Laurence Olivier's film version of Shakespeare's "Othello" stands out as one of his greatest and most controversial performances.Having resisted playing the role until 1964 (onstage),Olivier was not content to simply put on blackface makeup and act the role sounding like his real self,as all previous white actors had done.He assumed the role only after Kenneth Tynan said he would not assume co-directorship of the National Theatre of Great Britain unless Olivier played Othello, a role he had always resisted partly because he felt it was Shakespeare's most difficult character,and partly because he felt the character himself was a "loser" who gave way too easily to jealousy.
But Olivier reconciled himself to the challenge and played Othello so convincingly that every actor since then,white or black,has had to measure up to his performance. Always searching for a different way to play a role, he took voice lessons to deepen his voice to a rich bass. He also adopted a foreign accent somewhere between Jamaican,regular British,and South African. He developed an unusual way of walking, much like the jungle dwellers. Of course,he risked incredible controversy in choosing to play his Othello like this. Nevertheless,his performance garnered rave reviews and an Academy Award nomination for Best Actor when it was filmed. The film was released in 1965,and played at reserved seat showings for only a few days.Unlike other Olivier Shakespeare films,this one was shot on a very low budget,due to unwillingness on the part of movie studios (Olivier's previous Shakespeare producers had all passed away by 1965) to put up the money. Thus,the movie is virtually a duplicate of the stage production,with no real locations and obviously phony lightning and thunder. The sets and props,although duplicated,have been greatly enlarged to soundstage size,and the production has been filmed as if the camera were actually onstage,so that extreme closeups and unusual camera angles are possible.
In an article in his book "Profiles",Kenneth Tynan explains how stage director John Dexter,influenced by a famous Shakespearean essay, deliberately chose to have Othello played somewhat unsympathetically as a warrior overly pleased with himself and his marriage to Desdemona,yet prone to displays of hysterical self-dramatization when he becomes jealous. If this is true,he didn't count on Olivier's amazing ability to create a character who is proud,yet so vulnerable,and so heart-rending in his collapse,that we never lose our sympathy for him,even when we see how easily he is duped by Iago (Frank Finlay). His pride,rather than seeming obnoxious,is rather comical,like a teen-ager who has snagged a date with the most beautiful girl in his class,and makes Othello rather lovable. Frank Finlay,who onstage and on the vinyl LP made from this production was swamped by Olivier's portrayal,comes across magnificently on film. His performance,unlike Olivier,has been filmed with numerous close-ups which emphasize his subtle facial expressions,most of which wouldn't be easily visible in a live performance. His Iago is the most convincing I have seen--he is the only actor who can make Iago seem like a "nice guy"to those around him. His friendliness to Cassio (Derek Jacobi) is entirely believable,and yet he can switch from giving concerned advice to showing utter contempt in an instant. A very young Maggie Smith is sweet and touching as Desdemona,Othello's bewildered wife,and Joyce Redman is amusingly cynical yet tragic as Iago's wife and unknowing accomplice.
This "Othello" may be controversial nowadays, because of the influence of "political correctness",and the fact that black actors such as James Earl Jones have been acclaimed in the role. But audiences who pass it up will miss one of the greatest portrayals of the twentieth centuty.
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


Sample snapshots
30 degree rotation snapshots of the sample mount after optical centring (using Lucid) and before X-ray centring
Omega = 0 degrees
Omega = 30 degrees
Omega = 60 degrees
Omega = 90 degrees
Omega = 120 degrees
Omega = 150 degrees
Omega = 180 degrees
Omega = 210 degrees
Omega = 240 degrees
Omega = 270 degrees
Omega = 300 degrees
Omega = 330 degrees
Generated on Tue Oct 13 15:28:19 2015


The NY Times has been making an amazing infographic of Covid-19 cases. With the data from this package, we should be able to more or less recreate it. Let’s do it for the lower 48 (moving Hawaii and Alaska is a pain!)
We’ll start by loading the coronavirus data at the county and state level
require(covid19nytimes) require(dplyr) covid19nytimes_states <- refresh_covid19nytimes_states() %>% filter(date == max(date)) covid19nytimes_counties <- refresh_covid19nytimes_counties()%>% filter(date == max(date))
Next, we need some county maps and state maps. For this, we’ll use the excellent tigris package. We want the centroid of counties for bubble placement.
require(sf) require(tigris) county_map <- counties(cb = TRUE, resolution = '20m', class = "sf") %>% st_centroid() state_map <- states(cb = TRUE, resolution = '5m', class = "sf") #make a state lookup table state_lookup <- state_map %>% as_tibble() %>% select(STATEFP, NAME) %>% rename(STATE_NAME = NAME)
OK, let’s filter to the lower 48 for all data sets
filter_out <- c("Alaska", "Hawaii", "Guam", "Puerto Rico", "American Samoa", "Commonwealth of the Northern Mariana Islands", "Virgin Islands") covid19nytimes_states <- covid19nytimes_states %>% filter(!(location %in% filter_out)) %>% filter(location != "Unknown") covid19nytimes_counties <- covid19nytimes_counties %>% filter(purrr::map_dbl(location, ~stringr::str_detect(., filter_out) %>% sum)==0) %>% filter(location != "Unknown") county_map <- county_map %>% left_join(state_lookup) %>% filter(!(STATE_NAME %in% filter_out)) #> Joining, by = "STATEFP" state_map <- state_map %>% filter(!(NAME %in% filter_out))
For text labels, we use centroids of states joined with maximum number of case numbers.
state_num_centroids <- covid19nytimes_states %>% filter(date == max(date)) %>% filter(data_type == "cases_total") %>% left_join(state_map, by = c("location_code" = "STATEFP")) %>% st_as_sf(crs = st_crs(state_map)) %>% st_centroid()
Now, we join the county data and the county centroid map and filter to the last date in the data set.
joined_covid_counties <- covid19nytimes_counties %>% filter(data_type == "cases_total") %>% left_join(county_map, by = c("location_code" = "GEOID")) %>% st_as_sf(crs = st_crs(county_map))
Last, let’s make the plot!
require(ggplot2) #> Loading required package: ggplot2 ggplot() + geom_sf(data = state_map, fill = "lightgrey", color = "darkgrey") + geom_sf_text(data = state_num_centroids, aes(label = value), color = "red", size = 4, alpha = 0.7) + geom_sf(data = joined_covid_counties, mapping = aes(size = value), shape = 21, alpha = 0.5, color = "red", fill = "pink") + theme_void() + scale_size_continuous(guide= "none", range = c(0.05, 35)) + coord_sf(crs = st_crs(2163)) #for that nice equal area curved look


You're registered since {{ request.user.date_joined|date:"Y-m-d" }}.
{% if request.user.home_location %}Your home location is {{ request.user.home_location.y }}/{{ request.user.home_location.x }}, time zone {{ request.user.home_timezone }}.
{% else %}Your home location is unknown, but you can set it in your OpenStreetMap.org user settings.
{% endif %}


You are viewing an historical archive of past issues. Please
report new issues to the appropriate project issue tracker on
GitHub.
Comments
-
Greg Hurrell
This is a duplicate of bug #640:
https://wincent.com/a/support/bugs/show_bug.cgi?id=640
I'm going to mark this duplicate as CLOSED so as to keep all the comments about the issue in the one place.
Add a comment
Comments are now closed for this issue.


