J.K Avro Superstore has a web-store front which a customer can browse through the products and place a purchase order. After the customer checks out the products in his cart, he is given a form to fill in sensitive information like credit card number and other confidential information. Once submitted, this data is stored in the 'credit_card' table and the purchase order placed by the customer is stored in the 'PurchaseOrder' table. Every purchase order placed will have a corresponding entry in the 'sales' table.
Data in the 'credit_card' table contains confidential customer information which should be accessed by authorized users only. The data in the 'sales' table is monitored by sales managers of respective regions. J.K Avro Superstore has different users with restricted access to the 'credit_card' table based on the authorities and privileges granted to them. The different users are
To become more PCI compliant, J.K Avro Superstore decides to implement the following PCI DSS requirements :


public class VideoPlayer
extends java.lang.Object
| Modifier and Type | Class and Description |
|---|---|
private class |
VideoPlayer.myListener
The listener interface for receiving my events.
|
| Modifier and Type | Field and Description |
|---|---|
private Context |
act_context
The Context in which this activity is running in.
|
private TellMeMoreApplication |
app
Reference to the parent application, which contains the database with the
card information.
|
private java.lang.String |
cardId
The UUID of the audio card which is serving as the source for this video
player activity.
|
private int |
cardPos
The position of the card within the cardscroll view from which the user
launched this videoplayer activity.
|
private static int |
DEFAULT_POS
The Constant DEFAULT_POS.
|
static java.lang.String |
EXTRA_SELECTED_ID
The Constant EXTRA_SELECTED_ID.
|
static java.lang.String |
EXTRA_SELECTED_POS
The Constant EXTRA_SELECTED_POS.
|
private boolean |
isQuitting
Used to prevent the return
|
private static int |
KEY_SWIPE_DOWN
The Constant KEY_SWIPE_DOWN.
|
private AudioManager |
mAudioManager
The audio manager used to provide audio feedback to the user in response
to user actions.
|
private GestureDetector |
mGestureDetector
The gesturedetector used by this activity to handle user input.
|
private VideoPlayer.myListener |
mlistener
The mlistener.
|
(package private) com.google.android.youtube.player.YouTubePlayer |
player
A reference to the youtube player which resides inside the youtube player view.
|
static java.lang.String |
TAG
The Constant TAG.
|
private VideoCard |
thisCard
A reference to the video card containing all the information used in this
activity.
|
(package private) com.google.android.youtube.player.YouTubePlayerView |
ytpv
The youtube player view which handles the details of the video streaming for this activity
|
| Modifier and Type | Method and Description |
|---|---|
private GestureDetector |
createGestureDetector(Context context)
Creates the gesture detector.
|
protected void |
onCreate(Bundle savedInstanceState) |
boolean |
onGenericMotionEvent(MotionEvent event) |
void |
onInitializationFailure(com.google.android.youtube.player.YouTubePlayer.Provider arg0,
com.google.android.youtube.player.YouTubeInitializationResult arg1) |
void |
onInitializationSuccess(com.google.android.youtube.player.YouTubePlayer.Provider arg0,
com.google.android.youtube.player.YouTubePlayer arg1,
boolean arg2) |
boolean |
onKeyUp(int keyCode,
KeyEvent event) |
private void |
peaceOut(Context context)
Gracefully exits the activity, freeing up the resources as it goes out.
|
public static final java.lang.String TAG
private GestureDetector mGestureDetector
public static final java.lang.String EXTRA_SELECTED_POS
SelectCardActivity
so that when the user returns to it, the card
they just tapped on is in focus.private boolean isQuitting
public static final java.lang.String EXTRA_SELECTED_ID
private static final int DEFAULT_POS
private static final int KEY_SWIPE_DOWN
private VideoPlayer.myListener mlistener
private int cardPos
private java.lang.String cardId
private Context act_context
com.google.android.youtube.player.YouTubePlayerView ytpv
com.google.android.youtube.player.YouTubePlayer player
private TellMeMoreApplication app
private VideoCard thisCard
private AudioManager mAudioManager
protected void onCreate(Bundle savedInstanceState)
public void onInitializationFailure(com.google.android.youtube.player.YouTubePlayer.Provider arg0, com.google.android.youtube.player.YouTubeInitializationResult arg1)
public void onInitializationSuccess(com.google.android.youtube.player.YouTubePlayer.Provider arg0, com.google.android.youtube.player.YouTubePlayer arg1, boolean arg2)
private GestureDetector createGestureDetector(Context context)
context
- The context in which this activity is running. Used to access
system resources.public boolean onGenericMotionEvent(MotionEvent event)
public boolean onKeyUp(int keyCode, KeyEvent event)
private void peaceOut(Context context)
context
- The activity context in which this is called.


The abilities are: Strength, Agility, Focus, and Charm. Each are explained in detail below.
There are four parts to an Ability: Score, Modifier, Misc., and Bonus
Score
Score is how naturally adapt you are at performing actions related to the Ability.
A commoner would have each Ability score be an average of 10-11. Characters tend to have much higher, heroic, Ability scores.
When you first create your character, each score starts at 0. You then have a pool of 48 Points to distribute between the four scores. To increase a score by one, you reduce the pool by one, until the remaining Ability points is zero.
At this point, you cannot increase a score above an 18, though other sources such as Race or magical items may increase an Ability beyond that.
Modifier
When a score is higher than 11, a creature receives a bonus proportional to the score when attempting an action related to that Ability.
When a score is lower than 10, a creature receives a penalty.
This bonus or penality is referred to as the Modifier, or "Mod", for short.
Modifier is determined by the following table.
| Score | Modifier |
|---|---|
| 0 | Incapacitated |
| {{ score }} | {{ $ctrl.modForScore(score) }} |
Modifiers can also be determined by applying this formula:
- Take the Ability score.
- If the score is odd, round down to the nearest even number.
- Take the result of the previous step and reduce it by 10.
- Take the result of the previous step and divide it by two.
- The result of the previous step is the Modifier.
Misc.
Some magical items or Features give a Miscellaneous bonus to an Ability.
Bonus
Bonus is the sum of Modifier and Misc.


