public interface PathHandler
PathParser
instance in order to be
notified of parsing events.| Modifier and Type | Method and Description |
|---|---|
void |
arcAbs(float rx,
float ry,
float xAxisRotation,
boolean largeArcFlag,
boolean sweepFlag,
float x,
float y)
Invoked when an absolute elliptical arc command has been parsed.
|
void |
arcRel(float rx,
float ry,
float xAxisRotation,
boolean largeArcFlag,
boolean sweepFlag,
float x,
float y)
Invoked when a relative elliptical arc command has been parsed.
|
void |
closePath()
Invoked when a closepath has been parsed.
|
void |
curvetoCubicAbs(float x1,
float y1,
float x2,
float y2,
float x,
float y)
Invoked when an absolute cubic bezier curve command has been parsed.
|
void |
curvetoCubicRel(float x1,
float y1,
float x2,
float y2,
float x,
float y)
Invoked when a relative cubic bezier curve command has been parsed.
|
void |
curvetoCubicSmoothAbs(float x2,
float y2,
float x,
float y)
Invoked when an absolute smooth cubic bezier curve command has
been parsed.
|
void |
curvetoCubicSmoothRel(float x2,
float y2,
float x,
float y)
Invoked when a relative smooth cubic bezier curve command has
been parsed.
|
void |
curvetoQuadraticAbs(float x1,
float y1,
float x,
float y)
Invoked when an absolute quadratic bezier curve command has been parsed.
|
void |
curvetoQuadraticRel(float x1,
float y1,
float x,
float y)
Invoked when a relative quadratic bezier curve command has been parsed.
|
void |
curvetoQuadraticSmoothAbs(float x,
float y)
Invoked when an absolute smooth quadratic bezier curve command
has been parsed.
|
void |
curvetoQuadraticSmoothRel(float x,
float y)
Invoked when a relative smooth quadratic bezier curve command
has been parsed.
|
void |
endPath()
Invoked when the path ends.
|
void |
linetoAbs(float x,
float y)
Invoked when an absolute line command has been parsed.
|
void |
linetoHorizontalAbs(float x)
Invoked when an horizontal absolute line command has been parsed.
|
void |
linetoHorizontalRel(float x)
Invoked when an horizontal relative line command has been parsed.
|
void |
linetoRel(float x,
float y)
Invoked when a relative line command has been parsed.
|
void |
linetoVerticalAbs(float y)
Invoked when a vertical absolute line command has been parsed.
|
void |
linetoVerticalRel(float y)
Invoked when a vertical relative line command has been parsed.
|
void |
movetoAbs(float x,
float y)
Invoked when an absolute moveto command has been parsed.
|
void |
movetoRel(float x,
float y)
Invoked when a relative moveto command has been parsed.
|
void |
startPath()
Invoked when the path starts.
|
void startPath() throws ParseException
ParseException
- if an error occured while processing the pathvoid endPath() throws ParseException
ParseException
- if an error occured while processing the pathvoid movetoRel(float x, float y) throws ParseException
Command : m
x
- the relative x coordinate for the end pointy
- the relative y coordinate for the end pointParseException
- if an error occured while processing the pathvoid movetoAbs(float x, float y) throws ParseException
Command : M
x
- the absolute x coordinate for the end pointy
- the absolute y coordinate for the end pointParseException
- if an error occured while processing the pathvoid closePath() throws ParseException
Command : z | Z
ParseException
- if an error occured while processing the pathvoid linetoRel(float x, float y) throws ParseException
Command : l
x
- the relative x coordinates for the end pointy
- the relative y coordinates for the end pointParseException
- if an error occured while processing the pathvoid linetoAbs(float x, float y) throws ParseException
Command : L
x
- the absolute x coordinate for the end pointy
- the absolute y coordinate for the end pointParseException
- if an error occured while processing the pathvoid linetoHorizontalRel(float x) throws ParseException
Command : h
x
- the relative X coordinate of the end pointParseException
- if an error occured while processing the pathvoid linetoHorizontalAbs(float x) throws ParseException
Command : H
x
- the absolute X coordinate of the end pointParseException
- if an error occured while processing the pathvoid linetoVerticalRel(float y) throws ParseException
Command : v
y
- the relative Y coordinate of the end pointParseException
- if an error occured while processing the pathvoid linetoVerticalAbs(float y) throws ParseException
Command : V
y
- the absolute Y coordinate of the end pointParseException
- if an error occured while processing the pathvoid curvetoCubicRel(float x1, float y1, float x2, float y2, float x, float y) throws ParseException
Command : c
x1
- the relative x coordinate for the first control pointy1
- the relative y coordinate for the first control pointx2
- the relative x coordinate for the second control pointy2
- the relative y coordinate for the second control pointx
- the relative x coordinate for the end pointy
- the relative y coordinate for the end pointParseException
- if an error occured while processing the pathvoid curvetoCubicAbs(float x1, float y1, float x2, float y2, float x, float y) throws ParseException
Command : C
x1
- the absolute x coordinate for the first control pointy1
- the absolute y coordinate for the first control pointx2
- the absolute x coordinate for the second control pointy2
- the absolute y coordinate for the second control pointx
- the absolute x coordinate for the end pointy
- the absolute y coordinate for the end pointParseException
- if an error occured while processing the pathvoid curvetoCubicSmoothRel(float x2, float y2, float x, float y) throws ParseException
Command : s
x2
- the relative x coordinate for the second control pointy2
- the relative y coordinate for the second control pointx
- the relative x coordinate for the end pointy
- the relative y coordinate for the end pointParseException
- if an error occured while processing the pathvoid curvetoCubicSmoothAbs(float x2, float y2, float x, float y) throws ParseException
Command : S
x2
- the absolute x coordinate for the second control pointy2
- the absolute y coordinate for the second control pointx
- the absolute x coordinate for the end pointy
- the absolute y coordinate for the end pointParseException
- if an error occured while processing the pathvoid curvetoQuadraticRel(float x1, float y1, float x, float y) throws ParseException
Command : q
x1
- the relative x coordinate for the control pointy1
- the relative y coordinate for the control pointx
- the relative x coordinate for the end pointy
- the relative x coordinate for the end pointParseException
- if an error occured while processing the pathvoid curvetoQuadraticAbs(float x1, float y1, float x, float y) throws ParseException
Command : Q
x1
- the absolute x coordinate for the control pointy1
- the absolute y coordinate for the control pointx
- the absolute x coordinate for the end pointy
- the absolute x coordinate for the end pointParseException
- if an error occured while processing the pathvoid curvetoQuadraticSmoothRel(float x, float y) throws ParseException
Command : t
x
- the relative x coordinate for the end pointy
- the relative y coordinate for the end pointParseException
- if an error occured while processing the pathvoid curvetoQuadraticSmoothAbs(float x, float y) throws ParseException
Command : T
x
- the absolute x coordinate for the end pointy
- the absolute y coordinate for the end pointParseException
- if an error occured while processing the pathvoid arcRel(float rx, float ry, float xAxisRotation, boolean largeArcFlag, boolean sweepFlag, float x, float y) throws ParseException
Command : a
rx
- the X axis radius for the ellipsery
- the Y axis radius for the ellipsexAxisRotation
- the rotation angle in degrees for the ellipse's
X-axis relative to the X-axislargeArcFlag
- the value of the large-arc-flagsweepFlag
- the value of the sweep-flagx
- the relative x coordinate for the end pointy
- the relative y coordinate for the end pointParseException
- if an error occured while processing the pathvoid arcAbs(float rx, float ry, float xAxisRotation, boolean largeArcFlag, boolean sweepFlag, float x, float y) throws ParseException
Command : A
rx
- the X axis radius for the ellipsery
- the Y axis radius for the ellipsexAxisRotation
- the rotation angle in degrees for the ellipse's
X-axis relative to the X-axislargeArcFlag
- the value of the large-arc-flagsweepFlag
- the value of the sweep-flagx
- the absolute x coordinate for the end pointy
- the absolute y coordinate for the end pointParseException
- if an error occured while processing the pathCopyright (c) 2010 Gaudenz Alder. All rights reserved.


COVID-19 Vaccine Distribution Model
Imagine, it’s December 2020. There are long winded queues in
front of the pharmacies and vaccine distribution centres. The
Australian government only has a very limited number of
vaccines. The population is desperate to return to normality and
the vaccine is the only key to achieving this. For the
government to reach the minimum requirement herd immunity needs
to be reached. With a population of 25 million and barely 15
million vaccines. How will the government succeed in effectively
distributing the vaccines?
Fortunately, a small team called the Hacker-Knights come to the
rescue.
A novel way of achieving herd immunity and a simple prediction
tool with intuitive visualisation filters, that will assist the
government in taking control of COVID-19 in the most efficient
manner.
The idea is to achieve below outcomes with the government data:
- Forecast for the covid impact for future months with the existing covid factors (e.g: cases on daily basis, confirm, recovered, deaths and infection rate etc.)
- Predict the minimum number of vaccines required to achieve herd immunity of a particular region. We considered R-nought, a variable factor, as 2.2 (average according to available sources) hypothetically for the limitation of data and time constraint.
- Find out the optimal way of distributing available vaccines to the most affected regions based on multiple factors like business impacts, available distributed channels, social, economic conditions and also to achieve herd immunity across the region as fast as possible.
Data Story
The datasets used come from various sources including Australian
Bureau of Statistics, Github open source data and Australia
Population (2020) Worldometer. One of the major challenges has
been to extract precisely the most relevant data by combining
different unstructured datasets into a single effective
aggregated dataset that has been normalised. Having the monthly
statewise covid cases, deaths and population we extrapolated the
number of vaccines needed for each region and improved the
prediction efficiency using factors such as employment and
unemployment. An interactive visualisation web dashboard(shown
in the video) has been built for users.
This model takes into consideration covid cases that have been
confirmed and deaths per state over a time period to calculate
the R naught value. These cases have been forecasted upto march
21 using multiple linear regression.
After calculating R naught found to be 2.5 on average. We
calculated the number of vaccines needed to reach herd immunity
for each state according to its population. Formula for Herd
immunity: 1/(1-Rnaught) We have run this algorithm to our
forecasted range of Mar 21.
If we get limited number of vaccines, the algorithm calculates
the optimal distribution of vaccines to take smaller steps
towards herd immunity (no more a pandemic).
Future implementation could use more features(density, social
distance, house rents, food habits) to train a Neural Network
model for the weighted and variability factor to achieve the
real time R-naught more accurately to eradicate COVID-19.
Team
Knights who served for the hack
Nazmul
Naga
Mahesh
Afrida
Quang
Jan
Team DataSets
Datasets that were used
Frequently Asked Questions
For NSW the map displays 4,934,944 vaccines are needed, but how to achieve herd immunity, when we have 4 million vaccines only for the whole of Australia now?
According to the map NSW would be able to achieve herd immunity over cumulative months initially requiring 1,421,249 vaccines(See label with “number of vaccines available”). This helps the Government to rapidly detect the area to respond in an emergency.
We’d like to implement the tool outside of Australia. Whom to contact?
Hackerknights would be extremely happy to assist you. Please contact Nazmul (team leader) through LinkedIn for any questions regarding or expanding the project.
How accurate is Covidvaccineready’s prediction?
Currently its as accurate as any machine learning forecast but the accuracy can be strictly measured once we have the real number of covid cases until March 2021.