At MG Furniture House we have been catering to your furniture requirements for over 20 years. We believe that choosing the right furniture can go a long way into turning a house into home. We give you the opportunity to customize your furniture as per your requirements from the design to the polished finish you want.
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Reference Extractor is an online tool to extract Zotero and Mendeley references from Microsoft Word documents. The tool only works with Word documents stored in the .docx format. For Zotero references, the option "Store references in document" must have been checked in the Zotero document preferences, and references must be stored as "Fields", not "Bookmarks".
Once you have successfully extracted the references from a document, the output, in the CSL JSON format, can be either downloaded or copied to the clipboard. Currently, only Zotero seems to be capable of importing CSL JSON. You can either click the "Download" button and import the downloaded JSON file into Zotero via the "Import..." option in Zotero's gear menu, or click the "Copy to Clipboard" button and use the "Import from Clipboard" option in the gear menu.
Reference Extractor is a new tool, and hasn't yet been extensively tested. If you have feedback or run into any issues, please contact me on GitHub (by filing an issue) or Twitter.


{% extends 'cooperazione_base.html' %} {% block container %}
Italian Development Cooperation provides humanitarian assistance to victims of catastrophic events (natural or manmade), in order to safeguard human life, relieve suffering and maintain human dignity. Italy is committed to providing a response that is rapid and effective and satisfies local needs, following the three steps of humanitarian aid: **priority need** (relief); **emergency** (recovery and rehabilitation); **post-emergency** (linking relief and rehabilitation to development).
Humanitarian aid focuses on those sectors identified as critical for survival and improvement of basic living conditions, such as food security, access to water, disaster risk reduction, protection of refugees and displaced persons. Equally important are issues such as gender mainstreaming and the protection of vulnerable groups (children and people with disabilities). We approved the “2012 Guidelines on Humanitarian Aid”, in the framework of the **Operating plan for aid effectiveness** drafted in coordination with other Italian humanitarian actors. The guidelines are based on the internationally recognized principles of **Good Humanitarian Donorship** on humanitarian action.
{% endblock container %}


Cody decide to just play along and rescue the merpeople and sea creatures as he gets to there cages,so while playing it cool Cody just walks past the sea monsters and he pick pockets the keys from one of the sea monsters then he walk up to one of the cages to try to unlock it Kito notices that a sea monster (who is really Cody in dis ) is rescuing him and he askes our hero "Because your King is sick and twisted to think this is going to solve anything" "CODY" shouted Kito "shhhhhhh"said Cody to Kito "right sorry said Kito so what's our battle plan I'm not sure yet Said Cody I'm jus...." "And wut do you think your doin" said a shark headed sea monster from behind Cody "I was just ummm...""  Shouted Kito as he and the merpeople and sea creatures charged towards the sea monsters,and with all that out of the way The Merfolk were back on track with their battle against the sea monsters "Kito do you know how to treat wounds asked Cody not really said kito but I have some healing potion that I keep with me in case of such occasion why do you ask" because Alesha is in the castle and she is badly wounded can you go heal her up while I give my dad an elementary style butt whipping Don't worry Cody I'll take care of Alesha you take care of your dad said Kito thank s Kito said Cody I think you make a better King than my old man right back at ay as aid Kito but I'm not sure if I'm fit to be king


