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java.lang.Object com.googlecode.objectify.cache.EntityMemcache
public class EntityMemcache
This is the facade used by Objectify to cache entities in the MemcacheService.
Entity cacheability and expiration are determined by a CacheControl
object.
In addition, hit/miss statistics are tracked in a MemcacheStats
.
In order to guarantee cache synchronization, getAll() *must* be able to return an IdentifiableValue, even for entries not present in the cache. Because empty cache values cannot be made into IdentifiableValue, we immediately replace them with a null value and refetch (null is a valid cache value). If this refetch doesn't work, we treat the key as uncacheable for the duration of the request.
The values put in memcache are Key -> Entity, except for negative cache entries, which are Key -> String (the value NEGATIVE).
| Nested Class Summary | |
|---|---|
class |
EntityMemcache.Bucket
A bucket represents memcache information for a particular Key. |
| Constructor Summary | |
|---|---|
EntityMemcache(java.lang.String namespace)
Creates a memcache which caches everything without expiry and doesn't record statistics. |
|
EntityMemcache(java.lang.String namespace,
CacheControl cacheControl)
Creates a memcache which doesn't record stats |
|
EntityMemcache(java.lang.String namespace,
CacheControl cacheControl,
MemcacheStats stats)
|
| Method Summary | |
|---|---|
void |
empty(java.lang.Iterable<com.google.appengine.api.datastore.Key> keys)
Revert a set of keys to the empty state. |
java.util.Map<com.google.appengine.api.datastore.Key,EntityMemcache.Bucket> |
getAll(java.lang.Iterable<com.google.appengine.api.datastore.Key> keys)
Gets the Buckets for the specified keys. |
static java.util.Set<com.google.appengine.api.datastore.Key> |
keysOf(java.lang.Iterable<EntityMemcache.Bucket> buckets)
Basically a list comprehension of the keys for convenience. |
void |
putAll(java.util.Collection<EntityMemcache.Bucket> updates)
Update a set of buckets with new values. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public EntityMemcache(java.lang.String namespace)
public EntityMemcache(java.lang.String namespace, CacheControl cacheControl)
public EntityMemcache(java.lang.String namespace, CacheControl cacheControl, MemcacheStats stats)
| Method Detail |
|---|
public java.util.Map<com.google.appengine.api.datastore.Key,EntityMemcache.Bucket> getAll(java.lang.Iterable<com.google.appengine.api.datastore.Key> keys)
Gets the Buckets for the specified keys. A bucket is built around an IdentifiableValue so you can putAll() them without the risk of overwriting other threads' changes. Buckets also hide the underlying details of storage for negative, empty, and uncacheable results.
Note that worst case (a cold cache), obtaining each bucket might require three memcache requests: a getIdentifiable() which returns null, a put(EMPTY), and another getIdentifiable(). Since there is no batch getIdentifiable(), this is *per key*.
When keys are uncacheable (per CacheControl) or the memcache is down, you will still get an empty bucket back. The bucket will have null IdentifiableValue so we can identify it as uncacheable.
public void putAll(java.util.Collection<EntityMemcache.Bucket> updates)
updates
- can have null Entity values, which will record a negative cache result. Buckets must have
been obtained from getAll().public void empty(java.lang.Iterable<com.google.appengine.api.datastore.Key> keys)
public static java.util.Set<com.google.appengine.api.datastore.Key> keysOf(java.lang.Iterable<EntityMemcache.Bucket> buckets)
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Pairs of Korean speakers reading scripts with 3 distinct text sentiments (negative, neutral, positive), with 3 distinct vocal sentiments (negative, neutral, positive), are recorded.
The recordings took place in 3 different types of places, which are an anechoic chamber, studio apartment, and dance studio, of which the level of reverberation differs.
And, in order to examine the effect of the distance of mic from the source and device, every experiment is recorded at 3 distinct distances with 2 types of smartphone, iPhone X, and Galaxy S7.
The dataset is a subset(approximately 1%) of a much bigger dataset
which were recorded under the same environment as this public dataset.
This sample dataset only contains only a limited amount which contains a single pair of speaker recorded in 3 places, but only at single distance and with iPhone X.
Please visit our website Deeply Inc., GitHub, or contact us for more details and access to the full set of the dataset with commercial license.
Deeply makes products that anyone can use with audio AI technology and makes people's lives happier with those products. For more products and services, please visit Deeply Inc..
Structure
Korean_Read_Speech_Corpus.json {'AirbnbStudio': {'sub100100a00000': {'text_sentiment': 0, 'voice_sentiment': -1, 'subjectID': 'sub1001', 'speaker': 'a', 'age': 20, 'sex': 0, 'noise': 0, 'location': 0, 'distance': 0, 'device': 0, 'text': '저 식당 음식이 정말 맛있나 봐요.', 'text_code': 'aa0', 'rms': 0.024304501712322235, 'length': 2.71825}, ... }, ... } Text sentiment : {-1: negative, 0: neutral, 1: positive} Vocie sentiment: {-1: negative, 0: neutral, 1: positive} Subject ID : unique 'sub + 4-digit' key allocated to each subject group Speaker : unique key allocated to each indiivdual in the subject group. Sex : {0: Female, 1: Male} Noise : {0: Noiseless, 1: Indoor noise, 2: Outdoor noise, 3: Both indoor/outdoor noise} Location : {0: Studio apartment, 1: Dance studio, 2: Anechoic chamber} Distance : {0: 0.4m, 1: 2.0m, 2: 4.0m} Device : {0: iPhone X, 1: Galaxy S7} Text : the content of the script Text code : unique key allocated to each line of the script Rms : root mean square value of the signal Length : length of the signal in secodsYou can cite the data as follows:
@misc{deeply_corpus_kor, title={{Deeply Korean read speech corpus}}, author={Deeply Inc.}, year={2021}, url={https://github.com/deeplyinc/Korean-Read-Speech-Corpus} }
Contact
Tel: (+82) 70-7459-0704
Web: http://deeplyinc.com/
Email: contact@deeplyinc.com


title=Java Configuration with Spring Social date=2013-04-14 type=post tags=blog status=published ~~~~~~
In the first post in this series, I introduced Spring's Java configuration mechanism. There is the base configuration style, and - as we saw - there are annotations (like @EnableWebMvc
) that turn on container-wide, and often conventions-oriented features. In the case of @EnableWebMvc
, the annotation enables Spring MVC and turns on support for correctly exposing @Controller
-annotated beans as HTTP endpoints. Java configuration APIs of the same style as @EnableWebMvc
can often optionally implement interfaces that are used to contribute, change, or otherwise tailor the API. Most Spring projects today offer beta-or-better APIs that work in this same manner.
In this post, we'll look at Spring Social.
Spring Social a OAuth 1.0, 1.0.a and 2.0-compliant REST-services client and builds on top of this strongly typed Java bindings. Here's an example setup.
@Configuration
@EnableJdbcConnectionRepository
@EnableFacebook(appId = "${blog.facebook.appId}", appSecret = "${blog.facebook.appSecret}")
public class SocialConfiguration {
@Bean
public ConnectController connectController(ConnectionFactoryLocator connectionFactoryLocator,
ConnectionRepository connectionRepository) {
return new ConnectController(connectionFactoryLocator, connectionRepository);
}
@Bean
public ProviderSignInController providerSignInController(
ConnectionFactoryLocator connectionFactoryLocator,
UsersConnectionRepository usersConnectionRepository) {
return new ProviderSignInController(connectionFactoryLocator, usersConnectionRepository, new SignInAdapter() {
@Override
public String signIn(String userId, Connection> connection, NativeWebRequest request) {
return SignInUtils.signIn(userId);
}
});
}
@Bean
public DisconnectController disconnectController(Environment environment, UsersConnectionRepository usersConnectionRepository) {
return new DisconnectController(usersConnectionRepository, environment.getProperty("blog.facebook.appSecret"));
}
@Bean
public UserIdSource userIdSource() {
return new UserIdSource() {
@Override
public String getUserId() {
Authentication authentication = SecurityContextHolder.getContext().getAuthentication();
if (authentication == null) {
throw new IllegalStateException("Unable to get a ConnectionRepository: no user signed in");
}
return authentication.getName();
}
};
}
}
Once you've done this, you can simply inject instances of the Facebook API binding and make calls against it. Spring Social will attempt to make the call if there is an authenticated user, and if not will prompt the user to approve the API access and then proceed with the call.
@Inject private Facebook facebook;
@RequestMapping("/hello")
public void showProfileInformation( Model model){
model.addAttribute( "name", facebook.userOperations().getUserProfile().getName() );
}


