Boost mutex functionality in R.
| Version: | 1.3.5 |
| Imports: | methods, bigmemory.sri, Rcpp, uuid |
| LinkingTo: | BH, Rcpp |
| Published: | 2018-08-21 |
| Author: | Michael J. Kane |
| Maintainer: | Michael J. Kane <bigmemoryauthors at gmail.com> |
| Contact: | Michael J. Kane<EMAIL_ADDRESS>|
| License: | LGPL-2 | Apache License 2.0 |
| URL: | http://www.bigmemory.org |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Citation: | synchronicity citation info |
| Materials: | README NEWS |
| CRAN checks: | synchronicity results |
| Reference manual: | synchronicity.pdf |
| Package source: | synchronicity_1.3.5.tar.gz |
| Windows binaries: | r-devel: synchronicity_1.3.5.zip, r-release: synchronicity_1.3.5.zip, r-oldrel: synchronicity_1.3.5.zip |
| OS X binaries: | r-release: synchronicity_1.3.5.tgz, r-oldrel: synchronicity_1.3.5.tgz |
| Old sources: | synchronicity archive |
| Reverse imports: | BGData |
| Reverse suggests: | NMF, Rdsm |
| Reverse enhances: | bigmemory.sri |
Please use the canonical form https://CRAN.R-project.org/package=synchronicity to link to this page.


| Member ID | First Name | Last Name | Phone | |
|---|---|---|---|---|
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Select if the post-build item should be on each matrix axe or only once as an aggregate for all axes.
AXES runs the post-build item on each axe
MATRIX runs the post-build item only after all axes were executed
BOTH runs the post-build item on each axe and additionally after all axes were executed


This call is useful for accessing the information associated with a request, without freeing the request (in case the user is expected to access it later). It allows one to layer libraries more conveniently, since multiple layers of software may access the same completed request and extract from it the status information.
| MPI_REQUEST_GET_STATUS( request, flag, status ) | |
| IN request | request (handle) |
| OUT flag | boolean flag, same as from MPI_TEST (logical) |
| OUT status | MPI_STATUS object if flag is true (Status) |
int MPI_Request_get_status(MPI_Request request, int *flag, MPI_Status *status)
MPI_REQUEST_GET_STATUS( REQUEST, FLAG, STATUS, IERROR)
INTEGER REQUEST, STATUS(MPI_STATUS_SIZE), IERROR
LOGICAL FLAG
bool MPI::Request::Get_status(MPI::Status& status) const
bool MPI::Request::Get_status() const
Sets flag=true if the operation is complete, and, if so, returns in status the request status. However, unlike test or wait, it does not deallocate or inactivate the request; a subsequent call to test, wait or free should be executed with that request. It sets flag=false if the operation is not complete.


001/* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017package org.apache.commons.configuration2.convert; 018 019import java.util.Collection; 020import java.util.List; 021 022/** 023 *
 024 * Definition of an interface that controls the handling of list delimiters in configuration properties. 025 *
 026 *
 027 * {@link org.apache.commons.configuration2.AbstractConfiguration AbstractConfiguration} supports list delimiters in 028 * property values. If such a delimiter is found, the value actually contains multiple values and has to be split. This 029 * is useful for instance for {@link org.apache.commons.configuration2.PropertiesConfiguration PropertiesConfiguration}: 030 * properties files that have to be compatible with the {@code java.util.Properties} class cannot have multiple 031 * occurrences of a single property key, therefore a different storage scheme for multi-valued properties is needed. A 032 * possible storage scheme could look as follows: 033 *
 034 * 035 *
 036 * myProperty=value1,value2,value3 037 *
 038 * 039 *
 040 * Here a comma is used as list delimiter. When parsing this property (and using a corresponding 041 * {@code ListDelimiterHandler} implementation) the string value is split, and three values are added for the property 042 * key. 043 *
 044 *
 045 * A {@code ListDelimiterHandler} knows how to parse and to escape property values. It is called by concrete 046 * {@code Configuration} implementations when they have to deal with properties with multiple values. 047 *
 048 * 049 * @since 2.0 050 */ 051public interface ListDelimiterHandler { 052 /** 053 * A specialized {@code ValueTransformer} implementation which does no transformation. The {@code transformValue()} 054 * method just returns the passed in object without changes. This instance can be used by configurations which do not 055 * require additional encoding. 056 */ 057 ValueTransformer NOOP_TRANSFORMER = value -> value; 058 059 /** 060 * Parses the specified value for list delimiters and splits it if necessary. The passed in object can be either a 061 * single value or a complex one, e.g. a collection, an array, or an {@code Iterable}. It is the responsibility of this 062 * method to return an {@code Iterable} which contains all extracted values. 063 * 064 * @param value the value to be parsed 065 * @return an {@code Iterable} allowing access to all extracted values 066 */ 067 Iterable<?> parse(Object value); 068 069 /** 070 * Splits the specified string at the list delimiter and returns a collection with all extracted components. A concrete 071 * implementation also has to deal with escape characters which might mask a list delimiter character at certain 072 * positions. The boolean {@code trim} flag determines whether each extracted component should be trimmed. This 073 * typically makes sense as the list delimiter may be surrounded by whitespace. However, there may be specific use cases 074 * in which automatic trimming is not desired. 075 * 076 * @param s the string to be split 077 * @param trim a flag whether each component of the string is to be trimmed 078 * @return a collection with all components extracted from the string 079 */ 080 Collection<String> split(String s, boolean trim); 081 082 /** 083 * Escapes the specified single value object. This method is called for properties containing only a single value. So 084 * this method can rely on the fact that the passed in object is not a list. An implementation has to check whether the 085 * value contains list delimiter characters and - if so - escape them accordingly. 086 * 087 * @param value the value to be escaped 088 * @param transformer a {@code ValueTransformer} for an additional encoding (must not be null) 089 * @return the escaped value 090 */ 091 Object escape(Object value, ValueTransformer transformer); 092 093 /** 094 * Escapes all values in the given list and concatenates them to a single string. This operation is required by 095 * configurations that have to represent properties with multiple values in a single line in their external 096 * configuration representation. This may require an advanced escaping in some cases. 097 * 098 * @param values the list with all the values to be converted to a single value 099 * @param transformer a {@code ValueTransformer} for an additional encoding (must not be null) 100 * @return the resulting escaped value 101 */ 102 Object escapeList(List<?> values, ValueTransformer transformer); 103}


Our pizza loves you just as much as you love it.
Deluxe, Pepperoni and Margherita are a few of the many types of pizza we have that you can choose from.
From a mega size to small size you can choose your favourable pizza size depending on your prefence.
We have varieties of toppings & crust , choose your favorite for an added pizza flavor.
Decide on the number you want, choose whether you want it delivered or not and leave the rest to us.
JustPizza © 2020


This work was born by the collaboration between University of Naples "Federico II" and University of Bristol.
Prof. Mario di Bernardo
Mario di Bernardo (SMIEEE ’06, FIEEE 2012) is Professor of Automatic Control at the University of Naples Federico II, Italy and Professor of Nonlinear Systems and Control at the University of Bristol, U.K. On 28th February 2007 he was bestowed the title of Cavaliere of the Order of Merit of the Italian Republic for scientific merits from the President of Italy.He was elevated to the grade of Fellow of the IEEE in January 2012 for his contributions to the analysis, control and applications of nonlinear systems and complex networks. He was elected to the BoG of the IEEE Circuits and System Society in 2006 and then again in 2009. He is currently VP Financial Activities of IEEE CASS and President of the Italian Society for Chaos and Complexity. His research interests include the analysis, synchronization and control of complex network systems; piecewise-smooth dynamical systems; nonlinear dynamics and nonlinear control with applications to engineering and computational biology.
Dr. Gianfranco Fiore
In 2011 Gianfranco Fiore received his M.Eng. degree in Control Engineering by the University of Naples “Federico II”. In 2015 he received his PhD in Computational Biology and Bioinformatics from the University of Naples “Federico II”, defending a thesis on the “Identification and Control of living cells”. In 2015 he was hired as a Post Doctoral Research Associate from the BBSRC/EPSRC funded Bristol Centre for Synthetic Biology (BrisSynBio) at the University of Bristol. In Bristol he has been focusing his research activity on the design and fabrication of novel microfluidic devices for the real time control of bacterial cell population and, on the design and implementation of embedded cellular controllers.
Francesco Alderisio
Francesco was born in 1989 in San Paolo Belsito, Naples, Italy. He received his M.Eng. degree in Control Engineering in December 2013 by the University of Naples “Federico II”. Between January 2014 and July 2016 he was involved in the European Project “AlterEgo” and in October 2014 he started his PhD in Engineering Mathematics at University of Bristol, under the supervision of Prof. Mario di Bernardo. Since September 2015 he has been an assistant teacher at University of Bristol, teaching first year engineering students on the main mathematics, through both group and front-led sessions. His current research interests include human-robot interaction, and analysis and control of synchronization in complex networks with applications to human movement coordination. He has been a reviewer for conference papers (IEEE Conference on Decision and Control, IEEE Multi-conference on Systems and Control, European Control Conference, American Control Conference), as well as journal papers (IEEE Transactions on Automatic Control, IEEE Transactions on Control of Network Systems, Automatica).
Maria Lombardi
Maria was born in 1991 in Nola, Naples, Italy. She received her M.Eng. degree in Computer Engineering in April 2016 by the University of Naples "Federico II" with a thesis in Nonlinear Control on the “Development and Experimental Validation of a Software tool for the study of Human Multiplayer Coordination”. Her thesis found its context in the European Project “AlterEgo”, which she worked on until July 2016. Furthermore from April to December 2016, she had worked on the design and development of a network for the power supply of electrical vehicles at Ensto Chago, part of Ensto Group in Porvoo, Finland. In January 2017 she started her PhD in Engineering Mathematics at University of Bristol, under the supervision of Prof. Mario di Bernardo.


Taiwander needs your help!
Taiwander is cheering hard for
Taiwan every day! Now it's racing in the
YuruChara Grand Prix 2015! This the biggest competition
for mascot characters in Japan, the one that made Kumamon famous. The
competition is really tough, Taiwander is struggling and needs your
support!
You can help Taiwander by voting on
the Yuruchara Grand Prix website. It's all in Japanese, but it's very
easy after you've done it once. We've written some instructions to help.
You can cheer for Taiwander by voting!
Please see the instructions here for how to vote.
If you want more information, here are some FAQs!
Who is Taiwander?
Taiwander is a happy island who lives in Japan. Here is its biography.
It's a volunteer with the Cheer4Taiwan NGO working to tell people in
Japan about the beautiful island of Taiwan! Taiwander isn't connected
to the government or any business. Let's watch this video to see
Taiwander dancing live to its theme song, "Taiwan the Tropical Island"
(written and sung by Kenta Ebara).
Music Video Version | Taiwanese Version | Lyrics (JP) | Lyrics (TW)
Why is Taiwander struggling in the YuruChara Grand Prix?
Most people in Japan give their vote to their own local character and
business characters are supported by their company. Taiwander is in the
'overseas character' category, competing against characters working for
foreign governments (and 7-11!). Taiwander needs support from people in
Taiwan, but even though it loves Taiwan very much it's very shy about
speaking Taiwanese! It only has 900 votes so far: the top character has
more than 1 million!
Who are you?
We're just fans who want to help Taiwander! We think Cheer4Taiwan and
Taiwander do a good job of promoting Taiwan, but we're not affiliated
with them or any other organisation in Japan or Taiwan.