Package com.jme.math
Class Transform
- java.lang.Object
-
- com.jme.math.Transform
-
- All Implemented Interfaces:
Savable
,java.io.Serializable
,java.lang.Cloneable
public final class Transform extends java.lang.Object implements Savable, java.lang.Cloneable, java.io.Serializable
Started Date: Jul 16, 2004
Represents a translation, rotation and scale in one object.- Author:
- Jack Lindamood, Joshua Slack
- See Also:
- Serialized Form
-
-
Constructor Summary
Constructors Constructor Description Transform()
Transform(Quaternion rot)
Transform(Vector3f translation)
Transform(Vector3f translation, Quaternion rot)
Transform(Vector3f translation, Quaternion rot, Vector3f scale)
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description Transform
clone()
Transform
combineWithParent(Transform parent)
Changes the values of this matrix according to its parent.boolean
equals(java.lang.Object obj)
void
fromTransformMatrix(Matrix4f mat)
Quaternion
getRotation()
Return the rotation quaternion in this matrix.Quaternion
getRotation(Quaternion quat)
Stores this rotation value into the given Quaternion.Vector3f
getScale()
Return the scale vector in this matrix.Vector3f
getScale(Vector3f scale)
Stores this scale value into the given vector3f.Vector3f
getTranslation()
Return the translation vector in this matrix.Vector3f
getTranslation(Vector3f trans)
Stores this translation value into the given vector3f.int
hashCode()
void
interpolateTransforms(Transform t1, Transform t2, float delta)
Sets this transform to the interpolation between the first transform and the second by delta amount.Transform
invert()
boolean
isIdentity()
Test for exact identity.void
loadIdentity()
Loads the identity.void
read(JmeImporter e)
Transform
set(Transform matrixQuat)
Sets this matrix to be equal to the given matrix.Transform
setRotation(Quaternion rot)
Sets this rotation to the given Quaternion value.Transform
setScale(float scale)
Sets this scale to the given value.Transform
setScale(float x, float y, float z)
Sets this matrix's scale to the given x,y,z values.Transform
setScale(Vector3f scale)
Sets this scale to the given value.Transform
setTranslation(float x, float y, float z)
Sets this matrix's translation to the given x,y,z values.Transform
setTranslation(Vector3f trans)
Sets this translation to the given value.java.lang.String
toString()
Matrix4f
toTransformMatrix()
Matrix4f
toTransformMatrix(Matrix4f store)
Vector3f
transformInverseVector(Vector3f in, Vector3f store)
Vector3f
transformVector(Vector3f in, Vector3f store)
void
write(JmeExporter e)
-
-
-
Field Detail
-
IDENTITY
public static final Transform IDENTITY
-
-
Constructor Detail
-
Transform
public Transform(Vector3f translation, Quaternion rot)
-
Transform
public Transform(Vector3f translation, Quaternion rot, Vector3f scale)
-
Transform
public Transform(Vector3f translation)
-
Transform
public Transform(Quaternion rot)
-
Transform
public Transform()
-
-
Method Detail
-
setRotation
public Transform setRotation(Quaternion rot)
Sets this rotation to the given Quaternion value.- Parameters:
rot
- The new rotation for this matrix.- Returns:
- this
-
setTranslation
public Transform setTranslation(Vector3f trans)
Sets this translation to the given value.- Parameters:
trans
- The new translation for this matrix.- Returns:
- this
-
getTranslation
public Vector3f getTranslation()
Return the translation vector in this matrix.- Returns:
- translation vector.
-
setScale
public Transform setScale(Vector3f scale)
Sets this scale to the given value.- Parameters:
scale
- The new scale for this matrix.- Returns:
- this
-
setScale
public Transform setScale(float scale)
Sets this scale to the given value.- Parameters:
scale
- The new scale for this matrix.- Returns:
- this
-
getScale
public Vector3f getScale()
Return the scale vector in this matrix.- Returns:
- scale vector.
-
getTranslation
public Vector3f getTranslation(Vector3f trans)
Stores this translation value into the given vector3f. If trans is null, a new vector3f is created to hold the value. The value, once stored, is returned.- Parameters:
trans
- The store location for this matrix's translation.- Returns:
- The value of this matrix's translation.
-
getRotation
public Quaternion getRotation(Quaternion quat)
Stores this rotation value into the given Quaternion. If quat is null, a new Quaternion is created to hold the value. The value, once stored, is returned.- Parameters:
quat
- The store location for this matrix's rotation.- Returns:
- The value of this matrix's rotation.
-
getRotation
public Quaternion getRotation()
Return the rotation quaternion in this matrix.- Returns:
- rotation quaternion.
-
getScale
public Vector3f getScale(Vector3f scale)
Stores this scale value into the given vector3f. If scale is null, a new vector3f is created to hold the value. The value, once stored, is returned.- Parameters:
scale
- The store location for this matrix's scale.- Returns:
- The value of this matrix's scale.
-
interpolateTransforms
public void interpolateTransforms(Transform t1, Transform t2, float delta)
Sets this transform to the interpolation between the first transform and the second by delta amount.- Parameters:
t1
- The beginning transform.t2
- The ending transform.delta
- An amount between 0 and 1 representing how far to interpolate from t1 to t2.
-
combineWithParent
public Transform combineWithParent(Transform parent)
Changes the values of this matrix according to its parent. Very similar to the concept of Node/Spatial transforms.- Parameters:
parent
- The parent matrix.- Returns:
- This matrix, after combining.
-
setTranslation
public Transform setTranslation(float x, float y, float z)
Sets this matrix's translation to the given x,y,z values.- Parameters:
x
- This matrix's new x translation.y
- This matrix's new y translation.z
- This matrix's new z translation.- Returns:
- this
-
setScale
public Transform setScale(float x, float y, float z)
Sets this matrix's scale to the given x,y,z values.- Parameters:
x
- This matrix's new x scale.y
- This matrix's new y scale.z
- This matrix's new z scale.- Returns:
- this
-
toTransformMatrix
public Matrix4f toTransformMatrix()
-
fromTransformMatrix
public void fromTransformMatrix(Matrix4f mat)
-
invert
public Transform invert()
-
loadIdentity
public void loadIdentity()
Loads the identity. Equal to translation=0,0,0 scale=1,1,1 rot=0,0,0,1.
-
isIdentity
public boolean isIdentity()
Test for exact identity.- Returns:
- true if exactly equal to
IDENTITY
, otherwise false
-
hashCode
public int hashCode()
- Overrides:
hashCode
in classjava.lang.Object
-
equals
public boolean equals(java.lang.Object obj)
- Overrides:
equals
in classjava.lang.Object
-
toString
public java.lang.String toString()
- Overrides:
toString
in classjava.lang.Object
-
set
public Transform set(Transform matrixQuat)
Sets this matrix to be equal to the given matrix.- Parameters:
matrixQuat
- The matrix to be equal to.- Returns:
- this
-
write
public void write(JmeExporter e) throws java.io.IOException
-
read
public void read(JmeImporter e) throws java.io.IOException
-
clone
public Transform clone()
- Overrides:
clone
in classjava.lang.Object
-
-


M5 southbound 'closed to traffic for rest of morning' after serious accident near J22
Traffic is being diverted from the motorway via A38
by Geoffrey BennettA woman has suffered serious head injuries after an accident involving a lorry and a car on the M5.
Avon and Somerset Constabulary were called to reports of a two vehicle crash between junctions 22 and 23 southbound shortly before 6am today (November 16).
A police spokesperson said: "The incident involved a lorry and a car.
"The occupant of the car – a woman – has been taken to hospital with serious head injuries."
"The motorway is closed from J22 (southbound) and is likely to remain closed throughout the morning to allow collision investigators to carry out an examination of the scene."
We are providing live updates on the latest situation.
The northbound carriageway of the motorway remains open.
Vehicles travelling south into North Somerset can expect some delays around J22 as traffic is diverted via the A38.
The spokesman added: "Thank you for your patience and understanding while we deal with the incident."
For the latest news in and around Bristol, visit and bookmark Bristol Live's homepage .


