Class CommandPrompt
A command-line helper class that makes it easy to run commands.
Implements
Inherited Members
Namespace: DotNetHelper_CommandLine
Assembly: DotNetHelper-CommandLine.dll
Syntax
public class CommandPrompt : IDisposable
Constructors
| Improve this Doc View SourceCommandPrompt()
Declaration
public CommandPrompt()
CommandPrompt(Boolean)
Declaration
public CommandPrompt(bool hideWindow)
Parameters
| Type | Name | Description |
|---|---|---|
| Boolean | hideWindow |
CommandPrompt(String, String, Boolean)
Declaration
public CommandPrompt(string runAsUser, string password, bool hideWindow)
Parameters
| Type | Name | Description |
|---|---|---|
| String | runAsUser | |
| String | password | |
| Boolean | hideWindow |
Properties
| Improve this Doc View SourceCreateNoWindow
Declaration
public bool CreateNoWindow { get; set; }
Property Value
| Type | Description |
|---|---|
| Boolean |
RunAsUser
Declaration
public string RunAsUser { get; }
Property Value
| Type | Description |
|---|---|
| String |
Methods
| Improve this Doc View SourceCreateSecurePassword(String)
Declaration
public SecureString CreateSecurePassword(string password)
Parameters
| Type | Name | Description |
|---|---|---|
| String | password |
Returns
| Type | Description |
|---|---|
| SecureString |
CreateStartInfo(String, String, Boolean)
Declaration
public ProcessStartInfo CreateStartInfo(string command, string workingDirectory = "./", bool hideWindow = true)
Parameters
| Type | Name | Description |
|---|---|---|
| String | command | the command to run |
| String | workingDirectory | sets the working directory for the command to be run |
| Boolean | hideWindow |
Returns
| Type | Description |
|---|---|
| ProcessStartInfo | a new instance of ProcessStartInfo |
Dispose()
Declaration
public void Dispose()
RunCommand(String, String)
Starts a new instance of a command terminal and runs the specified command
Declaration
public Process RunCommand(string command, string workingDirectory = "./")
Parameters
| Type | Name | Description |
|---|---|---|
| String | command | the command to run |
| String | workingDirectory | sets the working directory for the command to be run |
Returns
| Type | Description |
|---|---|
| Process | the process |
RunCommandAndWaitForExit(String, String, Nullable<TimeSpan>)
Starts a new instance of a command terminal and runs the specified command and wait for it to exit
Declaration
public (Process process, bool? didProcessExit) RunCommandAndWaitForExit(string command, string workingDirectory = "./", TimeSpan? timeout = default(TimeSpan? ))
Parameters
| Type | Name | Description |
|---|---|---|
| String | command | the command to run |
| String | workingDirectory | sets the working directory for the command to be run |
| Nullable<TimeSpan> | timeout |
Returns
| Type | Description |
|---|---|
| ValueTuple<Process, Nullable<Boolean>> | the associated process and whether or not the process exited |
RunCommandAndWaitForExitAsync(String, String, CancellationToken)
Starts a new instance of a command terminal and runs the specified command
Declaration
public Task<Process> RunCommandAndWaitForExitAsync(string command, string workingDirectory = "./", CancellationToken cancellationToken = default(CancellationToken))
Parameters
| Type | Name | Description |
|---|---|---|
| String | command | the command to run |
| String | workingDirectory | sets the working directory for the command to be run |
| CancellationToken | cancellationToken | cancellation token for how long the process should wait to be exited |
Returns
| Type | Description |
|---|---|
| Task<Process> | the process |
UpdateDefaultUserAndPassword(String, String)
Declaration
public void UpdateDefaultUserAndPassword(string runAsUser, string password)
Parameters
| Type | Name | Description |
|---|---|---|
| String | runAsUser | |
| String | password |
Events
| Improve this Doc View SourceErrorDataReceived
event handler for error responses return during the execution of the command
Declaration
public event DataReceivedEventHandler ErrorDataReceived
Event Type
| Type | Description |
|---|---|
| DataReceivedEventHandler |
Exited
Occurs when the process exits
Declaration
public event EventHandler Exited
Event Type
| Type | Description |
|---|---|
| EventHandler |
OutputDataReceived
event handler for responses return during the execution of the command
Declaration
public event DataReceivedEventHandler OutputDataReceived
Event Type
| Type | Description |
|---|---|
| DataReceivedEventHandler |


{% load static %}
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Summary
Red Hat is an multinational software company providing open-source software products to the enterprise community
I am a Principal Software Engineer in the middleware business, focusing on MicroProfile,
SmallRye and Quarkus.
I am also an Eclipse MicroProfile committer, and part of the MicroProfile GraphQL workgroup where I help with the creation of the API, Specification and TCK
My responsibilities include looking after SmallRye GraphQL, an open-source
implementation of MicroProfile GraphQL, that I am also contributing too.
I also look after SmallRye OpenAPI, an open-source implementation of
MicroProfile OpenAPI, and more recently I got involved with Reactive Messaging
and I am working on a POC implementation of Async API.


About me
- I am a Computer science student from The Netherlands. Windows Phone has always been my favorite Mobile OS and since WhatsApp disappeared I really needed an alternative, which is why I created this application.
Features
- Whatsapp web on Windows 10 mobile
- Dark and light mode
- No ads, totally clean software
To do
Contact me
-------------------------------------------------
Click on the "refresh" button (in the bottom menu bar) to exit the "about me" page and go back to WhatsApp Web.
-By Jetspiking-


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{0} - date {1} - type {2} - name {3} - uniqueId {4} - severity |
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The earth's climate is constantly changing, with periods of warming and cooling alternating throughout history and prehistory. Scientists are able to measure the earth's climate by using temperature records for the last roughly 150 years, and using reconstruction from ice cores and seafloor cores before that. There are many factors that cause the earth's climate to change, including changes in the sun, changes in the tilt of the earth's axis, meteorites, volcanos, and changes in vegetation [<IP_ADDRESS>]. Some of these changes, like changes in the sun or the tilt of the earth's axis, happen slowly over millions of years and cause similarly slow changes in climate.
in-context


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contact information :
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language spoken :
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The images :


[Android] Unity Game Demo
Implemented with Unity and C#. This is a game I am currently working on. It uses components from the Unity framework such as RigidBody2D for physics and BoxCollider to detect collisions.
SDE II at Amazon
I am currently a Software Development Engineer II at Amazon.
At Amazon, I work on the Retention part of the retail website. My work affects all regions (i.e. www.amazon.com, www.amazon.ca, ...). On the frontend, I develop new experiences that customers see and click through. On the backend, I write microservices to power the frontend experience. For example, if an API that I need does not exist, I write it. I have also worked on the underlying web framework which runs all the Audible websites globally and created new experiences on the site. In addition, I worked on various backend services such as membership management and product catalog. I was responsible for setting up the production websites such as setting up the front load balancers, reverse proxies, auto scaling for the web application, and provisioning hosts for the web applications. I am now looking into serverless.
Implemented with Unity and C#. This is a game I am currently working on. It uses components from the Unity framework such as RigidBody2D for physics and BoxCollider to detect collisions.
Implemented with Swift using Xcode. This application uses various public APIs such as the Waterloo API, GitHub and Glassdoor to provide job openings and relevant information about them such as salary, reviews, interview process, etc.
Implemented using the Android SDK. This application is used together with a barcode scanner to allow users to scan items while shopping to create an electronic shopping list. Within this application, users can see reviews of products, insert reviews, and find items using an interactive map that lets you search for products. After scanning all the items, users can bypass lines and checkout directly at a station via transferring their created shopping list.
Implemented with Java and MySQL. This database application allows patients to find doctors based upon the recommendations of other patients. It provides an interface for doctor and patient profiles, storing a set of reviews, a social network graph, adding patients as friends, flexible doctor search, salted password and hash for security, and database security techniques such as SQL injection prevention.
Apr 2018 - Present
Software Development Engineer
Aug 2016 - Mar 2018
Software Development Engineer Intern
Sep 2015 - Dec 2015
Jan 2014 - Dec 2014
May 2013 - Aug 2013
Sep 2012 - Dec 2012


| PostgreSQL 9.0.10 Documentation | ||||
|---|---|---|---|---|
| Prev | Up | Chapter 13. Concurrency Control | Next |
PostgreSQL provides various lock modes to control concurrent access to data in tables. These modes can be used for application-controlled locking in situations where MVCC does not give the desired behavior. Also, most PostgreSQL commands automatically acquire locks of appropriate modes to ensure that referenced tables are not dropped or modified in incompatible ways while the command executes. (For example, ALTER TABLE cannot safely be executed concurrently with other operations on the same table, so it obtains an exclusive lock on the table to enforce that.)
To examine a list of the currently outstanding locks in a database server, use the pg_locks system view. For more information on monitoring the status of the lock manager subsystem, refer to Chapter 27.
The list below shows the available lock modes and the contexts in which they are used automatically by PostgreSQL. You can also acquire any of these locks explicitly with the command LOCK. Remember that all of these lock modes are table-level locks, even if the name contains the word "row"; the names of the lock modes are historical. To some extent the names reflect the typical usage of each lock mode — but the semantics are all the same. The only real difference between one lock mode and another is the set of lock modes with which each conflicts (see Table 13-2). Two transactions cannot hold locks of conflicting modes on the same table at the same time. (However, a transaction never conflicts with itself. For example, it might acquire ACCESS EXCLUSIVE lock and later acquire ACCESS SHARE lock on the same table.) Non-conflicting lock modes can be held concurrently by many transactions. Notice in particular that some lock modes are self-conflicting (for example, an ACCESS EXCLUSIVE lock cannot be held by more than one transaction at a time) while others are not self-conflicting (for example, an ACCESS SHARE lock can be held by multiple transactions).
Table-level lock modes
Conflicts with the ACCESS EXCLUSIVE lock mode only.
The SELECT command acquires a lock of this mode on referenced tables. In general, any query that only reads a table and does not modify it will acquire this lock mode.
Conflicts with the EXCLUSIVE and ACCESS EXCLUSIVE lock modes.
The SELECT FOR UPDATE and SELECT FOR SHARE commands acquire a lock of this mode on the target table(s) (in addition to ACCESS SHARE locks on any other tables that are referenced but not selected FOR UPDATE/FOR SHARE).
Conflicts with the SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE lock modes.
The commands UPDATE, DELETE, and INSERT acquire this lock mode on the target table (in addition to ACCESS SHARE locks on any other referenced tables). In general, this lock mode will be acquired by any command that modifies data in a table.
Conflicts with the SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE lock modes. This mode protects a table against concurrent schema changes and VACUUM runs.
Acquired by VACUUM (without FULL), ANALYZE, and CREATE INDEX CONCURRENTLY.
Conflicts with the ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE lock modes. This mode protects a table against concurrent data changes.
Acquired by CREATE INDEX (without CONCURRENTLY).
Conflicts with the ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE lock modes.
This lock mode is not automatically acquired by any PostgreSQL command.
Conflicts with the ROW SHARE, ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE lock modes. This mode allows only concurrent ACCESS SHARE locks, i.e., only reads from the table can proceed in parallel with a transaction holding this lock mode.
This lock mode is not automatically acquired on user tables by any PostgreSQL command. However it is acquired on certain system catalogs in some operations.
Conflicts with locks of all modes (ACCESS SHARE, ROW SHARE, ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE). This mode guarantees that the holder is the only transaction accessing the table in any way.
Acquired by the ALTER TABLE, DROP TABLE, TRUNCATE, REINDEX, CLUSTER, and VACUUM FULL commands. This is also the default lock mode for LOCK TABLE statements that do not specify a mode explicitly.

