Send to fixed email address¶
Choose mailto “Fixed Email Address”
This options is most usefull when you want to email the backoffice after a form was submitted.
Fill in the email addresses the mail needs to be send to.
You can specify multiple addresses by using a semicolon between the addresses. Make sure to leave no spaces.


You unleash a virulent disease on a creature that you can see within range. The target must make a Constitution saving throw. On a failed save, it takes 14d6 necrotic damage, or half as much damage on a successful save. The damage can't reduce the target's hit points below 1. If the target fails the saving throw, its hit point maximum is reduced for 1 hour by an amount equal to the necrotic damage it took. Any effect that removes a disease allows a creature's hit point maximum to return to normal before that time passes.


Shadow Maps
This sample implements a basic shadow map.
Use A, S, D, W, I and O to move the light. Press spacebar to see the shadow map.


| home | mailing lists | latest releases | documentation | mirroring | legal and licencing |
|
license Copyright (c) 2000 The Legion Of The Bouncy Castle (http://www.bouncycastle.org) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|


| Trees | Indices | Help |
|
|---|
|
|
This script adds the synset identifiers to some NAF files created with the semcor to NAF converter. In the original NAF files only senses and lexical keys are included. Different types of input is allowed: 1) A folder as generated by conver_corpus.py script 2) A list with paths to NAF files 3) A single file
The synset information is added to the same files, so no new files are created. Run the script with -h option to see the parameters
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__package__ = None
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Takes the path to the root of the wordnet plain text files, and reads from path/dict/index.sense the mapping from sensekeys to synsets
|
Process a single NAF file and adds the synset information the file.
|
Process a folder with the subfolders brown[12v] and *naf under each subfolder
|
Adds synset information to a list of files. The paths to these files is stored in the input file
|
| Trees | Indices | Help |
|
|---|
| Generated by Epydoc 3.0.1 on Fri Jul 11 16:47:58 2014 | http://epydoc.sourceforge.net |


public class WKTReader
extends java.lang.Object
Geometry
.
WKTReader
supports
extracting Geometry
objects from either Reader
s or
String
s. This allows it to function as a parser to read Geometry
objects from text blocks embedded in other data formats (e.g. XML).
A WKTReader
is parameterized by a GeometryFactory
,
to allow it to create Geometry
objects of the appropriate
implementation. In particular, the GeometryFactory
determines the PrecisionModel
and SRID
that is
used.
The WKTReader
converts all input numbers to the precise
internal representation.
WKTGeometry: one of WKTPoint WKTLineString WKTLinearRing WKTPolygon WKTMultiPoint WKTMultiLineString WKTMultiPolygon WKTGeometryCollection WKTPoint: POINT ( Coordinate ) WKTLineString: LINESTRING CoordinateSequence WKTLinearRing: LINEARRING CoordinateSequence WKTPolygon: POLYGON CoordinateSequenceList WKTMultiPoint: MULTIPOINT CoordinateSingletonList WKTMultiLineString: MULTILINESTRING CoordinateSequenceList WKTMultiPolygon: MULTIPOLYGON ( CoordinateSequenceList { , CoordinateSequenceList } ) WKTGeometryCollection: GEOMETRYCOLLECTION ( WKTGeometry { , WKTGeometry } ) CoordinateSingletonList: ( CoordinateSingleton { , CoordinateSingleton } ) | EMPTY CoordinateSingleton: ( Coordinate ) | EMPTY CoordinateSequenceList: ( CoordinateSequence { , CoordinateSequence } ) | EMPTY CoordinateSequence: ( Coordinate { , Coordinate } ) | EMPTY Coordinate: Number Number Numberopt Number: A Java-style floating-point number (including NaN, with arbitrary case)
WKTWriter
| Constructor and Description |
|---|
WKTReader()
Creates a reader that creates objects using the default
GeometryFactory . |
WKTReader(GeometryFactory geometryFactory)
Creates a reader that creates objects using the given
GeometryFactory . |
| Modifier and Type | Method and Description |
|---|---|
Geometry |
read(java.io.Reader reader)
Reads a Well-Known Text representation of a
Geometry
from a Reader . |
Geometry |
read(java.lang.String wellKnownText)
Reads a Well-Known Text representation of a
Geometry
from a String . |
public WKTReader()
GeometryFactory
.public WKTReader(GeometryFactory geometryFactory)
GeometryFactory
.geometryFactory
- the factory used to create Geometry
s.public Geometry read(java.lang.String wellKnownText) throws ParseException
Geometry
from a String
.wellKnownText
- one or more Geometry
specified by wellKnownText
ParseException
- if a parsing problem occurspublic Geometry read(java.io.Reader reader) throws ParseException
Geometry
from a Reader
.reader
- a Reader which will return a Geometry
read from reader
ParseException
- if a parsing problem occurs


Sequencer
MIDI
eWave studio
×
TRACK 1 - effects
Delay
Flanger
Tremolo
Distortion
Reverb
Load Sample - Track 1...
Select Sample...
Settings
Delete Channel
×
TRACK 2 - effects
Delay
Flanger
Tremolo
Distortion
Reverb
Load Sample...
Select Sample...
Settings
Delete Channel
×
TRACK 3 - effects
Delay
Flanger
Tremolo
Distortion
Reverb
Load Sample Track 3...
Select Sample...
Settings
Delete Channel
×
TRACK 4 - effects
Delay
Flanger
Tremolo
Distortion
Reverb
Load Sample...
Select Sample...
Settings
Delete Channel
×
TRACK 5 - effects
Delay
Flanger
Tremolo
Distortion
Reverb
Load Sample...
Select Sample...
Settings
Delete Channel
×
TRACK 6 - effects
Delay
Flanger
Tremolo
Distortion
Reverb
Load Sample...
Select Sample...
Settings
Delete Channel
×
Track 7 - effects
Delay
Flanger
Tremolo
Distortion
Reverb
Load Sample...
Select Sample...
Settings
Delete Channel
×
TRACK 8 - effects
Delay
Flanger
Tremolo
Distortion
Reverb
Load Sample...
Select Sample...
Settings
Delete Channel
USB input
❌


"SHOA "opulent, the mountain district lying to the north-east of Babylonia, anciently the land of the Guti, or Kuti, the modern Kurdistan. The plain lying between these mountains and the Tigris was called su-Edina, i.e., “the border of the plain.” This name was sometimes shortened into Suti and Su, and has been regarded as = Shoa (Ezekiel 23:23). Some think it denotes a place in Babylon. (See PEKOD.)
"SHOA "opulent, the mountain district lying to the north-east of Babylonia, anciently the land of the Guti, or Kuti, the modern Kurdistan.
The plain lying between these mountains and the Tigris was called su-Edina, i.e., “the border of the plain.” This name was sometimes shortened into Suti and Su, and has been regarded as = Shoa (Ezekiel 23:23). Some think it denotes a place in Babylon. (See PEKOD.)


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1"""Error classes for PostgreSQL error codes
2"""
4# psycopg/errors.py - SQLSTATE and DB-API exceptions
5#
6# Copyright (C) 2018-2019 Daniele Varrazzo<EMAIL_ADDRESS>7# Copyright (C) 2020 The Psycopg Team
8#
9# psycopg2 is free software: you can redistribute it and/or modify it
10# under the terms of the GNU Lesser General Public License as published
11# by the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# In addition, as a special exception, the copyright holders give
15# permission to link this program with the OpenSSL library (or with
16# modified versions of OpenSSL that use the same license as OpenSSL),
17# and distribute linked combinations including the two.
18#
19# You must obey the GNU Lesser General Public License in all respects for
20# all of the code used other than OpenSSL.
21#
22# psycopg2 is distributed in the hope that it will be useful, but WITHOUT
23# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
24# FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public
25# License for more details.
27#
28# NOTE: the exceptions are injected into this module by the C extention.
29#
32def lookup(code):
33 """Lookup an error code and return its exception class.
35 Raise `!KeyError` if the code is not found.
36 """
37 from psycopg2._psycopg import sqlstate_errors # avoid circular import
38 return sqlstate_errors[code]


$1A/26: Header block: Parity ROM-Reference: LDA $1A : $F944 STA $1A : $F4B6 $F531 Previous Page / Next Page


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VW Atlas Cross Sport R soars into L.A. with 480 horsepower
It's slated to compete in the 2020 Baja 1000
by Zac PalmerVolkswagen is headed to the 2020 Baja 1000 with this absolute beast of a vehicle. It’s called the Atlas Cross Sport R, and it’s the coolest SUV coupe we’ve ever seen. The resemblance to the regular Atlas Cross Sport is noticeable right away, but beyond a general roofline, nothing here looks like a production 2020 Atlas Cross Sport.
Actual details are scant. The powertrain consists of a 2.0-liter turbocharged four-cylinder and an unspecified transmission — VW says that the car is only in concept form now. Don’t let the small engine fool you, though. VW says it produces about 480 horsepower. The engine is actually a detuned version of the VW WRC 2.0-liter race engine that’s capable of running at 600 horsepower. For now, Volkswagen is dogging it for the sake of balance and reliability.
VW says it’s developing the Baja vehicle with both Tanner Foust and Rhys Millen, so we know it’s in good hands with those two. The team still has awhile to get everything in order, as next year’s Baja 1000 isn’t until November 2020. Foust is already slated to be one of the drivers for the race.
As of now, the livery is temporary, advertising the new Atlas Cross Sport. VW says it plans to invite designers from schools and colleges nationwide to help design the car’s final look next year. It won’t be hard to make this Baja truck look good, as the design is already fantastic. The photos are the real story here, so keep scrolling through and watch this VW fly through the air.


You should specify a future time.
You must specify a time.
One of the image URLs is invalid.
You must specify a list of urls.


This option allows you to specify a pattern that will be used to determine the
name of the baseline that will tag this build.
The pattern supports the following variables which will be substituted with
appropriate values in the name when the baseline gets created.
- [PROJECTID]
- the name of the Dimensions stream or project being monitored.
- [JENKINS_PROJECT]
- the name of the Jenkins project (can also use [HUDSON_PROJECT])
- [DM_BASELINE]
- the value of the ${DM_BASELINE} build parameter
- [BUILDNO]
- the Jenkins build number
- [CURRENT_DATE]
- the current date in "yyyy.MMMMM.dd hh.mm.ss aaa z" format,
e.g. "2018.May.29 08.47.59 PM BST"
For example, the pattern
"[PROJECTID]_P_[JENKINS_PROJECT]_B_[BUILDNO]_T_[CURRENT_DATE]"
might create a baseline with a name like
"QLARIUS:JAVADEV_P_My Jenkins Job_B_406_T_2018.May.29 08.47.59 PM BST"
If you specify a pattern that contains no variables in it, then the pattern
will be treated as a literal baseline name.