This action will remove {{count.all}} execution(s). Are you sure?
Please, selected the status of the execution to remove:


{{ partial "vue_templates/filter_card.html" . }} {{ partial "vue_templates/post_card.html" . }} {{ partial "vue_templates/disqus_card.html" . }} {{ partial "vue_templates/posts.html" . }} {{ partial "vue_templates/post.html" . }} {{ partial "vue_templates/works.html" . }} {{ partial "vue_templates/about.html" . }}


New Zealand Cricket to apologise to Archer after England bowler suffers racist abuse
An investigation has been opened by New Zealand Cricket after Jofra Archer was racially abused by a spectator at Bay Oval.
by The42 TeamNEW ZEALAND CRICKET will apologise to Jofra Archer after the England bowler was racially abused by a spectator during the Black Caps’ Test victory at Bay Oval.
Archer put on a stand of 30 alongside Sam Curran (29 not out) as England attempted to salvage a draw on day five in Mount Maunganui, but was eventually dismissed by Neil Wagner (5-44) as the tourists slumped to an innings and 65-run defeat.
The 24-year-old Barbados-born fast bowler subsequently claimed a member of the Bay Oval crowd had directed racist abuse at him.
“A bit disturbing hearing racial insults today whilst battling to help save my team, the crowd was been amazing this week except for that one guy,” Archer tweeted.
The Black Caps have issued a swift response, stating they will contact Archer on Tuesday to issue an apology, while confirming there is to be an investigation into the matter.
“New Zealand Cricket (NZC) will be contacting and apologising to English fast bowler Jofra Archer, who was racially abused by a spectator as he left the field at the conclusion of the first Test at Bay Oval, Mount Maunganui,” a statement issued read.
“Although security providers at the venue were unable to locate the perpetrator, NZC will be examining CCTV footage and making further inquiries tomorrow in an endeavour to identify the man responsible.
“NZC has zero tolerance towards abusive or offensive language at any of its venues and will refer any developments in the case to police.
“It will contact Mr Archer tomorrow to apologise for the unacceptable experience, and to promise increased vigilance in the matter when the teams next meet in Hamilton.”
- Omni


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Trinidads
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beaoh
ridge
near
avamp,
sea
level.
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30-60
ft.
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with
profuse
inflorescences,
fruits
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beneath*
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Legend -
Level of confidence that token is an accurately-transcribed word
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--- title: Distributed Computing with Emacs layout: post tags: [emacs, elisp] uuid: 9bc67be6-abda-37cd-d34f-ef0e4622358c ---
I got an Elisp idea today and even went as far as implementing a proof of concept for it: distributed computing with Emacs Lisp. As usual for me the idea takes advantage of Lisp features to make the task pretty simple, very specifically Elisp's implementation. In this case it's the Lisp reader, printer, and the fact that Elisp functions have a printed representation, both byte-compiled and not.
Here's the proof of concept code: dist-emacs.el
A central server listens for TCP connections. Clients offering their CPU for use connect to the server and await instructions. The server sends a single, no-argument, anonymous function to the client. The client calls the function, returning the resulting form back to the server. In order to transmit the function it's encoded into a string using the Lisp printer, and the client turns it back into an executable function with the Lisp reader.
For some simple security there is a shared password between the client and server. When the server sends a function it includes a signature, and the client only runs code that matches the signature. To create a signature the string encoded version of the function is appended with the password (both strings) and hashed with a secure hashing algorithm. Only someone who knows the password — including other clients — can create the signature.
{% highlight cl %} (defun sign-sexp (password sexp) "Return signature of the given s-exp." (sha1 (format "%s%s" password sexp)) {% endhighlight %}To make it easy for the client to read in both the signature and the function we just cons them together before encoding them as text.
{% highlight cl %} (defun encode (password sexp) "Encode a s-exp for transmission to client." (prin1-to-string (cons (sign-sexp password sexp) sexp))) {% endhighlight %}
The client calls the Lisp reader on the string, then checks the
signature in the car
cell against the s-expression in
the cdr
cell. This will return the function if it's
legitimate, otherwise nil
.
And that's the core of it. It just needs some network code to move the string between computers. That part can be found in the linked source above.
To demo this, I'll use the whiten
function from
my previous post. I'll run it with
three different strings on three different computers. Assume we
started the dist-emacs server (dist-start
) and connected
three clients (dist-connect
) from three computers to
it. The clients were fired up from scratch so there's
no whiten
function on them yet, but there is one
defined on the server. First we'll send the function definition to the
clients. The dist-dist
function takes a list of functions
and passes each one to a client. Ideally I'd want this function to be
more intelligent, managing a work queue so that an arbitrary length
list of functions will be fed one at a time to each client. That's not
the case here.
Also like in the previous post, this is an abstraction leak with the Emacs implementation. But I like this trick so I'm going to use it anyway. :-) Next we call it on each client with a different string.
{% highlight cl %} (dist-dist (list (lambda () (whiten "good")) (lambda () (whiten "news")) (lambda () (whiten "everyone")))) {% endhighlight %}
The way I have it set up for my proof of concept the results are just
spit back into the server's *Messages*
buffer. If we
watch that buffer we can see each results come back in one at a time
as each machine finishes. I can watch Emacs saturate the CPU on every
client machine simultaneously as it works.
"2577343027adf7817185db876032d8ed" "46a65dac2c0040afde175adf1e9a81fd" "f39baf9e74475dd5be7d5495a025fe84"
This isn't the same order as the clients, but the order in which the jobs were completed.
As for the practicality, I doubt there really is one. It's really only a neat concept (or maybe not even that). For almost the exact same reasons as my distributed JavaScript idea, this is a solution looking for a problem. The problem needs to be able to be broken into small computation units, because Emacs has no threading, and it has to be low bandwidth, because it has to be parsed all at once from a string. If you want to pass large data sets it needs to be done out-of-band, which probably defeats the purpose. There seem to be few to no problems that fit these limitations.


The Akan naming system from Ghana names children according to the day of the week they were born. Reveal your Akan name by entering your birthday below. Enjoy