Once acquired, a lock is normally held till end of transaction. But if a lock is acquired after establishing a savepoint, the lock is released immediately if the savepoint is rolled back to. This is consistent with the principle that ROLLBACK cancels all effects of the commands since the savepoint. The same holds for locks acquired within a PL/pgSQL exception block: an error escape from the block releases locks acquired within it.
Table 13-2. Conflicting lock modes
| Requested Lock Mode | Current Lock Mode | |||||||
|---|---|---|---|---|---|---|---|---|
| ACCESS SHARE | ROW SHARE | ROW EXCLUSIVE | SHARE UPDATE EXCLUSIVE | SHARE | SHARE ROW EXCLUSIVE | EXCLUSIVE | ACCESS EXCLUSIVE | |
| ACCESS SHARE | X | |||||||
| ROW SHARE | X | X | ||||||
| ROW EXCLUSIVE | X | X | X | X | ||||
| SHARE UPDATE EXCLUSIVE | X | X | X | X | X | |||
| SHARE | X | X | X | X | X | |||
| SHARE ROW EXCLUSIVE | X | X | X | X | X | X | ||
| EXCLUSIVE | X | X | X | X | X | X | X | |
| ACCESS EXCLUSIVE | X | X | X | X | X | X | X | X |
In addition to table-level locks, there are row-level locks, which can be exclusive or shared locks. An exclusive row-level lock on a specific row is automatically acquired when the row is updated or deleted. The lock is held until the transaction commits or rolls back, just like table-level locks. Row-level locks do not affect data querying; they block only writers to the same row.
To acquire an exclusive row-level lock on a row without actually modifying the row, select the row with SELECT FOR UPDATE. Note that once the row-level lock is acquired, the transaction can update the row multiple times without fear of conflicts.
To acquire a shared row-level lock on a row, select the row with SELECT FOR SHARE. A shared lock does not prevent other transactions from acquiring the same shared lock. However, no transaction is allowed to update, delete, or exclusively lock a row on which any other transaction holds a shared lock. Any attempt to do so will block until the shared lock(s) have been released.
PostgreSQL doesn't remember any information about modified rows in memory, so there is no limit on the number of rows locked at one time. However, locking a row might cause a disk write, e.g., SELECT FOR UPDATE modifies selected rows to mark them locked, and so will result in disk writes.
In addition to table and row locks, page-level share/exclusive locks are used to control read/write access to table pages in the shared buffer pool. These locks are released immediately after a row is fetched or updated. Application developers normally need not be concerned with page-level locks, but they are mentioned here for completeness.
The use of explicit locking can increase the likelihood of deadlocks, wherein two (or more) transactions each hold locks that the other wants. For example, if transaction 1 acquires an exclusive lock on table A and then tries to acquire an exclusive lock on table B, while transaction 2 has already exclusive-locked table B and now wants an exclusive lock on table A, then neither one can proceed. PostgreSQL automatically detects deadlock situations and resolves them by aborting one of the transactions involved, allowing the other(s) to complete. (Exactly which transaction will be aborted is difficult to predict and should not be relied upon.)
Note that deadlocks can also occur as the result of row-level locks (and thus, they can occur even if explicit locking is not used). Consider the case in which two concurrent transactions modify a table. The first transaction executes:
UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 11111;
This acquires a row-level lock on the row with the specified account number. Then, the second transaction executes:
UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 22222; UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 11111;
The first UPDATE statement successfully acquires a row-level lock on the specified row, so it succeeds in updating that row. However, the second UPDATE statement finds that the row it is attempting to update has already been locked, so it waits for the transaction that acquired the lock to complete. Transaction two is now waiting on transaction one to complete before it continues execution. Now, transaction one executes:
UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 22222;
Transaction one attempts to acquire a row-level lock on the specified row, but it cannot: transaction two already holds such a lock. So it waits for transaction two to complete. Thus, transaction one is blocked on transaction two, and transaction two is blocked on transaction one: a deadlock condition. PostgreSQL will detect this situation and abort one of the transactions.
The best defense against deadlocks is generally to avoid them by being certain that all applications using a database acquire locks on multiple objects in a consistent order. In the example above, if both transactions had updated the rows in the same order, no deadlock would have occurred. One should also ensure that the first lock acquired on an object in a transaction is the most restrictive mode that will be needed for that object. If it is not feasible to verify this in advance, then deadlocks can be handled on-the-fly by retrying transactions that abort due to deadlocks.
So long as no deadlock situation is detected, a transaction seeking either a table-level or row-level lock will wait indefinitely for conflicting locks to be released. This means it is a bad idea for applications to hold transactions open for long periods of time (e.g., while waiting for user input).
PostgreSQL provides a means for creating locks that have application-defined meanings. These are called advisory locks, because the system does not enforce their use — it is up to the application to use them correctly. Advisory locks can be useful for locking strategies that are an awkward fit for the MVCC model. Once acquired, an advisory lock is held until explicitly released or the session ends. Unlike standard locks, advisory locks do not honor transaction semantics: a lock acquired during a transaction that is later rolled back will still be held following the rollback, and likewise an unlock is effective even if the calling transaction fails later. The same lock can be acquired multiple times by its owning process: for each lock request there must be a corresponding unlock request before the lock is actually released. (If a session already holds a given lock, additional requests will always succeed, even if other sessions are awaiting the lock.) Like all locks in PostgreSQL, a complete list of advisory locks currently held by any session can be found in the pg_locks system view.
Advisory locks are allocated out of a shared memory pool whose size is defined by the configuration variables max_locks_per_transaction and max_connections. Care must be taken not to exhaust this memory or the server will be unable to grant any locks at all. This imposes an upper limit on the number of advisory locks grantable by the server, typically in the tens to hundreds of thousands depending on how the server is configured.
A common use of advisory locks is to emulate pessimistic locking strategies typical of so called "flat file" data management systems. While a flag stored in a table could be used for the same purpose, advisory locks are faster, avoid MVCC bloat, and are automatically cleaned up by the server at the end of the session. In certain cases using this advisory locking method, especially in queries involving explicit ordering and LIMIT clauses, care must be taken to control the locks acquired because of the order in which SQL expressions are evaluated. For example:
SELECT pg_advisory_lock(id) FROM foo WHERE id = 12345; -- ok SELECT pg_advisory_lock(id) FROM foo WHERE id > 12345 LIMIT 100; -- danger! SELECT pg_advisory_lock(q.id) FROM ( SELECT id FROM foo WHERE id > 12345 LIMIT 100 ) q; -- ok
In the above queries, the second form is dangerous because the LIMIT is not guaranteed to be applied before the locking function is executed. This might cause some locks to be acquired that the application was not expecting, and hence would fail to release (until it ends the session). From the point of view of the application, such locks would be dangling, although still viewable in pg_locks.
The functions provided to manipulate advisory locks are described in Table 9-61.


Blade (1998) 1/2 star out of 4. Starring Wesley Snipes
It used to be that not just anyone could become a vampire.
Usually, you had to be an aristocrat - a count such as Dracula or Karnstein.
To qualify, you'd have to have a modicum of sophistication, so you'd at least look cool - or suave - when biting into some young damsel's throat.
But today, in our overly politically correct world, any scuzzy-looking, long-haired, unshaven lout or any spiked-haired harridans can put the bite on you.
By the same token, to be a vampire hunter, one had to have some sort of medical training and knowledge of the occult or maybe perhaps be some sort of professional soldier, or at least one who retired with honors.
Not in these times. All you need are some sharp weapons, some bullets forged from silver and any yahoo can be a Buffy - or a Blade.
Without sounding elitist, the fear of vampires has greatly diminished because of this lack of exclusivity.
Which brings us to Blade, the latest in a long line of vampire movies in which, it seems, half the population is putting the bite on the other half.
Not only are there thousands of vampires, but they seem to move from city to city, pay off police departments and, most importantly, establish their own exclusive after-hours raves in which the highlight is the sprinkler system going off and dousing all the occupants in a blood shower. With all that blood, you wonder why they have to hunt for victims. You also wonder when they get the time to get their clothes dry-cleaned and why no cleaner ever complains about all the bloodstains.
Blade, which is based on a Marvel comic book character, is, like a comic book, all visual. The plot is basically Blade repetitiously slashing his way through the vampire army seeking their leader, Deacon Frost.
It's all hokum and nonsense, of course. But the filmmakers play it straight.
Unfortunately, at least at the advance screening I attended, the audience didn't see it that way and laughed through much of the proceedings.
You know a movie is in trouble when the scenes of blood and gore that should elicit screams of fright instead evoke peels of laughter.
Another tell-tale sign is when the audience seems to be admiring the hero's costume more than the hero.
Also it seems it's not enough for today's vampire merely to have fangs. He must also be proficient in the martial arts. Why a supernatural being, one of the undead, needs such skills is beyond me. (Of course, this concept dates to the 1974 Hammer Films-Run Run Shaw production of Legend of the Seven Golden Vampires, in which Dracula is defeated by seven karate-chopping siblings.)
Wesley Snipes is buff as Blade. He growls his lines -the few he has - and spends most of the time glowering - as if he was suffering from indigestion or having second thoughts about starring in and co-producing this turkey.
Blade is just another example of how the vampire film, a once honorable member of the horror family, has gone downhill. This dud mostly makes you yearn for those quaint old days when Christopher Lee could be stopped just by dangling a crucifix in his face.
Today's vampires lack the panache, the style of a Bela Lugosi or a Christopher Lee. They are not even worthy enough to carry those great bloodsuckers' capes. They are more farcical than frightful.
This whole enterprise is one dull Blade that could have used lots of sharpening.
Bob Bloom is the film critic at the Journal and Courier in Lafayette, Ind. He can be reached by e-mail at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>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.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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More than any other building in the world, the Empire State Building embodies the ambition of humans to build towers that reach for the skies. The skyscraper is probably New York's best known building and definitely a must-see during your visit.
Price: $32-52
The Statue of Liberty was a gift from France on America’s 100th birthday. A universal symbol of freedom, the copper-plated sentinel stands 305 feet tall from the bottom of her base to the tip of her torch. Reservations are required for a tour of the statue’s interior and to ascend its iconic, seven-pointed crown.
Remember: Be sure to book in advance if you want to see the view from Lady Liberty’s Crown
Price: $18 (+3 for Crown views)
For my fellow shop-a-holic’s, you can’t come to NYC and not visit Macy’s; it is the world’s biggest store after all! You won’t find pricey designer labels here, though mid-priced fashion lines for all ages are its bread and butter, along with all the big beauty names.
Remember: As a foreign visitor, you can get a 10% discount pass to use throughout the store, just take your passport to the information desk upon arrival.


