Welcome to Find UK government data
Find data published by central government, local authorities, and public bodies to help you build products and services.
See dataset collections
Registers
Maintained by HM government, use the Country, Territory, or the Local authority England register (among others) to help you build your services
Polling Stations
List of polling station datasets
National authorities
From public transport, healthcare, or schools, to household population data; we've got the details
Local authorities
Discover data about local councils, spending figures, land planning, or reserves, plus much more
Trending datasets
Immigrations statistics
This dataset replaces Volume 1 of the Immigration Statistics: before entry.
HM Land Registry Price Paid Data
Our price paid data tracks the residential property sales in England and Wales that are lodged with us for registration.
International comparisons of Productivity
An international comparison of productivity across the G7 nations, in terms of the level of and growth in GDP per hour and GDP per worker.
Upcoming datasets
Primary School Catchment areas
Catchment boundaries for Primary Schools in Gateshead Council.
Due: June 2017 Alert me
Statistics on Obesity, Physical Activity and Diet, England
This statistical report presents a range of information on obesity, physical activity and diet, drawn together from a variety of sources.
Due: July 2017 Alert me
Fixed Penalty Notice - Dogs
Due: August 2017 Alert me


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 So much so that you struggle trying to remember details about your previous visits to these restaurants? How long you waited? What the food was like? Or if the waitstaff was friendly, helpful or rude? Well look no further! We are here to help you keep track of the details!
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"Confused" - Training Sets
Use the links below to get training sets for the Machine Learning for Kids project "
Confused
".
Instructions for which you should use and how to use them are in the
project worksheet
.
You'll need to use this one first:
First
training Set
After that, you'll need this one:
Final
Training Set


Note that major changes were made to the subelements of
in version 1.04.location
For more information refer to:
The @ref attribute was introduced to provide a cross reference that a publisher can use to link back to their own internal system.
The @percentage attribute was deemed unworkable and deprecated in 1.04


{% extends "home-base.html" %} {% block title %} All Cars {% endblock %} {% block head%} {{super()}} {% endblock %} {% block page_content %}
| Car ID | Model | Car Type | Year | Description | Manufacturer | Edit | Delete | Ajax Delete |
|---|---|---|---|---|---|---|---|---|
| {{car.id}} | {{car.model}} | {{car.cartype}} | {{car.year}} | {{car.description}} | {{car.manufacturer.name}} | Edit | Delete |


NoPlate is a small experiment for designers who want to make a quick static html site, with simple template reuse, without having to run a full blown server side scripting solution.
NoPlate is a small experiment for designers who want to make a quick static html site, with simple template reuse, without having to run a full blown server side scripting solution.
Download from Github: https://github.com/cablehead/noplate


(
SELECT u.*
FROM friendships f JOIN users u
ON f.friend_id = u.id
WHERE f.user_id = 123
) UNION (
SELECT u.*
FROM friendships f JOIN users u
ON f.user_id = u.id
WHERE f.friend_id = 123
)
class User < ActiveRecord::Base
has_many :friendships
has_many :inverse_friendships, class_name: "Friendship",
foreign_key: :friend_id
def friends
friend_ids = friendships.pluck(:friend_id) +
inverse_friendships.pluck(:user_id)
User.where(id: friend_ids)
end
end
The (Non-Perfect) Mathematics of Trust
Cypher
MATCH (you {name:"You"})
MATCH (expert)-[:WORKED_WITH]->(db:Database {name:"Neo4j"})
MATCH path = shortestPath( (you)-[:FRIEND*..5]-(expert) )
RETURN db, expert, path
has_many :friendships
has_many :inverse_friendships, class_name: "Friendship",
foreign_key: :friend_id
# has_many :friendships
# has_many :inverse_friendships, class_name: "Friendship",
# foreign_key: :friend_id
has_many :both, :friends, type: :friend, model_class: 'User'
def friends
friend_ids = friendships.pluck(:friend_id) +
inverse_friendships.pluck(:user_id)
User.where(id: friend_ids)
end
# def friends
# friend_ids = friendships.pluck(:friend_id) +
# inverse_friendships.pluck(:user_id)
#
# User.where(id: friend_ids)
# end
@user.friends


Landing Page
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CARL FONTICELLA
02.28.17
This story has been updated to reflect the most recent information.
Had it been up to Quiera Lampkins, one of the best basketball players in Bobcat history would’ve never touched a basketball.
Lampkins is 17 assists away from being ranked in the top five all-time of four categories in the Ohio record books. She ranks second in points, second in steals, sixth in assists and fifth in rebounds.
On March 30, she will participate in the InsiderExposure.com Pro Combine. She has the opportunity to become the first WNBA player Ohio has ever produced.
But when she put on her first pair of basketball shorts at 10 years old, she was ready to end her career before it began. She was self-conscious about how she would look while playing.
“I didn’t wanna sweat,” Lampkins said. “I felt like my knees were ugly.”
Lauren Bacho
Ohio guard Quiera Lampkins (#5) dribbles the ball past Buffalo center Christa Baccas (#44) during the Ohio vs. Buffalo game on Mar. 13, 2015. Ohio beat Buffalo 63-55 during the MAC Tournament. (FILE)
Because of her height, her shorts revealed much more of her legs than her teammates'. She was so embarrassed by her knees showing that she wouldn’t take her pants off before one of her first games to reveal the shorts underneath.
When Andre, her father, threatened to take Quiera home if she didn’t get into uniform, she became more disinterested in playing.
After realizing that wasn’t a realistic option, she begrudgingly took the court. Twelve years later, as the end of her career at Ohio draws near, her name is rapidly ascending through the Ohio record books.
She has played her way onto the short list of greatest players in program history, but what changed?
As her career progressed, the knees Lampkins was so ashamed of became a part of her greatest asset.
When coach Bob Boldon was first hired to coach Lampkins in 2013, one aspect of her game stood out immediately.
Boldon, who had been coaching for 15 years, saw a level of explosiveness he hadn’t seen before, and hasn’t since.
“Her ability to go from stop to start is the best I’ve ever seen,” Boldon said. “That’s the first thing I noticed.”
Taylor Agler was introduced to Lampkins’ athleticism at an early age. Agler began playing against Lampkins in Amateur Athletic Union tournaments when each player was in middle school. Lampkins and Agler grew up 15 minutes apart in Gahanna and Westerville, respectively.
When the teammates would play against each other, Agler knew exactly how to point Lampkins out to her teammates.
“I’d be like, ‘she’s the really fast one,”’ Agler said. “'Don’t let her get by you.’ ”
Occasionally, Agler was tasked with guarding Lampkins. She said she would get frustrated whenever Lampkins would zip by her for a layup.
They were both future Division-I prospects, but Lampkins had an extra gear that Agler couldn’t match. Agler was experiencing the same problem that many in the Mid-American Conference players face today: She just wasn’t quick enough.
Lampkins used her athleticism as her main tool in dominating her early competition. Nobody could stay in front of her or get past her. Agler was thankful when they become AAU teammates in high school.
By the end of Lampkins' high school career, her father’s urging had paid off. She was a two-time All-Ohio Capital Conference First Team member, Gahanna Lincoln High School was 74-26 during her career and she was recruited to Ohio University on scholarship.
But her passion for basketball wasn’t where it is today.
She was good, so she liked that part. But the obsessive work ethic that would propel her to her current heights wasn’t there.
Not yet, at least.
On March 2, 2014, Lampkins played perhaps the most important game of her career. It was also one of her worst.
It was her freshman season with the Bobcats. She entered the matchup with Bowling Green averaging just under 11 points per game.
Her quickness had transferred to the next level. She found that she could still drive to the basket rather easily, even against Division-I talent.
But Bowling Green defended her in a way she had never seen before. They didn’t defend her at all.
She had only attempted 15 3-pointers through 26 games. Knowing how well she attacked the rim, Bowling Green decided to force her to shoot deeper shots.
Lampkins said her defender stood at the MAC logo, about eight feet away from the 3-point line in Anderson Arena, and dared her to shoot.
The result was a five-point performance in 29 minutes. Lampkins made 2-of-8 shots from the field, and went 0-of-3 from 3-point range. She recalls multiple 3-point attempts being airballs.
“She just looked at us like, ‘What am I supposed to do?’ ” assistant coach Marwan Miller said.
Lampkins had no confidence in her jump shot. After the game, she promised the coaches she would never experience a similar situation again.
Oliver Hamlin
Ohio's Quiera Lampkins goes in for a layup during a game against Northern Illinois University on Feb. 6, 2016, in The Convo. Lampkins will be graduating this year and will leave behind a big hole in the Bobcats' roster.
The following summer, she made sure she would keep her promise.
That summer was filled with long sessions in empty gyms with Kiyanna Black and Miller. She worked on her jump shot, but it was the discipline she developed that would help her the most.
Lampkins said her and Black were on three-a-day practice schedule. In the morning, they would go for a run. In the afternoon, they would work on skills.
They would participate in drills such as form shooting, where players shoot with one hand from different places on the court to improve their shooting technique. They would also work with the speed ladder, where they would chop their feet through small holes to improve their quickness.
In the evening, Black and Lampkins would find a gym where they could play pick-up basketball. They repeated the process everyday and pushed each other to improve.
Dating back to their AAU days, Lampkins had always shrugged her shoulders when Black outplayed her in a drill or scored on her in practice. After that summer, she stopped expecting Black to be better than her and starting getting upset about it.
Her unrelenting competitiveness, now so famous around The Convo, was born.
Matt Starkey
Quiera Lampkins (5) drives through contact against Kent State in The Convo on Jan. 14.
The following season, Lampkins shot 32.8 percent from 3-point range, which is still her career-high. Her scoring average has improved in each of her four seasons, and she has developed guard level ball handling skills.
As a result of one of her most embarrassing moments against Bowling Green, she discovered the work ethic that would propel her to become one of the best players in program history.
“I’m glad that it happened,” Lampkins said. “It allowed me to continue to work on my game and grow as a player.”
Lampkins may not have fallen in love with basketball from the beginning, but now she plans on dedicating her life to it.
Her uncertainty about her future isn’t about if she’ll play professionally, it’s about how many professional leagues she’ll play in. The WNBA offseason occurs during the same window as the in-season play for some overseas professional women’s leagues.
“Playing in both wouldn’t be the worst,” Lampkins said. “But playing in one is for sure gonna happen.”
Her coaches and teammates have just as much confidence in her. Agler, whose father coaches the Los Angeles Sparks, said any WNBA team could use Lampkins’ explosiveness.
Boldon believes her versatility is her biggest professional asset. He said she is smart enough to play point guard and run an offense, a good enough defender to be a defensive specialist and cited her career at Ohio as evidence for her scoring ability at the next level.
Miller, who has known Lampkins since she was in seventh grade, bases his faith in her around the fierce competitive nature she has developed at Ohio.
“She ain’t afraid of nobody,” Miller said.
Before she gets a chance to show her talents to pro scouts at the combine, Lampkins has work to do.
She has made progress with her jump shot, but she knows it can still improve. Her driving ability is unmatched by any player in the MAC, but she still believes she can be a more consistent finisher.
After the season is over, Lampkins will still be in the gym every day. More shooting drills, more running, more speed ladder work.
But for someone who never wanted to play in the first place, she seems to be ok with working on basketball for a few more weeks.
“I’m definitely glad (my dad) made me play,” Lampkins said. I’ve put a lot of work in, and to see myself grow as a player, I have respect for myself.”
Carl Fonticella
When Ohio senior guard Quiera Lampkins graduates, she'll be considered one of the best in Ohio's history, but she never would have become a basketball player unless her dad, Andre Lampkins, forced her to begin to play at the age of 10.
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Xduce is a JavaScript library that implements transducers in the spirit of Clojure transducers.
A transducer is a generalization of all of the map
, filter
, and similar functions that functional programming promotes. If there is a problem with these functions, it's that they can only be instructed on how to transform values in a custom way, and that's only one part of a collection-related function. They cannot be instructed on how to break a collection into its component parts for transformation (iteration), and they cannot be instructed on how to take the transformed values and put them back together into a new collection (reduction). So while you can use Array.prototype.map
to turn any type of array element into any other type of array element, they have to be array elements. You can't use this function on anything but an array, and you can't have it return anything other than an array.
Xduce seeks to improve this by providing an engine that allows customization of all three parts of the process: iteration, transformation, and reduction. This means the same map
function can be used on arrays, strings, objects, iterators, streams, channels, immutable lists, or anything else.
In node.js, or for use with Browserify, Webpack, etc., you can install the npm package.
npm install xduce
or, if you use yarn
yarn add xduce
For the browser, you can visit the releases page to download the latest archive. Inside there is a dist
folder that contains xduce.js
and its minified version, xduce.min.js
. These files are transpiled to ES5 code that's suitable for any modern browser, even Internet Explorer 11.
Xduce provides all of its functionality on a single exported object.
In node.js, Browserify, Webpack, etc., you can access this through require
or ES2015 import
.
import xduce from 'xduce';
const { map } = xduce.transducers;
const array = map([1, 2, 3, 4, 5], x => x + 1);
// array = [2, 3, 4, 5, 6]
const string = map('hello', x => x + x);
// string = 'hheelllloo'
If you're using require.js, you can use requirejs
.
requirejs(['xduce'], xduce => {
const { map } = xduce.transducers;
const array = map([1, 2, 3, 4, 5], x => x + 1);
// array = [2, 3, 4, 5, 6]
const string = map('hello', x => x + x);
// string = 'hheelllloo'
});
Finally, if you're using directly in an HTML page, you can use the global xduce
object.
<script src="js/xduce.js"></script>
<script>
const { map } = xduce.transducers;
const array = map([1, 2, 3, 4, 5], x => x + 1);
// array = [2, 3, 4, 5, 6]
const string = map('hello', x => x + x);
// string = 'hheelllloo'
</script>
Every public API function is documented the API documentation. Most of the functions have examples of their usage.
Transducers were introduced to me after they were brought into Clojure, particularly in conjunction with CSP, which I have also written a library for. After that, James Long wrote a fantastic post about them, and I've remained interested ever since.