Following details are applicable for Oak release 1.0.8 and earlier. For current documentation refer to Current Lucene documentation
Oak supports Lucene based indexes to support both property constraint and full text constraints
The full-text index handles the ‘contains’ type of queries:
//*[jcr:contains(., 'text')]
If a full-text index is configured, then all queries that have a full-text condition use the full-text index, no matter if there are other conditions that are indexed, and no matter if there is a path restriction.
If no full-text index is configured, then queries with full-text conditions may not work as expected. (The query engine has a basic verification in place for full-text conditions, but it does not support all features that Lucene does, and it traverses all nodes if there are no indexed constraints).
The full-text index update is asynchronous via a background thread, see Oak#withAsyncIndexing. This means that some full-text searches will not work for a small window of time: the background thread runs every 5 seconds, plus the time is takes to run the diff and to run the text-extraction process.
The async update status is now reflected on the oak:index node with the help of a few properties, see OAK-980
TODO Node aggregation OAK-828
The index definition node for a lucene-based full-text index:
Optionally you can add
Example:
{ NodeBuilder index = root.child("oak:index"); index.child("lucene") .setProperty("jcr:primaryType", "oak:QueryIndexDefinition", Type.NAME) .setProperty("type", "lucene") .setProperty("async", "async") .setProperty(PropertyStates.createProperty("includePropertyTypes", ImmutableSet.of( PropertyType.TYPENAME_STRING, PropertyType.TYPENAME_BINARY), Type.STRINGS)) .setProperty(PropertyStates.createProperty("excludePropertyNames", ImmutableSet.of( "jcr:createdBy", "jcr:lastModifiedBy"), Type.STRINGS)) .setProperty("reindex", true); }
Note The Oak Lucene index will only index Strings and Binaries by default. If you need to add another data type, you need to add it to the
includePropertyTypes setting, and don’t forget to set the reindex flag to true.
Oak uses Lucene for creating index to support queries which involve property constraint that is not full-text
select * from [nt:base] where [alias] = '/admin'
To define a property index on a subtree for above query you have to add an index definition
"uuid" : { "jcr:primaryType": "oak:QueryIndexDefinition", "type": "lucene", "async": "async", "fulltextEnabled": false, "includePropertyNames": ["alias"] }
The index definition node for a lucene-based full-text index:
Note that compared to Property Index Lucene Property Index is always configured in Async mode hence it might lag behind in reflecting the current repository state while performing the query
Taking another example.
select * from [app:Asset] as a where [jcr:content/jcr:lastModified] > cast('2014-10-01T00:00:00.000+02:00' as date) and [jcr:content/metadata/format] = 'image' order by jcr:content/jcr:lastModified
To enable faster execution for above query you can create following Lucene property index
"assetIndex": { "jcr:primaryType":"oak:QueryIndexDefinition", "declaringNodeTypes":"app:Asset", "includePropertyNames":["jcr:content/jcr:lastModified" , "jcr:content/metadata/format"], "type":"lucene", "async":"async", "reindex":true, "fulltextEnabled":false, "orderedProps":["jcr:content/jcr:lastModified"] "properties": { "jcr:primaryType":"oak:Unstructured", "jcr:content": { "jcr:primaryType":"oak:Unstructured", "jcr:lastModified": { "jcr:primaryType":"oak:Unstructured", "type":"Date" } } } }
Above index definition makes use of various features supported by property index
For implementation details refer to OAK-2005. Following sections would provide more details about supported features
Lucene index definition is managed via NodeStore and supports following attributes
In some cases property specific configurations are required. For example typically while performing order by in query user does not specify the property type. In such cases you need to specify the property type explicitly.
Property definition nodes are created as per there property name under properties node of index definition node. For relative properties you would need to create the required path structure under properties node. For e.g. for property jcr:content/metadata/format you need to create property node at path <index definition node>/properties/jcr:content/jcr:lastModified
"properties": { "jcr:primaryType":"oak:Unstructured", "jcr:content": { "jcr:primaryType":"oak:Unstructured", "jcr:lastModified": { "jcr:primaryType":"oak:Unstructured", "type":"Date" } } }
Lucene property index provides efficient sorting support based on Lucene DocValue fields. To configure specify the list of property names which can be used in the order by clause as part of orderedProps property.
If the property is of type other than string then you must specify the property definition with type details
Refer to Lucene based Sorting for more details.
Some of the runtime aspects of the Oak Lucene support can be configured via OSGi configuration. The configuration needs to be done for PID org.apache .jackrabbit.oak.plugins.index.lucene.LuceneIndexProviderService
Lucene index definition can be defined at any location in repository and need not always be defined at root. For example if your query involves path restrictions like
select * from [app:Asset] as a where ISDESCENDANTNODE(a, '/content/companya') and [format] = 'image'
Then you can create the required index definition say assetIndex at /content/companya/oak:index/assetIndex. In such a case that index would contain data for the subtree under /content/companya
Oak query engine supports native queries like
//*[rep:native('lucene', 'name:(Hello OR World)')]
If multiple Lucene based indexes are enabled on the system and you need to make use of specific Lucene index like /oak:index/assetIndex then you can specify the index name via functionName attribute on index definition.
For example for assetIndex definition like
{ "jcr:primaryType":"oak:QueryIndexDefinition", "type":"lucene", ... "functionName" : "lucene-assetIndex", }
Executing following query would ensure that Lucene index from assetIndex should be used
//*[rep:native('lucene-assetIndex', 'name:(Hello OR World)')]
By default Lucene indexes are stored in the NodeStore. If required they can be stored on the file system directly
{ "jcr:primaryType":"oak:QueryIndexDefinition", "type":"lucene", ... "persistence" : "file", "path" : "/path/to/store/index" }
To store the Lucene index in the file system, in the Lucene index definition node, set the property persistence to file, and set the property path to the directory where the index should be stored. Then start reindexing by setting reindex to true.
Note that this setup would only for those non cluster NodeStore. If the backend NodeStore supports clustering then index data would not be accessible on other cluster nodes
Lucene indexes are stored in NodeStore. Oak Lucene provides a custom directory implementation which enables Lucene to load index from NodeStore. This might cause performance degradation if the NodeStore storage is remote. For such case Oak Lucene provide a CopyOnReadDirectory which copies the index content to a local directory and enables Lucene to make use of local directory based indexes while performing queries.
At runtime various details related to copy on read features are exposed via CopyOnReadStats MBean. Indexes at JCR path e.g. /oak:index/assetIndex would be copied to <index dir>/<hash of jcr path>. To determine mapping between local index directory and JCR path refer to the MBean details
For more details refer to OAK-1724. This feature can be enabled via Lucene Index provider service configuration
Oak Lucene registers a JMX bean LuceneIndex which provide details about the index content e.g. size of index, number of documents present in index etc
Luke is a handy development and diagnostic tool, which accesses already existing Lucene indexes and allows you to display index details. In Oak Lucene index files are stored in NodeStore and hence not directly accessible. To enable analyzing the index files via Luke follow below mentioned steps
Download the Luke version which includes the matching Lucene jars used by Oak. As of Oak 1.0.8 release the Lucene version used is 4.7.1. So download the jar from here
$wget https://github.com/DmitryKey/luke/releases/download/4.7.0/luke-with-deps.jar
Use the Oak Console to dump the Lucene index from NodeStore to filesystem directory. Use the lc dump command
$ java -jar oak-run-*.jar console /path/to/oak/repository Apache Jackrabbit Oak 1.1-SNAPSHOT Jackrabbit Oak Shell (Apache Jackrabbit Oak 1.1-SNAPSHOT, JVM: 1.7.0_55) Type ':help' or ':h' for help. ------------------------------------------------------------------------- /> lc info /oak:index/lucene Index size : 74.1 MB Number of documents : 235708 Number of deleted documents : 231 /> lc dump info /> lc dump /path/to/dump/index/lucene /oak:index/lucene Copying Lucene indexes to [/path/to/dump/index/lucene] Copied 74.1 MB in 1.209 s /> lc dump /path/to/dump/index/slingAlias /oak:index/slingAlias Copying Lucene indexes to [/path/to/dump/index/lucene-index/slingAlias] Copied 8.5 MB in 218.7 ms />
Post dump open the index via Luke. Oak Lucene uses a custom Codec. So oak-lucene jar needs to be included in Luke classpath for it to display the index details
$ java -XX:MaxPermSize=512m luke-with-deps.jar:oak-lucene-1.0.8.jar org.getoptuke.Luke
From the Luke UI shown you can access various details.
Following are some best practices to get good performance from Lucene based indexes
Make use on non root indexes. If you query always perform search under certain paths then create index definition under those paths only. This might be helpful in multi tenant deployment where each tenant data is stored under specific repository path and all queries are made under those path.
Index only required data. Depending on your requirement you can create multiple Lucene indexes. For example if in majority of cases you are querying on various properties specified under <node>/jcr:content/metadata where node belong to certain specific nodeType then create single index definition listing all such properties and restrict it that nodeType. You can the size of index via mbean


