| testModels {mitml} | R Documentation |
Performs multi-parameter hypothesis tests for a vector of statistical parameters and compares nested statistical models obtained from multiply imputed data sets.
testModels(model, null.model, method = c("D1", "D2", "D3", "D4"), use = c("wald", "likelihood"), ariv = c("default", "positive", "robust"), df.com = NULL, data = NULL)
model |
A list of fitted statistical models (“full” model) as produced by |
null.model |
A list of fitted statistical models (“restricted” model) as produced by |
method |
A character string denoting the method by which the test is performed. Can be |
use |
A character string denoting Wald- or likelihood-based based tests. Can be either |
ariv |
A character string denoting how the ARIV is calculated. Can be |
df.com |
(optional) A number denoting the complete-data degrees of freedom for the hypothesis test. Only used if |
data |
(optional) A list of imputed data sets (see 'Details'). Only used if |
This function compares two nested statistical models fitted to multiply imputed data sets by pooling Wald-like or likelihood-ratio tests.
Pooling methods for Wald-like tests of multiple parameters were introduced by Rubin (1987) and further developed by Li, Raghunathan and Rubin (1991).
The pooled Wald test is referred to as D_1 and can be used by setting method = "D1"
.
D_1 is the multi-parameter equivalent of testEstimates
, that is, it tests multiple parameters simultaneously.
For D_1, the complete-data degrees of freedom are assumed to be infinite, but they can be adjusted for smaller samples by supplying df.com
(Reiter, 2007).
An alternative method for Wald-like hypothesis tests was suggested by Li, Meng, Raghunathan and Rubin (1991).
The procedure is called D_2 and can be used by setting method = "D2"
.
D_2 calculates the Wald-test directly for each data set and then pools the resulting χ^2 values.
The source of these values is specified by the use
argument.
If use = "wald"
(the default), then a Wald test similar to D_1 is performed.
If use = "likelihood"
, then the two models are compared with a likelihood-ratio test instead.
Pooling methods for likelihood-ration tests were suggested by Meng and Rubin (1992).
This procedure is referred to as D_3 and can be used by setting method = "D3"
.
D_3 compares the two models by pooling the likelihood-ratio test across multiply imputed data sets.
Finally, an improved method for pooling likelihood-ratio tests was recommended by Chan & Meng (2019).
This method is referred to as D_4 and can be used by setting method = "D4"
.
D_4 also compares models by pooling the likelihood-ratio test but does so in a more general and efficient manner.
The function supports different classes of statistical models depending on which method
is chosen.
D_1 supports models that define coef
and vcov
methods (or similar) for extracting the parameter estimates and their estimated covariance matrix.
D_2 can be used for the same models (if use = "wald"
and models that define a logLik
method (if use = "likelihood"
).
D_3 supports linear models, linear mixed-effects models (see Laird, Lange, & Stram, 1987) with an arbitrary cluster structed if estimated with lme4
or a single cluster if estimated by nlme
, and structural equation models estimated with lavaan
(requires ML estimator, see 'Note').
Finally, D_4 supports models that define a logLik
method but can fail if the data to which the model was fitted cannot be found.
In such a case, users can provide the list of imputed data sets directly by specifying the data
argument or refit with the include.data
argument in with.mitml.list
.
Support for other statistical models may be added in future releases.
The D_4, D_3, and D_2 methods support different estimators of the relative increase in variance (ARIV), which can be specified with the ariv
argument.
If ariv = "default"
, the default estimators are used.
If ariv = "positive"
, the default estimators are used but constrained to take on strictly positive values.
This is useful if the estimated ARIV is negative.
If ariv = "robust"
, which is available only for D_4, the "robust" estimator proposed by Chan & Meng (2019) is used.
This method should be used with caution, because it requires much stronger assumptions and may result in liberal inferences if these assumptions are violated.
A list containing the results of the model comparison.
A print
method is used for more readable output.
The methods D_4, D_3, and the likelihood-based D_2 assume that models were fit using maximum likelihood (ML).
Models fit using REML are automatically refit using ML.
Models fit in 'lavaan'
using the MLR estimator or similar techniques that require scaled chi^2 difference tests are currently not supported.
Simon Grund
Chan, K. W., & Meng, X.-L. (2019). Multiple improvements of multiple imputation likelihood ratio tests. ArXiv:1711.08822 [Math, Stat]. https://arxiv.org/abs/1711.08822
Laird, N., Lange, N., & Stram, D. (1987). Maximum likelihood computations with repeated measures: Application of the em algorithm. Journal of the American Statistical Association, 82, 97-105.
Li, K.-H., Meng, X.-L., Raghunathan, T. E., & Rubin, D. B. (1991). Significance levels from repeated p-values with multiply-imputed data. Statistica Sinica, 1, 65-92.
Li, K. H., Raghunathan, T. E., & Rubin, D. B. (1991). Large-sample significance levels from multiply imputed data using moment-based statistics and an F reference distribution. Journal of the American Statistical Association, 86, 1065-1073.
Meng, X.-L., & Rubin, D. B. (1992). Performing likelihood ratio tests with multiply-imputed data sets. Biometrika, 79, 103-111.
Reiter, J. P. (2007). Small-sample degrees of freedom for multi-component significance tests with multiple imputation for missing data. Biometrika, 94, 502-508.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. Hoboken, NJ: Wiley.
testEstimates
, testConstraints
, with.mitml.list
, anova.mitml.result
data(studentratings) fml <- ReadDis + SES ~ ReadAchiev + (1|ID) imp <- panImpute(studentratings, formula = fml, n.burn = 1000, n.iter = 100, m = 5) implist <- mitmlComplete(imp) # * Example 1: multiparameter hypothesis test for 'ReadDis' and 'SES' # This tests the hypothesis that both effects are zero. require(lme4) fit0 <- with(implist, lmer(ReadAchiev ~ (1|ID), REML = FALSE)) fit1 <- with(implist, lmer(ReadAchiev ~ ReadDis + (1|ID), REML = FALSE)) # apply Rubin's rules testEstimates(fit1) # multiparameter hypothesis test using D1 (default) testModels(fit1, fit0) # ... adjusting for finite samples testModels(fit1, fit0, df.com = 47) # ... using D2 ("wald", using estimates and covariance-matrix) testModels(fit1, fit0, method = "D2") # ... using D2 ("likelihood", using likelihood-ratio test) testModels(fit1, fit0, method = "D2", use = "likelihood") # ... using D3 (likelihood-ratio test, requires ML fit) testModels(fit1, fit0, method = "D3") # ... using D4 (likelihood-ratio test, requires ML fit) testModels(fit1, fit0, method = "D4")


This page tracks the new paper links made to my list of EGSR 2014 papers.
Page maintained by Ke-Sen Huang.
If you have additions or changes, send an e-mail.
July 28, 2014
- A Physically-Based BSDF for Modeling the Appearance of Paper
July 14, 2014
- Error analysis of estimators that use combinations of stochastic sampling strategies for direct illumination
June 30, 2014
- Color Me Noisy: Example-based Rendering of Hand-colored Animations with Temporal Noise Control
- Interactive Parameter Retrieval for Two-Tone Procedural Textures
- Bilinear Accelerated Filter Approximation
June 18, 2014
- Interactive Parameter Retrieval for Two-Tone Procedural Textures
June 17, 2014
- C-LOD: Context-aware Material Level-of-Detail applied to Mobile Graphics
- User-Assisted Video Stabilization
June 2, 2014
- Visibility Silhouettes for Semi-Analytic Spherical Integration
- Multiple light source estimation in a single image
- Robust Fitting on Poorly Sampled Data for Surface Light Field Rendering and Image Relighting
- Full Wave Modeling of Light Propagation and Reflection
- Layered Reconstruction for Defocus and Motion Blur
- Spectral Ray Differentials
- Vectorising Bitmaps into Semi-Transparent Gradient Layers
- Robust Image Denoising using a Virtual Flash Image for Monte Carlo Ray Tracing
- Spherical Fibonacci Point Sets for Illumination Integrals
- Semi-Automated Video Morphing
- Massively Parallel Hierarchical Scene Processing with Applications in Rendering
May 29, 2014
- Hero Wavelength Spectral Sampling
May 28, 2014
- Importance Sampling Microfacet-Based BSDFs using the Distribution of Visible Normals
- AutoStyle: Automatic Style Transfer from Image Collections to Users' Images
May 27, 2014
- Probabilistic Visibility Evaluation using Geometry Proxies
May 26, 2014
- Vectorising Bitmaps into Semi-Transparent Gradient Layers
May 25, 2014
- Spectral Ray Differentials
- Page published
kesen.huang@gmail.com


--- layout: documentation title: HistoryItemType ---
| Field Name | Type | Description |
|---|---|---|
| Action_Entry0..1 | COATakenType |
The Action_Entry field is optional and provides a record of actions taken during the handling of the Incident. |
| Journal_Entry0..1 | JournalEntryType |
The Journal_Entry field is optional and provides journal notes for information discovered during the handling of the Incident. |


|
ItMayBeAHack OOD Exercises
An exercise in OOD as guided by ItMayBeAHack.com.
|
Contract for constructing all of the bins for a wheel. More...
Public Member Functions | |
| void | BuildBins (Wheel wheel) |
| Creates all bins for a wheel. More... | |
| void | GenerateColumnBets (Wheel wheel) |
| Generates all column bets. More... | |
| void | GenerateCornerBets (Wheel wheel) |
| Generate all corner bets (i.e. a bet on the corner of a number). More... | |
| void | GenerateDozenBets (Wheel wheel) |
| Generates all 'dozen' bets (i.e. 1-12, 13-24, 25-36). More... | |
| void | GenerateEvenMoneyBets (Wheel wheel) |
| Generates all even money bets (i.e. Red, Black, Even, Odd, High (19-36), and Low (1-18)). More... | |
| void | GenerateFiveBet (Wheel wheel) |
| The top line bet (0,00,1,2,3). More... | |
| void | GenerateLineBets (Wheel wheel) |
| Generates all line bets (i.e. a bet between 2 rows such as 1-3 + 4-6). More... | |
| void | GenerateSplitBets (Wheel wheel) |
| Generates all of the split bets (i.e. bets on 2 numbers). More... | |
| void | GenerateStraightBets (Wheel wheel) |
| Generates all straight bets for the wheel (i.e. a bet on a single number). More... | |
| void | GenerateStreetBets (Wheel wheel) |
| Generates all stree bets for the wheel (i.e. bet on all 3 numbers in a column). More... | |
Contract for constructing all of the bins for a wheel.
| void Roulette.IBinBuilder.BuildBins | ( | Wheel | wheel | ) |
Creates all bins for a wheel.
| wheel | The wheel to add the bin-outcomes to. |
Implemented in Roulette.BinBuilderUS.
| void Roulette.IBinBuilder.GenerateColumnBets | ( | Wheel | wheel | ) |
Generates all column bets.
| wheel | The wheel to add the bin-outcomes to. |
Implemented in Roulette.BinBuilderUS.
| void Roulette.IBinBuilder.GenerateCornerBets | ( | Wheel | wheel | ) |
Generate all corner bets (i.e. a bet on the corner of a number).
| wheel | The wheel to add the bin-outcomes to. |
Implemented in Roulette.BinBuilderUS.
| void Roulette.IBinBuilder.GenerateDozenBets | ( | Wheel | wheel | ) |
Generates all 'dozen' bets (i.e. 1-12, 13-24, 25-36).
| wheel | The wheel to add the bin-outcomes to. |
Implemented in Roulette.BinBuilderUS.
| void Roulette.IBinBuilder.GenerateEvenMoneyBets | ( | Wheel | wheel | ) |
Generates all even money bets (i.e. Red, Black, Even, Odd, High (19-36), and Low (1-18)).
| wheel | The wheel to add the bin-outcomes to. |
Implemented in Roulette.BinBuilderUS.
| void Roulette.IBinBuilder.GenerateFiveBet | ( | Wheel | wheel | ) |
The top line bet (0,00,1,2,3).
| wheel | The wheel to add the bin-outcomes to. |
Implemented in Roulette.BinBuilderUS.
| void Roulette.IBinBuilder.GenerateLineBets | ( | Wheel | wheel | ) |
Generates all line bets (i.e. a bet between 2 rows such as 1-3 + 4-6).
| wheel | The wheel to add the bin-outcomes to. |
Implemented in Roulette.BinBuilderUS.
| void Roulette.IBinBuilder.GenerateSplitBets | ( | Wheel | wheel | ) |
Generates all of the split bets (i.e. bets on 2 numbers).
| wheel | The wheel to add the bin-outcomes to. |
Implemented in Roulette.BinBuilderUS.
| void Roulette.IBinBuilder.GenerateStraightBets | ( | Wheel | wheel | ) |
Generates all straight bets for the wheel (i.e. a bet on a single number).
| wheel | The wheel to add the bin-outcomes to. |
Implemented in Roulette.BinBuilderUS.
| void Roulette.IBinBuilder.GenerateStreetBets | ( | Wheel | wheel | ) |
Generates all stree bets for the wheel (i.e. bet on all 3 numbers in a column).
| wheel | The wheel to add the bin-outcomes to. |
Implemented in Roulette.BinBuilderUS.