All of the functionality for this demo is in core/subject.service.ts and the subjects folder. Click Start to begin.
Status: {{ status }}
Subject
{{ data.length }}
BehaviorSubject
{{ data.length }}
ReplaySubject
{{ data.length }}
AsyncSubject
{{ data.length }}
Subject
Only subscribers receive data
BehaviorSubject
Note how this picks up the last value emitted event though it subscribed after the value was sent out. That's because
BehaviorSubject allows an initial value to be sent to an observer as they subscribe.
ReplaySubject
Note how this stays in sync with everything above even though it subscribes 10 seconds after the subject. That's because
it's replaying everything up to that point from a cache it maintains.
AsyncSubject
This only plays the last item before it completes - nothing before that. It "completes" in the data service once the
customers array length is greater than 5.


The plugin loads url from 'plugin.callback_on_offline.url' property, adds 'to' & 'from' parameters and executes GET request. The request is async. Sample link: http://localhost:8080/user/offline/callback/url?from=1%40fotsum.com&to=2%40fotsum.com
If something don't work, set "plugin.callback_on_offline.debug" property to true and check the debug log.


--- title: My Timesaver utilities keywords: fastai sidebar: home_sidebar summary: "A set of simple utilities I found useful to import " description: "A set of simple utilities I found useful to import " nb_path: "nbs/index.ipynb" ---
This is the current list of functions
@profile_call(fname=None)
- a function or method decorator to record execution timesprint_prof_data(fname=None)
- prints out profile data for function fname
or all functions if fname
is Noneclear_prof_data(fname=None)
- clears out profile data for function fname
or all functions if fname
is Noneget_prof_data(fname)
- get exec times for fname
start_record(fname)
- start recording time for fname
(alternative to decorator if function cannot decorated)end_record(fname)
- stop recording and add elapsed time for fname
save_prof_data(file_name)
- save profile data to file_name
load_prof_data(file_name)
- load profile data from file_name
MyProfileCallback
- a fastai Callback
that provides a hierarchical view of model training time executionLearner.to_my_profile
- method added to a fastai Learner
if my_timesaver_utils.profiling_callback
is imported. Call it to add a MyProfileCallback
to the Learner
instance.Learner.my_profile
- MyProfileCallback
instance attached to the fastai Learner
object if to_my_profile
method is called.print_stats
- MyProfileCallback
method to show a hierarchical view of the training lifecycle execution stats (execution counts, avg time, max time)get_stats
- MyProfileCallback
method to get the execution stats as a listclear_stats
- MyProfileCallback
method to reset the execution statsreset
- MyProfileCallback
attribute to set so each call to Learner.fit
resets the execution counts before starting the training.class EnhancedImageClassifierCleaner
- a drop-in replacement for the fastai.vision.widgets.ImageClassifierCleaner
which adds Apply
and Reset
buttons to allow the user to apply or revert changes to actual dataset.(In the fastai, the application of the changes is done in a separate step, making it more cumbersome to apply across a lot of categories -- doubling it since it segregates the validation and training datasets as well)pip install git+https://github.com/butchland/my_timesaver_utils.git
Decorate method or function you want to profile
import time
@profile_call
def test_func(t=2.0):
time.sleep(1)
Call your method or function
for i in range(10):
test_func(i)
You can also add an optional funcname if you want to replace the name its stored in the profile data
@profile_call('wachacha')
def test_func2():
time.sleep(1.0)
for i in range(3):
test_func2()
Print your profile data
print_prof_data('test_func')
Print your profile data for the test_func2 (aka wachacha)
print_prof_data('wachacha')
Get your profile data (e.g. good for graphing)
times = get_prof_data('test_func'); times
If you can't add a decorator, you can start and end the recording manually and it will be added to the profile data
for i in range(10):
start_record('my_sleep')
time.sleep(1.)
end_record('my_sleep')
As an alternative, the decorator can be invoked this way
sleep = profile_call(time.sleep)
for i in range(5):
sleep(i)
You can also pass in an optional function name to replace the func name used in the profile data
sleep2 = profile_call(time.sleep, 'maluman')
for i in range(3): sleep2(1)
If you call print_prof_data
without any arguments, it will print all the timings for all the functions
print_prof_data()
You can also get the profile data for the manually recorded calls as well.
times2 = get_prof_data('sleep');times2
You can also save the profile data to a file
save_file = 'my_profile_data.pickle'
save_prof_data(save_file)
Calling clear_prof_data
for a function will clear out the data for that function
clear_prof_data('sleep')
print_prof_data()
Calling the clear_prof_data
with no arguments will clear out all the previously recorded timings.
clear_prof_data()
print_prof_data()
You can reload the profile data from a previously saved file
load_prof_data(save_file)
print_prof_data()
from fastai.vision.all import *
Import the whole my_timesaver_utils
package
from my_timesaver_utils.all import *
or as an alternative, just import the profiling_callback
package
from my_timesaver_utils.profiling_callback import *
Setup your path, data, datablock, dataloaders and learner as usual.
path = untar_data(URLs.MNIST_TINY)
Path.BASE_PATH = path
datablock = DataBlock(
blocks=(ImageBlock,CategoryBlock),
get_items=get_image_files,
get_y=parent_label,
splitter=GrandparentSplitter(),
item_tfms=Resize(28),
batch_tfms=[]
)
dls = datablock.dataloaders(path)
learner = cnn_learner(dls,resnet18,metrics=accuracy)
Importing the profiling callback adds a method to_my_profile
to the learner
object
learner.to_my_profile()
Calling the to_my_profile
method on the learner
object adds a callback called MyProfileCallback
which can be accessed through the learner
attribute my_profile
.
learner.summary()
learner.my_profile
Call the print_stats
method on the my_profile
attribute of the Learner
object displays a hierarchical list of the training lifecycle events -- in this case with no data yet as the fit
method has not been called.
learner.my_profile.print_stats()
learner.fit(1)
The print_stats
method now prints the execution counts, max time and avg time (in secs) for each part of the training lifecycle.
learner.my_profile.print_stats()
The stats can also be collected as a list of tuples where each tuple consists of the lifecycle event name, the level, and the elapsed times.
fit_stats = learner.my_profile.get_stats();fit_stats
The print_stats
can also print just the stats for one lifecycle event
learner.my_profile.print_stats('train_batch')
The get_stats
can also just collect the stats for one lifecycle event
train_batch_stats = learner.my_profile.get_stats('train_batch'); train_batch_stats
Call the clear_stats
to clear the stats. You can also pass a lifecycle event to clear a single event
learner.my_profile.clear_stats()
learner.my_profile.print_stats()
learner.my_profile.print_stats('train')
learner.fine_tune(1)
learner.my_profile.print_stats()
Setting the reset
attribute to true on the my_profile
attribute will cause the stats to be reset each time the fit
method of the Learner
is called. So only the accumulated stats for the last call to fit
(e.g fine_tune
calls fit
twice, but setting the reset
attribute to true will show only the stats for the second fit
call.
The default value of reset
is False
.
learner.my_profile.reset = True
learner.fine_tune(1)
learner.my_profile.print_stats()
learner.my_profile.reset
Import the utils -- You can either import all the utilities
from my_timesaver_utils.all import *
Or you can import only the enhanced image classifier cleaner package
from my_timesaver_utils.enhanced_imageclassifiercleaner import *
In order to use the EnhancedImageClassifierCleaner
(just like the fastai ImageClassifierCleaner
), we will need to have an existing fastai Learner
object.
Just create an instance of EnhancedImageClassifierCleaner
object.
cleaner = EnhancedImageClassifierCleaner(learner)
cleaner # on a separate line, this will display the widget
If you updated your dataset, you can refresh your dataloader by running
newdls = datablock.dataloaders(path)
learner.dls = newdls # assume learner was previously created or loaded
cleaner = EnhancedImageClassifierCleaner(learner)
This will then display the updated images from your dataset.
cleaner = EnhancedImageClassifierCleaner(learner)
cleaner
The default folder structure for the Enhanced Image Classifier Cleaner
assumes that you are using RandomSplitter
or GrandparentSplitter
as
your splitter
and parent_label
as the get_y
parameter in your datablock.
If you have a custom valid/train splitter and your image files are not segregated by categories within them, you will need to create a custom file mover in order to update your image labels (aka categories).
The default implementation of the file_mover
is shown below:
export
import shutil
def parent_move_file(fn, newcat):
new_path = fn.parent.parent/newcat
if new_path.is_dir():
shutil.move(str(fn), new_path)
new_file = new_path/fn.name
return new_file
return fn
The default implementation will work with either a RandomSplitter
or GrandparentSplitter
and if parent_label
is used to get the labels.
Otherwise, you will need to modify the file_mover argument with a custom one and pass it as follows
cleaner = EnhancedImageClassifierCleaner(learner, file_mover=custom_file_mover)


