git-apply - Apply a patch to files and/or to the index
git apply [--stat] [--numstat] [--summary] [--check] [--index] [--3way]
[--apply] [--no-add] [--build-fake-ancestor=<file>] [-R | --reverse]
[--allow-binary-replacement | --binary] [--reject] [-z]
[-p<n>] [-C<n>] [--inaccurate-eof] [--recount] [--cached]
[--ignore-space-change | --ignore-whitespace ]
[--whitespace=(nowarn|warn|fix|error|error-all)]
[--exclude=<path>] [--include=<path>] [--directory=<root>]
[--verbose] [<patch>...]
Reads the supplied diff output (i.e. "a patch") and applies it to files. With the --index option the patch is also applied to the index, and with the --cached option the patch is only applied to the index. Without these options, the command applies the patch only to files, and does not require them to be in a Git repository.
This command applies the patch but does not create a commit. Use git-am(1) to create commits from patches generated by git-format-patch(1) and/or received by email.
<patch>...
The files to read the patch from. - can be used to read from the standard input.
--stat
Instead of applying the patch, output diffstat for the input. Turns off "apply".
--numstat
Similar to --stat, but shows the number of added and deleted lines in decimal notation and the pathname without abbreviation, to make it more machine friendly. For binary files, outputs two - instead of saying 0 0. Turns off "apply".
--summary
Instead of applying the patch, output a condensed summary of information obtained from git diff extended headers, such as creations, renames and mode changes. Turns off "apply".
--check
Instead of applying the patch, see if the patch is applicable to the current working tree and/or the index file and detects errors. Turns off "apply".
--index
When --check is in effect, or when applying the patch (which is the default when none of the options that disables it is in effect), make sure the patch is applicable to what the current index file records. If the file to be patched in the working tree is not up-to-date, it is flagged as an error. This flag also causes the index file to be updated.
--cached
Apply a patch without touching the working tree. Instead take the cached data, apply the patch, and store the result in the index without using the working tree. This implies --index.
-3, --3way
When the patch does not apply cleanly, fall back on 3-way merge if the patch records the identity of blobs it is supposed to apply to, and we have those blobs available locally, possibly leaving the conflict markers in the files in the working tree for the user to resolve. This option implies the --index option, and is incompatible with the --reject and the --cached options.
--build-fake-ancestor=<file>
Newer git diff output has embedded index information for each blob to help identify the original version that the patch applies to. When this flag is given, and if the original versions of the blobs are available locally, builds a temporary index containing those blobs.
When a pure mode change is encountered (which has no index information), the information is read from the current index instead.
-R, --reverse
Apply the patch in reverse.
--reject
For atomicity, git apply by default fails the whole patch and does not touch the working tree when some of the hunks do not apply. This option makes it apply the parts of the patch that are applicable, and leave the rejected hunks in corresponding *.rej files.
-z
When --numstat has been given, do not munge pathnames, but use a NUL-terminated machine-readable format.
Without this option, each pathname output will have TAB, LF, double quotes, and backslash characters replaced with et, en, e", and ee, respectively, and the pathname will be enclosed in double quotes if any of those replacements occurred.
-p<n>
Remove <n> leading slashes from traditional diff paths. The default is 1.
-C<n>
Ensure at least <n> lines of surrounding context match before and after each change. When fewer lines of surrounding context exist they all must match. By default no context is ever ignored.
--unidiff-zero
By default, git apply expects that the patch being applied is a unified diff with at least one line of context. This provides good safety measures, but breaks down when applying a diff generated with --unified=0. To bypass these checks use --unidiff-zero.
Note, for the reasons stated above usage of context-free patches is discouraged.
--apply
If you use any of the options marked "Turns off apply" above, git apply reads and outputs the requested information without actually applying the patch. Give this flag after those flags to also apply the patch.
--no-add
When applying a patch, ignore additions made by the patch. This can be used to extract the common part between two files by first running diff on them and applying the result with this option, which would apply the deletion part but not the addition part.
--allow-binary-replacement, --binary
Historically we did not allow binary patch applied without an explicit permission from the user, and this flag was the way to do so. Currently we always allow binary patch application, so this is a no-op.
--exclude=<path-pattern>
Don(cqt apply changes to files matching the given path pattern. This can be useful when importing patchsets, where you want to exclude certain files or directories.
--include=<path-pattern>
Apply changes to files matching the given path pattern. This can be useful when importing patchsets, where you want to include certain files or directories.
When --exclude and --include patterns are used, they are examined in the order they appear on the command line, and the first match determines if a patch to each path is used. A patch to a path that does not match any include/exclude pattern is used by default if there is no include pattern on the command line, and ignored if there is any include pattern.
--ignore-space-change, --ignore-whitespace
When applying a patch, ignore changes in whitespace in context lines if necessary. Context lines will preserve their whitespace, and they will not undergo whitespace fixing regardless of the value of the --whitespace option. New lines will still be fixed, though.
--whitespace=<action>
When applying a patch, detect a new or modified line that has whitespace errors. What are considered whitespace errors is controlled by core.whitespace configuration. By default, trailing whitespaces (including lines that solely consist of whitespaces) and a space character that is immediately followed by a tab character inside the initial indent of the line are considered whitespace errors.
By default, the command outputs warning messages but applies the patch. When git-apply is used for statistics and not applying a patch, it defaults to nowarn.
You can use different <action> values to control this behavior:
h'-04'(buh'+03'c
nowarn turns off the trailing whitespace warning.
h'-04'(buh'+03'c
warn outputs warnings for a few such errors, but applies the patch as-is (default).
h'-04'(buh'+03'c
fix outputs warnings for a few such errors, and applies the patch after fixing them (strip is a synonym --- the tool used to consider only trailing whitespace characters as errors, and the fix involved stripping them, but modern Gits do more).
h'-04'(buh'+03'c
error outputs warnings for a few such errors, and refuses to apply the patch.
h'-04'(buh'+03'c
error-all is similar to error but shows all errors.
--inaccurate-eof
Under certain circumstances, some versions of diff do not correctly detect a missing new-line at the end of the file. As a result, patches created by such diff programs do not record incomplete lines correctly. This option adds support for applying such patches by working around this bug.
-v, --verbose
Report progress to stderr. By default, only a message about the current patch being applied will be printed. This option will cause additional information to be reported.
--recount
Do not trust the line counts in the hunk headers, but infer them by inspecting the patch (e.g. after editing the patch without adjusting the hunk headers appropriately).
--directory=<root>
Prepend <root> to all filenames. If a "-p" argument was also passed, it is applied before prepending the new root.
For example, a patch that talks about updating a/git-gui.sh to b/git-gui.sh can be applied to the file in the working tree modules/git-gui/git-gui.sh by running git apply --directory=modules/git-gui.
apply.ignorewhitespace
Set to change if you want changes in whitespace to be ignored by default. Set to one of: no, none, never, false if you want changes in whitespace to be significant.
apply.whitespace
When no --whitespace flag is given from the command line, this configuration item is used as the default.
If the patch contains any changes to submodules then git apply treats these changes as follows.
If --index is specified (explicitly or implicitly), then the submodule commits must match the index exactly for the patch to apply. If any of the submodules are checked-out, then these check-outs are completely ignored, i.e., they are not required to be up-to-date or clean and they are not updated.
If --index is not specified, then the submodule commits in the patch are ignored and only the absence or presence of the corresponding subdirectory is checked and (if possible) updated.
Part of the git(1) suite


Self Assessment end-to-end service guide
Version 1.0 issued 9 May 2019
This guide explains how you can integrate your software with our APIs to help complete and submit Self Assessment returns. It shows how the APIs fit into various end-to-end user journeys.
The intended audience is software developers, designers, product owners and business analysts.
The user journeys in this guide are only examples - other use cases are also possible.
This guide relates to the annual Self Assessment process, not the Making Tax Digital quarterly Income Tax process.
Overview
Self Assessment is a system we use to collect Income Tax.
Tax is usually deducted automatically from wages, pensions and savings via Pay As You Earn (PAYE). People and businesses with other income must report it in a Self Assessment tax return.
Some people and businesses submit their own Self Assessment return. Others have an agent who submits it on their behalf.
It is possible to submit Self Assessment returns using commercial software. To support this, we provide APIs to:
- get details from us to help complete a Self Assessment return
- submit a Self Assessment return
To use these APIs, the end user must:
- be an individual or an agent
- be registered for Self Assessment as an individual or an agent
- have an appropriate HMRC online account
End-to-end user journeys
These journeys show examples of how to use the APIs:
These journeys are only examples - other use cases are also possible.
Related API documentation
Help complete a Self Assessment return
- Individual PAYE API - currently in private beta
- Individual Benefits API
- Individual Employment API
- Individual Income API
- Individual Tax API
- National Insurance API
- Marriage Allowance API
Submit a Self Assessment return
Changelog
Version 1.0
9 May 2019
What changed:
- Guide first issued