Landing Page
Special Projects
This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.
Baxter Turain
11.06.17
Athens Township Trustee Steven H. Pierson is running for re-election in the Nov. 7 election.
In March, Pierson retired from his position as the assistant city manager of Nelsonville, where he previously served as code enforcement director. He also worked as the director of code enforcement for 14 years for the City of Athens.
Pierson has also been a member of the Athens County Regional Planning Commision for 21 years, and a member of Carpenters Local 200 in Columbus for 37 years.
“I moved here from the Sugar Grove area and started working with the city in 1994,” Pierson said. “In the area that I lived in ... I served as a planning commissioner and a board of zone appeals member. That was the experience that I had when (Athens) hired me 23 years ago.”
Pierson serves as the secretary of the Athens County Regional Planning Commission. He also serves as the secretary of the Athens Civitan Club and chairs the Board of Directors for the United Seniors of Athens County.
“If you have the time and the contacts to fund programs like this, that is where my experience lends itself well in the trustee position.”– Steven H. Pierson
“I have been involved with quite a few different things,” Pierson said. “I am a founding member of the Ohio Floodplain Managers Association. It originated from the group Water Management Association of Ohio. All of us who were doing floodplain administration had little in common with sewer operators and water operators, so we formed our own organization.”
Pierson is passionate about issues regarding flooding, public works and city maintenance. He said the Athens County Commissioners will soon enter an agreement with the City of Athens to construct a $20 million sewer project to serve approximately 1,100 residents in Athens Township.
“One of the main charges that township trustees have is to maintain township roads, and a lot of the work that is going to be done to install this sewer system will involve disturbance of the road surfaces to place sewer pipes,” Pierson said. “That’s a big concern that the trustees have over the next few years.”
About 75 percent of the Athens Township budget is based on property taxes, Pierson said. He is concerned about protecting the health and safety of residents in the Athens Township area and aiding in improvements of disposal of sewage.
“We get about 1.6 percent of everyone’s property tax that they pay,” Pierson said. “We get that in two disbursements from the county auditor, Jill Thompson, and that represents 75 percent of our total budget. ... I also want to make sure that there is potential to grow because the large part of our revenue is based on property tax.”
Pierson said his past experiences working with grants has helped him with his position as township trustee, dealing with “quality of life” questions on a daily basis.
“If you have the time and the contacts to fund programs like this, that is where my experience lends itself well in the trustee position,” Pierson said. “In the past, I’ve worked with Athens County Commissioners. It was a federal grant passed down to the state, and then distributed to each county.”
That grant funded a program known as Moving Ohio Forward, which acquires “distressed properties” for demolition. Since his time in office began, Pierson has worked with the program to demolish three houses.
Nelsonville City Manager Charles Barga has worked with Pierson for two months since he began working with the city in March.
“You’re not going to find anymore more knowledgeable about regulations regarding code enforcement, floodplain management or zoning,” Barga said. “He’s a wealth of knowledge in those areas. It was very helpful while we worked together. I still reach out to him occasionally for something I just can’t find the answers to; he’s always been very helpful with me.”
Barga said Pierson is passionate about government and is brings his passion to the position of being a trustee.
“When it comes to local (operations), and how they operate, and how they effectively get things done, he’s just a wealth of knowledge,” Barga said. “He’s just a good person.”
Pierson is one of three candidates running for Athens township trustee. The other candidates are Eddie Smith and Brian Baker.
Landing Page
This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.


DarkWiiPlayer/CSS is a collection of plain CSS files that can
easily be loaded from a CDN or served directly without any
compilation or pre-processing steps.
Import from CDN
@import
<EMAIL_ADDRESS>layer(framework);
@import
<EMAIL_ADDRESS>layer(theme);
Or serve the files yourself from a git submodule
Clone into your project
git submodule add git+https://github.com/darkwiiplayer/css /path/to/styles
Warning
Layers
The all.css
file makes use of import statements
with layers, which have less than ideal support in some CSS
preprocessors. If something is broken, check that any bundled
CSS is actually importing the files correctly.
Badges are a generic way of adding short (usually one-word) tags to the document.
They are colourful and attract attention, and should therefore be used carefully and sparingly.
Use them to display informationt hat might be of priority to a user.
Usage examples
Adding entity counts
Users 315
Listing short tags
An Article about CSS
Tags:
#Beginner
#Tutorial
#CSS
The .blob
utility class makes an element blob-shaped and fixes is aspect-ratio to 1 / 1
.
There are 9 different blob-shapes which will be picked based on the element's child count inside its parent element.
To manually set the shape of a blob, the classes .blob-1 through .blob-9 are available.
The CSS file also exposes the --blob-1 through --blob-9 custom properties which can be used in an elements border-radius property to make it blob-shaped.
Boxes are the main generic container element.
By default they appear inset into the document.
This can be changed by also adding the floating class which will elevate the box from the document.
Boxes can also have a coloured bar at their right edge to make them more attention seeking and help distinguish them vertically from their surrounding content.
Boxes also take their corner radius from the --corner-radius variable.
Content in a box
Content in a box
Drawer elements are used to hide content from the page like a navigation menu.
Open
Click anywhere outside the drawer to cose it again.
TODO
Script-controller drawers are not currently supported.
The floating-action-button
custom element can be used to position an element in one of the corners of its parent element.
The button itself will still have to be styled separately though.
Content in a box, button in the bottom right.
The page-hero
custom element is a large, full-width box at the beginning of the page.
Items in the hero section are arranged as a centered column.
Items are centered vertically and horizontally.
A single image that's the first child element of the hero section will be displayed as a background image to the box.
The minimum height of the page hero can be adjusted using the cover attribute,
which currently supports values in % of screen height from 40 to 100 in steps of 10.
Developers should generally aim to keep content within this range and avoid growing the hero beyond its intended size.
A special rule makes full-height hero elements preceded by a header shorter by the value of --header-height to ensure that both elements combined will cover the sreen entirely.
Please note that this element only provides a good baseline for a generic hero section.
It does not cover (nor could it) any more elaborate design, but should still provide a good foundation to add more interesting styling on top of.
Ordered
- First list item
- Second item on the list
- Third thing being listed
Unordered
- First list item
- Second item on the list
- Third thing being listed
Warning
Until native CSS scoping becomes available in browsers, it is near-impossible to reliably style nested navigations without writing insanely unmaintainable code.
As soon as this feature becomes available and somewhat widely supported, most of this framework will be updated to use it, but until then, nested nav elements might behave a bit weird.
Navigation breadcrumbs provide a way for displaying the users current location in a document hierarchy.
Navigation bars are horizontally arranged navigation items usually put in a header.
Vertical and Horizontal spacing can be added where needed using the vertical-spacer
and horizontal-spacer
custom elements.
Adding the attributes double
, triple
and quadruple
increases the spacing.
Basic HTML tables work out of the box.
| Greeting | Name |
| Hello | World |
| Greetings | Internet |
Vertical tables also work
| Name | Talia |
| Hobbies | Writing CSS |
Lines can be added with the borders
attribute:
-
horizontal
adds horizontal ilnes
-
vertical
adds vertical ilnes
-
outline
adds a border around the table
When the attribute is missing entirely, horizontal borders will be used.
Tables without horizontal lines will have every second row slightly lightened/darkened for easier reading.
This can be enabled explicitly by setting the alternate
attribute to rows
and disabled by setting it to anything else (including the empty string).
| Greeting | Name |
| Hello | World |
| Greetings | Internet |
| Greeting | Name |
| Hello | World |
| Greetings | Internet |
| Greeting | Name |
| Hello | World |
| Greetings | Internet |
| Greeting | Name |
| Hello | World |
| Greetings | Internet |
Many other common HTML elements have received smaller styling changes.
Summary of the subject
Further explanation on the subject.
This is a paragraph with an inline quote
.
As a very dumb person once said:
This is a longer quote with a citation at the bottom.
Me, just now


Jonathan Ohms
Democrat
Software Engineer - MetaRouter
Bachelors - Southern Illinois University Edwardsvill - Computer Engineering Masters - Denver University - Mechatronics Systems Engineer
80211
Donated to Bernie Sander's campaign in 2016, other than that mostly just paying attention to what is going on. Looking to have more impact by working with the redistricting.
FoodBank of the Rockies
I understand the concepts of gerrymandering and a large interest in participating in this commission is to avoid that from happening. Our state has some very diverse communities and I want to make sure that everyone's voice is heard. I love the idea at looking over a map, understanding the political, racial, financial, and religious differences of everyone in the state and getting them representation. Aside from donating to BLM I have not participated in these types of organizations.
Everyone counts! Everyone in our state matters, whether or not they have the same opinions as I do. I would like to believe that the entire point of this commission is to be fair and impartial to assigning districts. I believe that I can be fair and help bring others together. Aside from my current job, I would have plenty of time to focus on this commission and believe I could be a great asset.
I have experience with leading teams of software engineers and have grown greatly over the years in doing so. There are hundreds of ways to build software and everyone will do it differently. I've used many different techniques to gain agreement from others, whether it is simple pro/con lists, or a lot of times just walking through a problem step by step on how each person views it so that we all can better understand where they are coming from.  I once served on a 6 or 7 person jury, largely older men, in Denver that was a civil lawsuit for someone that got into a car accident and was suing the insurance company. When we went into deliberations they elected me as a foreman. I started off by going around the room and asking where everyone stood. Three of them were on the plaintiff's side, I was on on the defendant's, and the others were undecided. I spent time just listening to everyone's arguments, and we looked through all of the documentation. The man had pre-existing conditions in his sacrum region. We drew a timeline on the whiteboard of all of his shots he received for pain, when the accident was, how long the shots lasted, etc. We worked out as a team that it was over a month after his accident that he started feeling pain, this also coincided with the same duration of time when his shot would normally wear out. In the end, we all sided with the defendant.
Aside from the story from the last question, my career requires me to think through problems and analyze data. I work with SQL databases, both architectural design and querying them for relevant data. This also includes geospatial and geofencing type of data. I also architect and build end user systems that require a lot of thought on simplifying complex problems. I have a large amount of experience in "big data" tools such as streaming services like Kafka or NATS and data warehouse technologies like Snowflake and Google BigQuery. I could give you several examples and stories where I had to solve tough problems, such as on some Android phones when a user would take 1 picture with our application it would work fine. However if the user took more than 1 picture it would crash the app. Also debugging wouldn't work over the USB. It took some time but I guessed that we were trying to allocate memory for uploading the pictures at the same time and would cause the application to run out of memory. Sure enough, by using the same memory location for each upload it would work fine (but slower).
male