This topic describes system and user properties that can be set to configure the deployment process.
The deployment.properties
file is used for storing and retrieving deployment configuration properties shown in the Java Control Panel. The properties are also used for customizing runtime behavior for both Java Plug-in and Java Web Start.
This topic contains the following sections:
(deployment.properties)
A user-level deployment.properties
file always exists. Its location, which is non-configurable, is described in User Level. There can also be an optional system-level deployment.properties
file. If it exists, its location is determined by a System Administrator through the deployment.config
file, described in System Level.
The following table shows the location of the user-level deployment.properties
file.
Table 21-1 Location of the User-Level Deployment Configuration File
| Operating System | Location |
|---|---|
|
Windows |
|
|
Solaris, Linux |
|
|
OS X |
|
On Windows, <User Application Data Folder>
is typically C:\Users\
username. On Solaris and Linux, ${user.home}
is typically /home/
username. On OS X, the tilde (~) represents the home directory, which is typically /Users/
username.
The following locations provide examples for each operating system:
For user jsmith
running on Windows 7, the deployment.properties
file would be located in the following directory:
C:\Users\jsmith\AppData\LocalLow\Sun\Java\Deployment\deployment.properties
For user bjones
running on Solaris or Linux, the deployment.properties
file would be located in the following directory:
/home/bjones/.java/deployment/deployment.properties
For user jdoe
running on OS X, the deployment.properties
file would be located in the following directory:
/Users/jdoe/Library/Application Support/Oracle/Java/Deployment/deployment.properties
The deployment.config
file is used for specifying the system-level deployment.properties
in the infrastructure. By default no deployment.config
file exists, so no system-wide deployment.properties
file exists. If the deployment.config
file exists, it is located in one of the directories shown in the following table.
Table 21-2 Location of the System-Level Deployment Configuration File
| Operating System | Location |
|---|---|
|
Windows |
|
|
Solaris, Linux |
|
|
OS X |
|
${deployment.java.home}
is the location of the JRE from which the deployment products are run. Deployment products include Java Web Start, Java Plug-in, Java Control Panel, and others.
The deployment.config
file contains two properties: deployment.system.config
and deployment.system.config.mandatory
.
The deployment.system.config
property is the URL to the system (enterprise-wide) deployment.properties
file. This property can be used by system administrators to centrally administer or "lock-down" user-specific configuration settings. For local files, use the file
protocol in the URL, for example, file:///C:/Windows/Sun/Java/Deployment/deployment.properties
.
The deployment.system.config.mandatory
property is a boolean. If set to true
, the deployment.properties
file that is pointed to by the deployment.system.config
property must be found and successfully loaded, otherwise, nothing is allowed to run. If the property is set to false
, an attempt is made to find and load the deployment. properties file that is pointed to by the deployment.system.config
property. If successful, the file is used, otherwise, the file is ignored. The default for the deployment.system.config.mandatory
property is false
.
The following tables describe the properties that can be set in the deployment.properties
file.
|
Note: Any system deployment property, for exampleSomeKey=SomeValue , can be locked by including another key, SomeKey.locked . The key SomeKey.locked does not require a value, when the key is present, the property SomeKey=SomeValue is locked so that the user cannot change it. If a system deployment property is not locked, then a user is allowed to change it. |
Table 21-3 Configuration Properties Related to Infrastructure
| Property Key | Type | Default Value | Description |
|---|---|---|---|
|
deployment.user.cachedir |
String |
|
User-level cache directory. |
|
deployment.system.cachedir |
String |
null |
System-level cache directory. |
|
deployment.user.logdir |
String |
|
User-level log directory. |
Table 21-4 Configuration Properties Related to Certificate Stores and Policy Files
| Property Key | Type | Default Value | Description |
|---|---|---|---|
|
deployment.user.security.trusted.cacerts |
String |
|
User-level Root CA certificate store. |
|
deployment.user.security.trusted.jssecacerts |
String |
|
User-level JSSE CA certificate store. |
|
deployment.user.security.trusted.certs |
String |
|
User-level Trusted signer certificate store. |
|
deployment.user.security.trusted.jssecerts |
String |
|
User-level Trusted JSSE certificate store. |
|
deployment.user.security.trusted.clientauthcerts |
String |
|
User-level Client Authentication certificate store. |
|
deployment.user.security.exception.sites |
String |
|
Location of the exception site list. See Chapter 29, "Exception Site List" for information. |
|
deployment.system.security.policy |
String |
null |
System-level security policy file. The protocol of URL is either file, HTTP, or HTTPS. |
|
deployment.system.security.cacerts |
String |
|
System-level Root CA certificate store. |
|
deployment.system.security.jssecacerts |
String |
|
System-level JSSE CA certificate store. |
|
deployment.system.security.trusted.certs |
String |
|
System-level Signer certificate store. |
|
deployment.system.security.trusted.jssecerts |
String |
|
System-level JSSE certificate store. |
|
deployment.system.security.trusted.clientauthcerts |
String |
|
System-level Client Authentication certificate store. |
Table 21-5 Configuration Properties Related to Security Access and Control
| Property Key | Type | Default Value | Description |
|---|---|---|---|
|
deployment.security.level |
String |
|
Security level setting. The following values are valid:
|
|
deployment.webjava.enabled |
Boolean |
|
Set to |
|
deployment.insecure.jres |
String |
|
Setting for insecure JRE prompt. The following values are valid:
|
|
deployment.expiration.check.enabled |
Boolean |
|
Set to Note: To ensure that the expiration check is disabled, use the |
|
deployment.security.askgrantdialog.show |
Boolean |
|
Set to |
|
deployment.security.askgrantdialog.notinca |
Boolean |
|
Set to |
|
deployment.security.jsse.hostmismatch.warning |
Boolean |
|
Set to |
|
deployment.security.trusted.policy |
String |
"" |
Policy file that contains the ceiling policy of permissions granted to trusted applications and applets. The default is all permissions. Use this property to configure a lesser set of permissions. |
|
deployment.security.mixcode |
String |
|
Setting for mixed mode. The following values are valid:
|
|
deployment.security.sandbox.awtwarningwindow |
Boolean |
|
|
|
deployment.security.sandbox.jnlp.enhanced |
Boolean |
|
Set to |
|
deployment.security.sandbox.selfsigned |
String |
|
Setting for the prompt to run self-signed code in the sandbox. The following values are valid:
|
|
deployment.security.sandbox.casigned |
String |
|
Setting to enable users to turn off future prompts for a signed app running in the sandbox. The following values are valid:
|
|
deployment.security.blacklist.check |
Boolean |
|
Support for blacklisting signed JAR files that contain serious security vulnerabilities. This property is used to toggle this behavior. For more information see Blacklist Feature. |
|
deployment.security.revocation.check |
String |
|
Setting for revocation checks. The following values are valid:
|
|
deployment.security.validation.ocsp |
Boolean |
|
Specifies whether Online Certificate Status Protocol is enabled. |
|
deployment.security.validation.ocsp.url |
String |
null |
Specifies a URL string pointing to an OCSP response server. |
|
deployment.security.validation.ocsp.signer |
String |
null |
Points to a OCSP response signer certificate subject name. |
|
deployment.security.validation.crl |
Boolean |
|
Specifies whether to use certificate revocation list. |
|
deployment.security.validation.crl.url |
String |
null |
Specifies a URL in the Certificate Revocation List to perform a certificate validation. |
|
deployment.security.validation.clockskew |
int |
900 |
Acceptable time difference, in seconds, between the system clock and the clock on the server used for revocation checks. If the property is not set, or the value is negative, the default of 900 seconds (15 minutes) is used. |
|
deployment.security.validation.timeout |
int |
15 |
Maximum time, in seconds, that the system attempts to connect to the server for revocation checks before timing out. If the property is not set, or the value is negative, the default of 15 seconds is used. To never time out, set the property to 0. |
|
deployment.security.authenticator |
Boolean |
|
Normally Plug-in and Web Start install an Authenticator to handle communication with Authenticating web pages or Authenticating proxies. This is the default behavior (true). This option can be used to turn the normal behavior off if, for example, an application communicates directly with an authenticating web page and needs to install its own Authenticator. |
Table 21-6 Configuration Properties Related to Networking
| Property Key | Type | Default Value | Description |
|---|---|---|---|
|
deployment.proxy.type |
int |
3 for |
Type of proxy that should be used. The following values are valid:
|
|
deployment.proxy.same |
Boolean |
|
Set to |
|
deployment.proxy.auto.config.url |
String |
(no default) |
URL for auto-Auto proxy configuration JavaScript.proxy configuration JavaScript. |
|
deployment.proxy.bypass.list |
String |
(no default) |
List of host names that should bypass the proxy. Each host name is delimited by a comma in the property, for example, |
|
deployment.proxy.bypass.local |
String |
(no default) |
All local hosts should be bypassed. |
|
deployment.proxy.http.host |
String |
(no default) |
HTTP proxy host name. |
|
deployment.proxy.http.port |
String |
(no default) |
HTTP proxy port. |
|
deployment.proxy.https.host |
String |
(no default) |
HTTPS proxy host name. |
|
deployment.proxy.https.port |
String |
(no default) |
HTTPS proxy port. |
|
deployment.proxy.ftp.host |
String |
(no default) |
FTP proxy host name. |
|
deployment.proxy.ftp.port |
String |
(no default) |
FTP proxy port. |
|
deployment.proxy.socks.host |
String |
(no default) |
SOCKS v4 proxy host name. |
|
deployment.proxy.socks.port |
String |
(no default) |
SOCKS v4 proxy port. |
|
deployment.proxy.override.hosts |
String |
"" |
Proxy overridden list. |
Table 21-7 Configuration Properties Related to Cache and Optional Package Repository
| Property Key | Type | Default Value | Description |
|---|---|---|---|
|
deployment.cache.max.size |
int |
-1 |
Maximum size of the deployment cache in megabytes (MB). This is the cache size for each cache: Java Web Start and Java Plug-in. 0: Disables caching in Java Plug-in; cache size in Java Web Start will be unlimited. -1: Indicates unlimited cache size. |
|
deployment.cache.jarcompression |
String |
0 |
0-9 compression ratio to use in applet JAR compression. |
|
deployment.javapi.cache.enabled |
Boolean |
|
Specifies if the cache should be disabled. Set to |
Table 21-10 Configuration Properties Related to Java Web Start
| Property Key | Type | Default Value | Description |
|---|---|---|---|
|
deployment.javaws.associations |
int |
2 |
JNLP associations. The following values are valid:
|
|
deployment.javaws.shortcut |
String |
|
Create a desktop shortcut for Java Web Start. The following values are valid:
|
Table 21-11 Configuration Properties Related to Browser Selection and Path
| Property Key | Type | Default Value | Description |
|---|---|---|---|
|
deployment.browser.path |
String |
<No Browser Selected> |
Path to the browser for showing web pages from the application viewer, the About Box, and Java Web Start applications. Note that this property is used for Solaris and Linux only. On Windows, this property is ignored, and the default browser is determined in the same way as other applications that launches a browser on Windows. |
It should be noted that SSLv3 is obsolete and should no longer be used. See https://blogs.oracle.com/security/entry/information_about_ssl_poodle_vulnerability
. Starting with JDK 8u31 release, the SSLv3 protocol (Secure Socket Layer) has been deactivated and is not available by default.
If the user needs to use SSLv3 for applications, see the procedure to re-enable SSLv3 at http://docs.oracle.com/javase/8/docs/technotes/guides/security//SunProviders.html#enable-sslv3
.
More information about how to configure specific protocols can be found at:
http://www.oracle.com/technetwork/java/javase/documentation/cve-2014-3566-2342133.html


Preparation time
1-2 hours
Cooking time
10 to 30 mins
Serves
Makes 12 sausage plaits
Turn up to a picnic or party with these and you are sure to impress your friends and family.
For the shortcut puff pastry, mix the flour and salt together in a bowl. Rub in the chilled butter using your fingertips until the mixture resembles breadcrumbs. Gradually add enough water to form a dough (about 4-6 tablespoons of water).
Roll the dough out into a rectangle on a lightly floured work surface.
Coarsely grate the frozen butter over the bottom two-thirds of the dough. Fold down the top third and fold up the bottom third as if folding a letter.
Turn the folded dough 90 degrees on the work surface and roll out into a rectangle again.
Fold again in thirds, wrap the dough in cling film and set aside to rest in the fridge for 30 minutes. Repeat the rolling, folding and turning process a further two times, chilling in between each turn. In total you will have done four turns. Rest the pastry in the fridge while you make the filling.
Preheat the oven to 200C/400F/Gas 6.
For the filling, put the mushrooms in a food processor and season with salt and pepper. Pulse until the mushrooms are broken down to a rough paste. Add the thyme and give the mix a final pulse.
Put the mushroom mixture into a dry frying pan set over a medium-high heat and cook, stirring often, until all the moisture has evaporated from the mushrooms. Remove from the pan and leave to cool.
Meanwhile, using the same pan, heat the oil and butter over a medium-low heat. Add the onions and sugar and cook slowly until the onions are very soft and sweet. This will take at least 20 minutes. Once caramelised, stir in the sherry vinegar.
To assemble, roll out the pastry into a 60x45cm/24x18in rectangle, and cut into 12 squares.
Divide the mushroom mixture into 12 portions and spread a portion down the centre of each square of pastry, leaving a 2cm/1in gap at the top and bottom.
In a bowl, mix the sausage meat with the black pudding and mould into 12 sausage shapes. Place on top of the mushroom paste and then spread the caramelised onions on top of each sausage.
Make two small diagonal cuts from each corner of the pastry, to remove a small triangle. Then fold the top and bottom 'wings' over the ends of the sausage meat. Cut a 1cm/½in fringe all the way down the pastry on each side of the filling. Bring one strip over the filling from one side, then one from the other and so on, crossing the strips over to form a plaited effect. Tuck the ends of the pastry under the plait, trimming off any excess if necessary.
Repeat with the remaining squares of pastry until you have 12 mini sausage plaits. Place the plaits on a baking tray, brush with beaten egg and sprinkle with sesame seeds.
Bake for 20 minutes, until the pastry is golden-brown. Leave to cool for five minutes or so, then serve hot or cold.
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Rolling out pastry
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Rubbing in