Quantum Tetris plays just like regular tetris! Blocks fall one at a time, it's up to you to rotate and position them. Fill a full row to clear it. If blocks reach the screen top, then it's Game Over!
We searched for fun video games in the Quantum space and found nothing but chess and puzzles. So we built Quantum Tetris, and made it our mission to teach Quantum principles in a familiar game format. Confused by Quantum? Check out this video by Kurzgesagt and the Australian Academy of Sciences:
Click section headers to learn more about the topic.
Quantum Tetris plays just like regular tetris! Blocks fall one at a time, it's up to you to rotate and position them. Fill a full row to clear it. If blocks reach the screen top, then it's Game Over!
Superposition pieces exist in two states at once! While falling, they can be one of two pieces based on their quantum probability in the top left box. The only way to force a superposition piece to stay in one state is to 'measure' by placing it down on the tetris stack.
Position pieces with LEFT and RIGHT arrow keys. Rotate pieces with UP. Hold DOWN to fast fall, SPACE to instant drop. Press X or H to use power-ups!
Some Superposition pieces come in entangled pairs! They are NOTs of each other, so they move as mirror images. When the first piece lands, it measures as 1 and the other piece flips to 0! Checkout the visualizer to guess which piece the first to land will become.
Explore the IBM Qiskit backend that powers Quantum Tetris. Learn how to create Quantum games of your own!


LearningRateScheduler
public protocol LearningRateScheduler
Undocumented
-
Undocumented
Declaration
Swift
func learningRate<Element>(atStep step: Int) -> Element where Element : NumericType
DL4S Docs (68% documented)
public protocol LearningRateScheduler
Undocumented
Undocumented
Swift
func learningRate<Element>(atStep step: Int) -> Element where Element : NumericType


Java Control Panel |
The Java Control Panel is a multipurpose control panel. It allows you to view and set a wide range of parameters controlling how Java runs on your computer. It lets you view and delete temporary files used for Java Plug-in, which allows Sun Java to be used by your web browser to run applets, and Java Web Start, which allows you to run Java applications over the network. It allows you to control certificates, making it safe to run applets and applications over the network. It allows you to set runtime parameters for applets run with Java Plug-in and applications run with Java Web Start. It provides a mechanism for updating your version of Java so that you always have the latest. And it allows you to set options for debugging, desktop integration, applet handling, etc. The Java Control Panel includes the following separately viewable panels:
Each is discussed below.
The General panel looks like this:
It includes three subpanels: About, Network Settings, and Temporary Internet Files.
The About... button displays version information for the latest JRE installed on the computer.
These settings are for network connections. Press the Network Settings... button to get the Network Settings dialog. There are four choices:
Check this to use the browser default proxy settings. This is the default setting (checked).
You have two choices here:
You can specify the location (URL) for the JavaScript file (.js or .pac extension)
that contains the FindProxyForURL
function. FindProxyForURL
has the logic to determine the proxy server to use for a connection request.
Select this for situations where you do not want to use a proxy.
You can do the following:
Notes
|
The Security panel looks like this:
Press the Certificates... button to get the Certificates dialog, which looks like this:
It handles both User- and System-Level (enterprise-wide) certificates of the following types:
These are certificates for signed applets and applications that are trusted.
These are certificates for secure sites.
These are certificates of Certificate Authorities (CAs) for Trusted Certificates; Certificate Authorities are the ones who issue the certificates to the signers of Trusted Certificates.
These are certificates of Certificate Authorities (CAs) for secure sites; Certificate Authorities are the ones who issue the certificates for secure sites.
These are certificates for a client to authenticiate itself to a server.
Options
For Trusted, Secure site, and Client Authentication certificates, there are four options: Import, Export, Remove, and Details. The user can import, export, remove, and view the details of a certificate.
For Signer CA and Secure site CA, there is only one option: Details. The user can only view the details of a certificate.
Default Keystore Location
The default locations of the of the
keystore
files for Unix and Windows are as follows:
Operating System LocationUnix ${user.home}/.java/deployment/security
.Windows
${deployment.user.home}\security
For instance, on Windows 2000/XP, the default location of the
keystore
files for userjsmith
would be as follows:
C:\Docments and Settings\jsmith\Application Data\Sun\Java\Deployment\security
Non-Default Keystore Location
For non-default locations of the certificate
keystore
files, specify them in the User-Leveldeployment.properties file
with the following property names:
Certificate Type Property NameTrusted Certificates deployment.user.security.trusted.certs
Secure site deployment.user.security.trusted.jssecerts
Signer CA deployment.user.security.trusted.cacerts
Secure site CA deployment.user.security.
trusted.jssecacerts
Client Authentication deployment.user.security.trusted.clientcerts
Options
For System-Level certificates, the only options a user has are Export and Details.
Default Keystore Location
Trusted, Secure Site, and Client Authentication certificate
keystore
files do not exist by default. Thus there are no default locations for them.The default location for the Signer CA keystore is:
Operating System LocationUnix $JAVA_HOME/lib/security/cacerts
Windows
$JAVA_HOME\lib\security\cacerts
The default location for the Secure Site CA keystore is:
Operating System LocationUnix $JAVA_HOME/lib/security/jssecacerts
Windows
$JAVA_HOME\lib\security\jssecacerts
Non-Default Keystore Location
The location of the
keystore
files for the various types of certificates can also be set in a System-Leveldeployment.properties
file, if it exists. (The System-Leveldeployment.properties
file does not exist by default. It is specified in adeployment.config
file. See System-Leveldeployment.properties file
.) The following properties may be specified:
Certificate Type Property NameTrusted Certificates deployment.system.security.trusted.certs
Secure site deployment.system.security.trusted.jssecerts
Signer CA deployment.system.security.trusted.cacerts
Secure site CA deployment.system.security.
trusted.jssecacerts
Client Authentication deployment.system.security.trusted.clientcerts
The Java panel looks like this:
It has two subpanels: Java Applet Runtime Settings and Java Application Runtime Settings.
These settings will be used when an applet is launched in a browser. Press View... for the Java Runtime Settings for applets. On Windows you will see this:
Note that on Unix there will also be Add and Remove buttons.
NotesThere will always be at least one entry. It will be the most recently installed JRE; i.e., the JRE associated with the Java Control Panel. Windows will show all JREs installed on a computer. The Java Control Panel finds the JREs by looking in the registry. On Unix the situation is different. There is no registry so there is no easy way to find the JREs that a user may have installed. Thus, for Unix there are Add and Remove buttons. For Unix, only version 5.0 or higher should be added. For Windows, where all JREs are found in the registry, only version 5.0 or higher will be displayed. However, a user can still run older versions. To do so, launch the Java Plug-in Control Panel for the older version, then (re)select the browser in the Browser tab. Example:Assume you are running on Microsoft Windows with Microsoft Internet Explorer, have first installed version 1.4.2, then version 5.0, and you want to run 1.4.2.
|
On Unix you can add a JRE by pressing Add and specifiying its location (see notes above).
For Window and Unix you can optionally set Java Runtime Settings for the JRE.
You can override the Java Plug-in default startup parameters by specifying custom options in the Java Runtime Parameters field. With the exception of setting
classpath
andcp
(see Settingclasspath
andcp
below), the syntax is the same as used with parameters to thejava
command line invocation. See the java launcher for a full list of command line options:java launcher: Windows, Solaris and Linux.
Below are some examples of Java runtime parameters.
The following format should be used for setting
classpath
andcp
in Java Plug-in. It differs slightly from thejava
command line format, which uses a space instead of the equal (=
) sign.
-classpath=<path>
-cp=<path>
Enabling and disabling assertion support
To enable assertion support, the following system property must be specified in the Java Runtime Parameters:
-D[ enableassertions | ea ][:<package name>"..." | : <class name> ]
To disable assertion in the Java Plug-in, specify the following in the Java Runtime Parameters:
-D[ disableassertions | da ][:<package name>"..." | : <class name> ]
See Assertion Facility for more details on enabling/disabling assertions.
Assertion is disabled in Java Plug-in code by default. Since the effect of assertion is determined during Java Plug-in startup, changing assertion settings in the Java Plug-in Control Panel will require a browser restart in order for the new settings to take effect.
Because Java code in Java Plug-in also has built-in assertion, it is possible to enable the assertion in Java Plug-in code through the following:
-D[ enableassertions | ea ]:sun.plugin
Tracing and logging support
Tracing is a facility to redirect any output in the Java Console to a trace file (
.plugin<version>.trace
).
-Djavaplugin.trace=true
-Djavaplugin.trace.option=basic|net|security|ext|liveconnectIf you do not want to use the default trace file name:
-Djavaplugin.trace.filename=<tracefilename>
Similar to tracing, logging is a facility to redirect any output in the Java Console to a log file
(.plugin<version>.log
) using the Java Logging API. Logging can be turned on by enabling the propertyjavaplugin.logging
.
-Djavaplugin.logging=true
If you do not want to use the default log file name, enter:
-Djavaplugin.log.filename=<logfilename>
Furthermore, if you do not want to overwrite the trace and log files each session, you can set the property:
-Djavaplugin.outputfiles.overwrite=false
.If the property is set to
false
, then trace and log files will be uniquely named for each session. If the default trace and log file names are used, then the files would be named as follows
.plugin<username><date hash code>.trace
.plugin<username><date hash code>.logTracing and logging set through the Control Panel will take effect when the Plug-in is launched, but changes made through the Control Panel while a Plug-in is running will have no effect until a restart.
For more information about tracing and logging, see the chapter called Tracing and Logging.
Debugging applets in Java Plug-in
The following options are used when debugging applets in the Java Plug-in. For more information on this topic see the Debugging Support in the Java Plug-in Developer Guide.
-Djava.compiler=NONE
-Xnoagent
-Xdebug
-Xrunjdwp:transport=dt_shmem,address=<connect-address>,server=y,suspend=nThe
<connect-address>
can be any string (example:2502
) which is used by the Java Debugger (jdb
) later to connect to the JVMDefault connection timeout
When a connection is made by an applet to a server and the server doesn't respond properly, the applet may appear to hang and may also cause the browser to hang, since by default there is no network connection timeout.
To avoid this problem, Java Plug-in 1.4 has added a default network timeout value (2 minutes) for all HTTP connections. You can override this setting in the Java Runtime Parameters:
-Dsun.net.client.defaultConnectTimeout=<value in milliseconds>
Another networking property that you can set is
sun.net.client.defaultReadTimeout
.
-Dsun.net.client.defaultReadTimeout=<value in milliseconds>
Note
Java Plug-in does not set
sun.net.client.defaultReadTimeout
by default. If you want to set it, do so through the Java Runtime Parameters as shown above.Networking properties description:
sun.net.client.defaultConnectTimeout
sun.net.client.defaultReadTimeoutThese properties specify, respectively, the default connect and read timeout values for the protocol handlers used by
java.net.URLConnection
. The default value set by the protocol handlers is-1
, which means there is no timeout set.
sun.net.client.defaultConnectTimeout
specifies the timeout (in milliseconds) to establish the connection to the host. For example, for http connections it is the timeout when establishing the connection to the http server. For ftp connections it is the timeout when establishing the connection to ftp servers.
sun.net.client.defaultReadTimeout
specifies the timeout (in milliseconds) when reading from an input stream when a connection is established to a resource.For the official description of these properties, see Networking Properties.
These settings will be used when a JNLP application is launched. Press View... for JNLP Java Runtime Settings. You will see this:
The Table contains the following information:
The panel includes the following buttons:
The Choose... button allows user to choose content of the Path (Command) field. This button becomes enabled when the user clicks on Path (Command) field of an existing JRE.
The Find... button pops up a tool that allows the user to find JREs installed on the system.
The Add button adds a new line to the table of JREs.
The Remove button removes a line from the table of JREs.
When the OK button is pressed, the changes will be saved in the config file and the dialog will be dismissed. When the Cancel button is pressed, the changes will be discarded and the dialog will be dismissed.
The Update panel looks like this:
NoteThis panel is only available on Windows for 1.4.2_01 and higher releases and only for users with Administrative privileges. |
The Update panel, in conjunction with the Java Update Scheduler
(jusched.exe
), is used to provide the latest Java updates to the
end user.
There are two basic options on the Update tab:
Automatic update is performed on a scheduled basis and it is selected by checking the Check for Updates Automatically check box.
Manual update is performed by pressing the Update Now button.
If you select automatic update, you can then set the notification via the Notify Me: drop-down menu, and you can set the update schedule via the Advanced... button.
With notification, you can chose to be notified before an update is downloaded and before it is installed; or you can chose to be notified only before an update is installed (i.e., the download is automatic).
The Advanced... allows you to select the desired frequency for updates: daily, weekly (default), or montly. For daily updates, you can select the time of the day for the udpate; for weekly updates, you can select the day of the week and the time of the day; for montly updates, you can select the day of the month and the time of the day.
You can do manual updates at any time by presseing the Update Now button. This allows you to do immediate, unscheduled upates.
The Java Update Scheduler (jusched.exe
) is used for launching
automatic updates when Update Automatically is selected in the Update
tab. jusched.exe
runs as a background process that launches the
Update Manager at predefined intervals set by the user through the Advanced
... button of the Update tab. The Update Manager coordinates the update
process.
jusched.exe
is launched when the user reboots the computer after
installing the SDK/JRE. It is normally transparent to the user but can be viewed
in the Processes tab of the Windows Task Manager. Should a user for some reason
not want the scheduler to run, it can be killed via End Process button
of the Processes tab.
The Advanced panel looks like this:
It includes, as shown, options for Debugging, Java console, Java
icon, <APPLET>
handling, Desktop integration, JNLP File/MIME
Association, and Security.
You can enable tracing and logging. For more information on tracing and logging and how to set runtime parameters for tracing and logging, see Tracing and Logging.
There are three options:
See Java Console for information about it.
<APPLET>
tag support (Windows only)Here you can specify whether a particular browser should use the
Sun VM for the standard <APPLET>
tag.
NoteIf Mozilla and Netscape 7 are both installed and |
This provides options for Java Web Start for creating shorcuts on the desktop. The options are:
This allows you to associate files with the JNLP MIME type. The options are (radio button, select only one):
The options are (checkboxes, select any number, all checked by default):
Here you have the option to place the Java icon in the system tray (default).
Allows you to specify the location of the default browser to be launched.