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{% blocktrans %} ... but we still have a few details to polish before you'll be able to use it. {% endblocktrans %}
{% blocktrans %} Don't worry, we'll tell you as soon as we open our first beta. Just give us your e-mail address and you'll be the first to try it! {% endblocktrans %}
{% blocktrans %} The only e-mails you'll receive will be about the release. No spam, pinky promise. And you can change your mind anytime. {% endblocktrans %}


Note 1 : Recommended statistics for this type of classification highlighted in aqua
Note 2 : The recommender system assumes that the input is the result of classification over the whole data rather than just a part of it. If the confusion matrix is the result of test data classification, the recommendation is not valid.
| Actual | Predict
|
| Class | Anterior_nares | Buccal_mucosa | Stool | Supragingival_plaque | Tongue_dorsum | Description |
| ACC | 0.99687 | 0.97367 | 0.99875 | 0.99185 | 0.98119 | Accuracy |
| AGF | 0.99687 | 0.95731 | 0.99804 | 0.98783 | 0.96924 | Adjusted F-score |
| AGM | 0.99687 | 0.96932 | 0.99856 | 0.9908 | 0.97803 | Adjusted geometric mean |
| AM | 3 | -2 | 0 | 1 | -2 | Difference between automatic and manual classification |
| AUC | 0.99687 | 0.95768 | 0.99804 | 0.98785 | 0.96944 | Area under the ROC curve |
| AUCI | Excellent | Excellent | Excellent | Excellent | Excellent | AUC value interpretation |
| AUPR | 0.99222 | 0.93397 | 0.99687 | 0.97966 | 0.95284 | Area under the PR curve |
| BCD | 0.00094 | 0.00063 | 0.0 | 0.00031 | 0.00063 | Bray-Curtis dissimilarity |
| BM | 0.99373 | 0.91536 | 0.99608 | 0.97571 | 0.93887 | Informedness or bookmaker informedness |
| CEN | 0.0232 | 0.1362 | 0.00974 | 0.04773 | 0.09212 | Confusion entropy |
| DOR | 101124.0 | 847.8 | 405450.0 | 9457.07143 | 1707.08036 | Diagnostic odds ratio |
| DP | 2.75932 | 1.61445 | 3.09182 | 2.19195 | 1.78204 | Discriminant power |
| DPI | Fair | Limited | Good | Fair | Limited | Discriminant power interpretation |
| ERR | 0.00313 | 0.02633 | 0.00125 | 0.00815 | 0.01881 | Error rate |
| F0.5 | 0.98942 | 0.93573 | 0.99687 | 0.97874 | 0.95463 | F0.5 score |
| F1 | 0.9922 | 0.93396 | 0.99687 | 0.97966 | 0.95283 | F1 score - harmonic mean of precision and sensitivity |
| F2 | 0.99499 | 0.9322 | 0.99687 | 0.98058 | 0.95104 | F2 score |
| FDR | 0.01242 | 0.06309 | 0.00313 | 0.02187 | 0.04416 | False discovery rate |
| FN | 1 | 22 | 1 | 6 | 16 | False negative/miss/type 2 error |
| FNR | 0.00313 | 0.06897 | 0.00313 | 0.01881 | 0.05016 | Miss rate or false negative rate |
| FOR | 0.00079 | 0.01721 | 0.00078 | 0.00471 | 0.01252 | False omission rate |
| FP | 4 | 20 | 1 | 7 | 14 | False positive/type 1 error/false alarm |
| FPR | 0.00313 | 0.01567 | 0.00078 | 0.00549 | 0.01097 | Fall-out or false positive rate |
| G | 0.99221 | 0.93397 | 0.99687 | 0.97966 | 0.95283 | G-measure geometric mean of precision and sensitivity |
| GI | 0.99373 | 0.91536 | 0.99608 | 0.97571 | 0.93887 | Gini index |
| GM | 0.99687 | 0.95731 | 0.99804 | 0.98783 | 0.96924 | G-mean geometric mean of specificity and sensitivity |
| IBA | 0.99374 | 0.8676 | 0.99374 | 0.96281 | 0.90261 | Index of balanced accuracy |
| IS | 2.30389 | 2.22791 | 2.3174 | 2.29002 | 2.25676 | Information score |
| J | 0.98452 | 0.87611 | 0.99375 | 0.96012 | 0.90991 | Jaccard index |
| LS | 4.93789 | 4.68454 | 4.98433 | 4.89062 | 4.77918 | Lift score |
| MCC | 0.99026 | 0.91752 | 0.99608 | 0.97456 | 0.94109 | Matthews correlation coefficient |
| MCCI | Very Strong | Very Strong | Very Strong | Very Strong | Very Strong | Matthews correlation coefficient interpretation |
| MCEN | 0.04095 | 0.21803 | 0.01734 | 0.08066 | 0.14791 | Modified confusion entropy |
| MK | 0.98679 | 0.91969 | 0.99608 | 0.97342 | 0.94332 | Markedness |
| N | 1276 | 1276 | 1276 | 1276 | 1276 | Condition negative |
| NLR | 0.00314 | 0.07006 | 0.00314 | 0.01891 | 0.05071 | Negative likelihood ratio |
| NLRI | Good | Good | Good | Good | Good | Negative likelihood ratio interpretation |
| NPV | 0.99921 | 0.98279 | 0.99922 | 0.99529 | 0.98748 | Negative predictive value |
| OC | 0.99687 | 0.93691 | 0.99687 | 0.98119 | 0.95584 | Overlap coefficient |
| OOC | 0.99221 | 0.93397 | 0.99687 | 0.97966 | 0.95283 | Otsuka-Ochiai coefficient |
| OP | 0.99687 | 0.94584 | 0.99757 | 0.98511 | 0.96098 | Optimized precision |
| P | 319 | 319 | 319 | 319 | 319 | Condition positive or support |
| PLR | 318.0 | 59.4 | 1272.0 | 178.85714 | 86.57143 | Positive likelihood ratio |
| PLRI | Good | Good | Good | Good | Good | Positive likelihood ratio interpretation |
| POP | 1595 | 1595 | 1595 | 1595 | 1595 | Population |
| PPV | 0.98758 | 0.93691 | 0.99687 | 0.97812 | 0.95584 | Precision or positive predictive value |
| PRE | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | Prevalence |
| Q | 0.99998 | 0.99764 | 1.0 | 0.99979 | 0.99883 | Yule Q - coefficient of colligation |
| RACC | 0.04038 | 0.03975 | 0.04 | 0.04013 | 0.03975 | Random accuracy |
| RACCU | 0.04038 | 0.03975 | 0.04 | 0.04013 | 0.03975 | Random accuracy unbiased |
| TN | 1272 | 1256 | 1275 | 1269 | 1262 | True negative/correct rejection |
| TNR | 0.99687 | 0.98433 | 0.99922 | 0.99451 | 0.98903 | Specificity or true negative rate |
| TON | 1273 | 1278 | 1276 | 1275 | 1278 | Test outcome negative |
| TOP | 322 | 317 | 319 | 320 | 317 | Test outcome positive |
| TP | 318 | 297 | 318 | 313 | 303 | True positive/hit |
| TPR | 0.99687 | 0.93103 | 0.99687 | 0.98119 | 0.94984 | Sensitivity, recall, hit rate, or true positive rate |
| Y | 0.99373 | 0.91536 | 0.99608 | 0.97571 | 0.93887 | Youden index |
| dInd | 0.00443 | 0.07072 | 0.00323 | 0.01959 | 0.05134 | Distance index |
| sInd | 0.99687 | 0.94999 | 0.99772 | 0.98615 | 0.9637 | Similarity index |
Generated By PyCM Version 2.4