---
sort_order: 16
tei_id: rdx_b5h9g.p.idm192337104
annotation_count: 0
images:
small-thumbnail: https://readux.library.emory.edu/books/emory:b5h9g/pages/emory:pchr1/mini-thumbnail/
json: https://readux.library.emory.edu/books/emory:b5h9g/pages/emory:pchr1/info/
full: https://readux.library.emory.edu/books/emory:b5h9g/pages/emory:pchr1/fs/
page: https://readux.library.emory.edu/books/emory:b5h9g/pages/emory:pchr1/single-page/
thumbnail: https://readux.library.emory.edu/books/emory:b5h9g/pages/emory:pchr1/thumbnail/
title: Page 16
number: 16
---
{% raw %}9{% endraw %}
{% raw %}JACDB HODGES.{% endraw %}
{% raw %}who had already attained to ihe age of{% endraw %}
{% raw %}seventy years. By a process of tedious{% endraw %}
{% raw %}and vexatious law-suits,he had come into{% endraw %}
{% raw %}possession of property, by which another{% endraw %}
{% raw %}family was greatly disappointed and com¬{% endraw %}
{% raw %}pletely impoverished. Hostility, which{% endraw %}
{% raw %}had long existed between the parties, be{% endraw %}
{% raw %}came every day more and more violent{% endraw %}
{% raw %}Revenge was constantly meditated and{% endraw %}
{% raw %}threatened. At length, the malignity of{% endraw %}
{% raw %}the aggrieved party was so exasperated{% endraw %}
{% raw %}that actual violence and bloodshed were{% endraw %}
{% raw %}not only meditated, but planned for{% endraw %}
{% raw %}speedy execution. The old man of{% endraw %}
{% raw %}seventy years was selected as the victim.{% endraw %}
{% raw %}The individual whose pecuniary in¬{% endraw %}
{% raw %}terests he had injured secured two accom¬{% endraw %}
{% raw %}plices, who, from sharing in his feelings{% endraw %}
{% raw %}of revenge, or from promises of pecuniary{% endraw %}
{% raw %}reward, entered into his purposes of mur¬{% endraw %}
{% raw %}der. One of these was the poor, ignorant{% endraw %}
{% raw %}Jacob Hodges. He was selected as the{% endraw %}
{% raw %}immediate instrument of the fatal deed;{% endraw %}
{% raw %}as if murder, though perpetrated by othei{% endraw %}
{% raw %}hands, would secure them from the ven{% endraw %}


{% extends "base.html" %} {% block content_wrapper %}
Converting text to snowman morse code has never been easier. And it just got easier!
$ pip install snorse
$ snorse "abc"
☃ ⛄⛄⛄ ⛄⛄⛄ ☃ ☃ ☃ ⛄⛄⛄ ☃ ⛄⛄⛄ ☃
$ desnorse "☃ ⛄⛄⛄ ⛄⛄⛄ ☃ ☃ ☃ ⛄⛄⛄ ☃ ⛄⛄⛄ ☃"
abc
>>> import snorse
>>> print snorse.snorse("abc")
☃ ⛄⛄⛄ ⛄⛄⛄ ☃ ☃ ☃ ⛄⛄⛄ ☃ ⛄⛄⛄ ☃
>>> print snorse.desnorse("☃ ⛄⛄⛄ ⛄⛄⛄ ☃ ☃ ☃ ⛄⛄⛄ ☃ ⛄⛄⛄ ☃")
abc
With Snorse as a Service you get the convenience of snorse without none of the setup or configuration hassle. Snorsing has never been easier.
$ CURL -X POST "http://www.snorse-cloud.com/api/v1/snorse/" -d "abc"
☃ ⛄⛄⛄ ⛄⛄⛄ ☃ ☃ ☃ ⛄⛄⛄ ☃ ⛄⛄⛄ ☃
$ CURL -X POST "http://www.snorse-cloud.com/api/v1/desnorse/" -d "☃ ⛄⛄⛄ ⛄⛄⛄ ☃ ☃ ☃ ⛄⛄⛄ ☃ ⛄⛄⛄ ☃"
abc
>>> import requests
>>> response = requests.post("http://www.snorse-cloud.com/api/v1/snorse/", 'abc')
>>> print response.content
☃ ⛄⛄⛄ ⛄⛄⛄ ☃ ☃ ☃ ⛄⛄⛄ ☃ ⛄⛄⛄ ☃
>>> print requests.post("http://www.snorse-cloud.com/api/v1/desnorse/", response.content)
abc
POST http://www.snorse-cloud.com/api/v1/snorse/
Returns the body of the post request converted to unicode snowman morse code.
POST http://www.snorse-cloud.com/api/v1/desnorse/
Returns the body of the post request converted from unicode snowman morse code back to plain text.


Simulator launcher
Simulation Set up
100% - accuracy = [%]
Class Number Error
Simulator:
JMT
GreatSPN
Sim. duration [s] (min 60s)
Class Number Error
Number of repetitions:
Class Number Error
Min number of users:
Min Number of Users Error
Max number of users:
Max Number of Users Error
Increasing users with step:
Class Number Error
Min number of VMs:
Min Number of VMs Error
Max number of VMs:
Max Number of VMs Error
Increasing #VMs with step:
Class Number Error
Think Time[ms]:
Reduce Time Error
Submit
Executing simulations...


Requirements
The package does not depend on any other package.
No particular requirements for the Tcl/Tk interpreter exist.
The clTcl package is currently ported to CLISP, Lucid, Allegro, SBCL, CMU, Clozure CL and LispWorks, and should be easily ported to other Common Lisp implementations as well. The only implementation dependent part is starting the interpreter and setting up a two way stream to it. See function open-Tcl/Tk-stream for details.
Installing clTcl
You have to install the clTcl package, get a Tcl/Tk interpreter and tell the clTcl package were the interpreter is located. On a lot of systems the Wish interpreter is already installed in /usr/bin/wish but even in that case you might want to choose a different one.
You can download the package from GitHub. It contains all sources, an ASDF system definition file and an alternative system definition file that works for example for LispWorks. Just extract the sources from the archive and load the system in your own prefered way.
Connecting to an interpreter
Use keyword :interpreter to connect to an interpreter. For example
(cltcl:event-loop
#TCL[button .b -text Exit -command exit
pack .b]
:interpreter "/home/paul/.my-runtimes/tclkit85-linux-x86")
or change the default interpreter by setting *interpreter*
(setf cltcl:*interpreter* "/home/paul/.my-runtimes/tclkit85-linux-x86")
Argument :interpreter eventually gets passed to open-Tcl/Tk-stream. The exact meaning of this keyword is implementation dependent but the general idea is that :interpreter points to the executable of the interpreter. Keyword :options is available for command line options.
The default value for the :interpreter keyword is *interpreter* and its default value is /usr/bin/wish.
Choosing a Tcl/Tk interpreter
Tcl/Tk is available for many systems in many forms. Visit the Tcl Developer Xchange software page for an overview. Popular binaries are ActiveState’s ActiveTcl distribution.
System Install
A Tcl/Tk interpreter can be installed system wide. On Unixy systems it
is often installed. Type wish
on the command line to test if it is
available.
If a Wish (or any other) interpreter is pre-installed you can simply connect to it as explained in the previous section. If it installed as /usr/bin/wish, which is clTcl’s default, it even works out of the box.
If Wish is not pre-installed you can download ActiveTcl. You can for example install Wish 8.5 for Windows and connect to it with :interpreter “wish85”
Standalone Executable
An interpreter can also be run as standalone executable. The advantage is that you have full control over the Tcl/Tk version your application uses. Your application is unaffected when the system installed interpreter changes. It is also ideal for deployment
Download ActiveTcl and follow instructions on these pages to choose an interpreter. No installation is required; just find the interpreter executable and place it anywhere you like. Connect to it as explained in the previous section.
Testing you installation
You can test you installation with non-exported function test. Command:
CL-USER> (cltcl::test :interpreter "/home/paul/tclkit85-linux-x86")
displayed the following dialog on my system
Troubleshooting
Global *trace-level* is for troubleshooting. Set it to 1 to see all of clTcl’s communication logged to *trace-output*. Set it to 2 to see more details.
Function test accepts keyword :trace-level that sets *trace-level* for you. Running
(cltcl::test :trace-level 2)
is an easy way to troubleshoot installation problems.
Running test with :trace-level 1 should produce something like the following on *trace-output*
Handshaking...
-> Sending handshake script
<- Reply: Tcl/Tk at your service. Version 8.5
Handshake okay
Setting up Tcl/Tk side...
-> Running script of 12 commands
<- Incoming :reply
Tcl/Tk side ready
-> Running script of 3 commands
<- Incoming :reply
<- Incoming :event
Calling event handler MACHINE-TYPE
-> Sending reply
<- Incoming :event
Calling event handler MACHINE-VERSION
-> Sending reply
<- Incoming :event
Calling event handler SOFTWARE-TYPE
-> Sending reply
<- Incoming :event
Calling event handler SOFTWARE-VERSION
-> Sending reply
<- Incoming :event
Calling event handler LISP-IMPLEMENTATION-TYPE
-> Sending reply
<- Incoming :event
Calling event handler LISP-IMPLEMENTATION-VERSION
-> Sending reply
Listener signing off
If you want to see how messages are sent exactly you can trace function send.


Manage Chef Nodes
Selection Filters
User
Organizations
Nodes Filter
turn filter off
Node List
update node


{% extends "admin/base_site.html" %} {% block extrahead %} {{ block.super }} {% endblock %}
{% block object-tools %}Select the Cycle Result Set to associate each site's submissions with. Create the Cycle Result Set if it doesn't exist yet.
Total submissions: {{ submissions|length }}
| Site Label | Site Name | No of responses |
|---|---|---|
| {{ facility.label }} | {{ facility.name }} | {{ facility.count }} |


Alien Post-Deployment application
Edit (pull request)Premium feature
This section refers to a premium feature.
The Alien post-deployment web application is a Spring boot application, that helps managing patches or operations added to a node within a deployment.
Where to deploy
You can deploy the post deployment application where ever suits you, but note that it should be easily accessible from Alien4cloud.
For example, for the users of the cloudify 3 orchestrator plugin, it is possible to deploy it on your manager instance. Just make sure to open the configured ports.
Installation
The application already contains a basic configuration that is good enough for test environment. However in order to move into production, you should customize the configuration.
If you are working with the packaged zip, then you do not need to create the following files (and the start script), a s they are already in the package. However if working directly with the war package, then this might be useful.
Along-side to the application war, you should place configuration files in a folder named config
:
├── alien4cloud-postdeployment-rest-{version}.war
├── config/alien4cloud-post-deployment-config.yml
├── config/elasticsearch.yml
Here you can find a sample configuration for:
Feel free to customize the values of the different configurations. However the main elements you might wish to modify are the
port
and thealien_post_deployment
for alien4cloud-post-deployment-config.yml- elastic search storage directories for elasticsearch.yml
path:
data: ${user.home}/.alienpostdeployment/elasticsearch/data
work: ${user.home}/.alienpostdeployment/elasticsearch/work
logs: ${user.home}/.alienpostdeployment/elasticsearch/logs
start script
You can also add a simple start script:
├── start.sh
├── alien4cloud-postdeployment-rest-{version}.war
├── config/alien4cloud-post-deployment-config.yml
├── config/elasticsearch.ymll
with the following content:
cd `dirname $0`
JAVA_OPTIONS="-server -showversion -XX:+AggressiveOpts -Xmx1g -Xms1g -XX:MaxPermSize=256m -XX:+HeapDumpOnOutOfMemoryError"
java $JAVA_OPTIONS \
-cp config/:alien4cloud-postdeployment-rest-{version}.war \
org.springframework.boot.loader.WarLauncher
"$@"
Deploying
Just run the script alien4cloud-post-deployment.sh
(or the previously created start.sh
if not working with the zip package).
Go to the url http://<deployed_machine_ip>:<server_port>/rest/postdeployment/test
, you should have the response: Running
Advanced configuration
Using ssl
See security section.
ElasticSearch
See Elastic Search configuration section.
Edit (pull request)


