This post explains how to build a custom dumbbell chart with a gap column on the right hand side with R and ggplot2. Step by step code snippets with explanations are provided. Thanks a lot to Fred Duong for this contribution!
This tutorial by Fred Duong will show you how to create dumbbell charts with gap columns, similar to the charts you might have seen from Pew and YouGov.
The original dumbbell chart built by the Pew Research Center is shown below:
We can get pretty close to the chart with ggplot2()
(and
with a lot more tweaking that we won’t go into here, we could probably
exactly recreate the chart!)
Here is the final figure that we’ll create.
But first, let’s create the basic figure!
Let’s use the tidyverse
that includes
ggplot2
and `patchwork`` to combine plots.
library(tidyverse)
library(patchwork) #to combine plots
First, we create a tibble that contains the data from the figure.
=tibble(
rawlabels=c(
"Spirituality, faith and religion",
"Freedom and independence",
"Hobbies and recreation",
"Physical and mental health",
"COVID-19",
"Pets",
"Nature and the outdoors"),
Dem=c(8,6,13,13,8,5,5),
Rep=c(22,12,7,9,5,2,3)
)
=raw df
From this tibble, it’s easy to create the base figure. Though you can
keep the data in wide format, I find it simpler to wrangle the data
into long format first thanks to the
pivot_longer
function.
=df %>%
df_longpivot_longer(-labels)
Now let’s build the plot:
%>%
df_long ggplot(aes(x=value,y=labels)) +
geom_line(aes(group=labels), color="#E7E7E7", linewidth=3.5) +
# note that linewidth is a little larger than the point size
# so that the line matches the height of the point. why is it
# like that? i don't really know
geom_point(aes(color=name), size=3) +
theme_minimal() +
theme(legend.position = "none",
axis.text.y = element_text(color="black"),
axis.text.x = element_text(color="#989898"),
axis.title = element_blank(),
panel.grid = element_blank()
+
) scale_color_manual(values=c("#436685", "#BF2F24"))+
scale_x_continuous(labels = scales::percent_format(scale = 1))
That’s it! Not a ton of code! But, you’ll notice that this is missing a few key formatting features from the original Pew figure:
The y-axis is not sorted. By default,
ggplot()
alphabetizes character vectors that are used
for the axes. Then, it displays the y-axis values
reverse alphabetized. We can manually sort this however
we want, but sorting by descending gaps is common. To do this, we
will compute the gaps, and then turn the y-axis labels into a
factor so that ggplot()
respects the order.
There are no text labels. Some dumbbell figures
have data callouts to the left and right, and some have them
underneath or above the points. When it’s to the left and right,
it’s a little complicated to implement. There are two issues: 1)
Because Democrats and Republicans might switch orders in terms of
which is the higher value, you can’t rely just the categories
themselves to decide which is left and right (Democrats won’t
always be the lower value, for example); and 2) by default,
positioning the data callouts will place them right on top of the
points. But we will want to nudge the data callouts either to the
left or to the right of the points. To solve this, we will create
a variable that assesses the maximum value per label (or minimum)
to assign the left and right callouts. Then, we can either use two
geom_text()
s, with each one using
filter()
on the data to only show the maximum or not
the maximum and using the color
aesthetic
set to political party, or–more simply–use one
geom_text()
that uses an
if_else()
statement to assign that conditions on if
the value == the maximum value. I’ll show the latter case.
There is no color legend for the points. By
default, ggplot
will attempt to put a typical legend
for your color aesthetic that is placed to the right. You’ll see
above I turned this off in theme()
. It can be much
more readable to place the legend above the actual points
themselves, as you see from the Pew figure.
There is no gap column. The best way I can figure
out how to do this is to create a second
ggplot
object, and use patchwork
to
stitch it together with the main plot.
The complete figure will solve all four issues, but this requires a lot more tweaking. The complete figure will consist of the main dumbbell figure and a gap figure.
A few additional data wrangling steps are necessary to complete the final figure:
=raw %>% # raw is just the generated data
df
# compute the gap
mutate(gap=Rep-Dem) %>%
# find the maximum value by label
group_by(labels) %>%
mutate(max=max(Dem, Rep)) %>%
ungroup() %>%
# sort the labels by gap value
# note that its absolute value of gap
mutate(labels=forcats::fct_reorder(labels, abs(gap)))
# make into long format for easier plotting
=df %>%
df_longpivot_longer(
c(Dem,Rep)
)
df
%>% head() df_long
The left hand side part of the final plot can then be built as follow:
# set a custom nudge value
=.6
nudge_value
=
p_main%>%
df_long
# the following 3 lines of code are the same
ggplot(aes(x=value,y=labels)) +
geom_line(aes(group=labels), color="#E7E7E7", linewidth=3.5) +
geom_point(aes(color=name), size=3) +
# but we want geom_text for the data callouts and the legend
# data callout
geom_text(aes(label=value, color=name),
size=3.25,
nudge_x=if_else(
$value==df_long$max, # if it's the larger value...
df_long# move it to the right of the point
nudge_value, -nudge_value), # otherwise, move it to the left of the point
hjust=if_else(
$value==df_long$max, #if it's the larger value
df_long0, # left justify
1),# otherwise, right justify
+
)
# legend
geom_text(aes(label=name, color=name),
data=. %>% filter(gap==max(gap)),
nudge_y =.5,
fontface="bold",
size=3.25)+
theme_minimal() +
theme(legend.position = "none",
axis.text.y = element_text(color="black"),
axis.text.x = element_text(color="#989898"),
axis.title = element_blank(),
panel.grid = element_blank()
+
) labs(x="%",y=NULL) +
scale_color_manual(values=c("#436685", "#BF2F24")) +
#extend the y-axis otherwise the legend is cut off
coord_cartesian(ylim=c(1, 7.5)) +
#display percentages with % appended
scale_x_continuous(labels = scales::percent_format(scale = 1))
p_main
The Gap figure on the right hand side must be created too. Pew formats the gap values in a particular way:
Colors by party
Adds “+” to the beginning of the gap value if positive
Adds “R” or “D” to the end of the gap value
Let’s do that!
=
df_gap%>% # note i am using df and not df_long
df mutate(
label=fct_reorder(labels, abs(gap)), #order label by descending gaps
# we need a column for the party with the max value
gap_party_max=if_else(
==max,
Rep"R",
"D"
),
# format gap values
gap_label=
paste0("+", abs(gap), gap_party_max) %>%
fct_inorder() #turns into factor to bake in the order
)
df_gap
And then the plot:
=
p_gap%>%
df_gap ggplot(aes(x=gap,y=labels)) +
geom_text(aes(x=0, label=gap_label, color=gap_party_max),
fontface="bold",
size=3.25) +
geom_text(aes(x=0, y=7), # 7 because that's the # of y-axis values
label="Diff",
nudge_y =.5, # match the nudge value of the main plot legend
fontface="bold",
size=3.25) +
theme_void() +
coord_cartesian(xlim = c(-.05, 0.05),
ylim=c(1,7.5) # needs to match main plot
+
)theme(
plot.margin = margin(l=0, r=0, b=0, t=0), #otherwise it adds too much space
panel.background = element_rect(fill="#EFEFE3", color="#EFEFE3"),
legend.position = "none"
+
)scale_color_manual(values=c("#436685", "#BF2F24"))
p_gap
Use the package patchwork
to put together your final
figure. You’ll need to play around with plot_layout()
a
bit to get your figures to fit together how you want.
=
p_whole# syntax from `patchwork`
+ p_gap + plot_layout(design=
p_main c(
area(l=0, r=45, t=0, b=1), # defines the main figure area
area(l=46, r=52, t=0, b=1) # defines the gap figure area
))
p_whole
And there it is! To summarize: making the basic figure is quite easy, but adding all the little extras to make it more readable takes a lot more tweaking.
Some final notes:
If you look at the original Pew figure, they sort it by party, and
then gap. There is not a built-in tidyverse way to do this, but
reorder_within()
from the r package
tidytext
might be able to. See
https://juliasilge.com/blog/reorder-within.
To pull colors from existing figures, I use a free color picker program for Windows called Color Cop.
Your y-axis labels might be long statements, which don’t
automatically wrap and then push the plot area so that it’s more
narrow. I find it helpful to use str_wrap()
for those
cases. See
this tutorial.
The dumbbell chart is a variation on the more simple lollipop chart, which only has one datapoint per row. You can find a different implementation of a dumbbell chart on the wonderful resource R Graph Gallery: https://r-graph-gallery.com/web-extended-dumbbell-plot-ggplot2.html.
This was inspired by this tweet from Andy Luttrell asking for help with the name of the type of chart and how to create it. On the name: it’s called a dumbbell chart because the gap line, if unmodified, is thin.  Maybe it should be called a gap chart.
I took the patchwork
approach from Kat Hoffman, who
used it for forest plots created with ggplot:
https://www.khstats.com/blog/forest-plots.
If you have feedback or questions or just want to connect, you can find my contact info on my webpage: fuhred.com.
Thank you for reading! I hope you had as much fun following along as I did making this! – Fred Duong


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You can create your own filtering tool and use it instead of the built-in tools, or along with them. Usually all you need to implement such a filter is to insert an HTML code representing your filter form into a template, instruct PHP Generator to use the new template within the OnGetCustomTemplate event handler and handle entered parameters within the OnPreparePage event handler. In the example below we created a filter which allows you to filter countries by life expectancy.
Technical details are as follows (click a link below to see the appropriate code):
An advanced version of this filter is represented on this page.


