public class FreeCamController
extends org.jogamp.java3d.Behavior
implements java.awt.event.KeyListener, java.awt.event.MouseMotionListener, java.awt.event.MouseListener
| Modifier and Type | Field and Description |
|---|---|
protected org.jogamp.vecmath.Vector3d |
forward
Unit vectors for the directions of the camera
|
protected java.util.HashSet<java.lang.Integer> |
keys
The set of all keys currently held down
|
private int |
lastMX
x position of mouse last frame, used when mouse dragging
|
private int |
lastMY
y position of mouse last frame, used when mouse dragging
|
static double |
movSpeed
Units per second speed of the camera when translating
|
private static double |
PI_2
PI divided by two, save that math
|
protected org.jogamp.vecmath.Vector3d |
right
Unit vectors for the directions of the camera
|
protected org.jogamp.java3d.Transform3D |
targetT
The target Transform3D, should be the ViewTransform's transform
|
protected org.jogamp.java3d.TransformGroup |
targetTG
The target TransformGroup, should be the ViewTransform
|
protected org.jogamp.vecmath.Vector3d |
up
Unit vectors for the directions of the camera
|
protected double |
viewPitch
Current pitch rotation of the camera
|
protected org.jogamp.vecmath.Vector3d |
viewPos
Current position and therefore translation of the camera, in global worldspace
|
protected double |
viewYaw
Current yaw rotation of the camera
|
private static org.jogamp.java3d.WakeupCriterion |
wakecon
To run the behaviour on every frame
|
ALLOW_AUTO_COMPUTE_BOUNDS_READ, ALLOW_AUTO_COMPUTE_BOUNDS_WRITE, ALLOW_BOUNDS_READ, ALLOW_BOUNDS_WRITE, ALLOW_COLLIDABLE_READ, ALLOW_COLLIDABLE_WRITE, ALLOW_LOCAL_TO_VWORLD_READ, ALLOW_LOCALE_READ, ALLOW_PARENT_READ, ALLOW_PICKABLE_READ, ALLOW_PICKABLE_WRITE, ENABLE_COLLISION_REPORTING, ENABLE_PICK_REPORTING
| Constructor and Description |
|---|
FreeCamController(BasicView view)
The one and only constructor, pass in the BasicView
or some other subclass for it to control.
|
| Modifier and Type | Method and Description |
|---|---|
org.jogamp.vecmath.Vector3d |
getForward()
Gets the forward direction of the camera
|
double |
getPitch()
Gets the current pitch rotation of the camera
|
org.jogamp.vecmath.Vector3d |
getPos()
Gets the current position of the camera
|
org.jogamp.vecmath.Vector3d |
getRight()
Gets the rightwards direction of the camera
|
org.jogamp.vecmath.Vector3d |
getUp()
Gets the upwards direction of the camera
|
double |
getYaw()
Gets the current yaw rotation of the camera
|
protected void |
handleKeyInput()
|
void |
initialize() |
void |
keyPressed(java.awt.event.KeyEvent arg0) |
void |
keyReleased(java.awt.event.KeyEvent arg0) |
void |
keyTyped(java.awt.event.KeyEvent arg0) |
void |
mouseClicked(java.awt.event.MouseEvent arg0) |
void |
mouseDragged(java.awt.event.MouseEvent arg0) |
void |
mouseEntered(java.awt.event.MouseEvent arg0) |
void |
mouseExited(java.awt.event.MouseEvent arg0) |
void |
mouseMoved(java.awt.event.MouseEvent arg0) |
void |
mousePressed(java.awt.event.MouseEvent arg0) |
void |
mouseReleased(java.awt.event.MouseEvent arg0) |
void |
processStimulus(java.util.Iterator<org.jogamp.java3d.WakeupCriterion> arg0)
Checks key presses and updates the ViewTransform
|
protected void |
updateDirs()
Updates the unit vector directions to the current angles
|
protected void |
updateTargetTG()
Updates the ViewTransform to the current position and angle
|
getEnable, getNumSchedulingIntervals, getSchedulingBoundingLeaf, getSchedulingBounds, getSchedulingInterval, getView, getWakeupCondition, postId, setEnable, setSchedulingBoundingLeaf, setSchedulingBounds, setSchedulingInterval, updateNodeReferences, wakeupOn
cloneNode, cloneTree, cloneTree, cloneTree, cloneTree, cloneTree, cloneTree, duplicateNode, getBounds, getBoundsAutoCompute, getCollidable, getLocale, getLocalToVworld, getLocalToVworld, getParent, getPickable, setBounds, setBoundsAutoCompute, setCollidable, setPickable
public static final double movSpeed
private static org.jogamp.java3d.WakeupCriterion wakecon
private static double PI_2
protected org.jogamp.java3d.TransformGroup targetTG
protected org.jogamp.java3d.Transform3D targetT
protected java.util.HashSet<java.lang.Integer> keys
protected org.jogamp.vecmath.Vector3d forward
protected org.jogamp.vecmath.Vector3d right
protected org.jogamp.vecmath.Vector3d up
protected double viewYaw
protected double viewPitch
protected org.jogamp.vecmath.Vector3d viewPos
private int lastMX
private int lastMY
public FreeCamController(BasicView view)
view
- The BasicView for which to controlpublic org.jogamp.vecmath.Vector3d getPos()
public double getYaw()
public double getPitch()
public org.jogamp.vecmath.Vector3d getForward()
public org.jogamp.vecmath.Vector3d getUp()
public org.jogamp.vecmath.Vector3d getRight()
public void initialize()
initialize
in class org.jogamp.java3d.Behavior
protected void updateTargetTG()
protected void updateDirs()
public void processStimulus(java.util.Iterator<org.jogamp.java3d.WakeupCriterion> arg0)
processStimulus
in class org.jogamp.java3d.Behavior
protected void handleKeyInput()
public void keyPressed(java.awt.event.KeyEvent arg0)
keyPressed
in interface java.awt.event.KeyListener
public void keyReleased(java.awt.event.KeyEvent arg0)
keyReleased
in interface java.awt.event.KeyListener
public void keyTyped(java.awt.event.KeyEvent arg0)
keyTyped
in interface java.awt.event.KeyListener
public void mouseDragged(java.awt.event.MouseEvent arg0)
mouseDragged
in interface java.awt.event.MouseMotionListener
public void mouseMoved(java.awt.event.MouseEvent arg0)
mouseMoved
in interface java.awt.event.MouseMotionListener
public void mouseClicked(java.awt.event.MouseEvent arg0)
mouseClicked
in interface java.awt.event.MouseListener
public void mouseEntered(java.awt.event.MouseEvent arg0)
mouseEntered
in interface java.awt.event.MouseListener
public void mouseExited(java.awt.event.MouseEvent arg0)
mouseExited
in interface java.awt.event.MouseListener
public void mousePressed(java.awt.event.MouseEvent arg0)
mousePressed
in interface java.awt.event.MouseListener
public void mouseReleased(java.awt.event.MouseEvent arg0)
mouseReleased
in interface java.awt.event.MouseListener


Research and analysis
This report gathered evidence from all pathfinder areas, detailing progress from April to June 2012.
Ref: DFE-RR248 PDF , 8.73MB , 103 pages
This report is the fourth in a series of progress reports that will be produced throughout the course of the 18-month evaluation of the SEND Pathfinder programme. The report is based on evidence gathered through the second set of monitoring returns received from all Pathfinder areas, which detailed self-reported progress against the Common Delivery Framework (CDF) from April to June 2012.
The Pathfinder programme involves the development and delivery of alternative approaches that could enhance or replace the existing system. Each Pathfinder was tasked to develop and trial an assessment process; a single, joined up ‘Education, Health and Care Plan’; and personal budgets across education, social care and health, and adult services as appropriate for children and young people from birth to 25 years.
In addition, the programme is exploring how best to utilise and build the skill and resource of families and the voluntary and community sector (VCS), and the development of a local service offer.