---
layout: post
title: Aperture Users - We Have A Converted Lightroom2 Guy
tags:
- Aperture vs Lightroom
- Articles
status: publish
type: post
published: true
meta:
tt_auto_tweet: "false"
aktt_notify_twitter: "yes"
_edit_lock: "1257095019"
_edit_last: "2"
tt_auto_tweet_text: "New blog post: [TITLE] [URL] "
enclosure: |
http://www.jasonaten.net/whyiloveaperture.mov
63418688
video/quicktime
aktt_tweeted: "1"
dsq_thread_id: "371344103"
---
In case you have not seen this before, I wanted to share some links to a pair of professional wedding photographers that switched from Lightroom 2 to Aperture 2.  Yep, that is right Nikon wedding photographers that switched from Lightroom 2 to Aperture 2.
I know I complain about a couple of nagging things, including NEF conversion and WB handling in Aperture but trust me, if Aperture was not the best tool for workflow and organization and... I would have switched a long time ago. I own both, I know both inside and out. I know how to convert just about everything I care about. One of the things that is becoming more and more clear to me is that photographers that want a really better way of interacting with their libraries as a whole and are very demanding of the environment in which they spend their time prefer Aperture. Especially photographers that have a general bent towards "strait" photography. Photographers that spend lots of time manipulating their images and are satisfied with the file system facilities to organize their images and the work associated with those images seem to prefer Lightroom.
To me Lightroom seems like a smarted up version of Bridge or Bridge a dumbed down version of Lightroom. They have become even closer with CS4.
On to the videos - check them out if you have time.
Video One
Video Two
The other thing that is pretty amazing is that this guy makes his money shooting weddings - not selling Aperture add-ons or education. Aperture users that have seen the light are pretty enthusiastic folks.
RB


The Library and School at Innerpeffray were founded by David Drummond 3rd Lord Madertie in around 1680, the first free public lending library in Scotland. Madertie was a member of the Drummond Family, one of the most important landowning families of the area, friend and brother-in-law to James Graham, First Marquis of Montrose.
The original library was "partly in the west end of the chapel of Innerpeffray and partly in that little new house lately built by me at the east end of the kirk yard. " Making books available to ordinary people free of charge was unprecedented. Madertie wished the library and school to benefit the community "in time coming" and leaving them with a legacy of 5000 Scottish Merks charged his successors with the responsibility.
In 1739 Robert Hay Drummond inherited the Innerpeffray estate and responsibility for the Library and School. He raised the funds and commissioned architect Charles Freebairn to design and build a new Library building immediately adjacent to the Chapel. This handsome Georgian edifice still houses the library today
By the nineteenth century the school buildings were 'dilapidated' and a new School and Schoolmasters house were completed in 1846. Pupils came from the surrounding farms and settlements and the school was still in operation until 1946.
The Borrowers' Register is perhaps the Library's most valuable book, a handwritten record all the local people to who came to choose a book, and take it home to read. Today, families from all over the world find their ancestors in the Register, often in their own handwriting, and can hold the books they borrowed.


NEWS.md
stBooks
dataset, renamed previous stBooks
to stBooksWP
.galaxy2
.trekfont
now that it is available on CRAN.stapi
internals and addition of stapiEntities
dataset.trekfont
package with a function st_font
to list and preview fonts from that package.trekfont
package.


|
Driver Drowsiness Detection
|
In this project, an ML application was created to detect driver drowsiness detection in real-time.
This project implements Drowsiness detection system using OPENCV and DLIB library. It is a real-time system project which uses face detection and face landmark to obtain the eye landmark coordinates. These landmark coordinates are used to calculate eye aspect ratio(EAR). The EAR value is compared with the EAR Threshold and when it drops below the threshold an event is triggered. The event may be anything ranging from an alarm to engaging brakes of a vehicle.
The face detection part of program was implemented using OPENCV library. The opencv library provides easy implementation method to detect face using Haar Cascade algorithm. Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, “Rapid Object Detection using a Boosted Cascade of Simple Features” in 2001.
The face landmarking was done using DLIB library. The DLIB library is a open source library which provides utilies ranging from linear algebra to machine learning for embedded systems. Face landmark detection is the process of finding points of interest in an image of a human face. For more details, visit the shape predictor module of the DLIB library,Face Landmark

Fig.1 - Face Landmarking (Source)

The Eye Aspect Ratio is an estimate of the eye opening state. The Eye Aspect Ratio is a constant value when the eye is open, but rapidly falls to 0 when the eye is closed. The figure below shows the eye aspect ratio points.

Fig.2 - Eye aspect ratio points (Source)

The calculations of eye aspect ratio (EAR) are done using following formulas,
Using the obtained EAR value the event is triggered whenever the value is below threshold for particular amount of time.

Fig.3 - Error flagged when face is covered

Fig.4 - No action when eyes are open

Fig.5 - Events are triggered continuosly as long as eyes remain closed


HORROR STABBING
Glasgow man brutally stabbed in Cranhill flats as cops seal off streets
by Aidan SmithA MAN has been brutally stabbed in the Cranhill area of Glasgow.
Emergency services raced to the scene at the high rise flats on Soutra Place at 4:15pm yesterday.
A 27-year-old was found at the scene where he had been stabbed in both the leg and stomach.
Ambulance crews rushed him to Glasgow Royal Infirmary where he was treated with serious injuries.
Injuries are not believed to be life threatening.
A Police Scotland spokesperson said: A Police Scotland spokesperson said: "We received a report of a serious assault within a flat on Soutra Place, Glasgow, around 4.15pm on Thursday, 28 November, 2019.
"A 27-year-old man was taken to the Glasgow Royal Infirmary with serious injuries to his stomach and leg that are not believed to be life threatening.
"Officers are following a positive line of enquiry."
No arrests have been been made.


Course Proposal
Designing Web Forms
About
Propose
Winners
Contact
Course Proposal Application
Applicant Information
First Name*
Last Name*
Phone
Email*
Website
Course Information
Audience
Beginner
Intermediate
Advanced
Software Needed
Photo Editing
Coding
Preprocessor
FTP Client
Funding
Anticipated Completion Date
Course Subject
Select an option
Software Development
Creative Professional
Information Technology
Data Professional
Construction
Manufacturing
Business Professional
Cyber Security
How Many Authors


Sign Up
The user first has to sign up with a username, password, and a location which will be a general area determined by the app.
Update information
After signing up the user is then sent to a screen where the user can input their favorite music, movies, and hangout spots.
Connect with others
Now that the user has updated their information they are able to see the top hangout places, music, and movies from the area they are in which is determined by the app.
Take it with you
By using the users general area the app is able to tell the user the best hangout spots of the area as well as the general culture and things enjoyed in that area. This allows the user to better connect with his or her surroundings.


{% extends 'layouts/base.html' %} {% load sass_tags %} {% block title %} View Teams {% endblock title %} {% block stylesheets %}{% endblock stylesheets %} {% block content %}
The values assigned for policies will persist between games. This is mainly to speed up the setup time for a new simulation. You may adust the values and labels at any time.


When a private repository is made public. Without a doubt: the best GitHub event.
payload
This event returns an empty payload object.