Bases: TransferModules.TransferBase.TransferBase
Ftp transfer type
called by TransferBase to setup the command that pulls receipt files from the target
called by TransferBase in order to create a push command
called by TransferBase to setup the command that pushes a ThankYou file to the target
called by TransferBase in order to create the command to check for stop files
Bases: TransferModules.TransferBase.TransferBase
GridFTP transfer type
called by TransferBase to setup the command that pulls receipt files from the target
called by TransferBase in order to create a push command
called by TransferBase to setup the command that pushes a ThankYou file to the target
called by TransferBase in order to create the command to check for stop files
Bases: TransferModules.TransferBase.TransferBase
Rsync transfer type
called by TransferBase to setup the command that pulls receipt files from the target
called by TransferBase in order to create a push command
called by TransferBase to setup the command that pushes a ThankYou file to the target
called by TransferBase in order to create the command to check for stop files
Bases: TransferModules.TransferBase.TransferBase
Rsync transfer type
called by TransferBase to setup the command that pulls receipt files from the target
called by TransferBase in order to create a push command
called by TransferBase to setup the command that pushes a ThankYou file to the target
called by TransferBase in order to create the command to check for stop files
Abstract Base class for all transfer types This class uses command strings obtained from derived classes to push and pull files according to the following protocol
No Arrival Monitor :-
source pull stop file target t0 <——————————————- t1
push data file
t2 ——————————————>> t3
In the above protocol - the stop file is pulled from the target until the source detects that the stop file is no longer present at which point the data file is pushed to the target
With Arrival Monitor :-
source pull stop file target t0 <——————————————- t1
push data file
t8 ——————————————-> t8
In this case we start as begore - waiting for the stop file on the target side to be not present. Then the data file followed by a control file is sent once the control file is received by the target, the target creates a receipt file Once the receipt file is pulled to the source, the source pushes a thankyou file and the transfer is considered to be finished
remove files after they have been transfered or warn if for any reason they cannot be deleted
Initialise the logger client module, and then log a single line that says “startup”
pull a receipt file using transferData
take the push data command and call transferData if we are using an arrival Monitor then send a conf file, pull a receipt file and then push a thankyou file
push a thankyou file using transferData
transferData via subprocess.popen all data is piped back through stdout and stderr This method does not know about the concept of push or pull, it only executes the command string given to it
wait until a stop file is no longer present on the target machine - we only push data when we are allowed to do so
Utilities class for TransferBase
NOTE: THIS MIGHT NOT BE BEST METHOD FOR CALCULATING MD5 checksum!!!! comment from Alan - tested it and it agrees with md5sum command - haven’t tested performance
Return full path in specified directory
item is an entry in the dataset directory, which may be file or directory if a plain file, return the name if a directory, zip it up and return the zip file name if anything that can’t be transferred, return None
Move specified file into the quarantine directory, If necessary timestamp the filename to prevent overwriting.
A timestamp that is designed to be part of a filename to ensure uniqueness.


Go to the source code of this file.
|
| void | HMAC_CTX_init (HMAC_CTX *ctx) |
| |
| void | HMAC_CTX_cleanup (HMAC_CTX *ctx) |
| |
| int | HMAC_Init (HMAC_CTX *ctx, const void *key, int len, const EVP_MD *md) |
| |
| int | HMAC_Init_ex (HMAC_CTX *ctx, const void *key, int len, const EVP_MD *md, ENGINE *impl) |
| |
| int | HMAC_Update (HMAC_CTX *ctx, const unsigned char *data, size_t len) |
| |
| int | HMAC_Final (HMAC_CTX *ctx, unsigned char *md, unsigned int *len) |
| |
| unsigned char * | HMAC (const EVP_MD *evp_md, const void *key, int key_len, const unsigned char *d, size_t n, unsigned char *md, unsigned int *md_len) |
| |
| int | HMAC_CTX_copy (HMAC_CTX *dctx, HMAC_CTX *sctx) |
| |
| void | HMAC_CTX_set_flags (HMAC_CTX *ctx, unsigned long flags) |
| |
| #define HMAC_MAX_MD_CBLOCK 128/* largest known is SHA512 */ |
| int HMAC_Update |
( |
HMAC_CTX * |
ctx, |
|
|
const unsigned char * |
data, |
|
|
size_t |
len |
|
) |
| |
| int HMAC_Final |
( |
HMAC_CTX * |
ctx, |
|
|
unsigned char * |
md, |
|
|
unsigned int * |
len |
|
) |
| |
| unsigned char* HMAC |
( |
const EVP_MD * |
evp_md, |
|
|
const void * |
key, |
|
|
int |
key_len, |
|
|
const unsigned char * |
d, |
|
|
size_t |
n, |
|
|
unsigned char * |
md, |
|
|
unsigned int * |
md_len |
|
) |
| |
| void HMAC_CTX_set_flags |
( |
HMAC_CTX * |
ctx, |
|
|
unsigned long |
flags |
|
) |
| |


Course Outline: Introduction to Project management: Historical background and evolution, Terminologies, Software project management objectives, Scope, focus and basic rules Principles of software project management: Basic PM Skills, SPM framework, elements, stakeholders, boundaries, challenges of SPM Software Project planning: Planning objective, project plan, variations, structure of SPM plan, project estimation, estimation methods, models and decision process. PM organization and scheduling: WBS, types of WBS, functions, activities, tasks, life cycles, phasing and purpose of phasing, building project schedule, network diagrams: PERT, CPM, Bar charts, Gantt charts Software project management techniques: Use of methodologies, Managing risks and issues, Managing Quality, Configuration, Change, Crisis, Documentation, Release. Project monitoring and control: Dimensions of monitoring and control, earned value indicators (BCWS, CV, SV, CPI, SPI), backlog management, dispute and error tracking, RMMM charts Industry scenarios: Domain analysis, Business case analysis, Dynamicity, Success and failure factors, case studies.


- Local authority reference: 0930/09139385080004
- Property: OLD MILL CAFE, COAST ROAD, BARDSEA, ULVERSTON, CUMBRIA, LA12 9RB
{{title}}
You can propose changes to the parking spaces and areas for your property. If there is no change, leave the field blank.
Parking should only be recorded once, as either spaces or areas. If there are marked spaces you should count them. If there are no marked spaces you should measure the area.
You can record your parking as a combination of spaces and areas so long as they relate to different sections of parking. Recording the same section of parking as both spaces and areas may result in the parking being counted twice.
A marked parking space is assumed to be 20 square metres. If you have any spaces which are larger or smaller than this (for example a parking space for a lorry), please enter the total number of spaces and provide more details in your supporting documents.
You’ll need to select whether the parking is covered or uncovered. You’ll also need to select whether the parking is surfaced (for example concrete) or unsurfaced (for example grass or gravel).
| Description of spaces | Number spaces | Proposed number of spaces |
|---|---|---|
| 271 spaces | spaces | |
| 50 spaces | spaces | |
| 28 spaces | spaces | |
| 32 spaces | spaces | |
| 0 spaces | spaces | |
| Description of areas | Area (m²) | Proposed area (m²) |
| 0m² | m² | |
| 0m² | m² | |
| 65m² | m² | |
| 16m² | m² | |
| 28m² | m² |


Welcome to our Spotify Recommendation App
This is a tool to give users recommended songs based on their current taste in Music.
Click an artist below to get a similar recommended song:


Maui: Out of all the Hawaiian islands, Maui has the perfect mix of vast, sandy beaches and shopping/dining/sightseeing activities. The weather is warm and comfortable year-round, and it only takes a 6-7 hour flight from the west coast of the mainland to arrive in this tropical paradise. Sun and ocean abound!
Austin: Smack-dab in south-central US, getting to Austin shouldn’t take more than a half-day of traveling via airplane from anywhere on the mainland, ensuring that your travel time doesn’t interfere with your actual out and about vacation time. Chocked full of local adventures, shopping, sightseeing, and home to one of the best live-music meccas in the US, you can be sure to never have a dull moment in the original “Keep it weird” destination.
Reykjavik: Talk about an exotic escape! Reykjavik, Iceland is sure to thrill the adventurist and the stunning landscape is the stuff that dreams are made of. Be sure to catch the Aurora Borealis and soak in the local culture, and double check that you’ve brought your coat!


