FROM NENITA VILLARAN; Email<EMAIL_ADDRESS>Dearest one .
Dear i am in need of urgent investment relationship with you if you are willing.However i am childless widow who was married to the former late minister of finance in joseph regime who died of ulcerous illness while on exile for being working with the ex-president Joseph .E of Philippine. I, Nenita Villaran being his legitimately married wife after his death it happen I Inherited and entitle to some of his assets including the sum of Ten million, five hundred thousand united states dollars($10.500,000) by inheritance right from my late husband.Unfortunately this brought serious presure on me as my deceased husband's family member objected the my right of inheritance to this fund,
But fortunately for me this said fund was concealed in metallic truck box kept in security company in Manila for political reasons best known to him.But the rising tension and pressure from my brothers in -laws compelled me relocate this truck box to somewhere in a far Coted Ivoire State in Africa with collaboration of the operational manager.
This idea was able to check relinquish of the metallic box containing the fund to unauthorise person.And the terms of deposit permit only my my bussiness representive or married partner to have access or empower to request for the relocation/shipment of the box to some other state for either bussiness or investment purpose after due consent from me.
And besides the lapse of time i am still afraid of being killed some day if i dont leave for good. Hence i am become desperate and needed investment partner to assist me to contact the security company on behalf requesting for the shipment of the box to any investment friendly state while i join the him or her for joint bussiness investment.
On my scouting i found your profile suiting and somewhat exceptional having browsed this site sometimes. In summary, i honestly want you and i to have a versatile relationship and i will give you detalis regarding this deposited box containing the said fund in other for you to contact the security company as my bussiness partner and request for the diplomatic shipment of the consignment to your destination where i will join you on arrival of consignment/box.
The idea behind this is that i want to leave philippine by all means and it has being my dream to invest this fund instead of leaving it lying fallow,attracting all kind of charges,and at the end devalued.And this requesting for assistance i am seeking from you is somewhat urgent as every thing is set for the shipment only awaiting finally consent from me to whom and where the consignment will be diplomatically ship to.Besides the investment partnership opportunity you will benefit from your assistance.
I am also willing to give you 15% of the total fund which is approximately ($3Million United State Dollars Cash)for your kind urgent assistance to my plight immediately on arrival of the consignment in your place .
And do not entertain fear of paying for freight charge or demurrage neither shipment charge as all has being recently settled personally by me before contacting you.
I will like you to contact me in my altanative email adress<EMAIL_ADDRESS>immediately for more details on how to go about this .
Thanks for your audience and awaits your urgent response .
Your's faithfull Mrs NENITA VILLARAN
Gli affari migliori del Web? Solo su Lycos Shopping!


Unity developer
I am a Unity developper based in Belfast.
I have been working three years at Airbus Defense & Space (Toulouse, France) upon the completion of my master's degree at Epitech.
The experience I have gathered through my work in the space industry made me rigorous, a good team player and able to work with and manage my own clients in a professionnal manner.
For my next project, I’m looking to step out of my comfort zone and trying my hand at living and working abroad.
Regarding all my side projects you will find here, all of them are open-source, under a MIT license and can be found on my Github.
I would be glad to have some feedback and maybe help you using them.


Several options make sense only if you have table to play around.
Use the table below to play with the features.
Watch the styles being added to the cells.


Welcome! Please fill the application form to continue...
First Name:
Last Name:
Email:
Contact number:
Aadhar number:
Quota(CG/NTPC):
Category(Unreserved/OBC/SC/ST):
Date of Birth:
Gender(Male/Female/Others):
Board of Education:
Year of Passing(Class XII)
Result Status(Passed/Awaited):
Board Roll number:
Personal Details:
Mother's name:
Mother's Contact number:
Mother's Occupation:
Father's name:
Father's Contact number:
Father's Occupation:
Are you a resident of India?(Yes/No)
Address Details:
Correspondence Address:
Address Line 1:
Address Line 2:
Country:
State:
City:
Pin Code:
Permanent Address:
Address Line 1:
Address Line 2:
Country:
State:
City:
Pin Code:
Bank Details:
Name of the Bank:
Name of the Account holder:
Account number:
IFS Code:
Uploads:
(.jpeg,.jpg,.pdf,.png formats only)
(.jpeg,.jpg,.pdf,.png formats only)
(.jpeg,.jpg,.pdf,.png formats only)
Declaration by the applicant:
1. I have checked all the entries made by me in the form. Any wrong information given by me will lead to cancellation of my admission and also penal action against me.
2. The information declared here is true and accurate to the best of my knowledge.
3. I further affirm that I have not been involved in any malpractice/use of unfair means(UFM) in any examination taken by me and no judicial proceeding is pending against me.
4. I understand that in the event of my not fulfilling all requirements, the International Institute of Information Technology, Naya Raipur may not consider/may cancel my candidature.
5. I declare that I will submit myself to the disciplinary jurisdiction of the authorities of the International Institute of Information Technology, Naya Raipur which may be vested with the powers to exercise discipline under the Act, the Ordinances and the Rules that may be framed by the Institute from time to time.


--- layout: page title: Lean Prover Zulip Chat Archive permalink: archive/113488general/72857solveeachgoalinbymode.html ---
In by
mode, i.e. by xxx; yyy; zzz
, is there a way to not apply the tactics to every goal?
you can use seq_focus, i.e. xxx; [yyy, zzz]
, or use a big tactic block by { xxx, yyy, zzz }
aha
I was trying focus [yyy, zzz]
but it failed
turns out you don't say focus
how would you write this?
by simp [reduce.step, h] at H; cases L; injections; [cc, { rw ← h_2, from list.suffix_append _ _ } ]
begin simp [reduce.step, h] at H, cases L; injections, {cc}, rw ← h_2, exact list.suffix_append _ _ end
but it's three lines longer, lol
four lines longer. i can't count
If you have to represent complicated control flow, you probably shouldn't be using by
at some point it's saving lines by just deleting newlines
{% endraw %}


Laravel Install - The Popular PHP Framework to Code Web Applications.
Do you need Xampp or Mamp to Install this awesome Framework to build your own Apps. And the Composer to run Laravel correct on your localhost server.
Get Composer : ###View Application →
Laravel is a very Fast Popular PHP Framework for develop Web Applications.
Laravel over Xampp.
Download Xampp for Mac Os X. →
Download Laravel. →
/Applications/XAMPP/ htdocs/L33
ls
which php
/Applications/XAMPP/
xamppfiles/bin/php
sudo vi ~/.bash_profile
password:
*************
:wq
cd
then back to bash type
which php
/Applications/XAMPP/
xamppfiles/bin/php
composer install
wait, it need a bit time.
find app/storage -type d -exec chmod 777 {} \;
find app/storage -type f -exec chmod 777 {} \;
Install Composer
Let’s you download and install Composer before You need to install composer software on you OS , you can get from getcomposer.org and follow instruction on website i won’t teah you to install composer , Let’s you do it yourself.
https://getcomposer.org/
Get Laravel
Then download Laravel from github here and place your folder in XAMPP/htdoc
/Applications/XAMPP/htdoc/{you folder}
before you install it , you have to check php path that point to
/Applications/XAMPP/
xamppfiles/bin/php
Terminal Bash :
cd /Applications/XAMPP/htdocs/L4
Terminal Command Line :
sudo vi ~/.bash_profile
export Path :
“PATH=/Applications/
XAMPP/xamppfiles/bin:$PATH”
If finish use ESC to exit
Type composer install after you have finish installation go to browser and enter to
localhost/{your laraval}
Last problem and solve Boooom!!! you will get a error on your screen , Go to terminal again user both command
find app/storage -type d -exec chmod 777 {} \;
find app/storage -type f -exec chmod 777 {} \;
then refresh browser you will get complete to install Laravel , Hope to enjoy it!
Update
**Don’t forget restart apache service after set path in ~/.bash_profile
Laravel over Mamp
Download Mamp for Mac Os X. →
Download Laravel. →
Install Laravel on MAMP !
install and run Terminal .bash
type :
curl -sS https://getcomposer.org/installer | php
type:
php composer.phar
type:
composer
to exit : type
clear
exit
Open a New Terminal Bash
type:
sudo mv composer.phar /usr/local/bin/composer
type:
composer
type to exit :
clear
go back to localhost of the index
localhost/
localhost:8888/
Open New Terminal :
type:
cd /Applications/MAMP/htdocs/
ls
composer create-project laravel/laravel spaceg –prefer-dist
ls
rm -R ./*
ls
cd ~
ls - l
ls -ls
nano .bash_profile
export
PATH=/Applications/MAMP/
bin/php/PHP_VERSION/bin/:$PATH
enter the php version
export
PATH=/Applications/MAMP/
bin/php/php5.6.2/bin/:$PATH
php5.6.2
ctrl o
ctrl x
Open a New Terminal again:=)
cd /Applications/MAMP/htdocs/
ls
composer create-project laravel/laravel/ yourprojectname
Preload your Localhost and BOOOOMM! The index of your Laravel PHP Framework to build APPS!
Globally#
You can place this file anywhere you wish. If you put it in your PATH, you can access it globally. On unixy systems you can even make it executable and invoke it without php.
You can run these commands to easily access composer from anywhere on your system:
curl -sS https://getcomposer.org/installer | php mv composer.phar /usr/local/bin/composer Note: If the above fails due to permissions, run the mv line again with sudo.
export
PATH=/Applications/MAMP/
bin/php/PHP_VERSION/bin/:$PATH
<VirtualHost *:80>
DocumentRoot "/Path/To/New/Document/Root"
ServerName spaceg.github.io
<Directory "/Path/To/New/Document/Root">
Options All
AllowOverride All
Order, allow,deny
Allow from all
</Directory> </ VirtualHost >
</code>


One-third of Israelis have suffered from depression
A third of all Israelis say they have suffered from depression at one point in their life, new study claims.
by Arutz Sheva StaffA new study shows that more than 60% of Israelis know at least one person who is suffering from a depression, while one-in-three Israelis say they have suffered from depression themselves at one point in their lives, Yediot Ahronot reports.
The study, which was carried out by the Lishma organization - which helps Israelis who are suffering with mental health issues – shows that the primary causes of depression are illness, birth, unrequited love, family disputes, and problems at work.
A full 43% of respondents said they believe depression is a disease which cannot be managed without outside assistance. Nearly 30% said depression can be managed early on, but which eventually becomes uncontrollable, while a similar percentage believe that individuals suffering from depression can overcome it on their own “if they truly want to”.


My Gregorc Thinking Style leans toward the random, leaning more to Concrete Random than anything. Concrete Random learners like to experiment, solve problems independently, and use intuition when learning.
This is really true for me. I love to get inside of something, break it down and get to know not only how something works but why it works that way. This is really good for developing deep understanding. However, this is REALLY inefficient for rapid new learning.
 The rapid pace for Dev Bootcamp, in addition to my regularly scheduled life (which includes a full time job, running a company, being a husband and dog owner) barely leaves time for me to touch on the lessons once let alone reflect and dissect them.
I am frustrated...and overwhelmed. Today it really hit hard. I woke up and started filling in my assignments in the submitterator app. Are I went to submit the link for this blog post, I discovered that the entry I wrote yesterday had disappeared. This hasn't happened to me since...well...I can't remember a time since college, and that was half a lifetime ago. I am chalking it up to being, in general, overwhelmed.
I am very comfortable with the notion of adapting to optimize what I can in a given situation. I know that because I tend to approach problems in a certain way, and feel comfortable learning in certain ways, that I can adapt.
I am trying to get caught up enough to take a few moments to reflect on the bigger picture. I want to define 'spaces' in my day to reflect. I am busy enough that I feel like I am just bouncing from one task to the next without synthesizing...without getting to the higher ordered bit. I want to define times when I am not doing but just reflecting. And, to be clear, writing these blog entries can be a gift, a space for reflection...but I have been approaching them as another task. Now that I think about it though...if I pause for just a second, I can get a little bit of perspective.
I am not sure where this fits in, but I did just notice that I am getting more efficient using Sublime Text...so while I still don't really feel comfortable with JavaScript (I certainly don't feel confident writing it), there is that.