Package description |
|
|---|---|
pdqr: Work with Custom Distribution Functions |
|
Create |
|
Create new pdqr-function |
|
|
|
Get metadata of pdqr-function |
Convert |
|
Convert to pdqr-function |
|
Diagnose pdqr approximation |
|
Transform |
|
Create a pdqr-function for distribution of sample estimate |
|
Form mixture of distributions |
|
Change center and spread of distribution |
|
Change grid of pdqr-function |
|
Change support of pdqr-function |
|
Change type of pdqr-function |
|
Smooth pdqr-function |
|
Transform tails of distribution |
|
Transform pdqr-function |
|
Summarize |
|
|
|
Summarize distribution with center |
Summarize pair of distributions with classification metric |
|
Summarize pair of distributions with distance |
|
Summarize distribution with entropy |
|
Summarize distribution with Highest Density Region |
|
Summarize distribution with interval |
|
Summarize distribution with moment |
|
Summarize list of pdqr-functions with order |
|
Summarize boolean distribution with probability |
|
Summarize distribution with p-value |
|
Summarize distribution with quantiles |
|
Summarize distributions with ROC curve |
|
Summarize distributions with separation threshold |
|
|
|
Summarize distribution with spread |
Regions |
|
|
|
Work with regions |
Methods for other functions |
|
Pdqr methods for S3 group generic functions |
|
|
|
Pdqr methods for base plotting functions |
Pdqr methods for print function |
|
Other |
|
Represent pdqr-function as a set of points |


Here's a webpack.config.js file that does the trick.
TODO: explain webpack concepts so somebody new to the tool (and perhaps even npm) can know what's going on. This may be impossible since even the webpack team can't seem to do this.
const webpack = require('webpack');
const path = require('path');
module.exports = {
target: 'node',
entry: {
//the keys are the [name] in filename below
//the values are relative to this file
getSales: './getSales.js',
setSales: './setSales.js'
},
output: {
path: path.resolve('./'),
filename: './[name].bundle.js',
library: 'strat-library',
libraryTarget: 'umd'
},
plugins: [
// this bit is important--strat is a dependency injected at runtime
new webpack.IgnorePlugin(/strat/gi)
],
};
Since webpack is run by javascript hypebeasts it gets very upset unless you run it in the latest and greatest way:
You'll need npm 5+
npx webpack


A library for creating simple interactive maps with panning and
zooming, ipyleaflet supports annotations such as polygons,
markers, and more generally any geojson-encoded geographical
data structure.
Example
{% highlight python %}{% include_relative assets/ipyleaflet-example.py %}{% endhighlight %}
Installation
With conda:
{% highlight bash %}conda install -c conda-forge ipyleaflet{% endhighlight %}
With pip:
{% highlight bash %}pip install ipyleaflet
jupyter nbextension enable --py --sys-prefix ipyleaflet{% endhighlight %}
If you are using JupyterLab, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-leaflet{% endhighlight %}
A Jupyter widget to interactively view molecular structures and trajectories.
Example
{% highlight python %}{% include_relative assets/nglview-example.py %}{% endhighlight %}
Installation
With conda:
{% highlight bash %}conda install -c bioconda nglview{% endhighlight %}
With pip:
{% highlight bash %}pip install nglview
jupyter nbextension enable --py --sys-prefix nglview{% endhighlight %}
K3D lets you create 3D plots backed by WebGL with
high-level API (surfaces, isosurfaces, voxels, mesh, cloud points, vtk objects, volume renderer,
colormaps, etc). The primary aim of K3D-jupyter is to be easy for use as stand alone package like
matplotlib, but also to allow interoperation with existing libraries as VTK. The power of ipywidgets
makes it also a fast and performant visualisation tool for HPC computing e.g. fluid dynamics.
Showcase gallery: https://k3d-jupyter.readthedocs.io/en/latest/showcase/index.html.
Example
{% highlight python %}{% include_relative assets/k3d-example.py %}{% endhighlight %}
Installation
With pip:
{% highlight bash %}pip install k3d
jupyter nbextension enable --py --sys-prefix k3d{% endhighlight %}
If you are using JupyterLab, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager k3d{% endhighlight %}
A 2-D interactive data visualization library implementing the
constructs of the grammar of graphics, bqplot provides a simple
API for creating custom user interactions.
Example
{% highlight python %}{% include_relative assets/bqplot-example.py %}{% endhighlight %}
Installation
With conda:
{% highlight bash %}conda install -c conda-forge bqplot{% endhighlight %}
With pip:
{% highlight bash %}pip install bqplot
jupyter nbextension enable --py --sys-prefix bqplot{% endhighlight %}
If you are using JupyterLab, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager bqplot{% endhighlight %}
A 3-D visualization library enabling GPU-accelerated computer
graphics in Jupyter.
Example
{% highlight python %}{% include_relative assets/pythreejs-example.py %}{% endhighlight %}
Installation
With conda:
{% highlight bash %}conda install -c conda-forge pythreejs{% endhighlight %}
With pip:
{% highlight bash %}pip install pythreejs
jupyter nbextension enable --py --sys-prefix pythreejs{% endhighlight %}
If you are using JupyterLab, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-threejs{% endhighlight %}
3-D plotting for Python in the Jupyter notebook based on IPython widgets using WebGL.
Example
{% highlight python %}{% include_relative assets/ipyvolume-example.py %}{% endhighlight %}
Installation
With conda:
{% highlight bash %}conda install -c conda-forge ipyvolume{% endhighlight %}
With pip:
{% highlight bash %}pip install ipyvolume
jupyter nbextension enable --py --sys-prefix ipyvolume{% endhighlight %}
If you are using JupyterLab, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-threejs ipyvolume{% endhighlight %}
BeakerX includes widgets
for interactive tables, plots, forms, Apache Spark, and more.
The table widget automatically recognizes pandas dataframes
and allows you to search, sort, drag, filter, format,
select, graph, hide, pin, and export to CSV or
clipboard. This makes connecting to spreadsheets quick and
easy.
The table widget, shown below, is so fast because it's implemented with the PhosphorJS Data Grid,
part of Jupyter Lab's architecture.
Example
{% highlight python %}{% include_relative assets/beakerx-example.py %}{% endhighlight %}
Installation
With conda:
{% highlight bash %}conda install -c conda-forge beakerx ipywidgets{% endhighlight %}
With pip:
{% highlight bash %}pip install beakerx
beakerx-install{% endhighlight %}
Gmaps lets you
embed interactive Google maps in Jupyter notebooks. Visualize
your data with heatmaps, GeoJSON, symbols and markers, or plot
directions, traffic, or cycle routes. Let users draw on the map
and capture the coordinates of the markers or polygons they are
placing to build interactive applications entirely in Python.
Example
{% highlight python %}{% include_relative assets/jupyter-gmaps-example.py %}{% endhighlight %}
Installation
With conda:
{% highlight bash %}conda install -c conda-forge gmaps{% endhighlight %}
With pip:
{% highlight bash %}pip install gmaps
jupyter nbextension enable --py --sys-prefix gmaps{% endhighlight %}
If you are using JupyterLab, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager{% endhighlight %}
The Jupyter widget framework is extensible and enables developers to create custom
widget libraries and bindings for visualization libraries of the JavaScript ecosystem.
The cookiecutter
project helps widget authors get up to speed with the
packaging and distribution of Jupyter interactive widgets.
It produces a base project for a Jupyter interactive widget library following the current best practices.
An implementation for a placeholder "Hello World" widget is provided. Following these practices will
help make your custom widgets work in static web pages (like the examples of this page) and be compatible
with future versions of Jupyter.


