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相信未来


{% extends 'base.html' %} {% block content %}
VetCalc is not responsible for any deaths, comas, bodily harm, medical mishaps or miracles, emotional distress, or lawsuits that are the direct or indirect result of using any resources available on the site.
Always double check your doses.
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Keycloak Auth Demo
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|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
HTMLEditorKit
and supporting classes
for creating HTML text editors.
See:
Description
| Class Summary | |
|---|---|
| BlockView | A view implementation to display a block (as a box) with CSS specifications. |
| CSS | Defines a set of CSS attributes as a typesafe enumeration. |
| CSS.Attribute | Definitions to be used as a key on AttributeSet's that might hold CSS attributes. |
| FormSubmitEvent | FormSubmitEvent is used to notify interested parties that a form was submited. |
| FormView | Component decorator that implements the view interface for form elements, <input>, <textarea>, and <select>. |
| HTML | Constants used in the HTMLDocument . |
| HTML.Attribute | Typesafe enumeration representing an HTML attribute. |
| HTML.Tag | Typesafe enumeration for an HTML tag. |
| HTML.UnknownTag | |
| HTMLDocument | A document that models HTML. |
| HTMLDocument.Iterator | An iterator to iterate over a particular type of tag. |
| HTMLEditorKit | The Swing JEditorPane text component supports different kinds of content via a plug-in mechanism called an EditorKit. |
| HTMLEditorKit.HTMLFactory | A factory to build views for HTML. |
| HTMLEditorKit.HTMLTextAction | An abstract Action providing some convenience methods that may be useful in inserting HTML into an existing document. |
| HTMLEditorKit.InsertHTMLTextAction | InsertHTMLTextAction can be used to insert an arbitrary string of HTML into an existing HTML document. |
| HTMLEditorKit.LinkController | Class to watch the associated component and fire hyperlink events on it when appropriate. |
| HTMLEditorKit.Parser | Interface to be supported by the parser. |
| HTMLEditorKit.ParserCallback | The result of parsing drives these callback methods. |
| HTMLFrameHyperlinkEvent | HTMLFrameHyperlinkEvent is used to notify interested parties that link was activated in a frame. |
| HTMLWriter | This is a writer for HTMLDocuments. |
| ImageView | View of an Image, intended to support the HTML <IMG> tag. |
| InlineView | Displays the inline element styles based upon css attributes. |
| ListView | A view implementation to display an html list |
| MinimalHTMLWriter | MinimalHTMLWriter is a fallback writer used by the HTMLEditorKit to write out HTML for a document that is a not produced by the EditorKit. |
| ObjectView | Component decorator that implements the view interface for <object> elements. |
| Option | Value for the ListModel used to represent <option> elements. |
| ParagraphView | Displays the a paragraph, and uses css attributes for its configuration. |
| StyleSheet | Support for defining the visual characteristics of HTML views being rendered. |
| StyleSheet.BoxPainter | Class to carry out some of the duties of CSS formatting. |
| StyleSheet.ListPainter | Class to carry out some of the duties of CSS list formatting. |
| Enum Summary | |
|---|---|
| FormSubmitEvent.MethodType | Represents an HTML form method type. |
Provides the class HTMLEditorKit
and supporting classes
for creating HTML text editors.
Note: Most of the Swing API is not thread safe. For details, see Threads and Swing, a section in The Java Tutorial.
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


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Opening your HTML code through VS Code
Being a past Sublime PC user, I would right click the html file and select Open in Browser. Pretty simple. However, now I would like to go through this process in VS Code.
How would I open HTML code from VS Code into my web browser, particularly Google Chrome?
Well, boy oh boy, here is the solution for you, provided in this article:
-
Open VS Code
-
Enter CTRL+Shift+P
-
In the opened pallette selection, enter in Configure Task Run
-
You will then click build, in which a* task.json *file will appear.
-
Replace the text with the following:
{
“version”: “0.1.0”,
“command”: “Chrome”,
“windows”: {
“command”: “C:\Program Files (x86)\Google\Chrome\Application\chrome.exe”
},
“args”: [“${file}”]
}
Stack Overflow Link to “How to View HTMl Code in Browser
-
Save the task.json file
-
Enter CTRL+Shift+B (Similar to that of Sublime Text) [It’s okay to re-load]
And presto! You are done.
Cheers,
Raul Maldonado
Note: Mac user editing can be found in Stack Overflow link above, after raw json code.


Avocado
$1.50
Kiwi Fruit
$4.00
Broccoli
$5.00
Eggplant
Meat Stuff
$10.00
Bacon
Pizza
$8.50
Ice Cream
$3.00
Tater Tots


SetEntityMaterial()
Syntax
SetEntityMaterial(#Entity, MaterialID)Description
Assign a material to the specified #Entity. An entity can only have one material assigned at once.
'MaterialID' can be easily retrieved with the MaterialID() function.
Supported OS
All


A hotel is an establishment that provides paid lodging on a short-term basis.
Facilities provided inside a hotel room may range from a modest-quality mattress in a small room to large suites with bigger,
higher-quality beds, a dresser, a refrigerator and other kitchen facilities, upholstered chairs, a flat screen television,
and en-suite bathrooms.
Small, lower-priced hotels may offer only the most basic guest services and facilities. Larger, higher-priced hotels may provide additional guest facilities such as a swimming pool, business centre (with computers, printers, and other office equipment), childcare, conference and event facilities,
tennis or basketball courts, gymnasium, restaurants, day spa, and social function services.


Hello and welcome to Emergent, the ultimate opensauce Twitter clone (or something along the lines). Feel free to contribute stuff.
This site is so very under construction that it may, or may not, lead to catastrophic death of people and things dear to you. Please avoid using Emergent while drunk, high, pregnant and/or retarded. Thank you very much,
Project Emergent developers.
Register now for just ${{price}}.99
The "shut up and take my money" button


"{{ site.title }}"
{% if site.favicon_img %}
{% endif %}
{% if site.front_img %}
{% endif %}
{% if site.avatar %}
{% endif %}
{{ site.name }}
{{ site.description }}
This is a portfolio website where you can see some of my work as a musician, composer and session
player.Some of the recording processes are
described bellow the videos.
Be Still My Ever Beaten Heart | Neil Nathan
Be Still My Ever Beaten Heart is about that scary moment just before one risks trusting another and opens themselves up to potential heartbreak.
Vocals | Neil Nathan
Guitars | Thales Posella
Synth Strings | Thales Posella
Percussion | Thales Posella
Bass | Pemberton Roach
Drums | Beto Ferrari
Production and Arrangement | Thales Posella and Neil Nathan
Mix | Thales Posella
Master | @timothystollenwerk
The Guardrail | Neil Nathan
Neil lost a lifelong friend on 9/11. He wrote this tribute song for the 20th anniversary.
Vocals | Neil Nathan
Acoustic guitars | Thales Posella
Piano | Thales Posella
Hammond | Thales Posella
Harmonica | @studio1935philadelphia
Arrengement | Thales Posella and Neil Nathan
Mix | Thales Posella
Master | @timothystollenwerk
Please be Well | Neil Nathan
Vocals | Neil Nathan
Acoustic guitars | Thales Posella
Piano | Thales Posella
Virtual instruments | Thales Posella
Steel | Mike Brenner
Arrengement | Thales Posella and Neil Nathan
Mix | Thales Posella
Master | Thales Posella
Silver Angel | Ivan Busic
Vocals | Ivan Busic
Drums | Ivan Busic
Bass | Andria Busic
Rhythm acoustic guitars | Thales Posella
Piano | Thales Posella
Virtual instruments | Thales Posella
Percussion | Thales Posella
Backup vocals | Thales Posella and Marco Bavini
Acoustic guitar melody | Marco Bavini
Arrengement | Thales Posella
Mix | Thales Posella
Master | Thales Posella
Nothing Else Matters | Metallica (Piano version by Thales Posella & Eduardo Deberaldini)
This was a sad but beautiful project I've recorded as a tribute to the passing of a very close
friend, Eduardo Deberaldini.
Vocals | Thales Posella
Piano | Thales Posella
Virtual instruments | Thales Posella
Electric guitars | Eduardo Deberaldini
Arrengement | Thales Posella
Mix | Thales Posella
Master | Thales Posella
Klimbim | Don Ross (Cover by Thales Posella)
This is a cover from a beautiful song by Don Ross.It features a Blueridge guitar with 2 large
diaphram condensers.
M.A.T. | Thales Posella
This features a custom Strat straight into a Mesa Boogie
Mark IV going into a Laney 4x12-v30s
cabnet.Mic setup was one sm57 On-center, another SM57 Off-center and a MD421 On-center.
Sisters | Steve Vai (Cover by Thales Posella)
This is a cover from the amazing Steve Vai.It features an Ibanez JEM 77FP 1989 straight into a Mesa
Boogie
Mark V going into a Laney 4x12-v30s
cabnet.Mic setup was 2 sm57s, one On-enter and another Off-center. With an additional large diaphram
condenser as room mic.
Wicked Game | Chris Isaack (Piano version by Thales Posella)
This is a piano version I've made as a tribute to this beautiful song by Chris Isaack.It features an
original Steinwein 1929 upright piano.Mic setup was 2 small diaphram condensers on the piano and one
large
diaphram condenser on vocals.
You ain't the first | Guns n' Roses (Cover by Thales Posella)
This is an acoustic arrangement for this awesome song.It features a
Blueridge guitar, one small and one large diaphram condenser on the guitar and one large
diaphram condenser on vocals.
Home Is Where We Belong | Thales Posella
This is a guitar-driven instrumental I wrote for my parents.It features a
Gibson Les Paul straight into the computer.
The Tie and The Sign | The Mavens Telescope
This is an all-original project called The Mavens Telescope.I've composed, produced, engineered,
recorded all guitars,
piano, synths, virtual instruments,
backing vocals and mixed the album.It was mastered by Andy VanDette in NYC.Check it out through the links bellow...


The decimal value of this prime was computed with calc and the English name was computed with number.
NOTE: This is an abbreviated version of this really Big prime number!


iSOL runs on the LattePanda Board with Windows 10 inside
TODO
Download the dist_panda_win32_node0.12.zip
from http://yun.baidu.com/share/link?shareid=152966760&uk=4178147136
dist_panda_win32_node0.12.zip
run lattepanda demo
cd dist
./start_lattepanda.sh
Then you will see
start message broker ...
start center ...
start lattepanda hub ...
visit ip:8080 for develop, ip:3000 for ui view
Use ui-widget switch to control the blue led in lattepanda. - Open the browser in your lattepanda or other host machine which is in the same network with lattepanda. - Visit <your lattepanda ip>:8080
in browser - Create new App


 GOLF. MADRID… IN THAT ORDER”.
Gareth Bale risked incurring the wrath of Real Madrid with his celebration of Wales’ Euro 2020 qualification.
Wales – semi-finalists at Euro 2016 – booked their ticket to next year’s European Championship with a 2-0 win over Hungary on Tuesday.
Aaron Ramsey’s double in Cardiff ensured Wales leapfrogged Hungary and clinched the second automatic qualifying spot in Group E.
Bale and his team-mates celebratedly wildly post-game, with the former Tottenham talisman particularly catching the eye.
Tipped to leave Madrid and criticised for his priorities amid his love for golf, Wales fans have adopted the chant, “Wales, golf, Madrid”, which has been heard during the international break.
 GOLF. MADRID… IN THAT ORDER”.