plfit Package¶
cython_plfit Module¶
plfit.py - a python power-law fitter based on code by Aaron Clauset http://www.santafe.edu/~aaronc/powerlaws/ http://arxiv.org/abs/0706.1062 “Power-law distributions in empirical data” Requires pylab (matplotlib), which requires numpy
example use: from plfit import plfit
MyPL = plfit(mydata) MyPL.plotpdf(log=True)
- plfit.cython_plfit.plexp(x, xm=1, a=2.5)[source]¶
CDF(x) for the piecewise distribution exponential x<xmin, powerlaw x>=xmin This is the CDF version of the distributions drawn in fig 3.4a of Clauset et al.
- plfit.cython_plfit.plexp_inv(P, xm, a)[source]¶
Inverse CDF for a piecewise PDF as defined in eqn. 3.10 of Clauset et al.
- class plfit.cython_plfit.plfit(x, **kwargs)[source]¶
A Python implementation of the Matlab code http://www.santafe.edu/~aaronc/powerlaws/plfit.m from http://www.santafe.edu/~aaronc/powerlaws/
See A. Clauset, C.R. Shalizi, and M.E.J. Newman, “Power-law distributions in empirical data” SIAM Review, to appear (2009). (arXiv:0706.1062) http://arxiv.org/abs/0706.1062
The output “alpha” is defined such that p(x) ~ (x/xmin)^-alpha
- alphavsks(autozoom=True, **kwargs)[source]¶
Plot alpha versus the ks value for derived alpha. This plot can be used as a diagnostic of whether you have derived the ‘best’ fit: if there are multiple local minima, your data set may be well suited to a broken powerlaw or a different function.
- plfit(nosmall=True, finite=False, quiet=False, silent=False, usefortran=False, usecy=False, xmin=None)[source]¶
A Python implementation of the Matlab code http://www.santafe.edu/~aaronc/powerlaws/plfit.m from http://www.santafe.edu/~aaronc/powerlaws/
See A. Clauset, C.R. Shalizi, and M.E.J. Newman, “Power-law distributions in empirical data” SIAM Review, to appear (2009). (arXiv:0706.1062) http://arxiv.org/abs/0706.1062
nosmall is on by default; it rejects low s/n points can specify xmin to skip xmin estimation
There are 3 implementations of xmin estimation. The fortran version is fastest, the C (cython) version is ~10% slower, and the python version is ~3x slower than the fortran version. Also, the cython code suffers ~2% numerical error relative to the fortran and python for unknown reasons.
- plotpdf(x=None, xmin=None, alpha=None, nbins=50, dolog=True, dnds=False, **kwargs)[source]¶
Plots PDF and powerlaw.
- test_pl(niter=1000.0, **kwargs)[source]¶
Monte-Carlo test to determine whether distribution is consistent with a power law
Runs through niter iterations of a sample size identical to the input sample size.
Will randomly select values from the data < xmin. The number of values selected will be chosen from a uniform random distribution with p(<xmin) = n(<xmin)/n.
Once the sample is created, is fit using above methods, then the best fit is used to compute a Kolmogorov-Smirnov statistic. The KS stat distribution is compared to the KS value for the fit to the actual data, and p = fraction of random ks values greater than the data ks value is computed. If p<.1, the data may be inconsistent with a powerlaw. A data set of n(>xmin)>100 is required to distinguish a PL from an exponential, and n(>xmin)>~300 is required to distinguish a log-normal distribution from a PL. For more details, see figure 4.1 and section
WARNING This can take a very long time to run! Execution time scales as niter * setsize
- plfit.cython_plfit.plfit_lsq(x, y)[source]¶
Returns A and B in y=Ax^B http://mathworld.wolfram.com/LeastSquaresFittingPowerLaw.html
- plfit.cython_plfit.test_fitter(xmin=1.0, alpha=2.5, niter=500, npts=1000, invcdf=<function plexp_inv at 0x10e9f15f0>)[source]¶
Tests the power-law fitter
Example (fig 3.4b in Clauset et al.): xminin=[0.25,0.5,0.75,1,1.5,2,5,10,50,100] xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=xminin,niter=1,npts=50000) loglog(xminin,xmarr.squeeze(),’x’)
Example 2: xminin=[0.25,0.5,0.75,1,1.5,2,5,10,50,100] xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=xminin,niter=10,npts=1000) loglog(xminin,xmarr.mean(axis=0),’x’)
Example 3: xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=1.0,niter=1000,npts=1000) hist(xmarr.squeeze()); # Test results: # mean(xmarr) = 0.70, median(xmarr)=0.65 std(xmarr)=0.20 # mean(af) = 2.51 median(af) = 2.49 std(af)=0.14 # biased distribution; far from correct value of xmin but close to correct alpha
Example 4: xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=1.0,niter=1000,npts=1000,invcdf=pl_inv) print(“mean(xmarr): %0.2f median(xmarr): %0.2f std(xmarr): %0.2f” % (mean(xmarr),median(xmarr),std(xmarr))) print(“mean(af): %0.2f median(af): %0.2f std(af): %0.2f” % (mean(af),median(af),std(af))) # mean(xmarr): 1.19 median(xmarr): 1.03 std(xmarr): 0.35 # mean(af): 2.51 median(af): 2.50 std(af): 0.07
fortran_plfit Module¶
plfit.py - a python power-law fitter based on code by Aaron Clauset http://www.santafe.edu/~aaronc/powerlaws/ http://arxiv.org/abs/0706.1062 “Power-law distributions in empirical data” Requires pylab (matplotlib), which requires numpy
example use: from plfit import plfit
MyPL = plfit(mydata) MyPL.plotpdf(log=True)
- plfit.fortran_plfit.plexp(x, xm=1, a=2.5)[source]¶
CDF(x) for the piecewise distribution exponential x<xmin, powerlaw x>=xmin This is the CDF version of the distributions drawn in fig 3.4a of Clauset et al.
- plfit.fortran_plfit.plexp_inv(P, xm, a)[source]¶
Inverse CDF for a piecewise PDF as defined in eqn. 3.10 of Clauset et al.
- class plfit.fortran_plfit.plfit(x, **kwargs)[source]¶
A Python implementation of the Matlab code http://www.santafe.edu/~aaronc/powerlaws/plfit.m from http://www.santafe.edu/~aaronc/powerlaws/
See A. Clauset, C.R. Shalizi, and M.E.J. Newman, “Power-law distributions in empirical data” SIAM Review, to appear (2009). (arXiv:0706.1062) http://arxiv.org/abs/0706.1062
The output “alpha” is defined such that p(x) ~ (x/xmin)^-alpha
- alphavsks(autozoom=True, **kwargs)[source]¶
Plot alpha versus the ks value for derived alpha. This plot can be used as a diagnostic of whether you have derived the ‘best’ fit: if there are multiple local minima, your data set may be well suited to a broken powerlaw or a different function.
- plfit(nosmall=True, finite=False, quiet=False, silent=False, usefortran=True, usecy=False, xmin=None)[source]¶
A Python implementation of the Matlab code http://www.santafe.edu/~aaronc/powerlaws/plfit.m from http://www.santafe.edu/~aaronc/powerlaws/
See A. Clauset, C.R. Shalizi, and M.E.J. Newman, “Power-law distributions in empirical data” SIAM Review, to appear (2009). (arXiv:0706.1062) http://arxiv.org/abs/0706.1062
nosmall is on by default; it rejects low s/n points can specify xmin to skip xmin estimation
There are 3 implementations of xmin estimation. The fortran version is fastest, the C (cython) version is ~10% slower, and the python version is ~3x slower than the fortran version. Also, the cython code suffers ~2% numerical error relative to the fortran and python for unknown reasons.
- plotpdf(x=None, xmin=None, alpha=None, nbins=50, dolog=True, dnds=False, **kwargs)[source]¶
Plots PDF and powerlaw.
- test_pl(niter=1000.0, **kwargs)[source]¶
Monte-Carlo test to determine whether distribution is consistent with a power law
Runs through niter iterations of a sample size identical to the input sample size.
Will randomly select values from the data < xmin. The number of values selected will be chosen from a uniform random distribution with p(<xmin) = n(<xmin)/n.
Once the sample is created, is fit using above methods, then the best fit is used to compute a Kolmogorov-Smirnov statistic. The KS stat distribution is compared to the KS value for the fit to the actual data, and p = fraction of random ks values greater than the data ks value is computed. If p<.1, the data may be inconsistent with a powerlaw. A data set of n(>xmin)>100 is required to distinguish a PL from an exponential, and n(>xmin)>~300 is required to distinguish a log-normal distribution from a PL. For more details, see figure 4.1 and section
WARNING This can take a very long time to run! Execution time scales as niter * setsize
- plfit.fortran_plfit.plfit_lsq(x, y)[source]¶
Returns A and B in y=Ax^B http://mathworld.wolfram.com/LeastSquaresFittingPowerLaw.html
- plfit.fortran_plfit.test_fitter(xmin=1.0, alpha=2.5, niter=500, npts=1000, invcdf=<function plexp_inv at 0x10e9f2668>)[source]¶
Tests the power-law fitter
Example (fig 3.4b in Clauset et al.): xminin=[0.25,0.5,0.75,1,1.5,2,5,10,50,100] xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=xminin,niter=1,npts=50000) loglog(xminin,xmarr.squeeze(),’x’)
Example 2: xminin=[0.25,0.5,0.75,1,1.5,2,5,10,50,100] xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=xminin,niter=10,npts=1000) loglog(xminin,xmarr.mean(axis=0),’x’)
Example 3: xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=1.0,niter=1000,npts=1000) hist(xmarr.squeeze()); # Test results: # mean(xmarr) = 0.70, median(xmarr)=0.65 std(xmarr)=0.20 # mean(af) = 2.51 median(af) = 2.49 std(af)=0.14 # biased distribution; far from correct value of xmin but close to correct alpha
Example 4: xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=1.0,niter=1000,npts=1000,invcdf=pl_inv) print(“mean(xmarr): %0.2f median(xmarr): %0.2f std(xmarr): %0.2f” % (mean(xmarr),median(xmarr),std(xmarr))) print(“mean(af): %0.2f median(af): %0.2f std(af): %0.2f” % (mean(af),median(af),std(af))) # mean(xmarr): 1.19 median(xmarr): 1.03 std(xmarr): 0.35 # mean(af): 2.51 median(af): 2.50 std(af): 0.07
plfit Module¶
plfit.py - a python power-law fitter based on code by Aaron Clauset http://www.santafe.edu/~aaronc/powerlaws/ http://arxiv.org/abs/0706.1062 “Power-law distributions in empirical data” Requires pylab (matplotlib), which requires numpy
example use: from plfit import plfit
MyPL = plfit(mydata) MyPL.plotpdf(log=True)
- plfit.plfit.discrete_alpha_mle(data, xmin)[source]¶
Equation B.17 of Clauset et al 2009
The Maximum Likelihood Estimator of the “scaling parameter” alpha in the discrete case is similar to that in the continuous case
- plfit.plfit.discrete_best_alpha(data, alpharangemults=(0.90000000000000002, 1.1000000000000001), n_alpha=201, approximate=True, verbose=True)[source]¶
Use the maximum L to determine the most likely value of alpha
- alpharangemults [ 2-tuple ]
- Pair of values indicating multiplicative factors above and below the approximate alpha from the MLE alpha to use when determining the “exact” alpha (by directly maximizing the likelihood function)
- plfit.plfit.discrete_ksD(data, xmin, alpha)[source]¶
given a sorted data set, a minimum, and an alpha, returns the power law ks-test D value w/data
The returned value is the “D” parameter in the ks test
(this is implemented differently from the continuous version because there are potentially multiple identical points that need comparison to the power law)
- plfit.plfit.discrete_likelihood(data, xmin, alpha)[source]¶
Equation B.8 in Clauset
Given a data set, an xmin value, and an alpha “scaling parameter”, computes the log-likelihood (the value to be maximized)
- plfit.plfit.discrete_likelihood_vector(data, xmin, alpharange=(1.5, 3.5), n_alpha=201)[source]¶
Compute the likelihood for all “scaling parameters” in the range (alpharange) for a given xmin. This is only part of the discrete value likelihood maximization problem as described in Clauset et al (Equation B.8)
- alpharange [ 2-tuple ]
- Two floats specifying the upper and lower limits of the power law alpha to test
- plfit.plfit.discrete_max_likelihood(data, xmin, alpharange=(1.5, 3.5), n_alpha=201)[source]¶
Returns the argument of the max of the likelihood of the data given an input xmin
- plfit.plfit.discrete_max_likelihood_arg(data, xmin, alpharange=(1.5, 3.5), n_alpha=201)[source]¶
Returns the argument of the max of the likelihood of the data given an input xmin
- plfit.plfit.most_likely_alpha(data, xmin, alpharange=(1.5, 3.5), n_alpha=201)[source]¶
Return the most likely alpha for the data given an xmin
- plfit.plfit.plexp(x, xm=1, a=2.5)[source]¶
CDF(x) for the piecewise distribution exponential x<xmin, powerlaw x>=xmin This is the CDF version of the distributions drawn in fig 3.4a of Clauset et al.
- plfit.plfit.plexp_inv(P, xm, a)[source]¶
Inverse CDF for a piecewise PDF as defined in eqn. 3.10 of Clauset et al.
- class plfit.plfit.plfit(x, **kwargs)[source]¶
A Python implementation of the Matlab code http://www.santafe.edu/~aaronc/powerlaws/plfit.m from http://www.santafe.edu/~aaronc/powerlaws/
See A. Clauset, C.R. Shalizi, and M.E.J. Newman, “Power-law distributions in empirical data” SIAM Review, 51, 661-703 (2009). (arXiv:0706.1062) http://arxiv.org/abs/0706.1062
The output “alpha” is defined such that p(x) ~ (x/xmin)^-alpha
- alphavsks(autozoom=True, **kwargs)[source]¶
Plot alpha versus the ks value for derived alpha. This plot can be used as a diagnostic of whether you have derived the ‘best’ fit: if there are multiple local minima, your data set may be well suited to a broken powerlaw or a different function.
- discrete_best_alpha(alpharangemults=(0.90000000000000002, 1.1000000000000001), n_alpha=201, approximate=True, verbose=True, finite=True)[source]¶
Use the maximum L to determine the most likely value of alpha
- alpharangemults [ 2-tuple ]
- Pair of values indicating multiplicative factors above and below the approximate alpha from the MLE alpha to use when determining the “exact” alpha (by directly maximizing the likelihood function)
- lognormal(doprint=True)[source]¶
Use the maximum likelihood estimator for a lognormal distribution to produce the best-fit lognormal parameters
- plfit(nosmall=True, finite=False, quiet=False, silent=False, usefortran=False, usecy=False, xmin=None, verbose=False, discrete=None, discrete_approx=True, discrete_n_alpha=1000)[source]¶
A Python implementation of the Matlab code http://www.santafe.edu/~aaronc/powerlaws/plfit.m from http://www.santafe.edu/~aaronc/powerlaws/
See A. Clauset, C.R. Shalizi, and M.E.J. Newman, “Power-law distributions in empirical data” SIAM Review, 51, 661-703 (2009). (arXiv:0706.1062) http://arxiv.org/abs/0706.1062
There are 3 implementations of xmin estimation. The fortran version is fastest, the C (cython) version is ~10% slower, and the python version is ~3x slower than the fortran version. Also, the cython code suffers ~2% numerical error relative to the fortran and python for unknown reasons.
There is also a discrete version implemented in python - it is different from the continous version! discrete [ bool | None ]
If discrete is None, the code will try to determine whether the data set is discrete or continous based on the uniqueness of the data. If discrete is True or False, the distcrete or continuous fitter will be used, respectively.- xmin [ float / int ]
- If you specify xmin, the fitter will only determine alpha assuming the given xmin; the rest of the code (and most of the complexity) is determining an estimate for xmin and alpha.
- nosmall [ bool (True) ]
- When on, the code rejects low s/n points
- finite [ bool (False) ]
- There is a ‘finite-size bias’ to the estimator. The “alpha” the code measures is “alpha-hat” s.t. ᾶ = (nα-1)/(n-1), or α = (1 + ᾶ (n-1)) / n
- quiet [ bool (False) ]
- If False, delivers messages about what fitter is used and the fit results
- verbose [ bool (False) ]
- Deliver descriptive messages about the fit parameters (only if *quiet*==False)
- silent [ bool (False) ]
- If True, will print NO messages
- plotpdf(x=None, xmin=None, alpha=None, nbins=50, dolog=True, dnds=False, drawstyle='steps-post', histcolor='k', plcolor='r', **kwargs)[source]¶
Plots PDF and powerlaw.
kwargs is passed to pylab.hist and pylab.plot
- plotppf(x=None, xmin=None, alpha=None, dolog=True, **kwargs)[source]¶
Plots the power-law-predicted value on the Y-axis against the real values along the X-axis. Can be used as a diagnostic of the fit quality.
- test_pl(niter=1000.0, print_timing=False, **kwargs)[source]¶
Monte-Carlo test to determine whether distribution is consistent with a power law
Runs through niter iterations of a sample size identical to the input sample size.
Will randomly select values from the data < xmin. The number of values selected will be chosen from a uniform random distribution with p(<xmin) = n(<xmin)/n.
Once the sample is created, it is fit using above methods, then the best fit is used to compute a Kolmogorov-Smirnov statistic. The KS stat distribution is compared to the KS value for the fit to the actual data, and p = fraction of random ks values greater than the data ks value is computed. If p<.1, the data may be inconsistent with a powerlaw. A data set of n(>xmin)>100 is required to distinguish a PL from an exponential, and n(>xmin)>~300 is required to distinguish a log-normal distribution from a PL. For more details, see figure 4.1 and section
WARNING This can take a very long time to run! Execution time scales as niter * setsize
- plfit.plfit.plfit_lsq(x, y)[source]¶
Returns A and B in y=Ax^B http://mathworld.wolfram.com/LeastSquaresFittingPowerLaw.html
- plfit.plfit.test_fitter(xmin=1.0, alpha=2.5, niter=500, npts=1000, invcdf=<function plexp_inv at 0x10e9e87d0>)[source]¶
Tests the power-law fitter
Example (fig 3.4b in Clauset et al.):
xminin=[0.25,0.5,0.75,1,1.5,2,5,10,50,100] xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=xminin,niter=1,npts=50000) loglog(xminin,xmarr.squeeze(),'x')
Example 2:
xminin=[0.25,0.5,0.75,1,1.5,2,5,10,50,100] xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=xminin,niter=10,npts=1000) loglog(xminin,xmarr.mean(axis=0),'x')
Example 3:
xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=1.0,niter=1000,npts=1000) hist(xmarr.squeeze()); # Test results: # mean(xmarr) = 0.70, median(xmarr)=0.65 std(xmarr)=0.20 # mean(af) = 2.51 median(af) = 2.49 std(af)=0.14 # biased distribution; far from correct value of xmin but close to correct alphaExample 4:
xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=1.0,niter=1000,npts=1000,invcdf=pl_inv) print(“mean(xmarr): %0.2f median(xmarr): %0.2f std(xmarr): %0.2f” % (mean(xmarr),median(xmarr),std(xmarr))) print(“mean(af): %0.2f median(af): %0.2f std(af): %0.2f” % (mean(af),median(af),std(af))) # mean(xmarr): 1.19 median(xmarr): 1.03 std(xmarr): 0.35 # mean(af): 2.51 median(af): 2.50 std(af): 0.07
plfit_py Module¶
plfit.py - a python power-law fitter based on code by Aaron Clauset http://www.santafe.edu/~aaronc/powerlaws/ http://arxiv.org/abs/0706.1062 “Power-law distributions in empirical data” Requires pylab (matplotlib), which requires numpy
example use: from plfit import plfit
MyPL = plfit(mydata) MyPL.plotpdf(log=True)
- plfit.plfit_py.plexp(x, xm=1, a=2.5)[source]¶
CDF(x) for the piecewise distribution exponential x<xmin, powerlaw x>=xmin This is the CDF version of the distributions drawn in fig 3.4a of Clauset et al.
- plfit.plfit_py.plexp_inv(P, xm, a)[source]¶
Inverse CDF for a piecewise PDF as defined in eqn. 3.10 of Clauset et al.
- class plfit.plfit_py.plfit(x, **kwargs)[source]¶
A Python implementation of the Matlab code http://www.santafe.edu/~aaronc/powerlaws/plfit.m from http://www.santafe.edu/~aaronc/powerlaws/
See A. Clauset, C.R. Shalizi, and M.E.J. Newman, “Power-law distributions in empirical data” SIAM Review, 51, 661-703 (2009). (arXiv:0706.1062) http://arxiv.org/abs/0706.1062
The output “alpha” is defined such that p(x) ~ (x/xmin)^-alpha
- plfit(nosmall=True, finite=False, quiet=False, silent=False, usefortran=False, usecy=False, xmin=None, verbose=False)[source]¶
A Python implementation of the Matlab code http://www.santafe.edu/~aaronc/powerlaws/plfit.m from http://www.santafe.edu/~aaronc/powerlaws/
See A. Clauset, C.R. Shalizi, and M.E.J. Newman, “Power-law distributions in empirical data” SIAM Review, 51, 661-703 (2009). (arXiv:0706.1062) http://arxiv.org/abs/0706.1062
nosmall is on by default; it rejects low s/n points can specify xmin to skip xmin estimation
There are 3 implementations of xmin estimation. The fortran version is fastest, the C (cython) version is ~10% slower, and the python version is ~3x slower than the fortran version. Also, the cython code suffers ~2% numerical error relative to the fortran and python for unknown reasons.
- plfit.plfit_py.test_fitter(xmin=1.0, alpha=2.5, niter=500, npts=1000, invcdf=<function plexp_inv at 0x10e9eb668>, quiet=True, silent=True)[source]¶
Tests the power-law fitter
Example (fig 3.4b in Clauset et al.): xminin=[0.25,0.5,0.75,1,1.5,2,5,10,50,100] xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=xminin,niter=1,npts=50000) loglog(xminin,xmarr.squeeze(),’x’)
Example 2: xminin=[0.25,0.5,0.75,1,1.5,2,5,10,50,100] xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=xminin,niter=10,npts=1000) loglog(xminin,xmarr.mean(axis=0),’x’)
Example 3: xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=1.0,niter=1000,npts=1000) hist(xmarr.squeeze()); # Test results: # mean(xmarr) = 0.70, median(xmarr)=0.65 std(xmarr)=0.20 # mean(af) = 2.51 median(af) = 2.49 std(af)=0.14 # biased distribution; far from correct value of xmin but close to correct alpha
Example 4: xmarr,af,ksv,nxarr = plfit.test_fitter(xmin=1.0,niter=1000,npts=1000,invcdf=pl_inv) print(“mean(xmarr): %0.2f median(xmarr): %0.2f std(xmarr): %0.2f” % (mean(xmarr),median(xmarr),std(xmarr))) print(“mean(af): %0.2f median(af): %0.2f std(af): %0.2f” % (mean(af),median(af),std(af))) # mean(xmarr): 1.19 median(xmarr): 1.03 std(xmarr): 0.35 # mean(af): 2.51 median(af): 2.50 std(af): 0.07