File generated from /home/antongml/offline_projects/potato_game/_build/test/lib/potatoserver/ebin/../src/p2p/supersocket_tcplistener.erl by COVER 2018-10-27 at 23:14:36 **************************************************************************** | -module(supersocket_tcplistener). | -behaviour(gen_server). | | -record(state, {name, % connection id nonsense idk | next, % next step, used when initializing | socket}). % the current socket | | -export([start_link/1]). | -export([init/1, handle_call/3, handle_cast/2, handle_info/2, code_change/3, terminate/2]). | | | -define(TIME, 800). | -define(EXP, 50). | | start_link(Socket) -> 28..| gen_server:start_link(?MODULE, Socket, []). | | init(Socket) -> | %% Because accepting a connection is a blocking function call, | %% we can not do it in here. Forward to the server loop! 28..| gen_server:cast(self(), accept), 28..| {ok, #state{socket=Socket}}. | | %% Never used | handle_call(_E, _From, State) -> 0..| {noreply, State}. | | handle_cast(accept, S = #state{socket=ListenSocket}) -> | %% listen for connection 28..| {ok, AcceptSocket} = gen_tcp:accept(ListenSocket), | | %% once we get a connection, start a new listener to replace us 8..| supersocket:start_socket(), | | %% server initiates handshake stuff 8..| ok = gen_tcp:send(AcceptSocket, <<"Welcome to my 🥔🥔🥔 server\n">>), | | %% once we send out handshake, start actively listening for a response 8..| ok = inet:setopts(AcceptSocket, [{active, once}]), | | %% go to next step 8..| {noreply, S#state{socket=AcceptSocket, next=handshake}}. | | | %% Let's get rid of the white space and ignore whatever's after. | %% makes it simpler to deal with telnet. | line(Str) -> 0..| hd(string:tokens(Str, "\r\n ")). | | %% use handle info to pick up on active tcp messages | handle_info({tcp, _Socket, Str}, S = #state{next=handshake}) -> 0..| Name = line(Str), | %% TODO figure out who connected and send message to something else to start a worker to handle it | %% make sure to pass on ownership of socket to the next owner | %% make sure the next owner sts socket to {active, once} | %% make sure there's no race condition where tcp packets get lost in ownership transfer (would be awful if there was...) | | 0..| {noreply, S#state{name=Name, next=done}}; | | %% TODO log errors or whatever. | handle_info({tcp_closed, _Socket}, S) -> 8..| {stop, normal, S}; | handle_info({tcp_error, _Socket, _}, S) -> 0..| {stop, normal, S}; | handle_info(E, S) -> 0..| io:format("unexpected: ~p~n", [E]), 0..| {noreply, S}. | | | code_change(_OldVsn, State, _Extra) -> 0..| {ok, State}. | terminate(normal, _State) -> 8..| ok; | terminate(_Reason, _State) -> 0..| io:format("terminate reason: ~p~n", [_Reason]).


__construct()
__construct(string $filePath, boolean $isReadMode = true, boolean $autoDeletion = false)
Constructor.
Parameters
| string | $filePath | The file that is going to store the pipe. |
| boolean | $isReadMode | Indicates if the param is going to be read only |
| boolean | $autoDeletion | If it is set during the destruction of the GPhpThreadIntercom instance the pipe file will be also removed. |


Luna Raytracer is a simple multi-threaded C++ ray tracing library. It provides the basic building blocks for rendering simple scenes and hooks for extending its capabilites.
Its most relevant features are support for all major OS, 64 bit HDR multi-threaded core, area lights, sky light, glossy surfaces, dielectric fresnel reflectance and realistic depth of field camera.
Complete 3D game engine built from scratch using Java and GLSL. It features state of the art per pixel lighting and advanced materials, including support for normal mapping and parallax mapping.
The engine provides all the utilities for model and texture loading, particle systems, on demand dynamic shader creation and compilation, AdMob integration, sound fx and music, scene management, settings, and user input.
All the source code for the engine, incluing the tools, will be uploaded to GitHub eventually.
This is yours truly. The complete source is stored in GitHub.
On my spare time I enjoy taking and developing digital photographs. Some of them can be licensed at Getty Images.
Retro-style drawings, limiting the color palette. I use Aseprite for my drawings.
Development of new features and products for the Quantexa Platform.
First techinal hire in the company, I have been creating the proper infrastructure for developing technical projects while meeting the tight deadlines.
Consultant at CNP Partners (insurance and investment) working on the improvement, bugfix and refactor of a large multi-layered web application for the Italian market in a small agile team.
We were using Struts 2 + FreeMaker for the UI, Spring as application container, Hibernate and iBatis for the persistence layer (two MS SQL and one DB2 databases), JasperReports for reporting, JUnit for unit testing and Jenkins for continuous integration.
Telecommuting from Spain and reporting directly to the CEO, the fast paced time-critical project included these tasks:
Cofounder and technical leader, we succeeded at creating our own base technology for developing high-quality 3D games, and develop and publish a full featured game, with a small three people core team and several collaborators.
My main tasks were:
Creating the online presence for a law firm.
Analysis, design and implementation of mobility-based products. Introduced and promoted the usage of GWT, Hibernate and virtualization (Xen) for SaaS applications.
Designed the architecture and GUI for the company new flagship product, BeMo, a web-based mobile app creation tool for travel guides. Release manager.
Consultant at former Eliop, S.A. (DF Núcleo nowadays) working on:
Working at the core team for the main company product OpenGate, an IoT Platform.
Development of C++ embedded agents and mobility and remote control based solutions.
C++ RS-232 driver development for data loggers, meteorological stations and sonometers. Proposed and implemented an abstraction layer for data input sources.
Development of the site for the I Iberoamerican congress on ubiquitous computing.
IV and V MobiGame editions website maintenance (site). Substituted the old image based navigation with a JavaScript and CSS based interactive menu. Updated the sources to validate XHTML 1.0 Strict.
Coursera - DeepLearning.AI
Coursera - DeepLearning.AI
Coursera - DeepLearning.AI
Coursera - Stanford
edx - W3Cx
Zoni Languages Center Manhattan New York City, USA
The British Council Alcalá de Henares, Spain
Vodafone New Technologies Institute Alcobendas, Spain
The British Council Alcalá de Henares, Spain
Universidad de Alcalá Alcalá de Henares, Spain
4-year university degree focusing on Computer Science, Computer Engineering, Project Management, Digital Circuits, Mathematics, Information Technology, and Networks.
Awarded Best PocketPC game at Mobigame 2005 contest for a full isometric game engine built on top of the PocketPC platform (C++) and an example game to demonstrate the full capabilities of the engine.
Achieved the highest grade in the thesis: Ray tracing based multi-platform library for photorealistic 3D scene rendering.
I have been developing software for more than ten years now. During these years I have developed C++ embedded systems for an IoT platform and ray-tracing based rendering engines, Java large enterprise applications for investment and insurance and for visually creating travel guide mobile apps, desktop emulators for the GSM network and Android 3D games, C# 3D tools and Kinect-based gesture interaction systems, and JS web 3D visualization libraries based on WebGL.
All these years I have been driven by a constant will to face new challenges and learn new programming languages and frameworks, and jump out of my confort zone constantly. My broad and diverse experience is the result.
Born in Spain, now I live in London following the buoyancy of the tech scene here, where all kind of new and exciting technologies are within reach.
Interested in contacting me? There are a lot of options!


The Sakai Basic LTI tool allows you to include tools that support the IMS Basic Learning Tools Interoperability standard in your sites. The Basic LTI launch protocol securely sends user, site, and role information to the external tool using the OAuth (www.oauth.net) security mechanism.
The site owner or the system administrator can configure a Basic LTI tool. If the system administrator pre-configures a Basic LTI tool, it appears in the list of tools like any other tool that can be added to the site by the site owner.
If the system administrator gives the site owner access to an unconfigured Basic LTI tool, a site owner can set up a link to any external tool they like. When you make arrangements with an external tool provider, they will provide you with a launch URL for the tool, key, and a secret. These values must be entered as part of the configuration of the tool. Make sure not to reveal these values to the other members of the site. Once you have entered these values into the tool, it can properly sign launch requests. Site members who are not site owners cannot see the configuration screen for the tool.
The Basic LTI configuration also includes the ability to send or suppress user identity information to the external tool such as the user's name or E-mail address. In addition, you can set the frame size, as well as the button and title text for the tool from the configuration screen. If the tool you are working with was created by a system administrator, they may have already pre-configured and locked a number of the configuration options for the tool.


Frozen star Idina Menzel to entertain young fans on CBeebies
The actress will read a story about a robin’s first winter.
Frozen star Idina Menzel will continue to charm young fans as she has signed up to read a CBeebies Bedtime Story.
The actress will read Robin’s Winter Song by Suzanne Barton on the BBC channel for pre-school age children.
The heartfelt tale tells the story of a little robin’s first winter.
Menzel, 48, is best known for her role as Elsa in Disney’s Frozen films – the second of which will be in UK cinemas from Friday November 22 – and for her Broadway career in hit musicals including Rent and Wicked.
The US star follows in the footsteps of fellow big names such as Sir Elton John, Dolly Parton, David Hasselhoff, Mark Ronson and Tom Hardy in signing up for the star-studded TV slot.
Menzel’s role in settling down young viewers before bedtime comes after it was announced that US jazz singer Gregory Porter will become the first celebrity to sing a CBeebies Bedtime Story rather than read it.
Porter will sing Moon River, the classic ballad from the 1961 film Breakfast At Tiffany’s, on Friday November 22.
Menzel’s slot will air on the pre-school channel on Sunday November 24. CBeebies Bedtime Stories airs daily at 6.50pm.