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Our chatbot will be powered using the Microsoft Language Understanding Intelligent Service (LUIS), which is an algorithm used to identify the intent behind natural language. The idea behind the use of this algorithm is to analyse natural language and get what exactly a user is asking for.
It takes a sentence such as the one below and breaks it down into an intent (what the user wants) and an entity (something the user wants to do things with). We can then create individual functions for each type of user intent. The algorithm is key to allowing users to speak in natural language just as if they were talking to a real person; without it, there would be no way of understanding user speech and it would feel a lot more like a command line application, typing in set responses.
LUIS first needs to be trained with sample sentences for each type of intent. So, using the previous example, the user intent “Show Projects” can be written in many different ways:
"Find all projects related to AI"
"List projects that use Artificial Intelligence"
"Can you search for AI projects"
"What projects use Artificial Intelligence?"
Our testing consists of passing in a large dataset of these example sentences for each intent, so that LUIS is able to identify an intent for any user message. After testing,
LUIS should be able to recognise any message sent by a user and it is then up to us to write the code that responds to this request.
When passing in a sentence, a score between 0 and 1 is assigned to how close it matches with each intents:
LUIS then gets the intent with the highest score and returns it as the best match. To test our LUIS setup, we passed in test sentences and checked which intent they
were being mapped to. If the intent returned wasn't the correct one for the sentence, it was considered a test failure, and LUIS needed fixing to accommodate for this.
We will use a sample file of messages to test LUIS consisting of valid and invalid chatbot sentences. For the sentences that failed, we will record them and figure out
why LUIS isn't matching them.
Over the past few months, we were able to train our chatbot to recognise more than just one intent. It now consists of these complete intents supporting a range of functions within the chatbot:
The testing for LUIS was done over a period of time. By debugging our chatbot, every command sent was also saved by LUIS
for us to review. In the end, we recieved 1419 messages recieved by our LUIS app for us to train using. This ensured that
our chatbot was able to map as many messages as possible to the correct commands.
As part of our solution, we developed a parsing program that was able to take IXN forms, in word document format, parse them into a list data structure in Python and push them to a database. We did this by accessing the XML file encoding behind .docx files. Using this, we were able to isolate the table tags and find the text enclosed in this table. We then extracted this text and constructed a tuple of field and value pairs.
To compensate for the fact that people filling out forms may choose to change the spelling of fields, or even
add their own in, we created an algorithm that correctly mapped fields to the order we had them in our database schema.
We did this by using a fuzzy string matching algorithm, that compared how close to strings were related.
The issue with this was that a lot of times, collisions would occur, in which the same field would be mapped twice, to
2 different columns of our database schema. To fix this, we had to store the best matches for each column of the table and
whenever a collision occurred, swap out the field that mapped with the lowest similarity score to its next best alternative. The
issue with this was that sometimes the alternative swapped out also had already been mapped before. As a result, the algorithm
needed to resolve this was a complex recursive function that checked everything up to that value in the mapped fields
each time there was a swap.


JButton
Recall that many Swing classes start with J
.
Similar AWT classes do not start with J
.
A frequent error is to forget the J
and get the wrong class.
For example, there is an AWT class Button
.
Here, as a review, is how the class JButton
fits into the Swing hierarchy:


| Database Name | SQLite3 |
| Database Version | <IP_ADDRESS> |
| Mandatory Features | 0 of 164 (0%) |
| Optional Features | 0 of 364 (0%) |
| All Tests | 373 of 515 (72%) |
| Feature ID | Tests | Description |
|---|---|---|
| E011 | 100% (128/128) | Numeric data types |
| E011-01 | 100% (7/7) | INTEGER and SMALLINT data types (including all spellings) |


--- layout: post title: There are (at least) two things wrong with this Dart code date: '2014-04-26T15:45:00.000-07:00' author: Kevin Moore tags: - dartlang - Programming - dart modified_time: '2014-04-26T15:45:19.994-07:00' thumbnail: http://4.bp.blogspot.com/-Xj6x0dbVPxA/U1wrVMg0RZI/AAAAAAAALek/CcKw-Go-lqk/s72-c/dart_logo_simple.png blogger_id: tag:blogger.com,1999:blog-2115056243892631308.post-2758999054338105736 blogger_orig_url: http://work.j832.com/2014/04/there-are-at-least-two-things-wrong.html ---
operator ==
.class Foo {
int _a;
int _b;
int get a => _a;
int get b => _b;
...
bool operator ==(Foo other) {
return other._a == this._a && other._b == this._b;
}
}


Album downloads ({{ downloads.count }})
-
{% for download in downloads %}
-
{{ download.album }}
Downloaded at: {{ download.timestamp }}
Download failed: {{ download.failed }}
{% endfor %}
Downloaded at: {{ download.timestamp }}
Download failed: {{ download.failed }}


THE SHADOW A film review by James Berardinelli Copyright 1994 James Berardinelli
Rating (0 to 10): 6.7
Date Released: 7/1/94 Running Length: 1:47 Rated: PG-13 (Violence, mature themes)
Starring: Alec Baldwin, John Lone, Penelope Ann Miller, Peter Boyle, Ian McKellen, Tim Curry, Jonathan Winters Director: Russell Mulcahy Producers: Martin Bregman, Willi Baer, and Michael S. Bregman Screenplay: David Koepp Cinematography: Stephen H. Burnum Music: Jerry Goldsmith Released by Universal Pictures
There seems to be a common failing amongst the last decade's screen versions of the exploits of comic book-type heroes. Most of these genre movies--BATMAN, DICK TRACY, THE CROW, and now THE SHADOW - have been visually impressive (stunning, even), but lacking in the character department. Who is Bruce Wayne, really? Or Dick Tracy? Or Lamont Cranston?
Given its due, THE SHADOW probably does the best of the aforementioned movies in creating a personality for its title character, but it still makes one yearn for the not-so-distant days of Christopher Reeves' SUPERMAN, when there was more than the shell of a man beneath the cape. Alas, style has supplanted substance, and all this sort of film seems to need is a brooding atmosphere into which the avenging angel can swoop.
THE SHADOW tries to say something about the human condition ("Who knows what evil lurks in the hearts of men? The Shadow knows."), and it has an interesting reason for why its title character is who he is. Back in Asia during the early 1930s, Lamont Cranston (Alec Baldwin) was an evil drug lord, given to slaughter and debauchery until a priest shows him the error of his ways and forces redemption upon him.
By 1939, Cranston has returned to New York and, as the Shadow, is busy cleaning up the city's criminal element. However, while the thugs and mobsters of Manhattan don't offer much threat to someone who can "cloud men's minds," danger arrives in the person of Shiwan Khan (John Lone), the last descendant of Genghis and Cranston's psychic equal. Khan's stated goal is to conquer the half of the world that his ancestor failed to, and in order to do this, he plunders the United States' scientific laboratories, where he comes up with a pair of brilliant scientists (played by Ian McKellen and Tim Curry) and sets to work building an atomic bomb. Meanwhile, not only is Cranston having difficulty trying to locate Khan's hideout, but his identity has been uncovered by Margo Lane (Penelope Ann Miller), the daughter of one of Khan's captives.
The most interesting element of THE SHADOW is understanding what gives Cranston his powers: the harnessing of the evil in his own heart. With Khan, we get to see the antithesis--someone who allows that evil free reign. Ultimately, of course, the two must clash to see which facet of power has the greater strength.
THE SHADOW's problems have nothing to do with the basic premise; rather, they are flaws in execution. The setup promises something more invigorating than it delivers. The entire "use the atomic bomb to hold New York hostage" plotline is trite. There's nothing especially interesting about it, or the man behind it. As a villain, Shiwan Khan lacks a certain flair. Although John Lone's portrayal is anything-but- understated, Khan still doesn't seem all that impressive or threatening, even as he gets closer to achieving his desire. He's just another megalomaniac with unusual powers.
Alec Baldwin's portrayal of the invisible avenger is suitably creepy (although his makeup job could have been a little more impressive), but his version of Cranston is dull. Much as was true for Michael Keaton in BATMAN, the costume seems to bring out the performance. Penelope Ann Miller looks very fetching in her gauzy 30s costumes with their plunging necklines, but her role is essentially the thankless one of the hero's love interest. Tim Curry and Jonathan Winters are kept relatively in check in supporting roles. Veteran British actor Ian McKellen is vastly underused.
Will the Shadow be back? As always, box office take will be the determining factor. If there is a next time, it might be intriguing to further examine those dark forces prowling Cranston's heart instead of having him chase around a warrior with delusions of godhood. Or, failing that, the producers could do something that would render plot and all attempts at subtlety void: bring in Jack Nicholson to play the bad guy.
- James Berardinelli<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


constructFormdata() Constructs a FormData instance from an object.
the object to generate the FormData from
delete() DELETE request
generateHeaders() A function used to generate the headers that need to be passed to the request performee.
get() GET request
patch() PATCH request
Request body
patchFormdata() PATCH request (multipart/form-data, application/x-www-form-urlencoded)
post() POST request
Request body
postFormdata() POST request (multipart/form-data, application/x-www-form-urlencoded)
put() PUT request
Request body
putFormdata() PUT request (multipart/form-data, application/x-www-form-urlencoded)
Generated using TypeDoc
Defines an API request.


Google Store lists ‘CLAW’ Pixel holder for Stadia Controller, ‘coming soon’
by Abner LiDuring last week’s AMA, a Made for Google accessory that mounts Pixel phones onto Stadia Controllers was teased. The “CLAW” holder is now listed on the Google Store as “coming soon” for $14.99.
Unlike third-party solutions, this Claw holder is custom-made for the Stadia Controller. It straps onto the front of the first-party Wi-Fi accessory with cutouts for the Stadia button, and makes sure not to conflict with the Assistant or Capture keys. It also leaves space for the 3.5 mm headphone jack up front and USB-C port on the rear.
The Claw obscures most of the face buttons, but is specifically designed to put your Pixel’s screen right in front of you. Google notes how the “Claw positions the phone in a comfortable and balanced position, enabling ergonomic mobile game play.”
Predominantly made of black polycarbonate plastic with a matte finish, there is silicone on the phone holder portion that’s spring loaded. Officially, the Claw is designed for the Pixel 3, Pixel 3 XL, Pixel 3a, Pixel 3a XL, Pixel 4, or Pixel 4 XL.
Of course, it should work for the Pixel 2 and fit other third-party devices, not that non-Pixel Android phones or iOS devices are supported by Stadia at this stage. The “Phone Holder Extension Height” lists a minimum of 2.09 in (53 mm) and maximum of 3.19 in (81 mm).
Available for $14.99, it looks like no short USB-C cable is included in the box. The Stadia Controller Claw holder is listed as “Coming soon” and expected to be an exclusive.
More about Google Stadia:
- Stadia Founders invite codes not arriving as promised, Google working on a solution
- How to improve your internet connection for Google Stadia
- Google launches Stadia, an ambitious streaming service that could change gaming
FTC: We use income earning auto affiliate links. More.