|
JSONQL Data Source Overview
JSONQL stands for JSON Query Language and it is meant to replace the existing language(simply called JSON) for
querying JSON data, but in a separate implementation as they are not compatible in terms of syntax and complexity.
It is recommended that you choose JSONQL over JSON due to its extended capabilities of traversing and filtering JSON structures.
The table below gives an overview of some of the features found in the new JSONQL language in comparison to the existing ones in the JSON language.
The examples are based on the restructured data/northwind.json source file.
| Feature |
JSONQL |
JSON |
Description |
| Dot-separated path for simple object keys |
Northwind.Customers.Address |
[the same] |
The addresses of all customers |
| Path for complex object keys |
Northwind.Customers["Company Name"] |
[not possible] |
The company names of all customers |
| Array index based selection |
Northwind.Customers[0].Orders[0] |
[the same] |
The first order of the first customer |
| Object construction expression |
Northwind.Customers[0].Orders[OrderId, OrderDate] |
[not possible] |
Select only OrderId and OrderDate from the orders of the first customer |
| Deep traversal |
..[OrderId, OrderDate, ShippedDate, Freight] |
[not possible] |
Select the OrderId, OrderDate, ShippedDate and Freight from anywhere |
| Filtering with simple expression |
Northwind.Customers.*(City == "México D.F.") |
Northwind.Customers(City == México D.F.) |
Customers from México D.F. |
| Filtering with complex expression |
Northwind.Customers.Orders.*(Freight > 200 && OrderDate *= "1997") |
[not possible] |
Orders for which the Freight is greater than 200 and OrderDate contains 1997 |
| Going up the JSON tree |
..OrderDate(@val == "1997-10-03")^{3} or ..OrderDate(@val == "1997-10-03")^^^ |
[not possible] |
The parent customers(3 levels up) for which there is an order with OrderDate that equals "1997-10-03" |
The JSONQL Query Executer
It is triggered when the jsonql language is specified:
- at report level:
<queryString language="jsonql">
<![CDATA[ ..Orders(@size > 1).* ]] >
</queryString>
- or in the data adapter:
<jsonDataAdapter class="net.sf.jasperreports.data.json.JsonDataAdapterImpl">
...
<language>jsonql</language>
...
</jsonDataAdapter>
The JsonQLQueryExecuter uses the query string to produce a
JsonQLDataSource instance,
based on the same built-in parameters (or equivalent report properties) as for the existing
JsonDataSource. This query executer is registered via
JsonQLQueryExecuterFactory factory class.
As in the case of the existing JSON query executer, in order to prepare the data source, the JSONQL query executer looks for the
JSON_INPUT_STREAM parameter that contains the JSON
source objects in the form of an java.io.InputStream . If no
JSON_INPUT_STREAM parameter is provided, then
the query executer looks for the alternate net.sf.jasperreports.json.source
java.lang.String parameter or report property that stores the path to the location of the JSON source file.
JsonQLQueryExecuter runs the query over the input source and stores
the result in an in-memory JsonQLDataSource object.
During the JsonQLDataSource instantiation, the query executer also looks for the following additional parameters or report properties, containing the required localization settings:
In the next section you can see how these additional parameters are provided in the /src/JsonQLDataSourceApp.java class.
The JSONQL Data Source Sample
In our example, the data is stored as a hierarchy of Northwind.Customers.Orders objects in the data/northwind.json file.
The JSONQL configuration is done in the data/northwindDataAdapter.xml data adapter file that is provided in the
reports/NorthwindOrdersReport.jrxml via the report property:
<property name="net.sf.jasperreports.data.adapter" value="data/northwindDataAdapter.xml"/>
In the NorthwindOrdersReport we run a JSONQL query in order to retrieve orders from anywhere:
<queryString language="jsonql">
<![CDATA[ ..Orders(@size > 1).* ]] >
</queryString>
In reversed order of application, the query simply translates to: "Select the children(.*) of Orders - with size greater than 1 - from anywhere"
This query is possible since the Orders property of each customer is an array.
Therefore we can apply an array specific filtering function to each value for the Orders property.
In this case we are interested in the Orders that have more than one element.
The "get children" expression .* is necessary here because without it we would be
looking at an array of arrays(an array with the arrays that the Orders properties point to) and not an
array of objects that we are interested in.
The properties (fields) that we are interested in are:
- properties of the order itself:
OrderID , OrderDate and Freight
<field name="Id" class="java.lang.String">
<property name="net.sf.jasperreports.jsonql.field.expression" value="OrderID"/>
</field>
<field name="OrderDate" class="java.util.Date">
<property name="net.sf.jasperreports.jsonql.field.expression" value="OrderDate"/>
</field>
<field name="Freight" class="java.lang.Float">
<property name="net.sf.jasperreports.jsonql.field.expression" value="Freight"/>
</field>
- properties of the parent customer object:
City , CustomerID and Company Name
that are obtained by going up 2 levels
<field name="ShipCity" class="java.lang.String">
<property name="net.sf.jasperreports.jsonql.field.expression" value="^{2}.City"/>
<fieldDescription><![CDATA[ Go up by two levels then select City ]] ></fieldDescription>
</field>
<field name="CustomerID" class="java.lang.String">
<property name="net.sf.jasperreports.jsonql.field.expression" value="^{2}.CustomerID"/>
<fieldDescription><![CDATA[ Go up by two levels then select CustomerID ]] ></fieldDescription>
</field>
<field name="CompanyName" class="java.lang.String">
<propertyExpression name="net.sf.jasperreports.jsonql.field.expression"><![CDATA[ "^^[\"Company Name\"]" ]] ></propertyExpression>
<fieldDescription><![CDATA[ Go up twice by 1 level then select CompanyName ]] ></fieldDescription>
</field>
The rest of the configuration necessary for this sample to work is done in the data adapter file: data/northwindDataAdapter.xml
<?xml version="1.0" encoding="UTF-8" ?>
<jsonDataAdapter class="net.sf.jasperreports.data.json.JsonDataAdapterImpl">
<name>NorthWind JsonQL DA</name>
<fileName>/data/northwind.json</fileName>
<useConnection>true</useConnection>
<language>jsonql</language>
<datePattern>yyyy-MM-dd</datePattern>
<numberPattern>#,##0.##</numberPattern>
</jsonDataAdapter>
Here we specify:
- the source file with the JSON data:
/data/northwind.json
- the fact that we want to use the existing connection and the report's
queryString by setting the useConnection flag to true
- the language of our query:
jsonql ; it is optional in this case since we are using the report's queryString that already specifies the language
- the date pattern used to parse the date fields
- the numeric pattern used to parse the numeric fields
Running the Sample
Running the sample requires the Apache Ant library. Make sure that ant is already installed on your system (version 1.5 or later).
In a command prompt/terminal window set the current folder to demo/samples/jsonqldatasource within the JasperReports source project and run the > ant test command.
It will generate PDF and HTML document types containing the sample report(s) in the demo/samples/jsonqldatasource/build/reports directory.
|
|
Overview
JSONQL is a language used for querying JSON data structures. It appeared from the need to perform more complex JSON traversals and filtering.
JSONQL is not compatible with the existing JSON language due to its possibly complicated syntax.
We typically write JSONQL queries in queryString expressions marked with the appropriate jsonql language:
<queryString language="jsonql">
<![CDATA[ ..Orders(@size > 1).* ]] >
</queryString>
Syntax
A JSONQL query is an expression containing one or more chained members:
[member1][member2]...[memberN]
For example, in the following expression:
Northwind.Customers.Orders.*(Freight > 200 && OrderDate *= "1997")
we distinguish the following members:
- [Northwind]
- [.Customers]
- [.Orders]
- [.*(Freight > 200 && OrderDate *= "1997")]
Each member is applied on the result(s) produced by the previous member. If a member produces no result, the rest of the members, if any, are skipped and no results are returned.
When successful, the final result of the JSONQL expression is a collection of JSON nodes, where each node may be an Array, an Object or a Value(boolean, string, number or null).
Absolute expressions
Absolute expressions start with the $ symbol placed in front of the first member expression:
$.Northwind.Customers[0]["Company Name"]
or
$["Northwind"].Customers[0]["Company Name"]
etc.
When used in the report's queryString , it makes no difference if an expression is absolute or not.
The result is always calculated from the root of the JSON data.
The only place where the absolute expression matters is the field's property expression. For example:
<field name="FirstCustomerCompanyName" class="java.lang.String">
<propertyExpression name="net.sf.jasperreports.jsonql.field.expression"><![CDATA[ "$.Northwind.Customers[0][\"Company Name\"]" ]] ></propertyExpression>
<fieldDescription><![CDATA[ The company name of the first customer]] ></fieldDescription>
</field>
defines a field whose value is calculated directly from the root Northwind object and not
relative to the field in the dataSet produced by the queryString .
You may want to use an absolute expression like this when the value you are interested in is not part of any of the results produced by the main dataSet's query.
Comments inside expressions
Comments are supported inside JSONQL expressions. A JSONQL expression can be a single or multi-line expression, therefore we have single line comments and multi-line comments:
- Single line comments start with the // symbol:
Northwind
.Customers
.Orders
.*
//(Freight > 200 && OrderDate *= "1997")
Here we comment out the entire filtering expression.
Multi-line comments start with /* and end with */ symbols:
Northwind
.Customers
/*.Orders
.*
(Freight > 200 && OrderDate *= "1997")*/
Here we comment out everything that follows the Customers member.
We can also use the multi-line comment inside a single line JSONQL expression:
Northwind.Customers.Orders.*(Freight > 200 /*&& OrderDate *= "1997"*/)
Here we comment out only a part of the filtering expression.
JSONQL Members
A member is an expression that tells us how to navigate a single level (up or down or anywhere down) into the JSON tree.
The generic member expression has this form:
[direction][key(s)][filterExpression]
The tables below presents all the supported member types within a JSONQL expression. Only the [direction][key(s)] part
is shown here as the [filterExpression] is applicable to all member types and will be described separately.
| Member type |
Key(s) value |
Direction |
Example in context |
Description |
Expected result type |
Explanation |
| Symbol |
Description |
| Simple Object Key |
Legal JavaScript Identifier(1) |
. |
Down(implicit) |
Northwind |
Go down on Norhtwind key |
Object |
The dot symbol is not necessary at the beginning of the expression. Direction is "Down" by default.
The result is an Object because the JSON root is of type Object and the Northwind key also points to an Object. |
| Northwind.Customers |
Go down on Customers key of the object produced by navigating down Northwind key |
Array of Objects |
The result is an Array of Objects because Northwind member produces an Object whose Customers key points to an Array.
|
| Northwind.Customers.Address |
Select the addresses of all customers |
Array of String values |
The result is an Array of Strings because the Customers key points to an array that is traversed, and each object
is queried for the Address key, which in turn, points to a String.
|
| .. |
Anywhere Down |
Northwind..Orders |
Select Orders from anywhere down starting from the Northwind root |
Array of Arrays |
The Anywhere Down selection usually builds a collection(array) of nodes. The type of the key dictates the
type of the nodes contained. In this case each Orders key points to an array. |
| ..Orders |
Select Orders from anywhere down starting from the root of the JSON source. |
Array of Arrays |
| Wildcard |
* |
. |
Children from the first level |
..Orders.* |
Select the children of all orders. |
Array of Objects |
Because the Orders keys produce Arrays, we need to select their first level of children in order to have an Array with all of them. |
Consider the JSON structure:
{
"Products": {
"Product_1": {
"Id": 123,
"Category": "books"
},
"Product_2": {
"Id": 234,
"Category": "dvds"
}
}
}
and the expression: Products.*
|
Select the children of Products. |
Array of Objects
[{
"Id": 123,
"Category": "books"
}, {
"Id": 234,
"Category": "dvds"
}]
|
The first level children of the Products Object are the values for its keys. |
| .. |
All children from all levels |
Northwind.Customers[0]..* |
Select all children of the first customer. |
Array of Nodes(Array(s), Objects, Values) |
This kind of query:
- traverses arrays adding each child to the result
- goes down on each object key adding it to the result
- stops when the end of the JSON tree is reached
- can produce huge amounts of result Nodes if no filter expression is applied
|
| Member type |
Key(s) value |
Direction |
Example in context |
Description |
Expected result type |
Explanation |
| Symbol |
Description |
| Complex Object Key |
String Literal(2)
enclosed in square brackets |
[none] |
Down(implicit) |
Northwind.Customers["Company Name"] |
Select the company name of all customers |
Array of Strings |
Direction is "Down" by default. The result is an Array because the Customers array is queried on each of
its items for the "Customer Name" key/property. |
| .. |
Anywhere Down |
..["Company Name"] |
Select the company name from anywhere |
Array of Strings |
The result is an Array because applying Anywhere Down traversal usually produces a collection of Nodes
whose type is dictated by the value found at the requested key.
"Company Name" always produces a String in this case.
|
| Object Construction |
At least 2 - legal JavaScript identifiers or String literals - separated by comma and enclosed in square brackets |
[none] |
Down(implicit) |
Northwind.Customers[0].Orders[OrderID, OrderDate] |
Select the OrderID and OrderDate from the orders of the first customer |
Array of Objects
[{
"OrderID": 10643,
"OrderDate": "1997-08-25"
},
...
{
"OrderID": 11011,
"OrderDate": "1998-04-09"
}]
|
Direction is "Down" by default. The result is an Array because the Orders array is queried on each of
its items for the OrderID and OrderDate keys. |
| .. |
Anywhere Down |
..["Company Name", Address] |
Select the company name and address from anywhere |
Array of Objects
[{
"Company Name": "Alfreds Futterkiste",
"Address": "Obere Str. 57"
},
...
{
"Company Name": "Wolski Zajazd",
"Address": "ul. Filtrowa 68"
}]
|
The result is an Array of Objects because applying Anywhere Down traversal usually produces a collection of Nodes
whose type is Object in this case.
We are constructing objects with "Company Name" and Address keys.
|
| Member type |
Key(s) value |
Direction |
Example in context |
Description |
Expected result type |
Explanation |
| Symbol |
Description |
| Array Index |
[Integer] |
[none] |
Down(implicit) |
Northwind.Customers[2] |
Select the third customer |
Object |
JavaScript array indexes are 0-based. Direction is "Down" by default. The result is an Object because
the 3rd element in the Customers array is an object.
Indexes outside the valid range(from 0 to array size - 1) produce no results. |
| .. |
Anywhere Down |
Northwind.Customers.Orders..[0] |
Select the first order of each customer's Orders |
Array of Objects |
The result is an Array of Objects because applying Anywhere Down traversal usually produces a collection of Nodes
whose type is Object in this case.
We are selecting the first order(which is an object) from each Orders array.
|
| Array Construction |
[Integer1, Integer2, ...] |
[none] |
Down(implicit) |
Northwind.Customers[0].Orders[0,2] |
Select the first and third orders of the first customer |
Array of Objects |
JavaScript array indexes are 0-based. Direction is "Down" by default. The result is an Array of Objects because
we are constructing an array with only the 1st and 3rd orders.
Indexes outside the valid range(from 0 to array size - 1) are ignored. |
| .. |
Anywhere Down |
Northwind.Customers.Orders..[0,1,3] |
Select the first, second and fourth orders of each customer's Orders |
Array of Objects |
The result is an Array of Objects because we are constructing an array with only the
1st, 2nd and 4th orders.
Indexes outside the valid range(from 0 to array size - 1) are ignored.
|
| Member type |
Key(s) value |
Direction |
Example in context |
Description |
Expected result type |
Explanation |
| Symbol |
Description |
| Array Slice |
[Integer startIndex : Integer endIndex] |
[none] |
Down(implicit) |
..Orders.*[-1:] |
Select the last of all orders. |
Object |
Direction is "Down" by default.
Each of the start/end indexes is 0-based and optional, but not both at the same time.
When startIndex is missing it defaults to 0.
When endIndex is missing it defaults to the array size.
The element at endIndex is not included in the results list.
The indexes can have negative values that suggest operating at the end of the array.
-1 points to the last item. -2 points to the item before last and so on.
The indexes outside the array index range are ignored.
The result is Object because the last order is an object.
|
| .. |
Anywhere Down |
Northwind.Customers.Orders..[0:2] |
Select the first two orders(indexes 0 and 1) of each customer's Orders |
Array of Arrays |
The result is an Array of Arrays because applying Anywhere Down traversal usually produces a collection of Nodes.
Each node is an array in this case because from each Orders array we select only the first two items.
If we want to reach the deeper order nodes, the .* member(first level children) could be added to the expression, thus obtaining an Array of Objects.
|
| Multi Level Up |
[no key] |
^ |
One level up |
..OrderID^ |
Select the parents of all objects that have the OrderID property = Select all orders |
Array of Objects |
The result is an Array of Objects because the parent of each OrderID key is the object that contains the key. |
| ^{N} |
Up by N levels; N >= 1 |
..[OrderID,OrderDate](^{3}.City == "Berlin") |
Select the OrderID and OrderDate keys for objects(orders) that have the parent customer's(which is 3 levels up) city Berlin. |
Array of Objects
[{
"OrderID": 10643,
"OrderDate": "1997-08-25"
},
...
{
"OrderID": 11011,
"OrderDate": "1998-04-09"
}]
|
The query examples are equivalent: ^^^ produces the same result as
^{3}.
The result is an Array of Objects because we are constructing objects with the OrderID and OrderDate keys.
The filter expression travels up to the Customer parent to reach the City key.
The immediate parent of an object with the [OrderID, OrderDate] keys is the object that contains these keys - an order.
The parent of an order is the Orders array that contains the order.
The parent of the Orders array is the object holding the Orders key - the customer object. From here we go down the City key to perform the filtering.
|
| ..[OrderID,OrderDate](^^^.City == "Berlin") |
(1)A Legal JavaScript Identifier is a character sequence that:
- must start with one of: letter(lowercase or uppercase), $ or _
- and can be followed by any combination of letter, $, _ or digit(0-9).
(2)A String Literal is an escaped and double quoted JavaScript String.
The Filter Expression
Each member expression described above can be accompanied by a filter expression.
Let's consider the following JSONQL expression:
Northwind.Customers.Orders.*(Freight > 200 && OrderDate *= "1997")
It contains the following members:
- [Northwind]
- [.Customers]
- [.Orders]
- [.*(Freight > 200 && OrderDate *= "1997")]
Members #1, #2 and #3 contain no filter expression, only #4 does:
(Freight > 200 && OrderDate *= "1997")
In this case we have a compound filter expression made out of two basic filter expressions joined by the logical and(&&) operator.
The filter expression is evaluated as member results are added to the whole expression's result.
The result of a filter expression is a boolean true or false .
The Basic Filter Expression
The form of a basic filter expression is one of:
-
( [filterMember1][filterMember2]...[filterMemberN]@Function(*) Operator Value )
-
( [filterMember1][filterMember2]...[filterMemberN]@Function)
(*)The @Function element is optional
The filterMember is similar to the JSONQL Member except it lacks the filter expression. It contains only the:
[direction][key(s)] part.
This means that the above member type definitions also apply in this case.
The type and presence of the @Function element dictates the form of the basic filter expression as follows:
| Function type |
Function expression |
Example |
Description |
Observation |
| Path Type Check functions |
@isNull |
..Orders.*(["nonexistent key"]@isNull) |
Select Orders keys from anywhere and get the children that have a "nonexistent key" property with null value.
The result of the filter expression is true for all orders, so we are getting all of them.
|
The path type check functions are logical functions that return true/false. No Operator/Value can be used here. |
| @isNotNull |
..Orders.*(Products@isNotNull) |
Select Orders keys from anywhere and get the children that have a non-null Products property.
The result of the filter expression is false for all orders, so we are getting nothing as result.
|
| @isArray |
..Orders(@isArray).* |
Select Orders keys from anywhere that are arrays and get the first level children.
The result of the filter expression is true for all Orders keys, so we are getting all orders.
|
| @isObject |
..Orders(@isObject) |
Select Orders keys from anywhere that are of type Object.
The result of the filter expression is false for all Orders keys because each points to an Array, so we are getting no results.
|
| @isValue |
..*(@isValue) |
Select everything that is a value: string, number, boolean, null. The result is a huge collection of strings and numbers that probably needs additional filtering. |
| Value check function |
@val |
..OrderID(@val > 10600) |
Select the OrderID keys from anywhere that have a value greater than 10600.
This function can be used only at the beginning of the filter expression when we want to filter the resulting nodes directly. It is applicable only to value nodes.
If we want to check the value of a path we do not need to use this function as the function operators apply to values only.
|
|
| Array size check function |
@size |
Northwind.Customers.*(Orders@size > 10).Orders.* |
Select the orders that come from Orders arrays with more than 10 items. Both query expressions produce the same output.
|
|
| ..Orders(@size > 10).* |
| [none] |
[none] |
..Orders.*(OrderID > 10600) |
Select the orders for which the OrderID has a value greater than 10600.
We don't need functions to check the value of a key.
|
|
Operator to Operand type application
There is an enforcement at the JSONQL language level that requires the operators to be applied to the right kind of operands.
The query expression is not valid unless the matching is correctly done.
| Operator(s) |
Description |
Operand type |
JSONQL Expression Example |
Observations |
| ==, != |
Equal to, Not equal to |
Number, String, Boolean, null |
..Orders.*(OrderID == 10643) |
|
| >, >=, <, <= |
Greater than, Greater than or equal to, Lower than, Lower than or equal to |
Number |
..Orders.*(Freight >= 60) |
|
| *= |
Contains |
String |
..Orders.*(OrderDate *= "1997") |
String values are String literals(escaped and double quoted). |
Logical Operators for Basic Filter Expressions
Basic Filter Expressions can be negated and/or combined in order to allow more complex filtering capabilities.
| Operator |
Description |
JSONQL Expression Example |
| && |
AND |
..Orders.*(OrderID >= 10600 && OrderDate *= "1997") |
| || |
OR |
..Orders.*(Freight > 120 || ShipVia == 2) |
| ! |
NOT |
..Orders(!@size > 10).* |
| mixed |
Grouping expressions is possible |
..Orders.*(!(OrderID >= 10600 && (OrderDate *= "1998" || OrderDate *= "1997"))) |
|


