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You can compile .treetop
files into Ruby source code with the tt
command line script. tt
takes an list of files with a .treetop
extension and compiles them into .rb
files of the same name. You can then require
these files like any other Ruby script. Alternately, you can supply just one .treetop
file and a -o
flag to name specify the name of the output file. Improvements to this compilation script are welcome.
tt foo.treetop bar.treetop
tt foo.treetop -o foogrammar.rb
The Polyglot gem makes it possible to load .treetop
or .tt
files directly with require
. This will invoke Treetop.load
, which automatically compiles the grammar to Ruby and then evaluates the Ruby source. If you are getting errors in methods you define on the syntax tree, try using the command line compiler for better stack trace feedback. A better solution to this issue is in the works.
In order to use Polyglot dynamic loading of .treetop
or .tt
files though, you need to require the Polyglot gem before you require the Treetop gem as Treetop will only create hooks into Polyglot for the treetop files if Polyglot is already loaded. So you need to use:
require 'polyglot'
require 'treetop'
in order to use Polyglot auto loading with Treetop in Ruby.
If a grammar by the name of Foo
is defined, the compiled Ruby source will define a FooParser
class.
To parse input, create an instance and call its parse
method with a string.
The parser will return the syntax tree of the match or nil
if there is a failure.
Note that by default, the parser will fail unless all input is consumed.
Treetop.load "arithmetic"
parser = ArithmeticParser.new
if parser.parse('1+1')
puts 'success'
else
puts 'failure'
end
A Treetop parser has several options you may set.
Some are settable permanently by methods on the parser, but all may be passed in as options to the parse
method.
parser = ArithmeticParser.new
input = 'x = 2; y = x+3;'
# Temporarily override an option:
result1 = parser.parse(input, :consume_all_input => false)
puts "consumed #{parser.index} characters"
parser.consume_all_input = false
result1 = parser.parse(input)
puts "consumed #{parser.index} characters"
# Continue the parse with the next character:
result2 = parser.parse(input, :index => parser.index)
# Parse, but match rule `variable` instead of the normal root rule:
parser.parse(input, :root => :variable)
parser.root = :variable # Permanent setting
If you have a statement-oriented language, you can save memory by parsing just one statement at a time, and discarding the parse tree after each statement.
If a parse fails, it returns nil. In this case, you can ask the parser for an explanation. The failure reasons include the terminal nodes which were tried at the furthermost point the parse reached.
parser = ArithmeticParser.new
result = parser.parse('4+=3')
if !result
puts parser.failure_reason
puts parser.failure_line
puts parser.failure_column
end
=>
Expected one of (, - at line 1, column 3 (byte 3) after +
1
3
Please don't try to walk down the syntax tree yourself, and please don't use the tree as your own convenient data structure. It contains many more nodes than your application needs, often even more than one for every character of input.
parser = ArithmeticParser.new
p parser.parse('2+3')
=>
SyntaxNode+Additive1 offset=0, "2+3" (multitive):
SyntaxNode+Multitive1 offset=0, "2" (primary):
SyntaxNode+Number0 offset=0, "2":
SyntaxNode offset=0, ""
SyntaxNode offset=0, "2"
SyntaxNode offset=1, ""
SyntaxNode offset=1, ""
SyntaxNode offset=1, "+3":
SyntaxNode+Additive0 offset=1, "+3" (multitive):
SyntaxNode offset=1, "+"
SyntaxNode+Multitive1 offset=2, "3" (primary):
SyntaxNode+Number0 offset=2, "3":
SyntaxNode offset=2, ""
SyntaxNode offset=2, "3"
SyntaxNode offset=3, ""
SyntaxNode offset=3, ""
Instead, add methods to the root rule which return the information you require in a sensible form. Each rule can call its sub-rules, and this method of walking the syntax tree is much preferable to attempting to walk it from the outside.


BTS #JIMIN breaking gender stereotypes; inspiring fans as well as the world
by Manika18BTS Jimin was mentioned in an article by Harper's Bazaar Japan "An Era when men cross borders & wear earrings".
The article talks about how in recent years, the norm of masculinity has been loosened. Famous brands such as Chanel now sell cosmetics for men whilst Gucci and Balenciaga have introduced jewelry into the men's look. Many brands are beginning to break down stereotypes for men.
Jimin was mentioned along with other well-known celebrities like Harry Styles and Nick Jonas for sporting earrings.
The article's statement about Jimin:
"BTS Jimin has established his own style by adopting not only medieval makeup and costumes, but also various earrings from hoop earrings to fringe type."
The sense of incongruity in things has faded. Nowadays, men can wear earrings even if they aren't a celebrity or not part of the LGBT community.
But this isn't the first time Jimin broke the gender norms.
During an episode of Season 1 of Bon Voyage, BTS's reality show about traveling, Jimin shunned the stereotypical words of his group members Jungkook and V. The members who stated that men shouldn't check their selfies were corrected by Jimin, who asked a simply "there you go again..what on earth is masculinity?"
From talking freely about his affection towards his mother to wanting to do his father's makeup, Jimin has never worried about the general world's judgemental eyes.
Jimin is the member who began the tradition of celebrating birthdays and gifting things amongst the bts members as RM expressed how such things were considered unmanly by them.
Jimin is a man who doesn't worry about which section a set of clothes belong to. He wears them based on whether they look good on him and if he likes them.
The most sensational moment was when in 2018's BTS's stage at the Melon Music Awards, Jimin performed Buchaechum, a traditional form of Korean dance also called a Fan Dance, usually performed by groups of Korean female dancers.
Jimin has never bothered what the society or the world has to say about his choices as man, becoming an inspiration for his fans all across the globe.


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Firdous Ashiq Awan prays for Nawaz Sharif's health
by https://www.facebook.com/dunyanewsISLAMABAD (Dunya News) – Special Assistant to the Prime Minister (SAPM) on Information and Broadcasting Dr. Firdous Ashiq Awan Tuesday prayed for former PM Nawaz Sharif’s wellbeing.
“May Allah give health to Nawaz Sharif so that he could face the law after returning to the country,” the Pakistan Tehreek-e-Insaf (PTI) leader tweeted.
“PM Imran Khan has set a bright example of humanity and rule of law through his conduct. The premier is pursuing the mission of national development and prosperity.
“Significant actions will unfold in the coming days for people’s relief as facilities for the masses will start increasing along with the economic restoration.
“Imran Khan has always defeated his opponents and will continue to do so. There is mafia on one hand and people are standing under Imran Khan’s leadership on the other.”
Punjab’s Minister for Colonies Fayyaz-ul-Hassan Chohan also prayed for Nawaz Sharif’s health so that the people could get their money back which has been plundered by the PML-N supremo.


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May the force be with you!
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Insight Heroes OÜ Roosikrantsi TN 2-K139, Tallinn City, Harju County, 10119


DI in Action
Nested Service Dependencies
Limit service scope to a component subtree
Multiple Service Instances (Sandboxing)
Make dependencies @Optional and limit search with @Host
Supply a custom provider with @Inject
Modify the provider search with @Self and @SkipSelf
Class Provider with useClass
Alias Provider with useExisting
Value Provider with useValue
Factory Provider with useFactory


An Accessible Mega Menu
Content for the links and text within the mega menu comes from the Web Content Accessibility Guidelines (WCAG) 2.0.
Keyboard Accessibility
The accessible mega menu supports keyboard interaction modeled after the behavior described in the WAI-ARIA Menu or Menu bar (widget) design pattern, however it also respects users' general expectations for the behavior of links in a global navigation. The accessible mega menu implementation permits tab focus on each of the six top-level menu items.
- When one of the menu items has focus, pressing the
Enter
key,Spacebar
orDown
arrow will open the submenu panel, and pressing theLeft
orRight
arrow key will shift focus to the adjacent menu item. - Links within the submenu panels are included in the tab order when the panel is open. Links can also be navigated with the arrow keys or by typing the first character in the link name, which speeds up keyboard navigation considerably.
- Pressing the
Escape
key closes the submenu and restores focus to the parent menu item.
Screen Reader Accessibility
The accessible mega menu models its use of WAI-ARIA roles, states, and properties after those described in the WAI-ARIA Menu or Menu bar (widget) design pattern with some notable exceptions, so that it behaves better with screen reader user expectations for global navigation. The accessible mega menu doesn't use role="menu"
for the menu container and role="menuitem"
for each of the links therein; if it did, assistive technology will no longer interpret the links as links, but instead as menu items, and the links in the global navigation will no longer show up when a screen reader user executes a shortcut command to bring up a list of links in the page.
This approach maintains the semantic structure of the submenu panels in the accessible mega menu by omitting role="menu"
and role="menuitem"
for the global navigation; the links are organized into lists and separated by headings.
Usage
HTML
The HTML for the accessible mega menu is a nav
element — or any other container element — containing a list. Each list item contains a link which is followed by a div
or any other container element which will serve as the pop up panel.
The panel can contain any HTML content; in the following example, each panel contains three lists of links. You can explicitly define groups within the panel, between which a user can navigate quickly using the left and right arrow keys; in the following example, the CSS class .sub-nav-group
identifies a navigable group.