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When this policy is active, a frame or main page content will not be able prevent its content from scrolling vertically. This is done by:
touchstart
) are non-cancelable.touch-action: none
as if it were touch-action: pan-y
). Meaning the page still blocks pan-x
and zoom.el.scrollIntoView()
do not propagate outside of the frame.The frame below uses body{ touch-action: none; }
to
prevent its content from scrolling. However, when the policy is "on", it
will be scrollable!. In other words, the policy prevents
the page from blocking vertical scroll by treating the touch-action: none
as touch-action: pan-y
.
Try scrolling the iframe below on mobile (e.g. using touch events).


--- layout: post title: "Faster JavaScript through Category Theory" tags: - presentation status: publish type: post published: true meta: _edit_last: "1" ---
Update My final presentation of the material from JQuery UK has been posted. On a personal note the venue and speakers made for an awesome setting to retire this talk.
Update My presentation of the revised material from JSCONFEU 2012 has been posted. Thanks again the @jsconfeu crew for puting on one of the best conferences I've yet been to.
My presentation at JQCON 2012. Thanks again to the good folks from @confreaks for all the hard work they do.


Connected digital additive manufacturing
Application details
These are the default questions included in the Application details section.
Connected digital additive manufacturing
These are the default questions included in the Application details section.


API — The MG-RAST Application Programming Interface¶
URLs¶
https://api.mg-rast.org/
Further documentation, with a complete parameter listing for all resources available is at:
https://api,mg-rast.org/api.html
Github repository of script tools, examples, and contributed code for using the MG-RAST API:
https://github.com/MG-RAST/MG-RAST-Tools
Introduction¶
This site is open source. {% github_edit_link "Improve this page" %}
_Over 110,000 metagenomic data sets have been uploaded and analyzed in MG-RAST since 2007, totaling over 43 terabases (TBp). Data uploaded falls in three classes: shotgun metagenomic data, amplicon data, and, more recently, metatranscriptomic data. The MG-RAST pipeline normalizes all samples by applying a uniform pipeline with the appropriate quality control mechanisms for the various data sources. Uniform processing and robust sequence quality control enable comparison across experimental systems and, to some extent, across sequencing platforms. With the inclusion of standardized metadata MG-RAST has enabled meta-analysis available through its web-based user interface. This provides an easy-to-use way to upload and download data, perform analyses, and create and share projects.
As with most GUIs, however, there are limitations to what can be done, for example, regarding the number of samples processed in a single analysis, access to complete metadata, and easy access to raw data and quality metrics for each sample. As part of the DOE Systems Biology knowledgebase project (KBase) we have implemented a web services application programmers interface (API) that exposes all data to (authenticated) programmers, enabling access to available data and functionality through software applications. This makes user access to MG-RAST’s internal data structures possible.
The MG-RAST API enables programmatic access to data and analyses in MG-RAST without requiring local installations. Using the API, users can authenticate against the service, submit their data, download results, and perform extensive comparisons of data sets. The API uses the Representational State Transfer (REST) [3] architecture which allows download of data in ASCII format, allowing users to query the system via URLs and returning MG-RAST data objects in their native format (e.g. similarity tables or sequence files). For structured data (e.g. metadata or project information) the MG-RAST API uses JSON (Javascript Object Notation, a widely used standard) as its data format.
This allows users to use simple tools to download data files or view the JSON in their web browsers using one of the many available JSON viewers. In addition many programming languages have libraries for convenient HTTP interaction and JSON conversions. The API has a minimal number of prerequisites; and any language with HTTP and JSON support or command line utilities such as “curl” can easily integrate with the design.
If you are not a programmer or you are not willing to spend the time learning the API, the Example scripts (see chapter 7.)
Design and Implementation¶
The MG-RAST API enables programmatic access to data and analyses in MG-RAST without requiring local installations. Users can authenticate against the service, submit their data, download results, and perform extensive comparisons of data sets. We chose to use the Representational State Transfer (REST) [3] architecture. The REST approach allows download of data in ASCII format, allowing users to query the system via URLs and returning MG-RAST data objects in their native format (e.g. similarity tables or sequence files). For structured data (e.g. metadata or project information) the MG-RAST API uses JSON (Javascript Object Notation, a widely used standard) as its data format.
Using this approach users can use simple tools to download data files to their machines or view the JSON in their web browsers using one of the many available JSON viewers. In addition many programming languages have libraries for convenient HTTP interaction and JSON conversions.
Most of the API calls are simply URLs which can be entered in the address bar of a web browser to perform the download through the browser. These URLs can also be used with a command line tool like curl, in programing-language-specific libraries, or in command line scripts. The examples in the Results section illustrate the use of each of these methods. The example scripts are available on in the supplementary materials and on github (https://github.com/MG-RAST/MG-RAST-Tools) along with other useful illustrative scripts.
The MG-RAST API covers most of the functionality available through the MG-RAST website, with access to annotations, analyses, metadata and access to the MG-RAST user inbox to view contents as well as upload files. All sequence data and data products from intermediate stages in the analysis pipeline are available for download. Other resources provide services not available through the website, e.g. the m5nr resource lets you query the m5nr database.
Each query to the API is represented as a URI beginning with
https://api.mg-rast.org/
and has a defined structure to pass the requests and parameters to the API server. These URI queries can be used from the command line, e.g. using curl, in a browser, or incorporated in a shell script or program.
Each URI has the form:
https://api.mg-rast.org/{version}/{resourcepath}?{querystring}
where
{version}
explicitly directs the request to a specific version of the API. If it is omitted the latest API version will be used. The current version number is ‘1’.
{resourcepath}
is constructed from the path parameters listed below to define a specific resource.
{querystring}
is used to filter the results obtained for the resource, this is optional.
For example, in:
https://api.mg-rast.org/1/annotation/sequence/mgm4447943.3?evalue=10&type=organism&source=SwissProt
the resource path
annotation/sequence/mgm4447943.3
defines a request for the annotated sequences for the MG-RAST job with ID 4447943.3. The optional query string
evalue=10&type=organism&source=SwissProt
modifies the results by setting an evalue cutoff, annotation type and database source.
The API provides an authentication mechanism for access to private MG-RAST jobs and users’ inbox. The ’auth_key’ (or ’webkey’) is a 25 character long string, e.g.
j6FNL61ekNarTgqupMma6eMx5
which is used by the API to identify an MG-RAST user account and determine access rights to metagenomes. Note that the auth_key is valid for a limited time after which queries using the key will be rejected. You can create a new auth_key or view the expiration date and time of an existing auth_key on the MG-RAST website. An account can have only one valid auth_key and creating a new key will invalidate an existing key.
All public data in MG-RAST is available without an auth_key. All API queries for private data which either do not have an auth_key or use an invalid or expired auth_key will get a “insufficient permissions to view this data” response.
The auth_key can be included in the query string like:
https://api.mg-rast.org/1/annotation/sequence/mgm4447943.3?evalue=10&type=organism&source=SwissProt&auth_key=j6FNL61ekNarTgqupMma6eMx5
or in a request using curl like:
curl -X GET -H "auth: j6FNL61ekNarTgqupMma6eMx5" "https://api.mg-rast.org/1/annotation/sequence/mgm4447943.3?evalue=10&type=organism&source=SwissProt"
Note that for the curl command the quotes are necessary for the query to be passed to the API correctly.
If an optional parameter passed through the query string has a list of values only the first will be used. When multiple values are required, e.g. for multiple md5 checksum values, they can be passed to the API like:
curl -X POST -d '{"data":["000821a2e2f63df1a3873e4b280002a8","15bf1950bd9867099e72ea6516e3d602"]}' "https://api.mg-rast.org//m5nr/md5"
In some cases, the data requested is in the form of a list with a large number of entries. In these cases the ‘limit’ and ‘offset’ parameters can be used to step through the list, e.g.
https://api.mg-rast.org/1/project?order=name&limit=20&offset=100
will limit the number of entries returned to 20 with an offset of 100.
If these parameters are not provided default values of limit=10
and
offset=0
are used. The returned JSON structure will contain the
‘next’ and ‘prev’ (previous) URIs to simplify stepping through the list.
The data returned may be plain text, compressed gzipped files or a JSON structure.
Most API queries are ‘synchronous’ and results are returned immediately.
Some queries may require a substantial time to compute results, in these
cases you can select the asynchronous option by adding
‘&asynchronous=1’
to the end of the query string. This query will
then return a URL which will return the query results when they are
ready.
Most of the API calls are simply URLs which can be entered in the address bar of a web browser to perform the download through the browser. These URLs can also be used with a command line tool like curl, in programing-language-specific libraries, or in command line scripts. The examples below illustrate the use of each of these methods. The example scripts are available on the github site along with other useful illustrative scripts.
| Resource/Object | Description |
|---|---|
| annotation | taxonomic and functional annotations made by comparison with the M5nr database |
| compute | resource to compute PCoA , heatmap, and normalization for a set of input metagenomes |
| download | download results of the MG-RAST pipeline |
| inbox | upload and listing of data in the staging area prior to execution of the MG-RAST pipeline |
| library | library information for uploaded metagenome provided by the user |
| matrix | abundance profiles in BIOM (5) format for a list of metagenomes |
| M5nr | access M5 nonredundant protein database used for annotation of metagenomic sequences |
| metadata | creation, export, and validation of metadata templates and spreadsheets |
| metagenome | container for sample, library, project, and precomputed data for an uploaded metagenomic sequence file |
| profile | returns a single data object in BIOM format |
| project | project summary for metagenome provided by user |
| sample | sample information provided by user |
| search | search MG-RAST by MG-ID, metadata, function, or taxonomy; or implement a more complex search. |
| validation | validates templates for correct structure and data |
[table:upload_speeds]
Examples¶
The API provides index-driven access to data subsets using the following data types as indices into the data: functions, functional hierarchy data, and taxonomic data. Whenever possible we have employed standards to expose data and metadata, such as the BIOM standard for encoding abundance profiles. The examples below are intended to illustrate usage for the various resources available, they do not cover the entire functionality of the API, see the documentation at the API website for the comprehensive listing.
annotation
https://api.mg-rast.org/1/annotation/sequence/mgm4440036.3?type=function&filter=protease&source=Subsystems
Retrieve the reads from a metagenome with ID mgm4440036.3 which were annotated as protease in SEED Subsystems.
download
https://api.mg-rast.org/1/download/mgm4447943.3