Cards For Humanity
Game Log
Abandon Game
Take a tour
Tweet
Menu
Welcome!
You are about to start a new game session...


Uninstall something...like antivirus...
wmic product get name /value (this gets software names)
wmic product where name="XXX" call uninstall /Interactive:Off (this uninstalls software)


---
layout: post
date: 2010-10-30
time: "11:00:00"
authors: ["Greg Wilson"]
title: Would You Prefer...
tags: ["Content"]
---
Here are three instructional videos that I enjoyed watching:
What they have in common:
- They're informative.
- They're fun to watch.
- It must have taken a ton of work to create them.
So my questions are:
- Do you like them more than what we're doing?
- Are they "better enough" than what we're doing to justify the extra effort? We'd produce half as much material if we did this—would that be a good tradeoff?


We provide lessons from the convenience of your home. If you prefer, we also offer the option of hosting the lesson at a home studio in Syosset.
Our small business model allows us to keep overhead costs low, which means that we can pass those savings on to you and offer much lower rates than larger music academies.
15 years of proven results, including top grades at NYSSMA every year. Our students are able to use their accomplishments to give an extra boost to their applications to the most selective universities in the country. Students also have the option to prepare for exams at the Royal Conservatory of Music or prepare for recitals in local concert venues.
Each student will be given a unique lesson plan tailored to their ambitions, time commitment, and skill level. Whether the end goal is entrance to a music academy or simply performing a Christmas song for their family, we make sure that each student gets the results they want. No matter what their level, all students regularly have a chance to showcase their work at regular recitals.
Fill out the form below with your contact details and I will be in touch. First trial lesson is provided free of charge.


--- layout: page category: spec title: "Picture-in-Picture" ---
| Current Status: | Working Draft (WD) |
|---|---|
| Canonical URI: |
http://www.w3.org/TR/picture-in-picture
|
| Abstract: | This specification intends to provide APIs to allow websites to create a floating video window always on top of other windows so that users may continue consuming media while they interact with other content sites, or applications on their device. |
Picture-in-PictureUpdates:


Demo for cepto v0.1.0
-
text1
-
text2
-
text3
-
text4
-
text5
image.
text.
From now until May 19, Coca-Cola® will be pushing messages to their official account followers at 3:15pm on a few random days per week. By answering the fun and easy questions directly in their official account and participating in the mini-games, participants will have a chance to receive 7-Eleven offers with the correct answers.


problems with launch at login
-
scrag
I've got the "Launch Synergy at login" option ticked in the the Synergy pref pane, but it won't start when I log in.
I've looked in both my loginwindow.plist files, and there is only one copy of Synergy referenced, and I have only one copy of Synergy installed.
I've also tried un-checking the "Launch Synergy at login" option, clicking apply, and then re-checking the option, followed by apply. While this does remove and add the Synergy entry to my loginwindow.plist as it should, it doesn't actuallt fix the problem. Synergy just will not start.
I'm running Synergy 2.1 on OS X 10.3.9. Any ideas?
Thanks, - c -
-
Greg Hurrell
So, does the entry in your loginwindow.plist file have the correct path to Synergy.app in it?
If the correct path is in there and the system is still not launching it then I suspect a permissions problem. Have you tried replicating the problem from another user account? Does Synergy launch correctly when you do a manual launch (by clicking the "Start" button in the preference pane)?
Reply
This topic is now closed.


Main Concepts
About Templates
-
The templates syntax seems a little bit complex
-
Doesn't look like standard html
-
As an example the following syntax is correct

 {{ title }}

User List:

<user-cmp [id]="user.id"></user-cmp>

<input type="text" [(ngModel)]="filterCriteria">
And yes, this is valid ...


--- layout: page title: About header-img: "img/about-bg.png" ---
I'm a music teacher turned coder. I've always loved video games, which is what got me to think about coding in the first place. I always wanted to know how and why the games worked the way they did rather than just assuming that the game is "doing something."


{{ _("We are proud to have been given an opportunity by Loyds Industri to take part in manufacturing of this exclusive hardtop. The hardtop is designed so that it can include a variety of alternative furnishings - double retractable floor, drawers for storage of tools and possibilities for shelving on one or both sides, all of which is provided by Loyds. Three lockable doors, one rear and one on each side. Both side and rear hatches are fitted with gas struts for easy access. Customized body makes the load compartment water and dust proof. Solid roof rails for exterior roof transportation. And much more.") }}
{{ _("Please note that Hilux Cargo is a special model developed by Toyota Norway, and is therefore only sold in Norway. If you are located in Norway and already have a Hilux, this can of course be retrofitted on older cars. For more details please contact your Toyota dealer.") }}
{{ _("Specification") }}
| {{ _("Load compartment floor length") }} | {{ _("1800 mm") }} |
| {{ _("Load compartment floor width") }} | {{ _("1470 mm") }} | {{ _("Max length in load compartment") }} | {{ _("2850 mm") }} |
| {{ _("Side hatch opening size") }} | {{ _("1400x400 mm") }} |
| {{ _("Hardtop weight") }} | {{ _("108 kg") }} |


Install¶
Linux / Mac¶
git clone https://github.com/AmadeusITGroup/cPMML.git && cd cPMML && ./install.sh
Usage
// your_program.cc
#include <iostream>
#include "cPMML.h"
int main() {
std::cout << "cPMML version: " << cpmml::version << std::endl;
return 0;
}
g++ your_program.cc -lcPMML
Requirements
Git
Compiler supporting C++11.
CMAKE version >= 3.5.1.
Windows¶
git clone https://github.com/AmadeusITGroup/cPMML.git && cd cPMML && install.bat
The compiled library files will be placed under cpmml/cpmml_win
Usage
// your_program.cc
#include <iostream>
#include "cPMML.h"
int main() {
std::cout << "cPMML version: " << cpmml::version << std::endl;
return 0;
}
Supposing you cloned and built cPMML under some path like C:\Users\YourUser\cpmml.
copy C:\Users\YourUser\cpmml\cpmml_win\* .
g++ your_program.cc -L. -lcPMML
Requirements
Git
MinGW-W64 supporting C++11
CMAKE version >= 3.5.1


|
|||
| » | TIME IN / OUT |
||
| » | Employee Info View and edit employee information |
||
| » | Attendance View attendance information |
||
| » | Absence View and input absence proposal. |
||
| » | Leave View and input leave proposal. |
||
| » | Overtime View and entry overtime proposal and actual. |
|
||||||||
|
|
||||||||||
| ||||||||||
|
||
| [var.strDetailAlert] | ||
| Empl. ID | Name | Start Date | Finish Date | ||


--- type: formControls title: Dropdown usage: A dropdown control. Basically, it replaces the HTML select tag. The dropdown prepend is optional. stylesheet: '/scss/controls/dropdown.scss' ---


|
<colorSensor>
This library support the TCS3200 colorSensor using the included freqMeasuringtool.
|
colorSensor ADT More...
#include <colorSensor.hpp>
Public Types | |
| enum | colors : char { red = 'R', green = 'G', blue = 'B', yellow = 'Y', pink = 'P', brown = 'b', orange = 'O', empty = 'X' } |
Additional Inherited Members | |
| Protected Attributes inherited from freqMeasuringtool | |
| int | frequenty |
| uint_fast64_t | t1 |
| uint_fast64_t | t2 |
| hwlib::target::pin_in & | pin |
colorSensor ADT
This is an ADT that gives the user RGB values of colors. the RGB values are stored seperately as integers. The RGB values do not match the standard RGB codes. This ADT can also give the name of a few colors with two different modes as a char and is stored as a char. If a char is used multiple times the primary color is capitalized and the secundary color lowercase. this ADT is reliant on hwlb and freqMeasuringtool. https://github.com/wovo/hwlib
|
inline |
colorSensor constructor.
This constructor construct the colorSensor with the intput of an hwlib::target::in and four pins in an hwlib::port_out. It also sets the frequenty scaling to 2%.
| int colorSensor::calculateColor | ( | ) |
calculateColor() function.
This function takes 25 samples of a single color and takes the average frequenty and returns this as an int.
| void colorSensor::calculatelightIntensity | ( | ) |
calculatelightIntensity() function.
This function fils the int lightIntensity with the light intensity.
| void colorSensor::calculateRGB | ( | ) |
calculateRGB() function.
This function sets the hwlib::color using the calculateColor function.
| void colorSensor::flush | ( | ) |
flush() function.
This function flushes the chosen settings to the sensor.
| char colorSensor::getColor | ( | ) |
getColor() function.
This function returns the color as a char.
| int colorSensor::getlightIntensity | ( | ) |
getlightIntensity() function.
This function returns the lightIntensity as an int.
| hwlib::color colorSensor::getRGB | ( | ) |
getRGB() function.
This function returns the RGB values as an hwlib::color.
| void colorSensor::nameColorMode1 | ( | ) |
nameColorMode1() function.
This function fils the color char with either the color red, green or blue. It is consistent but can be influenced bij ambient light. If the color in front of the sensor cannot be defined as red, green or blue the color will be filled with an 'X'. The color is determined by putting shrinkValueRGB values into 8 different zones. the combination of three zondes correspond with a color. red = 'R green = 'G' blue = 'B'
| void colorSensor::nameColorMode2 | ( | ) |
nameColorMode2() function.
This function fils the color char with either the color red, green, blue, brown, yellow, orange or pink. It is consistent but can be influenced by ambient light. If the color in front of the sensor cannot be defined as one of the colors the color will be filled with an 'X'. The color is determined by putting shrinkValueRGB values into 16 different zones. the combination of three zondes correspond with a color. red = 'R', green = 'G', blue = 'B', brown = 'b', yellow = 'Y', orange = 'O', pink = 'P'.
| void colorSensor::printRGB | ( | ) |
printRGB() function.
This function prints the red, green and blue values to the terminal.
| void colorSensor::setBlue | ( | ) |
setBlue() function.
This function sets the color sensor to read blue values and flushes the sensor.
| void colorSensor::setClear | ( | ) |
setClear() function.
This function sets the color sensor to read clear values for light intensity and flushes the sensor.
| void colorSensor::setGreen | ( | ) |
setGreen() function.
This function sets the color sensor to read green values and flushes the sensor.
| void colorSensor::setRed | ( | ) |
setRed() function.
This function sets the color sensor to read red values and flushes the sensor.
| void colorSensor::shrinkValueRGB | ( | ) |
shrinkValueRGB() function.
this function shrinks the integers red, green and blue integers until their maximum value is 255 or the lowest value becomes 0.


