adapted from https://plot.ly/python/treemaps/, https://gist.github.com/gVallverdu/0b446d0061a785c808dbe79262a37eea, and https://fcpython.com/visualisation/python-treemaps-squarify-matplotlib
- Parameters
disparity_table – a disparity table. Output of bias.get_disparity or fairness.get_fairness function.
group_metric – the metric to plot. Must be a column in the disparity_table.
attribute_name – which attribute to plot group_metric across.
color_mapping – matplotlib colormapping for treemap value boxes.
ax – a matplotlib Axis. If not passed, a new figure will be created.
fig – a matplotlib Figure. If not passed, a new figure will be created.
label_dict – optional, dictionary of replacement labels for data. Default is None.
title – whether to include a title in visualizations. Default is True.
highlight_fairness – whether to highlight treemaps by disparity magnitude, or by related fairness determination.
min_group_size – minimum proportion of total group size (all data) a population group must meet in order to be included in bias metric visualization
significance_alpha – statistical significance level. Used to determine visual representation of significance (number of asterisks on treemap).
- Returns
A Matplotlib axis
-
plot_disparity_all
(data_table, attributes=None, metrics=None, fillzeros=True, title=True, label_dict=None, ncols=3, show_figure=True, min_group_size=None, significance_alpha=0.05)[source]¶ Plot multiple metrics at once from a fairness object table.
- Parameters
data_table – output of group.get_crosstabs, bias.get_disparity, or fairness.get_fairness functions.
attributes – which attribute(s) to plot metrics for. If this value is null, will plot metrics against all attributes.
metrics –
which metric(s) to plot, or ‘all.’ If this value is null, will plot:
Predicted Prevalence Disparity (pprev_disparity),
Predicted Positive Rate Disparity (ppr_disparity),
False Discovery Rate Disparity (fdr_disparity),
False Omission Rate Disparity (for_disparity),
False Positive Rate Disparity (fpr_disparity),
False Negative Rate Disparity (fnr_disparity)
fillzeros – whether to fill null values with zeros. Default is True.
title – whether to display a title on each plot. Default is True.
label_dict – optional dictionary of label replacements. Default is None.
show_figure – whether to show figure (plt.show()). Default is True.
min_group_size – minimum proportion of total group size (all data) a population group must meet in order to be included in metric visualization.
significance_alpha – statistical significance level. Used to determine visual representation of significance (number of asterisks on treemap).
- Returns
A Matplotlib figure
-
plot_fairness_disparity
(fairness_table, group_metric, attribute_name, ax=None, fig=None, title=True, min_group_size=None, significance_alpha=0.05)[source]¶ Plot disparity metrics colored based on calculated disparity.
- Parameters
group_metric – the metric to plot. Must be a column in the disparity_table.
attribute_name – which attribute to plot group_metric across.
ax – a matplotlib Axis. If not passed, a new figure will be created.
fig – a matplotlib Figure. If not passed, a new figure will be created.
title – whether to include a title in visualizations. Default is True.
min_group_size – minimum proportion of total group size (all data) a population group must meet in order to be included in bias metric visualization
significance_alpha – statistical significance level. Used to determine visual representation of significance (number of asterisks on treemap).
- Returns
A Matplotlib axis
-
plot_fairness_disparity_all
(fairness_table, attributes=None, metrics=None, fillzeros=True, title=True, label_dict=None, show_figure=True, min_group_size=None, significance_alpha=0.05)[source]¶ Plot multiple metrics at once from a fairness object table.
- Parameters
fairness_table – output of fairness.get_fairness functions.
attributes – which attribute(s) to plot metrics for. If this value is null, will plot metrics against all attributes.
metrics –
which metric(s) to plot, or ‘all.’ If this value is null, will plot:
Predicted Prevalence Disparity (pprev_disparity),
Predicted Positive Rate Disparity (ppr_disparity),
False Discovery Rate Disparity (fdr_disparity),
False Omission Rate Disparity (for_disparity),
False Positive Rate Disparity (fpr_disparity),
False Negative Rate Disparity (fnr_disparity)
fillzeros – whether to fill null values with zeros. Default is True.
title – whether to display a title on each plot. Default is True.
label_dict – optional dictionary of label replacements. Default is None.
show_figure – whether to show figure (plt.show()). Default is True.
min_group_size – minimum proportion of total group size (all data) a population group must meet in order to be included in fairness visualization
significance_alpha – statistical significance level. Used to determine visual representation of significance (number of asterisks on treemap)
- Returns
A Matplotlib figure
-
plot_fairness_group
(fairness_table, group_metric, ax=None, ax_lim=None, title=False, label_dict=None, min_group_size=None)[source]¶ This function plots absolute group metrics as indicated by the config file, colored based on calculated parity.
