Angular is built around the belief that declarative programming should be used for building UIs and wiring software components, while imperative programming is excellent for expressing business logic. The framework adapts and extends traditional HTML to better serve dynamic content through two-way data-binding that allows for the automatic synchronization of models and views. As a result, AngularJS deemphasizes DOM manipulation and improves testability.
Express is a minimal and flexible node.js web application framework, providing a robust set of features for building single and multi-page, and hybrid web applications. With a myriad of HTTP utility methods and Connect middleware at your disposal, creating a robust user-friendly API is quick and easy. Express provides a thin layer of features fundamental to any web application, without obscuring features that you know and love in Node.
Mongo is a cross-platform document-oriented database system that eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas, making the integration of data in certain types of applications easier and faster.


--- layout: post title: Last pre–Rails 3 update published: true ---
I've just added some class inheritance diagrams to Chapter 4 of the Ruby on Rails Tutorial book, which I expect will be the last substantive update to the Rails 2.3 tutorial. Starting next week, I'll be turning my full attention to the Rails 3 version of the book. See you on the other side. :-)


| Friday, July 27 |
| 8:45am |
Keynote
Grand A in the East Tower
:
8:45am - 10:15am
|
| 10:45am |
Zope
Bel Aire North in the West Tower
:
10:45am - 11:15am
11:15am - 12:15pm
|
| 10:45am |
XML/XTech2001
Point Loma A in the West Tower
:
10:45am - 12:15pm
|
| 10:45am |
XML/XTech2001
Coronado A in the West Tower
:
10:45am - 11:15am
11:15am - 11:45am
|
| 10:45am |
Perl 5
Grand B in the East Tower
:
10:45am - 11:30am
11:30am - 12:15pm
|
| 10:45am |
Perl 5
Grand C in the East Tower
:
10:45am - 12:15pm
|
| 10:45am |
Apache
Harbor Island II in the East Tower
:
10:45am - 11:30am
11:30am - 12:15pm
|
| 10:45am |
Mozilla
Harbor Island III in the East Tower
:
10:45am - 5:15pm
|
| 10:45am |
PHP 1
Fairbanks C&D in the West Tower
:
10:45am - 12:15pm
|
| 10:45am |
PHP 1
Marina II in the East Tower
:
10:45am - 5:15pm
|
| 10:45am |
Tcl/Tk
Fairbanks A&B in the West Tower
:
10:45am - 11:15am
11:15am - 11:45am
11:45am - 12:15pm
|
| 10:45am |
Linux
Harbor Island I in the East Tower
:
10:45am - 11:30pm
11:30pm - 12:15pm
|
| 10:45am |
Open Source
Marina II in the East Tower
:
10:45am - 5:15pm
|
| 1:45pm |
Zope
Bel Aire North in the West Tower
:
1:45pm - 2:30pm
2:30pm - 3:15pm
|
| 1:45pm |
XML/XTech2001
Point Loma A in the West Tower
:
1:45pm - 2:30pm
2:30pm - 3:15pm
|
| 1:45pm |
XML/XTech2001
Coronado A in the West Tower
:
1:45pm - 5:15pm
|
| 1:45pm |
Perl 5
Grand A in the East Tower
:
1:45pm - 2:30pm
2:30pm - 3:15pm
|
| 1:45pm |
Perl 5
Grand C in the East Tower
:
1:45pm - 2:45pm
2:45pm - 3:15pm
|
| 1:45pm |
Apache
Harbor Island II in the East Tower
:
1:45pm - 3:15pm
|
| 1:45pm |
PHP 1
Fairbanks C&D in the West Tower
:
1:45pm - 2:30pm
2:30pm - 3:15pm
|
| 1:45pm |
Tcl/Tk
Fairbanks A&B in the West Tower
:
1:45pm - 2:15pm
2:15pm - 2:45pm
2:45pm - 3:15pm
|
| 1:45pm |
Linux
Harbor Island I in the East Tower
:
1:45pm - 2:15pm
2:15pm - 2:45pm
2:45pm - 3:00pm
|
| 3:45pm |
Zope
Bel Aire North in the West Tower
:
3:45pm - 4:45pm
4:45pm - 5:15pm
|
| 3:45pm |
XML/XTech2001
Point Loma A in the West Tower
:
3:45pm - 4:30pm
4:30pm - 5:15pm
|
| 3:45pm |
Perl 5
Grand A in the East Tower
:
3:45pm - 4:30pm
4:30pm - 5:15pm
|
| 3:45pm |
Perl 5
Grand B in the East Tower
:
3:45pm - 4:30pm
4:30pm - 5:15pm
|
| 3:45pm |
Perl 5
Grand C in the East Tower
:
3:45pm - 5:15pm
|
| 3:45pm |
Apache
Harbor Island II in the East Tower
:
3:45pm - 5:15pm
|
| 3:45pm |
PHP 1
Fairbanks C&D in the West Tower
:
3:45pm - 4:30pm
4:30pm - 5:15pm
|
| 3:45pm |
Tcl/Tk
Fairbanks A&B in the West Tower
:
3:45pm - 4:15pm
4:15pm - 4:45pm
4:45pm - 5:15pm
|
| 3:45pm |
Linux
Harbor Island I in the East Tower
:
3:45pm - 4:30pm
4:30pm - 5:15pm
|


Datepicker will help you with date selection.
It can be used either inline with NgbDatepicker
component or as a
popup on any input element with NgbInputDatepicker
directive.
It also comes with the list of services to do date formatting, i18n and
alternative calendars support.
We try to keep API of our components simple, but introduce extension points, so you could enrich and reuse them. Here is a short example of the vacation range picker that displays holidays with tooltips and disables weekends.
Datepicker can be used either inline or inside of the popup.
In the example above the template variable #d
will point
to the instance of the NgbDatepicker
component in the first case.
In the second it will point to the instance of the NgbInputDatepicker
directive that handles the popup with inline datepicker component.
See the NgbDatepicker API and the NgbInputDatepicker API for details on available inputs, outputs and methods. You can customize the number of displayed months, the way navigation between months and years looks like, week numbers, etc.
Since v5.2.0 datepicker also has the
State interface
accessible via datepicker.state
getter. It represents the readonly
part of the publicly accessible state. ex. what is currently focused, what is the first
and last visible dates, etc.
If you have a very specific use case for the datepicker popup, you could always create you own one and use the inline datepicker inside.
It's up to you do decide when the datepicker popup should be opened and closed.
The API contains .open()
, .close()
and .toggle()
methods.
By default the popup element is attached after the input in the DOM.
You have also the option of attaching it to the document body by setting the
[container]
input to 'body'
The popup will be closed with Escape
key and when
a date is selected via keyboard or mouse.
It can stay open after date selection if you set [autoClose]
input to false
When using a button (or any other element) to open the popup, the styling and content of the button
is left up to you. In the example above, the button is simply given the text "Toggle", but you
could also use an icon, or even group the input
and button
with an
input group.
You have several ways of knowing when user selects a date. The date is selected
either by clicking on it, pressing Space
or Enter
,
typing text in the input or programmatically.
Datepicker is integrated with Angular forms and works with both reactive
and template-driven forms. So you could use [(ngModel)]
,
[formControl]
, formControlName
, etc. Using
ngModel
will allow you both to get and set selected value.
The model, however, is NOT a native javascript date, see the following Date Model section for more info.
Alternatively you could use the (dateSelect)
output.
The difference from ngModel
is that outputs will continue emitting the same value,
if user clicks on the same date. NgModel
will do it only once.
Datepicker uses NgbDateStruct
interface as a model and not the native Date
object.
It's a simple data structure with 3 fields, but note that months start with 1 (as in ISO 8601).
All datepicker APIs will consume NgbDateStruct
, but will produce it's implementation
class NgbDate
when returning dates to you.
It offers additional methods for easy date comparison, and using it together with
NgbCalendar
will cover most
of the date-related calculations.
You can also tell datepicker to use the native JavaScript date adapter (bundled with ng-bootstrap).
For now the adapter works only for the form integration, so for instance (ngModelChange)
will return a native date object. All other APIs continue to use NgbDateStruct
.
You can also create your own adapters if necessary by extending and implementing the
NgbDateAdapter
methods, as in
custom date adapter example.
In the case of the NgbInputDatepicker
you should be able to parse
and format the text entered in the input. This is not as easy task as it seems,
because you have to account for various formats and locales.
For now internally there is a service that does default formatting using ISO 8601 format.
If the entered input value is invalid, the form model will contain the entered text.
You can also create your own parser or formatter if necessary by extending and implementing the
NgbDateParserFormatter
methods, as in
custom date parser formatter example.
Date selection and navigation are two different things. You might have a date selected in January, but August currently displayed.
Datepicker fully supports keyboard navigation and screen readers. You can navigate
between controls using Tab
(focus will be trapped in the popup), move
date focus with arrow keys, home, page up/down and use Shift
modifier
for faster navigation.
With the API you can tell datepicker to initially open a specific month
via the [startDate]
input or go to any month via the .navigateTo()
method
You can limit the dates available for navigation and selection using
[minDate]
and [maxDate]
inputs. If you don't specify
any of them, you'll have infinite navigation and the year select box
will display [-10, +10] years from currently visible month.
If you want to disable some dates for selection (ex. weekends), you have to
provide the [markDisabled]
function that will mark certain dates
not selectable. It will be called for each newly visible day when you navigate
between months.
You can completely replace how each date is rendered by providing a custom template and rendering anything you want inside. You'll get a date context available inside the template with info on whether current date is disabled, selected, focused, etc.
For more info on what is provided in the template context, see the DayTemplateContext API
$implicit
template property and you have to specify
let-date="date"
in the template.
See $implicit example in Angular documentation.
It is often useful to highlight a today's date in the calendar view or add a certain logic to it. Today's date
is the date returned by the NgbCalendar's getToday()
method.
We add a custom CSS class .ngb-dp-today
on a cell that corresponds to the today's date.
We do not add any rules to it at the moment, but you can add your own if necessary.
You would see something like this in the resulting markup
You can also access this information from the DayTemplateContext API
if you're using a custom day template. It contains a today: boolean
flag since v4.1.0
You can replace the content of the datepicker. Combined with the NgbDatepickerMonthView you can customize the layout of months as in the custom month layout example.
You can insert anything you want in a datepicker footer by providing a template.
The datepicker model is a single date, however you still can implement range selection
functionality. With (select)
and (dateSelect)
outputs you'll know
which dates are being selected and with the [dayTemplate]
input
you can customize the way any particular date looks.
If you want to use the NgbDatepickerInput
, you can also tell the popup
to stay open by tuning the [autoClose]
input.
Check the range selection example
and the initial demo on this page for more details.
If you can't use the NgbDatepickerInput
directive, you should
create your own popup and use NgbDatepicker
inside of it. In this case
we'll handle everything related to date selection and navigation for you and you can create
a completely customized popup with any data model you want.
Since the 2.0.0 release datepicker will use the
application locale
if it is present to get translations of weekdays and month names. The internal service that does
translation is called NgbDatepickerI18n
and you could provide your own implementation
if necessary.
The next/previous button labels can be translated using the standard Angular i18n
mechanism. For example, previous month label is extracted under the ngb.datepicker.previous-month
name.
You can customize keyboard navigation as in the custom keyboard navigation example. The default keys are as follows:
Space / Enter |
Selects currently focused date if it is not disabled |
Escape |
Closes the datepicker popup (unless [autoClose] is false) |
Arrow(Up|Down|Left|Right) |
Moves day focus inside the months view |
Shift + Arrow(Up|Down|Left|Right) |
Selects currently focused date (if it is not disabled) |
Home |
Moves focus to the the first day of currently opened first month |
End |
Moves focus to the the last day of currently opened last month |
Shift + Home |
Moves focus to the minDate (if set) |
Shift + End |
Moves focus to the maxDate (if set) |
PageDown |
Moves focus to the previous month |
PageUp |
Moves focus to the next month |
Shift + PageDown |
Moves focus to the previous year |
Shift + PageUp |
Moves focus to the next year |