Methodology
Priority Areas
The One D Scorecard makes available over 30 baseline indicators through interactive data visualizations and reference tables. The indicators are categorized within the five priority areas of: Economic Prosperity, Educational Preparedness, Quality of Life, Social Equity, and Regional Transit. In order to provide the most robust and balanced information within each priority area for all metro regions, we have refined our indicator list from previous iterations for greater consistency across units of geography and annual availability.
Economic Prosperity
| Indicator | Data Source | Unit of Geography | Years Available | Index Score |
|---|---|---|---|---|
| Total Value of Exports | U.S. Census Bureau; U.S. Department of Commerce | MSA | 2009-2011 | |
| Per Capita GDP for Metropolitan Area (GMP) | U.S. Department of Commerce, Bureau of Economic Analysis | MSA | 2009-2011 | |
| Percent Change in High Tech Jobs | Bureau of Labor Statistics, Quarterly Census of Employment and Wages | CBSA | 2000-2009, 2001-2010 | |
| Percent Change in Knowledge Industry Employment | Bureau of Labor Statistics, Quarterly Census of Employment and Wages | CBSA | 2000-2009, 2001-2010 | |
| Per Capita Personal Income | Regional Economic Information System, Bureau of Economic Analysis; U.S. Department of Commerce | CSA/CBSA | 2009-2011 | |
| Number of Technology Patents per 10K People | U.S. Patent & Trademark Office - Electronic Information Products Division, Patent Technology Monitoring Team (PTMT) | MSA | 2009-2011 | |
| Business Tax Climate Index | Tax Foundation | State | 2009-2011 | |
Educational Preparedness
| Indicator | Data Source | Unit of Geography | Years Available | Index Score |
|---|---|---|---|---|
| Young & Talented Population, Education Attainment Bachelors+, 25-34 | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Education Attainment Bachelors+, 25+ | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Percent Population Enrolled in School, 3+ | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Education Attainment High School+, 18+ | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Percent Teens Not Enrolled in School, No HS Diploma, Unemployed | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| State Appropriations Per Pupil | NCHEMS Information Center for Higher Education Policymaking and Analysis | State | 2009-2011 | |
| State Expenditures Per Pupil | U.S. Department of Education, National Center for Education Statistics, Common Core of Data (CCD) | State | 2009-2011 |
Quality of Life
| Indicator | Data Source | Unit of Geography | Years Available | Index Score |
|---|---|---|---|---|
| Air Quality Index, Percent of “Good” Days | Environmental Protection Agency - AirData Air Quality Index Report | CSA/CBSA | 2009-2011 | |
| Percent Change in Population | U.S. Census Bureau, Population Division | CSA/CBSA | 2008-2009, 2009-2010, 2010-2011 | |
| Percent Population Without Health Insurance | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Total Violent Crimes per 100,000 Residents | FBI Uniform Crime Reporting (UCR) Data | MSA | 2009-2011 | |
| Average Volunteer Rate | The Corporation for National and Community Service | MSA | 2009-2011 | |
| Percent of Population neither Overweight nor Obese | Center for Disease Control - Behavioral Risk Factor Surveillance System | MSA | 2009-2011 |
Social Equity
| Indicator | Data Source | Unit of Geography | Years Available | Index Score |
|---|---|---|---|---|
| Percent Population Foreign Born | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Gini Index | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Income Level by Quintile (Lowest, Second Lowest, and Middle Quintiles) | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Income Level by Quintile (Highest and Second Highest Quintiles) | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Median Household Income (by Race) | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Percent Housing Owner Occupied (by Race) | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Poverty Rate of Children under 18 (by Race) | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 |
Regional Transit
| Indicator | Data Source | Unit of Geography | Years Available | Index Score |
|---|---|---|---|---|
| Annual Hours of Delay per Auto Commuter | Bureau of Transportation Statistics, Research and Innovative Technology Administration | Urban Areas | 2009-2011 | |
| Percent of Workers with No Vehicle | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Percent of Workers 16+ Driving Alone to Work | U.S. Census Bureau, American Community Survey 3-year Estimates | CSA/CBSA | 2007-2009, 2008-2010, 2009-2011 | |
| Annual Number of Transit Rides Occurring on a Bus, in Thousands | U.S. Department of Transportation, Federal Transit Administration, National Transit Database | Urban Areas | 2009-2011 | |
| Public Transportation Funding, State Dollars per 1,000 Residents | Bureau of Transportation Statistics, Research and Innovative Technology Administration | State | 2009-2010 |
One D Index
The One D Index, developed by Data Driven Detroit (D3) and NiJeL, offers a comprehensive and easily comparable score for each metro region. Using a single score, the One D Index captures a summary overview of each data indicator, and its respective priority area, included in the 2014 One D Scorecard.
We calculate the One D Index score by transforming the original values of individual indicators over a 0-5 range, with 0 as the lowest score and 5 as the highest. We then calculate the priority area indices by averaging the scores for the respective indicators. Similarly, we calculate the One D Index by averaging the priority area index scores for a given region and year. We equally weight each priority area in the One D Index score, independent of the number of indicators within that priority area.
The data available for download are the original values for each indicator as well as the One D Index and index score by priority area for each region.
Priority Area Indices
Normalized Indicators
We normalize each indicator by first selecting the minimum and maximum value of that indicator from across all regions for each year. We then assign a score of 0 to the region with the minimum value, 5 to the region with the maximum value, and calculate the scores of all other regions in-between on a linear scale between 0 and 5. Last, we sum the normalized indicator values and divide by the total number of indicators in the priority area to calculate each priority area index for each year.
Negatively-scored Indicators
In calculating each priority area index, some individual indicators were negatively scored where higher values indicate a more distressing situation, such as childhood poverty rates. We assigned these indicators a score of 5 to the region with the minimum score, 0 to the region with the maximum score, and the scores of all other regions are assigned on a linear scale between 5 and 0. You can see whether an indicator is positively or negatively scored in the Priority Areas data table.
Weighted Indicators
When comprising any priority area index, we equally weight each indicator in most cases. Three exceptions occur in the Social Equity priority area when an indicator is broken out into three categories of race and ethnicity: African American; White, non-Hispanic; and Hispanic/Latino. In these cases, we set each of the three race categories to be one third of the weight of the overall indicator.
Calculated Indices
We calculate a priority area index for a given year when 50 percent or more of the indicators that comprise the overall index are available, in the given year, across all regions. For example, the Economic Prosperity index is comprised of eight economic indicators; we will calculate an Economic Prosperity index for the year 2010 if four or more of the economic indicators are available in 2010 across all regions.
Geography
The One D Scorecard tracks and compares baseline data indicators over the 54 U.S. regions most populous in 2000 whenever possible. Data provided for the 54 metro regions are collected and reported for various regional units of geography; most commonly combined statistical areas (CSAs)*, metropolitan statistical areas (MSAs)**, and urbanized areas (UA).
In rare cases, key data indicators that we identified as vital to include were not available at the regional geography and we defaulted to applicable state level data. The unit of geography is specified for all indicators on an individual basis and displayed in tabular format in our Priority Areas.
* Combined statistical areas are comprised of one or more contiguous "core based statistical areas" (see below). For context, the Detroit-Ann Arbor-Flint CSA includes nine counties: Genesee, Lapeer, Livingston, Macomb, Monroe, Oakland, St. Clair, Washtenaw, and Wayne.
** Metropolitan and micropolitan statistical areas are geographic entities comprised of one or more counties. These entities are considered to have a high degree of social and economic integration, and are "defined by the U.S. Office of Management and Budget (OMB) for use by Federal statistical agencies in collecting, tabulating, and publishing Federal statistics" (U.S. Census Bureau). Collectively, metropolitan and micropolitan statistical areas are referred to as "core based statistical areas".
One Detroit Portal
The One Detroit Portal is home to the Kirwan Institute's Southeast Michigan Regional Opportunity Index mapping initiative. While we make available a list of the indicators by category included in the Opportunity Index, the full extent of the Kirwan Institute's research and methodology can be accessed at their site. All data provided by the Kirwan Institute is reported at census tract level of geography.
Education
| Indicator | Data Source | Year | Sign | Description |
|---|---|---|---|---|
| Childhood Poverty | ACS 2007-2011 | 2007-2011 | Poverty rate for people 18 and younger | |
| High School Dropout Rate | ACS 2007-2011 | 2007-2011 | Dropout rate; high school students | |
| Persons 16-19 No HS Diploma, Unemployed | ACS 2007-2011 | 2007-2011 | # of people 16-19 who are unemployed, out of high school, with no diploma | |
| High School Completion | ACS 2007-2011 | 2007-2011 | Graduation rate for high school students | |
| Reading | School District | 2011 | Student proficiency rates of three nearest in-district primary schools | |
| Math | School District | 2011 | Student proficiency rates of three nearest in-district primary schools | |
| Student Poverty | School District | 2011 | % of students receiving free or reduced price lunch |
Economy and Employment
| Indicator | Data Source | Year | Sign | Description |
|---|---|---|---|---|
| Public Assistance Rate | ACS 2007-2011 | 2007-2011 | % of households receiving a form of public assistance | |
| Median Household Income | ACS 2007-2011 | 2007-2011 | Median household income | |
| Unemployment Rate | ACS 2007-2011 | 2007-2011 | Rate of unemployed persons in the labor force | |
| Job Change | Business County Pattern | 2006, 2010 | Change in jobs within a five mile radius |
Neighborhood
| Indicator | Data Source | Year | Sign | Description |
|---|---|---|---|---|
| Vacant Property | ACS 2007-2011 | 2007-2011 | # of vacant houses in census tract | |
| Property Values | ACS 2007-2011 | 2007-2011 | Average value of homes per census tract | |
| Homeownership Rates | ACS 2007-2011 | 2007-2011 | Rate of people who own their home in comparison to the population as a whole | |
| Poverty Rates | ACS 2007-2011 | 2007-2011 | % of population living below the Federal poverty line | |
| Percentage Population Change | Census 2000-2010 | 2000, 2010 | The % change in population from 2000-2010 |
Data Download
Partners of the One D Scorecard and One Detroit Portal intentionally do not offer any subjective conclusions based on these data. Rather, by making these data accessible, we aim to show how metro Detroit compares to other regions nationwide in the One D Scorecard and track change over time within our own region at a finer level of geography through the One Detroit Portal. These data are provided with the intention to better understand the social health and economic competitiveness of metro Detroit, inform civic decision-making, and measure progress on regional goals moving into the future.
For further exploration and deeper analysis, we encourage One D Scorecard and One Detroit Portal users to download the datasets, by simply clicking the Download icon at the bottom left of any visualization. The full datasets download as a spreadsheet, specifically a .csv file.


[1]
there is a gym but we did not use this.
[2]
the hotel has so many activities that you also want.


Latest updates
May 11th 2016: HoloViews 1.5.0 released and now available on
PyPI and
Anaconda.
December 5th 2015: Now includes extensive support for the Bokeh plotting library.
May 14th 2015:
Talk
and paper on HoloViews to appear at the SciPy 2015 conference.
Mar 19th 2015: HoloViews now available on SageMathCloud.
Feb 25th 2015: HoloViews announced as the winner in its category at the UK Open Source Awards 2015.


@@include('../../includes/head.html', { "title": "Dylan Araps - 0717", "description": "The personal website of Dylan Araps, author of Neofetch, wal and other projects." })
@@include('../../includes/nav.html') @@include('../../includes/header.html', { "title": "", "background": "/img/rice/0717/2.png", "position": "center 25%" })