Portfolio
Order
Order Status
Trade Information
Market Feed
Watchlist 1
Watchlist 2
Watchlist 3
Watchlist 4
Report
Margin
Order Book
Holdings
Login
Asian Paints
Coal India
HDFC ltd.
HDFC Bank
ICICI
IIFL
ITC
L&T
OBC Bank
ONGC
5Paisa Portfolio


It really is that simple.
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This is a cryptic crossword; you can learn more about solving them here. In addition to the standard cryptic rules, one entry in each row must have a letter inserted before being entered in the grid. (Modified entries are all real words.) For all other across entries, the wordplay portion will yield a letter sequence with one extraneous letter.
Across
- Pamper before drinking red wine
- Drunkard swallowed nothing but ashes
- Slam boy President, who loses his head
- Join with regressive Martinez inside European Union
- I'm embroiled in brouhahas for so long
- Tabernacle architecture conceals organ
- Pete Seeger's group flutters around southeast
- Let turn yellow
- Foul-smelling row
- Search crazily for someone following you
- Beers in the west of Britain
- Angry about Greek woman showing up before the start of elegant party
- Trace of tin found among sulfur, argon, lead...
- It's a mistake to pry awkwardly
- Uptight Greek character removed from yearbook
- Actors Bruce and Laura voiced pitchers
Down
- Call before trimming cute hairdo (7)
- One of a nation's young leaders (3)
- In Bojack Horseman, gubernatorial candidate rejects leftist label and embraces extreme conclusion (4)
- Rabbit's head stuffed in uncooked beef produces something unpleasant to eat (4)
- Tribute to model stripped bare (3)
- Actress Reid's holding on to current collection of royal headwear (6)
- Employ second-person form of address for group of Brooklyn auditors (3)
- Change permanently to black! (4)
- Pretentious snobs at cross purposes lose bit of poise (7)
- Broke up with innately nice adult (6)
- Know about a good for sale (5)
- Eggs: large, round (4)
- Put another way, Laos is something else (4)
- "Hello, sailor!" A prostitute, yes? (4)
- Request to jump back to front (4)
- Ran around, excited at first, around, around (4)
- Snake was possibly hiding something (3)
- Feminist goal of another time (3)
- Endlessly confess wrongdoing (3)


An aspiring Full Stack Web Developer with extensive experience in Business Development, B2C Sales and Technical Recruitment.
Having worked with many awesome developers and inspiring tech companies during my career in B2B/B2C Sales and Recruitment, I have decided to finally realise my long standing goal of learning coding by attending General Assembly’s Web Development Immersive program. 12-Weeks, all day everyday, web development bootcamp, where I experienced working in an Agile environment, attending daily Scrums, learning the key aspects of programming while building full-stack web applications and APIs using JavaScript and Ruby.
Having now built a solid foundation of knowledge I seek a role where I continue my development and put into practice the skills that I have acquired.
HTML | CSS | Javascript (JQuery) | Bootstrap
Node.js(Express) | Ruby (Ruby on Rails | Sinatra)
AngularJS | Backbone.js | Meteor


DB2 offers many compatibility features that can be turned on individually based on your need. To enable all the compatibility features applicable to Oracle databases, set the registry variable DB2_COMPATIBILITY_VECTOR = ORA. You can then create an Oracle compatible database.
Follow the steps below to create a compatible database:


2.0.0
BTree main class
((BTreeRootAttrStruct | BTreeValueAttrStruct | T))
Can be of type object, string, number. In case of object root/value property is expected to be value of root node.
new BTree(10);
new BTree({ root: 10 });
new BTree({ value: 10 });
Returns an iterable of key, value pairs for every entry in the tree.
var tree = new BTree(10);
for (const node of tree) {
console.log(node.value); // 10
}
Reduces each node values using reduceFunction and returns final value.
(any)
(T2
= 0
)
Optional, Accumulator/Initial value.
T2
:
Returns reduceed value
var tree = BTree.fromArray([10, 20, 30, 40]);
var sum = tree.reduce((acc, node) => acc + node); // => 100
Returns string value of given tree.
var tree = new BTree(10);
tree.insert(10);
tree.insert(20);
tree.insert(30);
tree.toString(); // "10102030"
Returns JSON Form.
BTreeNodeStruct
:
Returns json form of a given tree.
var tree = new BTree(10);
tree.insert(20);
tree.toJSON(); // {value:10,lNode:{value:20,lNode:null,rNode:null},rNode:null}
Returns array value.
Array<BTreeNode>
:
Returns array form of given tree.
var tree = new BTree(10);
tree.insert(20);
tree.toArray(); // => [{value:10,...},{value:20,...}]
Returns array of values of the tree.
Array<T>
:
Returns array form of given tree.
var tree = new BTree(10);
tree.insert(20);
tree.toFlatArray(); // => [10,20]
Inserts the given value to the tree where first free left child node is found.
(any)
any type of value to be added to tree node.
BTreeNode
:
Returns newly created BTreeNode.
var tree = new BTree(10);
tree.insert(10);
tree.insert(20);
tree.insert(30);
tree.toString(); // "10102030"
Inserts the given value to the tree where first free left child node is found.
(T)
any type of value to be added to tree node.
BTreeNode<T>
:
Returns newly created BTreeNode.
Inserts the given value to the tree where first free right child node is found.
(T)
any type of value to be added to tree node.
BTreeNode<T>
:
Returns newly created BTreeNode.
Deletes given value from tree. Travarsal = Root -> L -> R.
(T)
Value to be removed.
BTreeNode<T>
:
Returns removed BTreeNode.
Inserts given element at given location. If location is already taken then it does not insert any value.
(T)
value to insert.
(number)
index at which to append new element to.
tree.insertAt(20,2);
Breadth first search. Executes given callback functions with parameters BTreeNode and path index for each node in BFS fashion.
void
:
no value.
Depth first search, Executes given callback functions with parameters BTreeNode and path index for each node in DFS fashion.
void
:
no value.
Breadth first search. Executes given callback functions with parameters BTreeNode and path index for each node in BFS fashion.
void
:
no value.
Breadth first search. Executes given callback functions with parameters BTreeNode and path index for each node in BFS fashion.
void
:
no value.
Returns an iterable of key, value pairs for every entry in the tree.
IterableIterator<[number, BTreeNode<T>]>
:
Iterable for iterations.
var tree = new BTree(10);
for (const [index, node] of tree.entries()) {
console.log(index, node.value); // 0, 10
}
Maps current tree values to a new tree with modifying the values using given callback function. Uses BFS.
BTree<T>
:
A new BTree
var tree = BTree.fromArray([10, 20, 30, 40]);
var tree2 = tree.map(n => n * 2);
var arr2 = tree2.toArray(); // [{value:20,...},{value:40,...},{value:60,...},{value:80,...}]
Filters each item based on given filter function
BTree<T>
:
New filtered instance of tree.
var tree = BTree.fromArray([10, 20, 30, 40]);
var tree2 = tree.filter(n => !!(n % 4 === 0 || n === 10));
var arr2 = tree2.toArray(); // [{value:10,...},{value:20,...},{value:40,...}]
Reverses the current Binary Tree, Left Node becomes Right node and vise versa. Does not return new instance, returns current tree instance.
BTree<T>
:
Returns current tree instance.
var tree = BTree.fromArray([10, 20, 30, 40, 50, 60, 70, 80]);
tree.reverse().toArray(); // => [10, 30, 20, 70, 60, 50, 40, 80]
Returns first index of a value matched, if it is not present, it returns -1.
(T)
Any value to find.
number
:
Returns index of given item.
var tree = BTree.fromArray([10, 20, 30, 40, 50, 60, 70, 80]);
tree.indexOf(30); // => 3
tree.indexOf(51); // => -1
Checks if given item exists or not, returns boolean.
(T)
Any value to check if it exists or not.
boolean
:
Returns true if it is present, otherwise false.
var tree = BTree.fromArray([10, 20, 30, 40, 50, 60, 70, 80]);
tree.includes(30); // true
tree.includes(51); // false
Checks if given item exists or not, returns boolean.
(T)
Any value to check if it exists or not.
boolean
:
Returns true if it is present, otherwise false.
var tree = BTree.fromArray([10, 20, 30, 40, 50, 60, 70, 80]);
tree.exists(30); // true
tree.exists(51); // false
Checks if given item exists or not, returns boolean.
(T)
Any value to check if it exists or not.
boolean
:
Returns true if it is present, otherwise false.
var tree = BTree.fromArray([10, 20, 30, 40, 50, 60, 70, 80]);
tree.has(30); // true
tree.has(51); // false
Sorts the tree based on compare function, Has option to sort only at children level.
(Function)
Function used to determine the order of the elements. It is expected to return
a negative value if first argument is less than second argument, zero if they're equal and a positive
value otherwise. If omitted, the elements are sorted in ascending, ASCII character order.
(a, b) => a - b)
(boolean)
Optiona, Flag to specify if first child of each node should sorted. Default is
false
.
void
:
Returns undefined.
var tree = BTree.fromArray([10, 200, 100, 50, 60, 90, 5, 3]);
tree.sort().toFlatArray(); // => [3,5,10,50,60,90,100,200]
Prints entire tree on the console, useful for logging and checking status.
void
:
Returns undefined.
var tree = BTree.fromArray([1, 2, 3]);
tree.print();
1 (1)
|- 2 (2)
|- 3 (3)
Returns the first matched tree node. Traverses using BFS.
(T)
any value to find inside the tree.
(BTreeNode<T> | null)
:
Returns the first matched tree node, if not found, returns null.
Returns index value from given path.
(Array<("U"
| "L"
| "R"
)>)
Array for U L or R, which represents the quickest path to get to a node.
number
:
Returns index value.
Returns Path equivalent to the given index.
(number)
Index number from which path to be calculated.
Array<("U"
| "L"
| "R"
)>
:
Path equivalent to the given index.
Converts given values into a Binary Tree.
(Array<T2>)
Any array of values.
BTree<T2>
:
Newly generated tree.
var tree = BTree.fromArray([10,20,30,40]);
Binary Tree node class, contains 2 child nodes and single value.
var node = new BTreeNode({ value: 15 });
var node3 = new BTreeNode({ value: 30 });
var node2 = new BTreeNode({ value: 20, rNode: node, lNode: node3 });
console.log(node2.lNode.value); // 30
Validates a BTree node, it must have a valid value (no undefined nor null).
boolean
:
new BTreeNode(); // throws error saying `A BTree node must have a valid value, cannot be null or undefined`
var node = new BTreeNode({ value: 10 });
console.log(node.validate()); // true
Converts current node and all children nodes in json format.
BTreeNodeStruct
:
var node = new BTreeNode({ value: 10 });
var lNode = new BTreeNode({ value: 15, lNode: lNode });
console.log(node.toJSON()); // {value:15,lNode: {value: 10,lNode:null,rNode:null},rNode:null}