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public class ServerImpl extends java.lang.Object implements Server
Server
, for creation by transports.
Expected usage (by a theoretical TCP transport):
public class TcpTransportServerFactory {
public static Server newServer(Executor executor, HandlerRegistry registry,
String configuration) {
ServerImpl server = new ServerImpl(executor, registry);
return server.setTransportServer(
new TcpTransportServer(server.serverListener(), configuration));
}
}
Starting the server starts the underlying transport for servicing requests. Stopping the server stops servicing new requests and waits for all connections to terminate.
| Constructor and Description |
|---|
ServerImpl(java.util.concurrent.Executor executor,
HandlerRegistry registry)
Construct a server.
|
| Modifier and Type | Method and Description |
|---|---|
void |
awaitTerminated()
Waits for the server to become terminated.
|
boolean |
awaitTerminated(long timeout,
java.util.concurrent.TimeUnit unit)
Waits for the server to become terminated, giving up if the timeout is reached.
|
boolean |
isShutdown()
Returns whether the server is shutdown.
|
boolean |
isTerminated()
Returns whether the server is terminated.
|
ServerListener |
serverListener()
Listener to be called by transport factories to notify of new transport instances.
|
ServerImpl |
setTransportServer(com.google.common.util.concurrent.Service transportServer)
Set the transport server for the server.
|
ServerImpl |
shutdown()
Initiates an orderly shutdown in which preexisting calls continue but new calls are rejected.
|
ServerImpl |
shutdownNow()
Initiates a forceful shutdown in which preexisting and new calls are rejected.
|
ServerImpl |
start()
Bind and start the server.
|
public ServerImpl(java.util.concurrent.Executor executor, HandlerRegistry registry)
setTransportServer(Service)
must be called before starting the
server.executor
- to call methods on behalf of remote clientsregistry
- of methods to expose to remote clients.public ServerImpl setTransportServer(com.google.common.util.concurrent.Service transportServer)
transportServer
should be in state NEW and not
shared with any other Server
s; it will be started and managed by the newly-created
server instance. Must be called before starting server.public ServerListener serverListener()
public ServerImpl start()
this
objectjava.lang.IllegalStateException
- if already startedpublic ServerImpl shutdown()
public ServerImpl shutdownNow()
isTerminated()
will likely
return false
immediately after this method returns.
NOT YET IMPLEMENTED. This method currently behaves identically to shutdown().
public boolean isShutdown()
shutdown()
,
isTerminated()
public boolean awaitTerminated(long timeout, java.util.concurrent.TimeUnit unit) throws java.lang.InterruptedException
isTerminated()
.java.lang.InterruptedException
public void awaitTerminated() throws java.lang.InterruptedException
java.lang.InterruptedException
public boolean isTerminated()
isShutdown()


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Join us any evening for any refreshing elixers and maybe a game or two of Dance Dance Revolution.Wireless acces is always provided by us. Bring your own web servers. Directions to us


The first step is coarse and fine universe selection. Using coarse selection, we create an investment universe with all non-financial U.S. stocks listed on NYSE, AMEX, and NASDAQ that contain fundamental data. The universes will be saved so that we can perform analysis on the annual changes of the total assets in the following year.
def CoarseSelectionFunction(self, coarse): if self.yearly_rebalance: filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData) and (x.Market == "usa")] return filtered_coarse else: return [] def FineSelectionFunction(self, fine): if self.yearly_rebalance: fine = [x for x in fine if x.FinancialStatements.BalanceSheet.TotalAssets.Value > 0 and ((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE")) and (x.CompanyReference.IndustryTemplateCode!="B") and (x.CompanyReference.IndustryTemplateCode!="I")] if not self.previous_fine: self.previous_fine = fine self.yearly_rebalance = False return [] else: self.filtered_fine = self.Calculate(fine,self.previous_fine) sorted_filter = sorted(self.filtered_fine, key=lambda x: x.delta_assets) self.filtered_fine = [i.Symbol for i in sorted_filter] self.previous_fine = fine return self.filtered_fine else: return []
During fine universe selection we take the stocks from the previous and current year and calculate their total asset growth. The stocks are then sorted in ascending order based upon the implemented calculation. Note that we account for size difference across the sample firms by scaling the growth by last year's total assets.
def Calculate(self, current, previous): growth = [] for stock_data in current: try: prev_data = None for x in previous: if x.Symbol == stock_data.Symbol: prev_data = x break stock_data.delta_assets = (float(stock_data.FinancialStatements.BalanceSheet.TotalAssets.Value)-float(prev_data.FinancialStatements.BalanceSheet.TotalAssets.Value))/float(prev_data.FinancialStatements.BalanceSheet.TotalAssets.Value) growth.append(stock_data) except: pass return growth
In OnData()
, we short top decile of the stocks in the sorted list and long the bottom decile of stocks. The portfolio is rebalanced every year at the end of June.


Arsenal hero explains reason Liverpool are so far ahead of title rivals City
Arsenal legend Lee Dixon believes the absence of Aymeric Laporte is the main reason Manchester City have slipped so far behind Premier League title rivals Liverpool.
Pep Guardiola’s side, who are bidding to win a third successive league title, fell nine points and three places behind leaders Liverpool after losing at Anfield before the international break.
France defender Laporte only made four appearances for City this season before suffering an injury that will keep him sidelined until 2020.
And Arsenal hero Dixon said: ‘I’m trying to put it down to one thing, I would probably say Laporte getting injured for me is the biggest factor.
‘I think he is a brilliant defender, I think he holds down that left-hand side of the defence, whoever is playing at left-back he can kind of control that side of the pitch and obviously his centre-half on the inside.
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‘So that’s a big area of pitch to replace somebody.
‘You can put a body in there and I think that’s been a problem at centre-back as is well-documented, with Fernandinho going in there who’s a brilliant footballer but he’s not got that sixth sense of somebody just nicking in-behind him off his shoulder.

Dixon, speaking on The Season So Far show, added: ‘I think Laporte, his absence, has caused a real problem with that area of the pitch.
‘And then taking Fernandinho out of [midfield] to put him elsewhere, there’s a kind of domino effect in the team.
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‘With the ball I don’t think they’re that different, they’re still brilliantly exciting to watch. But I think defensively that one area is affected a lot of the team and that’s probably why.’
Following the international break, defending champions City will look to return to winning ways against Frank Lampard’s in-form Chelsea side.


Check-in:
Fri, 19 Feb 2020
Total:
đ20M
Check-out:
Fri, 19 Feb 2020
| Sun | Mon | Tue | Wed | Thu | Fri | Sat |
|---|
| Sun | Mon | Tue | Wed | Thu | Fri | Sat |
|---|


SIMPLY IRRESISTIBLE
Reviewed by Harvey Karten, Ph.D. 20th Century Fox Director: Mark Tarlov Writer: Judith Roberts Cast: Sarah Michelle Gellar, Sean Patrick Flanery, Patricia Clarkson, Dylan Baker, Christopher Durang, Larry Gillard
One of the running gags in this movie is that men think of sex 278 times a day...that you can tell when a gentleman in contemplating its joys when he tightens his belt or puts his hands in his pockets. While no one is taking a guess on the figure attributable to women, I'd cut the digits in half and say that people think of food 139 times a day and sex the remainder. We don't wonder that sex has been the favorite theme of theater and film since Agamemnon's fooling around cost him his life and Medea's husband Jason's adulteries cost him his children. But perhaps only one out of forty movies shows people enjoying food. They may be eating to create busywork at the director's insistence, but centering a film around a meal is as rare as displaying actors in the bathroom.
We're grateful, then, that "Simply Irresistible" has come along, a paean to great food, a film that equates fine dining with sexual ecstasy. In no way, though, is Mark Tarlov's work in the same class as the 1966 stroke of genius, "Big Night," which substitutes Stanley Tucci for Sarah Michelle Gellar as that film's gourmet cook. While "Simply Irresistible" is on the right track, exchanging flotation and magic for the earthier tones of "Big Night," the film is simply too repetitious, featuring two actors who simply do not make chemistry together and, in the case of Ms. Gellar a nasal voice that is the equivalent of fingernails making their way up a blackboard.
Sorcery is in the air from the earliest scene, as Christopher Durang plays Cupid in the guise of a seller of crabs and a taxi driver. He starts the sparks flying, assuring a meeting between Amanda (Gellar) and a man who will become her gentleman caller, Tom (Sean Patrick Flanery). As with virtually every romantic comedy, the two must be made to split up each time they get together. In "Simply Irresistible" the fear of vulnerability takes the place of geographical estrangement: Tom, who is opening a large, elaborate restaurant uptown from Amanda's SoHo eatery, has worked his dating problems out on his computer and has discovered that he loses every prospective partner the fourth time out. He is as determined to avoid the mistake with Amanda as Amanda is bent on keeping his attention.
Gellar, who made a name for herself as Buffy the Vampire Slayer on TV, comes across like an 18-year-old kid to Flanery's urbane, alpha-male restaurateur. As Amanda, Gellar plays a chef whose long-established restaurant is about to go under simply because she cannot cook, but under the spell of a crab that Cupid has left in her kitchen, she has fallen in love. Her cooking suddenly takes on inspirational wizardry. She makes caramel eclairs that gives those who eat them unadulterated euphoria, while her Crabs Napoleon bring customers in by the score. When she dances with Tom, the floor takes on the qualities of a large ballroom with mirrors, filling with fog that literally transports the lovers to the ceiling. Is this what you were like when you first fell in love?
Opening shortly before Valentine's Day, "Simply Irresistible" is a date movie that probably seeks a twenty- something audience. The romance is old-fashioned, the sort that market researchers say is craved by youth now tuning out the edgy, satiric comedies about love. Its principal attractions are the side roles, particularly Larry Gilliard Jr. as Amanda assistant chef Nolan, who is easily the most amusing fellow in this slight work, while Dylan Baker, so vivid in Todd Solondz's "Happiness," is foolish to the point of embarrassment. For the most part, this eclair is no "Like Water for Chocolate."
Rated PG-13. Running Time: 100 minutes. (C) 1999 Harvey Karten
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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Related links: index of all rec.arts.movies.reviews reviews