Get in Touch
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本书pdf是文字版，书签良好。
1.1 History of Polymers
1.2 Why Polymers?
1.3 Today’s Marketplace
1.4 Environmental Assessment
1.5 Managing Sustainability
1.6 Summary
2.1 Stereochemistry of Polymers
2.2 Molecular Interactions
2.3 Polymer Crystals
2.4 Amorphous Bulk State
2.5 Polymer Structure–Property Relationships
2.6 Crystalline and Amorphous Combinations
2.7 Cross-linking
2.8 Summary
3.1 Introduction
3.2 Solubility
3.3 Average Molecular Weight Values
3.4 Fractionation of Polydisperse Systems
3.5 Chromatography
3.6 Colligative Molecular Weights
3.7 Light-Scattering Photometry
3.8 Other Techniques
3.9 Viscometry
3.10 Summary
4.1 Polysaccharides
4.2 Cellulose
4.3 Cellulose-Regenerating Processes
4.4 Esters and Ethers of Cellulose
4.5 Starch
4.6 Other Polysaccharides
4.7 Proteins
4.8 Nucleic Acids
4.9 Naturally Occurring Polyisoprenes and Other Synthetic Elastomers
4.10 Lignin
4.11 Melanins
4.12 Polymer Structure
4.13 Genetic Engineering
4.14 DNA Profiling
4.15 Asphalt
4.16 Summary
5.1 Comparison between Polymer Type and Kinetics of Polymerization
5.2 Introduction
5.3 Stepwise Kinetics
5.4 Polycondensation Mechanisms
5.5 Polyesters and Polycarbonates
5.6 Synthetic Polyamides
5.7 Polyimides
5.8 Polybenzimidazoles and Related Polymers
5.9 Polyurethanes and Polyureas
5.10 Polysulfides
5.11 Polyethers
5.12 Polysulfones
5.13 Poly(Ether Ether Ketone) and Polyketones
5.14 Phenolic and Amino Plastics
5.15 Synthetic Routes
5.16 Liquid Crystals
5.17 Microfibers
5.18 Summary
6.1 Cationic Polymerization
6.2 Anionic Polymerization
6.3 Stereoregularity and Stereogeometry
6.4 Polymerization with Complex Coordination Catalysts
6.5 Soluble Stereoregulating Catalysis
6.6 Polyethylenes
6.7 Polypropylene
6.8 Polymers from 1,4-Dienes
6.9 Polyisobutylene
6.10 Metathesis Reactions
6.11 Zwitterionic Polymerization
6.12 Isomerization Polymerization
6.13 Precipitation Polymerization
6.14 Summary
7.1 Initiators for Free Radical Chain Polymerization
7.2 Mechanism for Free Radical Chain Polymerization
7.3 Chain Transfer
7.4 Polymerization Techniques
7.5 Fluorine-Containing Polymers
7.6 Polystyrene
7.7 Poly(Vinyl Chloride)
7.8 Poly(Methyl Methacrylate)
7.9 Polyacrylonitrile
7.10 Solid-State Irradiation Polymerization
7.11 Plasma Polymerizations
7.12 Summary
8.1 Kinetics of Copolymerization
8.2 The Q–e Scheme
8.3 Commercial Copolymers
8.4 Block Copolymers
8.5 Graft Copolymers
8.6 Elastomers
8.7 Blends
8.8 Polymer Mixtures: IPNS and Alloys
8.9 Dendrites
8.10 Ionomers
8.11 Fluoroelastomers
8.12 Nitrile Rubber
8.13 Acrylonitrile Butadiene Styrene Terpolymers
8.14 EPDM Rubber
8.15 Summary
9.1 Introduction
9.2 Inorganic Reaction Mechanisms
9.3 Condensation Organometallic Polymers
9.4 Coordination Polymers
9.5 Addition Polymers
9.6 Summary
10.1 Introduction
10.2 Portland Cement
10.3 Other Cements
10.4 Silicates
10.5 Silicon Dioxide (Amorphous)
10.6 Sol–Gel
10.7 Silicon Dioxide (Crystalline Forms): Quartz Forms
10.8 Silicon Dioxide in Electronic Chips
10.9 Asbestos
10.10 Fly Ash and Aluminosilicates
10.11 Polymeric Carbon: Diamond
10.12 Polymeric Carbon: Graphite
10.13 Internal Cyclization: Carbon Fibers and Related Materials
10.14 Carbon Nanotubes
10.15 Bitumens
10.16 Carbon Black
10.17 Polysulfur
10.18 Ceramics
10.19 High-Temperature Superconductors
10.20 Zeolites
10.21 Summary
11.1 Reactions with Polyolefins and Polyenes
11.2 Reactions of Aromatic and Aliphatic Pendant Groups
11.3 Degradation
11.4 Cross-Linking
11.5 Reactivities of End Groups
11.6 Supramolecules and Self-Assembly
11.7 Transfer and Retention of Oxygen
11.8 Nature’s Macromolecular Catalysts
11.9 Photosynthesis
11.10 Mechanisms of Energy Physical Absorption
11.11 Breakage of Polymeric Materials
11.12 Summary
12.1 Spectronic Characterization of Polymers
12.2 Surface Characterization
12.3 Amorphous Region Determinations
12.4 Thermal Analysis
12.5 Thermal Property Tests
12.6 Flammability
12.7 Electrical Properties: Theory
12.8 Electric Measurements
12.9 Weatherability
12.10 Optical Properties Tests
12.11 Chemical Resistance
12.12 Summary
13.1 Rheology
13.2 Typical Stress–Strain Behavior
13.3 Stress–Strain Relationships
13.4 Specific Physical Tests
13.5 Summary
14.1 Fillers
14.2 Reinforcements
14.3 Nanocomposites
14.4 Plasticizers
14.5 Antioxidants
14.6 Heat Stabilizers
14.7 Ultraviolet Stabilizers
14.8 Flame Retardants
14.9 Colorants
14.10 Curing Agents
14.11 Antistatic Agents: Antistats
14.12 Chemical Blowing Agents
14.13 Compatibilizers
14.14 Impact Modifiers
14.15 Processing Aids
14.16 Lubricants
14.17 Microorganism Inhibitors
14.18 Summary
15.1 Monomer Synthesis from Basic Feedstocks
15.2 Reactants for Step-Reaction Polymerization
15.3 Synthesis of Vinyl Monomers
15.4 Summary
16.1 Polymer Processing
16.2 Secondary Structures: Mesophases
16.3 Fibers
16.4 Elastomers
16.5 Films and Sheets
16.6 Polymeric Foams
16.7 Reinforced Plastics (Composites) and Laminates
16.8 Molding
16.9 Casting
16.10 Extrusion
16.11 Coatings
16.12 Adhesives
16.13 Conductive Polymeric Materials
16.14 Drug Design and Activity
16.15 Synthetic Biomedical Polymers
16.16 Dental Materials
16.17 Emerging Polymers
16.18 Green Materials
16.19 Summary


Admin Panel
Import Data
Input course data as CSV, for more info, see:
http://help.com
(show github link for more docs later)


Dig A Pony is a warm and welcoming bar in the heart of Portland, Oregon. Putting a fresh take on an old space, we have one of the largest bar tops in town, DJs playing records every night, and an enticing and varied food and drink menu. Providing an inviting and friendly place for meeting old friends and making new ones.


A node list is a collection of nodes
The getElementsByTagName() method returns a node list. A node list is an array-like collection of nodes.
The following code selects all <p> nodes in a document:
The nodes can be accessed by an index number. To access the second <p> node you can write:
Note: The index starts at 0.
The length property defines the number of nodes in a node list:
Example explained:
The length property is useful when you want to loop through the nodes in a node list:
Change the background color of all <p> elements in a node list:
|
A node list is not an array! A node list may look like an array, but it is not. You can loop through the node list and refer to its nodes like an array. However, you cannot use Array Methods, like valueOf() or join() on the node list. |


Overview
This utility helps you import groups to your account using comma separated values (CSV).
Work flow
Initially a cache will be created of all the groups currently on your account. The input provided
is parsed and if a group does not exist in the cache, it will be added and then saved to your account.
Skill level
Intermediate


Musician Barbado:
Thank you so much! At last I get to play a duet with Bard Swan! My dream is coming true! You have made this possible.
Here! Take this priceless accordion music as a token of my appreciation!


The frame is custom.
The pricing is clear.
Each piece we frame passes through at least nine talented people’s hands before we ship it back to you. Every material is cut just for your art; nothing is off the shelf. A (human) designer looks out for your work from start to finish. This is real American design and manufacturing. And it’s not $500; it’s $199 or less + shipping is always free.
What will my frame cost?
Tell us the size of your art (in inches), and we'll tell you the price. Simple.

INSTAGRAM(printed at 5x5)$39
EXTRA SMALL(art up to 5x7)$59
SMALL(art up to 9x12)$79
MEDIUM(art up to 16x20)$99
LARGE(art up to 24x36)$149
EXTRA LARGE(art up to 32x40)$189
Yes, that includes the frame.
And the printing
(if you want it).
And the mat.
And the shipping. Both ways.
No hidden fees.
Frame your heart out.


Air Force ROTC prepares you to become an Air Force officer. Being an officer in the Air Force carries with it a high level of responsibility, accountability and professionalism.
There are typically three chances for students to compete for scholarships. First, during your junior year in high school you can
apply for the High School Scholarship Program. If you missed that opportunity, there are still two more chances to compete for an
In-College Scholarship.
Air Force ROTC Det 490
New Jersey Institute Of Technology
323 Martin Luther King Boulevard
Newark, NJ 07102
<EMAIL_ADDRESS>973.596.3626
Current as of 9 Apr 2014 | Privacy & Security Policy