Why is your site called "Lucifer"?
I just recommended your site to my friends, and then - oh horrors! - noticed this lucifer thing.
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The myth describes Lucifer as the Bringer of light, an angel who questioned the
authority of God and went out to explore things on his own. Of course, it is just a myth,
and no person worthy of following the ideas of great thinkers would take myths of Lucifer,
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There is a wonderful story by Max More
about Lucifer as a mythological figure of extropian movement
and this server was named after these ideas. I was just donated some space on it, and am
trying to provide some useful service to people who raise themselves above myths.
If you have not done so yet, please go to grade school, and then come back.


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Table of Contents
This library provides both stable and unstable sorting algorithms, in single threaded and parallel versions.
These algorithms do not use any other library or utility. Compiling this library requires a C++11 compliant compiler.
The algorithms use a comparison object, in the same way as the standard library sort algorithms. If you don't define it, the comparison object defaults to std::less, which uses the < operator internally for comparisons.
The algorithms are exception safe, meaning that, the exceptions generated by the algorithms guarantee the integrity of the objects to sort, but not their relative order. If the exception is generated inside the objects (in the move or in the copy constructor.. ) the results can be unpredictable.
This library is include only. There is no need to link with any external static or dynamic library. This doesn't depend on any other boost files, variables or libraries or any other external libraries. To use this library, just include the files in the boost/sort/parallel folder.
This table provides you a brief description of the sort algorithms in the library.
Table 1. AlgorithmDescription
|
Algorithm |
Parallel |
Stable |
Additional Memory |
Best, average, and worst case |
|---|---|---|---|---|
|
sort |
No |
No |
Log N |
NlogN, NlogN , NlogN |
|
stable sort |
No |
Yes |
N / 2 |
NlogN, NlogN , NlogN |
|
parallel_sort |
Yes |
No |
block_size * num_threads |
NlogN, NlogN , NlogN |
|
parallel_stable_sort |
Yes |
Yes |
N / 2 |
NlogN, NlogN , NlogN |
|
sample_sort |
Yes |
Yes |
N |
NlogN, NlogN , NlogN |
The block_size is an internal parameter of the algorithm, which in order to achieve the highest speed, change according the size of the objects to sort according the next table. The strings use a block_size of 128.
Table 2. BlockSize
|
object size (bytes) |
1 - 15 |
16 - 31 |
32 - 63 |
64 - 127 |
128 - 255 |
256 - 511 |
512 - |
|---|---|---|---|---|---|---|---|
|
block_size (number of elements) |
4096 |
2048 |
1024 |
768 |
512 |
256 |
128 |
There are two primary categories of parallelization in sorting algorithms.
SUBDIVISION ALGORITHMS
Filter the data and generate two or more parts. Each part obtained is filtered and divided by other threads, until the size of the data to sort is smaller than a predefined size, then it is sorted by a single thread. The algorithm most frequently used in the filter and sorting is quick sort
These algorithms are fast with a small number of threads, but are inefficient with a great number of HW (hardware) threads. Examples of this category are # Intel Threading Building Blocks (TBB) # Microsoft PPL Parallel Sort.
MERGING ALGORITHMS
Divide the data in parts, and each part is sorted by a thread. When the parts are sorted, they are merged to obtain the final results. The problem of these algorithms is they need additional memory for the merge, usually the same size as the data.
With a small number of threads, these algorithms have similar speed to than the subdivision algorithms, but with span style=font-weight: bold;many threads they are much faster/span . Examples of this category are # GCC Parallel Sort (based on OpenMP) # Microsoft PPL Parallel Buffered Sort
This generates an undesirable duality. With a small number of threads the optimal algorithm is not the optimal for a big number of threads. For this reason, the SW designed for a small machine is inadequate for a big machine and vice versa. But the main problem for the merging algorithms is the additional memory used, usually of the same size as the data.
This version have as a new parallel_sort algorithm (internally named Block Indirect), created for processors connected with shared memory. It is a hybrid algorithm. With small number of threads, it is a subdivision algorithm, but with many threads is a merging algorithms, which need a small auxiliary memory ( block_size * number of threads).
The block_size is an internal parameter of the algorithm, which in order to achieve the highest speed, change according the size of the objects to sort according the next table. The strings use a block_size of 128.
Table 3. BlockSize
|
object size (bytes) |
1 - 15 |
16 - 31 |
32 - 63 |
64 - 127 |
128 - 255 |
256 - 511 |
512 - |
|---|---|---|---|---|---|---|---|
|
block_size (number of elements) |
4096 |
2048 |
1024 |
768 |
512 |
256 |
128 |
This algorithm eliminates the duality. You compile your program using the new algorithms. When your program runs on a machine with a small number of threads the algorithm internally uses a subdivision algorithm and has similar performance to TBB, and when run on a machine with many threads, internally uses the new algorithm and has the performance of GCC Parallel Sort, with the additional advantage of reduced memory consumption.
The algorithm uses an auxiliary memory of block_size elements for each thread. The worst case for the algorithm is when there are very big elements and many threads. With big elements (512 bytes), and 12 threads, The memory measured was:
Table 4. MemoryUsed
|
Algorithm |
Memory used in MB |
|---|---|
|
GCC Parallel Sort (OpenMP) |
1565 MB |
|
Threading Building Blocks (TBB) |
783 MB |
|
Block Indirect Sort |
812 MB |
This new parallel_sort algorithm had been created and implemented specifically for this library by the author.
If you are interested in a brief description of the algorithm, you can find in the next link Block Indirect Sort Brief.
If you are interested in a detailed description of the algorithm, you can find in the next link Block Indirect Sort. .
If you want run the benchmarks in your machine, all the code, instructions and procedures are in (Sort Parallel Benchmarks)
The parallel algorithms have a parameter indicating the number of thread to use in the sorting process, which always is the last value in the call. The default value (if left unspecified) is the number of HW threads of the machine where the program is running.
The parallel algorithms have 4 invocation formats:
algorithm ( first iterator, last iterator, comparison object, number of threads ) algorithm ( first iterator, last iterator, comparison object ) algorithm ( first iterator, last iterator, number of threads ) algorithm ( first iterator, last iterator )
If no comparison object is specified, the default class ( std::less<value_t> ) is used.
If the number of threads is unspecified, the number of HW threads on the machine where the the program is running is used
You only need to include the file boost/sort/parallel/sort.hpp if you wish to use this
#include <boost/sort/parallel/sort.hpp>
All the functions and definitions are in the namespace boost::sort::parallel
This example uses the single threaded sort and stable_sort.
#include <iostream> #include <vector>#include <random> #include <boost/sort/parallel/sort.hpp>namespace bsp = boost::sort::parallel; int main (void) { //-------------- begin------------ std::mt19937_64 my_rand(0); const uint32_t NMAX = 1000000; std::vector <uint64_t> A, B; for (uint32_t i = 0; i < NMAX; ++i) A.push_back(my_rand()); B = A; bsp::sort (A.begin(), A.end()); bsp::stable_sort (B.begin(), B.end()); for (uint32_t i = 0; i < NMAX; ++i) if (A[i] != B[i]) std::cout<<"Error in the sorting process\n"; return 0; };
This example uses parallel_sort and sample_sort.
#include <iostream> #include <vector> #include <random> #include <boost/sort/parallel/sort.hpp> namespace bsp = boost::sort::parallel; int main( void ) { //-------------- begin------------ std::mt19937_64 my_rand(0); const uint32_t NMAX = 1000000; std::vector <uint64_t> A, B; for (uint32_t i = 0; i < NMAX; ++i) A.push_back (my_rand()); B = A ; //------------------------------------------------------------------------ // if the thread parameter is not specified, the number of thread used // is the number of HW threads of the machine where the program is running. // This number is calculate in each execution of the code //------------------------------------------------------------------------ bsp::parallel_sort (A.begin(), A.end()); bsp::sample_sort (B.begin(), B.end()); for (uint32_t i = 0; i < NMAX; ++i) if (A[i] != B[i]) std::cout<<"Error in the sorting process\n"; return 0 ; };
This example uses parallel_sort and sample_sort and specifies the thread count.
#include <iostream> #include <vector> #include <random> #include <boost/sort/parallel/sort.hpp> namespace bsp = boost::sort::parallel;int main (void) { //-------------- begin------------ std::mt19937_64 my_rand(0); const uint32_t NMAX = 1000000; uint32_t number_threads = std::thread::hardware_concurrency(); std::vector <uint64_t> A, B ; for (uint32_t i = 0; i < NMAX; ++i) A.push_back (my_rand()); B = A ; //------------------------------------------------------------------------ // If the result of number_threads / 6 is smaller than 1, internally use 1 thread //------------------------------------------------------------------------ bsp::parallel_sort (A.begin(), A.end(), number_threads / 6); //------------------------------------------------------------------------ // force to execute with 100 threads //------------------------------------------------------------------------ bsp::sample_sort (B.begin(), B.end(), 100); for (uint32_t i = 0; i < NMAX ; ++i) if (A[i] != B[i]) std::cout<<"Error in the sorting process\n"; return 0 ; };
Last revised: January 12, 2017 at 08:57:31 GMT |