BORN YESTERDAY
November 13-14, 1961 at the Wisner Building
LLT's very first production,
after organizing at the bar at the Pinehurst Hotel
Laurel's own Tom 'Tiger' Lester ('Eb' on TV's "Green Acres")
is credited as the Boot Black
Director - Elton Bateman
Special Consultant - Frank Hains
THE CAST
Billie Dawn - Dorris Miller
Harry Brock - Lowell Tew
Paul Verrall - Jim Gibbon
Ed Devery - Ernest Graves
Senator Hedges - Elmer Gossett
Mrs. Hodges - Farris Rowell
Eddie Brock - Billy Townsend
Assistant Manager - Betty Whitt
Bellhop - Charles Thomas
Barber - John Hunt
Manicurist - Charlotte Crews
Boot Black - Tommy Lester
Waiter - John Neill


[§266-2.2] Exemption from conservation district permitting and site plan approval requirements. Notwithstanding any law to the contrary, all work involving submerged lands used for state commercial harbor purposes shall be exempt from any permitting and site plan approval requirements established for lands in a conservation district. [L 2013, c 86, §1]


Market
Player Select
Hero Status
Return to Game
Logout
UserName
Ability
NAME
Power
205
Weapon
Weapon
Weapon
Weapon
Weapon
Weapon
Weapon
Weapon
Goku
Ability
Ultra Instinct
Health
Power
Health
Power
Health
Power
Health
Power
Update
Play


About:
On this page you may add, replace or delete resources - files that are used to find information.
Please ensure that the name of the file is informative, to assist students when looking through files.
Help:
To view a resource, click its name in the table.
To replace or delete a resource, click the relevant button in the resource's row.
Deleting a resource is final; please take care when doing so.


First, some design info. Windows Remote Management is the Microsoft implementation of the WS-Management Protocol. It uses SOAP (Simple Object Access Protocol) over HTTP and HTTPS, and thus is considered a firewall-friendly protocol. It was designed to provide interoperability and consistency for enterprise networks that have a variety of operating systems, to locate and exchange management information.
WinRM provides a command line interface that can be used to perform common management tasks, and also provides a scripting API so you can write your own Windows Scripting Host based scripts. In the background, WinRM relies on management data provided by WMI; however it makes the exchange of data much easier by utilizing the HTTP protocol.
Apart from WMI, WinRM utilizes the Intelligent Platform Management Interface (IPMI) driver for hardware management. The IPMI provider and driver enable you to control and diagnose remote server hardware through BMCs [Baseboard Management Controllers] even when the OS is not running or deployed. Effectively BMC is a chip connected to the processor board of a server; it has its own network adapter and hence can monitor the server in situations even when the server is malfunctioning.
Basic Configuration:
winrm quickconfig
This command performs configuration actions to enable this machine for remote management, which includes:
- Starts the WinRM service
- Set the WinRM service type to auto start
- Create a listener to accept requests on any IP address
Enable firewall exception for WS-Management traffic (for http only)
To list all the WinRM listeners, run this command:
“Winrm enumerate winrm/config/listener”
You can also get the configuration information of the Service, Client and WinRS by running the following command:
“Winrm get winrm/config”
Now let us look at the different operations that WinRM supports to access WMI data.
The list of currently supported operations is:
GET
PUT
ENUMERATION
INVOKE
Using the WinRM get command you can also query different services configuration running on the server.
Example
“Winrm get wmicimv2/Win32_Service?Name=spooler”
You can also use the WinRm get command to query the remote computer:
“Winrm get Winrm/config –r:remotemachinename”
Run this to query the service of remote computer:
“Winrm get wmicimv2/Win32_Service?Name=spooler –r:remotemachinename”
To reboot a remote machine:
“winrm invoke reboot wmicimv2/Win32_OperatingSystem -r:<some computer>”
Start a service on a remote machine
“winrm invoke startservice wmicimv2/Win32_Service?name=w32time -r:<some computer>”


Heat Recovery Type
None
Air to air heat exchanger
Earth to air heat exchanger
Earth to water heat exchanger
Heat pipe
Other
Run-around coil
Thermal wheel
Unknown
Energy Recovery Efficiency
Heat Recovery Efficiency
Manufacturer
Model Number
Quantity
Source
None
ASHRAE
Agent
Arch2030
Assessor
Auditor
Building Component Library
Default
Design files
Direct measurement
ENERGY STAR Portfolio Manager
Energy Management System
Estimate
Other
Product specification
Simulation
Target Finder
US EIA
US EPA
Utility transfer
Year Installed
Year of Manufacture


I am graduate from Oregon State University with a Bachelors of Science in Computer Science and a minor in Business and Entrepreneurship. I am currently pursuing a career in Software Engineering with Lockheed Martin. While studying at OSU, I worked as a Student Support Technician starting in 2018 until I graduated in 2020. I spent the summer of 2018 interning at Princess Cruises, automating test cases in Java for the EZair booking team. I spent the following summer in 2019 as a Software Engineering Intern at Lockheed Martin doing most of my work still in Java. I am currently utilizing these skills that I have learned at my current role at Lockheed Martin as a Software Engineer Associate. My position began after I graduated in June of 2020 and have worked there since. As of 2021, the primary languages I use and know are Java and Python.
I have really enjoyed working in R&D and hope to continue to work in that area. My internships have prepared my Java knowledge for my work as a full-time Software Engineer. My work has really been developing micro-services in Java.
For information pertaining to hobbies and outside work interests, please message me on Linkedin or on email me. I would be happy to share this information but prefer not to be an open book especially on a public platform. Reach
out to me for my most up-to-date resume.


Description: {{ recipe.Description }}
Viewes: {{ recipe.RecipeViews }}
Liked: {{ recipe.RecipeLikes }} {{likeText}}
Owner: {{ recipe.Owner }}


Before Change
DEFAULT_CONFIDENCE_LEVEL = ifEnvElse("DEFAULT_CONFIDENCE_LEVEL", 0.98)
TEST_OVERWRITE_MODEL = ifEnvElse("TEST_OVERWRITE_MODEL", True) // flip this to true if coding offline
EXEC_LEARN_IN_THREAD = ifEnvElse("EXEC_LEARN_IN_THREAD", False)
After Change
// These are the paths for storing data regarding mindsdb models and model info
MINDSDB_STORAGE_PATH = os.path.abspath(os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))+"/../storage/")
SQLITE_FILE = ifEnvElse("SQLITE_FILE", "{storage_path}/mindsdb.mdb".format(storage_path=MINDSDB_STORAGE_PATH))
LOCALSTORE_PATH = ifEnvElse("LOCALSTORE_PATH", "{storage_path}/local_jsondb_store".format(storage_path=MINDSDB_STORAGE_PATH))
// You can choose to store info about models in MongoDB or not By default it uses a local object store
STORE_INFO_IN_MONGODB = ifEnvElse("STORE_INFO_IN_MONGODB", False)


Beer Style Guidelines currently includes:
BJCP Style Guidelines:
2015 Beer, 2015 Mead, & 2015 Cider, and 2021 Beer
Brewers Association Beer Style Guidelines:
2016, 2017, 2020, 2021, 2022, and 2023
Finding your favorite beer style is easy. Search for your beer style using style name or keywords.
Read all of the included Style Guidelines directly on your iPhone, iPad or Mac.
Mark any style as a favorite with easy access to it later. Favorites are easily searchable and listable.
Tap the left and right arrows to quickly move to the next section of the style guide.


class: center, middle, inverse, title-slide # An Implementation of Automatic Differentiation in C++ ### Edward A. Roualdes ### 2019-09-06 --- ## Introduction Programming a computer to find the minimum of an aribtrary (differentiable) function `\(f\)` is a problem for modern mathematicians, statisticians, computer scientists, and data scientists. --- ## Outline 1. Part I - probabilistic programming language Stan [Car+15] 2. Part II - algorithms for automatic differentiation - reverse-mode automatic differentiation 3. Part III - reverse-mode automatic differntiation in C++ - complete code + build new operation --- ## Stan ```stan data { int<lower=0> N; // number of observations int<lower=0> K; // number of predictors matrix[N, K] x; // predictor matrix vector[N] y; // response vector } parameters { real alpha; // intercept vector[K] beta; // coefficients real<lower=0> sigma; // error } model { alpha ~ normal(0, 1); // priors beta ~ normal(0, 1); sigma ~ exponential(1); y ~ normal(alpha + x*beta, sigma); // likelihood } ``` --- ## Minimize `\(f\)` Goal is to minimize `\(f: \mathbb{R}^d \to \mathbb{R}\)` with respect to `\(\alpha \in \mathbb{R}, \beta \in \mathbb{R}^K, \sigma \in \mathbb{R}^+\)`. `\begin{align*} f(\alpha, \beta, \sigma| x, y) = \frac{N}{2}(\log{(2\pi\sigma^2)}) & + \frac{\sum_{n=1}^N(y_n - (\alpha + \sum_{k = 1}^K x_{nk} \beta_k))^2}{2\sigma^2} \\ & + \frac{\alpha^2}{2} + \frac{\sum_{k=1}^K \beta_k^2}{2 \cdot 10^2} + \sigma \\ \end{align*}` --- ## Stan, an ecosystem [Stan](https://mc-stan.org) accomplishes three main things: 1. defines a probabilistic programming language, named Stan, 2. transpiles Stan to C++, and 3. fits a statistical model by minimizing (or otherwise) using reverse-mode automatic differentiation on `\(f\)`. --- ## Part II Algorithms for automatic differentiation: 1. finite differences -- R, Python \\ {SymPy, JAX} $$ \frac{\partial f}{\partial x_n}(x) \approx \frac{f(x_1, \ldots, x_n+\epsilon, \ldots, x_N) - f(x_1, \ldots, x_N)}{\epsilon} $$ 2. symbolic differentiation -- Python's SymPy 3. reverse-mode automatic differentiation -- Stan, Python's [JAX](https://github.com/google/jax) --- ## Reverse-mode AD, example Consider $$ f(x_1, x_2, x_3) = \log{(x_1)} - (x_2 - x_3)^2 $$ --- ## Reverse-mode AD, general Let the `\(k\)`th node in the graph contain a value `\(v_k\)`, and let `\(a_k\)` be the value of the derivative at node `\(k\)`. Node `\(k\)`'s derivative is calculated as `$$a_k = \sum_{i \in \text{parent}(k)} a_i \frac{\partial v_i}{\partial v_k}$$` --- ## Part III 1. Topologically sorted graph => `std::vector` named `stack` 2. Data structure => `var` 3. Operations of `var`s 4. Traverse graph and calculate derivatives => `grad()` --- ## `stack` ```c typedef std::vector<double> adj_t; typedef std::function<void(adj_t&)> chain_t; std::vector<chain_t> stack_; ``` --- ## `var` ```c struct var { double val_; int idx_; var(double val) : val_(val), idx_(next_idx()) { } }; ``` --- ## Ops on `var`s ```c inline var operator+(const var& x1, double x2) { var y(x1.val_ + x2); stack_.emplace_back([=](adj_t& adj) { adj[x1.idx_] += adj[y.idx_]; }); return y; } ``` --- ## `grad()` ```c adj_t grad(var y, std::vector<var> x) { // init adj_t; root node = 1, rest = 0; // reverse iterate through stack_ // calc derivative each node // store each node's derivative return derivatives; } ``` --- ## End Thank you. --- ## References CppReference (2019). _CppReference_. URL: [https://en.cppreference.com/w/](https://en.cppreference.com/w/). Carpenter, B, M. D. Hoffman, M. Brubaker, et al. (2015). "The Stan math library: Reverse-mode automatic differentiation in C++". In: _arXiv preprint arXiv:1509.07164_.