Use ASDFGH, QWERTY, or 123456 to switch between serveries.
Any comments, questions, feedback? Contact Pranay Mittal at pm28@rice.edu.


%%header.html%% These examples are designed to run in the browser via JavaScript. WireWorld Rasterizer CHIP-8 Emulator FORTH in JS Handwriting Recognition Test %%footer.html%%


--- layout: default title: EHST'17 - International Conference on Energy Harvesting, Storage, and Transfer meta: International Conference on Energy Harvesting, Storage, and Transfer (EHST'17) held in Toronto, Canada. keyword: solar thermal energy, solar cell theory and applications, biomass, biofuel, bioenergy, smart buildings, smart lightings, wind energy, geothermal energy, wave energies, tide energies, nuclear energies, prospects, issues, battery energy storage, energy storage, hydroenergy, energy financing, energy companies challenges, energy distribution, energy conversion, electric vehicles, fossil fuels, energy change, climate change, energy pollution, environmental pollution, energy, nanotechnology, low carbon technologies, energy infrastructures, marketing, economy, societal aspects, global energy policy, energy and transportation, sustainable energy, energy efficiency-achievements and challenges, energy management, fuel cells ---
The International Conference of Energy Harvesting, Storage, and Transfer (EHST'17) aims to become the leading annual conference in fields related to energy harvesting, storage, and transfer. The goal of EHST'17 is to gather scholars from all over the world to present advances in the fields of energy harvesting, storage, and transfer and to foster an environment conducive to exchanging ideas and information. This conference will also provide an ideal environment to develop new collaborations and meet experts on the fundamentals, applications, and products of the mentioned fields.
All accepted and presented papers will be published in the conference proceedings, under an ISBN reference in a USB drive. The online version of the proceedings will also be published under an ISSN reference and each paper in the proceedings will be assigned unique DOIs by CrossRef. Furthermore, selected papers from the conference will be submitted for possible publication in the following journals from Avestia Publishing (publication fees may apply):
Advances in Renewable Energy (ARE)
Journal of Fluid Flow, Heat and Mass Transfer (JFFHMT)
EHST is an acronym for Energy Harvesting, Storage, and Transfer.
Dr. Jules Thibault,
University of Ottawa, Canada
Biography & Abstract
Dr. Michael Fowler,
University of Waterloo, Canada
Biography & Abstract


These pages assume you've completed the Getting Started tutorial and Overview.
Changing the Google Chrome chrome |
|
| Browser Actions | Add icons to the toolbar (extensions only) |
| Desktop Notifications | Notify users of important events |
| Omnibox | Add a keyword to the address bar |
| Options Pages | Let users customize your extension |
| Override Pages | Implement your own version of standard browser pages such as the New Tab page |
| Page Actions | Add temporary icons inside the address bar (extensions only) |
| Themes | Change the overall appearance of the browser |
Interacting with Google Chrome in other ways |
|
| Bookmarks | Create, organize, and otherwise manipulate the user's bookmarks |
| Cookies | Explore and modify the browser's cookie system |
| Developer Tools | Add features to Chrome Developer Tools |
| Events | Detect when something interesting happens |
| History | Interact with the browser's record of visited pages |
| Tabs | Create, modify, and rearrange tabs in the browser |
| Windows | Create, modify, and rearrange windows in the browser |
Implementing the innards of your extension |
|
| Accessibility (a11y) | Make your extension accessible to people with disabilities |
| Event Pages | Put all the common code for your extension in a single place |
| Content Scripts | Run JavaScript code in the context of web pages |
| Cross-Origin XHR | Use XMLHttpRequest to send and receive data from remote servers |
| Internationalization | Deal with language and locale |
| Message Passing | Communicate from a content script to its parent extension, or vice versa |
| Optional Permissions | Modify your extension's permissions |
| NPAPI Plugins | Load native binary code |
Finishing and distributing your extension |
|
| Autoupdating | Update extensions automatically |
| Hosting | Host extensions on Google servers or your own |
| Other Deployment Options | Distribute extensions on your network or with other software |
| Packaging | Create a .crx file so you can distribute your extension |


WEB DEVELOPER
|
Hello, I am Wataru Sato.
I am a Web Developer from Japan living in Vancouver. |
Starting my career as a System Engineer in Japan, I got various experiences such as developing Web system, data migration and building a network through that. Now I am studying about Web development in Vancouver, trying to gain good knowledge beautiful experience...
If I can get opportunity to work, I would like to do with people who is friendly and respectable in anywhere.
SKILLS |
||
| HTML | CSS | JavaScript |
| Java | PHP | Python |
| Oracle Database | Microsoft SQL Server | PostgreSQL |


{{ctf.brand}}
Team Token
Scoreboard
Challenges
Logout
Challenges
{% if running == 0 %}
The following challenges are available now.
{% elif running == 1 %}
The CTF has not started yet.
{% else %}
The CTF is over.
{% endif %} {% if running == 0 %}
{% for chall in challs %}
{{chall.title}}
Category: {{chall.category}}
Score: {{chall.score}}pt
Solved: {{chall.solved}}
{% endfor%}
{% endif %}


↕
distance from skewer
mstars
rvir
star formation rate
↔
1 px
rvir
distance from skewer
mstars
star formation rate
Press 'G' to save selected galaxy


Rajya Sabha might discuss economy, Pegasus next week
by Shemin JoyRajya Sabha is likely to discuss the status of the economy and the controversial snooping episode involving Israeli spyware ‘Pegasus’ next week when the government is expected to face tough questions from the Opposition.
Two notices to discuss the “grave economic crisis facing India and how to improve the situation” have been submitted to Rajya Sabha Chairman M Venkaiah Naidu by Congress and Trinamool Congress.
The Trinamool notice was signed by Shiv Sena, DMK, RJD, AAP, MDMK and NCP among others. The Opposition wants to put the government on the mat over PSU disinvestments, unemployment and scrapping surveys that were unfavourable to the government.
Another issue that the leaders want to discuss was the recent controversy over the use of Pegasus software to breach WhatsApp of at least 121 activists and journalists with the Congress even claiming that its general secretary Priyanka Gandhi was also a victim. The company’s claim that it sells the software only to government agencies has raised questions and accusations that the government was illegally spying on people.