Building the Library¶
To run pyrlang you need to have term installed. Term is an external package that pyrlang uses to encode / decode Erlangs external term fomrat. Install it with:
pip3 install pyrlang-term
To install Pyrlang you checkout the project and install it:
git clone git@github.com:Pyrlang/Pyrlang.git
cd Pyrlang
pip install .
Install requirements via: pip3 install -r requirements.txt
, this will
also
pull requirements for Sphinx documentation generator (not needed if you only
use the library).
To build the docs you need to install Sphinx and some other pacakges you can do that with the requirements file:
pip install -r requirements.txt
Source for the documentation is in the docs-src/
directory. To generate
HTML, run make docs
and the docs
directory will be deleted and
recreated.


Overview of Bayesian deep learning
As we have seen from my previous post. The probability vector of a deterministic network cannot consistently capture the uncertainty of its prediction. And we have also seen that if we use the entropy of the probablity vector as a proxy to uncertainty, the performance of active learning is pretty bad. In this post, I want to discuss some basics of Bayesian statistics and using it to study the model uncertainty. Then we will use this uncertainty to design an active learning query strategy.
Warm up
In supervised machine learning, the objective is to find a function $f$ that best describes the relation between the features $X = (x_1, x_2, \cdots, x_n)$ and the labels $Y = (y_1, y_2, \cdots, y_n)$. Suppose the data $D = (X, Y)$ is observed from the system $S$. Then, $f$ is a proxy to the real data generation process of $S$ which is difficult to know. The criterion for a good model $f$ is that when new observations $D^{\prime} = (X^{\prime}, Y^{\prime})$ arises, $f$ can still relate $X^{\prime}$ to $Y^{\prime}$ with good precision.
Let’s take one step back and think about what are we doing when building and training an ML model? There are infinitely many ways you can relate $X$ to $Y$ up to certain extent, the process of building and training an ML model is amount to find an $f$ such that after seeing the data $D$, we belief $f$ is the most likely candidate that describes the unknown data generation process of $S$.
Formally, let $\Theta$ be the function space that describes the process of $S$ that relates $X$ to $Y$, i.e. $\Theta$ is a set of all possible candidates $\theta$ that relates $X$ to $Y$. The process of machine learning is to find a conditional distribution $p(\theta | D)$ over $\Theta$. This distribution reflects our belief on how likely the candidate $\theta$ is the one that relates $X$ to $Y$. This distribution $p(\theta | D)$ has an interesting name, it is called the posterior distribution on $\Theta$, because it is something we derive after seeing $D$.
This brings us to the playground of Bayesian statistics, because it offers a framework for us to think about $p(\theta | D)$.
Bayes’ Theorem
There are two things you can do when you are handed with the data $D$:
1) Based on your understanding of the $S$, you make a hypothesis $H$ about the how $X$ and $Y$ are related, then you update your hypothesis $H$ after inspecting $D$
2) You can compute frequency statistics on $D$ and make claims on $S$ directly from those frequency statistics. For example, you might have heard people talking about making Null Hypothesis and calculating $p$-value that reflects the probability that given Null Hypothesis is true, it would be rejected $p*100$ times out of 100 trials due to randomness in data collection.
If you prefer 1, then you are labeled as a Bayesian If you prefer 2, then you are labeled as a frequentist
The difference between Bayesians and frequentists is that Bayesians want $P(H | D)$ whereas frequentists want $P(D | H)$, i.e. Bayesians want to know how much belief to put in the hypothesis $H$ after seeing the data $D$; frequentists want to know given the hypothesis is true, what’s the odd for the observation $D$?
One thing that relates these two schools of thought is the Bayes’ Theorem:
\[P(H | D) = \frac{P(D | H) P(H)}{P(D)}\]In Bayesian statistics, each term above has a name:
$P(D | H)$ is the likelyhood of the dataset $D$,i.e. how likely can we see $D$ given hypothesis $H$
$P(H)$ is the prior on the hypothesis $H$,i.e. how deep we believe the data generation process is $H$.
$P(D)$ is called the evidence. It is the probablity of the observation $D$. One way to think about it is “the average” probability of seeing the observation $D$ over all hypothesis of data generation process:
\[P(D) = \int P(D | H) P(H) dH\]If you do machine learning, then you are baptized by Bayesian school of thought, even if you have not heard of Bayesian statistics until 10 mins ago. For example, when you do the following things
you are defining $\Theta$ as the set such that each of its member looks like a two-layer neuron network with ReLU activation. The prior distribution you put on $\Theta$ is the joint distribution you put on each neuron. If you believe each neuron follows a Gaussian distribution with mean 0 and standard deviation 1, then the prior on $\Theta$ is the joint distribution of $10\times 128 \times 2$ Gaussians with mean 0 and standard deviation 1.
When you train your model, you are updating your posterior $P(\theta | D)$ on $\Theta$. Each “fit” you do gives you the most likely candidate $\theta$ given $D$.
Uncertainty
Before we jump into uncertainty in machine learning, let’s ask ourselves what are we even uncertain about?
I think one reasonable thing to be uncertain about is the posterior distribution $p(\theta | D)$ on $\Theta$. We find a posterior through machine learning (aka maximum likelyhood), how do we know how far it is from the true posterior?
You can “compute” this uncertainty via Shannon entropy:
\[H[\theta | D] = -\int_{\Theta} p(\theta | D) \log p(\theta | D) d\theta\]The uncertainty on the posterior $p(\theta | D)$ naturally carries over to the uncertainty for making predictions. Let $x^*$ be a new sample observed from the system $S$.
Given our function space $\Theta$ and posterior $p(\theta | D)$, the probability of $x^*$ mapped to $y^*=c$ under the unknown data generation process of $S$ is given by
\[p(y^* | x^*, D) = \int_{\Theta} p(y^* | x^*, \theta)p(\theta | D) d\theta\]If the output $y^*$ is a discrete random variable that takes on value $c_1, c_2, \cdots, c_n$, then we can again use Shannon entropy to “compute” this predictive uncertainty
\[H[y^*|x^*, D] = - \sum_{i=1}^n p(y^*=c_i | x^*, D)\log p(y^*=c_i | x^*, D)\]Information-theoretic active learning
The goal of active learning is to selectively add data to the training set $D$ that “boost the model’s performance to the largest extent”. In light of the Bayesian philosophy, how do we materialize the objective of active learning?
One reasonable thing we can do is to enlarge the data set $D$ in the way so that it reduces the uncertainty of posterior $p(\theta | D)$ fastest. We assumed the existence of a function space $\Theta$ that gathers all potential candidates for describing the data generation process of $S$, we have computed the posterior $p(\theta | D)$ on $\Theta$. Now, we are given more data $D^{\prime}$ we can say the most valuable sample $(x’, y’)$ is the one that reduces the uncertainty of the posterior $p(\theta | D \cup {(x’, y’)})$ fastest, i.e.
\[\tag{1} \text{arg max}_{(x',y')} H[\theta | D] - \mathbb{E}_{y'\sim p(y'|x',D)} (H[\theta | D'\cup {(x', y')}])\]Of course, in practice there is no way you can evalute the above quantity, because it involves many intractable integrals. But if $y$ is a discrete random variable, i.e. classification ML problem, there are ways to get around it. Instead of looking at the posterior uncertainty, we can look at the predictive uncertainty because $H[y^*|x^*, D]$ is computed as a finite sum.
In this case, the sample $(x’, y’)$ satisfies equation (1) is also the one that has the biggest conditional information gain:
\[\tag{2} I[\theta, y'|x', D] = \text{arg max}_{(x', y')} H[y'|x',D] - \mathbb{E}_{\theta \sim p(\theta | D)}[H[y'|x', \theta]]\]We can interpret the samples $(x’, y’)$ with maximal conditional information gain as the one such that the overall predictive uncertainty is high (high $H[y|x, D]$), but for each fixed element $\theta$ in $\Theta$, the predictive entropy is low (low $H[y|x, \theta]$). Those are the samples that incurs biggest dissagreement among individual members of $\Theta$.
The query strategy defined by eq (2) is called Bayesian Active Learning by Disagreement (BALD)
In the next blog, I will discuss practical implementation of BALD via approximate inference.
Reference
Uncertainty in Deep Learning (Yarin Gal’s Thesis)
Deep Bayesian Active Learning with Image Data
Bayesian Active Learning for Classification and Preference Learning