All of the methods return the Slice object to enable the chaining of configuration options
Alternatively, you can build an associative array, using the method names as keys, and pass the array into the Slice's constructor


So many nonprofits need technical help So many volunteers seek technical projects But ... why do both struggle to achieve their goals?
A new technical volunteer model that . . . Encourages technical volunteers of all ages, experience levels, and geographic locations to contribute to nonprofit projects Fosters a strong, supportive community within the diverse volunteer pool Connects volunteers to nonprofits based on their skillsets and interests


Installing NixOS
Contents
{% include toc %}
Get NixOS on the USB stick
Formatting the USB stick: 1. Find out the device:
- Unmount
- Format to FAT32
- Detach and reattach
- Copy NixOS on the USB
Install NixOS
My laptop is a Lenovo Thinkpad T440s and I’ve followed (mostly) the set of instructions on Chris Martin’s blog.
Booting and partitioning
First of all, I had to switch to only using UEFI in the boot menu and then boot from the USB stick. I wanted to wipe the whole disk and reinstall NixOS on an encrypted partition. The NixOS installer does not (yet!) have a graphical user interface for partitioning, but what I wanted to do is fairly straightforward to achieve using fdisk
. I followed the advice posted on StackOverflow1 and ended up creating the following partition table:
Disk /dev/sda: 238.5 GiB,<PHONE_NUMBER>04 bytes, 500118192 sectors
Units: sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disklabel type: gpt
Disk identifier: C57FCAC2-37B1-4E17-B70F-D1BE189BB48F
Device Start End Sectors Size Type
/dev/sda1 2048 4047 2000 1000K BIOS boot
/dev/sda2 4096 1028095 1024000 500M EFI System
/dev/sda3 1028096 500118158 499090063 238G Linux LVM
The LVM device will contain the OS and will be partitioned into the root folder /
and the swap.
Setting up the encryption, LVM groups and volumes
Let’s move on to encrypting the device where the OS will actually live, i.e. /dev/sda3
. I am here following the instructions reported on the NixOS installation instructions. The first command encrypts /dev/sda3
and will prompt you to insert a password, the second command opens the encrypted partition:
OK, great! Time to make space for /
and the swap:
pvcreate /dev/mapper/enc-pv
vgcreate vg /dev/mapper/enc-pv
lvcreate -n swap vg -L 8G
lvcreate -n root vg -l 100%FREE
and to format:
At this point, launching fdisk -l
you will see the following output (or at least I do!):
Disk /dev/sda: 238.5 GiB,<PHONE_NUMBER>04 bytes, 500118192 sectors
Units: sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disklabel type: gpt
Disk identifier: C57FCAC2-37B1-4E17-B70F-D1BE189BB48F
Device Start End Sectors Size Type
/dev/sda1 2048 4047 2000 1000K BIOS boot
/dev/sda2 4096 1028095 1024000 500M EFI System
/dev/sda3 1028096 500118158 499090063 238G Linux LVM
Disk /dev/mapper/root: 238 GiB,<PHONE_NUMBER>04 bytes, 499085967 sectors
Units: sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disk /dev/mapper/vg-swap: 8 GiB,<PHONE_NUMBER> bytes, 16777216 sectors
Units: sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disk /dev/mapper/vg-root: 230 GiB,<PHONE_NUMBER>00 bytes, 482304000 sectors
Units: sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Get the installation going!
Let’s mount the disks and volumes to /mnt
:
and activate the swap:
NixOS declaratively defines the configuration of the system. The starting point is to run the following:
that generates hardware-configuration.nix
(do not touch) and configuration.nix
in the directory /mnt/etc/nixos
. My configuration.nix
is available on GitHub and I can just clone the repo with and populate the /mnt/etc/nixos
directory with the contents of the repo. This will not only configure the system as I like, but also install all the software that I need in one go. Once the configuration.nix
is all set run:
and reboot when the process is done.
First login
Login as root user when the system reboots and add the 17.03 channel:
Add whatever you need to configuration.nix
and rebuild your system:
Moreover, the user that was created in the configuration.nix
file does not have a password set! Let’s do it now:
You can now login as a normal user, instead as root. The system is now ready for use!
Thanks to Radovan Bast for the tip.↩


Syntax:
set symbol value => value
Arguments and Values:
symbol---a symbol.
value---an object.
Description:
set changes the contents of the value cell of symbol to the given value.
(set symbol value) == (setf (symbol-value symbol) value)
Examples:
(setf (symbol-value 'n) 1) => 1 (set 'n 2) => 2 (symbol-value 'n) => 2 (let ((n 3)) (declare (special n)) (setq n (+ n 1)) (setf (symbol-value 'n) (* n 10)) (set 'n (+ (symbol-value 'n) n)) n) => 80 n => 2 (let ((n 3)) (setq n (+ n 1)) (setf (symbol-value 'n) (* n 10)) (set 'n (+ (symbol-value 'n) n)) n) => 4 n => 44 (defvar *n* 2) (let ((*n* 3)) (setq *n* (+ *n* 1)) (setf (symbol-value '*n*) (* *n* 10)) (set '*n* (+ (symbol-value '*n*) *n*)) *n*) => 80 *n* => 2 (defvar *even-count* 0) => *EVEN-COUNT* (defvar *odd-count* 0) => *ODD-COUNT* (defun tally-list (list) (dolist (element list) (set (if (evenp element) '*even-count* '*odd-count*) (+ element (if (evenp element) *even-count* *odd-count*))))) (tally-list '(1 9 4 3 2 7)) => NIL *even-count* => 6 *odd-count* => 20
Side Effects:
The value of symbol is changed.
Affected By: None.
Exceptional Situations: None.
See Also:
Notes:
The function set is deprecated.
set cannot change the value of a lexical variable.


Error 404
The page you requested doesn't exist.
{{ $posts := (where .Site.Pages "Section" "posts") }} Browse all posts ({{ len $posts }}){{ define "main" }}
The page you requested doesn't exist.
{{ $posts := (where .Site.Pages "Section" "posts") }} Browse all posts ({{ len $posts }})


Classes
The following classes are available globally.
-
The
Font
type acts as a reference-counted owner of an underlyingPangoCairoFont
instance. It provides the methods that can operate on this data type throughFontProtocol
conformance. UseFont
as a strong reference or owner of aPangoCairoFont
instance.
See morePangoCairoFont
is an interface exported by fonts for use with Cairo. The actual type of the font will depend on the particular font technology Cairo was compiled to use.Declaration
Swift
open class Font : Pango.Font, FontProtocol
-
The
FontMap
type acts as a reference-counted owner of an underlyingPangoCairoFontMap
instance. It provides the methods that can operate on this data type throughFontMapProtocol
conformance. UseFontMap
as a strong reference or owner of aPangoCairoFontMap
instance.
See morePangoCairoFontMap
is an interface exported by font maps for use with Cairo. The actual type of the font map will depend on the particular font technology Cairo was compiled to use.Declaration
Swift
open class FontMap : Pango.FontMap, FontMapProtocol


Algorithm implementing QuickSort Algorithm where the pivot element is chosen randomly between first and last elements of the array and the array elements are taken from a Standard Normal Distribution. This is different from the ordinary quicksort in the sense, that it applies more to real life problems , where elements usually follow a normal distribution. Also the pivot is randomized to make it a more generic one.
The array elements are taken from a Standard Normal Distribution , having mean = 0 and standard deviation 1.
>>> import numpy as np
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> p = 100 # 100 elements are to be sorted
>>> mu, sigma = 0, 1 # mean and standard deviation
>>> X = np.random.normal(mu, sigma, p)
>>> np.save(outfile, X)
>>> print('The array is')
>>> print(X)
>>> mu, sigma = 0, 1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, p)
>>> count, bins, ignored = plt.hist(s, 30, normed=True)
>>> plt.plot(bins , 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (bins - mu)**2 / (2 * sigma**2) ),linewidth=2, color='r')
>>> plt.show()
–
>>>import matplotlib.pyplot as plt
# Normal Disrtibution QuickSort is red
>>> plt.plot([1,2,4,16,32,64,128,256,512,1024,2048],[1,1,6,15,43,136,340,800,2156,6821,16325],linewidth=2, color='r')
#Ordinary QuickSort is green
>>> plt.plot([1,2,4,16,32,64,128,256,512,1024,2048],[1,1,4,16,67,122,362,949,2131,5086,12866],linewidth=2, color='g')
>>> plt.show()