Packaging binary extensions¶
- Page Status
Incomplete
- Last Reviewed
2013-12-08
One of the features of the CPython reference interpreter is that, in addition to allowing the execution of Python code, it also exposes a rich C API for use by other software. One of the most common uses of this C API is to create importable C extensions that allow things which aren't always easy to achieve in pure Python code.
Contents
An overview of binary extensions¶
Use cases¶
The typical use cases for binary extensions break down into just three conventional categories:
accelerator modules: these modules are completely self-contained, and are created solely to run faster than the equivalent pure Python code runs in CPython. Ideally, accelerator modules will always have a pure Python equivalent to use as a fallback if the accelerated version isn't available on a given system. The CPython standard library makes extensive use of accelerator modules. Example: When importing
datetime
, Python falls back to the datetime.py module if the C implementation ( _datetimemodule.c) is not available.wrapper modules: these modules are created to expose existing C interfaces to Python code. They may either expose the underlying C interface directly, or else expose a more "Pythonic" API that makes use of Python language features to make the API easier to use. The CPython standard library makes extensive use of wrapper modules. Example: functools.py is a Python module wrapper for _functoolsmodule.c.
low-level system access: these modules are created to access lower level features of the CPython runtime, the operating system, or the underlying hardware. Through platform specific code, extension modules may achieve things that aren't possible in pure Python code. A number of CPython standard library modules are written in C in order to access interpreter internals that aren't exposed at the language level. Example:
sys
, which comes from sysmodule.c.One particularly notable feature of C extensions is that, when they don't need to call back into the interpreter runtime, they can release CPython's global interpreter lock around long-running operations (regardless of whether those operations are CPU or IO bound).
Not all extension modules will fit neatly into the above categories. The extension modules included with NumPy, for example, span all three use cases - they move inner loops to C for speed reasons, wrap external libraries written in C, FORTRAN and other languages, and use low level system interfaces for both CPython and the underlying operation system to support concurrent execution of vectorised operations and to tightly control the exact memory layout of created objects.
Disadvantages¶
The main disadvantage of using binary extensions is the fact that it makes subsequent distribution of the software more difficult. One of the advantages of using Python is that it is largely cross platform, and the languages used to write extension modules (typically C or C++, but really any language that can bind to the CPython C API) typically require that custom binaries be created for different platforms.
This means that binary extensions:
require that end users be able to either build them from source, or else that someone publish pre-built binaries for common platforms
may not be compatible with different builds of the CPython reference interpreter
often will not work correctly with alternative interpreters such as PyPy, IronPython or Jython
if handcoded, make maintenance more difficult by requiring that maintainers be familiar not only with Python, but also with the language used to create the binary extension, as well as with the details of the CPython C API.
if a pure Python fallback implementation is provided, make maintenance more difficult by requiring that changes be implemented in two places, and introducing additional complexity in the test suite to ensure both versions are always executed.
Another disadvantage of relying on binary extensions is that alternative import mechanisms (such as the ability to import modules directly from zipfiles) often won't work for extension modules (as the dynamic loading mechanisms on most platforms can only load libraries from disk).
Alternatives to handcoded accelerator modules¶
When extension modules are just being used to make code run faster (after profiling has identified the code where the speed increase is worth additional maintenance effort), a number of other alternatives should also be considered:
look for existing optimised alternatives. The CPython standard library includes a number of optimised data structures and algorithms (especially in the builtins and the
collections
anditertools
modules). The Python Package Index also offers additional alternatives. Sometimes, the appropriate choice of standard library or third party module can avoid the need to create your own accelerator module.for long running applications, the JIT compiled PyPy interpreter may offer a suitable alternative to the standard CPython runtime. The main barrier to adopting PyPy is typically reliance on other binary extension modules - while PyPy does emulate the CPython C API, modules that rely on that cause problems for the PyPy JIT, and the emulation layer can often expose latent defects in extension modules that CPython currently tolerates (frequently around reference counting errors - an object having one live reference instead of two often won't break anything, but no references instead of one is a major problem).
Cython is a mature static compiler that can compile most Python code to C extension modules. The initial compilation provides some speed increases (by bypassing the CPython interpreter layer), and Cython's optional static typing features can offer additional opportunities for speed increases. Using Cython still has the disadvantage of increasing the complexity of distributing the resulting application, but has the benefit of having a reduced barrier to entry for Python programmers (relative to other languages like C or C++).
Numba is a newer tool, created by members of the scientific Python community, that aims to leverage LLVM to allow selective compilation of pieces of a Python application to native machine code at runtime. It requires that LLVM be available on the system where the code is running, but can provide significant speed increases, especially for operations that are amenable to vectorisation.
Alternatives to handcoded wrapper modules¶
The C ABI (Application Binary Interface) is a common standard for sharing functionality between multiple applications. One of the strengths of the CPython C API (Application Programming Interface) is allowing Python users to tap into that functionality. However, wrapping modules by hand is quite tedious, so a number of other alternative approaches should be considered.
The approaches described below don't simplify the distribution case at all, but they can significantly reduce the maintenance burden of keeping wrapper modules up to date.
In addition to being useful for the creation of accelerator modules, Cython is also useful for creating wrapper modules. It still involves wrapping the interfaces by hand, however, so may not be a good choice for wrapping large APIs.
cffi is a project created by some of the PyPy developers to make it straightforward for developers that already know both Python and C to expose their C modules to Python applications. It also makes it relatively straightforward to wrap a C module based on its header files, even if you don't know C yourself.
One of the key advantages of
cffi
is that it is compatible with the PyPy JIT, allowing CFFI wrapper modules to participate fully in PyPy's tracing JIT optimisations.SWIG is a wrapper interface generator that allows a variety of programming languages, including Python, to interface with C and C++ code.
The standard library's
ctypes
module, while useful for getting access to C level interfaces when header information isn't available, suffers from the fact that it operates solely at the C ABI level, and thus has no automatic consistency checking between the interface actually being exported by the library and the one declared in the Python code. By contrast, the above alternatives are all able to operate at the C API level, using C header files to ensure consistency between the interface exported by the library being wrapped and the one expected by the Python wrapper module. Whilecffi
can operate directly at the C ABI level, it suffers from the same interface inconsistency problems asctypes
when it is used that way.
Alternatives for low level system access¶
For applications that need low level system access (regardless of the
reason), a binary extension module often is the best way to go about it.
This is particularly true for low level access to the CPython runtime
itself, since some operations (like releasing the Global Interpreter Lock)
are simply invalid when the interpreter is running code, even if a module
like ctypes
or cffi
is used to obtain access to the relevant C
API interfaces.
For cases where the extension module is manipulating the underlying operating system or hardware (rather than the CPython runtime), it may sometimes be better to just write an ordinary C library (or a library in another systems programming language like C++ or Rust that can export a C compatible ABI), and then use one of the wrapping techniques described above to make the interface available as an importable Python module.
Implementing binary extensions¶
The CPython Extending and Embedding guide includes an introduction to writing a custom extension module in C.
mention the stable ABI (3.2+, link to the CPython C API docs)
mention the module lifecycle
mention the challenges of shared static state and subinterpreters
mention the implications of the GIL for extension modules
mention the memory allocation APIs in 3.4+
mention again that all this is one of the reasons why you probably
*don't* want to handcode your extension modules :)
Building binary extensions¶
Binary extensions for Windows¶
Before it is possible to build a binary extension, it is necessary to ensure that you have a suitable compiler available. On Windows, Visual C is used to build the official CPython interpreter, and should be used to build compatible binary extensions.
Python 2.7 used Visual Studio 2008, Python 3.3 and 3.4 used Visual Studio 2010, and Python 3.5+ uses Visual Studio 2015 or later. Unfortunately, older versions of Visual Studio are no longer easily available from Microsoft, so for versions of Python prior to 3.5, the compilers must be obtained differently if you do not already have a copy of the relevant version of Visual Studio.
To set up a build environment for binary extensions, the steps are as follows:
For Python 2.7
Install "Visual C++ Compiler Package for Python 2.7", which is available from Microsoft's website.
Use (a recent version of) setuptools in your setup.py (pip will do this for you, in any case).
Done.
For Python 3.4
Install "Windows SDK for Windows 7 and .NET Framework 4" (v7.1), which is available from Microsoft's website.
Work from an SDK command prompt (with the environment variables set, and the SDK on PATH).
Set DISTUTILS_USE_SDK=1
Done.
For Python 3.5
Install Visual Studio 2015 Community Edition (or any later version, when these are released).
Done.
Note that from Python 3.5 onwards, Visual Studio works in a backward compatible way, which means that any future version of Visual Studio will be able to build Python extensions for all Python versions from 3.5 onwards.
Building with the recommended compiler on Windows ensures that a compatible C library is used throughout the Python process.
Binary extensions for Linux¶
Linux binaries must use a sufficiently old glibc to be compatible with older distributions. The manylinux Docker images provide a build environment with a glibc old enough to support most current Linux distributions on common architectures.
Binary extensions for macOS¶
Binary compatibility on macOS is determined by the target minimum deployment
system, e.g. 10.9, which is often specified with the
MACOSX_DEPLOYMENT_TARGET
environmental variable when building binaries on
macOS. When building with setuptools / distutils, the deployment target is
specified with the flag --plat-name
, e.g. macosx-10.9-x86_64
. For
common deployment targets for macOS Python distributions, see the MacPython
Spinning Wheels wiki.
Publishing binary extensions¶
For interim guidance on this topic, see the discussion in this issue.
FIXME
cover publishing as wheel files on PyPI or a custom index server
cover creation of Windows and macOS installers
cover weak linking
mention the fact that Linux distros have a requirement to build from
source in their own build systems, so binary-only releases are strongly
discouraged
Additional resources¶
Cross-platform development and distribution of extension modules is a complex topic, so this guide focuses primarily on providing pointers to various tools that automate dealing with the underlying technical challenges. The additional resources in this section are instead intended for developers looking to understand more about the underlying binary interfaces that those systems rely on at runtime.
Cross-platform wheel generation with scikit-build¶
The scikit-build package helps abstract cross-platform build operations and provides additional capabilities when creating binary extension packages. Additional documentation is also available on the C runtime, compiler, and build system generator for Python binary extension modules.
Introduction to C/C++ extension modules¶
For a more in depth explanation of how extension modules are used by CPython on a Debian system, see the following articles:


MATLAB xUnit Test Framework: How to Write and Run Tests
This example shows how to write and run a couple of test cases for the MATLAB fliplr function.
Contents
Make a folder for your tests
To get started, create a folder (directory) that will contain your tests, and then make that your working folder. The test directory in this example is example_quick_start.
cd example_quick_start
Write each test case as a simple M-file
Write each test case as an M-file function that returns no output arguments. The function name should start or end with "test" or "Test". The test case passes if the function runs with no error.
Here's a test-case M-file that verifies the correct output for a vector input.
type testFliplrVector
function testFliplrVector %testFliplrVector Unit test for fliplr with vector input in = [1 4 10]; out = fliplr(in); expected_out = [10 4 1]; if ~isequal(out, expected_out) error('testFliplrVector:notEqual', 'Incorrect output for vector.'); end
The function testFliplrVector calls the function being tested and checks the output against the expected output. If the output is different than expected, the function calls error.
Here's another test-case M-file that verifies the correct fliplr output for a matrix input.
type testFliplrMatrix
function testFliplrMatrix %testFliplrMatrix Unit test for fliplr with matrix input in = magic(3); assertEqual(fliplr(in), in(:, [3 2 1]));
This function is simpler than testFliplrVector because it uses the utility testing function assertEqual. assertEqual checks to see whether its two inputs are equal. If they are equal, assertEqual simply returns silently. If they are not equal, assertEqual calls error.
Run all the tests using runxunit
To run all your test cases, simply call runxunit. runxunit automatically finds all the test cases in the current directory, runs them, and reports the results to the Command Window.
runxunit
Test suite: C:\Users\psexton\Documents\GitHub\matlab-xunit-doctest\doc\example_quick_start 20-Feb-2014 19:59:07 Starting test run with 2 test cases. .. PASSED in 0.014 seconds.