First off, let's talk about version control and its virtues. We love this principle because it gives code a lineage, the ability to see the what, when, and why of what we've written. These characteristics are important because coding is a very dense process, and when we add guidelines and regulations to this venture, we no longer have to rely on our faulty memories to pick right back up where we left off. Of course there is the pair or group programming aspect to consider as well, once others begin reviewing, correcting, and using our code, version control becomes essential. So let's look at how GitHub and git can help us acheive.
How does git help you keep track of changes?Let's begin with git because this file management system works at the local level, and in some ways it is the precursor to GitHub. An essential tool within git is the branch feature. Branches allow us to create files temporarily in the directory we are working in. We can then experiment with the changes that we have made and only when we are happy with the end result of our code to we add new files or edits to our permanent directory. The commands for git are very simple and they allow you to quickly incorporate your temporary files as well as leave descriptive comments to help you document modifications. We use git to apply a careful and calculated approach to coding.
Why use GitHub to store your code?GitHub is a relative of git which resides in the online world. The two programs work together seamlessly, and once our git code has been commited to our local directory, we can "push" changes online for the world to see. We use GitHub because it makes collaboration and sharing code with others really easy. Also, it is free, which is very appealing!


[gecode-users] (adaptative) Incremental search
Luis Quesada
luque at info.ucl.ac.be
Thu Jan 19 23:43:23 CET 2006
Dear all,
We want to implement an (adaptative) incremental search: we want to find
one solution, then depending on the solution found and on the
interactions with the user re-define the order constraint on the fly,
and then ask the search engine to find the next one and so on.
We think we can do this by using
http://www.gecode.org/gecode-doc-latest/classGecode_1_1Restart.html.
However, we don't know how to re-define the order constraint on the fly...
Another thing that we need to do is to define our own labeling strategy.
We think the idea is to use:
http://www.gecode.org/gecode-doc-latest/classGecode_1_1ViewValBranching.html
However, we wonder whether you could provide us with a simple example
that lets us get started.
Thanks in advance for your help,
Luis
More information about the gecode-users
mailing list


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Quote #3 - Martin Fowler on Good Code (and engineering in general)
Any fool can write code that a computer can understand. Good programmers write code that humans can understand.
Even though you might admire the elegance of code which uses the extremely succinct \(i, j, k, \mathrm{fft}(x)\)... notations for variables and assignments, the truth is they are incredibly difficult to manage in a larger-than-one-file codebase. And that's not because that math is complicated but rather because you're fooling yourself for thinking that making an abstraction visually succinct will make its complexity disappear.
Just because the letter \(i\) is smaller than the words "row" or
"column" doesn't mean your brain will automatically attach meaning to
it. In reality, every time you try to understand code with this sort of
scientific notation, in your head, you will try to associate the symbols
to their meanings, which creates a ton of mental friction when trying to
understand the whole. So why not simply write "row" instead of this
dreaded \(i\) and eliminate the friction altogether?
I would add to Fowler's quote that we should not only write code that humans can understand but rather understand it easily. Developing with a team in a codebase where every line contains a bit of mental friction is not only unpleasant but unsustainable. Mad looks, wtfs, anger and resentment will quickly get even bigger than the code itself.
The quote above can also be further generalized into the importance of not only taking care of the what — the product itself — but the how — the means. If, for example, you want to develop a wheel, ideally, you wouldn't have to also develop a vehicle, like a car, in order to test it. You would simply create a rod you could spin and attach it to the wheel. This equipment would be you taking care of the most optimal way of how to develop the wheel, which, arguably, is a product in and of itself, a metaproduct.
Developing a vehicle to test the wheel may seem like good enough. It tests the product in the end anyway, doesn't it? But it's much worse than you would initially believe it to be. One big reason is that it creates a multivariate issue, i.e., you're not testing the wheel in isolation anymore, you're testing the whole system; so, if something isn't playing out as expected, how would you unambiguously say it's due to the wheel and not any other part of the vehicle?
The other reason — and the one that is much closer to Fowler's quote — is that it adds a lot of noise, inconvenience and useless stuff to what you want to test. Imagine the routine of someone who wants to test the vehicle-wheel setup: every day the person would have to make the whole thing work in order to test even the smallest of the parts. If the setup were simpler, the management cost would be that of only a simple rod.
And all this discussion is, to me, (at) the heart of engineering. After
bashing my head against many walls throughout the past years, I've come
to firmly disagree with blunt definitions like the one propagated by the
American Engineers' Council for Professional Development


MTUC seeks genuine consultation with govt
KUALA LUMPUR, Nov 10 — The Malaysian Trades Union Congress (MTUC) hopes the technical committee meeting it will attend with the Human Resources Ministry in Putrajaya tomorrow, on amendments to labour laws will be held in a spirit of genuine consultation.
Its secretary-general J. Solomon said the meeting which will be the second one on the matter, will need detailed discussions as the proposed amendments which constitute law reforms, need to be studied comprehensively.
He explained that as discussions on the Trade Unions Act 1959 and the Employment Act 1955, will be undertaken tomorrow, one day would be insufficient.
“We need to look at the holistic impact of amendments, and when amendments are made after 60 years, we surely cannot rush to make decisions...we will need time,” Solomon told Bernama at MTUC’s 70th anniversary celebrations tonight.
Prior to this, Bernama reported Human Resources Minister M. Kula Segaran as stating that the meeting which was scheduled to be held on Monday (Nov 4), had to be postponed to tomorrow.
He said the meeting would be attended by all stakeholders including MTUC, the Malaysian Employers Federation (MEF) and the National Labour Advisory Council (NLAC). — Bernama


NAF training helicopter crash in Enugu
by Agency ReportA training helicopter belonging to the Nigerian Air Force has crashed in the NAF Base in Enugu State after returning from a routine training exercise.
According to NAF twitter account on Friday in Enugu State, the crash occurred on Thursday after a routine training.
It said that although there were casualties, the cause of the incident was still sketchy and unknown.
@NigAirForce twitter that was re-twitted by Air Commodore Ibikunle Daramola, reads; “NAF helicopter suffers mishap upon landing in Enugu.
“Helicopter has crashed on landing in Enugu after a routine flight today. Fortunately, there were no fatalities or injuries to any crewmember or persons on the ground.
“@CAS_AMSadique has directed that a Board of Inquiry be constituted to determine the causes of the accident.
“@NigAirForce continues to solicit the understanding and support of the general public as it daily strives to ensure the security of Nigeria and Nigerians. Details later’’.
The News Agency of Nigeria reports that efforts to get NAF officials on the ground at the NAF Ground Training Command, Enugu State, to speak on the incident were not successful.
Those that responded to calls, who spoke on condition of anonymity, said that it was only NAF Headquarters that can authoritatively speak on the matter.
(NAN)