Javascript request execution
Javascript ("create-element.js") execution
Text ("some-text.txt") execution
All Tests


Includes
The include
tag allows you to include the content from another file stored in the _includes
folder:
{% include footer.html %}
Jekyll will look for the referenced file (in this case, footer.html
) in the _includes
directory at the root of your source directory and insert its contents.
Including files relative to another file
You can choose to include file fragments relative to the current file by using the include_relative
tag:
{% include_relative somedir/footer.html %}
You won’t need to place your included content within the _includes
directory. Instead,
the inclusion is specifically relative to the file where the tag is being used. For example,
if _posts/2014-09-03-my-file.markdown
uses the include_relative
tag, the included file
must be within the _posts
directory or one of its subdirectories.
Note that you cannot use the ../
syntax to specify an include location that refers to a higher-level directory.
All the other capabilities of the include
tag are available to the include_relative
tag,
such as variables.
Using variables names for the include file
The name of the file you want to embed can be specified as a variable instead of an actual file name. For example, suppose you defined a variable in your page’s front matter like this:
---
title: My page
my_variable: footer_company_a.html
---
You could then reference that variable in your include:
{% include {{ page.my_variable }} %}
In this example, the include would insert the file footer_company_a.html
from the _includes/footer_company_a.html
directory.
Passing parameters to includes
You can also pass parameters to an include. For example, suppose you have a file called note.html
in your _includes
folder that contains this formatting:

 Note:
{{ include.content }}

The {{ include.content }}
is a parameter that gets populated when you call the include and specify a value for that parameter, like this:
{% include note.html content="This is my sample note." %}
The value of content
(which is This is my sample note
) will be inserted into the {{ include.content }}
parameter.
Passing parameters to includes is especially helpful when you want to hide away complex formatting from your Markdown content.
For example, suppose you have a special image syntax with complex formatting, and you don’t want your authors to remember the complex formatting. As a result, you decide to simplify the formatting by using an include with parameters. Here’s an example of the special image syntax you might want to populate with an include:

This is the Jekyll logo

You could templatize this content in your include and make each value available as a parameter, like this:

{{ include.caption }}

This include contains 5 parameters:
url
max-width
file
alt
caption
Here’s an example that passes all the parameters to this include (the include file is named image.html
):
{% include image.html url="http://jekyllrb.com"
max-width="200px" file="logo.png" alt="Jekyll logo"
caption="This is the Jekyll logo." %}
The result is the original HTML code shown earlier.
To safeguard situations where users don’t supply a value for the parameter, you can use Liquid’s default filter.
Overall, you can create includes that act as templates for a variety of uses — inserting audio or video clips, alerts, special formatting, and more. However, note that you should avoid using too many includes, as this will slow down the build time of your site. For example, don’t use includes every time you insert an image. (The above technique shows a use case for special images.)
Passing parameter variables to includes
Suppose the parameter you want to pass to the include is a variable rather than a string. For example, you might be using {{ site.product_name }}
to refer to every instance of your product rather than the actual hard-coded name. (In this case, your _config.yml
file would have a key called product_name
with a value of your product’s name.)
The string you pass to your include parameter can’t contain curly braces. For example, you can’t pass a parameter that contains this: "The latest version of {{ site.product_name }} is now available."
If you want to include this variable in your parameter that you pass to an include, you need to store the entire parameter as a variable before passing it to the include. You can use capture
tags to create the variable:
{% capture download_note %}
The latest version of {{ site.product_name }} is now available.
{% endcapture %}
Then pass this captured variable into the parameter for the include. Omit the quotation marks around the parameter content because it’s no longer a string (it’s a variable):
{% include note.html content=download_note %}
Passing references to YAML files as parameter values
Instead of passing string variables to the include, you can pass a reference to a YAML data file stored in the _data
folder.
Here’s an example. In the _data
folder, suppose you have a YAML file called profiles.yml
. Its content looks like this:
- name: John Doe
login_age: old
image: johndoe.jpg
- name: Jane Doe
login_age: new
image: janedoe.jpg
In the _includes
folder, assume you have a file called spotlight.html
with this code:
{% for person in include.participants %}
{% if person.login_age == "new" %}
{{ person.name }}
{% endif %}
{% endfor %}
Now when you insert the spotlight.html
include file, you can submit the YAML file as a parameter:
{% include spotlight.html participants=site.data.profiles %}
In this instance, site.data.profiles
gets inserted in place of include.participants
in the include file, and the Liquid logic processes. The result will be Jane Doe
.


A highly motivated and hardworking undergraduate in Computer Science & Engineering field.Interested in learning new technologies and to be updated with current trends in the field. Happy to help and support to anyone with my coding skills and knowledge.


""" script that scrapes and uploads DarkSky and USGS data
Examples:
python continuous_retrieval.py [--csv] --manual start-date end-date
* scrapes and uploads data over the specified date range (inclusive)
* start-date and end-date must be in ISO format, 'YYYY-MM-DD'
* optional argument to insert records into database from CSV file (for USGS data only)
python continuous_retrieval.py [--csv] --daily [days-back]
* scrapes and uploads data over date range
* date range is from days-back before yesterday to yesterday (inclusive)
* optional days-back parameter must be an integer (defaults to 0)
* optional argument to insert records into database from CSV file (for USGS data only)
"""
import datetime as dt
import dateutil
import json
import pandas as pd
import requests
from riverrunner import settings
from riverrunner.context import Context, Measurement
from riverrunner.repository import Repository
from sqlalchemy.exc import SQLAlchemyError
import sys
# data directory
DATA_DIR = "data/"
# base API URLs
DARK_SKY_URL = 'https://api.darksky.net/forecast'
USGS_BASE_URL = "https://waterservices.usgs.gov/nwis/iv/"
# USGS query constants
USGS_FORMAT = "json"
USGS_SITE_STATUS = "all"
# USGS parameter codes
PARAM_CODES = [
"00060",
]
[docs]def fill_noaa_gaps(start_date, end_date):
"""use as needed to fill gaps in weather measurements
Args:
start_date: the start day, included in API calls
end_date: the end day, inclusive
"""
context = Context(settings.DATABASE)
session = context.Session()
repo = Repository(session)
stations = repo.get_all_stations(source='NOAA')
# loop through each day retrieving observations
while start_date <= end_date:
content = stations.apply(
lambda station: make_station_observation_request(station, start_date.isoformat()),
axis=1
).values
# put them all in the db
added = 0
for station_measurements in content:
try:
repo.put_measurements_from_list(station_measurements)
except SQLAlchemyError:
session.rollback()
continue
added += len(station_measurements)
print(f'added {added} - {start_date.isoformat()}')
start_date += dt.timedelta(days=1)
[docs]def get_noaa_predictions(run_id, session):
"""retrieve NOAA predictions for run
Args
run_id (int): run
session (Session): database session
Returns
DataFrame: containing predictions
"""
repo = Repository(session)
run = repo.get_run(run_id)
lat = run.put_in_latitude
lon = run.put_in_longitude
r = requests.get(f'https://api.weather.gov/points/{lat},{lon}/forecast/hourly')
if r.status_code == 200 and len(r.content) > 10:
return pd.DataFrame(r.json()['properties']['periods'])
else:
return None
[docs]def get_usgs_site_ids():
""" retrieve USGS site ids from database
Returns:
[str]: list of site ids
"""
r = Repository()
sites = r.get_all_stations(source="USGS")
site_ids = [s for s in sites["station_id"]]
return site_ids
[docs]def get_usgs_json_data(site_id, start_date, end_date, param_code):
""" retrieve JSON data for a specific USGS site, parameter, and date range
Args:
site_id (str): string representation of site id
start_date (str): start date in ISO format, 'YYYY-MM-DD'
end_date (str): end date in ISO format, 'YYYY-MM-DD'
param_code (str): string representation of parameter code
Returns:
dict[str, str]: map relating timestamps to values
"""
params = {
"format": USGS_FORMAT,
"sites": site_id,
"startDT": start_date,
"endDT": end_date,
"parameterCd": param_code,
"siteStatus": USGS_SITE_STATUS,
}
response = requests.get(USGS_BASE_URL, params=params)
try:
response_json = response.json()
except json.decoder.JSONDecodeError:
print(response.content)
raise
# response_json = requests.get(USGS_BASE_URL, params=params).json()
time_series_list = response_json["value"]["timeSeries"]
values_list = []
if len(time_series_list) > 0:
values_list = time_series_list[0]["values"][0]["value"]
date_value_map = {elem["dateTime"]: elem["value"] for elem in values_list}
return date_value_map
[docs]def make_station_observation_request(station, day):
"""make a request for observations to the DarkSky API
Args:
station: (Station) weather station to retrieve measurments
day: (str) datetime in iso format
Returns:
[Measurements] if call was successful
None otherwise
"""
now = dt.datetime.now().isoformat()
r = requests.get(f'{DARK_SKY_URL}/{settings.DARK_SKY_KEY}/{station.latitude},{station.longitude},{day}')
# make sure the web result is valid
if r.status_code == 200:
content = r.json()
measurements = []
# generate hourly measurements
for obs in content['hourly']['data']:
timestamp = obs['time']
timestamp = dt.datetime.fromtimestamp(timestamp)
# add precip
measurements.append(
Measurement(
station_id=station.station_id,
metric_id='00003',
value=obs['precipIntensity'],
date_time=timestamp
)
)
# add temp
measurements.append(
Measurement(
station_id=station.station_id,
metric_id='00001',
value=obs['temperature'],
date_time=timestamp
)
)
# add humidity
measurements.append(
Measurement(
station_id=station.station_id,
metric_id='00002',
value=obs['humidity'],
date_time=timestamp
)
)
print(f'{now}: {station.station_id} complete')
return measurements
else:
print(f'{now}: {station.station_id} failed')
return None
[docs]def put_24hr_observations(session):
"""get yesterdays observations
Args
session (Session): database session
"""
# create a repo and pull all the weather stations from NOAA
repo = Repository(session)
stations = repo.get_all_stations(source='NOAA')
# setup the day to retrieve
yesterday = dt.datetime.now() - dt.timedelta(hours=24)
yesterday = dt.datetime(year=yesterday.year, month=yesterday.month, day=yesterday.day)
# apply the api request to each station
content = stations.apply(
lambda station: make_station_observation_request(station, yesterday.isoformat()),
axis=1
).values
# put them all in the db
added = 0
for station_measurements in content:
repo.put_measurements_from_list(station_measurements)
added += len(station_measurements)
return added
[docs]def scrape_usgs_data(start_date, end_date):
""" scrape data for all USGS sites and parameters, over the specified date range
Args:
start_date (str): start date in ISO format, 'YYYY-MM-DD'
end_date (str): end date in ISO format, 'YYYY-MM-DD'
Returns:
[str]: list of full paths of CSV files that were written to
"""
start_date_file_ext = start_date.replace("-", "")
end_date_file_ext = end_date.replace("-", "")
site_ids = get_usgs_site_ids()
param_codes = PARAM_CODES
out_files = []
for param_code in param_codes:
total_values = 0
out_file = DATA_DIR + "measurements_{}_{}_{}.csv".format(
param_code,
start_date_file_ext,
end_date_file_ext
)
out_files.append(out_file)
with open(out_file, "w") as f:
for site_id in site_ids:
json_data = get_usgs_json_data(
site_id=site_id,
start_date=start_date,
end_date=end_date,
param_code=param_code
)
total_values += len(json_data)
for date_time, value in json_data.items():
f.write("{},{},{},{}\n".format(
site_id,
param_code,
date_time,
value
))
print("{}: {}".format(param_code, total_values))
return out_files
[docs]def upload_data_from_file(csv_file, from_csv=False):
""" insert all records contained in file to database
Args:
csv_file (str): full path of CSV file containing records
from_csv (bool): whether to insert into database using CSV or ORM (CSV scales better)
Returns:
bool: success/exception
"""
r = Repository()
if from_csv:
success = r.put_measurements_from_csv(csv_file=csv_file)
else:
measurements = []
with open(csv_file, "r") as f:
for line in f:
site_id, param_code, date_time, value = line.strip().split(",")
measurement = Measurement(
station_id=site_id,
metric_id=param_code,
date_time=dateutil.parser.parse(date_time),
value=float(value)
)
measurements.append(measurement)
success = r.put_measurements_from_list(measurements=measurements)
return success
if __name__ == "__main__":
# python scrape_usgs_data.py [--csv] --manual start-date end-date
# python scrape_usgs_data.py [--csv] --daily [days-back]
from_csv = False
if sys.argv[1] == "--csv":
from_csv = True
if "--manual" in sys.argv[1:3]:
index = sys.argv.index("--manual")
start_date, end_date = sys.argv[index+1:]
elif "--daily" in sys.argv[1:3]:
index = sys.argv.index("--daily")
days_back = 0
if len(sys.argv) > index + 1:
days_back = int(sys.argv[index+1])
today = dt.date.today()
end_date = today - dt.timedelta(days=1)
start_date = end_date - dt.timedelta(days=days_back)
end_date = end_date.isoformat()
start_date = start_date.isoformat()
csv_files = scrape_usgs_data(start_date=start_date, end_date=end_date)
for csv_file in csv_files:
print("uploading {}...".format(csv_file))
success = upload_data_from_file(csv_file=csv_file, from_csv=from_csv)
fill_noaa_gaps(start_date, end_date)


Took Amber's cell phone this morning instead of mine. D'oh! 3/16/2009, 10:29:56 AM Favs: 0 Retweets: 0 link


WinPath Manager Release Notes:
February 21th, 2014, Release 1.0 Alpha
I have updated WinPathManger with important updates and bug fixes.
If you encounter any issues or bugs, please don't hesitate to contact me in the Dissusions /issues in https://pathmanger.codeplex.com/
New Features
February 4th, 2014, Release 0.8
New Features
January 28,2014 -Release 0.7
January 26,2014 -Release 0.4
First release