You must tribute levels for a level, 3 and lower may be placed with out tribute Tribute level = to monster tributing for, Lv3 Slime + Lv1 Slime = Lv4 Slime First one to hit the other wins Wait one turn after placing to use/attack Use takes your attack for that monster
Stages: Draw 1 Card (Optional) Place 1 Card (Optional) Use/attack Regenerate


state
invoice is active
:state
invoice is invalid
:state
invoice has been submitted
:state
invoice number
,invoice name
:invoice number
"owner
,recipients
,creation date
,start date
,end date
,timesheet
,columns
:type
=format
=first name
first letter
title
=type
=format
=day of week letter
month number
-day
-short year
"type
=type
=precision
=type
=format
=formula
:of
type
=shows column titles
the number of days start date end date day count
cell date total :
"(day count day)" row count 1
"(day count days)"
total hours
:total charge
:submission date
,invoice message
,
invoice is active :
update timesheet to today
the address of the owner
the first name
the recipients
, Please find *the start date
andthe end date
. A detailed breakdown of hours and activities follows:the timesheet
as html
*The total now due is $


Cristiano Ronaldo is one of the best soccer players to ever play the game.
He
was born on Feburary 5,1985 in Funchal, Madeira Portugal. He played soccer at a very
young age, in 2003 he went to Manchester United he played there for 6 years. After that
he went to Real Madrid and is still playing there. He has recently taken portugal
all the way in a recent cup and won.
I chose Cristiano Ronaldo because i think he is one of the best soccer player
to ever play the game i admire him very much


Mike Jones
Inside Sales
<EMAIL_ADDRESS>650.465.1576
Michael was born in Mission Viejo, California. A Son of a Lieutenant Colonel in the Marine Corps, Michael also grew up in Virginia, Tennessee, and Colorado. He attended the Air Force Academy, in Colorado Springs as a Division 1 Baseball Recruit and later graduated UCLA with a Bachelor’s Degree in Geography. He is a recent transplant from Southern California, and is a passionate Dodgers fan lost amongst a sea of Giants fans. He enjoys outdoor activities such as golfing, fishing, snowboarding and traveling. On the weekends you have a good chance of running into him at the local Bay Area casinos playing poker. He joined R&B Company in August of 2017.


George Macrae is a Designer and Developer who lives in Vancouver, BC. He is currently a 4th year Communication Design Student at Emily Carr University, and has a BSc major Computer Science minor Physics from McGill University in Quebec.
George’s focus is to combine his expertise in Development with his passion for Design to create practical and meaningful products. He looks forward to a career in User Focused Design and Development.


Class ConnectionsClient
Contains methods to access the /connections endpoints.
Implements
Inherited Members
Namespace: Auth0.ManagementApi.Clients
Assembly: Auth0.ManagementApi.dll
Syntax
public class ConnectionsClient : BaseClient, IConnectionsClient
Constructors
ConnectionsClient(IManagementConnection, Uri, IDictionary<String, String>)
Initializes a new instance of the ConnectionsClient.
Declaration
public ConnectionsClient(IManagementConnection connection, Uri baseUri, IDictionary<string, string> defaultHeaders)
Parameters
| Type | Name | Description |
|---|---|---|
| IManagementConnection | connection | IManagementConnection used to make all API calls. |
| System.Uri | baseUri | System.Uri of the endpoint to use in making API calls. |
| System.Collections.Generic.IDictionary<System.String, System.String> | defaultHeaders | Dictionary containing default headers included with every request this client makes. |
Methods
CheckStatusAsync(String, CancellationToken)
Retrieves the status of an ad/ldap connection.
Declaration
public Task CheckStatusAsync(string id, CancellationToken cancellationToken = default(CancellationToken))
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | id | ID of the connection to check. |
| System.Threading.CancellationToken | cancellationToken | The cancellation token to cancel operation. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task | A System.Threading.Tasks.Task that represents the asynchronous check operation. Will throw if the check fails. |
CreateAsync(ConnectionCreateRequest, CancellationToken)
Creates a new connection according to the request.
Declaration
public Task<Connection> CreateAsync(ConnectionCreateRequest request, CancellationToken cancellationToken = default(CancellationToken))
Parameters
| Type | Name | Description |
|---|---|---|
| ConnectionCreateRequest | request | The request containing the properties for the new connection. |
| System.Threading.CancellationToken | cancellationToken | The cancellation token to cancel operation. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<Connection> | A Connection containing the newly created Connection. |
DeleteAsync(String, CancellationToken)
Deletes a connection and all its users.
Declaration
public Task DeleteAsync(string id, CancellationToken cancellationToken = default(CancellationToken))
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | id | The id of the connection to delete. |
| System.Threading.CancellationToken | cancellationToken | The cancellation token to cancel operation. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task | A System.Threading.Tasks.Task that represents the asynchronous delete operation. |
DeleteUserAsync(String, String, CancellationToken)
Deletes a specified connection user by its email
.
Declaration
public Task DeleteUserAsync(string id, string email, CancellationToken cancellationToken = default(CancellationToken))
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | id | The identifier of the connection. |
| System.String | The email of the user to delete. |
|
| System.Threading.CancellationToken | cancellationToken | The cancellation token to cancel operation. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task | A System.Threading.Tasks.Task that represents the asynchronous delete operation. |
Remarks
Currently only database connections are supported and you cannot delete all users from specific connection.
GetAllAsync(GetConnectionsRequest, PaginationInfo, CancellationToken)
Retrieves every connection matching the specified strategy. All connections are retrieved if no strategy is being specified. Accepts a list of fields to include or exclude in the resulting list of connection objects.
Declaration
public Task<IPagedList<Connection>> GetAllAsync(GetConnectionsRequest request, PaginationInfo pagination = null, CancellationToken cancellationToken = default(CancellationToken))
Parameters
| Type | Name | Description |
|---|---|---|
| GetConnectionsRequest | request | Specifies criteria to use when querying connections. |
| PaginationInfo | pagination | Specifies pagination info to use when requesting paged results. |
| System.Threading.CancellationToken | cancellationToken | The cancellation token to cancel operation. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<IPagedList<Connection>> | An IPagedList<T> containing the list of connections. |
GetAsync(String, String, Boolean, CancellationToken)
Retrieves a connection by its id
.
Declaration
public Task<Connection> GetAsync(string id, string fields = null, bool includeFields = true, CancellationToken cancellationToken = default(CancellationToken))
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | id | The id of the connection to retrieve. |
| System.String | fields | A comma separated list of fields to include or exclude (depending on include_fields) from the result, empty to retrieve all fields. |
| System.Boolean | includeFields | True if the fields specified are to be included in the result, false otherwise (defaults to true). |
| System.Threading.CancellationToken | cancellationToken | The cancellation token to cancel operation. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<Connection> | The Connection. |
UpdateAsync(String, ConnectionUpdateRequest, CancellationToken)
Updates a connection.
Declaration
public Task<Connection> UpdateAsync(string id, ConnectionUpdateRequest request, CancellationToken cancellationToken = default(CancellationToken))
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | id | The id of the connection to update. |
| ConnectionUpdateRequest | request | The ConnectionUpdateRequest containing the properties of the connection you wish to update. |
| System.Threading.CancellationToken | cancellationToken | The cancellation token to cancel operation. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<Connection> | The Connection that has been updated. |


--- layout: post title: Workers and Tribe - Ruby threading simplified comments: false share: false date: 2013-01-22 type: post tags: - actors - jruby - ruby - threading ---
Recently I've created two new Ruby gems known as Workers and Tribe.
The Workers gem turns low-level operating system threads into simple to use event-driven workers and worker pools. This allows you to focus on message passing instead of complex thread management. As an added bonus, it includes a timer system that can tell your workers to execute work in the future.
The Tribe gem builds on top of Workers in order to implement the actor model. It specifically focuses on event-driven actors such that each thread manages one or more actors. This way the system can support many more actors than is possible with implementations that force a 1:1 thread to actor relationship. A simple benchmark demonstrating one million actors using 20 threads can be found on the performance wiki page.
Both gems have been tested with MRI Ruby 1.9.3 and JRuby 1.7. Due to MRI's thread limitations, it is highly recommended that you use JRuby if you are concerned about multi-core CPU performance.