Music

Alternative

Country

…

Dance

Electronic

…

Hip-Hop/Rap

Jazz

…

Movies

New Release

Comedy

…

Drama

Sci-Fi

…

Horror

Documentary

…

By default, the accessible mega menu uses the the following CSS classes to define the top-level navigation items, panels, groups within the panels, and the hover, focus, and open states. It also defines a prefix for unique ID strings, which are required to indicate the relationship of a top-level navigation item to the panel it controls.
defaults = {
// unique ID's are required to indicate aria-owns, aria-controls and aria-labelledby
uuidPrefix: 'menu',
// default CSS class used to define the megamenu styling
menuClass: 'menu',
// default CSS class for a top-level navigation item in the megamenu
topNavItemClass: 'menu-top-nav-item',
// default CSS class for a megamenu panel */
panelClass: 'menu-panel',
// default CSS class for a group of items within a megamenu panel
panelGroupClass: 'menu-panel-group',
// default CSS class for the hover state
hoverClass: 'hover',
// default CSS class for the focus state
focusClass: 'focus',
// default CSS class for the open state
openClass: 'open'
}
You can optionally override the defaults to use the CSS classes you may have already defined for your mega menu.
JavaScript
Be sure to include jQuery and the accessible-mega-menu.js plugin script. jQuery 1.10.0 is the highest version that will continue to support IE8.

The following initializes the first nav element in the document as an accessibleMegaMenu
, with optional CSS class overrides.
$("nav:first").accessibleMegaMenu({
// prefix for generated unique id attributes, which are required
// to indicate aria-owns, aria-controls and aria-labelledby
uuidPrefix: "accessible-megamenu",
// CSS class used to define the megamenu styling
menuClass: 'nav-menu',
// CSS class for a top-level navigation item in the megamenu
topNavItemClass: 'nav-item',
// CSS class for a megamenu panel
panelClass: 'sub-nav',
// CSS class for a group of items within a megamenu panel
panelGroupClass: 'sub-nav-group',
// CSS class for the hover state
hoverClass: 'hover',
// CSS class for the focus state
focusClass: 'focus',
// CSS class for the open state
openClass: 'open'
});
CSS
AccessibleMegaMenu
handles the showing and hiding of panels by adding or removing a CSS class. No inline styles are added to hide elements or create animation between states.
This CSS example enables the showing/hiding of and the layout of lists panels in the mega menu.
/* mega menu list */
.nav-menu {
display: block;
list-style: none;
margin: 0;
padding: 0;
position: relative;
z-index: 15;
}
/* a top level navigation item in the mega menu */
.nav-item {
display: inline-block;
list-style: none;
margin: 0;
padding: 0;
}
/* first descendant link within a top level navigation item */
.nav-item > a {
border: 1px solid transparent;
display: inline-block;
margin: 0 0 -1px 0;
padding: 0.5em 1em;
position: relative;
}
/* focus/open states of first descendant link within a top level navigation item */
.nav-item > a:focus,
.nav-item > a.open {
border: 1px solid #dedede;
}
/* open state of first descendant link within a top level
navigation item */
.nav-item > a.open {
background-color: #fff;
border-bottom: none;
z-index: 1;
}
/* sub-navigation panel */
.sub-nav {
background-color: #fff;
border: 1px solid #dedede;
display: none;
margin-top: -1px;
padding: 0.5em 1em;
position: absolute;
top: 2.2em;
}
/* sub-navigation panel open state */
.sub-nav.open {
display: block;
}
/* list of items within sub-navigation panel */
.sub-nav ul {
display: inline-block;
margin: 0 1em 0 0;
padding: 0;
vertical-align: top;
}
/* list item within sub-navigation panel */
.sub-nav li {
display: block;
list-style-type: none;
margin: 0;
padding: 0;
}
Browser Support
The accessible mega menu works in current versions of Chrome, Firefox, Opera, Safari, Edge, and Internet Explorer 11.


{% load static %}
Victim: A Kannada film actress
Date: 2017
Incidence: Vulgur pictures circulated on social media.A Kannada film actress was recently in the news for filing a police complaint alleging that unknown persons had morphed and circulated nude pictures of her on social media. Some months back, a legislator from a constituency near Bengaluru had approached the police against hackers who had siphoned off Rs 190,000 from his bank account.These cases made news because the victims were well-known personalities. Otherwise, cyber crime has become so routine in India’s IT capital that it hardly caused a ripple 1,001 cases were registered in 2015, close to three a day.Most complaints were of cheating by impersonation, followed by complaints of identity theft.In this concluding part of our series on crime in Bengaluru, based on police data accessed by 101Reporters and India Spend through Right to Information filings.


Lesson 4: Using the
Local Tuplespace
Each mote has its own independent tuple space. Agilla provides 6
instructions for
accessing the local tuple space. They are:
- out -- insert a tuple into the local tuple space
- inp -- remove a tuple from the local tuple space, do not block
if no match is present
- rdp -- copy a tuple from the local tuple space, do not block
if no match is present
- in -- remove a tuple from the local tuple space, block if no
match is present
- rd -- copy a tuple from the local tuple space, block if no match
is present
- tcount -- count the number of tuples in the local tuple space
matching a particular template
- regrxn -- register a reaction
- deregrxn -- deregister a reaction
Agilla agents use a stack architecture. Tuples and templates are
created by pushing their values onto the stack, followed by the number
of fields. The order of the values on the stack represents the order
they appear in the tuple. For example, suppose you want to create
a tuple <int:1, int:2, string:"abc", loc:(1,1)>, and insert it into
the local tuple space. Here's the code:
Note that you push the last field of the tuple first. Note also
that despite the fact that the agent dies immediately after performing
the OUT instruction, the tuple it inserted in the local tuple space
remains.
The maximum number of fields a tuple can have is limited by the
maximum size of the tuple, which is by default 25 bytes as defined
by AGILLA_MAX_TUPLE_SIZE within <agilla>/types/TupleSpace.h. This
value
was chosen to ensure
a tuple can fit within a single 36 byte TinyOS message. The size
of a tuple or template's field is the size of the variable within
the
field
+
1. Variables sizes are given here.
The extra byte within every field is used to indicate the variable
type. For example, the tuple in the example above would contains
3+3+3+5=14 bytes.
To retrieve a tuple from the local tuple space, you need to supply
a template that matches the tuple. A template is the same as a tuple
except some of its fields may contain types (also known as wild cards).
A match occurs when the template and tuple have the same number of
fields, and each field within the tuple matches that of a template.
If the template's field is a non-type variable, the tuple's field
must match exactly. This form of matching is called "match
by value."
However, if the template's field is a type variable, then the tuple's
field
must
simply be of
the
same
type.
This form of matching is called "match by type." Note
that a type-variable may match "ANY" other type, in which case a
match
always occurs. The template may contain any mix of match-by-value
and match-by-type fields. For example, all of the following templates
match the tuple above:
The following template is the exact same as the tuple, it matches
by value in all fields. If this agent executes on the same node as
the
agent
above, the green LED will light up.
The following template matches every field by value.
If this agent executes on the same node as the
agent
above, the green LED will light up.
The following template matches some fields by value and others by
type. If this agent executes on the same node as the agent above,
the green
LED
will
light up.
The following template does NOT match the tuple since
it's first field does not match.
An agent can also register reactions on its
local tuple space. A reaction allows an agent to immediately respond
to the presence
of a tuple in the local tuple space and helps prevent the agent from
having to poll for a tuple. Reactions consist of a template
and an
address. When a tuple matching the template appears, the middleware
immediately jumps the agent to the specified address in its instruction
memory. The tuple and return address are stored on top of the stack.
To register a reaction, the agent first pushes a template, then an
address, onto the stack. It then calls regrxn to register the reaction.
Here's an example.
The "reaction" agent above registers a reaction using
template <type:value> and
address BLINKRED. Thus, whenever a tuple containing a single value
appears in the local tuple space, it will jump to BLINKRED and toggle
the red LED. The agent is programmed to continuously blink the green
LED, and only blink the RED led when its reaction fires. The reaction's
code is on lines 10-14, as denoted by BLINKRED. Note that the first
two instruction on lines 10-11 are both pop. This is necessary because
the tuple that caused the reaction to fire is stored on the agent's
stack. The top of the stack is a value that indicates the number
of fields in the tuple which in this case it 1. The second variable
in the stack is the value field within the tuple.
The following "instigator" agent can be used to cause
the above agent's reaction to fire, assuming the above agent is injected
onto
node (1,1).
If the instigator agent is injected onto node (0,0),
while the reaction agent is injected onto node (1,1), the reaction
agent will react to the tuple and toggle its red LED.
Unlike tuples in a tuple space, reactions belong to
the agent that registered it and remains with the agent across strong
migrations and clones. Reactions can be explicitly deregistered using
the deregrxn instruction, which takes in the template
of the reaction that should be deregistered.
Note: One limitation of Agilla's reaction mechanism is that an agent
can only register one reaction per template. That is, it can only
register multiple reactions when the reactions contain different
templates.
|


If "Madeleinology" was a real world, it would mean "the study of Madeleines". It is one of my hobbies. On this site I will share some of my findings about this 250 years old recipe.