{{ config['SITE_NAME'] }}
Search by
GST Number
Business Registration
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{% block content %} {% endblock %}


{% extends "polls/wizard_create_poll.html" %} {% load staticfiles %} {% load tz %} {% get_current_timezone as TIME_ZONE %} {% block form_content %}
Please enter the general settings of the poll
If the period is 3 days, the timeframe of the poll will be repeated every 3 days. By default the votes are conserved from one iteration to another.


We'll present material we've workshopped and recorded at the Flaming Hakama studio throughout the year.
This includes some Afro-Cuban material from the alt.musica repertoire, arrangements of Brahms, classic Jazz ballads, and cues written for film & TV.
There are two Afro-Cuban and Jazz-based originals, an arrangement of Brahms piano rhapsody
Repertoire
Rhapsodie Op. 79, No. 2/Self-Immolation
- J. Brahms/D. Elaine Alt (score, recording)
Don't Wait For Me
- D. Elaine Alt (score, recording)
Stomping Ground
- D. Elaine Alt (score, recording)
Tension Cue
- D. Elaine Alt (score, recording)
Taken All My Life - Quartet Accompaniment
- Paula McMath, arr. D. Elaine Alt (score)
Taken All My Life - Orchestration
- Paula McMath, arr. D. Elaine Alt (recording)
Taken All My Life - Dramedy
- Paula McMath, arr. D. Elaine Alt (score, recording)
Smooch
- C. Mingus & M. Davis, arr. D. Elaine Alt (score, recording)
Production
Event page on facebook
Monroe on facebook
Classical Revolution on facebook


Image Functions module¶
image_fn.crop_patches_from_img (zstack, centroids) |
Crop image patches from a given input image of given width at given (x,y) centroid coordinates. |
This module contains scripts for image manipulation including denoising, enhancement and cropping functions
-
image_fn.
crop_patches_from_img
(zstack, centroids, width=25)[source]¶ Crop image patches from a given input image of given width at given (x,y) centroid coordinates.
Float centroids are first cast into ints.
Parameters: - zstack : numpy array
input (n_rows x n_cols x n_channels) numpy array.
- centroids : numpy array or list
array of (y,x) centroid coordinates
- width : int (odd)
size of cropped image patch is (width x width x n_channels)
Returns: - zs : numpy array
an array of cropped patches with length equal to the number of centroids.
-
image_fn.
detect_centrioles_in_img
(zstack_img, size, aniso_params, patch_size, CV_thresh=0.3, tslice=0, is_img_slice=False, filter_border=True, filter_high_intensity_bg=True, remove_duplicates=True, filter_CV=True, separation=5, invert=False, minmass=10, minoverlap=10, bg_min_I=0.2, bg_max_area=1000, bg_dilation=3, bg_invalid_check=0.5, debug=False)[source]¶ Primary function that wraps various functions in this module into one API call to detect centrioles given an image or image stack.
Parameters: - zstack_img : numpy array
- either
- a temporal z-stack (n_frames x n_z x n_rows x n_cols),
- a z-stack (n_z x n_rows x n_cols) or
- a grayscale image (n_rows x n_cols)
- size : float
Approximate expected width of centriole to detect in image pixels.
- aniso_params : Python dict
A Python dictionary giving the parameters for running the anisotropic filtering of Perona-Malik [1]. This dictionary should contain the following keys: ‘iterations’, ‘delta’, kappa’, see
image_fn.perona_malik()
- patch_size : int
size of the local image patch to crop for filtering by CV if used, see
image_fn.filter_centrioles_BCV()
- CV_thresh : float
coefficient of variation threshold for keeping high SNR detections as in
image_fn.filter_centrioles_BCV()
- tslice : int
if tslice 0, takes the corresponding time slice of the temporal z image and returns the max projection image over z. If zstack_img is just a zstack set tslice=-1.
- is_img_slice : bool
Set True if input is a grayscale image.
- filter_border : bool
If True, removes detections within a defined border zone
- filter_high_intensity_bg : bool
If True, removes detections from high intensity background areas.
- remove_duplicates : bool
If True, detects potential duplication of (y,x) locations that may by detecting the same centriole.
- filter_CV : bool
If True, keeps only (y,x) centriole detections whose CV evaluated over a local image crop is greater than a given threshold.
- separation : float
minimum separation distance in pixels between blobs.
- invert : bool
if True, features of interest to detect are assumed darker than background, used in trackpy.locate, see [2]
- minmass : float
minimum integrated intensity values of detected blob used in trackpy.locate, see [2]
- minoverlap : float
distance threshold for calling duplicate (y,x) coordinates, see
image_fn.remove_duplicate_centrioles()
- bg_min_I : float
intensity cut-off for defining ‘high’ intensity image areas as in
image_fn.produce_valid_img_mask()
- bg_max_area : int
area cut-off for defining ‘large’ background areas as in
image_fn.produce_valid_img_mask()
- bg_dilation : int
disk kernel size to dilate background noise mask as in
image_fn.produce_valid_img_mask()
- bg_invalid_check : float
this is a check to prevent everything in the image being regarded as being invalid if one knows centrioles should be present. It is an upper bound on the total area of the invalid image area mask output of
image_fn.produce_valid_img_mask()
.- debug: bool
if True, will produce all intermediate plotting graphics to help debugging.
Returns: - out_dict : Python dict
dictionary which collects the final output detections along with additional detection information.
- The dictionary has the following structure
- ‘centriole_centroids’:
(y,x) coordinates of detected centrioles
- ‘centriole_pos’:
table of all centriole detections with associated intensity statistics
- ‘max_proj_full_img’:
maximum projection image
- ‘max_proj_full_img_denoise’:
anisotropically filtered maximum projection image
- ‘background_mask’:
background image area mask
- ‘valid_detection_mask’:
non-background image areas where centrioles are being detected.
- ‘centriole_SNR’:
associated of detected centrioles
References
[1] (1, 2) Perona, P et. al, “Anisotropic diffusion.” Geometry-driven diffusion in computer vision. Springer, Dordrecht, 1994. 73-92. [2] (1, 2, 3) TrackPy Gaussian blob detection, http://soft-matter.github.io/trackpy/dev/generated/trackpy.locate.html.
-
image_fn.
filter_border_centroids_detection
(centroids, size, limits)[source]¶ Given (y,x) coordinates and the size of the border, removes all coordinates that lie within the defined border.
Parameters: - centroids : numpy array
array of (y,x) 2D coordinates.
- size : int
border size, how many pixels from the image edge do you consider the border. Isotropic border is assumed.
- limits : tuple-like
(y_max, x_max) pair that define the maximum number of rows, columns respectively of the image.
Returns: - filtered_centroids : numpy array
array of only valid (y,x) 2D coordinates that do not lie in the border zone.
- select : bool array
a binary array either 0 or 1 indicating which centroids lie within the border zone.
-
image_fn.
filter_centrioles_BCV
(centroids, max_slice_im, patch_size, CV_thresh=0.3)[source]¶ Given (y,x) centroid coordinates, the maximum slice whole frame image filter detections based on signal-to-noise (SNR) ratio within local image crops.
The SNR measure used is the coefficient of variation, where and are the standard deviation and mean of the pixel intensities in the image patch.
Parameters: - centroids : numpy array
array of (y,x) 2D coordinates.
- max_slice_im : numpy array
a grayscale 2D image
- patch_size : int (odd)
width of the local area to crop around the given (y,x) centroid
- CV_thresh : float
Signal-to-noise ratio cut-off where SNR is measured by CV i.e. centroids are kept if CV_thresh
Returns: - filtered_centroids : numpy array
array of only valid (y,x) 2D coordinates that have CV_thresh.
- select : bool array
a binary array either 0 or 1 indicating which centroids have CV_thresh.
- filtered_CV : array
array with the corresponding CV of filtered_centroids.
-
image_fn.
filter_masks
(mask, min_area=10, max_area=300, keep_centre=True, dist_thresh=0.5, min_max_area_cutoff=20)[source]¶ filters binary masks to identify the primary large area of interest.
- consideration of minimum and maximum area range.
- preferential consideration of areas near the image centre
if the 2nd option is used, and the found area is smaller than an expected area (min_max_area_cut_off) we default to finding the largest area.
Parameters: - mask : bool numpy array
input (n_rows x n_cols) binary image.
- min_area : int
minimum area of region of interest.
- max_area : int
maximum area of region of interest.
- keep_centre : bool
if True, preferentially consider the closest connected component to the image centre.
- dist_thresh : float (0-1)
what is the upper bound on the distance between the centroid of the segmented area of interest candidate and the image centre given as a fraction of the image patch width.
- min_max_area_cutoff : int
what is the minimum size below which we disregard the closest area to the image centre and fallback to the largest area. (only used if keep_centre=True)
Returns: - cand_mask : bool numpy array
either a blank image same size as input if nothing is detected or a refined binary mask with only one area of interest of same image size as the input.
-
image_fn.
filter_noise_centroids_detection
(centroids, mask)[source]¶ Given (y,x) coordinates and a binary mask of 0,1 of background regions, removes coordinates that lie in 1 areas (background).
Parameters: - centroids : numpy array
array of (y,x) 2D coordinates.
- mask : numpy array
boolean or integer mask with values 1 or 0 denoting invalid and valid spatial regions respectively.
Returns: - filtered_centroids : numpy array
array of only valid (y,x) 2D coordinates that lie in mask==0 regions.
- select : bool array
a binary array either 0 or 1 indicating which centroids are valid.
-
image_fn.
find_best_focus
(zstack)[source]¶ Finds the best focus slice by finding the z-slice that maximises the signal-to-noise ratio given by coefficient of variation (CV).
where and are the standard deviation and mean of the slice pixel intensities.
Parameters: - zstack : numpy array
an input (n_z x n_rows x n_cols) image.
Returns: - best_focus_slice : int
index of the z-slice of best focus.
-
image_fn.
find_best_focus_stacks
(zstacks)[source]¶ Finds the best focus slice of a series of z-slice stacks and constructs an array composed of the best-focus slices.
Parameters: - zstacks : numpy array
an input (n_stacks x n_z x n_rows x n_cols) image.
Returns: - best_focus_imgs : numpy array
a new numpy array (n_stacks x n_rows x n_cols) composed of the best-focus slices only.
- best_focus_slices : numpy array
list of the index of the z-slice of best focus for each z-slice stack.
-
image_fn.
locate_centroids_simple
(mask)[source]¶ Given an image, locates all centroids of connected components.
Note: This function inherently assumes a threshold of 0 and dilation with disk kernel of 3.
Parameters: - mask : numpy array
an input grayscale image.
Returns: - centroids : numpy array
an array of (y,x) coordinate pairs giving the peaks in the input image.
-
image_fn.
perona_malik
(img, iterations=10, delta=0.14, kappa=15)[source]¶ Runs Perona-Malik anisotropic on a given grayscale image.
Parameters: - img : numpy array
(n_rows x n_cols) grayscale image.
- iterations : int
Number of iterations to run the diffusion process. Higher gives smoother output.
- delta : float
This is the time step in the diffusion equation.
- kappa : float
This regulates the sensitivity to edges in the Perona-Malik formulation.
Returns: - filtered_img : numpy array
The filtered output image. Same size as input of type float.
References
[1] Perona, P et. al, “Anisotropic diffusion.” Geometry-driven diffusion in computer vision. Springer, Dordrecht, 1994. 73-92.
-
image_fn.
produce_valid_img_mask
(img, min_I=0.1, max_area=1000, dilation=3)[source]¶ Example Centriole images may have a ring of high pixel intensity of a much larger structure. This function is designed to identify such large continuous areas in order to filter detections.
Parameters: - img : numpy array
an input grayscale image.
- min_I : float
the lower threshold for identifying the bright intensity regions. Assumes normalised intensities i.e. image intensities should be between [0,1]
- max_area : integer
threshold for identifying ‘large’ region based on counting the number of pixels within the area.
- dilation : int
size of the disk kernel used to postprocess and smoothen resulting binary segmentation.
Returns: - invalid_regions : numpy array
a binary image of either 0, 1 pixel intensities indicating the large regions of high intensity i.e. invalid centriole zones.
-
image_fn.
remove_duplicate_centrioles
(centroids, min_dist, lam=1000)[source]¶ Removes duplicate (y,x) returning only one (y,x) instance given array of (y,x) centroid coordinates and a minimum distance threshold below which we call two (y,x) duplicates,
Parameters: - centroids : numpy array
array of (y,x) 2D coordinates.
- min_dist : float
two (y,x) coordinates are a duplicate if the distance between them is less than mid_dist.
- lam : float
a very large float, typically just a number larger than the image diagonal to exclude oneself in the pairwise pairing process of (y,x) coordinates.
Returns: - filtered_centroids : numpy array
array of unique (y,x) 2D coordinates.
- select : bool array
a binary array either 0 or 1 indicating which centroids are taken as unique (y,x) instances.
-
image_fn.
rescale_intensity_stack
(img_stack)[source]¶ rescales the intensity of a series of images given as a (n_imgs x n_rows x n_cols x channels) tensor such that it is [0,255] for uint8 and [0,1] for floats.
Parameters: - img_stack : numpy array
- an input image of 3 or 4 dimensions:
(n_imgs x n_rows x n_cols): gray-image stack (n_imgs x n_rows x n_cols x 3): rgb-image stack
Returns: - img_stack_rescale : numpy array
intensity rescaled images with range [0,255] for uint8 and [0,1] for floats
-
image_fn.
resize_img_stack
(img_stack, shape=(256, 256))[source]¶ Resizes a series of images given as a (n_imgs x n_rows x n_cols x channels) tensor.
Parameters: - img_stack : numpy array
- an input image of 3 or 4 dimensions:
(n_imgs x n_rows x n_cols): gray-image stack (n_imgs x n_rows x n_cols x 3): rgb-image stack
- shape : 2-tuple
(row_size, col_size) tuple giving the desired output image dimension
Returns: - img_stack_new : numpy array
- a numpy array of resized input:
(n_imgs x shape[0] x shape[1]): gray-image stack (n_imgs x shape[0] x shape[1] x 3): rgb-image stack
-
image_fn.
uint16_2_uint8
(vidstack)[source]¶ Casts any input image to be of uint8 type.
Note: Though named uint16, converts any input to uint8. We are just implicitly assuming with biological imaging uint16 input.
Parameters: - vidstack : numpy array
an input image (any size) as a numpy array.
Returns: - uint8_img : numpy array
a numpy array of same size as input rescaled to be of uint8 (range [0,255]).