-
The Secret Goldman Sachs Tapes
www.bloombergview.com
The public doesn't seem to understand just how dysfunctional our financial regulatory system is, Michael Lewis writes. That may change today with a ...
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Miss a Payment? Good Luck Moving That Car - NYTimes.com
dealbook.nytimes.com
The thermometer showed a 103.5-degree fever, and her 10-year-old’s asthma was flaring up. Mary Bolender, who lives in Las Vegas, needed to get her ...
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Computing Pioneer Ray Ozzie and Security Veteran Nico Sell Join ...
recode.net
The creator of Lotus Notes and former Microsoft chief software architect is now focused on mobile collaboration with Talko, the startup that came out ...
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A Watch Guy's Thoughts On The Apple Watch After Seeing It In The Metal ...
www.hodinkee.com
I'm not even sure we can call it a watch. Okay, it goes on the wrist, and it happens to tell the time, but that's about where the similarities ...
-
Inside Apple's Secret Testing Labs (Where iPhones Are Bent All Day Long)
recode.net
Apple, in an effort to show people the exhaustive tests it puts its products through, has given Re/code a tour of its labs. Take a look.
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Some iPhone 6 Plus Owners Accidentally Bending Their iPhones in Pockets
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As highlighted in a few reports shared in the MacRumors forums, a small but growing number of iPhone 6 Plus owners have reportedly bent their ...
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Talko | Talko is the best way to use your voice to get things done.
www.talko.com
TALK. SHARE. DO. Amazing things can happen when we talk with each other. Thoughts are shared, ideas formed and problems solved. Talko is the best way ...
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Apple's Beats Music Brand May Go Away. Apple's Beats Music Service Is ...
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That makes more sense.
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So you want to speak at Code/Mobile?
www.youtube.com
Rethinking, reinventing and recoding the mobile industry. Join us at Code/Mobile, the definitive tech industry event on October 27-28 in Half Moon ...
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Apple’s IPhone Software Snafu Has Links to Flawed Maps - Bloomberg
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Apple’s IPhone Software Snafu Has Links to Flawed Maps
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Dear News Corp & Google: An Open Letter On Their Open Letters To Each ...
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Last week, Rupert Murdoch's News Corp sent an open letter to the European Union outlining competition concerns it has about Google. This week, Google ...
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Troy Hunt: Everything you need to know about the Shellshock Bash bug
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Observations, musings and conjecture about the world of software and technology
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oss-sec: Re: CVE-2014-6271: remote code execution through bash
seclists.org
oss-sec mailing list archives
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vgvassilev/cling
github.com
cling - Cling is an interactive C++ interpreter, built on top of Clang and LLVM compiler infrastructure.
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San Diego Researcher Crowdfunding Patent-Free Cancer Drug - Times of San ...
timesofsandiego.com
Dr. Isaac Yonemoto founded IndySci as a crowdfunding platform for Project Marilyn— a campaign that seeks to develop a patent-free cancer drug.
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oss-sec: CVE-2014-6271: remote code execution through bash
seclists.org
oss-sec mailing list archives
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How to tell when a robot has written you a letter
medium.com
Or a human! The next Turing Test is … handwriting.
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Comcast Accuses Time Warner Cable Deal Opponents of “Extortion”
recode.net
Netflix and other opponents sought special favors to support the deal, the cable giant says.
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Larry Ellison Bought an Island in Hawaii. Now What?
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How the tech billionaire came to own 87,000 acres, three hotels, a wastewater-treatment plant, a cemetery and 380 cats.
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For Cinephiles, Netflix Is Less and Less an Option
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Code/red: What Will Apple Call Its Streaming Service? Beats Me.
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Plus, iOS 8 adoption, Xiaomi’s stuffed animal sales and a suggestion for handling in-flight cellphone calls.
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Layoffs hit Freedom Communications as it ceases publication of L.A. ...
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Layoffs hit the Orange County Register on Tuesday after owner Freedom Communications ceased publication of its Los Angeles daily five months after it ...
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Sometimes, written words just don't do the job.
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After the Alibaba IPO, Is Cash-Gorged Yahoo a Buyer -- Or a Seller?
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Can Yahoo CEO Marissa Mayer make it rain? Or will she get soaked?


Drupal is highly relational. When it comes to performance, MySQL has a
big role to play in Drupal world. As I explained in Drupal scaling and
performance tuning - Part 1, we were able tune Apache to handle much load during high
traffic hours. But MySQL didn't give a chance to rest.
As the first step, we decided to look at the MySQL slow queries to
identify bad queries. Server logged all queries that took more than 2 seconds to process and most
of them were node permissions, user sessions, access log, cache, comments, watchdog and node contents.
The server load was gone up and it was almost at the frozen state most of the
time. As a solution, I knew that we would have to end up with a modified MySQL configuration file.
But, What are the Parameters I need to change and what are the values for
them, like i did for Apache?
Then I came across with a tool called MySQLTuner-perl which is a script
written in Perl that allows us to review a MySQL installation quickly and make adjustments to increase
performance and stability. The current configuration variables and status data are retrieved and
presented in a brief format along with some basic performance suggestions
Tools used
- MySQLTuner-perl
- Maatkit ( Power tools for open-source databases)
Mysql Optimization - I will not list all configurations here as MySQL
Tuner gives you a good guide
- MySQL Tuner
- Download the Mysql Tuner (wget https://github.com/rackerhacker/MySQLTuner-perl/blob/master/mysqltuner.pl)
- Make it executable (chmod +x mysqltuner.pl)
- Run MySQL Turner - You need your MySQL root password in order to execute this (./mysqltuner.pl)
- You should carefully read the output, especially the recommendations at the end. It shows exactly which variables you should adjust in the [mysqld] section of your my.cnf (on Debian and Ubuntu the full path is /etc/mysql/my.cnf). Whenever you change your my.cnf, make sure that you restart MySQL. You can then run MySQLTuner again to see if it has further recommendations to improve the MySQL performance. This way, you can optimize MySQL step by step.
- Maatkit
- mk-duplicate-key-checker - helped me to find duplicate indexes and foreign keys on MySQL tables.
- Removed all duplicate indexes and foreign keys - This helped MySQL to process SQLs smoothly.
- mk-query-digest and mk-query-profiler helped to profile and test new configurations/ Modifications to the database.
- mk-variable-advisor to double check changes and recommendations made by MySQL Tuner.
- Converted comments, node, users tables from MyISAM to InnoDB
- mysqlcheck -o -A -p command optimized other tables in the Drupal Database
Other Optimizations used
- Disable watchdog, Statistic modules from Drupal to reduce the read/write load to MySQL
- Uninstalled all unused modules.
Finally I was able to get Our Oxygentank Database to a state where
it can breathe freely without running on full power all the time.
But I was not happy with this setting since the second MySQL master server was always running silently
without helping the Primary Master server to handle its load.
I used NGINX to solve this
problem.
I will discuss how I used NGINX to share MySQL load between Databases / Drupal nodes with the help of Memcached, in part 3.


| 033:030 Khan | : |
O wives of the Prophet! Whoever of you commits an open illegal sexual intercourse, the torment for her will be doubled, and that is ever easy for Allah. |
| 033:030 Maulana | : |
O wives of the Prophet, whoever of you is guilty of manifestly improper conduct, the chastisement will be doubled for her. And this is easy for Allah. |
| 033:030 Pickthal | : |
O ye wives of the Prophet! Whosoever of you committeth manifest lewdness, the punishment for her will be doubled, and that is easy for Allah. |
| 033:030 Rashad | : |
O wives of the prophet, if any of you commits a gross sin, the retribution will be doubled for her. This is easy for GOD to do. |
| 033:030 Sarwar | : |
Wives of the Prophet, if anyone among you commits indecency, her torment will be double. This is not at all difficult for God. |
| 033:030 Shakir | : |
O wives of the prophet! whoever of you commits an open indecency, the punishment shall be increased to her doubly; and this IS easy to Allah. |
| 033:030 Sherali | : |
O wives of the Prophet ! if any of you be guilty of manifestly dishonourable conduct, the punishment will be doubled for her. And that is easy for ALLAH. |
| 033:030 Yusufali | : |
O Consorts of the Prophet! If any of you were guilty of evident unseemly conduct, the Punishment would be doubled to her, and that is easy for Allah. |
Concepts/Themes Covered: أركان الإسلام > (محمد (صلى الله عليه وسلم > أخلاقه وصفاته Pillars of Islam> The Blessed Muhammad(PBUH)> His Manners and Attributes أركان الإسلام > (محمد (صلى الله عليه وسلم > أزواجه وبناته Pillars of Islam> The Blessed Muhammad(PBUH)> His Wives and Daughters أركان الإسلام > (محمد (صلى الله عليه وسلم > مآثره وخصائصه لله Pillars of Islam> The Blessed Muhammad(PBUH)> Memorable Qualities given to Him by Allah العمل > العمل الطالح > الفاحشة والزنى > الفحشاء Action(Work)> Bad Deeds> Adultry and Atrocity>Atrocity
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Agent working to get Stoke City client a move, looking unlikely at this stage
by Nihat Emre KocaaslanAccording to Fotospor, former Galatasaray sporting director Cenk Ergun is behind Trabzonspor’s interest in Stoke City midfielder Badou Ndiaye.
Ergun recently informed the Turkish media the Super Lig side were close to getting a deal done for the player.
It’s claimed Ergun, who worked with Ndiaye’s agent William D’Avila when he was at Galatasaray, is said to be working with him again to get the Senegalese a move to Trabzonspor.
The Turkish giants are just two points behind the league leaders Sivasspor and they will be looking to strengthen their squad in the new year to bolster their title chances.
Midfield is a priority for Karadeniz Firtinasi ahead of the next transfer window as their current options are struggling with injuries.
However, it’s been reported by Fotospor that a return to Turkey is unlikely at this stage given the Stoke City midfielder’s potential cost to Trabzonspor.
Last season Ndiaye spent the season on loan with Turkish champions Galatasaray but retuned to Stoke City in the summer.