This tool works with a time-enabled layer of
point features that are instant
in time. It first
determines which features belong to a track using an identifier. Using the time at each location, the tracks are ordered sequentially and the tool searches these paths of movement over time. Depending on the output type chosen, the resulting features can be time-enabled points (instant time type) or areas (interval time type) that represent locations where tracked objects travelled less than the specified distance within the specified duration of time. When the dwells are discovered, statistics about the included features are calculated and assigned to the output track. The
most basic statistic is the count of points within
the dwell location, but other statistics can be calculated as well.
Features in time-enabled layers can be represented in one of two ways:
For example, suppose you work for the Department of Transportation and have collected GPS readings for major highways. Each GPS record contains a unique vehicle ID, the time, location, and speed. Using the unique vehicle ID to define individual tracks, search for vehicles that have not moved more than 100 meters in at least 15 minutes to uncover troublesome areas in the road network. Additionally, you could calculate statistics such as the mean speed of the vehicles within the dwell location.
ADD HERE
The point layer that will be summarized into dwells. The input layer must be time-enabled with features that represent an instant in time, as well as have one or more fields that can be used to identify tracks.
In addition to choosing a layer from your map, you can choose Choose Analysis Layer at the bottom of the drop-down list to browse to your contents for a big data file share dataset or feature layer. You may optionally apply a filter on your input layer or apply a selection on hosted layers added to your map. Filters and selections are only applied for analysis.
Fields that represent the track identifier. You can use one field or multiple fields to represent unique values of tracks.
As an example, if you were finding idle locations for hurricanes, you could use the hurricane name as the track field.
Method used to calculate the dwell distance within tracks. The Planar method may calculate the results more quickly but will not wrap tracks around the international dateline or account for the actual shape of the earth when buffering. The Geodesic method will wrap tracks around the date line if required and apply a geodesic buffer to account for the shape of the earth.
The dwell distance tolerance is the maximum distance between points to be considered in a single dwell location.
For example, if you are finding dwells where traffic has not moved more than 20 meters within a certain time, the distance tolerance would be 20 meters.
Use the parameter Define the temporal search range to specify the time.
The unit of the distance tolerance.
The dwell time tolerance is the minimum time duration of a dwell to be considered in a single dwell location.
For example, if you want to know where traffic hasn't moved a certain distance within an hour, the time tolerance would be 1 hour.
Use the parameter Define the spatial search range to specify the distance.
The unit of the time tolerance.
You can calculate statistics on features that are summarized. You can calculate the following on numeric fields:
You can calculate the following on string fields:
Determines which features are returned and the format. Four types are available:
GeoAnalytics results are stored to an data store and exposed as a feature layer in Portal for ArcGIS. In most cases, results should be stored to the spatiotemporal data store and this is the default. In some cases, saving results to the relational data store is a good option. The following are reasons why you may want to store results to the relational data store:
You should not use the relational data store if you expect your GeoAnalytics results to increase and need to take advantage of the spatiotemporal big data store's capabilities to handle large amounts of data.
The name of the layer that will be created. If you are writing to an ArcGIS Data Store, your results will be saved in My Content and added to the map. If you are writing to a big data file share, your results will be stored in the big data file share and added to its manifest. It will not be added to the map. The default name is based on the tool name and the input layer name. If the layer already exists, the tool will fail.
When writing to ArcGIS Data Store (relational or spatiotemporal big data store) using the Save result in drop-down box, you can specify the name of a folder in My Content where the result will be saved.


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Portfolio
ClearShift
Microsoft community
Web tools
Adobe.CC
Icons8
Accesibility checker
Travel
Litrail
Trafi
Google maps


Ant Maze
Controls
Below you'll find various controls for this scene. They're broken up into the
following sections:
- Mouse
- Keyboard
- Buttons
Hope you enjoy the game!
Mouse
Click and drag the mouse to rotate the current component.
Keyboard
- w: moves component up along y-axis
- s: moves component down along y-axis
- a: moves component left along the x-axis
- d: moves the component right along the x-axis
- q: applies uniform scaling to shrink the component
- e: applies uniform scaling to scale the component
- 1: pans the camera down by center of interest
- 2: pans the camera up by center of interest
- 3: pans the camera left by center of interest
- 4: pans the camera right by center of interest
- 5: pans the camera in by view angle
- 6: pans the camera out by view angle
Buttons
The following controls are used to select the various body parts of the ant
for transformation.
Each subsection refers to the level of the object in
the scene graph. Try grabbing a leg and pulling it off the body!
If you don't like what you've done, consider resetting the scene!
Top Level
Tier 2
Tier 3
Reset


Provides pure functional combinators.
- Source:
- /home/queen/Projects/folktale/core/lambda/lib/index.js, line 22
Members
-
<static> apply
(a → b) → a → b
-
Applies a function to an argument.
- Source:
- /home/queen/Projects/folktale/core/lambda/lib/index.js, line 73
Example
var inc = (a) => a + 1 apply(inc)(3) // => 4
-
<static> compose
(b → c) → (a → b) → (a → c)
-
Composes two functions together.
- Source:
- /home/queen/Projects/folktale/core/lambda/lib/index.js, line 105
Example
var inc = (a) => a + 1 var square = (a) => a * a compose(inc)(square)(2) // => 5, inc(square(2))
-
<static> constant
a → b → a
-
The constant combinator. Always returns the first argument it's given.
- Source:
- /home/queen/Projects/folktale/core/lambda/lib/index.js, line 58
Example
constant(3)(2) // => 3 constant('foo')([1]) // => 'foo'
-
<static> curry
Number → ((a1, a2, ..., aN) → b) → (a1 → a2 → ... → aN → b)
-
Transforms any function on tuples into a curried function.
- Source:
- /home/queen/Projects/folktale/core/lambda/lib/index.js, line 120
Example
var sub3 = (a, b, c) => a - b - c curry(3, sub3)(5)(2)(1) // => 2
-
<static> flip
(a → b → c) → (b → a → c)
-
Inverts the order of the parameters of a binary function.
- Source:
- /home/queen/Projects/folktale/core/lambda/lib/index.js, line 89
Example
var subtract = (a) => (b) => a - b subtract(3)(2) // => 1 flip(subtract)(3)(2) // => -1
-
<static> identity
a → a
-
The identity combinator. Always returns the argument it's given.
- Source:
- /home/queen/Projects/folktale/core/lambda/lib/index.js, line 43
Example
identity(3) // => 3 identity([1]) // => [1]
-
<static> spread
(a1 → a2 → ... → aN → b) → (#[a1, a2, ..., aN) → b)
-
Transforms a curried function into one accepting a list of arguments.
- Source:
- /home/queen/Projects/folktale/core/lambda/lib/index.js, line 146
Example
var add = (a) => (b) => a + b spread(add)([3, 2]) // => 5
-
<static> uncurry
(a1 → a2 → ... → aN → b) → ((a1, a2, ..., aN) → b)
-
Transforms a curried function into a function on tuples.
- Source:
- /home/queen/Projects/folktale/core/lambda/lib/index.js, line 161
Example
var add = (a) => (b) => a + b uncurry(add)(3, 2) // => 5
-
<static> upon
(b → b → c) → (a → b) → (a → a → c)
-
Applies an unary function to both arguments of a binary function.
- Source:
- /home/queen/Projects/folktale/core/lambda/lib/index.js, line 176
Example
var xss = [[1, 2], [3, 1], [-2, 4]] sortBy(upon(compare, first))


{% extends 'email_template.html' %} {% block content %}
Hello {{ session.student.first_name }},
A training session has been scheduled! Here are the details:
Training Session:
If you believe there has been an error, please contact<EMAIL_ADDRESS>immediately!
{% endblock %}


Expanding textareas provide the same functionality as regular textareas however with the additional feature that they can grow in height as more content is added to them.
To create an expanding textarea use the expanding-textarea
directive.
Additional attributes can be specified to configure the behavior of the textarea:


Data Engineer
Machine Learning Engineer
Front End Developer
Web UI Design
React Developer
Node Backend Developer
Front End Framework Developer
Project Manager


High-level tools for working with 'data.world' data sets. 'data.world' is a platform where you can find interesting data, store and showcase your own data and data projects, and find and collaborate with other members. In addition to exploring, querying and charting data on the data.world site, you can access data via 'API' endpoints and integrations. Use this package to access, query and explore data sets, and to publish your insights. Visit <https://data.world>, for additional information.
| Version: | 1.2.2 |
| Depends: | R (≥ 3.3.0), dwapi (≥ 0.1.3) |
| Imports: | httr, ini, miniUI, shiny, stringi |
| Suggests: | covr, knitr, lintr, readr, rmarkdown, testthat (≥ 2.0.0) |
| Published: | 2018-04-04 |
| Author: | Rafael Pereira [aut, cre], Triet Le [aut], Bryon Jacob [aut], Scott Came [aut] |
| Maintainer: | Rafael Pereira <rafael.pereira at data.world> |
| BugReports: | https://github.com/datadotworld/data.world-r/issues |
| License: | Apache License 2.0 |
| URL: | https://github.com/datadotworld/data.world-r |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | data.world results |
| Reference manual: | data.world.pdf |
| Vignettes: |
query quickstart |
| Package source: | data.world_1.2.2.tar.gz |
| Windows binaries: | r-devel: data.world_1.2.2.zip, r-release: data.world_1.2.2.zip, r-oldrel: data.world_1.2.2.zip |
| OS X binaries: | r-release: data.world_1.2.2.tgz, r-oldrel: data.world_1.2.2.tgz |
| Old sources: | data.world archive |
Please use the canonical form https://CRAN.R-project.org/package=data.world to link to this page.