|
Contiki 2.5
|
Modules | |
| Configuration macros for endpoints | |
List of the standard macro used to configure pipes and endpoints. | |
| USB Pads Regulator drivers | |
Turns ON/OFF USB pads regulator. | |
| USB common management drivers | |
These macros manage the USB controller. | |
| USB device controller drivers | |
These macros manage the USB Device controller. | |
| USB endpoint drivers | |
These macros manage the common features of the endpoints. | |
| USB host controller drivers | |
These macros manage the USB Host controller. | |
| USB pipe drivers | |
These macros manage the common features of the pipes. | |
Defines | |
| #define | wSWAP(x) |
| wSWAP This macro swaps the U8 order in words. | |
| #define | Usb_write_word_enum_struc(x) (wSWAP(x)) |
| Usb_write_word_enum_struc This macro help to fill the U16 fill in USB enumeration struct. | |
Functions | |
| U8 | usb_config_ep (U8 config0, U8 config1) |
| usb_configure_endpoint. | |
| U8 | usb_select_enpoint_interrupt (void) |
| usb_select_endpoint_interrupt. | |
| U8 | usb_send_packet (U8 ep_num, U8 *tbuf, U8 data_length) |
| usb_send_packet. | |
| U8 | usb_read_packet (U8 ep_num, U8 *rbuf, U8 data_length) |
| usb_read_packet. | |
| void | usb_halt_endpoint (U8 ep_num) |
| usb_halt_endpoint. | |
| U8 | usb_init_device (void) |
| usb_init_device. | |
| U8 | host_config_pipe (U8 config0, U8 config1) |
| usb_configure_pipe. | |
| U8 | host_determine_pipe_size (U16 size) |
| host_determine_pipe_size. | |
| void | host_disable_all_pipe (void) |
| host_disable_all_pipe. | |
| U8 | usb_get_nb_pipe_interrupt (void) |
| Returns the pipe number that generates a USB communication interrupt. | |
| void | usb_start_device (void) |
| This function initializes the USB device controller. | |
Variables | |
| volatile uint16_t | g_usb_event |
| Public : U16 g_usb_event usb_connected is used to store USB events detected upon USB general interrupt subroutine Its value is managed by the following macros (See usb_task.h file) Usb_send_event(x) Usb_ack_event(x) Usb_clear_all_event() Is_usb_event(x) Is_not_usb_event(x) | |
USB Events | |
| #define | Usb_send_event(x) (g_usb_event |= (1<<x)) |
| Send event. | |
| #define | Usb_ack_event(x) (g_usb_event &= ~(1<<x)) |
| Ack event processed. | |
| #define | Usb_clear_all_event() (g_usb_event = 0) |
| Clear all events. | |
| #define | Is_usb_event(x) ((g_usb_event & (1<<x)) ? TRUE : FALSE) |
| Check for USB event. | |
| #define | Is_not_usb_event(x) ((g_usb_event & (1<<x)) ? FALSE: TRUE) |
| Check for USB event NOT occuring. | |
| #define | Is_usb_device() (g_usb_mode==USB_MODE_DEVICE ? TRUE : FALSE) |
| Check if USB is device. | |
| #define | EVT_USB_POWERED 1 |
| USB Event: USB plugged. | |
| #define | EVT_USB_UNPOWERED 2 |
| USB Event: USB un-plugged. | |
| #define | EVT_USB_DEVICE_FUNCTION 3 |
| USB Event: USB in device. | |
| #define | EVT_USB_HOST_FUNCTION 4 |
| USB Event: USB in host. | |
| #define | EVT_USB_SUSPEND 5 |
| USB Event: USB suspend. | |
| #define | EVT_USB_WAKE_UP 6 |
| USB Event: USB wake up. | |
| #define | EVT_USB_RESUME 7 |
| USB Event: USB resume. | |
| #define | EVT_USB_RESET 8 |
| USB Event: USB reset. | |
| #define | EVT_USB_SETUP_RX 9 |
| USB Event: USB setup received. | |
Standard requests defines | |
| #define | GET_STATUS 0x00 |
| #define | GET_DEVICE 0x01 |
| #define | CLEAR_FEATURE 0x01 |
| see FEATURES below | |
| #define | GET_STRING 0x03 |
| #define | SET_FEATURE 0x03 |
| see FEATURES below | |
| #define | SET_ADDRESS 0x05 |
| #define | GET_DESCRIPTOR 0x06 |
| #define | SET_DESCRIPTOR 0x07 |
| #define | GET_CONFIGURATION 0x08 |
| #define | SET_CONFIGURATION 0x09 |
| #define | GET_INTERFACE 0x0A |
| #define | SET_INTERFACE 0x0B |
| #define | SYNCH_FRAME 0x0C |
| #define | GET_DEVICE_DESCRIPTOR 1 |
| #define | GET_CONFIGURATION_DESCRIPTOR 4 |
| #define | REQUEST_DEVICE_STATUS 0x80 |
| #define | REQUEST_INTERFACE_STATUS 0x81 |
| #define | REQUEST_ENDPOINT_STATUS 0x82 |
| #define | ZERO_TYPE 0x00 |
| #define | INTERFACE_TYPE 0x01 |
| #define | ENDPOINT_TYPE 0x02 |
| #define | DEVICE_DESCRIPTOR 0x01 |
| #define | CONFIGURATION_DESCRIPTOR 0x02 |
| #define | STRING_DESCRIPTOR 0x03 |
| #define | INTERFACE_DESCRIPTOR 0x04 |
| #define | ENDPOINT_DESCRIPTOR 0x05 |
| #define | DEVICE_QUALIFIER_DESCRIPTOR 0x06 |
| #define | OTHER_SPEED_CONFIGURATION_DESCRIPTOR 0x07 |
| #define | FEATURE_DEVICE_REMOTE_WAKEUP 0x01 |
| #define | FEATURE_ENDPOINT_HALT 0x00 |
| #define | TEST_J 0x01 |
| #define | TEST_K 0x02 |
| #define | TEST_SEO_NAK 0x03 |
| #define | TEST_PACKET 0x04 |
| #define | TEST_FORCE_ENABLE 0x05 |
| #define | BUS_POWERED 0 |
| #define | SELF_POWERED 1 |
| #define Usb_write_word_enum_struc | ( | x | ) | (wSWAP(x)) |
| #define wSWAP | ( | x | ) |
| U8 host_config_pipe | ( | U8 | config0, |
| U8 | config1 | ||
| ) |
usb_configure_pipe.
--------------------------------------------------------- ------------------ HOST --------------------------------- ---------------------------------------------------------
This function configures a pipe with the selected type.
| config0 | |
| config1 |
Definition at line 350 of file usb_drv.c.
References Host_allocate_memory, Host_enable_pipe, and Is_pipe_configured.
| U8 host_determine_pipe_size | ( | U16 | size | ) |
host_determine_pipe_size.
This function returns the size configuration register value according to the endpint size detected inthe device enumeration process.
| SIZE_8 | pipe size register value. |
| SIZE_16 | pipe size register value. |
| SIZE_32 | pipe size register value. |
| SIZE_64 | pipe size register value. |
| SIZE_128 | pipe size register value. |
| SIZE_256 | pipe size register value. |
| SIZE_512 | pipe size register value. |
| SIZE_1024 | pipe size register value. |
| void host_disable_all_pipe | ( | void | ) |
host_disable_all_pipe.
This function disable all pipes for the host controller Usefull to execute upon device disconnection.
Definition at line 391 of file usb_drv.c.
References Host_disable_pipe, Host_reset_pipe, Host_select_pipe, and Host_unallocate_memory.
| U8 usb_config_ep | ( | U8 | config0, |
| U8 | config1 | ||
| ) |
usb_configure_endpoint.
This function configures an endpoint with the selected type.
| config0 | |
| config1 |
Definition at line 170 of file usb_drv.c.
References Is_endpoint_configured, Usb_allocate_memory, and Usb_enable_endpoint.
| U8 usb_get_nb_pipe_interrupt | ( | void | ) |
Returns the pipe number that generates a USB communication interrupt.
This function sould be called only when an interrupt has been detected. Otherwize the return value is incorect
| MAX_EP_NB | + 1 - pipe_number |
Definition at line 410 of file usb_drv.c.
References Host_get_pipe_interrupt.
| void usb_halt_endpoint | ( | U8 | ep_num | ) |
usb_halt_endpoint.
This function sends a STALL handshake for the next Host request. A STALL handshake will be send for each next request untill a SETUP or a Clear Halt Feature occurs for this endpoint.
| ep_num | number of the addressed endpoint |
Definition at line 289 of file usb_drv.c.
References Usb_enable_stall_handshake, and Usb_select_endpoint.
| U8 usb_init_device | ( | void | ) |
usb_init_device.
This function initializes the USB device controller and configures the Default Control Endpoint.
| FALSE | if not Is_usb_id_device() returns FALSE |
Definition at line 303 of file usb_drv.c.
References FALSE, Is_usb_endpoint_enabled, and Usb_select_endpoint.
Referenced by ISR(), and usb_start_device().
| U8 usb_read_packet | ( | U8 | ep_num, |
| U8 * | rbuf, | ||
| U8 | data_length | ||
| ) |
usb_read_packet.
This function moves the data stored in the selected endpoint fifo to the address specified by *rbuf.
| ep_num | number of the addressed endpoint |
| rbuf | aaddress of the first data to write with the USB data |
| data_length | number of bytes to read |
Example: while(!(Usb_rx_complete)); // wait new packet received usb_read_packet(4,&first_data,usb_get_nb_byte); // read packet from ep 4 Usb_clear_rx(); // acknowledge the transmit
Note: rbuf is incremented of 'data_length'.
Definition at line 265 of file usb_drv.c.
References Is_usb_read_enabled, Usb_read_byte, and Usb_select_endpoint.
| U8 usb_select_enpoint_interrupt | ( | void | ) |
usb_select_endpoint_interrupt.
This function select the endpoint where an event occurs and returns the number of this endpoint. If no event occurs on the endpoints, this function returns 0.
Definition at line 187 of file usb_drv.c.
References Usb_interrupt_flags.
| U8 usb_send_packet | ( | U8 | ep_num, |
| U8 * | tbuf, | ||
| U8 | data_length | ||
| ) |
usb_send_packet.
This function moves the data pointed by tbuf to the selected endpoint fifo and sends it through the USB.
| ep_num | number of the addressed endpoint |
| tbuf | address of the first data to send |
| data_length | number of bytes to send |
Example: usb_send_packet(3,&first_data,0x20); // send packet on the endpoint #3 while(!(Usb_tx_complete)); // wait packet ACK'ed by the Host Usb_clear_tx_complete(); // acknowledge the transmit
Note: tbuf is incremented of 'data_length'.
Definition at line 230 of file usb_drv.c.
References Is_usb_write_enabled, Usb_select_endpoint, and Usb_write_byte.
| void usb_start_device | ( | void | ) |
This function initializes the USB device controller.
This function enables the USB controller and init the USB interrupts. The aim is to allow the USB connection detection in order to send the appropriate USB event to the operating mode manager. Start device function is executed once VBUS connection has been detected either by the VBUS change interrupt either by the VBUS high level
Definition at line 149 of file usb_task.c.
References Usb_attach, Usb_enable_reset_interrupt, usb_init_device(), and Wait_pll_ready.
Referenced by ISR(), and PROCESS_THREAD().