Ionosphere
Ionosphere is a story about time series.
Dedicated to my father, Derek, a man of numbers if there ever was one.
What Ionosphere is for - humble beginnings
YOU can teach a system.
YOU can help a system to LEARN.
You want to monitor metrics on small VPSs that do not do a great deal, meaning there is no high, rate constant work or 3sigma consistency in the metrics. There are 0 to 7 apache.sending a day, not 7000 a minute. Or there are some peaks on the metric stats_counts.statsd.bad_lines_seen
This is NOT ANOMALOUS
Even in massive, workload intensive, dynamic systems there are always some metrics that are low rate and relatively low range. And at 3sigma… that means noisy.
Ionosphere’s goal is to allow us to train Skyline on what is not anomalous and thereby compliment statistical 3simga means with some new methodologies of trying to “teach” the system. Of course teaching statistics algorithms something new is unfortunately not possible, but luckily teaching the system is possible. Giving the system better contextual data to work with is the key factor.
While a lot of work and research in the anomaly detection and machine learning domain is focused at automatically finding anomalies and getting it right, Skyline Ionosphere actually does somewhat of the opposite.
With Ionosphere we are training Skyline on what is NOT ANOMALOUS, rather than focusing on what is anomalous. Ionosphere allows us to train Skyline as to what is normal, even if normal includes spikes and dips and seasonality.
Overview
Ionosphere gives the operator an input into Skyline to allow them to train it and compliment the existing statistical 3sigma anomaly detection methods.
Ionosphere then starts to learn, retrospectively.
Is Ionosphere the panacea for anomaly detection? No.
Is Ionosphere immediately useful for web scale anomaly detection? No. Unless you are already doing web scale anomaly detection with Skyline, then still no. However over time yes. You cannot rush time series.
Is Ionosphere a lot of work to train, yes.
Ionosphere uses a time series similarity comparison method to compare two different time series for a metric.
This method is based on summing the features that are calculated for the time series using tsfresh to “fingerprint” the data set.
This allows the operator to fingerprint and profile what is not anomalous, but normal and to be expected, even if 3sigma will always detect it as anomalous.
Further to this, Ionosphere uses the trained data to create 1000s of motifs (small patterns) of what normal is and supplements the feature profile comparisons by evaluating the current potentially anomaly against the 1000s of trained normal motifs from the features profiles.
Ionosphere also allows for the creation of layers rules which allow the operator to define ad hoc boundaries and conditions that can be considered as not anomalous for each features profile that is created.
How well does it work?
This is a graph of the impact that Ionosphere has had on the anomaly count of ionosphere_enabled metrics before and after the deployment of Ionosphere.
It is clear that when the features profiles and layers are introduced and Skyline starts to learn, the anomaly counts for metrics that have been trained drop significantly, but it is not perfect.
Significant events which happen infrequently and change machine metrics significantly are still difficult to handle. Things like the occasional importing of a DB, reboots, zipping up a bunch of things, however layers do allow one to “describe” these events on metrics as normal should they so wish.
And Ionosphere does not make handling downtime and missing data points in metrics any easier, in fact it can make it harder.
“Do I want to train Skyline to think that the hole in this metric for 10 hours yesterday while OVH had 2 x 20 Kv lines down and generator problems in France, is normal? Maybe I should wait a week before training Skyline on any metrics that have been OVH’d…”
Deploying and setting Ionosphere up
Requires - Panorama to be enabled and running.
Ionosphere has a specific section of variables in settings.py
that are
documented in the settings specific documentation see all the settings below:
settings.IONOSPHERE_CHECK_PATH
- http://earthgecko-skyline.readthedocs.io/en/latest/skyline.html#settings.IONOSPHERE_CHECK_PATH
The Ionosphere settings follow normal Skyline settings conventions in terms of being able to paint all metrics with certain defaults and allowing for specific settings for specific or wildcard namespaces. If you know Skyline then these should all be fairly intuitive.
If you are new to Skyline, then by the time you have all the other Skyline apps set up and running you should have a more intuitive feel for these to after having spent some time looking over your running Skyline. You cannot rush time series :)
Ionosphere static demo pages
The demo pages show examples of how Ionosphere presents the operator with the metric’s details for the specific point anomaly and the all the Graphite metric graphs at multiple resolutions so that the operator can evaluation the anomaly in the full context of the metric’s history.
Graphs demo page - multiple resolution Graphite graphs for context
The demo page uses an edge case anomaly one which is not easy to decide whether it is or is not an anomaly.
Would YOU consider it anomalous or not? Would you want an alert?
Note that this is demo page is from an older version of Ionosphere before learn was introduced. So it does not give the operator the 2 options:
Create features profile and DO NOT LEARN
Create features profile and LEARN
It only gives the one option, however this demo page is about the multiple resolution Graphite graphs giving the operator context, so do not worry about the buttons, look at the graphs, see the Graphite :: graphs NOW section on the below demo page.
See Ionosphere static multiple resolution graphs demo page for a clearer picture (opens in a new browser tab). Is it anomalous or not anomalous?
Features profile demo page with matched graphs
This demo page shows an existing features profile with all the graph resources that the profile was created, see the Graphite :: graphs WHEN created :: at 7h, 24h, 7d and 30d section in the below demo page.
A series of matched graphs, showing the instances where Ionosphere has
analyzed the Mirage detected anomalous time series and found it be not
anomalous because the calculated features_sum
of the Mirage anomalous
time series were within 1% difference of the features_sum :: 4764.3152004300
that was calculated for features profile 989 see
Graphite :: graphs MATCHED section in the below demo page.
See Ionosphere static features profile demo page with matched graphs for a clearer picture.
Features profile search demo page with generational information
See Ionosphere static search features profiles demo page with generation info for a clearer picture with generational, parent_id, number of times checked, number of times matched information.
Still interested?
Things to consider
Contextual anomalies - Earthquakes and Earth tremors
A point anomaly is only as contextually as the time frame in which it is considered to be anomalous in.
The following metaphor can be used to describe this concept. As this concept is important to understand in terms of Ionosphere and Mirage.
Let us take Bob as an example, Bob lived in the UK and decided to move to San Francisco because he landed a good job at a cool San Jose data analytics upstart. In the first week he is there, the Bay Area has a few Earth tremors, to Bob from the UK this is ANOMALOUS!!! Luckily Bob has a friendly co-worker called Alice and she directs him to http://earthquaketrack.com/us-ca-san-jose/recent and shows him it is not that anomalous, it is quite normal. Alice shows Bob to consider the context of these events in at a fuller duration.
5 earthquakes in the past 24 hours
5 earthquakes in the past 7 days
28 earthquakes in the past 30 days
381 earthquakes in the past 365 days
See also
Bob’s Hampshire earthquake data, if there is any…
And the penny drops for Bob that HERE in the Bay Area this is obviously this quite normal.
Bob then wonders to himself why he did not think about this before leaving his stable shire in the UK. He consoles himself by thinking “Well all the VCs and players are here…” so it obviously cannot be a big single point of failure.
bob = 'Skyline'
alice = 'You'
Skyline does not know all the contexts to the data, you do. Ionosphere lets us teach Bob that is not an earthquake!!! and enables Bob to look and ask, “Did Alice say this was not an earthquake, let me look”.
Negative values
It needs to be noted that the current implementation of the algorithm is only valid for positive time series. Any anomaly in time series that is identified as having a negative value in that specific time series period will not be trainable.
“Create” or “Create and LEARN”
With Ionosphere, you have the option to allow it to learn these things for itself, as long as you tell it what it is ALLOWED to learn at the fuller duration.
So Ionosphere gives you 2 options:
This means you do not have to ship that earthquake that happened 17 days ago into
Ionosphere’s features profiles and teach it BAD, badly. You can just tell it
to see the relevant Analyzer settings.FULL_DURATION
or Mirage the
SECOND_ORDER_RESOLUTION_HOURS
data as not anomalous and not learn at
the fuller duration of the metric’s learn_full_duration
.
You can teach Ionosphere badly, but to “unteach” it is just a click of the Disable features profile button.
Use Ionosphere learning sparingly, although it is a feature, it will also generates an ongoing requirement for you to review and validate what it has learnt.
Skyline learning is not perfect. There are certain metrics that learning seems not to work as well on as other metrics, 5min and 15min load_average metrics fall into this category. Do not go overboard on learning do it slowly and incrementally. All LEARNT features profiles have the ability to be validated, however this is not a hard requirement, unvalidated features profiles will still be used in analysis, the validated flag is currently simply there to give the operator a view of what LEARNT features profiles have not been assessed to determine their accuracy. Skyline will also LEARN from an unvalidated features profile. Therefore the operator needs to review and validate or disable features profiles every so often, this can be achieved when reviewing and training on not anomalous patterns is done, whenever training data for a metric is assessed it is easy to review all features profiles for that metric during this phase by using the Existing :: features profiles page link on the training page and using the Ionosphere Validate page for side-by-side comparisons.
To begin with try and start with only creating features profiles and only implementing learning very occasionally when you are certain that a metric has a very “normal” pattern over 30 days. Initially just creating features profiles and not trying to learn on all of them, will give you a good idea of what Skyline Ionosphere is doing when you look at the Ionosphere matches pages and review matches. Having a few learn features profiles as well, will also give you a good idea of what is being done when Skyline is learning, when you look at the Ionosphere features profiles page and reviewing the learnt features profiles on the Search :: features profiles page.
Bad LEARNT features profiles
Due to Ionosphere using a settings.IONOSPHERE_LEARN_DEFAULT_MAX_PERCENT_DIFF_FROM_ORIGIN
percentage difference to learn with, it is going to get some learning wrong as
far as we humans are concerned. Due to the algorithm using a percentage
difference and not exact matches means that occasionally Ionosphere is going to
learn badly, which is often the case with load_avg metrics it seems and this
will also happen sometimes when there is missing data in a time series.
Ensure that you review and validate LEARNT features profiles often to ensure
that incorrect LEARNT features profiles are disabled as soon as possible.
The validate features profile page is useful for this. See Ionosphere validate features profile demo page
Warning
A important note on learning. When you let Ionosphere learn
you create work for yourself in terms of validating every learnt
profile that Ionosphere learns. If Ionosphere learns badly and you do not
keep up to date with validating learnt features profiles, Ionosphere could end
up silencing genuine anomalies which you would want to be alerted on.
The minimise and mitigate Ionosphere learning badly too many times, it is
possible to set the settings.IONOSPHERE_LEARN_DEFAULT_MAX_GENERATIONS
variable to 2 instead of the current default of 16 (as of v1.2.5), but as a
general rule of thumb, using learning sparingly and validate often.
How Ionosphere works - a simple overview
Firstly one needs to understand there is a chicken and egg aspect to Ionosphere. Which if you have read up to this point, hopefully you have already got that point.
Ionosphere has a number of roles that are centered on feature extractions, feature calculations and comparisons and a role centered on learning.
The features role
The features role is based on the sum of the calculated features of a time series. What are calculated features? Between 150 and 210 features are calculated, here are a few examples:
['value__minimum', '1507.1']
['value__maximum', '3806.75']
['value__median', '1853.95']
['value__count_above_mean', '474.0'],
['value__skewness', '2.1725548997']
['value__number_peaks__n_3', '121.0']
['value__longest_strike_above_mean', '42.0']
['value__first_location_of_minimum', '0.714285714286']
['value__last_location_of_minimum', '0.715277777778']
['value__absolute_sum_of_changes', '71195.65']
['value__augmented_dickey_fuller', '-5.85928430205']
['value__large_number_of_peaks__n_3', '1.0']
For the current full list of all features that are calculated see tsfresh_feature_names.TSFRESH_FEATURES
Ionosphere calculates the features and then then use the sum of these values.
Ionosphere only analyses SMTP alerter enabled metrics.
Once Ionosphere is enabled, if Analyzer or Mirage detect an anomaly on a metric they:
Save the training data set and the anomaly details
If the metric is not an
ionosphere_enabled
metric and a SMTP alert enabled metric, an alert is triggered and all the created alert images are saved in the training data directory as well.If the metric is an
ionosphere_enabled
metric, Analyzer and Mirage defer the time series to Ionosphere, via a check file, for Ionosphere to make a decision on. More on that below.
Ionosphere serves the training data set for each triggered anomaly, ready for a human to come along in the Webapp Ionosphere UI and say, “that is not anomalous” (if it is not).
At the point the operator makes a features profile, all the features values that are created for the not anomalous time series are entered into the database and the metric becomes an
ionosphere_enabled
metric, if it was not one already.All the anomaly resources are then copied to the specific features profile directory that is created for the features profile.
Once a metric is
ionosphere_enabled
, both Analyzer and Mirage will refer any anomalies found for the metric to Ionosphere instead of just alerting.When a 3sigma anomalous time series is sent to Ionosphere, it calculates the features with tsfresh for the 3sigma anomalous time series and then compares the common features sums with those of previously recorded features profiles. If the two values are less than
settings.IONOSPHERE_FEATURES_PERCENT_SIMILAR
, Ionosphere will deem the time series as not anomalous and update the related training data as MATCHED and update the features profile matched count in the database.If the values are not matched Ionosphere will apply Min-Max scaling (ONLY if the features profile time series and the anomalous time series are of a similar range) and try the same technique with temporary Min-Max scaled time series from the original features profile time series (which is stored in the database) and the 3sigma anomalous time series. It creates a features profile for each Min-Max scaled (standardized) time series and then compares the features sums and determines whether the difference is below the
settings.IONOSPHERE_FEATURES_PERCENT_SIMILAR
and will deem it as not anomalous and update the related training data as MATCHED and update the features profile matched count in the database.If Ionosphere does not find a features profile match, it analyses the time series against any defined layers, if there are any and if a match is found Ionosphere will deem the time series as not anomalous and update the related training data as MATCHED and update the features profile layers matched count in the database.
If Ionosphere does not find a match, it tells the originating app (Analyzer or Mirage) to send out the anomaly alert with a
[Skyline alert] - Ionosphere ALERT
subject field.
The learning role
Once a features profile has been made for a metric with the LEARN option, for every unmatched anomaly that training_data is created for, after the
learn_valid_ts_older_than
seconds have been reached, Ionosphere will attempt to “learn” whether the anomalous event afterlearn_valid_ts_older_than
seconds and any subsequent aggregation has had time to occur, if the time series features atlearn_full_duration
seconds match any feature profiles that were created for the metric at thelearn_full_duration
.If Ionosphere finds a match to the features calculated from the metric time series that it surfaces from Graphite at
learn_full_duration
, it will use the anomaly training data to create a features profile for the metric at the metric’ssettings.FULL_DURATION
orSECOND_ORDER_RESOLUTION_HOURS
(whichever is applicable) and it will also create a features profile with thelearn_full_duration
data that matched, as long as thesettings.FULL_DURATION
orSECOND_ORDER_RESOLUTION_HOURS
features sum difference is within thesettings.IONOSPHERE_LEARN_DEFAULT_MAX_PERCENT_DIFF_FROM_ORIGIN
or the metric specificmax_percent_diff_from_origin
Note
Ionosphere does not currently use Min-Max scaling in the learning role, the learnt features profiles are only created with the time series at the original scale.
Input
When an anomaly alert is sent out via email, a link to the Ionosphere training data is included in the alert. This link opens the Ionosphere UI with the all training data for the specific anomaly where the user can submit the metric time series as not anomalous and have Skyline generate a features profile with tsfresh (and optionally some additional layers, which are covered further down on this page).
features profiles
When a training data set is submitted as not anomalous for a metric a features
profile is extracted from the time series using tsfresh. This features profile
contains the values of 210 features (currently as of tsfresh-0.4.0), such
as median, mean, variance, etc, for a full list of known features that are
calculated see tsfresh_feature_names.TSFRESH_FEATURES
.
This features profile is then stored in the Skyline MySQL database in the following manner. For every metric that has a features profile that is created, 2 MySQL InnoDB tables are created for the metric.
The features profile details are inserted into the ionosphere table and the features profile gets a unique id.
z_fp_<metric_id> - features profile metric table which contains the features profile id, feature name id and the calculated value of the feature.
z_ts_<metric_id> - the time series data for the metric on which a features profile was calculated.
These tables are prefixed with z_
so that they are all listed after all core
Skyline database tables. Once a metric has a z_fp_<metric_id> and a
z_ts_<metric_id> table, these tables are updated with any future features
profiles and time series data. So there is are 2 tables per metric, not tables
per features profile.
Note