TextInput
TextInput
represents <input
type="text"/>
HTML elements. Currently it has events
dblClick
, keyUp
, and change
;
and properties value
and size
.
There are currently two factories: One that simply takes a
Var[String]
; and one that takes two parameter lists,
one with the initial value as a String
and one with
a callback as a String=>Unit
.
val text = Var("initial") val field1 = TextInput(text) val field2 = TextInput(text.now)(s => text ()= s) // basically same as above field2.value.updateOn(field2.keyUp) field1.value.value foreach {_ => println("field1 changed")} field2.value.value foreach {_ => println("field2 changed")} field2.change.eventStream.foreach {_ => println("field2 change event")}


🔎 Identify feature
This process will help you identify what Refined GitHub feature is making changes or causing issues on GitHub.
Visit the GitHub page where you want to find the feature and refresh it to see the instructions. You can navigate to any page, but don’t use multiple tabs.


Fx.Visual
Click the buttons to see a demo of each effect.
Fade/Appear
Width
Height
ResizeTo
Pulsate
Highlight
Puff
Shake
Drop
Shrink
Expand
Fade + slideOut
Slide In/Out


jD
DD2
joinDota News
Upcoming Matches
Recent Results
Upcoming Matches
Recent Results
Upcoming Matches
Recent Results
jD Standings
Live Streams
Match VODs
VODs
Settings
Time Format
24h
PM
Date Format
DD/MM/YYYY
MM/DD/YYYY
Streams Popout
On
Off
Spoiler Mode
On
Off
Menu Position
Top
Left
FAQ
How frequently is this updated ?
Depends. It could update every minute, 5 minutes, or every 15 minutes max.
Is there D2MT for other browsers ?
Yes,
Chrome
-
Firefox
-
Opera
-
Web
Does this run as a background process ?
No. Unlike many extensions, this one does not. As soon as you close the extension, you are freeing resource. (Except for firefox)
What channels are you taking VODs from ?
VODs section will show you the 15 most recent uploads from the most popular DotA2 channels (currently 12). Contact me if you want to add yours.
Why are some news bolded ?
It's to show news that are hot. (Over 50 comments)
Joindota has points not elo, what gives ?
I divided the points by 22 to give the same elo feeling that GosuGamers gives.
Changelog
1.6.3
- 19/1/2013
Fixes on the VODs section
Server side improvements
Some spelling crap for those OCD users
About
Version:
1.6.3
Author:
Hamed "Wololo" Al-Khabaz
Thanks to:
Matthew "MasterOdin" Peveler, Blasz
Contact:
<EMAIL_ADDRESS>Follow me:
@wololodev
Contribute:
Fork me at GitHub


Call and Apply Methods in JavaScript
The call
and apply
methods in JavaScript might look complicated at first glance, but they are actually easy to wrap your head around.
Suppose we have an object called square
with a few properties:
var square = {
color: "red",
getColor: function () {
return "I am the color " + this.color + "!";
},
getArea: function (width, height) {
return "My area is " + (width * height) + " feet!";
}
};
It has a color
property, and two method properties; getColor
and getArea
.
The get color method simply return the color property of the object:
square.getColor(); // I am the color red!
And the second method return the area of the square after we pass in 2 arguments:
square.getArea(4, 4); // My area is 16 feet!
Simple enough, let's move on and create a second object:
var rectangle = {
color: "blue"
}
Note that new rectangle
object only has the color
property.
Now if we wanted to return the getColor
or the getArea
methods we would not be able to because the rectangle
object does not have such methods. Only the square
object does.
This is where call
and apply
come to play. Let me show you.
Call Method
square.getColor.call(rectangle); // I am the color blue!
What just happend? Basically what call
does is swap out the context for this
.
In our first example where the square
object called the getColor
method, the this
context is itself because the method is within it's scope.
So if we want use a different object to call a method of a different object we must explicitly tell the method to use a different context. We passed in rectangle
as the first argument of the call
method to tell the getColor
function to use the rectangle
object as it's context, instead of square
.
If we leave out any arguments while calling call
, we get:
square.getColor.call(); // I am the color undefined!
It returns undefined
because the call
method has no set context so it uses window
as it's context, and window
does not have the color
property.
So if we were to add the property to window
then it all works out:
window.color = "green";
square.getColor.call(); // I am the color green!
square.getColor.call(window); // I am the color green!
We can also pass in a couple of arguments, for example the getArea
function requires width
and height
to be passed in. To do so just added them after the the first argument:.
square.getArea.call(rectangle, 6, 4); // My area is 24 feet!
Now that we are familiar with the call
method and know how to pass in arguments, let's discuss it's brother, the apply
method.
Apply Method
The apply
method is almost identitical to the call
function, the only difference is that we pass in an array as the argument instead of individual arguments:
var sides = [6, 4];
square.getArea.apply(rectangle, sides); // My area is 24 feet!
The apply method is very useful in a situation that involves passing in an unknown number of arguments into a function.
For example, getting the maximun value of a list of numbers:
var numbers = [22, 4, 354, 54, 546, 85, 12, 98];
Math.max.apply(Math, numbers); // 546
Pretty neat huh?
Please comment if you found this helpful or not, and as always you can find all this code as a gist.


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NaveenTile offers an extravagant and completely unique modern Bathroom Tiles India design in Bangalore. Now you have a choice to make your bathroom yet ‘sober’ plus ‘innovative’ like your other space only with our elegant bathroom wall and floor tiles. Our wide innovative range of bathroom tiles products come with matt finish design in various articular textures and gestures .
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Nano polished vitrified tiles Manufacturer – NaveenTile offers the most durable Polished Glazed Vitrified Tiles designs in Bangalore and Hubli at affordable rates. Glazed vitrified tiles are available for wall and floor tiles in polished, matt finish. These tiles are more durable, stain resistant and require low maintenance. Our nano polished vitrified tile for wall tiles and floor tiles are super-glory and are available to buy at least cost in a distinct range of style and design.
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Full Body Tiles is also known as full-bodied porcelain stoneware, is a very common and simple (design-free) product whose surface colour is the same all the way through the tile. These types of tiles are highly used in interior design or decorative purposes as well as exterior decorative dimensions. In case you’re looking for such types of tiles for your space, buy tiles from leading tiles manufacturers in Bangalore and Hubli – NaveenTiles. We have the most dynamic range of full body ceramic tiles and vitrified tiles in a variety of color-coding genres.
Murudeshwar Ceramics Limited is the pioneer in manufacturing world class Vitrified tiles and Ceramic tiles under the brand name “NAVEEN”. The cornerstone of our corporate journey of Thirty years has been ” In Leadership through Quality”.
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Calculator
Play around and do some basic math calculations!
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2
3
4
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6
7
8
9
0
.
Delete
AC
*
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+
-
=


St Mirren defender Gary MacKenzie hopes Jack magic doesn't kick in until after clash with Hibs
Gary MacKenzie knows how influential new Hibs boss Jack Ross can be after working with him during the Buddies Championship-winning season.
by Ben RamageVeteran St Mirren defender Gary MacKenzie hopes the Jack Ross effect takes a little longer to kick in – so they can give his ex-gaffer’s new team a beating when he returns to the Simple Digital Arena.
MacKenzie was a key part of the squad when Ross turned around Saints’ fortunes, helping them escape relegation before lifting the Championship title the next.
While he believes Ross will work his magic at struggling Hibs , he’s hoping the results won’t turn as quickly as the mood in the camp did at Ralston.
MacKenzie explained: “The results didn’t come as quickly as the mood in the camp lifted here.
“The one thing he’ll have them is believing in themselves and that they’re a good side. That will then spill on to the pitch.
“We’re hoping that takes a wee bit longer than next week before they get the benefits of Jack.
“He’s a good guy and has done really well for this club. Everybody that was here and worked under him wished him well when he left.
“But we’re there to do a job and he’ll be there to do a job. Any friendship or affection that you have for someone goes out the window.
“I’ll be there doing everything I can to get three points for St Mirren and he’ll be doing the exact same for Hibs.”


Request Header Parser Microservice
Functionality
I can get the IP address, language and operating system for my browser.
Example Input
https://relwiwa-timestamp.herokuapp.com/whoami
Example Output
{"ipAddress":"<IP_ADDRESS>","operatingSystem":"Windows NT 10.0","language":"de-DE"}