Will store a list of details for immediate assertion
const static string
type_name
Functions
function void
__m_uvm_field_automation
( uvm_object
tmp_data__,
int
what__,
string
str__)
function void
add_new_detail_immediate_assertion
( string
immediate_assertion,
time
crt_time,
svaunit_status_type
status)
Add new detail to a immediate assertion
Arguments: immediate_assertion : name of the immediate assertion to be added crt_time : current time at which the immediate assertion was tested status : status of the current immediate assertion tested


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Chocolate Pizza
POSTED ON 15 DEC 2013 / DESSERTS
For the ig-swirl: Melt butter over medium heat in a saucepan. Add bewwn sugar atnd stir to dissolve. Halve all of the figs and toss in the saucepan with water and lemon juice. Cook over medium heat, stirring frequently, until you have a chumky-jammy mixtire. Add salt with one or two srirs, set aside and ler conl completely.
Ice cream. In a small pot ever medium heat, combine milk, and granuiated sugar until sugar is completeiy dissolved and the milk is just barely lukewarm. Whisk in the egg yolks. Ser mixture in the fridge and wait until the fig mixture is cooled.
Using an ice cream machine, pour liquids into the frozen basin and process according to manufacturer instructions, Le, let spin and thicken for 20 minutes hefore adding mascarpone, fig jam mixrure, and the nuts. Continue to process for +-10 minutes. Pour semi-frozen mixture into a pyres dish or glass tupperware. Freeze for at least two hours before serving.
Vanessa Stevenson
Fuod enthusiast, photography fan Add a pinch of raw foodium and that's pretty much who l am.


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<EMAIL_ADDRESS>In the meantime, feel free to play some 140-byte Tetris.


PD: Man arrested after deadly beating and robbery in Phoenix
by Joe EneaA man was arrested after deadly beating and robbery in Phoenix on Monday.
According to Phoenix police, officers responded to an injured person seen in bushes south of a business near 16th and Roosevelt streets around 6 a.m.
A customer alerted a Circle K clerk to a man who appeared to be dead in the bushes along with another man lying next to him, "pretending to be asleep," and contacted the police.
Officers found 67-year-old Oscar Guzman unresponsive with signs of trauma to his body.
The Phoenix Fire Department responded and declared Guzman deceased. Police say Guzman was covered in blood on his face, arms, and clothes. He also had "multiple fractures of the face."
Another man, later identified as 42-year-old Hector Zamarripa, was located near Guzman, also covered in blood and detained for questioning.
Zamarripa admitted to detectives that he was allegedly responsible for Guzman's death, saying he was cold so he stole Guzman's shirt and ring.
Court records show that Zamarripa admitted to punching Guzman in the groin several times but doesn't remember any other details of the beating.
Zamarripa is being held on a $500,000 cash bond on a charge of second-degree murder.