Just a note on when features are extracted. The user may notice that when they extract features on a training data set, that sometimes they are advised that the features were “previously calculated by Ionosphere” and sometimes not. This is because if the metric has been previously trained, it is an Ionosphere metric and Ionosphere will have extracted the features to analyse the metric. If the metric has not been previously trained then no features will have been extracted by Ionosphere and the features will be extracted when the user requests them. This is for the sake of efficiency, there is no need to add computational overhead to extract features on every anomaly.
How Ionosphere is “learning”?
Ionosphere may have had humble beginnings, but adding this seemingly trivial function was anything but humble, simple or easy. So to solve the seemingly simple problem, something completely new had to be pieced together.
Ionosphere “learns” time series and makes decisions based on a time series similarities comparison method using the tsfresh package.
This “learning” is base upon determining the similarities in time series that could be best described as attempting to determine how similar 2 time series are in terms of the amount of “power/energy”, range and “movement” there is within the time series data set. A fingerprint or signature if you like, but understand that neither are perfect. This time series similarities comparison method is not perfect in the dynamic, operational arena, but it achieves the goal of being useful. However, it must be stated that it can be almost perfect, a tsfresh features profile sum is (about as) perfect as you can get at 0% difference (there may be edge cases). However using it with 100% matching is not useful to learning and trying to profile something like the Active Brownian Motion (for want of a better way of explaining it). Lots of dynamic metrics/systems will exhibit a tendency to try an achieve Active Brownian Motion, not all but many and definitely at differing and sometimes multiple seasonality.
For a very good overview of Active Brownian Motion please see the @kempa-liehr description at
See also
https://github.com/blue-yonder/tsfresh/pull/143#issuecomment-272314801 - “Dynamic systems have stable and unstable fixed points. Without noise these systems would be driven to one of their stable fixed points and stay there for ever. So, fixed points resemble an equilibrium state”
Ionosphere enables us to try and profile something similar to Active Brownian Motion as the norm, again for want of a better way of trying to explain it.
Note
Ionosphere does not use Min-Max scaling in the learning context
However, contextually, Ionosphere nor the tsfresh implemented method, will ever be perfect, unless 2 time series have identical data, consistently, without change. But how challenging would that be? :)
Also it may be possible that an identical time series reversed may give the same or negative of a features sum and a mirror image time series can have very similar calculated feature sums.
Anyway, it is not perfect, by design. Evolution tends to not achieve perfection, attaining a working, functional state is usually the norm in evolution it seems.
Evolutionary learning - generations
Ionosphere uses an evolutionary learning model that records (and limits) the
generations of trained and learnt features profiles per metric. Limits can be
set in settings.py
and played around with. For veterans of Skyline, these
tend to be much like settings.CONSENSUS
, what is the correct CONSENSUS
?
They are tweak and tunable. Keep them low, you give Ionosphere less leverage to learn. But you will bump them up so that it can learn more and better.
Although this documentation may seem overly chatty and verbose, all things have stories. And why should documentation be overly dull, try explaining Skyline to someone, good luck. You should see me at parties. Anyway not many people read this, so it does not really matter :)
If you want to understand Skyline more, look at the code. But be gentle :)
Or better yet, set it up. Being able to teach a system and see it learn it pretty cool, just look at https://github.com/xviniette/FlappyLearning and NeuroEvolution.js (thanks for the inspiration from @nylar and @xviniette)
Lots of Skyline concepts are easy enough to get, some are not so easy and when they are all tied together with a splash of seasonality and a dash of similarities, it gets quite complicated.
However, all Skyline pieces, individually, are relatively simple. Seeing them work helps or hinders depending on your outlook… “Shit lots of stuff is anomalous” can often lead to lots of work, debugging, fine tuning and making better or polishing a turd or diamante.
DISABLED features profiles
Ionosphere learning is not perfect, sometimes it will get it wrong as far as a human is concerned. Luckily that does not happen often, but it will happen.
Ionosphere lets the operator disable any features profile that they deem as anomalous. This can be due to a features profile having been LEARNT and the operator thinks it to be anomalous or an operator could create a features profile that they decide was in error, this can especially be true when on re-evaluating after creating with the “and LEARN” option enabled, but looking at the 30 day data and thinking… “hmmm actually I do not really want it to learn that spike from 2 weeks ago”.
If a features profile is DISABLED, all its progeny features profiles are disabled as well. This ensures that every features profile was LEARNT from the profile and any that were LEARNT from any of them are disabled too so that the pattern is removed from evaluation during analysis of the metric in the future.
Layers
Ionosphere allows the operator to train Skyline a not anomalous time series in terms of generating a features profile to be compared to anomalies in the future, however Ionosphere also allows the operator to define “layers” rules at the time of feature profile creation.
Layers rules allow us to train Skyline on boundaries as well, on the fly via the
UI at the time of features profile creation, which puts all the work for the
operator in the one place. Think of them as metric AND feature profile specific
algorithms. A layer should only ever be used to describe the features profile
and ONLY at settings.FULL_DURATION
time series. The operator should
limit their layers values to within acceptable bounds of the range within the
features profile. The operator should not try and use a single layer to try and
describe the entire range they “think” the metric can go to, a layer is meant to
match with a features profile, not a metric, however see the Matching
approximately close section immediate below. If this methodology is followed,
layers and features profiles “retire” around the same time as metrics change
over time, an old features profile that no longer describes the current active
motion state will no longer ever be matched, neither should its layers. One of
the things some way down the road on the Ionosphere roadmap is
Feature #1888: Ionosphere learn - evolutionary maturity forget
Layers were added to reduce the number of features profiles one has to train Skyline on. They were introduced for humans and to make it easier and more useful. However they come at a cost. Every layer created reduces Ionosphere’s opportunities to be trained and learn. It is a compromise to save on the amount of monkeys you have to have or need to be to train Skyline properly. Unfortunately someone has to be the monkey, but for every features profile/layer you create, you create a Skyline monkey to watch that. A monkey with fairly simple instructions.
A layer consist of a series of simple algorithms that are run against a time series after Analyzer/Mirage and Ionosphere features comparisons.
Note
It is IMPORTANT to understand that layers are only evaluated against
the settings.FULL_DURATION
time series, regardless if it is a Mirage
metric being checked. The operator should define their layers values in
relation to the Redis graph that is presented in the Train :: layers section
and to the Last :: 30 datapoints :: at FULL_DURATION section just below that
section, to ensure they are using relevant ranges.
When you create a layers on a features profile, initially do try to validated them when they are matched, just to ensure they are behaving as you desire. Once you have validated that the layer is behaving as you wish, you should never have to worry about an invalid layer match thereafter. However, do ensure that you just try to validate a few layers every now and then.
Matching approximately close
The settings.IONOSPHERE_LAYERS_USE_APPROXIMATELY_CLOSE
variable
enables a margin of tolerance to be applied to layers D and E. This is enabled
by default and D and E boundary limits are matched if the value is approximately
close to the limit. This is only implemented on boundary values that are > 10.
The approximately close value is calculated as 10 percent for limit values
between 11 and 29 and within 5 percent when the limit value >= 30. It is only
applied to the D layer with a ‘>’ or ‘>=’ condition and to the E layer with a
‘<’ or ‘<=’ condition. This makes the layers more effect and means that if a
limit on a layer is set to 52 and the data point is 53, the layer will match
and you probably want it to as well. If approximately_close is not used you
will find it frustrating the number of times an anomaly is triggered where it
did not match a layer by small margin.
How layers are defined
The layers are defined as:
D layer [required] if last_datapoint [<, >, ==, !=, <=, >=] x
DISCARD - not_anomalous False
The D layer can be used as the upper or lower limit, e.g if value > x (probably or certainly anomalous). Or this can be used if this metric operates in the negative range or if you want it too not discard on 0 as you want to match 0, set it to -1 or > 0.1 or > 1. On a high constant rate metric the D layer can be used to discard if < x so the the layer does not silence a drop. This layer can be complimented by the optional D1 layer below. Remember a match here disables any of the other below layers being checked
D1 layer [optional] if datapoint [<, >, ==, !=, <=, >=] x in the last y values in the time series
DISCARD - not_anomalous False
The D1 layer can be used as an upper or lower limit, so the D layer does not silence a drop. Remember a match here disables any of the other below layer conditions from being checked.
E layer [required] if datapoint [<, >, ==, !=, <=, >=] x in the last y values in the time series
not anomalous
The Es, F1 and F2 layers shall not be discussed as NOT IMPLEMENTED YET.
An example layer
For instance, say occasionally we can expect to see a spike of 404s status codes on a web app due to bots or your own scanning, with layers we can tell Ionosphere that a time series was not anomalous if the datapoint is less than 120 and has values in the last 3 datapoints is less than 5. This allows for a somewhat moving window and an alert that would be delayed by say 3 minutes, but it is a signal, rather than noise. Let us describe that layer as gt_120-5_in_3
To demonstrate how the above layer would work, an example of 404 counts per minute:
D layer :: if value > 120 :: [do not check] :: ['ACTIVE']
D1 layer :: if value none none in last none values :: [do not check] :: ['NOT ACTIVE - D1 layer not created']
E layer :: if value < 5 in last 3 values :: [not_anomalous, if active Es, F1 and F2 layers match] :: ['ACTIVE']
Es layer :: if day None None :: [not_anomalous, if active F1 and F2 layers match] :: ['NOT ACTIVE - Es layer not created']
F1 layer :: if from_time > None :: [not_anomalous, if active F2 layer matchs] :: ['NOT ACTIVE - F1 layer not created']
F2 layer :: if until_time < None :: [not_anomalous] :: ['NOT ACTIVE - F2 layer not created']
Apply against
13:10:11 2
13:11:11 0
13:12:11 8
13:13:11 60
13:14:11 0
With the above described layer, this would be classified as not anomalous, however if the data was:
13:10:11 2
13:11:11 0
13:12:11 800
The layer would not ever report the time series as not anomalous as the 800 exceeds the gt_120, so the rest of the layer definition would not be evaluated.
Warning
Layers may seem simple, but the layers must be thought about carefully as it is possible for a metric to have multiple layers created on multiple features profiles, that could silence any anomalies on the metric. Specifically D layer, however layer D1 was added to remove this possibility, if the layers are properly implemented. The D1 layer is optional (and is reverse capable with with any existing layers that were created prior to 1.1.3-beta) and is there to let the operator set upper and lower bounds where necessary.
Be careful that you do not create another layer later that silences bad, e.g. dropped to 0, the above example is not a good example of that as we want and expect 0 on the 404 not found generally, but if it was status code 200, we would not want any layers silencing a drop to 0, please try and use layer D1 wisely where required.
However, in this example, if you want to ensure that your 200 status code count does not hit 0 or drop off a cliff, you would configure Boundary to watch it.
No machine learning
Ionosphere brings no machine learning to Skyline per se. It is merely making programmatic decisions based on the data it is provided with, things a human operator tells it are not anomalous. Ionosphere is an attempt to give Skyline an Apollo Program refit. Enabling the pilots to take control, have inputs.
A benefit here is that the method does not suffer from underspecification that is prevalent in machine learning models.
For Humans
If Ionosphere achieves the sentiments expressed in Brian L. Troutwine @bltroutwine seminal Belgium 2014 devopsdays presentation, then it has achieved a goal.
Automation with Humans in Mind: Making Complex Systems Predictable, Reliable and Humane - https://legacy.devopsdays.org/events/2014-belgium/proposals/automation-with-humans-in-mind/
Ionosphere first and foremost was created to give this dimension of human piloting where necessary. Giving Skyline that ability to allow human input in some from to “teach” Skyline what is not anomalous comes with a number additional benefits, like giving the Skyline the information needed to learn how to make decisions based on the input data it is provided within.
The initial goal has been achieved, but it comes at a price. Everything has a
cost and here the cost is the operator needs to
train_ionosphere_learn == time_in_seconds # about 12 seconds
.
Ionosphere can only be activated by the input from a human neocortex to tell it what is not anomalous. Some brain CPU cycles, opening emails and clicks, assess 1 or 2 more clicks. It is not easy, however that said it is effective at what it set out to achieve.
Current state
It appears that Ionosphere is better at doing what it was intended for than doing what it was not intended for. All time series not being created equal.
Ionosphere does low range, low rate metrics very well.
Ionosphere does them better than high rate, highly variable metrics, when it saw
first light at least. This is not to say that it does not do high rate, highly
variable metrics, it just needs a lot more features profiles for the metric
describing what is not anomalous. However it is possible that a larger
settings.IONOSPHERE_LEARN_DEFAULT_MAX_PERCENT_DIFF_FROM_ORIGIN
or metric
specific max_percent_diff_from_origin
may work quite well on large volume
and high variability metrics, time will tell.
Over the fullness of time and data, these learning efficiency metrics will be available via the database data for analysis.
UPDATE - using Min-Max scaling Ionosphere now handles high range, highly variable metrics much better.
tsfresh
The tsfresh package and features extraction functions, enabled this ability of features calculation on a wholesale scale, without having to design lots of algorithms to calculate the time series features for. The tsfresh package enabled Ionosphere to happen much FASTER, it calculates all the features that are required to make this method viable and work. They said:
"Spend less time on feature engineering"
They were not wrong. Skyline has added a lot of “checks” to ensure consistency in the tsfresh calculated features so that a features profile is not affected by any changes that may be implemented in the tsfresh package. All of this has been pushed back into tsfresh and may be one of the reasons why the actual development of Ionosphere took so long, but you cannot rush time series.
This overview of Ionosphere could not be complete without a special thanks to the tsfresh people @MaxBenChrist, @nils-braun and @jneuff who are some of nicest people in open source, on par with @astanway :)
Thanks to blue-yonder for supporting the open sourcing of tsfresh.
memcached
Ionosphere uses memcached and pymemcache (see https://github.com/pinterest/pymemcache) to cache some DB data. This optimises DB usage and ensures that any large anomalous event does not result in Ionosphere making all the DB metrics become anomalous :)
The architectural decision to introduce memcached when Redis is already available, was done to ensure that Redis is for time series data (and alert keys) and memcached isolates DB data caching. The memcache data is truly transient, where as the Redis data is more persistent data and memcached is a mature, easy to use and well documented.
Cached data
Ionosphere caches the following data:
features profile features values from the z_fp_<metric_id> table - no expiry
metrics table metric record - expire=3600
metric feature profile ids - expire=3600
features profile features time series from the z_ts_<metric_id> table - no expiry
Note
due to caching a metric and a features profile can take up to 1 hour to become live.
Operational considerations
No UI data update method
There is no method to modify the DB data via the UI. If you want to make any
changes, they must be made directly against the DB. Deleting features profiles,
changing any of the metrics values once set for metrics e.g.
learn_full_duration
, learn_valid_ts_older_than
, max_generations
or
max_percent_diff_from_origin
Backup
Backup the MySQL DB to another machine or better slave it and backup the slave.
rsync backup /opt/skyline/ionosphere/features_profiles to another machine, frequently (for the time being, until autobuild is available, however autobuild will not a able to recreate all the resources, but most).
MySQL configuration
There could be a lot of tables. DEFINITELY implement innodb_file_per_table
in MySQL.
IONOSPHERE_CUSTOM_KEEP_TRAINING_TIMESERIES_FOR
Enabling settings.IONOSPHERE_CUSTOM_KEEP_TRAINING_TIMESERIES_FOR
allows
you to not remove training data when it is settings.IONOSPHERE_KEEP_TRAINING_TIMESERIES_FOR
seconds old. Instead for any training data for metrics that match a namespace
substring declared in the settings.IONOSPHERE_KEEP_TRAINING_TIMESERIES_FOR
list will be moved to settings.IONOSPHERE_HISTORICAL_DATA_FOLDER
This is an advance feature and not for general purpose, please read the
documentation in the docstrings for more info, see:
settings.IONOSPHERE_HISTORICAL_DATA_FOLDER
and
settings.IONOSPHERE_CUSTOM_KEEP_TRAINING_TIMESERIES_FOR
Ionosphere - autobuild features_profiles dir
Warning
autobuild - TBD at some point in the future, for now see the Backup section above.
The number of features_profiles dirs that Ionosphere learn could spawn and the amount of data storage that would result is unknown. It is possible the operator is going to need to prune this data a lot of which will probably never be looked at. Or a Skyline node is going to fail, not have the features_profiles dirs backed up and all the data is going to be lost or deleted. So it is possible for Ionosphere to created all the human interrupted resources for the features profile back under a best effort methodology. Although the original Redis graph image would not be available, nor the Graphite graphs in the resolution at which the features profile was created, however the fp_ts is available so the Redis plot could be remade and all the Graphite graphs could be made as best effort with whatever resolution is available for that time period.
This allows the operator to delete/prune feature profile dirs by possibly least matched by age, etc or all and still be able to surface the available features profile page data on-demand.
Note
expire features profiles older than? Ionosphere forget.