The term blaster was often used interchangeably with the term laser in everyday conversation, both denoting the same type of particle beam weapon. However, technically speaking, the laser was a much older weapon design than the blaster. And, unlike those of lasers, blasters' firing cycles had more proficient recharge rates, allowing them to attain a much higher rate of fire, though at a loss of range and accuracy.
There was also some dispute as to which size restrictions denoted a "blaster." For example, blasters were commonly thought of as handheld weapons only, but starship-mounted blaster cannons could fall into this category as well.
Operation
Instead of firing a coherent beam of light like the archaic laser, the blaster fired a compressed, focused, high-energy particle-beam that was very destructive, commonly referred to as a "bolt."  When the blaster was fired, a small amount of gas moved from the cartridge through the Heter valve into the gas conversion enabler chamber, commonly called the "XCiter". In this chamber, the power pack energized the gas, before it passed into the actuating blaster module, where the now extremely high-energy gas was transformed into a compressed beam of intense energy particles, coupled with intense light. The particle beam was then focused through a prismatic crystal or a similar device, which generated a deadly high-energy particle beam, fired from the emitter nozzle as a bolt of glowing energy. The color of the bolt depended on the gas used and the type of focusing device, and could vary from red to blue to orange to white.
A side effect of firing blasters was the gas conversion enabler heating up as gas was energized by the power pack, which could cause blasters to overheat, sometimes to the point of destruction. Additionally, a small amount of ozone was emitted as a trace product of the bolt emerging from the emitter nozzle, lending to blaster bolts a distinctive smell that Han Solo characterised as being like "burnt meat".
Ammunition
Most personal blasters had two kinds of ammunition: a gas cartridge and a power cell. Blaster gas cartridges could typically provide for up to five hundred shots before needing replacement. Most blaster power packs could supply energy for up to one hundred shots. Certain more powerful weapons put greater demands on the gas cartridge, power pack, or both: less powerful weapons, like the Drearian Defense Conglomerate Defender sporting blaster pistol, relied largely on the power cell and used only a small quantity of gas, while very powerful weapons, such as the illegal disruptor, used much larger amounts of gas. Still other weapons, namely those dating from earlier times, employed a kind of removable power cell/cartridge which supplied enough energy (though at weaker levels) to make total expenditure seldom. Most blasters also had a stun setting, which fed power through a secondary emitter, bypassing the gas chamber to create a ring-shaped electromagnetic burst that disrupted the target's nervous system, often leaving them unconscious.
Blaster bolts themselves carried no heat. On impact, however, their displacement of matter produced kinetic energy that causes heat: the atmosphere
was displaced by the bolt's passage (causing the blaster's iconic sound); materials struck by bolts tended to deform and fuse; and liquids
inside organic bodies instantly changed state to steam, expanding and doing terrible damage to surrounding tissue. Consequently, bolts usually
dissipated in a small, but violent, explosion of extreme heat and force upon contact with an unshielded surface, sometimes leaving blast points.
Weapons such as Han Solo's DL-44
heavy blaster pistol and the E-11
blaster rifle could cause incredible destruction, and had the power to blow
large chunks from stone walls and smaller holes and pockmarks out of durasteel bulkheads, as seen in Han Solo and Luke Skywalker's raid on
Detention Block AA-23
in 0 BBY.
"So uncivilized."
--Obi-Wan Kenobi, after killing General Grievous with the latter's own weapon.
The oldest known weapon that could be called a blaster was used by an ancient droid of an unknown model employed by the Rakatan Infinite Empire. This weapon was considered to be extremely up-to-date compared to the blasters of the year 3956 BBY.
The first blasters developed by the greater galaxy were reverse-engineered from Rakatan technology after the collapse of the Infinite Empire. These were heavy artillery weapons that combined large energy conductors with huge power generators. These early blasters were destructive but were slow to fire, temperamental, and difficult to move. As more efficient energy sources were developed, blasters became smaller and more practical, becoming anti-infantry emplacements and then handheld weapons known as beam-tubes. These consisted of backpack power generators with a hose coming out of the side that connected to a handheld focusing tube. All of the equipment that created the blaster beam was contained in the backpack and was fired through the hose. The beam-tube was the dominant weapon of the elite infantry of Xim the Despot, the Duinarbulon Star Lancers, around 25,130 BBY and came to displace heatbeams and plasma cannons (both of which used lasers to heat plasma) because they were safer to carry and required less maintenance.
Beam-tubes were replaced by pulse-wave blasters in 7811 BBY. These weapons produced a more powerful beam and had a much greater rate of fire. The pulse-wave weapons of Waymancy Hollow dramatically overpowered the Galactic Republic during the Waymancy Storm conflict until the Republic was able to reverse-engineer the Sif-Alulan process to produce pulse-wave blasters of its own.
The next innovation in blaster technology was the triple blaster, which dated back to the early Galactic Republic. It worked by using three separate weapons connected to the same targeting system, often coordinated in sets of two or four and fired simultaneously at a target. Triple blasters had become exceedingly rare by the rise of the Empire. Pulse-wave weapons were still in extensive use at the time of the Great Sith War around 4000 BBY, but lost out to the triple blaster as they were imprecise and lacked range.
Further developments in the lead up to the Mandalorian Wars led to the decline of triple blaster technology as what is traditionally thought of as the blaster was created: a self-contained unit that could produce bolts by itself with no additional equipment. Blasters were a mature technology from this point, and blasters from the Clone Wars were considered identical in operation to their successors from the Galactic Civil War twenty years later.
Blasters rapidly displaced the slugthrower as the primary infantry weapon owing to their greater ammunition capacity, power, and lighter ammunition compared to slugs. However, slugthrowers were still considered to be more useful than blasters in certain environments, such as exceptionally humid locales like jungles which might destroy a blaster's delicate internal components. Slugthrowers could also be silenced more easily than blasters and did not produce a visible bolt.
An updated version of blaster technology developed during the Clone Wars were "stealth" blasters, exemplified by the DC-19 "Stealth" carbine used by the Republic's clone shadow troopers. The DC-19 had a sound suppressor unit that allowed the user to operate the blaster in virtually complete silence, and also had an alternate stealth firing mode that used a refined mixture of tibanna gas, making the bolt invisible to the naked eye. However, the invisible tibanna mix had to be reloaded after every ten shots, and there was a required cool down time between each individual shot to protect the blaster's dampeners from overheating. A further development of this was the Nightstinger sporting blaster rifle. Another development during the Galactic Civil War was the autoblaster. Designed for the B-wing starfighter, it had an even higher rate of fire than other blasters but did not become commonly available until after the Battle of Endor.
The most common personal weapon in the galaxy by the rise of the Empire, blasters were often used not only by military personnel, but by civilians as protection. They were more commonly seen in the Outer Rim worlds, though small, concealable hold-out blasters were frequently used by social elite, such as Baron Administrator Lando Calrissian. Many establishments, such as the Cantina in Mos Eisley, had restrictions against use of blasters, though these were often overlooked if the conflict remained small.
The most common manufacturer of blasters during the Galactic Civil War was BlasTech, which had contracts with both the Imperial Military and the Alliance to Restore the Republic. It also manufactured the Protector civilian fighter-grade blaster line
Many blasters had side attachments and accessories to aid in targeting, accuracy, rate of fire, and grip. Some known attachments
included targeting lasers, electronic scopes, and larger power packs. Delta Squad's DC-17m
interchangeable blasters had sniper and
anti-armor attachments for adaptability in unpredictable situations. Boba Fett's EE-3
carbine rifle had a small scope which could
interface with Fett's helmet for increased accuracy. Jango Fett's two WESTAR-34
blaster pistols had dallorian alloy plating, which
kept the weapon from overheating. Clone trooper's DC-15A
blasters could interface with their helmets.
- Blaster pistols
- Hold-out blasters
- Stingbeams
- Sporting blasters
- Heavy blaster pistols
- Blaster rifles
- Blaster carbines
- Repeating blasters
- Blaster cannons
- Autoblasters
- Blastswords


Thanksgiving is Here – The Jay Shapiro Show [audio] 29 Nov 2019, 13:26 by Israel News Talk Radio Thanksgiving is a secular holiday with Jewish religious roots. RELATED ARTICLES


services
package services
Type Members
-
class
ApplicationTimer extends AnyRef
This class demonstrates how to run code when the application starts and stops.
This class demonstrates how to run code when the application starts and stops. It starts a timer when the application starts. When the application stops it prints out how long the application was running for.
This class is registered for Guice dependency injection in the
Module
class. We want the class to start when the application starts, so it is registered as an "eager singleton". See the code in theModule
class to see how this happens.This class needs to run code when the server stops. It uses the application's
ApplicationLifecycle
to register a stop hook. -
class
AtomicCounter extends Counter
This class is a concrete implementation of the
Counter
trait.This class is a concrete implementation of the
Counter
trait. It is configured for Guice dependency injection in theModule
class.This class has a
Singleton
annotation because we need to make sure we only use one counter per application. Without this annotation we would get a new instance every time aCounter
is injected. -
trait
Counter extends AnyRef
This interface demonstrates how to create a component that is injected into a controller.
This interface demonstrates how to create a component that is injected into a controller. The interface represents a counter that returns a incremented number each time it is called.
The
Modules
class binds this interface to theAtomicCounter
implementation.