Second Year undergrad in Computer Science and Engineering at Indian Institute of Information Technology Nagpur.
I am passionate programmer broadly intrested in Data Structures, Algorithms, Sports Programming and their applications.
A Linux terminal implemented in C programming language using conceptsof fork, exec, signals, pipes etc.
Different kinds of interrupts were also handled
A django based video player app, using the YouTube Data API v3 which provides the Youtube result on the app.
Vehicle Management System implemented using concepts of OOPs and file handling for storing data in C++
A student record data management system implemented using AVL trees and Linked Lists
and file handling for storing data in C
A examination System implemented using the concepts of Linked List in C
Class 10 : 9.8 CGPA


Property |
Type | Notes |
|---|---|---|
|
java.lang.String |
Values: "plain", "striped" |
The Quaqua Look and Feel supports tables with plain style and with striped style (aka alternating row colors).
The picture below shows the two styles as well a table using a large font.
You can specify the desired style by setting the client property Quaqua.Table.style
to
striped
or to plain
. If you don't specify a value,
the plain style is used.
|
For best results with editable JTable's you may consider
using your own implementation of a TableCellEditor, because the default implementation
does only draw a focus ring around editable text fields (instead of around
all editable components) and it does not properly honour font settings. Please use the following class, as a replacement of the DefaultTableCellEditor. The
test
package of the Quaqua Look and Feel contains this class as well.
import java.awt.*; import javax.swing.*; import javax.swing.border.*; public class DefaultCellEditor2 extends DefaultCellEditor { /** * Constructs a |
If you implement a TableCellRenderer on your own, make sure that the renderer component is non-opaque. If your renderer component is opaque, the component obscurs the stripes of a striped table.


Before Change
// To properly test the performance of our model, we need to split the data
// according to time: we train on older pushes and evaluate on newer pushes.
def train_test_split(self, X, y):
pushes = OrderedDict()
for test_data in test_scheduling.get_test_scheduling_history(self.granularity):
rev = test_data["revs"][0]
name = test_data["name"]
if self.granularity == "label" and not name.startswith("test-"):
continue
if rev in pushes:
pushes[rev] += 1
else:
pushes[rev] = 1
train_push_len = math.floor(0.9 * len(pushes))
train_pushes = list(pushes.values())[:train_push_len]
train_len = sum(count for count in train_pushes)
print(
f"{train_push_len} pushes in the training set (corresponding to {train_len} push/jobs)"
)
return X[:train_len], X[train_len:], y[:train_len], y[train_len:]
After Change
// according to time: we train on older pushes and evaluate on newer pushes.
def train_test_split(self, X, y):
pushes, train_push_len = self.get_pushes()
train_len = sum(
len(push["failures"]) + len(push["passes"])
for push in pushes[:train_push_len]
)
print(
f"{train_push_len} pushes in the training set (corresponding to {train_len} push/jobs)"
)
return X[:train_len], X[train_len:], y[:train_len], y[train_len:]


arrow_back
Home
Total no of feedback is {{ stat1 }}
{% for feed in feedback %}
mood
Name
{{ feed['name'] }}
Email
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{{ feed['idea'] }}
Presentation
{{ feed['presentation'] }}
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{{ feed['objective'] }}
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{{ feed['review'] }}
{% endfor %}


at close range wikipedia
At Close Range | Facebook
Wikipedia. Content from the Wikipedia article At Close Range (contributors) licensed under CC-BY-SA. Freebase. Content from Freebase licensed under CC- BY .
http://www.facebook.com/pages/At-Close-Range/105664079466529?sk=wiki
At Close Range (1986) - Online Movie Wiki - ShareTV
At Close Range Wiki and Online Community (Movie, First Released Apr 18th, 1986). Keep track when At Close Range is released in theaters, DVD and Blu-ray .
http://sharetv.org/movies/at_close_range_1986
Close Range: Wyoming Stories - Wikipedia, the free encyclopedia
Close Range: Wyoming Stories is a 1999 collection of short stories written by E. Annie Proulx. The stories are set in the desolate landscape of rural Wyoming .
http://en.wikipedia.org/wiki/Close_Range:_Wyoming_Stories
Things You'll Need
- Certified copies of public records evidence in your case
Instructions
-
-
1
At Close Range Movie Review
Apr 1, 2009 . Bruce Johnson Sr. Wikipedia entry (pretty good details with a lot of reference links). Review: At Close Range Like Father. Like Son. Like Hell.
http://www.dreamindemon.com/2009/04/01/at-close-range/At Close Range - Wikipedia, the free encyclopedia
At Close Range is a 1986 American crime drama film based on the real life rural Pennsylvania crime family led by Bruce Johnston, Sr. which operated during the .
http://en.wikipedia.org/wiki/At_Close_Range -
2
at close range - The Free Dictionary
Information about at close range in the free online English dictionary and . dictionary. Financial dictionary. Acronyms. Idioms. Encyclopedia. Wikipedia .
http://www.thefreedictionary.com/at+close+rangeBruce Johnston (criminal) - Wikipedia, the free encyclopedia
From Wikipedia, the free encyclopedia. Jump to: . The movie At Close Range was based on the thefts leading up to the murders in 1977. Christopher Walken .
http://en.wikipedia.org/wiki/Bruce_Johnston_(criminal) -
-
3
eileen ryan | Tumblr
At Close Range Movie Downloads. At Close Range movie download. Actors: David Strathairn Sean Penn Chris Penn Christopher Walken R.D. Call Eileen Ryan .
http://www.tumblr.com/tagged/eileen%20ryan?language=nl_NLFull Cast of At Close Range Actors/Actresses - Ranker
This list of the At Close Range actors' names (actors in the movie At Close . on this page and throughout Ranker is supplied by , , Wikipedia, Ranker users, and .
http://www.ranker.com/list/full-cast-of-at-close-range-actors-and-actresses/reference -
4
At Close Range Trailer, Reviews and Schedule for ... - TV Guide.com
At Close Range Trailer, Reviews, Schedule, Photos and At Close Range Cast on . At Close Range Online Movie Wiki at ShareTV.org · At Close Range Videos, .
http://movies.tvguide.com/at-close-range/125907At Close Range
At Close Range is a 1986 American crime drama film based on the real life rural . Portions from Freebase, licensed under CC-BY and Wikipedia licensed under .
http://movies.amctv.com/movie/1986/At+Close+Range
-
1
References
Resources
- Photo Credit Justice image by MVit from 1950 s alphabet and numbers