- Parameters
fairness_table – a fairness table. Output of fairness.get_fairness function.
group_metric – the fairness metric to plot. Must be a column in the fairness_table.
ax – a matplotlib Axis. If not passed a new figure will be created.
ax_lim – maximum value on x-axis, used to match axes across subplots when plotting multiple metrics. Default is None.
title – whether to include a title in visualizations. Default is True.
label_dict – optional dictionary of replacement values for data. Default is None.
min_group_size – minimum proportion of total group size (all data) a population group must meet in order to be included in fairness visualization
- Returns
A Matplotlib axis
-
plot_fairness_group_all
(fairness_table, metrics=None, fillzeros=True, ncols=3, title=True, label_dict=None, show_figure=True, min_group_size=None)[source]¶ Plot multiple metrics at once from a fairness object table.
- Parameters
fairness_table – output of fairness.get_fairness functions.
metrics –
which metric(s) to plot, or ‘all.’ If this value is null, will plot:
Predicted Prevalence (pprev),
Predicted Positive Rate (ppr),
False Discovery Rate (fdr),
False Omission Rate (for),
False Positive Rate (fpr),
False Negative Rate (fnr)
fillzeros – whether to fill null values with zeros. Default is True.
ncols – number of subplots per row in figure. Default is 3.
title – whether to display a title on each plot. Default is True.
label_dict – optional dictionary of label replacements. Default is None.
show_figure – whether to show figure (plt.show()). Default is True.
min_group_size – minimum proportion of total group size (all data). a population group must meet in order to be included in fairness visualization
- Returns
A Matplotlib figure
-
plot_group_metric
(group_table, group_metric, ax=None, ax_lim=None, title=True, label_dict=None, min_group_size=None)[source]¶ Plot a single group metric across all attribute groups.
- Parameters
group_table – group table. Output of of group.get_crosstabs() or bias.get_disparity functions.
group_metric – the metric to plot. Must be a column in the group_table.
ax – a matplotlib Axis. If not passed, a new figure will be created.
title – whether to include a title in visualizations. Default is True.
label_dict – optional, dictionary of replacement labels for data. Default is None.
min_group_size – minimum size for groups to include in visualization (as a proportion of total sample)
- Returns
A Matplotlib axis
-
plot_group_metric_all
(data_table, metrics=None, fillzeros=True, ncols=3, title=True, label_dict=None, show_figure=True, min_group_size=None)[source]¶ Plot multiple metrics at once from a fairness object table.
- Parameters
data_table – output of group.get_crosstabs, bias.get_disparity, or fairness.get_fairness functions.
metrics –
which metric(s) to plot, or ‘all.’ If this value is null, will plot:
Predicted Prevalence (pprev),
Predicted Positive Rate (ppr),
False Discovery Rate (fdr),
False Omission Rate (for),
False Positive Rate (fpr),
False Negative Rate (fnr)
fillzeros – whether to fill null values with zeros. Default is True.
ncols – number of subplots per row in figure. Default is 3.
title – whether to display a title on each plot. Default is True.
label_dict – optional dictionary of label replacements. Default is None.
show_figure – whether to show figure (plt.show()). Default is True.
min_group_size – minimum proportion of total group size (all data) a population group must meet in order to be included in group metric visualization.
- Returns
A Matplotlib figure
-
-
src.aequitas.plotting.
assemble_ref_groups
(disparities_table, ref_group_flag='_ref_group_value', specific_measures=None, label_score_ref=None)[source]¶ Creates a dictionary of reference groups for each metric in a data_table.
- Parameters
disparities_table –
- a disparity table. Output of bias.get_disparity or
fairness.get_fairness functions
- param ref_group_flag
string indicating column indicates reference group flag value. Default is ‘_ref_group_value’.
- param specific_measures
Limits reference dictionary to only specified metrics in a data table. Default is None.
- param label_score_ref
Defines a metric, ex: ‘fpr’ (false positive rate) from which to mimic reference group for label_value and score. Used for statistical significance calculations in Bias() class. Default is None.