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The most basic RNN cell
tefla.core.rnn_cell.BasicRNNCell (num_units, reuse, trainable=True, w_init=
Args
- num_units: int, The number of units in the LSTM cell.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- input_size: Deprecated and unused.
- activation: Activation function of the states.
- layer_norm: If
True
, layer normalization will be applied. - layer_norm_args: optional dict, layer_norm arguments
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).
Methods
add_variable (name, shape, dtype=None, initializer=None, regularizer=None, trainable=True)
Arguments:
name: variable name.
shape: variable shape.
dtype: The type of the variable. Defaults to self.dtype
.
initializer: initializer instance (callable).
regularizer: regularizer instance (callable).
trainable: whether the variable should be part of the layer's
"trainable_variables" (e.g. variables, biases)
or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
Returns
The created variable.
apply (inputs, args, *kwargs)
This simply wraps self.__call__
.
Arguments:
inputs: Input tensor(s).
args: additional positional arguments to be passed to self.call
.
*kwargs: additional keyword arguments to be passed to self.call
.
Returns
Output tensor(s).
call (inputs, **kwargs)
Arguments: inputs: input tensor(s). **kwargs: additional keyword arguments.
Returns
Output tensor(s).
get_losses_for (inputs)
Arguments:
inputs: Input tensor or list/tuple of input tensors.
Must match the inputs
argument passed to the __call__
method at the time the losses were created.
If you pass inputs=None
, unconditional losses are returned,
such as weight regularization losses.
Returns
List of loss tensors of the layer that depend on inputs
.
get_updates_for (inputs)
Arguments:
inputs: Input tensor or list/tuple of input tensors.
Must match the inputs
argument passed to the __call__
method
at the time the updates were created.
If you pass inputs=None
, unconditional updates are returned.
Returns
List of update ops of the layer that depend on inputs
.
zero_state (batch_size, dtype)
Args
batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state.
Returns
If state_size
is an int or TensorShape, then the return value is a
N-D
tensor of shape [batch_size x state_size]
filled with zeros.
LSTM unit
tefla.core.rnn_cell.LSTMCell (num_units, reuse, trainable=True, w_init=
This class adds layer normalization and recurrent dropout to a basic LSTM unit. Layer normalization implementation is based on: https://arxiv.org/abs/1607.06450. "Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton and is applied before the internal nonlinearities. Recurrent dropout is base on: https://arxiv.org/abs/1603.05118 "Recurrent Dropout without Memory Loss" Stanislau Semeniuta, Aliaksei Severyn, Erhardt Barth.
Args
- num_units: int, The number of units in the LSTM cell.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- forget_bias: float, The bias added to forget gates (see above).
- input_size: Deprecated and unused.
- activation: Activation function of the states.
- inner_activation: Activation function of the inner states.
- layer_norm: If
True
, layer normalization will be applied. - layer_norm_args: optional dict, layer_norm arguments
- cell_clip: (optional) A float value, if provided the cell state is clipped by this value prior to the cell output activation.
- keep_prob: unit Tensor or float between 0 and 1 representing the recurrent dropout probability value. If float and 1.0, no dropout will be applied.
- dropout_seed: (optional) integer, the randomness seed.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).
Basic attention cell
tefla.core.rnn_cell.AttentionCell (cell, attn_length, reuse, w_init=
Implementation based on https://arxiv.org/abs/1409.0473. Create a cell with attention.
Args
- cell: an RNNCell, an attention is added to it. e.g.: a LSTMCell
- attn_length: integer, the size of an attention window.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- attn_size: integer, the size of an attention vector. Equal to cell.output_size by default.
- attn_vec_size: integer, the number of convolutional features calculated on attention state and a size of the hidden layer built from base cell state. Equal attn_size to by default.
- input_size: integer, the size of a hidden linear layer,
- layer_norm: If
True
, layer normalization will be applied. - layer_norm_args: optional dict, layer_norm arguments built from inputs and attention. Derived from the input tensor by default.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).
Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078)
tefla.core.rnn_cell.GRUCell (num_units, reuse, w_init=
Args
- num_units: int, The number of units in the LSTM cell.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- input_size: Deprecated and unused.
- activation: Activation function of the states.
- inner_activation: Activation function of the inner states.
- layer_norm: If
True
, layer normalization will be applied. - layer_norm_args: optional dict, layer_norm arguments
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).
RNN cell composed sequentially of multiple simple cells
tefla.core.rnn_cell.MultiRNNCell (cells, state_is_tuple=True)
Create a RNN cell composed sequentially of a number of RNNCells.
Args
- cells: list of RNNCells that will be composed in this order.
Operator adding dropout to inputs and outputs of the given cell
tefla.core.rnn_cell.DropoutWrapper (cell, is_training, input_keep_prob=1.0, output_keep_prob=1.0, seed=None)
Create a cell with added input and/or output dropout. Dropout is never used on the state.
Args
- cell: an RNNCell, a projection to output_size is added to it.
- is_training: a bool, training if true else validation/testing
- input_keep_prob: unit Tensor or float between 0 and 1, input keep probability; if it is float and 1, no input dropout will be added.
- output_keep_prob: unit Tensor or float between 0 and 1, output keep probability; if it is float and 1, no output dropout will be added.
- seed: (optional) integer, the randomness seed.
convolution:
tefla.core.rnn_cell._conv (args, filter_size, num_features, bias, reuse, w_init=None, b_init=0.0, scope='_conv')
Args
- args: a Tensor or a list of Tensors of dimension 3D, 4D or 5D
- batch x n, Tensors.
- filter_size: int tuple of filter height and width.
- reuse: None/True, whether to reuse variables
- w_init: weights initializer object
- b_init: a
int
, bias initializer value - num_features: int, number of features.
- bias_start: starting value to initialize the bias; 0 by default.
Returns
A 3D, 4D, or 5D Tensor with shape [batch ... num_features]
Adds a fully connected layer
tefla.core.rnn_cell._linear (x, n_output, reuse, trainable=True, w_init=
fully_connected
creates a variable called weights
, representing a fully
connected weight matrix, which is multiplied by the x
to produce a
Tensor
of hidden units. If a layer_norm
is provided (such as
layer_norm
), it is then applied. Otherwise, if layer_norm
is
None and a b_init
and use_bias
is provided then a biases
variable would be
created and added the hidden units. Finally, if activation
is not None
,
it is applied to the hidden units as well.
Note: that if x
have a rank greater than 2, then x
is flattened
prior to the initial matrix multiply by weights
.
Args
- x: A
Tensor
of with at least rank 2 and value for the last dimension, i.e.[batch_size, depth]
,[None, None, None, channels]
. - n_output: Integer or long, the number of output units in the layer.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- activation: activation function, set to None to skip it and maintain a linear activation.
- layer_norm: normalization function to use. If
-
batch_norm
isTrue
then google original implementation is used and if another function is provided then it is applied. default set to None for no normalizer function - layer_norm_args: normalization function parameters.
- w_init: An initializer for the weights.
- w_regularizer: Optional regularizer for the weights.
- b_init: An initializer for the biases. If None skip biases.
- outputs_collections: The collections to which the outputs are added.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). - name: Optional name or scope for variable_scope/name_scope.
- use_bias: Whether to add bias or not
Returns
The 2-D Tensor
variable representing the result of the series of operations.
e.g: 2-D Tensor
[batch, n_output].
Adds a Layer Normalization layer from https://arxiv.org/abs/1607.06450
tefla.core.rnn_cell.layer_norm (x, reuse, center=True, scale=True, trainable=True, epsilon=1e-12, name='bn', outputs_collections=None) "Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton Can be used as a normalizer function for conv2d and fully_connected.
Args
- x: a tensor with 2 or more dimensions, where the first dimension has
batch_size
. The normalization is over all but the last dimension ifdata_format
isNHWC
and the second dimension ifdata_format
isNCHW
. - center: If True, subtract
beta
. If False,beta
is ignored. - scale: If True, multiply by
gamma
. If False,gamma
is not used. When the next layer is linear (also e.g.nn.relu
), this can be disabled since the scaling can be done by the next layer. - epsilon: small float added to variance to avoid dividing by zero.
- reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
- outputs_collections: collections to add the outputs.
- trainable: If
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). - name: Optional scope/name for
variable_scope
.
Returns
A Tensor
representing the output of the operation.
LSTM
tefla.core.rnn_cell.lstm (inputs, n_units, reuse, is_training, activation=
Args
- inputs:
Tensor
. Inputs 3-D Tensor [samples, timesteps, input_dim]. - n_units:
int
, number of units for this layer. - reuse:
bool
. If True and 'scope' is provided, this layer variables will be reused (shared). - is_training:
bool
, training if True - activation:
function
(returning aTensor
). - inner_activation:
function
(returning aTensor
). - dropout:
tuple
offloat
: (input_keep_prob, output_keep_prob). The input and output keep probability. - use_bias:
bool
. If True, a bias is used. - w_init:
function
(returning aTensor
). Weights initialization. - forget_bias:
float
. Bias of the forget gate. Default: 1.0. - return_seq:
bool
. If True, returns the full sequence instead of last sequence output only. - return_state:
bool
. If True, returns a tuple with output and states: (output, states). - initial_state:
Tensor
. An initial state for the RNN. This must be a tensor of appropriate type and shape [batch_size x cell.state_size]. - dynamic:
bool
. If True, dynamic computation is performed. It will not compute RNN steps above the sequence length. Note that because TF requires to feed sequences of same length, 0 is used as a mask. So a sequence padded with 0 at the end must be provided. When computation is performed, it will stop when it meets a step with a value of 0. - trainable:
bool
. If True, weights will be trainable. - scope:
str
. Define this layer scope (optional). A scope can be used to share variables between layers. Note that scope will override name.
Returns
if return_seq
: 3-D Tensor [samples, timesteps, output dim].
else: 2-D Tensor [samples, output dim].
GRU
tefla.core.rnn_cell.gru (inputs, n_units, reuse, is_training, activation=
Args
- inputs:
Tensor
. Inputs 3-D Tensor [samples, timesteps, input_dim]. - n_units:
int
, number of units for this layer. - reuse:
bool
. If True and 'scope' is provided, this layer variables will be reused (shared). - is_training:
bool
, training if True - activation:
function
(returning aTensor
). - inner_activation:
function
(returning aTensor
). - dropout:
tuple
offloat
: (input_keep_prob, output_keep_prob). The input and output keep probability. - use_bias:
bool
. If True, a bias is used. - w_init:
function
(returning aTensor
). Weights initialization. - forget_bias:
float
. Bias of the forget gate. Default: 1.0. - return_seq:
bool
. If True, returns the full sequence instead of last sequence output only. - return_state:
bool
. If True, returns a tuple with output and states: (output, states). - initial_state:
Tensor
. An initial state for the RNN. This must be a tensor of appropriate type and shape [batch_size x cell.state_size]. - dynamic:
bool
. If True, dynamic computation is performed. It will not compute RNN steps above the sequence length. Note that because TF requires to feed sequences of same length, 0 is used as a mask. So a sequence padded with 0 at the end must be provided. When computation is performed, it will stop when it meets a step with a value of 0. - trainable:
bool
. If True, weights will be trainable. - scope:
str
. Define this layer scope (optional). A scope can be used to share variables between layers. Note that scope will override name.
Returns
if return_seq
: 3-D Tensor [samples, timesteps, output dim].
else: 2-D Tensor [samples, output dim].
Bidirectional RNN
tefla.core.rnn_cell.bidirectional_rnn (inputs, rnncell_fw, rnncell_bw, reuse, is_training, dropout_fw=None, dropout_bw=None, return_seq=False, return_states=False, initial_state_fw=None, initial_state_bw=None, dynamic=False, scope='BiRNN', outputs_collections=None) Build a bidirectional recurrent neural network, it requires 2 RNN Cells to process sequence in forward and backward order. Any RNN Cell can be used i.e. SimpleRNN, LSTM, GRU... with its own parameters. But the two cells number of units must match.
Args
- inputs:
Tensor
. The 3D inputs Tensor [samples, timesteps, input_dim]. - rnncell_fw:
RNNCell
. The RNN Cell to use for foward computation. - rnncell_bw:
RNNCell
. The RNN Cell to use for backward computation. - reuse:
bool
. If True and 'scope' is provided, this layer variables will be reused (shared). - is_training:
bool
, training if True - dropout_fw:
tuple
offloat
: (input_keep_prob, output_keep_prob). the input and output keep probability. - dropout_bw:
tuple
offloat
: (input_keep_prob, output_keep_prob). the input and output keep probability. - return_seq:
bool
. If True, returns the full sequence instead of last sequence output only. - return_states:
bool
. If True, returns a tuple with output and states: (output, states). - initial_state_fw:
Tensor
. An initial state for the forward RNN. This must be a tensor of appropriate type and shape [batch_size x cell.state_size]. - initial_state_bw:
Tensor
. An initial state for the backward RNN. This must be a tensor of appropriate type and shape [batch_size x cell.state_size]. - dynamic:
bool
. If True, dynamic computation is performed. It will not compute RNN steps above the sequence length. Note that because TF requires to feed sequences of same length, 0 is used as a mask. So a sequence padded with 0 at the end must be provided. When computation is performed, it will stop when it meets a step with a value of 0. - scope:
str
. Define this layer scope (optional). A scope can be used to share variables between layers. Note that scope will override name.
Returns
if return_seq
: 3-D Tensor [samples, timesteps, output dim].
else: 2-D Tensor Layer [samples, output dim].