Constructor
new exports(modelpath, testpath, weightsnullable, batches)
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
modelpath |
string | Path to directory containing model | |
testpath |
string | Path to director containing validation minibatches | |
weights |
Float32Array | ArrayBuffer |
<nullable> |
Weights to load into Validator |
batches |
number | Number of validation minibatches |
- Source:
Extends
Methods
backward(output)
Execute backward pass of error through model.
Parameters:
| Name | Type | Description |
|---|---|---|
output |
Tensor | Error from output |
- Inherited From:
- Source:
forward(input) → {Tensor}
Execute forward pass of input through model.
Parameters:
| Name | Type | Description |
|---|---|---|
input |
Float32Array | Tensor | Features input to model |
- Inherited From:
- Source:
Returns:
Tensor containing model output
- Type
- Tensor
load(weightsnullable)
Loads weights into model, or generates new ones if needed.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
weights |
Float32Array | ArrayBuffer |
<nullable> |
weights to load into model, if null then weights will be generated |
- Overrides:
- Source:
save() → {ArrayBuffer}
Saves model weights.
- Overrides:
- Source:
Returns:
ArrayBuffer containing weights and biases from all layers in model
- Type
- ArrayBuffer
train(learn, input, expect, callbacknullable)
Train model and pass new weights and model accuracy as arguments to callback.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
learn |
float | learning rate | |
input |
Float32Array | Tensor | Features input to model | |
expect |
Float32Array | Tensor | Expected model output | |
callback |
trainingCallback |
<nullable> |
- Inherited From:
- Source:
validate(input, expect, callbacknullable)
Validate model and pass output accuracy as argument to callback.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
input |
Float32Array | Tensor | Features input to model | |
expect |
Float32Array | Tensor | Expected model output | |
callback |
validtionCallback |
<nullable> |
- Inherited From:
- Source:
validateWeights(weightsnullable, callbacknullable)
Validate weights
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
weights |
Float32Array | ArrayBuffer |
<nullable> |
Weights to validate |
callback |
validtionCallback |
<nullable> |
- Source:


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Cannes Man (1996) *** out of ***** - Cast: Seymour Cassel, Francesco Quinn. Written by: Irwin Rappaport, Deric Haddad, Susan Shapiro, and Richard Martini. Directed by: Richard Martini. Running Time: 90 minutes.
With special appearances by: Lara Flynn Boyle, James Brolin, Jon Cryer, Ann Cusack, Johnny Depp, Robert Evans, Peter Gallagher, Menacham Golan, Dennis Hopper, Randal Kleiser, Jim Jarmusch, Lloyd Kaufman, Julian Lennon, John Malkovich, Chris Penn, Kevin Pollak, Jim Sheridan, Bryan Singer, Benicio Del Toro, Harvey Weinstein, Frank Whaley, Treat Williams.
An odd, yet interesting concept for a low budget film. A dim-witted ex-video store clerk becomes a courier for Troma Films and has to take promotional materials to the infamous Cannes film festival in France. Once there, he becomes the subject of a bet between two movie producers. One of the producers is the "legendary" Sy Lerner (Cassel), who bets the other producer that he can make this kid into the hottest thing at the festival before it's over. Sy hypes the kid as Frank Rhino, the most talented screenwriter ever born who's written the most brilliant screenplay ever written. The two then wander around all over the festival, trying to sell their non-existant screenplay "Con Man" (get it? Con Man? Cannes Man? It's funny!) and pursuading stars and directors to attach themselves to the project.
As I said, "Cannes Man" has an ultra low budget, and all it really is is two guys bothering celebrities at various events in Cannes. It would have been interesting to watch the making of this film, as stars must have been approached literally minutes before their "roles" were to be filmed. Everyone seems to enjoy their little moments however, particularly Johnny Depp and Jim Jarmusch who contribute the most screen time of all the "special appearances". And it does have a few laughs sprinkled throughout. It's an incredibly simple yet fairly entertaining film. [R]
-- Chuck Dowling
Visit Chuck's Movie Reviews at http://users.southeast.net/~chuckd21/ Over 1,600 movies rated and/or reviewed! Movie news, film related links, and reader's reviews.
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How to change the style of your application with the css-editor?¶
From Mapbender3 version <IP_ADDRESS> you find a css-editor for every application which easily allows you to change the style (colors, sizes, icons, ...). In the editor you can define additional css classes, that will overwrite the default style.
Firebug can help you to find out the css-classes you want to change.
With the following css you can change the color of the toolbar and navigation toolbar. You also set the background and width of the sidepane.
.toolBar {
background-color: rgba(0, 29, 122, 0.8) !important;
}
.toolPane {
background-color: rgba(0, 29, 122, 0.8) !important;
}
.pan{
background-color: rgba(0, 93, 83, 0.9);
}
.sidePane {
overflow: visible;
background-image: url("");
background-color: #eff7e9;
}
.sidePane.opened {
width: 350px;
}
Until Mapbender3 Version <IP_ADDRESS> you had to create your own template with your own css-file to overwrite the default style. The new css-Editor makes styling much easier. The documentation to create your own templates you find at How to create your own Template?.


Class: Cask::CaskLoader::AbstractContentLoader Abstract Private
Overview
This class is part of a private API. This class may only be used in the Homebrew/brew repository. Third parties should avoid using this class if possible, as it may be removed or changed without warning.
It cannot be directly instantiated. Subclasses must implement the abstract
methods below.
Loads a cask from a string.
Direct Known Subclasses
Instance Attribute Summary collapse
-
#content ⇒ String
readonly
private
-
#tap ⇒ Tap?
readonly
private
Method Summary
Methods included from ILoader
Instance Attribute Details
#content ⇒ String (readonly)
This method is part of a private API. This method may only be used in the Homebrew/brew repository. Third parties should avoid using this method if possible, as it may be removed or changed without warning.
30 31 32 |
# File 'cask/cask_loader.rb', line 30 def content @content end |
#tap ⇒ Tap? (readonly)
This method is part of a private API. This method may only be used in the Homebrew/brew repository. Third parties should avoid using this method if possible, as it may be removed or changed without warning.
33 34 35 |
# File 'cask/cask_loader.rb', line 33 def tap @tap end |