Your session has expired.
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Add instances to your playground.
Sessions and all their instances are deleted after {{ttl}} hours.


our holiday was good we went some where else to play.That same day we went to milk the cow.We were called to drink milk
The holiday was good we had fun.the third day we played alot and milked the cow and washed the cow shade after that we washed our gumboots
we went to the market and bought milk meat and a bulb we went to milk the cow and played alot and we and went somewer.
My holiday was good we even went to the market.And bought a big meat of a goat and we ate the meat that was rosted and had salt and it was soft
our holiday was good at our grand father place we had fun we plaed alot and watchedsa movie about a super heroe callde superman
The last day we played a little washed the car and we were milked the cow and watched a little and we went to nairobi
my holiday was fun we roasted a goats meat and played and milked the cow we washed the cow's shade we were splashing water
it was a good day we were playing a game at night it was called tag in the dark we milked the cow and wshed the cow shade


There are two ways to start using Capacitor: adding Capacitor to an existing frontend project, or starting a fresh project.
Capacitor provides a native mobile runtime and API layer for web apps. It does not come with any specific set of UI controls which you will most likely need unless you're building a game or something similar.
We strongly recommend starting an Capacitor project with your mobile frontend framework of choice (such as Ionic), though we also provide a blank starter for apps that aren't using a frontend UI framework, and a starter that uses Ionic and is ready for building a production-ready native app and Progressive Web App.
To start a fresh Capacitor project, first clone the starter base:
git clone<EMAIL_ADDRESS>my-app
cd my-app
npm install
Next, run the capacitor initialization command in the directory of the app.
npm run capacitor init
When prompted for the starter template to use, choose either the official Ionic template, or select blank. Note: blank does not come with any UI framework which you will most likely need!
Integration into the Ionic CLI is coming soon. For now, use our stock starter.
First, clone the project as shown above, then select the "Ionic" starter when prompted.
Capacitor was designed to drop-in to any existing modern JS web app.
To add Capacitor to your web app, run the following commands:
cd my-app
npm install @capacitor/core @capacitor/cli
npm run capacitor init
When prompted whether this is a new project, select "n"
Your app is ready to start developing and testing! Follow the Getting Started with Development Guide for information on the development process of a Capacitor app.


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|
|
| DoubleColumn (int newSize=0, std::string newColumnName="", int newColumnId=1, std::string newColumnType="", std::vector< double > newVector=std::vector< double >()) |
| |
|
void | deleteCells (std::vector< int > rowIndexes) override |
| |
|
void | printCell (int cellIndex) const override |
| |
|
void | setExtistingCell (std::vector< int > rowIndexes, std::string targetValue) override |
| | changes cell values with given rowIndexes to the targetValue
|
| |
|
void | addCell (int rowIndex, std::string targetValue) override |
| | not only adds cells but can change cell values and resize row vector
|
| |
| int | numOfDigitsCharacters (int cellIndex) const override |
| |
|
std::vector< int > | searchColumn (std::string searchedValue) const override |
| |
|
void | accumulate (mathop opearation, double base, int startValue, std::vector< int > rowIndexes) override |
| | a higher order function that performs an operation on the chosen rows
|
| |
|
std::string | getValue (int index) const override |
| |
|
ColumnTemplate * | clone () override |
| |
|
void | serialize (std::ostream &out) const override |
| |
|
void | deserialize (std::istream &in) override |
| |
| Public Member Functions inherited from ColumnTemplate |
|
| ColumnTemplate (int newSize=0, std::string newColumnName="", int newColumnId=1, std::string newColumnType="unknown") |
| |
|
void | setName (std::string newColumnName) |
| |
|
void | setSize (int colSize) |
| |
|
int | getSize () const |
| |
|
std::string | getName () const |
| |
|
std::string | getType () const |
| |
|
void | printType () const |
| |
◆ numOfDigitsCharacters()
| int DoubleColumn::numOfDigitsCharacters |
( |
int |
cellIndex | ) |
const |
|
overridevirtual |
returns how many digits or numbers a word or a number is made up of. The variable j is used to count the extra zeroes found in the precisioun 8 with which i resceived the doubles.
Implements ColumnTemplate.
The documentation for this class was generated from the following files:
- C:/Users/User/Desktop/DataBase/DataBase/DataBase/doubleColumn.h
- C:/Users/User/Desktop/DataBase/DataBase/DataBase/doubleColumn.cpp


Comments
-
bugzilla user 82
Created an attachment (id=12) Audioscrobbler menu
Shows Audioscrobbler 1.0.7 menu
-
Greg Hurrell
Thanks for submitting the request to the database. Others have mentioned to this privately by email before, but it's good that you've added it here because now it's on the public record. Marking as ASSIGNED.
-
Greg Hurrell
- ** Bug 239 has been marked as a duplicate of this bug. ***
-
Greg Hurrell
- ** Bug 412 has been marked as a duplicate of this bug. ***
-
Greg Hurrell
Here are the protocol notes:
http://www.audioscrobbler.net/wiki/Protocol1.1
Have already asked Russ about setting up a Client ID for use by Synergy and Synergy Advance.
-
Greg Hurrell
Changing assignment to reflect my new email address.
http://wincent.com/a/news/archives/2006/05/change_of_email.php
-
Greg Hurrell
Well, it's been months and still haven't heard back from Russ about the Client ID, so going to try contacting him again today.
-
Greg Hurrell
- ** Bug 450 has been marked as a duplicate of this bug. ***
-
Greg Hurrell
Just heard back from Russ and a Client ID has been assigned. My plan is to add last.fm support for version 3.0 of Synergy (currently scheduled for approximately mid-August).
To be informed when the new version comes out you can add yourself to the announcements mailing list:
-
Ronald Heft
What's the status on this? I'm looking forward to this feature being in Synergy.
-
Greg Hurrell
(In reply to comment #11)
What's the status on this?
Current plan is for inclusion in version 3.1, slated for release this October.
-
Greg Hurrell
Basic initial documentation is now up at:
http://wincent.com/a/products/synergy-classic/features/audioscrobbler/
-
Greg Hurrell
3.1b now seeded to beta testers. Announcement to beta testers mailing list available here:
http://lists.wincent.com/pipermail/synergy-beta/2006-November/thread.html
Download link:
http://wincent.com/download.php?item=Synergy3.1b.dmg
SHA1 checksum:
022255ebe3625f3abaf5499a830e10af427f91d9
Adding fixed-in-prerelease keyword.
-
Greg Hurrell
Adding "audioscrobbler" keyword so that I can easily find all Audioscrobbler-related feature requests and bug reports.
-
Greg Hurrell
3.1 is now officially out. Marking this one as FIXED/CLOSED. No bugs were found during the beta test cycle, so I'm putting out a 3.1 release. Feature enhancements will go into the next release.
Add a comment
Comments are now closed for this issue.


CONTEXT
This mini-documentary was released on January 23rd, 2041, and provides historical context that will allow you to see evidence and important chronological information.
This mini-documentary was released on January 23rd, 2041, and provides historical context that will allow you to see evidence and important chronological information.
Society has become a shell of what it once was. After the war, people outside the main, well-off cities struggled to survive and were forced to constantly wear masks outside out of fear of swarms and toxins.
In cities, life is difficult but possible. If you are rich and have power, you'll be alright. If not, at least you're in the city where there are more buildings to protect you from swarms. The slow death of trees and other plants around the world is still visible to the privileged, though. As food is extremely expensive.
Masks are now everywhere. People need them. The rich wear fancy, high-tech masks while the poor cover up
with what they can. A culture grew around masks. As the swarms got worse, goggles were needed. The world changed.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/Advocacy.20is.20Volunteering.html ---
Advocacy is volunteering, or so goes the open source mantra. I've advocated for better documentations so...
I've scraped the 89 thousand lines of this chat room (back to May 23, it wouldn't go back any further). Since a new semester has started it seems that new user questions are on the rise. Since I'm also struggling to climb the "smooth wall" of Lean I thought I'd write a new user guide based on questions and answers in this chat room. I hope to have a draft by the January meeting.
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


The menu Number Theory - Interactive contains the following commands:
| Learning Tool for Number Theory | Interactive introduction to number theory. |
| Point Addition on Elliptic Curves | Visualization of a point addition on elliptic curves. |
| Compute Mersenne Numbers | Computation of Mersenne numbers and display of up-to-date GIMPS results. |
| Generic t-adic NAF Key Generator | Generation of t-adic NAF keys. |


famous throughout Israel! 15 He will renew your life and sustain you in your old age. For your daughter-in-law, who loves you and who is better to you than seven sons, has given him birth.”
16 Then Naomi took the child, laid him in her lap and cared for him. 17 The women living there said, “Naomi has a son.” And they named him Obed. He was the father of Jesse, the father of David.
Ruth 4:1-17
Reflect and Respond
I know when I read the Bible I’m supposed to feel holier, wiser, closer to God. But I have to admit that sometimes, while I’m reading some of the greatest stories in Scripture, I actually feel a little jealous. I’m sure that’s not a very saintly response, but I can’t help it. I confess I have miracle envy. And I like to eat cookie dough ice cream right out of the carton. Bless me, for I have sinned.
When I read the awesome stories of how God worked in people’s lives in powerful and visible ways, I find myself wishing that God would show up and work in the same way in my life. Sometimes I think my faith would be stronger and my doubts weaker if I could just witness the kinds of events that people in the Bible were able to witness up close and personal.
But then I remember that even those who saw miracles with their own eyes didn’t always believe. Moses still doubted his ability to lead even after God spelled it out through the burning bush. The disciples had trouble understanding Jesus’ messages and believing in His power even after learning from Him and seeing Him perform miracles with their own eyes. They heard Jesus explain that He would die and rise from the dead, and yet they still didn’t understand what was going on when it happened. If the people who had front row seats to God’s visible miracles didn’t get it, what makes me think I would be different?
Read about the response of one eyewitness to Jesus’ life and miracles in John 20:24-31.
What did he say to Jesus in verse 25?
“Unless I . . . I will not .” (NIV)
Have you ever told God something similar? That you won’t believe until you can see or touch something tangible?


--- layout: post title: director board date: '2017-09-01T11:30:21+02:00' categories: - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/164864511790/director-board ---


--- title: "Multivariate regression analysis" pre: 7. weight: 25 ---
In this section we will learn how to run a regression analysis with more than one independent variables.
Almost all social phenomena have more than one cause. To control, statistically, for all possible causes social scientists employ multinomial regression analysis.
The multivariate regression model is the following:
\[Y_{i}=\alpha+\beta_{1}X_{i}+ \beta_{2}Z_{i}+ u_{i}\]
The interpretation of the slope coefficients for the multivariate- in this case three variable model- is similar to the bivariate model but with one major difference. The coefficient \[\beta_{1}\] represents the effect of X on Y while keeping Z (the third variable) constant. The same will hold if we had a model with four variables.