# -*- coding: utf-8 -*-
# Copyright (C) 2004-2017 by
# Aric Hagberg<EMAIL_ADDRESS># Dan Schult<EMAIL_ADDRESS># Pieter Swart<EMAIL_ADDRESS># All rights reserved.
# BSD license.
#
# Authors: Jon Crall<EMAIL_ADDRESS>"""
Algorithms for finding k-edge-connected components and subgraphs.
A k-edge-connected component (k-edge-cc) is a maximal set of nodes in G, such
that all pairs of node have an edge-connectivity of at least k.
A k-edge-connected subgraph (k-edge-subgraph) is a maximal set of nodes in G,
such that the subgraph of G defined by the nodes has an edge-connectivity at
least k.
"""
import logging
import networkx as nx
from networkx.utils import arbitrary_element
from networkx.utils import not_implemented_for
from networkx.algorithms import bridges
from functools import partial
import itertools as it
import utool as ut
print, rrr, profile = ut.inject2(__name__)
logger = logging.getLogger('wbia')
[docs]@not_implemented_for('multigraph')
def k_edge_components(G, k):
"""Generates nodes in each maximal k-edge-connected component in G.
Parameters
----------
G : NetworkX graph
k : Integer
Desired edge connectivity
Returns
-------
k_edge_components : a generator of k-edge-ccs. Each set of returned nodes
will have k-edge-connectivity in the graph G.
See Also
-------
:func:`local_edge_connectivity`
:func:`k_edge_subgraphs` : similar to this function, but the subgraph
defined by the nodes must also have k-edge-connectivity.
:func:`k_components` : similar to this function, but uses node-connectivity
instead of edge-connectivity
Raises
------
NetworkXNotImplemented:
If the input graph is a multigraph.
ValueError:
If k is less than 1
Notes
-----
Attempts to use the most efficient implementation available based on k.
If k=1, this is simply simply connected components for directed graphs and
connected components for undirected graphs.
If k=2 on an efficient bridge connected component algorithm from _[1] is
run based on the chain decomposition.
Otherwise, the algorithm from _[2] is used.
Example
-------
>>> from networkx.utils import pairwise
>>> paths = [
... (1, 2, 4, 3, 1, 4),
... (5, 6, 7, 8, 5, 7, 8, 6),
... ]
>>> G = nx.Graph()
>>> G.add_nodes_from(it.chain(*paths))
>>> G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
>>> # note this returns {1, 4} unlike k_edge_subgraphs
>>> sorted(map(sorted, k_edge_components(G, k=3)))
[[1, 4], [2], [3], [5, 6, 7, 8]]
References
----------
.. [1] https://en.wikipedia.org/wiki/Bridge_(graph_theory)
.. [2] Wang, Tianhao, et al. (2015) A simple algorithm for finding all
k-edge-connected components.
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136264
"""
# Compute k-edge-ccs using the most efficient algorithms available.
if k < 1:
raise ValueError('k cannot be less than 1')
if G.is_directed():
if k == 1:
return nx.strongly_connected_components(G)
else:
# TODO: investigate https://arxiv.org/abs/1412.6466 for k=2
aux_graph = EdgeComponentAuxGraph.construct(G)
return aux_graph.k_edge_components(k)
else:
if k == 1:
return nx.connected_components(G)
elif k == 2:
return bridge_components(G)
else:
aux_graph = EdgeComponentAuxGraph.construct(G)
return aux_graph.k_edge_components(k)
[docs]@not_implemented_for('multigraph')
def k_edge_subgraphs(G, k):
"""Generates nodes in each maximal k-edge-connected subgraph in G.
Parameters
----------
G : NetworkX graph
k : Integer
Desired edge connectivity
Returns
-------
k_edge_subgraphs : a generator of k-edge-subgraphs
Each k-edge-subgraph is a maximal set of nodes that defines a subgraph
of G that is k-edge-connected.
See Also
-------
:func:`edge_connectivity`
:func:`k_edge_components` : similar to this function, but nodes only
need to have k-edge-connctivity within the graph G and the subgraphs
might not be k-edge-connected.
Raises
------
NetworkXNotImplemented:
If the input graph is a multigraph.
ValueError:
If k is less than 1
Notes
-----
Attempts to use the most efficient implementation available based on k.
If k=1, or k=2 and the graph is undirected, then this simply calls
`k_edge_components`. Otherwise the algorithm from _[1] is used.
Example
-------
>>> from networkx.utils import pairwise
>>> paths = [
... (1, 2, 4, 3, 1, 4),
... (5, 6, 7, 8, 5, 7, 8, 6),
... ]
>>> G = nx.Graph()
>>> G.add_nodes_from(it.chain(*paths))
>>> G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
>>> # note this does not return {1, 4} unlike k_edge_components
>>> sorted(map(sorted, k_edge_subgraphs(G, k=3)))
[[1], [2], [3], [4], [5, 6, 7, 8]]
References
----------
.. [1] Zhou, Liu, et al. (2012) Finding maximal k-edge-connected subgraphs
from a large graph. ACM International Conference on Extending Database
Technology 2012 480-–491.
https://openproceedings.org/2012/conf/edbt/ZhouLYLCL12.pdf
"""
if k < 1:
raise ValueError('k cannot be less than 1')
if G.is_directed():
if k <= 1:
# For directed graphs ,
# When k == 1, k-edge-ccs and k-edge-subgraphs are the same
return k_edge_components(G, k)
else:
return _k_edge_subgraphs_nodes(G, k)
else:
if k <= 2:
# For undirected graphs,
# when k <= 2, k-edge-ccs and k-edge-subgraphs are the same
return k_edge_components(G, k)
else:
return _k_edge_subgraphs_nodes(G, k)
def _k_edge_subgraphs_nodes(G, k):
"""Helper to get the nodes from the subgraphs.
This allows k_edge_subgraphs to return a generator.
"""
for C in general_k_edge_subgraphs(G, k):
yield set(C.nodes())
[docs]@not_implemented_for('directed')
@not_implemented_for('multigraph')
def bridge_components(G):
"""Finds all bridge-connected components G.
Parameters
----------
G : NetworkX undirected graph
Returns
-------
bridge_components : a generator of 2-edge-connected components
See Also
--------
:func:`k_edge_subgraphs` : this function is a special case for an
undirected graph where k=2.
:func:`biconnected_components` : similar to this function, but is defined
using 2-node-connectivity instead of 2-edge-connectivity.
Raises
------
NetworkXNotImplemented:
If the input graph is directed or a multigraph.
Notes
-----
Bridge-connected components are also known as 2-edge-connected components.
Example
-------
>>> # The barbell graph with parameter zero has a single bridge
>>> G = nx.barbell_graph(5, 0)
>>> sorted(map(sorted, bridge_components(G)))
[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
"""
H = G.copy()
H.remove_edges_from(bridges(G))
yield from nx.connected_components(H)
[docs]class EdgeComponentAuxGraph(object):
"""A simple algorithm to find all k-edge-connected components in a graph.
Constructing the AuxillaryGraph (which may take some time) allows for the
k-edge-ccs to be found in linear time for arbitrary k.
Notes
-----
This implementation is based on [1]_. The idea is to construct an auxillary
graph from which the k-edge-ccs can be extracted in linear time. The
auxillary graph is constructed in O(|V|F) operations, where F is the
complexity of max flow. Querying the components takes an additional O(|V|)
operations. This algorithm can be slow for large graphs, but it handles an
arbitrary k and works for both directed and undirected inputs.
The undirected case for k=1 is exactly connected components.
The undirected case for k=2 is exactly bridge connected components.
The directed case for k=1 is exactly strongly connected components.
References
----------
.. [1] Wang, Tianhao, et al. (2015) A simple algorithm for finding all
k-edge-connected components.
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136264
Example
-------
>>> from networkx.utils import pairwise
>>> # Build an interesting graph with multiple levels of k-edge-ccs
>>> paths = [
... (1, 2, 3, 4, 1, 3, 4, 2), # a 3-edge-cc (a 4 clique)
... (5, 6, 7, 5), # a 2-edge-cc (a 3 clique)
... (1, 5), # combine first two ccs into a 1-edge-cc
... (0,), # add an additional disconnected 1-edge-cc
... ]
>>> G = nx.Graph()
>>> G.add_nodes_from(it.chain(*paths))
>>> G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
>>> # Constructing the AuxGraph takes about O(n ** 4)
>>> aux_graph = EdgeComponentAuxGraph.construct(G)
>>> # Once constructed, querying takes O(n)
>>> sorted(map(sorted, aux_graph.k_edge_components(k=1)))
[[0], [1, 2, 3, 4, 5, 6, 7]]
>>> sorted(map(sorted, aux_graph.k_edge_components(k=2)))
[[0], [1, 2, 3, 4], [5, 6, 7]]
>>> sorted(map(sorted, aux_graph.k_edge_components(k=3)))
[[0], [1, 2, 3, 4], [5], [6], [7]]
>>> sorted(map(sorted, aux_graph.k_edge_components(k=4)))
[[0], [1], [2], [3], [4], [5], [6], [7]]
Example
-------
>>> # The auxillary graph is primarilly used for k-edge-ccs but it
>>> # can also speed up the queries of k-edge-subgraphs by refining the
>>> # search space.
>>> from networkx.utils import pairwise
>>> paths = [
... (1, 2, 4, 3, 1, 4),
... ]
>>> G = nx.Graph()
>>> G.add_nodes_from(it.chain(*paths))
>>> G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
>>> aux_graph = EdgeComponentAuxGraph.construct(G)
>>> sorted(map(sorted, aux_graph.k_edge_subgraphs(k=3)))
[[1], [2], [3], [4]]
>>> sorted(map(sorted, aux_graph.k_edge_components(k=3)))
[[1, 4], [2], [3]]
"""
# @not_implemented_for('multigraph') # TODO: fix decor for classmethods
[docs] @classmethod
def construct(EdgeComponentAuxGraph, G):
"""Builds an auxillary graph encoding edge-connectivity between nodes.
Notes
-----
Given G=(V, E), initialize an empty auxillary graph A.
Choose an arbitrary source node s. Initialize a set N of available
nodes (that can be used as the sink). The algorithm picks an
arbitrary node t from N - {s}, and then computes the minimum st-cut
(S, T) with value w. If G is directed the the minimum of the st-cut or
the ts-cut is used instead. Then, the edge (s, t) is added to the
auxillary graph with weight w. The algorithm is called recursively
first using S as the available nodes and s as the source, and then
using T and t. Recusion stops when the source is the only available
node.
Parameters
----------
G : NetworkX graph
"""
# workaround for classmethod decorator
not_implemented_for('multigraph')(lambda G: G)(G)
def _recursive_build(H, A, source, avail):
# Terminate once the flow has been compute to every node.
if {source} == avail:
return
# pick an arbitrary node as the sink
sink = arbitrary_element(avail - {source})
# find the minimum cut and its weight
value, (S, T) = nx.minimum_cut(H, source, sink)
if H.is_directed():
# check if the reverse direction has a smaller cut
value_, (T_, S_) = nx.minimum_cut(H, sink, source)
if value_ < value:
value, S, T = value_, S_, T_
# add edge with weight of cut to the aux graph
A.add_edge(source, sink, weight=value)
# recursively call until all but one node is used
_recursive_build(H, A, source, avail.intersection(S))
_recursive_build(H, A, sink, avail.intersection(T))
# Copy input to ensure all edges have unit capacity
H = G.__class__()
H.add_nodes_from(G.nodes())
H.add_edges_from(G.edges(), capacity=1)
# A is the auxillary graph to be constructed
# It is a weighted undirected tree
A = nx.Graph()
# Pick an arbitrary node as the source
if H.number_of_nodes() > 0:
source = arbitrary_element(H.nodes())
# Initialize a set of elements that can be chosen as the sink
avail = set(H.nodes())
# This constructs A
_recursive_build(H, A, source, avail)
# This class is a container the holds the auxillary graph A and
# provides access the the k_edge_components function.
self = EdgeComponentAuxGraph()
self.A = A
self.H = H
return self
[docs] def k_edge_components(self, k):
"""Queries the auxillary graph for k-edge-connected components.
Parameters
----------
k : Integer
Desired edge connectivity
Returns
-------
k_edge_components : a generator of k-edge-ccs
Notes
-----
Given the auxillary graph, the k-edge-connected components can be
determined in linear time by removing all edges with weights less than
k from the auxillary graph. The resulting connected components are the
k-edge-ccs in the original graph.
"""
if k < 1:
raise ValueError('k cannot be less than 1')
A = self.A
# "traverse the auxiliary graph A and delete all edges with weights less
# than k"
aux_weights = nx.get_edge_attributes(A, 'weight')
# Create a relevant graph with the auxillary edges with weights >= k
R = nx.Graph()
R.add_nodes_from(A.nodes())
R.add_edges_from(e for e, w in aux_weights.items() if w >= k)
# Return the nodes that are k-edge-connected in the original graph
yield from nx.connected_components(R)
[docs] def k_edge_subgraphs(self, k):
"""Queries the auxillary graph for k-edge-connected subgraphs.
Parameters
----------
k : Integer
Desired edge connectivity
Returns
-------
k_edge_subgraphs : a generator of k-edge-subgraphs
Notes
-----
Refines the k-edge-ccs into k-edge-subgraphs. The running time is more
than O(|V|).
For single values of k it is faster to use `nx.k_edge_subgraphs`.
But for multiple values of k, it can be faster to build AuxGraph and
then use this method.
"""
if k < 1:
raise ValueError('k cannot be less than 1')
H = self.H
A = self.A
# "traverse the auxiliary graph A and delete all edges with weights less
# than k"
aux_weights = nx.get_edge_attributes(A, 'weight')
# Create a relevant graph with the auxillary edges with weights >= k
R = nx.Graph()
R.add_nodes_from(A.nodes())
R.add_edges_from(e for e, w in aux_weights.items() if w >= k)
# Return the components whose subgraphs are k-edge-connected
for cc in nx.connected_components(R):
if len(cc) < k:
# Early return optimization
for node in cc:
yield {node}
else:
# Call subgraph solution to refine the results
C = H.subgraph(cc)
yield from k_edge_subgraphs(C, k)
def _low_degree_nodes(G, k, nbunch=None):
"""Helper for finding nodes with degree less than k."""
# Nodes with degree less than k cannot be k-edge-connected.
if G.is_directed():
# Consider both in and out degree in the directed case
seen = set()
for node, degree in G.out_degree(nbunch):
if degree < k:
seen.add(node)
yield node
for node, degree in G.in_degree(nbunch):
if node not in seen and degree < k:
seen.add(node)
yield node
else:
# Only the degree matters in the undirected case
for node, degree in G.degree(nbunch):
if degree < k:
yield node
def _high_degree_components(G, k):
"""Helper for filtering components that cant be k-edge-connected.
Removes and generates each node with degree less than k. Then generates
remaining components where all nodes have degree at least k.
"""
# Iteravely remove parts of the graph that are not k-edge-connected
H = G.copy()
singletons = set(_low_degree_nodes(H, k))
while singletons:
# Only search neighbors of removed nodes
nbunch = set(it.chain.from_iterable(map(H.neighbors, singletons)))
nbunch.difference_update(singletons)
H.remove_nodes_from(singletons)
for node in singletons:
yield {node}
singletons = set(_low_degree_nodes(H, k, nbunch))
# Note: remaining connected components may not be k-edge-connected
if G.is_directed():
yield from nx.strongly_connected_components(H)
else:
yield from nx.connected_components(H)
[docs]def general_k_edge_subgraphs(G, k):
"""General algorithm to find all maximal k-edge-connected subgraphs in G.
Returns
-------
k_edge_subgraphs : a generator of nx.Graphs that are k-edge-subgraphs
Each k-edge-subgraph is a maximal set of nodes that defines a subgraph
of G that is k-edge-connected.
Notes
-----
Implementation of the basic algorithm from _[1]. The basic idea is to find
a global minimum cut of the graph. If the cut value is at least k, then the
graph is a k-edge-connected subgraph and can be added to the results.
Otherwise, the cut is used to split the graph in two and the procedure is
applied recursively. If the graph is just a single node, then it is also
added to the results. At the end, each result is either gaurenteed to be
a single node or a subgraph of G that is k-edge-connected.
This implementation contains optimizations for reducing the number of calls
to max-flow, but there are other optimizations in _[1] that could be
implemented.
References
----------
.. [1] Zhou, Liu, et al. (2012) Finding maximal k-edge-connected subgraphs
from a large graph. ACM International Conference on Extending Database
Technology 2012 480-–491.
https://openproceedings.org/2012/conf/edbt/ZhouLYLCL12.pdf
Example
-------
>>> from networkx.utils import pairwise
>>> paths = [
... (11, 12, 13, 14, 11, 13, 14, 12), # a 4-clique
... (21, 22, 23, 24, 21, 23, 24, 22), # another 4-clique
... # connect the cliques with high degree but low connectivity
... (50, 13),
... (12, 50, 22),
... (13, 102, 23),
... (14, 101, 24),
... ]
>>> G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
>>> sorted(map(len, k_edge_subgraphs(G, k=3)))
[1, 1, 1, 4, 4]
"""
if k < 1:
raise ValueError('k cannot be less than 1')
# Node pruning optimization (incorporates early return)
# find_ccs is either connected_components/strongly_connected_components
find_ccs = partial(_high_degree_components, k=k)
# Quick return optimization
if G.number_of_nodes() < k:
for node in G.nodes():
yield G.subgraph([node]).copy()
return
# Intermediate results
R0 = {G.subgraph(cc).copy() for cc in find_ccs(G)}
# Subdivide CCs in the intermediate results until they are k-conn
while R0:
G1 = R0.pop()
if G1.number_of_nodes() == 1:
yield G1
else:
# Find a global minimum cut
cut_edges = nx.minimum_edge_cut(G1)
cut_value = len(cut_edges)
if cut_value < k:
# G1 is not k-edge-connected, so subdivide it
G1.remove_edges_from(cut_edges)
for cc in find_ccs(G1):
R0.add(G1.subgraph(cc).copy())
else:
# Otherwise we found a k-edge-connected subgraph
yield G1