--- permalink: /demo/ ---
Your winter holiday experience will certainly become a cherished memory of a place you will always wish to return to!
Welcome to the lavish world of our luxury mountain resort, a 5-star hotel. Get ready for an all year round magic trip to a world of hospitality enriched with activities suitable for nature lovers, families and winter sports fans.
Natural beauty, combined with comfortable apartments of our resort will not leave you indifferent. Visit our hotel and spend your dream winter holidays in the picturesque mountains.
The spirit of the mountains is calling you to our luxurious mountain lodge in scenic mountains. Nestled in Colorado's Mountains, surrounded by acres of pristine ski terrain and majestic 14,000-foot peaks, our breathtaking resort exudes a chic, yet rustic charm that exudes serenity and luxury.
Beautiful guestrooms and suites and private log cabins feature warm Western design, balconies and modern lifestyle amenities. Designed with soft leather furnishings, rich wood accents, and warm interiors, our mountain accommodations are an oasis of alpine elegance.
Only best meal, cooked by the professionals. Visiting our hotel, don’t forget about breakfast. We have an incredible variety of dishes.
Breakfast $25.00
Breakfast to your choice, sandwiches, cold and hot beverages.
Lunch $30.00
Bar & Grill and the perfect hangout for aprés ski cocktails.
Dinner $28.00
The grilled menu, salads, a wide choice of charcuterie and cheese.
“ I am hard pressed to think of a more pleasant, elegant and delicious hotel stay. The only comparable event was the Grand Hotel in Taipei, Taiwan in the 1970's. Thank you both for a memorable occasion. “
ALEXANDER HOLMES
© 2019 Mountainking. All rights reserved.