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SHOWER A film review by Steve Rhodes Copyright 2000 Steve Rhodes RATING (0 TO ****): ***
Yang Zhang's SHOWER is a tale about the fragility of life and the importance of friendships and family. Set against the backdrop of a decades-old communal bathhouse, the story celebrates the simple pleasures of life, not the least of which being a long soak while kibitzing with your old buddies.
The movie opens with a suited gentleman getting quickly cleansed in a fully automated shower, almost exactly like a car wash. It is fast, efficient and mechanical, everything that the bathhouse owned all of his life by Mr. Liu (Zhu Xu) isn't.
This sweet little film charms us with its innocent images while never becoming saccharine. As it explores father-to-son and brother-to-brother relationships, the script and the acting do not have a moment that doesn't ring true.
Mr. Liu's son Da Ming (Pu Cun Xin) is a businessman who comes home for a visit by mistake. Da Ming's mentally-challenged brother, Er Ming (Jiang Wu), sent his brother a postcard that Da Ming took to mean that their father had died.
While there, an old friend pitches Da Ming a get-rich-quick business scheme -- not another dot-com, but the idea of fifty hot dog stands. With perfect irony, the two are inside an automated car wash at the time.
Most of the movie has the bathhouse regulars -- who have a median age of about 70 -- spend the day hanging out with each other as they bathe, talk, sleep, drink tea and get massaged. One of the major pastimes involves cricket battles in which -- horrors! -- one guy cheats.
The sweetly innocent times are interrupted by a story that harkens back to an even simpler time still. In a remote mountain region of China, it rains less than once a year we are told. In the story, the poor natives swap bowls of grain for bowls of water so that the young girl being married can have the traditional bath before her wedding. (The girl, miraculously, is sparkling clean even before she enters the water.)
Among the many delightful subplots is one concerning a shower singer. He belts out "O Solo Mio" with foghorn intensity so long as the water flows.
Nothing lasts forever. The bathhouse and all of the buildings around it are to be demolished in the name of progress. In their place will be a large high-rise apartment complex. The problem with such buildings, points out one of Mr. Liu's regulars, is that crickets don't go above the ground floor.
After watching SHOWER, progress doesn't look quite so sweet anymore.
SHOWER runs 1:32. The official reason for its being rated PG-13 is for "language and nudity." Well, excuse me. I don't recall any profanity and the nudity comes from some very discrete glimpses of men's rears in the bath. To lump this in with crude PG-13-rated films like THE IN CROWD and BIG MOMMA'S HOUSE is a travesty. Consider SHOWER to be a PG film. It should be fine for any kid old enough to be interested in the story and able to read the English subtitles -- the film is in Mandarin.
Email<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


Earlier this week I had recently purchased an eighth of gold capped psilocybin mushrooms from a friend who I had trusted and purchased mushrooms from before. This would be the second time doing mushrooms and I wanted to try them again but at night instead of day which I had done the first time.
I was going to trip with my friends M and R. They had bought some mushrooms from the same person but only had an eighth to share where as I ate a whole eighth. The setting of the place we were going to shroom was in R's room but we had to be careful not to be loud because R's parents were still around. R's room also has a door that leads outside so we could wander around his property fine.
At 10:00pm we chewed up the mushrooms with only a little Gatorade to wash them down. Knowing that I had done twice as much than either of my friends I informed them what to do if I started having a bad trip because my first experience with mushrooms was pleasant for the first hour but after that I went into a crazy psychoactive deja vu and informed my mom that I had done the illegal substance. We put on the movie Finding Nemo because we thought it would have some interesting visuals in it.
About a half hour later I felt this intense body high and an appreciation for the sounds in the movie. The movie itself was uninteresting so we put on Pink Floyd's 'Wish You Were Here' to get us tripped out. Fifteen minutes later I was looking at the carpet and trying to figure out where the ends of the patterns were. I look up and see patterns all over M's face and R is under his covers looking around his room. I began to become very annoyed by M because I felt that he was the only sober one (obviously he wasn't) and I went under my covers and just looked around the room.
I was becoming very paranoid because R's room had tons of posters and Spanish architecture. Voices appeared in my head saying that I was going to stay in this state of mind forever and never recover. I kept on telling myself that I would be fine but the voices persisted. I looked at the walls and realized the posters on the wall were the voices talking to me. I needed to get out of the room. M was enjoying his experience so much that all he wanted to do was go out and explore. I agreed to join him because I needed to get out of the room. R apparently was too scared to get out of his bed so we left him there because he wouldn't budge.
We walked outside and I told M how the voices were in the room and I needed to get out. Outside was very nice with the breeze and the stars. Though it had been over an hour since I had taken the shrooms, the visuals were not as strong as they had been the first time I had done them but I was feeling totally shroomed up and tracers were everywhere. We decided to go to my car and lay on the hood and windshield and just look at the stars. I don't know what it is about shrooming but every car looks like an aerodynamic speed demon.  We laid down on my hood and just stared at the stars.
For some reason it felt as if were laying totally flat even though we were at an angle. M was telling me how much he was enjoying the feeling. I agreed with him and told him tell me if he was scared or beginning to have a bad trip at all. We sat on the car and just talked about the beauty of this drug and why we did it. It was just fun to discuss topics we liked. We decided to go inside my car and listen to some music because it sounded so good. We put the AFI song Morningstar on repeat and analyzed how perfect that song was and how it related exactly to how we were feeling.
At 11:45 M said he needed to take a piss we walked back up to R's room and saw his dad talking to him. For some reason this made me laugh but made M go in a completely different place. He became very paranoid and didn't even want to go inside. When R's dad left I knocked on the window and R just looked at me and closed the blinds and walked away. It seemed as if R was in a horrible place. I felt bad for him and I started getting in a bad place. M would have totally freaked out if he had to go inside to take a leak so I told him to piss in the bushes instead. We decided to just leave R alone because he told us all he wanted to do was sleep. We wanted to explore so we left his room and continued our journey.
M and I made our way to the bottom of the street (R lives in a gated community) and just walked around for a little bit. Suddenly a rent-a-cop came around the street. I told M to act like we went outside to smoke a cigarette. The cop shined his flashlight on us and then drove away. M was angered because the flashlight but I was too busy staring at the neighbor's house because it looked like some Polynesian tiki mansion to me. M informed me that he was going to a bad place so we went back to my car.
The AFI music was too harsh so we put a sublime CD and talked about how Bradley Nowell did drugs and died. It reminded me of how numerous amounts of my friends had been strung out on drugs. My best friend had been strung out on pills and sent to Utah. My girlfriend was expelled from school because of her meth addiction. Other friends had been strung out on coke, heroin, LSD, a lot on meth, and one had been raped because of drugs.  My life was just too much fun and too important to use drugs as an escape. M and I got to the point where we said that all we would do was drink occasionally and if any drugs only safe ones like shrooms and once every four months. It also gave me a sense of hope by helping my friends.
Without knowing R's dad came out to the car around 1:00 and asked us what we were doing. For some reason I felt completely sober while talking to him and told him that we were listening to music in my car because R didn't want us around him. For some reason the dad believed us and said 'Ok just don't drive around because your mom doesn't want you to drive after midnight.' M was scared because he didn't know what to say but I assured him that he didn't think we were on anything because he said don't drive after midnight. I felt a since of accomplishment for pulling that task off. We were good for the night but I wasn't too sure about R.
 We talked for hours about random topics. We even got to a point where we thought that shrooms could make blind people see by using CEV or closed eye visuals. The entire time we were talking I was still seeing the car stretch out and little things in the car move and change. I remember going outside to take a piss and I pissed on a plant and the plant seemed to grow and bloom when I pissed on it. It felt like a burden was being pulled off my back and made my night. Earlier M left to take a piss and I remember feeling scared because I wasn't with anyone and I was going to die alone but he came back and I was fine.
We were both getting tired and agreed that at 3:30 we would go inside and go to sleep. When we went inside all the lights were off. I called R's name and all I heard was some evil laughter. M turned the light on and we saw R in the corner with an evil grin and he had pissed himself. We led him into the bathroom and told him to put some new pants on and go to sleep.  He seemed very amused at it. This was very weird because it had been almost six hours since we did the shrooms and it seemed like he hadn't come down at all, almost as if he was still peaking.
This scared me, he was almost possessed like having half as much shrooms as me and I was almost completely back to baseline. We eventually put him in the bed and M and I tried to go to sleep but couldn't. M went to sleep in his car. When M left I felt a since of loneliness. The room started tripping me out again. I needed to leave the room. I left the room and went to M's car to try to go to sleep.
I struggled for what seemed like an eternity to go to sleep though it was only an hour. During that time I made calls to everyone I knew and just lied down for a while. At about 5:00am I just decided I was completely sober, no need to be worried of the room or loneliness. I went back to the room and it was locked. I went back to my car to go to sleep. I got a call from M when I was in my car he said he couldn't go to sleep and I told him R locked his door. We both decided to go home. I didn't fall asleep until 7:00 in the morning in my own bed.
In conclusion, this trip was very much different than my first trip. It was more of an intellectual journey than a visual journey. Since that trip M and I are much better friends. We bonded so much that he is almost like my brother. We also agreed to stop doing any hard drugs. R's dad found out that he did the shrooms that night and called both M and my parents. He doesn't believe that we did shrooms but he did make us explain some stuff. Also a big thing I learned was that everyone has different reactions to shrooms and even though people may be experienced with psychedelics they still have bad trips. M and I had just the right amount of shrooms to have a good time but R being a lightweight had a little too much and had one of the most horrible experiences of his life. I do believe I will try shrooms again but it won't be for at least a few months.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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{% extends "index.html" %} {% block body %} Okay, added {{ participants.count() }} users. Now, what items are at stake. Again, one per box.