- return
A dictionary
src.aequitas.preprocessing
¶
-
src.aequitas.preprocessing.
check_required_cols
(df, required_cols)[source]¶ - Parameters
df – A data frame of model results
required_cols – Column names required for selected fairness measures
- Returns
None, or ValueError
-
src.aequitas.preprocessing.
discretize
(df, target_cols)[source]¶ - Parameters
df – A data frame of model results
target_cols – Names of columns to discretize
- Returns
A data frame


Chapter 8 Regularization
8.1 Bias-variance tradeoff
To set the stage for the remainder of this note we will briefly revisit the bias-variance trade-off in this section. In particular we will illustrate the effect of varying the sample size \(n\). Readers familiar with this topic may choose to skip this section.
As as in Bishop (2006) we consider synthetic data generated by the sinusoidal function \(f(x)=\sin(2\pi x)\). To simulate random samples of \(\mathbf{y}\) we sample \(n\) input values from \(\mathbf{X} \sim \text{unif}(0,1)\) and introduce a random noise component \(\varepsilon \sim \mathcal{N}(0,0.3)\). Figure 8.1 shows \(\mathbf{y}\) along with random draws \(\mathbf{y}^*_n\).
Following Bishop (2006) we will use a Gaussian linear model with Gaussian kernels \(\exp(-\frac{(x_k-\mu_p)^{2}}{2s^2})\) as
\[ \begin{equation} \begin{aligned} && \mathbf{y}|\mathbf{X}& =f(x) \sim \mathcal{N} \left( \sum_{j=0}^{p-1} \phi_j(x)\beta_j, v \mathbb{I}_p \right) \\ \end{aligned} \tag{8.1} \end{equation} \]
with \(v=0.3\) to estimate \(\hat{\mathbf{y}}_k\) from random draws \(\mathbf{X}_k\). We fix the number of kernels \(p=24\) (and hence the number of features \(M=p+1=25\)) as well as the spatial scale \(s=0.1\). To vary the complexity of the model we use a form of regularized least-squares (Ridge regression) and let the regularization parameter \(\lambda\) vary
\[ \begin{equation} \begin{aligned} && \hat\beta&=(\lambda I + \Phi^T \Phi)^{-1}\Phi^Ty \\ \end{aligned} \tag{8.2} \end{equation} \]
where high values of \(\lambda\) in (8.2) shrink parameter values towards zero. (Note that a choice \(\lambda=0\) corresponds to the OLS estimator which is defined as long as \(p \le n\).)
As in Bishop (2006) we proceed as follows for each choice of \(\lambda\) and each sample draw to illustrate the bias-variance trade-off:
- Draw \(N=25\) time from \(\mathbf{u}_k \sim \text{unif}(0,1)\).
- Let \(\mathbf{X}_k^*=\mathbf{u}_k+\varepsilon_k\) with \(\varepsilon \sim \mathcal{N}(0, 0.3)\).
- Compute \(\mathbf{y}_k^*=\sin(2\pi \mathbf{X}^*_k)\).
- Extract features \(\Phi_k\) from \(\mathbf{X}_k^*\) and estimate the parameter vector \(\beta_k^*(\Phi_k,\mathbf{y}^*_k,\lambda)\) through regularized least-squares.
- Predict \(\hat{\mathbf{y}}_k^*=\Phi \beta_k^*\).
Applying the above procedure we can construct the familiar picture that demonstrates how increased model complexity increases variance while reducing bias (Figure 8.2). Recall that for the mean-squared error (MSE) we have
\[ \begin{equation} \begin{aligned} && \mathbb{E} \left( (\hat{f}_n(x)-f(x))^2 \right) &= \text{var} (\hat{f}_n(x)) + \left( \mathbb{E} \left( \hat{f}_n(x) \right) - f(x) \right)^2 \\ \end{aligned} \tag{2.1} \end{equation} \]
where the first term on the right-hand side corresponds to the variance of our prediction and the second term to its (squared) bias. In Figure 8.2 as model complexity increases the variance component of the MSE increases, while the bias term diminishes. A similar pattern would have been observed if instead of using regularization we had used OLS and let the number of Gaussian kernels (and hence the number of features \(p\)) vary where higher values of \(p\) correspond to increased model complexity.


Money Play. How should Red play 61?
pips Red=208 White=166
(CorrectAnswer= 0/0, ErrorScore= 0)
Hitting here is relatively pointless. Red should advance his anchor with 23/22 and then use the six to leap White’s four-point prime with 20/14.