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2018-06-05–Remove Persistent Running App in Notification Background
2016-08-29–Thoughts on Internship with OpenDayLight


The smallest part of something that still is that something
Elements are made of even more fundamental things which are atoms
If Proton is changed, the element changes If Neutron or Electron is changed the Isotope changes
It is another measuring system
1 u = 1.660540 x 10-27
Take a look at the Hydrogen Atom it's mass is 1.0008
Many versions of these exists, different versions are called Isotopes
So a hydrogen with 1 Proton, 1 Neutron, and 1 Electron exists. u ≈ 2
The number on the bottom is actually all the Weighted Mass of All Isotopes
0.8(5 u) + 0.2(6 u) = 5.2
If you wanted to turn:
6.94 u of Lithium -------------> 6.94 g of Lithium
To do this you do 6.022 x 10-23 That number is Avogadro's number
A mole is a representation of the Avogadro's number
1 Mole of Lithium is = 6.022x10-23 LI
In this case a Mole of Lithium is 6.94 g


Cheryl slams claims she 'set Nadine up to eat testicles' in I'm A Celeb trials
Girls Aloud bandmates Cheryl, Kimberley Walsh and Nicola Roberts have said they never plotted in group chat to make Nadine Coyle eat 'turkey testicles' on I'm A Celebrity
by Irishmirror.ieNadine Coyle is not being set up to 'eat turkey testicles' on I'm A Celebrity by her former Girls Aloud bandmates.
After reports appeared in The Sun that Cheryl, Kimberley Walsh and Nicola Roberts had been plotting in a group WhatsApp chat, to ensure Nadine was nominated for a Bushtucker Trial in the jungle, the Girls Aloud stars have refuted the claims.
They have taken offence at the idea they have been "voting repeatedly" so that Nadine is faced with something nasty.
Speaking to the MailOnline, Nadine's former bandmates have spoken out against the claims of a source, with Cheryl insisting they "have love for each other", even if they have had differences in opinion.
Cheryl said: "We only want to see Nadine do well, and regardless of any differences we might have had, we will always all have love for each other.
"We did not enjoy watching her distressed when jumping out of the plane. And we don’t have time for group chats regarding the trials on the show!"
Kimberley also dismissed the claim and that when Nadine jumped from the helicopter in the launch episode, her heart went out to her.
She said: "I felt really emotional watching Nadine jump out of the plane and texted Cheryl saying how hard it was to watch. I think she’s doing great in there."
This was echoed by Nicola Roberts, who also slammed the reports that the Girls Aloud singers were hoping for bad things to happen to Nadine in the Australian jungle.
She also dismissed reports that there was a private group chat where they tear into Nadine behind her back.
Nicola said: 'We don’t have a group chat about I’m A Celebrity, this just isn’t true. We only wish Nadine the best in the jungle and hope she does well on the show.'
A spokesperson for the band also said the reports were "fabricated", telling MailOnline: "This story is completely fabricated and made up.
"Cheryl, Nicola and Kimberley have not been 'plotting' to get Nadine to do trials in I’m A Celebrity. To suggest they have been is quite frankly ridiculous."
There had been rumours that Nadine and Cheryl had a longstanding disagreement but Nadine denied this when asked about by Jacqueline Jossa.
Do you have a story to sell? Get in touch with us at<EMAIL_ADDRESS>or call us direct 0207 29 33033.