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The purpose of this SOP is to help users to carry out pan-genome studies with EUPAN strategy. The EUPAN SOP is divided into 2 parts:
The example data used in this SOP can be downloaded here. The example includes sequencing data of 3 individual. For each individual, there are 3 Illumina sequencing runs. Each run contains 500,000 paired-end reads. The read length is 83bp and the quality values follow phred+64 rule. Note these are only simple examples to help users understand the input data type and data structure. The real data may be much larger and more complex.
After download the data, follow the command:
tar zxvf eupanExample.tar.gz
and you will find two directories within the eupanExample/ directory:
data/ this directory includes the sequencing data with a per-sample-per-directory structure ref/ref.fa example of a reference sequence (chr12 of the rice IRGSP-1.0 genome) ref/ref.gtf gene annotation of the reference sequences unaln.gtf a blank file
The main analysis procedures include:
We'll introduce how to process your data step by step using EUPAN toolbox and other required tools.
Requirements
Eupan incorporates Fastqc tool to view the sequencing quality and Trimmomatic tool to filter and trim low-quality reads.
A recommended quality control procedure includes:
We provide the qualitySta tool to overview the overall qualities. "-t" indicates #cores used to run Fastqc parallelly. "-v" indicates pair-end or single-end data. Follow the commands:
cd eupanExample eupan qualSta -f /path/to/Fastqc -t 4 -v PE data/ preview_quality/
Results can be found in the preview_quality/ directory:
fastqc_output detailed outputs of each sample file.summary combined quality statistics of all files sample.summary combined quality statistics of all samples (A sample's "PASS" requires "PASS" of all files of the sample) file.summary_heatmap.pdf heatmap of file statistics from file.summary sample.summary_heatmap.pdf heatmap of sample statistics from sample.summary
In all output figures, green represents "PASS"; yellow represents "WARNING"; and red represents "FAIL".
The summary files together with the plots will provide a landscape of the overall sequencing qualities. We also recommends the users go through some detailed sample results to gain a better understanding of the data and data qualities.
We provide the trim tool to trim or filter low-quality reads. After preview the qualities, the users should have a basic understanding of the sequencing data including the quality score version (phred33 or phred64), read length, etc. To trim the reads, follow the command:
eupan trim -p 64 -t 4 data/ trim/ /path/to/Trimmomatic/
To filter low-quality reads, follow the comand:
eupan trim -p 64 -t 4 -w 83 -m 83 data/ filter/ /path/to/Trimmomatic/
Results can be found in the output directory (trim/ or filter/):
data/ directory of the high-quality reads of each sample err/ logs and standard errors
After trimming or filtration of reads, the quality should be scanned again. Follow the command:
eupan qualSta -f /path/to/Fastqc -t 4 trim/data/ review_quality/
Similar to step 1, check the qualities manually and decide if the trimming or filtration step is good enough for subsequent analyses.
If the trimming results do not match the user's expection, new parameters should be selected and step 2 and step 3 should be conducted for several times. We highly recommend the users to run step 2 with a set of parameters on a small subset of their data, evaluate their performance and apply the selected parameter on the whole data.
Requirements
To gain the comprehensive sequences of all samples, we need first to assemble the individual genomes from short reads. We provide two different strategies in this step: 1) direct assembly and 2) iterative assembly to select the optimal Kmer (recommended). See linearK usage for the detailed strategies for the iterative assembly method. Please note that assembly requires relatively large memory. Pay attention to the memory usage when running the following steps.
To assemble all samples directly with SOAPdenovo 2 and Gapcloser under a fixed Kmer (e.g. K=23), use command
eupan assemble soapdenovo -t 4 -k 23 -g data/ assembly_soap/ /path/to/SOAPdenovo2/
The assemblies of individuals can be found in the assembly_soap/ directory. To enable GapCloser, add -g option.
To assemble all samples directly with iterative use of SOAPdenovo,run with command
eupan assemble linearK data assembly_linearK/ /path/to/SOAPdenovo2/
The assemblies of individuals can be found in the assembly_linearK/
directory.
This program utilizes a linear model of sequencing depth to estimate the initialized Kmer. Therefore when applying to user's real data, the estimated genome size (reference genome size is OK) should be provided with -g
option. We recommend the user to re-estimate the linear model on a subset of samples and provide it to the program with -r
option when processing all samples. According to our experience, this method is about 2.5~3.5 times lower than the direct use of SOAPdenovo2.
In order to have an overview of the assemblies, we evaluate the major assembly statistics with QUAST software including assembly size, N50, genome fraction (how much of the reference genome is covered), unaligned size, etc. In this process, the contigs from each individual are aligned to the reference genome with nucmer tool within Mummer package, enabling the direct extraction of unaligned sequences in the subsequent analysis. Also the users can extract the unaligned sequences with their own parameters.
To evaluate the assemblies of the contigs, run with command
eupan assemSta -t 4 assembly_soap/data/ assembly_soap_sta/ /path/to/quast/ ref/ref.fa
To evaluate the assemblies of the scaffolds, run with command
eupan assemSta -t 4 -s assembly_soap/data/ assembly_soap_sta/ /path/to/quast/ ref/ref.fa
Note that the assembly step could take a long time. Here for our example data, it would take ~2h with 4 cores(-t 4). The assembly statistics of each sample can be found in the assembly_soap_sta/data/ directory and a summary can be found in assembly_soap_sta/total_statistics.txt
file. Note the sequencing data used in this example is of small size inorder to limit the running time, therefore the assembly size is also very small and the statistics is incomplete. It will work well on the user's large data.
There are several strategies to construct the pan-genome sequences.
If there's a reference genome of the studied organism, we reccommend the first strategy to benifit from the high-quality assembly of the reference together with the high-quality annotation. If there's no such reference, you may use the second strategy. Or still follow the first strategy by building a reference first. To do this, there should be a multi-library sequencing for the reference sample with high-sequencing depth and multiple insertion sizes.
In this SOP, we only describe how to build the comprehensive pan-genome sequences based on a reference.
Requirements
Construction of the pan-genome sequences based on a reference genome involves the following steps:
Actually this has been done in the evaluation of assemblies. Users can also carry out this step with Nucmer tool within Mummer package.
To do this, run command
eupan getUnalnCtg assembly_linearK/data/ assembly_linearK_sta/data/ unalnCtgs
The unaligned contigs can be found in unalnCtgs/total_unaln.fa file
Check the basic statistics of contigs (base number and contig number) with fastaSta command
eupan fastaSta unalnCtgs/total_unaln.fa
To remove redundant contigs, run command
eupan rmRedundant cdhitCluster unalnCtgs/total_unaln.fa unalnCtgs/nrUnaln /path/to/cd-hit/ eupan fastaSta unalnCtgs/nrUnaln/non-redundant.fa
Non-redundant contigs (identity<0.9) and the clustering information can be found in unalnCtgs/nrUnaln/
directory.
This is a very important step in such pan-genome analysis involving large number of individuals. The non-redundant contigs are the enrichment of sequences missed in the reference, and are also the enrichment of contaminates. Users should remove contaminates according to their studied species. For our rice study, we removed contaminates from bacteria, virus, archaea and even animals. Actually, we remove all contigs whose best alignment to nt database is not green plants. There is no gold standard in this step, therefore we provide no tool for this part. Users should remove contaminates by themselves.
The non-redundant novel sequences we obtained above is at identity of 0.9. Sometimes we want to know the size under a much lower identity (say 0.5).Here we provide a blast-based method to cluster sequences and remove the non-redundant contigs. To do this, run command
eupan rmRedundant blastCluster -c 0.5 unalnCtgs/total_unaln.fa unalnCtgs/nrUnalnBlast/ /path/to/ncbi-blast/bin/
The outputs are similar to those of "eupan rmRedundant cdhitCluster" command. The results can be found in unalnCtgs/nrUnalnBlast/ directory.
We recommend to use non-redundant sequences derived from step 4) instead of sequences derived at much lower identity for the subsequent analyses. We will map raw reads to the comprehensive sequences to determine gene presence-absence. Using low-identity contigs will result in loss of mapping of reads and further result in miss annotation of a gene existence or a gene family existence. To merge the reference and non-redundant contigs, simply run
mkdir pan cat ref/ref.fa unalnCtgs/nrUnaln/non-redundant.fa >pan/pan.fa
Annotation of the pan-genome sequences can be divided into two parts:
Gene annotation of the pan-genome sequences can also be divided into 2 parts: i)gene annotation of the reference genome; and ii)gene prediction of the novel sequences.
As described above, one of the advantages to build pan-genome sequences is that we can use the high-quality annotation of the reference genome. The annotation of the reference genome is in ref/ref.gtf file. To be consistent with the annotation of unaligned sequences and also to be convenient for subsequent analyses, the annotation should be re-arranged following per-transcript-per-gene pattern. For genes with multiple transcripts, only the one with the longest coding sequences are selected as represents. To do this run command
eupan pTpG ref/ref.gtf ref/ref-ptpg.gtf
Gene prediction of eukaryotes itself is a challenging and complex task. And the selection of gene prediction tools or pipelines depends on the target organism. In rice study, we used MAKER-P pipeline to integrate ab initio predictions, transcript evidence and protein homologies. For animals or other organisms, you may want MAKER or PASA. That's why we provide no tools for this step. Users need to carry out the gene prediction step carefully.
The reccomended pipelines are:
Here we use a blank file (./unaln.gtf
) to subtitute the gene annotation of unaligned sequences.
After these 2 steps, we should combine the annotation of the reference and the annotation of unaligned sequences. Simply run
cat ref/ref.gtf unaln.gtf >pan/pan.gtf
Pan-genome analyses are often carried out on gene families. Here we recommend cluster genes into gene families with OrthoMCL. OrthoMCL requires specific configuration, therefore we didn't integrate it into the toolbox. Users need to carry out this step by themselves.
Requirements