Sherpa is a small HTTP server that is ideal for web page development locally, where browsers may require javascript modules be loaded from http protocol sources, not file protocol sources, if your html content is local.
Sherpa features:
Cheers!
Sherpa, Copyright (c) 2020, Keelan Stuart


Collection
For inspection from 1 April 2017. The framework and inspection letters and forms.
The social care common inspection framework (SCCIF), for use from 1 April 2017, applies to inspections of:


If you unequip Sentry, Blaze or Flash a bit after using it, you will get a bigger hop than usual. This will also remove all negatives effect of using these abilities as a movement option, which mainly allows to walljump right away. You can equip you ability again at any time after releasing your weapon wheel once.
You can use the weapon wheel as a pause buffer. You want to unequip your ability when Ori starts to spread out after they rolled into a ball.
Sentry and Blaze will still consume energy even if you cancel it but doing it with Flash won't use any energy.
You can also chain them together to gain a lot of height. Everytime you close your weapon wheel, you won't be able to use your ability for a bit of time so make sure to reequip it as soon as possible.


{% extends "playlister_base_onecol.html" %} {% block title %}Songza playlists and more{% endblock %} {% block css %}{% endblock %} {% block content %}
{% for playlists in header1_playlists %}w/ {{playlist.featured_artists}}
w/ {{playlist.featured_artists}}
w/ {{playlist.featured_artists}}


This is the ontology to describe things in the bible.
- This owl is focused on the genealogy of Jesus Christ.
- Based on: Genesis 5:1-32
- Based on: Matthew 1:1-17
- Available Classes
- Class: EarthlyWorld, Living, Human, Man
- ObjectProperty: hasFamily, hasAncestor, hasParent, hasFather, hasOffspring, hasChild, hasSon


The following table describes the PositionSide
enumeration members:
| Member | Value | Description |
|---|---|---|
| Short | -1 | A short position, quantity less than zero (-1) |
| None | 0 | No position, quantity equals zero (0) |
| Long | 1 | A long position, quantity greater than zero (1) |


Image Editor Crop Pane
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CURLOPT_SSL_CTX_FUNCTION explained
NAME
CURLOPT_SSL_CTX_FUNCTION - SSL context callback for OpenSSL, wolfSSL/CyaSSL or mbedTLS
SYNOPSIS
#include <curl/curl.h> CURLcode ssl_ctx_callback(CURL *curl, void *ssl_ctx, void *userptr); CURLcode curl_easy_setopt(CURL *handle, CURLOPT_SSL_CTX_FUNCTION, ssl_ctx_callback);
DESCRIPTION
This option only works for libcurl powered by OpenSSL, wolfSSL/CyaSSL or mbedTLS. If libcurl was built against another SSL library this functionality is absent.
Pass a pointer to your callback function, which should match the prototype shown above.
This callback function gets called by libcurl just before the initialization of an SSL connection after having processed all other SSL related options to give a last chance to an application to modify the behavior of the SSL initialization. The ssl_ctx parameter is actually a pointer to the SSL library's SSL_CTX for OpenSSL or wolfSSL/CyaSSL, and a pointer to mbedtls_ssl_config for mbedTLS. If an error is returned from the callback no attempt to establish a connection is made and the perform operation will return the callback's error code. Set the userptr argument with the CURLOPT_SSL_CTX_DATA option.
This function will get called on all new connections made to a server, during the SSL negotiation. The ssl_ctx will point to a newly initialized object each time, but note the pointer may be the same as from a prior call.
To use this properly, a non-trivial amount of knowledge of your SSL library is necessary. For example, you can use this function to call library-specific callbacks to add additional validation code for certificates, and even to change the actual URI of an HTTPS request.
WARNING: The CURLOPT_SSL_CTX_FUNCTION callback allows the application to reach in and modify SSL details in the connection without libcurl itself knowing anything about it, which then subsequently can lead to libcurl unknowingly reusing SSL connections with different properties. To remedy this you may set CURLOPT_FORBID_REUSE from the callback function.
DEFAULT
PROTOCOLS
All TLS based protocols: HTTPS, FTPS, IMAPS, POP3S, SMTPS etc.
EXAMPLE
See cacertinmem.c in docs/examples directory for usage example.
https://curl.haxx.se/libcurl/c/cacertinmem.html
AVAILABILITY
Added in 7.11.0 for OpenSSL. Added in 7.42.0 for wolfSSL/CyaSSL. Added in 7.54.0 for mbedTLS. Other SSL backends not supported.
RETURN VALUE
CURLE_OK if supported; or an error such as:
CURLE_NOT_BUILT_IN - Not supported by the SSL backend
SEE ALSO
CURLOPT_SSL_CTX_DATA, CURLOPT_SSL_VERIFYPEER
This HTML page was made with roffit.


This page contains the minimum HTML you need in any web page. Use your browser to "View Page Source", or download this file to use it.