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{% load staticfiles %}
Precise Sacco Management System
Precise Verison [2.0]
Helpdesk:<PHONE_NUMBER>(Telkom),<PHONE_NUMBER>(Safaricom)
French
Spanish
Russian
German
Italian
Profile
Wallet
11
Settings
Lock screen
Logout
Menu
Maintenance
General Parameter
Bank Setup
Products Defination
Relationship Setup
Holiday Setup
Purpose Of Loan
Accounts Setup
Budget Setup
Member's BioData
General Posting
Member Receipt
Non Member Posting
Journal
petty cash
member Refund
Member Loan
Loan Application
Loan Approval
Loan Transfer
Member Offset
Debit Guarantor
Processes
Close Day
Close Month
Close year
Journal Transfer
Reference Checker
Utility
Count Queury
Search Member
Change User Password
Dividend Calculator
Internal Backup
Reports
Member's list report
Member Statement
Ledger Report
Monthly Contribution Reports
Trial Balance
Balance Sheet
Profit and Loss
Budget Report
Loans Application Report
Loan Approved Report
Member Deposit
Member Loans
Member Shares
Audit Trail
System Security
System Security
System users
Login Method
Login Form
Username
Password
Login
Remember me
You Do Have Account Register here?
Forgot
password?


--- layout: page title: About header-img: "img/about-bg.png" ---
Hi there everyone! I'm fairly new to the coding world, but I'm excited to be taking on these new challenges. I've been overseas working in Japan for the last couple of years as a teacher, but I'm currently based in the southeast US. I'm eager to get involved with the coding community out there, so if you want to talk about anything you see here in the blog, feel free to drop me a message!


| Package | Description |
|---|---|
| io.permazen |
Abstraction layer allowing access to a
Database using normal Java objects. |
| io.permazen.app |
Support for Permazen command line applications.
|
| io.permazen.cli |
Permazen Command Line Interface (CLI) classes.
|
| io.permazen.cli.cmd |
Permazen CLI
Command related classes, including pre-defined commands. |
| io.permazen.cli.telnet |
Support for Permazen Command Line Interface (CLI) over telnet connections.
|
| io.permazen.core |
Core classes for the Permazen Java persistence layer.
|
| io.permazen.core.type |
Permazen
FieldType s. |
| io.permazen.core.util |
Permazen core API utility classes.
|
| io.permazen.jsck |
Classes associated with
Jsck , the Permazen key/value database consistency checker. |
| io.permazen.parse |
Permazen parsing related classes.
|
| io.permazen.parse.expr |
Classes for parsing Java expressions with Permazen-specific extensions.
|
| io.permazen.schema |
Classes for modeling Permazen
Database schemas. |
| io.permazen.spring |
Spring Framework integration classes.
|
| io.permazen.vaadin |
Classes to facilitate Vaadin applications that interact with a Permazen database.
|
| io.permazen.vaadin.app |
Support for Permazen GUI applications based on Vaadin.
|
| Class and Description |
|---|
| Database
Provides an object database abstraction on top of a key/value database.
|
| DatabaseException
Superclass of all unchecked exceptions thrown by a
Database . |
| DeleteAction
Describes what action to take when an object that is still referred to by some other object is deleted.
|
| EnumValue |
| FieldType
Defines the encoding, ordering, and range of possible values for a
SimpleField . |
| ObjId
Object IDs.
|
| SnapshotTransaction
A "snapshot"
Transaction that persists indefinitely. |
| Transaction
A Permazen
Database transaction. |
| Class and Description |
|---|
| Database
Provides an object database abstraction on top of a key/value database.
|
| FieldType
Defines the encoding, ordering, and range of possible values for a
SimpleField . |
| Class and Description |
|---|
| Database
Provides an object database abstraction on top of a key/value database.
|
| Class and Description |
|---|
| Transaction
A Permazen
Database transaction. |
| Class and Description |
|---|
| Database
Provides an object database abstraction on top of a key/value database.
|
| Class and Description |
|---|
| CollectionField
Superclass of fields with types assignable to
Collection . |
| ComplexField
A complex
Field , such as a collection or map field. |
| CompositeIndex
Represents a composite index on an
ObjType . |
| CoreIndex
Core API
Index implementation representing a index on a single field. |
| CoreIndex2
Core API
Index2 implementation representing a composite index on two fields. |
| CoreIndex3
Core API
Index3 implementation representing a composite index on three fields. |
| CoreIndex4
Core API
Index4 implementation representing a composite index on four fields. |
| CounterField
Counter fields.
|
| CreateListener
Listener interface for notifications that an object has just been created.
|
| Database
Provides an object database abstraction on top of a key/value database.
|
| DatabaseException
Superclass of all unchecked exceptions thrown by a
Database . |
| DeleteAction
Describes what action to take when an object that is still referred to by some other object is deleted.
|
| DeleteListener
Listener interface for notifications that an object is about to be deleted.
|
| EnumArrayField
A field that contains an array (having one or more dimensions) of values chosen from
an ordered list of unique
String identifiers. |
| EnumField
A field that contains a value chosen from in an ordered list of unique
String identifiers. |
| Field |
| FieldSwitch
Visitor pattern interface for
Field s. |
| FieldType
Defines the encoding, ordering, and range of possible values for a
SimpleField . |
| FieldTypeRegistry
A registry of
FieldType s. |
| InconsistentDatabaseException
Thrown when inconsistent content is detected in a
Database indicating a corrupted or invalid database,
or a buggy underlying key-value store. |
| InvalidSchemaException
Thrown by
Database.createTransaction(io.permazen.schema.SchemaModel, int, boolean) when the provided schema is invalid. |
| ListField
List field.
|
| ListFieldChangeListener
Listener interface for notifications of a change in value of a
ListField . |
| MapField
Map field.
|
| MapFieldChangeListener
Listener interface for notifications of a change in value of a
MapField . |
| ObjId
Object IDs.
|
| ObjType
Represents a
Database object type. |
| ReferenceField
A field that references another
Database object. |
| Schema
Contains information describing one schema version.
|
| SchemaItem |
| Schemas
Contains the set of all
Schema s of objects visible in a Transaction . |
| SetField
Set field.
|
| SetFieldChangeListener
Listener interface for notifications of a change in value of a
SetField . |
| SimpleField
A simple
Field . |
| SimpleFieldChangeListener
Listener interface for notifications of a change in value of a
SimpleField . |
| SnapshotTransaction
A "snapshot"
Transaction that persists indefinitely. |
| Transaction
A Permazen
Database transaction. |
| Transaction.Callback
Callback interface for notification of transaction completion events.
|
| Transaction.ListenerSet
A fixed collection of listeners (
CreateListener s, DeleteListener s, VersionChangeListener s,
SimpleFieldChangeListener s, SetFieldChangeListener s, ListFieldChangeListener s, and
MapFieldChangeListener s) that can be efficiently registered on a Transaction all at once. |
| TransactionException
Superclass of exceptions associated with a specific
Transaction . |
| VersionChangeListener
Listener interface for notifications that an object's schema version has been changed to match the
current transaction.
|
| Class and Description |
|---|
| EnumValue |
| FieldType
Defines the encoding, ordering, and range of possible values for a
SimpleField . |
| ObjId
Object IDs.
|
| Class and Description |
|---|
| ObjId
Object IDs.
|
| ReferenceField
A field that references another
Database object. |
| Transaction
A Permazen
Database transaction. |
| Class and Description |
|---|
| FieldTypeRegistry
A registry of
FieldType s. |
| ObjId
Object IDs.
|
| Class and Description |
|---|
| ComplexField
A complex
Field , such as a collection or map field. |
| CompositeIndex
Represents a composite index on an
ObjType . |
| Database
Provides an object database abstraction on top of a key/value database.
|
| Field |
| FieldType
Defines the encoding, ordering, and range of possible values for a
SimpleField . |
| ObjId
Object IDs.
|
| ObjType
Represents a
Database object type. |
| Schema
Contains information describing one schema version.
|
| SimpleField
A simple
Field . |
| Transaction
A Permazen
Database transaction. |
| Class and Description |
|---|
| Field |
| ObjId
Object IDs.
|
| SimpleField
A simple
Field . |
| Class and Description |
|---|
| DeleteAction
Describes what action to take when an object that is still referred to by some other object is deleted.
|
| Class and Description |
|---|
| Transaction.Callback
Callback interface for notification of transaction completion events.
|
| Class and Description |
|---|
| FieldType
Defines the encoding, ordering, and range of possible values for a
SimpleField . |
| ObjId
Object IDs.
|
| Class and Description |
|---|
| ObjId
Object IDs.
|
Copyright © 2020. All rights reserved.