OCTOBER 19 Saints John de Brebeuf
and Isaac Jogues, Priests, and Companions, Martyrs (or St Paul of the Cross)
They
were Jesuit missionaries who died martyrs in
Common of Martyrs
COLLECT
O God, who chose to manifest the blessed hope of your eternal Kingdom by the toil of Saints John de Brebeuf, I saac Jogues and their companions and by the shedding of their blood, graciously grant that through their intercession the faith of Christians may be strengthened day by day. Through our Lord Jesus Christ, your Son, who lives and reigns with you in the unity of the Holy Spirit, one God, for ever and ever.
(1694-1775).
Born in
ENTRANCE ANTIPHON
I resolved to know nothing while I was with you except for Jesus Christ, and him crucified.
COLLECT
May the Priest Saint Paul, whose only love was the Cross, obtain for us your grace, O Lord, so that, urged on more strongly by his example, we may each embrace our own cross with courage. Through our Lord Jesus Christ, your Son, who lives and reigns with you in the unity of the Holy Spirit, one God, for ever and ever.
PRAYER OVER THE OFFERINGS
Look upon the
sacrificial gifts we offer, almighty God, in commemoration of
COMMUNION ANTIPHON
We proclaim Christ crucified; Christ, the power of God and the wisdom of God.
PRAYER AFTER COMMUNION
O God, who in


Hey! Welcome to our website about Roast Dinners. We are a group of students and young professionals who wanted to share our love of this amazing dish!
Originating in Britain, this dish is traditionally served on a Sunday afternoon hence why it is otherwise known as “Sunday Lunch”.
Feel free to browse this site where you can find a handy recipe and the best places for a Roast Dinner in Leeds!
The components of a Roast Dinner vary a lot between families and also the areas of Britain.
However, usually the centrepiece of the dish is a roasted meat like beef or chicken, roasted vegetables, stuffing, gravy and of course the best part (in our opinion!) Yorkshire puddings!