Retrieve information formatted as a JSON object about all the files available for download for metagenome mgm4447943.3 with information about the files and sequence statistics where applicable. Each file listed has a URL included which can be used to download the file, e.g.
https://api.mg-rast.org/1/download/mgm4447943.3?file=650.1
will download the protein.sims file containing the BLAT similarities.
inbox
curl -X POST -H "auth: auth_key" -F<EMAIL_ADDRESS>"https://api.mg-rast.org/1/inbox"
Upload the file ’sequences.fastq’ to your inbox. This API call requires user authentication using the auth_key described above. It can not be used in a browser, but needs to be run from the command line or from a script.
matrix
https://api.mg-rast.org/matrix/organism?group_level=family&source=SEED&evalue=5&id=mgm4440442.5&id=mgm4440026.3
Retrieve the taxonomic abundance profile on family level for 2 metagenomes based on SEED assignments with an evalue cutoff of 1e-5.
metagenome
https://api.mg-rast.org/1/metagenome/mgm4440026.3
List analysis submission parameters and other details for a metagenome. The metagenome resource can also be used to search metadata, function and taxonomy.
https://api.mg-rast.org/metagenome?function=dnaA&organism=coli&biome=marine&match=all&order=created
This call will find all marine metagenomes with reads annotated as dnaA and have taxonomic assignment containing the text ‘coli’, the results will be ordered based on creation date for the metagenome.
project
https://api.mg-rast.org/project/mgp31?verbosity=full
Retrieve available information about the project with ID mgp31.
sample
https://api.mg-rast.org/1/sample/mgs12326?verbosity=full
Retrieve available information about individual samples, including IDs and metadata.
metadata
https://api.mg-rast.org//metadata/template
Retrieve the static template for metadata object relationships and types used by MG-RAST.
https://api.mg-rast.org//metadata/export/mgp128
Retrieve all metadata for project mgp128.
https://api.mg-rast.org/metadata/cv
Retrieve a set of lists of all our controlled metadata terms, including the ontologies.
https://api.mg-rast.org/metadata/ontology?name=biome&version=2013-04-27
Retrieve a more detailed list (with relationships) for a specific version of the ontology.
m5nr
https://api.mg-rast.org/1/m5nr/md5/ffc62262a18b38671c3e337150ef535f?source=SwissProt
Retrieve the UniProt ID for a given sequence identifier.
Example scripts using the MG-RAST REST API¶
.
Introduction¶
As part of the RESTful API (see chapter 6), we are providing a collection of example scripts.
Each script has comments in the source code as well as a help function. This document provides a brief overview of the available scripts and their intended purpose. Please see the help associated with all of the individual files for a complete list of options and more details.
We believe these scripts to be the best starting point for many users, he we attempt to provide a listing of the most important tools.
URLs¶
The Examples are located on github at:
https://github.com/MG-RAST/MG-RAST-Tools
This is the base directory for the rest of this chapter, go here to find the tools and examples described below:
https://github.com/MG-RAST/MG-RAST-Tools/tree/master/tools/bin
Each script has a verbose help option (–help) to list all options and explain their usage.
Download DNA sequence for a function – mg-get-sequences-for-function.py¶
This script will retrieve sequences and annotation for a given function or functional class.
The output is a tab-delimited list of: m5nr id, dna sequence, semicolon seperated list of annotations, sequence id.
Example:
mg-get-sequences-for-function.py --id "mgm4441680.3" --name "Central carbohydrate metabolism" --level level2 --source Subsystems --evalue 10
Download DNA sequences for a taxon or taxonomic group– mg-get-sequences-for-taxon.py¶
This script will retrieve sequences and annotation for a given taxon or taxonomic group.
The output is a tab-delimited list of: m5nr id, dna sequence, semicolon seperated list of annotations, sequence id
Example:
mg-get-sequences-for-taxon.py --id "mgm4441680.3" --name Lachnospiraceae --level family --source RefSeq --evalue 8
Download sequences annotated with function and taxonomy – mg-get-annotation-set.py¶
Retrieve functional annotations for given metagenome and organism.
The output is a tab-delimited list of annotations: feature list, function, abundance for function, avg evalue for function, organism.
Example:
mg-get-annotation-set.py --id "mgm4441680.3" --top 5 --level genus --source SEED
Download the n most abundant functions for a metagenome – mg-abundant-functions.py¶
Retrieve the top n abundant functions for metagenome.
The output is a tab-delimited list of function and abundance sorted by abundance (largest first). ’top’ option controls number of rows returned.
Example:
mg-abundant-functions.py --id "mgm4441680.3" --level level3 --source Subsystems --top 20 --evalue 8
Download and translate similarities into different namespaces e.g. SEED or GenBank – m5nr-tools.pl¶
MG-RAST computes similarities against a non-redundant database (Wilke et al. 2012) and later translates them into any of the supported namespaces. As a result you can view your annotations (or indeed the similarity results) in each of these namespaces. Sometimes this can lead to new features and or differences becoming visible that would otherwise be obscured.
m5nr-tools can translate accession ids, md5 checksums, or protein sequence into annotations. One option for output is a blast m8 formatted file.
Example:
m5nr-tools.pl --api "https://api.mg-rast.org/1" --option annotation --source RefSeq --md5 0b95101ffea9396db4126e4656460ce5,068792e95e38032059ba7d9c26c1be78,0b96c92ce600d8b2427eedbc221642f1
Download multiple abundance profiles for comparison – mg-compare-functions¶
Retrieve matrix of functional abundance profiles for multiple metagenomes. The output is either tab-delimited table of functional abundance profiles, metagenomes in columns and functions in rows or BIOM format of functional abundance profiles.
Example:
mg-compare-functions.py --ids "mgm4441679.3,mgm4441680.3,mgm4441681.3,mgm4441682.3" --level level2 --source KO --format text --evalue 8
Standard operating procedures SOPs for MG-RAST¶
SOP - Metagenome submission, publication and submission to INSDC via MG-RAST¶
MG-RAST can be used to host data for public access. There are three interfaces for uploading and publishing data, the Web interface, intended for most users, command line scripts, intended for programmers, and the native RESTful API, recommended for experienced programmers.
When data is published in MG-RAST, it can also be released to the INSDC databases. This tutorial covers both use cases.
We note that MG-RAST provides temporary IDs and permanent public identifiers. The permanent identifiers are assigned at the time data is made public. Permanent MG-RAST identifiers begin with “mgm” (e.g. “mgm4449249.3”) for data sets and mgp (e.g.”mgp128”) for projects/studies.
The following data types are supported:
- Shotgun metagenomes (“raw” and assembled)
- Metatranscriptome data (“raw” and assembled)
- Ribosomal amplicon data (16s, 18s, ITS amplicons)
- Metabarcoding data (e.g. cytochrome C amplicons; basically all non ribosomal amplicons)
PLEASE NOTE: We strongly prefer raw data over assembled data, if you submit assembled data, please submit the raw reads in parallel. If you perform local optimization e.g. adapter removal or quality clipping, please submit the raw data as well.
This document is intended for experienced to very experienced users and programmers. We recommend that most users not use the RESTful API. There is also a document describing data publication and INSDC submission via the web UI.
An access token for the MG-RAST API, this can be obtained from the MG-RAST web page (http://mg-rast.org) in the user section.
You will need a working python interpreter and the command line scripts and example data can be found in https://github.com/MG-RAST/MG-RAST-Tools:
Scripts: MG-RAST-Tools/tools/bin Data: MG-RAST-Tools/examples/sop/data
Change into MG-RAST-Tools/examples/sop/data and call:
sh get_test_data.sh
to add additional example data.
Either download the repository as a zipped archive from https://github.com/MG-RAST/MG-RAST-Tools/archive/master.zip or use the git command line tool:
git clone http://github.com/MG-RAST/MG-RAST-Tools.git
We tested up to the following parameters:
- max. size per file: 10GB
- max. project size: 200 metagenomes
While there is no reason to assume the software will not work with larger numbers of files or larger files, we did not test for that.
SOP:¶
Upload and submit sequence data and metadata to MG-RAST using the command mg-submit.py Note: This is an asynchronous process that may take some time depending on the size and number of datasets. (Note: We recommend that novice users try the web frontend; the cmd-line is primarily intended for programmers) The metadata in this example is in Microsoft Excel format, there is also an option of using JSON formatted data. Please note: We have observed multiple problems with spreadsheets that were converted from older version of Excel or “compatible” tools e.g. OpenOffice.
Example:
mg-submit.py submit simple .... --metadata
Verify the results and obtain a temporary identifier E.g. by using the WebUI at http://mg-rast.org – you can also use that to publish the data and trigger submission to INSDC.
Publish your project in MG-RAST and obtain a stable and public MG-RAST project identifier
Note: once the data is made public the data is read only, but metadata can be improved
Example:
mg-project make-public $temporary_ID
Trigger release to INSDC/ submit to EBI
Note: Metadata updates are automatically synced with INSDC databases within 48 hours.
Example:
mg-project submit-ebi $PROJECT_ID
Check status of release to INSDC/ submission to EBI
Note: This is an asynchronous process that may take some time depending on the size and number of datasets.
Example:
mg-project status-ebi $PROJECT_ID
We include a sample submission below:
From within the MG-RAST-Tool repository directory
# Retrieve repository and setup environment
git clone http://github.com/MG-RAST/MG-RAST-Tools.git
cd MG-RAST-Tools
# Path to scripts for this example
PATH=$PATH:`pwd`/tools/bin
# set environment variables
source set_env.sh
# Set credentials, obtain token from your user preferences in the UI
mg-submit.py login --token
# Create metadata spreadsheet. Make sure you map your samples to your
# sequence files
# Upload metagenomes and metadata to MG-RAST
mg-submit.py submit simple \
examples/sop/data/sample_1.fasta.gz \
examples/sop/data/sample_2.fasta.gz \
--metadata examples/sop/data/metadata.xlsx
# Output
> Temp Project ID: ed2102aa666d676d343735323836382e33
> Submission ID: 77a1a1a5-4cbd-4673-86bf-f87c9096c3e1
# Remember IDs for later use
SUBMISSION_ID=77a1a1a5-4cbd-4673-86bf-f87c9096c3e1
TEMP_ID=mgp128
# Check if project is finished
mg-submit.py status $SUBMISSION_ID
# Output
> Submission: 77a1a1a5-4cbd-4673-86bf-f87c9096c3e1 Status: in-progress
# Make project public in MG-RAST
mg-project.py make-public $TEMP_ID
# Output
> # Your project is public.
> Project ID: mgp128
> URL: https://mg-rast.org/linkin.cgi?project=mgp128
PROJECT_ID=mgp128
# Release project to INSDC archives
mg-project.py submit-ebi $PROJECT_ID
# Output
> # Your Project mgp128 has been submitted
> Submission ID: 0cf7d811-1d43-4554-ab97-3cb1f5ceb6aa
# Check if project is finished
mg-project.py status-ebi $PROJECT_ID
# Output
> Completed
> ENA Study Accession: ERP104408
Acknowledgments¶
This project is funded by the NIH grant R01AI123037 and by NSF grant 1645609
This work used the Magellan machine (U.S.Department of Energy, Office of Science, Advanced Scientific Computing Research, under contract DE-AC02-06CH11357) at Argonne National Laboratory, and the PADS resource (National Science Foundation grant OCI-0821678) at the Argonne National Laboratory/University of Chicago Computation Institute.
In the past the following sources contributed to MG-RAST development:
- U.S. Dept. of Energy under Contract DE-AC02-06CH11357
- Sloan Foundation (SLOAN #2010-12),
- NIH NIAID (HHSN272200900040C),
- NIH Roadmap HMP program (1UH2DK083993-01).
| [1] | This includes only the computation cost, no data transfer cost, and was computed using 2009 prices. |
| [2] | We use the term cloud as a shortcut for Infrastructure as a Service (IaaS). |
| [3] | This would be for several metagenomes that are part of the JGI Prairie pilot. |
| [4] | An MD5 checksum is a widely used way to create a digital fingerprint for a file. Think of it as a kind of checksum, if the fingerprint changed, so did the file. The fingerprints are easy to compare. There are many tools out there for creating MD5 checksums, google is your friend. |