Indenpendence Day (also called Forth of July) is probably the most important federal holiday for Americans.
It commemorates publication of American Declaration of Indenpendence in 1776.
Many people use it as opportunity to show their patriotism by participating in parades and ceremonies,
wearing clothes in their national colours and hanging out flags.
History
In 1775 people living in New England began fighting the British Kingdom for their indenpendence.
On July 2, 1776 the Second Continental Congress secretly voted for indenpendence from Great Britain.
Two days later the final wording of the document was approved and it was published. On 2 August
delegates began to sign the Declaration of Indenpendence.
The Comitee of Five were men who drafted and presented
Declaration of Indenpendence to the Second Continental Congress.
They worked from June 11 to the publication of the document.
Members of the Comitee of Five:
John Adams
Thomas Jefferson
Benjamin Franklin
Roger Sherman
Robert Livingston
Customs
In the summer of 1776 some colonist celebrated independence
of USA by holding mock funerals for King George III. People
organized many festives, parades, bonfires and firing of cannons
during public readings of Declaration of Indenpendence. Philadelphia
held the first commemoration of independence on July 4, 1777.
In 1778 Massachusetts became the first state celebrating July 4th as
an official state holiday.
Today Americans celebrate Indenpendence Day by participating in
public events and parades.
Other activities associated with the day include
watermelon or hotdog eating competitions and sporting
events, such as baseball games, three-legged races,
swimming activities and tug-of-war games.
Symbols of USA
Flag
It consists of thirteen equal horizontal red and white stripes
whith a blue rectangle bearing 50 white, five pointed stars.
Stars represents states of the USA and stripes represents 13 colonies
that declared indenpendence from the Great Britain.
First version with 13 stars was adopted in 1777 and the current one in 1960.
Great Seal
It is a seal used by the US federal goverment.
Design of the obverse of the seal is used on
United States passports, military insignia, embassy
placards, and various flags. It is also national
coat coat of arms of the United States. It also
appears on the reverse of the one dolar bill.
Many goverment agencies use it's elements on their
seals.
The Star Spangled Banner
Oh, say can you see, by the dawn’s early light,
What so proudly we hailed at the twilight’s last gleaming?
Whose broad stripes and bright stars, through the perilous fight,
O’er the ramparts we watched, were so gallantly streaming?
And the rockets’ red glare, the bombs bursting in air,
Gave proof through the night that our flag was still there.
O say, does that star-spangled banner yet wave
O’er the land of the free and the home of the brave?
On the shore, dimly seen through the mists of the deep,
Where the foe’s haughty host in dread silence reposes,
What is that which the breeze, o’er the towering steep,
As it fitfully blows, now conceals, now discloses?
Now it catches the gleam of the morning’s first beam,
In full glory reflected now shines on the stream:
‘Tis the star-spangled banner! O long may it wave
O’er the land of the free and the home of the brave.
And where is that band who so vauntingly swore
That the havoc of war and the battle’s confusion
A home and a country should leave us no more?
Their blood has wiped out their foul footstep’s pollution.
No refuge could save the hireling and slave
From the terror of flight, or the gloom of the grave:
And the star-spangled banner in triumph doth wave
O’er the land of the free and the home of the brave.
Oh! thus be it ever, when freemen shall stand
Between their loved homes and the war’s desolation!
Blest with victory and peace, may the heaven-rescued land
Praise the Power that hath made and preserved us a nation.
Then conquer we must, for our cause it is just,
And this be our motto: “In God is our trust.”
And the star-spangled banner forever shall wave
O’er the land of the free and the home of the brave!