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Chapter 8 Semiparametric estimation and inference
Parametric: governed by parameters; e.g., normal distributions
Nonparametric: not governed by parameters; used when we cannot assume that the distribution of a random variable follows a particular probability distribution
Semiparametric: a model whose behavior is partially governed by parameters
Empirical distribution function: summarizes all the information that the data provide about a random variable’s distribution
The empirical distribution function \(\hat{F}_n(z)\) gives the proportion of observations in a sample that are less than or equal to a constant \(z\):
\[ \begin{aligned} \hat{F}_n(z) =& \frac{1}{n} \sum_{i = 1}^n I_{X_i \leq z} \end{aligned} \]
where \(I\) is an indicator variable that states whether observation \(X_i\) is less than or equal to \(z\).
8.0.1 Exercise set 8-1
- Comparing an ECDF with a CDF for the normal distribution
- As the sample size increases, the ECDF approximates the CDF of the normal distribution.
n <- 500
x.vals <- seq(-3, 3, length.out = 10000)
Fx <- pnorm(x.vals, 0, 1)
plot(x.vals, Fx, xlab = "z", ylab = "F(z)", type = "l")
x <- rnorm(n, 0, 1)
lines(ecdf(x), verticals = TRUE, do.points = FALSE, lty = 2)
- Comparing an ECDF with a CDF for the Poisson distribution
set.seed(22)
n <- 30
lambda <- 10
x.vals <- seq(0, 30, by = 1)
Fx <- ppois(x.vals, lambda = lambda)
plot(x.vals, Fx, xlab = "z", ylab = "F(z)", type = "p")
x <- rpois(n, lambda = lambda)
lines(ecdf(x), verticals = TRUE, do.points = FALSE, lty = 2)
- See handwritten notes.
8.1 Semiparametric point estimation using the method of moments
8.1.1 Introduction to moments
Methods of moments: a semiparametric approach to estimation
Method of maximum likelihood: a parametric approach to estimation
Suppose \(X\) is a random variable. The \(j\)th moment of the distribution is:
\[ \begin{aligned} \mu_j =& E(X^j) \end{aligned} \]
where \(\mu\) is used as the symbol for a moment. For example \(\mu_1 = E(X)\). It is easy to express the first moment of \(X\) as an expression using \(X\) - but what about \(\mu_2\)?
\[ \begin{aligned} \mu_2 =& E(X^2) \end{aligned} \]
Now we have expressed the second moment, \(\mu_2\), as the expectation of a new random variable, \(X^2\). At least this is how I interpret it. From my reading, I gather this is not so satisfying, because the second moment is then usually rearranged in the following way.
First, we need to recall the definition of Var(\(X\)):
\[ \begin{aligned} \text{Var}(X) = \text{E}(X^2) - [\text{E}(X)]^2 \\ \end{aligned} \]
So, if we rearrange we get:
\[ \begin{aligned} \text{E}(X^2) = \text{Var}(X) + [\text{E}(X)]^2 \\ \end{aligned} \]
We can continue on with 3rd, 4th, 5th, etc, moments. But expressing these in terms of \(X\) is, I am guessing, a harder challenge. These additional moments describe further the probability distribution, but typically knowing the first and second moments is enough for practical purposes (our purposes). As an aside, the third moment gives us the skewness, and the fourth moment gives us the kurtosis, of the distribution. Finally, it is worth noting the notation for a moment - here I have used \(\mu\), becuase it is often used in statistics notes (that I found online). This can be confusing, because we often use \(\mu\) for the population mean. But we won’t do that here, for clarity.
So, let us say that \(X\) follows a Normal(\(\theta\), \(\sigma^2\)) distribution. In this case, we know that E(\(X\)) = \(\theta\), and that Var(\(X\)) = \(\sigma^2\). So we can express the first and second moments as:
\[ \begin{aligned} \mu_1 =& ~ \text{E}(X) \\ =& ~ \theta \\ \mu_2 =& ~ \text{E}(X^2) \\ =& \text{Var}(X) + [\text{E}(X)]^2 \\ =& ~ \sigma^2 + \theta^2 \\ \end{aligned} \]
So we have expressed the moments in terms of the parameters of the distribution we are trying estimate. We can rearrange these, to express the parameters in terms of \(X\) - which is exactly what we want to do, since we are trying to estimate \(\theta\) and \(\sigma^2\)!
\[ \begin{aligned} \theta =& ~ \text{E}(X) \\ \sigma^2 =& ~ \text{E}(X^2) - [\text{E}(X)]^2 \\ \end{aligned} \]
To paraphrase Edge, we can estimate the moments of arbitrary distributions (i.e., distributions that are not described by parameters) by following these steps:
Write the equations that give the moments of a random variable in terms of the parameters being estimated
Solve the equations from (1) for the desired parameters, giving expressions for the parameters in terms of the moments
Estimate the moments and plug the estimates into the expressions for the parameters from (2)
8.1.2 Plug-in estimators
Plug-in estimation: perhaps I’m oversimplifying here, but this sounds like awesome jargon for ‘calculate your estimator from data’. In other words, if you want the population mean - collect some data and calculate the mean. Then ‘plug that in’ to your population mean.
We can express a plug-in estimator of the \(k\)th moment of \(X\), E(\(X^j\)) as the \(k\)th sample moment:
\[ \begin{aligned} \overline{X^k} = \frac{1}{n} \sum_{i = 1}^n X_i^k \end{aligned} \]
So to recap: a moment describes the random variable \(X\). A sample moment is an estimate of the moment using independent samples (\(X_1, X_2, X_3\)) drawn from \(X\).
8.1.3 Exercise set 8-2
- Supose \(X_1, X_2, ..., X_n\) are I.I.D observations.
- What is the plug-in estimator of the variance of \(X\)?
\[ \begin{aligned} \sigma^2 =& ~ \text{E}(X^2) - [\text{E}(X)]^2 \\ \hat\sigma^2 =& ~ \frac{1}{n} \sum_{i = 1}^n X_i^2 - [\frac{1}{n} \sum_{i = 1}^n X_i]^2 \\ \end{aligned} \] b. What is the plug-in estimator for the standard deviation of \(X\)?
\[ \begin{aligned} \hat\sigma =& ~ \sqrt{\frac{1}{n} \sum_{i = 1}^n X_i^2 - [\frac{1}{n} \sum_{i = 1}^n X_i]^2} \\ \end{aligned} \]
- What is the plug-in estimator of the covariance of \(X\) and \(Y\)?
Here is the formula for the covariance, as a reminder:
\[ \begin{aligned} \text{Cov}(X,Y) =& ~ \text{E}([X - \text{E}(X)][Y - \text{E}(Y)]) \\ =& ~ \text{E}(XY) - \text{E}(X)\text{E}(Y) \end{aligned} \] Let’s use the 2nd equation:
\[ \begin{aligned} \text{Cov}(X,Y) =& ~ \frac{1}{n} \sum_{i = 1}^n X_iY_i - [\frac{1}{n} \sum_{i = 1}^n X_i][\frac{1}{n} \sum_{i = 1}^n Y_i] \\ \end{aligned} \]
- What is the plug-in estimator of the correlation of \(X\) and \(Y\)?
Here is the formula for the correlation, as a reminder:
\[ \begin{aligned} \text{Cor}(X,Y) =& ~ \rho_{X,Y} \\ =& ~ \frac{\text{Cov}(X,Y)}{\sqrt{\text{Var}(X)\text{Var}(Y)}} \\ =& ~ \frac{\text{Cov}(X,Y)}{\sigma_X \sigma_Y}\\ \end{aligned} \]
Plugging in, we get:
\[ \begin{aligned} \rho_{X,Y} =& ~ \frac {\frac{1}{n} \sum_{i = 1}^n X_iY_i - [\frac{1}{n} \sum_{i = 1}^n X_i][\frac{1}{n} \sum_{i = 1}^n Y_i]} {\sqrt{ \frac{1}{n} \sum_{i = 1}^n X_i^2 - [\frac{1}{n} \sum_{i = 1}^n X_i]^2}] [\frac{1}{n} \sum_{i = 1}^n Y_i^2] } \\ \end{aligned} \]
- Though the plug-in estimator of the variance is consistent, it is biased downward.
- Demonstrate the downward bias in R with simulations.
This is what I did first:
n_obs <- 5
n_sims <- 10000
set.seed(1010)
x <- mat.samps(n = n_obs, nsim = n_sims)
x_var <- apply(X = x, MARGIN = 1, FUN = var)
mean(x_var)
## [1] 1.004699
The result is very close to 1. This surprised me, given that I was expecting something below 1. So I ran Edge’s code, and in it, he defines a function to calculate the variance himself.
var.pi <- function(vec){
return(sum((vec-mean(vec))^2)/length(vec))
}
vars.pi <- apply(x, 1, var.pi)
mean(vars.pi)
## [1] 0.8037593
Why the difference? Turns out this is a great demonstration of something I had read some time ago, and then buried away because it frankly was not super relevant. Let’s inspect the help function for var
:
And in the details you’ll see this note:
The denominator n - 1 is used which gives an unbiased estimator of the (co)variance for i.i.d. observations. These functions return NA when there is only one observation (whereas S-PLUS has been returning NaN), and fail if x has length zero.
Thanks to this book, this statement make sense. Let’s calculate the variance using n and n-1 as the denominator:
## [1] 0.4974519 1.1826813 1.0876947 -1.3540474 -0.9988671
## [1] 1.406506
## [1] 1.125205
## [1] 1.125205
# But now we change the denominator from n, to n-1
# Unbiased estimator for the variance
# Gives the same result as `var`
sum((vec-mean(vec))^2) / (length(vec) - 1)
## [1] 1.406506
I have not read ahead, but I suspect Edge will discuss this discrepancy soon..
- Repeating the simulation, but with samples sizes from 2 to 10.
n_sims <- 10000
n_vec <- 2:10
variance_vec <- vector(length = length(n_vec))
for(i in 1:length(n_vec)){
n_obs <- n_vec[i]
x <- mat.samps(n = n_obs, nsim = n_sims)
vars.pi <- apply(x, 1, var.pi)
variance_vec[i] <- mean(vars.pi)
}
variance_vec
## [1] 0.4879436 0.6578587 0.7378372 0.7852684 0.8369602 0.8502939 0.8715858
## [8] 0.8900192 0.9012010
plot(n_vec, variance_vec, type = "b",
xlab = "Number of samples",
ylab = "Mean variance",
ylim = c(0, 1))
abline(h = 1, lty = 2 , col = "red")
The bias is largest with a sample size of 2, and the plug-in estimator gets closer to the true value with increasing sample size.
I did not think to convert these decimal outputs to fractions as indicated in Edge’s solution, even having stumbled upon the unbiased estimator above. So this result is not yet intuitive:
\[ \begin{aligned} \text{E}(\hat\sigma^2_n) =& ~ \frac{n - 1}{n} \sigma^2 \\ \end{aligned} \]
This equation demonstrates the bias of the plug-in estimator for the variance. If we multiply it by \(n/(n-1)\), we get the sample variance (\(s^2\)), an unbiased estimator:
\[ \begin{aligned} s^2 =& ~ \frac{\sum_{i = 1}^n (X_i - \overline X_i)^2}{n - 1} \\ \end{aligned} \]
- See Edge solution. It explains how we get the expectation of the plug-in estimator.
- Did not try.
8.1.4 The method of moments
We apply the method of moments to the model for simple linear regresion:
\[ \begin{aligned} Y = \alpha + \beta X + \epsilon \\ \end{aligned} \]
If we make the assumption that the expectation of the disturbance term is 0 for all values of \(X\):
\[ \begin{aligned} \text{E}(\epsilon | X = x) =& ~ 0 \\ \end{aligned} \]
then the conditional expectation of \(Y\), given any \(x\), can be predicted using a line with a slope \(\beta\) and intercept \(\alpha\):
\[ \begin{aligned} \text{E}(Y) = \alpha + \beta_{\mu_X} \\ \end{aligned} \]
If this assumption is not true, then the relationship between X and Y is not linear! Now that we have made the linearity assumption (see Edge’s Figure 8-4), we can proceed with the MOM estimators for \(\alpha\) and \(\beta\).
We know that the covariance of \(X\) and \(Y\) is:
\[ \begin{aligned} \text{Cov}(X,Y) = \beta \sigma_X^2 \\ \end{aligned} \]
and that:
\[ \begin{aligned} \mu_X =& ~ \text{E}(X) \\ \sigma_X^2 =& ~ \text{Var}(X) \\ \text{Cov}(X,Y) =& ~ \text{E}(XY) - \text{E}(X)\text{E}(Y) \\ \text{Var}(X) =& ~ \text{E}(X^2) - [\text{E}(X)]^2 \\ \end{aligned} \]
we can get expressions for the parameters \(\alpha\) and \(\beta\) in terms of the moments of \(X\) and \(Y\):
\[ \begin{aligned} \alpha =& ~ \text{E}(Y) - \beta \text{E}(X) \\ \beta =& ~ \frac{\text{Cov}(X,Y)}{\sigma_X^2} \\ =& \frac{\text{E}(XY) - \text{E}(X)\text{E}(Y)}{\text{E}(X^2) - [\text{E}(X)]^2} \\ \end{aligned} \]
The expression for \(\beta\) is entirely in terms of moments, as opposed to the expression for \(\alpha\). So we’ll plug in sample moments for the moments in the expression for \(\beta\), multiply the numerator and denominator by \(\frac{n}{n}\), and get:
\[ \begin{aligned} \tilde{\beta} =& \frac{\sum X_i Y_i - \frac{1}{n} (\sum X_i) (\sum Y_i)} {\sum X_i^2 - \frac{1}{n} (\sum X_i)^2 }\\ \end{aligned} \]
(try working through the algebra to get this expression yourself).
Here’s the expression for \(\alpha\):
\[ \begin{aligned} \tilde{\alpha} =& ~ \frac{1}{n} \sum Y_i - \tilde{\beta} \frac{1}{n} \sum X_i \\ =& ~ \frac{\sum Y_i - \tilde{\beta} \sum X_i} {n} \end{aligned} \]
These expressions are essentially identical to the expressions for \(\alpha\) and \(\beta\) that we derived for the coefficients of the least squares line in chapter 3.
8.1.5 Exercise set 8-3
See solution on paper.
See solution in Edge.
8.2 Semiparametric interval estimation using the bootstrap