| Item | Changes | Source |
|---|---|---|
|
Type: Quote "If you think you understand quantum mechanics, you don't understand quantum mechanics." |
No Changes, direct quote does not require any |
Source: https://en.wikiquote.org/wiki/Talk:Richard_Feynman#%22 If_you_think_you_understand_quantum_mechanics, _you_don't_understand_quantum_mechanics.%22 |
|
Type: font-icon Icon: Class Identifier(s): fas fa-angle-double-right fa-second |
Scaled and coloured, used for bullet points and name designation | Source: https://fontawesome.com/icons/angle-double-right?style=solid |
|
Type: font-icon Icon: Class Identifier(s): fas fa-bars |
Scaled and coloured, used for hamburger menu icon | Source: https://fontawesome.com/icons/bars?style=solid |
|
Type: font-icon Icon: Class Identifier(s): fas fa-times |
Scaled and coloured, used for hamburger menu icon | Source: https://fontawesome.com/icons/times?style=solid |
|
Type: content reference |
Reference only, no copying or quotations | Source: https://www.pbs.org/transistor/science/info/quantum.html |
|
Type: content reference |
Reference only, no copying or quotations | Source: https://en.wikipedia.org/wiki/Matter_wave |
|
Type: content reference |
Reference only, no copying or quotations | Source: https://theconversation.com/explainer-what-is-wave-particle-duality-7414 |
|
Type: content reference |
Reference only, no copying or quotations | Source: https://en.wikipedia.org/wiki/Double-slit_experiment |
|
Type: content reference |
Reference only, no copying or quotations | Source: https://www.theguardian.com/science/2013/nov/10/what-is-heisenbergs-uncertainty-principle |
|
Type: content reference |
Reference only, no copying or quotations | Source: https://www.physicsoftheuniverse.com/topics_quantum_nonlocality.html |
|
Type: content reference |
Reference only, no copying or quotations | Source: https://www.livescience.com/28550-how-quantum-entanglement-works-infographic.html |
|
Type: image Content: |
Scaled to fit | Source: https://gifer.com/en/AYmV |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/Max_Planck#/media/File:Max_Planck_1933.jpg |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/David_Bohm#/media/File:David_Bohm.jpg |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/File:Niels_Bohr.jpg |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/File:Max_Born.jpg |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/Louis_de_Broglie#/media/File:Broglie_Big.jpg |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/Paul_Dirac#/media/File:Dirac_4.jpg |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/File:Einstein_1921_by_F_Schmutzer_-_restoration.jpg |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/Enrico_Fermi#/media/File:Enrico_Fermi_1943-49.jpg |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/Richard_Feynman#/media/File:Richard_Feynman_Nobel.jpg |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/Werner_Heisenberg#/media/File:Bundesarchiv_Bild183-R57262,_Werner_Heisenberg.jpg |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/Wolfgang_Pauli#/media/File:Pauli.jpg |
|
Type: image Content: |
Scaled to fit | Source: https://en.wikipedia.org/wiki/Erwin_Schr%C3%B6dinger#/media/File:Erwin_Schr%C3%B6dinger_(1933).jpg |
|
Type: Code Language: JavaScript Library: CodeMirror |
In-Browser code editor (CodeMirror), rescaled to suit page display. Note: the evaluation (execution of written code) is not included in this library, it was self-created | Source: https://github.com/codemirror/codemirror |


PICKER BOT
Problem:
High Labour scarcity
Our Solution:
We are developing an equipment to pick the cotton using the AI.
How it works?
Navigated by human
Automatically detects the cotton and pick the cotton.
Benefits:
No need to
run for labour.
Easy to Operate.
Wastage of cotton is reduced that ensures the
increased profit of sales.
Can be used on the day after raining when human cannot walk and pick the cotton from the plant
because the field will be wet and difficult to walk.
Will be soon available in market


;
Server Testing
Topics
-
automated testing.
-
jest testing framework.
-
supertest module.
Assignment
For this project you will use Test Driven Development
to create a RESTful API using Node.js
and Express
that publishes a set of endpoints to manage a resource of your choosing.
-
[X] Data can be stored in memory, adding a test database is optional.
Download Project and Install Dependencies
-
[X] fork and clone this repository.
-
[X] CD into the folder where you downloaded the repository.
-
[X] run
npm i
to download all dependencies.
-
[X] type
npm test
to run the tests.
Requirements
-
[X] use
jest
and supertest
to write the tests.
-
[X] Your API must be able to create and delete a resource of your choosing.
-
[X] Write a minimum of two tests per route handler.