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Welcome to UC Berkeley South Indian Society’s Website! Our club was initiated in April of 2016. We have successfully launched several events including Southside, our first dance party, and Dosas and Mimosas, our formal banquet event.
We’re very excited for the future of our club. If you are interested in joining as a member or have any general inquiries, please contact<EMAIL_ADDRESS>Thank you for visiting our page!
Please visit our social media pages!
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Hotel Gateway kicks off Rayalaseema food festival
Dinner will be served between 7.30 p.m. and 11 p.m.
by Special Correspondent VijayawadaFoodies fond of spicy flavours of Rayalaseema have a feast waiting at the GAD restaurant of Hotel Taj Gateway.
The hotel on Friday kicked off Rayalaseema Food Festival that offers traditional dishes made using authentic ingredients to cater to the taste buds of the local crowd.
Speaking to reporters, Food and Beverage Manager S.V. Ramana Murthy said the ingredients are fresh, authentic and hand-picked.
The hotel’s master chef Brahmaji, with a team of experienced chefs from Rayalaseema and the executive chef Shreedhar will whip up a gourmet’s delights during the festival period ensuring consistency of quality, taste and hygiene.
The dishes lined up in the menu are mutton pulao, miriyalu liver, Seema kodi fry, veta mamsam, masala quails (kamsu pitta mamsam), medada (brain) keema, Kadapa chicken curry, Kurnool chepala pulusu, alasandula wada, pachchi pulusu, gunta ponganalu (punugulu) and borugula upma besides jowar roti, ragi roti and ragi sangati.
The festival menu will be served for dinner between 7.30 pm and 11 p.m.


First, the weights and inverse Hilbert coordinates for each object are determined. If the objects do not have weights, unit weights are assigned. If the objects have multiple weights, only the first weight is currently used. The smallest axis-aligned box is found that contains all of the objects using their two or three dimensional spatial coordinates. This bounding box is slightly expanded to ensure that all objects are strictly interior to the boundary surface. The bounding box is necessary in order to calculate the inverse Hilbert Space Filling curve coordinate. The bounding box is used to scale the problem coordinates into the [0,1]^d unit volume (d represents the number of dimensions in the problem space.) The inverse Hilbert coordinate is calculated and stored as a double precision floating point value for each object. This code works on problems with one, two or three dimensions (the 1-D Inverse Hilbert coordinate is simply the problem coordinate itself, after the bounding box scaling.)
The algorithm seeks to cut the unit interval into P segments containing equal weights of objects associated to the segments by their inverse Hilbert coordinates. The code allows a user vector to specify the desired fraction of the total weight to be assigned to each interval. Note, a zero weight fraction prevents any object being assigned to the corresponding interval. The unit interval is divided into N bins, N=k(P-1)+1, where k is a small positive constant.) Each bin has an left and right endpoint specifying the half-open interval [l,r) associated with the bin. The bins form a non-overlapping cover of [0,1] with the right endpoint of the last bin forced to include 1. The bins are of equal size on the first loop. (Hence each interval or part of the partition is a collection of bins.)
For each loop, an MPI_Allreduce call is made to globally sum the weights in each bin. This call also determines the maximum and minimum (inverse Hilbert) coordinate found in each bin. A greedy algorithm sums the weights of the bins from left to right until the next bin would cause an overflow for the current part. This results in new partition of P intervals. The location of each cut (just before an "overflowing" bin) and the size of its "overflowing" bin are saved. The "overflowing" bin's maximum and minimum are compared to determine if the bin can be practically subdivided. (If the bin's maximum and minimum coordinates are too close relative to double precision resolution, the bin can not be practically subdivided.) If at least one bin can be further refined, then looping will continue. In order to prevent a systematic bias, the greedy algorithm is assumed to exactly satisfy the weight required by each part.
Before starting the next loop, the P intervals are again divided into N bins. The P-1 "overflow" bins are each subdivided into k-1 equal bins. The intervals before and after these new bins determine the remaining bins. This process maintains a fixed number of bins. No bin is "privileged." Specifically, any bin is subject to later refinement, as necessary, on future loops.
The loop terminates when there is no need to further divide any "overflow" bin. A slightly different greedy algorithm is used to determine the final partition of P intervals from the N bins. In this case, when the next bin would cause an overflow, the tolerance is computed for both underfilling (excluding this last bin) and overfilling (including the last bin). The tolerance closest to the target tolerance is used to select the dividing point. The tolerance obtained at each dividing point is compared to the user's specified tolerance. An error is returned if the user's tolerance is not satisfied at any cut. After each cut is made, a correction is calculated as the ratio of the actual weight to the target weight used up to this point. This correction is made to the target weight for the next part. This correction fixes the subsequent parts when a "massive" weight object is on the border of a cut and its assignment creates an excessive imbalance.
Generally, the number of loops is small (proportional to log(number of objects)). A maximum of MAX_LOOPS is used to prevent an infinite looping condition. A user-defined function is used in the MPI_Allreduce call in order to simultaneously determine the sum, maximum, and minimum of each bin. The message length in the MPI_Allreduce is proportional to the P, the number of parts.
Note, when a bin is encountered that satisfies more than two parts, that bin is refined
into a multiple of k-1 intervals which maintains a total of N bins.
The inverse transformation is computed by taking the highest order bit from each spatial coordinate and packing them together as 2 or 3 bits (as appropriate to the dimensionality) in the order xyz (or xy) where x is the highest bit in the word. The initial state is 0. The data table lookup finds the value at the column indexed by the xyz word and the row 0 (corresponding to the initial state value.) This data are the 3 (or 2) starting bits of the Hilbert coordinate. The next state value is found by looking up the corresponding element of the state table (xyz column and row 0.)
The table procedure continues to loop (using loop counter i, for example) until the required precision is reached. At loop i, the ith bits from each spatial dimension are packed together as the xyz column index. The data table lookup finds the element at column xyz and the row determined by the last state table value. This is appended to the Hilbert coordinate. The state table is used to find the next state value at the element corresponding to the xyz column and row equal to the last state value.
The inverse transformation is analogous. Here the 3 (or 2 in the 2-d case) bits of the
Hilbert coordinate are extracted into a word. This word is the column index into the
data table and the state value is the row. This word found in the data table is
interpreted as the packed xyz bits for the spatial coordinates. These bits are
extracted for each dimension and appended to that dimension's coordinate. The corresponding
state table is used to find the next row (state) used in the next loop.
The Point Assign function now works for any point in space, even if the point is
outside the original bounding box. If the point is outside the bounding box, it is first
scaled using the same equations that scale the interior points into the unit volume.
The point is projected onto the unit volume. For each spatial dimension, if the scaled
coordinate is less than zero, it is replace by zero. If it is greater than one, it is
replaced by one. Otherwise the scaled coordinate is directly used.
The query functions decompose the unit square (or cube) level by level like the Octree method. Each level divides the remaining region into quadrants (or octets in 3d). At each level, the quadrant with the smallest inverse Hilbert coordinate (that is, occurring first along the Hilbert curve) whose inverse Hilbert coordinate is equal or larger than the starting inverse Hilbert coordinate and which intersects with query region is selected. Thus, each level calculates the next 2 bits (3 bits in 3d) of the inverse Hilbert coordinate of the next point to enter the query region. No more than once per call to the query function, the function may backtrack to a nearest previous level that has another quadrant that intersects the query region and has a higher Hilbert coordinate.
In order to determine the intersection with the query region, the next 2 bits (3 in 3 dimensions) of the Hilbert transformation are also computed (by table lookup) at each level for the quadrant being tested. These bits are compared to the the bits resulting from the intersection of the query region with the region determined by the spatial coordinates computed to the precision of the previous levels.
If the user query box has any side (edge) that is "too small" (effectively degenerate in some dimension), it is replaced by a minimum value and the corresponding vertex coordinates are symmetrically expanded. This is refered to as a "fuzzy" region.
This function requires the KEEP_CUTS parameter to be set by the user. The Box Assign function now works for any box in space, even if it has regions outside the original bounding box. The box vertices are scaled and projected exactly like the points in the Point Assign function described above. However, to allow the search to use a proper volumn, projected points, lines, and planes are converted to a usable volume by the fuzzy region process described above.
This algorithm will work for any space filling curve. All that is necessary is to
provide the tables (derieved from the curve's state transition diagram) in place of
the Hilbert Space Filling Curve tables.
The parameters used by HSFC and their default values are described in the HSFC section of the Zoltan User's Guide. These can be set by use of the Zoltan_HSFC_Set_Param subroutine in the file hsfc/hsfc.c.
When the parameter REDUCE_DIMENSIONS
is specified, the HSFC algorithm will perform lower dimensional
partitioning if the geometry is found to be degenerate. More information
on detecting degenerate
geometries may be found in another
section.
The main routine for HSFC is Zoltan_HSFC in the file hsfc/hsfc.c.