LOCAL :
FIXED SENTENCE
FULL
YEAR
SHORT
YEAR
MONTH
NAMED
MONTH
NUMERIC
WEEK
NAMED
DATE
HOUR
⬇⬇⬇
Drag and drop below to arrange.
⬇⬇⬇
✔
Ok, I will do this.
✘
No, I won't.


componentDidMount
- This method is called after your component has been added to the component tree.
componentDidUpdate
- This method is called after your component has been updated.
componentWillUnmount
- This method is called just before your component is removed from the component tree.
When a class component is being added to the component tree, the following process occurs:
A component will update if it receives new props or if the setState
method is called.
When a component receives new props, the following process occurs:
When a the setState
method is called, the following process occurs:
Just before a class component is removed from the component tree, the componentWillUnmount
lifecycle method is called. This is often used for cleaning up any asynchronous functions from our React Component.
A common use case for the componentDidMount lifecycle method, is to fetch data from an API after a component has been mounted to the DOM. EX:
import React from 'react';
class FetchingData extends React.Component {
constructor(props) {
super(props);
this.state = {
repositories: [],
};
}
async componentDidMount() {
const url = `https://api.github.com/users/${this.props.gitHubUsername}/repos`;
const res = await fetch(url);
const data = await res.json();
this.setState({ repositories: data });
}
render() {
const { repositories } = this.state;
if (repositories.length === 0) {
return (

Fetching data...

);
} else {
return (

GitHub Repositories for {this.props.gitHubUsername}

{
repositories.map((repo) => (

{repo.name}

))
}

);
}
}
}
export default FetchingData;
In the above example:
.
Once the component is mounted to the DOM, the componentDidMount lifecycle method is called, which in turn uses the Fetch API to retrieve the public repositories for the provided GitHub username.
When the fetch HTTP request completes and the JSON response is parsed, the this.setState method is called to update the this.state.repositories property with the newly acquired data.
Updating the state causes React to re-render the component which then displays an unordered list of links.


GOOD WILL HUNTING A film by Gus Van Sant Starring Matt Damon, Ben Affleck, Robin Williams, Minnie Driver, and Stellan Skaarsgard Written by Matt Damon and Ben Affleck
One of the most stunning pictures of 1997, "GOOD WILL HUNTING" emerged from winter season as one of the best films of the year, and a blockbuster hit for Miramax. Featuring a hip, young cast, and a wonderful role by Robin Williams, "GWH" was rewarded with 2 Oscar victories, including Best Original Screenplay, and Best Supporting Actor, for Williams.
The plot is relatively simple and straightforward. Will Hunting (Damon) is the classic definition of a rebel. Born and raised in South Boston by foster parents, Will is a fighter, a kid of the streets, who gets into fights without being asked, who bums around bars with his friends, having a few drinks between jobs. Will is also a genius. His mind is brilliant, and he has the education of a Harvard student, without the degree.
His secret is discovered when one day, he is caught by Professor Gerald Lambeau (Skaarsgard) while working on a complex Fourier system Lambeau had put up on a blackboard as a challenge to his students. Lambeau immediately "rescues" the boy from an impeding jail term, and takes him to a number of therapists to heal his mind. Finally, he asks college roommate Sean McGuire (Williams), himself a Southie as well, to help the boy. What follows is a journey for Will and Sean, as both grow closer during the sessions. The story also deals with Will's development, his love for Skylar (Driver), and his complicated friendship with Chuckie (Affleck).
The screenplay is riddled with old movie cliches, and is as predictable as any other. What sets "GOOD WILL HUNTING" apart, however, is the direction of Gus Van Sant, as well as the acting of the principal roles. Van Sant uses all sorts of angles and lighting to acheive brilliant technical shots while drawing the audience into the story. While the story may be flawed, the dialogue certainly isn't. Damon and Affleck wrote an endearing script, one which rewards excellent performances with great pieces of dialogue, and fantastic set pieces.
The performances are nothing short of exquisite. Damon and Affleck are wonderful, absolutely wonderful. Driver is sweet and touching, and Skaarsgard is as good as ever. But the film belongs to Williams' tortured performance as McGuire. It is his story that is told as well, his change from sorrow to relief. The script tracks a parallel story in Sean McGuire's, and Williams pours more into this performance than any other in recent memory. Damon is nothing short of outstanding in his lead role, but Williams just steals every shot he has. With a wonderful score and a gorgeous ending, this film is certainly one of the finest of 1997, and a stunning debut for screenwriters Damon and Affleck.
RATING: ***1/2 out of ****
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--- title: Flexible ---
Creating digital products can be challenging leaving very little time managing an maintaining a DS. The DPS.KIT was designed to be flexible, easy to customize or extend, runs on any platform so you can build, run and test locally or quickly deploy to the web without worry.


I am currently studying web development full time at Epicodus, a code bootcamp in Portland, OR. We are pair programming all day, every day at Epicodus. Pairing strengthens and challenges your knowledge base, and also fosters great communication skills.


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We'll use acceleration
and
accelerationIncludingGravity
give the
acceleration of the device on the three axis X, Y and Z. Acceleration is expressed in m/s2.
window.addEventListener('devicemotion', function(event) {
const x = event.accelerationIncludingGravity.x;
const y = event.accelerationIncludingGravity.y;
const z = event.accelerationIncludingGravity.z;
...
});