Any other play is a blunder or worse and 14/7* is nearly a double blunder. Remember, have a plan and play to it unless the dice change the plan for you.
23/22, 20/14 gives Red a good 3-pt game and command of the outer boards. At the moment that is the best plan available.
Any other play is a blunder or worse and 14/7* is nearly a double blunder. Remember, have a plan and play to it unless the dice change the plan for you.
23/22, 20/14 gives Red a good 3-pt game and command of the outer boards. At the moment that is the best plan available.
1. Cubeful 2-ply 23/22 20/14 Eq.: -0.405 0.417 0.085 0.003 - 0.583 0.213 0.014 2. Cubeful 2-ply 23/22 14/8 Eq.: -0.478 (-0.074) 0.402 0.087 0.003 - 0.598 0.230 0.015 3. Cubeful 0-ply 24/23 20/14 Eq.: -0.559 (-0.154) 0.390 0.076 0.002 - 0.610 0.249 0.022 4. Cubeful 0-ply 14/7* Eq.: -0.624 (-0.220) 0.384 0.086 0.003 - 0.616 0.270 0.030 5. Cubeful 0-ply 20/13 Eq.: -0.652 (-0.247) 0.377 0.075 0.003 - 0.623 0.259 0.026


--- layout: post title: "No Title" ---
In an extraordinary feat of endurance the first round Wimbledon match between American John Isner and Frenchman Nicolas Mahut has gone to an epic 59 games each in the 5th set. At a match time of 10 hours, it’s already three and a half hours longer than the second longest tennis match ever played.
And it’s not over yet. Play was unfortunately suspended due to poor light early this morning and will resume on day 4 of competition tonight.


| DOSE: T+ 0:00 |
1/4 bowl |
smoked |
Cannabis |
(plant material) |
| T+ 0:14 |
1/3 bowl |
smoked |
Cannabis |
(plant material) |
| T+ 0:18 |
1/3 bowl |
smoked |
Cannabis |
(plant material) |
The following was the strangest, and most unexpected of my many experiences with Marijuana. Reflecting upon it, I still do not understand why this experience was so intense. The only thing I am truly sure off is that it changed my beliefs about Cannabis, and the people I knew.
Before I begin recounting the experience I will provide some Background information on myself and those present.
I - Male, age: 15, Weight 187lbs, Height: 6’1”, 2 years of experience with psychoactives. No prescriptions or Supplements.
Friend E- Female, Age: 17, Weight: 135lbs, Height: 5’ 6’, 1 year of experience with psychoactives, Regular user of Claritin, and prescription allergy medications.
Friend R- Female, Age: 16, Weight: 130lbs, Height: 5’2”, 3 years of experience with psychoactives, No prescriptions or supplements.
Friend L- Female, Age 17, Weight: 210lbs, Height 5’ 8”, 4 years of experience with psychoactives, no prescriptions or supplements.
Friend Q- Male, Age 15, Weight 115lbs, Height 5’4”, no experience with psychoactives, no prescriptions or supplements. Observer.
Friend M- Male, Age 15, Weight 120lbs, Height 5’3”, no experience with psychoactives, no prescriptions or supplements. Observer.
The date was November 24th 2006.
I and my friends E, R, and L sat it lawn chairs under the stars in J’s back yard. I was completely relaxed, my mind was at ease, and I was ready to smoke. I could hear the soft buzzing and thrumming of the air conditioner in the distance, as well as numerous small insect noises.
T 11:35 PM L brought out the bag of beautiful and highly potent hydroponic Cannabis. L prepared the first bowl of with a large green, silver, and orange bud, and lit it with a torch lighter. Next it was passed to me, and I took a single long draw. I coughed, surprised at the strength of the weed. Within 5 seconds of letting the smoke free I felt the high begin. The bowl was passed from me to E, and then to R. Each of us took a very large hit of smoke.
T 11:40 PM At this point the high truly began. I felt incredible, calm, peaceful, and at ease with my surroundings, that coupled with a warm “glowing” sensation to from the perfect high. So R, E, L and I sat and talked, about the world, about drugs, about life.
T 11:49-11:53 PM. Two more Bowls of equal or slightly greater size than the first are shared between R, L, and I. E sates that she has smoked enough for the night and so declines both bowls.