Parnas Assisted Nunes’ Investigations In Europe
Ed MacMahon, attorney for indicted Rudy Giuliani associate Lev Parnas, said that his client helped arrange meetings and interviews for Rep. Devin Nunes’ (R-CA) “investigations” in Europe in 2018.
According to the Daily Beast, Nunes traveled around Europe with a coterie of aides from November 30 to December 3, costing $63,000 in government funds.
It is not clear exactly what the investigations entailed, but the trip coincided with Nunes’ mania about discrediting the Mueller probe.
Nunes has just as ardently been trying to undermine the impeachment inquiry, reiterating day after day that it’s a Democratic “circus” born out of nothing more than hate for President Donald Trump. He has also infused the process with a steady stream of hard-to-decipher conspiracy theories, including the bogus notion that Ukraine — and not Russia — meddled in the 2016 election on Hillary Clinton’s, not Trump’s, behalf.


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public abstract class Fresnel
extends java.lang.Object
| Constructor and Description |
|---|
Fresnel()
Creates a new instance of Fresnel
|
| Modifier and Type | Method and Description |
|---|---|
RGBf |
approxFr(@NotNull
RGBf Fr,
@NotNull Vector3f N,
@NotNull Vector3f L)
Approximate the Fresnel reflectance of the surface for light incident from a specific direction.
|
RGBf |
approxFrFt(@NotNull
RGBf Fr,
@NotNull RGBf Ft,
@NotNull Vector3f N,
@NotNull Vector3f L,
@NotNull Vector3f T,
boolean bIn,
float n)
Approximate the Fresnel reflectance and transmittance of the surface for light incident from a specific direction.
|
static float |
approxK(@NotNull RGBf rgb)
Approximates the coefficient of extinction for a material from the reflectance for incident
light parallel to the normal.
|
static float |
approxN(@NotNull RGBf rgb)
Approximates the index of refraction for a material from the reflectance for incident
light parallel to the normal.
|
abstract float |
getAveReflectance()
Returns the average reflectance for an incident ray parallel to the normal.
|
public static float approxN(@NotNull @NotNull RGBf rgb)
rgb
- The reflectance for incident light parallel to the normal.public static float approxK(@NotNull @NotNull RGBf rgb)
rgb
- The reflectance for incident light parallel to the normal.public abstract float getAveReflectance()
public RGBf approxFr(@NotNull @NotNull RGBf Fr, @NotNull @NotNull Vector3f N, @NotNull @NotNull Vector3f L)
Fr
- (RGBf, modified) The reflectance, which will be set to Fresnel reflectance and be returned.N
- (vector3f, readonly) The normal.L
- (vector3f, readonly) The direction of the light. This vector is directed from the surface to the light.public RGBf approxFrFt(@NotNull @NotNull RGBf Fr, @NotNull @NotNull RGBf Ft, @NotNull @NotNull Vector3f N, @NotNull @NotNull Vector3f L, @NotNull @NotNull Vector3f T, boolean bIn, float n)
Fr
- (RGBf, modified) The reflectance, which will be set to Fresnel reflectance and be returned.Ft
- The transmittance.N
- (vector3f, readonly) The normal.L
- (vector3f, readonly) The direction of the light. This vector is directed from the surface to the light.T
- (vector3f, readonly) The direction of the transmitted light.bIn
- true if the ray is going into the material and false if
the ray is going out of the material.n
- The index of refraction on the transmitted side of the material boundary (normally 1.0 for air)


Planning responsibilities
- Because perceived contrast, and therefore readability, is tightly connected to font weight and font size. it is important to plan your CSS styles to provide predictable and consistent size and weight for the target display(s).
- This is particularly important for responsive designs that resize or reconfigure to different device resolutions.
- While designers are free to use any size units such as em, vw, calc(), etc., it is important for the calculated sizes as rendered meet or exceed the size values listed as px in the visual contrast chart.
- For non-standard fonts, compare the relative x height to the x height of a standard reference font, and adjust the font-size value for a match.
- The design of each font also plays a significant part in readability and perceived contrast. When planning, selected fonts should be compared against the
standardized
fonts in the visual contrast matrix. - The available useable colors to achieve readable visual contrast is largely constrained by font weight & font size. Alternately if a specific color palette is required, the color palette may limit the minimum font weight & size.
- The needs of font weight, size, and color extends to readable text content in images.
- The colorspace standard for web content is sRGB. Images with other profiles may give unpredictable results.
- Plan to get images early in the design process so they can be evaluated and adjusted if needed
- A suggested best practice is to use textless versions of images and CSS compositing techniques to apply dynamic text over the image.
Tips for collaboration
The planning and design phases will often be very interactive, and it is important to maintain communication and understanding of the available range of colors, sizes, and weights early in the process to allow adjustments to meet design goals and client needs.
Planning for each stage
Consider early design wireframes
and the space allocated for various elements, and how this will affect the desired font size in the final design.
Determine early on if the site will be responsive, to dynamically adjust and work on all device sizes, or if it will use a mobile separate
paradigm. This choice will significantly affect font weight & size.
To simplify early planning, remember that smaller and thinner (light weight) fonts require greater contrast between the font color and the background. Conversely, a lower contrast can be used with larger and thicker fonts.
How to get started early
- Still to be developed. We will include this in a future working draft.
How to remediate
- Still to be developed. We will include this in a future working draft.


Bluemix, a short introduction with demos
Right-mouse click on the links to not lose the main URL
At the end of each page are navigaton buttons
- IBM Bluemix is a cloud Platform-as-a-Service (PaaS) developed by IBM
- Built on open source compute technologies: Cloud Foundry, Docker,
and Open Stack
- Runs on Softlayer Infrastructure as a Service (IaaS)
- Supports Public cloud, Private cloud, On-prem cloud
- Supports the common programming languages and cloud services as well as
integrated DevOps
to build, run, deploy and manage applications on the cloud
Introduction, Bluemix overview charts
The High-level Bluemix architecture
Let's build a Bluemix app
We'll add public Bluemix services to an existing on-prem app that will allow bank customers to work with their accounts
on their smartphones
Communication between the public and on-prem app is handled by the Bluemix Secute Gateway service.
A Short Agenda
v3 2016/08/27


csv,conf is a group effort to put on a non-profit, community conference with dozens of people putting in time, including but not limited to the 2019 organizing team:
We strive to be a supportive and welcoming environment to all attendees. We encourage you to read the Conf Code of Conduct and will be enforcing it.
The code for this website is open source. View it on GitHub. Want to talk to us? Join us on Slack.


The sphinx-build script builds a Sphinx documentation set. It is called like this:
$ sphinx-build [options] sourcedir builddir [filenames]
where sourcedir is the source directory, and builddir is the directory in which you want to place the built documentation. Most of the time, you don’t need to specify any filenames.
The sphinx-build script has several options:
The most important option: it selects a builder. The most common builders are:
See Available builders for a list of all builders shipped with Sphinx. Extensions can add their own builders.
If given, always write all output files. The default is to only write output files for new and changed source files. (This may not apply to all builders.)
Don’t use a saved environment (the structure caching all cross-references), but rebuild it completely. The default is to only read and parse source files that are new or have changed since the last run.
Define the tag tag. This is relevant for only directives that only include their content if this tag is set.
New in version 0.6.
Since Sphinx has to read and parse all source files before it can write an output file, the parsed source files are cached as “doctree pickles”. Normally, these files are put in a directory called .doctrees under the build directory; with this option you can select a different cache directory (the doctrees can be shared between all builders).
Distribute the build over N processes in parallel, to make building on multiprocessor machines more effective. Note that not all parts and not all builders of Sphinx can be parallelized.
New in version 1.2: This option should be considered experimental.
Don’t look for the conf.py in the source directory, but use the given configuration directory instead. Note that various other files and paths given by configuration values are expected to be relative to the configuration directory, so they will have to be present at this location too.
New in version 0.3.
Don’t look for a configuration file; only take options via the -D option.
New in version 0.5.
Override a configuration value set in the conf.py file. The value must be a string or dictionary value. For the latter, supply the setting name and key like this: -D latex_elements.docclass=scrartcl. For boolean values, use 0 or 1 as the value.
Changed in version 0.6: The value can now be a dictionary value.
Make the name assigned to value in the HTML templates.
New in version 0.5.
Run in nit-picky mode. Currently, this generates warnings for all missing references. See the config value :confval:`nitpick_ignore` for a way to exclude some references as “known missing”.
Do not emit colored output. (On Windows, colored output is disabled in any case.)
Increase verbosity (loglevel). This option can be given up to three times to get more debug logging output. It implies -T.
New in version 1.2.
Do not output anything on standard output, only write warnings and errors to standard error.
Do not output anything on standard output, also suppress warnings. Only errors are written to standard error.
Write warnings (and errors) to the given file, in addition to standard error.
Turn warnings into errors. This means that the build stops at the first warning and sphinx-build exits with exit status 1.
Display the full traceback when an unhandled exception occurs. Otherwise, only a summary is displayed and the traceback information is saved to a file for further analysis.
New in version 1.2.
(Useful for debugging only.) Run the Python debugger, pdb, if an unhandled exception occurs while building.
Display usage summary or Sphinx version.
New in version 1.2.
You can also give one or more filenames on the command line after the source and build directories. Sphinx will then try to build only these output files (and their dependencies).
The Makefile and make.bat files created by sphinx-quickstart usually run sphinx-build only with the -b and -d options. However, they support the following variables to customize behavior:
The value for :confval:`latex_paper_size`.
The command to use instead of sphinx-build.
The build directory to use instead of the one chosen in sphinx-quickstart.
Additional options for sphinx-build.
The sphinx-apidoc generates completely automatic API documentation for a Python package. It is called like this:
$ sphinx-apidoc [options] -o outputdir packagedir [pathnames]
where packagedir is the path to the package to document, and outputdir is the directory where the generated sources are placed. Any pathnames given are paths to be excluded ignored during generation.
The sphinx-apidoc script has several options:
Gives the directory in which to place the generated output.
Normally, sphinx-apidoc does not overwrite any files. Use this option to force the overwrite of all files that it generates.
With this option given, no files will be written at all.
This option selects the file name suffix of output files. By default, this is rst.
This sets the maximum depth of the table of contents, if one is generated.
This option makes sphinx-apidoc follow symbolic links when recursing the filesystem to discover packages and modules. You may need it if you want to generate documentation from a source directory managed by collective.recipe.omelette. By default, symbolic links are skipped.
New in version 1.2.
This prevents the generation of a table-of-contents file modules.rst. This has no effect when --full is given.
This option makes sphinx-apidoc create a full Sphinx project, using the same mechanism as sphinx-quickstart. Most configuration values are set to default values, but you can influence the most important ones using the following options.
Sets the project name to put in generated files (see :confval:`project`).
Sets the author name(s) to put in generated files (see :confval:`copyright`).
Sets the project version to put in generated files (see :confval:`version`).
Sets the project release to put in generated files (see :confval:`release`).