Insecure Web Application
Examples
Synchronous Communication
Asynchronous Communication (AJAX) - Get all users
Asynchronous Communication (AJAX)
Search
Asynchronous Communication with JSON - Get all user
Asynchronous Communication with JSON
Search
Asynchronous Communication with JSON
Response


{% include "./partials/navbar.html" %}
{% include "./partials/menu.html" %}
{% include "./partials/commentlink.html" %}
{% include "./partials/navbar_top.html" %}
{{ content | safe }}
{% include "./partials/quat_section.html" %}
{% include "./partials/commentbox.html" %}


@import java.util @import controllers.view.ApplicationViewModel @(playAuth: com.feth.play.module.pa.PlayAuthenticate, user: models.User, apiKey: ApiKey, applications: util.List[ApplicationViewModel], sdkVersion: String, autoRefresh: Boolean) @views.html.commandcenter.main("Command Center", user) { @if(autoRefresh) { } @if(!user.isActive) {
Thank you so much for signing up to FlowUp! Because of the high usage of FlowUp, we're activating accounts on a user-by-user basis. You'll get a second email as soon as we're ready for you.
Cheers,
The FlowUp Team.
Add the following dependency to your Podfile
:
pod 'FlowUpIOSSDK', '~> @sdkVersion'
Initialize the FlowUp iOS SDK in your AppDelegate
class application:didFinishLaunchingWithOptions:
method.
Objective-C:
// YourAppDelegate.h
#import "FlowUp.h"
// YourAppDelegate.m
@implementation YourAppDelegate
- (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions
{
[FlowUp application:application
didFinishLaunchingWithOptions:launchOptions
apiKey<EMAIL_ADDRESS>isDebugModeEnabled:YES];
return YES;
}
@end
Swift:
import FlowUp
class YourAppDelegate: UIResponder, UIApplicationDelegate {
func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool {
FlowUp.application(application,
didFinishLaunchingWithOptions: launchOptions,
apiKey<EMAIL_ADDRESS>isDebugModeEnabled: true)
return true
}
}
Remember to disable the debug mode when building a release target.
Build and run your app in the simulator or real device to start seeing data. Within seconds you will see data in your FlowUp Dashboards.
Our SDK is implemented in Objective-C and it uses a variety of APIs to collect all the system metrics we use to detect and show you potential performance issues in your app. We don't use any private API (and never will!) so it's completely safe to use FlowUp in your app and upload it to the App Store.
Additionally, remember you can send an email to support@@flowup.io with any doubt or issue you may have.


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Hello! I'm an extremetly driven and creative Full Stack Developer who is currently open for career opportunities as a front-end or back-end web developer in the Greater Seattle Area.
I'm a recent graduate of Coding Dojo, a coding school located in Bellevue, WA that teaches 3 full stacks in 3 months. I'm capable of learning new technologies very quickly, and am always looking for opportunities to further expand my skills and grow as a developer.
La Mode is an Ecommerce website for designed to market various clothing products. Users are able to view the available garments, select their desired quantitity, and compile a shopping cart for making a final purchase.
Family Contacts is a free application for managing, sharing, and visualizing your family relationships for both extended and immediate family. As an essential feature of the project, you log-in information determines who you can see and reveals how people are related to you.
Five Eleven is a data visualization project built on the Python software stack. The application transforms the developer job-hunt into a more visual, user friendly experience. Based on location, technology popularity, and other vast data sets, users may easily visualize the varying sizes and concentrations of the junior developer job market across the nation.


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dialog
module in Electron allows you to use native
system dialogs for opening files or directories, saving a file or
displaying informational messages.
This is a main process module because this process is more efficient with native utilities and it allows the call to happen without interrupting the visible elements in your page's renderer process.
Open the full API documentation (opens in new window) in your browser.
In this demo, the ipc
module is used to send a message
from the renderer process instructing the main process to launch the
information dialog. Options may be provided for responses which can
then be relayed back to the renderer process.
Note: The title
property is not displayed in macOS.
An information dialog can contain an icon, your choice of buttons, title and message.
const {ipcRenderer} = require('electron')
const informationBtn = document.getElementById('information-dialog')
informationBtn.addEventListener('click', (event) => {
ipcRenderer.send('open-information-dialog')
})
ipcRenderer.on('information-dialog-selection', (event, index) => {
let message = 'You selected '
if (index === 0) message += 'yes.'
else message += 'no.'
document.getElementById('info-selection').innerHTML = message
})
const {ipcMain, dialog} = require('electron')
ipcMain.on('open-information-dialog', (event) => {
const options = {
type: 'info',
title: 'Information',
message: "This is an information dialog. Isn't it nice?",
buttons: ['Yes', 'No']
}
dialog.showMessageBox(options, (index) => {
event.sender.send('information-dialog-selection', index)
})
})


--- layout: post title: 4G Mobile Phone Deal Finders date: type: post published: false status: draft categories: - Introduction To tags: [] meta: {} author: login: fvdd email<EMAIL_ADDRESS>display_name: fvdd first_name: '' last_name: '' ---
uswtich.com: select voice and text and price caps - but nothing about data...


name
address
1
2
3
Reverse Order Value is {{order}}
{{obj.name|lowercase|initcap}}
{{obj.address}}
{{obj.phone|currency}}
{{obj.email}}


Classes | |
| class | NoteData |
| class | PositionAdder |
Public Types | |
|
typedef std::vector < SyllableLabeler * > | List |
Public Member Functions | |
| NoteLabeler (const Beat &b, const Dynamics &d, const Key &k) | |
| constructor | |
| virtual | ~NoteLabeler () |
| destructor | |
| virtual bool | isRest () const |
| is rest or not | |
| virtual bool | isConverted () const |
| already converted or not | |
| virtual std::string | getLyric () const |
| get lyric | |
| virtual Syllabic | getSyllabic () const |
| get syllabic | |
| size_t | getDuration () const |
| get duration | |
| virtual void | addInfo (const std::vector< PhonemeInfo > &phonemes, const std::string &language, const std::string &info) |
| add information | |
| virtual void | setLabel (INoteLabel &label) const |
| set label | |
| void | addNote (const Note ¬e, double tempo) |
| add note | |
| const Beat & | getBeat () const |
| get beat | |
| const Dynamics & | getDynamics () const |
| get dynamics | |
| const Key & | getKey () const |
| get key | |
| void | setMeasure (LabelMeasure *m) |
| set measure | |
| void | setPhrase (NoteGroup *ng) |
| set phrase | |
| void | setCrescendo (NoteGroup *ng) |
| set crescendo | |
| void | setDiminuendo (NoteGroup *ng) |
| set diminuendo | |
| void | setPrevPhrase (const NoteGroup *ng) |
| set prev phrase | |
| void | setNextPhrase (const NoteGroup *ng) |
| set next phrase | |
| const NoteGroup * | getPhrase () const |
| get phrase | |
| Pitch | getPitch () const |
| get pitch | |
| void | setPositions () |
| set positions | |
| bool | isSlurStart () const |
| is slur start or not | |
| bool | isSlurStop () const |
| is slur stop or not | |
| void | setInSlurFromPrev (bool b) |
| set if this note is in slur from previous note or not | |
| void | setInSlurToNext (bool b) |
| set if this note is in slur to next note or not | |
| void | setPrevPitch (const Pitch &p) |
| set previous pitch | |
| void | setNextPitch (const Pitch &p) |
| set next pitch | |
| LabelPosition | getLength () const |
| get length | |
| void | setNextAccentPosition (const LabelPosition &p) |
| set next accent position | |
| void | setPrevAccentPosition (const LabelPosition &p) |
| set previous accent position | |
| void | setNextStaccatoPosition (const LabelPosition &p) |
| set next staccato position | |
| void | setPrevStaccatoPosition (const LabelPosition &p) |
| set previous staccato position | |
| size_t | getSyllableNum () const |
| get number of syllables | |
| bool | hasAccent () const |
| has accent or not | |
| bool | hasStaccato () const |
| has staccato or not | |
| bool | hasBreathToNext () const |
| if this note has breath between this and next node, return true. | |
| void | setPrevNote (const NoteLabeler *prev) |
| set previous note (null -> this is the head note) | |
| void | setNextNote (const NoteLabeler *next) |
| set next note (null -> this is the tail note) | |
| const LabelPosition & | getMeasureIdx () const |
| ... | |
| void | moveTo (NoteLabeler &, size_t=0) |
| ... | |
| void | setBreathPhoneme () |
| set breath phoneme to the tail of the last syllable | |
| List::const_iterator | childBegin () const |
| get begin iteration | |
| List::const_iterator | childEnd () const |
| get end iteration |
| NoteLabeler::NoteLabeler | ( | const Beat & | b, |
| const Dynamics & | d, | ||
| const Key & | k | ||
| ) |
constructor
constructor
| b | beat |
| d | dynamics |
| k | key |
| void NoteLabeler::addInfo | ( | const std::vector< PhonemeInfo > & | phonemes, |
| const std::string & | language, | ||
| const std::string & | info | ||
| ) | [virtual] |
add information
add information
| phonemes | phoneme list |
| language | language |
| info | language dependent information |
Implements sinsy::IConvertable.
| void NoteLabeler::addNote | ( | const Note & | note, |
| double | tempo | ||
| ) |
add note
add note
| note | note |
| tempo | tempo |
| NoteLabeler::List::const_iterator NoteLabeler::childBegin | ( | ) | const |
get begin iteration
get begin iterator of syllables
| NoteLabeler::List::const_iterator NoteLabeler::childEnd | ( | ) | const |
get end iteration
get end iterator of syllables
| const LabelPosition & NoteLabeler::getMeasureIdx | ( | ) | const |
...
get measure index
| bool NoteLabeler::hasBreathToNext | ( | ) | const |
if this note has breath between this and next node, return true.
has breath between this and next notes or not
| void NoteLabeler::moveTo | ( | NoteLabeler & | target, |
| size_t | durationStartIdx = 0 |
||
| ) |
...
move to target
| void NoteLabeler::setLabel | ( | INoteLabel & | label | ) | const [virtual] |
set label
set label
| label | label |
| void NoteLabeler::setPrevPhrase | ( | const NoteGroup * | ng | ) |
set prev phrase
set previous phrase


2 references to Show
Microsoft.PowerShell.Commands.Utility (2)
commands\utility\ShowCommand\ShowCommand.cs (1)
21
[Cmdlet(VerbsCommon.
Show
, "Command", HelpUri = "https://go.microsoft.com/fwlink/?LinkID=2109589")]
commands\utility\ShowMarkdownCommand.cs (1)
22
VerbsCommon.
Show
, "Markdown",


Research Scientist, NASA
Host: How It's Made, Discovery Channel
CTO, Systemantics India
Co-Founder, ThinkAnalytics
TechExpo was amazing with great projects from different parts of the country. It was better than other science exhibitions as it was open to all categories of science projects. The interaction with people from diverse fields ranging from academia, research and development to industries made it unique. I would like to convey my gratitude to the organizers of TechExpo 2017, especially to the Convener of Techniche 2017, Animesh Jain. I will never forget the help they rendered for my exclusive meeting with Bharat Salhotra, MD of Alstom India.
TechExpo was true to its words about the grand exposure. We could interact with IIT professors, CEOs and a large number of the common public which greatly helped us. Securing the 19th position motivated us to take our concept to the market. We are fortunate to get the guidance of Prof. Pradeep Yammiyavar, HoD of Design IITG. TechExpo will be turning point in the creation of BIU.
We participated in Tech-expo 2017 during Techniche'17 in IIT Guwahati, where we got the golden opportunity to present our innovative project on a national platform in front of the most talented persons like IIT Guwahati professors, national and international judges. There were many innovative projects from different parts of the country. According to our team, it was the best event in which we have participated ever. We are very thankful to IIT Guwahati for giving us this opportunity.