Last updated: 2021-09-14
Checks: 7 0
Knit directory: myTidyTuesday/
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This week’s data comes from the Himalayan Database, a compilation of records for all expeditions that have climbed in the Nepal Himalaya mountain chain. The data covers all expeditions from 1905 thru the spring of 2019 for more than 465 significant peaks in Nepal.
Let’s first explore the details of the mountain peaks
peaks %>%
arrange(desc(height_meters)) %>%
head(20) %>%
mutate(peak_name = fct_reorder(peak_name, height_meters)) %>%
ggplot(aes(height_meters, peak_name, fill = climbing_status)) +
geom_col(show.legend = FALSE) +
labs(
x = "Height (meters)",
y = NULL,
title = "Top 20 Himalayan Peaks",
subtitle = "With both Climbed
and Unclimbed summits.",
fill = ""
) +
theme(
plot.subtitle = ggtext::element_markdown(
size = 11, lineheight = 1.2,
face = "bold"
),
panel.grid.major.y = element_blank()
)
Let’s tidy some of the expeditions dataset
na_reasons <- c(
"Unknown",
"Attempt rumoured",
"Did not attempt climb",
"Did not reach base camp"
)
expeditions <- tt$expeditions %>%
mutate(
time_to_highpoint = highpoint_date -
basecamp_date,
success = case_when(
str_detect(termination_reason, "Success") ~ "Success",
termination_reason %in% na_reasons ~ "Other",
TRUE ~ "Failure"
),
days_to_highpoint = as.integer(highpoint_date - basecamp_date)
)
Other questions that could be posed and possible answered with this dataset:
Fraction of successful climbs per year
Rate of death over time, deadliest peaks, by mountain
Death causes and rate of injury
Distribution of duration of climbs versus the height or time
Correlation between frequency of expeditions and death rate
Which are the most dangerous peaks that have been climbed?
summarize_expeditions <- function(tbl) {
tbl %>%
summarize(
first_climb = min(year),
success_rate = mean(success == "Success"),
n_climbs = n(),
across(members:hired_staff_deaths, sum),
.groups = "drop"
) %>%
mutate(
pct_death = member_deaths / members,
pct_hired_staff_deaths = hired_staff_deaths / hired_staff
)
}
peak_summarized <- expeditions %>%
group_by(peak_id, peak_name) %>%
summarize_expeditions() %>%
ungroup() %>%
arrange(desc(n_climbs)) %>%
inner_join(peaks %>% select(peak_id, height_meters), by = "peak_id")
What are the deadliest mountains?
Answering the question directly from the proportions may be misleading because some mountains have so few climbers.
Given the limited amount of data available on some mountains, the death rate is adjusted by adding the available priors from all climbing. The model could be further refined by regressing on information about oxygen use or other features.
For further reading: Introduction to Empirical Bayes: Examples from Baseball Statistics
peaks_eb <- peak_summarized %>%
filter(members >= 20) %>%
arrange(desc(pct_death)) %>%
add_ebb_estimate(member_deaths, members)
peaks_eb %>%
ggplot(aes(pct_death, .fitted)) +
geom_point(aes(size = members)) +
geom_abline(color = "red") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_color_viridis_c(trans = "log10") +
labs(
x = "Death Rate (raw)",
y = "Adjusted Estimate of Death Rate using empirical Bayes",
title = "How much adjustment compression using priors?"
) +
theme(legend.position = c(0.8, 0.2))
peaks_eb %>%
filter(members >= 500) %>%
arrange(desc(.fitted)) %>%
mutate(peak_name = fct_reorder(peak_name, .fitted)) %>%
ggplot(aes(.fitted, peak_name)) +
geom_point(aes(size = members)) +
geom_errorbarh(aes(xmin = .low, xmax = .high), color = "gray") +
expand_limits(x = 0) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
labs(
subtitle = "Estimate of Death Rate using empirical Bayes with 95% credible interval",
y = "", x = "",
title = "Deadliest Himalayan Peaks with more than 500 climbers",
caption = "Visual: @jim_gruman, Data: the Himalayan Database"
) +
theme(
panel.grid.major.y = element_blank(),
legend.position = c(0.8, 0.2)
)
How does death rate relate to elevation?
peaks_eb %>%
filter(members >= 500) %>%
ggplot(aes(height_meters, .fitted)) +
geom_point(aes(size = members)) +
labs(title = "There does not appear to be a relationship between death rate and height")
What is the distance between the base camp and the summit?
expeditions %>%
filter(
success == "Success",
!is.na(days_to_highpoint),
!is.na(peak_name)
) %>%
mutate(
peak_name = fct_lump(peak_name, 10),
peak_name = fct_reorder(peak_name, days_to_highpoint, mean)
) %>%
ggplot(aes(days_to_highpoint, peak_name)) +
geom_boxplot() +
labs(
title = "Duration to successfully summit the most popular Himalayan peaks",
caption = "Visual: @jim_gruman, Data: the Himalayan Database",
x = "Days from basecamp to summit",
y = ""
) +
theme(panel.grid.major.y = element_blank())
expeditions %>%
filter(peak_name == "Everest") %>%
ggplot(aes(days_to_highpoint, fill = success)) +
geom_density(alpha = 0.3) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
labs(
title = "Duration to successfully summit Everest",
caption = "Visual: @jim_gruman, Data: the Himalayan Database",
x = "Days from basecamp to summit",
y = "Percentage",
fill = ""
) +
theme(
panel.grid.major.y = element_blank(),
legend.position = c(0.6, 0.5),
legend.background = element_rect(color = "white")
)
When have the Everest expeditions been successful?
expeditions %>%
ggplot(aes(year, fill = success)) +
geom_histogram(alpha = 0.3, bins = 40) +
labs(
title = "Year of Everest climbs",
caption = "Visual: @jim_gruman, Data: the Himalayan Database",
x = "Year",
y = "Climbers",
fill = ""
) +
theme(
panel.grid.major.y = element_blank(),
legend.position = c(0.2, 0.5),
legend.background = element_rect(color = "white")
)
What is the median amount of time to climb?
everest_by_decade <- expeditions %>%
filter(peak_name == "Everest") %>%
mutate(decade = pmax(10 * (year %/% 10), 1970)) %>%
# limp prior to 1970
group_by(decade, peak_name) %>%
summarize_expeditions()
everest_by_decade %>%
ggplot(aes(decade, pct_death)) +
geom_line(aes(color = "All climbers")) +
geom_line(aes(y = pct_hired_staff_deaths, color = "Hired Staff")) +
geom_point(aes(color = "All climbers", size = members)) +
geom_point(aes(y = pct_hired_staff_deaths, color = "Hired Staff", size = hired_staff)) +
scale_y_continuous(labels = scales::percent_format(accuracy = .1)) +
scale_x_continuous(
breaks = seq(1970, 2010, 10),
labels = c("<= 1980", seq(1980, 2010, 10))
) +
expand_limits(y = 0) +
labs(
x = "Decade", color = "",
y = "Death Rate",
title = "Everest has been getting less deadly over time",
subtitle = "Though trends have reversed for hired staff",
size = "Number of climbers", caption = "Visual: @jim_gruman, Data: the Himalayan Database"
) +
theme(
legend.position = c(0.2, 0.4),
legend.background = element_rect(color = "white")
)
everest_by_decade %>%
ggplot(aes(decade, success_rate)) +
geom_line() +
geom_point(aes(size = n_climbs)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_x_continuous(
breaks = seq(1970, 2010, 10),
labels = c("<= 1980", seq(1980, 2010, 10))
) +
expand_limits(y = 0) +
labs(
x = "Decade",
y = "Success Rate",
title = "The Everest summit success rate has been increasing",
size = "Number of climbs", caption = "Visual: @jim_gruman, Data: the Himalayan Database"
) +
theme(
legend.position = c(0.7, 0.3),
legend.background = element_rect(color = "white")
)
Examine the death probability per expedition member. What causes climber fatalities?
members <- tt$members
members %>%
count(died, death_cause) %>%
knitr::kable()
| died | death_cause | n |
|---|---|---|
| FALSE | NA | 75413 |
| TRUE | AMS | 102 |
| TRUE | Avalanche | 369 |
| TRUE | Crevasse | 27 |
| TRUE | Disappearance (unexplained) | 49 |
| TRUE | Exhaustion | 41 |
| TRUE | Exposure / frostbite | 42 |
| TRUE | Fall | 331 |
| TRUE | Falling rock / ice | 26 |
| TRUE | Icefall collapse | 16 |
| TRUE | Illness (non-AMS) | 60 |
| TRUE | Other | 33 |
| TRUE | Unknown | 10 |
members %>%
count(sex) %>%
knitr::kable()
| sex | n |
|---|---|
| F | 7044 |
| M | 69473 |
| NA | 2 |
For Everest, what are the significant features in predicting death?
members %>%
filter(peak_name == "Everest") %>%
mutate(expedition_role = fct_lump(expedition_role, 5)) %>%
glm(died ~ year + age + sex + expedition_role,
data = .,
family = "binomial"
) %>%
tidy() %>%
mutate(p.value = format.pval(p.value)) %>%
knitr::kable()
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 49.8368629 | 7.0523777 | 7.0666752 | 1.5869e-12 |
| year | -0.0276627 | 0.0035435 | -7.8066459 | 5.8730e-15 |
| age | 0.0182624 | 0.0066097 | 2.7629740 | 0.0057277 |
| sexM | 0.3353777 | 0.2907061 | 1.1536656 | 0.2486373 |
| expedition_roleDeputy Leader | -0.0639574 | 0.5124297 | -0.1248120 | 0.9006724 |
| expedition_roleExp Doctor | -0.1399287 | 0.4634403 | -0.3019346 | 0.7627019 |
| expedition_roleH-A Worker | 0.3469201 | 0.1625223 | 2.1346007 | 0.0327936 |
| expedition_roleLeader | 0.6033960 | 0.1852958 | 3.2563929 | 0.0011284 |
| expedition_roleOther | 0.0700387 | 0.2232777 | 0.3136842 | 0.7537609 |
The significant features include the year (lowers likelihood), the leader role (more likely to die), age (more likely to die), and the H-Aworker (much more likely to die)
members %>%
filter(peak_name == "Everest", !is.na(age)) %>%
group_by(age = 10 * (age %/% 10)) %>%
summarize(
n_climbers = n(),
pct_death = mean(died)
) %>%
knitr::kable()
| age | n_climbers | pct_death |
|---|---|---|
| 10 | 263 | 0.0076046 |
| 20 | 5258 | 0.0125523 |
| 30 | 8079 | 0.0120064 |
| 40 | 5001 | 0.0115977 |
| 50 | 1897 | 0.0147601 |
| 60 | 446 | 0.0269058 |
| 70 | 48 | 0.0000000 |
| 80 | 5 | 0.4000000 |
members %>%
filter(peak_name == "Everest", !is.na(age)) %>%
group_by(hired) %>%
summarize(
n_climbers = n(),
pct_death = mean(died)
) %>%
knitr::kable()
| hired | n_climbers | pct_death |
|---|---|---|
| FALSE | 14624 | 0.0121718 |
| TRUE | 6373 | 0.0136513 |
It seems that expedition role H-A worker may be confounded with hired in some funky way
What happens when oxygen is included?
model <- members %>%
filter(peak_name == "Everest", !is.na(age)) %>%
mutate(leader = expedition_role == "Leader") %>%
glm(died ~ year + age + sex + leader + hired + oxygen_used,
data = .,
family = "binomial"
)
model %>%
tidy(conf.int = TRUE, exponentiate = TRUE) %>%
filter(term != "(Intercept)") %>%
mutate(term = fct_reorder(term, estimate)) %>%
ggplot(aes(estimate, term)) +
geom_point() +
geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), color = "gray") +
geom_vline(xintercept = 1, color = "red") +
labs(
title = "Relative risk factors for death on Everest summits",
caption = "Visual: @jim_gruman, Data: the Himalayan Database",
x = "Exponentiated estimate for log odds"
)
There may be an interaction, where added risk to hired workers is associated with climbing expeditions that use oxygen (maybe due to carrying the tanks). Maybe explore it further next time.
Calculate proportion of female climbers per peak per year
top_peaks <- members %>%
count(peak_name, sort = TRUE) %>%
slice(1:20) %>%
pull(peak_name)
prop_women <- members %>%
filter(peak_name %in% top_peaks) %>%
count(peak_name, year, sex) %>%
pivot_wider(names_from = sex, values_from = n, names_prefix = "num_") %>%
replace_na(list(num_F = 0)) %>%
mutate(
prop_F = num_F / (num_M + num_F),
total_peeps = num_M + num_F
) %>%
arrange(year)
Calculate proportion of female climbers per peak per year
prop_women <- members %>%
filter(peak_name %in% top_peaks) %>%
count(peak_name, year, sex) %>%
pivot_wider(names_from = sex, values_from = n, names_prefix = "num_") %>%
replace_na(list(num_F = 0)) %>%
mutate(
prop_F = num_F / (num_M + num_F),
total_peeps = num_M + num_F
) %>%
arrange(year)
Inspired by Danielle Ferraro @dm_ferraro
# Plot
ggplot(prop_women, aes(
x = year,
y = factor(peak_name, levels = rev(top_peaks))
)) +
geom_point(aes(size = total_peeps, color = prop_F), alpha = 0.7) +
scale_color_viridis_b(
n.breaks = 4,
labels = scales::percent_format(accuracy = 1),
option = "H"
) +
scale_x_continuous(breaks = seq(1900, 2010, by = 10)) +
labs(
x = NULL,
y = NULL,
size = "Total expedition\nmembers",
color = "Percentage of\nwomen",
title = "Women on Himalayan climbing expeditions",
subtitle = "Each point represents the number of expedition members and percentage of women on that peak for that year",
caption = "Data: The Himalayan Database\nFigure by @jim_gruman for #TidyTuesday"
) +
theme(
panel.background = element_blank(),
panel.grid.major = element_line(color = "grey30", size = 0.2),
panel.grid.minor = element_line(color = "grey30", size = 0.2),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.background = element_blank(),
axis.ticks = element_blank(),
legend.key = element_blank(),
plot.title = element_text(size = 22)
)
Strategies for handling class imbalances
Let’s make one last exploratory plot and look at seasons. How much difference is there in survival across the four seasons?
members %>%
filter(season != "Unknown") %>%
count(season, died) %>%
group_by(season) %>%
mutate(
percent = n / sum(n),
died = case_when(
died ~ "Died",
TRUE ~ "Did not die"
)
) %>%
ggplot(aes(season, percent, fill = season)) +
geom_col(position = "dodge", show.legend = FALSE) +
scale_fill_viridis_d(option = "H") +
scale_y_continuous(labels = scales::percent_format()) +
facet_wrap(~died, scales = "free") +
labs(x = NULL, y = "% of expedition members")
Let’s now create the dataset that we’ll use for modeling by filtering on some of the variables and transforming some variables to a be factors. There are still lots of NA
values for age but we are going to impute those.
members_df <- members %>%
filter(season != "Unknown", !is.na(sex), !is.na(citizenship)) %>%
select(peak_id, year, season, sex, age, citizenship, hired, success, died) %>%