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|
ADAPT 2015
|
@ HiPEAC 2015
|
Amsterdam, The Netherlands
|
Keynote by Dr.Aaron Smith
(Microsoft, USA)
|
Program
|
|
ADAPT 2014
|
@ HiPEAC 2014
|
Vienna, Austria
|
Keynote by Prof. Per Stenström
(Chalmers University, Sweden)
|
Program
|
|
ADAPT 2013
|
@ HiPEAC 2013
|
Berlin, Germany
|
Keynote by Prof. Markus Püschel
(ETHZ, Switzerland)
|
Program
|
|
EXADAPT 2012
|
@ ASPLOS 2012
|
London, UK
|
Keynote by Prof. Steve Furber
(Manchester University, UK)
|
Program
|
|
EXADAPT 2011
|
@ PLDI 2011/FCRC 2011
|
San Jose, USA
|
Keynote by Prof. Katherine Yelick
(U. of California, Berkeley / LBNL, USA)
|
Program
|


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KratosMultiphysics
KRATOS Multiphysics (Kratos) is a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface.
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The base class for the PfemFluidDynamicsAnalysis. More...
Public Member Functions | |
| def | __init__ (self, model, parameters) |
| The constructor of the AnalysisStage-Object. More... | |
| def | Initialize (self) |
| This function initializes the AnalysisStage Usage: It is designed to be called ONCE, BEFORE the execution of the solution-loop This function has to be implemented in deriving classes! More... | |
| def | InitializeSolutionStep (self) |
| This function performs all the required operations that should be executed (for each step) BEFORE solving the solution step. More... | |
| def | FinalizeSolutionStep (self) |
| This function performs all the required operations that should be executed (for each step) AFTER solving the solution step. More... | |
| def | Finalize (self) |
| This function finalizes the AnalysisStage Usage: It is designed to be called ONCE, AFTER the execution of the solution-loop. More... | |
| def | SetGraphicalOutput (self) |
| This function sets the settings for the graphical output. More... | |
| def | GraphicalOutputExecuteInitialize (self) |
| This function performs the initialize of the graphical output. More... | |
| def | GraphicalOutputExecuteBeforeSolutionLoop (self) |
| This function performs the ExecuteBeforeSolutionLoop of the graphical_output. More... | |
| def | GraphicalOutputExecuteInitializeSolutionStep (self) |
| This function performs the ExecuteInitializeSolutionStep of the graphical_output. More... | |
| def | GraphicalOutputExecuteFinalizeSolutionStep (self) |
| This function performs the ExecuteFinalizeSolutionStep of the graphical_output. More... | |
| def | GraphicalOutputPrintOutput (self) |
| This function prints the output for this time step. More... | |
| def | GraphicalOutputExecuteFinalize (self) |
| This function performs the ExecuteFinalize of the graphical_output. More... | |
| def | SetParallelSize (self, num_threads) |
| This function sets the number of threads. More... | |
| def | GetParallelSize (self) |
| This function returns the number of threads. More... | |
| def | StartTimeMeasuring (self) |
| This function starts time calculation. More... | |
| def | StopTimeMeasuring (self, time_ip, process, report) |
| This function ends time calculation. More... | |
| def | KratosPrintInfo (self, message) |
| This function prints info on screen. More... | |
| def | AddMaterialVariables (self) |
| def | AddAllMaterialVariables (self) |
| def | AddPfemVariables (self) |
Public Attributes | |
| model | |
| t0p | |
| TIME MONITORING START #### Time control starts. More... | |
| t0w | |
| echo_level | |
| TIME MONITORING END ####. More... | |
| report | |
| main_model_part | |
| Model_part settings start ####. More... | |
| problem_path | |
| problem_name | |
| constitutive_laws_names | |
| model_processes | |
| Processes settings start ####. More... | |
| computing_model_part | |
| processes settings end #### START SOLUTION #### More... | |
| graphical_output | |
| step | |
| Sets strategies, builders, linear solvers, schemes and solving info, and fills the buffer. More... | |
| time | |
| end_time | |
| delta_time | |
| clock_time | |
| output_settings | |
| post_process_model_part | |
The base class for the PfemFluidDynamicsAnalysis.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.__init__ | ( | self, | |
| model, | |||
| parameters | |||
| ) |
The constructor of the AnalysisStage-Object.
Keyword arguments: self – It signifies an instance of a class. model – The Model to be used parameters – The ProjectParameters used
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.AddAllMaterialVariables | ( | self | ) |
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.AddMaterialVariables | ( | self | ) |
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.AddPfemVariables | ( | self | ) |
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.Finalize | ( | self | ) |
This function finalizes the AnalysisStage Usage: It is designed to be called ONCE, AFTER the execution of the solution-loop.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.FinalizeSolutionStep | ( | self | ) |
This function performs all the required operations that should be executed (for each step) AFTER solving the solution step.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.GetParallelSize | ( | self | ) |
This function returns the number of threads.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.GraphicalOutputExecuteBeforeSolutionLoop | ( | self | ) |
This function performs the ExecuteBeforeSolutionLoop of the graphical_output.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.GraphicalOutputExecuteFinalize | ( | self | ) |
This function performs the ExecuteFinalize of the graphical_output.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.GraphicalOutputExecuteFinalizeSolutionStep | ( | self | ) |
This function performs the ExecuteFinalizeSolutionStep of the graphical_output.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.GraphicalOutputExecuteInitialize | ( | self | ) |
This function performs the initialize of the graphical output.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.GraphicalOutputExecuteInitializeSolutionStep | ( | self | ) |
This function performs the ExecuteInitializeSolutionStep of the graphical_output.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.GraphicalOutputPrintOutput | ( | self | ) |
This function prints the output for this time step.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.Initialize | ( | self | ) |
This function initializes the AnalysisStage Usage: It is designed to be called ONCE, BEFORE the execution of the solution-loop This function has to be implemented in deriving classes!
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.InitializeSolutionStep | ( | self | ) |
This function performs all the required operations that should be executed (for each step) BEFORE solving the solution step.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.KratosPrintInfo | ( | self, | |
| message | |||
| ) |
This function prints info on screen.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.SetGraphicalOutput | ( | self | ) |
This function sets the settings for the graphical output.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.SetParallelSize | ( | self, | |
| num_threads | |||
| ) |
This function sets the number of threads.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.StartTimeMeasuring | ( | self | ) |
This function starts time calculation.
| def pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.StopTimeMeasuring | ( | self, | |
| time_ip, | |||
| process, | |||
| report | |||
| ) |
This function ends time calculation.
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.clock_time |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.computing_model_part |
processes settings end #### START SOLUTION ####
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.constitutive_laws_names |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.delta_time |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.echo_level |
TIME MONITORING END ####.
PARSING THE PARAMETERS #### set echo level
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.end_time |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.graphical_output |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.main_model_part |
Model_part settings start ####.
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.model |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.model_processes |
Processes settings start ####.
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.output_settings |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.post_process_model_part |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.problem_name |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.problem_path |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.report |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.step |
Sets strategies, builders, linear solvers, schemes and solving info, and fills the buffer.
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.t0p |
TIME MONITORING START #### Time control starts.
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.t0w |
| pfem_fluid_dynamics_analysis.PfemFluidDynamicsAnalysis.time |


Created: 2020-01-12 Sun 01:08
Step One: Defining the Purposes or Objectives
Step Two: Gathering Data from Secondary Sources
Step Three: Gathering Information from Primary Sources
| Method | Costs | Flexibility | Response Rate | Speed | Depth |
| Telephone | |||||
| Personal | |||||
| Internet |
Step Four: Analyzing and Interpreting the Results
Marketing plan- A written statement of marketing objectives, strategies, and activities to be followed in business plan It is designed to provide answer to three basic questions:
Introduction Situation Analysis Target Market Marketing Goals and Objectives Product/service strategy
Pricing Strategy Distribution strategy Advertising and Promotion Sales Strategy Sales and Marketing Forecasts
The situation analysis is an assessment of where the organization currently stand both internally and externally To develop this assessment. Much analysis must be done
The organization's product(s), target markets, distribution channels, competitors, finances, strengths, weakness, opportunities and threats must all be considered
A marketing plan should: Provide a strategy Be based on fact/assumptions Describe an organization for implementation Provide for short-term and long-term continuity Be simple and short Be flexible Specify criteria for control
TODO
| Marketing Mix Variable | Critical Decisions |
| Product | Quality of components of materials, style, features, options, brand name, packaging, sizes, service availability, and warranties |
| Price | Quality image, list price, quantity, discounts, allowances for quick payment, credit terms, and payment period |
| Channels of distribution | Use of wholesalers and/or retailers, type of wholesalers or retailers, how many, length of channel, geographic coverage, inventory, transportation, and use of electronic channels |
| Promotion | Media alternatives, message, media budget, role of personal selling, sales promotion (displays, coupons, etc.), use of social networking, Web site design, and media interest in publicity |
Defining the Business Situation
Defining the Target Market/ Opportunities and Threats
Process of segmenting and targeting customers
Consider the strengths and weaknesses in the target market
Establishing Goals and Objectives
Defining Marketing Strategy and Action programs Product or service
Pricing
Degree of Directness of Channel
Number of Channel Members
Criteria in Selection of Channel Members
Number of Channels
Market Strategy: Consumer versus Business-to-Business Markets
Budgeting and Implementation
Monitoring the Progress of Marketing Actions