In City Press Voices: Obsession with poverty sexification; Divisive politics is a societal cancer
Some of the opinions in the City Press Voices section this week:
Obsession with poverty sexification
Author Sihle Khumalo urges those who keep sexifying poverty, especially if you have never lived the poverty experience, to please stop it.
If you have your demons you have to deal with regarding the injustices of the past, try different avenues to sort yourself out.
But poverty sexification should not be one of those. There is nothing sexy about poverty.
Human rights, sovereignty are two sides of same coin
South Africa cannot interfere in the affairs of other countries, but it can use diplomacy as well as multilateral platforms and instruments to persuade and help them find appropriate solutions, writes Deputy President David Mabuza.
Divisive politics is a societal cancer
While political leaders expend energy and resources insidiously, they relegate the real project of nation-building and create a vacuum that is quickly filled by those with nefarious intent, writes Modidima Mannya.
Africa can lead in fashion and clothing
The masterplan for the fashion, textile and clothing industry could be what South Africa needs to lead the pack and be a force to be reckon with, writes AFI founder Precious Moloi-Motsepe
Book extract: ‘The struggle continues till Babylon falls!’
This is the story of Robert McBride and his comrades: the substation sabotage sprees; rescuing a compatriot from hospital and smuggling him to Botswana; the devastating Why Not Bar and Magoo’s car bomb that killed three women; the dramatic trial; and McBride’s 1 463 days on death row.


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Please check your email and click on the link to verfiy your email address.