Ecliptic SCADA is a control system architecture comprising computers, networked data communications and graphical user interfaces for high-level supervision of machines and processes. It also covers sensors and other devices, such as programmable logic controllers, which interface with process plant or machinery.
Client-server communication is implemented with node-opcua library. Transport layer of node-opcua is websockets for web browser clients and tcp for Electron version of application.
Ecliptic SCADA's frontend is based on Qooxdoo, its backend uses the C++/C OPC UA implementation.
One of adaptations for the system is a tram traction substations' system for whole city. On the screenshots you may see the following.


This package can efficiently create and project species distribution models using the MaxEnt framework and parallel processing. It can find and download occurrence data for a list of species on GBIF (Global Biodiversity Information Facility), environmentally subsample the occurrences to mitigate spatial bias, generate background (pseudo-absence) points, train the model and project it to different times (incorporating dispersal rate of each species and intermediate range fluctuations), and create species richness maps for each time period and taxon.
This example briefly demonstrates much of the functionality of megaSDM and can be used as a template for other SDM analyses. For a more thorough discussion of the methods and novelty of the package, as well as a full list of arguments for each function, refer to https://doi.org/10.1111/ecog.05450 and the documentation of each function.
First Time Users: The modelling portion of megaSDM relies on the MaxEnt algorithm, using the “maxent.jar” executable file. This file is installed along with the megaSDM package, but if this is your first time running MaxEnt or megaSDM, you may want to download the “maxent.jar” file separately to ensure that it can open. The “maxent.jar” file can be found at https://github.com/mrmaxent/Maxent or along with this package at https://github.com/brshipley/megaSDM/inst/extdata/.
Once downloaded, double-click the “maxent.jar” file: If the maxent.jar dialog box does not load when opened (this is common when using MacOS), the Java Runtime Environment may need to be downloaded before running the modelling functions that megaSDM provies (https://www.oracle.com/java/technologies/javase-jre8-downloads.html).
If you are having trouble downloading the package or running this vignette, please first check https://github.com/brshipley/megaSDM/README.Md to make sure that you have the package dependencies up-to-date.
Finally, set your working directory to a location where you want all of outputs megaSDM provides to be written out.
<- "F:/EXAMPLE"
WD #CHANGE TO OWN WORKING DIRECTORY
setwd(WD)
Species distribution models use a training area and a study area. The training area is the area where the model will be trained on (i.e., where the occurrence and background points for the model generation are located). The study area is the region of interest (i.e., where the model will be projected and habitat suitability will be predicted for both the current time period and future/past time periods).
The first function TrainStudyEnv
creates and manages the
environmental rasters of the current time period for the desired
training and study areas.
First, make a list of environmental rasters that we will use to clip, resample, and reproject. In this example, the training area files are in extdata/trainingarea, which is downloaded within the megaSDM package
<- list.files(system.file("extdata", "trainingarea", package = "megaSDM"),
input_TA pattern = ".grd$",
full.names = TRUE)
# If you have your own data, replace the system.file command with
# a pathway to the training area files.
Next, define “envoutput”, which is where the training and study rasters will be printed out to (within the working directory). If the directory does not exist, megaSDM will create a new directory. In this example, we want the environmental layers to be output to the “TestRun” directory.
<- "TestRun" envoutput
For this example, our training and study areas will be exactly the same (corresponding roughly to the southeastern United States), and we don’t need to reproject or resample (the rasters are in the same projection and resolution). By assigning this function call to the list “TSEnv”, the two rasters are saved within the R environment for use later in addition to being written to the “TestRun” directory.
# Here we define the extent of the training and study regions in c(xmin, xmax, ymin, ymax) form.
<- TrainStudyEnv(input_TA = input_TA,
TSEnv output = envoutput,
clipTrain = c(-91.5, -75, 25.5, 36),
clipStudy = c(-91.5, -75, 25.5, 36))
After getting the training and study environmental layers for the
current time period, the next function PredictEnv
clips,
resamples, and reprojects the future/past environmental layers to match
the study region rasters created by the TrainStudyEnv
function.
PredictEnv
requires lists of the forecasted/hindcasted
environmental files.In this example, the future climate layers are
organized by scenario and then year. Rasters from two climate scenarios
(RCP4.5, RCP8.5) and two future years (2050, 2070) are provided with the
function.
.5 <- list.files(system.file("extdata", "predictenv/RCP4.5/2050", package = "megaSDM"),
Env2050_4pattern = ".grd$",
full.names = TRUE)
.5 <- list.files(system.file("extdata", "predictenv/RCP4.5/2070", package = "megaSDM"),
Env2070_4pattern = ".grd$",
full.names = TRUE)
.5 <- list(Env2050_4.5, Env2070_4.5)
Env4
# The "time_periods" argument must contain the current time (the time of the training
# and study rasters) first and then the time periods for the forecast/hindcast.
PredictEnv(studylayers = TSEnv$study,
futurelayers = Env4.5,
time_periods = c(2010, 2050, 2070),
output = envoutput,
scenario_name = "RCP4.5")
#Repeat with a different climate scenario (RCP8.5):
.5 <- list.files(system.file("extdata", "predictenv/RCP8.5/2050", package = "megaSDM"),
Env2050_8pattern = ".grd$",
full.names = TRUE)
.5 <- list.files(system.file("extdata", "predictenv/RCP8.5/2070", package = "megaSDM"),
Env2070_8pattern = ".grd$",
full.names = TRUE)
.5 <- list(Env2050_8.5, Env2070_8.5)
Env8PredictEnv(studylayers = TSEnv$study,
futurelayers = Env8.5,
time_periods = c(2010, 2050, 2070),
output = envoutput,
scenario_name = "RCP8.5")
The OccurrenceCollection
function acts as a wrapper for
gbif::occ_search
, downloading occurrence points from the
Global Biodiversity Information Facility (GBIF) from a defined extent.
However, this function is more efficient than occ_search
for a large number of taxa. It also checks the taxonomy of the given
species list against the GBIF taxonomy, renaming or merging taxa if
necessary. NOTE: This step interfaces with the GBIF server and
therefore requires an internet connection. Downloading large amounts of
data (e.g., many species) on an unstable internet connection may lead to
incomplete occurrence collection.
# This function only takes occurrences from the described trainingarea extent.
# The defined extent should be the same (or similar to) as the extent of the training area.
# Given in latitude/longitude coordinates:
<- c(-91.5, -75, 25.5, 36)
extent_occ
# A list of southeastern mammals for this example
<- c("Puma concolor coryi",
spplist "Podomys floridanus",
"Sylvilagus aquaticus",
"Sylvilagus palustris",
"Geomys pinetis",
"Neofiber alleni")
# Define the file folder where the occurrences will be written, within the working directory
# (if this folder doesn't already exist, megaSDM will make it)
<- "occurrences"
occ_output
<- OccurrenceCollection(spplist = spplist,
Occurrences output = occ_output,
trainingarea = extent_occ)
# NOTE: when running this using R Markdown, you may get "incomplete final line..."
# warnings. However, they do not appear to affect the total number or identity
# of the occurrence points and when the code is run off of the console, the
# warnings do not appear.
# Because one species was renamed, rename species list to reflect taxonomy changes
<- Occurrences$Scientific.Name spplist
After collecting the occurrences, they must be formatted so that they
are consistent and able to be read within MaxEnt. The
OccurrenceManagement
function takes a list of occurrence
files, extracts environmental data at each point, and, if requested,
environmentally subsamples the dataset for more accurate modelling. The
environmental subsampling method used in this function was developed by
Varela et al. (2014), dividing the environmental data into a given
number of bins and selecting one point from each unique combination of
bins. This helps to mitigate environmental bias that is intrinsic to
occurrence point datasets.
In this example, we want to extract environmental data from each point (envextract = TRUE) using the environmental data we generated in the previous steps. We also want to environmentally subsample the data (envsample = TRUE), using 25 bins for each environmental variable. Finally, we set the output to the same place as in the previous function so it will overwrite the original occurrence files.
# First, get the list of the occurrence files
<- list.files(occ_output, pattern = ".csv", full.names = TRUE)
occlist
OccurrenceManagement(occlist = occlist,
output = occ_output,
envextract = TRUE,
envsample = TRUE,
nbins = 25,
envdata = TSEnv$training)
To model species distributions based on presence-only data, a set of “background points” describing the environmental conditions of the training area is needed.
Although the best method of generating background points is still up
for debate, spatially-constrained background points can be more
effective than randomly-generated background points for SDMs. The
BackgroundBuffers
function in megaSDM generates buffers
around the occurrence points so that the background points are only
sampled within a certain radius of the occurrence points. This function
uses parallel processing to dramatically speed up processing time,
running a set of species simultaneously. The number of computer cores
used for this process (given by the “ncores” argument) should be set no
higher than the number of cores the computer has minus 1. If
ncores
is set to 1, no parallelization will occur.
# Get the list of occurrence files again, even if they were written out
# in the same folder as before. This ensures that the occurrence files
# are properly formatted.
<- list.files(occ_output, pattern = ".csv", full.names = TRUE)
occlist
# The location to print out the background buffers (.shp) (will be created if it doesn't exist)
<- "TestRun/buffers"
buff_output
# Generates buffers for each species.
BackgroundBuffers(occlist = occlist,
envdata = TSEnv$training,
buff_output,ncores = 2)
The BackgroundPoints
function generates a set of
background points for each species. This function offers flexibility in
the number of background points generated, the degree of spatial bias,
and whether or not the background poins should be environmentally
subsampled.
# Set the parameters for the background point generation
# (how many points, and how spatially-constrained)
# How many background points should be generated per species?
<- 1000
nbg
# What proportion of the background points should be sampled from within the buffers?
<- 0.5
spatial_weights
# Should the background points be environmentally subsampled (Varela) or
# randomly distributed (random)?
<- "Varela"
sampleMethod
# Because we want a partial spatial constraint (50% of points within the buffer), we must make a
# list of the buffer files to use in the creation of the background points. In the example,
# these files are created from the BackgroundBuffers function, but they can also be generated
# outside of megaSDM and brought in here.
<- list.files(buff_output, pattern = ".shp$", full.names = TRUE)
bufflist
# Define the location where the background points will be printed out to (.shp)
# (This directory will be created if it doesn't already exist)
<- "TestRun/backgrounds"
bg_output
BackgroundPoints(spplist = spplist,
envdata = TSEnv$training,
output = bg_output,
nbg = nbg,
spatial_weights = spatial_weights,
buffers = bufflist,
method = sampleMethod,
ncores = 2)
SDMs are generally better when a species-specific set of
environmental variables are used (with biological relevance). The
VariableEnv
function can change the set of environmental
variables used for each species to tailor the analysis to the habitat
suitability of each species.
# Define a list of the environmental variables to keep for each species
# In this example, we simply want all of the species to have the same environmental variables.
<- rep("Bio1,Bio12,Bio14,Bio6,Bio9", length = length(occlist))
envvar
# Define a list of the background point files
# (either created in the BackgroundPoints function or generated separately)
<- list.files(bg_output, pattern = ".csv", full.names = TRUE)
bglist
# In this example, megaSDM overwrites the occurrence and background points,
# but they could be placed in a different folder if requested.
VariableEnv(occlist = occlist,
bglist = bglist,
env_vars = envvar,
occ_output = occ_output,
bg_output = bg_output)
After the occurrence and background point management (if desired),
megaSDM will model the habitat suitability using the MaxEnt algorithm.
The MaxEntModel
function generates the actual MaxEnt models
for each species. The MaxEnt parameters (e.g., regualarization, feature
types, crossvalidation method) can be manipluted within this function:
see the documentation for a complete list of settings and defaults that
can be changed.
# First, define a list of all background and occurrence point files
<- list.files(occ_output, pattern = ".csv", full.names = TRUE)
occlist <- list.files(bg_output, pattern = ".csv", full.names = TRUE)
bglist
# Define where the results of the MaxEnt model runs will be printed out to (as .lambdas files)
<- "TestRun/models"
model_output
# "nrep" is set to 4, meaning that the MaxEnt algorithm will run 4 times with different
# subsets of occurrence points for a better representation of the habitat suitability.
MaxEntModel(occlist = occlist,
bglist = bglist,
model_output = model_output,
ncores = 2,
nrep = 4,
alloutputs = FALSE)
After generating the model parameters (or using .lambas files
generated in a different program), megaSDM takes the .lambdas files and
projects them onto the current and forecasted/hindcasted environmental
conditions in the study region. The MaxEntProj
function
takes the generated model parameters, projects them onto current and
future environments, removes poorer models from the analyses, creates
ensemble models from the multiple replicates conducted in the previous
step, and generates binary presence/absence distribution maps.
# First, create a list of the time periods and climate scenarios used in the analysis
# (starting with the year the model is trained on)
<- c(2010,2050,2070)
time_periods <- c("RCP4.5", "RCP8.5")
scenarios
# Define the directory where the current study area rasters are located
# (generated from the TrainStudyEnv function or brought in from a separate location)
<- "TestRun/studyarea"
study_dir
# Define the directories where the future study area rasters are location
# (generated from the PredictEnv function or brought in from a separate location)
# Define a list of directories for the projected climate layers,
# separated into the different climate scenarios and years:
# list(c(Scenario1Year1, Scenario1Year2),
# c(Scenario2Year1, Scenario2Year2))
<- list(c("TestRun/RCP4.5/2050",
predictdir "TestRun/RCP4.5/2070"),
c("TestRun/RCP8.5/2050",
"TestRun/RCP8.5/2070"))
# Define Where the results will be printed out.
# For this example, We'll define a new folder within
# the working directory that is specifically for the model
# projecions and analysis.
<- "Results"
result_dir
# Other options are also available (check the documentation page)
MaxEntProj(input = model_output,
time_periods = time_periods,
scenarios = scenarios,
study_dir = study_dir,
predict_dirs = predictdir,
output = result_dir,
aucval = 0.7,
ncores = 2)
The createTimeMaps
function uses the binary maps at each
time period to create “time maps”: These maps detail the step-wise
expansions and contractions of the species distribution through those
time-steps, allowing for the visualization of both unidirectional range
shifts (e.g., a range expansion across all time-steps) and more complex
dynamics (e.g., a range expansion from 2010-2050 followed by a range
contraction from 2050-2070).
# The time maps will be written out to the directory supplied in "result_dir"
<- "Results"
result_dir
createTimeMaps(result_dir = result_dir,
time_periods = time_periods,
scenarios = scenarios,
dispersal = FALSE,
ncores = 2)
The additionalStats
function generates statistics on
species range sizes and changes through the multiple time steps and
different scenarios. It also creates several graphs (written out to the
directory provided by “result_dir”) to visualize these changes.
additionalStats(result_dir = result_dir,
time_periods = time_periods,
scenarios = scenarios,
dispersal = FALSE,
ncores = 2)
Incorporating dispersal limitations into SDMs can help to more
accurately predict where a species could live in the future. The
dispersalRate
function uses data on the average yearly
dispersal rate of a species (given in km/year) to constrain the
continuous habitat suitability maps and the presence/absence maps by the
dispersal ability of each species.
# Add in dispersal data (normally you would read a .csv file with two columns, but in this example
# the data is just added in by hand here).
<- data.frame(Species = spplist, Rate = c(48.92,
dispersaldata 1.21,
6.07,
3.37,
0.96,
3.27))
dispersalRate(result_dir = result_dir,
dispersaldata = dispersaldata,
time_periods = time_periods,
scenarios = scenarios,
ncores = 2)
# Repeat the time map and additional stats steps for the dispersal constrained data.
# Set dispersal = TRUE this time.
createTimeMaps(result_dir,
time_periods,
scenarios, dispersal = TRUE,
dispersaldata = dispersaldata,
ncores = 2)
# The additional stats function will compare the species ranges between the
# dispersal-constrained and the regular data.
additionalStats(result_dir,
time_periods,
scenarios, dispersal = TRUE,
dispersaldata = dispersaldata,
ncores = 2)
The createRichnessMaps
function stacks the binary
distribution maps to create species richness maps for the set of species
examined across all time periods and scenarios. In addition, it compares
the species richness of the dispersal-constrained analysis to the
regular analysis.
createRichnessMaps(result_dir = result_dir, #taken from previous steps
time_periods = time_periods, #taken from previous steps
scenarios = scenarios, #taken from previous steps
dispersal = TRUE,
taxonlist = FALSE)
# A list of the higher taxa of each species (e.g., family) can be provided (taxonlist) to create
# separate richness maps for each higher taxon.