T 11:53 PM - This was when the experience became more than a simple smoking experience, and my when my high reached unprecedented levels. I took the final massive hit from the bubbler and felt as though I had punched through some barrier and was now higher than I had ever been. It felt as though a generator had been hooked up into my skeleton and now high voltage power was being passed through it. I could feel my bones buzzing, I saw everything with clarity and yet it seemed distant, and most of all I heard great noises. The thrumming and buzzing of the Air Conditioner shifted into a great booming which filled my ears and a shrill piercing cry which resonated in my skull. I could hear the insects as though they were perched upon my ears, and most of all I heard the voices of R and E (both of whom were nowhere near as high as I was).
They seemed to have changed in some way. R’s Boston accent gained a harping almost wailing quality to it, and E’s seemed to become dark and full of a hidden malice. L remained unchanged but for a more dull and timid quality to her voice. However she seemed to change.  I stared at her, intrigued by this, all the while feeling/hearing the great buzzing. I looked up startled to hear two voices, that of E’s brothers Q and M, coming towards us. They did not and could not know what we were doing, and thus E and R quickly devised a plan to distract them so that I and L (who were far too high to conceal it) could go inside. R began to speak with Q and M and I then noticed the same change of perception of her and E that I had just witnessed in L.
R began to chat in her now twangy and wailing tone. She seemed to me to be most like an older woman, a witch even. She was quick, powerful, bold, but her heart was as cold as Ice, and her voice seemed to be filled with corruption. E meanwhile had undergone a similar transformation in my perception of her. She seemed to be some great evil.  It terrified me for every time she spoke I had the thought of evil, something cunning beyond all else, with a heart as cold as ice, a bold and powerful mind, and a palpable malice. Q and M seemed to be unchanged, if a little more annoying. I sat outside with the worst elements of my friends, paranoid, and filled with great buzzing for 35 minutes.
T 12:48 AM I finally had an excuse to leave when R convinced Q to strip. L, E, and I then ran for the house to escape that and moved into E’s room. I had by this point come down significantly, and the buzzing no longer seemed to fill me, and the images that I had conjured in my mind had dissipated. By the time R re-entered the room at 1:00 all impression of the wailing Bostonian witch had vanished.
T 1:20 AM - I have now significantly come down all extreme effects of the Cannabis are now gone. I can simply relax and enjoy the high. I dismiss the images that I thought off as nothing more than the product of negativity, and paranoia. I sat and enjoyed the movie we were watching(Snatch) all the while growing closer to my friend R. I had now forgotten the Image that I had created of her earlier and now saw only the person for whom I had strong feelings.
T 1:35 AM - The high is still going strong and I feel wonderful. I have my arm around R now and we are pressed close together in a wonderful embrace while lying on E’s Bed. At this point another curious effect occurs. I feel amazing, and I feel a tangible connection between R and I. It seems as though we are sharing emotions through some link. No matter how I try I cannot overcome the urge to sleep, and my eyelids close and I pass into deep slumber wrought with lucid dreams.
T 10:30 AM- I awake, feeling wonderful and rested. My dreams are a swirling vortex of sight and sound.  I begin the day and spend several hours with E,R and L. I do not recall the images of the night before, or the feeling of the night before. The buzzing has left me.
I remembered the extent of that night a week later. At the time I again dismissed the Images as foolish, a product no doubt of very potent weed and naught else, I did not think at the time that they would provide insight into my friends character. I merely thought them to be irrelevant “pipe-dreams”.
Yet it is not simply because of the strangeness of the experience that I have filled this account. It is the truthfulness of my visions while in the midst of the Buzzing that truly motivated me to do so. Great examination of the character of E and R on my part over the next few months would bring me to a clear conclusion, the visions I had on that night were a reflection of my friend’s true dark sides. I realized this when I truly thought into who my friends were and how they acted.  E possessed the dark side that was apparent to me that night. L also was exactly the same as I perceived her to be. The visions of the Buzzing were completely accurate.
The only question that remained to me, and one I still have not been able to answer was this. Why was I so effected by this weed? I had smoked high potency weed before, and in far greater quantities. So then what made this experience so powerful? This is a question that I am still unable to answer. However one thing is for certain. I shall never forget the truth that was revealed to me on that night. In that single experience I learned more about my friends than I had in the months that I had known them for. I also learned that Cannabis can be far more than just a means of recreation. This experience was not a happy, funny, adventure, it was a dark vision of the truth that helped me to change my view of the people I knew.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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