Here is a useful identity relating the expectation of a non-negative random variable $X$ to the integral of its tail. $ \newcommand{\abs}[1]{| #1 |} \newcommand{\E}{\mathbb{E}} \newcommand{\ind}{\mathbf{1}} \renewcommand{\Pr}{\mathbb{P}} $
Proposition: Let $X \geq 0$ be a non-negative random variable. We have the equality $$ \E X = \int_0^\infty \Pr( X \geq t ) \; dt \:. $$
Proof: We simply write the right hand side as $$ \begin{align*} \int_0^\infty \Pr( X \geq t ) \; dt &= \int_0^\infty \int \ind_{\{X \geq t\}} \; d\mu \; dt \\ &\stackrel{(a)}{=} \int \int_0^\infty \ind_{\{X \geq t\}} \; dt \; d\mu \stackrel{(b)}{=} \int \int_0^X \; dt \; d\mu = \int X \; d\mu = \E X \:, \end{align*} $$ where in (a) we used Fubini's theorem to switch the order of integration and in (b) we used the fact that $X \geq 0$.
As an example, we can use this identity to compute the expected value of an exponential random variable without having to integrate by parts. Letting $X$ be a standard exponential random variable with density $f(x) = e^{-x}$, we have for any $t \geq 0$ that $\Pr(X \geq t) = \int_0^t e^{-x} \; dx = e^{-t}$. Hence, $$ \E X = \int_0^\infty \Pr(X \geq t) \; dt = \int_0^\infty e^{-t} \; dt = 1 \:. $$