We utilize a "map-to-pan" strategy to determine gene presence-absence and gene family presence-absence. Therefore we need to map the raw reads to the comprehensive sequences first. This can be done using "bwa mem" (recommended) or bowtie2.
To use bwa mem, run the following commands
cd pan/ /path/to/bwa/bwa index -a bwtsw pan.fa cd .. eupan alignRead -f bwa -t 4 trim/data/ map2pan/ /path/to/bwa/ pan/pan.fa
To use bowtie2, run the following commands
cd pan/ /path/to/bowtie2/bowtie2-build pan.fa pan cd .. eupan alignRead -f bowtie2 -t 4 trim/data/ map2pan/ /path/to/bowtie2/ pan/pan
The results can be found in map2pan/
directory.
Next, the .sam
output should be converted to .bam
and then sorted, merged and indexed. The .bam
and .bai
files can be used to view alignments in genomebrowsers. And .sam
files can be further removed to save space. To do this, run command
eupan sam2bam -t 4 map2pan/data/ panBam/ /path/to/samtools-1.3/ rm -r map2pan/data/
The results can be found in panBam/
directory. There are two files for each sample: .bam
and .bai
. We also provide a bam2bed tool to calculate the covered region of the genome. The output non-overlapped fragments are organized in 3-column .bed
files which can be utilized for visualization in pan-genome browser and also matained for further use. See our pan-genome browser of rice for example.
eupan bam2bed panBam/data/ covRegion/
The .bed
files can be found in covRegion/data/
directory.
What's more, the users can also use this tool to align reads to the reference genome alone and check the statistics. We show how to do this with bwa as an example. Similar as mentioned above, do the alignment with commands
cd ref/ /path/to/bwa/bwa index -a bwtsw ref.fa cd .. eupan alignRead -f bwa -t 4 trim/data/ map2ref/ /path/to/bwa/ ref/ref.fa
Convert .sam
to .bam
and remove .sam
with commands
eupan sam2bam -t 4 map2ref/data/ refBam/ /path/to/samtools-1.3/ rm -r map2ref/data/
Check the basic statistics from .bam
files with command
mkdir refBamSta eupan bamSta basic refBam/data/ refBamSta/basic/ /path/to/bamUtil/
The basic statistics can be found in refBamSta/basic/summary.txt
Check how much of the genome can be covered from .bam
files with command
eupan bamSta cov refBam/data/ refBamSta/cov/ /path/to/qualimap/
The coverages can be found in refBamSta/cov/summary.txt
After mapping the reads to the pan-genome sequence, we can calculate the gene body coverage and CDS coverage of each gene. Run command
eupan geneCov -t 4 panBam/data/ geneCov/ pan/pan.fa pan/pan.gtf
The gene body coverages can be found in geneCov/summary_gene.cov
and the cds coverages can be found in geneCov/summary_cds.cov
.
Next we can determine gene presence-absence considering genebody coverages and cds coverages. If we define genes with gene body coverage >0.8 and cds coverage >0.95 ere considered as present in the genome, gene PAV profile can be calculated by
mkdir geneExist eupan geneExist geneCov/summary_gene.cov geneCov/summary_cds.cov 0.8 0.95 >geneExist/gene.exist
The output file are organized as
gene sampleA sampleB sampleC ... geneA 1 1 1 ... geneB 1 0 1 ... geneC 1 1 1 ... geneD 1 0 1 ... ...
where 1 represent presents and 0 represents absence.
Obviously the coverages are influenced by the sequencing depth. Insufficient sequencing will result in mis-annotation of presented genes. The example sequencing files are from rices and has ovely sequencing deth below 1x. We recommend the users to add a sample selection step before the pan-genome analyses. The users can select a subset of individuals based on sequencing depth and mapping depth (based on reference) for the subsequent analyses. In our rice study, we required sequencing depths higher than 20 and mapping depth higher than 15.
Users can put the selected sample name in a list as
sampleA sampleB sampleC ...
With the subSample command, users can select a subset of samples from the PAV profile
eupan subSample geneExist/gene.exist list >geneExist/SampleFilteredGene.exist
The new PAV profile for the selected sample can be found in geneExist/SampleFilteredGene.exist
file.
Next we can further convert gene PAV profile into gene family PASV profile with a gene family annotation. An example of gene family annotation file can be found here. Gene family PAVs can be derived from gene PAVs with gFamExist command
eupan gFamExist geneExist/gene.exist geneFam.txt >geneFam.exist
The geneFam.exist
file is organized as the same pattern of gene PAV profile. Therefore the subsequent pan-genome analyses can be carried out for both gene PAV profile and gene family PAV profile.
Next we will do random sampling for pangenome simulation. For each iteration of simulation, we will randomly sample one by one, and calculate the core genome size and pan genome size.
eupan sim -n 100 geneExist/gene.exist simulation_pav
Here, -n indicates the number of random simulations (default=100). The result can be found in simulation_pav/sim_out.txt_PAV_plot.pdf
Traditional pan-genome analyses can be carried out based on the PAV profiles:
In addition, classifications of samples can be derived from the phylogenetic analysis if there's no such information before you do this analysis. Based on the grouping information of samples, you can carry out many comparisons among groups:
What's more, by integrating other knowledge, many customized analyses can be carried out. We give some examples here.
.......
.......
We recommend to use tools with the exact version we mentioned. If you got errors, please first read the error files and try the recommended version. There are logs and error files for each step. Bugs should be sent to HZQ. Pan-genome analysis using "map-to-pan" strategy is somewhat complex. We welcome the user's suggestion and discussion on any points involved in the pipeline.
The example data used in this SOP can be downloaded here. The length of sequencing reads is 83bp and the quality values follow phred+64. Note these are only simple examples helping users understand the data type and data structure. The real data may be much larger. After download the data, follow the commands:
tar zxvf eupanExample.tar.gz
and you will find two directories within the eupanExample directory:
data/ this directory includes the sequencing data with a per-sample-per-directory structure; ref/ref.fa example of a reference sequence (chr12 of the rice IRGSP-1.0 genome) ref/ref.gtf gene annotation of the reference sequences unaln.gtf a blank file
The main analysis procedures include:
We'll introduce how to process your data step by step using EUPAN toolbox and other required tools.
Requirements
eupan incorporates
A recommended quality control procedure includes:
We provide the qualitySta tool to overview the overall qualities.
cd eupanExample eupanLSF qualSta -f /path/to/Fastqc -t 4 data/ preview_quality/ eupanLSF mergeQualSta preview_quality/
Results can be found in the preview_quality directory:
fastqc_output detailed outputs of each sample file.summary combined quality statistics of all files sample.summary combined quality statistics of all samples (A sample's "PASS" requires "PASS" of all files of the sample) file_barplot.png barplot of file statistics from file.summary file_heatmap.png heatmap of file statistics from file.summary sample_barplot.png barplot of sample statistics from sample.summary sample_heatmap.png heatmap of sample statistics from sample.summary
In all output figures, green represents "PASS"; yellow represents "WARNING"; and red represents "FAIL".
The summary files together with the plots will provide a landscape of the overall sequencing qualities. We also recommends the users go through some detailed sample results to gain a better understanding of the data.
We provide the trim tool to trim or filter low-quality reads. After preview the qualities, the users should have a basic understanding of the sequencing data including the quality score version (phred33 or phred64), read length, etc. To trim the reads, follow the command
eupanLSF trim -p 64 -t 4 data/ trim/ /path/to/Trimmomatic/
To filter low-quality reads, follow the comand
eupanLSF trim -p 64 -t 4 -w 83 -m 83 data/ filter/ /path/to/Trimmomatic/
Results can be found in the output directory (trim/ or filter/):
data/ directory of the high-quality reads of each sample job/ job scripts, standard outputs and standard errors
After trimming or filtration of reads, the quality should be scanned again.
eupanLSF qualSta -f /path/to/Fastqc -t 4 trim/data/ review_quality/ eupanLSF mergeQualSta review_quality/
Similar to step i), check the qualities manually and decide if the trimming or filtration step is good enough for subsequent analyses.
If the trimming results do not match the user's expection, new parameters should be selected and ii) and iii) steps should be conducted for several times. We highly recommend the users to run step ii) with a set of parameters on a small subset of their data, evaluate their performance and apply the selected parameter on all the data.
Requirements
To gain the comprehensive sequences of all samples, we need first to assemble the individual genomes from short reads. We provide 2 different strategies in this step: 1)direct assembly with SOAPdenovo2 and 2)iterative assembly based on SOAPdenovo2 (recommended). See linearK usage for the detailed strategies for the iterative assembly method. Please note that assembly requires relatively large memory. Pay attention to the memory usage when running the following steps.
To assemble all samples directly with SOAPdenovo 2 and Gapcloser under a fixed Kmer (e.g. K=23), use command
eupanLSF assemble soapdenovo -t 4 -k 23 -g data/ assembly_soap/ /path/to/SOAPdenovo2/
The assemblies of individuals can be found in the assembly_soap/ directory. To enable GapCloser, add -g option.
To assemble all samples directly with iterative use of SOAPdenovo,run with command
eupanLSF assemble linearK data assembly_linearK/ /path/to/SOAPdenovo2/
The assemblies of individuals can be found in the assembly_linearK/
directory.
This program utilizes a linear model of sequencing depth to estimate the initialized Kmer. Therefore when applying to user's real data, the estimated genome size (reference genome size is OK) should be provided with -g
option. We recommend the user to re-estimate the linear model on a subset of samples and provide it to the program with -r
option when processing all samples. According to our experience, this method is about 2.5~3.5 times lower than the direct use of SOAPdenovo2.
In order to have an overview of the assemblies, we evaluate the major assembly statistics with QUAST software including assembly size, N50, genome fraction (how much of the reference genome is covered), unaligned size, etc. In this process, the contigs from each individual are aligned to the reference genome with nucmer tool within Mummer package, enabling the direct extraction of unaligned sequences in the subsequent analysis. Also the users can extract the unaligned sequences with their own parameters.
To evaluate the assemblies of the contigs, run with command
eupanLSF assemSta -t 4 assembly_soap/data/ assembly_soap_sta/ /path/to/quast/ ref/ref.fa eupanLSF mergeAssemSta assembly_soap_sta/data/ >assembly_soap_sta/total_statistics.txt
To evaluate the assemblies of the scaffolds, run with command
eupanLSF assemSta -t 4 -s assembly_soap/data/ assembly_soap_sta/ /path/to/quast/ ref/ref.fa eupanLSF mergeAssemSta assembly_soap_sta/data/ >assembly_soap_sta/total_statistics.txt