At long last, radio shows are now available as podcasts. We’ve been working towards that for some time, and we’re very excited to tell you all about it!
Look for us on Apple podcasts, Stitcher, or any other podcast apps. Look for Utopian Podcasts in podcast name, or for FOSSHub in episode name.
Our schedule, right now, contains 2 episodes per week. Thursdays will have that Monday’s IdeaHub, whereas Saturdays will feature that Wednesday’s FOSSHub. Episodes are likely to arrive fairly late in the day, so don’t worry if you don’t see them first thing in the morning.
If you want to add our rss feed manually to your podcatcher, that’s also quite easy. You’ll find us at https://utopian.podbean.com/feed.xml. If you like to listen to episodes on the web, our home is at https://utopian.podbean.com/. There you’ll also find show notes for each episode.
Finally, if you want a handy web player for Utopian podcasts, we’ve made a nice little hackmd page for it.
We’re very excited about this development. We believe the podcast will help us get greater exposure for open source projects and ideas on and off the Steem blockchain. Our shows are live conversations between open source experts and fans on a variety of topics with valuable insights from guests and audience. Open source is about community. These podcasts aim to give the community a place to speak and listen.
About Our Shows
FOSSHub, which has recently started its second season, is our flagship show. Host @jedigeiss, news presenter @techslut, and producer @buckydurdle talk with a variety of developers about their open source projects, and also deliver the open source news. The show airs live every Wednesday at 8PM UTC, and you can find it on Facebook, Youtube, Vimm, and on our discord server, where the conversation is happening in real time.
IdeaHub is our newer show, airing every Monday at 8PM UTC, with the same host and the same producer. Ideas category CM @knowledges is also part of the fun, as folks with new and exciting ideas for open source projects come on to talk about them, and maybe find developers to help them out with them. You’ll find it live everywhere you can find FOSSHub.
About Our Host and Crew
Our host, @jedigeiss is a developer, a banker, and a Steem veteran. He’s a member of the new steering committee aimed at creating the Steem Alliance foundation that will take over much of the managing of the blockchain from Steemit Inc.
Producer @buckydurdle is a veteran producer of both video and audio, and the CM of our Video Tutorials category. He edits the shows, runs the raffles, and makes sure everything works.
News presenter and occasional co-host @techslut is Utopian’s CMO. She’s really the boss, but she doesn’t make a big deal of it. She’s a recovering tech journalist and an open source enthusiast.
First Time Contributing in Utopian.io?
Learn how to contribute on our website
This page is synchronized from the post: Utopian Podcasts are LIVE