Anupama Chingacham
PhD Candidate(SFB 1102)
Saarland University(UdS)
Spoken Language Systems (LSV)
Chair of Computational Linguistics (CoLi)
About
Hello World! I am Anupama Chingacham, a doctoral student in the department of computer science at Saarland University, Germany. Under the supervision of Prof. Dr. Dietrich Klakow and Prof. Dr. Vera Demberg, my current research is on the topic of paraphrasing to improve speech perception in noise. Recently, we proposed a novel framework for noise-adaptive spoken dialogue systems, which utilizes paraphrases to generate better intelligible utterances. My research interest, however, spans across multiple areas in computational linguistics, especially in the lanscape of employing large pre-trained models for low-resource settings and controlled text generation models.
News
- June 2023 Invited talk at EXPRESSION team in IRISA, France.
- Jan 2023 Invited talk at COG-MHEAR team in Edinburgh Napier University, United Kingdom.
- Jan 2023 Poster presentation at IEEE SLT, Qatar.
- Sept 2021 Our work received a Best Student Paper award at Interspeech!
- Sept 2021 Paper presentation at Interspeech, Czechia (hybrid).
- Dec 2018 Paper presentation at CLiC-it, Italy.
Publications
A Data-Driven Investigation of Noise-Adaptive Utterance Generation with Linguistic Modification Chingacham, Anupama and Demberg, Vera and Klakow, Dietrich Ninth IEEE Spoken Language Technology Workshop, 2023
Exploring the Potential of Lexical Paraphrases for Mitigating Noise-Induced Comprehension Errors Chingacham, Anupama and Demberg, Vera and Klakow, Dietrich 22nd INTERSPEECH Conference, 2021
Generalizing Representations of Lexical Semantic Relations Chingacham, Anupama and Paperno, Denis Fifth Italian Conference on Computational Linguistics, 2018
Projects
- PiN: Paraphrases in Noise Dataset - annotated with sentence-level intelligibility of sentential-paraphrases in noisy environments.
- SiN: Synonyms in Noise Dataset - annotated with word intelligibility of synonyms in noisy listening setups.
- Relational Representations Python, PyTorch - an extended Skip-gram model to represent semantic relation between two lexical units.
A growing list of my volunteerism
- Feb 2023 - Present Member @ Toastmasters International
- Jan 2023 - Present Language teacher @ Malayalam School
- Oct 2021 - Dec 2022 Member @ ISCA-SAC Mentoring Team
- Feb 2019 - June 2022 PhD Representative @ SFB 1102
- Feb 2015 - June 2016 Mentor @ Mentor Together
- Feb 2011 - June 2014 Tutor @ Make A Difference
Experience
-
Doctoral studies
University of Saarland, Germany
Oct 2018 - present
Pi-SPIN: Paraphrase to improve Speech Prception in Noise. -
Research Intern
Loria, CNRS, France
Feb 2018 - July 2018
Explored an unsupervised method to represent lexical relations.
Published our findings at CLiC-it conference. -
MSc Natural Language Procesing
Lorraine University, France
Sept 2017 - July 2018
Acquired knowledge on data modeling techniques and its application on NLP problems like for semantic parsing.
-
Networking Engineer Consultant
Cisco Systems Pvt Ltd (India)
Sept 2014 - Aug 2017
Automated applications migration plan generation.
Orchetrated application migration for multiple service providers.
-
-
Masters in Software Engineering (5 Year Integrated)
Vellore Institute of Technology, India
2009 - 2014
A recipient of Top 10 Academic Performers award for 4 years.
CGPA: 9.2/10
Gallery


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{% extends "imagr_app/base.html" %} {% block body %}
The image sharing website that's totally not called imgur


This is a demo for the MPEG21 MCO to Smart Contract UPM Response to
N19504, based on Ethereum Smart contracts. The transformation from an
MCO contract into a smart contract is performed by considering
payments as transactions and actions to be performed on IP entities in
the form of assets, i.e. Non Fungible Tokens
In this demo:
1) Each IP entity has its own identifier and a dedicated smart
contract.
2) It is considered only one smart contract at the address
0xBbdA7C2fB654F901B38743A6A6D5afC27497791d of the Ropsten test
network. If you are reading here you should have
installed Metamask
, so switch to the Ropsten test network in the extension for the
correct functioning.
3) This smart contract holds all the infos regarding MCO contracts
related to this IP entity. Infos consist in a list of MCOContract
structures, in which each struct contains parties (Ethereum addresses)
and pointers to Non Fungible Tokens.
4)
ERC721 Tokens
are generally used for assets, debts or virtual collectables (games)
and are, together with ERC20, the actual main use case for smart
contracts.
5) When you click on "Upload MCO Contract to Ethereum Blockchain" a
new MCOcontract structure is created within the main smart contract,
taking in input the data that it is found on the left editor.
6) The left editor reads JSON-LD files encoded from MCO contracts.
Parties are associated to random addresses, while for each
DeonticExpression a new ERC721 token is minted. Each token will be
assigned to the Permission/Obligation/Prohibition assignee and an URI
will point to the action infos.
7) The right editor shows data that is ACTUALLY STORED ON BLOCKCHAIN.
Basically all the token issued since the start of the implementation.
8) The bottom left editor shows part of the final smart contract
created from the information found in the JSON-LD when payments are
involved.


Shilen's Stone Statue:
Righteous Elrokian warrior! I accept your oath on behalf of Shilen. You are reborn as a true Elrokian warrior, protected by Shilen and given power over the dinosaurs.
"The oath mentioned that I would wield Gazkh. Where is Gazkh?"


{{child_info.child.name}}'s Records
{% for detail in child_info.conditions %}
{{ detail }} {% endfor %}


#include <AggOOBrush.h>
Public Member Functions | |
| SolidBrush (const Color &color) | |
| Status | GetColor (Color *color) const |
| Status | SetColor (const Color &color) |
| virtual Brush * | Clone () const |
Protected Member Functions | |
| SolidBrush () |
| AggOO::SolidBrush::SolidBrush | ( | const Color & | color | ) | [inline] |
Constructor
| AggOO::SolidBrush::SolidBrush | ( | ) | [inline, protected] |
Create a style handler pointer for this brush
| Brush * AggOO::SolidBrush::Clone | ( | ) | const [virtual] |
Create a clone of this brush
Implements AggOO::Brush.


Design
Great code starts with the design. Whether it be the user interface, data models, or project
structure, I strive to design only the best.
Development
I strive to write clean, beautiful, scalable code. I have solid foundational knowledge of
programming practices, data structures, and algorithms.
Back-End
Data is very valuable, but only if it is used right. I can design data models, query/analyze data,
write APIs, and create web services.


--- layout: page title: About header-img: "img/about-bg.png" ---
I am excited to share my adventure switching careers from the corporate world to tech!


'Urban Legend'
by Simon Calloway
Cast: Alicia Witt; Jared Leto; Rebecca Gayheart; John Nevill; Robert England.
DVD Widescreen 2.35:1/Standard/Making-Of Featurette/Cast Bios/Dolby 5.1
In the upteenth slasher flick Hollywood has rushed out to the multiplexes since Scream smashed its way to the top of the box office in 1996, Urban Legend stands as either malarkey or a camp classic. I view it as both.
It opens in the tradition of "the girl gets it" with a women driving alone on a desolate road in the middle of nowhere, during a thunder storm. She goes to a run-down gas station, but is forced to go inside for the creepy attendant (Brad Dourif) says that her credit card won't work.
The film then takes us to a small university in Maine, where the local school newspaper reporter (Leto) claims that the girl was killed based on an urban legend. Everyone thinks he is crazy, except for smart Natalie (Witt, who, if she keeps her cards right, could become a Jamie Lee Curtis of the `90s) who believes him and is attacked by a stranger wearing a big, bulky coat, hood and all, carrying an axe!
After several more attacks and bloody murders, Natalie and reporter Paul try to find who the killer is and which urban legend he will use next.
Urban Legend reminded me of Scream many times, but not in the sense that Legend is a blunt rip-off, but in small details: The big frat house party that takes place at the end of the movie; the leading lady being the prime target of the psycho; the scene where a girl is being chased inside a radio station, which feature enormous sound-proof windows (Scream 2) for the killer to surprise her.
Legend isn't fresh in the brains department, but, lo, it is a lot of fun. It's stylishly directed by newcomer Jamie Blanks and is probably the best horror film featuring a masked maniac since the Scream movies and Halloween: H20. We can safely pass on Disturbing Behavior and Bride of Chucky.
The killer's identity is very hard to figure out, and when you think you know, you really don't. I was disappointed with the revealing of the murderer and the cause for the campus slaughter, yet, the actor/actress makes such fun of the role the thin plotline sort of creeps away from you.
Urban Legend isn't great, but the amount of sustained scares and the way the film is shot, it isn't the worst way to spend two hours. But, for a real treat in college carnage, rent Scream 2.
*** ~TeenViews (c) 1999~
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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write-excel-file
Write simple
*.xlsx
files in a browser or Node.js
Objects
Edit
Schema
Edit
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Cell data
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Columns config
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Chelsea appeal against transfer ban to be heard by CAS
The Blues were initially sanctioned by world governing body FIFA in February of this year.
Chelsea’s appeal against their two-window transfer ban will be heard at the Court of Arbitration for Sport today.
The Blues were initially sanctioned by world governing body FIFA in February of this year.
FIFA found the London club guilty of breaching article 19 of its regulations on the status and transfer of players (RSTP), which concerns the protection of minors.
They were also found to have breached article 18, concerning third-party influence.
Chelsea appealed to FIFA and the case was heard in April, but documents published by FIFA at the end of October showed its appeals committee found the Blues had committed 150 breaches of regulations.
The appeals committee agreed with the disciplinary committee that the breaches were “inexcusable”.
CAS will not make a decision today, but it has been reported that the verdict should come in time for the club to know whether they can be involved in the January 2020 transfer window.
Chelsea declined to comment on the CAS appeal when contacted by the PA news agency on Tuesday.
The club have made a good start to life under new head coach Frank Lampard despite not being able to sign any new players in the summer.
The likes of Tammy Abraham, Mason Mount and Fikayo Tomori, all Chelsea academy products, have returned to the club from loans elsewhere to make a big impression on the first team at Stamford Bridge.
All three have also been capped at England senior level by Gareth Southgate since the start of the season and the Blues are in third place in the Premier League table, behind second-placed Leicester only on goal difference.