Module: color
¶
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Stain to RGB color space conversion. |
|
Convert an image array to a new color space. |
|
Euclidean distance between two points in Lab color space |
|
Color difference as given by the CIEDE 2000 standard. |
|
Color difference according to CIEDE 94 standard |
|
Color difference from the CMC l:c standard. |
|
Create an RGB representation of a gray-level image. |
|
Create an RGB representation of a gray-level image. |
Make an educated guess about whether an image has a channels dimension. |
|
Haematoxylin-Eosin-DAB (HED) to RGB color space conversion. |
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HSV to RGB color space conversion. |
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CIE-LAB to CIE-LCH color space conversion. |
|
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Lab to RGB color space conversion. |
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CIE-LAB to XYZcolor space conversion. |
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Return an RGB image where color-coded labels are painted over the image. |
CIE-LCH to CIE-LAB color space conversion. |
|
Compute luminance of an RGB image. |
|
Compute luminance of an RGB image. |
|
RGB to Haematoxylin-Eosin-DAB (HED) color space conversion. |
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RGB to HSV color space conversion. |
|
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RGB to lab color space conversion. |
RGB to RGB CIE color space conversion. |
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RGB to XYZ color space conversion. |
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RGB to YCbCr color space conversion. |
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RGB to YDbDr color space conversion. |
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RGB to YIQ color space conversion. |
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RGB to YPbPr color space conversion. |
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RGB to YUV color space conversion. |
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RGBA to RGB conversion. |
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RGB CIE to RGB color space conversion. |
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RGB to stain color space conversion. |
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XYZ to CIE-LAB color space conversion. |
XYZ to RGB color space conversion. |
|
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YCbCr to RGB color space conversion. |
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YDbDr to RGB color space conversion. |
YIQ to RGB color space conversion. |
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YPbPr to RGB color space conversion. |
YUV to RGB color space conversion. |
combine_stains¶
-
skimage.color.
combine_stains
(stains, conv_matrix)[source]¶ Stain to RGB color space conversion.
- Parameters
- stainsarray_like
The image in stain color space, in a 3-D array of shape
(.., .., 3)
.- conv_matrix: ndarray
The stain separation matrix as described by G. Landini [1].
- Returns
- outndarray
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If stains is not a 3-D array of shape
(.., .., 3)
.
Notes
Stain combination matrices available in the
color
module and their respective colorspace:rgb_from_hed
: Hematoxylin + Eosin + DABrgb_from_hdx
: Hematoxylin + DABrgb_from_fgx
: Feulgen + Light Greenrgb_from_bex
: Giemsa stain : Methyl Blue + Eosinrgb_from_rbd
: FastRed + FastBlue + DABrgb_from_gdx
: Methyl Green + DABrgb_from_hax
: Hematoxylin + AECrgb_from_bro
: Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-Grgb_from_bpx
: Methyl Blue + Ponceau Fuchsinrgb_from_ahx
: Alcian Blue + Hematoxylinrgb_from_hpx
: Hematoxylin + PAS
References
Examples
>>> from skimage import data >>> from skimage.color import (separate_stains, combine_stains, ... hdx_from_rgb, rgb_from_hdx) >>> ihc = data.immunohistochemistry() >>> ihc_hdx = separate_stains(ihc, hdx_from_rgb) >>> ihc_rgb = combine_stains(ihc_hdx, rgb_from_hdx)
convert_colorspace¶
-
skimage.color.
convert_colorspace
(arr, fromspace, tospace)[source]¶ Convert an image array to a new color space.
- Valid color spaces are:
‘RGB’, ‘HSV’, ‘RGB CIE’, ‘XYZ’, ‘YUV’, ‘YIQ’, ‘YPbPr’, ‘YCbCr’, ‘YDbDr’
- Parameters
- arrarray_like
The image to convert.
- fromspacevalid color space
The color space to convert from. Can be specified in lower case.
- tospacevalid color space
The color space to convert to. Can be specified in lower case.
- Returns
- outndarray
The converted image.
Notes
Conversion is performed through the “central” RGB color space, i.e. conversion from XYZ to HSV is implemented as
XYZ -> RGB -> HSV
instead of directly.Examples
>>> from skimage import data >>> img = data.astronaut() >>> img_hsv = convert_colorspace(img, 'RGB', 'HSV')
deltaE_cie76¶
-
skimage.color.
deltaE_cie76
(lab1, lab2)[source]¶ Euclidean distance between two points in Lab color space
- Parameters
- lab1array_like
reference color (Lab colorspace)
- lab2array_like
comparison color (Lab colorspace)
- Returns
- dEarray_like
distance between colors lab1 and lab2
References
- 1
- 2
A. R. Robertson, “The CIE 1976 color-difference formulae,” Color Res. Appl. 2, 7-11 (1977).
deltaE_ciede2000¶
-
skimage.color.
deltaE_ciede2000
(lab1, lab2, kL=1, kC=1, kH=1)[source]¶ Color difference as given by the CIEDE 2000 standard.
CIEDE 2000 is a major revision of CIDE94. The perceptual calibration is largely based on experience with automotive paint on smooth surfaces.
- Parameters
- lab1array_like
reference color (Lab colorspace)
- lab2array_like
comparison color (Lab colorspace)
- kLfloat (range), optional
lightness scale factor, 1 for “acceptably close”; 2 for “imperceptible” see deltaE_cmc
- kCfloat (range), optional
chroma scale factor, usually 1
- kHfloat (range), optional
hue scale factor, usually 1
- Returns
- deltaEarray_like
The distance between lab1 and lab2
Notes
CIEDE 2000 assumes parametric weighting factors for the lightness, chroma, and hue (kL, kC, kH respectively). These default to 1.
References
- 1
- 2
http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf DOI:10.1364/AO.33.008069
- 3
M. Melgosa, J. Quesada, and E. Hita, “Uniformity of some recent color metrics tested with an accurate color-difference tolerance dataset,” Appl. Opt. 33, 8069-8077 (1994).
deltaE_ciede94¶
-
skimage.color.
deltaE_ciede94
(lab1, lab2, kH=1, kC=1, kL=1, k1=0.045, k2=0.015)[source]¶ Color difference according to CIEDE 94 standard
Accommodates perceptual non-uniformities through the use of application specific scale factors (kH, kC, kL, k1, and k2).
- Parameters
- lab1array_like
reference color (Lab colorspace)
- lab2array_like
comparison color (Lab colorspace)
- kHfloat, optional
Hue scale
- kCfloat, optional
Chroma scale
- kLfloat, optional
Lightness scale
- k1float, optional
first scale parameter
- k2float, optional
second scale parameter
- Returns
- dEarray_like
color difference between lab1 and lab2
Notes
deltaE_ciede94 is not symmetric with respect to lab1 and lab2. CIEDE94 defines the scales for the lightness, hue, and chroma in terms of the first color. Consequently, the first color should be regarded as the “reference” color.
kL, k1, k2 depend on the application and default to the values suggested for graphic arts
Parameter
Graphic Arts
Textiles
kL
1.000
2.000
k1
0.045
0.048
k2
0.015
0.014
References
deltaE_cmc¶
-
skimage.color.
deltaE_cmc
(lab1, lab2, kL=1, kC=1)[source]¶ Color difference from the CMC l:c standard.
This color difference was developed by the Colour Measurement Committee (CMC) of the Society of Dyers and Colourists (United Kingdom). It is intended for use in the textile industry.
The scale factors kL, kC set the weight given to differences in lightness and chroma relative to differences in hue. The usual values are
kL=2
,kC=1
for “acceptability” andkL=1
,kC=1
for “imperceptibility”. Colors withdE > 1
are “different” for the given scale factors.- Parameters
- lab1array_like
reference color (Lab colorspace)
- lab2array_like
comparison color (Lab colorspace)
- Returns
- dEarray_like
distance between colors lab1 and lab2
Notes
deltaE_cmc the defines the scales for the lightness, hue, and chroma in terms of the first color. Consequently
deltaE_cmc(lab1, lab2) != deltaE_cmc(lab2, lab1)
References
- 1
- 2
http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html
- 3
F. J. J. Clarke, R. McDonald, and B. Rigg, “Modification to the JPC79 colour-difference formula,” J. Soc. Dyers Colour. 100, 128-132 (1984).
gray2rgb¶
-
skimage.color.
gray2rgb
(image, alpha=None)[source]¶ Create an RGB representation of a gray-level image.
- Parameters
- imagearray_like
Input image of shape
(M[, N][, P])
.- alphabool, optional
Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created.
- Returns
- rgbndarray
RGB image of shape
(M[, N][, P], 3)
.
- Raises
- ValueError
If the input is not a 1-, 2- or 3-dimensional image.
Notes
If the input is a 1-dimensional image of shape
(M, )
, the output will be shape(M, 3)
.
grey2rgb¶
-
skimage.color.
grey2rgb
(image, alpha=None)[source]¶ Create an RGB representation of a gray-level image.
- Parameters
- imagearray_like
Input image of shape
(M[, N][, P])
.- alphabool, optional
Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created.
- Returns
- rgbndarray
RGB image of shape
(M[, N][, P], 3)
.
- Raises
- ValueError
If the input is not a 1-, 2- or 3-dimensional image.
Notes
If the input is a 1-dimensional image of shape
(M, )
, the output will be shape(M, 3)
.
guess_spatial_dimensions¶
-
skimage.color.
guess_spatial_dimensions
(image)[source]¶ Make an educated guess about whether an image has a channels dimension.
- Parameters
- imagendarray
The input image.
- Returns
- spatial_dimsint or None
The number of spatial dimensions of image. If ambiguous, the value is
None
.
- Raises
- ValueError
If the image array has less than two or more than four dimensions.
hed2rgb¶
-
skimage.color.
hed2rgb
(hed)[source]¶ Haematoxylin-Eosin-DAB (HED) to RGB color space conversion.
- Parameters
- hedarray_like
The image in the HED color space, in a 3-D array of shape
(.., .., 3)
.
- Returns
- outndarray
The image in RGB, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If hed is not a 3-D array of shape
(.., .., 3)
.
References
- 1
A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution.,” Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001.
Examples
>>> from skimage import data >>> from skimage.color import rgb2hed, hed2rgb >>> ihc = data.immunohistochemistry() >>> ihc_hed = rgb2hed(ihc) >>> ihc_rgb = hed2rgb(ihc_hed)
hsv2rgb¶
-
skimage.color.
hsv2rgb
(hsv)[source]¶ HSV to RGB color space conversion.
- Parameters
- hsvarray_like
The image in HSV format, in a 3-D array of shape
(.., .., 3)
.
- Returns
- outndarray
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If hsv is not a 3-D array of shape
(.., .., 3)
.
Notes
Conversion between RGB and HSV color spaces results in some loss of precision, due to integer arithmetic and rounding [1].
References
Examples
>>> from skimage import data >>> img = data.astronaut() >>> img_hsv = rgb2hsv(img) >>> img_rgb = hsv2rgb(img_hsv)
Examples using skimage.color.hsv2rgb
¶
lab2lch¶
-
skimage.color.
lab2lch
(lab)[source]¶ CIE-LAB to CIE-LCH color space conversion.
LCH is the cylindrical representation of the LAB (Cartesian) colorspace
- Parameters
- labarray_like
The N-D image in CIE-LAB format. The last (
N+1
-th) dimension must have at least 3 elements, corresponding to theL
,a
, andb
color channels. Subsequent elements are copied.
- Returns
- outndarray
The image in LCH format, in a N-D array with same shape as input lab.
- Raises
- ValueError
If lch does not have at least 3 color channels (i.e. l, a, b).
Notes
The Hue is expressed as an angle between
(0, 2*pi)
Examples
>>> from skimage import data >>> from skimage.color import rgb2lab, lab2lch >>> img = data.astronaut() >>> img_lab = rgb2lab(img) >>> img_lch = lab2lch(img_lab)
lab2rgb¶
-
skimage.color.
lab2rgb
(lab, illuminant='D65', observer='2')[source]¶ Lab to RGB color space conversion.
- Parameters
- labarray_like
The image in Lab format, in a 3-D array of shape
(.., .., 3)
.- illuminant{“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
The name of the illuminant (the function is NOT case sensitive).
- observer{“2”, “10”}, optional
The aperture angle of the observer.
- Returns
- outndarray
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If lab is not a 3-D array of shape
(.., .., 3)
.
Notes
This function uses lab2xyz and xyz2rgb. By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref=95.047, y_ref=100., z_ref=108.883. See function get_xyz_coords for a list of supported illuminants.
References
lab2xyz¶
-
skimage.color.
lab2xyz
(lab, illuminant='D65', observer='2')[source]¶ CIE-LAB to XYZcolor space conversion.
- Parameters
- labarray_like
The image in lab format, in a 3-D array of shape
(.., .., 3)
.- illuminant{“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
The name of the illuminant (the function is NOT case sensitive).
- observer{“2”, “10”}, optional
The aperture angle of the observer.
- Returns
- outndarray
The image in XYZ format, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If lab is not a 3-D array of shape
(.., .., 3)
.- ValueError
If either the illuminant or the observer angle are not supported or unknown.
- UserWarning
If any of the pixels are invalid (Z < 0).
Notes
By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref = 95.047, y_ref = 100., z_ref = 108.883. See function ‘get_xyz_coords’ for a list of supported illuminants.
References
label2rgb¶
-
skimage.color.
label2rgb
(label, image=None, colors=None, alpha=0.3, bg_label=-1, bg_color=(0, 0, 0), image_alpha=1, kind='overlay')[source]¶ Return an RGB image where color-coded labels are painted over the image.
- Parameters
- labelarray, shape (M, N)
Integer array of labels with the same shape as image.
- imagearray, shape (M, N, 3), optional
Image used as underlay for labels. If the input is an RGB image, it’s converted to grayscale before coloring.
- colorslist, optional
List of colors. If the number of labels exceeds the number of colors, then the colors are cycled.
- alphafloat [0, 1], optional
Opacity of colorized labels. Ignored if image is None.
- bg_labelint, optional
Label that’s treated as the background.
- bg_colorstr or array, optional
Background color. Must be a name in
color_dict
or RGB float values between [0, 1].- image_alphafloat [0, 1], optional
Opacity of the image.
- kindstring, one of {‘overlay’, ‘avg’}
The kind of color image desired. ‘overlay’ cycles over defined colors and overlays the colored labels over the original image. ‘avg’ replaces each labeled segment with its average color, for a stained-class or pastel painting appearance.
- Returns
- resultarray of float, shape (M, N, 3)
The result of blending a cycling colormap (colors) for each distinct value in label with the image, at a certain alpha value.
lch2lab¶
-
skimage.color.
lch2lab
(lch)[source]¶ CIE-LCH to CIE-LAB color space conversion.
LCH is the cylindrical representation of the LAB (Cartesian) colorspace
- Parameters
- lcharray_like
The N-D image in CIE-LCH format. The last (
N+1
-th) dimension must have at least 3 elements, corresponding to theL
,a
, andb
color channels. Subsequent elements are copied.
- Returns
- outndarray
The image in LAB format, with same shape as input lch.
- Raises
- ValueError
If lch does not have at least 3 color channels (i.e. l, c, h).
Examples
>>> from skimage import data >>> from skimage.color import rgb2lab, lch2lab >>> img = data.astronaut() >>> img_lab = rgb2lab(img) >>> img_lch = lab2lch(img_lab) >>> img_lab2 = lch2lab(img_lch)
rgb2gray¶
-
skimage.color.
rgb2gray
(rgb)[source]¶ Compute luminance of an RGB image.
- Parameters
- rgbarray_like
The image in RGB format, in a 3-D or 4-D array of shape
(.., ..,[ ..,] 3)
, or in RGBA format with shape(.., ..,[ ..,] 4)
.
- Returns
- outndarray
The luminance image - an array which is the same size as the input array, but with the channel dimension removed.
- Raises
- ValueError
If
rgb2gray
is not a 3-D or 4-D arrays of shape(.., ..,[ ..,] 3)
or(.., ..,[ ..,] 4)
.
Notes
The weights used in this conversion are calibrated for contemporary CRT phosphors:
Y = 0.2125 R + 0.7154 G + 0.0721 B
If there is an alpha channel present, it is ignored.
References
Examples
>>> from skimage.color import rgb2gray >>> from skimage import data >>> img = data.astronaut() >>> img_gray = rgb2gray(img)
rgb2grey¶
-
skimage.color.
rgb2grey
(rgb)[source]¶ Compute luminance of an RGB image.
- Parameters
- rgbarray_like
The image in RGB format, in a 3-D or 4-D array of shape
(.., ..,[ ..,] 3)
, or in RGBA format with shape(.., ..,[ ..,] 4)
.
- Returns
- outndarray
The luminance image - an array which is the same size as the input array, but with the channel dimension removed.
- Raises
- ValueError
If
rgb2gray
is not a 3-D or 4-D arrays of shape(.., ..,[ ..,] 3)
or(.., ..,[ ..,] 4)
.
Notes
The weights used in this conversion are calibrated for contemporary CRT phosphors:
Y = 0.2125 R + 0.7154 G + 0.0721 B
If there is an alpha channel present, it is ignored.
References
Examples
>>> from skimage.color import rgb2gray >>> from skimage import data >>> img = data.astronaut() >>> img_gray = rgb2gray(img)
rgb2hed¶
-
skimage.color.
rgb2hed
(rgb)[source]¶ RGB to Haematoxylin-Eosin-DAB (HED) color space conversion.
- Parameters
- rgbarray_like
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.
- Returns
- outndarray
The image in HED format, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If rgb is not a 3-D array of shape
(.., .., 3)
.
References
- 1
A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution.,” Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001.
Examples
>>> from skimage import data >>> from skimage.color import rgb2hed >>> ihc = data.immunohistochemistry() >>> ihc_hed = rgb2hed(ihc)
Examples using skimage.color.rgb2hed
¶
rgb2hsv¶
-
skimage.color.
rgb2hsv
(rgb)[source]¶ RGB to HSV color space conversion.
- Parameters
- rgbarray_like
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.
- Returns
- outndarray
The image in HSV format, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If rgb is not a 3-D array of shape
(.., .., 3)
.
Notes
Conversion between RGB and HSV color spaces results in some loss of precision, due to integer arithmetic and rounding [1].
References
Examples
>>> from skimage import color >>> from skimage import data >>> img = data.astronaut() >>> img_hsv = color.rgb2hsv(img)
rgb2lab¶
-
skimage.color.
rgb2lab
(rgb, illuminant='D65', observer='2')[source]¶ RGB to lab color space conversion.
- Parameters
- rgbarray_like
The image in RGB format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.- illuminant{“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
The name of the illuminant (the function is NOT case sensitive).
- observer{“2”, “10”}, optional
The aperture angle of the observer.
- Returns
- outndarray
The image in Lab format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.
- Raises
- ValueError
If rgb is not a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.
Notes
This function uses rgb2xyz and xyz2lab. By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref=95.047, y_ref=100., z_ref=108.883. See function get_xyz_coords for a list of supported illuminants.
References
rgb2rgbcie¶
-
skimage.color.
rgb2rgbcie
(rgb)[source]¶ RGB to RGB CIE color space conversion.
- Parameters
- rgbarray_like
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.
- Returns
- outndarray
The image in RGB CIE format, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If rgb is not a 3-D array of shape
(.., .., 3)
.
References
Examples
>>> from skimage import data >>> from skimage.color import rgb2rgbcie >>> img = data.astronaut() >>> img_rgbcie = rgb2rgbcie(img)
rgb2xyz¶
-
skimage.color.
rgb2xyz
(rgb)[source]¶ RGB to XYZ color space conversion.
- Parameters
- rgbarray_like
The image in RGB format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.
- Returns
- outndarray
The image in XYZ format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.
- Raises
- ValueError
If rgb is not a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.
Notes
The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts from sRGB.
References
Examples
>>> from skimage import data >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img)
rgb2ycbcr¶
-
skimage.color.
rgb2ycbcr
(rgb)[source]¶ RGB to YCbCr color space conversion.
- Parameters
- rgbarray_like
The image in RGB format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Returns
- outndarray
The image in YCbCr format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Raises
- ValueError
If rgb is not a 3- or 4-D array of shape
(M, N, [P,] 3)
.
Notes
Y is between 16 and 235. This is the color space which is commonly used by video codecs, it is sometimes incorrectly called “YUV”
References
rgb2ydbdr¶
-
skimage.color.
rgb2ydbdr
(rgb)[source]¶ RGB to YDbDr color space conversion.
- Parameters
- rgbarray_like
The image in RGB format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Returns
- outndarray
The image in YDbDr format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Raises
- ValueError
If rgb is not a 3- or 4-D array of shape
(M, N, [P,] 3)
.
Notes
This is the color space which is commonly used by video codecs, it is also the reversible color transform in JPEG2000.
References
rgb2yiq¶
-
skimage.color.
rgb2yiq
(rgb)[source]¶ RGB to YIQ color space conversion.
- Parameters
- rgbarray_like
The image in RGB format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Returns
- outndarray
The image in YIQ format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Raises
- ValueError
If rgb is not a 3- or 4-D array of shape
(M, N, [P,] 3)
.
rgb2ypbpr¶
-
skimage.color.
rgb2ypbpr
(rgb)[source]¶ RGB to YPbPr color space conversion.
- Parameters
- rgbarray_like
The image in RGB format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Returns
- outndarray
The image in YPbPr format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Raises
- ValueError
If rgb is not a 3- or 4-D array of shape
(M, N, [P,] 3)
.
References
rgb2yuv¶
-
skimage.color.
rgb2yuv
(rgb)[source]¶ RGB to YUV color space conversion.
- Parameters
- rgbarray_like
The image in RGB format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Returns
- outndarray
The image in YUV format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Raises
- ValueError
If rgb is not a 3- or 4-D array of shape
(M, N, [P,] 3)
.
Notes
Y is between 0 and 1. Use YCbCr instead of YUV for the color space which is commonly used by video codecs (where Y ranges from 16 to 235)
References
rgba2rgb¶
-
skimage.color.
rgba2rgb
(rgba, background=(1, 1, 1))[source]¶ RGBA to RGB conversion.
- Parameters
- rgbaarray_like
The image in RGBA format, in a 3-D array of shape
(.., .., 4)
.- backgroundarray_like
The color of the background to blend the image with. A tuple containing 3 floats between 0 to 1 - the RGB value of the background.
- Returns
- outndarray
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If rgba is not a 3-D array of shape
(.., .., 4)
.
References
Examples
>>> from skimage import color >>> from skimage import data >>> img_rgba = data.logo() >>> img_rgb = color.rgba2rgb(img_rgba)
rgbcie2rgb¶
-
skimage.color.
rgbcie2rgb
(rgbcie)[source]¶ RGB CIE to RGB color space conversion.
- Parameters
- rgbciearray_like
The image in RGB CIE format, in a 3-D array of shape
(.., .., 3)
.
- Returns
- outndarray
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If rgbcie is not a 3-D array of shape
(.., .., 3)
.
References
Examples
>>> from skimage import data >>> from skimage.color import rgb2rgbcie, rgbcie2rgb >>> img = data.astronaut() >>> img_rgbcie = rgb2rgbcie(img) >>> img_rgb = rgbcie2rgb(img_rgbcie)
separate_stains¶
-
skimage.color.
separate_stains
(rgb, conv_matrix)[source]¶ RGB to stain color space conversion.
- Parameters
- rgbarray_like
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.- conv_matrix: ndarray
The stain separation matrix as described by G. Landini [1].
- Returns
- outndarray
The image in stain color space, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If rgb is not a 3-D array of shape
(.., .., 3)
.
Notes
Stain separation matrices available in the
color
module and their respective colorspace:hed_from_rgb
: Hematoxylin + Eosin + DABhdx_from_rgb
: Hematoxylin + DABfgx_from_rgb
: Feulgen + Light Greenbex_from_rgb
: Giemsa stain : Methyl Blue + Eosinrbd_from_rgb
: FastRed + FastBlue + DABgdx_from_rgb
: Methyl Green + DABhax_from_rgb
: Hematoxylin + AECbro_from_rgb
: Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-Gbpx_from_rgb
: Methyl Blue + Ponceau Fuchsinahx_from_rgb
: Alcian Blue + Hematoxylinhpx_from_rgb
: Hematoxylin + PAS
References
Examples
>>> from skimage import data >>> from skimage.color import separate_stains, hdx_from_rgb >>> ihc = data.immunohistochemistry() >>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
xyz2lab¶
-
skimage.color.
xyz2lab
(xyz, illuminant='D65', observer='2')[source]¶ XYZ to CIE-LAB color space conversion.
- Parameters
- xyzarray_like
The image in XYZ format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.- illuminant{“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
The name of the illuminant (the function is NOT case sensitive).
- observer{“2”, “10”}, optional
The aperture angle of the observer.
- Returns
- outndarray
The image in CIE-LAB format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.
- Raises
- ValueError
If xyz is not a 3-D array of shape
(.., ..,[ ..,] 3)
.- ValueError
If either the illuminant or the observer angle is unsupported or unknown.
Notes
By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref=95.047, y_ref=100., z_ref=108.883. See function get_xyz_coords for a list of supported illuminants.
References
- 1
- 2
Examples
>>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2lab >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_lab = xyz2lab(img_xyz)
xyz2rgb¶
-
skimage.color.
xyz2rgb
(xyz)[source]¶ XYZ to RGB color space conversion.
- Parameters
- xyzarray_like
The image in XYZ format, in a 3-D array of shape
(.., .., 3)
.
- Returns
- outndarray
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.
- Raises
- ValueError
If xyz is not a 3-D array of shape
(.., .., 3)
.
Notes
The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts to sRGB.
References
Examples
>>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2rgb >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_rgb = xyz2rgb(img_xyz)
ycbcr2rgb¶
-
skimage.color.
ycbcr2rgb
(ycbcr)[source]¶ YCbCr to RGB color space conversion.
- Parameters
- ycbcrarray_like
The image in YCbCr format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Returns
- outndarray
The image in RGB format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Raises
- ValueError
If ycbcr is not a 3- or 4-D array of shape
(M, N, [P,] 3)
.
Notes
Y is between 16 and 235. This is the color space which is commonly used by video codecs, it is sometimes incorrectly called “YUV”
References
ydbdr2rgb¶
-
skimage.color.
ydbdr2rgb
(ydbdr)[source]¶ YDbDr to RGB color space conversion.
- Parameters
- ydbdrarray_like
The image in YDbDr format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Returns
- outndarray
The image in RGB format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Raises
- ValueError
If ydbdr is not a 3- or 4-D array of shape
(M, N, [P,] 3)
.
Notes
This is the color space which is commonly used by video codecs, it is also the reversible color transform in JPEG2000.
References
yiq2rgb¶
-
skimage.color.
yiq2rgb
(yiq)[source]¶ YIQ to RGB color space conversion.
- Parameters
- yiqarray_like
The image in YIQ format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Returns
- outndarray
The image in RGB format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Raises
- ValueError
If yiq is not a 3- or 4-D array of shape
(M, N, [P,] 3)
.
ypbpr2rgb¶
-
skimage.color.
ypbpr2rgb
(ypbpr)[source]¶ YPbPr to RGB color space conversion.
- Parameters
- ypbprarray_like
The image in YPbPr format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Returns
- outndarray
The image in RGB format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Raises
- ValueError
If ypbpr is not a 3- or 4-D array of shape
(M, N, [P,] 3)
.
References
yuv2rgb¶
-
skimage.color.
yuv2rgb
(yuv)[source]¶ YUV to RGB color space conversion.
- Parameters
- yuvarray_like
The image in YUV format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Returns
- outndarray
The image in RGB format, in a 3- or 4-D array of shape
(M, N, [P,] 3)
.
- Raises
- ValueError
If yuv is not a 3- or 4-D array of shape
(M, N, [P,] 3)
.
References