Pygments changelog¶
Issue numbers refer to the tracker at <http://bitbucket.org/birkenfeld/pygments-main/issues>, pull request numbers to the requests at <http://bitbucket.org/birkenfeld/pygments-main/pull-requests/merged>.
Version 2.0rc1¶
(released Oct 16, 2014)
- Dropped Python 2.4 and 2.5 compatibility. This is in favor of single-source compatibility between Python 2.6, 2.7 and 3.3+.
- New website and documentation based on Sphinx (finally!)
- Lexers added:
- APL (#969)
- Agda and Literate Agda (PR#203)
- Alloy (PR#355)
- AmbientTalk
- BlitzBasic (PR#197)
- ChaiScript (PR#24)
- Chapel (PR#256)
- Cirru (PR#275)
- Clay (PR#184)
- ColdFusion CFC (PR#283)
- Cryptol and Literate Cryptol (PR#344)
- Cypher (PR#257)
- Docker config files
- EBNF (PR#193)
- Eiffel (PR#273)
- GAP (PR#311)
- Golo (PR#309)
- Handlebars (PR#186)
- Hy (PR#238)
- Idris and Literate Idris (PR#210)
- Igor Pro (PR#172)
- Inform 6/7 (PR#281)
- Intel objdump (PR#279)
- Isabelle (PR#386)
- Jasmin (PR#349)
- JSON-LD (PR#289)
- Kal (PR#233)
- Lean (PR#399)
- LSL (PR#296)
- Limbo (PR#291)
- Liquid (#977)
- MQL (PR#285)
- MaskJS (PR#280)
- Mozilla preprocessors
- Mathematica (PR#245)
- NesC (PR#166)
- Nit (PR#375)
- Nix (PR#267)
- Pan
- Pawn (PR#211)
- Perl 6 (PR#181)
- Pig (PR#304)
- Pike (PR#237)
- QBasic (PR#182)
- Red (PR#341)
- ResourceBundle (#1038)
- Rexx (PR#199)
- Rql (PR#251)
- Rsl
- SPARQL (PR#78)
- Slim (PR#366)
- Swift (PR#371)
- Swig (PR#168)
- TADS 3 (PR#407)
- Todo.txt todo lists
- Twig (PR#404)
- Default lexer encoding is now “guess”, i.e. UTF-8 / Locale / Latin1 is tried in that order.
- Added a helper to “optimize” regular expressions that match one of many literal words; this can save 20% and more lexing time with lexers that highlight many keywords or builtins.
- New styles: “xcode” and “igor”, similar to the default highlighting of the respective IDEs.
- The command-line “pygmentize” tool now tries a little harder to find the correct encoding for files and the terminal (#979).
- Added “inencoding” option for lexers to override “encoding” analogous to “outencoding” (#800).
- Added line-by-line “streaming” mode for pygmentize with the “-s” option. (PR#165) Only fully works for lexers that have no constructs spanning lines!
- Added an “envname” option to the LaTeX formatter to select a replacement verbatim environment (PR#235).
- Updated the Makefile lexer to yield a little more useful highlighting.
- Lexer aliases passed to
get_lexer_by_name()
are now case-insensitive. - File name matching in lexers and formatters will now use a regex cache for speed (PR#205).
- Pygments will now recognize “vim” modelines when guessing the lexer for a file based on content (PR#118).
- The NameHighlightFilter now works with any Name.* token type (#790).
- Python 3 lexer: add new exceptions from PEP 3151.
- Opa lexer: add new keywords (PR#170).
- Julia lexer: add keywords and underscore-separated number literals (PR#176).
- Lasso lexer: fix method highlighting, update builtins. Fix guessing so that plain XML isn’t always taken as Lasso (PR#163).
- Objective C/C++ lexers: allow “@” prefixing any expression (#871).
- Ruby lexer: fix lexing of Name::Space tokens (#860) and of symbols in hashes (#873).
- Stan lexer: update for version 2.4.0 of the language (PR#162, PR#255, PR#377).
- JavaScript lexer: add the “yield” keyword (PR#196).
- HTTP lexer: support for PATCH method (PR#190).
- Koka lexer: update to newest language spec (PR#201).
- Haxe lexer: rewrite and support for Haxe 3 (PR#174).
- Prolog lexer: add different kinds of numeric literals (#864).
- F# lexer: rewrite with newest spec for F# 3.0 (#842), fix a bug with dotted chains (#948).
- Kotlin lexer: general update (PR#271).
- Rebol lexer: fix comment detection and analyse_text (PR#261).
- LLVM lexer: update keywords to v3.4 (PR#258).
- PHP lexer: add new keywords and binary literals (PR#222).
- external/markdown-processor.py updated to newest python-markdown (PR#221).
- CSS lexer: some highlighting order fixes (PR#231).
- Ceylon lexer: fix parsing of nested multiline comments (#915).
- C family lexers: fix parsing of indented preprocessor directives (#944).
- Rust lexer: update to 0.9 language version (PR#270, PR#388).
- Elixir lexer: update to 0.15 language version (PR#392).
- Fix swallowing incomplete tracebacks in Python console lexer (#874).
Version 1.6¶
(released Feb 3, 2013)
- Lexers added:
- Dylan console (PR#149)
- Logos (PR#150)
- Shell sessions (PR#158)
- Fix guessed lexers not receiving lexer options (#838).
- Fix unquoted HTML attribute lexing in Opa (#841).
- Fixes to the Dart lexer (PR#160).
Version 1.6rc1¶
(released Jan 9, 2013)
- Lexers added:
- AspectJ (PR#90)
- AutoIt (PR#122)
- BUGS-like languages (PR#89)
- Ceylon (PR#86)
- Croc (new name for MiniD)
- CUDA (PR#75)
- Dg (PR#116)
- IDL (PR#115)
- Jags (PR#89)
- Julia (PR#61)
- Kconfig (#711)
- Lasso (PR#95, PR#113)
- LiveScript (PR#84)
- Monkey (PR#117)
- Mscgen (PR#80)
- NSIS scripts (PR#136)
- OpenCOBOL (PR#72)
- QML (PR#123)
- Puppet (PR#133)
- Racket (PR#94)
- Rdoc (PR#99)
- Robot Framework (PR#137)
- RPM spec files (PR#124)
- Rust (PR#67)
- Smali (Dalvik assembly)
- SourcePawn (PR#39)
- Stan (PR#89)
- Treetop (PR#125)
- TypeScript (PR#114)
- VGL (PR#12)
- Visual FoxPro (#762)
- Windows Registry (#819)
- Xtend (PR#68)
- The HTML formatter now supports linking to tags using CTags files, when the python-ctags package is installed (PR#87).
- The HTML formatter now has a “linespans” option that wraps every line in a <span> tag with a specific id (PR#82).
- When deriving a lexer from another lexer with token definitions, definitions for states not in the child lexer are now inherited. If you override a state in the child lexer, an “inherit” keyword has been added to insert the base state at that position (PR#141).
- The C family lexers now inherit token definitions from a common base class, removing code duplication (PR#141).
- Use “colorama” on Windows for console color output (PR#142).
- Fix Template Haskell highlighting (PR#63).
- Fix some S/R lexer errors (PR#91).
- Fix a bug in the Prolog lexer with names that start with ‘is’ (#810).
- Rewrite Dylan lexer, add Dylan LID lexer (PR#147).
- Add a Java quickstart document (PR#146).
- Add a “external/autopygmentize” file that can be used as .lessfilter (#802).
Version 1.5¶
(codename Zeitdilatation, released Mar 10, 2012)
- Lexers added:
- Awk (#630)
- Fancy (#633)
- PyPy Log
- eC
- Nimrod
- Nemerle (#667)
- F# (#353)
- Groovy (#501)
- PostgreSQL (#660)
- DTD
- Gosu (#634)
- Octave (PR#22)
- Standard ML (PR#14)
- CFengine3 (#601)
- Opa (PR#37)
- HTTP sessions (PR#42)
- JSON (PR#31)
- SNOBOL (PR#30)
- MoonScript (PR#43)
- ECL (PR#29)
- Urbiscript (PR#17)
- OpenEdge ABL (PR#27)
- SystemVerilog (PR#35)
- Coq (#734)
- PowerShell (#654)
- Dart (#715)
- Fantom (PR#36)
- Bro (PR#5)
- NewLISP (PR#26)
- VHDL (PR#45)
- Scilab (#740)
- Elixir (PR#57)
- Tea (PR#56)
- Kotlin (PR#58)
- Fix Python 3 terminal highlighting with pygmentize (#691).
- In the LaTeX formatter, escape special &, < and > chars (#648).
- In the LaTeX formatter, fix display problems for styles with token background colors (#670).
- Enhancements to the Squid conf lexer (#664).
- Several fixes to the reStructuredText lexer (#636).
- Recognize methods in the ObjC lexer (#638).
- Fix Lua “class” highlighting: it does not have classes (#665).
- Fix degenerate regex in Scala lexer (#671) and highlighting bugs (#713, 708).
- Fix number pattern order in Ocaml lexer (#647).
- Fix generic type highlighting in ActionScript 3 (#666).
- Fixes to the Clojure lexer (PR#9).
- Fix degenerate regex in Nemerle lexer (#706).
- Fix infinite looping in CoffeeScript lexer (#729).
- Fix crashes and analysis with ObjectiveC lexer (#693, #696).
- Add some Fortran 2003 keywords.
- Fix Boo string regexes (#679).
- Add “rrt” style (#727).
- Fix infinite looping in Darcs Patch lexer.
- Lots of misc fixes to character-eating bugs and ordering problems in many different lexers.
Version 1.4¶
(codename Unschärfe, released Jan 03, 2011)
- Lexers added:
- Factor (#520)
- PostScript (#486)
- Verilog (#491)
- BlitzMax Basic (#478)
- Ioke (#465)
- Java properties, split out of the INI lexer (#445)
- Scss (#509)
- Duel/JBST
- XQuery (#617)
- Mason (#615)
- GoodData (#609)
- SSP (#473)
- Autohotkey (#417)
- Google Protocol Buffers
- Hybris (#506)
- Do not fail in analyse_text methods (#618).
- Performance improvements in the HTML formatter (#523).
- With the
noclasses
option in the HTML formatter, some styles present in the stylesheet were not added as inline styles. - Four fixes to the Lua lexer (#480, #481, #482, #497).
- More context-sensitive Gherkin lexer with support for more i18n translations.
- Support new OO keywords in Matlab lexer (#521).
- Small fix in the CoffeeScript lexer (#519).
- A bugfix for backslashes in ocaml strings (#499).
- Fix unicode/raw docstrings in the Python lexer (#489).
- Allow PIL to work without PIL.pth (#502).
- Allow seconds as a unit in CSS (#496).
- Support
application/javascript
as a JavaScript mime type (#504). - Support Offload C++ Extensions as keywords in the C++ lexer (#484).
- Escape more characters in LaTeX output (#505).
- Update Haml/Sass lexers to version 3 (#509).
- Small PHP lexer string escaping fix (#515).
- Support comments before preprocessor directives, and unsigned/ long long literals in C/C++ (#613, #616).
- Support line continuations in the INI lexer (#494).
- Fix lexing of Dylan string and char literals (#628).
- Fix class/procedure name highlighting in VB.NET lexer (#624).
Version 1.3.1¶
(bugfix release, released Mar 05, 2010)
- The
pygmentize
script was missing from the distribution.
Version 1.3¶
(codename Schneeglöckchen, released Mar 01, 2010)
- Added the
ensurenl
lexer option, which can be used to suppress the automatic addition of a newline to the lexer input. - Lexers added:
- Ada
- Coldfusion
- Modula-2
- Haxe
- R console
- Objective-J
- Haml and Sass
- CoffeeScript
- Enhanced reStructuredText highlighting.
- Added support for PHP 5.3 namespaces in the PHP lexer.
- Added a bash completion script for pygmentize, to the external/ directory (#466).
- Fixed a bug in do_insertions() used for multi-lexer languages.
- Fixed a Ruby regex highlighting bug (#476).
- Fixed regex highlighting bugs in Perl lexer (#258).
- Add small enhancements to the C lexer (#467) and Bash lexer (#469).
- Small fixes for the Tcl, Debian control file, Nginx config, Smalltalk, Objective-C, Clojure, Lua lexers.
- Gherkin lexer: Fixed single apostrophe bug and added new i18n keywords.
Version 1.2.2¶
(bugfix release, released Jan 02, 2010)
- Removed a backwards incompatibility in the LaTeX formatter that caused Sphinx to produce invalid commands when writing LaTeX output (#463).
- Fixed a forever-backtracking regex in the BashLexer (#462).
Version 1.2.1¶
(bugfix release, released Jan 02, 2010)
- Fixed mishandling of an ellipsis in place of the frames in a Python console traceback, resulting in clobbered output.
Version 1.2¶
(codename Neujahr, released Jan 01, 2010)
- Dropped Python 2.3 compatibility.
- Lexers added:
- Asymptote
- Go
- Gherkin (Cucumber)
- CMake
- Ooc
- Coldfusion
- Haxe
- R console
- Added options for rendering LaTeX in source code comments in the LaTeX formatter (#461).
- Updated the Logtalk lexer.
- Added line_number_start option to image formatter (#456).
- Added hl_lines and hl_color options to image formatter (#457).
- Fixed the HtmlFormatter’s handling of noclasses=True to not output any classes (#427).
- Added the Monokai style (#453).
- Fixed LLVM lexer identifier syntax and added new keywords (#442).
- Fixed the PythonTracebackLexer to handle non-traceback data in header or trailer, and support more partial tracebacks that start on line 2 (#437).
- Fixed the CLexer to not highlight ternary statements as labels.
- Fixed lexing of some Ruby quoting peculiarities (#460).
- A few ASM lexer fixes (#450).
Version 1.1.1¶
(bugfix release, released Sep 15, 2009)
- Fixed the BBCode lexer (#435).
- Added support for new Jinja2 keywords.
- Fixed test suite failures.
- Added Gentoo-specific suffixes to Bash lexer.
Version 1.1¶
(codename Brillouin, released Sep 11, 2009)
- Ported Pygments to Python 3. This needed a few changes in the way encodings are handled; they may affect corner cases when used with Python 2 as well.
- Lexers added:
- Antlr/Ragel, thanks to Ana Nelson
- (Ba)sh shell
- Erlang shell
- GLSL
- Prolog
- Evoque
- Modelica
- Rebol
- MXML
- Cython
- ABAP
- ASP.net (VB/C#)
- Vala
- Newspeak
- Fixed the LaTeX formatter’s output so that output generated for one style can be used with the style definitions of another (#384).
- Added “anchorlinenos” and “noclobber_cssfile” (#396) options to HTML formatter.
- Support multiline strings in Lua lexer.
- Rewrite of the JavaScript lexer by Pumbaa80 to better support regular expression literals (#403).
- When pygmentize is asked to highlight a file for which multiple lexers match the filename, use the analyse_text guessing engine to determine the winner (#355).
- Fixed minor bugs in the JavaScript lexer (#383), the Matlab lexer (#378), the Scala lexer (#392), the INI lexer (#391), the Clojure lexer (#387) and the AS3 lexer (#389).
- Fixed three Perl heredoc lexing bugs (#379, #400, #422).
- Fixed a bug in the image formatter which misdetected lines (#380).
- Fixed bugs lexing extended Ruby strings and regexes.
- Fixed a bug when lexing git diffs.
- Fixed a bug lexing the empty commit in the PHP lexer (#405).
- Fixed a bug causing Python numbers to be mishighlighted as floats (#397).
- Fixed a bug when backslashes are used in odd locations in Python (#395).
- Fixed various bugs in Matlab and S-Plus lexers, thanks to Winston Chang (#410, #411, #413, #414) and fmarc (#419).
- Fixed a bug in Haskell single-line comment detection (#426).
- Added new-style reStructuredText directive for docutils 0.5+ (#428).
Version 1.0¶
(codename Dreiundzwanzig, released Nov 23, 2008)
Don’t use join(splitlines()) when converting newlines to
\n
, because that doesn’t keep all newlines at the end when thestripnl
lexer option is False.Added
-N
option to command-line interface to get a lexer name for a given filename.Added Tango style, written by Andre Roberge for the Crunchy project.
Added Python3TracebackLexer and
python3
option to PythonConsoleLexer.Fixed a few bugs in the Haskell lexer.
Fixed PythonTracebackLexer to be able to recognize SyntaxError and KeyboardInterrupt (#360).
Provide one formatter class per image format, so that surprises like:
pygmentize -f gif -o foo.gif foo.py
creating a PNG file are avoided.
Actually use the font_size option of the image formatter.
Fixed numpy lexer that it doesn’t listen for *.py any longer.
Fixed HTML formatter so that text options can be Unicode strings (#371).
Unified Diff lexer supports the “udiff” alias now.
Fixed a few issues in Scala lexer (#367).
RubyConsoleLexer now supports simple prompt mode (#363).
JavascriptLexer is smarter about what constitutes a regex (#356).
Add Applescript lexer, thanks to Andreas Amann (#330).
Make the codetags more strict about matching words (#368).
NginxConfLexer is a little more accurate on mimetypes and variables (#370).
Version 0.11.1¶
(released Aug 24, 2008)
- Fixed a Jython compatibility issue in pygments.unistring (#358).
Version 0.11¶
(codename Straußenei, released Aug 23, 2008)
Many thanks go to Tim Hatch for writing or integrating most of the bug fixes and new features.
- Lexers added:
- Nasm-style assembly language, thanks to delroth
- YAML, thanks to Kirill Simonov
- ActionScript 3, thanks to Pierre Bourdon
- Cheetah/Spitfire templates, thanks to Matt Good
- Lighttpd config files
- Nginx config files
- Gnuplot plotting scripts
- Clojure
- POV-Ray scene files
- Sqlite3 interactive console sessions
- Scala source files, thanks to Krzysiek Goj
- Lexers improved:
- C lexer highlights standard library functions now and supports C99 types.
- Bash lexer now correctly highlights heredocs without preceding whitespace.
- Vim lexer now highlights hex colors properly and knows a couple more keywords.
- Irc logs lexer now handles xchat’s default time format (#340) and
correctly highlights lines ending in
>
. - Support more delimiters for perl regular expressions (#258).
- ObjectiveC lexer now supports 2.0 features.
- Added “Visual Studio” style.
- Updated markdown processor to Markdown 1.7.
- Support roman/sans/mono style defs and use them in the LaTeX formatter.
- The RawTokenFormatter is no longer registered to
*.raw
and it’s documented that tokenization with this lexer may raise exceptions. - New option
hl_lines
to HTML formatter, to highlight certain lines. - New option
prestyles
to HTML formatter. - New option -g to pygmentize, to allow lexer guessing based on filetext (can be slowish, so file extensions are still checked first).
guess_lexer()
now makes its decision much faster due to a cache of whether data is xml-like (a check which is used in several versions ofanalyse_text()
. Several lexers also have more accurateanalyse_text()
now.
Version 0.10¶
(codename Malzeug, released May 06, 2008)
- Lexers added:
- Io
- Smalltalk
- Darcs patches
- Tcl
- Matlab
- Matlab sessions
- FORTRAN
- XSLT
- tcsh
- NumPy
- Python 3
- S, S-plus, R statistics languages
- Logtalk
- In the LatexFormatter, the commandprefix option is now by default
‘PY’ instead of ‘C’, since the latter resulted in several collisions
with other packages. Also, the special meaning of the arg
argument to
get_style_defs()
was removed. - Added ImageFormatter, to format code as PNG, JPG, GIF or BMP. (Needs the Python Imaging Library.)
- Support doc comments in the PHP lexer.
- Handle format specifications in the Perl lexer.
- Fix comment handling in the Batch lexer.
- Add more file name extensions for the C++, INI and XML lexers.
- Fixes in the IRC and MuPad lexers.
- Fix function and interface name highlighting in the Java lexer.
- Fix at-rule handling in the CSS lexer.
- Handle KeyboardInterrupts gracefully in pygmentize.
- Added BlackWhiteStyle.
- Bash lexer now correctly highlights math, does not require whitespace after semicolons, and correctly highlights boolean operators.
- Makefile lexer is now capable of handling BSD and GNU make syntax.
Version 0.9¶
(codename Herbstzeitlose, released Oct 14, 2007)
- Lexers added:
- Erlang
- ActionScript
- Literate Haskell
- Common Lisp
- Various assembly languages
- Gettext catalogs
- Squid configuration
- Debian control files
- MySQL-style SQL
- MOOCode
- Lexers improved:
- Greatly improved the Haskell and OCaml lexers.
- Improved the Bash lexer’s handling of nested constructs.
- The C# and Java lexers exhibited abysmal performance with some input code; this should now be fixed.
- The IRC logs lexer is now able to colorize weechat logs too.
- The Lua lexer now recognizes multi-line comments.
- Fixed bugs in the D and MiniD lexer.
- The encoding handling of the command line mode (pygmentize) was enhanced.
- Added a
-P
option to the command line mode which can be used to give options whose values contain commas or equals signs. - Added 256-color terminal formatter.
- Added an experimental SVG formatter.
- Added the
lineanchors
option to the HTML formatter, thanks to Ian Charnas for the idea. - Gave the line numbers table a CSS class in the HTML formatter.
- Added a Vim 7-like style.
Version 0.8.1¶
(released Jun 27, 2007)
- Fixed POD highlighting in the Ruby lexer.
- Fixed Unicode class and namespace name highlighting in the C# lexer.
- Fixed Unicode string prefix highlighting in the Python lexer.
- Fixed a bug in the D and MiniD lexers.
- Fixed the included MoinMoin parser.
Version 0.8¶
(codename Maikäfer, released May 30, 2007)
- Lexers added:
- Haskell, thanks to Adam Blinkinsop
- Redcode, thanks to Adam Blinkinsop
- D, thanks to Kirk McDonald
- MuPad, thanks to Christopher Creutzig
- MiniD, thanks to Jarrett Billingsley
- Vim Script, by Tim Hatch
- The HTML formatter now has a second line-numbers mode in which it
will just integrate the numbers in the same
<pre>
tag as the code. - The CSharpLexer now is Unicode-aware, which means that it has an option that can be set so that it correctly lexes Unicode identifiers allowed by the C# specs.
- Added a RaiseOnErrorTokenFilter that raises an exception when the lexer generates an error token, and a VisibleWhitespaceFilter that converts whitespace (spaces, tabs, newlines) into visible characters.
- Fixed the do_insertions() helper function to yield correct indices.
- The ReST lexer now automatically highlights source code blocks in ”.. sourcecode:: language” and ”.. code:: language” directive blocks.
- Improved the default style (thanks to Tiberius Teng). The old default is still available as the “emacs” style (which was an alias before).
- The get_style_defs method of HTML formatters now uses the cssclass option as the default selector if it was given.
- Improved the ReST and Bash lexers a bit.
- Fixed a few bugs in the Makefile and Bash lexers, thanks to Tim Hatch.
- Fixed a bug in the command line code that disallowed
-O
options when using the-S
option. - Fixed a bug in the RawTokenFormatter.
Version 0.7.1¶
(released Feb 15, 2007)
- Fixed little highlighting bugs in the Python, Java, Scheme and Apache Config lexers.
- Updated the included manpage.
- Included a built version of the documentation in the source tarball.
Version 0.7¶
(codename Faschingskrapfn, released Feb 14, 2007)
- Added a MoinMoin parser that uses Pygments. With it, you get Pygments highlighting in Moin Wiki pages.
- Changed the exception raised if no suitable lexer, formatter etc. is found in one of the get_*_by_* functions to a custom exception, pygments.util.ClassNotFound. It is, however, a subclass of ValueError in order to retain backwards compatibility.
- Added a -H command line option which can be used to get the docstring of a lexer, formatter or filter.
- Made the handling of lexers and formatters more consistent. The aliases and filename patterns of formatters are now attributes on them.
- Added an OCaml lexer, thanks to Adam Blinkinsop.
- Made the HTML formatter more flexible, and easily subclassable in order to make it easy to implement custom wrappers, e.g. alternate line number markup. See the documentation.
- Added an outencoding option to all formatters, making it possible to override the encoding (which is used by lexers and formatters) when using the command line interface. Also, if using the terminal formatter and the output file is a terminal and has an encoding attribute, use it if no encoding is given.
- Made it possible to just drop style modules into the styles subpackage of the Pygments installation.
- Added a “state” keyword argument to the using helper.
- Added a commandprefix option to the LatexFormatter which allows to control how the command names are constructed.
- Added quite a few new lexers, thanks to Tim Hatch:
- Java Server Pages
- Windows batch files
- Trac Wiki markup
- Python tracebacks
- ReStructuredText
- Dylan
- and the Befunge esoteric programming language (yay!)
- Added Mako lexers by Ben Bangert.
- Added “fruity” style, another dark background originally vim-based theme.
- Added sources.list lexer by Dennis Kaarsemaker.
- Added token stream filters, and a pygmentize option to use them.
- Changed behavior of in Operator for tokens.
- Added mimetypes for all lexers.
- Fixed some problems lexing Python strings.
- Fixed tickets: #167, #178, #179, #180, #185, #201.
Version 0.6¶
(codename Zimtstern, released Dec 20, 2006)
- Added option for the HTML formatter to write the CSS to an external file in “full document” mode.
- Added RTF formatter.
- Added Bash and Apache configuration lexers (thanks to Tim Hatch).
- Improved guessing methods for various lexers.
- Added @media support to CSS lexer (thanks to Tim Hatch).
- Added a Groff lexer (thanks to Tim Hatch).
- License change to BSD.
- Added lexers for the Myghty template language.
- Added a Scheme lexer (thanks to Marek Kubica).
- Added some functions to iterate over existing lexers, formatters and lexers.
- The HtmlFormatter’s get_style_defs() can now take a list as an argument to generate CSS with multiple prefixes.
- Support for guessing input encoding added.
- Encoding support added: all processing is now done with Unicode
strings, input and output are converted from and optionally to byte
strings (see the
encoding
option of lexers and formatters). - Some improvements in the C(++) lexers handling comments and line continuations.
Version 0.5.1¶
(released Oct 30, 2006)
- Fixed traceback in
pygmentize -L
(thanks to Piotr Ozarowski).