With enough high quality data, the empirical distribution function is a reasonable approximation to the true cumulative distribution function. We can leverage this fact to estimate uncertainty intervals to accompany our plug-in estimators.
Bootstrap: a procedure that resamples a single dataset to create many simulated samples, which permits the calculation of standard errors and confidence intervals.
Take B ‘bootstrap’ samples, with each sample being a set of n observations drawn with replacement from the original observations. Label these samples \(D_1^*\), \(D_1^*\), …, \(D_B^*\).
For each of the simulated amples, calculate the statistic of interest \(\hat \vartheta\) for each simulated dataset. That is, calculate \(\hat \vartheta(D_1^*)\), \(\hat \vartheta(D_2^*)\), …, \(\hat \vartheta(D_B^*)\). These values form the bootstrap distribution of \(\hat \vartheta(D)\).
We will apply this procedure to our dataset of paired x, y data to calculate the bootstrap distribution of \(\tilde \beta\), the method of moments estimator of \(\beta\). We need to assume that the \(x, y\) pairs in the sample represent independent draws from a joint distribution.
<IP_ADDRESS> Assumptions
For all i and \(j \neq i\), \(X_i, Y_i\) are independent of \(X_j, Y_j\). This is the independence of units assumption
For all i, \(X_i, Y_i\) are drawn from the same joint distribution \(F_{X,Y}(x,y)\). This is the distribution assumption.
<IP_ADDRESS> Bootstrapping the Anscombe dataset
# Original fertilizer use and cereal yield data
d <- cbind(anscombe$x1, anscombe$y1) [order(anscombe$x1), ]
# One bootstrap sample
set.seed(8675309)
db <- cbind(anscombe$x1, anscombe$y1) [sample(1:11, replace = TRUE), ]
d; db
## [,1] [,2]
## [1,] 4 4.26
## [2,] 5 5.68
## [3,] 6 7.24
## [4,] 7 4.82
## [5,] 8 6.95
## [6,] 9 8.81
## [7,] 10 8.04
## [8,] 11 8.33
## [9,] 12 10.84
## [10,] 13 7.58
## [11,] 14 9.96
## [,1] [,2]
## [1,] 8 6.95
## [2,] 14 9.96
## [3,] 12 10.84
## [4,] 12 10.84
## [5,] 13 7.58
## [6,] 4 4.26
## [7,] 5 5.68
## [8,] 7 4.82
## [9,] 7 4.82
## [10,] 11 8.33
## [11,] 7 4.82
The function boot.samp
returns a bootstrap sample of either a vector or the rows of a matrix. The function beta.mm
calculates the methods of moments estimate of \(\beta\).
## function (x)
## {
## if (is.null(dim(x))) {
## x <- matrix(x, ncol = 1)
## }
## n <- nrow(x)
## boot.inds <- sample(1:n, replace = TRUE)
## x[boot.inds, ]
## }
## <bytecode: 0x7fb72b0bd6f8>
## <environment: namespace:stfspack>
## function (x, y)
## {
## n <- length(x)
## (sum(x * y) - (1/n) * sum(x) * sum(y))/(sum(x^2) - (1/n) *
## sum(x)^2)
## }
## <bytecode: 0x7fb72cc78a50>
## <environment: namespace:stfspack>
We use a for
loop to draw the bootstrap sample:
set.seed(8675309)
B <- 10000
boot.dist <- numeric(B)
dat <- cbind(anscombe$x1, anscombe$y2)
for(i in 1:B){
samp <- boot.samp(dat)
boot.dist[i] <- beta.mm(samp[,1], samp[,2])
}
We can plot a histogram and calculate the standard error of the estimator, \(\tilde \beta\) (recalling that the standard error of an estimate is its standard deviation).
# Plot histogram
hist(boot.dist, xlim = c(-0.2, 1.2), breaks = 30,
xlab = expression(paste("Bootstrapped ", beta)),
main = "", col = "gray", border = "white")
## [1] 0.165262
<IP_ADDRESS> Approximating confidence intervals for \(\tilde \beta\)
We consider three options:
- If we assume the sampling distribution is normal, then we can use the bootstrap estimate to identify a confidence interval (\(1 - \alpha\) interval).
# Normal bootstrap interval
a <- 0.05
b_mm <- beta.mm(dat[,1], dat[,2])
b_sd <- sd(boot.dist)
z <- qnorm(p = 1 - (a / 2), mean = 0, sd = 1)
b_mm - b_sd*z; b_mm + b_sd*z
## [1] 0.1760924
## [1] 0.8239076
- Use the percentiles of the bootstrap distribution to bound a confidence interval (\(1 - \alpha ~ percentile\) interval).
## 2.5% 97.5%
## 0.1630270 0.8459399
- Calculate bootstrap pivotal intervals (see Box 8-1; skipped for now).
8.2.1 Exercise set 8-4
- Sensitivity of a bootstrapped mean to n, B.
Try the boot.dist.1d
function:
## [1] -0.22323507 -0.01956961 0.04809411 -0.21668665 0.20288450 -0.01956961
## [7] -0.13718622 -0.26937485 -0.33757747 -0.10288316
Use the wrapper function:
## $`boot m`
## [1] -0.7622767
##
## $`boot se`
## [1] 0.2741837
Changing n:
# Adjust wrapper function
wrap.bm2 <- function (n, B, mu = 0, sigma = 1, FUN = mean, ...){
sim <- rnorm(n, mu, sigma)
boots <- boot.dist.1d(sim, B, FUN = FUN, ...)
hist(boots, main = paste("n = ", n, "; ", "B = ", B, sep = ""),
xlab = "Bootstrap distribution",
xlim = c(-1.5, 1.5), col = "gray", border = "white")
}
B <- 10000
n <- 5
par(mfrow = c(2, 2))
set.seed(3)
wrap.bm2(n = n, B = B)
n <- 10
wrap.bm2(n = n, B = B)
n <- 50
wrap.bm2(n = n, B = B)
n <- 100
wrap.bm2(n = n, B = B)
Changing B:
n <- 100
B <- 10
par(mfrow = c(2, 2))
set.seed(101)
wrap.bm2(n = n, B = B)
B <- 100
wrap.bm2(n = n, B = B)
B <- 1000
wrap.bm2(n = n, B = B)
B <- 5000
wrap.bm2(n = n, B = B)
- Sensitivity of a bootstrapped midrange to n, B.
New functions:
midrange <- function(x){
(min(x) + max(x)) / 2
}
wrap.bm3 <- function (n, B, mu = 0, sigma = 1, FUN = mean, ...){
sim <- rnorm(n, mu, sigma)
boots <- boot.dist.1d(sim, B, FUN = FUN, ...)
sd(boots)
}
Changing n:
B <- 10000
my_seq <- seq(1:10)
n_vector <- 2^my_seq
se_vector <- numeric(length = length(my_seq))
se_pred_vector <- pi / (sqrt(24 * log(n_vector)))
for(i in 1:length(my_seq)){
set.seed(101)
se_vector[i] <- wrap.bm3(n = n_vector[i], B = B, FUN = midrange)
}
par(mfrow = c(1,1))
plot(n_vector, se_pred_vector, type = "l", main = paste("B = ", B, sep = ""),
ylim = c(0, 0.8), xlab = "Sample size (n)", ylab = "SE of midrange")
points(n_vector, se_vector, col = "red")
Changing B:
n <- 100
my_seq <- seq(1:10)
B_vector <- 2^my_seq
se_vector <- numeric(length = length(my_seq))
se_pred_vector <- rep(pi / (sqrt(24 * log(n))), length(my_seq))
for(i in 1:length(my_seq)){
set.seed(101)
se_vector[i] <- wrap.bm3(n = n, B = B_vector[i], FUN = midrange)
}
par(mfrow = c(1,1))
plot(B_vector, se_pred_vector, type = "l", main = paste("n = ", n, sep = ""),
ylim = c(0, 0.5), xlab = "Bootstrap samples (B)",
ylab = "SE of midrange")
points(n_vector, se_vector, col = "red")
- Skipped.
8.3 Semiparametric hypothesis testing using permutation tests
Here we will test the hypothesis that the estimated slope from the Anscombe dataset differs from a null hypothesis, where \(\beta = 0\). This results in a simplified equation, where there is no assocation between \(X\) and \(Y\):
\[ \begin{aligned} Y = \alpha + \epsilon \end{aligned} \]
We will use a permutation test to test the null hypothesis. Let’s go over the assumptions of our test.
<IP_ADDRESS> Assumptions
Linearity: \(\text{E}(\epsilon | X = x) = ~ 0\)
Independence of units: for all i and \(j \neq i\), \(X_i, Y_i\) are independent of \(X_j, Y_j\)
Distribution: for all i, \(X_i, Y_i\) are drawn from the same joint distribution \(F_{X,Y}(x,y)\)
Independence of X and disturbances: for all \(i\), \(X_i\) and \(\epsilon_i\) are independent
Notice that we made no assumptions aboute the distribution of the disturbance term, \(\epsilon\).
The procedure for a permutation test is:
- Choose a test statistic and calculate it using the original data (\(s_d\)).
- Permute (i.e., shuffle) the data randomly and in such a way that if the null hypothesis were true, the hypothetical dataset is just as probable as the original data.
- Calculate the test statistic using the permuted data and save the value.
- Repeat steps (2) and (3) many times. The resulting test statistics are called a ‘permutation distribution’.
- Compare the original statistic \(s_d\) with the permutation distribution.
Note that the permuted sample is somewhat like a sample that might be observed if the \(X\) and \(Y\) values were independent.
Here’s the original dataset, and a permuted one:
# Original fertilizer use and cereal yield data
d <- cbind(anscombe$x1, anscombe$y1) [order(anscombe$x1), ]
# One permuted sample
set.seed(8675309)
db <- cbind(anscombe$x1, anscombe$y1[sample(1:11, replace = FALSE)])[order(anscombe$x1), ]
d; db
## [,1] [,2]
## [1,] 4 4.26
## [2,] 5 5.68
## [3,] 6 7.24
## [4,] 7 4.82
## [5,] 8 6.95
## [6,] 9 8.81
## [7,] 10 8.04
## [8,] 11 8.33
## [9,] 12 10.84
## [10,] 13 7.58
## [11,] 14 9.96
## [,1] [,2]
## [1,] 4 8.81
## [2,] 5 4.26
## [3,] 6 9.96
## [4,] 7 8.04
## [5,] 8 8.33
## [6,] 9 10.84
## [7,] 10 6.95
## [8,] 11 5.68
## [9,] 12 7.58
## [10,] 13 7.24
## [11,] 14 4.82
If we do this a number of times, we can plot the histogram of permuted samples, along with a line to represent the original \(\tilde \beta\):
set.seed(8675309)
nperms <- 10000
perm_dist <- numeric(nperms)
dat <- cbind(anscombe$x1, anscombe$y1)
for(i in 1:nperms){
samp <- perm.samp(dat)
perm_dist[i] <- beta.mm(samp[,1], samp[,2])
}
b_orig <- beta.mm(anscombe$x1, anscombe$y1)
b_orig
## [1] 0.5000909
# Plot histogram
hist(perm_dist, xlim = c(-0.6, 0.6), breaks = 30,
xlab = expression(paste(beta, " from permuted samples")),
main = "", col = "gray", border = "white")
abline(v = b_orig, col = "red", lty = 2)
We can compute a two-sided \(p\) value testing the null hypothesis that \(\beta = 0\) by calculating the proportion of the permuted samples for which the absolute value of \(\tilde \beta_p\) is greater than the absolute value of \(\tilde \beta\) from the original data:
## [1] 0.0033
In summary we tested the hypothesis that:
- \(\beta = 0\)
AND
- the \(X\) and \(Y\) data are linear
- the permuted units are independent
- all observations are drawn from a common distribution
- the \(X_i\) are independent of the disturbance \(\epsilon_i\)
Therefore, in principle, the low \(p\) value indicates that one of the above are unlikely to be true. Usually, we focus on the null hypothesis, but it is important to remember the assumptions.
8.3.1 Exercise set 8-5
- 20 wheat fields, 10 received treatment application (substance Z). Design a permutation procedure to assess the claim that substance Z changes the wheat yield.
The null hypothesis is that there is no association between yield and the treatment. Alternatively, the treatment may have altered the yield. To test this, we can randomly permute the control / treatment labels for the 20 fields. Then we can calculate the mean yield for each treatment, and compare the permutation distribution to the mean yield for each treatment for the original dataset.
Edge has a thorough explanation, and in it he highlights that a low p-value may also result from a change in the variance due to substance Z. Thus, we are really testing the null hypothesis that substance Z does not affect the distribution of wheat yield. This might be worth simulating.
# 10 pairs
beta_vec <- c(0, 0.1, 0.2)
n_pairs <- 10
n_datasets <- 20
p_mat <- matrix(nrow = 500, ncol = length(beta_vec))
for(i in 1:length(beta_vec)){
p_mat[,i] <- sim.perm.B(n = n_pairs, nsim = n_datasets, a = 3, b = beta_vec[i])
}
colnames(p_mat) <- c("b_0", "b_0.1", "b_0.2")
df10 <- as_tibble(p_mat)
# 50 pairs
beta_vec <- c(0, 0.1, 0.2)
n_pairs <- 50
n_datasets <- 20
p_mat <- matrix(nrow = 500, ncol = length(beta_vec))
for(i in 1:length(beta_vec)){
p_mat[,i] <- sim.perm.B(n = n_pairs, nsim = n_datasets, a = 3, b = beta_vec[i])
}
colnames(p_mat) <- c("b_0", "b_0.1", "b_0.2")
df50 <- as_tibble(p_mat)
# 100 pairs
beta_vec <- c(0, 0.1, 0.2)
n_pairs <- 100
n_datasets <- 20
p_mat <- matrix(nrow = 500, ncol = length(beta_vec))
for(i in 1:length(beta_vec)){
p_mat[,i] <- sim.perm.B(n = n_pairs, nsim = n_datasets, a = 3, b = beta_vec[i])
}
colnames(p_mat) <- c("b_0", "b_0.1", "b_0.2")
df100 <- as_tibble(p_mat)
# Get proportion of significant tests
colMeans(df10 < 0.05)
## b_0 b_0.1 b_0.2
## 0.05 0.10 0.10
## b_0 b_0.1 b_0.2
## 0.00 0.30 0.55
## b_0 b_0.1 b_0.2
## 0.1 0.6 1.0
# Plot
library(patchwork)
p10 <- df10 %>%
gather(key = beta, value = p) %>%
ggplot(aes(x = beta, p)) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 1)) +
ggtitle("n = 10 pairs") +
geom_hline(yintercept = 0.05, color = "red")
p50 <- df50 %>%
gather(key = beta, value = p) %>%
ggplot(aes(x = beta, p)) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 1)) +
ggtitle("n = 50 pairs") +
geom_hline(yintercept = 0.05, color = "red")
p100 <- df100 %>%
gather(key = beta, value = p) %>%
ggplot(aes(x = beta, p)) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 1)) +
ggtitle("n = 100 pairs") +
geom_hline(yintercept = 0.05, color = "red")
p10 + p50 + p100