Entity
with a custom
Shader
this custom shader is physically-based, rendering
MetallicMaterial
with Cook-Torrance and Lambertian specular and diffuse BRDFs, GGX microfacet distribution, and Schlick's Approximation for Fresnel term.


Imagine we have two large reservoirs of fluids. One contains the liquid ‘supertox’ and one contains purified water. We want to get certain proportional mixtures of the two.
There is also a drain for waste. Supertox is very dangerous, however, so we’d like to pour as little down the drain as possible, even in dilute form. Supertox is polar, so supertox and water mix perfectly and instantly whenever one is poured into the other.
In addition, we have two opaque measuring vessels, one of 10 liters and one of 7. We call these the 10-vessel and the 7-vessel for short. There are no measurement lines on the vessels. When we fill a vessel from a reservoir, we fill it from a faucet, as our client is nervous about contamination from handling.
So we can fill a vessel to completion from a reservoir or we can pour liquid from one vessel to the other, stopping either when the receiving vessel is full or the source vessel is empty. We are allowed to pour pure water back in the water reservoir and to pour pure supertox back into the supertox reservoir.
We want to create a 10-liter solution having 40% of supertox without pouring anything down the drain. How do we do it?
Fill the 7-vessel with supertox. Pour into the 10-vessel. Fill the 7-vessel with supertox again. Pour into the 10-vessel until the 10-vessel is full. So we have 4 left in the 7-vessel. Pour the contents of the 10-vessel back into the supertox container. Pour the contents of the 7-vessel into the 10-vessel, and then fill the rest of the 10-vessel with water.
We want a mixture having 2.7 liters of supertox in a 3-liter mixture. We don’t want to pour more than 10 liters of any liquid laced with supertox down the drain. How can we achieve this?
Can we get a mixture that is 2/9 supertox and the rest water without pouring any supertox down the drain?
Now, chemists require extremely precise mixtures, so let me explain our terminology with an example. A 25% concentration of supertox in the 10-liter container would correspond to 2.5 liters of supertox and 7.5 liters of water.
Also, we have a big empty bucket at our disposal that can hold over 100 liters, though we don’t know exactly how big it is.
Can we get a 26% concentration of supertox in the 7-liter container?
Suppose that we want to get mixtures which are all supertox, 1/2 supertox, 1/3 supertox, 1/4 supertox, … 1/50 supertox. You are allowed to specify the size of the two vessels. You may even include fractional sizes as long as the smallest vessel contains at least one liter and the largest contains at most 30 liters.  Which two vessel sizes would you choose in order to obtain each of these mixtures? Given those vessel sizes and given a target mixture, how would you obtain it?


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How to help
Development
We have many open tasks to do for the future development (see roadmap). Every developer who is able to help
out would be great to improve CuteFlow.
If need to know how to code PHP and using MYSQL to so.
Translation
At the moment there are only english and german versions available. We need translators which
are willing to translate the single "language-file" of cuteflow into more languages.
This file is a php file with about 200 Text passages to translate.
Documentation
The documentation of CuteFlow is a really "raw" version. A more advanced version would be great.
So we need people which understand CuteFlow in detail, so they can write an easy to understand
documentation.
Donation
If you use CuteFlow and find it useful it would be great if you help developing through donations
(Hard money ;-) ). For that you will be able to donate small amounts of money in the near future.
If you are interessted to help please contact timo.haberkern@fantastic-bits.de.


If defer_javascript is enabled, '*'s and images will appear before script text. If not the script text will be interspersed with '*'s and images.
*


I have moved over to tarikkdiry.com where you can see what I've been up to lately, including my personal blog.
As always, let's connect!


Xalan-Java Overview
- Introduction
- Xalan-Java Features
- Getting to work with Xalan-Java
- Getting up to speed with XSLT
- Glossary
Introduction
Xalan-Java fully implements XSL Transformations (XSLT) Version 1.0 and the XML Path Language (XPath) Version 1.0. XSLT is the first part of the XSL stylesheet language for XML. It includes the XSL Transformation vocabulary and XPath, a language for addressing parts of XML documents. For links to background materials, discussion groups, frequently asked questions, and tutorials on XSLT, see Getting up to speed with XSLT.
| XSL also includes a vocabulary for formatting documents, which is not part of Xalan-Java. For more information, see Extensible Stylesheet Language (XSL) Version 1.1 and the Apache XmlGraphics FOP (Formatting Objects Project). |
XSL stylesheets are written in the XSLT language. An XSL stylesheet contains instructions for transforming XML documents into XML, HTML, XHTML or plain text. In structural terms, an XSL stylesheet specifies the transformation of one tree of nodes (the XML input) into another tree of nodes (the output or transformation result).
| The XSL stylesheet may generate and refer to cascading style sheets (CSS) as part of its output. |
In the following example, the foo.xsl stylesheet is used to transform foo.xml into foo.out:
foo.xml:
<?xml version="1.0"?> <doc>Hello</doc>
foo.xsl:
<?xml version="1.0"?> <xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0"> <xsl:template match="doc"> <out><xsl:value-of select="."/></out> </xsl:template> </xsl:stylesheet>
foo.out:
<out>Hello</out>
By default, Xalan-Java uses Xerces-Java, but it may be configured with system properties to work with other XML parsers (see Plugging in a Transformer and XML parser). The input may be submitted in the form of a stream of XML markup (from a URI, a character or byte stream, or another transformation), a SAX InputStream, or a DOM Node.
Xalan-Java performs the transformations specified in the XSL stylesheet and packages a sequence of SAX events that may be serialized to an output stream or writer, used to build a DOM tree, or forwarded as input to another transformation.
Xalan-Java Features
- Includes an Interpretive processor for use in a tooling and debugging environment and
a Compiling processor (XSLTC) for use in a high performance runtime environment.
- Implements the relevant W3C specifications: XSL Transformations (XSLT) Version 1.0 and
XML Path Language (XPath) Version 1.0.
- Implements Java API for XML Processing (JAXP) 1.3, and builds on
SAX 2 and DOM level 3.
- Implements the XPath API in JAXP 1.3.
- May be configured to work with any XML parser, such as
Xerces-Java, that implements
JAXP 1.3 (see Plugging in an XML
parser).
- Can process Stream, SAX or DOM input, and output to a Stream, SAX or DOM.
- Transformations may be chained (the output of one transformation may be the input for
another).
- May be run from the command line for convenient file-to-file
transformations.
- Includes an applet wrapper.
- May be used in a servlet to transform XML documents
into HTML and serve the results to clients.
- Supports the creation of Java and scripting language extensions. and provides a growing library of extension elements and functions.
Getting to work with Xalan-Java
For instructions and some suggestions about how to get started using the XSLT Interpretive processor, see Getting Started with Interpretive Processing.
For instructions and some suggestions about how to get started using the XSLT Compiling processor, see Getting Started with XSLTC.
Getting up to speed with XSLT
If you are still working through the details of the XSLT spec (the W3C 1.0 Recommendation), you may want to consult one or more of the following:
- XSLT - XSL Transformations in
The XML Revolution: Technologies for the future Web by Anders Møller and Michael
I. Schwartzbach (Web pages, but designed for sequential reading)
- Crane Softwright's Free preview of
Practical Transformation Using XSLT and XPath
- Doug Tidwell's XSLT, O'Reilly, 2001
- Bob Ducharme's XSLT Quickly,
Manning Publications, 2001
- John Robert Gardner and Zarella Rendon's
XSLT
and XPath: A Guide to Transformations, Prentice-Hall, 2001
- Michael Kay's XSLT
Programmer's Reference, 2nd ed., Wrox Press, 2001
- Dave Pawson's XSL Frequently Asked Questions to search out particular answers and
techniques
- Miloslav Nic's XSL
Tutorial, a collection of stylesheet examples
- Elliotte Rusty Harold's
Chapter 17 of the XML Bible: XSL Transformations
- The Mulberry XSL-List -- Open Forum on
XSL (of interest to XSL users at all levels)
- Objects by Design's
Transforming XMI to HTML (oriented towards XMI, "an XML-based, stream representation
of a UML model," but also covers "generic" XML transformations) and their related
XSLT by Example
- OASIS (the Organization for the Advancement of Structured Information Standards):
Extensible Stylesheet Language (XSL)
by Robin Cover
When you come across other useful introductory or background materials, please email Xalan Development Mailing List, so we can add them to this list.
Glossary
XSLT Namespace
The XML namespace for XSLT. An XML namespace is a collection of element and attribute names, identified by a Unique Resource Identifier (URI), which often takes the form of a URL, but is really just a unique string, not a pointer to a web page. The XSLT namespace URI is http://www.w3.org/1999/XSL/Transform. In each XSLT stylesheet, you must declare this namespace in the stylesheet element tag and bind it to a local prefix. Like the XSLT specification, we always use xsl as the XSLT namespace prefix in our descriptions and examples, although you are free to bind any prefix to this namespace.
XSL Instruction
Any tag associated with the XSLT namespace.
Template
An element, usually with child elements, that specifies a "rule" or set of instructions to perform when a particular kind of node is encountered in the source tree.
XSL Template Instruction
Any tag that occurs inside an xsl:template element and is associated with the XSLT namespace.
Source Tree
The XML tree input to the XSL process.
Result Tree
The tree that is output by the XSL process.
Match Pattern
The part of a template that defines the kind(s) of nodes to which the template applies.