Parallel Hydrostatic MM5 for Distributed Memory Computers
Updated December 27, 1996
Project for Collaboration between
Iowa State University Atmospheric Science/Computer Science
and
Argonne National Laboratory (Global Change Program) Mathematical and Computer Science Division
The Penn State/NCAR Mesoscale Meteorological Model (MM5) is designed for high-resolution simulations or forecasts of mesoscale atmospheric circulation with four-dimensional data assimilation [1]. This model has been used for real-time forecasting on small scales, process studies, sensitivity studies, and climate studies. The MM5 includes a finite difference formulation of the time-dependent Navier Stokes equations plus physics computations for the simulation of clouds, radiation, moist convection, etc. in a cubic three-dimensional region representing the atmosphere [3].
The MM5 has two versions: hydrostatic and non-hydrostatic. The hydrostatic version solves for the vertical velocity using the incompressible continuity equation rather than a prognostic equation. Another approximation used by the hydrostatic version is a calculation of pressure directly from the temperature field rather than from a Poisson equation. These two approximations allow the hydrostatic version to run faster, but with some sacrifice of the accuracy for horizontal grid spacing below 5 km. For grids with spacing larger than 20 km the two versions give almost identical results. Another distinct difference between the two versions is that the splitting method for the fast waves is explicitly implemented in the hydrostatic version. Numerical stability of the hydrostatic equations is severely limited by the speed of external gravity waves, and the fast moving gravity waves are a small fraction of the total energy. Therefore the hydrostatic version requires the split-explicit method.
The parallel MM5 maps the three-dimensional domain onto a
two-dimensional array of processors so that the computations in a column
of nodes are assigned to a single processor (See Figure 1).
This decomposition is known to provide the best efficiency [5].
The RSL is a portable library which provides two supporting mechanisms
for parallelization of finite difference climate models: communication
interface and index transformation
[6,7].
The library handles all details of the underlying message passing
such as buffer allocation, copying, routing, and asynchronous communication.
The RSL manages routines for decomposition of multiple nested domains and for
specifying the communication to exchange data both within each domain and
between domains.
For index transformation, the RSL removes iteration over global
horizontal indices from the original program and inserts the local horizontal
indices at runtime. The MM5 code contains the time loops within
which all variables are recalculated for each grid point, and the subroutine
solve1 contains both horizontal and vertical indices.
Figure 2 shows the RSL index transformation that separated the
vertical loop. Since the library keeps both of global and local horizontal
indices, the parallel program does not contain the local index transformation
from the global index. Note that there exist vertical loops because we use
the horizontal two-dimensional data mapping.
Our objective is to create a parallel model that is consistent with the
sequential model. The differences between output
values from the parallel and sequential models are defined as errors.
The approximate error is the mean of the absolute errors.
3.2 Validation of the parallel hydrostatic MM5
Validation was performed on two different grid sizes,
32x32x23 and 64x64x23, by comparing
meteorologically important field data such as east-west wind velocity and
temperature. The two different grid sizes on the same domain show how
different resolution affects on the accuracy.
The test domain is from the `Great Flood' of 1993 in the US Midwest
(9 July 1993). We used 64 processors on the IBM SP1.
Figure 5 shows the approximate errors of the east-west
wind velocity field (u) and temperature (T), and
Figure 6 shows the approximately errors of the pressure (ps)
and mixing ratio for water vapor temperature (qv) field.
It is observed that the differences
between the two models are less than measurement uncertainties in establishing
initial conditions.
Figures 7 and 8 show execution time
on a 32x32x23 grid and a 64x64x23 grid,
respectively, for a 24-hour weather simulation.
The number of processors was varied between 2x2, 4x4 and 8x8.
The execution time excludes file I/O and message displaying time.
We run the sequential model on a single processor of the IBM SP1.
Figure 7 shows that
the parallel model running on 64 processor IBM SP1 reduced the execution time
from 46 minutes to
less than 2 minutes for a 24-h simulation with 23,552 grid points
(32x32x23).
As shown in Figure 8, for a 24-h simulation with 94,208
grid points (64x64x23) the parallel
model running on 64 processor IBM SP1 reduced the execution time from 5 hours
to just 7 minutes.
The speedups shown in Figure 9 are better for larger
domains because of smaller communication overhead and better load balance
among processors.
The parallel model has inter-processor communication overhead not present
in the sequential model. We measure the communication overhead in each
time step to show the percentage of the communication overhead as a part
of the execution time. For measuring communication timings, we consider the
time-marching routine
(solve1) since it is the dominant routine for the dynamics and
physics computations in each time step. Table 1
shows the communication overhead for the two different domains.
The communication overhead is less than 10%.
The larger size domain (64x64x23) has
lower communication overhead since the communication amount
is increased by a factor of 2 over that for the smaller domain
(32x32x23) while the computation time increased
by a factor of 4.
Computations in MM5 can be classified either as dynamics or physics.
The dynamics includes computations for solving the basic Navier Stokes equations
and equations for
conservation of mass and conservation of energy. Subroutines for
parameterizing convective precipitation, radiation and boundary layer
processes are considered physics. While the dynamics computations are
uniformly distributed across processors at all time steps, the physics
computations show significant spatial and temporal variations in terms of the
computational load per grid column depending upon meteorological conditions
in that column. We analyze the overall load balance to show the
utilization of processors.
A load balance of 1.0 means all processors take exactly the same amount
of time, whereas a number close to zero indicates that many processors are
idle waiting for the busiest processor to finish.
Figure 9 compares the load balance of the parallel MM5 using two
different domain sizes. The load balance values decline as the number of
processors increases.
Load distribution maps of processors can be used to relate load imbalance
to the physics computations and thereby to the areas meteorological
conditions being simulated. Figure 10 compares the maps
with the rainfall maps obtained from the parallel model.
We use a 64x64x23 domain on 4x4 processors and 8x8
processors. The load balance percentages are averaged over 160 time steps
(3 simulation h). Precipitation is one of physics computations causing
load imbalance, so rainfall regions define processors
NASA funds (project NAG 5-2491) of the HPCC program provide partial
support for this research. Proposals were submitted to support this
and follow on collaborative work on MPMM between Iowa State University,
Ames Laboratory, and Argonne National Laboratory.
[1] R. A. Anthes, E. Y. Hsie and Y. H. Kuo,
Description of the Penn State/NCAR mesoscale model version 4 (MM4).
Tech. Report NCAR/TN-282+STR, National Center for Atmosphere Research,
Boulder, Colorado, 1987.
[2] I. Foster and B. Toonen, Load-Balancing Algorithms
for Climate Models Proc. 1994 Scalable High-Performance Computing Conf.,
IEEE, pp. 674-681, 1994.
[3] D. O. Grell, J. Dudhia and D. R. Stauffer,
A description of the Fifth-Generation Penn State/NCAR Mesoscale Model
(MM5) Tech. Report NCAR/TN-398+STR, National Center for Atmosphere
Research, Boulder, Colorado, 1994.
[4] I. B. M. Corp., IBM AIX parallel environment -
Parallel programming subroutine reference (2.0) Kingston, New York, 1994.
[5] K. Johnson, J. Bauer, G. Riccardi, K. Droegemeier
and M. Xue, Distributed Processing of a Regional Prediction Model
Mon. Wea. Rev., 122 (1994), pp. 2558-2572.
[6] J. Michalakes, RSL: A parallel runtime system
library for regular grid finite difference models using multiple nests
Tech. Report ANL/MCS-TM-197, MCS Division, Argonne National Laboratory,
Argonne, Illinois, 1994.
2 Parallel Implementation of MM5
2.1 Data Mapping
Figure 1: Data mapping of parallel MM5
2.2 RSL (Runtime System and Library)
Figure 2: RSL index transform
2.3 Parallelization steps
Figure 3: Description of communication points for solve1
Figure 4: Schematic representation of parallelization steps
3 Validation
3.1 Absolute error
Figure 5: The approximate errors of the east-west wind speed and temperature
Figure 6: The approximate errors of the pressure and mixing ratio for
water vapor
4 Performance Analysis
4.1 Execution time
Figure 7: Execution time for the 32x32x23 domain
Figure 8: Execution time for the 64x64x23 domain
Figure 8: Speedups on different number of processors
4.2 Communication overhead
4.3 Analysis of load balance
Load balance=Tmean/Tmax
Tmean : Execution time at a processor averaged over all processors
Tmax : Maximum execution time at a processor
Figure 9: Overall load balance of the parallel MM5 on two difference domains
<4x4 processors>
<8x8 processors>
<9-12 hour average>
<4x4 processors>
<8x8 processors>
<15-18 hour average>
<4x4 processors>
<8x8 processors>
<21-24 hour average>
<4x4 processors>
<8x8 processors>
Figure 10: Load-distribution maps for the 64x64x23 domain
Acknowledgments
References