The assembly statistics of each sample can be found in the assembly_soap_sta/data/ directory and a summary can be found in assembly_soap_sta/total_statistics.txt
file. Note the sequencing data used in this example is of small size inorder to limit the running time, therefore the assembly size is also very small and the statistics is incomplete. It will work well on the user's large data.
There are several strategies to construct the pan-genome sequences.
If there's a reference genome of the studied organism, we reccommend the first strategy to benifit from the high-quality assembly of the reference together with the high-quality annotation. If there's no such reference, you may use the second strategy. Or still follow the first strategy by building a reference first. To do this, there should be a multi-library sequencing for the reference sample with high-sequencing depth and multiple insertion sizes.
In this SOP, we only describe how to build the comprehensive pan-genome sequences based on a reference.
Requirements
Construction of the pan-genome sequences based on a reference genome involves the following steps:
Actually this has been done in the evaluation of assemblies. Users can also carry out this step with Nucmer tool within Mummer package.
To do this, run command
eupanLSF getUnalnCtg assembly_soap/data/ assembly_soap_sta/data/ unalnCtgs eupanLSF mergeUnalnCtg unalnCtgs/data/ unalnCtgs/total_unaln.fa
The unaligned contigs can be found in unalnCtgs/total_unaln.fa file
Check the basic statistics of contigs (base number and contig number) with fastaSta command
eupan fastaSta unalnCtgs/total_unaln.fa
To remove redundant contigs, run command
eupanLSF rmRedundant cdhitCluster unalnCtgs/total_unaln.fa unalnCtgs/nrUnaln /path/to/cd-hit/ eupanLSF fastaSta unalnCtgs/nrUnaln/non-redundant.fa
Non-redundant contigs (identity<0.9) and the clustering information can be found in unalnCtgs/nrUnaln/
directory.
This is a very important step in such pan-genome analysis involving large number of individuals. The non-redundant contigs are the enrichment of sequences missed in the reference, and are also the enrichment of contaminates. Users should remove contaminates according to their studied species. For our rice study, we removed contaminates from bacteria, virus, archaea and even animals. Actually, we remove all contigs whose best alignment to nt database is not green plants. There is no gold standard in this step, therefore we provide no tool for this part. Users should remove contaminates by themselves.
The non-redundant novel sequences we obtained above is at identity of 0.9. Sometimes we want to know the size under a much lower identity (say 0.5).Here we provide a blast-based method to cluster sequences and remove the non-redundant contigs. To do this, run command
eupanLSF rmRedundant blastCluster -c 0.5 unalnCtgs/total_unaln.fa unalnCtgs/nrUnalnBlast/ /path/to/ncbi-blast/bin/
The outputs are similar to those of "eupan rmRedundant cdhitCluster" command. The results can be found in unalnCtgs/nrUnalnBlast/ directory.
We recommend to use non-redundant sequences derived from step 4) instead of sequences derived at much lower identity for the subsequent analyses. We will map raw reads to the comprehensive sequences to determine gene presence-absence. Using low-identity contigs will result in loss of mapping of reads and further result in miss annotation of a gene existence or a gene family existence. To merge the reference and non-redundant contigs, simply run
mkdir pan cat ref/ref.fa unalnCtgs/nrUnaln/non-redundant.fa >pan/pan.fa
Annotation of the pan-genome sequences can be divided into two parts:
Gene annotation of the pan-genome sequences can also be divided into 2 parts: i)gene annotation of the reference genome; and ii)gene prediction of the novel sequences.
As described above, one of the advantages to build pan-genome sequences is that we can use the high-quality annotation of the reference genome. The annotation of the reference egenome is in ref/ref.gtf file. To be consistent with the annotation of unaligned sequences and also to be convenient for subsequent analyses, the annotation should be re-arranged following per-transcript-per-gene pattern. For genes with multiple transcripts, only the one with the longest coding sequences are selected as represents. To do this run command
eupanLSF pTpG ref/ref.gtf ref/ref-ptpg.gtf
Gene prediction of eukaryotes itself is a challenging and complex task. And the selection of gene prediction tools or pipelines depends on the target organism. In rice study, we used MAKER-P pipeline to integrate ab initio predictions, transcript evidence and protein homologies. For animals or other organisms, you may want MAKER or PASA. That's why we provide no tools for this step. Users need to carry out the gene prediction step carefully.
The reccomended pipelines are:
Here we use a blank file (unaln.gtf
) to subtitute the gene annotation of unaligned sequences.
After these 2 steps, we should combine the annotation of the reference and the annotation of unaligned sequences. Simply run
cat ref/ref.gtf unaln.gtf >pan/pan.gtf
Pan-genome analyses are often carried out on gene families. Here we recommend cluster genes into gene families with OrthoMCL. OrthoMCL requires specific configuration, therefore we didn't integrate it into the toolbox. Users need to carry out this step by themselves.
Requirements
We utilize a "map-to-pan" strategy to determine gene presence-absence and gene family presence-absence. Therefore we need to map the raw reads to the comprehensive sequences first. This can be done using "bwa mem" (recommended) or bowtie2.
To use bwa mem, run the following commands
cd pan/ /path/to/bwa/bwa index -a bwtsw pan.fa cd .. eupanLSF alignRead -f bwa -t 4 trim/data/ map2pan/ /path/to/bwa/ pan/pan.fa
To use bowtie2, run the following commands
cd pan/ /path/to/bowtie2/bowtie2-build pan.fa pan cd .. eupanLSF alignRead -f bowtie2 -t 4 trim/data/ map2pan/ /path/to/bowtie2/ pan/pan
The results can be found in map2pan/
directory.
Next, the .sam
output should be converted to .bam
and then sorted, merged and indexed. The .bam
and .bai
files can be used to view alignments in genomebrowsers. And .sam
files can be further removed to save space. To do this, run command
eupanLSF sam2bam -t 4 map2pan/data/ panBam/ /path/to/samtools-1.3/ rm -r map2pan/data/
The results can be found in panBam/
directory. There are two files for each sample: .bam
and .bai
. We also provide a bam2bed tool to calculate the covered region of the genome. The output non-overlapped fragments are organized in 3-column .bed
files which can be utilized for visualization in pan-genome browser and also matained for further use. See our pan-genome browser of rice for example.
eupanLSF bam2bed panBam/data/ covRegion/
The .bed
files can be found in covRegion/data/
directory.
What's more, the users can also use this tool to align reads to the reference genome alone and check the statistics. We show how to do this with bwa as an example. Similar as mentioned above, do the alignment with commands
cd ref/ /path/to/bwa/bwa index -a bwtsw ref.fa cd .. eupanLSF alignRead -f bwa -t 4 trim/data/ map2ref/ /path/to/bwa/ ref/ref.fa
Convert .sam
to .bam
and remove .sam
with commands
eupanLSF sam2bam -t 4 map2ref/data/ refBam/ /path/to/samtools-1.3/ rm -r map2ref/data/
Check the basic statistics from .bam
files with command
mkdir refBamSta eupanLSF bamSta basic refBam/data/ refBamSta/basic/ /path/to/bamUtil/ eupanLSF bamSta mergeBasicSta refBamSta/basic/data/ >refBamSta/basic/summary.txt
The basic statistics can be found in refBamSta/basic/summary.txt
Check how much of the genome can be covered from .bam
files with command
eupanLSF bamSta cov refBam/data/ refBamSta/cov/ /path/to/qualimap/ eupanLSF bamSta mergeCovSta refBamSta/cov/data/ >refBamSta/cov/summary.txt
The coverages can be found in refBamSta/cov/summary.txt
After mapping the reads to the pan-genome sequence, we can calculate the gene body coverage and CDS coverage of each gene. Run command
eupanLSF geneCov panBam/data/ geneCov/ pan/pan.fa pan/pan.gtf eupanLSF mergeGeneCov geneCov/summary_ geneCov/data/
The gene body coverages can be found in geneCov/summary_gene.cov
and the cds coverages can be found in geneCov/summary_cds.cov
.
Next we can determine gene presence-absence considering genebody coverages and cds coverages. If we define genes with gene body coverage >0.8 and cds coverage >0.95 ere considered as present in the genome, gene PAV profile can be calculated by
mkdir geneExist eupanLSF geneExist geneCov/summary_gene.cov geneCov/summary_cds.cov 0.8 0.95 >geneExist/gene.exist
The output file are organized as
gene sampleA sampleB sampleC ... geneA 1 1 1 ... geneB 1 0 1 ... geneC 1 1 1 ... geneD 1 0 1 ... ...
where 1 represent presents and 0 represents absence.
Obviously the coverages are influenced by the sequencing depth. Insufficient sequencing will result in mis-annotation of presented genes. The example sequencing files are from rices and has ovely sequencing deth below 1x. We recommend the users to add a sample selection step before the pan-genome analyses. The users can select a subset of individuals based on sequencing depth and mapping depth (based on reference) for the subsequent analyses. In our rice study, we required sequencing depths higher than 20 and mapping depth higher than 15.
Users can put the selected sample name in a list as
sampleA sampleB sampleC ...
With the subSample command, users can select a subset of samples from the PAV profile
eupanLSF subSample geneExist/gene.exist list >geneExist/SampleFilteredGene.exist
The new PAV profile for the selected sample can be found in geneExist/SampleFilteredGene.exist
file.
Next we can further convert gene PAV profile into gene family PASV profile with a gene family annotation. An example of gene family annotation file can be found here. Gene family PAVs can be derived from gene PAVs with gFamExist command
eupanLSF gFamExist geneExist/gene.exist geneFam.txt >geneFam.exist
The geneFam.exist
file is organized as the same pattern of gene PAV profile. Therefore the subsequent pan-genome analyses can be carried out for both gene PAV profile and gene family PAV profile.
Next we will do random sampling for pangenome simulation. For each iteration of simulation, we will randomly sample one by one, and calculat e the core genome size and pan genome size.
eupanLSF sim -n 100 geneExist/gene.exist simulation_pav
Here, -n indicates the number of random simulations (default=100). The result can be found in simulation_pav/sim_out.txt_PAV_plot.pdf
Traditional pan-genome analyses can be carried out based on the PAV profiles:
In addition, classifications of samples can be derived from the phylogenetic analysis if there's no such information before you do this analysis. Based on the grouping information of samples, you can carry out many comparisons among groups:
What's more, by integrating other knowledge, many customized analyses can be carried out. We give some examples here.
.......
.......
We recommend to use tools with the exact version we mentioned. If you got errors, please first read the error files and try the recommended version. There are logs and error files for each step. Bugs should be sent to HZQ. Pan-genome analysis using "map-to-pan" strategy is somewhat complex. We welcome the user's suggestion and discussion on any points involved in the pipeline.