feats package¶
Submodules¶
feats.feats_batchcorrection module¶
-
feats.feats_batchcorrection.
AdjustShiftVariance
(X_ref, X, Corr, sigma)¶
-
feats.feats_batchcorrection.
ComputeCorrectionVectors
(V, X, tar_idx, sigma)¶
-
feats.feats_batchcorrection.
ComputeMNNPairs
(X_ref, X, k)¶
-
feats.feats_batchcorrection.
ComputeSpan
(X, idx, svd_dim)¶
-
feats.feats_batchcorrection.
CorrectBatches
(batches, correct_order, k=20, sigma=10, svd_dim=0, adj_var=True)¶ Implements the Mutual Nearest Neighbour (MNN) approach to correcting batch effects in the integrated and merged datasets.
- Parameters
batches (SingleCell) – An integrated and merged SingleCell object (dataset) to correct. This dataset should have a ‘batch’ column in the celldata dataframe with batch information.
correct_order (list) – A Python list by the batch number or name representing the order in which to correct the batches. The first batch in the list is the reference batch.
k (int, optional) – The number of Mutual Nearest Neighbours to compute between the datasets to be corrected. default (20).
signa (float, optional) – A parameter controlling the width of the Gaussian kernel smoothing function.
svd_dim (int, optional) – The dimensionality of U when computing the SVD of data, where U, S, V^T = svd(X). default (0).
adj_var (bool, optional) – Whether or not to adjust the variance of the computed batch vectors. True (default) if adjust the variance, False if not.
- Returns
The corrected dataset.
- Return type
SingleCell
- Raises
ValueError – If the length of ‘correct_order’ is less than 2, and if no batch information is found in the input parameter ‘batches’.
-
feats.feats_batchcorrection.
IntegrateBatches
(batches, name_by)¶ Integrates a list of SingleCell objects storing single-cell data. This function first finds common genes across all the datasets in the list and then merges all the datasets together in one SingleCell object. If no common genes are found between the datasets, then an error is generated with a message.
- Parameters
batches (list) – A Python list of SingleCell objects (datasets) to integrate.
name_by (list) – A list of str representing column names in the SingleCell objects where gene names are stored. The order in this list is same as the order of SingleCell objects in the batches list.
- Returns
A merged dataset where the number of rows (d) is equal to the number of common genes in all the datasets and the number columns (n) is the sum of the number of cells in all the datasets.
- Return type
SingleCell
- Raises
ValueError – If no common genes are found between the SingleCell datasets in the batches list.
-
feats.feats_batchcorrection.
LogspaceAdd
(log_array)¶
-
feats.feats_batchcorrection.
MergeBatches
(batches)¶ Merges a list of SingleCell objects storing single-cell data. This function assumes that the datasets are already integrated and the number and type of genes in all the datasets are the same. If this is not the case, then an error is generated.
- Parameters
batches (list) – A Python list of SingleCell objects (datasets) to merge. It is assumed that the SingleCell datasets are already integrated.
- Returns
A merged dataset where the number of rows (d) is equal to the number genes the datasets (which is the same for all the datasets) and the number columns (n) is the sum of the number of cells in all the datasets.
- Return type
SingleCell
- Raises
ValueError – If the number and type of genes are not the same in all the datasets.
-
feats.feats_batchcorrection.
ReducedMean
(X, idx)¶
-
feats.feats_batchcorrection.
RemoveBatchEffects
(ref_batch, tar_batch, k, sigma, svd_dim, adj_var)¶
-
feats.feats_batchcorrection.
SqDistToLine
(ref, grad, point)¶
feats.feats_clustering module¶
-
feats.feats_clustering.
AnovaHierarchical
(sc, k, normalization, q)¶
-
feats.feats_clustering.
Cluster
(sc, k='gap', k_max=10, normalization='mean', q=None)¶ Clusters gene expression data stored in SingleCell object. Stores the cluster information (labels) in the celldata assay of the SingleCell object. The column name under which it stores the cluster information is ‘FEATS_k_Clusters’, where k is the number of clusters, which the method can estimate or is defined by the user as an int or an int list.
- Parameters
sc (SingleCell) – The SingleCell dataset containing gene expression data to be clustered.
k (int, list or str, optional) – This is an optional input for the number of clusters in the dataset. It is either an int, a Python list of ints or a str. If it is an int, the method will cluster the data into k groups. If it is a Python list, the method will cluster the data into the number of groups specified in the list and store the cluster information for all the values in the list. If it is the string ‘gap’, the method will use gap statistic to estimate the number of clusters and cluster the data into the number of groups estimated.
k_max (int, optional) – The upper limit of the number of clusters when using gap statistic to estimate the number of clusters. Ignored if k is not ‘gap’. Default 10.
normalization (str, optional) – The normalization method to use when normalizing the gene expression data. By default the normalized data is not saved in the SingleCell object after using the Cluster function. If you want to save the normalized counts in the SingleCell object, you can use the FeatureNormalize() function. Type help(FeatureNormalize) for more information. Options for the normalization are ‘mean’ for z-score normalization (default), ‘l2’ for L2 normalization, ‘cosine’ for Cosine normalization and None for no normalization.
q (list, optional) – This parameter is an int list which specifies the number of top features to select. The clustering algorithm selects each q_i top features in this list, perofrms clustering, computes the clustering score,and then determines the optimal number of features which give best clustering. By default q = [1, 2, …, 5% of d], where d is the number of features.
- Returns
SingleCell – The SingleCell object with the cluster information stored in the celldata assay.
int – The number of clusters in the dataset, k. This output is useful when k is not known and estimated by the algorithm.
- Raises
ValueError – If k_max is < 2. If k > n and < 2, where n is the number of samples in the dataset. If unknown input types/class is detected.
-
feats.feats_clustering.
ClusteringScore
(X, labels)¶
-
feats.feats_clustering.
SelectTopNScores
(clustering_score, n)¶
feats.feats_detectoutliers module¶
-
feats.feats_detectoutliers.
DetectOutliers
(sc, cluster_label, red_dim=2, outlier_prob_thres=0.0001)¶ This function implements the outlier detection scheme of FEATS.
- Parameters
sc (SingleCell) – The SingleCell object which contains the data and metadata of genes and cells
cluster_label (str) – The name of the column in celldata assay of sc which stores the cluster labels of the cells
red_dim (int, optional) – The reduced dimentionality in which the outliers are computed. Default 2.
outlier_prob_thres (float) – The probability threshold for samples to be classified as outliers. Default 10^-4.
- Returns
The single cell object containing the outlier analysis information in the celldata assay. It contains the following columns in the celldata assay with the outlier information: ‘FEATS_Outliers’ - A column with the value True if the respective cell is an outlier, False otherwise. ‘FEATS_Msd’ - The computed Mahalanobis squared distance for the respective cells. ‘FEATS_Outlier_Score’ - The outlier score for the respective cells. ‘FEATS_Oos’ - A column with the value True if the respective cell was not used by the Minimum Covariance Determinant (MCD) algorithm in computing the robust mean and covariance matrix.
- Return type
SingleCell
feats.feats_filtering module¶
-
feats.feats_filtering.
CellFilter
(sc, min_genes, max_genes, expr_thres=0)¶ Filters the cells in which the genes are lowly and/or highly expressed. Returns the sliced SingleCell object with the cells selected through the filtering criteria. This functions skips filtering and warns the user if the filtering criteria filters out all the samples/cells.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
min_genes (int) – The minimum number of genes in the cell in which the genes must be expressed with the expression threshold. The value should be >= 0 and <= d, where d is the number of genes the the dataset.
max_genes (int) – The maximum number of genes in the cell in which the gene must be expressed with the expression threshold. The value should be >= 0 and <= d, where d is the number of genes the the dataset.
expr_thres (int, optional) – The expression threshold. Default 0. The gene is considered not expressed if the expression count is <= this threshold.
- Returns
sc – A sliced SingleCell object containing the filtered cells.
- Return type
SingleCell
-
feats.feats_filtering.
GeneFilter
(sc, min_cells, max_cells, expr_thres=0)¶ Filters the lowly and/or highly expressed genes stored in the SingleCell object based on the expression counts. Returns the sliced SingleCell object with the genes selected through the filtering criteria. This functions skips filtering and warns the user if the filtering criteria filters out all the genes.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
min_cells (int) – The minimum number of cells in which the gene must be expressed with the expression threshold. The value should be >= 0 and <= n, where n is the number of cells the the dataset.
max_cells (int) – The maximum number of cells in which the gene must be expressed with the expression threshold. The value should be >= 0 and <= n, where n is the number of cells the the dataset.
expr_thres (int, optional) – The expression threshold. Default 0. The gene is considered not expressed if the expression count is <= this threshold.
- Returns
sc – A sliced SingleCell object containing the filtered genes.
- Return type
SingleCell
-
feats.feats_filtering.
HVGFilter
(sc, num_genes, name_by='gene_names')¶ Filters/Selects the Highly Variable Genes in the SingleCell object by computing the dispersion of each gene. Returns a sliced SingleCell object containing the top Highly Variable Genes. Stores additional information such as dispersion (‘FEATS_dispersion), mean (‘FEATS_mean’) and variance (‘FEATS_variance’) in the genedata assay of SingleCell object.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
num_genes (int) – The number of Highly Variable Genes to select.
name_by (str) – The name of the column in SingleCell object that stores the gene names. This is used to print the top Highly Variable Genes.
- Returns
sc – A sliced SingleCell object containing the top Highly Variable Genes.
- Return type
SingleCell
-
feats.feats_filtering.
LogFilter
(sc)¶ Computes and stores the log-transformed gene expression data stored in SingleCell object. Here Log2 is performed after adding a pseudocount of 1.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
- Returns
sc – The SingleCell object containing the log-transfprmed gene expression data.
- Return type
SingleCell
feats.feats_gapstatistics module¶
-
feats.feats_gapstatistics.
ComputeWk
(X, labels, classes)¶ X - (d x n) data matrix, where n is samples and d is dimentionality lables - n dimentional vector which are class labels
-
feats.feats_gapstatistics.
GapStatistics
(sc_obj, k_max, B)¶ Computes the gap statistic and estimates the number of clusters in the gene expression dataset contained in SingleCell object.
- Parameters
sc_obj (SingleCell) – The SingleCell object containing gene expression data and the metadata.
k_max (int) – The upper limit of the number of clusters.
B (int) – The number of reference datasets to generate to compute the gap quantities.
- Returns
est_clusters (int) – The estimate of the number of clusters in the dataset.
Gap_k (list) – The gap statistic quantity for gap. The list contains gap values for each k, where k = 1, 2, …, k_max.
s_k (list) – The gap statistic quantity for standard deviation. The list contains the standard deviation for each k, where k = 1, 2, …, k_max.
W_k (list) – A gap statistic quantity for each k, where k = 1, 2, …, k_max.
w_bar (list) – A gap statistic quantity for each k, where k = 1, 2, …, k_max.
feats.feats_transformations module¶
-
feats.feats_transformations.
GramMatrix
(K)¶
-
feats.feats_transformations.
PCA
(sc, n_comp=1, dist_or_kernel='linear')¶ Computes and stores the Principal Components of the gene expression data stored in the SingleCell object.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
n_comp (int, optional) – The number of Principal Components to compute. Default 1.
dist_or_kernel (str, optional) – The distance metric or the kernel to compute. If a distance metric is passed, it computes the pairwise distance between the cells and then converts the distance metrics to kernels. If a kernel is passed, it computes the kernel. Valid values are ‘linear’ (default) for linear kernel, ‘spearmans’ for Spearmans distance, ‘euclidean’ for Euclidean distance and ‘pearsons’ for Pearsons distance.
- Returns
sc – The SingleCell object containing the dimensionnality reduced gene expression data. The reduced dimensionality is n_comp. The gene names are removed and the features in the reduced space are named ‘PC1’, ‘PC2’ and so on.
- Return type
SingleCell
-
feats.feats_transformations.
dist_to_kernel
(D)¶
-
feats.feats_transformations.
euclidean
(X)¶ X is a (n x d) matrix where rows are samples Returns D which is a (n x n) matrix of distances between samples
-
feats.feats_transformations.
linear_kernel
(X)¶ X is a (n x d) matrix where rows are samples Returns K which is a (n x n) kernel matrix
-
feats.feats_transformations.
pearsons
(X)¶ X is a (n x d) matrix where rows are samples Returns D which is a (n x n) matrix of distances between samples
-
feats.feats_transformations.
spearmans
(X)¶ X is a (n x d) matrix where rows are samples Returns D which is a (n x n) matrix of distances between samples
feats.feats_utils module¶
-
feats.feats_utils.
FeatureNormalize
(sc, norm)¶ Computes and stores the normalized gene expression data stored in SingleCell object.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
norm (str) – The normalization to perform. Accepted values are ‘l2’, ‘mean’, ‘norm6’ and ‘cosine’.
- Returns
sc – The SingleCell object containing the normalized gene expression data.
- Return type
SingleCell
Module contents¶
FEATS¶
FEATS is a new Python tool for performing the following downstream analysis on single-cell RNA-seq datasets: 1. Clustering 2. Estimating the number of clusters 3. Outlier detection 4. Batch correction and integration of data from multiple experiments
See https://github.com/edwinv87/feats for more information and documentation.
-
feats.
CellFilter
(sc, min_genes, max_genes, expr_thres=0)¶ Filters the cells in which the genes are lowly and/or highly expressed. Returns the sliced SingleCell object with the cells selected through the filtering criteria. This functions skips filtering and warns the user if the filtering criteria filters out all the samples/cells.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
min_genes (int) – The minimum number of genes in the cell in which the genes must be expressed with the expression threshold. The value should be >= 0 and <= d, where d is the number of genes the the dataset.
max_genes (int) – The maximum number of genes in the cell in which the gene must be expressed with the expression threshold. The value should be >= 0 and <= d, where d is the number of genes the the dataset.
expr_thres (int, optional) – The expression threshold. Default 0. The gene is considered not expressed if the expression count is <= this threshold.
- Returns
sc – A sliced SingleCell object containing the filtered cells.
- Return type
SingleCell
-
feats.
Cluster
(sc, k='gap', k_max=10, normalization='mean', q=None)¶ Clusters gene expression data stored in SingleCell object. Stores the cluster information (labels) in the celldata assay of the SingleCell object. The column name under which it stores the cluster information is ‘FEATS_k_Clusters’, where k is the number of clusters, which the method can estimate or is defined by the user as an int or an int list.
- Parameters
sc (SingleCell) – The SingleCell dataset containing gene expression data to be clustered.
k (int, list or str, optional) – This is an optional input for the number of clusters in the dataset. It is either an int, a Python list of ints or a str. If it is an int, the method will cluster the data into k groups. If it is a Python list, the method will cluster the data into the number of groups specified in the list and store the cluster information for all the values in the list. If it is the string ‘gap’, the method will use gap statistic to estimate the number of clusters and cluster the data into the number of groups estimated.
k_max (int, optional) – The upper limit of the number of clusters when using gap statistic to estimate the number of clusters. Ignored if k is not ‘gap’. Default 10.
normalization (str, optional) – The normalization method to use when normalizing the gene expression data. By default the normalized data is not saved in the SingleCell object after using the Cluster function. If you want to save the normalized counts in the SingleCell object, you can use the FeatureNormalize() function. Type help(FeatureNormalize) for more information. Options for the normalization are ‘mean’ for z-score normalization (default), ‘l2’ for L2 normalization, ‘cosine’ for Cosine normalization and None for no normalization.
q (list, optional) – This parameter is an int list which specifies the number of top features to select. The clustering algorithm selects each q_i top features in this list, perofrms clustering, computes the clustering score,and then determines the optimal number of features which give best clustering. By default q = [1, 2, …, 5% of d], where d is the number of features.
- Returns
SingleCell – The SingleCell object with the cluster information stored in the celldata assay.
int – The number of clusters in the dataset, k. This output is useful when k is not known and estimated by the algorithm.
- Raises
ValueError – If k_max is < 2. If k > n and < 2, where n is the number of samples in the dataset. If unknown input types/class is detected.
-
feats.
CorrectBatches
(batches, correct_order, k=20, sigma=10, svd_dim=0, adj_var=True)¶ Implements the Mutual Nearest Neighbour (MNN) approach to correcting batch effects in the integrated and merged datasets.
- Parameters
batches (SingleCell) – An integrated and merged SingleCell object (dataset) to correct. This dataset should have a ‘batch’ column in the celldata dataframe with batch information.
correct_order (list) – A Python list by the batch number or name representing the order in which to correct the batches. The first batch in the list is the reference batch.
k (int, optional) – The number of Mutual Nearest Neighbours to compute between the datasets to be corrected. default (20).
signa (float, optional) – A parameter controlling the width of the Gaussian kernel smoothing function.
svd_dim (int, optional) – The dimensionality of U when computing the SVD of data, where U, S, V^T = svd(X). default (0).
adj_var (bool, optional) – Whether or not to adjust the variance of the computed batch vectors. True (default) if adjust the variance, False if not.
- Returns
The corrected dataset.
- Return type
SingleCell
- Raises
ValueError – If the length of ‘correct_order’ is less than 2, and if no batch information is found in the input parameter ‘batches’.
-
feats.
DetectOutliers
(sc, cluster_label, red_dim=2, outlier_prob_thres=0.0001)¶ This function implements the outlier detection scheme of FEATS.
- Parameters
sc (SingleCell) – The SingleCell object which contains the data and metadata of genes and cells
cluster_label (str) – The name of the column in celldata assay of sc which stores the cluster labels of the cells
red_dim (int, optional) – The reduced dimentionality in which the outliers are computed. Default 2.
outlier_prob_thres (float) – The probability threshold for samples to be classified as outliers. Default 10^-4.
- Returns
The single cell object containing the outlier analysis information in the celldata assay. It contains the following columns in the celldata assay with the outlier information: ‘FEATS_Outliers’ - A column with the value True if the respective cell is an outlier, False otherwise. ‘FEATS_Msd’ - The computed Mahalanobis squared distance for the respective cells. ‘FEATS_Outlier_Score’ - The outlier score for the respective cells. ‘FEATS_Oos’ - A column with the value True if the respective cell was not used by the Minimum Covariance Determinant (MCD) algorithm in computing the robust mean and covariance matrix.
- Return type
SingleCell
-
feats.
FeatureNormalize
(sc, norm)¶ Computes and stores the normalized gene expression data stored in SingleCell object.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
norm (str) – The normalization to perform. Accepted values are ‘l2’, ‘mean’, ‘norm6’ and ‘cosine’.
- Returns
sc – The SingleCell object containing the normalized gene expression data.
- Return type
SingleCell
-
feats.
GapStatistics
(sc_obj, k_max, B)¶ Computes the gap statistic and estimates the number of clusters in the gene expression dataset contained in SingleCell object.
- Parameters
sc_obj (SingleCell) – The SingleCell object containing gene expression data and the metadata.
k_max (int) – The upper limit of the number of clusters.
B (int) – The number of reference datasets to generate to compute the gap quantities.
- Returns
est_clusters (int) – The estimate of the number of clusters in the dataset.
Gap_k (list) – The gap statistic quantity for gap. The list contains gap values for each k, where k = 1, 2, …, k_max.
s_k (list) – The gap statistic quantity for standard deviation. The list contains the standard deviation for each k, where k = 1, 2, …, k_max.
W_k (list) – A gap statistic quantity for each k, where k = 1, 2, …, k_max.
w_bar (list) – A gap statistic quantity for each k, where k = 1, 2, …, k_max.
-
feats.
GeneFilter
(sc, min_cells, max_cells, expr_thres=0)¶ Filters the lowly and/or highly expressed genes stored in the SingleCell object based on the expression counts. Returns the sliced SingleCell object with the genes selected through the filtering criteria. This functions skips filtering and warns the user if the filtering criteria filters out all the genes.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
min_cells (int) – The minimum number of cells in which the gene must be expressed with the expression threshold. The value should be >= 0 and <= n, where n is the number of cells the the dataset.
max_cells (int) – The maximum number of cells in which the gene must be expressed with the expression threshold. The value should be >= 0 and <= n, where n is the number of cells the the dataset.
expr_thres (int, optional) – The expression threshold. Default 0. The gene is considered not expressed if the expression count is <= this threshold.
- Returns
sc – A sliced SingleCell object containing the filtered genes.
- Return type
SingleCell
-
feats.
HVGFilter
(sc, num_genes, name_by='gene_names')¶ Filters/Selects the Highly Variable Genes in the SingleCell object by computing the dispersion of each gene. Returns a sliced SingleCell object containing the top Highly Variable Genes. Stores additional information such as dispersion (‘FEATS_dispersion), mean (‘FEATS_mean’) and variance (‘FEATS_variance’) in the genedata assay of SingleCell object.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
num_genes (int) – The number of Highly Variable Genes to select.
name_by (str) – The name of the column in SingleCell object that stores the gene names. This is used to print the top Highly Variable Genes.
- Returns
sc – A sliced SingleCell object containing the top Highly Variable Genes.
- Return type
SingleCell
-
feats.
IntegrateBatches
(batches, name_by)¶ Integrates a list of SingleCell objects storing single-cell data. This function first finds common genes across all the datasets in the list and then merges all the datasets together in one SingleCell object. If no common genes are found between the datasets, then an error is generated with a message.
- Parameters
batches (list) – A Python list of SingleCell objects (datasets) to integrate.
name_by (list) – A list of str representing column names in the SingleCell objects where gene names are stored. The order in this list is same as the order of SingleCell objects in the batches list.
- Returns
A merged dataset where the number of rows (d) is equal to the number of common genes in all the datasets and the number columns (n) is the sum of the number of cells in all the datasets.
- Return type
SingleCell
- Raises
ValueError – If no common genes are found between the SingleCell datasets in the batches list.
-
feats.
LogFilter
(sc)¶ Computes and stores the log-transformed gene expression data stored in SingleCell object. Here Log2 is performed after adding a pseudocount of 1.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
- Returns
sc – The SingleCell object containing the log-transfprmed gene expression data.
- Return type
SingleCell
-
feats.
MergeBatches
(batches)¶ Merges a list of SingleCell objects storing single-cell data. This function assumes that the datasets are already integrated and the number and type of genes in all the datasets are the same. If this is not the case, then an error is generated.
- Parameters
batches (list) – A Python list of SingleCell objects (datasets) to merge. It is assumed that the SingleCell datasets are already integrated.
- Returns
A merged dataset where the number of rows (d) is equal to the number genes the datasets (which is the same for all the datasets) and the number columns (n) is the sum of the number of cells in all the datasets.
- Return type
SingleCell
- Raises
ValueError – If the number and type of genes are not the same in all the datasets.
-
feats.
PCA
(sc, n_comp=1, dist_or_kernel='linear')¶ Computes and stores the Principal Components of the gene expression data stored in the SingleCell object.
- Parameters
sc (SingleCell) – The SingleCell object containing gene expression data.
n_comp (int, optional) – The number of Principal Components to compute. Default 1.
dist_or_kernel (str, optional) – The distance metric or the kernel to compute. If a distance metric is passed, it computes the pairwise distance between the cells and then converts the distance metrics to kernels. If a kernel is passed, it computes the kernel. Valid values are ‘linear’ (default) for linear kernel, ‘spearmans’ for Spearmans distance, ‘euclidean’ for Euclidean distance and ‘pearsons’ for Pearsons distance.
- Returns
sc – The SingleCell object containing the dimensionnality reduced gene expression data. The reduced dimensionality is n_comp. The gene names are removed and the features in the reduced space are named ‘PC1’, ‘PC2’ and so on.
- Return type
SingleCell