With dedicated web and iOS apps and syncing between them, you'll never have to worry about forgetting your passwords.
All of your Cryptonite data is encrypted according to our secure algorithm. Furthermore, our unique 2-factor authentication makes it extremely difficult for computers to brute force their way in.
Cryptonite's simple and distraction-free interface make it easy to find the features you need.


{% extends "base.html" %} {% block title %} {{ super() }} Help {% endblock %} {% block content %}
McBench introduces some XPath extensions to make queries more convenient.
is_stmt()
and is_expr()
predicates return whether the current node
is a statement or an expression, respectively.loopvars()
returns a sequence of the loop vars currently in scope. For example,
//ForStmt[loopvars()='i']
finds all for loops where the loop variable is i
(or
that are nested inside such a for loop).CellIndexExpr
, DotExpr
, or ParameterizedExpr
,
target()
returns the target (aka receiver) of the expression.AssignStmt
, lhs()
and rhs()
get at the
left and right hand sides, respectively.CellIndexExpr
or ParameterizedExpr
,
num_args()
returns the number of arguments, and arg(i)
returns
the argument at index i
(starting from 1).is_call(names)
predicate returns whether the current node
represents a call to a function whose name is in names
, which may be
an XPath path expression or a (possibly empty) comma-separated list of string literals. Some examples:
//ParameterizedExpr[is_call()]
selects all function calls.//ParameterizedExpr[is_call('eval', 'feval')]
selects all calls
to either eval
or feval
.//ParameterizedExpr[is_call(ancestor::Function/Name/@nameId)]
selects
all calls to functions that are ancestors of the call. In the simplest case this means
recursive calls; in the presence of nested functions, it could either the nested function
or the enclosing function.