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Provides access to the keyboard for event generation.
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Send a sequence of keys via keyboard events Usage:
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Send a keyboard event Usage:
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Send a key down event Usage: The key will be treated as down until a matching release_key() is sent.
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Send a key up event Usage: If the specified key was not made down using press_key(), the event will be ignored.
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Fake a keypress Usage: Uses XTest to 'fake' a keypress. This is useful to send keypresses to some applications which won't respond to keyboard.send_key()
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Wait for a keypress or key combination Usage: Note: this function cannot be used to wait for modifier keys on their own
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| Generated by Epydoc 3.0.1 on Fri Apr 20 20:26:25 2012 | http://epydoc.sourceforge.net |


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| Author | Sebastien Lelong, Joep Suijs, Copyright © 2008-2009, all rights reserved. |
| Adapted-by | Joep Suijs, Albert Faber |
| Compiler | >=2.4i |
this library provides an ISR to implement a stateful i2c hardware slave. This is a jalv2 implementation of Microchip Application Note AN734. According to AN734, there are 5 possible i2c states. During ISR, each of this states are detected. This ISR provides a standard skeleton to implement an i2c hardware slaves, while client code must implement several callbacks the ISR is expecting to call while processing states. Here there are: - procedure i2c_hw_slave_on_state_1(byte in _trash): called when i2c address matches (master starts a talk) - procedure i2c_hw_slave_on_state_2(byte in rcv): called when master is writing a byte. Slave is thus receiving this byte. This callback takes this bytes as argument - procedure i2c_hw_slave_on_state_3(): called when master wants to read a byte from slave. Thus, slave should send a byte (for instance, using i2c_hw_slave_write_i2c from i2c_hw_slave.jal library) - procedure i2c_hw_slave_on_state_4(): called when master still wants to read a byte from slave. That is, master required to read (state 3) and now still want to read a byte - procedure i2c_hw_slave_on_state_5(): called when master does not want to talk to slave anymore usually a good place to reset data or slave's logic - procedure i2c_hw_slave_on_error(): called when something wrong happens. You can do what you want in this case, like resetting the PIC, log some information using usart, ... => called any cases other than states 1, 2, 3, 4 or 5
- AN734: http://www.microchip.com/stellent/idcplg?IdcService=SS_GET_PAGE&nodeId=1824&appnote=en011798
No dependency found
i2c_hw_slave_isr_handler()
i2c ISR handler. Detects states a call appropriate procedures


Transparency data
These tables give detailed information on planned local authority education spending, in a form which enables easy comparison.
PDF , 286KB , 13 pages
MS Excel Spreadsheet , 2.12MB
MS Excel Spreadsheet , 586KB
MS Excel Spreadsheet , 554KB
MS Excel Spreadsheet , 2.18MB
PDF , 110KB , 4 pages
All tables are based on planned expenditure data recorded by local authorities in their Section 251 budget statements for 2014 to 2015 as of 2 September 2014.
Further information is provided in the explanatory note and the additional information document on this page.
Published: 25 September 2014


Documentation of the nanodiffraction toolbox¶
The nanodiffraction toolbox is a set of Matlab classes and functions used for the analysis of scattering and diffraction data. It is primarily suited for the batch analysis of small-angle scattering data obtained in scanning experiments. It’s main purpose is also described in [Nicolas2017a].
Note
Nicolas et al. (2017), Scanning X-ray diffraction on cardiac tissue: automatized data analysis and processing, J Synchrotron Radiat 24, 1163-1172.


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Note: this documentation is out of date and has been replaced by official documentation in qemu.git.
The QCOW image format is one of the disk image formats supported by the QEMU processor emulator. It is a representation of a fixed size block device in a file. Benefits it offers over using raw dump representation include:
The qemu-img command is the most common way of manipulating these images e.g.
$> qemu-img create -f qcow test.qcow 4G Formating 'test.qcow', fmt=qcow, size=4194304 kB $> qemu-img convert test.qcow -O raw test.img
Each QCOW file begins with a header, in big endian format, as follows:
typedef struct QCowHeader { uint32_t magic; uint32_t version; uint64_t backing_file_offset; uint32_t backing_file_size; uint32_t mtime; uint64_t size; /* in bytes */ uint8_t cluster_bits; uint8_t l2_bits; uint32_t crypt_method; uint64_t l1_table_offset; } QCowHeader;
With QCOW, the contents of the device are stored in clusters. Each cluster contains a number of 512 byte sectors.
In order to find the cluster for a given address within the device, you must traverse two levels of tables. The L1 table is an array of file offsets to L2 tables, and each L2 table is an array of file offsets to clusters.
So, an address is split into three separate offsets according to the cluster_bits and l2_bits fields. For example, if cluster_bits is 12 and l2_bits is 9, then the address is split up as follows:
Note, the size of the L1 table is a function of the size of the represented disk image:
l1_size = ceiling (disk_size / (cluster_size * l2_size))
In other words, in order to map a given disk address to an offset within the image:
If the offset found in either the L1 or L2 table is zero, that area of the disk is not allocated within the image.
The QCOW format can represent the notion of a snapshot. That is, a QCOW image can be used to store the changes to another disk image, without actually affecting the contents of the original image.
The representation is very simple. The snapshot image contains the path to the original disk image, and the snapshot image header gives the location of the path string within the file.
When you want to read an area from the snapshot, you first check to see if that area is allocated within the snapshot image. If not, you read the area from the original disk image.
The QCOW format supports compression by allowing each cluster to be independently compressed with zlib.
This is represented in the cluster offset obtained from the L2 table as follows:
The QCOW format also supports the encryption of clusters.
If the crypt_method header field is 1, then a 16 character password is used as the 128 bit AES key.
Each sector within each cluster is independently encrypted using AES Cipher Block Chaining mode, using the sector's offset (relative to the start of the device) in little-endian format as the first 64 bits of the 128 bit initialisation vector.
Mark McLoughlin. June 21, 2006.


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Study
Rodeo 2012 Castricini 2011 Gumina 2012 Antuna 2013 Bergeson 2012 Weber 2013 Zumstein 2016
Overall (I—squared = 0.0%, p = 0.517)
P=0.767
RR (95% Cl)
—.———_ 4.32 (0.22, 86.79)
—
<>
0.35 (0.01, 8.33) 0.95 (0.06, 14.62) (Excluded) (Excluded) (Excluded) (Excluded)
1.26 (0.28, 5.67)
%
Weight
17.70 48.42
33.88
0115