uiGridTreeBaseConstants
ui.grid.treeBase
constants available in treeBase module.
These constants are manually copied into grouping and treeView, as I haven't found a way to simply include them, and it's not worth investing time in for something that changes very infrequently.


How to work with Git in Hydrograph
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Set up your username and email address (required for Git operations). Go to Windows > Preferences > Team > Git> Configuration:
Click new entry and provide details as given below:
Click OK and add new entry for email and then click Apply + OK on preferences window.:
For further reference click on the links below:


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Welcome


Titanic
Chad'z rating: **** (out of 4 = excellent)
1997, PG-13, 194 minutes [3 hours, 14 minutes]
[drama]
starring: Leonardo DiCaprio (Jack Dawson), Kate Winslet (Rose DeWitt Bukater), Billy Zane (Cal Hockley), Gloria Stuart (Old Rose); produced by James Cameron, Jon Landau; written and directed by James Cameron.
Seen February 13, 1998 at 8 p.m. at Crossgates Cinema 18, theater #4, by myself, using Hoyts Cinema Cash. [Theater rating: ****: excellent seats, sound, and picture]
One thing all films considered to be "epics" and/or "classics" have in common is that they weren't made with the intention of being crowned as such. What's so remarkable about "Titanic" is that it seems as if it was made with such a goal in mind - a nearly unreachable, almost pompous level of greatness, and does manage to fulfill and even surpass its expectations.
The legend of the Titanic disaster is a story we've heard about for years, but does anyone really know the whole story? Director James Cameron spent years researching the material for this film, and his effort is clear from the get-go. We're introduced to Brock Lovett (Bill Paxton), a modern-day scientist exploring the wreckage who is looking for a diamond necklace worn by one of the passengers in a drawing dated the night the ship went down. When this news is broadcast on television, an old woman named Rose (Stuart) contacts Lovett to tell him she is the girl in the picture and proceeds to tell her story.
The first hour does an excellent job in establishing the reality of the situation. Cameron makes full use of the actual wreckage, sending robots and probes into the cracks and crevasses no one has seen in over 80 years. As the explorers and their submarines reach the ocean floor and turn their lights and cameras on, the sheer site of it all is awesome and yet frightening because it is real. Lovett's line, "Seeing her come out of the dark like a ghost ship gets me every time" accurately reflects the feeling all viewers will think. What's also fascinating is the use of modern technology to tell the story, and I don't mean special effects alone. One of the film's best scenes involves a computer-animated simulation of the ship hitting the iceberg and sinking while one of the explorers narrates in lighthearted, plain English, conveying the tragedy of it all and still creates for a moving moment.
But the film is not simply about the sinking of the ship, or even the ship itself, it's a humanistic drama about all the people involved. The Titanic was meant to be all things to all people - for the rich it was the ultimate form of elitism, but for the poor it was simply an escort to a better life. Rose was a member of the former group, a society in which she never felt a part of, and Kate Winslet's portrayal as 17-year-old Rose DeWitt Bukater is quite convincing in this aspect. She's engaged (somewhat against her will) to Cal Hockley, a multi-millionaire who epitomizes the image of the snobbery and elitist imagery associated with the type, especially as played by Billy Zane with just the right accent, facial expressions, and cold attitude. But of course where there's the uptight rich, there must also be the free-spirited poor, and this class is represented by the American teenage bounder Jack Dawson (DiCaprio), who gains entrance to the Titanic by winning a ticket in a poker game.
Taking these basic elements into consideration it's easy to tell where they will go and how the film will use them. Jack and Rose will of course get together and the differences in classes will be played up and used to develop character and plot. One thing will lead to another and Jack and Rose will completely fall for each other while Cal will become extremely jealous and retaliate. This sounds cliche and it is, but taking into account the scope and premise of the film, there doesn't seem to be much else that could be done. Epics such as this aren't exactly known for their idiosyncratic observations and terrific chemistry. I think Cameron realized this and carried out the film the way he did to spite the situation, not to work with it. However, he does manage to squeeze in just the right amount of one-liners and comedic situations to keep the mood easy-going (my favorite examples include: a situation in which Rose must hit something with an axe in just the right place before the water rises, but Jack suggests she take a few practice swings first; after the ship has sunk Jack says, "I don't know about you, but I intend to write a strongly worded letter to the White Star Line about all this!").
This is not a dialogue-driven film, nor is it too concerned with plot, it is a complex drama utilizing dozens of characters and showing how all of them relate to each other. Most of the second act focuses on Jack and Rose, but the script works perfectly to allow their "adventures" to take place throughout the ship in a wholly believable, realistic manner: from the first-class suites and ballrooms, to the third-class barracks; from the clean, precision of the captain's deck, to the dark, dirty, hellish boiler room with engines as big as houses. How the film manages to incorporate everything, and be so well-paced at that is amazing.
In the last few years we've been inundated with disaster films which work mostly as eye candy, not as the believable, suspenseful joyrides they intend to be. The first two hours are so intriguing by themselves, at times it's easy to forget where the ship's destiny lies. Throughout the film hints are dropped that perhaps the ship's executives have been too arrogant in their ways, and had they been more cautious the disaster might have been averted. The entire third hour of the film takes place in real time from the moment the iceberg is spotted to the last inch of the ship going under water. Cameron is a veteran when it comes to action and special effects, but usually with sci-fi movies, not dramas, so the payoff is even more impressive. Here he is able to create for gripping suspense as the ship sinks slowly at first but later breaks in half, speeding up the action tenfold.
What's most impressive is the reality of it all: the authenticity to the set of the ship; the people screaming and running for dear life; the atmosphere of panic and chaos. Never before have I felt such a connection with such horrific events taking place on a screen. The plausibility is vivid, giving one a shocking sensation, especially when we consider that this actually happened. One of the last scenes, involving a search for survivors, is more frightening than any horror movie I've ever seen.
"Titanic" accomplishes what it set out to do: to be an epic film in all ways possible. At first glance it seems to be trying too hard, but upon close inspection we realize it is everything a great film can and should be.
Please visit Chad'z Movie Page @ http://members.aol.com/ChadPolenz - over 190 new and old films reviewed in depth, not just blind ratings and quick capsules. Also, check out The FIRST Shay Astar Web Page @ http://members.aol.com/ChadPolenz/ShayAstar.html
e-mail<EMAIL_ADDRESS>(C) 1998 Chad Polenz
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在指定容器右下角，模拟一个添加按钮
An element with position: static; is not positioned in any special way; it is always positioned according to the normal flow of the page:
An element with position: relative; is positioned relative to its normal position:
An element with position: fixed; is positioned relative to the viewport, which means it always stays in the same place even if the page is scrolled:
An element with position: absolute; is positioned relative to the nearest positioned ancestor (instead of positioned relative to the viewport, like fixed):
在指定容器右下角，模拟一个添加按钮


Change Log: 2.071.0
Download D 2.071.0
released Apr 5, 2016
Compiler Changes
Language Changes
Library Changes
- A clear method has been added to associative arrays to remove all elements.
- The GC now uses a spinlock instead of a recursive mutex.
- Calls to GC.free are now ignored during finalization instead of throwing an InvalidMemoryOperationError, see Bugzilla 15353.
- GC.addRoot and GC.addRange now use a separate lock.
- GCAllocator.goodAllocSize was added.
- High-level API of std.net.curl now uses Appender for received content. Which generally makes all calls slightly faster. Up to 200ms for large amounts of data.
- Repeat was added to obtain a repeating AliasSeq consisting of template arguments.
- fold was added as an alternative to reduce with argument order swapped.
- Added nextPow2 and truncPow2 to std.math.
- bsf, bsr, and popcnt now work with ulongs on 32 bit systems.
- topN is now 5% - 7% faster.
- Unary overloads of startsWith and endsWith were added.
- maxCount and maxPos were added.
- Range support for the convenience wrapper redBlackTree was added.
List of all bug fixes and enhancements in D 2.071.0.
Compiler Changes
- Import access checks for fully qualified names were fixed.
It is no longer possible to bypass private imports by using fully qualified names, e.g. the following example will fail with package std.range is not accessible here.
import std.algorithm; static assert(std.range.isForwardRange!string);
To ease updating existing code, the old behavior was retained but deprecated.
- Protection for selective and renamed imports were fixed.
It is no longer possible to use private selective or renamed imports in another module, e.g. the following example will fail with a.File is not visible from module b.
module a; import std.stdio : File; // imports are private by default
module b; import a; File f; // File is private
To ease updating existing code, the old behavior was retained but deprecated. This fix relies on the visibility changes introduced with DIP22.
Language Changes
- Imports no longer hide locals declared in outer scopes.
These changes were made to the name lookup algorithm:
- Lookup for unqualified names is change from one pass to two pass. The first pass goes through the scopes but does not check import declarations. If not found, the second pass goes through the scopes and only looks at import declarations.
- Qualified name lookups, base class lookups, and WithStatement lookups no longer search import declarations, unless a module is the subject of the lookup, where the behavior remains as before.
This can break existing code, although reliance on the previous behavior tends to be unintended, and fixing it improves the comprehensibility of the code. Breakage tends to take the form of a symbol now being flagged as undefined. Fixing the breakage can be done by fully qualifying the name, or adding an alias to the import declaration.
The old lookup behavior can be restored using the -transition=import compiler switch. With -transition=checkimports the compiler will issue deprecation warnings when the old and new lookup find different symbols. You can combine both switches to use the old lookup and still get deprecation warnings.
See also Bugzilla 10378
- Private symbols are no longer visible in other modules.
Protection (private, package, protected) is now already enforced during symbol lookup in order to resolve any conflicts between private and public symbols that could occur with the old access check scheme.
The visibility of overloaded symbols with mixed protection is determined by the most visibile member, but additional access check against the exact symbol is performed after overload resolution.
To ease updating existing code, a deprecated search for invisible symbols will be performed. For the the old lookup behavior (using the -transition=import or -transition=checkimports compiler switch) symbol visibility is ignored. All existing access checks remain active during the deprecation phase.
See also DIP22
- Manifest constant can now be used for deprecation message.
Manifest constants (enum, static immutable) can now be used for deprecation message, as well as concatenated strings.
Example:
string generateMessage() { return "Some deprecation message"; } enum DepMsg = generateMessage(); deprecated(DepMsg) class Foo {} deprecated("Some long deprecation " ~ "message") class Bar {}
Library Changes
- A clear method has been added to associative
arrays to remove all elements.
One can now use aa.clear() to remove all elements from an associative array. This allows removing all elements from all references to the same array (setting to null just reset the reference, but did not removed the elements from other references).
auto aa = ["first" : "entry", "second": "entry"]; auto aa2 = aa1; // reference to the same AA aa.clear(); // still retains its current capacity for faster imports assert(aa2.length == 0); // other references affected
- The GC is no longer wrapped in a pthread_mutex, it
now uses a spinlock.
This results in a 5% faster GC on average, with the most benefits going to multi-threaded programs that use the GC. See druntime#1447 for more details.
- Added nextPow2 and truncPow2 to std.math.
nextPow2 and truncPow2 are functions for rounding numbers to powers of two.
import std.math; assert(nextPow2(10) == 16); assert(nextPow2(4000) == 4096); assert(truncPow2(10) == 8); assert(truncPow2(4000) == 2048);
- Unary overloads of startsWith and endsWith were added.
startsWith and endsWith can now be used just with a predicate.
import std.algorithm.searching; import std.ascii : isAlpha; assert("abc".endsWith!isAlpha); assert(!"ab1".endsWith!(a => a.isAlpha)); assert("abc".startsWith!isAlpha); assert(!"1ab".startsWith!isAlpha);
- maxCount and
maxPos were added.
Previous to this addition, in order to get the number of the greatest elements, you had to use minCount!"a > b", which was very confusing. This change adds convenience functions to fix this issue.
List of all bug fixes and enhancements in D 2.071.0:
DMD Compiler regressions
- Bugzilla 14965: [REG2.031] Forward reference to inferred return type of function call when using auto return type
- Bugzilla 14973: [REG2.068] compiler inference of contexts for nested map seems broken
- Bugzilla 15296: [REG2.069] cannot inline simple function that calls use non-inlinable statements
- Bugzilla 15422: [REG2.066] associative array of nested struct - crash on usage
- Bugzilla 15455: [REG v2.065] Compiler segfault for simple nested structure
- Bugzilla 15618: [REG-master] Class member layout has been changed, ABI needs to be updated?
- Bugzilla 15713: [REG-master] ctfe string concatenation is templates is broken
- Bugzilla 15729: [REG(master)] broken debug info for libraries
- Bugzilla 15733: [REG2.066] Forward reference issue involving inheritance
- Bugzilla 15744: [REG2.067] (SIGABRT) Error: overloadset t.Bar.__ctor is aliased to a function
- Bugzilla 15778: [REG2.064] polysemous string type doesn't work in array operation
- Bugzilla 15781: [REG2.069] Template type deduction failure with same-type variables with different constness
- Bugzilla 15785: [DMD HEAD] Spurious warning when calling protected super
- Bugzilla 15788: [REG2.069] ICE assert triggered on overloaded function
- Bugzilla 15814: [REG-master] Custom deprecation message is not shown
- Bugzilla 15815: [REG2.071-devel] deprecation for aliased template instance not shown
- Bugzilla 15817: [REG2.068] ICE (with no stacktrace) instead of 'cannot index null array counts' with CTFE AA
- Bugzilla 15839: [REG2.071-b1] this.outer is of wrong type
DMD Compiler bugs
- Bugzilla 143: 'package' does not work at all
- Bugzilla 313: [module] Fully qualified names bypass private imports
- Bugzilla 314: [module] Static, renamed, and selective imports are always public
- Bugzilla 1754: module member visibility does not affect conflicts
- Bugzilla 2991: [module] Import within aggregate causes conflicts with private identifiers
- Bugzilla 3254: [module] Module member visibility depends on declaration order
- Bugzilla 6907: ice(interpret.d) delete expressions crash interpreter
- Bugzilla 7516: Postblit not called for structs returned from a ternary operator
- Bugzilla 7517: Interface contracts broken
- Bugzilla 11581: Given T..., new T[0] does not work
- Bugzilla 12748: ICE: Using typeof(S.init[0]) in type name specialization.
- Bugzilla 12829: Wrong error line number for closure allocation in @nogc function
- Bugzilla 13702: One missed 'may cause GC allocation' error message
- Bugzilla 14442: Wrong this.outer reference in nested classes
- Bugzilla 14486: delete on classes ignores @nogc
- Bugzilla 14603: "cannot alias an expression" when opDispatch results in a template
- Bugzilla 14604: "cannot resolve forward reference" when opDispatch results in a template
- Bugzilla 14686: Postblit isn't sometimes called on concatenation
- Bugzilla 14730: Wrong closure var access with -inline
- Bugzilla 14788: Incorrect rejection of inout function call
- Bugzilla 14895: ICE on array operation when its result is concatenated
- Bugzilla 14989: Overload merge sometimes doesn't work
- Bugzilla 14992: static array local variables always require .init
- Bugzilla 15309: [dmd-internal] ScopeExp.semantic() should set its type always
- Bugzilla 15310: [dmd-internal] TemplateDeclaration should be converted to TemplateExp always
- Bugzilla 15332: ICE in e2ir.c: assert(irs->sthis) in visit(ThisExp), function literal with keyword 'function' calls method
- Bugzilla 15353: std.experimental.allocator cannot free memory in its destructor if the GC is an ancestor
- Bugzilla 15384: assignment is sometimes still accepted as a condition
- Bugzilla 15389: extern(C++) forward referencing problem
- Bugzilla 15407: Assert hit in toElem e2ir.c:1962
- Bugzilla 15411: Function literal accessing variable in declaration scope produces bad error message with inline call
- Bugzilla 15441: dmd segfaults using std.experimental.ndslice
- Bugzilla 15450: ICE during CTFE of legit function
- Bugzilla 15519: Circular imports leads to fwd ref error with aliased imports
- Bugzilla 15535: Emit error on "goto default" in final switch
- Bugzilla 15565: Forward reference error with namespaces
- Bugzilla 15578: Should have access to namespace private symbols from same module
- Bugzilla 15579: extern(C++) interfaces/multiple-inheritance
- Bugzilla 15610: extern(C++) multiple inheritance - calling with wrong 'this' ptr
- Bugzilla 15623: is(M!N) evaluates to true for M!N that fails to instantiate.
- Bugzilla 15626: extern(C++) calling crash
- Bugzilla 15638: no common type for const classes
- Bugzilla 15647: Casting from one C++ interface in a hierarchy to another is a noop
- Bugzilla 15665: Templated scope class with constructor don't compile
- Bugzilla 15667: dmd infinite loop on invalid syntax.
- Bugzilla 15669: Wrong line number in error message
- Bugzilla 15688: dmd segfault with an integer call through comma expression
- Bugzilla 15784: Overload set constructor call should be supported from inside a constructor
- Bugzilla 15789: ICE Assert in TemplateInstance semanticTiargs
- Bugzilla 15794: Lambda cannot get a chance to run its codegen
- Bugzilla 15811: -transition=import and -transition=checkimport have oddly behaviors
- Bugzilla 15825: dmd's -transition=checkimports reports a false positive for tuple __dollar
DMD Compiler enhancements
- Bugzilla 10378: Prevent local imports from hiding local symbols
- Bugzilla 12954: deprecated doesn't work with concatenated strings or anything else but a string literal
- Bugzilla 14383: [ddoc] documented unittests don't work for module declaration
- Bugzilla 15644: Change object layout ABI to MI style
Phobos regressions
- Bugzilla 15222: std.experimental omitted from phobos zip file
- Bugzilla 15782: [Reg 2.071-devel] Alias no longer strips qualifiers from user defined types
- Bugzilla 15807: Array!bool insertBack is broken
Phobos bugs
- Bugzilla 1238: Private identifiers in imported modules create conflicts with public ones
- Bugzilla 15543: [ndslice] assumeSameStructure has useless flag
- Bugzilla 15545: csv Reader line feed '\r' failure
- Bugzilla 15549: [ndslice] byElement is broken for packed slices
- Bugzilla 15553: topN very inefficient [slower than sort, even for topN(0)] but should be O(n)
- Bugzilla 15561: std.typecons.Proxy gets NaN comparisons wrong
- Bugzilla 15639: std.experimental.allocator enables abstract class instantiation
- Bugzilla 15654: SysTime.toISOString formats the time zones incorrectly
- Bugzilla 15655: SysTime.from*String incorrectly accept single digit time zones and minutes > 59
- Bugzilla 15659: SList: clear() can cause crash
- Bugzilla 15663: writefln("%#o", 0) should yield "0" instead of "00"
- Bugzilla 15677: std.experimental.allocators.expandArray overload with init should take the array by ref.
- Bugzilla 15683: broken link in DDoc
- Bugzilla 15686: std.uni overloads documentation
- Bugzilla 15715: [ndslice] rangeHasMutableElements is not defined
- Bugzilla 15721: free error when calling Mallocator.dispose on interfaces
- Bugzilla 15772: emplace works with abstract classes but it shouldn't
Phobos enhancements
- Bugzilla 15377: std.stdio: Use MSVCRT's _fseeki64 / _ftelli64 on Windows COFF
- Bugzilla 15532: [ndslice] iota(5).sliced(2,2) should throw error
- Bugzilla 15596: strip with delimiter?
- Bugzilla 15714: [ndslice] byElement seems to be missing some slicing primitives
Druntime regressions
- Bugzilla 15224: making 'clean' results in garbage commands
- Bugzilla 15779: DWARF EH fails when using stack stomping (-gx)
- Bugzilla 15822: InvalidMemoryOperationError when calling GC.removeRange/Root from a finalizer
Druntime enhancements
- Bugzilla 15628: Exceptions in fibers never caught with /SAFESEH
dlang.org bugs
- Bugzilla 10987: Add documentation for 'extern (C++)' classes
- Bugzilla 15344: m32mscoff switch missing from documentation.
- Bugzilla 15666: Grammar does not allow member function attributes on static constructors
- Bugzilla 15696: The website logo overlaps the Learn tab when using Microsoft Edge
- Bugzilla 15697: The script for This Week in D is served over HTTP even when accessing the website over HTTPs.