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Isherwood Cinema
[50, 70]
|
Haynes Square
[51, 70]
|
a factory
[52, 70]
|
Elliott Grove
[53, 70]
|
Boorman Walk
[54, 70]
|
the Vigors Motel
[55, 70]
|
St Matheos's Hospital
[56, 70]
|
Minchington Crescent
[57, 70]
|
Hosken Crescent
[58, 70]
|
Hook Avenue
[59, 70]
|
Rodd Square
[50, 71]
|
Tipper Crescent
[51, 71]
|
a junkyard
[52, 71]
|
the Shortland Arms
[53, 71]
|
St George's Hospital
[54, 71]
|
the Norwood Building
[55, 71]
|
Hensler Walk
[56, 71]
|
the Kilminster Monument
[57, 71]
|
Leader Avenue
[58, 71]
|
Knapman Row
[59, 71]
|
Sanford Towers
[50, 72]
|
Beatty Boulevard School
[51, 72]
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Spreat Alley School
[52, 72]
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Neot General Hospital
[53, 72]
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the Kitchen Monument
[54, 72]
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Laimbeer Plaza
[55, 72]
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Club Crook
[56, 72]
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St Ferreol's Hospital
[57, 72]
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Chalk Street
[58, 72]
|
Huckle Way
[59, 72]
|
Cave Walk
[50, 73]
|
Crampton Road
[51, 73]
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the Bearns Arms
[52, 73]
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McNeil Towers
[53, 73]
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Courage Cinema
[54, 73]
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Barnett Boulevard Fire Station
[55, 73]
|
Titus General Hospital
[56, 73]
|
Nunn Row Fire Station
[57, 73]
|
Byewell Bank
[58, 73]
|
Sheldrake Road
[59, 73]
|
Bramwell Auto Repair
[50, 74]
|
Ware Walk
[51, 74]
|
Furneaux Lane
[52, 74]
|
Doran Square
[53, 74]
|
Tyack Plaza
[54, 74]
|
a warehouse
[55, 74]
|
the Bornard Building
[56, 74]
|
a junkyard
[57, 74]
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Barne Place
[58, 74]
|
wasteland
[59, 74]
|
the Dobbs Building
[50, 75]
|
a factory
[51, 75]
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a junkyard
[52, 75]
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Club Blackburn
[53, 75]
|
Penni Drive Fire Station
[54, 75]
|
a warehouse
[55, 75]
|
the Jillard Building
[56, 75]
|
the Whitenoll Building
[57, 75]
|
Burdett Way Police Dept
[58, 75]
|
Jenys Place
[59, 75]
|
the Rawle Monument
[50, 76]
|
Pattemore Grove Railway Station
[51, 76]
|
Mundy Alley
[52, 76]
|
wasteland
[53, 76]
|
Barnerd Way Police Dept
[54, 76]
|
Boggis Avenue
[55, 76]
|
Julie General Hospital
[56, 76]
|
Ebbutt Road
[57, 76]
|
a junkyard
[58, 76]
|
Rolle Library
[59, 76]
|
Cridlin Drive
[50, 77]
|
Crawford Towers
[51, 77]
|
Whittern Towers
[52, 77]
|
wasteland
[53, 77]
|
a carpark
[54, 77]
|
Pipe Plaza
[55, 77]
|
Southall Place
[56, 77]
|
Firth Alley
[57, 77]
|
wasteland
[58, 77]
|
the Dike Building
[59, 77]
|
Cayley Walk
[50, 78]
|
Nunn Alley
[51, 78]
|
Cheal Lane
[52, 78]
|
a warehouse
[53, 78]
|
Bant Park
[54, 78]
|
Rowson Library
[55, 78]
|
Otto Square
[56, 78]
|
Styles Bank
[57, 78]
|
Wheaton Square
[58, 78]
|
St Luke's Church
[59, 78]
|
Carpendale Bank
[50, 79]
|
Cundham Drive
[51, 79]
|
Purt Walk Fire Station
[52, 79]
|
Keppel Boulevard
[53, 79]
|
Club Tremlett
[54, 79]
|
Wawer Cinema
[55, 79]
|
Alsoop Avenue
[56, 79]
|
wasteland
[57, 79]
|
Galloway Park
[58, 79]
|
a cemetery
[59, 79]
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Research Log:
Period 4 February
I heard an infernal racket outside today, and sent out a Probe Golem to investigate...
I'll wager those adena-grubbing dogs are drooling all over the Northern Mine. If it's not one thing it's another!
Of course the worst of the Dwarves haven't shown up yet, but...
Not only the Gray Pillar, but the Collectors and the Bronze Key are here! I heard some Dwarves knocking on the walls trying to find a way in...
It's only a matter of time until I'm discovered.
Turn the page


If you leave a glass of water somewhere and come back a few days later, some or all of the water will be gone. Here is a timelapse of the water slowly disappearing.
The water is turning from liquid to gas form, which is called evaporating.
Here is a model of a glass. Fill it with particles!
I’ve put the thermostat on really cold, so the “water” is now frozen solid. Heat it up!
As the contents of the glass melts, the particles start tumbling around. Sometimes, this random tumbling will give one of the surface particles a lot of kinetic energy. If the energy, and therefore the speed, is great enough, the particle will escape the glass! (You might have to wait a little while for this to happen.)
This also means that the rest of the particles will lose energy and get a little colder. But the temperature difference with the rest of the room will soon even out again.


Learn To Code Change it
Learning to code is simple with html and react for browser and simple pages.
Checkout examples from this Website:
https://gist.github.com/gaearon
Add React in One Minute
This page demonstrates using React with no build tooling.
React is loaded as a script tag.
This is the first comment.
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React Section