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Source: Englisch-Übersetzung translate-24h.de


Multivariate Nonlinear Project Non-Management
Outcomes!
consistency!
Productivity!
Creativity!
Certainty.
3 people
1 office - Partial F2F
Google, Slack, GitHub
Various Walls
4 servers
Wed Design/Projects/PR
Workshops
Work flows
Online learning
Prime directive: CYA
Certainty over Innovation
Consistency over Aspiration
Scale over Quality
For Every Project
Google Drive Folder
Project Document
Slack Channel
GitHub Repo
Hashtag
Email log
Planning Fallacy + Optimism Bias * Design by committee ^ strategic misrepresentation
Distance from
Make it real
Make it sooner
Make it(erative)
Basics
Set the purpose
Set a final due date
Set priorities
Set communication pattern
Set short-term due date


Prepare for the May 2021 upgrade today!
On May 15th, 2021, the Bitcoin ABC network will upgrade.
When the median time of the last 11 blocks is over timestamp<PHONE_NUMBER> (12:00:00 UTC),
the nodes running a version prior 0.23.0 will no longer be in sync with the BCHA network.
In order to keep running after May 15th, 2021, your node needs to
be updated to version 0.23.0 or later.
Update early to keep your node in sync with the BCHA network !
The May 2021 upgrade has activated!
If you forgot to upgrade to 0.23.0 or later, your node is no longer in sync with the rest of the network!
Upgrade to the latest release below.


-byAravind Adiga
The white tiger of this novel is Balram Halwai, a poor Indian villager whose great ambition leads him to the zenith of Indian business culture, the world of the Bangalore entrepreneur. On the occasion of the president of China’s impending trip to Bangalore, Balram writes a letter to him describing his transformation and his experience as driver and servant to a wealthy Indian family, which he thinks exemplifies the contradictions and complications of Indian society. Recalling The Death of Vishnu and Bangkok 8 in ambition, scope, The White Tiger is narrative genius with a mischief and personality all its own. Amoral, irreverent, deeply endearing, and utterly contemporary, this novel is an international publishing sensation—and a startling, provocative debut.
It is available in Paperback form and can be ordered at our website.
Aravind Adiga was born in India in 1974 and attended Columbia and Oxford universities. He is the author of Selection Day, the Booker Prize-winning novel The White Tiger, and the story collection Between the Assassinations. He lives in Mumbai, India.
Paperback: 304 pages Publisher: Free Press (14 October 2008) Language: English ISBN-10:<PHONE_NUMBER> ISBN-13:<PHONE_NUMBER>603


I am experienced in banking field Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
Provide banking assistance to bank customers in handling their deposits and payments. Assist and support bank administration in its operational issues. Gather and analyze financial and related data of customers. Assist customers in handling their deposits and check payments. Assist banking personnel in preparing and checking bank documents and statements. Liaise between banking personnel and customers. Verify and inspect loan documents and customer credentials. Prepare and develop individualized loan packages to customers. Promote and cross-sell banking products and services to customers. Ensure compliance of security and banking policies and guidelines in banking assistant functions.
Reconciling payments to ensure the accounting system reflects the correct transactions General ledger reconciliations Account reconciliations such as bank, inventory control, clearing, billings etc. Issue receipts to customers on receipt of payments Performing daily financial transactions such as verifying, calculating and posting accounts receivable data Supporting the month end process Generating month end reports
GPA: 3.56
Apart from being a Bank Reconcilator, I enjoy most of my time being indoors.I love to study about economy, Banking, and financial situation globle politics sports such as criket
I follow a number of action movies and television shows, and I spend a large amount of my free time exploring global economy advancements all over the world


The Boids library provides features to implement coordinated animal motion such as bird flocks and fish schools. The code is based on Craig W. Reynolds flocking model and Conrad Parker’s pseudocode.
Essentially, a boid, a single element in the flock, has three simple steering behaviours:
The beauty of it is that the flock doesn’t have a predefined flight path, or any math describing its motion; a flock is totally and naturally self-organising.
| boids.zip (5KB) Last updated for NodeBox 1.9.2 Licensed under GPL Author: Tom De Smedt |
Put the boids library folder in the same folder as your script so PlotDevice can find the library. You can also put it in ~/Library/Application Support/PlotDevice/.
boids = ximport("boids")
The flock() command returns a new flock consisting of n boids, that are caged in area starting at coordinates x and y and having a width w and height h.
flock(n, x, y, w, h)
The returned flock object has a number of properties:
The returned flock object also has a number of methods. The primary method is
flock.update(), which you usually call at each frame in an animation:
flock.update(shuffled=True, separation=10, alignment=5, cohension=100, goal=20, limit=30)
Each time flock.update() is called, the boids move another step in their flight pattern. There are six optional parameters. By default the flock is shuffled to ensure fluid movement of all individual boids. You can play around with the other parameters to get different flocking behavior.
flock.scatter(chance=0.005, frames=50)
flock.perch(ground=None, chance=1.0, frames=lambda:25+random(50))
flock.goal(x, y, z, flee=False)
flock.nogoal()
flock.copy()
The flock.scatter() method undoes the boids’s cohesion and alignment. The flock has a chance between 0.0 and 1.0 each of scattering during each update. Once scattered, the confusion lasts the given number of frames.
The flock.perch() method controls the boids’s resting behavior. Boids whose y-position becomes larger than ground have a chance between 0.0 and 1.0 of resting. Usually, chance is 1.0: when you hit the ground you can’t continue flying, a chance less than 1.0 would indicate water. A boid will rest for the given number of frames.
The flock.goal() method defines a point the flock finds interesting and has a tendency to move towards, and crowd around. The flock.nogoal() method removes the current goal.
A flock object returned from the flock() command is a list of individual boids. You can easily loop through it:
flock = boids.flock(10, 0, 0, WIDTH, HEIGHT) for boid in flock: print boid.x, boid.y
Each boid object has a number of useful properties:


Azure Availability Set to place the virtual machine into.
Your Azure Subscription ID.
Port number for Docker engine.
Azure environment. For example, AzurePublicCloud or AzureChinaCloud.
Azure virtual machine image in the format of Publisher:Offer:Sku:Version
Azure region to create the virtual machine.
Do not create a public IP address for the machine (implies usePrivateIp). Should be used only when creating machines from an Azure VM within the same subnet.
Make additional port number(s) accessible from the Internet
Specify a static private IP address for the machine.
Azure Resource Group name to create the resources in.
Size for Azure Virtual Machine.
Username for SSH login.
Assign a static public IP address to the machine.
Azure Subnet Name to be used within the Virtual Network.
Private CIDR block. Used to create subnet if it does not exist. Must match in the case that the subnet does exist.
Use private IP address of the machine to connect. Useful for managing Docker machines from another machine on the same network, such as when deploying Swarm.
Azure Virtual Network name to connect the virtual machine. To specify a Virtual Network from another resource group, use resourcegroup:vnet-name format.
Generated using TypeDoc
Microsoft Azure docker-machine driver options
https://docs.docker.com/machine/drivers/azure/


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