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| Facility | Tests Performed |
Positive | Negative |
|---|---|---|---|
| Albemarle CI | 1 | 0 | 1 |
| Alexander CI | 1 | 0 | 1 |
| Anson CI | 2 | 0 | 2 |
| Avery-Mitchell CI | 2 | 0 | 2 |
| Bertie CI | 17 | 0 | 17 |
| Burke CRV | 0 | 0 | 0 |
| Caldwell CC | 2 | 0 | 2 |
| Caledonia CI | 20 | 11 | 9 |
| Carteret CC | 0 | 0 | 0 |
| Caswell CC | 2 | 2 | 0 |
| Catawba CC | 0 | 0 | 0 |
| Central Prison | 26 | 1 | 25 |
| Columbus CI | 0 | 0 | 0 |
| Craggy CC | 3 | 0 | 3 |
| Craven CI | 4 | 0 | 4 |
| Dan River PWF | 0 | 0 | 0 |
| Davidson CC | 0 | 0 | 0 |
| Eastern CI | 3 | 0 | 3 |
| Foothills CI | 1 | 0 | 1 |
| Forsyth CC | 0 | 0 | 0 |
| Franklin CC | 0 | 0 | 0 |
| Gaston CC | 0 | 0 | 0 |
| Greene CI | 17 | 0 | 17 |
| Harnett CI | 0 | 0 | 0 |
| Hyde CI | 2 | 0 | 2 |
| Johnston CI | 17 | 3 | 14 |
| Lincoln CC | 0 | 0 | 0 |
| Lumberton CI | 1 | 0 | 1 |
| Marion CI | 0 | 0 | 0 |
| Maury CI | 7 | 0 | 7 |
| Morrison CI | 0 | 0 | 0 |
| Mountain View CI | 3 | 0 | 3 |
| NC CI Women | 16 | 8 | 8 |
| Nash CI | 1 | 0 | 1 |
| Neuse CI | 449 | 288 | 161 |
| New Hanover CC | 0 | 0 | 0 |
| North Piedmont CRV | 0 | 0 | 0 |
| Orange CC | 0 | 0 | 0 |
| Pamlico CI | 0 | 0 | 0 |
| Pasquotank CI | 49 | 19 | 30 |
| Pender CI | 3 | 2 | 1 |
| Piedmont CI | 11 | 0 | 11 |
| Polk CI | 1 | 0 | 1 |
| Randolph CC | 1 | 0 | 1 |
| Rutherford CC | 0 | 0 | 0 |
| Sampson CI | 2 | 0 | 2 |
| Sanford CC | 0 | 0 | 0 |
| Scotland CI | 16 | 2 | 14 |
| Southern CI | 2 | 1 | 1 |
| Swannanoa CCW | 0 | 0 | 0 |
| Tabor CI | 15 | 0 | 15 |
| Wake CC | 1 | 0 | 1 |
| Warren CI | 0 | 0 | 0 |
| Wilkes CC | 0 | 0 | 0 |
| Statewide Totals | 698 | 337 | 361 |