Firefox is a stand-alone browser based on the Mozilla codebase.
This package is known to build and work properly using an LFS-8.1 platform.
Download (HTTP): https://archive.mozilla.org/pub/firefox/releases/55.0.3/source/firefox-55.0.3.source.tar.xz
Download MD5 sum: bc55a68fdd6770215cfa7a49fe7e3c98
Download size: 226 MB
Estimated disk space required: 6.0 GB (143 MB installed)
Estimated build time: 26 SBU (with parallelism=4)
Optional patch to allow system versions of Graphite2-1.3.10 and HarfBuzz-1.4.8 - this should be regarded as experimental http://www.linuxfromscratch.org/patches/blfs/8.1/firefox-55.0.3-system_graphite2_harfbuzz-1.patch
The tarball firefox-55.0.3.source.tar.xz will
untar to firefox-55.0.3
directory. It will also reset the permissions of the current
directory to 0755 if you have permission to do that. If you do
this in a directory where the sticky bit is set, such as
/tmp
it will end with error
messages:
tar: .: Cannot utime: Operation not permitted
tar: .: Cannot change mode to rwxr-xr-t: Operation not permitted
tar: Exiting with failure status due to previous errors
This does finish with non-zero status, but it does NOT mean there is a real problem. Do
not untar as the root
user in a
directory where the sticky bit is set - that will unset it.
Autoconf-2.13, both GTK+-3.22.18 and GTK+-2.24.31, NSS-3.32, PulseAudio-10.0 (or alsa-lib-<IP_ADDRESS> if you edit the mozconfig; now deprecated by mozilla), rustc-1.19.0, UnZip-6.0, yasm-1.3.0, and Zip-3.0
ICU-59.1, libevent-2.1.8, libvpx-1.6.1, and SQLite-3.20.0
If you don't install recommended dependencies, then internal copies of those packages will be used. They might be tested to work, but they can be out of date or contain security holes.
With Firefox-31.0 and later
versions, you must have installed Openssl before Python 2 or the build system will quickly
fail with output including "ImportError: cannot import name
HTTPSHandler". If you are in any doubt about this (e.g. upgrading
from an older version of Firefox), check if /usr/lib/python2.7/lib-dynload/_ssl.so
exists.
If it does not, reinstall Python-2.7.13
(after installing OpenSSL-1.1.0f). The latest version of
any currently maintained
version of Openssl should be satisfactory if already installed.
cURL-7.55.1, dbus-glib-0.108, Doxygen-1.8.13, GConf-3.2.6, FFmpeg-3.3.3 (runtime), libwebp-0.6.0, OpenJDK-<IP_ADDRESS>, startup-notification-0.12, Valgrind-3.13.0, Wget-1.19.1, Wireless Tools-29, Hunspell, liboauth , libproxy, and (with the patch) Graphite2-1.3.10 and HarfBuzz-1.4.8
User Notes: http://wiki.linuxfromscratch.org/blfs/wiki/firefox
The configuration of Firefox is
accomplished by creating a mozconfig
file containing the desired configuration options. A default
mozconfig
is created below. To see
the entire list of available configuration options (and an
abbreviated description of each one), issue ./configure --help. You may also
wish to review the entire file and uncomment any other desired
options. Create the file by issuing the following command:
cat > mozconfig << "EOF"
# If you have a multicore machine, all cores will be used by default.
# If desired, you can reduce the number of cores used, e.g. to 1, by
# uncommenting the next line and setting a valid number of CPU cores.
#mk_add_options MOZ_MAKE_FLAGS="-j1"
# If you have installed dbus-glib, comment out this line:
ac_add_options --disable-dbus
# If you have installed dbus-glib, and you have installed (or will install)
# wireless-tools, and you wish to use geolocation web services, comment out
# this line
ac_add_options --disable-necko-wifi
# API Keys for geolocation APIs - necko-wifi (above) is required for MLS
# Uncomment the following line if you wish to use Mozilla Location Service
#ac_add_options --with-mozilla-api-keyfile=$PWD/mozilla-key
# Uncomment the following line if you wish to use Google's geolocaton API
# (needed for use with saved maps with Google Maps)
#ac_add_options --with-google-api-keyfile=$PWD/google-key
# Uncomment these lines if you have installed optional dependencies:
#ac_add_options --enable-system-hunspell
#ac_add_options --enable-startup-notification
# Uncomment the following option if you have not installed PulseAudio
#ac_add_options --disable-pulseaudio
# and uncomment this if you installed alsa-lib instead of PulseAudio
#ac_add_options --enable-alsa
# If you have installed GConf, comment out this line
ac_add_options --disable-gconf
# Comment out following options if you have not installed
# recommended dependencies:
ac_add_options --enable-system-sqlite
ac_add_options --with-system-libevent
ac_add_options --with-system-libvpx
ac_add_options --with-system-nspr
ac_add_options --with-system-nss
ac_add_options --with-system-icu
# If you are going to apply the patch for system graphite
# and system harfbuzz, uncomment these lines:
#ac_add_options --with-system-graphite2
#ac_add_options --with-system-harfbuzz
# Stripping is now enabled by default.
# Uncomment these lines if you need to run a debugger:
#ac_add_options --disable-strip
#ac_add_options --disable-install-strip
# The BLFS editors recommend not changing anything below this line:
ac_add_options --prefix=/usr
ac_add_options --enable-application=browser
ac_add_options --disable-crashreporter
ac_add_options --disable-updater
ac_add_options --disable-tests
# Optimization for size is broken with gcc7
ac_add_options --enable-optimize="-O2"
ac_add_options --enable-official-branding
ac_add_options --enable-safe-browsing
ac_add_options --enable-url-classifier
# From firefox-40, using system cairo causes firefox to crash
# frequently when it is doing background rendering in a tab.
#ac_add_options --enable-system-cairo
ac_add_options --enable-system-ffi
ac_add_options --enable-system-pixman
ac_add_options --with-pthreads
ac_add_options --with-system-bz2
ac_add_options --with-system-jpeg
ac_add_options --with-system-png
ac_add_options --with-system-zlib
mk_add_options MOZ_OBJDIR=@TOPSRCDIR@/firefox-build-dir
EOF
Compile Firefox by issuing the following commands:
If you have installed system versions of graphite2 and harfbuzz and
wish firefox to use those instead of its shipped versions, apply
the patch and uncomment the appropriate entries in the mozconfig
file. This also ensures that the system
versions linked by libmozgtk.so
match
those used in libxul.so
:
patch -Np1 -i ../firefox-55.0.3-system_graphite2_harfbuzz-1.patch
If the geolocation APIs are needed:
echo "AIzaSyDxKL42zsPjbke5O8_rPVpVrLrJ8aeE9rQ" > google-key echo "d2284a20-0505-4927-a809-7ffaf4d91e55" > mozilla-key
If you are compiling Firefox in
chroot, make sure you have $SHELL
environment variable set or prepend SHELL=/bin/sh
.
make -f client.mk
This package does not come with a test suite.
Now, as the root
user:
make -f client.mk install INSTALL_SDK= && chown -R 0:0 /usr/lib/firefox-55.0.3 && mkdir -pv /usr/lib/mozilla/plugins && ln -sfv ../../mozilla/plugins /usr/lib/firefox-55.0.3/browser
make -f client.mk
...: Mozilla products are packaged to allow the use
of a configuration file which can be used to pass the configuration
settings to the configure command. make uses the client.mk
file to get initial configuration and
setup parameters.
mkdir -p
/usr/lib/mozilla/plugins: This checks that
/usr/lib/mozilla/plugins
exists.
ln -sv ...
/usr/lib/firefox-55.0.3/browser: This command
creates a symbolic link to /usr/lib/mozilla/plugins
. It's not really needed,
as Firefox checks /usr/lib/mozilla/plugins
by default, but the
symbolic link is made to keep all the plugins installed in one
folder.
If you use a desktop environment like Gnome or KDE
you may like to create a firefox.desktop
file so that Firefox appears in the panel's menus. If you
didn't enable startup-notification in your mozconfig change the
StartupNotify line to false. As the root
user:
mkdir -pv /usr/share/applications &&
mkdir -pv /usr/share/pixmaps &&
cat > /usr/share/applications/firefox.desktop << "EOF" &&
[Desktop Entry]
Encoding=UTF-8
Name=Firefox Web Browser
Comment=Browse the World Wide Web
GenericName=Web Browser
Exec=firefox %u
Terminal=false
Type=Application
Icon=firefox
Categories=GNOME;GTK;Network;WebBrowser;
MimeType=application/xhtml+xml;text/xml;application/xhtml+xml;application/vnd.mozilla.xul+xml;text/mml;x-scheme-handler/http;x-scheme-handler/https;
StartupNotify=true
EOF
ln -sfv /usr/lib/firefox-55.0.3/browser/icons/mozicon128.png \
/usr/share/pixmaps/firefox.png
Last updated on 2017-08-29 08:44:13 -0700


{{- define "header" -}} {{- partial "back.html" . -}} {{- end -}} {{- define "main" -}} Oh no! 😱 This page can't be found! {{- end -}}
This page can't be found!


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React HOC
2019-08-24 23:00:00, published in Auckland
If you have any question, you can email me<EMAIL_ADDRESS>High-level components (HOC) are an advanced technique used in React to reuse component logic. The HOC itself is not part of the React API, it is a design pattern based on the combined nature of React. Specifically, a high-level component is a function whose parameters are components and whose return value is a new component.
For example:
import React, {Component} from 'react';
const SchoolHoc = (fileds) => {
return WrappedComponent => {
return class extends Component{
render(){
return (
<WrappedComponent
{...this.props}
school={...fileds}
/>
)
}
}
}
}
export default SchoolHoc
the object of the filed will pass itself to WrapperComponents’s props
import React from "react";
import DashboardLayout from "../../components/layout/dashboard_layout/dashboard_layout";
import { Switch, Route } from "react-router-dom";
import Content from './content/content'
import SchoolHoc from '../../hoc/SchoolHoc'
const Dashboard = props => {
const schools = [
{
name: 'uoa',
},
{
name: 'aut',
},
{
name: 'massey',
},
{
name: 'lincoln',
},
{
name: 'otago',
},
{
name: 'uc',
},
{
name: 'victoria',
},
{
name: 'waikato',
}
]
return (
<DashboardLayout>
<Switch>
{schools.map((school,index)=>(
<Route
key={index}
path={`${props.match.path}/${school.name}`}
component={SchoolHoc({name: school.name})(Content)}
/>
))}
</Switch>
</DashboardLayout>
);
};
export default Dashboard;
Here, the school object is field in School Hoc, so school object will bind to the Content Component’s props. We can console.log the props in Content Component to check whether the object is in or not.


Contains functions to set up risk-adjusted quality control charts in health care. For the variable life adjusted display (VLAD) proposed by Lovegrove et al. (1997) <doi:10.1016/S0140-6736(97)06507-0> signaling rules derived in Wittenberg et al. (2018) <doi:10.1002/sim.7647> are implemented. Additionally, for the risk-adjusted cumulative sum chart based on log-likelihood ratio statistic introduced by Steiner et al. (2000) <doi:10.1093/biostatistics/1.4.441> average run length and control limits can be computed.
| Version: | 0.2.0 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp (≥ 0.12.12) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | dplyr, ggplot2, parallel, rmarkdown, spcadjust (≥ 1.1), testthat, tidyr |
| Published: | 2018-12-06 |
| Author: | Philipp Wittenberg [aut, cre], Sven Knoth [aut, ths] |
| Maintainer: | Philipp Wittenberg <pwitten at hsu-hh.de> |
| BugReports: | https://github.com/wittenberg/vlad/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Citation: | vlad citation info |
| Materials: | README NEWS |
| CRAN checks: | vlad results |
| Reference manual: | vlad.pdf |
| Package source: | vlad_0.2.0.tar.gz |
| Windows binaries: | r-devel: vlad_0.2.0.zip, r-release: vlad_0.2.0.zip, r-oldrel: vlad_0.2.0.zip |
| OS X binaries: | r-release: vlad_0.2.0.tgz, r-oldrel: vlad_0.2.0.tgz |
| Old sources: | vlad archive |
Please use the canonical form https://CRAN.R-project.org/package=vlad to link to this page.


Earl's Market
Current flavors:
Vanilla
Chocolate
Stawberry
Mint Chocolate Chip
Cookies and Cream
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'Congrats on the win Aston Villa' - Newcastle United fans are angry with this decision
Latest Aston Villa news includes Newcastle fans unhappy with Lee Mason appointment for Premier League meeting
by Shane Ireland, https://www.facebook.com/ShaneIrelandJournalistNewcastle United fans are fearing the worst after Lee Mason was revealed as the referee for next week’s clash against Aston Villa.
Steve Bruce returns to Villa Park for the first time since he was sacked in October 2018 after two years at the helm.
Having been appointed by Newcastle in July, Bruce has won four of his 13 matches in charge of the Magpies, who sit four places and four points above Villa in the Premier League table.
Villa are hoping a number of injury concerns will be resolved in time for the clash, with Jack Grealish and Jota in line to return.
Aston Villa latest
This is your round up of all things Villa; the one-stop shop that will keep you updated on the latest goings on at the club and beyond.
Transfers, injuries, match-days and managers, we’ve got you covered.
We’ll be bringing the very latest on the Villains throughout the week and around the clock.
Make sure you keep yourself updated with our handy daily catch up...
Must reads...
- Director reveals John McGinn's reaction to failed transfer
- Martin Keown fires puzzling criticism at Tyrone Mings
- What Jarrad Branthwaite has said about Villa interest
- Dean Smith urged to have a 'quiet word' with Frederic Guilbert
For all your latest AVFC news, opinion, analysis and transfer gossip, click here
You can follow our Villa reporter Ashley Preece on Twitter, and his email address is<EMAIL_ADDRESS>Grealish is back in training having missed the last three games while Jota has been sidelined since the start of October.
Bjorn Engels was a late omission from the travelling squad who lost at Wolves before the international break while Tom Heaton also missed out.
Dean Smith said after that game: "What the international period will do will enable them to be fit for the Newcastle game.”
Matt Targett went off injured at Molineux and the defender is expected to be fit, but Jed Steer faces a longer period on the treatment table while Keinan Davis remains out.
But Newcastle fans have a big concern of their own following confirmation that Mason will be the man in the middle.
The Bolton-born official hasn’t taken charge of any Newcastle games so far this season but has been involved in controversies with VAR since the new technology was brought in.
One fan said: “Congrats on the win Villa.”
“Who's going to be rugby tackled this time?” asked another.
A third simply added: “Oh dear.”