Well, I'm not a regular user and certainly not an addict. But I also know that when I do it and get lit up, I have to do it smart. People are going to always use drugs and if they do they should take precautionary measures to make sure you get a great high, but not deplete your body of energy and nutrients so badly you become emaciated and an addict.
Once a month or so, I decide to get lit up. I'm not your typical user as I am not white, I am upper-middle class and Asian. I used to do coke but coming down from that was like the worst depression and paranoia of all my drug experiences. Plus its so expensive and you need to do more of it, more often, since technically and physiologically the high lasts only 20 minutes.
I tried speed about three years ago at a party. Noticing there were differences in colors from a brown sugar look to white powder and all yellowish colors mixed in between I took the browner powder and it burned my nose so bad I thought my nose hairs had fallen out. It definitely gave a rush like cocaine, but it was different...I liked it better. It was actually fairly mild and gradual. Having a hyper personality to begin with speed, like ritalin, actually slows me down. Most people I know who ingest it or snort it become hyper active clowns!! I become much more able to think clearly, move more deliberately, and become very focused. So maybe its true....ritalin and Adderall work on hyperactive minds like mine and it allowed me to think very clearly and not so quick on the draw.
I began to throw up later in the evening and din't like the fact that for the next four days I was up...and no matter what I did I couldn't sleep.
Then a friend showed me the right way to do it. Here's my recipe for a better and healthier speed experience:
- I always eat a good, healthy meal beforehand since it kills the appetite afterwards.
- I take a regular vitamin and then extra vitamin B 12 because any stimulant (as well as stress) depletes your body of B 12. I take one calcium supplement and drink lots of fluids.
- Then I take three regular aspirin so that my blood pressure won't surge, as aspirin thins the blood, lowering my chances of a cardiac arrest or something.
- Then I do some lines, as I need them, moderately, all the while drinking lots of gatorade because now I can't eat.
The key is to replenish what is lost. Brush your teeth frequently...at least 3 times because we all know it leaves that pasty rotten taste, not to mention it can rot your teeth over time ...
Then I'm also doing the white powder speed....a much smoother ride and does not make me as jittery. I will take a few more aspirins and then after being lit up...again, speed has the opposite effect on me...sure it gives me a rush, like a big intake of air, and makes my brain clear and sharp and actually makes me able to control my hyper, energetic behavior. I move more slowly and deliberately on it and find myself communicating to others in a way I might not be able to.
Limit thyself, replenish with fluids and vitamins, and just do it moderately. Why get off at 150th street when you can just get off at 10th street? Moderation of usage and proper replenshing of the body's depleted nutrients and fluids are the key.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Working with scala collections
Aug 16, 2015
One of the first things that makes people feel attracted to scala is its powerful collection’s library. For those developers coming from Java or C# backgrounds, having powerful collections at hand should not come as a surprise. But for others coming from C or PHP backgrounds it might open a world of possibilities that were not available to them before.
Immutability
For me, the cornerstone of scala collections is the fact that they are immutable. An immutable collection looks pretty much like any other collection, but the operations applied to them don’t change the original collection’s state but return a new collection instead:
// new immutable list with 4 elements
val myList = List(1, 2, 3, 4)
// append integer 5 to the list
val modifiedList = myList :+ 5
// size is 4
myList.size
// size is 5
modifiedList.size
Methods on immutable collections are side effect free. You can always assume that their state won’t change at any given point in time during your program’s execution, which makes them simpler to work with and less error prone.
Scala works by default with immutable collections, but if you really need to work with mutable ones, you can also do so. You only need to import the mutable collection’s package:
import scala.collection.mutable
Standard collections
The standard library comes with a good amount of collections, organized in the following hierarchy:
All of them inherit from Traversable and Iterable. This allows to loop through them using ‘foreach’ or with an iterator, to count their elements, to apply maps and reduces and all the other set of available functions.
Collection methods return the types that makes more sense to the operation applied to that collection. For instance, when operating on a List or a Set, we’ll get the same type when applying methods on them:
// flattening a list of lists results in a list
scala> List(List(1), List(2), List(3)).flatten
res0: List[Int] = List(1, 2, 3)
// flattening a set of sets results in a set
scala> Set(Set(1), Set(2), Set(3)).flatten
res1: scala.collection.immutable.Set[Int] = Set(1, 2, 3)
// the operation will result on the outer collection type
scala> List(Set(1), Set(2), Set(3)).flatten
res2: List[Int] = List(1, 2, 3)
Conversions
What we just saw doesn’t mean that once you start working with one collection type you’re bound to it for life. You can convert from one type of collection to another using converters:
// from List to Set
scala> List(1, 2, 3, 4).toSet
res0: scala.collection.immutable.Set[Int] = Set(1, 2, 3, 4)
// from Map to List
scala> Map("a" -> 1, "b" -> 2).toList
res1: List[(String, Int)] = List((a,1), (b,2))
// not all conversions work straight away
scala> List(1, 2).toMap
<console>:8: error: Cannot prove that Int <:< (T, U).
List(1, 2).toMap
// but if we add the pairs to the keys...
scala> List((1, 1), (2, 2)).toMap
res2: scala.collection.immutable.Map[Int,Int] = Map(1 -> 1, 2 -> 2)
Which one should I use?
I’ve met many developers who just fall in love with a single data structure and they use it always, everywhere. This actually defies the purpose of having such a rich collections library, since each of its types are better in certain scenarios and worse in others.
Sequences
The main properties of sequences is that they have a length and their elements’ positions are indexed. Within the Sequence trait there are two subtraits: LinearSeq and IndexedSeq. The former is more optimized towards operations over its head and tail, whereas the latter has better performance on apply and length.
This makes LinearSeq like Lists a better candidate for those algorithms that are going to process their elements accessing the head often, or prepending elements often. On the other hand, an IndexedSeq Vector might be better candidate if methods like apply are called often on the collection since its complexity is often O(1).
Sequences have a very interesting implementation of lazy lists, called Streams, which we’ll see in more detail later.
Sets
The most important characteristic of sets is that they do not contain duplicate elements. This property makes them ideal for those scenarios where it does not really matter how many times an element is in the collection, but only if it contains the element or not.
A very absurd scenario would be to determine if an article contains a given word:
val article = "This is a very interesting article about Scala collections. This article contains the word article several times."
//transforming a String to a Set without repeated words
def hasWord(w: String): Boolean = article.split(" ").toSet.contains(w)
scala> hasWord("article")
res0: Boolean = true
scala> hasWord("Java")
res1: Boolean = false
// given that String is also a collection, we could have done it directly, but this was just an illustrative example
scala> article.contains("article")
res2: Boolean = true
Beware!! There’s quite a big gotcha when dealing with very small Sets which I stomped on and could not figure out. Check this:
scala> val s1 = Set(1, 2, 3, 4, 5)
scala> val s2 = Set(5 ,4 ,3 ,2 ,1)
scala> s1 sameElements s2
res0: Boolean = true
All seems to work just fine, right? The elements are inserted in different order to the Set but they are, at the end of the day, the same elements. Now look at this:
scala> val s1 = Set(1, 2, 3)
scala> val s2 = Set(3, 2, 1)
scala> s1 sameElements s2
res0: Boolean = false
Wait, what!?
As it happens, the internal representation of a Set is one when it contains up to 4 elements and another one for 4 and more elements. As a result of that, the internal iterators used to make the comparison produce the elements in the order that they were originally added, hence failing the comparison.
Maps
The third collection trait is Map. The main property of maps is that they are represented using key - value pairs. This makes them very fast for lookups on known keys, pretty much like indexed sequences.
Typical scenarios where you’ll find maps are to represent countries and their capitals, train stations and the zone they are in, etc. In this example, we are mapping each floor of a building to their list of neighbours:
case class Neighbour(val name: String)
val building = Map(
"First Floor" -> List(Neighbour("Angela"), Neighbour("Mark")),
"Second Floor" -> List(Neighbour("Mikel"), Neighbour("Sara"),
"Penthouse" -> List(Neighbour("Pam"), Neighbour("Sandra"))
)
scala> building("Penthouse")
res0: List[Neighbour] = List(Neighbour(Pam), Neighbour(Sandra))
Streams
Streams are a type of collection that I think deserve their own section.
 Hence, they are part of the LinearSeq trait. The theory behind them tell us that the tail of a stream is only computed when needed. What this means in practice, is that you can have infinitely long Streams, such as a collection representing all natural or prime numbers.
It’s probably easier to understand with an example. Lets say that we want the first 10 natural numbers that are divisible by 7:
// we define a stream which contains all natural numbers
def naturals: Stream[Int] = {
def iterate(n: Int): Stream[Int] = n #:: iterate(n + 1)
iterate(0)
}
// then we simply pick the first 10 elements that are divisible by 7
scala> naturals filter (_ % 7 == 0) take 10 toList
res0: List[Int] = List(0, 7, 14, 21, 28, 35, 42, 49, 56, 63)
Gosh this is powerful! With these techniques, it’s possible to do computations over absurdly large sets of data, or even upon theoretical sets of data. How cool is that?
The “trick” here is on the implementation of the cons operator. This operator is used to build Streams, concatenating its elements. The implementation goes like this:
// the list tail 't1' is a call by name parameter!
def cons[T](hd: T, t1: => Stream[T]) = new Stream[T] {
def isEmpty = false
def head = hd
def tail = tl
}
Being the tail a call by name parameter, it does not get evaluated until it is actually accessed.
Performance
One always has to keep an eye on performance though. There is “danger” when using these collections naively. We need to be aware how to better traverse them to get the most out of them. For instance, in the previous example, in order to obtain the first 10 elements that are divisible by 7 from the naturals’ stream we’re iterating over the stream one by one and applying a check on each of them, only selecting those that are divisible by 7.
That means that we’re going to go through 64 elements before being done.
If we think on the relationship between natural numbers and the property of “being divisible by”, we can reach the same result as before if we multiply each natural number by the number that we’re looking to be divisible for, in this case 7.
scala> naturals map (_ * 7) take 10 toList
res0: List[Int] = List(0, 7, 14, 21, 28, 35, 42, 49, 56, 63)
This will result on a quicker execution, since only the first 10 elements of the naturals’ stream will be evaluated in order to yield the expected result.
Wrapping up
In summary. I like the scala collection’s library a lot. I think it is both powerful and with well designed APIs.
I also think it is important to understand what each collection is meant for, and also it doesn’t hurt to dig deeper into their implementations to know both how they are built and their limitations.
I’m now trying to come up with a small enough sized project to put all these collections at a good use and explore them further. I’ll keep you posted!
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GPGPU-Sim
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#include <XML_Parse.h>
Public Attributes | |
| int | number_entries |
| int | cache_policy |
| double | total_hits |
| double | total_accesses |
| double | total_misses |
| double | conflicts |
Definition at line 116 of file XML_Parse.h.
| int itlb_systemcore::cache_policy |
Definition at line 118 of file XML_Parse.h.
| double itlb_systemcore::conflicts |
Definition at line 122 of file XML_Parse.h.
| int itlb_systemcore::number_entries |
Definition at line 117 of file XML_Parse.h.
| double itlb_systemcore::total_accesses |
Definition at line 120 of file XML_Parse.h.
| double itlb_systemcore::total_hits |
Definition at line 119 of file XML_Parse.h.
| double itlb_systemcore::total_misses |
Definition at line 121 of file XML_Parse.h.


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The Best Players In Europe Revealed: CR7 Not in Top 20
by David KappelThe best-performing players across Europe's top 35 leagues in 2019/20 have been revealed, and while Lionel Messi tops the ranking ahead of an African superstar, Cristiano Ronaldo does not make the top 20.
Tired of reading? Click through the gallery above to see Europe's top performers this season, according to CIES and InStat.
The CIES Football Observatory and InStat recently developed an exclusive index to assess the performances of football players on an objective basis.
FC Barcelona captain Messi, who recently claimed the 2019 Ballon d'Or, leads the ranking with 401 index points, followed by Ajax Amsterdam's Hakim Ziyech with 377 points.
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Paris Saint-Germain's Kylian Mbappe completes the podium with 366 points, while Manchester City's Riyad Mahrez appears in fourth spot with 362.
PSG duo Angel Di Maria and Neymar are fifth and sixth with 360 and 358 points, respectively, while Red Bull Leipzig's Timo Werner is the highest-ranked Bundesliga player with 357 points.
Lazio forward Ciro Immobile is the top performer in Serie A so far, having collected 355 points, while Manchester City's Kevin De Bruyne, with 354 points, and Liverpool left-back Andrew Robertson, with 353 points, complete the top 10.
Read: Best XI To Never Win The Ballon d'Or
Further down the ranking is Ronaldo in 29th position with 332 points, which sees him occupy fifth spot among the best performers in Serie A.