Contains all the header files defining the Pantheios Core, Application Layer, stock Back-end, stock Front-end and Utility components.
The header files are divided into the following sub-directories:
| Sub-directory | Purpose |
|---|---|
b64 |
Header file(s) for the b64 library, which is bundled with Pantheios |
pantheios |
Header files for all Pantheios components |
pantheios/util |
Header files for the Pantheios Utility components |
pantheios/backends |
Header files for all Pantheios stock Back-end components |
pantheios/frontends |
Header files for all Pantheios stock Front-end components |
pantheios/implicit_link |
Header files for implicit linking to Pantheios libraries |
pantheios/inserters |
Header files for Pantheios Application Layer inserter classes |
pantheios/internal |
Header files for internal (implementation) components |
pantheios/quality |
Header files for quality assurance components / sub-systems |
pantheios/test |
Header files for test-related components |
shwild |
Header file(s) for the shwild library, which is bundled with Pantheios |
xtests |
Header file(s) for the xTests library, which is bundled with Pantheios |


In this post I will describe what is variable hoisting in JavaScript. But before we go into detailed explanations let’s look at a few code examples.
Let’s start with function bad()
that tries to
access not defined variable:
As we would expected attempt to read notDefinedVariable
ends with an error, namely ReferenceError
exception.
Now you may be wondering what will happen when we try to write value to not defined variable:
In this case we get no error and everything seems to work,
but after a call to bad2()
notDefinedVariable
is still visible.
What happened here is that we accidentally create
notDefinedVariable
global variable.
Using global variables is regarded by many programmers as
bad practice, especially when you can create them accidentally.
To prevent errors and encourage good programming style
ECMAScript 5 standard (which defines JavaScript language)
introduced so called strict mode.
We can enable strict mode by beginning function with 'use strict';
instruction.
With strict mode enabled we get ReferenceError
exception
when we try to write to undefined variable:
You may be wondering what will happen when we declare variable but didn’t assign any value to it, will we get an error while accessing variable value or not?
We didn’t get an error, that’s because variables declared using var
keyword
initially have value of undefined
until explicitly assigned by the user.
Now let’s look on a bit more complicated example:
You may wonder what happened here. We already know that reading
from undefined variables causes ReferenceError
exception, but
why first call to console.log(x)
didn’t throw any?
And what about undefined
value that was printed,
we know that this is the value of declared but not initialized variables.
So to sum up this function behaves as if variable x
was declared
at the very beginning of hoisting1
function:
And this is what hoisting is all about. In JavaScript it doesn’t
matter where you declare variables inside function body
because they declarations will be implicitly
moved to the beginning of the function.
For example func1()
when processed by JavaScript interpreter
behaves exactly the same as func2()
:
As you may expect hoisting can make troubles when we are not alert, for example can you spot a bug here:
If i
was accessible only inside for(var i ...)
loop
interpreter would spot problem with console.log(arrayJ[i])
line
and would thrown a ReferenceError
exception. Unfortunately
because of hoisting
variable i
is accessible through entire function body and only
wrong behaviour of our program can tell us that something is wrong.
So how can we make our programs secure against bugs caused by hoisting? The simples thing to do is to declare all function variables at the beginning of function body, thus making hoisting explicit:
This is simple and effective solution but a bit cumbersome to use,
it would be better to declare variables in the place of their first usage
right?
In some cases you really want to reduce visibility of a variable to some block of code, the popular solution to this problem is to use IIFE pattern:
Here variable i
is visible only inside IIFE expression.
Using IIFE has it’s own problems especially when we want to
access this
value inside IIFE expression.
We must either pass this
via local variable:
or use call()
to invoke function:
Almost every popular language (e.g. Java, C#, C++) follows block scoping rules, this means that variable is visible only inside a block of code in which it is declared. For example in Java:
Compare this with JavaScript version:
If you have this strange feeling that something is wrong here
you are not the only one.
JavaScript community decided to introduce proper lexical scoping
to JavaScript in ECMAScript 2015 standard (sometimes called ES6).
ECMAScript 2015 introduces a new JavaScript keyword let
that
works like var
but with lexical scoping.
Here’s how our example looks with let
:
let
also prohibits redeclarations of variables:
Support of ES6 among browsers is pretty good right now (you may also use it with node.js), but I guess if you have a chance to work with ES6 you most probably will be transpiling ES6 code into ES5 code (a plain old JavaScript) using Babel or some other transpiler.
I hope you now know what hoisting is and what troubles it can make. If you have any remarks how I can improve this article please leave a comment.


descr: Descriptive Statistics
Weighted frequency and contingency tables of categorical
variables and of the comparison of the mean value of a numerical
variable by the levels of a factor, and methods to produce xtable
objects of the tables and to plot them. There are also functions to
facilitate the character encoding conversion of objects, to quickly
convert fixed width files into csv ones, and to export a data.frame to
a text file with the necessary R and SPSS codes to reread the data.
| Version: |
1.1.4 |
| Imports: |
xtable, utils, grDevices, graphics, stats |
| Published: |
2018-01-19 |
| Author: |
Jakson Aquino. Includes R source code and/or documentation
written by Dirk Enzmann, Marc Schwartz, Nitin Jain, and Stefan
Kraft |
| Maintainer: |
Jakson Aquino <jalvesaq at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/jalvesaq/descr |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
descr results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=descr
to link to this page.


BJP workers erupts joy in Nagpur, Celebration galore
by Nagpur Today, Nagpur NewsNagpur: As the name of Devendra Fadnavis was announced as next Chief Minister of Maharashtra in a sudden twist and turn in the polity of Maharashtra, the joy was evident among BJP workers in Nagpur.
As the party workers woke up to pleasant surprise with the news of Fadnavis taking up the state government’s top job


Please, login via Facebook and keep this tab alive for some time to seed app to some more users from your browser


Gaucho Bingo Night
Home
Sign up
(current)
My Account
What are you waiting for?
Join us by filling out this short form.
Your information
(We'll only use these data to administer the Gaucho Bingo Night)
Full name (preferred)
Email
Address line 1
Address line 2
City
State (abbr.)
ZIP code
What to order
Select package:
$20: Basic package (includes 1 bingo card, delivered via email)
$60: VIP package (includes cards printed on thick cardstock, snacks, a commemorative drinking glass, and an ink stamper; delivered by hand to any address in the Greater Bay Area)
Additional cards
Summary
Item
Number of Cards
@
Unit Price
Price
Placeholder Item
2
N/A
$60.00
Additional cards
Quantity
@
$10.00
$1 000.00
Total
102
N/A
$1 060.00
Payment
Payment method
Token payment (select if you've been provided a token of the form
00000000-0000-0000-0000-000000000000
)
Informal payment (with cash, check, etc.)
PayPal (ACH, credit, debit, etc.)
Enter token here
Procced to payment


|
gpath − path resolver for OGDL streams |
|
gpath [options] path [filename] |
|
gpath is a structured text file processor that reads an OGDL file or stream and extracts parts of it according to the specified path. The input file or stream is loaded as a graph in memory, and printed out according to the given path. If ’.’ is given as path, the whole graph is printed to the standard output. Else the path is resolved against the graph and the result printed. |
|
-d n |
Depth of output graph |
|
-n number |
|
Number of spaces used for indentation (default 4). |
|
-r number |
|
Include the root node. |
|
The path language used is OGDL Path, as specified in <http://ogdl.org>. Basically, it is a set of tokens separated by dots, where the dots act as edges of the graph. |
|
Given the file ’network.g’: eth0 ip <IP_ADDRESS> mask <IP_ADDRESS> gw <IP_ADDRESS> dns <IP_ADDRESS>, <IP_ADDRESS> $ gpath eth0.ip network.g <IP_ADDRESS>. $ gpath -d 1 eth0 network.g ip mask gw $ gpath dns.[0] network.g <IP_ADDRESS> |
|
Only trees are currently supported; cycles are not yet stabilized in the OGDL specification. |
|
Rolf Veen. See <http://ogdl.org> for more information. |


Navigation
Navigation
The main navigation bar is on the top of the screen. Some menu items are hidden pending log-in.
- File
- Home - This is the first screen users see after login. It presents high-level information about ToF.
- Open from computer - Users can open a resource directly from their computers, either an XML file or JSON file, and edit the resource in ToF without saving the resource to the FHIR server. When saving, the browser prompts users to re-download the updated resource as an XML or JSON file depending on the format when opened.
- Documentation - Opens this help documentation in HTML format.
- Request Support - Opens the support page for ToF, where users can submit support requests (defects, new ideas for features/improvements and general questions).
- Settings - This opens the settings window for ToF.
- Browse/Edit - Search, select, delete, and create new resources depending on the resource type selected in the sub-menu.
- Implementation Guides
- Profiles
- Capability Statements
- Operation Definitions
- Value Sets
- Code Systems
- Questionnaires
- Import - Import resources from other locations into ToF.
- Export - Export implementation guides from ToF in various formats (i.e., bundles, FHIR IG Publisher package, GitHub, etc).
- Publish - Publish your implementation guide using the FHIR IG Publisher.
On the right-side of the navigation menu, users will find:
- A label indicating the currently selected FHIR server. You may click on this label to open the settings for ToF and select a different FHIR server. This is the same as clicking on the File > Settings menu.
- A label indicating the user that is currently logged in. You may click on your name to edit your profile. A user is represented as a FHIR Practitioner resource.
- An icon for logging out of GitHub (if you are logged into GitHub within ToF).
- An icon for logging out of ToF (if you are logged into ToF).
- An icon for getting real-time guidance on how to use each screen. When clicked, a tour of the page will be started, highlighting key points of interest. Note: not all screens support "tour" functionality.
FHIR Versions in the UI
Depending on the FHIR server and the version it supports, the screens for editing resources may appear (e.g., STU3 vs. R4). The screens in ToF reflect the changes between STU3 and R4 resources. In STU3, for example, ImplementationGuide has "packages" with "resources" inside each package. In R4, ImplementationGuide has "resources" parallel to "packages," and each resource references a package.