Attainment statistics team
Email<EMAIL_ADDRESS>Telephone: Tingting Shu 0370 000 2288
National Statistics
Provisional information on A level and other results for 16- to 18-year-olds at the end of their final year.
PDF , 826KB , 32 pages
XLSM , 846KB
XLSM , 1.37MB
XLSM , 1.08MB
XLSM , 270KB
XLSM , 92.8KB
PDF , 1.87MB , 1 page
PDF , 1.87MB , 1 page
PDF , 2.16MB , 1 page
PDF , 436KB , 31 pages
Plain text , 1.9KB
This statistical first release ( SFR ) provides provisional information on the overall achievements of 16- to 18-year-olds for the 2015 to 2016 academic year, including:
We’ve published revised figures , accounting for amendments made during the school and college checking exercise. We’ve also published the data in the 16 to 18 performance tables .
Email<EMAIL_ADDRESS>Telephone: Tingting Shu 0370 000 2288
Published: 13 October 2016
Updated: 13 October 2016
From: Department for Education


Electronic Out-of-School Youth Literacy Assessment (eOLA)
International efforts to promote literacy development for young adult readers are improving
outcomes for many out-of-school youth—particularly those living in extreme poverty or
post-conflict environments and with low literacy skills. But there is an urgent need for
easy-to-use literacy assessment tools in many low-resource communities to help determine
just how effective these programs are.
EDC is helping ministries of education document the literacy skill levels of out-of-school
youth through the electronic Out-of-School Youth Literacy Assessment (eOLA), a
research-based, one-on-one reading assessment that measures the functional literacy skills
that youth and adults may have acquired without formal literacy instruction as well as the
skills that are gained through direct instruction. The assessment can be administered using
either laptops or tablets.
EDC partners directly with ministries of education to support the implementation of eOLA. Activities include:
- Providing assessment data to program staff, stakeholders, and beneficiaries in implementing countries
- Using data to support curriculum and materials development, design educator trainings, and inform policy
- Providing professional development and capacity building on using eOLA for ministry of education officials in implementing countries


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All you have to do is name the time and the place. We're flexible.
All you have to do is name the time and the place. We're flexible.
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It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters,
Request a DemoIt is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout.
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Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
This succulent fish pie serves up sumptuous coconut and spices alongside prawns, mussels and smoked haddock.
For the pie, preheat the oven to 200C/400F/Gas 6.
First make the sauce. Heat the oil in a large frying pan, add the curry leaves, onion, ginger and green chillies and cook, stirring, until the onion is soft.
Add the mussels and turmeric, followed by the rice flour.
Mix the rice flour in evenly, then add coconut milk and salt, and bring to a simmer.
Cook for 2-3 minutes, until the mussels open up and sauce begins to turn glossy.
Remove the mussels and take the meat out of the shell, discarding the shells. Set the mussels aside, and continue to cook the sauce until it thickens and becomes very thick.
Remove the sauce from the heat, cool and mix the raw seafood and the mussel meat through the sauce.
Divide the pie filling equally among four shallow cast-iron skillets, garnish with the green chillies and curry leaves and cover with the puff pastry disc.
Brush the pies with egg, sprinkle with black onion seeds and bake in the oven for 15 minutes, or until the fish is cooked through and the pastry crisp and golden brown.
For the salad, mix together all the ingredients for the salad.
Serve the pie with the sprouted fenugreek and moong lentil salad alongside.


In a prior notebook, we performed a linear analysis of a data set consisting of physical measurements of abalones (sea snails). We saw in that notebook that linear models do not capture higher-order effects and the interactions of variables.
In this notebook we'll utilize models with higher-order interaction terms and explore different variable transformations. We mentioned the two principal ways to do this: the first way is to add, by hand, any variable transformations that we think might be important, and analyze the results by hand to determine if, in fact, the interaction is significant. The second way is to utilize different kernels to perform support vector regression, which (depending on the kernel) can automatically deduce which of the (potentially thousands of) variable transformations work best.
The snippets of code below will load and transform data, as well as remove outliers whose physical dimensions are not sensible.
%matplotlib inline
# numbers
import numpy as np
import pandas as pd
# stats
import statsmodels.api as sm
import scipy.stats as stats
# plots
import matplotlib.pyplot as plt
import seaborn as sns
# utils
import os, re
# Copy-and-paste from prior notebook
def abalone_load(data_file, infant=False):
# x data labels
xnlabs = ['Sex']
xqlabs = ['Length','Diameter','Height','Whole weight','Shucked weight','Viscera weight','Shell weight']
xlabs = xnlabs + xqlabs
# y data labels
ylabs = ['Rings']
# data
df = pd.read_csv(data_file, header=None, sep=' ', names=xlabs+ylabs)
if(infant):
new_df = df[ df['Sex']=='I' ]
else:
new_df = df[ df['Sex']<>'I' ]
return new_df
def infant_abalone_load(data_file):
return abalone_load(data_file,True)
def adult_abalone_load(data_file):
return abalone_load(data_file,False)
def abalone_removeoutliers(df,mse_tol,verbose=False):
df.loc[:,'Volume'] = df['Length'].values*df['Diameter'].values*df['Height'].values
X = df['Volume']
Y = df['Shell weight']
lin = sm.OLS(Y,X).fit()
Yhat = lin.predict(df['Volume'])
df.loc[:,'Predicted shell weight'] = Yhat
df.loc[:,'Residual'] = Y - Yhat
df.loc[:,'MSE'] = (Y - Yhat)**2
MSE = df['MSE']
original_length = len(df)
thresh = 0.5
df = df[df['MSE'] < thresh]
df = df[ df['Height'] < 0.30 ]
new_length = len(df)
records_removed = original_length - new_length
if(verbose):
print "Number of data points removed: %d"%(records_removed)
print "%0.2f%% of data was removed"%((float(records_removed)/len(df))*100)
del df['Predicted shell weight']
del df['Residual']
del df['MSE']
del df['Volume']
return df
def infant_abalone_removeoutliers(df):
return abalone_removeoutliers(df,0.5)
def adult_abalone_removeoutliers(df):
return abalone_removeoutliers(df,1.0)
# x data labels
xnlabs = ['Sex']
xqlabs = ['Length','Diameter','Height','Whole weight','Shucked weight','Viscera weight','Shell weight']
xlabs = xnlabs + xqlabs
# y data labels
ylabs = ['Rings']
inf_df = infant_abalone_removeoutliers(infant_abalone_load('abalone/Dataset.data'))
adt_df = adult_abalone_removeoutliers(adult_abalone_load('abalone/Dataset.data'))
adt_df.head()
fig = plt.figure(figsize=(10,4))
(ax1,ax2) = (fig.add_subplot(121), fig.add_subplot(122))
sns.distplot(adt_df['Rings'], bins=10, ax=ax1)
sns.distplot(adt_df['Rings'].map(lambda x : np.log(x)), bins=10, ax=ax2)
ax1.set_title('Histogram: Rings')
ax1.set_xlabel('No. Rings')
ax1.set_ylabel('Frequency')
ax2.set_title('Histogram: log(Rings)')
ax2.set_xlabel('log(No. Rings)')
ax2.set_ylabel('Frequency')
plt.show()
We covered support vector regression earlier - this is a regression technique that utilizes kernels (defined in terms of integral convolutions of functions) to find the most efficient way of combining variables; the possible combinations of variables is defined by the kernel choice.
Let's review again how we construct a linear support vector regression model, and how it performs:
from sklearn.svm import SVR
The C
parameter is the error penalty term: the larger C is, the longer it will take to train the model.
# ~5 seconds
svr_lin = SVR(kernel='linear', C=1e3)
svr_lin = svr_lin.fit( adt_df[xqlabs], adt_df['Rings'].values )
y = adt_df['Rings'].values
yhat_lin = svr_lin.predict(adt_df[xqlabs])
svr_lin_resid = yhat_lin - y
sns.distplot(svr_lin_resid, color=sns.xkcd_rgb['brick'], label='Linear SVR')
plt.title('Linear SVR Residuals')
plt.legend()
plt.show()
The state vector regression algorithm results in a linear model with large residuals that are not normally distributed - meaning our predictions will be off by a substantial amount. This was what motivated a need for higher-order models.
To introduce a more complex kernel, we have a couple of choices of pre-configured kernels:
'linear'
) - what we were using before, response is linear with respect to each variable'poly'
) - a kernel including polynomial interaction effects'rbf'
) - radial basis function (decaying squared exponential)'sigmoid'
) - S-curve function fitWe'll start with the polynomial kernel, and go from there.
We can pass 'poly'
to the kernel argument to perform SVR with a polynomial kernel, which will include interaction effects between variables. We can start with degree 2 (it turns out the fit gets worse as the degree increases) - when we do that, here are the residuals we get:
# ~15 seconds
svr_poly = SVR(kernel='poly', degree=2, C=1e4)
svr_poly = svr_poly.fit( adt_df[xqlabs], adt_df['Rings'].values )
y = adt_df['Rings'].values
yhat_poly = svr_poly.predict(adt_df[xqlabs])
svr_poly_resid = yhat_poly - y
sns.distplot(svr_lin_resid, color=sns.xkcd_rgb['brick'], label='Linear SVR')
sns.distplot(svr_poly_resid, color=sns.xkcd_rgb['cornflower'], label='Poly SVR')
plt.title('Linear vs. Polynomial SVR Residuals')
plt.legend()
plt.show()
This result is a bit disappointing - adding interaction terms to our sVR model still gets us no noticeable improvements.
print svr_lin.score( adt_df[xqlabs], adt_df['Rings'].values)
print svr_poly.score( adt_df[xqlabs], adt_df['Rings'].values)
Next we'll try state vector regression with a radial basis function as our kernel. The radial basis function is a squared exponential function.
# ~10 seconds
svr_rbf = SVR(kernel='rbf', C=1e4)
svr_rbf = svr_rbf.fit( adt_df[xqlabs], adt_df['Rings'].values )
print svr_rbf.score( adt_df[xqlabs], adt_df['Rings'].values)
#svr_rbf2 = SVR(kernel='rbf', C=1e6)
#svr_rbf2 = svr_rbf2.fit( adt_df[xqlabs], adt_df['Rings'].values )
#print svr_rbf2.score( adt_df[xqlabs], adt_df['Rings'].values)
# The score that results is 0.4562
y = adt_df['Rings'].values
yhat_rbf = svr_rbf.predict(adt_df[xqlabs])
svr_rbf_resid = yhat_rbf - y
sns.distplot(svr_rbf_resid, color=sns.xkcd_rgb['periwinkle'], label='RBF SVR')
sns.distplot(svr_lin_resid, color=sns.xkcd_rgb['brick'], label='Linear SVR')
plt.title('Linear vs. RBF SVR Residuals')
plt.legend()
plt.show()
The scores for these state vector regression models can be improved by scaling the input variables.
from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(adt_df[xqlabs])
X = scaler.transform(adt_df[xqlabs])
# This takes 3 minutes
svr_rbf3 = SVR(kernel='rbf', C=1e4)
svr_rbf3 = svr_rbf3.fit( X, adt_df['Rings'].values )
print svr_rbf3.score( X, adt_df['Rings'].values )
The $R^2$ statistic is a measure of how much of the variance in the data is explained by the state vector regression model.
A value of 0.589 means that there is still about 40% of the variance from the mean that is not explained (or, fit) by the model. This is much improved, however, over a vlaue of less than 0.4, which we saw with linear models.
y = adt_df['Rings'].values
yhat_rbf3 = svr_rbf3.predict(X)
svr_rbf3_resid = yhat_rbf3 - y
sns.distplot(svr_lin_resid, color=sns.xkcd_rgb['brick'], label='Linear SVR')
sns.distplot(svr_rbf3_resid, color=sns.xkcd_rgb['periwinkle'], label='Scaled RBF SVR')
plt.title('Linear vs. Scaled RBF SVR Residuals')
plt.xlim([-10,10])
plt.xlabel('Residual')
plt.ylabel('Frequency')
plt.legend()
plt.show()
The residual is more tightly clustered around 0, so the scaling helped - but the tails of the distribution are still significant. If we look at a quantile plot of the model residuals, we can see that a significant number of residuals don't follow a normal distribution:
fig = plt.figure(figsize=(4,4))
ax1 = fig.add_subplot(111)
stats.probplot(svr_rbf3_resid, dist='norm', plot=ax1)
ax1.set_title('SVR with Scaled Inputs: Residual Quantiles')
plt.show()
fig = plt.figure(figsize=(14,8))
ax1,ax2,ax3 = fig.add_subplot(231), fig.add_subplot(232), fig.add_subplot(233)
ax4,ax5,ax6 = fig.add_subplot(234), fig.add_subplot(235), fig.add_subplot(236)
ax1.plot(y,svr_rbf3_resid,'*')
ax1.set_xlabel('System Response')
ax2.plot(yhat_rbf3,svr_rbf3_resid,'*')
ax2.set_xlabel('Model Prediction of Response')
ax2.set_ylabel('Residual (Normalized RBF SVR)')
ax4.plot(adt_df['Length'],svr_rbf3_resid,'*')
ax4.set_xlabel('Length')
ax5.plot(adt_df['Height'],svr_rbf3_resid,'*')
ax5.set_xlabel('Height')
ax6.plot(adt_df['Diameter'],svr_rbf3_resid,'*')
ax6.set_xlabel('Diameter')
ax1.set_ylabel('Residual')
ax2.set_ylabel('Residual')
ax4.set_ylabel('Residual')
ax5.set_ylabel('Residual')
ax6.set_ylabel('Residual')
plt.show()
From the plot of system response $y$ versus residuals $\hat{y} - y$, we can see a pretty clear trend: model residuals still have functional dependence on the response value. Residuals that are more negative as the system response gets larger means that we are systematically overpredicting the age of younger abalones and underpredicting the age of older abalones. There is also a slight functional dependence of the residual on length, height, and diameter - as these quantities get larger, the residual also gets larger.
From this, we can conclude that support vector regression, which explores different variable interactions, together with scaling of variables, leads to a better fit - but still doesn't capture all of the higher-order effects. This information can help guide choices of next steps.
Methods for improving fit:


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useForm is the React hook for validating form inputs. It returns an object
with several helpers to retrieve error messages and form value, define
validations and more.
useForm returns an object that contains everything you need.
defaultValues: this is the object you provide while initializing the
form so you don't have to define beforehand.
errors: this is an object like {attribute: string[]} which contains all
error messages for invalid attributes. If you need to transform error
messages in any way, use the validations property to derive a new object.
handleChange: the input's onChange handler.
isInvalid(): a function that returns true if the validation fails.
isValid(): a function that returns true if the validation succeeds.
reset(): reset the form's state; i.e. resets validations and errors.
Also restores values to the defaultValues.
validations: an object that contains everything related to the
validation. For more info about the result check Validation.
useForm
is the React hook for validating form inputs. It returns an object with several helpers to retrieve error messages and form value, define validations and more.import React from "react"; import { useForm } from "@fnando/use-form"; type FormParams = { name: string; email: string; password: string; passwordConfirmation: string; }; export const SignupScreen: React.FC = () => { const { errors, values, ...form } = useForm<FormParams>(({ required, confirm, regex }) => ({ name: [required()], email: [required(), regex(/^\S+@\S+$/)], password: [required()], passwordConfirmation: [confirm("password")] })); return ( <form onSubmit={form.handleSubmit}>
  Your name
 <input name="name" onChange={form.handleChange} />

  Your email
 <input name="email" type="email" onChange={form.handleChange} />

  Choose a password
 <input name="password" type="password" onChange={form.handleChange} />

  Confirm your password
 <input name="passwordConfirmation" type="password" onChange={form.handleChange} />

 <button>Sign up</button>
 </form> ); };
useForm
returns an object that contains everything you need.defaultValues
: this is the object you provide while initializing the form so you don't have to define beforehand.errors
: this is an object like{attribute: string[]}
which contains all error messages for invalid attributes. If you need to transform error messages in any way, use thevalidations
property to derive a new object.handleChange
: the input'sonChange
handler.isInvalid()
: a function that returnstrue
if the validation fails.isValid()
: a function that returnstrue
if the validation succeeds.reset()
: reset the form's state; i.e. resetsvalidations
anderrors
. Also restoresvalues
to thedefaultValues
.validations
: an object that contains everything related to the validation. For more info about the result checkValidation
.values
: the current form's state.


{% load staticfiles %} {% include "base/links.html" %} {% include "base/less_headers/referrals_less.html" %}


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|
Intel(R) Threading Building Blocks Doxygen Documentation
version 4.2.3
|
Interface to be implemented by all exceptions TBB recognizes and propagates across the threads. More...
#include <tbb_exception.h>
Public Member Functions | |
| virtual tbb_exception * | move ()=0 throw () |
| Creates and returns pointer to the deep copy of this exception object. More... | |
| virtual void | destroy ()=0 throw () |
| Destroys objects created by the move() method. More... | |
| virtual void | throw_self ()=0 |
| Throws this exception object. More... | |
| virtual const char * | name () const =0 throw () |
| Returns RTTI name of the originally intercepted exception. More... | |
| virtual const char * | what () const __TBB_override=0 throw () |
| Returns the result of originally intercepted exception's what() method. More... | |
| void | operator delete (void *p) |
Private Member Functions | |
| void * | operator new (size_t) |
Interface to be implemented by all exceptions TBB recognizes and propagates across the threads.
If an unhandled exception of the type derived from tbb::tbb_exception is intercepted by the TBB scheduler in one of the worker threads, it is delivered to and re-thrown in the root thread. The root thread is the thread that has started the outermost algorithm or root task sharing the same task_group_context with the guilty algorithm/task (the one that threw the exception first).
Note: when documentation mentions workers with respect to exception handling, masters are implied as well, because they are completely equivalent in this context. Consequently a root thread can be master or worker thread.
NOTE: In case of nested algorithms or complex task hierarchies when the nested levels share (explicitly or by means of implicit inheritance) the task group context of the outermost level, the exception may be (re-)thrown multiple times (ultimately - in each worker on each nesting level) before reaching the root thread at the outermost level. IMPORTANT: if you intercept an exception derived from this class on a nested level, you must re-throw it in the catch block by means of the "throw;" operator.
TBB provides two implementations of this interface: tbb::captured_exception and template class tbb::movable_exception. See their declarations for more info.
Definition at line 138 of file tbb_exception.h.
|
pure virtual |
Destroys objects created by the move() method.
Frees memory and calls destructor for this exception object. Can and must be used only on objects created by the move method.
Implemented in tbb::movable_exception< ExceptionData >, and tbb::captured_exception.
|
pure virtual |
Creates and returns pointer to the deep copy of this exception object.
Move semantics is allowed.
Implemented in tbb::movable_exception< ExceptionData >, and tbb::captured_exception.
Referenced by tbb::internal::assert_task_valid().
|
pure virtual |
Returns RTTI name of the originally intercepted exception.
Implemented in tbb::movable_exception< ExceptionData >, and tbb::captured_exception.
Operator delete is provided only to allow using existing smart pointers with TBB exception objects obtained as the result of applying move() operation on an exception thrown out of TBB scheduler.
When overriding method move() make sure to override operator delete as well if memory is allocated not by TBB's scalable allocator.
Definition at line 182 of file tbb_exception.h.
References tbb::internal::deallocate_via_handler_v3(), and p.
|
private |
No operator new is provided because the TBB usage model assumes dynamic creation of the TBB exception objects only by means of applying move() operation on an exception thrown out of TBB scheduler.
|
pure virtual |
Throws this exception object.
Make sure that if you have several levels of derivation from this interface you implement or override this method on the most derived level. The implementation is as simple as "throw *this;". Failure to do this will result in exception of a base class type being thrown.
Implemented in tbb::movable_exception< ExceptionData >, and tbb::captured_exception.
|
pure virtual |
Returns the result of originally intercepted exception's what() method.
Implemented in tbb::movable_exception< ExceptionData >, and tbb::captured_exception.