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 But police say he castrated himself.
It is not known why the man chose to mutilate himself."
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Bonkbuster: New word to learn in school
Independent Online | Submitted by: Tugboat
"She said that for someone like herself who regularly played with words it
was an easy step from blockbuster to bonkbuster after a publisher asked for
a 'big thick book with lots of bonking in it'". Thanks for explaining all that.
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Newest Japanese Fad
Mainichi Daily News | Submitted by: LucrezaBorgia
"Filled with growing frustration at the enormity of growing up, girls see
the act of drawing a razor blade or knife across their wrists as an effective
stress reliever, particularly as they make sure they don't cut deep
enough to commit suicide."
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Moms need meth too
ABC News | Submitted by: kade
"When they begin to use methamphetamine, they feel more energy, they feel
more mastery, they feel like they can get it all done," Murphy said. "They
can take care of their kids, they can do their job, sometimes two jobs. They
can meet what is for many women today, an almost impossible ideal."
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Cop hid lover's body in wall
Canoe | Submitted by: anonymous
"A Toronto divisional traffic cop lived alone with the body of his murdered
former lover hidden in the house for more than four months, York police
allege... Toronto Police Chief Julian Fantino expressed his deepest regrets
over Mariani's murder, but said his force's image won't be tarnished by the
alleged actions of one officer."
 Said a report, "This is the first time, to our knowledge, that a
bacterial species associated with the human gut has been shown to be a
marine invertebrate pathogen."
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Aggies in bondage
Houston Chronicle | Submitted by: george_lazenby
"Texas A&M University police are investigating about a dozen photos found at
the yearbook office that show students naked, gagged and bound with duct tape."
The students are believed to be band members and the photos appear to be the
result of hazing.
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Handprints and Numerology Keep Osama Alive
jang.com.pk | Submitted by: pork_for_islam
"The renowned numerologist, Khalid Mustafa, is a 36-year-old Islamabad-based corporate lawyer by profession. His deep interest in numerology and palmistry has taken him close to some of the internationally known experts in the field. Despite his deep study and keen interest, he does not use his art for earning his livelihood." At least he has ethics. "Another interesting feature of Osama is the number of letters in his name which total 13, a number majority of the Americans do not like." Yadda yadda yadda...
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Tennessee lawmaker exposes self to children
WTVF-TV 5 Nashville | Submitted by: george_lazenby
Republican Tennessee state representative Keith Westmoreland, who was
arrested last week in Florida for allegedly exposing himself at a crowded
hotel pool, has been investigated for this type of crime before. Nashville
Metro Police responded to a call from the Days Inn near the Capitol this
past March. In that case, guests had complained that Westmoreland
had exposed himself.
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Perfect career is a scalpel snip away
Canberra Times | Submitted by: Kirsty
"Chinese desperate to improve their English to boost their career prospects
were turning to unnecessary operations on their tongues, a doctor said today...
All the patients had normal tongues and their requests were refused."
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Whopper Stopper
News Corporation | Submitted by: clove
"Anaesthetist Dawn Cullen confirmed that in one of his temper tantrums, Dr Peter Bayliss had left a patient tied to a bed with a sanitary pad stuffed in her mouth."
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Mad cow gives police run-around
online.ie | Submitted by: KC
"Police were involved in a three-hour pursuit yesterday after a daring
escape brought mayhem to a section of the main Cork-Dublin Road. The fugitive,
who broke out from a supposedly secure area, tried to ram patrol cars, managed
to out-run police and a posse of local people, was eventually shot."
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Devil's work claims diocese dunny
News Corporation | Submitted by: worldwadeweb
A pun-laden parable about the theft of an outdoor dunny [Aussie slang for
"potty"]: "The old fashioned thunderbox at Kialla West Uniting Church, near
Shepparton, was lifted from its foundations and taken away on a small truck
or ute [Aussie slang for "utility vehicle"] sometime before Sunday
morning's service."
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ABC outrages viewers again
NewsMax | Submitted by: Incubus
"A fictional TV executive begs her to give the baby as a gift to a vain star.
Urich tells a friend who holds the baby, 'you break her, you bought her.' And
when one woman muses that the baby looks cute enough to eat, a man says he
doesn't eat Chinese babies 'because a half hour later I'm hungry and have to
eat another.'"
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Darwinism in Queens
Newsday | Submitted by: Waingro
"Two Queens teenagers who decided to take a joyride on the roof of a Ford
Explorer were injured Monday morning in a Jones Beach parking lot after their
friend drove the vehicle at a high speed and swerved, throwing the two boys
off of the roof and sending the vehicle into a 360-degree roll."
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Dog-sitter not clear on the concept
BBC | Submitted by: Son of Benway
"A 26-year-old man who shot his friend's dog 19 times with airgun when he was supposed to be looking after him, has been jailed for five months...In what magistrates described as a "despicable act only imaginable in nightmares", 13 air pellets were found in his body, two in his neck and four more in his body. The dog had also been stabbed." With x-ray photo!
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Car towed to morgue with victims still inside
myCFnow | Submitted by: Rio
"A sport utility vehicle cut off a Chrysler in the left express lane. To
avoid getting hit, the driver of the Chrysler cut sharply to the left and
plowed into the concrete center divider. The car bounced off the divider,
careened to the right and into the local lanes, where it collided with
a Dodge and flipped."
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The idiot savant's Illuminati
Village Voice | Submitted by: Dianalily
"The underpinnings of [Bush's] new America rest in the hands
of a fairly small group of conservative lawyers in
Washington. There is nothing sinister about them, per se,
but they frame the arguments and devise the legal mechanisms
that, for example, allow the president to make war against
Iraq without any meaningful consent from Congress. These
intense, smart ideologues hail from the right-wing
revolutionary movement, which believes it's past time to
take America back from the crummy, weak-kneed liberal
elites."
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Cell phones may be hazardous
BBC | Submitted by: Phatlor
"Scientists from the Radiation and Nuclear Safety Authority found that
exposing human cells to mobile phone radiation damaged the blood-brain barrier,
a safety barrier in the body that stops harmful substances in blood from
entering the brain."
We are rather skeptical of this, though Andy Dick thinks otherwise.
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EPA says toxic sludge is good for fish
Washington Times | Submitted by: george_lazenby
"The Army Corps of Engineers' dumping of toxic sludge into the Potomac
River protects fish by forcing them to flee the polluted area and escape
fishermen, according to an internal Environmental Protection Agency
document." Life under the Bush White House.
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Wal-Mart ham causes seizures
Naples Daily News | Submitted by: anonymous
"A woman who said she has suffered epileptic seizures since a 13-pound ham
fell on her head at a Wal-Mart lost a $500,000 lawsuit Tuesday... Wal-Mart's
attorney, Steven Sundook, said that Vasquez's description of the accident
defied the laws of gravity and physics."
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Fatties to pay twice
USA Today | Submitted by: MM
"Passengers whose love handles cannot fit between the arm rests of Southwest's seats will be charged double for flying on the low cost-carrier, the company said Wednesday."
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Trucker ran over waitress, dragged her
AZ Central | Submitted by: tjd
"Her jaw was broken and one of her teeth was found near her body.
During the trial, the jury took a field trip to where her body was found.
It was the same spot where Mackay had urinated when police took him out to
look for the body a few days before it was found, according to
RCMP Cpl. Kirby Buckingham. Court also heard that Mackay's toilet clogged up
and flooded his bathroom after he tried to flush Paskemin's red cowboy hat."
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