Click or shift+drag to select grid cells and download the tiles of a 100x100 km image.
Be carefull to not select too many tiles. It might trigger a lot of parallel HTTP requests.


text-primary
text-muted
text-secondary
text-error
Changing an icon’s color involves changing the CSS color or using text-primary
, text-muted
, text-secondary
or text-error
.
The color will not be communicated to users using assistive technologies, so include an aria-label
if the color is used to convey meaning.


Timepicki is a light weight jQuery Time Picker plugin for using in form submission websites. User can easily pick time in the form with using it.
Learn more
Copyrights @Timepicki, by @senthil2rajan.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
For the watercress purée, heat the butter in a pan over a moderate heat. Add the watercress stalks and season well with salt and freshly ground black pepper. Cook for 2-3 minutes, until softened, then add the chicken stock and the majority of the watercress leaves (reserving a small handful of attractive leaves for garnishing). Cook for one minute, until the watercress leaves have just wilted.
Allow the watercress mixture to cool, then transfer to a food processor and blend until smooth. Pass the purée through a fine sieve and keep warm.
For the leeks, bring a pan of salted water to the boil, then add the leeks and cook for one minute. Drain and refresh in iced water.
Dry the leeks thoroughly, then place onto a hot griddle pan. Griddle the leeks for 1-2 minutes on each side, until brown griddle lines cover the leeks. Remove from the heat and keep warm.
For the halibut, heat a non-stick frying pan until moderately hot, then add the vegetable oil.
Season the halibut steaks on both sides with salt, then place into the pan and fry until lightly golden-brown, then gently turn over.
Add the butter, then cover the fish with the butter paper and remove from the heat.
Pour the hot chicken stock over the top of the butter paper and allow to rest for two minutes.
Place the pan back over a moderate heat and cook for 2-3 minutes, or until the fish is just cooked through. Remove from the heat and set aside to rest.
For the coddled eggs, place the butter into a separate pan over a high heat and cook until it starts to turn brown. Add a good pinch of table salt and the lemon juice.
Gently break the tops off the quails' eggs.
Heat a pan of water until simmering, then place small egg cobbling moulds, or ramekins, into the simmering water. Fill each with ½ tsp of the browned butter.
Add a quail's egg into each of the moulds and allow the water to simmer gently until the eggs have just set. Remove the moulds from the water and, using a teaspoon, gently scoop out the eggs onto kitchen paper. Season, to taste, with salt and freshly ground black pepper.
To serve, spoon the watercress purée onto the centre of each plate. Top with a piece of halibut, and three leeks. Place a coddled egg on top of each and scatter the watercress leaves around. Place two cobbled eggs either side of the halibut steaks.
Sprinkle the chopped cobnuts over and season well with salt and freshly ground black pepper and finish with a drizzle of olive oil.


Dwarf Explorer Droph:
Well, I have activated the necessary equipment. Behold, Hekaton Prime approaches!
You and your colleagues must defeat him. If 2 of you can do so without help, you will be richly rewarded.
Good luck!


flutter_chips_input
Flutter library for building input fields with InputChips as input options.
Usage
Installation
Follow installation instructions here
Import
import 'package:flutter_chips_input/flutter_chips_input.dart';
Example
ChipsInput
ChipsInput(
initialValue: [
AppProfile('John Doe'<EMAIL_ADDRESS>'https://d2gg9evh47fn9z.cloudfront.net/800px_COLOURBOX4057996.jpg')
],
decoration: InputDecoration(
labelText: "Select People",
),
maxChips: 3,
findSuggestions: (String query) {
if (query.length != 0) {
var lowercaseQuery = query.toLowerCase();
return mockResults.where((profile) {
return profile.name.toLowerCase().contains(query.toLowerCase()) || profile.email.toLowerCase().contains(query.toLowerCase());
}).toList(growable: false)
..sort((a, b) => a.name
.toLowerCase()
.indexOf(lowercaseQuery)
.compareTo(b.name.toLowerCase().indexOf(lowercaseQuery)));
} else {
return const <AppProfile>[];
}
},
onChanged: (data) {
print(data);
},
chipBuilder: (context, state, profile) {
return InputChip(
key: ObjectKey(profile),
label: Text(profile.name),
avatar: CircleAvatar(
backgroundImage: NetworkImage(profile.imageUrl),
),
onDeleted: () => state.deleteChip(profile),
materialTapTargetSize: MaterialTapTargetSize.shrinkWrap,
);
},
suggestionBuilder: (context, state, profile) {
return ListTile(
key: ObjectKey(profile),
leading: CircleAvatar(
backgroundImage: NetworkImage(profile.imageUrl),
),
title: Text(profile.name),
subtitle: Text(profile.email),
onTap: () => state.selectSuggestion(profile),
);
},
)
To-do list
X
Ability to limit the number of chips
Create aFormField
implementation (ChipsInputField
) to be used within Flutter Form Widget
Known Issues
- Deleting chips with keyboard on IOS makes app to crush (Flutter Issue with special characters used as replacement characters). Already reported #1
- Overlay doesn't move when input height changes i.e. when chips wrap
- For some reason Overlay floats above AppBar when scrolling
Libraries
Dart
- dart:ui
- Built-in types and core primitives for a Flutter application. [...]
- dart:async
- Support for asynchronous programming, with classes such as Future and Stream. [...]
- dart:collection
- Classes and utilities that supplement the collection support in dart:core. [...]
- dart:convert
- Encoders and decoders for converting between different data representations, including JSON and UTF-8. [...]
- dart:core
- Built-in types, collections, and other core functionality for every Dart program. [...]
- dart:developer
- Interact with developer tools such as the debugger and inspector. [...]
- dart:math
- Mathematical constants and functions, plus a random number generator. [...]
- dart:typed_data
- Lists that efficiently handle fixed sized data (for example, unsigned 8 byte integers) and SIMD numeric types. [...]
- dart:io
- File, socket, HTTP, and other I/O support for non-web applications. [...]
- dart:isolate
- Concurrent programming using isolates: independent workers that are similar to threads but don't share memory, communicating only via messages. [...]


How to enable RCS messaging on Google Messages for Android
by Ben SchoonRCS messaging is finally coming to Android smartphones in the United States after a trial in some other countries. If you want to get in on the action, here’s what you’ll need to do to enable RCS messaging on Android.
Note: Google’s RCS rollout in the United States started on November 14th but won’t be completed until the end of 2019.
How to enable RCS on Android
- Download Google Messages
- Set Google Messages as your default SMS app
- Enable Chat Features
Download Google Messages
While the carriers will have their own app at some point in the future, you can get RCS messaging right now using your carrier number with the Google Messages app for Android. On many current smartphones including Google’s Pixel lineup as well as Motorola and LG devices too. If the app isn’t pre-installed on your device, though, you’ll need to download Google Messages from the Play Store.
Set Google Messages as your default SMS app
Once Google Messages is installed, you’ll need to set it as your default SMS app if you haven’t already. If the app was pre-installed, it probably already is your default SMS app and you can skip this step entirely.
If the app wasn’t pre-installed, upon opening it you should get a prompt to set the app as your default SMS app. It looks like this:
Enable RCS features on Google Messages for Android
Finally, what we’re all here for. If you want to enable RCS on your Android smartphone using Google’s method, you can do so by tapping on a pop-up message when you open the app. This pop-up, seen below, will start the process of preparing RCS features on your device.
That message will say “New! See when friends are typing” or “Messages just got better.” In either case, there’s a big blue button that says “Agree” or “Upgrade Now.” Tapping that button will start the process of enabling RCS features. After a few minutes, going to Settings > Chat Features within Google Messages will tell you whether or not you’re connected to Google’s servers.
If you’re texting another user with RCS messaging enabled, the text field will show “Chat Message” instead of “Text message.”
Keep in mind, though, that this process will only work once the RCS rollout has arrived on your Android smartphone. Google says that the rollout should be completed by the end of 2019 for Android users in the United States.
More on RCS:
- Google officially rolling out RCS Chat in the US through Messages app
- Verizon, AT&T, Sprint, and T-Mobile join forces to bring RCS to Android in 2020
- Google Messages trick enables RCS on any Android phone and carrier
FTC: We use income earning auto affiliate links. More.


Gallery of all PyClaw applications¶
Contents
- Gallery of all PyClaw applications
- 1-dimensional advection
- 1-dimensional variable-velocity advection
- 1-dimensional acoustics
- 1-dimensional Burgers’ equation
- 1-dimensional shallow water equation
- 1-dimensional nonlinear elasticity
- 1-dimensional Euler equations
- 2-dimensional advection
- 2-dimensional variable-coefficient advection
- 2-dimensional acoustics
- 2-dimensional variable-coefficient acoustics
- 2-dimensional shallow water equations
- 2-dimensional shallow water on the sphere
- 2-dimensional Euler equations
- 2-dimensional KPP equation
- 2-dimensional p-system
1-dimensional variable-velocity advection¶
Advecting Gaussian and square wave with periodic boundary.
Source code ... Plots
1-dimensional acoustics¶
Acoustics equations with wall boundary at left and extrap at right.
Source code ... Plots
1-dimensional Burgers’ equation¶
Burgers’ equation with sinusoidal initial data, steepening to N-wave.
Source code ... Plots
1-dimensional nonlinear elasticity¶
Evolution of two trains of solitary waves from an initial gaussian.
Source code ... Plots
1-dimensional Euler equations¶
Woodward-Colella blast-wave interaction problem.
Source code ... Plots
2-dimensional acoustics¶
Expanding radial acoustic wave in a homogeneous medium.
Source code ... Plots
2-dimensional variable-coefficient acoustics¶
Expanding radial acoustic wave in a two-material medium with an interface.
Source code ... Plots
2-dimensional shallow water on the sphere¶
Wavenumber 4 Rossby-Haurwitz wave on a rotating sphere.
Source code ... Plots


Newton was the first to correctly describe the force of gravity that Galileo was studying. He found that near the surface of the Earth the force of gravity is indeed just proportional to the mass of the object
where the constant
If you take your mass in kilogram and you multiply by
\9.8 \;m/s^2 \, you will get your weight in Newtons. You can then convert Newtons to pounds if you want. 1 lbs is approximately 4.4 Newtons.
The force of gravity does depend on the mass and it is bigger on the hammer than on the feather. How is it that the time to fall is the same for both??
The fact that free fall time does not depend on the mass has to do with one crucial aspect that makes gravity special compared to the other forces.
The analog of electric charge for gravity is the mass but mass is also related to inertia. The more mass you have the harder it is to move. The more mass you have the harder it is to change your momentum.
So the force of gravity is stronger on the hammer and thus the change in momentum of the hammer is going to be bigger. But the momentum is also proportional to the mass
If you like equations,...
You remember that force is just change of momentum
The case of gravity is special because the force depends on the mass
The conclusion is that the time it takes to fall only depends on the velocity (and the constant g). The only thing that matters is whether the hammer and the feather are thrown downward or simply dropped. The mass of the object is irrelevant for the time it takes to free fall!
[ciscode|rev=1|tool=elmsmedia|item=2734|entity_type=node|render=display_mode|display_mode=h5p]


Website Name/Logo
Home
Page 1
Page 1-1
Page 1-2
Page 1-3
Page 2
Signin
Login
Change Password
Old Password
New Password
Confirm Password
Change Password
Left
CONTACT US
  Phone :
  Email :