UPPCB told to shut hot-mix plants lacking requisite consent
NGT issues the order on plea alleging violation of environmental norms by plants
by Staff ReporterFollowing a plea seeking closure of hot-mix plants burning charcoal, the National Green Tribunal (NGT) has directed the Uttar Pradesh Pollution Control Board (UPPCB) to ensure that plants which do not have the requisite consent be shut.
Noting that several of the units had shifted locations following previous orders of the tribunal, a Bench headed by NGT judicial member Justice Raghuvendra S. Rathore said, “In such circumstances, it is ordered that the remaining hot-mix plants shall continue to be sealed unless and until they obtain permission from the pollution control board under the relevant laws.”
“If any of the remaining units do not obtain consent from the pollution control board or shift to another location and starts functioning, the UPPCB shall immediately take steps for their closure and also inform the tribunal accordingly,” the Bench added.
Additionally, the green panel directed that any report pertaining to the matter, filed by the UPPCB or the Noida authority, has to be registered and listed before the tribunal separately.


This form will display information regarding a particular Facility's enrolment information.
If the record is currently associated to the Current Enrolment field on a facility, the sum values are pulled into the Capacity Utilization tab on Facility.


Python Packaging¶
A well structured project is easy to navigate and make changes and improvements to. It’s also more likely to be used by other people – and that includes you a few weeks from now!
Organization basics¶
We want to write a Python program that draws triangles:
We use the the Polygon
class of the matplotlib library and write a script called
draw_triangles.py
.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.set_xlabel('x')
ax.set_ylabel('y')
patch = plt.Polygon([
(0.2, 0.2),
(0.2, 0.6),
(0.4, 0.4)
])
ax.add_patch(patch)
ax.text(0.2, 0.4, '(0.2, 0.4)')
ax.text(0.2, 0.6, '(0.2, 0.6)')
ax.text(0.2, 0.4, '(0.2, 0.4)')
patch = plt.Polygon([
(0.6, 0.8),
(0.8, 0.8),
(0.5, 0.5)
])
ax.add_patch(patch)
ax.text(0.6, 0.8, '(0.6, 0.8)')
ax.text(0.8, 0.8, '(0.8, 0.8)')
ax.text(0.5, 0.5, '(0.5, 0.5)')
patch = plt.Polygon([
(0.6, 0.1),
(0.7, 0.3),
(0.9, 0.2)
])
ax.add_patch(patch)
ax.text(0.6, 0.1, '(0.6, 0.1)')
ax.text(0.7, 0.3, '(0.7, 0.3)')
ax.text(0.9, 0.2, '(0.9, 0.2)')
plt.show()
Do you think this is a good way to organize the code?
What do you think could be improved in the script draw_triangles.py
?
Functions¶
Functions facilitate code reuse. Whenever you see yourself typing the same code twice in the same program or project, it is a clear indication that the code belongs in a function.
A good function:
has a descriptive name.
draw_triangle
is a better name thanplot
.is small – no more than a couple of dozen lines – and does one thing. If a function is doing too much, then it should probably be broken into smaller functions.
can be easily tested – more on this soon.
is well documented – more on this later.
In the script draw_triangles.py
above,
it would be a good idea to define a function
called draw_triangle
that draws a single triangle,
and re-use this function every time we need to draw a triangle:
import matplotlib.pyplot as plt
def draw_triangle(points, ax=None):
if ax is None:
ax = plt.gca()
else:
fig, ax = plt.subplots()
ax.set_xlabel('x')
ax.set_ylabel('y')
patch = plt.Polygon(points)
ax.add_patch(patch)
for pt in points:
x, y = pt
ax.text(x, y, '({}, {})'.format(x, y))
draw_triangle([
(0.2, 0.2),
(0.2, 0.6),
(0.4, 0.4)
])
draw_triangle([
(0.6, 0.8),
(0.8, 0.8),
(0.5, 0.5)
])
draw_triangle([
(0.6, 0.1),
(0.7, 0.3),
(0.9, 0.2)
])
plt.show()
Python scripts and modules¶
A module is a file containing a collection of Python definitions and statements,
typically named with a .py
suffix.
A script is a module that is intended to be run by the Python interpreter.
For example, the script draw_triangles.py
can be run from the command-line
using the command:
$ python draw_triangles.py
If you are using an Integrated Development Environment (IDE) such as Spyder, PyCharm, or Visual Studio Code then the script can be run by opening it in the IDE and clicking on the “Run” button.
Modules, or specific functions from a module can be imported
using the import
statement:
import draw_triangles
from draw_triangles import draw_triangle
When a module is imported, all the statements in the module are executed by the Python interpreter. This happens only the first time the module is imported.
It is sometimes useful to have both
importable functions
as well as executable statements
in a single module.
When importing functions from this module,
it is possible to avoid running other code by placing it under
if __name__ == "__main__"
:
import matplotlib.pyplot as plt
def draw_triangle(points, ax=None):
if ax is None:
ax = plt.gca()
else:
fig, ax = plt.subplots()
ax.set_xlabel('x')
ax.set_ylabel('y')
patch = plt.Polygon(points)
ax.add_patch(patch)
for pt in points:
x, y = pt
ax.text(x, y, '({}, {})'.format(x, y))
if __name__ == "__main__":
draw_triangle([
(0.2, 0.2),
(0.2, 0.6),
(0.4, 0.4)
])
draw_triangle([
(0.6, 0.8),
(0.8, 0.8),
(0.5, 0.5)
])
draw_triangle([
(0.6, 0.1),
(0.7, 0.3),
(0.9, 0.2)
])
plt.show()
When another module imports the module draw_triangles
above,
the code under if __name__ == "__main__"
is not executed.
How to structure a Python project?¶
Let us now imagine we had a lot more code; for example, a collection of functions for:
plotting shapes (like
draw_triangle
above)calculating areas
geometric transformations
What are the different ways to organize code for a Python project that is more than a handful of lines long?
A single module¶
geometry
└── draw_triangles.py
One way to organize your code
is to put all of it
in a single .py
file (module)
like draw_triangles.py
above.
Multiple modules¶
For a small number of functions the approach above is fine, and even recommended, but as the size and/or scope of the project grows, it may be necessary to divide up code into different modules, each containing related data and functionality.
geometry
├── draw_triangles.py
└── graphics.py
import matplotlib.pyplot as plt
def draw_triangle(points, ax=None):
if ax is None:
ax = plt.gca()
else:
fig, ax = plt.subplots()
ax.set_xlabel('x')
ax.set_ylabel('y')
patch = plt.Polygon(points)
ax.add_patch(patch)
for pt in points:
x, y = pt
ax.text(x, y, '({}, {})'.format(x, y))
Typically, the “top-level” executable code is put in in a separate script which imports functions and data from other modules:
import graphics
graphics.draw_triangle([
(0.2, 0.2),
(0.2, 0.6),
(0.4, 0.4)
])
graphics.draw_triangle([
(0.6, 0.8),
(0.8, 0.8),
(0.5, 0.5)
])
graphics.draw_triangle([
(0.6, 0.1),
(0.7, 0.3),
(0.9, 0.2)
])
Packages¶
A Python package is a directory containing a file called __init__.py
,
which can be empty. Packages can contain modules as well as other packages
(sometimes referred to as sub-packages).
For example, geometry
below is a package, containing various modules:
draw_triangles.py
geometry
├── graphics.py
└── __init__.py
A module from the package can be imported using the “dot” notation:
import geometry.graphics
geometry.graphics.draw_triangle(args)
It’s also possible to import a specific function from the module:
from geometry.graphics import draw_triangle
draw_triangle(args)
Packages can themselves be imported,
which really just imports the __init__.py
module.
import geometry
If __init__.py
is empty,
there is “nothing” in the imported geometry
package,
and the following line gives an error:
geometry.graphics.draw_triangle(args)
AttributeError: module 'geometry' has no attribute 'graphics'
Importing from anywhere¶
sys.path¶
To improve their reusability,
you typically want to be able to
import
your modules and packages
from anywhere,
i.e., from any directory on your computer.
One way to do this is to use sys.path
:
import sys
sys.path.append('/path/to/geometry')
import graphics
sys.path
is a list of directories
that Python looks for modules and packages in
when you import
them.
Installable projects¶
A better way is to make your project “installable”
using setuptools.
To do this, you will need to
include a setup.py
with your project.
Your project should be organized as follows:
draw_triangles.py
geometry
├── graphics.py
└── __init__.py
setup.py
A minimal setup.py
can include the following
from setuptools import setup
setup(name='geometry',
version='0.1',
author='Geoffrey Lentner',
packages=['geometry'])
You can install the package using pip
with the following command
(run from the same directory as setup.py
):
$ pip install .
This installs your package in a place that is already on sys.path
.
If you’re using Anaconda this installs your package into the currently activated
environment. If you include the --user
flag it will install your package in
a specific location in your home directory, also on sys.path
.
If you include the -e
flag it will install your package in editable mode,
creating a link to it such that you can modify the code without having to reinstall.
Once your project is installed, you don’t need to worry about adding it manually to
sys.path
each time you need to use it.
It’s also easy to uninstall a package; just run the following command from the same directory as setup.py
:
$ pip uninstall .