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| <?php | |
| /** | |
| * This file contains classes implementing attribute collection feature. | |
| * | |
| * @author Qiang Xue<EMAIL_ADDRESS>| |
| * @link http://www.yiiframework.com/ | |
| * @copyright 2008-2013 Yii Software LLC | |
| * @license http://www.yiiframework.com/license/ | |
| */ | |
| /** | |
| * CAttributeCollection implements a collection for storing attribute names and values. | |
| * | |
| * Besides all functionalities provided by {@link CMap}, CAttributeCollection | |
| * allows you to get and set attribute values like getting and setting | |
| * properties. For example, the following usages are all valid for a | |
| * CAttributeCollection object: | |
| *
 | |
| * $collection->text='text'; // same as: $collection->add('text','text'); | |
| * echo $collection->text; // same as: echo $collection->itemAt('text'); | |
| *
 | |
| * | |
| * The case sensitivity of attribute names can be toggled by setting the | |
| * {@link caseSensitive} property of the collection. | |
| * | |
| * @author Qiang Xue<EMAIL_ADDRESS>| |
| * @package system.collections | |
| * @since 1.0 | |
| */ | |
| class CAttributeCollection extends CMap | |
| { | |
| /** | |
| * @var boolean whether the keys are case-sensitive. Defaults to false. | |
| */ | |
| public $caseSensitive=false; | |
| /** | |
| * Returns a property value or an event handler list by property or event name. | |
| * This method overrides the parent implementation by returning | |
| * a key value if the key exists in the collection. | |
| * @param string $name the property name or the event name | |
| * @return mixed the property value or the event handler list | |
| * @throws CException if the property/event is not defined. | |
| */ | |
| public function __get($name) | |
| { | |
| if($this->contains($name)) | |
| return $this->itemAt($name); | |
| else | |
| return parent::__get($name); | |
| } | |
| /** | |
| * Sets value of a component property. | |
| * This method overrides the parent implementation by adding a new key value | |
| * to the collection. | |
| * @param string $name the property name or event name | |
| * @param mixed $value the property value or event handler | |
| * @throws CException If the property is not defined or read-only. | |
| */ | |
| public function __set($name,$value) | |
| { | |
| $this->add($name,$value); | |
| } | |
| /** | |
| * Checks if a property value is null. | |
| * This method overrides the parent implementation by checking | |
| * if the key exists in the collection and contains a non-null value. | |
| * @param string $name the property name or the event name | |
| * @return boolean whether the property value is null | |
| */ | |
| public function __isset($name) | |
| { | |
| if($this->contains($name)) | |
| return $this->itemAt($name)!==null; | |
| else | |
| return parent::__isset($name); | |
| } | |
| /** | |
| * Sets a component property to be null. | |
| * This method overrides the parent implementation by clearing | |
| * the specified key value. | |
| * @param string $name the property name or the event name | |
| */ | |
| public function __unset($name) | |
| { | |
| $this->remove($name); | |
| } | |
| /** | |
| * Returns the item with the specified key. | |
| * This overrides the parent implementation by converting the key to lower case first if {@link caseSensitive} is false. | |
| * @param mixed $key the key | |
| * @return mixed the element at the offset, null if no element is found at the offset | |
| */ | |
| public function itemAt($key) | |
| { | |
| if($this->caseSensitive) | |
| return parent::itemAt($key); | |
| else | |
| return parent::itemAt(strtolower($key)); | |
| } | |
| /** | |
| * Adds an item into the map. | |
| * This overrides the parent implementation by converting the key to lower case first if {@link caseSensitive} is false. | |
| * @param mixed $key key | |
| * @param mixed $value value | |
| */ | |
| public function add($key,$value) | |
| { | |
| if($this->caseSensitive) | |
| parent::add($key,$value); | |
| else | |
| parent::add(strtolower($key),$value); | |
| } | |
| /** | |
| * Removes an item from the map by its key. | |
| * This overrides the parent implementation by converting the key to lower case first if {@link caseSensitive} is false. | |
| * @param mixed $key the key of the item to be removed | |
| * @return mixed the removed value, null if no such key exists. | |
| */ | |
| public function remove($key) | |
| { | |
| if($this->caseSensitive) | |
| return parent::remove($key); | |
| else | |
| return parent::remove(strtolower($key)); | |
| } | |
| /** | |
| * Returns whether the specified is in the map. | |
| * This overrides the parent implementation by converting the key to lower case first if {@link caseSensitive} is false. | |
| * @param mixed $key the key | |
| * @return boolean whether the map contains an item with the specified key | |
| */ | |
| public function contains($key) | |
| { | |
| if($this->caseSensitive) | |
| return parent::contains($key); | |
| else | |
| return parent::contains(strtolower($key)); | |
| } | |
| /** | |
| * Determines whether a property is defined. | |
| * This method overrides parent implementation by returning true | |
| * if the collection contains the named key. | |
| * @param string $name the property name | |
| * @return boolean whether the property is defined | |
| */ | |
| public function hasProperty($name) | |
| { | |
| return $this->contains($name) || parent::hasProperty($name); | |
| } | |
| /** | |
| * Determines whether a property can be read. | |
| * This method overrides parent implementation by returning true | |
| * if the collection contains the named key. | |
| * @param string $name the property name | |
| * @return boolean whether the property can be read | |
| */ | |
| public function canGetProperty($name) | |
| { | |
| return $this->contains($name) || parent::canGetProperty($name); | |
| } | |
| /** | |
| * Determines whether a property can be set. | |
| * This method overrides parent implementation by always returning true | |
| * because you can always add a new value to the collection. | |
| * @param string $name the property name | |
| * @return boolean true | |
| */ | |
| public function canSetProperty($name) | |
| { | |
| return true; | |
| } | |
| /** | |
| * Merges iterable data into the map. | |
| * | |
| * Existing elements in the map will be overwritten if their keys are the same as those in the source. | |
| * If the merge is recursive, the following algorithm is performed: | |
| *
 | |
| *
the map data is saved as $a, and the source data is saved as $b;
 | |
| *
if $a and $b both have an array indexed at the same string key, the arrays will be merged using this algorithm;
 | |
| *
any integer-indexed elements in $b will be appended to $a and reindexed accordingly;
 | |
| *
any string-indexed elements in $b will overwrite elements in $a with the same index;
 | |
| *
 | |
| * | |
| * @param mixed $data the data to be merged with, must be an array or object implementing Traversable | |
| * @param boolean $recursive whether the merging should be recursive. | |
| * | |
| * @throws CException If data is neither an array nor an iterator. | |
| */ | |
| public function mergeWith($data,$recursive=true) | |
| { | |
| if(!$this->caseSensitive && (is_array($data) || $data instanceof Traversable)) | |
| { | |
| $d=array(); | |
| foreach($data as $key=>$value) | |
| $d[strtolower($key)]=$value; | |
| return parent::mergeWith($d,$recursive); | |
| } | |
| parent::mergeWith($data,$recursive); | |
| } | |
| } |