Hello <%= name %> <%= surname%>! <%= inviter.firstName %> <%= inviter.lastName %> has just invited you to Indaba as a member of <%= company.name %> organization.
Your password for first login: <%= password %>
Please, login and change your password.


Make and send angry emails
Type whatever you like!

Email sent successfully!
Note: This is a joke. It doesn't actually send an email.


boost::quan::uncIOflags — Control of printing uncertain values, similar to std::ios
flags.
// In header: <boost/quan/unc.hpp> enum uncIOflags { defaults = = 0, firm = = 1 << 0, setScaled = = 1 << 1, autoScaled = = 1 << 2, plusMinus = = 1 << 3, addSISymbol = = 1 << 4, addSIPrefix = = 1 << 5, noisyDigit = = 1 << 6, useSetSigDigits = = 1 << 7, useSetUncSigDigits = = 1 << 8, degfree = = 1 << 9, replicates = = 1 << 0xA, limits = = 1 << 0xB };
Can be output for diagnostic use, for example:
outUncIOFlags(std::cout.flags(), std::cerr); // uncFlags (0x201) firm adddegfree.
defaults
firm
setScaled
autoScaled
plusMinus
bit 3 = 1 means add +/- uncertainty Usage:
std::cout << plusminus << u;
addSISymbol
addSIPrefix
noisyDigit
Add extra 'noisy' decimal digit to value & sd. Usage: std::cout << addnoisyDigit << u;
This means is suitable for input to other calculations because the random extra 2 to 3 bits will approximate a continuous function. Quantization to fewer digits would distort statistical calculations. Default is to use minimum decimal digits for clearer display. Extra digit also added when degrees of freedom > 10.
useSetSigDigits
useSetUncSigDigits
degfree
replicates
limits


The flight from {{(origin|string)[3:-28]}} ({{origin_code}}) to {{(destination|string)[3:-28]}} ({{dest_code}}) will be delayed by {{prediction}} minutes
The departure time was: {{(d_time|string)[:-2]}}:{{(d_time|string)[-2:]}} {{mer_d}}
The scheduled arrival time was: {{(sa|string)[:-2]}}:{{(sa|string)[-2:]}} {{arr_d}}
The predicted delay is {{prediction}} minutes
Expected total travel time would be: {{t_hrs}} hours {{t_mins}} minutes.
There could be a deviation of +/- {{error}} minutes in the predicted delay.
Live Map
OTP Analysis of the Airline {{carrierN}}
OTPs-{{carrierN}}
Possible Reasons for the Delay
The delay could be due to unforseen weather changes.
The airplane could have faced some departure delays(Delays due to airport congestion or traffic controls).
Historical Inferences
The airline {{carrierN}} has been recording these OTPs during the analysis of the data during the period January-March, 2015. There was a variance of {{error}} minutes while
predicting the delays.


Michael Scott
Regional Manager
https://en.wikipedia.org/wiki/Michael_Scott_(The_Office)
Played By:
Steve Carell
Eps. Appeared In:
139
Regional Manager
https://en.wikipedia.org/wiki/Michael_Scott_(The_Office)
Steve Carell
139
Regional Salesman
https://en.wikipedia.org/wiki/Jim_Halpert
John Krasinski
188
Receptionist
https://en.wikipedia.org/wiki/Pam_Beesly
Jenna Fischer
188
Regional Salesman
https://en.wikipedia.org/wiki/Dwight_Schrute
Rainn Wilson
188
Regional Salesman
https://en.wikipedia.org/wiki/Andy_Bernard
Ed Helms
152
Accounting
https://en.wikipedia.org/wiki/Angela_Martin
Angela Kinsley
188
Regional Salesman
https://en.wikipedia.org/wiki/Stanley_Hudson
Leslie David Baker
188
Regional Saleswoman
https://en.wikipedia.org/wiki/Phyllis_Vance
Phyllis Smith
188


Misty's Goldeen
Was one of the three original Pokémon acquired by Misty in the Kanto region prior to the start of the anime.


Threshold :
0.05
Vertex Count:
To zoom-in/out use the mouse wheel. The slider controls the threshold and the percentage represents the number of remaning vertices at the given value of the threshold


Enables data editing functionality in the grid similar to how a spreadsheet application works.
(boolean): Default false.
(boolean)
false
{editable: true}


One Day, One Thing is a very simple website generator for daily posts. It was designed to work everywhere, i.e. no dependencies except Bash.
posts/


{% extends 'game_base.html' %} {% block content %}
What is this?
This is a game that uses Geoguessr as inspiration.
Why does this exist?
Sadly, because of Google's predatory pricing strategy for their Maps api, geoguessr have had to modify their game in a detrimental way to cut costs.

Why do I need an api key?
To get around the aforementioned api pricing strategy I require you to upload your own api key.
Your api key will be used only for games that you play, each key has $200/month free usage, which is plenty for all but the most avid players.
How do I get an api key?
Go here and register to get an api key.
Once you have a key save it against your profile here.
Is this safe?
All I can do is offer assurances that I won't use the api keys for anything other than their stated purpose: namely, for each individual's games on this site.
This site uses django's built in authentication framework, and I have taken every care to look after your personal details.
What does the "Broken Streetview" button do?

If this occurs please click the "Broken Streetview" button and you will be provided with a new scene and the faulty coordinate will be removed from the database.
There isn't a great variety of countries, why?
I am relying on crowd sourcing of playable scenes, please consider helping out by adding a few coordinates of your own.
You get to keep track of the coordinates you add, and see how well (or badly) other people do on them!
For now I have managed to find some data on uk coordinates that I can use, but its not so easy getting data for other countries.
I didn't receive my email reset password, what gives?
I am using a free mail server to send password reset emails, the limit is 100/day.
If you require assistance accessing your account, please email<EMAIL_ADDRESS>This site is amazing, how can I contact you to discuss this, or another, site?
Thank you! Please get in touch: edward.wilding3@gmail.com


Level 1 / CEO
Level 2 / IT Manager
Level 3 / IT Developer
Level 3 / IT Developer
Level 3 / IT Developer
Level 2 / Sales Manager
Level 3 / Sales Exe
Level 3 / Sales Exe
Level 4 / Sales Jnr.
Level 4 / Sales Jnr.
Fork over Github: @mrdogra007
Follow me on Twitter: @mrdogra007


Mumbai
Mumbai (formerly called Bombay) is a densely populated city on Indiaís west coast. A financial center,
it's India's largest city.z On the Mumbai Harbour waterfront stands the iconic Gateway of India stone arch,
built by the British Raj in 1924. Offshore, nearby Elephanta Island holds ancient cave temples dedicated to the Hindu god Shiva.
The city's also famous as the heart of the Bollywood film industry.
Chennai
Chennai, on the Bay of Bengal in eastern India, is the capital of the state of Tamil Nadu. The city is home to Fort St. George,
built in 1644 and now a museum showcasing the cityís roots as a British military garrison and East India Company trading outpost,
when it was called Madras. Religious sites include Kapaleeshwarar Temple, adorned with carved and painted gods, and St. Maryís, a 17th-century Anglican church.
Kolkata
Kolkata (formerly Calcutta) is the capital of India's West Bengal state. Founded as an East India Company trading post,
it was India's capital under the British Raj from 1773Ė1911. Today itís known for its grand colonial architecture, art galleries and cultural festivals.
Itís also home to Mother House, headquarters of the Missionaries of Charity, founded by Mother Teresa, whose tomb is on site.