+++ title = "Dream Internet café in San Cristóbal" slug = "dream-internet-cafe-in-san-cristobal" date = 2007-03-15T11:00:00+10:00 tags = ["technology", "stunning"] locations = ["mexico/san-cristobal-de-las-casas"] draft = false summary = """
San Cristóbal has an enormous selection of Internet cafés, none of which are rubbish, and none of which charge more than 8 pesos / hr. But there's one that stands out way above the rest. Over 40 brand-new PCs. Very high speed Internet. Everything working perfectly. And all for just 5 pesos / hr! The cheapest and the best Internet in Mexico.
""" [[comments]] title = "Everything is about internet connections with u! LOL" date = 2007-03-28T05:15:02+10:00 author_name = "Liam" content = """I think ur up to 5 or 6 blog entries solely on internet café’s,
Also Jeremy, U giving directions with compass bearings!
I still can’t believe it, not sure what’s harder to believe. That ur using compass bearings or that u know where North is. :P
Sounds like ur learning a lot on this trip, I’ll let u navigate on the next canoe trip. :)
By the end of ur trip I’m going to be able to find a internet café anywhere in South America.
San Cristóbal has an enormous selection of Internet cafés, none of which are rubbish, and none of which charge more than 8 pesos / hr. But there's one that stands out way above the rest. Over 40 brand-new PCs. Very high speed Internet. Everything working perfectly. And all for just 5 pesos / hr! The cheapest and the best Internet in Mexico.
I'm not sure what this place is called, or exactly where it is. But it's about 1 block north and half a block west of Posada Mexico Hostel. If you see a large, air-conditioned interior, filled with banks of shiny, numbered terminals, you know you've found the place.


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A Lesson in Ballistics
In the previous lesson, we saw how one can calculate the vertical position of an object as a function of its initial velocity and the accelerationofgravity. In this lesson, we will extend that concept to allow us to calulate the horizontal position of the object as well. To demonstrate this we will use a common application of this principle: ballistics.
For our example we will use the cannon shown on the left. Our cannon fires a cannonball. This cannonball leaves the barrel of the cannon at some intial velocity V0. Recall that velocity is the vector that includes both the speed and the direction of an object. So initially, the cannonball has a velocity that includes both a horizontal component, Vx and a vertical component, Vy.
From trigonometry, it is easy to see that we can calulate the components from the velocity:
Vx = V0 * cos(θ)
Vy = V0 * sin(θ)
where θ is the angle of the initial trajectory of the cannonball.
The important point to grasp in this lesson is, as in the previous lesson, the forces acting on the object to not change with time (as before, we ignore the effects of friction from the atmosphere).
Therefore, the position of the cannonball at any time after time t0 can be calculated as:
xt = Vx * t
yt = Vy * t - 1/2 * g * t2
where t is the time in seconds from the firing the cannon and g is the acceleration of gravity (9.802m/s2).
Questions:
- If the initial velocity is 15m/s and the barrel angle is 50o above the horizon, what is the maximum height the cannonball will reach? (mouse over here for a hint).
- In the above example, how far will the ball travel before it hits the ground (assume the “ground”is the faint brown line, which is the elevation the cannon ball starts at time zero)? (mouse over here for a hint)


3.3 Amend
3.3.1 What to consider?
In the amend phase, the objective is to change the data values in order to improve data quality (see Figure 7). Units/ variables that were flagged in the select phase are amended to produce a transformed dataset, which should better meet the needs of customers.
A key objective of the amend phase is to use the appropriate methods and tools to transform the dataset. Most of the considerations in the amend phase will inform analysts on the appropriate methods and tools to use for amending the data.
In order to select the appropriate methods and tools for amending a dataset, it is important that analysts understand the data and its issues. These include:
- The type of error in the data; identifying the error source, which resulted in units being selected for treatment. Awareness of the error type in the dataset will help in identifying the root cause, which in turn will help resolve the issue and improve data quality. For example, a number of businesses in a dataset have been selected for treatment, because the reported revenue was considered too large in the context of other units. Further analysis of this selection reveals that it stems from measurement error; businesses did not report in thousands, which in turn inflated their actual revenue. As a result of identifying the error source, an amend function of converting the figures to thousands will improve the quality of the data.
- Identifying the error generating mechanism. Errors in a dataset may be systematic or random. The former are errors that result in a consistent deviation of estimates from true values; contributing to the bias of outputs. Whereas the latter don’t have any identifiable pattern but can increase the variation of estimates. Identifying the error generating mechanism will advise on what methods and tools to use in improving data quality; functions that manage bias with systematic errors and those that manage variance with random errors.
- Identifying the type of missingness in the data. The types of missingness include missing completely at random, missing at random or not missing at random. The pattern of missingness will advise how best to estimate for the missing units/ values in the dataset.
- Knowing what data are available to derive estimates of missing or incorrect units/ values. Data that could be used in the amend phase includes the units and variables in the dataset of interest, as well as other related data sources that analysts might have access to.
- The impact of amend actions on the frequency structure of data. Although the data is transformed during the amend phase, it is expected that these changes will not alter the distribution of the variable in question. That is, if the underlying distribution of the variable is assumed to be correct, an amend function should not introduce bias or modify the variance of the data as a result of changing incorrect values and/or filling in missing values.
- Ensuring that automatic changes are a result of edit rules, which are tested and are shown to improve data quality. Automatic corrections are generally used when there is confidence that the amended data are closer to the truth. As a result, edit rules that result in automatic changes should be verified through testing and specified in the design of the statistical product. Ad hoc/ untested automatic corrections have the risk of introducing more error into the data.
- Ensuring that all edit rules applied are still relevant and accurate for the statistical production. Edit rules that are built into the design of a production cycle should be reviewed; to determine whether the type or mechanism of the error it is addressing has not changed, and whether the edit rules need to be adapted.
3.3.2 How to amend data?
The aim of the amend phase is to improve the quality of data by addressing the missing and erroneous values and units. By managing the impact of the error sources, the ability to draw inferences of the target population and target concept is in turn improved. The amend functions can be categorised as follows (see Table 5):
- Variable amendment. To best capture the target concept, variable amendment is applied; where observed values are altered or missing values are filled in.
- Unit amendment. To best capture the target population, units in the data modified so that the final set of units are in scope, representative of the target population and are compiled correctly.
Table 5. Table presenting the methods of amending data for the purpose of correcting erroneous values or resolving missing values.


The following parameters are available :
- Field
- The field these permissions should apply to.
- Username
- The user to whom the permissions are granted. This user must already
exist in the User Permissions table.
- Hosts
- The hosts that the user can access the table from.
- Permissions
- The permissions granted to the user on the chosen column of the table.


In his book, "Outliers," Malcolm Gladwell explored the possibility of a correlation between the month of one's person, and his or her athletic proclivities. This report examines that correlation.
By understanding the media habits of potential voters, political candidates can focus their advertising spending and campaign messaging. This chart compares the voting intentions of Americans and their favorite shows.
Is there a correlation between arthitis and sleep quality? Studying different medical conditions and their co-morbidities can help researchers and pharmaceutical companies tap into unmet needs in healthcare and medicine.
Researchers have begun considering a connection between male sexuality and birth order. Having access to a global, anonymous dataset can help scientists and laypeople alike understand nuances of the human experience.
Large data collection allows us to explore connections between tastes, preferences, and habits we might not have previously appreciated. By seeing what other things people like us enjoy, we can discover new opportunities.
As users answer questions and sync their other online accounts, they can begin to see a clearer picture of how they fall into "typical" or "atypical" patterns. A broad view can direct them to new ways to push themselves out of their comfort zones.