@Beta var numCols: Int
the number of columns of the board
numCols - the number of columns of the board
numCols


In this example, build a genetic programming feature transformation model and a genetic programming regression prediction model using the following features and labels:
| Data Category | Description |
|---|---|
| Features | Daily percent change of the close price of the SPY over the last 5 days |
| Labels | Daily percent return of the SPY over the next day |
The following image shows the time difference between the features and labels:
Follow these steps to create a method to build the model:
function_set = ['add', 'sub', 'mul', 'div', 'sqrt', 'log', 'abs', 'neg', 'inv', 'max', 'min']
SymbolicTransformer
constructor with the preceding set of functions and then save it as a class variable.self.gp_transformer = SymbolicTransformer(function_set=function_set)
SymbolicRegressor
constructor to instantiate the regression model.self.model = SymbolicRegressor()


Peter Rabbit and Other Stories
by Beatrix Potter
“The Tale of Mrs. Tittlemouse”
Additional Information
- Year Published: 0
- Language: English
- Country of Origin: England
- Source: Beatrix Potter, The Great Big Treasury of Beatrix Potter
-
Readability:
- Flesch–Kincaid Level: 4.0
- Word Count: 1,236
- Genre: Fantasy
- ✎ Cite This
Downloads
Once upon a time there was a woodmouse, and her name was Mrs. Tittlemouse.
She lived in a bank under a hedge.
Such a funny house! There were yards and yards of sandy passages, leading to store-rooms and nut cellars and seed cellars, all amongst the roots of the hedge.
There was a kitchen, a parlor, a pantry, and a larder.
Also, there was Mrs. Tittle-mouse’s bedroom, where she slept in a little box bed!
Mrs. Tittlemouse was a most terribly tidy particular little mouse, always sweeping and dusting the soft sandy floors.
Sometimes a beetle lost its way in the passages.
“Shuh! Shuh!
And one day a little old woman ran up and down in a red spotty cloak.
“Your house is on fire, Mother Ladybird! Fly away home to your children!”