Releasing Rails
In this document, we'll cover the steps necessary to release Rails. Each section contains steps to take during that time before the release. The times suggested in each header are just that: suggestions. However, they should really be considered as minimums.
10 Days before release
Today is mostly coordination tasks. Here are the things you must do today:
Is the CI green? If not, make it green. (See “Fixing the CI”)
Do not release with a Red CI. You can find the CI status here:
http://travis-ci.org/#!/rails/rails
Is Sam Ruby happy? If not, make him happy.
Sam Ruby keeps a test suite that makes sure the code samples in his book (Agile Web Development with Rails) all work. These are valuable integration tests for Rails. You can check the status of his tests here:
http://intertwingly.net/projects/dashboard.html
Do not release with Red AWDwR tests.
Are the postgres tests green? If not, make them green
Currently Travis CI doesn't run the Active Record postgres tests. They are working to resolve this, but in the mean time, it is crucial to ensure that the tests are still green before release.
Do we have any git dependencies? If so, contact those authors.
Having git dependencies indicates that we depend on unreleased code. Obviously rails cannot be released when it depends on unreleased code. Contact the authors of those particular gems and work out a release date that suits them.
Contact the security team (either Koz or tenderlove)
Let them know of your plans to release. There may be security issues to be addressed, and that can impact your release date.
Notify implementors.
Ruby implementors have high stakes in making sure Rails works. Be kind and give them a heads up that Rails will be released soonish.
Send an email just giving a heads up about the upcoming release to these lists:
-
<EMAIL_ADDRESS>-
<EMAIL_ADDRESS>-
<EMAIL_ADDRESS>Implementors will love you and help you.
3 Days before release
This is when you should release the release candidate. Here are your tasks for today:
Is the CI green? If not, make it green.
Is Sam Ruby happy? If not, make him happy.
Are the postgres tests green? If not, make them green
Contact the security team. CVE emails must be sent on this day.
Create a release branch.
From the stable branch, create a release branch. For example, if you're releasing Rails 3.0.10, do this:
[aaron@higgins rails (3-0-stable)]$ git checkout -b 3-0-10 Switched to a new branch '3-0-10' [aaron@higgins rails (3-0-10)]$
Update each CHANGELOG.
Many times commits are made without the CHANGELOG being updated. You should review the commits since the last release, and fill in any missing information for each CHANGELOG.
You can review the commits for the 3.0.10 release like this:
[aaron@higgins rails (3-0-10)]$ git log v3.0.9..
If you're doing a stable branch release, you should also ensure that all of the CHANGELOG entries in the stable branch are also synced to the master branch.
Update the RAILS_VERSION file to include the RC.
Build and test the gem.
Run `rake install` to generate the gems and install them locally. Then try generating a new app and ensure that nothing explodes.
This will stop you from looking silly when you push an RC to rubygems.org and then realise it is broken.
Release the gem.
IMPORTANT: Due to YAML parse problems on the rubygems.org server, it is safest to use Ruby 1.8 when releasing.
Run `rake release`. This will populate the gemspecs with data from RAILS_VERSION, commit the changes, tag it, and push the gems to rubygems.org. Here are the commands that `rake release` should use, so you can understand what to do in case anything goes wrong:
$ rake all:build $ git commit -am'updating RAILS_VERSION' $ git tag -m'tagging rc release' v3.0.10.rc1 $ git push $ git push --tags $ for i in $(ls dist); do gem push $i; done
Send Rails release announcements
Write a release announcement that includes the version, changes, and links to github where people can find the specific commit list. Here are the mailing lists where you should announce:
-
<EMAIL_ADDRESS>-
<EMAIL_ADDRESS>-
<EMAIL_ADDRESS>Use markdown format for your announcement. Remember to ask people to report issues with the release candidate to the rails-core mailing list.
IMPORTANT: If any users experience regressions when using the release candidate, you must postpone the release. Bugfix releases *should not* break existing applications.
Post the announcement to the Rails blog.
If you used markdown format for your email, you can just paste it in to the blog.
Post the announcement to the Rails twitter account.
Time between release candidate and actual release
Check the rails-core mailing list and the github issue list for regressions in the RC.
If any regressions are found, fix the regressions and repeat the release candidate process. We will not release the final until 72 hours after the last release candidate has been pushed. This means that if users find regressions, the scheduled release date must be postponed.
When you fix the regressions, do not create a new branch. Fix them on the stable branch, then cherry pick the commit to your release branch. No other commits should be added to the release branch besides regression fixing commits.
Day of release
Many of these steps are the same as for the release candidate, so if you need more explanation on a particular step, so the RC steps.
Today, do this stuff in this order:
-
Apply security patches to the release branch
-
Update CHANGELOG with security fixes.
-
Update RAILS_VERSION to remove the rc
-
Build and test the gem
-
Release the gems
-
Email security lists
-
Email general announcement lists
Emailing the rails security announce list
Email the security announce list once for each vulnerability fixed.
You can do this, or ask the security team to do it.
Email the security reports to:
-
<EMAIL_ADDRESS>-
<EMAIL_ADDRESS>Be sure to note the security fixes in your announcement along with CVE numbers and links to each patch. Some people may not be able to upgrade right away, so we need to give them the security fixes in patch form.
-
Blog announcements
-
Twitter announcements
-
Merge the release branch to the stable branch.
-
Drink beer (or other cocktail)
Misc
Fixing the CI
There are two simple steps for fixing the CI:
-
Identify the problem
-
Fix it
Repeat these steps until the CI is green.
Manually trigger docs generation
We have a post-receive hook in GitHub that calls the docs server on pushes. It triggers generation and publication of edge docs, updates the contrib app, and generates and publishes stable docs if a new stable tag is detected.
The hook unfortunately is not invoked by tag pushing, so once the new stable tag has been pushed to origin, please run
rake publish_docs
You should see something like this:
Rails master hook tasks scheduled: * updates the local checkout * updates Rails Contributors * generates and publishes edge docs If a new stable tag is detected it also * generates and publishes stable docs This needs typically a few minutes.
Note you do not need to specify the tag, the docs server figures it out.
Also, don't worry if you call that multiple times or the hook is triggered again by some immediate regular push, if the scripts are running new calls are just queued (in a queue of size 1).


This example demonstrates a tool that can be built with an intermediate-level knowledge of the MyGeotab system. It is suitable for those who have a good understanding of the Geotab ecosystem and are comfortable with the MyGeotab SDK and leveraging the MyGeotab APIs within their projects.
You can use a spreadsheet application such as Microsoft Excel or Google Drive to import multiple routes at once.
Click here to download the Microsoft Excel template.


Musk suggests Tesla has 200,000 orders for Cybertruck
Tesla Inc Chief Executive Officer Elon Musk indicated in a tweet on Sunday that the electric carmaker received 200,000 orders for its electric pickup truck within three days of its launch.
REUTERS: Tesla Inc Chief Executive Officer Elon Musk indicated in a tweet on Sunday that the electric carmaker received 200,000 orders for its electric pickup truck within three days of its launch.
Musk, who has been regularly tweeting about the Cybertruck's features since its launch late Thursday, has also been updating his followers with the number of orders the company has received.
In an earlier tweet, Musk said the company had received 146,000 orders for Cybertruck, and tweeted again on Sunday saying "200K" - an apparent reference to the number of orders.
The company's website shows that an immediate payment of US$100 is required to reserve an order for the Cybertruck, which has a starting price of US$39,900.
The launch of its futuristic pickup on Thursday suffered a setback when the electric vehicle's "armored glass" windows shattered in a much-anticipated unveiling. The overall look of the electric vehicle had worried Wall Street on Friday, driving the automaker's shares to close 6.1per cent lower.
During the launch, Musk had taken aim at the design, power and durability of mainstream trucks, only to be shaken when his boast about his new vehicle's windows backfired.
Separately, Musk said the Cybertruck is Tesla's last product unveil for a while.
Tesla plans to start manufacturing the Cybertruck around late-2021.
(Reporting by Rama Venkat and Bhargav Acharya in Bengaluru; Editing by Alistair Bell and Sherry Jacob-Phillips)


|
threeP.games
0.1
A user interface to accelerate the process of building games in the Python programming language with PyGame and Panda3D
|
The parent class for all of the custom objects to be used when developing a a 2d game with the PyGame engine. More...
Public Member Functions | |
| def | __init__ (self, x=0, y=0, sprite=["images\\empty.svg"]) |
| The constructor for Stuff2d. More... | |
| def | getX (self) |
| def | getY (self) |
| def | getSprite (self) |
| def | getWidth (self) |
| def | getHeight (self) |
| def | setX (self, x) |
| def | setY (self, y) |
| def | setSprite (self, sprite) |
| Replaces the list of sprites for the object. More... | |
| def | setWidth (self, width) |
| def | setHeight (self, height) |
| def | loadSpriteSheet (self, sprite) |
| def | render (self, game, x, y, frame=0) |
| The method to render an object on the scene using the PyGame blit (block transfer) method, to be called every pass of the main loop in the game. More... | |
| def | bounds (self) |
| def | addScript (self) |
The parent class for all of the custom objects to be used when developing a a 2d game with the PyGame engine.
| def GameObjects.Stuff2d.__init__ | ( | self, | |
x = 0 , |
|||
y = 0 , |
|||
sprite = ["images\\empty.svg"] |
|||
| ) |
The constructor for Stuff2d.
| x | the x coordinate of the objects starting positiion |
| y | the y coordinate of hte objects starting position |
| sprite | the list of image files as strings to be used for the sprite animations, if only one single image it still must be passed in as a single element list. The design intention when creating this was that all sprite images are placed into a single directory and then retrieved with a list comprehension passed into the constructor. example: sprite=[("images\\sprite\\" + file) for file in listdir("images\\sprite") if isfile(join("images\\sprite", file))]. |
| def GameObjects.Stuff2d.render | ( | self, | |
| game, | |||
| x, | |||
| y, | |||
frame = 0 |
|||
| ) |
The method to render an object on the scene using the PyGame blit (block transfer) method, to be called every pass of the main loop in the game.
| game | The pygame display surface in the rendering window -object created by line "pygame.display.set_mode((x,y))" |
| x | The x coordinate of where to render the object at |
| y | The y coordinate of where to render the object at |
| frame | The frame of the sprite animation to render, design intentions are for this parameter to be filled with a ternary operator checking a global counter, example: for idle/walk animations with 20 frames each (counter%20, counter%20+20)[character.getIsMoving()] |
| def GameObjects.Stuff2d.setSprite | ( | self, | |
| sprite | |||
| ) |
Replaces the list of sprites for the object.
| sprite | Must be a list of filenames as strings, if only a single image, pass it in as a single element list. |