Move the mouse to exit the bonhomme to the right. The goal is to avoid collisions with trees. If the bonhomme is hit 5 times the game is over.


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WARNING: your version is outdated,
please update to the newest stable version at the SCC Web3.0 Github
Address QR
Welcome to the SCC Web3 wallet!
Be your own Bank!
The SCC Web3 wallet has no custody over your funds.
With this wallet, you can use SCC across the web, on-the-go, with full safety of your funds in your very
own protection. The wallet has many customization options, can be downloaded and ran locally, has a dedicated
offline mode, and can be cloned directly from GitHub!
Universal and Portable
With our built-in tools, you can generate cryptographically-secure addresses with the power of your browser and
hardware. You can generate 'random' addresses or you can generate vanity addresses with your own custom prefixes.
In addition, all keys are entirely universal and can be imported/exported directly to and from SCC Core to this
web wallet.
WARNING: if you don't save your private key you
will lose access to your funds!
Would you like to encrypt & save your wallet in your browser?
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I Love Programming , understanding how programming enables a developer to be a demigod in digital world is something that sparks me learning programming.
Hands on and Experience on Cloud computing technologies
With Basic Understanding of Web Development can create a moderate web page development
Hands on experience to build beautiful hybrid apps
Hands on experience to build Complex Server modules
Not Just about above programming, any topic about computer Let's talk


{% extends "base.html" %} {% load mathfilters %} {% block content %}
November 29, 2015
The Daily Californian sifted through eight years of UC Berkeley grades, looking for the most difficult classes, departments and academic disciplines.
Read the story
Title of story goes here
Explore the data
Browse all subjects, or explore the hardest courses and professors.
Build an app
The code for our analysis is open-source. Use it for your own app or investigation.
| {{ division.name }} |
| Discipline | Average |
|---|---|
| {{ stat.discipline.name }} | {{ stat.mean|floatformat:"2" }} |
| Subject | Average |
|---|---|
| {{ stat.subject.name }} | {{ stat.mean|floatformat:"2" }} |
| Subject | Average |
|---|---|
| {{ stat.subject.name }} | {{ stat.mean|floatformat:"2" }} |
| Class | Average |
|---|---|
| {{ class }} | {{ class.mean|floatformat:"2" }} |
| Class | Average |
|---|---|
| {{ class }} | {{ class.mean|floatformat:"2" }} |


Lukaku strikes again as Inter beat Torino in torrential rain
Inter Milan kept up the pressure on Serie A leaders Juventus with a comfortable 3-0 win at Torino in terrible weather conditions on Saturday.
ROME: Inter Milan kept up the pressure on Serie A leaders Juventus with a comfortable 3-0 win at Torino in terrible weather conditions on Saturday.
Goals from Lautaro Martinez and Stefan De Vrij gave Antonio Conte’s side the halftime lead and Romelu Lukaku’s 10th strike of the season put the result beyond doubt in the second half.
Kick-off was delayed at the Stadio Olimpico Grande Torino so water could be swept off the pitch amid heavy rainfall, and the players had to work hard throughout the game on a sodden surface that deteriorated as the downpour continued.
Inter are second with 34 points, one behind Juve who beat Atalanta 3-1 earlier on Saturday, while Torino are 12th on 14.
(Reporting by Alasdair Mackenzie)


Kara is a board certified nurse practitioner, certified holistic health coach, distance runner and digital nomad. She uses evidence-based science to empower YOU to make sustainable lifestyle changes in order to improve overall wellness and prevent, better manage and even reverse chronic disease. She believes health and wellness are about abundance, expanding your vision and vitality, not dieting and deprivation.
Full BioAs a lifestyle medicine and wellness expert, Kara empowers people to make sustainable, health-related, lifestyle changes adding years to their life and life to their years. She conducts sessions via audio and video platforms, so location is not a limiting factor!
Kara has published in peer reviewed healtchare journals and online publications, been quoted on wellness sites, such as Well and Good, and appeared as a guest on several podcasts. She is available for speaking engagements at conferences and retreats.
Having completed an intensive web development and coding course at the Institute of Code, Kara does front-end web development for health and wellness entrepreneurs. She developed and designed this site!


---
layout: post
title: What I add to Linux that helps make it better
tags:
- Linux
- Personal Opinion
- Work Practices
status: publish
type: post
published: true
meta: {}
---
As a follow up to a post I made back in 2002
What I add to Windows that helps make it better « Alec the Geek
I have recenlty had to rebuid my machine from scratch and this is what I feel the need to install.
I thought I would outline the extra goodies I use on my Linux system:
- GnuCash accounting software
- Eclipse (not used very much, but can be handy to have around sometimes)
- The Java SDK
- BOUML for UML modelling
- Freemind, and now VYM, mind mapping software
- I still prefer Adobe's Acrobat reader to the OSS versions supplied with Linux
- Evolution is my PIM and email client (but KDE Kontact is also good), not an extra goody per se, but you need to choose which PIM to use. Evolution is the default on Ubuntu anyway
- gworldclock to get the time in multiple time zones
- Skype to chat to my friends all over the world
- Automatix for Ubuntu to I can access all those pesky Windows media files people keep sending me
- Mozilla Composer is still my favourite HTML editor
- esvn for Subversion
- svk for distributed version control
- Envy to install my ATI Graphics driver -- it does in such a way that suspend now works correctly.
Pretty much everything else is already there by default.
powered by performancing firefox


| Author: | Mark Rowe |
|---|---|
| Contact: |<EMAIL_ADDRESS>|
| Web site: | http://pycap.sourceforge.net |
| Project page: | http://sourceforge.net/projects/pycap/ |
Packet injection works for Ethernet, ARP, IP and TCP.
Capture objects can be used as an iterator allowing code such as the following:
>>> import pycap.capture >>> capture = pycap.capture.capture() >>> for packet in capture: ... print packet >>>
pycap.constants package that contains constants for various protocols.