“Beg pardon, is this not Miss Muffet’s?”
“Go away, you bold bad spider! Leaving ends of cobweb all over my nice clean house!”
She bundled the spider out at a window.
He let himself down the hedge with a long thin bit of string.
Mrs. Tittlemouse went on her way to a distant storeroom, to fetch cherrystones and thistle-down seed for dinner.
All along the passage she sniffed, and looked at the floor.
 I am sure I can see the marks of little dirty feet.”
Suddenly round a corner, she met Babbitty Bumble—“Zizz, Bizz, Bizzz!” said the bumble bee.
Mrs. Tittlemouse looked at her severely. She wished that she had a broom.
“Good-day, Babbitty Bumble; I should be glad to buy some bees-wax. But what are you doing down here? Why do you always come in at a window, and say, Zizz, Bizz, Bizzz?” Mrs. Tittle-mouse began to get cross.
“Zizz, Wizz, Wizzz!” replied Babbitty Bumble in a peevish squeak. She sidled down a passage, and disappeared into a storeroom which had been used for acorns.
Mrs. Tittlemouse had eaten the acorns before Christmas; the storeroom ought to have been empty.
 Three or four other bees put their heads out, and buzzed fiercely.
“I am not in the habit of letting lodgings; this is an intrusion!” said Mrs. Tittlemouse. “I will have them turned out —” “Buzz! Buzz! Buzzz!”—
”I wonder who would help me?”
“Bizz, Wizz, Wizzz!”
—“I will not have Mr. Jackson; he never wipes his feet.”
Mrs. Tittlemouse decided to leave the bees till after dinner.
When she got back to the parlor, she heard some one coughing in a fat voice; and there sat Mr. Jackson himself.
He was sitting all over a small rocking chair, twiddling his thumbs and smiling, with his feet on the fender.
He lived in a drain below the hedge, in a very dirty wet ditch.
“How do you do, Mr. Jackson? Deary me, you have got very wet!”
“Thank you, thank you, thank you, Mrs. Tittlemouse! I’ll sit awhile and dry myself,” said Mr. Jackson.
He sat and smiled, and the water dripped off his coat tails. Mrs. Tittlemouse went round with a mop.
He sat such a while that he had to be asked if he would take some dinner?
First she offered him cherry-stones. “Thank you, thank you, Mrs. Tittlemouse! No teeth, no teeth, no teeth!” said Mr. Jackson.
He opened his mouth most unnecessarily wide; he certainly had not a tooth in his head.
Then she offered him thistle-down seed—“Tiddly, widdly, widdly! Pouff, pouff, puff.” said Mr. Jackson. He blew the thistle-down all over the room.
“Thank you, thank you, thank you, Mrs. Tittlemouse! Now what I really—REALLY should like— would be a little dish of honey!”
“I am afraid I have not got any, Mr. Jackson!” said Mrs. Tittlemouse.
“Tiddly, widdly, widdly, Mrs. Tittlemouse!” said the smiling Mr. Jackson, “I can SMELL it; that is why I came to call.”
Mr. Jackson rose ponderously from the table, and began to look into the cupboards.
Mrs.
When he had convinced himself that there was no honey in the cupboards, he began to walk down the passage.
“Indeed, indeed, you will stick fast, Mr. Jackson!”
“Tiddly, widdly, widdly, Mrs. Tittlemouse!”
First he squeezed into the pantry.
“Tiddly, widdly, widdly? No honey? No honey, Mrs. Tittlemouse?”
There were three creepy-crawly people hiding in the plate rack. Two of them got away; but the littlest one he caught.
Then he squeezed into the larder. Miss Butterfly was tasting the sugar; but she flew away out of the window.
“Tiddly, widdly, widdly, Mrs. Tittlemouse; you seem to have plenty of visitors!”
“And without any invitation!” said Mrs. Thomasina Tittlemouse.
They went along the sandy passage—“Tiddly, widdly—” “Buzz! Wizz! Wizz!”
He met Babbitty round a corner, and snapped her up, and put her down again.
“I do not like bumble bees.

“I shall go distracted!” scolded Mrs. Tittlemouse.
She shut herself up in the nut cellar while Mr. Jackson pulled out the bees-nest. He seemed to have no objection to stings.
When Mrs. Tittlemouse ventured to come out—everybody had gone away.
But the untidiness was something dreadful—“Never did I see such a mess—smears of honey; and moss, and thistledown—and marks of big and little dirty feet— all over my nice clean house!”
She gathered up the moss and the remains of the bees-wax.
Then she went out and fetched some twigs, to partly close up the front door.
“I will make it too small for Mr. Jackson!”
She fetched soft soap, and flannel, and a new scrubbing brush from the storeroom. But she was too tired to do any more. First she fell asleep in her chair, and then she went to bed.
“Will it ever be tidy again?” said poor Mrs. Tittlemouse.
Next morning she got up very early and began a spring cleaning which lasted a fort-night.
She swept, and scrubbed, and dusted; and she rubbed up the furniture with bees-wax, and polished her little tin spoons.
When it was all beautifully neat and clean, she gave a party to five other little mice, without Mr. Jackson.
He smelt the party and came up the bank, but he could not squeeze in at the door.
So they handed him out acorn cupfuls of honeydew through the window, and he was not at all offended.
He sat outside in the sun, and said—“Tiddly, widdly, widdly! Your very good health, Mrs. Tittlemouse!”


Have I Been Pwned: API v3 Have I Been Pwned allows you to search across multiple data breaches to see if your email address has been compromised.
dotnet/coreclr CoreCLR is the runtime for .NET Core. It includes the garbage collector, JIT compiler, primitive data types and low-level classes. - dotnet/coreclr
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Convert Array.IsPrimitiveTypeArray to C# by EgorBo · Pull Request #27302 · dotnet/coreclr Another baby steps towards https://github.com/dotnet/coreclr/issues/18111 (along with #27216) Ported line by line C++ to C# for these structs (missing some debug asserts, #if FEATURE_PREJIT parts a...
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LMGTFY For all those people who find it more convenient to bother you with their question rather than search it for themselves.
Invisible banana skins thrown at black people every day, says John Barnes After the alleged abuse of Manchester City winger Raheem Sterling, former Liverpool winger John Barnes tells BBC Sport the problem of racism has never gone away.


Pokemon cheatsheet
I am really sorry about the slowness. It is the API.
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Umpalumpa
Attack:
50
Defence:
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Speed:
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Hit Points:
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Question 0501 answer
A sequence
is such that
for all
(ai) Show that
(aii) Hence show that
(aiii) Give a reason why the series in (aii) is convergent.
(aiv) State the sum to infinity.
(b) Use your answer to part (aii) to find
This question is
worth
2+2+1+1+2
marks.

(b)
.
The answer:
(ai)
.
(aii)
(aiii) The series converges because
(aiv)
.
(b)
.
8
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» Commando Hoofing (Medium) - 4.0%
Cotleigh Brewery (Somerset)
A pale golden beer, refreshing and slightly sparkling. Originally brewed to celebrate the return of the Royal Marines 40 Commando from deployment in Afghanistan and to further support the unit that is based in Taunton, Commando Hoofing beer now enjoys widespread acclaim. When a number of Royal Marines from Termoli Troop 40 Commando RM tasted the beer, they said "It's a great beer with an explosion of flavours from hops to fresh fruit and it's far safer and far more palatable than ones we are used to". Hoofing' is Royal Marine slang to describe something fantastic e.g. a hoofing dancer. This beer is certainly that ... Hoofing!


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- PasswordEncoder - Class in org.jasypt.spring.security3
-
- PasswordEncoder() - Constructor for class org.jasypt.spring.security3.PasswordEncoder
-
Deprecated.
Creates a new instance of PasswordEncoder
- PasswordEncoder - Class in org.jasypt.springsecurity3.authentication.encoding
-
This class implements the Spring Security 3.x
org.springframework.security.authentication.encoding.PasswordEncoder
interface, allowing Spring Security-enabled applications to use JASYPT
for password encryption.
- PasswordEncoder() - Constructor for class org.jasypt.springsecurity3.authentication.encoding.PasswordEncoder
-
Creates a new instance of PasswordEncoder
- PBEPasswordEncoder - Class in org.jasypt.spring.security3
-
- PBEPasswordEncoder() - Constructor for class org.jasypt.spring.security3.PBEPasswordEncoder
-
Deprecated.
Creates a new instance of PBEPasswordEncoder
- PBEPasswordEncoder - Class in org.jasypt.springsecurity3.authentication.encoding
-
This class implements the Spring Security 3.x
org.springframework.security.providers.encoding.PasswordEncoder
interface, allowing Spring Security-enabled applications to use JASYPT
for password encryption.
- PBEPasswordEncoder() - Constructor for class org.jasypt.springsecurity3.authentication.encoding.PBEPasswordEncoder
-
Creates a new instance of PBEPasswordEncoder
- processAutoLoginCookie(String[], HttpServletRequest, HttpServletResponse) - Method in class org.jasypt.spring.security3.TokenBasedRememberMeServices
-
Deprecated.
- processAutoLoginCookie(String[], HttpServletRequest, HttpServletResponse) - Method in class org.jasypt.springsecurity3.authentication.rememberme.TokenBasedRememberMeServices
-


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You can pay through Cash on Delivery (COD) for transactions up to Rs. 25,000. Once all items you wish to purchase have been added to your cart, follow the steps below to pay with COD:
There are no additional charges for Cash on Delivery.
The COD service is available all over Pakistan. COD is not available internationally.
In cases where multiple items are ordered, typically we ship all the items in one package. The payment for the order will always be in full at the time of delivery. Customers will be notified in case of any exceptions, and payments will be adjusted accordingly.
As per courier arrangements, the cost of shipping increases with the weight of the order. For international shipping rates, please see Shipping Policy.
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Order delivery takes 3-5 days, depending on the size and availability of the product. Orders are processed before 1 PM on a working day and are generally scheduled for delivery the following working day.
Timely delivery is subject to availability of stocks and payment authorizations. In certain cases, we might request for some form of payment verification or source in order to process the order.
Shipping charges depend on the delivery location and package weight. CLOTHE-X provides free shipping for orders above PKR 3,000.
Currently we are unable to ship to any PO Box, military, protected area/location. Shipping charges for failure of attempted delivery to the restricted areas will be borne by the buyer. Please note that we are unable to publish all the restrictive measures imposed by the country of delivery as this is dependent on the country’s customs and restrictions.
Before making any purchase, we encourage you to check with your local customs on their regulations. You might want to ask about the restriction on:
If, for some reason, there are items that cannot be imported into your choice of delivery destination, you might still be able to checkout and pay for the items to be delivered. In such cases, shipping charges incurred for delivery of the package to the intended delivery destination, and for the intended delivery destination to return the goods to CLOTHE-X.com will be borne by the buyer.
Following are the payment options available for your convenience:
Absolutely. All payment information submitted by our customers is encrypted and is kept secure and confidential. CLOTHE-X store is PCI/DSS Level 1 Certified – the level of security used by banks. Only authorized personnel have the right to access this information. At no point will we share, rent or sell your personal information.
Yes. Our Cash On Delivery (COD) service enables you to do that.
No, you do not have to pay any additional tax on the order you place. All applicable taxes are included in the actual product price.
Only in certain cases where international orders are held by the local customs office, clearance charges are to be borne by the customer.