Research Projects Blueprint A Toolchain for Highly-Reconfigurable Microservice Applications Hindsight Distributed Tracing with Retroactive Sampling! Tracey Distributed Trace Comparison and Aggregation using NLP techniques Smooth Kronecker Kronecker Graph Generator with Smooth degree distributions TraViz Visualization of Distributed System traces Sifter Encoding-based sampling for distributed Tracing GoVector A vector clock logging library Dara Model checker for distributed systems Personal/Class Projects FPL Data Fantasy Premier League Data Extractor Block Art Blockchain based Art Canvas Battle Tanks P2P Battle Royale Game History Lab OCR Article Parser eTone Tone matching game Contractor DBMS A DBMS for Coast Capital Savings Inc. Basic OS A basic operating system project Music Plagarizer Music Plagarism Detector CFL Predictor A predictor for CFL games Old Projects See more Vancouver Gardens Check out Vancouver's fruitful gardens! Restaurant Quiz App Test your restaurant finding skills! JOKER Micorsoft Kinect powered AI(?) Event Manager App Assistance app for event managers! Monopoly World A text-based Monopoly World!


Framework
Examples that focus on implementing uses of built-in components provided by Bootstrap.Starter template
Nothing but the basics: compiled CSS and JavaScript.
Grids
Multiple examples of grid layouts with all four tiers, nesting, and more.
Jumbotron
Build around the jumbotron with a navbar and some basic grid columns.
Narrow jumbotron
Build a more custom page by narrowing the default container and jumbotron.
Navbars
Taking the default navbar component and showing how it can be moved, placed, and extended.Navbar
Super basic template that includes the navbar along with some additional content.
Static top navbar
Super basic template with a static top navbar along with some additional content.
Fixed navbar
Super basic template with a fixed top navbar along with some additional content.
Custom components
Brand new components and templates to help folks quickly get started with Bootstrap and demonstrate best practices for adding onto the framework.Album
Simple one-page template for photo galleries, portfolios, and more.
Cover
A one-page template for building simple and beautiful home pages.
Carousel
Customize the navbar and carousel, then add some new components.
Blog
Simple two-column blog layout with custom navigation, header, and type.
Dashboard
Basic admin dashboard shell with fixed sidebar and navbar.
Sign-in page
Custom form layout and design for a simple sign in form.
Justified nav
Create a custom navbar with justified links. Heads up! Not too Safari friendly.
Sticky footer
Attach a footer to the bottom of the viewport when the content is shorter than it.
Sticky footer w/ navbar
Attach a footer to the bottom of the viewport with a fixed top navbar.
Experiments
Examples that focus on future-friendly features or techniques.Offcanvas
Build a toggleable off-canvas navigation menu for use with Bootstrap.


Isar735.github.io
Doing literature review - Only HQ writing services provided by top professionals. original reports at affordable prices available here will make your education. Doing a Literature Review PS: Political Science. Doing a Literature Review - SlideShare.
Doing a Literature Review: Releasing the Research Imagination (SAGE Study Skills Series) Chris Hart on Amazon.com. FREE shipping on qualifying offers. The Importance of Literature Review in Research Writing. A literature review surveys scholarly articles, books and other sources relevant to a particular issue, area of research, or theory, and by so doing. Literature reviews - Example 1 - Student Services Carol Hurst's Children's Literature. Doing Your Literature Review: Traditional and Systematic.
Literature Reviews: An Overview for Graduate Students. What is a literature review? What purpose does it serve in research? What should you expect when writing. Literature Reviews: An Overview for Graduate Students. Literature Review - Green River College.
☑ Find out how you can submit a well written and perfectly formatted MLA literature review with our professional help and advice. Check out the infographic!. Doing a literature review - A Leading UK University.
Polyflam Doing literature review - Polyflam.
Challenges and success factors for large-scale agile. Ten Simple Rules for Writing a Literature Review
The literature review is not merely a simple compilation or a list of every item and resource with any possible relation to your topic. A good literature. Complete Review of Vedic Literature - Stephen Knapp. How to Write a Literature Review The literature review is no longer a brief preliminary to the real business of doing social research. There is no topic which has not been studied previously The literature review is not merely a simple compilation or a list of every item and resource with any possible relation to your topic. A good literature revie…. Academic Programs - Wagner College Academic Programs. Doing a Literature Review - Volume 39 Issue 1 - Jeffrey W. Knopf. The Literature Review - Communication Studies - Research. Provides a complete review of the Vedas and Vedic literature.
Doing a careful and thorough literature review is essential when you write about research at any level. It is basic homework that is assumed Book Review: Superforecasting Slate Star Codex. Doing Your Literature Review: Traditional and Systematic Techniques Jill Jesson, Lydia Matheson, Fiona M Lacey on Amazon.com. FREE shipping on qualifying offers. Examples - Literature Reviews - LibGuides at CSU, Chico.
Student Learning Development Doing a literature review www.le.ac.uk/succeedinyourstudies This Study Guide explains why literature reviews are needed Required Elements of the Literature Review. Length: At least five-six pages of APA-manuscript text (not including the References page). According to Stipek (1996), early approaches to the study of motivation were rooted in the literature on extrinsic reinforcement. Within this literature, all behavior.
How to undertake a literature review – Raul Pacheco-Vega Doing a Literature Review SAGE Publications. Writing the Literature Review (Part One): Step-by-Step Tutorial for Graduate Students - Duration: 5:22. David Taylor 921,421 views. Featured majors Are you undecided? It’s ok to explore. About 25% of each incoming class is undecided, and many change their majors. As you take different courses.
DOING A LITERATURE 3 REVIEW - SAGE Publications. A guide with information on how to write a literature review.
What is a review of the literature? A literature review is an account of what has been published on a topic by accredited scholars and researchers. Example of literature reviews from Helen M. Paterson (2004), “Co-Witnesses and the Effects of Discussion on Eyewitness Memory.” PhD Thesis submitted Doing a Literature Review: Releasing the Research. Motivation: A literature review - Assessment Instruction.
Philip Tetlock, author of Superforecasting, got famous by studying prediction. His first major experiment, the Expert Political Judgment experiment Free literature based classroom units, reviews of great books for kids, ways to use them in the classroom, free teaching guides and activities. Picture Books, Novels. Challenges and success factors for large-scale agile transformations: A systematic literature review. Doing a Literature Review - YouTube.
The Literature Review: A Few Tips On Conducting. CHAPTER 3 DOING A LITERATURE REVIEW 30 INTRODUCTION This chapter considers how one can discover what is already known about a particu-lar ﬁ eld of study Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications For example.
I have been asked a few times for a blog post on how to conduct a proper literature review. This is hard to do sometimes because a lot of people.


Leach Construction
The website for Leach Construction is currently under construction. Check back later for quality home repairs in the Greater Nashville Area of Tennessee!
The date and time is:
The color is:


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Profile
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Stood school 3rd in Chemistry in 12th standard.
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lpr
CUPS tool for printing files.
See also: `lpstat` and `lpadmin`.
More information: home page
- Print a file to the default printer:
lpr path/to/file
- Print 2 copies:
lpr -# 2 path/to/file
- Print to a named printer:
lpr -P printer path/to/file
- Print either a single page (e.g. 2) or a range of pages (e.g. 2–16):
lpr -o page-ranges=2|2-16 path/to/file
- Print double-sided either in portrait (long) or in landscape (short):
lpr -o sides=two-sided-long-edge|two-sided-short-edge path/to/file
- Set page size (more options may be available depending on setup):
lpr -o media=a4|letter|legal path/to/file
- Print multiple pages per sheet:
lpr -o number-up=2|4|6|9|16 path/to/file


What best describes your diet?
Meat in every meal
Meat very rarely
Vegetarian
Vegan
In a week, how much do you spend on food from restaurants and home delivery?
Rs. 0
Rs. 100 - Rs 1000
Rs. 100 - Rs 1000
Rs. 1000 - Rs 5000
Of the food you buy how much is wasted and thrown away?
None
0%-10%
10%-30%
More than 30%
What kind of vehicle do you travel in most often as driver or passenger? (if any)
Car
Bike
Public transport, walking, cycling
How many hours a week do you spend on the train for personal use including commuting?
Under 2 hrs
2 to 5 hrs
5 to 15 hrs
More than 15 hrs
How many hours a week do you spend on the bus for personal use including commuting?
Under 2 hrs
2 to 6 hrs
6 to 10 hrs
More than 10 hrs
What kind of house do you live in?

Yes
No
Which of these home energy efficiency improvements are installed in your home?
Solar heaters
Low watt led bulbs
Solar panels
Which of these types of waste do you recycle and/or compost?
Food
Clothes
Paper
Plastic cups, bottles
Glass
Submit


Welcome to The Butcher, your source of delicious meats. Please feel free to view our samples, sign up to our mailing-list or purchase our special BeEF-hamper!
Delicious delicious hamper, straight to your door!
Sign up to our mailing list for delicious meats delivered straight to your inbox!
Thanks to http://www.flickr.com/photos/bulle_de/ and http://dineSarasota.com for the BeEF images


1) Toggle Effect Button
+
2) Infinite rotater
3) On hover Effect upside down
Hover me..
I am animated upto :)


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Sports Blog
e-commerce shop
Analytics Dashboard
Podcast
Blog
Python
Django
Flask
PostgreSQL
JavaScript
Angular
React JS
A website where you get the latest sports and entertainment news on artists and athletes I love in the Kenyan streets.
E-commerce platform that is a subsidiary of the Rogue Nation App. A store where you get the latest sport apparel and styles you like.
Conversations about technology in the Nairobi technology scene.
Conversations about technology, events, life in the start-up ecosystem in Nairobi and many more youthful stories.
Blogs about experiences at Tech For Granted podcast and issues around technology, sports and so much more
A website where a data analyst can upload csv files, and the data is categorized into tables and visualized as graphs.