Country Star Sam Hunt Was Arrested for DUI
Yikes, guys. Country superstar Sam Hunt was reportedly arrested early this morning and charged for driving under the influence and being in possession of an open container. The “House Party” singer was allegedly driving the wrong way down a road in Nashville, according to WKRN. Police also observed the driver reportedly swerving in and out of his lane and claimed Sam had “difficulty providing his Tennessee license.”
E! News, USA Today, and WKRN all reported that Sam’s blood alcohol level was 0.173 (the legal limit is 0.08). According to each outlet, he also allegedly told police he had consumed alcohol “recently.”
The police who were present at the scene had reported to WKRN that the musician had a strong alcohol scent and appeared to have bloodshot eyes. Authorities also found two empty beer bottles next to Sam.
The 34-year-old entered the Metro jail around 6:30 a.m. and was released around 9 a.m. on a $2,500 bond. A court date has been set for January 17, 2020, although USA Today noted it’s not clear if he has legal representation yet.
Sam rose to fame with hits like “Body Like a Back Road,” “Break Up in a Small Town,” and “Take Your Time.” He married his wife, Hannah Lee Fowler, in April 2017, and the two don’t share any children.
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From: Country Living US
Megan Stein Megan Stein is the senior editor for CountryLiving.com, covering entertainment news ranging from outrageous moments on “The Voice,” to the latest happenings with HGTV stars.


| Day of the Week | Female Name | Male Name |
|---|---|---|
| SUNDAY | Akosua | Kwasi |
| MONDAY | Adwoa | Kudwo |
| TUESDAY | Abenaa | Kwabena |
| WEDNESDAY | Akua | Kwaku |
| THURSDAY | Yaa | Yaw |
| FRIDAY | Afua | Kofi |
| SATURDAY | Ama | Kwame |
In parts of Ghana, a person’s name is determined by the day of the week on which she/he was born.
Enter your birthday and gender below to read your day name in Akan, the most widely spoken language in Ghana;
You can view your name or day of birth from the table below.
| Day of the Week | Female Name | Male Name |
|---|---|---|
| SUNDAY | Akosua | Kwasi |
| MONDAY | Adwoa | Kudwo |
| TUESDAY | Abenaa | Kwabena |
| WEDNESDAY | Akua | Kwaku |
| THURSDAY | Yaa | Yaw |
| FRIDAY | Afua | Kofi |
| SATURDAY | Ama | Kwame |


@* * Copyright 2019 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.AppConfig @(appConfig: AppConfig, title: String, mainClass: Option[String] = None, mainDataAttributes: Option[Html] = None, bodyClasses: Option[String] = None, sidebar: Html = HtmlFormat.empty, contentHeader: Option[Html] = None, mainContent: Html = HtmlFormat.empty, serviceInfoContent: Html = HtmlFormat.empty, scriptElem: Option[Html] = None)(implicit request: Request[_], messages: Messages) @import domain.auth.AuthenticatedRequest @import layouts.{govuk_template => hmrcGovUkTemplate} @import uk.gov.hmrc.play.views.html.{helpers => uiHelpers, layouts => uiLayouts} @head = { @uiLayouts.head( linkElem = None, headScripts = None) } @headerNavLinks = {


When you want to buy any application from the Apple iTunes store you have more choices than you can handle. Most of the users scroll past the application description completely avoiding it like ads displayed on the right column of your webpage. Their choice is dependent on three important factors price, screenshot and reviews.
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This is my description of a low dosage in my opinion of Oxymorphone. I use fine cannabis daily and recreationally use opiods every 2 months or so. Along with that, I usually take Oxycodone but my usual dealer was out and suggested these Oxymorphone's in place explaining to me how they both work similar. This was my first experience with Oxymorphone. I mentioned how I used Cannabis. I used around 3 bowls and it maintained sedation mostly while eliminating most paranoia.
Description of mindset & setting
My mindset was to experience sedation and euphoria in excess amounts around 11pm on a weekday night. The setting was in a basement low temp of 62 degrees farenheit. This time, I was by myself playing video games all night and had nothing else planned.
Details of any preparations made for the experience
Cutting the outer tan layer off the 10 MG pill's using razor blade. Cannabis readily available as Cannabis reacts as a natural remedy to me. Drink and dry food which I rarely used.
Dosage & timing information
11pm - Cut the outer layer off the two 10mg pills using razor blade. I then insufflated 10mg of Oxymorphone.
11:15pm - Eager to feel effects, I insufflated the other 10mg of Oxymorphone I had.
12:00am - After sitting around playing video games I glanced around and noticed everything was slow motion kinda like Oxycodone. Comparatively feeling, I felt as if I had taken about 30mg of Oxycontin and was feeling it full on. I thought it would be like this the rest of the night...
1:30am - I wrote my own reports at the time (about 5 days ago) and this is what I was experiencing. Every 30 minutes, intermittent paranoia for around 3 minutes and then complete BLISS EUPHORIA imagining I could do anything.
Journal quote:
'Everything could change life..as far as reading an article vs another...maybe I would get some kind of inspiratoinal idea off of it.'
Adverse Effects: Paranoia
2:30am - I am estimating 2:30 AM as I do not completely recall cause i was so sedated to the point Iíve never experienced. I was trying to watch TV without completely falling over and trying to keep my eyes open. I was not tired, I probably woke up around 11 hours earlier and did nothing all day. I specifically posted this to my journal:
'I layed here closing my eyes listening to a movie that I interpreted by listening but completely missed the point and everything about the real thing [what was really going on in the movie].'
Basically completely in my own world with my eyes closed but open them and look around to reality. I specifically mentioned jitters which could be a immediate withdrawal symptom.
3:00am?:  Other than that withdrawal was not bad especially when I smoke bowls of Cannabis, but I do remember getting really weird heightened senses at times almost reminding me I wanted the drug again. I also looked up 'heroin addict' on youtube the next day and it reminded me of myself the night before. I also snorted an Oxycontin 40mg 2 days later that did absolutely nothing compared to what it used to do to me.
After affects/withdrawal personally wrapped up
1.Cold, cough, sick.
2.depression
3.possible dependence
4.tolerance built up
5.minor joint aches
This is the best feeling Iíve ever felt and it will be hard not to do it again or want it all the time.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.