Test the mail button for subscribing to list serve:
For list requests, send mail to<EMAIL_ADDRESS>and in the body, put (stuff in brackets optional): [un]unsubscribe village[-digest]


4. Annotator Test
This test checks if the Simple Language annotator functionality, implemented in the Annotator section of the Custom Language Support Tutorial, works as expected.
4.1. Define Input Test Data
The DefaultTestData.simple
properties file is reused for this test.
Create an input test file AnnotatorTestData.java
in the testData
directory.
This file contains two instances of Simple Language embedded in the Java code.
The first instance is a valid use of the simple:
prefix followed by the Simple Language key website
.
The second is a valid prefix but an invalid key, as noted by the test <error>
highlighting.
public class Test {
public static void main(String[] args) {
System.out.println("simple:website");
System.out.println("simple:<error descr="Unresolved property">websit"</error>);
}
}
4.2. Define a Test Method
Add the testAnnotator()
method to the SimpleCodeInsightTest
class previously defined.
Again, this method configures the test fixture by using the test files.
It then calls the checkHighlighting()
method to verify weak warnings.
public void testAnnotator() {
myFixture.configureByFiles("AnnotatorTestData.java", "DefaultTestData.simple");
myFixture.checkHighlighting(false, false, true, true);
}
4.3. Run the Test
Run the test and make sure it’s green.


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Here are some Linux commands that you will need to use with Git, Docker, and Azure.
LINUX
pwd - shows present working directory
ls - lists files in the current directory
cd - changes directory (use cd .. to go up a directory)
Example:
Current directory: /home
cd student
New directory: /home/student
mkdir - makes a directory
Example: mkdir test
Creates test directory in current directory
mv - renames files & directories
Example: mv index.htm index.html
Renames the index.htm file to index.html
rm - removes/deletes files (use -r to remove folder with recursion)
Example: rm file1
Removes file1 from current directory
touch - creates an empty file
Example: touch file1
Creates a file named file1
vi - opens vi editor to edit files
Commands to use in VI:
i - command to enter Insert mode to be able to type
Delete key - to delete anything typed to make the corrections
Esc key - to get back to Normal mode
ESC :wq - to enter normal mode, write the file (save it), and quit
Example to follow


The temple is nestled on a small hillock on the Vemulawada - Karimnagar highway. It is a small shrine dedicated to Lord Lakshmi Narasimha, an incarnation of Lord Vishnu. The idol was carved out of the rock around which the temple was built.
The visitors have to walk a few hundred steps that lead to the top from nearest the parking area at bottom. The climb is little steep may take up to 15 minutes. One the way to the Narasimha temple, there is a temple for Naga Devatha. The temple was built in a model of Snake.Visitors can enter the shrine through the snake's belly - a long, winding tunnel.
There are statues depicting the story of Prahalada and Hiranyakasipu. At the end of the tunnel, the statue of Lord Narasimha killing the demon Hiranyakasipu is present. There are also a few ancient idols of Naga Devatha. At the entrance of the temple, one can find a statue of Lord Narasimha breaking his way out of a pillar.It is 29.6km from Karimnagar
Visiting Timings:-9am to 5:30pm
Elgandal Fort is one of the best survived forts in Telangana. The Fort is located on a hillock. There are 300 well laid steps providing access to the fort. The East entrance of the fort, the Brundavan pond, the Mosques and graves in the fort are various points of attraction for tourists. Within the premises of the Fort exists various tombs of Muslim saints.
These include the dargahs of great saints like Syed Shah Munawar Quadri Saheb, Doola Shah Saheb, Syed Maroof Saheb, Shah Talib Bismilla Saheb and Vali Hyder Sahib. These attract devotees in huge numbers.The Alamgir mosque, built by the Mughal Emperor Aurangzeb is a major attraction. It is crowned with three minarets unlike the usual four minarets found on every mosque.
Brindavan tank on the outside of the eastern gateway of the fort was built by Zafar-ud-Doula in 1754. The temples of Nelakantha Swamy and Narasimha Swamy are also located inside this fort. The fort offers pleasing view of the country-side, the water reserve of the Lower Maner Dam and small hills around.It is 16.1km from Karimnagar
Visiting Timings:-10am to 7:45pm
The District Museum i.e, Gandhi Centenary Museum, Karimnagar was established in the memory of Gandhi Centenary celebrations in the year 1969 Karimnagar town. There are more than 1500 antiquities exhibited in the Museum. Those have been recovered from explorations and excavations and the sculptures from the ancient dilapidated temples and fields.
These objects range in date from Pre-historic to modern period (2,50,000 to 20th C.A.D.). The important displayed objects are Fossils, stone tools belong to Paleolithic, Mesolithic, Neolithic times and Iron objects from megalithic period.
The Early historic cultural material includes Terracotta beads and Moulds, Iron objects, Chinese porcelin, specially designed Bidri ware, Sculptures of stone and metal, copper plate inscriptions, manuscripts of Hindu, Islamic and Jaina, Weaposns Miniature paintings of Deccan, Rock brushings and engravings, important photographs of Archaeological sites and monuments of vario us periods and dynasties belonging to 2nd C.B.C. to 20th Century. They are displayed in separate galleries in this Museum for the benefit of researchers, scholars and students.It is 0.9km from Karimnagar
Visiting Timings:-10am to 5pm
One of the ancient temples of Rajahmundry, Sri Markandeya Swamy Temple, is located in Chanda Satram or Satram-Choultry. The pilgrim center was initially mistaken as the ruins of a mosque, but later archeological surveys found out the remnants as part of an ancient Shiva temple.
The temple was rebuilt in 1818 by Gundu Sobhanadrihwara Rao and the entrance to the temple starts from Gundu vari Street which was his former residence. A holy destination that has immense historical as well as religious significance, this temple has numerous legends attached to it and is frequented by pilgrims from all over the country.It is 0.6km from Karimnagar
Visiting Timings:-10am to 5pm


--- layout: page title: About header-img: "img/about-bg.png" ---
Many years working with large relational databases. Looking forward to applying Big Data techniques.