mutate(died = case_when(
died ~ "died",
TRUE ~ "survived"
)) %>%
mutate_if(is.character, factor) %>%
mutate_if(is.logical, as.integer)
We can start by loading the tidymodels metapackage, and splitting our data into training and testing sets.
members_split <- initial_split(members_df, strata = died)
members_train <- training(members_split)
members_test <- testing(members_split)
We are going to use resampling to evaluate model performance.
members_folds <- vfold_cv(members_train, strata = died)
Next we build a recipe for data preprocessing.
First, we must tell the recipe()
what our model is going to be (using a formula here) and what our training data is.
Next, we impute the missing values for age using the median age in the training data set. There are more complex steps available for imputation, but we’ll stick with a straightforward option here.
Next, we use step_other()
to collapse categorical levels for peak and citizenship. Before this step, there were hundreds of values in each variable.
After this, we can create indicator variables for the non-numeric, categorical values, except for the outcome died which we need to keep as a factor.
Finally, there are many more people who survived their expedition than who died (thankfully) so we will use step_smote()
to balance the classes.
members_rec <- recipe(died ~ ., data = members_train) %>%
step_impute_median(age) %>%
step_other(peak_id, citizenship, threshold = 0.01) %>%
step_dummy(all_nominal_predictors()) %>%
step_smote(died)
members_rec
Data Recipe
Inputs:
role #variables
outcome 1
predictor 8
Operations:
Median Imputation for age
Collapsing factor levels for peak_id, citizenship
Dummy variables from all_nominal_predictors()
SMOTE based on died
We’re going to use this recipe in a workflow()
so we don’t need to stress a lot about whether to prep()
or not. If you want to explore the what the recipe is doing to your data, you can first prep()
the recipe to estimate the parameters needed for each step and then bake(new_data = NULL)
to pull out the training data with those steps applied.
Let’s compare two different models, a logistic regression model and a random forest model. We start by creating the model specifications.
glm_spec <- logistic_reg() %>%
set_engine("glm")
glm_spec
Logistic Regression Model Specification (classification)
Computational engine: glm
rf_spec <- rand_forest(trees = 1000) %>%
set_mode("classification") %>%
set_engine("ranger")
rf_spec
Random Forest Model Specification (classification)
Main Arguments:
trees = 1000
Computational engine: ranger
Next let’s start putting together a tidymodelsworkflow()
, a helper object to help manage modeling pipelines with pieces that fit together like Lego blocks. Notice that there is no model yet: Model: None.
members_wf <- workflow() %>%
add_recipe(members_rec)
members_wf
== Workflow ====================================================================
Preprocessor: Recipe
Model: None
-- Preprocessor ----------------------------------------------------------------
4 Recipe Steps
* step_impute_median()
* step_other()
* step_dummy()
* step_smote()
Now we can add a model, and the fit to each of the resamples. First, we can fit the logistic regression model. Let’s set a non-default metric set so we can add sensitivity and specificity.
all_cores <- parallelly::availableCores(omit = 1)
all_cores
system
11
future::plan("multisession", workers = all_cores) # on Windows
members_metrics <- metric_set(roc_auc, accuracy, sensitivity, specificity)
# doParallel::registerDoParallel()
glm_rs <- members_wf %>%
add_model(glm_spec) %>%
fit_resamples(
resamples = members_folds,
metrics = members_metrics,
control = control_resamples(save_pred = TRUE)
)
Second, we can fit the random forest model.
rf_rs <- members_wf %>%
add_model(rf_spec) %>%
fit_resamples(
resamples = members_folds,
metrics = members_metrics,
control = control_resamples(save_pred = TRUE)
)
We have fit each of our candidate models to our resampled training set!
Let’s check out how we did
collect_metrics(glm_rs) %>% knitr::kable()
| .metric | .estimator | mean | n | std_err | .config |
|---|---|---|---|---|---|
| accuracy | binary | 0.6676891 | 10 | 0.0015514 | Preprocessor1_Model1 |
| roc_auc | binary | 0.7202278 | 10 | 0.0085022 | Preprocessor1_Model1 |
| sens | binary | 0.6798315 | 10 | 0.0148981 | Preprocessor1_Model1 |
| spec | binary | 0.6674792 | 10 | 0.0016366 | Preprocessor1_Model1 |
Well, this is middling but at least mostly consistent for the positive and negative classes.
collect_metrics(rf_rs) %>% knitr::kable()
| .metric | .estimator | mean | n | std_err | .config |
|---|---|---|---|---|---|
| accuracy | binary | 0.9780237 | 10 | 0.0003557 | Preprocessor1_Model1 |
| roc_auc | binary | 0.7594277 | 10 | 0.0104190 | Preprocessor1_Model1 |
| sens | binary | 0.1355960 | 10 | 0.0075674 | Preprocessor1_Model1 |
| spec | binary | 0.9903824 | 10 | 0.0004311 | Preprocessor1_Model1 |
The accuracy is great but that sensitivity is not what we’d like to see. The random forest model has not done a great job of learning how to recognize both classes, even with our oversampling strategy. Let’s dig deeper into how these models are doing to see this more. For example, how are they predicting the two classes?
glm_rs %>%
conf_mat_resampled(tidy = FALSE) %>%
autoplot() +
labs(title = "GLM Confidence Matrix across resamples") +
theme_jim(base_size = 10)
rf_rs %>%
conf_mat_resampled(tidy = FALSE) %>%
autoplot() +
labs(title = "Random Forest Confidence Matrix across resamples") +
theme_jim(base_size = 10)
The random forest model is quite bad at identifying which expedition members died, while the logistic regression model does about the same for both classes.
We can also make an ROC curve.
glm_rs %>%
collect_predictions() %>%
group_by(id) %>%
roc_curve(died, .pred_died) %>%
ggplot(aes(1 - specificity, sensitivity, color = id)) +
geom_abline(lty = 2, color = "gray80", size = 1.5) +
geom_path(show.legend = FALSE, alpha = 0.6, size = 1.2) +
coord_equal()
It is finally time for us to return to the testing set. Notice that we have not used the testing set yet during this whole analysis; to compare and assess models we used resamples of the training set. Let’s fit one more time to the training data and evaluate on the testing data using the function last_fit()
.
members_final <- members_wf %>%
add_model(glm_spec) %>%
last_fit(members_split)
members_final
# Resampling results
# Manual resampling
# A tibble: 1 x 6
splits id .metrics .notes .predictions .workflow
<list> <chr> <list> <list> <list> <list>
1 <split [57380/19127]> train/test split <tibble ~ <tibb~ <tibble [19~ <workflo~
The metrics and predictions here are on the testing data.
collect_metrics(members_final) %>% knitr::kable()
| .metric | .estimator | .estimate | .config |
|---|---|---|---|
| accuracy | binary | 0.6715115 | Preprocessor1_Model1 |
| roc_auc | binary | 0.7243124 | Preprocessor1_Model1 |
collect_predictions(members_final) %>%
conf_mat(died, .pred_class) %>%
autoplot() +
labs(title = "GLM Model Performance on Testing Data") +
theme_jim(base_size = 10)
Well, this is not so great. The GLM model predicts that all climbers survive.
The coefficients (which we can get out using tidy()
) have been estimated using the training data. If we use exponentiate = TRUE
, we have odds ratios.
members_final %>%
extract_workflow() %>%
tidy(exponentiate = TRUE) %>%
arrange(estimate) %>%
knitr::kable(digits = 4)
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.0000 | 1.0071 | -50.7877 | 0.0000 |
| peak_id_ANN1 | 0.0814 | 0.0529 | -47.4281 | 0.0000 |
| peak_id_DHA1 | 0.0983 | 0.0480 | -48.3149 | 0.0000 |
| peak_id_KANG | 0.1000 | 0.0570 | -40.3997 | 0.0000 |
| peak_id_PUMO | 0.1652 | 0.0537 | -33.5158 | 0.0000 |
| peak_id_MAKA | 0.1952 | 0.0504 | -32.4175 | 0.0000 |
| peak_id_MANA | 0.1973 | 0.0433 | -37.5068 | 0.0000 |
| peak_id_EVER | 0.2294 | 0.0377 | -39.0889 | 0.0000 |
| peak_id_other | 0.2550 | 0.0375 | -36.4663 | 0.0000 |
| hired | 0.4030 | 0.0575 | -15.8060 | 0.0000 |
| sex_M | 0.4672 | 0.0317 | -24.0209 | 0.0000 |
| peak_id_LHOT | 0.5757 | 0.0587 | -9.4069 | 0.0000 |
| peak_id_CHOY | 0.6400 | 0.0427 | -10.4613 | 0.0000 |
| citizenship_Russia | 0.8491 | 0.0698 | -2.3435 | 0.0191 |
| peak_id_HIML | 0.8916 | 0.0832 | -1.3781 | 0.1682 |
| peak_id_BARU | 0.9108 | 0.0656 | -1.4230 | 0.1547 |
| peak_id_ANN4 | 0.9647 | 0.0917 | -0.3919 | 0.6952 |
| age | 0.9904 | 0.0008 | -12.7651 | 0.0000 |
| year | 1.0267 | 0.0005 | 52.1618 | 0.0000 |
| citizenship_India | 1.0906 | 0.0635 | 1.3655 | 0.1721 |
| season_Spring | 1.1001 | 0.0168 | 5.6950 | 0.0000 |
| season_Winter | 1.1219 | 0.0444 | 2.5881 | 0.0096 |
| citizenship_Germany | 1.3270 | 0.0623 | 4.5435 | 0.0000 |
| citizenship_S.Korea | 1.4019 | 0.0591 | 5.7147 | 0.0000 |
| citizenship_Poland | 1.7852 | 0.0686 | 8.4455 | 0.0000 |
| citizenship_other | 1.8164 | 0.0521 | 11.4518 | 0.0000 |
| success | 2.3182 | 0.0166 | 50.7364 | 0.0000 |
| citizenship_Japan | 2.4260 | 0.0542 | 16.3549 | 0.0000 |
| citizenship_Spain | 2.8580 | 0.0603 | 17.4191 | 0.0000 |
| citizenship_Switzerland | 2.9370 | 0.0660 | 16.3193 | 0.0000 |
| citizenship_France | 2.9518 | 0.0578 | 18.7304 | 0.0000 |
| citizenship_Nepal | 2.9763 | 0.0747 | 14.5924 | 0.0000 |
| citizenship_Austria | 3.4269 | 0.0680 | 18.1080 | 0.0000 |
| citizenship_China | 4.0187 | 0.0714 | 19.4695 | 0.0000 |
| citizenship_New.Zealand | 4.4991 | 0.0960 | 15.6614 | 0.0000 |
| citizenship_Canada | 4.9039 | 0.0905 | 17.5721 | 0.0000 |
| citizenship_UK | 5.0950 | 0.0587 | 27.7364 | 0.0000 |
| citizenship_Italy | 5.3972 | 0.0662 | 25.4856 | 0.0000 |
| citizenship_USA | 5.6072 | 0.0581 | 29.6837 | 0.0000 |
| season_Summer | 6.4447 | 0.0974 | 19.1391 | 0.0000 |
we can also visualize the variable coefficients of the GLM model.
members_final %>%
extract_workflow() %>%
tidy() %>%
filter(term != "(Intercept)") %>%
ggplot(aes(estimate, fct_reorder(term, estimate))) +
geom_vline(xintercept = 0, color = "gray50", lty = 2, size = 1.2) +
geom_errorbar(aes(
xmin = estimate - std.error,
xmax = estimate + std.error
),
width = .2, color = "gray50", alpha = 0.7
) +
geom_point(size = 2) +
labs(y = NULL, x = "Coefficent from logistic regression")
The features with coefficients on the positive side (like climbing in summer, being on a successful expedition, or being from the UK or US) are associated with surviving.
The features with coefficients on the negative side (like climbing specific peaks including Everest, being one of the hired members of a expedition, or being a man) are associated with dying.
Remember that we have to interpret model coefficients like this in light of the predictive accuracy of our model, which was somewhat middling; there are more factors at play in who survives these expeditions than what we have accounted for in the model directly. Also note that we see evidence in this model for how dangerous it is to be a native Sherpa climber in Nepal, hired as an expedition member.
sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ranger_0.13.1 vctrs_0.3.8 rlang_0.4.11 themis_0.1.4
[5] yardstick_0.0.8 workflowsets_0.1.0 workflows_0.2.3 tune_0.1.6
[9] rsample_0.1.0 recipes_0.1.16 parsnip_<IP_ADDRESS>0 modeldata_0.1.1
[13] infer_1.0.0 dials_<IP_ADDRESS>00 scales_1.1.1 broom_0.7.9
[17] tidymodels_0.1.3 gt_0.3.1 ebbr_0.1 forcats_0.5.1
[21] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.0.1
[25] tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5 tidyverse_1.3.1
[29] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.2.2 R.utils_2.10.1 tidyselect_1.1.1
[4] grid_4.1.1 pROC_1.18.0 munsell_0.5.0
[7] codetools_0.2-18 ragg_1.1.3 future_1.22.1
[10] withr_2.4.2 colorspace_2.0-2 highr_0.9
[13] knitr_1.34 rstudioapi_0.13 stats4_4.1.1
[16] Rttf2pt1_1.3.9 listenv_0.8.0 labeling_0.4.2
[19] git2r_0.28.0 bit64_4.0.5 DiceDesign_1.9
[22] farver_2.1.0 rprojroot_2.0.2 mlr_2.19.0
[25] parallelly_1.28.1 generics_0.1.0 ipred_0.9-11
[28] xfun_0.25 R6_2.5.1 markdown_1.1
[31] doParallel_1.0.16 VGAM_1.1-5 lhs_1.1.3
[34] cachem_1.0.6 assertthat_0.2.1 promises_<IP_ADDRESS>
[37] vroom_1.5.4 nnet_7.3-16 gtable_0.3.0
[40] globals_0.14.0 timeDate_3043.102 BBmisc_1.11
[43] systemfonts_1.0.2 splines_4.1.1 extrafontdb_1.0
[46] lazyeval_0.2.2 selectr_0.4-2 prismatic_1.0.0
[49] checkmate_2.0.0 yaml_2.2.1 modelr_0.1.8
[52] backports_1.2.1 httpuv_1.6.2 gridtext_0.1.4
[55] extrafont_0.17 tools_4.1.1 lava_1.6.10
[58] usethis_2.0.1 ellipsis_0.3.2 jquerylib_0.1.4
[61] Rcpp_1.0.7 plyr_1.8.6 parallelMap_1.5.1
[64] rpart_4.1-15 ParamHelpers_1.14 viridis_0.6.1
[67] haven_2.4.3 hrbrthemes_0.8.0 fs_1.5.0
[70] here_1.0.1 furrr_0.2.3 unbalanced_2.0
[73] magrittr_2.0.1 data.table_1.14.0 reprex_2.0.1
[76] RANN_2.6.1 GPfit_1.0-8 whisker_0.4
[79] ROSE_0.0-4 R.cache_0.15.0 hms_1.1.0
[82] evaluate_0.14 readxl_1.3.1 gridExtra_2.3
[85] compiler_4.1.1 crayon_1.4.1 R.oo_1.24.0
[88] htmltools_0.5.2 later_1.3.0 tzdb_0.1.2
[91] ggtext_0.1.1 lubridate_1.7.10 DBI_1.1.1
[94] dbplyr_2.1.1 MASS_7.3-54 Matrix_1.3-4
[97] cli_3.0.1 R.methodsS3_1.8.1 parallel_4.1.1
[100] gower_0.2.2 pkgconfig_2.0.3 xml2_1.3.2
[103] foreach_1.5.1 bslib_0.3.0 hardhat_0.1.6
[106] tidytuesdayR_1.0.1 prodlim_2019.11.13 rvest_1.0.1
[109] digest_0.6.27 rmarkdown_2.10 cellranger_1.1.0
[112] fastmatch_1.1-3 gdtools_0.2.3 curl_4.3.2
[115] lifecycle_1.0.0 jsonlite_1.7.2 viridisLite_0.4.0
[118] fansi_0.5.0 pillar_1.6.2 lattice_0.20-44
[121] fastmap_1.1.0 httr_1.4.2 survival_3.2-11
[124] glue_1.4.2 conflicted_1.0.4 FNN_1.1.3
[127] iterators_1.0.13 bit_4.0.4 class_7.3-19
[130] stringi_1.7.4 sass_0.4.0 rematch2_2.1.2
[133] textshaping_0.3.5 styler_1.5.1 future.apply_1.8.1