--- layout: post title: "Closing database connections during test teardown" description: "How to close database connections to SQL Server during integration testing teardown." date: 2020-07-20 7:20 UTC tags: [Unit Testing, xUnit.net] --- {% include JB/setup %}
{{ page.description }}
Whenever I need to run integration tests that involve SQL Server, I have a standard approach that I've evolved since 2007. It involves
One problem with that approach is that SQL Server doesn't allow you to delete a database if it has existing connections.
I usually solve the problem by turning off connection pooling. For an integration test suite, this is fine. I usually use integration testing to verify functionality - not performance.
Turning off connection pooling is easily done by setting the flag to false
in the connection string:
Server=(LocalDB)\MSSQLLocalDB;Database=Booking;Integrated Security=true;Pooling=false
This means that when you get to the teardown phase of the test, you can issue a DDL statement to the master
database:
IF EXISTS (SELECT name FROM master.dbo.sysdatabases WHERE name = N'Booking') DROP DATABASE [Booking]
When connection pooling is turned off, no other connections are open when you attempt to do that, and the database (here named Booking
) is deleted.
Recently, however, I ran into a testing scenario where connection pooling had to be turned on. When you turn on connection pooling, however, the above DROP DATABASE
statement fails because at least one connection from the pool is still connected to the database.
To solve that issue, I forcibly close other connections during teardown:
IF EXISTS (SELECT name FROM master.dbo.sysdatabases WHERE name = N'Booking') BEGIN -- This closes existing connections: ALTER DATABASE [Booking] SET SINGLE_USER WITH ROLLBACK IMMEDIATE DROP DATABASE [Booking] END
Surprisingly, turning on connection pooling like this makes the integration tests slower. I suppose it's because throwing other connections off the database involves a bit of negotiation between the server and the clients, and that takes some time.
While slower, it does enable you to run the integration tests with connection pooling turned on. When you need it, you need it.
You can run integration tests against a SQL Server Express database. People do it in various ways, but I've found that setting up and tearing down a pristine database for each test case is a robust and maintainable solution to the problem.
SQL Server will not, however, allow you to delete a database if other connections exist. The easiest and fastest solution to that problem is to turn off connection pooling.
Sometimes, you can't do that, so instead, you can expand your database teardown script so that it closes existing connections before it deletes the database.
Comments
This sounds like a great approach. I have been on projects with tests that involved the database, but none of them were designed as well as this. I will be sure to come back to this post when we add a database to my current project.
My understanding is that SQL Server Express and LocalDB are not the same thing. Are you using SQL Server Express or LocalDB? Do you prefer one over the other for this database testing approach of yours?
Tyson, thank you for writing. It's not really my area of expertise. I use the one bundled with Visual Studio, so I suppose that's actually LocalDB, and not SQL Server Express.


Dota, is one of the most widely played online games in the world, with half a million users logged in every hour. Dota is more than just your average video game because It combines the complexities of chess with the mechanics of a video game. You need to be fast and, on your toes, to compete in this popular MOBA. Dota 2 is played in a team of 5 people, where each player chooses a character out of 117 different characters, each equipped with unique abilities. Once you have chosen the right combination of characters, you battle until one team destroys the other team’s ‘Ancient’. Heroes can equip themselves with up to 11 spells, each creating a different impact on the battlefield. Players then get to choose among 152 items. Each item is purchased using gold collected and either enhances the abilities of the characters or protects them during the battle. This wide range of options defines the beauty of Dota as a game. Each match is unique, action packed and an epic battle of wits.
Dota has 3 lanes where players battle. Lanes contain barracks that unleash CPU controlled creeps that move towards enemy base. Killing creeps lets player acquire gold that they can use to purchase items and improve their game. When your hero dies and simply respawns back in your base after serving a small penalty time. Willfully returning to the base can help heal players and restore mana. Each player attempts to move closer to the enemy’s Ancient which cannot be damaged until one lane of towers is cleared.
Source: https://dota2.gamepedia.com/Lane, Dota 2 map with main lanes where players of two separate team — Radiant and Dire clash together with creeps. Dots represent defensive towers. Back in 2003, Dota was just a custom map made with Warcraft 3 map editor, started by Eul, Steve Feak and IceFrog. In 2013, Valve officially released It as a separate game. It is well known as a precursor of MOBA genre and still holding the 1st place as a most played game on Steam and 7th place in the ranking of most played games in the world. Few months back, Valve published statistics that showed that in July 2019, more than 11 million unique players played the moba.
Source: https://steamcharts.com/app/570#
All, Chart displaying average and peak amount of players which has been logged in game at the same time over past 7 years.


Fill the following square with 1 circle that touches all 4 sides of the square.
Refer to the "finished-example.png" file to see what it should look like.


DIVISION 4. COURTS AND JUDICIAL PROCEEDINGS
Note
Pursuant to article VI, section 7 of the state constitution, the supreme court promulgated rules having the force and effect of law. Statutes relating to process, practice, procedure, and appeals remain in force and effect if, but only if, they are not in conflict with the rules of court. Accordingly, the procedural statutes set out in this division should be compared with the rules of court to ascertain whether they have been superseded in whole or in part. As to applicability of rules, see HRCP rules 1 and 81; HRPP rules 1 and 54; RCC rules 1.1, 32, and 34; HAR rule 2; RDC rules 1.1 and 31; DCRCP rules 1 and 81; RSCD rule 1; HFCR rules 1 and 81; HRAP rules 1, 2, and 2.1; see also Rules of the Supreme Court, Hawaii Rules of Professional Conduct, Hawaii Revised Code of Judicial Conduct, Rules of the Intermediate Court of Appeals, Rules of the Tax Appeal Court, and Rules of the Land Court.
Rules of Court
See also Hawaii Appellate Mediation Program Rules; Hawaii Probate Rules; Hawaii Civil Traffic Rules; Rules Governing Court Reporting; Hawaii Rules for Certification of Spoken and Sign Language Interpreters.
TITLE 32. COURTS AND COURT
OFFICERS
Chapter
601 Courts Generally
602 Courts of Appeal
603 Circuit Courts
604 District Courts
604A Environmental Courts
605 Attorneys
606 Clerks, Reporters, Interpreters, Etc.
607 Costs and Fees
608 Expenses and Salaries
609 Jurors--Repealed
610 Retirement and Removal of Justices and Judges--Repealed
611 Counsel and Other Services For Indigent Defendants--
Repealed
612 Jurors
613 Center for Alternative Dispute Resolution
Note
Community court outreach project; annual report to 2018-2020 legislature. L 2017, c 55.
CHAPTER 601
COURTS GENERALLY
Section
601-1 Judiciary
601-1.5 Emergency period; suspension of deadlines
601-2 Administration
601-3 Administrative director
601-3.2 Reporting of non-general fund information
601-3.5 Supreme court law library revolving fund
601-3.6 Spouse and child abuse special account; judiciary
601-3.7 Judiciary computer system special fund
601-4 Judicial council
601-5 Independence of judiciary
601-6 Repealed
601-7 Disqualification of judge; relationship, pecuniary
interest, previous judgment, bias or prejudice
601-8 Practice of law forbidden
601-9 Same, other instances
601-10 to 12 Repealed
601-13 Publication of notices and process
601-14 Repealed
601-15 Style of process
601-16 Repealed
601-17 Use of credit and debit cards to pay for court
costs, fees, expenses, and other charges
601-17.5 Collection of delinquent court-ordered payments
601-18 Interest income
601-20 Court annexed arbitration program
601-21 Substance abuse treatment monitoring program
Sheriff
601-31 to 38 Repealed
Security Personnel
601-51 Repealed
Note
Community court outreach project; annual report to 2018-2020 legislature. L 2017, c 55.
Decriminalizing minor offenses. L 2005, c 124; L 2008, c 101.
Cross References
Commission on salaries, see §26-56.
Intermediate sanctions for selected offenders and defendants, see §§353-10.5, 353-63.5, and 706-605.1.
Rules of Court
Court interpreter certification program, see Hawaii Rules for Certification of Spoken and Sign Language Interpreters.
Law Journals and Reviews
Judicial Independence: The Hawaii Experience. 2 UH L. Rev. 1 (1979).