public class Toolkit
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
static double |
eps
A constant holding the smallest positive
nonzero value of type double, 2-1074.
|
static double |
inf
A constant holding the positive infinity of type double.
|
| Constructor and Description |
|---|
Toolkit() |
| Modifier and Type | Method and Description |
|---|---|
static Matrix |
abs(Matrix A)
Compute element-wise absolute value of all elements of matrix.
|
static Vector |
abs(Vector V)
Compute element-wise absolute value of all elements of a vector.
|
static DenseMatrix |
eye(int n)
Construct an n-by-n sparse identity matrix.
|
static DenseMatrix |
eye(int m,
int n)
Construct an m-by-n sparse identity matrix.
|
static DenseMatrix |
full(Matrix S) |
static DenseVector |
full(Vector V) |
static Matrix |
hilb(int m,
int n)
Create a m-by-n Hilbert matrix.
|
static double |
innerProduct(Matrix A,
Matrix B)
Compute the sum of all elements of a matrix.
|
static double |
innerProduct(Vector V1,
Vector V2)
Compute the inner product of two vectors, i.e.
|
static void |
main(java.lang.String[] args) |
static double |
max(double[] V)
Compute the maximum of elements in a 1D
double array. |
static double[][] |
max(double[][] A,
int dim)
Compute the maximal value for each row or each column
and the corresponding indices of a matrix A.
|
static double |
max(double[] V,
int start,
int end)
Compute the maximum of elements in an interval
of a 1D
double array. |
static Vector[] |
max(Matrix A)
Compute the maximal value for each column and the
corresponding row indices of a matrix A.
|
static Vector[] |
max(Matrix A,
int dim)
Compute the maximal value for each row or each column
and the corresponding indices of a matrix A.
|
static double[] |
max(Vector V)
Compute the maximal value and the corresponding row
index of a vector V.
|
static Vector[] |
min(Matrix A)
Compute the minimal value for each column and the
corresponding row indices of a matrix A.
|
static Vector[] |
min(Matrix A,
int dim)
Compute the minimal value for each row or each column
and the corresponding indices of a matrix A.
|
static double[] |
min(Vector V)
Compute the minimal value and the corresponding row
index of a vector V.
|
static Matrix |
minus(double v,
Matrix A) |
static Matrix |
minus(Matrix A,
double v) |
static Matrix |
minus(Matrix M1,
Matrix M2) |
static Vector |
minus(Vector V1,
Vector V2) |
static Matrix |
mldivide(Matrix A,
Matrix B)
Calculate the left division of the form A \ B.
|
static Matrix |
mrdivide(Matrix A,
Matrix B)
Calculate the right division of B into A, i.e., A / B.
|
static double |
norm(Matrix A)
Compute the Euclidean norm of a matrix.
|
static double |
norm(Matrix A,
double type)
Compute the induced vector norm of a matrix (row or column
matrices are allowed).
|
static double |
norm(Matrix A,
int type)
Compute the norm of a matrix (row or column matrices
are allowed).
|
static double |
norm(Matrix A,
java.lang.String type)
Calculate the Frobenius norm of a matrix A.
|
static double |
norm(Vector V)
Compute the Euclidean norm of a vector.
|
static double |
norm(Vector V,
double p)
Compute the norm of a vector.
|
static Matrix |
plus(double v,
Matrix A) |
static Matrix |
plus(Matrix A,
double v) |
static Matrix |
plus(Matrix M1,
Matrix M2) |
static Vector |
plus(Vector V1,
Vector V2) |
static Matrix |
pow(Matrix A,
double p)
Compute element-wise exponentiation of a vector.
|
static Vector |
pow(Vector V,
double p)
Compute element-wise exponentiation of a vector.
|
static int |
rank(Matrix A)
Compute the rank of a matrix.
|
static void |
setMatrix(Matrix X,
Matrix Y)
Set all the elements of X to be those of Y, this is particularly
useful when we want to change elements of the object referred by X
rather than the reference X itself.
|
static Matrix |
sigmoid(Matrix A)
Calculate the sigmoid of a matrix A by rows.
|
static Matrix[] |
sort(Matrix A,
int dim,
java.lang.String order) |
static SparseMatrix |
sparse(Matrix A) |
static SparseVector |
sparse(Vector V) |
static Matrix |
sparseColumnVectors2SparseMatrix(Vector[] Vs) |
static Vector[] |
sparseMatrix2SparseColumnVectors(Matrix S) |
static Vector[] |
sparseMatrix2SparseRowVectors(Matrix S) |
static Matrix |
sparseRowVectors2SparseMatrix(Vector[] Vs) |
static SparseMatrix |
speye(int n)
Construct an n-by-n sparse identity matrix.
|
static SparseMatrix |
speye(int m,
int n)
Construct an m-by-n sparse identity matrix.
|
static double |
sum(double[] V)
Compute the sum of a 1D
double array. |
static DenseVector |
sum(Matrix A)
Compute the sum of elements for each row of a real matrix.
|
static DenseVector |
sum(Matrix A,
int dim)
Compute the sum of elements for each row or each column of
a real matrix.
|
static double |
sum(Vector V)
Compute the sum of all elements of a vector.
|
static Matrix |
times(double v,
Matrix A) |
static Matrix |
times(Matrix A,
double v) |
static Matrix |
times(Matrix M1,
Matrix M2) |
static Vector |
times(Vector V1,
Vector V2) |
static Matrix |
uminus(Matrix A)
Unary minus.
|
public static double eps
public static double inf
public static void main(java.lang.String[] args)
args
- public static Matrix abs(Matrix A)
A
- a matrixpublic static Vector abs(Vector V)
V
- a vectorpublic static Matrix pow(Matrix A, double p)
A
- a real matrixp
- exponentpublic static Vector pow(Vector V, double p)
V
- a real vectorp
- exponentpublic static double[] max(Vector V)
V
- a real vectordouble
array [M, IX] where M is the
maximal value, and IX is the corresponding
indexpublic static Vector[] max(Matrix A)
A
- a real matrixVector
array [M, IX] where M contains
the maximal values, and IX contains the corresponding
indicespublic static double[][] max(double[][] A, int dim)
A
- a 2D double
arraydim
- 1: column-wise; 2: row-wisedouble[]
array [M, IX] where M contains
the maximal values, and IX contains the corresponding
indicespublic static Vector[] max(Matrix A, int dim)
A
- a real matrixdim
- 1: column-wise; 2: row-wiseVector
array [M, IX] where M contains
the maximal values, and IX contains the corresponding
indicespublic static double[] min(Vector V)
V
- a real vectordouble
array [M, IX] where M is the
minimal value, and IX is the corresponding
indexpublic static Vector[] min(Matrix A)
A
- a real matrixVector
array [M, IX] where M contains
the minimal values, and IX contains the corresponding
indicespublic static Vector[] min(Matrix A, int dim)
A
- a real matrixdim
- 1: column-wise; 2: row-wiseVector
array [M, IX] where M contains
the minimal values, and IX contains the corresponding
indicespublic static DenseVector sum(Matrix A)
A
- a real matrixpublic static DenseVector sum(Matrix A, int dim)
A
- a real matrixdim
- 1: column-wise; 2: row-wisepublic static double sum(double[] V)
double
array.V
- a 1D double
arraypublic static double sum(Vector V)
V
- a real dense or sparse vectorpublic static double norm(Matrix A)
A
- a real matrixpublic static double norm(Matrix A, double type)
A
- a matrix or a vectortype
- 1, 2, or, inf for a matrix or a positive real
number for a row or column matrixpublic static double norm(Matrix A, int type)
A
- a matrixtype
- 1, 2public static double norm(Matrix A, java.lang.String type)
A
- a matrixtype
- can only be "fro"public static double norm(Vector V, double p)
V
- a real vectorp
- a positive double
valuepublic static double norm(Vector V)
V
- a real vectorpublic static double innerProduct(Vector V1, Vector V2)
V1
- the first vectorV2
- the second vectorpublic static double innerProduct(Matrix A, Matrix B)
A
- a matrixpublic static Matrix sigmoid(Matrix A)
[exp(a11) / exp(a11) + exp(a12), exp(a12) / exp(a11) + exp(a12); exp(a21) / exp(a21) + exp(a22), exp(a22) / exp(a21) + exp(a22)].
A
- a real matrixpublic static double max(double[] V)
double
array.V
- a 1D double
arraypublic static double max(double[] V, int start, int end)
double
array.V
- a 1D double
arraystart
- start index (inclusive)end
- end index (inclusive)public static Matrix mldivide(Matrix A, Matrix B)
A
- divisorB
- dividendpublic static Matrix mrdivide(Matrix A, Matrix B)
Note: X = A / B <=> X * B = A <=> B' * X' = A' <=> X' = B' \ A' <=> X = (B' \ A')'
A
- dividendB
- divisorpublic static int rank(Matrix A)
A
- a matrixpublic static DenseMatrix eye(int m, int n)
m
- number of rowsn
- number of columnspublic static DenseMatrix eye(int n)
n
- number of rows and columnspublic static SparseMatrix speye(int m, int n)
m
- number of rowsn
- number of columnspublic static SparseMatrix speye(int n)
n
- number of rows and columnspublic static Matrix hilb(int m, int n)
m
- number of rowsn
- number of columnspublic static void setMatrix(Matrix X, Matrix Y)
X
- a matrix to be setY
- a matrix to set Xpublic static DenseVector full(Vector V)
public static SparseVector sparse(Vector V)
public static DenseMatrix full(Matrix S)
public static SparseMatrix sparse(Matrix A)


Crer&Ser is a project started in NIEJ, an entrepreneur group composed mainly by CESUPA students. The project has the purpose of deliver health care and social support to the children of BENTO XV public school.


touch_app
Clicks
pageview
Impresseion
report
Reports
attach_money
Revenue
Account Summarization
Welcome, Alireza!
Let's Take a Small Tour!Device Transparency
OS Transparency
Connection Transparency
User Label Transparency
TRANSACTION TRANSPARENCY
- TODAY CLICKS 1 200 USERS
- YESTERDAY CLICKS 3 872 USERS
- LAST WEEK CLICKS 26 582 USERS
APPLICATION STATS
- TOTAL APPLICATIONS 12 TICKETS
- DISABLED APPLICATIONS 15 TICKETS
- ENABLED APPLICATIONS 90 TICKETS
PLACEMENT STATS
- TOTAL PLACEMENTS 12 TICKETS
- DISABLED PLACEMENTS 15 TICKETS
- ENABLED PLACEMENTS 90 TICKETS


Block Lists
Block lists resemble single-column tables.
- Item 1
- Item 2
- Item 3
Border Classes
Bottom borders are dotted.
Top borders are solid.
All-around borders are solid.
Extended Grid System
The Foundation grid system is pretty complete, but has a couple gaps. Here we keep additions we make to the Foundation grid system classes:
.large-offset-1-and-half
can be used for offsetting columns by one-and-a-half columns width on a large screen. This is useful for centering three groups of three columns (like for pricing tables).
Misc Helper Classes
Adding the class full-width
to something will make it width: 100%;
Adding the class inline
to something will make it display: inline;
Panels
White Panel
Example .la-panel
div.la-panel.columns.mbl
Adding the class la-panel
to a div will provide it a white
background. This div also has the classes columns
and
mbl
to provide padding and margin.
Contrast Panel
Example .contrast-panel
Adding the class contrast-panel
to a div will provide it a blue
background, white text, and the "header" font for all text. Links
even look okay!
Recommended Use:
.contrast-panel
.row
%h1 ...
%p ...
Custom Spacing Classes
To provide custom padding and margin to elements, add a class to the element by assembling one letter from each category below depending on your needs:
1. p, m
= padding, margin
2. a, t, r, b, l, h, v
= all, top, right, bottom, left, horizontal, vertical
3. s, m, l, n, a
= small(5px), medium(10px), large(20px), none(0), auto(auto)
Example:
.ptm
--> "padding top medium"
.mvl
--> "margin vertical large"
.pas
--> "padding all small"
Split Columns
Split columns allow you to have two pieces of content next to each other, separated by a vertical line. On small and medium screens, the will move to be vertically stacked and have a horizontal separator line.
You can really have whatever your heart desires here for content.
You can really have whatever your heart desires here for content.
Split Columns (Medium)
Split columns allow you to have two pieces of content next to each other, separated by a vertical line. On small screens, the will move to be vertically stacked and have a horizontal separator line.
You can really have whatever your heart desires here for content.
You can really have whatever your heart desires here for content.
This documentation generated using Hologram