Files
class can access the commonly used attributes of a file.
Files.isHidden(Path p)
method tests if a file is hidden.
The following methods in the Files
class can access various types of commonly used attributes of a file.
long size(Path) boolean isHidden(Path path) boolean isRegularFile(Path path, LinkOption... options) boolean isDirectory(Path path, LinkOption... options) boolean isSymbolicLink(Path path) FileTime getLastModifiedTime(Path path, LinkOption... options)
The java.nio.file.attribute
package contains the attribute-related classes.
It bundles the file attributes in the following six types of views.
BasicFileAttributeView
manages the basic file attributes
such as creation time, last access time, last modified time, size,
file type (regular file, directory, symbolic link, or other), and
file key (a unique number for a file).
This view is supported on all platforms.
DosFileAttributeView
extends the BasicFileAttributeView
accesses the file attributes that are specific to DOS.
It provides the support to check if a file is a hidden file, a system file, an archive file, and a read-only file.
It is available only on the systems that support DOS such as Microsoft Windows.
PosixFileAttributeView
extends the BasicFileAttributeView
and
adds support for attributes that are available on the systems that support POSIX standards such as UNIX.
It lets we manage owner, group, and [related access] permissions.
FileOwnerAttributeView
manages the owner of a file.
AclFileAttributeView manages
the ACL for a file.
UserDefinedFileAttributeView
manages a set of user-defined attributes for a file.
The name of an attribute is a String
.
The value of an attribute could be of any data type.
supportsFileAttributeView()
method from the FileStore
class tells
whether a specific file attribute view is supported by a file store.
If the specified file attribute view is supported, it returns true
; otherwise, it returns false
.
The following code shows how to check for file attribute support.
import java.nio.file.FileStore; import java.nio.file.Files; import java.nio.file.Path; import java.nio.file.Paths; import java.nio.file.attribute.PosixFileAttributeView; public class NIO_FileStore_Example_SupportedFileAttributeView { public static void main(String[] args) throws Exception { Path path = Paths.get(""); FileStore fs = Files.getFileStore(path); // Check if POSIX file attribute is supported by the file store boolean supported = fs.supportsFileAttributeView(PosixFileAttributeView.class); if (supported) { System.out.println("POSIX file attribute view is supported."); } else { System.out.println("POSIX file attribute view is not supported."); } } }
POSIX file attribute view is not supported.
import java.io.IOException; import java.nio.file.FileStore; import java.nio.file.Files; import java.nio.file.Path; import java.nio.file.Paths; import java.nio.file.attribute.*; public class NIO_FileStore_Example_SupportedFileAttributeViews { public static void main(String[] args) { Path path = Paths.get("C:"); try { FileStore fs = Files.getFileStore(path); printDetails(fs, AclFileAttributeView.class); printDetails(fs, BasicFileAttributeView.class); printDetails(fs, DosFileAttributeView.class); printDetails(fs, FileOwnerAttributeView.class); printDetails(fs, PosixFileAttributeView.class); printDetails(fs, UserDefinedFileAttributeView.class); } catch (IOException ex) { ex.printStackTrace(); } } public static void printDetails(FileStore fs, Class<? extends FileAttributeView> attribClass) { boolean supported = fs.supportsFileAttributeView(attribClass); System.out.format("%s is supported: %s%n", attribClass.getSimpleName(), supported); } }
AclFileAttributeView is supported: true BasicFileAttributeView is supported: true DosFileAttributeView is supported: true FileOwnerAttributeView is supported: true PosixFileAttributeView is supported: false UserDefinedFileAttributeView is supported: true
To read or update one file attribute, use Files
class.
Files
class has the following two static methods to read and update a file attribute using the attribute name as a string:
Object getAttribute(Path path, String attribute, LinkOption... options) Path setAttribute(Path path, String attribute, Object value, LinkOption... options)
To read or update multiple attributes of a file, work with a specific file attribute view.
For most of the file attribute views, we have to work with two interfaces named as TypeAttributes
and TypeAttributeView
.
For the basic file attributes, we have the BasicFileAttributes
and BasicFileAtrributeView
interfaces.
The TypeAttributes
reads the attributes.
The TypeAttributeView
reads/updates the attributes.
The following two methods of the Files
class read the file attributes in a bulk.
<A extends BasicFileAttributes> A readAttributes(Path path, Class<A> type, LinkOption... options) Map<String,Object> readAttributes(Path path, String attributes, LinkOption... options)
The last argument of both methods specify how a symbolic link is handled. By default, if a file is a symbolic link, the attributes of the target of the symbolic link are read.
If we specify NOFOLLOW_LINKS
as the option,
the attributes of the symbolic link are read, not the attributes of its target.
The first readAttributes()
method returns all file attributes of a specified type in an TypeAttributes
object.
To create the Path
object representing the path of the file
Path path = Paths.get("C:\\Java_Dev\\test1.txt");
To Read the basic file attributes
BasicFileAttributes bfa = Files.readAttributes(path, BasicFileAttributes.class);
To Get the last modified time
FileTime lastModifiedTime = bfa.lastModifiedTime();
To Get the size of the file
long size = bfa.size();
The second readAttributes()
method returns all or some of the attributes of a specific type.
The list of attributes to read is supplied in a string form. The string form of an attribute list uses the following syntax:
view-name:comma-separated-attributes
The view-name
is the name of the attribute view that we want to read, such as basic
, posix
, acl
, etc.
If view-name
is omitted, it defaults to basic
.
If view-name
is present, it is followed by a colon.
We can read all attributes of a specific view type by specifying an asterisk as the attributes list.
For example, we can specify "basic:*"
or "*"
to read all basic file attributes.
To read the size and the last modified time of the basic
view, we would use
"basic:size,lastModifiedTime" or "size,lastModifiedTime".
To read the owner
attribute of a file using an ACL
view, we would use a string "acl:owner"
.
To read all posix
attributes of a file, we would use "posix:*"
.
The following code prints the size and the last modified time of the file D:\tmp\test\abc.txt
.
import java.nio.file.Files; import java.nio.file.Path; import java.nio.file.Paths; import java.util.Map; public class NIO_Files_Example_AttributeList { public static void main(String[] args) throws Exception { Path path = Paths.get("D:\\tmp\\test\\abc.txt"); // Prepare the attribute list String attribList = "basic:size,lastModifiedTime"; // Read the attributes Map<String, Object> attribs = Files.readAttributes(path, attribList); System.out.format("Size:%s, Last Modified Time: %s %n", attribs.get("size"), attribs.get("lastModifiedTime")); } }
Size:49, Last Modified Time: 2020-12-30T21:12:45.741Z
The following code reads the basic file attributes of the file D:\tmp\test\abc.txt
and prints some of them on the standard output.
import java.io.IOException; import java.nio.file.Files; import java.nio.file.Path; import java.nio.file.Paths; import java.nio.file.attribute.BasicFileAttributes; public class HelloWorld { public static void main(String[] args) { Path path = Paths.get("D:\\tmp\\test\\abc.txt"); try { BasicFileAttributes bfa = Files.readAttributes(path, BasicFileAttributes.class); System.out.format("Size:%s bytes %n", bfa.size()); System.out.format("Creation Time:%s %n", bfa.creationTime()); System.out.format("Last Access Time:%s %n", bfa.lastAccessTime()); } catch (IOException e) { e.printStackTrace(); } } }
Size:49 bytes Creation Time:2021-02-25T07:47:18.146921Z Last Access Time:2021-02-25T07:47:18.146921Z
We can also read file attributes using a specific view object.
We can use the getFileAttributeView()
method of the Files
class to get a specific attribute view.
It returns null
if the file attribute view is not available.
<V extends FileAttributeView> V getFileAttributeView(Path path, Class<V> type, LinkOption... options)
After getting a view object of a specific view type, we can read all attributes of that view type using the view object's readAttributes()
method.
The following code reads all basic attributes for D:\tmp\test\abc.txt
file using a basic view object:
Path path = Paths.get("D:\\tmp\\test\\abc.txt"); BasicFileAttributeView bfv = Files.getFileAttributeView(path, BasicFileAttributeView.class); BasicFileAttributes bfa = bfv.readAttributes();
The following code shows how to use the basic file attribute view to read and update basic file attributes.
import java.io.IOException; import java.nio.file.Files; import java.nio.file.Path; import java.nio.file.Paths; import java.nio.file.attribute.BasicFileAttributeView; import java.nio.file.attribute.BasicFileAttributes; import java.nio.file.attribute.FileTime; import java.time.Instant; public class HelloWorld { public static void main(String[] args) { Path path = Paths.get("D:\\tmp\\test\\abc.txt"); try { BasicFileAttributeView bfv = Files.getFileAttributeView(path, BasicFileAttributeView.class); BasicFileAttributes bfa = bfv.readAttributes(); System.out.format("Size:%s bytes %n", bfa.size()); System.out.format("Creation Time:%s %n", bfa.creationTime()); System.out.format("Last Access Time:%s %n", bfa.lastAccessTime()); FileTime newLastModifiedTime = null; FileTime newLastAccessTime = null; FileTime newCreateTime = FileTime.from(Instant.now()); bfv.setTimes(newLastModifiedTime, newLastAccessTime, newCreateTime); } catch (IOException e) { e.printStackTrace(); } } }
Size:49 bytes Creation Time:2021-02-25T16:33:37.83Z Last Access Time:2021-02-25T07:47:18.146921Z