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Fractal
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normal
delay
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qualia2.functions package¶
Submodules¶
qualia2.functions.activation module¶
-
class
qualia2.functions.activation.
BReLU
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Bipolar rectified linear unit
-
class
qualia2.functions.activation.
ELU
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Exponential linear unit
-
class
qualia2.functions.activation.
Gaussian
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise Gaussian function
-
class
qualia2.functions.activation.
Identity
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Identity function
-
class
qualia2.functions.activation.
LeakyReLU
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Leaky rectified linear unit
-
class
qualia2.functions.activation.
ReLU
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Rectified linear unit
-
class
qualia2.functions.activation.
Sigmoid
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Sigmoid function
-
class
qualia2.functions.activation.
SoftMax
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
SoftMax function
-
class
qualia2.functions.activation.
SoftPlus
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
SoftPlus function
-
class
qualia2.functions.activation.
SoftSign
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
SoftSign function
qualia2.functions.array module¶
-
class
qualia2.functions.array.
Concat
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
concatenate given Tensors
qualia2.functions.complex module¶
-
class
qualia2.functions.complex.
CMSELoss
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
MSE Loss for complex neural networks
- Args:
input (Tensor): output of the network target (Tensor): label of the dataset reduce (bool): the losses are averaged or summed over observations for each minibatch depending on size_average. size_average (bool): the losses are averaged over each loss element in the batch.
- Model:
l_n = sum((y_n - x_n)^2/2)
- Shape:
Input: [N, C]
Target: [N, C]
- Output: [1] by default
[N] if not reduce
-
class
qualia2.functions.complex.
CReLU
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.complex.
CTanh
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise hyperbolic tangent function for complex neural networks
qualia2.functions.conv module¶
-
class
qualia2.functions.conv.
Conv1d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, kernel, bias=None, stride=1, padding=1, dilation=1)[source]¶ Applies a 2D convolution over an input signal composed of several input planes.
- Args:
x (Tensor): Input tensor with shepe of [batch, channel, width]
kernel (Tensor): Kernel with shape of [patch, channel, kernel_width]
bias (Tensor): Bias with shape of [patch] to add if needed. Default: None
stride (int): Stride of the convolution. Default: 1
padding (int): Padding controls the amount of implicit zero-paddings on both sides for padding number of points for each dimension. Default: 1
dilation (int): Spacing between kernel elements. Default: 1
- Returns:
(Tensor): Output tensor will have shape of [batch, patch, out_width]
- Shape:
Input: [N, in_channels, W]
Output: [N, out_channels, W_out]
\(W_out = (W+2*padding-dilation*(kernel_size-1)-1)/stride+1\)
-
static
-
class
qualia2.functions.conv.
Conv2d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, kernel, bias=None, stride=(1, 1), padding=(1, 1), dilation=(1, 1))[source]¶ Applies a 2D convolution over an input signal composed of several input planes.
- Args:
x (Tensor): Input tensor with shepe of [batch, channel, height, width]
kernel (Tensor): Kernel with shape of [patch, channel, kernel_height, kernel_width]
bias (Tensor): Bias with shape of [patch] to add if needed. Default: None
stride (tuple of int): Stride of the convolution. Default: (1,1)
padding (tuple of int): Padding controls the amount of implicit zero-paddings on both sides for padding number of points for each dimension. Default: (1,1)
dilation (tuple of int): Spacing between kernel elements. Default: (1,1)
- Returns:
(Tensor): Output tensor will have shape of [batch, patch, out_height, out_width]
- Shape:
Input: [N, in_channels, H, W]
Output: [N, out_channels, H_out, W_out]
:math:`H_out = (H+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0]+1 ` :math:`W_out = (W+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1]+1 `
-
static
-
class
qualia2.functions.conv.
Conv3d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, kernel, bias=None, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1))[source]¶ Applies a 3D convolution over an input signal composed of several input planes.
- Args:
x (Tensor): Input tensor with shepe of [batch, channel, height, width, depth]
kernel (Tensor): Kernel with shape of [patch, channel, kernel_height, kernel_width, kernel_depth]
bias (Tensor): Bias with shape of [patch] to add if needed. Default: None
stride (tuple of int): Stride of the convolution. Default: (1,1,1)
padding (tuple of int): Padding controls the amount of implicit zero-paddings on both sides for padding number of points for each dimension. Default: (1,1,1)
dilation (tuple of int): Spacing between kernel elements. Default: (1,1,1)
- Returns:
(Tensor): Output tensor will have shape of [batch, patch, out_height, out_width, out_depth]
- Shape:
Input: [N, in_channels, H, W, D]
Output: [N, out_channels, H_out, W_out, D_out]
\(H_out = (H+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0]+1\) \(W_out = (W+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1]+1\) \(D_out = (D+2*padding[2]-dilation[2]*(kernel_size[2]-1)-1)/stride[2]+1\)
-
static
-
class
qualia2.functions.conv.
ConvTranspose1d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, kernel, bias=None, stride=1, padding=1, output_padding=0, dilation=1)[source]¶ Applies a 1D transposed convolution over an input signal composed of several input planes.
- Args:
x (Tensor): Input tensor with shepe of [batch, channel, height]
kernel (Tensor): Kernel with shape of [channel, patch, kernel_height]
bias (Tensor): Bias with shape of [patch] to add if needed. Default: None
stride (tuple of int): Stride of the convolution. Default: 1
padding (tuple of int): Zero-padding added to both sides of the input. Default: 1
output_padding (tuple of int): Zero-padding added to both sides of the output. Default: 0
dilation (tuple of int): Spacing between kernel elements. Default: 1
- Shape:
Input: [N, in_channels, H]
Output: [N, out_channels, H_out]
\(H_out = (H-1)*stride[0]-2*padding[0]+dilation[0]*(kernel_size[0]-1)+1+output_padding[0]\)
- Reference:
-
static
-
class
qualia2.functions.conv.
ConvTranspose2d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, kernel, bias=None, stride=(1, 1), padding=(1, 1), output_padding=(0, 0), dilation=(1, 1))[source]¶ Applies a 2D transposed convolution over an input signal composed of several input planes.
- Args:
x (Tensor): Input tensor with shepe of [batch, channel, height, width]
kernel (Tensor): Kernel with shape of [channel, patch, kernel_height, kernel_width]
bias (Tensor): Bias with shape of [patch] to add if needed. Default: None
stride (tuple of int): Stride of the convolution. Default: (1,1)
padding (tuple of int): Zero-padding added to both sides of the input. Default: (1,1)
output_padding (tuple of int): Zero-padding added to both sides of the output. Default: (0,0)
dilation (tuple of int): Spacing between kernel elements. Default: (1,1)
- Shape:
Input: [N, in_channels, H, W]
Output: [N, out_channels, H_out, W_out]
\(H_out = (H-1)*stride[0]-2*padding[0]+dilation[0]*(kernel_size[0]-1)+1+output_padding[0]\) \(W_out = (W-1)*stride[1]-2*padding[1]+dilation[1]*(kernel_size[1]-1)+1+output_padding[1]\)
- Reference:
-
static
-
class
qualia2.functions.conv.
ConvTranspose3d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, kernel, bias=None, stride=(1, 1, 1), padding=(1, 1, 1), output_padding=(0, 0, 0), dilation=(1, 1, 1))[source]¶ Applies a 2D transposed convolution over an input signal composed of several input planes.
- Args:
x (Tensor): Input tensor with shepe of [batch, channel, height, width, depth]
kernel (Tensor): Kernel with shape of [channel, patch, kernel_height, kernel_width, kernel_depth]
bias (Tensor): Bias with shape of [patch] to add if needed. Default: None
stride (tuple of int): Stride of the convolution. Default: (1,1,1)
padding (tuple of int): Zero-padding added to both sides of the input. Default: (1,1,1)
output_padding (tuple of int): Zero-padding added to both sides of the output. Default: (0,0,0)
dilation (tuple of int): Spacing between kernel elements. Default: (1,1,1)
- Shape:
Input: [N, in_channels, H, W, D]
Output: [N, out_channels, H_out, W_out, D_out]
\(H_out = (H-1)*stride[0]-2*padding[0]+dilation[0]*(kernel_size[0]-1)+1+output_padding[0]\) \(W_out = (W-1)*stride[1]-2*padding[1]+dilation[1]*(kernel_size[1]-1)+1+output_padding[1]\) \(D_out = (D-1)*stride[2]-2*padding[2]+dilation[2]*(kernel_size[2]-1)+1+output_padding[2]\)
- Reference:
-
static
qualia2.functions.distance module¶
qualia2.functions.dropout module¶
-
class
qualia2.functions.dropout.
Dropout
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, p=0.5, training=True)[source]¶ During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution. Each channel will be zeroed out independently on every forward call. Args:
x (Tensor): Input tensor with any shepe p (float): probability that randomly zeroes some of the elements of the input tensor training (bool): True if the model is in training
-
static
qualia2.functions.linear_algebra module¶
-
class
qualia2.functions.linear_algebra.
Dot
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.linear_algebra.
Linear
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
qualia2.functions.loss module¶
-
class
qualia2.functions.loss.
BinaryCrossEntropy
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Creates a criterion that measures the Binary Cross Entropy between the target and the output
- Args:
input (Tensor): output of the network target (Tensor): label of the dataset reduce (bool): the losses are averaged or summed over observations for each minibatch depending on size_average. size_average (bool): the losses are averaged over each loss element in the batch.
- Model:
l_n = -y_n*log(x_n)-(1-y_n)*log(1-x_n)
- Shape:
Input: [N, 1]
Target: [N, 1]
- Output: [1] by default
[N] if not reduce
-
class
qualia2.functions.loss.
CrossEntropy
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
‘Creates a criterion that measures the Cross Entropy between the target and the output
- Args:
input (Tensor): output of the network target (Tensor): one-hot representation of label for the dataset reduce (bool): the losses are averaged or summed over observations for each minibatch depending on size_average. size_average (bool): the losses are averaged over each loss element in the batch.
- Model:
l_n = -sum_over_classes(y_n*log(x_n))
- Shape:
Input: [N, num_class]
Target: [N, num_class]
- Output: [1] by default
[N] if not reduce
-
class
qualia2.functions.loss.
HuberLoss
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
- Args:
input (Tensor): output of the network target (Tensor): label of the dataset reduce (bool): the losses are averaged or summed over observations for each minibatch depending on size_average. size_average (bool): the losses are averaged over each loss element in the batch.
- Model:
- l_n = sum((y_n - x_n)^2/2) for |y_n - x_n| < 1
sum(|y_n - x_n|) otherwise
- Shape:
Input: [N, C]
Target: [N, C]
- Output: [1] by default
[N] if not reduce
-
class
qualia2.functions.loss.
LogisticBinaryCrossEntropy
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Creates a criterion that measures the Binary Cross Entropy between the target and the logistic of output
- Args:
input (Tensor): output of the network target (Tensor): label of the dataset reduce (bool): the losses are averaged or summed over observations for each minibatch depending on size_average. size_average (bool): the losses are averaged over each loss element in the batch.
- Model:
l_n = y_n*log(1+exp(-x_n))+(1-y_n)*log(1+exp(x_n))
- Shape:
Input: [N, 1]
Target: [N, 1]
- Output: [1] by default
[N] if not reduce
-
class
qualia2.functions.loss.
MSELoss
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
- Args:
input (Tensor): output of the network target (Tensor): label of the dataset reduce (bool): the losses are averaged or summed over observations for each minibatch depending on size_average. size_average (bool): the losses are averaged over each loss element in the batch.
- Model:
l_n = sum((y_n - x_n)^2/2)
- Shape:
Input: [N, C]
Target: [N, C]
- Output: [1] by default
[N] if not reduce
-
class
qualia2.functions.loss.
SoftmaxCrossEntropy
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
‘Creates a criterion that measures the Cross Entropy between the target and the softmax of output
- Args:
input (Tensor): output of the network target (Tensor): one-hot representation of label for the dataset reduce (bool): the losses are averaged or summed over observations for each minibatch depending on size_average. size_average (bool): the losses are averaged over each loss element in the batch.
- Model:
l_n = -sum_over_classes(y_n*log(softmax(x_n)))
- Shape:
Input: [N, num_class]
Target: [N, num_class]
- Output: [1] by default
[N] if not reduce
qualia2.functions.normalize module¶
-
class
qualia2.functions.normalize.
BatchNorm
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, mean, std, weight, bias, axis, eps=1e-05)[source]¶ Applies Batch Normalization for each channel across a batch of data.
- Args:
x (Tensor): input tensor. mean (ndarray): running mean of the input tensor. std (ndarray): running std of the input tensor. weight (Tensor): weight to apply. bias (Tensor): bias to apply. axis (list): axis indicates the all the axis in the input except C dimention eps (float): a value added to the denominator for numerical stability.
- Shape:
Input: [N,C,*]
Output: [N,C,*]
-
static
qualia2.functions.pool module¶
-
class
qualia2.functions.pool.
AvePool1d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, kernel_width=2, stride=2, padding=0, dilation=1)[source]¶ Applies a 1D average pooling over an input signal composed of several input planes.
- Args:
kernel_size (int): the size of the window to take a max over stride (int): the stride of the window. Default value is kernel_size padding (int): implicit zero padding to be added on all three sides dilation (int): a parameter that controls the stride of elements in the window
- Shape:
Input: [N,C,W]
Output: [N,C,W_out]
W_out = (W+2*padding-dilation*(kernel_width-1)-1)/stride + 1
-
static
-
class
qualia2.functions.pool.
AvePool2d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, kernel_size=(2, 2), stride=(2, 2), padding=(0, 0), dilation=(1, 1))[source]¶ Applies a 2D average pooling over an input signal composed of several input planes.
- Args:
kernel_size (tuple of int): the size of the window to take a max over stride (tuple of int): the stride of the window. Default value is kernel_size padding (tuple of int): implicit zero padding to be added on all three sides dilation (tuple of int): a parameter that controls the stride of elements in the window
- Shape:
Input: [N,C,H,W]
Output: [N,C,H_out,W_out]
H_out = (H+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0] + 1 W_out = (W+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1] + 1
-
static
-
class
qualia2.functions.pool.
AvePool3d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 0, 0), dilation=(1, 1, 1))[source]¶ Applies a 3D max pooling over an input signal composed of several input planes.
- Args:
kernel_size (tuple of int): the size of the window to take a max over stride (tuple of int): the stride of the window. Default value is kernel_size padding (tuple of int): implicit zero padding to be added on all three sides dilation (tuple of int): a parameter that controls the stride of elements in the window
- Shape:
Input: [N,C,H,W,D]
Output: [N,C,H_out,W_out,D_out]
H_out = (H+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0] + 1 W_out = (W+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1] + 1 D_out = (D+2*padding[2]-dilation[2]*(kernel_size[2]-1)-1)/stride[2] + 1
-
static
-
class
qualia2.functions.pool.
GlobalAvePool1d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.pool.
GlobalAvePool2d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.pool.
GlobalAvePool3d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.pool.
MaxPool1d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, kernel_width=2, stride=2, padding=0, dilation=1, return_indices=False)[source]¶ Applies a 1D max pooling over an input signal composed of several input planes.
- Args:
kernel_size (int): the size of the window to take a max over stride (int): the stride of the window. Default value is kernel_size padding (int): implicit zero padding to be added on all three sides dilation (int): a parameter that controls the stride of elements in the window return_indices (bool): if True, will return the max indices along with the outputs.
- Shape:
Input: [N,C,W]
Output: [N,C,W_out]
W_out = (W+2*padding-dilation*(kernel_width-1)-1)/stride + 1
-
static
-
class
qualia2.functions.pool.
MaxPool2d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, kernel_size=(2, 2), stride=(2, 2), padding=(0, 0), dilation=(1, 1), return_indices=False)[source]¶ Applies a 2D max pooling over an input signal composed of several input planes.
- Args:
kernel_size (tuple of int): the size of the window to take a max over stride (tuple of int): the stride of the window. Default value is kernel_size padding (tuple of int): implicit zero padding to be added on all three sides dilation (tuple of int): a parameter that controls the stride of elements in the window return_indices (bool): if True, will return the max indices along with the outputs.
- Shape:
Input: [N,C,H,W]
Output: [N,C,H_out,W_out]
H_out = (H+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0] + 1 W_out = (W+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1] + 1
-
static
-
class
qualia2.functions.pool.
MaxPool3d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
fold
(delta, kernel_size, argmax, oh, ow, od, x_shape, padded_shape, stride, dilation)[source]¶
-
static
forward
(x, kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 0, 0), dilation=(1, 1, 1), return_indices=False)[source]¶ Applies a 3D max pooling over an input signal composed of several input planes.
- Args:
kernel_size (tuple of int): the size of the window to take a max over stride (tuple of int): the stride of the window. Default value is kernel_size padding (tuple of int): implicit zero padding to be added on all three sides dilation (tuple of int): a parameter that controls the stride of elements in the window return_indices (bool): if True, will return the max indices along with the outputs.
- Shape:
Input: [N,C,H,W,D]
Output: [N,C,H_out,W_out,D_out]
H_out = (H+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0] + 1 W_out = (W+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1] + 1 D_out = (D+2*padding[2]-dilation[2]*(kernel_size[2]-1)-1)/stride[2] + 1
-
static
-
class
qualia2.functions.pool.
MaxUnpool1d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, indices, kernel_size=2, stride=2, padding=0, dilation=1)[source]¶ Computes a partial inverse of MaxPool2d. Args:
x (Tensor): the input Tensor to invertc indices (ndarray): the indices given out by MaxPool2d kernel_size (tuple of int): the size of the window to take a max over stride (tuple of int): the stride of the window. Default value is kernel_size padding (tuple of int): implicit zero padding to be added on all three sides dilation (tuple of int): a parameter that controls the stride of elements in the window
- Shape:
Input: [N,C,W]
Output: [N,C,W_out]
W_out = (W-1)*stride+dilation*(kernel_size-1)+1-2*padding
-
static
-
class
qualia2.functions.pool.
MaxUnpool2d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, indices, kernel_size=(2, 2), stride=(2, 2), padding=(0, 0), dilation=(1, 1))[source]¶ Computes a partial inverse of MaxPool2d. Args:
x (Tensor): the input Tensor to invertc indices (ndarray): the indices given out by MaxPool2d kernel_size (tuple of int): the size of the window to take a max over stride (tuple of int): the stride of the window. Default value is kernel_size padding (tuple of int): implicit zero padding to be added on all three sides dilation (tuple of int): a parameter that controls the stride of elements in the window
- Shape:
Input: [N,C,H,W]
Output: [N,C,H_out,W_out]
H_out = (H-1)*stride[0]+dilation[0]*(kernel_size[0]-1)+1-2*padding[0] W_out = (W-1)*stride[1]+dilation[1]*(kernel_size[1]-1)+1-2*padding[1]
-
static
-
class
qualia2.functions.pool.
MaxUnpool3d
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, indices, kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 0, 0), dilation=(1, 1, 1))[source]¶ Computes a partial inverse of MaxPool2d. Args:
x (Tensor): the input Tensor to invertc indices (ndarray): the indices given out by MaxPool2d kernel_size (tuple of int): the size of the window to take a max over stride (tuple of int): the stride of the window. Default value is kernel_size padding (tuple of int): implicit zero padding to be added on all three sides dilation (tuple of int): a parameter that controls the stride of elements in the window
- Shape:
Input: Input: [N,C,H,W,D]
Output: [N,C,H_out,W_out,D_out]
H_out = (H-1)*stride[0]+dilation[0]*(kernel_size[0]-1)+1-2*padding[0] W_out = (W-1)*stride[1]+dilation[1]*(kernel_size[1]-1)+1-2*padding[1] D_out = (D-1)*stride[2]+dilation[2]*(kernel_size[2]-1)+1-2*padding[2]
-
static
qualia2.functions.recurrent module¶
-
class
qualia2.functions.recurrent.
GRU
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.recurrent.
GRUCell
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.recurrent.
LSTM
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.recurrent.
LSTMCell
(output_shape_h, output_shape_c, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
static
forward
(x, h, c, weight_x, weight_h, bias_x, bias_h)[source]¶ - Shape:
x: [N, input_size]
h: [N, hidden_size]
c: [N, hidden_size]
weight_x: [input_size, 4*hidden_size]
weight_h: [hidden_size, 4*hidden_size]
bias_x: [4*hidden_size]
bias_h: [4*hidden_size]
Output_h: [N, hidden_size]
Output_c: [N, hidden_size]
-
static
-
class
qualia2.functions.recurrent.
RNN
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.recurrent.
RNNCell
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
qualia2.functions.sparse module¶
-
class
qualia2.functions.sparse.
Embedding
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
qualia2.functions.trigonometric module¶
-
class
qualia2.functions.trigonometric.
Arccos
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise inverse-cosine function
-
class
qualia2.functions.trigonometric.
Arccosh
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise inverse of hyperbolic cosine function
-
class
qualia2.functions.trigonometric.
Arcsin
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise inverse-sine function
-
class
qualia2.functions.trigonometric.
Arcsinh
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise inverse of hyperbolic sine function
-
class
qualia2.functions.trigonometric.
Arctan
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise inverse-tangent function
-
class
qualia2.functions.trigonometric.
Arctanh
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise inverse of hyperbolic tangent function
-
class
qualia2.functions.trigonometric.
Cos
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise cosine function
-
class
qualia2.functions.trigonometric.
Cosh
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise hyperbolic cosine function
-
class
qualia2.functions.trigonometric.
Sin
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise sine function
-
class
qualia2.functions.trigonometric.
Sinc
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.trigonometric.
Sinh
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise hyperbolic sine function
-
class
qualia2.functions.trigonometric.
Tan
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
Elementwise tangent function
qualia2.functions.ufunc module¶
-
class
qualia2.functions.ufunc.
Amax
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.ufunc.
Amin
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.ufunc.
Cbrt
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.ufunc.
Exp
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.ufunc.
Log
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.ufunc.
Log10
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.ufunc.
Maximum
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.ufunc.
Mean
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.ufunc.
Minimum
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function
-
class
qualia2.functions.ufunc.
Sqrt
(output_shape, *args, **kwargs)[source]¶ Bases:
qualia2.autograd.Function


 The idea is that you have one place to keep a complete copy of all your job history contacts, recommendations, living history, etc. and you can apply it to customized designer resumes and print them out in pdf format.
The excellent part of all this is the designers we have on board to make your resume stand out, and the complete customization of your resume. For instance you can select a career field such as Administration or Shipping and have all your job histery pertaining to that industry used in the resume rather than everything, so that employers can see what's relevant.
Things to do.
- Justin, I need you to build a login system and create text on that top bar for logged in and not logged in people so I can edit it properly. I cant get the login thing working on my own side.


{% extends "melange/root.html" %}
{% comment %}
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
{% endcomment %}
{% block stylesheets %}
{% endblock stylesheets %}
{% block body %}


{% extends 'layout/layout.html' %} {% block main %}
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Train layout designer / maker by François CREVOLA
For more informations and instructions of use, see the
online help and watch the video tutorial
or
see example of what can done
. (Contact me at francois (AT) crevola (DOT) org)


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Show my inputs
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