Forky from 'Toy Story 4' stars in new series 'Forky Asks A Question' on Disney+
by Janet Davies, Marsha JordanForky, the breakout star of "Toy Story 4," is getting his own series on the new Disney+ streaming service that's launching next week.
Actor Tony Hale said the opportunity to bring the humble spork-turned-toy character to life on screen was both amazing and overwhelming.
Hale said it was an easy decision when he was asked to continue voicing the character for the "Forky Asks A Question" series on Disney+.
"I'm crazy about this character and I'm crazy about Pixar," Hale said.
"Forky was made simply, he looks at things very simply and the shorts are very simple questions," Hale said about the new series.
Hale said the episodes are "done beautifully and they're funny and it's a very simple format much like him."
Click here to learn more about Disney+.
The Walt Disney Company is the parent company of this station.


When classes have an inheritance relationship, the flow of logic between them travels via
constructor chaining.
Constructor chaining occurs when
the keyword super is explicitly used as the first line in the body of a subclass (as is
the case in this example – at line 23), with or without parameters, to reference the
syntactically matching constructor of a super class. Alternately, constructor chaining also occurs
when a subclass constructor explicitly (or implicitly, if no other class constructor exists) references the default constructor
of it's super class (this is what enables the constructor at line 16 to be chained to its
super class constructor at line 3).
This code in this example, also includes samples of
unboxing and casting. Specifically,
unboxing, from an Integer to a int type,
occurs simultaneously where the subclass at
line 21 is chained to its super class' constructor at line 16 via a reference to the super keyword
(at line 23) – used in the body of the constructor at line 22.
Implicit conversion is what enables the constructor
at line 16 to accept an int type (via the super constructor call, at line 23) as an argument.
In other words, an int can be implicitly converted
to a double without any mathematical loss of precision.
Following the execution of the constructor at line 3 (as constructor chaining reverses direction),
when the constructor at line 16 executes,
the cast that occurs (at line 17) converts the value of the argument supplied
to the constructor – to an int.
The final result of executing this code, is the output of the String, "double",
from the println statement at line 18.


Mother dumps day-old son in public pit - The Nation Newspaper
Precious Igbonwelundu
I already have five children that I am struggling to cater for. My husband abandoned me with them. So, I did not need the sixth child and that was why I dumped him inside the soak-away as soon as I had him.
The above were the words of a 30-year-old woman Risikat Olabintan arrested by the police in Ogun State for throwing her newborn baby into a pit.
It was gathered that Olabintan, a resident of Ibukun Oluwapo Community Ishaga Surulere in Owode-Yewa threw her son into the pit shortly after birthing him on Sunday morning.
Fortunately, children who were playing later that day heard a baby’s cries from the pit located at the back of the compound and raised alarm.
It was gathered that community leaders who assembled by the pit, alerted the police at Owode-Yewa Division who moved to the scene and evacuated the newborn.
“At the time the baby was rescued, no one knew it was the woman’s. She secretly gave birth around 5am and threw the child into the pit. It was later that detectives discovered she was the mother of the baby and she was promptly arrested,” said Abimbola Oyeyemi, spokesman for Ogun Police Command.
READ ALSO: Mother dumps baby at refuse dump in Lagos
Oyeyemi said the woman claimed she had no means of feeding all six children.
Asked if the woman had been examined for possible postpartum depression, Oyeyemi said she was taken to the hospital together with the baby for medicare.
“The baby and mother are presently receiving treatment at the hospital. The baby is responding to treatment. I do not know if her action had anything to do with postpartum depression but the woman said the newborn was additional burden to her.
“The Commissioner of Police, Imohimi Edgal has directed that the case be transferred to Anti-Human Trafficking and Child Labour Unit of the State Criminal Investigation and Intelligence Department (SCIID) for proper investigation.


Ocean [an84], ocean, in The Outer Sea ------------------------------------------------------------------------ Routes leaving Ocean: North, to Ocean [am84], 6 days East, to Ocean [an85], 5 days South, to Ocean [ao84], 6 days West, to Ocean [an83], 5 days


This piece is experimental ethnomusicology, involving everything from research to composition to performance in the performance of one work. Passers-by in three locations mentioned in the version title were asked to draw on index cards according to procedures that require no musical training, to make choices about the elements of harmony, rhythm, and dynamics. Within the parameters chosen by my audience, I improvise a piano piece to reflect the musical instincts of the community I currently find myself in.


The Felinae are a subfamily of the family Felidae, which includes the genera and species listed below. Most are small to medium-sized cats, although the group does include some larger animals, such as the cougar and cheetah. The earliest records of the Felinae are ascribed to Felis attica from the late Miocene, 9 million years ago (Mya), of western Eurasia.
Jaguarundi; public domain image from U.S. Fish & Wildlife Service [source]


Map: The map is colored by the median response time (the time for which half take more time and half take less) for the category selected in the drop-down box. When no area is selected, the other displays show citywide data. Select community districts by hovering over them on the map; lock the other displays by clicking a community district. The borough buttons on the left act similarly.
Histogram: The histogram area updates automatically with the selection. It indicates the distribution of response times for given incident types, dates and regions. The average and medians are shown, along with a slidable percentile indicator, which is adjusted with the percentile slider just above the histogram. Hovering over the histogram shows the percentage of calls less than and more than the selected time interval.
Trends: The trends area shows the median values over time. Hovering over the trend shows the median value at a given time; clicking and dragging opens a window that adjusts the histogram values indicating the call data in just the selected window. The window can be dragged and resized.
Note on medians and percentiles: The percentiles are estimated from the data in the histograms. This can lead to erratic results when there are small numbers of calls, so use care when interpreting these results.
How is this loading millions of calls worth of data? It isn't. This data has been prefiltered and summarized by creating histograms for each call type, community district, and month, greatly reducing the amount of data to transmit to the browser. You can see how this filtering is done, and how the resulting datasets are written, in this Observable notebook
Doesn't the City already provide response time information and trends? Yes, you can see the official reports (under "Local Law 119") here. However, this report focuses on a single number per borough per month: the average response time. While useful (and what is mandated by law!) showing the distribution of the data gives a fuller picture of how responses can vary, even within a smaller region or timeframe.
Who is responsible for this? Chris Prince (that's me!). I wrote the scripts to summarize the datasets and display the graphs. However, this could never have been made without the extraordinary work of NYC public servants: FDNY, who collected the data; the NYC Open Data team, who published it; and the NYC DoITT GIS team, who create the map tiles and community district shapes. That said, any errors in analysis or rendering are mine, and corrections should be sent to me (please do!). You can Tweet to @superdupercrisp or open an Issue on github.
Any other credit due? I was inspired by this exceptional visualization done by the LA Times Data Desk that looks at LAFD response times. Seriously, check it out!


Before Change
self.assertEqual(len(self.build_file_parser._target_proxy_by_address), 3)
def test_sibling_build_files(self):
with self.workspace("./BUILD", "./BUILD.foo", "./BUILD.bar") as root_dir:
with open(os.path.join(root_dir, "./BUILD"), "w") as build:
build.write(dedent("""
fake(name="base",
dependencies=[
":foo",
])
"""))
with open(os.path.join(root_dir, "./BUILD.foo"), "w") as build:
build.write(dedent("""
fake(name="foo",
dependencies=[
":bat",
])
"""))
with open(os.path.join(root_dir, "./BUILD.bar"), "w") as build:
build.write(dedent("""
fake(name="bat")
"""))
bar_build_file = BuildFile(root_dir, "BUILD.bar")
base_build_file = BuildFile(root_dir, "BUILD")
foo_build_file = BuildFile(root_dir, "BUILD.foo")
self.build_file_parser.parse_build_file_family(bar_build_file)
addresses = self.build_file_parser._target_proxy_by_address.keys()
self.assertEqual(set([bar_build_file, base_build_file, foo_build_file]),
set([address.build_file for address in addresses]))
self.assertEqual(set([":base", ":foo", ":bat"]),
set([address.spec for address in addresses]))
def test_build_file_duplicates(self):
// This workspace has two targets in the same file with the same name.
self.add_to_build_file("BUILD", "fake(name="foo")\n")
self.add_to_build_file("BUILD", "fake(name="foo")\n")
After Change
self.assertEqual(addressable.name, "foozle")
self.assertEqual(addressable.target_type, ErrorTarget)
def test_sibling_build_files(self):
self.add_to_build_file("BUILD", dedent(
"""
fake(name="base",
dependencies=[
":foo",
])
"""))
self.add_to_build_file("BUILD.foo", dedent(
"""
fake(name="foo",
dependencies=[
":bat",
])
"""))
self.add_to_build_file("./BUILD.bar", dedent(
"""
fake(name="bat")
"""))
bar_build_file = BuildFile(self.build_root, "BUILD.bar")
base_build_file = BuildFile(self.build_root, "BUILD")
foo_build_file = BuildFile(self.build_root, "BUILD.foo")
address_map = self.build_file_parser.address_map_from_spec_path(bar_build_file.spec_path)
addresses = address_map.keys()


The Hubble Space Telescope (HST) is a space telescope that was launched into low Earth orbit in 1990, and remains in operation. With a 2.4-meter (7.9 ft) mirror, Hubble's four main instruments observe in the near ultraviolet, visible, and near infrared spectra. The telescope is named after the astronomer Edwin Hubble.
Hubble's orbit outside the distortion of Earth's atmosphere allows it to take extremely high-resolution images with negligible background light. Hubble has recorded some of the most detailed visible-light images ever, allowing a deep view into space and time. Many Hubble observations have led to breakthroughs in astrophysics, such as accurately determining the rate of expansion of the universe.
Many Hubble observations have led to breakthroughs in astrophysics, such as accurately determining the rate of expansion of the universe.
Although not the first space telescope, Hubble is one of the largest and most versatile, and is well known as both a vital research tool and a public relations boon for astronomy. The HST was built by the United States space agency NASA, with contributions from the European Space Agency, and is operated by the Space Telescope Science Institute. The HST is one of NASA's Great Observatories, along with the Compton Gamma Ray Observatory, the Chandra X-ray Observatory, and the Spitzer Space Telescope.
Space telescopes were proposed as early as 1923. Hubble was funded in the 1970s, with a proposed launch in 1983, but the project was beset by technical delays, budget problems, and the Challenger disaster (1986). When finally launched in 1990, Hubble's main mirror was found to have been ground incorrectly, compromising the telescope's capabilities. The optics were corrected to their intended quality by a servicing mission in 1993.
Hubble is the only telescope designed to be serviced in space by astronauts. After launch by Space Shuttle Discovery in 1990, four subsequent Space Shuttle missions repaired, upgraded, and replaced systems on the telescope. A fifth mission was canceled on safety grounds following the Columbia disaster (2003). However, after spirited public discussion, NASA administrator Mike Griffin approved one final servicing mission, completed in 2009. The telescope is still operating as of 2016, and may last until 2030–2040. Its scientific successor, the James Webb Space Telescope (JWST), is scheduled for launch in 2018.
Source:
Hubble Telescope Wikipedia