Best possible project manager in the whole universe.
Congrats! You've run the Project Manager application.
To test things out create an account and create Your first project.
This sample application should do these things:
-
Can add, edit, display projects.
-
Each project has title, description and documentation.
-
There is required that project title has to be over 40 characters long, app has to follow good practices and show user when it's has not.
-
Projects are saved in database.
-
There is directive that before displaying project documentation will detect and linkify all external links.
-
There are tests.


Please wait...
SISTEM INFORMASI PERSURATAN
DASHBOARD
playlist_add_check
NEW TASKS
help
NEW TICKETS
forum
NEW COMMENTS
person_add
NEW VISITORS
CPU USAGE (%)
REAL TIME
OFF
ON
more_vert
Action
Another action
Something else here


White to play 26
pips White=167 Black=157
(CorrectAnswer= 0/0, ErrorScore= 0)
1. 13/5 eq: -0.159 46.92% (G:13.19% B:0.56%) 53.08% (G:17.39% B:1.12%) 2. 24/18 13/11 eq: -0.160 (-0.001) 47.40% (G:12.24% B:0.61%) 52.60% (G:19.46% B:0.83%) 3. 13/11 13/7 eq: -0.192 (-0.033) 46.43% (G:13.27% B:0.58%) 53.57% (G:18.77% B:1.56%) 4. 24/16 eq: -0.202 (-0.042) 46.34% (G:10.78% B:0.50%) 53.66% (G:18.41% B:0.72%) 5. 24/22 13/7 eq: -0.222 (-0.063) 45.57% (G:11.97% B:0.57%) 54.43% (G:19.16% B:0.93%)


White to play 11
pips White=167 Black=158
(CorrectAnswer= 0/0, ErrorScore= 0)
1. 8/7(2) 6/5(2) eq: +0.291 57.02% (G:17.35% B:0.92%) 42.98% (G:13.24% B:0.78%) 2. 24/22 6/5(2) eq: +0.130 (-0.161) 54.15% (G:14.57% B:0.73%) 45.85% (G:13.59% B:0.47%) 3. 8/5 6/5 eq: +0.087 (-0.204) 52.25% (G:16.08% B:0.76%) 47.75% (G:14.95% B:0.89%) 4. 24/23(2) 6/5(2) eq: +0.125 (-0.167) 53.26% (G:15.19% B:0.74%) 46.74% (G:12.44% B:0.44%) 5. 24/23 6/5(3) eq: +0.109 (-0.182) 53.27% (G:14.91% B:0.76%) 46.73% (G:13.74% B:0.53%)


protected abstract fun usePIDOutput(output: Double): Unit
Uses the value that the pid loop calculated. The calculated value is the "output" parameter.
This method is a good time to set motor values, maybe something along the lines of
driveline.tankDrive(output, -output)
.
All subclasses of PIDSubsystem must override this method.


A simple, mobile first, responsive base boilerplate for rapid development based on Kraken.
It has addons for style and animations:
In Apache HTTP servers, .htaccess (hypertext access) is the configuration file that allows for web server configuration.
The htaccess.txt
file provided includes a number of best practice server rules for making web pages fast and secure. Adapted from HTML5 Boilerplate.
If no .htaccess file exists, create one. If you're hosting a WordPress site, make sure that you add these snippets after any existing WordPress configurations


<o2 multiColumnLayout> allows you to lay out as many columns as you would like in a table-like way without the table-tag being used (which is good
for screen readers and search engine ratings). It's also a very easy-to-use alternative to writing div tags and styling them manually. This tag gives you
good control over the widths of each individual column and the width of all the columns as a whole. Javascript is used to set the width and height of the
columns.
.column {
padding : 10px;
}
Column 1 - lskdnfgksnd edkfbsdkjb d bsdjgfbs kdjb skjdb skjdb kjb
Column 2
Column 3
Column 4
width
Other attributes will be passed on to the <div> that's created.
width - you may specify the width in pixels or as a percentage value of the available width after absolute column widths
(specified in pixels) have been subtracted. All columns without a specified width are distributed evenly in the available horizontal space.
Other attributes will be passed on to the <div> that's created.


Jennifer Aniston Posts Hilarious Throwback Photo On Instagram
by Erika HarwoodJennifer Aniston hasn’t been on Instagram that long, but she clearly knows exactly what she’s doing. The actress posted a throwback photo of her and her longtime hair stylist Chris McMillan on Saturday, showing the two while they worked together on Friends, presumably filming one of the flashback scenes of Rachel back in the 80s. McMillan, it should be noted, is the one responsible for Aniston’s iconic “Rachel” haircut.
“Happy Birthday, Chrissy,” Aniston wrote. “We've come a long way, baby.”
Aniston joined Instagram last month, causing the app to crash. She posted a video addressing the technical issue, showing her slamming her phone on a table.
“I swear I didn’t mean to break it,” she wrote. “Thank you guys for the kind, glitchy welcome.”
Aniston now holds the record for the fastest account to hit the 1 million follower mark, though she’s currently at a cool 19.1 million followers. She beat out Meghan Markle and Prince Harry, who previously held the title with their @sussexroyal account.
Of course, she’s been keeping fans happy with plenty of Friends-related content. Her first post was a selfie of her with her former co-stars Lisa Kudrow, Courteney Cox, Matt LeBlanc, David Schwimmer, and Matthew Perry, the latter being the only one of the group not on Instagram.
“And now we’re Instagram FRIENDS too,” she captioned the photo, which has racked up over 15 million likes. “HI INSTAGRAM.”


Do you suffer from migraines, headaches, irritability, depression like conditions around your monthly cycle? You are not alone as many women suffer from these conditions every month in silence. It might be time for you to try out reflexology. Reflexology can help with PMS, migraines, anxiety, hormonal issues and other health issues. So what is reflexology?
It is a gentle, non-invasive technique which can help you relax, de-stress and bring into balance your state of mind. If done correctly by a qualified and experienced therapist it can help in prevention and recovery from various psychosomatic conditions. Reflexology works wonders. I have seen some profound emotional and physical changes in my clients after the reflexology sessions. From children to the elderly, people from all walks of life, everyone finds themselves energised and renewed after a session of this amazing technique.
During the therapy session, a reflexologist looks at the issue in a holistic manner looking into the root cause of the issue faced by the client. In the case of pre-menstrual syndrome (PMS) it is no different as the therapist works on the endocrine system together with other areas such as the nervous system, liver and the glands in the body. Around the menstrual cycle a woman can experience over 100 different types of conditions although there is still a lack of clarity as to why it happens.
These conditions can range from –
But the fact remains that this conditions effects women all around the world. Some studies have found that the hormonal changes that take place during the menstrual cycle contribute greatly to this debilitating condition.
The results of controlled studies such as the one done by Oleson T. & Flocco W. (Randomized Controlled Study of Premenstrual Symptoms – http://www.ncbi.nlm.nih.gov/pubmed/8233263) have demonstrated a significantly greater decrease in premenstrual symptoms for the women given true reflexology treatment than for the women in the placebo group. In this study 35 women with PMS condition participated and received reflexology sessions with positive results. Post-menstrual syndrome can upset normal life leading to issues in personal relationships as well. Reflexology can bring a balance into your life. It is possible that migraines, headaches, mood swings and other conditions can be a thing of the past.
What is stopping you? Try a session of reflexology today.


SSH, when inside of a connection, can specify "C" hotkey to enable pivoting/tunneling
normally you can issue: ssh -L yada yada
If you're in an ssh session, you have access to easy peasy ssh tunneling
on a NEW LINE if the VERY FIRST characters you enter are ~C
you drop into an ssh> prompt
ssh>
ssh> help
etc, and you can add a port forward from here
so if you are <IP_ADDRESS> and you are SSHd into <IP_ADDRESS> . . . press "enter" and then ~C and then:
ssh> -L 8001:<IP_ADDRESS>:80
your Kali box <IP_ADDRESS> is now listening on 8001 and it's being forwarded through the ssh target <IP_ADDRESS> to <IP_ADDRESS>:80
Now, let's set up another port forward to get requests back from another port
so if you are <IP_ADDRESS> and you are SSHd into <IP_ADDRESS>
ssh> -R 8002:<IP_ADDRESS>:8003
(this is slightly confusing, but)
The box you are SSHd into (<IP_ADDRESS>) will listen on ITS local host on 8002 and will take everything from ITS 8002 and forward it through the SSH connection TO kali local (<IP_ADDRESS>) 8003.
With the two commands above, we will be able to command inject a shell through the SSH connection on a third host back to Kali:
kali:8001 -> <IP_ADDRESS>:80 -> victim:80
victim:80 -> <IP_ADDRESS>:8002 -> kali:8003 shell
Make sure you adjust your reverse_shell accordingly. In this case, you want to call back to <IP_ADDRESS>:8002. And then listen on your Kali box on 8003.


-
mainWarehouse
Warehouse mainWarehouse
the main warehouse for the supermaket's use
-
numFastClients
int numFastClients
counter of fast/quick clients
-
numLargeClients
int numLargeClients
count of large clients
-
tickets
Lista<T> tickets
the tickets of this whole supermarket
-
singleLine
MinHeap<T extends java.lang.Comparable<T>> singleLine
the singe line where the clients may be form up
-
checkouts
MinHeap<T extends java.lang.Comparable<T>> checkouts
the large checkouts where the client can be form up in this structure by using it in the best way
-
formatter
java.text.SimpleDateFormat formatter
give us the formar for the report's date
-
date
java.util.Date date
This give us the date


Welcome to our experiment! _ people, that is you and _ others, have joined us.
Your decision.
You will receive 20 coins per round for _ rounds. Each round, you will have to choose how many coins to put directly into your personal account, and how many coins to put into a group account.
Ordered group matching.
Each round you will be assigned to a new group of 4 people, that is, you and three (possibly different) others. Which group you are assigned to depends on your and others contributions to the group account.
All contributions will be ranked from highest to lowest. The first 4 people in the list form Group 1, the next 4 people in the list form Group 2, the third 4 people in the list form Group 3, and the last 4 people form Group 4.
In case two or more players contributed equally in the same round, their relative position in the ranking will be decided randomly.
Return from personal account.
Each coin that you put into your personal account each round goes one-to-one towards your final earnings in the game.
Return from group account.
Each coin that you put into the group account will pay you back some positive amount of money as described below.
The total amount of coins in your group account is equal to the sum of the contributions to the group account by each of the group members. That amount is then multiplied by 2 and distributed equally amongst group members. In other words, you will get a return equal to half of the group account total.
| Total number of coins in your group | Your return |
|---|---|
| 0 | 0 |
| 10 | 5 |
| 15 | 7.5 |
| 28 | 14 |
| ... | ... |
| 80 | 40 |
| Any amount | Half of that amount |
Total return.
You will be paid according to the final amount of coins earned over the whole experiment. 20 coins are equal to 0.075 USD, and the maximum amount you can win is 800 coins, equivalent to 3 USD.


{%extends 'hood/base.html'%} {% block content %} {{block.super}}
Name : {{request.user | capfirst}}
Location : {{user_profile.location}}
Neighborhood : {{user_profile.neighborhood}}
Email : {{user_profile.email}}


{% extends 'base.html' %} {% block title %}{{ producer.producer_name }}{% endblock %} {% load staticfiles %} {% load directoryApp_tags %} {% block extra_css %}{% endblock %} {% load django_tables2 crispy_forms_tags %} {% load render_table from django_tables2 %} {% load bootstrap3 %} {% block content %}
{{ producer.title }}
{{ producer.county_name }}
{{ producer.actor }}
{{ producer.disambiguation }}
List of all the transactions at the Nairobi Coffee Exchange of the producer here as an extract of the {% total_transactions %} records in the Transaction List. This is still a raw presentation of what is to come. Maps and Analytics to be added. Would be nice to have pins or clusters of producers who have bookmarked the coop or that are claiming to be members
{% render_table table 'django_tables2/bootstrap.html' %} {% endblock %}