There is different ways to hunt for vulnerabilities, depending of your knowledge, your skills, your expectation and how you like to chase. I personally love programming and, as a true developper, I’m lazy, so I love to automate things. By writing some piece of code or by combining multiple tools, you can find a lot of interesting stuff. This article is about some scripts/tricks I wrote/use to perform massive tests:
- XSS with PhantomJS
- Heroku subdomain takeover
- Amazon S3 buckets theft
XSS with PhantomJS
The first thing to do is to get a list of urls to test, it’s like finding endpoints when you perform the recon of a new target. And since even an url without any parameter can be vulnerable, it doesn’t matter if our urls have one or several or none.
Possible sources:
- using the scanner INURLBR, you can retrieve a list of urls from many search engines, a simple dork as
inurl:"&"
would return many results - a list of subdomains in scope of different bug bounty programs
- the list of the most famous xxx websites in whatever country or all around the world
- the history of the Wayback Machine
- …
The second thing you need is a good list of payloads. You could use the awesome Brutal Cheat Sheet list or grab another one on the internet or create your own. Another good one is the list from RSnake because it contains payloads with a call of an external Javascript, using XSS Hunter here could be a good idea, you never know…
Finally you need a program to automatically test all the urls you grabbed. You could probably use Burp Suite but I’m not sure it would be the best tool to test XSS. @brutelogic created the magic extension Knoxss, you can use the free version. However it becomes very complicated when you deal with thousands of urls, I already tried to script it but I was faced to the rate limit of the firewall. So I finally created my own script, it was originally based on string comparaison but I recently changed my mind and I implemented PhantomJS which is much more reliable.
$ php testxss.php --prefix --suffix --sos --threads=10 --verbose=2
--phantom=/usr/local/bin/phantomjs --payload=brute-full.txt --urls=urls.txt
Heroku subdomain takeover
I recently discovered DNSTrails, “The World’s Largest Repository of historical DNS data”. Basically it’s a huge database of DNS history.
After reading the report #296907 on Hackerone, I had the idea to check the IPs of Heroku.
I checked my log files and I extracted all IPs I had, linked to herokuapp.com
.
I have 44 address in my list, they probably own much more but that was enough for a single test.
Calling the API of DNSTrails, you can retrieve many domains and subdomains configured with those DNS: https://app.securitytrails.com/api/search/by_type/ip/<ip>
Looping through the 44 IPs, I got +30k (sub)domains. Doh!
We all know that a misconfigured Heroku service leads to this page:
It would be impossible (or very very long) to manually test all domains. Fortunately Michael Henriksen wrote Aquatone. This tool can check if a given list of subdomains is vulnerable to subdomain takeover, it supports services like: Heroku, Cloudfront, Zendesk, Uservoice and many others… It comes very handy when you deal with so much datas.
$ aquatone-takeover --domain herokuapp.com
I also wrote my own script to test some 3rd party services a long time ago, so I also injected my list in it, the result was pretty much the same.
$ for d in $(cat domains.txt) ; do php 3rdparty.php -s heroku -d $d ; done
Let’s say 10% of the domains are vulnerable.
Amazon S3 buckets theft
Here I started to query Threat Crowd with every IP I was able to link to Amazon Service using https://bgp.he.net/ and some other resources.
https://www.threatcrowd.org/searchApi/v1/api.php?type=ip&query=<ip>
I locally stored the json result and more than 3000 requests later, I got 90k+ unique (sub)domains.
Ok, things change, so probably some of them are not linked to Cloudfront anymore but hey! It’s worth to try! So I wrote a quick script that basically requests every domain in the list and check the response from the server.
$ php cloudfront.php domains.txt
After extracting all buckets found, about 37000, I ran my tool to test their permissions, maybe some of them are writable? Really??
About 2300 buckets are world writable, and for 1950 of them any user could alter the permissions, so everything is possible.
Conclusion
I don’t know how far this can goes. I think there is no limit to automation when combined with a good imagination and some technical skills, you can always dig deeper and deeper. The problem is, as a hunter, how would you monetize the time you spend on this?
2 years ago, when I started bug bounty, I was confident enough to think that companies would be happy to be warned of that kind of problems. But the responses were very different from a firm to another. Sometimes you get a “WTF??  What are you doing with my website?!”, then take care. Sometimes a single “Thank you”, no matter if the issue is fixed or not, and sometimes nothing, silence, they simply ignore you…
As far as I can see, chasing in the wild is fun, you can learn alot, but you won’t make money this way.
Tools
The script waybackurls.py
My tool: testxss
My tool: 3party-services
The Cloudfront checker script
My tool: s3-buckets-finder


Hotspot IO API¶
The hotspots.hs_io
module was created to facilitate easy reading and
writing of Fragment Hotspot Map results.
There are multiple components to a hotspots.result.Result
including, the
protein, interaction grids and buriedness grid. It is therefore tedious to manually
read/write using the various class readers/writers. The Hotspots I/O organises this
for the user and can handle single hotspots.result.Result
or lists of
hotspots.result.Result
.
The main classes of the hotspots.io
module are:
hotspots.io.HotspotWriter
hotspots.io.HotspotReader
-
class
hotspots.hs_io.
HotspotReader
(path, read_superstar=False, read_weighted=False, read_buriedness=True)[source]¶ A class to organise the reading of a
hotspots.result.Result
- Parameters
path (str) – path to the result directory (can be .zip directory)
-
read
(identifier=None)[source]¶ creates a single or list of
hotspots.result.Result
instance(s)- Parameters
identifier (str) – for directories containing multiple Fragment Hotspot Map results,
identifier is the subdirectory for which a
hotspots.result.Result
is requried- Returns
hotspots.result.Result a Fragment Hotspot Map result
>>> from hotspots.hs_io import HotspotReader
>>> path = "<path_to_results_directory>" >>> with HotspotReader(path) as reader: >>> reader.read()
-
class
hotspots.hs_io.
HotspotWriter
(path, grid_extension='.grd', zip_results=True, settings=None)[source]¶ A class to handle the writing of a :class`hotspots.result.Result`. Additionally, creation of the PyMol visualisation scripts are handled here.
- Parameters
path (str) – path to output directory
visualisation (str) – “pymol” or “ngl” currently only PyMOL available
grid_extension (str) – “.grd”, “.ccp4” and “.acnt” supported
zip_results (bool) – If True, the result directory will be compressed. (recommended)
settings (hotspots.hs_io.HotspotWriter.Settings) – settings
-
class
Settings
[source]¶ A class to hold the
hotspots.hs_io.HotspotWriter
settings
-
compress
(delete_directory: bool = True) → None[source]¶ compresses the output directory created for this
hotspots.HotspotResults
instance, and removes the directory by default. The zipped file can be loaded directly into a newhotspots.HotspotResults
instance using thefrom_zip_dir()
function- Parameters
delete_directory (bool) – remove the out directory once it has been zipped
-
write
(hr)[source]¶ writes the Fragment Hotspot Maps result to the output directory and create the pymol visualisation file
- Parameters
hr (hotspots.result.Result) – a Fragment Hotspot Maps result or list of results
>>> from hotspots.calculation import Runner >>> from hotspots.hs_io import HotspotWriter
>>> r = Runner >>> result = r.from_pdb("1hcl") >>> out_dir = <path_to_out> >>> with HotspotWriter(out_dir) as w: >>> w.write(result)


What To Eat
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Click below for some inspiration.
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Breakfast
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Ingredients
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Recipe


Number Plate Detection Demo
License Plate Reading
Preprocess :
Clear
Browse
File name: {{name}}
Document Preview
Car Details
Number Plate No:
{{data}}


Responsive Sidenav Example
This example use media queries to transform the sidenav to a top navigation bar when the screen size is 900px or less.
We have also added a media query for screens that are 500px or less, which will vertically stack and center the navigation links.
You will learn more about media queries and responsive web design later in our CSS Tutorial.


React Password Demo
Source at:
https://github.com/marudhupandiyang/react-password
Author:
Marudhu Pandiyan G


Characteristic
A more advanced and expensive version of Pokeball. Not only the technical data has changed, but also the design. Better than a normal Pokeball.