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weeds
`Prev `__
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`Next `__
--------------
**weeds**: n.
1. Refers to development projects or algorithms that have no possible
relevance or practical application. Comes from ‘off in the weeds’. Used
in phrases like “lexical analysis for microcode is serious weeds....”
2. At CDC/ETA before its demise, the phrase go off in the weeds was
equivalent mainstream hackerdom's `*jump off into never-never
land* <../J/jump-off-into-never-never-land.html>`__.
--------------
+---------------------------+----------------------------+---------------------------+
| `Prev `__ | `Up <../W.html>`__ | `Next `__ |
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spatialBehaviour.Rd
This function takes in data for a recorder and calculates the spatial metrics. Note that the spatial projection of the data allows for estimates of distance in meters. Pay particular attention to the specification of the parameters crs
and new_crs
spatialBehaviour(recorder_name, data, y_col, x_col, crs, new_crs, recorder_col = "recorders", upper_percentile = 95, lower_percentile = 60, h = 5000, res = 1000, threshold = 5)
| recorder_name | the name of the recorder for whom you want to calculate the metrics |
|---|---|
| data | the data.frame of recording information |
| y_col | the name of the column that contains the y coordinate (e.g. latitude) of the observation. This should be a numeric. |
| x_col | the name of the column that contains the x coordinate (e.g. longitude) of the observation. This should be a numeric. |
| crs | the proj4 string that describes the projection your data are using. For GPS lat long this is "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs". You can find more at http://spatialreference.org/ |
| new_crs | the proj4 string that the describes the coordinate system your data should be reprojected to. THIS IS IMPORTANT. Your data must be on a projection that has units in meters so that results are comparable to other studies. An appropriate system in the UK is the UK national grid "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +datum=OSGB36 +units=m +no_defs". If your original crs (given in the argument |
| recorder_col | the name of the column that contains the recorder names |
| upper_percentile | The percentile used to create a polygon that encapsulates a proportion of the recorders observations using the kernel method ( |
| lower_percentile | see |
| h | a numeric smoothing parameter for drawing the kernels. See |
| res | a numeric giving the resolution for kernel estimation. |
| threshold | If there are less than this number of observations NA values will be returned for the metrics. Default is 5 |
A data.frame with seven columns
recorder
- The name of the recorder, as given in the recorder_name argument
spPoint
- The observation points as a SpatialPoints object
poly_upper
- A SpatialPolygonsDataFrame object giving the are containing the upper_percentile percentage of records using the kernel method
poly_lower
- A SpatialPolygonsDataFrame object giving the are containing the lower_percentile percentage of records using the kernel method
upper_n_poly
- The number of polygons that make up poly_upper
lower_n_poly
- The number of polygons that make up poly_lower
upper_area
- The area of the polygons that make up poly_upper
in km squared
lower_area
- The area of the polygons that make up poly_upper
in km squared
ratio
- The ratio of lower_area
to upper_area
calculated as lower_area/upper_area
n
- The total number of observations made by this recorder
# NOT RUN { # load example data head(cit_sci_data) # Run for one recorder using the UK grid ## get the proj4 strings from http://spatialreference.org # current form is lat long WGS_84 <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs" # I want to change to UK national grid as that is in meters UKNG <- "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +datum=OSGB36 +units=m +no_defs" SB <- spatialBehaviour(recorder_name = 3007, data = cit_sci_data, crs = WGS_84, new_crs = UKNG, y_col = 'lat', x_col = 'long', recorder_col = 'recorder') SB plot(SB$poly_upper) # Run for more than one recorder, this can be slow SB_all <- lapply(unique(cit_sci_data$recorder)[1:10], FUN = spatialBehaviour, data = cit_sci_data, crs = WGS_84, new_crs = UKNG, y_col = 'lat', x_col = 'long', recorder_col = 'recorder') # summarise as one table SB_all_sum <- do.call(rbind, SB_all) SB_all_sum # }


Data that’s spread all over can be consolidated into a single source. Data virtualization allows companies to treat data as a dynamic supply—providing processing capabilities that can bring together data from multiple sources, easily accommodate new data sources, and transform data according to user needs. Data virtualization tools sit in front of multiple data sources and allows them to be treated as single source, delivering the needed data—in the required form—at the right time to any application or user.
Easily confused with operating system virtualization—which allows you to deploy multiple operating systems on a single machine—desktop virtualization allows a central administrator (or automated administration tool) to deploy simulated desktop environments to hundreds of physical machines at once. Unlike traditional desktop environments that are physically installed, configured, and updated on each machine, desktop virtualization allows admins to perform mass configurations, updates, and security checks on all virtual desktops.
Servers are computers designed to process a high volume of specific tasks really well so other computers—like laptops and desktops—can do a variety of other tasks. Virtualizing a server lets it to do more of those specific functions and involves partitioning it so that the components can be used to serve multiple functions.
Operating system virtualization happens at the kernel—the central task managers of operating systems. It’s a useful way to run Linux and Windows environments side-by-side. Enterprises can also push virtual operating systems to computers, which:
Network functions virtualization (NFV) separates a network's key functions (like directory services, file sharing, and IP configuration) so they can be distributed among environments. Once software functions are independent of the physical machines they once lived on, specific functions can be packaged together into a new network and assigned to an environment. Virtualizing networks reduces the number of physical components—like switches, routers, servers, cables, and hubs—that are needed to create multiple, independent networks, and it’s particularly popular in the telecommunications industry.